Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "17293b26",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statistics\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from imblearn.over_sampling import SMOTE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ced79309",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"df_w_var.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bbc32134",
   "metadata": {},
   "outputs": [
    {
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       "      <td>8.212629</td>\n",
       "      <td>6.270775</td>\n",
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       "      <td>8.177457</td>\n",
       "      <td>4.041513</td>\n",
       "      <td>6.366196</td>\n",
       "      <td>4.369383</td>\n",
       "      <td>8.530794</td>\n",
       "      <td>...</td>\n",
       "      <td>6.562527</td>\n",
       "      <td>6.614429</td>\n",
       "      <td>8.047375</td>\n",
       "      <td>7.003226</td>\n",
       "      <td>6.268696</td>\n",
       "      <td>3.411819</td>\n",
       "      <td>3.078852</td>\n",
       "      <td>3.624622</td>\n",
       "      <td>5.927809</td>\n",
       "      <td>9.263549</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>5.883377</td>\n",
       "      <td>6.236439</td>\n",
       "      <td>6.029005</td>\n",
       "      <td>8.071778</td>\n",
       "      <td>5.224647</td>\n",
       "      <td>4.879736</td>\n",
       "      <td>4.881586</td>\n",
       "      <td>3.394249</td>\n",
       "      <td>6.981226</td>\n",
       "      <td>...</td>\n",
       "      <td>6.083050</td>\n",
       "      <td>5.891748</td>\n",
       "      <td>5.999268</td>\n",
       "      <td>5.481434</td>\n",
       "      <td>6.353627</td>\n",
       "      <td>5.667723</td>\n",
       "      <td>3.429725</td>\n",
       "      <td>3.859270</td>\n",
       "      <td>5.952769</td>\n",
       "      <td>10.339334</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>6.665956</td>\n",
       "      <td>8.035231</td>\n",
       "      <td>7.614323</td>\n",
       "      <td>7.692385</td>\n",
       "      <td>7.442830</td>\n",
       "      <td>5.849879</td>\n",
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       "      <td>4.431447</td>\n",
       "      <td>7.199892</td>\n",
       "      <td>...</td>\n",
       "      <td>8.006517</td>\n",
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       "      <td>6.308003</td>\n",
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       "      <td>5.653083</td>\n",
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       "      <td>...</td>\n",
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       "      <td>3</td>\n",
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       "      <td>6.812968</td>\n",
       "      <td>4.303145</td>\n",
       "      <td>8.041560</td>\n",
       "      <td>...</td>\n",
       "      <td>5.846897</td>\n",
       "      <td>6.589755</td>\n",
       "      <td>10.269227</td>\n",
       "      <td>7.748888</td>\n",
       "      <td>6.293694</td>\n",
       "      <td>8.436834</td>\n",
       "      <td>3.184355</td>\n",
       "      <td>5.008725</td>\n",
       "      <td>7.046908</td>\n",
       "      <td>10.482143</td>\n",
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       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>3</td>\n",
       "      <td>10.704805</td>\n",
       "      <td>6.485646</td>\n",
       "      <td>6.094978</td>\n",
       "      <td>9.212460</td>\n",
       "      <td>9.218975</td>\n",
       "      <td>5.511615</td>\n",
       "      <td>6.541559</td>\n",
       "      <td>3.438830</td>\n",
       "      <td>8.264806</td>\n",
       "      <td>...</td>\n",
       "      <td>9.220842</td>\n",
       "      <td>6.866817</td>\n",
       "      <td>8.604499</td>\n",
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       "      <td>2.974504</td>\n",
       "      <td>4.354122</td>\n",
       "      <td>6.664799</td>\n",
       "      <td>9.311126</td>\n",
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       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>3</td>\n",
       "      <td>6.450710</td>\n",
       "      <td>5.612284</td>\n",
       "      <td>5.766023</td>\n",
       "      <td>10.130702</td>\n",
       "      <td>7.201212</td>\n",
       "      <td>6.760974</td>\n",
       "      <td>8.377978</td>\n",
       "      <td>5.779441</td>\n",
       "      <td>8.114371</td>\n",
       "      <td>...</td>\n",
       "      <td>5.915235</td>\n",
       "      <td>6.767851</td>\n",
       "      <td>9.653469</td>\n",
       "      <td>6.974722</td>\n",
       "      <td>5.489107</td>\n",
       "      <td>6.878120</td>\n",
       "      <td>3.190270</td>\n",
       "      <td>4.028819</td>\n",
       "      <td>6.493181</td>\n",
       "      <td>10.290373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>3</td>\n",
       "      <td>7.456670</td>\n",
       "      <td>5.587341</td>\n",
       "      <td>5.885478</td>\n",
       "      <td>8.975262</td>\n",
       "      <td>7.278170</td>\n",
       "      <td>6.600490</td>\n",
       "      <td>8.601761</td>\n",
       "      <td>7.847652</td>\n",
       "      <td>9.493311</td>\n",
       "      <td>...</td>\n",
       "      <td>6.543639</td>\n",
       "      <td>6.514000</td>\n",
       "      <td>9.727747</td>\n",
       "      <td>6.188328</td>\n",
       "      <td>5.823914</td>\n",
       "      <td>3.935959</td>\n",
       "      <td>3.396742</td>\n",
       "      <td>4.461881</td>\n",
       "      <td>6.473156</td>\n",
       "      <td>9.652443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>3</td>\n",
       "      <td>8.118853</td>\n",
       "      <td>5.633060</td>\n",
       "      <td>6.214792</td>\n",
       "      <td>9.423301</td>\n",
       "      <td>7.472433</td>\n",
       "      <td>4.514391</td>\n",
       "      <td>7.105446</td>\n",
       "      <td>6.637282</td>\n",
       "      <td>8.415955</td>\n",
       "      <td>...</td>\n",
       "      <td>5.923473</td>\n",
       "      <td>6.450032</td>\n",
       "      <td>9.062178</td>\n",
       "      <td>8.846653</td>\n",
       "      <td>5.697126</td>\n",
       "      <td>4.662247</td>\n",
       "      <td>2.993329</td>\n",
       "      <td>6.269305</td>\n",
       "      <td>6.678276</td>\n",
       "      <td>9.969752</td>\n",
       "    </tr>\n",
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       "<p>130 rows × 301 columns</p>\n",
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       "1       0    8.319513      9.980202      6.826210      7.493678    7.775287   \n",
       "2       0    7.261455      8.212629      6.270775      8.045542    8.177457   \n",
       "3       0    5.883377      6.236439      6.029005      8.071778    5.224647   \n",
       "4       0    6.665956      8.035231      7.614323      7.692385    7.442830   \n",
       "..    ...         ...           ...           ...           ...         ...   \n",
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       "127     3    6.450710      5.612284      5.766023     10.130702    7.201212   \n",
       "128     3    7.456670      5.587341      5.885478      8.975262    7.278170   \n",
       "129     3    8.118853      5.633060      6.214792      9.423301    7.472433   \n",
       "\n",
       "     1553654_at  1554018_at  1554507_at  1554544_a_at  ...  242134_at  \\\n",
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       "..          ...         ...         ...           ...  ...        ...   \n",
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       "\n",
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       "2     6.614429   8.047375   7.003226   6.268696   3.411819   3.078852   \n",
       "3     5.891748   5.999268   5.481434   6.353627   5.667723   3.429725   \n",
       "4     6.688329   6.308003   6.349058   6.409389   3.632523   3.294988   \n",
       "..         ...        ...        ...        ...        ...        ...   \n",
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       "126   6.866817   8.604499   7.580293   5.951765   4.060489   2.974504   \n",
       "127   6.767851   9.653469   6.974722   5.489107   6.878120   3.190270   \n",
       "128   6.514000   9.727747   6.188328   5.823914   3.935959   3.396742   \n",
       "129   6.450032   9.062178   8.846653   5.697126   4.662247   2.993329   \n",
       "\n",
       "     244503_at  244751_at  53071_s_at  \n",
       "0     3.627346   6.001562    8.183152  \n",
       "1     4.951005   5.518132    9.191431  \n",
       "2     3.624622   5.927809    9.263549  \n",
       "3     3.859270   5.952769   10.339334  \n",
       "4     3.604101   5.653083    8.723818  \n",
       "..         ...        ...         ...  \n",
       "125   5.008725   7.046908   10.482143  \n",
       "126   4.354122   6.664799    9.311126  \n",
       "127   4.028819   6.493181   10.290373  \n",
       "128   4.461881   6.473156    9.652443  \n",
