Two Sigma Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying models on data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import lightgbm as lgb\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.metrics import confusion_matrix \n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "from xgboost import XGBClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "traindata=pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "testdata=pd.read_csv('test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "traindata=traindata.iloc[:,1:]\n",
    "testdata=testdata.iloc[:,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['time', 'assetCode', 'assetName', 'volume', 'close', 'open',\n",
       "       'returnsClosePrevRaw1', 'returnsOpenPrevRaw1',\n",
       "       'returnsClosePrevMktres1', 'returnsOpenPrevMktres1',\n",
       "       'returnsClosePrevRaw10', 'returnsOpenPrevRaw10',\n",
       "       'returnsClosePrevMktres10', 'returnsOpenPrevMktres10',\n",
       "       'returnsOpenNextMktres10', 'universe', 'urgency', 'bodySize',\n",
       "       'companyCount', 'marketCommentary', 'sentenceCount', 'wordCount',\n",
       "       'relevance', 'sentimentClass', 'sentimentNegative', 'sentimentNeutral',\n",
       "       'sentimentPositive', 'sentimentWordCount', 'noveltyCount3D',\n",
       "       'volumeCounts3D', 'headlinelength',\n",
       "       'returnsClosePrevMktres10_lag_3_median',\n",
       "       'returnsClosePrevMktres10_lag_3_max',\n",
       "       'returnsClosePrevMktres10_lag_3_min',\n",
       "       'returnsClosePrevMktres10_lag_7_median',\n",
       "       'returnsClosePrevMktres10_lag_7_max',\n",
       "       'returnsClosePrevMktres10_lag_7_min',\n",
       "       'returnsClosePrevMktres10_lag_14_median',\n",
       "       'returnsClosePrevMktres10_lag_14_max',\n",
       "       'returnsClosePrevMktres10_lag_14_min',\n",
       "       'returnsClosePrevRaw10_lag_3_median', 'returnsClosePrevRaw10_lag_3_max',\n",
       "       'returnsClosePrevRaw10_lag_3_min', 'returnsClosePrevRaw10_lag_7_median',\n",
       "       'returnsClosePrevRaw10_lag_7_max', 'returnsClosePrevRaw10_lag_7_min',\n",
       "       'returnsClosePrevRaw10_lag_14_median',\n",
       "       'returnsClosePrevRaw10_lag_14_max', 'returnsClosePrevRaw10_lag_14_min',\n",
       "       'open_lag_3_median', 'open_lag_3_max', 'open_lag_3_min',\n",
       "       'open_lag_7_median', 'open_lag_7_max', 'open_lag_7_min',\n",
       "       'open_lag_14_median', 'open_lag_14_max', 'open_lag_14_min',\n",
       "       'close_lag_3_median', 'close_lag_3_max', 'close_lag_3_min',\n",
       "       'close_lag_7_median', 'close_lag_7_max', 'close_lag_7_min',\n",
       "       'close_lag_14_median', 'close_lag_14_max', 'close_lag_14_min',\n",
       "       'closeopentovolume', 'meanvolume', 'meanclose', 'stdclose', 'dayofweek',\n",
       "       'quarter', 'month', 'year', 'close_30EMA', 'close_26EMA', 'close_12EMA',\n",
       "       'MACD', 'MA_7MA', 'MA_7MA_std', 'MA_7MA_BB_high', 'MA_7MA_BB_low',\n",
       "       'RSI', 'beta', 'sin_quarter', 'sin_dayofweek', 'sin_month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traindata.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "traindata['time']=pd.to_datetime(traindata['time'], format='%Y-%m-%d')\n",
    "testdata['time']=pd.to_datetime(testdata['time'], format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "universe_test = testdata['universe']\n",
    "time_test = testdata['time']\n",
    "universe_train=traindata['universe']\n",
    "time_train=traindata['time']\n",
    "y_train=(traindata.returnsOpenNextMktres10>=0).astype('int')\n",
    "y_test=(testdata.returnsOpenNextMktres10>=0).astype('int')\n",
    "y_train1=traindata.returnsOpenNextMktres10\n",
    "y_test1=testdata.returnsOpenNextMktres10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing news data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['volume','returnsClosePrevRaw1', 'returnsOpenPrevRaw1',\n",
    "       'returnsClosePrevMktres1', 'returnsOpenPrevMktres1',\n",
    "       'returnsClosePrevRaw10', 'returnsOpenPrevRaw10',\n",
    "       'returnsClosePrevMktres10', 'returnsOpenPrevMktres10',\n",
    "       'returnsClosePrevMktres10_lag_3_median',\n",
    "       'returnsClosePrevMktres10_lag_3_max',\n",
    "       'returnsClosePrevMktres10_lag_3_min',\n",
    "       'returnsClosePrevMktres10_lag_7_median',\n",
    "       'returnsClosePrevMktres10_lag_7_max',\n",
    "       'returnsClosePrevMktres10_lag_7_min',\n",
    "       'returnsClosePrevMktres10_lag_14_median',\n",
    "       'returnsClosePrevMktres10_lag_14_max',\n",
    "       'returnsClosePrevMktres10_lag_14_min',\n",
    "       'returnsClosePrevRaw10_lag_3_median', 'returnsClosePrevRaw10_lag_3_max',\n",
    "       'returnsClosePrevRaw10_lag_3_min', 'returnsClosePrevRaw10_lag_7_median',\n",
    "       'returnsClosePrevRaw10_lag_7_max', 'returnsClosePrevRaw10_lag_7_min',\n",
    "       'returnsClosePrevRaw10_lag_14_median',\n",
    "       'returnsClosePrevRaw10_lag_14_max', 'returnsClosePrevRaw10_lag_14_min',\n",
    "       'open_lag_3_median', 'open_lag_3_max', 'open_lag_3_min',\n",
    "       'open_lag_7_median', 'open_lag_7_max', 'open_lag_7_min',\n",
    "       'open_lag_14_median', 'open_lag_14_max', 'open_lag_14_min',\n",
    "       'close_lag_3_median', 'close_lag_3_max', 'close_lag_3_min',\n",
    "       'close_lag_7_median', 'close_lag_7_max', 'close_lag_7_min',\n",
    "       'close_lag_14_median', 'close_lag_14_max', 'close_lag_14_min',\n",
    "       'closeopentovolume', 'meanvolume', 'meanclose', 'stdclose','close_30EMA', 'close_26EMA', 'close_12EMA',\n",
    "       'MACD', 'MA_7MA', 'MA_7MA_std', 'MA_7MA_BB_high', 'MA_7MA_BB_low',\n",
    "       'RSI', 'beta', 'sin_quarter', 'sin_dayofweek', 'sin_month','year']\n",
    "X_train=traindata[cols]\n",
    "X_test=testdata[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ea4062aac8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = X_train.corr()\n",
    "\n",
    "mask = np.zeros_like(corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "f, ax = plt.subplots(figsize=(11, 9))\n",
    "cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, vmax=.3, center=0,\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining custom metric functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigma_score(pred,valid_data):\n",
    "    pred=np.clip(pred,-1,1)\n",
    "    if(len(pred)==len(y_test)):\n",
    "        df_time = time_test.factorize()[0]\n",
    "        universe=universe_test\n",
    "        labels=y_test1*universe\n",
    "    elif(len(pred)==len(y_train)):\n",
    "        df_time = time_train.factorize()[0]\n",
    "        universe=universe_train\n",
    "        labels=y_train1*universe\n",
    "    x_t = pred * labels\n",
    "    x_t_sum = x_t.groupby(df_time).sum()\n",
    "    score = x_t_sum.mean() / x_t_sum.std()\n",
    "    return 'sigma_score', score, True\n",
    "def sigma_score_2(preds, valid_data):\n",
    "    preds=preds*2-1\n",
    "    if(len(preds)==len(y_train)):\n",
    "        df_time= time_train.factorize()[0]\n",
    "        labels=y_train1\n",
    "        x_t = preds*labels*universe_train\n",
    "#    df_time = valid_data.params['extra_time']\n",
    "#    labels = valid_data.get_label()\n",
    "    elif(len(preds)==len(y_test)):\n",
    "        df_time= time_test.factorize()[0]\n",
    "        labels=y_test1\n",
    "        x_t = preds*labels*universe_test\n",
    "        \n",
    "   # labels=y_test\n",
    " #   preds=pd.DataFrame(preds).clip(-1,1).values\n",
    "  #  preds=preds.reshape(len(preds), )\n",
    "#    assert len(labels) == len(df_time)\n",
    "    #  * df_valid['universe'] -> Here we take out the 'universe' term because we already keep only those equals to 1.\n",
    "    \n",
    "    # Here we take advantage of the fact that `labels` (used to calculate `x_t`)\n",
    "    # is a pd.Series and call `group_by`\n",
    "    x_t_sum = x_t.groupby(df_time).sum()\n",
    "    score = x_t_sum.mean() / x_t_sum.std()\n",
    "\n",
    "    return 'sigma_score', score, True\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time series split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tscv = TimeSeriesSplit(n_splits=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chinn\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model=LogisticRegression()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.37416417744774055, True)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=model.predict_proba(X_test)\n",
    "sigma_score(y_pred[:,1]*2-1,y_test1.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression()\n",
    "\n",
    "param_grid = { \n",
    "    'C': [0.001,0.01,0.1,1,10],\n",
    "    'penalty':['l2']\n",
    "   }\n",
    "\n",
    "rfc = RandomizedSearchCV(estimator=clf, param_distributions =param_grid, cv= tscv,n_jobs=-1,scoring ='neg_log_loss')\n",
    "rfc.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=clf.predict_proba(X_test)\n",
    "y_pred=y_pred[:,1]*2-1\n",
    "sigma_score(y_pred,y_test1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chinn\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_jobs=-1)\n",
    "model=clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.3750540242407995, True)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=clf.predict_proba(X_test)\n",
    "y_pred=y_pred[:,1]*2-1\n",
    "sigma_score(y_pred,y_test1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calibrated on RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CalibratedClassifierCV(base_estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "            cv=TimeSeriesSplit(max_train_size=None, n_splits=2),\n",
       "            method='sigmoid')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "calibrated_clf = CalibratedClassifierCV(model, method='sigmoid', cv=tscv)\n",
    "calibrated_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ytrainpred=calibrated_clf.predict_proba(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ytestpred=calibrated_clf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrainpred=Ytrainpred[:,1]\n",
    "ytestpred=Ytestpred[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 1.1045316341808502, True)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_score(ytrainpred*2-1,y_train1.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.4862011486445091, True)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_score(ytestpred*2-1,y_train1.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances = pd.DataFrame(clf.feature_importances_,\n",
    "                                   index = X_train.columns,\n",
    "                                    columns=['importance']).sort_values('importance',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sin_dayofweek</th>\n",
       "      <td>0.006310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sin_quarter</th>\n",
       "      <td>0.003783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stdclose</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meanclose</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meanvolume</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               importance\n",
       "sin_dayofweek    0.006310\n",
       "sin_quarter      0.003783\n",
       "stdclose         0.000000\n",
       "meanclose        0.000000\n",
       "meanvolume       0.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid search on RF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=TimeSeriesSplit(max_train_size=None, n_splits=2),\n",
       "          error_score='raise-deprecating',\n",
       "          estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=-1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "          fit_params=None, iid='warn', n_iter=10, n_jobs=-1,\n",
       "          param_distributions={'n_estimators': [100, 200, 300, 500], 'max_depth': [4, 5, 6, 7, 8]},\n",
       "          pre_dispatch='2*n_jobs', random_state=None, refit=True,\n",
       "          return_train_score='warn', scoring='neg_log_loss', verbose=0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier(n_jobs=-1)\n",
    "\n",
    "param_grid = { \n",
    "    'n_estimators': [100,200,300,500],\n",
    "    'max_depth' : [4,5,6,7,8],\n",
    "}\n",
    "\n",
    "rfc = RandomizedSearchCV(estimator=clf, param_distributions =param_grid, cv= tscv,n_jobs=-1,scoring ='neg_log_loss')\n",
    "rfc.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 300, 'max_depth': 8}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=rfc.predict_proba(X_train)*2-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=y_pred[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.7134924265006625, True)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_score(y_pred,y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.5346658390165596, True)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=rfc.predict_proba(X_test)*2-1\n",
    "sigma_score(y_pred[:,1],y_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chinn\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ea4b178550>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GQVoMFQ1duFy6TYO/0OKu/EZ1Sw/f3LCbgrQY/v3ms3Q++wwqSIuhZ8BJXYeuEOkvdLaM8nkbiqsYcLj47ftH6R10cu2yLF7eU2t1rKBSkBYDDC1DMCsh0uI0yhPac1d+4a0Dp6hr7+OWolySY+xWxwk6w4u78g9a3JXPq2ruZtvRZlblJzE/I9bqOEEpOTqcxKgwKnR1SL/hyQbZOSKyRUQOikiZiHxrlDaXiEi7iOxx33402nspNVF9g05e+OQk8ZFhuoyvhUTk05Oqyj94MubuAL5jjNktIrHALhF5yxhzYES7D4wx13g/ogpm//nuERo7+7nzvDzsYSFWxwlqBWmxvLa/DmOMnsz2A+P23I0xdcaY3e77ncBBYNZ0B1Nq/8l2Hn7vGGfnJlKYrsMxVitIi6GtZ5Dm7gGroygPTGjMXUTygBVA8SgvrxGRvSLymogsHuPvrxeREhEpaWxsnHBYFTwcThf3vlhKYlQ4Vy/V4RhfoCdV/YvHxV1EYoAXgG8bYzpGvLwbmG2MOQv4T+Cl0d7DGPOIMabIGFOUmqq70qux/ddHJ9h/soOfXLeYqHCdsesL5mlx9yseFXcRCWOosD9tjHlx5OvGmA5jTJf7/mYgTERSvJpUBY3qlh5+8dZh1i5M0167D8mMjyA6PESLu5/wZLaMAI8BB40xvxijTYa7HSKy0v2+zd4MqoLD6UXBbAL3X79ET9z5EBFhrs6Y8Rue/L57PvBlYJ+I7HE/930gF8AY8zBwE/A1EXEAvcCtxhhdhEJNyIbiKnZXtfLBkSauXZbJ1kN6XsZXbCiuAiBEhNKatk8f37ZKN0jxVeMWd2PMh8AZu0/GmAeBB70VSgWnjr5BNpXWkpsUxao5yVbHUaNIi7XzSXUbfYNOInRqqk/TK1SVTzDG8NInJ3E4DTednY1Nh2N8Uqp7V6bGzn6Lk6jxaHFXPuHlPbWUn+rkikXppMTq2jG+Ki1u6GvToMXd52lxV5Zr6OjjxxvLyE2K4rwCnWTlyxKjwgmxCQ2duvSvr9PirixljOHeF/fRN+jkRh2O8XkhNiEt1k69ruvu87S4K0s9s7Oad8sbuPeqBZ9uxKx8W0ZcBKfatbj7Oi3uyjLHm7r5p00HuHBeCnesybM6jvJQRnwEHX0OevodVkdRZ6DXdStLPLm9kkfePwrAeXNTePbjaosTKU9lxA/NmNEt93yb9tyVJbYcaqC6tZfrl2cRHxlmdRw1ARlxQ8Vdh2Z8mxZ3NeO2HGpgS3kDZ+cmsiw7weo4aoJiI8KItodySnvuPk2Lu5pR1S09fPvZPWTER3DdWVlWx1GTlKknVX2eFnc1Y/oGnXz96d24jOG2lbmEh+q3n7/KiI+gvqMPh9NldRQ1Bv3pUjPCGMMP/ryffSfb+eVfLic5Rqc9+rOM+AgcLsOJ5h6ro6gxaHFXM+JX7xzhhd01fHvtPNYuSrc6jpqi0ydVy0+N3LdH+QqdCqmm1YbiKnZXtvL87hrOzk0gNcb+6XKxyn+lxdqxCRys6+CaZXruxBdpz11Nq4qGLl78pIaC1BhuWDFLN98IEKEhNlJj7ZTXdVodRY1Bi7uaNrsqW3lqRyVpsRHctiqXUJt+uwWSjLgIyk9pcfdV+tOmpsX+k+3c+V87iY0I5Svn5+nGDgEoIz6Sk229tPcMWh1FjcKTPVRzRGSLiBwUkTIR+dYobUREfi0iFSJSKiJnT09c5Q8O13fy5ceKiYsI464L8omN0CtQA5GeVPVtnvTcHcB3jDELgdXAN0Rk0Yg2VwHz3Lf1wG+8mlL5jbr2Xm5/bCdhITae/ptVJESFWx1JTZPM+NPFXYdmfJEne6jWAXXu+50ichCYBRwY1ux64An3ptg7RCRBRDLdf1cFgQ3FVfQPOnnkg2O09AzwdxfNYdvRZqtjqWkUGxFKYlQYB+u05+6LJjTmLiJ5wAqgeMRLs4Dhy/rVuJ9TQcLpMjz7cTX1HX3ctjKXzPhIqyOpaSYiLM6KZ9/JdqujqFF4XNxFJAZ4Afi2MWbkf9WjzW8zo7zHehEpEZGSxsbGiSVVPm3z/joO1XdyzbIsCtNjrY6jZsiy7HgOneqkb9BpdRQ1gkfFXUTCGCrsTxtjXhylSQ2QM+xxNlA7spEx5hFjTJExpig1NXUyeZUPeumTk2w/2sz5c5NZPSfZ6jhqBi3LTsDhMhzQoRmf48lsGQEeAw4aY34xRrONwO3uWTOrgXYdbw8OR+o7+d6L+8hLjmLdkkyr46gZdlZOPACl1W0WJ1EjebL8wPnAl4F9IrLH/dz3gVwAY8zDwGbgaqAC6AG+4v2oytd09zv42tO7ibaHcOu5uYTY9OrTYJMRF0FarJ29NTru7ms8mS3zIaOPqQ9vY4BveCuU8n3GGL7/530ca+ziybtWUamrAwYlEWFZdgJ7a7Tn7mv0ClU1KU/tqOTlPbX8/dpCzi9IsTqOstBZ2fEca+ymo0+vVPUlWtzVhD3wWjn3bTzA/PRYEqPDdZXHILcsZ2irxP06NONTtLirCWnpHmDDziriIkO5uSgbm67yGNQ2FFdxxH2F6uPbK/U/eh+ixV15zOkyfOvZT+jud3DbytlEhet2AAqi7KEkRYdT06rnXXyJFnflsV++dZgPjjRx7bIsZiXqFajqv2UnRlLT2mt1DDWMFnflkbcO1PPglgpuKcrh3Pwkq+MoH5OdEEl77yCdelLVZ2hxV+M63tTNPc/tYemseH5y/WKr4ygfNCsxCoCT2nv3GTpoqsa0obiKAYeL37xXgdMY1i3J4MXdJ62OpXzQrIRIBKhp0+LuK7Tnrs7ojbJT1Hf085dFOSTq2uxqDOGhNtLjIvSkqg/R4q7GVNHQxfZjzayZm6wrPapxzU6OorK5B4fTZXUUhRZ3NYb23kFe2F1DaoyddYszrI6j/EB+SjT9DhdltbpCpC/Q4q5G9eOX99PZN8jNRdmEhei3iRpffko0ANuP6Q5cvkB/atVnvFtez0t7arl0QRrZ7lkQSo0nNiKM1Fg7O7S4+wQt7up/6Bt0ct/GAxSkxXBJYZrVcZSfmZMSzcfHW3Tc3QdocVf/w+/eP0ZVSw8/uW6xrs+uJiw/JZruASf7ddzdclrc1adqWnt4aGsFn1+aqcv4qkmZkxoDoEMzPkAvYlKfruT3dHElTpdhcVacru6nJiXGHsq8tBh2HGvmqxfPtTpOUNOeuwKG5rSX1XZw6fw0EvRiJTUFq+ck8/HxFgZ13N1SnmyQ/XsRaRCR/WO8fomItIvIHvftR96PqaaTyxg276sjMSqMC3Q4Rk3R6jnJQ+PuJ3XzDit50nP/A7BunDYfGGOWu2/3Tz2Wmkm7Kls51dHHuiWZhOqcdjVFq+YMrRq641iLxUmC27g/ycaY9wH9KgWorn4Hbx2oZ3ZSFEuy4qyOowJASoydwvQYvZjJYt7qpq0Rkb0i8pqI6JqwfuThrUfp6ndw9dJMRLfMU15yQUEqO44109XvsDpK0PJGcd8NzDbGnAX8J/DSWA1FZL2IlIhISWNjoxc+Wk3FybZefvfBMc7KjicnSa9EVd6zbkkGAw4XW8obrI4StKZc3I0xHcaYLvf9zUCYiIx6Vs4Y84gxpsgYU5SamjrVj1ZT9Mu3DmOAK3RhMOVl58xOJCXGzutlp6yOErSmXNxFJEPcv8+LyEr3e+pgm487XN/Ji7truH31bF2nXXldiE24YnE6W8ob6Bt0Wh0nKHkyFfIZYDswX0RqROQuEfmqiHzV3eQmYL+I7AV+DdxqjDHTF1l5w7+/cYjo8FC+fmmB1VFUgLpqSQY9A07eP6xDsFYY9wpVY8wXx3n9QeBBryVS0253VStvHqjnO58rJClae+1qeqyek0x8ZBivl53SoT8L6PIDQebpHZU8+uFxou2hxESE6jIDyuuGf0/NTY1m8746luckcPuaPOtCBSG9YiXIHGno4nhTN5fNT8UeGmJ1HBXgFmfF0zfo4nhjt9VRgo4W9yCz9VAD8ZFhnJufZHUUFQQK0mIID7XpEsAW0OIeREpOtHCiuYcLClIItemXXk2/sBAb89NjKattp9+hs2Zmkv6EB5GH3ztKVHgI5+Zpr13NnHNmJ9Iz4OSNsnqrowQVLe5B4tCpTt4+2MCaucmEh+qXXc2cgrQYEqPC2FBcaXWUoKI/5UHidK99zZxkq6OoIGMTYWVeEjuOtVDR0GV1nKChxT0IVLf0sHFvLbetzCUqXGe/qpl39uxEwkJEp97OIC3uQeCxD49jE7jrwnyro6ggFRsRxpWLM3hhd40uRzBDtLgHuM6+QZ7fVcM1y7LIjI+0Oo4KYretyqW9d5BXS+usjhIUtLgHuD+V1NDV7+Ar5+dZHUUFuTVzkpmTEs1TemJ1RmhxD2BOl+Hx7Sc4Z3Yiy7ITrI6jgpyI8OU1s/mkqo2dx3Vzt+mmxT1AbSiu4r6NZVQ29zAvLYYNxVV6MktZ7tZzc0mJsfPrd45YHSXg6dSJAPbR0SbiI8NYnBVvdRSlPu1cnJuXyGv7T/GzzQeZnRzNbatyLU4WmLTnHqBOtfdxrLGb1flJhNh0b1TlO1blJxMdHsK7ugXftNLiHqC2HW0iLER0qQHlc8JDbVw4L5UjDV1Ut/RYHSdgaXEPQE1d/eypbmNFTiJRdh15U75n1ZwkorT3Pq20uAegDcVVOFyG8+bqUgPKN9lDQ7igIIVD9Z06c2aaeLKH6u9FpEFE9o/xuojIr0WkQkRKReRs78dUnup3OHlieyWF6TGkxUVYHUepMZ03N4W4iFD+5dUDuFy67bK3edJz/wOw7gyvXwXMc9/WA7+Zeiw1Wa/sraOpq5/z56ZYHUWpMwoPtXHF4gz21rTzSmmt1XECzrjF3RjzPnCm35uuB54wQ3YACSKS6a2AynPGGB778DiF6TEUpMVYHUepcS3PSWDJrDgeeK1c15zxMm+Muc8Cqoc9rnE/9xkisl5ESkSkpLGx0QsfrYbbcayFg3Ud/PX5+Yjo9Efl+2wi/ODqRdS29/HYh8etjhNQvFHcR6siow6gGWMeMcYUGWOKUlNTvfDRarhHPzhGUnQ4N6wY9f9WpXzSmrnJfG5ROr/ZepTGzn6r4wQMbxT3GiBn2ONsQAfQZtihU528U97AHWvyiAgLsTqOUh7bUFzF0lnx9A44+epTu3SZDC/xRnHfCNzunjWzGmg3xuianjPst+6dlm5fM9vqKEpNWEqMnfMKktld2UpNq17Y5A3jXuEiIs8AlwApIlID/BgIAzDGPAxsBq4GKoAe4CvTFVZ91obiKlp7Bnhpz0nWzEnmtf2nrI6k1KRcOj+NT6ra2FRaxz9eOV/PG03RuMXdGPPFcV43wDe8lkhN2IdHmgA4v0CnPyr/FREWwpWL03lh90le2nOSv1iRbXUkv6ZXqPq57n4HJZUtLM9JICEq3Oo4Sk3JitxEshMj+flr5XT3O6yO49e0uPu57ceaGXQaLpyns4+U/7OJcM2yLOo7+nloS4XVcfyaFnc/1t47yLajTSzKjCNdlxpQASI3KYovnD2LRz84zommbqvj+C0t7n7s9x8ep2/QxWUL0qyOopRX3btuAeGhNv5p0wGro/gtLe5+qr1nkN9/eJxFmXFkJURaHUcpr0qLi+Duywt4p7yBLbos8KRocfdTj314jM5+B5cv1F67Ckx3npfPnJRo7t90gH6HrjszUVrc/VBbzwC//+gE6xZnkBmvvXYVeDYUV/H8rhouKkzleFM3dz+zR69cnSAt7n7o0Q+O09Xv4Ftr51kdRalpVZgey8LMOLaUN9DeO2h1HL+ixd3PNHQMrZ73+aWZLMyMszqOUtPu80szcRnDa/t1VZOJ0OLuZ37x1mEcLhf/eOV8q6MoNSOSosO5uDCV0pp2th1tsjqO39Di7ic2FFfxizcP89zH1azMS2Lb0WYdg1RB46LCVBKjwrhvYxmDTpfVcfyCFnc/8npZHfYwG5fqvHYVZMJCbHx+aRaH67t4fNsJq+P4BS3ufuJIfSeH67u4bH4aUeHjrvemVMBZmBnLJfNT+Y+3j9DQ0Wd1HJ+nxd0POJwuNu+vIyk6nNVzkq2Oo5QlRIT7rl3MgMPFTzcftDqOz9Pi7gce315JfUc/Vy3JIDREv2TzChk2AAAL/UlEQVQqeOWlRPN3F8/hpT21FB9rtjqOT9NK4eMaOvr45VuHKUyPYZFOfVRBbkNxFcnRdhKiwrj72U94cnulTiwYgxZ3H/fTzQcZcLi4dlmW7kyjFBAeauOapZnUd/SzQ3vvY/KouIvIOhE5JCIVInLvKK/fKSKNIrLHffsb70cNPjuONfPSnlq+evEckmPsVsdRymcszIxjXloM75TX06Obeoxq3OIuIiHAQ8BVwCLgiyKyaJSmzxljlrtvj3o5Z9AZcLj40cv7yU6M5GuXFFgdRymfIiJcvTST/kEXb+uqkaPypOe+EqgwxhwzxgwAzwLXT2+s4LahuIr/9cwnHK7v4tL5afz5k5NWR1LK56THRbAyP4mdx5s5XN9pdRyf40lxnwVUD3tc435upBtFpFREnheRHK+kC1Kt3QO8W17Posw4XT9GqTNYuzD90009jDFWx/EpnhT30c7ijfxXfAXIM8YsA94GHh/1jUTWi0iJiJQ0NjZOLGkQeaW0FoBrlmVanEQp3xZtD+WyBel8cKSJrYe0pgznSXGvAYb3xLOB2uENjDHNxph+98PfAeeM9kbGmEeMMUXGmKLUVN3QeTRvlp2i/FQnaxemkxAVbnUcpXze6jlJzE6O4oHXy3G5tPd+mifF/WNgnojki0g4cCuwcXgDERnexbwO0MvHJqGr38F9G8vIiIvgvLkpVsdRyi+E2mzc87lCyk91smmfLgt82rjF3RjjAL4JvMFQ0f6jMaZMRO4Xkevcze4WkTIR2QvcDdw5XYED2b+9Xk5dRx83LM8ixKZz2pXy1LXLsliQEcsv3jykq0a6iVUnIYqKikxJSYkln+2LdlW2cNPD27ljTR6F6bFWx1HK7xys6+DJHZX8xYpZnJuXBMBtq3ItTuV9IrLLGFM0Xju9QtUH9Duc/O8X9pEVH6mbcCg1SQsyYslJjOTd8gbtvaPF3Sc89G4FFQ1d/PQLS4m263K+Sk2GiHDF4gzaewfZebzF6jiW00pioQ3FVdS09vDwe0dZkZPAydZeXQRJqSmYmxrDnJRo3j/cyMr8JKvjWEp77hbqdzh57uNqYiPCuGZZltVxlAoIly9Mp7PfEfS9dy3uFtq0t46W7gH+siiHyPAQq+MoFRDyU6KZkzrUe+8bdFodxzJa3C3yyt5adlW1csn8VPJToq2Oo1RAuXzBUO/96SAe5tTiboGq5h6+/+d95CRGctmCdKvjKBVwTvfeH37vaND23rW4z7DeASfrnyxBgFvOzdWLlZSaJpcvSKexs5+ndlRaHcUSWtxnkDGGe18s5VB9J7/64gqSonXtGKWmS35KNBcUpPDQlgo6+gatjjPjtLjPoD9sO8HLe2q5Z20hl85PszqOUgHv3qsW0NozyMNbj1odZcbpPPcZsKG4ioqGLv6w7TgLM2JJjA7X+exKzYAls+K5YXkWj314nC+vmU1mfKTVkWaM9txnQG1bL08XV5Iaa+fmohxsutG1UjPmO1fMxxj45VuHrY4yo7TnPs1qWnt4fPsJIsJCuPO8fCLCdD67UjPl9G/IK/OT+FNJDRlxkdxzRaHFqWaG9tynUXNXP3f+18cMOl3ceV4e8ZFhVkdSKihdMj8Ve5iNV0prcQbJhh5a3KfJoVOdXP/QR1S39PCl1bNJj4uwOpJSQSsqPJSrl2RyvKmb374fHCdXtbhPgy3lDdz4m20MOFz88e/WMCclxupISgW9c2YnsmRWPL948zB7qtusjjPttLh7UUNnH7c+sp2//sPHxEWE8pXz8ymr7bA6llKKoSWB/2L5LNLjIrj7mU/oDPC571rcvaChs4//++YhLv7Xrew83sLK/CTWXzRXx9iV8jGR4SH86tbl1LT2cM8f99I7ELhLE3g0W0ZE1gG/AkKAR40xPx/xuh14AjgHaAZuMcac8G5U32GMoaa1l/ePNLJpbx3Fx5txGbj2rCwK02JIjrFbHVEpNYaivCR+eM0i7t90gJse3sZvv3wO2YlRVsfyunGLu4iEAA8BnwNqgI9FZKMx5sCwZncBrcaYAhG5FXgAuGU6As+0QaeLqpYejtR3caS+k7LaDnZVtdLY2Q9ASoydS+ansWxWPGl60lQpv/CV8/OZnRzFt57Zw3UPfsQDNy7jsgVpAbXW07gbZIvIGuA+Y8yV7sffAzDG/GxYmzfcbbaLSChwCkg1Z3jzmdwg2xjDgNPFgMNFv8NFT7+Tzv5BuvuddPQO0t47SEffIK09g7R2D9DSPUB9Rx8n23o51d7H8INIig4nNymK3KQo8lKiSY+1I3pRklJ+qamznyeLK2ns7Cc5OpwrFqdz0bxUMuIjSI21kxQdTniIjRCb+MzPuacbZHsyLDMLqB72uAZYNVYbY4xDRNqBZKDJs7iee33/Kb7zxz2fPj5deI0Bg3H/OfSCyxicZug5TwhDY3JR4aHERoSSGR/Bwsw4kqLCSYuzkxprxx6qFyEpFShSYu1889ICyk910tk3yMY9tTyzs3rUtmEhQwVeABEYujfEk7o/vMldF+RzzxXzpxZ+HJ4U99FijyyXnrRBRNYD690Pu0TkkAefP1kpTMN/LhbQ4/Atehy+xS+P4zvu2wieHstsTz7Dk+JeA+QMe5wN1I7RpsY9LBMPfGYDQ2PMI8AjngSbKhEp8eRXF1+nx+Fb9Dh8S6AcB3j/WDyZCvkxME9E8kUkHLgV2DiizUbgDvf9m4B3zzTerpRSanqN23N3j6F/E3iDoamQvzfGlInI/UCJMWYj8BjwpIhUMNRjv3U6QyullDozj+a5G2M2A5tHPPejYff7gJu9G23KZmT4ZwbocfgWPQ7fEijHAV4+lnGnQiqllPI/uvyAUkoFoIAp7iKSJCJvicgR95+Jo7SZLSK7RGSPiJSJyFetyHomHh7HchHZ7j6GUhHxuauBPTkOd7vXRaRNRDbNdMYzEZF1InJIRCpE5N5RXreLyHPu14tFJG/mU47Pg+O4SER2i4hDRG6yIqMnPDiOe0TkgPvn4R0R8Wi64Ezz4Di+KiL73DXqQxFZNOkPM8YExA34V+Be9/17gQdGaRMO2N33Y4ATQJbV2SdxHIXAPPf9LKAOSLA6+0SPw/3a5cC1wCarMw/LFAIcBea4v2f2AotGtPk68LD7/q3Ac1bnnuRx5AHLGFob6iarM0/hOC4Fotz3v+bHX4+4YfevA16f7OcFTM8duB543H3/ceCGkQ2MMQPGmH73Qzu++ZuLJ8dx2BhzxH2/FmgAUmcsoWfGPQ4AY8w7QOdMhfLQSqDCGHPMGDMAPMvQ8Qw3/PieBy4XX7k+/b+NexzGmBPGmFLAZUVAD3lyHFuMMT3uhzsYuh7H13hyHMPXCI9mlItBPeWLxW2y0o0xdQDuP9NGayQiOSJSytByCQ+4i6Mv8eg4ThORlQz1Anxte5kJHYePGW3JjVljtTHGOIDTS274Ek+Owx9M9DjuAl6b1kST49FxiMg3ROQoQ7/93j3ZD/OrDbJF5G0gY5SXfuDpexhjqoFlIpIFvCQizxtj6r2V0RPeOA73+2QCTwJ3GGNmvOflrePwQV5bcsNi/pDREx4fh4h8CSgCLp7WRJPj0XEYYx4CHhKR24D/w39fIDohflXcjTFrx3pNROpFJNMYU+cueg3jvFetiJQBFzL0a/WM8cZxiEgc8Crwf4wxO6Yp6hl58+vhY7y25IbFPDkOf+DRcYjIWoY6FhcPG371JRP9ejwL/GayHxZIwzLDl0C4A3h5ZAMRyRaRSPf9ROB8YDoXL5sMT44jHPgz8IQx5k8zmG0ixj0OHxYoS254chz+YNzjEJEVwG+B64wxvtqR8OQ45g17+HngyKQ/zeozyF48E50MvOP+x3gHSHI/X8TQ7lEwtOFIKUNnqUuB9VbnnuRxfAkYBPYMuy23OvtEj8P9+AOgEehlqGdzpdXZ3bmuBg4zdC7jB+7n7meoeABEAH8CKoCdwByrM0/yOM51/7t3M7SLWpnVmSd5HG8D9cN+HjZanXmSx/EroMx9DFuAxZP9LL1CVSmlAlAgDcsopZRy0+KulFIBSIu7UkoFIC3uSikVgLS4K6VUANLirpRSAUiLu1JKBSAt7kopFYD+P8vmTjYh5Yc3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_pred[:,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[173933, 189148],\n",
       "       [136749, 217843]], dtype=int64)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test,y_pred[:,1]>=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calibrated on tuned model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CalibratedClassifierCV(base_estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=7, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=300, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "            cv=TimeSeriesSplit(max_train_size=None, n_splits=2),\n",
       "            method='sigmoid')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "clf = RandomForestClassifier(n_estimators=300,max_depth=7)\n",
    "calibrated_clf = CalibratedClassifierCV(clf, method='sigmoid', cv=tscv)\n",
    "calibrated_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=calibrated_clf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.5485461670781893, True)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_score(y_pred[:,1]*2-1,y_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.6121033369983073, True)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=calibrated_clf.predict_proba(X_train)\n",
    "sigma_score(y_pred[:,1]*2-1,y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=y_pred[:,1]>=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[      0, 1280993],\n",
       "       [      0, 1341474]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=calibrated_clf.predict_proba(X_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=y_pred[:,1]*2-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f8569cdd30>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_cols = X_train.columns.tolist()\n",
    "# Note: y data is expected to be a pandas Series, as we will use its group_by function in `sigma_score`\n",
    "dtrain = lgb.Dataset(X_train.values, y_train,feature_name=train_cols,free_raw_data=False)\n",
    "dvalid = lgb.Dataset(X_test.values, y_test,feature_name=train_cols, free_raw_data=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'subsample': 0.75, 'reg_lambda': 1.2, 'reg_alpha': 1, 'random_state': 501, 'objective': 'binary', 'num_leaves': 12, 'n_estimators': 150, 'learning_rate': 0.005, 'colsample_bytree': 0.66, 'boosting_type': 'gbdt'}\n",
      "-0.6895867716593379\n"
     ]
    }
   ],
   "source": [
    "params = {'boosting_type': 'gbdt',\n",
    "          'max_depth' : -1,\n",
    "          'objective': 'binary',\n",
    "          'nthread': 3, # Updated from nthread\n",
    "          'num_leaves': 64,\n",
    "          'learning_rate': 0.05,\n",
    "          'max_bin': 512,\n",
    "          'subsample_for_bin': 200,\n",
    "          'subsample': 1,\n",
    "          'subsample_freq': 1,\n",
    "          'colsample_bytree': 0.8,\n",
    "          'reg_alpha': 5,\n",
    "          'reg_lambda': 10,\n",
    "          'min_split_gain': 0.5,\n",
    "          'min_child_weight': 1,\n",
    "          'min_child_samples': 5,\n",
    "          'scale_pos_weight': 1,\n",
    "          'num_class' : 1,\n",
    "          'metric' : 'binary_error'}\n",
    "\n",
    "# Create parameters to search\n",
    "gridParams = {\n",
    "    'learning_rate': [0.005],\n",
    "    'n_estimators': [40,150],\n",
    "    'num_leaves': [6,8,12],\n",
    "    'boosting_type' : ['gbdt'],\n",
    "    'objective' : ['binary'],\n",
    "    'random_state' : [501], # Updated from 'seed'\n",
    "    'colsample_bytree' : [0.65, 0.66],\n",
    "    'subsample' : [0.7,0.75],\n",
    "    'reg_alpha' : [1,1.2],\n",
    "    'reg_lambda' : [1,1.2,1.4],\n",
    "    }\n",
    "\n",
    "# Create classifier to use. Note that parameters have to be input manually\n",
    "# not as a dict!\n",
    "evals_result = {}\n",
    "\n",
    "mdl = lgb.LGBMClassifier(boosting_type= 'gbdt',\n",
    "          objective = 'binary',\n",
    "          n_jobs = 3, \n",
    "          silent = False,\n",
    "          max_depth = params['max_depth'],\n",
    "          max_bin = params['max_bin'],\n",
    "          subsample_for_bin = params['subsample_for_bin'],\n",
    "          subsample = params['subsample'],\n",
    "          subsample_freq = params['subsample_freq'],\n",
    "          min_split_gain = params['min_split_gain'],\n",
    "          min_child_weight = params['min_child_weight'],\n",
    "          min_child_samples = params['min_child_samples'],\n",
    "          scale_pos_weight = params['scale_pos_weight'])\n",
    "\n",
    "# To view the default model params:\n",
    "mdl.get_params().keys()\n",
    "\n",
    "# Create the grid\n",
    "grid = RandomizedSearchCV(mdl, gridParams,\n",
    "                    verbose=0,\n",
    "                    cv=tscv,\n",
    "                    n_jobs=2,scoring='neg_log_loss')\n",
    "# Run the grid\n",
    "grid.fit(X_train,y_train)\n",
    "\n",
    "# Print the best parameters found\n",
    "print(grid.best_params_)\n",
    "print(grid.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chinn\\Anaconda3\\lib\\site-packages\\lightgbm\\engine.py:113: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 30 rounds.\n",
      "[25]\tvalid_0's binary_logloss: 0.692633\tvalid_0's sigma_score: 0.0795633\n",
      "[50]\tvalid_0's binary_logloss: 0.691774\tvalid_0's sigma_score: 0.273122\n",
      "[75]\tvalid_0's binary_logloss: 0.691024\tvalid_0's sigma_score: 0.397076\n",
      "[100]\tvalid_0's binary_logloss: 0.690331\tvalid_0's sigma_score: 0.477671\n",
      "[125]\tvalid_0's binary_logloss: 0.689849\tvalid_0's sigma_score: 0.52121\n",
      "[150]\tvalid_0's binary_logloss: 0.689309\tvalid_0's sigma_score: 0.551341\n",
      "[175]\tvalid_0's binary_logloss: 0.688996\tvalid_0's sigma_score: 0.562054\n",
      "[200]\tvalid_0's binary_logloss: 0.688644\tvalid_0's sigma_score: 0.572705\n",
      "[225]\tvalid_0's binary_logloss: 0.688447\tvalid_0's sigma_score: 0.573533\n",
      "[250]\tvalid_0's binary_logloss: 0.688145\tvalid_0's sigma_score: 0.579904\n",
      "[275]\tvalid_0's binary_logloss: 0.687972\tvalid_0's sigma_score: 0.579298\n",
      "Early stopping, best iteration is:\n",
      "[254]\tvalid_0's binary_logloss: 0.688098\tvalid_0's sigma_score: 0.580737\n"
     ]
    }
   ],
   "source": [
    "x_1 = [0.19000424246380565, 2452, 212, 328, 202]\n",
    "\n",
    "params_1 = {\n",
    "        'task': 'train',\n",
    "        'boosting_type': 'gbdt',\n",
    "        'objective': 'binary',\n",
    "        'learning_rate': 0.005,\n",
    "        'num_iteration': 500,\n",
    "        'n_estimators':150,\n",
    "        'colsample_bytree': 0.66, \n",
    "         'subsample': 0.75,\n",
    "        'verbose': 1,\n",
    "         'metric':None,\n",
    "    'reg_lambda': 1.2,\n",
    "    'reg_alpha': 1,\n",
    "    'random_state': 501,\n",
    "    'objective': 'binary',\n",
    "    'num_leaves': 12\n",
    "    }\n",
    "\n",
    "\n",
    "gbm_1 = lgb.train(params_1,\n",
    "        dtrain,\n",
    "        num_boost_round=1000,\n",
    "        valid_sets=dvalid,\n",
    "        feval=sigma_score_2,\n",
    "        early_stopping_rounds=30,\n",
    "        verbose_eval=25\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2880x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xrhxIzFC+bds2tm3bhiRatWpFo0aNdjveM5S7PeHr9DgH/wIuIKyYfAUwEjgjYf//I+TM+hK4HOgft/8BuNXMvgAwsy2EBQl3EZdI/6ukzsB5wPi8KuJZ1lNHKra7oDbnzlC+dOlSbrzxRk4//fR8y/UM5a6wFP49di41SdoI/AS4G7iKkGaiNyGY6RSP+TfwZ0LQM8ZCxnUkfQ0cY2bfJil3GPCahezr2dsGA6vN7MFcxyYmHD3l7sG7xU3l2hHV4MvvS7sWJS8V211Qm1vU3zXB5saNG7nrrrvo1asXxxwTOlkvv/xynnzyyaTJOOfOncvzzz/PoEGDirTe+2Ljxo05PVeppKTb3b59+1lm1rqg47ynx6U8M5snqRGhl+eNxH2SjgCOB94xM5O0XVKamS3Yi0sl7Xc3s6eApwCaNGliv7vy4r0oev+VmZnJpXGZ/lSSiu3emzbPmjWLdevW0a1bNwCqVq1Ku3btqF279m7HZmRkMHjwYNLS0pLuLw2JaShSSVltt8/pcS6YADxEGNpKdBlwCLBM0nJC5vXL476FwCl7cI1WhFxezrk8fPXVV6xfvx6A77//nn//+980bdo0z+OXLl1K9ojFhx9+yA8//MBhhx1WInV1+x8PepwLngXuNbP5ubZfAZxrZo3MrBEhyMkOevoDf5F0JICkKpJ65S5YQS+gLjCxuBrgXHmwevVq2rdvT3p6OqeeeiodOnSgU6dODBkyhAYNGrBq1SrS09NzntIaO3YsaWlptGzZkhtvvNEzlLt8+fCWc4CZrQIeSdwWh7waEub5ZB+3TNIGSaeb2Rtx+OvfCv/KGiF4yjZQ0l3AgbGM9mb2Q/G2xLn9W2KG8kS9evWiV6/d/k/hGcrdHvGgx6U0M9ttpp2ZZQKZ8eNua+uY2ckJ7/8B/CPJMV2Lqo7OOeeKhg9vOeeccy4leNDjnHPOuZTgQY9zzjnnUoIHPc4555xLCR70OOecKxYrV66kffv2NGvWjObNmzNmTFigfO7cubRt25YWLVpw4YUXsmHDBgDefvttTjnlFFq0aMEpp5zCpEmTSrP6rhzyoMfttyQ9I+nE0q5HNkm/SKyPpExJBS6L7lx5ValSJQYNGsRHH33EjBkzGD9+PIsWLaJHjx4MGDCA+fPn07lzZwYOHAhA7dq1efXVV5k/fz7PPfccV199dSm3wJU3/si622+ZWY/SrkMuvwBeAxbtbQGecDR1lPd2Lx9wAXXr1qVu3boA1KxZk4YNG/L555+TlZXFmWeeCUCHDh0455xz6NevH61atco5v3nz5mzZsoWtW7dSpUqVUmmDK3+8p8ftFyRVl/S6pLmSFki6LLEnRdJGSffH/TPiooF5lTVM0t8kTZb0qaSzJD0r6aOYKDT7uCskzY/XezBh+27XkvQT4CLCgoRzJB0XD/+lpPclLZZ0Bs6lqOXLl7N06VJOP/100tLSmDBhAgCjR49m5cqVux0/duxYWrVq5QGPK1KeZd3tFyT9P0I6iOvj51rAeEI29JmSDLjIzF6V9Bdgg5ndl0dZw4CqhBQTFwHDgXaEXFofANcBawirKJ8CfAO8BQwxs1fyulbuzOqSMoFZZvZ7SecDt5jZz5PUx7Osp1i2cSj/7U7MmP79999z00030aVLFzp27Mhnn33Go48+yrfffku7du14+eWXGT9+fM7xy5Yt48477+Qvf/kL9evvtj7ofsWzrJeMwmZZx8z85a8y/wIaA8uAB4Ez4rZMoHV8v5Ufg/jLgGfyKWsYcGV8fyywJGHf84RhqouB5xO2Xwc8nN+1YrldEs7JBNrF90cASwtqZ+PGjS3VTJ48ubSrUCpSpd0//PCDdezY0QYNGpS0zVlZWXbqqafmfF65cqWdcMIJ9s4775RgLYtPqvyccyvpdgMzrRDfJT685fYLZraY0OsyH+gv6e5ch2yLv/gAOyh4vtrW+OfOhPfZnysB+WUs3JNrbS3kcc6VO2bGddddR7Nmzbjllltytq9ZswaAnTt3ct9999GzZ08A1q9fzwUXXED//v1p165dqdTZlW8e9Lj9gqR6wGYzGwE8BJxcwCn76j3gLEm1JVUkDIVNKeCc74CaxVwv5/Yb7777LsOHD2fSpEm0bNmSHj168MYbbzBy5EgaN25M06ZNqVevHt26dQPgscceY+nSpfTr14+WLVvSsmXLnADJuaLg//N0+4sWhEnCO4FtwK8JwU+xMLPVkv4ATCb0+rxhZuMLOG0U8LSkXkCX4qqbc/uLn/70p/zYKQqZmZlkZGQAcNNNN+12/J133smdd95ZUtVzKciDHrdfMLM3gTdzbc5I2F8j4f0YYEw+ZXVNeL8cSMtj34vAi0nOT3otM3sXSFw3KLF+a4FGedXJOedc8fPhLeecc86lBO/pceWWpD8Bv8y1ebSZ3V8a9XHOOVe6POhx5VYMbjzAcc45B/jwlnPOuX2UO7HoI488AsCcOXNo06YNLVu2pHXr1nz00UcAvPDCC6Snp5Oens5PfvIT5s6dW5rVdynEg54ySFJvSQeWwHUOkDRY0ieSlkgaL6lBMVwnQ9K3kmbHVA99i6jcRpJMUr+EbbUlbZP0WPw8TNJuT1IV5T2W1FTSdElbJd2aa9+5krIkLZV0R1Fcz7myJndi0ccff5xFixZx22230bdvX+bMmcO9997Lk08+CcAxxxzDlClTmDdvHnfddRf/93//V8otcKnCg55SoiCv+98b2KMvZEl7M1T5AGFdmcZmdgLwCvCypPwW5ttb08ysFdAauErSKYk797L+AJ8CnRI+/5KQTqIged7juC7Pnvga6EWuR+hjOY8D5xGe6rqiLGWFd66o1K1bl5NPDktn1axZk2bNmvH5558jiQ0bNgDw7bffcthhhwHwk5/8hEMOOQSANm3asGrVqtKpuEs5PqenBElqBPyLsPZLW2CwpJ5AFeAToBvQHagHTJa01szaS9qY/Zh07LXoZGZdY66nr4FWwIeSvgMaElIrNAQGm9kQSdWBfwINgIpAP+DVeL1jzGwHgJn9Q1J34GxJnwATCYv0tQIWA9eY2eYYsDwM1ADWAl3jujaZ8fj2wMHAdWY2LfEemNkmSbOA4yS1AC4g5MGqHq/bB7g03pNxZtY3JvtcYWZPxHtwD2EhwLHA98BHklqb2UxCWoh/xnuY+/73A44CPkx2j2ObzgF+L+n7PNrYC+gJbAcWmdnlZrYGWCPpglyXPI2QeuLTeP1RhPQWeWZh9yzrqaO8tHv5gF1/7ZcvX87s2bM5/fTTGTx4MOeccw633norO3fuZNCgQbudP3ToUM4777ySqq5LcR70lLwmhGDjbuBl4OcxELidkJDyXkm3AO3j2i4FaRzL2BGDgaaEoKMmkCXpb8C5wBdmdgHkJOs8HvjMzDbkKm8m0JwQhDUhBC7vSnoW+I2kR4BHgYvN7CtJlxEmC3eP51cys9Nigs2+wC4JNiUdBrQhBF6nEoK/dDP7WlJH4ARCsCBggqQzCYv+DQaeiMVcGtuU3VM2Crhc0v8I6R6+IFfQExOD1gK6mZklucfVgQVmdrekyoTVl5O18Q5CoLhV0sF5/1gAqA8kpo9eBZye+6BcCUe5u8X2AootX46oFgKAVFNe2p2ZmZnzPjuxaI8ePfjwww8ZMmQI1113HWeddRaTJ09mwIABHHnkkTnHz549m0cffZQhQ4bsUk55snHjxnLbtvyU1XZ70FPyVpjZDEmdCEMe78bRpAOA6XtR3ujsnprodTPbCmyVtIaQ6HI+8FDsMXnNzKbFISxLUl7i9pVxwT2AEYQhnImExfzejvWuCKxOOP/l+Ocsdl2M7wxJswm5rQaY2UJJpwJvm9nX8ZiO8TU7fq4BnGBmQyXViakoDge+MbPPYs8ZsU79gC+Bl5K06S7gPTPLb+LADkLPEYRgL682zgNekPQKYTgwP8mGCXe752b2FPAUQMNjj7dB81Prr+XvW2wn1doM5afdy6/MAGDbtm106tSJnj175uTZuvjiixk7diySOOuss3jooYdyVmSeN28ejz32GG+//TaNGzcupdoXv8RVqFNJWW33/v83bv+zKf4pwhf+FYU4J/GLsmoe5WVLTJ65g9DzsjgOSZ1PSNb5FjAIOFpSTTP7LuGckwlDX7mvm/1ZwEIza5tHXfNKsDnNzDolOT6x/gL6m9mTSY4bQ0jtcCShZ+fHSpn9EIfMfk/opbow17kfAKdIOjQhwMptS0LwmF8bLwDOBC4C7pLU3Mzy+u/6KsJwWrYGhF6oPFWrXJGsAblHycq3zMzMnC/OVFKe2p1XYtF69eoxZcoUMjIymDRpEvXr1wfgs88+45JLLmH48OHlOuBxZY8HPaVnBvC4pOPNbGl8kqhBzCaenbgye+jlS0nNgCygc9xfaLGH5GszGxHnrnSNQ2rPAQ9L6hmHx64hTO6dBBwNNJTU1symExJuvhPrcHj29jgU1NjMCjN5uCBvAv0kvWBmGyXVJ2Q0X0PMawXUBs5Kcu4gYIqZrUsyD3tiLPt1SR1jkJf7HidK2kbgI+AoM5ss6R3gV4TeqPV5tOcD4ARJxwCfA5fHc5wrV7ITi7Zo0YKWLVsC8MADD/D0009z0003sX37dqpWrcrvf/97AO69917WrVvHb37zGyA8/TVz5sxSq79LHR70lJI4V6QrMFJSlbj5TsKE4aeAf0labWbtCfNIXiPMD1lA+KLdE8mSdQL8gfDE0eK472Ogc5zzAuFL/lpJTwJLgL/FXpUuwJA4N6gSYb7NPgc9ZvZWDO6mx+tvBK4C1sThsJrA52a2Osm5C/Org5mNjudPiPONct/jxGPzauNiYETcJuCvZrZe0pGEuVAHATsl9QZONLMNkn5LCLgqAs8WUXDoXJmSO7FoolmzZuW8z57j8cwzz/DMM8+URNWc24Xy+kV1qS3Ol3nNzNIKONQVoSZNmlhWVlZpV6NEldWx/+KWiu32NqeOkm63pFlm1rqg43ydHuecc86lBB/eckmZ2XLCE0zOOedcueA9Pc4555xLCR70OOeccy4leNDjnHPlQF6Zzu+66y7S09Np2bIlHTt25IsvwlJRH3/8MW3btqVKlSo89NBD+RXtXLnhQY9zzpUDeWU679OnD/PmzWPOnDl06tSJe++9F4BDDz2UIUOGcOutt5ZyzZ0rOaUe9EjqHRfmK+7rVJY0QNISSQskvS/pvLhvuaTaRXitYZKWSZoj6UNJea1evKfl3iPp81juIkmFWc25oDKbSpouaaukW3PtO1dSlqSlku4ooJxMSQU+LlgU9Yr7K0qaLem1orpmIep0T3ZdJN0r6ecFneNcSckr0/lBBx2Uc8ymTZvIXryzTp06nHrqqVSuXLlU6utcaSiRp7dinieZ2c4ku3sT8jpt3oPyKuWz9H9e+gF1gbSYLPIIkq/sW1T6mNmYmETzSSA9cedetgHCgngPSToBmCVpjJlt24d6fk3IqfWLXPWrCDwOdCCkU/hA0gQzyzNDeBFLWq8ENxEWTzwoj/3FyszuLo5yPct66ijKdueX6RzgT3/6E88//zy1atVi8uTJRXJN5/ZHxRb0xMXt/gVMJmTSHiypJ1CFkMG7GyFrdT1gsqS1ZtZe0kYzqxHL6AJ0MrOukoYRvghbAR9K+g5oCBwb/xxsZkMkVQf+SchzVJEQ7LwKXE/Mjg1gZl/G43LX+xZ+zBj+jJkNTlammb0U81k9TFgheS0hvUPu1YKnEjKaIykT+C/QjrAy8PPA32P9IQSA04FPgZZmtj6etzSek8PMlkjaDBwCrJF0PSFT9wHAUuBqQh6sJcBxhAzjXwMZZjZV0jRCxvGl8fzcCZ9OA5aa2aexDqOAi4ECgx6FzO6nAtWAMWbWN24/P96vtcCHwLF55OMipp5IVi8kNSDkwLofuCX3/lzHLgdeJGSer0y4R/0JP5OBZvb3eFwfQvb2KsC4hDr/CbiGsBr2V4REqsTfx9diYHs3Id9XNcLP94a4qnUm8F689sGEjPXTktTRs6ynWJuhaNudX6ZzgA4dOtChQwdeeOEFbr31Vrp165Zz/PLly6lWrVqJZMQuq5m3i1MqthnKcLvNrFhehAzbO4E2hHxJU4Hqcd/twN3x/XKgdsJ5GxPedwEMr51hAAAgAElEQVSGxffDCKkYKsbP9xC+YKrE8tcRvtT+H/B0Qhm1CL0ss/Op6/JYximEjOTVCYHMQkKQlazMyvH6h8dtlxHSDGTXtUt8/0tChm+ATOCJhHJeBH4a3zcEPorvHyEEJACnA/9OaPOt8f3JhCSe2WUdlvD+PuB38f1EQhLOToRcUH+K92xZrnuQU3bCvX8m4fPVwGP53MNMoHV8f2j8s2Lcnk5IlLqSEHgCjCQEDQX9Hu1Sr7htTPxZZRRURvzZ/jq+/yshS3pNQrb2NXF7R0JaChGGfF8jJBXN/n04kNCjtDTh/if+jA9NuN5w4MKEezIovj8/++eY36tx48aWaiZPnlzaVSgVxdHuH374wTp27GiDBg1Kun/58uXWvHnzXbb17dvXBg4cWOR1SSYVf9ap2Gazkm83MNMKEZsU9/DWCjObIakTcCLwbhxPPoDQo7GnRtuPmbABXrfQc7NV0hrgCMKX1EOSHiR8IU5LkoAyLz8l/C9/E4Ckl4EzCIFD7jLTCIv3vR3Lrwgk9vIMlHQnoXfguoTtLyW8/zlwYkL9Dor5oV4C7gb+QUhSmXjOzbFX51jg3ITtaZLuI/Qo1CDkewKYRvgCP4bQw3E9MIUQAOUn2U0rbM6SS2PvRSXCkOKJhGDiUzNbFo8ZSezd2BPxd2mNmc2SlFHI0ybEP+cDNSwmHJW0RdLBhKCnIzA7HlcDOIEQHI0zs83x2hNIrr2k2wjB0aGEYDk7U/3L8c9ZhP8IOFcsLI9M50uWLOGEE04AYMKECTRt2rS0quhcqSvuoGdT/FPA22ZWmIm3iV+sVfMoL9vWhPc7gEpmtjgOO50P9Jf0FiGpZkNJNeMXXl6SRkd5lDkOWGhmeU1S7mNmY5JsT2xDBaCtmX2/SyWk6cDxkg4nzGm5L2F39pyeS4DnJR1nZlsIPQ+/MLO5ColMM+Lx04CehGHEu4E+cd/UPOqdbRVwVMLnBsAXBZyDQkbxW4FTzeybOAxUlTzu7V5oB1wUh8qqEgLFEWZ2VT7nZP+e7GTX35mdhL8DAvqb2ZOJJ8XEofkGepKqAk8QerlWSrqHXX9vs6+3A18B3RWjvDKdDx06lKysLCpUqMDRRx/N3//+dwD+97//0bp1azZs2ECFChUYPHgwixYt2mXis3PlTUk9vTUDaCcpe27LgZIax33fEf5Hne1LSc0kVQA67+mFJNUDNpvZCEKwc3L8n/pQQtbsA+JxdSXl/qKcCvwi1q96vP60ZGUCWcDh2U9mKTwd1nwPq/sW8NuEurcEiF114wjzXz4ys3W5TzSzlwmZva+Nm2oCqyVVBq5MOPQ94CfAzhgczQFuIARD+fkAOEHSMfGeXc6PPSb5OYgQ2H0bJ4ufF7d/DBwb53pBGA7cY2b2BzNrYGaNYp0mFRDwFMabQHdJ2XPJ6kuqQ/h96CypWuyBuzDJudkBztp4fpd9rItzeyU703n24+lz5szh/PPPZ+zYsSxYsIB58+bx6quvUr9+fQCOPPJIVq1axYYNG1i/fj2rVq3ygMeVeyXyP08z+yr2PoyUVCVuvhNYTJhL8S9Jq82sPXAHYU7FSmABYahhT7QgDC3tBLYBv0643n3AIklbCF/MuzyBY2Yfxp6J9+OmZ8xstqRzcpdpZj/EidZDJNUi3MvBhKGNwuoFPC5pXjx/KqFXBsKQ1gdA13zOvxd4UdLTwF2EAGcFYRinZmzTVkkrCYEnhGDningMko4kBE8HATtj78aJZrZB0m8JAUFFwnylAtsWe5pmE+7Dp8C7cfv3kn4DTJS0lh/vcVL51augOuwpM3tLUjNgehxq3AhcFX8fXiIEiitIEiia2fp4/+cT5g8VNGzonHOulCh0KjhX/CTVMLONCpHF48ASM/traderLGnSpIllZWWVdjVKVGZmJhkZGaVdjRKXiu32NqeOkm63pFlmVuBacaW+OKFLKddLmkPoBapFWL/IOeecKxF7PLwl6RDgKDObVwz1cWWcpHGEJ8ES3W5mbyY7PlHs1dmlZ0dSN8JCg4neNbMbS6pezjnnUkOhgp64yNpF8fg5wFeSpphZvgvDufLHzPZ4cnkB5f2D8Gj+vpZTpPVyrrg9+OCDXHrppdSpU4cFCxYAMHfuXHr27MnGjRtp1KgRL7zwAgcddBAvvPACAwcOzDl33rx5fPjhhzlPaTnnCqeww1u14gTSS4B/mNkphDVmXBmgkstfdoCkwZI+UchhNj6ujlzU18mQ9K1Cbq2PJPUtonIbSfpeP+Yuez4+7bav5T4raY2kBbm2Hyrp7Xiv3o69pM4BcO655zJx4sRdtvXo0YMBAwYwf/58OnfunBPoXHnllTlPZA0fPpxGjRp5wOPcXihs0FNJUl3CMv0lluDR/UhBXj+v3oSF8fakvL15cu8BwlNhjc3sBOAV4GWp8Ks/7oFpZtYKaA1cFddJyrGX9Qf4xMxaEp7ya0D4nd5Xw9h1ochsdwD/iffqP/GzcwCcdNJJHHroobtsy8rK4swzzwRC6oixY8fudt7IkSO54op9zjXsXEoq7BfHvYRHl981sw8kHUvI6eSKkcpe/rJuhDQSOyAMTUnqDpwt6RPCytXvxfIXA9eY2WblkaNMhchNZWabJM0CjpPUgpBzqyohVcjZSpIzS2Hl7BVm9kS8B/cQ1oMam1DuDknvA/UT7vXwWC7Ab83sv5KeACaa2YQ4b+gbM+su6bp4L+60kMusUZIf4cX8uEjkc4S0FLcnOS6HJxwt/3InB02UlpbGhAkTuPjiixk9ejQrV67c7ZiXXnqJ8ePHF2cVnSu3ChX0mNloYHTC508J+ahc8WtCCDbuJqQ0+HkMBG4HbjGzexWSpLY3s7WFKK9xLGNHDAaaEoKOmkCWQrLQc4EvzOwCgLgO0fHAZ0nWyZlJyO31SazrdWb2rqRngd9IegR4FLg4rtd0GSFRaHZS10pmdprCCst9yTVsKukwQv62foQkpm2BdDP7WiGD/QmE5KgiJHE9ExhFWDPpiVjMpbFNFRLKrUrIa5Y9iXoN0MHMtihksB9J6GWaSkhFMoEQINWNx/80Xic/R1hMQBuDvDrJDpInHE2phKPZSRg3btzIjBkz2LRpU862nj17ct9999GnTx/atWtHhQoVdknauGjRIsyMtWvXls1kjgUos0koi1EqthnKbrsLO5G5MfA3wj/iaZLSgYvM7L4CTnX7rqzkLxPJUzIkbl9pZu/G9yMIiy9OJP8cZXnlpjojLnK4ExhgZgslnUpIZ/J1PCZpziwzGyqpjsJK2ocTemc+i70xx8XH5k8gZIDPfgqxMvCYwqrYOwjBIYQFCXtLOpGQYf6QONTbNrZvn5nZU4RFOml47PE2aH5qZav4fYvtpFKbl1+ZAYTgJy0tjerVq++ynsk111wDwOLFi1m4cOEu+8aPH0+PHj3223VfUnHNmlRsM5Tddhf2X5qnCTmbngQws3mSXmTXnFCueJSV/GWDgKO1e/6yk/kxuWbuoMhivfPLUZZXbqppZtYpyfGJ9U+aMysaQ0gJcSS79sh8YmYtY+CSKekiM5sA3Ax8CZxE6BHaAmBmn8cJyOcSen0OJfQcbSwgjxuElCp1Yy9PXUJvUr6qVa5IVj7DH+VRZmZmTiCQ6tasWUOdOnXYuXMn9913Hz179szZt3PnTkaPHs3UqQWlzXPO5aWwE5kPNLPcaQNSpz+6bCjt/GWbCPNSHpZUMR53DWEC9aR4akPFXGSEVBfvUDQ5yvKSV84sCIHO5YTAZ7fEr3HY6Q7gD3FTLWC1me0Erib0SGWbTpgsPpXQ83MrBecugzAklp0b7VrAJ2K4HP369aNt27ZkZWXRoEEDhg4dysiRI2ncuDFNmzalXr16dOvWLef4qVOn0qBBA4499thSrLVz+7fC9vSslXQc8X/ycXLs6vxPcUWpjOQv+wMhCFoc930MdDYzi0NXHwHXSnqSMNH9b0WUoyypvHJmAWvicFhN4PPseTVJvALcI+kMwvyfsZJ+SZg4ntijNA3oaGZLJa0g9PbkBD2SRhImLNeWtAroa2ZDgQHAP+Ok58+AX+5rm135cddddyXt/r/pptxrdQYZGRnMmDEj6T7nXOEUKvdWfFrrKUK27m+AZcCVZraieKvn9hdxvsxrZpZWylXZr3nurdSRiu32NqeOspp7q8CenjhE0trMfh4fZa5QiLkMzjnnnHNlSoFzeuIch9/G95s84HHJmNly7+VxzjlXlhV2IvPbkm6VdFRcWv9QSYcWfJpzzjnnXNlQ2InM2QvJJWa+NsJKvs4555xzZV6henrM7JgkLw94nHOlrnv37tSpU4e0tB9HV/v06UPTpk1JT0+nc+fOrF+/HoD333+fli1b0rJlS0466STGjRtXWtV2zpWCQgU9kq5J9iruyjnnXEG6du26W7byDh06sGDBAubNm0fjxo3p378/EHJbzZw5kzlz5jBx4kRuuOEGtm/3JcecSxWFndNzasLrDOAe4KJiqpNzu5H0TEwFUWZI+kVZq1MqOvPMM3fLVt6xY0cqVQqj923atGHVqlUAHHjggTnbt2zZQlzfyTmXIgqbcPR3iZ/jInPDi6VGziVhZj1Kuw6JJFUCfkFYBHLRnpxnZnl2LXiW9cLLL1t5omeffZbLLrss5/N7771H9+7dWbFiBcOHD88Jgpxz5d/e/m3fTEjY6FyRi+tB/RNoQEgH0Y+wKvStZjZT0kbgEaAT8D0hg/uXeZR1DPAi4Xd9InCzmdWQlBHL6xSPewyYaWbDJN0NXAhUA/4L3BBXnc6Mn9sBbxF6O8+SdCfw/+IlHyckOd0MXG9mH0saBnwNtAI+BH6fq46eZX0v2pyYwfl///vfLtnKs40YMYL169dTv379XfY9/vjjrFixgj/+8Y9Ur16dAw44YC9rv/fKahbq4uRtTh1ltt1mVuCLkFByQny9BnwKPFiYc/3lrz19EQKIpxM+1wIyCYtkQnhy8ML4/i/AnfmUNQG4Jr6/kZAoFELaiNcSjnsM6BrfH5qwfXjCtTKBJxL2DQO6JHz+DyHLO8DpwKSE414DKhbU9saNG1uqmTx58j6XsWzZMmvevPku24YNG2Zt2rSxTZs25XleRkaGffDBB/t8/b1RFO3e33ibU0dJt5vwn9YCv18K29PzUML77cAKM1tVyHOd21PzgYckPUgITKblmnvxAyGIAJgFdMinrHb82AszHHiwENdvL+k2QjLVQwl5wrIzyb+U7ISY9PQnwOiEulZJOGS0me0oxLVdEZg4cSIPPvggU6ZM4cADD8zZvmzZMo466igqVarEihUryMrKolGjRqVXUedciSps0HO+md2euEHSg7m3OVcUzGyxpFOA84H+kt7Kdci2GNkD7KDg3+NkCea2s+tE/qoAkqoSko+2NrOVku7J3hclJiJNVAFYb2Yt89if13luH11xxRVkZmaydu1aGjRowJ///Gf69+/P1q1b6dAhxMNt2rTh73//O++88w4DBgygcuXKVKhQgSeeeILatWuXcguccyWlsEFPByB3gHNekm3O7TNJ9YCvzWxEnL/TdR+Kexe4HBgBXJmwfQVwYsxYXxX4GfAOPwY4a2PvTRdgTB5lfwfUBDCzDZKWSfqlmY1W6O5JN7O5+1B3VwgjR47cbdt1112X9Nirr76aq6++urir5Jwro/J9ZF3SryXNB5pImpfwWgbMK5kquhTUAnhf0hzgT8B9+1DWTcCNkj4gzA0CwMxWEiZLzwNeAGbH7euBpwlDbK8AH+RT9iigj6TZko4jBFXXSZpLGBK7eB/q7ZxzrogV1NPzIvAvoD9wR8L278zs62KrlUtpZvYm8GauzRkJ+2skvB9D3j0xmNkyoG325/ikVfa+24DbkpxzJ3Bnku0ZuT6/C+Rep+fcJOd1zat+zjnnSk6+QY+ZfQt8C1wBIKkOofu/hqQaZvZZ8VfROeecc27fFTYNxYWSlgDLgCnAckIPkHNlgqQ/SZqT6/Wn3Mcl9hI555xLLYWdyHwf0Ab4t5m1ktSe2PvjXFlgZvcD95d2PZxzzpVdhc29tc3M1gEVJFUws8lAXo/mOudcoY0ZM4a0tDSaN2/O4MGDc7Y/+uijNGnShObNm3PbbbtNvXLOuT1W2KBnfXx8dxrwgqRHCOucuBIiqbekAws+cp+vU1nSAElLJC2Q9L6k8+K+5ZKKbFETScPiY95zJH0oqW3BZxWq3HskmaTjE7bdHLe1jp83JjmvkaRfFUUdYnm/lbQ0XtcXg0liwYIFvP7667z//vvMnTuX1157jSVLljB58mTGjx/PvHnzWLhwIbfeemtpV9U5Vw4UdnjrYkKOo96Ex3JrAfcWV6VSVVzbRWa2M8nu3oS1ZjbvQXn5JrfMQz+gLpBmZlslHQGctYdl7Ik+ZjZGUkfgSSA9cedetgHCI+eX8+Pj7l0oODFoI+BXhKcWd7GX9XiXsHJ0ZmFPSKWEo8sHXMBHH33EiSeemLNq8llnncW4ceOYOXMmd9xxB1WqhEWt69SpU5pVdc6VE4Xq6TGzTcBRQIaZPQc8Q0gF4PZR7F34SNIThGSUV0uaHns+RkuqIakXUA+YLGlyPG9jQhldYlLL7N6Th+NxD8Zej2clZUr6NJaFpOqSXpc0N/boXBZ7kq4HfmdmWwHM7Esz+2eSet8Sz1sgqXdeZcbtp0iaImmWpDcl1U1yK6YCx8fjMyU9IGkKcJOkwyWNlfRBfLWTVCH2PB2cUKelMUiDsMbOxXH7sYSnEL9K0o7a8X5fAAwAzog9TzdL6hp/Bq8SEowiqU+swzxJf86v3WY228yW5/sLkOLS0tKYN28e69atY/PmzbzxxhusXLmSxYsXM23aNE4//XTOOussPvggv+WSnHOucArV0yPpekIW6EOB44D6wN8Jq9i6fdcE6AbcDbwM/NzMNkm6HbjFzO6VdAvQ3szWFqK8xrGMHQppFJoC7QmrB2dJ+hthPZkvzOwCAEm1CEHHZ2a2Ib/CFVJEdCMk1RTwXgxQjs1dpqTKwKOETOhfxYDgfqB7rmIvJPTOZDvYzM6K5bwI/NXM3pHUEHjTzJpJGg90Bv4h6XRguZl9GTrM2ACslJRGCH5einVObMcRhISkd5rZ25I2sWvm9a6ENX7Szezr2Bt1AnBabPcESWcSsqrnvpeFphTNsp6dgblz5860bduWatWqcfTRR/O///2Pb7/9lvnz5zNgwAA+/vhjLrroIl588UW0aw62/VqZzUJdjLzNqaOstruww1s3Ev6hfw/AzJYorNnjisYKM5shqRNhsbt34z/uBwDT96K83MktX489N1slrQGOoOCknvn5KTAu9gAi6WXgDGBikjLTgDTg7Vh+RWB1QlkDFRYM/ApIzB2QmNjz54SUEdmfD5JUMx5zN/APwlBW7mSgo+L2cwgBemLQU5mQFf1GM5uST1vfTliIs2N8zY6faxCCoGm5251Pebsxs6eApwCaNGliv7sy9RZyHjJkCAB//OMfadCgAd999x29evUiIyOD9u3b89BDD5GWlsbhhx9eyjUtOpmZmWRkZJR2NUqUtzl1lNV2Fzbo2WpmP2R/6UiqRPIkjm7vZCejFOFLtjDLASTe/6q59uVObrk14f0OoFIeST0fAhpKqmlm3+Vz7aTRUR5ljgMWmllek5T7xFWVc0tsQwWgrZl9v0slpOnA8ZIOB37B7ukqXgUGAjNjbqzEfdsJGdrPIaw9lZfEegjob2ZP5j4od7vNzOe8FdI333wDwGeffcbLL7/M9OnTqVChApMmTSIjI4PFixfzww8/eGJQ59w+K+zTW1Mk/RGoJqkDMJrwheKK1gygneJTR5IOlNQ47stJbhl9KamZpAqEIZ49opDUc7OZjSAEOyeb2WZgKDBE0gHxuLqSrsp1+lTgF7F+1eP1pyUrE8gCDld8Mkvh6bDme1jdt4DfJtS9JUDMtD4OeBj4KC6rkCMGSbeTfP0eIwyxNZWUnWIl9z3O7U2gu8KTjEiqL6lOHu12hdS3b19OPPFELrzwQh5//HEOOeQQunfvzqeffkpaWhqXX345zz33XLka2nLOlY7C9vTcQRh6mA/cALxBmMzsilCc89IVGKmQ/RtCDqjFhOGPf0labWbtCT+T14CVwALCUMueaEEYWtoJbAN+nXC9+4BFkrYQejruzlXPDxUmTr8fNz1jZrMlnZO7zNhD2IUQSNUi/M4NJiTkLKxewOOS5sXzpwI9476XCElBuyY70cxG5VVonPN0OfCqpA2ERKPbFRKGDgO+yXX8W5KaAdPjF/BG4CrCXKjd7qXCpPHbgCOBeZLeMLMee9DulDBkyJDdusEPOOAARowYUToVcs6VWwr/Wc5jp9TQ82s5V3KaNGliWVlZpV2NElVWx/6LWyq229ucOkq63ZJmmVnrgo4raHjrlYQCx+5zrZxzzjnnSklBQU/iIPqxxVkR55xzzrniVFDQY3m8d84555zbrxQ0kfmkOMFThCe3shetE+HhmYOKtXbOuf1GVlYWl112Wc7nTz/9lHvvvZf169fz9NNP56yx88ADD3D++eeXVjWdcyks354eM6toZgeZWU0zqxTfZ3/2gKeMU8klKT1A0mBJnygkKh0vqUExXCdD0reSZiuk7uhbROU2kvS9QvqJRZKejytJ72u5z0paI2lBUdSzrGvSpAlz5sxhzpw5zJo1iwMPPJDOncNqCjfffHPOPg94nHOlpbDr9LgySkFeP8fewB4FPXHhyT31AGF9m8ZmdgJhAvzLKp6FVaaZWSugNXBVXBQwx17WH+ATM2tJeJS/AXDpvlUTCI+9n1sE5ex3/vOf/3Dcccdx9NFHl3ZVnHMux95+QbhSJKkR8C9gMiE31GBJPYEqwCeEdAvd+TFJ6Vozay9po5llL6zXBehkZl3jmjtfA62ADyV9BzQkTF5vCAw2syFxIcJ/EoKCioSM7K/G6x2TnfrCzP4hqTtwtqRPCOkp3ovlLwauMbPNMWB5mLDG0Fqgq5mtlpQZj28PHAxclzu1Q8xNNgs4TlIL4ALCytTV43X7EAKXKoSUGX1jmogVZvZEvAf3EBYkHJtQ7g5J7xPyy2Xf6+GxXIDfmtl/FRLETjSzCZLGAd+YWXdJ18V7caeZTY3nF9r+mmV9+YALdvk8atQorrjix4XFH3vsMZ5//nlat27NoEGDOOSQQ0q6is4550HPfqysJymdCTQnBGFNCIHLu5KeBX4j6RHyT0RaycxOk3Q+0JeQfyuHpMOANoTA61QKlxh0FGFhxCdiMZfGNlVIKLcqIZHqTXHTGqCDmW2RdAIwktDLNJWQb2wCIUDKzhz/03idQlM5SDiamFhw27ZtjB07lk6dOpGZmUl6ejpDhw5FEs8++yy/+tWvuP3223OOL6uJCYtbKrbb25w6ymq7PejZf5WVJKUi+ZN9idtXmtm78f0IwgrLE8k/EenL8c9ZQKOE7WdImg3sBAaY2UJJp1KIxKBmNjQhbcThhN6Zz2JvzHGS5hCCpTFmNi+eWxl4LKa+2EEIDiEkGe0t6URgEXCIpLqE4KtXkvuRp8SEow2PPd4Gzd///louvzIj5/348eM5/fTTueSSS3Y77thjj6VTp067LFrmi7elDm9z6iir7d7//nV12cpKktJBwNHaPUnpyfyYny13UGSx3vklIs2+/g52/T2dZmadkhxfqMSgwBigCyE1RGKPzCdm1jIGLpmSLjKzCcDNwJfASYQeoS0AZva5pEMIPUVTgUMJPUcbC0jWmq9qlSuSlWuoaH8zcuTIXYa2Vq9eTd26oSNs3LhxpKWllVbVnHMpzicy7/9KO0npJuA54GFJFeNx1xAmUE+KpzZUTDgKXAG8Q9EkIs1L0sSgcd8o4HJC4LNbdnczW03Ia/aHuKkWsNrMdgJXE3qksk0nTBafSuj5uTX+mbI2b97M22+/vUsvz2233UaLFi1IT09n8uTJ/PWvfy3FGjrnUpn39OznykiS0j8QgqDFcd/HQGczszh09RFwraQngSXA34ooEWlS+SQGXROHw2oCn8cAJ5lXgHsknUGY/zNW0i8JE8cTe5SmAR3NbKmkFYTenpygR9JIIAOoLWkV0NfMhu5r+8qyAw88kHXrdkl2z/Dhw0upNs45t6t8E446t6/ifJnXzMzHNArBE46mjlRst7c5deyvCUedc84558oFH95yxcrMlhOe0nLOOedKlff0OOeccy4leNDjnHPOuZTgQY9zrtDWr19Ply5daNq0Kc2aNWP69On06dOHpk2bkp6eTufOnVm/fn1pV9M555LyoKeMKcHM6JUlDYhZ0RdIel/SeXHfckm1i/BawyQti1nMP0xYs2dfy71HkmWvURS33Ry3tY6fNyY5r5GkXxVFHWJ5v5W0NF63dsJ2SRoS982TdHJRXbO03HTTTZx77rl8/PHHzJ07l2bNmtGhQwcWLFjAvHnzaNy4Mf379y/tajrnXFI+kbkUxNQNigve5dabkKph8x6UV8nM9jRhUz9Cvqg0M9sq6QjgrD0sY0/0MbMxMS/Wk0B64s69bAOE1BiXA/fFz10IaSHy0wj4FfBi7h17WY93CesfZebafh4hrcUJhHxef4t/5qmsJhxdPuACNmzYwNSpUxk2bBgABxxwAAcccAAdO3bMOa5NmzaMGbPbmo/OOVcmeE9PCYm9Cx/F7NwfAldLmh57PkZLqiGpFz9mRp8cz9uYUEYXhYzo2b0nD8fjHoy9Hs9KypT0aSwLSdUlvS5pbuzRuSz2JF0P/C7m18LMvjSzfyap9y3xvAWSeudVZtx+iqQpkmZJejOmdMhtKiFJKbGuD0iaAtwk6XBJYyV9EF/tJFWIPU8HJ9RpaQzSICwkePH/b+/e46yu6v2Pv96hqXkBDY0CaUANJYHxbkePoYXhJS9H+ylyzhEvlRc86cnbLwsr62iimam/LhreKqFME82D8kNR40gIOgOaUYT0EztZyd8AABu+SURBVDURUkCEQvz8/lhrw57NngswzN4z+/18PHjM7LW/l7W+jM6H9f3u9c7t/YGlwBtlxtEzX+9jgGtIGV4NeWZoVP47eBB4NG9/Se7DbEnfaGncEfFc/pRaqeOBuyKZDvRo5pp0CvPnz2fnnXfmjDPOYJ999uHss89mxYqm6SXjxo3jqKOOqlAPzcxa5pmejlXtyehNKOVsnUGanRDwu1yg9C89pqQtaTk1veCzpNmZgh4R8cl8nJ8DN0TEbyX1BR6JiL0kPUCKzbhd0kHAgoh4PU2YsQx4WdLepCJjQu5z8Tg+REpD/2pETJa0Ari4kOGltKJ1W1Lady5zLVvSm7T6dcHC3NZkJWh1gpT1qVOnMnfuXGbNmsWoUaMYNWoUN910E+eeey5nnpn+in/605/y1ltv0bt37w1KV67WNObNrRbH7THXjmodt4uejlUtyehtPf6hwP05XwtJ9wH/TEpILz3m3rScmj5W0ldJszBnFbVPKPr+08DAov7toBQZMYFUKN5OupVVvA+sy9P6DPApmhY9WwJTgPMj4okWxtpqSjspYqLJuFs4HqSCqdR6S6AXp6wPGDAgLhh5fCuHrYw999yTq6++mvPOOw+Abt26cc011zB06FDuvPNOXnjhBaZMmcIHPrBhj6R5xdra4THXjmodt4uejlUtyejXkUJAS5PRS5Wtjpo55v20nJp+SUSUe9ijeAzvAz4RESubdEJ6Gthd0s7ACax7fqfgQWAsMDMilpUUde8Cs0gFUUtFT5tS2kvHHRHfbOGYC4Fdi173AV5tYfuq1qtXL3bddVfmzp3LgAEDmDJlCgMHDmTSpEl85zvf4YknntjggsfMrCP5mZ7KqHQy+jvAT0hhn+/P231Y0r+W7P4kcELu37b5/E+VOybtk5r+KDC6qO/1AJEC4u4Hvgu8GBFNEi1zkXQZ6XZaqSDdYttT0uW5rfQalyqb0t7MuFsyEfh3JQcDS1sIOe0UbrrpJkaOHMngwYNpaGjgK1/5CqNHj2b58uUMGzaM+vp6zjnnnEp308ysLM/0VECVJKN/lTRj8ntJq0gzHWNK+vms0oPTM3LTbRHxnKTPlB6znVLT/wO4RdLsvP+TQOE36ATgGWBUuR0jYnxzB83PPJ0KPChpGXAr8K6kRuAO4M2S7ZtLad+9dNwASg+NXwr0AmZLejgizgYeJs0KzSN9Gq/Js0adUX19PTNnzmzSNm/evAr1xsxswzhl3ayKOGW9dtTiuD3m2uGUdTMzM7MKctFjZmZmNcFFj5mZmdUEFz1m1qy6ujoGDRpEfX09++/f9Hb5ddddhyQWL27LOppmZpXnT2+ZWYsef/xxevZsmj/78ssvM3nyZPr27VuhXpmZbbiqnemR08Y35Lhfl/RKPu7vJbVl0cPWjrmnUlbV3yVdXPLecElzlTKwLm/uGHnbqcqJ5+1BKROrIf95XtIaSTu11/FbOffasUh6WEV5YLXmoosu4tprr2UDVvc2M6u4is70SE4bp/3Sxm+IiOsk7QHMknRvRKzehH7+jbRuzgkl/esG3AIMI604/IykiRHRWrJ5u4iIsaTVl5H0WeCioviIDhMRR2+O41ZDyvqCa45Z+70kjjzySCTxxS9+kS984QtMnDiR3r17M2TIkAr20sxsw3V40SOpDvhv4HFSyOP3JJ0DbAX8mbSA25msSxtfHBGHS3o7Igor5J4MHBsRo/LieX8D9gGelbQc6EsKxewLfC8ivp9XFP4FKQqgG6nYeZCUNt6vOG08b1fa7/9kXXjmbRHxvXLHjIgJOargu6SFBBcDo8qsxNskbRz4H+AQUrjlXcAPc/8hFYBPA/OB+oh4K+83L++zVkT8SdI7wI7AIkmfJ4VZvp+0SN6/keIq/gTsBnTP129oRDwp6SngjIiYl/c/hqYOBOZFxPzch/GkoM9Wix6lANQDgG2AeyPiytx+dL5ei0kJ9P0LYaCtGAHc08L56kg5Yb8FDgYaSfld3wB2AUZGxIz893gTaSHHLYCvR8QDkrbJ2w8EXsz9Lhx7AbB/RCyW9GtS3MTWwI05SwtJbwM3AscCK0lhrK+X6WdVBY4WhwSOHTuWnj178uabb3LxxRezcuVKfvjDHzJ27FimTp3KqlWrmDZtGt27t5a92rxqDSbc3Gpx3B5z7ajWcVdqpsdp4+2fNl7o677AnyJiUW66LyJuze99CzgrIm6S9EfSL/N+pGyqf5b0O6BPLniaUy45/KCWrl+RK3KKeTdgiqTBpFWofwQcFhEvSWq2iCmWb30Opyi2ohm7A58jFRXPAKeRglSPA75Cmsm6AngsIs7Mt6xmSPq/wBdJsRODc1+fbeYcZ+ZxbUOa+fpVjsrYFpgeEVdIupZUYJfmhjUJHO3bf/e4fk5lH7VbMHJo2fbGxkaWLVvGkiVLGD06XfbFixdzwQUXMGPGDHr16rVR5/PibbXDY64d1TruSv3f1Wnj7Z82flGe1elPKgYK9s7FTg/SzNMjuf0p4DBS0XM16RfyE6TCoCVtSg5vxv/KsxpbkG4pDiQ9VzY/Il7K29xDnvVoxWeBaW24tfVSRMwBkPQCMCUiQtIcoC5vcyRwXNGzS1uTZtkOA74PEBGzleIxyvkPSYVctF1JiexLgH+QIkQgFZbDWhvUNlt2Y+41pZNrlbFixQree+89tt9+e1asWMGjjz7KmDFjWLRo0dpt6urqmDlz5noPOpuZVaNKFT1OG1/fpqaNF57p+RfgLkm7RcQqUrbUCRHRqJT3NTRv/xQp1+ojpELqkvzek830u2CjksMl9QMuBg6IiDfzbcmtaebatsGptHBrq0jxz8J7Ra/fY93Pv4CTIqJJ/kMuOlss6CQNJRWpn4iId/KtysLP5+pYl/Oyhk72acnXX3+dE09Mtdy7777LaaedxvDhw1vZy8yselX601tOGy9vo9LG8zb3ATOB03PT9sBr+bbbyKJNfwf8E/BeLo4aSLdznmqlb88Ae0jql6/ZqaQ08dbsQCrsluaHxY/K7X8A+ufnbwBOae1A+dbkJ4EH2nDetngEuEC5ypG0T25/knzN8gze4DL7dgfezAXPnqRnh7qE/v3709jYSGNjIy+88AJXXHHFetssWLDAszxm1mlU9F+eThtv1kanjWffBH4u6Vbga6QC5y+kW3zb5zH9XdLLpMITUrEzIm+DpF6k4mkH4D1JFwIDI2KZpNGkQqEbMC4iWh1bnml6jnQd5gPTcvtKSecBkyQtZt01bsmJwKOF243t4CrS39HsXPgsID18/APS81OzSUVhub5NAs7J28xl3fU0M7Mq45R1qzhJ20XE27nguIX0IPYNle5XJThlvXbU4rg95trhlHWz5n1eUgNpFqg76dNcZmZm7apTPVhp1UvS/aRPghW7LCIeKbd9sTyr02RmR9IZwJdKNp0WEeeXOfcHgSllDv2pcs89mZlZbXLRY+0iIjb44fJWjnc76aP5bdl2CVDfnuc3M7Oux7e3zGytNWvWsM8++3DssWlB7ClTprDvvvtSX1/PoYceyrx5La1baWZW3Vz0dGFyaOuGHLdDQ1ur1Y033shee+219vW5557Lz372MxoaGjjttNP41rfWW1DazKzTcNHTySlp7u/xQmCDih5JG3PLszi0dW/Sasnbt7zLJrkkIupJyxis99DzRo4B0gKP9aQssR/ltY02RSG09bpNPE6HWLhwIb/5zW84++yz17ZJYtmylNKydOlSPvKRj1Sqe2Zmm8zP9HRCcmhrQWcNbW1WJVLWC6nqF154Iddeey3Ll69bnPy2227j6KOPZptttmGHHXZg+nQvQ2RmnZeLns7Loa2dN7S19NpUNGV96tSpPP3006xevZrly5fT0NDAkiVLmDp1KmPGjOGqq65i4MCBjB8/nhEjRnDJJZe06/mrNY15c6vFcXvMtaNax+2ip/NyaGvnDW1tojhlfcCAAXHByOM3ZPd28cgjjzBr1ixGjRrFqlWrWLZsGWPHjuWVV17hvPPOA1IsxfDhw9t9wTEv3lY7PObaUa3j9jM9nVdpaGt9/jMwIs5qZp9NDm0F9iMVP1dLGkO63dM3FxQtaTa0tcwxRQptLYxpUEQcWbTbJbl9WEQ838wYCqGthWP0zqGypaGt9xXtc0NEDCDlf90lqXCN7gBGR8Qg4Busu3ZPkQq3A4GHSUXRUFoPba06V199NQsXLmTBggWMHz+eI444ggceeIClS5fyxz/+EYDJkyc3ecjZzKyzcdHT+Tm0tbxqDm3tFLbYYgtuvfVWTjrpJIYMGcLdd9/N2LFjK90tM7ON5ttbnZxDW5tV1aGtGzCODjd06NC109InnngiJ57YrutOmplVjANHzaqIA0drRy2O22OuHQ4cNTMzM6sgFz1mZmZWE1z0mJmZWU1w0WNWA1atWsWBBx7IkCFD+PjHP86VV14JwFlnncWQIUMYPHgwJ598Mm+//XaFe2pmtvm46OkCHCy6Qcft0GBRScMlzZU0T9Llm3qujbXVVlvx2GOP0djYSENDA5MmTWL69OnccMMNNDY2Mnv2bPr27cvNN99cqS6amW12Lno6CQeLdr5gUUndgFuAo0irZo+QNHATz7VRJLHddmmFgtWrV7N69WokscMOOwAQEaxcuZINWGHbzKzT8To9VUwOFi3orMGiBwLzImJ+7sN4UqH1+9JrVtDegaOFMFGANWvWsN9++zFv3jzOP/98DjroIADOOOMMHn74YQYOHMj111/fbuc2M6s2numpfgOAu4BhpJypT0fEvqRF7/4zIr4PvEoKFj28DccrBIt+Ob/eE/gM6Rf0lXnWoxAsOiTP6Exi44JFDwY+L2mfcscsChY9OSL2A8aRgkVLlQ0WjYjrgRtJMzcHACeRiqz3gEKwKMXBoiV9LRcsekBEDAFeJAWLriEt9DiQlB9WCBbditaDRXuTFoIsWJjbKqJbt240NDSwcOFCZsyYwfPPpwSP22+/nVdffZW99tqLCRMmtHIUM7POyzM91c/Bop03WLTcRVtvNVBtxpT15lKO6+rquOWWWzjllFPWtn3sYx/jxz/+Mf369Wu387dFtaYxb261OG6PuXZU67hd9FS/0mDRtjx4u8nBonnG5mhSCOijpOdV+kraPgd3NqfZYNEyx7yfFCza3EPKl0TEvWXaywWLrmzSCak0WPRbRW/fEBHXSfoXUrDobjk76w7ghIhozNEeQ/P2T5EiLD5CKqQuoW3BoguBXYte9yHNyjXRESnrb7zxBltuuSU9evRg5cqVfO1rX+PSSy+lT58+7L777kQEDz30EIccckiHrx7rFWtrh8dcO6p13L691Xk4WLS8ag4WfQbYQ1K/fM1OBSZu4PjaxWuvvcbhhx/O4MGDOeCAAxg2bBjHHHMMp59+OoMGDWLQoEG89tprjBkzpvWDmZl1Up7p6SQcLNqsqg4WlTSadJusGzAuIjZkbO1m8ODBPPfcc+u1T5s2rQK9MTOrDAeOmlURB47Wjloct8dcOxw4amZmZlZBLnrMzMysJrjoMTMzs5rgosfMzMxqgoses07ozDPPZJdddmHvvfde773rrrsOSSxevLgCPTMzq14uesw6oVGjRjFp0qT12l9++WUmT55M3759y+xlZlbbXPRYlyIpJN1d9HoLSW9Ieqhkuwfyqs2tHe8GSQ35zx8lFQJM6/K5riratqek1ZJuLjlGo6R7Nn106xx22GHstNNO67VfdNFFXHvttU5LNzMrw4sTWlezgpShtU2OphgGvFK8gaQepBWh35bULyJeau5gEXFR0X4XkBLqC+YDx5IWNQT4HCULK0rai/SPi8MkbVvIJGtOW1LWi5PTi02cOJHevXszZMiQFvc3M6tVLnqsK/pv4BjgXtLKyfeQQk8LTgIeBF4nRUNc3cbjjgCuLHq9EnhR0v4RMRM4BfgFKaOr4DTgbmAv4LjclyY2NHC0EOL317/+lRUrVjB16lRWrVrFZZddxtixY9e+njZtGt27d2/j0CqnWoMJN7daHLfHXDuqddwueqwrGg+Mybe0BgPjaFr0jAC+QSp67qUNRY+kj5JS1h8rc65TJf2VFNj6Kk2LnlNIs00DSBlh6xU9xYGjffvvHtfPafk/ywUjh6avCxaw7bbbMnToUObMmcOSJUsYPTrFkC1evJgLLriAGTNm0KtXr9aGV1FesbZ2eMy1o1rH7aLHupyImC2pjlTcPFz8nqQPAbsDv42IkPSupL0j4vlWDnsqcG9ErClpnwRcRSqgJpSc6wDgjYj4i6SFwDhJO0bEm82dZJstuzG3mdtXLRk0aBCLFi1a+7quro6ZM2fSs2fPDT6WmVlX5QeZrauaSEpzL51ZOQXYEXhJ0gKgjlTQtObUMsciIv4BzAK+DPyq5O0RwJ75PH8mBZKe1NYBtGTEiBF84hOfYO7cufTp04ef/OQn7XFYM7MuzTM91lWNA5ZGxBxJQ4vaRwDDI+JpAEn9gMmkBPmyJA0gFUrNfdrreuCJiFhS+NSUpPeRHmweHBGv5LbD83lu24RxAXDPPS1/GGzBggWbegozsy7HMz3WJUXEwoi4sbgt3/LqC0wv2u4lYJmkg1o43AhgfEREM+d6ISLuLGk+DHilUPBkTwIDJX24zQMxM7N245ke61IiYrsybVOBqfll7zLv79vKMb9epm0BsN5yyBFxB3BHfnlwyXtrABc8ZmYV4pkeMzMzqwme6TEDJF1Began2C8j4tuV6I+ZmbU/Fz1mQC5uXOCYmXVhvr1lZmZmNcFFj5mZmdUEFz1mZmZWE1z0mJmZWU1QM+utmVkFSFoOzK10PzpYT2BxpTtRAbU4bo+5dnT0uD8aETu3tpE/vWVWXeZGxP6V7kRHkjSz1sYMtTluj7l2VOu4fXvLzMzMaoKLHjMzM6sJLnrMqsuPK92BCqjFMUNtjttjrh1VOW4/yGxmZmY1wTM9ZmZmVhNc9JiZmVlNcNFjVgUkDZc0V9I8SZdXuj/tSdI4SYskPV/UtpOkyZL+lL/umNsl6fv5OsyWtG/ler7xJO0q6XFJL0p6QdKXcnuXHbekrSXNkNSYx/yN3N5P0u/ymCdIen9u3yq/npffr6tk/zeFpG6SnpP0UH5dC2NeIGmOpAZJM3Nb1f98u+gxqzBJ3YBbgKOAgcAISQMr26t2dQcwvKTtcmBKROwBTMmvIV2DPfKfLwA/6KA+trd3gS9HxF7AwcD5+e+0K4/778ARETEEqAeGSzoY+A5wQx7zm8BZefuzgDcjYnfghrxdZ/Ul4MWi17UwZoDDI6K+aD2eqv/5dtFjVnkHAvMiYn5E/AMYDxxf4T61m4h4EvhbSfPxwJ35+zuBE4ra74pkOtBD0oc7pqftJyJei4hn8/fLSb8Qe9OFx537/nZ+uWX+E8ARwL25vXTMhWtxL/ApSeqg7rYbSX2AY4Db8mvRxcfcgqr/+XbRY1Z5vYGXi14vzG1d2Yci4jVIBQKwS27vctci38LYB/gdXXzc+TZPA7AImAz8GXgrIt7NmxSPa+2Y8/tLgQ92bI/bxfeAS4H38usP0vXHDKmgfVTSLElfyG1V//PtGAqzyiv3L71aXUuiS10LSdsBvwIujIhlLfyjvkuMOyLWAPWSegD3A3uV2yx/7fRjlnQssCgiZkkaWmgus2mXGXORQyLiVUm7AJMl/aGFbatm3J7pMau8hcCuRa/7AK9WqC8d5fXC9Hb+uii3d5lrIWlLUsHzs4i4Lzd3+XEDRMRbwFTS80w9JBX+gV08rrVjzu93Z/3boNXuEOA4SQtIt6WPIM38dOUxAxARr+avi0gF7oF0gp9vFz1mlfcMsEf+xMf7gVOBiRXu0+Y2ETg9f3868EBR+7/nT3scDCwtTJd3Jvk5jZ8AL0bEd4ve6rLjlrRznuFB0jbAp0nPMj0OnJw3Kx1z4VqcDDwWnWy13Ij43xHRJyLqSP/dPhYRI+nCYwaQtK2k7QvfA0cCz9MJfr69IrNZFZB0NOlfiN2AcRHx7Qp3qd1IugcYCvQEXgeuBH4N/ALoC/w/4HMR8bdcLNxM+rTXO8AZETGzEv3eFJIOBZ4C5rDuWY+vkJ7r6ZLjljSY9PBqN9I/qH8REd+U1J80C7IT8BzwrxHxd0lbA3eTnnf6G3BqRMyvTO83Xb69dXFEHNvVx5zHd39+uQXw84j4tqQPUuU/3y56zMzMrCb49paZmZnVBBc9ZmZmVhNc9JiZmVlNcNFjZmZmNcFFj5mZmdUEFz1mZh1A0pqcSF34U7cRx+gh6bz2793a4x8n6fLWt2zXc57QxQJ2rYr5I+tmZh1A0tsRsd0mHqMOeCgi9t7A/brliIiqklclvo00pntb295sU3mmx8ysQnJA51hJz0iaLemLuX07SVMkPStpjqTj8y7XALvlmaKxkoZKeqjoeDdLGpW/XyBpjKTfAp+TtJukSTkg8ilJe5bpzyhJN+fv75D0A0mPS5ov6ZOSxkl6UdIdRfu8Len63NcpknbO7fWSpudx3S9px9w+VdJ/SXoCuAw4Dhibx7SbpM/n69Eo6VeSPlDUn+9L+p/cn5OL+nBpvk6Nkq7Jba2O12qPA0fNzDrGNkoJ5AAvRcSJwFmkJfkPkLQVME3So6RE6hNzSGlPYLqkicDlwN4RUQ9rVwFuyaqIODRvOwU4JyL+JOkg4P+QsqJasmPe5jjgQVLW1NnAM5LqI6IB2BZ4NiK+LGkMacXt0cBdwAUR8YSkb+b2C/Nxe0TEJ3O/9qBopkfSWxFxa/7+W/ka3ZT3+zBwKLAnKdrgXklHAScAB0XEO5J2ytv+eCPGa12cix4zs46xslCsFDkSGFw0a9Ed2IMU0Phfkg4jxVj0Bj60EeecAGvT3v8J+KXWJb1v1Yb9H4yIkDQHeD0i5uTjvQDUAQ25fxPy9j8F7pPUnVTYPJHb7wR+WdqvZuydi50ewHbAI0Xv/Toi3gN+L6lwPT4N3B4R7wDk2IONHa91cS56zMwqR6TZkEeaNKZbVDsD+0XEaqUU763L7P8uTR9TKN1mRf76PuCtMkVXa/6ev75X9H3hdXO/P9ryoOiKFt67AzghIhrzdRhapj+Qrl3ha+k5N3a81sX5mR4zs8p5BDhX0pYAkj6mlFrdHViUC57DgY/m7ZcD2xft/xdgoKSt8uzKp8qdJCKWAS9J+lw+jyQNaacxvI91ieKnAb+NiKXAm5L+Obf/G/BEuZ1Zf0zbA6/lazKyDed/FDiz6NmfnTbzeK0Tc9FjZlY5twG/B56V9DzwI9IMys+A/SXNJP3i/wNARCwhPffzvKSxEfEyKdV6dt7nuRbONRI4S1Ij8AJwfAvbbogVwMclzSI9M/PN3H466QHl2UB9UXup8cAlkp6TtBvwNVIa/WTyuFsSEZNIz/fMzM9MXZzf2lzjtU7MH1k3M7ONpnb4KL5ZR/FMj5mZmdUEz/SYmZlZTfBMj5mZmdUEFz1mZmZWE1z0mJmZWU1w0WNmZmY1wUWPmZmZ1YT/DwVDiG9nl/jSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40,20))\n",
    "lgb.plot_importance(gbm_1,max_num_features=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=gbm_1.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.6859160729981825, True)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_score(y_pred*2-1,y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=gbm_1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.5807365552305024, True)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_score(y_pred*2-1,y_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f8875b84e0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_pred*2-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = lgb.LGBMClassifier(task= 'train',\n",
    "        boosting_type= 'gbdt',\n",
    "        objective='binary',\n",
    "        learning_rate= 0.005,\n",
    "        num_iteration= 500,\n",
    "        n_estimators=150,\n",
    "        colsample_bytree= 0.66, \n",
    "        subsample= 0.75,\n",
    "        verbose =1,\n",
    "         reg_lambda= 1.2,\n",
    "    reg_alpha= 1,\n",
    "    random_state= 501,\n",
    "    num_leaves= 12\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chinn\\Anaconda3\\lib\\site-packages\\lightgbm\\engine.py:113: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n",
      "C:\\Users\\chinn\\Anaconda3\\lib\\site-packages\\lightgbm\\engine.py:113: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CalibratedClassifierCV(base_estimator=LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=0.66,\n",
       "        importance_type='split', learning_rate=0.005, max_depth=-1,\n",
       "        min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
       "        n_estimators=150, n_jobs=-1, num_iteration=500, num_leaves=12,\n",
       "        objective='binary', random_state=501, reg_alpha=1, reg_lambda=1.2,\n",
       "        silent=True, subsample=0.75, subsample_for_bin=200000,\n",
       "        subsample_freq=0, task='train', verbose=1),\n",
       "            cv=TimeSeriesSplit(max_train_size=None, n_splits=2),\n",
       "            method='isotonic')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calibrated_clf = CalibratedClassifierCV(clf, method='isotonic', cv=tscv)\n",
    "calibrated_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ytrainpred=calibrated_clf.predict_proba(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ytestpred=calibrated_clf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrainpred=Ytrainpred[:,1]\n",
    "ytestpred=Ytestpred[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.6237107583636623, True)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_score(ytrainpred*2-1,y_train1.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.5677100719773357, True)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_score(ytestpred*2-1,y_train1.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBClassifier(learning_rate=0.1, n_estimators=300, objective='binary:logistic',\n",
    "                    silent=True, nthread=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=xgb.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=xgb.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.48366598850149906, True)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_score(y_pred[:,1]*2-1,y_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.8113740249829478, True)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=xgb.predict_proba(X_train)\n",
    "sigma_score(y_pred[:,1]*2-1,y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=y_pred[:,1]>=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[601906, 679087],\n",
       "       [447189, 894285]], dtype=int64)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBClassifier(objective='binary:logistic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chinn\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:271: UserWarning: The total space of parameters 9 is smaller than n_iter=10. Running 9 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  % (grid_size, self.n_iter, grid_size), UserWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 9 candidates, totalling 18 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed: 167.0min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(-0.6870596950771145, {'n_estimators': 50, 'max_depth': 2})"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomizedSearchCV(xgb,{'max_depth': [2,4,6],\n",
    "                    'n_estimators': [50,100,200]}, \n",
    "                    verbose=1, \n",
    "                    scoring='neg_log_loss',\n",
    "                   cv= tscv,n_jobs=-1\n",
    "                     )\n",
    "\n",
    "clf.fit(X_train,y_train)\n",
    "clf.best_score_, clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=clf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.5466463282705594, True)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_score(y_pred[:,1]*2-1,y_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.6633845835678877, True)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=clf.predict_proba(X_train)\n",
    "sigma_score(y_pred[:,1]*2-1,y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=y_pred[:,1]>=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[      0, 1280993],\n",
       "       [      0, 1341474]], dtype=int64)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_train,y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calibrated on XG BOOST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb = XGBClassifier(objective='binary:logistic',n_estimators=50,max_depth=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CalibratedClassifierCV(base_estimator=XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=2, min_child_weight=1, missing=None, n_estimators=50,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1),\n",
       "            cv=TimeSeriesSplit(max_train_size=None, n_splits=2),\n",
       "            method='sigmoid')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "calibrated_clf = CalibratedClassifierCV(xgb, method='sigmoid', cv=tscv)\n",
    "calibrated_clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ytrainpred=calibrated_clf.predict_proba(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ytestpred=calibrated_clf.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "ytrainpred=Ytrainpred[:,1]\n",
    "ytestpred=Ytestpred[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.5896574335387855, True)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_score(ytrainpred*2-1,y_train1.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.549996122565269, True)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_score(ytestpred*2-1,y_test1.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_cols = X_train.columns.tolist()\n",
    "\n",
    "# Note: y data is expected to be a pandas Series, as we will use its group_by function in `sigma_score`\n",
    "dtrain = lgb.Dataset(X_train.values, y_train1,feature_name=train_cols, free_raw_data=False)\n",
    "dvalid = lgb.Dataset(X_test.values, y_test1,feature_name=train_cols,free_raw_data=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgb_params = dict(\n",
    "    objective = 'regression_l1',\n",
    "    learning_rate = 0.005,\n",
    "    bagging_fraction = 0.75,\n",
    "    bagging_freq = 2,\n",
    "    feature_fraction = 0.5,\n",
    "    num_iteration= 500,\n",
    "    n_estimators=150,\n",
    "    colsample_bytree= 0.66, \n",
    "    subsample= 0.75,\n",
    "    verbose =1,\n",
    "    reg_lambda= 1.2,\n",
    "    reg_alpha= 1,\n",
    "    random_state= 501,\n",
    "    num_leaves= 12,\n",
    "    lambda_l1 = 0.5,\n",
    "    lambda_l2 = 1.0,\n",
    "    metric = 'None', # This will ignore the loss objetive and use sigma_score instead,\n",
    "    seed = 0 # Change for better luck! :)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chinn\\Anaconda3\\lib\\site-packages\\lightgbm\\engine.py:113: UserWarning: Found `num_iteration` in params. Will use it instead of argument\n",
      "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[25]\ttrain's sigma_score: 0.418809\tvalid's sigma_score: 0.117999\n",
      "[50]\ttrain's sigma_score: 0.568597\tvalid's sigma_score: 0.307166\n",
      "[75]\ttrain's sigma_score: 0.620927\tvalid's sigma_score: 0.421572\n",
      "[100]\ttrain's sigma_score: 0.635875\tvalid's sigma_score: 0.482216\n",
      "[125]\ttrain's sigma_score: 0.636294\tvalid's sigma_score: 0.512729\n",
      "[150]\ttrain's sigma_score: 0.638908\tvalid's sigma_score: 0.531243\n",
      "[175]\ttrain's sigma_score: 0.636937\tvalid's sigma_score: 0.5422\n",
      "[200]\ttrain's sigma_score: 0.637198\tvalid's sigma_score: 0.547239\n",
      "[225]\ttrain's sigma_score: 0.635802\tvalid's sigma_score: 0.551443\n",
      "[250]\ttrain's sigma_score: 0.632717\tvalid's sigma_score: 0.554628\n",
      "[275]\ttrain's sigma_score: 0.633287\tvalid's sigma_score: 0.554167\n",
      "[300]\ttrain's sigma_score: 0.634656\tvalid's sigma_score: 0.556521\n",
      "[325]\ttrain's sigma_score: 0.63648\tvalid's sigma_score: 0.556715\n",
      "[350]\ttrain's sigma_score: 0.637532\tvalid's sigma_score: 0.55786\n",
      "[375]\ttrain's sigma_score: 0.636781\tvalid's sigma_score: 0.558729\n",
      "[400]\ttrain's sigma_score: 0.637117\tvalid's sigma_score: 0.558295\n",
      "[425]\ttrain's sigma_score: 0.637578\tvalid's sigma_score: 0.558153\n",
      "[450]\ttrain's sigma_score: 0.638075\tvalid's sigma_score: 0.558225\n",
      "[475]\ttrain's sigma_score: 0.637123\tvalid's sigma_score: 0.558062\n",
      "[500]\ttrain's sigma_score: 0.636048\tvalid's sigma_score: 0.557664\n"
     ]
    }
   ],
   "source": [
    "evals_result = {}\n",
    "m = lgb.train(lgb_params, dtrain, num_boost_round=1000, valid_sets=(dtrain,dvalid), valid_names=('train','valid'), verbose_eval=25, feval=sigma_score, evals_result=evals_result)\n",
    "df_result = pd.DataFrame(evals_result['valid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=m.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f88893d978>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cc8XHzBaGD727AtOit0mqCtQD/mNmJik3HEK/+GecSoWcv0SXmbFK4ndvPB5n8TnYMe5rmXnqqafSoUMHxo4dCwQ99CNGjGD69Omcfvrp+budAHxuZpsAJL1G8CBvHEHPc3599nXgefhx+Hr4fpqkf0mqZGabo88fDku/IMy3HXA6++byMKYNUWk3EMyPh+DBw3NFxQKcB3QKG/rHASdIGmdm10XtnyfpZaBfeG2rCerzqxUs6lcR+M6CXvTe+cdJ+i9Bx98WoGZUjDX5cZh93HgLU7L/D3eudHqOYGGQU4DRBL30D5nZ09E7hU/9LgFamdkOSakEXxoAPxQ1jz7a8UcfxbKHi5pSdPClpqZGCoySxOPaPyUxrqJiMjN69uxJo0aNClRO1q1bF5k3+Prrr9OkSZPItj179jBp0iQ+/vjjAxq3c46pBENIUwiGo+brDJwEfBXO3T2BYNjsvcASgmGqM/bxHGcQzlstTEksM2OVxO/eeDzO4nMoYiyszFyxYgX16wed5lOnTqVhw4YAfPPNN9x3331MmjQpukEPsBs4R1JZYCdBvXdeuO0N4CKC+nBrYDmApFOADeGDvBYEU8K/jY1RUpVwDvqxwF0Ec9v3RbxpPmvDGFLDmFYUFYuZ3U045D2qp/66cI78aWa2Mnx/BfBFeI6pBI3x2QTTBWaE+ZYlGCGfLaktkGtmGWHe2yWdA3wK/IEfh/LHjbcw3qh3rvi9DgwhGFrzOyAXeEDS+HBoTw2C+UcVCVbI3CGpIXDOIYvYucPAJ598wosvvkjTpk0jq9j//e9/Z+LEiaSnpyOJOnXq8PTTPz5f+/jjj6lZsyZ169Y9VGE7d6QYTTDXdpGifs6JoPJ9WdRCUacSLIh1L/AQ8A9JHc1sfVix/5OZDY/OOKxY3wpUA9498JfiXOlXWJk5atQoli1bRpkyZfj1r3/Nv//9bwCGDBnCtm3b+Mtf/gIEq+fPmzcPgsUw3wY+J6jzziccGQM8DIyX1BvIAnqF6dcCf5aUS/AgoEvYm42kaUCvcC2NfuEw+DLAU2Y2I9znFIIHBycAexT8bF1jM9sWNqDbEkx7jfZHYFjYg/4D4ZScomIphICx4bB9EcyX/3O4bRTwoqSVBCv0568RUgV4T9IegoVCoxf8/DMwhmDBvXfCV1HxxuWNeueKmZntljQT2Br2tr8vqREwO+yFyCKYV/gucJOkhcAygnmGzrmf6fzzzydeOZz/E3bxpKSkMGeO/+k5d6CFc0yHRaeFQ/JrE1X+mdlXCn5bumU4FLYq8EHYcDeChwP5HpU0kGC17TlAm6JWvnfO/Wh/y8znnnuO6667Lu5UNTMbBAyKk76VHxfJjE4fAYyIdx4zax/1vh/B0PbYfdZTcNh69LYdFBwNlJ/+H4KRP/scS9Q+qQQ95oQr+Z9XyH4/AL+Jk54JNCjkmHlAkzjpceMtjDfqnStm4QJ55xD1R21mw4ipzITirtJrZuUPTHTOOefcwROvPIuuIPPjSs/R28+Mev884TzcmH26FVeMzjlX2vlP2jlXjCQ1BlYCH5pZkXNfnHPOOeecc+6X8p5654pRuOiFT851zjnnnHPOHRTeU++cc84555xzzpVS3qh3zjnnnHPOOedKKW/UO+ecc84555xzpZQ36p1zzjnnnHPOuVLKG/XOOeecc84551wp5Y1655xzzjnnnHOulPJGvXPOOeecc845V0p5o94555xzzjnnnCulvFHvnHPOOeecc86VUt6od845V+KsWrWKNm3a0KhRIxITExk2bBgAo0ePJikpieTkZNq1a8fatWsB2LJlC1dffTVJSUm0aNGCxYsXH8rwnXPOHUF69OhBlSpVaNKkSSStc+fOJCcnk5ycTJ06dUhOTgYgJyeHG264gaZNm9KoUSMeeughAH744QdatGhBs2bNSExMZNCgQXud59Zbb6V8+fKRzx9//DFnnnkmCQkJTJ48udD4Lrvsski+N910E3l5eQCkp6dzzjnnkJycTPPmzfnss88A+P7777niiisixzz//PMF8tu2bRs1atTglltuiaRJSpW0TFJ6+KoSpteWNFPSfEkLJbUP09tKSpO0KPz3on3I60JJn0vKlXRtdEySHpG0OHx1jkofI+mrqLySC71RpZg36p1zzpU4CQkJPP744yxdupQ5c+YwcuRIMjIy6Ny5MwsXLiQ9PZ2OHTsyZMgQAP7+97+TnJzMwoULeeGFF7j99tsP8RW4kkrSHZLKHoTzHCNpqKQvJa2QNEVSzQNwnhRJ34cV5qWS9m4J/Lx860gySQ9EpVWSlCNpRPh5TGzFOkwvtnssqaGk2ZJ2Seobs+2ysOK/UtKA4jifcz9Ht27dePfddwukvfzyy6Snp5Oens7//d//cc011wAwadIkdu3axaJFi0hLS+Ppp59m/fr1HHvsscyYMYMFCxaQnp7Ou+++y5w5cyL5zZs3j61btxY4R+3atRkzZgy/+93viozvlVdeYcGCBSxevJhNmzYxadIkAPr378+gQYNIT09nyJAh9O/fH4CRI0fSuHFjFixYQGpqKn369GH37t2R/AYOHEjr1q3jner3ZpYcvjaGafcCr5jZGUAX4F9h+mbgCjNrCtwAvLgPeX0DdAMmRO8oqQNwJpAMtAT6STohapd+UXmlF3mzSqmEQx2AcweLpOeAf5pZxqGOBUDSVcDy/HgkpQJ9zWze/uSzMyePOgPePgAR/nx9mubSrYTFBB7X/joUcWU+3AGAatWqUa1aNQAqVKhAo0aNWLNmDeXKlYvsm52djSQAMjIyuPvuuwFo2LAhmZmZbNiwgapVqx7U+F3JoOB/DJnZnjib7wDGATv2I78EM8vdzzD+DlQATjezPEndgdcktTQz28+8fsosM+soqRyQLuktM0vL3/gz4wf4H9ARGBh+/g2wZB+OK/QeSzrKzPL2I4bvgNuAq2LzAUYCbYHVwFxJU3+qjC+JZWasklomxPI4fyyzLrzwQjIzM+PuY2a88sorzJgxAwBJZGdnk5uby86dOznmmGMoW7YskiK98Dk5OeTk5ETKuLy8PPr168eECRN4/fXXI3nXqVMHgDJliu6nPeGEoH2bm5vL7t27I/lKYtu2bUDQO1+9evVI+vbt2zEzsrKyOPnkk0lICJqNaWlpbNiwgcsuu4x58/apympAfgO7IrA2vC/zo/ZZAhwn6Vgz21VoRmaZYXyx3+2NgY/C77lcSQuAy4BX9iXAw4H31Lsjhpn1KikN+tBVBF9CzrkiZGZmMn/+fFq2bAnAPffcQ61atRg/fnykp75Zs2a89tprAHz22Wd8/fXXrF69+pDF7A6+sGd5qaR/AZ8D14c9vJ9LmiSpvKTbgOrATEkzw+OyovK4VtKY8P0YSf8M93tE0mBJo8Nhof8L80JSOUlvS1qQP+wz7KXuDvTOb8Ca2fPALuCiMNYvJI0Nh6NOzu/ZlnSWpI/C4ajvSaoWpqeGw0s/k7Rc0gWx98DMsoE04DRJ3cLrfhN4P8yjn6S54TnvD9MekfSXqHswWFKf8ONOYKmk5uHnzhRSSZb0QHjP4t5jSUMkfQq0KuIab5OUEcb3UnhNG81sLpATc8oWwEoz+5+Z7QZeAq6MF5tzh9KsWbOoWrUq9evXB+Daa6+lXLlyVKtWjdq1a9O3b99IozsvL4/k5GSqVKlC27ZtI+XeiBEj6NSpU+Rh989x6aWXUqVKFSpUqMC11wYDbIYOHUq/fv2oVasWffv2jUwFuOWWW1i6dCnVq1enadOmDBs2jDJlyrBnzx769OnDo48+WthpnlcwxH2g8p8cwGDgOkmrgWnArXGO+z9gfkyDPl5ehVkAXC6prKRKQBugVtT2B8PvlSckHfsTeZVK3lPvDkthb8UrQE3gKOAB4M+EPeFhJW4YQQ/ETuBKM9tQSF5jwn0aAr8mqKjdALQCPjWzbuF+XYG/AgLeNrO7wvS9zgWcBnQCWku6l+DLDOA3YYX0RKCnmc0qJKYbgRsBKlWqzH1Nf04HzIFT9fjgyXhJ43Htn0MRV2pqaoHPO3fu5Pbbb6dXr158/vnnZGVl0bZtW9q2bcv48ePp27cv3bt357zzzmPEiBHUq1ePunXrUq9ePebPn8/27dsPavzukGtA8B19H/AacImZZUu6C7jTzIZIuhNoY2ab9yG/08M88iQNJigH2hD0wC+T9BRBb9BaM+sAIKkiUA/4xsy2xeQ3D0gEvgxj7Wlmn0gaDfxF0jDgSYIyaZOCeaEPAj3C4xPMrIWCOamDgEuiM5f0K+AcgjLvbIJyKsnMvpPUDqhP0BgWMFXShQSN4aH8OCT2t+E15Xf8vAR0kbQeyCPoZasec95/EPTAdTczi3OPywGLzew+SUcDHxVyjQOAU81sl6QTC//PAkANYFXU59UEw273UtLLzFgltUyI5XEWLLPWr19Pdnb2XuXYE088QYsWLSLpixYtYvPmzUycOJHt27dz++23c99990X2Hzp0KFlZWQwcOJCGDRtSoUIFnnvuOYYOHUpqaip5eXl7nWP9+vUsWbKESpUqFRrr3Xffze7du/nb3/7GE088QfPmzRk+fDg9e/akdevWzJw5k2uuuYbHH3+cjz76iEqVKjFhwgTWrl1Lr169eO6553jzzTdp0KABX375JV988QVr1qyJPsXvzWyNpArAq8D1wAtAV2CMmT0uqRXwoqQm+SOpJCUCjwDt9iGvuMzsfUlnA/8FNgGzgfz/6HcD64FjgGeAu4Ahhd6oUsob9e5wFa+S9eeo7eWAOWZ2T1gZ+SPwtyLyOwm4iKAh/iZwHtCLYLhfMrCR4AvpLGAL8L6kq8zsjXjnMrO/SZoKvGVmk8MY4ScqbPnM7BmCLyZq161njy8qWX/KfZrmUtJiAo9rfx2KuDJ/nxJ5n5OTQ8eOHbnpppu48847gaAClZIS7HPqqafSoUMHxo4dC0CHDsEwSDPj1FNP5be//W2k98MdMb42szmSOhKMhPok/G49hqCSt78mxQwVfzvsSdolaSNQFVgEPCbpEYLv9Flhr1K8IfbR6avM7JPw/TiCIebvAk2A6WHcRwHroo5/Lfw3DagTlX6BpPnAHuBhM1sSVnCnm9l34T7twlf+kNfyQH0zGyWpiqTqQGVgi5l9Iyk//3cJHhJsAF6Oc00DCR5w3xhnW748goo5BA8zCrvGhcB4SW8AbxSRHwT3MlbcaQ0lvcyMVVLLhFgeZ8EyKzMzk3LlykXKKAiGu3fu3Jm0tDRq1gyW1Jg0aRI33HADl1wSVPHefPNNVq1aRa9evQrknZaWxrfffkuVKlXYtGkTPXv2BGDXrl306tWLlStXRvYdM2YMiYmJBc5dmHXr1jF37lz69u3LlVdeyauvvookWrduzRNPPEFKSgqPPvooAwYM4IILggFBo0aNonLlyqxcuZLly5fz3nvvkZWVlT/PvgaAma0J/90uaQLBA8QXgJ4E9XLMbLak44BKwEYF64y8DvzBzL7Mj7GIvAplZg8SPCAkPGZFmJ7//bJL0vNA3/g5lG4l/y/RuZ8nXiUrevtu4K3wfRrBnLyivBn2PiwCNpjZIgBJSwgqVr8GUs1sU5g+HriQoFKyP+cqrMJWqOOPPopl4ZyukiI1NbVAQVdSeFz751DGZWb07NmTRo0aRRr0QIEh9VOnTqVhw4YAbN26lbJly3LMMcfw3HPPceGFF3qD/siUHf4rggZt1304JroheFwh+eWLHhqaR/Agdrmks4D2wEOS3gceB34tqYKZRQ8XOZPgwXDsefM/C1hiZq0KiTX//HkUrMPNMrOOcfaPjl/AQ2b2dJz9JgPXAqcQ9Mz/GJTZbklpQB+CUQZXxBw7FzhL0slRDxBi/RD1cKSoa+xAUHZ2AgZKSixiLYDVFBxeW5Nwrm5RSmKZGauklgmxPM6f9sEHH9CwYcNIgx6Cxe1mzJjBddddx44dO5gzZw7nnXcemzZt4uijj+bEE09k586dfPDBB9x111106NCB9evXR44vX758gQb9T8nKymL79u1Uq1aN3Nxcpk2bFmmsV69enY8++oiUlBRmzJgRmSJQu3ZtPvzwQy644AI2bNjAsmXLqFu3Lvfee2/kwcGYMWOYN28eI0eOXCMpATg7ge9WAAAgAElEQVTRzDaHo3E6Ah+EIXwDXAyMkdSI4Ht2Uzga523g7qgHnPxEXnEpWGPjRDP7VlISkMSP046qmdm68GHrVcBh+fM4PqfeHZbMbDlBr/kigkrWfTG75EQtVBRbOYonvyK1h4KVuj3hsUXN9dmfcxVWYXPuiPLJJ5/w4osvMmPGjMhPAk2bNo1nnnmGJk2akJSUxPvvvx/5qbulS5eSmJhIw4YNeeeddyLp7og1BzhPUj2AcJ7l6eG27QTD5/NtkNRIUhng6v09UdjDvcPMxgGPAWeGc9vHAv8MK5tI+gNQFpgRHlo7HIoKwfDU/wDLgMr56ZKODoemFof3gB6Syod511D4M1GEQ+wJGvbxfhfrceAuM/s2zrZ3gYeBt8OhsrD3PY4W9xrD+1/LzGYC/QmmoZUvJA8IHibUl3SqpGPC+KcWsb9zB0zXrl1p1aoVy5Yto2bNmowaNQqAl156ia5dCz5bvPnmm8nKyqJJkyacffbZdO/endNOO41169bRpk0bkpKSOPvss2nbti0dO8Z7VvejuXPnUrNmTSZNmsSf/vQnEhN//LrI/wm97OxsOnXqRFJSEs2aNaNKlSrcdNNNADz77LP06dOHZs2a8de//pVnnnkGCFa3/+9//0vTpk25+OKLeeSRR4oc2g8cC7wnaSGQDqwBng239QH+qGDxuolAt7BefAvBVKWBKvjTdYXmJenscG7+b4Cnw841gKOBWZIyCEblXBf1QHB82Cm3iGCEQFEjc0stbzS4w1JYyfrOzMaFc9q7HeBTfgoMCxfn2EJQQXvyJ44pqtLj3BHt/PPPJ94C4WXLlo07vLBVq1asWLHiIETmSoNwrnY3YGLUokj3AssJKnzvSFpnZm0I5nG/RTA/ezFFNyTjaQo8qmA15hx+nOp1N0Ejf3m47Qvg6nDUF8BS4AZJTxMME30q7BW/FhgeThtLIJjvvi8rzhcpnHPaCJgdnj8LuA7YGA7XrwCsiRqqGn3skqJiMLNJ4fFTw+ljsfc4et/CrnE5MC5ME/CEmW2VdArBWgQnAHsk3QE0NrNtkm4heFhxFDA6jNO5g27ixIlx08eMGbNXWvny5SM/KZcvNTWVpKQk5s+fv9f+sbKyImt7cvbZZxe6KGx6evDLbVWrVmXu3Llx9zn//PNJS0vbK7169eq8//77RcbRrVs3unXrxsiRI/MX6Twr3n7hItXnxUn/G4U3sAvLay7BqJzY9B8oZPFpM7uokHMcVrxR7w5X8SpZjx2ok4XDeu4GZhJURqaZ2ZSfOOwl4FkFKwXv9Tu/zjnn9l34U0dNoj7PIFgsLna/J4l66Bqua7JX73T+IqhRnwfHfM4/VyZBwzL2+F0EqzzHW+kZYI+Z3RTnuHSCIeix6SlR7zcTTtEys1QgNc7+Y4AxMWnDCBZu3YsFvxUd/TmTqPsZL9/oe2Rmo4HR4cfYe1zgQUlh1wicH+d864lTiQ+3TSNYTds5545o3qh3hyUze4+9K1kpUdvLR72PW6GL2t4t6n0mBSuN0dsmABPiHB/3XOH8oeinitHxRSpszjnnnHPOOVcYb9Q755xzzh1EhfWCO+eccz+HN+qdC0m6h2DhjWiTwp/IcM4555xzzrkSxxv1zoWif9/SOeecc84550oD/0k755xzzjnnnHOulPJGvXPOOeecc845V0p5o94555xzzjnnnCulvFHvnHPOOeecc86VUt6od84555xzzjnnSilv1DvnnHPOOeecc6WUN+qdc84555xzzrlSyhv1zjl3mOvRowdVqlShSZMmkbT09HTOOecckpOTad68OZ999hkAZsZtt91GvXr16NmzJ59//vmhCts555w7JOKVmwBPPvkkDRo0IDExkf79+xfY9s0331C+fHkee+yxSNq7775LgwYNqFevHg8//HAk/auvvqJly5bUr1+fzp07k5OTUyCvyZMnI4l58+bFje+JJ54gMTGRJk2a0LVrV3744YcC22+99VbKly8f+bxr1y46d+5MvXr1aNmyJZmZmQDs3r2b7t2707RpU5o1a0ZqamrkmJSUFBo0aEBycjLJycls3LgRgN69e0fSTj/9dIDk/GMk1Zb0vqSlkjIk1YmOS9KTkrKiPl8o6XNJuZKujdm3yLxcQd6oPwxIukNS2YNwnmMkDZX0paQVkqZIqnkAzpMi6XtJ88M/5EHFlG8dSSbpgai0SpJyJI0IP4+J/VIJ04vtHktqKGm2pF2S+sZsu0zSMkkrJQ0ojvM5161bN959990Caf3792fQoEGkp6czZMiQSOXknXfeYcWKFaxYsYI+ffrw5z//+VCE7FyJ4uXsPudbR9JOSelhJfwFSUcXQ76jJW2UtDgm/WRJ08N7NV3SSb/0XM5B/HJz5syZTJkyhYULF7JkyRL69i1QhaN3795cfvnlkc95eXncfPPNvPPOO2RkZDBx4kQyMjIAuOuuu+jduzcrVqzgpJNOYtq0aZHjtm/fzvDhw2nZsmXc2NasWcPw4cOZN28eixcvJi8vj5deeimyfd68eWzdurXAMaNGjeKkk05i5cqV9O7dm7vuuguAZ599FoBFixYxffp0+vTpw549eyLHjR8/nvT0dNLT06lSpQoQPFDIT7v11lsBok/2AvComTUCWgAb8zdIag6cGHM53wDdgAlxLrXQvNzeEg51AG7fSBIgM9sTZ/MdwDhgx37kl2BmufsZxt+BCsDpZpYnqTvwmqSWZmb7mddPmWVmHSWVA9IlvWVmafkbf2b8AP8DOgIDw8+/AZbsw3GF3mNJR5lZ3n7E8B1wG3BVbD7ASKAtsBqYK2mqmWUUldnOnDzqDHh7P05/4PVpmku3EhYTHJlxZT7cgQsvvDDyVD6fJLZt2wbA999/T/Xq1QGYMmUKf/jDH5BE48aN2bp1K+vWraNatWoHJD7nSgovZ4utnP3SzJLDMm068Ftg/C+MdQwwgqCSH20A8KGZPRw+CB8A3FVURiWxzIxVUsuqWIdjnJkPdwCIW24+9dRTDBgwgGOPPRYg0sgFeOONN6hbty7lypWLpH322WfUq1ePunXrAtClSxemTJlCo0aNmDFjBhMmBO3YG264gdtuuy1y3MCBA+nfv3+BHv9Yubm57Ny5k6OPPpodO3ZEyvC8vDz69evHhAkTeP311yP7T5kyhcGDBwNw7bXXcsstt2BmZGRkcPHFF0eu58QTT2TevHm0aNFin+7XxIkTIajXIqkxkGBm0wHMLLpH/ijgUeB3wNX56WaWGW4v8L1bVF4uPu+pL8HCJ95LJf0L+By4Puzh/VzSJEnlJd0GVAdmSpoZHhf9R3StpDHh+zGS/hnu94ikweHT71RJ/wvzQlI5SW9LWiBpsaTOYQ9Fd6B3fgPWzJ4HdgEXhbF+IWmspIWSJuf3akg6S9JHktIkvSepWpieKukRSZ9JWi7pgth7YGbZQBpwmqRu4XW/Cbwf5tFP0tzwnPeHaY9I+kvUPRgsqU/4cSewNHxaCNAZeKWQ+/9AeM/i3mNJQyR9CrQq4hpvC3srFkp6KbymjWY2F8iJOWULYKWZ/c/MdgMvAVfGi825X2ro0KH069ePWrVq0bdvXx566CEg6AGoVatWZL+aNWuyZs2aQxWmcweUl7MHpJzNzzcP+AyoEXWvZ4X39nNJ54bp/5LUKXz/uqTR4fuekv4W5vUxYcMhxpXA2PD9WGIeljtXnJYvX86sWbNo2bIlrVu3Zu7cuQBkZ2fzyCOPMGhQwQEvhZWn3377LSeeeCIJCQmR9M2bNwMwf/58Vq1aRceOHQuNo0aNGvTt25fatWtTrVo1KlasSLt27QAYMWIEnTp12utBfHQsCQkJVKxYkW+//ZZmzZoxZcoUcnNz+eqrr0hLS2PVqlWR47p3705ycjIPPPAAsc8Vv/76a7766iuAbWHS6cBWSa8pGAX0qILGPMAtwFQzW/cTtzlfUXm5OLynvuRrQFDI3we8BlxiZtmS7gLuNLMhku4E2pjZ5n3I7/QwjzxJg4GGQBuCnoFlkp4CLgPWmlkHAEkVgXrAN2a2LSa/eUAi8GUYa08z+yQslP8iaRjwJHClmW2S1Bl4EOgRHp9gZi0ktQcGAZdEZy7pV8A5wAPA2UArIMnMvpPUDqhP0BgWMFXShQSN4aHAv8JsfhteU/5DrJeALpLWA3nAWoIKW/R5/wFUBLqbmcW5x+WAxWZ2n4KhhR8Vco0DgFPNbJek2CFHsWoAq6I+rwbijr2SdCNwI0ClSpW5r+nP6Uw5cKoeHzwZL2mOxLjy58etX7+e7OzsyOfhw4fTs2dPWrduzcyZM7nmmmt4/PHH2bx5M/Pnzyc3N5esrCy2bNlCWloaWVkl4yF5VlZWgTl/zhUDL2eLv5xF0nEEZdjtYdJGoK2Z/SCpPjARaA58DFwATCUoB/NbI+eH5ylK1fxGgpmtk1Ql3k4lvcyMVVLLqliHY5zR5Utsufn999+zaNEiHn74Yb744gs6derEhAkT+Pe//027du2YN28emZmZHH/88aSmprJ48WLWrVsXOX7p0qWsXbuW//znP+zcuTOSvnHjRsyMGTNmcOeddzJgwABSU1PZunVr3PJ3+/btjB07lnHjxlG+fHkGDx7MPffcwxlnnMFzzz3H0KFDSU1NJS8vL3KOrKwsZs+eTeXKlQH44Ycf+OSTTzjttNOYPn06DRs2pGrVqjRs2JClS5eSmprKzTffTOXKldmxYweDBg1ix44dnHfeeZE8J06cSKtWraJHBCQQ/C2fQTCs/mWgm6R3CEbGpuzjf7JC8wJG7UceRxRv1Jd8X5vZHEkdgcbAJ5IAjgFm/4z8JsUMFX/bzHYBuyRtBKoCi4DHJD0CvGVmsxScNN7Qv+j0VWb2Sfh+HMEQ83eBJsD0MO6jgOindK+F/6YBdaLSL5A0H9gDPGxmSySdDUw3s/yn9e3C1/zwc3mgvpmNklRFUnWgMrDFzL7RjwtsvEtQedlA8CURayDwqZndGGdbvjzg1fB9gyKucSEwXtIbwBtF5AfBvYwVd7ilmT0DPANQu249e3xRyfpT7tM0l5IWExyZcWX+PiX4NzOTcuXKkZISfL7yyit59dVXkUTr1q154oknSElJoVmzZlSqVImUlBRSU1PJzs6O+9T/UElNTY1cg3PFxMvZ4i1nT5OUTvAwYLKZLQyPPRoYISmZoAw9PUyfBdyhYLhtBnBSONKgVXh9v1hJLzNjldSyKtbhGGd+mQl7l5sNGjTgtttuIyUlhTZt2vDYY4/RpEkT1q5dy6effsrYsWPZunUrZcqUITExkUsvvZTZs2dHjp89ezZnn302V155Jb169eL8888nISEh0tg+66yzWL16NQMGBEsqrV+/nvvvv5+pU6fSvHnzSFyTJk3ijDPO4KqrgoEpa9euZc6cORx//PFs2rSJnj17AsHieL169WLlypU0aNCAmjVr0qpVK3Jzc9m1axedOnVCUmT4PcC5557LNddcQ+PGjQvcl40bNzJv3jzKly8fuZ7evXszcuTI6Eb9amC+mf0PIKz7ngOsJ3houTL8jioraaWZ1SviP0VheXmjvhAl/y/RZYf/iqCg7boPx0RXCo4rJL98u6Le5xE80V8u6SygPfCQpPeBx4FfS6pgZtujjjkTeDPOefM/C1hiZq0KiTX//HkU/P9xlpnFG3sUHb+Ah8zs6Tj7TQauBU4h5km/me2WlAb0Iej9uCLm2LnAWZJOjqrYxPohqtJW1DV2AC4EOgEDJSUWMUdxNVAr6nNNglEEzhW76tWr89FHH5GSksKMGTOoX78+AJ06dWLEiBF06dKFjIwMKlasWGIa9M4dIF7OFh7/zyln8+fUVwNSJXUys6lAb4KH6c0IevR/ADCzNQoWuLuMoNf+ZIKe/6yY+xDPBknVwl76avhCWu4Auuqqq5gxYwYpKSksX76c3bt3U6lSJWbNmhXZZ/DgwZQvX55bbrmF3NxcVqxYwVdffUWNGjV46aWXmDBhApJo06YNkydPpkuXLowdO5bzzjuPihUrRobhQ7D6/GOPPVagQQ9Qu3Zt5syZw44dOzj++OP58MMPad68OR06dGD9+vWR/cqXL8/KlSuBoGwfO3YsrVq1YvLkyVx00UVIYseOHZgZ5cqVY/r06SQkJNC4cWNyc3PZunUrlSpVIicnh7feeotLLvlxkM+yZcvYsmULrVoV+NqZS/BQrrKZbQIuAuaZ2dsE3xNAMH3pJxr0hea1L/+djlTeqC895gAjJdUzs5UK5tHVNLPlwHaCYX353wQbJDUClhEsRvFThWIB4ZP378xsnIJ5g93CoYhjgX9KuikcVvgHoCwwA/g1UFtSKzObDXQF/hPGUDk/PRyqfrqZ7cvidD/lPeABSePNLEtSDSDHzDYSVDCeBSoBreMc+zjwkZl9Gz41jPZumPfbktqFlYrYexwt7jUCS4FaZjZT0n8IFgcpT8FVQqPNBepLOhVYA3QJjynS8UcfxbJwYZeSIjU1tcDT7pLiSI2ra9eupKamsnnzZmrWrMn999/Ps88+y+23305ubi7HHXcczzzzDADt27dn2rRp1KtXDzPjlVfiLjnh3OHIy9m9/exyNmxoDwDuJhhaXxFYbWZ7JN1AMKIg32yCxQgvAn5F8MBg8j7ENxW4AXg4/HfKTx1QEsvMWCW1rIp1OMcZr9zs0aMHPXr0oEmTJhxzzDGMHTuWOHXIiISEBEaMGMGll15KXl4ePXr0IDExEYBHHnmELl26cO+993LGGWfwxz/+sch41q5dS69evZg2bRotW7bk2muv5cwzzyQhIYEzzjiDG28sanAp9OzZk+uvv5569epx8sknR1bL37hxI5deeillypShRo0avPjii0DQy3/ppZeSk5NDXl4el1xyCX/84x8jDzAmTpxIly5dClx/+J3VF/gwHHmURvAdUahwdNDrwEnAFZLuN7PEn5PXkc4b9aVEOE+uGzBR0rFh8r3AcoIhZe9IWmdmbQjmcb9FMD97MUFDcn80BR5VsBJlDpD/m1Z3A48By8NtXwBXh3POIWjE3iDpaWAF8FTYK34tMDycM5hAMA/vF1c2zOz9sFI1Ozx/FnAdsDEcRlgBWBNvUY6wslNoDGY2KTx+ajgPMfYeR+9b2DUuB8aFaQKeMLOtkk4heNp4ArBH0h1AYzPbJukWgkrUUcDoYqqUuSNcuDrtXtLS0vZKk8TIkSOBoCIU20Pg3OHKy9m9/ZJyNvQGMFjBAn3/Al6V9BtgJgVHBMwC2oUPU74m6K2PdH9KmkgwH7eSpNXAIDMbRdCYf0VST4J5t7/5pdfsHBRebo4bN67I4/JXmM/Xvn172rdvv9d+devW5bPPPot8jrdWTHRa9erVC/zs3f3338/9999fZCzRc/GPO+44Jk2atNc+derUYdmyZXullytXLm4dIV/sdeazYLX6pKLiMrPyUe/nEoxM/Vl5uR/Jiv0XUtyRKJxH95aZNTnEoRxxGjRoYPG+kA+lkjrv2ePaPyUxroMRk6Q0M/OnGa5E8XK2eJTEMjNWSfzujcfjLD6lIUaIH6eXmSWD/6Sdc84555xzzjlXSvnwe1cszCyTYPVd55xzzhUzL2edc84VxnvqnXPOOeecc865Usob9c4555xzzjnnXCnljXrnnHPOOeecc66U8ka9c84555xzzjlXSnmj3jnnnHPOOeecK6W8Ue+cc84555xzzpVS3qh3zjnnnHPOOedKKW/UO+ecc84555xzpZQ36p1zzjnnnHPOuVLKG/XOOeecc84551wp5Y1655wrwXr06EGVKlVo0qRJJG3w4MHUqFGD5ORkkpOTmTZtGgDjx4+PpCUnJ1OmTBnS09MPVejOOefcLxavHOzXrx8NGzYkKSmJq6++mq1btwKwe/duunfvTtOmTWnWrBmpqamRYyZOnEjTpk1JSkrisssuY/PmzQBMmjSJxMREypQpw7x58yL7f/vtt7Rp04by5ctzyy23FBrfwIEDSUpKIjk5mXbt2rF27VogKJOTkpJISkri3HPPZcGCBQAsW7asQFl9wgknMHny5CKv67PPPovs36xZM15//XUAVq1aRZs2bWjUqBGJiYkMGzbsJ+MCSE1NJTk5mcTERFq3bh1Jf/fdd2nQoAH16tXj4YcfjqR/+OGHnHnmmfTq1Yvzzz+flStXFrgHkq6VZJKaF3qj3IFlZv7yl7+K6QXUARbvx/7dgOq/5Jynn366lTQzZ8481CHEVRrj+uijjywtLc0SExMjaYMGDbJHH320yDwXLlxop5566gGL61A5GDEB86wEfJ/4y1+H+8vLzPhK4ndvPAcrznjl4HvvvWc5OTlmZta/f3/r37+/mZmNGDHCunXrZmZmGzZssDPPPNM+/PBDy8nJscqVK9umTZvMzKxfv342aNAgMzPLyMiwL774wlq3bm1z586NnCMrK8tmzZplTz31lN18882Fxvf9999H3g8bNsz+9Kc/mZnZJ598Yt99952ZmU2bNs1atGix17G5ublWtWpVmzhxYpHXlZ2dHUlfu3atVa5c2XJycmzt2rWWlpZmZmbbtm2z+vXr25IlS4qMa8uWLdaoUSP7+uuvI/cpP5a6deval19+abt27bKkpKRIXvXr17eMjAybOXOmjRw50m644YZI3sDnwMfAHKC5lYDvliPxlXBQnyA452J1AxYDa39iv0LtzMmjzoC3iy2g4tCnaS7dSlhMULriyny4AwAXXnghmZmZ+53nxIkT6dq1a3GE55xzJUU3DsMyM1ZJLatiHYw4Mx/uELccbNeuXeT9OeecE+npzsjI4OKLLwagSpUqnHjiiSxbtowLLrgAMyM7O5tf/epXbNu2jXr16gHQqFGjuOcuV65c3F7pWCeccELkfXZ2NpIAOPfccwvEuHr16r2O/fDDDznttNM45ZRTiryusmXLRtJ/+OGHyDmqVatGtWrVAKhQoQKNGjVizZo1NG7cuNC4JkyYwDXXXEPt2rUj9wmC0QD16tWjbt26AHTp0oUpU6bQuHFjJLFt2zYAvv/+e6pXrx59GTWAnkDfIm+UO6B8+L1zxS9B0lhJCyVNllRW0lmSPpKUJuk9SdUkXQs0B8ZLSpd0vKT7JM2VtFjSM8r/BnYuxogRI0hKSqJHjx5s2bJlr+0vv/yyN+qdc6WBl5nuFxk9ejSXX345AM2aNWPKlCnk5uby1VdfkZaWxsaNGzn66KN56qmnaNq0KdWrVycjI4OePXsWWwz33HMPtWrVYvz48QwZMmSv7aNGjYrEGO2ll14qtKyOvi6ATz/9lMTERJo2bcq///1vEhIK9s1mZmYyf/58WrZsWWRcy5cvZ8uWLaSkpHDWWWfxwgsvALBmzRpq1aoVObZmzZqsWbMGgOeee4727dvzm9/8hhdffJEBAwYAMH/+fIBjzOytfbhN7gDynnrnil8DoKeZfSJpNHAzcDVwpZltktQZeNDMeki6BehrZvMAJI0wsyHh+xeBjsCbsSeQdCNwI0ClSpW5r2nuQbmwfVX1+OAJfklTmuKKnge4fv16srOzI2lJSUmMGjUKSYwePZrf/e533HXXXZH9MzIyMDM2b95cIJ/9lZWV9YuOPxBKYkzOuV/kiC8zY5XUsirWwYgz//s+thzMN27cOLZu3UqNGjVITU3ltNNOY/r06TRs2JCqVavSsGFDcnJy+OCDD/j73//OU089RfXq1Rk+fDg33ngj119/fSSvrVu3kpaWRlZWVoFzfPHFF6xZs6bIsqdt27a0bduW8ePH07dvX7p37x7ZNn/+fJ588kmGDx9eII+cnBxeffVVOv4/e/ceZ1O9P3789R5DGOWSy8GImMaMuYowx4mR3KIkquPnFCGpo6Qi3VAqREUpXaTRhQq5dFO+2KijchvjkqEybsktktuYGe/fH2vNbs/MnkEGe/J+Ph7rYe3PWuuz3nvtsd/rsz6ftXaHDnlyW+73le2VV15hy5YtPProo4SEhFCiRAkAjh49Sv/+/enduzcrV64sMK4tW7aQmprK888/z/Hjx/nvf/+LiPDTTz+xc+dO7/5++OEHfvnlFzweD0OGDGH48OFcdtllfPrpp3Tt2pUHH3yQBx54AGBbvgfGnDPWqDem8G1T1W/c+feAR4FoYJ7biVAM2JnPti1EZBBQGqgArMPPCYqqvgG8AXBZ7TB9fk1g/Vd+MCaTQIsJilZcad0S/5xPSyMkJITExERyq127Nh06dMixbPbs2fTu3dvv+qfD4/GccR2FLRBjMsackQs+Z+YWqLkqt3MRZ3Yu9JcHJ0+ezLp165g/f36O4enZw+/BGQIfFhZG2bJlKV++PN26dQOgWLFijBw5Mkd95cqVo0GDBjRsmPNZb2lpaRw6dOiUcs/ll19O+/btmTx5MgApKSmMHz+eefPmER4enmPd2bNn07hxY2666aYcuS2/9+UrKSmJChUq0LBhQzIyMujQoQN9+/bNbmQXGNe3335LXFycdxTAnDlzKFmyJG3atGHp0qXeOJYuXcpVV11FVFQUO3bs4J577sHj8fDoo4/Stm1bGjRokH1LQV0RSQP+AcwRkRuyL7yZcyfwvzGMKXo01+s/gHWqmlDQRiJSEngV5yEj20RkGFDyZDsrVbwYqe7914HC4/HkaJQGir9LXDt37vTeQzdz5swcTwQ+ceIE06ZNY/HixYUdpjHGnA0XfM7MLVBzVW7nM865c+cyatQoFi1alKPhe+TIEVSVkJAQ5s2bR3BwMLVq1aJ69eqsX7+ePXv2UKlSJebNm5fvvfSna9OmTVxxxRWA00COiIgAYOvWrdx00028++67eRr04P/ZN/m9r82bN1OjRg2Cg4O9Pe21atVCVenVqxeRkZF5GvT5xdWxY0f69etHZmYmx48f57vvvmPAgAFERESwadMmNm/eTPXq1fnggw+YMmUK5cuX5/fff2fjxo0A3mNXtmxZ9u7di2d8UmYAACAASURBVIisUdWGIuLBZySNObesUW9M4btMRBJUdSnQFedpoHdml4lIcSBcVdfhnLxc7G6XfTKyV0TKAF2A6ec6eBNYunbtisfjYe/evYSGhvLkk0/i8XhITk5GRKhVqxavv/66d/3FixcTGhrqfdCNMcYEOMuZpkD+8uCIESNIT0+nVatWgPNQuddee43du3fTpk0bgoKCqF69Ou+++y6bN2+mWrVqDB06lGbNmlG8eHFq1qxJUlIS4Fwcv/fee9mzZw/t27cnPj6eL7/8EoBatWpx8OBBjh8/zqxZs/jqq6+oV68evXv3pm/fvjRs2JDBgweTmppKUFAQNWvW5LXXXgPgqaeeYt++fdxzzz0ABAcHe38y78iRI8ybNy9H/gbo16+f3/f19ddfM3LkSIoXL05QUBCvvvoqFStW5Ouvv+bdd98lJiaG+Ph4AJ599lmuu+66fOOKjIykbdu2xMbGEhQURO/evb2dA+PHj6dNmzZkZWXRs2dPoqKiAHjzzTfp3LkzR44coUaNGkyaNOmsfNbmrxPnlwiMMYVBRGoBn+P8tMc/gU3AbUA48BJQFudi2lhVfVNEOgPPAkeBBOAx4N9AGs49SltUdVhB+6xbt66mpqYW/ps5A4E6RNriOj2BGNe5iElEVqiq/dauMWeZ5Uz/AvG71x+Ls/AUhRjBf5yWMwOD9dQbU4hUNQ2o52dRMtDMz/ozgBk+RY+7kzHGGPO3ZjnTGGMKh/2knTHGGGOMMcYYU0RZo94YY4wxxhhjjCmirFFvjDHGGGOMMcYUUdaoN8YYY4wxxhhjiihr1BtjjDHGGGOMMUWUNeqNMcYYY4wxxpgiyhr1xhhjjDHGGGNMEWWNemOMMcYYY4wxpoiyRr0xxhhjjDHGGFNEWaPeGGOMMcYYY4wpoqxRb4wxxhhjjDHGFFHWqDfGmL+oZ8+eVK5cmejoaG/ZwIEDiYiIIDY2lk6dOnHgwIEc22zdupUyZcowZsyYcx2uMcYYc1rGjRtHdHQ0UVFRjB07FoDffvuNVq1a8Z///IdWrVqxf/9+AFSV++67j7CwMGJjY1m5ciUACxcuJD4+3juVLFmSWbNmAdCtWzfq1q1LdHQ0PXv2JCMjI08MycnJJCQkEBUVRWxsLB9++KF3WY8ePbj88su9dScnJwMwevRob9kdd9xBsWLF+O2339i2bRstWrQgMjKSqKgoxo0b561r2LBhVK9e3bvd559/DkBaWhqlSpXylvft29e7TWJiInXr1vUu2717NwBJSUlUqlTJWz5x4sQc7+ngwYNUr16dfv36AXDs2DHat29PREQEUVFRDB48+Aw+NXMhska9Mcb8RT169GDu3Lk5ylq1asXatWtJSUkhPDycESNG5Fg+YMAA2rVrdy7DNMYYY07b2rVrefPNN/n+++9ZvXo1n376KZs2bWLkyJG0bNmS9957j5YtWzJy5EgAvvjiCzZt2sSmTZt44403uPvuuwFo0aIFycnJJCcns2DBAkqXLk3r1q0Bp1G/YcMG1qxZw9GjR/M0fgFKly7NO++8w7p165g7dy73339/jgvmo0eP9tYfHx8POBfYs8vuvPNOmjdvToUKFQgODub555/nhx9+4Ntvv+WVV15h/fr13roGDBjg3e66667zltepU8db/tprr+WI7/333/cuq1y5srf81ltv9Zb37t07xzZPPPEEzZs3z1H20EMPsWHDBlatWsU333zDF198ceoflrngBZ/vAIw5W0RkIvCCqq4/6crniIjcCGwszJiOZmRRa/BnhVVdoXgwJpMeARYTFF5caSPbA9CsWTPS0tJyLMs+UQFo0qQJ06dP976eNWsWtWvXJiQk5IxjMMYYk5eIFFPVrPyWB2LOzC1QcujoBkdo0qQJpUuXBqB58+bMnDmT2bNn4/F4SE1NpXv37iQmJjJq1Chmz57N7bffjojQpEkTDhw4wM6dO6lataq3zunTp9OuXTtvnb4N50aNGrF9+/Y8cYSHh3vnq1WrRuXKldmzZw/lypU7pfcxf/58unbtCkDVqlW98Vx88cVERkayY8cO6tWrd5pH569bsWIFu3btom3btixfvhyAkiVLkpiYCECJEiW48sor/R4LY/JjPfXmb0tVewdYgz4YuBE4rczhbmeKoEmTJnl75Q8fPsyoUaMYOnToeY7KGGMCg4gMF5H+Pq+fEZH7RGSgiCwTkRQRedJn+SwRWSEi60Skj0/5IRF5SkS+AxLO8dv424qOjmbx4sXs27ePI0eO8Pnnn7Nt2zZ27drlbRhXrVrVO+R8x44d1KhRw7t9aGgoO3bsyFHnBx984G1g+8rIyODdd9+lbdu2Bcb0/fffc/z4cerUqeMte+yxx4iNjWXAgAGkp6fnWP/IkSMsW7aMzp0756krLS2NVatW0bhxY2/Z+PHjiY2NpWfPnt7bCgA2b95M/fr1ad68OUuWLMlRzx133EF8fDzDhw9HVb3lM2bMIDY2li5durBt2zYATpw4wYMPPsjo0aPzfY8HDhzgk08+oWXLlgUeC2N8WWPB/C2ISAjwERAKFAOGA3cDD6nqchE5BIwDOgBHgY6quiufui4HpuD8/5gLDFDVMiKS6NbXwV1vPLBcVZNEZAhwPVAK+B9wl6qqiHjc102Br4AbgOYi8jiQnWFeASoBR4A7VXWDiCQBvwH1gZXAg7li7AP0AahYsRJDYjL/6qE7K6qUcnoaAk1hxeXxeLzzv/76K4cPH85RBvDee+9x4MABqlevjsfjYcKECbRu3Zrly5d778/L3ubQoUN5tg8EgRhXIMZkjPnL3gI+BsaJSBDwb+BRoCXQCBBgjog0U9XFQE9V/U1ESgHLRGSGqu4DQoC1qjrE304CPWfmFig5dNeuXXTs2JGEhARKlSpFzZo1+fXXX8nMzMTj8Xi/j7Nf7927l1WrVpGZ6cS+f/9+VqxYwaFDhwDYt28fK1eupGTJknm+x8eMGUPt2rXJysrK9zt+3759DBgwgMGDB7N48WIArr/+erp3705GRgbPP/88ffv2pXv37t5tFixYQEREBCkpKTnqOnr0KP3796d3797ee/9jY2N56623EBEmTZrE//t//4+HH36Y48ePM2XKFMqWLUtqaiqdO3fm7bffJiQkhP/+979UqlSJI0eOMHToUI4cOUKbNm0oX748kydPpkSJEsyZM4eOHTvywgsvMHPmTOrWrctPP/3Ehg0b2LFjR45jmZWVxaOPPsp1113H1q1b2bp1a2F8lIXGcnDgska9+btoC/yiqu0BRKQsTqM+Wwjwrao+JiLPAXcCT+dT1zhggqq+IyL/PcX9j1fVp9x9v4tz8eATd1k5VW3uLrsC+FRVp7uv5wN9VXWTiDQGXgWucbcLB671N4xQVd8A3gC4rHaYPr8msP4rPxiTSaDFBIUXV1q3xD/n09IICQnxDpsDmDx5MuvWrWP+/PneIYZPPPEE3333HZMnT+bAgQMEBQURFRVFv3798Hg8ObYPFIEYVyDGZIz5a1Q1TUT2iUh9oAqwCrgKaO3OA5QBrgAWA/eJSCe3vIZbvg/IAmYUsJ+Azpm5BUoOTeuWSGJiordX+dFHHyU0NJSUlBTq1q1LamoqdevWpVq1aiQmJhIXF0fFihW939GHDx/mhhtu8Pbqjxs3jltuuYVrr702x36efPJJgoOD+eijjwgK8j+I+ODBgyQmJvL8889z8803+12nRIkSjBkzJkeOGDduHK1bt85RlpGRQYcOHejbty8PPPCA37pq165Nhw4d8uSbxMREpk6dSpUqVWjYsGGOZbt372b58uV5trn66qupUKECiYmJvPnmmyxZsoQvv/ySQ4cOcfz4cerWrUvbtm1JTEykZ8+eNG7cmJdeeslvXOeb5eDAdf6/MYwpHGuAMSIyCqfRvEREfJcfBz5151cArQqoqyl/9qK/C4w6hf23EJFBQGmgArCOPxv1H/rbQETKAP8EpvnEepHPKtMKui8wW6nixUh17/EOFB6PJ0fDN1Cci7jmzp3LqFGjWLRokbdBD+QYrjds2DDKlCnjfeqtMcZcwCYCPYB/AJNweulHqOrrviu5o+WuBRJU9Yg7Eq6ku/jYqeRLCMycmVsg5dDdu3dTuXJltm7dyscff8zSpUvZvHkzkydPpkmTJkyePJmOHTsCcMMNNzB+/Hj+/e9/891331G2bNkc99NPnTo1z8NjJ06cyJdffsn8+fPzbdAfP36cTp06cfvtt+dp0Gffs6+qzJo1K8ev0fz+++8sWrSIPn28d2qgqvTq1YvIyMg8DXrf+/9nzpzprWvPnj1UqFCBYsWK8fPPP7Np0yZq165NZmYmBw4coGLFimRkZPDpp596L1j41jVnzhwiIyMB56F62ZKSkli+fDkjR47E4/Hw+OOP8/vvv/t9WKAxJ2ONevO3oKobRaQBcB0wQkS+yrVKhv55o1MWJ//bVz9lmeR8DkVJABEpidPD3lBVt4nIMP480QA4nM8+goADqhqfz/L8tjMBomvXrt4hh6GhoTz55JOMGDGC9PR0WrVyrhs1adIkz5NyjTHGeM0EngKKA/8PJ9cOF5H3VfWQiFQHMoCywH63QR8BNDlvEV9AOnfuzL59+yhevDivvPIK5cuXZ/Dgwdxyyy2MHz+eunXrMm3aNMB56N3nn39OWFgYpUuX5u233/bWk5aWxrZt2/I88b1v377UrFmThATnUQg33XQTQ4YMYfny5bz22mtMnDiRjz76yHtvf1JSEuA0iOPj4+nWrRt79uxBVYmPj8+Rb2fOnEnr1q0pVaqUt+ybb77h3XffJSYmxvuk/GeffZbrrruOQYMGkZycjIhQq1YtXn/dua60ePFihgwZQnBwMMWKFeO1116jQoUKHD58mDZt2pCRkUFWVhbXXnstd955JwAvvfQSc+bMITg4mAoVKnjjzs+ePXt45plniIiI4MorrwSgX79+eZ6ab0y+VNUmm4r8BFQDSrrzNwKzAA9OQxvgkM+6XYCkAuqaA/zHnb87e1ucoX5pOL3pZYHNOL0L5YBdOPfTlwHWAsPcbbwxuK9fBu7wef0/4GZ3XoA4dz4J6HIq7z08PFwDzcKFC893CH5ZXKcnEOM6FzHhPCvjvH+v2WTThTIBrwEjfV73xxmBtwZYCtRxc+8XQAowzc2vie76h051X4GYM3MLxO9efyzOwlMUYlT1H6flzMCYrKfe/F3EAKNF5ATOFf27gTF/sa7+wBT3ibzee/TU6YX/COeEYhPu/X6qekBE3sQ5+UgDlhVQ9wfAmyJyH87FhW7ABPfBecXd5av/YtzGGGNMkeI+IK8J4B1XrarjcJ5vk1s7f3WoapmzE50xxhQN1qg3fwuq+iXwZa7iRJ/lZXzmpwPTyYeqbsbnJ3HcBnf2skHAID/bPA487qc8Mdfrb8j7k3Z5fr9FVXvkF58xxhjzdyAi9XCedzNTVTed73iMMaaoska9McYYY4w551R1PVD7fMdhjDFFnTXqzQVLRB7DZ7ifa5qqPuNbYMP6jDHGGGOMMYHKGvXmguU23p856YrGGGOMMcYYE6D8/yCkMcYYY4wxxhhjAp416o0xxhhjjDHGmCLKGvXGGGOMMcYYY0wRZY16Y4wxxhhjjDGmiLJGvTHGGGOMMcYYU0RZo94YY4wxxhhjjCmirFFvjDHGGGOMMcYUUdaoN8aYv6Bnz55UrlyZ6Ohob9nAgQOJiIggNjaWTp06ceDAAQDmzZtHgwYNiImJoUGDBixYsOB8hW2MMeZv7sCBA3Tp0oWIiAgiIyNZunQpt956K/Hx8cTHx1OrVi3i4+MB2LdvHy1atKBMmTL069cvRz3Hjx+nT58+hIeHExERwYwZMwBIT0/n1ltvpVu3bjRu3Ji0tLQ8MWzbto0WLVoQGRlJVFQU48aN8y574okniI2NJT4+ntatW/PLL78A4PF4KFu2rDfOp5566qR15Zd3fd+X7/oAU6dOJSYmhtjYWNq2bcvevXu9y15++WXq1q1LVFQUgwYNAgrO4QXVZcy5ZI36C5SI3C8ipc/BfkqIyFgR+UlENonIbBEJPQv7SRSR30VklYj8ICJDC6neWiJyVESSRWS9iLwjIsULod5JIrJbRNbmKq8gIvPcYzVPRMqf6b7M2dGjRw/mzp2bo6xVq1asXbuWlJQUwsPDGTFiBAAVK1bkk08+Yc2aNUyePJnbbrvtfIRsjDHmAtC/f3/atm3Lhg0bWL16NZGRkXz44YckJyeTnJxM586duemmmwAoWbIkw4cPZ8yYMXnqeeaZZ6hcuTIbN25k/fr1NG/eHIC33nqL8uXL8/777zNgwAAefvjhPNsGBwfz/PPP88MPP/Dtt9/yyiuvsH79esBpiKekpJCcnEyHDh28jXeAq6++2hvnkCFDTlpXfnk3v/eVmZlJ//79WbhwISkpKcTGxjJ+/HgAFi5cyOzZs0lJSWHdunU89NBDQP45vKC6jDnXgs93AObsEREBRFVP+Fl8P/AecOQ06gtW1czTDONZ4GIgXFWzROQO4GMRaayqepp1ncwSVe0gIiFAsoh8qqorshf+xfgBflLVeBEpBswDbgHeP8NYk4DxwDu5ygcD81V1pIgMdl/nzZY+jmZkUWvwZ2cYTuF6MCaTHgEWExROXGkj2wPQrFmzPL0TrVu39s43adKE6dOnA1C/fn1veVRUFMeOHSM9PZ2LLrrojGIxxpizSUQmAi+o6vrzHQuAiNwIbMyOR0Q8wEOquvxU6wjEnJnbmeSqlEevZvHixSQlJQFQokQJSpQo4V2uqnz00Ufe3uaQkBD+9a9/8eOPP+apa9KkSWzYsAGAoKAgKlasCMDs2bMZNmwY6enpdOnShX79+qGqOKedjqpVq1K1alUALr74YiIjI9mxYwf16tXjkksu8a53+PDhHNv5U1Bd+eXd/N6XqqKqHD58mEsvvZSDBw8SFhYGwIQJExg8eLA3N1euXBnIP4cHBQXlW5cx55r11P/NuD3LP4jIq8BK4DYRWSoiK0VkmoiUEZH7gGrAQhFZ6G53yKeOLiKS5M4nicgL7nqjRGSY28vsEZGf3boQkRAR+UxEVovIWhG51R0JcAcwQFWzAFT1bSAduMaNdYOITBaRFBGZnj16QEQaiMgiEVkhIl+KSFW33CMio0TkexHZKCJX5z4GqnoYWAHUEZEe7vv+BPjKrWOgiCxz9/mkWzZKRO7xOQbDROTBXPVmAd8D1X2O9RL32K4UkX+65a+KyA3u/EwRmeTO9xKRp926FgO/+fkIOwKT3fnJwI0Ffd4mcE2aNIl27drlKZ8xYwb169e3Br0xJuCpau9AadC7bgTqne8gAtnPP/9MpUqVuOOOO6hfvz69e/fm8OHD3uVLliyhSpUqXHHFFQXWkz2M/YknnuDKK6/k5ptvZteuXQDs2LGDGjVqAE4vetmyZdm3b1++daWlpbFq1SoaN27sLXvssceoUaMG77//fo6e+qVLlxIXF0e7du1Yt27dKdWVLb+866t48eJMmDCBmJgYqlWrxvr16+nVqxcAGzduZMmSJTRu3JjmzZuzbNmyPNv75vCC6jLmXLNG/d9TXZwe4FZAL+BaVb0SWA48oKovAb8ALVS1xSnUF+7Wkd3IjQDaAI2Aoe5w9LbAL6oap6rRwFwgDNiqqgdz1bcciPKJ9Q1VjQUOAve49b0MdFHVBsAk4Bmf7YNVtRHOaIM8w+xF5FKgCZCdDRKA7qp6jYi0Bq5wY48HGohIM+AD4Fafam4BpuWqtyTQ2H1vALuBVu6xvRV4yS1fDGRfbKjOnycg/wKW5I43lyqquhPA/bfySdY3AeiZZ54hODiYbt265Shft24dDz/8MK+//vp5iswYY/zL5+K8R0QaussPicgz7vJvRaRKAXUlicgEEVnodgA0dzsEfsjuNHDX6yoia9z9jfIpz7Mv98L5DcBocW6Jq+OufnNBF/ovNJmZmaxcuZK7776bVatWERISwsiRI73Lp06dSteuXU+pnu3bt9O0aVNWrlxJQkKCdzi6v4GW+fW2Hzp0iM6dOzN27NgcPfTPPPMM27Zto1u3bt4h61deeSVbtmxh9erV3Hvvvdx4442nVFd2ff7ybm4ZGRlMmDCBVatW8csvvxAbG+sdsp+Zmcn+/fv59ttvGT16NLfcckuO95o7hxdUlzHnmg2//3vaoqrfikgHnAblN+6XbQlg6V+ob1p2T7vrM1VNB9JFZDdQBVgDjHGT8qequsQd/u9viL1v+TZV/cadfw+4D6fRHA3Mc+MuBuz02f5j998VQC2f8qtFZBVwAhipqutE5Cpgnqpm94q3dqdV7usywBWq+paIVBaRakAlYL+qbhWRWjg9/sk4FwOmq2qKu21xYLyIxANZOBc/wGm43y8i9YD1QHl3pEGC+/7OmIj0AfoAVKxYiSExf+WugrOnSiln+GCgKYy4PB6Pd/7XX3/l8OHDOcrmzp3LJ598wvPPP8+iRYu85Xv27OGBBx5g0KBBbNu2jW3btnmXHTp0KEcdgSIQ4wrEmIz5m8i+ON8eQETKAnf7LA8BvlXVx0TkOeBO4OkC6isPXIPTEP8EaAr0Bpa5eXM3MApoAOwHvhKRG1V1lr99qerTIjIH5xxjuhsjuBf6ReQ6nAv91+YOJNBzZm5nkqu2bt1KxYoVOXr0KB6Phzp16jBlyhRatmxJVlYWH374Ia+//nqe79ENGzawY8cOb7mqUrJkScqXL4/H4yE0NJSXXnoJj8dD6dKlmT17NjVr1mT+/Pns3buXlJSUPA37zMxMHnnkERo3bkyFChX8fndffvnlPPLII7RokbOPqXTp0vzxxx/Mnj2bsmXLFlhXfnk3+31lZGR419+wYQP79+/35uErrriCqVOn8q9//YvSpUtTu3Ztbx3Hjx9n9uzZlCtXzm8OL6iu01VUcltRifNCZI36v6fscVaC06A9+SXZnI3vkvnUly3dZz4LJ6FuFJEGwHXACBH5CngeqCkiF6vqHz7bXImT4HPvN/u1AOtUNSGfWLP3n0XOv+ElqtrBz/q+8QswQlX9dZVOB7oA/8Dpuc+WfU99VcAjIjeo6hxgALALiMMZ9XIMQFV3iPOAu7Y4vfYVcHr+D+U6Dv7sEpGqqrrT3d9ufyup6hvAGwCX1Q7T59cE1n/lB2MyCbSYoHDiSuuW+Od8WhohISEkJjplc+fOZc6cOSxatIhKlSp51ztw4ADNmzdn7NixdO7cOU+dHo/HW0cgCcS4AjEmY/4m/F2c911+HPjUnV+BMxqwIJ+oqorIGmCXqq4BEJF1OBfkawIeVd3jlr8PNANmnea+8rvQ7xXoOTO3M8lVaSM78uKLL1K1alXq1q2Lx+Ph6quvJjExkblz5xITE8PNN9+cd7u0NA4dOpTj+7Vjx44AJCYmkpSUxFVXXUViYiI9evRgzZo1REVF8euvv9KmTZs8jXJVpXv37jRt2pSxY8fmWLZp0ybv8P+XX36ZBg0akJiYyK+//kqVKlUQEb7//ntKlCjBDTfcAJBvXfnlXd/3lZqa6n1f4eHhPPnkk0RFRVGpUiXmz59P06ZNSUxMpGfPnvzyyy8kJiayceNGgoKC6NixI7///rvfHF5QXaerqOS2ohLnBSn7gRE2/T0mnIS21p2vBGwFwtzXpXEeWAdO8r7cZ7sfgUicxukMIMktT8IZBp+93jCch9Jkv17r7rMaUNItuxGY5c6/ALwJFHNf344z/F7c7RRIcJe9CTyIM6LgR5/y4kCUO+8BGrrzFYE0dz4R5yQk9/HoAYz3ed0a+A4o476uDlR256OA/wEbgaq5j6f7uhOw1J1/EXjQnb/D+e/kXS8p+9jjDNnfBryY32flUzYaGOzODwaeO9lnHh4eroFm4cKF5zsEvwozrn//+9/6j3/8Q4ODg7V69eo6ceJErVOnjoaGhmpcXJzGxcXpXXfdpaqqw4cP19KlS3vL4+LidNeuXWclrsIUiHGdi5iA5RoA3+c22XSuJ5yL0P8BvgaG5Mq5h3zW65J9npBPPd5zBz95NMnd/kbgHZ/yXjgP5ct3X+Q9J/F7TlDQFIg5M7cz/Z5btWqVNmjQQGNiYrRjx47622+/qapq9+7ddcKECXnWr1mzppYvX15DQkK0evXqum7dOlVVTUtL06uvvlpjYmL0mmuu0S1btqiq6tGjR7VLly5arVo1veqqq/Snn35SVdUdO3Zou3btVFV1yZIlCmhMTIw373322WeqqnrTTTdpVFSUxsTEaIcOHXT79u2qqvryyy9rvXr1NDY2Vhs3bqzffPPNSevKL+/6vq+SJUvmeF8TJkzQiIgI7/737t2rqqrp6enarVs3jYqK0vr16+v8+fNVteAcnl9dpysQ860//uK0nBkY03kPwKZC/kDzJs9rgGVAijvd4JbfC2wAFrqvuwA/uQlyfAEJdBj+G/Vt3PqT3f1lJ9mLcO6P/wnYhNNDX8Mn1vXAa+62M4DS7rJ4nF7u1Tj3xt/plvtN4Jxio94t649zUWMNzu0IdXyWrck+JvkcT3FjuhpnOH4K8C0wItdJSC+cYYzgXJQ4DNzks3wqzi0FGcB2oJdbfikw3z1W84EKJ/vMA/EEJVCTk8V1egIxLmvU22TT2Znwc3Ges9uorwpscXN5MeD/gI4F7cs9n7jDZ5nfc4KCpkDMmbkF4nevPxZn4SkKMapaoz6Qp8Aef2ROm6qm4dyPnv16AXCVn/VexkmO2a+n4ww/z71ej1yvh+V6nb2vNOBLP9un41xAuDefkE+oal8/2yXjDMPLXZ7oM78Xd6idqnpwknvu9ZNwTiB8y8YB4/wFo6oxuV6nkfN4Ks5w+2yxPvOP+Kz3FvCWO5+Bc3+gb71+b4lQ1X1AS3/LjDHGmLMoBuchdCdwLjjfDeT98fJCos5tZo8AC3EumH+uqrNPstkHwJvi/PJOl7MVmzHGFDXWqDfGGGOM7b+NuwAAIABJREFUucCp6pfkvTif6LO8jM+8344An+U9fObTyHlx3HfZFGCKn+397kudB+v6/qSdb3zeC/3GGHOhsUa9OW9yJ3pjjDHGGGOMMafHGvXGGGOMMea0ichjQO5HqU9T1WfORzzGGHOhska9McYYY4w5bW7j3RrwxhhzngWd7wCMMcYYY4wxxhjz11ij3hhjjDHGGGOMKaKsUW+MMcYYY4wxxhRR1qg3xhhjjDHGGGOKKGvUG2OMMcYYY4wxRZQ16o0xxhhjjDHGmCLKGvXGGGOMMcYYY0wRZY16Y4wxxhhjjDGmiLJGvTHG5KNnz55UrlyZ6Ohob9m0adOIiooiKCiI5cuXe8u///574uPjiY+PJy4ujpkzZ56PkI0xxhQBx44do1GjRsTFxREVFcXQoUMBWLBgAX369CE6Opru3buTmZkJwP79++nUqROxsbE0atSItWvX5qgvKyuL+vXr06FDhzz7uvfeeylTpozfOObNm0eDBg2IiYmhQYMGLFiwwLvsww8/JDY2lqioKAYNGuQtHzBgAPHx8fTu3Zvw8HDKlSvnXTZo0CCioqKIjIzkvvvuQ1UBmDp1KjExMcTGxtK2bVv27t0LwOrVq0lISCAmJobrr7+egwcPAvD+++97c2p8fDxBQUEkJycDsGLFCmJiYggLC8uxj/zqyrZ161bKlCnDmDFjAEhNTc2xj0suuYSxY8cW+LkZE6isUW+MMfno0aMHc+fOzVEWHR3Nxx9/TLNmzfKUL1++nOTkZObOnctdd93lPRkzxhhjfF100UUsWLCA1atXe/PG//73P7p3784TTzzB2rVrqVmzJpMnTwbg2WefJT4+npSUFN555x369++fo75x48YRGRmZZz/Lly/nwIED+cZRsWJFPvnkE9asWcPkyZO57bbbANi3bx8DBw5k/vz5rFu3jl27djF//nwAXnzxRZKTk5k4cSL33nsvN910EwD/+9//+Oabb0hJSWHt2rUsW7aMRYsWkZmZSf/+/Vm4cCEpKSnExsYyfvx4AHr37s3IkSNZs2YNnTp1YvTo0QB069aN5ORkkpOTeffdd6lVqxbx8fEA3H333bzxxhts2rSJTZs2efN0fnVlGzBgAO3atfO+rlu3rncfK1asoHTp0nTq1OkUP0FjAkvw+Q7grxCR+4E3VPXIWd5PcWA40BlIB44AQ1X1CxFJAxqq6t5C2lcS0Bz4HTgB/FdVlxZCvcOAO4E9QAlguKpOPcM6I4C3gSuBx1R1jM+ytsA4oBgwUVVHFlCPB3hIVZfnt05hxeUuLwYsB3aoat5L2WeBe/wPqeoYEXkKWKyq/1eY+ziakUWtwZ8VZpVn7MGYTHoEWExw6nGljWwPQLNmzUhLS8uxzN9JE0Dp0qW988eOHUNE/nqgxhQiy5mnVe8wLGdmL7eceZakjWyPiHh7zzMyMsjIyKBYsWJcdNFF1KhRA4BWrVoxYsQIevXqxfr163nkkUcAiIiIIC0tjV27dlGlShW2b9/OZ599xmOPPcYLL7zg3U9WVhYDBw5kypQp+Y4eq1+/vnc+KiqKY8eOkZ6ezs8//0x4eDiVKlUC4Nprr2XGjBm0bNkyx/ZTp07lySefBEBEOHbsGMePH0dVycjIoEqVKqgqqsrhw4e59NJLOXjwIGFhYYDTW559kbxVq1a0adOG4cOH59lH165dAdi5cycHDx4kISEBgNtvv51Zs2bRrl27AuuaNWsWtWvXJiQkxO9xmD9/PnXq1KFmzZr5fGrGBLaA7akXR37x3Q+UzmdZfvX9lQsYw4GqQLSqRgPXAxf/hXpO1UBVjQcGA6/nXvgX3wPAi269HYHX3ROvM/EbcB/g7wTgFaAdUA/oKiL1znBfZxyXj/7AD+cunJxUdUhhn5yYwPLdd98RFRVFTEwMr732GsHBRfK6qSmCLGdaziysuHxYzjzLsrKyiI+Pp3LlyrRq1YpGjRqRkZFBamoqANOnT2fbtm0AxMXF8fHHHwPO7V5btmxh+/btANx///0899xzBAXl/AoYP348N9xwA1WrVj2leGbMmEH9+vW56KKLCAsLY8OGDaSlpZGZmcmsWbO8sWT79ddf2bx5M9dccw0ACQkJtGjRgqpVq1K1alXatGlDZGQkxYsXZ8KECcTExFCtWjXWr19Pr169AGeU25w5cwDn9rbc+wDnNoDsRv2OHTsIDQ31LgsNDWXHjh0F1nX06FFGjRrlvcXBnw8++MC7D2OKooA64xSRWsAXwEIgARgrIn2Bi4CfgDuAnkA1YKGI7FXVFiJySFXLuHV0ATqoag/3Sv5vQH1gpYj8AVwG1Hb/HauqL4lICPAREIpztXw48AnO1frLVTUdQFV3uevljvsBNy5wrrSP9Venqn4oIg2AF4AywF6gh6ruzFXlYiDMrdsD/A9oCswRkXeA19z4wTlZWwr8DMSr6gF3ux/dbbxUdZOIHAHKA7tF5E6gD05vxI/AbTi9K5uAOkBZ9/glqupiEVkC3KGqP7rbt88VdyPgR1X92Y3hA5yTovW5j5mfYzgBuAooBUxX1aFu+XXu8doLrARq59djoKq784kLEQkF2gPPAA+cJJY0YArQAiiOc4xG4Hwmo1X1NXe9gcAtOH+fM31ifgy4HdiG09uzwi1PAj5V1ekiMgTnhLcUzud7l6qq+3l/5+67HNBLVZf4ibGPGxcVK1ZiSExgDfOuUsrpFQ80pxqXx+Pxzv/6668cPnw4RxnAgQMHWLFiBYcOHcpR/sorr7BlyxYeffRRQkJCKFGixEn3d+jQoTz1B4JAjCsQYzpfLGd6Wc60nFmkcqbvd9jYsWM5dOgQTzzxBBEREQwaNIiXXnqJsWPH0rBhQ44dO4bH46Fp06aMHz+esLAwateuTVhYGKtWreKrr74iIyODP/74g+TkZPbt24fH42Hv3r1MnDiRsWPH4vF4yMrKKvC7c/PmzTz++OM899xz3vXuuece2rVrR1BQEFFRURw4cCBHHXPnziUhIYElS5xDvmPHDr7++mumTnUGtzz00ENUrlyZqKgonn32WSZMmEC1atV46aWX6NOnD7fddht9+/bl6aefZuDAgTRt2pSgoKAc+1i/fj2qyt69e/F4PGzYsIH9+/d710lJSeG3337D4/HkW9cbb7xB69atWb58OWlpaZQqVSrHPjIyMpgxYwYdOnQ4b/mlqOS2ohLnhSigGvWuujgnIkOAj4FrVfWwiDwMPKCqT7knBC1OcRhfuFtHljusKwLny/9iINVNjG2BX1S1PYCIlMVJRltV9aD/ah3uCccdQGNAgO9EZBHOSVCOOt2r/S8DHVV1j4jcipMwe+aq9npgjc/rcqra3K1nCk4vwtcichnwpapGishsoBPwtog0BtJUdZfvEGARuRLY5CZygI9V9U132dM4yfBlEdmI02twOU5yvVpEvgNC3ZOT/FTHScrZtrvH5VQ8pqq/uT0X80UkFtiI0/vSTFU3i8iZDIEcCwzi1HuNtqlqgoi8CCThnOyVBNYBr4lIa+AKnJMywTl5bAYcBv6Nc1IcjHNStcJP/eNV9SkAEXkX6IBzUgwQrKqN3JOzocC1uTdW1TeANwAuqx2mz68JrP/KD8ZkEmgxwanHldYt8c/5tDRCQkJITEzMsU65cuVo0KABDRs29FtHUlISFSpUyHe5L4/Hk6f+QBCIcQViTOeZ5UzLmZYzi1jO9M0x2VasWMG+fft46KGHiIqKIjExka+++or09HTvd1779s71F1Xl8ssv55ZbbmHEiBGsWLGCHj16cOzYMQ4ePMjEiRPp2rUre/bs8faIp6en07t3b378Me+f5Pbt2+nTpw8fffQRTZv+eW0rMTGRRx99FIA33niDH3/8Mcf375133snkyZP55z//CcDo0aNp37699771ZcuWkZ6eTtmyZSlfvjzdunUDoFixYowcOdJb1+233w7Axo0bWbduXY59zJ49m969e3vL6taty9ixY72vd+7cSUxMTIF13XfffaxatYrJkydz4MAB70WKfv36effRuHFj77MBzoeiktuKSpwXosA764YtqvqtiHTASZLfuEm2BM7V9dM1TVWzfF5/5vYipIvIbqAKzsnAGBEZhXNVeImc+v2w/8K54nwYQEQ+Bq4G5vqpMxqIBua59RcDfHscRovI4zhXqnv5lH/oM38tUM8nvktE5GJ3nSE498f9O9c2A9wehto4J2PZot0Tk3I4vSBfuuVLgGY4JygjcHpfFgHLTnIs/B00Pck22W5xr6QH4wzfrIdze8jPqrrZXWcq7pX20+H+Le1W1RUikniKm81x/10DlFHVP4A/ROSYiJQDWrvTKne9MjgnLBfj/D0ccfc9B/9aiMggnCGxFXBOfLJPUD52/10B1DpZoKWKFyN1ZJ6OlvPK4/H4PWk5385mXJs3b6ZGjRoEBwezZcsWUlNTqVWr1lnZlzE+LGdazrSc6ShSOXPPnj0UL16ccuXKcfToUf7v//6Phx9+mN27nWtI6enpjBo1isceewxwRoeVLl2aEiVKMHHiRJo1a8Yll1zCiBEjGDFiBODkuDFjxvDee+8BzkizbGXKlPHboD9w4ADt27dnxIgRORr0ALt376Zy5crs37+fV199lY8++nPgTWpqKn/88Yf33naAyy67jDfffJNHHnkEVWXRokXcf//9VK9enfXr17Nnzx4qVarEvHnzvM+nyd7HiRMnePrpp+nbt6+3vhMnTjBt2jQWL17sLatatSoXX3wx3377LY0bN+add97h3nvvLbCul156ydsQHTZsGGXKlPE26CHnPfvGFFWBeE/9YfdfAeaparw71VPVXvls45sES+ZTX7Z0n/ksnCu8G4EGOMlohDvM60fgMjf5F8TvmUw+dQqwzuc9xahqa5/NBrrlrVTV97dKfN9DEJDgU0d1N3kuBcJEpBJwI38mOXB6KeoCtwLviEj2MUoC+qlqDPAkfx67JTgnWY2Az3FOYBJxhjgWZDtQw+d1KPDLSbZBRC4HHgJaqmos8JkbS2E9aawpcIM7RPAD4BoRee8k22T/nZwg59/MCZyTKAFG+HwOYar6lrtOgSdl7vF/FejiHvs3yfl3m72/LALzwtsFo2vXriQkJJCamkpoaChvvfUWM2fOJDQ0lKVLl9K+fXvatGkDwNdff01cXBzx8fF06tSJV199lYoVK57nd2AuAJYzLWdaznQUqZy5c+dOWrRoQWxsLFdddRWtWrWiQ4cOjB49mu7duxMbG8v111/vvV/9hx9+ICoqioiICL744gvGjRv3l/c9Z84chgwZAjj33f/4448MHz7c+9Nu2RcW+vfvT7169WjatCmDBw8mPDzcW8fUqVO55pprcjwUtkuXLtSpU4eYmBji4uKIi4vj+uuvp1q1agwdOpRmzZoRGxtLcnKydwTA1KlTCQ8PJyIigmrVqnHHHXd461u8eDGhoaHUrl07R/wTJkygd+/ehIWFUadOHe/IgILqys+RI0eYN2/eee2lN6ZQqPtEykCYcK6wrnXnKwFbgTD3dWkg3J1fg3PfXvZ2PwKROMl7BpDklifhJIHs9YbhPDk2+/Vad5/VgJJu2Y3ALHf+OZyr+CXc11WB/7jzaUBFnKfGprjxhbh11vdXJ3/eh5fglhcHovzF6hOjB+eJwdmvp+CcyGS/jveZHw28C3xewHuejXMvGjj33FV245jnc9wuct/fAvf1BJwhgnG5YstddzDOfYqXu+91dfb7y+fz9gANgTh33SCcXqBdQA+ce+e2AbXc9d/H6cE52d9RjrhyLUs8WR3Zn6073wNn2F+OZTg9Dt/h9EiAM4yyss/fQymcHohN2bFkf8Y4J3y73HXKuH8zw3J/3u5+0k72fsPDwzXQLFy48HyH4JfFdXoCMa5zEROwXAMgJ55swnKm5UzLmd7PmyKcM3MLxO9efyzOwlMUYlT1HydFJGf+3aeAvaKpzv1zPYCpInKRW/w4zj1jbwBfiMhOVW2B8+TbT3GS2VqcL/3TEYMzjO8EkAHc7bO/p4H1InIM5+r/kFxxrnQf5vK9WzRRVVeJSJvcdarqcXEeSvSSew9iMM59a+tOI9b7gFdEJMXdfjGQPVbpQ5zhfj0K2P4pYIqIvAk8gZNkt+Cc9F3svqd0EdkGfOtuswTo6q6DiPwD52duLgFOiPNzSfVU9aCI9MMZklgMmKSqJ31vqrpaRFbhHIefgW/c8qMicg8wV0T28ucx9quguE4Ww+lS1a9EJBJY6l6lPoRz8rpSRD4EknGOa54H9qjqAff4r8E54TnZEE1jjCmQ5cx8Wc7Mh+VMY4z5+xDnAosxgUlEyqjqIXHOAl7BeWjRi+c7rkBSt25dzf75m0ARqA9SsbhOTyDGdS5iEpEVqnryJxwaE2AsZ55cIObM3ALxu9cfi7PwFIUYwX+cljMDQyDeU2+MrztFJBmnR6Isfn6L2BhjjDGA5UxjjLkgBezwe/P3ISIzce4Z9PWwqn7pb31fbg9Djl4GEbkD6J9r1W9U9b/nKi5jjDHmbLCcaYwx5nRZo96cdaraqZDrexvnYUxnWk+hxmWMMcacKcuZxhhjTpcNvzfGGGOMMcYYY4ooa9QbY4wxxhhjjDFFlDXqjTHGGGOMMcaYIsoa9cYYY4wxxhhjTBFljXpjjDHGGGOMMaaIska9McYYY4wxxhhTRFmj3hhjjDHGGGOMKaKsUW+MueCMGzeO6OhooqKiGDt2LACrV68mISGBmJgYrr/+eg4ePHieozTGGPN3cuzYMRo1akRcXBxRUVEMHToUAFXlscceIzw8nMjISGbMmAHA+++/T2xsLLGxsfzzn/9k9erVAKSmphIfH++dLrnkEm8umzZtGlFRUQQFBbF8+fJ8Y3nxxReJiooiOjqarl27cuzYMQA2b95M48aNueKKK7j11ls5fvw4AFu2bKFly5bExsaSmJjI9u3bvXUVK1bMG8sNN9zgLV+wYAFXXnkl0dHRdO/enczMTABmz55NbGws8fHxNGzYkK+//hqA5ORkEhISiIqKIjY2lg8//NBbV35xpaenc+uttxIWFkbjxo1JS0vzbpOSkkJCQgI9evQgJibG+x6nTp1KTEwMsbGxtG3blr17957uR2lM4FFVm2yy6RQnQIF3fV4HA3uAT3OtNxtY6mf724G1wDpgPfCQW54EbAZWAxuBd4DqpxJTeHi4BpqFCxee7xD8Wrhwoa5Zs0ajoqL08OHDmpGRoS1bttSNGzdqw4YN1ePxqKrqW2+9pY8//vg5jSsQBWJc5yImYLkGwPeNTYExAfcDpc/BfooDI4FNbp74HmjnLksDKhbivrJzTjKwEkgopHqHATvcetcDXQuhzghgKZCenTN9lrUFUoEfgcGnUt/5zJknTpzQP/74Q1VVjx8/ro0aNdKlS5fqpEmT9LbbbtOsrCxVVf34449VVfWbb77R3377TVVVP//8c23UqFGeOjMzM7VKlSqalpamqqrr16/XDRs2aPPmzXXZsmV+49i+fbvWqlVLjxw5oqqqN998s7799tve+alTp6qq6l133aWvvvqqqqp26dJFk5KSVFV1/vz5+p///Mf7fRwSEpJnH1lZWRoaGqqpqamqqvrEE0/oxIkTVVX1jz/+0BMnTqiq6urVq7Vu3bqqqpqamqobN25UVdUdO3boP/7xD92/f3+Bcb3yyit61113qarq1KlT9ZZbblFV1YyMDI2JidHk5GRduHCh7t27VzMzMzUjI0MrVaqke/bsUVXVgQMH6tChQ/0ep3MpEPOtP/7itJwZGFPwmV8WMOaCchiIFpFSqnoUaIVzAuMlIuWAK4FDInK5qm52y9vhnBy2VtVfRKQkcJvPpgNVdbqIiLveQhGJVtXjBQV0NCOLWoM/K7Q3WBgejMmkR4DFlDayPQA//PADTZo0oXTp0gA0b96cmTNnkpqaSrNmzQBo1aoVbdq0Yfjw4ectXmPMueN+74qqnvCz+H7gPeDIadQXrKqZpxnGcKAqEK2q6SJSBWh+mnWcjuyc0xp4HYj1XfgX3wPAi6o6RkSuAFaIyHRVzTiDOH8D7gNuzBVfMeAVnDy8HVgmInNUdX1BlZ2vnJk2sj0iQpkyZQDIyMggIyMDEWHChAlMmTKFoCBnAG358uUB+Oc//+ndvkmTJjl6x7PNnz+fOnXqULNmTQAiIyNPKZ7MzEyOHj1K8eLFOXLkCNWqVUNVWbBgAVOmTAGge/fuDBs2jLvvvpv169fz4osvAtCiRQtuvPFGevXqlW/9+/bt46KLLiI8PBxw8uqIESPo1auX9xgAHD58GOe/H951AapVq0blypXZs2cPZcuWzTeu2bNnM2zYMAC6dOlCv379UFW++uorYmNjiYuLw+PxcOmll3qPu6py+PBhLr30Ug4ePEhYWNgpHTNjApkNvzfm9H0BtHfnuwJTcy3vDHwCfAD826f8EZxehl8AVPWYqr6Zu3L3wueLwK9Au0KO/YIXHR3N4sWL2bdvH0eOHOHzzz9n27ZtREdHM2fOHMAZvrht27bzHKkx5mwSkVoi8oOIvIrTW32biCwVkZUiMk1EyojIfUA1nIusC93tDvnU0UVEktz5JBF5wV1vlIgME5FJIuIRkZ/duhCREBH5TERWi8haEblVREoDdwL3qmo6gKruUtWP/MT9gLvdWhG5P7863fIGIrJIRFaIyJciUtXPoVgMhLnre0TkWRFZBPQXkUoiMkNElrlTUxEJEpE09wJ2dkw/uhchvFR1E86FkPLuOne6dax26ywtIsXcYyMiUk5ETohIM3f9JSISpqq7VXUZkPvCQCPgR1X92b34/QHQseBP/fzLysoiPj6eypUr06pVKxo3bsxPP/3Ehx9+SMOGDWnXrp3fxvtbb71Fu3Z5Twk++OADunbteloxVK9enYceeojLLruMqlWrUrZsWVq3bs2+ffsoV64cwcFOn19oaCg7djj9FnFxcd7bAmbOnMkff/zB77//Dji3FTRs2JAmTZowa9YsACpWrEhGRob3FoDp06fnyKszZ84kIiKC9u3bM2nSpDwxfv/99xw/fpw6deoUGNeOHTuoUaMGAMHBwZQtW5Z9+/axceNGRIQ2bdrQp08fnnvuOQCKFy/OhAkTiImJ+f/snXmczuX6+N+XnYhqhowlHYwZs2IiCUMhjaWkOnKy5zjaKDQHlZZzxpISSqciTspOqL7K9iA/jl1ZGi2GkDXCDMZw/f74fObpmZlnxpBlpq736/V5PZ/PvVz3dd/PzHPd1719CAoKYtu2bTkOThhGfsFm6g3j4pkKvCAin+LMbEwAGvrEdwBeAg4AM4EENzwcWH8R5WzAWXY4N3OEiPQEegIEBATyQsSlTKZcOcoVd2br8xIej4eTJ52+eNu2balfvz7FixfnlltuYf/+/fTq1YtXX32V/v3706BBAwoUKIDH47kqup08efKqlXUx5EW98qJORr6mBtAVeAGYDdytqski8hzwjKq+LCLPAE1UNTcbb4NdGedEZAjOb3gToBSQKCLjcJaM71PVOAARKY3jVO9W1RwP8xCROq6+9QAB/uc64H/JLFNECgNjgLaqesh19P8FdMsktjXwjc9zGVVt7Mr5GGfm/SsRqQx8oaqhIjIXuB/4QETqAUmqeiB9xtXNWxv4TlUPukGz0weyReRVoLuqjhGRHUBN4FYcG9lQRP4HVFTV73NojgqA7+jrHrdd/LXbNbeZvr9bo0aN4uTJkzz//POEhISQkpLC3r17ee2111i+fDkJCQlUrFjRm37jxo2MGTOG0aNHZ5Bz9uxZZs2aRatWrbL8Lh47doz169d77Z4vJ06cYNKkSUyePJmSJUsyZMgQBg0axG233capU6e8sg4ePEhKSgoej4d27doxevRoxo4dS2RkJAEBAZw+fRqPx8PUqVMJCAhg37599OrVi+TkZCpUqMCAAQPo1q0bZ8+eJSYmxpsenNUI77zzDps3b+aJJ55g5MiRXv2OHDlC3759iY+PZ/ny5Rw7dixbvU6ePMmqVasIDAwEnAGGlStXkpiYyKJFi3jnnXc4e/YsL774IgULFiQqKop///vfjBs3jqCgIEaPHk3Pnj159FHfhZNXn/xi2/KLnn9GzKk3jItEVb8WkSo4zvvnvnHuTEU14CtVVRFJc5fQb7mEoiS7CFV9F3gXoPJfqunIb/LWv/KzEWnkNZ2SOsbi8XiIjY0lNjaWESNGADBw4EAqVqxIp06d6NSpEwA7duxg69atxMbGXhXd0vXKa+RFvfKiTka+ZpeqrhaRVjiO5UrXMS2Cs4/7Ypmhqud8nj9zZ97PiMhBoByOA/2aiAzDOY9lha8zfAHuBOaoajKAiMzGGVRe4EdmOM5g8kJXfkHgZx9ZI0RkMM65ML5TldN87u8Gavrod72IlHLTvAB8gLMizTdPXxF5DGeg4R6f8HDXmS8DlAS+cMNXAI1wnPoEnBULy4C1F2gLf42m/hLmBZuZ1DE2S9j69es5cuQIt9xyCwMGDKBKlSo0btyY4cOHe3/nvv76a8aOHcvChQszLE8H58C5evXq0a5duyyyy5QpQ506dYiJickSN2PGDGrVqsV99zk7Gvbt28fq1atp27YtPXr04M4776RQoUKsWrWK6tWre3Vp37494Dh2ISEhlCtXLsvv8ZdffknRokW9tvbxxx/3hp85cyZL+tjYWEaNGkV4eDgBAQEcP36c2NhYRo4cyYMPPgg4539lp1eNGjWoWLEi9evXJy0tjTNnztCmTRtOnTrFqVOnaNu2LR6Ph7/+9a+cP3+e0qVLc8MNN9CxY0fAOeRv6NCh19yu5Bfbll/0/DOSt3rdhpF/mAe8BsQCN/mEP4yz1HCn2wm6HqfDMxjncLw6wJJcllELWHyhRMULFyRxaNyFkl1VPB6P3w5MXuHgwYOULVuW3bt3M3v2bFatWuUNO3/+PK+++iq9evW61moahnGizRplAAAgAElEQVTlSXY/BVioqrlZx+zrOBbLRl46Z3zuzwGFVHWHO+N+L5AgIl/i2JPKIlJKVU/kULZf7z8bmXOArapaPxtZ/VV1pp9w3zoUwDlE71QGJURWAdVEJBBnr/urPtHpe+rbAf8VkaqqehrncL77VHWziHTBsZ/gOPW9cLY5vAD0d+OWZ6N3OnuASj7PFYF9F8hzTW3moUOHKFy4MGXKlOHUqVMsWrSI5557jvvuu48lS5bQrVs3li1b5p2l3717N+3atePDDz/M4tCDc4r7xS69B6hcuTKrV68mJSWF4sWLs3jxYmJiYhARmjRpwsyZM/nrX//KpEmTaNvW2dFw+PBhbrzxRgoUKEBCQgLdujkLPo4ePUqJEiUoWrQohw8fZuXKlQwYMAD4zdaeOXOGYcOGMWjQIAC+//57qlatioiwYcMGUlNTuemmm0hNTeX++++nU6dOXoceyFGvNm3aMGnSJOrXr8/MmTNp2rSpd9n98OHDSUlJ4dy5cyxbtoy+fftSoUIFtm3bxqFDhwgMDGThwoW5PofAMPIytqfeMC6NCcDLqvpNpvAOwD2qWkVVq+A48en76hOA4SJyM4CIFE3fY+mLu7fwKZwDkxZcqQr8mXnggQeoWbMmrVu35q233uKGG25gypQpBAcHExISQlBQEF27dr3WahqGcfVYDTQQkfS95SVEJN2LOoGzfD6dAyISKiIFcJagXxQiEgSkqOpkHGe+tqqmAOOB0SJSxE1XXkT+lin7cuA+V7/r3PJX+JOJcyp8oIjUd+UVFpGwi1T3S+AJH92jwTn7BWfQ4HVgu6oeyZxRVWcD64DOblAp4Gd3W0BHn6T/A+4AzrvO/ybg7zjOfk6sBaqLyK1um/0VZ8A9z/Lzzz/TpEkTIiMjue2222jWrBmtWrUiPj6eWbNmERERwT//+U/69esHwMsvv8yRI0fo3bu39/Vv6aSkpLBw4cIss/Rz5syhYsWKrFq1iri4OFq0aAE4s/H33nsvAPXq1aN9+/bUrl2biIgIzp8/T8+ePQEYNmwYr7/+OtWqVePIkSPe/eYej4caNWoQHBzMgQMHvA769u3biYmJISoqiiZNmhAfH0/NmjUBGDFiBKGhoURGRtK6dWuaNm0KwKxZswgPDyc6OprHH3+cadOmISJMnz6d5cuXM3HiRO8r8jZt2pSjXt27d+fIkSNUq1aN119/naFDhwLO8v5nnnmG2267jR49elC7dm3i4uIICgrixRdfpFGjRkRGRrJp0yYGDhx4mb9pw7gGXOvj9+2yKz9dwEk/YbHAp0AVnJPwJVP8BqCee9+V315ptwVnzyZkfKXdd8CHOPsJL6iTvdIu95heF0de1MteaWfX5brc3+wtPs9NcRzFr92rjRv+JPAtsNR9bg/8AHiAscBEN3wi0N5H3hB8XsHm/uZXAVq48je55cW48UWA4TivZ9uC4+y2cOOScF9pBzzjxm8B+rhh2cmMxhkI2Ozancf86eqjoyc9r/scgLO0/mucV9S94xMXg7NqoXMOda6DM7hQAPiHa+c8OHv9J/qkWwH8271/BDgGFHCfb8aZlT/uhu8Brnfj7sV5DewPwKDcfO950WZmJi/+9vrD9Lx85AcdVe2Vdnn5suX3hnERqGpJP2EenE4KOAf3ZI6v7XP/Ac4exMxpulwuHQ3DMIwLo6pJOHvO05+XALf5STcGxwlNf56Jcwhq5nRdMj0PyfScXlYSv+0n941PBQa4V+a4Kj73r+PMkPvGf5GNzE04+9Vz1NUnPDbT82GcbWX+0q4j03YAP3Vej3MYIcA49/Inq6HP/cfAxz7P+3GW1vvL9zmZzrYxDMP4M2LL7w3DMAzDMAzDMAwjn2JOvWEYhmEYhmEYhmHkU8ypNwzDMAzDMAzDMIx8ijn1hmEYhmEYhmEYhpFPMafeMAzDMAzDMAzDMPIp5tQbhmEYhmEYhmEYRj7FnHrDMAzDMAzDMAzDyKeYU28YhmEYhmEYhmEY+RRz6g3DMAzDMAzDMAwjn2JOvWEYhmEYhmEYhmHkU8ypNwzDMAzDMAzDMIx8ijn1hmHkOxITE4mOjvZe119/PaNGjWLz5s3Ur1+fiIgIWrduzfHjx6+1qoZhGMaflNOnT1O3bl2ioqIICwvjxRdfBGDs2LFUq1YNEeHw4cPe9B6Ph9KlS9OjRw+io6N5+eWXvXFVqlQhIiKC6OhoYmJivOGbNm3i9ttv94avWbPGry6TJk2ievXqVK9enUmTJnnDp02bRmRkJGFhYQwYMMAb/vrrr1OzZk0iIyO566672LVrl7e8+vXrExYWRvfu3Zk2bZo3j6oyaNAggoODCQ0NZfTo0QAcPXqU+++/n8jISOrWrcuWLVu8ed544w3CwsIIDw+nQ4cOnD59GoAlS5ZQu3ZtwsPD6dy5M2lpaQCMGDHCa/vDw8MpWLAgv/zyi1feuXPnqFWrFq1atbqgXobxh0JV7fqDXkAfoMRVKKcwMBT4DtgCrAFaunFJQMBlLGsisBPYBGwA6l8muUMABar5hPV1w2Lc55N+8lUBHrmM9XsC+N4tN1ftFhwcrHmNpUuXXrWy0tLStFy5cpqUlKQxMTHq8XhUVXX8+PE6ePDga6bXxWB65Z6roROwTvPAb7hddtl1+a+raTPPnz+vJ06cUFXV1NRUrVu3rq5atUo3bNigO3fu1FtuuUUPHTrkTb906VKNi4vz+zuXOW06zZo1088//1xVVT/77DNt3LhxljRHjhzRW2+9VY8cOaK//PKL3nrrrfrLL7/o4cOHtVKlSnrw4EFVVe3UqZMuWrRIVVWXLFmiycnJqqr69ttv60MPPaSqqomJibpjxw5VVZ0xY4befPPNevToUVVVnTBhgj766KN67tw5VVU9cOCAqqr269dPhwwZoqqq27dv16ZNm6qq6p49e7RKlSqakpKiqqoPPvigfvDBB3ru3DmtWLGiJiYmqqrq888/r++//36Wes2bN0+bNGmSIWzkyJHaoUMHjYuL84YNGDDAr155ibxob/3hT0+zmXnjKnSFxgqMq4SICCCqet5PdB9gMpByEfIKqWraRarxClAeCFfVMyJSDmh8kTIuhv6qOlNEmgP/ASJ9Iy+xDgDfAH8FXnWf2wPbLpCnCvAI8HHmiEvUYyXwKeDJbYZTZ89RJf6ziyzmyvJsRBpdroBOSUPjsoQtXryYqlWrcsstt5CYmEijRo0AaNasGS1atOCVV1657HoYhmH80RCR94HXVfVCdu+qISL3ATsup05Xy2YmDY1DRChZsiQAZ8+e5ezZs4gItWrVumzliIh3Vdqvv/5KUFBQljRffPEFzZo148YbbwQc+7hgwQKqVatGcHAwgYGBANx9993MmjWLu+66iyZNmnjz33777UyePBmA4OBgb3hAQABly5bl0KFDlClThnHjxvHxxx9ToICzELhs2bIAbNu2jX/+858AhISEkJSUxIEDBwBIS0vj1KlTFC5cmJSUFIKCgjhy5AhFixb1ltWsWTMSEhLo3r17hnpNmTKFDh06eJ/37NnDZ599xqBBg3j99de94fPmzWP+/PlZ9DKMPxK2/D4fIiJVRGS7iLyNM1v9qIisEpENIjJDREqKyFNAELBURJa6+U76yGgvIhPd+4ki8rqbbpiIDBGRCSLiEZEfXVmIyHUi8pmIbBaRLSLysIiUAB4DnlTVMwCqekBVp/vR+xk33xYR6ZOdTDe8jogsE5H1IvKFiJT30xTLgWpueo+I/FtElgFPi0igiMwSkbXu1UBECohIkoiU8dHpe3cQAuAToK0b/hfgV+CQn3oEuO0dh7NCoaGIbBKRviLSxf0O5gNfuun7uzp8LSIv5VRvVd2oqkk5/gEYGZg6darXqIeHhzNv3jwAZsyYwU8//XQtVTMMw8g3qGqPPObQFwLuA2peQr48w7lz54iOjqZs2bI0a9aMevXq5Zh+1apVdO/enZYtW7J161ZvuIjQvHlz6tSpw7vvvusNHzVqFP3796dSpUr069ePhISELDL37t1LpUqVvM8VK1Zk7969VKtWjW+//ZakpCTS0tL45JNP/NrN8ePH07Jlyyzh27dvJzU1lapVqwLwww8/MG3aNGJiYmjZsiXfffcdAFFRUcyePRuANWvWsGvXLvbs2UOFChXo168flStXpnz58pQuXZrmzZsTEBDA2bNnWbduHQAzZ87MoldKSgoLFizggQce8Ib16dOH4cOHe533dPbt2+dXL8P4I2FOff6lBvBfoBnQHbhbVWsD64BnVHU0sA9ooqpNshfjJdiV8az7HAK0AOoCL4pIYeAeYJ+qRqlqOLAAx6nerao5bl4WkTpAV6AecDvwmIjU8ifTLWsM0F5V6wATgH/5EdsaZ3Y9nTKq2lhVRwJvAm+o6m3AA8D77mqGucD9rk71gCRVPeDmPw78JCLhQAdgGplwBwA+A15Q1c+AeGCFqkar6htusvpAZ1Vt6q4mqO62YzRQR0QaZdOWxkWSmprKvHnzePDBBwGYMGECb731FnXq1OHEiRMUKVLkGmtoGIaR98hmkN4jIjFu/EkR+Zcbv9pn8NufrFvdge61IvJK+gSCiMSKyKc+6caKSBf3/gU3/RYReddddZh5gP45oA0wwh04r+peC9wB/xUiEuLmyzA5cYWa7ZIoWLAgmzZtYs+ePaxZsybDfvLM1K5dm127djF+/HiefPJJ7rvvPm/cypUr2bBhA//3f//HW2+9xfLlywEYN24cb7zxBj/99BNvvPFGltlscLbaZkZEuOGGGxg3bhwPP/wwDRs2pEqVKhQqlHFMZPLkyaxbt47+/ftnCP/5559JSEjggw8+8DrRZ86coVixYqxbt47HHnuMbt26ARAfH8/Ro0eJjo5mzJgx1KpVi0KFCnH06FHmzp3Lzp072bdvH8nJyUyePBkRYerUqfTt25e6detSqlSpLHrNnz+fBg0aeFcffPrpp5QtW5Y6depkqWtqaqpfvQzjj0SeGs00LopdqrpaRFrhjGKvdG1iEWDVJciboarnfJ4/c2fez4jIQaAcjgP9mogMAz5V1RVumbnhTmCOqiYDiMhsoCGOM5tZZjgQDix05RcEfvaRNUJEBuPMovtaL18n/G6gpo9+14tIKTfNC8AHOEvtMzvuU93wFsBdOAMR6RQGFgOPq+qyHOq6UFXTT21p7l4b3eeSOE7+isz1zkFeFkSkJ9ATICAgkBciLmW3wZWjXHFnCf7lxuPxZHj+6quvuPXWW9m+fTvbt28HYODAgQD89NNPlC1bNkOekydPZpGRFzC9ck9e1Mkw8iHpA8txACJSGviHT/x1wGpVHSQiw3FW5L2aVQzgDKKPU9X/isjjuSx/rKq+7Jb9IdAKmO/GlVHVxm5cdRwbOdN9Xgz0UtXv3IH5t4Gmbr70yQnfvgxuvqtuM/39TlWpUoW33nqLhx9+GHAO0lu5ciWlS5fOkO7kyZOULFmSEydOMHfuXG/8jh07AKhVqxZTpkzh/PnzTJgwgfvvvx+Px0NgYCCrVq3KUvbx48fZtGmTN3zNmjVER0fj8XgoVaoUw4Y54yDz58+naNGi3nTr169n9OjRjBo1ilWrfutaJicn07dvXx555BFOnz7tTX/jjTdSoUIFPB4PN9xwAxs3bvTGde7cmc6dO6OqdOjQgT179jBnzhyKFSvmXZEQGhrKjBkzqFixIoB3+9zatWspXbp0hnqNHTuWxo0be8OmTJnCl19+yezZs0lNTSUlJYVmzZoxaNAgbrrppmz1yivkF9uWX/T8M2JOff4l2f0UHCeyQ06JXXyHaotlIy+dMz7354BCqrrDnXG/F0gQkS+B14DKIlJKVU/kULZf7z8bmXOArapaPxtZ/dMNfA51KIBziN6pDEqIrAKqiUggzrK+zJ2U+cAInEM/jmcatEgD1uM4/Dk59b56CJCgqv/JnChzvdM7OLlBVd8F3gWo/JdqOvKbvPWv/GxEGldCp6SOsRme33nnHXr37k1srBN+8OBBypYty/nz5+nSpQv9+/f3xoHTyfJ9ziuYXrknL+pkGPmQCw3Sp+Kc7wKO3WuWg6wGOCviAD4kdzPlTURkAFACuBHYym9OfZZVcgAiUhK4A5jho2tRnySZJye8XAubmdQxlkOHDlG4cGHKlCnDqVOneP7553nuuee8v2HFihWjQYMGBAQEALB//37KlSvHsmXLKFGiBEWKFKFNmzakpKRw/vx5SpUqRXJyMgMHDuSFF14gNjaWSpUqISLExsayePFiQkJCsvxGRkZGUqdOHaKiogDYsmULkyZN4sYbb/TazaNHj9KnTx+mT59OcHAwGzdu5O2332bRokVUr17dKys1NZWWLVvSu3dvoqOjM5T1yCOPkJKSQmxsLB6Ph9DQUGJjYzl27Ji3Pu+99x7NmzcnLi6OgIAAZsyYQd26dSlevDgffPABd999N7GxsV69zpw5wyuvvOKtLzhnB2zdupUFCxZw3XXXAWSx9a+99hqffur8CTdq1MivXnmJ/GLb8ouef0bylidgXAqrgbdEpJqqfi/OHveKqroDOAGUAtLfl3JAREKBRJwl6Dk54VkQkSDgF1Wd7C6v66KqKSIyHhgtIn9X1VRx9r/fpaqTfbIvByaKyFAcR/d+nLMAssjE2aceKCL1VXWVuxw/WFW3knu+xDlJfoSre7SqblJVFZE5wOvAdlU94ptJVU+JyHPADj8yFeiG06GIV9Wh/NbG2fEF8IqIfKSqJ0WkAnAW538vc70vieKFC5Lo5wC5a4nH48nigF9uUlJSWLhwIf/5z2/jJVOmTOGtt94CoF27dnTt2jW77IZhGH9ashlQ9+Ws/rZm+xwX7i9mXd/tDIT7bvMsBiAixXBm2GNU9ScRGULGiYbMkwzpFACOqWp0NvHZ5cvA1bSZP//8M507d+bcuXOcP3+ehx56iFatWjF69GiGDx/O/v37iYyM5N577+X9999n5syZjBs3jtOnTxMYGMjUqVMREQ4cOMD9998POAfLPfLII9xzzz0AvPfeezz99NOkpaVRrFgx7377devW8c477/D+++9z44038vzzz3PbbbcB8MILL3iXrT/99NNs3rzZG55+OF3//v05efKkd3tb5cqVmTdvHtOnT2f58uUcOXLEu6Jg4sSJREdHEx8fT8eOHXnjjTcoWbIk77//PuDsve/UqRMFCxakZs2ajB8/HoB69erRvn17ateuTaFChahVqxY9e/YEnFfXffrpp5w/f55//OMfNG2aviAD5syZQ/Pmzb0O/YV45JFHvNsUfPUyjD8U1/r4fbsu/sI5cX2Lz3NTYC3wtXu1ccOfBL4FlrrP7YEfcE5WHwtMdMMn4uxfT5c3BOjn87zFLbOFK3+TW176q96KAMNxXsW2Bfgf0MKNS8J9NRvwjBu/BejjhmUnMxpnIGAzzgj+Y/509dHRk57XfQ7AGe3/GucE+3d84mJwOiCds6uzP7m4r7Rz6/sF0JvfluRvxnkFXhecZYW+Mp7GmRX5BmdrRNUc6v0UsAenM7QP5yyAHP8e/uyvtLsYTK+LIy/qZa+0s8uu33/hHKRbzL2/D+eg2Cz2zr1vn95fyEbWPOBv7v0/fGxlJbcPUBQojfM62i5AGeAAUBxnS9oWYIibJ7MtHwN09Xn+f8CD7r0AUe69376Bvysv2szM5MXfXn+YnpeP/KCjqr3SLi9fNlOfD1HndPRwn+clwG1+0o3BMYjpzzOBLMvWVbVLpuchmZ7Ty0rCcWYz508FBrhX5rgqPvev48yQ+8Z/kY3MTUCjC+nqEx6b6fkw8HA2adeRaTtA5jr7k6uqJd3PVBynPJ27MmWbmEnGmzh7Dn35Af/1Hg2M9qeLYRiGYVwmInDOpzmPs3rsHzjb6S6Fp4GPReRpYFZ6oDqz8NNxBrC/wz1bRlWPich7OAPdSTgD29kxFXhPnLfwtAc6AuPcc3UKu/GbL1FvwzCMPwzm1BuGYRiGYfyJyGZAPdYnvqTPvd8JAZ/4nThvfQHAdbjT47Ib8B8MDPYTHpvpeSVZX2l3j598XbLTzzAM48+AvdLOMAzDMAzDMAzDMPIpNlNvGIZhGIZh5IiIDAIezBQ8Q1X/5RvgO8tvGIZhXB3MqTcMwzAMwzByxHXe/3XBhIZhGMZVx5bfG4ZhGIZhGIZhGEY+xZx6wzAMwzAMwzAMw8inmFNvGIZhGIZhGIZhGPkUc+oNwzAMwzAMwzAMI59iTr1hGIZhGIZhGIZh5FPMqTcMwzAMwzAMwzCMfIo59YZhGIZhGIZhGIaRTzGn3jAMwzAMwzAMwzDyKebUG4aRJzl27Bjt27cnJCSE0NBQVq1axebNm6lfvz4RERG0bt2a48ePX2s1DcMwjD8xP/30E02aNCE0NJSwsDDefPNNAJ5//nkiIyOJjo6mefPm7Nu3D4CPPvqIyMhIIiMjueOOO9i8eTMAiYmJREdHEx0dTY8ePbj++usZNWoUAL/88gvNmjWjevXqNGvWjKNHj/rVZcCAAYSFhREaGspTTz2FqgIwbdo0IiMjCQsLY8CAAd70y5cvp3bt2hQqVIiZM2dmkFWwYEGvPm3atPGGd+nShVtvvdWr56ZNmwDweDyULl3am+fll1/OUq/o6OgM9dq0aRO333470dHRxMTEsGbNGm85Ho+H6OhowsLCaNy4sTe8W7dulC1blvDw8Az6Ztfec+fOpXv37t4yvvrqqwt/qYaRH1FVu+zyXkAfoMRVKKcIMAr4AfgOmAtUvALlxAK/AhuB7cCLl0luFeAUsAnYBvwXKHwZ5E4ADgJbcpsnODhY8xpLly793TI6deqk7733nqqqnjlzRo8ePaoxMTHq8XhUVXX8+PE6ePDgq67XlcD0yj1XQydgneaB32O7rsx1Fe1cYWCoa+O2AGuAlm5cEhBwGcuaCOx0bdIGoP5lkjsEUKCaT1hfNyzGfT7pJ18V4JHLWL8ngO/dcgN8wgUY7cZ9DdS+kKzLbTP37dun69evV1XV48ePa/Xq1XXr1q3666+/etO8+eab+ve//11VVVeuXKm//PKLqqp+/vnnWrdu3SwyFy1apOXKldOkpCRVVe3fv78mJCSoqmpCQoIOGDAgS56VK1fqHXfcoWlpaZqWlqa33367Ll26VA8fPqyVKlXSgwcPqqpjWxctWqSqqjt37tTNmzfro48+qjNmzMgg77rrrvNb386dO3vT+v4eL126VOPi4nJsq7S0tAz1atasmX7++eeqqvrZZ59p48aNVVX16NGjGhoaqrt27VJV1QMHDnhlLFu2TNevX69hYWEZZGfX3idOnNAlS5aoqurmzZu1Ro0aOep4rciL9tYf/vQ0m5k3rkKXMhBg5G9ERABR1fN+ovsAk4GUi5BXSFXTLlKNfwOlgGBVPSciXYHZIlLP/YG4nKxQ1VYich2wSUQ+VdX16ZGXqD/AD6oaLSIFgYXAQ8BHv1PXicBYnEGCXHHq7DmqxH/2O4u9vDwbkUaXS9QpaWgcx48fZ/ny5UycOBGAIkWKUKRIERITE2nUqBEAzZo1o0WLFrzyyiuXS23DMP4g5BE79wpQHghX1TMiUg5ofIE8v4f+qjpTRJoD/wEifSN/h637Bvgr8Kr73B5nMDsnqgCPAB9njrhEPVYCnwKeTOEtgeruVQ8Y535my+W0mUlD4yhfvjzly5cHoFSpUoSGhrJ3715q1qzpTZecnIzzJwl33HGHN/z2229nz549WeRu2LCBqlWrcssttwDObLPH4wGgc+fOxMbGMmzYsAx5RITTp0+TmpqKqnL27FnKlSvHjz/+SHBwMIGBgQDcfffdzJo1i7vuuosqVaoAUKDA1Vm4u3jx4gz1EhHvirtff/2VoKAgAD7++GPatWtH5cqVAShbtqxXRqNGjUhKSsoi+/rrr/fe+7Z3yZIlvfe+4YbxR8OW3/9JEJEqIrJdRN7GGcV/VERWicgGEZkhIiVF5CkgCFgqIkvdfCd9ZLQXkYnu/UQRed1NN0xEhojIBBHxiMiPrixE5DoR+UxENovIFhF5WERKAF2Bvqp6DkBVPwDOAE1dXb8VkUki8rWIzHTzICJ1RGSZiKwXkS9EpLwb7hGRYSKyRkR2iEjDzG2gqsnAeqCqiHRx6z0f+NKV0V9E1rplvuSGDROR3j5tMEREns0k9xzODEwFn7Ze4bbtBhG5ww1/W0TauPdzRGSCe99dRF51ZS0HfrmU7/iPxI8//khgYCBdu3alVq1a9OjRg+TkZMLDw5k3bx4AM2bM4KeffrrGmhqGkVfIg3buMeBJVT0DoKoHVHW6H72fcfNtEZE+2cl0w/3awEwsB6q56T0i8m8RWQY8LSKBIjLLtXVrRaSBiBQQkSQRKeOj0/fiDEIAfAK0dcP/grP67ZCfegS47R2Hs0KhoYhsEpG+F2Fz/dZbVTeqapKfurYF/utOGK4GymTTJleFpKQkNm7cSL16zrjCoEGDqFSpEh999JF3Obov48ePp2XLllnClyxZQocOHbzPBw4c8A4clC9fnoMHD2bJU79+fZo0aeIdZGjRogWhoaFUq1aNb7/9lqSkJNLS0vjkk09yZTtPnz5NTEwMt99+O5988kmGuEGDBhEZGclbb73FmTNnvOGrVq0iKiqKli1bsnXr1iwyp06dmqFeo0aNon///lSqVIl+/fqRkJAAwI4dOzh69CixsbHUqVOH//43d/Mc2bX3ihUrCAkJIS4ujgkTJuRKlmHkN2ym/s9FDRxn+gVgNnC3qiaLyHPAM6r6sog8AzRR1cO5kBfsyjgnIkOAEKAJzgx8ooiMA+4B9qlqHICIlMbpbOxW1cwbotcBYThL8msA3VV1pev89haRN4ExQFtVPeQa+38B3dz8hVS1rojcC7wI3O0rXERuAm7HmT25DagPRKrqL+LMbFQH6uIs55snIo2AqTjbBN52xTzk1qmAj9xiODMDT7tBBzoY0LgAACAASURBVIFmqnpaRKoDU4AYnI5WQ2AezgBAesfjTrecXCMiPYGeAAEBgbwQcSmTL1eOcsWd2fpLwePxkJiYyPr16+nSpQtdunRhzJgx/OMf/6BXr168+uqr9O/fnwYNGlCgQAHv7EVuOHny5EWlv1qYXrknL+pk5Cnyup3LgIjUcfWth2N7/uc64H/JLFNECpOzDUynNc7sejplVLWxK+dj4A1V/UpEKgNfqGqoiMwF7gc+EJF6QJKqHhBnVvM48JOIhOM40dNcnX3rUQ7Htg1W1YUikgz0U9VWbnwXcmdzA/20ZU5UAHw91D1u2M+Z9LsiNtP3t+jUqVM8/fTT9OjRgw0bNgDOirJmzZrx0Ucf0a9fP7p2/a3ZNm7cyJgxYxg9enQGOWfPnmXlypU89thj3vC0tLQMaTI/A+zdu5evvvqKKVOmANCvXz/Kli1LVFQUvXv3pmXLlhQoUICwsDCOHTuWIf/+/fvZunUrAQEB3rCpU6cSEBDAvn376NWrF8nJyVSoUIHWrVvTuXNnzp49y7Bhw+jVqxedO3cmOTmZyZMnU7x4cVavXk2LFi2YPHlyhnrNmjWLVq1aecsePXo03bt3p3HjxixdupR27doxcuRIdu3aRWJiIiNHjiQ1NZXHH38cEaFSpUpefZOTk7O0QXbtXatWLRo2bMjmzZt54oknGDly5AW/26tNfrFt+UXPPyPm1P+52KWqq0WkFVATWOka7CLAqkuQNyN9pt3lM3dG4oyIHATK4XQsXhORYcCnqrpCnEL9LbH3Df9JVVe695OBp4AFQDiw0NW7IBkN92z3cz3O0r90GorIRuA8MFRVt4rIbcBCVU2fFW/uXhvd55JAdVUdLyJlRSQIp7NxVFV3i0gVnBn/TTgdk5mq+rWbtzAwVkSigXM4nUKAFUAfEamJs3TxBndGob5bv1yjqu8C7wJU/ks1HflN3vpXfjYijUvVKaljLCEhISQkJNC7t7NIomDBggwdOpROnTrRqVMnwBnJ37p1K7GxsbmW7fF4Lir91cL0yj15UScjT5FX7Fxu5d8JzHFXkiEis3EGfxf4kRlOzjZwhIgMxplF7+4TPs3n/m6gpo9+14tIKTfNC8AHOEvtffOAM/D8V6AFcBcZnfrCwGLgcVVdlkNdL2hzcexkhnrnIA+cfkNmsvQvrpTNTOoYCzgOa6tWrejVqxfPPPNMlnS33norcXFxTJo0CYCvv/6asWPHsnDhQoKDgzOknTt3LjVq1KBdu3besAoVKlCjRg3Kly/Pzz//TFBQUJbfwREjRhAXF+ed+V+7di1nzpwhNjaW2NhYBg4cCMC7777L999/nyH/xIkTCQsLy/a39csvv6Ro0aJZ4rdt28aiRYuyhMfGxvLOO+8QHh7uHSiYO3cu9erVy1Cvtm3bMmvWLESExo0b88YbbxAbG8vq1au9M/4A8+bNo1ixYt5ykpKSuO6667LVN3N7p9uN2NhYRo0alUGvvEJ+sW35Rc8/I3nLEzCuNMnup+AY1w45JXbxNY7FspGXzhmf+3M4M+c73JmIe4EEEfkSGAncIiKlVPWET57awHw/5aY/C7BVVetno2t6+efI+Le9In22IAf9BUhQ1f/4STcTZw/hzWScUU/fU18e8IhIG1Wdh3OI0AEgCmdG/zSAqu4VkRtwZnWWAzfizPyfzNQOF0XxwgVJHBp3qdmvCB6Px9vZuRRuvvlmKlWqRGJiIjVq1GDx4sXUrFmTgwcPUrZsWc6fP8+rr75Kr169Lp/ShmH8Ecgrdu41oLIfO5cZv95/NjLnkLMN7K+qM/2E+9ahAM4heqcyKCGyCqgmIoHAffy2fz6d+cAInAOxjmcatEjDGUxvAeTk1OfK5maut6pmXbf+G3uASj7PFYF9OaS/7DZTVenevTuhoaEZHPrvvvuO6tWrA45TGhISAsDu3btp164dH374YRaHHmDKlCk0bdo0Q1ibNm2YNGkS8fHxTJo0ibZt22bJV7lyZd577z3++c9/oqosW7aMPn36AHht59GjR3n77beZPj3LLpAMHD16lBIlSlC0aFEOHz7MypUrvafm//zzz5QvXx5V5auvvvKeQr9//37KlSuHiLBmzRrOnz/PTTfdlKFevkvvAYKCgli2bBmxsbEsWbLE215t27bliSeeIC0tjdTUVP73v//Rt2/fHHXOrr2///779EMV2bBhA6mpqRn0Mow/Cran/s/JaqCBiKTvuSshIumW5QTOssJ0DohIqIgUwFmad1G4M9wpqjoZp5NT252RmAS8Ls4hc4hIJ6AEsMTNWllE0jsuHYCvgEQgMD1cRAqLSNjF6pQNXwDdRKSkK7uCiKSfzJI+Q9Eex8HPgKr+DMQD/3SDSgM/uwc0PYozm5LOKpxDmpbjzEj0cz+NTIwZM4aOHTsSGRnJpk2bGDhwIFOmTCE4OJiQkBCCgoIyLGU0DMPw4VrbuRRgPDBaRIq46cqLyN8yZV8O3Ofqd51b/gp/Mrk8NvBLnJPk03WPBnAPqJ0DvA5sV9UjvpncQYDncJb7Z0ZxtgCEiEi8G5a5jTPj1+ZmU++cmAd0EofbgV9dm3zVWLlyJR9++CFLlizxvrbt888/Jz4+nvDwcCIjI/nyyy+9r7p7+eWXOXLkCL179/a+Zi2dlJQUFi5cSMOGGY8Fio+PZ+HChVSvXp2FCxcSH+8087p16+jRowcA7du3p2rVqkRERBAVFUVUVBStW7cG4Omnn6ZmzZo0aNCA+Ph472DC2rVrqVixIjNmzODvf/87YWHOn9P27duJiYkhKiqKJk2aEB8f7z34r2PHjkRERBAREcGvv/7K4MGDAZg5cybh4eFERUXx1FNPMXXqVO+hdOn18p2lB3jvvfd49tlniYqKYuDAgbz77rsAhIaGcs899xAZGUndunXp0aOHd/CgQ4cO1K9fn8TERCpWrMj48eO9beSvvWfNmkXXrl2Jjo7m8ccfZ9q0aXZYnvHH5GKPy7crf144y9G3+Dw3BdbivALma6CNG/4k8C2w1H1uj7PH3YNzKvtEN3wi0N5H3hCc/XPpz1vcMlu48je55aW/Aqcozt7A9FfazQcq+ei6DXjHzTsL9/VDQDROJ2gzsBV4zA33+MgOwNkPCM4r7T710x5dgLGZwp7GWUb5DY7zXdUn7pv0NsmmPcXVqSHOEsKvcTqVCfi87gdnSeQ+974wzsxFO5/4KTjLKc/izEB0v9B3+0d9pd2VwPS6OPKiXvZKO7uyu/KgnSsCDMd53doW4H9ACzcuCffVbMAzbvwWoI8blp3M7GxgBl19dPTaRvc5AGdp/de4dtYnLgbHQe+cXZ39yU23cW59vwB689uS/M04q9e6kAubm0O9n3JtYhrOTPz7brgAb7nf3ze+dc3uyos2MzN58bfXH6bn5SM/6Khqr7TLy5c434Vh5B3c/eqfqmr4NVYlX1CjRg1NTEy81mpkIK/uuTK9Lo68qNfV0ElE1qtqzIVTGoaR38iLNjMzefG31x+m5+UjP+gI/vU0m5k3sOX3hmEYhmEYhmEYhpFPsYPyjDyHOu+itVl6wzAMwzAMwzCMC2Az9YZhGIZhGIZhGIaRTzGn3jAMwzAMwzAMwzDyKebUG4ZhGIZhGIZhGEY+xZx6wzAMwzAMwzAMw8inmFNvGIZhGIZhGIZhGPkUc+oNwzAMwzAMwzAMI59iTr1hGIZhGIZhGIZh5FPMqTcMwzAMwzAMwzCMfIo59YZhGIZhGIZhGIaRTzGn3jCMPMGxY8do3749ISEhhIaGsmrVKm/ca6+9hohw+PDha6ihYRiG8WemW7dulC1blvDwcG/Yww8/THR0NNHR0VSpUoXo6GgAFi5cSJ06dYiIiKBOnTosWbIEgJSUFOLi4ggJCSEsLIz4+HivrL59+xIdHU2PHj0IDg6mTJkyfvWYMmUKERERREZGcs8992SxjdnZzLVr11KwYEFmzpwJwKZNm6hfvz5hYWFERkYybdo0b9ru3bsTFRVFZGQk7du35+TJkwDs3r2bJk2aUKtWLbp3787nn3+eY31z0nfGjBmEhYVRoEAB1q1b502fk6zY2Fhq1KjhbfODBw/m+J0Zxp8GVbXLLrvy8RUcHKx5jaVLl150nk6dOul7772nqqpnzpzRo0ePqqrq7t27tXnz5lq5cmU9dOjQVdframB65Z6roROwTvPA/7Zdef8C+gAlrkI5RYBRwA/Ad8BcoOIVKCcW+BXYCGwHXrxMcqsAp4BNwDbgv0DhyyB3AnAQ2JLbPL/HZi5btkzXr1+vYWFhfuOfeeYZfemll1RVdcOGDbp3715VVf3mm280KChIVVWTk5N1yZIlqurYujvvvFM///zzDHKWLl2qo0eP1q5du2Yp4+zZsxoYGOi1h/3799cXX3zRG5+dzUxLS9MmTZpoy5YtdcaMGaqqmpiYqDt27FBV1b179+rNN9/stb2//vqrN2/fvn01ISFBVVUfe+wxffvtt1VV9YMPPtBbbrklx/rmpO+2bdv022+/1caNG+vatWu95WUnS1WzpM0NedGWZSY/6KjqX0+zmXnjKnQVxg2uGiLSB3hXVVOucDmFgVeAB4AzQAqO4fs/EUkCYlT1skwpishEoDGOkT0PPK6qq3LMlDu5Q4DHgEM4nYVXVHXK75QZAnwA1AYGqeprPnH3AG8CBYH3VXVoDnI8QD9VXZddmovUqz/Q0X0sBIQCgar6y+WQf4GyPbh1EZHPgUdU9djlLOPU2XNUif/scor83TwbkUaXXOqUNDSO48ePs3z5ciZOnAhAkSJFKFKkCODMXAwfPpy2bdteKXUNwzCyRUQEEFU97ye6DzAZpx+QW3mFVDXtItX4N1AKCFbVcyLSFZgtIvXcTvXlZIWqthKR64BNIvKpqq5Pj7xE/QF+UNVoESkILAQeAj76nbpOBMbiDBLkiku1mUlD42jUqBFJSUl+41WV6dOne2eVa9Wq5Y0LCwvj9OnTnDlzhhIlStCkSRPAsXW1a9dmz549WeRNmTKFl156yW85qkpycjI33XQTx48fp1q1at747GzmmDFjeOCBB1i7dq03LDg42HsfFBRE2bJlOXToEGXKlOH666/3lnfq1CmcfwMQEY4fPw5AcnIyQUFBOda3QIEC2eobGhrqty2zk1W0aFG/6Q3DyIfL78UhO737ACUuUt6lDGy8ApQHwlU1HGiNY2yvFP1VNRqIB/6TOfIS6wDwhiu3LfAfd7Di9/AL8BTwmm+ga8DfAloCNYEOIlLzd5aVa1R1hKpGu3X9J7Dsajj0fvS493I79H8UfvzxRwIDA+natSu1atWiR48eJCcnM2/ePCpUqEBUVNS1VtEwjD8RIlJFRLaLyNvABuBREVklIhtEZIaIlBSRp4AgYKmILHXznfSR0d4dmEdEJorI6266YSIyREQmiIhHRH50ZSEi14nIZyKyWUS2iMjDIlIC6Ar0VdVzAKr6Ac6kQlNX129FZJKIfC0iM908iEgdEVkmIutF5AsRKe+Ge0RkmIisEZEdItIwcxuoajKwHqgqIl3ces8HvnRl9BeRtW6ZL7lhw0Skt08bDBGRZzPJPQesASr4tPUKt203iMgdbvjbItLGvZ8jIhPc++4i8qorazlO3+Oas2LFCsqVK0f16tWzxM2aNYtatWplcUqPHTvG/PnzueuuuzKE79+/n507d9K0adMssgoXLsy4ceOIiIggKCiIbdu20b17d4BsbebevXuZM2cOvXr1ylb/NWvWkJqaStWqVb1hXbt25eabb+bbb7/lySefBGDIkCFMnjyZihUrEh8fz5gxY3Ksb0765gZ/bde1a1eio6N55ZVXuPxjWoaRP8kXM/UiUgX4P2ApUB8YJSK9gKI4S9G6At34zbgeVtUmInJSVUu6MtoDrVS1i2tkfwFqARtE5ARQGfiL+zlKVUe7o9TTgYo4M8yvAPNxZrhvVdUzAKp6wE2XWe9nXL3AmZ0e5U+mqk4TkTrA60BJ4DDQRVV/ziRyOVDNle0B/h/QAJgnIv8F3nH1B2eAYxXwIxCd7kyKyPduHi+q+p2IpAA3AAdF5DGgJ84M/vfAozidh++AqkBpt/1iVXW5iKwAuqrq927+uEx61wW+V9UfXR2m4gwkbMvcZn7acBxwG1AcmKmqL7rh97rtdRinw/UXVW11IXlAByDbFQnu39oC4CvgdmAzzuqDl4CyQEdVXeN+j2OACJz/oyGqOldEirvpa+IsXSzuIzsJdxWHiHwCVAKKAW+q6rtumpM4Kxpa4SxVbOv+fWXWsyfOd0RAQCAvRFzKpMmVo1xxZ7Y+N3g8HhITE1m/fj1dunShS5cujBkzhu7du7N582ZGjBiBx+Ph9OnTrFy5ktKlS1+yXidPnsTj8Vxy/iuF6ZV78qJOxh+WGjj9ixeA2cDdqposIs8Bz6jqy66db5LL1XnBroxz7mq5EKAJzqRAomvv7gH2qWocgIiUxrH7u1X1eCZ564AwnH5QDaC7qq50nd/eIvImjp1qq6qHRORh4F/81i8ppKp1XXv6InC3r3ARuQnHDr6CY4frA5Gq+ouINAeq49h3wemHNAKm4mwTeNsV85BbpwI+cosB9YCn3aCDQDNVPS0i1XFsdAxOn6chMA9nAKC8m/5Ot5xcczlsZvrvzv79+0lOTs7yO/TGG29Qt27dLOE7d+5k8ODBDB8+PEPcuXPnGDhwIPfeey+7d+9m9+7d3rgFCxZQv359VqxYkUWPtLQ0/v3vfzNu3DiCgoIYPXo0PXv25MEHH+S5557zazOHDBnCww8/zIoVK9i/fz9bt24lICDAK/PIkSP07duX+Ph4li9f7g3v3Lkzf/vb3xg9ejQvvfQSLVu2ZPr06TRs2JCHHnqIdevW8cADDzBhwgQKFCjgt77Z6fvoo496yzl27Bjr16/37tvPqe0ef/xxAgMDSUlJ4cUXXyQlJYUWLVrk+N3lB7uRH3SE/KPnn5F84dS75HXjmgHXSe+KY7gE+J+ILMMZOMgg050hz8nwptMa+MbnuYyqNnblfIwz8/6ViFQGvlDVUBGZC9wPfCAi9YAkVT0g7jIqN29t4DtVTT9tZLaqvufGvYrTURgjIjtwnNVbcUbvG4rI/3D29X2fQ3NUAH7yed7jtktuGOR2IAoCi0UkEtiBs2KhkaruFJFcbRtwZy7uAZ64QNJqwIM4HYC1wCM4nYg2wEDgPmAQsERVu4lIGWCNiCwC/g6kqGqkq+uGbMro5tarOLBWRGap6hHgOmC1qg4SkeE4A0ivZs7sDgK8C1D5L9V05Dd561/52Yg0cqtTUsdYQkJCSEhIoHdvZ4KnYMGCDBkyhCNHjvDEE87XdfjwYZ588knWrFnDzTfffEl6eTweYmNjLynvlcT0yj15USfjD8suVV0tIq1wbN9K13YWwRk0v1hmpM+0u3zmTg6cEZGDQDkcG/+aiAwDPlXVFeIU6m860jf8J1Vd6d5Pxlk1twAIBxa6ehcEfCcLZruf63H2vKfTUEQ24mz5G6qqW0XkNmChzyq35u610X0uCVRX1fEiUlZEgoBA4Kiq7nYHzKuKyCacwYCZqvq1m7cwMFZEooFzOP0zgBVAH3FW9m0DbnBXGtR365drLofNTOoY63wmJXHddddl+B1KS0vj4YcfZv369VSsWNEbvmfPHnr27Mn06dNp0CDDfArdunWjXr16jB49OktZjz32GJMmTeKOO+7IErd27VpuuOEGOnZ0dhUWLFiQoUOHUqFChWxt5q5duxg+fLg3fMOGDURFRXHfffdx/PhxYmNjGTlyJA8++KDfuhcqVIgRI0YwbNgwHn/8cRYsWEClSpUAKFCgAOHh4ZQtW9ZvfbPT17f9ypQpQ506dYiJiclV26Vz8OBB1q1bd0GbkB/sRn7QEfKPnn9G8pYnkDN5xbjmVv6dwBx3+RoiMhtnxHmBH5nh5Gx4R4jIYJz9775rlqb53N8N1PTR73oRKeWmeQFn9vivmfL0dWfl/4Lj7KYT7jrzZXAM9Rdu+AqgEY5Tn4DjcC7DcXxzwl+j5Xa91EPuCHshnFH6mjgj/j+q6k43zRTcEfgL0BpYmYul9ztV9RsAEdkKLFZVFZFv+K3j0xxoIyL93OdiOKskGgGjAVT1axH5Gv88JSL3u/eVcDo5R4BU4FM3fD3Q7EKVKl64IIlDMy+OuLZ4PB5vByg33HzzzVSqVInExERq1KjB4sWLqV27NosXL/amqVKlCuvWrcswu2AYhnEFSXY/Bceh7ZCLPL62rVg28tI543N/DmfmfIc7KXAvkCAiXwIjgVtEpJSqnvDJUxtn9WDmctOfBdiqqvWz0TW9/HNk7A+uyGblm6/+AiSoapYtgcBMoD1wMxln1NP31JcHPCLSRlXnAX2BA0AUjn0/DaCqe0XkBpz+yXLgRpyZ/5OZ2uGiuBI2c9GiRYSEhGRw6I8dO0ZcXBwJCQlZnNLBgwfz66+/8v7772eRlZiYyIkTJ6hf3//XVqFCBbZt28ahQ4cIDAxk4cKFhIaGEhERkeEkeF+buXPnTm94ly5daNWqFffddx+pqancf//9dOrUKYNDr6r88MMPVKtWDVVl/vz5hISEAFC5cmUWL15Mly5d2LVrF6dPnyYwMDDb+manb05kJystLe3/s3fmcTqX6+N/X9ZCJQ2FSRIGY8ZYTlJhJFGctGhB52DaV+lETgtKHWvLF6VNjVNRIkcrCYP6qZCxZiIma7YIY5sZ1++P+36ennnmmbGWGa736/V5zefervu6P88zn+u+7u1hx44dREVFkZmZyaeffsqVV16ZjyTDOHUoTHvqw41rgr/qqGpem3OO2bgCDXHO/QAR6YNbjl7FO8z5EdH7z0NmwPAG2hSnqleFFOvp41up6pI82lAEaBIio7I3enOA6iJSHjfD/FFImRdVNQa4BfivXxIH7uCZB1Q1DrfsPBA/GzcwcTHwOc7pT8QZ2/xYh3NcA0QDGw5RBhG5EHgUaKmq8cBnXpfDHlkJ41byWXofQuh34WBI+CB/dHwEuDHkeVdR1R99Wr4DFiKSiBuEaaKq9XAzHYFnnKl/bBAL72id1AwfPpzOnTsTHx9Pamoqjz/++IlWyTAMA+Bb4DIRCWx/KyUigdnkXeQ8U2eTiNQWd/bP9RwhfoZ7j6q+izufpoGfHBgNvOBXrSEi/8SdIRT4ra8qIhLwAjvitpClAeUD8SJSXERij1SnPJgCJIlIYItjZRGp4NPex9nbDjgHPwd+a2Fv3Bk34Lb0bVR3EOE/cBMbAebgthPOwvVBHvV/TwgdO3akSZMmpKWlER0dzahRowB4//336dgx55jPiBEjWLlyJf3798/x82vr1q3jueeeY9myZTRo0ICEhIQczv3YsWO54oorggfTBQj8VF6lSpXo27cvzZo1O2Z7OW7cuOAhtQEdU1NTUVW6dOlCXFwccXFxbNy4kT59+gDw/PPP88Ybb1CvXj369+9PcnIyIpJne/PTd+LEiURHRzNnzhzatm0bXEafl6z9+/fTunVr4uPjSUhIoHLlytx5551H1XbDOOnQAnAE/6Eu3OzoEn9fHlgDVPfhUrjTYME5yheGlFuJO+m8CDABSPbxyUCHkHz9cCeUB8JLfJ2VgNN83HXA//z9YNzMdwkfrgjc5u/TgSjcCPoir19pL7N+JJn8sXe9iY8vDsRG0jVExxTc/uxAeAzO+Q+EE0LuhwDvAJ/n0+ZJwN3+fitu/3hx3Am1gedW0rdvug+PxC2rrxemW7jsYri9/Rf6ti4MtC+PzzsFt5+uns9bBLdyYhPQFbdPfS1Q1ed/D7fqIb/vUOAcgNKH+10Lf/7k/B7+B3firvhwff/3Edz5CeBWX2QFPqeQ70Z74BMfVws3K5How7tD6u4QePb5XSfLT9r9FZheR0ZB1Mt+0s6uv+KKYAuuwK1KW+Sva338g8ByYIYPd8DtcU/xNiLZxwdtiQ+H28lAv6O1l5/q6wvYj5K4bXqBn7T7BDg/RNdluHN1FuH6O6V8WgLOIV4ILAXu9PEpIbKjcFvzwA3U57Kn3vaOCIvrjut3LcY53xeFpC0OPJM8nqd4nZriVqotwg2eDAizg7fjtiyC65NkADeEpI/FrWzMxE0g3H6oz7Yg2sxwCuK7NxKm5/GjMOioaj9pV5CvQjcLqG7PeVdgrIgEjsJ8ErfP+nXgCxHZqKotcCPBn+IcwCW4peRHQhxu6ftBnMG4N6S+Z4FlIrIPZ2T6hOn5g7gD+b73UW+q6gIRaR0uU1UPiDvIb5jft18Md9DM0iPQ9SHgZb/cuxjOiAeOOf0A1znomk/5Z4AxIvIG8BTwHfALzjCf4du0X0TW4gwvuNHyjj4PInIe7uCeM4GD4n5isI6q7hSRB3Aj+0WBt1T1kG1T1YV+X99S3KDANz5+r7jTdSeLyFb+eMb5cT3wpfrtEMeB/rjPaJHf75iOO9xuJO78gkCnLJJuk4F7fJ40/niehmEYRgFAVdNxA7OB8HTcYXHh+YbjnO1AeDyRZ6e7hoX7hYUDdaXzx5a30PT9uAGEB/NQ+aCq5jraXFVTcdvCwuMTQ+634reWqWoKzuEPz5+MG5gIjfs/3MGuuVC30i80nE7O56m4gfsA8SH3/w7JNwoY5e8zcZMkoXIPZ0uEYRjGSU+hcOoLoHE9APTyV3ha1ZD7F3AntIemT8lDZl6Gt2t4nI9PDAtvxS2jj5R3HmFL1iO0eT7uMEJwjunIPGQ1Dbkfg1shEAj/iltaH6nc57gl+4ckrLPRNY9sM1S1lneoX8YNJuQnM5mwDkke+dLJ+V3rGilNVffiDsULL78Xt+wwkuyqIcGr88hTJuQ+4vfXMAzDMAzDMAwjQGHaU28YodzpT9FdiltaH+mwHsMwDMM4qVHV9JDJCMMwDOMUpFDM1BsnJyIyEbfPPpTH/GqGfFHVF4EXw+R144/fvQ3wjareH6Huc4Bp0IuLdQAAIABJREFU4fG4Q/m2Hap+wzAMwzAMwzCMgoA59cYJQ1WP+GTgQ8h7G3eA4eHk3YY7QMgwDMMwDMMwDKPQYsvvDcMwDMMwDMMwDKOQYk69YRiGYRiGYRiGYRRSzKk3DMMwDMMwDMMwjEKKOfWGYRiGYRiGYRiGUUgxp94wDMMwDMMwDMMwCinm1BuGYRiGYRiGYRhGIcWcesMwDMMwDMMwDMMopJhTbxhGgWDHjh106NCBWrVqUbt2bebMmRNMGzp0KCLC1q1bT6CGhmEYxqlMUlISFSpUoG7dujnihw8fTkxMDLGxsfTq1QuAzMxMunTpQlxcHLVr12bAgAHB/JMnTyYmJobq1aszcODAYPzq1atp3Lgxt912G7fccgsHDhzIpcN7771HQkJC8CpSpAipqakAjB07lri4OOLj42nTpk3QZv7222+0atWKGjVq0KpVK7Zv3w6AqvLQQw9RvXp14uPj+eGHH4L1FC1aNFjHtddeG4yfPn06DRo0oG7dugwYMICsrCwAfv/9d/7+979Tr149YmNjefvtt4Nl2rRpQ9myZWnXrl3E5/rggw9SpkyZXPHjx49HRJg3bx4A6enpnH766UG97rnnnhx1BOq+5557yM7OjliXYZysmFN/CiIiD4tIqb+gnuIiMlBEVojIEhH5XkSu9mnpIhJ1HOtKFpHVIpIqIj+ISJPjJLefiKz3cpeJSMfjILOWiMwRkf0i8mhYWhsRSRORlSLS+1jrKkx0796dNm3asHz5chYuXEjt2rUBWLt2LVOnTqVKlSonWEPDME5mzDYekdx+IqIiUj0kroePa+TDuyOUqyoinY6HDl7eA95e6vF8bnnRtWtXJk+enCNuxowZTJo0iUWLFrF06VIefdSZ9Q8//JD9+/ezePFi5s+fz2uvvUZ6ejrZ2dncf//9fPHFFyxbtoyxY8eybNkyAB577DF69OjBu+++y9lnn82oUaNy6dC5c2dSU1NJTU3lnXfeoWrVqiQkJJCVlUX37t2ZMWMGixYtIj4+nhEjRgAwcOBAWrZsyYoVK2jZsmVwIOGLL75gxYoVrFixgtdff5177703WM/pp58erOfjjz8G4ODBg3Tp0oX333+fJUuWcO655zJ69GgAXn75ZerUqcPChQtJSUnhX//6V3BQomfPnrzzzjsRn+m8efPYsWNHrvhdu3YxbNgwGjdunCP+oosuCur16quvBuPHjRvHwoULWbJkCVu2bOHDDz/M62M0jJOSYidaAePPQUQEEFU9GCH5YeBdYM8RyCumqllHqEZ/oCJQV1X3i8i5QPMjlHEk9FTV8SJyFfAaEB+aeJRtAHhRVYeKSA1gvoiMV9XMY9DzN+Ah4Low/YoCLwOtgHXAXBH5WFWX5Sdsb2Y2VXt/dgzqHH/+FZdF18PUKX1gW3bu3MmsWbNITk4GoESJEpQoUQKAHj16MHjwYNq3b/9nqWsYximC2cbjahsXA7cCz/pwByBfewVUBToBY8ITjlKPb4BPgZTDLXC0NjN9YFuaNWtGenp6jviRI0fSu3dvSpYsCUCFChUAEBEyMjLIyspi7969lChRgjPPPJPvv/+e6tWrU61aNQBuvfVWJk2aRO3atZk+fTpjxozh66+/pkuXLvTr1y+Hox3O2LFj6djRzTWoKqpKRkYG55xzDjt37qR6dTfmMmnSJFJSUgDo0qULiYmJDBo0iEmTJvHPf/4TEeGSSy5hx44dbNy4kYoVK0asb9u2bZQsWZKaNWsC0KhRIyZMmMDtt9+OiLBr1y5Uld27d1OuXDmKFXNuRsuWLYP1h5KdnU3Pnj0ZM2YMEydOzJH21FNP0atXL4YOHZpn+0M588wzAcjKyuLAgQO4f3XDOHWwmfqTCD8C/qOIvAL8APzDzwj/ICIfikgZEXkIqATMEJEZvtzuEBkdRCTZ3yeLyAs+3yA/Mv+WiKSIyCovCxEpLSKfichCP+twi5/tuBN4UFX3A6jqJlUdF0HvR3y5JSLycF4yfXxDEZkpIvNFZIqIRLI8s4DqPn+KiPxHRGYC3UWkvIhMEJG5/rpMRIr42ZGyITqt9B2tIKq6AtfZO9vnudPLWOhllhKRov7ZiIiUFZGDItLM558tItVVdbOqzgXCBwYuBlaq6ipVPQC8D5wSnuyqVasoX7483bp1o379+txxxx1kZGTw8ccfU7lyZerVq3eiVTQMo5BitjHI8baN/8PbKBGpBvwObInQjij/vNsCA4Gm4lYO9BCRrv4z+AT40ufv6XVYJCJP59duVV2gqun5fgH+ZH766Sdmz55N48aNad68OXPnzgWgQ4cOlC5dmooVK1KlShUeffRRypUrx/r16zn//POD5aOjo1m/fj3btm2jbNmyQUc4EJ8fH3zwQdCpL168OCNHjiQuLo5KlSqxbNkybr/9dgA2bdoUdNQrVqzI5s2bAfLUBWDfvn00atSISy65hP/9738AREVFkZmZGVwOP3PmTNauXQvAAw88wI8//kilSpWIi4vj//7v/yhSJH83Y8SIEVx77bW5BhEWLFjA2rVrIy7XX716NfXr16d58+bMnj07R1rr1q2pUKECZ5xxBh06dMi3bsM42bCZ+pOPGKAb0Af4CLhSVTNE5DHgEVV9RkQeAVqo6uFsUK7pZWSLSD+gFtACOANIE5GRQBtgg6q2BRCRs3AdhzWqujM/4SLS0OvbGBDgO9/JqBYuU0SKA8OB9qq6xRv154CkMLF/x80gBCirqs29nDG4mfevRaQKMEVVa4vIJOB64G0RaQykq+omCRnpFZEGwApV3eyjPlLVN3zas8DtqjpcRH4C6gAXAvNxHZjvgGhVXZnP46gMrA0Jr/PP5aQnKyuLH374geHDh9O4cWO6d+9Ov379mDVrFl9++eWJVs8wjMKP2cbjbxt3AmtFpC7Ouf/A6xzajnOBj4EnVXWqiGQAj6pqO5/eFWgCxKvqb+JWE9TADXIL8LEfGC8f4VkWCLKysti+fTvffvstc+fO5eabb2bVqlV8//33FC1alA0bNrB9+3aaNm3KlVdeiarmkiEiecbnxXfffUepUqWC+/szMzMZOXIkCxYsoFq1ajz44IMMGDCAJ598Mk8Z+dW5Zs0aKlWqxKpVq7jiiiuIi4vjoosu4v3336dHjx7s37+fmJiY4CDElClTSEhIYPr06fz888+0atWKpk2bBmfQw9mwYQMffvhhrhn8gwcP0qNHj+DKvVAqVqzImjVrOOecc5g/fz7XXXcdS5cuDdYxZcoU9u3bR+fOnZk+fTqtWrXKs+2GcbJhTv3Jxy+q+q2ItMM5lt/4F3QJYE6+JSPzoaqGnjbymZ9d2C8im4FzcZ2EoSIyCPhUVWfnZ4jCuByYqKoZACLyEdAUmBxBZl2gLjDVyy8KbAyRNUREnsTNFNweEv9ByP2VQJ0Q/c4UkTN8nj7A27jlhKFleojInbjOVJuQ+LremS8LlAGm+PjZQDOcUz8ANyszE5h7iGcR6aHltriAiNwF3AUQFVWePnFHs3Lyz+Pc090S/MMhJSWF3377jaioKPbu3UtKSgoXXXQRycnJrF69mpiYGAC2bNlCbGwsI0eOpFy5ckel1+7duyMuATzRmF6HT0HUySgUmG08/rYR3IqyW4HWQEtyOvXFgWnA/ao6M5+2TlXV3/z9Vf5a4MNlcE7+7PB25yMvF8fDZgbeO7/++isZGRnBcKlSpahWrRozZ7omHjhwgEmTJpGcnEydOnX45ptvAKhWrRqjR4+mQoUKwX3nALNmzQII7gWfNm0ae/fu5eOPP+a0007L83338ssv07hx42D68uXL2b59O2vXrmXt2rXUqFGDsWPHcvnll3PmmWcyYcIEzjnnHLZt28YZZ5xBSkoKRYoUYcqUKcHD7lasWEF6ejq7du0C3CoEgFq1avHuu+/SvLnbJdK/f/+g7mvXriUlJYWhQ4fSqVOn4HM4++yzee+994Ln46SmprJt27agvnPmzGHZsmVER0cDsGfPHipXrsxrr73GggULuOSSSwB3yF+bNm147rnngv2BAOeccw5jx47NFV+jRg1eeeUVihcvDhQOu1EYdITCo+epiDn1Jx8Z/q/gDOXhHOwW6jieloe8APtD7rOBYqr6k59VuAYYICJfAkOBKiJyhqruyqfuiD2cPGROBJaqal4H/fRU1fER4kPbUARooqp7cyghMgeoLiLlcXvdnw1JDuypvwH4r4hcpKr7gGTgOlVd6GcbEn3+2cA9uKWcfYCePm1WHnoHWAecHxKOBjZEyqiqrwOvA1SpVl2fX1yw/pX/FZfF4eqU3jkRgBdffJGKFSsSExNDSkoKLVu2ZMiQIcF8VatWZd68eURFHf1ZSCkpKSQmJh51+T8L0+vwKYg6GYUCs425OVbbCPAJMASYp6o7wwYtsnCr1VrjBrbzIlQPAQao6mvhmcLbrarP5CMzB8fDZgZsVXp6OqVLlw6+h5KSktiwYQOJiYn89NNPFClShPbt25OWlsby5ctp3rw5e/bs4ZdffmHQoEHUqVOH559/ngsuuIDKlSvTvXt3xowZQ2xsLFdddRVbtmzhvPPOY/HixXTr1i3i++7gwYPcdtttzJo1K7g3v2bNmjz99NPExsZSvnx5pk2bxmWXXUZiYiK33HILK1as4MYbb2TgwIHceuutJCYmkpGRwYgRI3jmmWf47rvvOO+887jxxhvZvn07pUqVomTJkmzdupWff/6ZF154gTp16rB582YqVKjA/v37eeSRRxg6dCiJiYnUr1+f3377jcTERDZt2sSmTZu46aabctjsr776KtiexMRE/v3vfwfTypQpE1z6//vvvwfjExMTGTp0KI0aNWLLli2UK1eOokWLsmrVKrZs2cJNN91EiRIl2LVrFxUrViQrK4uRI0fSsmXLYF2FwW4UBh2h8Oh5KlKwPAHjePIt8LK4Pdwrxe3ji1bVn4BduCWCgSWGm0SkNpCGW2aXX0cjFyJSCfhNVd8Vtwexq6ruEZFRwDARuVtVD4jb49dSVd8NKT4LSBaRgThjfj1uv2Mumbi9eOVFpImqzvFLDmuq6tIjUPdL4AFcJwQRSVDVVFVVEZkIvAD8qKrbwguq6kci0gXogjts6Axgo9ejMxDY/PYd8F9glaruE5FU4G4g8m+5/MFcoIaIXOhl3Yo7UChfTi9elLSBbQ+V7S8lJSUl2AE6XIYPH07nzp05cOAA1apVy/FzOIZhGMcJs42ROSrbqKp7/RaGnyLIVNwWgA9FpLeqDuSPZ5wXU4D+IvKequ4Wkcq482eKRWj3UXEsNrNjx46kpKSwdetWoqOjefrpp0lKSiIpKYm6detSokQJRo8ejYhw//33061bN+rWrYuq0q1bN+Lj3RmFI0aMoHXr1mRnZ5OUlERsbCwAgwYN4tZbb2X9+vVceumlwT3xH3/8MfPmzeOZZ9w4xqxZs4iOjg469ACVKlWib9++NGvWjOLFi3PBBRcEl7D37t2bm2++mVGjRlGlSpXgyfDXXHMNn3/+OdWrV6dUqVJBu/vjjz9y9913U6RIEQ4ePEjv3r2pU6cOAEOGDOHTTz/l4MGDtGrViiuuuAJwB9t17dqVuLg4VJVBgwYFHfqmTZuyfPlydu/eTXR0NKNGjaJ169ZH/PxnzZpFnz59KFasGEWLFuXVV1+lXLlybNq0iWuvvZb9+/eTnZ3NFVdckePn7gzjlCBwWqZdhf/CnSq7JCR8Bc5RXOSva338g8ByYIYPdwB+xp0eOwJI9vHJQIcQef1we+EC4SW+ztZefqqvr5FPLwEMBlb6vN8BrX1aOhDl7x/x6UuAh31cXjITcJ2dhcBS4M5IuobomBIo68NRuOWDi3Cn9L4aktYI1wnpkk+bG+I6eEWAe4HVvo7hgefm880G/uPvOwE7gCI+fB5uVn6nj18HnOnTrsF1jn4Gnjicz71mzZpa0JgxY8aJViEipteRURD1+it0ws06nvB3ul3H5zLb+Ofbxkhygd0h7Z0C3McfS/IXAj1wzvmIMBndcVsXFuO2RlyUT7sf8jY0C7ey7c1DfR8Kos0MpyC+eyNheh4/CoOOqpH1NJtZMC5xn4VhGIWVmJgYTUtLO9Fq5KCgLs8yvY6MgqjXX6GTiMxX1UZ/aiWGYZwQCqLNDKcgvnsjYXoePwqDjhBZT7OZBQP7STvDMAzDMAzDMAzDKKSYU28YhmEYhmEYhmEYhRRz6g3DMAzDMAzDMAyjkGJOvWEYhmEYhmEYhmEUUsypNwzDMAzDMAzDMIxCijn1hmEYhmEYhmEYhlFIMafeMAzDMAzDMAzDMAop5tQbhmEYhmEYhmEYRiHFnHrDMAzDMAzDMAzDKKSYU28YhmEYhmEYhmEYhRRz6g3DMAzDMAzDMAyjkGJOvWEYhmEYhmEYhmEUUsypNwzjmMnOzqZ+/fq0a9cOgI0bN9K4cWNq1KjBLbfcwoEDB06whoZhGIZx9CQlJVGhQgXq1q0bjOvXrx+VK1cmISGBhIQEPv/8cwC+//77YFy9evWYOHEiAPv27ePiiy+mXr16xMbG0rdv36Cs6dOn06BBA+rWrcuAAQPIysqKqMeaNWu46qqrqF27NnXq1CE9PR2A1atXR7S7r776KnFxcSQkJHD55ZezbNkyAKZOnUrDhg2Ji4ujYcOGTJ8+PVjHBx98QHx8PLGxsfTq1SuXDuPHj0dESEtLy7e9aWlpwfiEhATOPPNMXnrpJQB69uxJrVq1iI+P5/rrr2fHjh0ApKenc/rppwfL3HPPPcF6ExMTiYmJCaZt3rwZgF9++YWWLVsSHx9PYmIi69atO6zP1DBOKlTVLrtQVYCHgVJ/QT3FgYHACmAJ8D1wtU9LB6KOY13JwGogFfgBaHKc5PYD1nu5y4COx0FmLWAOsB949HDL1axZU080zz//vHbs2FHbtm2rqqrNmzfXsWPHqqrq3Xffra+88sqJVC/IjBkzTrQKETG9Dp+/QidgnhaAd7Jddtl1/K+jtZkzZ87U+fPna2xsbDCub9++OmTIkFx5MzIyNDMzU1VVN2zYoOXLl9fMzEw9ePCg7tq1S1VVDxw4oBdffLHOmTNHs7OzNTo6WtPS0lRV9R//+Ie++eabEfVo3ry5fvnll6qqumvXLs3IyFBV1Ztuuimi3f3999+DZSdNmqStW7dWVdUffvhB169fr6qqixcv1kqVKqmq6tatW/X888/XzZs3q6rqP//5T/3qq6+CMnbu3KlNmzbVxo0b66uvvppve0PJysrSc889V9PT01VVdcqUKcE8vXr10l69eqmq6urVq3M84/C2z507N1d8hw4dNDk5WVVVp02bprfddluO9IJoy8IpDDqqRtbTbGbBuIr9yWMGRgFDRAQQVT0YIflh4F1gzxHIK6aqkYeT86Y/UBGoq6r7ReRcoPkRyjgSeqrqeBG5CngNiA9NPMo2ALyoqkNFpAYwX0TGq2rmMej5G/AQcN2RFNqbmU3V3p8dQ7VHR/rAtgCsW7eOzz77jCeeeIIXXngBVWXBggV06NABgC5dutCvXz/uvffev1xHwzDyRkQeBl5X1cN+5x9lPcVx7/0bcYOWe4C+qvqFiKQDjVR163GqKxlnT34HDgL3q+qc4yC3H3AnsAUoAfRX1bHHKLMW8DbQAHhCVYeGpLUB/g8oCrypqgPzkZOCGwiedyz6hMjrCXT2wWJAbaC8qv52POQfou4UfFtE5HOgk6ruOJ51HI3NTB/YlmbNmgVnxQ9FqVKlgvf79u3Ddb1ARChTpgwAmZmZZGZmIiJs27aNkiVLUrNmTQAaNWrEhAkTuP3223PIXbZsGVlZWbRq1QogKEtVmT59OmPGjAFy2t0zzzwzWD4jIyOoS/369YPxsbGx7Nu3j/3797Nq1Spq1qxJ+fLlAbjyyiuZMGECLVu2BOCpp56iV69eDB0a/Lrm2d5Qpk2bxkUXXcQFF1wAwFVXXRVMu+SSSxg/fvyhHmueLFu2jBdffBGAFi1acN11R9SNMoyTAlt+fwogIlVF5EcReQU3W/0PEZkjIj+IyIciUkZEHgIqATNEZIYvtztERgffWUJEkkXkBZ9vkIj0E5G3RCRFRFZ5WYhIaRH5TEQWisgSEblFRErhOkYPqup+AFXdpKrjIuj9iC+3xHc+I8r08Q1FZKaIzBeRKSJSMcKjmAVU9/lTROQ/IjIT6C4i5UVkgojM9ddlIlJERNJFpGyITiv9IEQQVV2B66Se7fPc6WUs9DJLiUhR/2xERMqKyEERaebzzxaR6qq6WVXnAscyMPCX8/DDDzN48GCKFHGvk23btlGmTBmKFXNjhtHR0axfv/5EqmgYpyz+nZOXrX8YKJVHWl7yjmYyIHQgty7wd+CMo5BzuPRU1QSgN24gNwdH2QZwA7kJQHvgNT9YcSwEBnKHhkaKSFHgZeBqoA7QUUTqHGNdh42qDlHVBN/WfwMz/wqHPoIe1xxvh/7PYMSIEcTHx5OUlMT27duD8d999x2xsbHExcXx6quvBm1idnY2CQkJVKhQgVatWtG4cWOioqLIzMxk3jw3LjNz5kzWrl2bq66ffvqJsmXLcsMNN1C/fn169uxJdnY227Zto2zZsnna3ZdffpmLLrqIXr16MWzYsFxyJ0yYQP369SlZsiTVq1dn+fLlpKenk5WVxf/+97+gLgsWLGDt2rXBrXah5NXeAO+//z4dO3aM+Azfeustrr766mB49erV1K9fn+bNmzN79uwcebt160ZCQgL9+/dHVQGoV68eEyZMAGDixIns2rWLbdu2RazLME5WbKb+1CEG6Ab0AT4CrlTVDBF5DHhEVZ8RkUeAFoc5Y1LTy8j2Mxi1gBa4jlqaiIwE2gAbVLUtgIichXOq16jqzvyEi0hDr29jQIDvvANeLVym71gNB9qr6hbv6D8HJIWJ/TuwOCRcVlWbezljcB22r0WkCjBFVWuLyCTgeuBtEWkMpKvqptBRaBFpAKxQ1c0+6iNVfcOnPQvcrqrDReQnXAftQmA+0FREvgOiVXVlvk879/O5C7gLICqqPH3ijmahwbGRkpLCnDlzyMzMZNeuXaSmprJt2za+/vprVJWUlBQANm/ezJ49e4LhE8nu3bsLhB7hmF6HT0HUqaAhIlWBL4AZQBPgJRG5BygJ/Ix7tybxx0DuVlVtISK7VbWMl9EBaKeqXf2A7m9AfeAHEdkFVMG9j6sAL6nqMBEpDYwDonEzzP2BT3ADuReGDuT6fOF6P8If7+03VfWlSDJV9QNvI14AygBbga6qujFMZI6BXOD/AZcBH4vIf4FXvf7gBjjmAKuAhIAzKSIrfZkgqrpCRAIDuZtF5E7c+7gEsBL4B25FwgrgIuAs//wSVXWWiMwGuvn3/mYRaRum98XASlVd5XV4HzeQsCz8mUV4hiOBvwGnA+NVta+Pv8Y/r624wf1qqprbM8tNRyDPFQn+uzYZ+Bq4BFiIW33wNFAB6Kyq3/vPcTgQh+t79lPVSSJyus9fB/jR6x2QnY5fxSEi/wPOB04D/k9VX/d5duNWNLQD9uL6AZsi6HlMNjPwzvn111/JyMgIhuPj4xk1ahQiwltvvUWnTp147LHHguVefvllfvnlFx5//HFKly5NiRIlAHjppZfYvXs3Tz31FLVq1eLCCy+kV69eJCUlkZmZSXx8PPv27cv1rlu4cCEpKSm8/vrrnHvuuTz99NP07t2byy67jL179+Zpd2NjYxk1ahRfffUVDzzwAP/+97+DMlevXs2TTz7J4MGDg/nvu+8+rr76aooUKUJsbCw7duxg+vTpPPLII/Tu3ZuUlBR27NiRy7bn1d7MzEwmTJhAu3btcrXp3XffZceOHVSuXJmUlBQOHDjAmDFjOOuss0hLS+PGG2/k7bffpnTp0tx///2UL1+ePXv20LdvX/bs2UPr1q254YYbGDZsWHCAJSoqijlz5gRXMhQGu1EYdITCo+epiDn1pw6/qOq3ItIOZzy/8Y5pCVxH5kj5UFWzQ8Kf+Q7bfhHZDJyLc6CHisgg4FNVnS0RlmTlweXARFXNABCRj4CmuM5DuMy6QF1gqpdfFAjt3A0RkSdxyyZD17J9EHJ/JVAnRL8zReQMn6cPrtNxa1iZHr4zVw03gBGgrnfmy+I6nFN8/GygGc6pH4Dr6M4E5h7uQwngOzSvA1SpVl2fX/zX/yund05kypQpzJ8/n65du7Jv3z527tzJhx9+SEZGBpdffjnFihVjzpw51KhRg8TExL9cx3BSUlIKhB7hmF6HT0HUqYBiA7mFdyC3MhA6TbvOP5fD4QlV/c3P9k8TkXjgJ9yKhWaqulpEDmvbgLiVdW2ABw6RtTpwE85pngt0wtnwa4HHcVvKngCmq2qSX/32vYh8BdwN7FHVeK/rD3nUkeTbdTowV0QmqOo2oDTwrao+ISKDcXb12fDCx2oz0zsnur/p6ZQuXTriO6hatWq0a9cuYlpycjLlypWjUaNGOeLnz5/Ptm3b6NatG4mJidx///0ADBkyBFXNJeu0005jxowZdOrUCYANGzbw7bff0r59e+64445D2t1mzZpx9tlnB+PXrVvHXXfdxbhx47jssj/GrhITE3n88ccBeP3111m5ciUNGzZk3bp19O7dG3ADHM899xyTJ0/O1a7w9k6aNInGjRtzww035Mg3evRoli5dyrRp03Is4Q/VY+zYsZx77rm56ti8eTPz5s0LtiWw5W/37t3UqlUrx2qCwmA3CoOOUHj0PBUxp/7UIcP/FWCqqkZeA5UTDbk/LQ95AfaH3GcDxVT1J99RuwYYICJf4pYZVhGRM1R1Vz51R/T+85A5EViqqk3ykNVTVSNt1gptQxHcIXp7cyghMgeoLiLlcR2T0M5CYE/9DcB/ReQiVd2HO5zvOlVdKCJdgUSffzZwD252rA/Q06fNykPvw+L04kVJGxg+0fPXMGDAAAYMGAC4F/3QoUN5771fqnTjAAAgAElEQVT3SExMZPz48dx6662MHj2a9u3bnxD9DOMUxgZyC+9AbqSHphHiInGzn5UuhtvyUAdn31ap6mqfZyx+1voQ/B345jCW3q9W1cUAIrIUmKaqKiKLgao+z1XAtSLyqA+fhlsl0QwYBqCqi0RkUR51PCQi1/v784EawDbgAPCpj58PtDpUo46nzdy4cSMVK7rdfhMnTgyejL969WrOP/98ihUrxi+//EJaWhpVq1Zly5YtFC9enLJly7J3716++uqr4Mz+5s2bqVChAvv372fs2LE59qwH+Nvf/sb27dvZsmUL5cuXZ/r06TRq1AgRoUWLFhHt7ooVK6hRowYAn332WfB+x44dtG3blgEDBuRw6EN12b59O6+88grjxo3jrLPOYuvWP8b/EhMT6dixI40aNcqzvQHGjh2ba+n95MmTGTRoEDNnzszh0G/ZsoVy5cpRtGhRVq1axYoVK6hWrRpZWVns2LEjuFXh008/5corrwRg69atlCtXjiJFijBgwACSksLH9wzj5Mf21J96fAtcJiKBJYmlRKSmT9tFzn2Om0Sktrj9mNdzhIhIJdwI/Ls4Z76BP5BpFDBMREr4fBVF5Law4rOA67x+pX39syPJBNKA8iLSxMsrLiKxR6jul4TMRohIAoC6DVsTccsWf/QzAzlQ1Y+AeUAXH3UGsNHPJnUOyfodcClw0Dv/qbhZipwbxk4C7rrrLl544QWqV6/Otm3bch32YxjGn074QG6Cv+qoal7/kMc8kAs0xDn3A0SkD245ehXvMOdHngO5EWQKbiA30KY4Vb0qpFhPH99KVZfk0YbAQG5ARmU/0Bw+kPtRSJkXVTUGuAU3kBt4RsnAA6oah1t2HoifjRuYuBj4HOf0J3Logdx1OMc1QDSw4RBlEJELgUeBlqoaD3zmdTnskZUwbiWfpfchhH4XDoaED/LH5JEAN4Y87yqq+qNPy3fAQkQScYMwTVS1HrCAP55xpgY2Vvvv4WHoe1R07NiRJk2akJaWRnR0NKNGjaJXr17ExcURHx/PjBkzgoe1ff3119SrV4+EhASuv/56XnnlFaKioti4cSMtWrQgPj6ev/3tb7Rq1So4ozxkyBBq165NfHw8l156KVdccQUA8+bN44477gCgaNGiDB06lJYtWxIXF4eqcueddwIwaNCgiHZ3xIgRxMbGkpCQwAsvvMDo0aOD8StXrqR///65fiKue/fu1KlTh8suu4zevXsHD/DLi7zaC7Bnzx6mTp2aa5b+gQceYNeuXbRq1SrHT9fNmjWL+Ph46tWrR4cOHXj11VcpV64c+/fvp3Xr1sTHx5OQkEDlypWDbU9JSSEmJoaaNWuyadMmnnjiiWP4pA2jkKIF4Ah+u/7cCzdSviQkfAVupmCRv6718Q8Cy4EZPtwBt/8yBRgBJPv4ZKBDiLx+hPwEG+5n6qoCrb38VF9fI59eAhiM6+wtwTm7rX1aOv4n7YBHfPoS4GEfl5fMBFxHaSGwFLgzkq4hOqYEyvpwFG5GZhFu3+KrIWmNcJ2OLvm0uSFucKEIcC/uZ/RScEtEk0PyzQb+4+87ATuAIj58Hq4zt9PHrwPOPNTnWxB+0i6cgvrTLKbXkVEQ9bKftDuydz5QHlgDVPfhUkBNf78Yt9c9UG4l7qTzIsCEo3jnVwJO83HXAf/z94NxM98lfLgicJuGvPNxA7SLvH6lvcz6kWTyx971Jj6+OBAbSdcQHcPf+WNwzn8gnBByPwR4B/g8nzZPAu7291tx+8eLA1NDnltJ377pPjwSt6y+Xphu4bKL4fb2X+jbujDQvjw+7xScnarn8xbBrZzYBHTF7VNfC1T1+d/DrXrI7zsUOAeg9OF+18KfPzm/h//B9SPEh+v7v4/gzk8At/oiiz/seuC70R74xMfVAvbhzicA2B1SdwdC7G1eV0G0meEUxHdvJEzP40dh0FHVftKuIF+2/P4UQFXTccYyEJ6OO0gnPN9wnBMaCI8Hci1bV9WuYeF+YeFAXen8sQwxNP0A0Mtf4WlVQ+5fwM2Qh6ZPyUNmKm4ZX766hsQnhoW34mZfIuWdR9hMR4Q2z8ftYQXXcRuZh6ymIfdjcB3LQPhX3IyMYRjGcUHdnvOuwFgRKemjn8Tts34d+EJENqpqC9xp8Z/iHMAluKXkR0Icbun7QdyveAR+x/JJ3NalZSKyDzdj3idMzx/8gXzf+6g3VXWBiLQOl6mqB8Qd5DfM79svBryEG9A9XB4CXvbLvYvhBoXv8Wkf4AaNu+ZT/hlgjIi8ATyFG5z+BTdQcoZv034RWYtbIQduULejz4OInIdb5XUmcFDcr7zUUdWdIvIAztYVBd5S1UO2Td2WrwW457AK+MbH7xWR+4DJIrKVP55xflwPfKl+O8RxoD/uM1okbs9DOu5wu5G48wsCg/WRdJsM3OPzpPHH8zQMwzA85tQbhmEYxkmEDeTmprAN5Krq57gl+4cktG15tR+3Aq+Wd6hfxg0m5CczGTfrfqi608n5XesaKU3deTV3Ryi/F7fMP5LsqiHBq/PIUybkPuL31zAM41TA9tQbhmEYhmGc3NwpIqm4WfyzcKfhG4ZhGCcJNlNvGIZhGIZRwBGRibh99qE85lcz5Iuqvgi8GCavG9A9LOs3qnp/hLrPAaZFEN1SIxwgaxiGYfy1mFNvGIZhGIZRwFHVI/4VmkPIext3gOHh5N2GO5DWMAzDKIDY8nvDMAzDMAzDMAzDKKSYU28YhmEYhmEYhmEYhRRz6g3DMAzDMAzDMAyjkGJOvWEYhmEYhmEYhmEUUsypNwzDMAzDMAzDMIxCijn1hmEYhmEYhmEYhlFIMafeMAzDMAzDMAzDMAop5tQbhnFU7Nu3j4svvph69eoRGxtL3759AZg2bRp33XUXCQkJXH755axcufIEa2oYhmEYh09SUhIVKlSgbt26wbjffvuNVq1aUaNGDVq1asX27dsBeO+994iPjyc+Pp5LL72UhQsXBsvs2LGDDh06UKtWLWrXrs2cOXMA6NevH5UrVyYhIYGEhAQ+//zziHocafmpU6fSsGFD4uLiaNiwIdOnTwdgz549tG3bllq1ahEbG0vv3r2DdaxZs4YWLVpQv3594uPjDylr165dwXrvuOMOoqKiePjhhwGYNWsWDRo0oFixYowfPz5Xe3bu3EnlypV54IEHgnFjx44lLi6O+Ph42rRpw9atW3OUGTp0KCKSK37u3LkULVo0Yj2GcSpiTv1JgIg8LCKl/oJ6iovIQBFZISJLROR7Ebnap6WLSNRxrCtZRFaLSKqI/CAiTY6T3H4ioiJSPSSuh49r5MO7I5SrKiKdjocOXt4DIrLS1xsVEi8iMsynLRKRBserzuNNyZIlmT59OgsXLiQ1NZXJkyfz7bffcu+99/LEE0+QmppKp06dePbZZ0+0qoZhGH8KZn+PSG4/EVnv5S4TkY7HQWYtEZkjIvtF5NHjoSdA165dmTx5co64gQMH0rJlS1asWEHLli0ZOHAgABdeeCEzZ85k0aJFPPXUU9x1113BMt27d6dNmzYsX76chQsXUrt27WBajx49SE1NJTU1lWuuuSaiHkdaPioqik8++YTFixczevRo/vGPfwTzP/rooyxfvpwFCxbwzTff8MUXXwDw7LPPcvPNN7NgwQLef/997rvvvnxlnXHGGcF633zzTS644AJuuOEGAKpUqUJycjKdOkXuLj311FM0b948GM7KyqJ79+7MmDGDRYsWER8fz4gRI4Lpa9euZerUqVSpUiWHnOzsbB577DFat24dsR7DOBUpdqIVMA4PERFAVPVghOSHgXeBPUcgr5iqZh2hGv2BikBdVd0vIucCzQ9R5ljoqarjReQq4DUgPjTxKNsAsBi4FQh4mx2AZYcoUxXoBIwJTzhKPb4BPgVSwuKvBmr4qzEw0v/Nk72Z2VTt/dkRVn/0pA9sC4CIUKZMGQAyMzPJzMxERBARMjIyAPj999+pVKnSX6abYRjG8cbs73G1vy+q6lARqQHMF5Hxqpp5DHr+BjwEXHe4BQ5lM9MHtqVZs2akp6fniJ80aRIpKSkAdOnShcTERAYNGsSll14azHPJJZewbt06wM1Kz5o1i+TkZABKlChBiRIlDlfNoypfv3794H1sbCz79u1j//79lCpVihYtWgTlNGjQIKiniLBz504gp83OS1bJkiWD8evWrWPz5s00bdoUgKpVqwJQpEjuOcP58+ezadMm2rRpw7x58wBQVVSVjIwMzjnnHHbu3En16sE5F3r06MHgwYNp3759DlnDhw/nxhtvZO7cufk+D8M4lbCZ+gKMnx3+UUReAX4A/uFHpH8QkQ9FpIyIPARUAmaIyAxfbneIjA4ikuzvk0XkBZ9vkB81f0tEUkRklZeFiJQWkc9EZKGfEbjFz0TcCTyoqvsBVHWTqo6LoPcjvtwSEXk4L5k+vqGIzBSR+SIyRUQqRngUs4DqPn+KiPxHRGYC3UWkvIhMEJG5/rpMRIr4mYuyITqt9J0ggP8B7X18NeB3YEuEdkT5590WGAg09TMMPUSkq/8MPgG+9Pl7eh0WicjT+bVbVReoanqEtrYH/quOb4GyeTyTAkF2djYJCQlUqFCBVq1a0bhxY958803+/e9/Ex0dzTvvvJNjmZ9hGEZhwOxvkONtf/H6r8ANhJzt89zpZSz0MkuJSFH/bEREyorIQRFp5vPPFpHqqrpZVecCxzIwcFhs2rSJihXdI6pYsSKbN2/OlWfUqFFcffXVAKxatYry5cvTrVs36tevzx133BEc8AYYMWIE8fHxJCUlBZfyh3Ks5SdMmED9+vVzOOHglvR/8skntGzZEnBL+d99912io6O55pprGD58+GHLmjZtGrfccgsikudzAzh48CD/+te/GDJkSI744sWLM3LkSOLi4qhUqRLLli3j9ttvB+Djjz+mcuXK1KtXL0eZ9evXM3HiRO6555586zSMUw2bqS/4xADdgD7AR8CVqpohIo8Bj6jqMyLyCNBCVbfmJ8hT08vIFpF+QC2gBXAGkCYiI4E2wAZVbQsgImfhjPoaVd2Zn3ARaej1bQwI8J3vAFQLlykixYHhQHtV3eI7Gs8BSWFi/46bXQ9QVlWbezljcCP/X4tIFWCKqtYWkUnA9cDbItIYSFfVTd7w7ATWikhdnBP9gdc5tB3nAh8DT6rqVBHJAB5V1XY+vSvQBIhX1d/EzWbUAC727f7Ydz7KR3iW+VEZWBsSXufjNobpdxdwF0BUVHn6xB3NhMnREZipCPDSSy+xe/dunnrqKWrVqsXbb79Nnz59aNiwIe+//z4dO3akZ8+ef5l++bF79+5c+hcETK/DpyDqZJy0mP09/vY3oGsDYIWqBjzjj1T1DZ/2LHC7qg4XkZ+AOsCFwHzc4Pp3QLSqHvaBLUdiMwPvl19//ZWMjIxgOCsrK8e7Jzy8YMEChg8fzrBhw0hJSSEtLY358+fTtWtXunbtyvDhw7n33ntJSkoiPj6eUaNGISK89dZbdOrUicceeywoa/fu3cdUfvXq1Tz55JMMHjw4h47Z2dk8/vjjXHPNNaxZs4Y1a9Ywbtw4mjZtys0338zSpUu58cYbeeutt4Kz7XnJAufUP/HEE7nif/31V5YuXUpUlNsVMnHiRGJiYvj5559Zvnw569evJyUlhaysLP7zn/8wcuRIKlWqxLBhw7jrrru46aabeOyxxxgyZAgpKSns27ePb775hrPOOot+/fpxyy23MHv27Fz15EVhsBuFQUcoPHqeiphTX/D5RVW/FZF2OMP2jTeMJYA5RyHvQ1XNDgl/5kf+94vIZuBcnAEfKiKDgE9VdfahRmFDuByYqKoZACLyEdAUmBxBZl2gLjDVyy9KTud1iIg8iZtFvz0k/oOQ+yuBOiH6nSkiZ/g8fYC3cUvtQ8sAvO/jWwMtyenUFwemAfer6sx82jpVVX/z91f5a4EPl8E5+bPD252PPHAdsXA0V4Tq68DrAFWqVdfnF/91/8rpnRMjxs+fP5+tW7eyfv16GjZsSGJiItWqVaNNmzYkJkYu81eTkpJSYHQJxfQ6fAqiTsZJi9nf429/e4jInbiBhjYh8XW9M18WZz+n+PjZQDOcUz8At2JhJnBE666PxGYGbFx6ejqlS5cOvm8qV65MTEwMFStWZOPGjVSqVCmYtmjRIkaMGMHUqVOpWbMmALVq1WLAgAHBPepFixZl4MCBud5f1apVo127djniU1JSaN++/VGVX7duHXfddRfjxo3jsssuy5E3KSmJxo0bM2zYsGDc/fffz+TJkzn//PNJTEzk+eefp27dulSoUCFfWQsXLkRVufvuu3M9w+TkZGJjY4M6vfHGG8yePZspU6awe/duDhw4QExMDDfeeCNnn302nTt3ztHGypUrs23btuCBelu3buXBBx/k+++/55dffmHw4MHB+B9++IF69epx3XV578AoDHajMOgIhUfPUxFz6gs+gbVWgnMiD+dgmVAn8LQ85AXYH3KfDRRT1Z/8iP81wAAR+RIYClQRkTNUdVc+dUfsfeQhcyKwVFXzOoSnp6pGOtY0tA1FgCaqujeHEiJzgOoiUh631y78tLZPgCHAPFXdGdZpysLNCLTGdR7yIlQPAQao6mvhmcLbrarP5CNzHXB+SDga2JBPfk4vXpQ0v8/9r2TLli0UL16csmXLsnfvXr766isee+wxfv/9d9audYsNpk6dmuNgH8MwjEKE2d/cHKv9DeypvwH4r4hcpKr7gGTgOlVd6FfCJfr8s4F7cNsc+gA9fdqsPPQ+JEdrM6+99lpGjx5N7969GT16dHCf95o1a7jhhht45513gg49wHnnncf5559PWloaMTExTJs2jTp16gCwcePG4FL+iRMn5jhl/1jK79ixg7Zt2zJgwIBcTviTTz7J77//zptvvpkjvkqVKkybNo2uXbvy448/sm/fPsqXL5+vLHCn1l9xxRWH9ezee++94H1ycjLz5s1j4MCBbNiwgWXLlrFlyxbKly8f7DPExcXl2N5QtWpV5s2bR1RUFKtXrw7Gd+3alXbt2uXr0BvGqYLtqS88fAtcJv7Udr/fLGA9duGW7wXYJCK1RaQIbgncESEilYA9qvourjPRQFX3AKOAYSJSwuerKCK3hRWfBVzn9Svt658dSSaQBpQXf7KuuNN9Y49Q3S+B4G+jiEgCgKoqrtPyAvCjqm4LLeQ7IY/hlhuGo7gliLVEJLAhPPwZhzMFSBKRMl6PyiJSIY9258fHwD/9HsJLgN9VdeMhypwQNm7cSIsWLYiPj+dvf/sbrVq1ol27drzxxhv07duXevXq8c477+TaQ2cYhlHIMPsbmaOyvz7PR8A8oIuPOgPY6LcFdA7J+h1wKXDQO/+pwN04Z/9Po2PHjjRp0oS0tDSio6MZNWoUvXv3ZurUqdSoUYOpU6cGz4t55pln2LZtG/fddx8JCQk0atQoKGf48OF07tyZ+Ph4UlNTefzxxwHo1atX8GfcZsyYwYsvvgjAhg0bcpyEf6TlR4wYwcqVK+nfv3/wZ+c2b97MunXreO6551i2bBkNGjQgISEh6Nw///zzvPHGG9SrV4+OHTuSnJyMiOQpK8C4ceOC+/IDzJ07l+joaD788EPuvvtuYmPz/0pVqlSJvn370qxZs1xtNAzjCAmcPGlXwbtwJ64vCQlfgVtytshf1/r4B4HlwAwf7gD8jDtZfQSQ7OOTgQ4h8vrh9okHwkt8na29/FRfXyOfXgIYDKz0eb8DWvu0dCDK3z/i05cAD/u4vGQm4DoiC4GlwJ2RdA3RMSVQ1oejcEv7FuFOsH81JK0RzkHvklebI8kFdoe0dwpwH38syV8I9AC6AiPCZHTHLZ1cjFuaeVE+7X4INyufhZuJf9PHC/Cy//wWh7Y1r6tmzZpa0JgxY8aJViEipteRURD1+it0wq3gOeE2wK4Td2H290+3v0BD3OBCEeBeYLWvY3jgufl8s4H/+PtOwA6giA+f523pTh+/Djgzv8+2INrMcAriuzcSpufxozDoqBpZT7OZBeMS91kYhlFYiYmJ0bS0tBOtRg4K6p4r0+vIKIh6/RU6ich8VW106JyGYRQ2CqLNDKcgvnsjYXoePwqDjhBZT7OZBQNbfm8YhmEYhmEYhmEYhRRz6g3DMAzDMAzDMAyjkGJOvWEYhmEYhmEYhmEUUsypNwzDMAzDMAzDMIxCijn1hmEYhmEYhmEYhlFIMafeMAzDMAzDMAzDMAop5tQbhmEYhmEYhmEYRiHFnHrDMAzDMAzDMAzDKKSYU28YhmEYhmEYhmEYhRRz6g3DMAzDMAzDMAyjkGJOvWEYhmEYhmEYhmEUUsypNwzjkOzbt4+LL76YevXqERsbS9++fQHo3LkzMTEx1K1bl6SkJDIzM0+wpoZhGIZx5GRnZ1O/fn3atWsHwPTp02nQoAF169alS5cuZGVlBfOmpKSQkJBAbGwszZs3D8ZPnjyZmJgYqlevzsCBAyPWM2vWLBo0aECxYsUYP358jrQ1a9Zw1VX/n73zDq+qyh72u0joRUCK9IAQCIkhYAZBpckEBBRpIzAWUFBhVBClzYciyMzQi4hdEUalCA7SFJwfJICggkBoKkUTJYBKFUJLW98f5+Ryk9w0DJAM632e89x9dll7nZ2bs3ZZe9929OnTh4YNGxIbGwtAixYtCAsLIywsjKpVq9KlSxcAPvzwQ0JDQwkNDeX2229nx44dABw8eJA2bdoQFBREcHAwL7/8sqeO6OhomjVrRlhYGOHh4WzevDnb53r55ZcJCQkhODiYGTNmeOIPHDjgU9bkyZM9+oaEhODn58eJEycy7UsAxMTEcNttt1GvXj169uxJQkICAHPmzKFixYoeee+88052f0rDuD5RVbvssqsAX4GBgXqlSUlJ0TNnzqiqakJCgjZt2lS//PJLXblypaakpGhKSor26tVLX3vtNVVVjYyMvOI6XQ6mV+7Ij3pdDZ2AbzQf/G/bZZddeX9lZjOnTp2qvXv31k6dOmlycrJWr15d9+7dq6qqL7zwgr7zzjuqqnry5EkNCgrSn376SVVVf/31V1VVTUpK0jp16ugPP/ygFy9e1NDQUN2zZ0+GemJiYnTHjh360EMP6aJFi9KktWrVSj///HONjIzUM2fO6NmzZzOU79atm86dO1dVVTdu3KgnTpxQVdVPP/1UmzZtqqqqhw8f1q1bt6qq6unTp7VevXoeXSIiIvTTTz9VVdWVK1dqq1atsnyuXbt2aXBwsJ49e1YTExO1bdu2um/fPlVVDQ8P9ynLm2XLlmmbNm1UNfO+hKrqX/7yF50/f76qqj7xxBOe/sR7772nTz75ZAa5uSE/2rL0FAQdVX3raTYzf1y2Um9kQETGiMjQK1xHaxFZkccy/ykiB0UkPpP0HiKiIhKel/Vmo1O8+1lVRBZnlz+/IiKUKlUKgMTERBITExEROnbsiIggIjRt2pS4uLhrrKlhGDlFRJ4RkRJXoZ7CIjJBRPaLyG4R2SwiHdy0WBGpkId1zRGRGBGJFpFtItI8j+SOEZFDrtxvRaR3HshsICJfisjF9DZXRO4Wkb0ickBERmYjJyov7ZqI3CciO91n/UZE7swr2Tmo2/N9EJFNV6veuLg4Vq5cSf/+/QE4fvw4RYsWJTAwEICIiAg+/vhjAObNm0e3bt2oWbMmAJUqVQJg8+bN1K1blzp16lCkSBF69erF0qVLM9QVEBBAaGgohQql7YJ/++23JCUlERERAUCpUqUoUSLtv+eZM2dYu3atZ6X+9ttvp1y5cgA0a9bMY4OrVKlCkyZNAChdujRBQUEcOnQIcOz56dOnAfj999+pWrVqls/13Xff0axZM0qUKIG/vz+tWrViyZIlHp18yfJm/vz59O7d21O3r76EqrJ27Vp69OgBQJ8+ffjkk08yyDIMI3P8r7UChpGHLAdmAfvTJ4hIaWAQ8PXVVgpAVQ8DPa6E7POJyQSMXHklRAMQO6ET4Lgm3nrrrRw4cIAnn3yS2267zZMnMTGR999/P42Ln2EY1x4REUBUNcVH8jPAB8C5XMjzV9Wk7HOmYRxQBQhR1YsiUhlolU2ZP8IwVV0sIu2AN4FQ78TLfAaA6ao6RUTqAVtFZLGq/pE9Rydw7FKXdPr5Aa8CEUAcsEVElqnqt3+grtywBlimqioiocBHQIOrVLcHVb39SshNbzNjJ3TimWeeYdKkSZw5cwaAChUqkJiYyDfffEN4eDiLFy/m4MGDAOzbt4/ExERat27NmTNnGDx4MA8//DCHDh2iRo0aHrnVq1fn669z3uXYt28fZcuWpVu3buzatYsuXbowYcIE/Pz8PHmWLFlC27ZtKVOmTIby7777Lh06dMgQHxsby/bt2z02e8aMGbRv356hQ4eSkpLCpk2bsnyukJAQRo0axfHjxylevDiffvop4eHOHNJTTz3FsGHDMshK5dy5c6xatYpZs2Z54nz1JY4dO0bZsmXx9/f3tF3qJATAxx9/zPr16wkMDGT69Olp2tkwDAdbqTcQkYfdWfkdIvJ+urQwEfnKTV8iIuXc+EHuasVOEVngxpUUkdkiskVEtovIfTmsv6mIbHLLbBKR+m58CRH5yK1joYh8ndVqhKp+papHMkkeB0wCLmSjS18R+URElrurPU+JyLOubl+JSHk3380iskpEtorIBhFp4MbXdldetojIOC+5ASKy2yu8wV1F2iYit7vxrd0Vl8Ui8r2IfOh2yPMFfn5+REdHExcXx+bNm9m9e7cn7W9/+xstW7akRYsW11BDwzDA8475TkReA7YBD7nvpW0iskhESonIIKAqECkikW65eC8ZPURkjhueIyLT3HwT3VXr2e776kdXVqoNWOnakt0i0lMcT4DHgKdV9SKAqv6qqh/50PtZt9xuEXkmM5lu/K0iss59B68WkSo+mmI9UNfNHyUi/xKRdXG8vXUAACAASURBVMBgEakoIh+77+otInKHiBQSZ6W4rJdOB9xJCA+quh9nIiTVHj7mytjhyiwhIn5u24iIlBWRFBFp6ebfICJ1VfU3Vd0CpJ8YaAocUNUfVTUBWADk1J6+Ls7q+h4RGesV39G1K1+IyEzJwlNOVeNdl1qAkoBmlte1W+tcW71PHI+MB8TxxtglIje7+TK0txt/o4h87trYNwHxkp3q6VZKRNa4399d4vYtvL7nb7vP+7mIFM9JO3mzYsUKKlWqxK233ur9XCxYsIAhQ4bQtGlTSpcu7RlwJiUlsXXrVlauXMnq1asZN24c+/bt41KTpWmfHOuRlJTEhg0bmDJlCm+88QY//vgjc+bMSZPHe9Xbm8jISN59910mTpyYJj4+Pp7u3bszY8YMz0TA66+/zvTp0zl48CDTp0+nX79+WT5XUFAQI0aMICIigrvvvptGjRp52mLp0qU+ZaWyfPly7rjjDsqXL++J89WXyKrt7r33XmJjY9m5cyd//vOf6dOnT47b1DCuJ2yl/jpHRIKBUcAdqnpMnEHrIK8s/8bpjK0TkZeAF3FWd0YCtd1Vl9QO0Chgrao+6sZtFpH/U9Wz2ajxPdBSVZNE5M/Av4DuwN+Ak6oaKiIhQPRlPmNjoIaqrpCcbSsIARoDxYADwAhVbSwi04GHgRnAW8AAVd0vIrcBrwF3AS8Dr6vqv0XkyUzk/wZEqOoFcVZ85gOpkxWNgWDgMLARuAP4wsczPQ48DlChQkVG33I5i045IyoqKkNcQEAAr776Kj179mTu3Lns37+fl156yZM3Pj7eZ7lrjemVO/KjXvlRp3xKfeARYDTwH+DPqnpWREYAz6rqSyLyLNBGVY/lQF6gKyNZRMbgrNy2AUoDe0XkdeBu4LCqdgIQkRtwBtU/q+rprISLyK2uvrfhDOy+dgfgddLLFJHCwCvAfap61B3o/xN4NJ3Ye4FdXvdlVbWVK2cezsr7FyJSE1itqkEishToCrznvttjVfVX78GZiDQB9qvqb27Uf1T1bTftH0A/VX1FRPYBDYHawFaghYh8DVRX1QNZNEc14KDXfZzbLjlhlKqeEGe1f404K+37cDwWWqpqjIjMz06IiHQFxgOVgE7ZZG8EBOF4HvwIvKOqTUVkMPA0Tp/hZdK1t1vmReAL9/vYCdeupeMC0FVVT4vjmv+ViCxz0+oBvVX1MRH5CKfv8IGP58nUZs6f/xGff/45//nPf0hISODcuXNEREQwatQoxo1z5ua3bNnCDTfcQFRUFAkJCTRo0IAtW7Y4CtSrx7x586hYsSI7duzwvJ/Wr18P+LahAL/88gt79uyhQgVn98lvv/1G7dq1+fnnnzl//jz169dn+fLl3HzzzYDj3r5p0yaGDBmSRuYPP/zA6NGjmTBhArt2Xfq6JyUl8fe//53bbruN8uXLe8rMnj2brl27EhUVRcWKFfnyyy+zfK7WrVtz8803M23aNADefvttihUrRlRUFKtXr+bpp5/OICuVWbNm0apVq0zbILUvcf/993P06FHWrFmDn58fe/bs8dThTb169di8eXOubUBBsBsFQUcoOHpej9ig3rgLWJzaqXM7A4CnQ1ZWVde5eecCi9zwTuBDEfkESN341A7o7DVwLgbUBL7LRocbgLnuAFeBwm78nTgdAVR1t4jszO3DiUghYDrQNxfFIlX1DHBGRH7HcesHp3MYKiKlgNuBRV4dvaLu5x04nQqA94G00+YOhYFZIhIGJON0llPZrKpxru7RQAA+BvWq+hbOxAI169TVqbuu3L9y7AOtOXr0KIULF6Zs2bKcP3+eF154gREjRnDgwAH27t3LmjVrKF780gJJVFQUrVu3vmI6XS6mV+7Ij3rlR53yKT+p6lcicg/OwHKj+74qAnx5GfIWqWqy1/1Kd+X9ooj8BlTGeUdOEZGJwApV3ZCLlco7gSWpk8Ai8h+gBbDKh8wQnMnX/7ry/QBvL63JIvI8cBTwXjpc6BX+M9DQS78y4mzTWogzEfIe0CtdmSEi8hjORMPdXvEh7mC+LFAKZ8AKsAFoiTOoH4/jsbAO2JJNW/hqtExXy9NxvzuA9cfZ8tAQxyvzR1WNcfPMx/fg+VJlqkuAJa53wTic9sqMLaleciLyA/C5G78LZ+IHMm/vlkA3t86VInLSh3wB/uXqkoIz6ZHqPRGjqqkT/ltxbKav58nUZsZ++KEnHBUVxZQpU1ixYgW//fYblSpV4uLFi4wbN47Ro0fTunVrKleuzFNPPcWdd95JQkICP//8M5MmTaJBgwZMnTqVWrVqUa1aNQYPHsy8efMIDg722Whz5swhODjY8z5r0aIFb775JsHBwezZs4cjR44QERHhSX/jjTfo0qUL7dq188j4+eef6d+/P4sWLeL22y/tVlBV+vTpwx133JHmtHqAGjVqICK0bt2aNWvW0KBBgyyfKyQkxNMWP//8M1u3buXLL7+kXLlyVKhQwacscCYh9uzZw6pVqyhZsiRApn2JNm3a0K5dO44ePUqvXr1YsGABjzzyCK1bt+bIkSNUqeI44ixZsoSQkJBc24CCYDcKgo5QcPS8HrFBvSHkvLPgTSccY9wZeMFd8Regu6ruzaWscTgD6a4iEgBEeen2RymN0/mLcjsTNwHLRKSzqn6TSZmLXuEUr/sUnP+ZQsApVQ3LpHx27TkE+BVndaMQabcEeNedTA7+R4sX9mPvhOwWUv4YR44coU+fPiQnJ5OSksL999/PPffcg7+/P7Vq1aJ5c+csqm7dujF69OgrqothGDki1UNKgP+qak4OdvN+dxXLRF4qGd5VqrrPXXHvCIwXkc+BKUBNESntTpZmhs/3fSYylwB7VDWzQ/CGqaqvg0m9n6EQ0FxVz6dRQuRLoK6IVMTZ6/4Pr+TUPfXdgH+LyM2qegGYA3RR1R0i0hdo7ebfAAzA2eYwGhjmpq3PRO9U4gDvTcPVcby3skREagNDgT+p6klxtk8U4w/YUlVdL852swpZeHRkZzMh8/aG7G3mA0BF4FZVTRSRWC59P9N/D7N1v8+pzZw8eTIrVqwgJSWFgQMHctdddwEQFBTE3Xff7Tnsrn///oSEhADOynT79u1JTk7m0Ucf9QzoR48eTXh4OJ07d2bLli107dqVkydPsnz5cl588UX27NmDn58fU6ZMoW3btsTHx9OyZUsee+wxjz4LFixg5Mi0Zya+9NJLHD9+nL/97W8A+Pv7880337Bx40bef/99brnlFsLCnK7Kv/71Lzp27Mjbb7/N4MGDSUpKolixYrz11lvZPlf37t05fvw4hQsX5tVXX/Uczjd06FCee+65DLLAGYC3a9fOM6CHzPsSABMnTqRXr148//zzNG7c2OPKP3PmTJYtW4a/vz/ly5fPsCXBMAyXK3m0vl35/8Jx9d4H3OjelwfGAEPd+x1ACzc8BmfVuxAQ4MYVxhmglsVxm5+FcygTQOMs6m2Ns+oCTgetu1cdsW54GI4rOzirDYlAeA6eKT6LtKisZOCs6M/yuo8FKqRPAzYBf3HDAjRyw8uAB93wwFRdcFYPdrvh6cBzbvgR598wbZu497OAvtk979X4Sbvckl9/msX0yh35US/7Sbscvde93zcVgZ+Buu59CSDQDe/C2UaVWu4Ajkt0IeBjYI4bPwfo4ZXPYyPc+91unVWBYm5cF+ATNzwJZ+W7iHtfxes9GQtUAJrgeICVwNnHvRtnO1IGmTjeBgdwBong2KFgX7p66Zjm3Q/Mwxn8p96HeYUn43hafZrFMy8FnnDDx3Dc1AsD//Vqt6Lu861171/HcatvlE639LL9cdzYa7vPuiP1+TL5e0fhbOFq5OYthLOS/SuO3Sru1ptqtz/Ey9b4kFeXS3a8CXAo9d5H3taktVuediatnffZ3sBM4Hk33AFngJ9qc1Pt52DgFTfcxs0TgNf33E0bCozJ7v8jP9rM9OTHd68vTM+8oyDoqGo/aZefLzso7zpHVffg7EVcJyI7gGnpsvTBcWXcCYQBL+G4On4gIruA7TirF6dwVtwLAzvFORRuHDljEs4KzEZXdiqvARXdukfgdPh+z0yIiEwSkTighIjEufs+rxQPAP3cNtvDpUOMBgNPisgWnG0FvngN6CMiX+G43md35oBhGMZloapHcQZ289136VdcOsn8LeAzcQ/KwzkrZQWwlrTu7DnlFpyzVKJxzlhJXeVOdYX/1rUNn7j33npuwxmQb8b5lZJ3VHW7L5nqHB7XA+fQvh04563k9qT0QUC4OAexfouzop7KQuBB0rrep+cl4Fl3i9cLrs7/xTkjJvWZLuIMpr9yozbgeI/tAhCRm1yb9SzwvGu3yqhzMv9TOG783wEfubY6S1R1B45N3gPMxjmXBXVWx/8GrBKRL3AG+5naUpwtZLvdNn8V6Ol23P8ImbX3WKCliGzD2cL3s4+yH7plv8Gxvd/7yGMYhnFdI3/8PW0YVwb3oJ/C6hwodzPOz+wEuh06w6V+/fq6d29udzxcWfLrnivTK3fkR72uhk4islVV8+x3vw3jWiMipVQ1Xhx/91dxDvqbfq31uhbkR5uZnvz47vWF6Zl3FAQdwbeeZjPzB7an3sjPlMD5uaXCOC7uA21AbxiGYRi55jER6YPjzr8d5zR8wzAM438EG9QbVxQRaU/GE+BjVLVrdmXVOVQpw8yf+5NARdNFP6Squ9LnvRJ6GYZhGMbVRkSW4Oyz92aEqq72ld8bd1U+zcq8iDyCs2XMm42qmuHnWEXkFpxzBry5qKo5/ak9wzAM4wpig3rjiuJ2NrLtcORS5h/uRFwJvQzDMAzjSpHXk86q+h7OAYY5ybsL51wdwzAMIx9iB+UZhmEYhmEYhmEYRgHFBvWGYRiGYRiGYRiGUUCxQb1hGIZhGIZhGIZhFFBsUG8YhmEYhmEYhmEYBRQb1BuGYRiGYRiGYRhGAcUG9YZhGIZhGIZhGIZRQLFBvWEYhmEYhmEYhmEUUGxQbxiGYRiGYRiGYRgFFBvUG4YBwMGDB2nTpg1BQUEEBwfz8ssvAzBs2DAaNGhAaGgoXbt25dSpU9dYU8MwDMPIW5KTk2ncuDH33HMPAGvWrKFJkyaEhYVx5513cuDAAQDmzJlDxYoVCQsLIywsjHfeeQeAn376iVtvvZWwsDCCg4N54403fNYzZswYqlWr5in/6aefAhAbG0vx4sUJCwujf//+DBgwAIBz587RqVMnGjRoQHBwMCNHjvTIGjJkiEdOYGAgZcuW9aSNGDGCkJAQQkJCWLhwoSe+b9++1K5d21MuOjo6jX5btmzBz8+PxYsXe+LuvvtuypYt62mbVI4cOcJtt91GvXr16NmzJwkJCQCsX7+eJk2a4O/vn0YOgJ+fn6fuzp07e+JVlVGjRhEYGEhQUBAzZ87M9G9lGEZG/K+1AoZh5A/8/f2ZOnUqTZo04cyZM9x6661EREQQERHB+PHj8ff3Z8SIEYwfP56JEydea3UNwzAMI894+eWXCQoK4vTp0wAMHDiQpUuXEhQUxGuvvcY//vEP5syZA0DPnj2ZNWtWmvJVqlRh06ZNFC1alPj4eEJCQujcuTNVq1bNUNeQIUMYOnRohvibb76Z6OhooqKiaN26tSd+6NChtGnThoSEBNq2bctnn31Ghw4dmD59uifPK6+8wvbt2wFYuXIl27ZtIzo6mosXL9KqVSs6dOhAmTJlAJg8eTI9evTIUH9ycjIjRoygffv2aeKHDRvGuXPnePPNN9PEv/nmmwwZMoRevXoxYMAA3n33XQYOHEjNmjWZM2cOU6ZMyVBH8eLFM0wkgDNZcvDgQb7//nsKFSrEb7/9liGPYRiZY4N647pGRBT4QFUfcu/9gSPA16p6j1e+pUAlVW2ejbzpQBv3toRbpqyIBAAxwD9U9QU3bwW3rjdV9SkvGTuAb1W1d06e4XxiMgEjV+Ykq09iJ3QCnA5JlSpVAChdujRBQUEcOnSIdu3aefI2a9Ysw6y7YRjG9YyIjAHiVTXjCCbv6mgNDPW2S3kg85/Aw0A5VS3lI70HsAj4k6p+k1f1ZqNTvKqWEpGqwExVzTjy/IOkt5mxEzoRFxfHypUrGTVqFNOmTUvVxTPA//33330Ozr0pUqSIJ3zx4kVSUlLyRN8SJUrQpk0bTx1NmjQhLi4uQ7758+czduxYAL799ltatWqFv78//v7+NGrUiFWrVnH//fdnWdcrr7xC9+7d2bJlS5r4tm3bEhUVlSZOVdm+fbtncqBPnz6MGTOGgQMHEhAQAEChQjl3CH799deZN2+ep0ylSpVyXNYwDHO/N4yzQIiIFHfvI4BD3hlEpCzQBCgrIrWzEqaqQ1Q1TFXDgFeA/3gl/wh4d8j+AuxJV1cQzv9lSxEpeRnPkyfExsayfft2brvttjTxs2fPpkOHDtdIK8MwDCMPWQ409ZUgIqWBQcDXV1UjF1U9fCUG9JnxzDPPMGnSpDSD0HfeeYeOHTtSvXp13n///TRu7x9//DGhoaH06NGDgwcPeuIPHjxIaGgoNWrUYMSIEZlOBMyaNYvQ0FAeffRRTp486YmPiYmhcePGDB48mA0bNmQod+rUKZYvX07btm3TxP/000/ExMRw1113AdCoUSM+++wzzp07x7Fjx4iMjEyj56hRowgNDWXIkCFcvHgRgEOHDrFkyRKP2392HD9+nFKlSuHv76wPVq9enUOHDmVTCi5cuEB4eDjNmjXjk08+8cT/8MMPLFy4kPDwcDp06MD+/ftzpIdhGA62Um8Y8BnQCVgM9AbmAy280rvjdH5+BXoB43Motzfwotf9eeA7EQl3Vz16Ah8B3lb/r8D7QBDQ2dUlAyLyOPA4QIUKFRl9S1IOVcpI+tn38+fPM3jwYPr378+2bds88R988AGnTp2iWrVqGcqkJz4+Pts81wLTK3fkR73yo07G9YWIPAwMBRTYCfzglRYGvIHjqfUD8KiqnhSRQcAAIAnHE6uXO3H7CnALTn9sjKouzUH9TYEZQHEcu/KIqu4VkRLAHKAB8B0QADyZ2Sq7qn7lyvOVPA6Y5D5nVrr0BboAfkAIMBUoAjwEXAQ6quoJEbkZeBWoCJwDHlPV792J8nnu86/ykhsArFDVEDf8PpA60f2Uqm5yvRfGAMfcurcCD6qq+tAzU5s5fvx4EhMTOXPmDNHR0Rw/fpyoqChGjx7NuHHjaNiwIQsWLKB3794MGzaMcuXKMXfuXIoUKcKyZcu47777PKv7ADNnzuTYsWO88MILVKlShfLly6fRJTQ0lHfffRcRYfbs2fz1r39lxIgRJCQkMG/ePG644Qaio6Pp3r077733HiVLOo+dnJzM//t//4+OHTvy888/8/PPP3tkzp8/n+bNm3smAooUKUJQUBChoaGULVuWOnXqEBMTQ1RUFPfeey99+vQhMTGRqVOnMmDAAM8qe8+ePdmwYQO//PILe/bsoUKFCp46vNsGnAkGVfXc//bbb5w7dy7N+9mXnAULFlChQgUOHz7MgAEDOHv2LNWqVePcuXMcOnSIKVOmsH79erp3755n++oLgt0oCDpCwdHzukRV7bLrur2AeCAUZ0BfDIgGWuN0JlLz/B/OID8Q2JlDubVwXOv93PsAYDfOQH0KUB1YA/QFZnmV2+eWbQcsy0ldNWrfrLVGrLjsy5uEhARt166dTp06NU38nDlztFmzZnr27FnNCZGRkTnKd7UxvXJHftTraugEfKP54P1kV/67gGBgL1DBvS+PM7Ac6t7vBFq54ZeAGW74MFDUDZd1P/+FMwgFKOu+/0tmUq/HLgFlAH83/GfgYzc8FGc7FziD3CQgPAfPFJ/uvrGXzKisZLg27ABQGmfA/jswwE2bDjzjhtcA9dzwbcBaN7wMeNgNP5mqS6rNdMMlgGJuuF7q/6fbJr+79rQQ8CVwZ3bPm95mjhw5UqtVq6a1atXSypUra/HixbVjx45ap04dTeWnn37SoKAgTU9SUpKWKVMmQ7yqat++fXXRokU+01KJiYnR4ODgDPGRkZHaqlUr3bJliyfukUce0aefftqnnLCwMN24cWOm9fTu3VtXrlzps55OnTqpqmpAQIDWqlVLa9WqpSVLltSKFSvqkiVLfOZVVU1JSdEyZcpoYmKiqqpu2rRJ27Vrl0Z+nz59smwD7/T69etrTExMGtl5RX60ZekpCDqq+tbTbGb+uGyl3rjuUdWd7kpAb+BT7zQRqQzUBb5QVRWRJBEJUdXd2YjtBSxW1eR08atwVkB+BRZ6J4jIn4CjqvqTiMQBs0WknKqeJAuKF/Zjr7sv/o+gqvTr14+goCCeffbZSwqvWsXEiRNZt24dJUqU+MP1GIZhFGDuwnm3HwNQZxUaABG5AWfAvs7NOxdnTzo4g/0PReQTINXnuB3QWURSV8OLATVxVtmz4gZgrojUw/EWKOzG3wm87Oq1W0R25vbhRKQQzmC8by6KRarqGeCMiPyO49kGsAsIFZFSwO3AIi+vgKLu5x043nDgrMb7OoW1MDDL9YJIxplgT2Wzqsa5ukfjTAZ8kZWyGW1mJ8aPdxzwoqKimDJlCp988gk33XQT+/btIzAwkP/+978EBQUBzonvqefPLFu2zBMfFxfHjTfeSPHixTl58iQbN25MY0tT8S6/ZMkSQkJCADh69Cjly5fHz8+Pw4cPs3//furUqQPA888/z++//+45ad+bvXv3cvLkSZo3v3TkT3JyMqdOneLGG29k586d7Ny503M+Tmr9qsonn3ziqT8mJsZTvm/fvtxzzz106dIl03YUERo3bszixYvp1asXc+fO5b777sui5eHkyZOUKFGCokWLcuzYMTZu3Mjw4cMB6NKlC2vXruXRRx9l3bp1BAYGZinLMIy02KDeMByW4aygtwZu9IrvCZQDYtzOSBmcAfvz2cjrhbPqkAZVTRCRrcBzOCs+93ol9wYaiEise18Gp7OT0YpfATZu3Mj777/PLbfcQlhYGAD/+te/GDRoEBcvXiQiIgJwDsvL7Kd6DMMw/scRnIF0bukEtMTx1npBRIJdWd1VdW8uZY3DGUh3dSeko7x0+6OUxlnlj3Jt3k3AMhHprJkflnfRK5zidZ+C088sBJxS56wZX2TXnkNwJsIbubIuZFJ3MnnUr/X39+ftt9+me/fuFCpUiHLlyjF79mzAca9ftmwZ/v7+lC9f3nMi/nfffcdzzz2HiKCqDB06lFtuuQXA8xN14eHhDB8+nOjoaESEgIAAz4ny69evZ/To0fj7+3P+/HneeOMNypcvT1xcHP/85z9p0KABTZo0AeCpp56if//+gON636tXrzTbKBITE2nRwtlFWKZMGT744APP3vcHHniAo0ePoqqEhYXlyJ63aNGC77//nvj4eKpXr867775L+/btefzxx5k2bRrPP/88jRs3pl+/foDzs3hdu3bl5MmTLF++nBdffJE9e/bw3Xff8cQTT1CoUCFSUlIYOXIkDRs2BGDkyJE88MADTJ8+nVKlSvmcwDAMIwuutauAXXZdy4tLrn7VgcFuuDWX3By/BJp75a8NHMhGZn0gFhCvuAAuuRIGA33ccF9gFk5H5SBQzatMG2BNds8QGBio+Y386kZmeuWO/KiXud/bdS0v9/29D7jRvU/vfr8DaOGGx+CsehcCAty4wjgD1LI47vezUm0F0DiLer3t0hKcyYDUOmLd8DDgdTfcEEjkMtzv06VFZSWDjFvIYrm0NcGTBmwC/uKGBWjkhpdxaQvCQHy7308HnnPDjzhd17Rt4t7PAvpm97z50WamJz++e31heuYdBUFHVXO/z8+XnX5vGICqxqnqy95x7gpITeArr3wxwGkRSXssfFp6AwvcF52vuvao6tx00S2BQ6rqfXTseqChiFTJ8YMYhmEYVwxV3QP8E1jn/vzotHRZ+gCTXdf3MJx99X7AByKyC9gOTFfVUzgr7oWBnSKy273PCZOA8SKy0ZWdymtARbfuETgu/79nJkREJrlbvUqISJz703xXigeAfm6b7QFS/bQHA0+KyBacbQW+eA3oIyJf4bjen72CehqGYRRIzP3euK5RH7/Nq6pRXHJnrOYjvUk2Msf4iIvFcWlMHz8H57RigGbp0pIBG9AbhmHkI9xJ2fQTs6lp0aR7l7vc6SPveeCJHNYZhWuXVPVL0u4rf8H9vICz6n3BPW1+DfBTFjKHA8Ozqbd1NulzuGTDUNUAX2nuhPjdPsrHAM29oia48bG4NlNV9+McaJvK3934KC7ZalT1qax0NQzD+F/GBvWGYRiGYRgFnxJApIgUxnFxH6iqCddYJ8MwDOMqYIN6w7gMRGQU8Jd00YtU9Z/XQh/DMAyj4CMi7cl4AnyMqnbNrqw6J9CH+5D5NZdOm0/lIVXddTX0MgzDMK48Nqg3jMvAHbzbAN4wDMPIM1R1NbA6j2VmdQZMTmXkuV6GYRhG3mEH5RmGYRiGYRiGYRhGAcUG9YZhGIZhGIZhGIZRQLFBvWEYhmEYhmEYhmEUUGxQbxiGYRiGYRiGYRgFFBvUG4ZhGIZhGIZhGEYBxQb1hmEYhmEYhmEYhlFAsUG9YRiGYRiGYRiGYRRQbFBvGNcxjz76KJUqVSIkJMQTFx0dTbNmzQgLCyM8PJzNmzdfQw0NwzAM48qTnJxM48aNueeeewBQVUaNGkVgYCBBQUHMnDkTgO+//57mzZtTtGhRpkyZkkaGL5uaHlVl0KBB1K1bl9DQULZt2+ZJGz58OMHBwfTp04dBgwahqmnKdu7cOY3sEydOEBERQb169YiIiODkyZMALF26lNDQUI8d/+KLLzxl5s6dS7169ahXrx5z5871xI8aNYoaNWpQqlSpNHXOmTOHihUrEhYWRlhYGO+8804afcuWLetps1T69etHo0aNCA0NpUePHsTHx3vSPvroIxo2bEhwcDB//etfAYiMjPTIDwsLo1ixYnzyySeZtqFhGBmxQb1hibH2WAAAIABJREFUXMf07duXVatWpYkbPnw4L774ItHR0bz00ksMHz78GmlnGIZhGFeHl19+maCgIM/9nDlzOHjwIN9//z3fffcdvXr1AqB8+fLMnDmToUOHZpDhy6am57PPPmP//v3s37+ft956i4EDBwKwadMmNm7cyM6dO5k9ezZbtmxh3bp1nnL/+c9/Mgy4J0yYQNu2bdm/fz9t27ZlwoQJALRt25YdO3YQHR3N7Nmz6d+/P+BMAowdO5avv/6azZs3M3bsWM9EwL333pvpJH7Pnj2Jjo4mOjraIys1/v3338+Qf/r06ezYsYOdO3dSs2ZNZs2aBcD+/fsZP348GzduZM+ePcyYMQOANm3aeOSvXbuWEiVK0K5duyzb0TCMtPhfawUMIzeIiAIfqOpD7r0/cAT4WlXv8cq3FKikqs2zkTcdaOPelnDLlBWRACAG+IeqvuDmreDW9aaqPuUlYwfwrar2voznaQ0kqOqmTNLjVbWUr7RUzicmEzByZa7qjZ3QCYCWLVsSGxubvk5Onz4NwO+//07VqlVzJdswjOsPEXkGeEtVz13hegoD44DuwEXgHPCiqn4mIrFAuKoey6O65gCtgN+BFOBJVf0yD+SOAR4DjgJFgHGqOv8PymwAvAc0AUap6hSvtLuBlwE/4B1VnZCFnChgqKp+80f08ZI3DHjAvfUHgoCKqnriD8jsDDTM6jmyIr3NjJ3Qibi4OFauXMmoUaOYNm0aAK+//jrz5s2jUCFn/atSpUqez0qVKrFyZUa768umpmfp0qU8/PDDiAjNmjXj1KlTHDlyBBHhwoULJCQkkJiYSGJiIpUrVwYgPj6eadOm8dZbb3H//fenkRUVFQVAnz59aN26NRMnTkwz+D979iwiAsDq1auJiIigfPnyAERERLBq1Sp69+5Ns2bNctOMANx6660+48uUKQM4Xgnnz5/31P/222/z5JNPUq5cOeBSm3qzePFiOnToQIkSJXKtj2Fcz9hKvVHQOAuEiEhx9z4COOSdQUTK4nRsyopI7ayEqeoQVQ1T1TDgFeA/Xsk/At4+ZX8B9qSrKwjn/6iliJS8jOdpDdx+GeWuGDNmzGDYsGHUqFGDoUOHMn78+GutkmEY+QBxyKzf8AzOxGhu5F3OwsI4oAoQoqohwL1A6cuQk1OGufZhJPBm+sTLfAaA6a7c+4A33cmKP8IJYBCQxh9cRPyAV4EOQEOgt4g0/IN15RhVnexlY/8OrPsjA3pX5rLLHdBnxjPPPMOkSZM8A3iAH374gYULFxIeHk6HDh3Yv39/ntR16NAhatSo4bmvXr06hw4donnz5rRp04YqVarQo0cP2rdv7/EceOGFF3juuecyDHR//fVXqlSpAkCVKlX47bffPGlLliyhQYMGdOrUidmzZ2dZd3Z8/PHHHlf6gwcP5ug5H3nkEW666Sa+//57nn76aQD27dvHvn37uOOOO2jWrJlPr4YFCxbQu3eu10gM47rHVuqNgshnQCdgMdAbmA+08ErvDiwHfgV6ATkdlfYGXvS6Pw98JyLh7qpFT+AjwHvp+q/A+zirD51dXXwiIoOAAUAS8C1OJ3EAkCwiDwJPA3HAPJz/zUx9+ETkceBxgAoVKjL6lqQcPqJD6sw+wC+//MLZs2c9cTNnzqRfv360atWKyMhIunXrxtSpU3MlPz4+Pk0d+QXTK3fkR73yo07/y7heS58BkUBzYIaIDACKAj8AjwCP4rwXI0XkmKq28fYyEpEewD2q2tdd/T4BNAa2icgZoCZQx/2coaoz3UnSj4DqOCvM43De648BtVX1IoCq/urmS6/3s65e4KxOz/AlU1UXisitwDSgFHAM6KuqR9KJXA/UdWVHAZuAO4BlIvJv4A1Xf3AmOL7EmRgOU9VTbrkDbhkPqrpfRM4B5YDfROQxnHd7EeAA8BCOR8J+4GbgBrf9WqvqehHZADyiqgfc8p3S6d0UOKCqP7o6LMCZSPg2fZv5aMPXgT8BxYHFqvqiG9/Rba9jwDagjrenXBak2uvM6gvAsXtfAM2AHTjeB2OBSsADqrpZRPrieGQ85X6fTgPhwE3AcFVd7EN2pjZz/PjxJCYmcubMGaKjozl+/DhRUVGcO3eOQ4cOMWXKFNavX0/37t09++oBYmNjKV68eIb3UXqbmp5jx46xfft2kpIcHU6ePMnWrVvZu3cvX3zxBfPnzyc+Pp4xY8ZQqVIlSpYsyddff819993HV199lUZ2UlJSmnq878uVK8cbb7zBjh07eOqpp5g6dSoHDhwgMTHRkycmJoZixYqlkZGcnJzmvly5csydO5ciRYqwbNky7rvvPo83Q3x8PAcOHPC0mTd9+vThwQcfZObMmYwdO5YOHTrw66+/cvz4ccaOHcvRo0d56KGHeO+99zyeBcePH2fbtm0ZdPqjFAS7URB0hIKj53WJqtplV4G5gHggFGdAXwyIxlntXuGV5/9wBvmBwM4cyq2F41rv594HALtxBupTcDqBa4C+wCyvcvvcsu2AZdnUcRgo6obLup9jcFwdU/MsAx52w08C8dnpXqP2zVprxIpcXd7ExMRocHCw575MmTKakpKiqqopKSlaunRpzS2RkZG5LnM1ML1yR37U62roBHyj+eB9lx8u912YgjPIqoAzuC3ppo0ARrvhWKCCV7l4r3APYI4bngOs8HrXjsEZIBd15R8HCuNMzr7tJeMG992/PQtdY10ZtwK7gJI4A/U9OJMIvmQWduuv6Mb1BGZ76drDDf8FZ5sXQBTwmpececCdbrgm8J0bfhlnwA1wG/B/Xs881A03ATZ4ybrRK/wP4Gk3vAoIxvEe2wKMctssJl0bpLcpPXAmNVLvH8LLhvlowyicATNAeffTz40PxbG7B3EmVsAZpK/ITJ6X3BI4kxHls/muJQG34HjAbQVmA4IzEfGJm69v6jO4f6NFbv6GOBMYubKZI0eO1GrVqmmtWrW0cuXKWrx4cX3ggQe0fv36GhMTo6qOLSxTpox68+KLL+rkyZM1Peltanoef/xxnTdvnuc+MDBQDx8+rJMmTdKXXnpJVZ333NixY3XixIn62muvaZUqVbRWrVparVo1LVy4sLZq1SpNWVXVw4cPa2BgoM86AwIC9OjRozpv3jx9/PHHM9VFVbVkyZKZ6p6UlJSmHSIjIzUyMlI7deqUaZmoqChP+hNPPKHvvfeeJ+2uu+7SzZs3e+5nzJihjz32WKayLpf8aMvSUxB0VPWtJ2Yz88Vl7vdGgUNVd+IY/97Ap95pIlIZZzXlC1XdBySJSObH0F6iF85KRHK6+FU4Lv69gYXp6voTcFRVf8IZ8DcRkXJZ1LET+NBdlc9saf0OLq1kZDx9xgfFC/sRO6FTrq6sqFq1qudwnrVr11KvXr2cqGEYxv8uP6nqVzgD+4bARhGJBvrgTGrmlkXp3rUrVfWiOnvhfwMq4wzK/ywiE0Wkhar+ngv5dwJLVPWsqsbjbKtqkYnM+kAI8F/3mZ7HmcRNZbIb/zjQzyve2x78GZjl5lsGlBGR0m6enm6eXunKDBGRvcDXOAPxVEJEZIOI7MLZix7sxm8AWrrXePcZ/4QzwM8K8RGnPuJ8cb+IbAO2u3o0BBoAP6pqjJsnp2cB3Ats1Oxd72NUdZeqpuBMxqxxBw27cOy+Lz5R1RRV/Rbnu5Ml6W3m+PHjiYuLIzY2lgULFnDXXXfxwQcf0KVLF9auXQvAunXrCAwMzOGjZk3nzp3597//jary1VdfccMNN1ClShVq1qzJunXrSEpKIikpiXXr1hEUFMTAgQM5fPgwsbGxfPHFFwQGBnpWSjt37uw5wX7u3Lncd999ABw4cCB1ooRt27aRkJDAjTfeSPv27fn88885efIkJ0+e5PPPP6d9+/ZZ6nvkyCWnlWXLlqU5TNAXqsqBAwc84eXLl9OgQQMAunTpQmRkJOB4LOzbt486dep4ys6fP99c7w3jMjH3e6OgsgxnBb01cKNXfE8cN8YY92CWMjidqeezkdcLZ2U8DaqaICJbgedwOjX3eiX3Bhq4hzOl1tUdeAffdMLpkHUGXhCR4Ezy5bTD9Yfp3bs3UVFRHDt2jOrVqzN27FjefvttBg8eTFJSEsWKFeOtt966WuoYhpE/Oet+CvBfzdmhoN7vsWKZyEvlolc4GfBX1X2uW3xHYLyIfI7zzq8pIqVV9UwWdfsayJKJzCXAHs38UNVh6sOdO90zFAKaq+r5NEqIfAnUFZGKQBeclfdUpqvqFBHpBvxbRG5W1Qs4K89dVHWH62be2s2/AWe7VlVgNDDMTVufid6pxAE1vO6r43iNZYl7Hs1Q4E+qetJ1cy9GJm2bA3qRswkA7+9Citd9Cpn3Wb3LXK5+GRg5ciQPPPAA06dPp1SpUp6fcvvll18IDw/n9OnTFCpUiBkzZvDtt99SpkwZnza1X79+vPHGGwAMGDCAjh078umnn1K3bl1KlCjBe++9B0CPHj1Yu3Ytt9xyC+fPn6dbt27ce++9meqXquP999/Pu+++S82aNVm0aBHg7IH/97//TeHChSlevDgLFy5ERChfvjwvvPACf/rTnwAYPXq059C84cOHM2/ePM6dO0f16tXp378/Y8aMYebMmSxbtgx/f3/Kly/PnDlzPPUPGjSII0eOEB8fT/Xq1Xn33XeJiIigT58+nD59GlWlUaNGvP766wCeSYWGDRvi5+fH5MmTufFGpwsXGxvLwYMHadWqVR79BQ3jOuNauwrYZVduLlyXTpyOyWA33BrX/Q9nH2Nzr/y1ycYdD2elJhYQr7gAYLcbDgb6uOG+wCycTtxBoJpXmTY4qwq+6igEBLjhwjj7/cviTBaM9cq3DHjQDQ8kB+73mbnbXUvyqxuZ6ZU78qNe5n5/1d+53u/CisDPQF33vgQQ6IZ34bpku/cHcM4aKQR8TFr3+x5e+caQ1l18t1tnVaCYG9eFS67Xk3D2WRdx76t4vTNjcdzvm+B4RpXAccHfjeN+n0Eml/auN9dL7+dgX7p66RiF66Lu3s/DGfyn3od5hSfjeF19msUzLwWecMPHcPaPFwb+69VuRd3nW+vev+7aoEbpdEsv2x9nb39t91l3pD5fJn/vKJz96Y3cvIVwVr9/xbF/xd16U+3Zh2Tjfs+lcwBK5vS7lr79Sfs97Eta93vv71OBtJnpyY/vXl+YnnlHQdBR1dzv8/Nl7vdGgURV41T1Ze8495CdmsBXXvligNMiclsW4noDC9wXk6+69qjq3HTRLYFDqup9bOx6oKGIVPEhxg/4wHWp3I6zSnMK5+CnriISLSItgMHAkyKyBacjZBiGkS9Q1aM4A6r5IrIT513bwE1+C/hMRCLd+5E4e+fX4pxXkltuATa7Lu2juLTK/TzOT8F9KyK7cQbmR9PpuQ1nsLcZx739HVXd7kumqibg7Duf6P48aTS5/0WSQUC4iOwUkW9xVtRTWQg8SLrtW+l4CXjW/WWBF1yd/wt87/VMF3EG06n2bQPOqf+7AETkJhGJA54FnheROBEpo6pJwFPAauA74CNVTfMrLr5Q1R04tmoPzr72jW78eeBvwCoR+QJnsJ/d1oiuwOeqmt5DwzAMw8gjJJNxjGEYBYT69evr3r17r7UaaYiKiqJ169bXWo0MmF65Iz/qdTV0EpGtqhp+RSsxjAKKiJRS1Xhx9ri9CuxX1enXWq+ckh9tZnry47vXF6Zn3lEQdATfeprNzB/YSr1hGIZhGIaRUx5zvR324HiUvXmN9TEMw7jusYPyjOsCERmF85NE3ixS1X9egbpeJd1vEQMvq+p7eV2XYRiGYeQWEVmCs8/emxGqujq7su6qfJqVeRF5BGf7mDcbVTXDAbQiciPOL8akp62qHs+ufsMwDCMjNqg3rgvcwXueD+AzqStDJ8YwDMMw8guq2jWP5b2Hc4BhTvIeB8Lysn7DMIzrHXO/NwzDMAzDMAzDMIwCig3qDcMwDMMwDMMwDKOAYoN6wzAMwzAMwzAMwyig2KDeMAzDMAzDMAzDMAooNqg3DMMwDMMwDMMwjAKKDeoNwzAMwzAMwzAMo4Big3rDMAzDMAzDMAzDKKDYoN4wDMMwDMMwDMMwCig2qDeM65hHH32USpUqERIS4omLjo6mWbNmhIWFER4ezubNm6+hhoZhGIZx5UlOTqZx48bcc889ADzwwAPUr1+fkJAQHn30URITEwGYPHkyYWFhhIWFERISgp+fHydOnGDv3r2e+LCwMMqUKcOMGTMyrW/Lli34+fmxePFiwLG9zZs3Jzg4mH79+rFw4cIMZZ5++mlKlSrluV+/fj1NmjTB39/fIyeVuXPnUq9ePerVq8fcuXMBOHfuHJ06daJBgwYEBwczcuRIT/4hQ4Z4dA8MDKRs2bKetOHDhxMcHExQUBCDBg1CVT3xjRo1Ijg4mAEDBpCcnAzAsGHDaNCgAaGhoXTt2pVTp055ZI0fP566detSv359Vq9eDcCFCxdo2rSpR9aLL76Y3Z/LMIz0qKpddl31C2gNrMhjmf8EDgLxmaT3ABQIv4rPGe9+VgUWX4k6AgMD9XJZt26dbt26VYODgz1xERER+umnn6qq6sqVK7VVq1a5lhsZGXnZOl1JTK/ckR/1uho6Ad9oPnhP5vYCngFKXIV6CgMTgP3AbmAz0MFNiwUq5GFdc4AYIBrYBjTPI7ljXHtQ1ytuiLeN8GVLgADgr3n4fE8BB9x6K3jFCzDTTdsJNMlCRgCwO4//xu8CO9y6FwOlrvT3yq3X0zcAOgMj87qOzGzm1KlTtXfv3tqpUydVdexfSkqKpqSkaK9evfS1117LUGbZsmXapk2bDPFJSUlauXJljY2N9VlXUlKStmnTRjt06KCLFi1SVdW9e/fqvn37VFV10aJFetNNN+nJkyc9ZbZs2aIPPviglixZ0hMXExOjO3bs0IceesgjR1X1+PHjWrt2bT1+/LieOHFCa9eurSdOnNCzZ8/q2rVrVVX14sWLeuedd3rsvTczZ87URx55RFVVN27cqLfffrsmJSVpUlKSNmvWzPMeXrFihaqqpqSkaLdu3XT+/Pmqqrp69WpNTExUVdXhw4fr8OHDVVV1z549GhoaqhcuXNAff/xR69Spo0lJSZqSkqJnzpxRVdWEhARt2rSpfvnllz7b7nLIj7YsPQVBR1XfehZUm/m/dvnncg7AMPIzy4FZOB3NNIhIaWAQ8PXVVgpAVQ/jTCrkOecTkwkYuTJXZWIndAKgZcuWxMbGpkkTEU6fPg3A77//TtWqVfNET8Mw/jgiIoCoaoqP5GeAD4BzuZDnr6pJuVRjHFAFCFHViyJSGWiVSxm5YZiqLhaRdsCbQKh34mU+A8AuoBfwD/e+B/BtNmUCgL8C89InXKYeG4EVQFS6+A5APfe6DXjd/bxaDFHV0wAiMg1n8mHCVawfVV0GLMtrud42M9UWxsXFsXLlSkaNGsW0adMA6Nixo6dM06ZNiYuLyyBr/vz59O7dO0P8mjVruPnmm6lVq5ZPHV555RW6d+/Oli1bPHGBgYGecIUKFahUqRJHjx6lbNmyJCcnM2zYMObNm8eSJUs8+QICAgAoVCit4+3q1auJiIigfPnyAERERLBq1Sp69+5NmzZtAChSpAhNmjTJ9LnGjh0LOH2CCxcukJCQgKqSmJhI5cqVAShZsiQASUlJJCQk4LyeoF27dh5ZzZo183gRLF26lF69elG0aFFq165N3bp12bx5M82bN/d4ICQmJpKYmOiRZRhGzjD3ewMAEXlWRHa71zMiEiAi34vIXBHZKSKLRaSEm/dWEVknIltFZLWIVHHjo0RkoohsFpF9ItIih3U3FZFNIrLd/azvxpcQkY/c+heKyNciEp6ZHFX9SlWPZJI8DpgEXMhGl74i8omILBeRGBF5ym2b7SLylYiUd/PdLCKr3DbYICIN3PjaIvKliGwRkXFecgNEZLdXeIOIbHOv29341m4bLnbb/kO5BlZtxowZDBs2jBo1ajB06FDGjx9/tVUwDMML953xnYi8hrNa/ZD7ntkmIotEpJSIDMLxCIoUkUi3XLyXjB4iMscNzxGRaW6+iSIyRkRmu++fH11ZiEhJEVkpIjtc29DTtQOPAU+r6kUAVf1VVT/yoXcau5KZTDfep11Jx3qgrps/SkT+JSLrgMEiUlFEPnbfvVtE5A4RKfT/2TvP8KqqrAG/CwIIhmqIQ5UmhARCUKQoSBABEYwoiGIjguOgMp8NBMWCDVFUkDKgUh2UgDgIooMiEEQUBSTS2xh6R4oJJSFZ349zcrm5ufcmIEIi632e87DPLmuvfXI4a5e19xWRLSJSxkunze4kBMBnwK1ufA3gCLDfTzvC3OfdAWdg20JEkkTkCddmfCIinwNfu/n7ujqsFJGXgrVbVVeo6hY/bb0V+NBdCFsClAnwTHx1DWRfConIv0RkjYjMFpEvRSTgRLPXgF6A4jieBIHqnCgio0Vkgfv+tHTfp3VZ75ybr63ve+vG3+TavO+A273yx4vISDd8izh9gBUi8k3W3zDQu3umPP7447z55ps5BsfgDDL//e9/c9NNN2WLP3bsGHPmzKFz5845yiQkJPgd7APs3LmTGTNm0KtXr4D6rFu3jrS0NGrWrAnAyJEjiYuLo0KFXF8BTx1VqlTx3FeuXJmdO3dmy3P48GE+//xzWrdunS1+69atJCcnc8MNNwDQrFkzWrVqRYUKFahQoQLt2rWjbt26nvzt2rUjPDyckiVL0qVLzldq/PjxtG/fPle9MjIyiImJITw8nDZt2tCkyfmcwzKMgo+t1BuIyNXAAzirAIKzmr0QqAP0VNXFIjIeeERE3gVGALeq6n63Y/Ia0MMVF6KqjUXkZuBF4MY8qLAeuF5VT4nIjcAgoDPwCHBIVaNFpB6O++XZtK8hUEVVZ4tInzwUqQc0BC7BcX3sp6oNRWQocD8wDHgf6KWqm0SkCfAv4AbgXWC0qn4oIo8GkL8PaKOqJ0TkSmAKkDVZ0RCIAnbhrOBcB3znp00PAQ8BhIWV54X6Z7Y4lJiY6Anv2bOH1NRUT9zw4cPp2bMnLVu2ZMGCBdx+++28/fbbZyQ/JSUlWx35BdPrzMiPeuVHnc4TdXC+0y8A/wFuVNVUEekHPKmqL4vIk0ArVT2QB3m1XRkZIjIQiABaASWBDSIyGrgJ2KWqHQBEpDTOoHpb1qAvEP7sijsAr+ErU0SKENyuZHELzup6FmVUtaUr52NgqKp+JyJVga9Uta6IzARuAya43+otqrrXGatyFNju2pdbgamuzt7tuBxntfg5VZ0rIqlAH1Xt6KbHA82AaFX9TRxvgiuBxm67Z4nI9UB5P88yGJVwtpNlscONCzRxnUUg+3I7jpdBfSAcWAeMDyZIRCYAN+N4LzyVS71lcWxgHI7X3HXAg8BSEYlx9X8On/dWRN4EPnDLbsb5G/jjO6CpqqqIPAg87aVTjndXVdN92uLXZiYmJvLDDz+Qnp7O77//TlJSEgcPHsz2jXnrrbeoUaMGGRkZ2eLnz59PREQEK1euzKZoeno6n376KR07dvT7rRo4cCB33nknixYtYs+ePaxZs4awsDBP+sGDB3nttdd49tln+fbbbzlw4ABjx45l2LBhJCYm5tADyCFn8+bNpKene/IlJydzySWXeO4zMjJ49tlnufnmm9m2bRvbtm3zyJoyZQrNmjVj0aJFgDMQ/+6775gyZQoAffr0ITw8nAYNGpCSksIzzzxDWloar776KkOHDqVRo9NrL5MnT+bw4cNUqlSJxMREduzYwbp16zx67N69O5vew4YNIyUlheeff56IiAiqV6+e4/mdDQXBbhQEHaHg6HkxYoN6A6A5MENVUwFE5D9AC2C7qi5280zGcV+fgzPonet2igqTvZPxH/ff5TgdiLxQGpjkdkAUZ79mll7vAqjqahFZGaB8QESkEDAUiD+DYgtU9XfgdxE5gtNBAaczGe2uLlwLfCKnF9KLuf9ehzMhAfBv4A0/8osAI92OTgZO5zqLn1R1h6t7Es4zzDGoV9X3cSYWqFqjlr696sz+K2+5J/Z0eMsWLr30UmJjnbhbb72VTz/9FBGhZcuWDB061JOWVxITE8+4zPnA9Doz8qNe+VGn88RWVV0iIh2BSGCx+/0pCvxwFvI+UdUMr/sv3JX3kyKyD7gc55v3loi8gbPPeZHk3XkokF2Z40dmPYLblSEi8hzOKnpPr3jvAeCNQKSXfqXE2XY1FWciZAKOq73voDHBjW8HtCb7oL4IMA94VFUXBmnrXFX9zQ23da8V7n0oziB/kW+7g8gDZ0LAl4Cr5T46+7MvzXH+5pnAHnG9OYKhqg+ISGGcCZc7cZ5hID53B9yrgL2qugpARNbg2LHK+H9vI4BkVd3k5p+MO/j2oTIw1fVWKIpzzkIW/t7dbD7lgWzmlnti+eqrr1i+fDnx8fGcOHGCo0ePMnbsWCZPnsxLL71ESEgI06ZNy7GK/+6779K7d+8c36OZM2fSpEkTbr/9dvyxdetW3nzzTQAOHDjAzz//TIMGDejUqRNHjx4lNjaWBx98kEceecRp3BdfsH//fnr2dF79kydP8uCDD7J582aPzIkTJxIVFeXRZffu3dm+lVOmTKFFixae+x49etCkSROGDx+eQ78nnniCUaNGce211wLOwYAdOnTwrLYvXbqUkydPEhsbm62O3bt3s3TpUvr0cdZOJk2axJo1a5g3bx4lSpQA4IcfnE9VVpnXX3+dtm3b0qxZs2w6LF++nIMHD/LAA9nm2M6agmA3CoKOUHD0vBixQb0B/jsPkLMDoW7eNarazE9+gJPuvxnk/f16BWcgfZuIVOP03sJz4XpeEqezmOh2JP6Gs3ISp6rLApQ56RXO9LrPxGlTIeCwqsYEKJ9bx+sJYC/QwJXlvSXAu+48PcPiRQqzwd0XeC6oWLEiCxcuJDY2lvnz53PllVdzDCpoAAAgAElEQVSeM9mGYZw1qe6/gjOI9O/bmx3vb9ElAeRlkePbo6ob3RX3m4HXReRr4C2gqoiUdCc/A+H3+x1A5gyC25W+qjrdT7x3GwrhHKJ3PJsSIj8AtUSkPNCJ0/vns/gcGIJz0NNRn0mLUzgT1O1wvNcC4a2HAK+r6nu+mXzbraovB5G5A6jidV8Zx4MrNwLZl7Oyp64nx1SgL8EH9d520teGhuC8UzneW3fyIS+TFSOAd1R1lojE4hx06Fs35MFu+trM119/3bPNLDExkbfeeovJkyczduxYvvrqK+bNm5djQH/kyBEWLlzI5MmTc8gPtM8+i+Tk0/MR8fHxdOzYkU6dOpGWlsZtt93G/fffT0zM6e5Fhw4d2LNnj+c+NDQ024DeH+3atePZZ5/l0KFDAHz99deeNj733HMcOXKEsWPH5ii3YcMGDh06lG2QXbVqVT744AOeeeYZVJWFCxfy+OOPk5KSwsGDBwFnT/2XX35JixbOrss5c+bwxhtvsHDhQs+AHiAuLo67776bJ598kl27drFp0yYaN27M/v37KVKkCGXKlOH48eN888039OvXL2gbDcPIju2pN8DZp9hJnD3sl+K4Ki7C6bhlfdm74awYbwDKZ8WLSBERifqD9ZcGsjZ7xXvFfwd0deuJxHEbPCNU9YiqhqlqNVWtBiwBgg3o8yLzKJAsIne4uomINHCTF+Os+gDcE0BEaWC3u2JyH86q1AWhW7duNGvWjA0bNlC5cmXGjRvHBx98wFNPPUWDBg149tlnef/99y+UeoZh5GQJcJ2IZO0tLyEiWauxv+NMZGaxV0Tquh5Lt51pRSJSETimqpNxBvNXqeoxnJPRh4tIUTdfBRG516e4X7viTybnxq58jXOYW5buMQCqqjiTBu8A61T1oHchdxKgH467vy+KswUgQkSyfvvL9xn78hXQQ07vF68kIuEB2h2MWcD9rn1pChwJcmaMN4Hsy3dAZ3H21l+Oc8q8X9w6s94vwdn2sD4PdQcj0Hu7HqguIjXdfIFGw979hO5/UJc80atXL/bu3UuzZs2IiYnh5ZdPz8HMmDGDtm3beg6Ky+LYsWPMnTs3xyr9mDFjGDNmTND6pk2bxrfffsvEiRN58MEHiYmJISkp+K7DpUuXUrlyZT755BP+8Y9/EBXl/LcpV64czz//PNdccw3XXHMNL7zwAuXKlWPHjh289tprrF27lquuuoqYmJhsg/spU6Zw1113ZTukrkuXLtSsWZP69evToEEDGjRowC233EJqaioDBgwgOjqaBg0aEB4e7jknoHfv3vz++++0adOGmJgYT3xUVBRdu3YlMjKSm266iVGjRlG4cGF2795Nq1atiI6O5pprrqFNmzaenxY0DCNv2Eq9gar+LM5hNlk/SD4WOISz5667iLyHc6L8aFVNE+dwneHunsAQnD3ma/6ACm/iuN8/Ccz3iv+XG78Sx5VxJc5BRn5x9+bdDZQQkR3AWFUd+Af0CsY9wGjXJbQIjgvnL8BjwMci8hjwaYCy/wI+dScFFpBzxey8kbVHzpfly5efZ00Mw8gL7p7zeGCKiGRt+3kO2IjjXvxfEdmtqq2A/jgnq2/H+em5UD8ig1Efx/U9E0gHHvaq71VgrYicwPmGveCjZw67oqorRKSdr8xzZFf+Dxjl2osQnEmFrJPIpgJLCbANS1UTAgl1V6rvAj4XkaM4+79PicgvOD+3d8gn/9ciUhf4wR0YpQD34pxFkONZinOw29M4XmQrReRLVX0Q+BJnVX8zzq8Z5NUPOZB9+RRne8FqnHflRwLbU8GxvaXc8C+c/tufFYHeW9dz4yHgCxE5gDP5UM+PiIE4W9524kwQnJvN1j7ExsZ6XItPnQp8Vk18fDzx8fE54kuUKOFZvfYm0KF4EydO9ITvvfde7r3XmRsL5uKckuI5/5JrrrnG7+n14LjY9+iR/ViKypUr48xz+WfgwIE54goXLsx77+VwPOHyyy9nzJgxfvUM5kkwYMAABgwYkC0uOjqaFStWBChhGEZekGD/uY2LF9cNfraq+jOu50uHwkAR98Cfmjh7G2uratqF0ik/UqdOHd2wYcOFViMb+XXPlel1ZuRHvc6HTiKyXFUD/tKGYRRERCRUVVNE5DKcyZbrVHVPbuX+auRHm+lLfvz2+sP0PHcUBB3Bv55mM/MHtlJv5GdK4Pw8UxGc1YKHbUBvGIZhGGfFbHF+3q8o8MrFOKA3DMP4q2KDesMv7u/m/uFVetfV0vcE+GRVzXV/p3sIU46ZPxH5kdOnzWdxX9Zpu3+2XoZhGIZxvhGR+ji/quLNSVXN0w96q2qsH5kzyOnK3k9Vv/KTdwBwh0/0J6rq7zwCwzAM4zxig3rjT8XtGOToHPxBmXnqwOQi45zrZRiGYRh/Fu7EdaBfXTlbmXmeyHYH7zaANwzDyIfY6feGYRiGYRiGYRiGUUCxQb1hGIZhGIZhGIZhFFBsUG8YhmEYhmEYhmEYBRQb1BuGYRiGYRiGYRhGAcUG9YZhGIZhGIZhGIZRQLFBvWEYhmEYhmEYhmEUUGxQbxiGYRiGYRiGYRgFFBvUG4ZhGIZhGIZhGEYBxQb1hnGR0qNHD8LDw6lXr162+BEjRlCnTh2ioqJ4+umnL5B2hmEYhvHnc+LECRo3bkyDBg2IiorixRdfBKBFixbExMQQExNDxYoV6dSpEwAzZ84kOjqamJgYGjVqxHfffeeRddNNN1GmTBk6duwYsL4xY8ZQv359YmJiaN68OWvXrgUgLS2NBx54gPr169OzZ08SExM9ZWJjY6lTp45Hn3379gFw8uRJ7rzzTmrVqkWTJk3YsmULAAcPHqRVq1aEhobSu3fvbPVPnTqV6OjoHDZ+4sSJlC9f3lPH2LFjAdi6dStXX301MTExREVFMWbMGE+ZsWPHUqVKFUJDQ7PV8c477xAZGUl0dDStW7dm69atnrTChQt76oiLiwv+xzEMI8+EXGgFDMO4MMTHx9O7d2/uv/9+T9yCBQuYOXMmK1eupFixYp6Og2EYhmH8FSlWrBjz588nNDSU9PR0mjdvTvv27Vm0aJEnT+fOnbn11lsBaN26NXFxcYgIK1eupGvXrqxfvx6Avn37cuzYMd57772A9d1999306tULgFmzZvHkk08yZ84cPvjgAwBWrVrFjBkzeOqpp1i6dCmFCjnrbx999BGNGjXKJmvcuHGULVuWzZs3k5CQQL9+/Zg6dSqXXHIJr7zyCqtXr2b16tWe/AcPHqRv374sX76c8uXL0717d+bNm0fr1q0BuPPOOxk5cmS2OipUqMD3339PsWLFSElJoV69esTFxVGxYkWuvfZahgwZwpVXXpmtTMOGDVm2bBklSpRg9OjRPP3000ydOhWA4sWLk5SUlMe/jmEYecVW6o1zgoioiPzb6z5ERPaLyGyffDNF5Ic8yBsqIknutVFEDrvx1dy6XvHKGyYi6SIy0kfGLyIyJQ91TRSRZLeu9SLyoldaoohscNPWichDuchKCRDfS0Tu95fmlSfetw154Xh6BtX6f5HnK4vrr7+ecuXKZZM1evRo+vfvT7FixQAIDw8/U3UMwzCMvygF2dZn4W0z3fKeleb09HTS09MREU/+33//nfnz53tW6kNDQz3pqamp2fK2bt2akiVLBq2/VKlSnrB3+bVr13oG12XLlqVMmTIsW7YsqKyZM2fSvXt3ALp06cK8efNQVS699FKaN2/OJZdcki3/r7/+Su3atSlfvjwAN954I59++mnQOooWLerpE5w8eZLMzExPWmRkJBUqVMhRplWrVpQoUQKApk2bsmPHjqB1GIbxx7FBvXGuSAXqiUhx974NsNM7g4iUAa4CyohI9WDCVPUJVY1R1RhgBPAfr+RfAW/ftjuANT511cV5v68XkUvzoH9ft64YoLuPfve4adcBb4hI0TzI823PGFX98EzLnW82btzIokWLaNKkCS1btmTp0qUXWiXDMAwj/1DQbb1fMjIyiImJITw8nDZt2tCkSRNP2owZM2jdunW2wfiMGTOIiIigQ4cOjB8//ozrGzVqFDVr1uTpp59m+PDhADRo0ICZM2dy6tQpdu/ezfLly9m+fbunzAMPPEBMTAyvvPIKqgrAzp07qVKlCgAhISGULl2agwcPBqy3Vq1arF+/ni1btnDq1Ck+++yzbHV8+umnREdH06VLl2zx27dvJzo6mipVqtCvXz8qVqyY57aOGzeO9u3be+5PnDhBo0aNaNq0KZ999lme5RiGERxzvzfOJf8FOgDTgW7AFKCFV3pn4HNgL3AX8Hoe5XYDXvS6Pw6sE5FGqroMuBOYBnhbmbuBfwN1gThXl7yQNa2d6ict1I3PCCZARF7D6YgcB25V1b0iMhBIUdW3ROQaYJwr6zugvapmbWyvKCJzgJrADFX1u6nd9Rh4CCAsrDwv1D+Vx+aRbZ/enj17SE1N9cQdOXKEVatWMXjwYNavX09cXBwff/xxtpWIvJCSkpKtnvyC6XVm5Ee98qNOhnGRUeBsfSCb6f0tGTZsGCkpKTz//PNERERQvbozHzFq1ChuvvnmbHnLli3LmDFj+OWXX+jduzdvv/22Jy0pKYmDBw8G/U5FRUUxbtw4vvnmG3r37s0zzzxDzZo1mTt3LhEREYSFhREREcG6detITEzk0UcfpXz58hw7dowXX3yRY8eO0a5dO1JSUvjhhx88K+8nTpxg8eLFlC5dGoD169ezc+fObLo88sgjtG/fnkKFChEVFcXhw4dJTEykbNmyTJo0iaJFizJr1ixuvfVW3nnnHU+54cOHc+DAAZ5//nkqVKhAuXLlPN/jjIwMv+2dO3cu8+fPZ9iwYZ70hIQEwsLC2LVrF7169SI1NZVKlSoFfFbngoJgNwqCjlBw9LwoUVW77PrDF5ACROMY+UuAJCAWmO2V5xscw18bWJlHuVcAu4HC7n01YDWO8X4LqAzMA+KBkV7lNrpl2wKzcqljIpDs6pwCDPJKSwQ2ACtxOhj/yEWWAre44TeB59zwQKCPG14NXOuGBwOr3XA8zspEafcZbgWq5PaMqlSvqVf0m53ny5vk5GSNiory3Ldr104XLFjgua9Ro4bu27dPzxRvGfkJ0+vMyI96nQ+dgGWaD76rdtmV366CbOuzLm+b6Y+BAwfqkCFDVFX1wIEDWq5cOT1+/LjfvKqq1apV0/3793vuFyxYoB06dAiY35uMjAwtVapUjvgFCxZos2bNdM2aNTnSJkyYoI8++qiqqrZt21a///57VVVNT0/Xyy67TDMzM/3m9cd7772nffv2zRF/6tQpv3qpqsbHx+snn3zi0VNV9dJLL82Rb+7cuRoREaF79+4NWH/37t09sv5M8qMt86Ug6KjqX0+zmfnjspV645yhqitFpBrObPuX3mkicjlQC/hOVVVETolIPVVdnVNSNu4Cpquq7+r4HOAVnJWAqT51XQPsV9WtIrIDGC8iZVX1UJB6+qrqdBEJBeaJyLWq+r2bdo+qLhOR8sD3IjJHVbcGkJMGZO0tXI7jmuitWxmgpJfsj8nuXjhPVY+4edfidFa2E4TiRQqzYXCHYFnyTKdOnZg/fz6xsbFs3LiRtLQ0wsLCzolswzAMo+BTwG19Dpu5f/9+ihQpQpkyZTh+/DjffPMN/fr1A+CTTz6hY8eO2famb968mZo1ayIi/Pzzz6SlpXHZZZfl0rzTbNq0yXOw3BdffOEJHzt2DFVnP/yyZcsICQkhMjKSU6dOcfjwYcLCwkhPT2f27NnceOONAMTFxTFp0iSaNWvG9OnTueGGG3L1rNu3bx/h4eEcOnSIf/3rX0ybNg2A3bt3e/bHz5o1i7p16wKwY8cOLrvsMooXL86hQ4dYvHgxTz75ZNA6VqxYwT/+8Q/mzJmT7WyeQ4cOUaJECYoVK8aBAwdYvHix/cqOYZwjbFBvnGtm4cyqxwLeVu5OoCyQ7BqcUjhG/Llc5N0FPOobqappIrIceAqIAm7xSu4GRIjIFve+FI474NjclFfVFBFJBJoD3/uk7ReRn4EmOKvo/kh3Zy3BcdP3/T+Wmx/7Sa+wv/LnjG7dupGYmMiBAweoXLkyL730Ej169KBHjx7Uq1ePokWLMmnSpDN2vTcMwzD+8hRoW+/N7t276d69OxkZGWRmZtK1a1fPT9IlJCTQv3//bPk//fRTPvzwQ4oUKULx4sWZOnWqx062aNGC9evXk5KSQuXKlRk3bhzt2rXjhRdeoFGjRsTFxTFy5Ei++eYbihQp4nF5B2ew3a5dOwoVKkSJEiX4z3+c4wVOnjxJu3btSE9PJyMjgxtvvJG///3vAPTs2ZP77ruPWrVqUa5cORISEjx6VqtWjaNHj5KWlsZnn33G119/TWRkJI899hi//PILAC+88AK1a9cGHPf6WbNmERISQrly5Zg4cSIA69at46mnnkJEUFX69OlD/fr1Aefn+e69916OHTtG5cqVefDBBxk4cCB9+/YlJSWFO+64A4CqVasya9Ys1q1bxz/+8Q8KFSpEZmYm/fv3JzIy8kz+XIZhBOJCuwrY9de4cPaLg+Mi95gbjsV1yQN+AJp55a8ObM5FZh1gCyBecdU47a4eBXR3w/HASJwDc7YDlbzKtMJZAQ9Uz0SgixsOwXG57+zeJwKN3HAJHFe/q3N7Dm64CzDRDQ8ku/t9Uzc8iOzu995uhbOB2Nyefe3atTW/kV/dyEyvMyM/6mXu93bZdeGugmzrs678aDN9yY/fXn+YnueOgqCjqrnf5+fLTr83zimqukNV3/WOc930qgJLvPIlA0dFpAmB6QYkuB8Mf3WtUdVJPtHXAztV1fs03m+BSBHJ+bsrpxkiIkk4e+dXkf0E3o/ctOU4g/TlQeTkhZ7A++7P/Qhw5A/KMwzDMIzzRgG29YZhGH9JzP3eOCeoaqifuESclW6AHEebqupVucgc6CduC1DPT/xEnBV3gKY+aRlAQCOvqvFB0mKD6egnf6hXeDrOYUK+bVmjqtEAItIfWObmmcjpNqCq3nvtDcMwDOOCUpBtvWEYxl8ZG9Qbxvmng4g8g/P/byuOO6FhGIZhGIZhGMYZY4N644IiIgOAO3yiP1HV1/6EukYB1/lEv6uqE85C1o9AMZ/o+1R1VW5lVXUqPqf4GoZhGMZflfNp6w3DMC5GbFBvXFBcg35ejLqq5jhZ9w/ICrY/0DAMwzAMl/Np6w3DMC5G7KA8wzAMwzAMwzAMwyig2KDeMAzDMAzDMAzDMAooNqg3DMMwDMMwDMMwjAKKDeoNwzAMwzAMwzAMo4Big3rDMAzDMAzDMAzDKKDYoN4wDMMwDMMwDMMwCig2qDcMwzAMwzAMwzCMAooN6g3jIqRHjx6Eh4dTr149T9zAgQOpVKkSMTExxMTE8OWXX15ADQ3DMAzjz+XEiRM0btyYBg0aEBUVxYsvvgjAyJEjqVWrFiLCgQMHPPk/+ugjoqOjiY6O5tprr+WXX37xpB0+fJguXboQERFB3bp1+eGHH3LUl5iYSOnSpT129uWXX/arx4QJEzxlAuly6NAhbrvtNqKjo2ncuDGrV6/OVZc777zTU3e1atWIiYkB4ODBg7Rq1YrQ0FB69+6dTee0tDQeeughateuTUREBJ9++ikAY8aMoUePHsTExNC8eXPWrl3rKfP6669Tq1Yt6tSpw1dffeWJHzp0KFFRUdSrV49u3bpx4sQJAFSVAQMGULt2berWrcvw4cPz9PczDOM0IRdaAcMwzj/x8fH07t2b+++/P1v8E088QZ8+fS6QVoZhGIZx/ihWrBjz588nNDSU9PR0mjdvTvv27bnuuuvo2LEjsbGx2fJXr16dhQsXUrZsWf773//y0EMP8eOPPwLw2GOPcdNNNzF9+nTS0tI4duyY3zpbtGjB7Nmzg+oRHR3NkiVLaNq0aUBdBg0aRExMDDNmzGD9+vU8+uijzJs3L6guU6dO9ZR/6qmnKF26NACXXHIJr7zyCqtXr842OQDw2muvER4ezsaNG8nMzOS3334D4O677yYiIoLY2FhmzZrFk08+yZw5c1i7di0JCQmsWbOGXbt2ceONN7Jx40b27NnD8OHDWbt2LcWLF6dr164kJCQQHx/PxIkT2b59O+vXr6dQoULs27fvDP6KhmGADeoDIiKPA++rqv+v8rmrpwjwCtAZOAkcA15U1f+KyBagkaoeCCLiTOqaCLQEjgCZwKOqmnMq+czlDgT+DuwHigKvqOqUPygzApgAXAUMUNW3vNJuAt4FCgNjVXVwEDmJQB9VXfZH9PGS1xe4x70NAeoC5VX1tz8gMw6IDNaOYBxPz6Ba/y/ylHfL4A4AXH/99WzZsuVsqjMMwzDOASKiwGRVvc+9DwF2Az+qakevfDOBcFVtlou8oUAr97aEW6aMiFQDkoFXVfV5N2+YW9d7qtrbS8YvwFpV7ZZLXRM53Z+4BJiiqi+5aYlABeA4UAwYqqrvB5GVoqqhfuJ7AcdU9cMgZeNx+km9A+XxJctmbhncAREhNNSpOj09nfT0dESEhg0b+i177bXXesJNmzZlx44dABw9epRvv/2WiRMnAlC0aFGKFi2aV5Vy6JGRkYGIAATUZe3atTzzzDMAREREsGXLFvbu3Uvx4sVz1UVVmTZtGvPnzwfg0ksvpXnz5mzevDlHPePHj2f9+vUAFCpUiLCwMABKlSrlyZOamurRd+bMmdx1110UK1aM6tWrU6tWLX766SeqVq3KqVOnOH78OEWKFOHYsWNUrFgRgNGjR/Pxxx9TqJDjQBweHp7nZ2cYhsNF7X4vDoGeweM4RvFM5J3NJMkrOMavnqrWA24BSp6FnLzSV1VjgP7Ae76JZ9kGcIx2DHAr8J47WfFH+A34P+At70gRKQyMAtoDkUA3EYn8g3XlGVUdoqoxblufARb+kQG9K3PW2Q7ozzUjR44kOjqaHj16cOjQoQutjmEYxl+ZVKCeiBR379sAO70ziEgZnMntMiJSPZgwVX3Cyz6NAP7jlfwr0NHr/g5gjU9ddXH6hdeLyKV50D+rPxEDdPfR7x437TrgDRHJ+wj3dHvGBBvQnysyMjKIiYkhPDycNm3a0KRJkzyVGzduHO3btwfg119/pXz58jzwwAM0bNiQBx98kNTUVL/lfvjhBxo0aED79u1Zs+b0n8Bbj6uvvjpXPRo0aMB//uP8iX/66Se2bt3Kjh078qTLokWLuPzyy7nyyiuD1nH48GEAnn/+ea666iruuOMO9u7d60mfMWMGNWvW5Omnn/a4zO/cuZMqVap48lSuXJmdO3dSqVIl+vTpQ9WqValQoQKlS5embdu2APzvf/9j6tSpNGrUiPbt27Np06agehmGkZOLbqXenbH+L7AAaAYMc2eDiwH/Ax4AegAVgQUickBVW3nPJItIF6Cjqsa7s9W/AQ2Bn0Xkd6AqUMP9d5iqDncN5DSgMs4K8yvA5zgr3NVV9SSAqu518/nq/aSrFzir08P8yVTVqSJyNfAOEAocAOJVdbePyG+BWq7sROB7HOM7S0Q+BMa4+oMzwfEDTqcgRlUPu+U2u2U8qOomETkGlAX2icjfgYdwVvA3A/fheCRsAmoCpd3nF6uq34rIIuABVd3slu/go3djYLOq/urqkIAzkbCWXBCR0cA1QHFguqq+6Mbf7D6vA8DPQA3vVZIgdAMCeiS479oc4DugKfALjvfBS0A4TqfnJ++VBvd9Ogo0Av4GPK2q0/3IfgjnuRIWVp4X6p/Kg7rOfr4s9uzZQ2pqqicuOjqacePGISKMHz+eu+++m379+uVJri8pKSnZ6sovmF5nRn7UKz/qZBh/gP8CHYDpnLYpLbzSO+P0FfYCdwGv51FuN+BFr/vjwDoRaeR6rt2J03+o6JXnbuDfOB5ocQSxbz5c4v7rbxQb6sZnBBMgIq/hTDocB25V1b2uF2CKqr4lItcA41xZ3wHt3YUQgIoiMgenTzFDVZ/2Iz+HzfT+jgwbNoyUlBSef/55IiIiqF7dmZ84ceIEixcv9ripZ7FixQpGjBjB8OHDSUxMZMOGDSxfvpz4+Hji4+MZMWIEDz/8MD169MhWLjU1lcmTJ1O8eHGWLFlCu3btmDx5cg49nn32WSZMmODRw58u1113nWe/fY0aNahVqxYrVqwgIyMjV12GDh1K48aNc3xL169fz86dOz3xR44cYceOHZQuXZp33nmHadOmcd999/Hss88C0KZNG2677Ta++eYbevfuzTPPPMOOHTtYt26dR8bu3btZs2YNxYoVY9KkSUyePJnQ0FAGDhzIgAEDaNOmDceOHWPnzp289dZbfPvtt3Tu3Pmc7qsvCHajIOgIBUfPixJVvaguoBqO63lTIAxncHupm9YPeMENbwHCvMqleIW7ABPd8ERgNlDYvR+IM0Au5so/CBTBMcwfeMkoDUQDK4LousWVcTWwCrgUx0CuwZlE8CeziFt/eTfuTmC8l65d3PAdOC5+AInAv7zkfAw0d8NVgXVu+F2cATdAE+Abrzb3ccNXAYu8ZF3mFX4V+KcbngNE4RjxpcAA95kl+zwDj2yvZz/W6/4+YGSQZ5iIM2AGKOf+W9iNj8bpjGzHmVgBpxMzOw/vUQmcyYhyubxrp4D6OKsfy4HxgOBMRHzm5ovPaoP7N/rEzR+JM4ERVJcq1WvqFf1m5+nyJjk5WaOiotQfwdLywoIFC8667J+J6XVm5Ee9zodOwDLNB/bKrr/2BaS4dmi6a4uSgFhvGwR8gzPIrw2szKPcK3Bc67P6JdWA1TgD9bdwFgLmedseN99Gt2xbYFYudUzEcelPctsxyCstEdgArMQZpP8jF1kK3OKG3wSec8PefYvVwLVueDCw2g3H4yw4lHaf4VagSrD6smymPwYOHKhDhgzx3F9xxRW6f//+bHl++eUXrVGjhm7YsMETt3v3br3iiis8999++63efPPNfuvwxp98VVCQVk8AACAASURBVNX4+PhsegTLq6qamZmpV1xxhR45ciRXXdLT0zU8PFy3b9+eQ86ECRP00UcfzSa3RIkSmpGRoaqq27Zt08jISE961vc4IyNDS5UqpaqqgwYN0kGDBnnytG3bVr///nudNm2a9ujRwxM/adIkffjhh1VVtU6dOpqcnOypM0vWuSI/2jJfCoKOqv71NJuZP66L1f1+q6ouwRnYRwKLRSQJ6I5j0M6UT1TVexb6C1U9qc5e+H3A5TiD8htF5A0RaaGqR85AfnOc2edUVU3BcalrEUBmHaAeMNdt03M4BjyLIW78Q0BPr/ipXuEbgZFuvllAKREp6ea5081zl0+ZJ0RkA/AjjiHOop6ILBKRVTh70aPc+EXA9e71utvGa3AG+MEQP3GaS5ksuorIz8AKV49IIAL4VVWT3Tx5XZm4BVisubveJ6vqKlXNxJmMmed+AFfhdLT88ZmqZqrqWpx357ywe/dpZ44ZM2ZkOxnfMAzDOPeo6kocW9ANyPaTIyJyOY5H3XequhE4JSJ5+TDfheON5rs6PgfHxb8b2e037kr4flXdijPgv0pEyuZST5b7/d+A1iJyrVfaPaoajbMw0EdEgvWt0nAWR8CZ/K7mo1sZoKSqfu9GfexTfp6qHlHVEzhee3nux+3fv9/jYn78+HG++eYbIiIiAubftm0bt99+O//+97+pXbu2J/5vf/sbVapUYcOGDY5C8+YRGZlzZ+CePXuyJjL46aefyMzM5LLLLsuhx/Lly4PqAY5rfFpaGgBjx47l+uuvp1SpUrnqktXGypUr+5XrjYhwyy23eFZmvWV5u8h/8cUXHlf+uLg4EhISOHnyJMnJyWzatInGjRtTtWpVlixZwrFjx1BV5s2bR926dQHo1KmTZ3//woULsz1bwzDyxkXnfu+S5SImwFzN5UAYF++B4yU+ab4uZye9whlAiKpudN3ibwZeF5GvcWbMq4pISVX9PUjd/gayBJA5A1ijgQ/U6at+3Ll92lAIaKaqx7MpIfIDUEtEygOdcFbesxiqjovc7cCHIlLTNbATgU6q+ovrZh7r5l8E9MJx/XsB6OumfRtA7yx2AFW87isDu3Ipg7vXrw9wjaoect3cLyHAs80Dd5G3CQDvdyHT6z6TwP//vMvkql/xIoXZMNh3l0JwunXrRmJiIgcOHKBy5cq89NJLJCYmkpSUhIhQrVo13nsvx5ELhmEYxrlnFk5/IBa4zCv+TpytbMnuIWSlcGzPc7nIuwt41DdSVdNEZDnwFM7E9i1eyd2ACPeA3qy6OgNjc1NeVVPcbXzNcTwFvdP2u5PpTXBW0f2RrlkjXbfP5JOemx3M0ecKltnbZu7evZvu3buTkZFBZmYmXbt2pWPHjgwfPpw333yTPXv2EB0dzc0338zYsWN5+eWXOXjwII888ggAISEhLFvmnMM7YsQI7rnnHtLS0qhRo4bnZ+nGjBkDQK9evZg+fTqjR48mJCSE4sWLk5CQgIjk0OOaa66hY0dnF2AgXdatW8f9999P4cKFiYyMZNy4cZ42BtIFICEhgW7dcnZ7q1WrxtGjR0lLS+Ozzz7j66+/JjIykjfeeIP77ruPxx9/nPLly3tkjRw5kpkzZ1KmTBnKli3LpEmTAIiKiqJr165ERkYSEhLCqFGjKFy4ME2aNKFLly5cddVVhISE0LBhQx566CEA+vfvzz333MPQoUMJDQ1l7NhcXzvDMHy50K4C5/vCdUNzw+WBbUAt974EUNsNr8J1yXbvN+PsMysEfEp29/suXvkGkt1dfLVbZ0XgEjeuE6ddr9/E2Wdd1L2vANzrhrfguN9fhePGVgLHBX81jvt9Dpmc3rvezI0vAkT509VLx0RcF3X3/mOcwX/WfYxXeAjOnrsvg7R5Jq67Hc4+9XBXj7lez62Y27757v1oHDf4Bj66+coOwXG1q+629Zes9gX4eyfi7E9v4OYthLP6vRfHba+4W281N/9H5OJ+z+lzAC7N67vm+/zJ/h7Gk9393vt9SglWh6pSu3ZtzW/kVzcy0+vMyI96mfu9XX+VK+v7jjM5/Zgbjs2yQThn2TTzyl+dXLZk4XjrbQHEK87b3kQB3d1wPDDStYvbgUpeZVrhrIAHqsfbnoW4traze5/I6W1vJXDc+q/O7Tm4Ye/tjR77j9PvaeqGB/mzn+79bJwzegI+o/xoM33Jj99ef5ie546CoKOqud/n5+tiXakHPDPI8cAUESnmRj+HY4DeB/4rIrtVtRXOafGzcQzfapy97WdCfRzX90wgHXjYq75XgbUicgJnxfwFHz1/dleWf3KjxqrqChFp5ytTnZn4LsBwESmNY2yH4XPKbS78HzBKRFa65b/FWVUHx2VvKY4hDcTLwMci8gHwPI5L/laciZKSbptOish2YIlbZhHOSsEqABH5G7AMZ7UgU5yfGIxU1aMi0hv4Cmdv/HhVzbVt6ngKrMB5Dr8Ci9344yLyCDBHRA5w+hkH4zbga1X1f7StYRiGYeQRVd2Bc2aNB/eg1aqctpGoarKIHBWRJqr6YwBx3YAEt6Ptr6415OwPXA/sVFXvk/e/BSJFpILmPGg3iyEi8hzOBPs8sp+2/5GIZP2k3URVXR5ARl7pCXwgIqk4kwZnsoXRMAzjL48E+O4bxkWDiISq4z4oOD+Xt0lVh15ovfJKnTp1NGvvXH4hMTGR2NjYC61GDkyvMyM/6nU+dBKR5ara6E+txDCMPJNlp91wf6CCqj52NrLyo830JT9+e/1hep47CoKO4F9Ps5n5g4v1oDzD8Obv7qGAa3Bc620zuWEYhmHkHzqISJKIrMY5KPjV3AoYhmFcTFzU7vfGXwcRmYGz39Cbfqr6VW5l3VX5bCvzIvIA4LsKsFhVcxw+JCKX4bge+tJaVQ/mVr9hGIZh5BURGYDzs7TefKKqr/0JdY0CrvOJfldVJ/jLn4usH3Hc8b25T1VX5VZWVafic2K/YRiGcRob1Bt/CVT1tnMsbwLOAYZ5yXsQiDmX9RuGYRiGP9zB+zkfwAeoK8dE9h+Q1eRcyTIMwzCyY+73hmEYhmEYhmEYhlFAsUG9YRiGYRiGYRiGYRRQbFBvGIZhGIZhGIZhGAUUG9QbhmEYhmEYhmEYRgHFBvWGYRiGYRiGYRiGUUCxQb1hGIZhGIZhGIZhFFBsUG8YhmEYhmEYhmEYBRQb1BuGYRiGYRiGYRhGAcUG9YZxEdGjRw/Cw8OpV6+eJ+75558nOjqamJgY2rZty65duy6ghoZhGIZxfti+fTutWrWibt26REVF8e677wKQlJRE06ZNiYmJoVGjRvz000/Zyi1dupTChQszffp0T1zhwoWJiYkhJiaGuLg4v/Vt27aNVq1a0bBhQ6Kjo/nyyy8B+OmnnzxlGzRowKJFizxlqlWrRv369T26ZPHbb7/Rpk0brrzyStq0acOhQ4dy1RHg6NGjVKpUid69e3vipkyZQv369YmOjuamm27iwIEDnrQRI0ZQp04doqKiePrppz3xr7/+Ovfccw916tThq6++8sQPHTqUqKgo6tWrR7du3Thx4kS2+v/5z38SGhrq9/kYhnH2/GUH9SLyuIiUOA/1FBGRwSKySURWi8hPItLeTdsiImHnsK6JIpIsIkki8rOINDtHcgeKiIpILa+4J9y4Ru59ip9y1UTk7nOhgyuvt4hsdusN84oXERnupq0UkauCyKgmIqvPlU6uzHEi8otb93QRKbDWKD4+njlz5mSL69u3LytXriQpKYmOHTvy8ssvXyDtDMMwDH+ISKyIzD7HMl8Tke3+7Lub3sW7H3A+yNJFRCqKyPTc8v9RQkJCePvtt1m3bh1Llixh1KhRrF27lqeffpoXX3yRpKQkXn755WyD2YyMDPr160e7du2yySpevDhJSUkkJSUxa9Ysv/W9+uqrdO3alRUrVpCQkMAjjzwCQL169Vi2bBlJSUnMmTOHd955h1OnTnnKLViwgKSkJJYtW+aJGzx4MK1bt2bTpk20bt2awYMH56ojOBP5LVu29NyfOnWKxx57jAULFrBy5Uqio6MZOXKkp96ZM2eycuVK1qxZQ58+fQBYu3YtCQkJTJgwgTlz5vDII4+QkZHBzp07GT58OMuWLWP16tVkZGSQkJDgqWvZsmUcPnw49z+MYRhnTIEe1LuDvUBteBw4o0G9iISchRqvABWAeqpaD7gFKHkWcvJKX1WNAfoD7/kmnmUbAFYBd3nddwHW5lKmGuB3UH+WeiwGbgS2+sS3B650r4eA0Wch+4/whKo2UNVoYBvQO7cC55Pj6RlU6/9F0CuL66+/nnLlymUrX6pUKU84NTUVETlvuhuGYRgXjM+Bxv4SRKQk8H/Aj+dVIxdV3aWqXf4M2Vk2E6BChQpcdZWzTlCyZEnq1q3Lzp07ERGOHj0KwJEjR6hYsaKn/IgRI+jcuTPh4eFnXHcguSVKlCAkxOk2nThxIk92eObMmXTv3h2A7t2789lnn+Wq4/Lly9m7dy9t27b1xKkqqkpqaiqqytGjRz16jR49mv79+1OsWDEAj7yZM2dy1113UbRoUapXr06tWrU83gynTp3i+PHjnDp1imPHjnlkZWRk0LdvX958880zfGqGYeSFAjeod1di14nIv4CfgftE5Ad35foTEQkVkf8DKgILRGSBWy7FS0YXEZnohieKyDtuvjfcVevxIpIoIr+6shCRS0XkC3fFdrWI3Ol6Avwd+KeqngRQ1b2qOs2P3k+65VaLyOOBZLrxV4vIQhFZLiJfiUgFP4/iW6CWmz9RRAaJyELgMREpLyKfishS97pORAq5ngNlvHTaLCKXu7efAbe68TWAI8B+P+0Ic593B2Aw0ML1HHhCROLdv8HnwNdu/r6uDitF5KVg7VbVFaq6xU9bbwU+VIclQJkAz8RX12oissh9N34WkWvd+EIi8i8RWSMis0XkSxEJ2HlQ1aNuOQGKAxqkzokiMlpEFrjvT0v3fVqX9c65+UaLyDJXh6znUlpENohIHfd+ioj8Pbd2ngsGDBhAlSpV+Oijj2yl3jAMIxd8bbprb9aLyCQ57dVVws3r16a7tvsNcTz8NopIizzW3VhEvheRFe6/WTajhIhMc+ufKiI/SpBVdlVdoqq7AyS/ArwJnAiQnqVLvIh8JiKfi+NJ2Nt9NitEZImIlHPz1RSROe4zWCQiEW58dbdPsVREXvGS6/G6C2LLY91nON199h+5dvqs2LJlCytWrKBJkyYMGzaMvn37UqVKFfr06cPrr78OwM6dO5kxYwa9evXKUf7EiRM0atSIpk2bZhtgezNw4EAmT55M5cqVufnmmxkxYoQn7ccffyQqKor69evzxBNPeAb5IkLbtm25+uqref/99z359+7dS4UKTleoQoUK7Nu3L6iOmZmZPPXUUwwZMiRbfJEiRRg9ejT169enYsWKrF27lp49ewKwceNGFi1aRJMmTWjZsiVLly711FGlShWPjMqVK7Nz504qVapEnz59qFq1KhUqVKB06dKeCYSRI0cSFxfn0dkwjHPL2a7qXmjqAA8ALwD/AW5U1VQR6Qc8qaovi8iTQCtVPRBMkEttV0aGiAwEIoBWOCvuG0RkNHATsEtVO4AzAMMZVG/LGvQFQkSudvVtAgjwozgD8Bq+MkWkCDACuFVV97sD3teAHj5ib8FZXc+ijKq2dOV8DAxV1e9EpCrwlarWFZGZwG3ABBFpAmxR1b2uDTwKbBeRejiD6Kmuzt7tuByYBTynqnNFJBXoo6od3fR4oBkQraq/iUhbnNX1xm67Z4nI9UB5P88yGJWA7V73O9y4QJ2RLPYBbVT1hIhcCUwBGgG343gZ1AfCgXXA+GCCRGQCcDOO98JTudRbFrgBiMNZCbkOeBBYKiIxqpoEDHCfUWFgnohEq+pKEekNTBSRd4GyqvpBAH0ewvFaICysPC/UP+Uvm4fExERPeM+ePaSmpmaLa9OmDW3atOGjjz6iT58+PPDAAzmFnAEpKSnZ5OcXTK8zIz/qlR91Mi4u/Nl0YCFO36Snqi4WkfHAI+63PJhND1HVxiJyM/AijrdabqwHrlfVUyJyIzAI6Aw8AhxS1WjXliedZfsaAlVUdbaI9MlDkXpAQ+ASYDPQT1UbishQ4H5gGPA+0EtVN7n9j3/h2Ml3gdGq+qGIPBpAfiBbjltvFLALx9vvOuA7P23KYTO9vyPHjx/nscce48EHH+Tnn39m+PDh9OzZk5YtW7JgwQJuv/123n77bQYOHMidd97JokWL2LNnD2vWrCEszNktmJCQQFhYGLt27aJXr16kpqZSqVKlbHpMmzaNFi1a0LVrV9asWUPnzp0ZP348hQo5a2yjRo1i69atvPbaazRp0oSiRYsyZMgQwsLCOHToEH369OH48eM0aNCAU6eytyHrPpCOM2bMoE6dOvzvf/9j/fr17Ny5k8TERE6dOsWgQYMYPXo0FStWZPjw4Tz00EPcd999HDlyhFWrVjF48GDWr19PXFwcH3/8MTt27GDdunWUKVOGxMREdu/ezZo1ayhWrBiTJk1i8uTJhIaGMnDgQAYMGEDDhg0ZO3Ysw4YNIzExkYyMjPP6HS8IdqMg6AgFR8+Lkiy3m4Jy4QzGkt1wR+AAjuFKwhlwjXPTtgBhXuVSvMJdgIlueCLQ3SttIM6AK+t+HVAZZ+CfDLwBtHDTooEVQXTdAoQBjwEve8W/guPW5k9mPZwBdlabVgFfe+ma7MbPxXH5B0gEWnrJ3+dVPgnYiTNBcS0wx80zFPi7V5v7AF2BV4GlQClXbiM3z0lgtU89scBsr/t4YILX/VvuM8jSYzPQ01+7/T03r/svgOZe9/OAq4O8H6vdcGng3+4zTAKOufHDgAe8yvwH6JKHd68wTkfkgSB5JgL3uOEawCavtA+BTm64F46nyUocj4i7vPK9DxwEKufl/0SV6jX1in6zg17eJCcna1RUlPpjy5YtAdPOhAULFvxhGX8GpteZkR/1Oh86Acs0H9g7u/LnFcSmb/OKuwHHAy6YTU8ErnPDlwObg9TpsbdAFWCGa5NXAevd+M9wFjOyyvycZcNzaY93/6iQq1c1Lx0DynDt/gde99uASm64h2tvQ4HjZO+XrHPzHASKuOFSWbrk0ZbHAnO96h4N3Jtbe7NsZhZpaWnatm1bffvttz1xpUqV0szMTFVVzczM1JIlS6qqarVq1fSKK67QK664Qi+99FItX768zpgxQ33p3r27fvLJJzniIyMjddu2bZ776tWr6969e3Pki4mJ0aVLl+aIf/HFF3XIkCGqqlq7dm3dtWuXqqru2rVLa9euHVTHu+++W6tUqaJXXHGFXnbZZVqyZEnt16+f/vTTT3rDDTd46li4cKG2b99eVVXbtWuX7Ztbo0YN3bdvnw4aNEgHDRrkSWvbtq1+//33Om3aNO3Ro4cn/6RJk/Thhx/W2bNn6+WXX+7RS0S0Zs2aOdr3Z5EfbZkvBUFHVf96ms3MH9cZr9SLSFmcGdyVZ1r2HJKapQ7OB71bHsp4u0xfEkBeFie9whk4M+kb3dn5m4HXReRrnEFrVREpqaq/B6nbrztYAJkzgDWqGugQvL6q6u/wGO82FAKaqerxbEqI/ADUEpHyQCecAbw3nwNDcP5zHvXxYjsFLAfa4axIBMJbDwFeV1V/e/+ztVtVg/l878DpxGRRGWdWPjeeAPYCDXCeSZYb4Vm556njyTEV6AtMCJI16/3JJPu7lAmEiEh1nEmUa1T1kDhu+ZeAszUAqIvTASqH0/agFC9SmA2DO5xha06zadMmrrzySgBmzZpFRETEWcsyDCM7+cRmGueWQDbEd2uWunmD2fQsG5FB3r0nXwEWqOptIlINZ+AdTK8zoSTORESi2wf4G46XXZyqLgtQxtfOedvAEBz7e1id84D8EXBLm0sgW+5bd56eobfNVFV69uxJ3bp1efLJJz15KlasyMKFC4mNjWX+/PkeG5mcnOzJEx8fT8eOHenUqROHDh2iRIkSFCtWjAMHDrB48eJsh+tlUbVqVebNm0d8fDzr1q3jxIkTlC9fnuTkZKpUqUJISAhbt25l+/btVKtWjdTUVDIzMylZsiSpqal8/fXXvPDCCwDExcUxadIk+vfvz6RJk7j11luD6tipUydP/MSJE1m2bBmDBw9m165drF27lv3791O+fHnmzp1L3bp1AejUqRPz588nNjaWjRs3kpaWRlhYGHFxcdx9991cffXVJCcns2nTJho3bkyhQoVYsmQJx44do3jx4sybN49GjRrRoUMH9uzZ46k/NDSUzZs3/z97Zx7nY7k+/vfF2LcSOsMQsswYsxiEFlExonR0tGhhLPkqSgulU0nb0XIcJX4pKVIkbVRSWSYSdQxjJw4jW2owmDHGLNfvj+eZj8985jMLZpip6/16PS/3cy/Xfd3PfDzXvVz3/RT0pzIM4zQo1J56d89SdXdv1Foc9+3/FK9qhWIlcIW4p7a7+8mauWnHyHlg3QERCXEHTb1OtyIRqYszO/w+zmA+SlWPA1OBCSJS3s0XKCJ3+RRfCvzd1a+KW/8yfzKBrUBtcU+2F+d0/dDTVPdbvA5zE5FIAHc27TPgPziz5Ae9C7mTAI/huAb6ojiz7sEiMsqN833GvnwDDBD3tHgRqScidfJod37MA/qKQ3vgiOa9D9CbGsB+Vc0C7sZZaQfHNe8f4uytvxhntt8vbp3Zvy/B2fawpRB150d1nMmPI27913ulPYTjHdIHeMfdjlFk9OnThw4dOrB161aCgoKYOnUqo0aNomXLloSHh/Ptt996PuljGMaZUYJtplE0+LXpOJP82YP3Pji2pihsui81cDzwwFkpz+YHHI87RKQFzhaz00JVj6hqLVVtqKoNcfpZ+Q3oCyPzKLBTRG5xdRMRiXCTl3PqkN478xCRly0/a5YvX86MGTNYvHix55Ny8+fPZ8qUKTzyyCNERETwz3/+M8dedn9s3ryZNm3aEBERQefOnRk1ahQtWrQAYPTo0Z7T8MeNG8eUKVOIiIigT58+TJs2DRHhhx9+ICIigsjISHr16sWDDz5IrVq1OHDgAFdeeSURERFcdtll9OjRg27dugEwatQovvvuO5o2bcp3333HqFGj8tQvP+rWrcvTTz9Nx44dCQ8PJz4+nn/+85+A8xncHTt20LJlS26//XamT5+OiBAaGsqtt95K//796datG5MmTaJs2bK0a9eO3r17ExUVRVhYGFlZWQwePPiM9DIM4zQpzHI+ros5zr7gZ9zwuvPhWoCXS5Z7fw2Ou/g69+rpxt+PM/ha4t73Bv6HM6M9kZzu97295I3B2Seefb/BrTPalR/v1pftll4e5zCZ7W7en4BoNy0B140ceNhN3wA86MblJTMSp9OwFtjIKTf5HLp66RiLl3scjsv/bFf2JmCyV1obnAF6v7za7E8up1ziyuMM1u8DyuG4wq/FGYzGABN9ZAzHcZlbD6wALs2n3Q/grExn4KzEv+3GCzDJ/futJ39XQM/vA2c//zqcTslYrzaUASa7z+Zz4Guc/Xr+5JXB6XSsd/92HwDV86nf8zci92/VO20azuD9Kxz3/xicbQmbgWpunv/g/n/L78p2uStJlFQ3MtPr9CiJepUG93tKkM20q3gufGy6+77f5NqWdcAnQGU3b1423dvG1sI55yav+jpxyv2+A/CLa5ueyy4HVAE+duufjuNd1zQfmS+7NjfL/XeMnzweHfOQEYOX3Sdnv8eTBjQCFrjPYBMw2it+BU5fYBT+3e/zsuWeZ+LeTwRiCvrblUSb6UtJfPf6w/QsOkqDjqrmfl+SL3H+FvkjIuuBrq6ReEJV/ysi69T5xJdhlDpEpKqqJovIRcDPOPsafyuoXEmkefPmunXr1vOtRg5iY2Pp1KnT+VYjF6bX6VES9ToXOolInKqe8be5zWb+9XDd4L9U59O250uHsjj700+IyKU4k+7NVPXk+dKpJFISbaYvJfHd6w/Ts+goDTqCfz3P1mYaRUNh9289i7M6u9ztnDQGthWfWoZR7Hwpzuf9ygPPldYBvWEYJRKzmcb5oDLOp3zL4Xi43WsDesMwjL8GhRrUq+ocYI7X/Q6cz6cYxnlBRMJwTsP1Jk1V2xWmvKp28iPzMxxXQG8eU9Vv/OR9ArjFJ3qOqvo7j8AwjL8QZjP/eqhqAs4Bc2eFiETjfBnGm52qWuBZQOoc2JtrtUxEfgIq+ETfrarrffMWh16GYRhG8VOoQb17+NwbwMWq2lJEwnH2rvuenm4Y5wS3M5LXSbpnKrPQnRN38G4DeMMwcmE20zhT3EnkXBPJZymzUJPdBcgocr0MwzCMoqNQp98DU4DHgXQAdT7Nc3u+JQzDMAzjr4nZTMMwDMMwzhmFHdRXVtWffeIyiloZwzAMw/gTYDbTMAzDMIxzRmEH9YnuSarO98VEegOF+U64YRiGYfzVMJtpGIZhGMY5o7Cn3w8F3gKCRWQvsBO4s9i0MgzDMIzSi9lMwzAMwzDOGQUO6kWkDNBGVa8TkSpAGfeEVcMwDMMwvDCbaRiGYRjGuaZA93tVzQKGueEU65wYhmEYhn/MZhqGYRiGca4p7J7670RkhIjUF5Ga2VexamYYhmEYpROzmYZhGIZhnDMKu6d+gPvvUK84BRoXrTqGYRiGUeoxm2kYhmEYxjmjUCv1qtrIz2WdE8MoZQwYMIA6derQsmVLT9zIkSMJDg4mPDycXr16kZSUdB41NIzSj9lMwygd7N69m86dOxMSEkJoaCivvfYaAGPGjKFevXpERkYSGRnJ/PnzAfjggw88cZGRkZQpU4b4+HgATp48yeDBg2nWrBnBwcF88sknuepLT0+nX79+hIWFERISwtixYz1p48ePJzQ0lJYtW/Lcc89x4sQJACZOnEiTJk0QERITEz35Dx8+TK9evQgPD+eyyy5jw4YNnrSkpCR69+5NcHAwISEhXtl4IwAAIABJREFUrFixIt92Aaxbt44OHToQGhpKWFiYp/5u3boRERFBaGgoQ4YMITMzE4DbbruNQYMGERkZScOGDYmMjATgu+++o3Xr1oSFhdG6dWsWL17sqSMuLo6wsDCaNGnCAw88gKp6ZGXr5C3LMIzCU6iVehHp6y9eVd8rWnUM48+LiGQC63H+3+0E7lbVJPdgrVeBa3BW804At6rqThFJwDl0KzEPsadFTEwMw4YNo2/fU/+lu3TpwtixYwkICOCxxx5j7NixvPTSS0VRnWH8JTGbaRilg4CAAMaNG0dUVBTHjh2jdevWdOnSBYCHHnqIESNG5Mh/5513cuedzocs1q9fz0033eQZgL7wwgvUqVOHX375haysLA4dOpSrvjlz5pCWlsb69es5fvw4LVq0oE+fPpQrV44JEyawadMmKlWqRKdOnfjwww+JiYnhiiuu4IYbbqBTp045ZP3rX/8iMjKSzz77jC1btjB06FAWLVoEwPDhw+nWrRsff/wxJ0+e5Pjx455y/tqVkZHBXXfdxYwZM4iIiODgwYOUK1cOgI8++ojq1aujqvTu3Zs5c+Zw++23M3v2bGJjY+nUqROPPPIINWrUAKBWrVp88cUX1K1blw0bNhAdHc3evXsBuPfee3nrrbdo37493bt3Z8GCBVx//fXMnj3bo4u3LMMwCk9h3e/beoUrAtcCqwHroBhG4UlV1UgAEZmO45r7AnAbUBcIV9UsEQkCUgotND2ThqO+yjdPwos9AOjYsSMJCQk50rp27eoJt2/fno8//riwVRuG4R+zmUaJQEQUeF9V73bvA4D9wE+qeoNXvrlAHVXtUIC88UBn97ayW+YCEWmIM1n9vKo+5eat5db1pqoO85KxFtikqn3OoD2dgJOq+mMe6cmqWjU/Gdk2M+HFHgQGBhIYGAhAtWrVCAkJ8QxAC2LWrFn06XOqCe+88w5btmwBoEyZMtSqVcuffqSkpJCRkUFqairly5enevXqpKameuLKlStHWloadevWBaBVq1Z+69+0aROPP/44AMHBwSQkJHDgwAEqVarE0qVLmTZtGgDly5enfPny+bbl22+/JTw8nIiICAAuuugiT1r16tUBZ+B/8uRJRCRHWVXlo48+8qzIe+sbGhrKiRMnSEtL49ChQxw9epQOHZyfWN++ffn888+5/vrr85RlGEbhKaz7/f1e1z1AKyD/N4RhGPmxAqjnhgOB/e6p2ajqHlU9fD6Ueuedd3IYWMMwTh+zmUYJIgVoKSKV3PsuQI5Rq4hcAEQBF4hIo/yEqepDqhrpTlC/DnzqlbwDuMHr/hZgo09dITh9z47uJx9Pl07A5WdQrkASEhJYs2YN7dq1Axy39/DwcAYMGMDhw7lN8uzZsz2D+uxta0899RRRUVHccsstHDhwIFeZ3r17U6VKFQIDA2nQoAEjRoygZs2a1KtXjxEjRtCgQQMCAwOpUqVKjgl3f0RERPDpp87j//nnn9m1axd79uxhx44d1K5dm/79+9OqVSsGDRpESsqpdQJ/7frll18QEaKjo4mKiuLll1/OUVd0dDR16tShWrVq9O7dO0fasmXLuPjii2natGkuHT/55BNatWpFhQoV2Lt3L0FBQZ60oKCgXBMo+ckyDCN/CrtS78txwP7HGcYZICJlcVbuprpRHwE/iMhVwCKcVZU1BcgYDAwGqFWrNqPDMvKtMzY21hP+7bffSElJyREH8P7775OUlES9evVypZ0uycnJZy2jODC9To+SqFdJ1KkQmM00zidfAz2Aj4E+wCzgKq/0fwBfAAeA24GxvgLyoA/wtNd9KrBZRNqo6iocL7SPcDzRsrkDmAGEAD1dXfwiIg8AQ4AMYBMwyr3PFJG7gPuBPcBMnP7sgnxk5bKZ3u+R1NRUhg8fzqBBg1i9ejXh4eFMnToVEeGdd97hjjvu4LHHHvPk37RpE6pKYmIisbGxHDlyhD179lCjRg3+85//8NFHH3H33Xfzz3/+M4ce69evJzExkVmzZnHs2DGGDx9O1apVqVatGtOnT+f999+natWqPPnkkzzxxBOerQAAJ06cYPny5R7X9CuuuMKz375x48Y0adKENWvWkJmZSVxcHDExMcTExPD6669z7733MmDAgDzbtXXrVhYuXMjkyZOpUKECjzzyCGXLlqV169YAPP7445w8eZLnn3+e8ePH06ZNG8B5H0+ZMoXLLrss13t5586dPPnkk7z88svExsayZcsWDh8+7Mm3bt06Dh06lKPc+PHj/co6W0qD3SgNOkLp0fMviaoWeOG87Oe515c4s7EvFaasXXbZ5VxAJhAPJOEM3st6pVUArgdeAQ4B17rxCUCt/OTWb3SpXvLYl/le3uzcuVNDQ0NzxE2bNk3bt2+vKSkpWhQsWbKkSOQUNabX6VES9ToXOgGr9Oz+r5vNtKtEXEAyEI4zoK/o2qBOwJdeeRbiDPKbAesKKfcSHNf6su59Q2ADzkD930CQa+digIle5X5xy3YF5hVQxz6gghu+wP13DDDCK888oK8bHgokF6R7ts3M5uTJk9q1a1cdN26c+sOfzXzwwQf1hRde8NxnZWVp5cqVNTMzU1VVf/31V23RokUuWffdd5++9957nvv+/fvr7Nmz9aOPPtIBAwZ44keNGqX33ntvjrKXXHKJ/vHHH351zMrK0ksuuUSPHDmi+/fv10suucSTtnTpUu3evXu+7Zo1a5b269fPk/bss8/qyy+/nKvMtGnTdOjQoZ77hQsXap06dXT37t058u3evVubNm2qP/zwgydu37592rx5c8/9zJkzdfDgwZ779PR0v7KKgpJoy3wpDTqq+tfzbG2mXUVzFXal/t9e4Qxgl6ruKWRZwzAcUlU1UkRq4HT0hwITAFQ1DWc15WsROQD8HadDVCCVypVlq7tn/kxYsGABL730Et9//z2VK1c+YzmGYXgwm2mUGFR1nbvnvQ8w3ztNRC4GmgA/qKqKSIaItFTVDbkl5eB24GNVzfSJXwA8h7PqP9s7QUTaAn+o6i4R2QO8IyIXat7bzdYBH4jI58DneeS5AsfTABwPgAJPefW2marKwIEDCQkJ4eGHH/bk2b9/v2ev/WeffZbjizFZWVnMmTOHpUuXereNG2+8kdjYWK655hoWLVpEixYtctXdoEEDFi9ezF133cXx48dZuXIlDz74IKmpqaxcuZLjx49TqVIlVq9ezQ033JCrvDdJSUlUrlyZ8uXL8/bbb9OxY0eqV69O9erVqV+/Plu3bqV58+Y5dMmrXdHR0bz88sscP36c8uXL8/333/PQQw+RnJzMsWPHCAwMJCMjg/nz53PVVaecPOLi4ggODs7hVp+UlESPHj0YO3YsV1xxhSc+MDCQatWqsXLlStq1a8d7773H/fff70lfuHBhLlmGYRSeQu2pB7qr6vfutVxV94iIHY9tGGeAqh4BHgBGiEg5EYkSkboA7kn44cCu4qi7T58+dOjQga1btxIUFMTUqVMZNmwYx44do0uXLkRGRjJkyJDiqNow/kqYzTRKGvNwJpt83d1vAy4Esr+20hBnwF4Qt/uRhaqeBOKARwDfb7r1AYLdev4HVOfUgNwfPYBJQGsgzj3kzx9aCH39snz5cmbMmMHixYtzfObt0UcfJSwsjPDwcJYsWcL48eM9ZZYuXUpQUBCNG+f8SuVLL73EmDFjCA8PZ8aMGYwbNw6AefPmMXr0aACGDh1KcnIyLVu2pG3btvTv35/w8HDatWtH7969iYqKIiwsDFVl8ODBAEyYMIGgoCD27NlDeHg4gwYNAmDz5s2EhoYSHBzM119/7fkcH8Drr7/OnXfeSXh4OPHx8Z5tAHm168ILL+Thhx+mbdu2REZGEhUVRY8ePUhJSaFnz56eQ/Tq1KmTo4+wePHiHIcFgrNnf/v27Tz33HOeZ/r7778D8MYbbzBo0CCaNGnCpZdemuMMnw8//DCXLMMwToPCLOcDq/3EFcpFyy677HIufNwCcVx07wa64XSCNrjXO0BFN08CBbjfN2vWTEsaJdWNzPQ6PUqiXqXE/d5spl0l4sq2Ozju8MPdcCdc93ucQ1s7eOVvBGwvQGZz1zaJV1xDYIMbDgX6ueEYYCLOItJuoJ5Xmc7AojzqKAM0dMPlcFb+L8CZLHjGK9884C43fK+vnfV3lUSb6UtJfPf6w/QsOkqDjqrmfl+Sr3zd70XkXuA+oLGIrPNKqgYsz6+sYRg5UZ/P7KjqjV63fg/4UdWGxamTYRhFh9lMo6SizvaP17zjXJf8BsBKr3w7ReSoiLRT1Z/yENcH+NDtzPurayM+p94DHYG9qup93PlSoIWIBKrqfp/8ZYH33e1qAoxX1SQR+QL4WERuwjkobzgwU0SGk9szwDAM4y9DQXvqZ+Ls8x2Lc+poNsdU9VCxaWUYhmEYpQ+zmUaJwncy2Y2LBWLd23p+0qMKkDnGT1wC0NJP/DRgmnvb3ictE+eTrv7qSAeu9BP/C84WNW86eIVfzEtvwzCMPzP5DurV2ft7BGdWFhGpg3N6alURqaqqvxa/ioZhGIZR8jGbaRiGYRjG+aBQB+WJyI0isg3YCXyPs5fq62LUyzAMwzBKJWYzjdKOiDwhIvE+1xPFVNckP3X1L466DMMw/qwU9pN2z+O4TS1U1VYi0hl3JcIwDMMwjByYzTRKNar6AvDCOapr6LmoxzAM489MYT9pl66qB4EyIlJGVZcAkcWol2EYhmGUVsxmGoZhGIZxzijsSn2SiFQFlgEfiMjvQEbxqWUYhmEYpRazmYZhGIZhnDMKu1J/E3AceBDn01v/A27Mt4RhGIZh/DUxm2kYhmEYxjmjUCv1qpoiIpcATVV1uohUxvmGqGEYhmEYXpjNNAzDMAzjXFLY0+/vAT4G3nSj6gGfF5dShmEYhlFaMZtpGIZhGMa5pLDu90OBK4CjAKq6DahTXEoZhmEYRinGbKZhGIZhGOeMwg7q01T1ZPaNiAQAWjwqGYZhGEapxmymYRiGYRjnjMIO6r8XkX8ClUSkCzAH+KL41DIMozgYMGAAderUoWXLlp64kSNHEhwcTHh4OL169SIpKek8amgYfwrMZhpGCWf37t107tyZkJAQQkNDee2114D8beK6devo0KEDoaGhhIWFceLECQA6depE8+bNiYyMJDIykt9//z1Xfenp6fTr14+wsDBCQkIYO3asJ61hw4aEhYURGRlJmzZtPPGHDh2iS5cuNG3alC5dunD48GEAVJUHHniAJk2aEB4ezurVqz1lunXrxgUXXMANN9yQo/6BAwcSERFBeHg4vXv3Jjk5GYCHHnrIo3ezZs244IILANi1axetW7cmMjKS0NBQJk+eDMDx48fp0aMHwcHBxMTEMGrUKE8du3bt4tprryU8PJxOnTqxZ8+eAvUyDKNoKOygfhTwB7Ae+D9gPvBkcSllGEbxEBMTw4IFC3LEdenShQ0bNrBu3TqaNWuWo6NhGMYZYTbTMEo4AQEBjBs3js2bN7Ny5UomTZrEpk2b8rSJGRkZ3HXXXUyePJmNGzcSGxtLuXLlPPI++OAD4uPjiY+Pp06d3Ltt5syZQ1paGuvXrycuLo4333yThIQET/qSJUuIj49n1apVnrgXX3yRa6+9lm3btnHttdfy4osvAvD111+zbds2tm3bxltvvcW9997rKTNy5EhmzJiRq/7x48ezdu1a1q1bR4MGDZg4caInPlvv+++/n5tvvhmAwMBAfvzxR+Lj4/npp5948cUX2bdvHwAjRoxgy5YtTJkyheXLl/P111974vv27cu6desYPXo0jz/+eIF6GYZRNOQ7qBeRBgCqmqWqU1T1FlXt7YbNldDIFxFREZnhdR8gIn+IyJc++eaKyIpCyBsvIvHu9YuIJLnxDd26nvPKW0tE0kVkoo+MtSIyqxB1TRORnW5dW0Tkaa+0WBHZ6qZtFpHBBchKEJFaBdV5pqSmZ9Jw1Ff5Xtl07NiRmjVr5ijftWtXAgKcD2G0b98+x8y6YRiF569gM0XkQfc0/+Kup5yIvCgi20Rkg4j8LCLXu2lF+k71ed+vFpEORSR3jIjsdeVuEpE+RSAzWERWiEiaiIzwSevm2qbtIjIqLxlu3lgRaZNfnqLSy00vKyJrfO3/GdZVV0Q+PtPy2TYzMDCQqKgoAKpVq0ZISAh79+7N0yZ+++23hIeHExERAcBFF11E2bKF/6iFiJCSkkJGRgapqamUL1+e6tWr51tm7ty59OvXD4B+/frx+eefe+L79u2LiNC+fXuSkpLYv38/ANdeey3VqlXLJSu7LlUlNTUVEcmVZ9asWfTp4/xMy5cvT4UKFQBIS0sjKysLgMqVK9O5c2cAypUrR1RUlOcZbdq0iWuvvRaAzp07M3fuXI/svPQyDKNoKGil3nNar4h8Usy6GH8+UoCWIlLJve8C7PXOICIXAFHABSLSKD9hqvqQqkaqaiTwOvCpV/IOwNun6xZgo09dITi/+Y4iUqUQ+o9064oE+vnod6ebdgXwkoiUL4S8Es8777zD9ddff77VMIzSyp/CZopDXv2DB4HTGtS7ZwqcLs8BgUBLVW0J3AgU54gg+30/ilNfLfBwhm0AGO/KvQl4U0TKFVSgAA4BDwD/9tGvLDAJuB5oAfQRkRZnWddZ6+XFcGBzUVSkqvtUtXdRyMomISGBNWvW0K5duxzx3jbxl19+QUSIjo4mKiqKl19+OUfe/v37ExkZyXPPPYe/ObzevXtTpUoVAgMDadCgASNGjPBMsosIXbt2pXXr1rz11lueMgcOHCAwMBBwVs6z3fr37t1L/fr1PfmCgoLYuzdH98ov/fv3529/+xtbtmzh/vvvz5G2a9cudu7cyTXXXOOJ2717N+Hh4dSvX5/HHnuMunXr5iiTnJzMF1984RnIR0RE8Mknzqvvs88+49ixYxw8eLBAvQzDOHsKMlLe03iNi1MR40/L10APnM879QFmAVd5pf8DZ6/pAeB2oLC+332Ap73uU4HNItJGVVcBtwEfAd4W6A5gBhAC9HR1KQwV3X9T/KRVdeMzCyNIRB4GBri3b6vqqyLyKHBCVSeIyHggQlWvEZFrgf6qepcfOYOBwQC1atVmdFhGvvXGxsZ6wr/99hspKSk54gDef/99kpKSqFevXq600yU5OfmsZRQHptfpURL1Kok6eVFqbaaINMR5Xy8BOgCvisgQoALwP6A/zrurLrBERBJVtbOIJKtqVVdGb+AGVY0RkWk4A71WwGoROQY0wHkuDYBX3XdeFZx3dRBQFmcw/wVwD9BIVdMAVPWAm89Xb3/v1FwyVXW2iLQG/oPz3k4EYlR1v4/IpUATV3Ys8CPO5O08EXkPmOzqD84ExwqcSeVIVc32HtvulvGgqttE5DhwIfC7+9nDwUB5YDtwN5AGbAMuBWq4z6+Tqi4VkWU49mC7W76Hj96XAdtVdYerw4c4EwmbfJ+Zn2f4BtAWqAR8rKpPu/Hd3eeVCKwGGquq3w3Rqvp7HnohIkE4/YAXgIcL0CUBmAl0BsrhPKOxOH+TV1R1svtb/VJVW4pIDI49r4zz3D5T1Uf9yM1lM7PfI6mpqQwfPpxBgwbl2JvuaxO3bt3KwoULmTx5MhUqVOCRRx6hbNmytG7dmqFDh1K7dm2OHz/O008/zfHjx4mOjs6hw/r160lMTGTWrFkcO3aM4cOHU7VqVerWrcsrr7xCrVq1OHz4MCNGjOCee+4BHJd/7/dd9n1iYiJr1qwhI8Ox/YcPHyYuLs6zTz4+Pp6DBw/melf269ePu+66iwkTJvDMM8/kmMSfNWsWHTp0YNmyZTnKTJgwgcTERJ566ikCAwM9ExGZmZk8/fTTdO/enV9//ZVff/2Vm2++mQkTJjBx4kTCw8OpVasWK1asoGrVqvnqVdyUcLsBlA4dofTo+VekoEG95hE2jMLyITDadbkLB94h56C+D/AMzqD+YwoxqBeRS4BGwGI/dd0uIr/hDLL3kXNQfxuOt0BzYBgFD+pfEZEncToTE9xOSzYfiEga0BR4UFULHNS7Hcr+QDuczv9PIvI9TifyEWAC0Aao4K7mXAks8ydLVd8C3gJo0LiJjluf/3/lhDs7nQonJFClShU6dToVN336dDZu3MiiRYuoXPnsPWtjY2NzyC8pmF6nR0nUqyTq5EVpt5nNcd5Ro3E8oa5T1RQReQx4WFWfdQfRnVU1sRDymrkyMkVkDBCMM1irBmx1B5PdgH2q2gNARGrgvHN/VdWj+QnP553a2Fem+059HbhJVf8QkdtwBpkDfMTeiHMWQjYXqOrVrpyZOCvvP7hbLb5R1RARmQv0At4VkXZAgqoe8HZvFpEoYJuXHflUVae4ac8DA1X1dRH5BWelvREQB1wlIj8BQe6APi/qAbu97ve4z6UwPKGqh9zV/kUiEg78guOx0FFVd0ohtq3lw6vAoxTe02K3qnZwJ7mn4UyQVMTxvpvsJ38kzuRRGs7v6nVV9X4Wfm1mwp2dSE9P54YbbmDIkCE8/PCp+QZ/NvG3334jNTWVm266CYD//ve/ZGVl5Xof/f7776xatSpX/Jw5c+jXrx/XXXcdAF988QUBAQG58q1du5Zdu3YxatQo6tWrR/PmzQkMDGT//v3UrVuXTp06ERERQa1atTxlU1JS6Nmzp2dVH2DhwoV5visDAgJ45ZVXeOmllzxxDz30EJMmTeLyyy/3W+arr77K0d4BAwZwySWXePbmZ9O7t+NEkZycTHBwcK6D8fLTq7go4XYDKB06QunR869IQe73ESJy1J1hD3fDR0XkmIjka2wNA0BV1wENcQbv873TRORinM7bD6r6C5AhIi1zCcnN7TirCb4D6QU4g/Y+wGyfutoCf6jqLmARECUiFxZQT7Y75t+Aa0XE29LdqarhOCs2I9yJhoK4EmcVIUVVk3E6zVfhdNxai0g1nE7JCpzB/VXkMagvShYsWMBLL73EvHnzimRAbxh/YUq7zdylqiuB9jgDy+UiEg/0AwrzjvNljs97+itVTXMnBH4HLsYZQF8nIi+JyFWqeuQ05Of1TvUnsznQEvjObdOTOCv52bzixg8GBnrFe9uS64CJbr55QHX3vT0bZ9IYHPvkXeYhEdkK/ASM8YpvKSLLRGQ9cCcQ6sYvAzq611i3jW2B/xbwLHJvkC78xNKtIrIaWOPq0QJnAmaHqu5085zRoF5EbgB+V9W40yg2z/13PfCTqh5T1T+AE+6WPV8WqeoRVT2B45lQqN+qqjJw4EBCQkJyDOjzsonR0dGsW7eO48ePk5GRwffff0+LFi3IyMggMdGZ40pPT+fLL7/M8YWZbBo0aMDixYtRVVJSUli5ciXBwcGkpKRw7NgxwBmcf/vttzRq5Oz269mzJ9OnTweciYbsCYWePXvy3nvvoaqsXLmSGjVq5BjQ+2vr9u3bPeEvvviC4OBgT/rWrVs5fPgwHTqcOk5iz549pKamAo4nwPLly2nevDkATz75JEeOHGHYsGE56klMTPTsvR87diwDBvjOmRmGUVzku7ynqoU/AcQw8mYezj67TsBFXvG34bgi7nRXNKrjdIgKOiX6dmCob6SqnhSROJxV71CcFZds+gDBrmtfdl3/AN4uSHlVTXbdMK/EccX0TvvD7Qy1A3YVIMpfpwtVTXf16u/KX4ezmnUphdiDWKlcWba+mMvj0S99+vTxuO4FBQXxzDPPMHbsWNLS0ujSpQvgHAyU/ekawzAKz5/AZmZvMRLgO1UtzMFu3gPHij5pvluW0rzCmUCAqv7irrh3B8aKyLc49qKBiFRT1WP51J3XO9WfzM+Ajaqa1yF4I1XV3+Fr3m0oA3RQ1dQcSjgHvTYRkdrA34HnvZLHq+q/ReRm4D0RudQdfE4D/q6qa10X8k5u/mXAEBwvs9HASDdtaR56Z7MHqO91H4TjrZYv4pwVMwJoq6qH3W0TFcnj2Z4BVwA9XVf+ijgTIe/721bmRfbvJIucv5ks/Pdbc/2u8lMo22b+8MMPzJgxw/MpOYB//etfPPDAA35t4oUXXsjDDz9M27ZtERG6d+9Ojx49SElJITo6mvT0dDIzM7nuuus87vPz5s1j1apVPPvsswwdOpT+/fvTsmVLVJX+/fsTHh7Ojh076NWrF+C4199xxx1cdtllAIwaNYpbb72VqVOn0qBBA+bMmQNA9+7dmT9/Pk2aNKFy5cq8++67nvZdddVVbNmyheTkZIKCgpg6dSpdunShX79+HD16FFUlIiKCN954w1Nm1qxZ3H777TkOz9u8eTOPPPIIIoKqMmLECMLCwtizZw8vvPACwcHBDB48mKpVqzJs2DAGDRpEbGwsjz/+OCJCx44dmTRpUr56+W5RMAzjLFBVu+wqlgtIdv8NAoa74U44e+HAWZHu4JW/Ec6ewPxkNgcSAPGKawhscMOhQD83HANMxOmI7QbqeZXpjDO7n1c904DebjgAiAX+4d7HAm3ccGUcN8XW+chKAGrhHAi4zi1TBdgAtHLzjAF+xVkJutgNf1aY59ysWTMtaSxZsuR8q+AX0+v0KIl6nQudgFVaAt6h5/LyeY/Wdt9BTfTUe66ZG16Ps9c9u9x2nHNKygCfANPU5x2qp95xI7zuN7h11gUqunF/Bz53wy8D7wLl3ftA4C4txDvVn0xO7V3v4MaXA0L96eqlo+dd797PxBn8Z99HeoVfwTmzZX4+bZ4L/J8bTgTquHp85/XcKrjtW+zev4FjvyJ8dPOVHYCzt7+R29a12e3L4+8di+MRFuHmLePangM4trOSW29DN/8HuLa7gN9RDr180joVJCP7b+uGY4CJvmnk/K365vkS5xyCUmUzfSmJ715/mJ5FR2nQUdW/nvwFbWZJvM70NFfDKDSqugd4zTvOPeimAbDSK99O11W1nar+lIe4PsCH7kvEX10b8Tn1HseNca+qeh8NuxRoISKBmvugpGyy99SXx3FjvvhcAAAgAElEQVTZ9z5t/wMRScXpgE3TQrgWqupqdxXkZzfqbVVd44aXAU8AK9TZw3qCc+B6bxiG4Ys6HkgxwCwRqeBGP4kzgfkW8LWI7FfVzjinxX+JMwDcgHMI3ekQhvOuzQLSgewPbj+Js+K9yX0fpuCsXHvr6fedKiLRvjLV8eTqDUxw9+0H4Oz19rUX+fEAMElE1rnll+KsqoPjcv9fnEFmXjwLzBSRKcBTOC75u3AmSqq5bUoTkd2cso3LcOzeegAR+RuwCsfbLEtEHgRaqOpRERkGfINzOOA7rj3MF3U8BdbgPIcdwHI3PlVE7gMWiEgip56xX/LTqyAdDMMwjLNH8hgbGYZRSmjevLlu3br1fKuRg5J6kIrpdXqURL3OhU4iEqeqRfYNb8MojYhIVXW2nwnO5/K2qer4863X2VISbaYvJfHd6w/Ts+goDTqCfz3NZpYMCjoozzAMwzAMw/jrcY97KOBGnE/svXme9TEMwzDywNzvjRKHiDwB3OITPUdVXyiGuibh8z1h4DVVfddf/gJk/YTjju/N3aq63l9+wzAMwyhOROQznH323jymqt8UVNZdlc+xMi8i/YHhPlmXq2quw2uLSy/DMAwjNzaoN0oc7uC9yAfwedR1Wh2RAmQV9pvAhmEYhlHsqGqvIpb3Ls4Bhmcrp0j1MgzD+Ktj7veGYRiGYRiGYRiGUUqxQb1hGIZhGIZhGIZhlFJsUG8YhmEYhmEYhmEYpRQb1BuGYRiGYRiGYRhGKcUG9YZhGIZhGIZhGIZRSrFBvWEYhmEYhmEYhmGUUmxQbxiGYRiGYRiGYRilFBvUG4ZhGIZhGIZhGEYpxQb1hvEXYsCAAdSpU4eWLVt64kaOHElwcDDh4eH06tWLpKSk86ihYRiGYRQ/u3fvpnPnzoSEhBAaGsprr70GwJw5cwgNDaVMmTKsWrXKkz8hIYFKlSoRGRlJZGQkQ4YM8aTFxcURFhZGkyZNeOCBB1DVXPV98MEHhIeHEx4ezuWXX87atWs9aUlJSfTu3Zvg4GBCQkLYuHEjAPHx8bRv357IyEjatGnDzz//DICq8sADD9CkSRPCw8NZvXq1R9b06dNp2rQpTZs2Zfr06Z74Tp060bx5c4/+v//+OwCTJ08mLCyMyMhIrrzySjZt2uTRNztvZGQkZcqUIT4+HoAnnniC+vXrc/311+doY1paGrfddhtNmjShXbt2JCQkAHDy5En69+9PWFgYERERxMbGesp069aNiIgIQkNDGTJkCJmZmfm23TCMPFBVu+yyqxRfzZo108Ly/fffa1xcnIaGhnrivvnmG01PT1dV1UcffVQfffTRQsvLiyVLlpy1jOLA9Do9SqJe50InYJWWgP/bdtllV9Ff2TZz3759GhcXp6qqR48e1aZNm+rGjRt106ZNumXLFr366qv1v//9r2azc+fOHLbTm7Zt2+qPP/6oWVlZ2q1bN50/f36uPMuXL9dDhw6pqur8+fP1sssu86T17dtXp0yZoqqqaWlp+sUXX6iqapcuXTyyvvrqK7366qs94W7dumlWVpauWLHCI+vgwYPaqFEjPXjwoB46dEgbNWrkqdO3PdkcOXLEE547d65GR0fnyrNu3Tpt1KiR537FihW6b98+rVixYo58kyZN0v/7v/9TVdVZs2bprbfeqqqqEydO1JiYGFVVPXDggEZFRWlmZmaO+rOysvTmm2/WWbNm5dv2M6Ek2jJfSoOOqv71NJtZMq4/1Uq9iDwoIpXPQT3lRORFEdkmIhtE5GcRud5NSxCRWkVY1zQR2Ski8SKyWkQ6FJHcMSKy15W7SUT6FIHMYBFZISJpIjLCJ62biGwVke0iMqoAObEi0uZs9fGSd5OIrHPbukpEriwq2YWo2/N7EJEfi6OO1PRMGo76Kt8rm44dO1KzZs0c5bt27UpAQAAA7du3Z8+ePcWhpmEYf2LM/p6WXLO/ZydziIj0PdPy2TYzMDCQqKgoAKpVq0ZISAh79+4lJCSE5s2bF1re/v37OXr0KB06dEBE6Nu3L59//nmufJdffjkXXnghkNPWHj16lKVLlzJw4EAAypcvT9WqVbPbytGjRwE4cuQIdevWBWDu3Ln07dsXEaF9+/YkJSWxf/9+vvnmG7p06ULNmjW58MIL6dKlCwsWLMhX/+rVq3vCKSkpiEiuPLNmzaJPn1M/0/bt2xMYGJgr39y5c+nXrx8AvXv3ZtGiRagqmzZt4tprrwWgTp06XHDBBR4viOz6MzIyOHnypKf+vNpuGIZ/St2gXhzy0vtB4LQ6FSIScAZqPAcEAi1VtSVwI1DtDOQUlpGqGgmMAt70TTzDNgCMd+XeBLwpIuXOQkeAQ8ADwL999CsLTAKuB1oAfUSkxVnWdTosAiLctg4A3j6HdXtQ1cvPR72nwzvvvJPLnc4wDAPM/mL290wocvurqpNV9b2z1syLhIQE1qxZQ7t27fLNt3PnTlq1asXVV1/NsmXLANi7dy9BQUGePEFBQezduzdfOVOnTvXY2h07dlC7dm369+9Pq1atGDRoEKmpqQC8+uqrjBw5kvr16zNixAjGjh3rqbN+/fq56swrPpv+/fsTGRnJc889h7PA6jBp0iQuvfRSHn30USZMmJBL39mzZ+cY1OeFd/0BAQHUqFGDgwcPEhERwdy5c8nIyGDnzp3ExcWxe/duT7no6Gjq1KlDtWrV6N27d75tNwzDP2dqjM4pItIQ+BpYAnQAXhWRIUAF4H9AfxxjURdYIiKJqtpZRJJVtaorozdwg6rGiMg0HAPYClgtIseABkBj999XVXWCiFQBPgKCgLI4nYkvgHuARqqaBqCqB9x8vno/7OoF8LaqvupPpqrOFpHWwH+AqkAiEKOq+31ELgWauLJjgR+BK4B5IvIeMNnVH5wO1gpgBxCpqkluue1uGQ+quk1EjgMXAr+LyD3AYKA8sB24G0gDtgGXAjXc59dJVZeKyDKgv6pud8v38NH7MmC7qu5wdfgQpyOzyfeZ+XmGbwBtgUrAx6r6tBvf3X1eicBqoLGq3uBPhqome91WAXJvdjtVXyfgGeAAEAl8CqwHhrs6/F1V/ycitfF53qq6XEQuAmYBtYGfAfGSnayqVUWkKjAX53mXA55U1blev/MfgMuBvcBNqprqR8/BOH8jatWqzeiwjLyaBJBj/9pvv/1GSkpKjjiA999/n6SkJOrVq5cr7XRJTk4+axnFgel1epREvUqiTn9mzP56MPtbcuzvGCBZVf/t/i1+AjoDFwADVXWZH9m5bGb2eyQ1NZXhw4czaNCgHHvTk5KSiIuLIznZacLJkyeZOXMmNWrUYOvWrfzjH//g3XffZffu3Rw+fNgjb926dRw6dCjP99SaNWt4/fXXmTBhArGxsWzdupW4uDhiYmKIiYnh9ddfZ/r06VSqVIkJEyYwcOBArr76apYsWcLNN9/MuHHjSExMZM2aNWRkOLb/8OHDxMXFsX37dtLT0z1179y5k4oVKxIbG8vQoUOpXbs2x48f5+mnn+b48eNER0cDEBoaytSpU1m4cCHDhg3j8ccf9+i7adMmVJXExES/bfKOS05OZsWKFdSuXRuAEydOsHz5ci699FK+++47goODufjiiwkODmbz5s2eso8//jgnT57k+eefZ/z48bRp0ybPtp8JpcFulAYdofTo+ZfkfPv/F+YCGgJZQHugFo5xreKmPQaMdsMJQC2vcsle4d7ANDc8DfgSKOvej8Ex0BVc+QdxBlv/AKZ4yagBhANr8tE1wZXRGscYVcHpKGzE6cT4k1nOrb+2G3cb8I6Xrr3d8C3AT244Fvh/XnJmAle64QbAZjf8Go7BB2gHLPRq8wg3HAUs85J1kVf4eeB+N7wACAVuAP4LPOE+s50+z8Aj2+vZv+11fzcwMZ9nGAu0ccM13X/LuvHhQEVgN07HDpxB9JcF/IZ6AVtwOkMd8snXCUjCWQmqgDOwfsZNG47T4czveU/g1O+xB04Hppb37xFnMq26G66F03ETnN95Bk4nEJzO510F/f+o3+hSveSxL/O9vPG3L3DatGnavn17TUlJ0aKgpO4NM71Oj5Kol+2pN/ubj64JmP31yPZ69n82++v9/GKBcW64e/Yzzu/KtpmqqidPntSuXbvquHHj1Je89qD7pu/bt0+bN2/uiZ85c6YOHjzYb5m1a9dq48aNdevWrZ64/fv36yWXXOK5X7p0qbZr105VVatXr65ZWVmq6uw5r1atmqqqDh48WGfOnOkp06xZM923b1+uun3zZfPuu+/q0KFDc8VnZmZq9erVc8Q9+OCD+sILL/htj++e+q5du+qPP/6oqqrp6el60UUXefT3pkOHDrpx48Zc8dOmTfPolVfbz4SSaMt8KQ06qtqe+pJ8lSb3+12quhKnY9ECWC4i8UA/4JIzkDdHVTO97r9S1TRVTQR+By7G6RRcJyIvichVqnrkNORfCXymqinqzFR/ClyVh8zmQEvgO7dNT+KsJGTzihs/GBjoFT/bK3wdMNHNNw+oLiLV3Dy3uXlu9ynzkIhsxZnlHuMV31JElonIeuBOnI4EwDKgo3uNddvYFqeDkR+5N2jlM1vvw60ishpY4+rRAggGdqjqTjfPrIKEqOpnqhoM/B1nxSc//quq+9VZCfof8K0bvx6ngwt5P++OwPtunV8Bh/3IF+BfIrIOWAjUw/m9gdNBi3fDcV715UmlcmVJeLFHvld+LFiwgJdeeol58+ZRuXKxb4k1DKP0YfbX7G9Jsr++fOr+e1o2U1UZOHAgISEhPPzwwwUV448//vCczL5jxw62bdtG48aNCQwMpFq1aqxcuRJV5b333uOmm27KVf7XX3/l5ptvZsaMGTRr1swT/7e//Y369euzdetWABYtWkTDhk4z6taty/fffw/A4sWLadq0KQA9e/bkvffeQ1VZuXIlNWrUIDAwkOjoaL799lsOHz7M4cOH+fbbb4mOjiYjI4PExEQA0tPT+fLLLz1fwdm2bZtHl6+++spTB0BWVhZz5szh9ttvL/D5ZOuVfeL+xx9/zDXXXIOIcPz4cVJSUgD47rvvCAgIoEWLFiQnJ7N/v+MUk5GRwfz58wkODs637YZh+KdUuN+7pLj/CvCdqhbmYBlvw1UxD3nZpHmFM4EAVf3FdcvrDowVkW9x9qs1EJFqqnosn7r9GVLykPkZsFFV8zqEZ6Sqfuwn3rsNZXBmwHO4aovICqCJ6y7+d5yZ/2zGq+O+djPwnohcqqoncFYn/q6qa0UkBmf2HJxOxRAcN8vRwEg3bWkeemezB6jvdR8E7CugDCLSCBgBtFXVw67bZkXyeLaFQR13xUtFpJbbgfSH928hy+s+i1P/Z/J63lBwh+lOHPf81qqaLiIJnPp9+v4OKxUg67To06cPsbGxJCYmEhQUxDPPPMPYsWNJS0ujS5cugHMAzuTJk4uyWsMwSjdmf3Nj9vc0KUL7m1eZzHzy5GL58uXMmDHD8zk3gH/961+kpaVx//3388cff9CjRw8iIyP55ptvWLp0KaNHjyYgIICyZcsyefJkz8Gzb7zxBjExMaSmpnL99dd79stn29IhQ4bw7LPPcvDgQe677z7A2XOefVjc66+/zp133snJkydp3Lix59C8KVOmMHz4cDIyMqhYsSJvvfUWAN27d2f+/Pk0adKEypUr8+677wJQs2ZNnnrqKdq2bQvA6NGjqVmzJikpKURHR5Oenk5mZibXXXcd99xzDwATJ05k4cKFlCtXjgsvvDDHZ/CWLl1KUFAQjRs3zvHsHn30UWbOnElaWhpBQUEMGjSIMWPGMHDgQO6++26aNGlCzZo1+fDDDwH4/fffiY6OpkyZMtSrV48ZM2YAzsF8PXv2JC0tjczMTK655hrPpwLzarthGHlwvl0FCnPhzLxucMO1gV+BJu59ZaCZG16P6xLm3m8HQnAM7ifkdP/r7ZVvDDnd1Ta4ddYFKrpxfwc+d8MvA+8C5d37QFw3aU65/0UB61z9qrgyW/mTyam9cx3c+HJAqD9dvXSMxXWRc+9n4nQ+su8jvcKvADOA+fm0eS7wf244Eajj6vGd13Or4LZvsXv/Bo4bXoSPbr6yA3D2FjZy27o2u315/L1jgTZAhJu3DM7KzQEgBmeguxto6Ob/gHzc/3D2QYobjsJx6ZM88nbylkVOV0RPWl7PG8f9/kk3fD3+3e+HA6+74c5unoZ4/c7dtBHAmIL+f5zOJ+3OFSXVjcz0Oj1Kol7mfm/2F7O/8Ne2v542+uSpBSQU9JsuiTbTl5L47vWH6Vl0lAYdVc39viRfpWmlHgBV/cOdvZ4lIhXc6CeBX4C3gK9FZL+qdsY5rfZLHAO0AWdv3ekQhuN6lwWkA/d61fc8sElETuDM2I/20XO1O7P9sxv1tqquEZFoX5mqetI9SGiCiNTAMcKv4uwDLCwPAJNcl+4AnNn7IW7abBwXvZh8yj8LzBSRKcBTOC6Bu3A6atXcNqWJyG5gpVtmGdDHzYOI/A1YBVQHskTkQaCFqh4VkWHANzh7895R1QLbps5KxRqc57ADWO7Gp4rIfcACEUnk1DPOi38AfUUkHUgFbnNfQmdDXs/7GZzf5mrge5wOsC8fAF+IyCogHmevoWEYRonG7G+emP3Nm+Kwv4ZhGIYPYu9WozQiIlVVNVkcf/dJwDZVHX++9TofNG/eXLP34pUUYmNj6dSp0/lWIxem1+lREvU6FzqJSJyqFtm3ug3jz0Rpt78l0Wb6UhLfvf4wPYuO0qAj+NfTbGbJoDQdlGcY3tzjHkq0EecE41zfDzYMwzAMo8gx+2sYhlHCKHXu98afBxH5DGefnzePqeo3BZV1VwVyrAyISH+c/ereLFfVoX7qDsPZ5+hNmqq2K1BxwzAMwyjFmP01DMP4c2GDeuO8oaq9iljeuzgHKBUm73ogsijrNwzDMIzSgNlfwzCMPxfmfm8YhmEYhmEYhmEYpRQb1BuGYRiGYRiGYRhGKcUG9YZhGIZhGIZhGIZRSrFBvWEYhmEYhmEYhmGUUmxQbxiGYRiGYRiGYRilFBvUG4ZhGIZhGIZhGEYpxQb1hmEYhmEYhmEYhlFKsUG9YfyFGDBgAHXq1KFly5aeuJEjRxIcHEx4eDi9evUiKSnpPGpoGIZhGMXP7t276dy5MyEhIYSGhvLaa68BMGfOHEJDQylTpgyrVq3y5D948CCdO3ematWqDBs2zK/Mnj175rCv3hw5coQbb7yRiIgIQkNDeffddz1pv/76K127diUkJIQWLVrw22+/AbBo0SKioqKIjIzkyiuvZPv27Z78nTt3plWrVoSHhzN//nwA0tPT6devH2FhYYSEhDB27FhPHePHjyc0NJSWLVvSp08fTpw4kW8d06ZNo3bt2kRGRhIZGcnbb7+doz1Hjx7llltuyfEsZs+eTXh4OKGhoTz66KM52udPX4CxY8fSpEkTmjdvzjfffOOJb9iwIWFhYURGRtKmTRu/z9QwjFPYoN4w/kLExMSwYMGCHHFdunRhw4YNrFu3jmbNmuXoBBiGYRjGn5GAgADGjRvH5s2bWblyJZMmTWLTpk20bNmSTz/9lI4dO+bIX7FiRZ577jn+/e9/+5X36aefUrVq1TzrmzRpEi1atGDt2rXExsbyyCOPcPLkSQD69u3LyJEj2bx5Mz///DMXXHABAPfeey8ffPAB8fHx3HHHHTz//PMAPP/889x6662sWbOGDz/8kPvuuw9wJiTS0tJYv349cXFxvPnmmyQkJLB3714mTJjAqlWr2LBhA5mZmXz44Yf51gFw2223ER8fT3x8PIMGDcrRnqeeeorw8HDP/cGDBxk5ciSLFi1i48aNHDhwgEWLFuWr76ZNm/jwww/ZuHEjCxYs4L777iMzM9Mjc8mSJcTHx+eYXDEMwz8ldlAvIg+KSOVzUE85EXlRRLaJyAYR+VlErnfTEkSkVhHWNU1EdopIvIisFpEORSR3jIioiDTxinvIjWvj3if7KddQRO4oCh1cecNEZLtbby2veBGRCW7aOhGJykdGQxHZUFQ6uTKXuc88XkT2icjnRSk/n3o9bRGRNiIyoTjqSU3PpOGor/K9sunYsSM1a9bMUb5r164EBAQA0L59e/bs2VMcahqGUcIxu3tacs3u5q/XVBFZ69b9sYjkPdo9x2TbzMDAQKKinMdSrVo1QkJC2Lt3LyEhITRv3jxXuSpVqnDllVdSsWLFXGnJycn85z//4cknn8yzXhHh2LFjqCrJycnUrFmTgIAANm3aREZGBl26dAGgatWqnjpEhKNHjwLOSn/dunULjE9JSSEjI4PU1FTKly9P9erVATxxGRkZHD9+vEBZ+REXF8eBAwdo27atJ27Hjh00a9aM2rVrA3DdddfxySef5FvH3Ln/n73zDq+q2B72u5JQJHQD/mihSAupFBEuShEJAiqXIop6JRQVFcVChO8qTb2igooIYqGLFBFBrBcuEFCkSEeqKKETiqEkkJCQ9f0xO8eTk5MCBEhk3ufZT2bPnlmzZvbOXlPWnvMVDzzwAEWKFKF69erUrFmTNWvW5Fi+xWLJzDUd1DtGJysdngUuqnMhIn6XoMarQAUgRFVDgHuAEpcgJ7dEq2oEMAj4yPPiJdYBYAvwgNt5V2BbDnmqAV47F5eoxwrgTmCvR3w7oJZzPAaMvwTZl4yq3q6qEU67rwS+vJrlOzqsVdVnrna5F8ukSZNo167dtVbDYrFcIazdtXb3KvGcqoarahiwD/Dur55PiI2NZcOGDdx6662XlH/w4MG88MILFCuW9b9Pv3792L59OxUrViQ0NJT33nsPHx8fdu3aRenSpencuTP169cnOjratVo9YcIE2rdvT+XKlfn0008ZNGgQAMOGDWP69OlUrlyZ9u3b8/777wPQtWtX/P39qVChAoGBgQwYMICyZctSqVIlBgwYQGBgIBUqVKBUqVJERkZmWwbA3LlzCQsLo2vXruzfvx+AtLQ0XnjhBUaOHJmhfjVr1mTHjh3ExsaSmprK/PnzXXmy0vfgwYNUqVLFJaNy5cocPHgQMBMBkZGRNGzYkI8//viS7ovFcj1xqYbskhGRasD3wFKgKTBaRPoCRYDfgZ5AL6AisFREjqtqKxFJUNXijoyuwN2qGiUiU4A/gfrAehE5AwQCNZy/o1V1jIj4A58DlQFfTKfia+BRoLqqJgOoapyTzlPv5x29ACao6mhvMlV1tog0BN4BigPHgShVPewhcjlQ05EdA/wMNAMWiMg04ENHfzAdrZXAH0CEqp508u128gDMBzoCr4lIDeAUkOKlHgFOvV8DBgNBIrIRmArEAx2AooA/cIeIRAPdnPszT1WHZlVvVd3glOFZbEdgmqoqsEpESotIBS9t4qlrNeBTRxeAfqr6s9MhHQu0APZgJqcmqeoXOcgrAdyBecaySjMMqI7pcNYGngeaYDpIB4F7VDUlq3vsxE8CzgI/ucltCQxQ1btFpDEwGrgBOAf0VNWdIhIF3IvpVN+Mae+/PkrLqOdjmI4aAQHlGBKaml3ViYmJcYWPHDlCYmJihjiA6dOnc/LkSSpVqpTp2sWSkJBw2TKuBFaviyM/6pUfdcrvWLvrwtrdq2R3VfW0I08wtk6zKXMKxhbWBapinscemGd1tapGOenGA7c48r5w2qUUsAa417GjM4ElqvqJRxmZbGb6e+TcuXP079+fPn36sH79eleekydPsm7dOhISMjpc7Nixg4MHD7ry7969m9WrV9OxY0dWrVrl1b4CLFu2jICAAGbMmMGhQ4fo06cPEyZMcLnjf/zxx9x0000MHz6c5ORkfH19GTJkCK+++ir16tVj1qxZdO/enejoaD7//HNuv/12unXrxtatW+nSpQuTJk1i69atHD9+nJkzZ3LmzBn69+9P8eLFKVGiBFOnTmX69OkUL16cYcOG8dJLL9GmTZssyyhTpgxTp06lcOHCLFiwgI4dO/LOO+8wb9486tSpw++//05SUlKGtnjyySdp164dPj4+BAcHc/LkSWJiYrLU98CBA2zfvt2V//Dhw2zdupWAgABGjhxJQEAA8fHxDBgwgHPnzhEeHp7VY5QtBcFuFAQdoeDoeV2iqlf1wMxSp2EGSgEYI+vvXBsIDHHCsUCAW74Et3BXYIoTngJ8A/g658MwhrqII/8EUAjoAnziJqMUEAZsyEbXWEdGQ8yMvD+mw7AV05nxJrOQU345J+5+jOFL17WrE74PY6wAYoAP3OTMAG5zwoHAdif8HmYACHAr8D+3Og/ArECHAC9hDGIM0Ci9/YCbgNVAGyeuJfCNW7lRwAGgrHMeCXwMCMaAfwM091Zvb+3mdv5Nen2c88XpemXxfPzqhIsBRZ1wLWCt2/3/ztHp/zCdoq65ePYewXQEskszDDMYLwSEYwbn7Zxr84B/5nCPNwMtnPBIt7q42hooCfg54TuBuW7t/wfmOSqKWXmpklO9qlS/WasO/Cbbw509e/ZocHBwhrgpU6ZokyZNNDExUfOCpUuX5omcvMbqdXHkR72uhk7p75q/y4G1u9buXgO7C0wG4jCTScWySTcFmOXUuSNwGgh1ylqHmVTBrY18nXYOc87bYCZgHgB+yOn/Id1mqqqeP39eIyMj9e2331ZPWrRoob/88kum+MmTJ+tTTz3lOv/ggw+0QoUKWrVqVa1UqZIWKlRIW7RokSlf+/btdfny5a7zVq1a6erVq3XlypUZ0k+bNk07duyoR48e1Ro1arji9+7dq0FBQaqqWq9ePd23b5/rWvXq1TUuLk6ffPJJnTZtmiu+Z8+eOnv2bP3888+1V69ervipU6fqE088kW0Z7qSmpmrJkiVVVfXBBx/UKlWqaNWqVbVkyZJaokQJHThwYKY8H330kUZHR2er7+uvv66vv/66Kz4yMlJ//vnnTLKGDh2qI0eOzBSfW/KjLfOkIOio6l1P/mY2s6Ae1/IqVdwAACAASURBVMr9fq+qrsJ0MOoBK5xZ6x6YWdqLZY6qXnA7/1ZVk1X1OHAUY1S3AHeKyJsicruqnroI+bdhZssTVTUBY8Rvz0JmHYyBX+TU6WXMzHo6I534x4DebvGz3cJ3AmOddAuAks4q82xMZwWM8XLPA8YoPoAZeM7zuFYIY9RfVNVF2dR1kar+6YQjnWMDsB4zi14ri3pnR6YlBLKZtffQ+RMR2QLMwTwrYO7HHFVNU9UjmA5DbugOzMxFuu9VNQVTT18gfWe5LZjOj9d77KwYlFbVZU76T7OQXwqY43zD+C4Q7HZtsaqeUtUkjBtnjv8PNxTyJfaNDtke2fHDDz/w5ptvsmDBgmxdBy0WS4HG2l1rd6+q3VXVnhjvj+381YZZ8bWqKqaecaq6RVXTMJM51Zw03URkPaZtgtN1c9p2CzAO6OMp2JN0m6mq9O7dm6CgIJ5//vmcsmXJE088waFDh4iNjeWnn36idu3aXlcyAwMDXRvHxcXFsXPnTmrUqMEtt9xCfHw8x44dA2DJkiVUrVqVMmXKcOrUKXbt2gXAokWLCAoKyiRr+/btJCUlUa5cOQIDA1myZAmqSmJiIqtWraJu3boEBgayatUqzp49i6qyePFigoKCsi3j8OG/nDoWLFjgiv/ss8/Yt28fsbGxPPHEEzzyyCO88cYbABw9ehSA+Ph4PvjgA9fmelnpe++99zJr1iySk5PZs2cPv/32G40bNyYxMZEzZ84AkJiYyMKFC7P8VQGLxWK46u73DonOX8EYs+65yONujDx3KUn0OE92C1/ArIructzz2gMjRGQhMAoIFJESqnomm7K9GUeykDkP2KqqWW3GE63e3dXc6+ADNFXVcxmUEFkJ1BSRcpgOxGtk5GvM6vBaVT3t4Y6XipnxbgssI2vc9RBghKp6+wYxQ71V9ZVsZB4AqridVwYOZZM+necws/zhmDZJctProhCRG4HGQKdcJE93CU0TkRSnowFmpcvPKT/TPRaR0uSu0/QqsFRVOzmujjGeZTtcII//R7t3705MTAzHjx+ncuXKDB8+nBEjRpCcnOzapKdJkyZ8+OGHeVmsxWK59li7mxlrdzOTZ3YXQFUviMhsIBqzcp8V6c9PGhmfpTTAT0SqYzwjblHVeMdlvyiA82lAEMaFvyym7jmyYsUKPv30U9fPpgG8/vrrJCcn8/TTT3Ps2DE6dOhARESE66fWqlWrxunTpzl//jzz589n4cKF1KtXL8sy0m1p3759GTx4MFFRUYSGhqKqvPnmmwQEmL0NR40aRevWrVFVGjZsyN13342fnx+ffPIJXbp0wcfHhzJlyjBp0iQA3n77bR599FHeffddRIQpU6YgIjz11FP07NmTkJAQVJWePXu6dqjv2rUrDRo0wM/Pj/r16/PYY49lW8aYMWNYsGABfn5+lC1blilTpuTYpv3792fTpk0ADBkyhNq1a2erb3BwMN26daNevXr4+fkxbtw4fH19iYuLo1Mn011LTU3lwQcf5K677srNbbVYrl+utmsAGd28ymE2UKnpnBcDajvhLZhv7tLz7ca8tH2AuWR0A+zqlm4Y5tvl9PNfnTIr8pdL2T+B+U74LYyhKeycVwAedsKxGDfABhi36mIYV8BfMW6AmWQChR1dmzrxhYBgb7q66RiDm1scxg0w2u08wi08ErMC/J23OmNWDBp4ysW4AfpiVjsGOXENgWVucqKAsW7nkRi3weLOeSWgfFZt6ZYvloxugB0w33MKZpVoTS6fj3eBF5xwTyB9fH0fxrXQB7Ma9Ke3dvWQ2xeYmovn0/P5SfC8lsM93sxfLpxv4t39fh7QxU1mbBbt/w3QMieda9eurfmN/OpGZvW6OPKjXtb93tpdT5k5vJMz6OqmYwzW7l4Ru+uUWdMtPAoYlU35rnvkrov7Ncwkwya38uMw+yYAvID5ZOF2YC1QKLv/h/xoMz3Jj+9eb1g9846CoKOqdb/Pz8e1WqkHQFWPOZuDzRSRIk70y8AuzAv6exE5rKqtMLvWfgPsxxj3i/15lFCMC14aZiObJ9zKew3YJiJJmBnzIR56rndmhdN/Z2OCqm4QkbaeMlX1vLOh0BjHHdsPsyna1ovQ9RlgnIhsdvIvxwxKwbj+/YLpCGRCVWdlJVTNjPkDwNcichr4BEgVkU0YwxnvkX6hiAQBK53VhwTgYcxGQ5naUkSeAV7EfG+3WUS+U9U+mO/w2mM6XWfJZqM6Dz4A5orIfRhXv/TVjLlAa8xzsAvTAcrJFfEB4I1clpstOdzjnsAkETkL/DcLEW8BU51NoJbkhU4Wi8WSG6zdzRJrdw15YXcFY+NKOuFN/HXvLwlV3SQiGzD39A/Mrv+ISG2My31jVT0jIssxz9fQyynPYrFYChpiJlgsloKFiBRX1QTHrX4N0EzNd37XHXXq1NGdO3deazUyEBMTQ8uWLa+1Gpmwel0c+VGvq6GTiKxT1UZXtBCLpYDxd7G7+dFmepIf373esHrmHQVBR/Cup7WZ+YNrulJvsVwG3zjfsBfG/LRPgetYWCwWi8VSgLB212KxWPIpdlBvuSaISCiZd4dPVtVbc5NfVVt6kTkP8xvz7gxU1Uxu8CLSE+jvEb1CVZ/KTfkWi8VisRQk8oHdfQnzbb47c1T1P7kp32KxWCxZYwf1lmuCqm4BIvJYZm52tk9PO5nsd+K1WCwWi+VvQz6wu/8B7ADeYrFYrgDX6nfqLRaLxWKxWCwWi8VisVwmdlBvsVgsFovFYrFYLBZLAcUO6i0Wi8VisVgsFovFYimg2EG9xWKxWCwWi8VisVgsBRQ7qLdYLBaLxWKxWCwWi6WAYgf1FovFYrFYLBaLxWKxFFDsoN5isVgsFovFYrFYLJYCih3UWyzXCb169aJ8+fKEhIS44ubMmUNwcDA+Pj6sXbv2GmpnsVgsFsvVxZtd3LRpE02bNiU0NJR77rmH06dPA3D+/Hl69uxJaGgo4eHhxMTEuPLMnDmT0NBQwsLCuOuuuzh+/Himsr766ivCwsKIiIigUaNG/PTTT65r+/btIzIykqCgIOrVq8eRI0cAGDt2LDVr1kREMsiMj4+nU6dOhIWF0bhxY3799VfXtWrVqhEaGuoqJ53Bgwe7yo+MjOTQoUMAjBw5koiICCIiIggJCcHX15c///yTpKQkGjduTHh4OMHBwQwdOtQlKyoqiurVq9OnTx8iIiLYuHFjjnX09fV1lXPvvfe64rOqI0BMTAwREREEBwfTokWL7G6lxWJRVXvYwx4F+Khdu7bmhmXLlum6des0ODjYFbdt2zbdsWOHtmjRQn/55ZdcyckNS5cuzTNZeYnV6+LIj3pdDZ2AtZoP/rftYQ975P3hbjO92cVGjRppTEyMqqpOnDhRX375ZVVVHTt2rEZFRamqalxcnDZo0EAvXLigKSkpWq5cOT127JiqqkZHR+vQoUPVkzNnzmhaWpqqqm7atEnr1KnjutaiRQtduHChK93333+vqqrr16/XPXv2aNWqVV3yVVUHDBigw4YNU1XV7du36x133OG65pk2nVOnTrnC7733nj7++OOZ0ixYsEBbtWqlqqppaWl65swZVVU9f/68Nm7cWFeuXKmqqj169NA5c+Zkeh9nV0d/f/9M5WVXx/j4eA0KCtK9e/eqqmnzSyU/2jJPCoKOqt71tDYzfxzXfKVeRJ4VkWJXoZxCIvKGiPwmIr+KyBoRaedcixWRgDwsa4qI7BGRjSKyXkSa5pHcYSKiIlLTLe45J66Rc57gJV81EXkwL3Rw5PUTkd1OuQFu8SIiY5xrm0WkQTYyqonIr1ldz0u93K7fIiIXRKRrXpabg06uZ0tEfr4SZZxLuUC1Qd9meaTTvHlzypYtmyFvUFAQderUuRJqWSyWK4C1mRcl19rMS9DL7frf1mam480u7ty5k+bNmwPQpk0b5s6dC8C2bdto3bo1AOXLl6d06dKsXbvW1aFOTExEVTl9+jQVK1bMVG7x4sUREQASExNd4W3btpGamkqbNm1c6YoWLQpA/fr1qVatWiZZ7rrUrVuX2NhY4uLisq13yZIlXWH38t2ZOXMm3bt3B0BEKF68OAApKSmkpKR4zZObOmZHVnWcMWMGnTt3JjAwEDBtbrFYsuaqDOodw5VVWc8CF9VBERG/S1DjVaACEKKqIcA9QIlLkJNbolU1AhgEfOR58RLrALAFeMDtvCuwLYc81QCvHZRL1GMFcCew1yO+HVDLOR4Dxl+C7MshK70QEV/gTeC/V1knF6r6j2tVtsViKThYm2lt5lXC2kwvhISEsGDBAsB8orZ//34AwsPD+eqrr0hNTWXPnj2sW7eO/fv3U6hQIcaPH09oaCgVK1Zk27Zt9O7d26vsefPmUbduXTp06MCkSZMA2LVrF6VLl6Zz587Ur1+f6OhoLly44DV/OuHh4Xz55ZcArFmzhr1793LgwAHADMYjIyNp2LAhH3/8cYZ8L730ElWqVOGzzz7jlVdeyXDt7Nmz/PDDD3Tp0sUVd+HCBSIiIihfvjxt2rTh1ltvzSCrd+/ePPfccyQnJ2dbR4CkpCQaNWpEkyZNmD9/frb1S2+X+Ph4WrZsScOGDZk2bVqOeSyW65krNqh3ZpW3i8gHwHrgXyKy0pmFnyMixUXkGaAisFREljr5EtxkdBWRKU54ioi846R705mBnyQiMSLyhyMLEfEXkW9FZJOzunC/s6rxKPC0qiYDqGqcqn7uRe/nnXy/isizWcl04huKyDIRWSci/xWRCl6aYjlQ00kfIyKvi8gyoL+IlBORuSLyi3M0ExEfZ6a6tJtOu0XkJud0PtDRia8BnAKOealHgNPeHYA3gNudVZDnRCTKuQdfAwud9NGODptFZHh29VbVDaoa66WuHYFpjjfOKqB0Fm3iqWs1EfnReTbWi8g/nHgfEflARLaKyDci8l12KwbZ6AXwNDAXOJqDLi2de/q5iOwSs1L1kJhVqi0icrOTLtO9c+JvFJGFIrJBRD4CxE12gvO3uIgsduq6RUTS72f6/8wnTp0XisgNObWfxWIp+Fib6cLazBywNvPK2sxJkyYxbtw4GjZsyJkzZyhcuDBgvr+vXLkyjRo14tlnn+Uf//gHfn5+pKSkMH78eDZs2MChQ4cICwtjxIgRXmV36tSJHTt2MH/+fAYPHgxAamoqP/74I6NGjeKXX37hjz/+4IcffshWx0GDBhEfH09ERATvv/8+9evXx8/PzDetWLGC9evX8/333zNu3DiWL1/uyvef//yH/fv389BDDzF27NgMMr/++muaNWuWwXPB19eXjRs3cuDAAdasWeP6dn/EiBHs2LGD8ePH8+eff/Lmm29mW0cw+wasXbuWGTNm8Oyzz/L7779nW8fU1FTWrVvHt99+y3//+19effVVdu3alW0ei+V65lJnvnNLHaAnMAT4ErhTVRNFZCDwvKq+IiLPA61UNfOuIpmp7ci4ICLDgLpAK8zqwU4RGQ/cBRxS1Q4AIlIK00HYp6qnsxMuIg0dfW/FGJbVTmeihqdMESkEvA90VNVjjvH+D9DLQ+w9mJWCdEqragtHzgzgXVX9SUQCgf+qapCIfAV0AiaLyK1ArKrGiXFjOg3sF5EQTIdgtqOzez1uAhYAL6vqIhFJBAao6t3O9SigKRCmqn+KSCRmpaCxU+8FItIcKOelLbOjErDf7fyAE3c4h3xHgTaqmiQitYCZQCOgM2bFJBQoD2wHJmUlJCtEpBKmPe8AbslFlnAgCPgT+AOYoKqNRaQ/pqPzLPAeHvfOyTMU+Ml5tjtgVl88SQI6qeppMW6Gq0RkgXOtFtBdVR8Vkc+BLsB0L3V6LF12QEA5hoSmZlkZ9818jhw5QmJiYoY4gJMnT7Ju3ToSEjJ5ol4SCQkJmcrID1i9Lo78qFd+1CkPsTbT2kxrMzOT5zYzJ7v473//G4D9+/dTvnx517WOHTvSsWNHAPr160d8fDwTJ04kPj6e/fv3s3//fmrVqsXMmTO57bbbsm20rVu38tVXX3H06FGqV6/Ovn372LdvH3Xq1GHTpk0Z9ElKSmLFihWUKvXXI9WjRw969OiBqtK9e3cOHDhAfHw8gGvwW79+fWbOnElaWlqGsqtXr87/+3//j1atWrnixo4dS4sWLbJ8v1arVo1x48Zx//33A+YzhfPnz1O/fn1mz57t+mTBWx3T9U7Xq27dukyfPj3D5needTx//jx169bll19+AaBWrVrMmDGDli1bZtuu3igIdqMg6AgFR8/rkSs9qN+rqqtE5G6gHrDCMbKFgZWXIG+Oqrr7JH3rrCIki8hR4CZMZ2CUiLwJfKOqP0ouvulxuA2Yp6qJACLyJXA78IMXmSFACLDIke9LRkM8UkRexqwIuPthzXYL3wnUc9OvpIiUcNIMASZj3Abd8wDMcuLbAq3J2EEpBCwGnlLVZdnUdZGq/umEI51jg3NeHGMof/SsdzbywG2G3Q3NIU+6zmNFJAK4gOmIgrkfc1Q1DTgizsrUJTAaGOh0bHOT/hdVPQwgIr/jrMxgnq10C5jVvWuO6Vihqt+KSLwX+QK87nQC0zCduPRVpT2qutEJr8N00DKhqh8DHwME1qipb2/J+l859qGWf4VjY/H3989kFEuXLk3Dhg0z7JR7OcTExFyS4b3SWL0ujvyoV37UKQ+xNtPaTGszM5PnNtP9HeJpF48ePUr58uVJS0sjKiqK6OhoWrZsydmzZ1FV/P39WbRoEWXLliUqKopDhw4xfPhwgoODKVeuHIsXL6ZZs2aZ3lO7d+/m5ptvRkRYv349Pj4+3HvvvaSlpfHRRx+58k+dOpWaNWtmyF+0aFGaNWtGQIDZ+uDkyZMUK1aMwoUL88knnxAZGUmHDh1ITEwkLS2NEiVKkJiYyL///W+GDBlCy5Yt+e2336hVqxYA77//Pg0bNnSVcerUKbZu3coPP/yAv78/AMeOHaNQoUKULl2ac+fOMXjwYAYOHEjLli05fPgwFSpUYOnSpcTGxtKiRQtatmyZZR3T9S1SpAjHjx/n999/55133qFevXpZ1vGmm26iX79+3HbbbZw/f559+/bx1ltvZfilgtxSEOxGQdARCo6e1yNXelCf6PwVjEHsnos87gataBby0kl2C18A/FR1l7N60B4YISILgVFAoIiUUNUz2ZTt1XplIXMesFVVs9rQJ1pVv/AS714HH6Cpqp7LoITISqCmiJQD/gm85iHja2AkZrfJ0x5GNxVj2NoC2XVQ3PUQYISqevuOMUO9VfUVzzRuHACquJ1XBg5lkz6d54A4zGy/D2ZWPl2vvKARMMtppwCgvYikqmpWH3W5P1dpbudp/PU/k9W9g5w7ZQ9hVnQaqmqKiMTy17Pu+Uzn6Ep4QyFfdr7RIadkdO/enZiYGI4fP07lypUZPnw4ZcuW5emnn+bYsWN06NCBiIgI/vvfa/YJpcVyvWNtZmaszcyMtZmXaTPT8WYXExISGDduHACdO3emZ08zB3T06FHatm2Lj48PlSpV4tNPPwWgYsWKDB06lObNm1OoUCGqVq3KlClTAPjwww8B6Nu3L3PnzmXatGkUKlSIG264gdmzZyMi+Pr6MmrUKFq3bo2q0rBhQ+6++24AxowZw1tvvcWRI0cICwujffv2TJgwge3bt/PII4/g6+tLvXr1mDhxIgBxcXF06tQJMO7rDz74IHfddRdgXPZ37tyJj48PVatWdekG5jv4yMhI14Ae4PDhw/To0YMLFy6QlpZGt27dXHo99NBDHDt2jISEBJo1a+aSlVUdt2/fzuOPP46Pjw9paWkMGjTINaDPqo5BQUHcddddhIWF4ePjQ58+fS5pQG+xXDfoFdpWHzNb+qsTLgfsA2o658WA2k54C1DdLd9ujEuWD+Z7rilO/BSgq1u6YRj3uPTzX50yKwJFnbh/AvOd8FuYWfzCznkF4GEnHIsxXA2AzY5+/o7M+t5kYlZOdmOMFJiZ82BvurrpGAM0cjufgenIpJ9HuIVHAp8C33mrM2bVoYGnXCABswLyJTDIiWsILHOTEwWMdTuPBFYDxZ3zShjXPa9t6ZYvFghwO+8AfI/pWDQB1uTy+XgXeMEJ9wTUCd8HfOM8CzdhXPsytasX2Rn08rjm9d64XW+JWWHJdM/cr2V174AxGBdOMJsgabouQILztz/wvhNu5aSp5t4mzrUBwLCc6pvbn7S7muTXn2axel0c+VGvv+tP2mFtprWZ1mZetzbTk/z47vWG1TPvKAg6qtqftMvPx1XZ/V5Vj2GM4kwR2QyswnzbB8Yd6ns3N7FBGKO0hJy/K/NGKLBGRDYCL/HXjH26W982MT8LMx+PzXJUdT3GgK3BGOwJqrrBm0xVPY/ZRfdNEdkEbAQudqfWZ4BGYjba2Qb0dbs2G3iYzG6E6brOcvT1du0CpgPTSkSexHS6UsVs3vOcl/QLMQZ3pYhsAb7AfHPptS1F5BkROYBZVdgsIhMcUd9hvqfbDXwCPJnLdvgA6CEiqzBuhOkrInMxKxm/YnZDXo3Z5Mgr2eh1Jcjq3g0HmovIekzHb5+XvJ85eddiViB2XEE9LRZLAcPazCyxNtNgbabFYrFYMiBmgsViyZ+ISHFVTRCRGzEdx2aqeuRa65WfqFOnju7cufNaq5GB/PrNldXr4siPel0NnURknarmzQYTFstVxNrMnMmPNtOT/Pju9YbVM+8oCDqCdz2tzcwfXOlv6i2Wy+UbMT9VVBh41XZOLBaLxWLJEmszLRaL5TrEDuotVxQRCcV85+hOsqrempv8qtrSi8x5QHWP6IGqmusd3i5XL4vFYrFY8hprMy0Wi8VyKdhBveWKoqpbgIg8ltkpD2TkuV4Wi8VisVwO1mZaLBaL5VK4KhvlWSwWi8VisVgsFovFYsl77KDeYrFYLBaLxWKxWCyWAood1FssFovFYrFYLBaLxVJAsYN6i8VisVgsFovFYrFYCih2UG+xWCwWi8VisVgsFksBxQ7qLRaLxWKxWCwWi8ViKaDYQb3FYrFYLBaLxWKxWCwFFDuot1gsFovFYrFYLBaLpYBiB/UWy3VAr169KF++PCEhIa64P//8kzZt2lCrVi3atGlDfHz8NdTQYrFYLJarhze7uHHjRpo0aUJERASNGjVizZo1AJw6dYp77rmH8PBwgoODmTx5siuPr68vERERREREcO+993ota9++fbRq1Yr69esTFhbGd99957q2efNmmjZtSnBwMKGhoSQlJQEwe/ZswsLCCA4O5sUXX3SlT05O5v7776dmzZrceuutxMbGAnDixAlatWpF8eLF6devX4by161bR2hoKDVr1uSZZ55BVQHYtGkTTZs2JTQ0lHvuuYfTp08DsGjRIho2bEhoaCgNGzZkyZIlLlktW7akTp06RERE0KdPH44ePQrAO++8Q7169QgLC6N169bs3bvXlWfgwIGEhIQQEhLC7NmzXfG9e/cmPDycsLAwunbtSkJCQk63zWKxZIEd1Fss1wFRUVH88MMPGeLeeOMNWrduzW+//Ubr1q154403rpF2FovFYrFcXbzZxRdffJGhQ4eyceNGXnnlFddgety4cdSrV49NmzYRExPDCy+8wPnz5wG44YYb2LhxIxs3bmTBggVey3rttdfo1q0bGzZsYNasWTz55JMApKam8vDDD/Phhx+ydetWYmJiKFSoEKdOnSI6OprFixezdetW4uLiWLx4MQATJ06kTJky7N69m+eee46BAwcCULRoUV599VVGjRqVqfwnnniCjz/+mN9++43ffvvNVe8+ffrwxhtvsGXLFjp16sTIkSMBCAgI4Ouvv2bLli1MnTqVf/3rXxnkffbZZ2zcuJEJEyZQvnx5AOrXr8/atWvZvHkzXbt2dbXdt99+y/r169m4cSOrV69m5MiRrsmDd999l02bNrF582YCAwMZO3bsxdxCi8Xihh3UZ4GIPCsixa5COYVE5A0R+U1EfhWRNSLSzrkWKyIBeVjWFBHZIyIbRWS9iDTNI7nDROSgI3ebiHTPA5l1RWSliCSLyACPa3eJyE4R2S0ig3KQEyMijS5XHzd5HUVks1PXtSJyWx7I7Csij1xq/nMpF6g26FuvRzrNmzenbNmyGfJ99dVX9OjRA4AePXowf/78S1XBYrFYLHmAiKiIfOp27icix0TkG490X4nIylzIe9exVxtFZJeInHTiqzllveqWNkBEUkRkrIeMTSIyMxdlufcxdojIULdrMY7d3igi20XksRxk5Wn/x51zKRcA73ZRRFwDzlOnTlGxYkVX/JkzZ1BVEhISKFu2LH5+frkuMyu5CxcuJCwsjPDwcABuvPFGfH19OXz4MLVr16ZcuXIA3HnnncydOxfIaLu7du3K4sWLUVX8/f257bbbKFq0aIayDx8+zOnTp2natCkiwiOPPOKy9zt37qR58+YAtGnTxlVG/fr1XToGBweTlJREcnJytnVs1aoVxYqZbnOTJk04cOAAANu2baNFixb4+fnh7+9PeHi4a1KhZMmSAKgq586dQ0Ry3aYWiyUj1/WgXgxZtcGzwEUN6kUk92/4v3gVqACEqGoIcA9Q4hLk5JZoVY0ABgEfeV68xDoAvOvI7Qh8JCKFLkNHgD+BZ4AMU84i4guMA9oB9YDuIlLvMsu6GBYD4U5dewETLlegqn6oqtMuW7OLJC4ujgoVKgBQoUIFlwudxWKxWK4ZiUCIiNzgnLcBDronEJHSQAOgtIhUz06Yqj6nqhGOzXof+NLt8h/A3W7n9wFbPcoKwvQVm4uIfy70T+9jRAA9PPR7yLnWDHhTRArnQt5VZfTo0URHR1OlShUGDBjAiBEjAOjXrx/bt2+nYsWKhIaG8t577+HjY7qPSUlJNGrUiCZNmmQ5OT5s2DCmT59O5cqVad++Pe+//z4Au3btQkRo27YtDRo04K233gKgUqVK7Nixg9jYWFJTU5k/fz77f7nKuAAAIABJREFU9+8H4ODBg1SpUgUAPz8/SpUqxYkTJ7Ks08GDB6lcubLrvHLlyhw8aB6pkJAQl3fBnDlzXGW4M3fuXOrXr0+RIkVccT179iQiIoJp06a5XPndmThxIu3atQMgPDyc77//nrNnz3L8+HGWLl2aoZyePXvyf//3f+zYsYOnn346y3pYLJbsudQBXIFFRKoB3wNLgabAaBHpCxQBfgd6YgZrFYGlInJcVVuJSIKqFndkdAXuVtUoEZmCGYDWB9aLyBkgEKjh/B2tqmMcY/g5UBnwxQzmvwYeBaqrajKAqsY56Tz1ft7RC2CCqo72JlNVZ4tIQ+AdoDhwHIhS1cMeIpcDNR3ZMcDPGEO7QESmAR86+oOZ4FiJ6QBEqGr6TP9uJ48LVf1NRM4CZYCjIvIo8BhQGNgN/AtIBn4DbgZKOe3XUlWXi8iPQE9V3e3k7+Chd2Ngt6r+4egwCzORsM2zzby04XjgFuAG4AtVHerEt3fa6ziwHqihqnd7k6Gq7h98+QOZrdlf5bUEhgNxmA7Ol8AWoL+jwz9V9XcRGQYkqOoo516sBloBpYHeqvqjF9mPYdqVgIByDAlN9apDTEyMK3zkyBESExNdcampqRmue55fDgkJCXkmKy+xel0c+VGv/KiTxZLHfA90AL4AugMzgdvdrnfB9B/igAeAEbmU2x0Y6nZ+DtguIo1UdS1wP6ZPUdEtzYPAp0AQcK+jS25IXy5O9HKtuBN/ITeCsuj/vAgkOf2rdzGT7XeISGtMH+JhDxkZbGb6O8TTLo4ZM4bevXvTokULli5dSufOnXn77bdZtmwZAQEBzJgxg0OHDtGnTx8mTJiAv78/s2bNIiAggEOHDtG3b18SExOpVKlShjp8/vnn3H777XTr1o2tW7fSpUsXJk2axM6dO/nf//7Hhx9+SJEiRXjhhRfw9fWlTp06PPnkk7Rr1w4fHx+Cg4M5efIkMTExJCQksHLlStcqflJSEitWrKBUqVIA7Nixg4MHD7rqtGPHDuLj413nmzdv5s8//yQmJoa+ffvy2muvER0dTbNmzfDx8cnwft2zZw8vv/wyb731liv+qaeeoly5cpw9e5aXXnqJf//737Rt29aVZ9GiRSxZsoTRo0cTExND4cKFCQoKIiwsjNKlS1OjRg327NnjktejRw8efvhhxowZw/Dhw12TAXlJQbAbBUFHKDh6Xpeo6nV1ANWANKAJEIAZ3Po71wYCQ5xwLBDgli/BLdwVmOKEpwDfAL7O+TDMALmII/8EUAhjhD9xk1EKCAM2ZKNrrCOjIWYw6I8xhlsxkwjeZBZyyi/nxN0PTHLTtasTvg9Y7YRjgA/c5MwAbnPCgcB2J/wexlgC3Ar8z63OA5xwA+BHN1k3uoVfA552wj8AwZhVgl+Al5w22+PRBi7Zbm0/we38X8DYbNowBmjkhMs6f32d+DBMx2M/ZmIFTIflmxyeoU7ADsxkRNNs0rUETmI8MYpgVluGO9f6YyZ8PNsvBnjbCbdPb+PsjirVb9aqA7/xerizZ88eDQ4Odp3Xrl1bDx06pKqqhw4d0tq1a2tesXTp0jyTlZdYvS6O/KjX1dAJWKv5wF7Z4/o7gATHNn3h2KeNji35xi3N/zCD/NrA5lzKrQoc5q++SjXgV8xAfRRmcWAxEOVuU4FdTt5IYEEOZUwB9jg6JwCvu12LAXYCmzGTCY/nICuW7Ps/TYA5TtofgTWY/s/QnGRXqX6zpuNpF0uWLKlpaWmqqpqWlqYlSpRQVdX27dvr8uXLXelatWqlq1evVk969Oihc+bMyRRfr1493bdvn+u8evXqGhcXpzNnztQePXq44l955RV96623Mr3nPvroI42OjlZV1cjISP35559VVTUlJUVvvPFGl86qqpMnT9annnrKdX7o0CGtU6eO63zGjBn62GOPZdJx586desstt7jO9+/fr7Vq1dKffvopU9p0Bg4cmKGsRYsWad26dTUuLi7LPN27d9dvv/02U3xMTIx26NAhy3yXQ360ZZ4UBB1VvetpbWb+OK5X9/u9qroKYxTqAStEZCPQA2O8LpY5quo+4/ytqiar6nHgKHATxijdKSJvisjtqnrqIuTfBsxT1UQ1K8VfYgy6N5l1gBBgkVOnlzHGOp2RTvxjQG+3+Nlu4TuBsU66BUBJESnhpLnfSfOAR57nRGQnZpV5mFt8iIj8KCJbgIcwA3kwRri5c4xw6ngLZoCfHd4+uMpytdyDbiKyHtjg6FEPqAv8oap7nDQ5rkKo6jxVrQv8E+NxkR2/qOphNZ4YvwMLnfgtmE6VN9LdI9dlk8bFDYV8iX2jg9cjO+69916mTp0KwNSpU+nYsWNORVksFovlCqOqmzHv/u7Ad+7XROQmjJfdT6q6C0gVkZBMQjLzAMZDzXN1/AeMi393Mtp0ROQW4Jiq7sUM+BuISJkcykl3v/8/oLWI/MPt2kOqGoZZLBggIrnpb2XV/1kHNHT6JskYb8JGzrVM3m3u3FDIN8trFStWZNmyZQAsWbKEWrVqARAYGOjaqC4uLo6dO3dSo0YN4uPjXd+aHz9+nBUrVlCvXuYvAt3zb9++naSkJMqVK0fbtm3ZvHkzZ8+eJTU1lWXLlrnyp38SFx8fzwcffECfPn2AjLb7iy++4I477sj2W/QKFSpQokQJVq1ahaoybdo0l71PLyMtLY3XXnuNvn37AnDy5Ek6dOjAiBEjaNbsL4fM1NRUjh8/DkBKSgorV650/XrAhg0bePzxx1mwYIFr8zyACxcuuD4P2Lx5M5s3byYyMhJVZffu3YBZYPz666+pW7dulvWwWCzZc9253zuku4MJsEhVc7Oxm/vAsajHNU/3MvfdRC4Afqq6y3GLbw+MEJGFmNnxQBEpoapnsinb69s6C5nzgK2qmtUmeNGq+oWXePc6+GBWoM9lUMJsylNTRMphBrSvuV1+V437eGdgmojcrKpJmJn7f6rqJhGJwqw4gDG6fTFufkOAaOfa8iz0TucAUMXtvDJwKIc8ON/1DQBuUdV457OJomTRtrlBzecCN4tIgDOB4w33ZyHN7TyNrP//0tNcyCbNRdG9e3diYmI4fvw4lStXZvjw4QwaNIhu3boxceJEAgMDmTNnTl4UZbFYLJbLZwGmj9ASuNEt/n7M5217nIFcScyA/eUc5D0APOUZqarnRWQd8AJmsvset8vdgboiEuucl8R4COa4l4yqJjifk92G8R50v3bMmWC/FdjrJbs7WfV/Uhy9ejryN2M+W7sZ2J6TfuDdLn7yySf079+f1NRUihYtyscffwzA4MGDiYqKIjQ0FFXlzTffJCAggJ9//pnHH38cHx8f0tLSGDRokGtQPmTIEBo1asS9997L22+/zaOPPsq7776LiDBlyhREhDJlyvD8889zyy23ICK0b9+eDh06EBMTQ//+/dm0aZNLVu3atQHzM3D/+te/qFmzJmXLlmXWrFmuOlWrVo3Tp09z/vx55s+fz8KFC6lXrx7jx48nKiqKc+fO0a5dO5eL+8yZMxk3bhwAnTt3pmfPngCMHTuW3bt38+qrr/Lqq2btYuHChfj7+9O2bVtSUlK4cOECdevW5dFHHwUgOjqahIQE7rvvPsBMZCxYsICUlBRuv918PVKyZEmmT5+On58faWlp9OjRg9OnT6OqhIeHM378+NzcOovF4o1r7SpwtQ8clzMnXA7YB9R0zosBtZ3wFhyXbOd8N+abMh9gLhnd77u6pRtGRnfxX50yKwJFnbh/AvOd8FvAZKCwc14BeNgJx2LczxpgDFYxjAvarxj3s0wy+evb9aZOfCEg2JuubjrG4LioO+czMIP/9PMIt/BIzPd132VT569w3N8w36mXd/RY5NZuRZz6LXHOx2Pc4MM9dPOU7Yf5tr+6U9dN6fXL4n7HYGbvw520PhjPiTiMm+ENTrnVnPSfkY37PWaFRJxwA4xLvWSRtiUZXSZd7ex+jczu9+lpAoDYnJ7pvHSbzyvyqxuZ1eviyI96Wfd7e/ydD5xP/TAT1v2dsLu9WInbZ1+OLdydg8w6jr0Vt7hq/NUXCgZ6OOEoYKxjK/cDldzytAIWZ1POFP76xM/PsWddnHN321YM49bfMBtZsWTT/3HSDMP04e507Po+zKp+tm2cH22mJ/nx3esNq2feURB0VLXu9/n5uF5X6gHXbHEUMFNE0rf1fBljbD4GvheRw6raCrNb/DcYI/cr5tuuiyEU4/qeBqQAT7iV9xqwTUSSMCvmQzz0XO+sLK9xoiao6gYRaespU82se1dgjIiUwhjW0XjsaJsDzwDjRGSzk385ZlUdjHveLxjDnxWvADNE5BNgMMYlfy9moqSEU6dkEdkPrHLy/IhZFdgCICL/B6zFrAykicizQD1VPS0i/YD/Yr6Nn6SqOdZNjafABkw7/AGscOLPiciTwA8icpy/2jgrugCPiEgK5rvA+50XmsVisVgseYKqHsDsY+PC2eg3kL/sJqq6R0ROi8itqro6C3HdgVlZ2SrHhnra0ebAQVV133l/OVBPRCpo5s130xkpIi9jJt0Xk3G3/c9E5BxmUn+Kqq7LQoa7bl77P074R8x+PCtVNdHpQ2Xrem+xWCx/V8SORyzXOyJSXI2roGB+Lu83VX33WuuVW+rUqaM7d+681mpkICYmhpYtW15rNTJh9bo48qNeV0MnEVmnqo2uaCEWi+WakB9tpif58d3rDatn3lEQdATvelqbmT+4XjfKs1jcedTZFHAr5hcEPrrG+lgsFovFYrFYLBZLrriu3e8tfx9EZB7m20J3Bqrqf3PK66zKZ1iZF5GemJ+dc2eFqmbaaEhEQjH7DLiTrKq35qi4xWKxWCyXiYi8hPmpWnfmqOp/rkBZ44BmHtHvqerkS5C1GuOO786/VHXLpepnsVgs1yN2UG/5W6CqnfJY3mTMBoa5SbsFiMjL8i0Wi8ViyS3O4D3PB/BZlJVpcvsyZNnJb4vFYskDrPu9xWKxWCwWi8VisVgsBRQ7qLdYLBaLxWKxWCwWi6WAYgf1FovFYrFYLBaLxWKxFFDsoN5isVgsFovFYrFYLJYCih3UWywWi8VisVgsFovFUkCxg3qLxWKxWCwWi8VisVgKKHZQb7FYLBaLxWKxWCwWSwHFDuotluuA9957j5CQEIKDgxk9evS1VsdisVgslmtKr169KF++PCEhIa64jRs30qRJEyIiImjUqBFr1qzJkOeXX37B19eXL774whX34osvEhwcTFBQEM888wyqmqmsYcOGUalSJSIiIoiIiOC7774DICUlhR49ehAaGkpQUBAjRoxw5Xn33XcJDg4mJCSE7t27k5SUBMCSJUto0KABISEh9OjRg9TUVADi4+Pp1KkTYWFhNG7cmF9//dUl6+TJk3Tt2pW6desSFBTEypUrs63vqVOnuOeeewgPDyc4OJjJkycDsHTpUlcdIiIiiIyMZP78+QA89NBD1KlTh5CQEHr16kVKSgoAqsozzzxDzZo1CQsLY/369V5lFS1a1CXLYrFcAqpqD3vYowAftWvX1uzYsmWLBgcHa2JioqakpGjr1q11165d2ea5XJYuXXpF5V8qVq+LIz/qdTV0AtZqPvjftoc97JH3R7rNXLZsma5bt06Dg4M1nTZt2uh3332nqqrffvuttmjRwnUtNTVVW7Vqpe3atdM5c+aoquqKFSv0H//4h6ampmpqaqo2adLE6ztq6NChOnLkyEzxn332md5///2qqpqYmKhVq1bVPXv26Oeff67VqlXTs2fPqqrqfffdp5MnT9YLFy5o5cqVdefOnaqqOnjwYJ0wYYKqqg4YMECHDRumqqrbt2/XO+64w1XOI488op988omqqiYnJ2t8fHy29f3Pf/6jL774oqqqHj16VMuUKaPJyckZdD9x4oSWKFFCExMTXfnT0tI0LS1NH3jgAf3ggw9c8XfddZempaXpypUrtXHjxpna4cSJE1qmTBmXrLwmP9oyTwqCjqre9bQ2M38c18VKvYg8KyLFrkI5hUTkDRH5TUR+FZE1ItLOuRYrIgF5WNYUEdkjIhtFZL2INM0jucNEREWkplvcc05cI+c8wUu+aiLyYF7o4MjrJyK7nXID3OJFRMY41zaLSINsZFQTkV+zun6Jek0UkU1O2V+ISPE8kPmdiJS+1PznUi5QbdC3mY50tm/fTpMmTShWrBh+fn60aNGCefPmXa7aFovlb4q1mRcl19rM7PXKlzYToHnz5pQtW9ZTNqdPnwbManXFihVd195//326dOlC+fLlM6RPSkri/PnzJCcnk5KSwk033XQxdSExMZHU1FTOnTtH4cKFKVmyJIArLjU1lbNnz1KxYkVOnDhBkSJFqF27NgBt2rRh7ty5AGzbto3WrVsDULduXWJjY4mLi+P06dMsX76c3r17A1C4cGFKly6dbX1FhDNnzqCqJCQkULZsWfz8/DLo/sUXX9C4cWOKFTOvivbt2yMiiAiNGzfmwIEDAHz11Vc88sgjiAhNmjTh5MmTHD58OJOsdu3auWRZLJaL528zqHcMV1b1eRa4qDeFiPjlnCoTrwIVgBBVDQHuAUpcgpzcEq2qEcAg4CPPi5dYB4AtwANu512BbTnkqQZ47aBcoh4rgDuBvR7x7YBazvEYMP4SZF8Oz6lquKqGAfuAfpcrUFXbq+rJy1fNOyEhISxfvpwTJ05w9uxZvvvuO/bv33+lirNYLAUAazOtzbxKFCibOXr0aKKjo6lSpQoDBgxwucMfPHiQefPm0bdv3wzpmzZtSqtWrahQoQIVKlSgbdu2BAUFeZU9duxYwsLC6NWrF/Hx8QB07doVf39/KlSoQGBgIAMGDKBs2bKUK1eOAQMGEBgYSIUKFShVqhSRkZEEBASQkpLC2rVrATMYTrfn4eHhfPnllwCsWbOGvXv3cuDAAf744w/KlStHz549qV+/Pn369CExMTHb+vbr14/t27dTsWJFQkNDee+99/Dxyfi6mDVrlmsSwZ2UlBQ+/fRT7rrrLlfbValSxXW9cuXKHDx4MJOs7t2753R7LBZLNlyqAcsXiEg14HtgKdAUGC0ifYEiwO9AT6AXUBFYKiLHVbWViCSoanFHRlfgblWNEpEpwJ9AfWC9iJwBAoEazt/RqjpGRPyBz4HKgC+mY/I18ChQXVWTAVQ1zknnqffzjl4AE1R1tDeZqjpbRBoC7wDFgeNAlKoe9hC5HKjpyI4BfgaaAQtEZBrwoaM/mM7aSuAPICLdMIrIbicPwHygI/CaiNQATgEpXuoR4NT7NWAwECQiG4GpQDzQASgK+AN3iEg00M25P/NUdWhW9VbVDU4ZnsV2BKY57j6rRKS0iFTw0iaeulYDPnV0Aeinqj87ndqxQAtgD2aia5KqfuFNjqqeduQJcAOQ+eO5v8qcApwD6gJVMc9jD8yzulpVo5x0sUAjzD3+HvgJ+AdwEOioque8yH4M00EjIKAcQ0JTM5UfExPjCnfs2JGmTZtyww03ULVqVY4cOZLhel6TkJBwReVfKlaviyM/6pUfdSooWJvpwtrM69xmpr9Djhw5QmJiout8zJgx9O7dmxYtWrB06VI6d+7M22+/zbBhw7j//vv58ccfOXLkCFu3biUgIICDBw/y008/MXPmTAAGDBhA+fLlCQ8Pz1B+WFgYEydORESYNGkSDz74IAMHDmTLli0cP36cmTNncubMGfr370/x4sXx8fFh6tSpTJ8+neLFizNs2DBeeukl2rRpw4svvuj6Zr1Ro0YkJSURExNDs2bNGDt2LDVr1qRGjRrUrFmTDRs2cOHCBdatW0dUVBRRUVG8//77PPHEE/Tq1SvL+i5btoyAgABmzJjBoUOH6NOnDxMmTMDf3zwOJ06cYP369TzzzDOZ3sejRo2iRo0aXLhwgZiYGI4fP86GDRsyfPu/bt06EhISMsgqWrToFXu3FwS7URB0hIKj53XJtfb/v5wDM9OdBjQBAjCG2t+5NhAY4oRjgQC3fAlu4a7AFCc8BfgG8HXOh2GMfRFH/gmgENAF+MRNRikgDNiQja6xjoyGmFl9f4xB2orpEHmTWcgpv5wTdz/GeKbr2tUJ34cxeAAxwAducmYAtznhQGC7E34P6OmEbwX+51bnAcCXQAjwEsaoxgCN0tsPuAlYDf+fvXOP87nKH//zFSKEZlGuM+Q+LkNUvpuQNELCTlu2i0tlte1q226+v42o9htl0yZ0UUxtKEkkhTCodUu5h2wmhcglzEyYy+v3xznz6TOf+XzGjOsMr+fj8X7MOed93q/zOufzmffrXF7nfOjo09oBs4LK7QP8AET5+A3Aq4DgOgGzgGvD1TtcuwXFZ2XXx8fnZ+sV4fux3odLA6V8uC5+/4///Gd7nS7DdawSjvO9mwDsxnWMS+eRbyIwxdf5ZuAQ0MSXtQrXQQz+bsQAGUHp7wJ3HO//oEatyzX6sVm5rkj87//+r44ZMybi/VNBYd0bZnoVjMKol+2pN5uJ2UyzmSdpM7PZtm1bjj315cqV06ysLFVVzcrK0osvvlhVVWNiYjQ6Olqjo6O1TJkyWqlSJZ0+fbo+++yz+uSTTwaeHzZsmI4YMULzIrjMP/3pT/rmm28G7vXt21ffeecdfeKJJ7Rfv36B9MTERL3vvvtyyZozZ47ecsstudKzsrI0OjpaDx48qLt27dLo6OjAvcWLF2vnzp3zrG/nzp118eLFgWfat2+vy5cvD8RfeOEFvffee3O9j4cOHao333yzZmZmBtL69++vkyZNCsTr1aunO3fuzCXrdFIYbVkoRUFHVdtTX5ivc8H9/jtVXYbrpDQCPvcz371xM70FZaqqZgbFP1LVo6q6F9iDM8zrgOtFZISItFHVgwWQfw1uxj1VVVNwHYE2EWTWx3US5vk6PY6bnc/mOZ/eH7g7KP2doPD1wEs+30ygnIhc7PPc6vPcFvIMOMN6G9AdCN2AXQLXMXhUVeflUdd5qrrfh2/w11fAl7iZ+LoR6p0XuZYhyGPmP0Tn10RkHTAV910B93lMVdUsVf0R1+nIE1Xti1vJ+ppf2zASH/oX3jpgt6quU9UsXMc0Jkz+baq62odXRciTg4tKFCN5eJdcVzB79uwBYPv27bz//vvm5mYY5y9mM81mnvc2MxJVq1Zl0aJFgDtlvm7duq6QbdtITk4mOTmZhIQExo4dS/fu3alZsyaLFi0iIyOD9PR0Fi1aFNb9PngP+fTp0wMn7tesWZMFCxagqqSmprJs2TIaNGhA5cqVWbZsGWlpaagq8+fPD8jNtudHjx5lxIgRgS0BP//8M8eOHQNg/PjxXHvttZQrV47LLruMGjVqsHnzZgDmz59Po0aN8qxvzZo1mT9/PgC7d+9m8+bN1K5dO1CHyZMn5+pHjB8/njlz5jB58uQcrvrdunXjzTffRFVZtmwZ5cuXp0qVKnnKMgyj4BRp93tPqv8rOIOYnzdDsEErFUFeNkeDwplAcVXd4l38OgPPiMhcYCRQU0QuVtXDeZQdzsASQeZ0YIOqRjrQ5xEN7/IWXIcLgNYa4o4mIkuBOiJSCdcJeTpExofAc7jZt0MhLn0ZOOMZDyyKoFuoHgI8o6rh9jHmqLeqPpmHzB+AGkHx6sDOPPJn8yBupaAZrk2OBOlVYFQ1U0TeAR7BrUJEIvv7k0XO71IW4f//Qr9vF52IfqH87ne/Y9++fZQoUYIxY8ZwySWXnAqxhmEUPcxm5sZsZm7OeZvZq1evgHt49erVGTZsGK+99hoPPPAAGRkZlCpVildffTVPGQkJCSxYsIAmTZogInTq1ImbbroJgHvuuYcBAwbQsmVLHn30UVavXo2IEBMTwyuvuI/1/vvvp2/fvjRu3BhVpW/fvjRt2pT9+/eTkJBAixYtKF68OM2bN6d///4APPfcc8yaNYusrCzuu+8+rrvuOsAdinvXXXdRrFgxGjVqxOuvvx7Qc/To0dx+++0cO3aM2rVrB36iLlJ9Bw8eTJ8+fWjSpAmqyogRI6hY0Z2/mJyczPfff0/btm1ZvHhxoIwBAwYQHR1N69bu369nz54MGTKEzp07M3v2bOrUqUPp0qUDZYfKMgzj5DgXBvXZLAPGiEgdVd0q7uTe6qq6BTiMO3xnr8+7W0QaApuBHv5+vhGRqsB+Vf23uFNt+6hqmoi8DrwoIn9U1WMiUgXooKr/Dnp8MTBRRIbjjGMP4M5wMoHhQCURaa2qS0WkBFBPVTcUQN25uINpnvO6x6nqalVVEZmO23v4taruC35IVX8RkceALWFkKm5/41QRGaSqw/m1jSMxB3hKRN5W1RQRqYbbc1g8TL3zYibwZxGZgnOBPKjH2RvoKQ/8oKpZItIbtxcR3F683iKSCFTCuUROCifA7wm83H+/BHeo06Z8lH3WWbJkydlWwTCMwoXZzPCYzXSc8zYzex98KKtWrcrzuYkTJwbCxYoVCwzQQxk/fnwg/NZbb4XNU7ZsWaZOnRr23rBhwxg2bFiu9Oeee47nnnsuV3rr1q355ptvwsqKi4sLHK4XzDXXXBO2vlWrVmXu3LlhZcXExOQ66A4I7JkPRUQYM2ZMgWQZhlFwzplBvar+JCJ9gMkiUtInP44zsK8CH4vILlVtjzv5dhbwPbAet0+vIDTBufFl4YzsfUHlPQ1sFJEjuFn3ISF6fukPg1nhk8ar6lciEh8q03dyEnCdnvK4z+sFnBtafhmI67it9c8vBrKPb30HWEmEToGqTokk1M+63wZ8KCKHgNeADBFZg9sXdyAk/1zfKVzqVzBSgDtwhxXlaksRGQg8ituzt1ZEZqvqPbi9fJ2BrUAa7iCd/DAWmCYit+DcBbNXRKYBHXDfgy24PY+R3BkFSBSRcj68hl8/e8MwjCKD2cyImM10mM00DMMoQojbumQY5y8iUtavhPwG13H8rd8rWCSoX7++Zu+VKywkJSXRrl27s61GLkyvglGYV8EoAAAgAElEQVQY9ToTOonIKlVteVoLMYwiitnM009hfPeGw/Q8dRQFHSG8nmYzCwfnzEq9YZwEs0SkAnAh7ueBikznxDAMwzDOMGYzDcMwChk2qDeKPCLSBPd7usEcVdWr8vO8qrYLI3M6UCsk+TFVnRMm799xP5EUzFRV/Ud+yjcMwzCMM4XZTMMwjHMPG9QbRR5VXQfEnWKZPQqQ9x+AdUYMwzCMQo/ZTMMwjHOPc+F36g3DMAzDMAzDMAzjvMQG9YZhGIZhGIZhGIZRRLFBvWEYhmEYhmEYhmEUUWxQbxiGYRiGYRiGYRhFFBvUG4ZhGIZhGIZhGEYRxQb1hmEYhmEYhmEYhlFEsUG9YRiGYRiGYRiGYRRRbFBvGOc4o0aNIjY2lsaNG9OrVy+OHDlytlUyDMMwjLNKv379qFy5Mo0bNw6krV69mquvvpq4uDhatmzJihUrAJgxYwZNmzYNpH/22WeBZzp16kSFChXo2rVrxLKOHj3KrbfeSp06dbjqqqtITk4GIDk5mYsuuoi4uDji4uIYMGBA4Jm///3v1KhRg7Jly+aQ9eCDDwby16tXjwoVKgTuJSYmUrduXerWrUtiYmIuPbp165ajvtmMHDkSEWHv3r0AqCoDBw6kTp06NG3alC+//DKQd/v27dxwww00bNiQPn36BOrSpk2bgF5Vq1ale/fux227Rx99lNjYWBo2bMjAgQNR1UCbNmvWjNjYWAYMGEBmZmbEtjUMw6OqdtllVxG+6tWrp5H44YcfNCYmRtPS0lRV9ZZbbtEJEyZEzH+qWLhw4Wkv40QwvQpGYdTrTOgEfKGF4H/bLrvO5wvIBFYD64EPgQo+/QLgRZ++DlgJ1PL3koGKecnNtpmLFi3SVatWaWxsrGbTsWNHnT17tqqqfvTRR9q2bVtVVT18+LBmZWWpquqaNWu0fv36gWc+/fRTnTlzpnbp0kUjMWbMGP3jH/+oqqqTJ0/W3//+96qqum3bthzlZ7Nw4UJdunSp7ty5U8uUKRNR7osvvqh9+/ZVVdV9+/ZprVq1dN++fbp//36tVauW7t+/P5B32rRp2qtXr1zlbd++XW+44QatWbOm/vTTT4G6d+rUSbOysnTp0qV65ZVXBvK3bdtW586dq6qqs2fP1tTU1Fx69ezZUxMTE1U1ctt9/vnn+j//8z+akZGhGRkZevXVVwfe7wcPHlRV1aysLO3Zs6dOnjw5Yhvkh8Joy0IpCjqqhtfTbGbhuM7rlXoR+auIlD4D5ZQQkeEi8o2IrBeRFSJyo7+XLCIVT2FZE0Vkm4isFpEvRaT1KZI7VER2eLkbRaTXKZDZQESWishREXk45F4nEdksIltFZNBx5CSJSMuT1Sc/evn7xUTkKxGZdQrKqioi752MjF/SM4kZ9FGuK5uMjAx++eUXMjIySEtLo2rVqiertmEYhmGcbn5R1ThVbQzsB+736bcCVYGmqtoE6AH8nG+h6W7V99prryUqKirHPRHh0KFDABw8eDBgL8uWLYuIAJCamhoIA3To0IGLL744zzJnzJhB7969AUhISGD+/PnZExcRufrqq6lSpUqeeSZPnkyvXq47NmfOHDp27EhUVBSXXHIJHTt25JNPPgEgJSWF559/nscffzyXjAcffJBnn302R51mzJjBXXfdhYhw9dVX8/PPP7Nr1y42btxIRkYGHTt2BOCiiy6idOmc3ejDhw+zYMGCwEp9pLYTEY4cOcKxY8c4evQo6enpXHrppQCUK1cOcP2XY8eO5dDNMIzwnPODenFEqudfgQIN6kWk+Amo8RRQBWjsjdNNQN4W4OR4RFXjgEHAK6E3T7AOAKO83JuBV0SkxEnoCM5IDwRGhuhXDBgD3Ag0AnqJSKOTLOuk9QriAeDrU1GQqu5U1YRTISsc1apV4+GHH6ZmzZpUqVKF8uXLc8MNN5yu4gzDMAzjdLAUqObDVYBdqpoFoKo/qOqBU1HICy+8wCOPPEKNGjV4+OGHeeaZZwL3pk+fToMGDejSpQtvvPFGgeTu2LGDGjVqAFC8eHHKly/Pvn37ANi2bRvNmzenbdu2LFmyJN8yv/vuO7Zt28Z1112XqwyA6tWrs2PHDgAGDx7MQw89lGsAPnPmTKpVq0azZs0i6hssa8uWLVSoUIGePXvSvHlzXn755Vyu8dOnT6dDhw6BgXl2WmjbtW7dmvbt21OlShWqVKlCfHw8DRs2DDwTHx9P5cqVufjii0lIOG3dJMM4ZzgnB/UiEiMiX4vIWOBL4E6/8vqliEwVkbIiMhA307tQRBb651KCZCSIyEQfnigiz/t8I/yq9Rt+hfhbLwsRKSMiH4nIGr8if6v3BLgX+IuqHgVQ1d2q+m4Yvf/mn1svIn+NJNOnXyEii0RklYjMEZFw07mLgTo+f5KI/J+ILAIeEJFKIjJNRFb667cicoH3HKgQpNNWEbk0WKiqfgOkAZf4PPd6GWu8zNJ+NftbP6lSQUSyRORan3+JiNRR1T2quhJID9H7SmCrqn6rqseAKbiJhOMiIuNE5AsR2SAiw4LSO4vIJhH5TERezGuVPQ+9EJHqQBdgfD50SfZtvtTr1MJ/Vv8VkQE+T4yIrPfhPiLyvoh8Is6r49n81DkvDhw4wIwZM9i2bRs7d+4kNTWVf//73ycr1jAMwzDOCH6ivwMw0ye9C9wkznPwnyLS/FSVNW7cOEaNGsX333/PqFGjuPvuuwP3evTowaZNm/jggw8YPHhwgeSGW5UXEapUqcL27dv56quveP755/nDH/4Q8BQ4HlOmTCEhIYFixYrlWcbq1avZunUrPXr0yHEvLS2Nf/zjHzz55JP51jcjI4MlS5YwcuRIVq5cyc6dO5k4cWKOfMHeA9mEa7utW7fy9ddf88MPP7Bjxw4WLFjA4sWLA8/MmTOHXbt2cfToURYsWJCvNjGM85kTXbEtCtQH+gJDgPeB61U1VUQeA/6mqk+KyN+A9qq6Nx/y6nkZmSIyFGgAtMetuG8WkXFAJ2CnqnYBEJHyuEH1dlXN8y0tIld4fa8CBFjuB+C1Q2X6FfLRwM2q+pMf6P8D6Bci9ibcfrNsKqhqWy9nEm7l/TMRqQnMUdWGIjID58o2QUSuApJVdXew65OItAC+UdU9Pul9VX3N33sauFtVR4vIFtxKey1gFdBGRJYD1VV1ax7NUQ34Pij+g2+X/PB3Vd3vOwHzRaQpsAXnsXCtqm4Tkcn5lBWOF4BHyb+nxfeq2lpERgETgd8CpYANwMth8scBzYGjuO/VaFX9PjSTiPQH+gNUrFiJIU0ycglKSkoiKSmJUqVKsWHDBgAaNmzI1KlTqV69ej7VPzFSUlJISko6rWWcCKZXwSiMehVGnQzDOC1cJCKrgRhcH2IeuJV5EakPXOev+SJyi6rOjyQo1GZmv0N+/PFHUlNTA/E33niDHj16kJSURKVKlVi6dGnY982GDRuYMWMG5cuXB9wBe/v27Yv4bipdujQzZswgNjaWzMxM9u7dy9q1a3O5lf/mN79h8uTJVKtWLSArMzMzrNzx48fzwAMPBO4dOnSI1atXB+IrVqwgLi6OxMREli5dymWXXUZmZiY///wzcXFxDBw4kC1btlC/fn0AfvrpJ2JjYxk3bhwXXHABc+bMISPD9S2++eYbkpOT2bNnD7Vq1WL79u1s376dli1b8uGHH3L55ZcDbsvCf/7zHx588MGIbZHddh9//DGXXnopX3zxBQANGjTg7bffJisrK0f+unXrMnbsWEqUOHHn0KJgN4qCjlB09DwvOdub+k/HhTMA23y4K7AXd9jKamAj8Lq/l0zQgSpASlA4AZjowxOB3kH3huIGj9nxr4HquIH/NmAE0Mbfawp8lYeuyUBFnEv3k0HpT+FcwMPJbAwcCqrTOmBukK7bfPo8nMs/QBLQNkj+nqDnVwM7cAPV/wE+8XlGAfcG1XkHsBm3gt0hSFZbYInXYxvwsk//O3Af8CzQE/gYuAZ4N6QNhgIPB8VvAcYHxe8ERufRhklASx8egPPOWAv8BNyGGygvCsrfDZiVj+9RqF5dgbE+3O54MvxnW82H+wGvBd3bDlTAfVfX+7Q+IXk+Bq45np41al2u0Y/NynWpqi5btkwbNWqkqampmpWVpXfddZe++OKLeroprAe+mF4FozDqZQfl2WXX+XHh+2RAed/HGBgh38PZfQTycVBejVqXazahB9U1aNAg8I759NNPtUWLFqqq+s033wQOe1u1apVWrVo1EFd176W8Dsp76aWXchyUd8stt6iq6p49ezQjI0NVVf/73/9q1apVdd++fTnec+EOytu0aZNGR0fn0GHfvn0aExOj+/fv1/3792tMTIzu27cvx3ORDuZTVY2Ojg4clDdr1qwcB+W1atVKVVUzMjK0adOmumfPHlVV7dSpk7700ksBGePGjdO77rorh9xIbTdlyhTt0KGDpqen67Fjx/S6667TmTNn6uHDh3Xnzp2qqpqenq6///3vdfTo0RHbNj8URlsWSlHQUdUOyivM17m8Up/q/wowT1Xzc7BbsL9RqQjysjkaFM4EiqvqFr/i3hl4RkTm4vZl1xSRi1X1cB5lhz0FJILM6cAGVY10CN4jqhru8LXgOlwAtFbVX3IoIbIUqCMilYDuwNNBt0ep6kgR6Qm8KSKXq+oR3ERCd1VdIyJ9cANecEZ4AG6bwxDgEX9vMXnzA1AjKF4d2HmcZxCRWjjj3kpVD4jbPlGKCG17AvwW6CYinb3cciLyb1W9I49nsr8nWeT8zmQR3lMm1/fqeEpdVKIYm4d3CXvvqquuIiEhgRYtWlC8eHGaN29O//79jyfSMAzDMAoFqnrQb3Oc4b0imwA/qupOcWcmNcVN5OeLi0o4d/VevXqRlJTE3r17qV69OsOGDeO1117jgQceICMjg1KlSvHqq68CMG3aNN58801KlCjBRRddxDvvvBNYZW/Tpg2bNm0iJSWF6tWr8/rrrxMfH8+QIUNo2bIl3bp14+677+bOO++kTp06REVFMWXKFAAWL17MkCFDKF68OMWKFePll18OHN736KOPMmnSJNLS0qhevTr33HMPQ4cOBZyL+2233ZZjpT8qKorBgwfTqlUrAIYMGZLrIMD80rlzZ2bPnk2dOnUoXbo0EyZMAKBYsWKMHDmSDh06oKpUq1aNe++9N/DclClTGDQo59nGkdouISGBBQsW0KRJE0SETp06cdNNN7F79266devG0aNHyczM5LrrrsvxU3+GYUTgbM8qnI6LnKuflXCronV8vDRQz4fX4X8Gxce3Ag1xA95p5FypTwjKN5ScK7jrfZlVgVI+rTvwgQ8/C0wALvTxKsAdPpyMW6lvgTNKpYEyXmbzcDKBC72urX16CSA2nK5BOibhV7N9fBJu8J8djwsKPwe8BczOo84zgD/68F6gstdjXlC7lfT1W+Dj43Bu9c1CdAuVXRz4Fue2fyGwJrt+ET7vJKAl0MznvQC4FNiNW/2+yJcb4/O/zQms1Ifca3c8GQStGHg9Xgq9R+6V+uA8s4B2x9Mzr5+0O1sU1hln06tgFEa9bKXeLrvOj4sg70kf/xDnudcJ546/3l9vBPWTAnY30lUYbWYohfHdGw7T89RRFHRUtZX6wnydyyv1AKjbc94HmCwiJX3y47h91q8CH4vILlVtjzstfhZuALgeKFvA4poAz4lIFs5F/b6g8p4GNorIEdyK+ZAQPb/0K8srfNJ4Vf1KROJDZarqMRFJAF70+/aL4/Z6byiArgOBMSKy1j+/GLeqDvAO7ndf++Tx/JPAJBF5DRgMLAe+w02UXOzrdFREvgeW+WeWAL18HkTkMuALoByQJe5wwEaqekhE/gzMAYoBb6jqceumzlPgK1w7fAt87tN/EZE/AZ+IyF5+beOw5KXX8XQwDMMwDOPkUdWyIfGbgqKfRHgm5nTqZBiGUVg5Jwf1qpqM23eeHV8AtAqTbzTuwLns+HtALrd1Ve0TEh8aEs8uKxk3EA19/hjucLVHw9yLCQo/Dzwfcn9OBJmrgWuPp2tQeruQ+F7cb72Gy/sFIS7rYeq8CncYIbgV+HERZLUJCk/CeQhkx3/EudaHe242MDvcvTB52wWF+0TItlBVG4jzVRuDG7RHkhdRr6A8STgPgbzyxASFJ+K8KELv7cV/V8Pk6ZqXfMMwDMMwDMMwjHPyJ+0MIwz3+lN0N+AO3XnlLOtjGIZhGIZhGIZx0pyTK/XGuYmITMftsw/mMe/NkCeqOgp3mn+wvL64Xx0I5nNVvf9M6WUYhmEYhmEYhnEy2KDeKDKoao9TLG8C7gDDk5VzSvUyDMMwDMMwDMPIL+Z+bxiGYRiGYRiGYRhFFBvUG4ZhGIZhGIZhGEYRxQb1hmEYhmEYhmEYhlFEsUG9YRiGYRiGYRiGYRRRbFBvGIZhGIZhGIZhGEUUG9QbhmEYhmEYhmEYRhHFBvWGYRiGYRiGYRiGUUSxQb1hGIZhGIZhGIZhFFFsUG8Y5yibN28mLi4ucJUrV44XXnjhbKtlGIZhGGedfv36UblyZRo3bpwjffTo0dSvX5/Y2FgeffRRAN5+++0c9vSCCy5g9erVALRr14769esH7u3ZsydXWfv27aN9+/aULVuWP//5zznuTZ48mSZNmtC0aVM6derE3r17Adi/fz8dO3akbt26dOzYkQMHDuR4buXKlRQrVoz33nsvkJaYmEjdunWpW7cuiYmJufTo1q1bvuoLsHbtWlq3bk1sbCxNmjThyJEjAHTq1IlmzZoRGxvL888/T2ZmZp76Hjx4kJtuuinwzIQJEwJlbN++nRtuuIGGDRvSqFEjkpOTc+lsGEb+sEG9YZyj1K9fn9WrV7N69WpWrVpF6dKl6dGjx9lWyzAMwzDOOn369OGTTz7JkbZw4UJmzJjB2rVr2bBhAw8//DAAt99+e8CevvXWW8TExBAXFxd47u233w7cr1y5cq6ySpUqxVNPPcXIkSNzpGdkZPDAAw+wcOFC1q5dS9OmTXnppZcAGD58OB06dOCbb76hQ4cODB8+PPBcZmYmjz32GPHx8YG0/fv3M2zYMJYvX86KFSsYNmxYjomA999/n7Jly+arvhkZGdxxxx28/PLLbNiwgaSkJEqUKAHAu+++y5o1a1i/fj0///wzU6dOzVPfMWPG0KhRI9asWUNSUhIPPfQQx44dA+Cuu+7ikUce4euvv2bFihVh284wjPxhg/ogROSvIlL6DJRTQkSGi8g3IrJeRFaIyI3+XrKIVDyFZU0UkW0islpEvhSR1qdI7lAR2eHlbhSRXqdAZgMRWSoiR0Xk4ZB7nURks4hsFZFBx5GTJCItT1afIHk3i8haX9cvROSaUyX7VPBLeiYxgz7KcYUyf/58Lr/8cqKjo8+ChoZhnIuYzSyQXLOZhYRf0t3K8rXXXktUVFSOe+PGjWPQoEGULFkSIOwgc/LkyfTqVbCPr0yZMlxzzTWUKlUqR7qqoqqkpqaiqhw6dIiqVasCMGPGDHr37g1A7969+eCDDwLPjR49mt/97nc59JszZw4dO3YkKiqKSy65hI4dOwYmLVJSUnj++ed5/PHH81XfuXPn0rRpU5o1awbAb37zG4oVKwZAuXLlADfwz8jIQETy1FdEOHz4MKpKSkoKUVFRFC9enI0bN5KRkUHHjh0BKFu2LKVLn/bXiWGcs5x3g3pxRKr3X4ECvVFEpPgJqPEUUAVorKqNgZuAi09ATn55RFXjgEHAK6E3T7AOAKO83JuBV0SkxEnoCLAfGAjkmMoWkWLAGOBGoBHQS0QanWRZBWE+0MzXtR8w/gyWfUqYMmVKgTshhmEYZjPNZp4ARdZmbtmyhSVLlnDVVVfRtm1bVq5cmSvPO++8k8ue9u3bl7i4OJ566ilUNd/llShRgnHjxtGkSROqVq3Kxo0bufvuuwHYvXs3VapUAaBKlSoBt/4dO3Ywffp0BgwYkEPWjh07qFGjRiBevXp1duzYAcDgwYN56KGHcg2aI9V3y5YtiAjx8fG0aNGCZ599Nsdz8fHxVK5cmYsuuoiEhIQ89f3zn//M119/TdWqVWnSpAn/+te/uOCCC9iyZQsVKlSgZ8+eNG/enEceeSTgym8YRsE5UcNUpBCRGOBjYCHQGnhBRAYAJYH/An1xhqcqsFBE9qpqexFJUdWyXkYC0FVV+4jIRJwxbQ58KSKHgZpAbf/3BVV9UUTKAO8C1YFiuI7Jh8C9QC1VPQqgqrt9vlC9/+b1Ahivqi+Ek6mq74jIFcDzQFlgL9BHVXeFiFwM1PGyk4D/AL8FZorIm8DLXn9wnbWlwLdAnKr+7J/b6p8JoKrfiEgacAmwR0TuBfoDFwJbgTuBo8A3wOVAed9+7VR1sYgsAfqq6lb/fJcQva8Etqrqt16HKbhO0cbQNgvThuOAVsBFwHuq+oRP7+zbay/wJVBbVbuGk6GqKUHRMkBEiy0i7YBhwG4gDngfWAc84HXorqr/FZGbgMd9G+0DblfV3SLyIrBXVZ8UkXjg776dskLK6Y9rYypWrMSQJhk59EhKSgqE09PTmTZtGl27ds2RfjpJSUk5Y2UVBNOrYBRGvQqjTucaZjMDmM08R21m9jvkxx9/JDU1NRA/ePAg69atY/jw4WzatIlu3boxadKkwGr0xo0bUVX27t0beOb++++nUqVKpKWl8cQTT5CWlpbDLT6YTZs2sWPHjsCzGRkZ/N///R/jxo2jatWqvPjii/Tv358ePXqQkZGR412XHR86dCi33norS5Ys4ccff2TDhg1UrFiRrVu3kp6eHnhm27ZtlCpVivHjx7N8+XJuvvlmli1blq/6bt68mU8//ZSXX36ZkiVL8tBDD1GsWDGuuOIKAP73f/+XY8eOMXToUEaNGkXLli0j6rto0SIqVqzIpEmT2LlzJ/fccw/jx48PuOO/+uqrXHrppQwbNoxBgwbRpUvo1/nkKQp2oyjoCEVHz/OSbNefc/kCYoAs4GqgIs5Ql/H3HgOG+HAyUDHouZSgcAIw0YcnArOAYj4+FGfsS3r5+4ASwO+A14JklAeaAl/loWuyl3EFzrCVwXU6NuA6ROFklvDlV/JptwJvBOma4MO3AMt9OAkYGyRnEnCND9cEvvbhf+E6DwBXAZ8G1flhH24BLAmS9Zug8NPAX3z4EyAW6AqsxBnfksC2kDYIyA5q+/FB8TuBl/JowySgpQ9H+b/FfHpToBTwPa6TCDAZmHWc71APYBOuY9U6j3ztgJ9xq0olgR3AMH/vAVznFVxnTnz4HuCfPlzaf9btgc3A5cf7fteodblGPzYrxxXMBx98oB07dtQzycKFC89oefnF9CoYhVGvM6ET8IUWAtt1ti7MZprNPMdtZjbbtm3T2NjYQDw+Pj7HO6Z27dq6Z8+eQPyvf/2r/uMf/9BITJgwQe+///5831+xYoVed911gfiiRYv0xhtv1IULF2q9evV0586dqqq6c+dOrVevnqqqxsTEaHR0tEZHR2uZMmW0UqVKOn36dJ00aZL2798/IKt///46adIkHTt2rFapUkWjo6O1WrVqWqJECW3btm2e9Z08ebL27t07kP7kk0/qs88+m6s+jz32WKA+kfTt3LmzLl68OPBM+/btdfny5bp06dKAHqqqb775pv7pT3+K2HYnQ2G0ZaEUBR1Vw+vJeW4zC8t1Prnff6eqy3CdlEbA5yKyGugNnMhG46mqGuwn9JGqHlXVvcAe4FJcB+N6ERkhIm1U9WAB5F8DTFfVVHWz3u8DbSLIrA80Bub5Oj2OW5XI5jmf3h+4Oyj9naDw9cBLPt9MoJyIXOzz3Orz3BbyzIMishlYjutUZNNYRJaIyDrgdlynBGAJcK2/nvF1bIXrrOSFhEnLr3/b70XkS+Arr0cjoAHwrapu83kmH0+Iqk5X1QZAd9zqUV6sVNVd6laV/gvM9enrcJ1lcJ/PHN9Gj3jdUNU03KrUPFwn7L/H0+2iEsVIHt4lxxXMiez/MwzjvMdsptnMc9ZmRqJ79+4sWLAAcC7ox44do2JFd2RDVlYWU6dO5bbbbgvkz8jICJxWn56ezqxZs3KdLp8X1apVY+PGjfz0008AzJs3j4YNGwLupPrsE+wTExO5+eabAbcCn5ycTHJyMgkJCYwdO5bu3bsTHx/P3LlzOXDgAAcOHGDu3LnEx8dz3333sXPnTpKTk/nss8+oV69eYKU1Un3j4+NZu3YtaWlpZGRksGjRIho1akRKSgq7du0K1H358uU0aNAgT31r1qzJ/PnzAeeiv3nzZmrXrk2rVq04cOBAoO4LFiygUaMzuUvEMM4tzgv3e0+q/yvAPFXNzygn2AiWCrmXGhI/GhTOBIqr6hbv4tcZeEZE5uL2vtUUkYtV9XAeZYczykSQOR3YoKqRDvR5RFXfC5MeXIcLcLPpv+RQQmQpUEdEKuGM89NBt0ep6kgR6Qm8KSKXq+oR3EpHd1VdIyJ9cDPx4DooA3Aum0NwhrkdbhUoL34AagTFqwM7j/MMIlILeBhopaoHvAtoKSK0bX5Q5/p4uYhU9J3RcAR/F7KC4ln8+j83GnheVWd698OhQc80wa1cVT1RPbNJS0tj3rx5vPJKrm2hhmEYeWE2MzdmMwtIYbaZvXr1Iikpib1791K9enWGDRtGv3796NevH40bN+bCCy8kMTEx4Hq/ePFiqlevTu3atX9V/OhR4uPjSU9PJzMzk+uvv557770XgJkzZ/LFF1/w5JNPAhATE8OhQ4c4duwYH3zwAXPnzqVRo0Y88cQTXHvttZQoUYLo6GgmTpzIunXrGDRoEL///e95/fXXqVmzZuCU+UhERUUxePBgWrVqBcCQIUNyHQQYSqT6XnLJJfztb3+jVatWiAidO3emS5cu7N69m27dunH06FEyMzOpX79+YG9/JH0HDx5Mnz59aNKkCarKiBEjAhMlI0eOpEOHDqgqV1xxRaDtDMM4AeOKmjQAACAASURBVM62q8CZuHAzvet9uBKwHajj46WBej68Du9e5uNbgYY44z2NnK6ECUH5hpLT9W29L7MqUMqndQc+8OFngQnAhT5eBbjDh5NxroQtgLVevzJeZvNwMvl1H15rn14CiA2na5COSXh3Ox+fhOvIZMfjgsLPAW8Bs/Oo8wzgjz68F6js9ZgX1G4lff0W+Pg4nEtfsxDdQmUXx+1TrOXruia7fhE+7ySgJdDM570Atwq0G+iD26f3PRDj879NHq6EuD2V2W5/LXDugRIhb7tgWeR0awzcw62CXOHDE4AkH44GtvjPeTVw1fG+39kuboWJwupGZnoVjMKol7nfm83EbKbZzHPMZoZSGN+94TA9Tx1FQUdVc78vzNf5tFIPgKr+5GfCJ4tISZ/8OM4ovAp8LCK7VLU97uTbWThjth63T68gNMG58WUB6cB9QeU9DWwUkSO42f8hIXp+6WfJV/ik8ar6lT8IJodMVT3mDyV6UUTK4wz6C7h9ZvllIDBGRNb65xfjVgjAuQ+uxBn3SDwJTBKR14DBOPfC73Cdvot9nY6KyPfAMv/MEqCXz4OIXAZ8AZQDskTkr0AjVT0kIn8G5uD2+b2hqsetm7pVj69w7fAt8LlP/0VE/gR8IiJ7+bWNI/E74C4RSQd+AW71L7GTYSgwVUR24NqjlrjlgNdxnbOdInI3MFFEWqlbzTEMwzijmM2MiNnMyJjNNAzDOMPIyb9nDaPoISJlVTXFdwrGAN+o6qizrdeJUL9+fd28efPZViMHSUlJtGvX7myrkQvTq2AURr3OhE4iskpVT9nvdhtGUcds5pmlML57w2F6njqKgo4QXk+zmYWD8+mgPMMI5l5/wNEG3GnItuncMAzDMMJjNtMwDKMQc9653xvnDiIyHbdnMJjHVHXO8Z71Kww5VhlEpC/uJ3SC+VxV7w9TdhPcnslgjqrqVcdV3DAMwzDOMGYzDcMwzl1sUG8UWVS1xymWNwF3AE9+8q4D4k5l+YZhGIZxujCbaRiGce5i7veGYRiGYRiGYRiGUUSxQb1hGIZhGIZhGIZhFFFsUG8YhmEYhmEYhmEYRRQb1BuGYRiGYRiGYRhGEcUG9YZhGIZhGIZhGIZRRLFBvWEYhmEYhmEYhmEUUWxQbxiGYRiGYRiGYRhFFBvUG8Y5yubNm4mLiwtc5cqV44UXXjjbahmGYRjGWadfv35UrlyZxo0b50gfPXo09evXJzY2lkcffRSAFStWBGxps2bNmD59eiB/TEwMTZo0IS4ujpYtW4YtS1UZOHAgderUoWnTpnz55ZeBe506daJChQp07do1xzO333479evXp3HjxvTr14/09PTAvaSkJOLi4oiNjaVt27aB9FGjRhEbG0vjxo3p1asXR44cAaBNmzYB/atWrUr37t1PWNbdd99Ns2bNaNq0KU888QQpKSkALF68mBYtWlC8eHHee++9HHVJTEykbt261K1bl8TExEB6u3btqF+/fkC3PXv2ADBx4kQqVaoUSB8/fnzYdjUMIwhVtescvYC/AqXPQDkXAi8A/wW+AWYA1U9DOe2Ag8BXwNfAE6dIbgygwFNBaRWBdOAlH58IJJzONgYaAEuBo8DD+X2uXr16ejwyMjL00ksv1eTk5OPmPRUsXLjwjJRTUEyvglEY9ToTOgFf6Fl+f9tll12n58q2mYsWLdJVq1ZpbGysZrNgwQLt0KGDHjlyRFVVd+/eraqqqampmp6erqqqO3fu1EqVKgXi0dHR+tNPP2lefPTRR9qpUyfNysrSpUuX6pVXXhm49+mnn+rMmTO1S5cugbSFCxfqRx99pFlZWZqVlaW33Xabjh07VlVVDxw4oA0bNtTvvvsuh44//PCDxsTEaFpamqqq3nLLLTphwoRcuvTs2VMTExNPWNbBgwcDshISEvSZZ55RVdVt27bpmjVr9M4779SpU6cG8uzbt09r1aql+/bt0/3792utWrV0//79qqratm1bXblyZS4dJ0yYoPfff3+ebVoQCqMtC6Uo6KgaXk+zmYXjspX6Io44In2OfwVKF1Be8RNQ4/+Ai4F6qloX+AB4X0TkBGQdjyWq2hxoCdwhIlcE3zxB/QG+BYKnyW8BNuTjuYhtLCLFCqjDfmAgMLIgD/2SnknMoI9yXKHMnz+fyy+/nOjo6AKqZBhGUUFE/ioiBXrnn2A5JURkuIh8IyLrRWSFiNzo7yWLSMVTWNZEEdkmIqtF5EsRaX2K5A4VERWROkFpD/q0lj6eEua5GBH5w6nQwcv7s4hs9eVWDEoXEXnR31srIi3ykBEjIutPlU5e5hLf5qtFZKeIfHAq5edRbqAuItJSRF481WX8kp4JwLXXXktUVFSOe+PGjWPQoEGULFkSgMqVKwNQunRpihd33YsjR45Q0O7NjBkzuOuuuxARrr76an7++Wd27doFQIcOHbj44otzPdO5c2dEBBHhyiuv5IcffgBg0qRJ9OzZk5o1a+bQESAjI4NffvmFjIwM0tLSqFq1ag6Zhw8fZsGCBYGV+hORVa5cOcAtCh47dizQFjExMTRt2pQLLsjZJZ0zZw4dO3YkKiqKSy65hI4dO/LJJ58UqP0Mwzg+Nqgvgnij97WIjAW+BO4UkaW+wzNVRMqKyECgKrBQRBb651KCZCSIyEQfnigiz/t8I3xn5w0RSRKRb70sRKSMiHwkImt8R+5W34HsCzyoqpkAqjoBt9p8ndd1k4gk+o7Je9mdThG5QkQWicgqEZkjIlV8epKIjPAdxS0i0ia0DVQ1FVgFXC4ifXy9PwTmehmPiMhKX+YwnzZCRP4U1AZDReQhH/0F+Dq7MwfcCrwbof2f8m0Wto1F5EkRWQ60zqOOA0Vko9dviq/THlVdifMQOKVMmTKFXr16nWqxhmGcYfxg72xP5D4FVAEaq2pj4CbcxO7p4hFVjQMGAa+E3jzBOgCsA24LiicAG4/zTAwQdlB/gnp8DlwPfBeSfiNQ11/9gXEnIPuEUdU2qhrn230p8P6ZLN/r8IWqDjyTZW7ZsoUlS5Zw1VVX0bZtW1auXBm4t3z5cmJjY2nSpAkvv/xyYJAvItxwww1cccUVvPrqq2Hl7tixgxo1agTi1atXZ8eOHfnSKT09nbfeeotOnToFdDxw4ADt2rXjiiuu4M033wSgWrVqPPzww9SsWZMqVapQvnx5brjhhhyypk+fTocOHQID8xOV1bdvXy677DK2b9/OX/7ylzz1P17d+/btS1xcHE899RRu0dcxbdo0mjZtSkJCAt9//32+2sowzmdO1BAaZ5/6uMH0EJyxvV5VU0XkMeBvqvqkiPwNaK+qe/Mhr56XkSkiQ3Gu4O1xHbXNIjIO6ATsVNUuACJSHqgDbFfVQyHyvgBicS759YG7VfVzEXkD+JOI/AsYDdysqj+JyK3AP4B+/vniqnqliHQGnsB1egKIyG+Aq3Gdy1ZAa6Cpqu4XkRtwHaErAQFmisi1wBTcNoGxXszvfZ2yO8hTgNtE5EcgE9iJG7QHl/ssUB7oq6oapo3LAOtVdYiIlAAWRajjIKCWqh4VkQqRP5bwiEh/XEePihUrMaRJRo77SUlJgXB6ejrTpk2ja9euOdJPJykpKWesrIJgehWMwqhXYdTpdCMiMcDHwELcu+4FERkAlMS9Y/vi3ivZk4x7VbW9iKSoalkvIwHoqqp9/ITufqA58KWIHAZqArX93xdU9UURKYOb3KwOFMO9bz8E7sW/vwBUdTdhJkH9+zH7nT5eVV8IJ1NV3xHndfU8UBbYC/RR1V0hIhfjbA4ikgT8B/gt7h3/JvCy1x/cBMdSnBdWnKr+7J/b6p8B51V2M/C0iNTGbe/KNakqbgX9Q+BpYDDQUERWA4nAAaALUAr3/r9ORB7B2ZeSwHRVfSJSvVX1K19GaLE3A