mnist_digit_classification.ipynb

{
 "cells": [
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # **MNIST - IMAGE CLASSIFICATION**\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "> ### ABOUT MNIST DATASET\n",
    "> - **Features**: MNIST consists of 70,000 images of handwritten digits. These digits range from 0 to 9.\n",
    ">\n",
    "> - **Image Details**: Each image is a grayscale, 28x28 pixel square, which means each image contains 784 pixels (28x28=784), with each pixel value ranging from 0 (black) to 255 (white).\n",
    ">\n",
    "> - **Structure**: 60,000 images, used to train machine learning models. 10,000 images, used to evaluate the performance of the models.\n",
    ">\n",
    "> - **Labeling**: Each image is labeled with the corresponding digit it represents, making it a supervised learning dataset where the goal is to train a model to correctly classify these handwritten digits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### IMPORTING REQUIRED LIBRARIES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # **DATASET OVERVIEW AND VISULIZATION**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>pixel1</th>\n",
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       "      <th>pixel9</th>\n",
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       "      <th>class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  pixel8  pixel9  \\\n",
       "0       0       0       0       0       0       0       0       0       0   \n",
       "1       0       0       0       0       0       0       0       0       0   \n",
       "2       0       0       0       0       0       0       0       0       0   \n",
       "3       0       0       0       0       0       0       0       0       0   \n",
       "4       0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel10  ...  pixel776  pixel777  pixel778  pixel779  pixel780  pixel781  \\\n",
       "0        0  ...         0         0         0         0         0         0   \n",
       "1        0  ...         0         0         0         0         0         0   \n",
       "2        0  ...         0         0         0         0         0         0   \n",
       "3        0  ...         0         0         0         0         0         0   \n",
       "4        0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel782  pixel783  pixel784  class  \n",
       "0         0         0         0      5  \n",
       "1         0         0         0      0  \n",
       "2         0         0         0      4  \n",
       "3         0         0         0      1  \n",
       "4         0         0         0      9  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist = pd.read_csv('data.csv')\n",
    "mnist.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### REPRESENTATION OF DIGIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "digit = np.array(mnist.iloc[:1, :784]).reshape(28, 28)\n",
    "plt.figure(figsize=(13, 7))\n",
    "sns.heatmap(digit, cmap='binary', cbar=None)\n",
    "plt.title(f\"Heatmap of Digit: {mnist.iloc[:1, -1:].values[0, 0]}\")\n",
    "plt.axis(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 5, ncols = 5)\n",
    "fig.set_figheight(15)\n",
    "fig.set_figwidth(15)\n",
    "\n",
    "idx = 0\n",
    "\n",
    "for i in range(5):\n",
    "    for j in range(5):\n",
    "        digit = np.array(mnist.iloc[idx:1 + idx, :784]).reshape(28, 28)\n",
    "        ax[i, j].imshow(digit, cmap='binary')\n",
    "        ax[i, j].axis(\"off\")\n",
    "        ax[i, j].set_title(f\"Heatmap of Digit: {mnist.iloc[idx:idx + 1, -1:].values[0, 0]}\")\n",
    "        idx += 1\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "1    7877\n",
       "7    7293\n",
       "3    7141\n",
       "2    6990\n",
       "9    6958\n",
       "0    6903\n",
       "6    6876\n",
       "8    6825\n",
       "4    6824\n",
       "5    6313\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist['class'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # **DATA SAMPLING - `StratifiedShuffleSplit`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "\n",
    "split = StratifiedShuffleSplit(n_splits=10)\n",
    "\n",
    "for train_idx, test_idx in split.split(X=mnist, y=mnist['class']):\n",
    "    train_set = mnist.iloc[train_idx]\n",
    "    test_set = mnist.iloc[test_idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = train_set.drop('class', axis=1)\n",
    "y_train = train_set['class']\n",
    "\n",
    "X_test = test_set.drop('class', axis=1)\n",
    "y_test = test_set['class']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "0    6213\n",
       "1    7089\n",
       "2    6291\n",
       "3    6427\n",
       "4    6142\n",
       "5    5682\n",
       "6    6188\n",
       "7    6564\n",
       "8    6142\n",
       "9    6262\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class\n",
       "0    690\n",
       "1    788\n",
       "2    699\n",
       "3    714\n",
       "4    682\n",
       "5    631\n",
       "6    688\n",
       "7    729\n",
       "8    683\n",
       "9    696\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # **TRAINING A BINARY CLASSIFIER**\n",
    "\n",
    ">Let’s simplify the problem for now and only try to identify one digit—for example,\n",
    "the number 5. This “5-detector” will be an example of a binary classifier, capable of\n",
    "distinguishing between just two classes, 5 and not-5. Let’s create the target vectors for\n",
    "this classification task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_5 = (y_train == 5)\n",
    "y_test_5 = (y_test == 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### MODEL TRAINING - `SGDClassifier`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "sgd_clf = SGDClassifier(random_state = 42)\n",
    "sgd_clf.fit(X_train, y_train_5)\n",
    "y_pred = sgd_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### CROSS VALIDATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.96747619 0.96519048 0.94004762]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "scores = cross_val_score(\n",
    "    sgd_clf,\n",
    "    X = X_train,\n",
    "    y = y_train_5,\n",
    "    cv = 3,\n",
    "    scoring = 'accuracy'\n",
    ")\n",
    "\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### MODEL TRAINING - `DummyClassifier`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.dummy import DummyClassifier\n",
    "\n",
    "dummy_clf = DummyClassifier(random_state = 42)\n",
    "dummy_clf.fit(X_train, y_train_5)\n",
    "y_pred = dummy_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.90984127 0.90984127 0.90984127 0.9097619  0.9097619 ]\n"
     ]
    }
   ],
   "source": [
    "score = cross_val_score(\n",
    "    dummy_clf,\n",
    "    X = X_train,\n",
    "    y = y_train_5,\n",
    "    cv = 5,\n",
    "    scoring = 'accuracy'\n",
    ")\n",
    "\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6369,)\n",
      "(631,)\n",
      "(7000,)\n",
      "(0,)\n"
     ]
    }
   ],
   "source": [
    "print(y_test_5[y_test_5 == False].shape)\n",
    "print(y_test_5[y_test_5 == True].shape)\n",
    "\n",
    "print(y_pred[y_pred == False].shape)\n",
    "print(y_pred[y_pred == True].shape)\n",
    "\n",
    "# This is also calculated using Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The reason behind `Accuracy Score > 0.9` - This is simply because only about 10% of the\n",
    "images are 5s, so if you always guess that an image is not a 5, you will be right about\n",
    "90% of the time. - `Class Imbanance`\n",
    "\n",
    "> This demonstrates why `accuracy` is generally not the preferred `performance measure`\n",
    "for `classifiers`, especially when you are dealing with `skewed datasets` (i.e., when some\n",
    "classes are much more frequent than others)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # **MODEL PERFORMANCE - `Precision, Recall and F1-Score`**\n",
    "\n",
    "> ### MODEL PREDICTION - `cross_val_predict`\n",
    "\n",
    "> This function performs `K-foldcross-validation`, but instead of returning the evaluation scores, it returns the predictions made on each test fold. This means that you get a clean prediction for each\n",
    "instance in the training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False, False, False])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "y_pred_cv = cross_val_predict(\n",
    "    sgd_clf,\n",
    "    X = X_train,\n",
    "    y = y_train_5,\n",
    "    cv = 3\n",
    ")\n",
    "\n",
    "y_pred_cv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### CONFUSION MATRIX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[55472,  1846],\n",
       "       [  827,  4855]], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion_matrix(y_train_5, y_pred_cv)\n",
    "# Each row in a confusion matrix represents an actual class, while each column represents a predicted class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### PRECISION - RECALL - F1 SCORE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision_score : 0.7245187285479779\n",
      "recall_score : 0.8544526575149595\n",
      "f1_score : 0.7841395461519826\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score, recall_score, f1_score\n",
    "\n",
    "print('precision_score :', precision_score(y_train_5, y_pred_cv))\n",
    "print('recall_score :', recall_score(y_train_5, y_pred_cv))\n",
    "print('f1_score :', f1_score(y_train_5, y_pred_cv))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Generally, Those models will be selectd which having both `Precision` and `Recall` to be `High`. It means `High F1-Score`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### PRECISION RECALL TRADE-OFF\n",
    "\n",
    "> `Scikit-Learn does not let you set the threshold directly`, but it does give you access to\n",
    "the decision scores that it uses to make predictions. Instead of calling the classifier’s\n",
    "predict() method, you can call its `decision_function()` method, which returns a\n",
    "score for each instance, and then make predictions based on those scores using any\n",
    "threshold you want"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(63000,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(3685,)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decision_scores = sgd_clf.decision_function(X_train)\n",
    "print(decision_scores.shape)\n",
    "\n",
    "decision_scores[decision_scores > 3000].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> `Defalut the threshold is 0` but as you `increase the threshold` - The model becomes more and more accurate about the `precise prediction` but it also looses to predict the data points which comes below that perticular threshold which to - `Precision will get increases` and `Recall get decreases`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_scores = cross_val_predict(\n",
    "    sgd_clf,\n",
    "    X_train, y_train_5,\n",
    "    cv = 3,\n",
    "    method='decision_function'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "\n",
    "precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)\n",
    "\n",
    "plt.figure(figsize=(13, 5))\n",
    "plt.plot(thresholds, precisions[:-1], label = \"precision\")\n",
    "plt.plot(thresholds, recalls[:-1], label = \"recall\")\n",
    "plt.axvline(x=0, color = \"yellow\")\n",
    "plt.title(\"precision_recall_curve\")\n",
    "plt.xlabel('Threshold ➡️')\n",
    "plt.ylabel('Precision and Recall ➡️')\n",
    "plt.legend()\n",
    "plt.xlim(-25000, 40000)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1300x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13, 5))\n",
    "plt.plot(precisions, recalls)\n",
    "plt.title(\"Precision vs Precision\")\n",
    "plt.xlabel('Precision ➡️')\n",
    "plt.ylabel('Recall ➡️')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### Creating a `Classifier` which having a `Precision = 0.90`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "threshold : 4877.135869633361\n"
     ]
    }
   ],
   "source": [
    "idx_for_90_precision = (precisions >= 0.90).argmax()\n",
    "threshold_for_90_precision = thresholds[idx_for_90_precision]\n",
    "print(\"threshold :\", threshold_for_90_precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train_pred_90 = (y_scores >= threshold_for_90_precision)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> `y_train_pred_90` is our new prediction which is done by changing `threshold` value so that we can achieve required `precision` and `recall` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9002326934264107\n",
      "0.5447025695177754\n"
     ]
    }
   ],
   "source": [
    "print(precision_score(y_train_5, y_train_pred_90))\n",
    "print(recall_score(y_train_5, y_train_pred_90))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As you can see, it is fairly easy to create a classifier with virtually `any precision` you want: `just set a high enough threshold`, and you’re done.\n",
    "\n",
    "> If someone says `“let’s reach 99% precision,”` you should ask, `“at\n",
    "what recall?”`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # **MODEL PERFORMANCE - `ROC Curve`**\n",
    "\n",
    ">  It is very similar to the precision/recall curve, but instead of plot‐\n",
    "ting precision versus recall, the ROC curve plots the `true positive rate (another name for recall)` against the `false positive rate`. The `FPR is the ratio of negative instances that are incorrectly classified as positive`. It is equal to one minus the `true negative rate which is the ratio of negative instances that are correctly classified as negative`. The\n",
    "`TNR is also called specificity`. Hence the ROC curve plots sensitivity (recall) versus\n",
    "1 – specificity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "tpr, fpr, thresholds = roc_curve(y_train_5, y_scores) # y_scores is the prediction thresholds made by model using cross_val_predict() method\n",
    "\n",
    "# Here threshold values are in decreasing order\n",
    "idx = (thresholds <= threshold_for_90_precision).argmax()\n",
    "# This is an index of threshold where precision = 0.90\n",
    "tpr_90, fpr_90 = tpr[idx], fpr[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.plot(tpr, fpr, linewidth = 0.7, label = \"ROC Curve\")\n",
    "plt.plot([0,1], [0,1], linestyle = 'dotted', label = \"Random Classifier's ROC Curve\")\n",
    "plt.plot([tpr_90], [fpr_90], 'ko', label = \"threshold for 90 precision\")\n",
    "plt.title(\"True Positive Rate(Recall) vs False Positive Rate(1 - Specificity)\")\n",
    "plt.xlabel('TPR ➡️')\n",
    "plt.ylabel('FPR ➡️')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Once again there is a `tradeoff`: the `higher the recall (TPR)`, the `more false positives(FPR)` the classifier produces. The dotted line represents the ROC curve of a purely\n",
    "random classifier; `a good classifier stays as far away from that line as possible (toward the top-left corner)`\n",
    "\n",
    "> One way to compare classifiers is to measure the `area under the curve (AUC)`. `A per‐fect classifier will have a ROC AUC equal to 1`, whereas a purely random classifier will\n",
    "have a ROC AUC equal to 0.5.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### ROC-AUC Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7693592054818718"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "roc_auc_score(y_train_5, y_train_pred_90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # **SECOND MODEL - `RandomForestClassifier`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9964476021314387\n",
      "0.8890649762282092\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(random_state = 42)\n",
    "rf.fit(X_train, y_train_5)\n",
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "print(precision_score(y_test_5, y_pred))\n",
    "print(recall_score(y_test_5, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The predict_proba() method returns an array containing a row per instance and a column per class, each containing the probability that the given instance belongs to the given class "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.  , 0.  ],\n",
       "       [0.99, 0.01]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_scores = cross_val_predict(\n",
    "    rf,\n",
    "    X=X_train, y = y_train_5,\n",
    "    cv = 3,\n",
    "    method=\"predict_proba\"\n",
    ")\n",
    "rf_scores[:2, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The `1st column` represents the `probability of negative class`, `2nd column` represents the `probability of positive class`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### `precision_recall_curve` and `roc curve`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_precisions, rf_recalls, rf_thresholds = precision_recall_curve(y_train_5, rf_scores[:, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows = 1, ncols = 2)\n",
    "\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(7)\n",
    "\n",
    "ax[0].plot(rf_precisions, rf_recalls, linewidth = 0.7, label = \"Random Forest\")\n",
    "ax[0].plot(precisions, recalls, linewidth = 0.7, label = \"SGDClassifier\")\n",
    "ax[0].set_title(\"Precision vs Recall - Random Forest and SGDClassifier\")\n",
    "ax[0].set_xlabel('Precision ➡️')\n",
    "ax[0].set_ylabel('Recall ➡️')\n",
    "ax[0].legend()\n",
    "\n",
    "rf_tpr, rf_fpr, rf_thresholds = roc_curve(y_train_5, rf_scores[:, 1])\n",
    "\n",
    "ax[1].plot(rf_tpr, rf_fpr, label = \"RandomForestClassifier\")\n",
    "ax[1].plot(tpr, fpr, label = \"SGDClassifier\")\n",
    "ax[1].set_title(\"True Positive Rate(Recall) vs False Positive Rate(1 - Specificity)\")\n",
    "ax[1].set_xlabel('TPR ➡️')\n",
    "ax[1].set_ylabel('FPR ➡️')\n",
    "ax[1].legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9984553913445013"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_train_5, rf_scores[:, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As you can see RandomForestClassifier's PR curve looks much better than SGDClassifier's: it comes `much closer to Top-Right corner`.ROC_AUC score is also significantly better\n",
    "\n",
    "> You now know how to train binary classifiers, choose the appropriate metric for your task, evaluate your classifiers using cross-validation, select the precision/recall tradeoff that fits your needs, and compare various models using ROC curves and ROC AUC scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # **MULTICLASS CLASSIFICATION - `SVM`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### ONE-VS-ONE STRATEGY\n",
    "\n",
    "> - Another strategy is to train a binary classifier for every pair of digits: one to distin‐ guish 0s and 1s, another to distinguish 0s and 2s, another for 1s and 2s, and so on. <br><br>\n",
    "> - This is called the `one-versus-one (OvO) strategy`. If there are N classes, you need to train N × (N – 1) / 2 classifiers. <br><br>\n",
    "> - For the MNIST problem, this means training 45 binary classifiers! `When you want to classify an image, you have to run the image through all 45 classifiers and see which class wins the most duels`. <br><br>\n",
    "> - The main advantage of OvO is that each classifier only needs to be trained on the part of the training set for the two classes that it must distinguish."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_clf = SVC(random_state=42)\n",
    "svm_clf.fit(X_train, y_train)\n",
    "\n",
    "svm_scores = svm_clf.decision_function(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction : 0\n",
      "Actual : 5\n"
     ]
    }
   ],
   "source": [
    "print('Prediction :',svm_clf.classes_[svm_scores[0].argmax()])\n",
    "print('Actual :', y_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### ONE-VS-ALL STRATEGY\n",
    "\n",
    "> - One way to create a system that can classify the digit images into 10 classes (from 0 to 9) is to train 10 binary classifiers, one for each digit (a 0-detector, a 1-detector, a 2-detector, and so on). Then `when you want to classify an image, you get the decision score from each classifier for that image and you select the class whose classifier outputs the highest score`. This is called the `one-versus-all (OvA)` strategy (also called one-versus-the-rest).<br><br>\n",
    "> - Scikit-Learn detects when you try to use a binary classification algorithm for a multi‐\n",
    "class classification task, and it automatically runs OvA (except for `SVM classifiers` for\n",
    "which it uses OvO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>OneVsRestClassifier(estimator=SVC(random_state=42))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;OneVsRestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.multiclass.OneVsRestClassifier.html\">?<span>Documentation for OneVsRestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>OneVsRestClassifier(estimator=SVC(random_state=42))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: SVC</label><div class=\"sk-toggleable__content fitted\"><pre>SVC(random_state=42)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "OneVsRestClassifier(estimator=SVC(random_state=42))"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "ovr_svm = OneVsRestClassifier(SVC(random_state=42))\n",
    "ovr_svm.fit(X_train[:2000], y_train[:2000])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ovr_svm.estimators_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction : 0\n",
      "Actual : 5\n"
     ]
    }
   ],
   "source": [
    "print('Prediction :', ovr_svm.predict(X_train.iloc[0:1, :])[0])\n",
    "print('Actual :', y_train[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> - In both `OVR and OVO` the `prediction is wrong` for first row\n",
    "<br><br>\n",
    "> - `Support Vector Machines (SVM)` scales poorly with the size of the training set so we can't use it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # **ERROR ANALYSIS**\n",
    "\n",
    "> Lets take `RandomForestClassifier` for prediction since it shows significant results on both `ROC curve` and `PR curve`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scale = StandardScaler()\n",
    "X_train_scaled = scale.fit_transform(X_train)\n",
    "X_test_scaled = scale.fit_transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### `ConfusionMatrixDisplay`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = cross_val_predict(\n",
    "    RandomForestClassifier(random_state=42),\n",
    "    X = X_train_scaled,\n",
    "    y = y_train,\n",
    "    cv = 3\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "fig, ax = plt.subplots(nrows = 1, ncols = 2)\n",
    "fig.set_figwidth(20)\n",
    "fig.set_figheight(7)\n",
    "\n",
    "sns.heatmap(confusion_matrix(y_train, y_pred), annot=True, cbar=None, ax=ax[0], cmap='crest')\n",
    "ax[0].set_title(\"Confusion Matrix\")\n",
    "ax[0].set_xlabel(\"True Labels\")\n",
    "ax[0].set_ylabel(\"Predicted Labels\")\n",
    "\n",
    "sns.heatmap(confusion_matrix(y_train, y_pred, normalize='true') * 100, annot=True, cbar=None, ax = ax[1], cmap='crest')\n",
    "ax[1].set_title(\"Confusion Matrix - Normalized\")\n",
    "ax[1].set_xlabel(\"True Labels\")\n",
    "ax[1].set_ylabel(\"Predicted Labels\")\n",
    "\n",
