lectures/13_computer-vision.ipynb

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     "slide_type": "slide"
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   "source": [
    "# CPSC 330 Lecture 13\n",
    "\n",
    "**Note to self**: part of this code takes a very long time to run, so better not Restart & Run All right before this lecture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Lecture plan\n",
    "\n",
    "- 👋\n",
    "- **Turn on recording**\n",
    "- Announcements (5 min)\n",
    "- Multi-class classification (20 min)\n",
    "- Intro to computer vision (0 min)\n",
    "- Intro to neural networks (15 min)\n",
    "- Break (5 min)\n",
    "- Neural networks for images (10 min)\n",
    "- Pre-trained networks (10 min)\n",
    "- Transfer learning (15 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning objectives\n",
    "\n",
    "- Apply classifiers to multi-class classification algorithms.\n",
    "- Explain the role of neural networks in machine learning, and the pros/cons of using them.\n",
    "- Explain why the methods we've learned previously would not be effective on image data.\n",
    "- Apply pre-trained neural networks to classification and regression problems.\n",
    "- Utilize pre-trained networks as feature extractors and combine them with models we've learned previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import os \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import Pipeline, make_pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.metrics import plot_confusion_matrix, confusion_matrix, classification_report\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "from plot_classifier import plot_classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['font.size'] = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation, Conv2D, MaxPooling2D, GlobalAveragePooling2D\n",
    "from tensorflow.keras.models import Sequential, Model, load_model\n",
    "from tensorflow.keras.preprocessing.image import img_to_array, load_img\n",
    "from tensorflow.keras import utils\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50, preprocess_input, decode_predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Announcements\n",
    "\n",
    "- Midterm grading in progress\n",
    "- hw6 will be posted at the end of this week or weekend hopefully\n",
    "- Lecture 12 screencast posted, please watch before hw6\n",
    "- Final exam format posted: https://piazza.com/class/kb2e6nwu3uj23?cid=429\n",
    "- We are now in the 2nd half of the course!!\n",
    "  - The format is a bit different"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multi-class classification (20 min)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Most of the classification problems we looked at were binary classification (2 possible labels)\n",
    "- There are plenty of multi-class classification problems as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = datasets.load_wine()\n",
    "X = pd.DataFrame(data['data'], columns=data[\"feature_names\"])\n",
    "y = data['target']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>12.29</td>\n",
       "      <td>2.83</td>\n",
       "      <td>2.22</td>\n",
       "      <td>18.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.45</td>\n",
       "      <td>2.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.99</td>\n",
       "      <td>2.15</td>\n",
       "      <td>1.15</td>\n",
       "      <td>3.30</td>\n",
       "      <td>290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>12.67</td>\n",
       "      <td>0.98</td>\n",
       "      <td>2.24</td>\n",
       "      <td>18.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.94</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.46</td>\n",
       "      <td>2.62</td>\n",
       "      <td>1.23</td>\n",
       "      <td>3.16</td>\n",
       "      <td>450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.39</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.45</td>\n",
       "      <td>14.6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.25</td>\n",
       "      <td>1.02</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1290.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>13.29</td>\n",
       "      <td>1.97</td>\n",
       "      <td>2.68</td>\n",
       "      <td>16.8</td>\n",
       "      <td>102.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.23</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.66</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2.84</td>\n",
       "      <td>1270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>13.05</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.04</td>\n",
       "      <td>12.4</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.72</td>\n",
       "      <td>3.27</td>\n",
       "      <td>0.17</td>\n",
       "      <td>2.91</td>\n",
       "      <td>7.20</td>\n",
       "      <td>1.12</td>\n",
       "      <td>2.91</td>\n",
       "      <td>1150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>13.03</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.71</td>\n",
       "      <td>16.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.03</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.46</td>\n",
       "      <td>4.60</td>\n",
       "      <td>1.19</td>\n",
       "      <td>2.48</td>\n",
       "      <td>392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>12.20</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.32</td>\n",
       "      <td>19.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.73</td>\n",
       "      <td>5.50</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.83</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>12.04</td>\n",
       "      <td>4.30</td>\n",
       "      <td>2.38</td>\n",
       "      <td>22.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.10</td>\n",
       "      <td>1.75</td>\n",
       "      <td>0.42</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.60</td>\n",
       "      <td>0.79</td>\n",
       "      <td>2.57</td>\n",
       "      <td>580.0</td>\n",
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       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>12.36</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.04</td>\n",
       "      <td>7.65</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.58</td>\n",
       "      <td>520.0</td>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>12.60</td>\n",
       "      <td>1.34</td>\n",
       "      <td>1.90</td>\n",
       "      <td>18.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.45</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.45</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.77</td>\n",
       "      <td>562.0</td>\n",
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      "text/plain": [
       "     alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "93     12.29        2.83  2.22               18.0       88.0           2.45   \n",
       "85     12.67        0.98  2.24               18.0       99.0           2.20   \n",
       "6      14.39        1.87  2.45               14.6       96.0           2.50   \n",
       "57     13.29        1.97  2.68               16.8      102.0           3.00   \n",
       "50     13.05        1.73  2.04               12.4       92.0           2.72   \n",
       "..       ...         ...   ...                ...        ...            ...   \n",
       "76     13.03        0.90  1.71               16.0       86.0           1.95   \n",
       "170    12.20        3.03  2.32               19.0       96.0           1.25   \n",
       "129    12.04        4.30  2.38               22.0       80.0           2.10   \n",
       "160    12.36        3.83  2.38               21.0       88.0           2.30   \n",
       "101    12.60        1.34  1.90               18.5       88.0           1.45   \n",
       "\n",
       "     flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "93         2.25                  0.25             1.99             2.15  1.15   \n",
       "85         1.94                  0.30             1.46             2.62  1.23   \n",
       "6          2.52                  0.30             1.98             5.25  1.02   \n",
       "57         3.23                  0.31             1.66             6.00  1.07   \n",
       "50         3.27                  0.17             2.91             7.20  1.12   \n",
       "..          ...                   ...              ...              ...   ...   \n",
       "76         2.03                  0.24             1.46             4.60  1.19   \n",
       "170        0.49                  0.40             0.73             5.50  0.66   \n",
       "129        1.75                  0.42             1.35             2.60  0.79   \n",
       "160        0.92                  0.50             1.04             7.65  0.56   \n",
       "101        1.36                  0.29             1.35             2.45  1.04   \n",
       "\n",
       "     od280/od315_of_diluted_wines  proline  \n",
       "93                           3.30    290.0  \n",
       "85                           3.16    450.0  \n",
       "6                            3.58   1290.0  \n",
       "57                           2.84   1270.0  \n",
       "50                           2.91   1150.0  \n",
       "..                            ...      ...  \n",
       "76                           2.48    392.0  \n",
       "170                          1.83    510.0  \n",
       "129                          2.57    580.0  \n",
       "160                          1.58    520.0  \n",
       "101                          2.77    562.0  \n",
       "\n",
       "[133 rows x 13 columns]"
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 2, 0, 2, 0, 1, 2, 1,\n",
       "       0, 0, 1, 1, 1, 1, 0, 1, 0, 2, 2, 0, 0, 2, 0, 1, 2, 1, 2, 0, 2, 2,\n",
       "       1, 2, 1, 0, 0, 2, 0, 1, 2, 1, 2, 1, 2, 1, 1, 0, 1, 2, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 0, 2, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 2, 1, 2, 0,\n",
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       "       2])"
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     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Note that we have 3 classes here: 0, 1, 2.\n",
    "- For some models, like a random forest, we don't have to think about anything differently at all:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 1, 0, 1, 2, 2, 0, 0, 2, 2, 2, 1, 2, 1, 1, 2, 0, 1, 2,\n",
       "       0, 2, 1, 2, 0, 0, 0, 2, 2, 1, 1, 1, 2, 0, 1, 1, 0, 1, 1, 2, 0, 2,\n",
       "       2])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <td>0.011068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ash</th>\n",
       "      <td>0.011244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proanthocyanins</th>\n",
       "      <td>0.024605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malic_acid</th>\n",
       "      <td>0.026463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <td>0.029272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_phenols</th>\n",
       "      <td>0.029771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magnesium</th>\n",
       "      <td>0.039384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hue</th>\n",
       "      <td>0.077145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <td>0.126174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color_intensity</th>\n",
       "      <td>0.136799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proline</th>\n",
       "      <td>0.157664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>0.163556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flavanoids</th>\n",
       "      <td>0.166854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Importance\n",
       "nonflavanoid_phenols            0.011068\n",
       "ash                             0.011244\n",
       "proanthocyanins                 0.024605\n",
       "malic_acid                      0.026463\n",
       "alcalinity_of_ash               0.029272\n",
       "total_phenols                   0.029771\n",
       "magnesium                       0.039384\n",
       "hue                             0.077145\n",
       "od280/od315_of_diluted_wines    0.126174\n",
       "color_intensity                 0.136799\n",
       "proline                         0.157664\n",
       "alcohol                         0.163556\n",
       "flavanoids                      0.166854"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_importances = pd.DataFrame(data=rf.feature_importances_, index=X_train.columns, columns=[\"Importance\"])\n",
    "rf_importances.sort_values(by=\"Importance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Everything is the same here.\n",
    "- For our linear classifier, on the other hand, things are a bit different:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(max_iter=10000)\n",
    "lr.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 1, 0, 1, 2, 2, 0, 0, 2, 2, 2, 1, 2, 1, 1, 2, 0, 1, 2,\n",
       "       0, 2, 1, 2, 0, 0, 0, 2, 2, 1, 1, 1, 2, 0, 1, 1, 0, 1, 1, 2, 0, 2,\n",
       "       2])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.39752052e-05, 9.99740367e-01, 2.35657958e-04],\n",
       "       [9.99873535e-01, 2.37692663e-05, 1.02696127e-04],\n",
       "       [1.00989031e-02, 6.19482314e-03, 9.83706274e-01],\n",
       "       [8.61318888e-05, 9.99283792e-01, 6.30076122e-04],\n",
       "       [4.07061730e-03, 9.95881251e-01, 4.81314305e-05],\n",
       "       [9.99630049e-01, 2.42154674e-04, 1.27796005e-04],\n",
       "       [2.88703424e-05, 9.99851695e-01, 1.19435097e-04],\n",
       "       [1.26646552e-02, 2.07483791e-01, 7.79851554e-01],\n",
       "       [8.35008443e-06, 7.34003439e-06, 9.99984310e-01],\n",
       "       [9.99970441e-01, 1.85369256e-05, 1.10218723e-05]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict_proba(X_test)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 1, 0, 1, 2, 2, 0, 0, 2, 2, 2, 1, 2, 1, 1, 2, 0, 1, 2,\n",
       "       0, 2, 1, 2, 0, 0, 0, 2, 2, 1, 1, 1, 2, 0, 1, 1, 0, 1, 1, 2, 0, 2,\n",
       "       2])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(lr.predict_proba(X_test), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 2, 1, 1, 0, 1, 2, 2, 0, 0, 2, 2, 2, 1, 2, 1, 1, 2, 0, 1, 2,\n",
       "       0, 2, 1, 2, 0, 0, 0, 2, 2, 1, 1, 1, 2, 0, 1, 1, 0, 1, 1, 2, 0, 2,\n",
       "       2])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 13)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.coef_.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is going on here?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's happening here is that we have one coefficient per feature _per class_. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>0.555204</td>\n",
       "      <td>-0.767944</td>\n",
       "      <td>0.212740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malic_acid</th>\n",
       "      <td>0.449106</td>\n",
       "      <td>-0.783460</td>\n",
       "      <td>0.334354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ash</th>\n",
       "      <td>0.614254</td>\n",
       "      <td>-0.575625</td>\n",
       "      <td>-0.038628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <td>-0.216354</td>\n",
       "      <td>0.085719</td>\n",
       "      <td>0.130635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>magnesium</th>\n",
       "      <td>-0.032878</td>\n",
       "      <td>-0.013642</td>\n",
       "      <td>0.046520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_phenols</th>\n",
       "      <td>0.107551</td>\n",
       "      <td>0.298781</td>\n",
       "      <td>-0.406331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flavanoids</th>\n",
       "      <td>0.681276</td>\n",
       "      <td>0.212330</td>\n",
       "      <td>-0.893606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <td>0.086516</td>\n",
       "      <td>-0.083456</td>\n",
       "      <td>-0.003060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proanthocyanins</th>\n",
       "      <td>0.099971</td>\n",
       "      <td>0.287858</td>\n",
       "      <td>-0.387830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>color_intensity</th>\n",
       "      <td>0.182485</td>\n",
       "      <td>-1.050647</td>\n",
       "      <td>0.868162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hue</th>\n",
       "      <td>0.024824</td>\n",
       "      <td>0.207952</td>\n",
       "      <td>-0.232776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <td>0.682607</td>\n",
       "      <td>0.071177</td>\n",
       "      <td>-0.753784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proline</th>\n",
       "      <td>0.011232</td>\n",
       "      <td>-0.006785</td>\n",
       "      <td>-0.004447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     0         1         2\n",
       "alcohol                       0.555204 -0.767944  0.212740\n",
       "malic_acid                    0.449106 -0.783460  0.334354\n",
       "ash                           0.614254 -0.575625 -0.038628\n",
       "alcalinity_of_ash            -0.216354  0.085719  0.130635\n",
       "magnesium                    -0.032878 -0.013642  0.046520\n",
       "total_phenols                 0.107551  0.298781 -0.406331\n",
       "flavanoids                    0.681276  0.212330 -0.893606\n",
       "nonflavanoid_phenols          0.086516 -0.083456 -0.003060\n",
       "proanthocyanins               0.099971  0.287858 -0.387830\n",
       "color_intensity               0.182485 -1.050647  0.868162\n",
       "hue                           0.024824  0.207952 -0.232776\n",
       "od280/od315_of_diluted_wines  0.682607  0.071177 -0.753784\n",
       "proline                       0.011232 -0.006785 -0.004447"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_coefs = pd.DataFrame(data=lr.coef_.T, index=X_train.columns, columns=lr.classes_)\n",
    "lr_coefs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It appears that the coefficients add up to 0 for each feature.\n",
    "- I am still thinking about why this is the case.\n",
    "- But it's definitely only the case of `multi_class='multinomial'` which it's defaulting to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#??LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### optional: sum of coefficients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "alcohol                        -1.935341e-12\n",
       "malic_acid                      3.724188e-12\n",
       "ash                             1.762375e-12\n",
       "alcalinity_of_ash              -3.006762e-13\n",
       "magnesium                       1.168926e-13\n",
       "total_phenols                  -1.365574e-12\n",
       "flavanoids                      2.219336e-13\n",
       "nonflavanoid_phenols            9.961563e-14\n",
       "proanthocyanins                 1.319111e-12\n",
       "color_intensity                -1.855960e-12\n",
       "hue                             4.462264e-13\n",
       "od280/od315_of_diluted_wines   -1.916689e-12\n",
       "proline                         1.821113e-13\n",
       "dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_coefs.sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Note that the coefficients sum to 0 for each feature.\n",
    "- This relates to a redundancy in the number of coefficients.\n",
    "- For 2 classes we have 1 coefficient per feature, so for 3 classes we only really need 2 coefficients per feature.\n",
    "- But it's easier to have $k$ coefficients per feature with $k$ classes, when $k>2$.\n",
    "- So there is this extra constraint for this extra degree of freedom."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Back to the main event"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The interpretation is a feature importance for predicting a certain class. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0444702480798304"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_coefs.loc[\"alcohol\", 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This means that if alcohol is larger, you are more likely to predict class 2. \n",
    "- The specific interpretation depends on the way the logistic regression is implementing multi-class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ?LogisticRegression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that there's a `multi_class` parameter, that can be set to `'ovr'` or `'multinomial'`, or you can have it automatically choose between the two, which is the default.\n",
    "  - In CPSC 340 we discuss in detail the difference between these two approaches.\n",
    "  - In CPSC 340 we make an argument for preferring `'multinomial'`, but in short it doesn't matter which one you choose.\n",
    "- If you want a general feature importance irrespective of class, you could try looking at the sum of the squares of the coefficients, which is what sklearn does:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color_intensity                 2.091115\n",
       "flavanoids                      1.267963\n",
       "malic_acid                      0.864091\n",
       "od280/od315_of_diluted_wines    0.625061\n",
       "alcohol                         0.470614\n",
       "proanthocyanins                 0.410495\n",
       "alcalinity_of_ash               0.310140\n",
       "total_phenols                   0.275350\n",
       "ash                             0.228691\n",
       "hue                             0.118358\n",
       "nonflavanoid_phenols            0.008761\n",
       "magnesium                       0.003966\n",
       "proline                         0.000268\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(lr_coefs**2).sum(axis=1).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the output of `predict_proba` now contains a probability for each class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2.28035966e-03, 1.61479206e-02, 9.81571720e-01],\n",
       "       [4.77009959e-06, 2.05359683e-05, 9.99974694e-01],\n",
       "       [9.98506957e-01, 1.38391908e-03, 1.09123426e-04],\n",
       "       [9.95210511e-01, 3.83497582e-03, 9.54513261e-04],\n",
       "       [9.99999056e-01, 1.34524401e-08, 9.30612563e-07]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict_proba(X_test)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you'll see that each row adds up to 1, as expected:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.predict_proba(X_test).sum(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can still make a confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12,  0,  0],\n",
