02_Dask_ml.ipynb

{
 "cells": [
  {
   "cell_type": "code",
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   "source": [
    "# Note that this code must be run from python3 or ipython3 in a session's \n",
    "# terminal, not run directly in the graphical console. See\n",
    "# https://github.com/dask/dask/issues/4612\n",
    "\n",
    "import cdsw_dask_utils\n",
    "import cdsw\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixedAcidity</th>\n",
       "      <th>volatileAcidity</th>\n",
       "      <th>citricAcid</th>\n",
       "      <th>residualSugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>freeSulfurDioxide</th>\n",
       "      <th>totalSulfurDioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Poor</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Poor</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Poor</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Excellent</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
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       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
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       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Poor</td>\n",
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      "text/plain": [
       "   fixedAcidity  volatileAcidity  citricAcid  residualSugar  chlorides  \\\n",
       "0           7.4             0.70        0.00            1.9      0.076   \n",
       "1           7.8             0.88        0.00            2.6      0.098   \n",
       "2           7.8             0.76        0.04            2.3      0.092   \n",
       "3          11.2             0.28        0.56            1.9      0.075   \n",
       "4           7.4             0.70        0.00            1.9      0.076   \n",
       "\n",
       "   freeSulfurDioxide  totalSulfurDioxide  density    pH  sulphates  Alcohol  \\\n",
       "0               11.0                34.0   0.9978  3.51       0.56      9.4   \n",
       "1               25.0                67.0   0.9968  3.20       0.68      9.8   \n",
       "2               15.0                54.0   0.9970  3.26       0.65      9.8   \n",
       "3               17.0                60.0   0.9980  3.16       0.58      9.8   \n",
       "4               11.0                34.0   0.9978  3.51       0.56      9.4   \n",
       "\n",
       "     Quality  \n",
       "0       Poor  \n",
       "1       Poor  \n",
       "2       Poor  \n",
       "3  Excellent  \n",
       "4       Poor  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #1. Load the data (From File )\n",
    "input_file = \"data/WineNewGBTDataSet.csv\"\n",
    "col_Names=[\"fixedAcidity\",\n",
    "    \"volatileAcidity\",\n",
    "    \"citricAcid\",\n",
    "    \"residualSugar\",\n",
    "    \"chlorides\",\n",
    "    \"freeSulfurDioxide\",\n",
    "    \"totalSulfurDioxide\",\n",
    "    \"density\",\n",
    "    \"pH\",\n",
    "    \"sulphates\",\n",
    "    \"Alcohol\",\n",
    "    \"Quality\"]\n",
    "\n",
    "\n",
    "wine_df = pd.read_csv(input_file,sep=\";\",header=None, names=col_Names)\n",
    "wine_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixedAcidity</th>\n",
       "      <th>volatileAcidity</th>\n",
       "      <th>citricAcid</th>\n",
       "      <th>residualSugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>freeSulfurDioxide</th>\n",
       "      <th>totalSulfurDioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Label</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   fixedAcidity  volatileAcidity  citricAcid  residualSugar  chlorides  \\\n",
       "0           7.4             0.70        0.00            1.9      0.076   \n",
       "1           7.8             0.88        0.00            2.6      0.098   \n",
       "2           7.8             0.76        0.04            2.3      0.092   \n",
       "3          11.2             0.28        0.56            1.9      0.075   \n",
       "4           7.4             0.70        0.00            1.9      0.076   \n",
       "\n",
       "   freeSulfurDioxide  totalSulfurDioxide  density    pH  sulphates  Alcohol  \\\n",
       "0               11.0                34.0   0.9978  3.51       0.56      9.4   \n",
       "1               25.0                67.0   0.9968  3.20       0.68      9.8   \n",
       "2               15.0                54.0   0.9970  3.26       0.65      9.8   \n",
       "3               17.0                60.0   0.9980  3.16       0.58      9.8   \n",
       "4               11.0                34.0   0.9978  3.51       0.56      9.4   \n",
       "\n",
       "     Quality  Label  \n",
       "0       Poor      1  \n",
       "1       Poor      1  \n",
       "2       Poor      1  \n",
       "3  Excellent      0  \n",
       "4       Poor      1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# #### Cleanup - Remove invalid data\n",
    "wine_df.Quality.replace('1',\"Excellent\",inplace=True)\n",
    "\n",
    "# ### encode labels \n",
    "wine_df.Quality = pd.Categorical(wine_df.Quality)\n",
    "wine_df['Label'] = wine_df.Quality.cat.codes\n",
    "wine_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 2. Build a classification model using MLLib\n",
    "# ### Split Test/Train\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(wine_df.iloc[:,:11],\n",
    "                                                    wine_df['Label'], \n",
    "                                                    test_size=0.2, \n",
    "                                                    random_state=30)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# ### parameters for grid search\n",
    "param_numTrees = list(range(10,50,5))\n",
    "param_maxDepth = list(range(4,16,2))\n",
    "\n",
    "rfc = RandomForestClassifier(random_state=10, n_jobs=-1)\n",
    "\n",
    "GS_params = { \n",
    "    'n_estimators': param_numTrees,\n",
    "    'max_depth' : param_maxDepth\n",
    "}\n",
    "\n",
    "CV_rfc = GridSearchCV(estimator=rfc, \n",
    "                      param_grid=GS_params, \n",
    "                      cv= 3,\n",
    "                      verbose = 1,\n",
    "                      n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 48 candidates, totalling 144 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    3.1s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 12, 'n_estimators': 35}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done 144 out of 144 | elapsed:    6.4s finished\n"
     ]
    }
   ],
   "source": [
    "CV_rfc.fit(X_train, y_train)\n",
    "# ### Show Best Parameters \n",
    "print(CV_rfc.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.88      0.83       170\n",
      "           1       0.84      0.75      0.79       150\n",
      "\n",
      "    accuracy                           0.82       320\n",
      "   macro avg       0.82      0.81      0.81       320\n",
      "weighted avg       0.82      0.82      0.81       320\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "# ## fit test dataset \n",
    "y_true, y_pred = y_test, CV_rfc.predict(X_test)\n",
    "# ## Print classification metrics \n",
    "print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
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       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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kQQIECBAgAABAgQIENgg0EzAO/MO3pl3wD55RiC7W0T8vkHzdOfuQ6rn/HNE/NuM81dW7zhNX/519XNsG7nECyIiBcnplQLN/2nk5Ih4TPVR0lOnpTX/oIEx0l3JtaHye6ufxzs1RDsD3nQ3dAqo0yt9XvFvI6IvIt4TER+LiDMi4uTq978SEc/fzLrSHcCfqX4/BbTpcdcz77Z+UkR8KSIeUDNOqlk6Pt0hnD6DeOqVHrW9rEHLBtgdSoAAAQIECBAgQIAAgeIJCHiLV1MrIkCAAAECBAgQIECAwOYEmgl4nxsRKfibes28wzY9svns6jfvmhHs1VuNr0bEc6oHpwDxyBknfj0inl392nBErKh34BYdlx4z/KnqWMlw64hY06Kx0zDtDHjT+Omu21dXH408M5BNdzanO5zTK91VvEP1kdyzLe8TEfGy6jfSY65T2F/7So9hvqrmjuAfRcRrIuLKmoPSncTpcdcHV7/2m4h4dESMtNDTUAQIECBAgAABAgQIECisgIC3sKW1MAIECBAgQIAAAQIECMwq0GjAmz6/9vs1d12mxyKnz3CtDQn/KyL+tnq1X0bEvk3Yp0c6T32W648j4qAZY1xRvYs2fXm2YLGJSzZ0SnqccbqLOL3S5xGngLeVr3YGvOPVz0T++WYmfF1E7F79fnps8qdnOTY9ivvWiNi2+r2nVPdG7aG1dbwsIp42R3CbPlP58ojYq3ryqyIifY6zFwECBAgQIECAAAECBAjMIyDgtUUIECBAgAABAgQIECBQLoF6A970WOYXRsRpEZEeuTz1Sp+F+4UZZO+LiPQZs+mVHlucHl/c6Ks2JL5mls+LTY8Vflh10HdExD81eoEFHv+WiPjX6hjp84jTo4lb+WpnwJvulv3reSb7HxHx99Vj5rpD+lkR8Y3qMekx1+mx07X7KT1+OX097Z302m/GY61nTiGFuh+ufvHiiDi0laDGIkCAAAECBAgQIECAQFEFBLxFrax1ESBAgAABAgQIECBAYHaB2kAuPUo3fS5q7Su9T0x3p6bH6Ka7LGtffxcRKYid+fpIRBxb/eJ3IuKQJvD/JSLeWj3vdzV3k04Nlb6WAsX0eltEpOM7+aqdX/p84drQuxXzaGfAW49X+nzin1UXkh49nR7TPPORyelzg0+sHpOC/7+ZsfDaR3VfGhHp0c+be+1S81nN6Zrp8c4zHx/dCltjECBAgAABAgQIECBAoFACAt5CldNiCBAgQIAAAQIECBAgMK9AbcA778HVA9JjmdNnrX55jhPSo3VfWf2egLde1enHdTvgTbNJgX8K9tPrRdXP652aZfr7wR9q7lx+QkSkRzDXvmo/n/e/ax7bPZdIeuTzWM030x3aaa95ESBAgAABAgQIECBAgMBmBAS8tgcBAgQIECBAgAABAgTKJdBowJs+Uzfd3TmxGaYP1jwCeLbPz61H+N8j4h+qB872iOZf1Xxe69sjIj0yuZOv/xcR6dHQ6XVDRDy0xRfPQsCb7qCeujM6hfbpEcpTrwMjItU2vVIIO/W47FqG9P10XLOvA6qfy9vs+c4jQIAAAQIECBAgQIBAKQQEvKUos0USIECAAAECBAgQIEBgg0A9n8Gb7t78fI1ZeuRyujN3rtfpEXFK9ZuzhbP18Nc+/jfdGZruEK191YaHZ9Zcr56xW3FMejRxmmN63R0R27Zi0JoxshDwPjwirq3O6Y6ISJ+pu676/6dwO4Xc6fXOmjC+lqE2hG+G5+kR8d1mTnQOAQIECBAgQIAAAQIEyiQg4C1Tta2VAAECBAgQIECAAAECEfUEvMnppxGR7qhMr+9FxNM2g/fGiFhZ/f7aiNhqnjt+ZxvqcxFxePUbF0TE8hkHfT0inl392vkRcVSHi5mu96nqNZPhlhFxfwvnkIWANy1nVUTsX13Xsoj4VvWf053cj6r+c/r+1Of11hJcV/PZyVdHxE0N+rwpIq5o8ByHEyBAgAABAgQIECBAoHQCAt7SldyCCRAgQIAAAQIECBAouUC9Ae/zI+JLNVZ/EREXzWH3rIj4Rs339o2IFAg28vp1ROxZPeFtNY8Knhrjv2o+07WZu4RTQJ3uUE2vW2asrZ55pnCzdk1PjIhL6zmxeky6GzaZTr0+ExH31Pz/WQl43xwR/1md19kR8bpqXVJ90ivd4fuIOdZ9eUTsV/1e+szmdzfg41ACBAgQIECAAAECBAgQqFNAwFsnlMMIECBAgAABAgQIECBQEIF6A9603BRgTj0q+UcRcfAcBltExJ0Rsaj6/X+MiPSZuvW+do+IdPfn1OsZEZECz9rXiyPiwuoX0hp2i4g/1HuBiPh+RDypevwnIuIVDZybDk3vn9Mapx7N/M8R8W8NjPHqiPhA9fj02ON0l/NIzfkXR0R6FHZ6zRZwz7xUbR1n82o0MJ4af9fqZ+ym9aY7cHeOiL+JiBSwp9e/RkT6rN7ZXrV3WZ8REenObi8CBAgQIECAAAECBAgQaLGAgLfFoIYjQIAAAQIECBAgQIBAxgUaCXgPi4iv1awn3YH6lTnWl8LXFMKm12+qj/Mdr9PiX2pCwz9FxENrPvt1aoj0SOQUOKZgNL0aCVgfEBE31wTQzd5d+uGIeFX1+umO1kc28Cjq9Njpv6yemx6D/LgZNsn1udWvvSsi0p20m3u1K+BN1/xBTZifQvH/rgnH94mIq+aYWAqmU13SKz2ieeqRznVuA4cRIECAAAECBAgQIECAQD0CAt56lBxDgAABAgQIECBAgACB4gg0EvCmVdeGfbMFk1MyT59x1+3fVoPB+eR2qj72NwW46bW5u1fTI39fXz0uPd5474hIgfB8r9rHDk9U70qt57yZ46ZQ9ic1X0yPL06PMZ7vlQLrFAgPVg+czeZ/IuKY6vdTWP6SzQy6S0T8vub7rbyDNw17UkScVR3/vIh4eUT0RsQvIuLRm5nXQRGR7vSeer0oIr44D04K7FMAX7ue+Tx9nwABAgQIECBAgAABAqUWEPCWuvwWT4AAAQIECBAgQIBACQUaDXhnfr5uCh6nHpU8k+/zEZFCvfRKjyFOx9Z+ju/M47eJiG/V3M2aQtf0Obn3zlGXFAankHFp9fvpEdLPjIj7NlPH9HnA6fHMW1ePSUHqsQuo+6cj4sjq+ekRy+mzib+3mfEGIuLL1ePSYekxz2mNd8w455SIOL36tTURkULcu2YZtz8ivlBzt286pNUBb/q84BuroW7tFN4SEW+fx672Udipnk+e8fjt2tPTo73Tnnls9e7mzTkuoGROJUCAAAECBAgQIECAQLEEBLzFqqfVECBAgAABAgQIECBAYD6BRgPeNN53I+Kp1YGvrAZyteNMXXOHiPhp9RHL6WuT1TtBU3BZ+3m56U7WF0ZEehRx+szX9Ep31j4nIi6aZwEpXE0h69Qr3RmbPus1ff5rGmPqlQLddEds+pzcqc/NvbV6B2r632Zf6W7Tn0fEQ6oDjFZDz/dFxO01g6b32+kzddNnET+x5uspXE4h88xX+hzi31Y/6zd973+rd87WBsEHRsR/VgPd2vNbHfCmsVPwfuiMSe5Vffz25uwOqN71PXW38i3V+gzXPHY72Tw7IlZWH3OdxvtmRKT/mMCLAAECBAgQIECAAAECBOYREPDaIgQIECBAgAABAgQIECiXQDMBbwoQv13DdFREnD8HWwoBU1iX7kCdeqVrps/lTWFfeiTvHhEx9UjmdEz6rN702bafqLMUJ0TEe2vC0HRauts1BaT3R0QKmtNdsn0146U7Z1NgeUWd19jcYbOtMd2x/KuIuC0i0p2p6fpTdw1PjTXf5wZ/ZMbdxekO4an5psc8pzuYp14pPE+PTU6vdgS8r46ID9Rcb3OP555p9YqISGtJdxtPvVZHxDXVmqQwe/ua76XHM6fwOtl5ESBAgAABAgQIECBAgMA8AgJeW4QAAQIECBAgQIAAAQLlEmgm4E1CF1fvSE3/nIK6fap36M6ml+5yTZ/h+rIZIexsx6Y7glOYmB633MjrsIhIn8m7Zx0npbth/zoibqjj2HoPSWHrGRGxvI4TboqIE6uPVt7c4Sn8To9fToHtXK90x3B6nPPf19z93I6AN9UwPWI5PWI6vf4uIv6rjrVOHZLuxj13RtA/2+lfqYb7wt0GcB1KgAABAgQIECBAgEC5BQS85a6/1RMgQIAAAQIECBAgUD6BZgPe9Ijm9KjmqdcrI+Jj8/A9KiLS3ZwpgJy6ozXdlfrHiPhxRKTH9n5tM0HxfNVZFBGHR8SLq5/jmz47Nj0aON3Ne131s3c/GRGXzzfQAr7/+IhYUf0s4J0jIgWj6TN0093KaY1fjYjPVu9Srucy6a7jo6uPZ96v+njpe6rrSXdGn131+12bA9401/TZwc+rTjrddZuu2cgr1SI9JvsFEbF/9c7qtdXP9/1RRJwXEd9pZEDHEiBAgAABAgQIECBAgECEgNcuIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQE4EBLw5KZRpEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAQMBrDxAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAnAgLenBTKNAkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQICDgtQcIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQEwEBb04KZZoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAQ8NoDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyImAgDcnhTJNAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQICHjtAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECOREQMCbk0KZJgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAS89gABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRyIiDgzUmhTJMAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQICXnuAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECOREQ8OakUKZJgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAa89QIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZwICHhzUijTJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAgIDXHiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBOBAS8OSmUaRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQEDAaw8QIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgJwIC3pwUyjQJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAg4LUHCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkBMBAW9OCmWaBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQEPDaAwRyJvDNb36zkrMpmy4BAgQIECBAgAABAgQIECBAgAABAgQIEGibwLJly+RdbdM1cBYFbPgsVsWcCGxGQMBrexAgQIAAAQIECBAgQIAAAQIECBAgQIAAgY0CAl67oWwCAt6yVdx6cy8g4M19CS2AAAECBAgQIECAAAECBAgQIECAAAECBFooIOBtIaahciEg4M1FmUySwEYBAa/dQIAAAQIECBAgQIAAAQIECBAgQIAAAQIENgoIeO2GsgkIeMtWcevNvcDMgPeAAw7I/ZqaXcDo6GhceumlG04/6KCDYnBwsNnhnEeAQAkE9I0SFNkSCbRQQM9oIaahCJREQN8oSaEtk0CLBPSMFkEahkBJBPSM6YVetWrVtC8IeEvyg2CZGwQEvDYDgZwJCHg3FswvNTnbvKZLIAMC+kYGimAKBHIkoGfkqFimSiAjAvpGRgphGgRyIqBn5KRQpkkgIwJ6hoA3I1vRNDIiIODNSGxthXkAACAASURBVCFMg0C9AgJeAW+9e8VxBAhsKuDNkF1BgEAjAnpGI1qOJUAgCegb9gEBAo0I6BmNaDmWAAE9Q8Drp4BArYCA134gkDMBAa+AN2db1nQJZErAm6FMlcNkCGReQM/IfIlMkEDmBPSNzJXEhAhkWkDPyHR5TI5A5gT0DAFv5jalCXVVQMDbVX4XJ9C4gIBXwNv4rnEGAQJTAt4M2QsECDQioGc0ouVYAgSSgL5hHxAg0IiAntGIlmMJENAzBLx+CgjUCgh47QcCORMQ8Ap4c7ZlTZdApgS8GcpUOUyGQOYF9IzMl8gECWROQN/IXElMiECmBfSMTJfH5AhkTkDPEPBmblOaUFcFBLxd5XdxAo0LCHgFvI3vGmcQIDAl4M2QvUCAQCMCekYjWo4lQCAJ6Bv2AQECjQjoGY1oOZYAAT1DwOungECtgIDXfiCQMwEBr4A3Z1vWdAlkSsCboUyVw2QIZF5Az8h8iUyQQOYE9I3MlcSECGRaQM/IdHlMjkDmBPQMAW/mNqUJdVVAwNtVfhcn0LiAgFfA2/iucQYBAlMC3gzZCwQINCKgZzSi5VgCBJKAvmEfECDQiICe0YiWYwkQ0DMEvH4KCNQKCHjtBwI5ExDwCnhztmVNl0CmBLwZylQ5TIZA5gX0jMyXyAQJZE5A38hcSUyIQKYF9IxMl8fkCGROQM8Q8GZuU5pQVwUEvF3ld3ECjQsIeAW8je8aZxAgMCXgzZC9QIBAIwJ6RiNajiVAIAnoG/YBAQKNCOgZjWg5lgABPUPA66eAQK2AgNd+IJAzAQGvgDdnW9Z0CWRKwJuhTJXDZAhkXkDPyHyJTJBA5gT0jcyVxIQIZFpAz8h0eUyOQOYE9AwBb+Y2pQl1VUDA21V+FyfQuICAV8Db+K5xBgECUwLeDNkLBAg0IqBnNKLlWAIEkoC+YR8QINCIgJ7RiJZjCRDQMwS8fgoI1AoIeO0HAjkTEPAKeHO2ZU2XQKYEvBnKVDlMhkDmBfSMzJfIBAlkTkDfyFxJTIhApgX0jEyXx+QIZE5AzxDwZm5TmlBXBQS8XeV3cQKNCwh4BbyN7xpnECAwJeDNkL1AgEAjAnpGI1qOJUAgCegb9gEBAo0I6BmNaDmWAAE9Q8Drp4BArYCA134gkDMBAa+AN2db1nQJZErAm6FMlcNkCGReQM/IfIlMkEDmBPSNzJXEhAhkWkDPyHR5TI5A5gT0DAFv5jalCXVVQMDbVX4XJ9C4gIBXwNv4rnEGAQJTAt4M2QsECDQioGc0ouVYAgSSgL5hHxAg0IiAntGIlmMJENAzBLx+CgjUCgh47QcCORMQ8Ap4c7ZlTZdApgS8GcpUOUyGQOYF9IzMl8gECWROQN/IXElMiECmBfSMTJfH5AhkTkDPEPBmblOaUFcFBLxd5XdxAo0LCHgFvI3vGmcQIDAl4M2QvUCAQCMCekYjWo4lQCAJ6Bv2AQECjQjoGY1oOZYAAT1DwOungECtgIDXfiCQMwEBr4A3Z1vWdAlkSsCboUyVw2QIZF5Az8h8iUyQQOYE9I3MlcSECGRaQM/IdHlMjkDmBPQMAW/mNqUJdVVAwNtVfhcn0LiAgFfA2/iucQYBAlMC3gzZCwQINCKgZzSi5VgCBJKAvmEfECDQiICe0YiWYwkQ0DMEvH4KCNQKCHjth9IILF++fL+IeE1EPH14eHifZhZ+5JFHPnFycvJvI+LJEbE0Iu7o6en5TqVS+ffh4eErmxmz0XMEvALeRveM4wkQ0DfsAQIEmhPwB5Tm3JxFoMwC+kaZq2/tBBoX0DMaN3MGgTIL6BkC3jLvf2vfVEDAa1cUWuDoo4/eYu3atS/t6elJwe6B1cXeODw8vHOjC1++fPnLIuK8iOiNiEpE3BcRW1bHGa9UKi+64IILvtbouI0eL+AV1DS6ZxxPgIC+YQ8QINCcgD+gNOfmLAJlFtA3ylx9ayfQuICe0biZMwiUWUDPmF79VatWTfvCsmXL5F1l/gEp4dpt+BIWvSxLPuKII87s6el5VURsNWPNDQe8y5cvf3BE/DYilvT09HxkfHz8by+88MI7jjrqqIdOTEx8ICIOi4jbRkdH9/jiF794bzuNBbyCmnbuL2MTKLqAN0NFr7D1EWitgJ7RWk+jESiDgL5RhipbI4HWCegZrbM0EoEyCOgZAt4y7HNrrF9AwFu/lSNzJrB8+fLVEbFNRFxRqVQ+1Nvb21+pVFZGRMMB7xFHHPFPPT09/xYRv4iI/YaHhyemONJdwiMjI9dHxA6VSuW1F1xwwfvbSSXgFfC2c38Zm0DRBbwZKnqFrY9AawX0jNZ6Go1AGQT0jTJU2RoJtE5Az2idpZEIlEFAzxDwlmGfW2P9AgLe+q0cmTOBFStW/HdPT8/5559//mVp6suXLz82Ij7STMC7fPny70fEkyqVyt9dcMEF/zWT4ogjjjinp6fntRHxheHh4Re3k0rAK+Bt5/4yNoGiC3gzVPQKWx+B1groGa31NBqBMgjoG2WosjUSaJ2AntE6SyMRKIOAniHgLcM+t8b6BQS89Vs5MucCCwx476k+6vkvhoeHL5ol4P3rnp6eD0bEb4aHh/dqJ5WAV8Dbzv1lbAJFF/BmqOgVtj4CrRXQM1rraTQCZRDQN8pQZWsk0DoBPaN1lkYiUAYBPUPAW4Z9bo31Cwh467dyZM4Fmg14X/7yl289NjZ2d1p+b2/vo88///z0mOZpr+XLlz8vIr4cEfcNDw9v2U4qAe9GXb/UtHOnGZtAMQX0jWLW1aoItEtAz2iXrHEJFFdA3yhuba2MQDsE9Ix2qBqTQHEF9IzptV21atW0LyxbtkzeVdztb2WzCNjwtkVpBJoNeI866qidJiYmbqxC7TU8PPybmWgrVqx4RqVS+XZEVIaHh/vS/7YLdmbAu88++7TrUpkfd2xsLC6//PIN89x///1j0aJFmZ+3CRIg0D0BfaN79q5MII8CekYeq2bOBLoroG9019/VCeRNQM/IW8Xqn++1t94fH/nxzXHbmvH6T3IkgXkElq6+Nfa8+qdx8aOfuv7IY564Szxv3weW1u2Xv/zltLULeEu7FUq7cAFvaUtfvoUvIODdbWJi4vqq2KwB7xFHHHFIT0/PxemY2267beCSSy5Z1y7hmQHvmjVr2nUp4xIgQIAAAQIECBAgQIAAAQIECBAg0IDAZCXirT/ti3vG/em9ATaHbkZg57tvidf+6IJYfuVF0VupxCGv+WDcuM0D48W7TsQzdmrbfUaZr8mWW05/kKaAN/MlM8EWC/i3TItBDZddAQFvdmtjZgQIECBAgAABAgQIECBAgAABAgSKILB6NOKtq/qLsBRr6LLAHnf8MU780XC86JeXRH9lcsNs/ueA58Vbn3WCgFfA2+Ud6vLdFhDwdrsCrt8xgU4FvBHRPzw8PNGuhbmDt12yxiVAgAABAgQIECBAgAABAgQIECCwMIG7RiP+RcC7MMSSn73PLb+NE3/4mXjOtT+I3lk+CXC0byCe8toPx5P32dodvDV7xR28Jf/BKeHyBbwlLHpZl9xswPuSl7xk576+vj8mt97e3r3PP//8X800rPkM3onh4eG2/id6PoN3o77PqinrT7N1E2heQN9o3s6ZBMoooGeUserWTGBhAvrGwvyKfPaVN62JK25aE+nRrV4EpgTWTayLm266aQPITjvtFP19bf2zEvwOCNw7ui7O+8ktG660dHF/vOuFe3Tgyi6Rd4FtfvbT2P1D58T23//OnEu5Z/sd4qfPOzx++bRl8aSDD4idlm6R92U3PX+fwds0nRMLIiDgLUghLWN+gWYD3pe97GVLx8fH70xXmJycPOCzn/3s5TOvtmLFiudWKpWvRMTdw8PD284/m+aPmBnwHnDAAc0PlvMzR0dH49JLL92wioMOOigGBwdzvirTJ0CgnQL6Rjt1jU2geAJ6RvFqakUE2i2gb7RbOJ/jX/yrO+LUC6/N5+TNmgCBBQs8aKtF8fUTH7/gcQxQUIFKJfovuSSGVq6Mge9/f85FTuy5Z6x5/evjOzvvHJX+P/+HIGX/W+iqVaumebmDt6A/I5Y1p4CA1+YojUCzAW9E9CxfvnxNRCyJiBcNDw9/cSba8uXLj4uIcyPi58PDw49tJ6qAd6OuP560c6cZm0AxBfSNYtbVqgi0S0DPaJescQkUV0DfKG5tF7Kyf/jir+LrV9++kCGcS4BAjgUeuu1QfPH48t6gkePStXfqk5Mx8PWvrw92+2cElbUXXrfvvjFy6qkx/oIXxOi6dW52qcER8LZ3ixo9+wIC3uzXyAxbJLCAgDeWL1+ebhN9QkS8Y3h4+J9mTmnFihXvrVQqJ0bEJ4eHh1/eoinPOoyAdyOLP560c6cZm0AxBfSNYtbVqgi0S0DPaJescQkUV0DfKG5tF7KyUz93TVz86/UPBvMiQKCEAq84cMd406G7l3DlljyrwMREDHz+87F45crou/rqOZHWHXhgrH3Tm2Lds54V0fPnGMfvGdO5BLx+xsouIOAt+w4o0foXEvCuWLHiXyqVylsj4g9bbLHF3h/96EdHpuiWL1++ZURcFxE7VCqVV1xwwQWfaCergHejrl9q2rnTjE2gmAL6RjHralUE2iWgZ7RL1rjdFqhUKnHj3aMxPjHZ7akU7vpjY+Px859fsWFdj3nMY2PRooHCrdOCGhN450XXx49/f/eGkw59+ANil6WLGxvE0YUUmFj/Gbw3b1jbTjvtGH0+g7dQtd5j+8XxnEftEH29/gxfqMI2s5ixsVh0/vkxdOaZ0Xdd+lPy7K/xpz99/R27657ylA3B7tSR3p9MNxPwNrMRnVMkAf9mKVI1rWWzAvMFvIccckj/Ax/4wPMqlcqDI+KVw8PDf5ga8KijjtppYmLi19XHNH9hYmLihM997nM3L1++/CHVRzMfFhF/XLNmzV5f+9rXRttZCgHvRl2/1LRzpxmbQDEF9I1i1tWqCLRLQM9ol6xxuylw+5qxeO35v4zf3r62m9NwbQKlFjjt8L3j0IdvV2oDi/+zgN817AQCJRC4//4YPO+8GHr3u6P3ppvmXPDYYYetD3YnHj/35zXrGdP5BLwl+PmxxM0KCHhtkNIIzBfwHnnkkQdOTk7+uAry5uHh4XfV4qxYseIVlUrlfyKit/r1eyNiq+o/pzt6DxseHv5Ou0EFvBuF/VLT7t1mfALFE9A3ildTKyLQTgE9o526xu6WwEcvvTHOvOT33bq86xIgEBECXttgSsDvGvYCgeILDFx4YWx53HGzLrTS0xPjL37xn4PdffaZF0PPmE4k4J13yzig4AIC3oIX2PI2CswX8L7whS/canBw8AcR8cDe3t7nnH/++atm+i1fvvzgiHhzRDwpIpZGxG2VSuXblUrlHZ/97Gev6YS3gFfA24l95hoEiirgzVBRK2tdBNojoGe0x9Wo3RX4729dH5/4ycbHgXZ3Nq5OoJwCX3jN/h7RXM7Sb7Jqv2vYCARKIDAxEVsfdNC0xzJX+vtjbMWKGDnllJjcc8+6EfQMAW/dm8WBpRAQ8JaizBZZJAEBr4C3SPvZWgh0WsCboU6Lux6BfAvoGfmun9nPLjAz4F26pD+2GfIZsa3aL5XKZKxdmx7w9OfX4sVD0dMz9RCoVl3FOHkV2GqwL1Yc8OB4/r4PzOsSzLvFAn7XaDGo4QhkVGDReefFFiefHJWhoRg9+ugYOemkqOy8c8Oz1TOmk7mDt+Et5ISCCQh4C1ZQyym+gIB3Y439UlP8/W6FBFotoG+0WtR4BIotoGcUu74LWd3Pbrgnzvm/P8at944tZJiunHvHfWNx7+jEhmuf+ozd4ugn7NSVuRTxovpGEatqTQTaJ6BntM/WyAQyJTA2FkNnnBGjr3xlVB70oKanpmcIeJvePE4spICAt5BltagiCwh4BbxF3t/WRqDdAt4MtVvY+ASKJaBnFKuerVrNZKUSz3rPT+LO+8dbNWRXxxHwtpZf32itp9EIFF1Azyh6ha2vyAK9v/pVDH7oQ7H2bW+LGBrqyFL1DAFvRzaai+RGQMCbm1KZKIE/Cwh4Bbx+FggQaF7Am6Hm7ZxJoIwCekYZqz7/mtNdu88++yfzH5iTI/79BXvFcx61Q05mm/1p6hvZr5EZEsiSgJ6RpWqYC4H6BPquvDKGTjstBr70peipVOK+006LsVe9qr6TF3iUnjEd0COaF7ihnJ57AQFv7ktoAWUTEPAKeMu2562XQCsFvBlqpaaxCBRfQM8ofo2bWWGRAt79d94q3rP8UbFkUV8zFM6ZRUDfsC0IEGhEQM9oRMuxBLor0HfppbF45coYuOiiaROZ2GWXuOeyyyIGBto+QT1DwNv2TeYCuRIQ8OaqXCZLwB28tXvALzV+IggQaFRA32hUzPEEyi2gZ3S3/t/9zZ1xxY33xmSlu/OYefX7xybiM5f/acOXt13cHx9++b7ZmmQdsxka6IsHb7Uoenr8WaAOrroP0TfqpnIgAQIRoWfYBgQyLlCpRP93vhNDKdj9v/+bc7JrPvShGD/88LYvRs+YTuwO3rZvORfIuIB3chkvkOkRmCngDt6NIn6p8fNBgECjAvpGo2KOJ1BuAT2je/X//M9vibd97bfdm0ADV95+y4G46HUHNnCGQ4ssoG8UubrWRqD1AnpG602NSKAlApVKDHz96+sfxdy/atWcQ67bd98YOfXUGH/BCyL62v9EFD1DwNuS/W2QwggIeAtTSgspi4CAV8Bblr1unQTaIeDNUDtUjUmguAJ6Rvdqe8pnr47v/Oau7k2ggSvvvO1gfOn4xzVwhkOLLKBvFLm61kag9QJ6RutNjUhgQQITEzHw+c/H0OmnR/9VV80d7B54YKx905ti3bOeFdHBp6HoGQLeBe1vJxdOQMBbuJJaUNEFBLwC3qLvcesj0E4Bb4baqWtsAsUT0DO6V9PXfeaq+MH1q7s3gQaufNzBD4nXP23XBs5waJEF9I0iV9faCLReQM9ovakRCTQlMDYWiz7zmRg688zo++3cT5EZf/rT19+xu+4pT+losDu1Jj1DwNvU/nZSYQUEvIUtrYUVVUDAK+At6t62LgKdEPBmqBPKrkGgOAJ6RntrObpuMn5z2/0xMcuH7P73t66PK29es2ECL9x3h9h1u8XtnVAToz9suyXxtD2XRm8H79xoYppO6aCAvtFBbJciUAABPaMARbSEfAusXRuD550XQ+9+d/TeeOOcaxk77LAYeeMbY+LA7n4sh54h4M33D5zZt1pAwNtqUeMRaLOAgFfA2+YtZngChRbwZqjQ5bU4Ai0X0DNaTrphwOtuvz+O++QvYvXadXVd5D3LHxlPftjSuo51EIFuCugb3dR3bQL5E9Az8lczMy6IwD33xOBHPhJDZ58dvbfdNuuiKj09Mf7iF6+/Y3din30ysXA9Y3oZVs34fORly5bJuzKxU02iUwI2fKekXYdAiwQEvBsh/VLTok1lGAIlEtA3SlRsSyXQAgE9owWIcwzxX9+8Pj7105vrvsB7lz8yniTgrdvLgd0T0De6Z+/KBPIooGfksWrmnGeBntWrY/Ccc2LwAx+I3rvvnj3Y7e+PsRUrYuSUU2Jyzz0ztVw9Q8CbqQ1pMl0XEPB2vQQmQKAxAQGvgLexHeNoAgRqBbwZsh8IEGhEQM9oRKuxY//fl34VX7vq9rpO6u/tia+d8LjYfstFdR3vIALdFNA3uqnv2gTyJ6Bn5K9mZpxvgd7rr4+tDzwweiYnN1lIZWgoRo8+OkZOOikqO++cyYXqGdPL4g7eTG5Tk+qggIC3g9guRaAVAgLejYp+qWnFjjIGgXIJ6BvlqrfVEliogJ6xUMG5z58Z8D5026HYdnH/JidsNdQfRz1ux3jqHh7P3L5qGLmVAvpGKzWNRaD4AnpG8WtshdkTWHL88TE4PLxhYpUtt4zRv/qrGDnhhKg86EHZm3DNjPSM6eUR8GZ6u5pcBwQEvB1AdgkCrRQQ8G7U9EtNK3eWsQiUQ0DfKEedrZJAqwT0jFZJbjrOzID3Hc/fK567zw7tu6CRCXRIQN/oELTLECiIgJ5RkEJaRq4Eeq++OrZ58pNjctttY/T442P0Na+JytJ8/MeEeoaAN1c/bCbbdgEBb9uJXYBAawUEvALe1u4ooxEol4A3Q+Wqt9USWKiAnrFQwbnPF/C2z9bI3RXQN7rr7+oE8iagZ+StYuZbFIGBL30pxg85JGKrrXK1JD1DwJurDWuybRcQ8Lad2AUItFZAwCvgbe2OMhqBcgl4M1SuelstgYUK6BkLFRTwtk/QyFkV0DeyWhnzIpBNAT0jm3Uxq/wJ9F12WQydfnqsfetbY3LvvfO3gDpnrGcIeOvcKg4riYCAtySFtsziCAh4BbzF2c1WQqDzAt4Mdd7cFQnkWUDPaF/13MHbPlsjd1dA3+iuv6sTyJuAnpG3iplvpgQqlej/7ndjaOXKGPje99ZPbfTII+P+c87J1DRbORk9Q8Dbyv1krPwLCHjzX0MrKJmAgFfAW7Itb7kEWirgzVBLOQ1GoPACesbCSvy9394V37/urlg3UdlkoB///u744+qRDV/3GbwLs3Z2dgT0jezUwkwI5EFAz8hDlcwxcwKVSgz87//G0GmnRf9PfzptepW+vrjnssticrfdMjftVkxIzxDwtmIfGaM4AgLe4tTSSkoiIOAV8JZkq1smgbYIeDPUFlaDEiisgJ7RfGl/cN1d8brhq+seQMBbN5UDMy6gb2S8QKZHIGMCekbGCmI62RaYmIiBL3xh/aOY+3/5yznnuvYtb4mRN74x22tpcnZ6hoC3ya3jtIIKCHgLWljLKq6AgFfAW9zdbWUE2i/gzVD7jV2BQJEE9Izmq3nat6+Pj192c90DrHzJI+IZez2g7uMdSCCrAvpGVitjXgSyKaBnZLMuZpUxgfHxWPSZz8TQmWdG329+M+fkxp/2tBg59dRY99SnRvQUM/bQMwS8GfvpNJ0uCxSz03UZ1eUJtFNAwCvgbef+MjaBogt4M1T0ClsfgdYK6BnNe/7nN6+LT//0T3UNsNM2g/HpYx8bWw3113W8gwhkWUDfyHJ1zI1A9gT0jOzVxIwyJLB2bQx+4hMxeNZZ0XfDDXNObOzZz14f7E4ceGCGJt+eqegZ011XrVo17QvLli2Td7Vn6xk1owI2fEYLY1oE5hIQ8G6U8UuNnxMCBBoV0DcaFXM8gXIL6BnN139mwPsXj9guDtx1m00G3GJRXzxxt21j6ZKB5i/mTAIZEtA3MlQMUyGQAwE9IwdFMsXOC9x7bwx+5CMxdPbZ0XvrrbNev9LTE+MvetGfg9199+38HLt0RT1jOryAt0sb0WUzIyDgzUwpTIRAfQIC3o1Ofqmpb884igABfcMeIECgOQG/azTnls6aGfD+7TN3j5c9fsfmB3QmgZwI6Bs5KZRpEsiIgJ6RkUKYRiYEeu66Kwbf//4Y/MAHonf16lnnVOnvj7Hly2PklFNicq+9MjHvTk5Cz5iuLeDt5O5zrSwKCHizWBVzIrAZAQGvoMYPCAECzQt4M9S8nTMJlFFAz2i+6gLe5u2cmW8BfSPf9TN7Ap0W0DM6Le56WRToueWW9Xfrprt2e9asmT3YHRyM0aOPjtGTTorJhz40i8voyJz0DAFvRzaai+RGQMCbm1KZKIE/Cwh4N+4Ev9T4qSBAoFEBfaNRMccTKLeAntF8/QW8zds5M98C+ka+62f2BDotoGd0Wtz1MiVwzz2x+O1vj8GPfzx6RkZmnVplyy1j9FWvipETT4zKgx6Uqel3YzJ6xnR1d/B2Yxe6ZpYEBLxZqoa5EKhDQMAr4K1jmziEAIE5BLwZsjUIEGhEQM9oRGv6sQLe5u2cmW8BfSPf9TN7Ap0W0DM6Le56mRIYH4+tH/e46Lvhhk2mNbnttjH6mtfE6PHHR2Xp0kxNu5uT0TOm6wt4u7kbXTsLAgLeLFTBHAg0ICDg3Yjll5oGNo5DCRBYL6Bv2AgECDQikPWeceEVt8T7v//HuPP+8UaW1ZFjJyYrMVnZeCmfwdsRdhfJgEDW+0YGiEyBAIEaAT3Ddii7wOC558aSN795A8PkDjvEyOtet/6u3dhqq7LzbLJ+PWM6iYDXj0jZBQS8Zd8B1p87AQHvxpL5pSZ329eECXRdQN/oeglMgECuBLLcM+4ZWRfPOOvH00LULOMKeLNcHXNrpUCW+0Yr12ksAgRaI6BntMbRKDkWWLs2ttl//4iBgRh5wxti9BWviFi8OMcLau/U9QwBb3t3mNHzJiDgzVvFzLf0AgJeAW/pfwgAEFiAgDdDC8BzKoESCmS5Z1x7y31x1EevyE1VTn/JI+KQvR6Qm/maKIFmBbLcN5pdk/MIEGifgJ7RPlsjd1mgUolI/9fbO+9Eeq+6Kib33DNi0aJ5jy37AXqGgLfsPwPWP11AwGtHEMiZgIBXwJuzLWu6BDIl4M1QpsphMgQyL5DlnpGXgHegrycOe+T28ZbD9oiBvvn/wJf5TWGCBOYRyHLfUDwCBLInoGdkryZmtECBSiUGvvGNGDrttBg99tgYe9nLFjig02sF9AwBr58IArUCAl77gUDOBAS8At6cbVnTJZApAW+GMlUOkyGQeYFu94yb7h6JT/7k5rhtzdgmVukRzT/63d0bvr7XDkvi48c8JnOmvb090d/rbWfmCmNCbRPodt9o28IMTIBAWwT0jLawGrQbAhMTMfDFL8bQ6adH/y9+sX4GE3vuGff88IcRfX3dmFEhr6lnTC+rz+At5Da3qAYEvNNuAMuhBLIgIODdWAW/1GRhR5oDgXwJ6Bv5qpfZEui2QDd7RqVSicPPvTx+f+dIXQwPf+CSOP9V+9V1rIMIEGifQDf7RvtWZWQCBNoloGe0S9a4HRMYH49Fw8MxdOaZ0ffrX29y2TUf+lCMH354x6ZT9AvpGdMrLOAt+o63vvkEBLzzCfk+gYwJOr7TygAAIABJREFUCHg3FsQvNRnbnKZDIAcC+kYOimSKBDIk0M2eceu9Y/Hss39St8YBD906PvSyfes+3oEECLRHoJt9oz0rMioBAu0U0DPaqWvstgqMjMTgJz4Rg2edFX1//OOclxo54YRY+453tHUqZRpcz5hebQFvmXa/tc4mIOC1LwjkTEDAu7FgfqnJ2eY1XQIZENA3MlAEUyCQI4Fu9ow/3TMazznnp3Vr/fsL9ornPGqHuo93IAEC7RHoZt9oz4qMSoBAOwX0jHbqGrstAvfeG4Mf/WgMnX129N5yy6yXqPT0xPgLXxgjb3xjTDwmex8h0haXDg2qZ0yHFvB2aOO5TGYFBLyZLY2JEZhdQMC70cUvNX5KCBBoVEDfaFTM8QTKLdDNnjEz4N1mqD/+4S8etklB0hu6hz9wi9htu8XlLpbVE8iIQDf7RkYITIMAgQYE9IwGsBzaVYGeu+6KwQ98IAbf//7oXb161rlU+vpibMWKGDn55Jh8+MO7Ot+iXlzPmF5ZAW9Rd7p11Ssg4K1XynEEMiIg4N1YCL/UZGRTmgaBHAnoGzkqlqkSyIBAN3vGzID3QVstiq+f+PgMqJgCAQKbE+hm31AZAgTyJ6Bn5K9mZZtxz623rr9bd/DDH46eNWtmD3YHB2P05S+P0Te8ISZ32aVsRB1dr54h4O3ohnOxzAsIeDNfIhMkMF1AwCvg9TNBgEDzAt4MNW/nTAJlFOhmzxDwlnHHWXMRBLrZN4rgZw0EyiagZ5St4vlZb88NN8TQu98dg+edFz0jI7MHu1tsEaPHHhsjr3tdVB784PwsLscz1TMEvDnevqbeBgEBbxtQDUmgnQICXgFvO/eXsQkUXcCboaJX2PoItFagmz1DwNvaWhqNQKcEutk3OrVG1yFAoHUCekbrLI3UGoHe3/42hs44Ixadf370rFs366CT22wTo69+dYy+9rVRecADWnNho9QloGdMZ/KI5rq2jYMKLCDgLXBxLa2YAgJeAW8xd7ZVEeiMgDdDnXF2FQKNCoxPTMbEZKXR09p+/OjYWFz248s2XOfAJxwYg4sWtf266QK33DsWL/7g5Ruu5RHNHWF3EQILFvC7xoIJDUCgVAJ6RqnKne3FTkzEkhNOiEWf+1z0TE7OOtfJHXaIkRNPjNFXvSpi662zvZ6Czk7PmF5YAW9BN7pl1S0g4K2byoEEsiEg4N1YB7/UZGNPmgWBPAnoG3mqlrmWQSAFu2/72m/jf6++PdZlMODNUg0EvFmqhrkQmFvA7xp2BwECjQjoGY1oObbdAlu88pWx6Etf2uQykzvtFCNveEOMvuIVEUuWtHsaxt+MgJ4h4PUDQqBWQMBrPxDImYCAV8Cbsy1rugQyJeDNUKbKYTIE4uJf3xmnfu4aEnUICHjrQHIIgQwI+F0jA0UwBQI5EtAzclSsEky172c/i60PPXTDSice9rAYOfnkGDvyyIgOPcWmBMwLWqKeIeBd0AZycuEEBLyFK6kFFV1AwCvgLfoetz4C7RTwZqidusYm0LjAJ39yc7zrW9c3fmIJz3j8LlvHB1+6bwlXbskE8iXgd4181ctsCXRbQM/odgVcf6bAlsuXR+9NN8XaU0+N8Re9KKK/H1KGBPQMAW+GtqOpZEBAwJuBIpgCgUYEBLwC3kb2i2MJEJgu4M2QHUEgWwIzA97enoiBvt7sTLJSicmazyDr7e2N6On8W6iHbb84/vW5e8aeO2yRHRszIUBgVgG/a9gYBAg0IqBnNKLl2KYF0u+z990XsdVW8w7Rc9ddUdlmm4j0e69X5gT0jOkl8Rm8mduiJtRhgc7/daLDC3Q5AkUTEPBurKhfaoq2u62HQPsF9I32G2f1Ct+4+vb4+tW3x8j4ZFanWMp53XzPaPzuzrUb1n7U4x4cf7fsYZmx0DMyUwoTIZAbAX0jN6UyUQKZENAzMlGG4k5ifDwWXXBBDJ1xRqw7+OC4/4wzirvWkqxMzxDwlmSrW2adAgLeOqEcRiArAgJeAW9W9qJ5EMijgDdDeazawud85U33xjHnXbnwgYzQdgEBb9uJXYAAgTYL+F2jzcCGJ1AwAT2jYAXNynJGRmLRJz8ZQ2edFX1/+MP6WVUGBuLuVaui8pCHZGWW5tGEgJ4xHc0dvE1sIqcUSkDAW6hyWkwZBAS8G6vsl5oy7HhrJNBaAX2jtZ55Ge2TP7kp3vWt3+VluqWe5/FP3jle+5RdMmOgZ2SmFCZCIDcC+kZuSmWiBDIhoGdkogzFmcSaNTH40Y/G0NlnR++f/rTJukaOPz7W/sd/FGe9JVyJniHgLeG2t+TNCAh4bQ8CORMQ8Ap4c7ZlTZdApgS8GcpUOTo2mY9fdlOc9m0Bb8fAm7zQ0iX98dFXPDp2Wbq4yRFaf5qe0XpTIxIouoC+UfQKWx+B1groGa31LOtoPatXx+AHPxiD73tf9N5116wMlb6+GDvmmLj/tNPKylSIdesZAt5CbGSLaJmAgLdllAYi0BkBAa+AtzM7zVUIFFPAm6Fi1jWt6s77x+OH16+O+8YmNlnkj3+3Or71qzs3fP0Zez0gjtjvQcXFyOHKBvp641EP3jK2GOzL1Oz1jEyVw2QI5EJA38hFmUySQGYE9IzMlCKXE+m57bYYPOecGDr33OhZs2bWNVQWLYqxl788Rt7whpjcdddcrtOk/S10rj3gEc1+OsouIOAt+w6w/twJCHj9UpO7TWvCBDIk4A8oGSpGC6dy99rxOOojV8Sf7h2ra9SXP37H+Jtn7l7XsQ4qt4CeUe76Wz2BZgT0jWbUnEOgvAJ6Rnlrv5CV99xwQwy95z0x+LGPRc/IyOzB7pIlMXrssTHyutdFZccdF3I552ZIQM+YXgwBb4Y2p6l0RUDA2xV2FyXQvICAd6OdX2qa30fOJFBWAX2jmJX/5rV3xN9+/tq6F/fKJ+wUpzxjt7qPd2B5BfSM8tbeygk0K6BvNCvnPALlFNAzyln3Zlfde911MXTGGbHo/POjZ3x81mEmt946Rl/96hh97Wujst12zV7KeRkV0DMEvBndmqbVJQEBb5fgXZZAswICXgFvs3vHeQQIRHgzVMxd8KVf3Br//JXf1L249x35qDhot23rPt6B5RXQM8pbeysn0KyAvtGsnPMIlFNAzyhn3Rtdde9VV8Xi00+PgQsvjJ7JydmD3e23j9ETToiR446L2HrrRi/h+JwI6BkC3pxsVdPskICAt0PQLkOgVQICXgFvq/aScQiUUcCboWxXfe3YRNw7uuln6M43629ee3u861u/23DYLkuH4gm7brPJaelzXp+6x9I4eHfh7nymvv9nAT3DTiBAoFEBfaNRMccTKLeAnlHu+s+3+r5Vq2Jo5cpY9NWvznno5E47xchJJ8Xo0UdHLFky35C+n3MBPWN6AT2iOecb2vQXLCDgXTChAQh0VkDAu9HbLzWd3XuuRqAIAvpGdqt45iW/j49fdlOsm6wseJLP22eHePvz91rwOAYgoGfYAwQINCqgbzQq5ngC5RbQM8pd/82tfsmJJ8bgpz895yETu+8eIyefHGNHHRWxaBHIkgjoGQLekmx1y6xTQMBbJ5TDCGRFQMAr4M3KXjQPAnkU8GYom1W75Z7ROOycn7ZscgLellGWfiA9o/RbAACBhgX0jYbJnECg1AJ6RqnLv9nFD77nPbHkn/95k2MmHvGIWHvqqTH+4hdH9PcDLJmAniHgLdmWt9x5BAS8tgiBnAkIeAW8OduypksgUwLeDGWqHBsmc+VN98Yx513Zssm9/mm7xHEH79yy8QxUXgE9o7y1t3ICzQroG83KOY9AOQX0jHLWva5Vr1kT2zz2sdF7113rD1+3//4xkoLd5zwnore3riEcVDwBPWN6TT2iuXh73IoaExDwNublaAJdFxDwbiyBX2q6vh1NgEDuBPSNbJZsZsDb39sT2y5p/L9G7+/pWf/Zu3//Fw+LxQN92VysWeVKQM/IVblMlkAmBPSNTJTBJAjkRkDPyE2pujLRoXe9K/q/+931we66Qw6J6PGn/K4UIkMX1TMEvBnajqaSAQH/VshAEUyBQCMCAl4BbyP7xbEECEwX8GaoezviFzfdG+f+8Ia4/b7xTSZx/9hEXH/H2g1f33fHLeO8Yx7Tvcm6MoGqgJ5hKxAg0KiAvtGomOMJlFtAzyhh/UdGovfGG2Nyjz3mX/zERESf/3B1fqjyHKFnTK+1O3jLs/etdHYBAa+dQSBnAgJeAW/OtqzpEsiUgDdD3SnHuslKPOs9l8XqtevqmoCAty4mB3VAQM/oALJLECiYgL5RsIJaDoE2C+gZbQbO0vBr1sTgRz8aQ2efHZWlS+Oe733Po5azVJ+czEXPEPDmZKuaZocEBLwdgnYZAq0SEPAKeFu1l4xDoIwC3gx1p+o3rB6JF7x/Vd0Xf+oeS+OsIx5Z9/EOJNAuAT2jXbLGJVBcAX2juLW1MgLtENAz2qGarTF7Vq+OwQ9+MAbf//7ovfPODZNb8/GPx/hzn5utyZpN5gX0jOklcgdv5resCbZZQMDbZmDDE2i1gIB3o6hfalq9u4xHoPgC+kZ3avzHu0bihR+oL+Ad6u9dH+4euOs23ZmsqxKoEdAzbAcCBBoV0DcaFXM8gXIL6BnFrX/PbbfF4DnnxNC550bPmjWbLHTdAQfEvRdd5HN1i7sF2rIyPUPA25aNZdDcCgh4c1s6Ey+rgIBXwFvWvW/dBFoh4M1QKxRnH2NkfCK+9as740/3jG5ywOq14/Hxy27e8PUdtlwUKw/fe5Pjenoidt9uSSxZ5HOm2lcpIzcioGc0ouVYAgSSgL5hHxAg0IiAntGIVj6O7bnhhhh6z3ti8GMfi56RkVknXVmyJEaPPTbWvuUtEYOD+ViYWWZCQM+YXgZ38GZiW5pEFwUEvF3Ed2kCzQgIeDeq+aWmmR3kHALlFtA32lf/N37umrjk1xsfOba5K+287WB86fjHtW8yRibQIgE9o0WQhiFQIgF9o0TFtlQCLRDQM1qAmJEheq+7LobOOCMWnX9+9IyPzzqrya23jtFXvzpGX/vaqGy3XUZmbhp5EtAzpldLwJun3Wuu7RAQ8LZD1ZgE2igg4N2I65eaNm40QxMoqIC+0Z7Crh2fiCevvDQqdQ6/+3aL43N/vX+dRzuMQPcE9Izu2bsygbwK6Bt5rZx5E+iOgJ7RHfdWXrX3qqti8emnx8CFF0bP5OSsQ09uv32MnnBCjBx3XMTWW7fy8sYqmYCeMb3gAt6S/QBY7iYCAl6bgkDOBAS8At6cbVnTJZApAW+G2lOOe0fWxdPO/HHdgx938EPi9U/bte7jHUigWwJ6RrfkXZdAfgX0jfzWzswJdENAz+iGemuu2bdqVQytXBmLvvrVOQec3HHHGDnppBg95piIJUtac2GjlFpAzxDwlvoHwOIFvPYAgbwLCHgFvHnfw+ZPoJsC3gy1R39mwDvQ1xNHH7jTrBfbc4cl8exHbh+96QN3vQhkXEDPyHiBTI9ABgX0jQwWxZQIZFhAz8hwcWabWqUS/T/4QQyddloMXHLJnJOf2G23GDn55Bg76iifsZuzEmd9unqGgDfre9T8OivgL2ud9XY1AgsWEPBuJPRLzYK3kwEIlE5A32hPyWcGvFsO9sX3TjmoPRczKoEOCugZHcR2KQIFEdA3ClJIyyDQIQE9o0PQC71MCna/+c1YvHJl9F966dzB7t57x8ipp8bY4YdH9Pcv9KrOJ7CJgJ4xncQjmv2QlF1AwFv2HWD9uRMQ8Ap4c7dpTZhAhgS8GWpPMQS87XE1avcF9Izu18AMCORNQN/IW8XMl0B3BfSM7vrXe/XF//APMfT+9895+Lr99lsf7I4/97kRvb31Dus4Ag0L6BkC3oY3jRMKLSDgLXR5La6IAgJeAW8R97U1EeiUgDdD7ZEW8LbH1ajdF9Azul8DMyCQNwF9I28VM18C3RXQM7rrX+/V+37849j6sMM2OXz84IPXB7vrDj00wkfQ1MvpuAUI6BkC3gVsH6cWUEDAW8CiWlKxBQS8At5i73CrI9BeAW+G2uMr4G2Pq1G7L6BndL8GZkAgbwL6Rt4qZr4EuiugZ3TXv5Grb/miF8XA9763/pTxZz7zz8HuwQc3MoRjCSxYQM8Q8C54ExmgUAIC3kKV02LKICDgFfCWYZ9bI4F2CZT5zdAd943FOy+6Pn51630t552sVOKG1aMbxvUZvC0nNmCXBMrcM7pE7rIEci+gb+S+hBZAoKMCekZHuRd0sf7vfjcGzz13fbA7sd9+CxrLyQSaFdAzBLzN7h3nFVNAwFvMulpVgQUEvALeAm9vSyPQdoEyvxn6uy9cG9+45o62G6cLCHg7wuwiHRAoc8/oAK9LECikgL5RyLJaFIG2CegZbaOta+Ceu++Ovp/8JNY985l1He8gAt0W0DMEvN3eg66fLQEBb7bqYTYE5hUQ8Ap4590kDiBAYE6BMr8Zesm5l8f1d6ztyO7Y/QGL43Ov3r8j13IRAu0UKHPPaKersQkUWUDfKHJ1rY1A6wX0jNab1jNiz223xeD73hdD554bMT4ed//sZ1F54APrOdUxBLoqoGdM51+1atW0Lyxbtkze1dUd6uKdFrDhOy3uegQWKCDg3Qjol5oFbianEyihQJn7RqcC3sH+3vi35+0Zz3rE9iXcYZZcNIEy94yi1dJ6CHRKQN/olLTrECiGgJ7R2Tr23HhjDL373TF43nnRs3bjf/w6cvLJsfatb+3sZFyNQBMCeoaAt4lt45QCCwh4C1xcSyumgIBXwFvMnW1VBDojUOY3QzMD3rOOeGTssnSo5fAP3nowUsjrRaAIAmXuGUWonzUQ6IaAvtENddckkF8BPaMzteu9/voYOuOMWPTpT0fP+PgmF61suWXc/fOfR2XbbTszIVch0KSAniHgbXLrOK2gAgLeghbWsoorIOAV8BZ3d1sZgfYLlPnN0MyA94Lj9os9tl/SfnRXIJBjgTL3jByXzdQJdFVA3+gqv4sTyJ2AntHekvVefXUMnX56LPrc56JncnLWi01ut12MnnBCjLzmNRFbbtneCRmdwAIF9AwB7wK3kNMLJiDgLVhBLaf4AgJeAW/xd7kVEmifQJnfDAl427evjFxcgTL3jOJW1coItFdA32ivr9EJFE1Az2hPRfsuvzyGVq6MRV/5ypwXmNxxxxg56aQYPeaYiCX+w9f2VMKorRbQMwS8rd5Txsu3gIA33/Uz+xIKCHgFvCXc9pZMoGUCZX4zJOBt2TYyUIkEytwzSlRmSyXQUgF9o6WcBiNQeAE9o7Ul7v/BD2LotNNi4OKL5xx4YrfdIn3m7thRR0UMDrZ2AkYj0GYBPUPA2+YtZvicCQh4c1Yw0yUg4BXw+ikgQKB5gTK/GRLwNr9vnFlegTL3jPJW3coJLExA31iYn7MJlE1Az2hBxSuV6P/mN9c/inngRz+aO9jde+8YOfXUGDv88Ij+/hZc2BAEOi+gZwh4O7/rXDHLAgLeLFfH3AjMIiDgFfD6wSBAoHmBMr8ZEvA2v2+cWV6BMveM8lbdygksTEDfWJifswmUTUDPWEDFJydj4MtfXh/s9l9xxZwDrdtvv/XB7vhznxvR27uACzqVQPcF9AwBb/d3oRlkSUDAm6VqmAuBOgQEvALeOraJQwgQmEOgzG+GBLx+LAg0LlDmntG4ljMIEEgC+oZ9QIBAIwJ6RiNa1WPXrYtFn/3s+mC371e/mnOA8YMPXh/srjv00IgefwJvQtopGRTQMwS8GdyWptRFAf926yK+SxNoRkDAK+BtZt84hwCBPwuU+c2QgNdPAYHGBcrcMxrXcgYBAmX/XcMOIECgcQG/azRuNnjOObHkH/9xzhPHn/nMPwe7Bx/c+ODOIJBxAT1jeoFWrVo17QvLli2Td2V8D5teawVs+NZ6Go1A2wUEvBuJ/VLT9u3mAgQKJ1DmviHgLdx2tqAOCJS5Z3SA1yUIFFJA3yhkWS2KQNsE9IzGaXvuvju2ecxjoufee6edPPb8568Pdif226/xQZ1BICcCeoaANydb1TQ7JCDg7RC0yxBolYCAV8Dbqr1kHAJlFCjzmyEBbxl3vDUvVKDMPWOhds4nUFYBfaOslbduAs0J6BnNuQ29/e2xeOXKqPT1xdhf/mWMnHJKTD7iEc0N5iwCORLQMwS8OdquptoBAQFvB5BdgkArBQS8At5W7idjESibQJnfDAl4y7bbrbcVAmXuGa3wMwaBMgroG2WsujUTaF5Az2jOruf222Pone+M0de/PiZ32625QZxFIIcCeoaAN4fb1pTbKCDgbSOuoQm0Q0DAK+Btx74yJoGyCJT5zZCAtyy73DpbKVDmntFKR2MRKJOAvlGmalsrgYUL6BkbDXtuuikWXXBBjJ50UkSPP1kvfHcZoYgCeoaAt4j72pqaF/Bvy+btnEmgKwICXgFvVzaeixIoiECZ3wwJeAuyiS2jowJl7hkdhXYxAgUS0DcKVExLIdABAT0jovf662PorLNi0ac+FT1jY3HvBRfEukMP7YC+SxDIn4CeIeDN364143YKCHjbqWtsAm0QEPAKeNuwrQxJoDQCRX4zNFmpxBd+fmv84uY1UalUNqnpRdfeEWtGJzZ8/YLj9os9tl9SmtpbKIFmBIrcM5rxcA4BAvML6BvzGzmCAAF/10gCvVdfHUNnnBGLPvvZ6Jmc3IAy/qQnxZovf9k2IUBgFgG/Z0xHWbVq1bQvLFu2TN7lJ6dUAjZ8qcptsUUQEPB6I1SEfWwNBLolUOQ3Qx/50Q1x1nf+UDetgLduKgeWWKDIPaPEZbV0Am0V0DfaymtwAoUTKGPP6PvZz2Jo5cpYtJkQ956vfjUmnvjEwtXbgggsVKCMPWNzZgLehe4o5+ddQMCb9wqaf+kEBLwC3tJtegsm0EKBIr8ZOv7Tv4wf//7uurW+esLjYsetB+s+3oEEyihQ5J5RxnpaM4FOCOgbnVB2DQLFEShTz+j/4Q9j6LTTYuDb356zgBO77hojJ58cYy99acSg9yrF2elW0iqBMvWMeswEvPUoOabIAgLeIlfX2gopIOAV8BZyY1sUgQ4JFPnN0F994hdx+Q331CX5hF23ifcftU9dxzqIQJkFitwzylxXayfQTgF9o526xiZQPIHC94xKJfq//e31d+wO/PCHcwe7D394jJx6aoy95CUR/f3FK7QVEWiRQOF7RoNOAt4GwRxeOAEBb+FKakFFFxDwCniLvsetL/8CN909Er+9/f5MLmR8fF1ce+21G+a29957x8BAMf6AcMbFv4/r7li7YW3HPGGn2HXp4k3q8IAtBuKJu20TQwN9mayRSRHIkoA/oGSpGuZCIB8C+kY+6mSWBLIiUNieMTkZA1/96vpgt/9nP5uTe91jH7s+2B1/3vMienuzUhbzIJBZgcL2jCbFBbxNwjmtMAIC3sKU0kLKIiDgFfCWZa9bZz4FvnbVbfGPX/p1VPI5/ULN+tyX7ROPe+g2hVqTxRDotIA/oHRa3PUI5F9A38h/Da2AQCcFCtcz1q2LRRdeGEOnnx5911wzJ+X4E5+4Pthd98xnRvT483Qn95xr5VugcD1jgeUQ8C4Q0Om5F/Bv0NyX0ALKJiDgFfCWbc9bb74Ejv7Yz+MXN6/J16QLOtuPvHzf2G/nrQu6Ossi0BkBf0DpjLOrECiSgL5RpGpaC4H2CxSmZ4yOxqJPfSqGzjor+n73u7mD3Wc8I0be9KZY96QntR/XFQgUUKAwPaNFtRHwtgjSMLkVEPDmtnQmXlYBAa+At6x737rzIfDiD66K3985ko/JFniWSxb1xjdOPDC2GPQY5gKX2dI6IOAPKB1AdgkCBRPQNwpWUMsh0GaB3PeM++6LwY99LIbe857ovfnmObXGnv/8GDnllJg44IA2ixqeQLEFct8zWlweAW+LQQ2XOwEBb+5KZsJlFxDwCnjL/jNg/dkWmBnwHvDQrWPJQHY+S2lycjJWr169AXHbbbeN3oJ91tM2iwfiyAMeHI/eaatsbxazI5ADAX9AyUGRTJFAxgT0jYwVxHQIZFwg7z2j///+L7Z64QtnVa709sbYX/7l+mB38pGPzHglTI9APgTy3jNarSzgbbWo8fImIODNW8XMt/QCAl4Bb+l/CABkWmBmwHvhX+8fu223ODNz9mYoM6UwEQK5ENAzclEmkySQKQF9I1PlMBkCmRfIfc+oVGKrww6L/ssu22BdGRiIsZe+NEZOPjkmd9898zUwQQJ5Esh9z2gxtoC3xaCGy52AgDd3JTPhsgsIeAW8Zf8ZsP5sCwh4s10fsyNAoDEBf0BpzMvRBAhE6Bt2AQECjQgUoWf0f+MbsdVRR0Vl8eIYPeaYGHn966PykIc0wuBYAgTqFChCz6hzqXUdJuCti8lBBRYQ8Ba4uJZWTAEBr4C3mDvbqooiIOAtSiWtgwCBJOAPKPYBAQKNCugbjYo5nkC5BQrRMyqVGDznnBhbvjwqO+xQ7oJaPYE2CxSiZ7TQSMDbQkxD5VJAwJvLspl0mQUEvALeMu9/a8++gIA3+zUyQwIE6hfwB5T6rRxJgMCfBfQNO4EAgUYEstozeq+5Jobe+95Y+6//GpWlSxtZkmMJEGijQFZ7RhuXvNmhBbzdknfdrAgIeLNSCfMgUKeAgHcjlF9q6tw0DiucwHd/c2d845o7YnTdZObW9v3r7oq14xvn5TN4M1ciEyJAoAEBv2s0gOVQAgTWC+gbNgIBAo0IZK1n9F1xRQyddloMfOUr0VOpxNo3vzlG/v7vG1mSYwkQaKNA1npGG5da19AC3rqYHFRgAQFvgYtracUUEPBurKtfaoq5x61q8wJX3nRvvPK8K6OSEygBb04KZZoECMwq4HcNG4MAgUYF9I1GxRxPoNwCWekZfT/6USxOwe63vjWtIJPbbht3X3FFxFZblbtQVk8gIwJZ6RkZ4QgBb1YqYR7dEhAwwoHFAAAgAElEQVTwdkvedQk0KSDgFfA2uXWcVhCB/7n0xjjjkt/nZjUXve7xsf2WizIzX2+GMlMKEyGQCwE9IxdlMkkCmRLQNzJVDpMhkHmBrvaMSiX6L744hlaujIEf/GBOq/tOOy3GXvWqzFuaIIEyCHS1Z2QQWMCbwaKYUkcFBLwd5XYxAgsXEPAKeBe+i4yQZ4EP/+iGePd3/pCLJRz68AfEaYc/IlNz9WYoU+UwGQKZF9AzMl8iEySQOQF9I3MlMSECmRboSs+YnIyBr31tfbDbf/nlc/qse8xjYuTUU2P8+c+P6O3NtKPJESiLQFd6RoZxBbwZLo6pdURAwNsRZhch0DoBAa+At3W7yUh5FJgZ8B6y1wPiuY/aPnNLSXftPnqnraK/N1u/angzlLmtYkIEMi2gZ2S6PCZHIJMC+kYmy2JSBDIr0NGesW5dDHz+87F45crou+aauYPdgw6KtaeeGuuWLYvoydb7ucwW0sQIdEigoz2jQ2tayGUEvAvRc24RBPxbughVtIZSCQh4Bbyl2vAWu4nAzID32IMeEicfsiupOgW8GaoTymEECKwX0DNsBAIEGhXQNxoV+//snQmYHHW19t/qnqUnmYQESJBNWQIiQSFABFkkSgTxA4Ska0IEJMgOmoRwxU+v3ov3u+4SSCCAgBBBhHT1BARFWdSoiCwaTCTIIoisgRAge/csXd9TwyQzPZmll6qu7dfPc59rev7LOb9z6qVq3qkqxkMg3gRqohltbWq44w6l5s1T8l//GhB4+6RJyl1yiToOOwxjN95tSfYBJlATzQhw/n1Dw+ANUbEI1RMCGLyeYGVRCHhHAIO3hy0nNd71GSsHlwAGb3W1QTeq48dsCMSNAJoRt4qTLwSqJ4BuVM+QFSAQJwKeasbGjWq85Ralrr5aiddeGxBr22c+0/Uo5s4DD4wTenKFQCgJeKoZISSCwRvCohGyqwQweF3FyWIQ8J4ABi8Gr/ddxg5BJoDBW111uBiqjh+zIRA3AmhG3CpOvhCongC6UT1DVoBAnAh4ohlr1yr14x+r8dprlXjrrX5x2omE2qZMUW72bBX23TdOyMkVAqEm4IlmhJgIBm+Ii0forhDA4HUFI4tAoHYEMHgxeGvXbewURAIYvNVVhYuh6vgxGwJxI4BmxK3i5AuB6gmgG9UzZAUIxImAq5rhGLtXXaXGG25QYu3a/o3d+nq1nXKKcrNmqbDHHnFCTa4QiAQBVzUjAkQweCNQRFKoigAGb1X4mAyB2hPA4MXgrX3XsWOQCGDwVlcNLoaq48dsCMSNAJoRt4qTLwSqJ4BuVM+QFSAQJwJuaoaxerW22X9/GRs3boXQbmpS/vTTlfviF2XvskucEJMrBCJFwE3NiAIYDN4oVJEcqiGAwVsNPeYGnsCMGTNSGzZsuETSNEnOnyYWJD0n6bbRo0dfdf3117eXk0RLS8vhtm076x0uaVtJb0v6k2EYl2cymT+Vs1alYzF4MXgr7R3mRYMABm91deRiqDp+zIZA3AigGXGrOPlCoHoC6Eb1DFkBAnEi4LZmNH3960pdc80WhHZzs3Jnn638BRfIHjMmTmjJFQKRJOC2ZoQdEgZv2CtI/NUSwOCtliDzA0vANM1mSb+TdHB3kDlJSUn13f/+jaTPWJbVVkoS6XT6S4ZhXCkp0Wu9VPf/Lti2PTubzV5VylrVjMHg7aHHSU01ncTcsBLA4K2ucuhGdfyYDYG4EUAz4lZx8oVA9QTQjeoZsgIE4kTAbc0wXn9d20yYIHv4cOXPP1/5c86RPWpUnJCSKwQiTcBtzQg7LAzesFeQ+KslgMFbLUHmB5ZAOp2eaxjGxZJW2bZ9WjabfWDSpEnJMWPGtEi6UVKTpK9alvXdoZKYNm3afoVCYVm3ubs4mUzOvuOOO14+5ZRTduvs7Jwv6QTn7uBCoTChtbV1+VDrVfNzDF4M3mr6h7nhJ4DBW10NuRiqjh+zIRA3AmhG3CpOvhCongC6UT1DVoBAnAh4oRl1S5ao4+CDpWbnvgc+EIBAlAh4oRlh5oPBG+bqEbsbBDB43aDIGoEj0P1o5rckDTcMY1omk8n0DjKdTl9qGMb3JL1mWZbz8hF7sCRM07xc0hxJ/5Q0vvddv917rXAeAe3c4ZvJZBxT2bMPBm8PWk5qPGszFvaZwO+eXa17nlyljW2dW0Xy2pq8Xn7XeSDBe58Zh+ysWZM+4HPE4dke3QhPrYgUAkEggGYEoQrEAIFwEUA3wlUvooWA3wRK0YzkI4+oae5c5WbPVsdhh/kdMvtDAAI+EihFM3wMr+ZbY/DWHDkbBowABm/ACkI47hAwTfNTku6XtG7VqlXbLlmypKP3ytOnT9+ho6NjZfd3EyzL+ttgO5umea+k4wzD+FEmkzm/79h0On2tYRjO9/dblnWsO1n0vwoGbw8XTmq87DTW9ovAP1dtUMtNywb/q5NewWHwllcpdKM8XoyGQNwJoBlx7wDyh0D5BNCN8pkxAwJxJjCgZti2nDtxU3Pnqv5Pf+pC1P6JT2h9a2uccZE7BGJPgPOM4hbA4I39IRF7ABi8sW+BaAIwTdO529a56/Zhy7IO7y9L0zRfkbRz9+ObbxuMhGmaiyWdPIjBe7VhGBdJutuyrM96SRWDF4PXy/5ibf8J3LX8DX3zV8+XHIhz965j8vIpjQAXQ6VxYhQEIPAeATSDToAABMolgG6US4zxEIg3ga00Y+JENf/ud13Gbt3SpVvBWfvgg+o88MB4QyN7CMSYAOcZxcXH4I3xwUDqXQQweGmESBLo9f7dVsuy0v0laZrm45IONgzjy5lM5oeDgTBN88uSvt/fI5rPPffc+nfeeedJSXuX+k7faqBj8PbQ46Smmk5iblAJLF72hv7fr0szeMc2N2jh6R/WjiMbg5pO4OJCNwJXEgKCQKAJoBmBLg/BQSCQBNCNQJaFoCAQWAJbNKOzUzv/6U/a/957Vf/00wPGu2n2bOX+678Cmw+BQQAC3hLgPKOYLwavt/3G6sEngMEb/BoRYQUE0un09YZhnGMYxm2ZTOa0/pYwTfP3kj5u2/Zl2Wz2m4NtY5rmNpKWSXJedNna2dk5e/Hixa+YprmzYRhX2LZtSnqpvb19/7vuuuvdCkIueQoGbw8qTmpKbhsGhohAX4P3sN1H6fSJO22VQX2doX3f16ym+mSIsvM/VHTD/xoQAQTCRADNCFO1iBUCwSCAbgSjDkQBgbAQyK9bp9e//33ttXixml9/fcCw2486SrlLLlHH4YdLBr/ODUt9iRMCbhPgPKOYKAav2x3GemEjwBlB2CpGvCURSKfTCw3DOGMIg3eJpKMkfcuyrK8PtfC0adP2LhQKiyQd0D12k6Sm7v/9RCKROGXRokXPDrVOtT/va/COHz++2iVDO7+trU1PPPHElvgnTJighoaG0OZD4BBwCPz8ybf0nQf/vQXGCeO3039+ajfguEQA3XAJJMtAICYE0IyYFJo0IeAiAXTDRZgsBYEoE9i4UcN+9jMNu+Ya1Q1i7OaOPVYbZs1S+4QJUaZBbhCAQIkEOM8oBrVixYqiLyZPnozfVWIvMSwaBGj4aNSRLPoQ8NDgvVnSYf0A/01nZ+dZixcv7nFlPKpKX4N3/fr1Hu3EshCAgB8E/vyGoTte6Lkr99CxBU3fs+BHKOwJAQhAAAIQgAAEIAABCEAAAi4SqNu4Ubv/6lfa4+67lVqzpt+V7URCrx5+uJ6dOlXrduOPfV3Ez1IQgEDECDQ3NxdlhMEbsQKTzpAEMHiHRMSAMBIox+A1DON/M5nMNwbLc8qUKbskk8nHJO0o6c5EIvHNpqamZ9atW7ebYRjfNAyjRdKLdXV1h95+++1veMkMg9dLuqwNAf8JYPD6XwMigAAEIAABCEAAAhCAAAQg4CaB+rVrtecvfqHdf/lLNWzY0O/Shbo6vTxpkp6bMkUbdtr6NT1uxsNaEIAABKJAAIM3ClUkh2oIYPBWQ4+5gSVgmuaNks6ybfv2bDb7uf4C7fUO3m9ks9n/HSwZ0zSdO3dnSFpiWdYn+ow1TNP8g6QjDMO4MZPJnOMlGAxeL+myNgT8J4DB638NiAACEIAABCAAAQhAAAIQgIAbBBrfflvjfv5z7XbffarL5fpdsrOhQf/+1Kf03EknKTdmjBvbsgYEIACBWBDA4I1FmUlyEAIYvLRHJAmk0+l5hmHMlHS3ZVmf7S9J0zSdO3In2rZ9cTabvXKw48Q0Tec5yMMMwzg5k8nc1XdsOp3+rGEYzvebxo8f33zZZZd59jxV3sHbQ5/3TkTy8I19UryD19sWQDe85cvqEIgaATQjahUlHwh4TwDd8J4xO0AgFAQ2bdLIb35TTXfcISOf7zfkQnOz1p1+uv58yCHKjxrVNWbChAlqaGgIRYoECQEI1J4A5xnFzHkHb+17kB2DRQCDN1j1IBqXCKTT6UsNw/iepCcsyzqwv2VN03xZ0i6SpliWdedAW0+fPn2Hjo6Olc7PC4XC/q2trcv7jp06der4RCLxpPN9Mpnc+Y477njNpVS2WqavwXvggf2m59X2gVo3n8/r0Ucf3RLTIYccosbGxkDFSDAQKJfA4mVv6P/9+vkt0076yFj993Hjyl2G8QMQQDdoDQhAoBwCaEY5tBgLAQg4BNAN+gACEOgiYNsa8clPqm7Zsq2AFEaPVv6885Q/91zlmpr4vQYtAwEIlEyA84xiVEuXLi36gnfwltxKDIwIAQzeiBSSNIoJpNPp4wzDuFdSWy6XG33PPfds7D3i5JNPHltXV+eYtkahUNi7tbX1uYEYnnjiiSMaGxvXvnd+bh+bzWbv7zu2paVlsm3bDzjft7e3j77rrrve9aomGLw9ZDmp8arLWNdPAhi83tJHN7zly+oQiBoBNCNqFSUfCHhPAN3wnjE7QCAsBOrvvlvNM5y3fb33Keywg3IXXqj8mWdKzc1d36EZYakmcUIgGATQjOI6YPAGoy+Jwj8CGLz+sWdnDwmccMIJw1Kp1FuSmmzbviibzV7Te7uWlpav2Lb9XUn/tCxrr6FCMU1zhaR9DcO4JZPJnNF3fEtLy09s2/68pGcty/rgUOtV83MM3h56nNRU00nMDSoBDF5vK4NueMuX1SEQNQJoRtQqSj4Q8J4AuuE9Y3aAQGgIFAoa+bGPSbmc8jNnKn/qqVIqVRQ+mhGaahIoBAJBAM0oLgMGbyDakiB8JIDB6yN8tvaWgGmaznt1Z0ly3p975vjx4xevWLHCsG3bNAzjZkmp3ubvpEmT6saOHXurbdvvk3SGZVkvbY7QNM2zJN3o/NswjO+3t7dffuedd77p3AlcX19/iW3bl3b/7NxMJnODl5lh8PbQ5aTGy05jbS8J/OaZ1co8sVLr8h1bbfPOxnatXNu25Xse0exuJdANd3myGgSiTgDNiHqFyQ8C7hNAN9xnyooQCBwB25Y6O6W6uiFDS/zrXyrssotUX9/vWDRjSIQMgAAEehFAM4rbAYOXwyPuBDB4494BEc6/+9HKSyRtfkltvjvdzS9pvUfSyZZldTrfT5s2bWKhUHise8yllmX9oDeelpaWBbZtX9jru03OHcK9/n2tZVm9f+4JXQzeHqyc1HjSYizqMYHX1uR0wo+WqmCXthEGb2mcSh2FbpRKinEQgIBDAM2gDyAAgXIJoBvlEmM8BEJEoFBQ/a9/rdTcuWo7/njlZ8+uOng0o2qELACBWBFAM4rLjcEbq/Yn2X4IYPDSFpEmYJpmk2EY/2Hb9jRJe0hyzNynJf1EkmPIdpm7zqfbEH5Y0thEInHcokWLit/SLimdTh9vGMYFkj4qaZQk5127jil8jWVZv6wFTAzeHsqc1NSi49jDbQK/e3a15tz5TMnLnnnozpp51AdKHs/AwQmgG3QIBCBQDgE0oxxajIUABBwC6AZ9AIEIEujsVP1dd6lp7lwl//GPrgQL22+vNX/7mzRsWFUJoxlV4WMyBGJHAM0oLjkGb+wOARLuQwCDl5aAQMgIYPD2FIyTmpA1L+F2Efjts6t1SYkG75jmBt106n7aZVTxe5pAWTkBdKNydsyEQBwJoBlxrDo5Q6A6AuhGdfyYDYFAEWhrU8OiRUrNm6fkCy9sFdrG73xH+fPOqypkNKMqfEyGQOwIoBkYvLFrehIelAAGLw0CgZARwODF4A1ZyxJuHwJ9Dd6Ddh2piz+x21ac6pKG9tiuSfXJBAxdJMDFkIswWQoCMSCAZsSgyKQIAZcJoBsuA2U5CPhBYONGNd56q1JXXaXEa68NGEF+xgxtnDu3qgjRjKrwMRkCsSOAZmDwxq7pSRiDlx6AQJQIYPBi8Eapn+OYS1+D9xN7bau5U/aJIwpfcuZiyBfsbAqB0BJAM0JbOgKHgG8E0A3f0LMxBKonsHatGm+6Salrr1Vi1ap+17MNQ+0nnaTcnDnqHD++6j3RjKoRsgAEYkUAzcDgjVXDk+yQBLiDd0hEDIBAsAhg8GLwBqsjiaZcAhi85RJzdzwXQ+7yZDUIRJ0AmhH1CpMfBNwngG64z5QVIeA1AePtt9V43XVqvOEGJdas6d/YratTW0uLcrNnqzBunGshoRmuoWQhCMSCAJqBwRuLRifJkglg8JaMioEQCAYBDF4M3mB0IlFUSgCDt1Jy7szjYsgdjqwCgbgQQDPiUmnyhIB7BNAN91iyEgS8JmCsXKnUggVqXLhQxoYN/Ru7qZTyp5+u3Je+JHuXXVwPCc1wHSkLQiDSBNAMDN5INzjJlU0Ag7dsZEyAgL8EMHgxeP3tQHavlgAGb7UEq5vPxVB1/JgNgbgRQDPiVnHyhUD1BNCN6hmyAgS8JpB46SU1zp+vxttuk5HP92/sNjcr/4UvKHfBBbJ32MGzkNAMz9CyMAQiSQDNwOCNZGOTVMUEMHgrRsdECPhDAIMXg9efzmNXtwhg8LpFsrJ1uBiqjBuzIBBXAmhGXCtP3hConAC6UTk7ZkLAawKJZ59Vat48NWQyMjo7+92uMGqU8uedp/y558oePdrrkIRmeI6YDSAQKQJoBgZvpBqaZKomgMFbNUIWgEBtCWDwYvDWtuPYzW0CGLxuEy1vPS6GyuPFaAjEnQCaEfcOIH8IlE8A3SifGTMg4DkB29awiy5Sw6JFMmy7f2N37FjlLrxQ+TPPlEaM8DykzRugGTVDzUYQiAQBNKO4jEuXLi36YvLkyfhdkeh0kiiVAA1fKinGQSAgBDB4ewrBSU1AmpIwyiKAwVsWLtcHoxuuI2VBCESaAJoR6fKSHAQ8IYBueIKVRSFQNYGmL39ZqR//eKt1OnfZRfmZM5U/9VSpqanqfcpdAM0olxjjIRBvAmhGcf0xeON9PJC9hMFLF0AgZAQweDF4Q9ayhNuHAAavvy3BxZC//NkdAmEjgGaErWLECwH/CaAb/teACCDQH4HEyy9r5EEHyejo6Ppx57hxys2apbaWFqm+3jdoaIZv6NkYAqEkgGZg8IaycQnaMwIYvJ6hZWEIeEMAgxeD15vOYtVaEcDgrRXp/vfhYshf/uwOgbARQDPCVjHihYD/BNAN/2tABBAYiIDzmObk8uXKzZmj9hNPlJJJ32GhGb6XgAAgECoCaAYGb6galmA9J4DB6zliNoCAuwQweDF43e0oVqs1AQzeWhMv3o+LIX/5szsEwkYAzQhbxYgXAv4TQDf8rwERxIxAZ6eMd9+Vvd12Qye+fr00fLhkBOfXoWjG0GVjBAQgwO9CB+oBHtHM0RF3AsE5o4l7JcgfAiUSwODlpKbEVmFYQAlg8PpbGH6B4i9/dodA2AigGWGrGPFCwH8C6Ib/NSCCmBBoa1NDJqPUvHnq3GsvbfjZz0KZOJoRyrIRNAR8I4BmFKPH4PWtFdk4IAQweANSCMKAQKkEMHgxeEvtFcYFkwAGr7914WLIX/7sDoGwEUAzwlYx4oWA/wTQDf9rQAQRJ7Bpkxp/+lOl5s9X4tVXtyS79o9/VOf48aFLHs0IXckIGAK+EkAzMHh9bUA2DxwBDN7AlYSAIDA4AQxeDF6OkXATwOD1t35cDPnLn90hEDYCaEbYKka8EPCfALrhfw2IIKIE1q5V48KFSl1zjRJvvrlVkm0nn6wNP/5x6JJHM0JXMgKGgK8E0AwMXl8bkM0DRwCDN3AlISAIYPCW2gOc1JRKinFBIoDB62810A1/+bM7BMJGAM0IW8WIFwL+E0A3/K8BEUSLgPH222r80Y/UeP31SqxZ029ydl2d2qZP18YrrpASiVABQDNCVS6ChYDvBNAMDF7fm5AAAkUAgzdQ5SAYCAxNgDt4exhxUjN0vzAieAQweP2tCbrhL392h0DYCKAZYasY8ULAfwLohv81IIJoEDDeeEOpBQvUePPNMjZs6N/YbWxU/vTTlf/Sl1TYdddQJo5mhLJsBA0B3wigGRi8vjUfGweSAAZvIMtCUBAYmAAGLwYvx0e4CWDw+ls/Lob85c/uEAgbATQjbBUjXgj4TwDd8L8GRBBuAomXX1bj/Pld79k18vn+jd3mZuW/8AXlLrhA9g47hDphNCPU5SN4CNScAJqBwVvzpmPDQBPA4A10eQgOAlsTwODF4OW4CDcBDF5/68fFkL/82R0CYSOAZoStYsQLAf8JoBv+14AIwkkg8dxzSl15pRosS0ZHR79JFEaNUv6885Q/91zZo0eHM9E+UaMZkSgjSUCgZgTQDAzemjUbG4WCAAZvKMpEkBDoIYDBi8HL8RBuAhi8/taPiyF/+bM7BMJGAM0IW8WIFwL+E0A3/K8BEYSLQPLvf1dq7lzV3323DNvu39gdO1a5Cy9U/swzpREjwpXgENGiGZEqJ8lAwHMCaAYGr+dNxgahIoDBG6pyESwEJAxeDF6Og3ATwOD1t35cDPnLn90hEDYCaEbYKka8EPCfALrhfw2IIBwEko891