sklearn/2. Basic Principles.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(Source: http://www.scipy-lectures.org/packages/scikit-learn/index.html#basic-principles-of-machine-learning-with-scikit-learn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimators\n",
    "\n",
    "Every algorithm is exposed in scikit-learn via an ‘’Estimator’’ object. For instance a linear regression is: `sklearn.linear_model.LinearRegression`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "model = LinearRegression(normalize=True)\n",
    "print(model.normalize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=True)\n"
     ]
    }
   ],
   "source": [
    "print(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.array([0, 1, 2])\n",
    "y = np.array([0, 1, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [1],\n",
       "       [2]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = x[:, np.newaxis] # The input data for sklearn is 2D: (samples == 3 x features == 1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/greg/code/gkiar/sklearn-tutorials/env/sklearn/lib/python3.6/site-packages/sklearn/linear_model/base.py:485: RuntimeWarning: internal gelsd driver lwork query error, required iwork dimension not returned. This is likely the result of LAPACK bug 0038, fixed in LAPACK 3.2.2 (released July 21, 2010). Falling back to 'gelss' driver.\n",
      "  linalg.lstsq(X, y)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=True)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Supervised Learning: Classifcation and Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import neighbors, datasets\n",
    "iris = datasets.load_iris()\n",
    "X, y = iris.data, iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = neighbors.KNeighborsClassifier(n_neighbors=1)\n",
    "knn.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> What kind of iris has 3cm x 5cm sepal and 4cm x 2cm petal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['virginica']\n"
     ]
    }
   ],
   "source": [
    "print(iris.target_names[knn.predict([[3, 5, 4, 2]])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
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       "        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",
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       "\n",
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       "            if (mpl.ratio != 1) {\n",
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       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
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       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
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       "    this.header = titletext[0];\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
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       "\n",
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       "\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",
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       "\n",
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       "        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",
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       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
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       "        event['data'] = 'scroll'\n",
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       "            event.step = -1;\n",
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       "        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",
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       "\n",
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       "    if (!this.waiting) {\n",
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       "\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",
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       "\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",
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       "    var cursor = msg['cursor'];\n",
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       "    {\n",
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       "        cursor = 'pointer';\n",
       "        break;\n",
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       "        cursor = 'default';\n",
       "        break;\n",
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       "        break;\n",
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       "        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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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"
    },
    {
     "data": {
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0/Dqq69q11131ZIlSwo/x/D8889rl112iT4hIxFAAAEEEHBQgA1CB5NKSAgggAAC/gms2Dxo1KiRFi5cWEA49NBDde+996p+/fr+oVQRMRuE3AYIIIAAAggggAACeRGgxo+XqRNPPFEjR44sTHLYYYdpzJgx8SZkNAIIIIAAAo4JsEHoWEIJBwEEEEDAT4EVmwdPPfWUDjrooJJsEt555516+eWXraL3799fm266qdU5l03GBmEirEyKAAIIIIAAAgggkIAANX481Pvuu09HHnlkYZJWrVrpiy++iDchoxFAAAEEEHBMgA1CxxJKOAgggAACfgqs2DyoqKjQ008/vcomofmAbF4/avPo3bu3Ro8ebXNKTZw4UV26dLE657LJ2CBMhJVJEUAAAQQQQAABBBIQoMaPhzp+/Hh17dq1MIl5o4r5fUIOBBBAAAEEEPhVgA1C7gYEEEAAAQQcEFi5eWBCMk8Sdu/evdKThLY3CdkgdODmIQQEEEAAAQQQQACBTApQ48dLi/kio/m8Yo6WLVvqyy+/jDchoxFAAAEEEHBMgA1CxxJKOAgggAACfgpU1TyoapPQ/PaG+U1C208S5kWdJwjzkinWiQACCCCAAAIIIECNH+8eOPzww5f/7qB5Q4l5UwkHAggggAACCPwqwAYhdwMCCCCAAAIOCFTXPGCTsHJy2SB04GYnBAQQQAABBBBAwBMBavxfE11eXq758+drzTXXDJT9cePGFX5ywfz8gjmuu+469e3bN9BYTkIAAQQQQMAXATYIfck0cSKAAAIIOC1QU/PABD5hwgT16NFj+etGfX2SkA1Cp/81IDgEEEAAAQQQQMApAWr8X9M5d+5cbbTRRoVNvqOPPlqbbbZZlbn+7rvvdP311+uiiy7STz/9VDhnk0020VtvvaWGDRs6dX8QDAIIIIAAAnEF2CCMK8h4BBBAAAEEMiBQW/Ogqk3CXr166Z577nH2daM33nijzD8rHosXL9Y777yz/P+1+eabq0GDBpXOOeWUU2T+4UAAAQQQQAABBBBAIE0Bavxf9c0GYfPmzZf/P1q3bq2tttpK6667rho3bqx58+Zp+vTpeu2112Rq/mVHq1at9PTTT8vU/RwIIIAAAgggUFmADULuCAQQQAABBBwQCNI8MGE++eST6tmz5/InCV3eJLzgggs0ZMiQ0NkdPHiwzFgOBBBAAAEEEEAAAQTSFKDG/1X/hx9+0FprrSXzqtGgx6GHHqphw4Zp/fXXDzqE8xBAAAEEEPBKgA1Cr9JNsAgggAACrgoEbR74tEnIBqGrdztxIYAAAggggAACfghQ41fOs9kkND+d8MILL+j111/Xhx9+qK+//rrwKtEmTZoUnjDcYosttOOOO+oPf/hD4dWiHAgggAACCCBQvQAbhNwdCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCHgkwAahR8kmVAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTYIOQeQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMAjATYIPUo2oSKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDABiH3AAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIeCbBB6FGyCRUBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABNgi5BxBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDwSIANQo+SnfdQzznnHA0dOnR5GBMnTlSXLl0ihXXBBRdoyJAhgcbGuU6gC3ASAggggAACCCCAAAKeClDje5p4wkYAAQQQQAABBBBAAIHUBdggTD0FLCCIwJQpU9SpUyeVl5ezQRgEjHMQQAABBBBAAAEEEMi4ADV+xhPE8hBAAAEEEEAAAQQQQMBpATYInU6vG8EtXbpUnTt31qRJk7Tuuutq9uzZhcDiPNm34hOEU6dOrRFqww03VJMmTdzAJAoEEEAAAQQQQAABBDIgQI2fgSSwBAQQQAABBBBAAAEEEPBagA1Cr9Ofj+CvueYanXnmmWrfvr0OOeQQXXrppVY3CCsqKvIBwSoRQAABBBBAAAEEEHBEgBrfkUQSBgIIIIAAAggggAACCORWgA3C3KbOj4XPmjVLW2yxhebNm6dnnnmm8NTgst8OtPUEIRuEftxLRIkAAggggAACCCCQDQFq/GzkgVUggAACCCCAAAIIIICA3wJsEPqd/8xH3717d40dO1bHHXecRo0apRVfDcoGYebTxwIRQAABBBBAAAEEEFhFgBqfmwIBBBBAAAEEEEAAAQQQSF+ADcL0c8AKqhG4//77dcQRR6hFixZ67733tPbaa7NByN2CAAIIIIAAAggggECOBajxc5w8lo4AAggggAACCCCAAAJOCbBB6FQ63Qlm7ty52nzzzfXll1/qlltu0YknnlgILoknCPfdd19NmTJF5prNmjVThw4d1K1bN/Xp00fNmzd3B5VIEEAAAQQQQAABBBBIUYAaP0V8Lo0AAggggAACCCCAAAIIrCTABiG3RCYFTj755MLG4C677KLnn39ederUSWyDsDoAs1loXmvas2fPSEaffvppjeMWLVqkd999Vy1bttQ666yjsrKySNdhEAIIIIAAAggggEC2BMrLy/X1118XFtWxY0etttpq2VpgSquhxk8JnssigAACCCCAAAIIxBagxo9NyAQZFGCDMINJ8X1JZkNwjz32UL169fT6669ryy23XE5i8wnCBx98UAcffLB22GEHtW7dWkuWLCm8yvSuu+7S+PHjC9c0a3j00Ue1//77h07Lsk3N0AMZgAACCCCAAAIIIOCMwKuvvqpOnTo5E0/UQKjxo8oxDgEEEEAAAQQQQCBrAtT4WcsI64kqwAZhVDnGJSKwePFibb311oUn6wYMGKChQ4dWuo6tDcJlrxOtLoibbrpJp5xySuGPzebhjBkzQn/zmw3CRG4RJkUAAQQQQAABBHIlQPNAosbP1S3LYhFAAAEEEEAAAQRqEaDG5xZxRYANQlcy6UgcyzYA27Vrp7fffltNmjRJZIMwCJf53cORI0cWTr3zzjt19NFHBxm2/JzaXjH6ySefaOeddy6c/+oll2g9fu8wlC8nI4AAAggkIzBWByUzMbMi4JHAd999oYEDdyhEPHPmTG2wwQYeRb9qqNT4Xqef4BFAAAEEHBPg84JjCSWcwALU+IGpODFHAmwQ5ihZri/VPDVonh403zD+97//rR49eqwSsq0nCINYTpo0qfD6UXOcdNJJuvnmm4MMC3yO2UBs27Zt4fxPbrhBbdZaK/BYTkQAAQQQQCApgX/p8KSmZl4EvBH45ptPdeqpv9R5n3yiNm3aeBP7yoFS41Pje3vzEzgCCCDgqACfFxxNLGHVKkCNXysRJ+RQgA3CHCbN1SX36dOnsAm30UYb6eKLL64yzDFjxuiBBx4o/NmgQYPUoUOHwv/evXv3VZ42jOs0f/58rb766oVpDjjgAD322GNxp6w0ng1Cq5xMhgACCCBgSYAP/JYgmcZrAZoHv6afGp8NQq//MiB4BBBAwEEBPi84mFRCCiRAjR+IiZNyJsAGYc4S5vJye/furdGjR0cKMYlXNy1YsGD5piMbhJHSwiAEEEAAgRwK8IE/h0ljyZkToHnwa0qo8dkgzNy/oCwIAQQQQCCWAJ8XYvExOMcC1Pg5Th5Lr1aADUJujswIZK15MHnyZHXq1KngY36P8JZbbrFqxROEVjmZDAEEEEDAkgAf+C1BMo3XAjQPsrtBSI3v9b+aBI8AAgggYEGAzwsWEJkilwLU+LlMG4uuRYANQm6RXAmU8jcIze8O3nrrrQWfO+64Q8ccc4xVKzYIrXIyGQIIIICAJQE+8FuCZBqvBWgehEs/NX44L85GAAEEEEAgTQE+L6Spz7XTFKDGT1OfayclwAZhUrLMm4hAkObBqFGjdPzxxxeuP3jwYJkxKx5Tp05Vo0aNtMkmm1S7RvNbiOb3UszRqlUrTZ8+3fpvHLJBmMgtwqQIIIAAAgEF+GAfEIrTEIggQPMgHBo1fjgvzkYAAQQQQKAUAnxeKIUy18iTADV+nrLFWoMKsEEYVIrzMiFgo3lgNhDNK0P33HNP7b///urYsaPWWmstlZeX691339Vdd92l8ePHF+KtV6+eHnroIXXv3t16/GwQWidlQgQQQACBEAJ84A+BxakIhBSgeRAOjBo/nBdnI4AAAgggUAoBPi+UQplr5EmAGj9P2WKtQQXYIAwqxXmZELDVPFj2hGFNQZlNw5EjR6pnz56JxM4GYSKsTIoAAgggEFCAD/wBoTgNgQgCNA/CoVHjh/PibAQQQAABBEohwOeFUihzjTwJUOPnKVusNagAG4RBpTgvEwI2mgezZ8/W2LFj9dJLL+n111/XV199pW+++UYVFRVq0aKFtt56a3Xr1k29e/dW06ZNE4ubDcLEaJkYAQQQQCCAAB/4AyBxCgIRBWgehIOjxg/nxdkIIIAAAgiUQoDPC6VQ5hp5EqDGz1O2WGtQATYIg0pxHgKWBdggtAzKdAgggAACoQT4wB+Ki5MRCCVA8yAUl1MnU+M7lU6CQQABBLwW4POC1+kn+CoEqPG5LVwUYIPQxawSUy4EaB7kIk0sEgEEEHBWgA/8zqaWwDIgQPMgA0lIaQnU+CnBc1kEEEAAAesCfF6wTsqEORegxs95All+lQJsEHJjIJCSAM2DlOC5LAKlFqiokJYulerVK/WVuR4CNQrwgZ8bBIHkBGgeJGeb9Zmp8bOeIdaHgCUBanxLkEyTZQE+L2Q5O6wtDQFq/DTUuWbSAmwQJi3M/AhUI0DzgFsDAYcFPv9cGj9eeuUVae5c6eefpYYNpbZtpb33lnbZRVptNYcBCC0PAnzgz0OWWGNeBWge5DVz8ddNjR/fkBkQyKzARx8Va/zJk6Xvv5fMJqGp6TfcUNp3X2nHHaX69TO7fBaGQFgBPi+EFeN81wWo8V3PsJ/xsUHoZ96JOgMCNA8ykASWgIBtgVmzpNGjpalTa565cWNpn32kI46giWA7B8wXWIAP/IGpOBGB0AI0D0KTOTOAGt+ZVBIIAr8KvP++dMcd0nvv1azStKm0//7SIYdIdesiiEDuBfi8kPsUEoBlAWp8y6BMlwkBNggzkQYW4aMAzQMfs07MTgu8+aZ0xRXSokXBw+zQQRowQGrSJPgYzkTAkgAf+C1BMg0CVQjQPPD3tqDG9zf3RO6owIsvSsOHS+XlwQPcfnvpjDOkBg2Cj+FMBDIowOeFDCaFJaUqQI2fKj8XT0iADcKEYJkWgdoEaB7UJsSfI5AjAfOt4iFDpCVLwi/abBKefz5PEoaXY0RMAT7wxwRkOAI1CNA88Pf2oMb3N/dE7qDAa69JQ4cWf0887LHDDtJf/sKThGHdOD9TAnxeyFQ6WEwGBKjxM5AElmBdgA1C66RMiEAwAZoHwZw4C4HMCyxeLPXrJ333XfSl9uwpHX109PGMRCCCAB/4I6AxBIGAAjQPAkI5eBo1voNJJSQ/BX74oVjjL1wYPf7jjpMOPDD6eEYikLIAnxdSTgCXz5wANX7mUsKCLAiwQWgBkSkQiCJA8yCKGmMQyKDAM89II0bEW5h5xeiNN0oNG8abh9EIhBDgA38ILE5FIKQAzYOQYA6dTo3vUDIJxW+Bhx+W7r47nsE660jXXcdThPEUGZ2iAJ8XUsTn0pkUoMbPZFpYVEwBNghjAjIcgagCNA+iyjEOgYwJnHeeNGNG/EWdcoq0117x52EGBAIK8IE/IBSnIRBBgOZBBDRHhlDjO5JIwvBbwLxS1Dw9+PXX8R3OPVfadtv48zADAikI8HkhBXQumWkBavxMp4fFRRRggzAiHMMQiCtA8yCuIOMRyIDARx9Jf/2rnYVsuql08cV25mIWBAII8IE/ABKnIBBRgOZBRDgHhlHjO5BEQkDgjTfs1eXbb2/v8wKZQaDEAnxeKDE4l8u8ACEYLOoAACAASURBVDV+5lPEAiMIsEEYAY0hCNgQoHlgQ5E5EEhZ4L//lYYNs7OIRo2k0aPtzMUsCAQQ4AN/ACROQSCiAM2DiHAODKPGdyCJhIDA2LHS7bfbcVhvPenaa+3MxSwIlFiAzwslBudymRegxs98ilhgBAE2CCOgMQQBGwI0D2woMgcCKQuMHy/dequ9Rdx7L79RYk+TmWoR4AM/twgCyQnQPEjONuszU+NnPUOsD4EAAvffL40ZE+DEAKc0bWr380KAS3IKArYE+LxgS5J5XBGgxnclk8SxogAbhNwPCKQkQPMgJXgui4BNgWeekUaMsDNjWZl0111SnTp25mMWBGoR4AM/twgCyQnQPEjONuszU+NnPUOsD4EAAg8/LN19d4ATA5yy9tr2Pi8EuBynIGBTgM8LNjWZywUBanwXskgMKwuwQcg9gUBKAjQPUoLnsgjYFJgyRbrkEjsz0jyw48gsgQX4wB+YihMRCC1A8yA0mTMDqPGdSSWB+Cxg80uAm2xi7/OCzzkh9kQF+FyQKC+TOyRAje9QMglluQAbhNwMCKQkQPMgJXgui4BNgcWLpVNPlX78Mf6s3btLxx4bfx5mQCCgAI2AgFCchkAEAZoHEdAcGUKN70giCcNvAVPbn3KKtGRJfIdjjpF69Ig/DzMgkKAAnwsSxGVqpwSo8Z1KJ8H8IsAGIbcCAikJ0DxICZ7LImBb4M47pUceiT/rsGFSq1bx52EGBAIK0AgICMVpCEQQoHkQAc2RIdT4jiSSMBC4/nrp2WfjOdSvL914o7TGGvHmYTQCCQvwuSBhYKZ3RoAa35lUEsgKAmwQcjsgkJIAzYOU4LksArYFvvpK+vOfpYqK6DNvs400cGD08YxEIIIAjYAIaAxBIKAAzYOAUA6eRo3vYFIJyU+B6dPj1+ddukinneanH1HnSoDPBblKF4tNUYAaP0V8Lp2YABuEidEyMQI1C9A84A5BwCGB++6THnggWkCNGkkXXyy1aRNtPKMQiChAIyAiHMMQCCBA8yAAkqOnUOM7mljC8lPg5pulCROixd6sWfG3B83vjHMgkHEBPhdkPEEsLzMC1PiZSQULsSjABqFFTKZCIIwAzYMwWpyLQMYFzNOD5vVBEyeGW2jDhtI550hbbhluHGcjYEGARoAFRKZAoBoBmgf+3hrU+P7mnsgdFCgvl666Spo8OVxwTZpIf/+7tOGG4cZxNgIpCfC5ICV4Lps7AWr83KWMBQcQYIMwABKnIJCEAM2DJFSZE4EUBcwm4b/+VXySMMjrRtdaSzr7bGnjjVNcNJf2WYBGgM/ZJ/akBWgeJC2c3fmp8bObG1aGQCSBn3+WRo+WHn882PD11it+AbB162DncxYCGRDgc0EGksASciFAjZ+LNLHIkAJsEIYE43QEbAnQPLAlyTwIZExg9uziq4ieekr68cdVF/fb30pdu0qdO0v162ds8SzHNQE+7LuWUeLJiwDNg7xkyv46qfHtmzIjApkQ+Owz6cknpWeekRYsWHVJHTtK++0nbbedVFaWiSWzCASCCvCZIagU5/kuQI3v+x3gZvxsELqZV6LKgQDNgxwkiSUiEEdgyRLpnXekuXOlxYulxo2LvzPYrl2cWRmLQCgBPuyH4uJkBKwJ0DywRpm7iajxc5cyFoxAOIFFi4o1/vffS+YVpOZ1ohtsIJknBzkQyKkAnxlymjiWXXIBavySk3PBEgiwQVgCZC6BQFUCNA+4LxBAAAEEkhbgw37SwsyPQNUCNA/8vTOo8f3NPZEjgAACeRXgM0NeM8e6Sy1AjV9qca5XCgE2CEuhzDUQqEKA5gG3BQIIIIBA0gJ82E9amPkRYIOQe6CyADU+dwQCCCCAQN4E+MyQt4yx3rQE2CBMS57rJinABmGSusyNQA0CNA+4PRBAAAEEkhbgw37SwsyPABuE3ANsEHIPIIAAAgjkW4DPDPnOH6svnQAbhKWz5kqlE2CDsHTWXAmBSgJsEHJDIIAAAggkLcCH/aSFmR8BNgi5B9gg5B5AAAEEEMi3AJ8Z8p0/Vl86ATYIS2fNlUonwAZh6ay5EgJsEHIPIIAAAgiUVIAP+yXl5mIILBegeeDvzcCXAP3NPZEjgAACWRCg/s9CFliDqwLU+K5m1u+42CD0O/9En6IAzYMU8bk0Aggg4IkADQJPEk2YmROgeZC5lJRsQdT4JaPmQggggAACVQhQ/3NbIJCcADV+crbMnJ4AG4Tp2XNlzwVoHnh+AxA+AgggUAIBGgQlQOYSCFQhQPPA39uCGt/f3BM5AgggkAUB6v8sZIE1uCpAje9qZv2Oiw1Cv/NP9CkK0DxIEZ9LI4AAAp4I0CDwJNGEmTkBmgeZS0nJFkSNXzJqLoQAAgggUIUA9T+3BQLJCVDjJ2fLzOkJsEGYnj1X9lyA5oHnNwDhI4AAAiUQoEFQAmQugUAVAjQP/L0tqPH9zT2RI4AAAlkQoP7PQhZYg6sC1PiuZtbvuNgg9Dv/RJ+iAM2DFPG5NAIIIOCJAA0CTxJNmJkToHmQuZSUbEHU+CWj5kIIIIAAAlUIUP9zWyCQnAA1fnK2zJyeABuE6dlzZc8FaB54fgMQPgIIIFACARoEJUDmEghUIUDzwN/bghrf39wTOQIIIFAqAWr8UklzHQQqC1Djc0e4KMAGoYtZJaZcCNA8yEWaWCQCCCCQawGaB7lOH4vPsQDNgxwnL+bSqfFjAjIcAQQQQKBWAWr8Wok4AYFEBKjxE2Fl0pQF2CBMOQFc3l8Bmgf+5p7IEUAAgVIJ0DwolTTXQaCyAM0Df+8Ianx/c0/kCCCAQKkEqPFLJc11EKDG5x5wX4ANQvdzTIQZFaB5kNHEsCwEEEDAIQGaBw4lk1ByJcAGYa7SZXWx1PhWOZkMAQQQQKAKAWp8bgsE0hGgxk/HnasmK8AGYbK+zI5AtQI0D7g5EEAAAQSSFqB5kLQw8yNQtQDNA3/vDGp8f3NP5AgggECpBKjxSyXNdRCoLECNzx3hogAbhC5mlZhyIUDzIBdpYpEIIIBArgVoHuQ6fSw+xwI0D3KcvJhLp8aPCchwBBBAAIFaBajxayXiBAQSEaDGT4SVSVMWYIMw5QRweX8FaB74m3siRwABBEolQPOgVNJcB4HKAjQP/L0jqPH9zT2RI4AAArYFqOVtizIfAvEEqPHj+TE6mwJsEGYzL6zKAwGaBx4kmRARQACBlAVoKqScAC7vrQDNA29TL2p8f3NP5AgggIBtAWp526LMh0A8AWr8eH6MzqYAG4TZzAur8kCA5oEHSSZEBBBAIGUBmgopJ4DLeytA88Db1LNB6G/qiRwBBBCwLkAtb52UCRGIJUCNH4uPwRkVYIMwo4lhWe4LsEHofo6JEAEEEEhbgKZC2hng+r4K0DzwNfNig9Df1BM5AgggYF2AWt46KRMiEEuAGj8WH4MzKsAGYUYTw7LcF2CD0P0cEyECCCCQtgBNhbQzwPV9FaB54Gvm2SD0N/NEjgACCNgXoJa3b8qMCMQRoMaPo8fYrAqwQZjVzLAu5wXYIHQ+xQSIAAIIpC5AUyH1FLAATwVoHniaeLFB6G/miRwBBBCwL0Atb9+UGRGII0CNH0ePsVkVYIMwq5lhXc4LsEHofIoJEAEEEEhdgKZC6ilgAZ4K0DzwNPFsEPqbeCJHAAEEYghQs8fAYygCJRSgxi8hNpcqmQAbhCWj5kIIVBZgg5A7AgEEEEAgaQGaDUkLMz8CVQvQPPD3zqDG9zf3