       "       [ 0, 16,  2],\n",
       "       [ 0,  0, 15]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, lr.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(lr, X_test, y_test, display_labels=lr.classes_, cmap=plt.cm.Blues, values_format='d');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Precision, recall, etc. don't apply directly.\n",
    "- But if we pick one of the classes as positive, and consider the rest to be negative, then we can."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        12\n",
      "           1       1.00      0.89      0.94        18\n",
      "           2       0.88      1.00      0.94        15\n",
      "\n",
      "    accuracy                           0.96        45\n",
      "   macro avg       0.96      0.96      0.96        45\n",
      "weighted avg       0.96      0.96      0.96        45\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, lr.predict(X_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can also make the plots we made earlier in the course, but this time with more classes.\n",
    "- I need to train a model with only 2 features for this to work:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_2f = LogisticRegression()\n",
    "lr_2f.fit(X_train.iloc[:,:2], y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_classifier(X_train.iloc[:,:2], y_train, lr_2f);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: the colours aren't very good - the red triangles correspond to the light blue predictions, the black X's correspond to the red predictions, and the blue circles (correctly) correspond to the blue circles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can do the same with a random forest:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_2f = RandomForestClassifier()\n",
    "rf_2f.fit(X_train.iloc[:,:2], y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_classifier(X_train.iloc[:,:2], y_train, rf_2f);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or an RBF SVM:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_2f = SVC(gamma=2, C=100)\n",
    "svm_2f.fit(X_train.iloc[:,:2], y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_classifier(X_train.iloc[:,:2], y_train, svm_2f);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lr.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro to computer vision (<5 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Computer vision](https://en.wikipedia.org/wiki/Computer_vision) refers to understanding images/videos, usually using ML/AI. \n",
    "- Computer vision has many tasks of interest:\n",
    "  - image classification: is this a cat or a dog?\n",
    "  - object localization: where are the people in this image?\n",
    "  - image segmentation: what are the various parts of this image?\n",
    "  - motion detection: what moved between frames of a video?\n",
    "  - and much more...\n",
    "- We will focus on image classification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intro to neural networks (15 min)\n",
    "\n",
    "- Remember the `Pipeline`? \n",
    "- A neural network is a model that's sort of like its own pipeline.\n",
    "  - It involves a series of transformations (\"layers\") internally. \n",
    "  - The output is the prediction.\n",
    "- The code looks like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rf = RandomForestClassifier(...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(10, input_dim=1, activation='tanh')) # Transformation 1\n",
    "model.add(Dense(15, activation='tanh')) # transformation 2\n",
    "model.add(Dense(1, activation='linear')) # linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Each time we do `model.add` we are adding on one of those transformations.\n",
    "- Imporant question: how many features before/after transformation.\n",
    "  - e.g. scaling doesn't change the number of features\n",
    "  - OHE increases the number of features\n",
    "- With a neural net, you specify the number of features after each transformation.\n",
    "  - In the above, it goes from 1 to 10 to 15 to 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: in scikit-learn, we use one line of Python code to set up the model and set the hyperparameters. For example:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(C=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In tensorflow.keras (the package we're using), we use multiple lines of Python code to set up the model.\n",
    "- This design decision just makes the code more human-readable.\n",
    "- One line of code per layer of the network.\n",
    "- But there's nothing fundamental about it, one could also put the architecture/hypers on one line, in a file, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Next, with tensorflow, we have an extra step not present in sklearn, called `compile`.\n",
    "- Let's ignore this for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='mean_squared_error', optimizer=\"adam\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try it out on some made up data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(5)\n",
    "N = 200\n",
    "X = np.random.rand(N,1)\n",
    "y = np.sin(2*X) + np.random.randn(N,1)*0.03\n",
    "\n",
    "plt.plot(X,y,'.',markersize=10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The rest is what we're used to: `fit`, `predict`, etc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X, y, epochs=1000, verbose=0);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Note how even fitting such a simple dataset can be slow with neural networks.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X,y,'.',markersize=10,label=\"data\")\n",
    "grid = np.linspace(0,1,1000)[:,None]\n",
    "plt.plot(grid, model.predict(grid),linewidth=5,label=\"model\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of `score` we use `evaluate`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0010326269548386335"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X, y, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Terminology\n",
    "\n",
    "- Neural network = neural net\n",
    "- Deep learning ~ using neural networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Why neural networks?\n",
    "\n",
    "- They can learn very complex functions.\n",
    "  - The fundamental tradeoff is primarily controlled by the **number of layers** and **layer sizes**.\n",
    "  - More layers / bigger layers --> more complex model.\n",
    "  - You can generally get a model that will not underfit.\n",
    "- The work really well for structured data:\n",
    "  - 1D sequence, e.g. timeseries, language\n",
    "  - 2D image\n",
    "  - 3D image or video\n",
    "- They've had some incredible successes in the last 10 years.\n",
    "- Transfer learning (coming later today) is really useful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Why not neural networks?\n",
    "\n",
    "- Often they require a lot of data.\n",
    "- They require a lot of compute time, and, to be faster, specialized hardware called [GPUs](https://en.wikipedia.org/wiki/Graphics_processing_unit).\n",
    "- They have huge numbers of hyperparameters are a huge pain to tune.\n",
    "  - Think of each layer having hyperparameters, plus some overall hyperparameters.\n",
    "  - Being slow compounds this problem.\n",
    "- They are not interpretable.\n",
    "- When you call `fit`, you are not guaranteed to get the optimal.\n",
    "  - There are now a bunch of hyperparameters specific to `fit`, rather than the model.\n",
    "  - You never really know if `fit` was successful or not.\n",
    "  - You never really know if you should have run `fit` for longer.\n",
    "- I don't recommend training them without further training (ha)\n",
    "  - Take CPSC 340 and other courses if you're interested.\n",
    "  - I'll show you some ways to use neural networks without calling `fit`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deep learning software\n",
    "\n",
    "- scikit-learn has [MLPRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html) and [MLPClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html) but they aren't very flexible.\n",
    "  - In general you'll want to leave the scikit-learn ecosystem when using neural networks.\n",
    "  - Fun fact: these classes were contributed to scikit-learn by a UBC graduate student.\n",
    "- There's been a lot of deep learning software out there.\n",
    "- The current big players are:\n",
    "\n",
    "1. [TensorFlow 2.0](https://www.tensorflow.org)\n",
    "2. [PyTorch](http://pytorch.org)\n",
    "\n",
    "- Both are heavily used in industry.\n",
    "- A lot of people I talk to say PyTorch is easier to use.\n",
    "  - However, I started teaching this in MDS 4 years ago, before PyTorch was created!\n",
    "- If interested, see [comparison of deep learning software](https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Break (5 min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "REMINDER: RESUME RECORDING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural networks for images (10 min)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let's load the MNIST handwritten digits dataset.\n",
    "- It's a very boring dataset by now - we can get extremely high accuracy, and we've probably overfit to the test set by now.\n",
    "- But it's a good example to see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the data, shuffled and split between train and test sets\n",
    "(X_train_img, y_train), (X_test_img, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display a random training example\n",
    "i = np.random.randint(0,len(X_train_img))\n",
    "plt.imshow(X_train_img[i],cmap='gray')\n",
    "plt.title('This is a %d' % y_train[i]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train_img[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(28, 28)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_img[i].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_img.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "784"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "28*28"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- How can we take our input ($28\\times 28$ numpy array) and map it out our output (a label $0,1,\\ldots,9$)?\n",
    "- Most naive approach: \"flatten\" the images, and then use whatever supervised learning method we feel like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "X_train = X_train_img.reshape(60000, 28*28)\n",
    "X_test = X_test_img.reshape(10000, 28*28)\n",
    "X_train = X_train / 255 # this is the same a min/max scaling, since the pixel intensities range from 0 to 255\n",
    "X_test = X_test / 255\n",
    "\n",
    "print(X_train.shape[0], 'train samples')\n",
    "print(X_test.shape[0], 'test samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 0, 4, ..., 5, 6, 8], dtype=uint8)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use our usual classification methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(multi_class='multinomial')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: setting `multi_class=\"multinomial\"` because we have more than 2 classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/envs/cpsc330env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "lr.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.93505"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9258"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = np.array([[1,2],[3,4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(z, (4,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Why is this a bad idea?\n",
    "  - By \"flattening\" the image we throw away useful information.\n",
    "- What the computer sees:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0,\n",
       " 0.0,\n",
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       " 0.07058823529411765,\n",
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       " 0.6862745098039216,\n",
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       " 0.6666666666666666,\n",
       " 0.9921568627450981,\n",
       " 0.9921568627450981,\n",
       " 0.9921568627450981,\n",
       " 0.9921568627450981,\n",
       " 0.9921568627450981,\n",
       " 0.8823529411764706,\n",
       " 0.6745098039215687,\n",
       " 0.9921568627450981,\n",
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       " 0.0]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(X_train[0])[150:200]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Hard to classify this!\n",
    "- [Convolutional neural networks](https://en.wikipedia.org/wiki/Convolutional_neural_network) (CNNs) can take in images without flattening them.\n",
    "  - We won't cover CNNs here, but they are in CPSC 340."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using pre-trained networks (10 min)\n",
    "\n",
    "- `tf.keras` has a bunch of pre-trained computer vision models available, see [here](https://keras.io/applications/).\n",
    "- Here we load a pre-trained model (uses our internet connection).\n",
    "- The amazing part is how much effort went into this base model. \n",
    "  - An insane amount of labelled data.\n",
    "  - An insane amount of compute time.\n",
    "  - An insane amount of human effort tuning the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "resnet = ResNet50(weights='imagenet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the important distinction between the choice of architecture (in this case, `ResNet50`) and the choice of dataset (in this case, ImageNet)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ImageNet\n",
    "\n",
    "- [ImageNet](http://www.image-net.org/) is an image dataset that became a very popular benchmark in the field ~10 years ago. \n",
    "- [Wikipedia article](https://en.wikipedia.org/wiki/ImageNet)\n",
    "- There are 14 million images and 1000 classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's my graduation photo from undergrad:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_path = 'img/gelbart-michael-adam.jpg'\n",
    "img = load_img(img_path, target_size=(224, 224))\n",
    "plt.imshow(img);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('n02883205', 'bow_tie', 0.99401516),\n",
       " ('n03838899', 'oboe', 0.0021414417),\n",
       " ('n03110669', 'cornet', 0.0010458583)]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)\n",
    "\n",
    "preds = resnet.predict(x)\n",
    "decode_predictions(preds, top=3)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z5Mgyu3t7rCwv89f+5t/gIx/+ICTwMfJX/tJf5Q/8wd9PSrC7u8/f/wf/kJ/9E3+MGCObm5tcvXyND3zw/fimYbQ/plN1OH3mLlIIpJg4e/YsJ06cwMeAcZZz95xjsNDHh0hRFNjCYYzl8SefZG1tjYceukCMkRdfeImisJy75x729/Z48oknuPvus6wurzAaj3nppef4Az/z+zEijEYjLr1+kelsj2634NixNb7whc8ynky5/74LvO997+PVl56nlEiKDSJQJUdKaD0u+sDHFBWTyuCm3jGt9UOMCBFjCwRN1dtAouWFlg0KjieMGN1I8n2SBEkiGEuUpC/OrBBGS6KUIpIExCp4aiLGWaLX9F9C1AVpNcuQDJSWZakB3vh5RhRCmGc+TQiAhRQoyjIDoED+HPpcR3LCMWdKlAVUENiQ8Ekw4jJuG4khUhUliUgMAXEGAkTzZnbrjcd3hD1JKV0G/jf/5m8AGamaA0DJWMQUQMx1YcCI4IPn+eef5ez5+xGrETQlyxvJGgWugvckFzkcLPS/CCnl2tYc+h6gSzynu4d2M1fwwfc8QuUc/+zvuznluLI05OPf8xFubuzz2uvXWVusePDCPfOffP3iDa5dv01ZQr9X0Ol0cEbwPmiIE2F7a5P777+AYAnJ87M/+/OEEHDO8gu/8H+kLAv2dnfY2d3iZ3/h5/nUpz/NRz/yYb745a8yXFiiCZGNjQ0+8YlP8t3f/V00PvL665d47rkX+PEf+734Zsbrr13EFQVnTp+mrme88PyLHD12lJWVFWKIvPzqSwyGQ8qiRCwsLC7y4EPvIhF59tnnKcqCBy7cz2h3zFe/8nUefOgBhgtLTEZjnn3mKb7rYx/B14H9vR2uXrnMhQfPE/wUP5thzYwf+b3fzZkzZ1laOcrrl27iCoekBiNaPlin99AWhrIqcM5hxGLboCGHmTEzD/WkgFiljY0xGKeZob5MF7IY/VpLs0JmUACJiRZ9Tfm+C6IYSsh4hDFYp8BqM6vnQUyMzQEovz6XQCnl32UN0etzC0HB1hBIaLmVSKQc6ELIQSLE/FwkUtTPHFOa41uzUGsW4nQjstbgg2IvzlkFYUW0nM/AbgjNt1x+3ynK9X/RIaKgVSSRksUkTUfFWF2cUf9dFhVFUTGbTjDWYm2BiKH2kb0pGW2OWAlUzh6i8A4HDX3cZP61N0fg9jVv/LoRy5HVFXZ3dg7Rc/r6whrOHF3k9NEFzKFAk4BTp45x6uQRjLE401KB+t3NzU1ijCwMh9y6fZuyLJk1NZPxhL29PawUuMIQk9AfLNHvL9I0NT/8e3+Uxnt+4id/ChHhzp1b7O2P+dEf+1GuXb3KaH/M/t6E4cISEcuTTz/N5ctX+OBjH2B3d5+vfe1rHD1xlGPHjzKdzHjl1Ve5+/zd1JMZ09mU6CMPPvgQwXtef/1VrHM8+NCD7I/HPP74k9xzz70sLq0yGu3y/Asv8IHHPkhKkcbPuHnjBhcu3Mt4PAaJ3Llzm9H+Pu9733sU2BNLUVYUzpGameZ4OUAbYzRYmJwpGIOIwVrbsqBzMNOIIKYEK/rQm1yKiCGKLkB9fhTPiKIgY0gRQsybTVRa+E3Egkh+QkzSc0kHX3fO5sWsDJCzBgxacsSImEwDR93520BELpMT7eanwYgUtcqx+RrAnH3R4BO11Ei62RWFJSXRIIOWKikzPULGbQyEhGZDtLqN/3WxJ//Lj5Q0zcODiVp9SokxDls4ZrMZ1nYBKJxjFoxmIWKJGK5uzXjqWk0qOmAaVsrAubUeXVcRI8zqRh+SmAihwQG9boGRmKHSw/Xe4QzjcOCIFE5fZ20BJIy18+AjfGPNOK0Dr1/fZeoN504OWepZDgeklZUDpvrY0aO64wExaRB98YXnGc+8AnYZrJWY8meByWxEEzxHjp9i7egJIHHixGl976PHKZ1jd3eH4XCJRx5dZTJrePoLn2dt7Qjnz93DcLDIV7/6GU6eOklRlEz3JzRNM6/5n3ryaXq9Ho8+8gg3b97ipZdf5cEHH6Q36LGzt8urr7zM+z/wfmIM1DPP7VvrnLvnHDs7W2xubnDkyDJHj6xxzz3n9HoZS5ISY1VP0V4JayyFdXlHhcJZXFHgigLrFCRsU/QoBhMDGEMKYEUQA8k4Suu0ZDEOk0vclvGwIkqloum6BUISLVnEIBLnNK2YXBJYm/eQqKWOWKwI0SRIdq4xsWJpvMc5l2/hYaFV/peBmJQJMZlKRSLBcwCviWbVWipHgsRcWh1gGyFnPpLCPCCZpNlSTIaUg0eKgYzmUtnqWy6/t2fQmIf6XH8CKXmMLZS1MCZTSJKRdE3VjC1ADGfWuqylLpd2oNvt4JxwuQ7cvLRBf1BRVpZuIUxmke3NKd044p4Tqxxb6uqNQLGHFFUz4PCsLvQ1IGQRkGYsjphgOqupSndQ4ujeModg9f+Fi9f2uHhzRL9nqWcl9Hq8MRAdDk8HX/e+piwKLjz4IIezo0ScP4EhBC5fuzbfkYSkYF5SMZX3DbO6ptftcebUGRofiDHw0Y98FyKJjY079AZDPvax72Z9Y5312xs4YyiKgokIKRqKsuKus2dJSfirf/Wv8XM/93MsLi7y2usXuX71Ku/7wGPcvnmbwUKP27dvcurUcbY2N7ly5Qp3nTmNM0IvK1xTxiJEhMJaiqKgafS6heAhJazRjxvmVyKL80RygM7ZaA4ixtmsnhVsu8seyloQwYkheq/CqIxbSMqLU9osRN8/RtWLpJxBipCZDFFEMy9cLT2E1ASsVVzN5uwoRsUu8onhnMV7jzUo8Gq03AghIBlLUeo3EYmaWYmQosK28xzZCikYnBFiVNgz5cyrfZEGioQPeq8RIfpEkviG5+vNx9szaByqFKTFNsRmQspCAN94mqZmd3ub/sIKIQVMiqgiDzqF0C0ytRUN1hm63YrVxYrBoMI5ofGBfm/Azu4q/+SzT/PhDz2EkBiRmE1mNDXs7XoW03W+5/0XsEmBskRiVkeaaWRre8pkOqVbDQ6t/wPwFdrPALWPjMY1iCOENqgclEvyhv9vaUMoy/IN79O+90H4iBhJnDt7Or/qjalnSjCrZ2zvbOt384PtQ2A2nbK5tcHS4irOFRw7epTjx09ACrzy8ss5Gws4V/ADP/h7+MpXvkSn2+X9H3iM4XABYx2f/NRn+CM/84fY3d/lyaee5p7zd3Pk6BGuXrvO/s4OK8srXL96jQsXzmuWlDQbEAxiLUVZ4KzDp8x0ZE2Fpte6E+t/VhkRYzDWkYhKuQZIZs6b4iwYcVgryiSkrHFAGZUgByrQiJYkJN14kglIUGyj1W+0V1xacFMgitVFnBkzLUW0fAop4KzTP51FkAMlKRGTEwvFHDwxgbUGgyGE/CyIIEmz4Xmym/QH22zDGCF6r5yfaLYxx18gs28QQxaZxUSMM5xzb2Cb3ny8LYNGyhdmXnvpE0TCECKQItYIdd2wfv0654dLmeLXi2aA1UGCyrDvM/NCwkmH1Z6jUwgeMAZSN+G9wQjs7UeW10oGSegUPWKTWFqCbnGBF3c1GCWraeWtOxNuXNqniDts14nlqKrE7UYVigBNCJQWljoOAXpdR1VZxqMpG5vbrAwLOuWBMGz+/2LesPDfiIq0X8vBJX+zZQgOXne4AyJRFo7jR4++6TrrD587e5b9/X1qn0E9IxAT165eVRzICIPBAmIcH/2u78EZwx/5I6eIMTEZT/j5X/iPmU3GjG+v893f+33cuHqZ2dTz1BPP8N73PMTGxjrrd27zyCMXEFElLmJyeeKymKoFMvMizh/MGIOzZi5/NsbMs4RWaBUO75omYUyBNRbjBGJUatQoGybAnIbIF9AkMmPSAqUCVrMeogaLmBQst2JJBqxJuYyWfG6o0AwwRaFBSFM9Ugr6daMUqEGDlRODTxFQihUDKYOqKeXrNO81SfNKVlmZfPrGICmq2CyFOYaWksYYyQxUjLqxpCw4O4Qif8PxtgwakNMsvec5dZQsFR+yN9nLpQCUZYGIQ4yb31gh4BAGzuFcxErCYrBVB4um7gEoraFQeQe9QUUi0q0AifgoNC4SEUqn4KaPGuGbaFgeDNnqNvR6J2m6RwlhlzoJL45gNgMSjGYNwwLuWXaITxS9AWtrcO36HW7c3uLEWp9OuTBX6bXg3uHg8OZsRa9N/oscDiyHwV0O/Ux+6OSNrzgomvRf2vjWf8M9KMpyzmQsLy+xvLyCs26+8EWEnd1drlxfp+qXnL+wiqSGY0dP4usR0w+N2LhzgwsP3MtLSWlp3QPaPgiTgUc9v5jvzcEHkKyoZN7op1mGnT8jIoK1Zr7JKI2pOz4iWdODLmISMQXFI+YL0iBWvx9TK/E2GFEptliTN6IWWNTW8SRCyFR7jC1TwsGCb/8hgsSDvhYNTChhJ6J4mGg2EFPCZrl3bPukUsrlR5r31xx+BlR/lAOXK0ghgFFcQ7AEH+bEgl4fq2XtN0803uZBo2U2xCCSKFxJWXVouQ4RtOvRutyBGQ82EMAKdIAScFrYzJdgyoGjQKCAYa+iU1mO9HTXaKLkuhtubnsWFiwBi0nQJEPl4M7tim7H8IEPfpD1r/86xIRNWZdhwZQ9vE88uwHTCcSpp5QuoRyyvNJj0Ovqgz1fRG++CprOvvUdViReDgWQlBJNU8/LmfluJG/sPz0o+uCNAerw701MJw2+UbHQ8eMnWF1Z/YazWFxe5rVre1hrqZzD2EgMU3b3Nrl2/SoffOx91LMxZVUq8yAHcu6UF6i1hV6DFh849HEViMw0qcJF+to28mUBlbEqhrM2X0+D3m9jkQiSZedzkDNfWZM1CxLywrOHMKn2R+Yl8sHC1/43Ow8StO8lKkgN81XdZo6JSFJqVeWZpHwuGMVgYvTaYNZ2RLcPxTyryPTvoTWfUpjfOf35tjs3s0rGEIM/yDqNIAc/8pbH2zZoQF4QbW2ba8dO1WfOcMZENCpy8d7TwjstA59DDg6hQNl8jdn6YDgiBYmAcOzIETqVMCTpa9vnMsFGU9Oji0UViJHEqITVxYrlBeFHf+CD/I2v/xqpaZDxHotHhiRgrFk4PiTEga0KpnViVvTZHN9mPJ3S6RTf4gok3szCtAg5vHHRtyHWe5+DRhtwOADr5tqGeOg9Du/tBzkIoA+viD6Y6c2BS//tRGhCoqg6FL0OfjrGugpb9fj+7/sBmmZKSpFur6cZwiGYIIrR9oCqwlg73yhaQZXQlpbtr2ypd/2mJEMUlWYDFCZTr1ZLGWnTfhGNIPOPrddGgtIUbYbX0qkpodqOXM/YvMmkRFZmtiekuIg4xQ9iUIo1xYRJQpL8X+46Ne2uJYJzTgNe0OfJHHxEFZ7JAS2a4+m8QjkUMvJ/UZvr2kw1HZQvIficeSn+gago8C12qPnx9gwaSgEA7X4ac3miCH5MygyYrJIjKQcejeQIa8hJZA4jymVkjHn+Sw7fgML1iFEoUJ6/RRUSCVNPWKKXnzsV4FgROoUwrISF0ycwCPV0wubF13j/qUeJKbHjDDtFBtsRbAWzWqgpaUaJxh/wAv8mx3wnon0WhV6vN/98Ldh14N72VtvLm8qahKbBAnXdqIRZWn2L0HbvzH8HKeMpiUsXX2NxOKTqVCyv3UW9c40k4ELAujKzW/MnH4NQuEo7V51iJ5LMXE8gaMrdCqT0NLO+QTT7MPNzPwyeAsblgiKfb4pgsvDbaEA0ViA6UqYrkySNs1GZmJB0s2opzvwoAQeaB8mxWTIwmWKmY41S5a2wTOa06IFSNIWgpVDb6JYzElJg3qqf4sE1Sy220WY7+poYfc6SVMAleCItFWtIaKNbSio8M7mc+2bH2zJopAxAtR8S0aJPsJRVD2hvplHvADLgYwsSwqSBWQ1NTGzNUg4uGlCKSiis0HGJUsCJLoHxrKZIJZHDZKn+78qtdR4+vUKCeYlTCCQjNE2gl0PbaDzm4ssv8b3f9QhJdBdqHIRKsZmeE6pagVc75luCUf+6K3Tw/3q0OcJbvWOL+s9f+Obvv/na59VRN9msaB7E23dvi0CVYhuBy6+/hDjHwl13K13pp9iiosTndnCLccU8bT7o/CzpdrvaR9Gm5ChQmfICEWlVoAqeim21DW1pp2AiyFxM1fafKJgpWddraFWgbScwWauVohwY11iLpDjHIozkxZ/SPOGSFh/Jz4E2wSTC4UAhgtY4FjERCVqeabDI9ySn0zEeYBdtqdlS57TFTZuF5M7ZmLRZz4jiTDFGPScO3tNaxV40zihM2tLB3+x4WwYNyBE2yXwJt5G0KItMObXlXmJ7Z4dr165x70MPY0WYTBt2RzClw42diJ/Xl5HFRUfpdPHblLAp0STdOXzjubbrEGvomMRST+iTWN+dEd9wdlo0GKcMSfvMjGczLl+9TrvzWRKVCKHQOrRjFGepOkKy5tCO/b+W44ANSEDjD+2mot9/8+vBcPvqRUIU7rv/QcQWWqeLUWbElFhb4soK50rdYWMkRFU3Omcpi3IePzWLPFxoxnlpZTLdqsrfNrswOdnSxYkk1ewYxcHmDEfUpsKUslgwxXkgbAVdiahsiU3EAJicqbYZRgtikjKekXPRvLmFmHDGaA8LKIiZfzTmbIQM8BtjMtgpma4ViBlXSSkzgRqwIhwSgGWqNTMgKpcXfFNrCRJbVsnlwKaf0RhtyCNnhv/OUa56tOCdzFuSrTPYoszfVzRZSNy5dYuvf/1xLjzyHhKwP6kZTRts37DcswyrtmtQKCvD/rRhawSj2qhbF5YQhDrMuLghNCnSccJC19A1MJ4Je0nVxrWHxsM0aO3aJEfIkmCJiXrqGYfExCdcpTe9sEIuuzFWKEpDY928z+DgePOi/MYb22YU3064eevXHH6Hb5XpJKbTmqZpKIx5w8+9EVERut0BJ47fparMpAs/IRhbEqP2kzhbYK326EQU8Es5g3DO4bJr1Zx0QDJIKXnHNnPmRMTOXbsOsyCaFZRIdu2af76kStF06GOnQyVwW45FBJuiJgexfQIz7pHLnvkVS4Kx2nqeUAxszlCl/GeMuFbDITaXHUZLBzkAo1NqW+sNpKAsirXzjKItqfW9W+zi4KtzWUI6wK20RNITbnGhGP084Hyr5+dtHDRa5MfM/21NgbWl3kxp/QrU9erO+sa87rSuZEbBaCrUDaQgua05MNur2Z9G9maRJlhKJgxc4IETQ4w4Cmdpoj50L12bMksVdVOyE/Wm7M0SmzuRWSPs7DR0Kse21+TYSGDazHjyOtQh0ekoSNbt590lP0wJwbquej/QYg56p5984jmQwIMXHqIsq3n6mmKYZwEHD7N5w83/9vKWb4VvvPGrTahpmjovzPY3vDFkCbBy4gzGtmYvurcKYG1JClMwmRYXNchJSZCU9dLtZ2pXZIaj9DwydTqXd2tZ0dr4aa9GxhIEtfgTRZ7ahdL2Y0juV9Hr3S5WILX2fJm1EYd4ryrRmMsoc0Dxtp9+/jmtYFEJOUH1HgrU5laI3ALQxt0Yo6pGQUtvyK5a+fvhoCTSTeWNTXUHTI2CpYmkOIaxxOCxzhJDUsYk5h4Y0ezNOasUcfjWWNrbNGjoVmCAkLSu1iZ5r6Bb1OYci0qMNWVt00+N0scHjv06cXnTc2dX5bgpbwmzBmY+946YDrMU2Jw4RuMZWIekQCBycy8S/IzJdIzf2uDIyiqdDiwImChsdC0XN4Q7+2habBKNn3F9SxeDG4MxkWJP6A+gsyCMdyJb64meT4fW3sEifOGlV4gpsD9q6HQ7WUKfEX4SIUTqpmFWNxw9ssYjD184uGbzVfdv4xBC0wCCcS6XAJrCvxkDaXygNBbfeFzR0QWMKI4hDvUwtBhTEFOYC610h1f9S0y5p2K+/85J5YNFnn9jq9eQLILT5i79Me2TMdpvQtTv50AFkJJKq1RQlb+eDlgmI3oJpV2UrdHvIRxG42fKCHf7vkAyCIFkEtYJTaMAvTEGKxYfcpfpfPNog4jVNveIZtQ2ZwjIXOiWsqlzGziJuskoqJpLZNpwnTKdrSitMaqQNcZoS0L1rRi7t2vQSBkRzjuMPhzqGeBcoRZuSdvjMRrxu51Su/9Q09WuScQCSCoXJ7cqd7slo2mk8TldtY46lqzfCezse4xTrX8kESgoSAxWTvHijS439wQpBQLMmsSkgZ1Rw7jR/hdrLEs9y0fOajZgRPApcX0PJjXgYTxKjEdR5dkxby8tt4wwCYbdcYPc3ufmndfolyWLwwFN41XIhqcqOzQxYsvJ/JL5kHj8xSvqWxomDHpdppN9dvf3WFtd5eEHL5Az6IOLPP/z4PfLoa/W0yYb5OhDFsNBH8XhjCSRCNEjxuBDDuztjts6bBmLGIeJhmRixgoD2r8jgD0Qd8U0P5skSdH+OX5xcJ7a2ZrdsUy7J2flaC5rUtIN4+AjH5RaqaWUo/p1WDSIIZJ7Qg6uSEpxLi2PtJ24KFia9GlpGROtfDLtm7ODxtcYp/dJBWGJaAKSBJub0kLyWkblPJK2zwVIOVDOsTyjv6NVJhkRAio/EFFZujInYS6GI6V5ifitjrdn0CBBChksMyQcLQ0VQtL2Y2nr30Sv1+fo0aPkfQZA69qUG5fQIGBEAUwfEt6DT5EmiPLlXq3bok8kC5oaa7rZ6S1zc2y4OQnZO0HwEeXYoyEGBapiDExnDS9cnzGeTVkcLtIkYb+Guk5s3lin9gYx/ZxymzlyA/oo27ULyMhgV3vYeDdTscSqQjrCXr3N7uY1Fop9Hr7/PN1uJy+UREzCi3cqQjQ40+Vkp0ed+jx1o+FEbXDLnvH+DtPphBgaTGwoxDOb7DGtpywvLHLf+XtYWlzIiwmms5m2c7feE5K34TeVNlZUE9GqRCHHQW0tzQsxu0VJ5jBEP3/MMUs1HBrI20pI0PQ6ZdbEYBEpNO0XrdmNcfPTsfnnTL7/reahBQPJV5z2E+TtOWVdRDIyF2+FFltpaeto2tYPRNEPLaNErfSUrdXmNxWKGaJJysQAJtaQLIVrO1A0C7ICxjoar2VgigfcnTGGQMY1TJaDHypP2oa6NrAdbiWQRH6vzCrRUsfhUPB96+PtGTQSJO9blgtPk8UrKRvtgBPd3cFge13uuusEoLiDyVG5fW6LwswbxKoOyH7SUQResxkxBkvCOm3qSall/1V240PApETEIhJo4VkjDrFJexdQxWnjPdf3DOOpYUTS/ChCWQh7E4OfJXqdRIzatnwYIUiAlCtIY3CDBWbrewiGkDS1pyyxSwM2b3+Zfr+HE5d/TogpMUldohgKEtM6sHZijcFWwcRaHr9U431J8FrzN5O9rBg8wv7+mDU/YNpLlOt7hMk+IUSu3drWFDtG/tVvfpqf+LEfoVu9ua1ad+f8WM6NmTEm1+Q5YCRAbJYaKAgK2Uk8d2AqrKPAd0y6ICVncGKsgqBGvUattRqPRHffNnVXD1elYecUY5Z2SspNajF3KImqiE3esZ21CIW2zwsZiMw3SLXjuQwJ6sMRNUKllHDGQaZpFeLQ55DMflTdiuA9MSgQWViLj1GFc9J24Dp8VAFWQgHR1ucjHDLWaf1bkjB/fnVtGJzRpNpZR9PMMPOObZXUF1YdyQ+3Jbz5eFsGjUTCh5oYNWULqDOzDypicVbdqE1OC40xLAyH8zQ0xUAMhpAdlIwIwSSiT9k2/gBtVpBR24Ubn/W5sUWhdU84zM9HNB21Ro1NTCEEr6pBK0JAsnu07ketgaxxYIqS0EAUN6+/33wsyAZHThzDdgvedf8iMcKoFm7enDCNQlH1mdWB6ExO6w9AvKKsiMEgTPnQfT2mAR4vLCkW7M9UI6G5F9Q0uKrDwtIKvWVdHFf39NpJ6rO/t8O1jV1mzQxSxSsbWosfyNrbhy6n6/lB9iFQ2Bb7OMj81FCppXNNBkQ1qIZIVnBqqRHwGcdqNQZZVm2smvJYp+xC1ti0Mnyt7V3r3KnYR8Yl2rEFusC1Uzmm1kowA6cxzHthglFExLTnDkqhJpCkmVUUNAN2VhmPrFsJSYFGE2Vu6iNZ1tkCtCFp679PGVvJ99A6Q+P9HBxWalUbNLWxL5dWCM46ohyMfiAeeGqk1AZBza+s1cBokXkP0Dc73p5BI0aaZkoMEJKliVCHSF3XzEZ7CiCFQHICVlhdXuD0yaPYIlNwzoIV6qln2gSMGF1oMTGeTahDpSl1K0cWg0igW5bsT+v8MKOAqzlYJnPLe1FO3loojDBOCSMx19Emz6CI2dcyzWtI6zSlbsEskTfGewGOLliefOkJztz7MDf2Kt1xgyBFoIy6RJ1zOCqcPdzAJIRZpE6ehTIy6HXoBvip9w0xJIJEtvdLnr845tq+o9+tGPQSZb8gxkQ9jUSfGIeEsxUhWaaTWQ40kcHx+zTbkZa1SXzhS19ieemIypPL8g2fJLVljBgwDudK5tiJsboji2pxUmjBTWWJ5gIzIcvLZV76COrAZq2yAhbJ/TcRa9yhUKbMmjFGm7gOP1+5gVEkZuZB5q3yiMnOVwabvUNbbwtlfdIcBrIZ9yC2ZbAhGg9RSEYzBJvZLhLZcUx/lxUVBkouOSxq8YcJWRmbyxARFcXl548M9M7/rt/Ra2WMAq+2bYpT5sZmBscZlx3Mv0XE4O0aNFIk1jMdQJQK6pCY1TXT0T71/j7OWpUgWyEY2Lx9jX/yd/48RdXFiOr6jxw/ybjo0z1yHw8+9H5cZ5FpiGxu7GI6JRQOGy0pTXHZ3KewBoKZA5NGHEQP2ZMgoiKZtpMwxUjhKkBwGYQv2sdWyCKlvJNEARwmaSs2pqXU3ngsLhRIavBAE5hbAYgIhQOJBnGF7swWBVak7YG2KlpyJYnEeJr4xON7hJQwZZcQhfG0IibBGM+5I8L5U3l5R+2wuLWxy6vXA/0j8KvNPk1Tg0Cv09FPllrgFh59+F1sjS1sXiTmB73FfFKuz1N2SAOlQyNxjk/N+16sZgnQNpJpJpPyQrPWURQlxhUZXNVduGiVjdnLwrYNJvkGSM5ExLQMjM3dqwpCtFqGFHWgEDbhiIccvDR7CilhklHjm6icjsmCrpgEwefrEvB52FLIuJApHBIjwR8uRAHS3BskxHgIUAVbGJ3ylgOZtOVX+2Bx8G8Vfx1gRcYWpBRUJp9fb4zB1zW2U1KUBSbmzeubHG/LoEFKpGZGCgEfAo2vmY1r9nc32Nm5zc3rNyhLnXnaHQ4xZp/d2Q7d7kB3NWuZ7t7ODVFf5/aXf1kRdmsIPhCiZh2FLSi7Q8apg104zqMf/SF+8ENnWN8Xeka4uZ5Y33NMpjNc4fBRS9tGjMaSytLpaC1ujHbFGmuxrqAyCvJpein4Rqhrw6wJdIo4ZwHmHzn/2Sks7334POu1BgORlPsotBnKJUGMJeBx5jB1pum0ywpBEcHHyNY4EomUHp1hIu0O67myEdlsVHZcx6ST3q7d5LGHTnD86ApFASnq4j5zfAnn3vigjUZjjq4e48kmYgplPlJKFEa7gXWCnU5Da9PyVojUeqWkJFhxFLnTNWaLuwMQUrJxj8FlMNVg59Jp0GzEStufIgqYmoBN5BJDWZi2uppnIyIacPL1nzMu0srNAREtO1LSrMho+YlE3USSyViNlkFFa36dNBWJGbdopesptD0hkCTMb37bSq+mUVpSJ2s1KLRDu/SEMm7XnmPEllY3tdDMnySXDZVFFAPpdrrzDy/2jRnum4+3ZdDQFu+GGBpCgDBt8NMxcbJHvbdFPZvhrKUoHGEy0U7SesLeaE8dntCUUk1YdfFq2RAYDBdYWlpRenCWaCZ3sMYSd1/j2X/8ZV76lyV4lfUGrzeiLAdsjfbw1Rq2c4Sf/Pf/APc9dBefei5x/fo2D50RXkg6SClm5NyalM1WUqbcwCRL6TxivJoKv8WdG/YH7M928PsziIO80xwoHsUkOkaNXHRTPQBti1LwbYs3uvOAYJJQ2MgsuTkGEKPggxrKJAPBO5og7O7t4mSNqrSERgc+pRS4emcXH9coWvYOYXV1FTEK+LXDtOfNZLmuthnXUWBTz5SkmIfP+oim0YFFMeMilty9Sna0mrfUW22emwcgizWH03ZyBnMwxCiT9hrYc6GBUWYshZidyWPWiWhZ2lLuLbv1BjoHwVj9MJLUeIekoypicmSjDJJxWUwWsbbAk3KGmjHV7NCllHDEFRbv49yfwxidXaIqTw1IqvlI8+yjBXXjIcBW2sCHUt5NU1MV+b7kGPWvU/K8LYMGKZLqmhgbYu0JsxlxNoY4od+xLCwOCU3INzM/IBksUhPng11PYm5LFu01me1vsD7emYOgIaldnLWCjw2I1QwhwKkzZ+j0epi6ps8MZmP87Bqf+dvP8VsYfDBIarieIoWNBDFM7jzLU3/l50hFwZg+5x76ED/1B36ExnR57WZk5CqtzwnfgGcALCwucXN3xKAnmCmEmDUPLf4hwg9+z8O4XAYgLTCWH+C2LtcLqWbMKWKyoCm0FJ1xWBtYWIRoLGFXCFFYXOpRlFap6SbiXKW1eAhz7KA9W5PHFSo3nQ1x8t9T9m2NmU1pPST0M5hcl0fq2ZSmmUIMWlykjBPklLz9OVVIoo7gc3GXZiIiuuBbyacoDQOAzWu+vX5qYNyWLQpIKrSlC1BXlMz7QBI5K0qtT4UcbtTV32ttDuC5gQ1BJM9IyXJwLSKEuThOoHSWJqiBcfCaVbvcM6M9JIrTpOgQCViXzZdTm3Xon2VREqPXjcA3mOwrGmPAliXRexJ5hkqrAvsWx9s0aCRSnBJ9gnpKamZEP6Ke7FFYQ2ELfF1rsFBJKC3Brxb16cDRTfPc+YXWHbp1QtLon5JoG7RV0KwUqEPD3voNdtvOx6hpcwiJZBW861Qd6mbCtPEMhsscPXaSYCKh2USCxbHFxpM3+Z+e/RViCIrWtyknhs+Q6FQVnV4fcRVrx47xwQ9/lCOnzjLe28WUXQjK9ggzAKIRSts7qHVpexMMIiWT6S694gB01InmgY6xNLOGQcdwbLlkOi0oSkjRZ1ZDh2sXLnsvALOmZjodA8s4sTQJOvMdjXlgOixnDynhjOIBeXqoZoIJ1VegzYHam6KdqN4HHWBlcidqzIxJps3JmSJRINo5s6CkiYKIORTSwkT5FOY7Mq1WI2ccGJDQirSEKIF5JDFt+djuLYqJxPn8xNyFmv9u0GxN4sE2ICJEyRQsrWmP4HO5Z2wGJVGMRYhU1uaAaXE2j4ckN7NlZaqQg1Gm84Q0n50ieZgUaE+My4E1lsXcrKkFfP+dK08gEepZbiYLkBokNTg8jZ9SVI7dnZrZZMLmxiZlt8PK4pCF4ZCy0DEHkOYejUB2l44EH/OgG8nIdFbwtSKmpJG/262om/rA6j7XEsYoqCkCzUxLo17hCOM9bl56+aBWR1QynSQb0ebmptxc6WNisLTIsWMnmO2sE0jcuvQCz3zls8QEtfdK34b8fkYgGgyBL1vDOArdqsdP/6E/zO/5ge+HFCmKSNUbUDptikpiSJn+CRKJ0VJY6JTCrBacE0pj8MbNacvlhQGdokSA4NO86Qlp9Q4HRxs4okRcHuNQFGWu/828jsfYvFAz02ENoVEdwnQ2Y380zfNLheQPGAER8nBtLThNYdQO75A4qaUgpc2yDsncNVUHWh06eQ5KXniS1cRaDrR1l5YXsdVBtCBkUjo0xpT7gPL4g5ZMyRYNB2MT9VfGjCMEr5lCe81CaA5Y/0xFY6CZ1vQGPep6hnVuvsiLotCNJ8YDqXsegNS6mbegqpXWhV0pYisHPubGmHkw+WbH2zJo6E3yOuC5aUihRvLuY43BWaEqHWU5YHV5Bef0oqj4UBdaTEEvXha+NE3DrVt32N/bJYbA8WMnGAyH2MJQFSUNmrnEwuCSI+IpbZ6xCYQUM16iD3/ry5BiRrBVgqfj74zNMz+zd4O1tKIzciZrYmK2v8PFve3MrOhNDCnoQOIoc+2I9xFjhOHyClW/p6pY16FuxvzNv/T/4W/94p9TYE4KpYhj4FdsoQ9LEiYB+sdOc+bhj3DX+36AolNiakGKgqIs8TM9V4uwNOzjSpeXoc5YMTmrUZbmcODQdnQbdYHqAmgDtWJK8/uJiubm1kei2UOMkRgateQL+sAjbdfIXDKmfRnZDcsgWXsRD6bFG+ZYhojHZkyjLWsU19AM087PXe+jsYoxWJPxlsxWzUsSAEn5x1vWwmbBlWZXc25DMpaVUmbHPKCdvDGPUYQ2DosG1KgCuRAiVVXhaxUdqlzd6fmkmDeurNWQg0ApWfMhSeaBxqAUrjUqhAshUBQFITR6jb/F8bYMGpBIsSHRgESs1Ih4TTmNoXKFUkvRa6aQLwig/HbeBSTqjE2yqvDk8WOYU8fzQ8dcER18Q1VVNLXHCWAiyavBzwvPvMDUz7DW8uC7HqbKQp4WR8nZq551PNjUNH02868d7lBUWbDMoTaMHMiDUx7RJwmflAoUp7vGaGeL6d4OIoHGW4zRrlJrUPu4LCZKOZ0uOl2GS6v0rCPdfo3Ln36V65/622DApoC1Fc71STahLXoGwbGwfIQPfvCDSKqZ7u+R4lE6ZZWLkMMycm0ZjDlFttbMgzbBv6HTtChKhQusZkztfZ7NJuzv7QGtcE4HAEHGJlBHjZiycrQNOnnZBaNBIBcBmKRAb3tPJP9+vScGMYEYyJRjK57SWiVFMEnmbMg8FWhp5hwcRHSzaPEBI2pKHPLDIOj9NUmIMWMawly4Bvkep/zQ5PMrjKPJDlvWait9SmkeDGmxGDnILJyxBEzuY4p55rCKGm2hnhpWNJj4LHRsy8Rvdrxtg4bB42OglIg3CQmeEBpKV2GcComcdThxJBN1sl72KdDUVvseNCtVjCNkME/VeDmxtUAUQuMREibq5O6iKolRuPCuB0iCTvzKegPtpNQd0aAKu+gbQvRMplMuX7lKaDwLC4ucOHWSYa/PZDKZU4IaQFo3scw25LTWiBDSlJAMuqsELfVRXAagCZHgve5SFkRyE9J8h8wKx3qf0e0RrY6jTUnVoV+pOCuioxG7fQYLC9iioN4a87lfu8pDp/uEk/dBgi/8rf+UL/3dDlVRYkrDoLtAp9enTg0vX9rkwrvew8d+6IexriQFT4xaX/sYMLbAFgW0prr5s6Wkkmafp7g5m9WiSYgeyGl362hl8mdLPhCcVeCbNmDkGmoe0NLcrS0XELmMUJVoe1jTUpoyl4oT2ueoNQjOAaM1BI7K9qRWTp5a1WnKauCkgZPcSJbUNDiGgDWOJjT5Xh6AqolAxM4/q3peaPaclCunda2fz6mFuYAMY0gha0NEe3JUJZryOEmDjQHDt1aDwts1aCS9Mdpc1mT9kqZ4IUGnP8AW+vdpjDirgimXx/Al3UrmNak1Zt6CrMEiD9Al5eh74JeAMbgqT+zK6WPKNzGkSJFU7wGqSm1pNyuCKQoK53j0XQ/OM4sETMbjvDg1EzDG4r3+qVLkyPb2Nrdv32Y8rRkMupy+626WFxfY2dlRI1iUm4/oHA+xLdMgWsYFMFYwySk9d0jr0NKGMveZ0B1SyzeAQJrtsr++hwKqQog+Z00Zh5CEcYVOQHeW6WCJxaUVfEqsmIYbz3yK//mpz5AkZhm3wXMAHiZJfCkvPMEwDXr+dRNYXT3CoOvmLlUkizEBMYmicAe9JamV9ydS9JCMMkZGP6fCpiqrxrQlTH6gJBcjWe2rGOpBCaXXqM0cpcVM1bQm5GJUbJ5a1hY2KScgVp+7XDJ4r+cZQtCuYxSbkZBNffMh+ZxiypueqDAstkBJ7ukRk7LwSze12GhHccyGwcaQ2R6XM7UGawqInmQLKuuIBCR1mNYTCtw8KL7V8fYMGiSyD/xcp2CcUJYl4+mMEBL7dcqOUAbfaGpZOPVtqJtAPRsxHPQ4fuQIhbPz0YRJoJmpQ3aBpvWtZbwuq6Sj+2LMnZgoQq78nCaS2cvAZi69xU3m8y4gNxEpOGZSO8dCQdWWIgyx0bRTYG11kbWVRfyh9xmPlS0ybbMUuRIvDHWdGO3tsbu7T2g8rrSsra0x7FeE4HEI4iTv7KDBoAXqlO+PaA3foukxted2CGCUzL6UlS6sUCNSMNm5w/7WrfxZVRgW82zUdvHFBJPGUxQFC8srWFciYvFAxxqiGEobaUa32dqt86KOhDgjpYiTSsHprEPwPjKbzCgKh81sgskGQc6oGjSglnvknTpl3YZGjRYn0Tb3dv5Hq+M4wGSEefdx7npN2Ui4tc1r8VV9UZiXGSnjMgkyaKnq0BBVDAYcZJSHSlZtTVERWvv86Guz0CsptmOMYCs3B3ZTpoxj1poIkdK6rHOxymAZgTqChc688/jfuaDBfGJUjF41BiGSQkNh3bzUbFLQnboVw+Sb0e/3WVxYwjrHOBqmXnC2wnR6RCDZng7YMdCIJTU1IQTu3LrNeH+XwWCBtWMrOKmIUWXmszDFjyZ64YsSQk3CIeLzHApVEkqK89qxPSdAF1JqEfU0p+0gEkKr8APm/6+CagyYIET8nHZMIVBax9LiAouLi4rRhAaM1V2m3Y0bBc+MdVjncMayvb3NjZvXEUmsLK9y9OhR9f3wHrGSz9NkoFIXYmEdMWTHKRGCb3IHpwJDvskqx/Zz5frbCgyqgkRiurtBWz6keCD2jlHt9xsfFFDM+EEIkW5PGaiYgcAQGrxXlkaSwRvUJoGkAjVpcjYW8mBm9QJNIjnapjlNPec3dHXnnTweuv4HJcwbMpz2v/b7AqFRnGLeNhAiPoRcWmnZ1GZKMaX5JnP4EDGYAE16I/ZlWyFbSwFDtgrMWVW+nvaQm1kKXoOoJFzOZqTITIwtwLQM1Vsfb8ugkUhIDPNWZnUm17TQFRrth8Mhm9vbTCYTYkwMBgOqSrJVvsq5U7swRYHG6LXWbqeU+6A9A8aWhNRw7PRd85ZncTm9TpYmCrbqUFXLBJIKcqxADEhKbO9ts7+zz9LKCqfOnGJ5ZZGicBTOsbe3y9VLlxhvb1JkOixl4LPtxBSTkKhGNjotrFU8FvjQqL1/yGBpRtETBpOzIU1PswiMNoDlxZFLPV/PSNbS6/W49557MqOjw6XnDVLR5NnGqpT09ZRr166xtzeiKrvc/8A9FL0ezXSmpVqn0mY1tCY/KJbT3PhWvT/bDKvFE/O2PqdNURowJnyMeK8O5rETiU0D3iMxEr0nuAYrhiCKi+giVERAhzxrVyjicsbTDp7Qxq2Wt1C61M8hxvaIbwA7I3NE5E3BIqb2M6rnR2iDQjaoDiGoNoUshxVLijWQfVLTIRcwfUPFqIL259hM8YfcbKdDzh0pB0QRZfZaijnm4NiqaiXLCTSOawZTCWQrML6VUuNtGTQgqf0ZQIiE5GndpdvGn7ppKMsOVdnRujBElQMH1TeYvAtJ9l+MmXFz1jKbzbh6ZYNZPWZhuMDp06fntbdpG6KUh9MH32i7snGVqlRTxDf59Qi94QrdhRVNm4tScRAj1CnSGQy4713vyg+0OfC4zADqdDrlxRdf4fXXXmF1dY1H3vUwq0eP6EjEJnL9+nU+/1ufoec93W6HKA1SlNrgXpYQPD6C2Ig1GbUXXRa+mRLF0FKlB1PrUO2JaNeoQNa0p0x5gkShcAX3nL0bY0WViaiC0xjRIdx1raCytcQm0u332d/f58aNm9y6tc7y4pATp06xsDikaRpAgcUQ8jhDgKQamLZ8a8VYKsUHZwQjnuCnpNAhNoZISSCqK1ZRaJaQsiis1SS0wUl8bljPWA65Y1U03W+xiTYwHNZlvGFRH/66MXN6OEQVAmoAbphOZ9mQeUbZqbDiMFZIsaGwXRo/y/cfkIjJ7FpbAtkMerfXSI11cqmVfD57PW/VGllSHjitdomaTVgpEBrN3Izg80R7kwJLS0s038In9O0ZNLSYnEd2gex61s6/EIqyJPopYrVNPYZA0wQKMXPdBmSkOZuxJKPy3E6nw5kzp7EOQKjrGmMdMSinbpxOKrM2Nz+JpSwdvV6P69evsr6+ST2dceTEMVYWF7XD0hqKstCsQdR9WvLDofWv7kIhNyyRIAZNVR+88ACPvPuRA+C08cyi7gWrx07wk3/wpymsRQpHCirR7pZKYT715FOIc1y4cAHjLC4Ger0OF195mU9+4jconBBFqKqOZkaxtdpTQ6PWGj/OaU6BoA8okpu3IvgYssENeZKY7s+lsTS5y3Q2meCc48ypk5w5c4q2W9gHD2j5IVmzkFA6uQmBre0dNrd2mM1qev0+i4uLei5iKAqLk0RsJvimonRWcZikwUKxJ80OlQHW2l0HCHEQBFpWjYC21cZ5JgYRH8MhpbBmaWEeYw9lcGQFZtQn8/bWBl966gXOnTrGYqdLPZvgfaQsLE3dIAUYoyMaQtTA6UNQiX1sA5XW2603KMh8pmubJWXnZO2StmrQrE2BB2MaXZ7Dk2ICq1lU1e3gvfCVr3+VxeVFTp86xY1L69TNv2tBAw4mgWf3YJuBPRdUc9HJowdnedqysVl4lY13nGSH6Ki7ys7eHt43lKVO9Op2uwqJxajDd6yoV2SKpKi98GIcSQyzeqbqxemUfn9Ivz+cB6+UAt43hKA/G0MH58qs0dAg0UqmnXN0e13q2vPKSy/z4vPPMVhc5F0PPcKpUyfxwWMw+NgQGo91uuDKoqKOEVt7fXACjPdG1MFz9333YhCapmG6pzNV932gu3qMj//YT1IUJa4o0QHquq1dv36dy1evcubMGU4cP8b2xia3b99gtLdDPRox2d+jXzhsoa3oRmAynZJiolt1ctepOl756BUgTqgMPyXN1EhoYxpz17PCSFb4SlYy6m66vLTI0sICIahGI+bU3lmrbI2gTMBsSrRGu3UtgINYE0U0ywqSWwqysL6lUEn5iUrzvwOkFEi1Z1ZP8T7iylI/Q5t/ZHwhxkQ7XlHNkBWY3J/U/N1//Kts7+7x4ssXWSsDd58+rQJE51heXsYsLgCKD0m2NCS1kPZBQRdTynRonGcXKSbth0lakpusGjVtRkYeshS05I7G8fgzz7O3M+L4yRMsLAzwYY+NrS2mMfLRR9/Dn/+rf51LV2+xubP7Tdfe2zZogGbMPkt2SQGbhGgsRWGpqpLJTNPC1l86kdRSvp0ajiLUxsDS0tJcPh5TRIzSZ87aPCmtwRVON5sYkDwRzMTEra1Ntrd36HQ6LC0tceTIEUJoaJomZz2O4GPuKgzs7O7RKUsNZE6VBClGmumMyWhMBI6fOM6p0ycRUf+P/fFIa/rgSUBpy+wwpuVUURYkkyhdSQqRW+u3ef7FF1heXqawBQ88cD+dTod2V8QUDPrDnKkpG9R4BRWXV9c4evIEgrC/t8fO3ojpzBOloFxa4/R9F1hbO4YtndLRCb74hc8yndW872Mfp1cW7I2m+HrMndu3uX7tCt00UfOXoE1bMWtPRBJN3tXUME07XFsgUnI6oGKqTEMag43Q7XYpiwJnwJoI1MRYKMXswZgIyaLTetEnIGkvEQmSNfOei5aODSk7XUXPZHeXl15+iU/+5qc5fvIEP/aTP0Wn26NlfhQeyPaDmcFJoJktwn/9P/wFtvd3OHvmOJ2qxPQrdnb36RS6w4/GjqrXzWVGRk5yKdFiD6DlmBoBB8Tmz5Ix2baUMQg+BkId5jYP49GES1euY6yl2+1hjaXB0l1c4Pip03zq81/iM5/9PL3+gMXlIU+9+BqbO/t0Bx3M+u8SpiEi3wt88i2+tZNSWjr0umXg/w38PqALfAH4v6aUnv52f5dJ2tOg6Hprrir4FEgpUDgdahDy7iloX0FqqafMCEwmY0KMDAdDhgsLmpLHlk7MPx/15kQfMM62WSqzmQqyVlaWOXr02DyVrOta60in5j2p/fkQiDEQfWQSZ5SVwwYVYQHs7uxy/fp1dvf2WFhc5N5772VhYUiIiaaezUU7xKyziCrlThjiVDOm2bTGCBRFybsfeQSMoVNWGamfauo6N29RbKCtwzudDjdv3uC5Z5/j7LlznDt3N2VRKv0XAk3TUGa6cDKbYb0qbWezmgcefBgBRvt7bE2muKpge3sHHw1HTpxVy37vCd7j65p6f4sUGiaTGbs7e0QfWFhaJJGoSqvO3kazibkuQrLdrrFYB/1+j26viyuKLFoLEGtCsBifiDbN3bVSNgOAtiqKuf08MW0anc+bAqPxiI2NdUJMrG9u8frFK8QQ6VQV9WysmFTGRVox3mEwNAYNTPvjmuXVIStrPcR0qOuAH5asb91hdaGvVL73TKdjyrJSnUTrLJ4CbZOfZDNgdXJToDumSAowmYxxhcPagqLscOvWJjdu3WJpaRnEsLW9w2gy473vfpR/8Wv/iqeefYFBf8jScMBzz79MEyMnjh3FmsR/8kf/t6ysHuFP/5W/wTjM4PI3X3u/U5nG/wn4yqF/+/YvotzQrwDngP8E2AL+K+CTIvKelNLVb+cXJIJ2lGZcIyVF1p11tNO9bU5f69mUFCLBKoBnvKfO6HdZVTjnMug40V1szoFz4JeYtKlIcjDAB5q6Zmtnl8lkRL/fZ2VlBWuL9nPS1DW2yDW/V1/Lqiqoa1VqxtTgrJuzBM4ZTpw8yUmj2UVKid3dfSDvKCZ7VqZExGHQmr+1BnTGUHULtra2eO211xiN9zhx/BTLy8ss5IBYZoEaiTwGMWQ9gyX6yGA45N3veTdl2aGe1fgUsxBcl4ZKmAuqsqLqdBARLl26wuXLl7jwwP0sLS/jbMnu3hZGHMY1xKAAdZs1TWczohiaqCMPBgtDFcfl4ns6C4dcqUIGQlFXMlR+v7i0RG9hmaIoMrDb0qER0kxJEp8l5kQkFVjncrCAENS20RJ56eVXGQ4GCIFRA5eub1IOhlTVEe59+Ai3rlxiuDCc6ynU6attP1f9SSK1I1ohCb/8K/+c7Y0NllcXOXfPOUbTGZPJmBeefYlzd5/i7lNH6HQ7ubxpsFIBcS4MlFzy2MwuzaYTSlcwq2cEDHu7E158+SWOHDuGGEvTBHZ3djl67BgvX77GV598jmi0hPzsMxexfsyRo2v80Pd/Dw/dd461xT7BJ/7bP/Pn6Tj49Kd+i5/6/T/FT/zI7+WXP/Hrc4n/Wx2/U0Hj+ZTSF7/J934C+Bjw/SmlTwKIyBeA14H/HA04/5qjhTEzmJkSPqe7Ks+VzIwoVVdn+jIlfW0IqvGoZzV13dDrdqmqiqqqNAPJ9alYizQHHHhMCRttljXrrj8c9BgMenOeHiKzmcqAJQmNz45MGZQbj2eEvFjaIU5FodTp1tYWN2/dJsTA0tIyx48fYzAYzmlRyD6SAtY6nDVMZw1lUc4Dz3g6gZg4ffo0TdOwv79PU9dMJhNtSGoygJkEa+v5XNSy7FCWJdevXefixdc5d+4ejh8/pqBmtuTzXqXOs1lNTNm1WoSzZ+9iZXVlTgP6qH0rYscZKG4pSS0HyqLAi84T9T4QfGQyGWv/SQ4sklW68ZD7ezuAueiUVP0+RVEo3mRU4KcjzLS0SNHqCAlJpGjwMiFZAyEiBC5dvU5Vdag6Fa9vTujPSsqyot/rcuHhsywOhiSTuH39CmKg2+mpwpaQR+lk/Ue7acWWkQEQrt24Q1GV1I3nve/9AF9/8ilu3ryDT8LWzogzJ44q7ZqHX+sMlYSVQkFYm8AnprOGmU88//yrnDl7htsbm0wmU8ajMcsrJ9jcr/nU5z9Hp7dAVRT4Fy+zP2lYOvsuVk6fUQc7Hxm9+iQf+cAF7rv7GIuDgs3NO3jToRr0MNM9/tgf/2PcunOLwjS8+vpr87X1Vsd3AtP4CeB6GzAAUko7IvJPgZ/k2woaLUB0oLPI5PncdkiNRhT8jCERQoOxhhCFGGw7YoOqLDULINHkP11mDTKgrq81BiOWhgYxgjNKh65vbmCto1NVLC4tEn1gOqtzBgEpJIyo/qPNiOomeyaYnNpmNaCIcGTtqNbVCbY2t9nd2de2cpNIIeGKIpvmytxzMzOIkBLdXo/oPVeuXmV3d4fZbMZ958+zeuQornCUZTkHAkGy7iMykSkA3W6P++57AO896+ublEVBPatVXJWZmdH+PlWny2AwwFrL7u4uz7/wPIP+gNOnzxCCaiaa2h/Qm3qDtL8nA4W+aUgp4Zyj3x9kw+ia2SzMLQnnJWKmSMUYhp0u/W6PonC5J0cZjxQjsZmSpMDHQKw1K6lJ7Oxu48VRWke3Knjt5oS1E8t0pcMDDzzC8tISZaGqXjLG02pG6rphPBnnMiljLFovtU/woadS6eu1I8s0t29z/r4LfOI3PokxhhPHjnDtJcVtRtOGpRgpi0J1MAKCJSaP2hVqqf38cy+yN60puwvszSKf/MozNLWn0ykprtzGFRVn7jnP4ORZquEKs7ohxjzvRUAmu2y98hSPvftBHrr/HAtdR1PP+PVf/ySn7z5LmEzp9Dpsj/bY2t7m5pUbnD5+nPWb37w++Z0KGn9HRNaAbeDXgP8ypdSexbuAZ97iZ54F/kMRGaSU9r/Vm8+xZVFjHH0QkwaMkKgKx27LmFiDdcJ0Msv1oSHSZBDrgEo0VoOEtZay0nGHrjCkkKh9IIZAp1PRzlfRszAM+32VRxthe2t7XtLoKL00331jSplVIEt7Je9W6LOWsYJ6NmM0Hun7iKHf69HpdXFWnaRDfp+YhMJZ3f3zOZWFmztXW1uwvLRMWVXs7O2zP57S6XYoC2VuQvA4W2CsDpiqSs1WnHNsb2+zsbHO0tISa6trTKdT6sZT+4AQ2NnZJWaQ1Dr197z/vgdICXZ3d9nb28NZpRK1F84QsllwDIprJLK79iHHrrppmEymtP4QrchOrQw0qA4HfTpdVey6UrtYtGzUAOVJpFliFoWAm7d+r+96estHqK3D2w7v/cB5BoPBXKSltLJkz80WNk8YaymKUsd6zkVn2p1yoObN+ixArfcitW/odCrqWWB3tMfZu8/RrQzWFYeoXP2crYq5PRdbqDjP2oQ1lvWNbXapmV65zUMf+35mjae0hk7h8Nu3mVx6Drd1GXzNsDvA9BYZ+0jHGeqb15nsbrC60meh53ACL7x2iWmISNkjmojH8PTzL3Px1Vf5g7/vx7DO8uKLd77p+vu3HTR2gD8NfBrYBd4L/D+AL4jIe1NKt4EV4OJb/Oxm/nMZ+IagISI/C/wswOqSzjARIoSsHWj7BqSl07QN2zpLv9cj+Fp32DyRq7WKa63vW8o0pYj3U5wzFMERks7qiE3DaDxWxNoaxEQsJdYk6pBB2KaBlChKLXM0aGnnmNKqgsm7WCsYiqF1oNId2Yiaz9rcNTubeab1Ls6Y3DEpB2P0aHUqulPNcmVPDLqYRRiNJmxtbWJMyXBhMMdKiqLIUI326LTxC1FMp98fEELkxo2bmmk0nhh1TGCMY5om4Ao310qUOWO7s77O5sYGi4uLijv0esTgiUlLxpCvZQxRWQav7exqJmPodLsErwY8oQmE0OjPFpbhwoD+YDAfVCwJ6ukIiR7plEyCUrlBAk0xwA2PKTVrDKfODlhcUkm9WKv9JylPewdaeVfMugzI6s9QaxmZxXcaLNoAIS3BcdA0l0VyKapSeWlpiSCJJ598gjMnjlO4grJ0OGdUZOVcLsesvodxHNgUamb26qVrnP3eH2fBGsYzVQWLdeAsg6Vljsa78DcuM1u/xurpu4idM7y2M+HEkWWKc3exfvsWi11HmZvdNjY32RlP2N4bYSu1l/ziV77M9tYu3ns+8L73Mv3Ff/hNF/m/1aCRUnocePzQlz4tIp8BvoyWHX+SNwnpDh3fnOPR9/5F4BcBzp0+nqx1EIIa34rSTKXT2ZRFUeCco6oqEkYnSYmZeyCEmAg+KwFFl10MkdDUucoxqsVAh/UU1hHFYEKeyOUsrrA4LBHBRk9hC1WppkNzJ5J2yfrG0/iGEDzWZDpNoPEqtLHWYa3kP1U/0kiTLQDbBjJLDBFXuIyR6HtoD4ulNYZRfUMkyTT7ZwiFVcR/Op7qZ8q8vc2ZmJY2bWNdngmarQGapqFpNDNwTgcVz5qa/dE+rfOWygt01yQF1lZXMUYY7e0xHU8IoSZGDnmaKIjs65CvmVrXqb9EmDtNaUem3v+q06Hb7WOdoyhUaBd9TUhCM/MUvSHSXcZlV7Zhp8OgP8zgp5aTxBygYyKZMDfNaafthYx7tUHYJPWNVZJEafqYsjBv/sCmuTBMcZtcooTE4nDIyhCGi0fpdPp85dO/yXDQp9/vUZQFZVVSljmLMczNjLLUQwVcMRCahtIY1YmkLIIzogPCUlRGr+xhFgtGswmzl59i7+Ztxg+9i6OnzrI87OsIBjS4Xb95EzHCaDrWzbaqSM5x/PQpau/5jd/6HP1e95uuxd9xTCOl9HUReQl4LH9pE8023nws5z+3/rVv2tKFRt2kYjLYpA1qkpmHbqei9om6OWAX1NvSYF32bQgNNsu2S+sYdjtKQRrJTVNpzpHrIu1B9q8k9wbUXidyx9DgGz8XiNV1rcYmwc8FNrPpDGM9MSWqqtJAZrW9u1OVFO0DlGk1ILt7peyHkNmAjNGMZ5ODsZHWZT/TSNNEZnGGBMmala66ilmdCq6OUFmKbbVMkgweK3U4v3cqPsuf1bdWeympUxnaEamlVhbYWTNfdCm/mfeqWanKSnGIQ0IynyXWIbbXUbMp7crU1VN1OlRVR+XqVqiqisn+DpNOQX/lBKYvUDoW+n06HQVTTZa7q/WeThqb91WIJ6ZshtyC3klUJ5KFWyIKmoYQ51PcJHd/tgpeONj9VKORMbYI3/PRx3juxRdZ6PexnQ7Jdzhx8jSlSywvL9IfDBkMFyhLfeZE5MB9Xi+yllbOqop0+warR4+xsryIOEud9NmI+2MmyeCOn6Kwlr3dPWr2GRwVjp84Tq9XUlaG0WSE957COYZLi1y5vcHm9lYWrFkMJR96/wdoQiQ1DStZdftWx+8WEHo4u3gW+KG3eM1DwOV/HZ4xf7OMA4DOWE1NBIlZ/JNb1Vt8NMncPt9HKKouzurQoU6vgyD0uj1VGFa6KzsjNLVnNN5jNpkqC5F1FjqYqNRdNg9FilHt/GNQnUj7sHbKiqKqcm8JCDoqoW2tFlFGIbalUk7VU1AlqfcN7XgCaxzJqPtUiEAOIocnhKmuQSeUmaS7e7fXzw9kIlEoiyQmm/vo4BwfArOpZ9bU1D53C2Py2MVIVZQqfss+FKSkTW8xZNGVEESQaOaDhgVtqLK5F2IyGTGdCFE9f5CU1ISnFXFlr4sUyCBxoqwqCucoSkfhdCyFKwvMrMC5HlXVxVmHdTZvBhCzqYwa8mopmaJRnUc8bFWQr0k0tB7kmmm1Has6tS+RG9BaSTcHwk0VlKZ5uRuCPgv333sPW1ubLC6v8NJr15mMa86cPElKM1ZXV1gYdFlYXMI5LUX1UQ4ksRzuUAVhMtqlu/4ay/3IwI0onGPs1cpBQqBYW2Kx3ycleHJrE6lK3vPQezm6tsL167cQE3jx9Vc5dWyFlcVllpYWWFlaYn8y4eTxE6wdOYqzwkK3y7A/4NTZC4x+7bBi4o3H73jQEJEPAPcDfz9/6VeAPyoiH08pfTq/ZgH4ceDvfjvvOZnVfPYrT/DR971bd8yY9M+gjTvWunmEMhlLmA/OyQ1b1WBAp3KZai0ZDobKtjh94J0V6lmN2bYIuzQhYn2jDEtKlJ2KwpXzeRSGiBgd0Ou9ZzDQHgLJi7PqdFhaPcrW+gbNZKSudsFTz2Y0XkuA+S4ePDGqtMW5ImcEFlvq0J+iKCmrxMLiAoj6m2rrvJZpRgw+ti6aB231YsFnbCKGQAgeUqJuapVn59kZhRhKp2bCzmnJoFmCshLWqZkQZFYkpQyI6mxV45RKjT4PdEoJCZam9lpWCCSvXa9GkkrBtdjBGp11ao32tbhCy5GibOv/Aucs0erins1qTNfqACZpR1m23Fqr/M3nOad+FS9p9Txahuq1l3lA0abIpp5m35S2fV03JQTEWK5evwMIq4sDZpOxXlPAuoLl5WXKosPK8jLTTs1CryL4Gd1uxXBhQK/bwbrMnpC0eRCl09VYWCico1M4Fkqh2VnnxvptHJHR3h4IDJZXufvoI5w+cYSUEht3likKy33nzgCGV2aXqEPg4tV19kYTlhYX+dgHH2N1+Qif/q0v0xus0e0OuefkKtcvXeT82RP8o1/9LTa3977p+vu3rQj9O6je4usoc/JeVLh1Dfgf88t+BVWA/m0R+c84EHcJ8D98O78nIeyOA1975hU+/MH3MR3t6FAeW6h9u8xymq10XKsTMKKq0Kos6Q2GWGfodjtYY6gb7eeYTKdz3CKGgA8eV1Ws9nrz3aTdpULGHIzVZiLvPTRxXgq4wlF2ukqF9oecuvsebt26zfbOHtao+Gs2m+FzP0UI2Roug6JlUdAxhrIslFlpEmE2BanxsaEouwwGfd7z2IdZXFohxMRw6QjXX32a/d09Ll++zNbGOt1S57HOvCdm6lmzpLwoKelagy1KjFEMp11gkCXSeoeZNTWzWinYsiiyYlQb3wtXYI2j6naYTmZE01BSkCTS7XWopjUR9UJpvJYl1ujCIEW8r7NNf8zXVhdQWZbKMpQFVVVSFQVNYbFZz9EClS04OTelyRmQsqNpXma1NL3OaT0QManM5qC9PcVIM5nMn6HW1Sxle8SXXrnEr37iC3SqktPHlji2usDScIhzls6gz91n76YJgaWFBcbFlKYqKAstCauyyCWqZJOcpJmk5NGTAiLaj1RWPdzSUarBkCIlOqVjUNdISowmU8b7I5wV1te32LyzTlE6dnb26A+H7IzHzIJnZfUI+s6Gfq9PCp5HLtzPl17d4OmnnmbxBz7C5ddfwX/wfZg4w5jfPZ3GM8DPoErPHnAT+EfA/yultK43JkUR+THgTwF/HuigQeT7UkpXvp1fUpYlH/rgB3n11ddZ3x7zzLMv8sH3PoLFE2JCp4dn2a0+ObQVkojoWMRuF5KnrmuaWZ2dkSJN47EiuFjifT2nZSWqXVvrDtPanGjNroY77dJaXl7BFpZOp6PdnbMJYkukqBhPpuoIjeDDlJTAFSWmsjSzWQYoC100IVL7gA8xe4YAaEqfSJgy4mNifX2L0VS7FrfHgdH+mL3dPfZGY/bGY5q6JMTIaDLFWsVvbKlzVlyZqDpdjp04yWMffIzz995PCpEnHv8qr7zyCisrRxAD+3u7jEZjZpsb7I/3ePTR93DkyBGsMXzuc7/F+fvuYzqdMNrbZ3trh9o3pMarTjNfw5RUBh6DpygcTd0QY1J3Nd8wHHTUrzIJEYtJCecsRVlQFLrIyrLEFQXelSQx9Ho9ihw8NGoYNZGRPDhZMi6UVZy0WguUvSFjCRogD5zGVVAXiH6i2dIcfNZn6cWXL/IP/vGv4gPcc/ddvHzxKqO9AaePrtDv9xCr5kbOOfpVSQyesnBUZUHpDIUzmMxwKWvSerweMGIkFZNJUbJy+m4WFhbw0yliEt1uh407W1y7+RpLJ7Wn6dq1G1y8doOy2+Guc3t0uj2G/QGPPvQgqyvLDBYWefqVa2yurzPoFjz0rgt89bXPE2PgC197nN//ez7KaKTO/nYOsHzj8W+bPfnvgP/u23jdJvDH8n//RkdRFNx3373sj0fYqsfm3pSXXnqB9z76LkyT5dkpZYwgIaImNCToLSzRHSywdfv6HLnXOlx3Vt1dw7xpKMQ4x1GamVJw3arLZDplOOyryCnX2yG3NVurD21bejgpKas+x8/exwPvfoxet8PFq9fxMdA0M2xKDIeLnD59mtlkxP5oRN8mTh9dY39nm1vXb3Dx0iV2tnfYm2xr6m8EX9c8+/Tj+DpQ1xNCElaWV5hOxiBopmMshRgqq+xGTELI7lGESBNhfXOb116/wqzRFv2rN26xvTti2uiDu7+7y2w2IwTPeDJlY3NTMyTfsLO3x3g00uY/a2ny5PGJj4ynOp9jPB5TFFapWx8YDrtsbW4TE+oD4j2rKwuYpGWLRRvLXNZJtNlGWerfa+toaUmxNmcc+UEXFeLpJONMx88degVEA/AbprAZADPXjbT0iDHamgC5f4jIS8+/xt/7B/8cnxIf/76PcfrUcTa3d3jm649Tj/e46/SJzLB1FFPqOHyo8PFgZECvU+GsKA6HYLPfRzueUrLJctPUiFhOH1uj6hSEplRxX4jcbGpi1AB8884GL1++Sp3VsT5G+t0uTgy3rm9w+ugxNjc3+fyXv87e5gbf993vpzdcYFbXLK2tIWnM7tYWt7cmmkl9i+Nt2+VqjMGHSL/f4/w9Z5lOpnT7C2zs7DPe38Eh7Gzv8vLr1zhz+gTtU5BEWFo9wmBxldvXLunDIto9arOwR8TSzv9qI7/6Qiot2ulULA4XqaqK/qAPSBby1DRSU89UlTeta1R8FDEuUhQlC8tHufvhD9PvlDQL16mbGbPpmGa8x/l7z/PYB97PrRs3uXnrNicW+3z00fspDOxsb7O+vs5kPOFTTzxODJ7FlaNs7+xnADYxq6esrB3lygvP8qH3P4xJkaauOXr0KIJR+jcGdsYNr77yOpt3NkjNHtvb2+zv7/HqSy/y8nPPZTty7bVZ37hNCp669oQkLCwMWVhc5Id+5Ec5cvocTz7zPL0TZ5DC8uPf8zGWFxcJwWdz5EDdBP7a3/p7XLtxg3pWI7XHz2oaPE1MHMuGQvVsijVWZ7tm8VzIfiJlmYHQoqDqqNx/jIrfqqqDKxTnqMoKBIrcS6QdrYodxYw1KI6pXbMKQLbsT85AWrUn2jBX+4aYVautOdK1G7d4+dXX+ch3PcbKkUWSTRw9vsrKsaPs377G3mifqirpDRbo9DoUhWPQ6+BjUlbN5kyjHU2R1LbQQtaCHGiNDNDvlBxdWQQjKhUwhq2tLTbu3GJh2M2KZUt/aZllKanrUQ48QlVWbNzepDCW1ZUVUvAsDrsMBwMF95spg26PnuuwurbGZ772nBpMfQsFxNsyaLQ6CEEbrbAJOxhwrtdjZ3ubZ557kfPnznHx8jWu3t6g6vfpWKEqsjTcVZiyMxdXGTlowZaUqJsZ1lnKsqJwlp4zdKpOblmOc2bGOUvEU88y09F4de3y6l0aYvY8CIHgPb1uB1eWjL1ltj1Ckkqp9/d2Ge/tMpuM2d3ZZnt7h+lsRh36JDEsLi+wuLzEXefuBuB6gCZ4zt/3CK9fvZ49QTTwFUXBrA587w/+AMZHptMp9957L2BoQsPT1/apo6d/6mWWeic5MpiRZhN2JhNeuXGbO7c3s6KxptMtcIVjvD/l65//CraeUI+2Ge3v8vEf/CFWz76b9ekv4Y2n2+/xse/+OMeOHj24T4oWcnPPsD8ZgTHaySnQ1FP+yl/4M6yt9Dl+/DhXrlxC8AePakKzB6u0o3MFnU5Fp+rQqbo451g5dpyTZ8/jskBKRVIHWYW2uEem4xHTyQiiVwEe+hprjXqCqF259qqkQOF0eFCsDfV0lu0IEj4kqkLFdWfvPsPSwpA7d9ZZPX6UKiUe++D7+fJnRuzu7jPo9ahnU0KMFMbS61rEqACuqScayHIHtHqtzslbWrsCEcWJjFODbGvVCDimSPAN2/s7uKrLtAmcXBjQGy5y6fYeS4MVOlWXlCLD4YDTJxYQqVlYXKEsC3xTc+P6DRq3TPQ1nWLImdNnOHPmNKPPflWzsLmA8BuPt2XQmEynvPjqRe4+c5qydHNuHdSxa2dnj7puuHLjNqbogim4tbXJoFOwtjTEe68a/ezl2UqvY0gUrqTTNVRlRadT4YqCTrdDU3t1B8+u2t57UojM6hmIMMmNYibbBZqsfqynM8qyxIihyBHce8/+3h5NavC+hhTo97pY57izsc72zhYpmRx8vhGQstZQe1gYDOZDffRIzKYjtjavsr29TiWdLInWdDNG4dLNDV54+rc4c/cDHDt9N2ePwMDB9nRGs3ad8sa6YimzKSurA1aOrLG1sc+LL19F9raxsaZpJmAMs1rLtcKabOLDAWXJQbF38sxdiLN0e31sElxVMqtn/Mt/9stUfosjR45w+/Yt0myEQQgSVJAlRrOLUhmuqqqoOhVlVVL7hqo/YOXI8ZzyZ2Vv6zKeQfDWCiEEj/cJmkzLi6Xb71MUJUVREkn4mc++sIlOt4ckT6/bVTuBfJ2NraiqPnu7ezhXce/5+9iejZk2nmNHh+yOa/z+HmsrS4TgdcaMtfzKbzzOQtexsbnFux9+kKXODGcMtnA4W1FUBVEcoH03rTmQcULVqZjOZnz+M7/FXceO4U1ksHqEae2pHNy6s8nScEBoAhu3bmNmXTY2ljh9fI2V5RWudzqM9vaYra0QvNLId505xc3dMcE3TEYjVpbO8cnPfo6yLAgyxdhvHhrelkGjLEvuOnOG116/qPb4PvLYB97HeDwmhMjW1jZRhO3xlLUjCxw9dpS6qdnZ3mZh0Cc0tfLSi4tUVYk1edZIUEaAFOZWezFFRvtjfNPggxrChhBBVGQVY1ID2tY3I2ogqTpdUkgqdjJq7rO9pUbHk8mE3Z1dTdGbBhGLdWrWMxqNmU6mVJ0uoWnyoJ43HWIwRLqly4LE1sJeSBLZ2rzDdDrGOEfIu2R7pJjYWL/BibvOESXgUwLUXi/iNTue+4nofFJrhdA0jHf2oW7m0uso2ireqTraBxMaDkJF/n1ZKCX5d1M4Wi/U1eUVJpu7uKrShVko+JmizVpbmQOgReEwVhkTVzh8Hsxtre7C86HFIpnmTkhS57SqLGmcdhIjnpRitk3o0OmrOU1ZVQQfsyBMIe2qdFRl92DMg4EnnniKX/7lf8E958/ygQ+9jxPHTsLGBi9ffp2F5SmzkKhngToLwgyBT37hKfZHM55/6XkG/SFrd3Z4dv0W7767i6u6dHtDBm6Rwqlxs0EFeyEmmkZBZFd2mEnJjVlkYVDRNxZswdGjaxxfWeSVF19mY2uXpSLw0Pm7iQlub2yyP/Zs7o/ZefFVdpuIt5bt6ZT+8hKDGKjHE7ZTw+ryIn/vE5+gv7DCcNClrutvuv7elkHDWsvi0hJ3nTunkuSm4aXXXmN7Y5P+oM/yyrKCfdlHs1tV9DodtqLXmrfs0O8v0Ov3MZIonPL7Jhh63R6j8YgQG6b19ECaHSM+NFjJKHrupfCoibARS7LanToYLjCdTJQKRPs6ps2UZ557CttZIkTBJ5Vot7TmbFYzGo0