mXsNtx//4ABd+6yi/IzZyp/6qlSU1M4EiszSjSjTGAMh0DMCaAZGLwxPwRIvw8BDF5aAgIhI4DBi8EbspYl3D4EMHj9bQkuhvzlz+4QCBsBNCNsFSNeCPhPAN3wvwZEEHwCw+bMUePChQMbu+PGKTdrltpaWqT6+uAnVEWEaEYV8JgKgRgSQDMweGPY9qQ8CAEMXtoDAiEjgMGLwRuyliVcDN5A9QAXQ4EqB8FAIPAE0IzAl4gAIRA4AuhG4EpCQAEk4Lxnd/h5520VWcf48crNmaP2E0+UkskARu5+SGiG+0xZEQJRJoBmYPBGub/JrXwCGLzlM2MGBHwlgMGLwetrA7J5SQTuWv6Gbn7kVa3Z1LHV+LbOgja1F7Z8/4m9ttXcKfuUtC6DqifAxVD1DFkBAnEigGbEqdrkCgF3CKAb7nBklYgT6OjQyI9+VMkXX+xKtOPgg5W75BK1H3OMZMTrV5VoRsR7nfQg4DIBNKMY6NKlS4u+mDx5crz+I+Jyf7Fc+AjQ8OGrGRHHnAAGb08DcFIT84MhoOm/tb5Nn1rwl5Kjw+AtGZUrA9ENVzCyCARiQwDNiE2pSRQCrhFAN1xDyUIRJ9CwcKEa7rqr647djiOPjJ2xu7m8aEbEG530IOAyATQDg9fllmK5kBPA4A15AQk/fgQweDF449f14cp46ctrddbPniw56FMOep++MnmPksczsDoCXAxVx4/ZEIgbATQjbhUnXwhUTwDdqJ4hK4SYwKZNSj7/vDr322/oJGw7tqZubzhoxtCtwggIQIDfhQ7UA9zBy9ERdwIYvHHvAPIPHQEMXk5qQte0MQu4HIN3rzHDNC/9Ie04sjFmlPxLl1+g+MeenSEQRgJoRhirRswQ8JcAuuEvf3b3icC6dWq8+Walrrmm6925a5xHZjZyjVNKNdCMUigxBgIQ2EwAzSjuBQxejo24E8DgjXsHkH/oCGDwYvCGrmkDGPDaXIcWLX1dr76bdz261Rva9NAL725Z98M7Nmu++aGt9kkahkak6lzfnwUHJ8DFEB0CAQiUQwDNKIcWYyEAAYcAukEfxImA8c47arz+ejX+6EdKvNtzDbRh7ly1zZgRJxQV54pmVIyOiRCIJQE0A4M3lo1P0gMSwOClOSAQMgIYvD0F46QmZM0boHAvXPSU/vxizy8gvAxtwi4jdNOpH/ZyC9YugwC6UQYshkIAAhg19AAEIFA2Ac41ykbGhBASMN54o+tuXeeuXWP9+q0y6NxtN6197DGpjj9oHaq8aMZQhPg5BCDQmwCagcHLEQGB3gQweOkHCISMAAYvBm/IWjZw4RZsWxN/8GcV7NqEdvgeo3S1uW9tNmOXIQlwMTQkIgZAAAK9CKAZtAMEIFAuAXSjXGKMDxMB45VXlJo/X40//amMXK7f0O3hw5U/80xtuvRSqbk5TOn5Eiua4Qt2NoVAaAmgGcWl4xHNoW1lAneJAAavSyBZBgK1IoDB20Oak5padV209uks2Dr4B3+uWVLf/+ze+tQ+29dsPzYanAC6QYdAAALlEEAzyqHFWAhAwCGAbtAHUSSQ+Oc/lbrySjVkMjI6OvpNsTBqlPLnntv1f/a220YRgyc5oRmeYGVRCESWAJpRXFoM3si2OomVSACDt0RQDINAUAhg8PZUgpOaoHRluOLoz+D9r0/v6XoSCUPad8dm7TVmuOtrs2DlBNCNytkxEwJxJIBmxLHq5AyB6gigG9XxY3awCCRXrFBq7lzV//znMgqFfoMrjBmj3EUXdd21qxEjgpVACKJBM0JQJEKEQIAIoBnFxcDgDVBzEoovBDB4fcHOphConAAGbw87Tmoq76M4z+xr8DpG7F8vPSzOSGKVO7oRq3KTLASqJoBmVI2QBSAQOwLoRuxKHsmEk48/3mXsNtx334D5FXbeWbmZM5U/7TSpqSmSHGqRFJpRC8rsAYHoEEAzMHij081k4gYBDF43KLIGBGpIAIMXg7eG7RbJrTB4I1nWkpPiYqhkVAyEAAR41Co9AAEIVECAc40KoDElGDgRKOcAACAASURBVARsW3V//ON7d+z+4Q8DxtS5557KzZqltpYWqaEhGLGHOAo0I8TFI3QI+EAAzcDg9aHt2DLABDB4A1wcQoNAfwQweDF4OTKqI4DBWx2/sM/mYijsFSR+CNSWAJpRW97sBoEoEEA3olDFmOVg26q//36lLr9cdX/5y4DJd+y7r3IXX6z2k06SksmYQfIuXTTDO7asDIEoEkAzMHij2NfkVDkBDN7K2TETAr4QwODF4PWl8SK0KQZvhIpZQSpcDFUAjSkQiDEBNCPGxSd1CFRIAN2oEBzTfCOQ+s531PSDHwxs7B54oHKXXKL2Y4+VEgnf4ozqxmhGVCtLXhDwhgCagcHrTWexalgJYPCGtXLEHVsCGLwYvLFtfpcSx+B1CWRIl+FiKKSFI2wI+EQAzfAJPNtCIMQE0I0QFy+moSeef14jDzlERqFQRKD9iCOUmzNHHUcdJRn8+tCr9kAzvCLLuhCIJgE0A4M3mp1NVpUS4AytUnLMg4BPBDB4MXh9ar3IbIvBG5lSVpQIF0MVYWMSBGJLAM2IbelJHAIVE0A3KkbHRB8JDD/7bDUsXtwVQdsxx3Q9irnzkEN8jCg+W6MZ8ak1mULADQJoBgavG33EGtEhgMEbnVqSSUwIYPBi8Mak1T1LE4PXM7ShWJiLoVCUiSAhEBgCaEZgSkEgEAgNAXQjNKUi0F4EEk891fWYZueO3c4Pfxg2NSSAZtQQNltBIAIE0AwM3gi0MSm4SACD10WYLAWBWhDA4MXgrUWfRXkPDN4oV3fo3LgYGpoRIyAAAc416AEIQKByApxrVM6Ome4SMN59V3VLlqj9pJPcXZjVXCWAZriKk8UgEHkCaAYGb+SbnATLIoDBWxYuBkPAfwIYvPzS1f8uDH4E63IdWvjoq3rx7U39BvvbZ9/e8n3CkP566WHBT4oIXSHAxZArGFkEArEhgGbEptQkCgHXCKAbrqFkoQoJGG++qdQ116jxppukDRu09s9/VmHvvStcjWleE0AzvCbM+hCIFgE0o7ieS5cuLfpi8uTJ+F3RanmyGYIADU+LQCBkBDB4ewrGSU3ImreG4V5y59PqbeIOtjUGbw0LE4Ct0I0AFIEQIBAiAmhGiIpFqBAICAF0IyCFiGEYxiuvKHXVVWq89VYZudwWAvnp07VxwYIYEglHymhGOOpElBAICgE0A4M3KL1IHMEggMEbjDoQBQRKJoDBi8FbcrPEeOCkeY9pTa6jJAIjU3X6/ayPljSWQeEnwMVQ+GtIBhCoJQE0o5a02QsC0SCAbkSjjmHKIvH880pdeaUaFi2S0bH1NZBdV6e1f/2rCrvuGqa0YhMrmhGbUpMoBFwhgGZg8LrSSCwSGQIYvJEpJYnEhQAGLwZvXHq9mjyPvPJRrc93lrTEOYftoguPfH9JYxkUfgJcDIW/hmQAgVoSQDNqSZu9IBANAuhGNOoYhiwSTz2lprlzVX/XXTIKhX5DLowZo9yFFyr/hS9II0aEIa3YxYhmxK7kJAyBqgigGRi8VTUQkyNHAIM3ciUloagTwODF4I16j5ea39NvrNc/V23sd/i37ntBuY6eX3L8z/8Zp6b65FZjdx2V0t5jh8kw+M9hqdzDPo6LobBXkPghUFsCaEZtebMbBKJAAN2IQhWDnUPyL39R6oor1PCrXw0YaGGnnZSbOVP5006Thg0LdkIxjw7NiHkDkD4EyiSAZmDwltkyDI84AX6jHfECk170CGDwYvBGr6vLzyjzxEp95/4XSp74h1kf1YhUXcnjGRhdAlwMRbe2ZAYBLwigGV5QZU0IRJsAuhHt+vqWnW2r7qGHlHLu2P397wcMo3OPPZSbNUtt06ZJDQ2+hcvGpRNAM0pnxUgIQEBCMzB4OQ4g0JsABi/9AIGQEcDgxeANWct6Eu4pNy/TM29uKHnth2YfouGNW9/BW/ICDIwMAS6GIlNKEoFATQigGTXBzCYQiBQBdCNS5fQ/GcfYfeABNV1+ueoef3xgY/dDH9KmOXPU/tnPSnX8Yav/hSs9AjSjdFaMhAAEMHj79sDSpUuLvpo8eTJ+FwdKrAjQ8LEqN8lGgQAGLwZvFPq42hxO/NFSvfxurqRl9tuxWbd+/iMljWVQ9AnwC5To15gMIeAmATTDTZqsBYF4EEA34lFnz7Ps7FT9Pfd0PYq57u9/H3C7jgMPVM4xdj/9aSmR8DwsNnCfAJrhPlNWhECUCaAZxdXF4I1yt5NbKQQweEuhxBgIBIgABm9PMTipCVBj1jiUvgbvUeNGq7lx679Uf9/IBp1y4I7avpnHk9W4RIHdDt0IbGkIDAKBJIBmBLIsBAWBQBNANwJdnuAH196uhmxWqSuvVPK55waMt/3ww7uM3Y5JkySDX+0Fv7ADR4hmhLl6xA6B2hNAMzB4a9917BhkApwFBrk6xAaBfghg8GLwcmBIfQ3en587Qe8f3QQaCAxJgIuhIRExAAIQ6EUAzaAdIACBcgmgG+USY3xvAg2LFmn4BRcMbOxOntz1KObOQw8FXEQIoBkRKSRpQKBGBNAMDN4atRrbhIQABm9ICkWYENhMAIMXg5ejAYOXHqicABdDlbNjJgTiSADNiGPVyRkC1RFAN6rjF/vZbW3a5sADlXjttS0obMNQ+wkndN2x2/kRXj0TtR5BM6JWUfKBgLcE0AwMXm87jNXDRgCDN2wVI97YE8DgxeCN/UEgDF56oHICXAxVzo6ZEIgjATQjjlUnZwhURwDdqI4fs6XG66/XsP/7f2Unk2ozTeVmzVLhgx8ETUQJoBkRLSxpQcAjAmgGBq9HrcWyISWAwRvSwhF2fAlg8GLwxrf7ezLnEc10QaUEuBiqlBzzIBBPAmhGPOtO1hCohgC6UQ095nYR2LRJTd/6lvLnnKPCBz4AlIgTQDMiXmDSg4DLBNAMDF6XW4rlQk4AgzfkBST8+BHA4MXgjUPX59o79aM/vaynVm7oN91lr65TvqOw5We8gzcOXeFOjlwMucORVSAQFwJoRlwqTZ4QcI8AuuEeyyitZLzyihp/8hPlLr1Uqq+PUmrkUiUBNKNKgEyHQMwIoBkYvDFredIdggAGLy0CgZARwODF4A1Zy1YU7g9/8y/d9pfXS56LwVsyqtgP5GIo9i0AAAiURQDNKAsXgyEAAUnoBm3Qm0DihReUuvJKNSxaJKO9XRuuuUZtp5wCJAhsIYBm0AwQgEA5BNAMDN5y+oWx0SeAwRv9GpNhxAhg8GLwRqyl+03n87cu199fW19yqr+bOVGjmvhL+JKBxXggF0MxLj6pQ6ACAmhGBdCYAoGYE0A3Yt4A3eknnnpKTVdcofo775RR6HnyUOdee2ntn/8sJRKAgkAXATSDRoAABMohgGZg8JbTL4yNPgEM3ujXmAwjRgCDF4M3Yi3dbzqn37JcT75emsF70kfG6r+PGxcHLOToAgEuhlyAyBIQiBEBNCNGxSZVCLhEAN1wCWRIl0kuXarU3LlquPfeATNYf8staj/++JBmSNhuE0Az3CbKehCINgE0A4M32h1OduUSwOAtlxjjIeAzAQxeDF6fW9C17f+xcr2cd+kW7K2XvPXxV7VybduWH3xl8u7afbumrQZu39ygPbZrkmHwnzPXChPxhbgYiniBSQ8CLhNAM1wGynIQiAEBdCMGRe6bom2r7uGHlbr8ctUvWTIggM7dd1du1qz3HtHc0BBDUKTcHwE0g76AAATKIYBmFNNaunRp0ReTJ0/mF4TlNBRjQ0+Ahg99CUkgbgQweHsqzklNeLv/d8+u1iV3PqN+vN1+k7rl9A/rwzuNCG/CRB4YAuhGYEpBIBAIBQE0IxRlIkgIBIoAuhGocngbjGPsPvigmubOVd2jjw5s7O6zjzbNmaP2k06S6uq8jYnVQ0cAzQhdyQgYAr4SQDMweH1tQDYPHAEM3sCVhIAgMDgBDF4M3igcI1++6xk9+MzqklO5fcZHtM8OzSWPZyAEBiLAxRC9AQEIlEMAzSiHFmMhAAGHALoRgz4oFFR/zz1KXXGF6pYvHzDhjgkTlHOM3eOO4527MWiLSlNEMyolxzwIxJMAmoHBG8/OJ+uBCGDw0hsQCBkBDF4M3pC1bL/hfsl6Sg+98G5JqbxvRIPuPu9A1ScTJY1nEAQGI8DFEP0BAQiUQwDNKIcWYyEAAQzeiPdAe7saWlu7jN3kc88NmGz7YYd1Gbsdn/iExKtkIt4U1afHuUb1DFkBAnEigGZg8Map38l1aAIYvEMzYgQEAkUAg7enHJzUBKo1ywqmr8H78T1Ha6dRjVutMbqpXsfvN0Y7bZMqa30GQ2AgAugGvQEBCJRDAM0ohxZjIQABhwC6EcE+yOXUcPvtSs2bp+RLLw2YYPvkyV2PYu489NAIQiAlrwigGV6RZV0IRJMAmlFcV97BG80+J6vSCWDwls6KkRAIBAEM3p4ycFITiJasKIi+Bu/89D46cs9tK1qLSRAohwC6UQ4txkIAAmgGPQABCJRLAN0ol1iAx2/YoMaFC5VasECJlSv7DdQ2DLUff3zXHbud++8f4GQILagE0IygVoa4IBBMAmhGcV0weIPZp0RVOwIYvLVjzU4QcIUABm8PRk5qXGkpXxbB4PUFO5tyVw09AAEIlEmAc40ygTEcAhDgDt4I9UDiqae0zRFH9G/sJpNqS6eVmzVLhX32iVDWpFJrApxr1Jo4+0Eg3ATQDAzecHcw0btNAIPXbaKsBwGPCWDwYvB63GI1WR6DtyaY2aQfAlwM0RYQgEA5BNCMcmgxFgIQcAigG9Hqg+Gnn66GX/5yS1J2Q4PaPvc55WbOVGG33aKVLNn4QgDN8AU7m0IgtATQDAze0DYvgXtCAIPXE6yxWtRoaWk5RtIRtm3vJWkbSQ2WZR29mYJpmmMKhcKOra2ty2NFxqNkMXh7wHJS41GT1WBZDN4aQGaLfgmgGzQGBCBQDgE0oxxajIUABBwC6Ea0+iD5xBMaefTRsocNU/6MM5S76CLZO+0UrSTJxlcCaIav+NkcAqEjgGZg8IauaQnYUwIYvJ7ijfbi6XR6imEYV0rauVemTk/ZlmUlN39nmuavJX1M0v+xLOuhaFPxPjsM3h7GnNR4329e7YDB6xVZ1h2KALoxFCF+DgEI9CaAZtAPEIBAuQTQjXKJBX98w09/qvbjjpO93XbBD5YIQ0cAzQhdyQgYAr4SQDOK8fMOXl/bkc0DQACDNwBFCGMIpmnOkfQDSb17qM25e7e3wTtp0qS6MWPG3CPpWEkv5/P5/e6+++51Ycw5KDFj8GLwBqUXq4kDg7caesythgAXQ9XQYy4E4kcAzYhfzckYAtUSQDeqJej9fOfduql585T72tdU+MAHvN+QHSAwCAE0g/aAAATKIYBmYPCW0y+MjT4BDN7o19j1DKdNm7Z/oVD4q6SEpH9I+no+n3+wsbFxT0lL+97Be+qpp45sa2tbIWkn27YvyWazzl2/fCokgMHbA46TmgqbKADTMHgDUISYhoBuxLTwpA2BCgmgGRWCYxoEYkwA3Qhu8ZNLlyo1d64a7r23K8j8mWdq4+WXBzdgIosFATQjFmUmSQi4RgDNKEbJHbyutRYLhZQABm9IC+dn2C0tLTfZtj1D0rMNDQ0fve2229Y68XQbv0/0NXidn6XT6a8bhvE/kh6yLOvjfsYf9r0xeHsqyElNeLsZgze8tQt75OhG2CtI/BCoLQE0o7a82Q0CUSCAbgSsiratuocfVuryy1W/ZElRcHZDg9Y88YTsHXcMWNCEEycCaEacqk2uEKieAJqBwVt9F7FClAhg8EapmjXKxTTNFyR9wLbts7PZ7M2btx3C4D3aMIwHJK20LGunGoUayW0weDF4o9DYGLxRqGI4c+BiKJx1I2oI+EUAzfCLPPtCILwE0I2A1M4xdh98UE1z56ru0UcHDGrTN76h3MUXByRowogjATQjjlUnZwhUTgDNwOCtvHuYGUUCGLxRrKrHOZmmucl5124ikTh00aJFj5di8E6dOvXgRCLxmKQ2y7JSHocY6eUxeDF4o9DgGLxRqGI4c+BiKJx1I2oI+EUAzfCLPPtCILwE0A2fa1coqP4Xv1DqiitUt2zZgMF0HHCAcnPmqP0zn5ESztun+EDAHwJohj/c2RUCYSWAZhRXjkc0h7WTidstAhi8bpGM0Tqmab4raYRt28dls9n7N6c+2B28pmmmJWUkvWFZFs8/qqJfMHgxeKton8BMxeANTCliFwgXQ7ErOQlDoCoCaEZV+JgMgVgSQDd8KntHhxpaW7uM3eSzzw4YRPvHPtZl7HZ88pOSwa/EfKoW2/YigGbQDhCAQDkE0AwM3nL6hbHRJ8DZbPRr7HqGpmk6L645UtLNlmWdXaLB2yrpZEkPWJZ1rOtBxWhBDF4M3ii0OwZvFKoYzhy4GApn3YgaAn4RQDP8Is++EAgvAXSjxrXL59Vw++1KzZun5L//PbCxe/TR7xm7H/tYjQNkOwgMTgDNoEMgAIFyCKAZGLzl9Atjo08Agzf6NXY9Q9M0z5J0g6SCbdsXZbPZHzmbDHQHb0tLy2m2bd8iyZZ0gWVZ17seVIwWxODF4A1KuxdsW/9+e5PaO51Du7zPdx94QU+8sm7LpPnpfXTkntuWtwijIVABAS6GKoDGFAjEmACaEePikzoEKiSAblQIrtxpGzao8Sc/UWrBAiVef33A2W3HH99l7HYecEC5OzAeAjUhgGbUBDObQCAyBNCM4lLyiObItDaJVEgAg7dCcHGeZppmUtJDkg7pNm1/K+lG27bfMQzj1853lmUlp06dOt4wjAsMwzhfktNr/1y1atX4JUuWdMSZX7W5Y/D2EOSkptpuqnz+m+vadM7tT+qld3KVL9JrJgavKxhZpAQC6EYJkBgCAQhsIYBm0AwQgEC5BNCNcomVN95Ys0aNN96oxuuuU2L16n4n28mk2qZOVW72bBX22ae8DRgNgRoTQDNqDJztIBByAmhGcQExeEPe0IRfNQEM3qoRxnOBk08+eWxdXd3DkvboNnn7gnBu6dvcX87/f8d5rLNlWU/Fk5h7WWPw9rDkpMa9vip3pR//+RVd/YeXyp024Pj56Q/pyD1Hu7YeC0FgIALoBr0BAQiUQwDNKIcWYyEAAYcAuuFRH6xb1/UY5tQNN8hY1/MkoN672Q0Naps+XblZs1TYbTePAmFZCLhLAM1wlyerQSDqBNCM4gpj8Ea948lvKAIYvEMR4ucDEjBNs9kwjCts254hybmrd6DPQ52dnTMWL178AjirJ4DB28OQk5rq+6nSFX74m3/ptr8M/Ci0ctZNGNK95x+kHUY2ljONsRCoiAC6URE2JkEgtgTQjNiWnsQhUDEBdKNidINP3LRJ20yYoMSbb241zm5qUv6MM5T74hdl77STRwGwLAS8IYBmeMOVVSEQVQJoRnFlMXij2unkVSoBDN5SSTFuQALO3bz19fWmbdsfkeRcTaUMw3inUCg8nUwm7120aNEj4HOPAAZvD0tOatzrq3JX6mvwbj+8XqOH1Ze7jEak6nTKge/Tp/bZvuy5TIBAJQTQjUqoMQcC8SWAZsS39mQOgUoJoBuVkht6XuP8+Rp22WVbBtojRih3zjnKn3++7O25nhiaICOCSADNCGJViAkCwSWAZhTXBoM3uL1KZLUhgMFbG87sAgHXCGDw9qDkpMa1tip7ob4G7yWf3E2nTeSv5csGyYSaE0A3ao6cDSEQagJoRqjLR/AQ8IUAuuEh9nXrtM3++0vJpPIXXKDc2WdLI0d6uCFLQ8B7AmiG94zZAQJRIoBmYPBGqZ/JpXoCGLzVM2QFCNSUAAYvBm9NG26AzTB4g1AFYqiEABdDlVBjDgTiSwDNiG/tyRwClRJANyokZ9uSMfSvqJKPP67OffeVhg+vcCOmQSBYBNCMYNWDaCAQdAJoBgZv0HuU+GpLYOiz59rGw24hIGCa5m8l2Z2dneeU+l5d0zSnSfqOpGWWZZ0cgjQDGyIGLwZvEJoTgzcIVSCGSghwMVQJNeZAIL4E0Iz41p7MIVApAXSjPHJ1Dz+s1OWXK3/aaWo/mV8VlEeP0VEggGZEoYrkAIHaEUAzMHhr123sFAYCGLxhqFLAYjRNs+AYvIVCYUJra+vyUsJLp9NTDMPISlprWdaoUuYwpn8CGLwYvEE4NjB4g1AFYqiEABdDlVBjDgTiSwDNiG/tyRwClRJAN0ogZ9uqe/BBpa64QvWPPNI1oWO//bTu978v6S7eEnZgCARCQwDNCE2pCBQCgSCAZhSXgXfwBqItCcJHAhi8PsIP69abDV5JB1iW9fcS8jBM07xJ0hmS8pZlNZUwhyEDEMDg7QHDSY1/hwkGr3/s2bk6AuhGdfyYDYG4EUAz4lZx8oVA9QTQjUEYFgqq/8UvuozdumXLthq4/vbb1X7ssdUXgRUgECICaEaIikWoEAgAATSjuAgYvAFoSkLwlQAGr6/4w7G5aZpP9Yl0H+cOXkkvOoZtCVmMlTS6e9zfLMs6qIQ5DBmAAAZvDxhOavw7TDB4/WPPztURQDeq48dsCMSNAJoRt4qTLwSqJ4Bu9MOwo0MNra1dxm7y2WcHhJw75xxt+t73qi8CK0AgRATQjBAVi1AhEAACaEZxETB4A9CUhOArAQxeX/GHY/Ned+xW2y8FwzBOzmQy94Qj82BGicGLwRuEzsTgDUIViKESAlwMVUKNORCILwE0I761J3MIVEoA3ehFLp9Xw+23KzVvnpL//veASNuPPlq5OXPU8bGPVYqdeRAILQE0I7SlI3AI+EIAzcDg9aXx2DSwBKo17AKbGIG5R8A0TedOXeeO3c2fD3T/e6WktiF2cuatkuTcBTy3xEc6uxd8BFfC4O0pKic1/jU4Bq9/7Nm5OgLoRnX8mA2BuBFAM+JWcfKFQPUE0A1JGzao8Sc/UWrBAiVef31AqG3HH99l7HYecED14FkBAiElgGaEtHCEDQGfCKAZxeC5g9enRmTbwBDA4A1MKcITyOY7eguFwoTW1tbl4Yk8GpFi8GLwBqGTMXiDUAViqIQAF0OVUGMOBOJLAM2Ib+3JHAKVEoizbhhr1qjxxhvVeN11Sqxe3S9CO5lU29Spys2ercI+ztuf+EAg3gTirBnxrjzZQ6AyAmgGBm9lncOsqBLA4I1qZT3MC4PXQ7glLI3Bi8FbQpt4PgSD13PEbOARAS6GPALLshCIKAE0I6KFJS0IeEggjrphvPWWGq+9Vqkbb5Sxbl2/dO2GBrVNn67crFkq7LabhxVgaQiEi0AcNSNcFSJaCASLAJqBwRusjiQavwlg8PpdgRDub5rmEucRzZ2dnWctXrz4hRCmEOqQMXgxeIPQwBi8QagCMVRCgIuhSqgxBwLxJYBmxLf2ZA6BSgnESTeMV19V6uqr1XjLLTI2berf2G1qUv6MM5T74hdl77RTpViZB4HIEoiTZkS2iCQGgRoSQDMweGvYbmwVAgIYvCEoEiFCoDcBDF4M3iAcERi8QagCMVRCgIuhSqgxBwLxJYBmxLf2ZA6BSgnEQjc6OjTsy19Ww89+JqO9vX9jd8QI5c45R/nzz5e9/faV4mQeBCJPIBaaEfkqkiAEakcAzcDgrV23sVMYCGDwhqFKEYnRNM0PGoaxdyaTuSciKfmSBgYvBq8vjddnUwzeIFSBGCohwMVQJdSYA4H4EkAz4lt7ModApQTiohvNpqn63/xmK0yF7bZT/oILlDv7bGnkyEoxMg8CsSEQF82ITUFJFAIeE0AzMHg9bjGWDxkBDN6QFSxI4ba0tBxu2/aFkj4sabikgfqpUdJ2kuol/d6yrE8GKY+wxYLBi8EbhJ7F4A1CFYihEgJcDFVCjTkQiC8BNCO+tSdzCFRKIC66kXzkEY38zGe2YCrsuGPXY5jzn/+8NNz59QAfCECgFAJx0YxSWDAGAhAYmgCaUcxo6dKlRV9MnjwZv2voNmJEhAjQ8BEqZi1TMU1zlqS5vfYstZeWYPBWVykM3h5+nNRU10vVzMbgrYYec/0kgG74SZ+9IRA+AmhG+GpGxBDwm0CcdKP5+OOVePVV5WbNUtv06VKj87fdfCAAgXIIxEkzyuHCWAhAoH8CaEYxFwxejpS4EyjVlIs7J/LvRWDq1KkTEonEY5KS3V+/IektSeMl2ZJekJTr/plz5+77ur9/zDCMmzKZzA0ArZwABm8PO05qKu+jamdi8FZLkPl+EUA3/CLPvhAIJwE0I5x1I2oI+Ekg9Lph21I+L6VSQ2I0Vq587/26dXVDjmUABCCAWUMPQAAC1RMI/XlG9QiKVsDgdRkoy4WOAAZv6Ermf8AtLS032bY9Q9J6SVMty3rAico0zYJj5BYKhQmtra3Lne9mzJiR2rhx4822bU+T9IBlWcf6n0G4I8Dg7akfJzX+9TIGr3/s2bk6AuhGdfyYDYG4EUAz4lZx8oVA9QRCqxuFgup/+UulrrhCHYceqk3f/nb1MFgBAhAYkkBoNWPIzBgAAQh4QQDNKKaKwetFl7FmmAhg8IapWgGJ1TTNf0ra3TCMb2cymW9sDqs/g9f5mWmaDZIcw3cvwzCmZzKZTEBSCWUYGLw9ZeOkZvAW/sfK9frFk6u0Pt/heq///fX1+tfqTVvWveSTu+m0iTu5vg8LQsBtAuiG20RZDwLRJoBmRLu+ZAcBLwiETjc6OtSweHGXsZt85pkuJHZTk9YsW/be3bl8IAABTwmETjM8pcHiEIDAUATQjGJCGLxDdQw/jzoBDN6oV9iD/EzT3CjJebnOpzffvetsY5qm4yIZhULhkNbW1r/03rqlpWWmbdtXchdv9QXB4O1hyEnNwP301vo2nXj9Um1qd26s9/6Dwes9Y3ZwhwC64Q5HVoFAXAigGXGpNHlCwD0CodGNfF4Nt9+u1Pz5Sr744lYANl18sXLf2PL33O4BYiUIQKCIQGg0g7pBAAKBIIBmFJcBgzcQbUkQxAVmMQAAIABJREFUPhLA4PURfli3Nk1znaRhiUTi8EWLFj2yOQ/TNN+VNMIwjM9kMpn7eudnmuYRkv4g6W3Lsvgz4CqKj8HbA4+TmoEb6XfPrtacO9/7C/xafL7x6T01Zf8darEVe0CgKgLoRlX4mAyB2BFAM2JXchKGQNUEAq8bGzao8ZZblLr6aiVef33AfPPTp2vjggVV82ABCEBgcAKB1wwKCAEIBIoAmlFcDgzeQLUnwfhAAIPXB+hh39I0zaedxy3btn1mNpu9ZXM+pmn+XdK+hmF8LZPJfK93nul0+hjDMH4tqc2yrFTYGfgZPwZvD31OagbuxAeefkuX/vzZmrTq9sPrddsZ+2vsCOdp7HwgEGwC6Eaw60N0EAgaATQjaBUhHggEn0BgdWPtWqVuvFGN116rxOrV/YK0Ewm1TZmi3OzZKuy7b/BhEyEEIkAgsJoRAbakAIEoEkAziquKwRvFLiencghg8JZDi7FdBNLp9LWGYZwn6ZHx48cfcdlll3U9A9Y0zZslnSHpH+PHj//w5u/7zPm3ZVm7g7JyAhi8PeziflLT3lnQk6+v17ubtn7H7rJX1uonj722BdaH3jdc0ya8r/LGG2Bmqj6pie8fqW2HY+66DpcFPSEQd93wBCqLQiDCBNCMCBeX1CDgEYGg6YaxerUar7tOjTfcoMTatf1mbdfXq236dOVmzlRhjz08IsOyEIBAfwSCphlUCQIQCDYBNKO4Phi8we5XovOeAAav94wjt4Npmh91zF1JtqT7CoXCnNbW1qdbWlqOtW37V873tm1nDcP430Kh0J5IJKZL+k/n/bySbrQsyzGH+VRIAIO3B1ycT2o6C7bOu2OF/vpy/7+k6dteR++9rX548j4Vdh3TIBAdAnHWjehUkUwgUDsCaEbtWLMTBKJCICi6Ybz2WtdjmJ3HMRsbN/aL125qUv7zn1fui1+UvfPOUSkBeUAgVASCohmhgkawEIgxATSjuPgYvDE+GEi9iwAGL41QEYGWlpabbNue4Zi5hmGck8lkbnIWMk3zt5ImdZu/vdd2em1DIpE4YNGiRc9XtCmTughg8PY0QpxPap5+Y72mL1xe8lFxzD7b6Xuf/WDJ4xkIgagSiLNuRLWm5AUBLwmgGV7SZW0IRJOA37qRePFFpebNU8Ptt8toa+sXsj1ihHJnn638+efLHjMmmoUgKwiEhIDfmhESTIQJAQh0E0AzilsBg5dDI+4EMHjj3gEV5j9p0qS6MWPG3OA8ktkwjLN7GbzbSrpXknOXb++Pc5vhKZZlOe/h5VMFAQzeHnhxPql5/N9rdO4dK0rupK9M3l2nHLRjyeMZCIGoEoizbkS1puQFAS8JoBle0mVtCESTgF+6kXj6aaWuvFINra0yOjv7hVvYdtsuUzd/zjmyt9kmmgUgKwiEjIBfmhEyTIQLAQh0E0AzilsBg5dDI+4EMHjj3gFV5j9t2rQ929vb84sXL35l81KXXXZZYsWKFcfbtn2kpOZEIvFse3v7bXfeeeebVW7HdO7gLeqBOJ/U9DV4RzXVaf+dR2x1jCQThg7adWSXuZswkHxEBAJx1g2qDwEIlE8AzSifGTMgEHcCfujGsNmz1XDrrTJs5y1KW38KO+6o3EUXdT2OWc3NcS8R+UMgUAT80IxAASAYCECgLAJoRjEuDN6y2ofBESTAb/sjWFRSijYB7uDtqW+cT2r6GrwHv3+kbpi+X7Sbn+wg4AKBOOuGC/hYAgKxI4BmxK7kJAyBqgn4oRupH/xATd/5zlaxd37gA8rNmqW26dOlxsaqc2MBCEDAfQJ+aIb7WbAiBCBQKwJoBgZvrXqNfcJBAIM3HHWKTJSmaTZYltX/i4Aik6W3iWDwYvA6BDB4vT3OWD26BLgYim5tyQwCXhBAM7ygypoQiDYBP3TDePddbfORj8hYv74Lbufeeyt38cVqmzpVqquLNnCyg0DICfihGSFHRvgQiDUBNAODN9YHAMlvRQCDl6Yoi8CJJ544or6+fvdEIpHq6Oh4/s4771xd6gKmac6QdJllWbuVOodxWxPA4O1hEueTGgxe1AEClRGIs25URoxZEIg3ATQj3vUnewhUQsAv3Wj65jdV97vfKTdnjtqPP15KJCoJnzkQgECNCfilGTVOk+0gAAGXCKAZGLwutRLLRIQABm9ECul1GlOnTh2fSCR+KOloScle+90vabZlWc8MFEM6nT7eMIxvSxrvjLEsq/d8r0OP3PoYvD0ljfNJDQZv5A5tEqoRgTjrRo0Qsw0EIkUAzYhUOUkGAjUh4KpudHTIePNN2TvtNHTs+bzU0CAZ/JpnaFiMgEBwCLiqGcFJi0ggAAGPCKAZxWB5B69HjcayoSHAmX9oSuVfoKZpHiHpXknDJfXtGVvSO4lEYtKiRYue7B2laZofk/Q9SYd3f+/Mbbcsi5f/VFFODF4MXocABm8VBxFTY02Ai6FYl5/kIVA2ATSjbGRMgEDsCbiiG21tarjjDqXmzZM9erTWPfAAxm3sOwsAUSXgimZEFQ55QQACWxFAMzB4OSwg0JsABi/9MCgB0zSbJP1D0vu7B74h6c+SnPfoTpS0e/f3L44ePfqD119/ffvUqVP3SSQS35F0Yi9j1zGC70gkEv+1aNGi52uFfcaMGakNGzZcImmapD0kFSQ9J+m20aNHX+XEW0kspmmOk+Q8cvrT3WxWWZbVdYey1x8M3h7CcT6pweD1+khj/agSiLNuRLWm5AUBLwmgGV7SZW0IRJNAVbqxcaMab71VqauuUuK117YAWrd4sTomTYomMLKCQMwJVKUZMWdH+hCIIwE0o7jq3MEbx6OAnHsTwOClHwYlkE6nzzQM48eSHIP2ytGjR//fXqaoYZrmf3TfpWvbtv0twzCcZ0d9vvsxzpv765eFQuE/W1tbl9cSt2mazZJ+J+ng7n1z3XHVd//7N5I+Y1mWY1aX/Emn0183DOPrkjbfieywedyyrENKXqSKgRi8PfDifFKDwVvFQcTUWBOIs27EuvAkD4EKCaAZFYJjGgRiTKAi3Vi7Vqkf/1iN116rxFtvbUWv/YgjtP7uu2NMldQhEF0CFWlGdHGQGQQgMAQBNKMYEAYvh0zcCWDwxr0DhsjfNM3Fkk6S9CfLso7sb7hpmrdIOq3bBHaGbO6rP9q2/dVsNvuwH5jT6fRcwzAulrTKtu3TstnsA5MmTUqOGTOmRdKNkpy7k79qWdZ3S42vpaXl205O3ePvknT5qlWrHlmyZElHqWtUOw6Dt4dgnE9qMHirPZKYH1cCcdaNuNacvCFQDQE0oxp6zIVAPAmUoxvG6tVqvO46Nd5wgxJr1/YLzK6vV9u0adr4wx++945dPhCAQKQIlKMZkUqcZCAAgYoIoBnF2DB4K2ojJkWIAAZvhIrpRSqmaTqPU95N0rmWZTl38m716X7X7p+6DV6np54wDONrmUzmPi9iKmXN7kczO3/6PNwwjGmZTCbTe146nb7UMAzn/cCvWZa1Sy9zesDlu99F/MfuAZdalvWDUmJxewwGbw/ROJ/UYPC6fWSxXlwIxFk34lJj8oSAmwTQDDdpshYE4kGgFN0wXn9dqQUL1LhwoYyNG/sFYzc1KX/66cp98Yuyd3EuWflAAAJRJFCKZkQxb3KCAAQqI4BmFHPD4K2sj5gVHQIYvNGppSeZmKbp/BnxcNu2jxzoTtwTTjhhWCqVWt9tkn7RsqxrPQmmjEVN0/yUpPslrVu1atW2fe+wnT59+g4dHR0ru5ecYFnW34Za3jRNx8Q+zDAMK5PJOHcB+/LB4O3BHueTGgxeXw4/No0AgTjrRgTKRwoQqDkBNKPmyNkQAqEnMJhuJP79b6Xmz1fDbbfJaOv/TUF2c7NyZ5+t/AUXyB4zJvQ8SAACEBicAOcadAgEIFAOATSjmBYGbzndw9goEsDgjWJVXczJNM2CY9wWCoUJg71Dt9RxLoY26FKmac5xHp8s6WHLsg7vb7Bpmq9I2rn78c23DbagaZrjJD3nsEgkEvssWrTo2Vrl0ncfDN4eInE+qcHg9esIZN+wE4izboS9dsQPAT8IoBl+UGdPCISbQH+60fTii0rNm6cGy5LR2dlvgoXRo5U//3zlzzlH9qhR4YZA9BCAQMkEONcoGRUDIQABSWhGcRtg8HJYxJ0ABm/cO2CI/Es1bksdVyvcvd6/22pZVnoAg/dxSQcbhvHlTCbzw8FiS6fTXzIMY77z+GnLsg6sVR797YPBi8HrEMDg9fMoZO8wE+BiKMzVI3YI1J4AmlF75uwIgbAT6K0b27zwgg79zW/UeO+9Mmy739QKO+yg3EUXKT9jhtTcHPb0iR8CECiTAOcaZQJjOARiTgDNKG4ADN6YHxCkLwxemmBQAqUat6WOqxXudDp9vWEY5xiGcVsmkzmtv31N0/y9pI/btn1ZNpv95mCxmaZ5o6SzbNteICnrmMKSDnUeXy3pBdu2f2YYxuWWZW3yOkcM3ngZvO2dBeU6nBvp+5zAvLxWs1uf3vLlwe8fqRum7+d1+7E+BEJPgIuh0JeQBCBQUwJoRk1xsxkEIkHA0Y3nFi7U3palHZYuHTCnzve/X7lZs9Q2fbqUSkUid5KAAATKJ8C5RvnMmAGBOBNAM/r8frTPudbkyZPxu+J8gMQwdxo+hkUvJ+VSjdtSx5WzdzVj0+n0QsMwzhjC4F0i6ShJ37Is6+uD7bfZDJa0TNJHpK4/jshLSkiq7577sKTJXpu8fQ3e8ePHV4Mq1HPb2tr0xBNPbMlhwoQJamhoCHVOvYP/6V9W6sePvq5N7VsbvH2TPHCXZl2T/mBkcicRCHhFIOq64RU31oVAXAmgGXGtPHlDoHICw/7rvzTyhhsGXKBj3Dit/9KXlDv5ZKl+86Vk5fsxEwIQCDcBzjXCXT+ih0CtCaAZxcRXrFhR9AUGb607kv38JoDB63cFAr7/ZuNW0o8krRwk3Muc99OWME6WZf2P12l7YPD+TdL+3XE/YRjGbNu2/7R+/fq65ubmL0hyHt9cZ9v2D7PZrHN3r2efvgbv+vXrPduLhf0jsKFd+s+/JGWX+KCFcSML+tL4oY1g/zJiZwhAAAIQgAAEIAABCESfwJhly3TYf//3Vom+u/vuetY09fohh0jJZPRBkCEEIAABCEAAAhDwmEBzn9dbYPB6DJzlA0cAgzdwJQlWQL0MXtcCsyzL86vZcgxewzD+N5PJfGOwBE3T/KekPSU9k8/nJ959993reo9Pp9PfNQzjK5Ict3Wsl3fxYvC61oqBXui1DdL3lteVHONhYwuaticGb8nAGAgBCEAAAhCAAAQgAAEvCNi2Pn7ppRr93HNdq6/eZx89m07rzYMOkgx+BeMFctaEAAQgAAEIQCCeBDB441l3su4hwNUF3TAogW6D101Kdi0M3l7vzL09m81+rr8Eer2D9xvZbPZ/B0vSNM0XJO0u6UuWZV3dd+yUKVN2TCaTrznf27b98Ww2+0c3ofVeC4PXK7LBWrevwWvIVmM/fxrhiPhuI2x9bs+CRkbn6dTBKgbRQAACEIAABCAAAQhAoAwC73vsMe3+y192Gbur99sPY7cMdgyFAAQgAAEIQAACpRLA4C2VFOOiSgCDN6qVdSmvlpaWM1xaassymUzmJ26v2Xe9dDo9zzCMmZLutizrs/3tZ5rmY5Im2rZ9cTabvXKwmEzTXC7pw7Ztz8xms1cNsN4aSSNt256ezWbv8CpH3sHbQzbK753451ubdNpPn9qS7J7bpXTb6fF937JXxxPrxo9AlHUjftUkYwh4TwDN8J4xO0AgNAQ2bVL9k0+qfeLEQUNGN0JTUQKFQCAIoBmBKANBQCA0BNCM4lLxDt7QtC6BekQAg9cjsCzrL4F0On2pYRjfk/SEZVkHDmDIvixpF0lTLMu6c7CITdO8R9LxkuZalnXJAOu9K2kb27Zbstms5RWBvgbvgQf2m55X2wdq3Xw+r0cffXRLTIcccogaGxsDFWOlwTy3aoNablq2Zfq47YfJOuuASpdjHgQg0E0gyrpBkSEAAfcJoBnuM2VFCISOwNq1arzpJqWuvVbGpk1as3y57FGjBkwD3QhdhQkYAr4SQDN8xc/mEAgdATSjuGRLly4t+oJ38IaupQm4SgIYvFUCZHowCaTT6eMMw7hXUlsulxt9zz33bOwd6cknnzy2rq5upSSjUCjs3dra+t4Lkgb4pNPprxqG8W1Jj1uW9dG+w0zTfJ+k153vE4nERxctWvS4V2QweHvIRvmkBoPXqyOIdeNOIMq6Effakj8EvCCAZnhBlTUhEA4Cxttvq/G669R4ww1KrHEe1vTeZ9NXv6rcl788YBLoRjjqS5QQCAoBNCMolSAOCISDAJpRXCcM3nD0LVF6RwCD1zu2rOwjgRNOOGFYKpV6S1KTbdsXZbPZa3qH09LS8hXbtr8r6Z+WZe01VKimab5fkvMe3qRt28dms9n7e89Jp9PfMgzja5LeWrVq1Y5LlizpGGrNSn+OwdtDLsonNRi8lR4hzIPA4ASirBvUHgIQcJ8AmuE+U1aEQNAJGCtXKrVggRoXLpSxYcNW4RZGj9aaZcuk5uZ+U0E3gl5h4oNAsAigGcGqB9FAIOgE0IziCmHwBr1jic9rAhi8XhNmfd8ImKbpvFd3lqT1ks4cP3784hUrVhi2bZuGYdwsKdXb/J00aVLd2LFjb7Vt27kb9wzLsl7qHXw6nZ5rGMbFklbZtn1GNpv9tWma9ZJmSLpaUr1t21/JZrPf9zJpDF4MXi/7i7UhEHUCXAxFvcLkBwF3CaAZ7vJkNQgEmUDipZfUOH++Gm+7TUY+32+odnOz8medpU0XXyyNHInBG+SCEhsEQkKAc42QFIowIRAQAmgGBm9AWpEwAkIAgzcghSAM9wmceOKJIxobG5dI2vyS2s1X6Ztf0uq8V/dky7I6nd2nTZs2sVAoPNYdyaWWZf2gd1SmaTZI+rmkT3d/v0lSnWPsOv82DMPad999T7nssssK7mfTsyIGbw+LKJ/UcAevl0cRa8eZQJR1I851JXcIeEUAzfCKLOtCIDgEEs8+q9S8eWrIZGR0dl0abvVx7trNn3ee8ueeO+j7d52J6EZwakskEAgDATQjDFUiRggEhwCaUVwL7uANTm8SiT8EMHj94c6uNSJgmmaTYRj/Ydv2NEl7SHKu2J+W9BNJ1242d51wug3hhyWNTSQSxy1atKj4Le2SLrvsssRTTz11gW3bZ0n6YPd6yyXdOH78+Fu8NnedODF4e5onyic1GLw1Egm2iR2BKOtG7IpJwhCoAQE0owaQ2QICPhFILl+u1Ny5qr/nHhm23W8UhR12UO7CC5WfMUMaMaKkSNGNkjAxCAIQ6CaAZtAKEIBAOQTQDAzecvqFsdEngMEb/RqTYcQIYPBi8EaspUkHAjUlwMVQTXGzGQRCTwDNCH0JSQACWxFIPvqomhxj94EHBqTTueuuys+cqfypp0qpVFkU0Y2ycDEYArEngGbEvgUAAIGyCKAZGLxlNQyDI08AgzfyJSbBqBHA4MXgjVpPkw8EakmAi6Fa0mYvCISfAJoR/hqSAQS6CNi26n7/+/fu2H3ooYGN3XHjlJs9W22mKdV3vYmn7A+6UTYyJkAg1gTQjFiXn+QhUDYBNKMYGY9oLruFmBAxAhi8ESso6USfAAZvT42jfFLDI5qjfyyToT8Eoqwb/hBlVwhEmwCaEe36kl0MCBQKqr/vPqUuv1x1S7d6A88WAB377afcnDlqP+EEKZmsCgy6URU+JkMgdgTQjNiVnIQhUBUBNAODt6oGYnLkCGDwRq6kJBR1Ahi8GLxR73Hyg4CXBLgY8pIua0MgegTQjOjVlIziRaBxwQIN+8Y3Bky6Y+JEbbrkEnV86lOS4c6vR9CNePUY2UKgWgJoRrUEmQ+BeBFAMzB449XxZDsUAXeuYIbahZ9HmsDUqVP3SSQSh9u2vZekbSQ1ZLPZszYnffrppw/P5XKjLMt6NdIgapQcBi8Gb41ajW0gEEkCXAxFsqwkBQHPCKAZnqFlYQjUhIDx1lvaZv/9ZWzaVLRf+1FHdd2x23HEEa4Zu5s3QDdqUlo2gUBkCKAZkSkliUCgJgTQjGLMPKK5Jm3HJgEmgMEb4OIEPbRp06ZNLBQKV0o6tFesTk/ZlmVtea6VaZq3SPqkpGMsy3oq6HkFPT4MXgzeoPco8UEgyAS4GApydYgNAsEjgGYEryZEBIFyCTR97WtKXXdd17S2T39auYsvVufEieUuU/J4dKNkVAyEAAQkoRm0AQQgUA4BNKOYFgZvOd3D2CgSwOCNYlVrkJNpmtMk3SrJMXL79tEWg9c0zQZJD0s6UNIKSQdZltVWgxAjuwUGb09po3xSwzt4I3sIk5jPBKKsGz6jZXsIRJIAmhHJspJUzAgYr72mpssuU37WLHWOH+959uiG54jZAAKRIoBmRKqcJAMBzwmgGRi8njcZG4SKAAZvqMoVjGBN09xdknMnbqOkNw3D+J5t2w/atj3WMIwH+t7BO2XKlB2TyeSTkkbZtn1eNpu9MRiZhDMKDF4M3nB2LlFDIBgEuBgKRh2IAgJhIYBmhKVSxBk3Asbbb6v+nnvUdsYZgUsd3QhcSQgIAoEmgGYEujwEB4HAEUAzMHgD15QE5CsBDF5f8Ydz83Q6fbVhGBdKek3SIZvfrTtt2rT9C4XCE30NXidL0zS/I+krkh60LOuYcGYejKgxeDF4g9GJRAGBcBLgYiicdSNqCPhFAM3wizz7QqB/AsbKlUotWKDGhQtlbNigtffeq85De78xyH9y6Ib/NSACCISJAJoRpmoRKwT8J4BmYPD634VEECQCGLxBqkZIYjFN8xlJ42zbnp3NZq/aHPZgBm86nT7GMIxfS3rVsqxdQ5JqIMPE4MXgDWRjEhQEQkKAi6GQFIowIRAQAmhGQApBGLEnkHjpJTXOn6/G226Tkc9v4dF+9NFab1mB4oNuBKocBAOBwBNAMwJfIgKEQKAIoBkYvIFqSILxnQAGr+8lCF8ApmlukJQqFAqHtba2PlqKwTt16tSDE4nEY5LaLMtKhS/r4ESMwYvBG5xuJBIIhI8AF0PhqxkRQ8BPAmiGn/TZGwJS4rnnlLrySjVYloyOjn6RrP3tb9V5wAGBwYVuBKYUBAKBUBBAM0JRJoKEQGAIoBkYvIFpRgIJBAEM3kCUIVxBmKa5TtIw27aPzmazS0oxeFtaWk6wbfvnkt6yLGtsuDIOVrQYvBi8wepIooFAuAhwMRSuehEtBPwmgGb4XQH2jyuB5N//rtTcuaq/+24Ztt0vhsLYscpdeKHyZ54pjRgRGFToRmBKQSAQCAUBNCMUZSJICASGAJqBwRuYZiSQQBDA4A1EGcIVhGmaj0iaKOlKy7IuKcXgNU3zZklnSPqjZVlHhSvjYEWLwYvBG6yOJBoIhIsAF0PhqhfRQsBvAmiG3xVg/7gRSD72WJex23D//QOm3rnLLsrPnKn8qadKTU2BQ4RuBK4kBASBQBNAMwJdHoKDQOAIoBkYvIFrSgLylQAGr6/4w7l5S0vLxbZtX+48blnSSZZlOe/W1UDv4E2n00d3v383Ydv2pdls1pnLp0ICGLw94KJ8UvPcqg1quWnZlmTHbT9M1lnBefRche3LNAj4TiDKuuE7XAKAQAQJoBkRLCopBY+AbavuD394747dP/5xwPg6x41TbtYstbW0SPX1wcujOyJ0I7ClITAIBJIAmhHIshAUBAJLAM0oLs3SpUuLvpg8eTJ+V2C7l8C8IEDDe0E14mvOmDEjtWHDBsd52kuS87ysmwuFwg2JRMJ5KdLjzneWZSVPPfXUke3t7efatv0/zjt7Jb2RSqXG3Xrrrc47fPlUSACDtwdclE9qMHgrPECYBoEhCERZNyg+BCDgPgE0w32mrAiBLQRsW/X33afU5Zer7q9/HRBMx/jxys2Zo/YTT5SSycADRDcCXyIChECgCKAZgSoHwUAg8ATQjOISYfAGvmUJ0GMCGLweA47q8lOnTt0rkUg4j2oe3W3yOqm2S2ro/vfLknaS5FyBO32Wl/Qpy7IeiiqTWuWFwYvBW6teYx8IRJEAF0NRrCo5QcA7AmiGd2xZOcYEOjtV//OfK3XFFapbsWJgY/fgg5W75BK1H3OMZITnVxfoRox7m9QhUAEBNKMCaEyBQIwJoBkYvDFuf1Lvh0B4rpIoX+AITJkyZZdkMum8W/foXsE5d/Q6n9699a9CofC51tbWRwOXRAgDwuDtKVqUT2q4gzeEBychh4JAlHUjFAUgSAiEjACaEbKCEW6wCbS1qSGTUWrePCWff37AWNs//vGuO3Y7jjwyVMbu5oTQjWC3IdFBIGgE0IygVYR4IBBsAmhGcX24gzfY/Up03hPA4PWeceR3ME3zINu2TzEM4yPdd+06j2N+R9LTtm3faxiGZVlWZ+RB1ChBDF4M3hq1GttAIJIEuBiKZFlJCgKeEUAzPEPLwjEkUPfAAxoxbdqAmbcde2yXsds5cWKo6aAboS4fwUOg5gTQjJojZ0MIhJoAmoHBG+oGJnjXCWDwuo6UBSHgLQEMXgxebzuM1SEQbQJcDEW7vmQHAbcJoBluE2W9WBOwbY046ijVPfnkFgy2Yaj9s599z9jdb79I4EE3IlFGkoBAzQigGTVDzUYQiAQBNAODNxKNTBKuEcDgdQ1lfBYyTXMby7LWxCfjYGWKwYvBG6yOJBoIhIsAF0PhqhfRQsBvAmiG3xVg/6gRqL/zTjWfdZbsujq1maZys2ersNdekUoT3YhUOUkGAp4TQDM8R8wGEIgUATQDgzdSDU0yVRPA4K0aYfwWME1zk6Rf2rb9M8MwfmFZVlv8KPiXMQYvBq9/3cfOEAg/AS6Gwl9DMoBALQmgGbWkzV6xINDZqdT3v6+2005TYdddI5kyuhHJspIUBDwjgGZ4hpaFIRBJAmgGBm8he5GlAAAgAElEQVQkG/v/s3cmYHaUZdp+6vRyThYS9k1QXFgGFCGCARFFaWTYCXR12BcRkCAkJI4zzjj/oPM7o47ZANmVJWzp6mYRFVQQREQQCQIG/AXBYZFVA2Q7p5eq/zpNJ92HdKfPUsv3Vd3nuriE7m953/t967G+PKlTJFU3AQzeutFld6Lrur6kYJDA25K6Jd3ged4vskslvswxeDF44+s2doJA+ghwGEpfTckIAlESQDOipMvaaSGQe+EF5S+9VKv/5V+kDTZIS1p154Fu1I2OiRDIJAE0I5NlJ2kI1E0AzcDgrbt5mJhKAhi8qSxrtEm5rvsTSZ+V1Dq40xqz92XHcW5yHOeGxYsXL4k2iuyujsGLwZvd7idzCDROgMNQ4wxZAQJZIoBmZKna5ForgdzTT6uwYIFaPU9OX59WnX++SueeW+syqRuPbqSupCQEgUgJoBmR4mVxCKSOAJqBwZu6piahhghg8DaEL7uTjz/++EmlUukwx3GOlnSgpHHvMnv/JOm6/v7+G2+++eZns0sq/MwxeNNj8C5b1asfPvGaXlu+7recv7m6Vz958o21yX5o0/HyTtst/IZiRQhkjACHoYwVnHQh0CABNKNBgExPJYGmJ55QYd48tfzwh3KCNX/XV/I331xvPfqoNG7N0TCV6Y+ZFLoxJiIGQAACwwigGbQDBCBQCwE0o5LWkiWVz5i1tbXhd9XSUIy1ngANb30Jk0/gsMMOG18oFA4um71BEBwiaeJgVGtO+791HOe6IAg6Pc97PfmI7Y4Ag3eofrbf1Jx07eN64uUVVTUkBm9VmBgEgTEJ2K4bYybIAAhAIFQCaEaoOFnMcgJNv/3tgLHb+rOfjZrJyvnz1XPyyZZn2lj46EZj/JgNgawRQDOyVnHyhUBjBNCMSn4YvI31E7PtJ4DBa38NjcrgoIMOyk+cOLH8RG+7pEMlbTjsfb39ku7yPO9go4K2LBgM3qGC2XxTU35697MXPlx19314q4ladNKuVY9nIAQgMDIBm3WDmkIAAvETQDPiZ86OhhEIAjXfd987T+z+6lejBtf/oQ+pOHOmejo6pJYWw5KINxx0I17e7AYB2wmgGbZXkPghEC8BNKOSNwZvvP3HbuYRwOA1ryapich13clBEMxzHOfUQZO33G+B53lNqUkygUQweIeg23xTU/5a5gMv/l3VHfSVtvfr2I9tVfV4BkIAAiMTsFk3qCkEIBA/ATQjfubsaAiBIFDLT3+qwty5an7kkVGD6ttlFxVnz1bv4YdLTRzzyqDQDUN6mDAgYAkBNMOSQhEmBAwhgGZg8BrSioRhCAEMXkMKkZYwTjnllMKqVasO8X2/w3Gc8pO64yWt6bNeSXd6nndEWvJNIg8M3nQavBNam3TWJ7ddp6UcR9ph8wna472Tk2g39oRA6ghwGEpdSUkIApESQDMixcviJhLo71fLbbepMH++mpcuHd3Y3WMPFefMUe/nPieVb1j5rCWAbtAMEIBALQTQjFpoMRYCEEAzKnuAJ3i5JrJOgJNY1jsghPxHMXXLK6/pr99Iuk7SYs/z/h7ClpleAoN3qPw239S8+wneTSe26Odn75np3iZ5CMRBwGbdiIMPe0AAApUE0Aw6IjMEenvV2tmpwsKFanrmmVHT7v3Upwae2O3bd1+M3VEooRuZuWpIFAKhEEAzQsHIIhDIDAE0A4M3M81OolURwOCtChOD3k1gjakbBIEr6ZDBJ3WHm7p/chzn+iAIrvM87zkIhkcAgxeDN7xuYiUIZI8Ah6Hs1ZyMIdAIATSjEXrMtYLA6tXKX3+98hdcoKYXXxw15J4DDxwwdvv35C8kjlVXdGMsQvweAhAYTgDNoB8gAIFaCKAZlbR4greW7mFsGglg8KaxqhHn5LruTaOYuq8FQbC4qanpusWLFz8ccRiZXR6DF4M3s81P4hAIgQCHoRAgsgQEMkQAzchQsbOW6ooVyv/gBypcfLFyr702YvaB46j3iCPeMXY//OGsEao7X3SjbnRMhEAmCaAZmSw7SUOgbgJoBgZv3c3DxFQSwOBNZVmjTcp1XX/YDqsk3VZ+UtdxnJ95ntcf7e6sjsGLwctVAAEI1E+Aw1D97JgJgSwSQDOyWPVs5Oy8/LIm7767nJ6edRIOmpvV47oqzpolf/vtswEkxCzRjRBhshQEMkAAzchAkUkRAiESQDMweENsJ5ZKAQEM3hQUMe4UXNftk3R3+b26hULh5kWLFq2MO4Ys74fBi8Gb5f4ndwg0SoDDUKMEmQ+BbBFAM7JV76xlO37OHOWvumpt2kE+r9KJJ6p0zjnyt902azhCyxfdCA0lC0EgEwTQjEyUmSQhEBoBNAODN7RmYqFUEMDgTUUZ403Cdd0tPc97Jd5d2W0NAQxeDF6uBghAoH4CHIbqZ8dMCGSRAJqRxapnJ+fc//6vJu2xhzRunEqnnqrijBkKttgiOwAiyhTdiAgsy0IgpQTQjJQWlrQgEBEBNAODN6LWYllLCWDwWlo4ws4uAQxeDN7sdj+ZQ6BxAhyGGmfIChDIEgE0I0vVzmauLT/6kfr22UfBRhtlE0AEWaMbEUBlSQikmACakeLikhoEIiCAZmDwRtBWLGkxAQxei4sXR+j77bdf8yabbPKRXC631PO8dV/QFEcQ7FFBAIMXg5dLAgIQqJ8Ah6H62TETAlkkgGZksep259y0dKkK8+apePbZ6p8yxe5kLI0e3bC0cIQNgYQIoBkJgWdbCFhKAM3A4LW0dQk7IgIYvBGBTcuyruveKekASfd4ntdWzst13VUN5Bd4njehgfmZn4rBi8Gb+YsAABBogACHoQbgMRUCGSSAZmSw6Jam3PTwwyrMn6/WO8vHN6nnkEO0ctEiS7OxO2x0w+76ET0E4iaAZsRNnP0gYDcBNKOyfkuWLKn4QVtbG36X3S1O9DUSoOFrBJa14a7rLpdUNmRXeZ43sZy/67p+AxzKBm9TA/MzPxWDd6gFbL6peW15jw68+Hdrk9l0Yot+fvaeme9vAEAgagI260bUbFgfAhBYlwCaQVcYTSAI1PyrXw08sdty333rhPrW/ffL33lno1NIY3DoRhqrSk4QiI4AmhEdW1aGQBoJoBmVVcXgTWOXk1MtBDB4a6GVwbHt7e3fchznbMdxLuvs7PxyGYHruvdKCurF4XneZ+qdyzwJg3eoC2y+qcHg5WqGQDIEbNaNZIixKwSyTQDNyHb9jc0+CNTys5+pMHeumn839BcG3x1v8dxztfr8841NI62BoRtprSx5QSAaAmhGNFxZFQJpJYBmYPCmtbfJqz4CGLz1cWMWBBIjgMGLwZtY87ExBFJAgMNQCopIChCIkQCaESNsthqbQH+/Wn74w4GvYm7+wx9GHd/3sY+pOGeOeg88UHI48o8NNtwR6Ea4PFkNAmkngGakvcLkB4FwCaAZGLzhdhSr2U6A057tFST+zBHA4MXgzVzTkzAEQiTAYShEmCwFgQwQQDMyUGQbUuztVavnqbBwoZqefnrUiHv33VfF2bPV96lPYewmWFd0I0H4bA0BCwmgGRYWjZAhkCABNAODN8H2Y2sDCWDwGlgUQoLA+ghg8KbD4H317ZL+8ZJH1ibDO3i57iEQDwEOQ/FwZhcIpIUAmpGWSlqaR7Go/PXXK3/BBWp64YVRk+j53OcGjN3+j3/c0kTTFTa6ka56kg0EoiaAZkRNmPUhkC4CaAYGb7o6mmwaJYDB2yjBDM53XfcX5Xfw9vf3n37zzTc/Ww0C13WnS/pvSY95njetmjmMGZkABq/9Bq8fBPq325/WnU+9sTaZzSe26qdn70HbQwACERPgMBQxYJaHQMoIoBkpK6gt6SxfrvzVV6tw8cXKvfrqiFEHjqPeww9/x9j9yEdsySwTcaIbmSgzSUIgNAJoRmgoWQgCmSCAZlSWecmSJRU/aGtrw+/KxJVAkmsI0PD0Qs0EXNf1ywav7/u7d3d3P17NAu3t7Uc5jtMl6W3P8zasZg5jRiaAwTvExcabmiAI9J27n9NNj7xSUeD9d9hY3522E20PAQhETMBG3YgYCctDAALrIYBm0B5xEnCWLVP+8suVv+wy5d58c8Stg6Ym9XR0qDhzpvwddogzPPaqkgC6USUohkEAAgME0AwaAQIQqIUAmlFJC4O3lu5hbBoJYPCmsaoR57TG4JW0m+d5T1SxneO67g8knVy+d/U8b1wVcxgyCgEM3iEwNt7UXP7rF3TJ/ZVfsbftRgVddfyHtcmEVvoeAhCImICNuhExEpaHAATWQwDNoD1iIbB6tcZ9+9vK/+AHclasGHHLIJ9X6YQTVDrnHPnvfW8sYbFJfQTQjfq4MQsCWSWAZmS18uQNgfoIoBmV3DB46+sjZqWHAAZvemoZWSau6z75rsXLjxkGkv5SNmyr2HhzSRsNjvu953kfq2IOQ0YhgME7BMa2m5rOR1/Rf/+s8lvNy+/evfr4j+g9GxboeQhAIAYCtulGDEjYAgIQWA8BNIP2iIWA72vS3nur6emn19kumDBBpVNPVXHGDAVbbhlLOGzSGAF0ozF+zIZA1gigGVmrOPlCoDECaEYlPwzexvqJ2fYTwOC1v4aRZzDsid1G+8V3HGdaZ2fn7ZEHneINMHjtNHh/9tQb+pcf/mngb0as+WyQb9L3j/+wtt9sQoo7ltQgYBYBDkNm1YNoIGA6ATTD9AqlJ77Wm27ShBkz1ibkT56s0hlnqHTmmQo23jg9iWYgE3QjA0UmRQiESADNCBEmS0EgAwTQjMoiY/BmoOlJcb0EGjXswJsBAq7rlp/UHe5LvW/wv8svEe0ZA0F53uuSyk8Bz6vyK50zQLX+FDF4h9jZclPzm+fe1LldT6nPH7qMCs05XTJ9Z+22zaT6m4GZEIBAzQRs0Y2aE2MCBCAQCQE0IxKsLDoSgd5eTdpzTzmrVw88rVt+aleTuE+0sVnQDRurRswQSI4AmpEce3aGgI0E0AwMXhv7lpijI4DBGx3b1K685ole3/d37+7ufjy1iRqaGAavXQbvE39drjNvWqrVvf7awJtzjuYdtZP2/eCaby43tNkICwIpJMBhKIVFJSUIREgAzYgQbpaWDgLJGfvonXvySfnbbSeNH58lOqnLFd1IXUlJCAKREkAzIsXL4hBIHQE0A4M3dU1NQg0RGPuU2dDyTE4jAQzeZKuKwWuPwfvsG6v0+ev/oLeKfRVN881Dt9fBu2yWbCOxOwQySoDDUEYLT9oQqJMAmlEnOKZJQaDm++9XYd489R500MDXLfPJBgF0Ixt1JksIhEUAzQiLJOtAIBsE0AwM3mx0OllWSwCDt1pSjFtLwHXde8tf0dzf33/azTff/Cxo4iWAwWuPwXvc1Y/pqVdXVjTIP+2/nY7bY+t4m4bdIACBtQQ4DNEMEIBALQTQjFpoMXaAQNnY/fnPNW7uXDU//PDAj/ytt9ZbS5ZIra1AygABdCMDRSZFCIRIAM0IESZLQSADBNCMyiLzDt4MND0prpcABi8NAgHLCGDwDhXM5Jua3n5fH//ugxXd9YW9t9HZn3qvZR1HuBBIFwGTdSNdpMkGAukggGako46xZNHfr5bbb1dh/nw1P/HEOluuXLhQPSeeGEsobJIsAXQjWf7sDgHbCKAZtlWMeCGQLAE0A4M32Q5kd9MIYPCaVhED4znuuOM2uuGGG5YZGFomQ8LgtdPgLb9397df3ktOFe9fy2RjkzQEYiLAYSgm0GwDgZQQQDNSUsgo0+jtVWtXlwoLFqjp6adH3al03HFaddFFUUbC2oYQQDcMKQRhQMASAmiGJYUiTAgYQgDNwOA1pBUJwxACGLyGFMLUMDo6Oq4JguAESTd5nnd8OU7XdZ9sIN7A87xdGpif+akYvEMtYPJNzbuf4C0bvA//096Z718AQCBpAibrRtJs2B8CEFiXAJpBV4xKoFhU6w03qHDBBWp6/vlRh/UecIBWz56t/qlTgZkRAuhGRgpNmhAIiQCaERJIloFARgigGZWF5iuaM9L4pDkqAQxemmO9BFzXXSFpvKRVnudNLA92Xdcvv11KUi39s2Z82eBtAnv9BDB4MXjr7x5mQgACHIboAQhAoBYCaEYttDIydsUK5a++WoWLL1bulVdGTDpwHPUedpiKZWN3110zAoY01xBAN+gFCECgFgJoRi20GAsBCKAZGLxcBRAYTqAWgw5yGSTguu71ko4NgsDr6uqaPmjw/mXQ4K2LiOd5769rIpMGCGDwDjWCyTc1PMHLBQsBMwmYrBtmEiMqCGSbAJqR7fpXHJzffFP5yy9X/rLLlFs28ttrgqYm9biuijNnyt9xR+BllAC6kdHCkzYE6iSAZtQJjmkQyCgBNAODN6OtT9qjEMDgpTXGJDBt2rRNbrnllr+NOZABsRDA4MXgjaXR2AQCKSXAYSilhSUtCEREAM2ICKxFyzqvvabCJZco//3vy1lR/nKjdT9Ba6t6jj9exXPPlf++91mUHaFGQQDdiIIqa0IgvQTQjPTWlswgEAUBNKOSKl/RHEWXsaZNBDB4baoWsUKAJ3gresDkmxqe4OVyhYCZBEzWDTOJERUEsk0Azchu/Z0XX1ThoouUv/ZaOcXiyMbuhAkqnXKKijNmKNhqq+zCInNrziiUCgIQMI8A9xrm1YSIIGAyATQDg9fk/iS2+Alg8MbPnB0h0BABnuAdwmfyTQ0Gb0NtzmQIREbAZN2ILGkWhgAE6iaAZtSNzt6JQaDxX/6yWq+7Tk5v74h5+JMnq3T66Sp98YsKNt7Y3lyJPBIC6EYkWFkUAqklgGaktrQkBoFICKAZGLyRNBaLWksAg9fa0pkT+CmnnFK4+uqr1/lr7eWvdm5ubt66t7f3hVtvvfVNcyK2OxIMXgxeuzuY6CGQLAEOQ8nyZ3cI2EYAzbCtYuHEO/7ss5W/8cZ1FvM33XTgad3S5z8vTZoUzmaskjoC6EbqSkpCEIiUAJoRKV4Wh0DqCKAZGLypa2oSaogABm9D+LI9ub29/UzHcb4iaYXneR9dQ6Ns+K5cufL7kqZLKvdYfxAEN4wbN+7sRYsWrcw2tcazx+DF4G28i1gBAtklwGEou7UncwjUQwDNqIea/XNyTz+tSXvtJScIBpLxt95axXPOUenEE6Xx4+1PkAwiJYBuRIqXxSGQOgJoRupKSkIQiJQAmlGJl3fwRtpuLG4BAQxeC4pkYogdHR3/GQTBvw7GttTzvF3XxOm67vWSjn1X3OU/HbnT87xDTMzHppgweIeqZfJNDV/RbNNVRaxZImCybmSpDuQKAVsIoBm2VCr8OCeceqqaHn9cxZkz1XPMMVJra/ibsGIqCaAbqSwrSUEgMgJoRmRoWRgCqSSAZmDwprKxSapuAhi8daPL7sTp06fv6fv+g4MEikEQXNvV1XVW+b8Hf/eQpLKh+1QQBD9zHOeTkvYY/Nk0z/N+mF16jWeOwYvB23gXsQIEskuAw1B2a0/mEKiHAJpRDzWD55SfyF2xQtpggzGDdJYtU1Ae19w85lgGQGA4AXSDfoAABGohgGbUQouxEIAAmlHZAzzByzWRdQIYvFnvgDryd133KkknS3rT9/39uru7H1+zTEdHxxVBEJwm6Q/FYnGv22+/fdUZZ5zRsmzZsl9J2lPSLZ7ntdexLVMGCWDwDrWCyTc1PMHLJQsBMwmYrBtmEiMqCGSbAJqRkvr7vlpuv12F+fPlf+hDWnnllSlJjDRMJIBumFgVYoKAuQTQDHNrQ2QQMJEAmoHBa2JfElNyBDB4k2Nv7c6u6z4j6f2S/o/ned8cnojrui9L2jwIgi90dXWVjeCBj+u6p0j6gaQXPc97r7XJGxA4Bu9QEUy+qcHgNeBiIQQIjEDAZN2gYBCAgHkE0AzzalJTRL29au3uHjB2m55+emBqkMvp7Ycekv/BD9a0FIMhUC0BdKNaUoyDAATKBNAM+gACEKiFAJpRSYsneGvpHsamkQAGbxqrGnFOruuukpSX9FnP8365ZjvXdXcuP7lb/nOTvr6+rW655ZbX1vyuvb39E47j3C+px/O8QsQhpnp5DN6h8pp8U4PBm+rLkOQsJmCybliMldAhkFoCaIalpS0W1XrjjSosXKim559fJ4nS8cdr1YUXWpocYZtOAN0wvULEBwGzCKAZZtWDaCBgOgE0A4PX9B4lvngJYPDGyzsVu7muu1JSwXGcz3R2dt43zOAtv4f3e5L+5HneTsOT7ejo+EwQBHdLetvzvA1TASKhJDB4MXgTaj22hUAqCHAYSkUZSQICsRFAM2JDHc5GK1cqf/XVKnzve8q98sqIawaOo95p07Tyiiskh+NwOOBZZTgBdIN+gAAEaiGAZtRCi7EQgACaUdkDPMHLNZF1Apxos94BdeTvuu5TknZwHOfLnZ2d84cZvHdKOiAIgsu7urrKZu/aj+u6MyWVx/7R87zyk7586iSAwTsEzuSbGp7grbPBmQaBiAmYrBsRp87yEIBAHQTQjDqgJTDFeest5a+4QvlLL1Xu738fMYKgqUk97e0qzpwpf6eKv4uaQMRsmWYC6Eaaq0tuEAifAJoRPlNWhECaCaAZldXF4E1zt5NbNQQweKuhxJgKAu3t7Rc5jjND0iu+7x/Q3d29tL29/VDHcW4rDwyC4KCurq6frZl02GGHjS8UCk9I2i4Igku6urq+BNL6CWDwDrEz+aYGg7f+HmcmBKIkYLJuRJk3a0MAAvURQDPq4xbXLOf115W/5BIVrrxSzooVI24btLaq57jjVDz3XPnbbRdXaOyTYQLoRoaLT+oQqIMAmlEHNKZAIMME0IzK4mPwZvhiIPUBAhi8NELNBI4++ujtc7nc45JaBycvk7TR4L+Xv575H8r/7rpuk6QzBv/5qKR+3/c/3t3d/WjNmzJhLQEM3qFmMPmmBoOXixYCZhIwWTfMJEZUEMg2ATTDzPo7L72kwoUXKr9okZzVq0cMMhg/XqWTT1bx7LMVbL21mYkQVSoJoBupLCtJQSAyAmhGZGhZGAKpJIBmVJYVgzeVbU5SNRDA4K0BFkOHCHR0dHw+CIIr3vWXBEqO47R1dnb+ujzyxBNPnFAsFpeXH+otj3Mc5386Ozv/GY6NEcDgNcvg7enz9dqKnnWKWjZ4j7ry92t/3pxz9PA/7d1Y8ZkNAQg0TIDDUMMIWQACmSKAZphV7tyzz6qwcKFab7pJTm/viMH5kyapdPrpKn3xiwo22cSsBIgmEwTQjUyUmSQhEBoBNCM0lCwEgUwQQDMqy4zBm4m2J8n1EMDgpT3qJtDe3r5vLpc7JwiC90v6s+/73+3u7v7dmgWHGbyrHcf5Vmdn53/WvRkT1xLA4B1qhqRvau76f3/Tv//oaRX7/DE7FIN3TEQMgEAsBJLWjViSZBMIQCA0AmhGaCgbWij35JMqLFig1ptvluOPfN/lb7KJSjNmqHjaadKkSQ3tx2QINEIA3WiEHnMhkD0CaEb2ak7GEGiEAJpRSQ+Dt5FuYm4aCGDwpqGKhuZQ/opmx3EO7O3tfeiWW275m6FhWhcWBu9QyZK+qTni8iV6flmxqh7C4K0KE4MgEDmBpHUj8gTZAAIQCJUAmhEqzroWG/fVr6pw2WWjzvW32krFc85R6aSTpPHj69qDSRAIkwC6ESZN1oJA+gmgGemvMRlCIEwCaEYlTQzeMLuLtWwkgMFrY9WIOdMEMHiHyp/0Tc0+8x/Uqp6xn94tR/wPW0zQDaeUX0XNBwIQSJJA0rqRZO7sDQEI1E4AzaidWdgzWq++WhNmz15n2f7ttlNx5kz1HHOMlM+HvS3rQaBuAuhG3eiYCIFMEkAzMll2koZA3QTQjEp0GLx1txITU0IAgzclhSSN7BDA4B2qddI3Ne82eLealFduBFXdZsOC/mn/9+uDm/FUSXauVDI1lUDSumEqF+KCAARGJoBmGNAZpZImT5mi3MsvDwTTv+OOKs6erZ5p06TmZgMCJAQIVBJAN+gICECgFgJoRi20GAsBCKAZGLxcBRAYTgCDl35oiMD555+fW7p0qSvpcElTJG02uOBLkp6StHijjTb60eWXX97b0EZMXksAg3eoGZK+qXm3wXv/rKmakG+iWyEAAYMJJK0bBqMhNAhAYAQCaIYZbZG/5BK1et6Asdt78MFSLmdGYEQBAXSDHoAABBokwL1GgwCZDoGMEUAzKgvOE7wZuwBIdx0CGLw0Rd0Epk+fPsX3/RslfWhwkeH9FAxbuGz0nux53iN1b8bEtQQweIeaIembGgxeLkwI2Ecgad2wjxgRQyDbBNCMCOvf16fc88/L/8AHxt6kr09qapIcjq9jw2JE0gTQjaQrwP4QsIsAmmFXvYgWAkkTQDMweJPuQfY3iwAnZLPqYU007e3t+zqO81NJ5Rderemjv0gqf3da+bvStpK0zbCESr7v79fd3f2QNUkaGigGLwavoa1JWBCwggCHISvKRJAQMIYAmhFBKUoltd54owoLFw4Ytm//9rd81XIEmFkyOQLoRnLs2RkCNhJAM2ysGjFDIDkCaAYGb3Ldx84mEsDgNbEqhsd0+OGHb5DP558Z/DrmPkkLJC30PK/8tcxrP0cdddQ2zc3NM4IgOG/QCH65VCrt9MMf/nC54SkaHR4G71B5kr6p4Qleoy8VgoPAiASS1g3KAgEI2EUAzQixXitXKn/NNSp873tr36dbXn3lZZepxy2/8YUPBNJBAN1IRx3JAgJxEUAz4iLNPhBIBwE0o7KOfEVzOvqaLOongMFbP7vMzmxvb/+q4zjflFQ2dw/3PO/O9cFob2/fb/Bp3+YgCGZ1dXVdmFl4ISSOwTsEMembGgzeEBqaJSAQM4GkdSPmdNkOAhBokACa0SDA8lf9vPWW8ldeqfyllyr3t7+ts2D/jjvq7V//mvfqNo6aFQwhgG4YUgjCgIAlBNAMSwpFmBAwhACagcFrSCsShiEEMHgNKYRNYbiu+1JVoYsAACAASURBVBtJH5d0med5M6qJvb29faHjOOdI+rXneftWM4cxIxPA4B3ikvRNDQYvVykE7COQtG7YR4yIIZBtAmhG/fV33nhD+UsuUeHKK+UsH/kLfILWVvUce6xW/ed/ShMn1r8ZMyFgEAF0w6BiEAoELCCAZlhQJEKEgEEE0AwMXoPakVAMIIDBa0ARbAvBdd03JG0UBMFBXV1dP6sm/o6OjrYgCMpjX/E8b+tq5jAGg3esHkj6pgaDd6wK8XsImEcgad0wjwgRQQAC6yOAZtTeH85LL6lw0UXKX3utnNWrR1wgGDdOpZNPVvFLX1KwNUeD2ikzw2QC6IbJ1SE2CJhHAM0wryZEBAGTCaAZldXhK5pN7lZii4MABm8clFO2h+u6KyUVcrncXosXL364mvRc1y0/8fugpJLneeOqmcOYkQnwBO8Ql6RvajB4uUohYB+BpHXDPmJEDIFsE0Azqq9/7rnnVFi4UK033iint3dkY3eDDVQ8/XSVvvhFBZtuWv3ijISARQTQDYuKRagQMIAAmmFAEQgBAhYRQDMweC1qV0KNgQAGbwyQ07aF67p/lLS9pFM8z1tUTX6u654o6RpJL3ie975q5jAGg3esHkj6pgaDd6wK8XsImEcgad0wjwgRQQAC6yOAZozdH7mnnlJhwQK1dnfL8f0RJ/ibbKLSWWep+IUvSJMmjb0oIyBgMQF0w+LiEToEEiCAZiQAnS0hYDEBNKOyeDzBa3EzE3ooBDB4Q8GYrUU6OjquC4LgOEm/22WXXfY6//zzR/6TnEEs559/fm7p0qXlp3c/FgTB4q6urvJcPnUS4AneIXBJ39Rg8NbZxEyDQIIEktaNBFNnawhAoA4CaMbo0Jp+/3sV5s1T649+NOogf6utBr6GuXTSSdKECXVUgCkQsI8AumFfzYgYAkkSQDOSpM/eELCPAJpRWTMMXvt6mIjDJYDBGy7PTKzmuu4nJd0nKZB0c2tr62nXX3/92yMlf9hhh43P5/NXOo5zTHm84zgHdnZ23pUJUBElicE7BDbpmxoM3oianGUhECGBpHUjwtRYGgIQiIAAmrEu1Obf/EaFuXPV8otfjEq8/33vU3HmTPUce6yUz0dQGZaEgLkE0A1za0NkEDCRAJphYlWICQLmEkAzKmuDwWturxJZPAQweOPhnLpdOjo6rgmCoPy1y2WT92+O49zk+/4DjuO87DhOn+/7W0ja23Gc4yWV/11BEHR1dXVNTx2MmBPC4B0CnvRNDQZvzM3PdhAIgUDSuhFCCiwBAQjESADNqIRd+Pa3Ne7b3x61Av077qjieeep56ijpObmGCvFVhAwhwC6YU4tiAQCNhBAM2yoEjFCwBwCaEZlLTB4zelNIkmGAAZvMtyt39V13dby07uSDh5Mpmz0jvRZ02M/XbFixZF33HFHyfrkE04Ag3eoAEnf1GDwJnwxsD0E6iCQtG7UETJTIACBBAmgGZXwm5Yu1aR9912nIn0f/aiKs2er95BDpFwuwYqxNQSSJ4BuJF8DIoCATQTQDJuqRawQSJ4AmlFZAwze5HuSCJIlgMGbLH/rd29vb/+C4zhflrTDKMk8HwTBd7q6ui62PllDEsDgHSpE0jc1GLyGXBSEAYEaCCStGzWEylAIQMAAAmjGukWYcNxxar3zzoFf9O6114Cx27f//pLD0dKAliUEAwigGwYUgRAgYBEBNMOiYhEqBAwggGZUFgGD14CmJIRECXAKTxR/ejZ3XXe3IAh2cRxn6yAInFwu90YQBEs8z3ts8Guc05Nswplg8GLwJtyCbA8BqwlwGLK6fAQPgdgJoBnrIm/63e807lvfUnHOHPXtvXfsNWFDCJhOAN0wvULEBwGzCKAZZtWDaCBgOgE0A4PX9B4lvngJYPDGy5vdINAwAQxeDN6Gm4gFIJBhAhyGMlx8UodAHQQyoxkrV6r5wQffeRKXDwQg0BCBzOhGQ5SYDAEIrCGAZtALEIBALQTQDAzeWvqFsekngMGb/hqTYcoIYPBi8KaspUkHArES4DAUK242g4D1BNKuGc5bbyl/5ZXKX3qpyv/+9iOPyN92W+vrRgIQSJJA2nUjSbbsDYE0EkAz0lhVcoJAdATQDAze6LqLlW0kgMFrY9USirmjo+PAIAjOkLSXpE0kvSWp/BXMN3iedw1fxRxPYTB4MXjj6TR2gUA6CXAYSmddyQoCURFIq2Y4b7wxYOoWrrhCzvLla/EVTztNq//nf6LCyboQyASBtOpGJopHkhBIgACakQB0toSAxQTQDAxei9uX0CMggMEbAdS0Lem6bpPjOFcEQXDysNzW9E4w+LNf9/b2Hn7rrbe+mbb8TcsHgxeD17SeJB4I2ESAw5BN1SJWCCRPIG2a4bz0kgoXXaT8tdfKWb16HcBBPq+3Hn1UwZZbJg+fCCBgKYG06YalZSBsCFhDAM2wplQECgEjCKAZGLxGNCJBGEMAg9eYUpgbSEdHx/wgCGYOi7Ao6e+SNpQ0fvDnZaP3x57nHW5uJumIDIMXgzcdnUwWEEiGAIehZLizKwRsJZAWzcg995wKCxeq9cYb5fT2jliOYIMNVDz9dJW+9CUFG5Zv8/lAAAL1EEiLbtSTO3MgAIHaCaAZtTNjBgSyTADNqKz+kiVLKn7Q1taG35XlCySDudPwGSx6LSm7rruzpCcG5zzvOM65r7322h333ntvnySnvb39047jLJT0kfJXNAdBsH9XV9e9tezB2NoIYPAO8Ur6pmaf+Q9qVY+/NqD7Z03VhHxTbQVlNAQgECuBpHUj1mTZDAIQaJiA7ZqRe+opFRYsUGt3txx/6J5lOBh/441VOusslb7wBQWTJzfMjAUgkHUCtutG1utH/hCImwCaETdx9oOA3QTQjMr6YfDa3c9E3zgBDN7GGaZ6Bdd1F0g6V9LfJO3med5L707Ydd2NB03g8ne5XeV53hdSDSXh5DB4hwqQ9E0NBm/CFwPbQ6AOAknrRh0hMwUCEEiQgK2a0fT736swb55af/SjUen5W22l4tlnq3TyydKECQlSZmsIpIuArbqRriqQDQTsIYBm2FMrIoWACQTQDAxeE/qQGMwhgMFrTi2MjMR13UfKxm4QBN/o6ur6+mhBdnR0/HsQBOXfL/U8r/w0L5+ICGDwDoFN+qYGgzeiJmdZCERIIGndiDA1loYABCIgYJtmNP/mNyrMnauWX/xiVBr973ufijNnqufYY6V8PgJqLAmBbBOwTTeyXS2yh0DyBNCM5GtABBCwiQCaUVktnuC1qXuJNQoCGLxRUE3Rmq7rlp/c3dBxnLbOzs57RkvNdd3PSrpL0lue522UIgTGpYLBi8FrXFMSEAQsIsBhyKJiESoEDCBghWYEgZrvvnvgid2WBx8c3djdYQcVzztPPUcfLTU3G0CXECCQTgJW6EY60ZMVBKwkgGZYWTaChkBiBNAMDN7Emo+NjSSAwWtkWcwJynXdgXft5nK5KYsXL35sPQbvbpLKbzX3Pc/jT4wiLCEGLwZvhO3F0hBIPQEOQ6kvMQlCIFQCRmuG76vlxz9WYf58Nf/+96Pm3bfrrirOnq3eQw+VcrlQ+bAYBCCwLgGjdYOCQQACxhFAM4wrCQFBwGgCaAYGr9ENSnCxE8DgjR25XRu6rutLCnzf3727u/vx0aKfPn36R33ff7Q81vO8JruytCtaDF4MXrs6lmghYBYBDkNm1YNoIGA6AZM1o/X66zXhnHNGN3anTtXq2bPV19YmORz7TO814ksPAZN1Iz2UyQQC6SGAZqSnlmQCgTgIoBkYvHH0GXvYQ4CTvj21SiRSDN5EsK93UwxeDF7zupKIIGAPAQ5D9tSKSCFgAgGjNWPVKk3ebTfl3nijAlXvZz6j4pw56vvEJ0xASAwQyBwBo3Ujc9UgYQiYTwDNML9GRAgBkwigGRi8JvUjsSRPAIM3+RoYHQEGr3nlweDF4DWvK4kIAvYQ4DBkT62IFAImEDBdM/ILFmj8N74xgKrnkEMG3rHbP2WKCeiIAQKZJWC6bmS2MCQOAUMJoBmGFoawIGAoATQDg9fQ1iSshAhg8CYE3pZtMXjNqxQGLwaveV1JRBCwhwCHIXtqRaQQMIGA8Zrx9tsa/2//puJZZ8nfeWcTkBEDBDJPwHjdyHyFAAABswigGWbVg2ggYDoBNAOD1/QeJb54CWDwxsvbut3WGLySLpP0ynoS2FLSF8vv4JX09fUl6nneO48Z8KmLAAbvELakb2r2mf+gVvWUX1P9zuf+WVM1Ic8rqOtqbCZBICYCSetGTGmyDQQgEBKBJDTD+dvf1HrjjSrNmCHlciFlwjIQgEBcBJLQjbhyYx8IQCB8AmhG+ExZEQJpJoBmVFZ3yZIlFT9oa2vD70rzBUBu6xCg4WmK9RIYZvCGRsrzPBywBmhi8A7BS/qmBoO3gUZmKgQSIpC0biSUNttCAAJ1EohTM5yXX1bhoouUv+YaOatWacVVV6n3iCPqjJxpEIBAUgTi1I2kcmRfCEAgPAJoRngsWQkCWSCAZlRWGYM3C11PjusjgMFLf1Rj8IZJKcDgbQwnBi8Gb2MdxGwIZJsAh6Fs15/sIVArgTg0I/eXv6hwwQVqveEGOT09a0Ps23VXLb/nHsnhyFZr3RgPgSQJxKEbSebH3hCAQLgE0IxwebIaBNJOAM3A4E17j5NfbQT404LaeGVudEdHx8lhJ93Z2XlN2GtmaT0MXgzeLPU7uUIgbAIchsImynoQSDeBKDUj98c/qrBggVq7u+X0948Icvnixeo74IB0QyY7CKSMQJS6kTJUpAMBCEhCM2gDCECgFgJoBgZvLf3C2PQTwOBNf43JMGUEMHgxeFPW0qQDgVgJcBiKFTebQcB6AlFoRtNjj6kwd65afvxjOUEwIiN/yy1VPPtslU4+WZo40XqOJACBLBGIQjeyxI9cIZA1AmhG1ipOvhBojACagcHbWAcxO20EMHjTVlHyST0BDF4M3tQ3OQlCIEICHIYihMvSEEghgTA1o+nBBzWubOzeffeopPrf9z4VZ85UzzHHSIVCComSEgTSTyBM3Ug/LTKEAATQDHoAAhCohQCagcFbS78wNv0EMHjTX2MyTBkBDF4M3pS1NOlAIFYCHIZixc1mELCeQMOaEQRqvuceFebNU8sDD4xu7O6wg4rnnaeeo4+Wmput50YCEMgygYZ1I8vwyB0CGSSAZmSw6KQMgQYIoBkYvA20D1NTSACDN4VFJaUhAqecckph5cqVcyRNl/QBSb6kpyVdv9FGG114+eWX9zbCa/r06VN8339YUs5xnK93dnae38h61czF4MXgraZPGAMBCIxMgMMQnQEBCNRCoG7N8H21/OQnKsyfr+ZHHx11y75dd1Vx9mz1HnqolMvVEhpjIQABQwnUrRuG5kNYEIBAtATQjGj5sjoE0kYAzais6JIlSyp+0NbWht+VtqYnn/USoOFpkNQScF23/MKyeyTtMZhkUVKTpJbB/y5/P97Bnuf11AnBcV3315L2Ls/H4K2TYgPTkr6p2Wf+g1rVU/47A+987p81VRPy5RbjAwEImEogad0wlQtxQQACIxOoWTP6+tR6yy0Dxm7TH/84Kta+qVO1evZs9bW1lW8iwQ8BCKSIQM26kaLcSQUCEKidAJpROzNmQCDLBNCMyupj8Gb5aiD3MgH+NIE+SC2B9vb2eY7jnCfp9SAITujq6vr5fvvt17TZZpt1SLpS0jhJX/U871v1QGhvbz/JcZxr1szF4K2HYmNzkr6pweBtrH7MhkASBJLWjSRyZk8IQKB+AlVrRqmk1ptuUuGCC9T03HOjbti7334qzpmjvk98AmO3/rIwEwJGE6haN4zOguAgAIG4CKAZcZFmHwikgwCaUVlHDN509DVZ1E8Ag7d+dsw0mMDgVzO/IWmC4zjTOzs7O4eH297e/hXHcb4t6a+e520jKaglncMPP3yDfD7/J0lblA1kSZtj8NZCMJyxSd/UYPCGU0dWgUCcBJLWjThzZS8IQKBxAtVqRtOjj2rS/vuPumHPwQcPfBVz/5QpjQfFChCAgNEEqtUNo5MgOAhAIDYCaEZsqNkIAqkggGZUlhGDNxVtTRINEMDgbQAeU80l4LruAZJ+Jmn566+/vvG9997bNzzaY489dou+vr5XBn+2u+d5v68lm46Oju8GQTAnCILrHMfZVtKnMXhrIRjO2KRvajB4w6kjq0AgTgJJ60acubIXBCDQOIFaNGPi0Uer5Z7y20He+QS5nHqnTdPq886Tv/POjQfDChCAgBUEatENKxIiSAhAIFICaEakeFkcAqkjgGZUlhSDN3UtTkI1EsDgrREYw+0g4LrubElzJT3ged4+I0Xtuu6Lkt4z+PXN11eb2dFHH71TLpd7XFJPU1PTDv39/Tdg8FZLL9xxSd/UYPCGW09Wg0AcBJLWjThyZA8IQCA8ArVoRvMDD2iDQw9V0NKinmOOUXHmTPkf+EB4wbASBCBgBYFadMOKhAgSAhCIlACaESleFodA6gigGZUlxeBNXYuTUI0EMHhrBMZwOwgMe/9ut+d57SNF7bruw5L2cBznnzo7O79bbWau65afDD7AcZx/6+zs/C/Xde/F4K2WXrjjkr6pweANt56sBoE4CCStG3HkyB4QgEB4BGrVjPzFF6vn8MMVbFN+AwgfCEAgiwRq1Y0sMiJnCEBgiACaQTdAAAK1EEAzKmlh8NbSPYxNIwEM3jRWNeacBp9o3ScIgu0lTZbU2tXVddqaME488cQJxWJxQ8/zXoortPb29ssdxzndcZzrOzs7TxhpX9d1fynpU0EQnN/V1fX1amJzXXeapJslPbtixYqd77jjjlLSBu8uu+xSTeipHNPT06NHH310bW677767WltbY8v1s997VKt6/bX73T1jN01obYptfzaCAARqJ5C0btQeMTMgAIEkCfQ/84x6/uu/9CfXVXHjjRX3vUaSubM3BCBQHwHuNerjxiwIZJUAmpHVypM3BOojgGZUclu6dGnFD9ra2vC76mstZllKgIa3tHAmhD19+vQ9fd9fIGmvYfGUeyrwPG+ty+W67rWSPivpc57nPRlH7O3t7Vc7jnPyGAbvwJO3kr7ped7XxorrlFNOKaxcufIpSds5jjOts7Pz1vKcpA3eFStWjBU6v4+IwFcealLJH5LRb3+8TwX83YhosywEIAABCEAgPgITX3xR23d3a5tf/lI539czhx+upZ//fHwBsBMEIAABCEAAAhCAAAQgAAEIrJfAxIkTK36PwUvDZI0ABm/WKh5Svq7rTpe0SFLZznp3H601eF3XLT9O+YCkKZLKf6XmY57n9YQUxqjLRGHwtre3/4fjOOdLutvzvLY1m2PwRl1Nc9fH4DW3NkQGAQhAAAIQqIfA5Gef1Q6ep60efFBOEKxdoi+f18+vuEI9kybVsyxzIAABCEAAAhCAAAQgAAEIQCBkAhi8IQNlOesIYPBaV7LkA3Zd9/2Syk/i5iW95jjOt4MguCsIgs0dx/n5u5/gPeqoo7Zqamr6g6QNgyA4s6ur68qos6jF4HUc5/92dnb++/piOuqoo97X1NRUfnq3xff93bq7u9d+/wMGb9TVNHd9DF5za0NkEIAABCAAgVoIbPzUUwPG7hZLlow6belJJ+mZo46qZVnGQgACEIAABCAAAQhAAAIQgEBEBDB4IwLLstYQwOC1plTmBNre3n6R4zgzJP1V0tQ179adPn36R33fL78QteIrmsuRu67735L+WdJdnud9LupsXNctm8inBUFwY1dX13Ej7TfsHbz/3tXV9X/XF5Prut2SjnIc58LOzs5zh49N2uDlHby8gzfq64n1IZAmAryvJk3VJBcINEggCNR6332auHChWn/zm1EXW77NNnr66KP14r77arc991Rra/kLavhAAAIQGJkA9xp0BgQgUAsBNKMWWoyFAATQjMoe4B28XBNZJ4DBm/UOqCN/13X/n6QPBUEwq6ur68I1S6zP4G1vb/+c4zh3SnrJ87xt69i2pint7e0LHccpG7E/9DzviJEmu677W0l7BkFwXldXV/ldwiN+Ojo62oIgKD+Z/LeWlpbtb7jhhmXDByZt8E6ZUv7262x+SqWSHnroobXJT506Vfl8+cHyeD77zH9Qq3r8tZvdP2uqJuR5CW889NkFAvURSFo36ouaWRCAQKgEfF8td96pwrx5al7PE7t9H/mIlp97ru7beGOp6Z3/f4/7XiPUvFkMAhCIhQD3GrFgZhMIpIYAmpGaUpIIBGIhgGZUYl7yrvMc7+CNpQ3ZxCACGLwGFcOWUFzXXSmp4Pv+J7q7u9e6a+szeI8++ug9crlc2VDt8TyvEHWu7e3tXyl/dbSkRz3PG9EBdV33BUnblJ/M9TzvltFiWmME1xKz53mRXVt33XXX0Avhyi83xuBdW5q4/9AVg7eWq4KxEDCDAIchM+pAFBBIhEB/v1puvVXj5s1T01PlN2+M/On7+Me1es4c9bW1qdTTk+hfJkuEE5tCAAINEeBeoyF8TIZA5gigGZkrOQlDoCECaEYlPgzehtqJySkgEJkJlQI2pDAKAdd1l0saHwTB/l1dXfeuGbY+g7ejo+OwIAhuk/SG53mbRw23vb39IMdxflI2lIvF4ka33377quF7Tps2bfPm5uZXJDm+7+/Q3d399Ggxua5bfhnbe9cT82RJzZJWSxrYx/O8TaPKEYN3iGzSNzUYvFF1OetCIDoCSetGdJmxMgQgMCqBnh61Ll6swsKFanr22VGH9X760yqWjd199pGcd45JaAZ9BQEI1EoA3aiVGOMhkG0CaEa260/2EKiVAJpRSQyDt9YOYnzaCGDwpq2iMeTjuu6D5a82lrTA87w5a7Yc4x28V0k6WdKvPM/7dNRhHnbYYeMLhcIbksYFQXB2V1fXxcP37Ojo+OcgCL4l6RnP87ZvJB6+orkReo3NTfqmBoO3sfoxGwJJEEhaN5LImT0hkFkCq1Ypv2iRChdeqNxf/zoqhp6DDlJx9mz1f+xj64xBMzLbPSQOgboJoBt1o2MiBDJJAM3IZNlJGgJ1E0AzKtFh8NbdSkxMCQEM3pQUMs40Ojo6zguCYG756VhJR3qeV363rkYzeNvb2/cffP9uLgiCr3R1dZXnRv5xXbf8Xt2ZklZIOnWXXXa5eenSpU4QBK7jOGXDuTDc/N1vv/2aN99880VBEGxZNqM9z3u+miAxeKuhFM2YpG9qMHijqSurQiBKAknrRpS5sTYEIDBIYOVK5a+4QoVLLlHu9ddHxBLkcuo98kitnj1b/s47j4oOzaCrIACBWgmgG7USYzwEsk0Azch2/ckeArUSQDMqiWHw1tpBjE8bAQzetFU0hnxOOeWUwsqVKx+TVH7ytfw+2Kt8378il8v1SXq4/DPP85qOP/74Sb29vWcEQfCNspkq6dVCofChRYsWld/hG/nn8MMP3yCfz5e/QnrNO3hLg5vmB//3dknTPM/rL//39OnT9/R9v/ye4PLnK57n/U81QWLwVkMpmjFJ39Rg8EZTV1aFQJQEktaNKHNjbQhAYJDA8uWa/NGPKvfmm+sgCZqb1TN9uoqzZsn/4AfHRIZmjImIARCAwLsIoBu0BAQgUAsBNKMWWoyFAATQjMoewODlmsg6AQzerHdAnfkfffTR2+dyufJXNW80aPKWV+qV1Dr43y9I2lpSU/k9t+VXmEk6wPO8