2+qG+UsE5EKIlIlTJuE6hoDvOV1Afizqv7HTwS9BLQFtuHs3huq+l44OUHyLgauw33HIuUZCtTCTfTUA/6Gs883AjuAm1Q1PdJn7NPfANKAz4LktsNtResqIlfi7PdFuEn4vqq6WUT6AN1wk1mX49r70TA65rCZ2e+QH3/8kdTU1ED84MGDrFu3juHDh7Np0ya6devGpEmTAp/PmDFj+O677/h//+//UaZMGS688EKee+45KlasyIEDB3j44Yf55ZdfaNasWY7y9+7dy1dffUVGhrPVBw4cYNWqVYH96KtXr2bfvn0BPYLfcyNHjqR27dpkZmaSlJTEd999x+bNm/nnP//JsWPHuP/++xERKlSoQGJiIv/+978pW7YsQ4cO5e9//zsdO3YM6DFmzBg6d+4ckH2isnr37s0dd9zBP//5T4YNG8aNN94YKOPHH39kw4YNVKzoHE+2bt1Kenp6oMxt27ZRqlQpkpKSuP/++6lUqRJpaWk88cQTpKWlER8fzyWXXEJiYiIXXnghM2fO5Oabb+b5558P/VjzTVGwG0VBRyg6ep6P2KC+6PKdqi4Tka5AI+Bzb3QuxHVkCsrU7JV2z0e+w3ZURPYAl+JWNUaKyAhglqouEVeohpEXnP69qn7uw//GuZh/AjQG5nm9iwHBnZXsVYFVuJWRbNqIyFdAFjBcVTeISCtgnqru93lu8NdXPl4WqKuqr4tIZRGpClQCDqjqdt8Jwuv0FLAbeCdMnQYDy1W1f5h72WQC03y4fh51XAu8Lc6lscBujar6KvAqQM3adfSf63L+Kyff3i4QnjFjBldddRU9e/YsaDEnTFJSEu3atTtuvjON6VUwCqNehVGnM0Rhn8jNgR+o9QWuwtmD5SKyCDdxkEOmnwDNa5I3m5twdiibCqra1suZBIxS1c9EpCYwR1UbisgMoAcwQUSuApJVdbd/Jx8CvheRxrhB9DuEDF5F5FJgJvC4qs4TkVT8QNPf70P+JpUrhWnLvKgGBC9P/uDT8hzUA3uAjqp6RETqApNx29V64mxpE6Ay7lyaN44jC1zbzT/e540bULfH9UeWAr9T1UdFZDrQRUQ+IvJnPAH4i6ouEpHnIsjfBFyrqhkicj1u29/v/L043ATVUdx3d7Sq5ljaDbWZ2e+Q5ORkypQpE3in1K9fn4EDB9KuXTvat2/PyJEjady4MZUqVcqhzMSJE4mKisp1MN6aNWtIT0/P9Y5q1qwZFStWDKSnpqbSrVs3qlSpEsjz6aefBu5nv+eGDRtG8eLFeffddwNu7cuWLaNZs2aBgfTMmTMpVaoUR44coXnz5gHX+p07d7Js2bKAzH379rF161Yee+wxSpUqdVKyslm3bh2ffvopI0aMyNE2sbGxgby7du3K8d6ePHkybdq0ySVrz549fPHFF7nS27RpQ1RU1Em994uC3SgKOkLR0fN8xNzviy6p/q/gBrRx/mqkqndHeCZ48F0qgrxsjgaFM3Er51uAK3CdqmdEZAiwFYj2s/nBtOBXN8bQQb96vTcE6d1EVYP9xLLLzyTn5NMSVW2uqleo6ssR9BfgmSDZdVT1dX/vPZyL5a24lflflVI9hptEeIhfB+bBrASuEJGoMPeyORI0OZJXHbsAY3Dtueok3Ee5qEQxkod3yXEFM3nyZHO9N4yiz3equgy3Apo9kbsa6A2cyGEZYSdy/YRA8ETu9eK2LrVR1YMFkH8NbtU0VVVTcBMRbSLIDJ4AXQ08jlvRzuY5n94fCLZvwZOv1wMv+XwzgXLeLr2De9+Dc7UPnbCd4tO7A9ND7pUA5gOPquq8POoaaVL5S9xkSd0I9c6LcJu2w02gh1ICeE1E1gFTcd8VcJ/HVFXNUtUfcV4f+aEXbmLgeHysqum4ehbDTZLj4zFE+Iz95EYFVV3k878VQX55YKq4/fajcJ6A2cxX1YOqegTX78jz/+GiEpGPu+nevTsLFiwAnGv6sWPHqFixItu2bQussmevbsfExJCamsrhw4cBN1CfO3durtP0Abp168abb76JqrJs2TLKly+fY0AfjvHjxzNnzhwmT56cY5/6zTffzJIlSwJ73ZcvX07Dhg2pWbMmy5YtIy0tDVVl/vz5NGzYMPDc1KlT6dq1a2BAfyKyVJWtW7cCbk/90qVLadCgQZ71iI+PZ+7cuRw4cIADBw4wd+5c4uPjycjIYO9eN/+Ynp7OrFmzAm2Xfd4AuImG4HoYhhEeW6kv+iwDxohIHVXdKm6/enU/AD+MW3XJXrXZLSINgc242ffDBSnIr3DvV9V/i9uf38evFCUCz4vIAL/qcxfOFW4BzrjWFJHWqroU10H4zOtQKTvdr9TUU9X8HE53POYAT4nI26qaIiLVgHRV3YPrwL2GO92+bZhn/wksUtV9YdwhP/GyPxKRG1T1MLnbOJiwdcStkNRQ1YUi8hluf2ZZ4OeTrHcu0tLSmDdvHq+8kmsbqmEYRYvQidz8zNSd9ESuX3HvjJvInYs7yLOmiFzs34GRCHuSWASZ03EToJEOwXskgpt4cB0uAFqr6i85lBBZCtQRkUq4gfvTITI+BJ7Dnd58KOS9n4Gb6I3HbaWKRLhJ5XB7/3PUW1WfzEPmD0CNoHh13Jaw4/EgztusGa5NjgTpVSDEbXO7EtdfOB7ZWzGyRCTdbxsA51VXnF8nuXN8xuK2n+VnsuIpYKGq9vDedUmhZXtCFwIi0qtXL5KSkti7dy/Vq1dn2LBh9OvXj379+tG4cWMuvPBCEhMTERE+++wzhg8fTokSJbjgggsYO3YsFStW5Ntvv6VHD9c8GRkZ/OEPfwjsfX/5ZbfuMGDAADp37szs2bOpU6cOpUuXZsKECQE92rRpw6ZNm0hJSaF69eq8/vrrlCxZkgEDBhAdHU3r1q7JevbsyZAhQ2jYsCGdOnUKHEp3zz33BAbDCQkJgZ+Va968Of37/+pYOGXKFAYNGpSjDQoqS1Xp3bs3hw4dQlW57LLLeO211wBYuXIlPXr04MCBA3z44Yc88cQTbNiwgaioKAYPHkyrVq0AGDJkCFFRUaSmphIfH096ejqZmZlcf/313HvvvQC8+OKLzJw5k+LFixMVFcXEiRPz85EaxvmNFoIj+O0q2IWb9V4fFL8Ot4q81l/dfPpfcC5rC308Abf/Mgm3t26iT59I0M+1AUMJ+kk1YL0vM97LX+3La+nvl8S51WX/pN2HuEFrtq4bcXsd1+JWwEv7e3G4PZJrcCfN3+vTk4JkV8S5S4L7SbtZYdqjD/6n54LSHsCtEKzDuQJeHnRvXXabhGvPcHKD2wjnLrgQt7cvtI1TQmTkqiNuJeUzr8d6YJDPexmuI3cIN8D/ASh3vO9Dfn7S7kxTWH+axfQqGIVRr/PxJ+2C31E4N+7tQB0fL42bEM1+t9UKem4r0BA3uJt2Au/8qkApn9Yd+MCHn8W5TF/o41WAO3w42b+3W/h3fmn8WSM4F+lcMnHbxrbiBuX4d2RsOF2DdEzC2wkfn4Qb/AfevUHh53ArwLPD1Rm3Ut8iVC6Qglt1fj/oPX0FbuI3rP3BrdIvB8r6eDWcu3vYtgx6LhmoGBTvgjtHQXDeGSvy+f0YBTzkw30B/5XmFmCW/y5cijtTIVe7hsgdACTm4/sZ+v1JCb13nM94LXCND48Iqks7vM3HTfz8Lkhmdr8gtP1nAe3y0rcw2sxQCuO7Nxym56mjKOioaj9pV5gvW6kvgqhqMs6NLTu+AHdYXGi+0bjBdnb8PZz7eWi+PiHxoSHx7LKScSvVoc8fxQ1uIx2BmqWqA8I8txq4Nkx6u6DwXvyeelVNIufsfHaeibiOX3Dav4B/hVNGVZuExJMJas9wcoPbSFXf4Ne9iKFtXDZERtg64lwhQ8v7kZwup4ZhGDlQtx+5DzBZREr65MeBLbg9wx+LyC5VbY87kHMWbm/2epxHUEFognN9z8IdIHdfUHlPAxtF5AhupXpIiJ5fijuQb4VPGq+qX4lIfKhMVT0m7iC/F707dnHcoWgF8dwaiPNaW+ufX4wblIJzuV+JGwDmQlWnhEv39zJF5DbgQxE5hPP0yhCRNTj7cCAk/1zvEbfUr/qnAHfgziLI1ZbifkXlUdyk7loRma2q9wCzcav6W3EHyEU8qC6EscA0EbkFN/mc7UUwDeiA+x5swU08HG8LwG3A8HyWmyfH+Yz7Am+ISBph+hieZ4FEf27EglOhk2EYxrmEqObH68kwTgzvJjcraGLAOMXUr19fN2/efLbVyEFhPUjF9CoYhVGvM6GTiKxS1ZbHz2kYRQcRKatuS9pvcJMtv/WTyecVhdFmhlIY373hMD1PHUVBRwivp9nMwoGt1BunlUir4IZhGIZhnFFm+T3sF+J+Uu+8G9AbhmGcq9ig3jAMwzAMo5AjIk3IfTr8UVX9/+zde5wP9f7A8debdXeElg65X2LtWrsoKexKLqWD2EKdk0vqRErO4dCpXykV0YnC0akUXVCUCNGFjaRc17ovseWStMKyi714//6Y+U7f3f3uWlJ26/18POZh5jOf+XzeM777/cxn5jPzbZGf7f0fbfMrcx7Ob8z7G6GqOYbBi0g/nPfV+Fulqvfnp35jjDG/HuvUG2OMMcYUcKq6GeflqxezzPy82d6X93WcFyQaY4wpYOx36o0xxhhjjDHGmELKOvXGGGOMMcYYY0whZZ16Y4wxxhhjjDGmkLJOvTHGGGOMMcYYU0hZp94YY4wxxhhjjCmkrFNvjDHGGGOMMcYUUtapN8YYY4wxxhhjCinr1BvzO3bs2DFiYmJo2LAhISEhrF69+lKHZIwxxlxy/fv3p3LlyoSFhXlpo0aN4sorryQiIoKIiAgWL17srRszZgz16tWjQYMGLF261Et/4YUXCAsLIzQ0lIkTJ+ZaX2xsLBEREYSGhhIVFQXAzp07vboiIiIoV66cV8acOXMIDQ2lSJEirFu3LktZgWLZt28fbdu2JSQkhNDQUF544QUvf1xcHNdeey0RERE0b96cNWvWALBjxw5atmxJiRIleO6557z8p0+f5pprrqFJkyaEhoby+OOPe+tat27txRsTE0O3bt0AePvttwkPDyc8PJzrrruOTZs2edssWbKEBg0aUK9ePcaOHRuwrKpVq3plGWMugKraZJNNhXi66qqrNDd33XWXvvLKK6qqeubMGT169GiueS+m5cuX/yb1nC+L6/wUxLh+i5iAdVoA/rZtssmmiz/52szPP/9c169fr6Ghoerz+OOP6/jx4zW7rVu3anh4uJ4+fVr37NmjderU0YyMDN28ebOGhoZqSkqKpqena7t27TQhISHH9kePHtWQkBD99ttvVVX1hx9+yJEnIyNDr7jiCk1MTNTly5frtm3bdMeOHRoVFaVr1649ZywHDx7U9evXq6pqcnKy1q9fX7du3aqqqu3bt9fFixerquqiRYs0KirKi2PNmjX673//O8t+nz17Vk+cOKGqqmlpaXrNNdfo6tWrc8TcunVrnTFjhqqqrlq1Sn/66SdVVV28eLFec8013n7VqVNHv/nmGz1z5oyGh4d7cfnr3r27V9bFVhDbsuwKQ4yqgeO0NrNgTHan3hRKIjJKRIb9ynVcLiLLReSkiEz2Sy8tIotEZIeIbBWRsX7rRonIARGJ85vKi0i0iKiI3O2XN9JNG+aXFiQiSSIyJr9xnkrPpNbIRd7kk5yczIoVK7j7bqfK4sWLU758+Qs/IMYYYwql36jNvMav3dskIrf6reskIjtFZLeIjPRLj3XTfdvN9YtXRaSeX96hblpzvzRfO9oxv3GeSs8EoE2bNlSsWDFf28yfP59evXpRokQJateuTb169VizZg3bt2/n2muvpXTp0gQFBREVFcW8efNybD9z5ky6d+9OjRo1AKhcuXKOPJ999hl169alZs2aAISEhNCgQYN8x1KlShWaNm0KwJ/+9CdCQkI4cOAAACJCcnIyAMePH6dq1apeHFdffTXFihXLUoeIULZsWQDS09NJT09HRLLkOXHiBBs3bvTurl933XVUqFABgGuvvZb9+/cDsGbNGurVq0edOnUoXrw4vXr1Yv78+TnKWrZsmd2pN+YXsE69Mbk7DfwfEOhE6DlVbQhEAteLyE1+6yaoaoTfdMxN3wz09MvXC9hEVh2AncDtkr0FPU979uyhUqVK9OvXj8jISAYMGEBKSsovKdIYY4zJzRaguapGAJ2A/7kXqosCU4CbgEZAbxFp5LfdnX7tZYxf+macdtInBtiWrc7ewBfuvxfF5MmTCQ8Pp3///hw9ehSAAwcOUL16dS9PtWrVOHDgAGFhYaxYsYIjR46QmprK4sWL2bdvX44yExISOHr0KNHR0TRr1ow33ngjR57Zs2fTu/e5dyO3WPwlJiayceNGWrRoAcDEiRMZPnw41atXZ9iwYYwZc+77BpmZmURERFC5cmXat2/vleUzb948mjZtSrly5XJsO23aNG666aZ8xztv3jzatWsXsCxjTP5Yp94UCiJyl4jEu1f/38y2LkJEvnLXzxORCm76gyKyzU2f7aaVEZHXRGStiGwUka651amqKar6BU7n3j89VVWXu/NpwAagWj524zugpIhc4XbYOwEfZcvTG3jBzXttPsrMVUZGBhs2bGDgwIFs3LiRMmXKZHmWzRhjzO/TJWozU1U1w10sCag7fw2wW1X3uG3mbCDXcvx84MsnInWA48CPfvshOB39vkAHESmZjzLzNHDgQL755hvi4uKoUqUK//znP337liOviBASEsKIESNo3749nTp1okmTJgQFBeXIm5GRwfr161m0aBFLly5l9OjRJCQkeOvT0tJYsGABt9122zljzC0Wn5MnT9KjRw8mTpzodZKnTp3KhAkT2LdvHxMmTPBG8OWlaNGixMXFsX//ftasWcOWLVuyrJ81axY33HBDju2WL1/OtGnTePbZZ/MVr6+s/FzQMMbkLuc3jzEFjIiEAo8A16tqkohUBB70y/IG8ICqfi4iTwKPAw8BI4HaqnpGRHzjzh8BlqlqfzdtjYh8qqoXdAvbLeMv6/uHYQAAIABJREFUOB1xn6Ei8ld3/qiqtvVbNxe4DdiIczHgjF9ZpYB2wN+B8jgd/IBvthORe4F7AYKDK/FY4wxvXWxsLAA//fQTwcHBnDp1itjYWOrWrcvMmTNp167dhezqeTl58qQXR0FicZ2fghhXQYzJmILkUraZItICeA2oCfxNVTNE5ErA//b1fsD/tu/bInLKnf9EVYe788nAPhEJw+ncvwP089vuemCvqn4jIrHAzcD7ucSVpc30fYccOnSIlJSUgN8pjRs3ZubMmcTGxpKWlsbnn39OtWrO9fv4+HiaNm3qta3PP/88AK+88golS5bMUV5aWhoNGzZk7dq1ANSvX5+ZM2cSHR0NwBdffEHt2rXZvn0727dvz/I9d+zYMdavX8/Jkye9snKLJSMjg4cffpgWLVpQsWJFr4zXXnuNW2+9ldjYWCpVqsTq1auzxJiYmEipUqVy/W6tVasWU6ZMoWdPZ7Dh8ePH+fLLL7nnnnuybPPNN9/w2GOPMXbsWDZv3gzA4cOH2bRpk5dvxYoVwM/nKr6yhg4d+qt9txeGdqMwxAiFJ84/pEv9UL9NNp1rAh4Ans6WNgpnWPxlwHd+6XWBDe78EpxO9F+Bsm7aOpwhgnHu9B0Qco76+wKTA6QH4dxpfyh7XAHyRgMLgT8DnwOTcYbae/lxOvtvu/OX45wEFT3X8aleu67WHLHQm/y1atVKd+zYoarOC4CGDRumv4WC+sIXi+v8FMS47EV5NtmU93Sp20x3uxBgDc4d+9uAV/3W/Q2Y5M7H4gzZz769L97bgaeAtUA5//w4Q/rvcee7AHPyc3yq166rPnv37s3yoryDBw96888//7z27NlTVVW3bNmS5eV0tWvX1oyMDFX9+aV33377rTZo0MB7WZy/bdu26Q033KDp6emakpKioaGhunnzZm99z5499bXXXvOW/b/nsr8oL7dYzp49q3/72990yJAhOepv2LChV+ann36qTZs2zbI++wsCDx8+7L1YNzU1VVu1aqUffviht37q1Kl61113ZYnz22+/1bp16+qqVauylJ2enq61a9fWPXv2eC/K27JlS46yfk0FsS3LrjDEqGovyivIk92pN4WB8PMwvvPRGWiD09j/n3v3QoAeqrrzIsT1MrBLVXP/DZtsVPWQiKQD7YEhwHV+q3vjPJ+f6C5fDrQFPs2rzFLFirJzbOeA6yZNmsSdd95JWloaderU4fXXX89vqMYYYwqnS95mqup2EUkBwnDuzFf3W10NOJjPoj4ExuN0GpJ9w7bd5/R7AF1E5BE3zstF5E+qeiKvAksVKwpA7969iY2NJSkpiWrVqvHEE08QGxtLXFwcIkKtWrX43//+B0BoaCi33347jRo1IigoiClTplC0qFNOjx49OHLkCMWKFWPKlCney+JeeuklAO677z5CQkLo1KkT4eHhFClShAEDBng/pZeamsonn3zi1eUzb948HnjgAX788Uc6d+5MREQES5cuzTWWL774gjfffJPGjRsTEREBwDPPPMPNN9/MK6+8wpAhQ8jIyKBkyZK8/PLLgDNSoXnz5iQnJ1OkSBEmTpzItm3b+P777+nTpw+ZmZmcPXuW22+/nVtuucWLbfbs2YwcOTJLvE8++SRHjhxh0KBBAAQFBbFu3TqCgoKYPHkyHTt2JDMzk/79+xMaGppnWcaYC3CpryrYZNO5JiAUSAAud5crkvUO9yagtTs/CpiA876IWm5aMeAHnCHtz+DcJRd3XWQ+6u9Ltjv1OHcO3gOKZEv34sqWHg0sdOevA7r558e5A3EYKOG3TT9g2rniy+sn7S6VgnrF2eI6PwUxLrtTb5NNeU+Xqs0EagNB7nxNnI57MM6otj3u+uJu/aFuvljyuFPvzvcCmvrnBzoCS7NtMwNnyH+ex6cgtpnZFcTv3kAszounMMSoanfqC/Jkd+pNgaeqW0XkaeBzEcnEeR490S9LH+AlESmNc+LQDygKvCUil+FcwZ+gqsdEZDQwEYh3X7KTCNxCLty75uWA4iLSDWfIfDLOc4Y7gA3unYPJqvqqu5n/M/UAWX6jRVW/DFBVd5znFs/4pc0HxolIiWzpxhhjTECXsM1sBYx0R6OdBQapahKAiAwGlrr1vKaqW/2283+mPklVb8y2P7MD1NUbyP7bce8BA4E3c2Y3xpjfN+vUm0JBVWfgXIUPtC6OwG+KbxUg7ymcF9Hlt95auawK+HNzqjoK5w5Ddok4dxgC5feZnm3dT0Clc4RojDHGZHEp2kxVfZNcOtSquhhYHCA9Opf8o3JJ9+XvG2DdAmBBfmI1xpjfG/tJO2OMMcYYY4wxppCyO/XmD09EOgLPZkveq6q3Xop4jDHGmILK2kxjjCl4rFNv/vBUdSnOs37GGGOMyYO1mcYYU/DY8HtjjDHGGGOMMaaQsk69McYYY4wxxhhTSFmn3hhjjDHGGGOMKaSsU2+MMcYYY4wxxhRS1qk3xhhjjDHGGGMKKevUG2OMMcYYY4wxhZR16o0xxhhjjDHGmELKOvXGGGOMMcYYY0whZZ16Y37Hjh07RkxMDA0bNiQkJITVq1df6pCMMcaYS65///5UrlyZsLAwL23OnDmEhoZSpEgR1q1b56Wnp6fTp08fGjduTEhICGPGjPHW1apVi8aNGxMREUHz5s3zrHPt2rUULVqUuXPnemkjRowgLCyMsLAw3nnnnRzbPPDAA5QtW9Zbfumll7z6WrVqxbZt284ZI0BmZiaRkZHccsstXlrr1q2JiIggIiKCqlWr0q1bN29dbGwsERERhIaGEhUVFXB///73v2epY9KkSTRo0IDQ0FD+9a9/AfDJJ5/QrFkzGjduTLNmzVi2bJmXPzo6mgYNGngxHD58OM/jZ4zJXdClDsAY8+sZMmQInTp1Yu7cuaSlpZGamnqpQzLGmAJLRB4CXlbVX/XLUkSKA+OAvwBngW3A/aq6/yLXEw3MB/YAJYHZqvrERSi3FrAXeEpV/89NCwa+B/6nqoNFZDqwUFXnZtv2oh1jEWkIvA40BR5R1efyu23fvn0ZPHgwd911l5cWFhbG+++/n6OzOmfOHM6cOcPmzZtJTU2lUaNG9O7dm1q1agGwfPlygoOD86wvMzOTESNG0LFjRy9t0aJFbNiwgbi4OM6cOUNUVBQ33XSTt37dunUcO3YsSzl33HEH9913HwALFizgH//4B0uWLDlnjC+88AIhISEkJyd7Za1cudKb79GjB127dgWcGwKDBg1iyZIl1KhRI0dn27e/sbGxWdLmz59PfHw8JUqU8LYJDg7mww8/pGrVqmzZsoWOHTty4MABb7u33377nBdDjDHnZnfqTaEkIqNEZNivXMc1IhLnTptE5Fa/dZ1EZKeI7BaRkX7psW66b7u5fvGqiNTzyzvUTWvulxbppv3c6p/DqfRMao1c5E0+ycnJrFixgrvvvhuA4sWLU758+Qs9HMYY87sgjtzOfx4CSp9neRdyg+QZ4E/AVapaH/gAeF9E5ALKOpeVqhoJNAf+KiLN/FdeYPzgXCi4xW/5NmBrPrbL9RiLSNHzjOEn4EEg3535U+mZALRp04aKFStmWRcSEkKDBg0CxUVKSgoZGRmcOnWK4sWLU65cufMKdNKkSfTo0YPKlSt7adu2bSMqKoqgoCDKlClDkyZNWLJkCeBcBBg+fDjjxo3LUo5/vSkpKfg+MnnFuH//fhYtWsSAAQMCxnbixAmWLVvm3amfOXMm3bt3p0aNGgBZYs7N1KlTGTlyJCVKlMiyTWRkJFWrVgUgNDSU06dPc+bMmXOWZ4w5P9apNyZ3W4DmqhoBdAL+JyJB7knHFOAmoBHQW0Qa+W13p6pGuFOMX/pmoJffcgzO3Rl/vYEv3H9/kT179lCpUiX69etHZGQkAwYMICUl5ZcWa4wxhY6I1BKR7SLyX2AD8DcRWS0iG0RkjoiUFZEHgarAchFZ7m530q+MGPfuMyIyXUSed/M96164fc29sLvHLQsRKSMii9wLw1tEpKeIlAb6AUNVNRNAVV8HzgA3uLHuEJEZIhIvInPdbRCRZiLyuYisF5GlIlLFTY8VkWdFZI2IJIhI6+zHQFVTgPVAXRHp6+73h8DHbhnDRWStW+cTbtqzIjLI7xiMEpF/uoungO1+F6Z7Au/mcvxHu8cs4DEWkSdF5GugZR77+KCIbHPjm+3u02FVXQuk5+NjcMFiYmIoU6YMVapUoUaNGgwbNsy7ICAidOjQgWbNmvHyyy8H3P7AgQPMmzfPu8Pu06RJEz766CNSU1NJSkpi+fLl7Nu3D4DJkyfTpUsXqlSpkqO8KVOmULduXf71r3/x4osvnjPGhx56iHHjxlGkSODT/nnz5tGuXTvvIkBCQgJHjx4lOjqaZs2a8cYbb3h5/ff3ww8/9NITEhJYuXIlLVq0ICoqirVr1+ao57333iMyMtLr+AP069ePiIgIRo8ejaoGjM8Yc242/N4UCiJyFzAMUCAe+MZvXQTwEs6V/2+A/qp61D15uA/IALapai8RKQNMAhrjfP5Hqer8QHVmGxpY0q0b4Bpgt6ruceufDXQlZwc9uw/cfE+JSB3gOH4nIu4dmhigPbBSREqq6ulcjse9wL0AwcGVeKxxhrfONxxu586drF+/nr59+9K3b18mTZrEwIED6d+//znC/OVOnjyZZVheQWFxnZ+CGFdBjMkUGg1wOtOPAe8DN6pqioiMAP6hqk+KyD+AtqqalI/yrnLLyBSRUUBDoC3OHfidIjIV54LwQVXtDCAilwH1gO9UNTlbeeuAUJx2rAFwt6quEpHXgEEi8gJO+9VVVX8UkZ7A04DvSz1IVa8RkZuBx4Eb/QsXkcuBa4HRwNVASyBcVX8SkQ5AfZz2TYAFItIGmA1MBP7rFnO7u0++3uFsoJeIHAIygYM4nXb/escBlwH9VFUDHOMywBZVfUxEigGf57KPI4HaqnpGRM5r2Fn2NtP3HXLo0CFSUlJyfKccO3aM9evXc/Kkc01n8+bNJCUlMWvWLE6cOMGQIUMoW7YsVatWZfz48QQHB3P06FGGDRvGqVOnaNKkSZbyRo0aRc+ePVm5ciWHDh1i69atBAcHU7x4cUJCQggPD6d8+fLUqVOHvXv3EhwczKuvvsrEiROJjY0lMzMzS4yhoaFMmzaNTz/9lMGDB/Pwww/nGuO3335Leno6J06cIC4ujiNHjuTY3ylTpnDzzTd76d9++y07d+7kP//5D2lpadx///2ICNWrV8+yv//4xz+oUaMGTZo04fjx42zevJmxY8eyY8cOunTpwsyZM72RBHv37uXRRx9l3LhxXj33338/lSpVIjU1lccff5zU1NQsjydcLIWh3SgMMULhifMPSVVtsqlATzgnOTuBYHe5IjAKGOYuxwNR7vyTwER3/iBQwp0v7/77DPBXXxqQAJTJo+4WOMMJTwK3umkxwKt+ef4GTHbnY91Y49xpvJs+CueixPtAGPAI0MfN39zN0wr4zJ2fCXTPz/GpXruu1hyx0Jt8vv/+e61Zs6a3vGLFCr355pv1t7B8+fLfpJ7zZXGdn4IY128RE7BOC8B3n00XbwJqAXvd+VuAJL/v6W3ANHddoq+tcZdP+s3HANPd+elAH791o3Ce6fYtbweq4XT89wLPAq3ddU2ADQFinAg84Mb6nV/6DTgXhcOAZL+4NwMfu3ligevd+StwLjwDRONcQN6Ic5f+Pje9L/C6Xx3PufvuK3s3zkUF375UdeNe5Xc8twDFgU3AcJxOc1+/9nC6u+7lbPuZ/RhnAEXd+bz2cQkwF/grUDZbmaNwzwnONVWvXVd99u7dq6GhoZpdVFSUrl271lseNGiQvvHGG95yv3799J133smx3eOPP67jx4/PkV6rVi2tWbOm1qxZU8uUKaOVKlXSefPm5cjXu3dvXbRokT7zzDN6xRVXeNuIiNatWzdH/szMTC1XrlyeMY4cOVKvvPJKrVmzpl5xxRVaqlQpvfPOO718SUlJWrFiRT116pSXNmbMGH388ce95f79++u7776bo/4+ffp4+9uxY8cs38916tTRw4cPq6rqvn37tH79+vrFF1/kKMPn9ddf1/vvvz/X9b9EQWzLsisMMaoGjtPazIIx2fB7UxjcAMxV96q+qv7kW+He9Sivqp+7STOANu58PPC2iPwV56QBoAMwUkTicE6CSgI1cqtYVb9W1VCcuxoPi0hJnLsYObL6zfsPvx+eLd9snCH43YB52db1dtf78uVrCH6pYkVJHNvZm3z+/Oc/U716dXbu3AnAZ599RqNGjXIrxhhjfu98zx8J8Inf93QjVb07l238v9tL5lKej/+Dwpk4d84TgGY4ndMxIvIYToe5poj8Kdv2Tfl5xFf2ccjqxr3VL+7GqtohQP2ZZB2JuVJVI1W1maq+lEv8AozxK7ueqk5z183FuaDRk5/bKCco1TSciwX/BN4jp7VAMxGpGGCdz2l1H0M4xz52xnn0rRmw/kLfBVCq2Pk+tg81atRg2bJlqCopKSl89dVXNGzYkJSUFE6cOAE4z7d//PHHWd6m77N3714SExNJTEwkJiaG//73v3Tr1o3MzEyOHDkCQHx8PPHx8XTo0IGWLVty6NAhb5vSpUuze/duAHbt2uWVu2jRIurXr59njGPGjGH//v0kJiYye/ZsbrjhBt566y2vjDlz5nDLLbdQsuTPH++uXbuycuVKMjIySE1N5euvvyYkJCTH/q5bt87b327dunlvtk9ISCAtLY3g4GCOHTtG586dGTNmDNdff71XR0ZGBklJzmCN9PR0Fi5cGPDYGWPyx4bfm8JAyHmCkx+dcTr4XYD/E5FQt6weqrrzfApS1e0ikoJzF2E/UN1vdTWcUQH58SEwHueqZrLfC26KAj2ALiLyiBvn5SLyJ1U9cT6x+ps0aRJ33nknaWlp1KlTh9dff/1CizLGmN+Lr4ApIlJPVXe7z6tXczvgJ3CGz/uGhv8gIiE4I7Buddfnm4hUBX5S1bfc5/P7qjPkfwbwvIjcp87w/btwHiFbBtQEaohIS1Vdzc/vWtkJVPKlu0PVr1LV/Lyc7lyWAqNF5G1VPSkiVwLpqnoYpyP/ChAMRAXY9j/A56p6RHK+52+JW/YiEengtmfZj7G/gPuIM1qguqouF5EvgDuAssCxAGXkS+/evYmNjSUpKYlq1arxxBNPULFiRR544AF+/PFHOnfuTEREBEuXLuX++++nX79+hIWFoar069eP8PBw9uzZw623Ou/QzcjI4I477qBTp06A89NzQI7n6P2lp6fTurXz+oNy5crx1ltvERSU96n55MmT+fTTTylWrBgVKlRgxowZALnGeC6zZ89m5MiRWdJCQkLo1KkT4eHhFClShAEDBhAWFpZjf1u2bOntb//+/enfvz9hYWEUL16cGTNmICJMnjyZ3bt3M3r0aEaPHg3Axx9/TJkyZejYsSPp6elkZmZy4403cs8995wzXmNMYNapN4XBZ8A8EZngnjR4V/xV9biIHBWR1qq6Emco/OfivNk40AnAUuABEXlAVVVEIlV1Y6BKRaQ2sE9VM0SkJs4zjok4JxH13fUHcO6835GfHVHVU+7zmwnZVt0IbFJV72Ey96SvG/BmfsoOJCIiIstv7RpjzB+dOs9q9wVmiYjvjV2P4nwvvwx8JCLfq2pbnOe4FwL7cIablw1QZF4aA+NF5CzOO1QGuukP4wx5T3DX7cB5xEvdjvF2oI+I/A/YBUxV1TQRiQFedEepBeEM2f/FnXpV/di9eLHarf8kzjD3w6q61R1VcEBVvw+w7da8YlDVOe72C9zn/bMfY/+8ue1jAvCWmybABFU9JiJ/xnkXQTngrDg/l9dIc76vIIdZs2YFTPd1Wv2VLVuWOXPm5EivU6cOmzZtClhObp356dOne/MlS5b0fmc+L75n+8H5abpAcovRX3R0NNHR0VnScns+evjw4QwfnnWwYfb99d+2ePHiWUYA+Dz66KM8+uijAetYv359nvEaY/LPOvWmwHNPKJ7G6axn4jwbmOiXpQ/wknu3ZQ/Oi5CKEvgEYDTOCUK8+2K6RLL+JI+/VjhD9dNxfkd4kO8RABEZjHOBoCjwWrY7JW+LyCl3PklVs7ysSFWzDF909SbncPz3cE4AL7hTb4wxBlQ1EWeklW95Gc5jVdnzTcJ5GZ1veS7O8PPs+fpmWx6VbdlXVyJOW5F9+zM4z88/kEvIZ1U1R69QVeP4+REz//Rov/kknGfeUdVYnEfNsuefjvPMu3/aC0DAHqOqNs62nIjf8QxUrv8xUtXXgNfcxezHOMuFktz2EadNzl7fIZzRcsYY84dmnXpTKKjqDJzn5QOti8N5o292gU4ATgF/z2edb5JLh1pVFwOLA6RH55J/VC7pvvx9A6xbACzIT6zGGGOMMcaYPybr1BtjjDHGFBC53QU3xhhjcmOdevOHJyIdcX5uyN9eVc35YJ0xxhhjjDHGFCDWqTd/eKq6lADPPBpjjDHGGGNMQWe/U2+MMcYYY4wxxhRS1qk3xhhjjDHGGGMKKevUG2OMMcYYY4wxhZR16o0xxhhjjDHGmELKOvXGGGOMMcYYY0whZZ16Y4wxxhhjjDGmkLJOvTHGGGOMMcYYU0hZp96Y37Fjx44RExNDw4YNCQkJYfXq1Zc6JGOMMeaS69+/P5UrVyYsLMxLmzNnDqGhoRQpUoR169bl2Oa7776jbNmyPPfcc17ahAkTCA0NJSwsjN69e3P69Okc202fPp1KlSoRERFBREQEr776qreuaNGiXnqXLl289L1799KiRQvq169Pz549SUtLA+Dbb7+lXbt2hIeHEx0dzf79+71tRowYQVhYGGFhYbzzzjs54njggQcoW7Zslv1p27YtkZGRhIeHs3jxYgDefvttL6aIiAiKFClCXFwcAJ06daJJkyaEhoby/PPPk5mZCcBPP/1E+/btqV+/Pu3bt+fo0aNZ6l67di1FixZl7ty5Werv0KEDISEhNGrUiMTERACWLVtG06ZNCQsLo0+fPmRkZOTYF2NMVtapN+Z3bMiQIXTq1IkdO3awadMmQkJCLnVIxhhjzCXXt29flixZkiUtLCyM999/nzZt2gTcZujQodx0003e8oEDB3jxxRdZt24dW7ZsITMzk9mzZwfctmfPnsTFxREXF8eAAQO89FKlSnnpCxYs8NJHjBjB0KFD2bVrFxUqVGDatGkADBs2jLvuuov4+Hgee+wxHn74YQAWLVrEhg0biIuL4+uvv2b8+PEkJyd75a1bt45jx45liempp57i9ttvZ+PGjcyePZtBgwYBcOedd3oxvfnmm9SqVYuIiAgA3n33XTZt2sSWLVs4duwYc+bMAWDs2LG0a9eOXbt20a5dO8aOHevVk5mZyYgRI+jYsWOW+u+66y6GDx/O9u3bWbNmDZUrV+bs2bP06dOH2bNns2XLFmrWrMmMGTMCHlNjzM8KbKdeRB4SkdK/QT3FRGSsiOwSkS0iskZEbnLXJYpI8EWsa7qI7BWROBHZICItL1K5o0RERaSeX9pQN625u3wywHa1ROSOixGDW95gEdnt1hvsly4i8qK7Ll5EmuZRRi0R2XKxYnLLnCYim9y654pI2XNvdVHqjRaRhe58FxEZ+WvUcyo9k1ojF3mTT3JyMitWrODuu+8GoHjx4pQvX/7XCMEYY0wB5p4nDPuV67hcRJaLyEkRmeyXXlpEFonIDhHZKiJj/daNEpED7nmRbyrvtp8qInf75Y1004b5pQWJSJKIjMlvnKfSnTvLbdq0oWLFilnWhYSE0KBBg4DbffDBB9SpU4fQ0NAs6RkZGZw6dYqMjAxSU1OpWrVqfkPJlaqybNkyYmJiAOjTpw8ffPABANu2baNdu3YAtG3blvnz53vpUVFRBAUFUaZMGZo0aeJdtMjMzGT48OGMGzcuSz0i4nX8jx8/HjD2WbNm0bt3b2+5XLly3n5nZGQgIgDMnz+fPn365IgXYNKkSfTo0YPKlSt7adu2bSMjI4P27dsDULZsWUqXLs2RI0coUaIEV111FQDt27fnvffeO/+DaMwfzCXt1LudvdxieAg4r069iARdQBijgSpAmKqGAX8B/nQB5eTXcFWNAEYC/8u+8gL3AWAz0MtvOQbYdo5tagEBO/UXGMcq4Ebg22zpNwH13eleYOoFlP1LDFXVJqoaDnwHDP6N60dVF6jq2HPnvHj27NlDpUqV6NevH5GRkQwYMICUlJTfMgRjjDF/HKeB/wMCXTx4TlUbApHA9b6bJ64JqhrhN/luJ28Gevrl6wVsylZuB2AncLv4epe/gpSUFJ599lkef/zxLOlXXnklw4YNo0aNGlSpUoXLLruMDh06BCzjvffeIzw8nJiYGPbt2+elnz59mubNm3Pttdd6HeHk5GTKly9PUJBzKlatWjUOHDgAQJMmTbxO7rx58zhx4gRHjhyhSZMmfPTRR6SmppKUlMTy5cu9eiZPnkyXLl2oUqVKlphGjRrFW2+9RbVq1bj55puZNGlSjrjfeeedLJ16gI4dO1K5cmVKlSrlXXj44YcfvPKrVKnC4cOHAWc0w7x587jvvvuylJGQkED58uXp3r07kZGRDB8+nMzMTIKDg0lPT/cef5g7d26W42WMCexCO5AXTERqAR8By4GWwEQRuQ8oAXwD9AP6A1WB5SKSpKptReSkqpZ1y4gBblHVviIyHfgJp6HYICIngBpAHfffiar6ooiUAd4FqgFFcTrzHwL3ALVV9QyAqv7g5sse9z/cuABeVdWJgcpU1XdEpBnwPFAWSAL6qur32YpcAdRzy44FvgSuBxaIyBvAS2784FzgWA3sAbwGT0R2u9sAfAB0BZ4SkTrAcSA9wH4Eu/v9FE7jGyIiccAM4CjQGSgJlAFuEJHhwO3u/888VX08t/1W1Y1uHdmr7Qq8oaoKfOVeha8S4Jhkj7UW8KYbC8BtDXRHAAAgAElEQVRgVf3SvRA0GYgC9uJcnHpNVecGKkdVk93yBCgFaB51TgdOAQ2Bmjifxz44n9WvVbWvm68D8AR+n1tVPSkinYCJOP/vG/zK7Qs0V9XBIvIX4FGgOHAEuFNVfxCRUQT47OYS5704F0gIDq7EY41/ft4sNjYWgJ07d7J+/Xr69u1L3759mTRpEgMHDqR///6BiryoTp486cVRkFhc56cgxlUQYzKmoBGRu3A61wrE47RTvnUROOcYpd30/qp6VEQeBO4DMoBtqtrLbe8nAY1xzhlHqer8QHWqagrwhf+oQTc9FeecD1VNE5ENOOcP5/IdUE5ErgAOA52Axdny9AZeAAYC1+KcKwU6HlnaTN93yKFDh0hJScnxnXLs2DHWr1/PyZPOIMepU6fSoUMH1q1bR2JiIqVKlSI2NpYTJ04wY8YM3nrrLcqWLcuoUaN45JFHvLvPPhUqVGDGjBkUL16cBQsW0LVrV55//nkAZs+eTXBwMAcPHuS+++7zLr6fOnXKi+vw4cOkpqYSGxtL9+7defHFF5k8eTLh4eEEBwezevVqypYtS0hICOHh4ZQvX546deqwd+9e5s6dy6uvvsrEiROJjY0lMzPTK/fdd9+ldevW3H777WzdupUePXrw2muvUaSIc79t27ZtqCpJSUlZjtHDDz9MWloao0aNYsKECTRv3pyMjIwseXzLo0aNomfPnqxcuZJDhw6xdetWgoOD2bRpE7Gxsbz88stcccUVPPHEE4wcOZLOnTvzr3/9i/79+5Oenk7z5s05ffr0L/reLwztRmGIEQpPnH9IqvqbTjh3h8/ifPkG43Ruy7jrRgCPufOJQLDfdif95mOA6e78dGAhUNRdHoXTQS7hln8EKAb0AF7xK+MyIBzYmEesiW4ZzXCuGJfB6ahvxbmIEKjMYm79ldy0njgdTl+sMe78bTidRIBY4L9+5cwEWrnzNYDt7vwLOJ1HgBbAp377PAx4HwgDHsHpiMbidCQBTgJXAF8D7d20aGChX719gf1ARXe5A/AyIDgd54VAm0D7Hei4+S0v9O2Pu/yZL65cPh9b3PnSQEl3vj6wzu//f7Eb059xLkbEnONz9zrwA86JRek88k0HZrv73BVIxjmZKQKsByLI5XOLczFknxur4Fz4WOh3bCe78xUAcecHAP/J67N7rr+p6rXras0RC73J5/vvv9eaNWt6yytWrNCbb75ZfwvLly//Teo5XxbX+SmIcf0WMfm+a2yyqTBOQCjO3etgd7mi7zzBXY4Hotz5J3EuIAMcBEq48+Xdf58B/upLAxJ8bV8e9XvtXYB15XFuUNRxl0cBB4A4d1rupke75w4P4oyuu95tx/33o5Qbc2mcDvuL+Tk+1WvXVZ+9e/dqaGioZhcVFaVr1671llu1aqU1a9bUmjVr6mWXXaYVKlTQSZMm6bvvvqv9+/f38s2YMUMHDhyYozx/GRkZWq5cuYDr+vTpo3PmzNFly5bp5Zdfrunp6aqq+uWXX2qHDh1y5D9x4oReeeWVAcvq3bu3Llq0SBcuXKhXXHGFF7+IaN26zjFo1KiRfvfdd942tWvX1h9++MFbfuihh/Tpp5/OdV9GjBih999/v6qqXnXVVXrw4EFVVT148KBeddVVqqpaq1Ytr+4yZcpopUqVdN68ebp69WqNioryynrjjTd00KBBOepYunSp3nbbbbnGkB8FsS3LrjDEqBo4TmszC8Z0qYbff6uqX+F07BsBq9y7xX1w7o6erzmqmum3vEhVz6hqEs7V3StwOuU3isizItJaVY+fR/mtcO5Sp6jqSZzOc+tcymyA07H+xN2nR8l6RXq8m34vcLdfuv9rSm8EJrv5FuBcqf6Tm8c3FK1Xtm3A6Yz2AroB87KtK4bTmf6Xqn6Sx75+oqo/ufMd3Gkjzl3nhjgd1vM9loGGxOV6tzxbzK+IyGZgDs5nBZz/jzmqelZVD+HeAciLqvbDGf2xnazD+QL50P2S2gz8oKqbVfUszsWcWuT+uW0I7FXVXe72b+VSfjVgqbtfw3FOwHwCfXbzVKpYURLHdvYmnz//+c9Ur16dnTt3AvDZZ5/RqFGj3Ioxxhjz+3ADMNdtR/Br0xGRy3A67J+7STNwLtaD09l/W0T+inO3HpxzgJFuWxeLc/HaN4rwvLiP9c3C6Xzv8VvlP/y+bbbN3sW5CdLb3dbfLTgXAVKB94BbRaToueIoVeycWXJYuXIliYmJJCYm8tBDD/Hvf/+bwYMHU6NGDb766itSU1NRVT777LOAL6T9/vufByYuWLDAy3P06FHOnDkDQFJSEqtWraJRo0aICG3btvXeFD9jxgy6du3q5Tt79iwAY8aM8UbfZWZmcuTIEQDi4+OJj4+nQ4cOdO7cmUOHDnnxly5dmt27dwNQo0YNPvvsMwC2b9/O6dOnqVSpEgBnz55lzpw59Or185OdJ0+e9PYlIyODr7/+moYNGwLQpUsX74V2/vHu3bvXqzsmJob//ve/dOvWjauvvpqjR4/y448/As4b733nKL6h+2fOnOHZZ5/NMXTfGJPTbz783uV7sFdwOpG988rs8u8ElsylPJ8zfvOZQJCqJrjD4m8GxojIx8BzQA0R+ZOqnsij7oDPaeVS5jxgq6rm9hK84Rp4mLj/PhQBWqrqqSxBiKwG6olIJZyO+1PZyvgQGI9zxSw52zD4DJw7zR2Bz8mdfxwCjFHVQM/+Z9lvVX0yjzL3A9X9lqvhXF0/l6E4d9eb4BwT3+/EXNBzc6qaKSLv4HSkX88jq+/zc5asn6WzOH8zmQT43LpDGvNzsWIS8LyqLhCRaJw7D9nrxq3nF/2NTpo0iTvvvJO0tDTq1KnD66/ntdvGGGN+B4T8tUXZdcbp4HcB/k9EQt2yeqjqzosQ18vALlWdmN8NVPWQiKQD7YEhwHV+q3vjPJ+f6C5fDrQFPs1P2b179yY2NpakpCSqVavGE088QcWKFXnggQf48ccf6dy5MxERESxdujTXMlq0aEFMTAxNmzYlKCiIyMhI7r33XgAee+wxmjdvTpcuXXjxxRdZsGABQUFBVKxYkenTpwNOR/rvf/87RYoU4ezZs4wcOZJGjRpx+PBhnn32WXr16sWjjz5KZGSk99Lb2NhYHn74YUSENm3aMGXKFADS09Np3bo14LzM7q233vKeyc/Nf/7zH+655x4mTJiAiDB9+nTvEcoVK1ZQrVo16tSp4+VPSUmhS5cunDlzhszMTBo0aOB1uEeOHMntt9/OtGnTqFGjhvdW/NwULVqU5557jnbt2qGqNGvWjHvuuQeA8ePHs3DhQs6ePcvAgQO54YYb8izLGMMlG37vG15dCeeZqXrucmngKnd+M86z7r7tdgMhOJ2798g6/D7GL98o3KFZ7vIWt86q/DyUuxvwgTs/DqeDV9xdrsLPQ80ScYZBN8W5gl0aZwj+Fpzh9znKxHlOejdOpxycu82hgWL1izEWv+HoOMPvh/stR/jNj8d5znxxoH3GuVPfNHu5OMPvi+KMMhjppjUDPvcrpy9+Q+ZwrtB/DZR1l68EKud2LP22SyTr8PvOOO9REJy73Gvy+fmYAPzTne8HuDfAuQ1nWF4RnDvZPwU6rm5e4efPl+BcyHkuj/q9/yP/WPzXkcvnFudi03dAXTd9FoGH328EmrnzrwOxeX12z/U35RviVpAU1GFkFtf5KYhx2fB7m2zKe8IZ/ZUAXO4uZx9+vwlo7c6PctvaIr72Bue85QecofLP4LzDxvfIWGQ+6s9yLuGmPYVz7lYkW3qWds8vPdqv/bwO6OafHyiHM5qthN82/YBp54qvILaZ2RXE795ALM6LpzDEqGrD7wvydKnu1AOgqj+6LxCbJSIl3ORHcRqjl4GPROR7dYZjjcTpyO3D6eyc78+SNcYZ+n4W5wVyA/3qewrYJiKnce5UP5Ytzg3uC9TWuEmvqupGEemYvUx1XgITA7zoDnMLwnlx2tbziPVBYIqIxLvbr8B5eQ04Q+7X4jSaOahq4B9IxbtT3Qv4UESSgVeADBHZhNNhPZot/8ciEgKsdq/cngT+ivOCvxzH0n3Jzr9wnnOPF5HFqjoA5/n3m3EudqTiNLz58V/gPRG5DWeIvW8UwXtAO5zPQQLOhYfcHgEQYIaIlHPnN/Hz//0Fye1zq87IjXuBRSKSBHyB8yhGdqOAOSJyAPgKqP1L4jHGGGN8VHWriDwNfC4imTgXkhP9svQBXhLnZ4P34LTJRYG33PMWwRkSf0xERuOcw8S7L5tNxBn2HpB717wcUFxEuuHcHEjGedfPDpwXGoPT6X/V3WyoO+Tfp1u2/fkyQFXdgWXqvuTYNR8YJyIlsqUbY8zvnu/KqzGFioiUVedt85fjXGy5Xp3n6/9wGjRooL7n5guK2NhYoqOjL3UYOVhc56cgxvVbxCQi61W1+a9aiTHmkiiIbWZ2BfG7NxCL8+IpDDFC4DitzSwYLumdemN+gYUiUh7ncYfRf9QOvTHGGGOMMeaPzTr15pIQkcY47wbwd0ZVW+Rne1WNDlDmPHIOZR+hqjneciMij+A8m+9vjqo+nZ/6jTHGmD8i99HDZ7Ml71XVWy9FPMYYY6xTby4RVd2M85vvF7PMfJ9QuJ1368AbY4wx58G9UJ77K+GNMcb85i7V79QbY4wxxhhjjDHmF7JOvTHGGGOMMcYYU0hZp94YY4wxxhhjjCmkrFNvjDHGGGOMMcYUUtapN8YYY4wxxhhjCinr1BtjjDHGGGOMMYWUdeqNMcYYY4wxxphCyjr1xvxOZWZmEhkZyS233HKpQzHGGGMKlFq1atG4cWMiIiJo3rx5lnXPPfccIkJSUhIAO3bsoGXLlpQoUYLnnnsu1zJbt25NREQEERERVK1alW7dunnrYmNjiYiIIDQ0lKioKC/92LFjxMTE0LBhQ0JCQli9ejUAmzZtomXLljRu3Ji//OUvJCcnA5CWlka/fv1o3LgxTZo0ITY21itr1qxZNG7cmPDwcDp16uTFP3z4cBo2bEh4eDi33norx44dO2dZjzzyCNWrV6ds2bJZ9vHMmTP07NmTevXqMXDgQBITEwE4cuQIbdu2pWzZsgwePDjLNtHR0TRo0MA7NocPH/bWvfvuuzRq1IjQ0FDuuOOOXI+tMSZv1qk35nfqhRdeICQk5FKHYYwxxhRIy5cvJy4ujnXr1nlp+/bt45NPPqFGjRpeWsWKFXnxxRcZNmxYnuWtXLmSuLg44uLiaNmyJd27dwecjvugQYNYsGABW7duZc6cOd42Q4YMoVOnTuzYsYNNmzZ57faAAQMYO3Ysmzdv5tZbb2X8+PEAvPLKKwBs3ryZTz75hH/+85+cPXuWjIwMhgwZwvLly4mPjyc8PJzJkycD0L59e7Zs2UJ8fDxXXXUVY8aMybMsgL/85S+sWbMmxz5OmzaNChUqsHv3bm677TZGjBgBQMmSJRk9enSuFz3efvtt79hUrlwZgF27djFmzBhWrVrF1q1bmThxYp7H1xiTO+vUmwJJRF4VkUYXoZyTFyOebGWOF5GtIjL+Vyg7WkQW/tJy9u/fz6JFixgwYMDFCMsYY8yvRERGiUjevcVfXsdFaVuylfm0iOzLrZ0VkRgRURFpHmh9QTV06FDGjRuHiHhplStX5uqrr6ZYsWL5KuPEiRMsW7bMu1M/c+ZMunfv7l0o8HVqk5OTWbFiBXfffTcAxYsXp3z58gDs3LmTNm3aAE6n/L333gNg27ZttGvXziunfPnyrFu3DlVFVUlJSUFVSU5OpmrVqgB06NCBoKAgAK699lr279+fZ1m+fFWqVMmxb/Pnz6dPnz4AREVF8dlnn6GqlClThlatWlGyZMl8HSNwLircf//9VKhQIctxMcacP+vUmwJJVQeo6rZLHUcu/g40VdXhlzoQgFPpmdQauYhaIxd5aQ899BDjxo2jSBH7EzfGGPOr+BC4JtAKEfkT8CDw9W8a0XkQETp06ECzZs14+eWXAViwYAFXXnklTZo0+UVlz5s3j3bt2lGuXDkAEhISOHr0KNHR0TRr1ow33ngDgD179lCpUiX69etHZGQkAwYMICUlBYCwsDAWLFgAwJw5c9i3bx8ATZo0Yf78+WRkZLB3717Wr1/Pvn37KFasGFOnTqVx48ZUrVqVbdu2eRcL/L322mvcdNNNeZaVlwMHDlC9enUAihYtymWXXcaRI0fOeUz69etHREQEo0ePRlW945KQkMD111/Ptddey5IlS85ZjjEmMDvjN5eciJQRkUUisklEtohITxGJ9V3dF5GT7h2BTSLylYhckUdZtUVktYisFZHRfullReQzEdkgIptFpKubPlpEhvjle1pEHhTHeDeezSLS012/ACgDfO3GucfNW15EzopIGzffShGp5+7ba248G/3qLeqWv1ZE4kXk7wH25Wp3mzrnczwXLlxI5cqVadas2flsZowx5jcgIne53/ubROTNbOsi3HYuXkTmiUgFN/1BEdnmps920wK2L/mo/xoR+dLd5ksRaeCmlxaRd9063hGRr/O6y66qX6nq97msHg2MA06fI5a+IvKBiHwoIntFZLCI/MON7SsRqejmu8fdz00i8p6IlHbT54vIXe7830Xk7fwcA4BVq1axYcMGPvroI6ZMmcKKFSt4+umnefLJJ/NbRK5mzZpF7969veWMjAzWr1/PokWLWLp0KaNHjyYhIYGMjAw2bNjAwIED2bhxI2XKlGHs2LGA0/meMmUKzZo148SJExQvXhyA/v37U61aNZo3b85DDz3EddddR1BQEOnp6UydOpWNGzdy8OBBwsPDvWH2Pk8//TRBQUHceeedeZaVF1+H3J//qIZA3n77bTZv3szKlStZuXIlb775pndcdu3aRWxsLLNmzWLAgAHe8/7GmPOT91+uMb+NTsBBVe0MICKXAQP91pcBvlLVR0RkHHAP8FQuZb0ATFXVN0Tkfr/008CtqposIsHAV24HfRrwPvCCiBQBeuHceegORABNgGBgrYisUNUuInJSVSPcWPsBjYDawHqgtYh8DVRT1d0i8gywTFX7i0h5YI2IfArcCRxX1atFpASwSkQ+9gUrItcBk4Cuqvpd9p0UkXuBewGCgyvxWOMMAK9h/Pjjj3n//fdJS0sjNTWV9u3b88gjj5zjv+HiOXnyZJYX7hQUFtf5KYhxFcSYjMkvEQkFHgGuV9Ukt9P6oF+WN4AHVPVzEXkSeBx4CBgJ1FbVM25bgltOjvZFVVPOEcYOoI2qZojIjcAzQA9gEHBUVcNFJAyIu8B9jASqq+pCyd9jBWFAJFAS2A2MUNVIEZkA3AVMBN5X1Vfc8p8C7sZpI+/FaT/3Av8Ers0lJq/NrFSpkvcdkpCQAEBkZCTTp08nISGBBg0aAPDjjz8SGhrK1KlTqVixIgCJiYmUKlUqz++g48eP8+WXXzJ06FAvX1paGg0bNmTt2rUA1K9fn5kzZxIeHk5wcDCnTp0iNjaWunXrMnPmTFq0aMGhQ4f497//DTjP+VeuXNkrr2vXrnTt6lzDGTx4MEePHmXatGkcPXqUffv2sW/fPurXr8+sWbNo1aoVAEuWLOHDDz/kP//5D59//rkXb6Cy/PcvMzMzy3Lp0qWZP38+oaGhHD9+nKSkJOLj472O/Y4dOzhw4ECOY7Rr1y4AmjZtyrx586hRowZFihShQYMGrFq1CnCG38+ePZuGDRvmenwvRGFoNwpDjFB44vxD8j2DY5NNl2oCrgL2As8Crd20WKC5O38GEHe+J/BqHmUdAYq58+WAk+58MWAyEI9zonIK+LO77hOcE4pOwFw3bQLQ36/cN4Eu7vxJv/RHcC5AjMO5EPAR0Ap4112/Dtji1hkHfAeEAHOBBL/0vUAHIBr4xt2man6OX/XadfX/2bv3OB/L/PHjr7dDcqiRYyGDpGGOpGRLTotyqIRfKzEdJH3btrROm5TtJGZDYtldiiKSUpZNyRjZUihjHCI2o6gwUw7jOGbevz+uaz4+M+bkEDP1fj4e98N9X/d9X/f7c8/4XPd1X+/7ntAhCzR0yALNaenSpdqpU6eTyn9pS5cuPefHLAyL69QUxbjORUzAai0C3402/fom4GHguRxlI4CBQAjwbVD5FcCXfn6RbzfuAir4slzblzyO2wpY4OcvB+b5fdcBm3z5u0DroH2+zGqHC/hMwW1iCd9+1/HLCfnVAdwN/Cto+Vugpp+/Fxjn51sCy32824DJQfvcCRwHuhTmZ9CgQQNNS0vT/fv3q6pqWlqaNm/eXN9//30NFhoaqnv27MlW9tRTT2lcXJzmZ9KkSdqnT59sZRs3btQ2bdpoenq6Hjx4UMPDw3XdunWqqnrDDTfopk2bAvUPHDhQly5dqrt27VJV1YyMDO3du7dOnTpVVVUPHjyoaWlpqqr64YcfaosWLVRVdefOnXrppZfq7t27VVX1iSee0Mcee0xVVd9//31t2LBhYF2WvOoKVr58+WzLEyZM0AceeEBVVYcPH649evTItv7VV1/Vhx56KLCcnp4eOI/Hjh3Tbt266aRJkwJxZZ2rPXv2aK1atTQlJSXPc3u6imJbllNxiFE19zitzSwak43Um/NOVb8WkauBjsDI4BFrL91/aQBkUHCGycm5YW5kvCpwtaqmi0gyblQAYAruwuJS4BVfln8u2QnLgf5ADeBJYBDu4unjoHq6qerm4J3E3dJ+WFU/yFHeCvjBx9YY+L6gAMqWLsnmFzoVMlxjjDHnkZB7G1WQTsCNwC3AcD/in2v7UgjPAEtVtauI1MF1vLNiO1MX4UbeE/zI7aXAfBG5RVVX57HP0aD5zKDlTE6099OA21R1rYjcjWtns0TibujXKGyQu3btomvXroBLAb/zzju56aab8tz+xx9/pGnTpuzfv58SJUowbtw4Nm7cyMUXX0zHjh2ZMmVK4KV0s2fPZujQodn2b9iwITfddBNRUVGUKFGCvn37EhERAcDLL79Mr169OHbsGPXq1ePVV19l7dq1zJo1i4kTJwJw++23c8899wCwe/duOnToQIkSJahZs2Yglb1GjRo89dRT3HjjjZQuXZrQ0FCmTZsGuBH4o0eP0q5dO8C9BG/y5Ml51gUwePBg3njjDQ4dOkStWrXo27cvI0aM4L777qN3797Ur1+f0qVLs3Dhiff51KlTh/3793Ps2DHeffddPvzwQ0JDQ+nQoQPp6elkZGTw+9//nvvvvx+ADh068OGHH9KoUSNKlixJXFwclStXLuyP0RgT7HzfVbDJJlxDfKGfvw03WpDAiZH64FGA7sC0fOqaD9zl5x/kxEj9I8DLfr417qKqjl++ANgMfAOU9GW3Ax8AJXE3A7ZzYmQ/OJ4yQDIuBRJgEvAdEO2Xn8dlCGRlGjT2//bznzMrq6AB7jGDVsACoDqwFmhV0Plr0KCBFjVF9Y6zxXVqimJcNlJvU3GegHBcllZlv1wJP1Lvl9dyImNtBC5rrERQe1Ua2AVUzKt9yeO4rTgxUj8PdzMg6xjJfn4Q7vE1cI+VpXOKI/W5rEvIrw7cDfUJQcvJQJWc64AUoJr//IuzrgNwj8sl4q4jtuAeUcg33qLYZuZUFL97c2Nxnj3FIUZVG6kvypO9KM8UBZG4ZwETcenseT0vXxiPAA+JyCpcKmOWmUBTEVmNG7XflLVCVY8BS3Ep8xm+eB4uVX8tEA8MVtUfcx5MVY/iOvGf+aLluJGKdX75GdxFSJKIrPfL4LIDNgJf+vJ/EJSBoKq7gC7ARBFpdhrnwRhjTBGjqhuA54BlIrIWGJNjk1ggTkSScO91eRp3c3mGiKwD1gBjVXUvebcvBRmNy4r7xNed5e9AVX/sIbg2cF9elYjIaBHZAZQTkR0iMqKQxz8dw3Fv0l+Mb7/9+2j+hXtU7nvcM/WvSEFvbTPGmF8hS7835526FPQPchS3ClpfIWh+Lu65wrzq2gY0Dyp6wZen5CgP8C/Iuw7oEVSP4kYtTvqzdcHx+OUWQfNvAG8ELR/G/Qm8nHVkAo/7KViCn1D3grzw3GI2xhhTPKnqdGB6HusSyf1lbzfksm2u7Use9SZwom1ZgcsOyzLc/3sEl+l2RESuAJbgstTyqnMwMLiA47YqYP00XGp91nKd3Nap6iRcJlxO0UHbz8dl6xljzG+OderNb5qINMKlu89T1S3nOx5jjDHmPCkHLBWR0rjn6x/0mWzGGGOKOOvUm2JJRIYRNLLuvaWqz51KPaq6ETilvwNvjDHGFEUi0gH3l2SCbVPVrgXtq6oHgJP+Lr3/M61lchT3VtV1Obf9JeIyxhhTMOvUm2LJd95PqQNvjDHG/Jrl8TjbmdZ5xu91+SXiMsYYc4K9KM8YY4wxxhhjjCmmrFNvjDHGGGOMMcYUU9apN8YYY4wxxhhjiinr1BtjjDHGGGOMMcWUdeqNMcYYY4wxxphiyjr1xhhjjDHGGGNMMWWdemOMMcYYY4wxppiyTr0xxhhjjDHGGFNMWafemF+pjIwMGjduTOfOnc93KMYYY0yRUqdOHSIjI4mJiaFp06YADB8+nKioKGJiYmjfvj3ff/89AHFxccTExBATE0NERAQlS5bkp59+OqnO++67j+joaKKioujevTtpaWkAjBkzhkaNGhEVFUXbtm3Zvn07AImJiTRv3pzw8HCioqJ48803A3XFx8fTpEkTIiIiiI2N5fjx4wD8/PPPdO3alaioKK699lrWr18f2Gfs2LGEh4cTERFBz549OXLkCADbtm2jWbNmXHnlldxxxx0cO3YsW9xz585FRFi9enWgLCkpKRBbZGRkoK5Zs2YRGRlJVFQUgwcPJiUlBYA77rgjcI7q1KlDTEwMAMnJyZQtWzawrn///qf7IzPG5MM69cb8Sr300ks0bNjwfIdhjDHGFElLly4lMTEx0JkdNGgQSUlJJCYm0rlzZ55++ulAeWJiIomJiYwcOZKWLVtSqVKlk1UuuyYAACAASURBVOobO3Ysa9euJSkpidq1azNhwgQAGjduzOrVq0lKSqJ79+4MHjwYgHLlyvHaa6+xYcMGFi1axKOPPsrevXvJzMwkNjaW2bNns379ekJDQ5k+fToAzz//PDExMSQlJfHaa6/xyCOPALBz507Gjx/P6tWrWb9+PRkZGcyePRuAIUOGMGDAALZs2cIll1zC1KlTAzEfOHCA8ePH06xZs0DZ8ePHueuuu5g8eTIbNmwgISGB0qVLc/z4cR555BGWLl1KUlIS9erVC3zGN998M3COunXrxu233x6o74orrgismzx58tn54RljsrFOvTnrRGSEiAz8hY/RSkQWnOU6nxOR70QkLY/13UVERaTp2TzumTqcnkGdoQupM3RhoGzHjh0sXLiQvn37nsfIjDHGnAsiMkVEGp2FenJt/86wzjgR2SAicb9A3Wf1WuDiiy8OzB88eBAROWmbWbNm0bNnz3z3V1UOHz4c2L9169aUK1cOgOuuu44dO3YA0KBBA6688koAatSoQbVq1dizZw/79++nTJkyNGjQAIB27drx9ttvA7Bx40batm0LQFhYGMnJyezatQtwnfHDhw9z/PhxDh06RI0aNVBV4uPj6d69OwCxsbG8++67gZiHDx/O4MGDufDCCwNlH374IVFRUURHRwNQuXJlSpYsiaqiqhw8eBBVDRwjmKoyZ86cPM+RMeaXYZ16Y074N3BtbitE5CLgT8Dn5zSi0/Too48yevRoSpSw/+LGGPNrp6p9VXXj+Y4jDw8ATVR10PkOJJiI0L59e66++mr++c9/BsqHDRvG5ZdfzsyZMwMj9VkOHTrEokWL6NatW5713nPPPVx66aVs2rSJhx9++KT1U6dO5eabbz6pfOXKlRw7dowrrriCkJAQ0tPTAxkEc+fO5bvvvgMgOjqad955J7DP9u3b2bFjBzVr1mTgwIHUrl2byy67jJCQENq3b09qaioVK1akVKlSANSqVYudO3cCsGbNGr777ruTHtP7+uuvERE6dOhAkyZNGD16NAClS5dm0qRJREZGUqNGDbZv3859992Xbd/ly5dTvXr1wM0KcOn/jRs3pmXLlixfvjzPc2eMOX2lzncApvgTkT7AQECBJOB/QetigMlAOV9+r6r+LCJ/AvoDx4GNqvoHESkPvAxE4n43R6jqe4U4/rXAOKAscBi4R1U3i0g5YBoQBnwF1AEeUtXVudWjqp/5+nJb/Qww2n/O/GK5G7gNKAlEAC8CFwC9gaNAR1X9SUTuB/r5dVuB3qp6SETeA95W1ddE5AHgRlXtlctx+vn9qVKlKk9GumftEhISWLFiBenp6Rw4cIDExERSU1NJSEjIL+yzLi0t7ZwfszAsrlNTFOMqijEZcy75tnIOUAvX1jwDPAgMVNXVfrT9JaAzrk28VVV35VFXXeANXJu7KKi8AvAecAlQGnhCVd8TkWeAFFV9yW/3HLAL13aPBm7GXQs8q6pvish8oDzwuYiMBEYCVwAhwE9AK1X9WESWA/cAP5DLdYCIlAReAFoBZYCJqvqPHJ/lGuCfQDdV/SbHukCbWbVqVRISEoiLi6NKlSr8/PPPDBw4kMOHDxMdHU27du1o164dM2fOZODAgdxzzz2BeuLj4wkLCyMpKSnPn09sbCx33XUX48eP569//Wu2DvzixYuJj49n3Lhx2b7HUlNTGTBgAEOHDuXjjz/m4MGDDB48mHvvvZf09HSaNm3KkSNHSEhI4Prrr2fChAnUr1+fevXqUb9+fdasWcP333/P9OnTmTFjBhUqVGDEiBEMGzaMa665hsOHDweOt3v3bg4dOkR8fDyPPfYYQ4cOJSEhgb179/LFF1+QlpbG5s2b+eijj5g8eTJlypThz3/+MyVLliQ6Oprnn3+eSZMmUaNGDV588UX69etH7969A59l7NixXHvttYHjHTt2jDfeeIOQkBA2b95Mt27dePXVVylfvnye5/BsKw7tRnGIEYpPnL9JWak0Ntl0OhMQDmwGqvjlSsAI3MUFuE5+Sz//NDDOz38PlPHzFf2/zwN3ZZUBXwPl8zhuK2CBn78YKOXnf4/rFIPrgP/Dz0fgbiA0LcRnSsux3DiozoT86gDuxnXSLwKqAvuA/n7dWOBRP185aJ9ngYf9fHW/fwv/+SsVFO/lda/Q0CELNHTIAlVVHTp0qNasWVNDQ0O1evXqWrZsWe3Vq5eeS0uXLj2nxyssi+vUFMW4zkVMwGotAt+vNtmU2wR0A/4VtBwS3DbhOtVd/PxoXIc8r7rmA338/ENZ7R+uQ32xn6/i2yXB3Rz/0peXwN2sr+xjWoy7yVAd+Ba4zG+XFnS8Rf66oTOwChiG66Rv8+tzvQ7Adcif8OVlgNVA3axrAeB3wBdA7YLOX4MGDTSnp556SuPi4rKVJScna3h4eLay2267TWfOnHnS/rlJSEjQTp06BZYXL16sYWFhumvXrmzb7du3Txs3bqxz5swJlOX8nvvggw+0R48eJx0jMzNTQ0NDdd++fTpnzhy99957A+umT5+uDz74oGZmZmrlypU1PT1dVVU//fRTbd++ve7du1crV66soaGhGhoaqmXKlNHLLrtMV61apbNmzdLY2NhAXU8//bSOHj1aV65cqW3atAmUjxs3Tm+++ebAcnp6ularVk2/++67PM9Ly5YtddWqVXmu/yUUxbYsp+IQo2rucVqbWTQmy801Z6oNMFdVUwBUNfA6WBEJwXXYl/mi6cCNfj4JmCkid+E62wDtgaEikoi7QLkQqF2IGEKAt0RkPa7jHO7LbwBm+7jW+2OeEhEp4ev88ynstlRVD6jqHlyn/t++fB3uggggQkSWi8g6oFdWzOpGU54ElgJ/Dj6feSlbuiTJL3Qi+YVOAIwcOZIdO3aQnJzM7NmzadOmDTNmzDiF8I0xxhRh64Dfi8goEWmhqvtyrD+G6+iC6+jWyaeu64FZfv71oHIBnheRJOAjoCZQXVWTgVQRaYxrs9eoaiquvZ2lqhm+HVsGXJPL8ZbjrgNuxI3a3+C3W+XX53Ud0B7o48s/x91IyMrvbogboe+iqt/m81kDDh48yIEDBwLzH374IREREWzZsiWwzfz58wkLCwss79u3j2XLlnHrrbfmWqeqsnXr1sD8v//978D+a9as4YEHHmD+/PlUq1YtsM+xY8fo2rUrffr0oUePHtnq2717NwBHjx5l1KhRgbfG7927N/D2+ilTpnDjjTdy8cUXU7t2bT777DMOHTqEqrJkyRIaNmyIiNC6dWvmzp0LwPTp07n11lsJCQkhJSWF5ORkkpOTue6665g/fz5NmzalQ4cOJCUlcejQIY4fP86yZcto1KgRNWvWZOPGjezZsweAL774ItsLeT/66CPCwsKoVatWoGzPnj1kZGQA8M0337Blyxbq1atXmB+TMeYUWPq9OVOCGxU4VZ1wjfotwHARCfd1dVPVzadY1zO4jnRXEamDuxDIiu1MXYQb5U/wafmXAvNF5BbNI40fl2afJTNoOZMT/+emAbep6lqfst8qaJ9IIBXI/vYZY4wxv3mq+rWIXA10BEaKyIc5Nkn3o2cAGRR8rZdbG94Ll212taqmi0gyroMNMAWXlXYp8IovK2x7uxz36F0N3A3sQbj27+Ogek66DhDXAD+sqh/kKG+FS9m/EJdV931hgti1axddu3YF3Mvl7rzzTm666Sa6devG5s2bKVGiBKGhodne1D5v3jzat29/Utp4x44dmTJlCpdeeimxsbHs378fVSU6OppJkyYB7u35aWlpgY577dq1mT9/PnPmzOHjjz8mNTWVadOmAQT+jYuLY8GCBWRmZvLggw/Spk0bAL766iv69OlDyZIladSoUeBN9s2aNaN79+40adKEUqVK0bhxY/r16wfAqFGj+MMf/sATTzxB48aNT3oOPqdLLrmExx57jGuuuQYRoWPHjnTq5AYOnnrqKW688UZKly5NhQoVGDt2bGC/2bNnn/SCvI8//pgnn3ySUqVKUbJkSSZPnpzrXw4wxpyh850qYFPxnnAjzF/j08k5Of1+LdDCz4/AjXqXAOr4stK45/Eq4tLuJgDi1zXO57itOJF+Pw93EZB1jGQ/PwiY5OcbAemcRvp9jnUJ+dWBu9CZELSczIlHEwLrgBSgmv/8i4FpvvxaIBF3wbMFqFtQvLmlEp5vRTWNzOI6NUUxLku/t+m3Pvn24UI/fxvwLtnT74PT3btntS951DWfE+nuD3Ii/f4R4GU/3xrX8a/jly/APXb3DVDSl90OfIBLv68KbAcuzSWeMr5djPfLk4DvgGi/nOt1AC79/l2gtF9ugEvLb4XLSqiOu95oVdD5K4ptZk5F8bs3Nxbn2VMcYlS19PuiPFn6vTkjqroBeA5YJiJrgTE5NokF4nwKXwzuufqSwAyfer4GGKuqe3Ej7qWBJJ9K/0whwxiNG634xNed5e9AVX/sIbj0+5xpigEiMlpEdgDlRGSHiIwo5PFPx3BcCuFiYJM/fhngX7iXCX6PS/l/RfJ4c58xxpjfpEhgpU9FH4Z7L8vpegR4SERW4R5lyzITaCoiq3Gj9puyVqjqMdwjYnNUNcMXz8O1sWuBeGCwqv6Y82CqehTXif/MFy3HZcSt88t5XQdMATYCX/ryfxCUgaAu5b8LMFFETvzBdWOM+Y2w9HtzxlR1Ou55+dzWJQLX5bLqhly2PYz70zeFOWYCPs1eVVfg7tpnGe7/PYIbgTgiIlcAS3CjB3nVORgYXMBxWxWwfhoutT5ruU5u61R1Em6EIqfooO3n40ZRjDHGGADUpaB/kKO4VdD6CkHzc4G5+dS1DWgeVPSCL0/JUR7g3zVzHRB4CNyP1g3yU85jVMix3CJo/g3c2/ezlnO9DlDVTOBxPwVL4MS1wLeceKeOMcb8plin3vyalQOWikhp3HN6D/oRBmOMMcacIhFphEt3n6eqWwra3hhjzLlhnXpTpIlIB2BUjuJtqtq1oH1V9QDQNJc6P8c91xest6quy7ntLxGXMcYYcy6JyDCCRta9t1T1uVOpR1U3AvbqcmOMKWKsU2+KtDzSDM+0zjN+3u6XiMsYY4z5JfjO+yl14I0xxhQf9qI8Y4wxxhhjjDGmmLJOvTHGGGOMMcYYU0xZp94YY4wxxhhjjCmmrFNvjDHGGGOMMcYUU9apN8YYY4wxxhhjiinr1BtjjDHGGGOMMcWUdeqNMcYYY4wxxphiyjr1xvyKHDlyhGuvvZbo6GjCw8N56qmnzndIxhhjTJGUkZFB48aN6dy5MwB33303devWJSYmhpiYGBITEwH4+eef6dq1K1FRUVx77bWsX78+1/patGgR2LdGjRrcdtttAMTFxQXKIyIiKFmyJD/99FO+bfa2bdto1qwZV155JXfccQfHjh0D4OjRo9xxxx3Ur1+fZs2akZycDMDixYu5+uqriYyM5OqrryY+Pj5Q17Fjx+jXrx8NGjQgLCyMt99+G4AxY8bQqFEjoqKiaNu2Ldu3b8/2efbv30/NmjX54x//GChr1aoVV111FTExMfTt25fdu3cD8PHHH9OkSRNKlSrF3Llzs9Vz0003UbFixcB5Luh8GWNOnXXqjfkVKVOmDPHx8axdu5bExEQWLVrEZ599dr7DMsYYY4qcl156iYYNG2Yri4uLIzExkcTERGJiYgB4/vnniYmJISkpiddee41HHnkk1/qWL18e2Ld58+bcfvvtAAwaNChQPnLkSFq2bEmlSpXybbOHDBnCgAED2LJlC5dccglTp04FYOrUqVxyySVs3bqVAQMGMGTIEACqVKnCv//9b9atW8f06dPp3bt3IK7nnnuOatWq8fXXX7Nx40ZatmwJQOPGjVm9ejVJSUl0796dwYMHZ/s8w4cPD2wbbObMmSQmJjJlyhSqVasGQO3atZk2bRp33nnnSdsPGjSI119/vdDnyxhz6qxTb4olERkhIgN/4WO0E5EvRGSd/7dN0LoLROSfIvK1iGwSkW5Bce0UkcSgqaKItBIRFZH7gupo7MsGBpWVEpEUERlZ2DgPp2dQZ+jCrP2pUKECAOnp6aSnpyMiZ34yjDG/WSLyqIiUOwfHKS0iL4jIFhFZLyIrReRmvy5ZRKqcxWNNE5Ft/jv6SxFpfpbqDW4DNopIz7NQZ5iIrBCRoznbPRG5SUQ2i8hWERlaQD0JItL0TOMpTFx+fUkRWSMiC87WMc+mHTt2sHDhQvr27Vvgths3bqRt27YAhIWFkZyczK5du/Lc/sCBA8THx+c68jxr1ix69nS/Fnm12apKfHw83bt3ByA2NpZ3330XgPfee4/Y2FgAunfvzpIlS1BVGjduTI0aNQAIDw/nyJEjHD16FIBXXnmFv/zlLwCUKFGCKlXcf6XWrVtTrpz7r33dddexY8eOQJxffPEFu3bton379gWeH4A6deoQFRVFiRIndy3atm3LRRdddFrnyxhTONapNyZvKUAXVY0EYoHg28zDgN2q2gBoBCwLWjdWVWOCpr2+fB1wR9B2fwDW5jhme2Az8P/kNHvjGRkZxMTEUK1aNdq1a0ezZs1OpxpjzG+IOHldEzwKnFKnXkRKnUYYzwCXARGqGgF0AfLuCZy5QaoaAwwF/pFz5Wl+BvBtAHAr8A8RKX0GMQL8BPwJ+FuO+EoCE4Gbce1QTxFpdIbHOuO4gjwCfHXuwjk1jz76KKNHjz6pEzps2DCioqIYMGBAoFMcHR3NO++8A8DKlSvZvn17tg5wTvPmzaNt27ZcfPHF2coPHTrEokWL6NatW6AstzZ7//79VKxYkVKl3K9grVq12LlzJwA7d+7k8ssvB6BUqVKEhISQmpqa7Thvv/02jRs3pkyZMuzd6y5Bhg8fTpMmTejRo0euNySmTp3KzTffDEBmZiZ//vOfiYuLy/Xz3XPPPcTExPDaa6+hqnmeh8LK63wZYwrvdBssY84pEekDDAQUSAL+F7QuBpiMu+j8H3Cvqv4sIn8C+gPHgY2q+gcRKQ+8DETifv9HqOp7uR1TVdcELW4ALhSRMqp6FLgXCPPbZeJuABTkW+BiEakO7AZuAv6TY5uewEvAg8B1wIo8zkc/oB9AlSpVeTLyOAkJCYH148aNIy0tjeHDhxMWFkbdunULEd7Zk5aWli2eosLiOjVFMa6iGFNxJSJ1gPeBpUBzYJyI9AfK4L5L78F919UAlopIiqq2FpE0Va3g6+gOdFbVu0VkGq6j1xj4UkQOALWBev7fcao63n8PzwFqASVxnfl/A/cDdf13LKq6y2+XM+7HfFwAU1R1XG51quqbInI1MAaogPuevltVf8hR5cdAfV93AvApcD0wX0Rew7Uvtf22j+K+l78BAjdtRWSr3ydAVbeIyCHgEmC3iNyP+96+ANgK9AaOAluAK4AQf/5aqerHIrIcuEdVt/r9O+WI+1pgq6p+42OYjbuRsDHnOcvlHE4CrgHKAnNV9Slf3tGfrxTgS6CeqnbOrQ5V3Z1HXIhILaAT8BzwWAGxJANvAK2B0rhzNBL3M4lT1ckiUgF4D3cuSwNPqOp7InINMNWfi5LASuAOVV2f4xiBNrNq1aqMHDmS9PR0Dhw4QGJiIqmpqSQkJNClSxdiY2NJT0/nxRdfpH///sTGxnL99dczYcIE6tevT7169ahfvz5r1qzhwIEDuX6miRMn0rFjx5O+q+Lj4wkLCyMpKSlbec42+4ILLuDw4cOB/Xfv3s2hQ4dISEggLS2NFStWULVqVcC9S+eTTz4hJCQEcM/iP/HEE4wePZqEhAT27dvHjh07CAkJYcyYMcyZM4fevXvz+OOPB46/ePFi4uPjGTduHAkJCcybN4+rrrqK//3vf2zatImdO3cGYnnooYeoWrUqhw4dYtiwYTz++ON06NAhUNePP/7Ihg0bAtkAWYLPc2HP19lSHNqN4hAjFJ84f5NU1SabivQEhONGr6v45UrACGCgX04CWvr5p3EXjgDfA2X8fEX/7/PAXVllwNdA+ULE0B34KGi/73AXPl8CbwHV/boRwE4g0U9LfXkrYAFuVOOPuIu/V3N8jrI+5nK4i4/xhTk/l9e9QkOHLNDcjBgxQuPi4nJd90taunTpOT9mYVhcp6YoxnUuYgJWaxH47vulJ6AOkIm7gVgF17kt79cNAZ7088lZ379+OS1ovjswzc9P899zJfXE9+GnuJsEVYBUXIesG/CvoDpCgChgTT6xJvs6rsZlPZXHddQ34G4i5FZnaX/8qr7sDuCVoFi7+/kewOd+PgH4e1A9bwA3+PnawFd+/iVchxugWVD7EPyd3gRYHlRX5aD5Z4GH/fwiXDvXGViFywQrA2zLcQ4CdQed+ylBy72BCfmcwwSgqZ+v5P8t6cujgAtxbVtdv24WsKAQv0fZ4vJlc/3PqlVBdfif7YN+fiyuTb8IqIrLiAN3E/5iP18Fd1NEgs7l33BZC38pKN4GDRro0KFDtWbNmhoaGqrVq1fXsmXLaq9evTTY0qVLtVOnTppTZmamhoaG6r59+05ap6qakpKilSpV0sOHD5+07rbbbtOZM2fmup/qiTY7Pj5eK1eurOnp6aqq+umnn2r79u1VVbV9+/b66aefqqpqenq6Vq5cWTMzM1VV9bvvvtMrr7xS//vf/2aLt1y5cpqRkaGqqt9++602atQosH7x4sUaFhamu3btCpTdeeedevnll2toaKhWrlxZL7roIh0yZMhJ8Q4ZMkQfeuihbGWxsbH61ltvnbRtXuczv/N1thTFtiyn4hCjau5x8htpM4v6ZOn3pjhogxtJSAFQ1Z+yVohICK7DnpX+Ph240c8nATNF5C7caD249PahIpKIu5C5kBMjMLkSkXBgFPCALyqFGw36RFWb4EZtgtMPg9PvW+eobg7uArIn7oIpWGfcTYBDwNtAV59ema+ypUuS/IIbKNmzZ08g1e7w4cN89NFHhIWFFVSFMea3a7uqfobr2DcCPvHfj7FA6GnU95aqZgQtL1TVo/77ezdQHdcp/72IjBKRFqq67xTqvwGYp6oHVTUNeAdokUedVwERwGL/mZ7AfXdnifPl/YD7gsrfDJr/PTDBbzcfl211kd8m63GqP+TYZ4CIbAY+x3V4s0SIyHIRWQf0wnXkAZbj2q0bcSPUN+BG0VcVcC5ye0SrsLnQ/09EvgTW+Dga4bLPvlHVbX6bnG1UoYhIZ1xn/ItT2G2+/3cd7gbLAVXdAxwRkYq4z/q8iCQBHwE1cb9L4G7mtwOaAqMLc7CRI0eyY8cOkpOTmT17Nm3atGHGjBn88INL4lBV3n33XSIiIgDYu3dv4O3zU6ZM4cYbb8wzVfytt96ic+fOXHjhhdnK9+3bx7Jly7j11lsDZXm12SJC69atA2+Rnz59emC/W265henTpwMwd+5c2rRpg4iwd+9eOnXqxMiRI7n++hNJIyJCly5dAqOrS5YsoVEj95TGmjVreOCBB5g/f37ghXfgXoT37bffkpyczN/+9jf69OnDCy+8wPHjx0lJcYmJ6enprFixInCOTlde58sYc2qsU2+KA6HwFyrBOuHu3F8NfOGfjxSgW1Cnu7aq5vnMn08hnAf0UdWslP9U4JAvBzdS36QwAanqj0A67gJkSY7VPXEXpcnAF0BlXDpiof3www+0bt2aqKgorrnmGtq1a3fSn5AxxpggB/2/AiwO+m5spKr35bFP8PdxzivxgzmWjwbNZwClVPVrToy4jxSRJ3Ejr7V9hzk/ub5rJI86BdgQ9JkiVTX4rV+DfHk7zZ6uHfwZSgDNg+qoqaoHcDdz64tIVeA23M2FLGNV9Spcp/81Eck6R9OAP6p7T8tfOXHuluNuTFyLeySrIm6E++MCzsUO4PKg5Vq4bK98iUhd3ONsbVU1CljoYzlbb1W9HrjFt2WzgTYiMqOAfbJ+TzLJ/juTibuR3gs3cn+1uvcV7OLE+auEy9q4iJN/H09Jr169iIyMJDIykpSUFJ544gkAvvrqK8LDwwkLC+P999/npZdeCuzTsWNHvv/+xGmfPXt24EV4webNm0f79u0pX758oCy/NnvUqFGMGTOG+vXrk5qayn33uf+O9913H6mpqdSvX58xY8bwwgsvADBhwgS2bt3KM888E/gzcVl/bm7UqFGMGDGCqKgoXn/9dV588UXAvZU+LS2NHj16EBMTwy233JLv+Tl69CgdOnQgKiqKmJgYqlSpwv333w/AqlWrqFWrFm+99RYPPPAA4eHhgf1atGhBjx49WLJkCbVq1eKDDz4o8HwZY07R+U4VsMmmgibcKMLX+NRFTk6/Xwu08PMjcOl7JYA6vqw07gKgIi79fgIn0vYa53Pcir7ubrmsmw208fN340anso4/MJftW+FTEIHfAbcFbw9cjBvFKhO0zz3A1ILOT4MGDbSoKappZBbXqSmKcVn6/Vn9bq0DrPfzVXHv/ajvl8sBDfz8OnxKtl/eCjT037Nvkz39vnvQdtm+D4H1/pg1gAt92W3Au35+NO6xpAv88mWceFwqGZd23QSXhVUOl4K/Hpd+f1KdnHh2vbmeaAvCc4s1KMYEfIq6X34D1/nPWo4Jmo/DvUD1P/l85veAB/x8ClDNx7E46LyV8Z8v3i9PwqXBR+eILWfdpXDP9tf1n3Vt1ufL4+edgBvNjvbblsCNdu/CtWNl/XGz2s6ZnGb6fdC6VgXVQdDjHT6OCTnX4V6697Iva427sZQV53zgTtxjC3k+fpA1FcU2M6ei+N2bG4vz7CkOMapa+n1RnuxFeabIU9UNIvIcsExEMnDpgslBm8QCk/2fXPoG1xkuCczw6fmCGznZKyLPAOOAJP92+WRc2ntu/oh7Uc9wERnuy9qreznQEOB1ERkH7PHHzDLAp/xnyfY3WlT101yOdTvugi54hOI9YHTQy/mMMeYXoap7RORuYJaIlPHFT+BuqP4TeF9EflD3SNFQ3LPz3+E61RVO8XCRuNT3TFzmWJIjxgAAIABJREFU0oNBx3sW2CgiR3Aj5k/miPNL/0K+lb5oiqquEZEOOetU1WP+RX7jfVtQCvf9v+EUYv0TMNGnfZfCjZ739+vexKXI353P/k8Db4jIv4DhuJT87bgbJRf5z3RURL4DPvP7LMdlbq0DEJFLgdW4m7+ZIvIo0EhV94vIH4EPcG3eK6pa4GdT1bUisgZ3Hr4BPvHlh0Xk/4BFIpLCiXOcq/ziKiiG0zAT+LeIrMa9r2aTj6EPcFxV3/CPq30qIm1UNf4XiMEYY4qsrNFKY0wxddVVV+nmzZvPdxjZJCQk0KpVq/MdxkksrlNTFOM6FzGJyBeqetb+nrcxxYWIVFDVNH/TeyKwRVXHnu+4zqai2GbmVBS/e3NjcZ49xSFGyD1OazOLBnum3hhjjDHGANzvXwq4AfcXBP5xnuMxxhhTCJZ+b37zfNrmqBzF21S16/mIxxhjjDldIjIP95x9sCGq+kFu2wfzo/LZRuZF5B7cM+3BPlHVh85VXMYYY/JnnXrzm+cvKOyiwhhjTLF3tm9Iq+qruBcYnmk9dqPcGGN+IZZ+b4wxxhhjjDHGFFPWqTfGGGOMMcYYY4op69QbY4wxxhhjjDHFlHXqjTHGGGOMMcaYYso69cYYY4wxxhhjTDFlnXpjjDHGGGOMMaaYsk69McYYY4wxxhhTTFmn3hhjjDHGGGOMKaasU2/Mr8iRI0e49tpriY6OJjw8nKeeeup8h2SMMcYUSRkZGTRu3JjOnTsDcPfdd1O3bl1iYmKIiYkhMTERgJkzZxIVFUVUVBS/+93vWLt2bb71Pvzww1SoUCGwfPToUe644w7q169Ps2bNSE5OBmDlypWBY0VHRzNv3rzAPi+99BIRERGEh4czbty4QPnatWtp3rw5kZGRdOnShf379wOQmppK69atqVChAn/84x+zxXPs2DH69etHgwYNCAsL4+233wZg+/bttG3blqioKFq1asWOHTsC+wwZMoSIiAgiIiJ48803A+W9evXiqquuIiIiglGjRpGeng7Apk2baN68OWXKlOFvf/tbtuOPHTuW8PBwIiIi6NmzJ0eOHAGgRYsWgc9fo0YNbrvtNgASEhIICQkJrHv66afzPd/GmN9op15EHhWRcufgOKVF5AUR2SIi60VkpYjc7Ncli0iVs3isaSKyTUQSReRLEWl+luodISI7fb0bRaTnWagzTERWiMhRERmYY91NIrJZRLaKyNAC6kkQkaZnGk9h4vLrS4rIGhFZcLaOebaVKVOG+Ph41q5dS2JiIosWLeKzzz4732EZY4w5j3xbflK7dpaP0U5EvhCRdf7fNkHrLhCRf4rI1yKySUS6BcWVdY2RNVUUkVYioiJyX1AdjX3ZwKCyUiKSIiIjTyfml156iYYNG2Yri4uLIzExkcTERGJiYgCoW7cuy5YtIykpieHDh9OvX78861y9ejV79+7NVjZ16lQuueQStm7dyoABAxgyZAgAERERrF69OtBeP/DAAxw/fpxt27bxr3/9i5UrV7J27VoWLFjAli1bAOjbty8vvPAC69ato2vXrsTFxQFw4YUX8swzz5zUoQZ47rnnqFatGl9//TUbN26kZcuWAAwcOJA+ffqQlJTEk08+yV/+8hcAFi5cyJdffkliYiKff/45cXFxgZsHvXr1YtOmTaxbt45jx44xZcoUACpVqsT48eMZODD7r9nOnTsZP348q1evZv369WRkZDB79mwAli9fHjjXzZs35/bbbw/s16JFi8C6J598Ms/zbYxxfrWdenHy+nyPAqfUqReRUqcRxjPAZUCEqkYAXYCLTqOewhqkqjHAUOAfOVee5mcAGOvrvRX4h4iUPoMYAX4C/gRka3lEpCQwEbgZaAT0FJFGZ3isM44ryCPAV+cunMI5nJ5BnaELARCRwOhAeno66enpiMj5DM8YY8xvQwrQRVUjgVjg9aB1w4DdqtoA174vC1o3VlVjgqasHvE64I6g7f4A5Bwibw9sBv6fnGJjt2PHDhYuXEjfvn0L3PZ3v/sdl1xyCQDXXXddthHtYBkZGQwaNIjRo0dnK3/vvfeIjY0FoHv37ixZsgRVpVy5cpQq5S7Njhw5Emivt2/fznXXXRdY37Jly8Ao/ubNm7nxxhsBaNeuXWDUvXz58txwww1ceOGFJ8X1yiuvBDrsJUqUoEoVN6a0ceNG2rZtC0Dr1q157733AuUtW7akVKlSlC9fnujoaBYtWgRAx44dERFEhLCwsMC5qFatGtdccw2lS598iXj8+HEOHz7M8ePHOXToEDVq1Mi2/sCBA8THxwdG6o0xp+5X1akXkToi8pWI/B34EujtR16/FJG3RKSCiPwJqAEsFZGlfr+0oDq6i8g0Pz9NRMb47Ub5O8qv+BHib3xdiEh5EVkoImv9iPwdPhPgfuBhVT0KoKq7VHVOLnE/5vdbLyKP5lWnL79aRJb5u+AfiMhluZyKj4H6fvsEEXleRJYBj4hIVRF5W0RW+el6ESnhMwcqBsW0VUSqB1eqqluAQ8Alfpv7fR1rfZ3l/Gj2N/6mSkURyRSRG/32y0WkvqruVtVVQHqOuK8FtqrqN6p6DJiNu5FQIBGZJCKrRWSDiPw1qLyjHxX4r4iMz2+UPZ+4EJFaQCdgSiFiSfbnfIWPqYn/Wf1PRPr7bSqIyBL/u7lORG715deISJKIXOh/BzaISERhzkGWjIwMYmJiqFatGu3ataNZs2ansrsxxphiTkT6+LZkrYi8nmNdjIh85tfPE5GsNv1P4jLykkRkti8r7697VonLVMuzTVbVNar6vV/cAFwoImX88r3ASL9dpqqmFOJjfOvrqO477DcB7+fYpifwkt/2ukLUGfDoo48yevRoSpTIfik8bNgwoqKiGDBgAEePHj1pv6lTp3LzzTfnWueECRO45ZZbuOyy7JdmO3fu5PLLLwegVKlShISEkJqaCsDnn39OeHg4kZGRTJ48mVKlSlG3bl0+/vhjUlNTOXToEP/5z3/47rvvADe6P3/+fADeeuutQHlesrIGhg8fTpMmTejRowe7du0CIDo6OnBTYN68eRw4cIDU1FSio6N5//33OXToECkpKSxduvSk46Snp7N48WJuuummfI9fs2ZNBg4cSO3atbnssssICQmhffv22baZN28ebdu25eKLLw6UrVixgujoaG6++WY2bNiQ7zGMMXC6I7dF2VXAPcCTwDvA71X1oIgMAR5T1adF5DGgdSEblQa+jgwRGQGEAa1xI+6bRWQSrqH5XlU7AYhICK5T/a2q7s+vchG52sfbDBDgc98Br5ezTnEj5C8Dt6rqHt/Rfw7XWAbrgrvDnaWiqrb09byBuyv+XxGpDXygqg1F5D2gK/CqiDQDklV1lwTd+BaRJsAWVd3ti95R1X/5dc8C96nqyyLyNe5OfF3gC6CFiHwO1FLVrfmcjppAcKuxw5+Xwhimqj+JG+1fIiJRwNe4jIUbVXWbiMwqZF25GQcMpvCZFt+panMRGQtMA64HLsRd6EwGjgBdVXW/uMcwPhOR+aq6SkTmA88CZYEZqro+Z+Ui0g/oB1ClSlWejDxOQkLCiWDHjSMtLY3hw4cTFhZG3bp1T+9Tn6a0tLRs8RQVFtepKYpxFcWYjClKRCQcNzJ+vaqmiEglXBZaltdwAw7LRORp4ClcBuNQoK6qHg26yT8MiFfVe33ZShH5SFUPFhBGN2BNjrqeEZFWwP+AP6rqLl8+QETu8vM/q2rroHrmAj2ANbjBmkAvW0TKAm2BB4CKuA7+ijzOSaDNrFq1KiNHjiQ9PZ0DBw6QmJhIamoqCQkJdOnShdjYWNLT03nxxRfp379/YIQdYM2aNbz88suMHz/+pO+hlJQUpkyZwrhx40hISCAjIyOwTVpaGitWrKBq1aqAG5X/5JNPCAkJAWDixIls376dxx9/nPLly1O5cmVuvfVWmjdvTtmyZQkNDeXHH38kISGB/v378+yzzzJo0CCuv/56SpQokS2WTZs2sXPnzkDZvn372LFjByEhIYwZM4Y5c+bQu3dvHn/8cW6//XbGjx/PhAkTiIqKokqVKqxYsYIKFSrQsGFDoqKiqFixIvXq1WPbtm3ZjvO3v/2Nhg0bZvucAMnJyZQtWzZQduDAAaZPn86MGTOoUKECI0aMYNiwYbRr1y6wz8SJE+nYsWNgn4MHDzJjxgzKli3LZ599RocOHZgxY0ZuP9pCKQ7tRnGIEYpPnL9JqvqrmYA6wDY/3xmXCpbop43AVL8uGagStF9a0Hx3YJqfnwbEBq0bges8Zi1/BdTCdfy3AaOAFn5dFK5ByyvWZKAKLqX76aDyZ3CNb251RgD7gz7TOuDDoFi3+fLFuJR/gASgZVD9u4P2TwR24jqqvwMW+W3GAvcHfeaduPS2dKBtUF0tgeU+jm3AZF8+DHgQGA3cjruzfgMwJ8c5GAEMDFruAUwJWu4NvJzPOUwAmvr5/rgGPwnYg0vTiwGWBW1/C7CgEL9HOePqDPzdz7cqqA7/s63p5+8F/hW07lvcxUdpYIKPNxE4DFzqt7kAl2L4OVCyoHgvr3uFhg5ZoLkZMWKExsXF5brul7R06dJzfszCsLhOTVGM61zEBKzWItCm2WTT6UzAw8BzOcpGAAOBENyAQ1b5FcCXfn4RrhN9F1DBl60G1gddM3wLNCzg+OG4jvsVfrkKoEA3v/wY8HpwXLnU0QpYAFyKS9WfgEu1D2zvrxlm+vnKuEGBAtvMBg0a6NChQ7VmzZoaGhqq1atX17Jly2qvXr002NKlS7VTp06B5bVr12q9evV08+bNmpsFCxZo9erVNTQ0VENDQ1VE9IorrlBV1fbt2+unn36qqqrp6elauXJlzczMPKmOVq1a6apVq076nvvLX/6iEydOPGn7zZs36zXXXJOt7NVXX9WHHnoosJyZmanlypXTjIwMVVX99ttvtVGjRifVdeDAAa1Zs2aun61nz566cOHCwPKIESP01ltv1SVLlpy07VNPPZXtumPOnDl67733BpanT5+uDz74YGA5JSVFK1WqpIcPH8712KqqoaGhumfPnjzXF6QotmU5FYcYVXOP09rMojH9qtLvvay7xwIs1hPPaDVS1fvy2EeD5nM+jJTzbnRwLlYGUEpVvwauxnVuR4rIk8BWoLaIFDSym+szYHnUKcCGoM8UqarBOUyDfHk7zT66G/wZSgDNg+qoqaoHcHe364tIVeA2XJZDlrGqehXu2bbXRCTrHE3D3W2PBP7KiXO3HGiBS6f/D64T2wr3WEB+dgCXBy3XAr7PY9sAEamLu1hpq6pRwEIfy9l6mPx64BYRScY9EtBGRAq6ZZz1e5JJ9t+ZTFyGTC+gKnC1uvcV7OLE+asEVMDdbDn54bgcypYuSfILnQDYs2dPINXu8OHDfPTRR4SFhRVUhTHGmF8PIft1TWF1wr3X5mrgC3Hv4RFcZzzrmqG2qub5bhn/qNo8oI+q/s8Xp+Ie3ct6tftbQJPCBKSqP+IGFNoBS3Ks7gn83rfNX+A69q0phJEjR7Jjxw6Sk5OZPXs2bdq0YcaMGfzwww9Zx+Xdd98lIsI9/fbtt99y++238/rrr9OgQYNc6+zUqRM//vgjycnJJCcnU65cObZudcmJt9xyC9OnTwdg7ty5tGnTBhFh27ZtHD9+HHDP0W/evJk6deoAsHv37sCx33nnHXr27JmtPDMzk2effZb+/fvn+1lFhC5dugRGV5csWUKjRu51RSkpKWRmZgbOyb33usTPjIyMwOMBSUlJJCUlBVLmp0yZwgcffMCsWbNOenQhN7Vr1+azzz7j0KFDqCpLlizJ9nLCt956i86dO2d7F8CPP/6YdXOHlStXkpmZSeXKlQs8ljG/Zb/GTn2Wz4DrRSTr2fJyIpL1TXyA7GnUu0SkobgX63U91QOJSA3gkKrOwL1krYmqHgKmAuNF5AK/3WVBKWZZPgZu8/GV98dfnluduNHyquLfbC/u7frhpxjuh0Dgb52ISAyAum/PecAY4CtVTc25o6q+g7trn5WLdhHwg38soFfQpp/jRv4zVfUI7u7+A7jOfn5WAVeKSF1/zv4AzC/EZ7oYd+Nin7j3AGQ97LYJqCcidfzyHSfvWjBV/Yuq1lLVOj6meFXN+XM8VSG4lwali0hrIDRo3T+B4cBMXKZGof3www+0bt2aqKgorrnmGtq1axf4Uz3GGGN+E5bgXhxXGcCn3wOgqvuAn0WkhS/qDSzz1z+Xq+pS3KNmFXE3lz8AHvbPtCMijfM6qE+zXwj8RVU/CTqmAv/G3dwHlzK/8RQ+z5PAEFXNCDrWxbgMwNqqWse3zw/hOvqnrVevXkRGRhIZGUlKSgpPPPEEAE8//TSpqan83//9HzExMTRteuIP73Ts2JHvv89//OG+++4jNTWV+vXrM2bMGF544QUA/vvf/xIdHU1MTAxdu3bl73//e+Aldt26daNRo0Z06dKFiRMnBl7UN2vWrMCfpqtRowb33HNP4Dh16tThscceY9q0adSqVYuNG91pHjVqFCNGjCAqKorXX3+dF198EXB/Ou6qq66iQYMG7Nq1i2HDhgHuefkWLVrQqFEj+vXrx4wZMwIv9Ovfvz+7du2iefPm9O3bN/Dn5n788Udq1arFmDFjePbZZ6lVqxb79++nWbNmdO/enSZNmhAZGUlmZma2vx4we/bswA2LLHPnziUiIoLo6Gj+9Kc/MXv2bHvprzEFOd+pAmdzwqXfrw9aboPrKCb56RZf/jCuw7fUL3fHpYol4FK8pvnyaUD3oPpGkD0te70/ZgdOpFGv4kRK+AW4FPStftvPgQ5+XTL+EQBcKtp6Pz3qy/KqMwZ3I2At7vns+3OLNSjGhKx9/XIV4E1f90Z8yrxf1xR3dz82n898Ne7mQglciv02f4yXs86b32458LyfvxPYC5Twy5fiRuX3+/IdwMV+XUfcs/D/I+hRhzx+3oHP5j//V7gLineAu315F/+z/i/uhsXMfOrLM66gbVpRuPT7rJ/t3cCEnOv8tAJ3k2SKj70O0Af3rgKAkv53pk1+x2vQoIEWNUU1jcziOjVFMS5Lv7fJpoIn3M339f5aYRrZ09ZjcAMfScC7uJfflvbt5Dq/31C/bVncu2myyvNs/4AncDfYgx/xq+bXheKuXZJwNx1q+/IRuEf8gvepk1dby4nHCO4GZudYVwn3+F2Z/M5NUWwzcyqK3725sTjPnuIQo6ql3xflSdzPwphfJxGpoKppfpRhIu5Ff2PPd1xn01VXXaWbN28+32Fkk5CQQKtWrc53GCexuE5NUYzrXMQkIl+oatOCtzTGFDdFsc3MqSh+9+bG4jx7ikOMkHuc1mYWDb/m9HtjAO4XkURcVkMIbsTBGGOMMcYYY34Vfo1/0s78yojIPNyfxws2RFU/KGhfPyqfbWReRO7B/dWBYJ+o6kPnKi5jjDGmOBKRDpz8vpdtqnrK7yQyxhhzdlin3hR5Z/tCQVVfBV49C/XYBYwxxpjfFH/j2m5eG2NMEWLp98YYY4wxxhhjTDFlnXpjjDHGGGOMMaaYsk69McYYY4wxxhhTTFmn3hhjjDHGGGOMKaasU2+MMcYYY4wxxhRT1qk3xhhjjDHGGGOKKevUG2OMMcYYY4wxxZR16o0xxhhjjDHGmGLKOvXG/IocOXKE/8/emcfpXO0P/P2xFiMUuhjrHTPGLJ5sk9tiC0mkm0KlsRT9btJyJTeJVrokKXRv3VBpuHTRdouMQSKyZKfFKEu2hBnMjJnP749znsczzzyzyc0M5/16fV9zvmf5nM/5Ps98P2f5nPO0aNGCxo0bExUVxciRI8+3Sg6Hw+FwFClys5V9+vShXr16eDwePB4P69evB2D+/PnExsbi8Xho1qwZX3zxRQ6ZJ06coHPnzjRs2JCoqCiGDRvmS1u6dClNmjShVKlSzJkzJ1u5H3/8kQ4dOhAZGUmjRo1ITk4GYOfOncTFxdGgQQN69OhBeno6AGlpafTo0YOwsDDi4uJ8+VetWuXTu3HjxsydOzdbPZmZmVx11VXcfPPNvrjXXnuNsLAwRIRDhw754seOHeuTFR0dTcmSJfnll18AeOWVV4iOjiYqKipHW1599VUiIiKIiopi6NChAGRkZBAfH09MTAyRkZGMHj0agO3bt/vq8Hg8XHbZZUyYMAGAUaNGUbNmTV/aJ598kt9H6nA4VNVd7nJXMb7Cw8PVS1ZWlh4/flxVVdPT07VFixa6YsUK/b1ZvHjx715nQXB6FY6iqNfvoRPwtRaB/213XTgXMAoYcr71KKTO3YBG/wO5SUCz89Wu8PDwXG1lfHy8zp49WwM5fvy4ZmVlqarqN998oxERETnypKamamJioqqqpqWl6bXXXquffPKJqqru3LlTv/nmG+3du3cO+a1atdIFCxb46klNTdXFixfr7bffrgkJCaqqOnDgQJ08ebKqqk6aNEkHDhyoqqoJCQl6xx13+OrPyMhQVdW9e/dq1apVffeqqi+99JL26tVLO3fu7Itbu3at7ty5U+vUqaMHDx7M0SZV1Q8++EDbtGmjqqobN27UqKgoX11NmjTRHTt2qKpqYmKitmvXTk+dOqWqqvv371dV1RkzZmiPHj18OtapU0d37tyZrY7Tp0/rlVdeqcnJyaqqOnLkSB07dmxQfc6GomjLAikOOqoG19PZzKJxXRQr9SLysIiU+x3qKS0iY0TkWxHZJCKrRKSTTUsWkSrnsK5pIrJTRNaLyFoRaXmO5I4SERWRML+4R2xcM3ufEqRcXRG581zoYOUNEpHvbL1V/OJFRCbatA0i0iQPGXVFZNO50snKXGaf+XoR2Ssi886BzDdFpNHZlj+ZkUndYR97ZRESEgKY2fGMjAxE5Leq6HA4HI6Lj27AWdumokxhbWVISIgvPTU1NWjecuXK0aZNGwDKlClDkyZN2L17NwB169YlNjaWEiWyd7u3bNnC6dOnad++va+ecuXKoaokJibSvXt3AOLj45k3z3Q35s+fT3x8PADdu3dn0aJFqCrlypWjVKlSgPFE8Ndx9+7dfPzxx9x7773Z6r/qqquoW7duns8qISGBXr16AbB161auvvpqX13+HgFTpkxh2LBhlC1bFoBq1aoB5lmnpqZy+vRpTp48SZkyZbjsssuy1bFo0SL++Mc/UqdOnTx1cTgcuXPBDOrtYC+39jwMFGpQLyKlzkKNZ4HqQLSqRgNdgApnIaegPKaqHmAY8I/AxLNsA8BGoKfffXdgSz5l6gJBB/Vnqcdy4AZgV0B8J6CBvQYAU85C9lmjqtepqsc+9xXAf86BzHtVNb/nW2AyMzPxeDxUq1aN9u3bExcXd65EOxwOh6MAiMg9duL5GxF5JyDNIyIrbfpcEals4weLyBYbP9PGlReRt0RktYisE5FbbPwlIjJVRDba+DY2vo+IzBeRT0Vku4iM9Kv3brvYsF5E/iEiJW18iog8b3VdKSJXisifgK7AWJv/j8H0FpFIEVnlV0ddEdlgw+2sbhttG8oGeU4pfuHuIjLNhqeJyBQRWSwiP4hIKytjqzePzddBRFbYxY3ZIhJS0M8oN1s5fPhwYmNjeeSRR0hLS/Plnzt3Lg0bNqRz58689dZbecr+9ddf+fDDD2nXrl2e+Xbs2EGlSpX485//zFVXXcVjjz1GZmYmx44do1KlSr5BemhoKHv27AFgz5491KpVC4BSpUpRsWJFDh8+DMBXX31FVFQUMTExvP76677yDz/8MH//+99zTCrkx4kTJ/j000+57bbbAIiOjmbp0qUcPnyYEydO8NVXX/HTTz/52rJs2TLi4uJo1aoVq1evBszEQ/ny5alevTq1a9dmyJAhXH755dnqmTlzpm/iwMtrr71GbGws/fr148iRI4XS2+G4GDnbQV+RQETqAv8FFgMtgQkicj9QFvge6Av0A2oAi0XkkKq2EZEUVQ2xMroDN6tqH2sofgGuAtaKyHGgNlDf/p2gqhNFpDzwbyAUKIkZzH8I3AfUU9U0AFXdb/MF6v2o1QvgTVWdEEymqs4SkabAeCAEOAT0UdV9ASKXAmFWdhLwJXAN8IGIvA28bvUHM8GxAvgB8Kjqr7bcd7YMwDzgFuA5EakPHAUygrSjim33c8AIIFJE1gPTgSNAZ+ASoDzQVkQeA+6wn89cVR2ZW7tVdZ2tI7DaW4C3rbvPShGpJCLVgzyTQF3rAu9YXQAGqeqXdiLoNaAVsBMz0fWWqs4JJsdPXgWgLeY7llueUUA9zERPOPAocDVmYmIP0EVVM+xnNkRVv7YdnFeAm4GTwC32exQoewBmUoMqVaryVMxpkpKSfOkTJkwgJSWFESNG0LBhQ+rVq5dXc845KSkp2fQpKji9CkdR1Kso6uRw+CMiUcBw4BpVPSQilwOD/bK8DTyoqktE5BlgJMY2D8P2IUSkks07HEhU1X42bpWIfA7cD6CqMSLSEFggIuG2TAsgGjgBrBaRj4FUoIfVKUNEJgN3WV3KAytVdbiI/B24T1WfE5EPgI+89tAO1rPpraoPi0gZEamvqj/YOv4tIpcA04B2qrrD9kX+D5hQiEdZGWNnu2L6GtcA99o2eYDdwJPADaqaKiKPY+zsM0E+E5/NrFq1qu8dEmgru3TpQnx8PBkZGbz00kvcf//9vlXxypUr8/rrr/PNN98waNAgXnrppaBKZ2Zm8sQTT3DTTTfx448/8uOPP/rSfv75ZzZv3kyVKsYB8ZtvviEpKYl//vOfXHnllTz99NMMGzYMj8fDyZMnfXoeOHCAEydOkJSUREpKCitWrKBq1aqAWZVfvnw5FStWBGDSpEns2rWLJ554gvLly7NmzRoyMjI4fvw469ev5/DhwzneoYEyvCQmJtKwYUM2bNjgi7vlllto2bIll156KbVr1+bnn38mKSmJo0ePsnHjRsaMGcO2bdvo2rUr7733Hps2beLQoUMkJCRw/Pg4NrcsAAAgAElEQVRxHnroIUJCQqhRowZgPCXef/99br75Zp9esbGx/Otf/0JEeOutt7jzzjt5/PHHgz7vglAc7EZx0BGKj54XJefb//+3XJjV4SzMQKkKZnBb3qY9Djxlw8lAFb9yKX7h7sA0G54GfASUtPejMAPkslb+YaA0cBvwhp+MikAssC4PXZOtjKaYlfDymIH6ZswkQjCZpW39VW1cD8yA06trdxu+HfjKhpOAyX5y3gOuteHawFYbfgXoa8NxwOd+bR6CWYGOxnQo4vHbAwekAFcCXwHtbVxrjPH31tsHY3Avt/cdgH8Cghk4fwRcH6zdwZ6b3/1H3vbY+0XksjfPfj822XA54BIbboDd/2M//0+sTn/ATEZ0L8B37x5gTj55RgFf2M+xMaaD1cmmzQW6+X1m3mermME+wN+BJ/PTpVa9P2qdxz/SYIwaNeqc7ksrKEV1b5jTq3AURb3cnnp3FfULeBB4PiDOa1srAj/6xf8RWGvDnwJzgLuBEBv3NbAJWG+vH4FIa0Pa+slZhumH9MFMfHvjn8FMGAwC9vrJ2Q6MsnnSALHhHpjFBsjez8hL7yeAYTa81trYxsBSv/ztgP/YsL/Ny6s/dpcN1we+9cv3NmZrwM2YxQ5vm7YA/8rv8/E/h8ZLMFu5ePHibPvP/albt26ue9D79u2rDz74YNC0wD37K1as0FatWvnu3377bf3LX/6iiYmJesUVV/j2xH/55ZfaoUMHVVXt0KGDfvnll6qqmpGRoVdccYVvv78/rVu31tWrV+uwYcO0Zs2aWqdOHb3yyiv10ksv1bvuuitb3tz21Hfr1k1nzJgRtC2qqnfeeadOmjRJVVU7duyY7f1cv359PXDggP7lL3/Rt99+O9vzmTVrlu9+3rx52r59+1zr2Llzp0ZFReWaXhCKoi0LpDjoqOr21Bfl60Jwv9+lqisxA/tGwHK7WhwPnM3mnNmqmul3/7GqpqnqIeAAZjC7EbhBRF4UketU9Wgh5F+LWaVOVdUUzOD5ulxkRmAG1gttm57ErGh7GWvjBwD9/eJn+YVvAF6z+T4ALrOrzLMwxhuMq71/GYCZNr4bpvPgT2nMYHqoqi7Mo60LVfUXG+5gr3UYo98QY/gL+yyDbXrTfMp4dX5DRDYCszmzT/BazGeepao/Y7w+CkIvIKEA+f6rqhmYdpbEdNqw93WD5E/HTFwArMklTzYuLV2S5DGdATh48CC//vorACdPnuTzzz+nYcOGBVDT4XA4HOcIoWB2KZDOwCTM5P8aMVvXBLhN7bYvVa2tqlsJbgu9BNatNv90PzkRqjrKpmfYjjlAJoX34pwF3GE9BVRVv81Hv9x0vSQgzev7nuUX9t57n81CvzY1UtX+FIDcbOW+fcbpT1WZN28e0dHRAHz33XfeCQXWrl1Leno6V1xxRQ65Tz75JEePHvWd4p4fzZs358iRIxw8eBAwK+ONGjVCRGjTpo3vdPnp06dzyy23ANC1a1emT58OwJw5c2jbti0iws6dOzl9+jQAu3btYvv27dStW5fRo0eze/dukpOTmTlzJm3btuXdd9/NV7ejR4+yZMkSX71eDhw4AJhT+5ctW+Zzm+/WrRuJiYmAccVPT0+nSpUq1K5dm8TERFSV1NRUVq5cma1f4r9n34v3cwCz7cH7OTgcjty5EAb1qfZvYV7ueRmR1IB7f0OSCZRS1R2cWXEfLSJPAd8Bte2AOS+CGrpcZAqw2a9NMarawa/YYza+var6Hwjn34YSQEs/GTVV9TjGBT9MRKpiBu6Be8M/BHpjZuaPBaSdxgw4O+bTVn89BBjtp0eYqv4rl3bnxW6glt99KGb1IT8eAfZjVg+aAWX89CoUInIFxr3x4wJk927FyCJ7x8nbKQnkN3Wu9u3bR5s2bYiNjaV58+a0b98+28/XOBwOh+N/ziLMIPcKAOt+D4CduD4iItfZqN7AErsVrJaqLgaGApUw3nyfAQ+K3YsmIlfZcksx7vPYwXRtzOo7QHsRuVxELsXY9+VWp+4iUs2rk4jkt/BxHHsuUG5627TvMfZqBGcWCLYBdeXMobu+/AHst/vySwC35qNPICuBa7x1iEg5vy0IeZKbrbzrrruIiYkhJiaGQ4cO8eSTTwLw/vvvEx0djcfj4YEHHmDWrFm+7YEejwcwh9E9//zzbNmyhSZNmuDxeHjzzTcBWL16NaGhocyePZuBAwcSFRUFQMmSJRk3bhzt2rUjJiYGVeW+++4D4MUXX2T8+PGEhYVx+PBh+vc3Xdr+/ftz+PBhwsLCGD9+PGPGjAHgiy++oHHjxng8Hm699VYmT57sc/PPjYkTJxIaGsru3buJjY3NdpDe3Llz6dChA+XLl89W5rbbbqNRo0Z06dKFhx56iMqVKwPQr18/fvjhB6Kjo+nZsyfTp09HRHjggQdISUkhOjqa5s2b07dvX2JjYwGzZ3/hwoX8+c9/zlbH0KFDiYmJITY2lsWLF/Pyyy8X5GN1OC5qivWe+gBWApNEJExVvxNz2n2oHTR6DZP3Rzj3i0gkxgDeatMLjIjUAH5R1XftHug+qnpCRP4FTBSRgaqaLiLVMfvJ/KdElwLTRGQMZkB5K9A7mExgDFBVRFqq6goRKQ2Eq+rmQqi7AON2N9bq7lHV9aqqIjIXs19/q6oe9i+kqift/rQdQWQq5kyA2SIyTFXH4Gf8c+Ez4FkRmaGqKSJSE7NPv1SQdufFB8AgMYcIxQFHNZ/99JaKwG5VzRKReMyqORj3+HgRmQ5UxWwjeC8fWbdjthqcKkC9vyuxsbGsW7fufKvhcDgcFy2qullEnscM1jMxHmrJflnigddtP+UHzNksJYF3RaQipm/wsqr+KiLPYvahb7AD+2SM2/lkK2MjZqK9j5q9+GDs2juYs3beU9WvAUTkScze+xIY+/sAOQ+j9WcmxsNtMMY1PpjeXmZh+hn17DM4JSJ9Mf2EUsBqzPk+gQzDeKf9hNlmUOCD7lT1oIj0ARLkzCF8TxK835KN3Gyld6U5kMcffzzXPd3e37IPDQ3lzJx8dpo3b+47CT+Q9u3bZ9uz7qV+/fqsWrUqR/wll1zC7Nmzc8T37t2b3r17B63DS+vWrWndurXvfvDgwQwePDho3j59+tCnT58c8cuWLfOF/fdWlylTJqgHQEhISFB9wfxigPeQP3/eeeedILkdDkdeXDCD+nxe7v8E/isi+1S1Db/BiFhiMK7vWRjD+H9+9T0HbBGRU5iV6mwrz6q6VsyBfN439Zuquk5EOgbKtBMD3TETBRUxn9cEzD78gjIYM9mxwZZfij1gB2OEV5PLQFpVZ+YmVFUzRaQn8KGIHAPeAE6LyDeYvXBHAvIvsBMpK2ynIwWzbzAssN1gTgHGrFb8AdOZ+URV78Xsf78J4xlxgjwOqgtgMvC+iNyOcbH3ehG8j9nrtwnzXfkKczBgXvTETLg4HA6Hw5EDVZ2OOTQ2WNp6zJbBQK4NkvckMDBI/ClynwQ/oKqDgpSZRc6tdqg9ONiG52D29aOqy8n5k3bB9EZVxwHjAuIWYc4MCszbOlh9AXn6+IWTMVsRg6UlAs2D6eRwOBwXE8V6UB/kRR/05a6qrwKv+t3na0Ts/aiAe29dyZiV58Dy6ZiB6NAgaXX9wuMxK+T+6Z/lInM95kC5PHX1i28dcH+IM3vnA/N+TYD7eWCbg8n1dgBse/1d8AN/u2VagIxXMAf0+fM9wds9EZgYJF4xqwv54v/9sHv8Yv2S/2bjs0RkiPUeuAIz2bIxH7mtC1j/qID7kGBpwZ6tDQf9njocDofD4XA4HA6Hl2I9qHc4zhEf2Z8KKoP5Sb2fz7dCDofD4XAUFlWdRsCEusPhcDgufNyg3lHsEZEYzP5Bf9JUNa4g5YOtvNvzBgJ/3P1x61ERmLcv8FBA9HJVLZBHgcPhcDgcDofD4XCcLW5Q7yj2qOpGwHOOZRb4FF5VnQpMPZf1OxwOh8PhcDgcDkdBuBB+0s7hcDgcDofD4XA4HI6LEjeodzgcDofD4XA4HA6Ho5jiBvUOh8PhcDgcDofD4XAUU9yg3uFwOBwOh8PhcDgcjmKKG9Q7HA6Hw+FwOBwOh8NRTHGDeofD4XA4HA6Hw+FwOIopblDvcDgcDofD4XA4HA5HMcUN6h2OC4iffvqJNm3aEBkZSVRUFK+88sr5VsnhcDgcjiLFqVOnaNGiBY0bNyYqKoqRI0cC0L9/fxo3bkxsbCzdu3cnJSXFV+bf//43jRo1IioqijvvvDOo3ISEBGJiYoiNjeXGG2/k0KFDAIwYMYLY2Fg8Hg8dOnRg7969AKgqgwcPJiwsjNjYWNauXQvAzz//TNOmTfF4PERFRfH666/76lizZg0xMTGEhYUxePBgVBWAHj164PF48Hg81K1bF4/H4yszevRowsLCiIiI4LPPPvPFf/rpp0RERBAWFsaYMWN88arK8OHDCQ8PJzIykokTJwJw9OhRunTp4ntu//3vfwHYtWtXrvq2bt2aiIgIn24HDhzwlWnXrh2xsbG0bt2a3bt3Z3uWx44do2bNmgwaNCj/D9ThcJh/XHe5q7AXMAoYcr71KKTO3YBG/wO5SUCz89Wu8PBw9bJ3715ds2aNqqoeO3ZMGzRooJs3b9bfm8WLF//udRYEp1fhKIp6/R46AV9rEXhnuctd7jr3V3h4uGZlZenx48dVVTU9PV1btGihK1as0KNHj6qXRx55REePHq2qqjt27FCPx6O//PKLqqru379fA8nIyNCqVavqwYMHVVX1scce05EjR6qqZpP7yiuv6MCBA1VV9eOPP9Ybb7xRs7KydMWKFdqiRQtVVV2wYIGeOnVKVVWPHz+uderU0T179qiqavPmzfXLL7/UrKwsvfHGG/WTTz7Jocujjz6qTz/9tKqqbt68WWNjY/XUqVP6ww8/aP369fX06dN6+vRprV+/vn7//fealpamsbGxvv7CW2+9pb1799bMzMxs7X3++ed16NChqqp64MABrVChgqalpWlaWlqu+rZq1UpXr16dQ8fu3bvrtGnTVFV10aJFevfdd2dLHzx4sPbq1UsfeOCBHGULS1G0ZYEUBx1Vg+vpbGbRuNxKveNiohvQ6Hwrca45mZHpC1evXp0mTZoAUKFCBSIjI9mzZ8/5Us3hcBRDRORhESn3O9RTWkTGiMi3IrJJRFaJSCebliwiVc5hXdNEZKeIrBeRtSLS8hzJHSUiKiJhfnGP2Lhm9j4lSLm6IhJ8uffs9BgkIt/Zeqv4xYuITLRpG0SkSR4y6orIpnOlk5W5zD7z9SKyV0TmnQOZb4rIb7LlIkJISAgAGRkZZGRkICJcdtllgFnwOnnyJCICwBtvvMEDDzxA5cqVAahWrVoOmd6OdWpqKqrKsWPHqFGjBoBPLkBqaqpP7vz587nnnnsQEa6++mp+/fVX9u3bR+nSpSlbtiwAaWlpZGVlAbBv3z6OHTtGy5YtERHuuece5s2bl0OPf//73/Tq1ctXR8+ePSlbtiz16tUjLCyMVatWsWrVKsLCwqhfvz5lypShZ8+ezJ8/H4ApU6bw1FNPUaJEiWztFRGOHz+OqpKSkkKFChUoVaoUZcqUCapvXmzZsoV27doB0KZNG1/dYLwR9u/fT4cOHfKV43A4DG5Q7ygQInKP7RB8IyLvBKR5RGSlTZ8rIpVt/GAR2WLjZ9q48iLyloisFpF1InKLjb9ERKaKyEYb38bG9xGR+SLyqYhsF5GRfvXebTuB60XkHyJS0saniMjzVteVInKliPwJ6AqMtfn/GExvEYkUkVV+ddQVkQ023M7qttG2oWyQ55TiF+4uItNseJqITBGRxSLyg4i0sjK2evPYfB1EZIXtdM4WkZCz/cySk5NZt24dcXFxZyvC4XBcoNjBXm59gIeBQg3qRaTUWajxLFAdiFbVaKALUOEs5BSUx1TVAwwD/hGYeJZtANgI9PS77w5syadMXSDooP4s9VgO3ADsCojvBDSw1wBgylnIPmtU9TpV9djnvgL4zzmQea+q5vd88yUzMxOPx0O1atVo3769z1b27duXP/zhD2zbto0HH3wQgB07drBjxw6uueYarr76aj799NMc8kqXLs2UKVOIiYmhRo0abNmyhf79+/vShw8fTq1atZgxYwbPPPMMAHv27KFWrVq+PKGhob6J+J9++onY2Fhq1arF448/To0aNdizZw+hoaFB83tZtmwZV155JQ0aNMizjrzq/v7775k1axbNmjWjU6dOfPvttwAMGjSIrVu3UqNGDWJiYhg0aJBv4B9MXy99+/bF4/Hw7LPPYhZ2oXHjxrz//vsAzJ07l+PHj3P48GGysrL461//ytixY/P7CB0Ohx9na8AcFxEiEgUMB65R1UMicjkw2C/L28CDqrpERJ4BRmI6hcOAeqqaJiKVbN7hQKKq9rNxq0Tkc+B+AFWNEZGGwAIRCbdlWgDRwAlgtYh8DKQCPaxOGSIyGbjL6lIeWKmqw0Xk78B9qvqciHwAfKSqc2y7NgTqraoPi0gZEamvqj/YOv4tIpcA04B2qrpDRN4G/g+YUIhHWRloi5lc+BC4BrjXtskD7AaeBG5Q1VQReRx4FHgmyGcyANNBo0qVqiQlJWVLP3nyJA899BD33nuvb4/e70lKSkoOnYoCTq/CURT1Koo6FRdEpC7wX2Ax0BKYICL3A2WB74G+QD+gBrBYRA6pahsRSVHVECujO3CzqvaxE5K/AFcBa0XkOFAbqG//TlDViSJSHvg3EAqUxAzmPwTuw9oIAFXdb/MF6v2o1QvgTVWdEEymqs4SkabAeCAEOAT0UdV9ASKXAmFWdhLwJeZ9/IF9t79u9Qdjy1YAPwAeVf3VlvvOlgGYB9wCPCci9YGjQEaQdlSx7X4OGAFEish6YDpwBOgMXIKxYW1F5DHgDvv5zFXVkbm1W1XX2ToCq70FeFvNSGqliFQSkepBnkmgrnWBd6wuAINU9Us7EfQa0ArYiVkcestrV/OQVwFj//rmkWcUUA8z0ROOsX9XYyYm9gBdrL1Pwmz/+9pOpL8C3AycBG6x36NA2T6bWbXqGZs5YcIEUlJSGDFiBA0bNqRevXrEx8dz9913M3HiRJ5++mk6derE/v37OXz4ME8//TQHDx6kd+/eTJ061bfaD3D69GleeOEFpkyZQo0aNZg4cSIDBgygd+/eALRv35727dszY8YMhgwZQt++fTl06BDr1q3j9OnTABw5coQ1a9ZQs2ZNvv/+eyZOnMihQ4cYMWIE1atX58CBAxw5csSn/4YNG/jll1+yvRNffvllWrRo4YvbvXs3W7du9d3v27ePzZs3o6rs27fPF79161b27t1LUlISJ06cYM+ePYwbN46lS5dy2223MXHiRJYsWUKVKlV477332Lt3L48++iixsbGUL2++JoH6Xn755TzwwANUrVqVEydOMHLkSE6cOEHHjh3585//zMSJE3nttdeIjY2lSpUqrFixgoULFxIREcH333/Ptm3b2LNnz29+5xcHu1EcdITio+dFyfn2/3dX0b+AB4HnA+JGAUOAisCPfvF/BNba8KfAHOBuIMTGfQ1sAtbb60cgEpgLtPWTswyIBfpgOiTe+GcwnaxBwF4/OduBUTZPGiA23APTCQQzKO9uw3np/QQwzIbXYlY4GgNL/fK3A/5jw0nYPfVAil+e7sA0v7rvsuH6wLd++d7GbA24GdMJ9bZpC/Cv/D6fWvX+qP6kp6drhw4d9KWXXtLzRVHdG+b0KhxFUS+3p/43vcvrAlmYgVIVzOC2vE17HHjKhpOBKn7l8nqvfQSU1DN24UvMILQKcBgoDdwGvOEno6J9v6/LQ9dkK6MpZiW8PGagvhkziRBMZmlbf1U98/5/y09X7/v/duArPfP+nuwn5z3gWhuuDWy14VeAvjYcB3zu1+YhmBXoaMzEdXygXQCuBL4C2tu41phJZm+9fTATu5fb+w7APwHBDJw/Aq4P1u5gz83v/iNve+z9InI5A8Z+PzbZcDngEhtugP2fsJ//J1anP2AmI7oX4Lt3DzAnnzyjgC/s59gYM5HfyabNBbr5fWbeZ6uYwT7A34En89PF/xwaL6NGjdKxY8dmi0tKStLOnTurqurAgQN16tSpvrS2bdvqqlWrsuVftWqVtm3b1ne/ZMkS7dSpU466kpOTNSoqSlVVBwwYoO+9954vLTw8XPfu3ZvjPdenTx+dPXu27t27VyMiInzx7733ng4YMMB3n5GRodWqVdOffvrJF/fCCy/oCy+84Lvv0KGDfvnll/rll19qhw4dguaLiIjQnTt3qqpqVlaWXnbZZaqqetNNN+nSpUt9Za666ir96quvcrTRq28gU6dODbpH/vjx41qzZk1VVb3zzju1Vq1aWqdOHb3iiiu0QoUK+vjjj+coUxiKoi0LpDjoqOr21Bfly7nfOwqCYAxnYekMTMJ0ytZYl0IBblPrjqeqtVV1q43PjcC61eaf7icnQlVH2fQM+5IByKTwHimzgDusp4Cq6rf56JebrpcEpKXZv1l+Ye+999ks9GtTI1XtTyFQVfr3709kZCSPPvpoYYo6HI6Lg12quhIzsG8ELLerxfFAnbOQN1tVM/3uP1bVNFU9BBzADGY3AjeIyIsicp2qHi2E/Gsxq9SpqpqCGTxfl4vMCMzAeqFt05OYFW0vY238AMD/3TrLL3wD8JrN9wFwmV1lnoWZJADjau9fBmCmje+GGYD6UxozmB6qqgvzaOtCVf3FhjvYax1mcrkhZnBd2GcZzHYVxJ6XBt4QkY3AbM6cR3Mt5jPPUtWfMV4fBaEXkFCAfP9V1QxMO0tiFgew93WD5E/HTFwArMklTw4OHjzIr7/+ChjPts8//5yIiAi+++47wNjSDz/8kIYNGwLQrVs3Fi82TT106BA7duygfv362WTWrFmTLVu2cPDgQQAWLlxIZGQkgM99HeCDDz7wye3atStvv/02qsrKlSupWLEi1atX5+DBg5w8eRIwq/fLly8nIiKC6tWrU6FCBVauXImq8vbbb3PLLbf4ZH/++ec0bNgwm4t+165dmTlzJmlpaezcuZNvv/2WFi1a0Lx5c7799lt27txJeno6M2fOpGvXrr72JiYmArBkyRLCw43jZO3atVm0aBEA+/fv56effqJ+/frs3r07qL6nT5/2/QJARkYGH330EdHR0b7n6N17P3r0aPr1M844M2bM4McffyQ5OZlx48Zxzz33ZDuZ3+FwBMe53zsKwiJgroi8rKqHrfs9AKp6VESO2M7FMqA3sMS66NVS1cUi8gVm72AI8BnwoIg8qKoqIlepcRtcinGfT7SD6dqY1fcmQHtb50lMh6kfZgZ/vtXpgE2voKqB+wn9OY7dr5mb3jbtexHJxLhHejtu24C6IhKmqt/55w9gv4hEWt1vtXUWlJXAJG8dYg6qClXVHXkVurR0SV94+fLlvPPOO8TExPh+zuaFF17gpptuKoQaDofjAibV/vVOIvYqQJm8JitTA+79JywzgVJqtiw1BW4CRovIAmAcUFtEKqhqXu/JoBOqucicC2xW1dwOwXtMg7uJ+7ehBNBSVU9mU0JkBRAmIlUxdui5ABkfAmMxK1bHAtzgT2MGnB0JbjeC6SHAaFUNtvc/W7tVNccWLT92A7X87kMxXm758QiwH7NiXgI45adXoRCRKzDb6G4tQHbvVowsEfGfoPdOfgdyVpP4+/btIz4+nszMTLKysrjjjjvo3Lkz1113HceOHUNVady4MVOmmCMIOnbsyIIFC2jUqBElS5Zk7NixXHHFFQB4PB7Wr19PjRo1GDlyJNdffz2lS5emTp06TJs2DYBhw4axfft2SpQoQZ06dXw/+XbTTTfxySefEBYWRrly5Zg6dSpgfu4tLi4OEUFVGTJkCDExMYA5xK5Pnz6cPHmSTp060alTJ1+7Zs6c6Tsgz0tUVBR33HEHjRo1olSpUkyaNImSJU2/4bXXXqNjx45kZmbSr18/oqKifPreddddvPzyy4SEhPDmm28C5qf5+vTpQ0xMDKrKgAEDqFKlCgsXLuSvf/1rDn1TU1Pp2LEjGRkZZGZmcsMNN3DfffcBkJSUxN/+9jdEhOuvv55JkyYV5KNzOBy5cb5dBdxVPC7MKs4m4BuMG+Mo7E/aAR7MgHQDZm9hZcws/xeY2fVNnHFnvxRzQJE3/iMb792zvhGzMtHGxvfB7B/8GDNQHumnUw+Mm/oGTIfpahufm6voNRiX9nUYd/scevuVG4LpyNb1i2tny24E3gLK2vgkzrgCdsfsTU3C7Dv01j2NM66fdbEujkHS2gKrrU4bgK75fTbBXAnPN0XVjczpVTiKol7O/f43vcd97x6gKmb7U5i9LweE2/BGzF53b7nvMNukSgDvB3uv2XufXbD3m2ydNTjjyt0NmGfDfwemAmXsfXXgbhtOxrjfN7HvwnIYF/xNGPf7HDKBMlbXlja+NBAVTFc/HX3vb3v/Hmbw7733+IXHYvaZfxKszZiV+iaBcjHu9yUxXgZeW9gUWOInpw/wmt99B4y7vnfrWk2gWm7P0q9cMtnd7ztjzlEQjHfGqgJ+P14G/mrDfTFea2C2LnxkvwtXYs5UyNP9HnNmzvQCfD8Dvz8pwdICn61fHp+9z+sqijYzkKL47g2G0/PcURx0VHXu90X5civ1jgKhqtMxh/kES1uP6SwEcm2QvCeBgUHiT2E6NcE4oKqDgpSZRU4XSNQe6GTDczD7+lHV5eT8SbtgeqOq4zArSf5xizCdycC8rYPVF5Cnj184GeMiGiwtEWgeTCeHw+E4V6jqQRHpAyT4/ZLHk8AOzF7u/4rIPlVtgzn09CPgJ8ygurC/yhGDcX3Pwhwg939+9T0HbBGRU5iV6qcC9FxrD+Tz/irJm6q6TkQ6BspU1XR7kN9EEamIWbWdgNmHX1AGYzymNtjyS7EHuWLszWpysVWqOreXR9wAACAASURBVDM3oaqaKSI9gQ9F5BjwBnBaRLwT5UcC8i+wXl8r7Kp/CuZ8mrDAdoP5tRlgKGaf+wYR+URV78Xsf78JM9lxgjwOqgtgMvC+iNyOcbH3ehG8j5ng3oT5rnyFORgwL3oCzn/a4XA4/oe4Qb3D4XA4HBcBQSYUg04iquqrwKt+9/lOVtr7UQH33rqSMVuvAsunYwaiQ4Ok1fULj8ecaO+f/lkuMtdjDpTLU1e/+NYB94c4s3c+MO/XBLifB7Y5mFzvRLNtb0e/bO0Cik0LkPEK5oA+f74neLsnAhODxCvwQDAdg+RNxn4/1JwlE+uX/DcbnyUiQ1Q1xbrVr8J4duQlt3UB6x8VcB8SLC3Ys7XhoN9Th8PhuBhwg3pHkUZVpxHQ0XE4HA6Hw3He+Mj+JG0ZzE/q/Xy+FXI4HI6LHTeodzgcDofD4bhIEJEYzNkA/qSpalxBygdbeReRuZjfmPfncetREZi3L/BQQPRyVS2QR4HD4XA4cuIG9Q6Hw+FwOBwXCaq6EXNQ7LmUWZCT7b15p2IOSHQ4HA7HOcL9Tr3D4XA4HA6Hw+FwOBzFFDeodzgcDofD4XA4HA6Ho5jiBvUOh8PhcDgcDofD4XAUU9yg3uFwOBwOh8PhcDgcjmKKG9Q7HA6Hw+FwOBwOh8NRTHGDeofD4XA4HA6Hw+FwOIopblDvcDgcDofD4XA4HA5HMcUN6h0Oh8PhcDgcDofD4SimuEG9w3EB8dNPP9GmTRsiIyOJiorilVdeOd8qORwOh8NRpMjLVr766qtEREQQFRXF0KFDAcjIyCA+Pp6YmBgiIyMZPXp0ULl9+vShXr16eDwePB4P69evB2Dbtm20bNmSsmXLMm7cOF/+U6dO0aJFCxo3bkxUVBQjR470pe3cuZO4uDgaNGhAjx49SE9PByAtLY0ePXoQFhZGXFwcycnJ+erYr18/qlWrRnR0dDZ9Z8+eTVRUFCVKlODrr7/2xc+YMcPXBo/HQ4kSJXxtSU9PZ8CAAYSHh3PPPffw/vvvA/DII4/48oeHh1OpUiWfvJIlS/rSunbt6otftGgRTZo0wePxcO211/Ldd9/l99E5HI7cUFV32Qt4GCj3O9RTGhgDfAtsAlYBnWxaMlDlHNY1DdgJrAfWAi3PkdxRwB4rdwvQ6xzIbAisANKAIQFpNwLbge+AYfnISQKancNn+Jht53r7eWUCl/+vvycFvcLDw9XL3r17dc2aNaqqeuzYMW3QoIFu3rxZf28WL178u9dZEJxehaMo6vV76AR8rUXgf7uoX85mFkqus5n/4+9JQa/w8PBcbWViYqK2a9dOT506paqq+/fvV1XVGTNmaI8ePVRVNTU1VevUqaM7d+7UQOLj43X27Nk54vfv36+rVq3SJ554QseOHeuLz8rK0uPHj6uqanp6urZo0UJXrFihixcv1ttvv10TEhJUVXXgwIE6efJkVVWdNGmSDhw4UFVVExIS9I477shXxyVLluiaNWs0Kioqm15btmzRbdu2aatWrXT16tU59FZV3bBhg9arV893/9RTT+nw4cNVVXXRokV68ODBHGUmTpyoffv29d2XL18+qOwGDRroli1bfO2Kj48Pmu+3UhRtWSDFQUfV4Ho6m1k0rotupV4MubX7YaBcIeWVOgs1ngWqA9GqGg10ASqchZyC8piqeoBhwD8CE8+yDQAvW7m3AP8QkdK/QUeAX4DBwDj/SBEpCUwCOgGNgF4i0ug31lVgVHWsqnpsW/8GLFHVX36v+vPjZEamL1y9enWaNGkCQIUKFYiMjGTPnj3nSzWHw1HMcTbT2czCUtRtJuRuK6dMmcKwYcMoW7YsANWqVQNAREhNTeX06dOcPHmSMmXKcNlllxW4vmrVqtG8eXNKl87+kYsIISEhgFlpz8jIQERQVRITE+nevTsA8fHxzJs3D4D58+cTHx8PQPfu3Vm0aBGqmqeO119/PZdffnkOvSIjI4mIiMhT94SEBHr16uW7f+utt/jb3/4GQIkSJahSpUq+ZXJDRDh27BgAR48epUaNGvmWcTgcwbkoBvUiUldEtorIZMzMe28RWSEia0VktoiEiMhgoAawWEQW23IpfjK6i8g0G54mIuNtvhdFZJSIvCUiSSLyg5WFiJQXkY9F5BsR2SQiPUSkHHAf8KCqpgGo6n5V/XcQvR+15TaJyMO5ybTxTUVkiYisEZHPRKR6kEexFAiz+ZNE5AURWQI8JCJVReR9EVltr2tEpISIJItIJT+dvhORK/2Fquq3wAmgss1zn5XxjZVZTkRK2mcjIlJJRLJE5Hqbf5mIhKnqAVVdDWQE6N0C+E5Vf1DVdGAmplOULyIyRUS+FpHNIvK0X/xNIrJNRL4QkYki8lFB5AG9gIQ86qtr5b5pP58ZInKDiCwXkW9FpIXN10JEvhSRdfZvhI1/VETesuEYK6NQnWYvycnJrFu3jri4uLMp7nA4LlKczfThbOaZ+AvWZvrbyh07drBs2TLi4uJo1aoVq1evBszguXz58lSvXp3atWszZMiQoINkgOHDhxMbG8sjjzxCWlpavvVnZmbi8XioVq0a7du3Jy4ujmPHjlGpUiVKlTLzR6Ghob4J+j179lCrVi0ASpUqRcWKFTl8+HChdCwMs2bN8g3Qf/31VwBGjBhBkyZNGDVqFPv378+Wf9euXezcuZO2bdv64k6dOkWzZs24+uqrfZMTAG+++SY33XQToaGhvPPOOwwbNuw36+twXKyc7WxzcSQC6As8BfwHuEFVU0XkceBRVX1GRB4F2qjqoQLIC7cyMkVkFMYNrg1m9WC7iEzBuL/tVdXOACJSEdNB+FFVj+UlXESaWn3jAAG+sp2J+oEyxcz2vwrcoqoHbafleaBfgNguwEa/+0qq2srKeQ+zivCFiNQGPlPVSBGZD9wKTBWROCBZVfeLiL+uTYBvVfWAjfqPqr5h054D+qvqqyKyA7NqUA9YA1wnIl8Boaqa10aqmsBPfve77XMpCMNV9RcxKxeLRCQW2IFZfbleVXeKSK4dDn9sR+FGYFA+WcOA24EBwGrgTuBaoCvwBNAN2GbrPy0iNwAvALcBE4AkEbkVGA4MVNUTQXQZYOVTpUpVkpKSsqWfPHmShx56iHvvvZe1a9cWpHnnlJSUlBw6FQWcXoWjKOpVFHW6QHE209nMC9JmVq16xmYG2sqjR4+yceNGxowZw7Zt2+jatSvvvfcemzZt4tChQyQkJHD8+HEeeughQkJCcqwsd+nShfj4eDIyMnjppZe4//77favqYCYQLr300hzvsAkTJpCSksKIESNo2LAhZcqU4eTJk758Bw4c4MSJEyQlJZGSksKKFSuoWrUqYAbMy5cv58cff8xTx59//pnU1NSg789ff/2VNWvWkJKSki1+y5YtqCqHDh0iKSmJo0ePsnv3bipWrMj48eN599136d27N0888YSvTEJCAi1btmTZsmW+uJkzZ1KlShX27t3L/fffT2pqKjVr1uSpp57i2WefpVGjRsycOZNevXrx2GOP5dDvt1Ic7EZx0BGKj54XIxfToH6Xqq4UkZsxRnK5NbJlMHvSCstsVc30u//YriKkicgB4EpMZ2CciLwIfKSqy/wNez5cC8xV1VQAEfkPcB3waRCZ0UA0sNDKLwns85M1VkSeBA4C/f3iZ/mFbwAa+el3mYhUsHmeAqYCPQPKPCIi92E6TTf6xUfbjkklIAT4zMYvA67HdFBGY1ZflmCMeF4Ee2iaTxkvd1hjXgrjvtkI46Hyg6rutHkSsMY+H7oAywvgRrhTVTcCiMhmYJGqqohsBOraPBWB6SLSwLalNICqZolIH2AD8A9VXR6sAlX9J/BPgNr1w7R169a+tIyMDG6++Wbuv/9+Hn300QI069yTlJSEv05FBadX4SiKehVFnS5QnM10NvOCtJkRERHaunXroLYyIiKCwYMH07p1a9q0acO4ceOIjo5mzpw5xMfHc8MNNwDw4YcfUqpUqTzfRWXKlGHcuHHZ8iQlJRESEpJruTVr1nD48GGaNm1KWloa1157LaVKlWLFihU0aNCA1q1bExERQWhoKC1btuT06dOkpaXRtWtXBg0alKeOycnJlC9fPmjdlSpVomnTpjRr1ixb/Pz587n33nt9ZVSVcuXKMWLECEqUKMGBAwd4+umns8l85JFHmDRpEn/605+CtnHBggWULVuWqKgo9uzZw1/+8hcA6tevz4033vg/eb8XB7tRHHSE4qPnxchF4X5vSbV/BVjo3e+lqo1UtX8uZfyN4CW5yPPi72OVCZRS1R1AU0xHZbSIPIU5tKa2Nf55EbQnk4tMATb7tSlGVTv4FXvMxrdX1U25tKEE5kAgr4yaqnoc03kLE5GqmNny//iVeVlVI4AewNsi4n1G04BBqhoDPM2ZZ7cM08lqAXyC6cC0xrg45sVuoJbffSiwN58yiEg9YAjQTlVjgY+tLgXuJQbQkzzcCP3w/y5k+d1ncWYi7Vlgsd/+UP/vVwMgBePami+Xli7pC6sq/fv3JzIy8rwN6B0OxwWBs5nOZl6QNhNyt5XdunUjMTERgB07dpCenk6VKlWoXbs2iYmJqCqpqamsXLmShg0b5pC7b98+n/x58+blOG0+kIMHD/pc2k+ePMnnn39Ow4YNERHatGnDnDlzAJg+fTq33GJ2UHTt2pXp06cDMGfOHNq2bYuIFFjHgpKVlcXs2bPp2bOnL05E6NKli2+ldu3atTRqdOa4hu3bt3PkyBFatmzpizty5IhvG8KhQ4dYvnw5jRo1onLlyhw9epQdO3YAsHDhQiIjI89aX4fjYudiGtR7WQlcIyLefXLlRCTcph0n++E7+0UkUswhQbcWtiIRqQGcUNV3MQfZNLFuYf8CJopIGZuvuojcHVB8KdDN6lfe1r8smEzMCbdVRaSllVdaRKIKqe4C/FzkRMQDoKoKzAXGA1tV9XBgQVX9D/A14PUxqwDssy6Od/ll/Qr4E5ClqqcwJ+MOxHRc8mI10EBE6tln1hP4oABtugzTCTsqZk9jJxu/DagvInXtfY/8BFk30FbA/ALUWxAqYk5CBugTUM8rmNWZK0Ske2GELl++nHfeeYfExETfz8d88skn50hlh8NxEeJsZnCczcyDom4zc7OV/fr144cffiA6OpqePXsyffp0RIQHHniAlJQUoqOjad68OX379iU2NhaAm266ib17zZzJXXfdRUxMDDExMRw6dIgnn3wSMK7voaGhjB8/nueee47Q0FCOHTvGvn37aNOmDbGxsTRv3pz27dtz8803A/Diiy8yfvx4wsLCOHz4MP37m7m0/v37c/jwYcLCwhg/fjxjxowByFPHXr160bJlS7Zv305oaCj/+te/AJg7dy6hoaGsWLGCzp0707FjR98zWrp0KaGhodSvXz/bs3vxxRcZNWoUsbGxLFiwgJdeesmXlpCQQM+ePfH3sNm6dSvNmjWjcePGtGnThmHDhtGoUSNKlSrFG2+8wW233Ubjxo155513GDt2bEE+PofDEYSLyf0eALt/rg+QICJlbfSTmD1j/wT+KyL7VLUN5uTbjzB70zZh3OIKQwzGjS8Lc5DN//nV9xywRUROYYzoUwF6rhVzyNAqG/Wmqq4TkY6BMlU13RqyidbAlcLsM9tcCF0HA5NEZIMtvxS436bNwnQS+uRR/hngPRF5AxiB6YzswqyOVLBtShORnzCdRDAdk142DyLyB0xH5zIgS8xBR41U9ZiIDMK4JJYE3lLVfNumqt+IyDrMc/gBWG7jT4rIX4BPReQQZ55xXtwKLPC6dp4D/o5xJXwUSPSLfxmYrKo7RKQ/5hCqpX57L/Pk2muvxfQpHQ6H47fjbGauOJuZN0XaZuZlK999990ccSEhIcyePTtofv+Jc+8qfyB/+MMf2L17d4742NhY1q1bF7RM/fr1WbUq56O+5JJLguqSl44JCcEdJm699VZuvTX4/Fvr1q1ZuXJljvg6deqwdKlxFklKSqJ27dq+tFGjRuXI/6c//YmNGzfmiM+vfofDUTjEDQAcFyMiEqKqKWKmkydhDi16+XzrdTZERETo9u3bz7ca2Siqe66cXoWjKOr1e+gkImtUtVn+OR2OiwNnM39fiuK7NxhOz3NHcdARguvpbGbR4GJ0v3c4AO4TkfWYFYmKBPktYofD4XA4HICzmQ6Hw1Gkuejc7x0XDiIyF3MqsD+Pq+pnwfL7Y1cYsq0yiEhf4KGArMtV9YEgdV8BLAoiul2wPZQOh8PhcJxPnM10OByOCxc3qHcUW1T1nG7EUtWpmJ8hKkjew4DnXNbvcDgcDsf/CmczHQ6H48LFud87HA6Hw+FwOBwOh8NRTHGDeofD4XA4HA6Hw+FwOIopblDvcDgcDofD4XA4HA5HMcUN6h0Oh8PhcDgcDofD4SimuEG9w+FwOBwOh8PhcDgcxRQ3qHc4HA6Hw+FwOBwOh6OY4gb1DofD4XA4HA6Hw+FwFFPcoN7hcDgcDofD4XA4HI5iihvUOxwXCP369aNatWpER0efb1UcDofD4SiyBLOXI0aMIDY2Fo/HQ4cOHdi7dy8AR48epUuXLjRu3JioqCimTp0aVGZ6ejoDBgwgPDychg0b8v777wMwfvx4GjVqRGxsLO3atWPXrl2+MjfeeCOVKlXi5ptvziZr3759xMXF0aBBA3r06EF6ejoAaWlp9OjRg7CwMOLi4khOTgZg4cKFNG3alJiYGJo2bUpiYqJP1vDhw6lVqxYhISHZ6ti1axft2rUjNjaW1q1bs3v3bgAWL16Mx+PxXZdccgnz5s0D4K677iIiIoLo6Gj69evH6dOnffKSkpLweDxERUXRqlUrX3zdunWJiYnB4/HQrFmzbDq8+uqrREREEBUVxdChQ/Nti8PhyANVdZe73FWMr/DwcFVVXbJkia5Zs0ajoqL0fLN48eLzrUJQnF6Foyjq9XvoBHytReB/213ucte5v8LDw4Pay6NHj/rCr7zyig4cOFBVVZ9//nkdOnSoqqoeOHBAK1eurGlpaRrIU089pcOHD1dV1czMTD148KCqqiYmJmpqaqqqqk6ePFnvuOMOX5nPP/9cP/jgA+3cuXM2Wa1atdKEhARVVR04cKBOnjxZVVUnTZrk0yshIcEna+3atbpnzx5VVd24caPWqFHDJ2vFihW6d+9eLV++fLY6unfvrtOmTVNV1UWLFundd9+do02HDx/WypUr+/T/+OOPNSsrS7OysrRnz5768MMPq6rqkSNHNDIyUnft2qWqqvv37/fJqFOnju9Z+JOYmKjt2rXTU6dOZSuTV1vOlqJoywIpDjqqBtfT2cyicV1QK/Ui8rCIlPsd6iktImNE5FsR2SQiq0Skk01LFpEq57CuaSKyU0T+v707j6+iPPs//rkAgUdAFAMuBEWKJUBYXZBqEbSsWtA21qW1YKCKltZqQdCHp2L1adj0QcVK3SougIqloj9kEQ0oolLZEYJUUoEiCAISkC1cvz9mcnqSnBNICJwc+L5fr3mdOffM3HPNfZK55j6znMVmttDM2pdTvcPMzM2scVTZXWHZheH7vBjLNTSzm8ojhrC+AWa2JlxvSlS5mdlj4bSlZta2hDoamtny8oqppLiipl9kZvlmllEO6+pvZr880no6dOhAnTp1jrQaETlBKGeWql7lzDLEFTW9QuXMWPnylFNOiYzv2rULMytYHzt37sTdycvLo06dOlSpUqVYnc899xz33nsvAJUqVSIlJWiGTp06cfLJwb/ZJZdcEjkjDnDllVdSq1atQvW4O4sWLSIjI2iq3r17R86Uv/HGG/Tu3RuAjIwMZs+ejbvTpk0bzj77bACaN2/Onj172Lt3b2SdZ511VrF4P/vsM6688spIjG+88UaxeSZPnkz37t0j8ffo0QMzw8y4+OKL+frrrwGYMGECP/nJTzjnnHMAqFevXrG6inryyScZMmQI1apVK7RMSdsiIvElXac+TFzx4v4dUKoDFDMrvmc+tAeBs4B0d08HfgzUKnmRIzLI3VsDQ4C/FJ1Yxm0AWAbcEPU+A/jsEMs0BGIeoJQxjnnAj4B/FSnvDpwfDrcCT5ah7iMRLy7MrDIwAphRHity93Hu/kJ51CUiEk05UznzGDkucmbBpeovv/wyf/zjHwEYMGAAK1eu5Oyzz6ZFixY8+uijVKpU+F9q+/btQHAJf9u2bbnuuuvYtGlTsfqfffZZunfvXmIMW7dupWbNmpEvDlJTU9mwYQMAGzZsoEGDBgBUqVKF2rVrs3Xr1kLLv/7667Rp0ybSWY6nVatWkVsEpkyZws6dO4vVNWnSJG688cZiy+7fv58XX3yRiy++GIDVq1ezbds2OnbsyAUXXMALL/zn4zEzunTpwgUXXMBTTz0VKV+9ejXvv/8+7dq14/LLL2fBggXF1nO42yIiUNbEdkyZWUPgbeA9oD0wxsz6A9WAfwK3AJnA2cB7ZrbF3TuZWZ671wzryACudvc+ZvY88A3QBlhoZjuBc4BG4esYd3/MzGoArwKpQGWCA5M3gV8B57n7XgB33xTOVzTuu8O4AJ5x9zGx6nT3V8zsAuARoCawBejj7huLVDkXaBzWnQ18CFwKTDWzF4BxYfwQHKzNB74AWrv79nC5NeEyAH8HegEPmVkjYAewP8Z2pITb/RDwP0BTM1sMjAe2AVcB1YEawBVmNgj4Wfj5THH3++Ntt7svCtdRdLW9gBfCy3o+MrNTzeysGG1SNNaGwIthLAAD3P3D8KB2LHA5sJbgC63n3H1yrHpKiAvgN8DrwEWHiKUj8ACwCWgN/I3goPBO4L+Aa9z9n2Y2DMhz99Hh5/ox0Ak4Fejr7u/HqPtWggM36tatS3Z2NgBfffUVu3btirxPlLy8vITHEIviKp2KGFdFjKmiUc6MUM5Uziyou1jOjJUvO3fuTOfOnXn55ZcZOHAgt9xyC3PmzCElJYUJEybw73//m379+vHMM89Qo0aNyHI7duxg/fr11K5dm0ceeYRXX32Vm2++mfvuuy8yz6xZs3j33XcZM2ZMoXUuXryYrVu3Rsq2b9+Ou0feb968md27d5OdnU1eXh7z58+nbt26AOzZs4d58+ZRu3ZtANauXcvQoUMZOXJksf1kfn5+obKf/OQnPPbYY4wdO5aWLVuSkpLC/PnzI/feb926lYULF1K9evVidY0ePZpGjRrRqFEjsrOz+de//kVOTg4PP/ww+/bt49e//jVmRoMGDRg1ahQpKSls27aNgQMH8t1339GqVSt27NjBsmXLGD58OKtWraJnz55MmDAh8jdU0raUVjLkjWSIEZInzhNSoq//P5yB4Jvug8AlQApBoq4RThsM/CEczwVSopbLixrPAJ4Px58H3gIqh++HEST7amH9W4GTgJ8CT0fVURtoCSwqIdbcsI4LCJJRDYKDjhUEB0Sx6jwpXH/dsOx6guRZEGtGOH4d8HE4ng38OaqeCcBl4fg5wMpw/FHglnC8HfBO1DYPJEia6cB/A73Dei8saD/gDIKE2Tks6wi8FbXePsB6oE74vgvwFGAEBwFvAR1ibXesdot6/1bB9oTvZxfEFefvY3k4fjJQPRw/n/A+n/DznxbGdCbBgVXGYfztFY2rPjCH4CDr+ZLqCNtqO8EZqmrABuCBcNqdBAfCkc8i6nN9OBzvUfB5lTQU3FPv7r527VrdU18CxVU6FTEu3VOvnIlyZqzPTjmzlDmzpHyZm5sbmdajRw+fO3duZFqnTp38448/LjT/wYMH/eSTT/b8/Hx3d//yyy+9WbNmkemzZs3ytLS0QveaF3jvvfcK3VN/8OBBP+WUU3z//v3u7v7hhx96ly5d3N29S5cu/uGHH7q7+/79+/3000/3gwcPurv7unXr/Pzzz/cPPvgg5jYVvac+2s6dO71+/fqFysaMGeO/+tWvis07bNgw79Wrl+fn50f2x1lZWX7//fdH5snMzPRXX3212LL333+/jxo1yt3du3btWmh/3qhRI9+8efNhbUtpVcRcVlQyxOiue+or8pBMl9//y90/IjhIaQbMC7/57g2cW4b6XnP3/Kj3/8/d97r7FmAzQWJeBvzIzEaY2Q/dfUcp6r+M4Bv3Xe6eR3Ag8MM4dTYhOEiYFW7TUIJv5wuMCstvBfpGlb8SNf4jYGw431TgFDOrFc5zfTjPDUWWAZgUll8DTCky7SSCA4N73H1WCds6y92/Cce7hMMiYCGQRnCgUNq2jPV1vx9imYKYnzazZcBrBH8rEHwer7n7QXf/iuAMVlmMAQYX+dspyQJ33+jBGap/AjPD8mUEB1ax/C18/bSEeURESqKcqZypnFkKn3/+eWR86tSppKWlAXDOOecwe/ZsADZt2kROTg6NGjUqtKyZ8eMf/zhyBnP27Nk0axY05aJFi7jtttuYOnXqYd1rbma0adOGyZODiyLGjx9Pr169AOjZsyfjx48Hgvvdr7jiCsyM7du3c9VVV5GVlcWll14at+5oW7Zs4eDBgwBkZWWRmZlZaPrEiROLXXr/zDPPMGPGDCZOnFjoFoRevXrx/vvvc+DAAXbv3s3HH39M06ZN2bVrFzt37gSC5xTMnDkz8osD11xzTeTJ9qtXr2bfvn2kpKSUaVtEJEkuvw/tCl+NICEWv8mnuOiEVj1OfQWin8KRD1Rx99XhJX49gCwzmwmMBs4xs1ruvrOEdce8Bi1OnVOAFe4e74E+gzz2JW/R21AJaO/u3xUKwmw+0NjM6hIchDxUpI43gVEE37J9W+TSuQMESbIrwTft8UTHYUCWu8e6j7HQdrv7H0uocz3QIOp9KvDvEuYvcBfBpXutCNpkT1Rc5eFCYFLYTilADzM74O5/jzN/9N/Vwaj3B4n//1cwT34J8xRz4403kp2dzZYtW0hNTeWBBx6gb9++h15QRI5HypnFKWcWd0LmzFj5ctq0aeTk5FCpUiXOPfdcxo0b67pv9gAAGcBJREFUBwT3yffp04cWLVrg7owYMSLyELzWrVuzePFiAEaMGMHNN9/M7373O+rWrRv56btBgwaRl5fHddddBwRfEkydOhWAH/7wh6xatYq8vDxSU1N59tln6dq1K7feeiuPPPIIQ4cOpU2bNpFc3rdvX26++WYaN25MnTp1mDRpEgBjx45lzZo1PPjggzz44IMAzJw5k3r16nHPPfcwYcIEdu/eTWpqKv369WPYsGFkZ2dz7733YmZ06NCBJ554ItI+ubm5rFu3rtBP0wH079+fc889l/btg3+9Nm3a0LFjR5o2bUq3bt1o2bIllSpVol+/fqSnp/PFF19w7bXXAnDgwAFuuukmunXrBgQ/K5iZmUl6ejpVq1Zl/PjxmFmJ2yIi8SVTp77AR8ATZtbY3ddY8OTeVHdfDewkePjOlnDeTWbWFMgBrg2nHzYzOxv4xt1fsuCptn3cfbeZPQs8Zma3ufs+MzsLuNLdX4pafC7wvJkNJ0iO1wI3x6oTGA7UNbP27j7fzE4Cvu/uK0oR7kxgAMHBBmbW2t0Xu7ub2RSCew9Xunuhp6C4+3dmNhhYHaNOJ7i/8TUzG+Luw/lPG8czA3jQzF529zwzq09wz2GVGNtdkqnAADObRHAJ5A4/xL2BodrAenc/aGa9CS75A/gA6G1m44G6BJf5TTiM+gpx9/MKxsP7TN8q4eDkmJo4cWKiQxCRikc5MzblzMAJmTNj5ct4X4KfffbZzJw5M+a0gg49wLnnnsvcuXOLzfPOO+/EjeP994vd/h9Z5yeffFKsvHr16rz22mvFyocOHcrQoUNj1jVy5EhGjhxZrDwjIyPyhP2iGjZsGHk4X7To36UHCt1bPWjQIAYNGlRoeqNGjViyZEnMdVStWpWXXnqpWHlJ2yIi8SVdp97dvzazPsBEMyt4HOZQggT7FPC2mW10904ET759C1gHLCe4T680WhBcxneQIMneHrW+h4DPzGwPwbfufygS58IwgRXslZ9x90Vm1rVoneFBTgbBQU9tgs9lDME9hYfrtwQHbkvD5ecC/cNprwALiHNQ4O6T4lXq7vlmdgPwppl9CzwNHDCzJQT3x20rMv/M8KBwfvjNfB7wC4KHFRVrSzP7LXAPwT17S81smrv3I7iXrwewBthN8GCnw/Fn4HUzu47gcsGCMyKvA1cS/B2sJrjnMe7ljCXEJSKSNJQz41LODChniogcB8z9cG65Ekl+ZlYzPBNyOsGB46XhvYJJrUmTJp6Tk5PoMArJzs6mY8eOiQ6jGMVVOhUxrmMRk5l96u4XHtWViFRwypmJUxH3vbEozvKTDDFC7DiVMyuGpDtTL3IE3jKzU4GqBD8PlPQHJyIiIkeJcqaISJJQp16Shpm1IPg93Wh73b3d4Szv7h1j1DkFOK9I8WB3n3Gs4hIRESlvypkiIicOdeolabj7MqB1Odd5bTnUUe5xiYiIHAnlTBGRE0cy/U69iIiIiIiIiERRp15EREREREQkSalTLyIiIiIiIpKk1KkXERERERERSVLq1IuIiIiIiIgkKXXqRURERERERJKUOvUiIiIiIiIiSUqdepHjRGZmJvXq1SM9PT3RoYiIiFRYsfLloEGDSEtLo2XLllx77bVs374dgK1bt9KpUydq1qzJgAEDSqz38ccfp0mTJjRv3px77rknUr506VLat29P8+bNadGiBXv27GH37t1cddVVpKWl0bx5c4YMGRKZf9++fVx//fU0btyYdu3akZubG5mWlZVF48aNadKkCTNmzABg3bp1dOrUiaZNm9K8eXMeffTRyPzDhg2jfv36tG7dmtatWzNt2rQS4wLo1q0brVq1onnz5vTv35/8/PxC2zl69GjMjB07dgAwatSoSP3p6elUrlyZb775BoDt27eTkZFBWloaTZs2Zf78+WWOS0TiU6de5AiYWUMzW57oOAD69OnD9OnTEx2GiIhITBUlZ8bKl507d2b58uUsXbqU73//+2RlZQFQvXp1HnzwQUaPHl1ine+99x5vvPEGS5cuZcWKFQwcOBCAAwcO8Itf/IJx48axYsUKsrOzOemkkwAYOHAgq1atYtGiRcybN4+3334bgGnTpnHaaaexZs0a7rrrLgYPHgzAZ599xqRJk1ixYgXTp0/njjvuID8/nypVqvDwww+zcuVKPvroI5544gk+++yzSGx33XUXixcvZvHixfTo0eOQcb366qssWbKE5cuX8/XXX/Paa69F6lq3bh2zZs3inHPOiZQNGjQoUn9WVhaXX345derUAeDOO++kW7durFq1iiVLltC0adMyxyUi8alTL3Kc6NChQySJioiISGyx8mWXLl2oUqUKAJdccgnr168HoEaNGlx22WVUr169xDqffPJJhgwZQrVq1QCoV68eADNnzqRly5a0atUKgNNPP53KlStz8skn06lTJwCqVq1K27ZtI+ucN28evXv3BiAjI4PZs2fj7rzxxhvccMMNVKtWjfPOO4/GjRvzySefcNZZZ9G2bVsAatWqRdOmTdmwYUOJ8caLC+CUU04Bgg72vn37MLPIcnfddRcjR44sVBZt4sSJ3HjjjQB8++23zJ07l759+0a289RTTy1zXCISnzr1IkWY2QgzuyPq/TAz+72ZjTKz5Wa2zMyuj7FcHzMbG/X+LTPrGI7nhfV+ambvmNnFZpZtZl+YWc9wnsrhOhaY2VIzu+0YbK6IiEiZHY8587nnnqN79+6lWmb16tW8//77tGvXjssvv5wFCxZEys2Mrl270rZtW0aOHFls2e3bt/Pmm29y5ZVXArBlyxYaNGgAQJUqVahduzZbt25lw4YNkXKA1NTUYp333NxcFi1aRLt27SJlY8eOpWXLlmRmZrJt27bDiqtr167Uq1ePWrVqkZGRAcDUqVOpX79+pMNd1O7du5k+fTo//elPAfjiiy+oW7cut9xyC23atKFfv37s2rXriOISkdiqJDoAkQpoEjAG+HP4/mfACKAb0ApIARaY2dxS1FkDyHb3wWY2BXgI6Aw0A8YDU4G+wA53v8jMqgHzzGymu68tWpmZ3QrcClC3bl2ys7MB+Oqrr9i1a1fkfaLk5eUlPIZYFFfpVMS4KmJMIie4pMyZ8fLlSy+9xPbt26lfv36haatWrWLDhg1x9z87duxg2bJlDB8+nFWrVtGzZ08mTJhATk4O77zzDuPGjaNatWr8/ve/p3LlylxwwQUA5Ofnc99999GjRw++/PJLvvzyS/Lz85k/fz5169YFYM+ePcybN4/169ezcuXKSAwbN25kxYoVpKSkAPDdd99x55130q9fPxYuXAhAy5YtefbZZzEznnvuOW666SYGDx58yLjuvfde9u3bx0MPPcT//d//kZ6ezuDBgxk1ahTZ2dns2bOn2P743XffJS0tjaVLlwKQk5PDp59+Sp8+fejTpw+PP/44t99+O5mZmWWOqyySIW8kQ4yQPHGeiNSpFynC3ReZWT0zOxuoC2wDWgMT3T0f2GRmc4CLgKWHWe0+oOAGvmXAXnffb2bLgIZheRegpZllhO9rA+cDxQ5Q3P0p4CmAJk2aeMeOHYHgG/oaNWpQ8D5RsrOzEx5DLIqrdCpiXBUxJpETWbLmzFj5cvz48axYsYLZs2dz8sknF6ojNzeXvLy8uPufJk2a8Nvf/paOHTvSqVMnRo8eTXp6Ops2beK7776jV69eACxYsICDBw9G6snMzKRdu3Y89thjkbrOOOMMUlNTad++PQcOHGDv3r307Nkzcp98wbJZWVl06dKF9u3bs3//fq6++mr69+/P3XffHTPGRo0acfXVV9OxY0e++uqrEuMqsHHjRhYsWEDXrl3ZunVr5GGBW7Zs4e6772bJkiWceeaZADz66KMMGDAgUkdaWhpZWVnccUdwIUflypUZPnx4sXWUJa7SSIa8kQwxQvLEeSLS5fcisU0GMoDrCc5CxL55rLADFP6fir4Bb7+7ezh+ENgL4O4H+c+Xawb8xt1bh8N57j7zCLZBRETkWEj6nDl9+nRGjBjB1KlTi3XoD8c111zDu+++CwSXkO/bt4+UlBS6du3K0qVL2b17NwcOHGDOnDk0a9YMgKFDh7Jjxw7GjBlTqK4f/OAHjB8/HoDJkydzxRVXYGb07NmTSZMmsXfvXtauXcvnn3/OxRdfjLvTt29fmjZtWqxDv3Hjxsj4lClTIk/8jxdXXl5eZJkDBw4wbdo00tLSaNGiBZs3byY3N5fc3FxSU1N56qmnIh36HTt2MGfOnEhnHODMM8+kQYMG5OTkADB79uzItpc2LhEpmc7Ui8Q2CXia4LLBy4H2wG1mNh6oA3QABlH4ICQXuMPMKgH1gYtLuc4ZwO1m9m54RuL7wAZ333WoBQFuvPFGsrOz2bJlC6mpqTzwwAORh9OIiIgcRUmVM2Ply6ysLPbu3Uvnzp2B4GF548aNA6Bhw4Z8++237Nu3j7///e/MnDmTZs2a0a9fP/r378+FF15IZmYmmZmZpKenU7VqVcaPH4+Zcdppp3H33Xdz0UUXYWb06NGDq666ivXr1/O///u/pKWlRR5yN2DAAPr168dVV13F008/TePGjalTpw6TJk0CoHnz5vzsZz+jWbNmVKlShSeeeILKlSvzwQcf8OKLL9KiRQtat24NwJ/+9Cd69OjBPffcw+LFizEzGjZsyF/+8heAuHFt2rSJnj17snfvXvLz87niiivo37//IT+MKVOm0KVLF2rUqFGo/PHHH+fnP/85+/bto1GjRvz1r38FKHVcIlIy+88XoSISLbzMb4u7d7LgMa8jge6AAw+5+ytm1hB4y93Tw3leIrjscDlwBjDM3bPNLM/da4b1DgPy3H10+D7P3WuGBzYPAT8mOAPxNXCNu+8oKc4mTZp4wbfgFUVFvTxLcZVORYzrWMRkZp+6+4VHdSUixxnlzPJTEfe9sSjO8pMMMULsOJUzKwadqReJw91bRI07wVmGQUXmyQXSo+b5eZy6akaND4s1Lbys8L5wEBERSRrKmSIiiaN76kVERERERESSlDr1IiIiIiIiIklKnXoRERERERGRJKVOvYiIiIiIiEiSUqdeREREREREJEmpUy8iIiIiIiKSpNSpFxEREREREUlS6tSLiIiIiIiIJCl16kVERERERESSlDr1IiIiIiIiIklKnXoRERERERGRJKVOvYiIiIiIiEiSUqdeREREREREJEmpUy8iIiIiIiKSpNSpFxEREREREUlS6tSLiIiIiIiIJClz90THICJHwMx2AjmJjqOIFGBLooOIQXGVTkWM61jEdK671z3K6xCRBKigObOoirjvjUVxlp9kiBFix6mcWQFUSXQAInLEctz9wkQHEc3M/lHRYgLFVVoVMa6KGJOIJJUKlzOLSpb9nOIsP8kQIyRPnCciXX4vIiIiIiIikqTUqRcRERERERFJUurUiyS/pxIdQAwVMSZQXKVVEeOqiDGJSPJIhn1IMsQIirM8JUOMkDxxnnD0oDwRERERERGRJKUz9SIiIiIiIiJJSp16ERERERERkSSlTr1IkjKzbmaWY2ZrzGxIgmPJNbNlZrbYzP4RltUxs1lm9nn4etoxiOM5M9tsZsujymLGYYHHwvZbamZtj2FMw8xsQ9hei82sR9S0e8OYcsys69GIKVxPAzN7z8xWmtkKM7szLE90e8WLK+FtJiLJK1E5szT5saT9rJn1Duf/3Mx6R5VfENa/JlzWDiOmcsmVpY2ptMcF5ZU/4332ZnaemX0cxvOKmVUNy6uF79eE0xuWEGO55dKj2Z4lxFmh2lOOgLtr0KAhyQagMvBPoBFQFVgCNEtgPLlASpGykcCQcHwIMOIYxNEBaAssP1QcQA/gbcCAS4CPj2FMw4CBMeZtFn6W1YDzws+48lGK6yygbTheC1gdrj/R7RUvroS3mQYNGpJzSGTOLE1+jLefBeoAX4Svp4Xjp4XTPgHah8u8DXQ/jJiOOFeWJaZ46yhlnKXKBSV99sCrwA3h+Djg9nD8DmBcOH4D8EoJMZZLLj3a7VlCnBWqPTWUfdCZepHkdDGwxt2/cPd9wCSgV4JjKqoXMD4cHw9cc7RX6O5zgW8OM45ewAse+Ag41czOOkYxxdMLmOTue919LbCG4LMud+6+0d0XhuM7gZVAfRLfXvHiiueYtZmIJK2KljNLu5/tCsxy92/cfRswC+gWTjvF3ed70GN6gcPIteWUK8sSU6mOC8opf8b87MOz3VcAk+Nsc0Gck4ErC86Ox4ixvHLpUW3PcsytR7U9pezUqRdJTvWBdVHv11Pyzvloc2CmmX1qZreGZWe4+0YIkglQL0GxxYsj0W04ILz07rmoS+YSElN4KVwb4GMqUHsViQsqUJuJSFJJ5H6iNPkxXpwlla+PUV4WxyKm8jouKE0uiFd+OrDd3Q/EiDOyTDh9Rzh/iY4wlx6z9jzC3HrM2lNKR516keQU6xvORP4+5aXu3hboDvzazDokMJbDlcg2fBL4HtAa2Ag8nKiYzKwm8DrwO3f/tqRZY5QdtdhixFVh2kxEkk4i9xOlyY/x4ixteXmqaDGVNheUJc5Sb0M55NJj0p7lkFuPSXtK6alTL5Kc1gMNot6nAv9OUCy4+7/D183AFILLszYVXJ4dvm5OUHjx4khYG7r7JnfPd/eDwNP853LxYxqTmZ1EkNxfdve/hcUJb69YcVWUNhORpJTI/X1p8mO8OEsqT41RXhbHIqYjPi4oQy6IV76F4NL3KjHijCwTTq9NCbcBlFMuPertWU659ai3p5SNOvUiyWkBcH74pNGqBA8emZqIQMyshpnVKhgHugDLw3gKnt7aG3gjEfGVEMdU4Jfhk2gvAXYUXMZ2tBW5F/1agvYqiOmG8Emx5wHnEzwg52jEYMCzwEp3fyRqUkLbK15cFaHNRCRpJSRnliE/xtvPzgC6mNlp4eXRXYAZ4bSdZnZJuO/8JWXPtccipiM+LihDLoj52Yf3p78HZMTZ5oI4M4B3w/ljxVNeufSotmc55taj2p5yBLwCPK1PgwYNpR8InqC6muAppP+dwDgaETz9dAmwoiAWgvulZgOfh691jkEsEwkuH9tP8M1w33hxEFwO9kTYfsuAC49hTC+G61xKkOzOipr/v8OYcjiMpxgfQVyXEVz+thRYHA49KkB7xYsr4W2mQYOG5B0SkTNLmx9L2s8CmQQPK1sD3BJVfiFBR+yfwFjADiOucsmVpY0p3jpKGWepc0G8zz78fD4J438NqBaWVw/frwmnNyohxnLLpUezPUuIs0K1p4ayDwV/FCIiIiIiIiKSZHT5vYiIiIiIiEiSUqdeREREREREJEmpUy8iIiIiIiKSpNSpFxEREREREUlS6tSLiIiIiIiIJCl16kUkKZhZvpktjhoalqGOU83sjvKPLlJ/TzMbcrTqj7POa8ys2bFcp4iIVGzKmXHXqZwpxyX9pJ2IJAUzy3P3mkdYR0PgLXdPL+Vyld09/0jWfTSYWRXgGYJtmpzoeEREpGJQzixOOVOOZzpTLyJJy8wqm9koM1tgZkvN7LawvKaZzTazhWa2zMx6hYsMB74XnrUYZWYdzeytqPrGmlmfcDzXzP5gZh8A15nZ98xsupl9ambvm1lajHj6mNnYcPx5M3vSzN4zsy/M7HIze87MVprZ81HL5JnZw2Gss82sblje2sw+CrdripmdFpZnm9mfzGwOMBjoCYwKt+l7ZvarsD2WmNnrZnZyVDyPmdmHYTwZUTHcE7bTEjMbHpYdcntFRCR5KGcqZ8rxq0qiAxAROUz/ZWaLw/G17n4t0BfY4e4XmVk1YJ6ZzQTWAde6+7dmlgJ8ZGZTgSFAuru3BjCzjodY5x53vyycdzbQ390/N7N2wJ+BKw6x/GnhPD2BN4FLgX7AAjNr7e6LgRrAQnf/vZn9AbgfGAC8APzG3eeY2R/D8t+F9Z7q7peHcZ1P1FkHM9vu7k+H4w+FbfR4uNxZwGVAGjAVmGxm3YFrgHbuvtvM6oTzPlWG7RURkYpBOVM5U04g6tSLSLL4ruDAIkoXoGXUN+i1gfOB9cCfzKwDcBCoD5xRhnW+AsFZDOAHwGtmVjCt2mEs/6a7u5ktAza5+7KwvhVAQ2BxGN8r4fwvAX8zs9oEByFzwvLxwGtF44ojPTwwORWoCcyImvZ3dz8IfGZmBe3xI+Cv7r4bwN2/OYLtFRGRikE5s0hccShnynFBnXoRSWZG8M38jEKFweWAdYEL3H2/meUC1WMsf4DCtyEVnWdX+FoJ2B7jAOlQ9oavB6PGC97H2/8ezoNOdpUw7XngGndfErZDxxjxQNB2Ba9F11nW7RURkYpLObO451HOlOOA7qkXkWQ2A7jdzE4CMLPvm1kNgrMPm8ODk07AueH8O4FaUcv/C2hmZtXCb/qvjLUSd/8WWGtm14XrMTNrVU7bUAkoOGtyE/CBu+8AtpnZD8Pym4E5sRam+DbVAjaGbfLzw1j/TCAz6j7COkd5e0VEJDGUM5Uz5TilTr2IJLNngM+AhWa2HPgLwbf5LwMXmtk/CJL0KgB330pwD+FyMxvl7uuAV4Gl4TKLSljXz4G+ZrYEWAH0KmHe0tgFNDezTwnuv/tjWN6b4GE+S4HWUeVFTQIGmdkiM/se8D/Ax8Aswu0uibtPJ7hX8B/h/ZcDw0lHa3tFRCQxlDOVM+U4pZ+0ExFJICuHnx0SERE5EShnisSmM/UiIiIiIiIiSUpn6kVERERERESSlM7Ui4iIiIiIiCQpdepFREREREREkpQ69SIiIiIiIiJJSp16ERERERERkSSlTr2IiIiIiIhIkvr/Y3qbaYYRl5YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(14, 14))\n",
    "lgb.plot_importance(m, ax=ax[0])\n",
    "lgb.plot_importance(m, ax=ax[1], importance_type='gain')\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = RandomForestRegressor(n_estimators=200, max_depth=5,random_state=0,n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=clf.fit(X_train,y_train1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('sigma_score', 0.3476250043396745, True)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_score(y_pred,y_test1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>returnsClosePrevMktres10_lag_14_min</th>\n",
       "      <td>0.267275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close_30EMA</th>\n",
       "      <td>0.132671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close_26EMA</th>\n",
       "      <td>0.131267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>returnsOpenPrevMktres1</th>\n",
       "      <td>0.044182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>returnsOpenPrevRaw10</th>\n",
       "      <td>0.037511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     importance\n",
       "returnsClosePrevMktres10_lag_14_min    0.267275\n",
       "close_30EMA                            0.132671\n",
       "close_26EMA                            0.131267\n",
       "returnsOpenPrevMktres1                 0.044182\n",
       "returnsOpenPrevRaw10                   0.037511"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importances = pd.DataFrame(clf.feature_importances_,\n",
    "                                   index = X_train.columns,\n",
    "                                    columns=['importance']).sort_values('importance',ascending=False)\n",
    "\n",
    "feature_importances.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\chinn\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:271: UserWarning: The total space of parameters 9 is smaller than n_iter=10. Running 9 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  % (grid_size, self.n_iter, grid_size), UserWarning)\n"
     ]
    }
   ],
   "source": [
    "#model = RandomForestRegressor()\n",
    "#param_grid = { \n",
    "#    'n_estimators': [100,250,500],\n",
    "#    'max_depth' : [3,5,7],\n",
    "#}\n",
    "#rfc = GridSearchCV(estimator=model, param_distributions =param_grid, cv= tscv,n_jobs=2)\n",
    "#rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#rfc.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_pred=rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sns.distplot(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sigma_score(y_pred,y_train1)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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