RI4AAghEFaBmjyrHOARKK0CNX1pvrlYaATYIS+PMVRBYRYDmATcFAggggEDSAjQbkhZmfgTYIOQeqCxAjc8dgQACCCAQVoCaPawY5yOQjgAbhOm4c9VkBdggTNaX2RGoVoDmATcHAggggEDSAjQbkhZmfgTYIOQeYIOQewABBBBAIJ4ANXs8P0YjUCoBNghLJc11SinABmEptbkWAisIsEHI7YAAAgggkLQAzYakhZkfATYIuQfYIOQeQAABBBCIJ0DNHs+P0QiUSoANwlJJc51SCrBBWEptroUAG4TcAwgkL1BeLk2eLL3/vjRvnlSnjrTGGtJWW0lbbinVrZv8GrgCAhkRoNmQkUSwDO8EaB54l/LlAfMlQH9zT+QJCyxeLL3yijRjhjR/vlSvntS0qbTtttJmmxVrfg4EcipAzZ7TxLFs7wSo8b1LuRcBs0HoRZoJMosCNA+ymBXWlGuBuXOl8eOlp56Svvuu6lDWW0/abz9pr72kRo1yHS6LRyCIAM2GIEqcg4B9AZoH9k3zMiM1fl4yxTpzIzBnjvT449LEidKPP1a97N/8RuraVdp9d6lBg9yExkIRWCZAzc69gEA+BKjx85EnVhlOgA3CcF6cjYA1AZoH1iiZCAHp3XeloUOLTwwGOVq2lAYOlMyGIQcCDgjQVHAgiYTglADNA6fSGSoYavxQXJyMQM0Cr70mXX219NNPwaTMRuF550ktWgQ7n7MQyIgAtXxGEsEyEKhFgBqfW8RFATYIXcwqMeVCgOZBLtLEIvMg8Pbb0sUXS0uWhFutee3oRRexSRhOjbMzKkBTIaOJYVneCtA88Db1osb3N/dEblng1VelK6+UKirCTbzOOsUav3nzcOM4G4EUBajlU8Tn0giEEKDGD4HFqbkRYIMwN6lioa4J0DxwLaPEk4rA7NnSOecUf4ckymGeJDRPHvK60Sh6jMmQAE2FDCWDpSAgieaBv7cBNb6/uSdyiwIffSSdf374LwAuW8KGGxa/QFhWZnFRTIVAcgLU8snZMjMCNgWo8W1qMldWBNggzEomWId3AjQPvEs5ASchcMst0pNPxpv5uOOkAw+MNwejEUhZgKZCygng8gisJEDzwN9bghrf39wTuUWBK66QzBOEcY4//1naddc4MzAWgZIJUMuXjJoLIRBLgBo/Fh+DMyrABmFGE8Oy3BegeeB+jokwYYEFC6Q+fYL/Jkl1yzG/Q2h+26Ru3YQXzPQIJCdAUyE5W2ZGIIoAzYMoam6MocZ3I49EkaLAN99Ip50W/tWiKy+5fXvpwgtTDIRLIxBcgFo+uBVnIpCmADV+mvpcOykBNgiTkmVeBGoRoHnALYJATIHHH5duuy3mJL8M/9vfpK22sjMXsyCQggBNhRTQuSQCNQjQPPD39qDG9zf3RG5J4L77pAcesDOZeRKxXTs7czELAgkKUMsniMvUCFgUoMa3iMlUmRFggzAzqWAhvgnQPPAt48RrXcD8duDkyXam7dFDOuYYO3MxCwIpCNBUSAGdSyLABiH3QBUC1PjcFgjEFDC/PfjBBzEn+WX4scdK3bvbmYtZEIgpQL0eE5DhCGRAgA3CDCSBJVgXYIPQOikTIhBMgOZBMCfOQqBagUGDpPfeswO0557SqafamYtZEEhBgIZDCuhcEoEaBGge+Ht7UOP7m3sityRgfjvwyy/tTHbwwdIf/mBnLmZBIKYA9XpMQIYjkAEBavwMJIElWBdgg9A6KRMiEEyA5kEwJ85CoFoB81rQ99+3A9SlS/G3TjgQyKkADYecJo5lOytA88DZ1NYaGDV+rUScgEDNAv36SV99ZUepZ0/p6KPtzMUsCMQUoF6PCchwBDIgQI2fgSSwBOsCbBBaJ2VCBIIJ0DwI5sRZCFQrcNll0muv2QE68EDpuOPszMUsCKQgQMMhBXQuiUANAjQP/L09qPH9zT2RWxI47zxpxgw7k5mnB81ThBwIZECAej0DSWAJCMQUoMaPCcjwTAqwQZjJtLAoHwRoHviQZWJMVODBB6V777Vzib/8Rerc2c5czIJACgI0HFJA55IIsEHIPVCFADU+twUCMQX++U/pP/+JOckvwy+8UGrf3s5czIJATAHq9ZiADEcgAwJsEGYgCSzBugAbhNZJmRCBYAI0D4I5cRYC1QrMnVv83cCff46H1Ly5dP31UllZvHkYjUCKAjQcUsTn0ghUIUDzwN/bghrf39wTuSWBTz+VzJf34h5t20pXXCHVqRN3JsYjYEWAet0KI5MgkKoANX6q/Fw8IQE2CBOCZVoEahOgeVCbEH+OQACBa66RXnwxwIk1nNKrl/T738ebg9EIpCxAwyHlBHB5BFYSoHng7y1Bje9v7oncosCQIdK0afEmPPFEab/94s3BaAQiCFCXR0BjCAI5EaDGz0miWGYoATYIQ3FxMgL2BGge2LNkJo8FZs6UBg6M/hTh6qtLV10lNWvmMSKhuyBAI8KFLBKDSwI0D1zKZrhYqPHDeXE2AlUKTJ0qXXSRVFERDWjttYs1/mqrRRvPKARiCFCXx8BjKAIZF6DGz3iCWF4kATYII7ExCIH4AjQP4hsyAwIFgeefl667LjxG/frSoEH8Lkl4OUZkUIBGRAaTwpK8FqB54G/6qfH9zT2RWxZ47DFp9OjwkzZuLJnfHmzXLvxYRiBgQYC63AIiUyCQUQFq/IwmhmXFEmCDMBYfgxGILkDzILodIxFYRcBsEo4YEfxJwiZNpAEDpA4dwETACQEaEU6kkSAcEqB54FAyQ4ZCjR8SjNMRqElg3LjiJmHQJwnNb4ufe6604Ya4IpCaAHV5avRcGIHEBajxEyfmAikIsEGYAjqXRMAI0DzgPkDAssCHH0qPPCK98kr1G4UNG0q77y716CG1bGl5AUyHQHoCNCLSs+fKCFQlQPPA3/uCGt/f3BN5QgLvvlus8f/3v+o3Cs2X//bcU+reXTKbhBwIpChAXZ4iPpdGIGEBavyEgZk+FQE2CFNh56IIsEHIPYBAYgJz50oTJ0qmmTB/vlSnjrTGGtJWWxU3B81rhzgQcEyARoRjCSWc3AvQPMh9CiMHwAZhZDoGIlCzwJw50lNPSTNmFGv8srJijb/tttIuu0jmi4AcCGRAgLo8A0lgCQgkJECNnxAs06YqwAZhqvxc3GcBmgc+Z5/YEUAAgWgCNByiuTEKgVIL0DwotXh2rkeNn51csBIEEEAgrgC1d1xBxiPglgA1vlv5JJqiABuE3AkIpCRA8yAleC6LAAII5FiAJkWOk8fSvRKgeeBVuisFS43vb+6JHAEE3BOg9nYvp0SEQBwBavw4eozNqgAbhFnNDOtyXoDmgfMpJkAEEEDAugBNCuukTIhAIgI0DxJhzcWk1Pi5SBOLRAABBAIJUHsHYuIkBLwRoMb3JtVeBcoGoVfpJtgsCdA8yFI2WAsCCCCQDwGaFPnIE6tEgOaBv/cANb6/uSdyBBBwT4Da272cEhECcQSo8ePoMTarAmwQZjUzrMt5AZoHzqc4XwFWVEhvvy09+aQ0fbo0f75Ut67UtKn0u99J++4rrbdevmJitQg4KECTwsGkEpKTAjQPnExroKCo8QMxcVKpBJYuld54o1jjf/xxscavX79Y43fqJO2zj7T22qVaDddBIHcC1N65SxkLRiBRAWr8RHmZPCUBNghTgueyCNA84B7IjMBzz0kPPyx9+mnNS9p6a+nII6WNN87M0lkIAr4J0KTwLePEm1cBmgd5zVz8dVPjxzdkBgsC5st/48dLY8dKX31V/YR16kjbby8ddZTUpo2FCzMFAm4JUHu7lU+iQSCuADV+XEHGZ1GADcIsZoU1eSFA88CLNGc7SPON4ttvl8aNC75O843jfv2kzp2Dj+FMBBCwJkCTwholEyGQqADNg0R5Mz05NX6m0+PH4srLpRtvlMyXAIMejRpJZ58tdewYdATnIeCFALW3F2kmSAQCC1DjB6bixBwJsEGYo2SxVLcEaB64lc9cRmM2B823isMe5pvG55wjbbtt2JGcjwACAQRoRARA4hQEMi5A8yDjCUpwedT4CeIyde0C5snB4cOl55+v/dyVzzBfBBw8WPrtb8OPZQQCORag9s5x8lg6AiUWoMYvMTiXK4kAG4QlYeYiCKwqQPOAuyJVgVdeka68MvoSGjaUrr1WatEi+hyMRACBKgVoUnBjIJB/AZoH+c9h1Aio8aPKMc6KwBNPSCNHRp9qzTWLNX7jxtHnYCQCOROg9s5ZwlguAikKUOOniM+lExNggzAxWiZGoGYBmgfcIakKnH++9MEH8ZbQq5f0+9/Hm4PRCCCwigBNCm4KBPIvQPMg/zmMGgE1flQ5xsUWMD8f0L9/zb85GOQiJ5wgde0a5EzOQcAJAWpvJ9JIEAiURIAavyTMXKTEAmwQlhicyyGwTIDmAfdCagIffiide278yzdrJo0YIZWVxZ+LGRBAYLkATQpuBgTyL0DzIP85jBoBNX5UOcbFFpgyRbrkktjTqG1b6YorJPOzAhwIeCBA7e1BkgkRAUsC1PiWIJkmUwJsEGYqHSzGJwGaBz5lO2Ox3nKL9OSTdhZ11lnSjjvamYtZEECgIECTghsBgfwL0DzIfw6jRkCNH1WOcbEFLr9cmjQp9jSFCS68UGrf3s5czIJAxgWovTOeIJaHQIYEqPEzlAyWYk2ADUJrlEyEQDgBmgfhvDjbooCN14suW85hh0lHHGFxcUyFAAI0KbgHEMi/AM2D/OcwagTU+FHlGBdb4PTTpa+/jj1NYYI//Unq1s3OXMyCQMYFqL0zniCWh0CGBKjxM5QMlmJNgA1Ca5RMhEA4AZoH4bw426LAmWdKn31mZ0LTODANBA4EELAmQJPCGiUTIZCaAM2D1OhTvzA1fuop8HcBxx8vzZ9vJ/4jj5QOPdTOXMyCQEYEqLEzkgiWgUCOBajxc5w8ll6tABuE3BwIpCRA8yAleC4rDRggffyxHYnu3aVjj7UzF7MggEBBgOYFNwIC+RegeZD/HEaNgBo/qhzjYgucdJL0/fexpylMYOp7U+dzIOCQADW2Q8kkFARSEqDGTwmeyyYqwAZhorxMjkD1AjQPuDtSE7joIunNN+1c/phjpB497MzFLAggUBCgecGNgED+BWge5D+HUSOgxo8qx7jYAja/BNi3r7T77rGXxAQIZEmAGjtL2WAtCORTgBo/n3lj1TULsEHIHYJASgI0D1KC57LSE09II0fakbjmGql1aztzMQsCCLBByD2AgCMCNA8cSWSEMKjxI6AxxI7A/fdLY8bEn6usTLrxRqlp0/hzMQMCGRJggzBDyWApCORUgBo/p4lj2TUKsEHIDYJASgI0D1KC57LSggXSKadIixbF09hqK+lvf4s3B6MRQGAVAZoX3BQI5F+A5kH+cxg1Amr8qHKMiy3wzTfS6adLS5fGm2q33aR+/eLNwWgEMihAjZ3BpLAkBHImQI2fs4Sx3EACbBAGYuIkBOwL0Dywb8qMIQRuvVUaPz7EgCpOPftsaYcd4s3BaAQQWEWA5gU3BQL5F6B5kP8cRo2AGj+qHOOsCFxxhfTqq/Gm+r//kzbbLN4cjEYggwLU2BlMCktCIGcC1Pg5SxjLDSTABmEgJk5CwL4AzQP7pswYQuDbb6XzzpO++y7EoBVONU8PDhwo1a0bbTyjEECA3xrkHkDAYQGaBw4nt5bQqPH9zX0mIv/ss2KNvnBhtOXsumvx6cE6daKNZxQCGRZggzDDyWFpCOREgBo/J4limaEE2CAMxcXJCNgToHlgz5KZIgrMnCldeKE0f364CTbcUBo8WGrcONw4zkYAgUoCNCm4IRBwV4Dmgbu5rS0yavzahPjzxAXeeku67DJp8eJwl9pyy+IXCOvXDzeOsxHIiQC1d04SxTIRyLAANX6Gk8PSIguwQRiZjoEIxBOgeRDPj9GWBD79tNhAmD072IS/+510xhlSo0bBzucsBBCoVoAmBTcHAu4K0DxwN7e1RUaNX5sQf14SgQ8+kC6/XJo7N9jlzJODp57K5mAwLc7KqQC1d04Tx7IRyJAANX6GksFSrAmwQWiNkokQCCdA8yCcF2cnKGC+XfzSS9ITT0jTp696IfMa0U6dpK5dpS224JVDCaaCqf0SoEnhV76J1i8Bmgd+5XvFaKnx/c195iI3rxl9/vlijf/JJ6sur6xM2nlnab/9pN/+NnPLZ0EI2Bag9rYtynwI+CdAje9fzn2ImA1CH7JMjJkUoHmQybSwKPPaUbNJaF47ajYGmzaVzO8NtmiBDQIIWBagSWEZlOkQyJAAzYMMJaPES6HGLzE4l6tdoKJCMk8UfvRRscY3rxA1Nf422xT/JwcCnghQe3uSaMJEIEEBavwEcZk6NQE2CFOj58K+C9A88P0OIH4EEPBdgCaF73cA8bssQPPA5ezWHBs1vr+5J3IEEMi2ALV3tvPD6hDIgwA1fh6yxBrDCrBBGFaM8xGwJEDzwBIk0yCAAAIZFqARkeHksDQEEhSgeZAgbsanpsbPeIJYHgIIOC9A/e18igkQgdQEqPFTo+fCCQqwQZggLlMjUJMAzQPuDwQQQMB9ARoU7ueYCBGoSoDmgb/3BTW+v7kncgQQyIYA9Xc28sAqEHBRgBrfxawSExuE3AO5ETjnnHM0dOjQ5eudOHGiunTpEnv999xzj/75z3/qzTff1Ny5c9WyZUvttttuOv3007XTTjvFnr+6CWgeJEbLxAi4I1BeLn35pTRvXjGm1VeX1ltPqlfPnRgdj4QGheMJJjwEqhGgeRD81qDGD27FmQgg4IjAkiXSF1/8+rvva6whtWpV/A14jtgC1N+xCZkAAQSo8bkHPBJgg9CjZOc51ClTpqhTp04qN83yX464G4QLFy5Ur169NG7cuCpp6tatq7///e8aPHhwInRsECbCyqQIuCHwzTfShAnSU09Jc+dWjql5c2nffaW99pJatHAjXoejoEHhcHIJDYEaBNggDHZ7UOMHc+IsBBBwRMB88e/JJ6WJE3/9AuCy0Fq2LNb45kvQTZs6EnA6YVB/p+POVRHwQYAa34cs+xcjG4T+5Tx3ES9dulSdO3fWpEmTtO6662r27NmFGOJuEB511FG69957C3Ptueee6t+/v1q3bq2pU6fqkksu0YwZMwp/dtNNN+nkk0+27sYGoXVSJkQg/wLmSxCjRhU3B5curTke8xThfvtJ6s1IIgAAIABJREFUxx4rlZXlP3ZHI6BB4WhiCQuBWgRoHtR+i1Dj127EGQgg4IjAokWmsSC98ELtAdWvL/XsKfXqxROFtWtVeQb1d0Q4hiGAQK0C1Pi1EnFCDgXYIMxh0nxb8jXXXKMzzzxT7du31yGHHKJLL720QBBng/Dpp5/W3nvvXZine/fueuihh1RvhVf2zZkzR9ttt51mzZqlZs2a6cMPP1Rz89SOxYMNQouYTIWACwI//SSZ1yhPnRoumm22kc4+W2rQINw4zi6JAA2KkjBzEQQyJ0DzoPaUUOPXbsQZCCDggID5qYCLLpI+/DBcMLvuKvXtyyZhOLXC2dTfEdAYggACgQSo8QMxcVLOBNggzFnCfFuu2aDbYostNG/ePD3zzDOFTcEhQ4YUGOJsEB5wwAH6z3/+o7KyMs2cOVNt2rRZhdY8XWieMjSH+e3DAQMGWOVng9AqJ5MhkG8B87TgFVdIkydHi8P8Xmr//jQQoulZGUUjwgojkyDgjADNg5pTSY3vzK1OIAggUJOA+a1Bszn4zjvRnLp1k/70p2hjHR9F7e14ggkPgYwKUONnNDEsK5YAG4Sx+BictIB5um/s2LE67rjjNGrUKF1wwQWxNwh//PFHrb322lq8eLG6detW2Cis6jB/vs466+iHH37QTjvtpBdffNFquGwQWuVkMgTyLfDcc9Lw4fFiOPNMyWwUcqQiQJMiFXYuikBmBWge1JwaavzM3rosDAEEbAo88oh0553xZrzgAqlDh3hzODia2tvBpBISAjkQoMbPQZJYYmgBNghDkzGgVAL333+/jjjiCLVo0ULvvfdeYVPPxgbhiq8XNa8rPffcc6sNqWvXrho/fnzhScMFCxaovvk9AEsHG4SWIJkGARcEBg6Upk+PF4lpHJgGAkcqAjQpUmHnoghkVoDmQfWpocbP7G3LwhBAwKaAeUPIn/8szZ4db1bzBUDzRUCOSgLU3twQCCCQhgA1fhrqXDNpATYIkxZm/kgCc+fO1eabb64vv/xSt9xyi0488cTCPDY2CIcPH65+/foV5jO/PXjwwQdXu8b+/ftr2LBhhT+fNm2aOlj85h4bhJFuDQYh4J6A+T2SGr6oECrgK6+U2rYNNYST7QjQpLDjyCwIuCJA86DqTFLju3KHEwcCCNQq8Npr0mWX1XparSfUqyeNGCE1b17rqT6dQO3tU7aJFYHsCFDjZycXrMSeABuE9iyZyaLAySefXNgY3GWXXfT888+rTp06hdltbBCaJwb/8Y9/FOabNGmStt9++2pXfsUVVyz/7cHHH39c5onCoIfZAKzp+OKLL7TDDjsUTvnkhhvUZq21gk7NeQgg4JLAqFHSuHF2IurZUzr6aDtzMUsoAZoUobg4GQHnBWgeVJ1ianznb30CRACBZQLXXCPZ+pmSP/5ROuggbFcQoPbmdkAAgTQEqPHTUOeaSQuwQZi0MPOHFjAbgnvssYfq1aun119/XVtuueXyOWxsEJ5++ukaYb6BJ/Nb4e+offv21a7xhhtu0GmnnVb48zFjxuiwww4LHM+yTc0gA9ggDKLEOQg4KnDFFdKrr9oJbtddi68y4ii5AE2KkpNzQQQyLUDzYNX0UONn+pZlcQggYFtg0CDpvffszHrggdJxx9mZy5FZqL0dSSRhIJAzAWr8nCWM5QYSYIMwEBMnlUpg8eLF2nrrrfXuu+8WntwbOnRopUvb2CA84YQTdNtttxXmnTFjhjbaaKNqwzPnmfPNcccdd+iYY44JTMEGYWAqTkTAb4GLLpLefNOOgXki+q9/tTMXs4QSoEkRiouTEXBegOZB5RRT4zt/yxMgAgisLDBggPTxx3Zc9t5b6tPHzlyOzELt7UgiCQOBnAlQ4+csYSw3kAAbhIGYOKlUAss2ANu1a6e3335bTZo0qXRpGxuEpXqCkFeMluqu4ToI5FyAJwhzlUCaEblKF4tFIDUBmgeV6anxU7sVuTACCKQlYPMJQvN6UfOaUY7lAtTk3AwIIJCGADV+GupcM2kBNgiTFmb+wALmqUHz9KD5hvG///1v9ejRY5WxNjYIS/UbhLUFbjYQ27ZtWziNV4zWpsWfI+CwwJ13So88YifAQw+VjjzSzlzMUqUAzQhuDAQQCCJA8+BXJWp8fmc8yL8znIOAcwLDh0vPPWcnrD/9SerWzc5cjsxCTe5IIgkDgZwJUOPnLGEsN5AAG4SBmDipFAJ9+vTRzTffXHjl58UXX1zlJc3vAD7wwAOFPxs0aJA6dOhQ+N+7d+++ytOG1a15+PDh6tevX+GPH3roIR188MHVhte/f38NGzas8OfTpk1bfj0bHmwQ2lBkDgQcEPjkE+mss+wEYv6+atXKzlzMUqUAzQhuDAQQCCJA8+BXJWp8NgiD/DvDOQg4J/DWW9KFF8YPq3596YYbpKZN48/l0AzU5A4lk1AQyJEANX6OksVSAwuwQRiYihOTFujdu7dGjx4d6TIzZ87UBhtsEGjs008/rb3NO/wlXXrppTJPFFZ3dO3aVePHj1dZWZkWLFig+qY4t3SwQWgJkmkQcEFg8GDpnXfiRbL11tL558ebg9G1CtCMqJWIExBAQBLNg19vA2p8Ngj5SwEBLwUqKqQzz5Q+/zxe+LvvLvXtG28OB0dTkzuYVEJCIAcC1Pg5SBJLDC3ABmFoMgYkJVCq5sGPP/6otddeu/Aq027duuk///lPlSGZP19nnXX0ww8/aKeddtKLL75oNXQ2CK1yMhkC+RZ4+WXpqqvixWC+7LDttvHmYHStAjQjaiXiBAQQYIOw0j1Ajc8GIX8pIOCtwOOPS7fdFi/8Sy6RNtkk3hwOjqYmdzCphIRADgTYIMxBklhiaAE2CEOTMSBNARu/QWjWf8ABBxQ2Bs2TgebpwzZt2qwS1r333qujjjqq8P8fOnSoBgwYYDV0NgitcjIZAvkWMN8wHjFCevbZaHHss4900klSnTrRxjMqsADNiMBUnIiA1wI0D8Klnxo/nBdnI4BATgR+/lm6/HLptdeiLfiww6Qjjog21vFR1OSOJ5jwEMioADV+RhPDsmIJsEEYi4/BpRYI0jwYNWqUjj/++MLSBg8eLDNm5WPF14z26NFDDz74oOrVq7f8tDlz5mi77bbTrFmz1KxZM3344Ydq3ry51XDZILTKyWQI5F+gvFy69lrplVfCxbLLLsXXDq3wd1i4CTh7ZQEaDtwTCCAQV4DmQThBavxwXpyNAAI5Eli0yHzjWDK/SRjm6NZNMn0Nz78ASF0e5qbhXAQQSFqAGj9pYeZPQ4ANwjTUuWZkAVvNA7MA83SgeUrQHHvuuafOOOMMtW7dWlOnTtXFF1+sGTNmFP7spptu0sknnxx5zdUNZIPQOikTIpB/gaVLpX/9Sxo7Vvrpp5rjWW01qUcP6dBDpbp18x97hiKgEZGhZLAUBHIqQPMgXOKo8cN5cTYCCORMwHwR8PbbpQkTJPO/13Q0aVJ8arBrV+83Bw0TdXnO7nWWi4DjAtT4jifY0/DYIPQ08XkN22bzYOHCherVq5fGjRtXJUfdunU1aNCgKp9AtOHHBqENReZAwFGBBQuKrxsdP1767LPKQbZtK+23n7T77lKjRo4CpBsWjYh0/bk6Ai4I0DwIl0Vq/HBenI0AAjkV+OEHaeLEYo3/9deVg9h44+Km4M47Sw0a5DRA+8umLrdvyowIIBBdgBo/uh0jsyvABmF2c8PKqhCw2TxYNv3dd98t81rSN954Q3PnzlXLli212267qW/fvtppp50SywMbhInRMjEC7giY3yb88Udp3rxiTKuvLq2xBt8mTjjDNCISBmZ6BDwQoHkQLsnU+OG8OBsBBHIuYGr8778v1vjmZwJMfW/qfI5VBKjLuSkQQCBLAtT4WcoGa7ElwAahLUnmQSCkABuEIcE4HQEEECiRAI2IEkFzGQQcFqB54HByawmNGt/f3BM5AgjYF6Aut2/KjAggEF2AGj+6HSOzK8AGYXZzw8ocF6B54HiCCQ8BBHIrQCMit6lj4QhkRoDmQWZSUfKFUOOXnJwLIoCAwwLU5Q4nl9AQyKEANX4Ok8aSaxVgg7BWIk5AIBkBmgfJuDIrAgggEFeARkRcQcYjgADNA3/vAWp8f3NP5AggYF+Auty+KTMigEB0AWr86HaMzK4AG4TZzQ0rc1yA5oHjCSa86ALTpknjxklz50rl5VLjxtJvfysddpjUoEH0eRmJQEABGhEBoTgNAQSqFaB54O/NQY3vb+6JvBaByZOlCROkH36Qli4t1vgdO0rdu0tlZfAhUKUAdTk3BgIIZEmAGj9L2WAttgTYILQlyTwIhBSgeRASjNPdFjAbgQ8+KI0fX2waVHXUqSN16CD17i395jduexBdqgI0IlLl5+IIOCFA88CJNEYKgho/EhuDXBVYvFi65x5p4kRpwYKqo6xXT9pqK+n446VWrVyVIK6IAtTlEeEYhgACiQhQ4yfCyqQpC7BBmHICuLy/AjQP/M09ka8kYJ4UPPvs6jcGqwL7/e+lXr2gRCARARoRibAyKQJeCdA88CrdlYKlxvc390S+ksBnn0kDB0oLFwajMV8GPOkkaZ99gp3PWV4IUJd7kWaCRCA3AtT4uUkVCw0hwAZhCCxORcCmAM0Dm5rMlVuB77+X+vWTFi0KH0LPntLRR4cfxwgEahGgEcEtggACcQVoHsQVzO94avz85o6VWxQwm4MDBhR/LiDsYd4WcsABYUdxvqMC1OWOJpawEMipADV+ThPHsmsUYIOQGwSBlARoHqQEz2WzI2B+e+SUU4q/NRj16NtX2n33qKMZh0CVAjQiuDEQQCCuAM2DuIL5HU+Nn9/csXJLAua1ouZJwKBPDlZ12UGDir9PyOG9AHW597cAAAhkSoAaP1PpYDGWBNggtATJNAiEFaB5EFaM850TGDdOGjUqXlhrrCGNHBlvDkZ7K0DDwdvUEzgCiQvQPEicOLMXoMbPbGpYWKkETH1v6vw4x7rrSsOHx5mBsY4IUK87kkjCQMARAWp8RxJJGJUE2CDkhkAgJQGaBynBc9nsCJx6qvTNN/HXwzeM4xt6OgMNB08TT9gIlECA5kEJkDN6CWr8jCaGZZVOwLwidMGC+Ne7+mpp/fXjz8MMuRagXs91+lg8As4JUOM7l1ICksQGIbcBAikJ0DxICZ7LZkPg44+Lv0ti49hoI+myy2zMxByeCdBw8CzhhItACQVoHpQQO2OXosbPWEJYTmkFJk2SLr/czjW32UYaONDOXMySWwHq9dymjoUj4KQANb6TafU+KDYIvb8FAEhLgOZBWvJcNxMCN98sTZhgZyllZdLdd9uZi1m8EqDh4FW6CRaBkgrQPCgpd6YuRo2fqXSwmFILmC/tvfaanas2bhz/5wjsrIRZUhSgXk8Rn0sjgMAqAtT43BQuCrBB6GJWiSkXAjQPcpEmFpmUwNCh0uTJdmavU0e67z47czGLVwI0HLxKN8EiUFIBmgcl5c7UxajxM5UOFlNqAfPE3/Tpdq7KlwDtOOZ8Fur1nCeQ5SPgmAA1vmMJJZyCABuE3AgIpCRA8yAleC6bDYFLLpGmTLG3lvvvtzcXM3kjQMPBm1QTKAIlF6B5UHLyzFyQGj8zqWAhaQicc440c6adK9erJ91zj525mCW3AtTruU0dC0fASQFqfCfT6n1QbBB6fwsAkJYAzYO05LluJgSuu056/nk7S6lbV7r3XjtzMYtXAjQcvEo3wSJQUgGaByXlztTFqPEzlQ4WU2qBIUOkadPsXLVhQ+mOO+zMxSy5FaBez23qWDgCTgpQ4zuZVu+DYoPQ+1sAgLQEaB6kJc91MyHw3/9Kw4bZWUqLFtKNN9qZi1mcE6Cp4FxKCQiBXAjQPMhFmhJZJDV+IqxMmheBBx6w9+r/tm2lK6/MS+SsM4YA9XoMPIYigEBJBajxS8rNxUokwAZhiaC5DAIrC9A84J7wXuDYY6WfforPcMwxUo8e8edhBicFaDg4mVaCQiDzAjQPMp+ixBZIjZ8YLRPnQaC8XDK1+dKl8Vd71lnSjjvGn4cZMi9AvZ75FLFABBD4RYAan1vBRQE2CF3MKjHlQoDmQS7SxCKTFDBP/T39dLwrlJVJd94pmdeMciBQhQANB24LBBBIQ4DmQRrq2bgmNX428sAqUhSw8VvjjRtLo0alGASXLqUA9XoptbkWAgjEEaDGj6PH2KwKsEGY1cywLucFaB44n2ICrE1g3jzpxBPjfcN4zz2lU0+t7Ur8uccCNBw8Tj6hI5CiAM2DFPFTvjQ1fsoJ4PLpC3zxhXTGGVJFRfS1HH64ZP7h8EKAet2LNBMkAk4IUOM7kUaCWEmADUJuCQRSEqB5kBI8l82WwKRJ0uWXR1uT+V0SM5anB6P5eTKKhoMniSZMBDImQPMgYwkp4XKo8UuIzaWyK/DEE9LIkdHWt8UW0uDB0cYyKpcC1Ou5TBuLRsBLAWp8L9PufNBsEDqfYgLMqgDNg6xmhnWVXOC556Trrw/3LeN27aTLLpPMK0Y5EKhBgIYDtwcCCKQhQPMgDfVsXJMaPxt5YBUZEHj0UemOO8ItxGwODhrEFwDDqeX+bOr13KeQABDwRoAa35tUexUoG4RepZtgsyRA8yBL2WAtqQu8/740YoT0+ec1L6VBA8m8VvT442kcpJ60bC2AxkK28sFqEPBdgOaBv3cANb6/uSfyKgSmTJFuvVWaPbtmnkaNpAMOkI44AkYPBajjPUw6ISOQUwFq/JwmjmXXKMAGITcIAikJ0DxICZ7LZltgzhxp9Gjprbekn34qPlVonhJs0UI6+GBpjz3YGMx2BlNbHY2F1Oi5MAIIVCFA88Df24Ia39/cE3kNAp99Jo0aJX3wgbR48a81/rrrSr//vbTjjvB5LEAd73HyCR2BnAlQ4+csYSw3kAAbhIGYOAkB+wI0D+ybMiMCCPgrQGPB39wTOQJZFKB5kMWslGZN1PilceYqCCDgjgB1vDu5JBIEXBegxnc9w37Gxwahn3kn6gwI0DzIQBJYAgIIOCNAY8GZVBIIAk4I0DxwIo2RgqDGj8TGIAQQ8FiAOt7j5BM6AjkToMbPWcJYbiABNggDMXESAvYFaB7YN2VGBBDwV4DGgr+5J3IEsihA8yCLWSnNmqjxS+PMVRBAwB0B6nh3ckkkCLguQI3veob9jI8NQj/zTtQZEKB5kIEksAQEEHBGgMaCM6kkEAScEKB54EQaIwVBjR+JjUEIIOCxAHW8x8kndARyJkCNn7OEsdxAAmwQBmLiJATsC9A8sG/q9YzvvSfdcIP05ZfS0qVFijp1pMaNpe7dpUMP9Zqn5MEvXCg9/7z0yivS3LnS4sXFXLRtK+29t9S+fTE/HKEFaCCEJmMAArkQmDNHmjBBeustad48qaJCWn11afPNpX32kVq1Sj4M81f1yy9LL7wgffON9NNPUqNGxWvvtZe01VZS3brB1kHzIJiTi2dR47uY1RRjmjJFuuWW4l9KK9b4a6whHXGEtO++KS7Ow0ub/0A984w0ebL0/fdSeXnxP1YbbFDMxUYbeYgSPGTq+OBWnImAKwKff16s8U3LyvwVatog5j9hHTsWWyNrrZV8pIsWSf/9r/TSS5XbM23aFNdgPm8Ebc9Q4yefL65QegE2CEtvzhURKAjQPOBGsCLw7LPSyJGSqXhqOky106mTdPbZVi7LJNUImM3ABx6QnntOMpuE1R1mo/DAA6U99wxeiYJeEKCxwI2AgFsCM2YU/9r83/+Km4LVHdtsU/yui/l+he1jwQLpoYekp5+Wfvyx+tlbtpS6dSv+U69ezaugeWA7S/mZjxo/P7nK9ErHjpXuvbf4JbOaDvOtBVNP9umT6XByv7jZs6UxY4rfIFmypPpwNt5Y6tlT6tw59yEnEQB1fBKqzIlANgWmTSvW12++Wf36zH/Ctt9e6tWr+D0L24f5Hof5nGHaZjW1Z8xGoWnPmC8E1rZRSI1vO0vMlwUBNgizkAXW4KUAzQMv02436FGjpHHjws3ZooV0/fW1dzbDzcrZRuDTT6VLLpHMYzBBjy5dpJNPlsrKgo7w/jwaC97fAgA4JPDii9Lw4cUHMIIcpolw0knFb/raOsxf2ZdeKn3ySfAZt91WOuMMabXVqh9D8yC4p2tnUuO7ltEU4rnmGsn8BRnmMF8+u/LKMCM4N6jA++9L//hHzd8gWXku8waXo48O/th50LXk/Dzq+JwnkOUjEFDgiSek226r+ct/K05Vv77Uv7+0ww4BLxDgtM8+K7Znvv46wMm/nLL77tIpp9TcnqHGD+7JmfkRYIMwP7lipY4J0DxwLKGlDue++4pfhYpymE3CG2+MMpIx1QmYV7v+7W/SDz+EN9p1V6lfv9q/qhZ+ZidH0FhwMq0E5aGAeZXn1VcHbxysSHTaaZL5fkXcw3yr2PzV/dVX4Wcyr0U677zqGwg0D8KbujKCGt+VTKYUx4gRxVdYRjnatZOuuCLKSMZUJzBzpjR4cO1va6lq/EEHSX/8I7YrCFDHczsg4L7Ak08W34wd9jBP7v31r9J224Uduer55qHv888vvgk67LHzztKf/1z99zuo8cOKcn4eBNggzEOWWKOTAjQPnExraYIyjzmcdVa8a5n3OJjqiyO+wM8/SwMGFJ8gjHqY5oFpInDUKkBjoVYiTkAg8wJffFF843VNb2mrKQjzek/zjeANN4wX6oUXFn/zMOpRU++X5kFU1fyPo8bPfw5Ti8C8a9k8qRbnOOAAqXfvODMwdpmA+QkH87j4t99GNzFdZvNlQI6CAHU8NwICbgt88EHxy3c1/WxATQINGxYfhl933ehO5ud6Tatr1qzocxxzjNSjR9XjqfGjuzIyuwJsEGY3N6zMcQGaB44nOMnwTFc1TrVj1ma+nmWeQuSIL/Dqq/G/rd28efHVr7xqtNZ80FiolYgTEMi8gPnpXPPqoTjHbrsVH76OekyfLg0cGHV0cZxpYtx0k9S48arz0DyIZ5vn0dT4ec5eyms37zWLsxlllm9qybvvTjkQRy4f9TGYFcP/zW+koUN5U8gvJtTxjvy7QRgIVCNw1VWSeUtInCPuw9c2vmvTrJlkHuivqj1DjR8nu4zNqgAbhFnNDOtyXoDmgfMpTiZA87TaUUfZmfvwwyXzD0c8gYsuqvmXt4PObp4K3XHHoGc7fR7NA6fTS3CeCyxcWPxtD/M/4xzmA/sNN0hrrhltljhv8VvxiscfL+2//6proHkQLS8ujKLGdyGLKcRg3oNmfmTVxmGeejPvSOOILmAefzGPoHz8cfQ5lo00j6u3bx9/HgdmoMZ3IImEgEA1Aub7LeZnAMwTfHGOJk2KX8Br0CDaLOa3xV9/PdrYFUdV959Savz4tsyQPQE2CLOXE1bkiQDNA08SbTtMG49dLFtTo0bS6NG2V+jXfOa3B82rg2wcW21VfB8HB68f4h5AwGGBCROkm2+2E+DRR0s9e4afa/586eSTo7/idMUrtmkjmW9Lr3zQPAifF1dGUOO7kskSx2Hemzxlip2L8nvj8R3ff99eXR73kff40WRmBjYIM5MKFoKAdYExY6T777cz7emnS3vsEX4u89uD5g0jUV9xuuIVt9ii+BO01Pjh88CI/AmwQZi/nLFiRwRoHjiSyFKHYb7G9Pnn9q5qq4Kzt6J8zfTii9I119hZs3lH3ahRdubK+Sw0D3KeQJaPQA0CN94oPf20HaLOnaW//CX8XO++K/397+HHVTfi9tul1Var/KdsENrzzdtM1Ph5y1hG1mueHjRPEdo4zA+13nOPjZn8nWPcOHt1eevW9j4v5Dwj1Pg5TyDLR6AGAfMTuub1njaOrl2lE04IP5N5vWlVX9wLP1Oxtjc1/soHNX4UTcZkXYANwqxniPU5K0DzwNnUJhuYjd8mWXGFbBDGy9f48dKtt8abY8XR994r1a1rb76czkTzIKeJY9kIBBC48krplVcCnBjglI4dpUGDApy40imTJxd/EsrWYV51utZalWejeWBLN3/zUOPnL2eZWPFxx8V/9/KyQPit8fgp/de/JPOPjcO8C/uWW2zMlPs5qPFzn0ICQKBaAfPlO/MlPBvHrrtGe1HTU08VX09q6zDftTHfuVnxoMa3pcs8WRJggzBL2WAtXgnQPPAq3faCNS91nzPH3nxsEMazNI/BmMdhbBym8rz7bsk0dTw/aB54fgMQvtMC5qFr8/C1jeN3v5POOy/8TOYtfuZtfrYO0/dd+bcQaR7Y0s3fPNT4+ctZJlbcu7e0YIGdpbBBGN/xwQcl88U9G4f5Bon5JgkHPyPAPYCAwwJDhkjTptkJsEuX4u8Zhj2eeUYyvzNu4zD/KTUbhCt/f5sa34Yuc2RNgA3CrGWE9XgjQPPAm1TbDfSvf5U++sjOnDQP4jvafAylWTN7P8wVP7JUZ2CDMFV+Lo5AogK33SY9/ridS+y+u9S3b/i5PvxQOvfc8OOqGmH+U3rXXVJZWeU/pXlgxzePs1Dj5zFrGVjzqadK33xjZyHmLyTzpTOO6AI2fzB3gw3sPrYeParUR1Ljp54CFoBAYgJXXy299JKd6bt3l449Nvxcr70mXXZZ+HFVjWjatOqXRVHj2/FllmwJsEGYrXywGo8EaB54lGyboT77rHT99XZmXGcde3PZWVH+ZjHf9O7TR/rpp/hr32svybxC1qODJoFHySZUBH4RsPn0Xv/+0i67hKf9+Wfp9NOlb78NP3blEdtsIw0cuOo8NA/i2+Z1Bmr8vGYu5XWbbxr8+992FrHJJnYfk7azqnzNYt7YYv5DUVERf92HHCIddVT8eRyYgdrfgSQSAgLVCNh8em/wYGmLLcJTL1xYbM8sWhR+7Moj9tij+J+BlQ9q/Pi2zJA9ATYIs5cTVuSJAM0DTxKdRJjmA6bpbsY9/vY3aaut4s7C+Jtvlsy3jOMe5qtuG20Ud5ZcjadJkKt0sVgLwDZFAAAgAElEQVQErAgsXSqZjb2vvoo3nXmlp3lj28pP7gWddcwYycZbts85R9puO5oHQd19OI8a34csJxCjqe3/8Ac7G1LDh0vrrpvAIj2b0vxYrXlbSJzDPGZuvty59tpxZnFmLLW/M6kkEARWEVi8uPh953nz4uG0aSOZ3yyP+ssrt94qjR8fbw1mtPk5AvN9m5UPNgjj2zJD9gTYIMxeTliRJwI0DzxJdBJhXnWV9PLL8WZu0EC68854czC6KGBe+Wpe/Rrn2HRT6eKL48yQy7E0CXKZNhaNQGyBRx6J/5+gQw+Vjjwy+lLM04PmW8Fxvm9jHsS/7rpVf5vErIrmQfTc5H0kNX7eM5ji+s3jyNOnx1vAGmtII0fGm4PRRYE33ohfn2+/ffzPCQ7lg9rfoWQSCgJVCNxxh/Too/Fo/vQnqVu36HPMmiWdfXb08WbkxhtLl15a9RzU+PFsGZ1NATYIs5kXVuWBAM0DD5KcVIjmq1l//KNkHsOIepxwgtS1a9TRjFtZwHxT+7nnornUqycNGiR16BBtfI5H0STIcfJYOgIxBMzrf847T/r882iTmAcxzEPX5rdB4hzmJ7oefjj6DGecIe28M82D6IJujqTGdzOvJYnqu++Kj1/Eea1ldY81lyQAxy5iPmuZ/9iYd2NHORo2lC66SPrNb6KMdnIMtb+TaSUoBJYLzJ0rmf8Mmf+cRTnati0+uWf++oxzmLeMTJwYbYa6dSXzsq0tt6TGjybIqDwKsEGYx6yxZicEaB44kcb0gvj4Y2nAgGjX33NP6dRTo41lVNUCS5YUv2L21lvhhMx7M0wuunQJN86Rs2kSOJJIwkAggoB5xaj5boRpJIQ5mjSRLrxQMg2EuIfp/Q4bJr34YviZzJsADz64+nF8uzi8qSsjqPFdyWRKccT5odbDD5fMPxz2BMzvjQ8ZIs2cGW5O8wVA81lt223DjXP8bGp/xxNMeAj88oKlCy6QzF+fYY611pL+7//svJG5vLz4/Y433wyzguK5pj1jWmbVHdT44U0ZkX0BNgiznyNW6KgAzQNHE1vKsMwHVfMIRpgnCc27Gsw7GzjsC5gnO81vjLz0UrC5zWte+/aVOncOdr6DZ9EkcDCphIRACIHZs4vfrfjss2CDWraUzj1XWn/9YOcHOcv8J3TUKOnxx4OcXXydqPnP6H771Xw+zYNgni6eRY3vYlZLHJPZJDR/OYZ5krC2by2UOASnLme63FdfXXzlaJCjcWPprLOkjh2DnO3VOdT+XqWbYD0WMK/5NBt0c+YEQzAPWpsa32wS2jrMd7hHjJBeeCHYjPXrF39+oLq3gyybhRo/mCdn5UuADcJ85YvVOiRA88ChZKYZitmUuvZa6bXXav4hpfXWk047TdpsszRX6/61TSNn6tTir2JPmlR1Y2fNNaW995b23dduBZxDXZoEOUwaS0bAsoD5z5j54G7+2pwxo+rJTdPAbMjttpu02mqWF/DLdO+/Lz3xRPE7HuZbxysf5slF87C3WYf5T2ptB82D2oTc/XNqfHdzW9LI5s0rbkpNm1bzlwHNX5D9+0tt2pR0ed5dzHyb5H//K/7HqrqNQvP+6332Kdb5pt7nWEWA2p+bAgF/BMxPCphfYTF/bX7ySdVxb7pp8ZdvdtpJMht0tg/TnjEveVrWnqnqu/Xmr+u99iq2Z8xf47Ud1Pi1CfHneRRggzCPWWPNTgjQPHAijdkKYsIEadw4af78YiPBvLi9fXvp5JMl87QaR2kFzNflzCaheX+e6YD/P3tXAu7ltL3f5nmeJIUmqUtRNIgyu0n36m+eypQyJhkSMlRIJEmmUIrMMnSbaKJoUITSJEOK0qx5+D/v+RydTuec3zfs7/t9w7uex8O97bX2Wu/6zmnttfZei9llJm+aNAEKFgxWlwB2e8tNRytXTAEYoy2EgBBICwJLlliHeObFeaAvWdIaz1q3LsCOzEHQxo3AF18Aa9cC27cDxYpZBcHjj3c2D0XJgyC8Fc49FOOH0y+R1mr0aGuYEl+y8Zcjb0o0agR07AiwlaUoWARWrQJmzwb4FwZvlPDFYM2alk/4zFwEFQL1EQgBIZCJAP/aWrjQ+ocxPmP6UqWsR9b81RkUMbafOdOaj5g1PdO4sbPipGL8oDymfYJEQAXCINHWXkIgCwJKHuhzEAJCIE4IuKr1uWKKE2qyRQgIgbgioORBXD2b2i7F+Kkx0gohIATijYAKhPH2r6wTAklGQDF+kr0fX9tVIIyvb2VZyBFQ8iDkDpJ6QkAIOELAVa3PFZMjtbRYCAgBIZAWBJQ8SAvsodhUMX4o3CAlhIAQSCMCKhCmEXxtLQSEgK8IKMb3FV4JTxMCKhCmCXhtKwSUPNA3IASEQJwQcFXrc8UUJ9RkixAQAnFFQMmDuHo2tV2K8VNjpBVCQAjEGwEVCOPtX1knBJKMgGL8JHs/vrarQBhf38qykCOg5EHIHRRV9Th/kPMwdu+25mGULRv8LIydO625e5xKzRkp1MHpDETOWKEdlMXZfemwI6rfQJr0dlXrc8WUJgO1rRAQAkLAAQJKHjgAK2ZLFePHzKFhMWfTJoD/cM44B7SWKRPccNZMDDi0iTH+tm3WgFbG54UKOUOIA6gY49MOxvi0Q3P7nGEYgdUqEEbASVJRCAgBVwgoxncFm5hCjoAKhCF3kNSLLwJKHsTXt4FbxiLal18C48dbk5+zUrlywGmnAaeeCpQv769qy5ZZOnz2mTX1OZMKFACaNgXOOgs44ojckxm7dgGzZ1syvv12f11LlwZOOQU44wygYkV/7ZB0Vwi4qvW5YnKlnpiEgBAQAoEioORBoHCHajPF+KFyR7SV2b4d+PxzYNw44Mcf97elShXg9NOB1q0Bxsl+0d69wA8/WPH5F18AjNcziRcATzgBOPNMoGbN3DXguYC8tGPx4v3XVaiw76zCgqMoFgioQBgLN8oIISAEckBAMb4+izgioAJhHL0qmyKBgJIHkXBT+JWcPBkYORLYsCFvXXkz96STgKuvBooUMWvXqlXA4MFW8iAVHXoo0KXLgUkEJg1eeQVYuzZvCfnyAc2bA506WS8kRYEjYLSmZ1RY4FBoQyEgBIRArggoeZDcj0MxfnJ9b8xyFuXGjAHefhtgd5C8iC/4WCi87DKgYEFjKmQIYlFyyBBg+fLUcuvWBa6/Hjj44P3XTpwIvP669foxL+KFwpNPBjp2dN55JLV2WuEDAioC+gCqRAoBIRB6BBTjh95FUtAFAioQugBNLELABAJKHphAMeEy3nzTShw4odq1gbvvtloTmaAlS4CHH0596M+6FwuUt90GNGpk/b8ffQQMH+5Mmxo1gJ49Ab6QFAWKgNGanlFhgcKgzYSAEBACeSKg5EFyPxDF+Mn1vRHL2XrzpZesF3tO6Oijge7drfb+Juibb4DHHgP4itEu8XzRowdQpw7AIudrrwGjR9vlttax2whl6CKgM9zSsFoFwjSAri2FgBBIOwKK8dPuAingAwIqEPoAqkQKATsIKHlgByWtyRUB3irmizs3VK8ecM893m/nrlxpyUl1IzgnHdmS6P77gV9+sW4mu6HDDwceeMBcIsSNDgnkMVrTMyosgc6QyUJACIQWASUPQusa3xVTjO87xPHegEW19993Z2OTJlaR0OtMP44N6NXLWXEwU2MWCXv3BmbOtAqEbuhf/7IuNJp+EelGF/HkioAKhPo4hIAQSCICivGT6PX426wCYfx9LAtDioCSByF1TBTU+u034NZbrZu5bumCC4DzznPLbfHdd9+BMw+dSKxUyWopunu3E67917ZtC1xxhXt+cTpGwGhNz6gwx6aIQQgIASHgGwJKHvgGbegFK8YPvYvCq+CiRdblOy/ENvycP+6W+IKxWzeA5w23dNhhwE8/eTursGVqu3ZuNRBfAAioQBgAyNpCCAiB0CGgGD90LpFCBhBQgdAAiBIhBNwgoOSBG9TEk4EAXw7yBaEXYmtOzg10ezOXM0nuvNOLBmZ42X7o2Wf1itAMmrakGK3pGRVmS30tEgJCQAgEgoCSB4HAHMpNFOOH0i3RUGrgQODzz73pWr060L8/wLndbmjePKBvXzecZnl4kXDQIO+vIc1qJWlZEFCBUJ+DEBACSURAMX4SvR5/m1UgjL+PZWFIEVDyIKSOCbta27YBnTsDW7Z415S3g5s1cyfnueeATz5xx2uai3iccoppqZKXCwJGa3pGhcllQkAICIHwIKDkQXh8EbQmivGDRjwm+61fD3Tp4q2zRiYUbMF/5JHugOnXD5g92x2vaS5eRmzc2LRUyTOEgAqEhoCUGCEgBCKFgGL8SLlLytpEQAVCm0BpmRAwjYCSB6YRTYi8zz4DnnrKjLHHHAP06OFc1q5dwJVXuptL4ny31BxHHAE89FDqdVphG4HA6naBbWTbdC0UAkJACBhBQMkDIzBGUohi/Ei6Lf1Kf/wxMGyYGT1OPtkqNjqljRuBa6/11hrU6Z55rT/+eGumoiiUCKhAGEq3SCkhIAR8RkAxvs8AS3xaEFCBMC2wa1MhACh5oK/AFQLvvguMGuWK9QCmatWAAQOcy/rzT3dJB+c72eNgu1S+aBQZQyCwul1gGxmDRoKEgBAQArYQUPLAFkyxXKQYP5Zu9d+ol14Cxo41s0+DBkCvXs5lLVsG3HWXcz6/OA4/HHj0Ub+kS65HBFQg9Aig2IWAEIgkAorxI+k2KZ0CARUI9YkIgTQhoORBmoCP+rYjRwKjR5uxokIFYMgQ57J+/RVge9KwULFi5m5ch8WmNOsRWN0usI3SDKi2FwJCIHEIKHmQOJf/Y7Bi/OT63pPlzzwDTJ7sScQ/zDVrAo884lzWd98BbE8aFjroIHOdU8JiU4z0UIEwRs6UKUJACNhGQDG+bai0MEIIqEAYIWdJ1XghoORBvPwZmDXvvAO88YaZ7dy+IFyzBrj+ejM6mJCiF4QmUNxPRmB1u8A2Mg6RBAoBISAE8kRAyYPkfiCK8ZPre0+Wm3xBWL8+cP/9ztVZutTd+AHnO9njOOwwgDMRRaFEQAXCULpFSgkBIeAzAorxfQZY4tOCgAqEaYFdmwoBtRjVN+ASgSlTgMGDXTJnYzv6aOCee5zL2rkT6NgR4L/DQLVrA337hkGT2OgQWN0usI1i4xoZIgSEQEQQUPIgIo7yQU0VCH0ANQkiP/gAGDHCjKUnnQTceKNzWevXA506Oefzi6NxY+DOO/2SLrkeEVCB0COAYhcCQiCSCCjGj6TbpHQKBFQg1CciBNKEgJIHaQI+6ttu2QJcdx2wfbt3S5g4YALBDT39NDB1qhtO8zxXXQWcdZZ5uQmWGFjdLrCNEuxMmS4EhEBaEFDyIC2wh2JTxfihcEP0lOCM7xtuAPbs8a47LwDyIqAb6tMH+PprN5zmeTjSoFkz83Il0QgCKhAagVFChIAQiBgCivEj5jCpawsBFQhtwaRFQsA8AkoemMc0MRJfeAGYMMGbuaVKWfMHCxd2J2fRInevD93tljtXkSLAc88BxYublpxoeYHV7QLbKNHulPFCQAikAQElD9IAeki2VIwfEkdEUY3+/YGZM71pXrUqMGAAkD+/OzmzZgGPPeaO1yQXRwiwa0rBgialSpYLBFQIdAGaWISAEIgtAorxY+vaRBumAmGi3S/j04mAkgfpRD/ie//8M9C9uzcj/vtf4JJL3MvYu9eaUbJsmXsZZcsCGzYAlOWWzjgDuOYat9ziywWBwOp2gW0kVwsBISAEgkVAyYNg8Q7Tborxw+SNiOny7bfAgw96U7pDB+Dss93L2L0buPlmYPVq9zJYpFy50j0/Oc8/3/pHlHYEVCBMuwukgBAQAiFCQDF+iJwhVYwhoAKhMSijKWjr1q2YMWMG5syZg2XLlmHVqlX466+/UKhQIZQtWxY1atRAgwYN0LRpU9SpUyeaRoZUayUPQuqYqKj15pvA22+70/aww4AHHgCKFXPHn8m1fDnQqxewdatzOQUKAD17ApQxfLhzfnIcfDDQuzdQsqQ7fnHlikBgdbvANpKzhYAQEALBIqDkQbB4h2k3xfhh8kbEdOGluRdfdN8ppEEDK772+uru+++tGHvXLucAFi1qnTOmTwdGj3bOT45atYD77wfYKUSUdgRUIEy7C6SAEBACIUJAMX6InCFVjCGgAqExKKMjaPPmzXjrrbfw+uuvY9q0adixY4ct5VksbN++PS699FIce+yxtni0KHcElDzQ1+EJASYQXnoJGDfOmZhq1YB77wXKl3fGl9tq3nTu1w/Yts2+PBYHb7oJaNHCej04ahTw3nv2+bmycmXgvvusf4uMIxBY3S6wjYxDJIFCQAgIgTwRUPIguR+IYvzk+t6I5XzBN2iQVWBzQrVrW8XBEiWccOW+9osvgKeeclYkZEHv9tut+Yecpfj888CnnzrTp0YN66xSpowzPq32DQEVCH2DVoKFgBCIIAKK8SPoNKmcEgEVCFNCFJ8FK1aswGOPPYZXXnkFmzZtwt4c2voVK1YM5cqVA18WbtiwAXuyDUnPly9fBiDHH388unXrhvPV9sP1B6LkgWvoxJiJAH+GP/4YeOMNYPv21Lg0bgzccIP5F3c//mglEFasSK1DxYrA9dcD//rX/ms5U3HECHuvEY86yiowskWpyBcEAqvbBbaRLzBJqBAQAkIgVwSUPEjux6EYP7m+N2Y5z+DsFvLBB6kLdDyft2wJdOpk/sUdXxJyDqCddqPs7MHWpDVr7oOBZxW+ImTXEzuXko8/3jonaLa4sU/JhCAVCE2gKBlCQAjEBQHF+HHxpOzIioAKhAn4HtavX4/7778fzz33XMZrQRYGCxQogBNPPBHNmjXLKPY1btwYlStXRpEsbTy4jkXCRYsWYdasWZg5cyYmTZoEHnpJLBay/SiLjmeeeWYCkDRropIHZvFMtLQtW4CpU63XhNmLdGwj2qoVwFl9hxziH0xMZPA1IXWYPfvAuYK8SczfE3x9zBeEORFfIX72mSXjp5/2X8HfTSeeaNnBFqkiXxEIrG4X2Ea+wiXhQkAICIEDEFDyILkfhWL85PreuOUbNwKTJgHjxx9YpCtVCjjlFOD00/3tqMEXjXPnWvH511/vbyKLk7yAyBifF/jy588Zgs2bgSlTLDuyzybki8fWrS07WGQUhQ4BFQhD5xIpJASEQBoRUIyfRvC1tW8IqEDoG7ThEVyxYkWsW7cuozDIguAll1yCCy+8EJUqVXKl5NSpUzFy5MiMNqUsPrJQOHDgQNx4442u5CWVScmDpHreR7t5S/f33wEmEzgzhLP5DjoIKFzYx01zEM3916yx2o5yDgnbmTp57Uc7eFN5wwZg506rVVKVKpYsUSAIBFa3C2yjQGDTJkJACAiBfxBQ8iC5H4Ni/OT63jfLeRFv1Spg0yardSdj/KpVvc8adKrw+vXA2rX7Ynx2Bild2r4U6v7HH+k/q9jXWCsBqECoz0AICAEhsA8Bxfj6GuKIgAqEcfRqNpvy58+f8cLvvvvuQ/PmzY1ZzFmGgwYNwoABAzKKg5Qvso+Akgf2sdJKISAE/EEg7fW5tCvgD66SKgSEgBBQ8iC534Bi/OT6XpYLgTgioAJhHL0qm4SAEHCLgGJ8t8iJL8wIqEAYZu8Y0m327Nlo0qSJIWkHitmyZQt++uknHHnkkb7tEUfBSh7E0auySQhEC4G01+fSrkC0/CVthYAQiA4CSh5Ex1emNVWMbxpRyRMCQiCdCKhAmE70tbcQEAJhQ0Axftg8In1MIKACoQkUJUMIuEBAyQMXoIlFCAgBowikvT6XdgWMwilhQkAICIF/EFDyILkfg2L85PpelguBOCKgAmEcvSqbhIAQcIuAYny3yIkvzAioQBhm70i3WCOg5AEAzpr74Qfgp5+Av/4CChUCypQBjjkGKFUqOv6nHUuWAD/+aNlRoIBlR8OG9mfvcdbevHnW7L3M2X2VK1syiItICPiAQNrrc2lXwAdQJTJRCOzeDcyfD6xcCWzdChQpAlSoADRqpLGpifoQcjBWyYPkfgGK8WHNyfv2W+C334AtW6x52JxJzV+OxYtH5+NgjL9gAfDzz/vs4Fxt2sE5gHaIcT1j/D//BLZvB4oVs+YHHnWUdWYQCYGQI6ACYcgdJPV8QYDpma+/tsamZqZnKlWyfv0rPeML5JERqhg/Mq6Sog4QUIHQAVhaKgRMIpDo5AETBVOnAuPGAStWHAgrI64WLYAzzwRq1zYJu1lZjBQ/+8yyg0XO7MRDf9OmwFlnAUccAeTLd+AaRpwTJwKffAJs2nTgn7PQeMopwOmnAxUrmtVf0hKPQNrrc2lXIPGfgABwicD69davbf76Zs43OzH/3aqV9dfYwQe73ERskUZAyYNIu8+T8omO8TduBCZNAiZMsLKq2aloUeCkk4AzzgBq1PCEs6/MvPA3ZYoV4/MGSHZiwfOEE6xf8jVr5qwKzzjjxwOTJ1s3SLITb5Ocdhpw6qn2LxT6arSEC4GcEVCBUF9GkhBYs8b61f3ppwD/SstOpUtb6Rn++uZ9blHyEFCMnzyfJ8FiFQiT4GXZGEoEEps8WLgQeOyxnIthOXmqdWugUyegYMFw+XHZMqBfP2DtWnt6NWsG3HijdYM6k8aOBYYNA/gEJRWx2HjllVZCRSQEDCGQ9vpc2hUwBKTEJAqB6dOBZ54BduxIbTbvhZx3nvVPTndEUkvQiqgioORBVD3nXe/Exvhz5gBPPZVzMSwnWNu2BS67DMif3zvoJiXw5eMTTwCbN9uTyizx1Vfvew3IV4dvvQW8847VLSUV8Wxw/fXW5UiREAghAioQhtApUskXBFgYfPll++mZDh2su+CiZCGgGD9Z/k6KtSoQJsXTedi5YMECPP/885g2bRqWLVuGTZs2YQ/bwuRB+fLlw65du4SeBwQSmTxgex0WB9mvwQmxj8Mdd4SnSMgiZ58+VpsgJ1SvHnDPPVaR8M03gbffdsJtrb3wQuD//s85nzgSi0Coa3ChVi6xn4wMzwMBPop54QXnEPFuB/PHKhI6xy6qHEoeRNVz3vVOZIzPjhqDBtkriGWFuGVL6wJdWIqEs2cDjz9uLzuc1Y4mTYDbbrPsePFF6wWlU+KFSBYbRUIgZAioQBgyh0gdXxDgnY433nAu+vzzAf4jSg4CivGT4+skWaoCYZK8nYOtjzzyCO677z7s3r0be+3ccPxbBguE5BG5RyBxyQPO7mBxjG053dDJJwNdurjhNMuzahXQo4c1a9ANseUoZyw++6wbbouHiRS2ZxIJARsIhLoGF2rlbICrJYlCYO5c4JFHnOe/M0G65BLgv/9NFGSJNlbJg+S6P3ExPmf0Pfig86Ja5ifCX4z8BZluYneQ++6z9zw8J115E4TjAF57zZ0lvEFy113WOUEkBEKEgAqEIXKGVPEFAU6/efpp96KZpmK6SpQMBBTjJ8PPSbNSBcKkeTyLvW+99RYu5Gsk8LJjfrRs2RINGzZE2bJlM/53KurVq1eqJfrzPBBIXPLg4YcBZle9EDOzuc358CLXCS9bJ/GWtBfi/BW3hVLuW6qUVWDUdGwvXkgMb6hrcKFWLjGfiAy1gQAbK9x6a86jqGywZyxhp2z+6ubsElH8EVDyIP4+zs3CRMX4vGDKi3MsrrklFsaYma1Uya0EM3wscrK9qBfiSAAvl2irVgUGDAjPi0ovWIg3NgioQBgbV8qQHBDgyAAW+DZtcg9PyZJWjJ91mox7aeIMOwKK8cPuIennBgEVCN2gFhMeFgSnT5+OatWqYcyYMTjqqKNiYlk0zEhU8uD334Gbb3b/7CLTpZwG3blz+hy8YYO1v5eDvyntb7oJOPFEU9IkJ8YIhLoGF2rlYvxRyDTHCHzzDdC7t2O2AxguvRT4z3+8y5GE8COg5EH4feSXhomK8ZcsAe6+2zuU554LXHyxdzluJfz6K9Ctm1tus3zsuHL00WZlSpoQ8ICACoQewBNr6BHw+now00COkm3dOvTmSkEDCCjGNwCiRIQOARUIQ+eS4BQqXbo0/vrrLwwdOhQdO3YMbmPtlIFAopIHI0YAH3zg3fO8ksWrWbyilQ567z3g9dfTsfOBex5xBPDQQ+HQRVqEGoFQ1+BCrVyo3SrlAkaA43NnzfK+aeXKAB+i22jU4H0zSUgrAkoepBX+tG6eqBh/8GBgyhTveJcpAzzzTPq6Y7z8MvC//3m3w4SE448Hunc3IUkyhIARBFQgNAKjhIQUAd7JWLTIu3K1awN9+3qXIwnhR0Axfvh9JA2dI6ACoXPMYsNRoUIFrF+/HnPmzEGjRo1iY1dUDElU8uCWW7z1Zcvq1K5dgRYt0uNm3pDmTemw0IsvqlddWHwRYj1CXYMLtXIhdqpUCxQBPhq/7DJzj8f79QMOOyxQE7RZGhBQ8iANoIdky0TF+LxkumWLGeTvvx+oX9+MLKdS+PRjzRqnXP6sZ5tSXq7kv0VCIAQIqEAYAidIBV8QYIOoa681J/q554By5czJk6RwIqAYP5x+kVbeEFCB0Bt+keZu3rw5Zs6cifHjx+PUU0+NtC1RVD5RyYMrrwT++suMm666CjjrLDOynEq54QZg9WqnXP6tf/xxoHp1/+RLcqQQiGStLZJKR+qzkLIGENi4EbjmGgOC/hbRsyfQsKE5eZIUTgSUPAinX4LQKjEx/q5dwCWXmIOUg16bNzcnz4kk9n/eudMJh79rdQnQX3wl/QAEVATUR5FEBH7+2eyDbV0CTMZXpBg/GX5OmpUqECbN41nsffbZZ3H99dfjhhtuwKBBgxKMRHpMT0zygPDy6QWnP5ugyy8HzjnHhCTnMjp1Atavd87nF8cjjwA1a/olXXIjhkAka22RVDpiH4bU9YzAn38CXbp4FvOPgDvuAJo0MSdPksKJgJIH4fRLEFolJsbfuhXo0MEcpDfeCJx0kjl5diXt3QtcdIHt+AYAACAASURBVJH3Wel297OzjiMVype3s1JrhIARBFQgNAKjhEQMgWXLgLvuMqc0W4yy1ago3ggoxo+3f5NqnQqESfU8eElyJ0477TR88cUXePvtt3FOuoouCfVBYpIH9O911wHr1pnxdOfOwCmnmJHlVApvNq9Y4ZTLv/Us7Fep4p98SY4UApGstUVS6Uh9FlLWAALbtgFXXGFA0N8iHngAOPJIc/IkKZwIKHkQTr8EoVViYnwW1i6+GNizxwysd94JNG5sRpZTKSa7nTjdO6f1w4cDRYuakCQZQsAWAioQ2oJJi2KGwKpVwM03mzPqySeBgw82J0+SwomAYvxw+kVaeUNABUJv+EWemzMIO3TogI8//hgXXnhhxj9169ZF8eLFU9pWo0aNlGu0IHcEEpM8IAQPPwzMnWvmc6CsWrXMyHIqhRHf9OlOufxZX6IE8MILQMGC/siX1MghEMlaWySVjtynIYU9IsAcOJMHv//uURCskVKcT1K6tHdZkhBuBJQ8CLd//NQuUTE+i3o//ugdznz5gKefBipV8i7LjQTe3PjuOzec5nl4+e+ppwBiIhICASGgAmFAQGubUCHATtmcQWhiGg5TqEzPFCoUKhOljA8IKMb3AVSJTDsCKhCm3QXpV+CTTz7BJZdcgjUOBrPny5cPu/i3qcg1AolKHsycCfTv7xqrfxgPPxxgW810HZiZOGACIQzUtq3ZJy1hsEk6eEIgkrW2SCrtyU1ijigCo0cDI0d6V75FC6BrV+9yJCH8CCh5EH4f+aVhomL8CROsjKhXOuYYoEcPr1Lc83/+OTBwoHt+k5wczdCunUmJkiUEUiKgAmFKiLQgpgjwwfZHH3k3rk0boGNH73IkIfwIKMYPv4+koXMEVCB0jlmsOLp27frP/MG9vCJvk1gg3L17t83VWpYTAolKHvBb4VwRDnLyQulsL0q9+TPSrVs42ozyZvFBB3lBU7wxQyCStbZIKh2zD0fm2EJg40ZrDuHOnbaW57pI7UW94RclbiUPouQts7omKsZnD2aOEuA8Qi+Uzvai1JsXX/lLfsMGL1Z45+XTkyFD9MzcO5KS4BABFQgdAqblsUFg5Urgllu8m6P2ot4xjIoExfhR8ZT0dIKACoRO0IrZ2hEjRuCKv4fqlCpVCueeey4aNmyIsmXLIn/+/CmtZWtSkXsEEpU8IEy8lsXrWW6pXDmr3U6RIm4lmOH79FPg2WfdyypZ0mqR+vXX7mUcfzzQvbt7fnFGGoFY1dRiZUykPyspbwMBPpLhYxm3VKcO0Lt3+h7Bu9VbfO4QUPLAHW5x4EpcjM/n1Xxm7ZYOOcTqNGLj/Ol2C1t8770HvP66raU5LmJ7VPaPXrrUvYzTT7f63YmEgE8I5Bp6Kyb3CXGJjQIC/CuITa/cEsfn8p6LKBkIKMZPhp+TZqUKhEnzeBZ7mzZtilmzZqFevXqYNGkSqnDegSgwBBKXPNizx2rdM2OGc4xZFOSzi5o1nfOa5uArQg6QYqHQKXFe4D33ANWrW//mdTWnVK0a8NBDAAuNokQiEKvze6yMSeTnmCijt28HHnwQWLzYudm849KnD1CxonNecUQTASUPouk3E1onLsbn6zvOCJ8/3zl8pUpZNyeqVnXOa5qDZxVmiWfPdi65WDHrLwjOCO/ZE1i3zrkM3iLp1QsoXNg5rziEgE0EVCC0CZSWJQqBzZuBe+911yjq4IOtv8aUnknOJ6MYPzm+TpKlKhAmydvZbC1Tpgw2b96M119/HRdccEGCkUiP6YlLHhBm9mYbPBiYPt0+6Dxo3347UL++fR6/V7JlKp+SOCkSssh5221Ao0aWdpz5yWTKL7/Y1/bQQ635LOXL2+fRytghEKuaWqyMid2nJoNyQIAJhH79gIUL7cPDRyV33w3wfocoOQgoeZAcX2e3NJExPluMPvGEsw4ZvDnBuPaww8LzsezYYXUscfKUhEVOPh2pW9eyY8UKoG9fYPVq+3bVqwfccYcyzPYR00qXCKhA6BI4scUegbVrrfTMTz/ZN5X3vvnXmC4A2scsDisV48fBi7IhOwIqECb4m6hYsSLWrVuHOXPmoFFm0SLBeARteiKTBwSZt3PHjgU+/jjvgzPbDB13HHDxxQCvZYWN+JLwk0+ADz/M+yVgvnzAscdadtSosb8VW7YAb74JTJqU9+yW4sWBk08Gzj8f4H+LEo1ArGpqsTIm0Z9looxn/vjdd612o5s25W46H4G0bAlcdBFQtmyiIJKx4NjlX9GlS/UMLH755RccwjaKokQgkNgYny8JP/jAivPXr8/d15yz17y5FRtXqBC+b4JnFZ5TxozJe356gQJA06aWHdk78dD+UaOAzz4D+JdGbsTiItuKtm+vl4Ph+xJiqZEKhLF0q4wyhADTM2+9ZaVn+N+5ER+NMz3DdxZKzxgCP0JiFONHyFlS1TYCKhDahip+C1u2bIkZM2bg448/xllnnRU/A0NuUWKTB5l+4eF73jxg4kTrmhYjMLbgZBaVhcHTTovGSzna8e23wPjxwPLlwF9/WXZwBgmb0fPQz+cjedG2bVYCYcoUq2jK/82ok1fRWrWyMsxFi4b8i5Z6QSEQq5parIwJ6gvQPmFBgI/iv/jCSiKwYzQf0PCxOB9589c2f32r3VBYvBW8HkoeBI95WHZMfIzPQuGsWVanDb6mY4zPX46M8Vu0sLKqjJPDTuwY8tVX1oVAdvxgjM+bH2XKAM2aAaeemvr2B5+dM75nnM/nKexVzRifLVWJA+WwYCoSAgEhoAJhQEBrm0gjkDU9w8ZPjPGZjmFaR+mZSLvWiPKK8Y3AKCEhQ0AFwpA5JEh1hg4dimuvvRaXX345hg0bFuTW2gtA4pMH+gqEgBBwhUCsamqxMsaVO8UkBIRATBFQ8iCmjrVhlmJ8GyBpiRAQAmlBQAXCtMCuTYWAEIgRAorxY+RMmfIPAioQJvxjaNeuXcYLwoEDB+LGG29MOBrBmq/kQbB4azchECUEElM3S4yhUfr6pKsQEAImEFDywASK0ZShGD+afpPWQiCsCAQSLgeySVgRll5CQAgIAfsIKMa3j5VWRgcBFQij4yvjmk6dOhU7duzAvffei5kzZ6Jp06a46KKLULduXRS30Uj7pJNOMq5TkgQqeZAkb8tWIeAMgcSc0RNjqDP/a7UQEALRR0DJg+j70K0FivHdIic+ISAEckIgkHA5kE3kXyEgBIRA9BFQjB99H8qCAxFQgTDBX0X+/PmRL18+VwiQbxfnS4hcI6DkgWvoxCgEYo9AYs7oiTE09p+sDBQCQiAbAkoeJPeTUIyfXN/LciHgBwKBhMuBbOIHOpIpBISAEAgWAcX4weKt3YJBQAXCYHAO5S4sELolFgh3c3C7Qdq4cSPGjBmDWbNmYfbs2VixYgVWr16NrVu3omzZsqhfvz7atGmDq6++GhUqVHC98yuvvIIrr7zSFv/LL7+Mjh072lrrdJGSB04Ri/H6PXuAsWOBceOAjRsBFt8LFgRKlwbOPBM46ywg1c8r+UaOBObNAzhVe+9eoFAh4OCDgUsvBerVSw3gn38Cn3wCLFkC/PWXtSd1OOYYoGVLazJ3FGj9euDTT4EffgA2b7bsKFUKOPpogC+fbbyQTreZiTmjJ8bQdH9R2l8ICIGgEVDyYB/iivHdn1uC/m61n2EEGNO//z4webIVk/L8zBi/XDngP/8BWrVKvSHj8+HDgQULrBifVLgwcOihwOWXA4cdllrGqlVWjL98uRXj84zAGP+444BmzSx5UaDVqy07li2z7ChQwLLj2GOBE04AihSJghWOdQwkXA5kE8emi0EICAEhEDoEFOOHziVSyAACKhAaADGqIqZMmeJJ9VZ2DjQOdpg4cSJOP/30lBwVK1bEiBEjcCYLJy5IBUIXoInFHwR27AAGDwZmzbKKgrkREwlNmwJduhx4gP/pJ2DIEOugnBfx8Ny+PdCmzYGrFi0CPvgAmD0bYLEyJypWDGjdGmjXDvBQoPcHyL+lEgPa8eWXVgImJ2Li4MQTraRMlSq+quNFeGLO6Ikx1MvXIF4hIASiiICSB/u8phhfBcIo/gx70pnFwKefti7u5RZbcwPGpby8dvXVB14GZEHwhReAX3/NWxXG5RdfbMnJTt98A3z4IfD117nL4CW6U04BzjnHKraFkRYutGL8OXOsS5A5UYkS+84qLMDGiAIJlwPZJEZOkSlCQAgkFgHF+Il1fawNV4Ew1u6NlnFMHlx11VU4+eST0bhxY1SvXh1Vq1bFnj17wNd2b7/9Nt59992Ml4uFCxfOmJvYsGFDx0ZmLRCOGzcOB/OFVS50yCGHZLxe9IP0gtAPVCMkc+1a4PbbgU2b7CvNQ3v//kDmNzl9OjBwYO4H5ZwkN2kC3HHHvj8ZPx4YOtS+jDJlgDvvBGrXtq93ECunTQOeeSb3wmB2HZhE6N4daNAgCO0c75GYM3piDHX8CYhBCAiBiCOg5ME+ByrGV4Ew4j/OztRfsQLo0WPfaz873JUrWzF+ZrcOdhZ56SU7nPvWnHoqcN111v9mEe2994BRo+zLqFTJ0vuQQ+zzBLFyzBhg2DD7ZxUWB++6Czj88CC0C2SPQMLlQDYJBC5tIgSEgBDwFQHF+L7CK+FpQkAFwjQBr20PRICFvwJsE5IHvf/++zj33HMzVvDfLBg6pawFwh9//BGH2WnL4nQTG+tVILQBUlyXbNkCXH89wH87JRa2+GLw22+Bfv2cclvr2YaHB2cWB1980bkMJi8eeCA8B28WBwcNcm4H2yvde6+99qvOpXviSMwZPTGGevocxCwEhEAEEVDyYJ/TFOOrQBjBH2F3Kq9ZA9xyC7Bzp3P+8uWtV4dsR/r88875yZFZJHzzTeDtt53L4GXE3r2Bgw5yzusHx8cfW8VBp8RxAg8+CNSo4ZQzlOsDCZcD2SSU8EopISAEhIAjBBTjO4JLiyOCgAqEEXGU1NyHQL169fDDDz+ArUY5o9ApqUDoFDGtN47ArbcCvF3slvjqlbNE8mpZlEo2WwlNmmT/Nm52ebyd+8QTAAuW6aQffwTuvtv+y8HsupYsadnh00tht9Ak5oyeGEPdfgniEwJCIKoIKHng3HOK8Z1jJo6QIdCpE8BZ2G6pVi1rbEBubTTtyOXscr5AdEs8Zzz2mDWnMJ3E9qh9+rjHomJFK8aPyAz1tIfEaVcgnR+b9hYCQkAI2EdAMb59rLQyOgioQBgdXxnXdMOGDRjI9oQArr322ox2nnnRypUr8QLnIAC47bbbUCJNhYHjjjsOs2fPRsmSJbHJSXvGv41TgdD4pySBThBYutRq35Nuyp/fW4GR+l9xBdC2bXotefJJgK1WvdB55wEXXOBFgnHexJzRE2Oo8U9EAoWAEAg5AkoeOHeQYnznmIkjRAjMmAEMGJB+hdiRJ7dZ3Ha1u/lmoGVLu6v9WcduJd995032NdcAZ5zhTUZA3GkPidOuQEBAaxshIASEgEcEFON7BFDsoURABcJQuiUYpZ555hnceOONqFOnTsaLvFS0d+9e8GbvkiVL8Pzzz+NqDlMPmKjnv/71L+zatQtNmjTBrFmzHGugAqFjyMRgEoFevYAFC0xKTJ8sXipgIoTFxnQQb2h36eI9CcLXkIMHAwULpsOKHPdMzBk9MYaG5tOSIkJACASEgJIHzoBWjO8ML60OIQLdugG//hpCxVyodMQRwEMPuWA0xEIciadXql7dmu2YL59XSb7zpz0kTrsCvkOsDYSAEBACRhBQjG8ERgkJGQIqEIbMIUGqc84552DMmDG4++678ZDNA0CvXr0y1rZr1w6cBxgEbdmyBStWrMCHH36Ifv364ffff8/YdsSIEbj00ksdq5C1QNi6deuM4uiaNWtQunRp1K5dG6eddhq6dOmCatWqOZbthEEzCJ2gFZO1O3YAl1/uvlVOGGG45x7g6KPToxlnkI4aZWZvJiGaNTMjy4CUxJzRE2OogY9CIoSAEIgUAkoepHaXYvzUGGlFRBBYuxbo3DkiytpUk4W1dM3we/ll4H//s6loimWcRVivnhlZPkpJe0icdgV8BFeihYAQEAIGEVCMbxBMiQoNAioQhsYVwStSvXp1/Pbbb/joo4/w73//25YCY8eORZs2bVCjRg0sX77cFo+bRVmLeDnx33XXXejbty/yubgNmEo29ytatCiefPJJXHfddW7Uz+BhATAvYsvW448/PmPJL0OG4JAKFVzvJcaIIDB/fnpv4/oB0/nnA/wnHfTII8BXX5nZ+eyzgQ4dzMgyICUxZ/TEGGrgo5AIISAEIoWAkgc5uytVHK4YP1KfuZTNRGDCBODvURyxAeXaa4HTT0+PORzHwLEMJuiSS4D//teEJF9lpD0kTrsCvsIr4UJACAgBYwgoxjcGpQSFCAEVCEPkjKBVKVKkSEarzjlz5qBRo0a2tp83bx6OPfZYkHfr1q22eNwsyi15QD3Z3pQzStwSZfMVZPv27dG8eXOwUEpatmwZ3nnnHbz99ttgO1XSc889h04cNu+CnBQvVSB0AXAUWSZPBp55Joqa565zmzZAx47psYmvFxctMrN369bA9debkWVASmLO6Ikx1MBHIRFCQAhECgElD3J2l2L8SH3GUtYuAm+8Abzzjt3V0Vh38cXAueemR9ebbgL+7hrkWYH//Adw0XXI874OBaQ9JE67Ag4B03IhIASEQJoQUIyfJuC1ra8IqEDoK7zhFl6mTBls3rwZ06ZNQ4sWLWwpO336dLRs2RLFixfP4PWL1q9f/88LPBYily5dijfffBPvvfceatWqlfG6r23btq6237BhQ0Y70dwKeHxRyeLhzp07M+zk3gcddJDjvVQgdAxZ/BmmTgWefjpedqbz5d299wI25qfaAvzkk615hiGhxJzRE2NoSD4sqSEEhEBgCCh5kDPUivED+wS1UZAIvPUWwH/iROl8eXfzzcCqVWbQ5OtB2hIwxSrEjZUxAX8I2k4ICIHYIaAYP3YulUEAVCBM8GdQr149LF68GAMGDMDNDMJt0FNPPYWuXbuiZs2aWLJkiQ0Os0teffVVdOjQIaO4N3ToUHT06eVS7969cS+LDwD43z179nRsiFqMOoYs/gwLFwL33RcvOy+6CGjfPj029esHzJ5tZu927YDLLjMjy4CUxJzDE2OogY9CIoSAEIgUAkoeOHOXYnxneGl1yBCYNAkYMiRkSnlUhzMVTznFoxCX7Dx7L17skjkbG+e/n3OOGVkOpMQqxI2VMQ6cqKVCQAgIgRwQUIyvzyKOCKhAGEev2rTp2muvzSiy1a1bF/Pnz0ehQoXy5OSLuqOOOiqjqHj55ZeDLYLSQRdeeGHGa8ISJUrg559/Rvny5Y2r8ccff2S8GmSr0dNPPx3jx483vgcLiJntTdVi1Di84RS4Z4/V4mb37nDq50arvn2B2rXdcHrnGTsWeOkl73Ioge1Kjz7ajCwDUhJzDk+MoQY+CokQAkIgUggoeeDcXYrxnWMmjpAgsGULcOWVwN9jKkKilXs18uUDBg0CKld2L8MLp8mWrf37AzVqeNHGFW+sQtxYGePKnWISAkJACPyDgGJ8fQxxREAFwjh61aZNme1C+RqPLTWHDRuW0VIzJ9qyZQuuuOIKvPvuuxmv9z799FO0atXK5k5ml7322mu49O85AiNHjsQlPrUMqVSpEtasWYP69evju+++M2sEkNFCVQVC47CGX+BjjwGzZoVfTzsa1qoFPPywnZX+rGEy5rrrgO3bvcmvWhUYMADIn9+bHIPciTmHJ8ZQgx+HRAkBIRAJBJQ8cO4mxfjOMRNHiBAw+eot3WYdeyxw113p0+LPP4EbbgB4udIL1asHPPigFwmueWMV4sbKGNcuFaMQEAJCIAMBxfj6EOKIgAqEcfSqA5tYXBs1alRG0e+QQw4BXxWeeOKJqMqEOYCVK1di6tSpePHFF/+ZCXjeeefhDd7qSxNNmDABZ5xxRsbuffv2RY8ePXzRpHLlyli9erUKhL6gm2Chv/8O3HRT+gHgi+GdO73pwZl9nN2XTnrhBWDCBG8adOgAcJZiiCgx5/DEGBqij0uqCAEhEAgCSh44h1kxvnPMxBEiBBYsAHr1Sr9ChQsDO3Z404PFQRYJ00l8+TdzpjcNOEalZUtvMlxyxyrEjZUxLh0qNiEgBITA3wgoxtenEEcEVCCMo1cd2LRt2za0a9cOEydOzCgS5kZstUliu83Ro0ejaNGiDnYxu5StTa9kCxcAnIl4kw/FFhYGq1SpktFi9LTTTgMTFqZJLwhNIxoheQ88AHh5ldqgAcAZoF5ezl18MfDuu+5lsFUP24syCZFO+uMP4M47gb/+cqdFlSoAZxkWK+aO3yeuxJzDE2OoTx+KxAoBIRBaBJQ8cO4axfjOMRNHyBC47Tbgl1/cK9W8uVUU8zKOgBffRoxwL+PII61CZ7o7ayxfDvBVptsLjYcfDvTpAxQs6N4fHjhjFeLGyhgPThWrEBACQkAvCPUNxBQBFQhj6lgnZrEINmjQIPTv3/+fV4LZ+dkK8/bbb8cNN9yQZyHRyb5u15599tkYM2ZMBvukSZPQunVrt6Jy5evTpw/u4UwyAA899NA//21yIxUITaIZMVm7dgG33AKsXu1ccRa0Bg60kg8sjLlpvdO2LXDFFcBXXwFseeo0CVGhAn8wgIoVnevvB8f331sJAKcJhFKlgN69gb9fTPuhmluZiTmHJ8ZQt1+C+A5A4Py3cgZF35I+lpAhoAKhc4coxneOmThChsC2bVZrzE2bnCvGmd68fDd/vhWfuplnePnlwDnnAFOmAIMHO9ehWjWrJSdj5DAQi6WPP+4ci0qVLAzLlUubFbEKS2JlTNo+CW0sBIRATBBQjB8TR8qM/RBQgVAfxD8IsFA4b948zJ07N2P2HqlixYo49thj0bBhQ98Lg7w1fNFFF+X5OnHAgAHo1q1bhm6HH344Fi9ejAIFCvxjw+TJk3Hy3y0PO3ToAMrMSsuXL8e6detwzDHH5Or5jz76CP/3f/+HHTt2oFixYhl7VONhyTCpQGgY0KiJY5GQBT4nt4wPO8xKHGTehOXNWhaynbQRat8euOiifWh984118N661R6C1asDbOsbluJgptYLF1ovATdvtmcHC6133x3K4iANSMw5PDGG2vsstcoGAioQ2gBJS8KAgJIH+7ygGL9CGD5J6RAUApyR3b078Pd52ta2Rx1lvZbLfLXHIiFjfieX+K6+GjjzzH3bzZgBPP20/Qt0desCd9wBlC5tS+XAFvFCI2eF2+2ccuih1lmlfPnAVMxpo1iFuLEyJq2fhTYXAkIgBggoxo+BE2XCAQioQKiPIjQIHHbYYdi0aVNGca5ly5aoVasWSpYsmfH/zZ8/HyNHjsTnn3+eoW/hwoXx8ccfZ7T/zEqpCoSZf968eXOcc845GYVPzhokLVu2DG+//XbGP5ktVQcPHozrr7/eF4xUIPQF1ugJ/fBD4KOPgHXrctedB9x27YA2bQ5cw4IYC+FffJF3obBWLYAth+rVO1DGn38C48YBn34KbNyYsx4sDDLp0KoVUKRIOHFev96aRzhxYu548rUg7eBFgpC1Fc0KamLO4YkxNJw/MpHUSgXCSLotiUorebDP64rxVSBM3O8Advh46y1g/Pi8XxPyHHreeUBOHXEY1774otXxgxcLcyKOCGFsf9VVAAtj2WnVKmDsWGDyZICFy5yILxfPOMOa1Zemdpwpvw8WW2nHpEm540n7GeOfdFL6RyDE7bKf4vWUn6gWCAEhkBwEFOMnx9dJslQFwiR5O+S2Mnnw008/pdTykEMOwUsvvZQxDzE72S0QptqkePHi4GvFTp06pVrq+s9VIHQNXTwZOVPwzTcBFut4Q5ZFOL7Su+ACgMW9VMREBAt8TACwaMgbxyyA1akDcN5gyZKpJFg3jNnKh7pwph9vMfMWcaNGAOeR5DGnNLXwAFcwiTJnDvDDDxYWtIP2H300wBvaEbAjMefwxBga4Pcf961UIIy7h2Njn5IH+1ypGF8Fwtj8YLsxhK8B33/furzGrh9FiwIHHWTF53a61DDG52VCXgZkfM7Wo8WLA/XrW+cEyktFbH1KfnYfoYxChawY/7jj7J0zUskP6s+J35dfAkuXWnawoMl2qI0bA3wBGaIYP1YhbqyMCepj1T5CQAjEFQHF+HH1bLLtUoEw2f4PlfU//PBDxqtAvhJcsmQJfv/9d/z5558ZbT75yq9Ro0Zo27YtLrjgArCAlxOlKhDyNeIHH3yAGTNmYPbs2Vi5cmVGO9Vdu3ahXLlyaNCgAU499VRcc801/7ws9AskFQj9QlZyhUD0EUjMOTwxhkb/mwyNBSoQhsYVUiRvBJQ82IePYnwVCPX7QggkDYFYhbixMiZpX6LsFQJCwDQCivFNIyp5YUBABcIweMFnHW6++Wb06NEDVdlazwdiS04W2Dg/UGQfARUI7WOllUIgaQgk5hyeGEOT9gX7aK8KhD6CK9EmEVDywCSa0ZKlGD9a/pK2QsAPBGIV4sbKGD+8LZlCQAgkCQHF+EnydnJsVYEwAb7Onz8/ihQpgquvvhrdunVDzZo1PVu9c+fOjFl9ffv2xffff49evXrhvvvu8yw3SQKUPEiSt2WrEHCGQGLO4Ykx1Jn/tToPBHIrELoBTd+fG9TEYxMBJQ9sAhXDZYrxY+hUmSQEHCIQqxAjVsY4dKSWCwEhIASyIaAYX59EHBFQgTCOXs1m0xVXXIGRI0f+8/82bdoUl112Gdq3b4+DOP/AJrEoOG3aNLz22mt49913sWHDBuzduzej4Dhs2DCccMIJNiVpGRFQ8iDLd8B5Elu2WPMwODePM+OCJs6tW7PGmsNXqZK9eR6mdeSMkY0bgfXrgbJlrdkgTrGgDGJJe0qUsDB1SuTlXI8CBawZJ051cLpfTuupA2e10Bb6I5e2wia2CqOMJacd1AAAIABJREFUsJ3D92IvtmIrtmM7SqAECqOwGdgcGur1VwXH9nAMD//hJ1W4cKjG1ZjBNO5SVCCMu4djY5+SB7FxpWNDFOPnEOPzL1zG+OmYEcfgYfVqSynGlNQlaGJ8zviecX758tZsbKfxNWUwPue/GeNz/p5T4jknc3ZfOmN8nrnol8qV03Pmcoqbi/UOQ1wXO5hh2YM9GTH+TuxEcRTPOcYPwBh+Dvw0ixRx96uCMf7WrdZnlRnjm0FIUoSAEBAC+yOgGF9fRBwRUIEwjl7NwaaZM2finnvuwcSJEzP+NN/fh7Pq1avjuOOOwzHHHJMxc49z+PjP1q1bsXbtWqxbtw6LFi3CrFmz8M0332AHIy5wNvpeVKpUCffeey86d+6Mgm4OKAnBPjczE5882LQJmDQJ+OQTYOXKfTCxoHXcccAZZwBHHulvIoFFqHfeASZMsA7sWYnJg6ZNgSuuAMqU8fdrnTcPeO014Kef+MO1by/+nB5+OHDxxUDDhrnrwETB/PnAuHHA3LnA7t371taoAZx2GnDSSXkX2VgxmTbNwmL58n38LBJyb/qjUSPnyQwnyNGOsWOBDz8E/vxzf07+juH+9IeDiw1Otg/T2gDO4bbMXYZlGIdxmIEZ2IZt//BUREWcjJNxKk5FeZS3JSvHRTYM5Y/m5Mk5/6o4/njgzDOBI47I+1fFzz8D48cDn31m1ZwzqVw5oHVr4PTTgYoV3ZshzgARUIEwQLC1lRcElDzwgl60eRMf4/OCF+P7Tz+1Lt9lEjP/zZtbf3HXquWvkxnXjhplBRBZ/+LnrkWLAieeaMXXLNT5SdOnA2+9BaxYsf8ujPHr1gUuv9z6d27EmH7OHCs+Z6zPWDmTiCEDGF7SJba50ebN+wKprHrwzNW4sRXjN2jg/5nr/fetOD/7mYt6MKAjFiyexoRshLhps5QX/xZiIcZjPGZiZkZxMJOqompGfN8arVEapa3/2ydj1q61flUwJZD1VwV/RDN/VeTV/IrH5qVLrSPwF18A27fvg5R3AU49FTjlFOverUgICAEhYAoBxfimkJScMCGgAmGYvBGALiz0PfnkkxkvALf/HUFlFgvz2p4FwUxq3LgxOnXqhEsuuQQleHtR5AqBxCYP+N0NH24dVHmLNS+qXh248krgX/9yhXGeTC+9ZFUMsh60c2NgofLee93d1M1Lie+/B/r3B3hwT0WlSgHdu1tF06w0e7aF56pVeUtg4oAJACZDshb0mXhg4uJ//7OuXeZFvOV76aXWic00MWnw5pvWy8dUxO/igQf8T+qk0sPHP/fpHG5bYxYGh2IoFmNxnjz5kR8t0AJX4SqUhIskWx6GMrfHT3vKlNS/KlgHv+oqoH79/dX99VfghReABQvyNp15Ot5LuOYaJRFsfyTpWqgCYbqQ174OEVDywCFgMVqe2Bif8ezLLwMsimW9rJaTb1nc4l+6pguFjOsHDbJ0yHrpLicd+Jc/C2SMr52+5kv1vX75JfDMM6lja8phUaxnT4DxbVaaOhV4/fUDL81l35vn8bZtgXPP3d8OXuwdMcIq1P59yTdXtatVAzp2zPtCYiqbc/tzBnNjxtg7c7FYyrEl6Xjl6da+XPjSHcvnZs53+A4v42X8jJ/ztLgQCqEVWuEKXIGib11uFB3+quBRnD+mqY7ideoAV18NZC8ULl4MDB0KLFuWt2q868oaOs8JCWtIY9RnEiYEhMA+BBTj62uIIwIqEMbRqzZs2rhxI0aPHo1JkyZltA1dyqtXuVDx4sXRrFkznHjiifjPf/6DRnzFI/KMQCKTB3w1+PDDwJIl9vFjVN+5M9CqlX2eVCsfesi6heuEePXwiSfMFaX4Wu/pp1MnL7LqyETGLbcALVpY/y8P28OGOZNx1FHA7bdbt6eZLKBNX33lBAngkkuA//7XGU9eq4cMsa6OOiHq/9hjQJUqTrgiszadSYW5mIsn8ERGO1G7VA3V0BM9wZeFjigXQ3m5nL8q8vir6YBt+KvihhuAli2tP2L9vV+/Ax8O5KUfbxszR3fwwY6s0OIgEVCBMEi0tZcHBJQ88ABexFkTGeOz80Pv3ge+lMvLlywC3XqrVaQzQaw03Hmn1ZHDCbEzBeNhUx1x2Anj1VedaGC19b/7boBxOgubb7wBvPuuMxnNmgE332zZwV6NjzwC/PCDfRkskl57rfXsyhQxmGN3EyfEEQv0B/8dYUpnLJ8bbNMwDc/gGexGlm4zKTCuhVroMfoLlN7hMMbPRS5fCvbp4+xXBe+5dusGHHOMJXTWLODJJ1NfIMyqAuvvjPFj9Eg1wj8dUl0IRBsBxfjR9p+0zxkBFQj1ZWQgsHr16oyZePw3W4sWLVo0o4Uo/+GMQbUQNf+hJC55wGIUC3NODqqZsLMwxqJWkybeHfH44wBv9bqhChWsW8leEwgsTjKJkupmc046EotevYA//rBuJrshnq54W/qpp9xjwVvffJHolTgfdfRod1J4DZQYxPA6aLqSCouwCA/ggf1aDdl1DouED+EhZy8JczCUj4wffBDgzWCnxNzWHXdYh3/+mKR6FJuTfBYJmbhQOyKn6Ae0XgXCgIDWNl4RUPLAK4LR5U9cjM9iFF99/fKLc6exvSS7dNSr55w3OwfluDlnUA5bEfBWkdeXhLwAyLOCG2KRkDqwoMaXf26IIwU6dbJuWX33nXMJPGewaMtio1ciDsTDDbEH/ODB3s9cbvY2xJOuWD439b/CV3gUj4LtRZ1SnT+bodfkT1F4TzGnrPut58tB/phm77hrRyh/VTC2Z7MZHqPtNJ3JLvfQQ60mNDE8OtqBUGuEgBAwhIBifENASkyoEFCBMFTukDJJQiBxyQPO2GMbSbdUrJj14o6tNt0S23Hy4O2FePC+8Ub3Eni7mS182D/RLfEaJVs3uTkZZe7JWR8zZ7rVwEqg8Havl6dWPB0yCeGFmFBiNSlmlI6kAueP3ISbsBZrXaN5Ek7CjXDw85GDocyJffCBaxXAXxW8dP777+5l8C4CC42iECKgAmEInSKVckJAyYPkfheJi/F5WYujA9wSi0EsJnlpK8muGq+84lYDi699e+Cii9zLYFzOOXqp2qvmtQPPOey44oW8xvj0A89cXm5Kff21ddvKC7FIyWdjEaV0xPK5QbUZm3EDbsBWpBgnkQfW7RbejsvmeztH87Ni51y3xF8V/PHKPsbSibzTTrNq6CIhIASEgFsEFOO7RU58YUZABcIwe0e6xRqBRCUP+HrwuuusdjdeiIfuc85xL4GHTA4l80K8vsi2QW5vGHMOyLPPetHADC9vKXtJYFCLf//bmhHplvr2BebNc8tt8fGmM+fdRPQqaJiSB5/hMzyFpzz5owAKYAiGoCzK2pOTDQDWzdlReMsWe+x+reJnxQe2Me1g6xdswcg1WSDMTeMw/WAGg6p28QEBJQ98ADUiIhMV4zNTz7+4vVxao195+Y6X8NwSzxnr1rnltvhKlrQGo7klzgt87z233Ob4TMT4LJSyYOqWeMtq+XK33BYf7eCZy2vnFm9auOYOUyjxMT7GMAxzbQsZS6AEnsWzKIIi++Q4MHL9eqBLF+/HT09GAOBR/rnnzE0N8aqP+IWAEIgeAorxo+czaZwaARUIU2OkFULAFwQSlTzgrWK37TCzos9s/cCB7opznI3CU4kJ4qT0M890J4kJELYHjQPxqRZPWJwH6JSYSLrsstST6e3IZatTtjyNIDk4V/tu3X24Dwux0PM+F+EitIfNpFI2AMJSPycI7dpZn6goZAioQBgyh0id3BBQ8iC530aiYnx2B2GXEK9Ut67VN9ANcbY55/eZoB499g06cyqP5wOvr/+c7unXeo5VYItPNxciN2ywZhmaINNzz03oZFNGWGL8PdiDW3ErVmKlTc1zX9YZnXEKTtm3wIGRHKk5apRnFYwIuOIKoG1bI6IkRAgIgQQioBg/gU5PgMkqECbAyTIxnAgkKnnAuSQLvRceMjzJ4QMNGjh3Kg+5U6Y458uJg4VKN/NFeHUybj1Nrr8eaN3aOa6mEkrcuUQJ6xVhBMnBudpX637Db+iKrkb2qIIqGASb83eyAXDPPcCiRUbU8CyEXb5eeMFdbszz5hKQOwIqEOrriAgCSh5ExFE+qJmoGP+WW4CV3gsPGW5g6/pDDnHuEc4453xvE1S7NsAOF07JZJHS6d5+rWfRtVEj59IZPE2Y4JwvJ46KFc1cMjWjjSMpYYnxF2ABeqGXI91zW1wXddEbWQr5DowM0x1Z/prhrxuREBACQsANAorx3aAmnrAjoAJh2D0k/WKLQKKSB7xFytukJogFNg4PcEo85PLwboL4Ym74cOeSTMxAdL6rvxznngtcfLHzPXgq++IL53w5cbAf5BtvmJEVsBQH52pfNZuDOXgUjxrb43W8DrYbTUnZALjqKmDz5pRcgS1gpzF2HBOFCAEVCEPkDKmSFwJKHiT3+0hMjM+52owB9+414+zbbweOO865LJOVBw45Y3cMp/Thh1Y7zDgRxwhwnIBTMnkxlPMQOZw6ghSWGP9TfJrRGtQElUIpDMXQfaJsGsnGMXwMGhZi91o+fOYRUiQEhIAQcIqAYnyniGl9FBBQgTAKXpKOsUQgMckDeu/SS4GdO834kT3/2PvPKZmYP5i5J2dhuGmn9Mkn7pIOTm0Ncv1ZZwGs6jgltpH65hunXLmvf/NNc7IClGTzXO27RibmD2ZV8iW8hJKwUVnLBgDzjF5HY5oE6+mngcqVTUqULM8IqEDoGUIJCAYBJQ+CwTmMuyQmxufA4I4dzbnA7RxCXh5klw4T5LYrBc8F7I4RJ7rwQuD//s+5RSbmD2buykoOZztGkMIS43+EjzAcLi625oA5L//xEuA/ZNNIXv5zc1z00+2sO7P+LBICQkAIOEVAMb5TxLQ+CgioQBgFL0nHWCKQmOQBvWfyWZDb+X933QUsW2bmWypSxN0t4RkzgAEDzOgQFiluB7X17w/MnGnGCr0g9IzjTMxEf/T3LCdTwEiMRCEUSi0vW2KhQwdg69bUbEGtYJesMmWC2k372EJABUJbMGlR+hFQ8iD9PkiXBomJ8U0/C+JlvmbNnLvthhuA1aud8+XEwb/0+Ze/UzLZOt/p3n6tdzuozWS/+EKFgJEj/bLQV7k2a2e+6kDhEzABL8DFN52DZsVRHK/glX1/YtPIHTvCNdebR0fWnd2M2PTdYdpACAiB0COgGD/0LpKCLhBQgdAFaGIRAiYQSEzygGCxZdBPP5mAzZLlpv1Qv34AW3yaoLJlgeefdy5pxQrg1lud84WZgzfH27RxriFbtH70kXO+nDjUfsgzjkuxFD3Qw7McCjig/VBeUrMlFkw+9PVqDHNS/Ex5eV0UIgRUIAyRM6RKXggoeZDc7yNRMb7JMQLsLlG3rvMPx+QYAbfDyebMAR4116rdOQg+cHTtCrRo4VwwL0PyUqQJKl0aePFFE5ICl2Gzdua7Xl/hKzyCR4zscwgOwRPIMrzPgZG847tpkxE1PAspXx541kzXVc+6SIAQEALRQ0AxfvR8Jo1TI6ACYWqMtEII+IJAopIH771npj0Mh4ExmnfTD2TRIoA3Wk3Q2WcDfOrkhky+pnSzv0keVk4GDwZ4ynJKnEnJpJIJOvZYgC9EI0gOztW+WrcHe9AVXbEKqzzvcxbOwlWw2XY2GwDvvBOecZInnQSw25koZAioQBgyh0id3BBQ8iC530aiYvxXXgHGjPHu7CpVgIED3T3pmT4dePJJ7zpQgtuLb5zHePnl5kYqmLHGvZRixayxCJy77pR4KZQXOk0Q586zhWwEKSwx/g7sQBd0wSZ4r85dhIvQHu33ecOBkUOHAuPGhcOR55xj/biKhIAQEAJuEFCM7wY18YQdARUIw+4h6RdbBBKVPGAxqEsXgK2IvJDXaP6664B167xoYE0zHzbM3YGZO8dpRgnbQPHJl1syNaOECaWqVd1qkVY+B+dq3/U0NaOEN4t5w9gWZQOAI4T4qyIMcwj79AHq1LFlhRYFiYAKhEGirb08IKDkgQfwIs6aqBj/t98AvjTzSm5njGfuy3aY27Z504KtA1591V2RkjsPGgRMm+ZNh7Bwu50xnqn/9dcDa9Z4s4ZnLlaVeEE0ghSmGH8ERuADfOAJRc4fHIIhKIuy++Q4MPLXX70dGz0pn4WZnxWPjgcdZEqi5AgBIZA0BBTjJ83jybBXBcJk+FlWhhCBRCUPTByaTUTzH3wAcCK5FzrySOCBB9xL4BAGJjF409gtEYu9e91yW3y1agFLl3qT0asX0KCBexlz5wIPP+yen5w83T31lDcZaeR2cK72XcvN2Jxxw3g7trveqwEaoBd62efPAQA+AuBjALfEHw9eePcyy7BmTevTpCxRyBBQgTBkDpE6uSGg5EFyv43ExfhsDfrNN+4dzsIcO4SUKuVeBttQjh/vnp+cbKfppdjJW05eX7sVLOj9QiWDGK9z1594AmC7VbfEp2Is7nmh2rWBvn29SEgrb5hi/D/wB27CTdgL9+fHFmiR0W1kP3JoJI/Q333n3i1sIsQjtJc7x8ccA/QwM1XBvSHiFAJCINIIKMaPtPukfC4IqECoT0MIpAmBxCUP1q4FOCOE/3ZDF1wAnHeeG859PDxR3HIL8Pvv7uQwgcHbwW5aambd8f33rZeEbok9Uf78031LJ76269nTmpXyyy/utDjlFKBzZ3e8WblYZFywwJ0cTpZ/5BHgsMPc8YeAy+G52neNP8WneBbuhnIUR3H0Rm/7rwdpTQ4A8MI5P0+3j30vvtiqG3MEjhsqUgS4/36rhi4KIQIqEIbQKVIpJwSUPEjud5G4GJ8zttnG/6+/3DmdRTW2kvRCrBgwLt240Z2U4sWtIqWblppZd3zpJWDsWHc6kOummwDOM3R7U4rBC4ucrMS4fcF37rkAgymvxLnr/DbcEAulvABYsaIb7lDwhC3GfxfvYhRGucKmHMqhD/qgIrL5w6GRfEXIXxVbtrhSI+NHfOdO97VnPkZlh5CINp5xB5q4hIAQMI6AYnzjkEpgCBBQgTAETpAKyUQgcckDupkzKR56yPnh/YwzAE42N/Gch+2HOFjMaQKB8/Z4Q9pU1eCFF4AJE5x//GeeaWHBYieLlZ9/7kxGhQoAi3KsoDBxwASC04LpcccBPPTz8O6VaMdttzlPIPBb6N4doC4RJofn6kAsdZNAKIqiuAt3oT7qO9MxFwCWL7d+VWxyOC7l3/+2xgfx8/jf/4CXX3amDu8A8HPkWEtRSBFQgTCkjpFa2RFQ8iC530QiY3xe9uKlLafP901cAMz81PiCjxcBnerAZ0mPPw5wDqIJ6tcPmD3buaSLLgLat7cqIJTx9dfOZFSrZsX4ZcsCrMQ8+CBATJwQLwByJIOJMxe7prDg6fTGFy8A8qZWvXpONA/d2rDF+Hw9+Apewf/wP0dYlUIp3It7cRhyuJDpwsjvv7d+VTjtCJz540HlR40C3n3XkRngWE3eVT7iCGd8Wi0EhIAQUIyvbyAJCKhAmAQvp7BxwYIFeP755zFt2jQsW7YMmzZtwp4U7Q/z5cuHXV56Owh3JDJ5QL+vWmUdwlksTEUsQDFx8J//mDmoZu7HAytPCD//nEoD689LlLCKgzx4m6S33wbeesteu1Ae1Hky4q3eTOLPKU9IbJ1qp2Vp3brW8IesLyBZKOVTKzv9XqhDmzbWVHce3k0Rded1zvnz7UlkIof+q++wGGVPuvFVLs7OxnVwKpAvCV/Gy7bajVZBFXRDNxyOw51uk+MLwkwh/FXRv7+9H1P+quCPB8eUZs1p8QL+88/bu6nM2jkv3Stx4NyNgXKoQBgo3NrMPQIqELrHLuqciY3xebuHrSn5F3gq4nP9Dh28vxzMvs/mzcBddwF//JFKA+vPy5WzOmqwqGaS2F6TbTbtEGNqvqJkcS6TeM7ma8RPPrF3TmjUCLj55v3n9a1ebZ257LQb5UVIdmphgdJEcTCrHXwyZkcH8vAlJwubNWrYQS7Ua8IY/7NI+CE+zHhJuAu7UuJXAzXQHd1xEHIZ2OfSyB9/tH5V2Lmjyl8VV165/48HFWdH4eHDAR7rUxFfDPICYAw+q1Sm6s+FgBAIAAHF+AGArC0CR0AFwsAhD9eGTzzxBHr06JFR7NvrYKYZC4S7d+8OlzER0yaxyQP6iQWhb7+1IvtZsw48+FaqBJx+OnDyyUCZMv55lredR44EFi/O+fDNV3Y8KLdu7Z8OTGRwLiJfAm7PYfYbWx21bAlccsn+h/6sGrFt68SJVhIh+y1dHvibNwf4CpOVj5wO/fzZX7LESmTMmGHdXM5K9MGpp1pJHD9b/bBgy5Mev42cCp5M4rRtC5x9ttkCpX/ezZDs8uzss1apxW/BFkzBFIzHeKzAgS2iGqIhzsAZOBbHogAKpBaY04oU4GT+quCnycv42f+aqlx536+K0qVzVoE3lKdNs37d5HQvgWM0+ePBx6gmHsW6A0JcthFQgdA2VFqYXgSUPEgv/uncPfEx/ldfWX/pzpt3oBsOPtj6S7dVK+sCnl9EHXiJjkXLnIjz9S68EGja1C8NrLEKjGt51skeW3NX2s+zDi9D5tbalMVWxviTJh3YVoEX5nhGIJ6cO5gTMXDiJcDMM1f28ztvR/HMxeKk6SJpVn0WLQJefRXgv3PKOTCg4yVI4mHyEqJ/3k0pOczx/0ZsBC8DTsAErMbq/WzJh3xogiY4E2fiX/gX8iOPS6EejOSnmPmrIqfHsryXm/mrgnXjnIjH6ClTrM975cr9V/DIy7o5m+/w3zH5rFJ+d1ogBISA/wgoxvcfY+0QPAIqEAaPeWh2HDt2LNrwNRBYM8iHpk2bonHjxihfvjzy24igerGFicg1AolOHmRFja1v2AaHc0vY34/FqMMPDzaK5yAEPjViy02eVnhAbtLEXKshu1/J3LkAr1TytFOqlIUDTzR2ibeNyc9XgfxvJh54VTK3ykluJy1WUegP/h4gL5MOQVZOqPtnn1m3v3ktlN9Ew4aRvfbp4exs1/O+ruNt45/wE9ZhXcaLQs4aPBgHHziHxI0WDsBh7TvzVwVzYvwx5fhJG39dZWjGfBRHbjJfx6Ihkw3sJmaqo5gb88XjAgEVCF2AJpZ0IKDkQTpQD8eeivH/9gPj6t9+s57x8xkQL3odeqjZF2qpXM6YmJffOLubgQB1aNbM+zzxVPtm/XPedmKRMDOIYWzNrh5OOmEwHmaMz97rlMdhagyCcquc5KTfhg1WIJR55qIePGvwMmFQxACMlyL5upHxPmN83tDipcyYkYMQN22W78EeLMdyrMd67MAOlECJjFninDloiwwZ6fVXBX8keHxlWoH3bfljwQIj698iISAEhIBpBBTjm0ZU8sKAgAqEYfBCmnQ466yzMH78eJQrVw4ffPABTjjhhDRpksxtlTxIpt9ldfIQMHR2jidwAieefvXTKhUI/URXsg0ioOSBQTAjJkoxfsQcJnWFgA8IJCLETYSRPnwcEikEhECkEVCMH2n3SflcEFCBMMGfRsWKFbFu3TqwzegtHOguChQBJQ8ChVubCYG0IaCzcx7QC5y0fZeR3VgFwsi6LmmKK3mQNI/vs1cxfnJ9L8uFQCYCiQhxE2GkvmkhIASEwP4IKMbXFxFHBFQgjKNXbdpUvHhxbN++HTNnzsxoLSoKFgElD4LFW7sJgXQhoLOzCoTp+vZiua8KhLF0axyNUvIgjl61Z5NifHs4aZUQiDMCiYj/E2FknL9S2SYEhIAbBBTju0FNPGFHQAXCsHvIR/3q1KmDZcuW4bPPPkPz5s193Emic0JAyQN9F/shwOEJixZZMzk4n6NYMaBSJaBOHftD1rZuBRYs2H8GIWeLxHCuR5S+Hp2dw1Eg3LFnFyau/BYr1v+FrTt2o0TRgqhVoSxOqnQk8ufLF6VPyrOunMM4caI1j3HnTmsEEO8JNWjgWbT/AlQg9B9j7WAEASUPjMAYSSGK8SPpNv+U5nzzhQutOYgckMYYv2pVa8a33fiD88kpgzMVOUuRc8Z5RtCQNf/85lFy4uP/AAHYtmsHxv72Lf7YtAXbduxByWIFcWSlimheoa5HL/7NnlvsGaCNdg1ZtQqYNAng7HamFziznam+WrXsStA6ISAEUiGgGD8VQvrzKCKgAmEUvWZI527dumHgwIF49NFH0b17d0NSJcYuAkoe2EUq5us2bQImTwbGjwd+//1AY5lAOP10oHVroGTJnMH45ReLf+pUgEXC7HT00cAZZ1gVgAIFYg5o+MwL4dkxPCAFAM7PW1dj2Kzv8N37dbDn12oH2F6o/mIc124lrjjqGJQvVCo82PigyZdfAm+8Afz6a87CS5UCTjsNOP98oGBBHxQwIVIFQhMoSkYACCh5EADIId1CMX5IHRO0WszQf/LJvhs52fevUcOKz0880Soa5kRLlwLjxgGff27d6MlKLC4ytj/zTOCoo+xfKAwah4TuF0CIG25kAwBg0aYVGP7lYix+vwH2/lHpADyKHPMdTmi7DpfVa4yShXL5GbODYgQKhCwKvvtuzukEmshCYZs2QNu2IY7x7fhCa4RACBBQjB8CJ0gF4wioQGgc0ugI/O2339CwYUMULFgQc+fOxUF6ZRSo85Q8CBTucG722WfAc89Zt4lTUZEiQJcuQIsW+1bu2gUMHWolH+xQtWrAnXfqRaEdrAyuCeB8bFDbgEX5DM7Ar6bi8yeaAjuKpDas3Dpc0GMJzjvsuNRrI7aCj5L5o79ypT3FWRzs2TOkLwpVILTnRK1KOwJKHqTdBWlTQDF+2qAPz8ZjxwLDhwOM1VMRLwDecgvQsOG+lTwbDB4MfPFFKm7rz2vXBm6/HShXzt56rfIdAZ9DXN/197yBjwDs2bsXvadNwbdDWgK7U99oy1fld1zb8w+cdtBR7swKcYFwwwbgjjusF4N2qGhR4KGI9CcDAAAgAElEQVSHgEMPtbNaa4SAEMgJAcX4+i7iiIAKhHH0qgObpk+fjv/+978oWbIknn76abThtSJRIAgoeRAIzOHd5H//A15+2bl+V19t3RRmwqFfP2DePGcy+ESoVy+At5ZFgSDg4/k4EP193cRHcO6fPBnfP9PamfoFduHM+2bi6iOzFOKdSQjd6i1bgBtvBNidzAnxYQKbCxwXtnqpCoRO3Ki1aURAyYM0gp/mrRXjp9kB6d5+1CjrKY8TYoePm26yLgKyG0jv3sDixU4kABUrAvffD1Su7IxPq31BwMcQ1xd9jQv1CQAWB7uPnoZfXzvJmcpFt+Kih75D+0ObOOPj6pAWCNm1uGtXe3eNsxqdPz/w4INAXUMdWJ0DKg4hEG0EFONH23/SPmcEVCBMwJdxyimn5GklXxIuWrQI+fLlQ9myZcHZhMWLF8+Th2s/sftqKQEYuzFRyQM3qMWEh33+Hn/cnTHM2t92GzBrFjBlijsZnFfSt69uGbtDzzGXT+djx3qEksEncJ7+ehqm9jnRncmFdqBjv+/Rplojd/wh4uLsEeYbOdrUDTGBwHsIobpPoAKhG1eKJw0IKHmQBtBDsqVi/JA4Ih1qTJgAvPCCu535fP+ee4DRo4G5c93JYLeQPn2AFGd5d8LF5QQBn0JcJyqkd61PADw0bTLmD3J4ATATiZKb0LXfb2hR8Qhn2ISwQMi7wp06Ob8AmGk4f90MGqQxps4+BK0WAhYCivH1JcQRARUI4+jVbDblz58/o/i3lwPNc6C8/iz78sy1/PduDlwXuUZAyQPX0EWbkdE8W4WyH4hb4itAzi70Qhw0xlOFyHcEfDof+653IBv4AM6fOzaiS+d8wGb38wQL1luCEQ/UQn4W5CNMH34IvPqqNwOqV3d/n8Hbzrlwq0DoC6wSah4BJQ/MYxoViYrxo+Ipw3r+9RfQubPz5zxZ1eArwDVrvCl23nnABRd4kyFuzwj4EOJ61ilQAT4AsOSvlbj7mkq22ormZmuJE+bi5VuOcQZFCAuEL70EsJOxF2rQwGosJBICQsAZAorxneGl1dFAQAXCaPjJk5atW7fOKBCapkmchCxyjYCSB66hizbj558DAwem3wbONOT8Q90wNuYLH87BxnQLrSAfQHtizhR88WgrzyZ3GDAPZ0f8FSHzlGvXeoYCzz4LlC/vXY4RCSoQGoFRQvxHQMkD/zEO6w6K8cPqGZ/1GjMGeOUVnzexIb5sWeCZZwA+ERL5ioAPYayv+gYq3Adw7vt0MhY+6/L1YBbj73h2CZqUrx0oHKY369DB6kbshZgi5EXCwoW9SBGvEEgeAorxk+fzJFisAmESvCwbQ4mAkgehdIv/SvGa3oIF/u9jZ4crrwT+/W87K7XGBgI+nINt7BrxJYZB41ySy3otxa6F3g/95c+egWc7NI8swMuWAXfdZUZ9jkTijJNQkAqEoXCDlEiNgJIHqTGK6wrF+HH1bB52sVMP/6JcuTIcxt96K9A8ujFMOEBMrYXhMDb1hlFaYRicbbt2oMOt67D39yqeUahx2RT0b+f9MqFnRVwKmDEDGDDAJXM2tnbtgMsuMyNLUoRAUhBQjJ8UTyfLThUIk+VvWRsiBJQ8CJEzglJl/fpwtfU88kjggQeCsj72+xg+B8cerwwDDYM2Z91SPHpdLTPYFdqB4cPzoWiBQmbkBSyFiQMmEExQiRLAyy+bkGRAhgqEBkCUiCAQUPIgCJTDuYdi/HD6xVetfv4Z6N7d1y0cCW/WDOjWzRGLFjtHwHAY61yBMHMYBufjFfMw7FYz88HzVfkDbwyqHGb08tTN5H1jdjXmg2OREBAC9hFQjG8fK62MDgIqEEbHV8Y1/ZkHGQDVqlVDgQIFbMnn3MEVK1ZkrK1Ro4YtHi3KGQElDxL4ZSxfDtxxR3gMr1o1HO1Ow4OIJ00Mn4M96RIZZsOgffjLXLx6m8O5InmA1X/YatQoVikycGZV9N57gR9+MKM6u5S99poZWZ6lqEDoGUIJCAYBJQ+CwTmMuyjGD6NXfNbp66+BPn183sSB+Hr1gAcfdMCgpW4QMBzGulEhvDyGwRm6YDrG9Wphxt58e/Da60DB/PnNyAtYCh8r//abmU05bSQMnZHNWCMpQiAYBBTjB4OzdgkWARUIg8U7VLvlz58f/Oebb75B/fr1bem2dOlS1KlTJ4Nv165dtni0KGcElDxI4JexeDHQs2d4DK9QARgyJDz6RFwTw+fgiKNhU33DoL314yy8dedxNjdPveyBob/iyFKHpF4YwhU9egBLl5pRjPmTUaPMyPIsRQVCzxBKQDAIKHkQDM5h3EUxfhi94rNOs2cD/fr5vIkD8TVrAo884oBBS90gYDiMdaNCeHkMgzNk/jRMeuhEY/a+NHIrShYqZkxekIJuvBH44w8zOxYtCgwfbkaWpAiBpCCgGD8pnk6WnSoQJsvf+1nLIl++fPkwf/58xwVC8vE1ocg9AkoeuMcuspx8fcuZIGGh6tWBxx8PizaR18PwOTjyeNgywDBo41Z+g6G3HG1razuLBo/YgEqFy9hZGro1fDjw7bdm1CpcGBgxwowsz1JUIPQMoQQEg4CSB8HgHMZdFOOH0Ss+6/T998D99/u8iQPxRx0FsJWAyFcEDIexvuoauHDD4IxY/AU+6NnMjBmFt2PUq4WRP18+M/IClsJuxn83A/O8c6lSwNChnsVIgBBIFAKK8RPl7sQYqwJhYlx9oKFuCoRLlixB3bp1M1qS7ty5M8HoeTddyQPvGEZOwo4dwNVXA9u3h0P15s3DVbAMByqutTB8DnatR6QYDYO2Yusa3HpVWWB3Qc8w5Ku6Eq8/eVBkkwcjRwKjR3uGIUNApUrA4MFmZHmWogKhZwglIBgElDwIBucw7qIYP4xe8Vknzhm/7jpg716fN7Ip/t//Bq680uZiLXOLgOEw1q0a4eQzDM5X65fhkU41jdha+KiFGHFvPSOy0iHkySeB6dPN7Hz44cCjj5qRJSlCICkIKMZPiqeTZacKhMny937WuikQTps2Da1atULZsmWxdu3aBKPn3XQlD7xjGEkJzz8PTJwYDtXvuQc42txrq3AYlT4tDJ+D02dIkDv7ANp1r8zAujHNPVtxZOfJeOCU1p7lpEvAtm1Ahw5mcpWUc/bZ6bIk274qEIbEEVIjFQJKHqRCKL5/rhg/vr7N0zK2GGWr0TBQ//5AjRph0CQWOvgQrsYClzyN8AG0Do9/ja1fNvSMXfM7p+DWxq08y0mXgNWrgRtuMLP7HXcATZqYkSUpQiApCCjGT4qnk2WnCoTJ8vd+1mYWCL/99lsceeSReSLB14KcP9i1a1eMHz8eTZs2xYwZMxKMnnfTlTzwjmEkJSxfDjASTzdVrQoMGABEdDh7uuHLaX8fzsFhNNOsTj6A9uEvc/Hqbcd407PALjz84mrUKlHVm5w0c993H7BwoTclCha02ouG5leFCoTeHCruwBBQ8iAwqEO3kWL80LkkGIW+/hro08fbXmx56PUVIs/1DzzgTQ9x74eAD+Fq/BH2AbShC6ZjXK8W3rAruQlDnt2LCoVLe5OTZu6uXYHffvOmRPHiwCuveJMhbiGQRAQU4yfR6/G3WQXC+Pv4HwvZFjQr7f378MF5gk6pX79+uO2225yyaX0WBJQ8SPDn4HU4WP36wNKl3lqVXnMNcMYZCXaCedN9OAebVzJsEn0Abc/evbii9w/YMd9966CK7T7HM5edEDa0HOvDXxM9ejhm24/hxBOBm27yJsMotwqERuGUMP8QUPLAP2zDLlkxftg95JN+e/YAt98O/PKL+w34lOerrwDKcks8ozdt6pZbfDkg4EO4Gn+cfQBt264d6HjnH9jzyyGu8TuswxT0Ozu6rwczDf/yS+Dxx13DkMF47rnAxRd7kyFuIZBEBBTjJ9Hr8bdZBcL4+/gfC/li0ARdcMEFGDlyZMYcQpF7BJQ8cI9d5DnZnrdnT+DPP52bwmFgvJ28eDHA9kFubhmfcIKV8Tf0O8G5EfHk8OEcHE+gslrlE2jzN/yE3j1KYO+aio4xLFhvCQbeUwmVCpdxzBtGhuHDgY8+cqdZ5crAU0+F7FeFCoTunCmuwBFQ8iBwyEOzoWL80LgieEX4pIct/Ddvdr43W4LyEiG79Dz3nHN+crRpA3Ts6I5XXLki4FO4Gm/EfQJt6h8L8PSd1YG/SjrGr+hx3+CZrnVQslAxx7xhZGCM/tln7jSrWRN45BF3vOISAklHQDF+0r+AeNqvAmE8/ZqjVQ9kazXC/83Xg507d0ZlZgFzIa4pWrQoqlatihYtWqBWrVoJQs0/U5U88A/bSEhmAoGFPg4RsEtVqgB33w2wPShp6lRgyBBg9267EoBmzaziYKFC9nm00hYCPp2D/5+96wCvqmjaLyGFUELvVaoUpfcqCigq+KGIUhULUvSzoCKIiAhSLFjooqAI0n4VpYuUj16kIyC9d0IogZDk/s97D4Ek3HJ2z7nnnnvvzvPwoGR3dvbdzZ7ZmdkZXWMHbCMfgrby3G58PTAnHKfz64YnosK/GPh2VpQO8NSi6SdMO+OSJbphcDbMnVvLQpwpk1g/n7dWDkKfQ6wGMAcBZTwwB8dA5KJ0/EBcNRNl5vN9Wt4vXdLPtHhx7cl/rlxan3nzgMmTxQIBmzUDXnjBZlE9+iGwc0sfqqt2nrYx2XwI2tzjWzD5w3uAS/qD+egcHPZqERTMdOt3zNjsbNNbpvRp0aLAsGEAywgoUggoBMQRUDq+OGaqh/0RUA5C+6+RzyRMqUG4fft2VGDKQkWWIqCMB5bCbc/BYmMBPu9hpLAnJx+193r1gE6dgOzpLkK7dgE//aS9KPRE7Pfoo0CrVspw4KPd4MN7sI8ktgFbH4O2+8oxfDHzGC4urAkke3j1Hn0NhVtvRL9HKyNPlH5jgw0Q1C3C3LnA9OnA9eueuzDreNWqQO/eNjUcKAeh7jVXDf2LgDIe+Bd/f46udHx/om+Tsc+c0Rx8Gzd6dvJFRQGNGgEdOgAsCJaamGp02jTg8GHPk8qTB3jiCYAOQonSITZBzNZi+FhdtfXcpYXzMWibLu7H6J8v4PLSmp5FjLmEkm024/1mtYPm5WD6CfOYYLaQmzc9Q8HkQUwk1LOnMgdI72vVUSEAJgI7hu7dizqxOHr0KIoUkU97rABVCNgFAeUgtMtK+EGOyby0OHOP/wcxMYFdpNkP8BkeUhkPDEMYPAzoKFy6FFi+XHtRSO2eL/z4srdxY+CBB+52DKaf/YEDwKJFwKZNwOXLWu2S6GiA+UNoMKhVy6bWfnsuo4/vtPactD+ksgjofVdP4sd1e7B34T1IOl4ASIgCoq8h4p5jqNLyJDpXroz8UTn8gYDlY/Lh8ezZwKlTaW2WWbIATZoA7drZ8NVgapSUg9DyPaMGlENAGQ/kcAuGXkrHD4ZVNGkO584Bf/6p5QFkiYHERCAyUssGQv2eej4/wO6IpQT27gUWLgS2btVSl/Lf6EwsV07T8atVU9Z+geWySPUUkChIm1oENMsKTF1zEAcXlUHyqXxAYgSQ+Sqiyh5GrUfOoVPFasgRKZ6ONNBWhVf/xYuB337TqpikrkJCU1/z5kCbNsocEGjrquS1JwJKx7fnuiipjCGgHITG8FO9FQLSCCjjgTR0wd+RxgMjOT94I+AtQdUJld4rFt1ppeULmo5+ADrZ4UBCciIyZVRpdnlMpNgqA2ZPKQdhwCxVqAuqjAehuwOUjh+6a+9x5mbo5/xwk1QdcelN5gfVU1rWgO7oB6Cp4yc6khAZpnJnUr8nGTEpBPT+U8IrBHyIgNLxfQiuYu03BJSD0G/Qq4FDHQFlPAj1HaDmb2cE/HCntTMcvpNNAe07bIOVs3IQBuvKBt28lPEg6JZU94SUjq8bKtVQIWA5Akr1tAhyBbRFQKthFAIKAasRUDq+1Yir8axAQDkIrUDZz2P8wBpnPqDOnTv7gGvosFTGg9BZazXTwENA3WktWjMFtEVAB9EwykEYRIsZ3FNRxoPgXl9Ps1M6fuiuvZq5/RFQqqdFa6SAtghoNYxCQCFgNQJKx7cacTWeFQgoB6EVKPt5jLCwMGQwuWA5+SWm5C3w8/wCdXhlPAjUlVNyhwIC6k5r0SoroC0COoiGUQ7CIFrM4J6KMh4E9/oqB2Horq+aeWAjoFRPi9ZPAW0R0GoYhYBCwGoElI5vNeJqPCsQUA5CK1D28xh0EJpNdBAmJSWZzTak+PnVQcgaGP/+C6xaBVy4ACQkANHRQKFCQJMmQL581qxFbCywbBlw5Ahw9SoQEQFkzw7UqgXcd5+++horVgBz5wKXLmnFtMgjd27gqaeAKlWsmYcZoxADzuXMGeDGDSBTJiBvXqBxY6BoUTNGUDwEEAiWO+05nMMyLMMxHEM84hGJSORETtRBHZRHeWRABgFUfNA0gIC+nnQT0/dvxN/bEhF/KRLJSRmQKVsCSpVNQuf7qiJ3ZIxXgOLigOXLgYMHtSOPdUF45NWooR1XPvhce5VJpsGVK9pxtX+/Ng/KHRMDVKsGVK+ur/zp8eMaFqdPA9evA1FRQJ48QKNGQIkSHqS65SBMRjK2Yis2YAPiEIebuIksyIISKIEmaIIYeF8Pt6MI7MtTp7TPGP+Oj9fmkSsX0KABULq0DLqqT7AgoIwHwbKS4vPwu46/cyewdi1APfvmTSBzZqBYMU3Hz5lTfEIyPc6dA5YuBXjY83CMjNTGrlcPKFcO8Ba8ylp7ixdrfy5fBnjvJI/8+YFnnwXKlpWRyvo+vHPxY7lypXbnoo7PO1eBAtp68G9FliIg8Im3VC7RwU7iJJZjOU7hlFPHj0IUciM3GqABSqGUKDvz2wcQ0FduxmPKnk3YtdOB+LhIJxaZYm6iQgUHOpSvjpjwzF7x4a839cGjR4Fr1zSTRI4cQJ06QMWK3o88rwNY1ICfDernhw+nNc/UrAncf7++uwrvOf/7H8DPQIpZg0c3jzyam7xRUoZEbCw0B5sLzEdc1FkkZ0hClps5UOZ8bTQ80tH531ZQinnm7Nk7dxWayXhX4SdVUegioHT80F37YJ65chAG8+remtthft3d0MWLF9GtWzds2LABlSpVQpcuXVCrVi3k5xccNNyddv5s8uTJ2L59O2rWrIlx48YhZ86cKF68eAig57sp+sV4QAcarboLFgCHDrmeHC/sVasCLVtqWqAvaN8+4I8/gHXrtAu/KypYEGjWTPtDi2tq4jy++07TPKl1uqOsWYEWLYC2bfVps76YqyeeNBqsWQMsXAj884/7lhUqAA8/DNSuHTi3C6uxlBwvgO6uQjPchV2Yi7nYiI1wwOGyb1EURQu0wAN4ABGIEOJvWuMAWIDj189j/Ort2P1LeThOa9/GuyjLFRRp/Tc6NymKKjnuuevHvCgzjmH1ai2OwRXxwsnjrnlzzXZoR6LRg/OgnZNxJa6I8RkPPaQdvTyCUxOPvA0btE/Qjh3uZ0jbMfvTjpzeaXq97Y9YhEVYjMU4jdMumYQj3OkEfwyPoSRKikOpY19u2QLMmwfwb3dUqpQ2j4YN9TlNxQVVPeyMgDIe2Hl1fCubX3R8Hsp//gksWgScOOF6ghkzArTyPvaY7xxsPNx5OG7aBPDQd0W8Q/Jj98ADWqRMamLEyPjx2h2Bzk13RMt769bAo4/6djFlufN+wzsXdfwDB1xz4Z2rcmXtzhVIQY2ymFjcT8en3GKJjA9HnX4zNmM+5juDpNzqHyjl1PEboRHCYH6wuK6ZBMAC7L96Et/+bw/2/3o/cCGX62nlvIiSrbeia8MyKJut8F1tdu/WdGPqt4xrcEWFC2v6YNOmWpyDHYlxDDTPMLbEnXmG8Qw8uqnnM5Y5NXHujD2njs84dHfEGPBHHtECCtPHiVyJuIAFpb/BnyXH40Lm4y5ZRCVmRoMjHfDo3jdQ5HJ506HkZ4ufn/nzPZtnypfX1rRuXWWeMX0RAoCh0vEDYJGUiMIIKAehMGTB0yEhIQH16tXD5s2bMXDgQPTr189tKlKHw4EhQ4agf//+qF69OlatWoVIu2o3AbJElhsPGMr26aeerbLpsePFm1G6Zj5rodFg8mT3RoP0MvApSZ8+2pMMEl8K9u6t/a2XihQBhg61l0ZOo8fo0ZomrZcYrvbKK3cbU/T2V+3uQiAA7q5Cq0bDwSzMwkzM1N2vHMrhHbyDbMimu49pDW2+AOvO78UXH2dF8nEd4a4EJfoanvhgO9qXqn0bItprv/3WvdEgPZY0IvTtqz0gthPRKcgjS292cTo8OY+USGH2mzBBe1CilxgT8eqrd47uc9FH8MljDXAUR3Wx4AvZF/EimqGZrva3G3nYlzSAsLQzP2V6iXbfN96wr+NX7zxUOzEElPFADK9gam25js8nH8OGaS/V9FLHjsDjj5tn2aRVdfp04P/+T68EAC2sb799J5qET7HffVd7caiXyGPAAHPvKnrHdteO8n/+ObDVvQPnrq50EnbubK95GMXBz/1trmIKo8PMCZMwCQuwQHffaqiG1/E6MiGdN0c3BwMNbb4AC09uw8SBRdw7BtNPPUcsnhtwCC0LaxmKeOT99hswdap+jMqUAd55R8seYieiU+/77/WbZxjjQfMMgwJJjNf+6ivNSaqX6GR84YU7AXTHsv2DTxo+grNZ3D9uSM07IikKvdb/iLrH2uod0ms73lXGjNFi0PUSM4Z07669GFUUOggoHT901jqUZqochKG02unm+tlnn+Htt9/G008/jZ9//lkXEs888wxmzpyJoUOHOvsqkkfAUuMBo3EHDhQzHKRMjaFRXbuaY0CgFv3TT+Kg8UXroEGalbhHDy1vhyjRWk3N1Uxnp6gMKe0ZljdiBPD33+IcmIuQDlI7zENcetv1sPndVRivKZiCOZgj3K8YiuEjfITM8J5CR5i5pw42XoANF/ZhRJ88QKxgGpsMyWj18Xp0LFPH+XBg4kRxxBgPMXjwnYu3OAdze/ARxDffiPPMlk2bB4/fL7/UHkyLEh+y0wgRl/U4+j1YF+cz63MOph7nOTyHlmipf2g3+5LGoHHjgL/+0s8qpSVfRfbvb684FfFZqB4iCCjjgQhawdXWUh2fuat5uJw8KQ5iu3bAk0+K93PVg5ETfH4iSrQ0845Cp9prr3l+NeiOd8mSWiCgHYgvOXln2bNHXBo+L+rWzZw7l/joQdfDxiqmMNYMAByN0c6UoqLEkgL90M9ZZsBSsvECOJ2D75YBrgum7Ii8gS7D/sGjhas44yFmzxZHlIGAPCLSZ9kQ52RODx7bPL5FiYGM1PE5jyFDxGLQU8aqX18LBDwVswf9m9bH5ajzomLgtbVT0eDos8L90ndgACBj6TduFGfFEgs0jfKRvqLQQEDp+KGxzqE2S+UgDLUVTzVfvgTcsmUL5s6di4eZvlAHLVy4EI888giqVq2KTUwdo0gaAUuNB4xiZb4IWWJ4Fx2FRoja1vDh8hwYcscaJIwuliVamt9/X7a3ef1kjSgpEvBlZ4cO5skTwpxsfHcVXhUaDUZhlHC/lA5VURV90MfauoQ2XYBzNy6hV9/LSD5aRA7PTPF46pVzmP1lUbcZ1rwxps30k0/8/2CYaYI++MB9uiFv82C2aL4E/PVXby3d/7xZiyTsm1YLB3NKBFUAzj3dF31RGZX1CeFmX8oaUVIGZapRGkIUhQYCyngQGuvsapaW6fi0aNK55ilNvbdlePNNrUiWEWLUxNix8hyYa44fGzo7ZYnFrRhE6G/6+mux5yfp5eUrQqaAVWQYAZuqmFLzYvAfgwBlibWZe8Di3w+bLgDTir73VpT+l4PpQc8Ri7ZPJ2Hm+FvP5yQWpVIlLa7DWylWCdZCXTZv1u4assRU+kz2tGSJLAegzTM3sGpCBZzO6iYVsxfW4UmRGLR0FUpdrCEvBLQYdsayyxKPbR7fikIDAaXjh8Y6h9oslYMw1FY81Xxz5MiBy5cvY+PGjU6Hnx5iOlI6FmNiYhDLdDaKpBGwzHjAGpRGX3uyzgdzy6WvE6J39nx2wScgLMTlb2KOO3/m9WBqVOah0JunzxVezGFBQwyf5ygyhIBN767Cc2LaodfwGs7gjHDf1B0GYzDKoIwhHkKdbboAw9ctw8bPmghNJX3j8NyXkHjeWA6h11/X6vD5k5i1zmg8ED8dRo68DBmT4ThSBCgk8ULmFnhMpTsIg/RB6WJf8lEIj27GqRihkSPvpF01wkf1tT8Cynhg/zXylYSW6fjbtgEff2xsGkWLas8mZC3VdFL27AmcF3/5YUzwdL2ZWWPKFPm7ihnCsPYjP9xGiLo9c9ypUh5GUHT2tamKKTyvG7iBV/AKruKqcN+UDgyU+hJfogAKSPMQ7mjTBei7aBn2fWtQx4+5hsQ4Y1lXPvwQqFBBGFVTO7AUwL59prIUZhaRJQE3T+YGsl0R7pvSodaxNui9RuI55y0G1O1ZxcVT2VtvwvGuQ/NMTIy3lurnwYCA0vGDYRXVHNIjoByEIbwnsmfPjitXrmDatGnONKN6aMaMGWCa0WzZsuGSSA04PcxDrI1lxgM6xBYvNo4uCyixCrMMMSq4Xz+Znub3YR2/Xr3M56uXI5/RiBQrcMe3UyetdowiQwjY9O4qPKe/8TeGwnh6rUZohF6w8PfDhguQmJyMTv0OI2n/PcLrYHYHGg5oQPAXnTmjvXhjjIff6cMBwICPDIkxHMNRAiW883CxL2XTrKYf7NFHgS5dvIugWgQ+Asp4EPhrKDsDy3R8OvbWr5cV804/vkJkLT8ZMpohRGZMd33atAGeecZMjmK8Jk0SK1DrjjvvKbyvKDKEgA1VTKn5LMVSjMEYqb6pO7VCK3RER8N8dDOw4QJcSbyOF167Ase5PLqn4auGNKvQvOIvYsna997z1+jpxh3zCvDKOGlhwpIzYtS8Q8gdL5f5Zc4cLb7EKLVvDzzxhGABXbYAACAASURBVFEuqn8gIKB0/EBYJSWjKALKQSiKWBC1r1OnDjZs2IDatWtj5cqVCPNS0yw5ORn169fHunXrUKtWLaw1krIyiHCUnYolxgPW83j5Za1ytFGqWBEYMECOC4tX0bpqB2JErhkaoMxcGGVNa/vZszK90/ZhXUbWVJSN+DYugW4ONrwf6pY9UBrSOUgnoVGKQITTCBEDP4c/+nHT/HpkE6b2rm4UStP6M0N0Ebn7riZD25nistzCn7EMRlKDig/soUfBE8CRYkB4kjTbh/AQXsbL3vu72H+McWGsi1HKkkWLMI6KMspJ9bc7Asp4YPcV8p18luj4Fy5oz5rNiODgU3XZl28sQrV1q+/AFOHM13cyhX9FxnDXlnct1g+UqZOenmfZssZfhpoxpwDg4Ud10TJ03sN72I/9hsfLiqwYi7HW1SK04eJM2LkKiwfWN4ylGQxYr46PhZmo6TbJ6OySwox5oQaWfuf/YEin+PdtA7bpLAPgZr5P7fwAT+8aKIwGP6Esf3v6tHDXuzqwLiOzTHsxqxofSHHwOwJKx/f7EigBfICAchD6ANRAYTl69Gj06tULGTJkwKOPPorx48ejQAHXaSdOnz6Nbt26Yc6cOc72X3/9NXrYoc5DoIDtQk5LjAc7d2q1ScwgOqKmTZPTeJh6yAynmBnzIA9q47nl6wZIi8HnOGa+XmTa1zz+j4D0hocN74feRA64n3dGZ1zHdVPkZh3CaqhmCi9pJn7cNP3/XIY9442lHpKet4uOhkvAyhgbbuHP+ih79pg5G4O8dpUHyu+WZlIQBZ0ptrxSuv3H9KIdTQy6Z0ZA2n8VBTcCyngQ3OvraXaW6PirVwPMWWwG0UI9frw4J1pWeTgaycsmPqrnHjNmmM1RH7+9e82tc86CWCwpoMgjAn5UFy1ZGer21PHNok/wCUqhlFnsPPOx4eL8d9YKnJxhn9e5b72l1em+TTI6u+Rqvlr6EZzen1Wytw+6nc8F5Loozbj82UYYuGy5cP9z58wtX8u4+Hz5hMVQHQIMAaXjB9iCKXF1IaAchLpgCs5GfBHYpEkT5+tBOv2ioqLQvHlz1KxZE/ny5XP+Gx2DfGW4aNEi3LhxAw6HAw0aNMCyZcu8vjgMTtTMm5UlxgO+8uTzE7Pou++ArBKKJCs2XzfHeWHKVIykUjIigNm5PIYP1yqD25xseD+0OWJi4iUiEe3RXqyTh9ZMMcpUo34lP26aN39dgWNT/Tz/VOC3awc8+aSB1ZAxNtzCnw9KWFLJNrSyPlB/tbQ42ZANEzHRe/90++/iRe1hiFnEkrzV/OyDN2suio97BJTxIHR3hyU6/sKF5r2WY/EkmfT3fDXHlPd2Itm7itE5/P03MNR4qvfbYtBhm+ZpkVEBg7O/H9VFSwA9h3PogR6mjdUP/VAZxl5q6RbGhovT7bu1uLigju4p+LohEz099FCqUWR0dkkhn8/ZGldjIyV7+6Dbv6WB0vIvZYteqoTPFm0XFuzQIeCdd4S7ue3Az0DJkubxU5zsiYDS8e25LkoqYwgoB6Ex/AK+99WrV9GhQwfny0ASnYKuiI5B0uOPP46ffvoJWWWcRAGPlrkTsMR4oByErhdNOQjN3cxeuNnwfmjp/H09mHIQmouwchACUA7CNJtKOQjN/R0LFW7KeBAqK333PC3R8ZWD0PUGUw7CkPrFC/Y7hnIQmrudlYPwDp7KQahhoRyE5v6OhQo3peOHykqH1jyVgzC01tvtbOfOnYsxY8Y4XwZeS1c7ITo62vnSsHv37njssccUYiYhYInxQKUYdb1aKsWoSbtYH5tgv7zrQ8G3rVSKUfPwVSlG7zgIVYpRbV+pFKPm/X6FEidlPAil1U47V0t0fJVi1PUGUylGQ+oXL9jvGCrFqLnbWaUYvYOnSjGqYaFSjJr7OxYq3JSOHyorHVrzVA7C0Fpvr7Nl2tH9+/fjAgvfA8iZMydKlSqFjKyirMhUBCwxHsTHA8xdwRRARqliRWDAADkuTMa+YoVcX7N7RUYCU6aYzVUfv+Rk4NVXzanHmD8/8NVXfParb2w/tgr2y7sfob099FAMxd/427AoEYjAWIwFUzH6lfy4aX49sglTe1f36/RTD84s0UWKGBBHJl3RLfyZce7XXw2MbWbXQseBw8WB8CRprg/hIbyMl733d7H/+vUD/v3Xe1dvLbJkAcaOBaKivLVUPw90BJTxINBXUF5+S3R83tVYD566pVGqVw9gTmkZGjwY2LpVpqf5fbJlMy/tqqh0vGsxF3W6QFtRNs72LFLLYrWKvCLgR3XRq2xmNXgP72E/5FMvpsiRFVmdOn4kLEoracPFmbBzFRYPrG/W0hjiQ/MaY5bTZBKW0dklpRjzQg0s/e4eyd4md7tvG7DNWOrbp3Z+gKd3DRQWjInSXnsNOH1auOtdHfLmBb7+GggLM85LcbA3AkrHt/f6KOnkEFAOQjncVC+FgGEELDEeUMoJE4DFiw3LizfeAOrWleNDqyqtq3agRo2AXr38Jwmt7TJ1XtJLzJovjz/uv3kIjGzD+6GA9IHRlM5BOgmNEmsPsgah38mPmyYxORmd+h1G0n7/X5orVAA+/NDgasgYG27hf/asdlzeyjJuUBCD3T8cAAz4yBCT4RiOEtBRt9XF/mOMC2NdjNKjjwJduhjlovoHAgLKeBAIq+QbGS3T8T/9FFi/3vgkjKTe37gRYE1sO1CbNsAzz/hPkkmTgHnzjI/PDy/vK4q8IuBHddGrbGY1WIqlGIMxhtm1Qit0REfDfHQzsOHiXEm8jhdeuwLHuTy6p+GrhjSr0LyShmR0dkkBD2zKgT41mkn2NrnbmFeAV8ZJMw1LzohR8w4hd7xcRCWrLZkRP96+PfDEE9LTUB0DCAGl4wfQYilRdSOgHIS6oVINFQLmImCZ8eDwYeDtt40Jz9C20aOB8HA5PrQu9+kDHDwo19/MXnSYZs9uJkcxXpcuAd27A4mJYv1St46I0J6gMFLaRmTDe6CN0PGtKMlIxmt4DWdwxtBAgzEYZVDGEA+fd7Zgow1ftwwbP2tiaCrhuS8h8byxs4YPOviwQxf5yKgwrFV9bPq9kC4R3DXip8PIkZchYzIcR4oAhU5Ky1EO5TAIg/T1d7HHmGaUR/fly/pYuGs1ciRQyBicxgRQvS1DQBkPLIPadgNZpuNv22b8pVnRogAdjbIZKfiCkQ4t5mnzJ/HJBi28sncVM2Q/cUL+JWbK+NTt+ayIGU9CjCxQ7wIS0Ru4gVfwCq7iqrT8GZABX+JLFEABaR4uO5q5aJ70WBPH6btoGfZ9a1DHj7mKxLgshrBkACADAdOQj3R5d4L2rd0U+9bnNjQPo50jsiTg5sncQLYr0qxqHWuD3mtmS/enbv/KK8DNm9IsnJ8emmdiYuR5qJ6Bg4DS8QNnrZSk+hFQDkL9WKmWCgFTEbDMeECpmaNu7Vp5+V94AWjRQr4/exqNMC5TRrPMnjolL8f99wPvvy/f36yeP/wA/PGHPLfWrYEOHeT7+6iniXc3H0kY3GyXYzlGYZT0JKuiKvqgD2hEsDVZsNHO3biEXn0vI/moXCQqMsXjqVfOYfaXRaVf3xUvDnzyiYCt00dGhX3rc6J/g6ZIuimXL6dgQaB2bWOpSpu3SMa/02riYE65NLrc033RF5WhM32Rmz3GY5vHtyw1bKhlmVYUGggo40ForLOrWVqm49M5x9d///wjD/abbwJ16sj3Z8+//tIso7JUvbqWwzkuTpYD0KSJlnLV38T8cv/7n7wUnTsDjz0m3z+Ae1qg3gUsOnMwB1MgXyKjCZqgB3zw+2HmolnkINx/9STeeysKuJBLbj/kiMXT7ZIwY5y8Y61SJYB1vu+Ky/CRLu9uopvnF8DQRxvA4ZC7+5UqBZQoASxZIgcle7V55gZWTaiA01kPSDEJT4rEoKWrUOpiDan+KZ1++gn47Td5Fjy2eXwrCg0ElI4fGuscarNUDsIQWPGuXbs6Z5khQwZMnDjx9oxT/l0GgvS8ZHiEeh/LjAcE+vp1zYCwX6J+AR2D3EOykcWpF5paF7UvUWK9Pdbi4Ms5Xv5l6nvky6fV7LNDUvikJGDECOBvCWN3jRpA7972mEe6dTTzjii6RVR7DQEaD2hEEKViKIaP8BEyI7NoV+vbW7TRNlzYhxF98gCxOcTmmCEZrT5ej45l6mDhQrlySLlyASzrlFvE9uBDo8KKKcXwTafaYjhAe+TMefD4/fJLYM0aYRaoXBl4910gLusJ9HuwDs5nPirM5Dk8h5Zoqb+fmz3Gx/Djxmm2cFEqV04zBoXgoxBRqIKmvTIeBM1SCk/EUh2fTjUeLiclXli3awc8+aTw/Fx2kA1+o3WZdxTq9iwEJfOEo2RJYKjxNOumAMHn5oMGAXv2iLNr2lSrY2jGnUt8dL/3sEi98/s8ZQRwwOFMM7oMy4S7l0d59EM/39QeNHPRLHIQEsCFJ7dh4rtlgOvRYnhG3kCXYf/g0cJVMH06MFvi0VrhwtoRkTWri6F9qMu7m+gfn5fBD29VEcMBAOvtUcfnPIYMAXbsEGaB+vW1wLlTMXvQv2l9XI46L8zktbVT0eDos8L90ndgvA0f0zOmXZSqVdMSdrGupKLQQEDp+KGxzqE2S+UgDIEVDwsLczoHSUl0TNyi1P8uAoPD4XDyS81LpL9qqyFgqfGAA/Li/dlnwPbt+peASdRZy8NMpxprc0yerL+oFQ0HTE9KizmJKTrpIOPfeonpk4YNE3iKo5exgXY0gDBt66pV+pmwHgnzX/gzfZIHac28I+oHRbVMjQANCLMxGzMwQzcw9+JevI23kQ32SlnrdgIWbrR15/fii4+zIvm4zpyQ0dfwxAfb0b7UHWcao2qZ2ZgXTz1UpAjw3nvaxVuIfGxUWPVzUYzqUhOJCfpuv3QK9u17J50mU4x++62Yc42PWpi5LsWpdi76CD55rAGOQp+TkC8HX8SLaAbBGise9hjXkXZwkTJTVapodWaiBe1QQuuvGtsOAWU8sN2SWCaQ5Tp+bKym54oEArKWNZ88mOWMYgTFjBliFnPm16NOn2IpZ5YQ6vwigYDlywMDBph7VzG6U+LjgS++ALZs0c+JBWq5JmbeufSPbouWFqp3tpivqBAsJzAJk7AAC3R3rYZqeB2vIxMy6e4j1NDMRbPQQcg50kn43UeF4TivMxovRyyeG3AYLQtr2Sh45DH2eepU/YgxIRKD3tymofSxLu9O0gWjSuH716rCkazvJSGznPCoTglkvHED4ONpkZK4Dz0EMElVilPtWLZ/8EnDljib5ZAuQCOSovDquimoc/wpXe31NOJdhRmeRR6BN2iglSBgHLui0EFA6fihs9ahNFPlIAyB1S5RosRtB+HBVDXgUv+7DAypecn0D/U+lhsPCDi1nhUr4HzW4q4eIA0FDIN65BGAKTl9Qfv2AXPnamlPUzmt0wzFvHTNmml/oqLSSsF5fP+9pr3xdaQ7osGBLyDbtrXnhZs3C2KwYIHn9FAVK2rzYJ4+sww5PlhXM++IPhAvpFjuwi7MwzxswAbQaeiKiqIoWqAFmqIpwiFZX9QfqFq80Y5fP4/xa7Zj9y/3wnHKTe2WrJdRpNVmdG5SFFVy3HMXKjxueeTxBZ27xxF0qPG4a95c0pFkgVHh6M4YzH2tBVauBPhIwhXRWJAyj/TR0TzyGJnLI89TrApf2z38MFC37t1H9/W2P2IRFmExFuM0TruUgfu5LuriUTyKkigpvkt17DHafefP1+y/nJcrYuolHt1MLaqiisWXIdB7KONBoK+gvPx+0fF5KDMiZdEi4Phx18LzIKpVC6AzqmxZ+Ql66slnJIyg2LTJ/eFI6zIPR6YFTR/0Rr1+/Hhg3TrPrwlZH50p9zkXOxLvN7yn8IN3wE3qPOr0jCDhnYt/hzjp+PSGOEJw6vSbsRnzMR9bsdUtHqVR2qnjN0RDhEEuRbwusM1cNIsdhJwf041++7892P/r/e5Tjua8iJJPbEXXBmVQNlvhu2DZvVs78ugccxcMyOA/6vd8JOwxk4QFury7dd2/MSfmflEGa2YWdVtWoEABbR507mVK53Pm3Fev1o68vXvd7x6al6jjM7N0erPGlYgLWFB6FP4sOQ4XMrv+jkUlZkbDwx3R8t/XUeRyeV3bVKQRdXp+fjiPXbvc92RsCufBYEYbm2dEpq7aCiCgdHwBsFTTgEFAOQgDZqmUoMGGgF+MBykgUvNhrQ++Xrt4EWDYV+bMAJ1yvKzTUm0FMeJ5+XLg8GEtWphGAl74abxgcn49UbR0eFIrJy86Dql187UhnYLMTRcodOSI5rw9e1ZzevKpSZ48QOPGAF9ABgCZeUcMgOkGhIjncM6Zjug4juMariEKUciBHE7nCV8O2r7eoCuU/bTRrifdxPT9G/H3tkTEX4pAclIYMmVLQOlyyehUqQpyR3qvSs9McDzy6DBMOfIYRVyzpnZc6Tny3G48q4wKM9vi6lXtuGKsB/+bNmfOo2pV7cKvxxlG+zWxOHMG4GMLGhroXORDaT4c9zZPRtJvwzasx3rEIQ6JSHSmyC2BEmCtnRh4Xw+3YwjsMT544TyY3Y/zYDwLP0GMKC5dOiCOCSWkjxBQxgMfARsAbP2u4+/cqQWgMdsGHYdZsmi6JHX8nDmtQfDcOWDZMs1ZyQ8eD0fq+PXqAYwC8WZRpbV58WLtz5Urd3R8lh149lnfOTh9gQ4/loysuXBBu3NRx+edizo+Le6KnAgIfHoVYgBO4ZRTx+ff8Yh36vi5kdvpFJQKjpJB1cxF84ODMGXKV27G46e9m7BzpwPX4yKd/0wdv3wFoEP56ogJ916Cgb/ePPKOHdN0Y5okeOTRgcTH0t6OPOegVunyHtb60pkoLJ9cHIe25sC12AiERyUj++UiTvPMfffpu6vwnsP4iPPn75g1aF7iJ6iQjqQsSRkSsbHQHGwuMB+Xo86B/581ISdKX6iFRoc7IXNidpndKtzn6FFNx+fnLOWuwgwvvKsUKybMTnUIIgSUjh9Ei6mmchsB5SBUm0Eh4CcE/Go88NOc1bCBj4CZ98DAR0PNIA0C7jaHVZfdUN+coY6zmfMP9b2kjjZTEFDGA1NgDEgmSscPyGVTQluEgPrEWgS0mcOoRTMTTfe8zNRlzZRYrb+ZaCpeQYCA0vGDYBHVFO5CQDkI1aZQCPgJAWU88BPwalhDCKj7gSH4gruzchD6d32tMirY9RAwc/52naN/d5gaXRABZTwQBCyImisdP4gWU03FdATUJ9Z0SH3PUC2a7zHmCGbqsmZKrNbfTDQVryBAQOn4QbCIagrKQaj2wB0E9u7di7K+qj+hgPaKgDIeeIVINbAhAup+YMNFsYtIykHo35Wwyqhg10PAzPnbdY7+3WFqdEEElPFAELAgaq50/CBaTDUV0xFQn1jTIfU9Q7VovsdYOQitwViNohAwAQGl45sAomJhOwTUC0LbLYl1AmXMmBH58+dH48aN0aRJE+ff9957r3UChPhIyniQbgMkJWlJ7XUl6PfR5kmpLG6kEBhrvbCWohEePpqeGWzV/dAMFIOURwg5CM04KkzfBRIOsoTkRCQlOxAdHqFfHB8cAldu3ER0RDgyhmXQL0f6lhLzdzuYD+YoPzHVM1ARUMaDQF0543IrHT8dhqwRzuK0Ssc3vrmCgIP6xAbgIobQovlVxzdRl6WOD0cGROopDO5tS0quP+8Y8TcTkTVK4J7hTRb1c4WADRBQOr4NFkGJYDoCykFoOqSBwzDslgMjQ6rLWr58+dCoUaPbDsMKrKisyCcIhLzxgI601auBJUuAQ4eAGzc0p1r27HBWwW7eHCha1CfY32bqcAD//gssWgRs2QJcvgzw36KjAb6ubdYMqF5dM2q4o2vXgGnTtErc/O8U4u9VwYLA008D9er5dh4Wcpe8H1gooRrKbwgEsYOQx8LevXeOiitXxI8Kn6+LTqPCrE0H8fvoooj/oylwLg+QnBHIFoewGptR+ZXVePWJYsgaGeleXBMOgT1nL2D0tIs4ub4IkMCx6Bh0ABGJyFv1GLq1z4r7C+UVg0zn/HUxNWGOusZRjYIaAWU8COrl9Ti5kNfx4+M1vZg6/vHjAHV+6tI5c2o6MfXr/Pl9u0H44d65U/twb9+u6ej8tyxZgPLltXvG/fd7DuiLiwN+/BFYtw64fj2tjl+8OPDss0DVqr6dRxByV5/YAFzUIF40Hgs7dgALF2pHRuqjgqYwHhX33WdR7K8BXTbJkYwfVx7En6PLImFxI+BCLsBB20oswuuvQ63uf6PHI/fIOQwF1n/LsTOYMO0qzm4uAiSG39HxIxNQuPYx9HwmF0rnzRmAvwRKZIXAHQSUjq92QzAioByEwbiqOuc0ffp0LF++HMuWLcPu3btT+TXuRPDnyZMnjcOwUqVKOrmrZt4QCFnjAaOIZ83StPCrVz3DRK28c2egZElvcIr/fOtWYOpU4OBBz31z5QJatwYefjht5DPnMXSoZnTgzcITZcoEdOgAtGghLqfNegjcD2wmuRLH5wgEqYNw82btqDh82DOCuXNrRwV/zf3ySMKLUWHisv1Y9G5TONbX8jyRAidR6M3pGP5WfkSG8WKfjgwcAvvOXsSg4TcQf5iGYU+vBR2ILHQevXsno0qRfPq2rgGjiplz1CesahUKCCjjQSissus5hqyOz2A/Bs399Vdah1p6mPiRrFIF6NIFKFTI/I2yZg0wY4bmnPREdFI+9RTQuHHaVvQQDB6sBRF6IzocX3opqIIBvU3Z6M8NqBFGh1b9ZREI0kVjrDKPihMnvKjGBYC2bYGGDWUB1NlPUpf9fM4+rO33OLDDi62u+CGU7jsTg14qhowZwnQKBUDH+m86ehpffJoRCSdze9Xxo0ucxgfvZEKpPDn0y6BaKgRshIDS8W20GEoU0xBQDkLToAxsRmfOnHE6C1Mchrt27bo9odQvDHPnzo2GDRvigQceQK9evQJ70n6WPiSNB4y+/fRTYNs2/ejzJcvrrwM1aujv460lo4knTvTu2EvNp0kToFs3LQKaz4feegu4eNHbSGl/Ts/BCy+I9bFZax33A5tJrMQxHQHRTSB52RWWW1QuHQMsWAB8/73YUdG0KfDyyz6MNJbAc8DkA/jnxTeBRP0pfiLb/YKxP1zz/JowNYZe8F+1/wS+/CAvcFO/DMiYiOf7ncAjlYp5Xy0JXPQYPLwPrFooBFwjoIwHobszQlLH52s7Bs7t26d/4elce+cd7UWfGcSAvdmzNYu/CLVqpQXy0XF57hzQu3farCB6eLVrBzz5pJ6WIdPGB2pZyGBnu4kG2WLyqJg5U4tZFqEnntAeDvssEFBCl33zs2M41vsNkWkga4/JGPt1pOtAQFecvKz/H9sO4YchRbWsJHopMgFvfnwedUoU1NtDtVMI2AYBpePbZimUICYioByEJoIZTKzOnTuHFStWOF8X0mm4Y8cOOFK9kqLTMIk14xRJIxByxgO+uBs2DODLPVFiTb/33wfMSHm7YgXwzTeiEmjtH3oIeP554NVXgQsX5HjQeEAjQoBSkN0PA3QV/Cy26CaQuOxKzVBULi+DLFsGjB4tJYkzHRFjAXxiQBDEc9jsfdjU9l0tzZAgRXWciUk/JOuLMvaA/57T59H/jRghB+VtUcOS0HvoWdQqUcCz9IK4OJmZvGcE4VXNgxwBZTwI8gX2ML2Q0/H5cnDgQDHnYAp+zLLx0UdAiRLGN8zvv2spQWWILwnpKOze3XuGE3f8+ZKQ6VMVqU9ssO2BINOXfv1Vyw4iQ6wewuPCJySoy/YZewgHur8tJUr2t77FhE+z6+vrYf2dAYB9C0jdMxBxE4NGXka5vLn0yaFaKQRsgoDS8W2yEEoMUxFQDkJT4QxOZlu3bsXs2bPx9ddfIy4uzukoVA5C42sdcsYDRvROny4PXLZswNdfA5kzy/M4dQp44w3AiHO7VClg/355GdiTryiL6XgRY2wUn/QOsvuhTzAKeqaim0DwsiuNn6hcHgZiqiE+EjZyVPDhs0/Kjwrg+ffx0xhapisQL39ulh4/HENeusf7snjAv33Pi0g8K19vJEPWq5j+XRbPMgjgcpuRiXvGO0CqRaghoIwHobbid+Ybcjr+d98BfHIvS/nyASNHAgwIlCW+XOzbV7a31o+1z48elefBqKAJE4CYGHkeQdRTfWLVYtoRAdYUZ9yxERowAKhY0QgHN30FdNm5Ow5jcuU3xF7tpRu2zm+f4M1Wpb1PxM0vc1KyA+27xsNxTf6eEZH/An76WjkIvS+CamEnBJSOb6fVULKYhYByEJqFZBDx2bZtm/PlIP/wFeHFW2kUU14QRkVFoW7duviL9SUUSSMQUsYDvh5kRO6lS9J4OTt27arVApSlyZOBuXNle5vXj5XO+/c3j5+FnNRl30Kw7TqU6CYQuOwamrKoXB4GM2rrJOsyZbQSRqaTAJ7dPziL84N6GBOh/C5M27nd+ytCN/gv3XsMY94vYkwGAG3eOoBnanuoRyuAy21hTNwzhieoGAQdAsp4EHRLqntCIaXjs574K68AfEVohN58E6hTR57DV18BK1fK9zerJ+sZ9uxpFreA5qM+sQG9fGmFD6LF/OILgGVKjVDNmsDbcg/3PA8roMs+9/INXJvQ0cg0ENZkOX5eesY7DzfrP2X1fswZWcp7f48tHPjvJydRv5QP6tEalEx1Vwi4Q0Dp+GpvBCMCykEYjKsqOCdvDsFMmTKhTp06aNy4MZo0aeL8bzoJFRlDIKSMB6tWAV9+aQww9i5SBPjsM7m8fTRc0IBBQ4a/KSwMmDLFWKS0n+YQRPdDPyEYBMOKbgKBy64hdETlcjMYS6Wy3Gh8vCFpnJ1ZjqmkB5+W1Ag68bySkICuxZsCp4zX9mixdCheaOLFAOAG/5cGHsWlnUWlppq6U6bip/HDiPzu+ejEQPluzwAAIABJREFUJQ0Dk/aM4ckpBkGJgDIeBOWy6ppUSOn48+YBkybpwsVjIz7H4bMcGWIAInV8I8/+ZcZ11Ye103/4wYeFiM0S1Pd81CfW9xhbNkKQLCbj3nv0MH5U8LHwqFFAnjwmr4BOXfZw7CW8XfhZ4JqX7Bo6xOu68zM8XMFLZiM369/pzTO4cSyfjlE8N8lx/xGMfz8wsysZnrxiEJAIKB0/IJdNCe0FAeUgDOEt0qZNG5cvBKOjo+9yCEbysqPIVARCynjA2iI7dpiD36BBQLly4ryM1B4UH817D58WMPA+vGyLILkfyk5f9SMCoptA52XXMLiicrkZcOlSYMwYw9I4GbAW4YsvmsPrNhedeI74ZR82tHnPlMGj2s/Gjz8le+blAv/4m4no0jFMri7JXaM58Pn4