    "plt.rc('font', size=10)  # make fonts great again\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This confusion matrix looks fairly good, since most images are on the `main diagonal`, which means that they were `classified correctly`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vv61z586sW7eO7777DrVaTYcOHVAqlXTq1EnfSNChQ4e/DdCEEEIIIcTDReKeBydxj84/iXtuTyd16dKle5bn9rl2r+Ps4uKCtbX1Pffh6OhY7lgUFRWRmJh437wJIURVkemGhKiBbleEVqxYUSZ9+fLlADz22GN/u420tLRyacHBwQAUFhb+bxlE13OhT58+/PLLL2WGgCYnJ7N582Y6deqEnZ3d/7yfB8nH3b0lVq1adc8eG/dz6dIlJk6cyDPPPMPMmTMrXGf48OEcPXqUnTt3lluWkZFBSUkJoDuGJSUlrF69Wr9crVazatWqSuXp888/p7i4WP9+9erVlJSU0L9//zLrjRo1CoVCwcyZM4mMjOSZZ56p1H7uNmDAAJKSkvj+++/1aSUlJaxatQobG5sygcTtaYTef/99mjVrph8q27lzZ/bu3cupU6dkqiEhhBBCCFGOxD2Vy4fEPf8s7mnZsiX+/v6sWLGi3A/5t/+mnp6eBAcHs2HDhjLrXLp0iV27dpVrrLhb/fr19c9puLNM/+T4CCGEIchIAiEM7I8//tD3rr5Thw4dyjzYqjKaN2/O2LFj+fzzz8nIyKBr166cOHGCDRs28OSTT1Y49+fdFi1axMGDB3nsscfw9fXlxo0bfPrpp3h7e9OpU6d/lK+7vfvuu+zevZtOnToxffp0TExM+OyzzygsLGTp0qX/yj7+zsCBA/n666+xt7encePG+uGyzs7Old7W7Yf3dunShU2bNpVZdvt4vvrqq2zbto2BAwcybtw4WrVqRW5uLhcvXuTHH38kOjoaFxcXHn/8cTp27Mgbb7xBdHQ0jRs3ZuvWrZWeL7SoqIiePXsyfPhwQkND+fTTT+nUqRODBg0qs56rqyv9+vVjy5YtODg4PFBAdT+TJ0/ms88+Y9y4cZw+fRo/Pz9+/PFHjhw5wooVK7C1tdWvGxAQgIeHB6GhoWUeUNalSxdef/11AGkkEEIIIYSo4STukbinpsY9SqWS1atX8/jjjxMcHMz48ePx9PTk6tWrXL58Wd8Q8sEHH9C/f3/at2/PhAkTyM/PZ9WqVdjb27Nw4cL77mPixIlMnTqVp556it69e3P+/Hl27tyJi4tLpf4OQghhKNJIIISBzZ8/v8L0devW/ePKMsCXX35JvXr1WL9+PT/99BMeHh7MmTOHBQsWPNDnBw0aRHR0NGvXriU1NRUXFxe6du3K22+/re/p/b9q0qQJhw4dYs6cOSxZsgSNRkO7du3YtGkT7dq1+1f28Xc+/vhjVCoV33zzDQUFBXTs2JE9e/bQt2/fSm8rJSWF3NxcJk+eXG7Z7eNpZWXFgQMHWLx4MVu2bGHjxo3Y2dnRoEGDMn9bpVLJtm3bmDVrFps2bUKhUDBo0CCWLVtWqbn5//Of//DNN98wf/58iouLGTVqFCtXrqxw3tYxY8bw22+/MXz48DIPHfsnLC0t2b9/P2+88QYbNmwgKyuLoKAg1q1bx7hx48qt37lzZ7Zs2VImEGvVqhVWVlaUlJRU2fkghBBCCCEMQ+IeiXtqctzTt29f9u3bx9tvv82yZcvQaDTUr1+fSZMm6dfp1asXO3bsYMGCBcyfPx9TU1O6du3K+++/f98HcgNMmjSJqKgovvrqK3bs2EHnzp3ZvXs3PXv2fOC/gRBCGJJCK08tEUKIh8Ivv/zCk08+ycGDB6XnvhBCCCGEEKJWkrhHCCEqTxoJhBDiITFw4EBCQkIIDw+vsMeNEEIIIYQQQtR0EvcIIUTlyXRDQghRy3333XdcuHCB33//nY8//lgqykIIIYQQQohaR+IeIYT452QkgRBC1HIKhQIbGxtGjBjBmjVrMDGR9mEhhBBCCCFE7SJxjxBC/HPSSCCEEEIIIYQQQgghhBBCPKSUxs6AEEIIIYQQQgghhBBCCCGMQxoJhBBCCCGEEEIIIYQQQoiHVI2eoE2j0ZCQkICtra08kEYIIYQQ/zqtVkt2djZ16tRBqTR+34qCggKKioqqZF9mZmZYWFhUyb6EEIYlcZMQQgghDEnippofN9XoRoKEhAR8fHyMnQ0hhBBC1HJxcXF4e3sbNQ8FBQX4+9qQdENdJfvz8PAgKiqqVlR4hXjYSdwkhBBCiKogcVPNVaMbCWxtbQHoxABMMDVybv5dCnNzY2fBIBSBvsbOgkEoricZOwsGoTCv2Te4imhcHYydBYO43tvR2FkwCJ91IcbOgkFM3X/S2FkwiM8G9TB2Fv51JZoi9l//Ul/nMKaioiKSbqiJOe2Hna1he+dkZWvwbRVNUVFRja/sCiFqd9yktKilcZON8b93DEGbk23sLBiGae26rgCUjvbGzoJB3OzoZewsGITjLxeNnQWDiJvWzNhZMAifNbXveJVoizmY96PETTVYjW4kuD1U1gRTTBS160tZUcvKc5tCVUsr8QozY2fBIBTK2lcuTS09B1W1sEEHwKSWXltWtipjZ8EgTJS18/oCqtX0HDa2CmxsDZsfDdWnvEKI/11tjpuUtbSuUBvr4QDaWnq8qGXXFYCyltbrVGYSN9UkEufWPBI31VzGnyRKCCGEEEIIIYQQQgghhBBGUaNHEgghhBBCPGzUWg1qreH3IYQQQgghhBA1lcRNlSMjCYQQQgghhBBCCCGEEEKIh5SMJBBCCCGEqEE0aNFg2C4xht6+EEIIIYQQQhiSxE2VIyMJhBBCCCGEEEIIIYQQQoiHlIwkEEIIIYSoQTRoMPTMl4bfgxBCCCGEEEIYjsRNlSMjCYQQQgghhBBCCCGEEEKIh5Q0EgghhBBC1CBqrbZKXkIIIYQQQghRU1XXuOmTTz7Bz88PCwsL2rVrx4kTJ+657tatW2ndujUODg5YW1sTHBzM119/XWadcePGoVAoyrz69etX6XzJdENCCCGEEEIIIYQQQgghhAF9//33zJ49mzVr1tCuXTtWrFhB3759CQ0Nxc3Nrdz6Tk5OzJ07l4YNG2JmZsZvv/3G+PHjcXNzo2/fvvr1+vXrx7p16/Tvzc3NK503aSQQQgghhKhBNGjRYNie/obevhBCCCGEEEIYUnWMm5YvX86kSZMYP348AGvWrOH3339n7dq1vPHGG+XW79atW5n3M2fOZMOGDRw+fLhMI4G5uTkeHh6VL8AdZLohIYQQQgghhBBCCCGEEOIfyMrKKvMqLCwst05RURGnT5+mV69e+jSlUkmvXr04evTo3+5Dq9Wyd+9eQkND6dKlS5ll+/fvx83NjaCgIKZNm8bNmzcrXQYZSSCEEEIIUYNo0KKuZj1ihBBCCCGEEKI6qcq4ycfHp0z6ggULWLhwYZm01NRU1Go17u7uZdLd3d25evXqPfeRmZmJl5cXhYWFqFQqPv30U3r37q1f3q9fP4YMGYK/vz8RERG8+eab9O/fn6NHj6JSqR64LNJIIIQQQgghhBBCCCGEEEL8A3FxcdjZ2enf/5NnAtyLra0t586dIycnh7179zJ79mzq1aunn4po5MiR+nUfeeQRmjVrRv369dm/fz89e/Z84P1II4EQQgghhBBCCCGEEEII8Q/Y2dmVaSSoiIuLCyqViuTk5DLpycnJ932egFKpJCAgAIDg4GBCQkJYsmRJuecV3FavXj1cXFwIDw+vVCOBPJNACCGEEKIGuf0ALkO/hBBCCCGEEKKmqm5xk5mZGa1atWLv3r2ledRo2Lt3L+3bt3/wcmk0FT7z4Lbr169z8+ZNPD09H3ibICMJhBBCCCGEEEIIIYQQQgiDmj17NmPHjqV169a0bduWFStWkJuby/jx4wEYM2YMXl5eLFmyBIAlS5bQunVr6tevT2FhIdu3b+frr79m9erVAOTk5PD222/z1FNP4eHhQUREBK+99hoBAQH07du3UnmTRgIhhBBCiBpErdWi1hq2p7+hty+EEEIIIYQQhlQd46YRI0aQkpLC/PnzSUpKIjg4mB07dugfZhwbG4tSWTrxT25uLtOnT+f69etYWlrSsGFDNm3axIgRIwBQqVRcuHCBDRs2kJGRQZ06dejTpw/vvPNOpZ+LII0EQgghhBBCCCGEEEIIIYSBzZgxgxkzZlS4bP/+/WXev/vuu7z77rv33JalpSU7d+78V/IljQR3eHxcKkOn3cDJtYTIK5Z8Os+L0HNWAExeEE+f4ekU5Cv56j1P9v3kqP9c54EZ9BqWzoKx/sbK+j01bZvF0MlJBDbNxdm9mLcnB3J0d2nen5qUyLDJiQD88JknW78sna8qKDiHGYuimTm4CRq1osrzfi/rN/yKu3teufRffw3g009aMWnyWXr3jqagQMW6tc3Yt89Pv06nznH06hnNwoWdqzDHD2b4pFg69ErFu14+RQVKQs7ZsXaZP/HRVvp1Jr0WQa/ByRTkKVn3kT/7f3PXL+vUN4Weg5J5+/mmxsj+Axk2LopxL17j5811+eLDhgBMnB1Kr8fjKchXsX5VA/b/UXoOduqVRI+BCSya1dJYWb6n2nAeTmx9hl4Bkfg7ZlBQouJcogcfHX6U6AzdPcLOvIDnHz1JB984PG1zSM+35M8If1YdbUNOkbl+ncV9/qStdzwxGQ68tacbV1Nc9fuY2+0g1zPt2HA22BhFBGrvtXVijTNHPnSjxbg0us3TPfgoI8aUg//nTsIpS9RFCny75NJ9QRLWLmoASgoV7H7Tk8g9Nli5ltDj7SR8O5aex6e+cCI7wZTuC5Ir3GdVGD0hlKcnXiuTFhdjzdSR3QGY+OJlej12XXfP+LQh+3d569fr1COBHv2vs+jVtlWa56qmufUy9D6EEKIitTJmapPF0MmJBNyKmRZNCeTobif98qcmJjJ0cgIAWz6rw9av7oiZmufw/KIoZg1pWq1ipuHPRdGhZwre/rkUFSoJOefA2hUBxMdY69eZ9EoYvQYlUJCvYt3HAezffkc9vHcyPR9P5O0Xg42Q+/urjccLoGnrTIZOuK4rl1sRi6Y34uheZ/3yp567ztCJ8QBs+cKLretK60BBzbJ5fkE4s4YHV6tyDRgczYAhMbh75gMQE2nDt2sbcPqYG1Bz6nXB/gk80+U8Db1ScLXL49WNfTl4pfRe1q1JJEPaXaGhVwr21oU88/FQriW6lNnGzMf+4rFWoRQUmfDJjnbsPNdAv6zHIxEMaBnGKxv6V1mZKtK0TRZDJyUQ0CRHd21NDeLonjuurQkJDJ186xz83IutX9XRLwtqns3zb0cx66lHjH4OtqqTwHMtz9HYNQU3mzxe+L0ff0aWHq/3ev3Jk41Cy3zmcIwPU7YNBMBUqWZRz/30qBdFaq4V7xzowrG40nNzfIuzeNrmsPigceP5pm0yGTrxjuM1LYije+64Z0yIZ+ikO47XWi/9sqDm2Ty/MJJZQ5sZ/XgZksRNlSONBLd0HZTO5AUJrHrDm6tnrBg8KYX3NkcyoXMQDVvm0X1wBnNG1cOrXiGzl8Vx+oAtWWkmWNmqGfd6Im+MrG/sIlTIwlJDVIgVu35wYf5n4WWW+TfM49mX4lkwoQEKhZa3vwrjzCF7okOtUKq0vPBuNCvf9Kt2N4yZL/ZGqSwdzuPrl8mSJQc4dMiHdu3i6dYtlrlvdqWOVzYvvXSS06c9ycoyx8qqiLFjL/DmnG7Gy/x9NG2dyW/f1iHski0qlZaxs6J578uLTHm8NYX5Ktp2u0nXgTeYN/ER6vjmM+vdMM4cdiIrwxQrmxLGzozmzQmPGLsY9xTYOJN+T8URGWajT2vb5Qbd+iUyb3orvOrmMXPBZc4cdSYrwwwrm2LGPB/O3GmtjJjre6sN52FrrwS+Pd+US8lumCg1zOxwnM8H/8YTX48kv8QUN5tc3Gxy+fBQByLTHPG0zWZ+j4O4Wucye7tubrvJbc9gbVbMsG+HMaLZZd7ueYAR3w0FoJlHEs08brDkQCdjFrNWXltJFyy4+J0DLg0L9GnFeQq2jquLa6MChm6KBeCvj1z5ZbIPo36MRqGEi987cOOSBSO2RBN90IY/XvJiyvFrKBSQGWfKxe8dGP1TtJFKVSo6wpZ5L7bTv1erdcMt23ZKplufBObNbIeXTy4z557nzHE3sjLNsLIuZsyUUOa++Kixsi2EELVerY2ZrDREhlixa4srb60p21Dt1zCPZ166zsKJDVAoYOGXoZw5fGfMFMXKuf7VLmZq2jqD3773Juyyna7+80I47605y5Qh7XX1n64pdO2fxLypLalTN49Zb1/hzF+36+EljH0hgjcnV7+OOlA7jxeAhZWayFAbdv3Xnbc+uVpmmV9QLs+8GMvCqY1RAAs/u8KZI45Eh1nryvV2OCvnB1S7cqWmWLL+04YkxFmDAnoNuM5bS0/y4tgueHjl1Zh6naVpCdcSnfn1VEOWPlu+x66lWQnnYzzYc7E+c586UG55p0bR9A2+xotfPUZdl0zmDt3PsTAfMvMssTYvZFqfE8z4cmBVFOW+LCzVpdfW6rAyy/yCcnlmVhwLJzXUXVtfhOh+P7p9Dr4Tycq59avFOWhpWkxoqjNbrzRk5WMV97A+FOPDvD099O+L1Cr9/4c1vUITtxRGbxlCZ99YlvbZTZevxgEKvOyyGNokhOHfDzVwKf6ehaWGyKvW7PrRjbc+Ldvo4ReUyzMz41g4uREKhZaFn1/lzGGH0uO1KJKV8+pVi+Mlqg/l369ieJ988gl+fn5YWFjQrl07Tpw4UeV5GDI5lR2bndj1vROx1yxY+bo3hfkK+o5Ko25gIReO2nDtghX7f3YkL0eFh08RABPnJfDbRhdS4s2qPM8P4tQBBzYs8+avXU7llnnXzyfqqiXnj9px7i97oq5a4VNf94PTsMmJXDphS9gFm3KfM7bMTAvS0y31r3ZtE0hIsOHiBVd8fLK4eMGVa9ecOLDfl7w8Ezw8cgCYMPE8v/8eQEqK9d/swTjmT3mEPT97EBtuTVSoDcvfbIBbnUICG2cDULdeHhdPOHDtsi0HtruRl6PC3Vt3vJ57JZLfv/MkJdHCmEW4JwvLEl597yKr3mlCTpapPt3HP5cLp50ID7HnwE5P8nJNcK+j62Xy3MxrbP/Rh5QkS2Nl+75qw3k49ZeB/BLSkIg0J0JTXZi7uwd17HJo7JYCQPhNZ176vR8HovyIy7TnxHVvVv7Vjm7+0agUuvbyek7p/BEWQEyGAz9ebIy/UzoAJko183scZNGfXdBojftVU9uuraJcBX/MrkOv9xKxsFPr0xNOW5EVb0qf9xNxCSrEJaiQvh8kkHzRgtijuh6eaeHm1OuZjUuDIoKfSSc/zYT8NF2FeO98Dzq/loK5rfH7QmjUCtLTLPSvrEzdd6yPbzYXzjgTftWBA7u9bt0zdCMhnpsRwvaffElJrp73jH+TGm2VvIQQ1YvETIZz6oADG5f7VBgz+dTLJ/qqJeeP2utjJu96uvrq0MkJXKymMdP86S3Ys60OsRE2RIXZsnx+E9zqFBDYKAuAuv65XDzlyLUrdhzY4aH7TvW6VQ9/6Rq//+BFSlL1qf/cqTYeL4BTB53YuMKXv/a4lFvmUy+P6FBrzh9z4NwxB6JC7yjXhOtcPGVH2EXbqs7y3zpx2J1TR91JuG5DQpwNGz9rSEG+CQ2bpteoet3RsLp8tqstBy5XPBLqj7MN+Gpva06Ge1W43M81nTORdbga78au84HkFZhRx0kXi7ww4Bj/Pd6Y5EzjH79TBx3Z+FFd/trtXG6ZT/18okOtOH/MnnNH7Ym6ao33rd+Phk5K4OIJO8IuVo9r63CMLyuPtWNvZL17rlOkVpGaZ6V/ZRWWzt1e3zGdfVF+RKQ58e2FpjhbFeBooSvr/G4HWf7Xo+QWG//77L7Hq96dx+uue8bEeC6erJ73jH+bxE2VY/RGgu+//57Zs2ezYMECzpw5Q/Pmzenbty83btyosjyYmGoIbJbHmUOlF4hWq+DsIVsat8oj8rIFDZrlYWNfQsAjeZhZaEiINqNJ2xwCHsnnl6/Kf4nXBNFXrfD2L8C1TiFuXoV4+RcQHWqJZ90Ceg9LYcMy77/fiJGZmKjp3iOGXTv9AQWRUQ4ENkjHxqaIgIA0zM3UJCTY0qRJCgEB6Wz7JdDYWX5g1ra6H/+yM3U/qkeGWhPYNBsbu2ICGmdjbqEhMdaCxi0zCWiUw7ZNFVdIqoNpb4Rw8rAL506U/fKKCrMlsHEmNrbFBDTKwtxcTWKcFY2D06nfMItt39Y1Uo4rp7achzZmukA+s/DeD7exNS8kp8gM9a0f/sNSnGnrHY9KoaGDbyxhqbpj/Fyrc5y8XofLN9wMn/FKqunX1p8LPfDvllNmmiCAkiIFKEBlVlpJUZlpUSgh4ZSukcC1UQEJp60oKVAQfcgaa7diLJ3UhPxih4m5loA+2VValnup45PLxm27+erHP3ll4Rlc3XUV2qhwOwIbZWBjW0RAUAbm5hoSr1vRuFka9Rtksu2H6jeFhRBC/BskZjKe6FArvG7HTHV0MVNMmJUuZhqaysblPsbO4gOxtikBIPtWh53IMFsCG2eVrYfHWtG4RQYBDbPZtrlm1MPvVluO192iQ63x8svH1bMAtzoFePnl68rlk0/vIclsXOFr7Cz+LaVSS5de8VhYqAm56PhQ1euuJbrQ0CsFW8tCGnqlYG5awvWb9jT3TSSoTio/HKleo5YrEh1qdescvH1t5RMTduv3o6dusPGjmnXPaOOVwMEJ6/jtmc281e0A9halI7SvpjrT0jMRc1UJHX3juJFjRXqBBY81CKNQrbpv40N1ER1mhZdfwa3jdeuecc2qxh4vUTWMPt3Q8uXLmTRpEuPHjwdgzZo1/P7776xdu5Y33nijSvJg56RGZQIZKWX/HOmpJvgEFHL6gB17t+axavs1CguUfDizLgV5Sl5YEs+Hs3wYOPYmg55LJStNxcev+hATVj17XNwtLsKSdR/4sORr3bCkdUt9iIuwZMnXV/lqSV1adcnkmZnxlJQoWLOoLpdO2Bk5x+W1bx+PjU0xu3frKhBnTnvy55++fLxyN4WFKpYta0dBgYrnZ5xm+bK2PPZYBIOeuEZmphkrV7YhNsbeyCWomEKhZcobEVw+bUdMuK7H+ZkjTuz71Y0VP5ylqEDJsjlBFOSrmDH/GsvfDGLAyAQGPZ1AVropKxcGEhtu/J7qAF36JBLQMJtZz7Yrt+zMURf2bffko03HKCpQsXxBUwryVTw/J4SPFjZlwLA4Hh8RS1aGGavebUxsZPXomXC32nAeKtDyRtcjnEnwIPxm+Z4IAA4W+Uxpe5ofLzXWp315qgVv9TjIH+O+IT7Llvl7ulHXIYNBjUJ5+ochzO9xgPZ147ic7MbCvV31zzIwlpp+bYX+ZseNyxYVTgnkGZyPqaWGwx+40fHlG6CFwx+4oVUryL31/dZkaAapV83Z0K8elo5qHlsZT2GmkqMrXBn2TQxHlrsS+psdDnWL6PN/idh4lFRxCSH0siMfvduc6zE2OLkUMHrCNZau/ovpz3TlzHE39u3w4qO1hykqVLH8neYU5Jvw/GsX+eid5gwYEs3jQ6PJyjRj1f81IzaqdvaOUWt1L0PvQwhRfUjMZDxxEZas/9CHxRt107+s/0AXMy3+OoS1/+dDqy4ZPP1iPOoSBWsW+XLpZPWLmRQKLVNeC+PyWXtiwnX16TN/ObPvdw9WbD5BUaGSZW810dV/5oaw/K0mDBh+nUGj4nT1n3caERtRPevhd6sNx6sicZFWrP/Il8XrLgOwfrkfcZFWLF53kbUf+NOqUwZPz4jVleu9elw6Zfz44jbf+lks+/wIZmYa8vNVvPtGK+KibYmLtn1o6nXHr/mw41wg657/L4UlJry9pQf5RSa8NvgQ72zpzpBHrzC8w0Uyci1YsrUrUTfKj5IxtrgIK9Yvq8viDVcAWP9hXeIirFi84Qpr3/elVecMnn4xTncOvuNfra+twzE+7Inw53qWHT72Wcxqf5zPBv3O6C2D0WiV/BTSkCCXm2x7+jsyCix4eUcf7M0LmfHoScZvfYIXHz1O/8Bw4jLtmbe3Gzdyq9/9MS7CivXL67J4/a17xjJf3fFaf5m1S31p1Tmdp1+IQ12iZM27flw6WX3uGf8miZsqx6iNBEVFRZw+fZo5c+bo05RKJb169eLo0aPl1i8sLKSwsFD/Pisrq0ryCbBpmQeblnno3z89O4mzh2xQlygYNTOZqT2CaNc7i1dXxjKjX4P7bKl62b7Zje2bS3v69hqSQl6uipCzNny59wIvPtEEF48i5qyMYFyX5hQXGX3wSRl9+0Vx6qQnaWmlwxC/2dSUbzaVPmB09NOXOHfWnRK1kpGjrjB9Wl/atk3glVeO8+ILfYyR7b81/a1wfANzeeWZ4DLp33zixzef+Onfj54ew9mjjpSUKBg5NZbpT7SiXbc0Xl4Sysxhxp9D1MW9gMmvhjJveiuKi1QVrrP5swA2fxagfz9qcgTnTjjpyjQhkunDO9C2Swovv3ORmU+3r6qsV0ptOA/ndT9IgHMaY7Y8WeFya7MiPn1iOxFpjnx6vLU+PafInNd39C6z7ldDfmHZ4fYMDArD2y6LxzeOYmHPA0xtd5oPD3UwZDH+Vk2+trITTNj/jjtDNsRiYl6+JmLlrGbgqnj2zvfg7AZHFEoIGpiFW5N8FLdu3SpT6PF2MlD6YOKdr3sSPDaNG1csiNhtw7O/RXLyc2f2vePO45/EV1HpSt1+kB1AdIQdoZcdWffTXjr3TGDXr3XZ/FUQm78K0q8z6rkwzp10oaREychx4Ux/pgttO97g5fnnmDm++j2kXgghKquyMRMYL26qvTGTO9s3u+vf9xqSQn6OipCztnyx5zwzn2yKi0chb6wMZ3zX4GoXM01/8yq+9XN4ZVzrMunfrKnPN2tKnxMxekokZ4/dqodPimL60Edp1yWVl9+9zMxR5Tv8VFc1/Xjdy/bvPNn+XelDmHs9mUx+roqQc7Z8seM0M4cG68r1USjje7SmuLh6lCs+xoYXxnbB2rqYjj0Smf3WeV6f3p64aNuHql735Z42fLmnjf79hJ6nOBnuRYlayXM9TjN6xXA6NYxh4fA/Gfsf4893X5Ht33qw/dvSe3yvwTfIz1USctaGL3afY+bgR3DxLOKNFWGM796y2l5bf1wrHdl/7aYzYanO7Bz7DW28Ejh+3ZsSjYp3D3Qp85l3e/7JN+cfoZFrCj3qRTHk2+E81+osb3Y5zKw/+lV1ER5Ixcfr1r1w11lmDmmGi0cRb3wUxvgerart8RJVx6hnQGpqKmq1Gnd39zLp7u7uJCUllVt/yZIl2Nvb618+Pv/OUMGsNBXqEnBwLdtj0tGlhPSU8u0oPgEF9BySzoalHjTrkMOlY9ZkpplwYJs9gc3ysbRWl/tMTWDnWMzTMxNYvdCXhsE5xEdZkBBtwYVjdqhMtHj5F/z9RqqQm1suwcHJ7Nhx76Fe3t5Z9OgRw8aNTWnW7AaXLrmSmWnBwYN1CQxMx9KyuApz/GCmzQ2nbdebvDGuGTeT793r2ts/j+6PJ/P1Kj+atc3k8il7stLNOLjDlcAmOVhaVX0P4LsFNMrC0bmIld8cY9uJ3Ww7sZtmrdMZNDKWbSd2l3nwL4C3Xy7dByTy9acBPNIqjUtnHMnKMOPQLncCGmVXizLdrTach292O0RX/xie++8gknPK94KwMi3isyd+I7fIlJm/9aNEU3GDD8CTja+SXWjOvkh/2ngnsDfSnxKNil3X6tPGq+p/cL5TTb+2ki9bkHfThG+e8GdFUENWBDXk+glrzm5wZEVQQzRq8O2cy3P7Iph6/BpTT4bRf1kCOcmm2N+aE/pucUetuHnNnOBn07l+3Aq/brmYWmlpMCCL68etqriEFcvNMSU+1hpP79xyy7x9c+je7zpffx7EIy1vcumcE1kZ5hza60lAw8xqec/4N2iq6CWEqB4qGzOBYeImiZl07ByLGf1iPKvf9iWo+Z0xkz0m1TBmmjbnKm27pPLGpFbcvHHv0Rvefrl0fyyRrz+pT7M26Vw+7aCr/+xyJ7Bx9ayHP4iadrwelJ1jMaNnxLL6nfoENc8mPtqShBhLLhx3wMREg5d/vrGzqFdSoiTxujXhoQ5sWN2IqHA7nhgRVW69h6le5+uaTv8WYXy2qy0t6yVwNsqTjFxL9lyoT0PvVKzMKq67Vyd2jsWMfuE6qxf5E3T796MYS921ZarFy6/6nIN/53qWHWn5FtS1z6xweVuveAKc09l8oSltvBI4FO1LfokpO64F0MYroYpz+8/ojlccq9/xv3UvvH3PqHnHqzIkbqqcGtVMNGfOHDIzM/WvuLi4f2W7JcVKrl2wokWn0rmYFQotwZ1yuHL67h9JtLz4/nU+e7sOBXkqlEpQmep+6DS59TxW5b1/P6vWprwVy09r3UlNMkOp1KIyuWNeaxNtuR90ja13nygyM805ccLzHmtoefHFU3zxeTAFBaYolVpMVLrL18RE92/1KpOWaXPDad8rlTnPNSc5/n4PadLywsJrfPF+/VvnYenxMrn1b3U4D8+fcGL6sPa8MOpR/Svssh37//DkhVGPotEo7lhby4y5V/hyeQMK8k1QqShfpmp1vHRq9nmo5c1uh+hZP4rntg4iPqv8kFBrsyI+H/wbxRoVL/zanyL1vQegOVrmM7XtKRbv7wSAUqHFVHmrrEoNKiOWszZcW3Xb5/Hs9kie+TVK/3J/JJ+Gg7J45teoMvmydFJjYach9qgVeTdV1OuZU257JYUK/lzoQa93ElGqQKNRoLnVXqUpUaBVK8p9xhgsLEvw9M4jLfXuHza0zHj9Al+ubHLrnqFFZXL72qq+9wwhhKgKhoibJGbSmTwvhp/XepCaZI5KpdV/5wAoq1XMpGXanKu075HCnEmt/r7+81YIXyzT1cOVSm3p8bpdX1VVl3JVTs05XpUzeU4kP6/3IjXZHJXyrnKpqne5FAotpqZ3/6z2MNXrtLwx+CArfu9AfpEpSqWmND5UGTs+fHCT50bz8zpP3bWl1GJietc5WIPu8e7WOThYFJCaV76TlJmqhHndDrHwzy5otEqUCi0mt2JcU6WmRhwrgMlvRvPzujql98IafLyE4Rh1uiEXFxdUKhXJycll0pOTk/Hw8Ci3vrm5OebmhpnPeuvnLryyIo6w81aEnrVi8KQULKw07Pqu7Fxw/UenkXnThOO7dfN1XTlpzbMvJ9GwZS5temQTE2pOblb1ubosrNTU8S3tHeHhU0i9RrlkZ5qQklD6t2zRKRMv/wI+fFnXGzrsgg0+9fNp3TUD1zpFaNQKrkfer2JZtRQKLb17R7Fntx8aTcVtXf36RZKZac7x47qHjl657MIzz1ymYcNUWrdOIibGjtxc4z+R/rbpb4XT7bEbLJrRhPxcFY4uut4DudkqigrLnlN9hyaRmWbKif26ueOvnLXj6edjCGqWResuacSEW5GbbfRHjpCfZ0JMRNm5IwvyVWRlmpZL7zs4nsx0U04c1E01cuWcA6MnRxD0SAatO6YSE2FNbo5pleX9QdT083Be90MMCLrGi7/2J7fIDGcr3YNwcwrNKFSb6BoInvwVS9MSZu7sibVZMdZmul+R0/Mt0GjLlvn1LkfYcKa5fk7Gs4kePN4wjL9ifBj6yBXOJpS/r1eF2nJtmdlocGlQWCbN1FKDpaNan375R3uc6hdi6aQm8awl+991p+X4NJzqle+NdPw/Lvh3y8Gtie6zdVrmceh9d5oMzeT8107UaWWc3iQTXrjC8cPu3Ei0xNm1gKcnhqFRKziwu06Z9foOiiUz3YwTh3U9a69ccGT0hDCCmqTTuv0NYiJtqt0949+iQYEawzbiaAy8fSHEg6tszASGi5selpjJ/W9ipmWv6KboCbtgjfftmMmzesVM098MpVv/JBbNaq6r/zjrvu9zc0zK13+GJOi+Uw+4Arp6+NNTIwl6JJPWnW7Vw7Orz3dqbTxecKtcdUvrX+7eBdRrmKMrV2JpZ4kWHdLx8stn2eu6KbvCLtriXS+f1l3ScPUoQqNRcD2qepRr7LQQTh11IyXJEkvrErr1ieeRljd5a1bZ6auqe73O0qwYb+fSXuZ1nLII9EwlK8+c5Exb7CwLcHfIwdVON/LV1zUDgJvZVqTllP3h+Yk2IWTkWnI4xA+AC9EeTOp1mqY+ybQPiiUy2ZGcAuM8x638tVWgu7YyTEhJvOPa6piBl18By17VTRscdtHm1jmYfse1ZbznzliZFpcZFeBtl0VDl1QyC8zJLLRgWtuT7A6vR2qeFT72Wbzc8SixGfYcjin/MN+pbU5zMLouV1N198eziR680vEoP4U0ZFSzi5xNNE6MCxUcL+/Cex8v/3yWvVbR8So0+vEyJImbKseovyKamZnRqlUr9u7dy5NPPgmARqNh7969zJgxo0rzcmCbI/bOasa8moSjawmRly2Z+7Q/GamlX0QOLsWMnJnMS4NK5y8LPWfFfz9z5Z2NUWTcNOHDmdXrCeENHsll6XdX9e+nvBULwO4fXVj2qq5BwMxcw/Nvx7B4Rn20Wt3JnZpkxuqFvsz+IIriIgXLXqlHUWH1GXjSokUy7u557NpV8RQvDg4FjBx1hdkv9dKnhYU5s/W/Qby96BAZGeYs+7B6zas5cFQiAEs3XiiTvvzNBuz5ufSLx8G5iJFTYnl5dLA+LeyiHVvXe/P2mktk3DRl+ZtB1CQOToWMmBDJK+Pa6tPCLtvz0yZfFn58lsx0M5bPb3qfLRhHTT8PRza79RCjob+USZ+7qzu/hDSksWsKzT1vAPDHuM1l1umz9mkSsktHHnSoG0tdh0zm7OypT/v2fFOauKWwecR/uZjsxurjZefBrSoP07WVFmnG4Q/dKMhUYedVRNtpN2n5XFq59VLDzAnbbsczv0bq0xr0z+b6cWt+GOmLY70iBiw3zvRQzq4FvPb2Gezsi8nMMOPyeSdmT+pIVkZpRdfBsZAR48J5ZXJHfVrYFUd++rYeC5edIDPdnOXvNDdG9oUQ4l8nMZPhBT6Sy9JvQ/Tvp8wrjZmWv6b7gdnMXMP0hdEseSHgjpjJnNUL/XhpaaQuZnq1+sRMA0dcB2Dp2tNl0pe/1Zg920ob3h2cChk5MYqXx5bOlR52yZ6tX/vy9n/OkZFmyvK3mlRNph9QbTxeAIFNs1n69SX9+ylv6qbk2b3VjeVzdA0CZuZqps+PZMmsoNJyJZuz+p16vLT4GsVFSpa93qBcQ5CxODgW8fL8czg5F5KbY0J0hB1vzWrHuZOud6xT/et1jbxvsHryr/r3Lw3UPQ/mt9MNeGdLDzo3jmb+sP365e+N3gPAF3talXkOgZNNHuN6nGHSp4P1aVeuu7P5UDOWj9tOWq4li37obuDS3FvgIzks/eaK/v2UuTEA7P6vK8tf1/3AbGauZvqCKJbMbFD22lrkz0vvR+iurdcCjHoONnG7wfoh2/TvX+/8FwA/hwSxaF8XgpzTeKJhKHbmRdzIteavWG9WHWtL8V3T6gY43aRfYARPfTtMn7YrvD5tvRLY+NTPRGc48NrOXhhLYNMcln5zWf9+ytxoAHZvdWX567rv4ArvGbeP1/+F647X68Y9XqL6UGi1WqOOjfn+++8ZO3Ysn332GW3btmXFihX88MMPXL16tdy8m3fLysrC3t6ebjyBiaL69Gz4NygMNGLC2BRB/sbOgkEoYhONnQWDUFjUvtZkjZujsbNgEHH9nf5+pRqo7meX/36lGujFU8eMnQWDWNXT+A/h/reVaArZE/spmZmZ2NmVn5KrKt2u95y67I6NrWF/1MjJ1tC6SXK1KLcQ4n+LmaB2x03KWlhfBVDY2v79SjWQNjv771eqiUxr13UFoHRyMHYWDCK1q7exs2AQTj+eN3YWDCJ2ZrCxs2AQdVfWvuNVoi3iz9xvq0X8IHHTP2P0+UhGjBhBSkoK8+fPJykpieDgYHbs2PFAlV0hhBBCCCGEqO0kZhJCCCGEEIZk9EYCgBkzZlT5UFkhhBBCiJpIXQVzaxp6+0KIypOYSQghhBDiwUncVDnVZwI+IYQQQgghhBBCCCGEEEJUKWkkEEIIIYQQQgghhBBCCCEeUtViuiEhhBBCCPFgZNisEEIIIYQQQtyfxE2VIyMJhBBCCCGEEEIIIYQQQoiHlIwkEEIIIYSoQTRaBRqtYXusGHr7QgghhBBCCGFIEjdVjowkEEIIIYQQQgghhBBCCCEeUjKSQAghhBCiBpG5NYUQQgghhBDi/iRuqhwZSSCEEEIIIYQQQgghhBBCPKRkJIEQQgghRA2iRonawP081AbduhBCCCGEEEIYlsRNlSMjCYQQQgghhBBCCCGEEEKIh5SMJBBCCCGEqEG0WgUarWHnvtQaePtCCCGEEEIIYUgSN1WOjCQQQgghhBD/ik8++QQ/Pz8sLCxo164dJ06cuOe669evR6FQlHlZWFhUYW6FEEIIIYQQQoCMJBBCCCGEqFHUKFBj2B4r/2T733//PbNnz2bNmjW0a9eOFStW0LdvX0JDQ3Fzc6vwM3Z2doSGhurfKxS1pyeOEEIIIYQQwniqa9xUXclIAiGEEEII8T9bvnw5kyZNYvz48TRu3Jg1a9ZgZWXF2rVr7/kZhUKBh4eH/uXu7l6FORZCCCGEEEIIATKSQAghhBCiRlFrlai1hu3nodbq/s3KyiqTbm5ujrm5ebn1i4qKOH36NHPmzNGnKZVKevXqxdGjR++5n5ycHHx9fdFoNLRs2ZLFixfTpEmTf6cQQgghhBBCiIdWVcZNtYGMJBBCCCGEEBXy8fHB3t5e/1qyZEmF66WmpqJWq8uNBHB3dycpKanCzwQFBbF27Vp++eUXNm3ahEajoUOHDly/fv1fL4cQQgghhBBCiHuTkQRCCCGEEDWIBgUaA/fz0KDrEhMXF4ednZ0+vaJRBP9U+/btad++vf59hw4daNSoEZ999hnvvPPOv7YfIYQQQgghxMOnKuOm2kAaCYQQQgghRIXs7OzKNBLci4uLCyqViuTk5DLpycnJeHh4PNC+TE1NadGiBeHh4f8or0IIIYQQQggh/hmZbkgIIYQQogZRo6iSV2WYmZnRqlUr9u7dq0/TaDTs3bu3zGiB+5ZLrebixYt4enpWat9CCCGEEEIIcbfqGDdVZzKSQAghhBBC/M9mz57N2LFjad26NW3btmXFihXk5uYyfvx4AMaMGYOXl5f+uQaLFi3i0UcfJSAggIyMDD744ANiYmKYOHGiMYshhBBCCCGEEA8daSSoprSFhcbOgkFcX6gydhYMwv7bhsbOgkFY3Sgydhb+daapecbOgkF4/5lt7CwYRGGrAGNnwSA+Hupl7CwYhFKZa+wsGED1G3Sp1ipRaw2bL7W28nNrjhgxgpSUFObPn09SUhLBwcHs2LFD/zDj2NhYlMrSfKenpzNp0iSSkpJwdHSkVatW/PXXXzRu3PhfK4cQovbTFBQYOwsGcf2llsbOgkF4HK+dca5pVu0rlza39pUJwP5a7YwHi9vVzt8k/L69buwsGITW4t97zld1odAooJqFg9U1bqqupJFACCGEEEL8K2bMmMGMGTMqXLZ///4y7z/66CM++uijKsiVEEIIIYQQQoj7qX7d44QQQgghhBBCCCGEEEIIUSVkJIEQQgghRA2iQYHGwA/IMvT2hRBCCCGEEMKQJG6qHBlJIIQQQgghhBBCCCGEEEI8pGQkgRBCCCFEDaJBidrA/Tw01J4HcAkhhBBCCCEePhI3VY6MJBBCCCGEEEIIIYQQQgghHlIykkAIIYQQogZRa5WotYbt56HW1p4eMUIIIYQQQoiHj8RNlSMjCYQQQgghhBBCCCGEEEKIh5SMJBBCCCGEqEE0KNHI3JpCCCGEEEIIcU8SN1WOjCQQQgghhBBCCCGEEEIIIR5SMpJACCGEEKIGUWsVqLUKg+9DCCGEEEIIIWoqiZsqR0YSCCGEEEIIIYQQQgghhBAPKRlJIIQQQghRg6hRojZwPw91LZpbUwghhBBCCPHwkbipcmQkgRBCCCGEEEIIIYQQQgjxkJKRBEIIIYQQNYhGq0SjNWw/D4229vSIEUIIIYQQQjx8JG6qHBlJIIQQQgghhBBCCCGEEEI8pGQkgRBCCCFEDSJzawohhBBCCCHE/UncVDkykkAIIYQQQgghhBBCCCGEeEjJSAIhhBBCiBpEA6i1CoPvQwghhBBCCCFqKombKkdGEgghhBBCCCGEEEIIIYQQDykZSXCHx8elMnTaDZxcS4i8Ysmn87wIPWcFwOQF8fQZnk5BvpKv3vNk30+O+s91HphBr2HpLBjrb6ys31dNL5f1jjSsd6ahulEMQImPOVnDXSlsaQuAy1tRmF/OK/OZ3D6OZEytA4AiuwTHVfGYX8qjxNOMjOfrUFzPUr+u/ecJqN3NyHnCpYpKpNO8fiKje5ynoU8qLvZ5vPFlHw5d9NMvnzt6PwPahZX5zLEQb15eMwAAU5WaN0YdoPMjMdzMsmLZlo6cCvPWrzu6x3ncHXP46L8dq6Q8tz3SMIlhAy/RoN5NnB3zWbCsO3+d8r1jDS1jh56jf48wbKyLuBzqxsq17YlPstOVy0TN7MlHaN8qjvRMS1aufZSzl+roPz1s4CXcXHL4ZP2jVVquu637+jfcPfLKpf+2rT6frmrFpCnn6NUnmoICFeu+asb+P0v/Bp26xNGzVzRvz+9clVl+IJaWxYwZfZ4Oj8bhYF9ARJQja75oTVi47vp46skrDBt8GYAftjZh6y+N9Z8NapDKjCknmPlqPzQa47ZBPxKUxIjHLhLol4qLYz7zV/TkyOk7jkHraB7vcZUGfjexsy1k8twniIh1LrONaaOP06fzNQoKTfnyh9bs/au+flmXtlH06RTOvOW9q6xMd1MqNTz99CV6dI/G0bGAtDRLdu/x59tvmwC63hJPDQlh6NAQALZsacTWnxrpPx8UlMrz008x66U+Rj9edxr93FWenlD23hcXY8PU0T0AmPjCJXoNiKMgX8X6NY3Zv6v0vtepewI9+sWx6PV2VZpnIYR4mNT02OJeakO5WtVJ4LmW52jsmoKbTR4v/N6PPyNL8/Verz95slFomc8cjvFhyraBAJgq1SzquZ8e9aJIzbXinQNdOBZX+j07vsVZPG1zWHzQeHXYbz76AQ/XnHLpv+xuyMoNHZj29B31t++rX/3tfpyd8pgw7iytWyZgbq4mIdGG5Svbcy1cV0d96skrDBtyBbhVD//5jnpdg1RmTD3JzFf6Vqt63d2GjbzK+EmX+Pm/AXz+aTAAk6advxU3mbDuy0fYv7eufv1OXa7Ts08Mb8+r2pj2QTg75THxmdO0aRmPuZmahCRbPvykA9cidHHT0EGXGf6kLm76/qcm/PfXJvrPNgxM4YVJx3nhjQHV6nhtWrHlntfXqvXtmfr0Cfp0uUZBoQlffteaP++6vnp3juCtZb2qMssPZMDgaAYMjsbdMx+AmChbvl0byOlj7gBMfPFyaXyxulH5+KL/dRa91tYoeb+f4RNi6NArBW//PIoKlISct2ftR/WJj7bSrzPp1Wv0eiKJgnwV61bUY//vHvplnfrcoOfjSbz9QjNjZF9UQ9JIcEvXQelMXpDAqje8uXrGisGTUnhvcyQTOgfRsGUe3QdnMGdUPbzqFTJ7WRynD9iSlWaCla2aca8n8sbI+n+/EyOoDeVSO5uS+Yw7JZ5mKACrfRk4/18cNz6sR0ldCwByezuSNdJV/xmteekXre2PqSjzNaR8WA/rHWk4rE4g5QNduUxD8zC7lk/KBM8qLROApVkx4fHO/H48iCUTdle4ztErPize3FX/vrhEpf//Ex1CCPJJZcpHT/BoozgWjvmTgfOeBRR4OmXxePurTPhwsKGLUY6FeQmRsU7s3B/Iwpf3lVs+4vFLPNnvCktXdyYpxYZxw86y5I1dTHj1SYqLTRjQM4xA/5vMXDCANs3jmTPjIMOnjgAUeLhmM6BHGM/PHVjl5brbzBm9UClLH1Dj65fF4qUHOHTAh7aPJtC1Ryzz5nShjlcOs14+yZlTHmRlmWNlVcTY8Rd587Wu99m68cyacQy/uhl88FEHbqZZ0bNbJEsW7WXyjMexsy3k2dHnWfBOdxQKLW/P28+Zc55ExziiVGp4YdpxVn7SrlpUdC3Ni4mIdeKPA4EsmvVnueUW5iVcCnPnwHF/Xp54pNzy9i1i6dE+kteX9sPLPZNXJx3m5AUvsnIssLYsYsLQ07z6fr+qKMo9DRsawmMDrrFs+aPExNjTIDCNl146Tm6uKdu2BeHnl84zz1xk4dtdUAALFx7kzFlPoqMddMdrxklWrmxbLY7X3aIjbZk3s73+vVqta/Ro2zGJbr3jmfdSe7y8c5j55jnOHHclK9McK+tixkwOYe6s9vfabK2hQYnGwINBDb19IUTNVBtii4rUlnJZmhYTmurM1isNWfnYzgrXORTjw7w9PfTvi9Sl8cWwpldo4pbC6C1D6Owby9I+u+ny1ThAgZddFkObhDD8+6EGLsX9TZ//OMo76uD+3ul8MGcnB074l9bf3u+Hl0cF9bdhp3n1/4xbf7sXG+tClr+/i/MX3Zn3dncysyzw8swmJ8cMAH+/dJ59+gIL3umGAnj7rf2cOetRWg+ffoKV/6ke9fB7CQxKo//ASCIj7PVpbdvfipte76yLm149xZmT7rq4ybqYsRMu8earXYyY64rZWBfy0Xt/cP6SB3Pf7UVmlvmt42UOgL9vOmNGnmP+4h6ggHfm/Mnp83WIjtUdrxcnH2PFmvbV7ng9/9bjKJWlk6f4e2ew9M2dHDzux6MtYunRIZI3/q8vXh5ZvDL5MKfuuL6eG36G15b0NWLu7y31hgXrVzciIc4aFNBrQBxvvX+SF8d1xaNOri6+mPUoXj655eOLKVeZO9O4HRTvpWnrDH77zouwS3aoVFrGzozgvc/OMeXJdhTmq2jbNZWuA24wb3Jz6vjmM2vRVc4ccSIrwwwrmxLGvhDJm5OCjV0Mg5K4qXKMWpKDBw/y+OOPU6dOHRQKBT///LPR8jJkcio7Njux63snYq9ZsPJ1bwrzFfQdlUbdwEIuHLXh2gUr9v/sSF6OCg+fIgAmzkvgt40upMSbGS3v91MbylXQxpbCVrao65hTUsecrKfd0VooMQvL16+jNVOgcTTVv7RWpZVd0/hC8jrZU1LHnNw+TphcL9QtKNHi8FkiGVPqgMqwc5RV5FhIXb7Y3oaDF+7d66i4RElatpX+lZ1vrl/m65HB4Uu+RCU58d/DTXC0LcDBugCAV4YfZvWvbckrrPrjd/K8N+t/aMmRMqMHbtMyuP8VvvmpOUdP1yUq1on3P+2Ms2MeHVvHAlDXK4Ojp32Iue7Itl0NcbQvwN5Wd8xenHCULze3Ii/f+OdlVqYF6emW+lfbRxNIiLfh4gVX6tbN4uJ5V66FOXFgX13y8kxw98gF4LlJF/j91/qkpFgbuQTlmZmV0Kl9LF+tb8GlK+4kJtmy6bvmJCTaMrB/GN7eWURFO3L+ogfnLngSFeOAj1cWAMMGX+HSZTf9iANjO3HBh3U/tuLIab8Kl+85EsDXP7fg9OU6FS6vWyeD81c9CItyYd+x+uTlm+Lpmg3A5JEn2fZnQ27ctDFU9h9Io8apHDvmzcmTXty4YcPhI3U5c9aDoAY3AfDxySY62oHz5z04d96DqGgHvL11x2vo0BAuXnIj7Jrz/XZhNBq1gvQ0C/0rK1N37/PxzebCWWfCrzpwYI83ebkmuHvqRvQ8N/0K23/2IyXZ6n6bFkKIGqm6xE21IbaoSG0p1+EYX1Yea8feyHr3XKdIrSI1z0r/yiosjS/qO6azL8qPiDQnvr3QFGerAhwtdPHF/G4HWf7Xo+QWG7esmdmWpGda6V+PtogjPtmW8yEeuvpbyK3629Fb9Te3W/W3USfZttf49bd7GfbUFVJSrVi+sj1h11xITrbhzDlPEpN0o+e9vbKIinbg/AUPzl3Q1et8btXrhg25wqVLboSFV896HYCFRQmvvXmClctbkZNtqk+vWzf7rrjJFHfPW3HT5Av8vq0eKTeqX91u+OBLpKRas+yTjoSGu5B0w5bT5+uQmKw7Xj5emUTFOHLukifnLnoSFeOIj1em7rNPXOZiiDthEdUjbrpTZrZFmeurXYs44pNuXV9emXdcX/XKXF+TRp3k1z3V9/o6ccSDU0fdSbhuQ0KcDRs/a0RBvgkNm6Tj45dTGl/s9tLFF3V0vzU99/wVtv/kW23ji/nTmrPnF09iI6yJCrNh+bxGuNUpJLCx7rjUrZfLxZMOXLtix4E/3MnLVeHupbunPzc7gt9/8CIlycKYRRDVjFEbCXJzc2nevDmffPKJMbOBiamGwGZ5nDlkq0/TahWcPWRL41Z5RF62oEGzPGzsSwh4JA8zCw0J0WY0aZtDwCP5/PJV9bu5Qy0tl1qL5eFMFAUaioJKpwyyPJSJx9iruM0Mx25TMorC0tbvYj8LzC/mglqL+dkcSnx1N0Gbn1MpamJFcYBlud1UFy0CEvnt3Y18++b3vDLsEHZWBfpl4fFONKuXhJlpCe0aXic104qMXAv6tLpGUbHqvo0PxuLhloOzYz5nL5WO3MjLN+NqhCuNA1MAiIxxomnQDcxMS2jdPJ6b6ZZkZpvTo2MERUWqezQ+GJeJiZruPWPYtdMPUBAZ6UBgg3RsbIoICEzD3ExNYoINjZukEBCYzrafA42d5QqpVFpUKi1Fxaoy6UVFKpo0ukF0jAPedbJwdcnFzTUHrzrZRMc64OmRTe+eEWz4Jtg4GTeAiFgnGvinYmNVSKBfKmZmauKT7WjaIIkAv5v8tLPx32/EwEKuuBAcnIzXrYYaf/90mjRO4dQpXcNHdLQ9Xl7ZuLrm4uaWi1edLGJi7HXHq3cUGzdW32Gldbxz2fjLTr76YQ+vLDiNq7uuISAq3J7AhhnY2BYREJSBubmGxHhrGje7Sf2gTLZtufePIrWJWquskpcQovqoDnFTrYwtqL3lupc2XgkcnLCO357ZzFvdDmBvURpfXE11pqVnIuaqEjr6xnEjx4r0AgseaxBGoVp138YHYzBRqenVMYIdBxoACl39rd5d9bek6lV/u5dH214nLNyZua8f4ruNP/KfFdvp1ydcv1xXD88urYd7ZRMdc7seHsmGb5obMfd/b/rMs5w45sG5M+5l0iMj7O+Im9J1cVO8DY2bphIQkMG2n6pn3NS+9XWuRTgz7+UD/LD2Bz794Ff69yqdLjMqxgEvzyxcXXJuxU1ZurjJPZs+PcJZv7mFEXP/YExUanp1imDHgUBAQWRMBfFRkh1NGyQT6JfGTzsb/e02qwOlUkuXXvFYWKgJueRIVLhd+fjiulWNjC+sbUoAyM7UTRoTGWpDYJNsbOyKCWicrStbnCWNW2QQ0Cibbd94329ztYLETZVj1OmG+vfvT//+/Y2ZBQDsnNSoTCAjpeyfIz3VBJ+AQk4fsGPv1jxWbb9GYYGSD2fWpSBPyQtL4vlwlg8Dx95k0HOpZKWp+PhVH2LCqkdLXG0ql0lMAa5zolAUadBaKLn5ug8lPrr85HW2R+1qhtrJBNPoAuy/TsYkvpC013VzGWYPdsHh80Tcp11D7WZK+vN1UCUUYr0vg5T/88dhTQLm53Moqm9JxrQ6aK1V98tKlTkW4s2BC34k3LTDyyWLKQNPsGzqH0z56Ak0WiW/HWtI/TppfDNnC5m5Fry1vhe2VoVMHHCKGaseZ9KAk/RqGUF8qh2Lv+1Kaqbxe6472eta5NMzyzbMpGda4uigW7ZjfyD16qbz5Yc/k5Vtzjsfd8PWuoixw87xyqJ+jBt+hm7to0hMtuXDzzpyM9345WrfIQEbm2L27NI1zJw55cG+vXVZ8Z89FBWpWPZBWwoKVMyYeYblH7RhwOMRDHoinKwsM1Z+1JrYGPu/2UPVyM835cpVF0YPv0jsdXsyMizo1jmahkGpJCbZEHfdnnWbglmyaA8A674OJu66PUsW7eGrDS1p1SKBZ0ZeoEStZM0Xrbl0xf1v9lh9nbrozZ4j9fl00TYKi0x4/7POFBSaMHPcUZZ+3pnHe15lcJ8rZGZbsHxtR2LiHf9+o/+yH7Y0xsqqmM8/+x2NRoFSqWXDxmbs2+8HQFycPes3NGPxe7ppv9ZvaE5cnD2L3/uTtWuDadUykaefvoRarWDNZ624dMmtystQkdArjnz0Xguux1rj5FzI6OdCWfrpEaY/250zJ9zYt9Obj748SFGhiuXvtqAg34TnX7nAR++1YMDgaB4fGklWhhmrljYnNsrO2MURQoh/RXWIm2pTbHGn2lquihyO8WFPhD/Xs+zwsc9iVvvjfDbod0ZvGYxGq+SnkIYEudxk29PfkVFgwcs7+mBvXsiMR08yfusTvPjocfoHhhOXac+8vd24kWvcXsMdW8dgY1XEzoO6H5L19bd37qq/jT/K0s8683ivO+pvXxmn/nYvnh45DOwfxtZfGvHdliY0CLzJtEmnKClRsufPerp6+NfNWbJoLwDrNja/VQ/fy1frW9CqRSLPjLpdD2/FpcvVpx7epXscAQHpzJzes9yyM6c82LenLis+3UtRoYpl77ehoMBEFzctvRU3DQ4nK9OclctbVpu4ydM9m4F9Q/nvr435dmtTggJuMv25k5SUqNi9vz5x8Q6s29yC/5uvi5vWftOCuHgH/m/BLr78uhWtgxN4dsR5SkoUrF7XlovVMG7q2DoWG6sidumvLy/2HqnPJ+/8SlGxiqVrbl1fz/3F0jW66+vJPiFk5ljw0ZcdqtX1BeBbL4tlnx/GzExDfr6Kd+e0Ji7alrhoW1188dUhXXzxTrAuvnj1Ih+9G3wrvogiK9OMVe83JzbK9u93ZgQKhZYpr4dz+Yw9MeG6e/OZv5zZ91sWK749TVGhkmVzG1GQp2LGW2Esn9eIASPiGTTqOlkZpqx8uyGxEcb/bUUYV416JkFhYSGFhYX691lZWVW2703LPNi0rPQBH0/PTuLsIRvUJQpGzUxmao8g2vXO4tWVsczo16DK8vW/qinlKqljxo1l9VDmabA8moXjqnhS3/GjxMeCvD5Opev5WqB2MsF1QQyqpCLUHmZorVWkv1S2hdRlfjSZY92xPJiJKrmI5FWBOKxOwPaHFLLGe9y9e6PYezZA///IRCciEpzYMv87WgQmcjrMC7VGyfIfO5X5zJuj97PlYFMaeKfSpVk0Y5c+xdM9z/PSU0eYu7ZPVRfhH1GrlaxaV3bOv1emHObnHY0I8L9Jh9axTH1jEMMfv8TzY0+waEV3I+W0VJ/+kZw64UHazdLGj2++bso3XzfVvx/9zGXOnnGjpETJyNEhTJ/ch3aPJvLyayeY+Xz1eXjaBx915KUXjrJ53VbUagXhEU4cOORLQP00ALbvaMD2HaX3gl7dI8jLNyXkqgtffrqNF1/pj4tLHnNePcy4SU+WeY5GTbPxp5Zs/Kml/v2zg89y5nId1GolzzxxnolvPsmjwXG8MeUg0+Y/UeX569I5lu7dY1i6tAMxsfbUq5fOlMlnSLtpyZ69uh4v27cHsn17aQ+sXj0jyc83JSTEmS8+/52Zs/ri4pLHG6//xfjxj1eL43X7AWIA0RG6RoN1/91N5x7x7PrNl81rG7J5bUP9OqPGh3LulCslJQpGjg1j+phutO2QzMvzzjJzQvV89sf/SoMCDYadJs/Q2xdCGJax4qaaEltUVm0p1x/XSusE1246E5bqzM6x39DGK4Hj170p0ah490DZ+d/f7fkn35x/hEauKfSoF8WQb4fzXKuzvNnlMLP+MO78/v27XuPEeW9uZpROBbJxa0s2br2r/nbpjvrbnCd5tEUcb0w9yLS3qr7+di8KBVwLd2L918EAREQ64Vc3k8f6XWPPn7fqdXfXw3tEkpdvQkioC19++isvvtxPVw9/5QjjJj1RLep1Lq55THn+HHNf60xxccX5+WZjE77ZWPpQ39HPXtHFTWoFI58JYfrEW3HTGyeZOa16PBRXoYCwCGfWbdadaxFRzvj5ZPBYn1B279c9o+T3XUH8vitI/5ne3SJ0nbJCXVm76mdmvP4Yrs55vPnSQcZMG1Itjted+ncLq+D6asHGraWjIJ4doru+StRKnn7yApPe0F1fr087xPR5g4yR7XuKj7XhhbFdsbYppmP3RGbPO8frz3cgLtqWzV8Fsfmr0mM16rlQzp100cXu464x/dmutO2YzMtvnWXmc9XvGRkA0+eG4RuQyytjy45S+Wa1P9+sLp1lYvTUKM4ec9TFTpNjmD6kDe263uTlxVeYOaJNVWfb4CRuqpwaNSZiyZIl2Nvb618+Pj7/ynaz0lSoS8DBtaRMuqNLCekp5dtRfAIK6DkknQ1LPWjWIYdLx6zJTDPhwDZ7ApvlY2mt/lfy9b+qVeUyVaL2NKe4viVZz7hT7GeBzW9pFa5aHKj7EjNJLKpwudXedDTWSgra2mF+KZeCtnZgoiC/vR3ml3MNVoT/VcJNO9JzLPB2yaxwecuABPw90vnvwSa0CEjk6BUfCopM+fNsPVoEJFZxbiuWdmsEgaN9fpl0R/t80jMqnvapeeNEfL0z+GVnQ5o1SuLEOW8KCk05cMyP5o2TDJ7nv+Pmlktwixvs/OPewxC9fbLo3iuGr9c3pVnzG1y+6EJWpgUHD/gQ2CAdS8viKszx/SUm2fLa3D48MXwkz04YzMxX+6My0ZKUXL6nmJ1tAU+PvMjqz1vTMCiV+AQ7EhLtuHDRA5VKo58Gpzbw8cygV4cI1v3YkuaNErkQ6k5mtiUHTvjTwP8mlhZVfwwnTDjHD1saceCgL9HRDvz5pz8//RzE8OFXKlzfzq6Q0aMvsXp1K4KCbhIfb0tCgi0XLrhjYqLByzu7ikvwYHJzTImPs8HTu/z92btuNt37XufrLxrySIubXDrnTFaGOYf+rENAw0wsrUoq2KIQQtR+hoibalVscYfaWq4HcT3LjrR8C+raVxxftPWKJ8A5nc0XmtLGK4FD0b7kl5iy41oAbbwSqji3Zbk559CyaQLb99+7UcbHM4NeHSuovx03Xv3tXtLSLYiNK9tLPva6Ha6uFcenpfXwNjRscLNsPdyk+tTDAxuk4+hYyKo1e/l113/5ddd/aRacyqDB4fy6679lHkINt+OmWL5e15RmzVO4fEH3ANmDB7wJbJBRbeKmtAxLYq/fdbzi7XFzuffxemb4eT75si0NA1O5fut4nb90K26qUz2O121uLjm0aJrIH/vvPd2Tj2cGPTtGsG5LS5o3TuLiVXcysy04cNyv2l1fACUlShLjrQkPdWDDmkZEhdvxxPDIcut5+2bTvW+8Lr5omcqlc066+GJv9Y0vpr0ZRtuuN3ljQjA3k+89ms3bP5fuA5P5epU/zVpncPm0PVnpZhzc6UZg45xqWTZRtWpUI8GcOXPIzMzUv+Li4v6V7ZYUK7l2wYoWnUp/IFEotAR3yuHK6bsfUKLlxfev89nbdSjIU6FUgspU98Vmcuv5O8pq0gBcW8sFoNAAJdoKl5lG6ebVVDuWr9QrM0uw3ZJCxsRbc+JrALVuOwq1Vve+mnK1z8HeqoCbWeUfmmNmUsLsYYdZ+n1nNFolSqUWE5WuMCYqDUpFxX+rqpZ0w4ab6Za0aFraaGFlWUTD+ilcueZabn1T0xJeGH+MFV+1R6NVorq7XErjH7DefaPIzDDnxHHPe6yh5YVZp/liTTAFBaYolbp5/wF9We6uHFcHhYUmpKVbYWNdSKvgBI4eL//jwpQJp/lpW0NSb1rrymVSejxUKm21LNc/o+Wl5/5izea2FBSalrm+VPpjWPXnorl5CVpN2V4LGo0CxT2+2SdPPsPPPzck9aaV7lq643gplZpqe7wsLEvw9MolLfXuCq+WGa9d4MtVTSjIN9E9U+NWmUxMdGWprmX6X8ncmkKIv2OIuKm2xha1tVwPwt06BweLAlLzKogvVCXM63aIhX920cUXCi0mt+o7ptWg3tCvaxgZWRYcO3evBrBb9bdvqlf97V6uhLjifdcP+151srlxo+LpP6ZMPM1Pv+jqddW5Hn7ujBvTJvRmxuRe+lfYVUf2763LjMm90JSpy2p54aUzfLGmGQUFJmXKVd3ipstXXfG+64d9b88sklMqnoJr6vhTbP21Ealp1mViWqhex+u2fl2ukZFpwbGz976+Zk34izWbdNeXSqHVX1fV8fqqiEKpxdT07jzeii9WNtbFF0otqltxRfWML7RMezOM9j1SmDMhmOT4+z1vU8sL80P54oMACvJNUKruLNutY6aqTmX7d0jcVDk1arohc3NzzM3NDbLtrZ+78MqKOMLOWxF61orBk1KwsNKw6zunMuv1H51G5k0Tju/WtRpfOWnNsy8n0bBlLm16ZBMTak5uVvWpGdaGctltSqaghQ1qV1MU+RqsDmVidjmX7Ld8USUVYXUwg4JWtmhsVbpnEqxLorCxFSV+5VtQ7dcmkTPIGY2zrhZf1NAKqwMZFAbbYL07naKGVfcQY0uzYrxdS3vt1HHOItArlaw8C7JyzXmu32n2n/fnZrYVXi5ZTB90nOup9hwPKf9FPa7vGY5eqcu1eN2D0y5GuvP8E8f5/XgQT3W+zMWoqpvj0MK8GC+P0gqTh2sO9X1vkpVjTspNG376ozGjn7xAfJIdiTdsGTfsDDfTrThyqm65bT0z+AInznkTEe0MwKUwNyaPPsXOAwE80ecql8OMO3ejQqGld99o9uz2Q6Op+Iuhb/9IXSPCMd3DZK9cduHpMVcIanST1m0SiYm2IzfXrCqzfV+tWuh6hV2Pt6OOZzYTx50hLt6eXXvrl1mvRfNEvLyy+PDjDgCEXXPGxyuL1i3jcXXJQ6NRcD3eePPBW5gX4+V+53mYTf26N8nONefGTRtsrQtxc87B2VH3QFwfT921mJZpSXpm2UB5QLcwMrMsOHpWd45eCnNnzOCzNKp/g7bNrxN93YHcPMN8N93P8eNejBx5mRspVsTE2BNQP50hg0PZtav8qJYWLRLx8spm2TLdVF5hYc54e2fTunVC6fG6Xj3m15zw/GWOH3HnRpIVzi4FPD3xKhq1ggN7vMqs1/fxWDIzzDhxRDf9w5ULTox+LpSgJmm0fvQGMVG25OaYGqMIQghhdIaKm2pDbFGR2lIuK9PiMqMCvO2yaOiSSmaBOZmFFkxre5Ld4fVIzbPCxz6LlzseJTbDnsMx5evhU9uc5mB0Xa6m6jrynE304JWOR/kppCGjml3kbKLxpmhVKLT063KNXYcC7lkHH9AtjMzsu+pvQ4xff7uXn35pxPKlOxkx7BIHD/sSFJjKgL7X+PiTduXWbRGciFedbD5cUUE93NX49fA75eebEhNdtsd9QYGKrCyzcul9B0SRmWnOiaO34qZLd8RNbZOqVdy09dfGrFj8ByOHXOTgX74EBaQyoPc1Vqx5tNy6LZsl4O2ZxQerOgIQGq47Xm1axOPqkqs7XgnV43iB7vrq2/Uau+93fXXXXV/H9NeXG2OeOkujgBu0aR5f7a6vsVNDOHXMjZQkSyytSujWJ55HWtzkrZfKHq++g2J1sfud8cWEMIKapOvii0ibahVfTJ8bRrcBN1g0syn5uSocnXXTDObmmFBUWPa7qO9TiWSmmXHigO43oytn7Xl6WhRBzTJp3SmNmHArcrOrT9mEcdSoRgJDOrDNEXtnNWNeTcLRtYTIy5bMfdqfjNTSi8TBpZiRM5N5aVDpkKvQc1b89zNX3tkYRcZNEz6cWb6CZUy1oVzKzBIcV8ajSi9BY6Wk2M+Cm2/5Uhhsgyq1GPMLudj8loaiUIPaxZT89nZkDy3fK938bA4mSUWkzyz9oSl3gBOmEfm4vh5JUaAl2cOr7sGdDeum8J8XftO/f3HwMQC2H2/AB1s6Ub9OGv3bhmFjWURqphUnQr35YntritVlb/b+nmn0aBHJuKVP6dP2na9Hi8BEPn1xG7E3HFi4sUfVFApoUC+VZfN36t9PG3MSgF0H6vPBms58/2tTLMxLmDXxL2ysirgU6s6c/+tNcXHZ25GfdzpdH41i6pzSuQwPHddNMfTRgj+IS7BnyX+MOx9gcMtk3Nzz2L3Dv8LlDg4FjBwdwsuzSh/SFRbqzNYfG/D2u4fIyDBn+dK2VZXdB2JlVcz4Z8/i4pJHTrYZh4/WZf2mYNTq0gqimVkJz085weIPOqPV6nr/pN60ZvUXrZn94lGKi1UsW9GeoiLjfcUE+aeyfO4f+vfTnz4BwM5DASz9vAsdWsby2uRD+uVvzdgPwIatwWWeQ+Bol8/Tg87z4qKB+rTQSFd+/KMpi1/eTXqWBe9/bpzzcPWaVox59gLPP38KB/tC0tIs2f5HAJs3NymznplZCdOnnWbJ/3W843hZsXpNK16adZziEiXLlj9q1ON1J2e3fF57+zR2dsVkZphx+YITs6d0JiujNNBwcCxgxNgwXpnaWZ8WFuLIT9/VZ+EHx8lMN2f5uy0q2nytoEaJ2sCDQQ29fSFEzVQbYouK1JZyNXG7wfoh2/TvX+/8FwA/hwSxaF8XgpzTeKJhKHbmRdzIteavWG9WHWtLsaZsfBHgdJN+gRE89e0wfdqu8Pq09Upg41M/E53hwGs7jTc3fMsmCbi75LLjQMVTDTna5fP0E+d58e276m/bm7L4lVv1t8+q17ziYeHOLFrchfFjzvH0iIskJduw5svW7DtQNs4wMyvh+cknWfxBp7L1us9bM3vmMYqLlUavh/8TDo4FjHz6Ki+/WPq8ubBQJ13ctPgIGenmLH+/+syXHhbhwttLu/Pc02d4Zth5km7Ysnpda/48VLazjplZCc9PPMF7y7uUHq80az75qi0vP3+E4hIVH6zqWK2OV8umuuvrjwMVTzXkYJfP6CcuMHPhY/q00EhXtmxvynuv7CE9y4KlazpX+FljcXAs5OW3zuLkXEhurgnR4Xa89dKjnDvpWmadEWOv8cqU0uc+hoU48tO39Vn44a344p1gI+T+3gaO1HXwW7ruXJn05fMasueX0pkOHJyLGDkphpefLY1zwy7ZsXWjD29/coGMNDOWz21UJXmuahI3VY5Cq9UabTxJTk4O4eHhALRo0YLly5fTvXt3nJycqFv37ytYWVlZ2Nvb040nMFFIi1dNEL+1yd+vVAPZf1vxsMKazupGxc91qMlMU/OMnQWD0FhVj141/7Ziu9pZLrOb+X+/Ug2kzKy+z3X5p0o0heyJ/g+ZmZnY2Rm3l9ftes+HpzphaWPYYDI/p4RXWh+uFuUWQkjc9DC6PqeDsbNgEB7HC/9+pRrINKv2lUuZW/vKBFDiUH56rdpAY1F9Rmf9m8yjUo2dBYPQZucYOwv/uhJNEXvT1leL+KG6x02ffPIJH3zwAUlJSTRv3pxVq1bRtm3FHUi3bt3K4sWLCQ8Pp7i4mMDAQF5++WWeffZZ/TparZYFCxbwxRdfkJGRQceOHVm9ejWBgfd+rkhFjNrccerUKVq0aEGLFrrefrNnz6ZFixbMnz/fmNkSQgghhKi2NFpFlbyEENWHxE1CCCGEEJVTHeOm77//ntmzZ7NgwQLOnDlD8+bN6du3Lzdu3KhwfScnJ+bOncvRo0e5cOEC48ePZ/z48ezcWTqDx9KlS1m5ciVr1qzh+PHjWFtb07dvXwoKCiqVN6OOaerWrRtGHMgghBBCCCGEENWexE1CCCGEEDXf8uXLmTRpEuPHjwdgzZo1/P7776xdu5Y33nij3PrdunUr837mzJls2LCBw4cP07dvX7RaLStWrGDevHk88cQTAGzcuBF3d3d+/vlnRo4c+cB5qz0TJwkhhBBCPAQ0t+bWNORLI1VEIYQQQgghRA1WlXFTVlZWmVdhYfkp24qKijh9+jS9epU+10epVNKrVy+OHj36t+XRarXs3buX0NBQunTRPVsnKiqKpKSkMtu0t7enXbt2D7TNO0kEKIQQQgghhBBCCCGEEEL8Az4+Ptjb2+tfS5YsKbdOamoqarUad3f3Munu7u4kJSXdc9uZmZnY2NhgZmbGY489xqpVq+jduzeA/nOV3WZFqs8j1IUQQgghxN/SaJVotIbt52Ho7QshhBBCCCGEIVVl3BQXF1fmwcXm5ub/2j5sbW05d+4cOTk57N27l9mzZ1OvXr1yUxH9r6SRQAghhBBCCCGEEEIIIYT4B+zs7Mo0ElTExcUFlUpFcnJymfTk5GQ8PDzu+TmlUklAQAAAwcHBhISEsGTJErp166b/XHJyMp6enmW2GRwcXKkySDcxIYQQQogaRI2iSl5CCCGEEEIIUVNVt7jJzMyMVq1asXfvXn2aRqNh7969tG/f/oG3o9Fo9M888Pf3x8PDo8w2s7KyOH78eKW2CTKSQAghhBBCCCGEEEIIIYQwqNmzZzN27Fhat25N27ZtWbFiBbm5uYwfPx6AMWPG4OXlpX+mwZIlS2jdujX169ensLCQ7du38/XXX7N69WoAFAoFs2bN4t133yUwMBB/f3/eeust6tSpw5NPPlmpvEkjgRBCCCFEDSLPJBBCCCGEEEKI+6uOcdOIESNISUlh/vz5JCUlERwczI4dO/QPHo6NjUWpLN1mbm4u06dP5/r161haWtKwYUM2bdrEiBEj9Ou89tpr5ObmMnnyZDIyMujUqRM7duzAwsKiUnmTRgIhhBBCCCGEEEIIIYQQwsBmzJjBjBkzKly2f//+Mu/fffdd3n333ftuT6FQsGjRIhYtWvQ/5Uu6iQkhhBBCCCGEEEIIIYQQDykZSSCEEEIIUYOoweAPFlYbdOtCCCGEEEIIYVgSN1WOjCQQQgghhBBCCCGEEEIIIR5SMpJACCGEEKIGqY4P4BJCCCGEEEKI6kTipsqpPSURQgghhBBCCCGEEEIIIUSlyEgCIYQQQogaRK1VojZwjxVDb18IIYQQQgghDEnipsqpPSURQgghhBBCCCGEEEIIIUSlyEgCIYQQQogaRIsCDQqD70MIIYQQQgghaiqJmypHRhIIIYQQQgghhBBCCCGEEA8pGUkghBBCCFGDyNyaQgghhBBCCHF/EjdVTu0piRBCCCGEEEIIIYQQQgghKkVGEgghhBBC1CAarQKN1rBzXxp6+0IIIYQQQghhSBI3VU6taCTIf6wVJqYWxs7Gv8r2QrKxs2AQXkMuGzsLBpE7tJ2xs2AQ+a5mxs7Cv87kVJixs2AYjesZOwcGkRJsbuwsGIRFau27tgBctqcYOwv/Oq2myNhZEEKIf034R61QWtauuMnzQO0cHO+95C9jZ8EgbjzfwdhZMAi1Re2rs3pvvGbsLBhEZhsXY2fBILQjUo2dBYMo2uFt7CwYRJ3vauP1pTJ2BsT/qFY0EgghhBBCPCzUKFEbeMZIQ29fCCGEEEIIIQxJ4qbKqT0lEUIIIYQQQgghhBBCCCFEpchIAiGEEEKIGkTm1hRCCCGEEEKI+5O4qXJkJIEQQgghhBBCCCGEEEII8ZCSkQRCCCGEEDWIBiUaA/fzMPT2hRBCCCGEEMKQJG6qnNpTEiGEEEIIIYQQQgghhBBCVIo0EgghhBBCCCGEEEIIIYQQDymZbkgIIYQQogZRaxWoDfyALENvXwghhBBCCCEMSeKmypGRBEIIIYQQQgghhBBCCCHEQ0pGEgghhBBC1CAarQKNgXusGHr7QgghhBBCCGFIEjdVjowkEEIIIYQQQgghhBBCCCEeUjKSQAghhBCiBtFqlWi0hu3noTXw9oUQQgghhBDCkCRuqpzaUxIhhBBCCCGEEEIIIYQQQlSKjCQQQgghhKhB1ChQY9i5Lw29fSGEEEIIIYQwJImbKkdGEgghhBBCCCGEEEIIIYQQDykZSSCEEEIIUYNotKDRGrbHikZr0M0LIYQQQgghhEFJ3FQ5MpJACCGEEEIIIYQQQgghhHhIyUgCIYQQQogaRKNVotEatp+HobcvhBBCCCGEEIYkcVPl1J6SCCGEEEIIIYQQQgghhBCiUmQkgRBCCCFEDaJBgQYDz61p4O0LIYQQQgghhCFJ3FQ5D2UjQfOAREb2vkBQ3VRcHPJ4c01vDp/3q3Ddl0cd4okuV1m15VG2/PkIAKYmal575iCdmsWQlmXJ8u86cfqql/4zI3ufx90xh49/6FgVxbmn0c9d5ekJYWXS4mJsmDq6BwATX7hErwFxFOSrWL+mMft3eevX69Q9gR794lj0ersqzXNlPD4ulaHTbuDkWkLkFUs+nedF6DkrACYviKfP8HQK8pV89Z4n+35y1H+u88AMeg1LZ8FYf2NlHYDm9RMZ3fM8QT6puNjnMeeLPhy66Ffhuq8MP8STnUL4eGt7tuwvPQ9fH3WAzo/EkJZlxbIfOnIqrPQYjuqhOw9X/Ldqz8PaWq67DZ9ynY59buJdL5+iQiVXztix9gNf4qMs9etMmhNF7yEpFOQrWfehL/u2ueqXdeqXSq/BKSyc0sgY2b8nS8tixow+T4dH43CwLyAiypE1X7QmLNwFgKeevMKwwZcB+GFrE7b+0lj/2aAGqcyYcoKZr/ZDozHeQLUJbc/QMzASf6cMCktUnEvwYMXBR4lOL70PPPXIFQY0ukYjtxRszIvp+J/nyC401y83ValZ2Gc/3etHkZprxXt7u3A8tvQ8HNf6LB52Ofzfn52rrFwt/BJ4pst5Gnql4GqXx6tf9+XAldL72Iklayr83Mrtj7LpUDCmKjVzn9pPl0bRpOVY8f7PnTkZUVqmZzqfw8Mhhw9/7WTootzXgGHXeWz4ddzr5AMQE2HDt5/5c+qI7hyc9EoYvQYlUJCvYt3HAezf7qn/bKfeyfR8PJG3Xww2RtaFEKLWsbyWhePuJCxiczHJLCZ+SiC5waXfp6qsYlx+isM6JBNlnpr8QFtujPCl2M1Cv47rjzHYHU1FY64i9Ulvstu66JfZnE7D7ngqCdMbVGm5guslMLr7eYK8U3G1z+ONtX04eOnO2EDLxH6nGPToVWwtC7kQ5cEHP3bmeqo9oKsnzBlxgM5No7mZZcWH/+3EqWul36mju5/D3SGHj34y7ncq1PyYqaVPAmPan6OxRwqutnm8tKUf+8NK8zSl80n6Ng7Hwy6HYrWSkCRX/rO/HZcS3AHdsZr/2H66NYjiZo4VS3Z04Xh06bEa8+hZPO1yeH9X1dXpAFp5JTCuzTkauafgZpPHzF/6sS+84r/1vF4HGN78Ckv3dWDTmeZA9ayrVmT4c1F06JmCt38uRYVKQs45sHZFAPEx1vp1akLdLtj/rnr4xr4cvKMe3q1JJEPaXaGhVwr21oU88/FQriW6lNnGzMf+4rFWoRQUmfDJjnbsPFd63+vxSAQDWobxyob+VVYmAIsf0jD9KwfV9SK0ZkpKGlmQP94FjbdZ+ZW1WmwWJGB6Oo+ceZ4Ut7cBQJGtxmp5MqYX8lDXMSNvlhvq+qXfAZaf3kDjYUrhEMfy2zSQlj4JjG13jkYeKbjZ5vHSj/3Yd630eE3tdOu+Yau7b1xJcuU/B8veNxYM2E+3wChu5lqxeGfZ+8bYdrrr6/3dcn2J2uWhnG7IwryEiHgnPvquw33X69w8isb+N0jJsCqT/ninEILqpjLtg0H8ergR85/7E9A9ztrTOYvHO17li21tDJX9SomOtOWZx/voX69N0/2w2rZjEt16xzPvpfas+7QxL75xDjv7QgCsrIsZMzmE1csfMWbW76vroHQmL0jgm+UePN+3AZFXLHhvcyT2zsW0651J98EZzBlVjy/f9eSlD+OwcyoBwMpWzbjXE/nPm15/swfDszQrJjzemeVb7v9jd5dmUTTxK38eDuoQQpBPKlOXP8G2vxqyYOwd56FTFoM6XOXz36v+PKyt5brbI22z+PUbT14a1ow3xzXBxFTDe+suY26pBqBdjzS6PZ7K3PGN+WqpLzPfi8DOsRgAK5sSxs6O5ZOF9YxZhArNmnGMlsGJfPBRB6a+OJAzZz1Zsmgvzk55+Pum8+zo8yz5sDP/t6wTY58+j59vOgBKpYYXph1n1eq2Rm0gAGjtncB355ryzOYhTP7xcUyUGtYM/Q1Lk2L9OpamxRyJ9uHLEy0r3MbQZldo7J7Cs98O4b8XG/P+Y7u5fR562WUx5JEQVh2u2kZUC7MSriU688EvFVdG+783psxr0Y/d0Gjgz0u682xw2ys0rJPKxNWD+elEI94ZuYfbZarjmMWTbUNYvattVRXnnlJvmLPu4wBeHNWOmaPbcv6EI299fJ669XNo2zWFrv2TmDe1JWs/CmTmghDsHIqAW9fVCxF8urihkUtgeGqtokpeQgihKNRQ6GXFjZG+5RdqtdRZE4ZpaiHxUwOJebMJxU5meH98FUWhrj5kfSEd25NpXH8xiNTBPrhvikKZo/s+VuaX4LItruJtG5iFWQnhCc4s21rxj/jP9DjPsM6X+GBLZyauGExBkQkfTfkdMxNdTPFE+xCCvFOY/PGT/HKsEW8/s5cy9dV2V/lsu/G/U2tLzBSW7MySnRXXf2LS7Hl/Z2eGfTGC8RsHk5Bpy6ejfsPRStfZ4KkWV2jskcLY9UP479nGLH6ytE5Xxz6LIcEh/Gd/1XeMszQtJjTFmcV77/8jY4+ASJp5JpOcbV0mvTrWVSvStHUGv33vzexn2zB3SktUJhreW3NWHzPVlLqdpen96+GWZiWcj/HgPzserXB5p0bR9A2+xotfPcZ//niUN586gP2tc9TavJBpfU7wwc9V36hocjGfwsccyFrmQ867XihKtNjMi4cCTbl1zX/OoKIO0xbfp6HI15C1si4lj1hitfKGfpnqaj4moQUUPuFguEJUwNK0mLAbziy5R+NfTJo9/7erM0O/GsH4Tbr7xuoRv+Foeeu+EXyFRh4pjN2ou28sGVTBfeOAXF81gcRNlWPUX3KWLFlCmzZtsLW1xc3NjSeffJLQ0FCD7/f4ZR++3NaGQ+fv3SvCxT6XmSOO8s667pSoy/6ZfD0yOHLBl+hEJ7YeaIyjbQH2NgUAzB51hDU/tSWvoIKWVyPQqBWkp1noX1mZup6yPr7ZXDjrTPhVBw7s8SYv1wR3zzwAnpt+he0/+5GSbHW/TRvVkMmp7NjsxK7vnYi9ZsHK170pzFfQd1QadQMLuXDUhmsXrNj/syN5OSo8fHQ3wonzEvhtowsp8cY/PsdC6vLF7204eOH+5+GsoX+xaGP589DPPYMjF32JSnLiv4ea4GhbgMOt8/CVEYdZvc0452FtLdfd3prQmD1b3YgNtyLqqjXLXw/E3auIwKY5APjUz+ficTuuXbLhwG+uuvPQW1eOCa/H8PtmD1ISze+3iypnZlZCp/axfLW+BZeuuJOYZMum75qTkGjLwP5heHtnERXtyPmLHpy74ElUjAM+XlkADBt8hUuX3fQjDoxp2taBbLvckIibToSluPDWjh7UscuhsXuKfp1NZ5qz9kRLLtzqLXK3ek7p7I/wI+KmE9+da4qTVQGOlrrjN6/XQVYcepTcoqo9D4+G1WXN7rbsv1LxtXUzx6rMq2ujaE5HepGQbgeAn1s6h0J8ibzhxI9Hm+JkU4CDta5Mrz95iP/88Si5hca/tk4ccOXUYRcSYq2Ij7Fm438CKMhT0bBZJnX9c7l4ypFrV+w4sMND993lpavMP/fSNX7/wYuUJIu/2YMQQtQsxoqZAPKaOnDzCW9ygp3KLTO9UYBlVC43RvlS6GdDsYclN0b5oSjSYHvyJgBmSQXkBdpS6GtDdhtnNBYqTFN1HZNctsaR0cWdEqeqrw8du1qXz/9oy8GLFX2nahne5SLrd7fk0GU/IhKdWbS5Oy52eXRpGg2An3s6hy/7EZXsxH+P3Kqv3vpOfXXoIT79vR151eA7tTbETEcifPn0QDv2hVbcuWbH5QYcj/YmPsOOyFQnlu3uiK1FEYFuunPQ3yWdA9f8iEx14ofTTXGyLsDRSnes3ux/kI/3VX2dDuBwtC//OdKOP8Pv3WnIzSaHOT0OM2d7L0ru6oRTHeuqFZk/vQV7ttUhNsKGqDBbls9vgludAgIb6WKImlK3OxpWl892teXA5Yrr4X+cbcBXe1tzMrzihjU/13TORNbharwbu84HkldgRh2nbABeGHCM/x5vTHKmrcHyfy8573hR1NsOja856nrm5M52R5VSgkl4YZn1VBGFWPyUQe7M8rGTKq6Ioi42aLzMKOxnjypOdx+hRIvVf26QN8MNVFX7I+qRSF8+OdiOfWEVX19/XCm9b0SkOrFsb9n7Rr1b942IVCe+P3PrvnHr+prb7yArjHTfuFttub5E9WHURoIDBw7w/PPPc+zYMXbv3k1xcTF9+vQhNzfXmNlCodAyb/w+vtvdjOjE8hXiiOvOPFI/CTPTEto2vk5qhhWZORb0bhNOUbHqvo0PVa2Ody4bf9nJVz/s4ZUFp3F11zUERIXbE9gwAxvbIgKCMjA315AYb03jZjepH5TJti3Vr4fzbSamGgKb5XHmUOmXqFar4OwhWxq3yiPysgUNmuVhY19CwCN5mFloSIg2o0nbHAIeyeeXr4z/I+aDUCi0vPXsPr7d24yopPLnYXi8E83q6c7Ddg2vk5ppRUaOBb1bX6OwWHXfH+mNqbaWy8pG1/MqO0M3i1tkiBWBj+RiY1dCQJMczC00JMRY0qRVFgGNc9i20fN+mzMKlUqLSqWlqFhVJr2oSEWTRjeIjnHAu04Wri65uLnm4FUnm+hYBzw9sundM4IN3wQbJ+N/w8ZcV1HNLHjwHyFCU5xp4ZWIuUkJHfziuJFjRXq+BQMahlGoVt03oKsOnGzy6Ngwlm2nSnuGXEt0oblfEuYmJTzaII6ULCsyci3oGxxGUbHqno0PxqRUaunSLwkLSzUh5+2JDLMlsHEWNrbFBDTKwtxcTWKsFY1bZBDQMJttm+saO8tVQqNVVslLCFE9VNuYqUTXq1Jresf9QqlAa6rEMkLXaaLQ2xKL2FyUuSWYx+SiKNZQ7GqBRXg2FnF5ZHSvuLHemOo4ZeNil8epsNIf+3ILzLkS60ZTv2QAriU408xfV199NCiOlEzdd2qfltcoKja5R+ND1XpYYqY7mSjVDGlxhewCM8KSnQEIS3Ym2EdXp2tfL46UbCvS8yzo3ySMohLVPRsfjE2BlsX997L+ZDARN8vHTDW1rmp9O2bKMgV4aOp21xJdaOiVgq1lIQ29UjA3LeH6TXua+yYSVCeVH45Uj1kcFLm6EQQamzvu6wUarD9IIm+aK1qn8jOWl/ibY3o+H9RaTM/kovbXxVwWP6ZT0swKdWD1/hHaRKnmqeBb940buvtGaLIzLbxvXV/+cdzIvnV93b5v3KPxwdge1uvrfiRuqhyjPpNgx44dZd6vX78eNzc3Tp8+TZcuXcqtX1hYSGFhaYtmVlaWQfI1us951GolP+5rUuHy3/8Kor53Gl/P30JGjgULvuyJrVUhzz1+ipkfDWTioJP0aBVJQqot/7exK6mZ1hVux9BCrzjy0XstuB5rjZNzIaOfC2Xpp0eY/mx3zpxwY99Obz768iBFhSqWv9uCgnwTnn/lAh+914IBg6N5fGgkWRlmrFranNgoO6OUoSJ2TmpUJpCRUvb0TU81wSegkNMH7Ni7NY9V269RWKDkw5l1KchT8sKSeD6c5cPAsTcZ9FwqWWkqPn7Vh5iw6vml9XSvc6g1CrYcaFrh8t+ONaS+Vxqb3txCZo4F89f1wtaqkIkDTvHCyseZ9NhJeraMID7VjiWbjXce3q02lkuh0DJlXjSXT9kSc02XnzOHHfnzl2w+3nqBwgIly14LoCBfyfNvR7L89QAeG53EoGcTyUw3ZeW8+sSGG3/kTn6+6f+zd9/hTVVvAMe/Sbr33i2ltLTsDaKCbHAA4mDIEmXIEgFlOEBQRFki/lQURGUpKqAsRdkgyC6bttC9d9Pdpsnvj2BLbUGqtGnh/TxPHsjNzc05zc25571ncfmqE88NvEB0rC2ZmWZ06RRJUGAqCYlWxMTa8tX6liycvweAr9a1JCbWloXz9/DlN61p0yqeYYPPoylRsnJVWy5eNnzgr0DHjC5/cCbOjWtpjnf8vp8uBtHQOY2fnv+OjHwzXtvRCxuzQiY+dJIXvu/PpIeO0yfwGjFZtszd3YXkHKtqzEXVPd46hNxCY/bf1Ntp26lA/N3S2DR1E5l5Zry+sSc25oWM63GKl1b146WeJ+jZ4hpxaTa8s7kLKWrD5cnXP4el605iYqIlP0/FO1NbEBNuRUy4Fft3urF84wmKCpUsfasJBfkqJr1xhWVvNeGxgbH0GxKDOsOYFe80Ivp67fpehBDi36hqzAQ1EzcVuZlR7GCC00+xJD3ni9ZUif3eRIwzijDK0jfQ5zW2I7t9Dj4fXEJnrCRppB9aUyWu30aSOMIPu0PJ2O1PosTKiKShvhR5GL4+5GCj71SVnm1ebnt6tjkO1vrXdhwPxN89jY0zvicr14y31urrq2P6nGLip30Z++gJerTS11cXbOpikPrq/RIzAXTyj+T9Ab9jZqwhNceSlzb2JTNf//39fC6IAJc0No/7jsw8M2Zs0dfpxj9ykjHr+jPhkeP0bnyN2Axb3t7ZhZTs2lF3eKH9WTRaJRvOVn7zuC7WVRUKHeNmhHLprC1R1/TpOXPU8b6o2x0P8+bX4AC+mriZQo0R837oRn6RETMGHOadH7ry1AOXGfjgBTJzzVi45REikis2DFU7rQ7zL1LQNDZD61vWucpiVQqaRmalaxD8XcGz9lh+kozNi5FoXY3JneKCMq4Ik71qspd6Y/G/JIzO5FESYEbuyy5gqar0ODWtk38kH/S/qdz47qZy43wQDV3S2DLmOzLzzZjx041yo9NJRm/oz8TO5csN+X2Je0WtWrg4KysLAAeHygvEhQsXMm/evGpNQ0OfFJ7pepHRCwdQ6YRrQIlWyYffPQSUzbk+a/hBNu9vSoB3Gg+3iOKFBU8xpNd5pgw6yltf9KzWNN/K6T/LbtBFXtc3Gny1+Xc6dYvjtx312LgmiI1rynqZDhkVQvApZzQaBYNHhjJhRBfaP5jE9DfPMuXFRwyRhX9t/VI31i91K30+dFoiZw9bUaJRMGRKEi91C6RDTzWvrYhmUp+aXSjtTgR6p/DsIxd5YdFT3O48XPZD+XkLZz93gB8PNiXAK5VOzSJ5/oOnea77OV55+g/eXNOrBlJ+e/dqvia+HY5vQB6vDinf8LHhYx82fFzWOv/cpBiCj9rqf2MTYpnwREvad03n1cVhvDygRU0nu1KLP3yIqZOPsfGrLZSUKLh23YGDh+vh3yAdgF2/NmTXr2W/mR5dr5OXb8yVq06s/nQbL7/6KE5Oecx+7QjPj3mSYo1hK4FvdD+Ev1M6z3/3ZJXep9GqeG9v+Rsv83vvY+OZZjRySaGbfwTPrh3IqPZnmdX1CNO297mLqf7v+rYJYXdwAEWasst8iVbF4m2dWHzTfm89vZ9NR5sS6JHKI40jGPrRswx/JJjpff9g1obeNZ/wG2IjLZg0sAOWVhoe7pnM9HcuMePFNsSEW7FhZQM2rGxQuu9z48I5+6eD/nc1JoIJzzxAh86pTH/3ElOGGH6u0OqgRYG2mue+1N6ijBZCGN4/xUxQM3ETKiXxYwNwXR+B/6tn0CkhL8iW3Ca2f03dDEDaE16kPVG24KPDjjjygmzQqRQ4/BJP1JtNsbyQidvX4US/XnknktqmRKti6Zbyc12/MXg/PxxuSkPPVDo3jWTEkmcY2jWYqQP+4I2vDV9frUxdj5n+cjLKk8GrB2Jnns9Tra6w6KnfGP7VU2TkWaDRqnh/d2fYXbb/20/s49uTzQhyS6FrwwgGrR7I8x3PMrPXEV7dbPg6XSOXFIa2Ps+gdc9yq5ipLtZVJ7x+lXoNcnj1+bbltt8vdbvVe9qxek/Z2novdj/FyWueaEqUvNDtNM8tH8jDQVG8PXAfI//3TI2nz+KzFFRRRWQvLiuvjf/Mweh8PuoVt+lxbqkid0b5UfJWs2PJf8EJkwPZKBM1qL/wxWJFEubfppM/2rm6slAlJ6M8GbTmRrnR8gqLnvyNYd+UlRsLfyv/+5r3+D6+PdWMIFd9uTHwy4GMeuAsM3oe4dWt8vuqrSRuqppaMyZCq9Xyyiuv8NBDD9G0aeWVw9mzZ5OVlVX6iImJuevpaOGfiL11Pj8s+JZ9/1vNvv+txt0xhwlPH2fTu99W+p5WDeOp75HBlgONaRkQz58XvSkoMmb/6fq0DEi462n8t3JzjImLscLdq+LQZC+fbLr2jmXdqiCatUrjYrAj6kxTDu/zwD8oC3MLjQFSXDl1uooSDdg5l0+TvZOGjJSK7V7e/gV0fyqDbxa50fzBHC7+aUlWuhEHt9kS0Dwfc8uSmkr6HWveIBF7q3w2z9vIgQ9XceDDVbg75jDpyT/5Ye7GSt/TKiCe+u4ZbD7UhFYBCRy7fOM8POtHq1pyHt6L+Ro/J5z2XTOYObwJqYm3ns7Gyy+Pbv1TWLvch+Yd1Fw8aUNWujGHdjkR0DS31pyHCYnWzHijF/0HDmb4iwOY8tqjqIx0JCZV7FVgY13A0MEX+OyLtgQFphIXb0N8gg3nL7ihUmnx9Kye0V53ana3w3RuEMXo7/uR9B97d7TzjqOBYwbfBjelrVc8hyPqka8xZneIP2294+9Siu+Olr4J+Lpk8vPJ2y9C1cYvDj/XdH441pQ2fvEcDfGhoNiYvecb0MbPsHnSaJQkxFhw7YoNX6/wJzzUmv5DK17zvXxz6fp4Aus+aUDzdhlcOm2HOsOEQ7+5EtA4u1Zdu4QQ4m64k5gJaiZuAiisZ0n0G025tqw14e+3Im5yIMpcDcVOldeJjBPzsTmRSmpfLyxC1eT7W1NibUx2GwfMYvJQFBi+PpSu1o9mcLDOL7fdwTqf9OzKRzq09o+jvlsGPx5pQusG8Ry94kNBkTH7ghvQuoFhrqn3Q8z0l4JiY2IybLkQ78a8nV0p0SoZ0PJqpfu2rRdHA+cMNp1qStt68Ry5Xo+CYmN+u+xPG5/aUadr4xWPg0U+u8eu48zUlZyZuhJP22ymP3KMX0avr/Q9tb2uOn72Vdp3TmXWmDakJd96VMr9Urer55zBo61C+fy39rT2i+dshDuZuebsOd+AIK9ULEyKajQ95p8lY3wil5yFXuicjEu3G53PR5lQjN3A69j1DcOubxgAlu8lYDUrttJjmfyehc5SSXFHK4zO51H8gCUYKSh62Bqj8/mVvscQypUbu26UGy1uUW74xNHAKYPvTv+t3LjiT9t68vsS945aM5Jg4sSJXLx4kSNHjtxyH1NTU0xNq3dhq93HAzh1tfxiM0sm/8JvxwPYdaxi7wkTIw1TB//BO2u6otUpUSl1KBT6edyMVDqUSl2F9xiKmbkGd89c9v3q9bdXdEyacZ7VHzehIN9IPy+50Y08GOnTX5vyoSlWEnbeglYPZ3PsV1tAP7Sq5cM5bPv679OJ6Hj5g1g+n+dBQZ4KpRJUxvq8GN249ilrx2i3cnafCOBUSPnzcNn4Xew+GcDO44EV9jcx0jDt2SPMX9tNfx4qdChU+nyqVFqUitrx/d1b+dIxfk4ED/ZMZ+awJiTF3m4Ito6X3wln1ULfG+ehrvS3VRt/YwCFhUYUFhphZVlIm5bxfPlN6wr7jHvxNFu3BZGaZknDgLTScgP06xsYLk86Znc7Qjf/CF78vh9x6v82XZqJSsPr3Q8ze1f3snJed6OMVBr6PKyoX9srXIl1Jizx1nMJmxhpeK3fEeZs0udJqdCiuLGgmOF/WxUplTqMjbV/26pj8ltXWLW0IQX5RiiVurLy/ca5qFTVrnwIIcR/dScxE9RM3HQzrbk+rDROLsAsKpe0vn+PNwCdDteNkaQ844POTAU6UJToy+nSf7U6DF1yx6dbk6q2oG1AHGHx+muphWkRjX2S2fpH4wr7mxhpmP7UEeZtuHFNVeowKo0HtQarD90PMdOtKBQ6jFUVGzVMVBpm9z7M6z//Vf/RYaQs+65UtaQ+vv1yIH9Glf8Nffb0TnZcacjPFyuJmWp1XVXH+NkhdOyWwqwX25AUZ37bfe+Pup2OWQMOsXzng+QXGaNUajFSlZ2HUIOxoU6H+coUTI7lkL3QC62bcbmXC56xp7BX+VjKdmI0+WOcKW5fcRo1RZYGs2/TyV7krd+gBW4u57W19/tTKHSY3K7c2Na97J6Esuz3pZLfl7iH1IpGgkmTJrFjxw4OHTqEl1clFcq7zNy0GE/nsh6u7o7Z+Huloc41JTnDCnVu+Zt9mhIl6WpzYpLsKhxrxGNn+fOiN2Gx+grkheuujH/qBLuONeSpLpe4eN1wc3K/OPESx/9wJTnRAkenAoaOvoq2RMHBPeVv0vbuG01Wpgkn/tAPNb183oHnXgghsEk6bR9IJirCmtwc48o+wmC2fOHEq8tjCD1nQchZCwaMScHMQstv35Ufdv3oc+lkpRlx/Hd9xfjySUuGT08kqHUu7bplExViSq7aMDVec5NiPJ2zSp+7O6rx90wlO8+MpAwr1HkVz8O0bAtiku0qHOv53mf485JP2XkY4cqE/sfZdTyQpztf4kJEzZ2H92q+/m7i2+F06ZvK/PFB5OeqsHfS9/bIzVZRVFj+nOozMJmsdCOO79Ofn5fPWDPs5RiCWmbTtnMGUWHm5GbXiuKYNq30PSFi42zwcM9m9PNniImz5be9Dcrt16pFAp6eapZ89CAAoWGOeHuqads6DmenPLRaBbFxhlnL5I3uh3k0KIwpPz9KbpEJjhb6+YNzikwovDH9jqNFHk6WefjY68/VAKc0cotMSMi2Ql1Q/hwd98BpjkT4cDVZPzT2bJwb0x45xk8XgxjS6gLB8W7UBHOTYrwcy35bHvZqAtxTUeeZkpSlX5TQ0rSI7s3C+Whnx9se68Vupzka4kNogv63dT7KjcmP/sn200EM7HiRc1E1k6fKPP/yNU4dcSQ50QwLixK6PJZIs7YZvDW+Vbn9ej8VT1aGCScO6r+Xy8F2DH0pnMBmWbR9OJWo65bkZteua9fdokNR7cNadffQsFkh7hU1HTMBKApKMEkpKH1unFaIaUwuJZZGaBxMsTqdTom1ERp7E0zi83H5PoqcFvbkNbatcCzbP1IosTIit7k9AAUNrHDcEYdZeA6WlzIpdDdHa1Ez9SFzk2K8nG6qrzpkE+Bx45qaac33h5oxsucZYlJtiU+3ZmyfU6SqLTh00bfCsUb1PMOxKz6Ext24pka4Manvn+w8EcjTD1/iQoThrqn3RMxkXIy3Q9l35WmnpqFrKup8UzLzzRj90GkOhvqSmmOJnUUBA9texMU6l9+vNKhwrDGdTnPkug8hSfq6Q3CsG1O7H2Pb+SAGt71AcGzNfVfmxsX42N2ULxs1gc6pZBWYkphtTdbf6qMarZK0XHMiM+wrHKu21FUrM+H1ELo8msj8V1roYyZH/VopuTlGFWKm2ly3q1APdyhfD7cxL8DVLgdnG/2sDfWcMwFIy7YgPaf8CKT+7a6QmWvOkSu+AJyPdGNMj9M09U6iY2A04Un25BTUTCOv+acpmBzMJvctd3TmShTp+p7kOkslmCrRORhVulix1tmoQoMCgPkXqRQOsEfnpH+PprEZJvuyKW5liemvWWga3+4m9t1jblxcGueBvtwIdNH/vjLzzRjz4GkOhJWVG4Na3yg3rlYsN8Y+VLHceKXbMX4+H8SgNjVbbvzdvfL7qk4SN1WNQe9K6XQ6Jk+ezNatWzlw4AD169f/5zfdBYE+KayYtrP0+eRn/wTgl2MBLFzb5Y6PU98jnW5twnlhwVOl2w6c9aNlwwT+N307MUl2zF/T9a6lu6ocXfKZMe80NjbFZGWacOm8A9PGdUKdWXbBsbMvYNDIUF59qWxOzdAr9mz9rgFvLz5OVoYpy95tVdnhDergNntsHUsY8Voi9s4awi+Z88bQ+mSmlhVqdk7FDJ6SxNR+AaXbQoIt2Py5M++sjSAzzYglUwy3mnuQTwofv7yj9PnLT+nPw13HG/Lehi53fJz67ul0bRXOqEVPl27bH+xHK/8EPpmyjehkO+Z90+2upfuf3Kv5+rsnhiYBsGjDpXLbl870Z88Wl9Lndo5FDB4fy7RBZYuOhZ63ZssaD+Z9cYXMdGOWzvCvmUTfAQuLYkYNP4uTUx452SYcOebD1+tbUlJSNjudiYmGieNO8N7iTuhuzO+XmmbJZ6vaMu3lYxQXq1i6vCNFRYa5xAxqqf9Ovhr0c7ntb/7alW2X9FPwDGxxifEPnip97evBP1fYB8DfMY1egdcZuPbZ0m2/hzagnXc8Xw/+ich0O2bt6lFteblZI89kVo7dXvp86hPHANhxuiHzf9T/Fno2v4YC2H3u1ueUn2s63ZtdZ9iKsjztvdiA1n7xfDHuZ6JSbHlrU83kqTK2DkVMf/cSDs6F5OYYERFqzVvjW3H2z7Jej3YOhQweHcH0kWXzuoZetGXLunrM+18wmenGLHuriSGSL4QQd52hYiYAs+hcvD8sm37B5cdoALIecCJppB9GWUU4b47GSF2MxtYYdQcn0h7zqHAclboYh1/iiX6trCd+ga8VGT3c8Pw0BI21MUkj/ao/QzcEeafwycSya+qUJ/XX1J0nGrLgu66s39cCM5NiZj57CCvzIs5HuDHti8fKrfUD4OeWTreW1xm5tGz+8P3n/WjtH89nk7YRnWLL3PXdayZTlbgXYqbG7smsHr6t9PmrPY8CsO1cIAt+6YyvYyZ9n/kNO/N8svLNuJTgwgtrnyQ8tXxDSAPnNHo1us6g1WX1nz1XGtC2XjxfDv+JqHQ7Xv+p5uo/TVyTWTOoLF8zuurz9fPFQN7afecxTm2qq1bmiUH6KWkWrTldbvuytxqzZ1tZWVHb63aNvJL57Bb18Hd+6EanxpHMefZA6esLntsDwKo9bcqtQ+Bglcfz3c4w5tMBpdsux7qy8XBzlj2/i/Rcc+Z/X3P3kMx26W+kW8+KK7c99xVXinpWrcOX0elcVPFF5E0v68hX+IQdRmGF2EyLQdPQlILnamZB5ibuyaweelO50eNGuXE+kHd/1ZcbS5vpy43Mv8qN9U9y/e/lhpO+3Bi45qbf11V9ubFmmL7cmL1Nfl/i3qHQ6XQGG08yYcIENm7cyM8//0xgYNmwOVtbW8zN/7mFUa1WY2trS4fH52NkfLupPuoe6/NJhk5CtdBERBk6CdUi95l7b4GXe5X1LxcNnYRqoWtcc4F1TYrvbG3oJFQLs9R7cyin065rhk7CXafRFrE39UuysrKwsTHM6Ji//FXveXrPSIwtTar1s4pzi9jc45takW8h7nf/NWaCsvLD68P5KM3vrbjJ/WCtWWbvrrL+7k9DJ6FaJE980NBJqBYl99bPCgCvtWGGTkK1SHu09nTQupt0g1INnYRqUfRr7Vjo+G7z+O7e+31J3GT4fP9XBq1RffbZZ2RlZdGlSxfc3d1LH5s2bTJksoQQQgghhBCiVpCYSQghhBBCVDeDTzckhBBCCCHunFanRKur3n4e1X18IcSdk5hJCCGEEKLqJG6qmnsnJ0IIIYQQQgghhBBCCCGEqBJpJBBCCCGEqEO0OkWNPP6NTz75BF9fX8zMzOjQoQMnTpy4o/d99913KBQKnnzyyX/1uUIIIYQQQghxs9ocN9VG0kgghBBCCCH+s02bNjFt2jTmzp3LmTNnaNGiBb179yY5Ofm274uMjOTVV1+lU6dONZRSIYQQQgghhBA3k0YCIYQQQog6RIuiRh5VtWzZMsaMGcOoUaNo3LgxK1euxMLCgjVr1tzyPSUlJQwdOpR58+bh5+f3X/4sQgghhBBCCFGqtsZNtZU0EgghhBBCiEqp1epyj8LCwkr3Kyoq4vTp0/To0aN0m1KppEePHhw7duyWx58/fz4uLi68+OKLdz3tQgghhBBCCCHujJGhEyCEEEIIIe5cTcx9+dfxvb29y22fO3cub7/9doX9U1NTKSkpwdXVtdx2V1dXrl69WulnHDlyhC+//JLg4OC7kmYhhBBCCCGE+EtNxk33AmkkEEIIIYQQlYqJicHGxqb0uamp6V05bnZ2NsOHD2fVqlU4OTndlWMKIYQQQgghhPh3pJFACCGEEKIOqckeMTY2NuUaCW7FyckJlUpFUlJSue1JSUm4ublV2P/69etERkbSt2/fss/UagEwMjIiJCSEBg0a/JcsCCGEEEIIIe5jMpKgamRNAiGEEEII8Z+YmJjQpk0b9u7dW7pNq9Wyd+9eOnbsWGH/oKAgLly4QHBwcOmjX79+dO3aleDg4ArTHAkhhBBCCCGEqD4ykkAIIYQQog6prT1ipk2bxsiRI2nbti3t27dn+fLl5ObmMmrUKABGjBiBp6cnCxcuxMzMjKZNm5Z7v52dHUCF7UIIIYQQQghRVbU1bqqtpJFACCGEEEL8Z4MGDSIlJYU5c+aQmJhIy5Yt+fXXX0sXM46OjkaplEGsQgghhBBCCFHbSCOBEEIIIUQdUpt7xEyaNIlJkyZV+tqBAwdu+96vv/76X32mEEIIIYQQQvxdbY6baiPpziWEEEIIIYQQQgghhBBC3KekkUAIIYQQQgghhBBCCCGEuE/JdENCCCGEEHWIDtBSvcNaddV6dCGEEEIIIYSoXhI3VY2MJBBCCCGEEEIIIYQQQggh7lMykkAIIYQQog6RBbiEEEIIIYQQ4vYkbqoaGUkghBBCCCGEEEIIIYQQQtynpJFACCGEEKIO+atHTHU/hBBCCCGEEKKuqq1x0yeffIKvry9mZmZ06NCBEydO3HLfVatW0alTJ+zt7bG3t6dHjx4V9n/++edRKBTlHn369KlyuqSRQAghhBBCCCGEEEIIIYSoRps2bWLatGnMnTuXM2fO0KJFC3r37k1ycnKl+x84cIAhQ4awf/9+jh07hre3N7169SIuLq7cfn369CEhIaH08e2331Y5bffEmgSWB65gpDAxdDLuKp1KZegkVIvrSx8wdBKqRYPpfxo6CdVDee+dh/EvdzB0EqqF55qLhk5CtTBr3MTQSagWxZb3Zi9tjb+HoZNw12k0BZBq6FSUJ3NrCiH+rcBPMzBSmRo6GXeVxsnK0EmoFqNCogydhGrxVaChU1A9lGZmhk7CXfdJyB5DJ6FaTOwzytBJqBatp101dBKqxebO995vCyA71s/QSbjrNMUFsN3QqSivNsZNy5YtY8yYMYwapS+LVq5cyc6dO1mzZg2zZs2qsP+GDRvKPV+9ejWbN29m7969jBgxonS7qakpbm5u/yIHZWQkgRBCCCGEEEIIIYQQQgjxL6jV6nKPwsLCCvsUFRVx+vRpevToUbpNqVTSo0cPjh07dkefk5eXR3FxMQ4ODuW2HzhwABcXFwIDAxk/fjxpaWlVzoM0EgghhBBC1CG1dW5NIYQQQgghhKgtajJu8vb2xtbWtvSxcOHCCulJTU2lpKQEV1fXcttdXV1JTEy8ozzNnDkTDw+Pcg0Nffr0Ye3atezdu5cPPviAgwcP8uijj1JSUlKlv9c9Md2QEEIIIYQQQgghhBBCCFHTYmJisLGxKX1uanr3p3d8//33+e677zhw4ABmN01zN3jw4NL/N2vWjObNm9OgQQMOHDhA9+7d7/j40kgghBBCCFGH6HQKdNXc07+6jy+EEEIIIYQQ1akm4yYbG5tyjQSVcXJyQqVSkZSUVG57UlLSP64nsGTJEt5//3327NlD8+bNb7uvn58fTk5OXLt2rUqNBDLdkBBCCCGEEEIIIYQQQghRTUxMTGjTpg179+4t3abVatm7dy8dO3a85fsWLVrEO++8w6+//krbtm3/8XNiY2NJS0vD3d29SumTkQRCCCGEEHWIFgVaqrdHTHUfXwghhBBCCCGqU22Mm6ZNm8bIkSNp27Yt7du3Z/ny5eTm5jJq1CgARowYgaenZ+maBh988AFz5sxh48aN+Pr6lq5dYGVlhZWVFTk5OcybN4+nn34aNzc3rl+/zowZM/D396d3795VSps0EgghhBBCCCGEEEIIIYQQ1WjQoEGkpKQwZ84cEhMTadmyJb/++mvpYsbR0dEolWUT/3z22WcUFRXxzDPPlDvO3Llzefvtt1GpVJw/f55vvvmGzMxMPDw86NWrF++8806V10WQRgIhhBBCiDpEq1Ogrea5Nav7+EIIIYQQQghRnWpr3DRp0iQmTZpU6WsHDhwo9zwyMvK2xzI3N2f37t1VTkNlZE0CIYQQQgghhBBCCCGEEOI+JSMJhBBCCCHqEJ1Oga6ae8RU9/GFEEIIIYQQojpJ3FQ1MpJACCGEEEIIIYQQQgghhLhPyUgCIYQQQog6pLbOrSmEEEIIIYQQtYXETVUjIwmEEEIIIYQQQgghhBBCiPuUNBIIIYQQQgghhBBCCCGEEPcpmW5ICCGEEKIOkQW4hBBCCCGEEOL2JG6qGhlJIIQQQgghhBBCCCGEEELcp2QkgRBCCCFEHaKrgQW47qUeMUIIIYQQQoj7j8RNVSMjCYQQQgghhBBCCCGEEEKI+5SMJBBCCCGEqEN0gE5X/Z8hhBBCCCGEEHWVxE1VIyMJhBBCCCGEEEIIIYQQQoj7lIwkAAaOi+WhXml4+eVTVKjk8hkb1iyuR1yEeek+Y2ZH0POpFArylXy1pB77tzmXvvZwn1R6DEjh7XGNDJH8Wxo4NoYHe6bq81Wg5MpZG9Ys9SUuwqJ0nzGzwunxZJI+X0vrc2CHS+lrD/dOofuTycwb38QQyS/1UuOz9PKKwM8mk8ISFWdS3VgU3IGIbDsAPC2zOdhvY6XvnXykB7/ENMDWpIDFD+yng0s8UTm2zDrehcsZTqX7vd3mMDG5Nnx5tUVNZOm2+j6fyjPjk3Fw1hB+2ZxP3/QkJFj/nY2dG0evgRkU5Cv5coE7+7fal76v0xOZ9Hg2g7kj6xsq6ZVq2iGbZ19KIqBZPo5uxbz9oh/HdtuVvv7MuCSeHZ8EwPefurL5C9fS1wJb5TJ5QTQv9w1CW2K4ed5e6HCG7g3Dqe+YSWGxiuB4N5YffICo9LK/v4lKw/SuR+nT6BomqhKORniz4PfOpOfpvzsbswLefWwf7XziiM6wY+4vXbiaXFaOzO5xiLgsG9aebFnT2SvnXik3WvnGM6zzOYI8U3C2yeO1db05eLnst3Fi4cpK37di1wOsP9wSY1UJbzx9gM6NIknPseCDnzpx8rpX6X7DOgXjZpfDku0PV3dWSrX2iWfEA8E0dk/B2TqPqd/34UBoWZ4cLPOY0u1POvrFYGVWxJlodxb9+jDRGXal+0zv8Qd9W4SQX2zEin0P8MvFhqWv9Wh0nSeahfDK94/VWJ5uxdysmJFDgnmoQzR2NgVci3DgszXtCL2uL7ef6XeJgU9eAmDT1iZs3l52vgUFpDB5zHEmz3oMrfbe7AuhRYGC6i0TtdV8fCFE3ffskBBGjb3ITz/688Un+jr0mAnn6NE7ioICI75a1ZQDe3xK93/4kVi694pi3hsPGSrJt+TokMvooWdo1yoOU1MN8YnWLPnkIcLCb1x3+l5kYP8b152fmrJ5x03XHf8UJo/5k8mzHzfodefqRiuufmtNTpw+xLcLKKblhEy8HikAIGSTFeE7LEm7ZEJxrpLnTkZjalPW/7GkCP54w5HovRaYO5fQcW46Hg8WlL5+YbUNuQkqHngro2Yzdgv3WswE0LSdmmfGJuDfNBdH12Lmjwvg2O8Opa8/PTqBZ8bGA/DD5x5s+dK99LXAFjlMnB/BK081NWjcdHCdG4fWu5MWawqAe0Aej0+JoWnX8ueNTgf/G9mYSwcdeOmLy7TsnQ5AbqYRX09rSOgxW1x88xm+OAyfprml7/v2TT+cfArpOTau5jJ1B54dfJVRYy7y02Z/vvi0JQBjxp+jR69IfXm4uhkH9t5UHna+UR6+abjyMP5LBRl7FRREgtIUrFqA1ytazH3L9imIgZhlSnKCQVsEtg/qqDdLh7Gj/nVtEUTOU5BxQIGxI9R7XYvtA2XvT/haQVEi1JtVc32tbbYmY35cjXFcIToTBYWBlmQOdUPjqT8nldkabL9PxuxcNqrUYrQ2RuS3tyFzkCs6S1XpPo6fxGJ6MReNuwlpE7worl92f9B+dRwaVxOy+zpXmoaaZG5axOgnTtG5ZST2VvmExjqx4oeOXI3Wx+iDu5/juZ7nANjwe0s27W1e+t7GvslMG3SEcYufpETipv/0GfcKg54Fn332Gc2bN8fGxgYbGxs6duzIL7/8UuPpaNZezfYN7kx9tjmvP98EI2MtC766hKl5CQAduqXTpW8qb4xqzJeL6jFlwXVs7IsBsLDSMHJaNJ+87Vfj6f4nTdtlsWOjB9MGteCNF5qiMtKyYPXF0ny175rGI48n8+bopqxZUp8p74ZhY3dTvqZG8en8BobMAgDtXeJZH9aEZ397kpH7n8BIoeXrrjsxV+nTmpBnyQNbh5d7LD/flpxiYw4m6C/EE5qcxdKomP67n+Z4kgcL2h0sPX5LxyRaOCbzVUgzg+TvZo/0y2Ds3Hg2LHNjYu+GhF82Y8HGcGwdi+nQM4uuAzKZPcSP1e+6M3VJDDYOGgAsrEt4fmYC/3vd08A5qMjMQkv4ZQv+96Z3hdfqN8pj+KvxvDehPgsn+jJyRjy+QfkAKFU6Xl4YzYrZPgat6AK09Y5n09mmDF/3FOO+74uRUsvKZ3dgblxcus9r3f7gEf8oXvu5Fy98+yTOVnkse3J36etjOp7BwqSYwd88y8kYD+b0KTsHm7kn0swjmfWnmmNo90q5YWaiISzBkcU/d6r09UcXjCj3mP9jF7Ra2HdRX5YPaH+ZII9URn82gK0nGvHO4D38NZDQw17Nk+2v8Nlv7WsqOwCYGxcTmuzIwl8ry5OOD5/9FS97Na98/yhDVj1DQpY1K4dtx+zGedo5IJI+TcOYsPEJPtrbkTmPH8DOXP97szItZFKX47xf6bFr3tQJR2ndIp5FKx5m3LS+nDnnzgdzf8fRIY/69TIYMTiY95Z14r0PO/H8kGB8ffSBp1Kp5eWxf/LR5w/csw0EQoj7T22JmW4WEJjOo33DCb9uW7qtfcd4Hukew5uvdWLN582Y8uppbGwKAbCwLGbki5f49KNWhkryLVlZFvLhO7+gKVHwxnvdGTO1P19805acXBMA6vukM2JQMO8t78x7yzvz/JCzFa87X3Q0+HXHwq2ENq9m0HdLAn03J+D+QAF7J7qQEWYMgCZfgWenfJq/lFXp+0M2WZN6yYTHNyXScGAOB6c7lU7RkB1jROgPVrSemllDubm9ezFmghtx0xULPp3rW+E136A8hk2N5f0p/nzwij8jpsfgG5gH6OOmye9G8L+36hs8brJ3L+LJmZHM3hHM7O3BBD6YxWdjGhEfalFuv71felDZfbVfPvamIFfF6zvPEtAxi/Wz/EtfCz9jTUSwNd1frF0NBAGB6Tz6RCXlYbdo3pzZiTVfNGPK9FN/Kw8v8ukKw5aH2acVuA7S0XitlsCVWnQaCB2vpEQfHlCSr3+OAgK/0NLoay26YgVhLyvRafX7pGxWkHtFQeNvtDg/rSN8trK03CiMg5QtCrwm1exkLKaXcsnp7UjSew1Ifqs+Co0Ol3cjUBToE63K0KDKKCZzhDuJywJIm+iFWXA2jp/Flh7DZksKinwtiYv8KWhiicPKsnPOJDQPk7B8sh9zqvDZhjBz6CHaNYrj3W+6MvK9Zzh5xZMPX96Jk20uDTzSePGJU7y9pjvzvurOmCdO4uehb5BTKbVMH3yYJd8+fM82EIiqM+iZ4OXlxfvvv8/p06c5deoU3bp1o3///ly6dKlG0/HWi43Zs8WF6GsWRFy1ZNnMAFw9iwhomgOAd4N8Lhy3IeyiFQd3OJOXo8LNS9+r4sWZUezc6EZKgmmNpvlOzBnTlD1bXYm+ZklEiBXLZjfExbOQgCb6fPn45XHhhC1hF605uNOFvBwVrjfy9cJrEez81p2UBDNDZkGflgOPsyUikDC1A1czHZl5vAueljk0dUgBQKtTklpgUe7RyzuCX6L9yNPoK8UNbDLYEe1PZLYd311vRAPbTACMFCXMb3eYt051RqszfMH41NhUft3owG+bHIgOM2PFTC8K8xX0HpKOT0Ah549ZEXbeggM/2evPQ+8iAEa/Gc+OtU6kxJkYOAcVndpvyzeLPTj6q12F17wbFBJxxZxzR60J/sOGiCvmeDfQn4PPvpTEheNWhJ6zrOEUVzThxyfYdjGI62kOhKY4MWdXNzxsc2jkqj8HrUwKGdD8Kkv2PciJaC+uJDkz55eutPJKpJl7IgD1HTLYfdWfqAw7Ngc3xs9BH1waKUt4s9ch3v2tdpyD90q5cSzUh5W/t+fA5cp7iaXlWJR7PNIoktPhnsRn2ADg65LB4Sv1CE924MdjTXGwKsDOUp/PmU8e5n+/PEBuYc3+3v64Xo9PD3Rgf0jFRmkfhyyaeyWxYFdnLie4EJVuz3u7OmNqpOHRJmEA1HfK4HSUJ5cTXPj1UgC5hSZ42GUDMKX7n/xwugmJausazVNlTEw0dHogmtVr23DhsivxiTas+74l8YnW9O0dgrdnFhFR9gRfdCf4gjsRUfZ4e+pveAzsf4kLV1xLRxzcq3Q6RY08hBC1Q22Jmf5iZqZhxhsnWbGkNTnZxqXbfeplcyHYmbBQew7u8yYv1xhXd30P4BfGXWDnNj9Ski1udViDGfjkRVLSLFn66cOEXHMmMdma0+c9SUjS1wm8PdVl152LN647HjeuO/1qz3XHp1s+3o8UYOurwba+hjZTMzGy0JISrI9TmzyfTfOxapxbFFb6/qzrxvh0y8c+oJhGQ7MpSFdRmKGvmx5724G2r2ZgYlU7Zl6+F2MmgFMH7Vi7zJujvzlUeM3bL5/Iq+acO2ZL8FFbIq5a4OWnv5v7zNh4LpywJvS8VU0nuYLmPdJp1i0D1/oFuPoV8OSMKEwtSog4U1bHjLlkyZ5VnoxYHFbh/QnXzGnXNwVXvwI6DUkk8Zq+zCgpVrDxjQYMfe86SlWNZecfmZlpmPH6CVYsa1O+PPTJ5sI5Z8JCHTi434e8vJvKw7Hna0V5GPipFqf+Osz9wSIQ6s/XUpSgIO+y/vWcs1AYD37ztVgEgEUA1H9HS+5lUJ/Q75MfDnaP6I/hOkiHJkOB5sagkcgFSrxf0aKq4dMy5c365Ha1p9jbjGJfc9ImemGUWoxJuP73UuxjRuqr9chva4PGzZTCZlZkDnHD/HQ2lOjLOOO4AvIeskXjYUpODweM426MqtLocPgijoyxHqAyfF3ZxFjDIy0j+GxrB85dcycuxZavdrUlLsWWJztdxsctk+txjpwJ9eR0iCfX4x3wcc0EYEiPc5y75l464uBeJXFT1Rj0jlTfvn157LHHCAgIoGHDhixYsAArKyv+/PNPQyYLCyt9T4PsTP1QzfArFgQ0y8XKRoN/kxxMzbTER5nTpI0a/8Y5bFvrfrvD1RqW1vqewNlZN/IVYkVA0xysbIrxb5KNqZmWhGgzGrfO0udrnYchk3tL1sb6Sl5mUeU3IpvYp9DYPo3vw4NKt13NdKSjaxwqhZZO7jGEZOorXmManeN4sjsX0w0/TMzIWEtA8zzOHC6rQOl0Cs4etqZxmzzCL5nRsHkeVrYa/JvlYWKmJT7ShCbtc/Bvls/PXxo+OKmqiKtmePkV4uxRhItnIZ71C4kMMcO9XiG9BqXxzaLaeQ5amerPQXWBPuhq7JaCsUrL8aiy6Wgi0+2Jz7Kihad+KqXQFEfa+ejPwQfrRxOaoh+j+Xz7YE7FeHA5sXZenO+VcuN2HKzyeCgomm2nysqMsAQnWvgmYmqk4YGGMaSoLcjMNaN3y1CKilW3bHwwFBOV/nsqKimLmnQoKCpR0dJb31AVmuRII/dkrM0KaeSWgqmxhpgMW1p6J9DILYVvTxp+NBWASqlDpdJRVFw+AiwsUtEkKJmIKDs83dU4O+Xg4pyDp4eayGg73F2z6dXtGl9vrH29VIUQ4r+obTHThFfOcuJPN4LPuJbbHn7dloDADKysivBvmIGpaQkJcVY0bpqKf0Am27b43+KIhtWxbQxh1x15c9oBvl+9iU8XbefR7qGlr0dE2+HpceO645SDp7uayBg73F3V9Op6ja+/rX3XHW0JhO+0QJOnxKVV5Y0Cf2cfVETSaVM0BQrijphh7qzB1F7L9W2WqEx11OuZX82pvjP3Y8wEEBligWf9Apw9CnHxKMSzfgFRoRa4+xTQ85lU1i6rOGrb0LQlcHKbE0X5Kuq3VgNQlK/ky5cDGfzOdWxdiiu8x6txLleP2lKigcuH7PEM0t9Y373Sk4YPZFGveU6N5uGfTJhym/Kw4Y3yMCADU5ObykP/TLZtDTBQim+t5MafVnVjQISuGFCA4qY2NaUpoIScs/qbohaB+v9rCyDrKBg76zCyh7SdCpQmYN+tRrNQKWWePk7SWt26dUmZV4LWXFl647+onjmmF3OhRIf5uRyKffT3nmx+TqGgiSVFDWpHg7dKqcVIpaNI87e4qVhF8waJhMc54O2ShYt9Dq4O2Xi7ZBERb4+Hk5rHHghl1fa2Bkq5qK1qzZoEJSUl/PDDD+Tm5tKxY8dK9yksLKSwsKySo1ar73o6FAod496M5NIpa6LC9D2YzxyxZ9/P2Xy05TyFBUqWzvCnIF/JxHnhLJvpz+PPJdJveAJZGcaseLMB0ddqR4FxM4VCx7jXw7l02qZcvvZvd2H5D8EUFSpZOqshBfkqJr19jWWzG/LYkAT6DYtHnWHMijn+RF8zfI9uBTreaH2UUyluhGVV7GEBMLDBVa5l2XE21a1028rLLZnf9gj7+n5LXK41s493oZ5VFk/VD+XZ359kfttDPOwey8V0Z14/0Zmc4pofGWLjUILKCDJTyv8sM1KN8PYv5PRBG/ZuyePjXWEUFihZMsWHgjwlkxfGseQVb54YmUa/F1JRp6v46DVvokIN35v7n8RcM+er9z1Y+K2+F8lX73sQc82c978NY/UCT9p0UTN8agIajYLP5npx8bjhezkr0DGj+x+cjXXjWqr+Rr+jZR5FGiXZheXPm/Q8C5ws9cOA1/zZijd6HWLH2A3Eq615+9cu+Nhn0q9pCMPXP8WbvQ7S0TeGS4kuzP/1EXKKDD866V4pN/7J461DyC00Zv+lshv/204F4u+Wxqapm8jMM+P1jT2xMS9kXI9TvLSqHy/1PEHPFteIS7Phnc1dSFEbtudWZJodCVlWTO56nHd3PUJ+kRHDOpzHzSYXJyv9OXgs3IddFxuy/oUfKdQYMWdbN/KLjHj90UPM3daNZ9tcYnC7C2TmmfPOzkcIT628jK1u+QXGXLrqzNBnzhMda0tmlhldH46kUcNU4hOtiYmz46uNrXh/zh4A1mxoRUycHe/P/Y3V69rQtmU8wwed05cbX7XnwmXXf/jEukerU6Co5h4r2nuoR4wQ95I7iZmg+uKmzl1j8A/IZMpLFe/8nDnpxv7ffVi+ch9FhSqWvt+WggIjJk09y7IP2vJYv+v0G3AdtdqUFUtbEx1pc1fS9F+5u2TzRK8QNu9owrdbmhHon8aEF06g0Sj5/aD/jetOa95/63cA1mxsrb/uvPUbq9e3oW3LOIY/ew5NiZLPvmrHhStu//CJ1Sc9xJidg90oKVRgbKGj2yfJ2PlXvBFbmYZP55ARYsLWxzwwsy+h6/JUirKUnF1hS591SZz+0I6IXRZY+2h4+L00LF1Lqjk3lbsfYyaAmOvmfL3Em/fWXgXg68XexFw35711V1jzvjdtOmcy9OU4SjQKVs6vx8WThvt9xV21YNGAFhQXKjG1LGHc51fwaKhvZPphfn0atFHTsld6pe/tMz6WjW804K3ObXH0KmT4ojCSIsz4c7MrM7aeY8PrDbhyyB6f5tkMf/8a5jaGOQ/hRnnon8GUCd0rvHbmlBv79/iw/NO9+vLwg3b68nDKGZYtasdjfa/Tb8A11FmmrFjWmugo20o+oebotBC9WIlVSx0WN9pzLZuByhxilyvwnKzvYR/7kQJKFBSn6p879deRHwoXnlJiZAcNFmkpUUPcZwoCV2uJ/Z+C9N0KTL2g/ttaTGq6Wq7VYf91AgWBFqU3+v9OqdZg+2MyOT3KYh/1k844rIrDY1IIGmcT0sZ7YZRQiOXBDJIWNMD+izjMz2VT2MCC9HGepWsZ1LT8QhMuhLsyss8ZIhPtyFCb06PtdZrUTyYuxYaoJHu+2NaODyfvBODzn9sTlWTPh5N38tlPHejQOJZRj51GU6JkxY8Pcu5a3egAXRUSN1WNwRsJLly4QMeOHSkoKMDKyoqtW7fSuHHjSvdduHAh8+bNq9b0THw7HN+APF4d0rTc9g0f+7Dh47KFZp6bFEPwUVs0GgWDJ8Qy4YmWtO+azquLw3h5gOEXv/27CXOuUS8gl1efK5+2Df+rx4b/1St9/tzEKM4etUNTrGTwS9FM6NeaDl3Tmf5BKFOeNnwvmbfbHqGhbTqD9/Sv9HVTlYa+9a7xyaXW5bbnFJsy7Vj5i/e6btv5IPgB+vmG4WOVTa8dg1jQ/hCTm55h4dlbB12GtH6pG+uXlgUeQ6clcvawFSUaBUOmJPFSt0A69FTz2opoJvVpeJsj1R471zuzc33ZSI4ez6SRl6vkymlLvjx4mclPBOLsXszrn0Qy8sEmFBcZdkqe13seooFTOs9veLJK78spMmX2jp7ltq0a9DPLDnTk8caheNmq6b96CHN6H2TcQ6dZuv/Bu5jqf+deKTf+Sd82IewODqBIU3ZJLNGqWLytE4tv2u+tp/ez6WhTAj1SeaRxBEM/epbhjwQzve8fzNrQu+YTfhONVsX0H/ow94n9HHp1DRqtguMRXhy55oOCsqkBPj/Ujs8PtSt9PrbTSY5HeKHRKhn98GkGfjGITgFRvNN/L0O/fNYQWQFg0YqHmT7xKN+t/pGSEgVh4Q4cOOJLQAN9QLnzt0B2/hZYun/PLtfJzzfmcogzaz7+iUkzH8fZMY/Xpx5ixPinKNbUonHpQgjxL1QlZoLqiZucnPMYN+kcb7zWieLiysvVDd80ZsM3Zel6bsRlzp52QaNRMnj4VSa80JMOHROYPvskU8ZVvLFmCAolhF535Ktv9fHD9UhHfL0zeLxXKL8f1N8t2/l7IDt/v+m688g18guMuBzqzJqPtjJp1hM4O+by+iuHGDHxaYNdd2zrF9P/pwSKspVE7rbg8EwnHlufdEcNBUpj6Di3/I3bw7MdaTQ8m/TLJkTvNaf/zwlcXG3D8Xft6fZxanVl4z+7F2MmgF0bXdm1sewua4+nUsjPUXHlrDWr9pxjypNNcXIrZNaKa4x6pKXB4iZXv3ze+OUs+dkqzuxy4pvpDZm26TwpUeZcPWrHG7vO3vK95jYlvPhxaLltHw5uylOvR3DiJ2dSo82Yt/8062b5s/MjH555K6K6s1MpJ+c8xk0M5o0ZtykP1zZhw9qyRc6fG36Zs2dc0JQoGDzsChNG96LDAwlMn3WSKeN71FTSKxW1UEH+NWj0tbZ0m7GD/qZ/1HtKkr5VgBIc++iwaKQrnZNEaQz1XtdR76Z4I2KOApchOvKuQuZ+BU2+15L4lYLoRUr8l2r//tHVyn51PMYxBSS9U/maeYq8EpwXRlLsZUrWwLLfls5SRdorPuX2dXk7nMzh7lgczsQoqYj4jwJxWBmL7Y/JZI403M31d7/pyuxhB/npvQ1oShSExjix91QDGvroy+ifjzTm5yNl1+U+HULJKzDmYoQLG+Z8z9hFA3C2y+XtUXsZOHeIxE33OYNPgB0YGEhwcDDHjx9n/PjxjBw5ksuXL1e67+zZs8nKyip9xMTE3NW0jJ8TTvuuGcwc3oTUxFv34vXyy6Nb/xTWLveheQc1F0/akJVuzKFdTgQ0zcXc0nCt2ZUZ/9Y12ndJZ9aI5qQl3SZf9fPo2jeZdSt8ad4hk0unbFFnmHDoF2cCmuRgbqmpwVRXNLfNEbp5RDFsX18S8yvvtfuodzhmKg1bI25f2Xu6/lXURSbsifOlg0s8v8f6otGp+CXGjw4u8dWR/H+kTldRogE75/J/Z3snDRkpFdvzvP0L6P5UBt8scqP5gzlc/NOSrHQjDm6zJaB5fq07D++Ejb2GYVMT+PQtb4Ja5RIXYUp8hBnnjlqjMtbh6Xdnw6Wry+weh+ncIIox3/UjOafsHEzLtcDESIu1afn0OVjkkZpb+cii/k2vkl1oyoFr9WnrE8++sPpotCp+D2lAW2/DL8Z1r5Qb/6SlbwK+Lpn8fDLotvu18YvDzzWdH441pY1fPEdDfCgoNmbv+Qa08TNMmfF3VxKdGbx6IJ0Wv0Cv5SOZ9O0T2JoXEJtZeU8yX8cMHm8WxqcH2tO2Xhxnoj3IyDPnt8sNaOyeioVJUQ3noExCkjWvzulNv+eGMHTs07w863FURloSkiqW/TbWBQwbeI5PVrcnKCCV2Hgb4hNsOHfRDZVKi6fH3R91aGg6Xc08hBC1R1ViJqieuCmgYQb2DoV8/MVetu/ZwvY9W2jeMpV+T11j+54tKJXlCw4vbzVde0azbk0TmrdM4dJ5J9RZphw64EVAw0zMze+sh3t1S88wJzrWrty26DhbXJwqn9bExrqAYc+e45MvOxAUkEJsgg3xiTacu+SOysiw1x2VCdjU0+DUtIi20zNxCCri0tp/NxI34U9TMsOMaTQsm8QTZnh1zsfYQofvo3kknjBc73uJmfRs7It57uU4PptXj8AWOcRFmBEfacb5P20xMtLhWb/AYGkzMtHh4ltAvWa5DJgZhVejXPZ/5UHIUVtSo8yY1qwjE/weYoLfQwB8/lIjlg6qfNrLo9+7YG6joWWvdEKP2dKiVxoqYx1tHksl9E/D9b4PaJiBvX0hH6/cy/bfNrP9t8368nDANbb/trny8rBHNOu+akrzFilcOu+sLw8PGr48jFqoIPOQgqDVFXv62z4IzXdoablPS6v9WvwW6ChKBtNbrP2tPgn51xW4DtaRfUqB7cM6VObg0EuH+lT15+Vm9qvjMD+TTdJcP0ocjSu8rsgvwWVBJDpzJSmv1QOjW/cGt9yfjtZSRX47G8wu5ZLf3gaMFOR1tMXskmGnwIpPtWHy8r70nDqKZ94cyrjFA1CptCSkViz7bS0LGPXYaZb/8CCNfZOJSbYlNsWWs2EeGKm0eLtUvrB9XSZxU9UYfCSBiYkJ/v76Hhpt2rTh5MmTfPTRR3z++ecV9jU1NcXUtDqm4NAxfk4ED/ZMZ+awJiTF3q7So+Pld8JZtdCXgjwVSqUOIyP9GfHXv3+/IBiOjvFvXadjjzRmjWhOUtzt8zV5fhirPvC7kS9QVchXDST5Fmmb2+YPenpFMHRvP2Jzbz108lm/q+yLq0d6ofkt93EwzWdS0zOloxFUCh1GSn2LtpFCi1JhmO9PU6wk7LwFrR7O5tiv+gqPQqGj5cM5bPva8W9763j5g1g+n+dR9n0Z3/i+blz/atOCTndq3NuxbFntQmqCCQ1b5JWegwAqlc6Avy0ds3scoVtABC9+14+4rPLn4OVEZ4pLlLSvF8veUH0vhXoOGXjY5nAuruKYSnvzfMY9eIrnNz4JgFKhw0h14xxUGu4c1LtXyo0706/tFa7EOhOWeOv5aU2MNLzW7whzNnVHq1OiVGhR3JivUqUy9PdVUc6Naa987DNp7J7CpwfbV7KXjjcfO8jS3x8kv9hYfw4qy85BoFbkq6DQmIJCY6wsC2nbMp7V69pU2OelUafYsr0RqemWBPqnlf6WwNDlhhBC3D1ViZmgeuKm4DMujB9Vvrfr1JmniY225odvG6LV3nyDRcfk6WdZ9WlzCgqMUN5YbwYoLadrS/l8KcQFL4/yN0a83NUkpVTeKeml50+yZUfjG9edVIxUN9VXlbXruqPTKtAWVX0aBE0hHJvvwCNLUlGq9HPL/zVdg1YDuhLDTa0gMZPe2Dej+GmNG6mJpjRsnlta9wZQGtW28xCKi5Q8MTWahwYnlXvtnV6teXZOOM27V5x+KDvNiJ0f+fDq5vMAaLUKSjT64KJEo0Bbs53Sywk+48L4F8uPEp/62iliY6z54bvAiuXh1DOsWnlTeWh0o95twPJQp4Po9xVk7NM3ENzqxj+Asb3+X/UJ0KSDXZeK6dUWQtRCJX7vaVGoQFcCfw0w0GmAmmqP0+mw/zIe8xNqkuf5UeJacaFyRV4JLu9GoDNWkjLTF0xuHbQqszTY/pBcNhpBq0Oh0WdMUaIDA56HNysoMqagyBgr80LaN4rls586VNhn8jNH+X5fM1IyrQiql/K3uEmLUllLMiMMxuCNBH+n1WrLzZ9ZEya+HU6XvqnMHx9Efq4Keyd978ncbBVFheVrDX0GJpOVbsTxffr5yi6fsWbYyzEEtcymbecMosLMyc2uHX/WCXOu0+WJZOZPbPyP+er9bCJZ6cac2K+vWF0+Y8PQSVEEtlDfyJeFwfI1r+0R+ta7xkuHepOrMcbJTD+/dnaxCYUlZWmqZ5VFO5cERh989LbHe6P1UdZcbU5Svn6u9NOpbjzpG8qRRC8G+1/hTIrh5q/e8oUTry6PIfScBSFnLRgwJgUzCy2/fVd+bvBHn0snK82I47/rK8aXT1oyfHoiQa1zadctm6gQU3LVtaPGa2ZRgodv2W/azbsQv8Z5ZGcakRJfdsFu3UmNl18BS17RT2MTes4Cb/8C2nbNwtmjGK0WYsMN02vp9Z6HebRRGK9sfZTcIhMcb6wzkFNoQqHGiJwiU7aeD+LVrkdRF5iRU2jCrB6HCY5z5UJCxXlpX+v+B2tPtigdjRAc58YTTUI5FuHN0y0uExxnuLls75Vyw9ykGC/HsoDfw15NgHsq6jxTkrL0vSosTYvo3iycj3befnqxF7ud5miID6EJ+oaE81FuTH70T7afDmJgx4uci6qZ78vcuBhvh7I8edqpaeiaijrflES1NT0aXScjz4zELGsCXNJ4rdcfHAjx5c/wigvZDWh1hYw8cw6F+QIQHOvGuM6naOaZyEMNormeYl/a2GAIbVrGoQBi423wcMtmzIjTxMTZsntf+UUvWzePx8tdzeKP9T3RQq454u2ppl2rOJydctFqFcTG1445r+8mnU6Brprnvqzu4wsh/htDxEz5+cZERZbvuVtQoEKtNqmwvffjkWRlmnLimAcAly86MnTkZQIbpdG2QxJRkdbk5la8cWMIW3Y0Zvm7uxg84DyHjvkS6J/KYz3CWP55xfpB6XXnfw8DEHLNCW/PLNq1jMXZKc+g151TS+3w6pyPpbuG4lwl4TssSTxhSq8v9XWHvBQl+akqsqP1d8czQk0wttRi5V6CqV35G0PnPrXD65F8HBvreze7ti7k5GJ7Ap7K4cp6a1xaG66XOtybMRPciJvqlf1tXb0L8WuUS3aWESnxZfWyVg9n4Vm/gKWv6m9ahp63xKtBPm0fycTZvQhtiYLY8Ft3mqtOWz+oR9MuGdh7FFKYq+LEz86E/mnL5HWXsHUprnSxYgePQpx8KpZn38/zo8eYOOzd9PFIg7Zqjm9xpnGnDA5vdKNBG8ON2qlSefhYBFlZN5eHTgwdcaM8bJ9IVKSNQcrDqPcUpP+iwH+5FpUlFN+YQUxlBcobYXfKTwrM/fSLEeecVxC9SIHrMB3mvhWPF/+FfuSA5Y0B2tYtIWa5Aqf+OpI2KbBqWQOZQj/FkOWRTFJm1ENrpkSZoT/ndBYqdKbK0gYCRaGOtJc9UeSVoPhrcWMbo9LFi0uP93U86r5OpaMRCoMssTiUSX4LK6z2pFMYZNg1Sds3igEFxCTZ4umsZsKA40Qn2bHrWGC5/doGxeLtksWCtV0BuBrlTD3XTDo0jsbFPpcSrYLoJDsD5KB6SdxUNQa9mz179mweffRRfHx8yM7OZuPGjRw4cIDdu3fXaDqeGKpvzV604VK57Utn+rNni0vpczvHIgaPj2XaTUPhQs9bs2WNB/O+uEJmujFLZ5S/gWFITzyXAMCidRfKbV82uyF7tpbdCLdzLGLwSzFMH1I273joBWu2fOXJvM8vkZlmzLJZ5QuYmjQ0QD+UemOP7eW2z/izC1siytL1jN9VEvOsOJxQ8YbYXzq5xVDPSs2rx8oWW1sX2oRmDils7rWVc2kurLhouBXeD26zx9axhBGvJWLvrCH8kjlvDK1PZmrZ8Dg7p2IGT0liar+A0m0hwRZs/tyZd9ZGkJlmxJIpPpUd3iAatshj8Q9hpc9fels/lc5v3zuwdJovACZmWia8G8N74+uXFrCpCSZ8+pY305dGUVykZMkrvhQVGKZb+qBW+rJhzZCfy21/a1dXtl3U14IW73sIrU7B0v67MVGVcDTSmwW/d65wrAd9o/Gxy+KNHWXz8H53pimN3VJYP3wzFxNc+Pyo4c7Be6XcaOSZzMqxZWXG1CeOAbDjdEPm/6j//fdsfg0FsPvcrcttP9d0uje7zrAVZfPz773YgNZ+8Xwx7meiUmx5a1PNzCHa2COZ1cO3lT5/tddRALadC2Tu9m44W+UyvecfOFrmk5pjwY7zgXxxuGLPewfLPEY/dIbnvx5Quu1SvCvrj7dgxaBdpOeZM2dbxQUpa5KlRTEvDD2Dk2Me2TmmHPnTh682tqKkpKwMMDHRMHH0CRYs61xWbqRb8smX7Zk+8Q+KNSoWf/wQRUW1o+FeCCH+rdoSM90pO/sCBg+7yvRJXUq3hV51YMsPDZm38CiZmaYse99wdZ2/C73uxLzFXXlh6BmGPXOOxGRrPvu6HfuO+JXbz8REw8QXj7Pgw0cqv+4Uq1j8ycMGu+4UpKk4PNOJvGQVJtZa7AOL6PVlMp4P6W86h3xnTfD/7Er3/2WovpPDwwtTCXgqt3R7RqgxEb9Y0P+nhNJtvn30UwztGuqGbf1iHllq2PUI7sWYCSCgWS6Lvr1S+nzcm9EA/P6jE8tm6BsETEy1THg7koWT/cvOw0RTPnvbl6mLwikuUrD0NT+KCg0TN2WnGvPVtIaok00wt9bgGZTH5HWXaNwps0rHuXTQjpQoc0YtL1ufoOvIBKLOW/HBky3wbZHD46/c3emnq4OdfQGDh15l+stdS7eFhjiw5ceGzHvvDzIzTFn2QbvbHKH6pPygP0dCRpdvKKs/T4tTf31P+YIoiP1YSUkWmHiAx2gdrsMqjiLIuwbpv+nXH/iLfU/9FENXX1BiVg/8FtZML3Xr3/SjUlzfLr9eRdoEL3K72mMSkY9pmH4hbY/J5de/iPskkBKXsgYbs+BsjBKLSJtcdo8pp48jJtfzcXv9OkX+5mQ9a7hOpgCW5kWM63cCZ7tcsvNMORBcn1Xb2lOivSluMtYwdeAfzF3TvbTcSMm0YvkPDzF7+EGKNSreW9uVomKJm+53Cp3OcLMnvfjii+zdu5eEhARsbW1p3rw5M2fOpGfPnv/8ZkCtVmNra0s3yyEYKWpHT5S7RaGqPT0a7qbQubdeYK0uazD9T0MnoXrU1fG3t5H4csVhd/cCzzUXDZ2EapE6oMk/71QHFVveO70NbuZ8Nvefd6pjNJoCDp5YQFZWFjY2hh2V8Fe9p9G3M1FZVO9Ij5K8Qq4M+aBW5FuI+91/jZmgrPzoHjAVI5XhRopVB41T5dMC1XUvrPn5n3eqg74KrGfoJFQLpZnh1mmoLp+G7DF0EqrFxD6jDJ2EatH626uGTkK12BzW0tBJqBZOGw07AqE6aIoLOLH9rVoRP0jc9O8YtJnoyy+/NOTHCyGEEEIIIUStJjGTEEIIIYSobrV8SUkhhBBCCCGEEEIIIYQQQlQXmXBKCCGEEKIO0eoUKKp5gSztPbQAlxBCCCGEEOL+I3FT1chIAiGEEEIIIYQQQgghhBDiPiUjCYQQQggh6hCdTv+o7s8QQgghhBBCiLpK4qaqkZEEQgghhBBCCCGEEEIIIcR9SkYSCCGEEELUIfoeMdU79+W91CNGCCGEEEIIcf+RuKlqZCSBEEIIIYQQQgghhBBCCHGfkpEEQgghhBB1iE6nqIEeMdV7fCGEEEIIIYSoThI3VY2MJBBCCCGEEEIIIYQQQggh7lMykkAIIYQQog7R3XhU92cIIYQQQgghRF0lcVPVyEgCIYQQQgghhBBCCCGEEOI+JSMJhBBCCCHqEJlbUwghhBBCCCFuT+KmqpGRBEIIIYQQQgghhBBCCCHEfUpGEgghhBBC1CUyuaYQQgghhBBC3J7ETVUiIwmEEEIIIYQQQgghhBBCiPuUjCQQQgghhKhLamBuTe6huTWFEEIIIYQQ9yGJm6pERhIIIYQQQgghhBBCCCGEEPcpGUkghBBCCFGH6HT6R3V/hhBCCCGEEELUVRI3VY2MJBBCCCGEEEIIIYQQQggh7lMykkAIIYQQog7R1cDcmtU+d6cQQgghhBBCVCOJm6rmnmgkUHi5o1CZGjoZd1d6lqFTUC0arkw2dBKqRYlSZegkVA9tiaFTcNe5LT9q6CRUi9TnOxo6CdXCeU+0oZNQLXS2VoZOQrXY9fsmQyfhrlNna7FvaOhUCCHE3ZETaI+RsZmhk3FXmaUUGToJ1WLtgJ6GTkK1UBiFGzoJ1UJbUGDoJNx1L9V72NBJqBZhX9sYOgnVQjeiiaGTUC3qqbSGTkK1WP3zUkMn4a7LztbSdLuhUyH+C5luSAghhBBCCCGEEEIIIYS4T90TIwmEEEIIIe4bOoX+Ud2fIYQQQgghhBB1lcRNVSIjCYQQQgghhBBCCCGEEEKI+5SMJBBCCCGEqEN0Ov2juj9DCCGEEEIIIeoqiZuqRkYSCCGEEEIIIYQQQgghhBD3KRlJIIQQQghRl+huPKr7M4QQQgghhBCirpK4qUpkJIEQQgghhBBCCCGEEEIIcZ+SkQRCCCGEEHWITqdAp1NU+2cIIYQQQgghRF0lcVPVyEgCIYQQQgghhBBCCCGEEOI+JSMJhBBCCCHqmnto7kshhBBCCCGEqBYSN90xGUkghBBCCCGEEEIIIYQQQtynZCSBEEIIIUQdInNrCiGEEEIIIcTtSdxUNTKSQAghhBBCCCGEEEIIIYS4T8lIAiGEEEKIukRH9c+tKXN3CiGEEEIIIeoyiZuqREYSCCGEEEIIIYQQQgghhBD3KRlJIIQQQghRpyhuPKr7M4QQQgghhBCirpK4qSpkJIEQQgghhBBCCCGEEEIIcZ+SkQRCCCGEEHWJzK0phBBCCCGEELcncVOVyEgCIYQQQgghhBBCCCGEEOI+JY0EQgghhBBCCCGEEEIIIUQ1++STT/D19cXMzIwOHTpw4sSJW+67atUqOnXqhL29Pfb29vTo0aPC/jqdjjlz5uDu7o65uTk9evQgLCysyumSRgIhhBBCiLpEV0MPIYQQQgghhKiramHctGnTJqZNm8bcuXM5c+YMLVq0oHfv3iQnJ1e6/4EDBxgyZAj79+/n2LFjeHt706tXL+Li4kr3WbRoEStWrGDlypUcP34cS0tLevfuTUFBQZXSJmsS3ODolM+oMRdo2z4RU1MNCXFWfLi4LWGhDgA89WwIzwwKBeCHTYFs/aFh6XsDg9KYMOUsUyd2Q6utve0uz46KYNTL1/hpgw9fLAkEYMz0EHr0jacgX8VXKwI48It76f4P90ii+xPxzHullaGSfEuOTvmMGneJth0SMTUr0X9f77cmLMQegKcGhfHMkBvf18aGbP0+oPS9gY3SmTD1HFPHP4K2pPZ9X31HpvDMS0k4OBcTfsWcT9/yJiTYEoCxc2LpNTCNgjwlXy70ZP9Wh9L3dXo8gx7PpDF3lL+hkn5bfZ9P5ZnxyTg4awi/bM6nb3oSEmwBwNi5cfQamEFBvpIvF7izf6t96fs6PZFJj2czmDuyvqGSflt1PV+tfOMZ1ukcQZ4pONvk8dq63hy8UpamE++trPR9K355gPWHW2KsKuGNpw7QuVEk6TkWfPBzJ05e9yrdb1inYNzscliy/eFqz8udenbEdZ6fFMJP3/qy6sPGAIx+5TI9Ho+joEDF1/8L5MBuz9L9H+6eQLfH4pg/va2hklypr9btwNUtr8L2Hdsa8OnHbRgzLpgevSIpKFDx1ZfNObCvXuk+D3eOoXuPSObN6VSTSa7UuiVurF/mVm6bV4MCvjx8FYDXnvbn/DGrcq8/NjyVKR/EAqDOULFkig/njlrhWb+Qacti8G+WX7rv/2Z74laviGdeSqnmnAghxL3P3LSI0X1P06lFJPbW+YTFOrLihwe5GuUMwOAe5xnS8xwAG39rwaa9zUvf28g3mWmD/+ClRf0pqWUxk6NDLqOHnqFdqzhMTTXEJ1qz5JOHCAt3AuCZvhcZ2P8SAJt+asrmHU1K3xvkn8LkMX8yefbjtTsWHHyVUaMv8NPmAL74rCUAY176q65gxFerm1WsK/SMYt5btacOd7O+I5J5ZlwS9n/FTHN8CD13I2Z6K4aez+pjpjXve7L/J8fS93V6PIPuT6fx9gsSM9Wkup4v+x0JWJ3OxCShAK2xkgJ/S1IHelHsbla6j82BFKyPpWMalYeqQMv1T1qgtSy75aYo1uKyJgrLs5mU2BqTPMKH/CY2pa/b7UrEOK2IlOE+NZq3mw0ddpFhwy6V2xYTY83YMY8BMGbsWXr2vBFfrGnO/v2+pfs93CmGHt0jefttw8cXfzfsufMMG3qx3LaYGBvGvPQEAGNHn6ZnjwgKCoxY83UL9h8oO986PRxN927hvD2/S00muYL969w4sM6d1FhTADwa5tFvSgzNumaU20+ng+UjG3PxgAMTV12mde90AHIyjVgztSFXj9niUj+fUYvDqNc0t/R969/0w9mnkN5j4xDVa9myZYwZM4ZRo0YBsHLlSnbu3MmaNWuYNWtWhf03bNhQ7vnq1avZvHkze/fuZcSIEeh0OpYvX86bb75J//79AVi7di2urq789NNPDB48+I7TdkeNBNu2bbvjA/br1++O973Z+++/z+zZs5kyZQrLly//V8f4t6ysiljy0X7OBzszZ9bDZGWZ4uGZTXa2CQC+fpkMe/4y8954CBQ63l7wB2dPuRIZYYtSqWXSK2dZ8WHrWl0pDGicxaNPxxIeWnajpX3nFB7pk8ibE1rj4ZPHK3Mvc+aYI+pMEyysihk56Rqvv9TagKmunJVVEUv+d4jzwU7MmfEgWZmmeHjlkJ1tDICvXxbDXrjCvFkd9d/X+8c4e8qFyHBblCotk6YHs2JJq1rZQPBI33TGzonl49k+XD1rwYDRySxYf40XH2lMUKtcuj6Zzuzn/PU3wpZGcfqADeoMIyysS3h+ZjyzBgf884cYwCP9Mhg7N56PZ3lx9YwFA8aksGBjOC92CiSodR5dB2Qye4gfnn6FTFsaw+mD1qjT/8pXArMGNzB0Fip1L+TLzERDWKIj208HsWjY7gqvP/reiHLPOzaM5s2nDrDvoh8AA9pfJsgzldErB9CxYTTvDNpDn/dGAgo87NU82e4KIz95uiayckcCGmXS56lowsOsS7e1fziJLr3jefPldnh65zHlzfOc+dMZdZYJFpbFjBgfwhsTOxgw1ZWbMqkHKmVZt4V6vmreW3SQwwe9af9API90i+bN2Z3x8MzhleknOXPKDbXaFAuLIkaOusDrMx4xYOrLqxeYz/ubrpc+V6nKd8d4dGgqI15LLH1uaq4t/f+3K1zJz1Xxye5Qdqx1ZPlr3vzvV30j8ZXTFlw9a8n4d++xyq5OoX9U92cIIaqkJmImMGzcNHPYYeq7p7Pgmy6kZlnQq/01lr28kxHzn8XGqoAXnjjFrE97gwI+GL+bk1e8CI93QKXUMn3IEZZs7FTrGgisLAv58J1fOHfJjTfe606W2gxPNzU5ufpYsL5POiMGBTPn/e4AvDN7L6fPexAZbY9SqeXlsX+y/POOtTsWDEzn0cevE37dtnRbaV1h1o26wqs31RUsixn5wsVaVVe4Wee+6Yx5K5aPX/chJNiSJ19MZsH6MEZ3aUJQq1y69E/n9WEBePoWMnVJJKcP2pbGTCNfi2P2cw3/+UMM4F6ILSpzL+TL/GoOmd2cKfSzhBIdTj/G4bkkjKj3GqMzVQGgLNSS18yWvGa2OP1Yse5pcyAV06g8Yt8KwuJ8Fm4rI4hY0RwUCoxSCrE9mErM241qOmsVREba8PrsLqXPS27cN+nQIY4uXaJ54/VH8PDMZurUk5w+7V4WX4w8X+59tU1kpC2z3+xW+rykRF/X7dA+li5donj9za54emYzdcpxTp9xR6020+drxDlmv9HtVoetMfZuRTw9KxLX+vnodHD0R1c+Ht2IubuC8Qws6zj2+5ceKCqpxu/82JuCXBVzdp3lwDp3vpnpz5yd+kb962esCT9rzXPzwmsqOzWnBuMmtVpdbrOpqSmmpqblthUVFXH69Glmz55duk2pVNKjRw+OHTt2Rx+Xl5dHcXExDg76TsMREREkJibSo0eP0n1sbW3p0KEDx44du/uNBE8++eQdHUyhUFBSUnLHH/6XkydP8vnnn9O8efN/3rkaPDM4hJQUcz5c3K50W1KiZen/vb2ziQy35VywCwCR4bZ4eWcTGWHLM4NCuXjBibAQhwrHrS3MzDXMeO8iK95pzODREaXbferncuG0PWGXbQm7bMvYV0Nx9chHnWnCC1PC2PmDFymJ5gZMeeWeeS5U/32936Z0W7nvq142kddtOHdW36Mp8rotXj45RIbb8szgMC6ecyLsqn2F49YGT41N5tdvnfjte31vlxWzfGjfXU3vwWnotHD+mDVh5y0JO2/JS2/H4uZTiDrDiNFvxLFjrRMp8SYGzkHlnhqbyq8bHfhtk/53smKmlz5fQ9LRaRWcP2ZF2HkLws5b8NK8eNy8i1CnGzH6zXh9vuIkX9XlWKgPx0Jv3VslLcei3PNHGkdyOsKT+Ax9rxdf5wwOX6lHeLIDcek2THnsT+wsC8jMNWdm/8P879cHyC00fD5BXxa+9k4wHy9oxqAXrpVu966fw/nTjly7Yse1K3aMmXoZV4881FkmvDD5Krs21yMlqfaVheoss3LPnx18lfg4Ky6cd+aZgSFcOOdMWKgDYaEOjB1/Fle3XNRqU14Yc56d2xuQkmJ5iyPXPJUKHFw0t3zd1Fx3y9djwsx4pH8GXg0KeWxYGrvW68tPTTGsmOnN1KXRqFTVkmwhhCinumMmMGzcZGKsoXPLCF7/vBfnrulHH3+1sw0PNovmyc6XuR7nwPU4R86E6kfjXY9zwMc1k/B4Bwb3PM/5a26lIw5qk4FPXiQlzZKln5b1mE9MLutM4O2pJiLKnuCL+jxHRNnj7ZFFZLQ9A/td4sIVV0KvO9V4uu+UmZmGGbOPs+LDtgweeqV0u4+PmgvnXMrqChOCcXX/W10h2eI2Rzacp0Yn8eu3Tvz+g/7v/vFsH9p3y6L3oDS0Wjj/Z1nMNG5uDG7eN2Km12PZuc5ZYqYadi/kK/7V8p3xkkb74vfyeUwj8ygI1JcXmb1dATC/kl3pMUwSCshtaUuRpznFzqY4b4pDla2hxMYYl2+iSR3oidbc8JXWkhIlGRkVYx9vbzUXzjsTFuZAWJgD48adxc0tB7XalBdHn2PnTv9aFV/8XYlWcct8nT/vQtg1R8KuOTJuzBncXHNRq80Y/UIwO3cF1Ip8teyZXu75UzOi2L/OjfCz1qWNBNGXLPntC0/e2hHMtLblO7klXDOnfd8U3PwK6PxcIgc36kdya4oVrHu9Ac9/cA2l4U+/Os3b27vc87lz5/L222+X25aamkpJSQmurq7ltru6unL16tU7+pyZM2fi4eFR2iiQmJhYeoy/H/Ov1+7UHXV30Gq1d/T4N5XdnJwchg4dyqpVq7C3N8yN2wcejCcsxJ7Zc46x8cftfLxyD70fK2tBi4ywxdMrG2eXPFxccvHwyiEq0gY39xx69olk7Zomtzm64U2YfZUTh50IPu5Ybnt4qBUBjdRYWRfj30iNqWkJCTEWNG6ZgX+jbLZ9a7hhbrfzwEOJhF21Y/a842z8aScfr95H7yfKGj8iw23w9M7Rf1+ueXh45xAVYY2bRw49H41i7WrDt85XxshYS0CzPM4cLgtKdDoFZw9b07h1LuGXLWjYIg8rWw3+zfIwMdMSH2lKk3Y5+DfN4+c1LgZM/a0ZGWsJaH6LfLXJI/ySGQ2b/z1fJjRpn4N/s3x+/rJ2Bl33ar5ux8Eqj4cCo9l2Kqh0W1iiEy3qJWJqpOGBgBhS1BZk5prRu0UoRRoVBy7XniHP42dc4uQfLgSfLP+3jwizIaBRlr4sDMrC1FRLQqwljVuk0yBIzbZNvoZJcBUYGZXQtXsUv+32BRSEh9sR0DADK6si/APSMTUpISHeisZNUvAPyGDbT7Vr1FFchAlDWjVh5AONeH+iD8mxxuVe37/FnmebNGVs10DWvOdOQV5ZbxC/xvkE/2FFiQZOHbDBr7F+3sXvP3Wl+YM5NGyRz71Gp6uZhxCiaqozZgLDx00qpRYjlY6i4vJ3EQqLVDRrkER4vAPeLlm42Ofg6pCNt2sWEQn2eDipeeyBEFZtq13T9v2lY9sYwq478ua0A3y/ehOfLtrOo91DS1+PiLbD00ONs1MOLk45eLqriYyxw91VTa+u1/j629o3NevNJrx8hhPH3Qk+U/7mgb6ukH6jrpChryvEWdG4aaq+rrC1dtUV/vJXzHT2SNk0LTqdgrNHrGnUOoeIy+Y31cFz9XXwqJtipq8kZqpJ92q+lPn6cvzm6YT+SaG3OeZhOSiKtFhcUKOxM6bE2gjro2nojBXktqkdHRk9PbNZv+Fn1ny1gxkzjuHsrJ+SJjzipvjCXx9fxMdb06RJCv7+GWz7uXaWGX/x9Mhmw9qtfPXlz8x49Y/SfEVE2NEwIL00XyamGuITrGnSOBn/Bun8vK32jTzSlsDxbU4U5ato0Frfe70wX8kXkwMZ+u51bF2KK7zHu1EuV47aUqKBSwft8Wqkz/+vKz0JfCAL3xY5NZqHmlKTcVNMTAxZWVmlj5tHC9wt77//Pt999x1bt27FzMzsn99QRf9pTYKCgoL/nKiJEyfy+OOP06NHD959993b7ltYWEhhYWHp878P5fi33NxzebxfOFt/DGDTxiAaBmbw0qRgNBole3/zJSbahm++bMqCRYcA+GZ1U2KibViw6BBrvmhG63ZJDB1xmRKNks8/acHFC7Wnh0zn3on4B2UzZVj7Cq+dOebE/l3uLF9/nKJCFUvnNKUgX8Wk16+ybG4THns2hn6DY1BnGrPincZEh1tV8gk1z809l8f7R7D1B382rQ+kYVAGL718Hk2xkr276xETZcM3q5qwYOkfAHzzRRNiomxYsPQIa1Y2pXX7ZIY+f4WSEiWfr2jOxfO1o+Jh46BBZQSZKeV/lhmpRnj7F3D6oA17t9jz8c4QCgsULJnqS0GekskLo1ky1ZcnRqTQb1QK6nQjPprpQ1Ro7ej5bONQcpt8Fd7IVx4f7wqjsEDJkik+N/IVx5JXvHliZBr9XkhFna7io9e8iQq9+wXhv3Gv5ut2Hm8VQm6hMfsvld3433YqEH+3NDa9sonMPDNe/7YnNuaFjOtxipdW9+Olnifo2fwacWk2vLOlCylqw5QjnXvG4x+YxSvPP1ThtTN/OrP/Vw8+/PoPigqVLJvfnIJ8FRNnXuTD+S147Oko+g6MQp1pwscLmxIdbl3JJxhWxwfjsbIqZs9v+u/mzCk39u/1Yfn/9lBUpGLp4vYUFKiYNOUMyxa347G+1+nX/xpqtQkrPmxLdJTtP3xC9Qlqncury/PxalBIerIx65e6MX1AAJ/vv4qFlZauAzJw8SrC0bWYiCvmfLnAndjrpsz5MhKAQZOSWDHLm+c7NsbVu4ipS6OJCzdhzw/2LN8WxkczvThz0JqAFnlMXRyDpY329gkS/8knn3zC4sWLSUxMpEWLFnz88ce0b1+xDgKwZcsW3nvvPa5du0ZxcTEBAQFMnz6d4cOH13CqhahedyNmAsPHTfmFJlwMd2Hko2eJSrQjQ21O93bXaeKXTFyKDVGJ9nyxrS3LXt4FwOc/tyMq0Z5lL+/ks5860L5xLKMeP4OmRMnHP3QsHY1gaO4u2TzRK4TNO5rw7ZZmBPqnMeGFE2g0Sn4/6E9MnB1fbWzN+2/9DsCaja2JibPj/bd+Y/X6NrRtGcfwZ8+hKVHy2VftuHDF7R8+seZ07hKNf0AGUyb0qPCavq5Qj+Wf3KgrLGpPQYERk6acZtmi9vq6wpNhqLNMWfFhG4PWFW5WGjOllq+DZ6Ya492ggNOHbNm3NZcV269SWKBg6TR9zDRpQRRLp/vy+PAU+j+fTFaGEStm1ZOYqZrdk/nS6nDeGEt+gCVFXnd+/qg7OWEak0+91y9RYm1EwgQ/lLklOGyNJ25WII6b47A6nkGxiylJL9ajxL7mR1CEXHVk6dIOxMZa4+CQz9Chl1i8ZB/jX+rDmdPu7NtXj49W/E5hoYqlSztQUKBi4qTTLFvanscfv06//mFkZZmwYkW7WlNmAFwNcWLphx3L8vXcRZYs+p2XJjzO6TMe7Nvvy4oPf6WwSMXSZR31cdPEkyz9sCOPPxZG/76hZKlNWfFxe6Ki7QyWj9irFrz3ZAuKC5WYWpYw8YsreDTUd4jaNK8+/m3VtOqVXul7H50Qy/o3GjCrU1ucvAoZtSiMpAgzjv7oyus/nWPt7AZcOmSPb/NsRn5wDQubf9eh4X5mY2ODjY3NbfdxcnJCpVKRlJRUbntSUhJubrevPyxZsoT333+fPXv2lBtR+tf7kpKScHcvq1slJSXRsmXLKuWhyo0EJSUlvPfee6xcuZKkpCRCQ0Px8/PjrbfewtfXlxdffPGOj/Xdd99x5swZTp48eUf7L1y4kHnz5lU1yf9IodARFmrPN182AyD8mj31fNU81jecvb/5ArBrRwN27SibC697r0jy8424ctmRL77ezSsTuuHknM/MN48zatijaIoNP07HybWAca+F8Mb41hQXVZ6eDZ83YMPnZfl6bux1zh53QKNRMHh0BBMGdqRDpxSmv3ORKUMfqKmk35ZCqSMsxJ5vVulHcISH2VGvvprH+kewd7d+sa1d2+qza1vZjczuvaP039clB75Yt4dXxnXBySWfmXNPMmpwr1rxfd2J9cs8WL/Mo/T50KkJnD1sQ4kGhrycyEs9GtGhh5rXlkcy6bHaOWKiMuuXurF+aVmBOHRaImcPW1GiUTBkShIvdQukQ081r62IZlKf2teSfyv3Wr76tg1h97kAijRll44SrYrF2zqx+Kb93np6P5uONSXQPZVHGkcwdMWzDO8czPQn/mDWxt41nm4nl3zGTrvMm5Pb37Is3LiqIRtXlX0HQ0aHEXzSSV8WvnCNCUM60f7hZKbPPceUkbVvAb9ej4Zz6oQb6WllgcqGdU3ZsK5p6fPnhl3i7BkXNBolg5+7woSxvejwQALTZ5xgysSehkg2AO26lQ3J9mtcQFCrPIa3b8yhbXb0eS6dx4allb5ev1EBDi7FzBzoT3ykCR6+RVjaaJn9aVS5Y854tgGj34xn3xZ7EqNM+PLwFZa/5sP6D90YNze+xvJWbXQ3HtX9GVW0adMmpk2bxsqVK+nQoQPLly+nd+/ehISE4OJSseemg4MDb7zxBkFBQZiYmLBjxw5GjRqFi4sLvXvXfFkhxN10N2MmqD1x07tfd2XW8INsXbgRTYmCsBgn9p5qQKBPKgDbDjdm2+HGpfv36RBKXoExl8JdWD/3B8Z98CTO9rnMfWEfg+YMplhj+Dq4Qgmh1x356lv9WmzXIx3x9c7g8V6h/H5Qv7jtzt8D2fl7YOl7ej5yjfwCIy6HOrPmo61MmvUEzo65vP7KIUZMfLpW5MvJOY9xE4N5Y0Znim8R62xY24QNa8tGxT83/BJnz7jq6z9DLzNhTG86PBDP9JknmDLBcHWFqlr/oQfrP7wpZnolnrNHbCgpVjBkcgLjezWmffcsXv0wksmPS8xkaHUtX87rojGJzSf2jcB/3vlmRgpSRviQctMm19WRZPV0wTQ6D8szmUS/0wj7XUm4rI8hYXLNr8Vw6lTZDcbICDtCrjryzdoddOocw2+7/diwvikb1t8UXwy9SPBZVzQlSgYPucyE8b1p3z6eV189zsuTe9V4+m/l1Omy8iAi0p6rIU6s/epnOneKZvdvDVi/sTnrN5bddB065AJng9305+DgS4yf8Bjt28fx6vRjTJ7yqCGyAICbXz5zfz1LvlrF6V1OfDmtITO/P09ypDlXjtox95ezt3yvhU0JYz8OLbdt8eCmPPtGBH9udSYl2owFB07zzUx/tn/kw6C3Im5xpDqmlsVNJiYmtGnThr1795ZOU6nVatm7dy+TJk265fsWLVrEggUL2L17N23blh+ZWb9+fdzc3Ni7d29po4Bareb48eOMHz++Slmp8upKCxYs4Ouvv2bRokWYmJS1bDZt2pTVq1ff8XFiYmKYMmUKGzZsuOOeNbNnzy43dCMmJqaqya9URro5MVHlW3tioq1xdsmrdH8bm0KeG36Fzz5uSWBQOnGxVsTHWXM+2AUjIy1eXrVjmE5AIzX2jkV8vPE420/uYfvJPTRvm0G/IdFsP7kHpbL8mezlm0vXxxNY92kDmrfN4NIZe9QZJhz6zY2AxtmYW9x6vuialJFmRkxk+Z68MVHWOLtUPqWEjW0hzz1/lc8+akFgo4wb35cV5886678v79rxfanTjSjRgJ1z+b+zvZOGjGTjCvt7Nyig+1PpfLPYneYdc7h43IqsdGMObrcjoHk+5pa1o+VXna66db5SKrZTevsX0P2pDL5Z5EbzB3O4+KclWelGHNxmK/kyoJa+Cfg6Z/LzyaDb7tfGLw4/l3R+ONaUNn7xHA3xoaDYmL0XGtDGzzA3Z/0bZWHvWMSKtX+w7egvbDv6C83bpNNvUCTbjv5SsSysl0PXPnGsW9mQZq3TuXjWAXWmKYf3uOPfSF1rysK/uLjk0rJVMrt/8bvlPl7earr2iGLd101p3iKZSxecUGeZceigNwENMzA3rzgk1VCsbEvw8iskPtK00teDWuuvzbd6ffd3DljZlPBgHzXnj1nxYJ8sjIyh0xOZnD9aO0bE3auWLVvGmDFjGDVqFI0bN2blypVYWFiwZs2aSvfv0qULAwYMoFGjRjRo0IApU6bQvHlzjhw5UsMpF+Luu1sxE9SuuCk+1YaXP+xLr1ee59k3nmPcoicxUmmJT604ys7WsoDnHz/DR98/SGPfFGKTbYlNseVsqAdGKi3eLll3JU3/VXqGOdGxduW2RcfZ4uJUeYxgY13AsGfP8cmXHQgKSCE2wYb4RBvOXXJHZaTF0+PujHb/rwICMrC3L+TjlXvYvvtHtu/+keYtUug3IIztu3+sWP/xVtO1ezTrvmpK8xYpXLrgjDrL9EZdIbPW1BVKYyan8vUxO6diMlIqxkxeDQroNiCdtUs89DHTCWuy0o05tMOegGZ5taYOfq/GFvdavpzXRWN5LovYWQ3ROPy3nv7mV7Ixicsns4cL5lezyW1ui85URU57e8yvVr6uQU3LzTUhLs4KD4+K5aGXl5pu3aJYu7YpzZsnc/GiM1lZZhw65ENAQO2KL/5Ony9rPNwr/p29vLLo1jWCteua07xZMhcvupClNuPQ4XoE+Bs2X0YmOlx9C/BtnsvTs6LwbpTLnjUeXDlqS0qUGZObdmRM/YcYU18/ev7TcY1YNLBZpcc68r0LFjYaWvVKJ+RPW1r1TsPIWEfbx1MJOVZ7RoHci6ZNm8aqVav45ptvuHLlCuPHjyc3N5dRo0YBMGLEiHJTFX3wwQe89dZbrFmzBl9fXxITE0lMTCQnR/+7VCgUvPLKK7z77rts27aNCxcuMGLECDw8PO54vay/VHkkwdq1a/niiy/o3r07L730Uun2Fi1a3PEiCwCnT58mOTmZ1q1bl24rKSnh0KFD/O9//6OwsBDV31YarGxl6Lvh8kVHPL3LFw6eXtkkJ1W+UNPYCef4aXMAaakWNAzMwEhVVsFSqnQVKlyGEnzCgfHPdCy3beq8S8RGWPLD175otTev8K1j8puXWbU0kIJ8I5RKHSoj/ZQMRjf+rS35unzREU+f8hcpT6+cW39fky7w0w/+pKWY0zAoozQ/ULu+L02xkrALFrR6OJtju+0A/SiXlg9ns+3rv09hpePlD6L5fJ4nBXkq/fdlrM+H0Y1/lapalK/zN/L1q/5io89XDtu+dvzb3jpe/iCWz+d53MgXN+VLv0dtWUznXs3XrfRrc4Ursc6EJd56ei4TIw2v9TvCnE3d0eqUKJVaFOjLGZVKi1JhmHPy3EknJgzuVG7bK3POExtpyY9rG1QoCyfNvsjq5Y0oyDdCpdKhMrrxXd34t7aUGX/p2TuCrExTThy/1bQNOia/cppVK1tSUGCsLy9ulA9GqtpVvgPk5yqJjzKh+9OVV8CvX9SPlnCoZK7NzDQVGz50ZdlP+oWpS0oUaDT677ekWIG2RFHhPXWSTqF/VPdnUHGKklvVxYqKijh9+nS5Cq1SqaRHjx4cO3bsnz9Op2Pfvn2EhITwwQcf/MfEC2F4dytmgtoVN/2loMiYgiJjrMwLadcolpVbK04rNumZY3y/rykpmVYE1UtBpSqrg6tU2lpz7bkU4oKXR/kGCy93NUkplTcsv/T8SbbsaExquiWB/qnlYkGVsvbEFsFnXRg/unxP3qmvnSQ22pofNgVVjAVfOc2qlS0oKDBCqdKVfl+1ra7wV8zU8iE1x36zA27UwR/KZvs3fx+1puPlhVF88Y5XWcxkJDFTTbpn8qXT4bw+BqvTmfoGAuf/Vr4qirQ4r4smaVx9UCpAC4q/JjbX6KCWzI5pZlaMu3sue/f+vYFax8svn2LVF2XxxV9lRW27f1QZfb5y2Lvv79NF6Xh50km+WN1an6+by8JamC+dDoqLlPSfFk2nIeWnr5nbszWD54TTokfF6Yey04zY/pEPszafB0BboqCkWN+HvESjQFtLzr+7ogbjpjs1aNAgUlJSmDNnDomJibRs2ZJff/21dOHh6OholMqyPv2fffYZRUVFPPPMM+WOc/PCyDNmzCA3N5exY8eSmZnJww8/zK+//lrl6S6r3EgQFxeHv79/he1arZbi4jtvUevevTsXLlwot23UqFEEBQUxc+bMChXd6rR1cwBLV+xn4HNXOHzAm8CgdB59PIIVH7apsG+rNkl4emWz9IN2AISG2OPlo6Zt+wScnPPRahXExtSO+arz84yIul6+cluQr0KdZVxhe+8BcWRlmHDikP5m9OVgO4aOCyewWSZtH0oj6roluTkVe2YYwtYf/Fn6yUEGDgvh8H5PAhtl8GjfSFYsqbhoWKu2yXh65bD0Pf13GXrVHi+fbNp2SMTJJR9tiYLY6NrxfQFs+cKFVz+MIvScBSHBFgwYnYKZuZbfNpWvQD36XBpZaUYc32MHwOVTVgyflkBQ61zadc0iKsSMXPV/WnLkrtryhROvLo/R5+usBQPGpGBmoeW37xzK7ffoc+n6fP2ur0BePmnJ8OmJ+nx1yyYqxJRcdS2p8XJv5MvcpBgvx7LA2MNBTYB7Kuo8U5Ky9L8NS9MiujcL56NdHW91GABe7HqaoyE+hCboGxLOR7kxuc+fbD8TxMAHLnIuyjBz9ObnGRH1t3UE9GWhSYXtvfvH6MvCI/oL9OVz9jw3JozAphm07ZhCVLhVrSkLQR9k9ewdyZ7ffdFqKx8c2PvRcH0jwp/6YbaXLzkxdMRlAhul0bZdAlGRNuTm1vycp3/5Yp4HD/TKwsWrmLREI9YtcUelhC4DMoiPNGH/Vnvad1djbV9CxGUzPn/bk2YP5JQuUHyzlXM8eXpcCk7u+vpIk3a57P3RgTaPZLNrgyNN2tWOkWN1ibe3d7nnN1dGb5aamkpJSUlp5fYvrq6ut70hmpWVhaenZ+lNzk8//ZSePevOlBZC3MrdipmgdsVN7RrFoFBATJItns5qxg84TnSSHbuOlZ92o21QLN4uWby3tgsAV6OcqeeaSYfGMbjY51CiVRCdVDt6Km7Z0Zjl7+5i8IDzHDrmS6B/Ko/1CGP55xXrPa2bx+Plrmbx//RTD4Zcc8LbM4t2LWNxdsrTx4Lxt5+PuKbk5xsTFVn+b1xQYIRabVphe+/HIsjKuqmucNGJoSMu3agrJBq8rvB3W1a78urSSMIuWOpjpheT9XXw78vHTH2GpJKVXhYzXTplxbCp8QS1yqFtVzVRoRIz1YR7IV/O62KwPpZOwpQGaM1UqDL15bjWQoXORF8HV2UWo8oqxjhZvx6MSWw+WjMVGkcTtFblzzOHbQnkNbelsJ6+o2NBgCVOm+JQd3LEbm8yBQGWNZi7MqNHB3P8uAdJyZY4OuQzbPhFtCUKDh7wKbdfnz7hZGWZcvy4J6CPL4YNu0RQUCpt2yYSFVW7yozRL57h+HFPkpMtcXDMZ/jQC5RoFRw4WK/cfn16X9fn64QXAJcuOzHsuQsEBabStm08UVG2BsvX5vfr0bRrBo4ehRTkqjj+kzMhx2yZuu4Sti7FlS5W7OBZiLNPYYXt387zo9eYOOzdigDwb6vm2BZnmnTO4NBGN/zb1o4RcfeySZMm3XJ6oQMHDpR7HhkZ+Y/HUygUzJ8/n/nz5/+ndFX5iti4cWMOHz5MvXrlf0w//vgjrVpVvEl7K9bW1jRt2rTcNktLSxwdHStsr25hIQ68O7cjz794keeGXyExwZLPP23Bgb3lC0ITkxLGTz7L++88gO5GS1FaqgUr/9eKV147haZYxbIP2lF0izmvays7h0IGj45g+vPtSreFXrJly3of5q0IJjPdhGVzmtzmCDUr7Ko9777ZgefHXua5EVdJTLTg8/8148Ce8jcyTExKGD/lHO/Pa1f2faWYs/KjFrwy8wyaYiXLFrapVd/Xwe0O2DpqGPFqAvbOxYRfNueN4f5kppbdlLRzKmbw5ESmPlk2H2NIsCWbv3DlnW+ukZlqxJKpvgZI/a0d3GaPrWMJI15LxN5ZQ/glc94YWr9ivqYkMbVfQOm2kGALNn/uzDtrI8hMM2LJFJ/KDm8w90K+Gnkms3LM9tLnUx/X9/jdcboh8zd3A6Bn82sogN3nKt7s+Iufazrdm11n2MfPlm7be7EBrevH88XYn4lKseWtTRUXzatN7BwKGTTqOq+OLrspEHrZjq0b6vP2h6fISjdh2bwWBkxhRS1bJ+Himsfvv9av9HU7uwIGP3eF6a90L90WGuLIlh8bMu/dw2RmmrJsUeWLytaU1ARjFk7wJTtDha2jhibtclm+IxQ7xxKKCpScPWzN1tXOFOQpcfYo5uHHMhnySlKF45w6YE18pCkzPo4u3dZvVAqh582Z8nhDGrbMY9j0iu+rixQ6/aO6PwP005zcvADX3e6ZbG1tTXBwMDk5Oezdu5dp06bh5+dHly5d7urnCFHT7lbMBLUrbrIyL2Js/5M42+WSnWfKwbP1WbWtHSU3NVSbGGt4ZdBR3v6ye2kdPCXTiuXfP8is4Qcp1qh4b20Xioprx83Z0OtOzFvclReGnmHYM+dITLbms6/bse9I+Wn8TEw0THzxOAs+fKQ0X6nplnzyZXumT/yD4mIViz95mKKi2pGvO1VaV5jSrXRbaIgDW34IZN6CI2RmGL6u8HeHtjtg66Bh+LT40pjpzeEBFergQyYlMvWpsgas0HOWbF7lyvyvr5GZasySab4GSP2t3QuxRWXuhXzZ7dOvJOD1fvk53RNfrEd2J30HKdv9KTj+nFD6mvfC0Ar7gL7xwOpkBtHzy9bDyGlrj/nVHLzeC6HYzYzElyqv21c3J6c8Zs46ho11EVlZply65MTUqT3IyirrjWxnV8DgIZeZNrUstgsNdWTL5kDmzdfHF0uXdDBE8m/JyTGPWTOOYm1TeCNfzkyd1oss9c35ymfIoEtMfbWss0poqBObtwYx/+0DZGaZsWSZ4dbpVKcZ8+XUhmQlm2BurcErKI+p6y7RpHNmlY5z8aAdyZHmjF5edi53ez6ByPNWLOjfgvotcuj3yt2ZorA2qMm46V6g0Ol0VcrOzz//zMiRI5k9ezbz589n3rx5hISEsHbtWnbs2PGfen916dKFli1bsnz58jvaX61WY2trS/fAaRipqm84rUGk1445Ou86m3tzPuiS61H/vFNdpK0dc1mKf5bx/O17+ddVznui/3mnOkhne2+Whbt+32ToJNx16mwt9g3DycrKKnez3CBpuVHv8f5oPkrzqg0drSptfgExU+bccb6LioqwsLDgxx9/LDf35ciRI8nMzOTnn3++o88dPXo0MTEx7N69+98mXYhaoTpjJvj3cVOHJ+ZjZFy95UdNM0spMnQSqoVRRuXr49V12tBwQyehWug0tWvNKnFrYV9XnDHiXhC4LNfQSagWuhocLVeTVv/8uaGTcNdlZ2tp2jhZ4qY6rMoLF/fv35/t27ezZ88eLC0tmTNnDleuXGH79u3/ubJ74MCBO67oCiGEEELcl3Q19KgCExMT2rRpw969e0u3abVa9u7dS8eOd96IqdVqKSysOCxaiLqmOmMmkLhJCCGEEOIf1cK4qTb7V2MhO3XqxO+//3630yKEEEIIIeqoadOmMXLkSNq2bUv79u1Zvnw5ubm5jBo1CoARI0bg6enJwoULAVi4cCFt27alQYMGFBYWsmvXLtatW8dnn31myGwIcddIzCSEEEIIIeqKfz1h4qlTp7hy5Qqgn3OzTZt7c8iWEEIIIUStolPoH9X9GVU0aNAgUlJSmDNnDomJibRs2ZJff/21dDHj6OhoqtofcQABAABJREFUlMqyQay5ublMmDCB2NhYzM3NCQoKYv369QwaNOiuZUMIQ5OYSQghhBDCQGpp3FRbVbmRIDY2liFDhvDHH39gZ2cHQGZmJg8++CDfffcdXl5edzuNQgghhBCiDpg0aRKTJk2q9LUDBw6Ue/7uu+/y7rvv1kCqhKh5EjMJIYQQQoi6pMprEowePZri4mKuXLlCeno66enpXLlyBa1Wy+jRo6sjjUIIIYQQ4i8yt6YQtZ7ETEIIIYQQBiZxU5VUeSTBwYMHOXr0KIGBgaXbAgMD+fjjj+nUqdNdTZwQQgghhBBC1DUSMwkhhBBCiLqkyo0E3t7eFBcXV9heUlKCh4fHXUmUEEIIIYS4hZrosXIP9YgRwhAkZhJCCCGEMDCJm6qkytMNLV68mMmTJ3Pq1KnSbadOnWLKlCksWbLkriZOCCGEEEIIIeoaiZmEEEIIIURdckcjCezt7VEoylZrzs3NpUOHDhgZ6d+u0WgwMjLihRde4Mknn6yWhAohhBBCCCFEbSUxkxBCCCGEqKvuqJFg+fLl1ZwMIYQQQghxR2TYrBC1ksRMQgghhBC1iMRNVXJHjQQjR46s7nQIIYQQQgghRJ0lMZMQQgghhKirqrxw8c0KCgooKioqt83GxuY/JUgIIYQQQtyGTqF/VPdnCCHuComZhBBCCCEMQOKmKqnywsW5ublMmjQJFxcXLC0tsbe3L/cQQgghhBBCiPuZxExCCCGEEKIuqXIjwYwZM9i3bx+fffYZpqamrF69mnnz5uHh4cHatWurI41CCCGEEOIGha5mHkKIf09iJiGEEEIIw5K4qWqqPN3Q9u3bWbt2LV26dGHUqFF06tQJf39/6tWrx4YNGxg6dGh1pFMIIYQQQggh6gSJmYQQQgghRF1S5ZEE6enp+Pn5Afq5NNPT0wF4+OGHOXTo0N1NnRBCCCGEKE9XQw8hxL8mMZMQQgghhIFJ3FQlVW4k8PPzIyIiAoCgoCC+//57QN9bxs7O7q4mTgghhBBCCCHqGomZhBBCCCFEXVLlRoJRo0Zx7tw5AGbNmsUnn3yCmZkZU6dO5bXXXrvrCRRCCCGEEEKIukRiJiGEEEIIUZdUeU2CqVOnlv6/R48eXL16ldOnT+Pv70/z5s3vauKEEEIIIYQQoq6RmEkIIYQQQtQlVW4k+Lt69epRr169u5EWIYQQQgjxDxSAoprnvlRU7+GFuO9IzCSEEEIIUbMkbqqaO2okWLFixR0f8OWXX/7XiRFCCCGEEEKIukhiJiGEEEIIUVfdUSPBhx9+eEcHUygUBqnwFjtYoDMyq/HPrU7GiamGTkK1UJRoDZ2EanFtSTtDJ6FaBC2ONHQS7jpNQqKhk1At8p3vpfbrMrrcPEMnoVqk9vAxdBKqRe8nhxs6CXedRlMALDR0MsrTKfSP6v4MIUSV1PaYCSCtkREq0/88mLxW8dl9b9YV7lUme50MnYRqoRlrZegk3HUlIdcMnYRq8ULrPwydhGpxTN3Q0EmoFtHPehk6CdXihcETDZ2Eu04fNy0wdDLKk7ipSu6ohhgREVHd6RBCCCGEEEKIOktiJiGEEEIIUVfdW91IhBBCCCHudbobj+r+DCGEEEIIIYSoqyRuqhKloRMghBBCCCGEEEIIIYQQQgjDkJEEQgghhBB1ifSIEUIIIYQQQojbk7ipSmQkgRBCCCGEEEIIIYQQQghxn5JGAiGEEEIIIcT/2bvv6CqKNoDDv1vSeyM9BEijdxClV0FBQSkKiqigIogCdhSxgAVE8VOxYsWOCoqI0pFO6CW9935Tb//+uJAYE5AoyQ3xfc7Zo3frDLuZ2XdnZ1YIIYQQQgjxH/WPGgl27drFtGnT6NevHxkZGQB8+umn7N69+7ImTgghhBBC1KYwN80khPh3JGYSQgghhLAeiZsapsGNBN999x2jRo3CwcGBI0eOoNVqASgpKWHp0qWXPYFCCCGEEEIIcSWRmEkIIYQQQlxJGtxI8Pzzz7N69Wree+89bGxsqudfc801REdHX9bECSGEEEKIvzA30SSE+MckZhJCCCGEsDKJmxqkwY0EMTExDBw4sM58Nzc3iouLL0eahBBCCCGEEOKKJTGTEEIIIYS4kjS4kcDPz4/4+Pg683fv3k3btm0vS6KEEEIIIcQFyBsxQjR7EjMJIYQQQliZxE0N0uBGgpkzZzJv3jz279+PQqEgMzOTzz//nIULF3Lfffc1RhqFEEIIIYQQ4oohMZMQQgghhLiSqBu6wWOPPYbJZGLYsGFUVFQwcOBA7OzsWLhwIXPnzm2MNAohhBBCiHMUZsvU2McQQvxzEjMJIYQQQliXxE0N0+BGAoVCwZNPPsnDDz9MfHw8ZWVldOjQAWdn58ZInxBCCCGEEEJcUSRmEkIIIYQQV5IGNxKcZ2trS4cOHS5nWoQQQgghxN8xKyxTYx9DCPGvScwkhBBCCGElEjc1SIMbCYYMGYJCceF/gK1bt/6rBAkhhBBCCCHElUxiJiGEEEIIcSVpcCNBt27dav3W6/UcPXqUkydPMn369MuVLiGEEEIIUR/zuamxjyGE+MckZhJCCCGEsDKJmxqkwY0EK1eurHf+M888Q1lZ2b9OkBBCCCGEEEJcySRmEkIIIYQQVxLl5drRtGnT+PDDDy/X7oQQQgghRD0U5qaZhBCXn8RMQgghhBBNQ+KmhrlsjQR79+7F3t7+cu1OCCGEEEIIIVoUiZmEEEIIIURz1ODhhiZMmFDrt9lsJisri0OHDvHUU09dtoQJIYQQQoh6yNiaQjR7EjMJIYQQQliZxE0N0uBGAjc3t1q/lUolkZGRPPvss4wcOfKyJawxdW6fzcRxp4hoU4CXZyWLXxnCnoMhf1rDzPRJRxk9LA5nJx2nzrZi1ftXkZHtCoCN2sj8e/fQr1caRcUOrPqgL0dOBFRvPXHsSVp5l/Pmmr5NnLPaxkzO4LrJmfgGVgGQEu/EF2+35tBuLwBmPhLP8BuzqapQsWZlW7b/7Fu9bf+RuQy7IYcl93e2Stov5tY7zzL1zpha89JSnLl36jAA7p5zkuFjUqmqVPHR6g5s/y24er3+QzIYem0azz56VZOm+a/ubX+EUUFJtHUtRmtUEZ3vx0vH+pJU6g5AoFMpO8eurXfbOX8M55e0drjZVvFK321c1SqT5DI3Hts/mNPF3tXrPdNzF2llrnwQ07UpsnRJJt6RyB1z4/hhbQjvrWgPwN0PnWX42AyqKtV89L9wtv9S87fUf3g2Q6/L5NmHelgryX9r7B353HxfLp4+BhJPO/DWokBijjoCMGtxBiMnFVFVqeSDF/zZ9r1H9XYDri9m+MQiFk9vY62kc1fvaIaHJdLGs5gqg4pjmX6s3H0VyUWWdLraVXF/v4P0a52Gv2sZRRUObE1ow//29KZMZ1e9zgvXbqVPUAYpxe48vXkwZ/N8qo/x5JCdpJW48kl0N2tksVpLKA+7h2YybcAxogLz8HGt4OFPR7HjTM31c2Dp6nq3W/XLVXy2qxs2KiNPTtjOwPbJFJY58tKPAziYEFS93rQBR/FzL2P5hv6Nnpe/42CvZ/rUY1zdNw13tyoSkjx4+/1exMZbyribbzjNxPGnAPj6+45892OH6m0jw/OZe+8BHnj4Wkymy9ZhUgghGqQlxEwAPf0zubP7UTq2yqOVUwVzN17LlqSauueFoVsZ3772ffmulGDu+el6AGyURp4bup2hbZLIr3DkuR0D2ZteU/fc2f0I/s5lvLBrQNNk6AK8PCu4644j9OqRiZ2dkcwsZ15d1Y+4eMt9wk03nmbihNMAfL2uI+t+aF+9bWREPnPuPci8haOadb0zccpZZtx9gh++C+fdt7sBMPPeowwfmUxVlZo173dm+9bW1ev3H5jGsBEpLHnKuvcFhs/KMO2swpxqBDsFyk42qO5xQRlS+1GG6aQOw/tlmM/oQQmKMDU2yz1R2Ckw68wYXi7B9IcWhacS9UOuKHvZ1Rzji3LMOUZsHnRt6uxd0MRbYpgx6yQ/fBvGu29a4rmZs48xfFSK5Xy914ntv9c8w+g/KJ1hI1NY8uQ11kryRV3JMRNA2vsq8reoqExSoLQD124mQh804Nim5ulgZZqCpBVqSo4oMevA4xoT7R7XY2spRjDpIO4ZGwq2KbH1NtPuSQMeV5mqt09fo6IqW0HY44amzl61MeOTGTM+GV//SgBSklz44sNwDu+zxEZ3P3CK4WPSLM9a3m7P9s015Xn/IZkMHZ3Os4/0sUra/6xnYCZ39DpKh1Z5tHKuYN76a9maUP819NSwHUzqcpqXtl/NZ0csf2s2KiNLRmxnSFtL3fXC1oHsS63J6x09j+DvUsay7datu8BSf9097TC9u2dgZ2skM9uF5W9dTVzCubhp3Ckm3WCJm776oSPfbehYvW1UeB5z797P3MfHNOv6SzSdBjUSGI1GZsyYQefOnfHw8Pj7Df7GM888w5IlS2rNi4yM5OzZs/963xdjb2cgMdmDX7eG8czD2+ssn3zDSW4cfYaX3+xPdq4zd0w+yrInf+Ou+Tei16sYMzyW8LYFzFs0mt7dM3j8gV1MmjkJUODnU8qYYXHc//h1jZqHS5GfY8ealW3JTHFAoYBhN2Tz1P9OMvemXvgFVzLouhwWzexCQOtKHnwuhug/PNAU2+LobGD6vCSeuLv5PFz+q+REFxY9eHX1b6NRAUCfa7IZPCKdRQ/1IzC4nHmPHyH6QCs0JXY4Oum5fdYZnvzTdtbSt1Umn8V35HiBDyqlmYVdDvDx4J8ZtXESlUYbsiqc6PvDbbW2mdLuDDOjjrEjy3IzOLvDEZxs9IzbfBNTw06ztM8Obtx8EwDdvHLo5pXLs9HN5yYxvEMJ105IJzHWuXpenwG5DL42i0X39yIwpIJ5T58keq/3uetQz+2z43hydi8rpvriBo0rYtbiTN54LIiz0Y6Mn5nHC2sTuWtAJFE9KhgyvpjHb2lLYFst81ekcXiHC5pCNY4uRu54NIvHprSzavp7BWXy5bFOnMxphUphYt41+3lnwk/c+PEUKg02tHIux8e5nBW7riahwIMA11KeGrYTH+dyFvw0CoBZfaNxstEz6fOJTO56imdG7GDK2psB6OKXTWf/XJZtt/5D55ZQHtrbGojL9mLD4ShenvZrneWjl95e63e/iFQWTdjO1pNtARjf5zRRgfncvXo8/SJSeW7y71y7dDqgIMBDw429zzD9zZuaIit/66E5+wgNKebl166msNCRoYMTeXHJFmbOHYuri5bbbj3G088PQaEw8+yT2zl81J/kFA+UShMP3Lef19/q27JvdJti7MsW9EaMEE2tpcRMAI42emIKvFh3Joo3xtSte8DSKPDk1qHVv3VGVfX/T+p4mo4+edz63QQGhKTy8ojfGLDmDkBBoIuGmzucYeLXNzdyLi7O2UnLqy9t5tgJXxYtGUKJxp5A/1LKymwBaBNaxG1Tj7P4ucEogCVPbSf6iF91vTN39gFW/a951zvhkYWMvi6BxISaxqs+V2UyaGgqix4bSEBgGQ8uPEj0IT80GkvcNP3OkzzxyCArptrCdEyHarwjiigbMILxvTL0Cwux/dgbhYPl39x0Uof+kSJUU51QznMBlQJzvB4U5/axoQJzrB6btzwx7dehf64E2x98UCgUmLMMmH6qwOZdLyvmsrbwyEJGj02sfb76ZTJoWBqLHh5AQFAZDz5yiOgDvjXn665TPLHQ+g8s63Olx0wAJYeUBEwx4tzRhNkIyavUnLzXlp7fa1E5grECTt5jg1OkmS7v6QBIeVPNqbm2dPtMh0IJWd+qKD2toOunOop2K4l51Ia+27UoFFCVriB7nYpuX+isms/8XHs+ers9mWlOoIDhY9J46qWDPHDHIPwCyhk8IoNFD15ledbyxFGi9/vUPGu55yxPzrPuy5jnOdjoic3z4vuTUbw+rv66C2Bou0S6+OWQU+ZUa/7Ezqfp0CqPaV9NoH9oKi+O/o3B79wBKAh01XBT5zPVMa81OTtpWfn8Lxw76ceTLwynRGN3rv6yNIK2aV3E7ZOP8vSyoaCA5x7byuFjASSnnoubZu3jtdX9mnX99a9J3NQgDboSVCoVI0eOpLi4+LIloGPHjmRlZVVPu3fvvmz7vpCDR4P46Kse/HGwdT1LzYwfc4bP13Vh76EQklI9eel//fHyqOCa3qkAhASWsPdQMCnpHqzfFIWHWxVuLloAHpi5j/c/70FFpW2j5+PvHNjuzaFdXmSmOpKR4sgnq9pSVaEiqquGkLYVnDjgTtwpV3Zs9KWiTIVvkOUN2zsXJPDzVwHkZTXf8VJNRgVFhfbVk6bEUggGty7l+BFv4mM82PF7EBXlNvj6VwBw5+xTbPy+DXk5jtZMOgAzdlzHd0mRxGk8OVvsxSP7BxPoVEYnzzwATGYl+VWOtaaRQUlsTGtLhcEGgDDXIn5KCSO51J0vE9rTzrUYALXCyHO9dvHUwYGYzM2jsLd3MPDw88d54/mOlGlsqucHtynn+GFP4s+4seNXfyrK1fgGWN5auPOBWDZ+G0xetoO1kv23JszKZ9NaTzZ/5UlqnD2rHg1CW6lg1C2FhIRrOb7Xmbjjjmz/wYOKMhV+wZabvrsXZfLTJ97kZVi3nLjv++v58XQUCQWexOZ7s2jzUAJcy+jga7kO4wu8mP/TtexIDCW9xI0DaUG88UdfBrdJRqWwvPXS1rOIX2LDSCl255sTHWjjWQSAWmnkqWE7eXZL87gOW0J5uDc2hNW/9WH76frfgikoc6w1DeqQzOGkQDKLLG/FhfoUsetMaxJzPfl2Xyc8natwd7Lk89EbdvG/TVdRrrV+3WVra6B/v1Te/7g7J0/7kpntwmdfdiUz24Xrr40lOEhDUrIHx074cfS4P0kp7gQHagCYOP40J061qu5xIIQQ1tBSYiaAXamtWbW/L1uS2l5wHZ1RRX6FY/Wk0da8od3Wo4itSaHEF3qy9kQnvByr8LC31D1PD9rJij1XUa63bt0z8abT5OU78uqqfsTGeZOT40z0UX+ysl0ACArUkJTszrHjfhw97kdSsjvBQefqnQmnOXmyFbHxzecB81/Z2xt45PH9rFrZq7rhAyAkRMOJY62Ii/Vkx7YQKips8PUvB+DOmcf5eUM78nKtHzfZvuKJarQjyjY2KMNsUD/uBjkmzLE1b1sb3ixFdZMj6qnOlvVC1KiGOqCwtbQSmFIMKK+xR9nGBtV4Ryg2QYnlqY7+VQ2qe1xQOFn/fhXOna8nD7JqeQ/KSmvippDWpZw46kNcrAc7tgafi3PPna97TvDz+rbN4nzV50qPmQA6rdbje4MRpzAzzpFmIp7To81SUHbaco1pjiqpylQQ8ZwepwgzThFmIp7XU3ZKQfEBy7VVmajAa7AJpzAz/lOM6IsU6C2hE/HPqwl90IDa+UIpaBoH/vDj0F5fMtOdyUxz5pN32lNVqSaqYxHBoWUcP+JF/Fl3dvwWWDt2v/80G79v3SyetQDsTm7NG3v6sjXhwnVXK6cynhiym8c2DcdgrP3338aziO2JoSQUePLl0XN1l4Ol7lo0bCcrd11Fuc761+WkG0+SV+DEireuISbem+xcFw4fCyArx1J/BQeUkJTiwdGT/hw94U9SqgfBgSWWbW84xYnTvsQmSNwkajS4JuzUqROJiYmXLQFqtRo/P7/qydvbuheoX6syvDwqOXK8ZsiTikpbzsb70CHC8uAsMcWDTlG52NoY6NUtk4JCB0pK7RjaPxGdXnWBxgfrUirNDBydg72DkTPHXEmMcSa8UynOrnrCOpRiZ28iK9WBDj2KCetQxvrPgv5+p1YUEFTOJz9s4oOvf2Ph04fx8bU0BCTFuxEeVYyzi46wyGLs7IxkZTjRoUsB7SJKWP/thSsJa3KxsdwIlejqfxDZySOPjh4FfJMQVT3vTLEX/XwzUClMDPBL42yxJwCz2h9jf64/J4p86t2XNdz32BkO7vbh6IHaAVRSnAvhHUpwdtETFlViOV9pjnToVkS7KA3rv2x+f0vnqW1MhHepIHqXS/U8s1nBkV0udOhZQeIpeyK6VODsZiCscwW29iYyk23p2KeMsM6V/PhB86uMnW3PXYdVdhdex05Lmc4W47kH/zF5XvQNtlyH17ROJS7Pco5n9DrKwfQATue0avyEN1BLKw/r4+lcwTWRqaw/VFNmxGV707V1NnZqA1eFp5GncaS43J5RXWPRGVQXbHxoaiqlGZXKjE6vqjVfq1XRsUMuSSnuBAVo8PEup5VPGYEBpSSnuuPvV8rIoQl8/Hk36yS8KZmbaBJC/GMtPWb6s96BmeyasYafb13L04N24GZXVb0spsCLHv5Z2KkM9A9JI7fckaIqe66PiEVnVF208aGpXNUnndh4L558dBdffvIt/3ttI9eOjK9enpziTlBAaU29E1hKcoql3hkxLJGPP7d+b8OLmf1ANAf2+3M02rfW/MREd8IjCnF21hEWXoSdrZGsDGc6dMonLLyI9d+HWynFf6Ps3PAsLpaHs+YiI+bTehTuSnSzC9DemIvugQJMx2veyFaG2WA6ocOsNWM6oAUvJbgpMP5WicJWgWqg9V8GOW/2g0c4sM+v7vlKcCM8sshyviKKzsW5589XMevXhVkpxRfXEmMmAGOZ5fpTn+vsYdIBClD+6bmx0g5QgibaEjc5RZrRHFFirIKiPUpsfczYeEDuz0qUduA9zERzolSaGTg8A3t7I2dOepAU7/qXZy0mstIdLc9aIktY/431y/NLpcDM0mu3sOZwNxIKPOssj83zonuApe66JjSN3DJHiirtuS7KEjddrPGhKfXrlU5cgheLFuzg6w++5q1XNjB6eGz18qRUdwL9Nfh4l9HKu4xAf40lbvItZeSQeD76orsVU99EJG5qkAZ/k+D5559n4cKFPPfcc/Ts2RMnp9rdclxdGzaOX1xcHAEBAdjb29OvXz+WLVtGSEhIvetqtVq0Wm31b41G09Dk/y1Pd0tLaFFJ7RuFohJ7PM4t27QtnLati3h/5Y9oNHY8t3IQLk46pk86wsIl13LH5GgGX5NMVrYLy9++moIipzrHaSqh4WWsWBuNra2JygoVzz3QibQEJ9ISnNi2wZfXvjqMrkrFiieiqKpUMeepOF59MooxUzIYd2sGmmIbVi2OJDXBenn4q5jTHqxc2p30VGc8vbTcOuMsL7+5m9m3DSH6QCu2bQ5i5Xs70WmVvPpCd6oq1dy/4Bgrl/ZgzI1JjL05EU2xHW+80pXUJOuPO6nAzKLueziU50dsSd0KCmBi27PElbgTXeBXPW/1mW4812s3267/gvRyFx4/MJhQ5xImhMZy8+838lyvnfT3S+dEoQ9PHBxImf7CD34b08CRWYRFaXjwtrpdD6P3erNtYwArP92LTqvi1Wc6U1Wp4v7HT7NycSfG3JzK2MmpaIpteeOFjqQmWvnVij9x9TSiUkNxXu1itChfTXCYlsM7XNmyroI3NsahrVKyfF4IVRVK5i7LYPmDwVw/vYBxd+ajKVTx+sPBpMRaNzhRYObRwX8QneFHfEH9b8O521dyT9/DfHuiZvz3Dw5256lhO9l45+dkalx4+rfBhLgXM65DDNO+nMBTw3Zwdes0TuW04pnfBlV/y8AaWmJ5eCHXdY+hXGvDtlM1D/7XH4okzK+Arx78iuIKe574YgSuDlruGX6Ie98fx70jDjCiSzwZBa48t24weRrr/L1VVtlw+qw3t046QWqaG8Ul9gwekEz7yHwys51JS3djzWfdWLbkdwDWfNqNtHQ3XlzyO+9/0oOe3TO5bcpxDEYlb7/fi5Onff/miEIIcflZM2aCpombAHanBvN7YhvSNa6EuGl48Kr9vDP2Z279bjwms5J1Z6KI8Cpgw61fUlRpz/xfR+Jmp2VOn4Pc8cMNPNB3P2PC40ktcWPR1sHkljd93ePvV8b1o2NZ92N7vvymIxHhBdw38xAGg5Lft7a11DufdmXZs1sAWPNJV9LS3Vj27BY++Kg7PbtnMe0WS72z+r2enDzVfOqdgYNTCQsvYt7s4XWWRR/yY9uW1rz25u/odCpWvNyHqio1c+Yd5tWX+zBmbALjboxDU2LHqpU9SU1xq+cITctsMmP4XymKzjYo21resjdnGgEwfFSG+j4XFGE2mDZXop9fiM1H3iiD1CjHOGBKMKC7PR+FmwKbZ9yh1IzhwzJsX/fE8H4pxq1VKAJU2DzqhsJHdZFUNJ6BQ9IICy9m3r1D6yyLPujHtt9CeG31VnRaFSte7GU5Xw8d4dWXejFmXALjxieg0dixakUPUpOtH+dCy4uZAMwmSHxZjWt3E07hlqeDLl1MqBwgaaWa0AcMYIak19VgVKDLt2zne6OR8lgFh2+0xcYDol7RYdBYhiXq8qGO5DfU5G1SYh9sJmKJHjsrFSWt22pY8e5uS8xUqeL5x3uRluxCWrIL234NYuUHuyyx+3PdLM9aHj7Byue7MWZ8MmNvTkJTYssbL3UlNcnl7w9mJXf2PoLRrOTzI/V/c+77U1FEeBfww/QvKa60Z+HPI3G103J/v4PM+OYG5l69n2sj40krduPpzdapuwD8fUu5fmQM3/3UgS/WdSKyXQGzZxzEoFfx2452pGW4s+aL7rz4lCVu+nBtd9Iy3Hnx6c28/2lPenXL5LZJxzAYFbz9YR9OnGk+9ZewjktuJHj22WdZsGABY8aMAWDcuHEoFIrq5WazGYVCgdFovOSD9+3bl48++ojIyEiysrJYsmQJAwYM4OTJk7i41C1Qli1bVmc8TmswGpW88UHtB54L79vND7+0Jyy0gKt7p3Hvw2OZNO4U9995gGdXDLFSSiE92ZE5N/XCydlI/5F5LFh6lkfu6EZaghOfv9WGz9+qeYB0633JHNnngcGgYMo9Kcy+sTd9BxWwYNkZ5k1qPuPCn/9oDkBygqXRYM23mxkwNJPNP7dm7YdRrP2w5u3ZW2ac5eghH0u+pscye/oQ+lydzYJF0cy7a7AVclDbkp67iXAvZPLvN9S73E5lYFzreP53qvbHe8v0djy0d1iteZ8N2cCLx67ihtZxBDuXMuLnySzts5O5HaNZdrRfo+XhQrx9K5m18CyLZvdCr6v/Znvtu2GsfbfmzZdbZsZzdL8XBoOSKXclMnvyNfQZkMeCZ08wb1rT5+Hf+GyFH5+tqGnYmTo/myO7nDEaFNwyL4d7h0bSd4SGh1elMufaCCumFJ4cupMwr0Kmf31jvcudbHW8eeNGEgs8eHtfTXlQprPj0V9G1Fr3/Zt+5NWd/bguKpYgNw1jP7qFZ4bv4N6rDrN8p/W+CdISy8MLGdsrhl+PhaMz1FTzRpOKV9YP4JU/rffUTdv4am8nIv3zGdQhiamrJnLbwKMsuP4PHls7qukTfs7Lr13D/Dl7+WLNOoxGBfEJnmzf1ZrwdoUA/PxrBD//WvM3M3xIAhWVNpw5680Hb61n7sLR+HhV8MTC3UyfdSN6g3WCfSHEf09ziJmg6eKmX+Jr3jaPK/QipsCLzbd9Tp/ATPalB2EwqXh+58Ba27wwdCufHe9Me+88hrVJYvyXk7irxxGeGLCbBzdd2+hp/iuFAuLiPfno024AJCR6EhpSwnXXxvH7Vsvbohs3RbBx05/qnaGJVFSqORPjzftvbeCBBdfi7V3B4wv/4I6ZNzSLesfbp4J77j/Kk48MRK+vPz2ff9KRzz+p+Yjlrbed4ki0r+X+Z+ppZs8cRd+rMlnw6AHmzR5R7z6akmGlBlOSHts3/vRCy7k3OFVjHVGNsQx1ooywwXRYh2ljJcpZLijUCmweqv3QXL+sBNVNjpji9Jh2a7H9wAvjF+UYVmmwee7ff0ukobx9KrhnzjGefHjAhc/Xxx34/OOal3Vuvf00Rw63ssRNt51l9p0j6NsviwWPH2TePcPq3UdzdCXFTADxL6gpj1fS9aOahlhbT2i/XE/882oy19qBElqNNuHc3lT9bQylDYQ9WfujxLFPqQm41UjZGSUFW5X0+EZH+ho1CS/a0GGlvimzVS0j1Zm50wfh5KznmiFZzF90lEfvv5q0ZBfWfhDJ2g8iq9e95c4Yjh70tlyDd8Qx+7ZB9LkmhwVPHWHenQMvchTr6dAqj2ndjzPp84lUn5y/MJhUvLBtIGyrmffcyK18frQz7VvlMaRdEjd/OokZvY/w2JDdzP+p6esusNRfsYlerFlreVaUkORFaEgx142M4bcdlm95/Lw5kp8315yzEYMSqKy04XSsDx+u+oE5j15niZse2sntsyc0i/pLWM8lDze0ZMkSysvL2bZtW/W0devW6un874YYPXo0EydOpEuXLowaNYqNGzdSXFzM119/Xe/6jz/+OCUlJdVTWlpag453KQqLLeOfe7hV1Zrv4VZFUXH9Y6N37ZhF6+BiftwURZeOORw4EkiV1oYde1vTtUPOZU9jQxj0SrJSHYk/7cJHr7UlMcaJG6al11kvqE05Q8bm8OkboXTpXcypQ+5oimzZ+WsrwjuW4eBoqGfvzUN5mQ0Zac74B5XXWRYUUsqQkel8+n57OnfP5+QxLzTFduzaGkhYZAkODtapeM9b3GM3QwNTmLp1LNmV9bc+jw5OxF5l4Pvki98Q3dTmLBqdLb9nhNK3VSa/pYdiMKvYmNqWq1plNkby/1ZYew0eXjpWfb6X9fs3s37/Zrr0KmLclFTW79+MUlm7X1ZQaBlDxmTx6dthdO5VyMkjlo/H7vrNl7D2mmZ1HWoKVRgN4O5TO00e3gaK8uq2vwaHVTFsQhEfv+xHl6vLOLnPiZJCNTvWuxHepRIHp0t/WHC5PTFkF4PapnDXt+PIKat7HTra6Fg9/icq9DbM23AtBtOFbxxu7HCWUq0d2xLb0Dsok63xbTCYVPwa147eQRmNmY2/9V8oDwG6hWYR6lPMjwejLrpez7YZtG1VyDd7O9GzbSZ7YkKo0tuw5UQ7era1TplxXla2Cw8vGsm4yVOYdvd4HnhkNGq1maycutenq0sV0yaf4K33ehEVkU9GhiuZWa4cO+mHSmUiMLBx3p61Kuk2K0Sz1RxiJmiauKk+6RpXCivtCXErqXd5n8AM2nkWsfZEJ3oHZrIzpTWVBhs2xYfRJ9A6dU9hkT2pabXfkk9Nd8XHp25sAZZ6Z+qUE7z9bm+iIgrIyLTUO8dP+KFSN596Jzy8CA8PLW+s/p0Nv37Lhl+/pUvXPMaNj2PDr9/WvQ8P1jBkWCqfrulEl655nDph+SDpzh3BhEcUWz1u0r+mwbRXi+1rniha1dyLKrwsjzQUobXvvxWt1Zhz6r+/NkVrMScbUI13xHxEh7KvHQoHJcoh9piOWufDseERRXh4annj3S1s+H0dG35fR5du+YybEM+G39fVf75GpPLphx3p0i2PU8e9Ledre1CzOF/ntaSYCSB+qZrCnSq6vK/Dzq/2Mo+rTfTeqOOq7Vr67dASuVSPNleBQ1D9N1XFB5SUJygJuMVIySElngNMqBzBe5Tlt7UYDEqyMpyIj3Hn49XtSYp35YZJdYfPC2pdypBRGXz6XhSde+Rz8qin5VnLlgDCokqabczUIzATT8dKNt/9KUfmrebIvNUEupWycOBeNt35Wb3b9A7KoJ1XEV8c7UTvoEx2JVvqrl9jw+gdZL24qbDYoZ76y41W3heuv6ZNOsabH/QhKjyf9ExXMrNdOXbqXNwU0Dzqr8tK4qYGueSeBGazJdeDBg1qtMS4u7sTERFBfHx8vcvt7Oyws2vcoSqyc50pKHKge+csElIsQ784OuiICstjw59a386zsTEy9679LFs1AJNZiUppRnHu09lqlRmlsrmNKwc2tn9Nk5m5i2N57+V2VFWoUarMqNTn8qC2rKtUNd+r3t7BgH9gOVt//eu44WbmPHyM9//XiapKNSolqM7lR30uf0qrNZKaWdzjD0YGJTF16zjSyy/cHXRi27NsyWxNofbCH/D1tKtkbsdoJm+x9EZQKszYnLv2bJQmlI3+Off6HTvgxexJtd8cf3DxSdKTnfj24zaYTH9uuTcz54nTvP9q5Lnz9efr8Nz5Ujaf69CgVxJ33JHu/UvZu8lSMSsUZrr1L2P9R38drsfMAy+l886SAKoqVCiVoLI5l7dz3yOzzrVo5okhuxkalsSd34wjQ1P3OnSy1fHO+J/QGVXM/XE0OuOFqw0Ph0ruueoQ07+6EbCMLa9WWf86vJCWWB4CjOt5hjPpPsRlX3gMV1u1gYfH7ebpr4ZhMitRKk0ozr1Jo1I1n3Ol1arRatU4O2np2T2T9z/uUWede+46zLoNUeQXOBERVlBdzgOoVOZmVW4IIVq+5hAzQdPETfXxdSrD3b6KvPK6H6+0VRl4auAuHvnNUveoFGYU5+5X1Va8Tzh9xoegvzzYDwwoJTe3/uEF77n7MN//GEV+gSMR4c233jl6pBX33T2y1ryHHj5IeqoL33wVVec+fO6Dh3lvdVeqqs7d/5y7hzt/L2etfJnNZgyvl2LaVYXN654o/P9yL+qnAm8l5rTaDyTNaQaUfev+DZi1ZgyvaVAvckehUoAJqp/wGDj3u+kdjW7FfTNqDwv10KOHLefri4i652vBEd57q4vlfJ37nhNY/3z9VcuImcBshoRlagq2qujygQ77Czz4B7A51xGleL8SfSF4Dq57UZm0lgaHqGV6FCowGy3HADAbLEMaNRcKpRkbm7ox05xHjvP+qg5XROz+ZxvORLIvtfazo9UTfuanMxH8cKruMz9blYEnh+7isV/Ox01mbMzWr7sATp2tW38FBWjIya//BdR77zjEup/ak1/oRGRYQXV8C82r/hLW06DmyT93lW0MZWVlJCQk4O/v36jHsbfT0651Ie1aW4Yt8GtVSrvWhfh4lQEKvt/YnlsnHKdfz1RCg4t4ZM5uCooc+eNg3XE/p910jANHAklItlRwJ2Na0b9PKm1CCrnh2rOcirHeRzvveDCRTj2LaRVQSWh4GXc8mEjn3sVs/6n2OGOjbs6ipMiGA9stD5ROH3Gja98iIruUcOPt6aTEO1JeamONLNTrrvtP0qlbPq38KmjfqZBFSw9gMirY8Xvtgn7U2BRKim058Ielif/0CU+69sgnsmMhN05KICXJhfIy6+RrSc/d3Bgax0N7h1FmsMHbvgJv+wrsVLVvbls7l9DHJ4uvEy7+RvCi7nv4IKYLOZWWYCY6348bQ2Np51rElHZnOJxvnbHlKivUpCS41JqqKlVoSmxISajdPX7U+HRKimw5sMvyN3P6mDtdexcS2amYG6emkJLgZLXzdSHr3vVm9K2FDJ9YSHBYFXNfTMfe0cTmL2t/W2L0rYWUFKjZ/5vlxvj0QSe6XVNGVI9yJszKIyXGjnJN09/xPjl0F9dFxfLYxuGU62zxcqzAy7HmOnSy1fHOhA042Oh5+rfBONnqq9dRKureuT466A8+Ody1ekzGI5l+jG0fSxvPIm7ufJqjmX51tmkqLaE8dLDVE+6fT7i/ZWDTAE8N4f75+LqVVq/jZKdjWOdEfjx08TLjriGH2RMTQmyWJZ/HU/wY3DGJML8CJl11kmMp1jtXAD27ZdKreya+rcro0TWLl5//nbR0NzZvaVdrvR5dswgK0LBho+WGPjbei+BADb16ZDB6ZBwmk4L0jOYxJu/lpDA3zSSE+GdaSswE4GijJ8o7nyhvS90T6Kohyjsff+dSHG30LLx6D118swlw0XBVUDr/G/MLqSVu7E6tGzfd1+swO1NCOJPvA8CRbD9GtEskwquAWzuf4EiWdeqe739sT1RkPpMnnsTfv5TBA5MYMyqODRvr9uLt3i2LwIDS6mWxcX+qd0Y1r3qnstKGlGS3WlNVlRqNxo6U5Npvno4ak0RJiR0H9gUAcPqkN1275xLZvoAbb4ojJdmV8nLb+g7T6AwrNZh+q8TmKXcUDgrMBUbLpLVUVAqFAtUUJ4zfVWDcXoU53YDhg1LMqQZU19V9ycr4SRnKq+xQRlju5xSdbDDurMKUoMf4fQXKTtbJZ/3nS4VGY1v3fF2XTEmxHQf2nj9fXjXna2I8KckuVjtf9bnSYyaAhBfU5P6sIvJFHSonM7p80OWD8U8DUGT/oEJzTEFlmoLcn5ScWWhD4G1GHNvUvalKfUeNZ38Tzu0ty1y7myjYoqI8VkHmFypcu1mnlWD6vWfo2K2AVn4VtG6rYfq9Z+jcvYBtm//yrGVcquUaPP+s5bgnXXvmE9mxiBsnJ5KS6GzV2N3BRk+kTz6RPjV1V6RPPn4upZRU2RNf4FVrMhiV5Jc7kFxUd6ixe/oeZldSCGfzLHXX0Uw/hoUlEuFdwC1dT1g1xl33Uwfah+cxZcIJAvw0DOmfyJjhcWzYVLexo0eXTIICNKzfZIkTY+K9CA7Q0Lt7BmOGx1rqr8zmUX9dThI3NUyDPlwcERHxtze9hYWFl7y/hQsXMnbsWFq3bk1mZiaLFy9GpVJxyy23NCRZDRbRroAVz/xa/fu+6YcA2Ly9Ha+81Z+vfuyEvZ2BB+/Zi7OjjpNnfXl86fA6YwOGBhcxqF8y9z4ytnrern2t6dohm5XPbiIt05Vlr1tvHDY3Tx0Llp3B00dHeamapFgnnprVhSN7aypjdy8dU2alsGBqzduZsSdcWfdxMEvePkFxgS2vPnnxh01NzcunikeeOYSrq56SYltOHfdi/j0D0RTXvCni7lHF5NtjWXjvgOp5sWc8+P7LMJ55eR8lRXa8+kLdN1KbyrTw0wB8MWxDrfmP7B/Md0k1BfrNbc+SXeHMruzgC+5rgF8arV00LNhX84GrT+I60skzj+9GfM/xgla8cbJ5j6Hu7qll8p2JLJzRt3pe7Cl3vv+sNc+8Hk1JkS2vLu5kxRTWb8d6D9y8jNz+cDYePgYSTznw5NQ2FOfX3BC5e+uZMi+Hh8bVjNkbc9SR797x4blPkiguULN83oU/PNiYpnQ9BcCaST/Wmr/o1yH8eDqK9q3y6OqfC8Avd66ttc6oD6aS+aeeB1e3TiXYvYTHN9WMf/rF0U509M1j7ZTvOJnTqta3DJpaSygP2wfmsnpmTZnx0HV7AfjpcATPfmf5+x/RJR4F8OuxsPp2AUBb30KGdU5g2hsTq+dtOdmOHm0yeXfWj6TkufHUV3U/cNiUnJz0zLjtCN5eFZSW2vLH3hDWfN4No7Hm3QZbWwOzZx1g6fIBmM2We5P8Aifeeq8XC+buRa9Xsfz1fuh0DbrVEUKIf62lxEwAHX1y+Xj8+urfj/XfA8D3ZyJ5dsdAIrwKuSEyBlc7HbnlTvyRFsQb+/ug/8vQhGGeBVwblsCEr2rqnl/j29E7IJNPx/9AUrE7j/xmnbonNt6LZ5cOZMbtR5k6+QTZOc6sfr8X23a0qbWera2B+2cdZOkr/f9U7zjy9ru9mD9vH3q9khWvXXn1jrt7FVNuPcOCeTWxRGyMJ+u+iWTJC7spLrLj1Zf7WC19ph8rAdDPq/03o37MFdVoS48V9UQn0Jkx/E8DpWYU7dTYrPBEEVj7XJgS9Zi2VWHzQc0b7MrB9iiP6tDPLUQRrMbmqdoP5Jsbd48qpkw7y4I5g6vnxZ71ZN03ESxZtofiYjtefbF5xX5XeswEkPW15Vo6cWft3ikRz+nxvcEyBFJlsoLk120xlIB9oJngmQYCb6s7PFJ5nIK8zUp6fF0ztJX3CBMlB40cu8MWh1AzUS9aZ7godw8tC546gqeXlvJyNcnxrjz10FUcPehTa53J0+NYeE//6nmxZzz4/ot2PLN8v+VZy3PdrJD6Gh19c1kzsabuemSwpe768VQkizbX/TD4hYR5FTAqIoGJn9XUXZtj29ErKJOPJv1AcpE7j260XtwUm+DNkleGcOet0Uy7+RjZuS68/VEvtu5qW2s9W1sD9991gBdWDqypvwqdePPDPiyY/Qd6g4pX/nfNFVd/ictPYT7fJ/ZvKJVKXnvtNdzcLl5pTp8+/ZIPPmXKFHbu3ElBQQE+Pj7079+fF154gXbt2v39xoBGo8HNzY2B/RahVlv/K/eXk82pFGsnoVEoPJr3Tdc/dXaOdd+6bSxRryRbOwmXnSEr29pJaBSZD1vvg8CNKfj9M9ZOQqPIv6F5Nb5eLh6ny6ydhMvOYKhi++FllJSU4Opq3bdrzt/3tHtiKSr7xr3vMVZVkbD0iWaRbyGuJM0xZoKa8iPs4aWo7FpW3BTyawscQxlQVlhnbPzGpn67ZZ4vw6z6h/e4khljLjyk2ZVswPGqv1/pCrR3nPU/6twYUif+dVjplsH/j/q/G3AlMxiq2LH/hWYRP0jc9M80qJloypQptGp1+YbP+fLLLy/bvoQQQgghhBDC2iRmEkIIIYQQV5pLbiRo7LE1hRBCCCHEJTBT/Y3FRj2GEKLBJGYSQgghhGgmJG5qkEv+cPEljkokhBBCCCGEEP9JEjMJIYQQQogr0SX3JDCZrPN1dSGEEEIIUUNhtkyNfQwhRMNJzCSEEEII0TxI3NQwl9yTQAghhBBCCCGEEEIIIYQQLUuDPlwshBBCCCGagRb0xooQQgghhBBCNAqJmy6Z9CQQQgghhBBCCCGEEEIIIf6jpCeBEEIIIcSVxEzjvxEjb9wIIYQQQgghrmQSNzWI9CQQQgghhBBCCCGEEEIIIRrZm2++SWhoKPb29vTt25cDBw5ccN1Tp05x0003ERoaikKh4LXXXquzzjPPPINCoag1RUVFNThd0kgghBBCCHEFUZibZhJCCCGEEEKIK1VzjJu++uor5s+fz+LFi4mOjqZr166MGjWK3NzcetevqKigbdu2vPjii/j5+V1wvx07diQrK6t62r17d8MShjQSCCGEEEIIIYQQQgghhBCN6tVXX2XmzJnMmDGDDh06sHr1ahwdHfnwww/rXb9379688sorTJkyBTs7uwvuV61W4+fnVz15e3s3OG3SSCCEEEIIcSUxN9EkhBBCCCGEEFeqJoybNBpNrUmr1dZJjk6n4/DhwwwfPrx6nlKpZPjw4ezdu/