classification_graphs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# classification es el notebook oficial de clasificación\n",
    "\n",
    "### - Limpiamos los datos según conclusiones del notebook main\n",
    "### - Probaremos distintos algorítmos de clasificación y compararemos los resultados\n",
    "### - Utilizamos el dataset de train ya recortado por la cátedra\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACA TODOS LOS IMPORTS DEL NOTEBOOK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, cross_val_predict, validation_curve\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression, Perceptron\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, VotingClassifier\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, precision_recall_curve, auc, roc_curve\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LECTURA Y LIMPIEZA DEL DATASET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_orig = pd.read_csv(r\"DS_G3_HeartFailure_FULL_train.csv\",index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformaciones necesarias al df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>age</th>\n",
       "      <th>creatinine_phosphokinase</th>\n",
       "      <th>ejection_fraction</th>\n",
       "      <th>platelets</th>\n",
       "      <th>serum_creatinine</th>\n",
       "      <th>serum_sodium</th>\n",
       "      <th>DEATH_EVENT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>95.0</td>\n",
       "      <td>371</td>\n",
       "      <td>30</td>\n",
       "      <td>461000.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>132</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>40.0</td>\n",
       "      <td>478</td>\n",
       "      <td>30</td>\n",
       "      <td>303000.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>136</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>70.0</td>\n",
       "      <td>232</td>\n",
       "      <td>30</td>\n",
       "      <td>173000.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>70.0</td>\n",
       "      <td>2695</td>\n",
       "      <td>40</td>\n",
       "      <td>241000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>137</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>59.0</td>\n",
       "      <td>176</td>\n",
       "      <td>25</td>\n",
       "      <td>221000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>136</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age  creatinine_phosphokinase  ejection_fraction  platelets  \\\n",
       "55   95.0                       371                 30   461000.0   \n",
       "180  40.0                       478                 30   303000.0   \n",
       "237  70.0                       232                 30   173000.0   \n",
       "280  70.0                      2695                 40   241000.0   \n",
       "181  59.0                       176                 25   221000.0   \n",
       "\n",
       "     serum_creatinine  serum_sodium  DEATH_EVENT  \n",
       "55                2.0           132            1  \n",
       "180               0.9           136            0  \n",
       "237               1.2           132            0  \n",
       "280               1.0           137            0  \n",
       "181               1.0           136            1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cleaned = df_orig.drop([\"time\",\"anaemia\",\"diabetes\",\"high_blood_pressure\",\"sex\",\"smoking\"],axis=1)\n",
    "#df_cleaned = df_orig.drop([\"anaemia\",\"diabetes\",\"high_blood_pressure\",\"sex\",\"smoking\"],axis=1)\n",
    "# NOS QUEDAMOS CON AGE, EJECTION_FRACTION, SERUM_CREATININE, SERUM_SODIUM Y DEATH_EVENT\n",
    "df_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_train, targ_train = df_cleaned.drop([\"DEATH_EVENT\"],axis=1),df_cleaned[\"DEATH_EVENT\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(326, 6)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "feat_train = scaler.fit_transform(feat_train)\n",
    "#APLICAMOS SMOTE PARA AUMENTAR LA CANTIDAD DE SAMPLES Y BALANCEAR EL TARGET\n",
    "feat_train, targ_train = SMOTE(random_state=420).fit_resample(feat_train, targ_train)\n",
    "feat_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACA TENEMOS EL DATASET RECORTADO Y STANDARIZADO "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_metric = \"recall\" # Queremos pocos FN, es decir pacientes q decimos q NO tienen riesgo pero SI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ALGUNAS FUNCIONES UTILES PARA GRAFICAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "def aoc_curves (model,feat,targ):\n",
    "    try:\n",
    "        predicted_p = cross_val_predict(model,feat,targ,cv=10,method=\"predict_proba\")\n",
    "    except:\n",
    "        return\n",
    "    fpr, tpr, thres = roc_curve(targ, predicted_p[:,1])\n",
    "    precisions, recalls, thres = precision_recall_curve(targ, predicted_p[:,1])\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "    pr_auc = auc(recalls, precisions)\n",
    "\n",
    "    fig,(ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize=(20,8))\n",
    "    ax1.plot(fpr,tpr,label = \"{}:{:.3f}\".format(str(model)[:3],roc_auc))\n",
    "    ax1.plot([0,1],[0,1],'--k', alpha=0.5)\n",
    "    ax1.set_xlabel('False Positive Rate')\n",
    "    ax1.set_ylabel('True Positive Rate')\n",
    "    ax1.set_title('Curva ROC')\n",
    "    ax1.legend()\n",
    "    ax2.plot(recalls,precisions,label = \"PR AOC:{:.3f}\".format(pr_auc))\n",
    "    ax2.plot([1,0],[0,1],'--k', alpha=0.5)\n",
    "    ax2.set_xlabel('Recall')\n",
    "    ax2.set_ylabel('Precisión')\n",
    "    ax2.set_title('Curva PR')\n",
    "    ax2.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_setup (model):\n",
    "    model.fit(feat_train,targ_train)\n",
    "    scoring = cross_val_score(model, feat_train, targ_train,cv=10, scoring=score_metric)\n",
    "    print(\"Cross Validation Score: {:.3f} +/- {:.3f}\".format(scoring.mean(),scoring.std()))\n",
    "    predicted = cross_val_predict(model,feat_train,targ_train,cv=10)\n",
    "    print(classification_report(targ_train, predicted))\n",
    "    cm = confusion_matrix(targ_train, predicted)\n",
    "    disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "    disp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelos = [\n",
    "    Perceptron(fit_intercept=False, max_iter=40000),\n",
    "    Perceptron(fit_intercept=False, max_iter=40000,penalty=\"l2\",random_state=420,alpha=1e-5,eta0=0.05),\n",
    "    LogisticRegression(fit_intercept=False, dual=False, max_iter=40000,C=2),\n",
    "    SVC(probability=True,gamma=0.61,C=0.85),\n",
    "    RandomForestClassifier(n_estimators=400,min_impurity_decrease=2e-4),\n",
    "    AdaBoostClassifier(base_estimator=DecisionTreeClassifier(),learning_rate=0.2),\n",
    "    VotingClassifier(estimators=[('lr', LogisticRegression(fit_intercept=False, dual=False, max_iter=40000,C=2)), ('tree', RandomForestClassifier(n_estimators=400,min_impurity_decrease=2e-4)), ('svc', SVC(probability=True,gamma=0.61,C=0.85))],voting='hard'),\n",
    "    VotingClassifier(estimators=[('lr', LogisticRegression(fit_intercept=False, dual=False, max_iter=40000,C=2)), ('tree', RandomForestClassifier(n_estimators=400,min_impurity_decrease=2e-4)), ('svc', SVC(probability=True,gamma=0.61,C=0.85))],voting='soft')\n",
    "]\n",
    "scores = []\n",
    "for model in modelos:\n",
    "    model.fit(feat_train,targ_train)\n",
    "    scores.append(cross_val_score(model, feat_train, targ_train,cv=15, scoring=score_metric))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "scores = pd.DataFrame(np.array(scores).T)\n",
    "scores.columns = [\"Per (BL)\",\"Per_op\",\"LR_op\",\"SVC_op\",\"RF_op\",\"ADA_op\",\"VotHard\",\"VotSoft\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "meaned_scores = [0.657,0.711,0.801,0.877,0.865,0.781,0.871,0.862]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 8 artists>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.bar(scores.columns,meaned_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>Per (BL)</th>\n",
       "      <th>Per_op</th>\n",
       "      <th>LR_op</th>\n",
       "      <th>SVC_op</th>\n",
       "      <th>RF_op</th>\n",
       "      <th>ADA_op</th>\n",
       "      <th>VotHard</th>\n",
       "      <th>VotSoft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Per (BL)    Per_op     LR_op    SVC_op     RF_op    ADA_op   VotHard  \\\n",
       "0   0.545455  0.545455  0.636364  0.818182  0.727273  0.636364  0.818182   \n",
       "1   0.545455  0.363636  0.727273  0.909091  0.727273  0.909091  0.818182   \n",
       "2   0.636364  0.363636  0.727273  0.818182  0.909091  0.636364  0.818182   \n",
       "3   0.545455  0.454545  0.909091  0.909091  0.727273  0.727273  0.818182   \n",
       "4   0.727273  0.727273  0.818182  0.818182  0.818182  0.727273  0.818182   \n",
       "5   0.727273  0.727273  0.818182  0.818182  0.818182  0.727273  0.818182   \n",
       "6   0.636364  0.727273  0.818182  0.818182  0.727273  0.727273  0.818182   \n",
       "7   0.727273  0.818182  0.909091  0.909091  1.000000  0.909091  1.000000   \n",
       "8   0.818182  0.727273  0.818182  0.818182  1.000000  1.000000  0.909091   \n",
       "9   0.636364  0.727273  0.818182  1.000000  0.909091  0.818182  1.000000   \n",
       "10  0.727273  0.636364  0.727273  0.818182  0.818182  0.727273  0.818182   \n",
       "11  0.636364  0.727273  0.636364  0.818182  0.909091  0.818182  0.818182   \n",
       "12  0.818182  0.272727  0.818182  1.000000  0.818182  0.727273  0.909091   \n",
       "13  0.600000  0.600000  0.800000  1.000000  1.000000  1.000000  1.000000   \n",
       "14  0.500000  0.900000  1.000000  1.000000  1.000000  0.900000  1.000000   \n",
       "\n",
       "     VotSoft  \n",
       "0   0.818182  \n",
       "1   0.818182  \n",
       "2   0.818182  \n",
       "3   0.818182  \n",
       "4   0.818182  \n",
       "5   0.818182  \n",
       "6   0.818182  \n",
       "7   1.000000  \n",
       "8   0.909091  \n",
       "9   1.000000  \n",
       "10  0.818182  \n",
       "11  0.727273  \n",
       "12  0.909091  \n",
       "13  0.900000  \n",
       "14  1.000000  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"whitegrid\")\n",
    "plt.figure(figsize=(20,8))\n",
    "sns.violinplot(data=scores)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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f48ePR25uLiIjIw0FpA7JyckoLi5GfX09zp07Z7h+7+Dq6opp06YBACZNmoRJkyYBaL+m9/f3N2x3s2t6V1dXPPnkk5gxYwbkcjmGDx+OIUOGwNXVFZMnTwbQ3qn+448/NtQYnnzySSxcuBBff/01NBoNvvvuuzs8ip1ZdRFpYvKdVSqbm5vh4uLSJxleffVV/P73v8eBAwdw4MAB3HvvvXj//fcxZcoU1NXVobi4GHK5HAUFBbjrrrsAtI88EgShW/u/2XbXr7BeVVWF999/Hxs3bjTMkXz77bfx17/+FQCwf/9+REREICYmppfvloiIiIiIiGzVnV6D96XKykqkp6cDANRqNQYPHow//vGPhsffeOMNjBgxAsePH8fSpUsxfvx4yGSyTvvQaDTYsGGDYR3jmTNnYtWqVRg/fjwkEgl0Ot0Nr6vRaAx/7kkX8+5c058/fx4//PADdu/eDTc3NzzzzDP46quv8Pvf/x5A+/rGv//97xEbG4s5c+YAAJ577jm89tprmDx5MrZt24bHH38c69ev73WHda6JZER79uzB5s2boVAoMG/ePCxfvhwvvfQSvv/+e0gkEmRkZGDjxo3YuHEj0tLSDD8oCQkJyM3N7bQvvV6Pxx9/HNXV1Z3uVygUqKqqMtyuqqrqVMUEgOzsbAwYMAD9+vWDVCrFvffei6NHjxoe//HHHzstAEZERERERERkiTrWRMrKysKWLVvw7rvvwtPT84btEhMT8cADD+C55567YQ2iPXv2oLGxEY8//jgmTpyItWvXYu/evSgvL8fgwYNRUFCAhoaGTs85efIkQkND4enpiYSEBJw5c6bT40qlEr/73e+gVqs73d+da/oDBw5g1KhR8PHxgUwmw9y5c5GTkwOgvWi2ePFixMbGGtY0rq2tRX5+vmGU0rRp01BVVYW6uroeHMmbM3oRSalUYvbs2SgpKbnhsXPnzmHevHmYNm0aXnzxxRv+4iydXC7H3/72N8N7FwQBly9fRlxcHABgzpw52LFjB7Zu3Yq5c+canrdgwQLs3bsXe/fuNTzvX//6F2pqam5YMX7cuHHYvn07WltbUVtbiyNHjmDUqFGdthkwYABOnz5tKEDt3Lmz0xS4kydPIjk5ue8PABEREYnCls+/iIiIuuuRRx5Ba2srVq1a1en+H374AU8++SR27dqFXbt2Yf/+/UhKSsKaNWsQFBSE1NRUvPjii2hubgYAFBUV4e233zY0w3r44YexcuVKw/rCGo0G77zzDlxdXW8Y9TRu3Dhs2LABOp0OhYWFKCgo6HS9DrQvlXPo0CG0tLRAEATs2rUL8fHx0Ol0+N3vfocZM2bgxRdfNIwy8vLygqOjI7KzswEAOTk5cHFxuWm3954yahHp1KlTWLRoEQoKCm76+J/+9Ce8/PLL2LZtGwRBwOrVq40Zx+RGjhyJxx9/HL/73e8wbdo0TJ8+HXq9Hn/4wx8AAIGBgfDy8oKzszNCQ0MNz/Pz88Mnn3yCzz//HKmpqZg1axYKCgrwz3/+EwBw5swZPPbYYwCAwYMHIy0tDfPnz8eiRYuwdOlSw6Ja6enpqKioQGRkJJ588kk8+OCDSE1NRW5uLp599lnD6xUXFyMgIMBUh4WIiIiMyNbPv4iIiLpLJpPhqaeewj/+8Q80NTUBAKqrq/HTTz9h/vz5nbZ95JFHsGbNGuh0Ovz1r39FfHw85s+fj1mzZuGpp57Ck08+aWhwFRMTg/feew9vvvkm0tLSkJ6eDkdHR7zxxhsA2gd2vPjiiwCA6dOnIzo6Gmlpafj973+PN998E3K5HBUVFYZpeWPGjMGsWbMwd+5cpKWlQavV4pFHHsGuXbtw9uxZbNu2Denp6UhPTzcUk/7xj3/g3XffRWpqKt577z1D17bekgjdXXznDrz44ouYM2cOnn32WXz11VcICQkxPFZaWoqHHnoIP/74I4D2KVcffvghvvrqqy73q1KpkJubi4SEhBu6jJ07d84w0udO9eWaSLagt8c8JycHSUlJfZiIusJjblo83qbF420cWw4XoKSiqdN9ba3N0Kia8OQDk2Bn17ffS93udz3dnrHOvwDj/r2cvlyF1RsPIiw8EpAAU0eEISzAvU9fgzrj56Vp8XibHo+58V17LcjraNPqq+N9/fV8V7/rjbqwdsd8vJuprKyEn5+f4bafnx8qKip6tP/r1w0C2lvndQwp642+2IetUKvVhvmYd6q3z6ee4zE3LR5v0+Lx7nsf/VACqQSwt2sfJq1qqUfNlWPw8lVgVJwnZPZcZtFcGPv8C7j5OVhvZe3Nw44dP8JdEQ1H72hUVlRiepJnn78OdcbPS9Pi8TY9HnPjuv76m9fRptUXx7un1/OidWe72QConq4SfquRSL2txrGC2jMymQxDhgy54+fzGwLT4zE3LR5v0+Lx7jsqjQ61DW0AAEEowfzJA3D/9DiUlpbi+++/h/OgwYiMjMSoESl9/9q/fAtGfasvzr+Am5+D9dbQoUOhcKxCW1sbtp46B3//2UhKGtT1E+mO8fPStHi8TY/H3Piuvf7mdbRp9dXxvv56vqtzMNGKSAqFolOnsZutQE5ERETiee3TIzh9+dff1TJ7OwDto1fi4uJw11134fz582LFoztgzudfdnZ2GDFiBHQ6HfYVnBU7DhEREd2EaGPPg4OD4ejoaBg2tW7dOowbN65P9m3EZZ7oOjzWRETWq0GpQlSIB55elIinFw2Dv6wKarUaMpkMU6dO5beNFsiY5199QSKRYMSIEXByby9s5ebmora2VuRURER0O3q9XuwIdIfu5O/O5CORHnvsMSxduhSDBg3C+++/j5deegnNzc2Ij4/Hgw8+2Ov9y+Vy1NTUwMfH546GZ1P3CYKAmpoayOVysaMQkQ2pqG3BZ+tzodbobvp4Q0MjNhw/bOJU1qmyrgVDB/jjrkH+2LhxIwoKCuDn44mBAweKHY16yNjnX8ag0aixf/9R6PV6pKend1ognIiIzIOLiwtKS0uhUCg4wMCCCIIAjUaDioqKHn8paJIi0q5duwx//uSTTwx/jo2Nxffff9+nrxUSEoKSkhJUVVXd8T46vmWlrsnlcp7UEZFJnbtSg8NnyhAe6A6Hmyzm3KLSQdKsFiGZ9Qnxd8OgcFesWrUKtbW1mDp1KgtIFsSU51/G4OAgw/yFC7Fu3TqsWbOGP39ERGYoJCQE1dXVKCwsRHNzMzuqmlBv6xb29vbw8PCAr69vz553x69ophwcHBAREdGrfeTk5PRqoWgiIktUXd+KgrJGsWN0Kf9qe8Y/P5SCID/XGx7nIpp9p6KiApmZmdBqtZg7dy7CwsLEjkQ2xsvLCwsXLsSGDRuwdetWNDY2YtSoUWLHIiKiX0ilUvj7+8Pf3x85OTmdWsWTcYlVt7C6IhIREd2Zv32bg9y8GrFjdItEAjg58leYsTk6OsLd3R3Tpk2Dj4+P2HHIRjk5OWHevHn48ccfOVKciIhIZDwDJyIiAECbWofYMC/8Jj1B7ChdcnORwcud67EZgyAIyMvLQ2RkJDw9PbFo0SKuMUiis7Ozw9SpUw23S0tL4e3tDScnJxFTERER2R4WkYiIyMDVWYaYMG+xY5BI9Ho9du7cidOnT2PmzJmIi4tjAYnMRsfPolqtxvr16yGTyTBnzhx4e/Mzi4iIyFRuXJGUiIiIbI5KpUJmZiZOnz6NlJQUxMbGih2J6KZkMhnS0tKgUqmwcuVKlJSUiB2JiIjIZnAkEhGRmTpwqhQ1DW0me736xjZ4urKjhi1qbGxEZmamoQPboEGDxI5EdFvBwcFYvHgxMjMzsWbNGkybNg3x8fFixyIiIrJ6LCIREZmhphY13v0q2+SvmzIwwOSvSeJraGhAS0sLO7CRRfH09DR0bisuLmYRiYiIyARYRCIii6XTC2JH6Ba9XuhxVo1WDwB4ZPZATB1puot6Fzl/LdiS2tpaeHt7IzQ0FEuWLGHnK7I4HZ3bOjQ2NsLZ2Rn29vwsIyIiMgb+hiUii/T1lnNY/eNFsWN036rSO3qak9werk4OfRyGbJ0gCMjOzsb+/fuRkZGB/v37s4BEFsvOzg4AoNVqsWbNGri6uiItLY2d24iIiIyARSQiskillUp4uMowe0x/saN06WrpVQQFB/X4efZ2UoweFGiERGTLru3AFh0djdDQULEjEfUJe3t73HXXXdi6dSu+/fZbdm4jIiIyAhaRiMhilFYpsXF/PnSCgLzSeri7OGLhlBixY3UpJ0eJpCTzz0nWT6VSYePGjSgoKEBKSgrGjh1raJtOZA1iY2Ph5uaGrKwsrFy5Eunp6QgJCRE7FhERkdWQih2AiKi79h0vwcaDV3Do9FW0qrSIj+A3zEQ9kZ+fj6KiIkydOhXjxo1jAYmsUnBwMBYtWgRnZ2ccPHgQgmAZ6+cRERFZAo5EIqJbqqxrgUqtEzuGQb1SBQD45tUZIichsixqtRoymQxxcXFQKBSc4kNWz8vLCwsXLoQgCJBIJNBoNLC3t2fhlIiIqJdYRCKim7pYVIc//u8+sWPcQGbPAZREPXH58mXs2LEDc+fOZQGJbErHwtp6vR7r1q2Di4sLpk6dys5tREREvcDfokR0U8oWDQDgvumxCPZ1FTnNrxQ+zmJHILIIgiAgJycH+/btg0KhgKur+fw7JjIliUSCfv364cCBA2hqamLnNiIiol5gEYmIbmtotB9iwzlygciSXN+BbcaMGXBwcBA7FpEoJBIJRowYAQ8PD3ZuIyIi6iXOCyEiIrIyp06dwunTp5GSkoLU1FQWkIjQ3rntnnvuMXQp5ILbREREPceRSETUyfnCWtQ3qZBX0iB2FCLqoY5FhIcMGQJ3d3dERkaKHYnIrHR0btNqtZBIJIZ/M0RERNQ9LCIRkUGDUoU/fbi/030uThzBQGQJysvLsWvXLqSnp8PFxYUFJKJb8PLyMvx59+7dkMvlGDVqFItJRERE3cAiEhEZaLR6AMCCKQMwKiEQznIHBPq6iJyKiLpy+fJlbNq0Cc7OzlCpVHBx4b9boq7o9Xqo1WqcOHEC9fX17NxGRETUDfxNSUQ38PN0RmSIp9gxiKgL13dgy8jIYAGJqJukUimmTZsGLy8vdm4jIiLqJi6sTUREZKFOnDiBvXv3IioqCvfeey8LSEQ91NG5bdasWSgrK8Pq1auh1+vFjkW90KrS4tyVWlytVoodhYjIKnEkEhEh+1wFisoboWzViB2FiHogLi4OOp0OycnJXM+FqBdiY2Ph5uaG1tZWSKX8jtVS1Da2Ib+0AVeuNiCvtAFXShtQVtMMQQAigtzx4R8niB2RiMjqsIhERPifb4+jqUUNAJBKAD8vDuUnMleNjY04cuQIJk6cCCcnJ6SkpIgdicgqBAcHG/6cm5sLqVSK+Ph4ERPRteqa2nCpuB4Xi+pwqage+VcbUN+kMjyu8HZG/2AP3J0UipzzFWhqVouYlojIerGIRGTmNFo9BEHo233qBKg1OsNtnV6PmaPD8cjsgZBKJZA52PXp6xFR3ygvL8e6deug0WgwdOhQ+Pv7ix2JyOoIgoDz58+jsLAQ9fX17NwmglaVFpdL6nGpqB4Xi+twqagOlXWtANq/7OoX4I6kWH/0D/JARLAHIoI84HpNN9mSyiYWkYiIjIRFJCIzdvxCJV795DD0fVtDavddaaebMgc7yB35kUBkrq7twDZ//nz4+vqKHYnIKkkkEsyZMwc7duzA4cOH2bnNBGob2/Bzfg3O5tfg5ys1KCxrNJz7KLydERPmjdSxnogO9UJksEefnq9otHq0qrRwd5H12T6JiKwZfxsSmbHK2hboBeCeSdFw6sMTptLS0k7D9iUSCcYODb7NM4hITGfOnMGOHTvYgY3IROzs7DBt2jR4enri4MGDaGpqwvz582Fnx5G6vSUIAq5WNxsKRmfza1FW0wwAcJTZITbMC/dOjkFMmBeiQz3h4erYZ6+t0epQUNaIyyUNyCupR15JPQrKmqDX6/H5y1Ph48Hp/EREXWERichMfLfjgmGodofiiiYAwKy7Ivr0xCYnpwlJSQP6bH9EZFxBQUGIj4/HpEmT4ODg0PUTiKjXJBIJRo4cCU9PT9TX17OA1As1Da04ebEKJy9W4dSlKtT9spaRm7MM8RHemDE6HAP7+6B/sAfs7fpmYXO9IPwyJa4Ol4rrkVfSgMLyRuh+GeLk6uSAyBAPDI72xfHzlVC2alhEIiLqBhaRiMxAS5sG32w9D2e5PeSyzv8so0I9OcSayAapVCqcO3cOQ4YMgY+PD6ZPny52JCKbFBsba/hzaWkpBEFASEiIiInMX5tKi9z8Gpy4WImTF6tQVN7+pZiHqwxDovyQEOWLgRHeCPF3g1Ta9+tNSSBBeU0Lnl6+F0B7sSoqxANzJ0QhMtgTkSEeUHg7QyKR4MCpUhw/X9nnGYiIrBWLSERGIggCSquU0Gj1XW7bqtICABZNjUXG+EhjRyMiM9fY2IjMzEzU1tYiODgYfn5+YkcisnmCIGD//v0oKyvDtGnT2LntGoIgoKRSiaM/lyPnfCXOFdRAqxPgYC/FwAgfTEwKxdABfogI8jBK0eh6M0aHw9dTjsgQTwzo5wV/Lycujk5E1EdYRCIykp9+LsebXxzt0XMcHfpmCDcRWa5rO7DNnTuXBSQiMyGRSJCeno7169djy5YtNt+5TavT4+yVGhz9uQJHfy43rGsUEeSOtLGRGDrAD/H9feAoQsfXgf19MLC/zx09V6fTo7C8CReL6hDs54pBUWxiQER0LRaRiIxE2aIBAPx+3uBuLQppbyfFkAG8WCSyZezARmTenJycMH/+/E6d26ZNm2Yz6yVptHqculSF/SdL8dPP5Whu1bSfv0T7IuPuSKTEBcDPyzLXFVrz4yVU1rUgr7QBao0OABAe6I6/PzNB5GREROaFRSQiI0uMVUDh7Sx2DCKyEP7+/khLS2MHNiIz1dG5zcvLC+Xl5ZBKrXsUsUarx8mLlThw6qqhcOQit8eIhECMTAjA0AH+fdpB1tTcnNrXnTx85ioiQzwxfVQYBoR6YftPhahXqkROR0Rkfiz3E5+IiMgK6PV6XL16FSEhIYiKikJkZKTNTo8hshQSiQQjRoyAIAiQSCRobGyEVquFt7e32NH6hCAIuFBYh13Zxdh/shTKawpHY4YEYegAPzjYW8foq8HRvvjsxSnw9pB36gx36MzVbhWRmlrUuFhUB2WLBuMTueA6EVk/FpGI7lBxRRMKyxtv+fjF4joTpiEiS6RSqbBx40YUFRXhoYcegre3NwtIRBak49/rtm3bUFlZifT0dIvu3FZR24LdOcXYlV2MsupmyBzsMHpQIMYNC8bQAf5wsLe+UVcSiQT+3RwxrtPpUVDWiItFdThfWIcLhXUorVIaHo8N9+bocyKyeiwiEd2hd746ZmhZeytSqcSih3gTkfFc24Ft0qRJVjOCgcgWTZ48GevWrcOaNWssrnObVqfHT7nl2HL4Ck5dqgYADI7yxb2TBmD04EA4yx1ETigetUaHw2eu4kJhe9Hockk9VOr29ZI8XR0RE+aFSSmhaGxWY93ePOh0XXfkJSKydLy6JbpDao0OKfEKPDTr1ieKbs4yuLvITJiKiCzBtR3Y5syZg/DwcLEjEVEveHl5YeHChRbVua2qrhW7Tjfgw43bUduogp+XE+6bHouJSaHdHpljzaQSCcprWvDWl8dgbydBZLAnpo4IQ0w/L8SEeUHh7Wz4+92TUyxyWiIi02ERiWyaIAh44aODKKlUdr3xdRqVKsSFeyMswN0IyYjImhUXF8POzo4d2IisyLWd2y5duoTk5GTIZOb1RZIgCMjNr0HW3jwcO1sOvQAkxynwh3vCkRSrgJ3UfItepnbPpAGIj/BBdD9P9A/ygMxB/DWg1BodCsoacam4HiWVTUgfF4kAHzZhICLTYhGJbJogALl5NYgK8UBUqFePnz8pOdQIqYjIGgmCAKVSCTc3NyQnJ2PQoEGQy+VixyKiPtTRuU2lUkEmk0Gj0UCr1cLJSdy29zqdHofOlCFzz2VcKq6Hu4sM8yZGI8ilCZPHjxA1m7nqH+yB/sEeor2+RtteMLpcXI/LJQ24XFyPwvJG6PSCYZsAHxekj4sULSMR2SYWkYgADB8YiEVTY8SOQURWSq/XY9euXbhw4QIeeOABuLu7s4BEZKUkEonh3/f27dtRUVGBjIwMUdY9a1Np8eOxIqzbm4eK2hYE+brg9/OHYGJyKBwd7JCTk2PyTHQjnV5AUXn7gt2XiutxuaQehWWN0OraC0Zuzg6ICvHE3AlRiArxRKCvC5b+bY+4oYnIZrGIREREZEQdHdgKCgqQkpICNzc3sSMRkYkMHToUWVlZWLlypUk7t6k0Omw+eAU/7L6EBqUasWFeWJI2EMMHBnLKmhlQtqhxvrAO5wtrcaGgDheK6tCq0gIAXJ3aC0YZ49sLRlGhnvD3cuq0vpayVdNpf4IgoKymGZeL66HR6jEppZ9J3w/dXptaC4lEAkczmBJJ1BdYRCIiIjKSazuwTZkyBYMHDxY7EhGZUHBwMBYtWoTMzEyTdG7TaHXYdqQQa3ZeRG2jCkMH+GHhlBgM7O9jtNek29PrBRRXNuF8QR0O5tTis507UVzRvhanVAKEB3lgQlIIYsO9ERPmhUAfl24vyH7o9FUcO1uOyyUNaL6msJQcp4CHq6NR3o81EAQBtY1tKK1SorK2BSnxAb0+Xi1tGpTXtKCsuhlXq5Uoq25GWU0zyqqbUdPQhiBfF/zfnyf30TsgEheLSEREREZy7NgxNDY2sgMbkQ3z8vLCokWLsH79euzZswf9+/fv8+msWp0eO48VY9WOC6iub8XA/j740/3JSIjkwv2mptHqcbm4Hrn51fg5vwbnC2rR3NY+yshJJsXASD/cnRiK2HAvRId6wcmx55djMnspnBztcLGoDuGB7hg7NBhRIZ4orVIic89l6K9ZN8mWqTU6VNRrcPDUVZRUNqGkSomSSiVKK5WGkV8A8PCseMybGN3l/pStGpR1FIiqm3H1l/+X1TSjvknVaVtPN0cE+rhgSLQfiiuabmjio9cLaGhWwcPFEVKODiQLwyISERFRH9NqtbC3t8f48eMxbNgwUdZCISLz0dG5rb6+HnK5HIIgQK/Xw86u99Nbjl+oxKdZZ1BcoURMmBeeXDAUQ6L9uj2ahfrOeytyUFTeBLVGBwAIVbhhzNBgxIZ5Iy7CG2WF55GcnNzr15E52OGLl6dB5iCFg/2vP0NbDl3ptF2DUmVYYyk2zAtDB/jf8WtqtHoUlTcir7QBReVNmJAUgsgQzzval14voKahDd4e8j6ZXtmgVKHol0JNaaUSJZVNKK1SoqK2BYIAABUAAF9PJ4T4uWJScihC/F0R6OuKv35yGFqd3rAvlUaHsurm9v1UNaG0Uomr1c24WtWMphZ1p9f18ZAj0NcFKXEKBPq6IMjXFYG+LgjwcYaz3MGw3adZubhytRGfrc81FJ3Kq5uh1upxz6RoPDiz69GJQvsb4b9rMgssIhEREfURQRCQk5OD3NxcLFy4EHK5nAUkIgLQ3rnNx6d9Wtnhw4dRXFyMtLS0O+7cVlbdjM/W5+Knn8sR6OuClx4ZjuEDA3iRKQI/L2c42Euh1wuYPjIMCZE+iI/wuWGKVHlR3/3duDg53PKxD1efRFFFEyprWwz3JUT6dLuI1KbSoqCsEXkl9cgrbfilcPTrQt8dulNEalVpUVjWiCtXG3Dlavv/C8sb0arS4ZHZ8Zg7oesRQB2UrRoUlTeiqLwJhb/8v6i8CfXKX0cByeylCPZ3RXSoF+5ODIWmuQpjRgxCsJ/rDaO+dL8Uj47kluHn/BqUVjejqq6j8NTO10OOID9X3DUkCIE+Lr8Ui1yg8HGGXNa9S2kPVxm0Oj02HypAoI8zAn1ckBjjj21HCtGg/LUwpdcLqG5oRfkv0+B+LTi1oKxGifBADyx7Ymy3jxeRsbCIRERE1Ac6OrCdOnUK0dHRfTLCgIisx/4Tpfh0/Rm8vGQkvLy8cPToUaxcuRJz5syBl5dXt/fT0qbBmp2XsG5vHhzsJXh4VjzSxvXvNCKFTGtgfx98//Zs0acl+Xi0FySLyhsR3c8Ls0aHIzrUC//ZfLZTYeRarSot8kraRyvllTQgr7QepZVKdMyIc3eRITLYA+njIhEZ4onIEA889T97IaDzDgVBQFVda3ux6JqiUXlNs+G1neX2iAjywKTkfth8uACNzWrcTEubBsUVTb8Ui5raC0cVTahpaDNsI5fZIVThhuQ4BfoFuKFfgBtC/d3g6+nU6e8hJ6cZUbcodkkkEvh6OqG0SolgP1fEhnlhcnIogv1dEezX/p/8DqYbXm/+xGhMGR4GD1dZpyLvvhOl+Dm/Bq9+egTlNc0or2npNCrK3k4ChbczAn1dodHpbpgS19KmQVl1+/PKapp/2Uczympa0NqmwfKn74bC27nX+YmuxyISERFRL6lUKmzatAlXrlxBSkoKxo4dy9EARASgfXTBt9vO47sfLwIAKmtbMHpwHNzd3Tt1bgsODu5yX4fPlOHfa0+htlGFicmheGhWPLzd+3Z9JbozYheQAGD4wAD88M5syK7rAiazt4NeEKDV6VFY1oiLxfW4VFSHS8X1KCpvNBSMfD3kiAzxxJghwYgM9kBkiCd8POQ3/D6TSICahjb8eLQIeaX1uHK1EQVljZ0W9w70cUF4kDsmJIUiIsgdEUEenbrMbfupEHoBKKlsQkFZY/s+rjaioKwBlXWt12SXIkThhkFRvggLcG8vGCnc4O/l3OtjLpVK8OmLUyCVGHeamEQigafbjQt3B/m54HJxPRzspQhVuGF4fAACfF0Q5OOCAF8X+Ho6Gab7/XvtaWz/qRDvfZP9y0illhum17m7yBDo4wI/Tyf8nN+C6vpWFpHIKFhEIiIi6qVdu3ahsLCQHdiIqBOVRof/+TYHh06XYVCkL87kVRseu7Zz2w8//IDHHnvsllPbGpvV+DjzDPaeKEFEkDteeHg4YsI4VZZudH0BqcOlojoseGET1Nr2kS5uzjIM6OeJUYMCER3qiehQr5sWOm7GTirBwVNXcfDUVTjK7AyLe0cEuSMi0ANhgW6d1gS6lcw9l5G55zKA9oJOsJ8rYsK8MXWkG/op3BEW4AaFj0ufrJt0u/cilrf++y4A3StgBfi4QKcXcKGwDoE+Lhg9OBCBvxSbAn06r8N06mIVXvq/Q0bNTraNRSQiIqJeGjt2LOLj4xEWFiZ2FCIyE8oWNV7//CecK6jFkrSBGBLth6V/29Npm47ObVevXr1lAenwmTL864dTaGpWY/G0WNwzKRr2dlITvAOyFslxCugFAdGhnhgQ6oXofp5QeDvf8eibP96XhKYWDSKDPRDk53pHhZhFU2PQoFQjIsgd4YHuCFW43bIAZq16cvwzxkcibWx/sxjxRsQiEtmM3TnF2HSwc9cKsAMqEd2hy5cv49y5c5g1axZcXV3h6uoqdiQiMhM1Da3468eHUVrVjD/dn4yxQ4Nx5WrDTbd1cnJCZGQkAODixYvIy8vDlClT0Nymw8frzmDfiVL0D/LAa/81ChFBHqZ8G2Ql5k6IwtwJUX22v6RYRa/3cc+kAX2QxLawgETmgkUkshk/5ZajoKwR8eGdh38nxfojKfbOW54SkW3p6MC2b98+KBQKqFSqO+6uRETWp7K2BS98dBCNzWq88thIDIn26/ZzGxoacPbsWeReKsHJSn80qyQcfURERGaFRSSyKf5eznjtt6PFjkFEFuraDmxRUVGYOXMmHBy6XveBiGxDZW0L/vzRQTS3avDmf49GdGj3u64BQGJiEvacqsTq79fD29sT77z4ewwbyGmyRERkPlhEIiIi6qbt27fj559/Zgc2IrrBtQWk1387qscFpPomFZZ9nY0zeVqkps+Bh+YyDu3ZiOiwhzhdloiIzAaLSERERN00ZMgQBAUFsQMbEXVS19SGl/596I4LSJeK6/DWl8fQqFTh6UXDMDG5H+rq6nDp0iUWkIio1wRBQGOzGg720m51ziO6HRaRiIiIbqO8vBzFxcVISUlBYGAgAgMDxY5ERGakpU2DVz89gprGtjuawrYnpxgfrj4JLzdHvPvEWESFeAJo79w2fPhwAEBlZSWuXLmC4cOHcwQkEXXLzmNF2P5TIUqrlCitVELZqkF4oDv+/swEsaORhWMRiYiI6BYuX76MTZs2wcnJCYMGDYJcLhc7EhGZEY1Wh7e+PIorVxvx8qMjEBvm3fWTfiEIAlZtv4Bvt1/AoEhfPPdgMjxcHW+67fnz53Hs2DHU1NRg6tSpsLfnKTwR3Zyrc/tIox1Hi+DjIUewnyvGDg3GxeI61DWq0NSiRmmVEoE+Lrf8zCG6Hf4GIqtWVdeKndlF0OsFFFU0AuC3d0TUNUEQcPz4cezduxcKhQIZGRksIBFRJ4Ig4B9rTuHUpWo8vWgYkuO63/Zco9Xhw9UnsSenBJNSQvGH+UPhYH/r7mtjx46FTCbDwYMH0dTUhLS0NHaFJKKb6h/sgc9emgI3ZxmcHH+93P/76pPYXlKIxS9vAQCMGhSIFx4efsPz1RodrlY3o6VNg7hwb45+pBuwiERWbVd2EVZsPW+4fdfgIBHTEJGl2Lt3L3JyctiBjYhuKWtfHnZlF2PxtFhMTO7X7ee1qXV47bOfcPJiFe6fEYt7Jw3o8iJNIpFg5MiR8PDwwNatW7Fy5Urcc889cHNz6+3bICIrI5FI4O/lfMP9k1P6wU4qQaCvC7YcLkBdYxtOXaxCSZXSMOWtpEqJqroWCEL7c/7xpwkIC3A38Tsgc8ciElmlljYNtDoBrSotAGD9+2msohNRt/n7+7MDGxHd0vHzlfhiw88YPTgQCyYP6NFzP153Bq1tGjy5YBgmD+9+8QkA4uLi4O7ujhMnTsDZ+caLRCKiW4mL8EZcRPuU2yO5ZTh7pRYv/d8hAIBcZocgP1fE9vPCpORQtLRpkbUvDyq1TszIZKZYRCKrotcL+GbrOXy/65Khgm4n5QUgEXWtsbERVVVViIyMRHx8vNhxiMhMlVU3Y9k32egX4I6nFiZC2sPzDJVah+cfGo5Rg+5skf7g4GAEBwcDAFpaWlBSUoIBA3pWyCIi2/Zo6kBcLmlAiJ8rgv1d4eMh7/SlWfa5CmTtyxMxIZkzFpHIaqg0Onyw8jgOnLqKu5NCMOCX7iiBvi4cSUBEt1VRUYHMzEwIgoAlS5ZAJpOJHYmIzJBGq8eyb7IhAfDSoyM6rTfSFYW3M4YN8MO8CdEYMsCvT/IcPXoUOTk5GD16NEaOHMnzHSLqlpgwb8T0oBEA0bVYRCKroNPp8fK/D+F8YS0emT0Qc+6O5IkUEXXLtR3Y5s6dywISEd3SV5vP4nJxPV54eDgU3j2bTuYsd8Brvx3dp3nGjh2L1tZWHDp0CPX19ZgyZQo7txERkVHxtwxZhbKaZpwrqMVDs+Ixd0KU2HGIyELk5OR06sDm4uIidiQiMlPZ5yqwbm8eZo4Ov+OpaH3Nzs4O06dPh5eXFw4ePIjGxkZ2biOiPqPXCyivaUZxRROKK5QoqWxCeJA70sZGih2NRMQiElkVP0+eNBFR9ymVSkRGRrIDGxHdVn2TCh+sOo7wQHc8mpYgdpxOru3c9tNPP4kdh4isyHP/PAC9XjDclkoAXy9nFpFsHItIRERkU1QqFZRKJXx8fDBu3DgA4PRXIrqtj9edQXOrFm/+dxIcHezEjnNTcXFxiImJgVQqhU6nQ3V1NRQKhdixiMgCRYd6YkJSCDxcHRHi74ZQhStC/N3w2fpc5ObXiB2PRMYiEhER2YzGxkZkZmZCpVLh0Ucf5dohRNSlI7ll2H+yFPdPj0VYgLvYcW5LKpUCAA4fPoxjx45h2rRp7DZJRD3m4eqI/7c4SewYZKZ49kwWbfnK4ygsb4Raoxc7ChGZuY4ObBqNBqmpqSwgEVGXlK0afPTDaYQHumPuhGix43RbUlISSktLsWXLFjQ0NLBzGxER9Rmp2AGIemPP8RK0tGkR4OOMuwYHIT7CR+xIRGSGLl++jFWrVkEqlWLRokUIDw8XOxIRWYAvN/6M+qY2LF0wFA72lnPa7OTkhPnz5yM+Ph6HDh3C1q1bodVqxY5FRERWgF/DksUbOzQYD8yIEzsGEZkpQRBw5swZ+Pj4YM6cOezARkTdcr6wFtuOFCJjfCSiQ73EjtNjHZ3bPD09kZ2djREjRsDb21vsWEREZOFYRCIiIquk1+uhUqng5OSEmTNnQiqVsgMbEXWLIAj4dF0uvNwcsWhqjNhx7phEIsGoUaMwePBgQwG9paUFzs7OIicjIiJLZdRxuRs2bMDMmTMxZcoUrFix4obHf/75Z8ybNw9paWn47W9/i8bGRmPGISIiG6FSqbBu3TqsXbsWOp0Ojo6OLCCRzeD5V+/tPV6CC0V1eHBmHJzllv/Z0VFAOnnyJL788kuUlpaKnIiIiCyV0UYiVVRUYPny5Vi7di1kMhkWLlyIESNGICoqyrDNm2++iaVLl2L8+PF455138Nlnn+Hpp582ViSyAEXljXjxo0NQabo3b1+vF8B1IonoWi0tLVi1ahVqa2sxadIk2NmZZztuImPg+Vfvtam0+HLTWUSGeGBicj+x4/SpsLAwHD9+HKtXr8b06dPFjkNERBbIaEWkQ4cOYeTIkfD09AQATJs2DVu3bsXjjz9u2Eav16O5uRkA0NraCg8PD2PFIQtRXtOCeqUKdyeGwNPNscvtJRIJJiaHmiAZEVmCiooKbNu2DQqFAnPmzOEC2mRzeP7Ve2v3XEZNQxv+dH8ypFLr+qbKy8sLixYtwvr167F582b4+voiMTGRnduIiKjbjFZEqqyshJ+fn+G2v78/Tp8+3Wmb559/Ho888gjeeustODk5YfXq1T16jdzc3D7JejM5OTlG2zfdqON4Xy5pBQAM8FcjqJtrP5YVXkBZobGSWS/+jJsWj7fxCYKAbdu2QSqVIj4+HjU1NaipqRE7lk3gz7f5MMX5F2D8czCtToeKykqT/2w1t+nw/a5yxIc6oa2uADk5BSZ9fVOJiIhAVVUVzpw5gx07dsDHh91tTYWfl6bHY953ampqoVapbntMebxNS4zjbbQikiAIN9x37bccbW1tePHFF/Gf//wHgwcPxhdffIHnnnsOH3/8cbdfIyEhAY6OXY9W6amcnBwkJSX1+X7p5q493jp5ObCvBnGxcYgK9RQ3mBXjz7hp8XgblyAIEAQBUqkUUVFRyM3NxZgxY8SOZTOM9fOtUqmMWqiwVqY4/wKMfw5mv7YcCn9/JCUN6vPXuJ0vN/4MrU7A44tGIVThZtLXNrWUlBRs27YNU6dOBdA+Qk0qNepyqTaP5wOmx2Pet/ZdPI6yhppbHtPs7GwkDB4KuYz9u0xBrHMwo/2mUCgUqK6uNtyurKyEv7+/4fbFixfh6OiIwYMHAwAWLFiAo0ePGisOERFZIb1ej127dmHr1q0QBAGenp5wcnISOxaRaHj+decalCpsOngFY4cGW30BCWgvLnaMWisoKMBXX32Furo6kVMRkaVoU2txqbgOO34qxCdZZ/DiRwfx3toy3PPnTTh1qUrseGRERisijR49GocPH0ZtbS1aW1uxfft2jBs3zvB4WFgYysvLkZ+fDwDYuXMnBg0y7bdNRERkuTo6sJ08edLQeYjI1vH8685l7rkMlUaHhVNixI5icg4ODmhpacHKlSvZuY2IbquusQ2/e+dH3PvCJvy/D/bhw9Unse1IIVpVWvTzkwEAahvbRE5JxmS0cWYKhQJPP/00HnzwQWg0GsyfPx+DBw/GY489hqVLl2LQoEF4++238dRTT0EQBPj4+OCtt94yVhwiIrIiTU1NyMzMRE1NDSZPnowhQ4aIHYnILPD8687Y2iik6wUHB2PRokXIzMw0dG6Li4sTOxYRmZnYcG9cLKpDqMIN44eFICzQHeFB7gjwdoFUKsH2PT/hfEm52DHJyIw6WTE1NRWpqamd7vvkk08Mfx4/fjzGjx9vzAhkZHq9gE/WnUFt051Xm+vq6rE9t30ofW0Dq9ZEdHt6vR4//PADmpqakJGRgYiICLEjEZkVnn/13Lq9eTY7CqnD9Z3bXF1dERrKDrhE9KsZo8IxY1S42DFIZFzxinqlrqkNGw9egbe7I1ydZXe0j9ZWDZo1SsPtuHBvBPg491VEIrIyUqkUkyZNglwu79SFiojoTrSptNhyuACjBwXZ5Cikazk5OWHevHk4e/YsQkJCxI5DRERmiEUk6hOLp8Vi2sjwO3ouuyYQUVcEQcDx48eh1+uRkpLCb8eJqM/syilGc6sGaeP6ix3FLNjb2xsWXq+rq8O+ffswdepUNi0gIiIALCIREZGZ0+v12L17N06ePIno6GgIgtCpZTkR0Z3S6wVs2J+PqBAPxIV7ix3H7NTU1CA/Px8rV67EnDlz4OXlJXYkIrIQbSotCsobUXC1EVeuNsDJ0R4Pzx4odizqAywiERGR2VKpVNi0aROuXLmC5ORkjBs3jgUkIuozJy9WoaRSiacXJfKz5SaioqJw7733IisrCytXrkR6ejqCg4PFjkVEZu6TdWewfOVxCEL7bYkEEATg7qRQFJU3oqCsEVeutv/f38sJqWP7Y1RCIOzsjNY8nvoQi0h0WwdPXcWqHRdu+bhWpzdhGiKyJTqdDqtXr0Z1dTU7sBGRUazfnwcvN0eMHRokdhSzdX3ntvnz53NKMRHdlLuTHYYO8IOToz0iAt0RHuSBiCB37D1egm+2nscT7+8GANhJJQhVuCE+or3b27tfZcPPywl/ui8ZcREcFWruWESi2zp1qf0bupR4xS23iQjywJBoLm5LRH3Lzs4OgwcPhru7OzuwEVGfK6lsQs75SiyeFgsHezux45i1js5thw8fRkBAgNhxiMhMOdhL8PpvR99w//jEEKi1egT7uSAiyAMh/m5wsG8fdaTTC8g+W443vjiKXTnFqFe2oaquFVNGhMHJ8cZyhbJVg8KyRvh4yBHg42L090Q3YhGJuuTq5IAXHh4udgwishGXL1+GVCpF//79OfqIiIxm6+FC2NtJMH1UmNhRLIKTkxMmTpwIoH2qcXZ2NkaOHAk7OxbgiOj2Anxc8MCMuJs+ZieVYERCIKRSCbYeLsDWwwUAAG8POcIC3NvXVCprQEFZ+/S3qrpWAEBMPy+8/+S4br1+R+GpsLwRbs4yjB3Kabm9wSISERGZjePHj2PPnj0ICQlBREQE1yghIqPQ6vTYe7wEKfEB8HKTix3H4uTn5+PIkSMoKSlBWloaO7cRUa/9cXEiWlU6ODpI8bdvj+Pdr7INj9lJJQjxd0V8uA8iRrvjwOmrUGl0N+xDo9WjtEqJgrJGFP5SdCooa0R1fathG0eZHYtIvcQiEhERie7aDmxRUVGYOXMmC0hEZDTHz1eiXqnC5JR+YkexSHFx7SMKtm7dys5tRNQnxg0LAdBeCDqTVwMXJweEB7ojIsgdIf6unaYdXyiqQ0FZI46dLf+lYNSEwvJGlFQ2QatrX827Y92lgRE+CAt0Q3igOw6fKcOe4yWivD9rwiISERGJSqvVYv369ezARkQmszO7CB6uMiTG+osdxWLFxcXB3d3d0Lltzpw5CAwMFDsWEVk4B3spnrh36G23kUolKKtuxmuf/QQA8PV0QnigO5LjFAgLdEd4oDuC/VwN6y51+Dm/xlixbQqLSEREJCo7Ozu4ubmxAxsRmURjsxpHf67AzLvCYc920r3S0blt+/btcHHhArdEZBqLpsZgSLQf+incEBboDlcnB7Ej2RQWkegGtY1t2LA/H1qdHucKasWOQ0RWqqKiAg4ODvD29saUKVPEjkNENmL/iRJodXpOZesjXl5eWLBgAYD2qckXL15ETEwMR5QSkdGEBbgjLMBd7Bg2i0UkusHhM2X4ftclOMrsIAGQEOkrdiQisjKXL1/Gpk2bEBAQYLj4ICIyhZ3ZxYgIckdEkIfYUazOhQsXsHnzZhQUFGDKlCns3EZEZIVYRCKDBqUKKrUOjc1qAMBnL06Bh6ujyKmIyJoIgoDjx49j79698Pf3x6xZs8SOREQ2pLiiCZeK67EkLUHsKFYpNjYW9fX1OHToEBoaGti5jYjMkl4voLKuBa7OMk6FuwMsIhEAoLC8EU+8vxuC8Ot9XCeAiPrS9R3YZsyYAZlMJnYsIrIhB05dhUQCjB/G9s7GIJFIMGrUKHh4eGDbtm3s3EZEZkej1WPhS5vQqtJhaLQfXv/daLEjWRwWkQgA0KhUQxCAeROiEOLvCm8PJ7iwKktEfUiv16OqqgrJyckYO3YspFIWqonItI7kliE2zBte7nKxo1i1+Ph4uLu7Y9OmTWhpaWERiYjMwrAB/sgraUCIvytOXqqCslUtdiSLxCISdZIUq8CgKK6BRER9p6mpCfb29nBycsL8+fNhb89fPURkehW1LcgvbcAjsweKHcUmhISEYMmSJYbP/Orqavj68hyTiMQzZIAfhgzwAwC8+ukR1De1iZzIMvFMnoiIjKaiogKZmZkIDAxEeno6C0hEJJojuWUAgJGDAkROYjs6PvPz8/ORmZmJu+66CyNGjGDnNiIyGyqNDkXljbhytRFXrjagrLoZ90+PQ1Sop9jRzBbP5q1Yq0qLC4W1ndY5upUrVxuNH4iIbMrly5exefNmyOVyjB7N+eZEJK4juWUIC3BDkK+r2FFsTr9+/RAXF4eDBw+ivr6enduIyCzkX23EvX/eCP0v18syBzuoNToMjvJjEek2WESyYmt2XsSanZd69BwnOX8kiKj3jh8/jj179sDf3x8ZGRlwdeVFGxGJp0Gpwtn8GtwzeYDYUWySvb09ZsyYAS8vL3ZuIyKzMHZoMOykEoQHuiMiyAMRQe7wdHPEghc3ix3N7LFiYMXa1Do4OdrhlcdGdWt7J0d7hAe6GzkVEVm7trY2ZGdnIzIykh3YiMgsHDtbDr0AjEwIFDuKzbq+c1tRURFiYmLEjkVENmpicigmJod2uq9VpRUpjWVhEcnKSaVSxEf4iB2DiGyAWq2Gvb095HI5Fi1aBBcXF3ZgIyKzcPhMOfy9nBAZ7CF2FJsXHx+PkJAQuLu3f3HZ1tYGuZzd8oiILAXP7omIqNeampqwatUq7NmzBwDg5ubGAhIRmYU2lRYnLlZiZEIgF3Q2Ex0FpLKyMnz66ac4d+6cyImIiH5VXtOMbUcKsXLbebRxdNINOBKJiIh6paMDm0ajwdixY8WOQ0TUSW5+DTRaPZLjFGJHoet4enrCz88PmzdvRkNDAzu3EZGoJJL2/7YcLjDcFxPmjcRYf/FCmSEWkYiI6I5d24Ft4cKF8PPzEzsSEVEnpy5VwcFeivj+nN5vbpycnDBv3jxs376dnduISHRymT3+/FAK9AKg1wlY9k02NFodCsoaodboMKCfl9gRzQKLSEREdEdaW1uxefNmeHt7swMbEZmtU5eqEBfuDUcHFibMUUfnNk9PTxw+fBgKhQLDhg0TOxYR2ahRg4IAABcKawEAb355FILQPkLp61emw8PVUcx4ZoFFJCIi6hFBECCRSAzfIPv5+bEDGxGZpfomFa5cbcQDM+LEjkK3IZFIMHr0aAQHByM0tL1bUsfvGiIiMYQFuGNicig8XB3RoFRhV3Yx1Bq92LHMAlc9JSKiblOpVFi3bh3OnDkDAAgODmYBiYjM1pnL1QCAIdG+Iieh7ggLC4NUKkVTUxO+/vprlJaWih2JiGyU3NEeTy9KxKOpAzGQ06E74UgkK1PX2IbMvXnQ6vTIzasWOw4RWZGmpiZkZmaipqYG/fv3FzsOEVGXTl2ugrPcHlEhnmJHoR7QarXQarVYvXo1pk+fjrg4jiQjIjIXLCJZmaNnK5C55zKc5faQSCSIDePiX0TUe9d2YMvIyEBERITYkYiIunTqUhUGRfrCzo6D7y2Jl5cXFi1ahPXr17NzGxGRmWERyeoIAIB/PTsRPh5OImchImugVCrx3XffsQMbEVmU8ppmlNe0IHUsR05aous7t9nb2yM5OVnsWERENo9FJCIiui1XV1dMmDABERER7MBGRBbj1KX2af1Do1n4tlQdndsCAwM5pY2IyExwbC8REd1Ar9djz549hkVNBw0axAISEVmU05eq4O3uiFCFm9hRqBckEgmGDRsGuVwOjUaDLVu2oK6uTuxYREQ2i0UkIiLqRK1WIysrCzk5OSgsLBQ7DhFRjwmCgNOXqzE4yo/r6FiR+vp65OfnY+XKlezcRkQkEhaRiIjIoKmpCatWrUJBQQEmT56M0aNHix2JiKjHKmpbUK9UIT7CW+wo1If8/PywaNEiyOVyrFmzBufOnRM7EhGRzWERiYiIAACNjY1YsWIFGhoakJGRgSFDhogdiYjojlwobJ/uNKAfu9RaG29vbyxatAiBgYHYvHkzTp48KXYkIrIhgiBArxfEjiEqLqxNREQAADc3NwwYMACDBg1iBzYismgXi+ogc7BDeKC72FHICDo6t+3Zswf9+vUTOw4R2Yh3vz6GkkolFF7OeO23o5BX0oC80nrklTSgoKwRcydEYXxiCAquNiCvtAF5JQ2obWzDUwuHwcPVUez4fYZFJCIiG3f69GmEh4fD3d0dEydOFDsOEVGvXSiqQ1SIB+zsOOjeWtnb22Py5MkA2kcGZGdnIyEhAU5OTiInIyJrE+jrAhcnB+j0AjxdZci/2oD7/7r118d9XFBV34KP153BP78/ZRipJLOXQq3Vo6CsEUOsqFMoi0hWoK6pDX/9+DBa2rRoadOKHYeILERHB7YTJ04gOTkZ48ePFzsSEVGvabR65Jc2YNZdEWJHIROprq7GgQMHkJubi4yMDHh5cRojEfWdQZG+WPXGTADAuSu12HqkAOGB7ogM8UD/YE+4Ojngs/W5KKlUIjLEA5HBnogM9kBFXQte+NdBkdP3PRaRrEB5dQuuXG3EkGhfeLvLDf8REd2KWq3Gpk2bkJ+fj+TkZIwdO1bsSEREfeLK1QZotHrEhLGQYCv8/Pxwzz33ICsrCytXrkR6ejqCg4PFjkVEViguwhtxN2nasCQt4Yb7KutaTBHJ5FhEsiJzJ0QjMcZf7BhEZOaUSiXWrl2LmpoaTJo0CUOHDhU7EhFRn7lYxEW1bVFISAgWLVqEzMxMrFmzBjNmzEBMTIzYsYiIrA4nihMR2RgHBwc4OjoiIyODBSQisjoXiurg5eYIP0+ujWNrvL29sXjxYgQFBUEmk4kdh4jIKnEkkgXT6wUIggCdXi92FCKyAEVFRQgICICjoyPuvfdeSCQSsSMREfW5i4V1GNDPi59xNsrJyQn33HOP4e+/oKAAoaGhsLOzEzkZEZF1YBHJQml1ejz25g5UN7QZ7rPjyRIR3cLx48exZ88eJCcnY9y4cby4IiKr1NSixtXqZkwezrbvtqzjd1xtbS3Wrl2LkJAQpKamsnMbEVEf4HQ2C6XW6FDd0IbEWH/cPz0Wv0lPuOkCX0Rk2/R6PXbt2oXdu3cjMjISI0eOFDsSEZHRcD0kupa3tzemT5+O0tJSrFq1CnV1dWJHIiKyeByJZOGGDfBDxvgosWMQkRm6WQc2qZTfHRCR9bpYWAeJBIgO9RQ7CpmJ+Ph4uLu7s3MbEVEf4dUEEZGVamlpQUVFBSZNmoTx48ezgEREFker02P5yuMorVJ2a/sLRXUIVbjBWe5g5GRkSTo6tzk6OqKsrEzsOEREFo0jkYiIrEx9fT08PDzg6emJRx99lB1qiMhiVde3Yld2MeIjvBHs59rl9leuNmJItK8JkpGl8fb2xgMPPAAHh/YCY8fvSq4RSETUM/xamojIiuTl5eGrr75CdnY2ALCAREQWrU2t6/a2TS1q1Da2ITzQ3YiJyJLJZDJIJBI0NDTg66+/xrZt26DTdf9njIiIWEQiIrIax48fR1ZWFry9vREXFyd2HCKiXmtTa7u9bVF5EwCgXwCLSHR77u7uSEpKws8//4wffvgBra2tYkciIrIYLCIREVm46zuw3XvvvXB17XraBxGRuVOpuj9KpLC8EQAQxiISdUEikWD06NGYMWOGoXNbfX292LGIyEppdXpcLq7H2Ss1YkfpE1wTiYjIwlVVVeHUqVNISkrCuHHjuIA2EVmNnoxEKixrhLPcHr6eciMmImtybee248ePY+LEiWJHIiIr9MonRwx/njk6HGXVzQjwdcHv5w0RMdWdYxGJiMhCaTQaODg4QKFQ4KGHHoK3t7fYkYiI+lRP1kQqLG9CWIA7F0qmHgkJCcF9991nGMHb8buViKi3IoI8MH5YCHw95SitUuJIbjk2HyqAk6M98kobWEQiIiLTqaiowLp16zBhwgQMGDCABSQiskrdLSIJgoCi8kaMHhxk5ERkjTw9PQEAra2tWLlyJeLj4zFixAgWJImoV1ycHPDM/UkAAJ1eQGllExQ+Lvh8fS4OnLoqcro7xzkPREQWJi8vD9999x0AwMvLS+Q0RETGo+rmdLa6JhWaWjRcD4l6xcHBAQEBATh48CA7txFRn7KTStAvwB2ODnZiR+k1jkQiIrIgx48fx549e+Dn54c5c+ZwAW0ismrdHYlUWPbLotqBbsaMQ1bO3t4eM2bMgKenJw4fPozGxkakpqbCyclJ7GhERGaDI5GIiCxESUmJoQPbggULWEAiIqvX3YW1C8ubALAzG/VeR+e26dOno7S0FLt37xY7EhGRWeFIJCIiMycIAiQSCUJCQpCeno7+/fuzAxsR2QRVN0ciFZU3wtPVER6ujkZORLZi4MCB8PT0NKyX1PG7mIjI1vEqhIjIjDU1NWH16tWorKwEAERFRbGAREQ2o7vT2YrKm9AvgFPZqG8FBwfDxcUFOp0OWVlZOH/+vNiRiIhExysRIiIzVVlZiW+//RYVFRVoaWkROw4Rkcl1ZzqbXi+gqKIRYYGcykbGodFo0NbWhk2bNuHIkSMQBEHsSEREomERiYjIDOXl5WHVqlUAgIULFyI8PFzcQEREIujOdLaq+la0qnQI40gkMhK5XI758+cjLi6OnduIyOZxTSQiIjNTVFSErKwsdmAjIpvXnZFIheW/dGbjotpkRNd3btPr9Zg5c6bYsYiITI5FJCIiMxMcHIxRo0YhKSkJMplM7DhERKLpzppIhWXtRSSuiUTG1tG5zdPTEz4+PmLHISISBaezERGZAbVajR07dqClpQV2dnYYNWoUC0hEZPO6M52tuKIJvh5yOMsdTJCICIiPj4dCoQAAHD58GKWlpSInIiIyHRaRLExTixr5pQ24crVR7ChE1Eeamprw3Xff4cyZMygpKRE7DhGR2ejOdLbymhYE+XHaL5leW1sbzp49izVr1rBzGxHZDE5nszAv/OsgCsp+LSA5OtiJmIaIequyshKZmZlQqVTIyMhA//79xY5ERGQ2ujOdrbymGclxChOkIepMLpdj8eLFyMrKwqZNm9DQ0IDhw4dDIpGIHY2IyGi6HInU3NyMV199FQ899BDq6+vxl7/8Bc3NzabIRjehbNVgcJQvXnh4OP6yZAQmpvQTOxIR3aGSkpJOHdhYQCIi6kzVxUikNrUWdU0qBPi4mCgRUWdOTk6Gzm0HDhzAzp07xY5ERGRUXY5EeuONN+Dv74+amho4OjpCqVTiL3/5C/72t7+ZIh/dhL+XM0YNChQ7BhH1ko+PDyIiIjBhwgR2YCMiuo6ArkciVdS2AAACfJxNkIjo5q7t3MYFt4nI2nU5EuncuXN4+umnYW9vDycnJ7z//vs4d+6cKbIREVkdvV6PEydOQKfTwcnJCampqSwgERHdhFqjgyDcfpuKmo4iEkcikbg6OrfFxMQAAC5duoS6ujqRUxER9b0uRyJJpZ3rTDqd7ob7iIioa2q1Gps2bUJ+fj6cnJwQGxsrdiQiIrPVnc5sZTXtSyywiETmRK1WY+fOndDr9UhPT0dwcLDYkYiI+kyX1aCUlBS89957aGtrw/79+/HEE09g+PDh3dr5hg0bMHPmTEyZMgUrVqy44fH8/Hw88MADSEtLw5IlS9DQ0NDzd0BEZAE6OrBduXIFkyZNYgGJiIzKGs7ButeZrRnOcnu4OTuYIBFR98hkMtx7771wdHRk5zYiuim9XsCZvGqs3X0ZP+fXiB2nR7osIj3zzDNwdnaGm5sbli9fjpiYGDz//PNd7riiogLLly/Ht99+i6ysLHz33Xe4fPmy4XFBEPDf//3feOyxx7B+/XrExcXh448/7t27ISIyQ3V1dfj2229RV1eHjIwMDB06VOxIRGTFrOUcrHud2VoQ4O3Cblhkdry9vbF48WIEBARg06ZN+Omnn8SORERmQiqRQNmqwQv/OogvNv6Mb7dZVqG5y+lse/fuxR/+8Af84Q9/MNy3bt06ZGRk3PZ5hw4dwsiRI+Hp6QkAmDZtGrZu3YrHH38cAPDzzz/D2dkZ48aNAwD87ne/Q2Nj4612R0Rk0RwdHTFnzhz4+/uLHYWIrJy1nINdP51NrxeQfa4CKfEKQ9GoorYZIf5uYsQj6lJH57Zt27ZBo9HA3r7LSy8isgHTR4fD090RkcGeWLHtfJfr/5mbW36S7dq1C1qtFsuWLYMgCBB+eWdarRbLly/vsohUWVkJPz8/w21/f3+cPn3acLuoqAi+vr547rnncPbsWQwYMAAvv/xyj8Ln5ub2aPueyMnJMdq+e0OtVqO6ptps890pa3s/loDH3PgqKirg7+8PLy8veHp6ori4GMXFxWLHsgn8+TYtHm/zYg3nYFqdDrX1TYb7CgsLsaa6FN/srsZ/TfdHkLcMekFAWbUSod78GewNHjvjUygUhj//+OOPcHZ2hqOjo4iJbAt/xk2Lx7t7orwAtCihUbVAq5Lc8XET43jfsoh07tw5HDlyBDU1Nfjqq69+fYK9PZYsWdLljoWblNOuHWqs1Wpx9OhRfPPNNxg0aBA++OADvPPOO3jnnXe6HT4hIcEoH8A5OTlISkrq8/32Bdnm7fD18UVS0jCxo/QZcz7e1orH3Lj0ej327NmDc+fOYcCAAWhoaEBycrLYsWwGf75Ny1jHW6VSGbVQYc2s4RzMfm05pPYyABoAQFhYGBzspQCqER0dg9hwb9Q0tEKrK8XQ+P5ISoro8yy2gJ+XpnX06FHk5eVBJpNhzpw5htGCZDz8GTctHu+eW3PkAKQSyR0dN7HOwW5ZROqYwrZixQrcd999PX5hhUKB7Oxsw+3KyspO0zj8/PwQFhaGQYMGAQBmz56NpUuX9vh1iIjMiVqtxubNm5GXl4ekpCT0798fJ06cEDsWEdkQazkHU123sHZTi6bT7fKaFgCAgp3ZyELY2dlh1qxZyMrKwsqVK5GWlsbObURkcbpcWPuee+7Bjh07sG7dOqxbtw4//PADli9f3uWOR48ejcOHD6O2thatra3Yvn27Ye49AAwbNgy1tbWGbgW7du3CwIEDe/FWiIjEpVQq8d133yE/Px+TJk3C3XffDam0y49ZIqI+ZS3nYNcvrN3UrO50u7ymGQAQ4ONsskxEvRUSEoJFixZBJpOxcxsRWaQuV3d7+umnUVxcjKqqKsTHx+PUqVMYPnx4lztWKBR4+umn8eCDD0Kj0WD+/PkYPHgwHnvsMSxduhSDBg3CP//5T7z00ktobW1FQEAAli1b1idviohIDBUVFWhoaEBGRgb69+8vdhwislHWcg52QxGp5foiUgukEsDPk0UksiwdnduysrJw4cIFxMTEsMMgEVmMLotI586dw/bt2/HKK6/gkUcegSAIePXVV7u189TUVKSmpna675NPPjH8eciQIfj+++97GJmIyLwolUq4uroiMjISS5YsgZOTk9iRiMjGWcM5mF7feW0n5fXT2Wqb4evp9MtaSUSWpaNzm16vh0QiQUtLCxwdHWFnZyd2NCISgbJVg0tFdbhYXIeLhfUoqWzCUwsTERfhLXa0G3RZRPL394e9vT3Cw8Nx8eJFzJgxA62trabIRkRk9k6cOIG9e/di/vz5CAkJYQGJiMhIrh+JVFHTggCuh0QWzN6+/VJMp9Ph+++/h1wuR2pqKs8liGzM2Ss1WPTSZsNthbczKmpbUFDWYJlFJGdnZ2zYsAGxsbFYvXo1+vfvj/r6ehNEIyIyX3q9Hnv37sXx48cRGRnZadFaIiLqe9cXkcpqmjE8PkCkNER9x87ODklJSdi+fTtWrVrFzm1ENmTs0GC4Ojkgup8nYvp5ISrUCxqNDg++uk3saLfU5fjfv/zlLzh37hzuuusuSKVS3H///ViyZIkpshERmSW1Wo3169fj+PHjSExMRFpaGmQymdixiIis2rXd2dpUWtQ3qbioNlmNgQMHYv78+WhpacHKlStRWloqdiQiMoFZd0XgpUdHYMHkGAwd4A9XJwexI3WpyyJSeHg4nn32WUgkEnzwwQc4duwYUlJSTJGNiMgsnT9/Hvn5+Zg4cSImTJjADmxEREZib/frYsPKa0YiVdS2AAACvDmdjaxHaGiooXPb7t27IQhC108iIjKxW05nKykpwfLly+Hp6YlnnnkGTk5OUCqV+Pvf/44VK1YgNzfXlDmJiESn0+lgZ2eHQYMGQaFQQKFQiB2JiMiqOcrsoW3VQKcX0NymNdxfXtMMAFBwJBJZmY7ObRqNBhKJBDqdDlKplN3biMhs3PLr8xdeeAFeXl6oqqrC//3f/yE7OxszZszAgQMH8Omnn5oyIxGR6PLy8vD555+jtrYWEomEBSQiIhOQy9o7VV3fma1jJJLCm0Uksj5OTk5wd3eHIAjYtGkTtm/fDp1OJ3YsIiIAtxmJVF5ejq+++gptbW2YO3cuVq9ejYcffhiPPvqooZMAEZEtOHHiBHbv3g0/Pz+ufUREZEIdRaTrF9WuaWiDzF4Kdxd+JpN18/HxwZEjR9DQ0MDObURkFm5ZDXJ2bv9mRy6Xo6GhAcuWLcOYMWNMFoyISGx6vR579uzBiRMnEBkZiZkzZ7KIRERkQo6y9lPV60ci1Ta2wctdzik+ZNUkEgnuuusueHp6snMbEZmNbq0G6+PjwwISEdmcnJwcnDhxAklJSezARkQkgluNRKptbIO3u1yMSEQmd23ntszMTOj1erEjEZENu+VIpGu/2bGzszNJGCIiczJ06FC4ubkhNjZW7ChERDZJ/stIpJsVkcIC3cWIRCSKjs5tra2t7ApLRKK6ZRHpwoULSExMBAC0tbUZ/iwIAiQSCY4fP26ahEREJlRZWYn9+/dj1qxZkMvlLCAREYnI0TAS6cbpbMNi/MWIRCQab29vw5+PHDkCiUSC4cOHc1onkZVqbtPixIVK1DS0YkJyP9hJzePf+i2LSDt27DBlDiIi0eXn52Pjxo1wdHRES0sL5HJOlSAiEtPNprO1qrRoadNyOhvZLEEQUFtbi3PnzqG+vh6TJ0/mzBEiK/SfTWcNfw5VuCEmzPs2W5vOLYtIwcHBpsxBRCSqazuwzZkzB66urmJHIiICABw9ehQNDQ0QBMFw39SpU0VMZDpyx46FtX8tItU1tQEAi0hksyQSCWbMmAEPDw92biOyQp5ujrhnUnT7aFwB+GbreWh1QtdPNJFbFpGIiGxFdnY29u7dyw5sRGR2nn32WRw5cgRhYWGG+yQSie0UkX5ZE+nak+faRhUAwNvdUZRMRObg+s5tq1evxv33388RSURWQCKR4MGZ8QCAkxcrRU5zIxaRLIBeLxiGcev15lOBJLIWMTExUKvVGDlyJBerJCKzkp2djS1btsDFxUXsKKLomM52rdpGjkQi6jBw4EC4u7ujoaGBBSQiMgkWkSzAv344hW1HCg237ezMY0EtIkumVCpx/PhxjBkzBm5ubhg9erTYkYiIbhAUFGSzBSSgcxFJKpVArxdQ2/BLEcmDU3eIgPbObaGhoQCAy5cvQ6fTISYmRuRURGStuiwiVVVV4cUXX0RhYSFWrFiB5557Dm+//Tb8/dkRw1Sq61vh5+WEeXdHAQBS4gNETkRk2SorK5GZmQmVSoX4+Hj4+vqKHYmI6KYSExPx9NNPY8KECZ0W+7eV6WyOsl9PVd2cHdCgVKO2sQ0yeylc5PwulOhagiDg1KlTKCgoQH19PTu3EZFRdPnb99VXX8XkyZPxzTffwMPDA7GxsXjppZfw8ccfmyIf/cLT1RGzxvQXOwaRxbu2A9vChQtZQCIis3bixAkAwJo1awz32daaSL+ORHJ1khmKSN4ecl4cE11HIpEgPT0d27Ztw4EDB9i5jYiMossiUmlpKe699158++23cHBwwJ/+9CekpqaaIhsRUZ/Kzc3F9u3b4efnh4yMDLi5uYkdiYjotr7++msAgFarhSAIcHBwEDmRacmvGYnk7iJDaVX7mkhRIZ7ihSIyY/b29pg5cyY8PT0Nndvmzp0Le3uO3COivtHlp4lEIoFerzfcViqVnW4TEVkKLy8vREdHY9q0aezARkQWoaamBs899xyOHDkCnU6HlJQUvPfee1AoFGJHMwnHa0ciObcX0DRaPbw9uKg20a1c27mtsrKSI5GIqE912YZo6tSpeOaZZ9DU1IRVq1bhoYcewowZM0yRjYio19RqNc6dOwcACA4ORmpqKgtIRGT2Oqavvfrqqxg6dCgOHTqEQ4cOITk5Ga+88oq44Uzo2ulsbs6/fnb7sDMbUZcGDhyICRMmQCKRoLq6GqWlpWJHIiIr0OVIpN/97ndYt24d9Ho9Dh06hAULFuCee+4xRTYiol5RKpXIzMxEVVUVFAoFvL29xY5ERNQtn332GY4fP47CwkJ8+OGHhvuXLl2KWbNmiZjMtOSdFtb+tYjkxSISUY/s2rULV69exYwZM9i5jYh6pcsi0qpVqzB79mxkZGSYIA4RUd+4tgNbeno6C0hEZFG2bt2KM2fO4Pnnn4dKpYKjoyMAoLW11aYWlHbsNBLp1/WgvFlEIuqR1NRUZGVlYePGjWhoaEBKSopNfZYQUd/psoh09OhRfPDBB5gwYQIWLFiAoUOHmiAWEdGdu74Dm7+/v9iRiIh6bNCgQZg5cyYefvhhzJ07FwCwdu1aTJs2TeRkptN5TSROZyO6U05OTpg/fz62bduG/fv3o76+HpMmTeJ6SUTUY10Wkf7nf/4HDQ0N2LhxI9544w2oVCrMnz8fDz30kCnyERH1WEtLC7y8vNiBjYgs3h/+8AcEBARg//790Ov1mDt3LubPny92LJOQ2UthJ/11pIR7p+lsjmJEIrJo13Zu4/pIRHSnutXr0cPDAwsWLIC/vz8++eQTfPLJJywiEZFZ0ev1hrWPEhISEBcXx2/XiMhiKZVKuLq6GkYLTJo0yfBYQ0MDPD09xQtnIo7XrIcklQBO8l9ve3s4iRGJyOJ1dG7T6XSws7NDa2srVCqVTXymEFHf6LKI9PPPP2Pt2rXYunUr4uPj8Zvf/AYTJ040RTYiom5Rq9XYvHkzCgoK8Mgjj8DDw4MFJCKyaA888AAyMzMxcuTITuuWCIIAiURi6DppzeSOv36OuzjJIP3lOMgc7OAi79b3oER0Cx3nSdu2bUNZWRnS09MRFBQkcioisgRd/gb+wx/+gHnz5mHNmjX8YCEis3NtB7YJEybAw8ND7EhERL2WmZkJADh//rzIScQjv2Y9JHeXaxfVduSCwER9ZNy4ccjMzMTq1avZuY2IukXa1QZ79uzBE088wQISEZmdyspKrFixAnV1dUhPT8ewYcPEjkRE1Keqq6uxc+dOAMBbb72FBx980GYKS9dOZ7t2UW12ZiPqO97e3li8eDECAgKwceNG/PTTTxAEQexYRGTGbllEWrRoEQBg2LBhSExMNPzXcZuM62qVEl9tPov/bDqLkkql2HGIzFLHhdTChQsRGRkpchoior73/PPPo7i4GIcPH8aRI0eQkZGBN954Q+xYJnHtSCQ3FpGIjKajc1tsbCzOnDkDlUoldiQiMmO3nM72v//7vwCAjRs33vAYq9PGt/2nQvyw+zLs7drrfEOi/URORGQ+Wltb4eTkhDFjxiA5ORnOzs5iRyIiMor6+no8/PDDePfddzF79mzMnTsXK1asEDuWScivGYnk5nztdDYWkYj6WkfntpaWFsjlcuh0Omg0Gsjl/PdGRJ3dsojk7+8PAPjrX/+KTz/9tNNj9957L1avXm3cZDZOLwCOMjt8//ZssaMQmQ29Xo+9e/fiwoULeOCBB+Di4sICEhFZNY1GA41Gg/379+Odd95Ba2srWlpaxI5lEo4ciURkUhKJBC4uLgCA3bt3o6SkBBkZGezcRmQmdHoBGq0Ocpk9NFo9dHpxBvfcsoi0dOlSXLlyBcXFxUhNTTXcr9VqIZV2uZQSEVGf6ujAlpeXh8TERDg5sb0zEVm/SZMmYdSoUYiLi0NCQgJmz56N2bNt4wuma6ezdVoTyYNFJCJji4mJwYULF7By5Up2biMyAx/9cAqF5U0AgP7BHiiuaEKwjwOGp5g+yy2LSM8++yxKS0vx8ssv4+WXXzbcb2dnh+joaJOEIyICOndgmzhxIhfQJiKbsXTpUtx7771QKBQAgPfffx+xsbEipzIBiaTTdDZ3Zwfgl4Zs3m4sIhEZW2hoKBYtWoS1a9eycxuRiPy9neHhKoODgx0CfJxRXtMCmb0Us8f0h5+8UZRMtywihYSEICQkBNu2bWMbVSIS1YEDBwwd2LiANhHZgqysLKSnp+OLL7644bHDhw/jkUceESGV6fxXRgL6B3vC212OR2YPxF1DgiF3tMP902MR399H7HhENqGjc1tWVha2b9+Ofv36cSQ4kYkF+brim1dn3PSxnJwcE6dpd8si0qJFi7By5UokJiZ2KiIJggCJRILjx4+bJCAR2S69Xg+pVIq7774bSUlJ8PPjAvNEZBsKCwsBABcvXhQ5iTgmJvcz/HnuhCjDnxdM4UgIIlNydnbGPffcg6qqKkMBqeP8jIhs0x11ZyMiMraTJ0/i/PnzmDdvHuRyObuDEJFNWbp0KQDg7bffxrFjx5CSkoL6+npkZ2dj8uTJIqcjIltib2+PwMBAAMCJEydw+fJlpKam8tyMyEbdsoTc0Z3N29sbVVVVCA4Oxo8//oh//OMfnN5GREaj1+uxZ88e7Ny5E3K5HIIgTtcBIiJzsHz5cnz44YcAgLa2Nnz88cf417/+JXIqIrJVMpkMJSUlWLVqFerr68WOQ0Qi6HIc4p///Gfs3LkTp0+fxldffYWgoKBOC20TEfUVtVqN9evXIycnB4mJiUhLS4NMJuv6iUREVmrnzp34/PPPAQABAQH45ptvsHnzZpFTEZGtGjhwIObPn4/m5masXLkSV69eFTsSEZlYl0Wk4uJi/PGPf8Tu3bsxZ84cPPHEE6w6E5FRbN++Hfn5+Zg4cSImTJjA+fZEZPM0Gg0cHBwMtx0cHDginIhE1dG5TSaTYc2aNVAqlWJHIiITuuWaSB00Gg2A9u5Izz33HHQ6HVpaWowejIhsz+jRoxEfH4/+/fuLHYWIyCwkJibij3/8I+bPnw+JRIJ169ZhyJAhYsciIhvX0bntypUrcHV1FTsOEZlQl1/zJyYmYubMmWhra0NiYiIefvhhjB492hTZiMgG5Ofn48cff4QgCPD29mYBiYjoGi+//DJ8fX3x9ttvY9myZfDx8cGLL74odiwiIjg5OSE+Ph4AUFRUhO3bt0On04mcioiMrcuRSC+//DJOnDiB2NhYSKVSLFmyBOPGjTNFNiKycidPnsSuXbvg5+cHlUrFLh9ERNdxdnbGn//8ZzQ0NMDDw0PsOEREN1VeXo4zZ86gvr4eaWlpPKcjsmJdjkSys7NDZWUl3nzzTTzzzDOoq6vjOiVE1CvXdmCLiIjAggULeLJBRHQT+fn5mDVrFmbPno2KigrMmDEDeXl5YsciIupk+PDhmDZtGkpLS9m5jcjKdVkN+uyzz/B///d/iImJwcCBA/Hll1/io48+MkU2IrJS27ZtQ05ODoYNG4b09HR2YCMiuoU33ngDL7zwAnx8fKBQKHD//ffjL3/5i9ixiIhukJCQgPnz50OpVGLlypVoaGgQOxIRGUGXRaR169ZhxYoVePjhh/HII4/gm2++wfr1602RjYisVFxcHCZMmICJEydyZCMR0W3U19fjrrvuMty+77772AmJiMxWaGgoFi9ejIEDB8Ld3V3sOERkBF2uiQSg04r7bm5usLfv1tOohxqUKmTty4NGq0duXrXYcYj6VFVVFcrLyzFo0CCEh4cjPDxc7EhERBZBpVJBIpEAaP8s1ev1IiciIro1b29vwxq69fX1yMvLQ2JiouFzjIgsW5fVoODgYPznP//B4sWLAQArVqxAUFCQ0YPZopzzlViz8xIcZXaQSoABoV5iRyLqE/n5+di4cSPkcjliYmI4fY2IqJsWLVqEJUuWoKamBn/729+wadMm/OY3vxE7FhFRt5w+fRrHjh1DTU0NJk2aBDs7O7EjEVEvdVlEevXVV/HMM89g2bJlAIAhQ4bg/fffN3owWyQIAgDgH89MQICPi8hpiPrGtR3YMjIyWEAiIuqBe+65B+Hh4dizZw+0Wi1ee+01jBkzRuxYRETdMnbsWEilUvz0009oaGhAamoqm6kQWbgui0gKhQJff/01Wltbodfr4eLC4gYRdc/evXuRnZ2NyMhIzJw5kwUkIqIeeuihh/Cf//wHKSkpYkchIuoxiUSCMWPGwNPTEzt27MCqVaswb948uLm5iR2NiO7QLVe0LSgowLx585CYmIjf//73aGlpYQGJiHrE1dUViYmJSEtLYwGJiOgONDU1oaWlRewYRES90tG5zdXVFY6OjmLHIaJeuOVIpNdeew1z5szBqFGj8P3332PZsmV49913TZnNJrSptfjn96fQ3KpBdX2r2HGIek2pVKK+vh4hISFISkoSOw4RkUVzcnLChAkTEBMTA2dnZ8P9//73v0VMRUTUc6GhoQgNDQXQ3jCgtLQU/fv3FzkVEfXULYtI1dXVuP/++wEAzzzzDNLT000WypaUVCixJ6cEAT7OcHFyQGKsP3w8OE+YLFNVVRXWrl0LQRDwm9/8hp0ciYh64eLFi5g0aRLGjBmDgIAAseMQEfWZn376CceOHcPYsWORkpLCzm1EFuSWV3jXXvzZ2dnxYtDIHksfhOEDeYJIlqujA5ujoyPmzp3Lzwwiol744Ycf8O677yIsLAxFRUV4//33MXbsWLFjERH1idGjR6OxsRH79+9HfX09O7cRWZBbXuV1dArrwOowEd1KRwc2X19fzJkzh4slEhH10tdff40NGzZAoVDgxIkTWL58OYtIRGQ17O3tMWvWLHh6erJzG5GFuWURqby8HG+88cYtb7/00kvGTUZEFkEQBJSXlyMiIgKzZs3iAtpERH1EoVAAAIYNG4a6ujqR0xAR9a1rO7cdOnQIarWaRSQiC3DLItJ9991329t05/R6Aa9//hPKqpVQa/VixyG6I2q1Gq2trfDw8MCUKVMgkUggld6y4SMREfXA9SPAOc2DiKxVQkICYmNjYW9vD0EQUFtbCx8fH7FjEdEt3LKI9Pjjj5syh03R6vTIPleB8EB3RAZ7Ymi0HWLDvcWORdRtSqUSmZmZ0Gg0eOihh3hxQ0RkZFxWgIisWcdamseOHcOhQ4cwY8YMxMTEiJyKiG6GK9+KaNywYNwzaYDYMYh6pKMDm0qlwqxZs1hAIiIyggsXLiAxMdFwu62tDYmJiRAEARKJBMePHxcxHRGRcSQkJCAvLw8bN25EQ0MDO7cRmSEWkYio2zo6sMlkMixYsMCwXgcREfWtHTt2iB2BiMjknJ2dcc8992Dr1q3s3EZkplhEIqJuEQQBR48ehaenJzuwEREZWXBwsNgRiIhEcW3ntmPHjmHIkCH84pLIjHRZRNLr9fj8889x6dIlvPzyy1ixYgV+85vfsBpMZCP0ej20Wi1kMhnS0tJgb2/PDmxEREREZDQdndsGDRoEDw8PAIBKpYKjo6PIyYioy1ZKy5Ytw8WLF3H69GkAwP79+/H2228bPRgRiU+tVmPDhg3IysqCXq+Hs7MzC0hEREREZBIdBaTz58/jiy++wNWrV0VORERdFpEOHz6Md955B46OjnB1dcXnn3+OgwcPmiIbEYlIqVRi9erVyMvLQ2RkJKTSLj8uiIiIiIj6nL+/P+zt7bF69WpcvHhR7DhENq3Lq0J7e/tOF48ymczQgpGIrFNVVRW+/fZb1NbWIj09vVOHICIiIiIiU/L29sbixYuhUCiwYcMGHDt2DIIgiB2LyCZ1WUQaMGAAVqxYAZ1Oh/z8fPzlL39BbGysKbIRkQj0ej02bdoEvV6PBQsWIDIyUuxIRERERGTjOjq3xcTEYN++fSgtLRU7EpFN6nJI0Ysvvoi33noLNTU1WLRoEcaMGYOXXnrJFNmIyMQEQYBUKsWsWbMgl8vZgY2IiIiIzEZH57aBAwciJCQEQPv5q0QiETkZke3osojk6uqKt956yxRZiEgker0e+/btgyAImDBhAvz8/MSORERERER0A4lEgoiICABAWVkZdu7cidTUVMMi3ERkXF0Wkd54442b3s/RSETWQa1WY8uWLbh8+TKGDRvGb3OIiIiIyCJotVrU19fj22+/RXp6OoKCgsSORGT1ulwTydPT0/Cfi4sLTpw4YYpcRGQC13ZgmzBhAiZOnMgCEhERERFZhNDQUCxevBgODg7s3EZkIl0WkR5//HHDf08//TS+/vprXLhwoVs737BhA2bOnIkpU6ZgxYoVt9xuz549mDhxYvdTE1Gv6XQ6rF69mh3YiIisDM+/iMiWXN+5rbCwUOxIRFaty+ls13N2dkZlZWWX21VUVGD58uVYu3YtZDIZFi5ciBEjRiAqKqrTdtXV1Xj33Xd7GoOIesnOzg7jxo2Dm5sbFAqF2HGIiKgP8PyLiGxRR+e2EydOIDQ0VOw4RFaty5FIr7/+Ot544w288cYbeP3117Fw4UL079+/yx0fOnQII0eOhKenJ5ydnTFt2jRs3br1hu1eeuklPP7443eWnoh67OTJk8jPzwcAREVFsYBERGRFeP5FRLbK3t4eKSkpkEqlUCqV2LJlC9RqtdixiKxOlyORvLy8Ot1OS0tDWlpalzuurKzs1OHJ398fp0+f7rTNV199hfj4eAwZMqS7eTvJzc29o+d1R05OjtH2rdEJAIDS0lLk5DQZ7XUsiTGPN7XT6/U4efIkzp8/j9DQUGRnZ3P9IxPiz7hp8XibFo+3+TDF+RdguedgdCMeb9Pi8TaNkpISHDhwAG5ublCr1XB1dRU7ks3gz7hpiXG8uywiFRUVYdmyZT3esSAIN9x37QXrxYsXsX37dnz55ZcoLy/v8f4BICEhAY6Ojnf03NvJyclBUlJSn++3g1qjA74rRXBwMJKSBhjtdSyFsY83/dqBrbGxEbNmzYKbmxuSk5PFjmUz+DNuWjzepmWs461SqYxaqLBWpjj/Aiz3HIw64/E2LR5v00lKSsKwYcPw0Ucf4ezZs+zcZiL8GTctsc7BupzOduHChZuekHRFoVCgurracLuyshL+/v6G21u3bkVVVRXmzZuH//qv/0JlZSUWL17c49chotvTarU3dGCTSrv8p09ERBaI519ERO369euHqVOnGjq3FRUViR2JyCp0ORLJ19cXs2bNwpAhQ+Di4mK4/6WXXrrt80aPHo2///3vqK2thZOTE7Zv347XX3/d8PjSpUuxdOlSAO3DDR988EF8++23d/o+iOgW7O3tER0djVGjRiEyMlLsOEREZEQ8/yIi+pW7uzsWL16Mffv2dZrqS0R37pbDEToWIRs2bBhmzpyJ4OBgeHp6Gv7rikKhwNNPP40HH3wQGRkZmD17NgYPHozHHnsMZ86c6bM3QEQ3d+XKFZSWlgIARowYwQISEZEN4PkXEVFnzs7OmD59OpycnKDVanHs2DHodDqxYxFZrFuORFqwYAEyMzN71bkjNTUVqampne775JNPbtguJCQEu3btuuPXIaLOTp48id27dyM0NBTz588XOw4REZkQz7+IiG4uPz8f+/btQ2FhIWbPng25XC52JCKLc8uRSHeyDhIRiUuv12PPnj3YuXMnwsPDu9VJkYiIiIjIFgwYMADTpk1DcXExVq1ahYaGBrEjEVmcW45EUqlUOHv27C2LSQMHDjRaKGty+nIV9p+82uk+nU4vUhqyZlqtFps2bcLly5cxbNgw3H333VxAm4iIiIjoGgkJCXB3d8f69evx7bffIiMjA4GBgWLHIrIYtywiFRcX44knnrhlq9idO3caNZi12LA/H0fPVsDdRdbpfh8POSKDPcUJRVZJKpVCIpFgwoQJSExMFDsOEREREZFZ6tevHxYvXozNmzdDJpN1/QQiMrhlESkqKgrr1q0zYRTrJAhAWIAbPvzjBLGjkJWqqqqCo6Mj3N3dkZqaColEInYkIiIiIiKz5u3tjfvuuw8SiQSCIODKlSuIiIjguTRRFzjXhciCXblyBatWrcKPP/4IAPylR0RERETUTR3nznl5ecjMzMSOHTvYuY2oC7csIiUnJ5syBxH10MmTJ7Fu3Tp4eHhgypQpYschIiIiIrJIkZGRGD58OM6cOYO1a9eira1N7EhEZuuWRaSXXnrJlDmIqJuu78C2YMECuLm5iR2LiIiIiMgiSSQSjB07FtOmTUNJSQk7txHdxi3XRCIi86TValFYWIihQ4diwoQJ7MBGRERERNQHOjq3bdy4EbW1tfDw8BA7EpHZYRGJyEI0NzdDJpNBJpNh4cKFcHR0FDsSEREREZFV6devH5YsWWI4166vr4enp6e4oYjMCIcwEFmAqqoqrFixwrCANgtIRERERETG0XGuXVxcjC+++ALHjh2DIAgipyIyDywiEZm5jg5ser0eiYmJYschIiIiIrIJAQEBiI6Oxr59+9i5jegXnM5mBEdyy/B/mWcgCAIam9UI9eeix3RnTp48id27d8PHxwdz5szhAtpERERERCbi4OCAWbNmwcPDA0ePHkVjYyNmz54NuVwudjQi0bCIZASXiutRXd+KKcP7AQCGxfiLnIgsUWtrKw4dOoTw8HDMmjULMplM7EhERERERDalo3Obl5cXduzYgYsXL2Lw4MFixyISDYtIRiKVSrB0wTCxY5AF0mq1sLOzg5OTExYuXAhPT092YCMiIiIiElFCQgICAwPh7e0NANBoNHBwcBA5FZHp8cqUyIwolUqsWrUKR44cAQB4e3uzgEREREREZAZ8fHwgkUhQU1ODzz77DBcvXhQ7EpHJ8eq0D6k1OrSptNBq9WJHIQtUVVWFb7/9FrW1tfDz8xM7DhERERER3YSTkxM8PDywYcMGdm4jm8PpbH3k7JUa/PlfB6HXt3+AONizPkfdd+XKFWzcuBEODg5YsGABFAqF2JGIiIiIiOgmnJ2dMX/+fGzbtg379u1DXV0dJk2aBDs7O7GjERkdi0h9pKquFXq9gDl3R8HTVYZgP1exI5GFUCqVyMrKgre3NzuwERERERFZgOs7t3l5eSElJUXsWERGxyJSH5s6oh9C/FkEoK4JggCJRAJXV1ekp6cjKCgIjo6OYsciIiIiIqJu6OjcFhgYiPDwcAC/nuMTWSvOuSISgVqtxvr163Hp0iUAQEREBAtIREREREQWKCoqCvb29mhtbcXq1atRVlYmdiQio2ERicjElEolVq9ejby8PCiVSrHjEBERERFRH2hra0NTUxO+++47dm4jq8UiEpEJdXRgq6mpQVpaGoYNGyZ2JCIiIiIi6gNeXl5YvHgxFAoFO7eR1WIRichEGhoasGrVKuj1eixcuBBRUVFiRyIiIiIioj7U0bltwIAB2LdvH7Kzs8WORNSnuLA2kYm4u7tj5MiRiI2NZQc2IiIiIiIr5eDggNmzZ+PYsWOIj48XOw5Rn+JIJCIjEgQB+/fvR1VVFSQSCVJSUlhAIiIiIiKychKJBMOHD4eLiwv0ej127dqFhoYGsWMR9RpHIvWCIAg4cbEKzS0anC+sFTsOmRmNRoPNmzfj8uXLsLOzg5+fn9iRiIiIiIjIxOrq6nD27FlcvHgR6enpCAwMFDsS0R1jEakXyqqb8dePDxtuSyWAi9xBxERkLpqbm5GZmYnKykpMmDABiYmJYkciIiIiIiIR+Pj4YNGiRcjMzMTq1asxc+ZMREdHix2L6I6wiNQLaq0eAPBYegKGxfjDxckBXu5ykVOR2BoaGvDdd9+htbUVaWlpXECbiIiIiMjG+fj4YPHixcjKysKGDRswadIkDBkyROxYRD3GIlIf8PF0QqiC69xQO1dXV4SEhCApKQkKhULsOEREREREZAY6Orft3LmT1wlksbiwNlEfOXv2LFpaWmBnZ4eZM2fyFwMREREREXXi4OCA6dOnIyAgAACQm5uLtrY2kVMRdR+LSES9JAgC9u7diy1btiA7O1vsOEREREREZAFqa2uxY8cOrFq1ip3byGKwiETUCxqNBuvXr0d2djaGDh2KMWPGiB2JiIiIiIgsgLe3N+bNmwelUomVK1eirKxM7EhEXWIRiegONTc347vvvkNeXh7uvvtuTJw4EVIp/0kREREREVH39OvXD4sWLYK9vT1Wr16NS5cuiR2J6LZ4xUt0hyQSCXQ6HdLS0pCUlASJRCJ2JCIiIiIisjAdndv8/f2h1WrFjkN0W+zORtRDV69ehUKhgLOzMx544AGOPiIiIiIiol5xdnbGggULDNcWpaWlCAgIgJ2dncjJiDrj1S9RD5w6dQrfffcdjh49CgAsIBERERERUZ/ouLZoaGjA6tWrkZmZyc5tZHZ4BUzUDR0d2H788UeEhYUhMTFR7EhERERERGSFPDw8MGXKFBQXF7NzG5kdFpGIunB9B7aMjAw4OjqKHYuIiIiIiKxUQkIC5s6dy85tZHZYRCLqQmNjI4qLi9mBjYiIiIiITCYsLMzQuS0vL0/sOEQAuLA20S0plUq4urrCx8cHS5YsgZOTk9iRiIiIiIjIhvj4+OC+++6DXC4H0H6N4uLiws7QJBoOqSC6iStXruCLL77A6dOnAYAFJCIiIiIiEoWTkxMkEgmam5vx9ddfY8eOHdDpdGLHIhvFIhLRdU6fPo1169bBw8MDERERYschIiIiIiKCs7MzEhIScObMGXZuI9GwiET0i44ObDt27EBYWBgWLFgANzc3sWMRERERERFBIpFg7NixmDZtGju3kWhYRCL6xdWrV9mBjYiIiIiIzNq1ndsOHz4sdhyyMVxYm2yeTqeDnZ0dgoODcd9990GhUHChOiIiIiIiMlthYWFYvHgxXFxcAPx6TUNkbByJRDaturoaX375JQoLCwEAAQEBLCAREREREZHZ8/b2hqOjI9RqNVauXIljx45BEASxY5GVYxGJbNaVK1ewcuVKaDQaTl0jIiIiIiKLJJFI4OHhgX379rFzGxkdp7ORTTp9+jR27twJb29vzJkzB+7u7mJHIiIiIiIi6jEHBwfMnj0bBw4cwNGjR9HY2IjZs2dDLpeLHY2sEEcikc0pKCgwdGBbuHAhC0hERERERGTRru/ctn37drEjkZXiSCSyOWFhYZg2bRri4+MhlbKOSkRERERE1iEhIQHu7u78opyMhlfQZBOam5uxdu1a1NXVQSKRICEhgQUkIiIiIiKyOv369YOnpycEQcC2bdtw6dIlsSORFeFVNFm96upqrFixAsXFxWhoaBA7DhERERERkdGp1WrU1NRgw4YN7NxGfYZFJLJqBQUFWLlyJfR6PRb8//buPD7K8tz/+HeyECCQELaAyCYBUiABBCqyyi6QBBI2A6dYTkUttpzSo6dUsNCiYCkvrYeetgfU03pMWE8Iy0tZlU2shIAsKoWISUibjUWykGWW5/dHm/kZWSaBzDyZyef9l5MnZL65My/nypX7ua9Zs9SlSxezIwEAAACA2wUFBWnGjBnq3r27Dh8+rP379zO5DfeNJhJ81ldffaVt27YpJCREs2fPVrt27cyOBAAAAAAeUzW57bvf/a7OnDmj999/3+xI8HIcrA2f9eCDD+rhhx/W4MGDFRQUZHYcAAAAAPC4qsltYWFhCg0NNTsOvBw7keBTrFarDh8+rIqKCgUGBmrkyJE0kAAAAAA0eH369FHHjh0lSadOnVJubq7JieCN2IkEn1FaWqrU1FTl5+frgQceUEREhNmRAAAAAKBeqaysVHp6ukpLSzVp0iR1797d7EjwIuxEgk+omsB25coVxcXF0UACAAAAgNto1KiREhMT1aZNGya3odZoIsHr5eTkVJvARgMJAAAAAO4sODi42uS2Dz74wOxI8BLczgavFxISogceeEDjxo1TSEiI2XEAAAAAoN6rmtx25MgRfo9CjdFEglcyDEOfffaZevfurZCQEE2bNs3sSAAAAADgVSwWi0aMGOF8nJWVpRYtWjDFDXdEEwlex2q16r333lNGRoaCgoI4CA4AAAAA7pPNZtPu3btlGIamTJmi9u3bmx0J9RBnIsGrlJaWavPmzfryyy/12GOPcf4RAAAAANSBgIAATZ8+Xf7+/tq8ebMuXrxodiTUQzSR4DW+PYFtwIABslgsZscCAAAAAJ/QqlUrzZ49m8ltuCOaSPAaFRUV8vPzYwIbAAAAALhJ1eS2iIgIff3112bHQT3DmUio9/Lz8xUeHq4OHTpo3rx58vf3NzsSAAAAAPiswMBAxcbGyjAMWSwWXb9+XU2aNFHjxo3NjgaTsRMJ9ZZhGDp8+LDeffddZWZmShINJAAAAADwAIvFIj8/P9ntdm3btk0bN25UUVGR2bFgMppIqJesVqvzHty+ffuqU6dOZkcCAAAAgAbH399fY8aMUUlJiZKTk5WXl2d2JJiIJhLqnaoJbBkZGXrsscc0ZswY+fnxUgUAAAAAM3Tu3FmJiYny9/fXpk2bmNzWgPGbOeqdrKwsJrABAAAAQD3yzcltJ0+eZGpbA+XWg7V37typP/zhD7Jarfr+97+vOXPmVLu+f/9+rV27VoZh6MEHH9SqVasUGhrqzkiox8rKytSkSRP16tVLnTp1UrNmzcyOBACAV6IGAwC4Q9XkNpvNJovFooqKCgUGBnLnSAPitp90fn6+Xn/9dSUnJ2v79u3atGmTMjIynNdLSkq0fPlyrVu3Tjt27FDPnj21du1ad8VBPXfmzBm9+eabzvtraSABAHBvqMEAAO4UGBioJk2ayOFwKDU1Vdu2bVNFRYXZseAhbmsiHTt2TIMHD1aLFi3UtGlTTZgwQbt373Zet1qtWr58ucLDwyVJPXv2VG5urrvioJ4yDEOnTp3Svn379MADDygsLMzsSAAAeDVqMACAJ/j5+alXr17Kzs7Wxo0bVVpaanYkeIDbbmcrKChQmzZtnI/btm2rM2fOOB+HhYVp7NixkqTy8nKtW7dO3/ve92r1HOfOnaubsLeRnp7u8nPyv7ZKki59+aUaWym+astms+njjz/W5cuX1b17d3Xq1MmtP1NUV5PXOOoO6+1ZrLdnsd71S0OowVB3WG/PYr09jzV3vx49eujo0aO6ePGiysvL1apVK7MjNRhmvL7d1kS63SFbtzsgubi4WAsWLFBkZKTi4+Nr9Rx9+vRRUFDQPWe8k/T0dA0YMMDl52XmFknv5euhbt00IPqBOs/h606cOCGHw6GHH35Ys2fP5gBtD6rpaxx1g/X2LNbbs9y13hUVFfxh4R41hBoMdYP19izW2/NYc88ZPHiw3njjDf3973/X2LFjOSPJA8yqwdz2kw0PD9eVK1ecjwsKCtS2bdtqn1NQUKDZs2crMjJSr7zyiruioJ5xOBySpIcfflizZs1SZGQkDSQAAOoINRgAwNNatWql8ePHKy4uTn5+fnI4HExv81FuayINGTJEH3/8sa5du6aysjLt3btXI0aMcF632+169tlnNXHiRC1ZsoQmQgORmZmpd955RyUlJfLz81OHDh3MjgQAgE+hBgMAmKFJkyZq2bKlDMPQgQMHtH//fucGAvgOt93OFh4erkWLFmnu3LmyWq2aPn26oqOjNX/+fC1cuFB5eXn6/PPPZbfbtWfPHkn/2BrNX8N815kzZ3TgwAG1bNmS/5kAAOAm1GAAALMFBQUpLS1NRUVFiomJccst0DCH25pIkhQbG6vY2NhqH1u/fr0kKSoqSufPn3fn06OeMAxDR44cUVpamrp06cL/RAAAcDNqMACAWSwWi0aMGKGwsDDt379fGzZsUEJCgkJCQsyOhjrAaVdwu+PHjystLU19+/ZVfHw8DSQAAAAA8HFRUVFKSEhQSUmJtmzZIrvdbnYk1AG37kQCJKlv374KDg5W7969OXcBAAAAABqIzp07KzExUUVFRfL39zc7DuoAO5HgFleuXNGuXbtks9nUuHFj9enThwYSAAAAADQwrVq1UteuXSVJp0+f1okTJ5jc5sXYiYQ6l5mZqZ07dyowMFBFRUVq2bKl2ZEAAAAAACYyDEOXL1/WX//6V12/fl1jxoyRnx/7WrwNTSTUqW9OYIuPj+fwNAAAAACALBaLJk+erJCQECa3eTHafqgzJ06c0L59+9SpUyc98cQTNJAAAAAAAE5Vk9vGjx+v7Oxsbdy4UTabzexYqAV2IqHOPPTQQyotLdXw4cPZlggAAAAAuK2oqCiFhISosLBQAQG0JbwJv+njvpSWlur48eMyDEMtW7bUyJEjaSABAAAAAO6qc+fOGjhwoCTp8uXLunjxosmJUBO0/HDPrly5om3btunmzZuKiIjgAG0AAAAAQK2lpaUpMzNTw4cP18CBA5nsXY+xZQT3JDMzUxs2bJDdbtesWbNoIAEAAAAA7klsbKwiIiJ0+PBh7d+/Xw6Hw+xIuAN2IqHWzp07p3379jGBDQAAAABw3wIDAxUbG6sjR444J7fFxcUpMDDQ7Gj4FppIqLXg4GB16dJFkyZNYhwjAAAAAOC+VU1ua9GihXJycjhwu57ip4IasVqtys7OVrdu3dS1a1d16dKF+1QBAAAAAHUqOjpaUVFRslgs+vrrr1VeXq527dqZHQv/xJlIcKm0tFSbN2/Wjh07dOPGDUmigQQAAAAAcIuq3zc/+OADbdq0SRkZGSYnQhWaSLirK1euKDk5WVeuXFFsbKxCQ0PNjgQAAAAAaAAmTJig1q1ba8eOHTpx4oQMwzA7UoNHEwl39O0JbBEREWZHAgAAAAA0EMHBwZo5c6YiIiJ06NAhJrfVAzSRcEeFhYUKCQnR7NmzuQcVAAAAAOBxVZPbBg0apLy8PNlsNrMjNWgcrI1qDMPQ9evX1bJlSw0cOFD9+vVjrCIAAAAAwDRVk9usVqsCAwNVWVmp8vJyhYSEmB2twWEnEpysVqt27typpKQklZSUyGKx0EACAAAAANQLVb+f7tu3T8nJycrLyzM5UcNDEwmS/v8EtoyMDD366KMKDg42OxIAAAAAALd45JFH5O/vz+Q2E9BEQrUJbHFxcRo4cKBzpCIAAAAAAPVJ69atNXv2bCa3mYAmEnTq1CkmsAEAAAAAvMY3J7elpaWpvLzc7EgNAgdrN2AVFRUKCgrSqFGjNHjwYDVv3tzsSAAAAAAA1EjV5LaioiI1adJEhmHIarWqUaNGZkfzWexEaoAMw9Dhw4eVlJSksrIyBQQE0EACAAAAAHgdi8Wi0NBQSdJHH32kDRs2qKioyORUvoudSPdgXepZXc4vVlmFzewotWa1WvX+++/r4sWLio6OVlBQkNmRAAAAAAC4bw8++KBOnTql5ORkTZ06Ve3atTM7ks9hJ1ItGYahnUcuKSe/WBZJ0RGtFfFgC7Nj1cg3J7CNGDFCY8eOlZ8fLwEAAAAAgPfr0qWLEhMTmdzmRuxEukfjH+msxAmRZseolQ8++MA5gY0DtAEAAAAAvqZqcltqaqp27dqlp556Ss2aNTM7ls+gidQAGIYhi8Wi0aNHa9CgQWzpAwAAAAD4rKrJbbm5uc4GUtXvxbg/3Mvk486ePavU1FTZ7XYFBwfTQAIAAAAA+LzAwEB16tRJknT+/Hlt27ZNFRUVJqfyfjSRfFTVBLa9e/fK4XDIbrebHQkAAAAAAI+z2WzKysrSxo0bmdx2n2gi+SCr1aqdO3cqLS1N0dHRio+PV6NGjcyOBQAAAACAx/Xp00fx8fEqKipScnKy8vLyzI7ktWgi+aD33ntPGRkZGjlyJBPYAAAAAAAN3rcnt924ccPsSF6Jg7V90ODBg9WrVy91797d7CgAAAAAANQLVZPbLly4oNDQULPjeCW2qPiIrKwsffTRR5Kk8PBwGkgAAAAAAHxLcHCw+vfvL0nKy8vThx9+KIfDYXIq70ETyQecPXtWKSkpunjxoiorK82OAwAAAABAvZedna2TJ08yua0WaCJ5sW9OYOvYsaMSExM5QBsAAAAAgBr47ne/q3Hjxik7O5vJbTVEE8mL7d692zmBLSEhQUFBQWZHAgAAAADAa1RNNK+a3Pb111+bHale42BtL9a1a1e1bt1aAwcOlMViMTsOAAAAAABep2py28mTJxUSEmJ2nHqNJpKXuXr1qq5evaoePXooMjLS7DgAAAAAAHi91q1ba/z48ZKk0tJSZWRkKDo6mg0b30ITyYtkZWVp586datSokR566CEFBPDjAwAAAACgLp06dUqffPKJCgsLNXr0aPn5cRJQFboQXuLs2bPav3+/wsLClJCQQAMJAAAAAAA3GDp0qBwOh9LS0nTjxg3FxMRwBvE/0U6r5243gY17NAEAAAAAcA+LxaIRI0ZUm9xWXFxsdqx6ge0s9ZzFYpHFYlF0dLTGjBnDNjoAAAAAADwgOjpaISEh+uijj7gb6J9YhXrq5s2bKi4uVnh4uIYNGyZJHOgFAAAAAIAHdenSRZ07d5bFYpHNZlNubq46duxodizTsK2lHrp69aqSkpK0Y8cO2e12524kAAAAAADgWVW/j6elpWnLli1KT0+XYRgmpzIHO5HqmaoJbAEBAZo6dar8/f3NjgQAAAAAQIM3cOBAFRQU6ODBg7p+/XqDnNxGE6ke+eYEtvj4eIWGhpodCQAAAAAASAoMDFRcXJyOHDmitLQ0FRUVafLkyQ1qchtNpHr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      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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279+vjh07XncfVqv1mvuut/r9008/rd/97ncaPXq0Nm3apEcffVSrV6/W3/72N/Xq1UuLFy9WcnKyHnzwQXXt2lWRkZH18h4BAAAAALjda/D6lJmZqfj4eElX1gfu0aOHfv7zn9sef/HFFzVgwAAdOnRIjz32mIYPHy4PD48a+6ioqNCaNWv0zjvvSJImTpyoJUuWaPjw4TKZTKqqqrrmdSsqKmxf30q3uutd73+3a9qJEyf08ccfa/v27fLz89OTTz6p9957T4888ogkac2aNRo9erRCQkJsz/n73/9u+/oHP/iBRo8efdOZboQ1kRpYv379lJWVpYsXL2r16tWaMWPGNd8kVqv1ut+E1cLDw2ss9PXdSqUk5eTkKCkpyfaNMW7cOGVlZeny5cvaunWrpk+fLpPJpJiYGPXs2VNHjhypx3cJAAAAAIDxqtdEWrVqlTZs2KBXXnnFtrzM1fr06aP77rtPTz/99DVrEO3YsUP5+fl69NFHNXLkSC1fvlw7d+5Uenq6evTooeTkZOXl5dV4zuHDh9WyZUsFBgaqW7duOnr0aI3HCwsL9aMf/Ujl5eU17g8PD6+xRnFWVtY11/t79uzRoEGDFBISIg8PD02fPr3GAtyffPKJJk6caLtdUFCg//znP7bbVqtVbm71M4aowYtIhYWFmjx5slJTU6957Pjx45oxY4bGjRunZ5999pp/OGcxbdo0vfnmmwoICFB0dLQGDhxoW5grJydHW7duVf/+/eXq6nrdY9CzZ0+dPXtW586dU1VVldauXathw4bV2CYoKEienp46ePCgJCkhIUE+Pj4KDg5Wp06d9Mknn9he79ixY+rcuXMDv2sAAGAUzr8AAKjbQw89pJKSEi1ZsqTG/R9//LEef/xxbdu2Tdu2bdPu3bvVt29fLVu2TC1atFBcXJyeffZZFRUVSZLOnz+v+fPn69FHH5UkPfjgg1q8eLFt8EZFRYVefvll+fr6XjPqadiwYVqzZo2qqqp07tw5JScnq3v37jW26dSpk/bu3avi4mJZrVZt27ZNXbp0kXSlQPTll1+qd+/etu29vb31r3/9S1988YWkK13mxowZUy/HrEGLSF988YXmzJmj5OTk6z7+i1/8Qs8//7w2bdokq9WqpUuXNmQcw0ydOlUff/yxZsyYIUn68Y9/rNzcXMXFxen73/++fvSjH6lr165q27atCgoK9Itf/EKSFB8fr4yMDHl6eurll1/WT37yE02cOFFt2rTR+PHjJUnPPvustm7dKpPJpL/97W965ZVXFBcXpz/+8Y+29Y5++ctf6ujRo5o0aZIeeOAB/exnP1Pr1q0NORYAAKBhcf4FAMDN8fDw0E9/+lP97W9/U0FBgSQpOztbn3/+uWbOnFlj24ceekjLli1TVVWVfvOb36hLly6aOXOmJk2apJ/+9Kd6/PHHNXXqVElSx44d9cc//lEvvfSSpkyZovj4eHl6eurFF1+UdGVh7WeffVaSNH78eLVv315TpkzRI488opdeekleXl7KyMiwTcsbMmSIJk2apOnTp2vKlCmqrKzUQw89JOnKQBF3d/caC227urrq9ddf1wsvvKAJEyboyy+/tNUZ7pTJer0JePXk2Wef1bRp0/TUU0/pvffeU1RUlO2xtLQ0PfDAA7YRMgcPHtRf//pXvffee3Xut6ysTMeOHVO3bt2uWZH8+PHjdzzKpj7XRHIW9XFca5OQkKC+ffs2yL5xfRxz++J425ejH++N+5K1M/Ha0SGNUXlpsTq2cNMPZ4+s933f6Hc9bqyhzr+khv13OXI6S0vXfqpWrdvW634bg+gIP40b2Nrur+voPy+dDcfb/jjm9lF9rch1tH3V1/H+7rV+Xb/rG3Rh7ZdeeqnWxzIzM2t0HAsNDb1ux7EbOXbs2DX3ubm52YaU3Yn62IczKS8vrzHnsr415L5xfRxz++J425cjH++1uzKVfrlCEUHuRke5IyWFl5V6ar9MHVoqIeHO28Wi/jT0+Zd0/XOwO7Vq5xlt2fKJ/MPbyz+8wy0tStqYVVZd+Ty3mfslQ17fkX9eOiOOt/1xzBve1dfgXEfbV30c71u91jesO9vNdhy7kdpGIt1pNY4K6rU8PDxqtBCsT3xCYH8cc/vieNuXox/vjz7fIz8/af4jQ4yOctvS0tL00UcfaWD0lanYDXG8qz8FQ/2qj/Mv6frnYHeqV69eCvfMUmlpqXr08K63tRsc3Xvrv9KKHacN+bnl6D8vnQ3H2/445vZRfQ3OdbR91dfx/u61fl3nYIYVkb7bcex6K5ADAOAsqqexnU3LU0xk4x65Exoaqs6dO+t73/ueTpw4YXQc3AJHPv9ydXXVgAEDVFVVpfDwcKPjAACA62jw7my1iYyMlKenp23Y1MqVK6/pOHa7GnCZpyaJ4wkAd+7qAtLw3lF1P8HBWCwWff755yovL5eHh4fGjh3Lp42NUEOef9UHk8mkAQMG2BqAHDt2TDk5OcaGAgDUiWvGxslisdzyc+xeRHr44Yd19OhRSdJrr72m+fPna8KECSopKdH9999/x/v38vLSpUuX+CauJ1arVZcuXZKXl5fRUQCg0YuJDND8R4Zo/KDWRke5JWVlZVqxYoX27Nmjr7/+2ug4uA0Nff7VEEpLS7V7924tXrxYqanOsRg9ADgjrsEbH6vVqvLycqWlpd3yh4J2mc62bds229dvv/227etOnTrpo48+qtfXioqKUmpqqrKysm57H9WfsuIKLy+vGp1dAABNR35+vlasWKGcnByNHTtWXbt2NToSbpI9z78agpeXl2bPnq2VK1dq2bJlfP8BgIOqvgZPTU2lo6od3Wndws3NTQEBAWrWrNmtPe+2X9FBubu7KyYm5o72kZCQ0GCLSAPAnXCm9vANpaCgQB99vsfoGNdojGshZWRkaMWKFaqsrNT06dPVqlUroyOhiQkKCtLs2bO1Zs0abdy4Ufn5+Ro0aJDRsQAAV6m+Bk9ISKjRKh4Ny6i6hWFrIgEAbl31ujpofBrjWkienp7y9/fXnDlzKCDBMGazWTNmzFC3bt0YKQ4AgMGcbiQSADi76nV1cH20870zVqtVZ86cUdu2bRUYGKg5c+bcVgt4oD65urpq7NixtttpaWkKDg6W2Ww2MBUAAE0PRSQAsJP6mIrWGKdEofGwWCzaunWrjhw5ookTJ6pz584UkOAwqr8Xy8vLtXr1anl4eGjatGkKDg42OBkAAE0H09kAwE7qYypaY5wShcahugPbkSNHFBsbq06dOhkdCbguDw8PTZkyRWVlZXRuAwDAzhiJBAAN7ODpQn30+R7bKCKmosHRfLcDW/fu3Y2OBNxQZGSk5s6dqxUrVmjZsmUaN26cunTpYnQsAACcHiORAKCBHU0uthWQGEUER5SXl6fi4mJNnz6dAhIajcDAQM2ePVuRkZFKSUkxOg4AAE0CI5EAwA4YgQRHlJOTo+DgYLVs2VLz5s2j8xUanerObdXy8/Pl7e0tNzfnPsXNKyxT8oV8nb2Yp6S0PJ29kK9mgWb95gcDjY4GAHByzv0bFkCTUB8LVjek9MsV8vMzOgXwLavVqoMHD2r37t2aOnWq2rRpQwEJjZarq6skqbKyUsuWLZOvr6+mTJniNJ3bsnNLdPLcZZ1Jy9XZC/k6eyFPl/JKbY8H+3tKMin9UpFxIQEATQZFJACNXvWC1Y7atSwiyJ1pbHAYV3dga9++vVq2bGl0JKBeuLm56Xvf+542btyoDz74oFF2bquotOjshTwdT87RiW/+ZH9TMHJxMallmK+6t22mmBYBimnhr5gWAQr089Q7q49p475kY8MDAJoEikgAGo3aRhw5+oLVCQkJ6tu3tdExAJWVlWnt2rVKTk5WbGyshg4damubDjiDTp06yc/PT6tWrdLixYsVHx+vqCjHL+JXWaz69f/t1VfJOSorr5IkNQs0q3NMiDq1ClKn1sFq3dxfHu6uBicFADR1FJEANBq1jThiwWrg5iQlJen8+fN0YINTi4yM1Jw5c7Ry5Up9+umnuueeexy6WBro5ymrVcrJL9WY2Gh1bRuizq2DFRLgHNPxAADOhSISgEbFkUccAY6qvLxcHh4e6ty5s8LDwxvdFB/gVgUFBWn27NmyWq0ymUyqqKiQm5ubQxaT4oa00Zj+rWT25LQcAOD4+G0FwCFdb+qaI697BDiq06dPa8uWLZo+fToFJDQp1QtrWywWrVy5Uj4+Pho7dqzDdW4zmUwUkAAAjYaL0QEA4Hqqp65djWlrwM2r7sC2evVq+fv7y9fX1+hIgCFMJpOio6N1/PhxffzxxyopKTE6EgAAjRYfewAwVGNdLBtwZN/twDZhwgS5u7sbHQswhMlk0oABAxQQENCoO7cBAOAIGIkEwFDXG3EkMeoIuBNffPGFjhw5otjYWMXFxVFAAnSlc9vdd99t61JotVqNjmSIguJyfX7sot5ZfUxvbczQj17eqrKKKqNjAQAaCUYiAah3tY0uuh5GHAH1p3oR4Z49e8rf319t27Y1OhLgUKo7t1VWVspkMtn+zziz4tIKHTmdrS9OZelY0iUlX8yXJLm7ucjsYVJ+caGKSirk6e5qcFIAQGNAEQlAvaseXXQzi2Az4gioH+np6dq2bZvi4+Pl4+NDAQmoRVBQkO3r7du3y8vLS4MGDXKaYlKVxarTKZeVeCpLiSczdeLcZVksVnl6uKpL62AN6dVJ3do0U/uWgXr34z1aeyDX6MgAgEaEIhKABsHoIsB+Tp8+rXXr1snb21tlZWXy8fExOhLg8CwWi8rLy5WYmKjc3FyH7Nx2KyqrLPrzBwk6eDxTBcXlMpmktlGBmjGinXp3DFOnVsFyd7P/ShYWi1WZl4sVEmA25PUbs6oqi7JyS5SRU6yMnGJlXS7R4B7NFdOCTrUAjNN4f1MCMFxdi2IDaFhWq1UJCQnatWuXwsPDNXXqVApIwE1ycXHRuHHjFBQUpD179qigoEBTpkyR2Ww2Otot8/Z0U2WVVQePZ6hv53DFdg5Xz/ahCvD1tHuWopIKnTx/WSfPXdaJczk6de6yCksqNHdcJ80Z29HueRyZ1WpVbkGZMnKKlZ5TrIycImVcKrbdzs4tkcVSc+2uywWlevTuXsYEBgBRRAJwB2qbtsYUNcA+EhMTtXPnTjqwAbfpu53bli5dqvvuu08uLo1rxMzUu9optmuEYpr7y9XVftmtVqsuXirSsTOXdCI5RyfPX1ZKRoGsVslkkqLD/fS9ni207WCKCovL7ZbLkVgsVl3KK9WFrEId+LpQianHdCG7UOnfFIvKv7OoeaCfpyKCvdW5VbDC+3grPPjbP0+9sVtNdD14AA6EIhLgRG52QeuCggJ99PmeO349FsUGjNW5c2dVVVWpX79+TrOeC2CETp06yc/PTyUlJY2ugCRJZk83tYsKtMtrZV4u1pGvs3X0TLaOfJ2l7LxSSZKv2V0dWwVpaK9IdWoVpA7RQfL2ulLY3nM4zS7ZjGK1WpVXWK60rEJdzC5UWlbRN18X6UJWocorLbZtPdwL1KKZj6LCfNW3U1iNIlFYsLe8PGq/PLvZn/OFJRVKv1QkX7O7IkIYnQqgflFEApzIrSxoXR8YcQTYX35+vj777DONHDlSZrNZsbGxRkcCnEJkZKTt62PHjsnFxUVdunQxMJFjuFxQqiNfZ+vI6WwdOZ2l9EvFkiR/Hw91b9dMd7drpu5tmyky1FcuLs5dzLZarbpcUKaU9AKdy8hXSkahUjIKdD49XwXFFbbtXF1MigjxUYtQH/XqEKoWob6KDPVR9sUk3fW9/nd8nKosVl3KLVF6TpEuZl+ZBpd+qVgXLxUp41KRLUtYkFnvPDf2jl4LAL6LIhLgZG5mZFBCQoL69u1rp0QA6kt6erpWrlypiooK9erVS2FhYUZHApyO1WrViRMndO7cOeXm5jpV57abUWWx6uuUyzp4PEMJxzN0OjVPkuTj5aZubZspbkgb9WgfquhwP6cuGl3OL1XyxfwrRaKMAp1PL1BKRoEKS74tFvmY3RUd7qfBPVqoZbifIkN91SLUR+FB3tedVpiQd/6Oj9nOxFRtO3helVXfzmtzdTEpLNhbEcHeat8rUhHBPko8lakzqbl39FqlZZVydTXJ3c31jvYDwLlQRAIMdLPTz24WC1oDzuvqDmwzZ85Us2bNjI4EOCWTyaRp06Zpy5Yt2rdvn1N0bqtLSVmldh9O0/6v0nXoRKbyi8rlYpI6tgrW/RM7q1eHULWJDJSrExaNqixWXcwu1Nm0fJ1Jy9XZC/lKupCn3IIy2zZ+3h6KjvDT0F6Rahnup+hwP0VH+CnQz9OuBcbxg1rrXHq+IoK91byZjyKCfRTRzEfNAryuKVplXS6us4hksVh1uaD0m/WZroxqSv9mce+Ll4qUW1CmtlEBev2JuxrsPQFofJz3tyHQCNT39DOmlwHO6ejRo9qyZQsd2AA7cXV11bhx4xQYGKhPP/1UBQUFmjlzplxdnXNExiOvbpPFYpWft4f6dg5Tv07h6tMpTH7eHkZHq1el5ZU6n16gpLS8K38u5Cn5Yr7Kyq8sbu3malJ0uL/6dgpTmxYBat3CX9Hh/grw9XCI0Wi3092uorLKNtUtPbtI6TnFSr90ZfpbxqWiGus1mUxSs0CzIoJ9FNs5XGdS83Q5v7Q+3wIAJ0ARCahDfY8WuhoLUwO4GS1atFCXLl00atQoOrABdmIymTRw4EAFBgYqNzfXKQtIzYM91K5loLrGhGhgtwh1jglpsNFGX6fk6o/vH9TZC3n66ew+6hAd1CCvU63KYlVKRoFOnb9s+3MuvUAWy5VpYD5md7VpEaBxA1upTYsAtYkMUFSYn9zdGt/C6rUpKK7QzGfWynJVRzcvD1dFhPgoMtRHfTuFKSLERxEh3ooI8VFYkLnG1LW/LTusA1+l17r/0rJK5RaWKTzY2yGKbADsgyISUIeGXKyakUMAalNWVqbjx4+rZ8+eCgkJ0fjx442OBDRJnTp1sn2dlpYmq9WqqCjn+N0dGeKhBT8d1OCv4+nhpuPJOUrNdFdBcYXOXsiv9yLSpbwSHU/O0anzuTp1/rLOpOaq9JsRRj5md3VoGaj+IyPUNipQbSIDFBZkdurCx5BekSoqrVB48JUFvpuH+CgixOeWR1VVWaw6kZxjG8l08dK3i3hXT/n71YOxGtS9RUO9FQAOhiIScBMYLQTAnvLz87VixQrl5OQoMjJSoaGhRkcCmjyr1ardu3fr4sWLGjduHJ3bbsFL/ztYFotVZk93/c+Lm+94f1brlVFGX53N0VdnL+nLsznKzLnSNc7N1UVtIwM0ZkArdWgZqA7RQWrezMepC0bX07VNiLq2Cbmjfbi4mJRXWK5fvLHbdl+zAC9FNLsy3c3X20Mrdpyu0ZkOgPOjiARcx9VT2FisGoA9Xd2Bbfr06RSQAAdhMpkUHx+v1atXa8OGDU2yc9vtigrzkyRl55bc1vOrqiw6nZqrL5MufVM4ylFBcbkkKdDXU13aBGvK0Dbq3DpYMS0CnGpKmpGmDW+n6HA/hQV7q3mIj8KDveXh/u10t6zLJVqx47SBCQEYgSIScB1XT2FjyhkAe6EDG+DYzGazZs6cWaNz27hx45xyvSQjVY80+uLrbH3xdZaOnslWcWmlJCky1EcDu0WoS0ywusSENMlRRvbSvJmPJg9pU+d2CScydCY1VyVllXpkRk95eXKJCTgz/oejSaprsWwWvAZglLCwME2ZMoUObICDqu7cFhQUpPT0dLm4MOqlPmTnluiLr7N0+OssHfk6Szn5V9bbaR7io6G9ItWzfai6tQ1RkJ+XwUlRzezpKlcXk/YeuSg3VxdVVlk06Xsx6tgq+Jb2U1llUeblYl3MLrL9uZBdJF+zu342tw9FQsDBUERCk1TXYtmMPgJgLxaLRRcuXFBUVJTatWuntm3bcsIMODiTyaQBAwbIarXKZDIpPz9flZWVCg6+tYvnpqyyyqLjyTk6+FWGDp7I0Pn0AklSgK+HerYLVc8OoerZPlThwd4GJ0VtfL099M5zY+Tm6qKvU3L12399Vuu2FZUWZeQU1SwUXSrSxawiZVwutnXNk650kHN3c1VBcbken91bbq78TgQcCUUkOLXaRhwx0giAIygrK9PatWt1/vx5PfDAAwoODqaABDQi1f9fN23apMzMTMXHxztN57aGtGrXGb275piKSivl5mpS1zYhGh0brV4dQtUqwl8uLvwcbCxCAsw1bl/KK1XiyUxdyCpUalah0jILdSG7SFmXi3VVnUhmTze1CPVRu5aBGto7Us1DfNS8mY9aNPNRoJ+nlm49pfc3nLDzuwFwMygiwanVNuKIkUYAjHZ1B7ZRo0YxggFoxEaPHq2VK1dq2bJldG67AW8vN3l6uKqopFyDe7RQbJdw9WwfKm8vd6Oj4Q65fFNQnf/fA7b7zJ6uigz1VcfoIN3VN0otmvmoRTNfNW/mI38fj5v60GTh+uNKzylSx+ggTR/RvsHyA7h5FJHg9BhxBMDRXN2Bbdq0aWrdurXRkQDcgaCgIM2ePZvObXXw9nLXot9NkIebC8fGyXSJCdb3J3SSv4+nokJ91SLUR8H+Xrf97+zv4ylJWr37jFxMJp27mE8RCXAQFJHQqN3sAtkA4EhSUlLk6upKBzbAiVzdue3rr79Wv3795OHhYXQsh+PpTic7Z+Tl6aZZozvW2/7GD2yl/l3CFejrqT9/cEhn0nJrPF5SVqkLWYW6kFWktOxC5RWUadaYjgr086y3DACujyISGjUWyAbQWFitVhUWFsrPz0/9+vVT9+7d5eVFlyHAmVR3bisrK5OHh4cqKipUWVkps9lc95MB2JhMphrrLeUXletvyw5fKRplFSonv/Sa53RpE6KhvSLtGRNokigioVFggWwAjZnFYtG2bdt08uRJ3XffffL396eABDgpk8lk+/+9efNmZWRkaOrUqax7BtymQH9PFRRXaN/Ri2rRzEe9OoQqMtRXkd9Mm6ussuhnr+8yOibQZFBEQqPAAtkAGqvqDmzJycmKjY2Vn5+f0ZEA2EmvXr20atUqLV68mM5twG2aF9dNc8Z2kq/5+guwn0/PlyRt/vycPjlwXuUVVfr1vIEye3KpCzQE/meh0WDEEYDG5uoObGPGjFGPHj2MjgTAjiIjIzVnzhytWLGCzm3AbXJxMdVaQJKuLMLt7uai48k58jW761JeqbIuFys6wv+621utVuXklyo1s1BZl4sV2yVCAb6spQTcLIpIAAA0kAMHDig/P58ObEATFhQUpDlz5mj16tXasWOH2rRpw3RWoB4F+nlqyYsT5ebqok+PXNCrCw9KkkrLK3Xxcrl2J6YpNatQaZmFSssqUFpWoUrKqmzPf3BSF80YSec34GZRRAIAoJ5VVlbKzc1Nw4cPV+/evVkLBWjiqju35ebmysvLS1arVRaLRa6udCoD6oPHd7r+PfvmXuUWln1zK1OSFBpkVlSor0bFRisq1FfNm/nqN2/vU2WVxfa8sooqXcwuUlpmoVKzCpSWWaiQALMemMQIQqAaRSQAAOqJ1WpVQkKCjh07ptmzZ8vLy4sCEgBJVzq3hYSESJL27dunlJQUTZkyhc5tQD1qFxWoHu2aKcjPS5FhviovzNSwgT3UvJmPvDxqXvpWfVM8+uzYRX2ZdElp2UXKulwsq/XbbdzdXCSp1iKSxWJVQXG5/H08ZDKZGuZNAQ6GIhIAAPWgugPbF198ofbt2zPCAECtgoKCtH//fi1evFjTpk1TUFCQ0ZEAp9C8mY9e+t/v2W4nJBQqpkXAdbc1mUxqFmhWWlahWoT6qlOrII3u11KRYdWd33z14ZaTWr07SUUlFUrLKrzyJ7PQNj3uQnaRyiuq9MSc3hrZL9pebxMwFEUkAADuUFlZmdatW6ezZ88qNjZWQ4cO5RNJALXq3Lmz/P39a3Rui4yMNDoW0KS4uJj0r2fHyMWkG/7Orqi0aPZz6799nkkKD/ZRZJivusQEa/3eZOUWlNX6fMDZUEQCAOAObdu2TefOnaMDG4CbdnXnto8//lgPP/wwU9sAO3N1ufEHPkN7RaqotFLhwd6KDPVVVJivIkK85e52ZbRxaVml1u9NtkNSwHFQRAIA4A4NHTpUXbp0UatWrYyOAqARqe7cduHCBQpIgANqGxWoH88MNDoG4FAoIsEhbNyXrJ2JqbU+fjYtTzGR15/PDABGOH36tI4fP65JkybJ19dXvr6+RkcC0AiZzWa1bdtWknTq1CmdOXNGY8aMkZsbp+kAAMfjYnQAQJJ2JqbqbFperY/HRAZoeO8oOyYCgOuzWq06ePCgVq9erfz8fJWVsQ4CgPqRl5enr776Sh9//LFKSkqMjgMAwDX4iAMOIyYyQPMfGWJ0DACo1dUd2Nq1a6eJEyfK3d3d6FgAnERsbKx8fX21ceNGLV68WFOnTlVwcLDRsQAAsGEkEgAAN2nz5s364osvFBsbqylTplBAAlDvOnfurHvuuUelpaVavHixCgsLjY4EAIANI5EAALhJPXv2VIsWLejABqBBVXdu+/rrr1lvDQDgUBiJBADADaSnp+vAgQOSpObNm1NAAmAXQUFB6t+/vyQpMzNTn3/+uaxWq8GpAABNHSORAACoxenTp7Vu3TqZzWZ1795dXl5eRkcC0ASdOHFCBw4c0KVLlzR27Fg6twEOJvFUls6lFyi3oEyPzeqlkACz0ZGABsNvIAAAvsNqterQoUPauXOnwsPDNXXqVApIAAwzdOhQeXh46NNPP1VBQYGmTJkis5mLVMBorq4u8vJw1eFTWfLxclNRaaXOXsiniASnRhEJAIDv2LlzpxISEujABsAhmEwmDRw4UAEBAbbObXfffbf8/PyMjgY0ae5uLnrrl6Pl6uqii9mFevKvu3Xgq3QlnsxURZVF/29aD7m6mIyOCdQrikgAAHxHWFiYYmNjNXToUJlMnPwBcAydO3eWv7+/EhMT5e3tbXQcAJKC/K+MVL5ccOXSev3eZJlMktUqzRzZXmFB3/5fzS0o07n0fJ27mK/UzEIN7xOlrm1CDMkN3C6KSAAASMrPz1dWVpbatm2rLl26GB0HAK4rMjJSkZGRkqTi4mKlpqaqQ4cOBqcCEB3up9ceGyo/Hw8dPX1Jf1t2WLsS03S5oFTnLubr3MUC5RaW1XhOlcVKEQmNDkUkAECTl5GRoRUrVshqtWrevHny8PAwOhIA1Gn//v1KSEjQ4MGDNXDgQEZOAgYymUzq2CpYknQ6JVeS9N91X8nD3VXREX7q1zlcrZr7qVWEv1o399dPF+y45deoqKxSambhlaJUeoFSMws06Xsx6tUhrB7fCXBjFJEAAE3a1R3Ypk+fTgEJQKMxdOhQlZSUaO/evcrNzdWYMWPo3AY4gEHdm+v3/2+QwoK8FR7iU8u6SLUXfS0Wq9JzinTuYoHOp+cr+ZuiUVpWoSwWqyTJ1cWkKotVIQFmWxEpr7BM59MLlF9crkHdmsuF9ZjQAPgtAwBoshISEmp0YPPx8TE6EgDcNFdXV40fP15BQUH69NNPlZ+fT+c2wAG4u7ne1Oggq9WqnPxvprulX5nylpyer5SMApWVV9m2iwjxVqsIfw3sFqHWzf3VKsJfLUJ99cBvN+mLr7P07Juf6nx6zelyf3p8mDpEBzXI+0PTRhEJANBkFRYWqm3btnRgA9BoXd257fPPPzc6DoBb8MmB89qy/7ztdqCfp1pF+GncwFa2aW8tw/1k9rz+ZXvzZt5KySiQj5e7YruEKzrCT2XlVXp/4wl9/mW6Pv8yXa4uJs0Z25Hprqg3FJEAAE1KWVmZCgsLFRISomHDhkkSJ1YAGr3OnTurY8eOcnFxUVVVlbKzsxUeHm50LAC1iB/WVheyC9Uqwt+2VlKAr+ct7eOPPxkmk6nmeczxszmSTmjpJ6ds900Z2ka+3kzXR/2giAQAaDLy8/O1YsUKlZWV6X/+539YOwSAU3FxcZEk7du3TwcOHNC4cePoNgk4qOkj2t3xPq635lHHVkH63Q8HKdDPU4dOZOo/676649cBrsbZMwCgSajuwFZRUaG4uDgKSACcVt++fZWWlqYNGzYoLy+Pzm1AE+LiYlLvjlfWYzp6Ovuax6s7vIUGmm9qdJLValVuwZUFu89l5Ot8eoHOpxcoJaNA4we11gOTKFQ3NZxBAwCc3tUd2ObMmaNmzZoZHQkAGozZbNbMmTO1efNmOrcB0MfbT+vipSKdT89XWlaRLBarBvdorl8+0L/GdtXd3c6nX+kGdz7jytcFxRW2bfy8PRQd4SerpHPp+XZ+J3AE/CaBXW3cl6ydiak17isoKFB2vkUxkQEGpQLgzKxWq44ePaqQkBBNmzaNDmwAmoTqzm2BgYE6ePCgBgwYoODgYKNjAbAjb68rTUM+3v61IoJ9FB3hp8HdW2jHoVSlZxdrw96zV4pF6QU6n5GvvMJy23N9vNwUHeGvwT1aKDrCT63C/RUd4adAP0+ZTCb9dMGOWl+3sLhc5zMKVFxaqb6dwup9JGRBcbk83V3l4e5ar/vFzaGIBLvamZiqs2l51xSMYiIDNLx3lEGpADgji8WisrIymc1mTZw4US4uLnRgA9CkmEwmDRo0SD169LAV0IuLi+Xt7W1wMgD2MKJvlNpHByo8yFteV3V4O56coyOns/WPj4/I7Omm6HA/9e8SoeiIK4WiVhF+Cvb3qrP4U1ZepS+TLikl48qopS+/ztJf125UTn6ZbZt/PDVSLcP9bjm71WrVpbxSpWQUKCWzQKkZhba/cwvL1KNdM730v9+75f3izjVoEWnNmjV68803VVFRoQcffFD33ntvjce//PJL/frXv1ZFRYWaN2+uP/7xj/L392/ISHAAMZEBmv/IENvthIQE9e3b18BEAJxNWVmZ1q1bp5KSEs2ePVuenrfW7QRozDj/wndVF5AOHz6svXv3Kj4+XpGRkQanAtDQXF1d1Cri2p/vP767py5kFSk6wk+hgebbGilkMpl05HS2jpzeI0ny8nBVsK+LeneMUHS4n/KLyvXx9tOqqLTccD9VVRZl5BR/UywqvPJ3RoFSMwtVUlZp287H7K6WYb6K7RKur87mKL+o/AZ7RUNqsCJSRkaGFixYoOXLl8vDw0OzZ8/WgAED1K7dt6vQv/TSS3rsscc0fPhwvfzyy3rnnXf0xBNPNFQkAEATUFxcrCVLlignJ0ejRo2SqytDndF0cP6FG2nVqpUOHTqkpUuXavz48UbHAWCQFs181aKZ7x3t48GJXXQmLU/REX6KDvdTs0CzEhMPqW/fPpKkfUcv1ti+vKJKaVnVRaLqUUUFSssqUmXVt4WmYH8vtQz31ah+LRUV7qeW4b5qGfbtNDpJ+sN/9utidtEd5cfta7Ai0t69ezVw4EAFBgZKksaNG6eNGzfq0UcftW1jsVhUVHTlH7+kpEQBAayJAwC4fRkZGdq0aZPCw8M1bdo0tW7d2uhIgF1x/oUbCQoK0pw5c7R69WqtX79ezZo1U58+fejcBuCW9ewQqp4dQuvc7q2VR5WTX6qMS0WyWK/c52KSwoN9FBXuq76dwq8UisL9FBXmJx9z/Sw9UFxaodTMQqVmFqii0qqxA6L5WVdPGqyIlJmZqdDQb7+pwsLCdOTIkRrbPPPMM3rooYf0hz/8QWazWUuXLr2l1zh27Fi9ZL2ehISEBtt3U1ZQUCDp2uPL8bY/jrl9cbwbntVq1aZNm+Ti4qIuXbro0qVLunTpktGxmgS+vx2HPc6/JM7BGruYmBhlZWXp6NGj2rJli0JCQoyO1GTw/W1/HHP7qj7eOZfL5eluUkZ2nsIC3NWhq59CA9zVzN9NIf7ucnetLuiUSipVYXa2TmTf3GtcvnxZxSVVOnjwoApLLcrOr1BWXqWy8yttXxeUVNV4jmtZuoJ8nW9JaCO+vxvsKFqt1mvuu7ryV1paqmeffVb//e9/1aNHD7377rt6+umn9dZbb930a3Tr1q1B1rlgjZ6G89HnV+bMXn18Od72xzG3L453w7JarbJarXJxcVG7du107NgxDRkypO4nol401Pd3WVlZgxYqnJU9zr8kzsGcQWxsrDZt2qSxY8dKujJCzcXFxeBUzo3vb/vjmNvXd4/3pNEN8zqbju7X6YsZem1lpopKKmz3mz1dFRnmp35dqkc2+ep8eoHe33hCXbt2U/NmztWh16hzsAYrIoWHh+vgwYO225mZmQoLC7PdPnXqlDw9PdWjRw9J0qxZs/SXv/yloeIAAJyQxWLR9u3bVVZWpgkTJigwMFBms9noWIBhOP/CzTKZTLZRa8nJydqxY4fi4+MVFBRkcDIAuLH+XSJUVFKhyLAr6yW1DPdVVJifQgKu7ShXUpYiSUpKy9NXZy/J38dDsV0ijIjtNBqsiDR48GC98cYbysnJkdls1ubNm/X73//e9nirVq2Unp6upKQktWnTRlu3blX37t0bKg7sbOO+ZO1MTL3m/rNpeYqJZO0FAHeuugPb2bNn1a9fP6PjAA6B8y/cDnd3dxUXF2vx4sV0bgPg8Eb3j9bo/tE3ta2ry5Wi0svvHZB0pYvcsvmTGyxbU9CgI5GeeOIJ3X///aqoqNDMmTPVo0cPPfzww3rsscfUvXt3zZ8/Xz/96U9ltVoVEhKiP/zhDw0VB3a2MzH1ugWjmMgADe8dZVAqAM6ioKBAK1as0KVLlzR69Gj17NnT6EiAQ+D8C7cjMjJSc+bM0YoVK2yd2zp37mx0LAC4Y7FdwvWjad0VHGDWZ8cuavfhNKMjNXoNurJUXFyc4uLiatz39ttv274ePny4hg8f3pARYKCYyADNf4R1SQDUL4vFoo8//lgFBQWaOnWqYmJijI4EOBTOv3A7vtu5zdfXVy1btjQ6FgDcEW8vd00a0kaSdPJcjsFpnIPzLU8OAHBqLi4uGjVqlLy8vGp0oQIA3Bmz2awZM2boq6++UlQUI8cBANeiiAQAcHhWq1WHDh2SxWJRbGwsn44DQANxc3OzLbx++fJl7dq1S2PHjqVpAQCnUFVl0Z8WJeh8RoH6dAzTA5O6GB2p0aGPJwDAoVksFm3btk07duzQxYsXr9vCHABQ/y5duqSkpCQtXrxYly9fNjoOANyR0ECzLFbpy7OXlJFTrMOnMo2O1ChRRAIAOKyysjKtXLlShw8fVr9+/RQXF3dN61YAQMNo166d7rnnHpWWlmrx4sVKS2NBWgCN16QhbbT8lcn693Nj1bl1sO3+8ooqPqS8BRSRAAAOqaqqSkuXLtW5c+c0evRoDR8+nAISANhZdec2Ly8vLV26VCkpKUZHAoDb5u7mavs6JbNQ817crBnPrNX7G08YmKpxYU0kAIBDcnV1VY8ePeTv708HNgAwUHXntn379ikiIsLoOABwx3q0a6bs3BJFh/upoDhD2bklRkdqNCgiAQAcyunTp+Xi4qI2bdqoZ8+eRscBAOhK57aRI0dKujLV+ODBgxo4cKBcXV3reCYAOJ5pd7XTtLvaSZLmvbjZ4DSNC9PZAAAO49ChQ1q9erUOHjzI3HQAcFBJSUn67LPP9NFHH6mkhE/vAaApoYgEADCcxWLR1q1btX37drVt21bTpk1j/SMAcFCdO3fWxIkTdeHCBTq3AXAKSWl5WrD4kJ75+x6dSM4xOo5DYzobAMBQlZWVWr16tc6ePat+/fpp2LBhFJAAwMF17txZ/v7+WrVqlRYvXqxp06apefPmRscCgFvm7+Oh06l5yi0sU25BmT785JRCAryUW1CmH9/dU0F+XkZHdCgUkQAAhnJ1dZWfn59Gjx7NGkgA0IhUd27bvHmzfHx8jI4DALdl/iNDVFFlkZeHq2Y/t0EHj2fIw91V5RVVGj+otfp1poh0NYpIqDcb9yVrZ2KqJOlsWp5iIgMMTgTAkWVkZMjd3V3BwcEaM2aM0XEAALchKChIs2bNknRlavKpU6fUsWNHRpQCaDS8PN1UXSZ68+mRcnN1UeblYv3ir7sNzeWoWBMJ9WZnYqrOpuVJkmIiAzS8d5TBiQA4qtOnT2vJkiXasmWL0VEAAPXk5MmTWrdunTZt2qSqqiqj4wDALQsL8lawv5cog9eOkUioVzGRAZr/yBCjYwBwUFarVYcOHdLOnTsVFhamSZMmGR0JAFBPOnXqpNzcXO3du1d5eXmaMmWKzGaz0bEAAPWIkUgAALuwWCzatm2bduzYobZt2+qee+6Rr6+v0bEAAPXEZDJp0KBBmjBhAp3bAMBJMRIJAGAXFotFWVlZ6tevn4YOHSoXFz7HAABn1KVLF/n7+2vdunUqLi5WUFCQ0ZEAAPWEIhJuy9WLaFdjMW0A11NQUCA3NzeZzWbNnDlTbm786gEAZxcVFaV58+bZfuZnZ2erWbNmBqcCANwpPgbGbbl6Ee1qLKYN4LsyMjK0aNEibd68WZIoIAFAE1L9Mz8pKUn//e9/9dlnn8lqtRqcCgBwJzibx027evRR9agjFtEGUJvTp09r/fr18vLy0uDBg42OAwAwSHR0tDp37qxPP/1Uubm5GjNmjFxdXY2OBQC4DRSRcNOqRx/FRAYw6gjADR06dEg7duxQWFiYpk6dygLaANCEubm5acKECQoKCqJzGwA0chSRcEsYfQSgLqWlpTp48KDatm2rCRMmyMPDw+hIAACDVXduCwgI0KZNm3T+/Hl17NjR6FgAcEMXs4u041CqKiurNLp/K6PjOASKSACAelFeXi43Nzd5eXlpzpw58vHxoQMbAKCGLl26KCoqSv7+/pKufPDg5eVlcCoAqMlkMkmS3lp51HZf745hCglgBCVn9wCAO1ZQUKAlS5Zox44dkiQ/Pz8KSACA66ouIF28eFH/+te/dPz4cYMTAUBNbSMDNG9KN/1sbh/dPaq9JMliMTiUg2AkEm7oeotpA8DVMjIytGLFClVUVGjo0KFGxwEANBKBgYEKDQ3V+vXrlZeXpwEDBtg+/QcAI7m6umjq8LaSpM2fnzM4jWPhY2LcUPVi2pJYTBvANU6fPq0PP/xQLi4umj17tmJiYoyOBABoJMxms2bMmGHr3LZp0yZVVVUZHQsAcAOMREKdWEwbwPWUlJRo/fr1Cg4OpgMbAOC2VHduCwwM1L59+xQeHq7evXsbHQsAUAuKSACAW2K1WmUymWyfIIeGhtKBDQBw20wmkwYPHqzIyEi1bNlS0re/awAAjoXpbACAm1ZWVqaVK1fq6NErnSoiIyMpIAEA6kWrVq3k4uKigoICLVy4UGlpaUZHAgB8B0UkAMBNKSgo0Icffqjk5GRZaE8BAGgglZWVqqys1NKlS+ncBgAOhiISAKBOGRkZWrRokfLy8jR16lT17NnT6EgAACcVFBSkOXPmqEWLFlq/fr0+++wzWa1Wo2MBaOLOZ+Rr64HzOng8w+gohmJNJADADRUWFurDDz+Ul5eXZs+erdDQUKMjAQCcXPW6e5s3b9ann34qNzc39evXz+hYAJogl2+WZ3vh7c8kSX7e7vrg9xMNTGQsikgAgBvy9fXViBEjFBMTQwc2AIDdVHdua968uTp37mx0HABNVGyXCN07vpMiQny0/8t0JZxo2iORmM4GALiGxWLRjh07bIuadu/enQISAMDuTCaTevfuLS8vL1VUVGjDhg26fPmy0bEANCEBvp6aPaaj7uoTpSB/T6PjGI4iEgCghvLycq1atUoJCQk6d+6c0XEAAJAk5ebmKikpSYsXL6ZzGwAYhCISAMCmoKBAS5YsUXJyskaPHq3BgwcbHQkAAElSaGio5syZIy8vLy1btozObQBgAIpIAABJUn5+Ph3YAAAOLTg4WHPmzFHz5s21fv16HT582OhIANCksLA2AECS5Ofnpw4dOqh79+50YAMAOKzqzm07duxQdHS00XEAoElhJBIANHFHjhxRfn6+TCaTRo4cSQEJAODw3NzcNHr0aAUHB8tqterAgQMqKSkxOhYAOD1GIkGStHFfsnYmpl5z/9m0PMVEBhiQCEBDq+7AlpiYqH79+mn48OFGRwIA4JZlZ2drz549OnbsmKZOnaqgoCCjIwGA02IkEiRJOxNTdTYt75r7YyIDNLx3lAGJADSk6g5s1QWkoUOHGh0JAIDbEhoaqrvvvlvFxcV0bgOABsZIJNjERAZo/iNDjI4BoIEVFhZq+fLlunTpkkaNGqVevXoZHQkAgDsSFRWlOXPmaMWKFVq2bJkmTJigjh07Gh0LAJwOI5EAoIlxd3eXp6enpk6dSgEJAOA0goODNXfuXLVo0UIeHh5GxwHgpKxWq06n5GrTZ8k6eS7H6Dh2x0gkAGgizp8/r4iICHl6euqee+6RyWQyOhIAAPXKbDbr7rvvtv2OS05OVsuWLeXq6mpwMgDOwCSTSsqq9MTrOyVJXduE6OUfN63ZPBSRAKAJOHTokHbs2KF+/fpp2LBhFJAAAE6r+ndcTk6Oli9frqioKMXFxclsNhucDEBjN2ZAtLw8XNW6hb+Wbz+tvMIyrd59RskX8jWiX0t1b9vM6IgNjulsAODELBaLtm3bpu3bt6tt27YaOHCg0ZEAALCL4OBgjR8/XmlpaVqyZIkuX75sdCQAjVyrCH99f0JnDekZqQBfT6VmFurtlce0Zf957Tx0bbdzZ8RIpCZu475kW2e2mMgAo+MAqEfl5eVat26dkpKSbB3YXFz47AAA0HR06dJF/v7+WrVqlRYvXqz4+HhFRkYaHQuAE/jh1O6aMKi12kYF6IkFO42OYzdcTTRxVxeQhveOMjoOgHpUXFysjIwMjRo1SsOHD6eABABokqo7t3l6eurixYtGxwHgJJo381H/rhEKCTCrKa0UwUgkKCYyQPMfaVqLgQHOLDc3VwEBAQoMDNT//M//0KEGANDkBQcH67777pO7u7ukb39XskYggPqSlVuitXuSlJpZqKnD2yoixMfoSA2CIlITVD2FTRLT2AAnc+bMGa1bt06DBg1SbGwsBSQAAL5R/TsxLy9PCxcuVPv27TVmzBg6twG4Y26uLjp0IlOHTmRKklqE+mjK0LYGp2oYFJGaoKunsDGNDXAe1R3YwsLC1LlzZ6PjAADgkPz9/dW3b1/t27dP+fn5dG4DcMd+fm9f5RWWKyLEW4/9aYd2JKRqR0KqLuWV6JVHhzrVqCSKSE7i6tFFdakuIDGFDXAOFotFO3bsUGJiotq1a6cJEyYwAgkAgFqYTCYNHjxYgYGB2rRpk5YsWaJp06YpMDDQ6GgAGqkuMSGSpPKKKvmY3ZWWVajQQLNy8suUllWowuIKlVdW2bZrzCgiOYlb6bDG6CPAuWRlZemLL75Q3759NWzYMBbQBgDgJlzdue3QoUMaOXKk0ZEANHIe7q5a+MI4ubq46Kuzl/TLf3yqF97+TJJkMkkf/H6ifM3uBqe8MxSRnAiji4CmpaKiQu7u7goPD9cDDzyg4OBgoyMBANCoREVF6d5775Wvr6+kb3+3AsDtcne7ss5aTIsrgzeaBXrpUn6pdiSkatXOM7qQXajwYG/dP7GLwUlvDx9XA0AjlJGRoX//+986deqUJFFAAgDgNgUGBsrNzU0lJSVauHChPvvsM1mtVqNjAWjkfMzuevL7ffXg5K7q1T5UkrRky0ntPXJBmz47Z3C628dIJABoZKo7sHl6eiooKMjoOAAAOAV3d3dFRETo008/VW5uLp3bANSbYb0jFRHioxahPlqy+aT2fHHB6Ei3jSISADQi1R3YQkNDNW3aNNvwewAAcGfc3Nw0YcIEBQYG0rkNQL1yd3NV1zaNf1FtielsANBopKamavv27Wrbtq1mzZpFAQkAgHpW3blt/PjxSktL0/bt242OBAAOhZFIjdjGfcnamZgqSTfdmQ1A42O1WmUymRQVFaX4+Hi1adOGDmwAADSgrl27KjAwUIGBgZK+/V0MAE0dVyGN2M7EVJ1Ny5N0pTPb8N5RBicCUN8KCgq0dOlSZWZmSpLatWtHAQkAADuIjIyUj4+PqqqqtGrVKp04ccLoSABgOEYiNXIxkQGa/8gQo2MAaACZmZlasWKFysrKVFxcbHQcAACapIqKCpWWlmrdunXKzc3VgAEDGJUEoMni42wAcEBnzpzRkiVLJEmzZ89W69atjQ0EAEAT5eXlpZkzZ6pz58769NNPtWnTJlVVVRkdCwAMwUgkAHAw58+f16pVq+jABgCAg/hu5zaLxaKJEycaHQsA7I4iEgA4mMjISA0aNEh9+/aVh4eH0XEAAIC+7dwWGBiokBDnaNUNALeK6WwA4ADKy8u1ZcsWFRcXy9XVVYMGDaKABACAA+rSpYvCw8MlSfv27VNaWprBiQDAfigiAYDBCgoK9OGHH+ro0aNKTU01Og4AALgJpaWl+uqrr7Rs2TI6twFoMpjO1shs3JesnYlXLjLPpuUpJjLA4EQA7sTVHdimTp2qNm3aGB0JAADcBC8vL82dO1erVq3SunXrlJeXp/79+9O5DcBNsVqtysgplr+Ph7y93I2Oc9PqLCIVFRXptddeU1JSkv7yl7/oz3/+s55++mn5+PjYIx++Y2diqq14FBMZoOG9o4yOBOA2paamavny5fL09NTs2bMVFhZmdCQAAHALzGazZs6cqc2bN2vPnj0qKCjQ6NGjjY4FwMEVFpfr3l9vVEFxuSRp3MBWOnshT9Pvaq/v9WxhcLobq7OI9OKLLyosLEyXLl2Sp6enCgsL9etf/1p/+tOf7JEP1xETGaD5jwwxOgaAOxQSEqKYmBiNGDGCDmwAADRSV3duY8FtAHXp2iZEJ89fVpsWAdqy/7wk6dMvLqi4tELHzmQ3/iLS8ePHNX/+fO3cuVNms1mvvfaaJk+ebI9sAOB0LBaLvvjiC/Xo0UNms1lxcXFGRwIAAHeounNbta+//lrNmjVTUFCQgakAOKJhvaM07JsZRfOmdFN+UbkiQrx17683GJzs5tRZRHJxqbn2dlVV1TX3AQDqVl5ernXr1ikpKUlms1mdOnUyOhIAAKhn5eXl2rp1qywWi+Lj4xUZGWl0JAAOysfsLh9z41kPSbqJ7myxsbH64x//qNLSUu3evVs/+clP1L9//5va+Zo1azRx4kSNGTNGixYtuubxpKQk3XfffZoyZYrmzZunvLy8W38HANAIVHdgO3v2rEaNGkUBCUCD4hwMMI6Hh4fuueceeXp60rkNgNOps4j05JNPytvbW35+flqwYIE6duyoZ555ps4dZ2RkaMGCBfrggw+0atUqffjhhzp9+rTtcavVqv/93//Vww8/rNWrV6tz585666237uzdAIADunz5sj744ANdvnxZU6dOVa9evYyOBMCJcQ4GGC84OFhz585VRESE1q1bp88//9zoSAAaga+Sc/Tivz/Xj17+RMfP5hgd57rqnM62c+dO/fjHP9aPf/xj230rV67U1KlTb/i8vXv3auDAgQoMDJQkjRs3Ths3btSjjz4qSfryyy/l7e2tYcOGSZJ+9KMfKT8//zbfBgA4Nk9PT02bNo0ObAAaHOdggGOo7ty2adMmVVRUyM2tzksvAE2Yv4+HktLyVFRSoYycYiVfzFPnmGCjY12j1p9k27ZtU2VlpV599VVZrVZZrVZJUmVlpRYsWFBnESkzM1OhoaG222FhYTpy5Ijt9vnz59WsWTM9/fTT+uqrr9ShQwc9//zztxT+2LFjt7T9rUhISGiwfd+JgoICSY6b73Y52/tpDDjmDS8jI0NhYWEKCgpSYGCgUlJSlJKSYnSsJoHvb/vieDsWzsFwKzjeDS88PNz29SeffCJvb295enoamKhp4Xvcvjjet+++4QGSAlRRZdWfVhTr3PnzSvC88WgkI453rUWk48eP67PPPtOlS5f03nvvffsENzfNmzevzh1XF52uZjKZbF9XVlZq//79ev/999W9e3e9/vrrevnll/Xyyy/fdPhu3bo1yA/ghIQE9e3bt973e7s27kvWzsRUSVJ2vkUxkQEOle9OOdrxbgo45g3LYrFox44dOn78uDp06KC8vDz169fP6FhNBt/f9tVQx7usrKxBCxXOjHMw3CyOt33t379fZ86ckYeHh6ZNm2YbLYiGw/e4fXG868fl/FJpxUW1io5W374xtW5n1DlYrWsi/fjHP9bChQv15JNPauHChbY/7777ru6///46Xzg8PFzZ2dm225mZmTWmcYSGhqpVq1bq3r27JGny5Mk1PiXDt3Ympups2pUFL2MiAzT8m3aAABxPeXm5Vq9ercTERPXt21dt2rQxOhKAJoZzMMAxubq6atKkSSouLtbixYuVlpZmdCQAuGV1Lqx99913a8uWLVq5cqVWrlypjz/+WAsWLKhzx4MHD9a+ffuUk5OjkpISbd682Tb3XpJ69+6tnJwcW7eCbdu2qWvXrnfwVpxbTGSA5j8yRPMfGaLxg1obHQfAdRQWFurDDz9UUlKSRo0apbvuuksuLnX+mAWAesU5GOC4oqKiNGfOHHl4eNC5DUCjVOfqbk888YRSUlKUlZWlLl266IsvvlD//v3r3HF4eLieeOIJ3X///aqoqNDMmTPVo0cPPfzww3rsscfUvXt3/f3vf9dzzz2nkpISRURE6NVXX62XN+UMrp7CdjYtTzGRAQYnAlCXjIwM5eXlaerUqYxAAmAYzsEAx1bduW3VqlU6efKkOnbsWGPKKQA4sjqLSMePH9fmzZv1wgsv6KGHHpLVatVvf/vbm9p5XFyc4uLiatz39ttv277u2bOnPvroo1uM3DRUT2GLiQxgChvg4AoLC+Xr66u2bdtq3rx5MpvNRkcC0MRxDgY4turObRaLRSaTScXFxfL09JSrq6vR0QDghuosIoWFhcnNzU2tW7fWqVOnNGHCBJWUlNgjW5NXPYUNgONKTEzUzp07NXPmTEVFRVFAAgAAN8XN7cqlWFVVlT766CN5eXkpLi6OcwkADq3OxTq8vb21Zs0aderUSRs2bNDJkyeVm5trh2gA4LgsFou2b9+ubdu2qXXr1jUWrQUAALhZrq6u6tu3r9LS0rRkyRKutQA4tDqLSL/+9a91/Phxfe9735OLi4u+//3va968efbIBgAOqboD26FDh9SnTx9NmTJFHh4eRscCAACNVNeuXTVz5kw6twFweHUWkVq3bq2nnnpKJpNJr7/+ug4cOKDY2Fh7ZAMAh3TixAklJSVp5MiRGjFiBB3YAADAHWvZsqWtc9v27dtltVqNjgQA16h1TaTU1FQtWLBAgYGBevLJJ2U2m1VYWKg33nhDixYt0rFjx+yZEwAMV1VVJVdXV3Xv3l3h4eEKDw83OhIAAHAi1Z3bKioqZDKZVFVVJRcXF7q3AXAYtRaRfvWrX6lDhw7KzMzU//3f/2nIkCF64okn5O/vr3/961/2zNhkbNyXrJ2JqZJk68wGwDGcOXNG27Zt04wZMxQcHEwBCQAANAiz2Syz2Syr1ap169bJ09NTo0ePpnMbAIdQaxEpPT1d7733nkpLSzV9+nQtXbpUDz74oP7nf/7H1kkA9WtnYqqteBQTGaDhvaOMjgRAVzqwbd++XaGhoax9BAAA7CYkJESfffaZ8vLy6NwGwCHUWg3y9vaWJHl5eSkvL0+vvvqqhgyh3XxDqB6BVF1Amv8IxxlwBBaLRTt27FBiYqLatm2riRMnUkQCAAB2YTKZ9L3vfU+BgYHavHmzlixZomnTpikwMNDoaACasJtaDTYkJIQCUgO6uoDE6CPAcSQkJCgxMVF9+/alAxsAADDE1Z3bVqxYIYvFYnQkAE1YrSORrl68jfm3DY8RSIDj6dWrl/z8/NSpUyejowAAgCasunNbSUkJXWEBGKrWItLJkyfVp08fSVJpaanta6vVKpPJpEOHDtknIQDYUWZmpnbv3q1JkybJy8uLAhIAAHAIwcHBtq8/++wzmUwm9e/fn85tAOyq1iLSli1b7JkDAAyXlJSktWvXytPTU8XFxfLy8jI6EgAAQA1Wq1U5OTk6fvy4cnNz6dwGwK5qLSJFRkbaMwcAGOrqDmzTpk2Tr6+v0ZEAQJK0f/9+5eXlyWq12u4bO3asgYkAGMlkMmnChAkKCAigcxsAu6u1iAQATcXBgwe1c+dOOrABcDhPPfWUPvvsM7Vq1cp2n8lkoogENHHf7dy2dOlSff/732dEEoAGRxEJQJPXsWNHlZeXa+DAgSxWCcChHDx4UBs2bJCPj4/RUQA4oK5du8rf3195eXkUkADYBVdLAJqkwsJC7dq1SxaLRX5+fho8eDAFJAAOp0WLFhSQANxQy5Yt1a1bN0nS6dOndfLkSYMTAXBmdV4xZWVl6Yc//KHGjRun7OxszZs3T5mZmfbIBgANIjMzU4sWLdLhw4eVk5NjdBwAqFWfPn30xBNPaPXq1dq8ebPtDwB8l9Vq1RdffKG1a9fq888/r7GOGgDUlzqLSL/97W81evRoeXp6KiAgQJ06ddJzzz1nj2wAUO+SkpK0ZMkSSdLs2bPVrFkzgxMBQO0SExOVnZ2tZcuWaeHChVq4cKHef/99o2MBcEAmk0nx8fHq1KmT9uzZo82bN6uqqsroWACcTJ1rIqWlpemee+7RBx98IHd3d/3iF79QXFycPbIBQL06duyYNm/erNDQUE2dOlV+fn5GRwKAG1q4cKEkqbKyUlarVe7u7gYnAuDI3NzcNHHiRAUGBto6t02fPl1ubiyFC6B+1PnTxGQyyWKx2G4XFhbWuA0AjUVQUJDat2+vcePG0YENQKNw6dIlPf300/rss89UVVWl2NhY/fGPf1R4eLjR0QA4qKs7t2VmZrLgNoB6Ved0trFjx+rJJ59UQUGBlixZogceeEATJkywRzYAuGPl5eU6fvy4JCkyMlJxcXEUkAA4vGXLlkm6sqxAr169tHfvXu3du1f9+vXTCy+8YGw4AI1C165dNWLECJlMJmVnZystLc3oSACcQJ0jkX70ox9p5cqVslgs2rt3r2bNmqW7777bHtkA4I4UFhZqxYoVysrKUnh4uIKDg42OBAA35Z133tGhQ4d07tw5/fWvf7Xd/9hjj2nSpEkGJgPQGG3btk0XLlzQhAkT1LFjR6PjALgFVqtVF7OLdPL8ZVVVWdWzfahOpVxWVlaZ+hqQp84i0pIlSzR58mRNnTrVDnEAoH5kZmZqxYoVKisrU3x8PAUkAI3Kxo0bdfToUT3zzDMqKyuTp6enJKmkpEQmk8ngdAAam7i4OK1atUpr165VXl6eYmNj+VkCOLi9Ry/q8y/Tder8ZRUUV1zzePsWXpo63v656iwi7d+/X6+//rpGjBihWbNmqVevXnaIBQC3LykpSWvXrpWnp6dmz56tsLAwoyMBwC3r3r27Jk6cqAcffFDTp0+XJC1fvlzjxo0zOBmAxsZsNmvmzJnatGmTdu/erdzcXI0aNYr1kgAH5OnhKjdXF33xdZZaRfhrUPcW6hAdJH8fdyWcyFSrCH91iA5UbkaSIfnqLCL9+c9/Vl5entauXasXX3xRZWVlmjlzph544AF75HNaG/cla2diqiTpbFqeYiIDDE4EOI/i4mIFBQXRgQ1Ao/fjH/9YERER2r17tywWi6ZPn66ZM2caHQtAI3R15zbWRwIcl7eXu/717GiZPd3k7VWzK+ug7i1sXydkn7V3NEk3UUSSpICAAM2aNUthYWF6++239fbbb1NEukM7E1NtxaOYyAAN7x1ldCSgUbNYLLa1j7p166bOnTvz6RqARquwsFC+vr620QKjRo2yPZaXl6fAwEDjwgFotKo7t1VVVcnV1VUlJSUqKyvjZwrgYEICzEZHqFWdRaQvv/xSy5cv18aNG9WlSxf94Ac/0MiRI+2RzenFRAZo/iNDjI4BNHrl5eVav369kpOT9dBDDykgIIACEoBG7b777tOKFSs0cODAGuuWWK1WmUwmW9dJALgd1edJmzZt0sWLFxUfH68WLVrU8SwAuIki0o9//GPNmDFDy5Yt4wfLHWIKG1D/ru7ANmLECAUE8P8KQOO3YsUKSdKJEycMTgLAmQ0bNkwrVqzQ0qVL6dwG4Ka41LXBjh079JOf/IQCUj2onsImiSlsQD3IzMzUokWLdPnyZcXHx6t3795GRwKAepWdna2tW7dKkv7whz/o/vvvp7AEoN4EBwdr7ty5ioiI0Nq1a/X555/LarUaHQuAA6t1JNKcOXO0ePFi9e7d+7rDqA8dOmSXgM6GKWxA/am+kKIDGwBn9cwzz2jIkCHat2+fPvvsMz344IN68cUX9f777xsdDYCTuLpz29GjR9WzZ095eXkZHQuAg6q1iPSXv/xFkrR27dprHqM6DcBIJSUlMpvNGjJkiPr16ydvb2+jIwFAg8jNzdWDDz6oV155RZMnT9b06dO1aNEio2MBcDLVnduKi4vl5eWlqqoqVVRUUEwCcI1ap7NVf6r/m9/8RpGRkTX+/OxnP7NbQACoZrFYtH37dv33v/9VUVGRXFxcKCABcGoVFRWqqKjQ7t27NXjwYJWUlKi4uNjoWACckMlkko+PjyRp+/btWrJkiXJzc40NBcDh1DoS6bHHHtPZs2eVkpKiuLg42/2VlZVycalzKSUAqFfVHdjOnDmjPn36yGx23LaXAFBfRo0apUGDBqlz587q1q2bJk+erMmTJxsdC4CT69ixo06ePKnFixfTuQ1ADbUWkZ566imlpaXp+eef1/PPP2+739XVVe3bt7dLOACQanZgGzlyJAtoA2gyHnvsMd1zzz0KDw+XJL322mvq1KmTwakAOLuWLVtqzpw5Wr58OZ3bANRQaxEpKipKUVFR2rRpU42FtXFrNu5L1s7EVEnS2bQ8xUTSfhy4VXv27LF1YGvbtq3RcQCgwa1atUrx8fF69913r3ls3759euihhwxIBaApqe7ctmrVKm3evFnR0dGMBAdQd3e2Pn360J3tDuxMTLUVj2IiAzS8d5TRkYBGw2KxyMXFRXfddZf69u2r0NBQoyMBgF2cO3dOknTq1CmDkwBoyry9vXX33XcrKyvLVkCqPj8D0DTdVnc23JqYyADNf2SI0TGARuXw4cM6ceKEZsyYIS8vL7qDAGhSHnvsMUnS/PnzdeDAAcXGxio3N1cHDx7U6NGjDU4HoClxc3NT8+bNJUmJiYk6ffq04uLiODcDmqg6u7MFBwcrKytLkZGR+uSTT/S3v/2N6W0AGozFYtGOHTu0detWeXl5yWq1Gh0JAAyzYMEC/fWvf5UklZaW6q233tI//vEPg1MBaKo8PDyUmppK5zagCatzHOIvf/lLbd26VUeOHNF7772nFi1a1FhoGwDqS3l5uVavXq2EhAT16dNHU6ZMkYeHh9GxAMAwW7du1b///W9JUkREhN5//32tX7/e4FQAmqquXbtq5syZKioq0uLFi3XhwgWjIwGwszqLSCkpKfr5z3+u7du3a9q0afrJT35C1RlAg9i8ebOSkpI0cuRIjRgxgvn2AJq8iooKubu72267u7szIhyAoao7t3l4eGjZsmUqLCw0OhIAO6p1TaRqFRUVkq50R3r66adVVVWl4uLiBg8GoOkZPHiwunTpojZt2hgdBQAcQp8+ffTzn/9cM2fOlMlk0sqVK9WzZ0+jYwFo4qo7t509e1a+vr5GxwFgR3V+zN+nTx9NnDhRpaWl6tOnjx588EENHjzYHtkANAFJSUn65JNPZLVaFRwcTAEJAK7y/PPPq1mzZpo/f75effVVhYSE6NlnnzU6FgDIbDarS5cukqTz589r8+bNqqqqMjgVgIZW50ik559/XomJierUqZNcXFw0b948DRs2zB7ZADi5w4cPa9u2bQoNDVVZWRldPgDgO7y9vfXLX/5SeXl5CggIMDoOAFxXenq6jh49qtzcXE2ZMoVzOsCJ1TkSydXVVZmZmXrppZf05JNP6vLly6xTAuCOXN2BLSYmRrNmzeJkAwCuIykpSZMmTdLkyZOVkZGhCRMm6MyZM0bHAoAa+vfvr3HjxiktLY3ObYCTq7Ma9M477+j//u//1LFjR3Xt2lX/+c9/9Oabb9ojGwAntWnTJiUkJKh3796Kj4+nAxsA1OLFF1/Ur371K4WEhCg8PFzf//739etf/9roWABwjW7dumnmzJkqLCzU4sWLlZeXZ3QkAA2gziLSypUrtWjRIj344IN66KGH9P7772v16tX2yAbASXXu3FkjRozQyJEjGdkIADeQm5ur733ve7bb9957L52QADisli1bau7cueratav8/f2NjgOgAdzU1dvVK+77+fnJza3OpZQAoIasrCwdPXpUktS6dWv16dPH4EQA0DiUlZXJZDJJuvKz1GKxGJwIAGoXHBysYcOGyWQyKTc3VwkJCbJarUbHAlBP6iwiRUZG6r///a8qKipUUVGh//znP2rRooU9sgFwEklJSVq8eLH27dun8vJyo+MAQKMxZ84czZs3T5cuXdKf/vQnzZo1S3PmzDE6FgDclCNHjmjHjh3asmULndsAJ1HnkKLf/va3evLJJ/Xqq69Kknr27KnXXnutwYMBcA5Xd2CbOnUq6x8BwC24++671bp1a+3YsUOVlZX63e9+pyFDhhgdCwBuytChQ+Xi4qLPP/9ceXl5iouLo5kK0MjVWUQKDw/XwoULVVJSIovFIh8fH3vkatQ27kvWzsRUSdLZtDzFRNKSF03Tzp07dfDgQbVt21YTJ06kgAQAt+iBBx7Qf//7X8XGxhodBQBumclk0pAhQxQYGKgtW7ZoyZIlmjFjhvz8/IyOBuA21TqdLTk5WTNmzFCfPn30yCOPqLi4mALSTdqZmKqzaVe6EcREBmh47yiDEwHG8PX1VZ8+fTRlyhQKSABwGwoKClRcXGx0DAC4I9Wd23x9feXp6Wl0HAB3oNaRSL/73e80bdo0DRo0SB999JFeffVVvfLKK/bM1qjFRAZo/iMMN0fTU1hYqNzcXEVFRalv375GxwGARs1sNmvEiBHq2LGjvL29bff/85//NDAVANy6li1bqmXLlpKuNAxIS0tTmzZtDE4F4FbVWkTKzs7W97//fUnSk08+qfj4eLuFAtA4ZWVlafny5bJarfrBD35AJ0cAuAOnTp3SqFGjNGTIEEVERBgdBwDqzeeff64DBw5o6NChio2NtXWgBOD4ar3Cu/riz9XVlYtBADeUlJSktWvXytPTU9OnT+dnBgDcgY8//livvPKKWrVqpfPnz+u1117T0KFDjY4FAPVi8ODBys/P1+7du5Wbm6tRo0bJ1dXV6FgAbkKtV3lWq7XGbarDAGpT3YGtWbNmmjZtGoslAsAdWrhwodasWaPw8HAlJiZqwYIFFJEAOA03NzdNmjRJgYGBdG4DGplai0jp6el68cUXa7393HPPNWwyAI2C1WpVenq6YmJiNGnSJBbQBoB6Eh4eLknq3bu3Ll++bHAaAKhfV3du27t3r8rLyykiAY1ArUWke++994a3ATRt5eXlKikpUUBAgMaMGSOTySQXl1obPgIAbsF3R4AzzQOAs+rWrZs6deokNzc3Wa1W5eTkKCQkxOhYAGpRaxHp0UcftWcOAI1IYWGhVqxYoYqKCj3wwANc3ABAA2NZAQDOrHotzQMHDmjv3r2aMGGCOnbsaHAqANfDyrf1ZOO+ZO1MTJUknU3LU0xkgMGJgIZR3YGtrKxMkyZNooAEAA3g5MmT6tOnj+12aWmp+vTpI6vVKpPJpEOHDhmYDgAaRrdu3XTmzBmtXbtWeXl5dG4DHBBFpHqyMzHVVjyKiQzQ8N5RRkcC6l11BzYPDw/NmjXLtl4HAKB+bdmyxegIAGB33t7euvvuu7Vx40Y6twEOiiJSPYqJDND8R4YYHQNoEFarVfv371dgYCAd2ACggUVGRhodAQAMcXXntgMHDqhnz558cAk4kDqLSBaLRf/+97/19ddf6/nnn9eiRYv0gx/8gGow0ERYLBZVVlbKw8NDU6ZMkZubGx3YAAAA0GCqO7d1795dAQFXlgkpKyuTp6enwckA1NlK6dVXX9WpU6d05MgRSdLu3bs1f/78Bg8GwHjl5eVas2aNVq1aJYvFIm9vbwpIAAAAsIvqAtKJEyf07rvv6sKFCwYnAlBnEWnfvn16+eWX5enpKV9fX/373//Wp59+ao9sAAxUWFiopUuX6syZM2rbtq1cXOr8cQEAAADUu7CwMLm5uWnp0qU6deqU0XGAJq3Oq0I3N7caF48eHh62FowAnFNWVpY++OAD5eTkKD4+vkaHIAAAAMCegoODNXfuXIWHh2vNmjU6cOCArFar0bGAJqnOIlKHDh20aNEiVVVVKSkpSb/+9a/VqVMne2QDYACLxaJ169bJYrFo1qxZatu2rdGRAAAA0MRVd27r2LGjdu3apbS0NKMjAU1SnUOKnn32Wf3hD3/QpUuXNGfOHA0ZMkTPPfecPbIBsDOr1SoXFxdNmjRJXl5edGADAACAw6ju3Na1a1dFRUVJunL+ajKZDE4GNB11FpF8fX31hz/8wR5ZABjEYrFo165dslqtGjFihEJDQ42OBAAAAFzDZDIpJiZGknTx4kVt3bpVcXFxtkW4ATSsOotIL7744nXvZzQS4BzKy8u1YcMGnT59Wr179+bTHAAAADQKlZWVys3N1QcffKD4+Hi1aNHC6EiA06tzTaTAwEDbHx8fHyUmJtojFwA7uLoD24gRIzRy5EgKSAAAAGgUWrZsqblz58rd3Z3ObYCd1FlEevTRR21/nnjiCS1cuFAnT568qZ2vWbNGEydO1JgxY7Ro0aJat9uxY4dGjhx586kB3LGqqiotXbqUDmwA4GQ4/wLQlHy3c9u5c+eMjgQ4tTqns32Xt7e3MjMz69wuIyNDCxYs0PLly+Xh4aHZs2drwIABateuXY3tsrOz9corr9xqDAB3yNXVVcOGDZOfn5/Cw8ONjgMAqAecfwFoiqo7tyUmJqply5ZGxwGcWp0jkX7/+9/rxRdf1Isvvqjf//73mj17ttq0aVPnjvfu3auBAwcqMDBQ3t7eGjdunDZu3HjNds8995weffTR20sP4JYdPnxYSUlJkqR27dpRQAIAJ8L5F4Cmys3NTbGxsXJxcVFhYaE2bNig8vJyo2MBTqfOkUhBQUE1bk+ZMkVTpkypc8eZmZk1OjyFhYXpyJEjNbZ577331KVLF/Xs2fNm89Zw7Nix23rezUhISLil7QsKCm7rebiC49bwLBaLDh8+rBMnTqhly5Y6ePAg6x/ZEd/j9sXxti+Ot+Owx/mX5FjnYLgzHG/74njbR2pqqvbs2SM/Pz+Vl5fL19fX6EhNBt/j9mXE8a6ziHT+/Hm9+uqrt7xjq9V6zX1XX7CeOnVKmzdv1n/+8x+lp6ff8v4lqVu3bvL09Lyt595IQkKC+vbte0vP+ejzPZJ0y8/D7R1v3JrqDmz5+fmaNGmS/Pz81K9fP6NjNRl8j9sXx9u+Gup4l5WVNWihwlnZ4/xLcqxzMNw+jrd9cbztp2/fvurdu7fefPNNffXVV3RusxO+x+3LqHOwOqeznTx58ronJHUJDw9Xdna27XZmZqbCwsJstzdu3KisrCzNmDFDP/zhD5WZmam5c+fe8usAuLHKysprOrC5uNT5Xx8A0Ahx/gUAV0RHR2vs2LG2zm3nz583OhLgFOocidSsWTNNmjRJPXv2lI+Pj+3+55577obPGzx4sN544w3l5OTIbDZr8+bN+v3vf297/LHHHtNjjz0m6cpww/vvv18ffPDB7b4PALVwc3NT+/btNWjQILVt29boOACABsT5FwB8y9/fX3PnztWuXbtqTPUFcPtqHY5QvQhZ7969NXHiREVGRiowMND2py7h4eF64okndP/992vq1KmaPHmyevTooYcfflhHjx6ttzcA4PrOnj2rtLQ0SdKAAQMoIAFAE8D5FwDU5O3trfHjx8tsNquyslIHDhxQVVWV0bGARqvWkUizZs3SihUr7qhzR1xcnOLi4mrc9/bbb1+zXVRUlLZt23bbrwOgpsOHD2v79u1q2bKlZs6caXQcAIAdcf4FANeXlJSkXbt26dy5c5o8ebK8vLyMjgQ0OrWORLqddZAAGMtisWjHjh3aunWrWrdufVOdFAEAAICmoEOHDho3bpxSUlK0ZMkS5eXlGR0JaHRqHYlUVlamr776qtZiUteuXRssFIBbV1lZqXXr1un06dPq3bu37rrrLhbQBgAAAK7SrVs3+fv7a/Xq1frggw80depUNW/e3OhYQKNRaxEpJSVFP/nJT2ptFbt169YGDQbg1ri4uMhkMmnEiBHq06eP0XEAAAAAhxQdHa25c+dq/fr18vDwMDoO0KjUWkRq166dVq5caccoAG5HVlaWPD095e/vr7i4OJlMJqMjAQAAAA4tODhY9957r0wmk6xWq86ePauYmBjOpYE6MNcFaMTOnj2rJUuW6JNPPpEkfukBAAAAN6n63PnMmTNasWKFtmzZQuc2oA61FpH69etnzxwAbtHhw4e1cuVKBQQEaMyYMUbHAQAAABqltm3bqn///jp69KiWL1+u0tJSoyMBDqvWItJzzz1nzxwAbtJ3O7DNmjVLfn5+RscCAAAAGiWTyaShQ4dq3LhxSk1NpXMbcAO1rokEwDFVVlbq3Llz6tWrl0aMGEEHNgAAAKAeVHduW7t2rXJychQQEGB0JMDhUEQCGomioiJ5eHjIw8NDs2fPlqenp9GRAAAAAKcSHR2tefPm2c61c3NzFRgYaGwowIEwhAFoBLKysrRo0SLbAtoUkAAAAICGUX2unZKSonfffVcHDhyQ1Wo1OBXgGCgiAQ6uugObxWJRnz59jI4DAAAANAkRERFq3769du3aRec24BtMZwMc2OHDh7V9+3aFhIRo2rRpLKANAAAA2Im7u7smTZqkgIAA7d+/X/n5+Zo8ebK8vLyMjgYYhpFIgIMqKSnR3r171bp1a82ePZsCEgAAAGBnV3duS0lJ0alTp4yOBBiKkUiAg6msrJSrq6vMZrNmz56twMBAOrABAAAABurWrZuaN2+u4OBgSVJFRYXc3d0NTgXYH1emgAMpLCzUkiVL9Nlnn0mSgoODKSABAAAADiAkJEQmk0mXLl3SO++8w6gkNElcnQIOIisrSx988IFycnIUGhpqdBwAAAAA12E2mxUQEKA1a9bQuQ1NDkUkwAFc3YFt1qxZateundGRAAAAAFyHt7e3Zs6cqY4dO9K5DU0OayIBBissLNSqVasUHBxMBzYAAACgEfhu57agoCDFxsYaHQtocBSRAINYrVaZTCb5+voqPj5eLVq0kKenp9GxAAAAANyE6s5tzZs3V+vWrSV9e44POCumswEGKC8v1+rVq/X1119LkmJiYiggAQAAAI1Qu3bt5ObmppKSEi1dulQXL140OhLQYCgiAXZWWFiopUuX6syZMyosLDQ6DgAAAIB6UFpaqoKCAn344Yd0boPToogE2FF1B7ZLly5pypQp6t27t9GRAAAAANSDoKAgzZ07V+Hh4XRug9OiiATYSV5enq0D2+zZs+nABgAAADiZ6s5tHTp00K5du3Tw4EGjIwH1ioW1ATvx9/fXwIED1alTJzqwAQAAAE7K3d1dkydP1oEDB9SlSxej4wD1ipFIQAOyWq3avXu3srKyZDKZFBsbSwEJAAAAcHImk0n9+/eXj4+PLBaLtm3bpry8PKNjAXeMkUhAA6moqND69et1+vRpubq6KjQ01OhIAAAAAOzs8uXL+uqrr3Tq1CnFx8erefPmRkcCbhsjkYAGUFRUpA8//FBnzpzRiBEjNHjwYKMjAQAAADBASEiI5syZIzc3Ny1dulRff/210ZGA20YRCahneXl5WrRoka0DW58+fYyOBAAAAMBAISEhmjt3rsLCwrRmzRp98cUXRkcCbgvT2YB65uvrq6ioKPXt21fh4eFGxwEAAADgAKo7t23dupXrBDRajEQC6slXX32l4uJiubq6auLEifxiAAAAAFCDu7u7xo8fr4iICEnSsWPHVFpaanAq4OZRRALukNVq1c6dO7VhwwYdPHjQ6DgAAAAAGoGcnBxt2bJFS5YsoXMbGg2KSMAdqKio0OrVq3Xw4EH16tVLQ4YMMToSAAAAgEYgODhYM2bMUGFhoRYvXqyLFy8aHQmoE0Uk4DZd3YHtrrvu0siRI+Xiwn8pAAAAADcnOjqazm1oVLjiBW6TyWRSVVWVpkyZor59+8pkMhkdCQAAAEAjc3XntsrKSqPjADdEdzbgFl24cEHh4eHy9vbWfffdx+gjAAAAAHfE29tbs2bNsl1bpKWlKSIiQq6urgYnA2ri6he4BV988YU+/PBD7d+/X5IoIAEAAACoF9XXFnl5eVq6dKlWrFhB5zY4HK6AgZtQ3YHtk08+UatWrdSnTx+jIwEAAABwQgEBARozZoxSUlLo3AaHQxEJqMN3O7BNnTpVnp6eRscCAAAA4KS6deum6dOn07kNDociElCH/Px8paSk0IENAAAAgN20atXK1rntzJkzRscBJLGwNlCrwsJC+fr6KiQkRPPmzZPZbDY6EgAAAIAmJCQkRPfee6+8vLwkXblG8fHxoTM0DMOQCuA6zp49q3fffVdHjhyRJApIAAAAAAxhNptlMplUVFSkhQsXasuWLaqqqjI6FpooikjAdxw5ckQrV65UQECAYmJijI4DAAAAAPL29la3bt109OhROrfBMBSRgG9Ud2DbsmWLWrVqpVmzZsnPz8/oWAAAAAAgk8mkoUOHaty4cXRug2EoIgHfuHDhAh3YAAAAADi0qzu37du3z+g4aGJYWBtNXlVVlVxdXRUZGal7771X4eHhLFQHAAAAwGG1atVKc+fOlY+Pj6Rvr2mAhsZIJDRp2dnZ+s9//qNz585JkiIiIiggAQAAAHB4wcHB8vT0VHl5uRYvXqwDBw7IarUaHQtOjiISmqyzZ89q8eLFqqioYOoaAAAAgEbJZDIpICBAu3btonMbGhzT2e7Qxn3J2pmYqrNpeYqJDDA6Dm7SkSNHtHXrVgUHB2vatGny9/c3OhIAAAAA3DJ3d3dNnjxZe/bs0f79+5Wfn6/JkyfLy8vL6GhwQoxEukNXF5CG944yOg5uQnJysq0D2+zZsykgAQAAAGjUvtu5bfPmzUZHgpNiJFI9iIkM0PxHhhgdAzepVatWGjdunLp06SIXF+qoAAAAAJxDt27d5O/vzwflaDAUkW5D9RQ2SUxjaySKioq0adMmjRgxQkFBQerWrZvRkQAAAACg3kVHR0uSrFarNm/erDZt2qh9+/YGp4KzYBjGbaiewiaJaWyNQHZ2thYtWqSUlBTl5eUZHQcAAAAAGlx5ebkuXbqkNWvW0LkN9YaRSLeJKWyNQ3JystasWSN3d3fNmjVLERERRkcCAAAAgAbn6empu+++Wxs3btSuXbuUm5urkSNHytXV1ehoaMQYiQSndfbsWa1YsUL+/v6aO3cuBSQAAAAATUp157b+/fvryJEj2rBhg9GR0MgxEglOKyoqSn369NHAgQPl6elpdBwAAAAAsLvqzm1BQUEKCGA9X9wZRiLBqVRUVGjXrl0qKyuTu7u7hg8fTgEJAAAAQJPXrVs3tWzZUpKUmJioixcvGpwIjREjkeA0ioqKtHLlSmVkZKhFixZq166d0ZEAAAAAwKGUl5crISFBRUVFmjhxIp3bcEsYiQSnUN2BLTs7W1OmTKGABAAAAADX4eHhoTlz5ig0NJTObbhlFJHQ6KWmpmrx4sWyWCyaNWsWBSQAAAAAuAEfHx/dfffdat++vXbt2qVt27YZHQmNBNPZ0Oj5+/urRYsWGjNmjPz9/Y2OAwAAAAAOr7pz2+7du7mOwk2jiIRGyWq16ssvv1TXrl3l7++vGTNmGB0JAAAAABoVk8mkYcOG2W6fO3dOgYGBdHFDrSgiodGpqKjQ+vXrdfr0aXl6erIQHAAAAADcocrKSm3cuFFWq1Xx8fFq3ry50ZHggFgTCY1KUVGRli5dqjNnzuiuu+5i/SMAAAAAqAdubm6aOXOmXF1dtXTpUn399ddGR4IDooiERuO7Hdj69u0rk8lkdCwAAAAAcAohISGaO3cundtQK4pIaDTKysrk4uJCBzYAAAAAaCDVndvatWun3Nxco+PAwbAmEhxeRkaGwsPDFRkZqYceekiurq5GRwIAAAAAp+Xu7q64uDhZrVaZTCZdvnxZZrNZXl5eRkeDwRiJBIdltVq1a9cuvf/++0pOTpYkCkgAAAAAYAcmk0kuLi6qqqrSihUrtGTJEuXn5xsdCwajiASHVFFRYZuD27NnT0VHRxsdCQAAAACaHFdXV40aNUqFhYX64IMPlJ6ebnQkGIgiEhxOdQe206dP66677tKoUaPk4sK3KgAAAAAYoVWrVpozZ45cXV314Ycf0rmtCePKHA7n3LlzdGADAAAAAAdydee2Q4cO0bWtiWrQhbXXrFmjN998UxUVFXrwwQd177331nj8k08+0RtvvCGr1aqoqCjNnz9fAQEBDRkJDqykpERms1ldunRRdHS0fH19jY4EAECjxDkYAKAhVHduq6yslMlkUllZmdzd3Zk50oQ02L90RkaGFixYoA8++ECrVq3Shx9+qNOnT9seLyws1AsvvKC33npLq1evVseOHfXGG280VJw7tnFfsn75jz365T/26GxantFxnM6RI0f0r3/9yza/lgISAAC3x9nOwQAAjsXd3V1ms1kWi0UrV67UihUrVFZWZnQs2EmDFZH27t2rgQMHKjAwUN7e3ho3bpw2btxoe7yiokIvvPCCwsPDJUkdO3bUxYsXGyrOHduZmGorHsVEBmh47yiDEzkHq9WqxMREbdmyRS1atFBQUJDRkQAAaNSc7RwMAOCYXFxc1KVLF50/f15LlixRUVGR0ZFgBw02nS0zM1OhoaG222FhYTpy5IjtdlBQkEaPHi1JKi0t1VtvvaX77rvvll7j2LFj9RP2OhISEmrcLigoUDN/F80cYP7mnktKSLjUYK/fFFRWVmrfvn1KSUlR+/btFR0d3aD/pqjpu9/jaFgcb/vieNsXx9uxONs5GBoWx9u+ON72xzFveB06dNCePXv09ddfq7S0VCEhIUZHajKM+P5usCLS9RbZut4CyQUFBXrkkUfUqVMnTZs27ZZeo1u3bvL09LztjLVJSEhQ3759a9z30ed7JOma+3H7Dh48KIvFoj59+mju3LksoG1H1/seR8PheNsXx9u+Gup4l5WV8cHCbXK2czA0HI63fXG87Y9jbj8DBw7UX/7yF124cEGjR49mjSQ7MOocrMH+ZcPDw5WdnW27nZmZqbCwsBrbZGZmau7cuerUqZNeeumlhooCB2OxWCRJffr00axZs9SpUycKSAAA1BPOwQAA9hYSEqKxY8dqypQpcnFxkcVioXubk2qwItLgwYO1b98+5eTkqKSkRJs3b9awYcNsj1dVVelHP/qRJkyYoGeffZYiQhORnJys9957T4WFhXJxcVFkZKTRkQAAcCqcgwEAjGA2mxUcHCyr1aqtW7fqk08+sQ0ggPNosOls4eHheuKJJ3T//feroqJCM2fOVI8ePfTwww/rscceU3p6ur766itVVVVp06ZNkq4MjebTMOd15MgRbd26VcHBwfwwAQCggXAOBgAwmqenpw4cOKD8/HxNnjy5QaZAwxgNVkSSpLi4OMXFxdW47+2335Ykde/eXSdOnGjIl4eDsFqt2r17tw4cOKDWrVvzQwQAgAbGORgAwCgmk0nDhg1TUFCQPvnkEy1evFjTp0+Xv7+/0dFQD1jtCg1u//79OnDggHr27Klp06ZRQAIAAAAAJ9e9e3dNnz5dhYWFWrZsmaqqqoyOhHrQoCORAEnq2bOnfHx81LVrV9ZdAAAAAIAmolWrVpozZ47y8/Pl6upqdBzUA0YioUFkZ2dr7dq1qqyslJeXl7p160YBCQAAAACamJCQEMXExEiSvvjiCx08eJDObY0YI5FQ75KTk7VmzRq5u7srPz9fwcHBRkcCAAAAABjIarUqJSVFJ0+e1OXLlzVq1Ci5uDCupbGhiIR6dXUHtmnTprF4GgAAAABAJpNJkyZNkr+/P53bGjGKSDewcV+ydiamSpLOpuUpJjLA4ESO7eDBg9q5cycd2AAAAAAA1/hu57YlS5bo3nvvlZsbpYnGgn+pG9iZmGorHsVEBmh47yijIzm0Nm3aqKioSEOHDmVYIgAAAADgurp37y5/f39lZWVRQGpk+NeqQ0xkgOY/MsToGA6rqKhIX375pWJjYxUcHKzhw4cbHQkAAAAA4OBatWqlVq1aSZJSUlJUWlqq9u3bG5wKdaGIhNuWnZ2tFStWqLi4WO3atWMBbQAAAADALTtw4ICSk5M1dOhQ9evXj87eDow5R7gtycnJWrx4saqqqjRr1iwKSAAAAACA2xIXF6d27dpp165d+uSTT2SxWIyOhFowEgm37NixY9qyZQsd2AAAAAAAd8zd3V1xcXHavXu3rXPblClT5O7ubnQ0fAdFJNwyHx8ftW7dWhMnTqQDGwAAAADgjlV3bgsMDFRqaioLbjso/lVwUyoqKnT+/Hm1bdtWMTExat26NfNUAQAAAAD1qkePHurevbtMJpNyc3NVWlqqiIgIo2PhG6yJhDoVFRVp6dKlWr16tfLy8iSJAhIAAAAAoEFUX29u27ZNH374oU6fPm1wIlSjiIQbys7O1gcffKDs7GzFxcUpICDA6EgAAAAAgCZg3LhxatasmVavXq2DBw/KarUaHanJo4iEWn23A1u7du2MjgQAAAAAaCJ8fHx0zz33qF27dtq5cyed2xwARSTUKisrS/7+/po7dy5zUAEAAAAAdlfduS02Nlbp6emqrKw0OlKTxsLaqMFqtery5csKDg5Wv3791KtXL9oqAgAAAAAMU925raKiQu7u7iovL1dpaan8/f2NjtbkMBIJNhUVFVqzZo0WLVqkwsJCmUwmCkgAAAAAAIdQfX26ZcsWffDBB0pPTzc4UdNDEek7Nu5L1rufZOqX/9ijs2l5Rsexm+oObKdPn9agQYPk4+NjdCQAAAAAAK4xYMAAubq60rnNABSRvmNnYqrSL1dIkmIiAzS8d5TBiRre1R3YpkyZon79+tlaKgIAAAAA4EiaNWumuXPn0rnNAKyJdB0RQe6a/8gQo2PYTWJioq0DGwtoAwAAAAAcXXXntg0bNujAgQPq2rWrzGaz0bGcHkWkJqysrEyenp4aMWKEBg4cKD8/P6MjAQAAAABwU6o7t+Xn58tsNstqtaqiokIeHh5GR3NaTGdrgqxWq3bt2qVFixappKREbm5uFJAAAAAAAI2OyWRSQECAJOnTTz/V4sWLlZ+fb3Aq50URqYmp7sB24MABtWzZUp6enkZHAgAAwP9v707DoyrMNo7fkwSCBAh7oBiFylaWCIUUBAQChDWBLCwNtFiquKClYuUVDQiKBEQurNW2FipV2iCbAYRLgYKEVQFBIVEpIJKAJYtgScg+M+f9YDMlgk4izDmZyf/3yeQkM/c8jNc8PJxzHgDADbv11luVl5fH5jYPYohUg1y9ga1///4aMmSI/Px4CwAAAAAAvF/r1q2VkJDA5jYPYoJQg7z33nuuDWzh4eFsYAMAAAAA+JSrN7dt2bJFV65csTqST+HG2jWAYRiy2WwaNGiQwsPD2cAGAAAAAPBZ5ZvbLly4oHr16kn639+LcWM4E8nHpaWlaePGjXI4HAoKCmKABAAAAADwebVq1dJtt90mSTpx4oQ2bNigkpISi1N5P4ZIPqp8A9v27dvldDrlcDisjgQAAAAAgOnsdrsyMjK0evVqNrfdIIZIPujqDWxhYWGKjY1V7dq1rY4FAAAAAIDpunTpotjYWDa33QQMkXzQO++8o9OnT2vAgAFsYAMAAAAA1Hjf3tx2+fJlqyN5JW6s7YN69+6tTp06qV27dlZHAQAAAACgWijf3Hby5EkFBwdbHccrcYqKj8jIyND+/fslSSEhIQyQAAAAAAD4lqCgIHXv3l2SlJWVpV27dsnpdFqcynswRPIBaWlpSklJ0alTp1RaWmp1HAAAAAAAqr3MzEwdPXqUzW1VwBDJi129gS00NFQJCQncQBsAAAAAgEr42c9+psjISGVmZrK5rZIYInmxrVu3ujawxcXFKTAw0OpIAAAAAAB4jfKN5uWb2/7zn/9YHala48baXqxNmzZq2rSpevbsKZvNZnUcAAAAAAC8TvnmtqNHj6pBgwZWx6nWGCJ5mYsXL+rixYtq3769OnbsaHUcAAAAAAC8XtOmTTV06FBJUkFBgU6fPq2wsDBO2PgWhkheJCMjQ5s3b1bt2rX14x//WAEB/PEBAAAAAHAzffTRRzp48KByc3M1aNAg+flxJ6ByTCG8RFpamnbs2KFGjRopLi6OARIAAAAAAB7Qt29fOZ1OHT58WJcvX1ZUVBT3IP4vxmnV3PU2sHGNJgAAAAAAnmGz2dS/f/8Km9vy8/OtjlUtcDpLNWez2WSz2RQWFqbBgwdzGh0AAAAAACYICwtTgwYNtH//fq4G+i+qUE0VFhYqPz9fISEh6tevnyRxQy8AAAAAAEzUunVr3X777bLZbLLb7bpw4YJCQ0OtjmUZTmuphi5evKjk5GS9/fbbcjgcrrORAAAAAACAucr/Pn748GGtW7dOR44ckWEYFqeyBmciVTPlG9gCAgIUExMjf39/qyMBAAAAAFDj9ezZUzk5OUpNTdXXX39dIze3MUSqRq7ewBYbG6vg4GCrIwEAAAAAAEm1atXS6NGjtXfvXh0+fFh5eXkaNWpUjdrcxhCpmjAMQ59//rlCQ0MVFRWlOnXqWB0JAAAAAABcpXxzW8OGDbVv3z4VFBQwRIJ5ysrKVFpaqqCgII0aNUp+fn5cwgYAAAAAQDUWFhamDh06KDAwUIZhKC8vr0ZcTVSzLt6rZgoKCrRu3TqlpKTI6XSqVq1aDJAAAAAAAPAC5WcgHTt2TK+//rpOnz5tcSLPY4hkkYsXL2rVqlXKzc1V7969a9zNuAAAAAAA8AXt2rVT06ZN9fbbb+vDDz/06c1tTC4skJGRoTfffFMOh0Pjx49Xu3btrI4EAAAAAAB+gKCgII0fP1533HGHdu/erZ07d8rpdFodyyMYIpnMMAzt3r1b9erVU0JCglq2bGl1JAAAAAAAcAPKN7eFh4fr+PHjunDhgtWRPIIba5vEMAw5HA4FBAQoJiZGtWvXZgMbAAAAAAA+onxzW6dOndS0aVNJkt1uV0CA74xeOBPJBGVlZdqyZYveffddGYahBg0aMEACAAAAAMAHlQ+Qzpw5oxUrVig7O9viRDcPQyQPKyws1Lp163Tq1Cm1aNHC6jgAAAAAAMAEDRo0kCStXr3aZza3MUTyoIsXLyo5OVm5ubmKjo5WeHi4bDab1bEAAAAAAICHNW3aVJMmTXJtbjty5IjXb25jiOQhDodDGzZsYAMbAAAAAAA11NWb21JTU3X27FmrI90Q37m7UzXj7++vESNGqF69egoODrY6DgAAAAAAsED55raTJ0+qdevWVse5IZyJdBMZhqG9e/fqgw8+kCS1atWKARIAAAAAADWczWZThw4dZLPZdOnSJa1Zs0b5+flWx6oyhkg3SfkGtkOHDik/P9/rr3MEAAAAAAA335UrV5STk6Pk5GSv29zGEOkmuHoDW//+/TVkyBBuoA0AAAAAAK5x2223KSEhQX5+fl63uY0h0g2y2+1688032cAGAAAAAAAqpXxzW5MmTfT222/rzJkzVkeqFG6sfYMCAgLUq1cvNWnSRC1btrQ6DgAAAAAA8AJBQUGaMGGCDh06pNtuu83qOJXCmUg/UHp6uuuUsy5dujBAAgAAAAAAVVKrVi317dtXAQEBKi4u1s6dO1VSUmJ1rO/EEKmKDMPQnj17tG3bNn3yySdWxwEAAAAAAD7g/PnzOn78uFavXq28vDyr41wXQ6QqKN/AdvjwYXXt2lVRUVFWRwIAAAAAAD6gbdu2iomJUV5enlatWlUtN7cxRKqksrKyChvYIiMj5e/vb3UsAAAAAADgI9q0aVNhc1tmZqbVkSrgxtqVFBAQoB/96EcKDw9Xu3btrI4DAAAAAAB8UPnmth07dqhx48ZWx6mAM5HcyMzM1FdffSWbzaaBAwcyQAIAAAAAAB4VFBSkMWPGqF69enI6nTp27JicTqfVsTw7RNq8ebNGjhypyMhIJScnX3P8s88+U3x8vIYNG6bExETZ7XZPxqmy9PR0vfXWW9qzZ4/VUQAAACrN23swAADwP2fOnNGOHTu0ceNGyze3eWyIlJ2drRdffFGrVq3Spk2btGbNGp0+fbrCz8ycOVNz5szRtm3bZBiG1q5d66k4VWIYhvbu3att27YpNDRUI0eOtDoSAABApXhzDwYAAK7Vtm1bRUZGKiMjQ2vWrFF+fr5lWTw2RDpw4IB69+6thg0bqm7duho2bJi2bt3qOv7ll1+quLhY3bp1kyTFxcVVOG4Vp9Ohf58+okOHDiksLEyxsbGqU6eO1bEAAAAqxVt7MAAA8N3K5xOXL19WcnKyLl26ZEkOjw2RcnJy1KxZM9fXzZs3r7Ce7tvHmzVrVm3W19ntJerfv7+GDBnCBjYAAOBVvLkHAwAA361169ZKSEhQYGCgDMOwJIPHtrNd7wXZbLZKH6+M9PT0qgdzo32ITW1HDJGfn5+OHj160x8f13fkyBGrI9Q41Nxc1Ntc1Ntc1Lt68dYerBzvJ3NRb3NRb/NRc3NRb3N06dJFfn5+ltTbY0OkkJAQffjhh66vc3Jy1Lx58wrHv/rqK9fXubm5FY5XRpcuXRQYGHjjYa/So8c3b/wePXrc1MfFd6Pe5qPm5qLe5qLe5vJUvUtKSjw6qPBl3tqDSfz/azbqbS7qbT5qbi7qbS6rejCPXc7Wp08fvf/++7p06ZKKioq0fft29e/f33W8VatWCgwMdE3ONm7cWOE4AAAAqo4eDAAAeIrHhkghISGaMWOGJk+erJiYGEVFRSksLExTp05VWlqaJGnJkiVauHChRowYoaKiIk2ePNlTcQAAAGoEejAAAOApHrucTZKio6MVHR1d4XvLly93/XfHjh21fv16T0YAAACocejBAACAJ3jsTCQAAAAAAAD4DoZIAAAAAAAAcIshEgAAAAAAANxiiAQAAAAAAAC3GCIBAAAAAADALYZIAAAAAAAAcIshEgAAAAAAANxiiAQAAAAAAAC3GCIBAAAAAADALYZIAAAAAAAAcIshEgAAAAAAANxiiAQAAAAAAAC3GCIBAAAAAADArQCrA/wQhmFIkkpLSz32HCUlJR57bFyLepuPmpuLepuLepvLE/Uu/4wv/8xH9UAP5nuot7mot/moubmot7ms6MFshhd2Z/n5+Tp58qTVMQAAgIe1b99e9evXtzoG/oseDACAmuG7ejCvHCI5nU4VFBSoVq1astlsVscBAAA3mWEYKisrU1BQkPz8uPq+uqAHAwDAt7nrwbxyiAQAAAAAAABz8U97AAAAAAAAcIshEgAAAAAAANxiiAQAAAAAAAC3GCIBAAAAAADALYZIAAAAAAAAcIshEgAAAAAAANxiiAQAAAAAAAC3GCIBAAAAAADArRo9RNq8ebNGjhypyMhIJScnX3P8s88+U3x8vIYNG6bExETZ7XYLUvoOd/XesWOHxowZo9GjR2vatGm6fPmyBSl9h7t6l0tNTdWgQYNMTOa73NX8zJkz+uUvf6nRo0fr3nvv5T1+g9zV+5NPPlF8fLxGjx6tBx54QHl5eRak9C1XrlxRVFSUzp8/f80xPjNRFfRg5qIHMxc9mLnov8xHD2a+atWDGTVUVlaWERERYXz99ddGQUGBER0dbZw6darCz4waNcr46KOPDMMwjCeffNJITk62IKlvcFfv/Px8o2/fvkZWVpZhGIbx+9//3pg/f75Vcb1eZd7fhmEYubm5xvDhw42IiAgLUvoWdzV3Op3G0KFDjd27dxuGYRgvvPCCsXjxYqvier3KvMcTEhKM1NRUwzAMY+HChcbSpUutiOozPv74YyMqKsro3Lmzce7cuWuO85mJyqIHMxc9mLnowcxF/2U+ejDzVbcerMaeiXTgwAH17t1bDRs2VN26dTVs2DBt3brVdfzLL79UcXGxunXrJkmKi4urcBxV467eZWVlmjdvnkJCQiRJHTp00IULF6yK6/Xc1bvc7Nmz9cgjj1iQ0Pe4q/knn3yiunXrqn///pKkBx98UJMmTbIqrterzHvc6XSqoKBAklRUVKQ6depYEdVnrF27VnPnzlXz5s2vOcZnJqqCHsxc9GDmogczF/2X+ejBzFfderAaO0TKyclRs2bNXF83b95c2dnZ33m8WbNmFY6jatzVu1GjRhoyZIgkqbi4WMuWLXN9japzV29JWrlypTp16qQ777zT7Hg+yV3NMzMz1bRpUz3xxBOKjo7W3LlzVbduXSui+oTKvMdnzZqlxMRE9evXTwcOHNDPf/5zs2P6lAULFqhnz57XPcZnJqqCHsxc9GDmogczF/2X+ejBzFfderAaO0QyDOOa79lstkofR9VUtp75+fmaOnWqOnbsqNjYWDOi+SR39T558qS2b9+uadOmmRnLp7mrud1u16FDh/SLX/xCmzdvVmhoqBYtWmRmRJ/irt7FxcVKTEzUG2+8oX379mnixIl64oknzIxYo/CZiaqgBzMXPZi56MHMRf9lPnqw6sWKz8waO0QKCQnRV1995fo6Jyenwulh3z6em5t73dPHUDnu6l3+vYkTJ6pjx45asGCB2RF9irt6b926Vbm5uYqPj9f999/vqj1+OHc1b9asmW6//XZ17dpVkhQVFaXjx4+bntNXuKv3yZMnFRgYqLCwMEnShAkTdOjQIdNz1hR8ZqIq6MHMRQ9mLnowc9F/mY8erHqx4jOzxg6R+vTpo/fff1+XLl1SUVGRtm/f7rpWVpJatWqlwMBAHTlyRJK0cePGCsdRNe7q7XA49OCDD2rEiBFKTEzkXxxvkLt6T58+Xdu2bdOmTZu0bNkyNW/eXKtWrbIwsfdzV/Pu3bvr0qVLOnHihCTpvffeU+fOna2K6/Xc1fv2229XVlaWzpw5I0nauXOnq4HEzcdnJqqCHsxc9GDmogczF/2X+ejBqhcrPjMDPPro1VhISIhmzJihyZMnq6ysTGPHjlVYWJimTp2q6dOnq2vXrlqyZIlmz56tgoICderUSZMnT7Y6ttdyV++srCx9+umncjgc2rZtmySpS5cu/GvYD1SZ9zdursrU/I9//KNmz56toqIitWjRQosXL7Y6tteqTL0XLlyoRx99VIZhqEmTJkpKSrI6ts/hMxM/BD2YuejBzEUPZi76L/PRg1UPVn5m2ozrXUQHAAAAAAAAXKXGXs4GAAAAAACAymOIBAAAAAAAALcYIgEAAAAAAMAthkgAAAAAAABwiyESAAAAAAAA3AqwOgAAz+rQoYPat28vP7//zYzdre5NSUnRtm3b9Je//OWGn//ll19WcnKyQkJCZLPZ5HA41KRJE82dO1dt2rSp8uNlZ2frt7/9rVavXq1z585p8eLFevnllyt8/0adP39ekZGRat++vet7hYWFatGihZKSkhQaGvq9v//KK6+oY8eOGjJkyA1nAQAAuFFX94M2m01FRUWqV6+e5s2bp65du97U5zp//ryio6P10Ucf6eWXX9bXX3+tp59++qY+BwDrMEQCaoA33nhDjRs3tuz5R44cWaF5+Pvf/67f/e53SklJqfJjhYSEuAZF//73v/XFF19c8/2boU6dOtq0aZPra8Mw9Nxzz+nFF1/U0qVLv/d3Dx48qLZt2960LAAAADfq2/3ga6+9pueee05r1qyxMBUAb8PlbEANtn79eo0bN04xMTGKiIjQqlWrrvmZ7du3KzY2VnFxcRo3bpwOHz4sScrPz9esWbMUFxen6OhoJSUlyW63V+p577rrLtfwJysrSw8++KCio6MVFRWlv/71r5Iku92uuXPnKjo6WnFxcZo+fboKCgp0/vx5de/eXQ6HQ7Nnz1ZmZqbuvfde1/edTqcGDBigtLQ01/PNmDHD9dr+/Oc/KzY2VmPGjNG0adOUnZ1dqcwlJSXKzc1VcHCwJOmLL77QlClTNGHCBEVEROihhx5SSUmJkpOTlZ6ersWLF+uf//ynSktLlZSUpNjYWI0ePVqzZs3SlStXKvWcAAAAnmC323XhwgVXXyN9d4+Um5uradOmafjw4Ro5cqRWrlwpSfr44481adIkjRs3TgMHDtRTTz1lyWsBYC7ORAJqgHvuuafC5WwrVqxQnTp1tG7dOi1btkyNGjXSxx9/rClTpmjixIkVfnfx4sVasmSJunXrpn379ungwYMKDw9XUlKSOnfurEWLFsnhcGjWrFn629/+pqlTp35vFrvdrvXr16tXr16SpMcff1yDBw/WlClTlJ+fr0mTJqlly5YKCQnRoUOH9M4778hms+mFF17Qv/71LzVv3lyS5O/vr+eee07z58/Xa6+9pvPnz0uS/Pz8FB8frw0bNqhr1666fPmyDhw4oGeffVYbN27UyZMntW7dOgUEBGjNmjWaPXu2li9ffk3O4uJijRkzRk6nUxcvXlRwcLCGDh2q+++/X5K0du1axcTEaMyYMSorK1NcXJxSU1M1adIkbd26VZMmTVJkZKReeeUV+fv7KyUlRTabTUuXLtWSJUs0b968H/znCQAAUFX33HOPbDabLl26pMDAQEVERGjhwoWS9L090jPPPKPWrVvrT3/6k/Lz85WQkKABAwZo5cqVmj59unr16qWCggINHjxY6enpatiwobUvFIBHMUQCaoDvupzt1Vdf1e7du3X27FmdOHFChYWF1/zMqFGj9Mgjj2jAgAHq27eva0iUmpqqtLQ0rV+/XtI3Q5fv8s477+jIkSOSpLKyMnXu3Fnz589XYWGhjh49qhUrVkiS6tevr7i4OO3Zs0eJiYny9/fXuHHj1K9fPw0bNkxhYWGuYdH3iY+P19ixYzVr1ixt2bJFERERql+/vnbt2qW0tDTFx8dLkpxOp4qKiq77GFdfzrZ3717NnDlTffv2VVBQkCRp5syZ2r9/v5YvX66zZ88qJyfnuvVLTU1Vfn6+Dhw44Hr9TZo0cfsaAAAAbqbyfvDTTz/V1KlT1b17d1dP8n090oEDBzRz5kxJ3/RqW7ZskSQtWrRIe/bs0auvvqozZ86ouLhYhYWFDJEAH8cQCaihsrKyNGHCBI0fP149evTQ8OHDtWvXrmt+bsaMGRo7dqz27dunlJQULVu2TCkpKXI6nXrppZd0xx13SJLy8vJks9mu+1zfvidSuStXrsgwjArfczqdstvtatCggTZt2qSjR4/qgw8+0KOPPqrJkydX6mbVrVq1UqdOnZSamqqUlBTX6dVOp1P33Xef62yr0tJSXb582e3j3X333ZoyZYoee+wxvfvuu6pfv74ee+wxORwOjRgxQgMHDtSFCxeueS3lz/nUU09pwIABkqSCggKVlJS4fU4AAABP6NSpk5588knNnj1bd955p2699dbv7ZECAgIq9Hjnzp1To0aNNGXKFHXs2FF33323RowYoWPHjl23FwLgW7gnElBDpaenq3Hjxpo2bZruvvtu1wDJ4XC4fsZut2vQoEEqLCxUQkKC5s6dq88//1x2u139+vXT66+/LsMwVFpaqoceekj/+Mc/qpShXr16uvPOO5WcnCzpm/ssbdy4UX369NGuXbv0q1/9St27d9dvfvMbxcTE6MSJExV+39/fX2VlZdd97PHjx2v58uUqLi5Wjx49JEn9+vXT+vXrXfckeumll/R///d/lcr661//Wg0aNNAf/vAHSdK+ffv08MMPa+TIkbLZbDp27Jirdv7+/q77Q/Xr10/JyckqLS2V0+nUnDlz3N6YGwAAwJOioqLUrVs3JSUlSfr+Humuu+7SW2+9JembXu2ee+7R2bNnlZ6erscff1xDhw5Vdna2MjMz5XQ6rXlBAEzDmUhADdW3b1+tX79ew4cP1y233KKwsDA1btxYGRkZrp8JCAjQU089pccff9z1r1BJSUmqXbu2EhMTtWDBAkVHR6usrEx9+vTRfffdV+UcS5Ys0bPPPquUlBSVlpa6bqTtdDq1Z88eRUVFqW7dugoODtb8+fMr/G67du3k7++vsWPH6sUXX6xwbNCgQXrmmWcq3KNp3Lhxys7O1vjx42Wz2dSyZUstWrSoUjlr1aqlOXPm6L777tO4ceM0Y8YMPfzwwwoODtYtt9yi8PBwZWZmSpIiIiL0/PPPq6ysTNOmTdPzzz+v2NhYORwO/eQnP9GsWbOqXCcAAICbac6cORo9erT27t37vT3S008/rXnz5ik6OlqGYeiBBx5Qly5ddP/99ys2NlYNGzZUo0aN9NOf/lQZGRkKDQ21+JUB8CSbwTmHAAAAAAAAcIPL2QAAAAAAAOAWQyQAAAAAAAC4xRAJAAAAAAAAbjFEAgAAAAAAgFsMkQAAAAAAAOAWQyQAAAAAAAC4xRAJAAAAAAAAbv0/Dxfg5b2HzRQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model in modelos:\n",
    "    aoc_curves(model,feat_train,targ_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ADA BOOSTING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cambia levemente la metodologia, usa early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_train_2, feat_val, targ_train_2, targ_val = train_test_split(feat_train,targ_train,random_state=420)\n",
    "# NECESARIO CREAR DATOS DE VALIDACION PARA USAR XGBC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[1]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[2]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[3]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[4]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[5]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[6]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[7]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[8]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[9]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[10]\tvalidation_0-error:0.52459\tvalidation_1-error:0.42683\n",
      "[11]\tvalidation_0-error:0.18443\tvalidation_1-error:0.29268\n",
      "[12]\tvalidation_0-error:0.15574\tvalidation_1-error:0.29268\n",
      "[13]\tvalidation_0-error:0.11885\tvalidation_1-error:0.26829\n",
      "[14]\tvalidation_0-error:0.10656\tvalidation_1-error:0.28049\n",
      "[15]\tvalidation_0-error:0.10656\tvalidation_1-error:0.26829\n",
      "[16]\tvalidation_0-error:0.10246\tvalidation_1-error:0.26829\n",
      "[17]\tvalidation_0-error:0.09836\tvalidation_1-error:0.25610\n",
      "[18]\tvalidation_0-error:0.09426\tvalidation_1-error:0.25610\n",
      "[19]\tvalidation_0-error:0.09016\tvalidation_1-error:0.23171\n",
      "[20]\tvalidation_0-error:0.08197\tvalidation_1-error:0.23171\n",
      "[21]\tvalidation_0-error:0.06967\tvalidation_1-error:0.23171\n",
      "[22]\tvalidation_0-error:0.06557\tvalidation_1-error:0.24390\n",
      "[23]\tvalidation_0-error:0.06557\tvalidation_1-error:0.25610\n",
      "[24]\tvalidation_0-error:0.06557\tvalidation_1-error:0.25610\n",
      "[25]\tvalidation_0-error:0.06557\tvalidation_1-error:0.23171\n",
      "[26]\tvalidation_0-error:0.06557\tvalidation_1-error:0.23171\n",
      "[27]\tvalidation_0-error:0.06148\tvalidation_1-error:0.24390\n",
      "[28]\tvalidation_0-error:0.06148\tvalidation_1-error:0.23171\n",
      "[29]\tvalidation_0-error:0.06148\tvalidation_1-error:0.21951\n",
      "[30]\tvalidation_0-error:0.06148\tvalidation_1-error:0.21951\n",
      "[31]\tvalidation_0-error:0.05738\tvalidation_1-error:0.21951\n",
      "[32]\tvalidation_0-error:0.04918\tvalidation_1-error:0.21951\n",
      "[33]\tvalidation_0-error:0.04508\tvalidation_1-error:0.21951\n",
      "[34]\tvalidation_0-error:0.03689\tvalidation_1-error:0.21951\n",
      "[35]\tvalidation_0-error:0.03689\tvalidation_1-error:0.21951\n",
      "[36]\tvalidation_0-error:0.03689\tvalidation_1-error:0.21951\n",
      "[37]\tvalidation_0-error:0.03689\tvalidation_1-error:0.21951\n",
      "[38]\tvalidation_0-error:0.03689\tvalidation_1-error:0.21951\n",
      "[39]\tvalidation_0-error:0.03279\tvalidation_1-error:0.19512\n",
      "[40]\tvalidation_0-error:0.02869\tvalidation_1-error:0.18293\n",
      "[41]\tvalidation_0-error:0.02869\tvalidation_1-error:0.19512\n",
      "[42]\tvalidation_0-error:0.02869\tvalidation_1-error:0.18293\n",
      "[43]\tvalidation_0-error:0.02459\tvalidation_1-error:0.18293\n",
      "[44]\tvalidation_0-error:0.02869\tvalidation_1-error:0.17073\n",
      "[45]\tvalidation_0-error:0.02869\tvalidation_1-error:0.17073\n",
      "[46]\tvalidation_0-error:0.02459\tvalidation_1-error:0.17073\n",
      "[47]\tvalidation_0-error:0.02459\tvalidation_1-error:0.18293\n",
      "[48]\tvalidation_0-error:0.02459\tvalidation_1-error:0.18293\n",
      "[49]\tvalidation_0-error:0.02459\tvalidation_1-error:0.18293\n",
      "[50]\tvalidation_0-error:0.02459\tvalidation_1-error:0.18293\n",
      "[51]\tvalidation_0-error:0.02459\tvalidation_1-error:0.18293\n",
      "[52]\tvalidation_0-error:0.02459\tvalidation_1-error:0.18293\n",
      "[53]\tvalidation_0-error:0.02459\tvalidation_1-error:0.18293\n",
      "[54]\tvalidation_0-error:0.02459\tvalidation_1-error:0.18293\n",
      "[55]\tvalidation_0-error:0.01639\tvalidation_1-error:0.18293\n",
      "[56]\tvalidation_0-error:0.01639\tvalidation_1-error:0.18293\n",
      "[57]\tvalidation_0-error:0.01230\tvalidation_1-error:0.18293\n",
      "[58]\tvalidation_0-error:0.01230\tvalidation_1-error:0.18293\n",
      "[59]\tvalidation_0-error:0.01230\tvalidation_1-error:0.18293\n",
      "[60]\tvalidation_0-error:0.01230\tvalidation_1-error:0.18293\n",
      "[61]\tvalidation_0-error:0.00820\tvalidation_1-error:0.18293\n",
      "[62]\tvalidation_0-error:0.00820\tvalidation_1-error:0.18293\n",
      "[63]\tvalidation_0-error:0.00820\tvalidation_1-error:0.18293\n",
      "[64]\tvalidation_0-error:0.00820\tvalidation_1-error:0.18293\n",
      "Cross Validation Score: 0.813 +/- 0.146\n"
     ]
    }
   ],
   "source": [
    "xgbc_model = XGBClassifier(use_label_encoder=False,objective='binary:hinge',learning_rate=0.05,n_estimators=500)\n",
    "xgbc_model.fit(feat_train_2,targ_train_2,eval_set=[(feat_train_2, targ_train_2), (feat_val, targ_val)],eval_metric=\"error\",early_stopping_rounds=20,verbose=True)\n",
    "xgbc_scoring = cross_val_score(xgbc_model, feat_train, targ_train,cv=10, scoring=score_metric)\n",
    "print(\"Cross Validation Score: {:.3f} +/- {:.3f}\".format(xgbc_scoring.mean(),xgbc_scoring.std())) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RED NEURONAL SIMPLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [],
   "source": [
    "keras.backend.clear_session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "input = keras.layers.Input(shape=[6,])\n",
    "flatten = keras.layers.Flatten()(input)\n",
    "hidden1 = keras.layers.Dense(100, activation=\"relu\")(flatten)\n",
    "dropout = keras.layers.Dropout(rate=0.2)(hidden1)\n",
    "hidden2 = keras.layers.Dense(100, activation=\"relu\")(hidden1)\n",
    "output = keras.layers.Dense(1, activation=\"sigmoid\")(hidden2)\n",
    "\n",
    "model = keras.models.Model(inputs=[input], outputs=[output])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 6)]               0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 6)                 0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 100)               700       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 100)               10100     \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 10,901\n",
      "Trainable params: 10,901\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=tf.keras.losses.BinaryCrossentropy(),\n",
    "              optimizer=keras.optimizers.Adam(learning_rate=1e-3),\n",
    "              metrics=[tf.keras.metrics.Recall()])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2000\n",
      "9/9 [==============================] - 0s 16ms/step - loss: 0.6872 - recall: 0.6701 - val_loss: 0.6978 - val_recall: 0.4545\n",
      "Epoch 2/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.6260 - recall: 0.4227 - val_loss: 0.7402 - val_recall: 0.3788\n",
      "Epoch 3/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.5887 - recall: 0.4639 - val_loss: 0.7525 - val_recall: 0.3788\n",
      "Epoch 4/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.5598 - recall: 0.5361 - val_loss: 0.7265 - val_recall: 0.5000\n",
      "Epoch 5/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.5340 - recall: 0.5773 - val_loss: 0.7359 - val_recall: 0.5152\n",
      "Epoch 6/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.5204 - recall: 0.5979 - val_loss: 0.7278 - val_recall: 0.5758\n",
      "Epoch 7/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.5072 - recall: 0.6289 - val_loss: 0.7237 - val_recall: 0.6212\n",
      "Epoch 8/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4987 - recall: 0.6701 - val_loss: 0.6882 - val_recall: 0.6061\n",
      "Epoch 9/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4930 - recall: 0.7113 - val_loss: 0.6370 - val_recall: 0.6818\n",
      "Epoch 10/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4883 - recall: 0.7526 - val_loss: 0.6467 - val_recall: 0.6818\n",
      "Epoch 11/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4819 - recall: 0.7113 - val_loss: 0.7083 - val_recall: 0.6212\n",
      "Epoch 12/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4756 - recall: 0.7113 - val_loss: 0.6927 - val_recall: 0.6364\n",
      "Epoch 13/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4716 - recall: 0.7216 - val_loss: 0.6700 - val_recall: 0.6364\n",
      "Epoch 14/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4648 - recall: 0.7216 - val_loss: 0.6883 - val_recall: 0.6515\n",
      "Epoch 15/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4610 - recall: 0.7010 - val_loss: 0.7285 - val_recall: 0.6364\n",
      "Epoch 16/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4570 - recall: 0.6701 - val_loss: 0.7170 - val_recall: 0.6364\n",
      "Epoch 17/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4538 - recall: 0.7113 - val_loss: 0.6311 - val_recall: 0.6515\n",
      "Epoch 18/2000\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4492 - recall: 0.7526 - val_loss: 0.6648 - val_recall: 0.6515\n",
      "Epoch 19/2000\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4433 - recall: 0.7526 - val_loss: 0.6666 - val_recall: 0.7273\n",
      "Epoch 20/2000\n",
      "9/9 [==============================] - 0s 4ms/step - loss: 0.4396 - recall: 0.7423 - val_loss: 0.7173 - val_recall: 0.6212\n",
      "Epoch 21/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4389 - recall: 0.7113 - val_loss: 0.7400 - val_recall: 0.6061\n",
      "Epoch 22/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4352 - recall: 0.6907 - val_loss: 0.7175 - val_recall: 0.6364\n",
      "Epoch 23/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4314 - recall: 0.6701 - val_loss: 0.7142 - val_recall: 0.6212\n",
      "Epoch 24/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4256 - recall: 0.7216 - val_loss: 0.6403 - val_recall: 0.7121\n",
      "Epoch 25/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4192 - recall: 0.7320 - val_loss: 0.6928 - val_recall: 0.6970\n",
      "Epoch 26/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4154 - recall: 0.7320 - val_loss: 0.6627 - val_recall: 0.6970\n",
      "Epoch 27/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4123 - recall: 0.7526 - val_loss: 0.6372 - val_recall: 0.7121\n",
      "Epoch 28/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4084 - recall: 0.7629 - val_loss: 0.6131 - val_recall: 0.7273\n",
      "Epoch 29/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.4048 - recall: 0.7526 - val_loss: 0.6656 - val_recall: 0.6970\n",
      "Epoch 30/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4099 - recall: 0.6701 - val_loss: 0.7391 - val_recall: 0.5909\n",
      "Epoch 31/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.4089 - recall: 0.7010 - val_loss: 0.6293 - val_recall: 0.7121\n",
      "Epoch 32/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3990 - recall: 0.7423 - val_loss: 0.6836 - val_recall: 0.6970\n",
      "Epoch 33/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3978 - recall: 0.7423 - val_loss: 0.6159 - val_recall: 0.7121\n",
      "Epoch 34/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3951 - recall: 0.7835 - val_loss: 0.5302 - val_recall: 0.7273\n",
      "Epoch 35/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3893 - recall: 0.7835 - val_loss: 0.5773 - val_recall: 0.7273\n",
      "Epoch 36/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3849 - recall: 0.7938 - val_loss: 0.5709 - val_recall: 0.7273\n",
      "Epoch 37/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3795 - recall: 0.7732 - val_loss: 0.6356 - val_recall: 0.7121\n",
      "Epoch 38/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3814 - recall: 0.7423 - val_loss: 0.6513 - val_recall: 0.7121\n",
      "Epoch 39/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3744 - recall: 0.7629 - val_loss: 0.5322 - val_recall: 0.7273\n",
      "Epoch 40/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3723 - recall: 0.7732 - val_loss: 0.5524 - val_recall: 0.7424\n",
      "Epoch 41/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3711 - recall: 0.7526 - val_loss: 0.6170 - val_recall: 0.7121\n",
      "Epoch 42/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3652 - recall: 0.7526 - val_loss: 0.5000 - val_recall: 0.7424\n",
      "Epoch 43/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3711 - recall: 0.7835 - val_loss: 0.5058 - val_recall: 0.7424\n",
      "Epoch 44/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3636 - recall: 0.8041 - val_loss: 0.5587 - val_recall: 0.7273\n",
      "Epoch 45/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3569 - recall: 0.7629 - val_loss: 0.6209 - val_recall: 0.7121\n",
      "Epoch 46/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3622 - recall: 0.7423 - val_loss: 0.6329 - val_recall: 0.6970\n",
      "Epoch 47/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3570 - recall: 0.7423 - val_loss: 0.5303 - val_recall: 0.7273\n",
      "Epoch 48/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3531 - recall: 0.7526 - val_loss: 0.5167 - val_recall: 0.7273\n",
      "Epoch 49/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3521 - recall: 0.7629 - val_loss: 0.5160 - val_recall: 0.7121\n",
      "Epoch 50/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3475 - recall: 0.7629 - val_loss: 0.5521 - val_recall: 0.7121\n",
      "Epoch 51/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3429 - recall: 0.7629 - val_loss: 0.6544 - val_recall: 0.6818\n",
      "Epoch 52/2000\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.3436 - recall: 0.7526 - val_loss: 0.5745 - val_recall: 0.7273\n",
      "Epoch 53/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3361 - recall: 0.7629 - val_loss: 0.4858 - val_recall: 0.7273\n",
      "Epoch 54/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3358 - recall: 0.7732 - val_loss: 0.5050 - val_recall: 0.7273\n",