yyiSw03qNp24pGFr0p4xBWTFJOgQUMaDoFtS3RMKKR2f+biNpOVMjSpfARbwUm/W1SrMmaMF3tmFWKu8YUO7SOM3OdQn1m/Qmz9wkCymkdqD6UH1yVVepy7bZ8whHOhhzhPGmDcm4tvPvaRFdrH+hy9ewtvd2C+D8f2WIRlTfnIgMjyjcV6Kg0LAAgSUjm8ByGoIyxFQDkLLIbfPgGFhYc5agkwdyteBTZs2db4QrF27NpRD0PfrFFLGg5dfBmJjzQGVT3sefFCcFyuR81ZgF6peHXj3XbtIo1uOILkf6p6vLRsG2iLovOz6DGtBvH78Efj9d3OkqVAB+PBDc3jd5qITz14fn8KZ/q+aMniGKlswffO/nnm5wHnT0dMY9paHV3+C0nXoewitq5QQ7KWaKwT8g4AyHvgHdzuMGjI6fnIy8OyzgMNhDuzvvAPUqCHOa/RoYNky8X6+6tGsGfDSS77ibiu+giqWrWQPemECbXE86bcmzeWbbwDGDJtBdesCb7xhBicdPNJh0/XVq7jyzXM6OnpvkrHFn5i24KKwjj9r40HMGK6jRrl3EZwt+o88h/sKmf0kU+fgqplCQBABpeMLAqaaBwQCykEYEMvkGyFTHITknj17djRo0MDpIHzggQdQpUoVp/NQke8QCBnjASHs2BFISDAHTPJq1Uqc17ffAosWiffzVQ++guRryAAjk+5nATZrm4kbaIug06HlM5QF8Ro3DliyxBxpihcHRowwh9dtLjrxfOntWFz61CQDZYmDmHFwo7DxYP6OI/j+I/NSBj3c8190bVzGZEAVO4WAbxBQxgPf4BoIXENGx2cu7i5dzFuSXr2ARo3E+X36KbB+vXg/X/VgLUXWVAwBElSxQgARG00x0BbHAgfh8OHARi/qrN4VrFwZ6NdPb2uD7dJh07mzA9d/fNogU617htrrMX3tYWEdf9ySf7FknHk6OUsSNCtvvCSBKaAoJgoBLwgoHV9tkWBEQDkIg3FVdc7pww8/dKYYXbt2La6z6BIVhFtOwdQOQzoNq1atqhyGOnHV2yxkjAcE5Pnnzav917Ur8PDDemG+027yZGDuXPF+vupx331A//6+4u4zvoF21/QZEP5kHGiLoNOh5TNIBfH67jtgwQJzpClTBhg82Bxet7noxLN7/7M4/3EPcwYvvwszdu0UNh4s33cco/oWNkcGAG3eOoBnaptd1NE08RQjhUAaBJTxIHQ3RMjo+ImJQPv25i00nWp0rokS65yz3rldqHFjoGdPu0jjUzkEVSyfyqKYp0Mg0BbHAgfhF18Aa9aYs1P42JmPni2hdNg89/INXJvQ0ZShw5osx89Lzwjr+FNW78eckV7qkwtI+Obwk6hTwnjddIEhVVOFgDQCSseXhk51tDECykFo48WxSrSEhASsW7cOy5Ytw/Lly7FmzRrEMyJUOQx9ugQhYzwgirzwHztmDp6yxgO71Sdp0kSrkh5gFGh3zQCDV5+4gbYIOh1a+iYv0UoQr19+AaZNkxjHRZeaNYG3zSkRcoe7TjzfHX0YB3v2NmUiYQ8sx89/iRsPTsRdxusvZjWnPgmA3sNPoVYJifpUpqCgmCgExBBQxgMxvIKpdUjp+C+8AFy+bM7ysWb5vfeK8/rhB+CPP8T7+arHU08BLFAWAiSoYoUAIjaaYqAtjgUOwkmTgHnzzFmjhx4CWEXFEkqHTY8BZ3HuI3PsCJHP/B+mTEvyPA0Xe2nV/hP48r1CJk3fgVHfxyNvlswm8VNsFAK+RUDp+L7FV3H3DwLKQegf3G096s2bN7F+/Xqns3Dp0qVOh+G1a9fSvCDMkSMHzp8/b+t52F24kDIezJgBzJplfEmio4GxYwH+LUqnTgGvvSbay3ftGcJY2LyXNb4TNC3nQLtrWoWLpeME2iLodGj5DENBvE6cAF5/3RxpeOQ0aGAOr9tcdOK55cQZDCnWDUgKNyxAydEjMLS7l9p/bnBu3z0WiedzGJYhQ+ZrmD5JGQ4MA6kYWIaAMh5YBrXtBgopHX/8eODPP42vQa5cwKhRQMaM4rz27gXef1+8n696MBVBVgbHBD8JqljBD4idZhhoi2OBg3D3buCDD8xZJPKpVMkcXl65pMNmzrZDmFLZnAjEGrM/wTttSnsWwcVeSkp2oP3z1+GIl7DLpBstPO9FTB2V0ysMqoFCwC4IKB3fLiuh5DATAeUgNBPNIOUVGxuLL774Al999RXi4uLgcDiczsKkJC+RRkGKh1nTCinjwYUL2mu55GRj8LVoATBSWZaGDAG2bJHtbV6/fPkAVkkPQAq0u2YAQuxd5EBbBJ0OLe8Tl2whgRfLg27fLjnerW4xMcCYMUBEhDE+d/UWwLND20jcnNXamADZ4jDy+CwUypZN2HjADiPn7sXqyWWNyQCgfOu9GNjBOB/DgigGCgGdCCjjgU6ggrBZSOn4hw+b81SeL+748k6GHA6gTx/g4EGZ3ub2KVkSGDrUXJ425iahYtl4NkEmWqAtjgUOQh4VzOxx5IixtS5UCGCs763qPMaY6entAptnGhZC8sr6enq7b1P4GCYfWoHocC+XFTd76f3Je7F3rnHdvPGLe9GzuXE+xsBQvRUC+hFQOr5+rFTLwEFAOQgDZ60sk5SvBVeuXOlMOco/mzZtQiJrTABO5yBJOQiNL0dIGQ8I12efAevWGQOOPIoaKF69aRMwbJgxGbJkMV5PkflImJckACnQ7poBCLF3kQNtEQQcWt4nL9FCAq/164FPP5UYK1WX//wHePZZYzxc9hbAc8LSfVjc9D1DQmTtOQnffZPlDg9BPBMSk9Cxk8PYS8YMyRj3fQJyZs5kaC6qs0LASgSU8cBKtO01Vsjp+HxKw6c5ssRXg4yoyWHgtfmSJcC4cbISaP2iooAbN4zxYH1x1hkPERJUCUIEFQunGYgLIKDHekVSYv6LFwMTJnjl7LHBc88BLVsa4yHU2wVmg3/ej63P9hFik75x3o++xqj+OlL3u8H57NVr6Nk1E+AIk5cj/Cam/JARkeEGeMiPrnoqBKQQUDq+FGyqk80RUA5Cmy+QFeLpcQhSjsyZM6NevXpo0qQJGjdujPr1DUYsWTE5G48RcsYDpvjs10++TsljjwGdOxtbUTq4afXfsEGOT4ECWrEBPi+65SwXZlSwIPDll8Ld7NJB4h5mF9GDR45AWwQzDQEyqyiBFx87jxgBMKZAhhhZ/PHHPsowJoBnkiMZz3XKiBs/Sb7KKHYYA9bPQ8X8ee7AIIHnNwv3YsXEMtK1CO9/ai/ef1pFFsvsRdXHfwgo44H/sPf3yCGn4x84AAwYIO9c69ABaG3wtTuDWamf//OP3PLz5R8jexiMKEtly2of/xAiCZUghNCxYKqBuAACeqxXBCXmf/MmwHKne/Z45e6yQalSwMCBQGSkXH+pXi4wS0hKQpeWeZC0SDLouOIOfL56HYow5Yk38oDzwKl7sPPXct44uPm5Aw+8/C+6P6R0fEkAVTc/IaB0fD8Br4b1KQLKQehTeO3NvG/fvm5fCFLyrFmzpnEI1qxZE+HhxmsZ2RsV66QLOeMBoaUmzjSf8fFiQNerp9UPDDMhsoyRwZRB1ICQM6d2G6CTcPVqzckn6iSkAs7UopkC9xWMxD1MbK1Va+8IBNoimGkI8I7O3S0k8bp+HRg8WNyAkDs38OGHQP78MsLq6COI56Ub1/FKq4LiBoS8Z/D80h/xSMViaYWSxFNLQyTuJCzccD++eLWUDmBUE4WAvRBQxgN7rYeV0oSkjv/335pzjdZ3EXrkEYDPcczI1XfliqarM+2pCDF4j/34gnHePGDSJJHeWlveDz7/HAixu7KkSiCOr+rhGoFAXABBPdbj0kvOPy5O+5U/elRsYxUurMVCGHnsLDbirdZuMDsWF4e3Hrofjg01xdiWOIjey/9ArWI6Xg+SsxecXxt5AKdWlxSTAQ6Ub/2vKh8giJpqbg8ElI5vj3VQUpiLgHIQmotnQHELCwtzpgpNSRuaLVs256vAlBeCNWrUQEaZQvEBhYL/hA1J4wHh5qV9+HDg7Fl94LdqBbRvb45zMGXEhARg7Fhg5Up9MtxzD/DOOwAt/ym0dauWrvRW+l2vjIoU0WqSWBpu6FUq4QaS9zDhcVQHDwgE2iKYaQiQ2RgG8OJRMXq0FhOghxhVzNomuXLpaS3ZRgLPKwkJ6NkjA+Inttc3aMUdeG3OEjQoWeju9gbwHPHrHmyYyijhDDrkUIYDHSCpJjZGQBkPbLw4PhYtZHV8phmlk/DSJe8IM+jvmWe0l4NmOAdTRrx2TQvi27zZuwxsUbEi8OabQOo6uytWaB9/vbXTy5XTvAYh5hwkfAZUAn3ro1p5RiAQF0BCj3ULgoH5X70KjBwJ8EqvhypV0o6KrFn1tDa5jQfMzly9ijc65cbNXx7XNWiGOuvw/i+bcF+BvLraOxvpwLn/D3ux5w+9gYAO1O38L954TL0c1L8IqqWdEFA6vp1WQ8liFgLKQWgWkgHIJ3v27GjYsOFth2D16tVBp6EiaxAIWeMB4aVTjUW+Fi50/ZKPmvcDDwDNmmkRub6igweBRYs0R2H6miM0VlSrBrRoAdx/v2sHJb0HM2cCrHvCqGVXVLy4ZgCpXt1Xs7CUr477gaXyhORggbYIZhoCZBbcBLyYPS3lqOCvfWriUcFf7+bN3R8VMmK77WMAz7k7DmP2mLy48kMb4Eq2u4bI+PBi1OqxET1b3oNIdwFCBvE8evESvppxBof/VxxIcJGfKTwRheocRvdnc6JcXl96Wk1dFcVMIXAXAsp4ELqbIqR1fOrTjKqhjs+PZ3ri0xvW4X7wwbSBd2ZuF2b4+PdfTYY1a+4O5uP3rVYtTccvX961g5KOxqlTAToLmVIgPfHjz6igjh2BChXMlD6geBlUCQJqrrYUNhAXwIAee9caGJw/jwomOKKOz6MiKSntCDwqatfWjop77zU3lkFoP+nAbPqGA5g3ujjif24NXI9Oyz4sCRGt56Fhj+3o2vQeRIYJZgXTifOe0+cx+udYnFxb3HXt8cgbKNH4CF5rmx9FcuhIbSoEkmqsELAOAaXjW4e1Gsk6BJSD0DqsbTdScnKycgj6cVVC2niQGvfjx4EjRwCG8UVEANmzaxdtK1/acWxGPV++rN0MsmQBSpcG8qSqu+Vtr2zfrkUrM2qa8+BrQzo4Lc9B4k1QYz/XeT8wNojq7RmBQFsEHZdany65iXgxDoBHBf+WPSoMz9UEPE9cvoyZK07jwrkw3EzIgJicSWhQLbPrF4PpBTYJz6RkB6avO4ADR5Nw7QoQndmBooUzon3dkogMV8FKhveJYuB3BJTxwO9L4DcBlI5/C/pDh4ATJwA626jXM10/HXJWvrRjLkF6AKjjkxiEyFqBIvo5a5fv3KnxiIoC8ubVdHy/PCXy27Z2ObBJKoG9JhVI0gTiApigx95eIhPnzyv83r3GjgqfbR0BzPZfuIjfVp5H7PmMzkfQ1PEfqp0N1QobqH0giHNCYhKmrjmIo8eTEH8tAzJnBUoVz4ina5ZExjA9WUR8hqRirBAwBQGl45sCo2JiMwSUg9BmC6LECR0ElPEgdNY6mGYqeD8IpqnbZy6BtggCl1qfgBxoeHkDQeHpDSH1c4WALRBQxgNbLINfhFA6vl9gV4P6AYFgU7H8AKGxIQNxAczUYwNx/jIrbiZmMuOHCs4y2Kg+IYmA0vFDctmDftLKQRj0S6wmaFcElPHAriuj5PKEgLof2GB/BNoiqEutuZtGFM9A2y/moqW4KQT8hoAyHvgNer8PrHR8vy+BEsAiBJSKoYAWRkBUj1UXU0BhJrzNVAeFgC8RUDq+L9FVvP2FgHIQ+gt5NW7II6CMByG/BQISAGUIsMGyBdoimHmplYE/0PDyNkdRPINt/t7wUT9XCNgEAWU8sMlC+EEMpeP7AXQ1pF8QUCqGRbAHE9CieqxyECoHoUW/ZmoYhYBeBJSOrxcp1S6QEFAOwkBarSCXNS4uDvPmzcOGDRuwceNGHD9+HGfPnkV8fDxy5MiBChUqoGXLlnjhhReQm/XdTKD58+dj/PjxzjE5Vt68eVGzZk28/PLLeOSRR0wYwT0Lw8aD8+cB1u9LqevBWholSgBhOms3sSr34cPAhQtAQgKQOTNQsKBWVyPUiBiuXg2cPQtnsn5iWaMGkF8gVz9rnKTUUmRFc9ZSvOcesTorrNNy5gxw/TqQKROQLx9QqJCtVsOK+2kyknEAB3AJl3ATN5EFWVAURZEDOSzF4h/8g93Yjcu4jMzIjMIojNqojTDo+x3jPA7hEGIRiwQkOHkUQRHkQi5j8xBchH1XT2LH+ROIu56A6IhwFMyWDXVylUW4zrPC1VHBbam7RGfbmYi7log1f98Ajy3yYxmiOtUikStbhDEs9PQWxEsPSyNtdu0CVq4ELl7USjLx2G3dGoiO1sm17Uwcx3GcxVlcx3VkQibkQz4UgpuzwsX8ryfdxKqze3D22jUkJiUje3QUauQpjoKZ9O9NlmLiJ4QlXHnkxcQAJUuKHXk6Z+zTZpSfZbL4d4YMQLZs2jxEyuDy2D55EoiP18pTUUUpWlTjF0pk+KwAcOMGcOCAVueT/Fjii59S3b8fNgJcGQ/uLIbS8QXvLUYPFeqyPNhiY+/o+EWKALn0n/E2+lUyJgr1c+r4vO/cVkDqiGHBvseOGbtzHT0KpxLEQ44HGj/+1PMDnERVrEQkOnX8OMQhCUlOHb8ESiArslqKxBZsccpxBVecMtyDe1AN1XTLQNkP4qBTx+ecKD/vKtmRXTcPoYZugN4ZdwR7Y8/i8vUEZI4MR9GYHKiZszTCdCogPCoOHgRYfy/FHED9hXq6XjqfEId1Zw/gYvwNp96TOzoadfOVRkx4ZtcsgthB+PffwPr1Gp68yvPYffxx/TqlAw4cyb4dF6KP40bGq4hOjEHBy2WR79ENepcDV+KTsOrveJw/lwGO5AzImcuBWlUikSf7rTuXjl9afjp4ZFE3jojQzBrUjXVeHXXL6uuGIWKe8TWMTv4pZwX3xs2bQJYs2l1HpJwvTW5UTajjy965LJmsjkGUjq8DJNUk4BBQDsKAW7LgFfjPP/9EMxZ890J58uTBlClT0KJFC29N3f48OTnZ6QScOHGi2zYvvvgixo0bhzAfaUJSDkJ+mbdsARYu1P7mRTc1FSgANG8ONGmiWdVcEb/MK1ZoPOhgTE/33QcQ2+rVNatvMNM//wBTpgD79t2NJedNB2GbNkDjxq41YuJPHosWAevWAUlJadHi7eqhh4AHH3RviOBtjIYLrsf+/XejXaaMtqZ16+q/XfhwzXTcKaRH5yX7L/yFP/EnzuFcGj50ytVADbRAC1RCJWSAbyzvdLjMwAwsxVJcxdW75hKOcNRETXRGZ+SGa4MfHYrsvxiLcRqn0/Cg3FVR1TmPyqis29mYhomORUhMTsKsQ5uweGEGXF5WHXCkdWpmLHUQVZ84jC5VqiB/lGvHKy+Fy5cDixe7PioqVwZ4ZHs6KjbHHsC0XdtwaFJjIDadtSHLFRTuuAxtu59DvcpZpPeN14468PLKw2ADHg2TJgFLl2oGGFfE2Ixu3YD773f98xsZr2F10elYVHMw9uPus6IsyqI5mqMO6iASkXeYpJo/ncU/rNuDPb+Uh+N0ugCIiATkbbkBrZpmQ7MC97k0LvHI27tXO67WrgUSE9PKSgMCjzsee7odyAaxle1OJxTnsWrV3WtCJ+EDD2jz4GfVFXHuGzZox//OnXe34KWZn9KGDQPTuSWCa8pZQSwY55KeeFYQi2rV3BuXqI6wP88cqimpiUY24kgexYqJSObftsp4cAd/pePrcBB6O1S4+akPejpUaA3lh4YfbjoZUxOtcfxgkwc/ND663/j3ty7V6LTUT5umRbK4Ilru27UDatd2/XPeubZt0z4U5JX+zsU7ApUg3rkYIeOKeJj973/a4UZre3qqWFE72BiUGB5uG+hEBNGrYlGvp36/BEucAYCpibp1XdR16sZlUMZnOj6dgVMxFSux0hlglZ6iEIV6qIcO6IAYuF7Ti7jonAPncgEX0rDIiIzOOwLnUQEVzJ1HKqCvJyZg2r6NWL4gM66trnLXPCIq7kHtVqfRuWI15Ih0bQ/gUfHXX9pRwfhYV0cFtybNAu6OitXn9mDmitM4/ns14Gq6cXLEokTrrWhfvxiq5Lgn7QBB5iCkXj92rGu9OGXihQsDPXsCpUu7OSrC47Ci+I9YXGoMjma/W6nk3Zc6Pu/C/H1xRdv3xeOnsdlw4LvGwMV0wSDR11Cww1I81f0sGu5/zmV/HnHbt2vH1caNmkMoNVGvp17ctKmYU0jkPDGjLeexe7d2dLszz/Cuwj/u3hxwTdes0bD499+7peI68lNar54tzDNmwOaWB53dPCv+/PPus4JnAz9fxKJSJfdnBYMQiCUDZBkjk5roaOSdi59Txs0ECikdP1BWSskpgoByEIqgpdr6FAEaD7p27YoHHngA1atXR9GiRVGwYEHQmUdn2qxZs/B///d/SEpKQmRkJNavX4/KtDpJ0HvvvYehQ4c6e1atWhXvvPMOSpUqhf3792P48OHYvHmz82dsN2TIEIkRvHcRdhDyYvnZZ66tb+mHY6hXp07Aww+n/cmyZcB332kv1LwRo1rffFMLFws2otbXt6/24k8PUXP5+GOA2n0KMQr4889da43peVJ7euIJ4Omn02pO3GfffAPwKY43ovHh1VcByT3vjb3en+s1BOjlx3aMlvwFv2AmZjqjib0Ro3zfwlvOV1Nm0jIsw1iMBV/+6aFmaIaX8FKapvMwDz/hJ+fLR2/EF4m90dv5MlGIvCzCuvN7MfKzCCTtS3chdzVI9DXU/+9G/LdaozQ/5UXg++/vVuJdsaCNjEcFX/mk0JXE63hv9kacnt1A19RytF6GT344gdwxPnhR6ItNq2tWWiNGEvPoTm9bdMeCF/Cvv04bn/F3gbkYVasLLked9zoyo9dfxau4H7c8jTPbgg7j/ov+h/2TGgLJ3gM/omttw0c9CqB45ju/Y4wY/eILLSbCG9EO/eijQMeO9rNBM2r1q6+0GBtvxHnQgNC1a1rbLZ2kI0cC59LGMbhkx4ciL70ENND3q+BNJNv9nAaDyZP1nRV0tvKsYLKDFOLneNw4zYauh2rV0oxsgfCiUBkP7qyo0vG9OAh5qPCApW7pjVwdKvzAzJ2rOcQY2u+NGMHw1lu2y1LhTWxdP+ch36fP3Q5Sd50ZzDdsWFqLNyMd+OF25dRLz4d3rmef1T56qV9t0RI6YYL2tNwb8cP/xhsAgwIDjLypWNSpp2AK5mKuU9/3RuVRHm/gDdOzhvyKXzEN03TJwGC+J/EknsbTt8Wl7Lyn8L6i565SCqWcd5U8yONtyvp+fgvoxae2Y+Kw3Eg+riPDTPZLeOStnXj+3np35uEAfv8d+Pnnu4O8XAnCuITevdMGS/HF4Hs//oPYhW6c6+kY5X9yJT55sgayhmfSfhJEDkI6oTzEm98FKfH89NO0/7yi2I+YWK0n4iO82wPyIq/z96M07ngaryck492eWXHy20d17aWYBzfgk+fLIm/kndeujCfhkUdHjjdi/HjbtsB//mO/TBl87E3zDD+p3ojmmVatgGeeSXtX4f2A5hk60b0RAwp79aI90VvLwPs51YrffgNmzNB3VhQvrqkVqQMrGSPDOxdjbPQQHYUvvqi9WrU7KR3f7iuk5JNBQDkIZVBTfXyCAB1/Gb28WPv111/xH2ojoFLyH6fDUJT27t2LihUrIjExETVq1MCKFSsQncrSdO3aNTRu3NiZ5jQ8PBz//PMPSrsL9xIdPFV7IQchtRw6KtOH1Xsbn06p9u21Vr/+Ckyd6q1H2p8zV9q772ohQcFCdI5Sk9Oj9aWeM/cmnYSlSmm55AYO1NIViRCjvmnZpEbKV5yjR98dnueJH/tRdj9amr0ZAkTgYFteuCdggjMSV4ToBPkAHzjT+ZhBNBwwqliUmI6oD/o450HH4BzMEWLBtEb90C/NRc8rAw+LQMPBhPeLAXFiKY7KdVuGQQ82cQ7NY5WGAxHiCx/a4ypUAC7dvIr/fvMvrq25O6rZE8+Iyrvw6ZLNKJg71es3ESHctTV70wrIROfJ+PECHW415SeJdkWmuVxWfBLG1HwBjgz6HNdkwSh2OgkZCZ8w/T/475T1OP/HHQORHonCih3DBx84UCGmqDO6nEde+gcp3vjQmUObp10eo9PJyXm4ejzvaS6My3jnHe3CSsPBiBH6bPCpeXbuDDz2mDfEAuvnM2cC/CNC3Ns8K8qX12KVBg8G9uwR4aAFI/Tv7z5Rghg337VWxoM72Cod34ODUPZQ6dJFc0rRivfDD5qDUISYaeT994MrEJCH/H//q88plxorfmxpHWfEE5+XU9+no1GEeMAzOJNOwj/+0NZEhCgDPTFVxHQnkSF80daTikVH2hf4AuuxXmhoBgAOwADQGWIGfY/vMR/zhVk1RVO8glecgYNjMAbLsVyIR07kdM7DbQrPXyJbAAAgAElEQVR4EW4z22LGwQ2Y9cF9wI1bjjad/Wu/sxxv1WjsPCoY/Ldggc6Ot5rRCcJvLoN7Tl6/gN7Dz+LmjnJCTDLX3YKvXi2rpR0NEgfh9OnA7NlCMDgbMyXjmDGabjyn7KeYUvltISZ86fo23nYGAjKd6KuPl8DVJXWEeISX/xfD+uRA0ei8znjpQYO0tKgixNeEDIDTmc1WhLVU21OnNB1fT5xN6gHq19disGlmYVwHnYPpX096Eoj9evQAGqWNtZWag1068ayg45uv/kSI8ew8K+gspKnto4/0x+OnjMOH9e+9Z/+XmUrHF9kZqm2gIKAchIGyUkrO2wjce++92LNnD5hqlHUDRalHjx4YQ60MTB2wBnXq3K1QrV27FnWZ0hH84PfAqFGjRIfx2l63g5DaTr9++l6ZuRr1uee0+oJ0RskQrXnUGgMpr5e7eVLbo+HgdNq0j7phoXX4k08067AsDxoQePmnw1dE+0wRkrcJ7gc/OW3N9rUwnecszNK9BKkbMsXnEAwBL+BGaDVWYyRGSrPgS8KCKIgfIGgMujUi0xhxHrpfRLpZhC2xBzGkT3bgglyNobrvLkflS42daXJkiMfMhx8lY8iiDYhdpC+qOP040Q02Ysyf+5A5yvsrN90ymr1pdQ7MWoMffqizsYtmNCD0+G0BhjZ4DMlh3l/WpmdBJ2F/9MfETk1wdEpjKUHCy+7HkLdy46tBOZzll2SIBoSXX5bpaW4fprQZMECz/coQ0wixjgzXNH16HL38+PmhISIYSNb5zbnzUT6NBj/9pD+qOD1mDEagb8POWfmU8UB8p4ecjs8DiQeTkUOFT5n5yyRDzAtNfTQY6o8zResrr4gHAKbgRiWG9x0eTqKW8hQefDbPOo98MiFDDMzk+KlTMsjwkexjtro0HuOFAwBTRGfN7o/xsbN+txHiy8XJmCzNoh3aIR7xwgGAKQNSt6eO7y5lqV7Blo+qiFFvlwDi5fB47OM1yLqjrnAAYIp8fGj7/qAbeP+HPYhf7yYXvpfJMFvI6P87rbsGui5szN60ugbVHEmyv+YcghmOn5g1Bd/U7qRzxLTNWH98oOMjDGtXDRdmPijHo/oODO5aCkM+iBZ2qqUM+NRTWoIkfxOTMVEnZAy3DLVsqaXKZNBa+ooxevjRPEOnlrsyEXp42KnNrFnay0EZ4ieQjlpmw9HzktPVGAwwZcYRO2dCVzq+zO5QfeyOgHIQ2n2FlHx3IVCzZk3n676sWbPisp7UjKk4OBwOFClSBCdOnACNEHwd6I5SjBSFCxfG0aNHkcHk8CjdDkKmvdm0SX4n8MtKx5as8YEjM9SfX/pApzlztJqDRog3pIsXjXDQQgcZ5SxLTP/KW4kftCYz72EncMKZJkVPyiF3UDVBE/RAD1kknf1YT9BVLRIRpqyRqDc1qSu+rFnCaFBd5GYRXhy7AXF/1dTFwmWjqOuIcEThZoJ8fcfc91zC+YNirxfTy1Lry5/Q+zUTXxGauWkF0H3+eYC12YxQ9OCBiO8r72WMWdsccXUXGhEB2e87gkvbjRV+o73z3nsNiWG48y+/aNn3jBBrY8gaHzgu7c90wPPFbSATo4K7dxd/RZl6zsxwqCd7nyecXnhBK99lV1LGA/GVCTkdnxZFV/Wn9ULHw4R5emUCzlLGYKAkLXGBTt9+K/7cIf2cadkUzQ6SmgfvilwTPWlF3eHNrDU+Km/hbYnNVJd2Y7cz04cRao3WznqAspSIRHRCJ10pQWXH0NPPVUkCPf1S2iQ7HOhc9T4kbC0v0i1t2+yxWnYRh7yOn+veM7iw21h5h5bTJ+O5p+WcnC4nb+amFUC3QwdjOhBz6ET+1gYJrX4VGDVt05y/PYeLT3wv3Z8dc1U4iQu75Au/8chjduxCOjLeGhLUS2fWeZ83z9gIRk08zBTN14d+MM8Ym3i63nybwIBGvaUxXA3OCkWyAZkp/KiWuHjHYepcjTBTOr4R9FRfuyKgHIR2XRkll0sE+HKwUqVKt9ODbtiwQQipAwcOOGsNkrp164axHp7K8Ofjb+WGY797TI7m1OUgZD435jww8oUWQshDYybMD/RXhN26GXfumYWnUT7M0VatmlEuwv3NvIdNwiSwZp8RikCEs25gNmSTYrMSK/EVJCO9pUZ03Yn1TkZhlL5aJS4WYXvcYQx6sbiJEvmPVXi5fZjyz98IMysow8xNqxMWOj5Yh8Ew5bwAXPBSu8rTIJ1+AKbIRSffZsvUpo4wQ1PhqzleNv1FtJ0zu7No2iFfyMvXlHxVGchkRqyNGfOnk5GqiVlHhRkypeahjAdiiIacjr9vn1YP29/Epw/MlELnWCAT8zjrqbEeCHNktpJb91UrxTVTXWJmDmboMELU7ZnaMxJyQWOzMRvTMd2ICKb0ZUrIcRgn/RpyydorGFf3eVNkMcaENSTlHYwcO3Pj9Zi07LAxMVL3NnPT6pSKtcXT1xHU2TVts+KHgEM66sW7Y958IbC4udTQdzoZX1O+vmPCKn8Rj32aeIzEZZglO8sR8CViIBOzYzNLtr+JybI+MBZj4tMpKB3fp/Aq5n5CQDkI/QS8GlY/AqwJePz4cfz+++8YPnw4Tt9K7ThlyhR0YPiWAP3xxx94nDnCwGinL/D666+77c2fv3kronbu3LloSe3HRNLlIGTKIFYHtgM1b65VDQ5UYo4D5p4IFmI1bEaeW0xm3cP4Yo91Pa7hmuEZdERHtEIrKT7/xX9xEpL5SKRGdN+pDdrgGTzjnauLRegzfxkOfK/VEAwG6rRkEh5vmsWcqZi1aQWkeftt4LAp9g8HsOteoPxegdFvNT2bByhyDEiIEu9rcg/an5nZm4+n/UEbNwLDh/tj5LvHZF0OymJXp5Y3lOhsfe018XqU3vjK/pwpX5lu1I6kjAfeVyWkdXym/l+2zDtIVrRo2xbgn0Alo/n+7DbvJk20olYWk1nqUixi0R3dTXm51wu90Ahyxb1ewku4BMHCaj7CvCu64mE8LMW9V6cYnJli4+fygrN6a/sk1K4UuDo+MxkbeWh8By4HEBsDZBesd0oGe8sA5STuBoJrpae5v7NjGEl5r2d+Im1Ys5yVYAKVmHCM+9toBhyz5v/551o6XjuS0vHtuCpKJqMIKAehUQQDoP+KFSt8ImUjH1binTRpEp5nfjY31KdPHwwZMkQ47SdfDHZnXiwAM2fOxFNMnO6GZs2ahba3LsvsxxeFIkQHoCc6efIkajHBNoCjY8agSG4Xr0T45EGizqKInLrbUvtjZfNAtWrSIksrcbAQ1+G777RiThaSWcaDtViLz/G5KZLfg3swDMOEeTElqC6HnDBnuQ75kR9f42vvnV0sQrvXT8JxQj5FjPdBrW1R8IV5+PJbg/k5U0Q2a9MKQNCunYkPvx9cDPwpESE8/iWg23gBqX3b1J/pIEeOBFYbe8hgKjh2vvB6m6jdYm3sUuPSFW7KeOB6NykdH1pK0C5djKX/9/bLKvJz5ofjQRmoxJeYfJEZLMRahJMnW56rzix1aSEWYiImmrIaVVEV70E8IPI8zjudlHahciiHQRgkLM71m0noHNMauB4t3NeuHcr1m4VBH/Plmglk1qYVEMXUmnsvjQXGS+zTQe8DH4jvJ4FpCjX1ZzpIVsHZuVNIXJ82ZrbrmBifDuEz5kzONmKEz9gLM7ZLjUul4wsvneoQoAgoB2GALpyI2GFhYcKONG/8WY8vkcXgfUTujAdVqlRxpv1kjRIZGjFiBN7h238A8+fPx8MPu4/k489TXg1++umneEswX5xIzUKXDkKmFX32WWN1RWRA8tSHOQcCtXgSU3IaTYZuNp5G+fnBymzWPYypRZli1AzKjuyYgAnCrM7hnOH6hcKDeujAdKk/4SfvLNMtQmJyMto/C8NpIL0PbF2LbA+vwsT5J8wZ0KxNKyCNqcaDcv8AuyWeSH0wEBhkn9ws//mP9knzBzFFze7d/hjZ9Zh8zH7//faRR0SStWsBfnrsQsy0zc+7HUk5CF2vitLxAbCQp52yclC3p44fqGSngEqzMGQQYNasZnHTxccsdWkqpuJXyNdWSy1scRTHCIhbrLdgC4ZgiK55W9EoL/I6SwmI0pHTN9C7QEfRbrZuX+C5