dfZTUuLo6AgADatm3L1KlTSU1NbfA+pJFACCGEEEIIIYQQQgghhPgHgoODcXNzq56WLVtWZ538/HyMRiO+vr615vv6+pKdnf2Pj923b18++ugjNm3axNtvv01SUhIDBgygtLS0QftR/+MUCCGEEEIIIYQQQgghhBD/YWlpabi6ulb/vtjQQJfb6NGjq/+/S5cu9O3bl9atW/P1119z1113XfJ+pJFACCGEEOIK0hQfFpYPFwshhBBCCCGuZE0ZN7m6utZqJKiPt7c3KpWKnJycWvNzcnIu+lHihnJ3dyciIoL4+PgGbSfDDQkhhBBCCCGEEEIIIYQQjcTW1paePXuyZcuW6nkmk4ktW7bQr1+/y3acsrIyEhIS8Pf3b9B20pNACCGEEOJK0hQfFpaeBEIIIYQQQogrWTOMm+bPn8/06dPp1asXffr04bXXXqO8vJwZM2YAcPvttxMYGFj9TQOdTsfp06er/z8jI4OjR4/i7OxMWFgYAAsXLmTs2LG0bt2azMxMFi9ejEql4pZbbmlQ2qSRQAghhBBCCCGEEEIIIYRoRJMnTyYvL4+nn36a7OxsunXrxqZNm6o/ZpyamopSWTPwT2ZmJt27d6/+vXz5cpYvX86gQYPYvn07AOnp6dxyyy0UFBTg4+ND//792bdvHz4+Pg1KmzQSCCGEEEJcSZrhGzFCCCGEEEII0aw007hpzpw5zJkzp95l5x/8nxcaGorZfPGDfPnllw1PRD3kmwRCCCGEEEIIIYQQQgghxH+U9CQQQgghhLiCKMyWqbGPIYQQQgghhBBXKombGqZFNBKkXuuA0t7e2sm4rNoYQ6ydhEahPpNs7SQ0irD5ydZOQqPQDulh7SRcdskr/KydhEbR7tY91k5CozBaOwGNxGhn7RQ0kgMnrJ2Cy8+st3YKhBDistFHVmJ0bEHRLKBJcLZ2EhqF2/rj1k5Co9AOqrB2EhrHVV2snYLLLu7jlhcLAtAl2topaCSp1k5Ao/A65WvtJDQKxZ5j1k7CZaeQuOmK1yIaCYQQQggh/jOa6diaQgghhBBCCNFsSNzUIPJNAiGEEEIIIYQQQgghhBDiP0p6EgghhBBCXEnkjRghhBBCCCGEuDiJmxpEehIIIYQQQgghhBBCCCGEEP9R0pNACCGEEOIKojBbpsY+hhBCCCGEEEJcqSRuahjpSSCEEEIIIYQQQgghhBBC/EdJTwIhhBBCiCuJjK0phBBCCCGEEBcncVODSE8CIYQQQgghhBBCCCGEEOI/SnoSCCGEEEJcQWRsTSGEEEIIIYS4OImbGkZ6EgghhBBCCCGEEEIIIYQQ/1HSk0AIIYQQ4koiY2sKIYQQQgghxMVJ3NQg0pNACCGEEEIIIYQQQgghhPiPkkYCIYQQQgghhBBCCCGEEOI/SoYbEkIIIYS4kki3WSGEEEIIIYS4OImbGkR6EgghhBBCCCGEEEIIIYQQ/1HSSCCEEEIIcQVRNNH0T7z55puEhoZib29P3759OXDgwAXXfe+99xgwYAAeHh54eHgwfPjwi64vhBBCCCGEEJeqOcdNzZE0EgghhBBCiH/tq6++Yv78+SxevJjo6Gi6du3KqFGjyM3NrXf97du3c8stt7Bt2zb27t1LcHAwI0eOJCMjo4lTLoQQQgghhBD/bdJIIIQQQghxJTE30dRAr776KjNnzmTGjBl06NCB1atX4+joyIcffljv+p9//jmzZ8+mW7duREVF8f7772MymdiyZUvDDy6EEEIIIYQQf9ZM46bmShoJhBBCCCFEvTQaTa1Jq9XWu55Op+Pw4cMMHz68ep5SqWT48OHs3bv3ko5VUVGBXq/H09PzsqRdCCGEEEIIIcSlkUYCIYQQQogriMLcNBNAcHAwbm5u1dOyZcvqTVN+fj5GoxFfX99a8319fcnOzr6kfD366KMEBATUamgQQgghhBBCiH+iKeOmlkBt7QQIIYQQQojmKS0tDVdX1+rfdnZ2jXKcF198kS+//JLt27djb2/fKMcQQgghhBBCCFG//2QjwT2dohkZkkRbt2K0BhXReX68En0VSRr3Wut1885mfvcDdPXOxWRWcKbImxm/X4fWqMZWaeSFftsZHpxMXpUjz+wfwJ6soOpt7+54FH+nMp470L+Jc1ebg72e6bcc5Zq+qbi7VhGf5MnbH/YmNsEbgJvHnWLSjacA+Or7jny3oWP1tlHhecyduZ+5j43BZGo+nU7GTM7kuilZ+AZWAZAS78gXb7fm0C7L8AQzH0lg+PgcqiqUrFnZhu0/1bzV2H9UHsPG5bDk/k5WSfulGHtHPjffl4unj4HE0w68tSiQmKOOAMxanMHISUVUVSr54AV/tn3vUb3dgOuLGT6xiMXT21gr6QB0jspm0nUnCW+Tj7dHJU+/OpQ9h1tXL+/fK5nrh8cQEVqAq4uWe54YR0KKV6193Dv1ACMHxlGlVfP+l73Yuqdd9bKBfZIYMSCBp1Y03Zumrr/l4/p7Pjb5OgB0gfYUTfCjopvlwZn3+2k4nixFVaTHbK+kKsKJgikB6AMtD7qUZQZavZ2Kw+ky9H525N4TjC7UsXr/3mvS0beypeS6Vk2Wp4u50q/BC7nS89U9JJPbrz5G+4A8fFwqWPDlKLbH1KTJwUbP3OH7GByVjJtDFZnFrny5vxPfHa4p1x8auYex3WKo1Kn535a+/HIionrZ8A4JXNclloe+HN2k+bqQK/18NaqmGPvy3P5dXV1rNRJciLe3NyqVipycnFrzc3Jy8PPzu+i2y5cv58UXX+T333+nS5cu/zjJQojLy/3HbJwOlWCbWYXZVklV+Ln7m4CahjyXrfm47CnCLqkCZZWJpHc7Y3L6U4ipN9HqvVScDpdgcLchf0YQlZ1qyhT3n3JQF+jInx7clFmjW9tMbh1yjMigfHzcKnjsw5HsPPnnesPM3dceYtxVZ3Fx0HI8yY9Xvh1Aer4bADYqI49P3sGATskUaBxZ/l1/DsXVxIK3DjmKr3sZK7+3bizYqbeGm2dmEtaxDC9fPc/eG8ne32uGdLvprkxunmX5WPw37way7oOA6mWRXUu5f0kSD97UGZNR0eRp/zst8T7By7OCu26LpnePDOxsjWRmu7Dif1cTl2CJlW6+4RQTz8XuX3/fie/Wd6jeNjI8j7mzDvDAo6OtGrt7bMjG+XAxtllVmGws5Ub+pED0/jXlhuu2fFz2FWKXXIGqykTCW11qlRsKvYlWH6biFF2M0c2G3OnBVHb8U7mxMQebAh15tzVtuVGflngdQsvLl1Jh4o5x0Yy4Kh5Pt0ryix3ZtCeCT3/qBljKt8kjjzPl2uMAfLGpC19vrrknbd8mlwen/cHsF27A2IyejZ3X0s7XZdWEcVNLYPWrOyMjg2nTpuHl5YWDgwOdO3fm0KFDjXrMPr5ZfB7TkYkbx3PH79djozSxZvhPOKj11et0887mw+Eb2Z0VzE0bJzBh4018erYjZvO5AiTiNJ288pn4y3i+im3Pq/1/5/yVEeSsYVL4GVYe6dOo+bgUD83eQ4+umby8qj/3zB9L9DF/Xlr8G16eFbRpXcTtU46y9NUBLF05gDtuOUpoSBEASqWJB2bt4/V3rmpWDQQA+Tl2rFnZhgcm9mDexO4c2+/OU/87RUhYOX0GFzDo+lwW3d2ZD1e0Zd6zcbi6W86ro7OB6fOSeev5MCvn4MIGjSti1uJMPn/Vj/tHRZB42p4X1ibi5qWn74gShowv5vFb2vL+8/48tDwNV08DAI4uRu54NIv/PRFo5RyAvZ2BxFQP3vioX/3L7Q2cjPHlvS971bv8qu6pDL06kcdeHMV7X/Rmwcw/cHW2NAg5Oei4c1I0b3x0VaOlvz4GTxsKpwSQ/nwk6c9HUNnRBb8VSdikVwKgbeNA7j0hpC2PIvOxdmAG/xcTwGQpEzx+yEFZZSR9aQSVHZzxeS+tet92ceXYxZdTMtqnSfN0IS3hGqxPS8iXg62B2BwvXto4oN7l80ft4eqwNJ5aN5Sb35zM2n2deWTMbgZGJAMwICKZazvHcf+n17Hq96tYNHYH7g6Wa9jZTsvsoQd4aaN1H2ac1xLO13+Nra0tPXv2rPXR4fMfIe7Xr/76AODll1/mueeeY9OmTfTqVX+9IISwTszkcLYMzXBv0pdEWO5vjGb8X4xHUWWsXkepNVHRxYWiG3zr3Yfr1gLskipJXxKBZqgXvm+mgNlyf6TO1eKyrYCCiQH1btuY7G0NxGd6sWJd/fXetKHHmDjgJK98M4C7XxtPlU7Nynt+xlZtqW9u6HeGyKA8Zr1+Iz/ua8+SaVs4Hwv6e2oY1/cs72y0fixo72Ak8Ywjbz1T9wFQaGQ50x5M48V5Ebz0YAS3P5RKaEQ5AEqVmbnPJfK/p9o2ywaClnif4Oyk5dWlmzAalSx6bhgz543l3Y96UlZmC0Cb1kXcNuUYS1cMYNmrA5j+19j9nv2seqev1WN3h5gyiof5kPZUJBmPhKEwmgl8JR6F9k/lhs5ERWdXisbW/xKB6/Z87JIrSH8qkpLB3vi9nVxTbuRpcdueT8HNTV9u/FVLvA6hZebrltHHuWHwGV5fezXTn7qZd7/rwy3XHmfCMEujW9ugAmbccJhn3x3Kc+8O4a4bD9MmsBAAldLE/Nv+4NVP+zfLBoKWeL6E9Vj1Ci8qKuKaa67BxsaGX375hdOnT7NixQo8PDz+fuN/4a4t17EuIYr4Ek/OFnnz6B9DCHQuo5NnXvU6T/bewydnO/Huye7El3iSpHHnl5QwdCYVAO3citiS3pr4Ek8+i+mEl0MVnnaWB5lL+u7ilcNXUaa3bdR8/B1bWwMDrkrl/U96cuK0L5nZrnz6dTcys10YOyqG4MASklI8OHrSn6Mn/ElK8SA4sASASTec4sQZ3+oeB83Jge1eHNrpSWaKAxkpjnzyehuqKlREddEQ0raCEwfciTvlwo6NragoU+EbZDkvdy5M5Ocv/cnLar7DGEyYlc+mtZ5s/sqT1Dh7Vj0ahLZSwahbCgkJ13J8rzNxxx3Z/oMHFWUq/IItb7bfvSiTnz7xJi/DutccwMFjQaz5pid/HGpd7/Lfd4fx2ffdiD7pX+/ykMASjp3xIzbJm21721JRaYN/q1IAZt5ykA2/R5Fb4Nxo6a9PRU83Krq7ove3Q+9vT+Fkf0z2SuzjKgAoHeZNVXtnDD526No4UjjJH5sCPeo8y/mxyaiirJ8Hen97NEO9sM089+FPgxmfD9PIvysYlM0jAGsJ12B9WkK+9sSH8Pa2Pmw7W/+bHl2Cs/npWCSHUwLJKnHl++gOxGV70TEwF4A23kUcTg7gTFYrfj0ZTrnWlgAPy9/WAyP28e2hDmRrXJosPxfTEs5XozM38vQPzJ8/n/fee4+PP/6YM2fOcN9991FeXs6MGTMAuP3223n88cer13/ppZd46qmn+PDDDwkNDSU7O5vs7GzKysr+WQKEaKGsFTNlPRpG6SAv9EEO6Fo7kntPCDYFeuySKqvXKRndiuJxflSFOdW7D9vMKsp7uqIPckAzwgeVxoCy1PKAwmdNGoVTAjA7qho1H/XZdzaEd3/pw84T9dWpZiYNPMFHv/Vg16lQErK8eHbtELxdKxjYKRmAUN8idp8KJSnHk+/+6IiHSxXuTpaY4+Gbd/HWz32p0Fq/Ljq004NPVoaw5zevOsuC21WSHOPIsX1uHN3rRtJZJ4LaWfJw88xMThxwJfZE095zX6qWeJ8wafwp8vOdWPG/q4mJ9yYn14XoYwFk5Vjuzc7H7seqY3d3ggM1AEy88RQnTvsSG2/92D1zYRilA7zQBTmgC3Ek5+7W2BTosEuqqF6neFQriq73o6rdRcqN7m7oghwoGe6DutSA6ly50erjNPInBWJyaPpy469a4nUILTNfndrlsPtoa/adCCG7wIUdh9tw8FQg7dtYngGG+JWQmO7JkbMBRJ8NJCHdkxC/YgAmjzrOsVg/YpKbx0t9f9USz9dl1wzjpubKqo0EL730EsHBwaxZs4Y+ffrQpk0bRo4cSbt27f5+48vI2dbyR1Ksszw89rSvpJtPLgVVDnx17ffsnfgxn4/8kZ6tsqq3OVvoTc9W2dipDAwISCOnwpFCrT3j2sSiNar4Lc363XVUSjMqlRmdvnYFqtWp6BiVS1KKO4H+Gny8y2jlU0ZggIbkVHf8fUsZOTSej9Z2t1LKL51SaWbg6FzsHYycOeZKYowT4Z1KcXbVE9ahFDt7E1mp9nToUUJY+zLWf9Z8W0nVNibCu1QQvavmIZ3ZrODILhc69Kwg8ZQ9EV0qcHYzENa5Alt7E5nJtnTsU0ZY50p+/MD6N4WXQ2KKJxFt8nF21BIemo+trZGMbFc6ReQQHlrI97+2t24CTWac9xSh1JqoCq97Y6uoMuKyoxC9jy0GLxsAdK0dcDhVBkYzjsc16IItZY37TzlUtndG29axzn6soaVegy01X391PM2PgRHJ+LiUAWZ6hWYQ4lXCvgTL8AdxOd50CMjDxV5LlH8edjYG0grd6BacRZRfPl/u72zdDJzzXzlfLdHkyZNZvnw5Tz/9NN26dePo0aNs2rSp+mPGqampZGXV3Eu9/fbb6HQ6br75Zvz9/aun5cuXWysLQjRLzSVmUlaYADA5X/rDOV2IA/Yx5Sh0JhyOazC4qzG5qHH+oxCzjZLy3u6NlNp/LsCzFG/XCg7F1sQN5VV2nE5tRadQy5BqcZledGmTja2Ngasi08grcaS43J6RPeLQ6dUXaHxoXpJjHAkMrcTHX0urAC2BbSpJiXXAP6SKETfl8snKEGsnsV4t9T7hqt7pxCZ48uTCHXy15mveXP4To4fHVS9PSvEgyF+Dj3f5udi99E+xewIfr+1mvcRfhLLS0oPA5HzpI11rQxxwiC1DoTPheEKDwd0Go4salz2FmG0UlPdyb6TUXrqWeh221HydTPClZ/tMgnwtL8W2Cyqgc3g2+09YhqxKzPAgyE9DK88yfD1LCfYtISnTgwAfDaOvieWD73taM/kX1FLPl7Aeq36TYP369YwaNYqJEyeyY8cOAgMDmT17NjNnzqx3fa1Wi1arrf6t0Wj+dRoUmFnU+w8O5foRV2wZnzHE2bLfuV0P8dKhfpwp8ubGtjF8MmIDY9ZPIqXUnW/jI4n0KOCXcV9RpLVn3s4RuNlqmdftEFN/HcdD3Q5wXWg8qWWuPP7HYHIqm/4tjMoqG06d9WHqzcdJTXejuMSeIf2TaR+RT2a2C2kZ7qxZ250Xn/4dgA8/705ahjsvLt7M+5/2pFe3TG6bfAyDQcHba/pw4nT93YitITS8nBVfHMHW1kRlhYrnHuhIWoITaQlObNvQite+PoKuSsmKxyOpqlQx5+k4Xn0ikjFTMhk3NRNNkQ2rngknNb7+txeswdXTiEoNxXm1/yyL8tUEh2k5vMOVLesqeGNjHNoqJcvnhVBVoWTusgyWPxjM9dMLGHdnPppCFa8/HExKbPPtMXExh04EsuWPdrz53AZ0ehUvrx5AlVbNvDv38PLqAYwdfpYbR56hpMyele9fTUpG475Fd55taiWBi+NQ6E2Y7JVkP9QGfdCfxtb8LR+vtZkotSZ0/nZkPtEO1JZ22OJxvnh/mEbIQ6cxeNuSOysEmywtLjuLyFgSjvcHaTgeL0Xb1pG8mcGYrPBGHbTca7Cl5uuvXv6lP4uu38Gm+Z9hMCoxmeH5DYM4kmrpjr03IZiNx8P5dOZ3aPVqnvlhKJU6NY9ft4vFPw7h5l6nmdznBMUV9rzw0yAS8zz/5oiN479yvv4NhdkyNfYx/ok5c+YwZ86cepdt37691u/k5OR/dhAh/mMaGjNBI8RNJjPen6ZTGeGELtjhkjfTDPLCNrWS4EfOYHRRk/NAG5TlRjy/zSJjUTieX2fivK8IfSs7cmeFYPS0/huNnq6WN54LS2vns7DUAU8Xy7Kf9kcS5l/A2ke+pqTcnqc+GY6Lo5aZ1x7i/rfGMmv0AYZ3TyAj35UXvhpMfknziTnOS0tw5KMVISz9+DQAHy0PIS3BkaUfn+bDl1rTc0AxUx9Iw2hQsPq5Npw8+Pffp2kKLfU+wd+3lOtHlbJuQwe+/K4zEWH53HfXQfQGJb9vb0dahhtrPu/OssWW2H3NZ91Jy3DjxcW/8f4nPejZ/XzsruTtD3tzsjnE7iYzPp+nUxnuhC6oAeXGAG/s0ipp/fhpjC5qsu4/V26syyTj8Qi8vs3EeX8R+la25NzV2irlRku9Dltqvtb+0hUnBx2fPPcNJpMCpdLM+9/34vf9lqGoU7M8eH9dL5bP/wWA99b1JjXLgxXzN/LOt33o0ymDO8ZFYzAqeeOLqzgeV/+oCE2tpZ6vy6k5x03NkVUbCRITE3n77beZP38+TzzxBAcPHuSBBx7A1taW6dOn11l/2bJlLFmy5LKm4Zm+uwh3L+SWTTdWz1OcO8Nfxnbgu4QoAE4XetPPP4Obw2JYcaQvBrOKJQdqjwv94tXb+ORMJzp45jM8OImxP01kZsejPNXnD+bsGHVZ032pXl7VnwX37+HL97/FaFQQl+jJ9t2hhLezjK/28+ZIft4cWb3+iMEJVFbacDrGhw/f+IE5j16Hj1cFTzy0k9vvm4DeYP1ufQDpyQ7MmdATJ2cD/Ufls2BpDI9M70JaghOfvxnK52+GVq976+wUjuz1wGBQMOXeVGbf0JO+gwtZsCyGeRN7WC8T/8BnK/z4bEXN2I1T52dzZJczRoOCW+blcO/QSPqO0PDwqlTmXBtxkT01b5+s684n62p6stw24QjRJwMwGJVMvfE4Mx+7kau6p/HofbuYvWhck6RJF2BH2rJIlBVGnA8U02p1ChlPhVc3FJRd40FlJxdUxXrcf87F7/VkMp4Jx2yrxOSoIndOaK39BTwfT8GtAbj8UYRNro7UFe3xeT8Vj3XZFExrvj1eWuo12BLyNaXPCToF5fDgF9eSVexCj9ZZPDpmN3mlThxIsvQmeHdHb97d0bt6m5mDDrE/KRCDUcldAw8z+e1JDIhI4dkbtzLtvZutlZW/1RLOlxBCXKqGxkxw+eMm74/SsU2vIuPp8IZtqFaQP6P2x0V93kmhZJQPdsmVOB0uIW1pFO4/5eL9STo5D7a9bGluTEaTihXraseCT07Zxje7OhERmM/ATsncvvxmpg45ykPj/+DJj0ZaKaUXt/ELPzZ+UVOfDh+fS2W5kjNHnHnvt6PMG98Zb38dj70Wy4whPdDrmt943PW5Eu8TFAqIS/BizeeWGCghyZPQkGKuGxXL79stvYZ+3hzBz5tr0jt8cAIVVTacifHhg//9yNxHxlhi9/m7mH7veKvH7j6fpGGbUUX6kw38N1YryLs9hLw/zfJ9L5mSka2wS6nAKbqY1Oej8Pg5h1afp5M1t3mWG1fidXgprsR8DemVyPC+CTz/3hCSMj0ICy5gzpR9FJQ48useSxrX72jP+h01oxaMujqWiiobTiW24tPnv+We52/Ax6Ocp+/Zxi2PTbb639eluhLPl7Aeq9byJpOJHj16sHTpUrp3786sWbOYOXMmq1evrnf9xx9/nJKSkuopLS2t3vUu1dN9djEkKIXbNo8ju6LmTf+8SsvQH/HFtd9QTijxIMCptN599fXNIMy9kE9jOtHXL5MdGSFUGmzYmNyOPn6Z/yqd/0ZWjgsLnx7FuFtvYeqsm3jgsetQqU1k5dTt2eDqUsW0Scd48/0+RIXnk57pSmaWK8dO+qFSmQgM+Pc9Ny4Xg15JVqoD8add+GhlGxJjnLjhtow66wW1qWDI2Bw+fSOULn1KOHXIDU2RLTs3+RDesQwHR4MVUl8/TaEKowHcfWqnycPbQFFe3fa84LAqhk0o4uOX/ehydRkn9zlRUqhmx3o3wrtU4uBkrLPNlSjYv5hh1ySw5psedO2QzYmzvpSU2rNjfygRbQpwsNf//U4uB7USg58duraOFE4JQBvigNummltXk6MKvb8dVe2dyX4wFJssLU6HSurdlcv2AoxOKip6uWF/pozyXm6gVlDe1x2H09Ybi7ulXoMtNV9/Zqc2cP+wA6zcfDW7YkOJz/Xi64Od+O1UO267+li924R6FTGmcyxvb+1Dr9BMjqT4U1zhwG+n2tE+IB/Hc0PxNbX/wvn61xp7XM0WOL6mEFeyhsZMcHnjJu+P0nA6UkLmk2EYvf7dG7v2p0qxTa+iZKQPDmdKKe/qitleRdlV7jicaR7fIynUWGJBT5fKWvM9XSopLK1/iMgeYRm08Svi290d6dEukz1nQqjS2bD1aDt6tLNeLNgQrh56bp2bztvPtiGyWxkZSfZkpjhwfJ8bahszgaGVf7+TJtBS7xMKix1ISXerNS8t3Y1W3uX1rm+J3Y/z1vu9iYrIJ6OZxe4+n6ThdKyE9MfCMfzLN/0dzpRim1FF8XAfHM6WWcoNOxVlfTxwOFP/85nG1lKvw5aar3snHmDtL13ZerAdSRme/LYvnG9/68TU0fXHSW7OVUwfe4RVX1xN+zZ5pOW4kZHrxtGYANQqU/WwRdbWUs/XZSVxU4NYtZHA39+fDh061JrXvn17UlNT613fzs4OV1fXWtM/Y+bpPrsYEZLEbZvHkl5Wez/pZS5kVzjS1q241vw2rsVklNf9qKOt0sAzfXfz9L5BmMxKVAoTaqVlzE4bpQlVM+h7UqW1obDYEWcnLb26ZbL3YHCdde6dcYh1G9qTX+iESmlGrTJVL1OpzCiV1s/HhSgVZmxs/po+M3OfieO9l9pRVaFCqTSjUlvWUZ/7r7IZNf4a9ErijjvSvX/NjY5CYaZb/zJOH/5rQGLmgZfSeWdJwLm8gepc/tWWYfCbVd7+OTMP3rWH1Z/1oUprg0phRnXuujz/X6XSdLEdNBqFGRSGCxzbDJjNKPR1lys1Bjy+zyF/uqW3gMJkRmE4d+0azGC23t9ZS70GW2q+/kytNGGjMmEy1/4AttGsQFlvHWTmiet38urmq6nU26BU1tRb5/9b/3aN779wvoQQoiEaGjPBZYqbzGZLA8EhSwOBoZVdw/fxJwqdCZ+P0sm7KxiUCjCBwmgpsxUGM1jnlq6OzEIX8jWO9AqveQHJ0U5Hh5BcTibXHcLFVm1gwYTdvPzNQExmJco/xVFqlalZx1B/NuvJZH5Y409+tp0lFvxTbKVUmZtNfdpS7xNOn/Eh+C8P9gMDNOTm1T9s8T0zDrHup/bkFzhZ4tzmErubzfh8kobz4WIyHg3H4HMZyo1P0sidEXKu3DCjOP881Gi22sO5lnodttR82dkaMP3lWjGaFNWjiPzV/ZP38c1vncgrcqpVpgOolCZUzaRcb6nnS1iPVYcbuuaaa4iJiak1LzY2ltatWzfqcZ/pu4uxbeK5b9u1lOtt8ba3jC1ZqrdFa1QDCj441Y0Huh7ibKEXp4u8mdAuhrauxczdXrer6P1dDrM9I4TThZaPfhzO9ePRnvv4Lj6KaVEnic71q7NNU+nZLQMFkJ7pSoBfKTNvP0xahhu/bg2rtV6PLpkE+Wt45Y1rAIiJ9yI4UEPv7hn4eJdjMilIz2we41De8VASh3Z6kJtlj6OTkcHX59K5TwlPzaz9ca1RN2dTUmjDge1eAJw+4srU+1OI7KKh18BCUuIdKS+16p9AHeve9Wbha2nEHnMk5ogj42fmYe9oYvOXtccGH31rISUFavb/Znnb5PRBJ25bkE1Uj3J6Dy0lJcaOco11Snh7Oz2BfjU3uP4+ZbRrXUBpmR25Bc64OGlp5V2Gl7vl7y7Y39IKX1jsQFFJ7YpszJBYSkrt2XfEcm5Pxrbi9puO0D4sl95dM0hOd6e84t/deF4Kzy8zqejqisHbBmWlCec9RdifKaPosXaoc7Q47yumorMLJlc1qkI9HutzMNsqqehW92/G+5MMisf4VI+dWRXhhPPuQiq6uOC6tYCqiKb/fsmftYRrsD4tIV8ONnqCPWveWgnw0BDhm4+m0o5sjQuHkv2ZN2IvWr2KrBIXerbO5LousazcfHWdfY3vcYaiCgd2xYYCcCzVj3sGHaZTYA7XhKeSkOtBmbbx/7YupCWcr8YkY2sK8d9irZjJ+6N0nPcUkT2/DSZ7FapiS+9Nk6MKs63lXTNVsR5VsR6bHMv3D2zTqjDZKzF429b5UKnHD9lUdHNFF2q536uKcMJrbQalg7xw+y2fqoimG7ffwVZPkHdNnervWUp4QD6aCjtyil34emdnpo+IJi3fjcxCF2Zde4h8jSM7T4bW2deMEdHsPRNCbIYlFjye5Mecsfv4+UAkN/U/xYkk68WC9o5GAlpXVf/2Da6ibftySovV5GXV1PPdrykmMLSKFQ9bYsTYE84Eta2k18AifPx1mIwK0hObz1jVLfE+Yd1P7Vm5dBNTbjrBzj9aExlewJgRcby2+qo66/bomklQgIbl52L32HOxe69mELv7fJKGy74isua1vXi5UfKnciP9XLnhVbfc8FyfRUUXV7Stz5Ub4c54f5WBZoAn7r/nURVuve99tMTrEFpmvvYeC+G2MUfJLXAmOdODsJACJo08ycbddYfX6dkhnSDfEpZ9OAiAmGQfQvyK6dMpjVaelr+v1Gy3OttZS0s8X5eTxE0NY9UnpA899BBXX301S5cuZdKkSRw4cIB3332Xd999t1GPOzXS8nGmz0etrzX/0T8Gs+7cNwg+OtMFW5WRJ3rvwc1Wy9kiL+74/XpSy2oXBuHuhYwJTWDcTxOr521KaUdfv0y+uPZHEjVuzN81vFHzczFOjnrunBqNt1cFpWV27N4Xwpq13TEaazqR2NoauP/uA7zw6kDM595CzS904s0P+rDg/j/QG1S88sY16HTN44G6m6eOBS/G4Omjo7xUTVKsE0/N7MyRvTXDQ7l76ZhyTyoLbu1WPS/2hCvrPgpiyeqTFBfY8OoTkfXs3bp2rPfAzcvI7Q9n4+FjIPGUA09ObUNxvk31Ou7eeqbMy+GhcTVjwsYcdeS7d3x47pMkigvULJ8XUt/um0Rk23xWLNpU/fu+2w4A8OvOMF55ZwD9eqbyyD27q5cvmrsDgE++61brOwTurpXcesNx5j1zXfW8mEQfvtnYiRcW/k6Rxp6XV9ceC7axqDQGWr2dgrrYgMlRhTbYnqzH2lHZ2QVVkR77s2W4/ZKHqtyI0U1NZZQzGc+EY3SzqbUfh2MabHK05M6uOT8lI32wS6wk6OlYqto6UjTBuh8ZawnXYH1aQr46BOTy7h0bqn8vGLUXgA1HI3jmx6E88e0I5gzbz/MTtuDqoCW7xIW3tvbh20O13z71dKrgzgHRzPhgfPW8U5m+fLa3C6/fupGicgcW/zCkaTJ1AS3hfAkhxOVirZjJ7fd8AAKfj681P3dWCKWDLC/huG7Jx3NddvWywOfi6qwDYJtWidO+YtKX1tx/l/exDDEU8Gwsen97cu4Pbays1BEVnMeb99fUqfNutNSpPx+I4IUvh/DZ1q7Y2+p5dOJOnB10HE/yY/67Y9AZasdDbf0KGdotgekrar7js+14W3qEZfL2nPWk5rmx+LNhTZOpeoR3LuPlz09X/77nyRQAfvvOh1cftTQI2NoZmb04iWXzImpiwWw73n62DQ+9lIBep2DFI2HotM3n4VFLvE+Ijffm2ZcGM2PaEaZOPE52rjOrP+zNtp21x9u3tTUw++6DLF0xoOZ8FTjx1ge9WTBnD3qDiuVvXG212N19q6XcCFoWV2t+9t2tKR1gKRPctuXh9UNNuRG8NLbOOgC26ZU4Hygm9bmo6nllvd1xOFtK0NJY9H72ZN8X2lhZ+Vst8TqElpmv19f2464bD/PgtD14uFSSX+zIhh1RfLyhe631bG0MzLt1L8++M7T67yuvyIlVX/TjsRk70RlULPtwEDp983g2Bi3zfAnrUZjNVhzbAvjpp594/PHHiYuLo02bNsyfP5+ZM2de0rYajQY3NzdCl7yA0r75vNlwObT5scLaSWgU6jPJ1k5CozAWN48x6S4345Ar68POlyJ5ZjPpy36Ztbv1qLWTIBog/55+1k5Co/B+Z6+1k3DZGcx6tvMjJSUl/2KYw8vj/H1P57uXorJt3Pseo66KE+8/0SzyLYT4dzET1JQfrd9/CqVjy4qbWm2wXs+3xuS2/ri1k9AoTBUtM87lqi7WTsFlF3df83kQejmFT4+2dhJEA2jH9LZ2EhqF3caD1k7CZSdxk/Xz/W9ZvdS//vrruf76662dDCGEEEKIK0NTfCCrBXWbFaIlkJhJCCGEEKKBJG5qEKt+uFgIIYQQQgghhBBCCCGEENZj9Z4EQgghhBDi0skHuIQQQgghhBDi4iRuahjpSSCEEEIIIYQQQgghhBBC/EdJTwIhhBBCiCuJjK0phBBCCCGEEBcncVODSE8CIYQQQgghhBBCCCGEEOI/SnoSCCGEEEJcSeSNGCGEEEIIIYS4OImbGkR6EgghhBBCCCGEEEIIIYQQ/1HSk0AIIYQQ4gqiMFumxj6GEEIIIYQQQlypJG5qGOlJIIQQQgghhBBCCCGEEEL8R0lPAiGEEEKIK4mMrSmEEEIIIYQQFydxU4NITwIhhBBCCCGEEEIIIYQQ4j9KehIIIYQQQlxBFGYzCnPjvrLS2PsXQgghhBBCiMYkcVPDSE8CIYQQQgghhBBCCCGEEOI/SnoSCCGEEEJcSWRsTSGEEEIIIYS4OImbGkR6EgghhBBCCCGEEEIIIYQQ/1HSk0AIIYQQ4gqiMFumxj6GEEIIIYQQQlypJG5qGOlJIIQQQgghhBBCCCGEEEL8R0lPAiGEEEKIK4mMrSmEEEIIIYQQFydxU4NITwIhhBBCCCGEEEIIIYQQ4j9KGgmEEEIIIYQQQgghhBBCiP+oFjHcULvX41Arba2djMvK1DbQ2kloFGeWRVo7CY2iw5IUayehUWj8W9bfFUC7qfutnQQh8NuYZu0kNIqUdR2tnYTLzlihhak/WjsZtcgHuIQQ/1S716pQq1rWH/jZ2TbWTkKj8JvtZO0kNIqqu/ysnYRGEXtTyztfEXcdtHYSGkXLKgFbPseEImsnoVE47vK2dhIuO325DkZZOxW1SdzUMNKTQAghhBBCCCGEEEIIIYT4j2oRPQmEEEIIIf4z5ANcQgghhBBCCHFxEjc1iPQkEEIIIYQQQgghhBBCCCH+o6QngRBCCCHEFUTG1hRCCCGEEEKIi5O4qWGkJ4EQQgghhBBCCCGEEEII8R8lPQmEEEIIIa4kMramEEIIIYQQQlycxE0NIj0JhBBCCCGEEEIIIYQQQoj/KGkkEEIIIYS4wpwfX7OxJiGEEEIIIYS40jXHuOnNN98kNDQUe3t7+vbty4EDBy647qlTp7jpppsIDQ1FoVDw2muv/et9Xog0EgghhBBCCCGEEEIIIYQQjeirr75i/vz5LF68mOjoaLp27cqoUaPIzc2td/2Kigratm3Liy++iJ+f32XZ54VII4EQQgghxJXEbG6aSQghhBBCCCGuVM0wbnr11VeZOXMmM2bMoEOHDqxevRpHR0c+/PDDetfv3bs3r7zyClOmTMHOzu6y7PNCpJFACCGEEEIIIYQQQgghhPgHNBpNrUmr1dZZR6fTcfjwYYYPH149T6lUMnz4cPbu3fuPjns59ymNBEIIIYQQV5DGHldTvksghBBCCCGEuNI1ZdwUHByMm5tb9bRs2bI66cnPz8doNOLr61trvq+vL9nZ2f8oj5dzn+p/lAIhhBBCCCGEEEIIIYQQ4j8uLS0NV1fX6t8XGhqoOZNGAiGEEEKIK4n53NTYxxBCCCGEEEKIK1UTxk2urq61Ggnq4+3tjUqlIicnp9b8nJycC36U+O9czn3KcENCCCGEEEIIIYQQQgghRCOxtbWlZ8+ebNmypXqeyWRiy5Yt9OvXz+r7lJ4EQgghhBBXEIXJMjX2MYQQQgghhBDiStUc46b58+czffp0evXqRZ8+fXjttdcoLy9nxowZANx+++0EBgZWf9NAp9Nx+vTp6v/PyMjg6NGjODs7ExYWdkn7vFTSSCCEEEIIIYQQQgghhBBCNKLJkyeTl5fH008/TXZ2Nt26dWPTpk3VHx5OTU1FqawZ+CczM5Pu3btX/16+fDnLly9n0KBBbN++/ZL2eamkkUAIIYQQQgghhBBCCCGEaGRz5sxhzpw59S47/+D/vNDQUMzmv/+wwsX2eamkkUAIIYQQ4koiHy4WQgghhBBCiIuTuKlB5MPFQgghhBBCCCGEEEIIIcR/lPQkEEIIIYS4gijMlqmxjyGEEEIIIYQQVyqJmxpGGgmAMZMyuG5yBr4BVQCkJDjxxepQDu32AmDmw3EMvyGbqkoVa15ry/af/aq37T8yl2Fjs1kyt4tV0v53HBz03H7rMa6+Kg13tyoSkjxY/V4vYuO9AbjpxtNMHH8KgK/XdWTdjx2qt42MyGfOPQeY9/C1mEzW63TisSkTl6NF2GZXYrJRUtXOmbwbg9H7OVSvY5NXhc93adgnlKIwmKjo4E7u5NYYXW0AUOhN+H6WhNPxIoyuNuROCaWivVvNMTZnoS7Skjc5tKmzd0ET70jijgfi+GFtCO8tjwLg7vkxDB+bQVWlio/eiGD7L/7V6/cfns3Q6zN59sEe1koyAN3aZjJ18DEiA/Pxcavg0TUj2XmqTa11Wrcq4v7r9tO9bRYqlYmkHA+e+HgEOcUuADwwdg/X9Y6lUqfmrZ/7svlIePW2Q7skMLpXLA9/OLpJ83UhY6fncfN9uXj6GEg87cBbTwUSc9QJgFmLMxg5sZCqSiUfLPVn2/ee1dsNuL6Y4TcXsviOttZK+gWNvSO/dp4WBRJz1BE4l6dJRZY8veDPtu89qrcbcH0xwycWsXh6mwvt2qpaWr5uvTuWqTPjas1LS3bi3smDAbh73mmGX5dOVZWKj96MYvuvgdXr9R+axdAx6Ty7sHdTJrleTpsKcfq1EFWuHgBDsB2aST5oe1jKA++nkrA7VVFrm/KRHhTfGwCAotSAxxsZ2J2swOBvS/H9Aejb1tQPbu9mYvS1pewG7ybKkRBCtFxTp51k2rRTtealpbkwa+YYAGbOOsKIEclUValY82EXtm0LrV6v/4A0hg9L5plnBjRlkuvlEKfBY3M29qnlqEv0ZNwbTnm3mrpfpdHjvS4NpzMlKCuMVIa7kDu5NXpf++p1fL5JwXVvPiY7Ffk3BlHat6aecT5ciOu+fDLvj2iyPOk+q8C4U4spxQh2oOpkg+29TihDaof8xpN6dO+VYzqjB6UCZZga+xVuKOwUmHVmtC+XYtytQ+GpxG6+M6petjXH+KICc44Ruwddmixff2firTHMuOc0P3zTjnf/Z4nJZ95/nOHXplJVpWbNOx3Z/ntw9fr9B2cwbFQqSx7vZ60kA9C7VSYz2x+jo2cevo4V3LtjFL+n19xrOqr1PNxtHyOCk3G3rSK93JWPYzrxRVzH6nWe6LGHCW1jqDSoeeVoX9Yn11xvo0MSGN8mllk7rB8zdepTys335hDeuQIvXz1L7m7H3s3u1ctvmpXNxHtzAPj6bT/WvVfzoc3IbuXMeSGVeeOiMBkVTZ30v9XS4ovzWmq+zpt4SwwzZp3kh2/DePfNrgDMnH2M4aNSLOXGe53Y/ntI9fr9B6UzbGQKS568xlpJRvtpBYadOkwpRhTnyni7+xzrLeO171VgPG0p41XhKhz+VMZXvVSGYbcOhacC+wXOqP9cxq+twJRjwv4h56bOnmgmrNpIEBoaSkpKSp35s2fP5s0332yydOTn2LHmtXZkpjigUMCwcdk8teoEcyf2xi+okkFjclk0qysBrSt58NmzRP/hiabYFkdnA9PnJvLEzG5NltaGenDOPkJDinll5dUUFDoybHAiy57dwqw5Y3F10XLbrcdY/NwQFAozSxZtJ/qoP8kpHiiVJubet59Vb/a1agMBgGNcKcWDWlHV2glM4P1jGkFvxJD8dGfMdioUWiOBq2LQBjmS/qDlYbr3hnQC34ol9ZEOoFTgtjsXu9Ry0h7ugNOpEvw+TCDx5e6gUKDO1+L2Ry6pj3Wyaj7/LLxDCdfelEZibE3h3GdgLoOvzWLR7J4EhlQwb/Epovd6nbsW9dx+fzxP3tfTiqm2sLc1EJfpxU8Honjxjs11lgd6lfDO/T+y4UAU7//ai3KtDW18i9AZLMVR/w7JjOwez7x3ryPYu4QnJ29nf0wQJRUOONlruWf0QR5457qmzla9Bo0rYtbiTN54LIizR5wYf3ceL3yeyF0Do4jqUcGQG4t4/NZ2BLbRMn9FKoe3u6IpUuPoYuSOR7N4bHI7a2ehjlp5inZk/Mw8XlibyF0DIi15Gl/M47e0JbCtlvkr0ji8wwVN4Z/yNKX55Qlabr6SE5xZNKdv9W+j0VJe9+mfw+BRmSx6oA+BIeXMe/I40ft80JTY4uik5/b7YnjyT9tZk9HLhpJpvhj8bVEAjtuK8XoxjdzlbTGEWB7GlI/wQDPFp3obs11NveTybT7KShN5y9vitKkQ97czyXvFcr5sYiqwjask7y5/WhSz2TI19jGEEM1Cc4mZzktOduWJxwdX/z5f9/Ttm8Hgwak8+cQgAgJLeeihgxw+7I9GY4ejo47p04/X2s6aFFoT2iBHNFd7E/BOfO2FZjMBb8diVinJuC8ck70Kjy3ZBL1+luTFlvjD6XgRLgcLSZ8XiW2uFt9PEinv6IbJ2QZlpQHvH9Oq45KmYjqqQz3eAVWUGrMR9O+WU7WgBIdPPFE4WB6uGk/qqXq4BJupjtg96AwqMMUb4NyzV8OGKkwxBhzedse4T0fVsxocf/RCoVBgyjRi2FCFw3vuTZqviwmPKmL0uGQS412r5/W5OotBw9JZtPAaAoLKePDRaKIPtkJTYoejk57pd5/iifn9rZhqCwe1gTPFXnyTEMXbg36ts/yJHnvo55fBgj+Gkl7uQn//dJb03kVuhRNbMkIZGpjM2NA47th6HaEuJbx41XZ2ZQVTpHXA2UbL/K4HuH3L9VbIWV32jiaSTjuw+Ssvnn4vsdayNlEV3LYgk8UzwlAoYMmaeKJ3upIc44BSZWbu0hRWPda6WTYQtNT4oqXm67zwyEJGj00kMaHmpdE+/TIZNCyNRQ8PsJQbjxwi+oCvpf5y0jP9rlM8sdC6DdzGo3psx9ujbK8GI2jfKadivganTz1qlfEVCzXYTnPA7kEnFCowxhury3j9eksZ77jazVLGLynFab1ndRmv21CF0/vu1stkY5C4qUGs+vT34MGDZGVlVU+//fYbABMnTmzSdBzY4c2hXV5kpjqSkeLIJ2+0papCRVSXEkLalnPioDtxp13Z8YsvFeUqfAMtPQ7unJ/Az18Hkpdt/zdHsA5bWwP9+6XywUfdOXnal6xsFz77siuZWS5cPzqWoCANSckeHDvhx9Hj/iSluBMcqAFg4vjTnDzVqrrHgTVlzI1E088HXYAjuiBHcm5vi02hDvvUcgAcEsqwKdCSc3tbdIGO6AIdyZ7eFrvUchxjLPmxza6ivIsHugBHigf5oi4zoCozAOD7RTL544MxOaislsc/s3cw8PALJ3jjuY6UaWyq5we3Kef4YU/iz7ix41d/KsrV+AZUAnDnvDg2fhtMXrbDhXbbZPadDeHdTX3YcbL+twfuufYge86G8ObPVxGb6U1GgRu7T4dSVGZJe+tWxUQnBHA23YffjoZRXmVLgFcpAHOu28/3ezpU9ziwtgkz89i01ovNX3uRGmfPqseC0FYqGTWlkJCwKo7vdSbuuCPbf/SgokyFX4gOgLsXZfLTJ17kZdr+zRGa3oRZ+Wxa68nmrzwteXo0CG2lglG3FBISrq3J0w/n8hT85zx5k5fR/PIELTdfJqOSokL76klTYklncGgZx6M9iT/rzo7NgVRUqPENsLyNf+fcs2z8LoS8HOuXFwBVvV3Q9nTBGGCHIcAOzVRfzPZKbGMrq9cx2yowedhUT2bHmvLaJkNLRX83DAF2lI/0RJ2utSwwmHF/J4viewJA1fwCSyGEuFTNJWY6z2hUUlTkUD1pNHYABAdrOHHch7g4T3Zsb01FhRo/vzIA7rr7GD//HEZenpNV0vxXFZ3cKbghiLLunnWW2eRW4ZBUTu6trdGGOqP3cyD3llAUehMuBwsAsM2qoiLCBW1rZ0p7e2GyV2GTb6l/vNelUTzQF4OnXZPmyX65Ozaj7VG2UaMKU2P3hAvmHBOmGH31Orr/lWFzkwO20xxRtlGjDFGjHmqPwtZST5pSDKivsUXZRo16ggMUm6HE8vBD92optvc6oXBqHp81tHcw8Miig6x6pTtlpTX3aSGtSzlx1Ju4GA92bAmmotwGX/9z90D3nuTnH9uSl+torWRX25kZwspjffgtvf6YqYdPNusSI9mfG0hGuStfxXfgbJEXXbxzAWjnVsT+nABOFrbip5RwyvS2BDlZYqZHu+9jbVwHsiqaR8x0aLsbHy8PZM+vHnWWBYVVkXTGkWN7XDn6hytJZxwIDrM8b5l4bzYnD7gQe7x5lBt/1VLji5aaLwB7ewOPPHmQVct7UFZa86zFUm74EBfrwY6t58sNy/OmO+85wc/rrV9uOK5ww2aMPapzZbz9uTLeGGOoXqfqjXJsb7bHbpojqnNlvM1Quz+V8UbU/W1RtVFjM8EBc7EZc7GljK9aUYbdfc2njBfWYdWz7+Pjg5+fX/X0008/0a5dOwYNGmS1NCmVZgZem4O9g5Ezx9xIjHEmvGMpzq56wjqUYmdnIivNgQ7diwlrX8r6z4Oslta/o1KZUanM6PS1H37rdCo6ts8lOcWdoAANPt7ltPIpIzCglORUd/z9ShkxLIGPP+9mnYT/DWWlEQCjo+XNc4XBBAowq2seApnVSlCAQ4LlRkkb5IhDQikKnQnH0yUY3GwwOqtxOZCP2UZBWbe6AYK13PfYGQ7u9uboAa9a85NiXQjvUIKzi56w9hrs7IxkpTnSoVsR7aI0rP8i5AJ7bD4UCjNXt08lLc+NlTN/5udnPub9B75nYMek6nXiM71oH5yHi4OWyMA87GwMpOe70SU0i4igfL7e3Tx6fKhtTIR3qSB6V01vD7NZwZHdznToWU7iaQciulTg7GYgrHMFtvYmMpNt6di7jLBOFfz4gc9F9m4dNXmqCSjMZgVHdrnQoWcFiafs689TnzLCOlfy4wfWb1SsT0vNF0BAcDmf/PQ7H6zbysIlR/DxtTxYT4pzJTzqXHkRVWKpu9Kd6NC1kHaRJaz/upl2ATaacdhdgqLKhC6yphHDYVcJftPP0mpePK6f5aDQmqqX6UPtsTtRDkYzdkfKMLS2NNw7/5CPrqMj+rDm0RhyOZ0fW7OxJyFE89DcYqbAwFI++/xHPlzzE488shcfH8uDlMQkd8IjinB21hEWVoidrZHMTBc6dswjLKyI9T+G/82emweFwVIAmm3+FCorFZjVShziLY0e2iAH7FPKUZYbsEspR6E3ofexxz6+FPvUCoqH+ta36yZlLrPkQ+FqyYe5yITptAGFh5LK+4oovyGfyrnFGI/XNCIo26kxntBj1poxHtCh8FKCmwLD5iqwVaAe2LQNHxcz+8GjHNjrx9HDrWrNT4x3Izyy2HIdRhRZYqZ0Jzp0zicsopj13zXvt5zPi87zY1hQMr4OZYCZq3wzCHUtYXeW5fnD2SJvOnvl4WqrpaNnHvZqAyllbvT0yaKjZz4fx3S2bgYuUfJZB4LaVuEToKNVoJbAtlqSY+zxb61lxMQCPn4lwNpJrFdLjS9aar7Om/3gEQ7s8+NodO0yOjHBjfDIotrlRoYzHTrlExZezPp1YVZK8UWUny/jzzUAnC/j3ZWU31dM2bgCKuYUY/hzGR+mwnjcUsYb9lvKeIW7Av3mKhS2CmyaURl/uUjc1DDN5psEOp2Ozz77jPnz56NQ1P/Gn1arRavVVv/WaDSX7fih4WWs+CwaW1sTlRUqnnuwM2mJTqQlOrHtJw2vfXEYnVbJiifbU1WhYs5Tsby6qD1jJmcw7pZ0NMU2rFoSRWpC82nlrqy04fRZb26ddILUdDeKi+0ZPCCZqMh8srKdSUt3Y81n3Vj27O8ArPm0G2npbix79nc++LgHPbtnMm3KcQxGJavf68XJ09a/2cVkxuebFCrbOaMLtLTkVrVxxmSrwvv7NPJvDAIzeP+QhsIEqhJLgVhytTd26RWEPnsco7MNmXeHoaww4rUhg/SHovD6MR2XwwXove3Iub0tBnfrtH4PHJlFWFQpD95WdxiQ6L3ebNvoz8rP9qGrUvHq4k5UVaq4//EzrHymE2MmpjF2ciqaYlveeL4DqYnNbxw5D+dKnOz13Db0KO/+0pu3fu7LVZFpLJu+mTmrx3IkMYD9scFsig7nw3nr0OrVPPflECp1ah65aTfPfTmYCVef5uZrTlJSbs+L3w4kKcc6DTyunkZUaijOt6k1vyjPhuB2Wg7vcGXLOg/e+DkWbZWS5Q+GUFWhZO6ydJY/FML1t+cz7s58NIVqXn8kiJRY6z/IrM5TXu2qoShfTXDY+TxV8MbGOEue5p3PUwbLHwzm+ukF5/Kk4vWHg0mJbR69rFpqvmJOubPy2a6kpzrh6aXl1rtjefmdvcy+dSDR+33YtimQlWt2o9OqeHVJV0t58chJVj7XlTETUhg7KdlSXizrTGqSdd80U6dU4fN4EgqdCbO9koJHgzEEW/6dKwa4YfSxxeipxia5CrdPc1BnaCl81NIwWjreG/d3s/C9Lw5jKxuK7g9AlanFaVsxeS+2wX11JnbHytC1c6D4vgDMTs2j15gQQvwTlxIzQePFTTFnvVixoi/p6S54elYydeopXlm+lfvuvZbow/5s3dqa11f9hlarYsWKvlRVqbh/zmFeXdGH665LYNwNcZSU2LJqVW9SU9z+/oBWoPOzR+9pi/f36eRMDcVkp8RjSzY2RTrUGssbsxUd3SntU0bIi6cw2yjJmd4Wk50S37XJZE9vi/uOXNy352B0UpMzLRRdQNO+gWo2mdG9UYaysxplW8v9jynT8qKVbk05trOdUYapMPyqpeqhYhw+8kAZrEZ9nT2mBAOVtxWicFdit8QVSs3oPizH/nV3dO+VY9hahTJAhe1jLih9rFOnDhyaTlhECfPuGVxnWfRBX7b9Fsxr72xHp1OxYllPqqrUzJl/jFeX9WDMDYmMm5CIpsSWVcu7k5rsWvcAzcCzh/rzfN8d/DHhM/QmJWYzPLF/EAdzLQ/Nd2UF82NSON9f+x1VRjUP7xlKpUHNs3128cjeIUwNP81tkSco0tqzaP8g4kqaz0txf5YW78CalwNZ9nksAGteCiQt3oFla2P5YGkQPQdpmPZQJga9gtXPBHPyQPPoHdFS44uWmi+AgUPSCAsvZt69Q+ssiz7ox7bfQnht9VZ0WhUrXuxlKTceOsKrL/VizLgExo1PQKOxY9WKHlYvN8wmM1WrylB1VqM6V8abz5Xx2jUV2M92QhmuRr+pisoHS3D62ANlsAqb6+wxJRgpv60IhZsS+2ddoNSM9oMKHFe5oX2vHP0WLcoAFfaPO1utjBfW02waCX744QeKi4u54447LrjOsmXLWLJkSaMcPz3JkTk398LJxUj/EbkseP4Mj8zoTlqiE5+/3YbP36556/LWe5M4ss8Dg0HBlFkpzJ7Qm76DCliw9DTzJlv/I5B/9srKa3ho7l7WrlmH0aggPsGTHbtaE9auEICNmyLYuKnmA0fDhyRQUWnDmbPevP/Weh5YOBpv7woef3g3d8y8Eb3BuoVEqy9TsMusJG1hzQeWjS42ZM0Mo9UXybhvzwEFlPbyoirYEc4HTyolubeE1tqX7yeJFA/xxS6tAudjRaQ82QnPzVn4fJVC1j1N/6aTt28Vsx6OYdHsnuh19f87r30njLXv1LRi3zIrgaMHPC3X4l2JzJ50NX0G5rHguRPMm2rdj3HVR3muiXXXyVC+3GX5sFhcpjedQ3O4sd9pjiRabno/2NyLDzb3qt7uzhGHOBgXiMGk5I5h0UxbMZFrOqTw9C3bmPHaTU2fkUv02av+fPZqzVjoUx/K5shuF4wGBbfMy+HeYVH0HV7Cw6+nMmd0pBVTeuk+W+HHZytqPt4+dX42R3Y51+RpaCR9R2h4eFUqc65tuo/1/VtXYr4O7615cy453tJosObHrQwYlsnmDSGsfT+Cte/XpPWWu2I5etDbUl7cGc/sWwfQp38uC545yrzp1h1j0xBgS+6KtigrTDjs1eDxRgb5z4ViCLanYmRNUGtobY/RU43P4hRU2TqMfraYnVQUPVS7V5/308mUTPfFYWcJqhwdOW+E4/52Ji5f56GZ4ffXw195zOemxj6GEKLZuZSYCRovbjp0qOa+JjnJnZizXnz8yU8MGJjG5l/b8vlnnfj8s5pen7dOPcnRI74YjEqm3HKa2feNok+fTBYu3M8Dc0de9vRdFiolmfeE4/tpEmELojEroSLKjfKOtRs1CsYGUTC2pv7x/CmDiihXzCoFnr9kkvJUJ5xOFOP3USKpTzRtT1jdyjJMSQbs/+deM/NcJzybcfbYjLE8uFNF2GA8rMOwsQrbe5xRqBXYza/9EFa7TIPNTQ6Y4gwYdmtx+NAT/RcV6F4vw/75pm/o8fap4J65x3lywTUXjJk+/6g9n3/Uvvr3rdPPcOSQDwaDkim3xTB7xjD6Xp3NgicOM2/WkKZKeoPcFnmCbt45zNp+LRnlLvRplcUzvXeTW+nEnmzLdbfqRG9Wnah5/jC38yH2ZFliptmdDnPdz5MYEpjCK/22cuOmm62Vlb+18TMfNn5W08t6+M0FVJSpOBPtxPvbTvHA2Ci8/fU8/mYSd1zTCb3uyhgO5UqMLy7FlZgvb58K7plzjCcfHoBef4Fy4+MOfP5xzXOmW28/zZHDrc6VG2eZfecI+vbLYsHjB5l3z7CmSnq9tK+WY0oy4vjmn8rgc2W87Th7bK47X8Y7YzysR/9zFXb3OqFQK7CfX/tl0sqlpdjefK6M36XDaY0HurUVaF8vx+H55tmI2iASNzVIsyldP/jgA0aPHk1AwIW7kz3++OOUlJRUT2lpaZft+AaDkqw0R+JPu/DR6+1IjHXmhmnpddYLalPOkOtz+PSNNnTpVcypw25oimzZ+WsrwjuU4eBoqGfv1pOV7cIjT47khklTuO2u8cx7eDQqtZnsnLpvmbu6VDF1ygnefrcXUZH5ZGS6kpnlyvETfqhUJgIDL1/PjX+i1ZfJOJ0sJu2h9hg8ar/pX9HBjeTnupLwcncSXulB9ox2qEv06L3r7y7lEKPBLrOS4sG+OMZpKO/khtlORWlPTxzjrJPPsPYaPLx0rPp8H+sP/Mb6A7/RpVcR46aksv7AbyiVtUueoNByhozJ4tO3wujcs5CT0R5oim3ZtdmXsPalze5aBCgut8dgVJKUU3s8yuRcd/zcy+rdprVPEdf2iOPdTb3p0S6To0n+FJc7sOVYO6KC8nG00zVF0uvQFKowGsDdW19rvoePnqK8uu2vwe2qGHZTIR+/7EeXfmWc3O9MSaGaHRvcCe9SiYOTsamSfkHVefKpfe14eBvqz1NYFcMmFFnydHUZJ/c5WfK03q3Z5Alabr7+qrzMhoxUJ/yDK+osC2pdxpDRGXz6TgSdexRw8ognmmI7dv3uT1iUxvrlhY0So78d+nYOaKb5og+1x/mnwnpX1Ydb3sZUZ9X/t++4pQiTk5KqPq7YnSynqo8rqBVU9nPF7lR5o2VBCCGawqXETNC4cdOflZfbkpHhTEBA3fu4oCANQ4em8MknnejSJZeTJ30oKbFn584QwsOLcHDQ17PH5kHb2onURZ2IX9mDxJe6k/FAJMpywwVjC5vsSlz355M/LgjHWA2VYS4YXWwo7emJfWoFiqqmu3fQrizFuEeH/WvuKFvVPAxTeFlCf2Vo7XsfZWs1phwT9TFG6zAlGVFPcMB4VI/6KlsUDgrUQ+wwHrXO+QuPLMbDU8sb721jw5Yf2LDlB7p0z2fcTQls2PJD3ZgppJQhI9P49MMOdOmez6nj3mhK7Ni5LZDwyOJmeR3aqQws6HqApYevZmtGKDHFXnwa24mNKe24u/2xerdp61rEDaGxrDzeh76+mRzM9adQ68DGlHZ08srHSW2dmKmhXD0MTH0wk7efDiaqezkZSXZkJttzfK8LKrWZwDbav99JE2ip8UVLzVd4RJGl3Hh3Cxt+X8eG39fRpVs+4ybEs+H3dXXLjWANQ0ak8umHHenSLa+m3NgeRHiEdcuNqpVlGPbqcHzd7QJlfO1GEGWoClNu/WW8IVqHKdmIzQR7DEf0qM6X8UPtMBxpfmWj+D979x3eVPUGcPyb0TTde7dQaAtl7z1kCQiigAMQRQVFBRQEtyxFRVAQ90IRVJwMUREEZMreo0ALdNHSvUfSrN8fwZbSovYnbdryfp4nD+Tm5uac3uTe855Z/WrFSIL4+Hg2b97M6tWr/3Y/e3t77O1rZo4spcKCnebqH5KFx2ef5dM3wtEVq1GqLKjU1ouJWm3dV6mqnU1Ier0avV6Ns5OeDm2T+Wx5+wr7PDLhEGvWRZKR6USTiExU6rL8q1SWChfOGmOx4PtdPM5Hs0mc3gzjNQrnAGZn69QvDmfyUOUbKGjtXmEfhcGM77dxpIwPA6UCzKC4vBq5wmQpbYGtacf2ezLprvK9/6fNPcXFOCd+/CIUs/nKIeUWprwYxdLFTdAVq1GpuOK7aP3XZufrbxhNKk4n+tDAN6fc9gbeuaRkVzZ01MKzd+7knZ+7UVxih0phQa20nqC//lXaaAI4o0FJzHFH2vUsYM9Gd8C65kLbngWsW3b1XIwWnliQyMcvBaErUpW/dthdPl+14NpRlqd89myw9koozdMXXlftbeGJBRf5+KVAa56UoLL7K0/WPZS1ZHRifc3X1bQORgKCivjjt6uvkRamPHeCpUuaW68XSkvp9b22Xi8UZsBYeZrsYq2L2Zk8KhZhlLlGXH5IJ/3Vy6P/zIDJ9tf3660m5r6sT3NrClFf/NuYCWoubtJqDQQEFLJly9VTSlh44omDfPpJW3Q6O5RKC2rVX/eey2W4WnbvqYzZwXqvsUvVoY0vJPO2Stajs1jw+zqO9LsaYNGqrLHFlfceQGG2VH9HRouFkiUFmHaWoH3bDWVg+QKLIkCJwluJOaF8pZ3loglVl/LTZwJY9Bb0bxVgP8sFhUoBJrgcMmExYrN76tFDPjz2QPlevE8+d4iLCS78sLJJhZjp8RlH+PT9Vtb4vVwZqPbG73YKMxqVGTPlpxQzWRTXiH0svNJ5B68d7k6R0Q6VwlxrYqaqemROImuW+pGRoqFJm8LSmAms8W5tOV/1Nb6or/k6etiXxx4cUG7bk89evm58c43rxget0ekuXzcuf+/+uo/Z4v5lsVjQLynEuKMEx3f+5hqfWP4ab040oe5ScTpti96CfnEh2tmXr/Fmyi7yNrzGX28SN1VNrWgkWLZsGb6+vgwdOtQmn//A1PMc3OVF2iV7HJ1M9BmSSqtOOcx6tE25/QbdcYncLA37t1srAKOOuDH2sViats6lY88s4s85UphfsYBlSx3aJQNwMcmVwIB8HnrgMIlJbvy+pfyCTe3aXCIoKI833+4OQHSMFyFBeXRsn4SPdxFms4KLSbYZauT7bTwuBzJJfjQCs70SVa61F4TZQY1FY20tdd2dTom/AyYXNdoLBfj+EE92P38M/hXnefdcn0RhS3f0Idb1I4rDnPFZnUhuNx/ct6VRHGabufyLi9TEny9fUa4rVpGXa1dh+6ARSeRm27F/h3W6kaij7twz8TxNW+XQsUcG8eedKCywzXfRQWMg2Du39HmgZz4RgRnkFdmTmuPC19vaMO/ezRy9EMDhc4F0jUykR/N4Jn84rMKxbutyhuxCLbuiQgE4HufPhIGHaNEglW6RCVxI8aBAZ7vFdVZ/6sNTbyUQfdyRs0ccGfFwOloHM79/V37Oz1vuySI3S82+TdaCVtQBJ+6bnkJk+0I69c0j/qyWwrxacTlm9SfePLUkkehjV+TJ0czv31aSp8yr8jTjcp765RN/1p7CvFpSKqR+5mvCE1Hs2+lHWooDXt46xj4cg9msYPvv5XuXDro9kdwcDft3WdeViTruyT0Px9C0ZTYdu6UTf8HZZtcLANevUtG1c8bkY4ei2Izjzlw0pwrJn9UQVUoJjjty0HVwweyisq5JsCwFfXNHjKEV5zh1+zyFgtu8MHtZ81MS6Yjj9hz0bZ1x2pRNSaTt1/4QQoj/l61jJoCHHjrKvn2BpKY54eVZzL33ncRsUrB9W4Ny+w0efIHcXHv27QsCIOqUN/fee4rIyAw6dkwhPt6VwkLbrAEGoNCZ0KTrSp/bZeixTyzE5KTG6GmP86EsTM5qjJ4aNEnF+H4fT0FbD4qaV5xex21XOiZnNYWtrSNldWHOeP2ShPZCAU6nctAHOGB2rP5yXslbBRg369G+5gqOSsyZ1hoehbMChb0ChUKB3WgHSpYVoQxXowxXY9ygwxxvxP7linGeYUURqq4aVE2s91RVKzUlHxZiHqLFuLoYVUvblB2Ki+2Ijy3/2bpiNXm5GuJjy+dj0K1x5ObYs3+3dZqsqBOejH3gNE2bZ9GxSyrxsS4UFtjme+ioNtDQpSxmCnHOo5lHBjl6ey4VubAvNYDn2u1Bb1RZpxvyS2ZEo2heO9y9wrFGhZ0mS+/AH0mhABxK9+eJVodo65XKTYEJxOR4kG+wXcykdTQRGFo2AsA/RE/j5kXk56hJTy77+7frlUdQIx1vPhkKQPQxJ0LCdXTsk4tPYAlmE1w8X3vmuK+P8QXUz3wVF9sRH1f++q3TqcjL01TYPmjo5evGHmtMFXXSi7H3R9G0Wab1uhHnYpP7l35xIYbNehxecwVHRaXXeM0YB/SfF6EMU6O6vCaBOd6E3byKv5uS5UWoumlQNbHen1St7NB/UIhpiBHDqmJULWtH/YSoWTY/62azmWXLlnH//fejVtsmOW6eBma8ehpPHz2F+WpiY5yZ9Wgbjuwpuwi6e5Uw+uF4ZtxX1gM/+qQrq1eE8NL7x8nJ0rD4xWaVHd6mHB0NPHjfEby9iyjI17BrTwO++KotJlPZTFMajZHJj+zntTd6YbFYW1AzMp348NOOTH9iDwaDikVLulFSYpvz474jDYCQt86U254yrhF53axzF2pSdXj/dBFVoRGDl4bMwYHk9K8477QmqQiXQ1nEv1g2L2hBO08co/MJWXQag5+WS+PDKryvNnH31DNqwgWeeqBz6bboU26s+aohc98+Qm62hsWza3be0ytFhqTzwWM/lz6fevseAH490IRXvuvL9pONWLiqF+P6HWH68D+JT3PnhRUDOR4XUO44Hs5FPND/MBPfG166LSrRl292tGbRhN/ILnBg3re2nUN0+zoP3DyNjHvqEh4+Ri6ccuDFexuXW8zY3dvA6CdSePL2svkYzx51YtXHvsxbcYGcDDVvTmtQ2eFtYvs6D9y8TIx7OqUsT2MbVczT1FSevK1s7Y6zRx1Z9bEP81bEkpOp5s2ptSdPUD/z5eWr45l5R3B1M5Cbo+HUMQ+mT+hOXk5ZEOjuqWfUA+d46uGygDI6yp01Kxszd/EBcrM0LH65rQ1SX0aZa8TjnSRU2UbMjkoMoVoyZzVE39YZVYYB++OFOP+ShUJvxuRtR3E3V/Lv9KlwHPsjBahTSsieGlS6rXCIJ3bni/F59gIlEQ7k3+1b4X11ksVS1tOnOj9DCFFr1IaYCcDbu4hnn9uDq0sJubn2nDrlzZNPDiA3t6wCwt1dx+gxUUx/sqzXZnS0F6tXNeWll3eSk2PPoje72CL5pbTxheViC98fEwDI7epN6gONUeeW4PNjAuo8A0Y3O/K6epM5pOIUT6o8A56/JZPwTNk81rpGzmTf7E/Q+2cxutiRen/j6s8QYFxrbfTQPZFbbrvmeRfsbrGeH7u7HbGUQMm7BVjyzSjD1GgXu6MMKl+RZ75gxPiHHofPy6YIVfWxR3XUQPGUHJQhKuxn1+65qt09dIy+N5oZk3uXbos+48nq7yN46fXd5OTYs/i1DjZLXyvPNL6+uSxmerGDNWZadb4Jz+7tx9RdN/NU230s6rEFd42epEIXFh/rzMqY5uWO46Ut4rGWh7n79xGl245n+vHZ6dZ82nc9mToHntlj25ipSesiFn4fXfr8kTnWaZ03/eDFohmhAGjszUx+OYHXJjcuq5NI0fDh7AZMfzMOQ4mSRdMbUaKvNTNm18v4Aupvvv4N63XjDDOm9CndFn3Gk9U/NOGl+ZevG693vPYBqpHh8jW++KprvPZ559J1ZjR3O2ApsaB/rxBLnhlluBqHt9wqXONNF4wYtupxuuIar+6jwXTEQNGUXJQNVDjMrh2LhP9nEjdVicJisW1ufv/9dwYNGsTZs2dp0qRqi5rk5eXh5uZGf88HUCtt1xOlOpgbB/3zTnXQ2fH1sxdn85fibZ2EapHVr9E/71THuH6zz9ZJqB716MZ0I1CHVDJdQT0Q/3bNL2BY3UxFeqLHvk5ubi6urratEPmr3NN1yMuo7aq3J53RoGPv+tm1It9CiP8WM0HZ9aNfy6dRq2zXo7g6nJlUP69R7ZrH2joJ1UI3oZ5UPF0leqKfrZNw3YU/f8DWSagWFmPtW7tPXJuqabitk1AtHJfm2DoJ152hsIRfBn1eK+IHiZv+PzYfSTBw4EBs3E4hhBBCCFFnyNyaQtx4JGYSQgghhKgaiZuqpvaM1RJCCCGEEEIIIYQQQgghRI2y+UgCIYQQQghRBZbLj+r+DCGEEEIIIYSoqyRuqhIZSSCEEEIIIYQQQgghhBBC3KBkJIEQQgghRB0ic2sKIYQQQgghxN+TuKlqZCSBEEIIIYQQQgghhBBCCHGDkkYCIYQQQgghhBBCCCGEEOIGJdMNCSGEEELUJWaL9VHdnyGEEEIIIYQQdZXETVUiIwmEEEIIIYQQQgghhBBCiBuUjCQQQgghhKhLLJcf1f0ZQgghhBBCCFFXSdxUJTKSQAghhBBCCCGEEEIIIYS4QclIAiGEEEKIOkQBKKq5x4qieg8vhBBCCCGEENVK4qaqkZEEQgghhBBCCCGEEEIIIcQNSkYSCCGEEELUJRaL9VHdnyGEEEIIIYQQdZXETVUiIwmEEEIIIYQQQgghhBBCiBuUjCQQQgghhKhDFJYamFuz/nSIEUIIIYQQQtyAJG6qGhlJIIQQQgghhBBCCCGEEELcoGQkgRBCCCFEXWK5/KjuzxBCCCGEEEKIukripiqRkQRCCCGEEEIIIYQQQgghxA1KRhIIIYQQQtQhCosFhaV6u6xU9/GFEEIIIYQQojpJ3FQ1MpJACCGEEEIIIYQQQgghhLhB1YuRBAoXJxRKe1sn47oyOtnZOgnVImKF3tZJqBYptzW2dRKqhUd0/Ttf+aO62DoJ1cI1Jt/WSagWygvJtk5CtTAmXrR1EqqFemsDWyfhulPodbZOQkXmy4/q/gwhRL2T29wNlZ3W1sm4rh7tscXWSagWa+f3t3USqoXuzRxbJ6Fa2O9V2DoJ113RryG2TkK1yNgRYOskVIvQ1em2TkK1MJ2OsXUSqsXhY/WvXsJcLHFTXScjCYQQQgghhBBCCCGEEEKIG1S9GEkghBBCCHGjkLk1hRBCCCGEEOLvSdxUNTKSQAghhBBCCCGEEEIIIYS4QUkjgRBCCCGEEEIIIYQQQghxg5LphoQQQggh6hLL5Ud1f4YQQgghhBBC1FUSN1WJjCQQQgghhBDXxfvvv09oaCharZYuXbqwf//+a+576tQp7rjjDkJDQ1EoFCxZsqTmEiqEEEIIIYQQopQ0EgghhBBC1CUWS808qui7775j+vTpzJkzh8OHD9OmTRsGDRpEWlpapfsXFRXRuHFjXn/9dfz9/f/rX0UIIYQQQgghytTSuKm2kkYCIYQQQgjxny1evJiHH36YBx98kObNm/PRRx/h6OjI559/Xun+nTp14o033mD06NHY29vXcGqFEEIIIYQQQvxF1iQQQgghhKhDFBbro7o/AyAvL6/cdnt7+0or9EtKSjh06BDPP/986TalUsmAAQPYs2dPtaZVCCGEEEIIIa5Wk3FTfSAjCYQQQgghRKVCQkJwc3MrfcyfP7/S/TIyMjCZTPj5+ZXb7ufnR0pKSk0kVQghhBBCCCHE/0lGEgghhBBC1CU1Mffl5eMnJibi6upaulmmBRJCCCGEEELUCTUYN9UH0kgghBBCCCEq5erqWq6R4Fq8vb1RqVSkpqaW256amiqLEgshhBBCCCFELSfTDQkhhBBC1CEKc808qkKj0dChQwe2bNlSus1sNrNlyxa6det2nf8CQgghhBBCCPH3amPcVJvJSAIhhBBCCPGfTZ8+nfvvv5+OHTvSuXNnlixZQmFhIQ8++CAA48aNIygoqHRdg5KSEqKiokr/n5SUxNGjR3F2diY8PNxm+RBCCCGEEEKIG400EgghhBBC1CW1dG7NUaNGkZ6ezuzZs0lJSaFt27Zs2LChdDHjhIQElMqyQazJycm0a9eu9Pmbb77Jm2++yU033cS2bdv+cxaEEEIIIYQQN7BaGjfVVtJIIIQQQgghrospU6YwZcqUSl+7uuI/NDQUSz0qVAshhBBCCCFEXSWNBEIIIYQQdYnl8qO6P0MIIYQQQggh6iqJm6pEFi4WQgghhBBCCCGEEEIIIW5QMpJACCGEEKIOUVgsKKp5mp7qPr4QQgghhBBCVCeJm6pGRhIIIYQQQgghhBBCCCGEEDcoGUkA3DPhLGMnRJfblhjvxKNj+gHw0BOnGDAkEV2xii8+bMa234NL9+vZN5l+t1zk5Wc612iaK9MqMoW7bz1JRKMMvD2Kmb24H7sPNix9vWenOG7tf5YmjTJxddHzyPO3cT7eq9wxHr13PwN7x6DTq1n6bUf++DOs9LXeXWK5udd5Zr05oMbydC0OWgP3jzlKjy4JuLvqOBfryYefdyL6vDcAd952iruHnwLguzUtWPVzi9L3Rkak8/jD+3j8uSGYzbZrJ2vXMJlx3Y/RLDAdH5ciZnw7iG1nGpW+7qAx8PiAvfSJjMPNQUdyjivf7mvJqoNleXly0G6GtT1LcYma9zZ34bcTTUpfG9D8PEPbRPPkN7fUaL7q6/ewbeNk7ul7jKbBGfi4FfHc5wPZcbLsfL04eitDO5e/juw9E8z0T4YCYKcy8fyo7fRqGUdmniNvrurJwZiya8k9fY/i517AW2t61kyGrkGpNHPvmBP06xuHh7uOzCwHNm9pxMrvWgIKAO4YcZq7RkYB8P2q5qxe26z0/U2bZDDlsQNMnTHIpr+vqw0ZlcTQUcn4BekAiD/nxDcfNuTgLut37+FnzjFgeAq6IhXL3mrMtl/9St/bc2Aa/W9P5aXJrWyS9n8y7IEM7nwsDU8fIxeiHPhgZhBnjzoCMHFOEgPvzkZXrOSzVwPYusaj9H29bs1hwF3ZzLm/0bUOXSPGdztM/6YXCPXMQW9UcSzJnyVbuxKfVZZWjcrIjP67GdT8HBqVid0XQnhtY2+yiqz5dNXqmHfrH3RqmERCljtz1vfhbKpP6fufH7iDizmufLm/bU1nr3pYLNZHdX+GEOKG17ZxMmP7HKNpkLX88+yygew4deV9w8LDgw5yW5czuDjoOR7rz8LVvbiY4QZcLv/cvZ3eLeLIzHfkzdU9OXBF+WdsH2v5Z/Hamiv/xC21I32ziqJYJUotuLUxEfZkCU6Nyq57RYkKzr2pIfeICnMJePUw0eR5PRprqIG5BE7PsSdjqwqNt4WmL+rx7GYufX/8Mjv0lxQ0eaGkxvIF9fN8Of6Yif2efFQXS8BegSHSgYJxPpiC7Uv3cfkgBc2xQpRZRixapXWf+8v2UeSbcH07GbsTRZgCNOQ/EYCxsbb0/c4fpWDyt6N4uFeFz68uHQKTGd/+KM190vF1LuLxXwfzx4Wyc/XqgD8Y3uxsuffsig/hkXW3AmCnNPFy/230axxLRqEj87b3Zm9i2bl6sN0RAlwKeG1HrxrJz1/U3+ag+rMQRaIBNArMzbUYJnhgCdFU3NliQTMzFdXBYvRzfDF3d7JuzzOheTMd5TEdliA7SqZ7YwkvO99272Vg8bfDeKdbDeUKOgQkM77dUVr4puPrVMTj6wezJfaK89XvD0Zcdb52xofwyC9l52tev230axRLRpH1fO25WHa+xrc7QoBzAa/urNnz9U/uGn2GBx8+ydpV4XzyQVsAHn7sGAMGxqHTqVm2tBXbtjQo3b9n74v0HxjPSzN72CjFf6+ux03ac3l4bL6ENqEQdZ6B5IcjKGzjWfq6Ks+A908JOJ7ORVlsojjchfS7QjH4ll3vvFfF47ovHbNGRebtIeR38i59zflwJi77M7j0aNMazVe1kripSmpPDY6NxV1w4d5bby59PPOo9aLWuUcKfW5OYua0riz7oDlPPH8MVzc9AI5OBsY9coYPF7W0ZdJLae2NXIj34N1l3a75+smzfnz6TcdKX+/aPoF+3S/w3PxBfLqyEzMe/hNXF2uFmpNDCePvPsy7y7pWW/qr4slJu2nfJpmF7/TkkenDOHwsgAVzNuHlWUSjhtmMG32U1xb34rW3evHAmKOENsgGrBWgT0zcy9sfd7V5BaaDnZHoVC8W/Fp5QWD6oN10D09k1up+3Pn+KFbubcUzQ3bRu2kcAL2axDG4VQyTvxzKO5u6MvO27bg7FgPgbK9nUv/9LFhf8xXO9fV7qNUYOZfsxaLV1/6b7jkdwq1z7it9zPmyrCHj9m6naRqczsS3h/PT3ma8dO8W/lrhJsAzj9u6nOHj9bZvbLzrjtMMHXKODz7qyMRJQ/n8i7bcOfI0tw+zNoA0Cs3mvrHHmf9GD15/owf333uc0IY5gPX39fikA7z7QWeb/76ulpFqz7K3GvPEXR2YencHju1zZ9Z7J2kQVkjnPhncNDSVmQ+35vPFjZn68llc3a1BvaOzkfunxvLBKxE2zkHlbrotm4lzkvl6sT+TBzXhQpSWV1dewM3LQJebc+k7IofnxzRm6SsBPPlmIq6eRgAcXUw88Owl3nshyMY5gA4NkvnuUEvGrRjJo98OQ6008+HoX9DaGUr3eWrAn/QOj+fpNQOZ8PVwfFyKWHzHxtLXH+p+GCeNgdHL7uJgQiCzb9le+lqrwBRaBqbx9YHWNZovIYSoD7QaIzHJXiy6RieGe/se466eJ1m4qhcT3hlBcYmaJQ//ikZtvd/c3vU0kcHpPPyutfwzd2zF8s9Hv9Vs+SfnoJLg0UY6fF1M2090WIxw9BEtpiLr66YiODpRi0IB7ZYW02FFMWYDHHtci+VyO0DSD2ryo5R0+KqYoDsNnHpOW1pHUHxRQfIqNY2fqNkGAqif58vuZBHFQ9zJfqMhOS+FgNGC+9xE0JU1yhjCtOQ9EUDme43ImRsCFnCfkwgma9qdfshAUWwme3EjDC0dcXkvpfS96rPF2EXrKB7mWeGzq5ODnYGzGV68sv3alcI740O46bP7Sx9Pb7y59LW7WkbRwjede34YyQ+nmrNw4Cb+OldBrnnc2eI0b+/pUt3ZqEB5XIdxmCv6JYHo5/uDyYLmhZRy5+svqjV5f/VBKkf9TQ4UW9C/H4S5tRbNkozS1xSndSjP6DGOcK3GXFTkaGfgbKYX8/7hfPVedn/p4+lNZefr7hZRtPBJ555Vl8/XzVecL5c87mx+miV7a/58/Z2IplnccusFLpwva4zp3C2Zm/olMPPZXnz+SSumzjiIq2tZ/dj9E07ywTvtbJXkv1Uf4ial3kxJkCNpo0IrvmixEPBJNHYZepIfaULCcy0xetoT9O5pFHoTAE4nsnE5mEnS5Egyhofgu/ICygJrzKUsNuL180XS767k2OKGYdNaHJPJxKxZs2jUqBEODg6EhYUxb948LDZohTEbFWRnaUsfebnWluqQ0AKOH/Hi3Bl3tm8KoqhQjV+gtSJ2/OQo1q9pSHqqY42ntzIHjgWz7IcO/HlFr+0rbd4Vzldr2nL4ZEClrzcIzOXYaX+iY73ZuqcxRcV2BPjkA/DwPQf4eXMkaZnO1Zb+f0ujMdKrawJLV3TgRJQfySmufPl9W5JTXBg26CwhQbnExntw9GQAR08EEBvvQUhQLgB3336KE6f9Skcc2NLucw348I/ObD1TeWt065AUfjnalENxQVzKcWXNoebEpHjRIigNgEY+2RyKC+R0si8bT0ZQqNcQ6G49X0/cvJcfDzQnJdelxvLzl/r6Pdx7pgGf/NaZHSeu3XvAYFSRle9Y+sgvLuvxEuqXza5TocSmerLqzxZ4uOhwd7I2fjx9504++LULRfpKetjUsObN0tm7N4j9B4NITXNm1+4GHD4aQNOITACCg/OIjXXn2HF/jh73JzbOnZDgPADuGnmak6d8iI6puV5Y/9b+bd4c3OlFcoIjSfGOrHinMboiFZFt8mjQuIgT+92JOeXK9vV+FBWo8Au2npvxM87z63eBpF/S/sMn2MbIiRlsWOnJ7995khCj5Z1ng9EXKxg0JosGEXqO73Em5rgj29Z6UFSgwj/EWmHx0MxkflnhTXqS7b9zk7+7lXUnIjmf4Ul0mjezf+lHoFsBzf3TAWuj54g2Z1i0pTsH4oM5neLDnF/60jY4hVaB1iC/sXc2G06Hk5DlzqqjzWnsZW0YVitNzBy8g1c39MZsqV0NV0II8W/ZMmbae6YBn2zozPaTlZV/LIzqdYIvNrdn56lQzl/y4uVv++LtWkTvlnGAtfyz84ryj6dzWfnnmZG2Kf+0/UhPwHAjzuEWXJqaafaKHv0lJXlR1vtEzlEVumQFzV7R49zEgnMTC81f1ZN/Skn2Pus+RbFKvPtYjxE02oghS4HBeuvh7Csawp8sQW2DsKk+nq/cuSHo+rtjamCPsZGWvKkBqNKN2J3Xle6jG+SOoYUjZj8NxjAtBfd6o8owokqzVn6pLpag6+mKKUhD8SB31BetlZoYLbh8mEL+Y36gqqS2uhrtim/IO3u7sOVC42vuU2JSkVHkWPrI05fFFmEe2WyNDeV8liffHG+Jl6MOD631bzK7zw4W7+5KoaHmy3klr/ljGuiCJVSDJcyekhk+KNNMKGP05fZTnNdjtyqXkukV43JlogHTTU5Ygu0wDnFBkXC544jRguadTEqe8K7x87UzoSHv7OvCltj/73w19sjmj9hQzmV5svLEVefrph0sstH5uhat1sgzL+znncUdKMi3K93eoEE+J475EBPtyfatDSgqssMvoBCA8ROP8+u6xqSn1Y76savVh7ipqIU7mcNCyo0e+Itdmg6HuALSRoeib+iMwc+BtFGhKAxmXA5ZY3lNSjHFES7oGzpT0NEbs1aFXab1t+m9NpHcXr4YPe0rHFvcOGwaMS9YsIAPP/yQ9957j9OnT7NgwQIWLlzIu+++W+NpCQwpZMVPv/PZD1t4as5hfPysXUliz7kSEZmDs0sJ4U1zsLc3c+miI81bZxLWNJd1P1z7JlHXXEjwpEmjDJyd9EQ0ykBjZyIp1ZWWTVOJCM1izYZm/3yQGqBSWlCpLJQYVOW260tUtIhMIzbenaCAPHy8C/D1KSAoMI+4BHcC/PIZ2O8cX6ysnS3bVzue6E/vpnH4uBQAFjqGJtHAK5e9563DEmNSvGkemI6LVk9kQDr2dkYSs9xo2+ASkQEZfLuvdk6N8k/qyvewMu3Ck/n1peV889y3PHXHTlwdy4KXmGQvWjdKQWNnpGvTRNJzHckp1DKwfQwlBvXfNj7UpKjTPrRtk0pQoLXiv1FoNi2apXPgkLVRJy7OneCgfHx8CvH1KSQoKI+4eDcC/PO5ecAFln/VxpbJ/1eUSgu9b0lF62Di9DFXLpx1JqJlPs6uBsKb52OvNXMpwYHm7XMIb17Auq+C//mgNqC2MxPRuojDO8saAy0WBUd2utC8QxEXTmlp0roIZzcj4a2K0GjNJMdpaNG5gPBWxfz0me0bSyvjrLUWyHMvN7I180/HTmVmX1zZeYjL8iA515k2QakAnE31onPDJFQKM90bJxCdbm2oeqDrUQ4mBBKV4lvDuahmFsBczY/6M2pWiDqvNsVMVwr0zMfbtYgDMWW9Kwt19kQl+NKyofX6fC7ZizaNUrBXG+lyZfmnXQx6o/oaldk1y1hgrWy0c7Ne+CwloFCA8or6IKU9KJSQc8Qafzg3MZN7RIVJB1l/qtD4mLHzgJRfVCg14NPfVOP5+Cf15Xwpi6w90s3Oqsp30Jlx2JyLyc8Ok7e1ctMYao/mRJG1V/uRQoyh1jKG4+pMDC0dMUY41Ejaq6pTUDI7Jizjl3tXMqvPdty0ZbHFmQwv2gdcwl5lpEfDRNIKHMnWaRnaJBq9SfW3jQ81SVFoPV8WlyvOl86M5vV0SiZ7gWfFGbDNjTUoj+rAZEF5sBhLY+uPUf19LubWWixNamclZqegZHY+uIxf71nJ7Ju242Zfdr7OZpadr54NEkkrtJ6vW5tEU2JS/W3jgy1MmnqE/Xv9OXrYr9z2C+fdiGiSjbNzCeER2dhrTFxKcqZ5ywzCw3NYt6Z2jr6ur3HTlRTGy/cw9RXVvEoFFrUSh/PWTpf6IEfsEwpRFhmxTyhEYTBj8NGiPZ+PfWIhOX38bZH06iVxU5XYdE2C3bt3c/vttzN0qHXO7tDQUL755hv2799fo+k4e8qdt15py8UEZzy9ddwzPpqFH+5m0r19OLzPl60bg3nrs52U6FUsntcWXbGayU+f4K1X2jJkRBzD7owlL1fDuwvakBBb8z23r5eDx4PY8mcY78/7mZISFQs/6oVOp2bqg7tZ+HEvht18huEDT5Obr+Wtpd2JT/L454NWg2KdHafO+DD2zuMkXHQjJ1dL355xNGuSQXKKC4lJ7ixb2Y7XZ28G4POv25GY5M7rc35n6Zcd6Ng2mftGHcNoVPDhss6ciPL7h0+0jYXrezJz2HY2zPgKo0mJ2QKv/HwTR+IDAdhzPoT1xyP4cuIq9AY1c9f0o9ig5vmhO5mzti93dopiVOcT5BRpefXnm7iQXrNDaP9fdeV7eLV9Z0LYfqIRyVkuBHvl8ciQ/SyeuJ6Jbw/HbFHyy76mhAdksvKZ78kt1DJrxQBcHPU8PPggkz8YxsRb9jOg3XmSMlx59bs+ZOQ62SQf3//YHEdHA59++AtmswKl0sLyL9uwdbs1KEy86MayFW2Y//IfACxb3pbEi27Mn7eFz75oS4d2l7j3nhMYjUo++rQDJ0/VnsrZ0IgCFq08jEZjprhIxbwnWpJ43onE805s/dmPJd8dokSnYtELkeiKVUyZFcPiFyMZMjqJ2+5JIi/HjnfmNCXhvG3OzdVcPU2o1JCTXv5Wnp2hJiRcz6HtrmxZXcS762PQ65S8ObUBuiIlj89P4s1pIdx6fya3jc8gL0vF20+HEB9t+9ESCiw8PeBPjiT6cz7DWtHv7VREiVFJvr58QJhV6IiXk7VBf9nedrw4aAc/P/Y1ybkuvPRrHxp45DCs1VnGrRjJi4O2061RIlEpvrz8200U6GtncCmEEJWpLTHT1bxcrNfgrPzyFaxZBQ6lr/28v6z8k1OoZeaXA3B10PPwoINM/nAYEwfv5+a257mY6cpr3/UhPa9m77EWM8Qs0ODWzoRzhDXKd21tQukA597SEPZECVjg/BINFpOCknRrg0LACCMF0Ur2DXfAzt1Cyzf1GPPgwvsa2i/Tcf4dO9I2qHEIMdPs5RLs/Wxfg1AfzhdmC85LUylp5oCpYfl7ucP6bJyWp6HUWTAGaaxTE9lZz1fRHV64fJSK1yPnMfnakTclAFVyCdqtuWQvCLWuaXC0EEO4lvzJ/licrtEAUYN2xYew+XwjLua5EuKWx7Ru+/j4tl+554cRmC1K1pyOpKl3JuvGfkuOTsuMDQNxs9czpesBHlx9O0903cctEedIzHVj5pY+pBXaYGiL2YLdR5mYWthjCS1rdbP7OAtzc/uyNQiuYhzljt07Gdg/kIjFz46SJ71RJBlQbc5HvyQQu7czUB4uxhxhj+FJb3Cy/WjRXQkhbL5gPV8N3PKY1nUfHw/7lXtWWc/X6tORNPHK5Od7viW7WMv0jZfPV+cDPLD2dp7oso8hEedIyHVj5h82Ol+X9e6bSHh4NlMn9a/w2uGD/mzd3IAlH2yhRK9i0YJO6HRqpkw9zOKFnRgy7Dy3jThHXq497yxuT0J8za0b8XfqY9x0tRJ/LQYPDV7rEkkb0wizRonH1hTsckooybWOxilq7k5+J29CFp7EYqck9b4wzBolvt/GknpfGG47U3HfnorJWU3amEaUBNTOUSGi+ti0kaB79+588sknREdH06RJE44dO8auXbtYvHhxpfvr9Xr0+rJhanl5edclHYf2llUSx5135ewpD5at3kyvfsn8/ksDVn7WlJWflS3cMWb8WY4e8MZoVDL6gRgm3XcTnXukMmPWEaaO731d0mQrK1a1Y8Wqsp729408wuGTgRiNSsYOP87Dzw6na/tEnp20k0kv3mazdC58pyczJu/m26U/YjIpiLngybZdoUSEZQHw6+9N+fX3snN2c5/zFBfbEXXWh8/fXcuUZ4fi41XEC0/uYNxjIzEYbV8QvNroLidoGZzKtJWDuZTrQvuGl3h2yC7S853Yf8Hao/aTbZ34ZFun0vc8fNNB9l0IwmhWMqH3IUZ9cDe9msTz8og/uPeTO22VlSqrK9/DK20+Gl76/wuXvDiX7MWPM7+hXXgyh2KCMZlVLFpdfg7LF0dv5YedLWkSlEHvlnGMe/NOxvY9ypMj/uTFLwbWdBYA6N0znn43xbHgze7EJ7gT1jibRx46ZF3A+A9rD5f1GyJYv6Gsl8iAfhcoKrbj9Blvln74C09MH4S3dzHPP/0nDzx0W635fV2Mc2TKHR1xcjbRc2A6M147wzMPtCXxvBNff9CIrz8o6x13z2NxHNnrgdGoYPQj8Uwa3okuN2UyY/5ppt5d+XoatdFXi/z5alFZj5Cx01M4stMZk1HBmKmpPNqvKV1uzuPpdxKYMrjJ3xypZjw/aAfh3lk88NXwKr2vQG/P8+tuLrftkzE/8dYf3RjSIppgjzyGfzKG2bdsZ2KPQyz+o/t1THXNU1gsKKp5mpHqPr4Q4t+raswE1Rc3VZXJrOLNNb1gTdm2F0dt5YddZeWf+xbfyb19jvLk8D95YUXNln+iX9VQeE5J++VlPX41ntBykZ6z8zRc/NoRhRJ8bzHh0syE4nJdpNIOms4sv+ZA1EwNIWMN5J9WkrFVTecfi4lfZkf06xpavVV+qpXaqrafL+ePU1En6MmeX3FaU91NrpS0dUKZbcRxTRaubySR/XpD0CixOKnImxFYbn/3mQkUPOCLdnsuqtQSMj9ojMv7l3D6LoOC8bbvRPZbTFlZOybTi+gMLzbe/zWdgpLZdzEYo1nFK9vL1z280v8Pvj7WimY+6fRrHMvIb+5mfIcjvNB7F9N+G1zTWcDuvUwU8Qb0i8qmmVXuKUR5tBj9B38zv7uTEsPz5TsaaZ65hOEhT1R/FKBIMaD/LBi7JRmov8rG+Ijtpzr97dwV5yvLi7OZXvx+39d0Dkpm71/na0f58/Vqvz/46ngrmnmn079RLCO+vZsJ7Y/wQq9dTNtQ8+cLwNuniEcmH+XFZ3phMFQew329ogVfr2hR+vye+6I4ctgXo0nB6HtPM+mhgXTpeokZzx1g6mMDKj1GbVTX4qYKVEouPdwEv68vEPbMISxKKGrqRmHz8g01WUODyRpaNkLbc/1FiiLdsKgUeG5IJuGFVjidzMFvxXkSn62bs1NcSeKmqrFpk+tzzz3H6NGjiYyMxM7Ojnbt2jFt2jTGjh1b6f7z58/Hzc2t9BESElIt6SossCMp0YmA4MIKrwU3zKfvoCS+/DSSVu0zOHnUk7wce3ZuCSQ8MhcHR2O1pMkWQgJz6N/zPMt+aE+b5imcOONHbr6W7XtDadIoEwet4Z8PUk0upbrw1OxB3HbPGMZOvIMnnhuKSm3mUmrFFndXFx333n2M95d2JjIig4vJriRfcuXYSX9UKnPptCq1ib3ayOT++3lrY3d2RodyLtWL7/e3ZNOpMO7rfqzS94R6ZzOkdTQfbu1Mx9BkjsQHkFPkwKZTYTQLzMBRU/OLp10Ptfl7+HeSs1zJLtAS7F3596t9eBKN/LP5cVcL2ocls/t0A3QldvxxNIz2Yck1nNoyDz14lO9/bM72naHExbuzZWsj1vwUyai7oird39VVx9gxJ/jw445ENskkKdmF5EuuHD/hh0ptJigov4ZzcG1Gg5JLCY6ci3LhiyWNuXDWidvvvVhhv+BGhfQdlsqX74bSulMOpw66k5etYcdGXyJaFNSa63xelgqTEdx9yqfHw9tIdnrFPgAh4Tr6j8xm+UJ/Wncv4OReJ3Kz1Gxf50ZE62IcnGw7NcJzA3fSOzyeh1beRlp+2bU8o9ARjdqMi335ChZPpyIyCyvv3XJ7qzPk6+3ZFtOIjg2S2RrdCKNZxaYzYXRskFSt+RBCiOutqjET1EzclJlvvQZ7uhSX2+7pXFz62tXahyXR2C+bH/+0ln/2XC7/bDlW8+Wfs69qyNiuot1nOrT+5QN8r+4muv9WTM/tRfTcUUSL+Xr0aQq0wZVXBGTvV1J4XknwGCM5B1R49TKicgTfQUayD9SOzhJ1/Xw5f5yC/YECsl9pgNnbrsLrFicVpkANhhaO5D4bhPpiCfZ7Cyo9lnZzDmYnJSVdXLA7WYS+iwuoFeh7uGJ3sqi6s/J/uZjnSlaxlgZuuZW+3jkoiXCvbFYeb2md9iauIcVGOzbEhNMpqOZjC7v3MlDuK6JkoT/4lJVLVUd1KC4Z0Y6MR3tLLNpbYgHQzEtD8/SlSo+l2pgPzkrM3Z1QHddh6u4EagWmXtbntdG/OV9hntmsPGE9XzviL5+vc+F0tsH5+ktEk2w8PPS8+9EWfv59FT//vorWbTO4bcQ5fv59FUpl+WtgcEgefQck8OWylrRuk86p4z7k5dqzY3swEU1ycHCoHbF6fYubrkXfwImE51tx/o0OxL7anuTJkagKjRi8Kh9FbZdSjMuBDDJvDcYhJo/icBdMLnbkt/dEm1iEQlc78ymqj00bCb7//nu+/vprVq5cyeHDh1m+fDlvvvkmy5cvr3T/559/ntzc3NJHYmJitaRL62AkIKiIrMyrf0gWpjxznKXvNEdXrLbOja+2XiTVl/+9+qJZd1mYNmE3H33VGZ3e7vI6ANb5BP/6V6k02zKBAOj0dmTlOOLspKdj22T2HKgYAD364EFW/9yMjCwnVEoLalVZulUqS608Z2qVGTuVGbOl/IJMJrMCpaKy9Fp44dYdLN7YneISO5QKM+rL50dder5qXz7/Wd34HlbGx60AN0cdmXkVgy6N2siMkbtY+IN1MVXlFd9Ltcps03Nlb2+s8L0zmxUoKv3ewSMPHWbNT5FkZDqivLxeyF9UNs7LP1EqwU5z9ffHwuNzovl0YRi6IjVK1ZXX+cvfOVXtyJPRoCTmuCPtepY1xCgUFtr2LCDq0NXfOwtPLLjIxy8FoitSoVSCyu5yvi7H2Uqb1WFYeG7gTvo1iWXiyttIznUt9+rpFB8MJiWdQ8sadBp6ZhPoVsCxpIo9/TwcipnY8yALfu8JgFJhKbseKs2oavF38l+zABZLNT9snUkhxF+qGjNBzcRNyVkuZOQ50jGirPHV0b6E5g3SOBlf8fqsURt5auQuFvxo2/KPxWJtIEj/w9pA4HCNin8AjQfYuULWPiUlWQq8+1TsKGDSw9lX7YmcXYJCZZ3CyGKwlqUsRoV1vuJaoK6eLywWawPB3gJyXmmA2e/fLB5qvY8pDBX/+IpcI07fZVIw0ZpnhRkwXc6L0VJrztfV/JwKcNfqyCiqJLZQGZnZZydz/7h8rq4o+9gpa7g8brFg914Gqt1FlCwMwOJfvkHHMMoN/UdB6D8sewAYHvHEMKOSed9zTKi/zqFk0uXRAias5wms562Wn6/0Sjq0aFRGZvXeyUvbrOdLdVVZtfJ4v2YcPezLYxNuZsrEAaWP6DMebNvSgCkTB2A2XxknWnj8ycN8+lFrdDq1NRZU1846iPoTN/07Zgc1Jhc77NJ02CcUUti6kimaLRZ8v40lY2RDLPYqFGYLisvXwtJ/zbXj/P0nEjdViU2nG3r66adLe8YAtGrVivj4eObPn8/9999fYX97e3vs7a//PMITppxi3y4/0lIc8fLWMfahs5hNCrZvKj8EbtBtCeTm2LP/T+sQpKjjntwzIZqmLbLp2DWN+AvOFBZU7NVQU7T2BoL8y3otB/gUENYwk/wCe9IynXFx0uPrXYCXh7V3REiAtVU7K8eB7NzyF8YhfaPJzdOy93ADAE5G+zLujiM0C0+jU5sk4i66U1hkuzmdO7RNQgFcTHYl0D+fh8cdIjHJjY1/hJfbr33rZIID8njj3R4AnD3nRUhQHp3aJeHjXYjZrOBismsln1D9HDQGQjzLehYEuufRxD+DvGJ7UnJdOBgXwNSBe9AbVVzKcaFDaDJD20Tz1saK02SMaH+a7CIHdkaHAnAs0Z9H+hyiZXAqPcITOJ/mQYGuZs5Xff0eOmgMBHuXna8Az3wiAjPIK7Inr0jL+EEH2Xa8MZl5jgR55zL51n1czHBj35mKDVcP3nyYPacbEJ1kLQgfj/VnyrC9/Lq/KXf0PMWJWNstGLTvQBCj7z5Jeroj8QluhDXOZsTwM/y+qeJiWu3aXiIoMJ833+oGQHSMFyHBeXTskIyPdxFms5KLSbVjnZYHpl3g4E5P0i7Z4+hkos/QNFp1ymHWxNbl9ht05yVys+3Yv816bqKOuDF2UhxNW+fSsVcW8eccKcy33XX+aqs/8eapJYlEH3Pk7BFHRjycjtbRzO/fll+D5JZ7ssjNVLNvk3W4adQBJ+6bkUJk+0I69csn/qw9hXm2Ke2+MGgntzSPYdqPt1BYoildZ6BAr0FvVFOgt2fNsUhm9N9NbrGWwhINz928k2MX/TiRXPG38vTNf7JifxvSCqyjEY5d9Gdoy2j2xIZwR7sojl6shwtyCSHqtarGTHD94qaryz+BV5R/UnNc+G5nKx7of5jEdDcuZbnw8OCDZOQ5suNkaIVjPTjgcvkn2XqPPRHnz+Rb9/LLgabc2eMUx+Nq5voc/aqG1PVqWr2tQ+UE+gxrpZfa2YLq8jTTyWvUODU2Y+dpIe+okugF9oTcZ8SpUcWagLiP7fDqZcKlmbVSzK2tiXOLNQQMV3DxGzVubWuuJ2Z9PF/OH6ei3ZFH7gvBWByUKLOtDTVmRyXYK1GmlKDdlU9JWyfMbiqUGQacVmVhsVeg71BxlLnL0jSKhnti9rKW5wzNHNBuzaOkrRMOv+dgiKyZRYwd7QzlepkHu+YR6Z1Brs6eXL2WxzofYNO5xmQUORLilseMHntIyHFjV3yDCsd6tNMhdsQ14EyGDwBHLvnzVI89rDkdyZjWJzhyqebKPnbvZaLaWkjJXF8sDgrIutyw5mQ9X3iqsVSyVJ7FV12hQQHA7qNMjHe4gbe16srcwh71lgLMHRxQr8/H3KJmYsGrz1fQVedrUqcD/H7eer4auOUxo9seEnLd2JVQ8Xw91vEQO+IbcPqv85Xiz1Pd97DmTCT3tKrZ83W14mI74uPKT0+j06nIy9NU2D5oSCy5ufbs32OdyivqpDdjx0XRtFkmHTunEB/nSmHhv2nUqxn1IW5S6E3YpZeNnrHL1KO5WIjZUY3R0x7nw5mYnO0weGqwTy7C58d4Clt7UNTMvcKxXHenY3JWU9jK2oBQ3NgFz/VJaGPzcYzKRe/vgNnRplXG9dr777/PG2+8QUpKCm3atOHdd9+lc+fO19z/hx9+YNasWcTFxREREcGCBQsYMmRI6esPPPBAhc4jgwYNYsOGDVVKl03PeFFREUpl+cEMKpUKs7lmm4O9fHU889JhXN0M5OZoOHXck+kTe5KXU3bDcffQM+r+GJ56pGfptujTHqz5Joy5b+4jN9uexfPa1mi6r9a0cQaLZpV9AR67z7qY2cbt4bzxcS+6dUjgmUd3lb4+84ntAKxY1bbc/O/ursXcM/w4U+cMLd129rwPP/zaklef3kx2npaFH5WfW72mOTkaGD/2MN5eReQX2LNrbwOWrWyHyVT2fdJojEx+aD+vLu6N5XLP6IwsJ97/rDMzJv+JwajijXd7UFJim59B88A0Pnng59LnMwbvAeDno02Yu7YfL/x4M1P67+OVkVtwddCTkuvCB3905seDzcsdx9OpiPG9D/PgZyNKt51K8uOrPa15+571ZBc6MGdt35rJFPX3exgZks77k8vO19Th1vP16/4mvLGqF+EBWQzpGI2zQwkZeY7sPxvMJ791wmAqX4Bo7J9Fv7bnuX9R2RoRW483pn14Mh9OWUdCuhtzvqq4SFRN+eDjjowbe5zJjx3A3U1PZpYDv20I5+tvW5bbT6MxMvmRg7y2sGfZ7yvTkQ8/6cD0qXsxGJQsequrzX5fV3PzLGHG/NN4+pRQmK8mNtqJWRNbc2RPWaHQ3auE0RPjmTG2fem26BOurF4ewksfniAnU8PiFyNtkfxr2r7OAzcvE+OeTsHDx8iFUw68OLYRORllQZa7t4HRU1N58rayuVLPHnVk1cc+zFsRS06mmjenVgxgasrd7U8B8Nm9P5XbPvuXvqw7Yf17v7m5BxaLgkUjN6JRmdgdG8JrGyuuAdStUQIhHrm8uK7sN/TtoZY0D0jny/tXcTLZl4921Z01Ja7pr14r1f0ZQohawZYxU2RIOh88dkX55/bL5Z8DTXjlu758tbUNDhoDz925A2eHEo7H+vPkp0MoMZa//zf2z6J/m/OMe6us/PPH8ca0C0vmo0mXyz9f10z5J+k76z3yyPjylcHN5ukJGG6t0CyKU3DhbXsMuQq0QRZCHy4hZFzFUQQFMQrSNqrp/EPZFD6+A03kHDRx6AEHHEPNtFhQc+sR1Mfz5fhbDgAeLyaU2573hD+6/u5gp8AuqgjHdVkoCk2Y3dQYWjiS/XpDLO7l86U5XIDqUgl5T5bNkV801AP1OR0eT8djjNBSOLqS3uzVoIVvGl+MXFf6/NleuwFYe7opL2/tTVOvLG6PPIurfQlphU7sTgjm3b2dMZjLxxbhnpkMjjjPHd/cVbrt93NhdA5KZsUda4nLceeZjTU3L7z6F2tPbfunU8ptL5nhjWlg1ToPKQ8WoUw2Ynim7H3G21xRRJdgPzUZcxN7DPdW0kO6GrTwSWP5iLLz9VxP6/lac7opL2/vTROvLG5vWna+/kwM5t191zhf4ecZ+V3Z+dp4LoxOgcl8OWItsTnuPLOp9s/j7+6hY/TYM8x4oqyeIfqsJ6t/bMJLr/1JTrY9ixd0+psj1Lz6EDdp4wsJfud06XOf1dbrYl4Xb1LvC0OVZ8B7dQLqfANGVzvyuniTNbji+h+qPAOeG5NInF62toQ+1Jmc/v4EfhiNyUVNyn1h1Z+hmlAL46bvvvuO6dOn89FHH9GlSxeWLFnCoEGDOHv2LL6+vhX23717N2PGjGH+/PnceuutrFy5kuHDh3P48GFatiyrpxk8eDDLli0rff7/dBZRWCy2iwIfeOABNm/ezMcff0yLFi04cuQIEydOZPz48SxYsOAf35+Xl4ebmxsDGk5GrbRdr/bqoA+tmcJJTVPq6+ecZpmt6ueq7x7RdWORtaoo8q89vRmuJ9eY2jP3//WkvGC7OTmrkyk729ZJqBapj9ftBYErY9LriPr4BXJzc3F1tc3Is7/8Ve7p1+ZZ1KrqLfcYTXr+OLagVuRbiBvdf42ZoOz60eHuV1DZaas5xTVr+PNbbJ2EarF2vu06jVQn3agcWyehWpTsraR7fB3n1afyNQLquowdAf+8Ux0Uujrd1kmoFqbTMbZOQrWIea+LrZNw3ZmLdSQ+NatWxA+1OW7q0qULnTp14r333gPAbDYTEhLC448/znPPPVdh/1GjRlFYWMgvv/xSuq1r1660bduWjz76CLCWFXNycli7du1/yotNu3i+++67zJo1i0mTJpGWlkZgYCCPPPIIs2fPtmWyhBBCCCFqLzOg+Me9/vtnCCFqBYmZhBBCCCH+DzUYN+Xl5ZXbXNnUjyUlJRw6dIjnn3++dJtSqWTAgAHs2bOn0sPv2bOH6dOnl9s2aNCgCg0C27Ztw9fXFw8PD/r168crr7yCl5dXlbJi00YCFxcXlixZwpIlS2yZDCGEEEIIIYSolSRmEkIIIYSo3UJCyq9HOWfOHObOnVtuW0ZGBiaTCT8/v3Lb/fz8OHPmTKXHTUlJqXT/lJSyad0GDx7MyJEjadSoEefPn+eFF17glltuYc+ePahU/34NjdoxWbQQQgghhPhXFBYLimqeLbK6jy+EEEIIIYQQ1akm46bExMRy0w39P2sC/L9Gjx5d+v9WrVrRunVrwsLC2LZtG/37//vpC5X/vIsQQgghhBBCCCGEEEIIIa7m6upa7lFZI4G3tzcqlYrU1NRy21NTU/H396/0uP7+/lXaH6Bx48Z4e3tz7ty5KuVBGgmEEEIIIeoSi6VmHkIIIYQQQghRV9WyuEmj0dChQwe2bNlSus1sNrNlyxa6detW6Xu6detWbn+ATZs2XXN/gIsXL5KZmUlAQNUWapdGAiGEEEIIIYQQQgghhBCiGk2fPp1PP/2U5cuXc/r0aR577DEKCwt58MEHARg3bly5hY2nTp3Khg0bWLRoEWfOnGHu3LkcPHiQKVOmAFBQUMDTTz/N3r17iYuLY8uWLdx+++2Eh4czaNCgKqVN1iQQQgghhKhLaqKnv4wkEEIIIYQQQtRltTBuGjVqFOnp6cyePZuUlBTatm3Lhg0bShcnTkhIQKks69PfvXt3Vq5cycyZM3nhhReIiIhg7dq1tGzZEgCVSsXx48dZvnw5OTk5BAYGMnDgQObNm1fldRGkkUAIIYQQQgghhBBCCCGEqGZTpkwpHQlwtW3btlXYdtddd3HXXXdVur+DgwMbN268LumSRgIhhBBCiLqkFvaIEUIIIYQQQohaReKmKpE1CYQQQgghhBBCCCGEEEKIG5SMJBBCCCGEqEvMgKIGPkMIIYQQQggh6iqJm6pERhIIIYQQQgghhBBCCCGEEDcoaSQQQgghhBBCCCGEEEIIIW5QMt2QEEIIIUQdorBYUFTzAlnVfXwhhBBCCCGEqE4SN1WNjCQQQgghhBBCCCGEEEIIIW5QMpJACCGEEKIusVisj+r+DCGEEEIIIYSoqyRuqhIZSSCEEEIIIYQQQgghhBBC3KBkJIEQQgghRF1itoCimnusmOtPjxghhBBCCCHEDUjipiqRkQRCCCGEEEIIIYQQQgghxA1KRhIIIYQQQtQlMremEEIIIYQQQvw9iZuqREYSCCGEEEIIIYQQQgghhBA3KBlJIIQQQghRp9RAjxjqT48YIYQQQgghxI1I4qaqqBeNBGY3Z8wqe1sn47qyj0mxdRKqh8bO1imoFqZOTrZOQrVIvLl+/a4Awr7KsHUSqoUlPsnWSagWWSNb2zoJ1cI+x2zrJFQLv3d32zoJ153RYiDK1okQQojrJDtSgVKrsHUyrqsf3hlg6yRUC69zBbZOQrUI8qyfZfEfH//W1km47ro+86itk1AtGm1PsHUSqoXvD7m2TkK12P9Td1snoVpETKmfcVOirRMh/pN60UgghBBCCHHDkLk1hRBCCCGEEOLvSdxUJbImgRBCCCGEEEIIIYQQQghxg5KRBEIIIYQQdYnZQrXPfWmuPz1ihBBCCCGEEDcgiZuqREYSCCGEEEIIIYQQQgghhBA3KBlJIIQQQghRl1jM1kd1f4YQQgghhBBC1FUSN1WJjCQQQgghhBBCCCGEEEIIIW5QMpJACCGEEKIusVisj+r+DCGEEEIIIYSoqyRuqhIZSSCEEEIIIYQQQgghhBBC3KCkkUAIIYQQQgghhBBCCCGEuEHJdENCCCGEEHWJ2QJU87BWc/0ZNiuEEEIIIYS4AUncVCUykkAIIYQQQgghhBBCCCGEuEHJSAIhhBBCiLpEFuASQgghhBBCiL8ncVOVyEgCIYQQQgghhBBCCCGEEOIGJSMJhBBCCCHqEgs10COmeg8vhBBCCCGEENVK4qYqkZEEQgghhBBCCCGEEEIIIcQNSkYSCCGEEELUJTK3phBCCCGEEEL8PYmbqkRGEgghhBBCCCGEEEIIIYQQNygZSSCEEEIIUZeYzYC5Bj5DCCGEEEIIIeooiZuqREYSCCGEEEIIIYQQQgghhBA3KBlJIIQQQghRl8jcmkIIIYQQQgjx9yRuqhJpJACUSjNjx56kX984PDx0ZGU5sGlzI775pgWgAOCOkae5887TAPzwQzNWr2lW+v6mTTOYPOkg054ciNlcewdn3HX/eR6YEs3abxry6eLmADw07TQDbk1Cp1PxxXtN2LYhqHT/nv0v0W9oEi9P72irJFfqnvFnGDshuty2xHhnHr2nHwAPPX6SAUMS0RWr+OKj5mz7Pbh0v559k+k3OJGXn+1So2m+2viuh+nf9AKhnjnojSqOJfmzZFtX4rM8SvfRqIzM6LebQc3PoVGZ2B0bwmsbe5NV5AiAq1bHvFv/oFODJBKy3Zmzvg9nU31K3//8zTu4mOvKl/vb1li+JrY6zMCGsTR2z0FnVHEkzZ83D3YlNs+9dJ8Vg3+iS8Clcu/79kxz5uzpDYCbRsfrvbbSJSCJ+Dw3XtjVl9NZ3qX7zu66k8R8V5adalMjefo7Xt7FPPjwCTp2TsHe3silJGfeeqMjMdGeAIy86yx3jrJ+V3/4rilrfmhS+t6mkZlMmnqEJyf3q1XXjbsfTaLHwEyCGxdTolcSddiFzxc2JCnWoXSfh1+I4+aRaeiKVSx7owFb15V973reksmAEenMnRhpi+SXatsomXtvOkbT4Ax8XIt4ZvlAdpxqVG6fUN9sJg/ZR7tGl1CpzMSmevD8lzeTmuMCwNRbdzOkYzS6EjUf/NaFjUciSt/br9V5hnSI5qkvbqmxPLWOuMSYgcdp0jADb/ciXvzgZnYdDS19/YFhh+jX6Ty+HoUYjUrOJnizdG0nTsf6AmCnNvHMuB30aBNPVp4Db63syaHTZdf80QOP4edZwNvf9qixPP2dYQ9kcOdjaXj6GLkQ5cAHM4M4e9R6/Zs4J4mBd2ejK1by2asBbF1Tdu3sdWsOA+7KZs79ja51aCGEEP9SR79kJrQ8RkvvdHwdi5i0ZRBbEspfXxu7ZfN0x7108r+ESmHmfI4Hj28dyKVC6/30uU67GRFxlmKjmkUHu/DzhbLy0ODQ89weFs1jW2rufgrQrmEy9/U4RrOAdHxci5jxzSC2n7HmS6U0Man/AXpEJBDkkUeBTsP+C8G8u7kLGflOANipTMy6fRu9m8aRWeDIgl97sf9CWcxxX4+j+LsV8Mb6njWar6t5eRYxYdxhOrVPxt7eRHKKC4ve6UbMeS8A7rw9irtGnALg+zUtWPVT89L3No3I4PFH9/PE04NtWlbVf1mEcUcJ5ngTCntQtbTD/jFHlA3KV2WYThrQf1qEKcoASgWqCBUOi9xQ2CuwlFjQLSjAuKsEhacC7Qxn1B01pe8tWVmEOdWM9knnGsvXz8u9+HWFN6mJ1nQ0bKpj7JMpdOqXX7pP1EFHvlgQwJnDjqhU0LhFMa+tPI+9g4USvYIlT4WwZ6MbHr4Gprx2kfa9C0rf+8MHPqQlaZj8alKN5Qn+uQzep+UFRnQ9TWRQOm5Oeu576w5iLnmXO0ZtK4NXZsjIeIaMjMcvsBiA+AvOfPNZBIf2WMvdD02NYsDQi9Z6lvcj2bbxinqWfpfoN+QiLz/VySZpv1L+cj26bUaM8WYU9go0rVS4TrZH3dD6mzcmm0kbWVjpez1e1eLQ3w5zroXsl4spOWxCFaLE40Utdk1VpfvlvKFDHajEeaym0uNUhw6ByYxvf5TmPun4Ohfx+K+D+eNC2ffw1QF/MLzZ2XLv2RUfwiPrbgXATmni5f7b6Nc4loxCR+Zt783exLJr/IPtjhDgUsBrO3rVSH7+icRN4nqRRgLgrjtPM3RIDIsWdyU+3o0mEVk8+eQ+CgvtWLeuKaGh2dx77wnmvtQbBTB37g4OHwkgLs4dpdLM41MO8M47nWtVRd/VIprnMHhEIheiXUq3de6VSp/Bycx8vBNBIYVMnXWCw3t8yMvV4OhkYNxj0bw4ubMNU31tcRdcmDm1W+lzk8namNO5Rwp9bk5i5pPdCAouYOoLRzm8z4e8XHtrniae5sVp3a512BrToUEy3x1uyalLvqiUZh7vvY8PR/3CyKWj0RnsAHiq/5/0Ckvg6bUDKdDZ89zAnSweuZEHvhoBwEPdD+OkMTB62V3c3f4UswdvZ+zyOwFoFZhCy8A0Fmyu2cCks/8lvj7TghMZvqgUZqZ32M9ng35h6JpRFBvtSvf77mwz3jlSVigqNpZdih5tcxgnuxJGrruTMZGneKXHdu74+Q4A2vik0sYnjVf22b4S09m5hDff3srxoz7Mfq4nubn2BAblk59vLfyENs7h3geieOnFHqCwMPfVPzly0I+4WDeUSjNTph3hnbfa17rrRqvOufz8lT/RJ5xRqSw8MCOBV7+I4pHBbdEXq+jSL4s+wzJ48YHmBIbqePL1cxza6U5eth2Ozkbun57AC+Oa//MHVTMHjZGYS178fCCSBff/XuH1IM9cPn7sJ34+EMmnv3ekUGdHY/9sSgzW72LPZnEMbHeOqUuHEuKdy4t3bWPv2WByixxw0up5dPABHv90aM3myd7IuYuerP+zCa9M2lzh9Yupbrz9TQ+S012wtzNy14CTvDltPfe8OIrcAgeG9TpNkwYZTHr9Nrq0vMisCX8w/Kl7AQX+Xnnc2usME18dUaN5upabbstm4pxk3n0umDOHHRnxcDqvrrzAhF5NiWxfRN8ROTw/pjFBjfVMX5TIoe0u5GWpcXQx8cCzl3hudJits1C9pEeMEKKGOKqNnM32YlVMJO/331jh9RCXXFYOWcuqmEjeOdKJAoMdEe7Z6E3W+2nfkDhubRzDhI1Daeiay2s9t7ErKYRsvQPOdnqmtd/Pgxtvrels4WBnJCbFi3WHI3lzTPl8ae2MRAaks3R7e2JSvHFx0PPULX+yeMwGxn1iLZOO7BhFZEAG45eOoHtEAq/csZmBb9wPKAh0z2N4+9Ol+9qKs5Oexa9v5PgJP2bO60dOrpagwDwKCq1l1UYNs7nvnmPMfqUvCoWFl1/cxqGjAcTFe6BUmnnisX28/UEXm5dVTUcNaEZoUTZTgwn0HxdSND0Ppy89UDhYY0DTSQNFT+WhudcB+2lOKFRgOmf6q78fhnU6zGeNOH7khmlvCbqX8nFa54lCocCcbKLkZx1OS91rNF8+AQbGv5BMUCM9FouCTT94MPfBRrz/ezShTXVEHXTkxbFhjJ6SyqRXklCpLFyIckBx+XT89pUXMccdeevnGA784cLrkxvy3fFTKBSQkqDht5VevPtb9N8nohr8UxlcqzFyLM6fLccb88KdOyq8XhvL4JXJSNPyxQeRJCc6ARYGDL3IrDcO8sR9vfAPKqLPoGRmPtGZoAaFTH3xOIf3XlnPcpYXp9i20+JfSo6YcLpDg11zJZgg70M9mVOL8PnGCaWDApWfAr9fncq9p2itgYKvS7DvZr3O53+hx1IEPsudKFxdQs58HT5fWN9TctKE4ZQJt+n2NZovBzsDZzO8WB0VyTtDK967AHbGhzBzc7/S5yWmsoaNu1pG0cI3nXt+GEmvhgksHLiJ3p89ACgIcs3jzhanufu7O6s5F/+OxE3/QOKmKrFpI0F+fj6zZs1izZo1pKWl0a5dO95++206darZFtVmzTPYuzeYAwesrbtpac7c1Ceepk0yAQgJyScuzp1jx/wBiI1zJzg4j7g4d+688zQnTvoSHeNVo2muCq2DkadfPsa7r7Vk1PjzpdtDQgs5fsiLc6fdOHfajYenn8YvqIi8XA3jnzjL+lUNSE91+Jsj247ZpCA7S1the0jDfI4f8eLcGXfOnXHn4akn8QsoIi/XnvGToli/NpT0VEcbpLi8yd+XD4Zm/9qPrVO/oLl/OocTA3G21zOizRmeXzeAA/HWFus5v/Zl7cRvaRWYwolkfxp7ZbMhKpyEbHdWHW3OHW2iAFArTcwctIOXfuuD2VKzhfqHNpUvsD23sy9771lOC690DqYGlm7XGdVkFFd+HsLcs1kfG05cnjvfnW3O3U2sI3jUChMvddvBzD9vqvF8VebO0WdJT3fgrTfKrlepKWUFqJCQfOIuuHHsqLU3SdwFN4JD8omLdePOUdGcPOFNzFnPGk/3P5k1vnwF/+Jnw/l2/0EiWhZy8oArIWHFnNjnSsxJZ2JOOvPIzDj8g/XkZdsx4dl4fl3pR/qlmi0EVmbP2QbsOdvgmq8/OvgAu8804L31XUu3JWW5lf4/1DeHw+cDOXPRhzMXfXhy2G4CPfPJLXJgypB9rN7bvHTEQU3ZdzKEfSdDrvn65v3h5Z6//0NXbu11lrDgLA6fCaJhQA5/HmtI3CVPkjNcmXTXPtycdeQWODB97J98vKozRbqa6+Hzd0ZOzGDDSk9+/876G3nn2WA6989j0JgsLGYFx/c4E3PckZjjjjz6UjL+ISXkZal5aGYyv6zwJj2pduRDCCGuF1vFTTuSGrAj6dr30yfb72fHxQa8cbCsE05iftn9NMwtm/0pgZzM9OVkpi8vdNlNsEs+2XoHnu60l2/ONC8dcVCTdp9rwO5zleerUG/P5BXDym1b+GtPVjyyGj+3fFJzXQj1zmbH2YZcSPckKduVaYP24u6oI6fIgedu3cm7m7pSqLftvejukVFkZDiy6N3updtS08p6yocE5xEb58GxE5dj3Hh3QoLyiIv34K4RUZw45Uv0Oe8Kx61pjovcyj3XvuBC4W1ZmM4aUbe1dkLSvVuI5k4t9veWxRdXjjQwx5tQ99SgaqRGGahC/0ERlhwLCg8FukUF2D/mhMKpZuOLrgPzyj1/8LkUflnhzZlDjoQ21fHx3CCGT0hn1ONppfuEhOtL/59wTkvXgbmENtUR0EDP0nlB5GapcPcy8c5zwUx48RJOLjW/mOY/lcE3HLaOJArwyK/09dpYBq/M/l1+5Z6v+CiSISMTiGyZjYubgeOHPcvqJJ6Mwi/wcj3L42dqVT2L15LyMbn7LC2ptxRiOGPCvp0ahUqByktRbp/i7UYc+tuhdLRuN8aZcbhZjbqBEsfb7ShaawDAYrSQs0CH+wtaFKryx6huu+Ibsiu+4d/uU2JSkVF0jToJj2y2xoZyPsuTi7muPN1zDx5aHdk6B2b32cHi3V0pNNSOeEPiJnE92bSm7aGHHmLTpk18+eWXnDhxgoEDBzJgwACSkmp2SNzpKG/atk0lKMh6o27UKJsWzdM5eNBaqRkX50ZQUD4+PoX4+hYSFJhHfLwbAf753HxzLCtWtK7R9FbVY89EceBPX47uL1/Ii41xIaJZLs4uBsIjc7G3N3Ep0YnmbbIIi8xj3XehtknwvxAYXMiKnzby2febeWrOIXz8igCIPedGRGQOzi4lhDfNwd7ezKUkJ5q3ziSsaS7rfmhs45RXztm+BIDcYmvlajP/dOxUZvbFlQ1pi8vyIDnXmTZBqQCcTfOic8MkVAoz3RslEJ1ubah6oMtRDiYEEpXiW8O5qMhFczlf+vINOsPCYtg75gt+Hv4d0zvsQ6sylL52JsubrgHWfPUKSuRstjVfD7U6Whpk1gZduycTc9aD52fvYeWPP/PuR5sZNORC6etxsW4EBefj41uEr28hgcEFxMe54h9QwM2D41jxeQsbpv7fc3QxApCfYw20LpxxIqJVAc6uRsJbFGCvNZMcr6VFhzzCWxSybnmALZP7rygUFro3SyAhw40lE35l/ezlfDZlDb1bxJbuE3PJi8jgdFwc9DQNSsfezsjFTDfahF6iaVAG3+9qacMc/DO1ysSwXmfIL9Jw/qL1N3TuohetIlLQ2Bnp3OIiGTmO5BZoGdD5HCVGFTuP1o5hpmo7MxGtizi8sywAtFgUHNnpQvMORVw4paVJ6yKc3YyEtypCozWTHKehRecCwlsV89Nntq/QqHZmS808hBC1Rm2Jm66kwEKfkATi8txZOvAXdo/+gu9vXU3/BmX30zNZ3rT0TsdVo6eFVzpalZH4PDc6+F6ihWcGX55uZbP0V4WztgSzGQp01rJ6TKo3bRukYK820jU8kfQ8R3KKtAxuFU2JUcW2M7a/p3btfJHoc168+PQOvvviB95f/Cu33BxT+npsvDvBgXn4eBfi61NAUGA+cQnuBPjnM7DfeZZ/3dZ2if87hdb7k8LVWulozjZjjjKicFdS+FgOBbdlUjQlB+PxsvhCGa7CdNyARW/BuK8EhZcShbsCw+86FBoFdr1t28HFZIJta93RFylp1rGQnAw1Zw474e5lZNqwCEa1bsFTI8M5ua+sQ1Lj5sWc2u+EvljBoW2uePoZcPM08cdqDzT2FnrckmvDHP3/6mIZXKm00PvmZLQOJk6f9CA2xpWIyCvrWcxcuni5nqVpLuu+t/314Vosl2esUrpWXqlfcsaEMdqM47CyWQLsIlToD5mwGC3o95lQh1urGQu+LMG+vQpNM1Wlx7K1TkHJ7JiwjF/uXcmsPttx0+pKXzuT4UX7gEvYq4z0aJhIWoEj2TotQ5tEozep2HKhdtQrSdz0L0jcVCU2G0lQXFzMqlWr+Omnn+jd2zoX+dy5c/n555/58MMPeeWVV2osLd//0BxHRwOffPwrZrMCpdLC8hWt2botFIDERDe+WN6a117dCsAXy9uQmOjGa6/+weeft6VD+0uMHXsSk0nBRx934OTJ2lGJCdD75mTCI3OZdn/3Cq8d3uvD1t8CeWv5bkr0Sha/1BpdsYrJz53irZdaM+SOBIaNiicvx453X2tJwgXbt9gDnI3y4K1X23ExwQlPLz33jD/Lwg/+ZNJ9fTm835etG4N5a+kOSvQqFr/SDl2xmslPHeetV9sxZEQcw+68QF6OhncXtiEh1tXW2UGBhacH/MmRRH/OZ1gr87ydiigxKsnXly+wZhU64uVkbRBZtrcdLw7cwc+Pfk1yrgsvre9DA48chrU6y7gVI3lx0Ha6NUok6pIvL2+4iQJ9zRZ+FVh4ocufHEr1JyanrMf8LxciSC5wIa3YkaYemTzVcR+N3HJ4/I9BAHxyvC1zu+1k050rSSpw4cVdN9HQNYfh4dGM/nUEL3XbQY+gRE5m+DDzz5soMNimUO8fUMjQ2y6w5scIvlsZSZOm2Tw65ShGo5Itv4eSmODK8s9a8upC6zDa5UtbkpjgyqsLd/D5J61o3ymVseOiMBmVfPx+G06e8PmHT6x5CoWFR16M49RBF+JjrL0sDu9054+ffHh7zXH0OiWLng5DV6xk8ssXWPxMOEPHpnDbfSnkZtvxzszGJMTYfuTO1TycinGyNzCu71E+3tiJ99d3oWvTRF6/73cmfzKMIxcC2RcdwsYjEXz++Gr0BjUvf9eX4hI1T4/Yxbzv+zCyWxR39ThJbqGW+at6E5taO0aFdGsVz+yH/0CrMZKZ68hTbw0ht8DaSLf+z6aEBWWx4qUfyC3QMveT/rg46hl/+0GmvnkrE24/QL9OF0hOd2HB8pvIyHH6h0+rHq6eJlRqyEkvX0TJzlATEq7n0HZXtqwu4t31Meh1St6c2gBdkZLH5yfx5rQQbr0/k9vGZ5CXpeLtp0OIj6446kwIIeqS2hQ3XcnLoRgnOwMPtzrCksOdePNgV3oFJfJev42M++02DqQGsis5hHXnI/hx2Cp0JjXP7uxHsVHNnO47eX5nX8ZERnFfsxNk67TM2n0T53Jqx/30Shq1kcdv3svGk+GlowN+OtyUcL9Mvp/yHTlFWp774WZcHfQ82u8gjyy7jcf67Wdgy3NczHbl5bV9SM+vubnu/xLgl8+tg/NZva4Z3/7YkiYRmTz20EEMRiWbt4aReNGNZV+1Zf5L1ikMl33ZlsSLbrz+0maWrmhPh3bJ3Df6OEaTkg+XduRklN8/fGL1s5gt6N4pQNVKjaqxtZxgSTYBoF9WhHaSE8oINYYNOoqn5eK03ANliAq7oVrM500U3peNwk2J9mUXyLeg/6wIx3fc0H9aiGGLHmWgCu3zzih9aqZSM/a0lmnDIijRK3FwMjP7s1gaNtFz+pC1/PzlYn8enpVMWItiNv/owXOjwvj4jzMENS5h0OhMYqO0PNwnEjdPEy9+FEd+jooVb/iz8MdzfLHAn20/eRDYUM/0xYl4Bxj+ITW1Q10qgzcMy2PR0t1oNGaKi1W88mwHEmNdSIx1YeuGIN5atstaJ/FSG2s9yzMneWteG4aMjGfY3XHWOon5rUiIrR31LBazhdwlOjStVdiFVf4bKFpnQB2qRNO67HXncRpyF+pIu7MQVYAS9xe1GBPMFK034LPUiZwFOvT7jNg1U+H+vBalc82OKqjMrvgQNp9vxMU8V0Lc8pjWbR8f3/Yr9/wwArNFyZrTkTT1zmTd2G/J0WmZsWEgbvZ6pnQ9wIOrb+eJrvu4JeIcibluzNzSh7TCmr/Gg8RN4vqzWSOB0WjEZDKh1Zb/Ejo4OLBr165K36PX69Hry4bY5eXlVbpfVfXulUDfvvEsXNid+AQ3GjfO5pGJh8nKdGDzFmsL4fr1EaxfX7ZgzoD+FygutuP0aS8+/eRXpk4bhLd3Ec89u5sHHxyGwWj71lJvv2ImzjjNzCmdMJRUnp6Vn0aw8tOyfI15KIaj+70xGhWMnnCOSaN70rlXOjPmHmfqONvPAw9waG9ZATXuvLXRYNmqTfTql8TvvzRk5eeRrPy8bNHUMQ+e5ehBH2ue7o9m0rg+dO6eyoyZR5g64SZbZKGc5wfuINwniwe+Gl6l9xXo7Xn+55vLbftkzE+8tbUbQ1pEE+yex/BPxjD7lu1M7HGIxX9UbCiqTnO67STCPYt71g8vt/376LLpbKKzvUgvdmL54J8JccklMd+NAoM9T+0YUO49ywev442DXRnWOIZglzwGrxrNvB7bmdz2EAsO1Gy+/qJQWIiJ9mD5Z9YecBfOedAwNI8hwy6w5fdQANb/Esb6X8rm+Os/MI7iYjWno7z45IuNTJvUD2+fYp6duY8H770Fo8H2140rTZ4bS2iTYp4aXX7Uw9fvhPD1O2XT3tzzeCJHd7tbf2OTkpg0tA2d+2bz1BvneGJ47RtppVRaW/p3nArl253W9MVc8qZ1aCojukZx5IJ1FNnSTR1Zuqls4fYJAw5y4FwQJpOSB/sfZuziu+jRLJ45o7bywDu2nXf4L0fOBvLQvJG4Oeu4tdcZ5j6ymUfnDycn3wGTScmSb3rAN2XX8ufu386qLS2JaJBJz7bxTHh5JGMGHeeJ0buZ/dHNf/NJtvXVIn++WuRf+nzs9BSO7HTGZFQwZmoqj/ZrSpeb83j6nQSmDG7yN0eqmywWMxZL9U4hUN3HF0L8e7UpbrqSEuv9dEtCKMuj2gDWkQPtfVMYHRnFgctTTb53tBPvHS2bFmly24PsSQ7CaFbyWOtDDFt7N31D4lnQ6w/u+Ll2zPH8F5XSxOt3bUIBvP5L79LtJrOKhb+WX7By9vCtfLuvJU0DMugTGcuYD+9iXM+jPD3kT575blANpxwUCog578myr9oBcD7Wk9AGOQwdFMPmrdby6a8bm/DrxrL75IC+5ykqtuP0GW8++2Adjz91Cz5eRbzw1C7unzjc5jGufnEh5lgTju9fMQXR5duV5jYtdkOtvxFVE2dMhwwYftVh/6gTCrUC7fTylXjFr+WjudMBc4wR484SnJZ5ULKyCP3bhTi8UjMdyYLD9Hyw6SxF+Sp2/uLOm1Mb8sbqGMyX8zTk3kwGjc4CILxVMUd3ubDxWy/Gv3AJtR1MmZ8ElI0menNaCLdPSOf8SQd2b3Djo81n+f59Xz6YFcTspXE1kqfroa6UwZPinXn8vl44ORvp0e8S02cf49nHupIY68LKpU1YubTstzVmQjRHD1yuZxl/jkn39KJzzzRmzD3K1Ptrx+K3uW/oMZ434/1J5Z28LDoLxb8bcHmwfCc9pbMCj5fLT5+UMbkI18ftKdpgwJRkxvd7J3Je05H/mR63qbaviP4tpqwOLCbTi+gMLzbe/zWdgpLZdzEYo1nFK9t7l3vPK/3/4OtjrWjmk06/xrGM/OZuxnc4wgu9dzHtt8E1nYV/TeImiZuqwmbTDbm4uNCtWzfmzZtHcnIyJpOJr776ij179nDp0qVK3zN//nzc3NxKHyEh156buSomTDjK9z80Y/uOhsTFufPHH41Ys7Ypd98dVen+rq567rnnJB9+2IGmTTNJSnIhOdmF48f9UKvNBAVXPr9eTQuPzMPDq4R3vtzNuj0bWLdnA607ZHHbqHjW7dlQWlH2l+CGBfS9JZkvP4qgVYcsTh72JC/Hnp2b/AlvloeDo9FGOfl7hQV2JCU6ExBcWOG14Ab59B10kS8/jaRVu0xOHvWy5umPQMIjc22ep+du3knv8HgeWnkbaVf0MMoodESjNuNiry+3v6dTEZmFld+0b291hnydPdtiGtGxQTJboxthNKvYdCaMjg1qdij6rK476RMSz/0bbiO16O9b1Y+lW0feNHSpPHgdGX6GvBJ7tiQ0onNAMlsSQjFaVGyIC6Ozf/J1T/u/lZ3lQGJ8+QAiMcEFH9+iSvd3ddVzz32n+fDdtjSNzCLpojPJSS4cP+qLWm0mOLigJpL9rz025wKd+2Xz7L3NyUi59miN4MbF9Ls9gxVvhdC6Sx4nD7iSm2XHjvVeRLQsxMHJVIOp/ndyCrUYTUriUj3KbY9LdcffvfLz0NAnm8HtY/hkYyfahyVz5EIAOYUObDkWRmRwBo6XpwyzNV2JHUnpbkTF+rFwxU2YTEqG9jhb6b7tmiYTGpjNmq3NadckmX0nQ9CV2LH1YCPaNqn8PlwT8rJUmIzg7lP++uzhbSQ7vWLfhpBwHf1HZrN8oT+tuxdwcq8TuVlqtq9zI6J1ca38DgohRFXUprjpStl6LQazkvO55e+n53M9CHSqPB5q7JbNbWHRvH24M539kzmYGkC23oHf4sJo6Z2Bk7p23E/hcgPB3Zvwdy9g8opb/3aNgQ6hSYT5ZPH9vpZ0CE3mz5gG6Ax2bD4ZRvtQ25RXs7IdiE8sP59/4kU3fH0qxkwAri467h11gg8+7UhkkwySklxJvuTKsZP+qFTm0ql5bUX3VgHGPSU4vu2G0ressULhZa3SUIaWb8BQhqowp1VecWM8XII5zoTdSC3GIwZUXTUoHBSo+9ljPFJzPe7tNBaCGpUQ0bqY8S9colHzYtYu9cHLz1oGathEV27/kHAdaUl2lR2Ko386Ex+t5bYHMzi+x5nO/fLQOprpfVsOx/fYppfz9VCby+BGo5JLF504d8aN5R9EEhvjwu2j4irsZ61nSeLLj5vQqn0mJ49crmfZHEB4ZO2oZ8l5U4fuTyNeHzii8q28mrB4qxGLDhyG/H1f46JfDCidFTj0tqPksAltbzUKtQKH/tbntdHFPFeyirU0cKt8mq7OQUmEe2Wz8nhLOgUlszOuIcVGOzbEhNMpyHZ1EhI3ievNpmsSfPnll1gsFoKCgrC3t+edd95hzJgxKJWVJ+v5558nNze39JGYmHhd0mFvb8RiLj/kyWxWoLjGX2fixMOsXRtJRqYjKqUFtbqs8KFUmitUvtvKsQNeTBrdk8fv7VH6iI5yY9uGQB6/twfmcnm2MOWFkyxdEomuWI1KaUGltuZDffnf2pKvq2kdjAQEFZKVcXWLtIUpzxxn6bstrHlSWVBdPle2z5OF527eSb8msUz85jaSc8tXNp9O8cFgUtI59GLptoae2QS6FXAsqeJQXw+HYib2OMiCTT0Ba77Uqst5VZpRKWoqnxZmdd3JzQ1iuX/DMC4W/HMvnGaeGQCkV7KQsYd9MZPbHmLeXmvPZ5XCglppi3xVFHXSi6CQ8gFwUHA+addYGHvipGOsXRVBZobj5fNTlnalylKLfl8WHptzge43Z/Hcvc1Jvfh3PT0sPPHKeT59rSG6IhVKVdn10Pa/sWszmlREJfrQwCen3PYQn1wuZVc23NfCs3fs5O2fu1FcYlf+93X5X6UNv4t/R6G0YGdXsbCnURuZNuZPFn3VE7NFiVJpQVWaJ9t+H40GJTHHHWnXs+z3pVBYaNuzgKhDV/++LDyx4CIfvxRo/Q4qQWV3+d51OYZW1q4BOteHpQbm1bTUzu+0EDeq2hI3XclgVnEiw4dGrjnltoe65pBUUPn99KXuO3h9f3eKjHYoFeZy5TqoPeWGvxoIGnjmMmn5reQWX7s8pFEbeXboLl77+abL91RzuXKCrcqrUWd8CLmqYj8oMI+09MqnE3xkwiFW/xxJRqaTtVxwRYyrsmHZwGKxWBsIdpTguMQNZWD5G7siQInCW4k5sXx5x5xoQulX8fdh0VvQLy5E+5SzdTFVM2C8nDcjpSMTbMFiAUOJEr+QErz8S7h4vnxHnaQL9vgGV2zEKNEpeP+FYKYuuIhKBWaTAqPRGuubDArMJttP7/L/qVtlcIUS7Oyu/gJZmPLcCZYuaX5FPUvtiZcsFou1gWC7Ee/3HFEHXruKsGidAW0vNSqPa+9jyjZbRwvMuPzdNYPl8k/TYrRQWztc+zkV4K7VVbqQsUZlZGafncz9o7f1Gn9FnYSdjev/JG76FyRuqhKbNhKEhYWxfft2CgoKSExMZP/+/RgMBho3rnwREHt7e1xdXcs9rod9+4IYPfoUnTol4etbQPduiYwccZY9u4Mr7Nuu3SWCgvL5+Rfr8KToaC+Cg/Pp2DGZWwafw2xWcPFi7ZhTrrhITfx5l3IPXbGKvFw74s+XT+Og4RfJzdawf6e1AjrqmAdtOmXStGU2w++JI/6CM4UFlfdaqGkTJp+iZdsMfP2LaNYyi5nz92M2Kdi+OajcfoOGJZCbo2H/n9ahVVHHPWnTIYOmLbIYPuo88bEuNsvTCwN3MrRFNM+vG0BhiQYvpyK8nIqwV1tbgAv09qw5FsmMfrvp2CCJZn7pvDxkK8cu+nEi2b/C8Z4e8Ccr9rchrcDaS+TYRX+GtoimkVc2d7SN4ujFiu+pDnO67uS2xjHM2D6AQoMGb4civB2KsFdZ8xXiksukNodo4ZVOkHMe/ULiWNBrK/tTAkoXKL7Si13+5PNTbUi7PBrhcKoft4fF0Ngtm1FNozicVjP5qsyaVRFENsvi7ntOExBYQJ9+CdwyNJZffgqvsG+7DqkEBefzy0/Wod3RZz0IbpBHx86XGDz0gvW6kVg7rhuTX4ql3+0ZLJweQXGhCg/vEjy8S9DYV6xoHjwqjdwsO/b9YZ0LNOqQC2265RHZNp8R4y8RH+NAYb5tZrVz0BiICMggIsDaCBXomU9EQAZ+7tYC1Nfb2zCgzXlu73yaYK9c7ux+kp7N4lm9p3mFY93e+Qw5BVp2nQ4F4HicPx3DkmnRIJXRvY5zIcWjdCHDas2TvYHw4EzCgzMBCPDOJzw4E1/PArQaAw8PP0DzRqn4eebTpEE6z96/HW/3IrYdrLg42rhbj7D3ZAgxidbFqk6e86N3uzgaB2Uyou8pTp637bzDqz/x5pZ7shhwVxYh4Toef/0iWkczv39bft7ZW+7JIjdTzb5N1p6SUQecaNujgMj2hYycmE78WXsK8+pjaVcIcaOxVdzkqDYQ6ZlB5OVOHcHOeUR6ZhBweaTAZyfackuj89zVJIoGLrmMbXaSviHxfHOmRYVj3dXkNFk6B7YmhgJwOM2frgHJtPFJ5YHmx4nJ9iC/pGbWmnLQGGjin0ETf2u+gjzyaOKfgZ9bPiqliYWjNtEsMJ2Zq/qjUlrwci7Cy7kItapieeihmw7xZ0wDzqZY76nHEvzp2yyWcL9M7u58kmOJtimvrl4XSWSTDEbfeZJA/3z69o5lyMAY1q1vWmHf9m0uERyYx8+XX4s+50VIUB4d2ydxy8AYa1k1yTZruekXF2L4XY92tgs4KjBnmjFnmrHoLy9grFCgGeNAyY86DFv1mC+a0C8txBxvwu7Wio07JcuLUHXToGpiLaOqWtlh3FGC6ZwRw6piVC1rpuz6+WsBnNjrREqihtjTWj5/LYDju53pOyILhQLufCydtZ/5sPMXN5JiNSxf6E/ieS2Dx2RWONbXS/zo1C+P8FbFADTvVMif6925EKVl3TJvWnSquRHL/1QGd3XQERGQQahfNgANfXOICMjA07niaOzaUgavzP2TztCibSa+AUU0DMvj/klnaNU+k60bA8vtN+j2RGudxK7L9SzHPWnT8XI9y5hYm9ez5L6hp3iDAY+XtCicwJRpxpRpxqIrX+lpTDRTctSE421/n9a8t/Q436MpHY1g11pJ8W8GDLEmitYayq1lUJ0c7QxEemcQ6X353uWaR6R3BgHO+TjaGZjRYzet/VIIdMmjS/BF3r31NxJy3NgV36DCsR7tdIgdcQ04k2FdQ/DIJX8GhF2giVcmY1qf4Mgl29VJgMRN4vqy2ZoEV3JycsLJyYns7Gw2btzIwoULa/TzP/yoA+PuO87kyQdxd9OTleXA+t/CWbmyfOFWozEy6bFDzH+9BxaLtTU+I9ORDz/qwJPT9mEwKlm0uCslJbXiz/qvuXvqGfXgeZ6a0LV0W3SUO2u+DmXuW4fIzdaweG7tmVfcy7eYZ146hKurgdwcDaeOezL9kV7k5ZQVENw9dIy6P5qnHi2b3y/6tAdrvg1j7hv7yM22Z/Er7WyRfADubn8KgM/G/lRu++xf+7LuhHU9hTe3WL9ni0ZsRKMysTs2hNd+713hWN0aJRDikcuLP/cv3fbtoZY090/ny3GrOHnJl4/+7FjhfdXhnmbWKbq+GrKu3PbndvZhzblIDGYV3QIvMq75cRzVRi4VOfF7fCM+ONahwrF6BibSwDWPp3eU5eur0y1p6Z3OD7eu5niGL+8drZl8VSbmrCevzOnGAxNOcs99p0m55MTHH7Rh25byBQuNxsRjjx/h9XldS68bmRmOfPReO6Y9fRCjQcXiBZ0ouca6ITXt1rGpACxcWX66tUXPhLF5ddmi7O5eJYx+LInpd7cs3RZ93IXVnwXw0qdnyMm0Y9EzYdhKs+B0Pnj059Ln04btAeDXg02Y931ftp9qxILVvbi/3xGevP1PEtLdef7LgRyLCyh3HE/nIh7od5iHPxheui0q0ZeVO1uz+MHfyC5w4OXv+9ZInpo2TOftp34tfT7l7r0A/LY7gsVf9aSBfw6DukXj5qwjr1DLmTgfnlg4jLhL5QuIjQKz6NvhAhPmjSzdtu1wY9o2vcS7z/xMYoo785bWTJ6uZfs6D9y8TIx7OgUPHyMXTjnw4thG5GSUBSbu3gZGT03lydvK5hQ9e9SRVR/7MG9FLDmZat6cWrGgL4QQdVlNx00tvdP48pay++kLXaz309UxTXh+Vz82JzRi7p7eTGx9mJld/iQ2150ntg7kUFr5+6mXtohHWx9mzK8jSredyPBj2cnWfDxgPVk6B57dWXP3nuaBaXz8YFm+pg+25uvnI034ZFtHboqMA+CbST+We98jy4ZxKK6sY1KYbxYDWpznng/vKt22JSqMDqHJLB3/E/EZbry4qvxaWzUl+pw3L79+Ew/ed5Sxdx8nJdWZjz7ryNYd5TsPaDRGJk3cz2tv9roixnXig087MuPxPRgMKt58u5vNYlzDWuuUO8VPlJ8GRPu8M3ZDrI0AmrsdsJRY0L9XiCXPjDJcjcNbbiiDypevTReMGLbqcfq8bIosdR8NpiMGiqbkomygwmF2zXTcyclQ88YTDclKU+PoYqJRMx2vrjxPh5usFfojH07HoFPw0Zwg8nNUNG6uY/435wkMLT+9TtwZLTt+9uDDTWXTS/a61TrF0IwREQSH6Xju/fgayRP8cxm8V/N4Zo3aVvr6K2O3ALB0U4dy6xDUpjJ4Zdw99MyYcwxPbz2FBWrizrkwa2pnju73KdvHU8+oB87x1MNla+hFR7mzZmVj5i4+QG6WhsUvt7VB6ssUrbaOTMmcVFxuu/tMLY63lpW7i34xoPJVYN/l2jGrbq8R40Uz7nPLGuec7tJgOGMmY0IRds1VuEy49rRt11ML3zS+GFlWJ/Fsr90ArD3dlJe39qapVxa3R57F1b6EtEIndicE8+7ezhjM5fMX7pnJ4Ijz3PFN2TX+93NhdA5KZsUda4nLceeZjba5xv9F4iZxPSksFtuNi9i4cSMWi4WmTZty7tw5nn76abRaLTt37sTO7p9bU/Py8nBzc6Nf62dRq2zTglxdlOk5tk5C9dDUjtEI11vSsKB/3qkOKvarP8Om/hL2VYatk1AtLPE1u+5ETckeWXsaKK8n+5xaOtb2P9L+st/WSbjujBYD2/iJ3Nzc6zaC8f/1V7mnv9t9qBXVG2QZLSVsyf2yVuRbCHH94qbGs19FqbX9opHXk0ucrVNQPbyO1a71qq4Xl0W2W/eoOv0YttnWSbjuuj7zqK2TUC28tl/8553qIN8fKp9Pv67b/1MrWyehWgTP323rJFx3EjfZPt//lU2nG8rNzWXy5MlERkYybtw4evbsycaNG/9VQVcIIYQQQgghbgQSNwkhhBBCiOpk03lx7r77bu6++25bJkEIIYQQom4xm0FRzaNRauvKckLcoCRuEkIIIYSoIombqsSmIwmEEEIIIYQQQgghhBBCCGE7dWuFXSGEEEKIG53FAlTzmjG2W7JKCCGEEEIIIf47iZuqREYSCCGEEEIIIYQQQgghhBA3KBlJIIQQQghRh1jMZizVPLempR7NrSmEEEIIIYS48UjcVDUykkAIIYQQQgghhBBCCCGEuEHJSAIhhBBCiLpE5tYUQgghhBBCiL8ncVOVyEgCIYQQQgghhBBCCCGEEOIGJSMJhBBCCCHqErMFFNIjRgghhBBCCCGuSeKmKpGRBEIIIYQQQgghhBBCCCHEDUpGEgghhBBC1CUWC2Cugc8QQgghhBBCiDpK4qYqkZEEQgghhBBCCCGEEEIIIcQNSkYSCCGEEELUIRazBUs1z61pqUc9YoQQQgghhBA3HombqkZGEgghhBBCCCGEEEIIIYQQNygZSSCEEEIIUZdYzFT/3JrVfHwhhBBCCCGEqE4SN1WJjCQQQgghhBBCCCGEEEIIIW5Q0kgghBBCCCGEEEIIIYQQQtygpJFACCGEEKIOsZgtNfL4f7z//vuEhoai1Wrp0qUL+/fv/9v9f/jhByIjI9FqtbRq1Yr169f/X58rhBBCCCGEEFeqrXHT9Y6ZLBYLs2fPJiAgAAcHBwYMGEBMTEyV0yWNBEIIIYQQ4j/77rvvmD59OnPmzOHw4cO0adOGQYMGkZaWVun+u3fvZsyYMUyYMIEjR44wfPhwhg8fzsmTJ2s45UIIIYQQQghR/aojZlq4cCHvvPMOH330Efv27cPJyYlBgwah0+mqlDZpJBBCCCGEqEss5pp5VNHixYt5+OGHefDBB2nevDkfffQRjo6OfP7555Xu//bbbzN48GCefvppmjVrxrx582jfvj3vvffef/0LCSGEEEIIIW50tTBuut4xk8ViYcmSJcycOZPbb7+d1q1bs2LFCpKTk1m7dm2V0qau0t61jMViHdJhNOltnJLrT2muf3kCwFx/Vv2+kklftda5usKs+/+mm6jN6uP1AsBiKbF1EqqFqaR+/raMhvp5LTRaDLZOwnVnxJqnv8octYERA1Rzcv7Kd15eXrnt9vb22NvbV9i/pKSEQ4cO8fzzz5duUyqVDBgwgD179lT6GXv27GH69Onltg0aNKjKhVkhxD/76xpmrodlVlP9LAJhNNa/cwVgKKyfJywvv/6V7eptObye1rWUFNTP31Z9rWuRuKlm1La4qTpiptjYWFJSUhgwYEDp625ubnTp0oU9e/YwevTof52XOt1IkJ+fD8COU0tsmxAhPrJ1AsS/dcHWCRBV852tEyCEVX5+Pm5ubjZNg0ajwd/fn10pNTNvv7OzMyEhIeW2zZkzh7lz51bYNyMjA5PJhJ+fX7ntfn5+nDlzptLjp6SkVLp/SkrKf0u4EKKCv+KmuAXzbJwSccMbZOsEVA8PWyegWsy0dQJEVfS3dQJEVVR9tvi6Q+Imq8ripuqImf7693rEVXW6kSAwMJDExERcXFxQKBTV+ll5eXmEhISQmJiIq6trtX5WTZJ81S2Sr7pF8lV31Mc8geTrerBYLOTn5xMYGFitn/NvaLVaYmNjKSmpmZ5iFoulQvmqslEEQojaT+Km/6Y+5gkkX3WN5KtukXzVLZKv/07iprofN9XpRgKlUklwcHCNfqarq2u9umD8RfJVt0i+6hbJV91RH/MEkq//ytY9Ya6k1WrRarW2TkYF3t7eqFQqUlNTy21PTU3F39+/0vf4+/tXaX8hxP9P4qbroz7mCSRfdY3kq26RfNUtkq//RuKmv1cdMdNf/6amphIQEFBun7Zt21Ypfcv+UuUAABQeSURBVLJwsRBCCCGE+E80Gg0dOnRgy5Ytpdv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4OGOMMQ6Hw2zbts34+vqaadOmOdd37NjRVFVVOd+zatUqExYWZhwOh3NZVVWV8fPzM4WFhcYYYwICAsy7777rXF9dXW06derkPJYxxgwYMMAkJSUZY4wpKSkxksy2bdv+NueOHTuMJHPx4kXnsmvXrpnmzZubPXv2uGybmJhoRo8ebYwxZubMmaZHjx4u62fMmFFrX38lyWzatOmm69977z0THR3tfD179mxjsVjMuXPnnMs+++wz06RJE1NeXm6MMaZbt24mNzfXZT9paWkmNjbWGGPMyZMnjSRz6NChmx4XAAAAwH+LzvT36EwAcPfjmQQA6qygoEAtW7ZUdXW1HA6HXnjhBc2ZM8e5PjIy0uWemocPH1ZpaalsNpvLfq5du6aysjJdunRJ5eXl6t+/v3Nd06ZNFRMTU+vrs38qLi6WxWLRgAED6py7tLRUV69e1eOPP+6y/Pr16+rbt68k6dixYy45JCk2NrbOx/jTunXrlJWVpbKyMlVWVurGjRu69957XbYJDg5WUFCQy3EcDodKSkpks9lUVlamxMREjRs3zrnNjRs31KpVq9vOAwAAAOC/Q2e6NToTANx9GBIAqLOBAwdq0aJFslqtCgwMVNOmrv+FtGjRwuV1ZWWloqOjtXr16lr7at++/R1l8PPzu+33VFZWSpK2bNnicqIp/XHP0Iayd+9excfHKzU1VUOGDFGrVq20du1azZ0797azLlu2rNYJuMViabCsAAAAABoenemf0ZkA4O7EkABAnbVo0UKhoaF13j4qKkrr1q1Thw4dal0Z8qeAgAB9++23evTRRyX9cfXHgQMHFBUV9bfbR0ZGyuFwqKioSIMHD661/s+rcmpqapzLevToIV9fX505c+amV9NEREQ4Hyj2p3379t36Q/6PPXv2KCQkRLNmzXIuO336dK3tzpw5o/PnzyswMNB5nCZNmigsLEwdO3ZUYGCgTpw4ofj4+Ns6PgAAAAD3ojP9MzoTANydeHAxgH9NfHy82rVrp7i4OO3evVsnT57Uzp07NWnSJJ07d06SlJSUpHfeeUf5+fn68ccfNX78eFVUVNx0n/fff7/sdrsSEhKUn5/v3GdeXp4kKSQkRD4+PiooKNCvv/6qyspK2Ww2TZs2TVOmTNHKlStVVlamgwcPasGCBc4HW7366qv66aefNH36dJWUlCg3N1c5OTm39XkfeOABnTlzRmvXrlVZWZmysrL+9oFizZo1k91u1+HDh7V7925NmjRJI0eOlL+/vyQpNTVVmZmZysrK0vHjx3XkyBFlZ2dr3rx5t5UHAAAAwN2NzkRnAoC7AUMCAP+a5s2ba9euXQoODtaIESMUERGhxMREXbt2zXmVzOuvv66XXnpJdrtdsbGxstlsevbZZ/9xv4sWLdJzzz2n8ePHKzw8XOPGjdOVK1ckSUFBQUpNTVVycrI6duyoiRMnSpLS0tKUkpKizMxMRUREaOjQodqyZYu6dOki6Y97Xm7YsEH5+fnq3bu3Fi9erIyMjNv6vE8//bSmTJmiiRMnqk+fPtqzZ49SUlJqbRcaGqoRI0boySef1BNPPKFevXpp4cKFzvVjx47V8uXLlZ2drcjISA0YMEA5OTnOrAAAAAA8A52JzgQAdwMfc7Mn3QAAAAAAAAAAAI/GNwkAAAAAAAAAAPBSDAkAAAAAAAAAAPBSDAkAAAAAAAAAAPBSDAkAAAAAAAAAAPBSDAkAAAAAAAAAAPBSDAkAAAAAAAAAAPBSDAkAAAAAAAAAAPBSDAkAAAAAAAAAAPBSDAkAAAAAAAAAAPBSDAkAAAAAAAAAAPBSDAkAAAAAAAAAAPBS/wctFDZiTAh09wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x700 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay\n",
    "\n",
    "sample_weight = (y_train != y_pred)\n",
    "\n",
    "fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))\n",
    "fig.set_figwidth(20)\n",
    "fig.set_figheight(7)\n",
    "\n",
    "plt.rc('font', size=10)\n",
    "ConfusionMatrixDisplay.from_predictions(y_train, y_pred, ax=axs[0],\n",
    "                                        sample_weight=sample_weight,\n",
    "                                        normalize=\"true\", values_format=\".0%\")\n",
    "axs[0].set_title(\"Errors normalized by row\")\n",
    "ConfusionMatrixDisplay.from_predictions(y_train, y_pred, ax=axs[1],\n",
    "                                        sample_weight=sample_weight,\n",
    "                                        normalize=\"pred\", values_format=\".0%\")\n",
    "axs[1].set_title(\"Errors normalized by column\")\n",
    "plt.show()\n",
    "plt.rc('font', size=14)  # make fonts great again"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> `Row Normalization` : The 49% in row 0 and column 8 means that 49% Error made my model by predicting 0 insteed of 8\n",
    "\n",
    "> `Column Normalization` : The 15% in row 0 and column 8 means that 15% of 0's are missclassified as 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> # **MULTILABEL CLASSIFICATION**\n",
    "\n",
    "> Multi-label classification is a type of classification task where each instance can be associated with multiple labels simultaneously, allowing for the assignment of zero or more labels to a single data point. This is in contrast to traditional classification tasks, where each instance is assigned to one and only one class label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "y_train_large = (y_train >= 7) # Label 1\n",
    "y_train_odd = (y_train % 2 == 1) # Label 2\n",
    "\n",
    "y_multilabel = np.c_[y_train_large, y_train_odd]\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n",
    "knn_clf.fit(X_train, y_multilabel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted : [[False False]]\n",
      "Actual : [False False]\n"
     ]
    }
   ],
   "source": [
    "y_pred = knn_clf.predict(X_train.iloc[0:1, :])\n",
    "print(\"Predicted :\", y_pred)\n",
    "print(\"Actual :\", y_multilabel[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9662710249675888"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_score(y_train, y_pred, average=\"macro\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> ### `ClassifierChain`\n",
    "\n",
    "> This in scikit-learn is a useful tool for handling multi-label classification problems. In multi-label classification, each instance can be assigned multiple labels simultaneously. The ClassifierChain method extends the standard classification approach to account for the dependencies between labels.\n",
    "\n",
    ">- Captures label dependencies, which can improve performance compared to treating each label independently. <br><br>\n",
    ">- Provides a structured approach to multi-label classification problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ClassifierChain(base_estimator=SVC(random_state=42), cv=3, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;ClassifierChain<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.multioutput.ClassifierChain.html\">?<span>Documentation for ClassifierChain</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>ClassifierChain(base_estimator=SVC(random_state=42), cv=3, random_state=42)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">base_estimator: SVC</label><div class=\"sk-toggleable__content fitted\"><pre>SVC(random_state=42)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.svm.SVC.html\">?<span>Documentation for SVC</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SVC(random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "ClassifierChain(base_estimator=SVC(random_state=42), cv=3, random_state=42)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.multioutput import ClassifierChain\n",
    "\n",
    "chain_clf = ClassifierChain(SVC(random_state=42), cv = 3, random_state=42)\n",
    "chain_clf.fit(X_train[:2000], y_multilabel[:2000])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The `cross-validation` uses the previous training for next steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted : [[0. 0.]]\n",
      "Actual : [False False]\n"
     ]
    }
   ],
   "source": [
    "print(\"Predicted :\", chain_clf.predict(X_train.iloc[0:1, :]))\n",
    "print(\"Actual :\", y_multilabel[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  }
 ],
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