       "129   6.269305   6.678276    9.969752  \n",
       "\n",
       "[130 rows x 301 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "991fcf6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Count and plot the number of samples of five subtypes.\n",
    "def count_plot(label, title):\n",
    "    ax = sns.countplot(x=label)\n",
    "    for p in ax.patches:\n",
    "       ax.annotate('{:.1f}'.format(p.get_height()), (p.get_x()+0.25, p.get_height()+0.01))\n",
    "    plt.title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f4757a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.iloc[:,1:]\n",
    "y = df.iloc[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35f929f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "subtype_counts = count_plot(y, \"Five different subtype data counts\")\n",
    "subtype_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f626c2de",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(x)\n",
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c63894cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mean centering ((x-mean)/std)\n",
    "# it is the same as standardization\n",
    "x = np.divide((x-x.mean(0)),x.std(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa651150",
   "metadata": {},
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "efc2ba0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3-fold Cross Validation\n",
    "kf = KFold(n_splits=3, shuffle=True)\n",
    "\n",
    "# four classification methods\n",
    "model_KNN = KNeighborsClassifier(n_neighbors = 5)\n",
    "model_LG = LogisticRegression(solver = 'liblinear',penalty = 'l2')\n",
    "nb = GaussianNB()\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "# store accuracy\n",
    "KNN_accuracy = []\n",
    "LG_accuracy = []\n",
    "nb_accuracy = []\n",
    "rf_accuracy = []\n",
    "\n",
    "# store F1\n",
    "KNN_F1 = []\n",
    "LG_F1 = []\n",
    "nb_F1 = []\n",
    "rf_F1 = []\n",
    "\n",
    "# store confusion matrix\n",
    "cm_KNN = []\n",
    "cm_lg = []\n",
    "cm_NB = []\n",
    "cm_RF = []\n",
    "\n",
    "for train_index, test_index in kf.split(x):\n",
    "    x_train, x_test = x[train_index], x[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    \n",
    "    # SMOTE: control for imbalanced class\n",
    "    sm = SMOTE()\n",
    "    x_train, y_train = sm.fit_resample(x_train, y_train)\n",
    "    \n",
    "    #After doing the cross validation and SMOTE, we do the KNN.\n",
    "    model_KNN.fit(x_train, y_train)\n",
    "    y_pred_KNN = model_KNN.predict(x_test)\n",
    "    KNN_accuracy.append(metrics.accuracy_score(y_pred_KNN, y_test) * 100)\n",
    "    KNN_F1.append(metrics.f1_score(y_pred_KNN, y_test,average='macro') * 100)\n",
    "    cm_knn_i = metrics.confusion_matrix(y_test, y_pred_KNN)\n",
    "    cm_KNN.append(cm_knn_i)\n",
    "    \n",
    "    #Logistic Regression with L2 penalty\n",
    "    model_LG.fit(x_train, y_train)\n",
    "    y_pred_LG = model_LG.predict(x_test)\n",
    "    LG_accuracy.append(metrics.accuracy_score(y_pred_LG, y_test) * 100)\n",
    "    LG_F1.append(metrics.f1_score(y_pred_LG, y_test,average='macro') * 100)\n",
    "    cm_LG_i = metrics.confusion_matrix(y_test, y_pred_LG)\n",
    "    cm_lg.append(cm_LG_i)\n",
    "    \n",
    "    #Naive Bayes\n",
    "    nb.fit(x_train, y_train)\n",
    "    y_pred_nb = nb.predict(x_test)\n",
    "    nb_accuracy.append(nb.score(x_test, y_test) * 100)\n",
    "    nb_F1.append(metrics.f1_score(y_pred_nb, y_test,average='macro') * 100)\n",
    "    cm_nb_i = metrics.confusion_matrix(y_test, y_pred_nb)\n",
    "    cm_NB.append(cm_nb_i)\n",
    "    \n",
    "    #Random Forest\n",
    "    rf.fit(x_train, y_train)\n",
    "    rf_pred = rf.predict(x_test)\n",
    "    rf_accuracy.append(metrics.accuracy_score(rf_pred, y_test) * 100)\n",
    "    rf_F1.append(metrics.f1_score(rf_pred, y_test,average='macro') * 100)\n",
    "    cm_rf_i = metrics.confusion_matrix(y_test, rf_pred)\n",
    "    cm_RF.append(cm_rf_i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "16138880",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate mean accuracy and F1 for each classifier\n",
    "KNN_acc_score = statistics.mean(KNN_accuracy)\n",
    "KNN_F1_score = statistics.mean(KNN_F1)\n",
    "\n",
    "LG_acc_score = statistics.mean(LG_accuracy)\n",
    "LG_F1_score = statistics.mean(LG_F1)\n",
    "\n",
    "nb_acc_score = statistics.mean(nb_accuracy)\n",
    "nb_F1_score = statistics.mean(nb_F1)\n",
    "\n",
    "rf_acc_score = statistics.mean(rf_accuracy)\n",
    "rf_F1_score = statistics.mean(rf_F1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "720d8b23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----KNN-----\n",
      "The accuracy score for KNN is  90.78576462297393\n",
      "The F1 score for KNN is  91.10062294161216\n",
      "\n",
      "-----Logistic Regression-----\n",
      "The accuracy score for Logistic Regression is  96.15926708949965\n",
      "The F1 score for Logistic Regression is  95.79871746704863\n",
      "\n",
      "-----Naive Bayes-----\n",
      "The accuracy score for Naive Bayes is  83.05144467935166\n",
      "The F1 score for Naive Bayes is  80.11319609817942\n",
      "\n",
      "-----Random Forest-----\n",
      "The accuracy score for Random Forest is  88.42494714587737\n",
      "The F1 score for Random Forest is  88.61533564474742\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"-----KNN-----\")\n",
    "print(\"The accuracy score for KNN is \",KNN_acc_score)\n",
    "print(\"The F1 score for KNN is \",KNN_F1_score)\n",
    "print()\n",
    "\n",
    "print(\"-----Logistic Regression-----\")\n",
    "print(\"The accuracy score for Logistic Regression is \",LG_acc_score)\n",
    "print(\"The F1 score for Logistic Regression is \",LG_F1_score)\n",
    "print()\n",
    "\n",
    "print(\"-----Naive Bayes-----\")\n",
    "print(\"The accuracy score for Naive Bayes is \",nb_acc_score)\n",
    "print(\"The F1 score for Naive Bayes is \",nb_F1_score)\n",
    "print()\n",
    "\n",
    "print(\"-----Random Forest-----\")\n",
    "print(\"The accuracy score for Random Forest is \",rf_acc_score)\n",
    "print(\"The F1 score for Random Forest is \",rf_F1_score)\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4f2b7767",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate mean confusion matrix\n",
    "cm_knn = np.mean(cm_KNN,axis = 0)\n",
    "cm_LG = np.mean(cm_lg,axis = 0)\n",
    "cm_nb = np.mean(cm_NB,axis = 0)\n",
    "cm_rf = np.mean(cm_RF,axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bcaed0bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'ependymoma'),\n",
       " Text(0, 1.5, 'glioblastoma'),\n",
       " Text(0, 2.5, ' medulloblastoma'),\n",
       " Text(0, 3.5, 'pilocytic_astrocytoma'),\n",
       " Text(0, 4.5, 'normal')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(15, 12))\n",
    "sns.set(font_scale = 1.3)\n",
    "sns.heatmap(cm_knn, annot = True,linewidths = 1, cmap = \"YlOrBr\", ax = ax)\n",
    "cell_type = ['ependymoma', 'glioblastoma',' medulloblastoma', 'pilocytic_astrocytoma','normal']\n",
    "ax.set_title(\"Confusion Matrix for KNN\")\n",
    "ax.set_xlabel(\"Predicted class\")\n",
    "ax.set_ylabel(\"Actual class\")\n",
    "ax.set_xticklabels(cell_type)\n",
    "ax.set_yticklabels(cell_type)\n",
    "#fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0cd01dc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'ependymoma'),\n",
       " Text(0, 1.5, 'glioblastoma'),\n",
       " Text(0, 2.5, ' medulloblastoma'),\n",
       " Text(0, 3.5, 'pilocytic_astrocytoma'),\n",
       " Text(0, 4.5, 'normal')]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QoJiYGN24cUMFChRwPJYuXeqIr1ixYtq3b1+ixM6Z6V1FihTRkSNHlC9fPsd5M2TIoFGjRunIkSOKjIzUyJEjdf78eb322msKDg7Wxo0bFRUVpS1btujs2bOOe6vefPNNffzxx1q+fLlOnjyp3377LUl/AQEBSf69h4SEKCoqKsnUTACwApIcwELeffdd+fn5qXnz5lq+fLmOHz+u0NBQjR8/XhMnTtR7770nX19fpU+fXi1atND06dMdx61YsUKTJ09Ws2bN5O3t/cgxlC1bVkuXLtX+/ft1+PBh9e3bN9HyzLdu3dK4ceO0atUqnT59Wrt27dKePXuSvXemRo0aKlGihPr27atffvlFx44d08SJE7V582anqz7OqFOnjux2uyZPnpzsVDVJ8vHxUXh4uHbu3KnTp09r7dq1Gjp0qKT/Var+Wfr6wIEDioqKcqrvDRs2aPny5Ro5cqQqV66sTp06aezYsTp69KhTr3/Sn9GNGzf0008/JXlcvnxZjRs3VrZs2dSjRw/9/vvvCg8P15AhQ3T48GG1bdtWkvTOO+9oy5YtmjFjhv7++29t2LDBsQhAcgnLg8bDP59pWFiYbty4keT177zzjr7//nvNnj1bx48f1+bNmzV9+nTHfTXOcnNz0+jRo3Xr1i0NHz5c0v8+2169emnbtm06efKkpk2bpjlz5jj+I6BDhw46duyYRo0apaNHj2rbtm2O198vQWvfvr2OHTumQYMG6fDhwzp48KB69OihkJAQFSlSROnTp9fvv/+uIUOG6I8//tCpU6e0bNkyxcTEqGzZssqaNat++OEHffDBBwoLC9OJEye0ZMkSubu7q2TJkkn6e/vtt/XTTz/pk08+0bFjx7Rjxw717t1bAQEBqlmz5kN9VgCQGjBdDbAQLy8vff7555o/f77mzZunkSNHys3NTf7+/po4caJeeuklx7H9+/dXlixZNGXKFJ0/f165c+fW22+/rQ4dOjxWDMOGDdOHH36oli1bytvbW23btnUsjyzdWU735s2bmjx5ss6dO6dMmTKpfv366t27d5Jzubq6au7cuRo/frx69eqlmzdvqkiRIpo8eXKi9/K4vLy8VLduXW3YsOGe+6N0795dly5dUteuXRUfH6+CBQuqd+/emjRpkv744w/Vq1dPVatWVYUKFdSmTRu9//77yf6x+W8REREaMmSImjZtqtq1a0u680f7Dz/8oN69e2vJkiUPnD70pD+jY8eOJZsczZ49W7Vq1dKiRYs0duxYvf3224qLi1OpUqU0f/58lS9fXpJUtWpVjR07VtOmTdOUKVPk6+urwMBATZ48Odn38qDxUKRIEb344ovq16+fmjdvrjZt2iR6fe3atTV27FjNnj1bEydOVI4cORQUFKR33nnnod97kSJF9M4772jy5Mlat26dXnnlFc2dO1cTJkxQnz59FBUVpQIFCmjcuHFq3LixpDv3sEyfPl0TJkzQV199JR8fH7Vs2VJjx46978+udOnS+uyzzzRp0iS98cYb8vT0VPny5fX55587/pNhypQpGjNmjN555x3duHFDhQsX1kcffaQKFSpIulNdHDdunFq3bq3bt2+rWLFimjVrVrKLS7z44ouaPHmypk+frrlz5ypjxoyqV6+e3n//faaoAbAkm/1RN0sAAOAuu3btUqZMmVSkSBFH28qVK/XBBx/o999/T1TVs4L9+/fLbrc7VvaTpN27d6tly5b66aefEi3SAAAwDtPVAABPzI4dO9SuXTtt27ZNZ86c0fbt2x3TAK2W4EjSkSNHFBQUpA0bNujMmTPas2ePgoODVbVqVRIcADARlRwAwBMTExOjjz76SBs2bNDFixeVPXt2vfrqq+rWrdsDV5lLjex2u2bMmKHly5fr7Nmzypw5s+rUqaNevXo5tWcVAODpIMkBAAAAYClMVwMAAABgKSQ5AAAAACzFeneBAgAAAM+wTkXMvwdy5uHoBx/0FFkmyUkJP0ykLHf+cZ0xOwykOD5iXCApH8n+t9lBIKWxFRTXCyTmY3YAcJJlkhwAAAAA3I8i8RkAAAAAsBiSHAAAAACWwnQ1AAAAwEJsNrMjMB+VHAAAAACWQiUHAAAAsBCqGHwGAAAAACyGJAcAAACApTBdDQAAALAQFh6gkgMAAADAYkhyAAAAAFgK09UAAAAAC6GKwWcAAAAAwGKo5AAAAAAW4sLCA1RyAAAAAFgLSQ4AAAAAS2G6GgAAAGAhzFajkgMAAADAYqjkAAAAABbiYrObHYLpqOQAAAAAsBSSHAAAAACWwnQ1AAAAwEJYeCCFVXJu3rxpdggAAAAAUjnDKznnzp3T5MmTFR4ervj4eEmS3W5XTEyMTp48qX379hkdEgAAAGAZLpRyjK/kDB48WLt371blypUVFhamatWqKW/evAoPD1fv3r2NDgcAAACAxRheydmzZ4/mzJmj8uXLa9u2bapTp46ee+45TZ8+XVu3blVgYKDRIQEAAACwEMMrOQkJCcqdO7ckydfXVwcPHpQkNWzYUAcOHDA6HAAAAMBSXFLAw2yGx+Dv768tW7Y4vt61a5ck6cKFC457dAAAAADgURk+Xa1bt27q0qWLXFxc9Nprr2nGjBlq1aqV/vrrL9WqVcvocAAAAABLsbHwgPFJTu3atbV+/XrFx8crZ86c+uqrr7RixQrVrFlTrVu3NjocAAAAABZjymagefPmdXxdpEgR9e3b14wwAAAAAFiQ4UlOWFiYxo8fr/DwcMXExCR5fseOHUaHBAAAAFhGSrjx32yGJzn9+vVTmjRp9M4778jLy8vo7gEAAABYnOFJzvHjx7Vs2TL5+fkZ3TUAAACAZ4Dh1azKlSsrNDTU6G4BAACAZ4LNZv7DbIZXcoYNG6ZmzZrp+++/V548eWS761NgEQIAAAAAj8PwJGfChAm6du2azp49qytXriR67u6EBwAAAMDDSa0LD8TExKhJkybq3bu3XnjhhSTPT5w4UStXrtTmzZsfeC7Dk5xNmzZp9uzZqlq1qtFdAwAAAEiBbt++rffff1/h4eHJPh8SEqJZs2YpZ86cTp3P8EQvZ86c8vb2NrpbAAAAACnQwYMH1axZM505cybZ52NjY9W/f3+VLVvW6XManuQMHDhQgwcP1g8//KCwsDCFh4cnegAAAAB4dC428x8P49dff1XdunW1ZMmSZJ+fNm2a8uXLp5dfftnpcxo+Xa1Tp06SpK5duzrabDab7Ha7bDYbK68BAAAAqdz169d1/fr1JO0ZM2ZUxowZE7W1b9/+nucJCQnRkiVLtHLlSm3YsMHp/k25JwcAAADA05ESlvJasGCBpkyZkqS9a9eu6tatm1PniImJUf/+/dWnTx9lz579ofo3PMnJkyePJOnKlSv666+/lJCQoEKFCilr1qxGhwIAAADgKQgKClKTJk2StN9dxbmfadOmKUeOHMme50EMT3Kio6M1atQoLVu2TAkJCZIkFxcXNWjQQKNHj5aHh4fRIQEAAAB4gpKblvawVq1apQsXLjgWHIiNjVVcXJzKli2r2bNnq0KFCvd8reFJzvjx4/XLL79oxowZKleunBISEvT7779rxIgRmjhxovr06WN0SAAAAIBlpNZ9cu62cOFCxcXFOb5ftWqVli5dqoULFz5wKWnDk5w1a9bo448/TrRPzvPPPy9PT0/16dOHJAcAAACA4zaXf3h7e8vNzU0FChR44GsNT3Li4uKUI0eOJO3Zs2dXZGSk0eEAAAAAlmJLCSsPmMzwalb58uU1bdo0xcbGOtpiYmI0ffr0h9rgBwAAAIC1HDp0SC+88EKyzwUGBmrz5s1OncfwSk7fvn0VGBio559/XkWLFpUkhYWFyc3NTXPnzjU6HAAAAAAWY3iSU7hwYa1du1arV6/W0aNH5enpqfr166tRo0ZKkyaN0eEAAAAAlmKVhQceh+FJjiRlzpxZrVq1MqNrAAAAABZneJITFham8ePHKzw8XDExMUme37Fjh9EhAQAAAJbhwsIDxic5/fr1U5o0afTOO+/Iy8vL6O4BAAAAWJzhSc7x48e1bNky+fn5Gd01AAAAgGeA4fclVa5cWaGhoUZ3CwAAADwTbCngYTbDKznDhg1Ts2bN9P333ytPnjyy3bVbUd++fY0OCQAAAICFGJ7kTJgwQdeuXdPZs2d15cqVRM/dnfDgwSq++qbeGDhefasXcLTVf7u3mvYeleTYIS+XVsSxQ0aGBxPZ7XbNmLFIS5as1uXLV1WyZIAGDeqqEiWKmB0aTMS4eHbY7XbNmLlYS5as1eXL11SypL8GDXxXJUr4J3v8qVPnNG7cbO3c+YdiYuNUsWIp9e7VXkWKFHQcs3HjNk2b/qWOHTspb++MevXVF9Sje2t5enoY9K5gJK4XqRcLD5iQ5GzatEmzZ89W1apVje7acio0aKagMbN18/rVRO15A0rp9w3favGHPRK137h8wcDoYLYZMxZp3ryvNWpUHxUsmFczZ36ptm17a+3a+cqWLYvZ4cEkjItnx4yZizVv3jcaNbKnChbMo5mzlqhtu/5au2aOsmXzTnRsTEys2rbrLx+fnJo3b4w8PNz16cQFCmrTV99vnKf06dNp+/bf1eO9UXr/vTZ65ZVaOn78jPoPmKDIG1EaPrzHPaJAasb1AqmZ4ffk5MyZU97e3g8+EPeUzjurOnyyUG3HzdO5ZCozeQJK6WTIPl2/GJHoYU9IMCFamCE2Nk5z536lzp1b6cUXa8rfv5DGjOmvtGnTaPHiVWaHB5MwLp4dd37Wy9T53ZZ68cXq8vcvqDHBvf/7s/4uyfHnzl1Q8eJ+Ch7dS8WL+8nPr4C6dmmpy5evKSzsmCQpIuKS2gQ10dtv/5/y5cutGjXKq0GD2tq+Y6/Rbw8G4HqB1M7wSs7AgQM1ePBgvfPOO8qbN6/c3BKHwKprD+bjW0zunl4a1bSKytVvolrNOzqec3V3V65CRXT2KIs7PMtCQ8N140aUqlQp52hzc3NVhQql9dtvf5gYGczEuHh2hIYdvfOzrvqco+3Oz7qUftu1P8nx+fP7aOKngx3fX7x4RZ/N+0Y5cmRV0aKFJUlNmrzoeN5ut2v//kP64fvteuGFyk/vjcA0XC9SN5vsZodgOsOTnE6dOkmSunbt6miz2Wyy2+2y2WysvOaEI7u36cjubZKkcvWbJHout18xubq7q2TtV/Ta+8PllT6D/t6/W99OGKyIvw6bES5MEBFxZ2pi7tw5ErXnyJFVf/wRYkZISAEYF8+OiHMXJUm5c2VP1H7nZ33/37M93hup9et/loeHu6ZOHar06dMlej4yMkqVq7yhuLh4FSvmq+7dWz/Z4JEicL1AamfKPTl4evIUKSlJio+L09yereWVPqMadB6gvl/9qOGNyuva+bMmRwgj3Lx5W5Lk4eGeqN3T00PR0TFmhIQUgHHx7Lh5614/a3dFR8fe97Wd322p9u3f0BdfrFSXLh9q4efj9dxzxRzPu7q66uslE3X+/CWNDp6htm37a+nSSXJ1dX3ybwSm4XqB1M7wJGfWrFlq3Lixypcvb3TXz4SdK7/UwZ82KvLKRUfb9M57FPxjuGq80VZrpo42MToYxcvrzkpHMTGxSpPGy9EeHR2jtGnTmBUWTMa4eHZ4ed7rZx2rtGm97vUySVJAQCFJ0pjg3jpw4LAWLlyRKMlJk8ZLJUr4q0QJf2XKlEEt3uqpHTv2qUYNfq9bCdeL1I3V1UxYeODatWvq0KGD6tSpo48//lhHjhwxOgTL+3eCI0kxt27q4qm/5J07r0kRwWg+PrkkSRERicfC+fOXlOuu6St4djAunh0+Pjkl3Vks4N/u/KyzJTn+9OkIrVnzY6I2FxcX+fsXdJxj164D2rs38TSlokV9/9tP4jGF1I/rBVI7w5OcTz/9VNu3b9f777+vI0eOqGnTpmrcuLHmzp2rc+fOGR2O5fyn53CN2nRINpf//Wi90mdUzoL+OnOYObTPioCAwsqYMb127vzfqkdxcfHavXu/KlV6zrzAYCrGxbMjIKDQf3/W/7tB/M7P+oAqVSqT5Pjw8OPq2StYoaFHHW2xsXE6ePCI/P+7T84Xi1bqw+FTEr1u3747v1f8/7WXDqyB60Xq5pICHmYzfLqaJKVJk0aNGjVSo0aNFBkZqfnz52vSpEn66KOPVKFCBbVo0UKvvPKKGaGler9v+Fb12r2n1qNmaP3sj5QmfUY16TVCUVev6Oev55odHgzi4eGuoKBmmjjxM2XN6i1//4KaOfNL3bp1W82bNzI7PJiEcfHs8PBwV1DrJpo4aYGyZsssf78CmjlriW7dilbz/2sgSbpw4bLSpk2jdOnSqFq1cipRwl8DBn6kYUO7KU0aL02bvkg3bkSqfbtmkqR2bZvprZY9FRw8Uy1aNNTx46c17MPJqlevmkqXCjDz7eIp4HqB1M6UJEeSDh8+rLVr12rdunU6c+aMatWqpYYNG+r8+fMaPXq0tm/frhEjRpgVXqp14uBeTWrXUI17DNGAZdtkT0hQyLYfNL9/R8XevmV2eDBQ586tFB8fr9GjpyoyMkqlSgVo/vyPlCVLZrNDg4kYF8+Ozp3fUnxCvEaPnnHnZ10yQPPnjXH8rGvUbKGuXQLVrVsrubu7afaskRo/fo46dxmmmzdvqUKFUvpy0cfKm/fOtKUyZYpqzpzR+vST+Vry9RqlT59OjRvX1Xs9WF3NqrheIDWz2e12QxfSnjp1qtatW6djx46pbNmyaty4sV5++WVlypTJcczq1as1ZMgQ7d3r/AZjnYp4Po1wkYrNPBwt6YzZYSDF8RHjAkn5SPa/zQ4CKY2toLheIDEfswNwyqTy7g8+6Cnrvuf+Kzk+bYZXctauXeuYqpYnT55kjylatKiGDx9ucGQAAAAArMDwJGfNmjUPPMbf31/+/v4GRAMAAABYS0q48d9shiQ5PXr0cPrYiRMnPsVIAAAAAFidIYle2rRpHQ8XFxdt2LBBZ86cUdasWZUzZ05dvHhR33//vTJkyGBEOAAAAAAszJBKTnBwsOPrnj17qnPnzurevXuiY2bOnKk9e/YYEQ4AAABgWTab2RGYz/Ape5s3b1bjxo2TtNevX187d+40OhwAAAAAFmN4kpM3b15t2LAhSfvy5ctVuHBho8MBAAAALMUlBTzMZvjqan369FGXLl20adMmFStWTHa7XQcOHNDff/+t2bNnGx0OAAAAAIsxPNGqXbu2Vq5cqfLly+vcuXOKiIhQzZo1tXr1alWoUMHocAAAAABYjOGVHEny9fVVv379zOgaAAAAsDQXFh4wPsm5cuWKZs6cqf379ys2NlZ2uz3R88uWLTM6JAAAAAAWYniSM2jQIO3bt0+NGzdW+vTpje4eAAAAsDQKOSYkObt27dLUqVNVqVIlo7sGAAAA8AwwfOGBDBkyKGPGjEZ3CwAAAOAZYXiS061bN40YMUIHDhzQ9evXdevWrUQPAAAAAI/OxWb+w2yGT1f76KOPdPXqVb355pvJPh8aGmpwRAAAAACsxPAk5+OPPza6SwAAAOCZYUsBlRSzGZ7k/LPgQEJCgk6fPq3cuXMrISFBHh4eRocCAAAAwIIMvycnPj5eEyZM0HPPPaf69evr7Nmz6tOnj3r37q3bt28bHQ4AAAAAizE8yZkyZYo2b96s6dOny9PTU5LUokUL7du3T2PHjjU6HAAAAMBSXFLAw2yGx7B69WoNGzZM1atXd7RVqVJFwcHB2rhxo9HhAAAAALAYw5OcixcvKleuXEnavb29dfPmTaPDAQAAAGAxhic55cuX1+LFixO1xcTEaNq0aSpXrpzR4QAAAACWYvYeOc/kPjmDBg1Shw4d9OOPPyomJkYDBgzQ33//LVdXV82dO9focAAAAABYjOFJTuHChbV+/XqtWrVKR48eVXx8vBo1aqTGjRsrTZo0RocDAAAAWAr75JiQ5EiSh4eH6tSpo/z588vFxUX+/v4kOAAAAACeCMOTnMjISA0cOFA//PCDEhIS7gTh5qbXXntNQ4cOZVNQAAAAAI/F8IUHhg4dqmPHjumzzz7Tnj17tGvXLs2YMUO///67xo0bZ3Q4AAAAgKWYvehASlh4wPAk58cff1RwcLCqVKmidOnSKUOGDKpRo4ZGjRql1atXGx0OAAAAAIsxfLpapkyZFBUVlaTdxcVFnp6eRocDAAAAWEoKKKSYzvAk57333tPgwYPVs2dPlS9fXm5ubgoJCdGYMWP01ltvKTw83HGsn5+f0eEBAAAASOVsdrvdbmSHRYsW/V/n/13f7t8h2Gw22e122Ww2hYaGOn3eTkWoAiGxmYejJZ0xOwykOD5iXCApH8n+t9lBIKWxFRTXCyTmY3YATllUzd3sENRye6yp/Rteydm0aZMk6dy5c/rrr7/03HPPKTIyUtmzZzc6FAAAAMByXGyG1jBSJMOTnCxZsmjgwIFat26dXFxctGHDBn388ce6evWqpkyZoixZshgdEgAAAAALMXx1tXHjxuncuXNat26dY6GBXr16KTo6WqNHjzY6HAAAAMBSzF4++plcQnrTpk0aMGCAChUq5Gjz9fXVhx9+qJ9//tnocAAAAABYjOFJTmRkpNKnT580EBcXxcXFGR0OAAAAAIsxPMmpUaOGpk2bliihuXz5ssaOHavq1asbHQ4AAABgKTab+Q+zGZ7kDB48WCdOnFDlypV1+/ZttW3bVs8//7xu3LihQYMGGR0OAAAAAIsxfHW1HDly6Ouvv9aOHTt07NgxxcXFydfXV9WrV3fsmwMAAADg0RhexUiBDE9y/lG1alVVrVrVrO4BAAAAWBSJHgAAAABLMa2SAwAAAODJ4w4QKjkAAAAALIYkBwAAAIClMF0NAAAAsBAXpqtRyQEAAABgLVRyAAAAAAuhkkMlBwAAAIDFkOQAAAAAsBSmqwEAAAAWwmw1KjkAAAAALIZKDgAAAGAhLDxAJQcAAACAxZDkAAAAALAUkhwAAADAQmw28x+PIiYmRq+++qq2bNniaDt37py6d++uypUrq3r16howYICuXbv2wHOR5AAAAAAw1e3bt9WjRw+Fh4c72hISEtS5c2dFRUVpwYIFmj59ug4dOqR+/fo98HwsPAAAAABYSGpbeODgwYPq16+fXF1dE7WHhobq4MGD2rZtm7Jnzy5JGjRokN566y1dv35dGTNmvOc5qeQAAAAAMM2vv/6qunXrasmSJYnafXx8NHv2bEeCI0m2/86Fi46Ovu85qeQAAAAAeKKuX7+u69evJ2nPmDFjkgpM+/btkz2Ht7e3atWqlaht/vz5KliwYKLEJzmWSXJmHr5/NodnlY/ZASBFYlwgGbaCZkeAFInrBVKflDBVa8GCBZoyZUqS9q5du6pbt26PdM5Zs2Zp48aNmjlz5gOPtUySI50xOwCkOD460TGT2UEghck/+5pk/9vsMJDS2AoyLpCUraAUH2Z2FEhJXIuaHUGqERQUpCZNmiRpv999NPczdepUTZo0SUOGDFHt2rUfeLyFkhwAAAAAj7qE85OU3LS0RzVq1CgtXLhQw4YNU4sWLZx6DUkOAAAAgBRp4sSJ+uKLLxQcHJxsZeheSHIAAAAApDhhYWGaMWOG2rVrpxo1aujChQuO57y9veXmdu9UhiQHAAAAsJDUtk/OvWzYsEEJCQmaM2eO5syZk+i51atXq0iRIvd8LUkOAAAAgBTh0KFDjq979OihHj16PNJ5SHIAAAAAC0kJCw+YLSUsow0AAAAATwxJDgAAAABLYboaAAAAYCFUMfgMAAAAAFgMSQ4AAAAAS2G6GgAAAGAhLja72SGYjkoOAAAAAEuhkgMAAABYCPvkUMkBAAAAYDEkOQAAAAAshelqAAAAgIW4MF2NSg4AAAAAa6GSAwAAAFgIhRwqOQAAAAAshiQHAAAAgKUwXQ0AAACwEBYeoJIDAAAAwGKo5AAAAAAWQiWHSg4AAAAAiyHJAQAAAGAppkxXu337ts6cOaOEhARJkt1uV0xMjEJCQvTGG2+YERIAAABgCTamqxmf5Kxbt04ffPCBoqKikjyXM2dOkhwAAAAAj8Xw6WoTJ05UgwYNtGbNGmXMmFFLlizRjBkzlDt3bnXv3t3ocAAAAABLcUkBD7MZHsOpU6fUvn17FS5cWMWLF9elS5dUu3ZtDR06VPPmzTM6HAAAAAAWY3iSky5dOsXFxUmSChUqpNDQUEmSv7+/Tp06ZXQ4AAAAACzG8CSnWrVqGjt2rM6cOaOyZctq7dq1OnPmjL777jt5e3sbHQ4AAABgKTab+Q+zGZ7kDBw4ULGxsdq8ebNeeeUVZcmSRXXq1NGkSZPUpUsXo8MBAAAAYDGGr66WPXv2RPfefP755woPD1eGDBmUM2dOo8MBAAAALMWWEkopJjNln5xz587pr7/+UkxMTKL2sLAw1a5d24yQAAAAAFiE4UnOvHnzNH78eMdGoP9ms9kcCxEAAAAAwKMwPMmZNWuWunTpog4dOsjT09Po