      "Epoch 55/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3286 - recall: 0.7732 - val_loss: 0.4995 - val_recall: 0.7424\n",
      "Epoch 56/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3249 - recall: 0.7629 - val_loss: 0.5486 - val_recall: 0.7121\n",
      "Epoch 57/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3289 - recall: 0.7732 - val_loss: 0.4752 - val_recall: 0.7424\n",
      "Epoch 58/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3293 - recall: 0.8041 - val_loss: 0.4951 - val_recall: 0.7424\n",
      "Epoch 59/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3231 - recall: 0.7835 - val_loss: 0.5040 - val_recall: 0.7424\n",
      "Epoch 60/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3191 - recall: 0.7732 - val_loss: 0.5257 - val_recall: 0.7121\n",
      "Epoch 61/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3139 - recall: 0.7835 - val_loss: 0.5056 - val_recall: 0.7273\n",
      "Epoch 62/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3149 - recall: 0.8041 - val_loss: 0.4266 - val_recall: 0.7879\n",
      "Epoch 63/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3110 - recall: 0.7938 - val_loss: 0.5315 - val_recall: 0.7424\n",
      "Epoch 64/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.3088 - recall: 0.7835 - val_loss: 0.4707 - val_recall: 0.7273\n",
      "Epoch 65/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3086 - recall: 0.8247 - val_loss: 0.4361 - val_recall: 0.7576\n",
      "Epoch 66/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.3032 - recall: 0.8351 - val_loss: 0.4786 - val_recall: 0.7273\n",
      "Epoch 67/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2973 - recall: 0.7938 - val_loss: 0.5224 - val_recall: 0.7273\n",
      "Epoch 68/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2988 - recall: 0.7732 - val_loss: 0.4830 - val_recall: 0.7424\n",
      "Epoch 69/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2923 - recall: 0.8144 - val_loss: 0.4541 - val_recall: 0.7576\n",
      "Epoch 70/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2873 - recall: 0.8144 - val_loss: 0.4625 - val_recall: 0.7424\n",
      "Epoch 71/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2851 - recall: 0.8144 - val_loss: 0.4722 - val_recall: 0.7424\n",
      "Epoch 72/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2824 - recall: 0.8144 - val_loss: 0.4543 - val_recall: 0.7424\n",
      "Epoch 73/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2791 - recall: 0.8351 - val_loss: 0.3947 - val_recall: 0.8182\n",
      "Epoch 74/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2856 - recall: 0.8763 - val_loss: 0.3684 - val_recall: 0.8333\n",
      "Epoch 75/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2746 - recall: 0.8454 - val_loss: 0.4738 - val_recall: 0.7576\n",
      "Epoch 76/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2724 - recall: 0.8041 - val_loss: 0.5005 - val_recall: 0.7273\n",
      "Epoch 77/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2720 - recall: 0.8041 - val_loss: 0.4461 - val_recall: 0.7879\n",
      "Epoch 78/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2728 - recall: 0.8041 - val_loss: 0.4649 - val_recall: 0.7576\n",
      "Epoch 79/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2725 - recall: 0.8454 - val_loss: 0.3970 - val_recall: 0.8788\n",
      "Epoch 80/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2618 - recall: 0.8557 - val_loss: 0.4489 - val_recall: 0.7879\n",
      "Epoch 81/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2694 - recall: 0.7938 - val_loss: 0.4632 - val_recall: 0.8182\n",
      "Epoch 82/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2623 - recall: 0.8041 - val_loss: 0.3811 - val_recall: 0.8182\n",
      "Epoch 83/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2585 - recall: 0.8660 - val_loss: 0.3988 - val_recall: 0.8333\n",
      "Epoch 84/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2567 - recall: 0.8454 - val_loss: 0.4562 - val_recall: 0.7879\n",
      "Epoch 85/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2531 - recall: 0.8144 - val_loss: 0.4427 - val_recall: 0.8030\n",
      "Epoch 86/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2508 - recall: 0.8454 - val_loss: 0.3789 - val_recall: 0.8788\n",
      "Epoch 87/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2532 - recall: 0.8763 - val_loss: 0.3800 - val_recall: 0.8636\n",
      "Epoch 88/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2465 - recall: 0.8454 - val_loss: 0.4392 - val_recall: 0.7879\n",
      "Epoch 89/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2379 - recall: 0.8351 - val_loss: 0.3600 - val_recall: 0.8485\n",
      "Epoch 90/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2408 - recall: 0.8763 - val_loss: 0.3968 - val_recall: 0.8182\n",
      "Epoch 91/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2468 - recall: 0.8041 - val_loss: 0.4561 - val_recall: 0.8182\n",
      "Epoch 92/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2415 - recall: 0.8247 - val_loss: 0.3101 - val_recall: 0.8939\n",
      "Epoch 93/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2372 - recall: 0.8969 - val_loss: 0.3686 - val_recall: 0.8333\n",
      "Epoch 94/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2326 - recall: 0.8660 - val_loss: 0.4087 - val_recall: 0.8182\n",
      "Epoch 95/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2284 - recall: 0.8763 - val_loss: 0.3378 - val_recall: 0.8939\n",
      "Epoch 96/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2263 - recall: 0.8660 - val_loss: 0.3696 - val_recall: 0.8333\n",
      "Epoch 97/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2235 - recall: 0.8454 - val_loss: 0.4053 - val_recall: 0.8182\n",
      "Epoch 98/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2237 - recall: 0.8866 - val_loss: 0.3327 - val_recall: 0.8636\n",
      "Epoch 99/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2194 - recall: 0.8557 - val_loss: 0.4173 - val_recall: 0.7879\n",
      "Epoch 100/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2176 - recall: 0.8763 - val_loss: 0.3815 - val_recall: 0.8939\n",
      "Epoch 101/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2185 - recall: 0.8969 - val_loss: 0.2934 - val_recall: 0.9242\n",
      "Epoch 102/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2232 - recall: 0.9278 - val_loss: 0.3143 - val_recall: 0.9091\n",
      "Epoch 103/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2114 - recall: 0.8866 - val_loss: 0.3776 - val_recall: 0.8485\n",
      "Epoch 104/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2125 - recall: 0.8763 - val_loss: 0.3472 - val_recall: 0.8485\n",
      "Epoch 105/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2061 - recall: 0.8866 - val_loss: 0.3160 - val_recall: 0.8788\n",
      "Epoch 106/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2067 - recall: 0.8557 - val_loss: 0.3736 - val_recall: 0.8182\n",
      "Epoch 107/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2027 - recall: 0.8866 - val_loss: 0.3386 - val_recall: 0.8939\n",
      "Epoch 108/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2056 - recall: 0.9072 - val_loss: 0.3313 - val_recall: 0.9242\n",
      "Epoch 109/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2003 - recall: 0.8969 - val_loss: 0.3312 - val_recall: 0.8636\n",
      "Epoch 110/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.2017 - recall: 0.8866 - val_loss: 0.3738 - val_recall: 0.8333\n",
      "Epoch 111/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1994 - recall: 0.9175 - val_loss: 0.2859 - val_recall: 0.8939\n",
      "Epoch 112/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1955 - recall: 0.9175 - val_loss: 0.3493 - val_recall: 0.8788\n",
      "Epoch 113/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1946 - recall: 0.8763 - val_loss: 0.3666 - val_recall: 0.8636\n",
      "Epoch 114/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1880 - recall: 0.8866 - val_loss: 0.2923 - val_recall: 0.9091\n",
      "Epoch 115/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1853 - recall: 0.8969 - val_loss: 0.3386 - val_recall: 0.8636\n",
      "Epoch 116/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1871 - recall: 0.8763 - val_loss: 0.3521 - val_recall: 0.8636\n",
      "Epoch 117/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1862 - recall: 0.8866 - val_loss: 0.3023 - val_recall: 0.9242\n",
      "Epoch 118/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1828 - recall: 0.9175 - val_loss: 0.3031 - val_recall: 0.8939\n",
      "Epoch 119/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1828 - recall: 0.8660 - val_loss: 0.3898 - val_recall: 0.8030\n",
      "Epoch 120/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1806 - recall: 0.8660 - val_loss: 0.2991 - val_recall: 0.8636\n",
      "Epoch 121/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1876 - recall: 0.9381 - val_loss: 0.2793 - val_recall: 0.8788\n",
      "Epoch 122/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.2060 - recall: 0.8557 - val_loss: 0.4525 - val_recall: 0.8182\n",
      "Epoch 123/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1886 - recall: 0.8660 - val_loss: 0.2189 - val_recall: 0.9394\n",
      "Epoch 124/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1817 - recall: 0.9588 - val_loss: 0.2678 - val_recall: 0.8939\n",
      "Epoch 125/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1744 - recall: 0.8866 - val_loss: 0.3544 - val_recall: 0.8788\n",
      "Epoch 126/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1714 - recall: 0.8763 - val_loss: 0.3195 - val_recall: 0.8788\n",
      "Epoch 127/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1655 - recall: 0.9072 - val_loss: 0.2844 - val_recall: 0.8939\n",
      "Epoch 128/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1630 - recall: 0.9175 - val_loss: 0.2677 - val_recall: 0.8939\n",
      "Epoch 129/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1658 - recall: 0.9175 - val_loss: 0.2537 - val_recall: 0.9242\n",
      "Epoch 130/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1657 - recall: 0.9381 - val_loss: 0.2415 - val_recall: 0.9394\n",
      "Epoch 131/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1659 - recall: 0.9381 - val_loss: 0.2654 - val_recall: 0.9091\n",
      "Epoch 132/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1633 - recall: 0.9072 - val_loss: 0.2839 - val_recall: 0.8636\n",
      "Epoch 133/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1615 - recall: 0.9485 - val_loss: 0.2852 - val_recall: 0.9091\n",
      "Epoch 134/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1600 - recall: 0.8866 - val_loss: 0.3197 - val_recall: 0.8636\n",
      "Epoch 135/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1508 - recall: 0.8969 - val_loss: 0.2409 - val_recall: 0.9394\n",
      "Epoch 136/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1519 - recall: 0.9278 - val_loss: 0.2581 - val_recall: 0.9394\n",
      "Epoch 137/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1491 - recall: 0.9072 - val_loss: 0.3514 - val_recall: 0.8939\n",
      "Epoch 138/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1537 - recall: 0.9072 - val_loss: 0.2684 - val_recall: 0.9394\n",
      "Epoch 139/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1504 - recall: 0.9485 - val_loss: 0.2218 - val_recall: 0.9394\n",
      "Epoch 140/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1470 - recall: 0.9588 - val_loss: 0.2899 - val_recall: 0.9091\n",
      "Epoch 141/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1475 - recall: 0.9072 - val_loss: 0.2870 - val_recall: 0.9242\n",
      "Epoch 142/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1428 - recall: 0.9381 - val_loss: 0.2325 - val_recall: 0.9394\n",
      "Epoch 143/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1390 - recall: 0.9485 - val_loss: 0.2543 - val_recall: 0.9394\n",
      "Epoch 144/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1364 - recall: 0.9278 - val_loss: 0.2335 - val_recall: 0.9394\n",
      "Epoch 145/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1359 - recall: 0.9381 - val_loss: 0.2420 - val_recall: 0.9394\n",
      "Epoch 146/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1418 - recall: 0.9175 - val_loss: 0.3632 - val_recall: 0.8333\n",
      "Epoch 147/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1413 - recall: 0.9278 - val_loss: 0.2688 - val_recall: 0.8788\n",
      "Epoch 148/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1328 - recall: 0.9278 - val_loss: 0.2303 - val_recall: 0.9242\n",
      "Epoch 149/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1327 - recall: 0.9381 - val_loss: 0.2177 - val_recall: 0.9394\n",
      "Epoch 150/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1279 - recall: 0.9588 - val_loss: 0.2503 - val_recall: 0.9242\n",
      "Epoch 151/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1260 - recall: 0.9485 - val_loss: 0.2340 - val_recall: 0.9242\n",
      "Epoch 152/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1248 - recall: 0.9588 - val_loss: 0.2389 - val_recall: 0.9242\n",
      "Epoch 153/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1256 - recall: 0.9278 - val_loss: 0.2354 - val_recall: 0.9394\n",
      "Epoch 154/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1228 - recall: 0.9485 - val_loss: 0.2319 - val_recall: 0.9394\n",
      "Epoch 155/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1235 - recall: 0.9381 - val_loss: 0.2634 - val_recall: 0.9091\n",
      "Epoch 156/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1207 - recall: 0.9381 - val_loss: 0.2114 - val_recall: 0.9394\n",
      "Epoch 157/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1199 - recall: 0.9691 - val_loss: 0.2247 - val_recall: 0.9394\n",
      "Epoch 158/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1201 - recall: 0.9381 - val_loss: 0.2392 - val_recall: 0.9394\n",
      "Epoch 159/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1178 - recall: 0.9485 - val_loss: 0.2000 - val_recall: 0.9394\n",
      "Epoch 160/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1147 - recall: 0.9588 - val_loss: 0.2487 - val_recall: 0.9394\n",
      "Epoch 161/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1161 - recall: 0.9691 - val_loss: 0.2301 - val_recall: 0.9394\n",
      "Epoch 162/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1197 - recall: 0.9588 - val_loss: 0.2289 - val_recall: 0.9242\n",
      "Epoch 163/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1149 - recall: 0.9485 - val_loss: 0.2239 - val_recall: 0.9394\n",
      "Epoch 164/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1116 - recall: 0.9588 - val_loss: 0.1988 - val_recall: 0.9394\n",
      "Epoch 165/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1107 - recall: 0.9691 - val_loss: 0.2138 - val_recall: 0.9394\n",
      "Epoch 166/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1123 - recall: 0.9691 - val_loss: 0.2104 - val_recall: 0.9394\n",
      "Epoch 167/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1066 - recall: 0.9691 - val_loss: 0.2036 - val_recall: 0.9394\n",
      "Epoch 168/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1058 - recall: 0.9588 - val_loss: 0.2268 - val_recall: 0.9394\n",
      "Epoch 169/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1049 - recall: 0.9691 - val_loss: 0.2161 - val_recall: 0.9394\n",
      "Epoch 170/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1038 - recall: 0.9588 - val_loss: 0.2276 - val_recall: 0.9394\n",
      "Epoch 171/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1042 - recall: 0.9691 - val_loss: 0.2004 - val_recall: 0.9394\n",
      "Epoch 172/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.1023 - recall: 0.9691 - val_loss: 0.2162 - val_recall: 0.9394\n",
      "Epoch 173/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1008 - recall: 0.9691 - val_loss: 0.1913 - val_recall: 0.9394\n",
      "Epoch 174/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0989 - recall: 0.9588 - val_loss: 0.1903 - val_recall: 0.9394\n",
      "Epoch 175/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0979 - recall: 0.9691 - val_loss: 0.1809 - val_recall: 0.9394\n",
      "Epoch 176/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0961 - recall: 0.9691 - val_loss: 0.1973 - val_recall: 0.9394\n",
      "Epoch 177/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0954 - recall: 0.9691 - val_loss: 0.2032 - val_recall: 0.9394\n",
      "Epoch 178/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0946 - recall: 0.9691 - val_loss: 0.2052 - val_recall: 0.9394\n",
      "Epoch 179/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0933 - recall: 0.9691 - val_loss: 0.2043 - val_recall: 0.9394\n",
      "Epoch 180/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0923 - recall: 0.9691 - val_loss: 0.1809 - val_recall: 0.9394\n",
      "Epoch 181/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0948 - recall: 0.9691 - val_loss: 0.1990 - val_recall: 0.9394\n",
      "Epoch 182/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0989 - recall: 0.9588 - val_loss: 0.2343 - val_recall: 0.9394\n",
      "Epoch 183/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0906 - recall: 0.9588 - val_loss: 0.1649 - val_recall: 0.9394\n",
      "Epoch 184/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0905 - recall: 0.9691 - val_loss: 0.1912 - val_recall: 0.9394\n",
      "Epoch 185/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1048 - recall: 0.9278 - val_loss: 0.2492 - val_recall: 0.9242\n",
      "Epoch 186/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.1049 - recall: 0.9794 - val_loss: 0.1505 - val_recall: 0.9394\n",
      "Epoch 187/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0947 - recall: 0.9691 - val_loss: 0.2107 - val_recall: 0.9242\n",
      "Epoch 188/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0894 - recall: 0.9691 - val_loss: 0.1574 - val_recall: 0.9545\n",
      "Epoch 189/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0893 - recall: 0.9691 - val_loss: 0.1937 - val_recall: 0.9394\n",
      "Epoch 190/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0853 - recall: 0.9691 - val_loss: 0.1841 - val_recall: 0.9394\n",
      "Epoch 191/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0884 - recall: 0.9691 - val_loss: 0.2067 - val_recall: 0.9394\n",
      "Epoch 192/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0888 - recall: 0.9794 - val_loss: 0.1617 - val_recall: 0.9394\n",
      "Epoch 193/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0823 - recall: 0.9691 - val_loss: 0.2168 - val_recall: 0.9394\n",
      "Epoch 194/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0824 - recall: 0.9588 - val_loss: 0.1641 - val_recall: 0.9394\n",
      "Epoch 195/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0800 - recall: 0.9794 - val_loss: 0.1647 - val_recall: 0.9394\n",
      "Epoch 196/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0778 - recall: 0.9794 - val_loss: 0.2041 - val_recall: 0.9394\n",
      "Epoch 197/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0776 - recall: 0.9794 - val_loss: 0.1800 - val_recall: 0.9394\n",
      "Epoch 198/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0760 - recall: 0.9794 - val_loss: 0.1708 - val_recall: 0.9394\n",
      "Epoch 199/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0740 - recall: 0.9794 - val_loss: 0.1816 - val_recall: 0.9394\n",