++vcsq5pruuOcxzny0EQTJf0AUllM/ePkq6RdMkac7e8+KAh/ICkzXO53EGLFy8uv3t3zA8G75iIIhuQ9E0NBm9kpWVhCERGIGndiCwxFoYABCoIFL71LY37znfW/iwoFFQ66SQVv/QlBdtsUzUtNKNqVAyEAAQGCaAbtAIEIFALATSjFlqMhQAE0IzKHsDg5ZrIOgEM3qx3QAP5H3XUUds0NTWVv+p4/2HLlJ/oLX+G99Zzvu8f193d/VAD2zF1kMBdd921hvHAT6ZMWfOAcvYQJX1Tg8GbvZ4jY/sJJK0b9hMkAwjYQcBZtmzgKd7yp3TaaSqedZaCzTevOXg0o2ZkTIBA5gmgG5lvAQBAoCYCaEZNuBgMgcwTQDMqWwCDN/OXROYBYPBmvgUaB+C67seCIDjGcZxdB5/aLX8d87Lyk7JBEPzEcRxv+JOyje+Y7RUweIfqn/RNDQZvtq9FsreTQNK6YSc1ooaAnQSa771X/bvtpmDDDetOAM2oGx0TIZBZAuhGZktP4hCoiwCaURc2JkEgswTQDAzezDY/iY9IAIOXxoCAZQQweDF4LWtZwoWAUQQ4DBlVDoKBQE0Emp54QoV581Q67jj1HXBATXPrHYxm1EuOeRDILgF0I7u1J3MI1EMAzaiHGnMgkF0CaAYGb3a7n8xHIoDBS19AwDICGLwYvJa1LOFCwCgCHIaMKgfBQKAqAk0PPaRx8+ap5ec/Hxjft+eeWn7nnZIT/VEGzaiqRAyCAASGEUA3aAcIQKAWAmhGLbQYCwEIoBkYvFwFEBhOIPo/FYE3BCAQKgEMXgzeUBuKxSCQMQIchjJWcNK1l0AQqPmXvxx4Yrfl/vvXyWP5bbepb999I88PzYgcMRtAIHUE0I3UlZSEIBApATQjUrwsDoHUEUAzMHhT19Qk1BABDN6G8GVzckdHxw/qzdz3/aCrq+u0euczT8LgxeDlOoAABOonwGGofnbMhEAsBHxfLT/9qQpz56p5yZJRtyydeqpWzZ0beUhoRuSI2QACqSOAbqSupCQEgUgJoBmR4mVxCKSOAJqBwZu6piahhghg8DaEL5uTXdf1JQV1ZF/ut8DzvKY65jJlkAAG71ArJH1Ts8/8B7Wqp3w5vPO5f9ZUTcjT3lysEDCZQNK6YTIbYoNAogT6+9Vy660qzJ+v5iefHDWU8tczr54z55138PIVzYmWjM0hAIGRCXCvQWdAAAK1EEAzaqHFWAhAAM2o7IElvd4+sAAAIABJREFU7/pLwW1tbfhdXCaZIkDDZ6rc4SQ7aPDWuljZEB74x/O8llonM36IAAbvEIukb2oweLkyIWAfgaR1wz5iRAyBiAn09Ki1s1OFhQvV9Oc/j7pZ76c/reLs2er75CdjMXbXBIJmRFx/lodACgmgGyksKilBIEICaEaEcFkaAikkgGZg8KawrUmpAQIYvA3Ay+rUadOmbVJt7s3NzZtKulTSpyXd4XneoXU+/Vvtlqkfh8GLwZv6JidBCERIgMNQhHBZGgK1EFi9WvlFi1S48ELlXnpp1Jk9//iPA8Zu/x571LJ6aGPRjNBQshAEMkMA3chMqUkUAqEQQDNCwcgiEMgMATSjstQ8wZuZ1ifRUQhg8NIakRM47LDDxhcKhUclfUjScZ7nLY580xRvgMGLwZvi9iY1CEROgMNQ5IjZAALrJ/D228pfdZUKF1+s3Ouvjzg2cBz1HnnkO8buLrskShTNSBQ/m0PASgLohpVlI2gIJEYAzUgMPRtDwEoCaAYGr5WNS9CREcDgjQwtCw8n0NHRcV4QBHMHn+I9BDr1E8DgxeCtv3uYCQEIcBiiByCQDAHn739X/rLLlL/8cuXeemtkY7e5WT0dHSrOmiX/Q+W/F5j8B81IvgZEAAHbCKAbtlWMeCGQLAE0I1n+7A4B2wigGZUV4wle2zqYeMMmgMEbNlHWG5FAR0fHZ4IguFvSq57nbQWm+glg8A6xS/qmhnfw1t/HzIRAUgSS1o2k8mZfCCRGoK9P477xjYGndp2VK0cMIygUVDrxRBXPOUfBNtskFupIG6MZRpWDYCBgBQF0w4oyESQEjCGAZhhTCgKBgBUE0IzKMmHwWtG2BBkhAQzeCOGy9BAB13U/Kek+Sb2e5+VhUz8BDN4hdknf1GDw1t/HzIRAUgSS1o2k8mZfCCRJYOKhh6rlgQfWCSGYOFGlz39exbPOUrDFFkmGOOreaIaRZSEoCBhNAN0wujwEBwHjCKAZxpWEgCBgNAE0o7I8GLxGtyvBxUAAgzcGyGwhtbe3f9VxnG9KWuZ53iYwqZ8ABi8Gb/3dw0wIQIDDED0AgfgJNP/iF9qgvX3txv6GG6p05pkqnXGGgo02ij+gGnZEM2qAxVAIQGCAALpBI0AAArUQQDNqocVYCEAAzajsAQxeromsE8DgzXoHRJz/+eefn/vDH/5wguM4l0oqP7n7C8/zDoh421Qvj8GLwZvqBic5CERMgMNQxIBZHgIjEQgCbbD//sr99a8qzpih0qmnShtsYAUrNMOKMhEkBIwigG4YVQ6CgYDxBNAM40tEgBAwigCagcFrVEMSTOIEMHgTL4F9Abiu+2SVURckvUdSs6RyrwVBELhdXV03VzmfYSMQwODF4OXCgAAE6ifAYah+dsyEwIgE+vulpqYx4eSee07+lltK48aNOdakAWiGSdUgFgjYQQDdsKNORAkBUwigGaZUgjggYAcBNKOyTjzBa0ffEmV0BDB4o2Ob2pVd1/XLZu2gaVt1no7jfKuzs/Nfq57AwBEJYPAOYUn6poZ38HKRQsA+Aknrhn3EiBgCIxAIAjXfd58K8+ap7+MfV/Hf/i21mNCM1JaWxCAQGQF0IzK0LAyBVBJAM1JZVpKCQGQE0AwM3siai4WtJIDBa2XZkg3add2/DBq8YwXSK+lvjuMsCYLgGs/zfjvWBH4/NgEMXgzesbuEERCAwGgEOAzRGxBogEAQqOWnP1Vh7lw1P/LIwEL+pEl66/HHpUmTGljY3Klohrm1ITIImEoA3TC1MsQFATMJoBlm1oWoIGAqATSjsjI8wWtqpxJXXAQweOMizT4QCIkABu8QyKRvaniCN6SmZhkIxEggad2IMVW2gkB4BPr71XLbbSrMn6/mpUvXWXf1176m4uzZ4e1n0EpohkHFIBQIWEIA3bCkUIQJAUMIoBmGFIIwIGAJATQDg9eSViXMmAhg8MYEmm0gEBYBDF4M3rB6iXUgkEUCHIayWHVyrptAT49aOztVWLhQTX/+86jL9EybppXf/37d25g8Ec0wuTrEBgEzCaAbZtaFqCBgKgE0w9TKEBcEzCSAZmDwmtmZRJUUAQzepMhbvK/rujcPhv8fnuc9YXEqVoaOwYvBa2XjEjQEDCHAYciQQhCG2QRWr1b+uuuUv/BCNb344ujG7oEHDjy527/nnmbn00B0aEYD8JgKgYwSQDcyWnjShkCdBNCMOsExDQIZJYBmYPBmtPVJexQCGLy0Rs0EXNftk+T4vj+1u7v7dzUvwISGCGDwYvA21EBMhkDGCXAYyngDkP76CSxfrvxVV6lw8cXKvfbaiGMDx1HvEUe8Y+x++MOpJ4pmpL7EJAiB0AmgG6EjZUEIpJoAmpHq8pIcBEIngGZg8IbeVCxoNQEMXqvLl0zwruuWH+XYStIhnufdmUwU2d0VgxeDN7vdT+YQaJwAh6HGGbJC+gg4y5Ypf9llyl9+uXJvvjmysdvcrJ6ODhVnzpS//fbpgzBKRmhGZkpNohAIjQC6ERpKFoJAJgigGZkoM0lCIDQCaAYGb2jNxEKpIIDBm4oyxptEe3v71Y7jnCTpUs/zZsS7O7th8GLwchVAAAL1E+AwVD87ZqaPgPPqqwNP65af2nVWrBjZ2M3nVTrxRJXOOUf+ttumD8IYGaEZmSs5CUOgYQLoRsMIWQACmSKAZmSq3CQLgYYJoBkYvA03EQukigAGb6rKGU8yxxxzzHb9/f2PSJoUBMGJXV1dN8WzM7uUCWDwYvByJUAAAvUT4DBUPztmpodA7oUXBt6vm1+0SE6pNLKxO3GiSqeequKMGQq22CI9ydeYCZpRIzCGQwACQjdoAghAoBYCaEYttBgLAQigGRi8XAUQGE4Ag5d+qJlAe3v7DY7j7CRpN0mBpIcl/b3KhQLP8w6pcizDRiCAwTsEJembmn3mP6hVPf7agO6fNVUT8k30LQQgYDCBpHXDYDSElhEC4776VeW//305fX0jZuxvuKFKZ5yh0plnKthoo4xQGT1NNCPzLQAACNRMAN2oGRkTIJBpAmhGpstP8hComQCaUYlsyZIlFT9oa2vD76q5q5hgMwEa3ubqJRS767plR6ts7Nb18TwPB6wucu9MwuAdgpf0TQ0GbwONzFQIJEQgad1IKG22hcBaAuO+/nUVFi5ch4i/+eYDT+uWn9rVBhtAbJAAmkErQAACtRJAN2olxngIZJsAmpHt+pM9BGolgGZUEsPgrbWDGJ82Ahi8aatoDPkMGrz17lR+gheDt156GLwV5JK+qcHgbaCRmQqBhAgkrRsJpc22EFhLwHntNU3ebTc5xeLAz/z3vEfFc89V6YQTpHHjIPUuAmgGLQEBCNRKAN2olRjjIZBtAmhGtutP9hColQCaUUkMg7fWDmJ82ghg8KatouSTegI8wTtU4qRvajB4U3+5kWAKCSStGylESkoWEhj3L/+ilrvvVnHWLPW4rtTaamEW8YSMZsTDmV0gkCYC6EaaqkkuEIieAJoRPWN2gECaCKAZGLxp6mdyaZwABm/jDFkBArESwODF4I214dgMAikjwGEoZQUlnSECQSBn2TIFG288NpUVK955WreJL1UZCxaaMRYhfg8BCLybALpBT0AAArUQQDNqocVYCEAAzcDg5SqAwHACGLz0w3oJdHR0fKo8YPXq1b+7/fbbV4EreQIYvBi8yXchEUDAXgIchuytHZGPQqC/Xy233abC/PkD5u6K224DVYgE0IwQYbIUBDJCAN3ISKFJEwIhEUAzQgLJMhDICAE0A4M3I61OmlUSwOCtElRWhw2+b9f3fX9Kd3f341nlYFLeGLzxGby9/b5uefw1PfvGyH+3ofv3r6rPD9YGdP+sqZqQ52kok64XYoHAuwlwGKInUkOgp0etnZ0qLFyopj//eW1ab99xh/qnTk1NmkkngmYkXQH2h4B9BNAN+2pGxBBIkgCakSR99oaAfQTQDAxe+7qWiKMkgMEbJd0UrD1o8Aa+7++OwWtGQTF44zN4/+eu53TDIy9XXXgM3qpRMRACiRHgMJQYejYOi8Dq1cpfd53yF16ophdfXGfV3gMO0IrFi8PaLfProBmZbwEAQKBmAuhGzciYAIFME0AzMl1+kodAzQTQDAzempuGCakmgMGb6vI2nhwGb+MMw14Bgzc+g/eIy5fo+WXFqkrYnHP06/OmqrU5V9V4BkEAAskQ4DCUDHd2DYHA8uXKX3WVChdfrNxrr424YOA46j3ySK289FKppSWETVkCzaAHIACBWgmgG7USYzwEsk0Azch2/ckeArUSQDMqiS1ZsqTiB21tbfhdtTYV460mQMNbXb7og8fgjZ5xrTtg8A4Ri/qm5uBLHtHLb5eqKtHBO2+qbx62Q1VjGQQBCCRHIGrdSC4zdk4rAWfZMuUvu0z5yy9X7s03R0wzaG5Wj+uqOGuW/O23TyuKRPJCMxLBzqYQsJoAumF1+QgeArETQDNiR86GELCaAJpRWT4MXqvbmeBDIIDBGwLENC+BwWtedTF4kzN4v7jPtpo8rnmdpthqcl6feP+Gamni6V3zrhgigkAlAQ5DdIQtBJxXXx14Wrf81K6zYsXIxm4+r9IJJ6h07rnyt93WltSsihPNsKpcBAsBIwigG0aUgSAgYA0BNMOaUhEoBIwggGZg8BrRiARhDAEMXmNKYWYgGLzm1QWDNzmD98dfnKKtJxfMawoiggAEqibAYahqVAxMiEDuhRcG3q9bfs+uUxz5NQHBhAkqnXqqijNmKNhyy4Qizca2aEY26kyWEAiTALoRJk3WgkD6CaAZ6a8xGUIgTAJoBgZvmP3EWvYTwOC1v4aRZrDG4A2C4D8kPR/GZl1dXdeGsU5W18DgxeDNau+TNwTCIMBhKAyKrBEFgdwzz6gwf75aPU9OX9+IW/iTJ6t0xhkqnXmmgo03jiIM1nwXATSDloAABGolgG7USozxEMg2ATQj2/UnewjUSgDNqCTGVzTX2kGMTxsBDN60VTTkfNYYvCEuG3iet+533Ia4QdqXwuAdqnDUNzXvfgcvT/Cm/eoivywQiFo3ssCQHMMl0PSHP6gwb55abrtNThCMbOxuttnA07rlp3Y1aVK4AbDaegmgGTQIBCBQKwF0o1ZijIdAtgmgGdmuP9lDoFYCaAYGb609w/h0E8DgTXd9G85umMEbVq+UDd6mhgPL8AIYvBi8GW5/UodAwwQ4DDWMkAVCJFD4xjc0bsGCUVf03/MeFc85R6UTT5TGjQtxZ5aqlgCaUS0pxkEAAmsIoBv0AgQgUAsBNKMWWoyFAATQjMoe4AleromsEwjLtMs6x9Tmv8bgdRzn/LC+ormzs/Oa1AKLITEMXgzeGNqMLSCQWgIchlJbWisTa7njDk08/vh1Yu//wAdUnDVLPR0dUmurlbmlJWg0Iy2VJA8IxEcA3YiPNTtBIA0E0Iw0VJEcIBAfATQDgze+bmMnGwhg8NpQpQRjXGPw+r6/e3d39+MJhsLWgwQweDF4uRggAIH6CXAYqp8dMyMg4PuatO++anrqqYHF+3beWcXzzlPvkUdKTXzhSQTEa14SzagZGRMgkHkC6EbmWwAAEKiJAJpREy4GQyDzBNCMyhbgCd7MXxKZB4DBm/kWWD8ADF7zGgSDF4PXvK4kIgjYQ4DDkD21ykqkLd3dKlxyiYpz5qj3wAOlXC4rqVuRJ5phRZkIEgJGEUA3jCoHwUDAeAJohvElIkAIGEUAzcDgNaohCSZxAhi8iZfA7AAweM2rDwYvBq95XUlEELCHAIche2pldaS9vWr605/Uv8suY6fh+5LjvPMPH+MIoBnGlYSAIGA8AXTD+BIRIASMIoBmGFUOgoGA8QTQDAxe45uUAGMlwJ8kxYrbvs0weM2rGQZveAZvnx/opkde1lOvrFAwQqnvefrvKvb6a3/z4y9O0daTC+Y1BRFBAAJVE+AwVDUqBtZDoFhU/vrrlb/gAjkrVuitxx6TJk6sZyXmGEIAzTCkEIQBAYsIoBsWFYtQIWAAATTDgCIQAgQsIoBmVBaLr2i2qHkJNRICGLyRYE3Pohi85tUSg3eoJo3e1Fzwy//VVQ++VHWRMXirRsVACBhLoFHdMDYxAkuWwIoVyl91lQoXX6zcq6+ujWXVN76h0pe+lGxs7N4QATSjIXxMhkAmCaAbmSw7SUOgbgJoRt3omAiBTBJAMzB4M9n4JD0qAQxemmO9BDB4zWsQDN7wDN6Trn1cT7y8ouoi33PuntpwXEvV4xkIAQiYR4DDkHk1sTkiZ9ky5a+4QvnLLlNu2bJ1UvG32EJvPfqoVODbH2ytM5pha+WIGwLJEUA3kmPPzhCwkQCaYWPViBkCyRFAMzB4k+s+djaRAAaviVUxKCbXdT9dDqdYLD58++23rzIotMyGgsEbnsF73DWP6alXVlbVS/vvsLG+O22nqsYyCAIQMJcAhyFza2NTZM5rrw08rZv/wQ8Gvop5pE+Qz6t0/PEqfu1rCjbc0Kb0iHUYATSDdoAABGolgG7USozxEMg2ATQj2/UnewjUSgDNqCTGVzTX2kGMTxsBDN60VZR8Uk8Ag3eoxI3e1Lzb4D3rk9tqmw3Xfcpqs4mt2m2bDdTSlEt9f5EgBNJOoFHdSDsf8ls/AefFF1W48ELlFy2SUyyObOxOmKDSKaeoePbZCrbcEqSWE0AzLC8g4UMgAQLoRgLQ2RICFhNAMywuHqFDIAECaAYGbwJtx5YGE8DgNbg4hAaBkQhg8EZn8F530q7aZauJNB4EIJBiAhyGUlzcCFPL/fnPKixYoNbFi+X09Y24kz95skpnnKHSmWcq2HjjCKNh6TgJoBlx0mYvCKSDALqRjjqSBQTiIoBmxEWafSCQDgJoBgZvOjqZLMIigMEbFknWgUBMBDB4MXhjajW2gUAqCXAYSmVZI0sq9+STGjdvnlpuvVWO749s7G62mYozZqh06qnSpEmRxcLCyRBAM5Lhzq4QsJkAumFz9YgdAvETQDPiZ86OELCZAJqBwWtz/xJ7+AQweMNnyooQiJQABi8Gb6QNxuIQSDkBDkMpL3BI6TX97ncqzJun1jvvHHVFf+utVTz3XJVOOEEaPz6knVnGNAJohmkVIR4ImE8A3TC/RkQIAZMIoBkmVYNYIGA+ATQDg9f8LiXCOAlg8MZJm70gEAIBDF4M3hDaiCUgkFkCHIYyW/qxEw8CNd9//4Cx2/LLX446vv8DH1Bx5kz1TJ8utbaOvS4jrCaAZlhdPoKHQCIE0I1EsLMpBKwlgGZYWzoCh0AiBNAMDN5EGo9NjSWAwWtsaQgMAiMTwODF4OXagAAE6ifAYah+dmmfmb/4Yo3/2tdGN3b/4R+0evZs9R5xhNTcnHYc5DdIAM2gFSAAgVoJoBu1EmM8BLJNAM3Idv3JHgK1EkAzMHhr7RnGp5sABm+660t2KSSAwYvBm8K2JiUIxEaAw1BsqK3byHnpJU2eMkVOb29F7H1Tpqg4Z456DzxQyuWsy4uAGyOAZjTGj9kQyCIBdCOLVSdnCNRPAM2onx0zIZBFAmgGBm8W+56cRyeAwUt3QMAyAhi8GLyWtSzhQsAoAhyGjCqHccGMnzVL+WuvHYir95OfVHH2bPV9+tOSwy2zccWKKSA0IybQbAOBFBFAN1JUTFKBQAwE0IwYILMFBFJEAM3A4E1RO5NKCAT406oQILIEBOIkgMGLwRtnv7EXBNJGgMNQ2ioabj65557TuH/9VxVnzVL/1KnhLs5qVhJAM6wsG0FDIFEC6Eai+NkcAtYRQDOsKxkBQyBRAmgGBm+iDcjmxhHA4DWuJAQEgfUTwODF4OUagQAE6ifAYah+dtbOXLFCLXfdpd4jj7Q2BQJPjgCakRx7doaArQTQDVsrR9wQSIYAmpEMd3aFgK0E0AwMXlt7l7ijIYDBGw1XVoVAZAQweDF4I2suFoZABghwGMpAkQdTdN58U/krrlD+0kuVW7ZMb//iF+rfbbfsACDTUAigGaFgZBEIZIoAupGpcpMsBBomgGY0jJAFIJApAmgGBm+mGp5kxySAwTsmIgZAwCwCGLwYvGZ1JNFAwC4CHIbsqlc90Tqvv678JZeocOWVclasWLtEz2GHaeU119SzJHMyTADNyHDxSR0CdRJAN+oExzQIZJQAmpHRwpM2BOokgGZg8NbZOkxLKQEM3pQWlrTSSwCDF4M3vd1NZhCIngCHoegZJ7WD8+KLKlx0kfLXXiunWFwnjMBx9PYDD8jfccekQmRfCwmgGRYWjZAhkDABdCPhArA9BCwjgGZYVjDChUDCBNAMDN6EW5DtDSOAwWtYQQgHAmMRwODF4B2rR/g9BCAwOgEOQ+nrjtyzz6qwYIFaFy+W09s7YoL+pEkqnX66SmedpWDjjdMHgYwiI4BmRIaWhSGQWgLoRmpLS2IQiIQAmhEJVhaFQGoJoBkYvKltbhKriwAGb13YmASB5Ahg8GLwJtd97AwB+wlwGLK/hmsyyD35pMbNn6+WW26R4/sjG7ubbjpg6hZPO02aNCk9yZNJbATQjNhQsxEEUkMA3UhNKUkEArEQQDNiwcwmEEgNATQDgzc1zUwioRDA4A0FI4tAID4CGLwYvPF1GztBIH0EOAzZX9OmJUtUmDdPrT/5yajJ+FtvreI556h04onS+PH2J00GiRFAMxJDz8YQsJYAumFt6QgcAokQQDMSwc6mELCWAJqBwWtt8xJ4JAQweCPByqIQiI4ABi8Gb3TdxcoQSD8BDkOW1jgI1PzrXw8Yuy333jtqEv3vf7+KM2eq55hjpNZWS5MlbJMIoBkmVYNYIGAHAXTDjjoRJQRMIYBmmFIJ4oCAHQTQDAxeOzqVKOMigMEbF2n2gUBIBDB4MXhDaiWWgUAmCXAYsqzsZWP3rrs0bu5cNf/2t6MbuzvtpNWzZ6v3yCOl5mbLkiRckwmgGSZXh9ggYCYBdMPMuhAVBEwlgGaYWhnigoCZBNAMDF4zO5OokiKAwZsUefaFQJ0EMHgxeOtsHaZBAAKSOAxZ0ga+r5bbb1dh/nw1P/74qEH37b67imVj96CDpFzOkuQI0yYCaIZN1SJWCJhBAN0wow5EAQFbCKAZtlSKOCFgBgE0A4PXjE4kClMIYPCaUgnigECVBDB4MXirbBWGQQACIxDgMGRHW7T85CeaeMIJowbb+4lPDBi7fZ/5jORwO2tHVe2MEs2ws25EDYEkCaAbSdJnbwjYRwDNsK9mRAyBJAmgGRi8SfYfe5tHgD8RM68mRASB9RLA4MXg5RKBAATqJ8BhqH52sc7s79ekvfdW0zPPVGzb29Y28FXM/XvtFWs4bJZdAmhGdmtP5hColwC6US855kEgmwTQjGzWnawhUC8BNAODt97eYV46CWDwprOuZJViAhi8GLwpbm9Sg0DkBDgMRY44tA1ab7hBE770JQWOo95DDx14Yrf/ox8NbX0WgkA1BNCMaigxBgIQGE4A3aAfIACBWgigGbXQYiwEIIBmYPByFUBgOAEMXvoBApYRwOCt3uDt6fN18a+e12MvLZcfrFvoP72+UsVef+0vrjtpV+2y1UTLOoJwIQCBWghwGKqFVsJje3s17v/8H5VOPln+TjslHAzbZ5UAmpHVypM3BOongG7Uz46ZEMgiATQji1UnZwjUTwDNwOCtv3uYmUYCGLxprCo5pZoABm/1Bu/37nteV/7mxar7AYO3alQMhIC1BDgMJVs65/XXlb/qKhXPOUcaNy7ZYNgdAlUQQDOqgMQQCECgggC6QUNAAAK1EEAzaqHFWAhAAM3A4OUqgMBwAhi89AMELCOAwVu9wXt255N64Lk3q67wj784RVtPLlQ9noEQgIB9BDgMJVMz56WXVLjwQuUXLZKzerVWfec7Kn3hC8kEw64QqIEAmlEDLIZCAAIDBNANGgECEKiFAJpRCy3GQgACaEZlDyxZsqTiB21tbfhdXCaZIkDDZ6rcJJsGAhi8Q1Uc66ZmxuIn9Zu/VGfwHrzzpvrmYTukoUXIAQIQWA+BsXQDeOESyD37rAoLF6r1ppvk9PauXbx/m2309iOPSC0t4W7IahAImQCaETJQloNABgigGxkoMilCIEQCaEaIMFkKAhkggGZg8GagzUmxBgIYvDXAYigETCCAwTtUhbFuat5t8M757Hb68Ajv2N1ofIveu1FBjoMkmtDjxACBKAmMpRtR7p2ltXNPPqnCggVqvflmOf7Qu86HM1j5ve+p59hjs4SFXC0kgGZYWDRChkDCBNCNhAvA9hCwjACaYVnBCBcCCRNAMzB4E25BtjeMAG6GYQUhHAiMRQCDt36D93vuP+gTH9hoLMT8HgIQSDEBDkPRFrfp0UdVmDdPrT/+8agb+VttNfAO3tJJJ0njx0cbEKtDoEECaEaDAJkOgQwSQDcyWHRShkADBNCMBuAxFQIZJIBmYPBmsO1JeT0EMHhpDwhYRgCDF4PXspYlXAgYRYDDUDTlaH7gARXmzlXLPfeMukH/dtupOHOmeo45RsrnowmEVSEQMgE0I2SgLAeBDBBANzJQZFKEQIgE0IwQYbIUBDJAAM3A4M1Am5NiDQQweGuAxVAImEAAgxeD14Q+JAYI2EqAw1CIlQsCNd91lwrz56vlwQdHN3Z33FHF2bPVM22a1NwcYgAsBYHoCaAZ0TNmBwikjQC6kbaKkg8EoiWAZkTLl9UhkDYCaAYGb9p6mnwaI4DB2xg/ZkMgdgIYvBi8sTcdG0IgRQQ4DIVQTN9Xy49+NGDsNj/22KgL9u2224Cx23vwwVIuF8LGLAGB+AmgGfEzZ0cI2E4A3bC9gsQPgXgJoBnx8mY3CNhOAM3A4LW9h4k/XAIYvOHyZDUIRE4AgxeDN/ImYwMIpJgAh6EGitvXp9bu7gFjt+lPfxp1od699x4wdvs++1nJ4VazAeJMNYAAmmFAEQgBApYRQDcsKxjhQiBhAmhGwgVgewjwDMj3AAAgAElEQVRYRgDNwOC1rGUJN2IC/KlbxIBZHgJhE8DgxeANu6dYDwJZIsBhqI5ql0pqvfFGFRYuVNP//u/oxu7++79j7O69dx2bMAUCZhJAM8ysC1H9f/bOA1yOqnzj394eElIIoYkGAcUIUkIVEBADIi0C9xJ6U0ogpKKAKNIRNYWAgQSkCPkj2QCBUCQIggioQToKBAVBpSWBhNxkb93/8w45e8/dO7NTdmZ35sx7niePcnfmlN/55tud857vOyQQZwL0G3GeHfaNBOJHgD4jfnPCHpFAnAnQZ1DgjbN9sm+VJ0CBt/LM2SIJlEWAAi8F3rIMiDeTQMoJ8GXIvwHU/PvfMnCnnSTT1WV7c/vBB1vCbtf22/uvnHeQQMwJ0GfEfILYPRKIIQH6jRhOCrtEAjEmQJ8R48lh10gghgToMyjwxtAs2aUqEqDAW0X4bJoEghCgwEuBN4jd8B4SIIHPCPBlKJglrDN2rDTeeWfh5nxtrbQfcYTkJk6U7q98JVilvIsEEkCAPiMBk8QukkDMCNBvxGxC2B0SiDkB+oyYTxC7RwIxI0Cf0XtCnnvuuV5/GDVqFPWumNksuxMtARp8tHxZOwmEToACbw9Stx81Z975d3nm7U8KN/yqZYTsvvmQ0OeEFZIACSSHgJvfSM5IKtvTmtdfl4G77y5SXy/tRx8tuQkTpHuzzSrbCbZGAlUgQJ9RBehskgQSToB+I+ETyO6TQIUJ0GdUGDibI4GEE6DP6D2BFHgTbtDsftkEKPCWjZAVkEBlCVDg7eHt9qOGAm9lbZOtkUASCLj5jSSMoVp9bJg7Vzq++U3Jb7JJtbrAdkmg4gToMyqOnA2SQOIJ0G8kfgo5ABKoKAH6jIriZmMkkHgC9BkUeBNvxBxAqAQo8IaKk5WRQPQEKPBS4I3eytgCCZhLgC9DPXNb89Zb0jRzpuROO026R4wwd9I5MhIogwB9RhnweCsJpJQA/UZKJ57DJoGABOgzAoLjbSSQUgL0GRR4U2r6HLYDAQq8NA0SSBgBCrwUeBNmsuwuCcSKAF+GRGr+8Q9pmjFDGu66SzLd3dLW0iKrZ8+O1TyxMyQQFwL0GXGZCfaDBJJDgH4jOXPFnpJAHAjQZ8RhFtgHEkgOAfoMCrzJsVb2tBIEKPBWgjLbIIEQCVDgpcAbojmxKhJIHYE0vwzVvvCCNE2bJg33399r3vM1NbJy8WLp/uIXU2cPHDAJuBFIs89wY8PPSYAE7AnQb9AySIAE/BCgz/BDi9eSAAnQZ1Dg5VNAAjoBCry0BxJIGAEKvBR4E2ay7C4JxIpAGl+G6p55RpqmTpX6xx5znIvc+PGy5qKLYjVX7AwJxIFAGn1GHLizDySQZAL0G0mePfadBCpPgD6j8szZIgkkmQB9BgXeJNsv+x4+AQq84TNljSQQKQEKvBR4IzUwVk4ChhNIzctQPi91jz5qRezW//nPjrPatdVWkps0SdoPP1ykrs7w2efwSMA/gdT4DP9oeAcJkIADAfoNmgYJkIAfAvQZfmjxWhIgAfoMCrx8CkhAJ0CBl/ZAAgkjQIGXAm/CTJbdJYFYETD+Zai7W+ofeECapk+XuhdecGTfud12kps8WToOOkikpiZWc8TOkECcCBjvM+IEm30hAUMI0G8YMpEcBglUiAB9RoVAsxkSMIQAfQYFXkNMmcMIiQAF3pBAshoSqBQBCrwUeCtla2yHBEwkYOzLUGenNNx9tyXs1r7+uuPUdey2myXsdn7rWyIZ/gw00cY5pnAJGOszwsXE2kiABDQC9Bs0BxIgAT8E6DP80OK1JEAC9BkUePkUkIBOgCt7tAcSSBgBCrwUeBNmsuwuCcSKgHEvQ21t0nDHHdI0c6bUvv22s7C7776SmzJFOr/+9VjNBztDAnEnYJzPiDtw9o8EDCBAv2HAJHIIJFBBAvQZFYTNpkjAAAL0GRR4DTBjDiFEAhR4Q4TJqkigEgQo8FLgrYSdsQ0SMJWAMS9Dra3S+JvfSNO110rNe+85Tlf7wQdbZ+x27bCDqVPKcZFApASM8RmRUmLlJEACOgH6DdoDCZCAHwL0GX5o8VoSIAH6DAq8fApIQCdAgZf2QAIJI0CBlwJvwkyW3SWBWBFI/MtQLidNs2ZJ43XXSc2yZbZs8zU10n7EEZKbOFG6R4yIFX92hgSSRiDxPiNpwNlfEjCAAP2GAZPIIZBABQnQZ1QQNpsiAQMI0GdQ4DXAjDmEEAlQ4A0RJqsigUoQoMBLgbcSdsY2SMBUAol/GerokIE77yy177zTZ4ry9fXSfvTRkpswQbq/+EVTp5DjIoGKEki8z6goLTZGAiQAAvQbtAMSIAE/BOgz/NDitSRAAvQZFHj5FJCAToACL+2BBBJGgAIvBd6EmSy7SwKxImDCy1DDzTdL/ylTClzz/fpJ2wknSG7cOMl/7nOx4s3OkEDSCZjgM5I+B+w/CSSNAP1G0maM/SWB6hKgz6guf7ZOAkkjQJ9BgTdpNsv+RkuAAm+0fFk7CYROgAIvBd7QjYoVkkCKCBjxMpTLyaCRIyXT2iq5739f2s44Q/LDhqVoFjlUEqgcASN8RuVwsSUSIAFG8NIGSIAEfBLgbw2fwHg5CaScAH0GBd6UPwIcfhEBCrw0CRJIGAEKvBR4E2ay7C4JxIqAKS9DtYsXS/eXvyz5QYNixZedIQHTCJjiM0ybF46HBOJMgH4jzrPDvpFA/AjQZ8RvTtgjEogzAfoMCrxxtk/2rfIEKPBWnjlbJIGyCFDgpcBblgHxZhJIOYG4vgzVvvCCNE2bJh3f/ra0H3tsymeJwyeB+BCIq8+IDyH2hARIoJgA/QZtggRIwA8B+gw/tHgtCZAAfQYFXj4FJKAToMBLeyCBhBGgwEuBN2Emy+6SQKwIxO1lqO6ZZ6Rp6lSpf+wxi1PX5pvLyj//WaSuLlbc2BkSSCuBuPmMtM4Dx00CSSJAv5Gk2WJfSaD6BOgzqj8H7AEJJIkAfQYF3iTZK/saPQEKvNEzZgskECoBCrwUeEM1KFZGAikjEIuXoXxe6h591IrYrYeYW1RW3XCDdBxxRMpmhsMlgXgSiIXPiCca9ooESMCBAP0GTYMESMAPAfoMP7R4LQmQAH1Gbxt47rnnev1h1KhR1Lv4mKSKAA0+VdPNwZpAgAIvBV4T7JhjIIFqEajqy1B3t9Q/8IA0TZ8udS+84IigraVFVs+eXS1EbJcESEAjUFWfwZkgARJIJAH6jUROGztNAlUjQJ9RNfRsmAQSSYA+gwJvIg2XnY6MAAXeyNCyYhKIhgAFXgq80VgWayWBdBCoystQZ6c03H23JezWvv66I+jOXXeVNZMnS+eoUSIZ/kRLh0VylHEnUBWfEXco7B8JkEBJAvQbNBASIAE/BOgz/NDitSRAAvQZFHj5FJCAToCrh7QHEkgYAQq8FHgTZrLsLgnEikBFX4ba2qThjjukaeZMqX37bUcOHd/8puSmTJHO3XePFSt2hgRIQKSiPoPASYAEjCBAv2HENHIQJFAxAvQZFUPNhkjACAL0GRR4jTBkDiI0AhR4Q0PJikigMgQo8FLgrYylsRUSMJNARV6GWlul8Te/kaZrr5Wa995zBNl+0EGSmzRJukaONBM2R0UCBhCoiM8wgBOHQAIk4P0dhaxIgARIQCfA3xq0BxIgAT8E6DMo8PqxF15rPgEKvObPMUdoGAEKvN4XT8688+/yzNufFG74VcsI2X3zIYZZBIdDAiTgh0CkL0MrV0rTjTdK43XXSc2yZbbdytfUSPvhh0tu4kTp/upX/XSd15IACVSBQKQ+owrjYZMkQALRE6DfiJ4xWyABkwjQZ5g0mxwLCURPgD6jN+Pnnnuu1x9GjRpFvSt6M2QLMSJAg4/RZLArJOCFAAXeHkpuP2oo8HqxKF5DAuki4OY3gtDILFsmjddfL4033CA1K1faVpGvr5f2o46S3IQJ0r355kGa4T0kQAJVIBCFz6jCMNgkCZBABQnQb1QQNpsiAQMI0GcYMIkcAglUkAB9Rm/YFHgraHxsKpYEKPDGclrYKRJwJkCBt4eN248aCrx8kkiABIoJuPkNX8Tyeel34YXSePPNklm92vbWfL9+0nb88ZIbN07ym27qq3peTAIkUH0CofqM6g+HPSABEqgAAfqNCkBmEyRgEAH6DIMmk0MhgQoQoM/oDZkCbwWMjk3EmgAF3lhPDztHAn0JUODtYeL2o4YCL58gEiCBYgJufsMvsf4nnywN997b57b8gAGS+/73pW3sWMkPG+a3Wl5PAiQQEwJh+4yYDIvdIAESiJAA/UaEcFk1CRhIgD7DwEnlkEggQgL0Gb3hUuCN0NhYdSIIUOBNxDSxk0EJnHTSSU2tra1TRGSMiCAnZreILBGRuUOGDLlmzpw5HX7qbm5uPjyTyZwmIjuJyEARwSGLT4nI1Gw2+4yfuoJeS4G3h5zbjxoKvEGtjPeRgLkE3PyG35HXvvyyDNx778Jt3eutJ21nnCFtp54q+UGD/FbH60mABGJGIGyfEbPhsTskQAIREKDfiAAqqyQBgwnQZxg8uRwaCURAgD6jN1QKvBEYGatMFAEKvImaLnbWD4GWlpYBIvKHtWIsbs2JSK2I1K+t51EROTCbzba71XvRRRfVvPLKK7dmMpnj1l4LoRj1raP+O5PJnDVv3rzr3eoq93MKvD0E3X7UUOAt19p4PwmYR8DNbwQZcf+jjpK6l16S3FlnSduJJ4oMwNcPCwmQgAkEovAZJnDhGEiABJwJ0G/QOkiABPwQoM/wQ4vXkgAJ0Gf0tgEKvHwm0k6AAm/aLcDg8Tc3N0/LZDKTROSjfD5/3Pz58x/ZZ599aocNG3akiNwoIv1E5PxsNvszNwwtLS0XiMhlItImIuc0NTXdfNttt7WOGTPmy93d3bNFZB8R6RKRbbPZ7N/d6ivncwq8PfTcftRQ4C3H0ngvCZhJwM1v9Bp1W5tIY6MriMx770l+yBCRpibXa3kBCZBAsgj48hnJGhp7SwIkEBEB+o2IwLJaEjCUAH2GoRPLYZFARAToM3qDpcAbkaGx2sQQoMCbmKliR/0QWJuaeamI9M9kMmPmzZs3T7+/ubn5h5lM5ioR+V82m91URPJO9a+t6wOkZM7n8z+ZP38+hN5COfbYYwe2t7e/ISIbZjKZqfPmzTvHT1/9XkuBt4dYLpeTmfcvliUrMtKdFxk6dKjU1CJI+7Py7DsrZFlrTxbuX7WMkN03H+IXOa8nARIwiIDry1A+L3V/+IM0TZsm3ZtvLqtnzjRo9BwKCZCAXwKuPsNvhbyeBEjAeAL0G8ZPMQdIAqESoM8IFScrIwHjCdBn9J5iCrzGmzwH6EKAAi9NxEgCLS0t+4nIIhH59KOPPlrv8ccf79QHevTRR2/Y2dn5/tq/7ZDNZl9wAtHS0oLDFR9f+/nQbDa7vPja5ubmWzKZzIki8nA2mz0gSqgUeHvo3vXcf+WyR/7tGTcFXs+oeCEJGEvA8WWou1vqH3xQmqZPl7rnn7fGn6+vlxV/+5vkN8U+IBYSIIE0EuACShpnnWMmgfII0G+Ux493k0DaCNBnpG3GOV4SKI8AfUZvfhR4y7Mn3p18AhR4kz+HHIENgZaWlskiMlVEns5ms3vYQWppafmPiHxubfrmuU4gW1pa9hWRC0WkNZvNHmR33ZFHHnlVPp//oYgszGazh0Y5KRR4e+j+6L7X5KF/9NHbHfHfctzXZLvPrRvl9LBuEiCBmBPo8zK0446y7lpht/a11/r0PnfaabLmZ66Z/GM+anaPBCpDoLu7W/CMrVmzxvpf/Hc+75gkpTKdKrMV9L+1tbVQS//+/SWT4StUmVh5OwkYTYB+w+jp5eBIIHQC9BmhI2WFJGA0gST6DLw/1dTUSGNjo/Tr18/6X/x3GIUCbxgUWUeSCXB1Ismzx747EtDO370rm802213Y0tKyWER2ymQyP5g3b94vy8HZ0tJyn4gcIiKXZ7PZH5dTl9u9FHh7CJ274DVZ9Lo3gferG/UXCLz1teH8gHCbJ35OAiQQTwJK4K3p6JDP/+EPss2DD0rd2287drb9wAOl9bbbRCjoxHNC2avYEMCztWzZMkvUNa3oYwprIcI0RhwPCZBAbwL0G7QIEiABPwToM/zQ4rUkQAJJ9xl4p8IxexB6yy0UeMslyPuTToACb9JnkP23JdDc3Dwnk8mcmslk5s6bN+84u4taWlqeEJG98vn8RfPnz784KMqWlpYviMgSEWkQka9ks9nXg9bl5T4KvD2UigXeI7YdJjsOH9wH46CmOhn5+YHSVN9zPq8X1ryGBEjAPAJtH38sH11xhWy5YIH0W7bMcYDtBx0kuUmTpGvkSPMgcEQkEDIBiLtLly61onUbGhqsXdn4V1tbm/hoVyyeFEfwUuQN2YBYHQkYRoB+w7AJ5XBIIGIC9BkRA2b1JGAYgST6DLwndnV1WZme8K+9vd16T1x//fXLFnkp8Bpm4ByObwIUeH0j4w1JIKDOxHUReHGuLs7XLSvqtqWlZaGIHJzP5+fNnz9/TNR8igXerbfeOuomY1v/jx94U36/ZEWhfz8etakcvM2Gse0vO0YCJFA9ApmVK2WdW26RdebMkVoHYTdfUyO50aOldfx46fzKV6rXWbZMAgkigJd1iLsoEHUHDx6ceFFXx4/xrV69uvCnddZZx6jxJcjU2FUSSAwB+o3ETBU7SgKxIECfEYtpYCdIIDEEku4z0P9PPvnEEnpRIPKWcwTOq6++2mvuRo0aRb0rMdbMjoZBgAYfBkXWETsClRJ4m5ubp2QyGaR3XllbWzvit7/97f+ihlEs8K5atSrqJmNb/2+W1MjflvakXD5+yy7ZaViyz/qLLWx2jAQSSqBh5UrZfOFC2fyBB6ReE2n04XTX1ck73/ymvHn44dK68cYJHSm7TQLVIYC0WkOGDBGcTbvuuuuW9XJenRGwVRIgARIgARIgARIgARIgARIggUoRgMj76aefWpmSPv74Y0FGqKBlwIABvW6lwBuUJO9LKgEKvEmdOfa7JAE/Am8mk7ls3rx5P/GLtLm5+VuZTOZ3IlKbyWQOnzdv3gK/dQS5ngJvDzUKvEEsiPeQQDoINC1fLlssWCCbPfyw1Dm8LHQ2NMi/999f3hw9WnLDhqUDDEdJAiETGDhwoOAfxN2mpqaQa2d1JEACJEACJEACJEACJEACJEACphHI5XKWyLty5UrrX9BCgTcoOd5nCgEKvKbMJMfRi0BLS8uNIvK9fD5/x/z584+xw6OdwfuT+fPnX+YH4RFHHPGVmpqaZ0RkcFCB2E97+rUUeHtoUOANakW8jwTMJbDOBx/IlnffLV949FGp7ey0HWhHv37y1oEHyj8POUTaB/c9t9tcOhwZCYRPACm1EL07aNAg68xdFhIgARIgARIgARIgARIgARIgARIoRQBn8q5YscKK4lVH/gQhRoE3CDXeYxIBCrwmzSbHUiDQ3Nx8dSaTGS8i92Wz2dF2aFpaWv4qIjvn8/lJ8+fPn+EV32GHHTa0rq7uLyKyRSaT+e28efMgIFcsLzDP4O2ZKZ7B69VqeR0JpIPAgEsvlf6zZ0umq8t2wN1DhsjKk0+Wp0eOlI61aXx22GEHaWhoSAcgjpIEIiDw4YcfWmmZN954YyPTMyf9jKsIppxVkgAJuBCg36CJkAAJ+CFAn+GHFq8lARIwxWd0d3fL+++/b03osDIyqvEMXj4TaSdAgTftFmDo+Jubm3+YyWSuEpHns9nsSAeB910R2VREDs9ms/d4QdHS0gIV4BER2UtE/rRq1apRDz30UPCDArw0WnRNscA7cqTt8ALUnLxbzl3wmix6fXmh4xcdsJmM3m6T5A2EPSYBEgiFQON118k6F1zQp67ujTaS3FlnSduJJ0pbfb385S/Yo/NZ2XXXXQVniLKQAAkEI/Cf//zHunHTTfGTyryChQfsKlcF0co1NTXmDZQjIgESCI0A/UZoKFkRCaSCAH1GKqaZgySB0AiY4jMgVP/3v/8t+13yueee68WWZ/CGZmqsKCEEKPAmZKLYTX8Empubv5PJZB4UkfZcLjdk4cKFq/UaDjvssA3q6uqwTSjT3d395bvuumuJlxZaWlpuFpGTROSNzs7O3e+5555lXu4L8xoKvD00KfCGaVmsiwQMINDaKoO2315qln3mmru+8AXJTZgg7UcfLbL2bNC2tjYKvAZMNYcQHwIUeOMzF+wJCZBAPAiYsvAaD5rsBQmYT4A+w/w55ghJIEwCJvmMMN4lKfCGaV2sK4kEKPAmcdbYZ1cChxxyyDpNTU1LRaRfPp8/a/78+bP0m4488shz8/n8z0TkzWw2+yXXCkWkubn5/Ewmc4WIfFRTU/P1O++8859e7gv7Ggq8PUQp8IZtXayPBJJPoGnqVGmYN09ykyZJ+xFHiNTX9xoUBd7kzzFHEC8CYbyUx2tEvXtj0gJKnDmzbyRgEgH6DZNmk2MhgegJ0GdEz5gtkIBJBEzyGWG8S1LgNcm6OZYgBCjwBqHGexJBoKWlBefqThCRVSJy8tZbb333q6++msnn8y2ZTAaRuE26+LvPPvvUbbDBBrfl8/mNROTEbDb7jhpoc3Pz4ZlMZr6IIB3zvtls9plqQaDAS4G3WrbHdkmgagS6uyXzwQeS33hj9y60tYnU1YnU1tpeS4HXHSGvIAE/BMJ4KffTXqWvNWkBpdLs2B4JpJUA/UZaZ57jJoFgBOgzgnHjXSSQVgIm+Yww3iUp8Kb1SeC4FQEKvLQFYwkceuih6zY2Nj4uIuqQWnVWrjpscaGIHJbNZrsAYcyYMTt3d3f/dS2QH2az2V8oOC0tLTh8bR0R6RCRlS7Q7shms2dHBZYCbw9ZRvBGZWWslwRiQqCzU+oXLJB+06eLdHfLyqeeEinz7EsKvDGZW3bDGAJhvJTHGYZJCyhx5sy+kYBJBOg3TJpNjoUEoidAnxE9Y7ZAAiYRMMlnhPEuSYHXJOvmWIIQoMAbhBrvSQyBlpaWfplM5px8Pj9GRDbHkYwi8pqI3Coi1ylxFwNaKwg/LSIb1NTUfOfOO+8snNLe0tKS9zrofD5/6/z583FObySFAm8PVgq8kZgYKyWB6hNob5eG3/5Wmq6+WmrfeqvQn1W/+Y10HHxwWf2jwFsWPt5MAn0IhPFSHmesJi2gxJkz+0YCJhGg3zBpNjkWEoieAH1G9IzZAgmYRMAknxHGuyQFXpOsm2MJQoACbxBqvIcEqkiAAi8F3iqaH5smgWgJrF4tjbfdJk3XXCM1//tfn7Y6t99ePn30UZFM8J8vFHijnULWnj4CYbyUx5maSQsocebMvpGASQToN0yaTY6FBKInQJ8RPWO2QAImETDJZ4TxLkmB1yTr5liCEAi+QhqkNd5DAiRQNgEKvBR4yzYiVkACcSOwcqU03nSTNM2aJTVLl9r2Ll9TIx3f/a60zpghMmBA4BFQ4A2MjjeSgC2BMF7K44zWpAWUOHNm30jAJAL0GybNJsdCAtEToM+InjFbIAGTCJjkM8J4l6TAa5J1cyxBCFDgDUKN95BAFQlQ4KXAW0XzY9MkECqBzPLl0nj99dJ4ww1Ss2KFvbBbVyftY8ZIbuJE6d5ii7Lbp8BbNkJWQAK9CITxUh5npCYtoMSZM/tGAiYRoN8waTY5FhKIngB9RvSM2QIJmETAJJ8RxrskBV6TrJtjCUKAAm8QaryHBKpIgAIvBd4qmh+bJoFQCGTef1+afvUrabzlFsm0ttoLu01N0nbCCZIbN07ym24aSruohAJvaChZEQlYBMJ4KY8zSpMWUOLMmX0jAZMI0G+YNJscCwlET4A+I3rGbIEETCJgks8I412SAq9J1s2xBCFAgTcINd5DAlUkQIG3B/65C16TRa8vL/zhogM2k9HbbVLF2WHTJEACpQjUvPOONM6cKY1z50qmrc320vyAAdL2ve9JbuxYyW+wQehAKfCGjpQVppxAGC/lcUZo0gJKnDmzbyRgEgH6DZNmk2MhgegJ0GdEz5gtkIBJBEzyGWG8S1LgNcm6OZYgBCjwBqHGe0igigQo8FLgraL5sWkSCESg5o03pGnGDGnIZiXT1WVbR/eQIdJ2+unSdtppkh88OFA7Xm6iwOuFEq8hAe8Ewngp995a5a80aQGl8vTYIgmkkwD9RjrnnaMmgaAE6DOCkuN9JJBOAib5jDDeJSnwpvM54Kh7CFDgpTWQQMIIUOClwJswk2V3U0yg9qWXpGnaNKlfuFAy+by9sLvhhpI780xpO/lkkQEDIqdFgTdyxGwgZQTCeCmPMzKTFlDizJl9IwGTCNBvmDSbHAsJRE+APiN6xmyBBEwiYJLPCONdkgKvSdbNsQQhQIE3CDXeQwJVJECBlwJvFc2PTZOAZwJNv/yl9LviCsfruz7/eWkbP17ajj1WpKnJc73lXkiBt1yCvJ8EehMI46U8zkxNWkCJM2f2jQRMIkC/YdJsciwkED0B+ozoGbMFEjCJgEk+I4x3SQq8Jlk3xxKEAAXeINR4DwlUkQAFXgq8VTQ/Nk0CngnUPvusDNx//z7Xd33pS5KbOFHam5tF6us91xfWhRR4wyLJekjgMwJhvJTHmaVJCyhx5sy+kYBJBOg3TJpNjoUEoidAnxE9Y7ZAAiYRMMlnhPEuSYHXJOvmWIIQoMAbhBrvIYEqEqDA2wP/3AWvyaLXlxf+cNEBm8no7Tap4uywaRIgAZ3AgMMOk/onnrD+1LnNNpKbPFk6DjlEpLa2aqAo8FYNPRs2lEAYL+VxRmPSAkqcObNvJGASAfoNk2aTYyGB6AnQZ0TPmC2QgEkETPIZYbxLUuA1ybo5liAEKPAGocZ7SKCKBCjw9sCnwFtFQ2TTJOCBQN2TT0q/yy6TNVOmSOd++4lkqv+zgwKvh4njJSTgg0AYL+U+mqv4pSYtoFQcHhskgZQSoN9I6cRz2CQQkJhen5kAACAASURBVAB9RkBwvI0EUkrAJJ8RxrskBd6UPggcdoFA9VdaORkkQAK+CFDgpcDry2B4MQmETaC9XWpfeEG6dtnFveZ8/rNrYiDsqs5S4HWfNl5BAn4IhPFS7qe9Sl9r0gJKpdmxPRJIKwH6jbTOPMdNAsEI0GcE48a7SCCtBEzyGWG8S1LgTeuTwHErAhR4aQskkDACFHh7JowRvAkzXnY32QTWrJHG226TpmuukczSpbLi+eclv9FGiRsTBd7ETRk7HHMCYbyUx3mIJi2gxJkz+0YCJhGg3zBpNjkWEoieAH1G9IzZAgmYRMAknxHGuyQFXpOsm2MJQoACbxBqvIcEqkiAAi8F3iqaH5tOI4GVK6Xx5puladYsqfnoowKB3LhxsuaSSxJHhAJv4qaMHY45gTBeyuM8RJMWUOLMmX0jAZMI0G+YNJscCwlET4A+I3rGbIEETCJgks8I412SAq9J1s2xBCFAgTcINd5DAlUkQIGXAm8VzY9Np4hAZvlyaZw9WxrnzJGaFSv6jDzfv7+seOklyQ8ZkigqFHgTNV3sbAIIhPFSHudhmrSAEmfO7BsJmESAfsOk2eRYSCB6AvQZ0TNmCyRgEgGTfEYY75IUeE2ybo4lCAEKvEGo8R4SqCIBCrwUeKtofmw6BQQy779vResiajfT2mo74nxTk7Qdf7zkfvADya+/fqKoUOBN1HSxswkgEMZLeZyHadICSpw5s28kYBIB+g2TZpNjIYHoCdBnRM+YLZCASQRM8hlhvEtS4DXJujmWIAQo8AahxntIoIoEKPBS4K2i+bFpgwnUvPuuNM6cKY233y6ZtjZ7YXfAAGk75RTJjR0r+Q03TCQNCryJnDZ2OsYEwngpj/HwxKQFlDhzTlvf1ltvvcKQ77vvPtlzzz3ThsDo8dJv2E/vn/70Jzn00EMLHy5fvtxoOwhrcO+9957Mnj1bHn30Ufn3v/8t7e3tMmzYMLn88st78QyrPdZTeQL0GZVnzhZJIMkETPIZYbxLUuBNsjWz72EQoMAbBkXWQQIVJECBtwf2uQtek0Wv9ywMXHTAZjJ6u00qOBtsigSST6BmyRJpmjFDGrJZyXR22g6oe/BgaTv9dGk77bTEpWQuHhAF3uTbLEcQLwJhvJTHa0S9e2PSAkqcOaetbxR4zZ5x+g37+aXA69/un3jiCTnppJNkhc1xKRB4x44d679S3hE7AvQZsZsSdogEYk3AJJ8RxrskBd5Ymys7VwECFHgrAJlNkECYBCjwUuAN055YV3oJ1L78sjRNmyb1990nmXzeXtjdYAPJnXmmtJ18ssi66xoBiwKvEdPIQcSIQBgv5TEaTp+umLSAUinOunhp12Ymk5F1111XcN3WW28te+yxh7S0tMjQoUMr1cWqt5NGgXe77baTd999NzD7JEV70m9Q4C1l6Oedd5783//9n3z5y1+W2267TTbeeGPby//73/9a/nHlypWFzxG5+8UvflHWrFkjp512mhx77LHWZ/Pnz5fzzz9fmpqa5LrrrmNWgMCepjo3VsJn/Otf/7Ls7rHHHhP8dsOmgcGDB8vnP/95+da3viXHH3+8bLrppqEDQHvz5s2TZ599Vj788EPp6Oiwvv/xnXDQQQdZ3/8NDQ2B233mmWdk7ty5gmwY77zzTuB61I0vvfSSxQnPFPr+hS98wXOdJ598stx7772er8eFP/zhDwU+wUsBu6eeekoeeeQR+etf/ypLly61/h1++OFy9dVXe6nCuibKOcEc//73v7f+LVmyRJYtW2b1EfOz2267eeojMhXcfvvt8sc//lHefvtt+eSTT6Rfv36Wr9xll13kqKOOkq9//euOdR1yyCEWp3KKn3lR7eRyOdlrr73kzTfftP6EZ+vFF1907Qa+C8DIT1mwYIHssMMOhVv69+8vNTU1jlWUY9d++hXk2jDeJSnwBiHPe0wiQIHXpNnkWFJBgAJvzzQzgjcVJs9Bhkyg9q9/tYTdhkWLHGvu2nRTaRs/XtqwaNSvX8g9qG51FHiry5+tm0cgjJfyOFOpxKJrnMcfpG9uAq9dnY2NjTJp0iTrX319fZBmE3UPBV7/00WB1zszXUy/9tpr5ZhjjvF+c4RXMoJX5I033uglckycOFEuvPBCW+o//vGPZdasWdZnWLifPn26HHfccYJNMsXlK1/5iiWeoey0006yqMTv/AinmFUHJBD1b42ZM2fKlVdeKXgPcioQ0C666CI59dRTA46i920ffPCBnHHGGYIo9FIFGxZuvPHGXmKVWwfw2/POO++UO+64QyBcqxL0ewIiZDabtYTdV155pVDfCy+84Evg/fa3vy2LFy92636vz70KiRD0MD92Ijb8AubYrUQ5J9iI8stf/lLmzJljpZEvLg8++KCrwNvZ2SmXXnqptUkF/79UOeCAAwTfb3a/OcMQeM8991zBPz/lkksukRkzZhRu8SLw4pncZJNNJO+w4d6pfS8Cb1h27YdBkGvDeJekwBuEPO8xiQAFXpNmk2NJBQEKvD3TTIE3FSbPQYZBIJ+Xuj/+8bOI3SefdKyxa8stJTdhgrQfeaSIoQvsFHjDMCjWQQI9BMJ4KY8zz6gXXeM89qB90xfbttpqK9loo416VYVFrE8//dSKcMD/6gWLcjfddJPU1tYGbT4R96Vd4B0+fLhsttlmvubqnnvu8XV9NS+utt+gwFvN2S/d9muvvSa777574aLx48dboo1dwXW4HuXEE0+0BF6nokeAjRw50oqeY0kOgSh9BkQwfRMBNgjAXvA9hPOdESGpl6lTpwoiUcspiLj8zne+I6+//nqhGgjII0aMsDZx4e+4RpUBAwbI/fffL9tuu61js4iOfOCBBywRFqIxmBUXPwIvBESca43o34cfftiKLC4ufgXer33ta4LIe5Rdd93Viqh3K2PGjLEiUp0K+oVo/eLI4Lq6OiuaH1HYONv8rLPOKtlUFHOiGoSfOuKIIyx70sugQYOsqPAhQ4bIz372M/nqV7/q2Meuri4rivx3v/td4RrY6pZbbmmdOY7+ox193vFdt3DhQoH96AWbY1599VU39H0+R8SwElrRD0QLey0vv/yyFQmvC9NeBF48f/DZKLAX2I2XcvHFF1vzr4qK4I3Crr30p5xrwniXpMBbzgzwXhMIUOA1YRY5hlQRoMDbM90UeFNl+hxsGQQabr9d+o8f71hD5zbbSG7SJOk49FARwxfVKfCWYUi8lQRsCITxUh5nsFEuusZ53OX0TRcvS0UPYhHt8ccftxae9YU4RECMGzeunC7E/t60C7xeI5ZiP5EOHay236DAG2/LOeecc6zoQwgXSNHslBYX4kBra6s1GEQ4Ig2rU0EkI0QNiGjwu/vss0+8IbB3vQhE5TMgHiEtroqo3Hvvva1UvnraYYiYZ555ZmEzAYQipP91Sh3uZep+8IMfyK9//evCpSpDhxLiIEL95je/kQsuuKAQVbzNNttYvwns0swi+hiRoXZnUev98SrwYiPZL37xC0FEa6niR+DFHGJDmxL4kGYYx1GUU1DXCSec0Ev0RArg008/Xb7xjW/0ETZLtRX2nKi2/v73v8t3v/vdQorhddZZx8o0gH8QdEulDdb7e8UVV1gRwKog88SPfvQjK7pVFQjIiKrFZgBVIGwj6rfcgrTXENtR/G6SgTg9atSoPumYvQi8SDGOVOUo+O7+wx/+4Gkodj7jlltuCd2uPXWmzIvCeJekwFvmJPD2xBOgwJv4KeQA0kaAAm/PjFPgTZv1c7yBCaxcKYO23VZqtDO8UFfnTjtJbsoU6dh/fxGbdG+B24vxjRR4Yzw57FoiCYTxUh7ngUe16BrnMZfbN68Cr2oHAgYWt3A+GAqiURDdY3KqZgq83s8cLNceq3F/tf0GBd5qzHr4babRT4RPMRk1RuUzIKzeeuutFgREzyJi1S6qFMIZBEMlkE6YMEF++tOfBoK3evVqa/MCIm5RSglwOJsXaZxVwTmte+65Z592IWrqqZM32GADK2IUmSD0NLpeBV6Ij0gZrMrAgQMtkRIM9BTVfgTe999/vxChisjVt956KxA//SakXVYR/ti8AVE6SMr9KObEWkvo7JRvfvObhU16EOlvvvlm2WKLLXyN/aOPPrJSdKOfKGPHjpXLL7/ctg5sDmxubi4IodiQgIwwOOqjnIL5RwQvyg033GDZl9eiz9P6669fELu9CLx33XVXwebwWxibfrwUO5+BzQBh27WXvpR7TRjvkhR4y50F3p90AhR4kz6D7H/qCFDg7ZlyCrypM38OuAwCTZdfLv2mTrVq6NhrL8lNniyd3/hGaoRdhY4CbxlGxFtJwIZAGC/lcQYb1aJrnMdcbt/8CrxoD+kWDzvssELTTou85fYtLvenUbjRRUdG8EZriRR4o+VbqdrT6CcqxTZu7UTxWwNRhTibGedwoiCiVv+eLWZw1VVXCf6hQDgtFky8MtPP2kaKXaTVRYpdp4Izo9U5uk7nnkLghYiHM26PPvpo2XfffQUpioOe6w2BF2mZUQ+iNg888EBL+Mb5tttvv32hq34EXvBCFCfK1ltvLU+WOBbJC0v0BWnaIXri2ApEPCPtdZASxZygH7qw+aUvfUkeeugh2zNx3fqMyNPJkydblyGdM9IdIxLYqWBeMHeqIHX317/+dbdmHD/HBkOV9QCR6y+++KJlX14KbBcbA9asWWP1HZsjlCjvReDVGSIVN1JZeylOAm/Ydu2lL+VeE8a7JAXecmeB9yedAAXepM8g+586AhR4e6acAm/qzJ8DLoNAZtkyWWfyZMmNGyddO+9cRk3JvpUCb7Lnj72PH4EwXsrjN6qeHkWx6Brn8YbRtyACL6JAkDJSRfyUOgMQi9ZYzEOUwt/+9jf58MMPrfSTaBfn9+EcX0R3NDQ0OA4H1zz11FPW5yqNNGwZkSeLFi2Sd99916oTC32IJkIEEs4s9FKwMImFdCzuIioKUTc4J+2AAw6wztFDpJBf4QaLmbfffrvVZ/QNdokFcwh5GAtSt7qdW6y3iT4i7SGEdJxti4WxpUuXWn3FgiSiSHAGIyJRVMH5d0gDu2DBAkHqyY8//tiKtsaZgy0tLRbzUn2IUuD93//+Zy1+IzptyZIl1kIr+o4oIggCxx57rNXXUkW3CUQOnXTSSXLddddZ5x4iEmvVqlVWRBEii+wKos6R9hMpRjFHYIEoM5ynh6gapEn1UpDuEhF3aq7xTGDRGKkuMRZEbxWn/cSZlF7SmntZbHbrIxbCdVvEczJ06FBLFME5kIh6cloY9yvGIL3s/PnzrfNkIXZAqMKiP3wFhA/MEc759lIQIQYR4bHHHpM33njDOv8bHPFsIm0t7N0pVXJx/bCz3/72t/Lss89a/gcRZXgeEYGGZxHPT6lnodTzr39WalzF6e91+/WygQIs4e9wziR4wAdjHjGG0aNHW+OAOGdX9HmEP8M8IaUvnpe//OUvAtaIqis+29XLPHm5Bm3guYSPRdTkypUrLb8KH40zMHFeMcbiVODP4ctQIBb+6le/Ktms3w0TYJrNZi2/ihTASFG74YYbWr4AvmiPPfbo014UvzV00QoZMf75z3+WTOkL36NHz+LohCBpmvWIRIz7H//4R0m+p5xyivW9goJnetq0aX2uh2gFH4rIWL349SnqXvhp+FT4aL2UI/DidwnOkEWBr1Y25sWm7a5B+mo8Uyjf+973rOjdoCWKOYHPgJCN5x2+Ar+ddtxxx0Bd1J9JbELQ03s7VYjvAXwvo7ilsHfrFH6b4bsG5Sc/+Ykg8t1rgb9UYj78Mor6PvbynXv++efL7Nmzrfv8HFFi5zMQgRy2XXvlUM51YbxLUuAtZwZ4rwkEKPCaMIscQ6oIUODtmW4KvKkyfQ7WjsCnn0rDggXSftxxqYvEDWoQFHiDkuN9JGBPIIyX8jizjWLRNc7jDaNvQQRetIuFQgiiKDhLUkVz6H1avHixTJw40XXBGFFLEPyQJtKuFAu8mGec9aYWC4vvgViMhdZS0U+4B+fHIQIKIrRdgeiIfiFaSJVS0croDyKa3BaKMd5Zs2b1ijwqbl+fFwhmWFQET6eChX0IhxA3sBiOMxohpjgViGRILajOWCy+LgqBF8IaFlQR8QJR16lg7Djf78gjj3S8RrcJ2Bg2EED40oudwIs+oO4ZM2Y4zjvqgJCEa5xSj8MGEfWDecT/dyo44xHX6GesVkLgxWI+nhEsvGPMTgWCK4RupIMtLl7FmI6ODmuhG2ky1dmhdu1BRMW5khA0SxWIG/AnEHWdCtJ7YmEfdu5UkP4VZ19C4C1VcH4jOA0fPtz2smoLvDi/EgIEhFGngjHgebYT+IoFXvgosNPtVgm/JUH5/BD+B/MIAbVUwWYOPCPYVGNXohJ4sUkG4jLO1CxVsAEAfksXK6P4raGnP8Z3xNNPP12yX3iuP/e5zxU2WuG5QfpdvwXPBzb9oGBzCoTlUgUbYNSZqn4iGFGnV5/idQzlCLzwVypddLmCrC6eYsMMxPZSUdBu44tiTvAdiUhoFEQWz507160bjp9jg406exabqPS020434bsG4jIKflcdfPDBgdrHewz8HZhjkxtSgcNuvRS0i98LKPgNhA1z+vexF4EXPmPhwoVWHfjuRKpoL8WPzyjHrr30pdxrwniXpMBb7izw/qQToMCb9Blk/1NHgAJvz5RT4E2d+XPAawlkPv5YGmfPlsY5c6Tmk0/k0zvvlM799iMfDwQo8HqAxEtIwAeBMF7KfTRX8Uv9LKBUvHMxbTCIwFscwQvBDgu9ekEaZ0Q/qTPa8Bmig7CAhr9hAUsXaLFQjXvsIuJ0MQ8RS1gkRoHIs/nmm1sL70gHiQV7VbDwh6hKpK60K4g6Lj4zDgIPBBIsQiKNHxbQUY8uRjoJvBCjICjri1boHxY18b+oT6XeRH8grGJh0e78Qnyuc9DPiEPUIliBIVJpQlxTBWOFeImoKghtiNJB+4iOQ+QiGOliHxYqp0+fbssnCoF3ypQpVhSiKugf0kRirIjqhU3opVR0jG4TiLhTYhVYgQP++/vf/74cddRRhSoh5EMQRLSeKpgHiCn4DNHEuk1C2Ef0q12BcIUIU1WQLhT2g/ow14iYVgXzD/EGaSFREJV6zTXXFD5HhKOKhsd8QRRGQbSaihSy7YTDH/HbCZHDagFePSuoGxG1xc8KBDYs/oODXryIMbA/RMEhGkwfL1ig3v/+97/W3Op2B2F8/Pjxtr2HSAVfoq7H3CLyCywQaa02laibnTaX4JmFYIjod1XgJ5RtgIH+XOO5whgUe71zpQRefRMJ/JcqeH70KPSzzz67V4pSrxG8EJ4hnij7hkiOiC9E3CIyX+cB5rCtYrGjON2rYgshChHReGZg/25CrB8bxLwjQlw/11RFYKNdPOu6v8amHESF2kXORyHwgh2EGfyvKpgvbDICH0TH6vaBiHf4frUhJorfGvge/fnPf251Z//997eizt0Koozht1CuvvrqQkSq233653g+kdlB2QW+N+02fOAePO+wbbV5CBupkDLZa/HiU7zWhevKEcIuvvhiixkKzi+GOA4fgO/VFStWWL8r8PsCfgSCYqmCTVWIokdBKmJEl+I7AH4V0fL4/oWN4xlFtLBdVHjUc4LNLuq7T51ZCyEafYQNYczwHbAFRLl6zZDgZb5aW1stP65sDNlc4HuCFPh8bAhBKfUbprhu2CzSQmOc+F0HW0Qf/Aq8++23n5WNBgUprvGbARHB+N7H9zj8Kc42hoCNMavix2eUY9dBmPq9J4x3SQq8fqnzetMIUOA1bUY5HuMJUODtmWIKvMabOwdYRCDzwQfSNGuWNN58s2TWpiTCJZ277iqfPvggo3g9WAwFXg+QeAkJ+CAQxku5j+YqfqmfBZSKdy6mDQYReB955JFei7pYINQX5hFphtR/StDE4jjEOl3MhH9H9APEHiVSQny77LLL+pDSxRD1IaJGzjnnnIKYgblHqlxECCoxBAuaV155ZZ/6kAoTqUFV5C4WsyG46Yu4iGJCykVduEJFTgKvnrYSohSiRCBiITIORaWqhliDNKAoWAjEImNx2kl8Vix0IxUrRGn9vEFEKCKyVxcs1WCxgIyz4vRFVCxAQvRECmkUCC1gYSdqhS3wYq4xX6oggghitB41iX7hGrXwBwEYgp8e/aruL7YJiNiIuEW94G9X9LPzEJmLKMZTTz3VitLFAjTsEFGQ6JeySdSJiDW9ILJOjz7CIvOFF15YsEUsYt95552W/SihFwu9iMC2iwj2m1LWzZVg4wJsBQUMIUZDYFS2iP6BK1irqFAsSEOk0Nl5EWOQnlU9s7AniPgQaHWRESIC2oKQjQKRGXNcbPfoC1hgAR4FPgX89XTrEEswHghwyoaxuF6c+hlCmTobEaIo/j/mUfHHBglsbsA/5S+QtnzOnDl98HpN0e71OjTgReBFSmmIS6p/iCqHv9QjAxHdDz+jBDdEYhZvCiieR7QPUQ72j9TvURSIXbAn5ecuvfRSSwDT7R/iCOxCidSYe31TgupX2AIv7B/8VYQshF3YB/qn0pXDH8D2YN9KlIKvUGfeqt8asHeVKtYvR/hvvSDi/vrrr7f+hL4gja1bwRmyyl+WSknvVo8elQuBEv7LLm05sl7AP6Lg+YXQhc0GXosXn+K1LlxXjhCmC54YC0RYpwJRD/bglAIbfgQ2jnLeeedZmzswH05ZCPD9jFTjugBY3HbYc7LLLrtYm3tQ8GziO0JFoha3DTEa3xn4feH0fepnnvTodPzeUkdu+KkD1+I7AgK04opnuHhjklOdOk99k5FfgVfPXlPKbuBL8HsLGwng9/y8n5Rj136ZBrk+jHdJCrxByPMekwhQ4DVpNjmWVBCgwNszzRR4U2HyHKSI1Lz7rjRec4003n67ZHI5WyafLlwonTZnOhFgbwIUeGkRJBAugTBeysPtUbi1+VlACbfl5NbmV+CFaAsRTS0UQqDDIq++CKgvmGGh8/nnn+9zFp8ipi8YY/EUYlBxKRbzSkV24iw2CL0oONPVLq0xRBCVthVRKoh608+vVe3DniDe4aw+VewEXj16B9fh7D2kfLQrEI4RwaMW95GGWC3q69fr84IoV/TXLp0yhGOI1RBqVYGoiz4Vn/2Kz7FwCPFdidtOEVhhCrwYK8RzJSYi6hHihd2ZoRDeEFkHcQsFC+Cwr2KxQbcJLJ5iDrHw6lQQqYdFdRVRjuhbRBiiFPsNlSIYn8G+sRCp91W3sZ122klw3qTdWP785z/3Su8NIQjiSXEJU+BFmmpECiuBupTog/5BqFYCIlJ24tlWxYsYo/cdwoZT+mXMPTYpKMHbTjjXz50slXoT9oQzfZUgg/mAWKkXXcyYMGGCFaFnV/RIfogaiGQrfm68Crder0M/3AReZElAClF1HmqpVLgQu+ED8JsZdojnRc9cUDyPXs/MDPqtVixOIJ0v5suuFD8jeO4RNamXsAVebOKATaDAd2CDktNZpLpwB8EGZ3dj80IUAi82BSGFLIqXs4ZxHZ5fJVRDINY30fiZP0TH43tJbRRApCM2S8G/gRH8CqI+VUYDPCs4isBvSmgvPsVPv8sRwuD/VTYQL23iuwCCqF1kK1IW45xpFAiOiAJ2K8jEgd8WTiJvmHOCKFOVvQC/1RCpXnykgV1/sRFEnSvsNh6nz/FbA3aCVMooeKbwuypIwUY85ctRJ74zvBT8bkNmExQIxPg9pTZz+BF4MRZsiHM61sOuL/DNeFbAHRtHVMHGCCfxvBy79sKj3GvCeJekwFvuLPD+pBOgwJv0GWT/U0eAAm/PlFPgTZ35p27ANW++KU3Tp0tDNiuZzk7b8XcPHixtp50mbaefLnmP58WkDqQ2YAq8aZ59jj0KAmG8lPvp1w5XlT5Dzk9dabj2+XPtF8GjHLtXgReCAyKuIKToqXSx4KufUYu+YnFTCbUQTiFUOBWIE3pk79tvv12INFT36GIIRDqnqBNcr4utEDuQfhIpclVB6lCIUkrUQrSbOn/Qro8QtCGwqmIn8B5xxBGFyDN1rlupOdMXFLF4DgG8OJJOnxeIoSr9o129WIBFtKgqbmPCQr4Svp3EL124w8K2U6rr4v4gignRoHrRRRJE2kLE0c+zLK4Dmwcwz0qktDvnTrcJnCuI6NxSBZHTEChQIIooIQX/XSzwQlyEsKHsvFigQqQnBGUURJIXp/rW+wFhSy32I9LV7szYMAVefZzbbrutZZd24rPqI4RDFYFYfA6lFzEGZxiqZwnCsl00umpLj6Cy4wbRF5s3UNB3FQFqN68QklWEo915ktgsgt+QKMXCtV5f8fONNtG2XrwKt16vQ91uAq8uSGAsEG2RBtypwK5USt9iob14HpG2GuJSVAVM9bmD7yplg9iIo4R/u7kKW+CFb1HCuVOWB8UGQg42JqjfTsoXKZ+BzTl//OMfrQ0opcZoxxrPjl78jtOLHfmZY/gpRCnbbbLS68EzDn+LNNJ+ixef4qfOcoQw+HhktUDB5in4JDyX+L5D1DYEKKRw1lOvw3Yg5BYLc/gNo7IKqP4j8wAEd2SgwKYRbB6BveD7XxUI6fh+cbKdsObkxRdftBXj8V2GKFOInvi+RWQtfDA2MqhitxnHzxzp2RTABBkXnM62L1UvfoMigwl+06EgyhyR1W4FGSHAGZsX8JwiKwueaVX8CLxoW/99A3EeUfzINoHjR7CRCb4PY1a2hXZgB/DLFHh7ZosCr5vl8nPTCVDgNX2GOT7jCFDg7ZlSCrzGmTcHtJZA7SuvSNO0aVJ/772SyedtuXQPGya5s86StpNPFll3XbLzSIACr0dQvIwEPBKgwOsRVJUuq7bAq5//qRBgoRPp8BDtoS9O4fNSZ2l6RVi8QIuFSCyU6cVNDNGvhTiI6D1VsECqi06IpMBCOgoWdbHoqgvAdv0uJdx88sknlgCsIjpK4FErSAAAIABJREFUiUmqbixUYpFQRSAiihARQHrxIxYVp8yGeIHzjp0KFnTvvvtu62OIbljALS666Oh1LnGdnQCOBW4VYewmiKq2EOGjhHy7iEM/NgEbRlpIxRvpIpHaVBW7yH+kvVUL8cV2fsYZZ1hn6qK4LdDjLGQlgGJR2y7taZgCr14X0pNDwCpVkN5bic4QbGC/qoQtxrgJWHpkJZ5NCJFOGwHwDOEfCpgWCwZ4vpQQgAV4fQNEMQ91/jH+Dl9QLLZ4fRa9Xod23OxXT/leKgJZjQXirppH2LayT3we9jz68QdertVtFmlrEb2qFze7KeW7rr32Wus8alWKv28gNpWK/Md9iKxVzwV8AfoTRbYQv+P0Ykde+OvXQHRBBgX9LHL9czwbSGOOTU1BSti2WI7Ai41GEDQRWYpzXfXjAtTY8N2BlPAqKwj+brepDQIpNo+pgmcQm4jsNmXoWUtwPeqGP3AqYcwJNiFgXlWBQA3x+thjj+3TLH7vYROUOr8cm/Tw/91+J9n1H5GyiALG84I2IWbbnbPtxZb0NM/4zYXofy+bKvBdriLP7Y4B8SPw4vscgv8zzzxjHSWATVvYEFZcIPTidws276GAHY6gwPEEqjCC97le2EaNGkW9y8uDwGuMIUCDN2YqOZC0EKDA2zPTFHjTYvXpGWft4sWWsNvw8MOOg+7+3OckN368tB13nIjNC0B6aAUbKQXeYNx4Fwk4EaDAG2/bqLbA65UOFkJxZiEiQb2Ujz76yEqJB5EPURRY/FKCKMQVdTYn6sIiWHHKQjcxRO+Dm2CMKAp1ziYiDlVaxVLjKCXc6OIqFjDRvr6I51SvLhLapWn2Ixb5XTT3IiSEJfAiJTIEe3WOJQRFpCx0K7rYh+hmld5R3efHJpAWe+edd7ZuxYIwosT1NLx2Yg3OmcT5myhHHXWUzJo1q9DlBQsWCAQ4VRChCLtC2ssgJSyBtzgaFdG7qDto8WtXmGMsfCM6HBtCsACubwpBVJhKAWuXghaCLCKrVOQ2+o4FdETN+S1IL/vrX//aug0CMCKbMWfFZ1t7qdfrs+j1OrTpZr/6OY8Qb92iJTHXSnRDClk9ZbvfefTCxM81a9assSLJIXYgVTr8v4quRj3w/0pkLxZk8bkXf6X3p9TzBH+vNtPAB8AXeBGJisdrosCLTATY7KO+m7HJApu+8PwgGnHp0qUFDBCvkDkCqZr9lLBtsRyB12u/IerttddehZTGxRsoUA8yXKgjCPA9gI0DToIobAffgdjMhlIq1XBYc4JU0Mcff3xhyMiAgO8spwJBV8+8guwXfkV9RDQju4I6Lxc+GWf6Bi3ojxKd7TbF2dWrC9v4XQlBv/jMaD8Cr5++v/XWW5aYrb7PLrzwwl5Hd1DgpcDrx554rXkEKPCaN6cckeEEKPD2TDAFXsONPS3Dy+el7sknP4vY/eMfHUfdtcUWkpswQdqPPFLE58tvWlB6GScFXi+UeA0JeCdAgdc7q2pcmQSBF+fLYfHSLhKxmNl7771nnZcGQUxF27lxDVvgLa4P57AhyhQFC/1YKHQrpYQbiMVqoRRnR6qzY93qRIpLRIOh7Lrrrlb6a734EYv8Lpp7EUzCOoO3OAU3hNridNR2rJCSVk+/iOhbdWYerncTyPQ6iyOc3eam+PPiFMBYoIftFKcK/+pXv2oJAYhYhihZvJDs1G5YAm9xGk4sMJdKhe3GwY9dIc0zxFg9fXup+p3OGEUKU5wlqhekKAZTxbZUdLq6D2IUUsers8Lxd9gPnjWkU0V9SNHqxY95fRa9XudmvxDFi7MYuM2V/jmESwipqviZRz/tuF0L0Rbp2ZEyXolfbvdELfAiQlid14wobwhAQUocBV5sYsC/IEWPKoXfQqp9RHeqyHhs3oAfRTSrioxHlOfNN9/s6RmKyhYrIfCi7xgnMgGgQPjGxgA9TTOiXFWmBmwGwqagUgXRvYgMRwFv+Gr9+w1/D3NOkM4Y2TNQ4B+QhhvjKFXwvac23yGjBp5lrwXvGtgAiN+AKAcccIAVBR9kMwXu14/ewBnYEHrdNtJhYwl8PdiiFGfuUGOJSuBF/Tj2QKVix8YAtelIzTvP4O2xKEbwen26eJ0pBCjwmjKTHEdqCFDg7ZlqCrypMXszB5rPS/2iRdI0darUPfus4xg7t95acpMmScfo0QgZMJNFBUdFgbeCsNlUKghUWuCtNNQoFl0rPYZKt+flDN4HH3xQcM6pKhC23KLqsICIlICIKvRTohZ4EW2pzof0kvoUfS8l3EAgVmewjhw5Un7/+997Gi7SMk6aNMm6FufSId2gXvyIRX4FnEoKvBiXfkYz0ljapTQshqZH3eIzRH8OGzascJkfgRfiI86aDVrsIr0RlYMFb0S86Sl+VRuIbINAjahRRGiVKmEJvIiePeiggwpNIXLei4Dp1DcvdgXhB8LHLbfc4guvk8CLSrABA5smVLRvccU77rijYKMGzs4uFUEIkRcRY3fddZdt3yB+4wxKLMJj44pT8foser0O7ZSyX4gibmmDS8GGiKL7XS/z6GvyPFyM6G1E/BWfs+h2a9QC7y9+8QtB6nIURNfhuy1IieK3xtlnn11IBV2cNcCpj7odIUJQiYZ+xoTvXESU4lmG4ARBCj7PruA35L777luI5kUmDz++NWxbrJTAi6McsDlEFdi1fi69n2cfdRTXh41h2CCmSthzoh9Ngc0jKnq4lJ3gLF51fIPdJjSne5cvX25F7mKMKPhdhMh5rxue7OrVz733+rsNzwP8CQq+K7DRxK5EKfDiexGbIlBw7vnTTz9d6AIjeBnB68dP81rzCFDgNW9OOSLDCVDg7ZlgCryGG7upw+vqkvr77pOm6dOl7pVXHEfZueOOkpsyRTqQrjLDr+uwzIECb1gkWQ8JfEaAAi8toZiAF4EX90CkUouCOHcUKf+cCtLy4pp3333XugQiExb7R48ebQkXiHZR0RdeFmj9iHlu9aEfSBeK4nY2pxpfqcVbLIBiIRTFzyIoIjmQshDFLvLXz4Kx30XzSgq8SNerp/IujsR1siGcnbz77rsXPi6O/PVjE/r5pEibWXwGIIQNlZZU2aseaQSbRXSqXcF4UD/OFkQKWr0edT2il6655hoZOnSobR1hCbyItsLCuioQOJ0ihLx4Qi92deONNwrSfaryxS9+0YpuxgYQ/H9EianINC92p+qBaA6hCcIA0moWn/+N65A+Fov22267bcnhIIocc/S73/2uV0Svugn+ady4cVZkpx0vr8+i1+vQbin7xfe0PiacKe5lU4QOQUWN4W9e5tGLPfi5Rs+UgPtgDxD1Ic5D6ID/V6zd7N+P3aCtUvUh7bp6lt2+x0qNVwm8iDhHFDBsyG90oj5HaEs/PuDQQw/1tGkCUYFKRL/qqqvk1FNP9TNN1rWI7ESEJ4rdeefFFepZK5D2FmKk1xK2Lbp933vtl9t1+E2D1Oeq4MgJpJNXBZ/hGhSnSFG9jeIo/UWLFlkZBVQJe070YwU22GADwferW9F9O8681Y/ScLoXDPA7Dxk4UHAfNlE4ffe59QGf61lA8F2C71n4kFIFNokNVvg+hl/GRjP87rQrUQq8evYQCLoYiyoUeCnwerF/XmMuAa4Ymzu3HJmhBCjw9kwsBV5DjdzwYdX++c8y8MADHUfZ8Y1vSG7yZOncay8KuxHYAgXeCKCyylQToMCb6um3HbxXgffhhx+2FuhVQVScU2QizmtTqSKR4hGLx0iJale8LND6EfPc6oP4pM7dReQRIpDcSinhRo/QQBSgHqFRql49kmz77beXxx57rNflfsQiv4vmXgSTsFI0F0cq4b+9LPYWR6NiXvWUkn5sQo9AR3pHRAfrJaxovFWrVlnnzz7xxBOWOKk2OKAtRDGhH3YRp24Cl5t9qs+LRXGkER04cKDX2/tc52ZX4DZixAhBpDAKxCakHm1qarJt04vd2d2I9O7YXAKhF9G9elQo5hOL6Hr0W6kBf/DBB9Y5wRBo4AcwZ6o4+QOvz6LX69BeKftFOmM9OhDzgPTfQYvbPAat1+m+4rTsbqmD3ezfr92Uqk/fWPO1r33NelaDFOUzsEkIGQKCFEQ66gWZINSRAThvtFgAtmsDIjWyG6BAbMZ57n4LOCCzAgrO1R0zZkzJKuDX9LO9IbgNHz7cU7Nh26Lb972nTnm4CO+jSBWvCjaLYOOFKvqZ2dh0gojRUgXn+m600UaFS+DDsPlBlbDnRD+jG5ucMN9um3/03zZ2WUaKxwc/jbTeGAsKRFgcPaEL4x5Q97lEf/69bEBABfp5vUHaVPe4nVXsVje+Z5Qt4LtfRTXjPgq8FHjd7Iefm02AAq/Z88vRGUiAAm/PpFLgNdDA0zCkfF7WPfBAqfvLX3qNtn3//S1ht0t7uUsDjkqPkQJvpYmzPdMJUOA1fYb9j8+rwIua9WghRJsg6sSuYJFZpSpGamecN+tUvCzQ+hHz3Oq74IILrEVsNR5E27iVUsINhFmkD0TBwinOviyVNla1pZ/NhlTWN910U69u+BGL/C6aexFMwhJ4EdEDsUqdwewlvTdA6GceIjWzEjEUJD82oUcRI8oOAu/gwYMLvMMSePUJROQQxBp9AwGeAz3VubreTeBys0/1+SeffNJL6IR4BaEgaHGzK/2cZHCFCFtqMd+L3XnpKwQEnAmpUmNDkFLPtJf71TUQUs844wwrshcFggei1LbYYotAz6KfZ9bNfnFOtRrf7bff3ivNuZ8x4lq3efRbn9v1+vniEN7x/JWKbnWzf792U6o+ZJ44/vjjrSEgRbc6n9NtTMWfRyHw3nbbbYL0syheI2P1yNFikdDrmHCmNVLOo9x9992Om7FUfbhWPwcbIp6evrhUu2Hbotv3vVNfkLIYmz1QEHGKbAOlCgRR3ZfCTyA6VRVEXGNsKF5SCBenYS9O0Rz2nBSL8ohohWhbqujR7m7ZSZAFA88pMiWgYBMXnjW3NtxsFGn6sQEOgjgKNhruvPPObrf1OlbD9eISF+gCL+weZ3irgvGqM6qdqsDvS3zHoCByGr5QFQq8FHjLsU3em3wCFHiTP4ccQcoIUODtmXAKvCkzfoOGW/fII7LumDGSz2Sk49BDPxN2y1gwMwhN5EOhwBs5YjaQMgIUeFM24R6G60fghWirRwhhMW///ffv0wpS66oUgBC5EDXrVLws0LqJIXrdbvVls1k5/fTTrVsQ/Yd+ui3SlRJuIBJBEFKpeUtFNqt+YqEQEXnqnMwrrriisAiorvEjFvldNPcimIQl8GI8ON8RUV4oOI/uxz/+satlIhIIwgEKzpWF+KEXPzYBoQwL+PhNgYLUkziLWRU/Ai98qEo/iTSzSANZqkC8RwpXFKdzAN0ELldY2gWIKnvzzTetv/z85z+X73//+yVvR0SRSrcOUUnn4mZXiIBVz7aXtJ+l7A5zhMV7VbCZpNSZjXoEPCLrcOa3KhCaEb2MgvMmET3tVLABAZH3KpIXKddPOOGEXpd7fRa9XofK3ez34IMPLmQDAGMV2enHFtS1bvMYpM5S90AUwTOG4iXizs3+kcoekbcoEOOw+aNUKVUfBCM9GhpR4YhCL1UQ/aiOJ8B3G54xPz7DK199wwTu+cc//tFLSC2u54033iikm4eADrE6SMQ+RHhsDkEBWzAuVRCxj/Toun15jTAP2xbdvu+dxoFnHGn1UZCy+8wzzyw5Zj0LBNIEw7+oYyZUHRBEUcBCib1OlWJTyTHHHGN9jHowd/rvkCjmRI8yvvTSSy1BtlTRGWFTxNVXX+14uX7eLTJt4LtBT2Ht9Rkovk4/BxgRzio62K0++B2vBT5BbSBD9gl9swI2D+E8bFXwW+/jjz+2/vPxxx93PR7gRz/6kRVZjwK/gd+fqlDgpcDr1UZ5nZkEKPCaOa8clcEEKPD2TC4FXoMN3fSh5fPSdOWV0t7cLN0uu11NR1Hp8VHgrTRxtmc6AQq8ps+w//H5EXhRO85TVVEIWEhX59nqLetpIy+++GI5++yzHTumi0S4CGenQWjSi5sYol/rtuCLM1O32WabQkQphINSi4HFkTtIEbvnnnv26p9+ri8EP3WeodOg9TNhETUI8RNClF78iEV+F80rLfBOnz5dsKCMgsikv/71r7Luuus62sS//vUv6wxnFVVml3rUj02gIYirSJeIgoVnLBSrFJVOYg1SzRYLF08++WRBAMG5qFiYLxWx/dOf/tQ6fxcFQre+wKsAIEIJdoty7bXXFhb+/T/Nvc/xRL0Yc6noST0tLDZvqMVotO1mV/q5jljUR/S6U1vYAIGU7jhLGQXp3vVoKMw1UnqqSG8IKsVnJes89LMNsSj/v//9r/DxlVdeKRCAUbykutUzE2C+VBSlqtDrs+j1OtTrZr8QU+A7USAEwOfqKV3tbAOiE56v4vN63eYxiJ2VukcXZLEBSEX12d2D3yQQVNasWeNo//p84mxiCCtO5dNPP7UiLbHxxul5+sY3vlHYEABhz+l8bdUGzrVW549iXiB2RSHw4hmAcKrEVkT/I224U9H9aqmMGm7zq9s/MkuoZ8fpPmxiUmf9QuxERoRS/lyvJ2xbdPu+dxqDHmUO4U1F8Ttdrx/tgA0jKkOJuv6ll17qFfkM37gXjm9yKKeccorgGicfFcWc6Gc8Q0DGcRJO310QMfE7ST2XpdJ/w4/j/HIUZDFB1CqesXILNt3geVfPA47+wG+tsIufM3j17DSTJk0qjNuuT3ie8R2MaG0U/A4+55xzCpdS4KXAG7Yts75kEaDAm6z5Ym9JAD/+8jqGUjuITcdFgdf0GU7W+DIffCCNs2dL21lnSX7o0GR1PkW9pcCbosnmUCtCgAJvRTAnqhG/Aq9+lhsGijM3EW2mFz3yA+n0sHhqJ/xgYRhCByIoVMH5mvr5k/i7mxiit+1lwRdpciEgoUBMRpplnYOqD2IThCglDOLvdgKvfs4arlEigJ0hoH8QPSA0ozhFdfoRi/wumlda4MViMRZqW1tbrTEjIgaLwnY2ge99LOKqs4xhCzgzFQvHevFjE7iv2G6x2KoENDuxBgIi7Bh9gZCi2sdiMyKh1ML3L3/5S8FivVPRIzGRVhh1FRcImYjGQ7noootk/PjxgX0IInJRH1JmopQSiSBOQ3RVYlhxZLObXSFqVl/Ix8I+hInigr5AOIV4rYpdWmVsjlDR0XZpy/V6EfUO9ihbbrmltWlAFT3TAAQoCIJOEYaYT2xUgTCIYicieH0WvV6HdtzsF/3CM6Mii/fYYw9LOCkWb9WYMZeoE5HUiHaHWK6K2zwGNjaHG3EG6fnnn299CuEfG1jszt3G2BDFqEc72qUx19Mqw2dAbMWc2xWILrfeemvhI7sNE/rZohC48P0EIcau6BuQamtrrUhepM+OQuBF++PGjRMITiiITEf0vx07RNFio5E6/9pLRKbTPOM5wvOEgmhSfJ/pEbr6fZgzbFRRZ4l62UCh3x+2LXr5vrcbN+5DRKjKvFHs+/R7MAfYBKZ8KgRwCOHFBZkm8F2FgvTNOMICacCLCzYJwb+p+uzajmJOkNkBG6fUmPE9g+8bu6L/RkCmk5dffrlXxLK6Bz5p7Nix1ljwfCACvPj3YFD/AlEZEbAoeOaw+Q/+POziR+DF5j2MV/k2fLc4nf+ub0zB9bB9fSMfBV4KvGHbMutLFgEKvMmaL/aWBCjwajZAgZcPRBwIZP7zH2maOVMab79dMrmcrDnnHMmtfXmIQ//Yh94EKPDSIkggXAIUeMPlaUJtfgVejFlfyIRwggVLXay75557ei2AQiRFlBQWClGQjhVRXRDYVqxY0QsjFlMROaIXNzFEv9bLgi/SMmNhWkWIQrCDEAChRxWk7MPiYnGEsp3Ai3uQChfnF6JgoROpiLFIqlJmQhCAaIgIDkQFo2DhHgvC66+/fh9T8iMW+V00r7TAi8Hpogr+G4vAiB7VFzwRMQs+OB8QBTaFKCe7aCA/NqHgnnTSSZZArwrEW6SWhBimxGcI79i0AAEac4YoX9yD1Kyq6JFQWHCGqKTPNa6DHV511VWCsx5VwXMBuysuEDtV6kmki4WoBJvABoMgC9p6/9B/MEX/VJQdFuOR/hp/V5srIHChD7BdVbzYlR6tD3ER0cp62mrMKeZZpdtWddul3UbKVD09MkQVpPPWz8eEGA0RAYKUen6nTJkiOFtbFYwPnFW0MPqFeQZnfXyIisUzrkRliJEQMopFGa/Potfr0E8v9gvhB+mOVcEcQbBHxKEqSDENkUX3pWABJn7mUbdJL/Pex4i1P+B3BiJK1bmZ2OCO50mJhpgfiPAQ/VUqf3W7Xbp6iIr4nlFiN/w0RGz9vGf4a7DBs6MXO4EXdoNMFBCMUDBveFaRmlg9b/iOgtgP21XjwFmaSgiNSuDFpif4GmXbiEaeNWuWtalEFWysgMik2zfOcIX9FhfMJZ59bGCAX7CLCMZmAoidKvUsnhekd4ePVlkOUC82X8FnKG74G8R3+ACvpVzbKm7Hy/e9U9/0zQDYwAN7RKSu2swDIRTf6XiWlO1hQxg2H+npmVX98HX77rtvwV6wQQNzpzaXwGZgn2hXbaqBLcPvFvv5qOYEGwH0TUaIYD/33HML3w34PQZfgu9rVZxSWGMjADZoKFuF78fRCmEUsIdNqswW8N8TJ04Mo+o+dfgRePGdjN8jKqUzhGfw1L/z8FsCqf71tPrwLcgWo35noBMUeCnwRmLQrDQxBCjwJmaq2FES+IwAI3h7LIECL5+KahKoefNNaZoxQxrmzZNMZ2ehK92DBsmKF18UGTiwmt1j2w4EKPDSNEggXAIUeMPlaUJtQQRePU0tGBRHoGABHxEquE4VpFFF1BUWjJGCVy2YYlFa/X9ci8W2Aw44oBdaL2KIusHrgi8WXovPgkUkBha3IfIhIg7jwLl4EIWw4I/iJPBiwRaCoRKLcC3GDFED/4v6VNQuPsO4ISTus88+tmbkRyzyu2heDYEXg8RCuX5+JgTcL3/5y5bA8sEHH1h2oQo+Q9pHp0VdPzah6sQc4YxZCBV6O4i2Gjx4sHUmMtIMqzTBuMYuohaiGhZs9blGJCAW/yHMYp6RLhcL+qoUpz/WJx0Lv0hrqwoW+5Fqd+nSpYX0jn58DWy1ubm5EAWt2yKECYhIui1CKEMUIxar9eLFrp544gmrLRUVhvsReQjhHqkp33333UKV+rPulN4dYgOENVXgL9AviPAQHzA/Knoa12DuEIFffF4vIgwPPPDAwjnX6pkbPny4FQWLTRYqdaZqyyny3uuz6PU6tOfVfoufGdwLHrA1iHawM100gHA5d+7cXsKcl3n0O+9u9gjBFP/0eYRQj00+6DNsGwXzC/+IZwrFKWVycX14RrARCM9dsZ2hTvXsOaU8xzNw6KGH9rIB9A1nbML34Pxb/XsJUavYrKGEuKgEXjDQ0wcrfvjuHDZsmPXcou+q4LsJYhw2TNgVZIuA+Itid3asugcbmZBZQYl1+Ds2OuD5wn34XtXToOPz4o0EbjaBz/3aoludXr/v7erB8wMRG5s6VIEfwXc2vvcR8YrvBFXgOyH46hssiuvFs4fIWBWdi89RH75fin8DYIMXMomAsV2JYk6wWQGiLHymPmacQw6bhkitzqrH50hPfjs2xWd6SxF4hiF0Kt+DzUN+MxUWn2+rMwBndX48uGOO1AZBN5vw+7kfgRd1gxG+W5RIj7/h2YTvAF/4Dv07Cn4PG1rwPFHg7Zkd/XcY/jpq1CjqXX6Nl9cnmgANPtHTx86nkQAF3p5Zp8Cbxieg+mOuffVVaZo2TervvVcy2kKb3rPVl1wibePGVb+z7EEfAhR4aRQkEC4BCrzh8jShtiACL8aNxXGVWhMLlIhE1aN9IMYgYhICkFNBtCLSaSKtq1osszvXzKsYgnb8LPhC0EGEli7o6X3FouycOXMsIVil0HUSeHEfFu8gyOCcVX2Bt3j8WPBDBJpT2lhc70cs8rtoXi2BF+MCc6S4VIKOnW1ACEQEDMRYp+LHJnr95lu92hJTEUFeao6wYD1t2jTHM/9g30hDW+p8UbSLZwLnVSIKSo8e1fsEQRYCEhaGi8vy5csDuRnUCVt0GyfaBWs9pa9q0KtdYTEeoobTnEIsQeQfxBO1qQJ/g1BVHHUIkQECF2xEXyS3g4AoXTyfWFy3K4gORb+UwOUEEnONdJoQPuyK12fR63Vow4/9QqSEwKmLA8X9hJ1hrIhILo4G9DqPdvOOepUY68cQ8Wwh6k4/Z7n4fvDC5/CFKmU+Uvo//PDDfZqCj0YErcqSYNcX9BXfH8gA8NRTT1mXlDrTGt8VeDbVmfJOdZ5++unWZhMI0apEKfCiDTwDsEldbCvuHwQjpPItdS6pfpYr/A9ERrtIX9QNbhirvinDyT/jexNnEfstfm3RrX4/3/d2dSFqGWmxizMMFF+LTTA33XSTFZnuVnBGMb5j1NmxdtdD9MUGLydxV90TxZyo7wY9u0RxHyHoIpsCIrnhq4tL8Ty6MbH7HNkJEFVuV3S7xe9IfBdHVfwKvOgHRF4czaB+Fzr1DdH4sBtsHPTjM8q166hYqXrDeJekwBv1LLH+uBOgwBv3GWL/SKCIAAXeHiAUePl4VJJA7bPPWsJuw+9+59hs9yabSG78eGk77jgcOlTJ7rEtjwQo8HoExctIwCOBMF7KPTZVlcv8LKBUpYMxbDSowAtBV48aQkQsIoD0gvlAWlqITDi7EKIYRFOknYR4p1KmQrxQUTSIglBno6q6/IghfhfGkO4SIhGEaESRIroPAiwin7D4Dz6IKFZnfJYSeFV/ceYkzmpDnYgug1CFaB2kbMRYcO4uIs9KFT9ikd9F82oKvBgzoicRFYSoFkRkYiEcogOiiJDqEAvLdudO6rz82IQdZ6Q3xQI3UoKjPxAnIRQi8hSbFzBHbn1AvUhXigV91IO5xlhV2wnoAAAgAElEQVQgZMKGsLiL854xLreCSDEIOhAacK4mUntjA4CeUtqtDrvP8dypcaJ/+F2FCFAIFXhenSLIUZcfu4IohDMTkbYT/19F3uKMXzxHeK4RVas/2xBB9LToev/xXQWuEPtgI+CDZwbRweg7ooZRt1tRqdGRGhUR13jG8TxCHMMGE6RUhbDrJBKjfq/PotfrUKdf+0XkpnpmEOkOYQoRbZhLcMBcOolFfuYRfUPKWPhmFPCGjwxa4DcRoY60trBr+FfYAiLgcI4p5gE2jjTTKLAb+GNdTNXbRkpgcICPBQNE92FzAs6jRXpdCGc621ICr6oX4jLsA0IvOEMIhR/As+H0/FbitwbEWIjfiLbE84CIU8w5otDx/QSBFcJRqYIoUAj/iEZWqdpLXY8IRHxngwl8G547iOtoF88LbA3t4ns8SPFri25t+P2+d6oPGwLgJ3G+M9LWY8x4nhEljihWHDNRfA58qb7B1rF5DZkREO0K/vpvgOJ08dWYE4wVEcd4NjFmPHt43vEswe532GEHx25FKfCiP+ocXwjN+G+nM6Hd7MPL50EEXtQLG8GzsnDhQisrCJ4VbK7BdwmivHHEADIqqOLHZ4Rl117GH+SaMN4lKfAGIc97TCJAgdek2eRYUkGAAm/PNFPgTYXJV3eQ+bzU/elPn0XslogY6tp8c8lNmCDtWLxwWWCt7oDYOgVe2gAJhEsgjJfycHsUbm1+FlDCbZm1kQAJJJUA/UZSZ479joKAfvbvXnvtZZ2DzdKbAH0GLYIESMAPAZN8RhjvkhR4/VgPrzWRAAVeE2eVYzKaAAVeCrxGG3hcBgdh95FHpN/UqVK3eLFjr7pGjJA1kydLx+jROIgoLr1nP0oQoMBL8yCBcAmE8VIebo/Crc2kBZRwybA2EiABJwL0G7QNEughgOg9lTJ56tSpVmQsS28C9Bm0CBIgAT8ETPIZYbxLUuD1Yz281kQCFHhNnFWOyWgCFHh7ppcRvEabenUG19Ul9ffdJ03Tp0vdK6849qFz5EjJTZkiHUiTU1NTnb6y1UAEKPAGwsabSMCRQBgv5XHGa9ICSpw5s28kYBIB+g2TZpNjKYcA0muPHDlSurq6rLS8WIRHGmUWCry0ARIggeAETPqdEca7JAXe4LbEO80gQIHXjHnkKFJEgAIvBd4UmXvlhtrRIQ3ZrDRdfbXULlni2G7HnntKbvJk6dx7b5EMv0IrN0HhtUSBNzyWrIkEQCCMl/I4kzRpASXOnNk3EjCJAP2GSbPJsZRD4MQTT7TOlESZNm2anHTSSeVUZ+y99BnGTi0HRgKREDDJZ4TxLkmBNxIzY6UJIsDV6QRNFrtKAiBAgbfHDhjBy2eibAK5nDTOnSuNM2dK7bvvOlbXsd9+Virmrl13LbtJVlBdAhR4q8ufrZtHIIyX8jhTMWkBJc6c2TcSMIkA/YZJs8mxlEPg6aeflrFjx8o+++wjV199dTlVGX0vfYbR08vBkUDoBEzyGWG8S1LgDd3EWGHCCFDgTdiEsbskQIGXAi+fgvAIZJYvl0HbbSeZ1tY+leYzGek45BArYrdr223Da5Q1VZUABd6q4mfjBhII46U8zlhMWkCJM2f2jQRMIkC/YdJscizlEvj000+lf//+UsNjbRxR0meUa2W8nwTSRcAknxHGuyQF3nTZP0fblwAFXloFCSSMAAVeCrwJM9nYd7ffhRdK07XXFvqZr62V9pYWyU2YIN1bbRX7/rOD/ghQ4PXHi1eTgBuBMF7K3dqo5ucmLaBUkyPbJoE0EaDfSNNsc6wkUD4B+ozyGbIGEkgTAZN8RhjvkhR402T9HKsdAQq8tAsSSBgBCrwUeBNmsrHvbub992XQDjuI5PPSfuyxkhs/XrqHD499v9nBYAQo8AbjxrtIwIlAGC/lcaZr0gJKnDmzbyRgEgH6DZNmk2MhgegJ0GdEz5gtkIBJBEzyGWG8S1LgNcm6OZYgBCjwBqHGe0igigQo8FLgraL5Gdt0/cKF0rnTTpLfeGNjx8iBfUaAAi8tgQTCJRDGS3m4PQq3NpMWUMIlw9pIgAScCNBv0DZIgAT8EKDP8EOL15IACZjkM8J4l6TAy2ci7QQo8KbdAjj+xBGgwEuBN3FGW4UO1/zzn9I0Y4a0jxkjnXvuWYUesMm4EqDAG9eZYb+SSiCMl/I4j92kBZQ4c2bfSMAkAvQbJs0mx0IC0ROgz4ieMVsgAZMImOQzwniXpMBrknVzLEEIUOANQo33kEAVCVDgpcBbRfOLfdM1f/+79Js2TeoXLJBMd7d07L23rLrnntj3mx2sHAEKvJVjzZbSQSCMl/I4kzJpASXOnNk3EjCJAP2GSbPJsZBA9AToM6JnzBZIwCQCJvmMMN4lKfCaZN0cSxACFHiDUOM9JFBFAhR4KfBW0fxi23Tt3/4mTdOmScNDD/Xp48pFi6Rrp51i23d2rLIEKPBWljdbM59AGC/lcaZk0gJKnDmzbyRgEgH6DZNmk2MhgegJ0GdEz5gtkIBJBEzyGWG8S1LgNcm6OZYgBCjwBqHGe0igigQo8FLgraL5xavpfF7q/vQnS9itf+IJx761nXiirJ4+PV59Z2+qRoACb9XQs2FDCYTxUh5nNCYtoMSZM/tGAiYRoN8waTY5FhKIngB9RvSM2QIJmETAJJ8RxrskBV6TrJtjCUKAAm8QaryHBKpIgAIvBd4qml88moaw+8gj0m/qVKlbvNixT10jRsiayZOlY/Rokbq6ePSdvag6AQq8VZ8CdsAwAmG8lMcZiUkLKHHmzL6RgEkE6DdMmk2OhQSiJ0CfET1jtkACJhEwyWeE8S5Jgdck6+ZYghCgwBuEGu8hgSoSoMBLgbeK5lfdpru6pH7hQmmaPl3qXn7ZsS+dI0dKbsoU6fj2t0VqaqrbZ7YeOwIUeGM3JexQwgmE8VIeZwQmLaDEmTP7RgImEaDfMGk2ORYSiJ4AfUb0jNkCCZhEwCSfEca7JAVek6ybYwlCgAJvEGq8hwSqSIACLwXeKppfdZru6JCG+fOlacYMqV2yxLEPHXvuKbnJk6Vz771FMvx6q85kxb9VCrzxnyP2MFkEwngpj/OITVpAiTNn9o0ETCJAv2HSbHIsJBA9AfqM6BmzBRIwiYBJPiOMd0kKvCZZN8cShABXwINQ4z0kUEUCFHgp8FbR/CrbdC4nDf/3f9I0c6bUvvOOs7C7335WKuauXXetbP/YWiIJUOBN5LSx0zEmEMZLeYyHJyYtoMSZM/tGAiYRoN8waTY5FhKIngB9RvSM2QIJmETAJJ8RxrskBV6TrJtjCUKAAm8QaryHBKpIgAIvBd4qml9lml61ShpvuUWaZs2Smvfft20zn8lIxyGHWBG7XdtuW5l+sRUjCFDgNWIaOYgYEQjjpTxGw+nTFZMWUOLMmX0jAZMI0G+YNJscCwlET4A+I3rGbIEETCJgks8I412SAq9J1s2xBCFAgTcINd5DAlUkQIGXAm8VzS/apjs6rDTMjddfLzUff2wv7NbWSntLi+QmTJDurbaKtj+s3UgCFHiNnFYOqooEwngpr2L3XZs2aQHFdbC8gARIIBQC9BuhYGQlJJAaAvQZqZlqDpQEQiFgks8I412SAm8oZsVKEkyAAm+CJ49dTycBCrwUeI21/Hxe1h01Suqef77PEPMNDdJ+7LGSGz9euocPNxYBBxY9AQq80TNmC+kiEMZLeZyJmbSAEmfO7BsJmESAfsOk2eRYSCB6AvQZ0TNmCyRgEgGTfEYY75IUeE2ybo4lCAEKvEGo8R4SqCIBCrwUeKtofpE3XX///TLghBMK7eT795e2k06S3JlnSn7jjSNvnw2YT4ACr/lzzBFWlkAYL+WV7bG/1kxaQPE3cl5NAiQQlAD9RlByvI8E0kmAPiOd885Rk0BQAib5jDDeJSnwBrUk3mcKAQq8pswkx5EaAhR4KfAabezd3TJwzz0l89570nbqqdJ2xhmSX289o4fMwVWWAAXeyvJma+YTCOOlPM6UTFpAiTNn9o0ETCJAv2HSbHIsJBA9AfqM6BmzBRIwiYBJPiOMd0kKvCZZN8cShAAF3iDUeA8JVJEABV4KvFU0v/KazudFMu5fOzV//7t0b7qpyMCB5bXHu0nAhgAFXpoFCYRLIIyX8nB7FG5tJi2ghEuGtZEACTgRoN+gbZAACfghQJ/hhxavJQESMMlnhPEuSYGXz0TaCbivtKedEMdPAjEjQIGXAm/MTNK1O7XPPSdN06ZJ1447Sm7SJNfreQEJREmAAm+UdFl3GgmE8VIeZ24mLaDEmTP7RgImEaDfMGk2ORYSiJ4AfUb0jNkCCZhEwCSfEca7JAVek6ybYwlCgAJvEGq8hwSqSIACLwXeKpqf96bzeal7+mlpmjpV6h9/3Lqve+hQWfHCCyL9+3uvh1eSQMgEKPCGDJTVpZ5AGC/lcYZo0gJKnDmzbyRgEgH6DZNmk2MhgegJ0GdEz5gtkIBJBEzyGWG8S1LgNcm6OZYgBCjwBqHGe0igigQo8FLgraL5uTcNYff3v5d+06ZJ3V/+0uf61ZdfLm1jx7rXwytIICICFHgjAstqU0sgjJfyOMMzaQElzpzZNxIwiQD9hkmzybGQQPQE6DOiZ8wWSMAkAib5jDDeJSnwmmTdHEsQAhR4g1DjPSRQRQIUeCnwVtH8nJvu7pb6hQulafp0qXvpJcfr2g88UFpvvz2WQ2Cn0kGAAm865pmjrByBMF7KK9db/y2ZtIDif/Txu2O99dYrdOq+++6TPffcM36dZI9ST4B+I/UmQAAk4IsAfYYvXLyYBFJPwCSfEca7JAXe1D8SqQdAgTf1JkAASSNAgZcCb6xstqNDGu66yxJ2a5cscexaxx57SG7yZOncZx+RDL96YjWHKesMBd6UTTiHGzmBMF7KI+9kGQ2YtIBSBobY3EqBNzZTwY6UIEC/QfMgARLwQ4A+ww8tXksCJGCSzwjjXZICL5+JtBPgKnvaLYDjTxwBCrwUeGNhtLmcNNxxhzRdfbXUvvOOs7A7apSsmTxZunbbLRbdZidIgAIvbYAEwiUQxkt5uD0KtzaTFlDCJeOvtnw+L48++qjce++9gkUY2M2aNWtk0KBBsskmm8iuu+4q3/3ud2X33XcvWTEFXn/ceXV1CNBvVIc7WyWBpBKgz0jqzLHfJFAdAib5jDDeJSnwVscO2Wp8CFDgjc9csCck4IkABV4KvJ4MJaqLWlul8ZZbpOlXv5Ka99+3bSWfyUjHwQdbEbtd220XVU9YLwkEIkCBNxA23kQCjgTCeCmPM16TFlCqxXnx4sUyZcoUeeWVV1y7sNtuu8l1110nw4cPt72WAq8rQl4QAwL0GzGYBHaBBBJEgD4jQZPFrpJADAiY5DPCeJekwBsDo2QXqkqAAm9V8bNxEvBPgAJvD7NzF7wmi15fXvjDRQdsJqO328Q/VN7hSiCzYoU03nCDNF5/vdQs72Gu35ivrZX25mbJTZwo3Vtt5VonLyCBahCgwFsN6mzTZAJhvJTHmY9JCyjV4Dx37lyZNGmSdHZ2Fprv16+fbLbZZjJkyBD55JNPZMmSJdLR0VH4fODAgXLPPffIDjvs0KfLFHirMYts0y8B+g2/xHg9CaSbAH1GuuefoycBvwRM8hlhvEtS4PVrQbzeNAIUeE2bUY7HeAIUeHummAJv9Oae+egjabzuOmm68UbJrFpl22C+oUHajzlGcuPHS/dmm0XfKbZAAmUQoMBbBjzeSgI2BMJ4KY8zWJMWUCrN+cEHH5Tjjz9ekJ4ZZcMNN5Sf/OQnMnr0aOnfv3+hO62trXLHHXfIJZdcIqvW/tYYNmyYPPHEE7LRRhv16jYF3krPItsLQoB+Iwg13kMC6SVAn5HeuefISSAIAZN8RhjvkhR4g1gR7zGJAAVek2aTY0kFAQq8PdNMgTdak2+65BJpmj1bMmvW2DaUX2cdaTvxRMmddZbkN2HkdLSzwdrDIkCBNyySrIcEPiMQxkt5nFmatIBSSc5Lly6VXXbZxYrQRdlqq61k4cKFsv766zt24+WXX5YDDzxQIPiijBkzxkrXrBcKvJWcRbYVlAD9RlByvI8E0kmAPiOd885Rk0BQAib5jDDeJSnwBrUk3mcKAQq8pswkx5EaAhR4e6aaAm+0Zt/vvPOkac6cPo10DxwobaeeKm1nnCH5oUOj7QRrJ4GQCVDgDRkoq0s9gTBeyuMM0aQFlEpyPu+882TO2t8QSMn89NNPO56rq/dr2rRpctlll1l/qqmpsc7t1aN4KfBWchbZVlAC9BtByfE+EkgnAfqMdM47R00CQQmY5DPCeJekwBvUknifKQQo8JoykxxHaghQ4KXAWyljz/znPzJoxx0ls/ZcvO7115e2sWMl973viQwcWKlusB0SCJUABd5QcbIyEmAEL22gDwFE4I4YMaKQbnnKlClywQUXeCK1bNky6151Zu/PfvYzOe200wr32gm8L7zwgtx6663y5JNPynvvvSe1tbXWGb/777+/nHnmmaLfU6oTjz76qNx///3yzDPPyIcffiirV6+WwYMHW/35zne+I8ccc4wMGDDAsYqzzjrLSjWN8sMf/lAgci9fvlxuvvlmeeihh+Stt96y6kT66d12201OP/102XHHHT1xAY8FCxZY/zBeREg3NDTIJptsIrvvvrucfPLJ8rWvfc1TXUiZfd9998m8efMEC2Lo46BBgyxm++23n5xyyikylBv4PLF0usikhdeyQPBmEiABTwToMzxh4kUkQAJrCZjkMyjw0qxJoHwCFHjLZ8gaSKCiBCjw9uBmBG/0prfO+PFS/+ijkjv7bGk74QSRddaJvlG2QAIREqDAGyFcVp1KAmG8lMcZnEkLKJXi/MADD1hn76JkMhlLRBw+fLjn5iHMPvvss9b1Rx55pFx//fWFe3Wx9t5775VFixb9P3t3AiZnUeB//Fc9k8wkIWDQhPvwAJGgIAqooEZEJQiBJF09idwrIHgsggrrf901roLoyiVyCAgR5JiungQSlF1wMajLLrDKIVFARAQ5dAgxJIGZSabr/1TbPekMc3XP2zNvv/19n4cnyfR7VH2qpvR9f131FpZxDu000BZm/3Z0dBRC2sG2xx9/XKeffrruvffeIcu4ww476JprrtF+++034H79A959991Xn/3sZwth7EBbsPn6179eCKGH2kIwfPTRR+vRRx8dcr8TTjhB5557rlpbWwfd74UXXtBxxx3X5zvQjiHsvfDCC3XUUUeNuM3YcXMBxg16BAIIVCLAmFGJFvsigECSxowo7iWZwcvvRKMLEPA2eg+g/nUnQMC7qckIeEfRfcP77aZMGfYEZvVqhXftqqVl2H3ZAYF6ECDgrYdWooz1JBDFTXmc65ukByhj5Rxm65benfvmN79Z999/f0WX/o//+I/C0sxhC6HqwoUL+44vD3j33ntvPfTQQ4XPttxyS73lLW8p/P13v/udXn311b5jwvt/w+ze5ubm15Rj5cqVhSAzzBwubeEaYTZrmDX7pz/9SWvWrOn7LFwnzPQN9eq/lQe8BxxwQCHY3rBhQ+G6b3zjGwvvH37qqacKs4xLW1iGOgTiYf+BtgceeKAQcpeXb8aMGYXy9fT0KJQ/XKO0hXD8Rz/60YB1/f3vf690Oq1nnnmmb/8dd9xRO+20U+FdyY899lhfUB7KFcLsOXPmVNR27Px3AcYNegICCFQiwJhRiRb7IoBAksaMKO4lCXj5nWh0AQLeRu8B1L/uBAh4NzUZAW+F3dd7Nf/0p2q98EL5rbbS+uIyghWehd0RqGsBAt66bj4KH0OBKG7KY1itviIl6QHKWDmHEPGuu+4qXO7II48sLFEc1dZ/ueUpU6YUZsGGGa4TJkwoXGbt2rX6yle+ouuvv77vstddd50OP/zwzYrR29urAw88UGEGb9hCaHrOOefoYx/7WOH9v2EL7R+WMj777LML5w1bOE84X/+tPOAtfWat1b/+678WgurStnz5cp122mmF5ZrDdthhhxVC2f5bV1eX3v/+9+sPf/hD4aNwjvPPP7+wjHKY/Ru21atX62tf+9pm5fm3f/u3wszh8i2E1QcffHBfcL7bbrvp4osvLiwVXdrC73JYWjoE7GGbNm1aYbnqECizVSbAuFGZF3sj0OgCjBmN3gOoPwKVCSRpzIjiXpKAt7L+w97JEyDgTV6bUqOECxDwbmpgAt4RdvZ8XhNuu60Q7DYXZ7qEI1/++c/Vu9deIzwJuyGQDAEC3mS0I7WIj0AUN+Xxqc1rS5KkByhj5RxCyTCzNGzh/bnhPbpRbeUBb0tLS+GdtAPNfu0f3oZliS+66KLNivHLX/6yb4ZqeG9vmGkcQt6BthDynnrqqYWPwrtvQ+gawuXyrX/Ae/LJJ+tb3/rWgOcLSyCHYDpskyZN0tNPP114d3D5FsLmEOiGLbwLOITmg5XvjDPOKLyHOGzh/blhNm4ppA4/C2FuCILDFpbL/ulPfzrge3aDW5i1G4LdsJXeJRxV+zXKeRg3GqWlqScC0QgwZkTjyFkQaBSBJI0ZUdxLEvA2Ss+nnoMJEPDSNxCoMwEC3k0NRsA7TOfduFETOzoKwW5TcXZK+RE9Rx2l9ddcU2e/ARQXgdEJEPCOzo+jEegvEMVNeTWq07beuprDhj1m/QUXqOeEE/r2G+wBylhdf7ACD3f91S+9NGxda7VDePdsWIo4bF/4whcUlmyOaisPeIcLH8sD0v33379vZmqpLGGp4vvuu6/wz8mTJ2v27NmDFjPM3i1/j3BY8nnmzJmb7V8e8IblosM+IYQeaHvyySf17ne/u++jEC6XL/scll/ec8899VKxHUNdwqzfwbaXX3658J7h0tLUIcAN7RC28L97oaylc91www1D1jWUO8y8Dluoc1gmmq0ygSQ9eK2s5uyNAALVCDBmVKPGMQg0rkCSxowo7iUJeBv3d4Ga/12AgJeegECdCRDwbmowAt5BOm93tybedJNaL75YTX/606A9vOejH9X6sCTgAO+kq7NfC4qLwIgFCHhHTMWOCIxIIIqb8hFdqN9OwwWc1ZwzHEPAW63cpuPCu3FL73kdLoSt9GrlAe+yZct00EEHDXqKxYsX68wzzyx8HpYkvvfeeyu93Gb7l187LLMclncu38oD3vDe4EsvvXTw/w/W06Ntt9227/M77rhjs8C3fHZxeIdvmJEblkweaps/f75+/vOfF3YJ78894ogjCn+/88471dbWVvh7uGZ4v3H57N7+5wzv9A3v5Q0hc9jCtadPnz4qu0Y7OEkPXhut7agvAuMhwJgxHupcE4H6FUjSmBHFvSQBb/32ZUoejQABbzSOnAWBMRMg4N1ETcDbr9utX6+WH/5QrZdeqtTzzw/+UPHww9V15pnq3WefMeu3XAiBuAgQ8MalJShHUgSiuCmvxoKAd+gZzOM5gzcuAe+NN97Y9y7anXfeWQ8++OCQXS3Mgg0zXx9++GGFfh3+HcLO0nb33Xf3/X2gcLmSgDecaKjA+JJLLtFXv/rVwvXCTN4Q+Fa7feMb39AFF1xQODwsvxyC7+G2MOP3+eL/l7z11lsL7wJmG7lAkh68jrzW7IkAAtUKMGZUK8dxCDSmQJLGjCjuJQl4G/P3gFpvEiDgpTcgUGcCBLybGoyA9+8WZs0atVx9tVquuEKpVasG7NG+qUk98+er6/OfV36PPeqs11NcBKITIOCNzpIzIRAEorgpr0aSgDe+Ae9+++1XeEdt2Gq5RPNwM3jLA94wI/Whhx4asKutWbNGYQnksHRxaYnj4fpk1AFv//P90z/9k6688spCMQ477DD9KKy4UuV24oknKoS01W7XXXedDj/88GoPb8jjkvTgtSEbkEojMMYCjBljDM7lEKhzgSSNGVHcSxLw1nmHpvijFiDgHTUhJ0BgbAUIeDd5N3rAa158US2XX67Wq6+WWbt2wI7oJ05Uz8KF6jr9dOV33XVsOytXQyCGAgS8MWwUilTXAlHclMcZIEkPUMbK+ZBDDlHpQcunPvUpffOb34zs0pUs0TySgDf03/C+2T/+8Y8VlbHWAe/nPve5QuActkwmoyuuuKKi8pXvPG/ePK1YsaLq4y+77DItWLCg6uMb8UDGjUZsdeqMQPUCjBnV23EkAo0okKQxI4p7SQLeRvwtoM7lAgS89AcE6kyAgHdTgzVqwGuefVat3/ueWq67TubVVwfswX7SJHUff7y6PvtZ+e23r7NeTnERqJ0AAW/tbDlzYwpEcVMeZ7kkPUAZK+d0Oq277rqrcLm5c+fqBz/4QWSXjjLgDW37sY99TL/61a/6yjd79myFd9mGZabD+2onT54sY/5+yzzctUezRHP/wPjzn/+8wszZsIX3515++eVVG4YA+xe/+EXh+F122UW7VviFvxA2H3zwwVVfvxEPZNxoxFanzghUL8CYUb0dRyLQiAJJGjOiuJck4G3E3wLqXC5AwEt/QKDOBAh4NzVYowW8qT/+Ua0XX6yJN90kU/ZOuPIu7KdOVdfJJ6v71FPl3/CGOuvdFBeB2gsQ8NbemCs0lkAUN+VxFkvSA5Sxcg7vjg3vkA3bbrvtpnvvvbeiS99zzz164oknCsfMmDFDhx56aN/xw4Ws5Rcabgbvf/7nf2rhwoV9h1x66aWb/bt/oYe7dpQBb7nhxz/+cV1//fUVGZbvfOyxx+rHP/5x4Uef/vSnFd7Jy1ZbAcaN2vpydgSSJsCYkbQWpT4I1FYgSWNGFPeSBLy17W+cPf4CBLzxbyNKiMBmAgS8mzgaKeBt/eY31Xr++TL5/IC/EfnXv17dp52mrpNOkrbckt8aBBAYRICAl66BQLQCUdyUR1uiaM+WpAco0coMfrYQJoZQMWxh9mt49+2OO+444suHQPe+++4r7G+t1fe///2+Y4cLWcsvMlzA+8UvflHXXHNN4ZAPfvCDWrp06ZBlHIngucUAACAASURBVO7aUQa8YcbuP//zPxfKs9dee+nnP//5iP3673jmmWdq8eLFhR+PNiyuuhANdiDjRoM1ONVFYJQCjBmjBORwBBpMIEljRhT3kgS8DfYLQHVfI0DAS6dAoM4ECHg3NVgjBbwTOjq0xcknv6a35rfbrrAMc/dxx0lTptRZb6a4CIy9AAHv2JtzxWQLRHFTHmehJD1AGSvndevWac8991T4M2xf/vKX9aUvfWlEl1+9erXe9ra3qaenp7D/eeedp1NOOaXv2OFC1vKLDBfwhvfK3nHHHYVDTj/9dIVZs0Ntw107yoA3BNylmcsTJkzQ448/rq222mrI8t1666197xIOx+6xxx6F/cO7fMMyy2F73etep9/+9rdqbW0dUXuwU3UCjBvVuXEUAo0qwJjRqC1PvRGoTiBJY0YU95IEvNX1I45KjgABb3Lakpo0iAAB76aGbqSAV7292vKAA9T05JMFgN5dd1XXP/6jesLSgi0tDdL7qSYCoxcg4B29IWdAoFwgipvyOIsm6QHKWDqfffbZuuqqqwqXnDp1amFG7jbbbDNsEb773e9q0aJFhf2ampr0yCOPbHbccCFr+QWGC3jDu23vvPPOwiEnnXSSvv3tbw9avgceeEAf/vCH+z7v/87c8EGUAW8IuGfOnKlVq1YVrtk/6O5f0NBPw3uDn3322cJHYTZymJUctueee0777LOPNm7cWPj3N7/5TX3qU58asi3Cvk8++aR23333YduMHV4rwLhBr0AAgUoEGDMq0WJfBBBI0pgRxb0kAS+/E40uQMDb6D2A+tedAAHvpiZrqIBX0sTrr1frZZep64wz1DNvntTcXHf9lwIjMN4CBLzj3QJcP2kCUdyUx9kkSQ9QxtL5+eef14EHHqi//e1vhcu+613v0i233KIpQ6w2EmaWzp49W2vXri0cE96PG96LW75FGfCGJZDDUshh22mnnQohdMsAX5r761//qqOOOkqPPvpoX1FCXT7wgQ9sVrYoA95w4q9//eu68MILC9cI9f7Zz35WKOdAW3jncWkGcpilu3LlSk2aNKlv1zALOpfLFf49efJkhYB63333HfBcIdwNM36XL1+uyy67THPmzBnLrpOIazFuJKIZqQQCYybAmDFm1FwIgUQIJGnMiOJekoA3Ed2aSoxCgIB3FHgcisB4CBDwblJPRMCbzyv19NPK77rr8N0pzLxIpf7+HxsCCFQlQMBbFRsHITCoQBQ35XHmTdIDlLF2XrJkSWFmbGl785vfrHPOOacwEzbMzi1tL7/8strb23XuuedqzZo1hR9Pnz5dv/zlLwt/lm9RBry/+tWv9JGPfKTv9OHvF1xwgXbYYYfCz3p7ewuhdJhRXJoZW9r5Rz/6kQ477LDNyhZ1wBssDjrooL5rh3D3/PPPL/iFdxuHLdiFMoeA13tf+FlwPPXUUzcrW/g9DTN6wxLYYdtiiy0KgfDRRx+92XLN9957b+HnpXcghxm8v/jFLxSWiWYbuQDjxsit2BMBBCTGDHoBAghUIpCkMSOKe0kC3kp6D/smUYCAN4mtSp0SLUDAu6l56zrg3bhRE5csUeuFF8qsWaM1v/61xPvQEv27S+XiIUDAG492oBTJEYjipjzOGkl6gDIeztdcc03h/bul8DGUIcwwfeMb31iYYfriiy/qD3/4QyFMLW3Tpk1TeJ/sXnvt9ZoiRxnwhpOXh7Lh3yHIfNOb3lSYaRzKVQqcwztrw8zW0jLHAy2ZHHXAG8oTglZrbd+s5vCzsNT1rrvuqg0bNhRm6ob/XSttxxxzjMIy1wNtd999t8Ln69ev7/s41POtb32rUqmUwu/yCy+80PdZeOdvWML6LW95y3h0nbq+JuNGXTcfhUdgzAUYM8acnAsiUNcCSRozoriXJOCt6+5M4SMQIOCNAJFTIDCWAgS8m7TrMuDt7tbEm25S63e/q6annuqrzPrzz1fPiSeOZVfiWgg0pAABb0M2O5WuoUAUN+U1LN6oT52kByijxqjyBD/96U/1T//0T4V3ug63HXzwwbr44ov7ZtH23z/qgDe86zYEsx0dHYMWbccdd1QIqs8888zCO4HDNn/+/L53DJcOrEXAG879m9/8RmGJ5ccee2zQMoalpcN7j8PSyuWzo/sf8NBDD+m0007bbLnpgU4altQO71AOQTJb5QKMG5WbcQQCjSzAmNHIrU/dEahcIEljRhT3kgS8lfchjkiWAAFvstqT2jSAAAHvpkauq4B3/Xq1XHedWr/3PaWef/41PbV355318v33h6kjDdCLqSIC4ydAwDt+9lw5mQJR3JTHWSZJD1DG0zkEqbfddpt+8pOfKISMf/nLXxR+Fmbz7rLLLjrggAOUTqf1jne8Y8hiRh3wli4WQujrrrtO//d//6dVq1Zp6tSp2mOPPXTEEUfo+OOPLyxjHELeu+66q3BImN3a//3AtQp4w/XCDOcQQgfDBx98sDDzOZQp2H30ox8tLLW88847j6iJS0tPh3M98MADhXOFGbzbbbed3v72tyuTybxmGe0RnZid+gQYN+gMCCBQiQBjRiVa7IsAAkkaM6K4lyTg5Xei0QUIeBu9B1D/uhMg4N3UZHUR8L78slqvvlotl1+u1KpVA/Y3n0qpZ/58vXreefLTptVdn6TACNSTAAFvPbUWZa0HgShuyuNczyQ9QImzM2VDIEkCjBtJak3qgkDtBRgzam/MFRBIkkCSxowo7iUJeJPUu6lLNQIEvNWocQwC4yhAwFsfAa958UW1XHGFWq+6Smbt2oGD3QkT1LNwobpOP135N75xHHsVl0agcQQIeBunranp2AhEcVM+NiWt7ipJeoBSnQBHIYBApQKMG5WKsT8CjS3AmNHY7U/tEahUIEljRhT3kgS8lfYg9k+aAAFv0lqU+iRegIA33gGvee65wjLMYTlm88orAwe7kyap+7jj1PXZz8rvsEPi+ywVRCBOAgS8cWoNypIEgShuyuPskKQHKHF2pmwIJEmAcSNJrUldEKi9AGNG7Y25AgJJEkjSmBHFvSQBb5J6N3WpRoCAtxo1jkFgHAUIeOMZ8KaeekqtF1+siTfdJNPTM3CwO3Wquk46Sd2nnio/ffo49iIujUDjChDwNm7bU/PaCERxU16bkkVz1iQ9QIlGhLMggMBwAowbwwnxOQIIlAswZtAfEECgEoEkjRlR3EsS8FbSe9g3iQIEvElsVeqUaAEC3ngFvKlHH1XrRRdpYkeHTG/vgH0vv/XW6j7tNHWfdJL8Vlslun9SOQTiLkDAG/cWonz1JhDFTXmc65ykByhxdqZsCCRJgHEjSa1JXRCovQBjRu2NuQICSRJI0pgRxb0kAW+Sejd1qUaAgLcaNY5BYBwFCHjjEfA2PfigWi+4QBNvu23Q3pDfbjt1feYz6j7+eGnKlHHsNVwaAQRKAgS89AUEohWI4qY82hJFe7YkPUCJVoazIYDAYAKMG/QNBBCoRIAxoxIt9kUAgSSNGVHcSxLw8jvR6AIEvI3eA6h/3QkQ8I5/wNtyxRWa/P/+36B9p3eXXdR1+unqWbhQammpuz5GgRFIsgABb5Jbl7qNh0AUN+XjUe6RXjNJD1BGWmf2QwCB0QkwbozOj6MRaDQBxoxGa3Hqi8DoBJI0ZkRxL0nAO7r+xNH1L0DAW/9tSA0aTICAd/wD3tSTT2rL/feXyec36329u++urjPOUM/8+VJzc4P1TKqLQH0IEPDWRztRyvoRiOKmPM61TdIDlDg7UzYEkiTAuJGk1qQuCNRegDGj9sZcAYEkCSRpzIjiXpKAN0m9m7pUI0DAW40axyAwjgIEvOMf8IYSTD7lFLXkcoXCbHzHO9R15pnacPjhUio1jr2DSyOAwHACBLzDCfE5ApUJRHFTXtkVx3bvJD1AGVs5roZA4wowbjRu21NzBKoRYMyoRo1jEGhcgSSNGVHcSxLwNu7vAjX/uwABLz0BgToTIODd1GBn3/Ko7njspb4fLDp0Vx259/Zj0qKp3/1Ok7/whcKM3Y2HHCIZhtMxgeciCIxSgIB3lIAcjkA/gShuyuOMmqQHKHF2pmwIJEmAcSNJrUldEKi9AGNG7Y25AgJJEkjSmBHFvSQBb5J6N3WpRoBEoho1jkFgHAUIeGsY8HZ3q/mee7TxQx8axxbm0gggUEsBAt5a6nLuRhSI4qY8zm5JeoASZ2fKhkCSBBg3ktSa1AWB2gswZtTemCsgkCSBJI0ZUdxLEvAmqXdTl2oECHirUeMYBMZRgIC3BgHvK6+o5brr1HrJJTIvvKCX77lH+be+dRxbmUsjgECtBAh4ayXLeRtVIIqb8jjbJekBSpydKRsCSRJg3EhSa1IXBGovwJhRe2OugECSBJI0ZkRxL0nAm6TeTV2qESDgrUaNYxAYRwEC3ggD3pdfVusPfqCWyy9X6sUX+07c3damVy6/fBxbmUsjgECtBAh4ayXLeRtVIIqb8jjbJekBSpydKRsCSRJg3EhSa1IXBGovwJhRe2OugECSBJI0ZkRxL0nAm6TeTV2qESDgrUaNY+pG4IQTTmhdv379FyS1SXqTpLyk30u6Ydq0aZdceeWVGyqpTFtb23vy+fyXJB0oaZqkVcaYu7335zrnflPJuardl4B39AGvWbVKLVdcoZarrlLq5Zdf0xS+qUkv33+/8rvuWm0zcRwCCMRUgIA3pg1DsepWIIqb8jhXPkkPUOLsTNkQSJIA40aSWpO6IFB7AcaM2htzBQSSJJCkMSOKe0kC3iT1bupSjQABbzVqHFMXAtbaLST9TNK7iwXuktQkaULx3/8l6TDnXM9IKmSt/YSk6yWlJHlJ6yWFa4Rtg/f+yFwud/tIzjWafQh4N+mdfcujuuOxl/p+sOjQXXXk3tsPymuef16tl16qlsWLZV55ZcD9/KRJ6j72WHWdeab8jBmjaSqORQCBGAoQ8MawUShSXQtEcVMeZ4AkPUCJszNlQyBJAowbSWpN6oJA7QUYM2pvzBUQSJJAksaMKO4lCXiT1LupSzUCBLzVqHFMXQik0+kLjDFnSOr03h+Ty+XunDVrVtP06dMzkq6WNEnSl51z5w1XIWvttpL+IGmyMebaDRs2fGnp0qWrFixYsFNvb++Vkg4N1+nu7n7zsmXL1g53vtF8TsC7SW+kAW/qT39S68UXa+KNN8r0DJzn+y22UNdJJ6n7tNPkp08fTRNxLAIIxFiAgDfGjUPR6lIgipvyOFc8SQ9Q4uxM2RBIkgDjRpJak7ogUHsBxozaG3MFBJIkkKQxI4p7SQLeJPVu6lKNAAFvNWocE3uB4tLM4aWqU4wxbdlsNlte6HQ6fZYx5luSnnPO7VickTtovdLp9FeMMV+X9IikfZxzvaWdjz322CldXV1/lDTde39qLpf7fi2BCHhHHvCmHntMrRddpIm5nExvX5Nt1jz5adPUfeqp6j75ZPnXva6WTce5EUAgBgIEvDFoBIqQKIEobsrjDJKkByhxdqZsCCRJgHEjSa1JXRCovQBjRu2NuQICSRJI0pgRxb0kAW+Sejd1qUaAgLcaNY6JvYC19iOS7pC0trOzc+sVK1ZsLC/0woULt9m4ceMLxZ+90zn34FCVstb+t6T3ee/PzuVy3+6/bzqdvtwYc6qkW51zR9USiIB3k+5gM3ibHn5Yreefrwm33Sbjw2rar93y226rrs98Rt3HHy9tUVppu5Ytx7kRQCAOAgS8cWgFypAkgShuyuPskaQHKHF2pmwIJEmAcSNJrUldEKi9AGNG7Y25AgJJEkjSmBHFvSQBb5J6N3WpRoCAtxo1jom9gLX2TEnnS7rHOXfgQAW21v5Z0g7F5ZtvGKpS1tqXJU2V9FHn3J39902n0ycZY66S9IRzbrdaAhHwbtLtH/BetsNL+siy6zThpz8dtAl6d95ZXaefrp6FC6XW1lo2FedGAIEYChDwxrBRKFJdC0RxUx5ngCQ9QImzM2VDIEkCjBtJak3qgkDtBRgzam/MFRBIkkCSxowo7iUJeJPUu6lLNQIEvNWocUzsBcrev9vhnEsPVGBr7f2S3m2M+VI2m/3OYJU6+uijt+zp6VkTPk+lUm9vb28PyzRvtllrPy7pNknrnXM1nQ5KwLuJvhDwPrpK73/qAX32f7I64JnXNE3fzr277aauM85Qz/z50oQJse/DFBABBGojQMBbG1fO2rgCUdyUx1kvSQ9Q4uxM2RBIkgDjRpJak7ogUHsBxozaG3MFBJIkkKQxI4p7SQLeJPVu6lKNAAFvNWocE3uBdDp9pTHmZGPMDdls9piBCmytvVvSB7z3i3K53NcGq9SCBQu27+3tfbb4+W7OuSf675vJZD7kvb8rvMvXOdc03Dt9RwPYP+CdOXPmaE5X18d+5cdPqOm22/X9W84dtB4b9tpL604/Xd2zZ0tNoWnYEECgkQV6enr0wAMP9BG8853v1MSJExuZhLojMCqBv/71rzLGaPvttx/VeeJ6sPder7zySl/xJk+eXKgvGwIIIDCYAOMGfQMBBCoRYMyoRIt9EUAgSWPGc889p1CfGTNmVN2wK1eu3OzYQw45hJu1qjU5sB4F6PD12GqUeViBdDq92Bhz/DAB7wpJH5R0jnPuK4OddMGCBbv29vb+sfj5gAFvOp2eZYz5Wdins7NzQv93/g5b4Ap26B/wrlu3roKjk7Xrdb9P6eG/9GrF90/RDms7N6vcqj320OPptP76rndJPIhNVsNTGwQQQACB2AiEm/EQek6bNi02ZaIgCCCAAAIIIIAAAggggAAC8RZYvXp14cu04UvD1W5bbLH5QpoEvNVKcly9ChDw1mvLUe4hBQh4G6ODhID3Vy+mdNyvluvffvr9QqX/uvfeetxarQozmwl2G6MjUEsEEEAAgXETIOAdN3oujAACCCCAAAIIIIAAAgjUrQABb902HQWPkQABb4wag6JEJ1BJwGuM+UY2m/2Xwa5e6QxeSc3Oud7oarP5mZjBu8mjFPC2bOjWuXdcqs45s7Xd/rvVip7zIoAAAggggEA/AQJeugQCCCCAAAIIIIAAAggggEClAgS8lYqxPwKvFSDgpVckUsBae7WkT3rvb8rlcp8YqJJl7+D9l1wu943BIObNm7djU1PTM+HzVCr11vb29sf771v2Dt5e51xzLVF5B+8m3Ve6uvXgAw/2/WDfffdRa0tLLfk5NwII1LkA7+Ct8wak+LET4B28sWsSCoQAAuMskKR3440zJZdHoCEEGDMaopmpJAKRCSRpzOAdvJF1C07UwAIEvA3c+EmuejqdvtgY84+Sljnnjhyortba+yTt570/I5fLXTSYxyc+8YlpGzZseCl8ns/n9+3o6Hig/76ZTOYw7/2PJa1xzr2ulrb9A9599923lpeL9bm7u7t177339pXxgAMOUAsBb6zbjMIhMN4CjBvj3QJcP2kCf/7znwtV2nHHHZNWtUJ98vm81q9f31e3KVOmhC/8JbKuVAoBBKIRYNyIxpGzINAoAowZjdLS1BOBaASSNGZEcS/561//ejNY3sEbTT/jLPUjQMBbP21FSSsQSKfTZxljviXpAefcgAmotTbMyg1PI+c555YOcXpjrV0nabKkI51zy/rva639pKQwa/hh59zeFRS14l0JeDeREdRU3H04AIGGF2DcaPguAEDEAlHclEdcpEhPl6QHKJHCcDIEEBhUgHGDzoEAApUIMGZUosW+CCCQpDEjintJAl5+JxpdgIC30XtAQuufTqdnG2N+Iqmnq6tr2vLly18pr+rcuXNnNDc3vyDJ5PP53Ts6On4/FIW1NkwT3V/SOc65r/TfN5PJXOq9/7SkG51zR9eSlYCXgLeW/YtzI5B0AQLepLcw9RtrgShuyse6zJVcL0kPUCqpN/sigED1Aowb1dtxJAKNKMCY0YitTp0RqF4gSWNGFPeSBLzV9yWOTIYAAW8y2pFa9BM44ogjJre2tr4oaZL3/jO5XO6y8l0ymczZ3vvzJD3hnNttOMBMJrPIe/9VSU9PmTLlrYsXL+4qHWOt3ULSk5Kme++PyeVyNwx3vtF8TsC7SY+gZjQ9iWMRaEwBxo3GbHdqXTuBKG7Ka1e60Z85SQ9QRq/BGRBAYCQCjBsjUWIfBBAoCTBm0BcQQKASgaSMGeFdws8++2yh6qN53Q8BbyW9h32TKEDAm8RWpU4FAWtteK/u6ZLC8sonzpw5c8nKlSuN994aY66V1Foe/s6aNat5xowZ13vvt5V0vHPu6RLlggULtu/t7Q2zfMMyzbf29vaetmTJkuettTsUl2Y+VNIz69at2+3222/vrmUTEPBu0iWoqWVP49wIJFOAcSOZ7Uqtxk8g3JSHm/MddthBxiTv1iIpD1DGr4dwZQQaT4Bxo/HanBojMBoBxozR6HEsAo0nkJQxI9TjueeeK9xDhnvJajcC3mrlOC4pAsl7CpOUlqEeoxaYM2fO1JaWlhWSSu/gLQWvLcWTL5c01znXG/7d1ta2Xz6fv6/42VnOuX8vL0QmkznGe/9DSaniz9dKmlr8e5jRe6hz7u5RF3yYExDwbgIiqKl1b+P8CCRPgHEjeW1KjcZX4Pnnn1dvb6+23XZbNTc3j29hanD1pDxAqQENp0QAgUEEGDfoGgggUIkAY0YlWuyLAAJJGTM2btyoF154QU1NTdpuu+2qblgC3qrpODAhAgS8CWlIqjGwgLV2kjHmi977NklvkhTC3EclhaD28lK4G44uBsL3SJqRSqVmt7e3/7r/Wa2175V0lqT3SZomqdN7f5f3/pyOjo5w3ppvBLybiAlqat7duAACiRNg3Ehck1KhcRZ46aWX9Morr2irrbbS1Kml772Nc6EivHxSHqBESMKpEEBgGAHGDboIAghUIsCYUYkW+yKAQFLGjLVr12rNmjWaPHmytt5666obloC3ajoOTIgAAW9CGpJqNI4AAS8Bb+P0dmqKQPQCBLzRm3LGxhZ49dVXtWrVKk2cOFHTp09P3DLNSXmA0ti9lNojMLYCjBtj683VEKh3AcaMem9Byo/A2AokYcwIr/jp7OxUT0+PXv/612vSpElVIxLwVk3HgQkRIOBNSENSjcYRIOAl4G2c3k5NEYhegIA3elPO2NgC4QFDWFor/Bm+fT1t2rREhbxJeIDS2D2U2iMw9gKMG2NvzhURqGcBxox6bj3KjsDYC9T7mBHC3dWrVxdWgUqlUoVX/YQ/q90IeKuV47ikCBDwJqUlqUfDCBDwEvA2TGenogjUQICAtwaonLLhBcLv1Ysvvqhwsx5m8oZvYIf/wo26MaauA996f4DS8J0TAATGQYBxYxzQuSQCdSzAmFHHjUfRERgHgXobM8I9YvgvlDus/hT+CzN3w33iG97wBrW0tIxKkYB3VHwcnAABAt4ENCJVaCwBAl4C3sbq8dQWgWgFCHij9eRsCJQEwu9WWKo53LgnbSuv02i+XZ40F+qDAAKDCzBu0DsQQKASAcaMSrTYFwEE6n3MCPdUYWnm0Ya7oScQ8PL70OgCBLyN3gOof90JEPAS8NZdp6XACMRIgIA3Ro1BURInEB40hN+x8K3s8Gf4d/i2dj1vofzr16/vq8KUKVPqekZyPbcFZUegXgQYN+qlpSgnAvEQYMyIRztQCgTqRaAex4wwWzeEuiHQDSs9hT+j+uIsAW+99FzKWSsBAt5ayXJeBGokQMBLwFujrsVpEWgIAQLehmhmKolAZAKMGZFRciIEGkaAcaNhmpqKIhCJAGNGJIycBIGGEWDM2LypCXgbputT0UEECHjpGgjUmQABLwFvnXVZiotArAS4GYpVc1AYBGIvwJgR+yaigAjEToBxI3ZNQoEQiLUAY0asm4fCIRA7AcYMAt7YdUoKNK4CBLzjys/FEahcgICXgLfyXsMRCCBQEuBmiL6AAAKVCDBmVKLFvgggEAQYN+gHCCBQiQBjRiVa7IsAAowZBLz8FiBQLkDAS39AoM4ECHgJeOusy1JcBGIlwM1QrJqDwiAQewHGjNg3EQVEIHYCjBuxaxIKhECsBRgzYt08FA6B2AkwZhDwxq5TUqBxFSDgHVd+Lo5A5QIEvAS8lfcajkAAgZIAN0P0BQQQqESAMaMSLfZFAIEgwLhBP0AAgUoEGDMq0WJfBBBgzCDg5bcAgXIBAl76AwJ1JkDAS8BbZ12W4iIQKwFuhmLVHBQGgdgLMGbEvokoIAKxE2DciF2TUCAEYi3AmBHr5qFwCMROgDGDgDd2nZICjasAAe+48nNxBCoXIOAl4K2813AEAgiUBLgZoi8ggEAlAowZlWixLwIIBAHGDfoBAghUIsCYUYkW+yKAAGMGAS+/BQiUCxDw0h8QqDMBAl4C3jrrshQXgVgJcDMUq+agMAjEXoAxI/ZNRAERiJ0A40bsmoQCIRBrAcaMWDcPhUMgdgKMGQS8seuUFGhcBQh4x5WfiyNQuQABLwFv5b2GIxBAoCTAzRB9AQEEKhFgzKhEi30RQCAIMG7QDxBAoBIBxoxKtNgXAQQYMwh4+S1AoFyAgJf+gECdCRDwEvDWWZeluAjESoCboVg1B4VBIPYCjBmxbyIKiEDsBBg3YtckFAiBWAswZsS6eSgcArETYMwg4I1dp6RA4ypAwDuu/FwcgcoFCHgJeCvvNRyBAAIlAW6G6AsIIFCJAGNGJVrsiwACQYBxg36AAAKVCDBmVKLFvgggwJhBwMtvAQLlAgS89AcE6kyAgJeAt866LMVFIFYC3AzFqjkoDAKxF2DMiH0TUUAEYifAuBG7JqFACMRagDEj1s1D4RCInQBjBgFv7DolBRpXAQLeceXn4ghULkDAS8Bbea/hCAQQKAlwM0RfQACBSgQYMyrRYl8EEAgCjBv0AwQQqESAMaMSLfZFAAHGDAJefgsQKBcg4KU/IFBnAgS8BLx11mUpLgKxEuBmKFbNQWEQiL0AY0bsm4gCIhA7AcaN2DUJBUIg1gKMGbFuLRdxbwAAIABJREFUHgqHQOwEGDMIeGPXKSnQuAoQ8I4rPxdHoHIBAl4C3sp7DUcggEBJgJsh+gICCFQiwJhRiRb7IoBAEGDcoB8ggEAlAowZlWixLwIIMGYQ8PJbgEC5AAEv/QGBOhPoH/DWWfEpLgIIIIAAAggggAACCCCAAAIIIIAAAggggAACkQoccsgh5F2RinKyuAvQ4ePeQpQPgX4CBLx0CQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEENgkQ8NIbGk2AgLfRWpz61r0AAW/dNyEVQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhQgIA3QkxOVRcCBLx10UwUEoFNAgS89AYEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBDYJEPDSGxpNgIC30Vqc+iKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQN0KEPDWbdNRcAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaDQBAt5Ga3HqiwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACdStAwFu3TUfBEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECg0QQIeButxakvAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjUrQABb902HQVHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFGEyDgbbQWp74IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFC3AgS8ddt0FBwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBpNgIC30Vqc+iKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQN0KEPDWbdNRcAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaDQBAt5Ga3HqiwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACdStAwFu3TUfBEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECg0QQIeButxakvAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjUrQABb902HQVHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFGEyDgbbQWp74IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFC3AgS8ddt0FBwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBpNgIC30Vqc+iKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQN0KEPDWbdNRcAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQaDQBAt5Ga3HqiwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACdStAwFu3TUfBEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECg0QQIeButxakvAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjUrQABb902HQVHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFGEyDgbbQWp74IxFjghBNOaF2/fv0XJLVJepOkvKTfS7ph2rRpl1x55ZUbKil+W1vbe/L5/JckHShpmqRVxpi7vffnOud+U8m52BcBBOIpEPW4kU6n5xljTpH0bklbhnFD0n9LOt859z/xVKBUCCAwUoGox4z+121ra9s3n8/fLylljPlaNptdNNKysR8CCMRToFbjhrX2LZJOkHSopJ0ldTrnZsZTgVIhgMBIBaIeMzKZzIHe+/CcJDzX2FrSS+H+xBhzfjabDfcpbAggkBABa+0+ksLziA9W+/8JeBaakM5ANRAYoQAB7wih2A0BBGorYK3dQtLPiqFKuFiXpCZJE4pX/i9JhznnekZSEmvtJyRdHx6wSvKS1ksK1wjbBu/9kblc7vaRnIt9EEAgngJRjhuLFi1KPfLIIz80xhxTrG34gkkYhyaX/m2M+Uw2m70inhqUCgEEhhOIcswY5FrGWhsetL43fE7AO1yL8DkC8Reo1biRTqe/Yoz5iqSWokK4X7nfOXdA/FUoIQIIDCYQ9ZiRTqc/Z4y5qPhco/ScpLV0f+K9/3wul7uEFkEAgfoVOPbYY6e8+uqrC4tfNN+vWJNnnXM7VlornoVWKsb+CNS/AAFv/bchNUAgEQLpdPoCY8wZ4Zvr3vtjcrncnbNmzWqaPn16RtLVkiZJ+rJz7rzhKmyt3VbSH0IwY4y5dsOGDV9aunTpqgULFuzU29t7ZfFb8p3d3d1vXrZs2drhzsfnCCAQT4GIx41/lvQNSd2Svtja2nrt9ddfv76trW33fD7/fUmzJPVKeodz7rfxFKFUCCAwlECUY8ZA10mn08cZY35Y+oyAl/6IQP0L1GLcyGQy53rvv1zUuSWsEtLZ2fm/K1as2Fj/YtQAgcYWiHLMaGtr2yufzz9UDHeXNDU1ff7mm29+ZsGCBbv29vZ+V9IRYdWzfD7/zo6OjocbW57aI1CfAul0+mJjzImSpvarQcUBL89C67MPUGoERitAwDtaQY5HAIFRCxSXMHpR0hRjTFs2m82WnzSdTp9ljPmWpOeK32AL33AfdCt+I/7rkh6RtI9zLoQyhS18M66rq+uPkqZ770/N5XIhuGFDAIE6E4hy3Cie6y9hSWbv/b/kcrkQ9PZtRx999JY9PT2PS9qmuBTaF+uMi+Ii0PACUY4ZA2HOmTNnaktLS2GcCF9WkzSDgLfhux0AdS5Qi3HDWnuQpF8Uac5yzv17nTNRfAQQKApEPWZYa8+XdKakJyTNLF/NrHitleHVVmGGbzabDV+WZ0MAgToTsNb+TdJWkh7y3v8glUo1e+8vkFRxwMuz0DprfIqLQEQCBLwRQXIaBBCoXsBa+xFJd0ha29nZuXX/b68vXLhwm40bN75QvMI7nXMPDnW14vKI7/Pen53L5b7df990On25MeZUSbc6546qvuQciQAC4yUQ5bhhrf2gpBXFurzeORfea7XZlk6nFxtjjpf0n8658K48NgQQqCOBKMeMgaqdyWS+E96P573/kTFmp/DeLALeOuogFBWBAQRqMW6U7lOMMS6bzYaVitgQQCAhAlGPGdban0iabYz5fjabDc8v+t+flJ5r3OGc+1hCGKkGAg0lEO4hjDHt7e3t94eKW2tPkHRtNQEvz0IbqutQWQT6BAh46QwIIDDuAtba8K3U8O3Ue5xzBw5UIGvtnyXtUFy++YahCm2tfbm4vMlHnXN3DnAjdJIx5qrwTVjn3G7jDkABEECgYoEoxw1r7cGS/jW8q9s59/GBCpPJZL7lvT9L0nLn3JyKC8wBCCAwrgJRjhn9KzJ//vw9UqlUWBqxp6mpaffe3t4bCXjHtbm5OAKRCEQ9blhr3yLp95J8KpXao729Pcz6Z0MAgYQI1GDMWCJp7hAB7/eMMZ+RtMw5d2RCGKkGAg0tMMqAl2ehDd17qHyjChDwNmrLU28EYiRQ9p6aDudceqCiWWvDt9nebYz5Ujab/c5gxS8upbomfJ5Kpd7e3t4elmnebLPWhgDntmKYs0WMKCgKAgiMUCDKcWMkl7TWLiu+5+oc59xXRnIM+yCAQHwEajlmWGvDKiQfMcb8czabPddaG1YEYAZvfJqfkiBQlUDU40Y6nf6cMSa8N/MB59y+VRWKgxBAILYCUY8Z1tovSQorkr1mieZTTjllwurVq8Ozjt0lfdk5d15sYSgYAgiMWKDagJdnoSMmZkcEEidAwJu4JqVCCNSfQDqdvtIYc7Ix5oZsNnvMQDWw1t4t6QPe+0W5XO5rg9VywYIF2/f29j5b/Hw351y4Gdpsy2QyH/Le3xW+Pe+cawp/1p8aJUagsQWiHDeGk7TW7lyccTNR0h7OuceGO4bPEUAgXgK1GjOstXMlhRk2T65bt27P22+/vZuAN15tT2kQqFYg6nHDWnu1pE967y+VlAtfXJX0HklTwhjivb/RGHO+c+7VasvMcQggMH4CNRgzCu/llLSLpI7e3t7PL1my5M/W2h2MMRd6762kpzds2LD3LbfcEt7jyYYAAnUuUG3Ay7PQOm94io/AKAQIeEeBx6EIIBCNQOndlsMEvIXZMJKGnD23YMGCXXt7e/9YLNmAAW86nZ5ljPlZ2Kezs3NC/3f+RlMrzoIAArUUiHLcGK6c1trlkg733mdzuVzbcPvzOQIIxE+gFmPGCSec0Lp+/frfSdrVGDM3m83eEmpOwBu/9qdECFQjEPW4UfrCajGweYek8DymOyw8JGlCsYz3SDqEkLeaFuMYBMZXIOoxI9Smra1t93w+3y5pn2LtwhdAJhX//kAqlVrAcu/j2+5cHYEoBUYR8PIsNMqG4FwI1JEAAW8dNRZFRSCpAlHeCBHwJrWXUC8ENheIctwYyjadTn/BGBOWhX+5qanpbTfffPNztAUCCNSfQC3GjHQ6/VVjzCJJ/+WcO6SkQsBbf/2DEiMwkEDU44a19kFJe5eCGWPM5733/71u3brmLbbY4h8kheWbm73338nlcmF2LxsCCNSRQNRjRqh6MeC9VtL7BqD4r97e3k8uWbLkT3XERFERQGAIAQJeugcCCFQqQMBbqRj7I4BA5AKV3AgZY76RzWb/ZbBCVBrwhocozrneyCvFCRFAoKYCUY4bgxU0nU5/2BjzH5KajDHzSrPzaloxTo4AAjURiHrMmDdv3i5NTU1h9u6EfD6/T0dHx8pSwQl4a9KEnBSBMReIetyw1oZXx7xZ0mPd3d37LVu2bG15pdLp9HnGmLMlrZM0g1m8Y97kXBCBUQlEPWbMmzdvx6ampvskbSdpaSqV+tqkSZMeW7t2bVg55GvGmIykp5qbm99z0003/WVUhedgBBCIhcBYBbw8C41Fc1MIBCIRIOCNhJGTIIDAaATK3kd1Uy6X+8RA5yp7B++/5HK5bwx2veJN0DPh81Qq9daBlisqewdvr3OueTRl51gEEBgfgSjHjYFqMH/+/D1SqdT/SHrdcF8sGR8BrooAApUIRD1mWGs7JM0zxlySzWb/sbwsBLyVtAz7IhBfgRqMG09KeqOkzznnvte/5vPmzduuqampsFKI9/4DuVzuF/HVoWQIINBfoAZjRpi5e4KkFc65D/W7nrHW/lzSQcaYq7PZ7Mm0CAII1L9AtQEvz0Lrv+2pAQLVChDwVivHcQggEJlAOp2+2BgTHo4uc84dOdCJrbXhm6v7ee/PyOVyFw128U984hPTNmzY8FL4PJ/P79vR0fFA/30zmcxh3vsfS1rjnHtdZBXhRAggMGYCUY4b/Qs9d+7c1zc3N98bZtkYY27OZrPhiyd+zCrHhRBAIHKBKMeMTCZziPf+TkmrJkyYsNuNN964urzABLyRNx8nRGBcBKIcN0IFrLUPS3q79/4fc7ncJYPc86yRtKX3fmEul7t5XCrORRFAoCqBiMeMEOCG2fyTjTFzB1pJKJ1OH2mMuUXSqzNnztxi0aJF+aoKzkEIIBAbgWoDXp6FxqYJKQgCYy5AwDvm5FwQAQT6C6TT6bOMMd+S9IBzbt9BHnaEWbk7htkyzrmlQyj23QhJOtI5t6z/vtbaT0q6WtLDzrnSe7BoGAQQqCOBiMeNvppbaydKCsHNByT9ct26dYfcfvvt3XVEQ1ERQGAAgSjHjNKXziqBds5x31UJGPsiEAOBKMeNUB1r7XJJh0u6wDn3hUHuef4maSvvfSaXy7kYMFAEBBAYoUCUY8bChQu32bhx4wvh0vl8fu+Ojo7wBZHNtvnz589MpVKPhB82NTXtcPPNNxdWAGBDAIH6Fag24JXEs9D6bXZKjsCoBHjQMCo+DkYAgSgE0un0bGPMTyT1dHV1TVu+fPkr5eedO3fujObm5nBzY/L5/O4dHR2/H+q61tow825/Sec4577Sf99MJnOp9/7Tkm50zh0dRR04BwIIjK1A1ONGqfTW2tJSaI9v3LjxfUuXLl01tjXjagggUAuBKMcMa+2vJe08RDm3Cu+1CjNqJBX+P41z7g21qBfnRACB2glEOW6EUqbT6S8bY86VdL9zLtyrbLZZa7eV9Hz4YSqV2r+9vf3+2tWOMyOAQNQCUY4Zc+bMmdrS0vJyKKP3/mO5XO6OAZ5rlFYU0YYNG6bdcsst4QsibAggUMcCowh4wxfJeBZax21P0RGoVoCAt1o5jkMAgcgEjjjiiMmtra0vSprkvf9MLpe7rPzkmUzmbO/9eZKecM7tNtyFM5nMIu/9VyU9PWXKlLcuXry4q3SMtXYLSeH9V9O998fkcrkbhjsfnyOAQPwEoh43Qg3LHrx2plKp97a3t/8hfjWnRAggUI1ALcaMwcrBEs3VtBDHIBA/gajHDWtt+GJIuA9pGiiwSafT5xhj/p+kFzs7O7dbsWLFxvipUCIEEBhMoAZjxkpJexpjrstms8f3v24mk/mh9/44SY87595KyyCAQP0LjCbg5Vlo/bc/NUCgGgEC3mrUOAYBBCIXsNaG9+qeLim8Z+bEmTNnLlm5cqXx3ltjTJhR11oe/s6aNat5xowZ13vvwzfdj3fOPV0q1IIFC7bv7e0Ns3wnS7q1t7f3tCVLljxvrd2huDTzoZKeWbdu3W4svRp5U3JCBMZMIMpxI51OzzPG5CSF5ZgPds79z5hVhAshgMCYCEQ5ZgxVYALeMWlOLoLAmAhEPW6k0+kLjDFnSOr03h+fy+X+w1o7QdIJkr4naYL3/uxcLvftMakgF0EAgUgFohwzyl4tJWPMtzds2HD+0qVL/xpWOJswYcIXvPdnhcIbY07JZrNXRVoRToYAAuMiMFzAy7PQcWkWLopArAUIeGPdPBQOgcYRKC5BtEJS6R28pXdethQVwjur5jrnesO/29ra9svn8/cVPzvLOffv5VqZTOYY7/0PwwpnxZ+vlTS1+Pcwo/dQ59zdjSNMTRFInkCU44a1dn3xSyEbJBWWQxtiu8k597nkiVIjBJItEOWYMZQUAW+y+xG1ayyBqMcNa+3E8AXUcC9SlAxLuYcl3UPIG4Iat+eeey5YtGhRvrGkqS0CyRCIeswoe71UCSiMGZPKtC53zoXXT7EhgEACBIYLeHkWmoBGpgoIRCxAwBsxKKdDAIHqBay1k4wxX/Tet0l6k6QQ5j4qKQS14calEO6GrXjjdI+kGalUanZ7e3t4H95mm7X2vZLCt1rfJ2la8Zvyd3nvz+no6AjnZUMAgToXiGrcsNb6kVKEL4/kcrkw04YNAQTqTCCqMWOoahPw1lmnoLgIDCMQ9bixaNGi1G9/+9vTvPeflBSWVQ33OA+HlYZmzpx5HeEuXRKB+haIesxIp9OHG2NOkxTe3f06SeFdu+HL7pc5535c31qUHgEEygWGC3h5Fkp/QQCB/gIEvPQJBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoE4ECHjrpKEoJgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIEDASx9AAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE6kSAgLdOGopiIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgS89AEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgTgQIeOukoSgmAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggQMBLH0AAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTqRICAt04aimIigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACBLz0AQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKBOBAh466ShKCYCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBAwEsfQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOpEgIC3ThqKYiKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIEvPQBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAoE4ECHjrpKEoJgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIEDASx9AAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE6kSAgLdOGopiIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgS89AEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgTgQIeOukoSgmAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggQMBLH0AAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTqRICAt04aimIigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACBLz0AQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKBOBAh466ShKCYCCCCAAAIIIIAAAgggMJyAtXaFpA9K6nbOtQ63P58jgAACCCCAAAIIIIAAAggggED9CRDw1l+bUWIEEEAAAQQQQAABBBCIoUAmk1nkvf9qhUW71Tl3VIXHDLp7vQa81toTJF07hENe0hpJT3nvf+G9v7Kjo2NlVG5Rn8dae4Ok0K4dzrnjqjl/Op0+zxjzWUn3SDrcOddTzXnG8pgRtOM6SZ2SHjLGLH/11VdvXr58+StjWUauhQACCCCAAAIIIIAAAggkQYCANwmtSB0QQAABBBBAAAEEEEBg3AUIeKtvghEEg/1P7iV9t7Oz84srVqzYWP2Voz9y4cKFb9i4cWMIMQvbxo0b37B06dJV5Vey1j4laRdJjznn9hioFNbaEIZOKX72bufcr6IvbbRnrKIdn/beH5PL5X4RbUn+fraRONfiupwTAQQQQAABBBBAAAEEEKi1AAFvrYU5PwIIIIAAAggggAACCDSEQL+AN8zkXTaCiq9xzv1xBPuNaJckzOA1xnzfe39FeYXz+bwxxmxtjPmApDCrdevwuff+h7lcLsz+jdVmrXWSPh76gHNuQf/CjSR4TKfTFxhjTpV075QpU2YvXry4K1aVHKAw5QHvQO0oaQtjzNu896HN3lc8xXpJ73XO/Sbq+o3EOeprcj4EEEAAAQQQQAABBBBAYCwECHjHQplrIIAAAggggAACCCCAQOIF+gW8JzrnFo91pRMS8H4tm80uGsxuwYIFO/X29oYZn2EGbAh5D8vlcrePtfVorpfU4LFfwDtUO5p0On2+MeaMouMdzrmPjcZ0oGOT6hy1E+dDAAEEEEAAAQQQQACB+hMg4K2/NqPECCCAAAIIIIAAAgggEEMBAt7qG6WCYLBwEWvtHEm3Fq94i3NubvVXH/sjkxo8VtKO1tqJkp6WtI2kXkmvd86F9yxHtiXVOTIgToQAAggggAACCCCAAAJ1K0DAW7dNR8ERQAABBBBAAAEEEEAgTgJRBrzpdPqjxphjJB0kaVtJeUnPeO//y3t/cUdHx+8HqvtIZvC2tbXt3tvb+4/GmEMk7Swp3Bc+573/hTHmMufcfUO5WmsnhaWDvfdh6eGZklKS/myMuXPjxo3nL1my5MlK26WSYDCc+5RTTpmwevXqlyW1SnrBObfdQNdMp9PvLy5z/G5JO0l6VdLvjTHLJkyY8L0bbrghnGPQrVqrgYLFsrYZ7Hp3O+dmlT4cqC3nzZu3XVNT0zOSmowxLpvNZgY7mbX2J5JmS+qZMGHCtjfeeOPq8n3j0I7W2qWSjiqW653OuQf712f+/PkHNDU1nei9/5CkHULdQ3+V9PNUKnVJe3v7r/vVa4WkDw7RrJs5l/abNWtW8/Tp04+XdJykfSS1SHpeUjjfxQOVrdJ+zv4IIIAAAggggAACCCCAQFQCBLxRSXIeBBBAAAEEEEAAAQQQaGiBKALeOXPmTG1pablZ0mFDYHYZY47PZrPZ/vsMF/BmMpl/8N5fVgyvBrqE995/M5fL/fNAH86fP3+3VCp1m6TdBylfd3i/ai6XC3UY8VZpwBtObK19VtL2kjY458Js0L5t9uzZLVOnTr3Sex/CusG2TmNMOpvN/nygHUZjVauAt1jvOyR9RNIrXV1d05cvX/7KAP1gK0l/lRRcXjPDOS7tmE6n240xhZA6lUq9t729/X9LdQkh/t/+9rfwPuYTh2jDXu/9F3O53EWlfSoN0sNxCxcu3Gbjxo23SHrPINfKe+/PyuVy54+4U7MjAggggAACCCCAAAIIIFBDAQLeGuJyagQQQAABBBBAAAEEEGgcgSgCXmvtXZLCTMWw3eG9v7apqempfD7/ekmHSvqUpAlhVmYqldqzvb39D+XCQwW8xdmsdxdn7P7FGPOdfD7/f6lUKtwXHuS9/4KkEAyG7dPOucvLz118922YYbl1CFUlhaB4WXFW7P6SzpYUZtL25vP5Azs6Ou4daetXGvAuWrQotXLlyrWSJkt60Tk3vZ9DCKE/XvzZr40x3+3t7X00lUpNM8Yc5b0/qTgTtDufzx/U0dHxf+XHj9ZqkID3LZK2KF4nzK4NVk9JKi0vvc4590SpHIO1ZTqdPs4Y88Own/c+k8vlXH/nTCZzjPf++uLPrXMuV9onLu1YbMNQ3zcWyzbDOddZVv9rJZ1Q/HeYVX659/7xVCo1NZ/Pf8AYc7qkKUWHD+RyufBe5hD8V+Rc/FLFA5LeHEglBdv2fD6/OpVK7VXs17sNZDnS/s1+CCCAAAIIIIAAAggggEDUAgS8UYtyPgQQQAABBBBAAAEEEGhIgdEGvOl0+sPGmJ8WA6trcrncJ/tDWmtPkfT94s/Pc859uXyfoQJea20IAtNh/3y35tyjAAARZElEQVQ+v3dHR8fD5ce2tbXtnc/n/0fSpBA8OudKwVthN2vtnZLCss5hO9w59+Py4+fNm7djU1PTryTNkPS/zrn3jrQjVBrwptPpWcaYnxXP/1/OuVK5lE6nTzLGXFX8bGlnZ2dmxYoVG8vLkk6nZ4dlmiU1S3qis7PzbeX7RGAVgttdJD3mnNtjgHYc8vOid2mp4W7nXFiKutQOIST+Swi3B1umuWzp45enTJmyzeLFi7vKjo9FO1prvyTp28Vy3emc+2hZGUNIW1qG/I7Ozs6PD9CGYRnz/wzHGGNuzmazC6txzmQyVxUD/xCYfyaXy4UvLvRt1trwpYfwe/E2SWHW+C7OufDOYDYEEEAAAQQQQAABBBBAYNwECHjHjZ4LI4AAAggggAACCCCAQJIE+gW8Xy3Obh2qik8459aVdmhra3tbb2/vgeHfxpg7nHNP9z/42GOPndLV1RXeGxvee/tj59zh5fsME/CGWarvkrTaORdm4b5ms9aG8LhQhq6urv1Ly//Onz//3alU6v7iAYudcwMum2utPVNSaRnbmc65346kjSsJeK21oewh/Hx78dwnOed+ULqOtfax4hLSqzZs2PCWW2655W8DlSGdTv+7MeaLRe9PZLPZm8rOUbVVOMdAM3j7tVPVAW84TzqdvtEYEwLN1yzTXOwjYSZsCOo3a6vxbsdQtldffXXv4nuRjy2ahJmyB7a3t/+uZBRmGW/cuPFjxba5Z7B+ZK19QdI2klY658Js28224dqhuDRzCG3De31/5pw7eJC+Ms8Y0xE+894flsvlbh9Jv2YfBBBAAAEEEEAAAQQQQKBWAgS8tZLlvAgggAACCCCAAAIIINBQAv0C3mHr7r3/UC6XC0FlRZu1NoTCYWnaFc650nLOhXMMs0Rz3/tOjTFzstns8pFeOJPJfKe4hHM45H3OuTCj8TXb/Pnz35FKpR4KHxhjTslms6WZtENeql/AG967ekX5Afl83hhjtk6lUu/z3p9WXN447HKPpA+UZlTOnz//nalU6tfhA+/9pblc7rODXbg44/iZYlk3mwFa/m7YSq2K7VCzGbzh/JlM5jDvfWEGdf9lmtPptDXGFN7PbIz5SDabLcwKLx43Zu04wr71dCqVmtPe3l7oM5Vu1tpHJM0caMb5CNsh9I9LilabhfzlZTn66KO37OnpWVP0Pnewd1RXWn72RwABBBBAAAEEEEAAAQSqFSDgrVaO4xBAAAEEEEAAAQQQQACBMoGoAl5rbZP3PswYnC9pX0nbFt/d2v/+7W7n3KzyRhgq4G1ra9svn8+H95S2FN81eqf3/lbv/d0dHR0rh2pMa+0dkj4S3r2bz+ffl0qlNlvyuHSs9/71pWWmJf27c+6skXSS8oB3JPsX9wnvsT3WOfdS6Zh+75492jl34zD1+qOkXSU97px7a2nf0ViFcww3c3S4z4vnGHCJ5vDZrFmzmqdPnx5mnoblsHPOOVsqu7X2Zkltkp6fOXPmjosWLcqXfRandjxn3bp1X7/99tu7h2mjQ733C4wx75a0U/F3IcxgL9/+5JwL7bjZNpxzOp2+0hhzcvGg2ZLCjODBtvBO6YmDLYtdQb9lVwQQQAABBBBAAAEEEEBg1AIEvKMm5AQIIIAAAggggAACCCCAQGFW5SLvfViaOWwnOucWV+pirQ3vvV0qae8RHFtRwBvOl06n32+Mubq4hHH5JcI7XW+TdKVz7r7+1y6bKTmCYvXtEs71qZEcMIKA10taK+nPxph7vfc/cs7d1f/c6XT6LGPMt8LPjTEHZ7PZ0nt6ByyGtTbMAA7vCn7NstXVWoULDRcsDvd58RyDBrzh80wm813v/efKl2k+4YQTWtevXx+WZ97CGHNRNps9o7ziY9mOxpjXzMQ2xhzovf9esX2uzWaz/zBY/5g7d+7rm5ubw3ujN5ulPsj+VQW81trQ5z8+kj5atk9YPr2wfDQbAggggAACCCCAAAIIIDBeAgS84yXPdRFAAAEEEEAAAQQQQCBRAqMNeK21W0j6TXFGabC5zxhzs/f+kXw+H95TWpo1G0LJ8H7VigPecNJFixalVq5c+VFJc4vh2W7lDeG9v8kY80nn3Kuln1trn5D05koazHv/w1wud8JIjqnkHbxDnS+TyZztvT8v7OO9/0AulwszlgfdrLUhzN5vsPcSV2MVLjZcgDvc58VzDBnwWmv3lxRmlfYt05xOp480xtwSfpbP5/fr6OgI7xLu22LQjsZa+ytJ75TUK+kdA71fN8xil/Tfkg4oFv5R7/11kh40xqyS1FP8ea7YL6sNeMPy1R8eSR8t2+c1v3cVHs/uCCCAAAIIIIAAAggggMCoBQh4R03ICRBAAAEEEEAAAQQQQACB0c/gtdaeLumiouXFzrnPD+Ra9g7eqgLe/ue01oYloMMsxjDbM7zPNMx+3eydtNba+yWFJXKfcc7tHHV7RxXwWms/KSnMUA6bdc6FAHDQzVob3sG7o6RHnHNvH65eI7EqXNjamr6Dt1ROa+1jxdnYhWWarbUhBD22/5LTZfuPeztaaw+VdHuxTMucc0f2dy8PqiV1dHZ2LlixYsVrlgUvm5FcbcAbZgini0uWTyi9y3m4fsDnCCCAAAIIIIAAAggggMB4CxDwjncLcH0EEEAAAQQQQAABBBBIhEAEM3hDGBneu6sNGzZMu+WWW/42EEw1AW/xna2t4XydnZ1dA4Vlxx577JSurq4wo3XPYuC1jXMuLPcblna+3hhzTJh1uWHDhjcMVrZqGzKqgHf+/PnvTKVSvy6W4wLn3BcGK9OCBQt26u3tfTp8Xv5e1dFahfONYcD7r5K+FpZp3rBhww4TJkwI7xR+naSvOuf+rX/d49KO1tqfS3p/0f6gbDYbZuv2bZlM5jve+0LbpVKpfdrb2x8a5HfhkeKXEqoKeDOZzNe9918pnjvMJg4z6NkQQAABBBBAAAEEEEAAgdgLEPDGvokoIAIIIIAAAggggAACCNSDQAQB739ICu/2DO+b3dI5t65/vefOnTujubn5z5ImVLJEs7U2zNAN7xsNYeZns9nspQOZZjKZC733hZnD5cFaJpNZ6L2/Mfzce392Lpf79mBtMnv27Jbbb7+9u5I2iyrgLS6pHELOMMs4vFf4LQM5hrJlMplzvfdfLpqks9lsR/j7aK2K5xhuBm8o466SHnPO7TGQlbV2yCWawzHz5s17U1NT0x+KdbjWe39i8Vy7OefCstqbbXFpx0wmE97F+8ti4f7bOXdQeUEzmcwV3vvS+5tnDrKMc1im/ElJYQb6YAHvkM7W2vD+5bDkedgud859erB+e8opp0y48sorwyzi8PvJhgACCCCAAAIIIIAAAgiMqwAB77jyc3EEEEAAAQQQQAABBBBIisBoA95MJvNd7/3nih4nOed+UG5z1FFHvW7ChAlLiu/NDR/9r3MuBFR922Ch4MKFC9+wcePGMFs1hGIPt7a2vu/6669f3+/YicX3noalmMP7UWc4514K+xRntT4u6Y2Suo0xh2ez2fD+0s22efPmbdfU1LTce/+7np6eTy9btmztSNo3qoA3XCuTyXzGe/+94nXbJR3df+nddDo9yxgTAvUWSY+uW7dun1IoPVqrcN0RzODte/evpOkDLQ08koC3eK0QUJb3g/ucc6V3127GH6d2tNaGLxyELx6E7Ujn3LJSYa21Z0o6P/zbGPONbDb7L+UVCV8imDp16mLv/YLiz19wzm3Xv6+Vv2N5COfwnuYQMHvv/Qm5XC4sc73ZZq3dStLN4XsPzc3Nx910003hywNsCCCAAAIIIIAAAggggMC4CRDwjhs9F0YAAQQQQAABBBBAAIEkCYw24G1ra9srn88/KKkphKje+4uMMXcYY3w+n3+PMeYzknYoM3vCObdbueFQoaC1trScbzjkj9777xpjHjbGdOfz+d2NMadK2j986L3/US6XC+9y7dva2trek8/n75YUguDesI8xZkkqlXree7+1936WpDDrcpqk55uamg64+eabwztuh92iDHhDJmitDe94DbOhw/Z/3vtLvPePpVKpLb33hxtjTivOgu4JSwU750Lg2reN1mq4gDedTi82xhxftP5hKpVans/nJ+RyuRAiFraRBrzpdPrTxpi+GdnGmNOz2ex3B0OPSzu2tbXtnc/nHwjtJWnlzJkz37Fo0aJ8KPfChQu32bhxY/hCwZaS8saYqyQtzefzr6RSqXd678NM27eW1XGDcy70y822kTi3tbW9OZ/Ph3cTh34btqXe+5uampqe8t5vVfzdC78b4XcvzKoP/SX8nrIhgAACCCCAAAIIIIAAAuMmQMA7bvRcGAEEEEAAAQQQQAABBJIkMNqAN1hkMpl/8N5/X1LzQDbGmBu892G24S7hVb3FWYlrSvsOFwqm0+l/N8aE2ZGpIez/s7u72w40+zaTyXzAe+/C7N4hjv+dpLZK3mcaccAbwtEwUznMxEwPUc6/eO/n5XK50hK9m+06GqvhAt758+e/I5VKhXCzvB02W2Z4uLYsFXbu3Lmvb25ufr4YWPc2NzfvMNwM07i0YzqdvtEYszDUxXv/D7lc7tpSvdLp9GxjTHgv9eRB2jDMIA8BcOFLCalUas/29vbQ9/q2kTiHnYtfrlgalvQeor88W5wNHr7kwIYAAggggAACCCCAAAIIjKsAAe+48nNxBBBAAAEEEEAAAQQQSIpAFAFvsLDWvssY88XijNg3SFodlmP23l+ey+VuL84uPTjsm8/nz+7o6Li3ZDiSUDCcX9InJYVzbC+pVdJfJYVZrNc750LQNeg2Z86cqS0tLWFG4xxJbyuGbJ2SfhfC3y222OKHixcv7qqkXaMOeEvXTqfTHzXG/IOkA4uhdLD8QyinMeZa51xfOD5Qeau1Gi7gDddKp9NHGmMWSdozzNiWtMI5F0wL20jasmzfsLzxEZLucM6VZi4P2QRxaEdrbQhUQygbvtDwzJQpU3Yv7zvz58/framp6Szv/UclhSWYwwzaXxljrslmszdba0+W9IlQUe/9hblc7tb+lR7OucxwYugr3vv5kt5RnNEblih/wnu/1Bhz9XD9pZI+z74IIIAAAggggAACCCCAwGgECHhHo8exCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwBgKEPCOITaXQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBEYjQMA7Gj2ORQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMZQgIB3DLG5FAIIIIAAAggggMD/b8+OTQAAABCG/f+1RxSccoAIWUuAAAECBAgQIECAAAECBAgQIECAAIEiIPAWPVsCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgcBQTeI7YrAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIFAGBt+jZEiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBA4Cgg8B6xXREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKAICLxFz5YAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJHAYH3iO2KAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECRUDgLXq2BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQOAoIvEdsVwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECgCAm/RsyVAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMBRQOA9YrsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAERB4i54tAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEjgIC7xHbFQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBIrAAO+ca+nX8z1NAAAAAElFTkSuQmCC\" width=\"956\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ## Show ROC Curve \n",
    "# Calcultate points for ROC Curve\n",
    "from sklearn.metrics import roc_curve,roc_auc_score\n",
    "\n",
    "# get Area Under ROC curve metrics\n",
    "y_pred_proba = CV_rfc.predict_proba(X_test)[:,1] \n",
    "auc = roc_auc_score(y_true, y_pred)\n",
    "\n",
    "# get False Positive Rate and True Positive Rate  \n",
    "fpr, tpr, _ = roc_curve(y_true, y_pred_proba)\n",
    "\n",
    "#Plot ROC Curve\n",
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(fpr,tpr,label=\"RandomForest classifier, auc=\"+str(auc))\n",
    "plt.plot([0, 1], [0, 1], linestyle='--',color='r',label='Chance')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waiting for Dask scheduler to become ready...\n",
      "Dask scheduler is ready\n",
      "IDs ['226ihncnytzz5pi7', '9jqifjtdp38g3unt']\n"
     ]
    }
   ],
   "source": [
    "# Run a Dask cluster with three workers and return an object containing\n",
    "# a description of the cluster. \n",
    "# \n",
    "# Using helper library \n",
    "#\n",
    "# Note that the scheduler will run in the current session, and the Dask\n",
    "# dashboard will become available in the nine-dot menu at the upper\n",
    "# right corner of the CDSW app.\n",
    "\n",
    "cluster = cdsw_dask_utils.run_dask_cluster(\n",
    "  n=2, \\\n",
    "  cpu=1, \\\n",
    "  memory=2, \\\n",
    "  nvidia_gpu=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a  target=\"_blank\" rel=\"noopener noreferrer\" href=\"http://read-only-k3x1dryj90vo32l4.mlamairesse-webinar-4.vpc.cloudera.com\">http://read-only-k3x1dryj90vo32l4.mlamairesse-webinar-4.vpc.cloudera.com</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the Dask Scheduler UI\n",
    "import os \n",
    "engine_id = os.environ.get('CDSW_ENGINE_ID')\n",
    "cdsw_domain = os.environ.get('CDSW_DOMAIN')\n",
    "\n",
    "from IPython.core.display import HTML\n",
    "HTML('<a  target=\"_blank\" rel=\"noopener noreferrer\" href=\"http://read-only-{}.{}\">http://read-only-{}.{}</a>'\n",
    "     .format(engine_id,cdsw_domain,engine_id,cdsw_domain))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #### Connect a Dask client to the scheduler address in the cluster\n",
    "\n",
    "from dask.distributed import Client\n",
    "client = Client(cluster[\"scheduler_address\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 48 candidates, totalling 144 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend DaskDistributedBackend with 16 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  18 tasks      | elapsed:   10.0s\n",
      "[Parallel(n_jobs=-1)]: Done 144 out of 144 | elapsed:   58.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 12, 'n_estimators': 35}\n"
     ]
    }
   ],
   "source": [
    "# ### Fit Model with Dask\n",
    "from joblib import Parallel, parallel_backend\n",
    "with parallel_backend('dask'):\n",
    "  CV_rfc.fit(X_train, y_train)\n",
    "\n",
    "# ### Show Best Parameters \n",
    "print(CV_rfc.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate model performance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.88      0.83       170\n",
      "           1       0.84      0.75      0.79       150\n",
      "\n",
      "    accuracy                           0.82       320\n",
      "   macro avg       0.82      0.81      0.81       320\n",
      "weighted avg       0.82      0.82      0.81       320\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "# ## fit test dataset \n",
    "y_true, y_pred = y_test, CV_rfc.predict(X_test)\n",
    "# ## Print classification metrics \n",
    "print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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\" width=\"952\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ## Show ROC Curve \n",
    "# Calcultate points for ROC Curve\n",
    "from sklearn.metrics import roc_curve,roc_auc_score\n",
    "\n",
    "# get Area Under ROC curve metrics\n",
    "y_pred_proba = CV_rfc.predict_proba(X_test)[:,1] \n",
    "auc = roc_auc_score(y_true, y_pred)\n",
    "\n",
    "# get False Positive Rate and True Positive Rate  \n",
    "fpr, tpr, _ = roc_curve(y_true, y_pred_proba)\n",
    "\n",
    "#Plot ROC Curve\n",
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.plot(fpr,tpr,label=\"RandomForest classifier, auc=\"+str(auc))\n",
    "plt.plot([0, 1], [0, 1], linestyle='--',color='r',label='Chance')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}