pXJkXmWoD3qq2jNMJzz35HN/90e/ima99iel4Qq9TMRmPCGGbjWuXNfW22jXZHk095Yuf+ruMfQJXZw1DYprg4quv8Pf+0v+IKQ2dhQ77G/sMqyWMsxRW2LtzlVhHbAzcun2H1y7e5P6182zcvsXmnascP7FICM1b3q8YE2WhA4NMgvnM1KzM3d3ZnVvhxrzoJYv3jNGhVjEmppMZ+2bE17/wFW5ceZ17H/3oPLtoMQeVlbb2juplUjcNo3GtRjpxqh3DCcRZXKkNY65wuMIeCLuIauqTtF+oZTm2tje5fv0qDz/6ICdOnODO7S02b27St116ZQfBsLmtoHGKiX/xr77G6zc3Wd/c4v4H7ufc+XOM93cYR/gHv/pVfuzjDxG9x7iCxcEQm53c5sGPhMXxzAsvUmN5/pWL3HP2FFVV4qxha2OD06tDTp08xtLigHqyy9JCn73ZjC8+/iyNr7mzoc1n6+MxneGQE8Mz/MbnPseJ0w9TlHr9Ll67QXcwZDodc+auM3z9ide+6fp7WwaNtu4bDocQI3U9Y7iwSK/T5aUXXubBB9+VB82oNNll7wrQUqRwJVYs9UzVjrPcmeibAAlm0ylNXdNkNaazVuvx3AznmwNwrKlrtJ8loAOSMtIuWp60szSaac1kusFgtcoK0pCl51N8U5NiUKfqzmw+2Ca9dcyg3+0yHk9BYLy3z2Rvj9CtGE32Ge/dZqE/wImb97rM1cnG0Dt2jA6GwkasE5IVmpTo9zo8cP4MN27c5vILr2FMQeonVlcWVDXaTPFeTXGm0wkrSz26nYLVo6fZ3rrCkaOnv2kd3DIcrRvV/EMFrZ0n4zHOWppav9OyVtYUNJMZN196lU1nOXriBMsXHmK0N6GZNfOSpCi0mzUByUelYEVHHRbW0e9WjKuSEGb5VysonJLObCnLcn59WvMZFcpBk2qlt3PJ1WJfGNi4s8G1SzcITeLoXcdZGg6UVk6qPKbqkaoOr7zyEhHLtJ6xuLiIKw3rW9ucvf8hTPRM9vcpiorKFVS9LiI2D5XS/0xhOHbsKN3uIlev3GBhOGBxMGCp38XHyNEja3Sqitu3blG4ilkD26MRV157JU8ZVBB7fXuPGAMrK0vE4HHddaaTMQu9Psura4Ro2d/f5fLlG0ym02+6+t6WQUMQHBYxER+jNjDFxOLKMg+/5xGm0wk3rqoBWIpqZIJYbLbVC76hqWc03uNEkXRtb7dsbW0rSCaSPTW0M9BIIvqIiAqCTOb1ye3kja9JIeGjx6FpPZIzFO9pvNexCgJFnDFwlr1pZH+k+IsB/KzGJh0NcFhM9OZjaWGBbqczH4cogE+JaAJWDDPUC6I/GHLINE3PtdOH3MtCaojRaXt0mDHa22O8N6Jbdmi8Nkzt7O1jbNDyAR2yPZlMdNSlNdy6/RK9gWFnc1OVufN7pEfKqk+blZqBhENl869dvshqJ7A/nii9KZJLCmVOxqMd9u/cYrHXw/b6TMcjbt++wd7ODvWsxhmhU7rsvaELupSivS1IMsSoJWencuzvT5RuFcHYttkv/+cOuktTBCMF4pzKaIXciKafy+RScGtjk52NLfrDRaZ1w0zUEb7TrQgIL1/e4PLVGwwXFrlw4QHe/f73U1QFRwYnuevsOWLw/P/+6t/kR7/rbib7u3S6KotPVjOdmCIxBUb1hMe/9jXqBkZ72+xsbbFz6xprR1cxxrCyskLpLJP9Ef1ej63tbYp6ylrVYewsy4Mh3X6XI0eOUDnLzTt32Nre5bnnnmU2rplI4NOf/S3uu+88n/rUp/nhH/9xvvb4i990/b0tgwYIz730Cg8/9AApJWZ1UNotQXepwsgyt65fo7QWZ0q6nU5uFNDAcOXS62xt7dDMaoLVmj167bhsoqe0haa5KtVTsDS7fRly4AkBIWY/i5hp20C32819KIGyrLCunRNbYKcNTTOlnoypSsPYZdVqaIhC9q1o8rwR8IcmjaX5J4fVlSNUnR4pCr1uyfa4w+/73/3v+eRf//9yZbxPr98HG/Gx1lGUkDGLyMQ38/knIp6UHI337I7GuF6Xxz70Hm7dXufoqQssLq5x7dJzXLr8Cok9Zo2nKoX77r2Pfn+AMYaFlWUqU7B+ZXPuTfLGI5+5rmL9BLn0mE0nmK5jNptgQsoZW8RnG8cmBDqDBe56+AKGyNLiAkeOLNMbdJnsJSpnKAsdCJ1Exw9iDUZSNufJGgzJpvXBZ8o1URYdUvR5sJDM5ef51IhWmbRpE/C+9b5XtWmnKNje3mNvNKWqSkb7+/SmA/76X/yfePHZp7j75CrJdvj8577C5u4eH/vYR3np1Wu811i6VcX1G3d49bVX+MN/5Gd4/PlX+ejDSxhjmM2mNLGmsI6USgSUeXGGD3/0I/zLf/4JlhcXOXb8CFVp+dwXv8CJY8fZ3d9jeTBgOpnQNFNu37qN8TU3NnehW3F0aYG9nV1u376DEdjZ2sF4z088+iD1iQU+99Jr7O6NuXjlCg889DCTesbs3zVMI5HY3Rvx1aeeIYbA+bvvZjDUmlqA4GsNIsZojWwNzuZ2ZxF2t7do6sjSsFBGPOnMVhMFm12PVFSTzXJ8okkeZxyzup73JkRsBgAVrOx2KqpOyc72PktLC3S6HWJul2+ahrrZUafwm9c4dfqUjgNEBVjaO2G0XwWhCAczXN58nDy6xh/9D/4A3aLi/e/+CEfXN1mYlKysnqeeQbdyLA5WePMa7hYVP/3RH+LFzevs7u1gC0OsA4NCsN0B9z72Ee5aWObk5Vfxs232a4Mtqjz7VuXuYiz9Xh+d6i74ENm9uc9kqpnbNx7tNLe5DALQFvvz588z3bwEOC2VmqzQtDorBQRXVSwfWWPY61AIFEWejBZ1kpzLoyKBeaaQf2um5fX+Z8/ePG9M45cYLTOMMToESvMIRCJWwBWWbq83l6JD4j3vfTd1DTfX17n3gXuoJ547t9c5duoYVREpjDrYX7p0B1OUPPLIw6ytrXL1xh1+9RNf4j/7v/88n/3iE/yRP/yHuHTtOt/73R8hNA3T6ZTpeB9hLZfB+vkVPnOQhP29bbqDRZaXV1lZHPB7fvDfoygt23sjvvi1JyhIPHDfvVy+cYvZ9oTHHrybraZh4ht2JlN298cMexUGy3efO0aa7HPm5DHkxm2IDa9dusi7H36ET336M4TmrTYAPd6WQUOMwZY2ZxkNL7/+OsfHY27duMm9991D6XSylCsrEO0GbLzX0oKA+DFxCk03t/9GP5cIR98wGPQxeTZFUaioKJFYXFjEGqHsdOiUpY41KAsFvVJrZhO5desOVVVqM5VYxtMRMYYMciXitGbmAzEq2OW9pzcY0l9emrflG2dyX8QbUAAANm5ewYsOdTp3epETR/vgb/DhD76HenY/zWyEdv57Op1e/nkdsPPA2WOsLFU888pNxlsT4mydi3s71LMp9/VWceI4duE9rKysMBgsQdI5qn/6z/0lfvM3P51FSJHKJgqB/+gP/XFsgslkSs+N5+d4iK/R+V850yO3ohtjOHbkFJc3LrM/mjDotLu8hdQgOdBe21vnWprwUG+N8cY6490R9UT9UQy5nFFuJk97j/Pfb61DogLlxlkFfrVfQIV3YvMYBZkHHHXe0qs9m065dunKnNJVEV/BwrDL9esNzz39Kr3hkPvvv1tZrpDodXssLSxwe2MKCJV1nD19hl/555/k/Y+9j1deeo2f+xN/HKzlC3/5b5CSzAd5Tac10SeocqOdtoqwuDBkOm2Y+sDx5QVc6fj8177Giy+9hJFE3TSsLS4wNHB18w5gWN/cws9mDAY9nAhNM+Kx+47gLNy46bn+2kvsDVZ45dIVku2QKKlcwayume2Pce5331j4d/QQ1M4uxkgTInWjsuZJPeP5Z5/n3Y9coKoKlhe6hCRZ9ZeBTAHjhE63orSWEANlp4s4XVidsqTfH+QuVqMCL2uZ1Q3ee4wVqrKTxxTAZDzN7dMxt98nev0e08mUXqfL/li9JWMCH212h2qQ5AmpZpJHE4iz8xbphGoJXO4cffORcv1uSCwt9NnamtAET7dyWOngomIb2GKeBbUXzlnLcn/ISm+Hd58ZQFpgc3uLqzdu8uiDD8xfZzJ6D4YO0Ks6mJjnjjrDxu4eA18z3h2RYk1oPLL41kDobKbXovWtNCJMm4avfO3LnFhyVMki2XuVqD4SMasv+03CXlvnq6+uMzw24D0PPMDg1dfZWr9NSi04bSBPXU0EWn8KkgaSGFPu3dGO35hi1r8cdLTGGN9gQgSagXR6nQx8yjxwj6djXn3hOZaWFjl+YpGN27f4Z//813np+ef5/3P339G+peldH/h5ww6/cPI5N4eqWzl0TupWS63QFgoII5GWAMNge5jBrLGBRRJmlgcbz9gDs/BiLVheDFjYsAZL1jBIgpbULam71TlVdap8q26O5578Czu8Yf543r3Pqe5bLTEjy5S2dLvuPecX93738z7P9/k+3+/xY8sUwwHTm7usbZxkc2sTk434C3/xz3Pn1m2uXb/KKxcv8sRTb+UdH/puRoMFvvmJ/wlVwaipk8taKW9pVTKZsjzzzDNUs5oXn3+J2zv7zKsZp0cFp5dKprXD1Y48KvYrz/7+hDIfULUBNa0ITaDA8OrNfd7x2Bkee7Bkb2VAYXPGS2NO7d6jVWM+/bUJt69cYrK3R1kWb3j/vSmDBkA1b4h4URtvA3c275LbjDYEgjKgNEVZMG0QjQwjqX+mDSbLWF5fZePYMarZjEFZpJMkrdSyKJjP58QYmM/nTNq2H40u8ox7+/cEIQgOhcX5tl9886omRjAa5tVcSg7naNoGqzTOi2k0ypBlGaPxSIBQrbFZzsLiCJ+AUYXvU++jh6iWOYw2TGeN2CF6+glPEe1RomzWi9+mjCVBCiY2dP481mrRpPi27keiUxNZGA8YFDlGO6yVG9EqhXgFeDT+28qh7hgNBv35izHKxCmBycEEvzBiXtWUI/EucTKjjlKRcpDTorg1nfPQ+QdZXRxQz2ZoY8gzUTvrmMGSlSVwujtSaRd9cj5Lp0EpOYeFzdKg2KFOBiAGy2jaWcUrL7+cpmhtL8v4vve9hwzDVz7/DHdvbrJfO/Yn+5w8ucHG+hIXHjrPvV3hg1x44EF+9dd/hQ99zw/wvT/wvQzKkq2tfT7/pWd4/NFH+MIXvkhdNWQ2P5KRSbCwJkNZw6uvXERFzwff+yRRBfLoyMwIYmS+P2UxswQfcVGzMcxZMIapaxkYwzwoLt/bImIYDQJfe+4qD54asTQecWdScfvOFmujHNfMeMeDi6ysLnNzc5+f/8T9ZHzTennD3/w7fMQojt4y3iwKXjrq5JEphkiDsmQ8HjLdmomRb5LDR4mVoXcOEZeRdp0/YmRUzStIjhnehQSgy853MJ0k1qFOzmAiqFO3Tc8XkG6JgHEuOsluorRltYkUNrC2usHxbAM9ztmfTuU9laLMC9oQcU2DSTfB6+lSIGm0IsOTa+kG1InV2kbwGmIQ4ljjupLhsGDQSlGU9si/Ta+PQUr1D58jf19YHJMNCqgb2Y1VclP3Pp2LhkD5bddKMKaIKTxWWTp9C6U15XhEZoz4w4wzQKfqQb7x7t6UfWUYDC03qxmqgWLDgrGiipZIWArxn+0CYvcdOsKbMRrn4pEpVRm/f/Yb3+RDH/qutKbS77VCJzOquavZ3t5mtLCQRvnl+cZa3vrud3Jn2nDlxiZPvONxmmpG28xZWRqjzJiNjQ1u3r7N8y+8hAvQeMdnP/sVHn7oQQ72p5w9c4wnn3iMS5cu8ZVvzBmPRod+uV2wC+IAH4xmYZThfU3TeLwJ2NzK421OnoHTiunelO2dCXXUPHj6OHs7e9y8tUXdRJ589DgrY83iKCO4mjbIRpPZks3deSLIQW4MZQxk/xv4nvyveyjAaFSIZIXFzzxt3WCNwVCyurrGwe42S6MF7u022FwmIknTqlqJMdDu9h4aGA4HoEjBRIKAUmJw1I/TJ8aoWAZE6rbua2GrDYFIXTU445lOJywuLFI1NVZbvBN/C+8Vg6FmdWA5tTZkcXnAwtKYY2urrCyMKfMMULx87S63N/do5jOubs/YCxlnlzMGmSymM6dOppQ88tD5c68LKhHw6H5QX/UD+4enLs8znnzk4f4nSwtjlhZGb3CiAQLH1lZZLAum9QSrNMdXFji5oDm5cAJBEgIdAhO/5dkkQPKo67pRhne85Wn2777KieMbuNkBWoPxclN7FShKi9mbcJqcxzYe5N52hR2Phd9CJGotvNFEUlPKSFaBP8weUoYR0s9a12KUKJ0/9fQTeCfXsNOPUEHOmDKp7TtvGI5SJpM6aioqTKb5rve9g8cPZtRNg3MNGkVuNXc3d5lVcybTKafPnOKRR56kqma877u+i1deeYXJZMaP//iPY7UiM5aV1WUpI7WmrV3yZlG46AihoZm1VHXG8xfv0jSOqqo4sTzm+GrB6kpSw88US4slmQ2UuWY8qFkqMh558Bw2K6jTGMTqyiI7O3sobTl1YsyNu1t84ovP8s4nH+XksXVu3NkSAej7MZHT8aYMGgqVuneKEBWtb1hbP4ZWioXxQEhaStOEgLYWmxcy+JTJLENUmrqtKEMJBGYz4fu3rcdaUe/yXlqUpDq49eIU1rZtT1vu0Hnnhcqe5cKvGI/GxBjJbdbrMhij0Vbq6Z3JDt945RKzg32mtYy5F+WYtY3jXLjwAAe7OzzzzJd4+sknOb6kqd0WcfQAZIPuBKCO3JqvL0AiNk3odmpYR4NGV783TcPe3l4vgyiaq8ItscZijbRir97a4dVrt9nZqRmOFpjvbaUgkN4vmRR9O5rRfRqFtkpS7mSAZJOyms6HhCCU68Ewh9qJFCAWH73shMUCC2rMidmrbBc5H/nKXZrXLrNoBHgOUUMUHGM+b3pJBGNEfFhFiErho6wVk+X4xkOQ7+x8BOVwUYSpOxwkQ8qUEBxt68SQKib6vA5YpRmUltoVycFdrCGs1pwyQy5evMoDG6uURc7nPvMZ/vr/+b/gV//VL/PDP/Ej/Mw//RmCa3ni6bfw6U9/hnXVovOSuq6xWbpaRjPMcgajJVbWVphV82QEphkMx9yZVOxNJ5ye1SwMB6wt5hTWkY9LlDGsH1/FRM3q6kliaLmxs82ZB89y/eYmW9UeRnnms8jCyjHe994Psr+9yZde2mbz7i2ch+n8/uxeeJMGDZSMPw/KEdoaxqOB7OTO0VaOna0diqJkdW2VG5s7yQdEUWRFkrCXm0IYm4iRkjVoK7gH7nDa0oeAsRHXJPm9LKNxgu5rk4nhTwL3XOt7ZevOVjASWFhYYDqd4oMQg6bTCrZ2KfOc6axlNtlnOGwZjhdw3qGU4uELD7G2vEzwLcVw1INzqr8Zv+2UpP92eYf6th2/5xpoEQgSQ+g3PnIUi4uO4+sNozJnvLDAjhVdEmmGHNonfFt2ceQIvgNvIwrTYw0dhpJnOU11gGpl+C0kjxSbWZZHA84/dZ7lpQnv/uZ1dBzx2XIB7aeInYAS53QkcDsnGWfnYuedJ3gt7FMtAUVbhTYZAbGGhAwVoG0BHTHGSPoeNbnNpDUduq6MaLSGGDDWkmcOQkwK85HMan7t1z7B1Wt3WFpcojqY8r73fxfXrl1l8cQKuzsH/I2f/it89GOfZG97iz/0R/4wv/nzfxfvcwnoUfc4y3Q6x3nPlctXqdqW8WhVFOIaR9MEZhFmvqGZ79A0jrzI8L6hqmqiKtCICJE1lsm0Zmd3j2GWk+eWejojGxQsLoyo6zlryyssLeQMTpzggXNn+Jef+N9QI/R/jUNFcQzXRlzOmqYSUhUwaypubd6UdHpxGY0VkhUinx9xqCjBofFiOkOE2HqiDklPQ/XliTEG1wq5J8+Emt2JA8vCMXgnxKG09abxh9ibU89mM+QmFjn9leUVHn77W9Aazntp12U2Y1AWrK2u4pYWyQpRrNLGEJQgLF086JSwD60gSeUKHGpiKpKeuvy8P3mH/7pvKUE3MSxoiiEmo2RpWRpt0OrQQPro673hIekWwUc66CQQmUz2McagTY4PBUq3hLYmosW4WRl2pw1fvHiFL969w0PDMefefZ7tuMX1iztMqoa6FWKdilE8QxBxITGtThPJMdC2PmEFhht3b3Pq2Bo+7dyTyYSlpRXRaVUyaiBUfwFMm7qWlqsSUR4VBc+xQGFNP7hojaYoLH/0j/xB/s1HPsHe3oyDg11+/l/+G06e+CKPP/YY62vHmX1lzg//8A/xz3/mn/HU29+Bd4IdiSmTnOcQAlU14we//4f4kR/9g70Jt7UGrZHhOq0wKopVhQzNp6suolIqCCNa9EJEF9a1vmfPaiSLBpk/ES1d+fsnv/7yG17ON2XQEPtEzXwq7UwVNbvbe2S5DBy5NlDkJUZLij2bzSkHOYOy6LUzVFag8oLoarzzZNpSVfPDGZPgE2uxQmnRNWiSOrVSiqzIcU1FVfvkeRJo25ZBWaZRe8WsnpHlA4IP6SI2WC1istpkaA0qCMreNKI25tLMidYyJEVIEvrdThe/Nccg/dz3szFHs5Hub93TusfIc9KjOuDtaASJ8hehXKc2pBFadVLyfx2W8kZhQwFRibRebkz3snjv+cYzX+PJB5YYDIdsH+wy1AbfK7K3GDxtXWFNyQd+34+S7W1y9ZVLbF65STHIqV2gdiJNpzl0RlMxJDFguWnqRm6oEBwhwGhcCFU9sU/zroUOEhDa9BnDoSEzqSsVoxDhldKYTDFIN2AMIk2ojXz+nXtbbO9OOHH2ND/9h/4A5eI6r1y5zvnzZ6ibmus3bvHFZ78BaLCWvCzIy4JBuSBXUGkWVtap6po7W9vgIx7Hzs4eu7u7VLMZu7vbVFWVgqHotnQ4nDGmL6XbtsFVHrRci9xk7B8ccGxjjbZtMTrgnWJWzRkMBhiluXPnd89h7XftiFGmU2fVHO9ShkCOdy2q1ChriRGaumJ7+x5FnrOxviGq4UQefPhRjM3YuvoKphzQupZBNqSeN3jvWFpa7IFP59ok/Kp7+ftU3QJQDAraRjgcJPCtdg5jsh5UbV0C5xCjonw0xGgwrcY3MnxVlmVSa8qknaiEWHVwcEDw4vd6KPcvRx8oVPeJXh9Uuvv/9R2Yb4sOR89s/9+IoklzMwEhzJnEVwjh23KY+18nSN4oct66KU5rDO95zzuZ7VxmY3WN/bs3BUNSkOiaKGV46Px5zp3Y4KWXX+BtawOCr5nUc8ZAdI6qcVgjFgSSjguZrE03D8kOovFObB+i52B7wvjkQMyaykNvWaFjpBWiIs53g4XyHUOEztA6pDVgjWVYHmJcEngDJjOcPXuK4bBke2uHj/7Cx3lg4zhbd7Z5x9uf4LnnX+RP/Mmf4m/99F/mB951htxmDMoRvsvuIkwmB1y5eoP5vJYSWosIlI8OYyMnThzHuZQdJV3PLlOWry7ld3QO5wIxZYiuaVl3a+RFlij4gjkJ01bK8LzI3vCavimDRtM2NE3DYDBCt00CiI78vpGx76gUk8mU555/iaefuICPTsAqYxgUJafPPcBSFsU5zBg6yXsfXHIvN0QirvUis28Vbd1QZDkqRBovdgV5UdLUM0KAuq4py5KqlsnVuq5p25rpvOJgMqWuRXkcB00UHEZu3bQwfRCJf2XSQhbJNhn1fn3A6A71ur8f/utwplRxn6eh1OtDiepLjSPZhw/MZlPmjQzq2czK6HnXkouHj73fEYgIJ0wMo4INaGVpg+ejv/4xfvC9T6KznGjk3GvExpIoSll7s4aZCywWA9bWz/Hq3Rk+SumANkQ03kfEwsqjlZRCk+mEcTkgRi+G2oitpY6K48fXsXlORIYQHdI6l8Dm+nPsfWA0GrG2sY42Fn1k+lSEiumd7Lt2bQyB4AJnHzjLC89d5FgzZGtrkXe97VHe+a73sb+/x/lz53j5lYsMh0MeO7coz8MwXlqWMxY7q43IqCzJs4K6mcvPlUoOeoroXRLQlusm2sqqd2rzXpjOzjUiGhXF/MoYzcAMIYgVaZabxEwFFUzSi/k91nJt25ayLEWQ11iCjeSqSDuZ+GKWA1HCCs6zv7/X+3daK5Z6GoVrHXU9p23TeLXJpFTJMuEAxED0LToTyrjxIu/fNJW05hSUuaVNaa/Ns15yPyKO9HXbUM1m1HXDsChxrQQ0HwWYs2VB9C5xT0S2TwEHe/vY4UD8ThSH5Uk6B996m35r7tAVIfcHQ++fYxx9RPf4LDMMyoKtnXvUaVxamSSPl9qafW5yqOPbHwaFVQJ46oQX6BjF8Kltqdqay69dxCiL9vM0UWySd2ogN5bWeb7+wksMtzcZZSUr4yVcOxHg0wfR4oiHil2ZkpLDd0HB+eQAL5qhHQbiQ6BN6lrCMHYpgxKhYeccVmsxitYiBShdIDlNwgNJ3ZkQxHE+aXG0k4bHH3mY8dIiejDg7IkTYt+pPP/w7/wd3vuh7+bXP/KvKXPNaDRieXmZssyxWS42BtpS5CWZLdOgnUw1d+LVovkq3roSmj0miT1F75JPsVAFQiIfdhmJ1WLI1LaeoGST9V6yjKZ1gCYzv8eChoCTLt38hjwXwd8syynLjDOnT8h8ipIaWvw/M/E4oUUc94TrP5vMsVkG0VDHOW3bcuL0Kfa2t/GVI/qItVLvam2Sy7pnPp2xOFqgNuJCVmQ5k90JJjOoqci2TaaT5GfSkuUZB5OptBIVaDz1dEZVH2A0ZFmB0WKWrAIMBwOK3GK0ZDz6O+zmR48IdAJ/v71nfPvzOfJcFUSvsihycqvoZnl6m8Ijx87ODisrSz0Y2x1eHapRaW06l2VUBIN0S+p2Tpml0iAIcW84KJm2Fc8//zyPnn+IBy6c5vnbV5gUhqwSn9fgo5zTGOkcWV3q7vjW43yTtFUiWlmiDqi0k3YuajHd7HBY7kUn7fV53abSQ6eT2wVj1XNOuvKQFLy0Unz4h97HM19+ibqt+MLnPs+5U6c4ceI02ztbfM/3/QBXb97hC5/8KE8+tEY5HLOwuEznEWu1xaDwCMnKGAF4O/Eia8RK1ITumiX19XT9tM0T+Awky0lptUdsAosjEoBi8OSjgQSfGFIWqcjt77HyRCnVqykvrSyjEYxuNhc9ztXVdbY2bxPwtK4SwpY2GJOJI7yxaCPzD40LKO2ZTA6oqorxeIH1tXXu3rpL29QYY2iCIzOG2f5B7wZms5xpNcegqFyDy1xaVAGtBaitqhpCoG1lAlQWL8wme7z4lc9y49oldrY3KYuC5ZUVNo6d4tGnHsd7z2w6YeHEiV59CqVft6t/2zk58nfzBj8/+u83ylhe/++IsgZtFbkVDEmjCMkDpNu9uufIkNO3IidpZL3r8kTAyGNal7o9Knm264CJlmAUWRRTpems5vzZh/H1mLuTwLsefISdqzd5becezntaJxIFAdAqYmOgVUgbNAQICu9F01XFmD6P0ME7U6VIhBB7rEJ4KJKVShszOb5FxIGt6zCl54aIdG9kcaayxXHj9nW+8uyLPPXWt3Ls5DpmOOTR4+uoqPjYR3+ZMycXWVpdZZAPGC8uMBwOISmOeNIAnhLVdR87bnCCupWkOWLJqZKCeMowQ0xKaPF1wLnw02QOSsd0DrROGaJ0krQRsPSN8lB4kwaNoigZDAbkeUlAPFRdI8rbZVmyt7/PM888g81ztM4oiwzdNRJVV+kbvBcNi1ZWGQrF/v4e48GAQZlzMDnAtw5jRKvS+UAzmWBNRowe14Y0AeuZTmcsjMc0rbi9d12Guq4Py4uoJGUMnrapWVxaYmlhhNXJOEfLQrQpyscQsNaQjM/5Vjjz6NEtYiAh/d3jjx6/Vci4z1KJorW5MBoyGA2Tb65nf3cH79qeLBZjZD6fsLCw2D91NpsxGAyk7o8kJqh0u5SSTpcPjqg0yqRukej4y4xOiIyHYx595HEu3phx+2BKk+/y8t2b4J3U7F5uL5HGC30mJ+VISC55LTI+ADp6gtY9OtxR1nsdU5KDHj7BNUIIlLalZCbCCo1MJnPmVcOwLPo2u+lMm7Thh37ouxmPV/jEZ55hZXUD73e5cm+Tb37zRU6vWVaWhqytrXHuwoOMxwsCgiev3o5hC1K2Hg4TSDYTQjcVrF+XVcaYjKE6cyrF67thdAxZuhNA1HJNiFKuqHj/NdYdb8qgkec5VV1JP93LCW6aGpAgsL29w3PPPc+73/0uEaiKYticZRZr8t4vI+Y5PnraeZOMeIXgVTeNvL73SUDH9HqeMYqSV+vEpCZEacvpGDk4OAAlruZ1VTEcSjekbsU3xFhLnmwQbNRUVcPB7jYrK0tkIVAMh0my30ovPkqrMoTI/QbXIpHJdJ52uwgh7RJRplNjbGX39AL0ra6sEyNUtWNYys7knGd/MhGbRpvRNFW3lxGB3cmUuqlo6oq2ccQgXZXJZB/nW166+AK7e3vM5wc8fO4EGxsn6UbVv/zVr/Du97xdsomE7AtmI3NAznuMtUQUZTkg+jYt2PQKSgbpBjpjXu+xUxvGegFtjLSCkx4GyE3S3RI6BYMYxeoheBkfz3NDU0UyLTiEtoloFiOyUSd3t64tbywRuf7GJtlELTzb/emcn/uFj/L886/wgx98LxurQ3KrWV1dYWVtjSwr0DHytrc+yIuXrnH+oQt87Tc/ySvXr7O+BOOlZS489CCnT55gNFoiz0SwSSHBrnOIE4J+xKZ/SxWk0hWSMkP8uFPXpOviyGmRc4EYW6skC0BSjBceisEFkblsvZP5oN/i/ntTBg1QDEcDtrd3sTpLU6aBosj71tfe/n5CuCUOizG0xnZ1ZwyoKBwIwQAivmmIDXzsY78Gqe3VncCutWWtxjVNfwFF6i+k4TJB02ezGdZkHBwc9K7vPsiCLkdjlM0YjEfMm7mMjY8XRbXJeZRWtE1L23i8C9Jqy2QmJX5bouH56Ge+SOMcdRtpm4YQhQMBCtcckOcl+zuXUbT85f/kr9E6xzcv7/Hex5cBzdbuDr/4a7/BwsIKG6fO8fyL30wovfAUvHdM93bZWFojErAGnIts3dvGtZ47B1PubW2xeesGD505jXcNICP973j7W3j52ksoNUwNmeSfmmV9DZ4Zg/NQe0WeblwVZce1Bu7d2+TVF19gZX2NxY0BJ8crPLB8jEt378prkWjkMh0P3X8ScNy1IMtygGtq8ZoJkZFNRkmJPm8Si9R0LfXE1XGtp6rrJDEgcyd7kwm/9Cuf5GOf/AIxan7jc1/hsbNrnFxdZLK7RwiRlbV1lNEUheEnf+x7af0Bb3vP4zz42ElUdCyMh4xGI4Z5QZ4bTJYylKgJCM9H1FdiwrMCQUUsSXk9Suba6ZTEFCAOM9JOhCgcWTeHTvTRe+lseXpEN89yvPfE7+DjCm/SoBFj4MSxk9zb3KINkr61dY33nqIoGA5LlpaWpP4moHQQEBONa8VQaOvuLfRggs2EOQcQTapXQ4eCIyCnFLM458iyAqXBRGlTEVNIigFtrLBHo0psUovJ5XmKSFGUnDl3jtvbM4yVnd2nFLJuHbqS7xCJIoasYp+y+iBWBOp1UcOwtbXP7mRKNW9p2za5lmuyXGNVQ15U7O/dlNkJhEK+vr4k5ZI22GLExvnHWV8ccfL4Me5MGlybDKmDJ/iGLB/KKPX1q2m4SzObV3gfGA4XWFoJmKDIMsP+zk3K4YjRwjovPPciLmuBESoKLbzTsEBJ21KyDxGiMVoRdccaSxgCGUsLC9Qzz/YLV7l8covJ7r5IAiowVkG0xCgELh0NHp+wB09mbL+pCECgaZqKEBd7Gns/R5S+Wz+Lo6KMKyT2cSASg+cLX/4av/yxT7G0tIw2lmOnzrK1v4ev71LkGXvbWxTDAePRoozfDzSTecNwlDEYrGO1kTa/giIT3+EYI5qUTWhQUTpD1mp0UiUXy8pUpHTBLaUUR8tRlTI9yTy0qKoHafmIT66ogolfq04/65O2Q5LfGxxvzqAR0k7gA9GA0dJb9qFNrmiW82fPoRLlWR8B7ozNUTpy9/YNTp1/hLe+5WmCl/rVuZamacSurm6Z1zWBgHPCJowKJrMp0SfNT2NwjcKlgTRjLM4HFJ0dYABNb3hkrWE0KnF3tkFBlucUgyHGZOTGgvfU83ny8pAAVNc14+LQEvD1h+bMqRMszWY4l1qKIUgwM2DbGlNYXvUHLAxzemgyZVgmzyjyjDMnNhhkllFRcPbYBq1zVE2g8YEYPBOjWRwWDMqBnFNtmExmeO9ZXVlhUBaEpUViiGR5TtsmJWtjeM/b3sXHPvdqwgMifYYcY/pO0pY1JhNcQFjhabOMLB9f5cQT59m+cp1wp2YYFskGWQK2u9ZsymBMeoEA4AlBugyi2So7g9Y6SfjpPs23WgsBLZ2b1I+grSucb3uBng420tqwsXGccw8+SDkoWVlZQRNpt65SVXMmB/usNy1qnIR8Ci2evS5D0wlCgdURY1XfDRGDakMMYvItOq4dDwNya/EhYnQH2HYBL01vQ49fGGWISsSJux6xUhKsbUxK+UG8a4hdAJdXCerbS+Gjx5syaID0lp33DAclKmryIifPS8YLi4wXFnngoQtCnPKSJRhrMFkp5B0afD2Ddk5m11FZQZ6nIOCciOdog/OO/d19hoMBWzvb4sxWz4kemqZGG0MzryEKOu+cCONEpXtjXh8FqNPWoo3FZAMZdlOiI4H3TPZ22d/ZZnFpERUC5WDI7mTG1vYWp9eGxMU0sPZtxWbk8YfO0TjZO2IEHYVVqRO1+IFzp/jUN59gkGpyRUT5ivk8MM6HFEZzfmWRLMuxVnN+Vez8Jl6xW4utwyiLLJc5RT5M5UBkMjnA+cDF1y5yMN1jurPHD7zjKfIsY15LGnzu3HniEYoZSZpPaZ0sS7Xs4CGS5VaYl0oRlUcbhTGKzcldXnjtWR5bWEQ9cgI7XmV48zZKlygMvbCZFqMlr5E2Y8rUvIOASRl4kgZM8xZGC/bTUcUVMoUcI/jo8HXipSjBAwKBtnYczGpu3rmDzjKeeutbGI0XcG3DTuVQrUjstW2TAhlYpRiXpQw6pqwUFHmmk0p7d5gewATpmqCS/WcqOa1JxLqQZk2UwjvhhnQdtl7bNmqUEzFtEu7hk2atbKRS4njvMOpIpiEf7w2PN2nQEBLXcDhkPB6TmYyl5cW0excMhiMeeuhhLr70ooCKUSYgd3f3oJ1xbLVEx0DVVExnlfDzXSOsPy0LxBpLOSxZXlrGaMO582fR2jKbTWgbx3w+pW4d9+7eYXd3j7ZtaFsngKwyNM0cNRSNDgAUlENN0JnU0NYImaau0WQ0bcXkANq2JsQhgYhrWpzrxvZbijx+i/xf5KOf/Cy7ezUOcXyb13OsUpRljlaev/Bn/xRf+cJH+bEf/qmummWoZuzt1oyXVpjVLc9d3WR5NGRlccCnn7+MDyJQFJynbRvqyS7veOIJ8qKQbEcp6nlF8IHBcEjrWqZqH6WQ+j9IprW+ttaDqt0Q4KFsniZ6nTAHTYwSQGKIqJD4HEpR2pLlvCRWc6oljSslFRGrxEMD5w670CEStQGfwFCVyqKyoG1qVJAytbcdVB31O4IK/fn13lFND+TzawG/Y4zcunmHb3ztefIs49z5s1y4cEHmm6aBWd1CnLLWLtFUFd41CdxM5EEbRTQqDRK+ns1yRHbwSJnkY8BHsbEEAS77gcGocKkkNdYSQDxfu3MconSjEq08ho7c5vHJnl4kFoWED1GA7m6w6A2ON2XQ0EYzHI1ZX18nK3LqqkYpTdu2uNkM53w/x6GUdDq2dnbZ3tpilEW0HmIS+Wc6rdA6YLTG5kUihYmPqmk1WS6KUtVcSFbG6ISbDJg3DYMiZ2VljbqpaZoW17Y0dc3+wR5N3SYpP9WTmjrAtHWiORqDo2nETVyjeksErUXP1Cf6797eHmU5JNf5kTMRsVlG1HUyHlIQIrOmJs8LSTdj5Otf/k1+9MN/CBihlCLPCg72D6Q0sJbBwhJKR2yes7K6Tt22MsNDxLU1M+UZLwzJ8rwf6JvNZrhEwhIhHJEYEKpy0ulMAF0MQl3WSa+DKJ4pnRRwVIlnoAwqOgGwnYSbhWLMmQcf5WB3k3x/i+WlwHais0vgSPmDEpf7zgcmaWABor1iTIZTLVFAAHRiT3Ykr242pjuvIXgmO/cQy8wsvY9ie2uHS6++xurxkzz22KMorajqlhMnTnLtpZL9rU3ZPFopa/qWJiI8pIxkOzGVrkTVW2B0Jgoqkciika7HwawmRitYWxMBIZzhW+b72ygdWV5dl+DmQp+9KKQFnOUZ0Qe8j+RK0dSJ7Rzke4rrpE9Dfxypc+5/vDmDhtKsrqxw9fIlqBt8kOlD57wg/k52L2NMv0tX1YyVpQGlEgHbXGd417C/t4vWos9h8zaxFkVG36SMIDMGYpQSxloyk2Ms5OWIYmmZjY3juODwXujJk+mUrXub7O7ucu/eJtPpVCjrTQUhSA2rRIjGpZF6m2W9U1jTNDjnezVqSJO99zODtkdEeJSoclV1RUc6iql9WdUzYAWUmDt1N4jU3Bk6eLTRlIOSLMtlwpMoO2Y1xVrReUjdU2GFpqlSlCyxkNLehXEpgaPTHO0yAS0iwBHZ9WwSfI4q1fURMDLvoxLOEFGsFWPGaxadl6wOFrnYpe9GrlX0SRGM2J8LybCTWnkkDatJ8O4+p8gLailnEjbQtWo7gYEYY3LeE76KsTmjhSVGoxE+eGbTKUTY2dtnMq+IQd6r61PECEYpYvSi7Zo6HioF39C10qOcxK6FKh6+kRANEcud3X3m84rMGJpqynBYsLE4Enc6q4QZG8RblyhFoTaKzIq0gveSic5276GUSCDe3dpjNFyU4bTQosiktIlC93+j400ZNITJhkwhxphcwT1ZbmkmM+bzGXmmWVpa4r3veS9KRR44exIb5jTzfbSRu9HiCa7BK5VqzRqP1JAoAVhRmix5fDoXMLYls074F7VHaxgOAq1ryfICqwyDQcnqyiqrKyusr62xu7vD/v4+UVmaqsbHQDYcUpS5ZEJNjdYKk2VgDPsH+7SNY2FUCqCa2mrfeihk+M4Yg/dSQ3cerhCxVrgWxlh2d7aB05C+X0j6mq5t2Ll7AxUj4+wENy9dpJ7PpbxCpofnkx0eObUi6T+d7YLvb7CYuCSihOUpTJl4I2k31aLLGnptB7m9o003PZ3xsZXfJW5Mbg1WKxrX4FrPeGWDYnVFcjWdMg2l0v2mxf09eDygki1niIjgTusET1EaGZ2R1N8qc4T2rnq+Q0zYAFFankZLjnj69Cne/o638aWvPcdXn/kab3/nO1lZXkFrw3g0YjK9J4FCWzqJQ+H6qJQJggqGgO8BcjlVAWMS5oR53bRyVTdcu73Fwf6EqpqzOM5ZGBasjgaJtVz0g4HXbm5y8/ZdtLYMypILD55lYSFL8yieS5cvs7QwYnFhkVtXXuHhx98pVIBiwL1r18n0nLkLtKkcut/xJg0a4rPRtG0vTNK2ba9psb19j7X1ddZW1/j+D32Ina3bxNDiZnXKPiQVdd6J8rcxxCg6GWiNa2WnEO+THNdKGh9MwAO5lRmJIpOuRtu0RCBrHcNySIwi3jNaGLG0sszx48eZTA7Yn1YJiANlNFVd9z6mMQFwIQZ29/aIPrK0MKR1vk9f73d0uqIK6eJELyTnwCHFO/jA/v4mpDTeGN13E5qm4cbV19AhsjzMeOWrn+fgYJfZdJvgW1CWpm546pELGFTPr+iCTlEOyasKqYe7m1GUvuUxwjcISVtV0nS5obS2aG3widehQgHBpXIxMh7muKrm+ovfZDgcUqxvMNMW3zqstf2Alk7dgZi2aSlYNG2UTKMLaEhlhNGivK60PjRNAuKRwBxjpKqmMvtjDjOS5aUhZ09v8MLFEWU5wHvPaDyS0ii0nRRSb+SN0mTAq19/ntIFTjz9MK0H8GhT0gnuqBDTWLwWViuHn2l3b4fbd+8SnaaaTlhbPcHe/lTKcdfpqAhb9dKVG3zpq89TlAMWFxaYt57FhRGLQ8P6yhKzeU2RGYaDEXdvbGKOTzi+sca1WxMG917D5BWzeUtwv8e8XInQNg7nWkD3wjggPqP7Bwesrq2jtSbPM/LMSvszs4Q2iakoaEjiKdGnrAUB+5Skd61zwqSzFoKjUh5lM5kvsTI01+kqGJtRz2Y0zmGIlOWAg8kB1lqEmyvKX3sTcUivZxWzA1EhH4+GFHkOSNDzzgs9OsqEJh297z7HYFCkVp0EjhBDaqcdzqk0Tcv21mb/CrL7Sc/faEVuLa5qMFpR5hpfWjI1ohyUDIcr3Lh5G/EY6ajWqdyIcOfGLXYO9jjY2SP6wGi8QlaO5Zwk6UOtNNpm/Th9IIr8ofC005RqZ1pkMFkBEcZL6+QnVsjzAXubd5nt7vDgI08SvKIoBhibHZ6XrgwAAfg4lBHoqOIh4QXi0at7b1+FJsYkDxgEB1NKkZlMzKFVehsl38noiGsqDiYT7tzbYv3kaV557pvcun6NtXGOtgabiciSVZF6d8LXvvA1Jne2eOfWXVYunEFnlsFolXwwkECspFXuYhSgN0JERIJu377D5StXWFvcQMfItet3yIxcQx9CkjyM0hlREZNlFLl0w65evwHAWx+7wNqKlrWVOEgqRL7yym3ONRm379xldOMKGxuL6NgmMZ/7H7+toKGUOgP8NeDdwNuAAfBgjPHytzxuBfg7wB9Mj/kc8BdjjN/4lseVwH8F/ElgGfgq8NdijL/52/k8RJlSjUhbqnENRVEk3CHH2oLxaEw92ZWJvczilQdr8UgvOirNysoai6snUARu3b7JbDrvSUBFJovFtU52UASxj61HRxiMSkJUiYcxpyhE6cuHQGYMo7Flun9A56BltXiX7m7vE5TsoJlSjMqc6cEufjBgYWkFm2UY22UCXV0sO/T9jnI8llMSxVZBKS9dBcCkbzteGHPr5l3kW8T+HCpEtyHLDM1cSq2ysGR2EcWYqqoJEVZW1xgMh0mNW1D/rua+/Oolbt69y+qoIKpAlg96L9r+htZCUOrwBKKArmk1CB8hKAEKtZZswxiGwyVOPvJO1tfXqSe7xNCyvL7BOwZLbN3bZjga9QNvpPkT59POqw7P2aHIjmQLOrmlSedFvhapBRkS50GRytQY+0ywI5xpFammu9yoHNN5S1M33Lr0Ms18QrlxgoWFxbT5aF55/gWe+8arTPf2mDQtL714g7cuDVFFQVt7FtbWGJQLqfuDzOEk/ZGIqNDt7OzQimEOIShu393i7Kk1wWq8xwWfuCgCnC4OSgZFLiCXko7LysYqWZYzTA2CECN5brl5/TqbW7u0dcUF1/LiwTYPHR/ivwNV47ebaTwM/FHgK8CngB/61gcoCeu/CDwI/J+AHeCngY8rpd4eY7x+5OH/BPgx4K8ArwF/HvhVpdT7Y4xf/e18IG2E0JXlhmPHjjMYlOzs7LK+PmA0HjMoB0kW34iDezB4reiZMsDa6nHOXniMPIeNY8epqznz+Zy9/T0OJgc0dQMRmtaltN6QW0vjAjbTKAzGCp/DB1HANjYSTGBeVdS1w7WiLOVVpGlafNOQDUvKzGKNYWE8xrUVmdUsLIzIM9H0cEmNV5DtNzoCw/FYtB1dYvWgMEaMmAwutXoH7E8mR69WAgIFCN3YOM4DJ85igMFoFUdGGbel+6Isp06uMx4v9EzJrusB8F3vfAdbO7vE2GAzfRiU+neSzkka8UAZKQnKskxKaALUaZtjvBQWPl0jY7T4444WGS+vUxQZZZGzeupBykuXMCZLNzOyS/v2UFmt4yyk7EY0KOhbmSa1dHXCN6S8EQEbtCZoReta0ZlIGEVIgfnhhx7kBz70Ab7x4hV2Dna48sqMUntWV5dZW1tjZXWVrMiJCj77qc+zfXeHJx96iLOnC+7cvcfOlRsUJ9dxPpANBhTZgG7KtM8Gkc3Ch0Bbt+RZKcTBqJjO5tR1nXyCgwCs6UnWWAZZTlvNCcFi8zFWK4IXkDcvUtkUNXXdUt28yRaG9x8vGBWWRa24dnuX+e+Al+tvxhiPAyil/mPuEzSAPwB8EPiBGOPH02M/B1wC/irwn6afvQ3448B/GGP8mfSzTwLPAf9lep3veCilGI/HrK9vsLy4zLnz57hx8zpm/wBrMpqmFbZgcg1TPRYOvVp4BDsYEWxJ66Ysr65ijSy46XTK3t4+u7t32d05oKpqvBd90MyKSU3jPE1dMRqNxOYvRmLTMhwOCSEwmUypG4fRKjnBR1xwGC1DYpPJVLRMi5y19fXkByuzHsH7I+pVApjdv0JRHFsesbq8yM7eAd3cwo//0Pdg8px7sykqKubTmrbdoaMjwCFLVSvN6miB4+WATV8zWjmDKcfs3fbcvHObIi/JyyVIYr9aK+qmpVN2Onf6JOury0wmB2SZv8+HVIl0lGYqOg5AN98RI1pb4QakP1qZPkB1LSSlJBMxysqgmbHSiYlHsgltyLXuy0qHEnnBRkBtmR+S9dAFsv4zHslKdOqgWWtl9i89JKZu1NrqCu9/33s4fvIUO3sT6rrGNxVlpji2vsri0rIMuEXNrI1kvuWV7Qlve+pBnjq+Qe4mTF2Nqi3T/X0WFlZAeZTO0EqudeJwEgPs7wmRrnINC6OF1P0wUp54l3gYkh9prYhaytb9uqauG8qyIHhHN9gYY6QoS5aHlpXgeHo545Fx4NV7e5QnzhGrGUfdPL/1+G0Fjdh72n3H4w8AN7uAkZ63p5T6JeDfJwWN9LgW+Nkjj3NKqf8Z+OtKqSLG+MbQLbLjD8ohg8GQLMu4dv0as9kUpTXT6QylFQeTCRiF1llKYaGTQpN1rVG2oHGOan+XvCgYFIVY/pUDTozGHDu+QZW8WvcP9rl29Rp1XdHUIsrjY6RpPW3TMBjKIq/rGlDs7e8zHI4wJIwheubzGXVVEaxi/951omqo6hpjFBhNVhgWFwcMy1JsDrTqU2WFuU/KoTi9usbDD5xktPQkl67cZmd3j3c88TDPvPQqG2cvYIzhj/7Un2HvoKZrRKLEg0VamzAY5YyXFrB6ke8bLmCtZefgBOcvPM5k2tIk3odCyqDbd27z6BPvwtqCqhGhIaUN/al93dHJ/+v+3x2NU7KWFNKVgI1aQQxdV0UI3X0A6cojrTFJqb0HDNMrRULiVIjIjGsiPnQzOwoXAwMj2YTSgnP0cSG9T0A6Jrkx/UAbUgGljCmyMB7y0IPnmMwrmlp4LUbDIMsZDgcYbWjrhrppKKMiLq7y/O0tfvTD38udb34eV89QumE+n9L6FmM1OkQ8AaOkmxSjTOdevnYDu3IcFz33du4Jlyd58NS1dN5CDNSNw7mGpeUFjq2tEZVhbzoVYF4LfmeUwtqM1jU0deDhY0sshpaXGs3ysVXyhYyRXcKY3x1h4aeA+xlAPgf8KaXUOMY4SY+7FGOc3edxOVIKPfed3kgbgw+O2jmmd+5QN46ysAIcekd0gel0xrg0qc2nEzCWNEC9WC2iFbVrOZhNCZMJC+Mxg6LEZNKuymyOXSohRhYWFxkMRly7eoWt7S1pLZZDQusSBVtOpffCnWi9FzxEC3HJtQ0xWiEDTfe4dXeTE+ePkeWRwdCSF0MZKgtzvJ8TopRE1mSJHRnvm2kM8oy3P/4wWTng/Poadd2ggHMnjlEOcoyCD7z7bUwnUwIyJKWATHeeIxnH11axec4IWBiI8e/G8pgHTh7j3u6Uy9dukinAB7TSNHXN7s5dGQRTOvEgFC6kO6sLTglV6OZDBK+IYCJNVUuGkbI0rQQ/0EaLkoQy0l1JLVhIDM8ocoGZzTE2Ryd+w6FZQ2d/oEFHopG0PMRI0zoKU/QdFh1V6sAElDGya0f6UQCV8IzODLvjgUh2JFhZ6XMyo1FKJBeM1r1DvUIo3jYb8vb3vodvvvwSn//MZzizvsB07x5ZVqK9o20rtBkKLqWNKOEnIDdE2Nze4vjSOouLi9y6eZMiBXDvPW3TktRjewB9cXGRY8eOM68qdvb2SIqA6CBSllppmjby8m7Fw+Oca/OMm23Lts54UgXKQr2uk/Stx+9k0FgFLt/n59vpvyvAJD1u5zs8bvW3eqPJwYS6cVTzitA6iJGqbphXcxSa06fPcO3qNZ547CFpzVmbiDPChNNG4XwgNo56VjOfzsmsZTqdioZGhKaU3TXLczJrKIoBJ08POXbyBAcHB0ync65dfo3JbI4hMpvPKYqSqqokA7KFkMwyURsPARrnaJqWGBWjQc7CKOfu5h7HT6wn8ddAXe9y/vwGs7mGqHAdxTcNKL3+SKzJ0RBQlEsjYARE1pfGKemOjLKM4dLi63ZUCXJKFnrSNO3ahSDiWoNMy59cBHddiIlwpalmE4J35MWYeQamNexsH+BjxDctWZb16b8swO7DBzRCbGuaBmutuOS1LYN0nZSKmARSdoHSJ22TTqLPBXFM66c96RTA5MYT1zXk74kCnuV5AjWltY3WotCtpXuiVNdSDsTEYHVJnFf+TzoVpIzVaJUo2zJLo6EPHCDnqp42LB1b58SxDV589SIvv/gSZvA42bzC2gw9KBPdHYgOFU06X10YjKysrXDh1AlqH/Ftw2hxwOnTJ9BK2J42szjvmU3ntHWDURYXhWOzs71DXha0zuHaFhcCGsPO3pStVvPAwOCHlpNas1FaLt6a8M7zY75TafE7GTQOC8Nv//n/L497/S+V+rPAnwUoywGzyZS2qkGJ0LACHn3kUYzO8FGmQ7WxCfGWnexwBkLRNA3z+QG5LaXW1aC8pmqa5FOik6KUtF6LXIyRymLI2uqA9XVhX1567Qqz6QGzSkx1jTaE0EoqazWuFc6E9wGvhK+QD0usizz/8nWgJSsMdV1x5sxZ8mIMGAoXxG2rH9O+38