7gEAAABLs6WEjWpMZspH0KBBAxIcAAAAAE+F4UlOUFCQpk6dqqioKKO7BgAAAPAMMHy6Wq1atfTZZ5+pQoUKypgxo1xcEudZO3bsMDokAAAAwDJYXc2EJKdv377y9fVVo0aNlCZNGqO7BwAAAGBxhic5J0+e1OrVq5U/f36juwYAAAAsj0KOCffkVKtWTfv27TO6WwAAAADPCMMrOWXLltXQoUO1fv165cuXT66urome79u3r9EhAQAAALAQw5Ocn3/+WSVLltSNGzcUEhKS6DlukgIAAAAeD39Tm5DkLFy40OguAQAAADxDDE9yJCkyMlLLli3T0aNHlZCQIF9fX7322mvKmjWrGeEAAAAAlkElx4SFB8LCwvTSSy9p/vz5unr1qq5cuaIFCxaoQYMGCg8PNzocAAAAABZjeCVn9OjRqlGjhkaNGiV3d3dJUmxsrAYOHKjg4GDNnTvX6JAAAAAAWIjhlZw//vhDnTp1ciQ4kuTu7q5OnTrp999/NzocAAAAwFJsNvMfZjM8ycmaNasiIiKStJ87d05p06Y1OhwAAAAAFmN4ktOoUSMNHjxYP/zwg86fP68LFy7o+++/15AhQ9SwYUOjwwEAAAAsxWazmf4wm+H35HTp0kUXLlxQjx49lJCQIElydXXVW2+9pV69ehkdDgAAAACLMTzJ8fDw0OjRozVgwAD99ddf8vDwUIECBZQmTRpdvHhR2bJlMzokAAAAABZi+HS1YsWK6fLly8qQIYNKly6tokWLKk2aNDp58qRefPFFo8MBAAAALMXsRQdSwGw1Yyo5y5Yt01dffSVJstvtat++vVxdXRMdc+nSJeXIkcOIcAAAAABYmCFJToMGDXTu3DlJ0p9//qkqVaooXbp0iY5Jly6dXnrpJSPCAQAAACzL5pICSikmMyTJSZs2rbp27SpJypMnj1599VV5eHgY0fUzwW63a8aMRVqyZLUuX76qkiUDNGhQV5UoUcTs0GCStJVel/ebwTrd+39jwNU7j7xbjJVXQC3ZE+J1c9c3urpsiOwxN02MFE+C3W7XjJmLtWTJWl2+fE0lS/pr0MB3VaKEf7LHnzp1TuPGzdbOnX8oJjZOFSuWUu9e7VWkSEHHMRs3btO06V/q2LGT8vbOqFdffUE9ureWpyfX7tSCcYHk3BkXS7Xk6w26fOW6Spbw06CBHVSiuO89X/P5wtX6YtEanYu4pAL5c6tHt7dUr14VSVKroEH6bdefyb6uW9cW6tq5+VN5H8CDGH5PTpMmTbRt2zZduHBBkrRo0SIFBgZqzJgxio6ONjocS5gxY5HmzftagwZ11TffzJCPT061bdtbFy9eNjs0mCBthabK2mZa4kYXV+V4b7lcPDMoYnwDXZzVVmlKvaQsrSeaEySeqBkzF2vevG80aOC7+mbZ5DvXgHb9dfHilSTHxsTEqm27/rp2PVLz5o3R0q8nysPDXUFt+ioyMkqStH377+rx3ig1eKW21nw3S6NG9tSqVZs0atR0o98aHgPjAsmZMXOp5s1fqUEDO+ibrz+Sj092tW03RBcvXk32+Dlzl2v8Rwv0Tqc3tHrFJNWrV1k9eo7T/gNHJEmTJ/bXtq3zEz1ebVBT2bN7641m3GsN8xie5EydOlW9evXS6dOn9dtvv2nUqFHy9fXVzz//rODgYKPDSfViY+M0d+5X6ty5lV58sab8/QtpzJj+Sps2jRYvXmV2eDCQS/osytrxM2VtP1Ox5w4nei5tucZyy+Gri3PaK/bUn4oO/VGXF76ndJXflGvW/CZFjCfhzjVgmTq/21Ivvlhd/v4FNSa493+vAd8lOf7cuQsqXtxPwaN7qXhxP/n5FVDXLi11+fI1hYUdkyRFRFxSm6Amevvt/1O+fLlVo0Z5NWhQW9t37DX67eERMS6QnNjYOM2dt0Kd331TL9arKn///BozuofSpvXS4q/WJTn+1q1oTZvxtbp1baGmTeqqQIHc6tGtpcqVLaYdO/6QJGXOnEHZs3s7HvsPHNbaddv00fheypkjq9FvEf9l9qIDz8zCA/+2dOlSffrpp3ruuec0ePBglS9fXh9++KH279+vt99+W8OGDTM6pFQtNDRcN25EqUqVco42NzdXVahQWr/99oeJkcFo7rmLyubuqXMjayttucZKX7ud4znPItUVezpECTcuOtpuH/pZ9oR4eRWprqgdJ8wIGU9AaNjRO9eAqs852u5cA0rpt137kxyfP7+PJn462PH9xYtX9Nm8b5QjR1YVLVpYktSkyf/+99Vut2v//kP64fvteuGFyk/vjeCJYlwgOaFhf/33b4YyjrY746KEftuddMrZ7j0HFRV1S681ej5R+8IFo5I9/+3b0RoVPEevN62rypVKPdHYgYdleJJz5coVFSly5z6BH3/8UW3btpUkZcqUSTExMUaHk+pFRNyZ9pc7d+KV6XLkyKo//ggxIySYJPrIdkUf2X7nm3KNEz3n6u2juCunEr8gPlYJkZflmiWPQRHiaYg4dydxzZ0re6L2O9eA0Pu+tsd7I7V+/c/y8HDX1KlDlT594gVhIiOjVLnKG4qLi1exYr7q3r31kw0eTw3jAsmJiPhnXCTekzBHjiz6449DSY7/++8z8vLy0LlzF9W3/6c6fOS48ufLpc7v/p9q1yqf5Pivvt6gixev6r3ugU/nDcBptpRQSjGZ4dPV/P399dVXX+mLL77QxYsXVbduXd2+fVszZsxQiRIljA4n1bt587YkycPDPVG7p6eHoqNJGnGHi0c62eOSjgd77G3Z3L1MiAhPys1b97oGuCs6Ova+r+38bkstXTpJr7xSS126fKh9+xL/8evq6qqvl0zUjOkfKirqptq27a/4+Pgn+wbwVDAukJybN+/c+5zsuIhJOi4iI28qIcGu3n0/1v+9+ZLmzhqmChVKqNO7I/TrzsQVwfj4eH3++Wq92exFZc/u/fTeBOAkwys5/fr1U9euXXXt2jV16tRJBQsW1LBhw7Rt2zbNmjXL6HBSPS+vOyvaxMTEKk2a//2xGh0do7Rp05gVFlIYe+wt2dySrn5kc/eS/XaUCRHhSfHyvNc1IFZp094/gQ0IKCRJGhPcWwcOHNbChSv03HPFHM+nSeOlEiX8VaKEvzJlyqAWb/XUjh37VKNG0v/BRcrCuEByEv/N4Olov9e4cHN3U0xMrHr3ClL9l6pJkooXL6wDfx7RZ/NWqErl0o5jf915QKfPnFeL5q885XcBOMfwSk7FihW1Y8cO7dy5U++//74kqWvXrtqyZYuKFSv2gFfjbj4+uST9rwT9j/PnLynXXdMU8OyKu3RSrpl9Eje6usslfRbFXTltTlB4Inx8ckq6c1P4v925BmRLcvzp0xFas+bHRG0uLi7y9y/oOMeuXQe0d2/i6a5Fi/r+t5/E1xqkTIwLJMfH587fBRHn7x4Xl5UrZ9JFAv5pK/rfxPcfRfzy69SpiERt3//wq4oXKyxf33xPMmQ8IrMXHUgJs+UMT3Ik6ezZs9q/f7+2bt2qrVu36s8//9TmzZv16aefmhFOqhYQUFgZM6bXzp3/W90mLi5eu3fvV6VKz5kXGFKU6CPb5ZG3hFzS/W8KgVfRWv99bodZYeEJCAgo9N9rwP8WGrlzDTigSpXKJDk+PPy4evYKVmjoUUdbbGycDh48Iv//7ofyxaKV+nD4lESv27fvzh+3/v/aMwUpF+MCyQkoUlAZM6bTzp0HHG13xsVBVUpmoYAK5YvLZrNp31336xw6fFz5C+RO1LZ790FVrZp0bAFmMXy62hdffKHRo0crISFBNptNdrtd0p0bpMqU4R/Hw/LwcFdQUDNNnPiZsmb1lr9/Qc2c+aVu3bqt5s0bmR0eUoib+9Yo04W/le3dhbqyZKBc0nkrS+Anurnza8VfPml2eHgMHh7uCmrdRBMnLVDWbJnl71dAM2ct0a1b0Wr+fw0kSRcuXFbatGmULl0aVatWTiVK+GvAwI80bGg3pUnjpWnTF+nGjUi1b9dMktSubTO91bKngoNnqkWLhjp+/LSGfThZ9epVU+lSAWa+XTiJcYHkeHi4K6hVY02c/KWyZs0sf7/8mjl7mW7djlbzN+tLki5cuKK0ab2ULl0a5c6dXW82e0ljxn2mdOnSyN8vv5av2KTde0K0YN4Ix3ljYmJ19Ngpvd3xdbPeGpCEzf5PlmGQOnXqqEmTJurUqZPq1Kmjb775Rjdu3FCfPn3UsGFDtW/f/hHPfOaJxpmaJCQkaPLk+Vq6dK0iI6NUqlSABg7sqmLF/MwOzWQ+OtExk9lBmCJTo/5KX7udTvcu4mhzzZpfWd4aL8+AWrJHR+nm7m91ZelgKe7Z2oQ3/+xrkv1vs8N4ohISEjR5ykItXbr+zjWgZIAGDnxHxYrdmUoUULS+unYJVLdurSRJly5d1fjxc/TTz7t08+YtVahQSv37vS0/vwKOc+74dZ8+/WS+Dh0+pvTp06lx47p6r0dreXhYdGd7W0HGBeMiKVtBKT7M7CieqISEBE2eulhLl32vyMibKlXSXwP7t1exYneWCg8o/pq6dm6ubl1bSLpT6Zk+42stW/69Ll++Ln+//Ore7S09X7uC45xnzlzQC/U6aPbMoapVs1yy/VqGa1GzI3DKkVbpHnzQU+a/0Nx7fg1PckqWLKl169YpX7586tixo9544w299NJL+u233/TBBx9ow4YNj3jmZzfJwb08u0kO7s2KSQ6eAAsmOXgCLJjk4DGR5DjN7CTH8HtyMmfOrBs3bkiSChUqpNDQO0tT5smTRxEREfd7KQAAAIAHsNlspj/MZniS88ILL2jIkCEKCwtTlSpVtHLlSu3atUvz589X7ty5H3wCAAAAALgPw5Oc/v37q2jRogoLC1OdOnVUqVIlBQUFafny5erfv7/R4QAAAACwGMPvyUlOVFSUPD095eb2OIu9cU8O7sY9OUiKe3KQLO7JQXK4Jwd3SyX35BwNSm92CPJdEGlq/4YsIb1o0SKnj23ZsuVTjAQAAACA1RmS5MydO9ep42w2G0kOAAAA8BhSwo3/ZjMkydm8ebMR3QAAAACAMUnO3a5evaqVK1cqPDxc7u7u8vX1VdOmTZUmTRozwgEAAABgIYavrnbgwAHVqVNHCxcu1NWrVxUREaE5c+aoXr16jj1zAAAAADwam4v5D7MZXsn54IMP9Prrr2vgwIGO+YLx8fEaNmyYhg4dqq+//trokAAAAABYiOF51rFjx9SyZctEN0S5urqqbdu2CgtjmUYAAADgcdhsNtMfZjM8yalYsWKyCxFs375dZcqUMTocAAAAAClATEyMXn31VW3ZssXRFhsbq+HDh6ty5cqqXLmyJkyYoISEhAeey5DpauPGjXN8nS1bNk2YMEG//fabypQpI1dXV4WFhWnjxo0KDAw0IhwAAAAAKcjt27f1/vvvKzw8PFH7xx9/rF9++UWzZs1SZGSk+vXrp4wZM+rtt9++7/kMSXIOHDiQ6Pvy5csrKipK27dvd7SVLVtWISEhRoQDAAAAWFYKmC32UA4ePKh+/frJ1dU1UXt0dLQWL16sTz75xDHjq1evXpowYYI6dOggF5d7T0ozJMlZuHChEd0AAAAASGV+/fVX1a1bV++++26i21dCQ0N169YtVahQwdFWoUIFXbx4USdOnFDBggXveU5T9sk5deqU5s+fr2PHjikhIUG+vr5q0aKF/Pz8zAgHAAAAsIyUcOP/9evXdf369STtGTNmVMaMGRO1tW/fPtlzREREKG3atMqQIYOjLXv27JKkc+fOpawkZ+fOnerYsaOKFi2qcuXKKT4+Xnv37tXSpUs1b948lS9f3uiQAAAAADxBCxYs0JQpU5K0d+3aVd26dXPqHLdu3ZKHh0eitn++j4mJue9rDU9yJkyYoMDAQPXt2zdR+9ixYzVhwgQtXrzY6JAAAAAAPEFBQUFq0qRJkva7qzj34+XllSSZ+ef7NGnS3Pe1hic5hw4d0vjx45O0/9///R8JDgAAAPCYUsJ0teSmpT2sXLly6ebNm4qKilK6dOkkSRcuXJAk5cyZ876vNXyfnNy5c+vQoUNJ2sPCwpQ5c2ajwwEAAACQAhUtWlRp0qTRnj17HG27d+9WtmzZlD9//vu+1vBKTosWLTRkyBCdO3dOpUuXls1m0759+zR9+nS1bdvW6HAAAAAApEBeXl5q1qyZRo4cqTFjxig6OlofffSRgoKCHvhaw5OcoKAgRUVFacaMGbpy5YqkO6skdO7cWa1btzY6HAAAAMBSUsBstSemT58+io6OVocOHeTp6almzZqpY8eOD3ydzW632w2IL1mXLl2Sp6en0qdP/wTOduYJnAPW4qMTHTOZHQRSmPyzr0n2v80OAymNrSDjAknZCkrxYWZHgZTEtajZETjlVKfMZoegvDOvmtq/IZWcRYsWqVmzZvL09NSiRYvue2zLli2NCAkAAACwpJSw8IDZDEly5s6dqwYNGsjT01Nz586953E2m40kBwAAAMBjMSTJ2bx5c7JfAwAAAMCTZkiSEx4e7vSxfn5+TzESAAAAwNpshm8Sk/IYkuQ0bNhQNptND1rjwGazKTQ01IiQAAAAAFiUIUnOpk2bjOgGAAAAeOax8IBBSU6ePHkcXxctWvSeH7y7u7ty5sypV199Vd26dZOrq6sR4QEAAACwEMM3Ax0+fLgmTpyobt266bnnnpMkHThwQJMmTdKbb74pf39/TZkyRXa7Xe+//77R4QEAAABI5QxPcubOnatRo0bp+eefd7QVLVpUuXLl0qhRo7R+/Xplz55dPXv2JMkBAAAAHhKz1STD1144f/688uXLl6Q9V65cOnv2rOPra9euGR0aAAAAAAswPMkpV66cxo8fr6tXrzrarly5oo8++kjlypWTJG3cuFEFCxY0OjQAAAAg1bPZbKY/zGb4dLWRI0eqY8eOqlWrlvLkyaOEhASdPXtWvr6+mjRpkn788Ud98sknmjx5stGhAQAAALAAw5Oc3Llza+XKldqxY4cOHz4sNzc3+fv7q2rVqpKkdOnS6ccff1SWLFmMDg0AAACABRie5EiSq6uratSooRo1aiR5juQGAAAAeHQpYbqY2Qy/JwcAAAAAniZTKjkAAAAAng4KOVRyAAAAAFgMSQ4AAAAAS2G6GgAAAGAhLDxAJQcAAACAxVDJAQAAACyEQg6VHAAAAAAWQ5IDAAAAwFKYrgYAAABYiM2F+WpUcgAAAABYCkkOAAAAAEthuhoAAABgJSyvRiUHAAAAgLVQyQEAAAAshEIOlRwAAAAAFkOSAwAAAMBSmK4GAAAAWAn75FDJAQAAAGAtVHIAAAAAC7Gx8gCVHAAAAADWQpIDAAAAwFKYrgYAAABYCLPVLJXk+JgdAFKg/LOvmR0CUiJbQbMjQErEuEByXIuaHQGAR2ChJOeM2QEgxfER4wJJ+cg+t5TZQSCFsbU/IK4XSIrfI7hbKvlPdUo53JMDAAAAwFpIcgAAAABYioWmqwEAAACwuTBdjUoOAAAAAEuhkgMAAABYCYUcKjkAAAAArIUkBwAAAIClMF0NAAAAsBAb++SknErOiRMnzA4BAAAAgAUYWsk5dOiQgoODFR4ervj4eEmS3W5XTEyMbt26pdDQUCPDAQAAAKwnxZQxzGPoR/Dhhx/q9u3b6tatm6KiotS1a1c1bdpU8fHxCg4ONjIUAAAAABZlaCXn4MGDWrx4sYoXL65vv/1W/v7+atmypfLly6dly5bpP//5j5HhAAAAALAgQys5Li4uypQpkySpUKFCCgkJkSTVqlVLhw8fNjIUAAAAwJJsNpvpD7MZmuSULFlSX3/9tSSpaNGi+vnnnyVJR48elYsLkwcBAAAAPD5Dp6v17t1bb7/9tjJlyqTXX39dc+bMUd26dXXp0iU1a9bMyFAAAAAAWJShSU6ZMmW0efNm3bp1S5kyZdLy5cu1ceNGeXt765VXXjEyFAAAAMCSUsJ0MbMZvhlounTp5OnpqVu3bil9+vRq2rSpJOn27dtKkyaN0eEAAAAAsBhDk5wdO3Zo2LBhOnnypOx2e5Ln2ScHAAAAeEwUcoxNcoYNGyY/Pz8NGjRIXl5eRnYNAAAA4BlhaJJz/vx5zZgxQ4UKFTKyWwAAAADPEEOTnBdffFFbt24lyQEAAACeEpsL89UMTXJ69uypxo0ba9WqVcqbN2+SlR8mTpxoZDgAAAAALMjQJGfQoEGy2WzKnz8/K6kBAAAATwNLSDuf5Kxdu1bly5dXzpw5NXfuXH377bcqV66cBg0aJE9PT6fOsXv3bn3xxRcqVarUIwcMAAAAAPfj4sxBs2bNUt++fXXy5Ent27dPEyZMUPHixfXLL79owoQJTndWsGBBxcTEPHKwAAAAAPAgTlVyli5dquDgYFWoUEGjR49WiRIlNG7cOO3atUu9evXSoEGDnOrs3XffVf/+/RUYGKi8efPKzS1x97Vr1374dwAAAADAgdlqTiY5586dU4UKFSRJ27dvV926dSVJefLk0bVr15zu7L333pMkBQcHJ3nOZrOxGSgAAACAx+ZUkpM9e3adOHFCMTExCg8P1wcffCBJ2rNnj3LlyuV0Z2FhYY8WJQAAAADnsIS0c0lOw4YN1b17d3l4eChv3ryqWLGivvzyS40fP17vvvvuQ3caEhKio0ePKiEhQb6+vipZsuRDnwMAAAAAkuNUkvP+++8rZ86cOn78uAIDA+Xi4iJ3d3f17NlTrVq1crqzq1evqlevXvrll1+UKVMmxcfHKzIyUuXKldPMmTOVIUOGR34jAAAAACA5meTYbDa1bNkyUdsbb7zx0J2NGjVKV65c0Zo1a+Tr6ytJOnLkiPr166dx48ZpxIgRD31OAAAAAP9jY+UB55aQjo2N1axZs3T8+HFJ0ogRI1S2bFm1b99eV65ccbqzH3/8UcOGDXMkOJLk7++voUOHauPGjQ8ZOgAAAAAk5VSS8/HHH2v27Nm6du2atm3bpi+//FJt2rTR1atXNW7cOKc7c3Nzk4eHR5J2Dw8PxcbGOh81AAAAgGTZbOY/zOZUkrNu3Tp99NFHKl26tL7//ntVqFBBPXr00NChQ/Xjjz863Vm1atU0ZswYXb582dF26dIljR07VtWrV3/o4AEAAADgbk7dk3Pp0iUVKVJEkvTLL7/o9ddflyRlyZJFt2/fdrqzfv36qXXr1qpdu7Z8fHxks9l0+vRp+fv7a8yYMY8QPgAAAAAk5lSS4+Pjoz///FOXLl3SqVOnHFWXLVu2KG/evE53liNHDn333Xf66aefdPToUXl6esrX11fVqlXjBikAAADgSeDvaueSnBYtWui9996Tq6urihcvrtKlS2v69OmaPHmyPvzwQ6c7a926taZOnao6deqoTp06jvZLly6pffv2WrFixUO/AQAAAACp1/Xr1zV69Ght2bJFrq6uqlevnvr166d06dI98jmdSnLatGmjQoUK6e+//1bjxo0l3VkVbcqUKYmSleRs2rRJe/bskSTt2rVLn3zyiby8vBIdc/LkSZ09e/ZR4gcAAACQin344Yc6efKkFixYoNu3b6t///4KDg7WyJEjH/mcTiU5klS7dm3Vrl3b8X29evWcel3RokW1YMEC2e122e12hYSEyN3d3fG8zWZT2rRpNXbs2IcIGwAAAEBybC6pa7rajz/+qOHDh6to0aKSpJYtW2revHmPdU6nkpzbt2/riy++0KFDh5SQkCBJstvtiomJ0Z9//nnfFdby5Mmjzz//XJI0YMAADRo0SOnTp3+soAEAAABYg7e3t7777jvVrl1b8fHx2rhxo0qVKvVY53QqyRk5cqRWrlypkiVL6o8//lDZsmV1/PhxXbp0SW3atHG6s+DgYF2+fFm3b9+Wl5eX9u/frx9//FElS5Z84LQ3AAAAAE5IAYWc69ev6/r160naM2bMqIwZMyZqGz58uPr06aOKFSvKbrerSJEimjZt2mP179Q+OVu2bNGoUaO0ePFi+fj4aPTo0dq8ebNq166tuLg4pzv74Ycf9Pzzz+v333/X8ePHFRQUpA0bNqhXr15asGDBI78JAAAAACnHggULVLdu3SSP5P7m/+uvv1S4cGF9/vnnmjdvnux2u/r37/9Y/TtVybl27ZrKly8vSfLz89PBgwdVoEABvfvuu+rVq5cGDRrkVGcTJ05Ut27dVK1aNX300Ufy8fHRmjVrtHnzZo0aNUpBQUGP/k4AAAAApAhBQUFq0qRJkva7qzgnTpzQyJEjtX79ehUoUECS9Mknn+jVV1/VgQMHHnnamlNJTubMmXX9+nXlyZNH+fPn15EjRyRJ2bJl04ULF5zu7O+//1bDhg0l3akO1a1bV5IUEBCgixcvPmzsAAAAAO6SEvafTG5aWnL+/PNPubu7OxIc6U5RxcvLSydPnnzkJMep6WpVq1ZVcHCwTp48qeeee05r167V2bNntWrVKmXLls3pznLlyqWDBw8qJCRE4eHhjtXaHnZTUQAAAACpX86cORUdHa2///7b0Xbq1Cndvn1b+fPnf+TzOpXk9O7dW5GRkdq0aZNeeuklZciQQXXq1NGkSZPUrl07pztr166dunfvrjfffFNVq1ZV+fLlNXnyZI0ZM0ZdunR55DcBAAAA4L9cbOY/nFSmTBmVKFFCgwYN0sGDB/Xnn3+qd+/eqlSpkkqWLPnIH4HNbrfbnT04JiZGHh4eunXrln7++Wflzp37oUtIISEhOnPmjGrUqCEvLy/t3btXadOmVUBAwEMHn9iZx3w9rMdHjAsk5SP73MdblhLWY2t/QFwvkBS/R3A3H7MDcErspCJmhyD37oedPvbChQsaM2aMtm/fLpvNptq1a6t///7KlCnTI/d/zyQnJibG6ZN4eHg8VKcxMTGKj4+X9L/9dkJDQ1W1atWHOk9iXIRwN345ITkkOUiKJAfJ4/cI7kaS46yHSXKehnsuPFC6dGmnb1oKDQ116ridO3dq8ODBOnXqVNJA3Nx04MABp84DAAAAIHkpYN0B090zyRk9evQTX5lhzJgx8vX11ZAhQ9SjRw+NGzdOERERmjRpkoYOHfpE+wIAAADwbLpnktO0adNE38fHx+vKlSuO1dR2796t0qVLP9RUtfDwcI0fP15+fn4qUaKEvLy81LJlS2XOnFmfffaZGjRo8IhvAwAAAIAkSjlycnW1EydO6OWXX9bcuXMdbV27dtV//vMfnTnj/FxVT09PR1JUqFAhxzS3smXL6tixYw8TNwAAAAAky6kkZ/To0SpYsKDatm3raFuzZo1y586tsWPHOt1Z+fLlNWPGDEVGRqpkyZL64YcfFBsbq127dilt2rQPHz0AAAAA3MWpJGfPnj3q37+/cuTI4WjLmjWr+vbtq19//dXpzvr166c9e/bo66+/VuPGjRUVFaXy5curf//+CgwMfPjoAQAAACRis9lMf5jtnvfk/JvNZtPNmzeTtMfHxysuLs7pzgoXLqwNGzbo1q1b8vLy0tKlS/Xbb78pc+bMKlOmjPNRAwAAAMA9OJXkVKlSRePHj9fEiRPl7e0tSbpy5Yo++ugjVa5c+b6vDQ8Pv+/zefLkcRzn5+fnTDgAAAAA7sHm1Fwta3Mqyenfv7/eeustPf/888qbN68k6dSpU8qWLZs+++yz+762YcOGstlsuseeo47nbDab0/vtIDG73a4ZMxZpyZLVunz5qkqWDNCgQV1VooT5G0HBPIyLZ9fOEwkK+ir5KrtNUmjfpKtinrpm17gtcdp5wq7YeKliPpt61XZVkez8pnwWcL1AchgXSM1s9ntlH3eJjIzU2rVrdejQIbm7u8vX11eNGzeWp6fnfV93+vRpp4P5p6rzaJ7dHYmnT/9C8+Z9rVGj+qhgwbyaOfNL/fTTTq1dO1/ZsmUxOzwTPds7VTMu7sVH9rmlzA7iqYqJt+varcRtJ6/a1fbrOLWv5KLuNdySHN9wbqx8MtrU9wVXebjaNPHnOO05bdfGju5K72n+3Oqnzdb+gLhecL1Iit8jjIu7+ZgdgFMSphc1OwS5vBtmav9OJzlPUlhYmI4cOSJ3d3f5+fk9oWlqz+ZFKDY2TlWr/kdduwapTZs3JElxcfGqV+8tvf76K+rWrY25AZrq2f3lxLi4H+snOXdLsNv1fwvjlM5Dmvd/bkluCD1xxa6Pf4pX3xdc5ZPxznNh5xP0n/lx+qKFmyrks34151lOcrhe3A+/RxgXd0slSc6MYmaHIJd3zJ2h5dR0tSfl0qVL6tKli/bt26dMmTIpISFBkZGRqlWrlsaMGeO43wfOCw0N140bUapSpZyjzc3NVRUqlNZvv/1hYmQwE+MC/7ZkX4IOX7RrZRv3ZFe8ye9t06ev/e/XwcUou+btSlCO9FLRHNav4jzruF4gOYwLpHaG/vfcoEGD5O7urh9++EE7d+7Url27tGbNGl29elXDhg0zMhTLiIi4IEnKnTtHovYcObLq3LnzZoSEFIBxgX/ExNk1dXu8mpdxUcEsD05Y3lsZpxpTY7UuLEGjXnZ7JqaqPeu4XiA5jIvUzezlo1PCEtKGJjk7duzQkCFDHIsXSHeWlR46dKh++uknI0OxjJs3b0uSPDzcE7V7enooOjrGjJCQAjAu8I+VBxN0/bbUvrKrU8e/W81FS1u56ZWiLurybZz2nUl4yhHCbFwvkBzGBVI7Q5OcfPny6cSJE0naL168qOzZsxsZimV4ed1ZJSkmJjZRe3R0jNKmTWNGSEgBGBf4x/I/E1TXz0U50jv3v2oB2V1UKreLghu4Km8maeEekhyr43qB5DAukNrd856cbdu2OX2SGjVq3PO5rVu3Or5+8cUXNXDgQL3zzjsqXbq0XF1dFRYWpsmTJ6tjx45O94f/8fHJJUmKiLioTJkyONrPn7+kXLlIHJ9VjAtI0oVIu/44Y1fH/9y/inP6ml37ziTo1WL/O87FZpN/dpsibhi+Ng0MxvUCyWFcpHIu5k8XM9s9k5wOHTrcd3+bfzxof5tOnTolaRs7dmyybW3atLlvX0gqIKCwMmZMr50796pIkUKS7qx+snv3fr35ZkOTo4NZGBeQpD2n71y/Kxe4/y+78It29Vodr8JZbCqW806BPzberoPn7KpV2Porqz3ruF4gOYwLpHb3THI2bdr0RDoICzN3jWyr8/BwV1BQM02c+JmyZvWWv39BzZz5pW7duq3mzRuZHR5MwriAJIVGJChfJimdR9Ik50KkXWk97jxXraBNJXLaNHBdvIa+JKVxl6ZvT9CNaKldJefu5UHqxfUCyWFcILW7Z5Lj7MackZGRD9VhQkKCNm3apKNHjyohIUG+vr56/vnnH7ipKO6tc+dWio+P1+jRUxUZGaVSpQI0f/5HypIls9mhwUSMC1yIkjKnSb6KU3NarLpUc1G3Gm5yd7VpVjM3jd8ary7L43QzVqqQ16ZFb7krbyamPDwLuF4gOYyLVCwFrG5mNqc2A718+bImT56sQ4cOKSHhzk2odrtdMTEx+uuvv7Rv3z6nOjt9+rQ6deqk06dPq1ChQoqPj9fx48eVM2dOff7558qZM+djvJVnc7Mu3M+zu4kb7ufZ2wwUD/YsbwaK++H3CO6WOjYDtc8paXYIsnX409T+nZps/eGHH2rdunXy8fHR/v37lS9fPsXGxio0NFSdO3d2urORI0cqZ86c2rJli5YvX66VK1dq8+bNypMnj0aPHv3IbwIAAADAf9ls5j9Mds/pav/266+/Kjg4WHXq1FFISIg6duyoIkWKaMCAATp27JjTnf3666/66quvlDlzZkdblixZ1LdvXwUGBj508AAAAABwN6cqOVFRUSpWrJikO5t3/rOaWlBQkHbu3Ol0Z+nTp9fNmzeTtN+8eVMuLqzgAwAAAODxOZVZZMuWTREREZLubOh5+PBhSVK6dOl0+fJlpzurW7euPvzww0QrroWEhGj48OGqW7fuw8QNAAAAIDkuNvMfZn8Ezhz0/PPP64MPPlBISIgqV66s1atXa/fu3Zo3b57Tq7BJUq9evZQpUyb95z//UenSpVW6dGm9/vrrypMnjwYOHPjIbwIAAAAA/uHUPTm9e/fWgAED9Mcff6h58+YqXry4AgMD5ebmpvHjxzvdWYYMGbRgwQIdPnxY4eHh8vT0lK+vrwoWLPio8QMAAAD4txRw47/ZnFpC+m52u12hoaHKnj27smfPft9jw8PDnT6vn5/fw4byLyzxiLux9CeSwxLSSIolpJE8fo/gbqlkCen5ZcwOQbY2f5jav1OVnL/++itJW5o0aRQZGanIyEgVKlTonq9t2LChbDab7s6lbP/KMO12u2w2m2NBAwAAAAB4VE4lOa+88kqipORu90tONm3a9PBRAQAAAHg0NlYtdirJ+fzzzxN9HxcXp7///lvz5s3TBx98cN/XZsmS5dGjAwAAAICH5FSSU6lSpSRt1apVU548eTRt2jTVqlXrnq8tW7bsfatAEtPVAAAAgCcmBSzhbDankpx78fX1VUhIyH2PubsKBAAAAABPk1NJTkxMTJK2yMhIzZ49Wzlz5rzva5OrAgEAAADA0+JUklO6dOlkp5zZbDaNGDHC6c5ef/31+05dW7ZsmdPnAgAAAJAM9slxLskJDg5O9L3NZpO7u7vKlCmjvHnzOt3ZCy+8kOj7uLg4nTx5Ulu3blXXrl2dPg8AAAAA3ItTSU7evHlVtmxZubklPjwmJkbr16/Xyy+/7FRn90pkli1bps2bN6tNmzZOnQcAAADAPVDJkVOLaLdu3VrXr19P0n7y5En17dv3sYOoUqWKtm/f/tjnAQAAAIB7VnIWLlyomTNnSrqzxHOjRo2S3E8TFRWlggULOt3ZrVu3krRFRUVp/vz5yp49u9PnAQAAAIB7uWeS88Ybb+jatWuy2+2aOnWqGjdurHTp0iU6Jn369E5PVZPuvWeOp6enRo8e/RBhAwAAAEgW++TcO8nx8vJy3ENjs9nUvn17pUmT5rE6W7BgQaIk558FDPz8/JQ+ffrHOjcAAAAASE4uPNC5c2dNnjxZ2bJlU8uWLSVJTZo0Ud26dR9qVbTKlSs/WpQAAAAAnGNz6rZ7S3MqyZk0aZIWL16caE+c1157TdOnT5e7u7s6dep0z9c+aG+cf2OfHAAAAACPy6kkZ8WKFRo/frxq1arlaGvTpo3y58+v0aNH3zfJ+ffeOFeuXNGSJUtUr149lSpVSm5ubvrzzz+1YcMGtWrV6jHeBgAAAADc4VSSc+XKFRUoUCBJu5+fn86fP3/f1/57Olv79u01YMAAx5S3f1SqVEnffPONM6EAAAAAuB8WHnBunxxfX1+tW7cuSfvGjRuTTX7uZffu3apevXqS9vLlyyssLMzp8wAAAADAvThVyXn77bf1/vvv6+DBgypTpowkaf/+/fr+++81btw4pzsrUqSIvvjiCw0cOFAuLnfyq9jYWM2ePVvFixd/hPABAAAAIDGnkpyXX35Zbm5umjt3rqZOnSoPDw/5+flp5syZ8vb2drqzwYMHq2PHjvr+++/l7+8vu92uQ4cOyWazad68eY/8JgAAAAD8l5OLflmZzW632x/2RdHR0fruu++0ePFiHTx4UKGhoU6/9sqVK1q7dq2OHj0qSQoICNCrr776BPbJOfOYr4f1+IhxgaR8ZJ9byuwgkMLY2h8Q1wskxe8R3M3H7ACcYl9a1ewQZHtjh6n9O1XJ+ceJEye0ePFiLV++XNevX1fmzJnVpUuXh+rQ29tb9erVk6+vr8qUKaOoqCg2AgUAAACeFCo5D05y7Ha7tmzZoi+//FLbt29XQkKCbDabevToobZt28rT09Ppzm7evKmBAwdq/fr1cnFx0YYNGxQcHKyrV69qypQpypIly2O9GQAAAAC45+pqly9f1syZM1W3bl117txZYWFhatWqlZYtWyYXFxfVq1fvoRIcSRo/frwiIiK0bt06x2t79eql6OhojR49+vHeCQAAAADoPpWc559/Xt7e3nr++ef18ssvq3Llyo4V0R7Vpk2bNGXKFBUqVMjR5uvrqw8//FDt27d/rHMDAAAAEPvk6D6VnPTp0ysyMlIXL17U8ePHdfXq1cfuLDIyMtn7b1xcXBQXF/fY5wcAAACAeyY5P/30k4KDgxUTE6Phw4erVq1a6tChg7799lvZHvFmpho1amjatGmJEprLly9r7NixyW4SCgAAAOAh2WzmP8z+CJxZQvrcuXNatmyZli9frjNnzshms6lBgwbq2LGjihYt6nRn58+fV9euXXX06FHdvHlTefLk0fnz5+Xn56fp06crZ86cj/FWWOIRd2PpTySHJaSRFEtII3n8HsHdUskS0t/WMDsE2ZpsM7f/h9knx263a9u2bVq6dKm2bNmiuLg4VahQQQsXLnyoTnfs2KFjx44pLi5Ovr6+ql69+iNXh/6HixDuxi8nJIckB0mR5CB5/B7B3UhynGV2kvNQ++TYbDbVrFlTNWvW1JUrV/Ttt9/qm2++eehOK1asqOeee87x/e3btyVJadKkeehzAQAAAPgX2+MtFmYFD5Xk/Ju3t7fatWundu3aOf2aHTt2aNiwYTp58qT+XUCy2+2y2WwKDQ191HAAAAAAQNJjJDmPYtiwYfLz89OgQYPk5eVlZNcAAADAs4ElpI1Ncs6fP68ZM2Yk2icHAAAAAJ4kQyfsvfjii9q6dauRXQIAAAB4xhhayenZs6caN26sVatWKW/evElWVJs4caKR4QAAAADWkwL2qTGboUnOoEGDZLPZlD9/flZSAwAAAPBUGJrk7N69W1988YVKlWKPCgAAAOCpoJJj7D05BQsWVExMjJFdAgAAAHjGGFrJeffdd9W/f38FBgYqb968cnNL3H3t2rWNDAcAAACABRma5Lz33nuSpODg4CTPsRkoAAAA8ASwT46xSU5YWJiR3QEAAAB4Bhl6Tw4AAAAAPG2GVnIAAAAAPGU26hh8AgAAAAAshUoOAAAAYCXsk0MlBwAAAIC1kOQAAAAAsBSmqwEAAABWwj45VHIAAAAAmCcuLk7jx49XtWrVVKFCBfXq1Us3btx4rHOS5AAAAABWYrOZ/3gIEyZM0KpVq/TJJ59owYIFOnz4sEaNGvVYHwFJDgAAAABT3LhxQ1988YVGjBihypUrq0SJEurdu7cOHjwou93+yOflnhwAAAAApti9e7fc3NxUo0YNR1vt2rVVu3btxzovSQ4AAABgJSlgn5zr16/r+vXrSdozZsyojBkzOr4/fvy4cuXKpU2bNmnq1Km6evWqnn/+efXt21fp06d/5P5t9sepAz2CuLg4rVmzRuHh4YqPj5ck2e12xcbGKiQkRF9++aWR4QAAAACWYv/hZbND0JTQVzVlypQk7V27dlW3bt0c30+bNk1z5sxRwYIF1bdvX0nSiBEjVLhwYU2ePPmR+ze8kjN8+HCtWLFCxYsX1/79+1W2bFmdOHFCFy9eVMuWLR/jzGeeWIywCh8xLpAU4wLJ8ZF979tmB4EUxlZ2lrheIDEfswNwjov5t90HBQWpSZMmSdr/XcWRJDc3N0VFRSk4OFgBAQGSpGHDhikwMFDnz59Xjhw5Hql/w5OcjRs3avz48apfv77q16+v4cOHq0CBAurTp49iYmKMDgcAAADAE3b3tLR7+SeJ8fX1dbT98/XZs2cfOckxPM2LjIxU6dKlJUkBAQHav3+/3Nzc1KlTJ23dutXocAAAAACYpGzZspKkkJAQR9vRo0fl4uKiPHnyPPJ5DU9yfHx8dOzYMUlSoUKFdPDgQUmSu7t7sjcnAQAAAHgIZu+R8xALHxQoUEAvvfSSBg8erP3792v//v0aPny46tevr2zZsj3yR2D4dLU333xTPXv21JgxY/Tiiy8qMDBQmTNn1s6dO1W8eHGjwwEAAABgorFjx2rMmDFq37697Ha76tevr4EDBz7WOQ1fXU2SVq1apVy5cqlSpUpavny5vvzyS2XOnFmDBg1SoUKFHvGs3BiIu3GDOZLDuEByWHgASbHwAJJKHQsP2H9saHYIsj3/nan9m7JPTuPGjR1fN23aVE2bNjUjDAAAAAAWZHiSExkZqUWLFik8PDzZ1dQmTpxodEgAAAAALMTwJKdnz576448/VK1aNaVNm9bo7gEAAABrc3H+xn+rMjzJ2bVrl+bMmaPy5csb3TUAAACAZ4DhSU7+/PllwloHAAAAwLPhIZZwtirDk5wRI0bogw8+0JtvvikfHx+5uCTeqqd27dpGhwQAAADAQgxPcn799VcdPnxYI0aMSPKczWZTaGio0SEBAAAAsBDDk5y5c+eqZ8+eatWqlby8vIzuHgAAALA2m8uDj7E4wz8BFxcX1a9fnwQHAAAAwFNheJLzzjvvaOLEibp+/brRXQMAAAB4Bhg+XW3t2rUKCQnR2rVrlSFDBrm6uiZ6fseOHUaHBAAAAFgH++QYn+Q0b97c6C4BAAAAPEMMT3KaNGni+Nput8vGOt4AAADAk8PCA8bfkyNJa9asUePGjVWmTBmVLFlSDRs21NKlS80IBQAAAIDFGF7JWbRokcaPH6/AwEC99957SkhI0J49ezR69GhJ0htvvGF0SAAAAAAsxPAkZ/78+RoyZIiaNm3qaKtXr578/Pw0Z84ckhwAAADgcTBdzfjpahcuXFD58uWTtFeoUEGnT582OhwAAAAAFmN4kuPn56eNGzcmad+wYYMKFChgdDgAAACAtdhczX+YzPDpat26dVPnzp21d+9elSlTRjabTXv37tXPP/+sTz/91OhwAAAAAFiM4ZWc2rVr67PPPpObm5tWrlypdevWKW3atPr6669Vr149o8MBAAAAYDGGV3JWrFihBg0aqHLlyonab968qfnz56tNmzZGhwQAAABYCAsPGJLkRERE6MaNG5KkAQMGKG/evMqcOXOiYw4dOqSPP/6YJAcAAADAYzEkyTlw4IC6du0qm80mu92uwMDAZI9r0qSJEeEAAAAA1sUS0sYkOfXq1dPmzZuVkJCgevXqaenSpcqSJYvjeZvNprRp0yap7gAAAADAwzLsnhwfHx9JUs+ePZUtWzblzp3bqK4BAAAAPEMMr2X98MMPqlu3rgIDA7VkyRJdu3bN6BAAAAAA67K5mP8wmeERfP3119qwYYNq1qypRYsWqUaNGurcubPWrVunmJgYo8MBAAAAYDGGLyEtSfny5VOnTp3UqVMnHT16VKtWrdKAAQPk6uqql156SS1atFDp0qXNCA0AAABI3VJAJcVspiQ5knTt2jVt3LhR69ev186dO+Xn56eGDRvqwoULatu2rdq2bauuXbuaFR4AAACAVMqUzUDXrVun7du3K3v27GrYsKEGDBggPz8/xzF+fn4KDg4myQEAAADw0AxPckaPHq369evrs88+U8WKFZM9JiAgQO+8847BkQEAAAAWwHQ145Ocbdu2KS4uTlevXk3U9txzzyl9+vSSpNKlS3NPDgAAAIBHYniaFxYWpjp16mjRokWOthEjRuiVV15RWFiY0eEAAAAA1mL28tEpoJJkeASjRo3Sa6+9pp49ezra1q9fr5dfflkjR440OhwAAAAAFmN4knPo0CEFBgbK1dXV0Waz2dSqVSsdPHjQ6HAAAAAAWIzh9+TkzJlTe/bsUb58+RK1HzhwQN7e3kaHAwAAAFhLCpguZjbDk5ygoCANGzZMhw4dUqlSpSRJISEhWrx4sbp37250OAAAAAAsxvAk56233pKnp6cWL16sr776Su7u7ipYsKBGjBihBg0aGB0OAAAAAIsxPMmRpNdff12vv/66GV0DAAAA1sZ0NXOSnL1792rWrFk6duyYEhISVLhwYbVu3VrVq1c3IxwAAAAAFmJ4mrd+/Xq1bNlSXl5eCgwMVMuWLeXp6am3335bmzZtMjocAAAAwFrM3iMnBVSSDK/kTJkyRb169VL79u0dbW3atNHcuXM1ZcoU1a1b1+iQAAAAAFiI4WnWyZMnVa9evSTt9erV09GjR40OBwAAAIDFGF7JyZ8/v3777TcVKFAgUfvOnTuVK1cuo8MBAAAArCUFTBczm+FJTvv27TVkyBAdOXJEpUuXls1m0969e7V06VINGjTI6HAAAAAAWIzhSc5//vMfSdLnn3+uJUuWyMvLS4UKFdJHH32U7DQ2AAAAAA+BSo7xSc68efPUoEEDR7IDAAAAAE+S4Wne1KlTFR0dbXS3AAAAAJ4Rhic5tWvX1qJFi3T16lWjuwYAAACsz+w9clLAdDnDp6sdP35ca9as0eeff640adLI09Mz0fM7duwwOiQAAAAAFmJ4ktOyZUujuwQAAACeHSmgkmI2w5Oc06dPq3379kqTJk2i9sjISE2cOFFNmjQxOiQAAAAAFmJIkhMWFqaIiAhJdxYeKFiwoDJkyJDomPDwcPbKeUR2u10zZizSkiWrdfnyVZUsGaBBg7qqRIkiZocGEzEukBzGBZJz8WqMaryTdLp4x8b51OutwiZEhJSA6wVSM0OSnBs3bqhTp06O73v37p3kmLRp06pdu3ZGhGM5M2Ys0rx5X2vUqD4qWDCvZs78Um3b9tbatfOVLVsWs8ODSRgXSA7jAskJOx4pF5v0w+TKcnf73zSXtF6uJkYFs3G9SMWYrmbM6moVK1ZUWFiYwsLC5OPjo+3btzu+/+fx+++/q3v37kaEYymxsXGaO/crde7cSi++WFP+/oU0Zkx/pU2bRosXrzI7PJiEcYHkMC5wL4dORClvDi/5ZPNS9swejkc6kpxnFtcLpHaG35OzefPmRN9HRkbqzz//VJEiRZQlC/8r8LBCQ8N140aUqlQp52hzc3NVhQql9dtvf5gYGczEuEByGBe4l8MnouSbN53ZYSAF4XqRytn4DwrDa1nh4eFq2rSpdu/erevXr6tp06Zq06aN6tSpw/LRjyAi4oIkKXfuHInac+TIqnPnzpsRElIAxgWSw7jAvRw6EanIm3EKGvGHqnfarmYDf9fKnyLMDgsm4nqB1M7wJGfEiBHKly+fChcurG+++UY3b97Utm3b9M4772j8+PFGh5Pq3bx5W5Lk4eGeqN3T00PR0TFmhIQUgHGB5DAukJy4eLuOnr6pqzdi1bFxPs3uX0p1K2TVwBlh+ur7M2aHB5NwvUBqZ/h0tT/++EOrV69WlixZtGnTJtWpU0fZsmVTo0aNNG3aNKPDSfW8vDwkSTExsUqTxsvRHh0do7Rp09zrZbA4xgWSw7hActxcbfp1TnW5ukheHnemuBQvlEHnLkdrzuqTav6ij8kRwgxcL1I5Fh4wvpKTLl06Xb16VZcvX9bevXtVu3ZtSdLRo0e5J+cR+PjkkiRFRFxM1H7+/CXlypXdjJCQAjAukBzGBe4lnZerI8H5R5F86RRxKVp2u92kqGAmrhdI7QxPcl566SX16NFDgYGBypkzp2rWrKlVq1apT58++s9//mN0OKleQEBhZcyYXjt37nW0xcXFa/fu/apU6TnzAoOpGBdIDuMCyTl0PFLl227TT3svJWrff/SG/PKlk81mMykymInrBVI7w6erDR48WAUKFNDp06cVGBgoDw8PJSQkqHv37nrrrbeMDifV8/BwV1BQM02c+JmyZvWWv39BzZz5pW7duq3mzRuZHR5MwrhAchgXSI5/vnTy9UmrUQuOysPdRbmyemrdjgv6bluEpvYuaXZ4MAnXi1SO6Wqy2VNQHfrmzZtKmzbtI7762b05MiEhQZMnz9fSpWsVGRmlUqUCNHBgVxUr5md2aCbzEeOCcZEU44JxkRwf2fe+bXYQprl8PUaffPW3tu69pKuRsfLLk05dmhVQ3QrZzA7NVLays8T1gutFYqnjHjX7oUFmhyBbwChz+zc6yTl37pwmT56s8PBwxcfHS5LsdrtiYmJ08uRJ7du37xHP/OxehHAvz/Yfs7gXxgWS82wnOUjes57kIDmpJMk5/IHZIchWZISp/Rteyxo8eLB2796typUrKywsTNWqVVPevHkVHh6u3r17Gx0OAAAAAIsx/J6cPXv2aM6cOSpfvry2bdumOnXq6LnnntP06dO1detWBQYGGh0SAAAAAAsxvJKTkJCg3LlzS5J8fX118OBBSVLDhg114MABo8MBAAAArMXmYv7DZIZH4O/vry1btji+3rVrlyTpwoULjnt0AAAAAOBRGT5drVu3burSpYtcXFz02muvacaMGWrVqpX++usv1apVy+hwAAAAAGtJAZUUsxme5NSuXVvr169XfHy8cubMqa+++korVqxQzZo11bp1a6PDAQAAAGAxhic5kpQ3b17H10WKFFHfvn2THFOpUiV98803ypcvn5GhAQAAAEjlTElynBEbG2t2CAAAAEDqw3Q14xceAAAAAICnKcVWcgAAAAA8Aio5VHIAAAAAWAtJDgAAAIAUYeLEiapTp85jn4fpagAAAICVpNLpaiEhIZo1a5Zy5sz52OdKnZ8AAAAAAMuIjY1V//79VbZs2SdyPlOSnN9//12//fab4/vJkydr//79iY4ZO3assmXLZnRoAAAAQOpmczH/8ZCmTZumfPny6eWXX34iH4HhSc6KFSsUFBSkQ4cOOdpOnjypwMBAbdiwwdH20ksvKU2aNEaHBwAAAOAxXb9+XadOnUryuH79epJjQ0JCtGTJEg0bNuyJ9W/4PTnTp0/XiBEj9J///MfRNm7cOFWtWlUTJ05U/fr1jQ4JAAAAwBO0YMECTZkyJUl7165d1a1bN8f3MTEx6t+/v/r06aPs2bM/sf4NT3IiIiKSnWtXrlw5nTp1yuhwAAAAAGtJAQsPBAUFqUmTJknaM2bMmOj7adOmKUeOHMke+zgMT3ICAgL0zTffqGfPnonaV69ercKFCxsdDgAAAIAnLGPGjEkSmuSsWrVKFy5ccBRBYmNjFRcXp7Jly2r27NmqUKHCI/VveJLTs2dPdejQQdu2bVOpUqUkSaGhoTp06JCmT59udDgAAACAtaSASo6zFi5cqLi4OMf3q1at0tKlS7Vw4cLHWkra8CSncuXKWrlypZYuXapjx47J3d1dlSpV0qeffiofHx+jwwEAAABgkjx58iT63tvbW25ubipQoMBjndeUzUALFy6sfv36mdE1AAAAAIszJMlp1qyZ5s6dq0yZMun111+XzWa757HLli0zIiQAAADAmlLRdLW7BQYGKjAw8LHPY0iS8/zzz8vDw0OS9MILLxjRJQAAAIBnlCFJTteuXR1fV65cWc8995zc3d0THRMTE6MtW7YYEQ4AAAAACzOklhUTE6Nbt27p5s2bat26tc6fP69bt24lehw4cEB9+vQxIhwAAADAumwu5j9MZkglZ+XKlfrggw9ks9lkt9tVr169ZI+rXr26EeEAAAAAsDBDkpw33nhDBQsWVEJCgoKCgjRp0iRlypTJ8bzNZlPatGlVpEgRI8IBAAAALMz8SorZDFtCumLFipLubAZas2ZNeXl5GdU1AAAAgGeI4WnewoULVa1aNfXr10+//PKLEhISjA4BAAAAgIUZvhnoTz/9pJ07d2rt2rXq1auX3Nzc9Morr6hRo0YqXbq00eEAAAAA1nKfPSmfFYYnOTabTVWqVFGVKlU0ZMgQbd++XZs3b1br1q2VM2dONWrUSM2aNVOuXLmMDg0AAACABZh2V5LdbteuXbv0ww8/6IcfflDatGlVrVo1hYWF6ZVXXtHSpUvNCg0AAABIvcxePvpZWUL633bv3q21a9dqw4YNunnzpurUqaORI0eqZs2acnV1lSTNnz9f48aN0xtvvGF0eAAAAABSOcOTnNatW6tKlSrq06ePXnzxRaVLly7JMSVKlNCrr75qdGgAAAAALMDwJGfr1q3Knj27IiMjHQlOeHi4/Pz8HMdUrFjRseQ0AAAAgIfBwgOGT5iLiorSK6+8omnTpjnaWrdurddee02nT582OhwAAAAAFmN4kjN8+HAVK1ZMnTp1crRt3LhRhQsX1vDhw40OBwAAALAWm838h8kMn662d+9erVy5UpkyZXK0pU+fXt27d1ezZs2MDgcAAACAxRheycmUKZPCw8OTtJ84cUJp0qQxOhwAAAAAFmN4JeeNN97Q4MGD1aVLF5UsWVKSFBISomnTpun11183OhwAAADAWlLAPjVmMzzJ6dy5s+Lj4zV16lRdvnxZkpQ1a1YFBQWpffv2RocDAAAAwGIMT3JsNpu6d++u7t276/Lly/Lw8FD69OmNDgMAAACwKPNv/Deb4UmOJJ06dUrz58/XsWPHlJCQIF9fX7Vo0SLRXjkAAAAA8CgMn7C3c+dONWjQQPv371eRIkXk7++vP/74Q02bNtWePXuMDgcAAACAxRheyZkwYYICAwPVt2/fRO1jx47VhAkTtHjxYqNDAgAAAKwjBexTYzbDKzmHDh3Sm2++maT9//7v/xQaGmp0OAAAAAAsxvAkJ3fu3Dp06FCS9rCwMGXOnNnocAAAAACLcUkBD3MZPl2tRYsWGjJkiM6dO6fSpUvLZrNp3759mj59utq2bWt0OAAAAAAsxvAkJygoSFFRUZoxY4auXLkiScqePbs6d+6s1q1bGx0OAAAAAIsxZZ+cLl26qEuXLrp06ZI8PT3ZJwcAAAB4Ulh4wJgkZ9GiRU4f27Jly6cYCQAAAACrMyTJmTt3rlPH2Ww2khwAAAAAj8WQJGfz5s1GdAMAAADAZv7qZmYzJMnZunWrqlWrJnd3d23duvW+x9auXduIkAAAAABYlCFJTqdOnfTLL78oa9as6tSp0z2Ps9lsbAgKAAAAPBYWHjAkyQkLC0v2awAAAAB40gxfQvoff/75p44cOSJXV1cVLVpURYoUMSsUAAAAABZieJITERGhLl266M8//1SmTJmUkJCgGzduqEqVKpo8ebIyZMhgdEgAAACAdbBPjgxfeuGDDz6Ql5eXvv/+e+3cuVO7du3SunXrFB0dreHDhxsdDgAAAACLsdntdruRHZYtW1ZLlixJMj0tNDRUgYGB2rNnj5HhAAAAAJZiv7TI7BBky2ru3peGT1fLnTu3jh8/niTJuXz5srJly/YYZz7zeIHBgnzEuEBSjAskh3GB5PjIfmSo2UEgBbH5f2h2CHCS4UlOUFCQhg4dqr///lvly5eXm5ubQkJCNG3aNDVp0iTRPjrsmQMAAADgYRk+Xa1o0aJOHffwe+bwP3C4G/8zi+QwLpAcxgWSQyUHiaWWSo790pdmhyBb1rdM7d/wSg775AAAAAB4mkzbJwcAAADAU8AS0sYvIQ0AAAAATxNJDgAAAABLYboaAAAAYCE2G3UMPgEAAAAAlkIlBwAAALAUFh6gkgMAAADAUkhyAAAAAFgK09UAAAAAK2GfHCo5AAAAAKyFSg4AAABgKdQx+AQAAAAAWApJDgAAAABLYboaAAAAYCUsPEAlBwAAAIC1kOQAAAAAsBSmqwEAAABWwnQ1KjkAAAAArIVKDgAAAGAp1DH4BAAAAABYCkkOAAAAAEthuhoAAABgJSw8QCUHAAAAgLVQyQEAAACshEoOlRwAAAAA1kKSAwAAAMBSmK4GAAAAWAp1DD4BAAAAAJZCJQcAAACwEhYeoJIDAAAAwFpIcgAAAABYCtPVAAAAAEthuhqVHAAAAACWQiUHAAAAsBJb6qpjnDt3TqNHj9bOnTvl5uamWrVqqX///sqUKdMjnzN1fQIAAAAALCMhIUGdO3dWVFSUFixYoOnTp+vQoUPq16/fY52XSg4AAAAAU4SGhurgwYPatm2bsmfPLkkaNGiQ3nrrLV2/fl0ZM2Z8pPOS5AAAAABWkor2yfHx8dHs2bMdCY4k2f4bf3R09COflyQHAAAAwBN1/fp1Xb9+PUl7xowZE1VnvL29VatWrUTHzJ8/XwULFkyU+DwskhwAAAAAT9SCBQs0ZcqUJO1du3ZVt27d7vm6WbNmaePGjZo5c+Zj9U+SAwAAAFiK+dPVgoKC1KRJkyTt97vHZurUqZo0aZKGDBmi2rVrP1b/JDkAAAAAnqi7p6U9yKhRo7Rw4UINGzZMLVq0eOz+DUlyXn/9dccNRA+ybNmypxwNAAAAYGGpbJ+ciRMn6osvvlBwcHCy1Z9HYUiS88ILLxjRDQAAAIBUJCwsTDNmzFC7du1Uo0YNXbhwwfGct7e33NweLV0xJMnp2rWrEd0AAAAASEU2bNighIQEzZkzR3PmzEn03OrVq1WkSJFHOq/h9+RERkbq888/V3h4uOLj4yVJdrtdMTExOnTokLZs2WJ0SAAAAICFmL/wgLN69OihHj16PPHzGj5hb/DgwVq4cKFsNpu+//57ubu76+zZs/rxxx/VrFkzo8MBAAAAYDGGV3J++eUXffrpp6pevboOHz6s9u3bq1ixYho5cqSOHDlidDgAAACAxaSeSs7TYngl5/bt2ypcuLAkyc/PTwcOHJAkvfXWW9q9e7fR4QAAAACwGMOTnIIFC2rv3r2SJF9fX/3xxx+SpOjoaN28edPocAAAAABYjOHT1dq1a6d+/fopLi5Or776qho3bqyEhATt379fFStWNDocAAAAwFpS2T45T4PhSU6TJk2UP39+eXl5qVChQpo5c6aWLFmiChUqqHv37kaHAwAAAMBiDE9yJKl8+fKOr6tVq6Zq1aqZEQYAAABgQSw8YHiSc/bsWU2fPl3h4eGKiYlJ8vyyZcuMDgkAAACAhRie5PTq1UsXLlxQ/fr15eXlZXT3lmS32zVjxiItWbJaly9fVcmSARo0qKtKlHi0HWJhDYwLJIdxgeQwLiBJ5y/f0mtd1+utV/3UrWWpZI85FRGpcXP3aeeB84qNS1DFEtnVq00ZFSmY2dhggQcwPMkJCQnRV199paJFixrdtWXNmLFI8+Z9rVGj+qhgwbyaOfNLtW3bW2vXzle2bFnMDg8mYVwgOYwLJIdxAbvdrn4f/aor16PveUxMbLzaDf5RPtnTat7IF+Th7qKJCw+ozaAt2ji7odKndTcwYtwf09UMX3qhWLFiioiIMLpby4qNjdPcuV+pc+dWevHFmvL3L6QxY/orbdo0Wrx4ldnhwSSMCySHcYHkMC4gSXO+CdPVG9HKnNHjnsecu3hTxX29Nfq9yiru6y2//JnUpUUJXb4WrbBjVwyMFngwwys5wcHB6tChg3799VflyZNHNlviTLNly5ZGh5SqhYaG68aNKFWpUs7R5ubmqgoVSuu33/4wMTKYiXGB5DAukBzGBQ4cvqRZS0P01YR6Chqw5Z7H5c+dQZ/2r+74/uKV25r37SHlyJJGRQt7GxEqnMYS0oYnOV988YVOnTqllStXJrknx2azkeQ8pIiIC5Kk3LlzJGrPkSOr/vgjxIyQkAIwLpAcxgWSw7h4tkXdilXv8TvUK6iMfPNlcvp17435Reu3nZSHu4