      "Epoch 200/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0743 - recall: 0.9794 - val_loss: 0.1676 - val_recall: 0.9394\n",
      "Epoch 201/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0760 - recall: 0.9588 - val_loss: 0.2043 - val_recall: 0.9242\n",
      "Epoch 202/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0778 - recall: 0.9897 - val_loss: 0.1559 - val_recall: 0.9242\n",
      "Epoch 203/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0755 - recall: 0.9897 - val_loss: 0.1717 - val_recall: 0.9394\n",
      "Epoch 204/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0730 - recall: 0.9794 - val_loss: 0.2033 - val_recall: 0.9242\n",
      "Epoch 205/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0735 - recall: 0.9691 - val_loss: 0.1752 - val_recall: 0.9242\n",
      "Epoch 206/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0714 - recall: 0.9897 - val_loss: 0.1482 - val_recall: 0.9545\n",
      "Epoch 207/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0701 - recall: 0.9897 - val_loss: 0.1624 - val_recall: 0.9545\n",
      "Epoch 208/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0707 - recall: 0.9897 - val_loss: 0.1491 - val_recall: 0.9545\n",
      "Epoch 209/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0682 - recall: 0.9897 - val_loss: 0.1650 - val_recall: 0.9394\n",
      "Epoch 210/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0663 - recall: 0.9897 - val_loss: 0.1576 - val_recall: 0.9394\n",
      "Epoch 211/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0662 - recall: 0.9794 - val_loss: 0.1883 - val_recall: 0.9394\n",
      "Epoch 212/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0671 - recall: 0.9691 - val_loss: 0.1816 - val_recall: 0.9242\n",
      "Epoch 213/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0683 - recall: 0.9794 - val_loss: 0.1568 - val_recall: 0.9545\n",
      "Epoch 214/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0680 - recall: 0.9897 - val_loss: 0.1615 - val_recall: 0.9545\n",
      "Epoch 215/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0664 - recall: 0.9897 - val_loss: 0.2103 - val_recall: 0.9242\n",
      "Epoch 216/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0665 - recall: 0.9794 - val_loss: 0.1690 - val_recall: 0.9545\n",
      "Epoch 217/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0628 - recall: 0.9897 - val_loss: 0.1426 - val_recall: 0.9545\n",
      "Epoch 218/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0614 - recall: 0.9897 - val_loss: 0.1653 - val_recall: 0.9545\n",
      "Epoch 219/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0625 - recall: 0.9897 - val_loss: 0.1785 - val_recall: 0.9394\n",
      "Epoch 220/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0606 - recall: 0.9897 - val_loss: 0.1532 - val_recall: 0.9545\n",
      "Epoch 221/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0620 - recall: 0.9794 - val_loss: 0.1783 - val_recall: 0.9394\n",
      "Epoch 222/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0649 - recall: 0.9794 - val_loss: 0.1554 - val_recall: 0.9394\n",
      "Epoch 223/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0731 - recall: 0.9897 - val_loss: 0.1562 - val_recall: 0.9545\n",
      "Epoch 224/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0868 - recall: 0.9381 - val_loss: 0.2340 - val_recall: 0.9242\n",
      "Epoch 225/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0674 - recall: 0.9691 - val_loss: 0.1057 - val_recall: 0.9697\n",
      "Epoch 226/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0606 - recall: 0.9897 - val_loss: 0.1637 - val_recall: 0.9394\n",
      "Epoch 227/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0577 - recall: 0.9897 - val_loss: 0.1766 - val_recall: 0.9394\n",
      "Epoch 228/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0572 - recall: 0.9897 - val_loss: 0.1090 - val_recall: 0.9697\n",
      "Epoch 229/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0569 - recall: 0.9897 - val_loss: 0.1430 - val_recall: 0.9545\n",
      "Epoch 230/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0556 - recall: 0.9794 - val_loss: 0.1433 - val_recall: 0.9545\n",
      "Epoch 231/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0642 - recall: 0.9897 - val_loss: 0.0828 - val_recall: 0.9848\n",
      "Epoch 232/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0562 - recall: 0.9897 - val_loss: 0.1922 - val_recall: 0.9394\n",
      "Epoch 233/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0562 - recall: 0.9897 - val_loss: 0.1259 - val_recall: 0.9697\n",
      "Epoch 234/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0517 - recall: 0.9897 - val_loss: 0.1224 - val_recall: 0.9697\n",
      "Epoch 235/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0511 - recall: 0.9897 - val_loss: 0.1517 - val_recall: 0.9394\n",
      "Epoch 236/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0538 - recall: 0.9897 - val_loss: 0.1508 - val_recall: 0.9545\n",
      "Epoch 237/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0500 - recall: 0.9897 - val_loss: 0.1196 - val_recall: 0.9697\n",
      "Epoch 238/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0492 - recall: 0.9897 - val_loss: 0.1483 - val_recall: 0.9545\n",
      "Epoch 239/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0476 - recall: 0.9897 - val_loss: 0.1479 - val_recall: 0.9697\n",
      "Epoch 240/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0469 - recall: 0.9897 - val_loss: 0.1438 - val_recall: 0.9697\n",
      "Epoch 241/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0462 - recall: 0.9897 - val_loss: 0.1469 - val_recall: 0.9697\n",
      "Epoch 242/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0464 - recall: 0.9897 - val_loss: 0.1315 - val_recall: 0.9697\n",
      "Epoch 243/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0526 - recall: 0.9897 - val_loss: 0.0940 - val_recall: 0.9697\n",
      "Epoch 244/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0547 - recall: 0.9897 - val_loss: 0.1467 - val_recall: 0.9697\n",
      "Epoch 245/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0482 - recall: 0.9897 - val_loss: 0.1539 - val_recall: 0.9697\n",
      "Epoch 246/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0471 - recall: 0.9897 - val_loss: 0.1425 - val_recall: 0.9545\n",
      "Epoch 247/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0444 - recall: 0.9897 - val_loss: 0.1419 - val_recall: 0.9697\n",
      "Epoch 248/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0446 - recall: 0.9897 - val_loss: 0.1344 - val_recall: 0.9697\n",
      "Epoch 249/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0435 - recall: 0.9897 - val_loss: 0.1700 - val_recall: 0.9394\n",
      "Epoch 250/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0459 - recall: 0.9897 - val_loss: 0.1296 - val_recall: 0.9697\n",
      "Epoch 251/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0425 - recall: 0.9897 - val_loss: 0.1607 - val_recall: 0.9545\n",
      "Epoch 252/2000\n",
      "9/9 [==============================] - 0s 5ms/step - loss: 0.0418 - recall: 0.9897 - val_loss: 0.1266 - val_recall: 0.9697\n",
      "Epoch 253/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0422 - recall: 0.9897 - val_loss: 0.1373 - val_recall: 0.9697\n",
      "Epoch 254/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0407 - recall: 0.9897 - val_loss: 0.1537 - val_recall: 0.9545\n",
      "Epoch 255/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0402 - recall: 0.9897 - val_loss: 0.1267 - val_recall: 0.9697\n",
      "Epoch 256/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0451 - recall: 0.9897 - val_loss: 0.1162 - val_recall: 0.9697\n",
      "Epoch 257/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0443 - recall: 0.9897 - val_loss: 0.1887 - val_recall: 0.9394\n",
      "Epoch 258/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0431 - recall: 0.9897 - val_loss: 0.1156 - val_recall: 0.9697\n",
      "Epoch 259/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0385 - recall: 0.9897 - val_loss: 0.1315 - val_recall: 0.9697\n",
      "Epoch 260/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0387 - recall: 0.9897 - val_loss: 0.1453 - val_recall: 0.9545\n",
      "Epoch 261/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0380 - recall: 0.9897 - val_loss: 0.1240 - val_recall: 0.9545\n",
      "Epoch 262/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0383 - recall: 0.9897 - val_loss: 0.1274 - val_recall: 0.9697\n",
      "Epoch 263/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0373 - recall: 0.9897 - val_loss: 0.1395 - val_recall: 0.9697\n",
      "Epoch 264/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0414 - recall: 0.9897 - val_loss: 0.0881 - val_recall: 0.9697\n",
      "Epoch 265/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0433 - recall: 1.0000 - val_loss: 0.1658 - val_recall: 0.9545\n",
      "Epoch 266/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0414 - recall: 0.9897 - val_loss: 0.1713 - val_recall: 0.9545\n",
      "Epoch 267/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0374 - recall: 0.9897 - val_loss: 0.1169 - val_recall: 0.9697\n",
      "Epoch 268/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0399 - recall: 0.9897 - val_loss: 0.1711 - val_recall: 0.9697\n",
      "Epoch 269/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0366 - recall: 0.9897 - val_loss: 0.1345 - val_recall: 0.9697\n",
      "Epoch 270/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0369 - recall: 0.9897 - val_loss: 0.1197 - val_recall: 0.9697\n",
      "Epoch 271/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0379 - recall: 0.9897 - val_loss: 0.1853 - val_recall: 0.9545\n",
      "Epoch 272/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0360 - recall: 0.9897 - val_loss: 0.0980 - val_recall: 0.9697\n",
      "Epoch 273/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0365 - recall: 0.9897 - val_loss: 0.1244 - val_recall: 0.9697\n",
      "Epoch 274/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0349 - recall: 0.9897 - val_loss: 0.1427 - val_recall: 0.9545\n",
      "Epoch 275/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0337 - recall: 0.9897 - val_loss: 0.1177 - val_recall: 0.9697\n",
      "Epoch 276/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0348 - recall: 0.9897 - val_loss: 0.0995 - val_recall: 0.9697\n",
      "Epoch 277/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0351 - recall: 0.9897 - val_loss: 0.1656 - val_recall: 0.9697\n",
      "Epoch 278/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0337 - recall: 0.9897 - val_loss: 0.1444 - val_recall: 0.9697\n",
      "Epoch 279/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0321 - recall: 0.9897 - val_loss: 0.1142 - val_recall: 0.9697\n",
      "Epoch 280/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0317 - recall: 0.9897 - val_loss: 0.1451 - val_recall: 0.9545\n",
      "Epoch 281/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0308 - recall: 0.9897 - val_loss: 0.1295 - val_recall: 0.9545\n",
      "Epoch 282/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0315 - recall: 0.9897 - val_loss: 0.1370 - val_recall: 0.9545\n",
      "Epoch 283/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0288 - recall: 0.9897 - val_loss: 0.1019 - val_recall: 0.9697\n",
      "Epoch 284/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0308 - recall: 0.9897 - val_loss: 0.1279 - val_recall: 0.9545\n",
      "Epoch 285/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0288 - recall: 0.9897 - val_loss: 0.1335 - val_recall: 0.9545\n",
      "Epoch 286/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0285 - recall: 0.9897 - val_loss: 0.1275 - val_recall: 0.9545\n",
      "Epoch 287/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0281 - recall: 0.9897 - val_loss: 0.1291 - val_recall: 0.9545\n",
      "Epoch 288/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0282 - recall: 0.9897 - val_loss: 0.1305 - val_recall: 0.9545\n",
      "Epoch 289/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0297 - recall: 0.9897 - val_loss: 0.1456 - val_recall: 0.9545\n",
      "Epoch 290/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0280 - recall: 0.9897 - val_loss: 0.1053 - val_recall: 0.9697\n",
      "Epoch 291/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0281 - recall: 0.9897 - val_loss: 0.1197 - val_recall: 0.9697\n",
      "Epoch 292/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0272 - recall: 0.9897 - val_loss: 0.1354 - val_recall: 0.9545\n",
      "Epoch 293/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0268 - recall: 0.9897 - val_loss: 0.1416 - val_recall: 0.9545\n",
      "Epoch 294/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0279 - recall: 0.9897 - val_loss: 0.1526 - val_recall: 0.9545\n",
      "Epoch 295/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0275 - recall: 0.9897 - val_loss: 0.1361 - val_recall: 0.9697\n",
      "Epoch 296/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0266 - recall: 0.9897 - val_loss: 0.1351 - val_recall: 0.9697\n",
      "Epoch 297/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0259 - recall: 0.9897 - val_loss: 0.1238 - val_recall: 0.9697\n",
      "Epoch 298/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0271 - recall: 0.9897 - val_loss: 0.1090 - val_recall: 0.9697\n",
      "Epoch 299/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0268 - recall: 0.9897 - val_loss: 0.1675 - val_recall: 0.9545\n",
      "Epoch 300/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0267 - recall: 0.9897 - val_loss: 0.1322 - val_recall: 0.9697\n",
      "Epoch 301/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0264 - recall: 0.9897 - val_loss: 0.1430 - val_recall: 0.9697\n",
      "Epoch 302/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0271 - recall: 0.9897 - val_loss: 0.1316 - val_recall: 0.9545\n",
      "Epoch 303/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0250 - recall: 0.9897 - val_loss: 0.1071 - val_recall: 0.9697\n",
      "Epoch 304/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0248 - recall: 0.9897 - val_loss: 0.1358 - val_recall: 0.9697\n",
      "Epoch 305/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0251 - recall: 0.9897 - val_loss: 0.1086 - val_recall: 0.9697\n",
      "Epoch 306/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0227 - recall: 0.9897 - val_loss: 0.1384 - val_recall: 0.9545\n",
      "Epoch 307/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0231 - recall: 0.9897 - val_loss: 0.1272 - val_recall: 0.9697\n",
      "Epoch 308/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0227 - recall: 0.9897 - val_loss: 0.1289 - val_recall: 0.9697\n",
      "Epoch 309/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0228 - recall: 0.9897 - val_loss: 0.1148 - val_recall: 0.9697\n",
      "Epoch 310/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0232 - recall: 0.9897 - val_loss: 0.1341 - val_recall: 0.9545\n",
      "Epoch 311/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0220 - recall: 0.9897 - val_loss: 0.1101 - val_recall: 0.9697\n",
      "Epoch 312/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0218 - recall: 0.9897 - val_loss: 0.1202 - val_recall: 0.9697\n",
      "Epoch 313/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0214 - recall: 0.9897 - val_loss: 0.1364 - val_recall: 0.9545\n",
      "Epoch 314/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0218 - recall: 0.9897 - val_loss: 0.1178 - val_recall: 0.9545\n",
      "Epoch 315/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0218 - recall: 0.9897 - val_loss: 0.1176 - val_recall: 0.9697\n",
      "Epoch 316/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0215 - recall: 0.9897 - val_loss: 0.1424 - val_recall: 0.9394\n",
      "Epoch 317/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0229 - recall: 0.9897 - val_loss: 0.1031 - val_recall: 0.9697\n",
      "Epoch 318/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0216 - recall: 0.9897 - val_loss: 0.1241 - val_recall: 0.9697\n",
      "Epoch 319/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0212 - recall: 0.9897 - val_loss: 0.1036 - val_recall: 0.9697\n",
      "Epoch 320/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0216 - recall: 0.9897 - val_loss: 0.1249 - val_recall: 0.9697\n",
      "Epoch 321/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0203 - recall: 0.9897 - val_loss: 0.1213 - val_recall: 0.9697\n",
      "Epoch 322/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0210 - recall: 0.9897 - val_loss: 0.1242 - val_recall: 0.9697\n",
      "Epoch 323/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0201 - recall: 0.9897 - val_loss: 0.1239 - val_recall: 0.9697\n",
      "Epoch 324/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0193 - recall: 0.9897 - val_loss: 0.1210 - val_recall: 0.9697\n",
      "Epoch 325/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0190 - recall: 0.9897 - val_loss: 0.1239 - val_recall: 0.9545\n",
      "Epoch 326/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0190 - recall: 0.9897 - val_loss: 0.1192 - val_recall: 0.9545\n",
      "Epoch 327/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0192 - recall: 1.0000 - val_loss: 0.1078 - val_recall: 0.9545\n",
      "Epoch 328/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0194 - recall: 1.0000 - val_loss: 0.1335 - val_recall: 0.9545\n",
      "Epoch 329/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0189 - recall: 0.9897 - val_loss: 0.1088 - val_recall: 0.9697\n",
      "Epoch 330/2000\n",
      "9/9 [==============================] - 0s 3ms/step - loss: 0.0182 - recall: 1.0000 - val_loss: 0.1025 - val_recall: 0.9697\n",
      "Epoch 331/2000\n",
      "9/9 [==============================] - 0s 2ms/step - loss: 0.0184 - recall: 0.9897 - val_loss: 0.1245 - val_recall: 0.9545\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(feat_train, targ_train, epochs=2000, batch_size=32,\n",
    "                    validation_split=0.2,\n",
    "                    callbacks=[keras.callbacks.EarlyStopping(patience=100, restore_best_weights=True)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11/11 [==============================] - 0s 500us/step - loss: 0.0677 - recall: 0.9877\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0676703229546547, 0.987730085849762]"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(feat_train,targ_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
    "plt.grid(True)\n",
    "plt.gca().set_ylim(0, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([126.,  24.,   7.,   3.,   4.,   2.,   1.,   5.,  13., 141.]),\n",
       " array([1.5837648e-07, 1.0000014e-01, 2.0000012e-01, 3.0000010e-01,\n",
       "        4.0000010e-01, 5.0000006e-01, 6.0000008e-01, 7.0000005e-01,\n",
       "        8.0000001e-01, 9.0000004e-01, 1.0000000e+00], dtype=float32),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(model.predict(feat_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([163.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 163.]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(targ_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x23b09237970>"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = confusion_matrix(targ_train, model.predict(feat_train)>0.5)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d9579fd63862c719ce91d1dd1fb1c44b2ef43bdaa2f86d843d9fd8562746d35b"
  },
  "kernelspec": {
   "display_name": "Python 3.9.6 64-bit",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}