CanLZ+MFrRn1r1ut+qfiirO/FH/Tzue5HUYad3NBwmurHobJ44eYZBLq3C8VgEntdXF7FqTTgT5vAVZ1UtAr2ZJjiHzgYC2GWiThYSASxP3ipppE3+HiG6NC9Bqtu7+JM4IZK9dGjxodpV9/7BS2kUgowKdJlCnueiGeKcBM0uYKT3Cl6xv7/fd3wiyNxI18YloFUgM+Bi18oFawRcBUDDsRPL6OmE2y98jXNnztLevknZeHwS1WmblhiQiW0lYC4Id0Qn9uj66jLrayu8fPFVtAqA5+BgF7WxQJZldAN4PrjE6Aw0dS3gZxI7ihHm0wm+bQlZxtbOAQeTOVeWlkVFblRw89Yd8hymk6FgT29w/E4GjW3unyWspP/uHHncue/wuO37/I4Y4z8C/hGAtVksy5zpZEIg8sSTb0EbyPMB+zu7VM1URHhIRMrY+XQcdhhc8DIE1NbUVS1q41lDnucYDPfmFcPBkLyQ0sOVJdrKSHFZllhtWVvbYHFhmWo+5+btG9y4fpPJdILzBbnOkgZt6AVtovMYLSLHjz30EN997gy5jiwujinynNFokIbC4LOf/Tx3b2+x/p7vSXwEfb/T0p0d4LeIuv3dpjtYoz/eKIoDGI2IChcDBoMBKE2ZjxMmAgvjEZnRlEUp9PsjLxxQ3NnZ6z/53lbg2MkobeoYiEF2VRXFmDnYIBoPRwKl3KziPO+NiAn7GHFtpG0ivhVugkogZYgenfZo17Yc7AsgTRTmqlORYVak9rSwRbWSDYLo+5ZrcIF79+5JeWWMTH4mxa+YBHiN0mTd71LJotOiCyHQes+F82e5e+Uysar54FPHePfGh5nN53zqS9do25Ysz5G4F6RUUp36mMIDLni2dybcvHlTJBtcQ+stVVWhlabMB7KenadOA44Hk23yYsjq0jJnTpzgGy++zO7uDsF68UexhmvXbhKahp29fU4cP8bNW3dQ8ynvP3sC5ysWB28cGn4ng8Zz3L+r8iRwNeEZ3eN+Qik1/BZc40mgAS7+Vm8UvOfWnbvYPOPRRx9lYXGB7Z0dDA1vfdtbKQcl58+fI/oGTEz6mzpxE2RRjkYjBuMxQVvR+kQTdaSa11iT8cSTT3Ljxk1iVLStI8simdFMZzVV3TIcDVldWWc8HmJtzqNPPMH0YMoXvvB5Lr56iUpVLA7HZFbTtA0x8RS0DuTWokPk1Vdf4ca1y2RpRkDpyJNPP8bi4ioBGJQFKytjQr6Ciwffqff6b3UYbVhekhh9mJHI8a1vkWcZSskwGVrjfHJnTzWv1ZbRUEqh12UtMfAHf/KvU9kLXH/l3zCvS7TKWDj2MJPJVzm/kbMyaBPV3CQx29crhgilQ9qFSit8iLQuENwcbRX704pwa6czc08TygEVPUYp6ralrcUoSAGegGpT1uclc1SJaSv/6YbYArVvuHXrlgyWKZnF8CG5t8VOVFne06Y2cofV9tVYjCzmQ3jibSxu3+PWc8/RrB/nld/8OPtqwmhxEZNlKIwwV4ligwB4IxtNpg1vfeopXr18BZsZBmbE4sICo9GQoMSk3LsWrGY+n3Pz1i2mVcDqjHpnlxt3txnYSGEtVTVNhWFklDWcPH0MoxpeeP4ii4uL7MxhTsFk2lA1vzuu8b8I/Bml1IdijJ+UNaQWgR8H/l/f8ri/BfwR4H9Mj7PAHwM++lt1TkCuRz2v+d7v+z4WhgucOHmCYjAgBvHAqOqKJ558ilee+0aqrQ/l6ru03CjFIB8w9ZpZVaObBq01w/EACAwGQ4wxTCYHhKiYVzXj8RiNphyWYs7r7rC/P0BpS5FbMmN57PHHGY8XuH79OlWVVLCCyLNVdUtmhb2XZxkPPHyBE+ceoCxyBoMS72uGg5wb168Qiayur7G+tkRRZrh5NwL1/8+RoEKtWVxc/A6POAwegzLn7IllFKKt2WEH97bu8Y/++39Clsu8h9ZdmxTwjn/9y19icf0BXr32GbQ6yeWLX2S4/oNsbj7P2z/wg2y+9o/5nve/l6auyLVNI+nyp+NTgGSIrQ+ghR2ZRQhthfIR38K8blMbNxklIepaHS8kJEyiY8DqGJkczHCuZWNjlSLPCMZLd4oU95SirWu27m1SDIToJMN27vAzHgkOijSur3U/IqSUZDan3v4Ytz77JcKFR9nNFe84f55Pq5bBaIGsyBmn6xBiIHhECxR6ISJjDO9+59O89S2PYFTO3DV85Fd+BXtyVRTUdSo9o3zvoDQqM9zZ2+cu0FQ10+mcxnmUE61YokZvT7m9F4hGcUDGyeUFyvGYeSzYrg46p4b7Hr/toKGU+sPpr+9K//0RpdQmsJmCxC8iDNB/rpT6KxySuxTwf+9eJ8b4VaXUzwL/nVIqQ3gcfw4hhf2J385nMUbzU3/ij2NtRuM8ubVMqxk3b9ymrh0qCullNF5gOtlNFObUww6C0MumaDl95gHOnNrg1rWrbN66Q/SAMewdTJnOZjRNS5YN5KZwgTp6rMvTgJGlURVFOcYoSzQFG8eOMRgtoEzG1auXmU4nGG3xPrmZJ1Ctch4TFOVwjEa6D3m5SLSBaAzowHC0QJ5ljArF0vJpivx+XhSyQx+aJB52Lr79kNKsS/8PSxbomAF9CdP9VJFamxFrbM9vWByOuX7lCtZmaYpSAF5tLB/5+C0GZo+rt75BvvEj7BxMGK5/D3lpcftTnvvCR/ju95zn2rXrnD93DqLHtwHS+8j3MGglnYTgOnEdmSWZHBwwqWBYLKLJJCDEiFfiki7lQSQEBz6xPJWBEPAEmsbhQyvGQcZy/PgKAZecFQzOz6mmU+qmxWTih6rDIaYUZCEfbkRK+BRN41KnJfmuRFH8esvbn2Kjcfzar36Sr3z5S7R4VhcXWFpZw9pSasB01jsecPAOvGZz8x7r68dYGI5QCrI28hO///eRaYOrZ2kwTkq2g+mc2byBsqTxYqQUtKEYlSgcTT3FpHmpV/bntHpI2zhGS4u8dveAvJ7jwxqvXdthWrdveP/922Qa/8u3/Psfpv9+Evi+GGNQSv1+4O+m35VIEPn+GOO1b3nunwH+a+BvIzTyrwE/HGN85rfzQVSKxvP5jGbuub5/j2ef/RobGxs984/gyax04buJB2EDCigWMOR5zsnjJ1lbHfLAAw/y0gvP8eorF3Gt48XnXyDLcpTJZey96syCRVuhah02i1SziqKN7M9mjIeLVJmSjOOJJzh3/jzXrt3g4qWXiXOfNDw90QYsSkxxrFCbY9pVQEhKxmoGeZHqXGH6cV9w6n448xueuW/LJGK/ux+2SA+P7gYW5DEEaSNrYDwe9dIDKFG8UhG0toTmDjvzm2TFaaq9isn+FmGyTbl+D4DGn6Gu99g4tpaU6DS4Bh81yotcv/MelJNSMHYZgNykTd0QQ4ZSSrIAFGIKFJNehuy88rvDVm0HYbroiUGzP5mRa40xQqmGpDTfVNzb3MQ3LYwkyErn7eg8S+gWo5gtIThtiCHNjUgnxdFiTMY1C8fe+06W2zmtcxxfXSLPszSbBFF7iDkutBht0EHMkqy1XLt2hapqqeo52lre8tRbRMA5JvQ1odZPPvk4jz/xBN94/gVeuHhVbCCSgDbRYEyOsckCYbyEmTkq71laWgZgeWuL09px7uQ6/2Lz4A1X0W87aMT4W5ghyGO2gf8w/flOj5sDfyn9+bc+YvT8s3/6z3j3+97L3tYWV2/e4MEHH0gC1IqqaiFWXLt6g42VpSQ/n5wjtGgPaBWZbG2yc+8ey8vnGSwsce7CoxTjRa68epH9vSkhytDOeDSgjRLNW+/Z2ZtQFAX7synDbIBvAkVRoK1mXjmysaVBk2cl62trXLtqUHnBbDbFuRatSrSFwsqNWhRFQs49BsXJU6d5et2xu6fTzmAIbxgMDqnb9++DdIf6tn/F1/3sty59tLaYrBCTocRK7MqIznqRqFlef4TLL94kNgp2rzM+ucJgYZvQKmx5Bje7xMriacajAh0i0+kEHz3DYU5UmrpV7MwKsqzGTubc27vEwd6cU2fWePqtTzBvatqk+NWJ7qSeOp0uBiqg0eLf2+3hscvBNNFJN6Wua7a3W7QOEBQutFgC1WyfsixEciHLD+0f07kQGcd0HpOEHjEk0p8WSwUjW5YyGt1GFpZGeF8yKIvErtWp5Swub1En4R46LohlNMhomhxtFHkhG8eVSwL7WQsbywsyFh8DrYtopXnqsUd54rFH6SjAZ06f4WCyy6Xnd3HJf3hhMMTFOUsDS1E3tCFixwXb+/s0VWTRvjHw/qacco1RrAM+/elPs7ywzJkzZ/GNp3JNQqEj9+5u88C5B6mm+2hEOTz6kLwthdtx4/plDsKQnf1txgtDhuMReZZz/pG30M72uXXjOm3tmFYtg7wkNxkBmfB0TUujI6N8SASmdUW4V6OKgvkkojOFyjOWT5zgQz/wYT7x6x8TwpLSFFazvb3PV179OPvbmzR1zcnTx1kYDtjZ2eadH3gvs50hd27f44+ceUpq9OghwVhwBHs4suN9y1mC1z067YZpVybaVDh76RykV5zOW7781ef5tY99jtu3tnnyySd48snHmU4nXH31Gsq1ifQUiLFFIf62xGTKjOPC257m9vVfR43PsX58iXuXP0m2cp5YnhZ+TD7i1Wuet408qEA5HKIxmBho5nPquy3Xtmdc3c957NE5b3mopYo7NLsHXHx2F9/MWTr+IJnSGJ0RfJs0UVMoVAofFCH6w8ASUuDQWqwmtbBHI8KsNCqVBUF8TX00fVYRUrmjdBKniZ0mq4jkJZIFIGI70cv3UsECIgRsUmclK2xqyUbRQYku5cLyRxQEpBsUvKPMLMdWFnEuZalA48Woi+hkCjdqZrMZN29vikPg+oZM/CZpgps3b0hQC9JqF3ymZilTBKWZzWqOZ5Y9lbOjC1oD0/C703L9XTwUZ8+el/aoMcQoxB+dHDDrquKBcw/gfdVfaCEKyYm0SkRomB+g24rJdEo2yMlDRBcDYt1ixyscf2DA7ZtXCLM5TV3L7pVk+LV2ZAw4OJiirGI0WqByDbm1tG2NUjD3c/RUMZtPGC9tsLu7h3ctLogf7Hg4YH3tYW5eu8axjZM8cP5BLl58mTIfYe2IhRVNViQXuX5f647u34JhJA4h//pff4KPfOyTbM9acjXg6bc+wQfe81ZOnDxGU9V8/Nc+zs7WDbybYbwXrxFb4GJEBZJ1Iei6ZnnQMtm7zs2rGYsLQwaFJab5meDkM7UOKhyRGu+ENGazM6yefhfzqWfn8scxmadqhuS5R8eWMl9kbx4wNMTo0UFuOhcN9azlxqXrLGTwWGyYv/wyN/wZFlYWaasBd3avs3z8AbbrjNOLx8kyqA/2wDuUknrksL0u5ylhm0kPM3YiWbLDIz2TbmrXKo1XQZSsEp9HEwUniUdYpym/E6WspGCWfidgo0rtX43VMCjy1N2REkh+rtIgXExK7C79PpAZmXDVRh6ndU7EE5VipJL6uhK2avAeFSPDQcnBZMLurmQU3nnmsxmPPvYow9GYmISkQlRsb++wN6l48OxZFoclJi/R0wNWhyVmZHjx5u9Oy/V37VBKUeSKJrS4ZHxUVY0oI+mchXEhu0uHZseuH3aUQwCtnzCdbaP3SrRr0VWNKQvIMkLrKYucRx59GOc9t67e4O6dW2S1KEOXK6s09SZ+sIYNgf3JHoPhmGpWw8ASW0dRFNi85PjaOqfOnuSVtQU+/pF/xfLCkKWFFYxZoXYV73zPeR46e15wC73Apz75WZ5//jXaJvIvfuY3+OEf/EF+5Ie+m2PrizTTCd/42jM899KLDMKM3Gicm2H1gEZLO/D0ouLc0hDXVGy9/Bk+8tqXKEdjFtfPcOzESY4fW2d3f5sXv/YMs+kuRSY+rSroPrWPQF1XTDavsnX5WUajkSzS2FDmAiaKbF3CWnzAak0MDfuTHczCI/iDr4NdRTHEuSnGak6uLnBrc5Nrr95Cve1hXIxYJbdgO2/ZurzHjQJ+6vf/QV7+8idYfegErm6ASKhrVNsy273O1Vevc/bsWb7+7DM8df4c3/39P8rtrbtU1Q5tXVE3c4IXPVlFTOMuCQQm0cWVCAwH39CNz4cAN2/eZGlgk0VFQ1ApNEffl0IqCrciJIWwXnQgCgYUiOnvEowyrSDLhJGZxvY73xXpeiTF86Tb6VwkN1bmTEjNYGWSObZJMIbHe9k4slyxvDxgMMyp6oa2aWnqluGg4N69ezQ3b7GxNGBheQGjLT/+Q9/P9bu7/OpvfILBYJHlwhAauFVNWRgOaNvfGSD036nDO413NUoFZq7m689+lfd94Lt7RmEXK7QWlWsFGCI+7Rgo4UHE4IRJZzVVcNBELJCpZMVncubTXQYLS+jNO1y/eonV0YA7s120zrH5hGkDy4sZU6vQUeMXRphswG7dsnnrFQ52t8Wub3HAhz/4Hl66fIdY7TMIu6zmGeFu5Mqdy3LDRs27jg94x8mnaOoZbVPT3Pg8//p/+jLrJy+wvHqccrzAU0+8h73tW1x67WW8s+B2EMOgGuMdCpssGh2hiUxmd1GTm0yuGbTyRDQLJjAcdrukuJSJhZ+UPEWpiXkOEVw9E42PKKplvWRgEKJTRHZWUDRNzaAo2W33xN8URzPZx80abseN1JKZEmKDiiYJEStiq3g4u8BjiwX/7Od/jvd+1/sZZIrWOLyTobCqbnHOcu70Ml/6tX/K5r19vvGsJt94gJ/9uWfZ/Ob/SCg8jz75NIaW93/Ph9k4tobGSAGgYyKQOlIXFmWMdFrS2jl16iwHO3fRRqd5IJ3ayjrlE0kwOGEkJB/Z6NPP6aaIhUfR+bBqFSmNxQeHNipJGbaJ10Kisid/kiiuftooTITONwYUjoCJXadJrpWA6obceBaHiZruxfSqn7dC2LfKKGLbMMzhx37wg+zv7qGUompqqqphendXBJLe4HhTBo0YYzI4CsTg+PIXvsh3f/B7CT6m2tSnIZ00NxACbS06k6ITmRSfo6KaHDCdX2Hr1iXKwYDRcJHReMRgVGK1IWpLO9vh9qWXybXn3ELEmCmV8+xub7I7qTBKc8+OyDIj7d4sx0VDWRTY4TJ5scKNzSkvX7rFcJBz/ORpHn7saRoP1197ldvXL1HPD8gLQ2kjmZGuT4wZyigGyXN0cu9lqr2r6KyQ74BlZSA0beUD86YhNg0YUFRSzx8pa3yo6EaoVe8Ol7gNSfSlc0TvRIxjSuEj8jIhpgwjaHSULlZMWpqiianY37uHjZZ2ts37P/AWXnn5NYbHjrOz3zCfXOMn/8CP8gu/dIN8uEF1sJW8PiLRKYIOHH/wUf7Ck2+hGh7wr37jnzNva5YWFhiPSxaGJSuLK+TDFW7dusNonHP82JD/4e/9Hzl+5mmuFTk3tzbZ/tynIc559mvP8Of+3H/KmdNnUCaD6DHaE4LuLRq988keUoBv2fE9ddNg8zwJMSPfMc25dCPmYrwdeog0pDZshyl1DmgxtXPFzlOuSeO63dygCbKhdUbhKV8RcR6TsiUjgKwCFwTHiYjHTYySRWklnzEEyWSaKIEstxkxOKKW2SDXNmRJNmJhMASjGQ6HRAXt+gpfvn77De+/N2XQUAoCLU1dcfXGdd7/we8hOI/4OciUZYMntDXTgz2q+YQYGojuiDp7ROsWHydUB3cJTcMUxbZqUTYSWtDFEGNG2MFQtBz0CK8c2mkMLYPlC4wWD/ChRiuHUiPa4IhApqCNHlXvyEKNnuWBjHM3+3d56XO/hLGG1jUs5QHy5HruuzmKFhUblFc0HvK8oLQZS0//MaaXfonpdMaZt/4HqPl1YvDc+PrP4dvO/iCNnqlAJ+4iXzsmjkZyMlMRGfeVHavjCfik5UHsAo7UzX2lFw5vjE4fK/Y3EVhvcEE6VF999lUaV7NzcIff//seoywf5IXnX8J5xb/81Zf4kQ+s4AFCYB4Ul8ucUw89wNc/+qvM7HXe+q63CADoPb6pwLXEZo6rK86cf4K2njLZv8f6+gkWBw0fevc5nv3GjO/+/h/gox/7FCdPnuSVu44Tb3mScydPcPulz5AbAzrimgYdhCZx58Z1jp88SW4tVTXDWnHqGy8u9nMwIQqaIYGyB0Yg/a6jx3SBw2iIXmjhAqqKIbePEZuCViCC8glT8hIsutQjIOW3PrRTVEffuwstIaRyKPRYjieAFzUwDSgj6yDEgDaGE8dWmFc1k/mc8WCJyXRO3Xi01WQ2Ode9wfGmDBoAbd3ycz/7s/zUf/Af4RsvqXmUSUStFNG3NJUYGLV1S0iDTyIym4CyCGV05MMcNcwxMekxmKS5CGjtsbqmbT3EXQqrkpmNAGrD0ZD5XBG9JVCjOKQoWxGHICpNmaXsBpmNMCotktAIS0IbRuMFFpY32Ly3xb/8V7/EhXPHeejBs8QYefWV13j3D/8lLr52jaz8ACeOL+OVYuH4k+jCMJpbbnzhZ1gcREKs0SpDJUtKFWUYSkEa0opJN0KhY6dzGnCpESP5iahGoTSt871osNwQqmc+epfMiTgMHOXiAru7E9m5vcPYjIPd66jmFD////kIo7VHGS8u4WNDdB5CoHVwc7tkHg/4/AvPcml2h/PrE9Q+FLlNJCqpBxrXYOoZ5XhIPl6gKArGS8ep6zmzyS7vfvfbme7c4Pvfd4GdGWxe/BTH/8CPc/Hiaxw7+ziDbEBpPPdeewYVpAQ5e/48EU+eZYRYkCmNi67vO4myY9dN6VqsksUKsBq7qgUS/nAYaOX8gozbO9cSlBbFNZXkAgiCd2iN8j18K6VPEMC1G+sJjdiCdlPbh812+bQhpOChxEw7RE9Xi4l/q5SWeZ6xmmQX15YWCMB0WjGr5j3ecr/jTRk0lFL8zD/5x/zv/uyfF8afguAjVqcLGw/T664TFxBPiBgDKvXCjVZoHBHRgHSxkbLAK1SIFEaBjwTXYI1FxYiJER9JeheRWRVonWJWeS5fvsTnv/w8B7M5KlqwkUFRMl5cIM9zBoOcc6dO8I63voWHHr3A89/4Jr/wi7/B5Zt3yLTpB5QSDZOHHjpF8C1t02Cs5etf/FkWF9e5c/cWX57Mca3h4Ucf4QvPXmRl5FkqGhbOr8pcewxpUSUXeaNR2tA0AswpSdcInRJ3Uhfv0toYg7BjiajgCIH0vaHb4WLU+AjKOwI2tSYd+xNHIMPmOWVR8IH3XuCXfvEFsEuUK2cIoYZoKUvFpG4xdsB+47hy74BK57z2xZuM45z3PLVGU1XcvXOXne1tbt+9w/kzZ7hw4QIqOogeok0dm4zhsGRtbYOqdjTVPtub1xiPM7Jhzr/4B3+JkC3ywguv8P/82Y9x7eZNhmffynAwYEDN9deeZbo/5b/5e3+XP/0n/xS6LCmzkoXxGGu6idyI0h22kNYiiBERQaZ4ldC6ow/E0OE8EnisUrjopVSga4l2510YxjE9PiCBSZAYRUiNMqME3O04RN0RE/eIbow/4VJdZt2VPZ3cQNRabA0So7XTdV0elyyOcqz5PZZpnD1zjj/zZ/8PxOSQTvTiwq0jFtUjzjFpCngv6soqIVfSj4+EYIjaMGs8N29v8c3nX+DmrXtUbWA0LBkMBygjhsFRCdhUDkccHEw4ONinbcWCsa2d2Oe1LokqCAZhjWXuW+Y7uxil8NFz8fJVfv0zX6CfuY0RspwmfTeVqNRaG6J3zGZz6rZhMpti1T6z2UwG5B59kBvXb3Nv8wbjbJ/pwYx6p+HBk+L7qjMBfF2blKyCp1WuN8F5nUxeYqSqFGFDwoJCTCZDKLwPaJuzt7+PeK8K9yVEkxa0IysLKrUiEoJry9wDtm7f4fq1RQbDJX7lNz6HsTmT2ZSyyLBmBbv4MChYGHgeMY4XX93hxJkCG07Rul18VBSjEScGQ46fOonRkaaZk9k5qp6Sl8nnNooBkLWWoihoBzmra+vM5hNmk21aN6E0U975xBn+2O97iu/78I/xzDNf42/87X/IyvoKiyefZOOBgv/sr67D7Baf/s2PCR/HebwPBJeMl323fkIiY3ZZV0w3fSD4w4E2lIEYxBs2aXaooCAFhUDs1dg7rE5KRskafIQQhFUKovXS+al0cond30OPsaSMJ3VlelgrRryXTcF0u2nPPemai4KnvN7+8/XHmzJooEhiOQqtPUZJLxxkglFmA3QC+xRaWXIrfqkdyKeIfPn5V/nKi9fwyVskRIhZSZZBQ8Al/v32/gStLWIx4Okk4bwXVyqiIOtolbqPwswz1kAUsx+x1fNHJOo8CbEEEA/NdPFRMrfggubjX3qVUxs5qwsLfPHLXwGT8ba3PMHQnODpJy5gbMad9QWuX7/JfH+zF7ap61rs+AjgNDYz4s0RNRiDirCzt4cxFmUNymaoqFBaqPNKWQIWnYsEYlCGoA2LI1lse3vblAvr2GwAUcqgGBTa51z+hf+Bc0/9GMVwgM4Mr165TFaOgJaV1RVCCBTFAOcdg8E4gatwqrSsHTvJ9m7F5q3buJBz6cYNLl+6xOJ4gXPnTlJkCu/mjLManTYDa6y0IJUSdqaCPM9RSrGUZZTlgJW1E7TVlL3tO3z4ez9IPd3ku952jv/Lf/aT/PhP/mF++SMf50/8qf89T7/vu9Ec5/evbfBP/9svUpQlRXqt7m5UMWJ66FMmXb3vBJfTxtQBol1wToN+pJ/76IXerTReViVKIlJyXUvZsBKNFkJIHqsSlNAyfCdVkkqvneZgjtwoPiT3OZ9KVWRqOIQAJtk5BJF9FMzmaCC8//HmDBrpMEYR8UlWX2r0DvZrQ9ufPJOUqTmyKyhk12hbcd8GLeld8n89jOQh0aWN3Ei+Sjt3xNgs1fcZnW+GUmCT/kWuwUVDZktcm3YL1fXzRS3KGCu+pkF2AZnxkPdvqpqzJ5Ypjaeqan7w+7+XEAOrKyvU9YzPfvYznHvwAebzigfOn+bF57bYmzQUA/nedXBgc8CCGoqBj86wZY5SY5aXMmI02DzHaFEED1qLwG3UEni12CJ0n0v4DYbx6gPEqGicQyFkNaMNsQ2sHStoD76AtRPq+Sb4OSsrpxiORrzliYf55Kc/j288Yb7HnRsj1tbPkpVjdPKwPX1syMm1C4R2hwuPPMEDDz+Cb2vaZFc5dQ17m3ushZzV1RxPRGvRxXDekyXPVMGKNGWaxM1tRlEUVFVDnO+yu32Xd73z7dy+9AoffPdjXHnpc7x28ct8+lOfwkTNI6uKthFf1tIHQlLakfAhtVpIOWMnzQdI9yNKw7XL3vAerzpTyuQf28sHixxixKfALa54zrX4CBmiAxKcT1lAkibstEvTe3WudoeeLzI84aMYWQVCcocXanvXncEnNBjJcg+HGe9/vImDhsfoghAgS/qNMSS15a4Bj2AcPgZc26a2YHdzijaCSlRipSMd1CWKUFki00ikdkEIQXmeJW+TrM82uu5EJ6ArTL1IG8FqjVIBow+VqnSUroVoYCYvEANZJorZPklBb08qCmOlm2Myrly/QV3NqeoabWW25fKV64SomMxqTl94muF4A1OUGD2QORFjsVlBEyHTmehypptMpCwy0EZc44LoeWqjwQeWlpdZX1/tqfeqd3pL4JsyvHbpNWrvadtU4jSe7/+Rn6Ce74GfUs/vUU8OsNbTNjMOdi/zjqeOg9IMivOsLC1CrHG1+LVkeYsKirt3bnDx4tcZjwbkmYCxPgjlOyQPWdfskxnFaHEdm0VQFkMmHjdKYfNM1Lq0JUaD0pAXlixrGI2GLK1uMJvPiNWUzc1bjEZDqnnFA8fWWFpdp7r9Qm/L0AZRuMeLTwtpQE9iR1pvnWp8jHSIQkjXUmktnKAovikxtahd0gPVOogncXAYZURcCN3f9IcizDLXopUmePH/Uek9j3bYfZqH6cygtZFJ3AhEnaZo5ZNJEEqZT9RarCt+L2YaxpRsbJxka+s6BoVLvf5ORxKlaIMXT40kbBuTOa7SMoeiTI6xJaEj2KR2pE/0XwkoktZZZXpz3RgjQclikIEmKy7z6X207joVRpiWJhNlpeRVYbRhUJbYNFnZtJ62bfDJYq8oCkLwTOvI20+NQWdMl05hhwNhBAZPPiiEGEVBVi6RD8ZYU1AMl1DGkGcjXPRolRG1ZiEz0lExJgn6IiVFjMn0WmYtFhbGLAxyqskOw8KgfcV8MqVualrXiPGTsZTlgKgsqyuLqAyu3biXLAJbiiJHq5J6XlEMFhmWGYujnNde/Qazg6mI22rNdLLH9RuXJFR3+hkRydqUZjAoxGS5EpHl+bzm1q1b1HXF2ZPH0W6Iq6b4ckFq8AjoiDZJKs8BWZb4C2knR5HnOeJub8i0htGYcrSIczXG7mCsZmQa2rxE25wYuxpfuiBHcYSOB9SrpSdSl0YqE6GIq1TCda1uWR9tkMJEKbFcIMZeJUxhiaGlm8iPXsqgGAM+iO1nd9q6NW+xCfzuODPyGbRSkiX15VGyfjjs3IpRdirvv2PE4E0bNBR5voqnQEdpjQK98rg4aEu9J8CSXAyRUJHU0cdIZhRFZqjporWcxeg9QUWiCv0FMclsuJtCzLKCSItWBa5tKYucPM9xzlFV4oM5KC1lWdB6h7GakoLgnBgZ5RZrLFXT9p6cMXUuOnryzZv3uPPIezhz/kGWzRCdlyImm2UEbbFZSYyGiGE4Fj5BN3ma56WYUmcDUAISggCtYkdoiEqClNEaa5PqNoFPffbTfP1zH+F7336OpaHl4sVvcunKa9y8u8n+/pyNY8d44vHHwWi27t0DFfDOY7XF09K24NsWtKWN4uGikGtS5gXz6YSiLNHa8tqla2LYnWdAGrVPDvMgJag1huFoQJ7lMhEcI4Miw+qIczO52b2VelzbnmOidNIG7ToBPWlKpomV1uhiSAyOzGicyxkNRrRtxXSyz/hESblQpCwBESFONyNdq/NI8IC02ydJwj6YILhD9PIcp3w/xxKRtqhPJakxmoD8PqiIDqKsHiOoRAlAKXyUtS7/1CgT+1ma3lkOuf1dDCk7EQHnPM8lg9YSLHxMHTtiKo+6aHL/400ZNLQyjJY3BLXHEYPI35u023TMuq6H7lO7MKLwXrQavBOswhpN1XR4VbrwJH8UknwbKc1TmrZpSMgSmc1ovSOzNlkDyA0tXRup/1vnZW2lXry0yyK4QAxt6lxIIFJGo4wR1WgfcG3DYOUCK6ffQVEORGbftXjvUEZj8iFFOUQnUyhrbBK3FcVtYzRaBzKtcMFx/eolrBW5P61MQr08CotRkcyCpcbtPs90+0U+/6WLKUOLNMGwtHaMpVVZXK9dvZwys8QuRWFMxIWWS5eu4pzDaCtpceyG92WhZsaKCpc1jEcDNtZXU3aVyEvd+UBahALcCcAZg+9VuIjQ1hWunoqCWFIVD0l7VIY24uEAcPLU1UkPtMO2BBMzZLlCBXHtK/Ics7bBeGCSsI88L0ICMBOOluT/5PXBpw2oY2V2DnC9JqG0NWQJpSG5rpiBgPf0G1MXjHzwCDHL94+NIfRU9a4kSXFLGopBArnYYSoMMlektE7UcgFE5XzINY5B6GPmt1DBeFMGDWNzjFJcu32PxbQjBbo0LQWL9McHpKMSI20ITOuW6bySi26gzDUH09jXpr3sXExGwFp2fhcEl+hQeZW8VIyR3dRHCE2VUn2NNkBUojat0kX0TuYcfIsOXmCqKDuJMSmohQRopdts5dhpTj34MFbLWHzdSIkwHA4pBwVtPeHmtddQeLS2InSrRHQFHcliTZHD3s4mP/tP/gGjQcQqT5YJAcn7SNM08p7JDoAYKWxkOhP3Luc9W1vbTCZTmrarwTVFkckNgkrEI0nFrbUsjMeUg1LEbTqEML2+0YbpfMZwMCDLMrz3NE2Lc4FRWaSgrNIsjBw+eKr5nLZpiCGwtb3L6rhgYdSCr1LnoWOpphIylZnBd943slJi+rwGK9kACmW6a65QHkyhyJRkYFZLZ0LuykMeUEjRqMcSQMDRVB6rFCQEo1KoGBO2oFGd34sx+N75TbLcXuck0AcaGXBTOK+OfBZ6tXrZqHxq3kiZJGz1lHWrNJGVpnyjkg6f0lpwEaUQPK6TLfw9CIReur3F//tnf43/6KeePrxeaWDIJwftkOYKQlCEoGh9oI0Rm2VEYGNjnaV7U+5sT3rA0/vUPu1uhzQtGyLihJTaacRI29QJpJL01zuHtlmvaB2jTu2z1PYKkZAk6rU+suOk1hkofCR5doLWNfP9y+zcXkGRg5KOkFYaPzFUmWXn3lU+8vP/mGp6j2JQUhQlykTms4bGeQFikTZeYcB7Te0ctjEExF92a2u7ZwHJpLfsZiFlXzH18Y3NZNEF0TNZX1tJ8wsqySmmNNoaVAjUTm7wuqoYDkdkVlrAWhn0eISEXdlRq6riYDJlePK4vE4qC5US/QdrDaPhkLoWlq+1mjzP0gBaTdtMJSAbiwqeGEzi6AjtIfiU5YWY1PV0kn5Mk6NoRLhHAkjUPpU7XRtdbkydRH+6GytGep6F0O9lND7GQ9mkLmAFldiacu/Kze67EuZQfEfWjxhkRa9ABYySTg6q83lJQsRKFOAPvV+62Bl7PdzgnWQQEouTLYMEraaqyDIrWYu8VTK9euPjTRk0IvC/fOo55tMqlZYqsfUsvm2FkJRqRFRCrhEla6IRJSUF1ubJHDdFaKPRKhFfND1RJ3Tlqwr9RZaJxpDK51aWv4LgHIooswQp8EhACD3qFFPqKgFDdi4XvCh9p2dFpcizyNc+8z/z2td/gRgtPkScr3FN5wViiVradMZm1I2jTiPouzv77O1NyDK5xB11vhsr6XDDDjTuavVOJFek7aCXyVOqX/wdcUkhNgEms1ib9yWd4BGaQTkQQ+29A8rRSJiH2vZy/ahOGEmztrrC6soyiphuqNSOVGLoFBBjaJ3wl+FwIDgMER09+JbQVpAVQuLr/5B8bExfYki1ERJfPhwuKqVAdd/NoPXhOZOjkw7oNo4UXEPKbkLH0+gGAl0PmHaDbjHNJvVNzcP/SR9Z1pvVqbQJ9MFOuh+C0WklJSje00a5ZgK+qteVeapfUWnyVqskpZiCHaKhSgf2qjQP8x2ON2XQmDctDke5OMBH1QvHxBho2zYlqPR4gdKGqCwuKkwmXHu0wmQlpJ1MSkO5nD4qEWY5WpemsXW5xtIOI/EsnHOcP3Ma7xyDMkOj2DqYsbO/m4KLTjcm6XNClyb3iyZ2goTdklIobZnOa0LUoBpa55nPK+bzGZ2Bc+9zGtON0INxKgUMaQMf6lseLohDkK7LdLqfHeo8HD6YtNCk368UNHWNd46xGfW0Y601wUnNb63FDIeMzw/pZn0IIlDsU4vQpBawvGHiOxAgdEpgHbYhv5P7XPRAZdQdNB6NQ8UWgoOQSZqtgHT9fboBlSZZSSp0R4ZSSm5oXGptpoCTsIhufXXnTj5XPAIivO609t+n65L0DnOJERpSm1VG6hMo2nm8pM6Mj+mmj5Lg6iOv003AEmP/Xjqtnt7USnfLKk289mxfWYAm+b8aY4l9CJMgHvs1ev/jTRk09iYzrNFMKydDZWl8uIvCHV9DrpPUlD6AzQpMJ22nFcpY0KYnU2ktYjAKj/LmcNowAsrQzXIA0hbUup9pefrxB2mqmjNnTuOd57Vrt/j0l59FgegnaJH6k5oy3aQhpt1XdrWQGundLh5jZHtnH60nqZzuiESp7D+yOEMiockuE5OuqLTXuhHqvvWYgoFkR4eg16F9onwApXS/MI/yDAX2UcxmU06cOE5R5Kl+VqkDkCIMUXQ3YrewUyZiFMnmpJ/IPZrRWCXet0of3kTdOek6m9aoxHdAkO7gILTidWNz+dwxHgZGHwiaI9Obh+peWqWgnizi5QyEvlvWPUNzKPenVPd9u2cc/m9Xvh6WnhKA+mCjUrmUMuCjVHSlBLwGaX/qqED7/voRk8xz7IbgUraqu85OB5R2j5Er7X2Q6xK7wE+fKYsTYdogwxFg9w2ON2XQAMW7n36Ij79yhQSBplpcvqy4aie2WwKkWh/JihExBPb29miaFm2tsANTemmMwSWNg9CPLKbVmo4+2qfF6FoJWJeu3sIoqFykbR17kxnBpclE1WUo/eyiuJLnliKzrC+OKLRn8+4marCANwU7++IjpVKQU6prvR3uekczBa0kOCQE9mjWS8dReX350D33MOMghYf+yUkMRoDfVJ6kYCJvpXvtS6NTV0jpwziUHqNC7LGL7vOLW93h/EboZ8v59sempkPogofW0hlKbfAYPcHVsgn4VkhowXNUK0R3gGFIwjj9Td+Nk8f+pjv8FHIaug6J4KHx9SEifcZUABx95uuuVYdZyc+PZnxdGXYkSKRzorWRgczg0camauiQGHY0/wld3ZnWSkz/lqwpiU+Hnr8KKIxNymDeiaZojL1OyOtSp2853pRBYzwcUBhD1BlZ6IZ6ZH7E+dCzKpu2oa3n7GzdZfPeFijxZd3a2hJPC2OZTGcCdkmhSucwD4d1p/fuyIZ8eKP2P1GKi69dgxho/EVGgyGLiwucOr6ONYbFpQVGoxFlUaAizKs504MJW5ubZMGxmnkWcoVezImjEc4Mca3HDJZ53/vfz4vf/Ap379x6/UlIOER3wyuSVFySBugxgW4Jq8MSDkRJPCT5ui6g0dXeXQCIoX+eLEYJYF3mkZlM2rykToGX7lMvrUeXQYS0mx89haH3UIFuR0zcmphqbp3GAiKkikdkCVS37NNzEDEmFQLRN0RfE0MmfA8j7fikACxOaMTEeZCfSWA8LENVaqd25UcXQ0OMvb7oUXJX95gu6BzaJRweklEcNpI6K43DE9KFnTQVGw7XXyf2I6B66qYYUdVHKaIKve9rtx67IUTJdFz/WcVmQb6rtlka5/dELZ0kn87H77nyJMbAJz7/Ddo2gpf2uw8B71xvude2LU3TMp9OuHPrCq9cus7BZJa6E1JThhiZTGYcsufkhMliTXVCN5l4ZOd7HWKdev7veuc7uXr5VXYP9jhxYo2F4RDnA3lmGA9HlMMh1mT9a7SNtNka57i5PaMsLdlgmcm0xtiINZIqtt73C5IOgOuyAXW4I3c6IgkN6RezTloXWsvwXLcSu+wkkhZSEKEXWaQqpdcqLXTZfbquSrdpxrRjiy6EZBV9CaJVEpnhsN4iZS1R9a8halQOo8UnV6vDulswiS5IHc6SaK2xWgt3psOaUqs9+Fak+7yTHTrq1M7U/Z2ogrRctZIJXZVkCntOhTqSqXUJR3w9hvC6siTxJVRiBHc0+0PAOwWh7oSk15Fv1Y3OW7qaTXWvHQ8/QgjJYS+VRh12IUSWKJylKIpvgShWlt2GcmTD6xTaQrpfIhCVklmW9DgZzvw9lmlM5nO8DwwHQo913tF6j29bWtfStKJBUTcts6piNpuxd3DArbtbtG2n7qXwQQhaxlppzcaQzJZsogRDDzIc7slpcR1iIcTI0089xuXXXgHSDhI89byhbRV7uxNciJIBhZh2CMBYGGaQaUwp0vbON0zmU+ato26m/Pov/yvKosB0QKAS1qTz7kiWcTTZ7TgkpDaydEJit1MbkyjJh7uJ4rBD0e13qk+p6crwfsfT6XNk1qaAIZ9AKyEReZVwANXt1jKLE6IEGFzH1jx8ve7hdN8lZQndIJYM9aVypv+cSnbaDgBG9CGid4QgJDgbhDDXlYT9oYD0OSNdkdDJ+x+V71PYbikgE64dD+Rbt+PD9F5u3sMvE3v8Iyah4cOyMGEU3QyJMnTDgQolvA4n2a+UICHJK0IIUqTIKExn2xkOl2sCyJWSgcKQlnIggu4+j3CRIq7Hfru86Y2ON2XQiBGeeuwB1ouHiHpKqDwhuFSWONq2oe0CSNMCSmjCIeDSriLnTfe7aY92pePwRkxTr13fNZkF+D6SR1yaSTlz+gxf/cZz3L6zzfKFc+S5xUXo21pp0XqErde0rQjvKphMJVWv6oaqdj1H0IrYKaozI0oXVGtxZNNKCGHdx1dd2zCqtBgixmpce7hYDxP7pDoVD5OBw5kE2SU70V+jTT/F23UzjBEeisUeKYNEbAa61DgJFncsw5j6Rj1Q2NkaHqb1WqkkDiSPMUr150M+i7xPSMJBMd1MIThUcMTYEnyLaluCEdNt+Y42EcY6jEO0LHxS1JLR8UhMWUmnjBWNdFfk3HqCD/KefenWgYcpEnfZUVofIaZg07V3le4zhm7iVaU1YpKOqHyvTq1Lgn5T1zK3o3Ta+NI17EoTuYB9dkMqlbwPfRjQnZKXRA/ZPHpgXD6PVke3oW8/3pRBAxTbezU//CPfRXP3WWZTGfaKXlIuyRgCvvXiqB0C1qTMILVQtTKv29rEP6IjNYU0sJYEW+PhiHOMKs0iSCqbJTLXr33iUzgvN/usarhy8y4hCsbSXbjDTEVuKH+ECtyVLf0GpiJ5Jj6dAkDKjqS07D6krkh3Ux+CmYcof0e26uppYxIN2hwukF64JRw23l6XYhw55x2uIZ9DLC6N1vTOgFFcz3xIlH6iaIqQKgMtPJbayYBW0zTE4AVf8R7nHGurq31nQt5VCV8qzXHYjnGrJWAFL92aGOWm01E2Du0D1jpc22CMlUClZJBNp/5Bd88bpYnR9dlXdx6UlmDSAeyyNlJg1h2v5UhG1n3io0EzBV/FYQdIwOKkXp4o36LDCP18rNaiDRJJBEIw1vbXobskPS5zeLZS5hL6rlnPxVGdeE9nTZq2xRhxLmCteAjF/nrf/3hTBo3F8ZCLly8zMN9PKE/g/T2p8VIGEFygaRqca2iamr2DA+7t7NMpcUNKwWIEZaSb4A9R8j6/S3Wi3NRpZ9KHjDzvAy7KyPzF166kzNKjlGF77+AIBpJEeYwWWfp+wXWL6xAY656T2Yyzp04wm+yzsr7B1vYudS0mTDIIpRPtvZtW9T3eoSLYflzfH+IapK8fu8UVsUqqasEShHkokEbCOlTqmmgZbJNV2H32ZDvYJ/nd7yTFlqzAcm97W4bpdMdFULSuwbeezhpAa5nbSSesx2W6UqDDm7o9UynVG1OTgqSJnVKbxwRHTMNy3QyS1iFlPEqGkdL30ul7dp0YySgSBhKPrJU0EEY8DBQhHIKeXUu+7z6k6CMZbeJftC0my3psoc+wwiENKxyJQpE0vBcC1th+mLHTh+mCdY/JdEtOdWXPIWNZum8hXbeut3QkkKSfeB/4TsebMmhUVUPjWqZVJYK+kUTNEfp4x6V33uOamrpuOJhO+wUagu/TYpNs6kKbInxqXWqtRIs1xsRg7Ny5uxSzq8O78kF3he8RvKC7YLJQRcdDWl0+Dc2lJFxuVA65BdaKGtnS4gL3trZpXZdRpJOQshGlNCbV3CqVWxqFSuw/kwaWokrm1yktjkphQ6JCI0NuQXepvhbKcyJAdQDr4eKUlWl04mzIndUvTp1q6Y4labRIA06nM06fPsnBwZTxeEWEd31yxzOGHkdMrxHSzam06l3YtE4j4Sk77AHqGMUUWWUYG2SwLQZUcHjnsAnc63bgEFQ3jS/4EIhYb/SC/aST3A/bpW7Et23AOgW3rlpJmUiMMmHddbhi2tmVEZ8XUY1Pg3UI8azLSmIUq0wNicsjbyzlpJSeWkuJ0kEmmm78P3aEECEnprEIEZQWsNMYK0GTSPDyebVWuCBKYUcVx+53vCmDRtM2vO3hc+h6j509kfBvnRM9x3AIaAJ4V2ONITOWSRAnLaVlhxRgLgm3JbCNSL8IvG/EtZ1O/LXzxTgkLElA0fhAXw4YY2lbMd8pkmZG2zb9c5q6mz+RmQrnpU62WnHs2AaZzRmNBhAddV0xm04o8kJKAa16lmKn8yDAoDBAY+I+YGTRaJvxp//j/4TTp07x3/yXfwMZpU/1sEoj1gm3IZVeorWTMA0OU+AQOZINRckcQoREtyam2Z0jQtYxBsYLQwiKsszxaTTbaoXHgOkyHMkaBO8gnR/D3u4um5ubrK+vsba2LDT+VMMH74mZSqP+EnyDb4jR8ezXv8q73/0+iArjjUxCB2lVShYl568bJtNGsh+dvod0iIUt2gvdIIE0TSD2tYzgHaEvYyIdAa8LsB1mEBNwmzKaLiClDMOg8XSKXEoChJKBMp2A+eAD1nSdu5jeu5uDiT0O029qiRoqGIwGDW3bYFK2a7WwY30UYWJ5ateOvv+hv8Pv/p0+PvzOx9D+gOrgXpog9T3iTBCkOTqpmb1zfUoX06LUWmGNpsjypGZFIoTFfqxdhGLTJY1pB46H2IfwDFR/IwluYHqQtJtRaVuXbrgj2AXp3x4KkzEqSx5/9BGWl5aYz2ccHEzYP5hS1S3Li4uU1pIZhTUKoxRGBeleKHHqyo0UCdaalO7LY1Gat73/w9jhUs+h0Fp275jKB5PwCWlLqkTaOpRI1EpKkywzadxesjFjbJKNS4FFq95bNx4JqiDkLxG/SVTztLtbm9O2gdcuX2ZrZ5fRYIj3HTnPMxwOOHniBMPhABGbSrlZAlJ6bCjGHtANvuXChfN8+tOfpm0bwZq8TMH2o+d9GSGvKJqz4TBAhkCf7UcpqToWpUSKVOZ0lUsk4So+ja2HvqTqNu2okGwzhiN8IPFXTWwzKWWkB06Mvg+inbq5wE9p8E3p9F7usIQLQLqOERLCLYFOeDkdVV+utwTOlo5tLK311yudf+vxpsw0Sqs4tjTgxsufkwEg3cn0RQkSvpWT6aRvrzo1Jy0XLWotaGA33KM6OncXwRWR5JOSiEZdSk9KHSUrlxrZGINGY5NkXsDR8XACEoxieg9FJNOaspQBr9XlZVaWl7l6/Sb3Nrd6hagQpLsym88ZDweUmdgK6KSyFSNoZQV38N1EJiSDDlQUxuTf/L/9Az75m78J7SyVMIjTerrVO02IruXftwbTdK/R6XOnRS7PF7ZiluUkTF4WbB8sBTCUdFljlebll1+mdp4f++Hfx7UbN6ibVrK9WjLB48eOoY24n3cBOEYBUgstWhuNq0UIRx0heEUBZ7UyUn6iCb7Fh8je/g7eN8RQ4H2DaxQqU+k6IoI0ydA7KtU7rhkt7E/fTb2m+l/1HaAucNCT4HoNkIQbHP47cqShK7IFdCCp8IuMsbgYUD4Qkzp4qnpTWchhRpL0RZTS/eYlv09oT7cphcNsIwQpuYj06nAgfjxdmSSSjvJc747MHN3neFMGjaXRiEsvfIrgG0AnP45IcNJ2lVpU2IzdRGVmRTsiJMWirq/dtE3CENLviWSJE9DJ9/dppBINhtY1Quvt9qU0HDavKqxOI81pclDyXJECXFocYogsLyzy9BOPc/nya6Jn0FYsjEciWYhm3rTMJhOcCyyMc8pcWqtRdSPjwpMI0aFJJCeT5lpIk7haY43hVz75CarZjHJxxCPv/gAvf/lzqd2pUocDCCKq3A+IKU1MHqLdeLw2itxmGG1wXvRWu2xExI80V69dYWtnwr/34e9jc3OTEMTRLgTH6dOnCBGuXbtG3bZEH4nG9SVRWWRC0kvAs+9usngkO4tC0e+JTimaS+bgRJMkpo3DpRsoiBeL95ZIS5HG8wXTUGmKuUUbUQnTyVxZ8ADBGzpSG/Fw7fR+JjEeSdd1v64Ex6DPRCXAhB4ER14Zm3CgkLQ8iAEDfbZ6+F3ltYyyvQ1mRMpBCbLd+EAHHEuaJCC46qeq5b2ktMtsKlFDICQ1dasUJvvOYeFNGTRCqFGulRQ7RqyyeN/0KaHYEYQ0sHUI9Elai6DqUUC/qq5FxzO1D60yCRNxKaULfddDQEeddkHk9ZTCZJrG1QBHUG1Nkee8922PcXx1lZdeeoXFxVFff+9u3yW3FpRmazLl1uYmrQ8pu4n9bmtU2Q9ZGW0ISNqK0gLadi1JSPPusW/R+ghfv/Ua+cICdjZnvDwQfUltUNqkdD4h9kFEiYyS2Z25U/zYv/8n+cmf+KOC6fiG4GpC2/Jf/7d/m6tXXk3itSSwDo6fOM7GsWPcu3unB6NDDLgYKIqcEKCqKkyWUbdtAv/g4GBC07ZorTi2vibZTpDds28lR9n1rda4bi4oBgiq94pRyqSbxTOZzGmamhiFGRqDlwwziHFUcG3S31AJwRQLBnTaWKJMjqIOZ3xCVHjnRNG7FyzqVmXaypWUsxqIOvS7t5QLh0pfPd6h5HuKmC3gU/s4lUjiFq9ROh7JEg7fVmvT/6vremh96Mrmk8ZMJ5/YdQStMaiomEwOGC0sHt5b9KHnDe+/N2XQkP61g4T8+igzF65Tfo4RHSNtW4ksnjHJZq6zVBSOhtJyUuORgCI7izhtDwYlo8GA8bBkMCi4ePkK3kdUND0p6Oi16KzsvI8oA3mWcWxllYPdPY6vrnDxynX2pzOOrS8zWljkldeuUbcOFwRk7KGTI21eEnFJJ2dzmYzMiERMurCZyQlBjJCMthJIraauao5vLBGsxRIovGdUlKm7I/W9DwVve9cH+at/9T+naZu02AQIHpQl5aCUnTY0hGaGq2b8rZ/+m/y9v/93ube7iSLSOieZjZX3dl5sHDGyE7rg2d47oGla8izDZhkhBLKsoK7nKbgHYtB9LJAyUPXnQnboxJPpRARUFzA77oGwI5VRXL5yte9EqIQP4A3O1WifYzPbzZvTSQtgkmgwWrpGXTtMiRtZx7WQkvawkdJltSplLipKxtp9RvlLp3Gh6KwDtOqkC+TFNJGGVsBNbREQJ+EmToJBN+IPXefuqJi2ZDN4IcVofWRKOQGzwUecD1grs0DD8QJt26YSO2Uo5ghZ7z7HmzJogGQL0SNO4GhaX8sOEUThyPlW2m5eMItO76FbSJ2DVCfvZxItXMVAbgzztsXHQOsdVmsymzEshuzs76GV4oPvfRebW/fY2dvl4Ycf5tOf+2KPZluTdthmxkuvvgIhcurYMcbLi9ze3uP6nW2OOzhxYo3LNzbJrGZe191GBSSgMuEtNsvp7BG6DEerbifOEsFLUHVjMnwrbEaTD6gne0SgGI8ZjEf8s5/9DcFYYpBOShTdi8FgxDAtsPT/iJ5DJCrfe8EqFVleXOQ//2t/k7/39/8f3LlzXfCOjiuipHsQlMdEg1IiZ1cUJYPBUHAKpVhaWcFqQ+Na6roGFVBB7CZQEaMzdMp6Otaj1hCNTiyDDoQ8LA4ioBKL8r1vfwJlSrTWtK5GZzaBlIaoO7anFnFlrLTonRdrz4RjBUAFDnVFlIz0d9MF3SxIVw5H32W2KetAOisxBQTfdzKlnS9xwaGVZE/RaHSUW9J7h1XyPb2K6KBEYkEZsSCIya8nrZme/HdUH8YJkIqSElqngNdtbjLV7RL1oKMEgOoJXvc/3pxBI4oor1I5IcpOFqK0W+tqDmnOQ0rJiDWZyOr3u1VHCT+yq6cFULuGTGUJwQyEtuHyteu0HpSW9DDPc5578WWaek4bIs9+7et0XA1rFTFaieRBS7rpWhZXVvjNZ54nKiPMPuW5fPUezkWGmefsqma8ssHWbmTz3hZLoyGKkoOpI88cRS4lhe49NruU1JPlJX/8J36cn/uFj/LH//Rf5Af+vR8mOvF3+cv//U+jDOhMUY4GlKMFdDT9mHXf34zxcCdUJBwn/V2q7J5zEkMkz4f8tb/yX/Df/f3/K1evXaIfbgmpPNJpNftAmVuywZAmeDJjaEOLq2rq/y93/x1nW3bWd8LfFXY4sfLNsW/fzrkVu5VaEkqAkMgYAQYbY4ztsfGAsXkdsAc8xmODPR4HxiRjMMIgkBBIaoVWSy11jrfzzbHq3sp14t57hfePtfa5F72tMPPa877NlvpTdatOndp1zt5rPc/v+QUfeMw+4iPOC5yvEHi8EFSxNXC2/ntlmIa4Ck/oxbWzl3UpSmF9yNuVQBIzbbVKI8HL4axBygRrPVIGbMPLCoSK7NboNiFcGIH6Kr48gXMiIKLi9esXFwJfa2Dqtyd6ZRB2/5CRG4yoJxMM50CF3+ljQLkXhMXCBezD+BAn6pQGexnjCByYeBqxTReERa6+LsL5iYjJGGxsbaqqIomy+KC1UhMOkbcGxH8HTEMIsQf4u8BrgFuBBnDQe3/qisccAE5+laeY8d5vXPHYHPinwIeAaeAp4O9677/wjZyPJ7ywhgBWVZEFGpycNNaPEc6jRVg0guFqfFFimRj61bAreueiStAjpUZKSZqFXM5RWaGzDF9V6DQJmZ5KMDLjMGHAgYVGmmB98LyyzlCNQ1+7sbHJTTffxPaFLj/3s+/k4qURv/eRs3RnhvzjH34b3gRxU5pojrywzifvP8b2nfu5/pa3sWPftaRpBsLz8H1/gB2cxTrFP//ffplrr7sjTInw4CS//9u/xK//9meQWqGlxiWxRBYBkK1Vv8LXXOnL4+dQHtd7i4jlNfUsErhcQYhIEvM40iTjp37m5/m5f/Q/sba2Gr0l61bjMnofzIpsIKE5Q4IOGqArplXWBqq1ipwDY+3ECT7s2yGYSgofc1NqNmQYgSsdVLLBaMbi444d3MkNmFDyC+8xtiRR4FwShICTDSTyOFB4ayMwzWRBrVm4oZfwcc2dAFwBU6Gev4ZX04m42EqF9Sa0lDUSGvGTIooltdQ4Y6iwmLIMFYEK7l5YE6dEPji5xWrOO4+w8vLrXrdb8e8JER7RY6MyoOLiGFXFWkdf1/gzpfPo/04eoVcD3w08DnwReNfXeOw/Az72FV/rfcW/fxX4ZuCngBPATwCfEkK80Xv/1Nc9Gx/duHAYq/Eu0LStN5FmC84bhqM+pS2pszH9ZIZ1xWjVhxJSCgkqTGDKIlB3lQoUxaIoAs+i8CQ63DRpEhLKRsWAf/qP34GzFVoqKmMpSscv/fvPkTZ2Md1Jeejxp7h4ocPBq28L+IQ27NyxjXYji/oRQ6ZT8iwhUwnrW1uY0Trvf/dbuPa665DGMfqB7+f0JgjnuWp7mMZopXEIkkTyvg/+CEmWE0rWILnO/JnwedyJnCg4Yv4DmWpzNT8Qx7T1BRbatHqXr8tuEVmLEhuIX0JE+rVEakWSZfyTX/w/+Uc/81dYurgUniuK+yJMM5l+hAlPwEyCxs8g4hg54DpBu1M7abkIy9WTgjACTiiqESYmntcYlYygqxAKIfVEz6JV2FlzlQTDYalCfIQMLZdFx8VQIRS0OlPc/J7v5eHf/ddRNh8Ng+NETEwCh/zE7bu2wnI2tEZCCLz0gZkZKxIXpec1bdsD3tSM4ahY9uG111KjkjA+ricwUiiMq6LWJkzo6hAkVwsiJ5WNDaAplxmzngAYi/jamnq8amOVLRJwJTpVcTz71ReOb5Tc9QXv/Xbv/fuA//Z1HnvCe//QV/w3GfwKIW4F/gLwt733/6f3/rOEBekM8E++wfOZrKIi6g3CaM3hsSgdLlitNJpwc6VJgq5xAhn+ciFlkHarmqgVgoORgXVaFJ7RqKSsAh+/KCzDkWFzq8/K6nowNpbB5TkRCi0VWaLotDL+4g/9AM3mDGcvLjMoSpzSdNs7UcKztrbMoN+j00hpZppOq80XvngW2+/wn//9v+ejH72Xv/nT/xtlYx9Hzg55fqnk+IbAoGi2NetLZxHC8U9/8T9y880H2FxfD+ftYn0rQCiN8uHvCwClY1yM+ZWP/j4f/v0/JtNJuFAjAnsl8FXvqjUtPRjXXP7+lcCalIpUpfziL/8X5ud2xAlGIKCFSMpg6OtdiF8IwHUNJNZTiRrH0EgRZAEhJhNATmwMjanB3gQtk4mrdk1qqq0DrQ2jd0FoU6WKzE8fq07sZQ0HgAibkCkNS4srfPg//Et+65PPxilGeEVqlrCrQdXIwwjYDyACXydI2C9nqtR/W03mct5N2glrr6yyLk9frDeR9WpB+tim2LCgqgB/m0hokzLAwjpaSUI4NRctL533kWPjQQSBorUB7K8V0s45vA1qcOHBlNXXvPe+oUrDTzS9/12O9wMV8OErnt8IIX4X+BkhROa9L772+YQ3T+sMhMRUnqoMc30tEko7AiwCN9EVyMjgq0Eg7+sZdwAFq8rEWMHwhoYJS0Cyp6ZnmZ2do9nqcPMtt/DF+/40YAlSsbS0RJKUeJHTaChMFRSVt1yT8Z+WToIKqs+NoeHc2VXyJMGbiiMvrPCT//izCAlaBdDvvW/ew6DXo5uuML64SDJziJ3TknMXLvBvfvl/5dzSWb73rfuhqvjd+54iUSl/+Jv/grvfdBt/6yd+iF/8l/+FdtLAINi5axu/9+HfxjkXU7U0SijeePPdPPfgUbyLF4a8ArSLu1CdDFaDrmEViTu9CulsSoWIR0dYbK21/Idf/QP+0g98C71+D+cNZlyi02wyelVEVa53k7hMby5PFCZjy1rujQ9s3jj1qiXgq1sDkiyn1WnRTcLPBnKUgJp4h6MyJXme4axFSWI7F/5O60pUFchWxjn+9b/+11hh+JEPvIdjD32Zt916F81Wk0F/ECqiKDoLeQixBap7EB9+X81pqTdpEfUjzlw2+fG1nD9OuAK5KuSgSBTeVDgpIocjXKtOCmoU1Yr4HM4htI7tI4CYtBw24ktCKJQSExBdRFxGKoUmRJk6a9Fag/GgQwVYa4i+2vE/Agj9Z0KI/wAMgPuBn/XeH7ni+zcCJ733w6/4ueeAlNAKPfe1f0X44zGG0nms8XgvqaqKwpQUVRmCdaoylI/Ch7KUGvgMl5WoLfDiGxp677rVsVy172BwznYCU1lWls5x//JZyrLAWstmMaLRbrJjx6386Z/ez6/+1kM0pzrUSlnvPVkaKN3Lq1v8r//2E9REnGp0+c8XFGSJDgowXyKlZO3kpzl17y/x1MtLHD17AS81W4MRew++jYtLawwGYxpt+NAP/09823vfiSxWeeyZl0hVEB79T3/tL6Nb06H1kmGR7Q0G/NZ//mO+77u+n8J4En259yVSjcFPtC1XBHRMsD8X6eahUgjYRmB0S4ajMf/+1/6Av/TD72c8HEISJirhtQ40bGs9QYauwbog+w7vKJU3WBN2T6GIE5OooI3Tino8fWmjz9pgzG0Hd7Aw1ZgID2u2po9J6NYE4lY4j2DGEzRECUYYXBUWmp/66b+D955HH32S807znoVZxqMxPjZJwttYccWWqT7reqwb3sg/gys4X+t6Il5yhdo1tDmXQ5ScIICkQkZWZ4wmt6GNCi+AmEBPIhpiR2glLmihdZSWyDx1GPNnfT18rFBqUDSQ8xJKMUa48HlR9mO788rHf0/tSQH8R+DHgHuA/xm4GfiyEOL6Kx43C6y/ws+vXfH9/49DCPFXhBCPCSEe6w+GAfH1fuJX4SLgKaWMfglMwL3o00yidJwEiMlOGrlYk9VVCcFUt8PB/fux1lMUBdZYhv0tymLAcDCgZilqneIqyw/+6H/iwx97hqzdCEne1lKVJdYYTGkxxlNVIVrPWk9ZmBj67DBVKJWzNCVNEqqioCzHfObJc/zWnz5J2pkma7XY6A+oKsPcwTt5+3f+CEkjY6s3hmabT37+Qe761p/ijbddx+Ig4YPvvI0Pfdtb8STgwZjwO3bPz/Ojd9zJjaMMoSvyLIjppFBR+lAbw1xmYVL3t5dx0gCEOmKWR3h8oEt4bOXZ3MwjFBEXZR85JwK89IgkD2CndzHuMljuSxG4HkDAqbjiFOpqJ55P7aD15OlFVnpDHDEgy4Z33BHanQk5zNmgTzImjn2ZnLuOieqmMpw7eZpdRvJrDz4ayviIodcvgb38IkwwMiEV1lmMqS4/9gqdkRCB4ToZiQqJ8BpJiLsQQhMg9BpIDrkmkjgx8wLhY6QGUaRGbXUQ/grrHPYrgND44sezjm2oCC3r5RtLMTbF5GdcXND/H+FpeO8Xgb96xZe+KIT4JKFq+FnCpAQu16BfeXz1swzP/yvArwDs273LV7YK5ZcMrtYB34CqCCxAoruSFBrpi/CiuyAmkmGsghQKW1lMZUiA+abk7/zY93Djob386scf4cnnjjI9Pc3W1gbFcAOR6IBtVGXQvETtircFKlGx3akmHgk7tu1kWAwn5XuWJEilkYkn0ZpmnpFoiUxS8qzJH977BT5x30M4Yzh06BAH9u/h2RePY7HBa8Jbdu46zJ7de0mTlK1qk5uvu4kLFy7wfR94C1/68qP89Z/8Mf7wobPI3gZDW9FFs1YVVMZy4cgR3vaDbwe1m7tfezPvfdcH+Ic//8/D/N7HhfMylzx+Hi/CAHQE3E9EdXBsBYSXV2hrBL/3336PH/ze99LQEuNKBBHHiDeUqwx4R9bIsWVoGWxlkSoBAjjqvWd1Y4NOp8Pc7CxbG5vUO3uohgJDVgjBM6cucXjPNvbOt8iUw3mJtxYrgqIWEXxPtFd4pcHbSNAKdPBQBXnKsuDc0gWM8Lxx30LkiIRVQ7gwiaqzaYPoUYcd2bnLvppXLLZaJtQsNy0MQfruqCpLkiiMC7TyycKMCDqqCH5W1sZRsUdNWjQ/qfomCzt1Hszlqk1FCb4XwXhbeIetAs9Dk0RbgMhZqieJ3mFNSKL7Wnfj/1Cehvf+rBDiAeC1V3x5Ddj3Cg+fueL7X/MQkSloHFHi7Ce08VpdWusKQiSBI0kESSbJnOU9d9/KB7/9nWid8bn7HuAjn3yAkeoi84x/9TufRktFqG4dvY018jQlbzVZ39jAI9CJpCwNQWZyWW6cJppE6aiWFEhlmZuZYjgaI4Vnz44pmqlgJk/Z0c2YzIeLWQAAj0xJREFUm2qye8csT59c576nziOkpz/a4vbrDnLj4RkO7LuJJ46v8MnP3o+tSnbs2MEP/sD3BSOXzJNlKc88/wK7ZqdoNxqkjQ44z3/9zd/gB77jO0hcxXdc3eTv/7P/htaSmd37+M1/eS8//fd+jqm5Lj/5d/7mJEQnUBquGDFOWqzLU5DAX7i8c0/6dxGIYtKDE+GG+q0P/yk/8Ve+l82tlYjOA9ahpcLE9rIYF3hr2dzcBOC2227j1KlTk0S8+bk5hJRsxAUDAmCdZ1ls5S7TnY9fWOP0pQ1ef91uGolDCYlxFVVVIXWC9ICwSOsDuUtacJJ6nRRek2UJP/4TP8ZH/uTj3Htxhb9cu3g7G6ZGIpgeyzjZkTEj9rKZXn1Dh8XNuioCnoFxWcsadA3MAlXl0DIg88aZiY+pcwH78bFtFDKI1SprAt4RgSZTc/Gdu/x+ROwoUNgVIniMo3SKFA4ha31KaO11Esin4f6xSJl+zfvv/wly11dWFs8BHxRCNL8C17gBKIFjX+8JQ2/mo67B4ITGRGVeQKuD8lNLgcayfTrjJ77nPTGNveSBI6f4mV/+ownGl83uo5MEgZe1llFRMB70GJmKgXekSc6oKLHO02wkaCVpJgqtRBibyoRm5HVMdVo0dEqWS44cO8/1hw5z9vwiu+am+MF7bmdlrce9n32U9XbO2tCwMJ1zYOcMP/iBt/LYixfYt32ad739bTx/7BJ64ToWH/x9sJ48y7FW8PM//79z7U23sFZ6fvxDb6e3eol/86t/zF//kQ8Cnn/5Cz/LB773r/Gh7/5mEutYmG7zS/+v70PojOViO+/89h/nzNkz5FZz/uXnmL5rXz0IoPZyCp/Vn/s4dfBhB4zTKhcl8FrWN1KAMgODIKDy//Y//jZ//a98D0U1xlrL008f4Zu+6R1srG2y3ttAEaYA8/PzWGs5feZ0lGCISXiy8J7BYIAxVVD3OkuaQJKmSJUwrsrJGLOqLPc/c5q7b9xBJ2uQ6QxThVG40sEu2GJRTkVujkF4HX05Dc4ER/Bvess9PP7kY9TWBhOnrOhSr33cuETtwRLJVjYwdaVQWBu0UUiBJLkM9EYVbFgbJImSAaAVoKSf+F5IKTA+TEWstRjjSJRGf6VVX/3Mk8UmVCa+fv1iO2N9HGHVDnHxXkpSFUWetSRCxn9/9eN/6KIhhNgH3A384RVf/hjwc8B3Ab8ZH6eB7wHu/XqTEyCWcmHREASZuJIaUxVoLanKQBOfm2rxzne/BlcOKctAhHr82CqfevQsRGZofUEYH3r0yhjKsgw7sNZU4xKrwuryxlsPoyUIa5hvWWba02ybbpJ1cuZbMwgtcJUEaVjuGaYW9rB971V89kuPozXs3t5m53ybW677AL3SsbjSY9AbM72wje17D2N8k8eeeIHjV494/ZvfS3dhJ1/68heQMnA5/tH/8n/wn3/tP5Onf0DHn+HQQothezv/7hf+Kv/LP/8NnE+Ynt/DD79rgcRBJUN2arM1Ra8/5Fd+/dd573f9KDPdKf6PX/w5Fg69FlwdFlSv7GF6UYN39YU3Ho2QmODsVAvjhIi4xmWrOjzBlCfaBvzzf/Fv+Gt/7S9SFAWHrz7EyZPH8XE8aIWalOHGWIbDfmxh/AQEzLOcbqvNcDSiKEukkGRJQlAwV2RZTlnVzMawaD347EVed/0+ZqQhTXUoza1H+ipUDFqSTHJgLKgUYVzU9mhazYx73vwmnKmzWTRC+OgdG/5ID3jrEdKxvLrK/v37yRLN+tpK0C4pNdnYwqsrwbsoZ4+rtDe44EcARCaRFyhCSyFjZeII1axzYGqZezxc5HZAYOp6Qpvi3GVxWz1Kremik6YmVk7O2ej5GgHkrwYgxOMbXjSEEN8ZP70zfnyvEGIZWPbe3y+E+Jf1ewYsA9cCfy++Qr9QP4/3/ikhxIeBXxZCJAQW6Y8DB4Hv/0bPp4zstrq/DPZ5NvhDGIOW0MksphqGlK/I6yjKgrIsov7ispRYWEElwVYl3jvarS5J7qmGYYHSWL7rbbehUORyzNAqzropBmKWvDPNEqGHbeRNlE6YTRPepDMGw40IbGny5gzDcYUQ0Eqb3LL7Vo6ePMlv/dePMBx9nDff/Vq+9zu/hTTtsHLpAtnUdt7wlvdz4thLLF44wRc+/M/4W3/jHzE3P8NP/NC38d53vo4//sQXefHi8/zjf/D3+a+/93skWvJDP/0rOBwCR5Z1gQ2mum3+wV99D4/f+4fces8HEXuuR2WNSY8MV14n9cQi7rBe0Gg2wJS4KkwURJ07AggRWK2Rl4jAYYstCqP433/hJymKImhArAcHS0sXkImmkeWBlZgmWGPJkpSiKKiMCXQTISitwQ2H8Xnj6FYEkaL3BkpLpnNK7zHOgFc4YXnkxdNo4bn7pkPMzmYoYSaXu3OGqoIsC0X+yZePsffA/jDVsiHQWuKxwuDNFX9j5Dk474KnCBJbOeZm51lbXSfPdMxlCcY2ddWLByl99M6Qlz0440078Vh1wYoSGUe1QkbuWKhSHHbCjwl/R1yQotq4NqkWcEWbA2EUUHcxbqI/scaglAraJmvBKSZMta9x/F+pNL6S1PXv4sf7gbcR2o4fB/4i0AFWgM8BP+e9f+krfvaHgZ8H/hcCjfxp4D3e+ye+0ZNpZDmDsgheGkITfCQMhS1x1tBpN7jrzmuCGzkB5Hn8ufN8+N5nUMLSyBMaeR7APUA4T+EC7m5GW3zPu2/jdbfdwOlxF6e72Moy9kGKPBYCZxU7lUNmTeaTii3bjExJRZqkSJmgkxRreighKFGs0eb+x57ipWOn2L9jhunpNt/xHd/JzbfewSOPPs0DDzzIb/zWR6gQbG2V7Lr6GnZOFdy2L6VLl6dfPMlnfvS7GJcV3/xNb+Iz9z3InYd38swxwfe86wCtTE2o7aEordCtNsYOSaSmM7Wdbbd+ENC0p7cjVSR4EST5tdnMZKQoZBwLXvk9Jt4eeJDWMh4P8dFawAKq2uDRz/4eR14+xn2PPoNBMtXukCQZK2srpFmLPFOMqwopFKPBAERCUVQYU5E3Upz1ZDpMPwaDAXVqnPUOneVo75EumjW7UfShCsKvmqlhvOD+507z3tdktFodhFQUtiIVaWxnKqRSfOFLX+L79+9BiNAuIFwEfEPKO1KFxcCH1snVjlnCBrAyErh6wwIBXHv4EGsry/T6/VDNWod1YeKkYwWnZWgTlFbB9Mb5ANPIy1Z7ztYYXW0IHN6HujqWuhachc1TqYCxGGO4TK2qw5hE9NIN9ZiLZDjnTDSSFkAAi78eKesbXjT8JLjiq37/14Bf+wafawT8ZPzv/9ZRlGNs5XESqnKEJYyLqjKi3TaqYEXd20maecJCR0I14K4bFnjPW19HnjewTtIbjUnzDInDlAacpT8o0GkDKxQyS0kgtkKB5JTojMpLqrRJW2nwgrzZRHhPmiQMBkNwJUpJLixe4t/8yu/Sbbf50He8l+lGSrfbYbS5weNPH2VcWn72Z/8eSWOO3niE1gmf+sxn8Osv00wSXn/tbnbv2s61t7+Z5bUBW8snuGbWcs1VB3j/+zp0WklYtISKN3h467O0hU3y8KIlbQrnaGo50X14fxnmrFmPEL40UZLW/3Mlw8E6VVEiVEKSZsjxEp//7Me5sLIcrAmMRCSara0+937pYbrT01DaYG7jHVqnTE91EV4iRkMqa5CJJlES58Mi0MwyhsNx3D0tWgeX8on03xmSJMU6QZokZAQfz9JYjEopS3P5b0Ly6cePc/cd1zPfSUmUCtYJNU8B+Hs//dOcPXMqbBi+QvkERO09GsyGvQM7GXQEkNhGxZi1DqU1QkhGwwHPPPMsM82UcSmZn5+lKAZhJFv5gKmI4HWihI5tVbihPQIiNd4JN2lPiCQvLyUigq+Bt+5JlAoGwxGANaaMXJhaFX2ZjyEiiU7KQOc3kRXqrCMRGiNsIMkJ//+76cn/sMN7yqoKIJaUlNKCCeWjkI5W2uDtdx7EVP1omx90J7ddvZPbr3l/2D2ilHk8GuMQPPnYk+zdt5fdu3YGqq6UbLENqRsgVDDfSTTCKVqdPPhBCM25C2cZCUmepWidYKseWkPpC/74ox/jgQceQAhFtyX51jceZt/+wzg3Zue2HSBSdNbhbW/djUHR6kyTNtv88cf/FFEs8+Y3vB7X85S9CySpRudTjHtbTKeSmQM3sP/AjWjhSNo7ccNzKGdwkcQmom5BqQZC5XgEaT5FFhPRAmeAr5jrX06VL0Y9RoOt0O86j7Mjys0TPHj/n3B2aRUhcoQO3qHGer745UcokCRCUjhHI8vpdDoIQXDlchWjypKlOcYbisKQ5AlFf0wjb5AozVZ/C60Vg9EIpTWVMaQ6Cd4gVQkQPUg9w9E4ANdSUlUhjlMqBbYgSzJKE+UFhJ7/y08fZefcFDfu28fcbAu8DTJ0bzlz9lSIhiTGNQRgIBgTiQB+umjYXJv9uojlKKU4cfIEjUbO3Pw2Wq021bjP2Do+8vH7+Ef/6z9nbfEl1peWCNPeUNnhPdbbmOjmcTLEYk4OC+Gmt2H64aNGrsZiYttRi+rqxcy5erooENRpa6G+sL6emtg4vnV4gqds0MoQAeEreDqvcLwqFw3nPd5UoASm8uAEpjKRXBMAMuGLKGa6zLQNWgt7meUowcTFpt1to3VI4NI6wcuU3EiyZhNjLRfOL+IQNPMGplRoCTbJ+Q//+hdZWV1harpN3tA4J1jd6JMkgm6mWJjKWV8ZkimFcJ5TZ87x3ne/EylTZNJCZ11Uo4tMuyjdQeiEH/zhv4bAI1PFxskvs3Ym2Ou1Zg4zc9VddOZ3MtzYYNy/hCBkpJh8BiOTSWle+5eSzyDHAWDU2TRaqkCU4rK1vhkP6PXWqWxgw2JLLp56mice/Swr65uTak3phKVLKxw7eZZRZZBZinAO6zyVc0gJJnpNVs6AkIxG4/C7tApK4STFj1OqssCUJVonmMoGL43SoDJBolTQyyiFVkE5GiI0QxSk9x7ZqKMAPHmaBpBURQWHKWimDYbjMuThibCDL631Wbz4PG9/4/UsTHURroq7fR1x6QmWiQEw9U5M3NJlvNeJlPigJwntwYH9B0i0pjLRvNiEPJrZ6QY/+3f+Bj/81/82633Prj1zjFYvRZAz3NjOhvGwEir4ccgAgAod+Br1aHbC+qzBTkFomeKEq464FHyloVNUYkewtJZJhJwmMcFF6lZNiPD6f40149W5aAB4IYPTFBof4/ZUNGiZa4dVthahaS0oCkNpK6Y6XQLfILgxqSqw+K49fDWVsVjnsZWALKOlNIONZU6cOc1v/JffIcsEM+1mcOXSGZVXTE13aeqKt9x+LTfdfICXTl7kU/d+nlRr2q0El2msqWjNT3P3299BM8sBgVQpIskRugEyQ8g0SKVFYJpKKXHCoWevozXYpBqu05w7RKM7h1IJSbuDTFIEwYczkZeDhmqiEEKQpFPkrQE4idJNBmuL9Ecj0GlwgXEFvXMv8eCD93Lm4iXStIE3Hq01y+sbnDx7nlFR0shzprotprttRJqCdZiixFlHo9VgNBwhhUc3dZg8RQql1oosazAqiliSB9ZnWQqajRZlFcyCrTW0Oh28hzzRjMbFZCFIVEIjbzAaD4NLmBBkWRrS26zFeU+apbQaDYajIV4BwuJSxaiwJCqjcg4tJCoXfO6xl7jnzuvZMTvFuCpJRYrWKXUjFl/FgFvUHhtRz+EIDl21c/d4PKaoKtrdNkIJTp86RZ7KwDERjkaq+N3/9G+xCP5fv/CLPH364zSiqbSNN7n1QeJOjEeoR8githLGx8qEmmMX2MzWBlDY1atEfM/xTKosFyMaaneyOr7D+DAx0dHIWBMAZO+ZuLp/teNVuWiEasLGcsqB8iFX1EnyVPGa6xaAEPt3YWmR4bhibbOPcI63vvUeAglHUJlNvC9pNFusrK4zGPTYnu7gwvnjnDh5lh075/niQy9Q6oTr981ww/7t3HnLVWTNNt1Ol2eOnsS7lBdPHOeed7+DXKf0+pabr72OqjTkjQ4XK1B6E49ibZxw6uwpms0u1159DUIkeJlgZYYjATQJgVIczOY0aXcHfu/dVJtL5FMLwabOh5m9ygX4Emc9OmmgRZ0CL8AYVhePs7V6EWE2sF6T9PucP3Uvzzz9NANrcCQBiPOCldV1nnz2JfJWi/F4iFaKVrOF0gqlE4SUIZ/F14Y5AS9K85TBYECSZdErQpA1MoSHsqzIs5yp7hS98xcmCWpFUaCThOFwTJopBpubLMzPURmL1JLReIQxhlRrkkRTGsNgOCTRoQKxNianEVi/zWaLwWBAVVVoqdm+bZbzi8tBOmPGyLxBM8koqoBBJFLx+Sde5C133oQZjdi7awEla5FXqNCc9zgpEdEXREQBnaTGNAAhyPOcjc1NrDO02h2mp+cx463A2ZBBAKiURFjLP/uZv8PuHbO89jV3ICObVXiHUAm2xjK8wXmLFDqGIflJ+1F7uvrIufDO46PxEO7ymNy7CAYLIFYPPo7Wg0VCzd6NExchsUTlKxonLo+BX+l4VS4aQAhPFgpEMC4J9m2GTi45cfIMN914Pb1enyeeep4LK1uU3jPTzEmTB2g0s9CjekMzTdi2ME+qFdlUB03FwX3zHNwXFp6bDu8jSVOee/Yltu/cwdgZWo0ZhHQ88cQL3HTjNbzvLa/Bl32k6nDrTddx+603UVQV/WHJz/zL34wjNcmZ5QG//TufYtf23bz/PQl5a4Ek63HVNTcjlUZ5HXUIEullmFAoRTa1g0ari5bxZlFR8wAgJP31JQbDLdBJ2KWExw17PP7FP+Dx556j2ZgG6dHRUPj80honTp/GieAArqRESUGa51S2Is3SiBuMUFKSZVnIO4k08dqsVsogWVcyCa2BExOS06gokUIw6A8pyuDTEBy/0ijVdoxsgQeyvMFoXJCmgSCXZSmp1sEX1QeruyxRpFmOsYaqLCFJENJTjCuazSbjUUErz1FZwnyrw3MrR2nOztHKcqpqRCvPMc5jShtGqgI+/8jTXHf4MGqlx4HtSeCfUJO1wsTNmWAJGchmTEb0jkDskgJ2bNvGYDBkPBiipGBclWglJhTxy47hgkNXHZzwYqQAawXCm7AQ25pzETwxArlTgIv+o8ZOSG/B4yQsBkrIEI5VRzAKH2mu4fxqUR1xyiLi+NpPuHuBzi58GjkedSDWKx+v0kUj6ASESpFaY0w9MvSI/gYn1s5x0w3XYauK977rrTgs5WjE0tIKWifML8xinCJXgq31NUbDEbOzXZQUgUGYKBLdiPkTQeEpkpz57du574EHuT6botto8he+4104L6isAKXZ6A/CmE5oXr40ZGZuO697zev45OfvZ1wUnLiwzt6b3ogtKn7nc8+xY9sCu9sZB6+5dUJ/D/P4K9b5uFto1SQRJVur51lbX8KJkNYqVcqJI1/m4oWXOHpuCSkUpTUoIWm0mjx+5ASl1KRSIeuci0RhUchYNWgVsIKqGiIlwZLPWJTWaCnRWkVqftiBpJQkSgXEP4Jq7XjjW2EZFWNM5UgSSdLIqIqSPE2w0tPr9+lOTbGxvkWr1aIoSxqNDFNWjMdFAHCtpdVuUVUjsixn/+59rK+ts76xSVlUDPtDpNTsWJjFmAEri+dpd5r0t1bwJmFTpOzJMy6urZN2OzSaCXbcp9OaZsMMo12CwFjH0dNnOeoSuNmwb9fC5IYOHAobby5FvMAi1frylVj7a7ZaTUxVsb65PmEnJ2kancEdGo+pRnRaDTJVB1bBqCLyOsI7H326ED6I6jwSIeoWI1DWA1ArI49GXiaQ+VpBKye4FRNeh0IIjXOXDX9qfMbHvJhaIvi1WhN4lS4a3oOtDIlMqEyYleMcK0tnOLtyhh/7y9/NyvIya2vLTDMDztDQkqt2TYdIO2Hol560Mc3BuS4XFxeprKHZ7JA1Gpw6dYaLl9bZu283J89exDvDmbMXOHjNVdx91+uwpWW9F+Txo9JS2TAOM8ZxcqmPUxlHLlRkumC+1WXPtikO7tvJDfvnaO17PTvmmuzaPcdobRm9dYmg8oyGKiqkmBX9dTaWz1AUwxhSLEiF5dKZZ/jcZz9BKRo4b9BKB1duJF9++EnSPGc8rmhPdciSlKTRwlchQQvlqUxF6gRZmlAUBakKu3tRFCRaU5iK0bgI7U8NQloHETJRUqJ0CBcqizF5s0EVRWfNZpPhqA9KBcWuqciSFFsZvIfxaEyapDhvmJ2dwViLsoJGkjC1ME+qBFoLzp9fZtfOnTz+5BNsW5in2cjppwmJCDGCg8rR0HD17CxFu8XGYI3Z3S02lvu0siluvOkQ54/n7OgP6CM5ubxCd3oKqJhqJWyNLFtbPTpTXby1KCV54MjL3OUlB3fOhgU8On/VeSIgYzQETCwEYuo7AqSvmGk45ptTDIeCTgtuP7SdjfkmtqoQtsRVA+Zzy0wHvAvA45YzIYJBBjfw4GwG0Yo4COScZHNoMNFYOqwHoVmqWc21LcpkKhZYX5ElKpAhpzMsLLGSkELGxUthBXgT2jedKL7W8SpdNBzeGYy1DF2FtZYLZ45z854Wh2+5k3I0IEsku3dtm1BrtQgRhFKHi6EYjziztkplLbunFNNZKF89gtOLl/iTT3yJD377ezh6bplxf8C3vOtuBmOL9yHvc1wGAHVYmuiwEAJ5H3lpifmFBZJqwIP3PcAHvvXtXL13O3/jx38QIbqopQ55syQ3F9FqxMKuBXI5YnP5LOubPaRK0UoxWFvk4pkjvHzsKL3BGJVm4YKQ8MwLZxjZwDpUQpDkWSD0JBkGyJoN0ixjc2Nr4rqeaBWCf33otZMkQakYGWBtSPmywRlbR28KX/thXqHcFDKEJlnbRylNokOS+eZWj0aek+gUT0miFM1mg9FoSKfTYmurj1YJrVaLPXsW2Nzos2fPbpaWLnLm7BIzs3NILUkSTZqkTLeaqAKqrT5nj77AcFTRbc0wtiN2zk6TCcnzLx1nYfsClfGcP7NGOXTo7V2ePfEiO+d2orsZhxpdls5dYGOrh5SC6alpMu0RGJTUVySkS5548TTWWg7u2UZSj9xUzRoOBCjvfXAr945mQzCVR75LVTHT8DSzBOY6CDxXT+/AsTO6xUNNP3c+xE5KBbNpMG2SuAAUWxnMhGVw9FKJx1tHChhfB3Y7rBcMSk9h6vcm0A2Ijl1+gn8ES4bw5sX8k7jy+HBKYShgTFCFK/11S41X6aIRyrDSOMZlhbMVpix415veTFmWCAFShZhE7zxK5dR2/NYHw9iqHHD8hZM8dXSRd9x9J3jodlvoJOHQtdfxuvWSufk53n1gL2VpKK2hGo0I5bmY9KlBwRnn4MawZ8cC2xZm2dqELPEsnj9FVVQ8/OhzvPzCC6yt9VgbDZibnubA7r3cdutNbC6f5OLiKV48foL1zT5EIA4BZ84vsroxIMsSEDAaF0ilcbjgPRGZf61WEwZDHI4s0Qx7gyDUSiTel2HMZi15muFwlGUZxEqlQRDp0c6TpUnIPvWBJavkZTNhGe3/yrIKJKToHzoB1byjGgdFZ1WW3HzDNTz3XCA67dt5FTpp8NCjT9GZ7XJ28RIz8wtIU9JJCy6cOIYQCfsPHmI6b3PkwSfY2+2w76p5di5knDu7jMqaDLopvf6A/Xv3cfLEWRYvLHHjtYdRKuHchXOsL63hFys2OusMhyXb2122NRJO9AaMhKeZt3HlkIWZNuNyRLMzMxkLW+DIifOUHnbMz7Ctk1MT4wO7y9NMBHPNoPDtNgXTeZ3AFlNofPAOcc5R2hBKFNyzApxZG/0CYedXMgj3rItsUxNbDRtT70LrMd8O0nvpRWiJvaBfeAZltAIQInxe+MDHoPY6qWWIfqITmjA+4+zWeRvxo5g6WBZfi9v16l00hAg+nt5rms2U73z/uxBKkubhBg4MxsAdcLjgm0gw7bFe052a5rYbD5E3chp5wspmDycVaRrETXfceSPee/qD0WT2LWVN4/UhtFdcsSgLj8TTkmMunDrKcNzHeMPJs4tcd+gA9973BV48cQHrHO2Gpt1qcOTll3n22FFSLUm0Zm2zx8bm5mVtQUTsw67jUEkIfQoxkyK2FZaGjiCdD+CcMUHfUlWGNGuE7zmPxZBlwV9zaMaU44oqqketdSQ6iYshE4fvOjEOogengCRJSdMs/A5jsMYFgFNYlIYDu7dx+vRpzp+/xKA/oigtu5MGuQKxNeTUkWNU1ZjlE2cwZoXr9s6RqxkunL7AO2+/gfPnLrJ67CTo4HfS3xginGSq06IjLP2Vi9hRj73bZrCDEdcePkx/q48dDVlfXUOnIuAACrbW15mZbrDH5awMx6xfuoRNBI2WpBwXdKe7+GbOeFxgXHDJeu74OZY3C/YttLl6zzzCefLEsaMraWhYaEOtL63DpGuvCw+TqIx60qEI6g9rTZTSBz+SUKKGSiHUAj5yQCJlX4Sv+fqjD9UIQiC9pZtAS4JQYcEbJIIi1Xgp2RwZ+uPg+eKjVCBMUcJ7qxONNWYC7ioR+E8iBmV9DZrGq3PRgOD3aEyQVi9euMjht9+AL0dhbDVxww5kauOZmLiW1mMMlEaR5m2uv+aqqA2A0XBEWZYh3EYERysfdRnOOHSWkCiFsZZEiXAheEOn3UYnCSePn6Ap+/TdmMoYeqOSQbVGZ2qKYjymXxrKosC6lOeOn+fSyiZJqpjqNOg2G5NxWYhNjPZ4xlJWBq3VRM2YaE1RjknSHCENU9PTrK+tE5LnRHS0DqBWohPGjBBR+lxVITNERu2D1hqlNMW4T5okWBd2pEQHTCMQ3mLavAg7pfeO6e4MVx3YQaozPvP5L1E4hzMlMtf0tgYMe2POrDwLokGlK8x2xdKxZ9jVbFO6ir6xHNrR4sXjFxgUita2hBElL5x6gfPnVugVBdPAYGWTZDphNDAcmp3HS8HpMxc4ef40N954M9d3r2Wrt4FPFZW17NjRpipGTE3lnL+0RWc+5/zpda59/Z0cHpQMVcJnH30MrxKmpzuYcY8kb6Nabfq9QTA09o7ltVW2+ptIN+Z9d+4j0565Vhy3RtZo3bXV7QuRRFZL1euFxPjadTzGKHoHMlhKCu/jwuMxLjybkrWUPWxS4f+hwriyc/D4SVqeFjCVCcjCpK6lPIMkkLh644qhCZMZqeRkOuJ8iIwgcj1EBEa00n8eK41QXo9Kj0oyzGCAL4ehpL/CFNfFkZRzgesfVmpBGb0pQQSyURJEOlLIYLMfS3UpPUqGUaGxjjzRdDptNjZ7rG6sM64MSZrgncSrhI1hxcNPH6Vwkn5RUlQGaQRPPvty6CIioLk1GrM1KkgTTafVoJGGYGXrLJUNilFrDFUVdqY0DcQjZwMTsdHK6Q8HWDuK1UC4INMkZVSMSZIUY2wcs4WdL8Qgplhj0PH5rLUkSYoQgkaeR/5LIBSliWZ+YY5G3mR9fQOwcQzn2Oz1GJXB5HZXo02rcoiyAgGq8jCyzGiBdJA1FePSc/ftN3O2A88/cRwpBK00Z2NxmSnXQAwcZy+coz3d5aVnX6IcWtI09OjNdovrbr+BlY0xNJqgDHtuPsjauYvMzM2xY+dOlk4exWjBwWv2sXTqFMn0DrYd3M3LZ7/AtoUGjVySyDFZGxamZtkpNbYy9AdDdu7YQWUrSl8yNTXFoLcZLCO9YzaH1x1osG/W462M+g4mI1kxWXzrqUWoMPD1VCW6ZxE8QVVkWgaHLR9T6N2k3Uaqy4xPT3QalxP/GOoJW83JkKAiOOqiDiZscpaGFrR04HC0E8W4Aqs1G31DWcWQqJoQ5utcl0gtn6Ctr3y8KhcNCPRv5xzKW77tnjsuv3GRGmttCEmuPQ08kcjiI4LsbZha6DByVFLFVT2gyjrRKK05d34JY0PlMq4svbGhNI77HnqMURn+7RFYobAWXjp+NniSSo8QDnwk18jLN6+PCJTWipmpNtL7kHOaJbSazYAZCIHSisoYtEhCpkb4MYqiDKO2SHDb2NhkPCpJsyTucBKVagYDw7goUDqJCV6eJE0Dl8NF89to7yeVitmfgBQkacb6Ro+lYh2tBa1GGg2EBdOzGeXpS1w61iNpT3No2zYuraxS2pKDVx+glTleWvGMK4WuKm68YRfSXmR+voXwfa6+8w5OPvYCm5cGzM502Li0RplCI8lInGPf3h1Mbd/G1uYyeSZoTrc4sOcqjINnjjzL8yfOUw5GLA9eIElPcvdth7CDVVpZxY59Ozm+uM66hWuuvpal06dxxnPq2Ekyo3DJCm95091sbW7x4PPHWF1dZW5uhmFvi+nZHC2nKPp99k3Bt7/lEDfs64SFQoqJaHxiJyAIIuC4UYmahRWvw8nnkUkaSoZooUiI77Qu5pcQjZGpyVs+rAox16RW+dbPJURwCq1DsqULxL6aDSxkxFG8o5EomqnCCWhIyWwzR6QNzq30wuQvptSFewYuR7e98vHqXDQ8sWTP8Aiu2j2Pi7oJEZ23a5u2qizxcb5trY2ZD8HXER9Ugt1uh9G4ZGV1g6IoUDosGFIlFE7zhS8/SGkFSMm4MmRZxvnzK1TOU5Quxo0o1je3gpmPqF/3uBPVaHWdlSkgTTQz3XasABJSEYBbIPhpEhaZOrFeRl5Bq5VTjCsSneC9DBhNZbHOYJ0k0SmlteQylKJlWU1IRqE1gU53ilG/T2U9OlHkeY73ISdmMBjggco4llfX6Q8GbJ+fodsMwU7RmI49O2Yo1kq60bu0KIaAYOPSBmLfPIdvvYUvP/IYC50ug60hqStZW1vEk7B4bgmXJbz9fe/EmJIXTx5ja9Dj6r37OPbCM3Rnmtxwyy0Mx0MqSs6tD/jyfZ/m0MFteDVA6jGDsk9LJjRaKWPVYnpbh2L1GC1VcsONh3jp6WN4W7HVH3P41lt5/IUn6eo2qhqy79ABjr/4El1rUP0Bl8oCYxx5IwORc3h7h+988y4ObO+GyksFVAI/uefDTemDx2iYKoUFV/ia/BWA5JC7opj4rEbX+zqBrja/ccYGr9XADEHgY3tsg1hSiIlILmw+oRTwsYUJBUg95XHB3cRfxlsCE97STgWdLARD6emEkVUsbfaCKTEeQdzkvsbt96pcNJxzGK9wVQgyqqwNqdcCrAtjQhtpvF6KaAMfJiphB+2zvr5OkmdIJVkfllgLZ85d4olnj1IZS5onIRQawaWVVRKdUtiK0bgk0QmNLMdXJVmqkFKRZRnr6+uIKD4KlmzghZ0Y0Hrv0Ymm223SzhKUCEbIwfBVUFXhwgpSg9BzZmkaWyWJ91Uof7E4Z5mfm2V5dR2tVdBhQMiJdY5Gs4GQkrIswHuarRbjcRlUpIMBXkRDGGMYDcdU1tJutkIFosJCl2Upo6IM4c8yVGFKSszGmOltDczYoLogtCdZhfHQsLW+xeHX3sHUwjTtJ59hOK5Y2ewxGD3H7mu3c/e738bJ86scunaKk6sbzM82uPbWG+ldusS23fs4cvQkD718jsHoUXze4MJgwNBYllbXMX7A7GzCa197J889c5yz586z0bdcuvQw3/bud+Jbu8hkQbc5S6fd5PjRE1x707WUyYj5w7sYvrjKO973Pi6dP8/WxhqNjufO22/liw8/zrFhSbKWMddp8n333Mj+HZ1JzilxhFmDht67SK6KACjhRq2Jb/6KdkJFgV2NLnrCzYuonc4iECrq1sJNiHQhULz2zwi8jQnFndpsR0YqOH/GP6d+ntrSPUji43N5B76im0laHjKR4lCcX9lkFAPB/xxiGuGFdwhEAlYEibMScgJeJirwLo69fDxQkbVGygSVpIxLx/nVPheWjodIJakYjgr6gxGXllfxHtqdNkUxhpgbYquKRt7E2WAqWxmDVpH66x3j8Yjdu7bT6/UpKkenPYVxlpX1DSxxjCaC6jbXKiDqLlQpmdZorRiXFVJAkiickzhTURQV7U4HY0qSVIeqJV6cw+EIhKesSnbt2sXFixcjrVswGhUxP0QjnJ/gI1VlaDZyCkPAbyCkuY/HFOMyTEVkqDRazSbDcUGtfwhpXJZm2mB9sU/lPOtlH0qNjeQ0gIXOFI20wTXXHObw9YdxuuLC8jlaCwvMNtssbN9Je34bS0vLfPwTH8frFKxAHznNuNyi2W3y+MsvMttsszguyVVKA8HiYo/lS5Le2jHW19fYNt1k53yD4+c2+finPoUyJR/41nchkzFju8Zdb3kDR4+8zKi3TtrI0EmDtcVFLg23mNoxw+6rdnH/A0/T9p49WjGuRvyNb3szu+cbk5bDGhOncNHg6Eoat5SxYHWxmvSTSEkgisOCaMxZN6k2hJRXXMux1YhksjApkRN8AYg6HBX0JHX1gMReYf1Xc2om5kEierrGcGzvY+q8jNwcHwhrCst0nmC9R881cSrl9GoP/zVqjVflohH6Povz0BAJpy6uUY424w5vgs+CFzihWBlanjt2iqXFJfK8ifcwOzOFVpLeuKSywXC2PxxTWReMTuqLBYFWgUZtnWc0HpNqTRXJOkpAEi3jxmUVEOx2iwvLazSyhMpEjQLhjRPxDUNKkjQNHBPvQsujNCpJQ5lpLYUpY18pMVUVdy9wWBrNBmmSsraxEc6nqhgOx0ipSZRia2tAmidUlSNvNBgMh+QypdHKGQwGFIWlNCWtZk5ZGoqioJlnE/KSTjTWVSSJppE34s4aQTgVUtFGmwbhYNAvwQs0OXe8/hpOHz/LUw8/StZtcvbMad7+3ndQmCHduR382u99lEylbJufZXq+zajvWFxdI0lyBqOQ3K5TwR4lObh9F4+cPYfxEulLbt43yw0H9nL0wiVOLV5kMDbcft1uds+3ubQ8Yse2lMVVx4mTF+hM9VjY12X/1bvZt28/9/7OH8JayfzCDEeefoF+f0xjKqWZtzC9AS5LmNKWG3cnXL13LgKXIB346EbmoyMfkW1Zg8Yukq1qLUc9ggUmwjGpFMaHdtW5EF9grQ1UfRmmUyFJLmIX1B4nIlrz6fCrvZiMX4lAaVg0xCT1PTBFI3VcuEn+Sni20FzWot3QeoW2WUlJpxmIXVfN5eRfgxX6qlw0PGGqYcqAWfznD3+C2+64DelG2KrWPpSgBOPRiJdOX2RUFOitAryjKA07d8yjkpDIXpiKJE0Y9UeMxiVponBakUe1po+re3CghspZtJdkacK4MnRbbSoTdhKtU9qtJgJBWRpSGUKakaGETLMmXnbYKARaN8my4DplfHADs97ipEVleaQbeUprqSqD8oE70e8PmO7qsDNFaXNva0BlCmSe02imCKkwzgR8RiWMygKkIEsbjClJnEOrjOZ0F+c87U6b5YsXGY1HKKnJGjlSKbI0QatAH9dCIhEcumo/ay3BxcVFBoOS7tQcGsXzLx/l2n27ePblC3zL3e9h5/YGSvYYDR3/4bc/xtgb5tqaE6fOMn6phxCC3dtTrr/xBv7k04/HyZVmq6g4nQwYGMtcK+P23XMcbHumlOG4h42+wUjJI89d5Ja5PstLW8zNz7HWL/jcw8/w/R98B1cdaDEYrtPtdBjKYAVZrm9SVSVCGLZvbzLVUjTyeW695VrOnDnOt7/vbqwLtPIrtKVxd748lfPexda1TrXUkwlILfSShGvFxmlIsCW0IWMmthNKhfGrd1HX48M4lEksYmh9nKhbmoh4RHhECKJ3h4oq76jBqmPY4ij4chhWxNSkwDiDUMEzRMQIBUGYFLayNEZbvPLxqlw0AIwzQdhjPetrm2xtjZEycAiKMpgMUzoGw5LSGrIkDdgGUbodR2LOVbGHDL2rkIJms4Ug5LuGWL3Q0KaxOtAy2PcPRxVJlrK11Y+pVZLSWtqdDg7B2DgSnWK8w1omqtD19ZVozxDyPGsBUQBx/WTcVrMsIUxEtk23UUrSaKQMRmOytEGeJWjvKMsK4xxCaMqyQikBUYg23enisIyHRdhhvGdsDJRF8PZUiqIokVqj0wzvg1fDcDhCa0Wio6WcDyO+i8un6DYUN996DVm2i899/su0MkGvMDx34gyz+3fwwH0P8b7v/BbW1pc4/fITfOiD38S//Z2PsVL1qSoDSuOcZ3nVMnzqHDfsv4qDecbj64u0ZjLOn9ugkWeUVcFmtcF6Y4HHT23w8vkNti10KSvLvizl3MqYTVvQL7cYD0f0eiW/99HP8Nd/+HvYPrWXiyfP8ro7buPq224mF5JeMeTlpz7D6TMn2TAV7/3A97OwbTuPP/gMs9NTWFtRM6tcxAFs9Lqox634y74a3l9uI+pd31uHV5EUV4dPOT9JuSSOtYUSE38QPBOhYi1ZrCefMk60iC7uUohA+ZcyiNrqxUBIhKz5FwG4l7KmGwSH+NKE6kIIGc2pbGD7yiCDEEJFK8Q/Z+1JvZpnSUplPNffckt8sUK0HkSGrk5oNGOmapZSjEcU4yLayHuUlhgrkdLhKwc4Ou0Wg+GIPNM08ybee4aDEaUpySpLmqWXJc3CRtp6GJlJJINizLgMIT3WBvKUt5ct9awLAU5hGmex9oqJDzUuFnviOGOtvSsq30SiUElOUgVqe6PV5NLKGolUbJ/fzmg0ChJvpdBCIHwAO4squGSVpsRYR5bnjIZDpFBMdadYvLDI9OwMs50OG2vrGFPRbnfieUXCWVRPXixSTl4q6LDJjoUtDh2awRjHgrB0ug06O+YZjJu4MrRjg/46N853uGq+w8mVPg6BUpq7mxlqvs2tOw5z9NiLbEx3+JZvfjfr6+uMN57i4vImYyF4+kzBCxfOY7zDObi4PsI6R08V3Pma7YyfL3jNLXeze2ePXlHw8IMP80u/8bv89R/8btwk2Q4+9vsf5d3f/B52zDR57FEDwvHRD3+cnTvneM97bkJGHo/0TPw/fWQB+5pXIYLnhquVofGGvBzBGNuXiIPUwKiLWAUiVDIgMDbEDBTjcTAVihWpdSJmtNaVQ7gwvPOTImQinlORsOEDtiYirjEZn0YDaCUFxoesHiUFXgoUDmQSdWyXFd1fc3TCBFt9dR0hnwGsKZESWs2c2itggl57z2A44vnjp0mUCi+aC9F41gXjWWsDEUZpSZIFgpUCTFniLRhTUlVF8OdME3SSUZRldKeGysK48GyNLGubfVbWe/R6Q4qipKpCZqi1NgBTPlQR1gSxkrNB+Wljxqg1hlazyfaFeeZnusxOd5idnWJmdiaWtJairEIuRRzb5XlKmob/xuMxhTF0p6fJsiy2UoJxWSBTFXJTlUIimZqaCipWERr1jc11UIKNrR7OelqtDkkSVLCIwCqVUoSRsPeMhiXCFkxPzXD6TI/p2X0sLo45c3rAyZdWeOy+47z0yBE++uE/QmYLDMVuvvjI07z7nW+FoqCRar7v2+7EzrW5cGGTxaWzfOniJZ59+QTrZ1dY/vwTzIxL7rj+KoSp2L1nN4euuQYhYGqqxdvf+Tauue4wqp3ywtkBvbGjv7TGyvoWeaq5602vw1nPv/6N32Nqz166uzXj0Xne8E1vJp3tcN8DZzAm5+733cO+a65mbeMct95yQ+3mF0KQpJiMQomLgK/HktFcx4aY+MCcleryoj8BJy/7UoQFg+gJGm5qGfEznSQTApiUgQZeIDFokAleaJyUYTIWla2BVl7TCEyQx0cQVspQOciYKwNxqCJBahkCqeLvTuLmIqNZdr1Rfa3jVbloBLl2NfFPdM5hTDCeDQi/j7F0TMpxY0qEC9x6IVScqcddxAVDExXf9W6nRaORInSC0CFOT0odWkIr2ewN2RyM6Y8M/XHBcDzEOMe4KilNFWzlPZGiHRO0nIv5r4Yk1SRZjsDQajXZt2cPe3fvDpVTZWlPzdDIm0iR0G63SdMEKSTtVoPSGobDMePSsDUY0u/3cc5RRVVmu9WKF5KlrCo63S6bW8G7oj3VQac5/X4/GOMk+cSUd3Z2jlazwfr6OqNihE5SkiSjKCqc8yilEVKhk4RZqZCDknNnTmMKw5nVJbo3zVEBu/Zex/T8FO/+9vdiqoJhv+Qt93wz23YLhuVp/v7f/BGcq1g+vcijp5ZZzRP+5Nw5BlKy1d/gk198hMc2+hwdjHnq5dNMzUxjqx6u7HPXa1/DG+44wJ7mKXz/FKN+wdpin1EleXFphZeOn+L0uWWyvMHrX38nOM9v/OG9NKb30pmdp5UZNi48x7f/wPdjnefCiUWef/7L3HX3eyKPpnYxFxP2K4AzkfxUZ8BOrsPQDltrYxvpQyRAXEystdRRjMZXyNhGFMU4sD1liH2ob3AI+NSIJo88v0Rrfg9V0sHlsxSqw3Nn1ji9VrA1dqBSvExB5XiZxhiPmK1LjbXYGIbl4gIhUWGIHDNiBZV1OCzeVkgfqe5XBkS/wiG+lkPP/78eM92Of8sdt4LQWBECZ55++gh33nkrzlmsKfHWsbK1xdLKKq1mhquqqDR0zHRabF+YxVlHZarg6ORgY2OL8XhEnjcZjPogE7JUMRgEwte49FhvJuE/tqxwIgjLrLUkSlNZE7GKFCE8zhpK6yfJYVJKlFZIlZLqjCwVtJpZNKZ10SYulME2KiKXl1coTcnOhXnSJKWqCjrNFt4FspqTkGoNUmGqikazQZ5krG6to6Tmqv17eenYqUBiG41xzrJtbpZRUTHo9Wl02jFHNJjZJlqzuLREljeYm5sj1ZArx0ynwVS3zdGnj1JVhuFoyN4dO7jke5T9McpLXN9w0803s3/fPj7yB79Pq9nmQz/6Azx37nM0R022bbsKkc/yv/3qr1MZR7vR4AfecxUpBReWDY8+12PQ3yLt5JxfXgv2+lnKHTfsRVQjnjm6gpMaZwu+/QMfYM+2GVY3LvGFB+5nbn6BijZz060wyk6bfOGLD+JMxd/9sQ/RbhqSRLK6eJ6LZyq+9IWHAEGn1eIf/Ox3IcohQilEnMLVrWIdMGR9tNHzgaVZj1Hre6i+lWoOxSS5zvv4s3EPv+JasPYyblLT0NXB13PmxEsc3DYL8fmdD/iWc5ZGs8XczBS9/oCLF1cxZUmvv0U5HtBuZFxz9UH27dnNcDxmbX2DpdPHGPc3Q4Vcn5UA72L2i05RWuBLg1OhNfp7v/E5Tiyuv+Lq8arENOo3LVGKVrfD6vJa6OliGY+UjMclZxcv0my1aDcbrK0MIbIu87yJVgmlHQdD1eiPIIREKU1pKtY3R8xOaWwV3Mm3+kNGZUWSJBhjgthLBA2DTJNJS6CSFDB4G1DrygVR0uWYgHpyFyTUXjWCwlI7rLOxzFVkecZ4VNDvh8wM5zyDcQFCMjc7i04StjZ6eBHMiMajMQs7drCyvIazMBaW3Tv2cn7xPKfOnGVqaor19XWyLGNmboHVjVWcC2Yzly5eCipVpWnmKb1+nztecxd3v+ntNJsZTz76ec6deJZYlYfyuPAcuvU6zi6dJp9rMDi/xgjBdD4FxnHfZz/D933v93Ju8Twf/d2PUIx7HH79QTJW2N9s44xHecF4OOLj953jtbfewiNPP8z+ndvRu7bzwJGnaXen6A/6eCd46sXFSMe0HNp/gNffdisvvfA09/7RS7hU84bX7ebxZ5fY6JVs376TG649gMTgTQky5V/9x9/mb/+tv0yTlIc/82EurBtuu/U2nn3+Of7G3/6bpPlFShN2YKSgTj3z3kXfEQ/RLVzUDvcRwJR1pIEgUrrDqJ0ak4qeHS56W4i4IdjoY0K8Hpx3CK/xeA4e2EceF5aiKimKkuC+BYPBkK2tHuPKkqcJeSOl1WkgCO3uan/M2osncB6SRDMuLdHmHG8NPlY1UgqyLADfznuQnn5/QKvVmix4r3S8KiuN6U7bv/XOW/FxWqHTVgDpXIlzAZwsjeHY2bM0koxuK2M4HuOsAQHb5+eY6TQwNuIOlaOwho3egOEoxAJubvZYmJtFSkFROda2euGNjnLm7fMLKCUCNdt7BsMxaR4MeDc3eyiZUZkRSIEpoz08QJzbSxHyZ3WS02y30DIUl3mqGQzHVFWJsRXGWfCCTGukDiE+Wil2bJtlPC4pixFTU1Nsbm4Enw0HidLkzQYba+voNKEsxyRJhlKKsigZjQuazQaNRk5VWZrtkJXq4ljRGc91N76G17z2zTQbmiOP3c/Fsy8w123R7jR44pEXGa5toYVg5+wsZTkmn+kwLvskjZwLly5w3eHDLG+s0HUNxiPDPe95B8+fe5juzDQ3HrgFXM4//Ff/jhKJFJqpTNMfDDAKpFcoLbnjpqv4Cx94C48+f4QvP3USqTPWVrZYXd5kx7btNNOEN7zmFh55+jlOnF7Ce8PCrGKzD/v37OHO229gPBpjX3qO3sYGD29W/MLf+Uv8n7/8S7zmtbdwcXnIBz7wvfzDn/vnfOi738Y7X381AXhgwm8I2IOYtCHCe4RUEKtLh48t6GXX7zA2De+1r6cnAnCBWzSZsABeiJjNE7gXVWl5emmL7/iu78UM+8FYuaionOX4iTNorcnylCMvn+BX/svHcN7zltfezo98z3spY9ujVAidklKS6pyqGLF24Rjj3mbEXfyfEdSFYGuJN1U0DBL87H++n5NLr1xpvDoXjW7b333bTSgVLOq1bqISzXg8xNjgGqXSlKeef5E8U8x2mmxu9iiLcIPv3D7P7u1zGGuCdsNYSmsZjQu2tnqU1lEZT57nWG8Z9IdsDkYAzM3OkErBcFSQ5BlJkiCcZ2n5Et1ul15/CNSCOhOMcgisPqUUnU4H5zybm1voJEQuTE3P0e1OMehtUVUB3E20wpgKYw1ZEujuXki8dYEZ6oIALU9SkkRHT4wqLBo6JUkTxuOCUTFG+OB3URrDYDCi2WiEc7KWNM0g+kwmiQ5VT2Vpdad5w13v5IbrbuLlI19i6fTTzHQbzM5M89mHnkWs99mWZdx2x2s4+dLLLK2ucMvdt7Pv0EHuf/yLMCxYPb6KEh68Yr2/ybu/9d3QG3H/l77EX/uxH0IuzPOz/+LfhbmTVOycbTIz1eYDH3grTzzzOJ9/9GXKquLOG27inXe9hi89eYTPfPEIzlQTcpNUkk4zo98fhjJ/PCSfniZNM646sI9tM1Nopbj3/i8H71Qr+Cc/+Rd5/JlH2DU7RzJus33fTp468ig/+J47Jh6aE+a3INzg/rLQUEJ4L7yPVgFRdOZ9xNsCdwLqSV/EOCDGL8oJMSwsNERrweD4/tiLF5jevp/5XbsoTQWVoT/YROgUa1zAzLTGoekPhzz8yBPMT7X57ve/k6MvvcTc7Cy33XYTWsuofYKzLz1Lb20Z6QNu40RwBJExvc35CoGmTqT/B795P8cX1/4ctSdAkiR4J6jKAqks5ajiwoVz7N6zi6oyPHvsJbI0QyvJ6sY6uc4wyl02mYl8iGByYtFaRXVs6Ft1EqYt1jgqH1idxtl40zZpt5NApjGW3mjI9m07WV1fC1wMD95XdDrtQMBxjqpvJpoCpSVzc3NsbGyA8PS2VkmyjNbULIOtVSRgTBWiCZSmshXS68Dew+OsR6kEaz394ZiFmWmMszgH49IwNp6kqlhdXaXVaKDTlE6nQ1FZqsoipKbRyhgMRtiYNapUWDCkVCRZgsTT39pkMDaMxiPyPKHTaQMOYQ39skJWls9//v5IV4cnv/Q4vc0XaPQGLJ71NBqa17/+Zi68+DynL3XZt3sfnU6H6+7YT5oNGA9aNNOU/TtncQ7e/Y5bufdzD9FqtZGtJm+953X4os2f/umn+dJjLyOA7XsSDl99iL1TC0wXiidPH2d4cR2z4yCXNre4et8taKV58ehxTp86x6Df4+qD+/nmd72Tj33yM0ht+Ll/9Wv8k5/6UZ54+vPs2rsL34K11UuAw3mDlAm1oc5lkWEw3YmCIpBhMlJbiLoYESCQCMVkshJwizoPNxw6CeP/iXFPzfOQEqzHVQX33/cZxh5uv/MOEqk4dOggOEFvOACpGBclxpVoBW98/e1Ya3j8+eNINP1L65z5zJeQUjI302XPjm1053dRlIaytxwMf6I2xnkDSBCKkPYo/gxO80rHq3PR8B5rKup8GRtTs3u9Pt4GbEBE+7JEaaRuMCrGAdWWgrKKXhUq9uceTLQB1IlmVBgsAbwSHvpbfXxM8K4MjMsCYLII9HtjytIGko3zWGvodrtIGReaqKjVWpNlGUVV4fG0Oy3KoqAqK1YvnqfRmgrEnWpInibBDTwNE5UsyyjLIuSaCo1xFUpKpme69AZDhBYIFFVV0Z3pUPaHNFptWp0OaZriRcL61gat7hRbW33sILCJkiRDJpLKhowSoqZha2ODM6fPsvfgLXgHVRmMlFuNFKVSNoWnIeGD3/UBZuZm+eP/9kfs2d9ACoVpatrtHsNhSX/5DC7N+cC338NH/tsf8EM/9sM0G7OcOfYAl5aP8lN/6UP843/36yjhOfrriwgcP/l3f4m03aDRbtJKm9xyw3Xs27+fBx95io2lizy0+BzHppoINPPbp2hfM8Pzzy2yuTlk+dIyxno67SZaSi5cuERZjjmwdy83H5xmVAw5dnqZv/8vfpWf/6kf5MzFEzz+3Kd467vuYeRTclHTwCUeg7hyr3Vxcidc1IfEYOZY7geqd/hZKf0E+5JRaxKcvx3jIkRDhEvZR4FiqEYFnn27trG0OWBsFKnSjHoDnnjscdIs4a7Xv4GrD+5FaUGSJAxGA+793IP88aceYG5+nm0LU7Q6bdI0o9FssjVy3PfQ0/QGA+ZmprhhTtNQYbxrTBFDomLSnoueH9Gv46sdr872pNP2b7vjRrYKiyeMRb2xpI0cVxmkljx79AR4RyosNrpV6VRHAFMwP9NhbqaDsx5jDRbB0upaiHi0sLaxSaPRxHpLbzAMocIeprrdmm2DiGWmEILe5hZpnmGcpd3q4p0J7YlMqKyh1+shJXS601GJGlyX8J7BcIipgq1+pzuNSjSmKqJBjqSONwgYiqXVak1AzWaekSjFem8LbyRJFmjjxpgwKhaebrfNZvQe1amiKiukCoDw5uZmNBpO6XTaVGXF1uYmQkqmpjrccuc7sKN1hsvPs3fXPNPdJn96/+OcXlxjykPbVGR5SnfKoLVka91zy+1voLujyxc/8UUkltKWCKGZ27bAC8dP8dM/87eolo+wsGueE+fXuNhv8Aef+BwznYxvft0MZxeXOLHc4LbXfxMUG6wur5K1Onz6/gfwElpNxa1X7eTk0jrnl7fQSnDVgQ7XXbNAliueebZgfX1EUYyDfZ2zXHv4EDu2zZI3cv7g459FIbjx6oN87/vv4fS54+hEsHz6PD/wza8j5CbV3JpI5JKBvemFRNbpZXGSUtcQxhQx0yW6hkd8w9bK1QicXnnP1VUIhAmK97C22eOZE4ucWxuxur7J2UtrbJ9qcutN11OOhyyvbzLY7LFz+wLf/d3fyd5925FeMR4XlNWI0ahgZXODj997PxsbJZub6yyvrzIcl/z4B97C/oUuXgTWqEzCvSOlprSGVIWP//A3P8/xP1fTE8A7iU4Ew7JCG4FQkqqqqCrD8WPnQ3SeCAVjmiToJMVYQ5omCBHyy0J6twmKUw94GUJ+pKI73aU0Fqwkq3f7PMP6EA5kK4NKJf1+P4xY09CuNNMmZTGa2OlZX2BKS5alkcQVZMkQwoeUFKSJnnyv19ug2W6TJilSBNu3qiwnu5X3MF7boJk3sa5gMBSkmQ47nZakaU5RlXSnp1ldvYRMMtKkhWCI0opWq81muYEpLZujTaRmUiKPxmMQITRpNBxx4cISu/ev84a3vo/Fl3Ns/yRSaKa701xaG9MfDLnh4CF2zRteeGGdTQuCiqcee4J3fvP1FGaNG265FeUrLi5vcs+738ro90dkhWfUPcjp8yeZyi15tpdWU1EYw588vEgjaeCqEc89fh975xIO757l0aMXg7WdgOHI8ugL5ymKAlNV7N63k0MHb2Z6do4La89y7sISzbwDUgT6u/M89syztFPNPfe8mW/+prfyufu/xAunz7Llcg4duJrR1oBVscR64ZhSAimizaN3E1Pg2kryShm88xbnTGxHrjDigUmZr6VCC0kZA59lFAY6F4azog6Bd2GRb+Yp7VQz19KcOr5MIhWX1vt88v5HGdvw91TWcnx5yFrxEaan2gzHfV566Szryxvc9Ybb+Zb3vo0f+q73k+cpm/0xZmxYXF5BDy9SmGDiJLXCVQ6tgml0qpLgtqaSy3/EKxyv2krjzbfeSGENIyPwQqO1xhjDy0ePItMGeabpNjP6/S0kYZdQiUYrxaED+9m5bZaLi2cQkf5dWM/ZSysMxmO2Noc0W02kEIzLMXUmVZblISrAe1IddvPxeEzlLRJPnjcRPrBL+/3e5KIRUYYQGHcJiAC+JvVERYQFrzRVWHiaLZIsDVElMg0O1jK8T7WzPhBqYS/IGnkgXwnBoNcHoNVps7K+SqYVjaxJu9tleW0FV1RMTU/RaDa5tHSJvJlPwn2UqjM+wk5rjaGVSu5807fRTge03SJz020+/dCLPPfSWcpqzBSS2UTx+rfdw9zcLOfPn6d34SRJs+LoiydoNtpMzaQsL65y17veS0vP8cVPfZrv/KG/gKLgk3/0R8zOTXPda9/Av/kv/4283eKOW29lbnaBzd4Wn/v850m05rpDTW5/zR6efXGLJx4/hbcBjJ6d77B3126uv+F6jp0/wq2HNZ/40wsMBpak2Zi8X8aEgPCFqS7NdpfD1+znySdfYLS5xc/+zP9MubnE0489RKLh+9/7uoA3uDDhwAb1qYncjbr6g8jBsAZbT0km4/UgRHQ2tKUhuzWM4lVMjrPOIuIUppbNl2MD0nNxZYtTlzbYHIw5t7TK0saAwlhGhaGqQittfVB71/GNxoFDohzsmJ9m36552s0GzbzBqLdJ3tTcedVOGsrTbuVIrcA6hBZEr/PJ3/Yzv/aZr1ppvCoXjalOy7/5tpvZGBQgQ7p4nuaMijG9rT5rvY1QLTRT+r2taKYSAoKMCzjFtpk222a7oeQkEBDOXlxlczgmSTOED2FAZWmio3mwW4PQe3amprmwdJ5GEjJHVKJpNttUVRFLWhNApQiKBfMsgdIhdUiryEoVBKNaIRgOB5RlGUKb8kb0p5AEO/vAJxGRrqyTlKooyRo5RVHSaDTCuNlYyvGY7dvmWL60FjgHIgDGeZYHBzMTIv+yJPgoJEmQ1zvnUEmC8CJGXUKea259/XvY0TFMiUvs37uTXYfu5N/9p9/m8SeexFQFt+7cy8FrD7Nnx3mWVvZgeiOuvu5qPv3op+gd6zEarpCnjSh+a+DwNDtdtm9rMrct4fCBm9DtKS72Kv7tb/3XsBMDrUyxc6HLvn3zoApW1tdYviRZvNhn+855tja36A8GIIIOQ0rHu163nb1ty/bma/hPDz5Kvz+c+JYYZ6iqikwnTHW7XH/tVTz7+GP0nebdb349r7v5Wp569CE++K430k0K8GBdFcqbMKyMlYaYjFSJkoV6mjXRDdX6lXikKqFyV0gLapwjPtbFLNcgFgz+pWcvrLI6qOiNCo4cPY3xCuM91nnGpaEwnsKYSU3gIldJxiBpKUFJRyPRdNsNbj20m70Lbdp5SrfdIFUSrRMcDuEcSucIEa61n/n1z/45a098WBXzTDMoPRjLyI9DOe8sSiiEt3gXMjKFECipKKqSqw8dYmZqmtPHX8ILUGkQlBnjSNKM8domZWniIpMhExcXAY+MxDEPbGyskag04AZakSaK8ThQuqUIN7YTwYfCO4/Q0bDFRCMUpdFJiAzQkeAjpQZCPGGSphOPBO8gSUOCG7iolh2DDyHXVRUs/arKTDCWtfVNZG2SbIOvxnhcxJAkhXGx57aW0Wgc+m0RUPVGlqOaTYbjERubWzzz8CeZfs0N7N43jXUJ5WBMK8tQSQgpXl65xPnTp9i9cwc7D3Y58eKLHH32CJUtSdoJSs7hneED7/lWbKJpz0zxJx/5Q+64+y56doOT1TIXX3yK2w68gXaWU5Ql1xzYx12vu4EvPfE5Tp0+yfqlIXfcdhuNXXDu4hEWL1ygXnW1lljj6DQkLx89w6NDAcln0a25iHf5CV8iTcLGcXZxifXVS2ybn+fwwYN86oGH+OgnPss9t13NH33mS3zPu1+HwqJ9DJxWtYhQXuZiUOfeQM3RMC5EYiJlyLYN2nRMpKCDiBzvy+rY4OwVJ2ORx5GlCQf2LmDOLjMaj7jt2oOs9wZcXO0xMgatBIlx5C69bGMpoiraBQl9oiSJhjwLwVijYkxRNmhlivHIoFtpaLeFRsjaN1f++RWsVWVBVQW/CQ9U1mGdYTAaoRPFjm3zVGWIF0ykitmYkhMnT/LkU0+hdZBm16pYLz29ra2wGypNnmV4PDNTswgEzjrG1ZiqCm9+OS6QhFI0y7KYJ1u/2uHmps5ODVh7wDBUSDqTUiNQJDqL6umAbUghMKakLIo4tahAiShWqntqj5BhBFsWJXmeT4R6wbkcbBVaDusD0aiqAps1STTjYozAkeYhRCpNU1rtVuzRHcNiyGAUqp5Wq0maJSRZjk6auIh9jIZDZqa6CCW4WI5ptxpY5zj+0ossbJ/j8C172Hn7AmOxxYHbDrL/tdfwyBcfYNWPwJXc8/Z7+PjvfIwdc3s5uPsqWkmTygz5ke9+X2C/VgabaLJWgzPLJSNSHjlylKefe4FUCnAwlTeYyxpkKiFJNf1eSdrexd1vuotve/cbyLRBKY2OnIrwKoNSmlarQWEd670BZ8+f4c1338XUwhxPHl9iMKz4r3/6AEVV4oQL9ofGYmzN1WBi90+kYUhx2T28BkEnRy2rD/8IokP8BGwVInhaWOuigtpOgNOr9yxw01V7mO802DXV5qptU1y1Y5r9Cx0OLHTY3kmYbSq6uWC6kTDTzGjnmkYqyVNJliSkSpEnmqIwjIuQdWONDyzTKK3weJyxE57G1zpelZWG9x4vJEkiJ1WBlCri2Yre5jpz3SYh8NbRmZpic2ODbdu3MTs1y9GjR5HRDUmpYGOkhaDbbVNujlBChvGrTmJ4kUbrEKjsrWM4DkxP6x2JDGNOicBUDqXDja2TBKynckFzUNnwfFdeTDZ6RVpr8cZTFkVgqYblCK2CwbHwAlMakiQBNNbYaDZMULgWZQCzpCRPG6RpyqgoEF6Qpwk6TRiNhhMhVTNvYJxlNBwGD0vhQ+KaCmpeH6sTdFhslpaWWby0wf5d2xAywZqCv/qjf4l/+yv/iZXVVSqZ4PC8/VvexUc//AfM3J4iKsXopEEME84du8BrXnM7D/WPsvjxT3J+736GxZCkmbN6cZ257bPcfMOdnF88xfbuNuZacPrsWX7zt8+zY8cc7/2mt7IwO48vDWZjmWdfPMbzZy/x9je+lrwpKFRGb2R48MsP8+zRC7x4/CLf8c7raLtNtnwDryTaKcoo0qq30jTP2RqO6Y9HZEnOm990F5+77352XH0jp154lN//5IN897vvIpXByRuiruQKwla0QglELV9bAFqssxMw3BhDkmhCYFFQqwYnt8vXQl25EK/JUNx48JaZbk47T5Bas7nZZ3F9DeMkg8GIUVEF/kUZWM3W+0jiG4ZNKU0jO9QjcCEPxzpE6sDrWL0G+8DJ/WVrZ49XPr7uoiGE+E7g+4DXANuAM8BHgF/w3veueNwM8C+ADwAN4EHgb3vvj3zF8+XAPwU+BEwDTwF/13v/ha93Ln/m8J7KleDrpCtYWrqElJJmo0G72WJzs09pKrZ6PRCS5ZU1BsMSoVUcg0lU0J1T2ZAWnyYJo3GIdjSmjKxigfUeFUtcFc1Q6hU53MzxtGI+iBCS0hbRsCeYm1hT4lWKFI7CGHSSIUTMVTHlhEmY561oa+/I0pyqqo2Cgg+F9zAqC6Y7XbwNMQ7Bu0MxGPXpdDqossDjKKoqWPTZ4Ho+MzPD8sXlwBlJM4QUJIlibW2TLEspy8AhkfhJKyOE5+iJU1xzcBezM1Mh/Lmq2LVtOxcWtrN48SLr1vFHv/sHvPOd97C8coHlzWW272mw/mJCMRry5NNfJu8k3HToKl48fpIPfOi7OHXyJJ/92Gf4wPd9B2NX0Rtt4ZzjbW+5hXvvf4kyjjs/f/9D9HoDrjpwgE6riWh22bc35f5Hn+DNd7+eoy8dp7SWPXv3YKND+0fuO0Y7s6SdGapyCFqifEKwiayzTiHPE0ajMWcuLOGRtFtNfvePPsHNV82SJoojx89w69UHiN1bHItGJ3FqclbMvCXwfqRSExp6sOtjcg0EPkdd9VwOrRIQ1Ne11C0g54FibgwkCmcdU42UVM8ilGI4HmNNIPiNRmWgFACDccmoKAI4G3NYlFZMT3eZzjOyRKGlINHBTsIZi9YCK5hkwX6tHuUbqTT+Z8JC8feBc8DtwD8G7hFC3OW9dyJsnx8DDgJ/A1gH/h5wnxDiNu/9uSue71eBbwZ+CjgB/ATwKSHEG733T30D54MnhOgG9qaksBaVapwHLSStqSnWNjaC+Q0+KkgNWig2NtdjT9qIrkph59E65p7ENsbjJ/khpqpi6HEFKIajAdY4pFYx1SwYDAcTV4kggIrS+4BLSIFwHhlBzMpUSCEpyhE6OpVXVchHVUoF13SdIlUyGeXJmKlajCtQkGcNhsMhzWYT3chiFoogSTT9ra1AbnMGKTWj4QAQ2HHB8vIySklmZ2fZ3NzEVpbxyJFlGS4S0LQKkm1TVWE8LQRra+usb26BvArhgwnuh77/+7i4vsbq+ipr4zFtK/jcpz+LThw7WwssHV9BY5nd1WRdOA5c3+XFC8dQM4Yj5z/Py19aRPsWn/7je3nnt34TpvQ0m1Nce/3NtLKd/OYffYKsTGm2W4wLw7nFZV5zw43cdv1hekUPgeHRp5/lzPklRqXDWJiammbvvn1sba4wk49ZGWtkpTGuCF4W0ZsmSYImRylNu9liVIxYXlll/969XHQrvHRmi+t2tjlybIWdC3PsmJlC+CAz9zDhXEBN7Ao3ujUWV1qECpTwIIFXYUQfpyZKSky8KevcGSEACzbK2OtwbnwMe3c2LjIJKCiqikaaoPOEVp4wLorgzRFDt0pbL1JhLCziJpQoGf0/ND6SH4FQ4Ua2cuBJf/XjG1k0vtV7v3zFv+8XQqwBvwm8Dfgc8H7gTcDbvff3hRdBPAicBH4a+Jvxa7cCfwH4Ee/9r8ev3Q88B/yT+Dxf9xAEoY8UitIHea+TKtBwpaSZpWxsrOMJ4c9FYWg0cxp5M/Z0JuZRCIiuVBZo5in93KISggGN88FrlOj3aKAs+tRglow3VpIkOO9JpQzVhA8xjkIGDMPUhisE45/gyRhtXhVY49BKoHWGkGoyPsXZyCgM+oBQcYBCkSSakXeU44q8nWOKgk57KoCxwc+NJMmpqjKY9eiMcVlirWFcFWxsbE5IY2mWUZUVaaIRIuAkSbTe10qjcoGpSh554ln27NnPwX1dHI7xuGDvzt2cPXsGe+kSl5zjkEi4eucCV081SLIdnF9d4rGzl5jetcDTnz3L1LThtjfcxktPPMeeg7tZfGGNYV8w6o/Y3TrEuc3TDIdb9JY2mEkTVlY2w2JsHd6XfOGJR3j6yBPsnMu57YZ5XnPdNDdetYMHnjjO4uom+/YdoNnq0hBn2TWdoUbznDgZApMCPhls7YypYuKZx/jQOm72tjhz5gzbdmzjwrk+T5/ewGL57KMv8d433cl06pEqyNfraoUrqhZXj2iVDCCHZaJoRcavucs4k/c2Pl/IG3HKBm/RemzrfWAUE50ydPha0BalExNjZcXETgE8yiqSOJEJl2ow7zE2tEnh73aI2iw6xm4SE9/C5vnV77+vC4R+xYJRH4/Gj7vjx/cDF+oFI/7cJvDHwLdd8XPvByrgw1c8zgC/C7xbCJF9vfMJPwMISenC1EBpzWZ/iPVhApFIzdbWACRoGYClwWDA+QvnsDbQr6VQEauMYJQP5rnXXXsdb3jDG5ienmFchrbACyjLgmI8Is0ykohNGGNAiGhUE4JzrDXULvZaBYcvJYMhT23Ugg8CJedt8HokZLIEN6UQxlwH7Djvo7GPJYplJm2Rc5bKltjKUFaWjY3VMImJ+RZSShqNRqB993popWi3mjSynDxK+fMsDSYwSoUcW+doNNJwMeMoyhBhkOcZIrZGSifgQojT93zHB9m7ayc+ou5eKc5eXOGxZ05y9PQS8/MLWJPTFR0O7dmH7zV56nMvs3kcTp8o0N15emPL5z97H9t2znP71dexbR6uu63Ld73/Xcx1g4I5yzN271pg52yXw1ftRKQZJxYNzz67hJBdGkmGBJ478iS5hltuuJ0vPLGIkBl53grxlUTHKmITIMJCTvy694LNrS3WV9fYvXsHlbOcvLjF+eUNPn7/wzx/donVzX4kAjIRq9W8jNoNCxGJWnHSFq6hwBUh/jt2AqG9rO3BCV/0PhhnJ0qjEAgfgr21DCQsxWWWcHAglOR5gzwLLm55IyNLFXmW0mrkZGlKlia0mw2yNEFFF3OdBjvCRCaBrxS1L+oK3OeVjv+705O3xo8vxI83As++wuOeA/YJIdpXPO6k9374Co9Lgau/0RMIUwRJMQqEm7W1jfjmeWSiQ+/qPIUpUamm1WqBDDtLVZWUVRk8ELyoBQPBlKcq6fUHzG/bFnbzoqCqykmvKggK1JqspaJNmpSSqrRolQT3Zx0cniEYCkuCpF1pNfFXmLz88aLzCLRWQaRWFoTSt2YWBswiTQLpq6jK0JcmSTQhtrRbLYbDYQhwThKK0ZjxeMxoOGCq08FUFVneCIHPZaA8m8piqgprDWmaRn8Fj1YKLRMaeR6T3zyXLl3i1OmzFGUV3bPDorZjYRtT7RbWeS7aMVuV54KpEHnKZ546TiI95y5tsLGxxsL2LlJLmt0Wh173Fq57491ku69l997DXFq8xMmzR1lcP48xivmpjLe//g4aiWT3Qpv33H0773zjbQy21sCXHDt1gam5GT7/5Yd5+ex5bFwon3r0y3zx80+Ru5QjTz5OluhI+feTdiCU55E/E/k3WZZhvGMwHDIuRrQ6DSoUFzfHnF/d4tMPP8fnHnmWtY2t+L7KSVVYA9wCEUeeYWVxsVqsxy7OWWw9sYutzUTQJgSoYAbsfNRQRRxLQNiM4sI0cZyL15/Wwb5RSYmWAQBNYuiRkjJosCL1QClFnmWkSRL5LSE13nsfBwRfHQS94qr9xg8hxG5CK/EZ7/1j8cuzBBzjK4+1+HHmG3zc7DdyDsF7IJqvyjDg1HFBUNJzfukiaRbLce9RWpPmOUJKdBr0HKEHBaWYeD4micIZg60KNtbWGI9LnDMhd8S6KD+3OEIgUZ5lIFRkUoKWMljzyUCaCWcmUTJ0gTpJgoo00ag0iUlWoVWZAGAotA6AmPcBx0i1Ris9ccWWKvASlNDkaYa3nk67M9nJVAzICePglDRJmJ6exnvPcDgiSTQOR6vViMK5YCmoYoxjnfCW5Slaa6xxCBd8I5585hnW1tcDD0FJKmN5091vZPvCAlIKNq2jEAKZJAyUZt+B/WAdetsUzZbh6hvmkNpTiZLDh/axvZPx5tddx+mlZR6+78vs3nYV3U6bY8dP8cLLTzLTkVy/Z5odU4rHH3uMk+fO05yaZ9feBRa2z/HY0yfpNLosNFskQiKVQFvHbYcOcE2zhcKzuHgOZ0I7NdF6xJvcMaFggPBkWcZgXHDx4jJ7di0wPZezNhqyvFVQGs/ZS5t84YkXWO+PYpUa2hNHnYHiI+YWIxIjH+byxi0m11s94rxSVequ8Iz18Vp3oh7hhggJEd9jpVUQudWXjwqTGSmDR4fSsdWNbbvU+vKiUzNencU4E3U0kTbwdQif/5dGrrFi+ChggB++8lu8cj3zlUvWN/q4V/rdfwX4K0BgdlobZN3Osb4VTG6cc4zGw5ARgcDYCqUztE64tLyC956yqrCxV3SxGZUE5qZSEuEdWgrW11eDP0XsC2vT1UC59kEbIgW+Ci+0lIIsSSlNiXXgqknkLxBpQO5yIreMJCFJcPCSQpGkOqgfBehEU4cIq4jGO+8oTUWz2aAcF3gfnK2LsmB2ZpaNjXWEgEaeMzYG7yyNRoORG7CyukLWyJienqLf20RKFbQ31lCWJWmaUkRpfKvVoqoqxuMxxhoUgTXqcVxcXuH8+TNMdbKYOOe47/P3Mhj1UYnEVY7VskBYx+4De9i25yDHjj3F3sYaWT7Fk4+dYN/+q3ns6ed45NMfZ/dV19JbWWVw9mXkbJcLZ9bYvedmRlNHaHQE7RbckdzKJ794hNOLmwixTrMNB5MFysrSEpJr9+3ljLOcX9/ASsFaWfLJx55gTgWMppnnIaNXKqrKkudpmCxIOfHKrG8ipRRVZRhXhvPnL6K0oagMKyaM1adzzenlLb7w5Evcdcu1zHWbhBToy1d2fYGHiAMfbPa8n+S3ely8FphUDQjxZ1qdwBmr25ZwJdmoPq2DorVUoCObVELtCua8jzEEAjtR7QbQPyxQoc3BO0QS+D+IoHR2xoX2+/+bkesVN21OmJBcBbz1KyYia7xylVBXGOtXPG7f13jc2it8DwDv/a8AvwLQaTW9jWMvh2Q0HgeLfakwpkSKYDBjrUfJhGarxdbWVrjpq0Ch9lJgIhMvvOCBeels8L4cjkahyrAhEcs7j9S1tZsjSTI8hiRNwlxbBgvCQBWXk8pEKIVwcb4vYoaKlxOQNGx4AryIK6rD2tA2eVxsc0LsgU4TUhVQ76yRgfcU4xKtFOPxKFQ8QrDV69NsNRFKUxUlWmmMC27nzgSP0yxtsLXZI02y0C75AIgKQnwfIoCFIYVLBK2DsySJ5MtfeoCls8c4uH8PJ8+c5+GnnidvNtm9fQenz51n7C1eSb780JPsv77H9Pw0C902KxeH2Crl2sPXUhVruHKDM888ysqlZdJGxqgY8MKTR8iTO7n6qltZH5xgXMHC9m289rZbuHDpMzghKEvLpbUxWZZQTms+c+QJksqxs9VhYWGWkxeW6BeGs2WYKGyfmuL0mTO0Wi3GpaWqQvZHuDEv4z/EVjVJEqqyZDgume62KcZjClFwqiiZzjV7Z3NOL66BOM6hXfNsm24wN92OrW54D2uiaO2ZESBGEPHG9z5McGp+xkQxjUBpxbiq8M4EkLImnbr6xpdxcONj4FKcrk1AVBld2CZ37+R3BuavCFwkH7JbAtHZk2rNuCjwXKYQvNLxDbUnQogE+APgdcD7vpJ7QcAkbnyFH70BOOO971/xuINCiOYrPK4Ejn0j5wMEenT0cwyxBFUkVtmJx4FSiuGgz8mTpyirEMHo49zaGk9Rmjh3rzUFob/t9beCtNxDkib1Qh+YejKkq7to6ut9OAeJCFWImNjHhgWJaHGv9ASEC74KgUDlore8VIra9QmIhkAGvGcwGEQNRBjXFeMiBj0FzCNN09ifxj5XqFiBxPwV52k1mwGDcI40TbDWoBMdsRJNouUkgjLLQkK8tYZGIwstlwqtTqfd4dJGjyefO8bjz77El594DqU1zUaT6Zlp0iwLbRySdtakt7JCO5/lwIEb6W8l7Nixg8/d+yfceMMu5hda7N7TJm9YZmcVC1MdsJ6HH3iQU8fOs7wC589bysLjneTwvl0Ir/AmRYqULM9ptHKE9GgRFv+r5heYSlIG3iKkJNcJmxubKK0YFrGiKgrKoqSmc9ZVRqD/B0Aw0QnD4Yhef8j09DQAs92cq/fvQaRTbAwKTl64yGcfPcIjz53k4lqPytnL7613kScBdf5qqCJ9rDaY4FXU2ShXtAWyxjqidaCL5ymiQtZzORtWEpSzssZVatKYZ4J5SKkRSsSgdBHpG6EVCQMUMbFI+Hpl/9ddNERAiX4beAfwbd77h17hYR8Ddgsh3nrFz3WBb43fu/JxCfBdVzxOA98D3Ou9L77e+dSHdR5rHeNhAbG8s5HQFHCMcKPqRMcFJOj46n5uVJQMR8VE+6Hji6kTzbA/wNXxfD7mXHDZTEVIj7FVeAGFCOG8cWKRZRnOxtSsqEK9ErwK4OllALVG0r1zuPoa8QKpFc1WgyxNJ2My51zgiNgqTF+cjeNjGyIZpaQsKqSE8bgkSVQUo5UoqRiPh6RZiEMIo9+4Q8Uy2sUFS0kdfEzj1xOd0Gm1aOQN8jxjdm4WlWUcO3U2LDBSsrCwjdFgwI5t8+A9y9ZyqbfF1uIaM93tvPzyBjJN2b97LwhotXbR6M6z1TPs2DnF7FzO9NQUrXabrbVNXn7uJFPtqxgOFS+8dBLrFO944+vYs30B5zwXL6xz7MWLnD+3RVWW7NjfobNbsTI+y9SujMNXL5BEd61Wpx0zb4MqWWvFaFxQVmXk4wQQGuGi/oKQERIT2Rt5g2ZDcdXeKa7Zv4BIc4xsMjUzj0NxbHGNh547yePPn2Bts/9n3tMJaCkuQwXOu7Co2Do4KWxaIt7A9orFowZL65F93S6LCdQaDuMs1ke3sXjN1ouHgDCBESEpz8XfVy84SkWOiXcTHdTXWjm+kfbk/yDc5D8PDIQQb7jie+dim/IxAgP0vwghforL5C4B/OLkBfD+KSHEh4FfjtXLSeDHCaSw7/8GziU8D2FG6oxhdX0LqZJIRioRKvZyNhBXsrxFWZlQsjkTBG1SUlaGUVFGICmY/W71N+ikTVbX16mqKtBv42peLzY18aYsA0FLJSFgycYzc9bGVkmQ6Azrgio2jONqNabgsry6TlsXE51K0CIE1DzRCdbZmPou6Hanw4QkIv5BsRos/BIRckkQoX/OsxBrAJ7haEy702Y0HKGTBCEURVmQahU5GQ5ThTm+tRVlVM6Ox2OcMzSbjUkJnWUabRJy30BGX8uapzA7M0Ov32d9vceyNXRlg2uvu47nXngegBdffBHncs6cXqPCc/bMCju2NVhbKfl/t3fmsZZlV3n/7X3mOw/vVXXNc1dXt3twd7ttuj3gAbvBjs1MFAERSUhQpCQSIlJCoogkRERKFClBAYUASv4g4MRMDRhsjG1sY3fTs91TVddcb57uu/M9w947f+x9X5WbdkMzuOvZd0lXr3TeebfOPeeedfb61re+r7O5QBjH+JHPqD9g0tngxOE5XnjpOVp+xv7GSR568xlG4zEbnR5SaE4dP0ajFjFfLRj0O+SkvOeht/LywjLppMeFy33mwwgvCJCTjNypaQmwA3yVwLViHTAqhMObNNL3GY3GRGGPQ4daHDvZYmFphbMXl2m3W3hJjT23BGx3tri22uHS4jKb/TH33X6SZjnEs1+dHexM6+tdEoul2NXiV3VPsJwJ49qr1z1lvxoPs3/gEpMnMWrqZC/QRuwkFRxd3QBCX5+89abgu5ukdsoeDjN57aG1v0jS+Hb381+5143xb4GfcqzQDwH/Gfg5IMYmkXcbY6694m9+BJuAfhpLI38WeNgY89Rf4FhcuKe2b2dLhLSMTlVcX7rpQiEMRK7lpQq71BewQ8ZRCrSQLK9tWlOiKKQobCvWl1bR2Y1r2PkPnRMGEUpZI2YJO0s9mzWsDKEQlsHnyYhCTS0Y9Y6Ai00+rrElhPO+kNaazzH6jLHtX6Wmx2/Zhaoo8L2AorArnen/VeT2PcPQcizG4zEQMBmNiaPYvbdPlo0IQg9fSlJnHCURBGFE4VsgVjg7QSkkhZ8hsAS0PMsoJQlZmuP7oWXOaover66uYYxmY2OdRr3GxsY2hYSx1jz37LMIT1DkOStLy8SlhKeeeppgT4Q3H9JZHZMJQblR5sCxo6xsFeyJFU998TFuvfMIdx2rAjlKjzh9ZB8Xr+xlq9O1q7OiwKiAly9uYYDJeMyRU4pQGPAiZGAFkSpVK/ossHhXURQ7Ky00O0rsU1tDKT0rxKMlRaGQIubSlR6q8Gi1m6xvbvL0CzkP3HkrJ07MsbR0lc624vzSBrmRNJOYu08eoFaJMabY8W+1XRVzPenvzJxYAHx641usxc4mCYzjIHlMx8mMu8ndI9SuijBO+9bc0Ma/zkmZckGmDFat1E7nr8hzQqdmP5Ui/Frx5yYNY8zRv8BdjDFmC/h77vVa+42BH3evv3RI6THJM1t2CNyAkBMMxraujCeYpLbisU97u0bUpkAbiTKCtY0ekQ/NhqAZV9jqj7HK01Y3wQKDypJspG2QaiMI/RDlDJj8QO4MKk1XJVPhH3vBDMpY6XuVW0zBE5byrZ2rVaHVDqagJ3ZFJLRhnI/dpbc9+P5gaOtjz9WgoV1l5XmG8Dx8LyQOQ8aDgSUYuX5ikRcMC0snn4zTne6PtXxI3ZIW0qIgjiLsqllTLZcZDIb2c2vFaDQkSWLQmqSUMBoOEUIRBgHaaHq9PnGcsG/fHpZWVlg3BRcuXcQoQ7e3TavZ5NDJQ/SzHnLep7M9IpiPyHtjJnmK8CVn7jjNfFSQjcZsrA+49Y476W8vkeVjWq0233LPHaxv9Th/dYGV5RXq9ZNsdkYkpYiTt+2nt3mZc5e3ubbYRykJuiCKY/u02LlprcmWKqyeLA6onro029JMIqRkMLTHsSUMk1GfZr1MEhjWN7Z5+oXLPHD3aQ4dPgbC0O12uLK8ylUF/dGYJBDcd8cJKknowEvLB7KyBTi/FMuf0MriWF40BUjttZsSxOz9w07imTq2mSnLeJowpvcajnlq+zC2U+iYh5bM6DmA1JbReoqpSf+vXJ7cfOEydb83se1IV6MVhfVq9Tw7Zo6AXCmnN2EVl4V7soNiNBpRj6sc2j9HtZIwKRTByE4BWjFga8lolyaWOWemw0VYYo8RAg9JqnI8YT1jcUY70gNPWiQ6LXIkEi8ICKMAU2iiKGQwTAn8gECaHZHhXBXEYYAx4HtW0DgMQsBQTkp2BaIV1UqNwWCAH1ptjvFkDK6G11oxmaT4vhXY8YPQJqVAMBkVlKsxg25OUi6RphNrxeh6+lJKosC2J5VLIvV6DSkFW1tbVMoVfE8ySTNbsjijdaOs0Xa73SKKI1ZXVxkbzVh7nDl6lEz3OXnH7Tz93LOksSaegJkoqvUab7nvPj7zB59jtLHF/XfezaDTYTwa067NU63OM8lS0nGPjaVz7Knv4y13naDT6zLOCja2BgwzQ7ffIYpy8vkqZy+vkWeaN99/hM1lTV6kltzlXN79ILAPBq3R0hKqhLHJ3ng4cNzs1PrjNOXgwQMMBmWkZ6i361S0wPghT567RmAKTh1uU9WKfreHFoKLy+ugNaNcUwkMD9x1Bs+3Eo7S2NWb8IRLBLZzIqV1c7er0es2o0jL6zByWtq41ZARKJy2nBQ4t3B7mzhdDQHg2vZyulIxxrVdJVOiohGO4/NatQm7VE8DHKg0XfJrq/cpPdxSWpGmmWulCaQndvQPjDCu7amZa1Q4fvgWqpWENEuR7klZKNtK9XwfTwr73pIdIRejrS7klAOAsdTbHfk+oy3eISRaFdaGEfvFTJKEwonlGG0sxdxzZY5WzkTpOv5RSmKSpETgXNzK5cRRw63PS61WpchyKtUqGEFRWGHgZr1BlmUUeUajUSPLJqTpmCxNka600sYaUueZpalrY6hWygRS4ns+SZTsaI4YbSiXK0RRgjEwSlOGwzH9wcDiNqoAFKowjIcjBv0BtWoFKSSbWjMcDnjLW9/G3oNzHLztFrxQUoljxFZOb6nPyaOnkVqwtbrBF//oc1w5f4lTZ46TxAEXX7hKq3mIclJjONzg6sUnOdwKOX3yGArJlWuLzO2Neeih/axvDqhWStx5YJ4kgEpVo4qh/dK4roVlPgrKcWRLeq3xsA8f46jh0zapHQIMGKc54zQnjMuIoIoMSwRJCS/0SbVgfViw3hfs3X+Me+65lzCK7M1u4NLSGi9eXeMzT7zEpx/9CnlhOyWFKpBTjojRFosyU3V9i0tJLO9iSgJz2DrGGDtDY5TF94x7mMnrgsjguCLTFYS43inUbqYFYVeQwp0HIa385V8V07j5QoAyilIpYtIfI4SV5iu0svxKYbn7RoMf+kRBYE8MVtdCCIEnDLVSQBIHFIXayfadbo80zZwqlXXU2pkpcAQZTwqrz+iwhjTPXHVpl3wYjR+FoBRZrjAmRStNFPq2hFL2AmdZRuh7lBN7Iw5H4x0bhCwv8KS1+DPCdjBs92SMzm3nRAtQSpPEMUWWEUcxk8mI8WhIaf8+PGe7YGtyQZ7mlKsVJpMxw94Qoy1uEgXWdKdUTjBGs93rW/Gi0EM4jsgkTamUK0RhwLDfRwtl1dqV7cRMxhPiKARh2NrcYjgZk4Qhvd4QLQQvrK3hP/YURmgKCvJBRheFGSpUXHB26XGaR0okMmJ5fZGNrmFuNI/K4fLL19BacfhYnX4Y4okK841beOjuNivrW6xsb5IXBTIpcer2QwyynM3JkP0nEybDLU6dqnHxsg8oB0ZOjZctqUlOQUOMBdKnT2rhOitGUGQp250OSamCa63g/gSwbe/V7ggtoVSKaO/ZQxQolq6sWLIfhmtrHbRWpI+/hNAFD917kqRSosiUw1MsnzTPc8IgYIdc4QDVabLAHsFX+QOb6Y1urmNl0+S3g/Mp25a131X7v+npzIsUFOOcMIp3umhfK3Zn0jCwtd2jWitZDILCyZzpHVcp49pTKs+tqpJn6dqFKlAaDu7by8EDexHSVnylqMTGVofJxGIgWqsdPr7n+w4LscQfVRRIBGHok7kZDp0rhKcJfKv4laUpaOWmVu2SFGPnVgLfc+1buwROU/sefmA7Eb7rniitMFpYR7UixxjwAp9xNsFISZoVSJkihST2LaAVxhESwfrGOr4v8IOAySQDY5C+lcYzWqE8SblcIstzwjimDEhj6Ha2UUWBDhVZIXYUv4rcMkc96ZE0qnS7XYSAcrnCaDQkjmMC6TEcDPHDiHazSej5DEZjBsMJWxguXbmKLywB7WSjydnNDeYbZU68eR8bS2ucPHmUYbfPxatLmElBJsbsieaQk4y1xWWOnziA6tdYWVzlyrk/5ciZU7SqZbaHPeIk4NLFLofKgn2lJne97Z382hc+y/q1nCTuEiRzsEN+smQ2y42xK0mNwRMe03FAmxIsGU8Ia0OQpimVat3qyhY3MjjBmIJCFWR5ikBQL/vc1RK0my327N3HufMvW/c8IVlc72CM5jNPvkwUhZw6vI/j+9oUDvTI88JiRG51YYln9vsjnAWksUtapDd1crO3hm2fs9MRUsW0tS5ReYGWniUdCtuN0bnB90D4HiKMATv/9FoFyq5MGgYYjjP23DKP0msId0P7vufAUCuOKo1AZTkymM4IWARbKUWjVmZPu4kqcoq8wA88DB7S89z7TJW57UyL73sYV1bkRUEURShdYIymHMek5GhpxWmmIi3GAU0YkML+/9POue/5RGFAEEX0ewOklISRHfI12jqoVZMaSmmyLKXdbrK5uUXgeTQb1vBIBD5ZlnPkyBGG/S4To4ijiF6vTzlJ8IKARqtOvzciyzPmWnsYD3tEoU+SREzSHKVyhkOFL60iehyG9CdjtGcTmu8HlkmZpqTpBM/3SdPcMREVG+ur3Hr6DEuLi0gHhgZhiMRDeJJ9e/dw4fJVlC5YzQoOVcok2vDwB7+dW1cW+eLnv8TSuQ3KUYlT++b55KMvceL4PkSpYNzL6CwNIZdcuXCN5lwDWcBESvq9Hv0nnuL+u86w3R+yvLyFyjW9QBAcrfHMtWfpdzKUEkxSRcsvdsqSItc7JCsUGM/ekMbNXhjp+DWuw+W7h0aWZYwnY4SwJs3TOY2d2RFV2C6dEEwyyeMjjyj0oDLg0NGjNCsVnnrqWQdQClY7fYwxbPWGPHvuEiIb88H3vJWkXN7pdIBr2e6UKtfvg+nsy3RlpLRTNZ9mECmur1KcLsvUpf66gpxbiSiFq1ktUPoaQOiuxDTs4FhBuRRZt3MhMNqqTAdBYGnRAutx4rllnetzK1WgVEEgNbHvkcQxcWxZj5myup5yeqJN4SYAPdI03alzfTc9aLSdBs3zYqcjYnkYlrgVJxGe9NwAkdPEKDRRFFCv14giOxOhjaZQOXlmtTg8z7rbp+mELEuJY1sfe55HmtmJ27l2y06m+h5LSwsYIMus2vb+/QfY2tymWq+ytrpOZ3OL8XjMsN9nvj3PZDxiNBzheZJyqUQlKVEplRkNh2AUzVaDpFQiSWLK5YQg9HeoRFoXzq7B2jomSUy/12UyGVOtVmnWmlSqJbb72xil2TM/T61aIfA98jDEB/a05vjSF/6ELz/1ZQIka8tdVte3WV5YZbQ5YHtxwPbFFNUNaB8+QjBXpz8ako9ybrvzdt7+7ge5684ztA63mLulzg988FtpV6qkWcHWWPHoxUXOr6ySFYpTx+p88B138/e/9wOEvu9Ykh5FoSxWJbBUf0uLxA8Dp/065TLsPOTJ85x0PELrgh31cTHlYUBhDIUjHaZ5zmaq2Bgqnrm4Sa1Zp9lo8ba3voXBxpBiZHaAyt5wTGeQsjFSPPK55/l/n3qUj/3+Z5kUYOlX2C6IkDsapEYLjFI7wLWFJ+S0QrGgr8KtaAVW4c6W7pbX5fRr3bLBQ7jOo2NGv0b7ZFeuNIrCyuIZYYk5ylgKcBA4nYAgIIkiWu06i4vLrhPiQC0/IPAkse+7JaAh9Dw8X9Dd7pPlOZ4fWBqvS+vaWIGaKW17qpMwHZEv0oIsFAitKZfKZM74ORuP7QWSEin9HYapFTW2Q3XGGBLHfSgKgyoykFCtlCiUZjCYkKYZo9HIGl5PRlCqkMT2Zg8jnyBKGPSG1qZv0MMTmjAJmYzGFCqn2aiQZTmVUsxgOEDKgH3793PhwgXqtQYCi4sE0mc4HOIFntVliEIHavYplKJZr2NUzigdW6TfDeZtbW0ReJLt7W2KoiDJQ1rtBoH0WFxcBDTDyYQoCpmkivVel61+1+p3uF5UOsm4cO4Cd9xR5drVHvmoglcFFUD70DylcsKzz3yFcjVGNhRPXnqak6dPEPqSchzha4U0BiMEx48cpLO1ye1vOsUTjz4GvT4LC6toNfUtwd7kSl2nXgs746GVsjeYcW3M6UyQsWBvXigi44hgxrImdtqcxuIgCIuXFHmGkAHbheb3Hz3P0XqN++44wLvf/x7+6Hc/y9vvfZDeeJMLly9RaI00Hpu9vh1RMJpH/vhpwNAuhbzrbXfjYb14PafXYiyKiZkyjqXjb0jccYudzyuEcexPH6OnsytWqUthKJTB80OMciD9axQouzRpFC6b5uRpju8YooPhyAJIaKQvqFSrSLFsiTJIJ+MHJ44c5s4zp+yTRzgsS3poY7kVtuUYQGGFZsIwsoCiqwN9z5YxcRQwSTPCckDuBHuEJzG5IVcTEB5R5FNKEgLHnUhzSwHP3WSpME4dK7FjzuV2fYe9GggPX1oXeYyloNdrdYIoZHNrE60k43RErS4YDbfRBuq1OnlhMYxhf2hnYtwMyfr6OghBEMUMhmP27TtAt9sl8H0Ggz5hFOzofYReyGg4YjgaEScxUgq2+12M0kR+SBCHoA3bvR6Nco1Br0sR+IzGI0rlmDxN0V5EKYkYpT7tRo3haMI1oThVWKkBz5/OO3icPHOUZjlj9doSh480Kdf3sD0YYYIBJ45pwsNtnheSx77wBPe+/S4efu/7yWTK2soC6YVn+b533c+vfOZxTp0+zFwJamcOcOLWMzz6uS/xnT/4YeJyjafOXmZSKLdqk1gpr+ly3t0mRoAnEMrNFCnjJPqsRJ5WGhkIily5suSGMfNpd9515zCQaas3u77dYzScsDIa8OGH7uCh9z3IFz79Jd7yLfdx350PsLi4zNrWyg7JLPAi+/mBfpqz/sdPgFLEIuDe247SapepJBFGWUMmjJUvENwAnGK7eIGTN9BakevcKXnZj6+UwsNDmYIiZ8dZ7rViVyYNg0WMrXdlSq2SuKeItRrIHYvS5VPHU7CSfEIKynFAvVrCw9axAC9fWWC710c6NN2CjBLft/obUhh0WlCuVR3bEoZjRSDtkFjsSo1sMiIIIlRuEIFP4EhUQgiSchmZZbbcmaRMJgVa58g0J4lji5c4wthgMMRzwjHZeEStXgdsa7a/3cPzpWWFKsvU9HyfaqnEZJyCyTFGkCQxlUqFSjnh4sUrSE+yZ36elaVV+r0upSQhDAJarTq9bp8oqdLtbFtpxEpCoXKSJLGzOaFHfzjEk55lzuYZk0nK6RMnGacpushRCiqVGtnE2kiqIkP4Pm868yb6gwFPPvMswo/I/YBG4vNPf+LHOH/hJT718U9TjTOWlq6wsdWnUCG1vqDQIfe85W5iGTMcTPD8PuNRymOff5Isu4u+7FCuJ5x5530ElPiHf+e7+OWP/R5f2ugQej588jEeuOso/+djn+BHf+h7LVnLgUz2xrQtceGEZ7RwfAntluc3iEd7voc21sWvyIw1qdDKdcvcF1Pa75MVd7JzRJapCX4gGKYpo9WU//uZZ/nB993LB7/7YX7ro7+DxMMoza23n+DChQvIyCdsJjt0caU13d7YcjPI+dTTL2FEgdCGchLzphMHOX30IGHoobIUrXJAuIE5ZXkf0rJCjSutpHDexkWB0rnD+q4zRl8rdmXSEMKa0iRJCWmMnSGYCqIKq7fpSWlVwgUEUWizp3DTfMLgY7siBh+jCoT0rLCMo/CiHZ8DOxMiPEG1WiVXOdUkZqK1HYnXtv0LWADV90nTFF9IdGZ1GXQQUij7BG5USqRpwVjgqN0R49GYPLe05kF/YLklwlj5QG1otFpgNM1Gg7zI8T3PiqbIIVsb2wgBjUaLwaBHnCTs33MLG+trJBXL1RgNU9qtFp1Oh263R5JEgJWEU8awuroKRhAUOa1WDV96DLo9At+nUq4wGPQtK9a3Ldw8zymXS3S3t1laWaZSKbm2tGE46FGrNej1tqmWSyxcW8LzPCZpju9JlMpZNYa6gF/6H79Ino+5484qKysXCPwq6XhAVmQcPdXgzKl7+MTvfBqwJWlrrk2z3eDQ4YPcfvpWnnnxi2ytr/D57XW0kdxx5B5KRpFmGRNhgc/PPrOABH7mZ38ZYWxrXEyrCcfShenMBXhutYAQGCHwhbdTSvrOHU9aDNjK9GEFeGy5Y+00LNvUppvcAYs6zZDO12azN+R//u4T/MB77+dD3/Md/Oav/jaekFy+fIV9Bw6ycO0aHb/MkfmIa0tr1KoxkScR0pBrg1IC41af6SDnsZcWePzsIkYrq/imDY1KwrEDe9g316JRkcShXYEbx0fxA99iJH6IL4X1pFUF2tip6lxdB2JfGbszaQDS8ymyMeVKiSydYLRAGYXvWciu1+tz9twFQi9Ei+v96jzLEEbbNuZ0nFhIllZX6ff7eMI+ofNcgbb8DSOs9sEkzYhD34r/FDlSWs3MKAopigKllCUCjcdUynWyYkISlx15xjDsDVCZ7YWrokAojfF9pC9JylbjcTgYWeMlLE8jCgN0UVBkE1bHY8aFmxEwMDc3R+lgie3tbZQqaM3PsbW+xnhUpTnf5sqVRWq1Ktu9LocO7qfb7dJqNamWYhauLVqdjXRCOYnsjTYZICXUq1Vrq5AXZFl3R3C42dpLnqW0W3MsLS9QbzbwpaA/6KPynMFgyJnb76BaSTh7dsKxEyeRvkez3eCF516kVa+z3umSCkOqQXdHvO/bHyT0twjDAEzB/n138NnPnmfh7AYMn+Do8RJXr6ySyJi5PVX2nz7IfL3NV54+x9yeI8zXYHO4zjAbsNB5ge/60NtZ+bVPsdkbIKQkCMt824N30e2u8eSXLzPI+o6w64YPhWfH2TEINQU8HBdCWEKfwrInjQOjvalil3N5l9NhNOE6Gtq46sSWzShh5/YVaBR5oRkUio9++k/5wIP38qHv/whriyvcevIUv/Px3+L7//Z38xu/9QgiHXJpuUe62CXwJXtaZQ7NVUkCz6qHuwdhmmYW7NcGre1Db62fsvnyIt6FZVcy2XKp0OBJ2y0pxRFCSNJMo0XhQFIfzzN0++Ovff/tRi/XIAhMrVGnXUmYDEd0xxNq1ZLtSQOlJOL0rcd55pnnLeFLWvTfE5LbThzhPQ/eQzmy3ifGGCTwm3/4J7x0ZQnt/EGiMCTLC3xpuQpxlJBrjRCGaqVCOhkxGE0oxTFGSzJlb2ar2SlJ0wyFbdXmk5xWq01/0AUJzVqd/mBCrhSh71nVc2OsHBsGXdhyShlDFMaOlFYwGY/xZIDvS5rzc5SThEsXL1OtVdjc3CTwQg4e3E+RZvR6A/wkIEvHDEcTylFMe36eOPRZW1mh2aqRFhqVK7Y6W7TmWpipTmgY093ukqYTfM8yEmu1Ou12G4RhdX2T44f2c+3aAllmOHz0MMPhgO3tDdrzexiNRlxbXKVeLpHnOb1eD1VMuPX0m3jsqWcJvYC3tubYHnQ4detR1tcvo0cR7UrIwuYYpRXNRsQt+wo6XcEttzQYbA84cnKOS9c6XHhpG5AcP3mYPSebRG0PI1IOtg5T9udZ2hzx8x99hHQC998xxzPPLVAUKVFUYW04IMtzkiim2ai6cXS7MtDKoHSB5/RDd4Sz5PX5Hd8LaLZb5Hm+47hmuyhuLH3qG2KwDN2pNYBFLhFCUqiUMIgJA4849Hn/g/dxuF7io//7ozSaNeqVGiudJdrtiD94cUyRj2w3VEDF9zjWLBEFgij28TwfLwhuGHfAtY9tN2jagTXOxsASxCx7edpitpR1D6vxbfd5/NwavVH2qnXKrk0ac3NzpJMRse/hhxHZZGy1QAPJnbfdxhNPfxkPgxYKTwZ4XkCuCt5823He/9B9GJNbGTYhEdLnN//gc7x0+SpSOr9P5Zig2pA7QGkyHBFFCVJKykmJ7aHts0eBRxAlTK9YZCW+6PcHzO2do7O1TTkpMckzwNLEdWEH4gpl0XAcaScOQwqjyHJNrVolz3Pr0aoUkW8/A06fNCmVqCYxKxubVCsJW2sdSrUSvV6PY8eOEQchL1+6SLNap5jkjCZDRwpybV3PI4kTRpMx7VablZUVKpUKtVqNKIro93oYnbH3lnmWlpfoDyb0+yOq1TKlMKBcLZFnGi0EBsVoMMYYw2QyoZRUeduDD/D0U0+xvr7KrcePcXXxCgcPneLpZ55He5JbDBysl8mzjLgW0IgiFq6uoP0AYeyq5+iRfdRqTa5eu8Ktp9sklJirNfAr+6m09uOVBErkrL54ls8/8zxzSY0H3v9evGaLn/6vv0C/P3Q3s+LEkf2cv7LKME15xzvezqXzZ63Un7sH8qJw1pkBWZE5HgZgrMbFVP9ifn4vRWHJbsqtNqax0/I02irEGkfO0raMudEe0vd9O2LgQez7/PB7voVHfvU3eO+HP4A2Gb3hBv/r977MpBjtlFVz9ZjvuGs/S9e2KDcbbHUGJKWYRruBQXDx0jXCShnhkkLgun7oAmUsh0Tg7eAWnjA7imHGSQYaIXny3PLXTBq7sjwBCHyJCUMwMBoO8D2P2Pd4x9vewu//0R8T+oFTLILA86yorLEAlvQESvnWXRvN+UtX2Oh0CTwr52+EFVz1fJ9MF1RKZdI0pVFrMckm1quEgjj0CYOYrY1NEmG7H4H0mGAHxTSGYa9HqVwGY+nBSRSjjcI4Q+DA8/D8gKzIEcZQqVi5/V5vSJamhGFAqVQmH2cMRn18aRXNQTM/16TT2SYIPBYWFqlXqqhUc/TQYQbbA0r75zm6/yDnLpynVK4Chr3ze6mUS6xtrCK9gDTNKLKczfV1qpUySudsdjatQpRWFEoxGme0Wg2yQlCqVChFJUpxxLWFy6hCEMURhhwhJOVymTffcYIvPf48n/rEJ/GDiLm5Oa4srnDo0H6Wl5fIdYYnIsqAynIatZCkInjve97O1Wef55lzCzz8kXdw7uwVXj57mYWr6xw9WufwgXnGQ02v3+XlL19jeT3lvm+5h9NvOs7KcMT7vuOdXH3heT758Y/znve9HaEVudH4QqKE5PK1DXuj5hlFntKe38Pm2trOasFqU1jZAaEd3V9adqQvQGkw0s4h2da7Z9v5UtrJae0SBQ5IVddbssKxTQGnk4G1n1AenicwyvDJr5zjA9/3MF/8w8fodLoYH6bC/QZb5WSTnPW1LmESMuh3iGNBsybpbqzR7Yw5tG8fG6OUF84vkWpDo1mzA5JK0axV6Q+GpLmi3a5hvIDV9S5hIBF+gMozIk8wyJTjiLx67NqkYZS9kJUkoa9TVKHI0zFPffk5Hn74W+l1hzz/3EtM0hGplcPCF9IRdgxgVa+iMCGMynh+gCqGCN8OD007GQJ7oYLAJ0utyI/WMBmNUUZTKpWo1WoU2KV9URTEQcgoTamWShS5Aa8gCEPKcUKWpeQqt+Ktvmf749Jj7y1tiiJne2uDdJwi/QjjRteHw7HV8dDFzmCc9D1UllvLPmNo1prM3zLPoD+iNxxQLldZXlpD5wX1Wo00TSmUYm1jja2OT61apdPdptVqUU5i1tZXiJIQg6Feq9BuzLG+uUUQBtSrIesbm3bYLRNko5S+m9s4cGA/V65eodlqOFwkZ3l9hFIZ8/Pz1Go1NjtbjIYTlIo4fvwopUqZ85cWeKlQHCwEP/DOB9DtFo9ffJG50HDnwTk+/Ycvc+bNxzl6d4g/LrG1/jL94RbnXpxw9dLYqsgjePKLT9He2+TMmdNcWrhGd2h477e9nTgO+Ml/9J381M99jPGkIHBSe1OZxu3tHsINiE2/ElbvQmENm115IuxT2E7IKoxiR1PUUNiWvRP/FQKEts7v1vJCWLq6AE1hU4bWltXpZoyEb2njxhjOX1lk7colvutdb+OF51/m8uVFPEKMsStUO97uY7wK/UGHUNoH4NbWJuVyk+axNiurm2yvD5gPQzZUwdpWh6OHDoMv2BxmKOPheYbtYUZcSai25ul2epjCIPyINHdp7zUKkF1ZnoRhaObaTSgyAjxH5zYMRyOajRqeJ0jzjEIZ6rU6w5HVpJDS494zx/i2d9yPwbC6vMJ2t88Tz13j4uIqXgBB4JPmOaFvW6V5XqB1gRcEKGUIAs8K7ghDlqWUSiVAYAqHogNeGDKZTIj8gMKNX4d+QF5o22L1fUqlEkJIhr0hYRBY4xohyLLMKpsnJcvyTDM8T1Jv1K3eZ+50Qwdj6o0K1VrCtaUNQj+0RLFSxMbKBo1Gg7AUcuCWfWysrdMf9snznGa9YQfsDPTHffa02zQbdZ597jl86TE31wYjmGu3qNdb9AYDJpMB/X6fIIwZjobkk5RGo87m1hZGCEIvwg8kRsPcfJsiS7l65RJ79x9gbm4PGxtrrKytM9dqUq836fW2eeHCIoEHd3kRlUjy7vsPQ7nK559+gXRzRE955EqhCkUUWAUzYSw/8sDeOapRSJanrHW6lNoV3vque6lWE4bFBl4gWe8scPLYt1L4Vf7Ff/zvTNIMU+RkSlvR6UJzzz13YYRmMhrT7W4D9sZUxonZuCmU6UTweDQmDEP23LIXY7S9nkojkE4RbErYA0fRtHodQiClT6YVU7Nxi7IKx+60Dxy0QXoRqrPKj3zP+/n4b36KF0dDLAfXdupKARyOIkqVkGPHjxAHgrXlS6gcNjZyCtx4v9FsFZqushaN9XqVRrVGuVRiPB5bg3PpIeMKwg/pdTvkUzMnJOsbK+TZNxCmIYToA2ff6OP4Jo85YOONPohv8vibvAZHjDHzr/aL3VqenDXG3P9GH8Q3cwghnphdgzc23qhrsCsH1mYxi1m8cTFLGrOYxSxeV+zWpPELb/QBzGJ2DW6CeEOuwa4EQmcxi1m8cbFbVxqzmMUs3qDYNUlDCHFICPExIURXCNETQvyGEOLVzKRn8TpDCHFQCPGzQogvCSFGQggjhDj6Kvs1hRC/KITYEEIMhRCfEkLc+Sr7xUKI/ySEWBZCjN37vvPr8mF2YQghvlcI8etCiCvufJ0VQvyMEKL6iv1uivO/K5KGM4z+NHAb8HeBHwJOAZ8RQpTfyGP7BomTwPdj7TQ//2o7CDus8AjwMPBPgO/B+vJ+Rghx8BW7/xLwo8C/AT4ELAOfEELc8zdx8N8A8RNYyuBPYs/vz2PtSv/QeSnfXOf/q2TOb9IX8M/cST15w7ZjQAH8+Bt9fLv9Bcgb/v0PsCTio6/Y5yNu+7tv2FYHtoD/dsO2u91+P3LDNh9Lxnvkjf6sN+MLmH+VbT/szuN7brbzvytWGsCHgUeNMeenG4wxl4A/wZ7MWfwVwkzly147PgwsGWM+c8PfdYHf4auvwYeBHPjoDfsVwK8BHxBCRH8tB/0NFMaY9VfZ/Lj7ecD9vGnO/25JGncAz73K9ueB27/Ox/LNGq91DQ4LISo37HfJTMczv3q/EFsKzeLPj3e5ny+6nzfN+d8tSaOFrbdfGVtA8+t8LN+s8VrXAK5fhz9vv9Zf83F9w4UQ4gDw74BPGWOecJtvmvO/W5IGvPqw7msroM7irzOu6+D92e1/mf1m8SrhVgy/jcXrfuTGX3GTnP/dkjQ6vHqGbPLqWXUWf/2xxde+BnD9Ovx5+229yu9mgW2VYjskx4EPGGMWbvj1TXP+d0vSeB5bq70ybgde+DofyzdrvNY1uGqMGdyw3zHXJn/lfhlwnln8mRBCBMCvAw8A32GM+cordrlpzv9uSRqPAG8TQhyfbnDko4fc72bxNx+PAAeEEFOADiFEDfhbfPU1eATLH/i+G/bzgR8APmmMSb8+h7t7wnExfgV4L/ARY8yjr7LbTXP+d8XsiSNwPQuMgX+Nrdn+PVAF7rohy87iLxlCiO91/3wv8GPAPwbWgXVjzB+7L/YXgEPAP8cuh/8lcBdwtzHm2g3v9WvAB9x+l7BEpQ8BDxpjnvr6fKLdE0KIn8ee8/8A/O4rfr1gjFm4qc7/G01seR0EmMPY5VsP6AO/xSsISLPXX+n8mq/x+uwN+7SAX8bWxSPgj9wX9pXvlQD/BVgBJsBjwLe+0Z/xZn0Bl1/j/P/UzXb+d8VKYxazmMXNE7sF05jFLGZxk8QsacxiFrN4XTFLGrOYxSxeV8ySxixmMYvXFbOkMYtZzOJ1xSxpzGIWs3hdMUsas5jFLF5XzJLGLGYxi9cVs6Qxi1nM4nXF/wfjZPOZqmwC5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img_path = 'img/office.jpg'\n",
    "img = load_img(img_path, target_size=(224, 224))\n",
    "plt.imshow(img);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('n04005630', 'prison', 0.32494283),\n",
       " ('n03179701', 'desk', 0.07232616),\n",
       " ('n04239074', 'sliding_door', 0.045325305)]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "x = preprocess_input(x)\n",
    "\n",
    "preds = resnet.predict(x)\n",
    "decode_predictions(preds, top=3)[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We got these predictions without \"doing the ML ourselves\".\n",
    "- Can be useful for cases like sentiment analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kXIVN3thVXPh"
   },
   "source": [
    "## Transfer learning (15 min)\n",
    "\n",
    "- Let's look at the [Kaggle dog breed identification](https://www.kaggle.com/c/dog-breed-identification/data) dataset. \n",
    "- But let's imagine we only have 2000 examples, to simulate only having a small dataset available to us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "F3uwOqqNVXQl",
    "outputId": "d94e6d24-0181-4d29-9fe1-33650217167a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>breed</th>\n",
       "      <th>image_path</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>000bec180eb18c7604dcecc8fe0dba07</td>\n",
       "      <td>boston_bull</td>\n",
       "      <td>data/dog-breed-train/000bec180eb18c7604dcecc8f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>001513dfcb2ffafc82cccf4d8bbaba97</td>\n",
       "      <td>dingo</td>\n",
       "      <td>data/dog-breed-train/001513dfcb2ffafc82cccf4d8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>001cdf01b096e06d78e9e5112d419397</td>\n",
       "      <td>pekinese</td>\n",
       "      <td>data/dog-breed-train/001cdf01b096e06d78e9e5112...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00214f311d5d2247d5dfe4fe24b2303d</td>\n",
       "      <td>bluetick</td>\n",
       "      <td>data/dog-breed-train/00214f311d5d2247d5dfe4fe2...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0021f9ceb3235effd7fcde7f7538ed62</td>\n",
       "      <td>golden_retriever</td>\n",
       "      <td>data/dog-breed-train/0021f9ceb3235effd7fcde7f7...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 id             breed  \\\n",
       "0  000bec180eb18c7604dcecc8fe0dba07       boston_bull   \n",
       "1  001513dfcb2ffafc82cccf4d8bbaba97             dingo   \n",
       "2  001cdf01b096e06d78e9e5112d419397          pekinese   \n",
       "3  00214f311d5d2247d5dfe4fe24b2303d          bluetick   \n",
       "4  0021f9ceb3235effd7fcde7f7538ed62  golden_retriever   \n",
       "\n",
       "                                          image_path  \n",
       "0  data/dog-breed-train/000bec180eb18c7604dcecc8f...  \n",
       "1  data/dog-breed-train/001513dfcb2ffafc82cccf4d8...  \n",
       "2  data/dog-breed-train/001cdf01b096e06d78e9e5112...  \n",
       "3  data/dog-breed-train/00214f311d5d2247d5dfe4fe2...  \n",
       "4  data/dog-breed-train/0021f9ceb3235effd7fcde7f7...  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/dog-breed-labels.csv')\n",
    "data = data[:2000]\n",
    "data['image_path'] = data.apply( lambda row: (os.path.join(\"data/dog-breed-train\", row[\"id\"] + \".jpg\") ), axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SWFmLeZtO2qv"
   },
   "source": [
    "Above: you can see some of the breeds that we're predicting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Hw0kE6BgVXQr",
    "outputId": "df467e37-9ac0-4727-d561-73edb636ea4e",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of dog breeds: 120\n"
     ]
    }
   ],
   "source": [
    "target_labels = data['breed']\n",
    "total_classes = len(set(target_labels))\n",
    "print(\"number of dog breeds:\", total_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xTCKP_rhVXQ5"
   },
   "outputs": [],
   "source": [
    "# read images from the image directory. \n",
    "images = np.array([img_to_array(\n",
    "                    load_img(img, target_size=(256,256))\n",
    "                    ) for img in data['image_path'].values.tolist()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "EtOYBdEiVXQ9",
    "outputId": "d068b3a0-733c-4873-8fe6-674c7dfa2668"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2000, 256, 256, 3)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bkPvTMP1O2q5"
   },
   "source": [
    "Above: we have 2000 images, each of size $256 \\times 256$ and with 3 colour channels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XXvcDjzKO2q6"
   },
   "outputs": [],
   "source": [
    "images = images.astype('float32')/255.0 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T-OqM5tQO2q8"
   },
   "source": [
    "- Above: it's very important to scale the images!\n",
    "- We need to set things up the same way they were set up when the model was trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 263
    },
    "colab_type": "code",
    "id": "rkbauZ2CO2q9",
    "outputId": "46a895d1-47a2-4a9c-8efe-f07afefc752a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(images[0]);\n",
    "plt.grid(True);\n",
    "plt.xticks([]);\n",
    "plt.yticks([]);\n",
    "plt.title(\"Breed = \" + target_labels[0]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "c49AJfCuO2q_"
   },
   "source": [
    "Above: this is a sample image from the dog breed data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r8s5GlKHVXRF"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(images, target_labels, \n",
    "                                                    stratify=np.array(target_labels),\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "4AxPhnaUVXRI",
    "outputId": "ce3d1213-8f17-4aa5-953d-b88ecea3f0b4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1500, 256, 256, 3)\n",
      "(500, 256, 256, 3)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset seems fairly balanced:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bernese_mountain_dog           20\n",
       "basenji                        19\n",
       "bedlington_terrier             19\n",
       "lakeland_terrier               19\n",
       "samoyed                        18\n",
       "                               ..\n",
       "doberman                        8\n",
       "german_shepherd                 7\n",
       "great_dane                      7\n",
       "briard                          6\n",
       "soft-coated_wheaten_terrier     6\n",
       "Name: breed, Length: 120, dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Meaning `DummyClassifier` gets a horrible accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc = DummyClassifier(strategy='prior')\n",
    "dc.fit(X_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.013333333333333334"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This is because of the large number of classes.\n",
    "- A **separate** issue is the small amount of training data per class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.5"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_train) / len(y_train.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CCL5JmShO2rF"
   },
   "source": [
    "We have roughly $1500/120=12.5$ images per class. **This is tiny compared to what we're used to.** What can we do??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can use **transfer learning**.\n",
    "- We'll use a model trained on one task as a starting point for learning to perform another task. \n",
    "- Why is transfer learning helpful to us? Why can't we just train a model with the second task's objectives from the beginning? \n",
    "  - It's difficult to obtain labeled data: ususally we need a whole lot of data in order to solve complex problems, and it can be hard to collect the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_inception = InceptionV3(weights='imagenet', include_top=False, input_shape=(256, 256, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_ACKgXs7O2rf"
   },
   "source": [
    "- Note the `include_top=False`. \n",
    "- This throws away the last layer. \n",
    "- It wasn't useful to us anyway. \n",
    "- ImageNet has 1000 classes, but we're not interested in those classes. \n",
    "- We don't want their predictions; we want every step in their \"pipeline\" except the predictions.\n",
    "- We will the feature extractor and then train our own model from the tools we know."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The next line turns the pre-trained model into something we can use.\n",
    "- The syntax and details are out of scope here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_extractor = Model(inputs=base_inception.input, outputs=GlobalAveragePooling2D()(base_inception.output))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next lines may take a long time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8min 21s, sys: 47.1 s, total: 9min 8s\n",
      "Wall time: 1min 30s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "Z_train = feature_extractor.predict(X_train) # we think of this as .transform()\n",
    "Z_test = feature_extractor.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This code is meant to be run on a GPU - that would make it much faster.\n",
    "- Interestingly, based on the above, it seems to be multithreading on my 8 cores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1500, 2048)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/envs/cpsc330env/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "lr = LogisticRegression(multi_class=\"multinomial\", solver=\"lbfgs\")\n",
    "lr.fit(Z_train, y_train);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9993333333333333"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(Z_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.score(Z_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This is an incredible accuracy for so little effort!!!\n",
    "- I challenge you to get accuracy anywhere near this high with only 2000 examples using other methods, including deep learning!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare this with logistic regression on the **original** flattened features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1500, 256, 256, 3)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 256, 256, 3)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_flat = X_train.reshape(X_train.shape[0], X_train.shape[1]*X_train.shape[2]*X_train.shape[3])\n",
    "X_test_flat = X_test.reshape(X_test.shape[0], X_test.shape[1]*X_test.shape[2]*X_train.shape[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/envs/cpsc330env/lib/python3.8/site-packages/sklearn/linear_model/_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  warnings.warn(\"The max_iter was reached which means \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1h 10min 39s, sys: 9.6 s, total: 1h 10min 48s\n",
      "Wall time: 1h 10min 59s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=10, multi_class='multinomial', solver='saga')"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %%time\n",
    "# lr_flat = LogisticRegression(multi_class=\"multinomial\", solver=\"saga\", max_iter=10)\n",
    "# lr_flat.fit(X_train_flat, y_train);"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "CPU times: user 1h 10min 39s, sys: 9.6 s, total: 1h 10min 48s\n",
    "Wall time: 1h 10min 59s\n",
    "Train score: 0.5\n",
    "Test score: 0.03"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.486"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_flat.score(X_train_flat, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.03"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_flat.score(X_test_flat, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random cool stuff\n",
    "\n",
    "- Style transfer: given a \"content image\" and a \"style image\", create a new image with the content of one and the style of the other.\n",
    "  - Here is the [original paper from 2015](https://arxiv.org/pdf/1508.06576.pdf), see Figure 2.\n",
    "  - Here are more in [this 2016 paper](https://arxiv.org/pdf/1601.04589.pdf); see, e.g. Figures 1 and 7.\n",
    "  - This has been done for video as well; see [this video from 2016](https://www.youtube.com/watch?v=Khuj4ASldmU).\n",
    "- Colourization: see [this 2016 project](http://iizuka.cs.tsukuba.ac.jp/projects/colorization/en/).\n",
    "- Inceptionism: let the neural network \"make things up\"\n",
    "  - [2015 article](https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html)\n",
    "  - \"Deep dream\" [video from 2015](https://www.youtube.com/watch?v=dbQh1I_uvjo)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- Multi-class classification refers to classification with >2 classes.\n",
    "  - Most sklearn classifiers work out of the box.\n",
    "  - With `LogisticRegression` the situation with the coefficients is a bit funky, we get 1 coefficient per feature per class. \n",
    "- Flattening images throws away a lot of useful information (sort of like one-hot encoding an ordinal variable!).\n",
    "- Neural networks are a flexible class of models.\n",
    "  - They are hard to train - a lot more on that in CPSC 340.\n",
    "  - They generally require leaving the sklearn ecosystem to tensorflow or pytorch.\n",
    "  - They are particular powerful for structured input like images, videos, audio, etc.\n",
    "- The good news is we can use pre-trained neural networks.\n",
    "  - This saves us a huge amount of time/cost/effort/resources.\n",
    "  - We can use these pre-trained networks directly or use them as feature transformers. \n",
    "- My general recommendation: don't use deep learning unless there is good reason to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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