umDq7JVDWkOIYnOcuXL9f48ePVqFEjo7u2pJs3b0uSPDwSX1w8PT0UHZ109To8GxgXSA7jAslhXDzbhk/bo8L5Mqp5A7+Het27zUuofdOi+mL1EXUZ+bM+D66j54pme0pRAg/P8FpWhgwZVLx4caO7tSwvrztzZ2NiYhO1R0fHKG3aNGaEhBSAcYHkMC6QHMbFs2v1j39r+75zGt2j8kO/NqBgZpUqklXB71dW3pzptXD14acQIR6ZzWb+w2SGJzn9+vXTqFGjFBYWpsjISN26dSvRAw/HxyeXJCki4mKi9vPnLylXruxmhIQUgHGB5DAukBzGxbNr6YZjunI9WnXbr1a5ZstUrtkyXbp2W7OWhqpcs6T7Fp4+H6U1W48nanNxscm/QCZFXOJvOKQshk9XGz16tK5evaomTZok+3xoaKjBEaVuAQGFlTFjeu3cuVdFihSSJMXFxWv37v16882GJkcHszAukBzGBZLDuHh2je9VRbdj4hO1Ne/9g16tnV+tGiXdIyn8+DX1Gr9DhfNlVLH/LjQQG5egg+GXVatCbkNihrPMr6SYzfAk5+OPPza6S0vz8HBXUFAzTZz4mbJm9Za/f0HNnPmlbt26rebNue/pWcW4QHIYF0gO4+LZlTNb2iRtri42ZUrvoQI+GSRJF67cUlovN6VL465qZXOphJ+3Bn66U0M7V1AaLzdN/+qgbkTFql1T9j9EymJ4krNw4UK9//77Kly4sNFdW1bnzq0UHx+v0aOnKjIySqVKBWj+/I+UJUtms0ODiRgXSA7jAslhXOBearZaqS4tSqhby1Jyd3PRrGG1NX7ePnUZ+bNu3opThZI5tGhcXeXNmd7sUIFEbHa73W5kh5UrV9ayZcuUL1++J3zmM0/4fEj9fMS4QFKMCySHcYHk+Mh+ZKjZQSAFsfl/aHYIzon51ewIJI8qpnZveCWnTZs2GjBggIKCguTj4yNPT89Ez/v5PdwShgAAAADwb4YnORMnTpQk7d69O8lzNpuNhQcAAAAAPBbDk5xNmzYZ3SUAAADwDDF8l5gUx/AkJ0+ePJKkkJAQHT16VAkJCfL19VXJkiWNDgUAAACABRme5Fy9elW9evXSL7/8okyZMik+Pl6RkZEqV66cZs6cqQwZMhgdEgAAAGAdNvbJMbyWNWrUKF25ckVr1qzRzp07tXv3bq1evVq3b9/WuHHjjA4HAAAAgMUYnuT8+OOPGjZsmHx9fR1t/v7+Gjp0qDZu3Gh0OAAAAAAsxvDpam5ubvLw8EjS7uHhodjYWKPDAQAAACyG6WqGV3KqVaumMWPG6PLly462S5cuaezYsape/f/bu++oKK73f+Bv7GDDfIKaaCwh7mLoiLKKil2DCBERRcVeYqzYYo9Ysf0+RoyKnVgTFezYULHEgiXYAGUBETRi+0RFEJTn94eH+bqCFE1QN+/XOZ7jzsze+8ydmcs+e+/MOhZ2OEREREREpGcKfSTnhx9+QPfu3eHk5ITPP/8cBgYGSEpKQq1atTB79uzCDoeIiIiISM/wEdKFnuRUrFgRu3btwtGjR6HValGyZEmYmpqiQYMGMOCTIIiIiIiI6B0VepIDvLwvx8nJCQ0aNICIAADS0tIAAIaGhu8jJCIiIiIi0hOFnuRcvHgRU6ZMQWRkpM5yEYGBgUG25UREREREVBCcHVXoSc6MGTNgaGiIn3/+GWXKlCns6omIiIiISM8VepJz7do1bNq0CWq1urCrJiIiIiLSf7zPvfAfvVCzZk3cvXu3sKslIiIiIqJ/iUIfyenRowcmT56Mrl27onr16ihevLjOeicnp8IOiYiIiIiI9Mh7+Z0cAJg7d262dXzwABERERHRu+J0tUJPcqKiogq7SiIiIiIi+hd5L7+TQ0RERERE/5RCv+3+g8MWICIiIiIivcIkh4iIiIiI9AqnqxERERER6RU+eIAjOUREREREpFc4kkNEREREpE8MOJLDkRwiIiIiItIrTHKIiIiIiEivcLoaEREREZFe4TgGW4CIiIiIiPQKkxwiIiIiItIrnK5GRERERKRX+HQ1juQQEREREZFe4UgOEREREZFe4UgOR3KIiIiIiEivMMkhIiIiIiK9wulqRERERET6xIDT1TiSQ0REREREeoUjOUREREREeoXjGGwBIiIiIiLSK0xyiIiIiIhIr3C6GhERERGRXuGDBziSQ0REREREeoUjOUREREREeoUjORzJISIiIiIivcIkh4iIiIiI9AqnqxERERER6RMDjmOwBYiIiIiISK9wJIeIiIiISK/wwQMcySEiIiIiIr3CJIeIiIiIiPQKp6sREREREekVTlfjSA4REREREekVJjlERERERKRXOF2NiIiIiEivcLoaR3KIiIiIiEivMMkhIiIiItInBkXe/78CyMjIwNSpU+Hg4AAHBwfMmzcPmZmZ79QEnK5GRERERETvzf/7f/8PJ06cwLJly/DkyRP88MMPKFeuHPr37//WZXIkh4iIiIiI3otnz55h48aNGDt2LKytreHo6IiRI0ciMDDwnUZzmOQQEREREekVgw/gX/5ERkYiNTUV9vb2yjJ7e3vcu3cPCQkJb7PzADhdjYiIiIiI/maPHj3Co0ePsi0vV64cypUrp7y+c+cOjIyMULZsWWWZiYkJAODPP/9EjRo13qp+PUpyPn/fAdAHiecF5YTnBeWE5wVlZ1DL932HQPQW3n9/Fhjoj0WLFmVbPnjwYAwZMkR5nZqaihIlSuhsk/U6PT39revXoySHiIiIiIg+BD169ED79u2zLX91FAcASpUqlS2ZyXptaGj41vUzySEiIiIior/V69PS3qRy5cp4+vQpUlJSULp0aQDA3bt3AQCVKlV66/r54AEiIiIiInovzMzMYGhoiHPnzinLzp49i08//RTVqlV763KZ5BARERER0XtRqlQpeHh4YPr06Th//jxOnjyJ+fPno0ePHu9UroGIyN8UIxERERERUYE8e/YM06dPx+7du1GyZEl4eHhgxIgRMDDI/6OoX8ckh4iIiIiI9AqnqxERERERkV5hkkNERERERHqFSQ4REREREekVJjkfobFjx2Lo0KHvOwzKp8TERKjValy7dg0A0KxZM6xbty5f7/X29sbs2bPfuL4gZeUmJSUFmzdvfudy6N35+/vD3d09X9uePn0aarUaKSkp2c6zvKjVahw+fDjHdQUtKzeJiYk4ePDgO5fzPr3aVn/XNZcbEcFvv/2GZ8+eASjYOfFPOnPmDCIjI99L3QcOHMDt27ffS9309/tQzmnSb0xyiArZli1b0KFDh/cdho7Vq1dj48aN7zsM0kPjxo3T+e2Dj9Hx48fh6OhYaPWFh4dj0qRJeP78OQCgd+/eWLlyZaHV/ybe3t74888/C73epKQkDB48GI8fPy70uono41XsfQdA9G/zySefvO8QsuFDFonezMTEpFDre/16LF26tPIr4P9G7J+I6G1wJCef7ty5g6FDh8LW1haNGjXClClTdKaIbN++Hc2aNYOdnR1GjhyJJ0+eKO/VarXo3bs3rK2t0axZMyxYsAAZGRkAXk43cXR0RFBQEJo1awZ7e3sMHDgQDx48UN4fFhaGtm3bwsrKCj4+PkhLS1PWOTs7Y9GiRTqxDh8+HJMnT1ZiCwsLQ6tWrWBtbQ0fHx/cunUL/fv3h7W1NVxdXXWmH2i1WgwYMAD29vbQaDSYNm2aUt/blBcWFoYOHTrAysoKtra26NOnD27duvX3HpwPxK1bt9CnTx/Y2NigVatW+PXXX6FWq7Nt9+p0l8zMTKxZswatWrWCpaUlOnTogFOnTulsf//+ffTp0weWlpZwdXXFxYsXc6z/yZMnmDRpEhwdHWFubo5mzZph7dq1yvrw8HDlWDg5OWHRokUQEQQFBWHRokW4cuWKEm96ejr++9//omnTprCysoK3tzeioqKUsry9vbFs2TJ89913sLKygrOzM65cuYKlS5dCo9FAo9Fg1apVyvbJycnw8fGBg4MDLCws0Lp1a+zZs+ftG/s9UqvVCAkJgaurK6ysrNC7d2/cvn0bo0ePVo7977//rmz/pr4jS0REBDw8PGBlZYVevXrh4cOHyrpXp6Nlye80j0ePHmHKlClo2LAhbGxs8N133yExMVFnm4iICKVvGTBgAO7du5djWbGxsejfvz/q1KkDCwsLfPvttzrn6caNG9GiRQtYWFjAxcUFBw4cAPByau2ZM2ewatUqeHt7K+0xcuRIaDQa2NvbY9SoUTr7XND2jYiIgLe3N2xsbGBlZQUvLy+dczU/1Go1Nm3ahG+++QY2Njbo168f7ty5o7M+p6l9eV2/mZmZ+Pnnn+Hk5ARbW1v06tULcXFxSE5ORu3atXHmzBmdbRs2bIiAgAB0794dAGBnZ4fTp09nO+anT5+Gp6cnrK2t0apVKwQHB+d7X3Prk58/fw5fX180aNAA1tbW6N69O65fvw7gZb8FAN999x38/f0RFBSEDh06YMSIEahTp44y0rRt2za4uLgo/UJISIhO/Rs2bFD+fnh6eiIiIgLp6emoW7dutv3o2LEjAgIC0Lx5cwBAu3btEBQUpLMf1tbWaN68OdavX6+8LygoCJ6enlizZg00Gg3q1auHZcuWISIiAq6ursoxfvTokfKeFStWoGXLlrCwsICDgwMmT56M9PT0fLfrh0atVmPbtm1o3749bGxs4OnpqfO3I7frMOtv/eLFi1GvXj0MGjRI+ayye/duNG7cGLa2tpg+fTri4uLQuXNnWFtbw8vLC0lJSUodwcHBcHFxgYWFBezt7TFs2DCdNif6xwnlKTMzUzw8PMTHx0euX78uf/zxh3Tq1EmGDh0qN2/eFJVKJU2bNpUTJ05IRESEODs7y+DBg0VEJC0tTZo0aSIzZsyQ2NhYOXnypLRu3Vpmz54tIiKnTp2Sr7/+Wjp37iyXL1+WkydPikajkRkzZoiISExMjHz99dfy888/i1arlQULFohKpZIhQ4aIiMjixYvlm2++UWJNSUkRa2trOXPmjBJb+/bt5fLly3L8+HExNzcXjUYjwcHBEh0dLZ6entKzZ08REXnw4IFoNBoZPny4REdHy4kTJ6Rp06YyceJEEZECl5eQkCDm5uYSGBgoN2/elPDwcGnVqpWMHTu2cA5cIcrIyJC2bdtK3759JTo6WkJDQ6V+/fqiUqmUdouOjhYRkaZNm8ratWtFRGTRokVSt25d2b17t2i1WvHz8xMLCwtJSEgQEZFu3bqJWq2WZcuWiVarlWnTpomNjY3cv38/W1njx4+X9u3by8WLF+XGjRuyYMECMTMzk4SEBHn+/LnUq1dP5s2bJzdv3pTDhw+LlZWVHDhwQFJTU8XPz09cXV0lOTlZRETGjRsnTZo0kaNHj8r169dl1KhRotFo5K+//lLisra2luDgYImLi5NOnTqJvb29jBgxQmJiYsTf31/MzMwkKSlJRER69Oghffr0kejoaImNjZUJEyaIra2tpKSkFN5B+puoVCpp1qyZnDlzRiIiIqRevXpib28vK1eulJiYGBk4cKC0bNlSRHLvO0RE7t+/L/b29jJlyhTRarWybt06+frrr6V9+/Yi8rJ/UKlU8uTJE6X+hQsX5rj+9fOse/fu0q5dOwkPD5fIyEjp1auXODs7S0ZGhrIfdevWlf3790tUVJR069ZNPD09RUR0ysrMzJRWrVrJDz/8ILGxsRIdHS0DBgyQZs2aiYjIlStXxMzMTPbu3SuJiYkSEBAg5ubmcu/ePXn06JF06tRJfvzxR3n48KGkp6dL69atxdvbWy5fviwXLlyQb7/9Vnr16vVW7fv48WOpW7euzJs3TxISEuTSpUvi6ekp3t7eBT6mDg4OEhISIpGRkdKlSxdxd3eXzMxMZf2hQ4dEpGDX708//SQODg5y4MABiYuLk+HDh0urVq0kMzNTevbsKT/++KMSw8mTJ8XGxkZSUlJk3759olKpJCEhQZ49e6ZzzLVarZibm8ucOXMkNjZWgoODxdzcXMLDw/Pcz7z65NWrV4uTk5P88ccfcuPGDRkyZIi4ubmJyMtzVaVSybZt2+TJkyeydetWUalUMm3aNImLi5Pbt29LcHCwWFhYyKZNmyQ2NlaWL18uZmZmcvbsWRER2bJli1hZWcnWrVslPj5eZsyYIfXq1ZPHjx/LhAkTpG/fvjqxqtVquXnzpkRERIhKpZLTp09LamqqnDp1SmrXri1Lly6V2NhY2bx5s1hZWcmOHTtERGTr1q1ibm4uw4cPl7i4OAkICBC1Wi0uLi5y+vRpCQ8Pl7p168rixYtFRGTbtm1St25dCQsLk8TERNmzZ49YWFhIcHBwgc6jD4lKpRInJyc5cuSIREZGipeXl3Is87oOs65/Ly8viYuLk5iYGOWzSo8ePeT69euybds25bNPaGioXLlyRZo3by4TJkwQEZHw8HAxNzeXnTt3SmJiohw9elQcHBzE399fRHT7MaJ/CpOcfPj999/FxsZGnj17piyLjY0VlUol4eHholKpZOfOnTrbq9VquXv3rmzevFlatWqlU96xY8fEwsJCMjIylA8pf/zxh7J+5syZ0qlTJxER8fPzkw4dOui8v0OHDkqSc/PmTVGr1RIZGSkiIjt27BAnJyfJzMxUOqp9+/bpvHfQoEHK6/Xr10vDhg1FROSXX34RjUYjaWlpyvojR46ImZmZ3L9/v8DlxcXFybp163Rinzdvnnh4eLy5sT9Sx44dE3NzcyX5EBHZsGFDrklOZmamODg4yKpVq3TK8vDwkKlTp4rIy2SiX79+yroXL15Is2bNZPXq1Tplibz8w3716lVl22fPnolKpZIjR47Iw4cPRaVSSWBgoPLB7dy5c/Lnn3+KiO4fnL/++ktq166tc5yfPXsmjRs3lpUrVypxDRgwQFm/bt06UalU8ujRIxERSU1NFZVKJcePHxcRUT5UZdFqtaJSqSQmJqZgDf0BUKlUOsds6NCh8u233yqvjxw5Imq1Wp49e5Zr33H79m1Zt26dNGzYUEk8ssp71yQnKipKVCqVXL58WXnfgwcPxNraWvbu3avsx/Lly5X1CQkJolKp5MqVKzplpaSkyPLly5VjKyJy9OhRUalUkpqaKvv375fatWvLpUuXRORlYnfs2DElge3WrZv4+fmJiEhoaKiYm5srybTIyy9yVCqV8v6CtG9ycrIsX75cXrx4oazfuHGj1K9f/43HLycqlUqWLl2qvL5x44aoVCqJiIhQ1r+e5OR1/WZmZopGo5HAwEBl3cOHD8XPz0/++usvCQoKEo1GI8+fPxcRkYkTJ8qoUaNEJPtxf/WY+/n56bSHiMiaNWvk5MmTee5nXn3ytGnTpHXr1nLv3j0REbl3756cOXNGp52y2iEryXm1z2vfvr1MmzZNp/xhw4YpfYW7u7vMnDlTWZeRkSF+fn5y8+ZNOX36tJibm8uDBw9ERGTJkiXi5eUlIpKtDx08eLBO/yMiMmfOHHFxcdGJLausp0+fKv3fq3GNGDFCRF4mmAcPHtQpz9PTU+bMmZNbc37QVCqVBAQEKK8PHjwoKpVK0tPT87wOs9o7JCREWZ91Tr7ap2g0Gp3jOWfOHOWzy6VLl7IliT4+PjJs2DARYZJDhYP35OSDVqtFamoqHBwcsq2Li4sDANjb2yvLLC0tISKIiYmBVqvFzZs3YWtrq6wXEaSnp+tM26pevbry/zJlyijT2bRaLb7++mudOi0tLXH//n0AQNWqVWFnZ4eQkBCYmZlh9+7dcHFxgYGBgbL9F198ofzf0NAQVapUUV6XKlVKGZKPiYmBmZkZSpYsqayvU6cOMjMzERsbi8qVKxeovBo1asDQ0BDLli3DtWvXEBsbi+jo6ByncH3soqOjUaVKFZ37bV495jm5f/8+Hj58CCsrK53ldnZ2iI6OVl5bW1sr/y9SpAhq166tTCF5Vfv27XHo0CEEBQUhLi5OmTaYmZkJY2Nj9O/fHzNmzMCyZcvg5OQEV1dXVKpUKVs5cXFxePHihU69JUqUgKWlJbRarbLs9fOgfPnyKFu2LICX5wEA5Vzw8vJCSEgIVq5cibi4OFy9ehUA8OLFi1zb6EOV1zUgIsjIyMiz74iJiYFKpUKxYv/XFVtaWuLmzZvvFJ9Wq0Xx4sV1+o4KFSqgZs2aOsfQxsZGZ5/Kly+P69evo06dOspyIyMjdO3aFTt27MDly5cRFxeHK1euAHh5/Bo2bIi6deuiQ4cO+Oqrr9C0aVN4eHjAyMgoW1wxMTH4/PPPde5xMTU1Rfny5aHVamFhYaHEkiW39jUxMYGHhwd++eUXREdHK7HlVHdeXt3natWqwdjYGNevX892fWbJ6/p9+PAhHjx4AEtLS2WdsbExfvjhBwBAy5Yt4evri1OnTqFevXrYv38/5s6dm2ecWq1Wp0wA6NGjR772Ma8+uVu3bjh48CAaNWoEW1tbNG/ePNeHpBgZGen0eTExMejZs6fONnZ2dsq0Wa1Wi169einrihUrprRHlSpVYGJiggMHDsDT0xO7d+9Gly5dcqw3JiYGrq6u2eoJDAxU+pSyZcuiQoUKAF6eQ1l1ZClVqhSePn0KANBoNLh06RL++9//IjY2FteuXcONGzdgZ2f3xn3/GNSoUUP5f5kyZQAAGRkZeV6HWdfCq+2VJb9//y0sLGBkZAR/f39otVpotVrExMSgZcuWf+s+EuWGSU4+PH/+HJ9//jlWr16dbV3WBV20aFFlWWZmprLs+fPnsLGxwaxZs7K9t3LlysojMYsXL66zTl650VJeu+ny1Q9EAODq6opVq1ahb9++OH78OHx8fHTWvxob8PKDck5KliypkxwB//ch9NUY8lvetWvX0KlTJzRq1Aj16tVDly5dcPjwYZw4cSLH7T9mxYoVK/DNsVmJQE5tnnUOAdnbOzMzM9v5Arx8itWpU6fg5uYGDw8PWFtbo0mTJsr6kSNHon379ggNDUVYWBh69OiBiRMnolu3bjrlvJrk5hbX6+fh6/uRRUTQp08f3Lt3D87OzmjYsCFMTEzQsWPHHLf/GOT3Gsit7zAxMcH+/ftzvb5zatOsJ27lpkSJEjkuf/0Yvh53TufW06dP4enpCUNDQ7Rs2RItW7ZEeno6Bg0aBODlB501a9bg3LlzOHLkCEJDQ7F+/XqsWbNGJ1EGcu5jsuJ6mz4mOTkZ7u7uUKlUaNSoEdzc3KDVarFw4cIct8/N6+fzixcvssXxqryu35yu0VeVKVMGzZo1Q0hICJ4/f46iRYuiQYMGecaZV7m5yatPrlGjBvbt24djx44hLCwMK1aswMaNGxEUFKR8gfGq18+zUqVKZWuPzMxM5ZzLLXYDAwO4uLggJCQEderUQVxcHNq0aZPjtjmdR1n1ZC3P6di96TwKCgqCr68vPDw80KRJEwwbNgyTJ09+Y6wfi5zaW0TyfR3m9Lcgv9fm77//jv79+6Ndu3bQaDTo168fVq1apXyBS1QY+OCBfDA1NUVycjJKly6N6tWro3r16nj+/Dn8/PyUBwxkfTMNABcvXkTRokXx1VdfwdTUFDdu3EDlypWV996+fRvz58/P14ditVqd7UbzrG9Rs7Rp0wa3bt3CmjVrULNmzbceKTE1NUVkZKTy2wwAcOHCBRgYGOh8I5RfwcHBMDc3x8KFC9GtWzfY2dkhISHhrWL70KlUKty6dUvngRGXLl3K9T1lypSBiYkJ/vjjD53lFy5cwJdffqm8fvUm6ufPn+Pq1aswNTXVec+TJ0+wfft2+Pn5wcfHB23atFFuVhcR3L17Fz/++CMqV66Mfv36Yd26dfD09FRu/n/1D161atVQvHhxnbjS09Nx5coVnbjyKyYmBqdPn0ZAQAAGDx6M5s2bKze4FjQx/Njk1Xeo1WpERUXp3OD86vWd9SHl1QcPvP7wgDfVm5GRoVPWgwcPcOPGjTeeW7GxsXj8+HG2c+vMmTOIj4/HunXr0L9/fzRu3Fi5KV9EcP78efj7+ys3L+/ZswefffZZjjfqm5qaIikpCXfv3lWWXb9+HU+ePHmrc+vAgQMoUaIEVq5ciV69ekGj0ejc+FwQr/bhcXFxePz4MczMzN64fV7Xb9myZfGf//xHp9wnT56gfv36yu8PtWvXDocPH8ahQ4fg7OysJFpv+sIAeJmIvFomAIwZMwY//fRTnvuYV58cHByMvXv3okWLFpg2bRq2b9+OhIQEXL58Oc+yAeDLL7/M1h7nz59Xju3rsWdmZqJ169Y4evQogJdf2IWHh2PHjh1o2LChMhLzenuYmprmWE+NGjXe+KE7N+vXr0efPn0wadIkdOjQATVq1EBCQoLe9k9/93WYk02bNqFt27aYNWsWOnfuDHNzc9y4cUNv25Q+TExy8sHR0RG1atXCiBEjcOXKFVy+fBmjR4/Gw4cPleFePz8/XLhwAefPn8f06dPh5uaGChUqwNXVFUWKFMHYsWNx/fp1hIeHY8KECShWrNgbvzF/laenJ+Lj4zFv3jzExcUhICAAFy5c0NnG2NgYjRs3xvLly9GuXbu33s927dqhWLFiSqwnT57E1KlT4ezs/FaPUK1QoQJiY2Nx7tw5JCQkYMmSJdi3b99H/cSaN9FoNDA1NcW4ceNw7do1HDt2LF8fOvr27YulS5ciJCQEcXFxmD17NqKjo+Hl5aVsc+DAAfzyyy/QarXw9fXFs2fP0L59e51ySpYsCUNDQxw8eBCJiYk4c+YMRo0aBeBlgmJsbIzQ0FBMmzYN8fHxuHjxIs6ePQtzc3MAL6ed3L9/Hzdv3oSRkRG8vLzg5+eHY8eOISYmBhMmTEBaWhrc3NwK3DblypVD0aJFERISgqSkJISFhcHX11eJTZ/l1ndUrFgRbdu2RZEiRTBp0iRotVps3rwZe/fuVd5fq1YtlCpVCgEBAbh58ya2bNmCI0eO5FlvzZo10bx5c4wfPx7nzp1DVFQURo0aBRMTEzRt2lTZzt/fH2FhYbh69SrGjh2LRo0aZfuSxNjYGBkZGcrx2717tzJSkp6eDiMjIwQEBGDt2rVITEzE4cOHkZSUpEw9K126NBISEnDv3j00aNAAtWrVwsiRI3H16lVERERgzJgxsLW1zTYFKz+MjY1x7949HD16FImJidi4cSPWrVv3VufV4sWLcfz4cURGRmLcuHGoV69erkkOkPf127NnTyxZsgRhYWGIi4vDpEmT8Omnn+Krr74CADRq1AiZmZkIDg7W6buzptu9/qUTAHTp0gXR0dHw9/dHfHw8goODsWfPHjRu3DjPfcyrT3769ClmzpyJsLAwJCYmYuvWrShZsqQSr5GREWJiYt74ezV9+/bFb7/9hl9//RXx8fFYuXIlDhw4oDxZr2fPnti4cSN27dqFGzduYNasWUhJSVGm9taqVQumpqZYvXq1znS0rPaIjo5GSkoKevfujaNHjyIgIADx8fHYunUrNmzYoDyVrqCMjY1x+vRpaLVaREVFYfTo0bh7967e9k9/93WYE2NjY1y8eBFXr15FbGwspk+fjkuXLultm9KHiUlOPhQpUgSLFy9GuXLl0K1bN/Tu3Rs1atTQeXRzu3btMHToUAwYMAANGjTAlClTALzsnFeuXIn//e9/8PDwwNChQ+Ho6Ijp06fnq+4vvvgCy5cvx/Hjx5VvuV7/gAsALi4uSE9Ph4uLy1vvZ1asDx48QIcOHTBq1Ci0bt06x6l2+eHt7Q2NRoP+/fujY8eOOHv2LMaPH4+EhASdR2zrAwMDAyxatAhpaWno0KEDpk+fjo4dO+Y5taR79+7o2bMnZs2aBTc3N1y4cAGrV6/W+aDZtWtX7Nu3D25uboiMjMTy5cuV+dVZihcvjvnz5+PYsWNwdnbGxIkT0bZtW9jY2CAyMhLFixdHQEAAEhIS0L59e/Tr1w/29vYYMWIEAKBVq1YoWbIkXFxckJycjNGjR6NFixYYM2YMPDw8cP/+faxbtw4VK1YscNtUqlQJvr6+2LBhA5ydnTF37lx8//33qFSp0nv79fTCklffUbZsWaxatUo5Llu2bNGZPlimTBnMnDkTR44cQdu2bXHw4EEMHjw4X3XPmjULtWvXxoABA9ClSxeULl0aa9euVe5PAIB+/fph+vTp8PLyQqVKlTBnzpxs5djY2GDYsGGYO3cuXFxcsHLlSkyePBnFixdHZGQkzMzMMGfOHGzcuBHffPMNpk+fjuHDh6NFixYAgE6dOiE8PBy9e/dW2qNs2bLo2rUr+vXrh9q1ayMgICDX0Ys3+eabb9CxY0eMGTMG3377LXbu3AlfX188ffoU8fHxBSrL3d0dU6dORZcuXVClSpVsj+bPSV7Xb+/eveHu7o7x48fD3d0dT548weLFi5XRhmLFiqF169aoVKmSztQ+lUqFhg0bomfPnjh06JBOnVWqVMGSJUtw8OBBuLi4ICAgAHPmzMnzHkAg7z7Zy8sLnTt3xqRJk9CmTRvs3bsXixcvVr7k6tmzJ/z9/d/4BU6LFi0wYcIELF++HC4uLtixYwcWLlyoJGBt27bFsGHDMH/+fLi6uuLKlStYtmyZzlQ4FxcXFC1aVHlkNfAyOXN3d8e4cePw66+/wtzcHD/99BN27twJFxcXLFu2DOPHj0fnzp3zbIOcTJgwAZmZmXB3d0e/fv1QunRpdO3aNduImb74u6/DnAwdOhRVq1ZF165d0a1bNyQnJ2PIkCGIioriaA4VGgPh2fZOEhMT0bx5c+zcuRMqleq9xREYGIiDBw/q/C4KFZ779+/j4sWLOt+Sh4SEYN68eQgNDX2PkRFRXtRqNZYuXapz/RaWQYMGwczMDEOGDCn0uj9EM2fOxKNHj+Dn5/e+QyGijxwfPPCRu3btGqKjo7F8+XKMGzfufYfzr2VgYIChQ4fCx8cHrVq1wp07d7Bo0SK0bdv2fYdGRB+gM2fO4OrVqzh+/DgmTJjwvsN57y5evIjo6Ghs3rw5xwd1EBEVFJOcj1xUVBQmTZqEtm3bwtnZ+X2H86/1ySefYOHChViwYAEWLFiA8uXLw83Njd/OElGOdu/ejV27dmHcuHH4/PPP37m8ffv2YezYsW9cX7RoUZw9e/ad6/mnnDp1CkuWLEHPnj11Hm1ORPS2OF2NiIjoI5eSkoJ79+69cb2BgQGqVatWiBEREb1fTHKIiIiIiEiv8OlqRERERESkV5jkEBERERGRXmGSQ0SUT97e3lCr1co/MzMzWFtbw83NDZs3b/5H6vT394ejo6PyWq1WY+PGjfl6b0ZGBn755RdkZGS8UwxBQUFQq9XZfpgyv8aOHQtPT893ioGIiKgg+HQ1IqICaN68OXx9fZXXqamp2Lp1KyZOnIhy5cqhdevW/2j9x48f1/nxxNzs2rULM2bMQKdOnf7RmIiIiD40HMkhIiqAkiVLwsTERPlXrVo1+Pj4oEaNGtixY8c/Xr+JiQlKlSqVr235XBkiIvq3YpJDRPQ3KFq0KEqWLAng5fSs77//HgMGDICtra3y6+1arVZZVr9+fQwaNAgJCQlKGSKCZcuWoWnTprCyssLgwYPx119/6dTz+nS1U6dOoUuXLrCxsYGjoyOmTJmC1NRUBAUFKT8QbGVlhaCgIADAnTt3MHLkSNSrVw9169ZFr169cOXKFZ06tm7ditatW8PS0hLe3t64fft2nvv/pjhycv78efTq1Qv29vawsLBA8+bNsWLFCmV9WloaJk+ejIYNG8LCwgJt27bVmQ744MEDDB8+HBqNBpaWlnB3d0doaGieMRIR0b8Hkxwionfw5MkTBAQEQKvV6vwgb2hoKCwtLbF9+3Z07doVd+7cQZcuXWBiYoJff/0Vq1evhqGhITp27Ig7d+4AAFasWIGff/4ZQ4YMwfbt22Fubo5169a9se5Lly6hT58+MDMzw5YtW7BgwQIcO3YMkyZNgrOzM8aPHw8AOHToEJydnfH06VN4e3sjNTUVq1evxqZNm6BWq9G5c2dERUUBAPbs2YOJEyeic+fO2LlzJ5ydnbF06dJc2yC3OF6XnJyMPn36oGbNmti8eTN27tyJNm3aYO7cuYiIiAAALFy4EL///jv8/f2xd+9eeHh4YOLEibhw4QIAwNfXF0lJSVi1ahX27NkDBwcHDB06FLdu3SrAkSMiIn3Ge3KIiApg//79sLW1BQBkZmYiLS0NJiYmGDt2LFq0aKFsZ2RkhEGDBsHAwAAAsGDBApQvXx7Tpk1Tls2ePRtOTk7YvHkzBg0ahMDAQHTp0gXu7u4AgIEDByIiIgKXLl3KMZY1a9agVq1amDx5srJsxowZOHv2LEqVKqXcu/Ppp5+iZMmS2Lx5M+7evYtdu3ahRIkSAF6OOp07dw6BgYGYNWsWAgMD0bJlS/Tq1QsAUKNGDWi1Wqxdu/aNbZJbHK979uwZBg4ciN69e6NYsZd/goYNG4YVK1YgOjoa1tbWiI+Ph6GhIapUqYKKFSuiV69eqFWrlvJjlvHx8TA2NkbVqlVRrlw5+Pj4oEGDBvm+V4mIiPQfkxwiogJo2LChMkJSpEgRlC5dGp988km27b744gslmQGAq1evIjExEXZ2djrbpaWlISYmBg8fPsTdu3dhaWmps97Ozu6NSU50dDTq1Kmjs0yj0UCj0eS4/dWrV5GamgoHBwed5enp6cr/r127ppOsZcWQW5JTkDi++OILeHp6YtOmTbh27RoSEhIQHR0NAHjx4gUAoH///hg4cCCcnJxQu3ZtODo6wsXFBf/5z38AAEOGDMHo0aNRv359WFtbw9HREa6urkxyiIhIwSSHiKgAjIyMUL169Ty3e/3hAJmZmbCzs8OMGTNyLPNNskZcclK8ePE843g9hqpVq2LlypW51vP6AwvyqqcgcWi1WnTt2hVfffUVGjVqBCcnJ1hYWKBx48bKNjY2Njhy5AhOnjyJkydPIjQ0FKtWrcLs2bPh4uKCFi1a4NixYzh+/DhOnTqFrVu3YsWKFVi6dGm2BI6IiP6deE8OEVEhUKlUiIuLQ8WKFVG9enVUr14dn332GebOnYvw8HB88skn+Oyzz7JN8cq6TyUnpqam2UZ59u/fDycnJzx9+lRnJCkrhj///BOlSpVSYqhevTqWLl2KQ4cOAQBq166Nc+fO5TuG/MTxqo0bN6J06dJYu3YtBgwYgObNm+Phw4cA/i+5WrZsGUJDQ9GkSROMGzcOe/bsgbW1NbZv3w4Rwdy5c3Hp0iW0adMGU6ZMwf79+1GhQgWEhITkGicREf17MMkhIioEXbp0QVpaGoYPH47Lly8jJiYGo0aNwokTJ2BmZgYAGDBgAH777TesX78e8fHxWLNmDQ4cOPDGMvv27YuoqCj4+fkhNjYWp06dwuzZs6HRaGBkZITSpUsDePlggJSUFLi6uuLTTz/FkCFDcPbsWcTHx2Pq1KnYsWMHatWqpcQQFhaGRYsWIS4uDsHBwdiwYUOu+5ZXHK/67LPPcPfuXYSGhuLWrVsICwuDj48PgP+bNnf//n1MmzYNhw8fRlJSEsLCwhATEwNbW1sYGBjgxo0bmDJlCk6dOoWkpCSEhIQgOTlZuVeKiIiI09WIiApB1apVsX79esyfPx/e3t4oUqQILCwsEBgYiC+//BIA4OXlBQBYtWoV/Pz8YGdnhz59+mDLli05lmlmZoalS5di4cKFWL9+PYyNjeHs7KwkDfXr14e9vT169uwJHx8f9OnTB+vXr8fcuXPx/fffIz09HSqVCkuWLEG9evUAAE5OTvjpp5+waNEiBAQEQKVS4fvvv8fcuXPfuG95xfEqb29v3LhxAxMnTkRaWhqqVq2KTp064eDBg7h48SIAYOTIkTAwMICvry/u3buHihUromvXrhgwYACAlw81mD17NkaNGoX//e9/qFKlCsaMGQM3N7e3PDpERKRvDIS/FkdERERERHqE09WIiIiIiEivMMkhIiIiIiK9wiSHiIiIiIj0CpMcIiIiIiLSK0xyiIiIiIhIrzDJISIiIiIivcIkh4iIiIiI9AqTHCIiIiIi0itMcoiIiIiISK/8f6gon9VHrgJ9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 12))\n",
    "sns.set(font_scale = 1.3)\n",
    "sns.heatmap(cm_LG, annot = True, linewidths = 1, cmap = \"YlOrBr\", ax = ax)\n",
    "cell_type = ['ependymoma', 'glioblastoma',' medulloblastoma', 'pilocytic_astrocytoma','normal']\n",
    "ax.set_title(\"Confusion Matrix for Logistic Regression\")\n",
    "ax.set_xlabel(\"Predicted class\")\n",
    "ax.set_ylabel(\"Actual class\")\n",
    "ax.set_xticklabels(cell_type)\n",
    "ax.set_yticklabels(cell_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d1a39119",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'ependymoma'),\n",
       " Text(0, 1.5, 'glioblastoma'),\n",
       " Text(0, 2.5, ' medulloblastoma'),\n",
       " Text(0, 3.5, 'pilocytic_astrocytoma'),\n",
       " Text(0, 4.5, 'normal')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 12))\n",
    "#sns.set(font_scale = 1.2)\n",
    "sns.heatmap(cm_nb, annot = True, linewidths = 1, cmap = \"YlOrBr\", ax = ax)\n",
    "cell_type = ['ependymoma', 'glioblastoma',' medulloblastoma', 'pilocytic_astrocytoma','normal']\n",
    "ax.set_title(\"Confusion Matrix for Naive Bayes\")\n",
    "ax.set_xlabel(\"Predicted class\")\n",
    "ax.set_ylabel(\"Actual class\")\n",
    "ax.set_xticklabels(cell_type)\n",
    "ax.set_yticklabels(cell_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fa2145f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'ependymoma'),\n",
       " Text(0, 1.5, 'glioblastoma'),\n",
       " Text(0, 2.5, ' medulloblastoma'),\n",
       " Text(0, 3.5, 'pilocytic_astrocytoma'),\n",
       " Text(0, 4.5, 'normal')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 12))\n",
    "#sns.set(font_scale = 1.2)\n",
    "sns.heatmap(cm_rf, annot = True,linewidths = 1, cmap = \"YlOrBr\", ax = ax)\n",
    "cell_type = ['ependymoma', 'glioblastoma',' medulloblastoma', 'pilocytic_astrocytoma','normal']\n",
    "ax.set_title(\"Confusion Matrix for Random Forest\")\n",
    "ax.set_xlabel(\"Predicted class\")\n",
    "ax.set_ylabel(\"Actual class\")\n",
    "ax.set_xticklabels(cell_type)\n",
    "ax.set_yticklabels(cell_type)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}