+fjqe4lXc65mZdam1YlYUpLJumzjv4BlD+ocPVWzbnQsigWviw+ivwdTjJqcbEv34CxhYKQ+uO6BdDYM5Ko8CxZo8ed2oaZNtReNdiSl49txVZRMRhFQDkKjCAZAfzoIzSY6v5KoIfmIYmNjkfICLz4+Hvv378eMGTPwyy+/OGsIjhw5Eo899pjw6IMGDcIHt5JZL1myBE351XFDf/31Fx58UFPY2O99wfSUhh2EdMC2by88R592COSQKIa2eXnV6VPsfMF86FCAVaAtJLPuYb/gF0zDNFMkj0Y0JmOyMK9DOIR3oAUM2IV+xs8Ig5czO90ixCVew4vtM9tlCqbIkbnRBkxafsgUXjBr06ZI03ama7lSjWOqg1C2RknvEcBnvc3B0AQu/qxR8u67wMGDJkzCJBaBXKNk+XKtXJldyM41SpTxwPUuUTo+gHPn/JJC0u3vLbNSTPd/rTbpc4XFXWNjpbvbsiPzcrvKLmOCsGarRelFMqPGeArPAiggVSuc9Q9ZB9EuFIMYfItvhcXZdTAeH5bsLNzPzh1yP/0nxky/6D8RDfwCJCQAHc3011bfAGzUMkoJkRk1xoUG9NyYJqsnnjCRoQAr81K+CgzqoengwUCZMubwspqLXWqMp8zb3zXsPeGvdHyrd6cazwoElIPQCpT9PMZyWnN8QI0bN/YBV88sf/zxR3Tp0sX5InLixIl4TrAispUvCE1JMcoaizdvWo6z2wFZEzEiwj7yiEgSbOmHOPcvvwQKWptW0sCdKs1qLcACfAdzQtRyIRfGYqzIbnC2ZV0S1iexC0UhCj/iR+/ipFuEZIcDz7RPApLCvfcNkBYxj63At7+fNkdaszZtijRWOwgr7gB23CeOxUf9gQEfiffzUQ9/pomxW/qhAQOAihV9BLSP2a5fDzA62i7EhBKs+WlHUsYDsVUJKR2fBX48lFIQQ86E1kxXzzICgUq9etmnMKpZGE6aBLC8gw/IbLUovYjTMR2zMdsUyWXLCOzETgzEQFNkMIOJ7jIC6QY7eT4B/83TwQwRbMOj4Itz8eUE4zXopSdk8BfA1CDAhxYBiyXqS/b8BhjdUxoCszv6s4zAG28Ax4+bPSN5fn5I8CQvbLqeixcDE8STMpk2fnpGqoyAz6BVjBUCLhFQDkK1MQIOgXbt2jlfE2bJkgVHjhxBrly5dM/ByhqE3oSiA7Fo0aLOZm5rEPbuDRw54o2VNT/PkwcYPdqasXwxCp88+MhZ7gtxvfKMjAQmTgRYp8RCMninui3pNmzDx/jYFMkroiIGYIAwL9YgfBbPwgGT6mAIS5C2w/+zdx5QUlTNAv6W3SXnnDMGlChBEEkqSBBMJEXABIqCCRFMoAiIiCiKYALUHwyYHjkpiCQBQUCQnIPkHDe9Uz27sLvsznTf7pnpmb31jsfnv7fq1q3q6a5b0bQTJA0lPPDaNmI3VbDJgXvQyz//M2+PcKiNtVMPbZJ4TAQIpZWmY0X2HSbBtwrOoe/aQ0f3VIT06gW33hqcZ+zTT2HevODsndaufiwM8fshpRBfCvLdAm3aOJzN7+DBdIDQujAzjI0vYwSk6u3kSetC8gdGxYowxD3tGC0fcdAgWLfOMpprEfLkAflw+Wnuu9NmUWo5LmQhH/GRI+KtT32e5VnLtM5xjm50s4znLwTVWYoyRuDBIreRcKSgv1gLON0a73xD/xeDmNRo8wfg6Jzxga/DAIVZgh/0hmc/CLju0tswmK3z33kHVq50hygkj17cM6E6keeff+BN9+SW0qULKDSNC8jDoG38gIhZbxJgCegAYYAFrrezL4FJkybxoFTWARMnTuQBC204p02bxl133WXgjhw5kmefTf/CIX9/PtELNn369MvzCO2fwEPBVIBwxgyQDFI3gKTLSQlIqMLRo/CkwhBwt543SE3Zbd6pLktTgnO96MVhDtuWsNC5FbXIw+u8zkbcMZzsUR6lOSaySNNQwrBlC/jrvca2ZekWAn3Xf0mtyg5lzjv10CYJx0SAULr/rlrlhDQT4HQOyHneOrELWaDEPjhWwDquwxhSnDJ2bMDzGS6fYutWkCJyN0D16u7hRVUer7wCW7aoYjuL9/77ULy4szSdoqadB9YlmaFs/EmT4Bdn5rRZl3QqDAlWSqp+qIKbXvJOyNDPQ3udNotSH/kiF+lBDyRIZxde5VWqoja09zmeYx/uKC16nue5mZuVxNG/byTbht+rhOs6pKgYhu/5jjJFA5vgmkIONn8A0iZ+pxNTECLiISYSIhW0dKQAlNwLF7MqIDuLUrgwjBoFfphqZIpRCQ5KkNANIA3WZCRuqEJ8PMhMx4MONfGxI4eoKJCESsmXcSNoG9+NWtE82ZWADhDalaDGD7gE5s6dS7NmzYx9hwwZQv/+/U3zsH37dmOGoUCPHj2QisL0QP7+qWRvAoJXrlw50/uYWWgqQCgtiHr0AGl4H0yIjPRUD+bLF0wu7O8dTnMIgzB/UBRg806VQoe/8AuTmGRLr7nIZbQXjUat9e1WtvIywY8cZCWrcY7smAiKpaGEAxeO8Uz3bHAhmy152kaWy26Cvbm32RsvZ8L8XbZZuUzAyYdWiJoIEJ45A4884sARym6HHTYqQ/sOg+E252xGxUCs2u8rSQKtWkHXrg7IwwYJMRW2bbNBwCHUUJ4/mCSChQvhI5vFIVIYI0VUdqBKFXjtNTsU/IurnQfW5ZuhbPzDh0FaY9r9IVgXc0qMbNngk09Ct+Qh6TRyXzoexLlmdvWQhC8vR3nBFirkFMWr6DhtFqXFqBNzCItRjJGM9D2bOx1JuWUOYT7yMZrRRKFWNbdu2zkGVXrItn1t+4GKjLU9zqBAh3mM+TbIv1ObPwDHOinU/hOWqwWNDV12+RK+tjmfMioWYtWey6TnSfL227a1/XQpE5CglnxKZbRvsEEK8aUgP5TBiTmEEiwWvdiBBg2gd287FPyLq218/8pXUw+OBHSAMDhy17vakMCECRN4OHFux6hRo+glfctMQkJCAiVLlmT//v1cd911/Pvvv+liXn/99WzcuJESJUqwZ88eY+6hk2AqQCgbfv45zJmjvnXOnCDtKI8dU6fh9i+02ZM5kWJWtqy9tEFJhypZ0h6N664LWv8Hm3eqFJo6xSme4ikk01gV7uM+OtBBFd3Ae5InOcpRZRoRRJCDHJzhjDKNO7mTRzAZUUpHCc/+uJD93zVU5iGi0GHyxBbkxHH1d12lOsfZstxeIkHbn77kwXtMBErNntTJh1b2NBEglGXyabKXgZlA0e+f5r926q2dy2xvwq7rZkFMZrPSumpdmaZb2PVbJWV8yS+ReRwBHpd6Fb9OBLVuvBGk/Y4qFCniGR0brCxrVb5T40nOkjzfdnzxNWrA6tX2OHJ7sFU7D6zrN8PZ+DLQUwZ7qoIEkc6dA0koVAXp3yV9vEIdnOi6IkmkdjJJZGag3LsOHVKXZt268MIL6vgmMJ02i9La8gAHkKq5OOJMcJT2ErGLxT62A9Jm1E4lY2YyG6MIYohRZqMjHbkXexWA3ZuX5sScuso8RJbfSfT+Mly4oG7jV7x1P1v/sFey//jiCdxRP4fyORxBdOAHIEmAkgyoDgnkX9uIY1X+UCZRcWVHttb+RhlfECs03MO2hZ6RNyogk05kikvu3CrYzuHYDWqJe6Z0aSkKUOepUiV46y2/dYZWZ8wi5unTnoDreYXmNUlbyXzwFSssbpxq+eDBIDJ1K2gb362a0XzZkYAOENqRXhjhnj59mnnz5rFmzRqOHDnC+fPnkWBaeiDBsi+kwXYQoFWrVsyQSyAwf/58Gje21lavZ8+ejJF6dWDp0qXcfPPVmVvLli2jXr16xhpZP1osH4fBdIBQvs4DBqgFlMQzK73AxHp74w21SkTp3SXWjlx4wwFGjIA//1Q7SYEC0psWhMaaNWo0JBVKUsukWf+pU9ZpSJ8FsZikn0cQwIE7VQqul7OcEYxQmgNYmcq8wivK1YNJjEj7oT70UXZiPM3T5CUvQxmqRKM85RnIQKSK0BSko4Sjl07Re+gBYtZfa4pMikWZL9Jt2EYqnq1mvCpiFPwgJUqAjAEavmIB/46x9l5O4qXkk1N5d/R5MjmZkOH0Q5tegDCV1M8ci+TxIncTF6tWUSmtKLsN2cxrTetzOov1ALY8k4MZzOTXqrDgLbUWvHnu+JPRD9fig5GRyhc9t3SuE5NGPuUSKFSBzp1BWgfJq1sl8CvzSOS35XAzApWjOIKzcaPn967yrpDxy4IrDp2fflJj5847QXLFnHxVqHGSPpZ2HliXaIaz8WUGodjpKgGlpJeK0JCuEirp+mKPyh0jwPOsrT8ZJjGkpHjTJpOLUy0T77AMX5KXk0qQUDI/pFRdPOWvvw4XFZLfJItEbHyHvO1Omz9WBTuPeXyKpxuPVahDHSPAmAk1GyppPxkjIHPKVeaNSwKgdBmRBMAPUJv3Ju1R+9FPuXow6Ry7vmzESwMuEr9LIZiT6xTPvfMfWXZdw7BhakXLlW4+ymu/LuDlFzKzd6xnXItVqDzwewYOUA9QWt3P8noLP5j//vO0YlQtAJdJIXf0X8nAxo24GGW9Fa9U1w5iEB++nYM1/TtaPqogFGv3B++0bcCQwRF4yZlPl7bYX5LLUKeO0vaOIsncd2kzqpp4JgExyb+Wz7HKaGB5ZYu7rGhRR48VNGKSUz98uNrzfe21nrvS+PHw229qR+jYEe61l1OhtrEFLG3jWxCWXhoyEtABwpBRlX8YjY+PZ9CgQYwYMYKzJrNPJXAoAcI4+RI7CJI13LFjR7J6meqbfC6gtPzcsmULkRIES4QFCxbQpEkT47+6du2K0EwNmzdvpnLlygb/tWrVYuHChWST9jqJIMHRhg0bsnLlSqKiotiwYQOV/JC+YjpAKHxJmr70LNhloe2eTEkWyzXJapOAlmQqW7mwSsmHWEpBCkY5+HilJCWX/3XrrJHPm9dTBiOBUgnaihW6fr15GmJFS7ph88QZc5KiNnSoNStUeJBBWlLFGCSwcHcyzeECFhjtNWUuoVm4lmt5iZfIiTOB6/Ws5y3eshzg60xn2tDGYHsZyxjFKGKJNXsMylGO/vQ3AoymwYsStp09wOvDzxCzwULKXdbz3D1gLQ9U8GQmy+VKHnUrrwoJDsqromBBiE9I4OWZv7N9grUgYeEus3jvi5NkjrLnDLpKjk4/tCYDhMLH3n9z0ufGFpb9tlLEIK8Hga35VvB2g1acymp+Xqe0sxKnVhnKGH2B3/lzAStHWNNHzkYrGdn9evJE5zCeBXkmrF68JajWxvPzcAXExnrmpCxbZo2d++4DGcMrr3FxCon/1kqQUEyMF18EqUAMJ5A5m/JcWOmCLgX08q6QfBtxrImZNnOmNalIXtgTT7i/ElM7D67oVdv4XmbBOvFSWbIEPvwQrNzNypf32JQOBaOs/Yr9tFqCpC+9ZO2+JKyIV1deZlJGIt5huXPt2GGeScETD3P9+h4cKTUX76qVEgwJDsrL0UEPs9Pmj3mBXFk5hSn8j/9ZQq1BDSM4mAVn5tQtZSnv876lIKEEB3vSk0Y0Mnify1w+53NLNCSRsS99zY0P8CWhye34+8QO3h4cZS1ImOck3QbupGWJasYOixbB6I/jLSWvVah9jP4z/iB3wUtcio3n+UfzcOgra5Wd5Z//mSHvxjibAOhLZlb/bvEHI24AySmwGiSsWRP69fMwt7bwPEbUv5fz0adNc1uUoshszsIUNu5cr70cyZa37zONLwsL3LWEkQ/UJmtktFEJKTkmmzebJyH5EGKHWczTN7+BwsoLFzyvXSsuHrHru3WDFi08G8psSbHxrQQJJXdbckPkkxpO8PvvnvnxVswKcZnKs50rlwdPEjPlnWMF5N4obWvdnAAo59E2vhWt6rWhIgEdIAwVTfmJzy5dujBx4kSjWlACbQUKFODQoUNGAFBacR4/fpwzif0T5H8rWLAg2aV9CnJvsnBxMsF/2bJlkUrG++67jwYNGhizAnPmzGn8b+vWrTP4XLx4sUEpc+bMTJ8+ndtvvz0FZTMBQkGQuYVviyUE1KhRg5deesnYb9u2bQwbNozViV5QWSdzDv0BlgKEwoC0EBJP2h9/+P5SS4nCo4/CNdekZF2sHmlZ6ssCFKtPKiuFhnzhwxHGjfO0bjWTcX399Z5BR+IASALxNE+aBHPn+o6kSKBVBnDJjSA5SMb4Z5+Zq0aUfmyijyAHay3enUw/OWtZi8wr2cterzgya7ApTXmIh5C2P06CVBJKkNBMu9FsZOMJnqAenkrjJJBM5XGMYyfep9fLHJKGNKQrXRFalsCHEo5cPMnAn9dxaGodn60ls1TfwMOPxtO0SMrIhcSvpUh8yxbvnEl+hhRbS+w7dZHxh38vZNH4SiQcKOaVSESBo9ToN4e+L0T6x3Hg9ENrIUAoBz/5eTsjw9ZswXDDhh4/Y3L4L8c2vqjZkzVFvbebFoeWONYe4zEKUtBDIvH8E7f+ybTPixC33UeCQc7TVOz4F6/fdovhOEgCueh99x3Mnu3b5yl+TrncpVGgb+lR98dieeX//DNMm+a7K58EsTp1AtFJchDHgfw+pBjdl2NIMpIfe8zTuigcQQptRBZbt/p+V4jvXN4VOZJ1FxP5yWf0hx/gxAnvNMQcuecekJmWbnccyEm08+CKPrWN7yVAaHwoLLxUxCYVezD1S0VKQMS29ZVMKLaslEOLXeolKTNk31fykpdgqQRNfb2g5UVy003Qp0/KjAPxNMudS0rOxd73BmXKeF5sopfksHu3584l5dbeQHiQtqKiU/E0OwhOmz+qrMI20M0AACAASURBVEkC3UQmcpCDXkmIPdyc5sbogEiuJACr7pscbzObeYd3kPEGvkCSD5/jOapQJcXS1azmS75kP/u9kpDA5m3cxoM8aLvLyeWNEpW569whhny3neOza0O8dxllr/c3T3fNSa38KYeibSg8n3G9arB7nffkxOgscTTqtpMuI9aQNceVxHAJSg17N46/hzUj4aj3d1tE8f00GPgrvR432SXFl3L8+XeFH8z+/Z7gkJlcAPmpi/2SuqPzrjxr+bxmTzYV9Pi50gOppq1LXR7lUXKTsqfn2AkXWPBaU+L3lvAuoXzHqNJ5Lf1vaUhUsn73kuj19deeii9fnSEk0UuCalWr+lMZarTldf3NNx4Xj69EV3HPPPQQ1KqVci9xz4hdayYpslo1ePzxoLtn1IRlAktyXaQScM8e74ulLkFqNESeyRsSyOdYuoXIP75a8ubP70nGlOraUABt44eCljSPViWgA4RWJRZG62fPnk2LFi2MYKBU20kV4b59+6hatWqKCsFNmzYZLTmlzaYE0X755Rdjfp/TIM6DXb4utcj4tpKMGzeOO+644yoWzAYIpXLy8ccfN+ikB48++iiffvopmfw0LMhygDCJUfGgzZ8Pv/6asiWRfI3FGysValJ+4s17JsFdsZzk8pzcohVvqHyVb7sN5Csd7iBWpPQ3E1mk9t7L3EaRp1g63i7sErgVB4J4OJNbT+KAkYCg6EPKRrzpQ24Xgi90pPF7Eog3VJw4zZo5mlFsR60KdyfT20n7n3/5lznMYQUrUsz7KEEJ47LdmMaOVQ2mx9hWtvI1XyPBvtQtiYSPdrSjPolZ4mkQEZwtbDHOIU6RS1y6vKoIRYxzSJAz9eXOtKBMKkGcCF+t3MD6qRVSZhvnOEPRO1dzb5MCNC5c2eu2EihMelWIvywJpFJQXhXyj7dXxYW4GL6Pnsj8j6/j7IKUzoys9VZxa891dLo/MzmzJgu+mxaEyYUm5WWSWvozCNMjkOTU2eXxWYrPMDXIxUqycMXHmKwo/qp1+3NuYk6L51nIwhQzL+VZkt/G7dyOZBangGTnj42PZ+re1cycE8uJ+TVSBJCjb9hE7bsO0qVKDfJHp58YIp8MyVOZNy9l52vhWy7K0vpRnAZ++nSaVpOvheI4kE+gPN/Ju8kJ31WqeF7dkpfhTR9Hjng+xeJQST6PTyoGZXSvvLrFd5wRQGSY9K5I7pSREWlJZoUUwacH8jmWeSVCY8OGK359+XRKrpPIUj7J8lsJFdDOgyua0ja+jwBhkqjkpSIvV7HzVV4qEhCTJEDJ5JDZhsnLe6UyTRIr5WMTTlWD6b0QxGiRrJYFC67OBpGX9K23wgMPQGLia5pk5G6QdOeSSs8kkDuCBPXkQyElE95sfLnbyotNyiiS37ny5bvychSjyg/gtPljh0XpEiLJgLOYxRrWpOjYUZay3MEdNKCB9aQ5i0ytYx2TmMR2tl9l4wsfnehkJFqlB2LjS9cRsfFXsjJF15CSlDTsMKk6lNnkjkIqZW4+vY+v/9zC5inXkfBfMrsvz0lKtf6b9rcWp27+dLqJtJtsxM43LS7AnDEV+POnksRcuBJsLFbpNLd1306Th3eSq8CVO0zq85y5EMukyTEsGnMjF5YmS4LNFEeOxito0nMj7dtkJWu0s8FeR+WanJiNH4x0Nv74Yzhw4GruJA9DbOMOHbzblDvzrGFOsz4sZjHnuTIALj/5jXuj3B8LkP63RKo7J087z68fX8uZ32pD3JW7Vda6f1P/ybV0bp+ZnNPSb0kqQRx5ZYptu2/flbOI7SVz5cQWk1wItydpJbln5HOa/N4lNn1y94y3u4roUtwzUkmXlntGXJHBnq/ut99CMsLyrpA8FzErxE5PHkCWSURJZoW3aURiiixd6vkUJk8+ludI3GTyKZVcHW93rkCc1coe2sa3Ii29NlQkoAOEoaIpP/Ap7Ty///57brzxRtauXWvssH79eqpUqZJmC9GpU6dy7733UqpUKaPCLo/DWY4SiJSqQKkS3Lp1KwcPHuTo0aNG+8/ChQtTvXp1WrduTfv27S9XMaYWi9kAYRKezDKUIOCKFSuM2YtSIVm7dm169OhhBE/9CcoBwuRMyddWLCC5qMpl16q1Jl98uUCLN08uyEIno4J4J48e9ThTxKOpklUtNMSyFita9GHVQy76EF2ITmR/Cfpa1amf9Wfj7mSJM3EkXOCCEVyTS7ZUDgYDZO6IVBTmIpfRBtTqLBRxJMglT86RnezOVD0qKOFM7AVkRmHOqKzki85luVLP1qsiseIuNi6BwydijKLdQvminG8lmt4DoiAvr8+axQrCpAq+5DTlNSE+Q7lMyeXS0qtXHDskcJGLxm9EMu6lmlaqB9OEdM4vwcKjMae4FB9Doeg8ZI2y/v6XV550J5cLnXxCrL7ygvGbTmtPOYd8SuV1K9VtKudI+hy79NUdMFHbelckcinviKRMY9FHKDkMkgtaOw+uSEPb+CYDhGnZ+KovFfkhiT0pLyf5IYVSdN3pN5a85JP6QouNb+mjm8iME3euJBs/QHcup80fp9QiNv45zhlBQrHxpatGoEF4EBv/OMcN+17sfKs2vtAQGz+GGCN50a/n8KLMMzHnORpzmjxR2ckdncO3jZ/KjjVeFaejuHQhkhx5Y4jOYn7kQ5LeJDh1+HisYT8VyhtNVKSLZw36+b4guR1794LkAIiNb8mGSbTxxb4XO1/17pj8zlUgb2TKIK3JF4MEgsTGl9elfIZUbONA/67Ts/HD3D0TMDE7YVY4cecK2IG9bKRtfDdoQfPgtAR0gNBpiYYQPcnm3bNnDx9//LEREBPwFiCUvz/22GOMHz+egQMH8pq0XNSgLAFHAoTKu2tELQE1CZi8U6gR11jmJBBqSrAaUDMnBfOrnJaX1fOE2/7mJa9XaglkaAlo50HGVb+28TOu7t10cqfNDzedLcPx4qQyrdqxGUXYTspYVWaB0I0bzqkqH42nJeASCWgb3yWK0Gw4KgEdIHRUnKFFTGYJXrx4kXnz5tFEmkYj5eMbqVy5slFBeO7cObIkbyINzJo1i5YtWxrVfKtWrQqtA7uMW+08cJlCNDumJKDvFKbE5N9FoaaEQFx2vUncaXlZPU+47e/fp1tT1xIIGwlo50HYqNLyQbSNb1lkGsEPEnDa/PADi5qkWQk4qUyrdqxZHkN9nZMyVpVFIHTjhnOqykfjaQm4RALaxneJIjQbjkpABwgdFWdoEUsKEEqgr5oMDkJ6je8zWohKgHDnzp3G/58cZG2tWrXImzcvx44dC60Du4xb7TxwmUI0O6YkoO8UpsQUnEUqygnERTQ40riyq4pcvPHspMzS483JPbydxWnZBFvXen8tARdJQDsPXKSMALOibfwAC1xvl6YE9Cc+jB6MYNuLwRalysOcni2tQitQ5w+E/e/m8wdKznofLQGbEtA2vk0BanRXSkAHCF2plsAwVa5cOXbv3p2igjA2NpacOXMSExPDlClTaNWqVQpmfv75Z+677z6yZs1qVBhqUJdA0J0H+/fDkiUggV6ZqyHzMKRRfoMGkCuX+sE0ZlhLIKPcKU5xikUs4gAHjPkiWciCDImvT32KUcyUjnezm2Us4wQnLs8gLEUpbuEWY6aE46CiHBMX0X3sYylLOcaxy+coTnEa0MCYueILZEbLWtbyD/9wmtPG/DzBu47ruImbiCTSFwlDB0tYwi52GXNrZOZeHvJQl7qUpax3/ES5yPihxYs9o0ZlDJCMCS1a1PPKMzNSV/heX2gBaxp/YJxDziWzcypRiTrUUZs9o+DwOcIR49mUf8uMkqxkpTCFDX3IM2oJVJ4ZSxvoxWYksHMnLF8OJ05c+RyXLg3163s+zRpCUwLaeRCaenOC66Db+Pql4oQaQ56G/sSnrUKxZ8WOOsShy3ZUQQoadpT82xeIPbiNbaxgBSc5SSyxhl0r9mg96hl3BsdBwV40w8NOdvInfxrnSJqXXoYyxn1HZlz7Apkj+Rd/sZGNxkxHmYct8xxv5EaqUtXUXEfBE33sZ/9lGz/pziX3DQN8PMx79sCyZSDz/5LcGiXb/MUtnfYYcxVTQBq04onj76KzWF94AaczHzWW57yUn8qHG1HzQEsymbir+JKVmb8faDfKuO/IMyozCEUHcu+UZzM3uc2Q8L1Gvxh8y8ihFTKXe8sW+OsvOHkS4uI8I4HLlYN69dTG4TrEmiZjUwLaxrcpQI3uSgnoAKEr1RIYptq0acP06dMZNWoUTz311OVN69Wrx/Lly7nnnnv44YcfUjDTrFkzI6B47bXX8u+//waG0TDdJSjOA7FSVqyA2bNh3bq0JRsd7fFKtmgB5cuHqfT1sVQlEO53Crnwz2SmERCLIdWFMlFo1ahGc5obwS25CCcHCRoJ7mxmG5fltEAcB7dyKy1ogQQMHQMV5aQTIJRzrGQls5hlBPbSgmiijWCnnKMc5a5aIoG8X/mVOczhIAfTpCEOgNu5nWY0S/PiK86CGcxgIQsNJ05acA3XGPjCS+pgo7zyVr/cznjl/f03yH+nhshIzyVNXnmVKl399wuRZ5hfbjxzKnzMvtxp61SClbdxm8GHpSCdSYePOKNED/JsiiNG/js1ZCKTEai8kzupTGVzj5XKM2OOsl7lQwLiJFi61PM53rQp7cVZs0LDhnDnnVCypBZpqElAOw9CTWPO8RsUG1+/VJxTYJhQ0p/4lIrcwAbDrl3OciPJKzWITV+LWoYdVYUqV/1dAoFij4pdu53taT4lkjjWmMYGjSIUce5JMmkvmtlQgnqLWWycYzOb00SRwFRDGho2/uUgXbKVkkgp+POYZwSz0gI5v9jFYh+nlRi5gx2GXSu8pHfnEj3InavW5GFXBRvj4+HPPz121IYNaZ88S/ZYbnlgNy16baVM1ZOeRclkeSb6OPPKf8rcCmM5nGNnmkQKni3NHduf4Pbt3cl1qYAZEVtaIzb9X8WmMrviaNYUnZMmrty5JPgs+qhABUv0r1qsXwz25GcCOyYGFi6EOXNgx460ESRQKFOexMYvXNgEUb3EVRLQNr6r1KGZcUgCOkDokCBDkcx7771Hnz59aNu2LVIZmASjR4+mV69eRpvRzp070759e86ePcuXX37JzJkzjf/9xRdf5O233w7FY7uG54A7DySd7sMPPZa0WejcGe66CyJSBkHMout14SeBcL1TyOXsF37hG74xrbSbuZmnedqoZhOQgNh7vGdUy5kBCeg8xmNGgMwRUFFOGgFCyVgdxSgjM9oMiEPlIR6iFa0uB0z3spehDOUwh82QMIKD/ehHRSpeXr+ABXzCJ4gjwwzcwA30oY9R1ScQeymCjx+pzaKJZcygG2vuvx/atbvyyjuYYztDG7Rkf+50IjipKIsD5EVeRHgxBSYcPnL+8Yw3HDFmoTWt6Uxn35nbKs+MWSb0unQlIA0Y3n0X/kk79n4VXqZM0L07NG2qhRpKEtDOg1DSlrO8BtzGt/pSkcwYeakkzqB39vSamlskoD/xHk1IMPB//I9pTDOtGglsPczDlxPPpMJuGMPYylZTNCSg05veRqcLR8CEvWhmH6nWe5d3kWCpGZDEuyd4gkY0urxcZPA2byNBQjNQiEL0pz8l8WQ6yZ1LdCE6SSvhLS2atffdTe8/J5IlztNW4cIFGDkSVq82wwFEZErg4VGrufOpbZcDhLvyrDVs/GPZ95kiku98Mfotmk65EzVMrTez6FKmC3xUpwvLSk02s9xY04lO3M3dVyWpmiagXwymRaWyULqBDBsG27aZw86cGZ55BmrXNrder3KHBLSN7w49aC6clYAOEDorz5CitmPHDipUqECWLFmMeYNFiniy3KTN6M0334zMG5RgYHJISEigTJkyxt/y5csXUud1G7MBdR5IGtPQoea9kcmFdd990KGD28Sn+QmSBML1TjGJSUaA0CpI+xwJbElW8Ru8YbQcsgpd6WoE12yDinJSBQglg3cwg007DpLz3J723M/9SHDwNV7jLGctHUkqKwVPKgIlGPY5n1vCl8XSFkn0kCU2B8Pvrs+q6YmtiSxQat0aunSBQ9l38GrT+pzI9p8FbAxnkjhC5NnwCT4cPuLUkmCttBuyCk1oYjh1Ule5pqCj8sxYZUSvTyGB8+dh4MD0M4q9ieuRRzyZxhpCQwLaeRAaevIHlwG18fVLxR8qDAua+hPvCUaNZSzzmW9Zp9JmU4J8ElQT+1TGDlgBsb960ctoD2kbHAgQSiLjAAYY7fqtgiQ0StB0E5sYxCCjHakVkOQ9wZMg4eTE/7OCL2srH2rEK3/MJv58FgYNgs1pFz96JfvgsLW0LVeVnXnWMKBJQ85HmwtyJhHNGpOTAb/Pp8LxWlbZv2p9bMQl3m7QmrVF51qmdQ/3GIFCJdAvBiWxmUGSNqKvvgoy1sIKiMtVgoTSxEtDaEhA2/ihoSfNpTUJ6AChNXmF3WoJDMbFxVG8eHGyyUCmRDh+/Di9e/fm+++/N+YRCkiwsGXLlowZM4aSuteV7WchoM6DMWNgvvWL0eVDPv20p8+ZhgwvgXC8U0il2sd8rKxbaZ0jmcXSklMFxIHwEi9Rk5oq6FdwVJSTKkA4mtH8zu/KfPSgBz/yozEfTwVkbkk3uvERH5nOKk69T3WqU+yZ75g56hoVFgych3tcYs471dJtKeqLsLRmkgrKtNoypcD14fD5ju8MearCAzxgZBmnCyrPjCozGs9ocStZxatWqQlDHAj9+0P16mr4GiuwEtDOg8DK2027BczG1y8VN6nddbzoTzxG8p8kAarCvdzLetYbgTEVkKQxSVyT5DdbYDNAKIHSIQxhDWuU2EgKdkpHC5nDrQJSSSiBrU/5VAXdwGm0sysxD05gifW8uct79ux3im9ev57j2fYr8ZHnQhGGz/mbvBeLKuEnIX1aswfzKqjLoic9jXa2lkG/GCyLzAyCdPl+/XXPzEEViIqCN9+Eilea6aiQ0TgBkoC28QMkaL1NQCWgA4QBFXfobXb69Gm2bNliVBVWrFiR/Pnzh94hXMpxwJwH+/fDs8/ak0LBgvDRRyB9zjRkCAlklLuDtG98iqfSnZ8RKGVL5ds7vGOuXYyTykkWIJTKv+d53taRJUPYauVg6g1lnp8EXJVhT0kiyu4iIV79fZU1/zkuHMgLmdOeQ2mGN7m0y+VdFcQBIxWA6c1lMUNXApWSOS//tgTenrF05lZaom9nsZPPvx0+FHA3bvQ4D+xAuXIgHd515287UgwMrnYeBEbObtwlYDa+Ey+VChU8XUY0hJ0EQvhz6YguznMeSVxLb4a1mU0kwGe21X169GR2+Su84ns7FYWZtMkkyCmBSjuQl7yc4IQdEsYYAFv3hNXVoabJvqLpcJqr3GFObytMqjHyls5197/9eOCfoWBS/qmJ/8d/RnWqHShAASOhMvX8dTs0k89ntEUnAyIvWwbvvWfv4DVqeBIBNbhfAtrGd7+ONIfWJaADhNZlpjG0BByRQMCcBxMmwIwZ9nnu2xdq2W+nYZ8RTSEQElC5owaCL6f3WM5yYxaHG0Ba71zLtb5ZcVI5yS624xjHLGb53t/tK157E956zT6X33SEjt8p05H5MzJDMSc5lWhMYYoxn8UuJLWFskRHBwgticvs4lGjYNEis6vTXzdkiM4wti9F/1PQzgP/y9itOwTMxv/gA1i82L4Y9EvFvgxdSMFJc9GFx/PJkmq7ep+EFRZIu/ii+Kg4U1GYyQCVzEhfxjIFzl2G0v0T+Ky7faZ+awJNFijTyXWxIGOn7SX6vilKNL7ma6YyVQk3OZLMXq9DHdt0LhNQeQad2z2kKUn1n9nZ4ukdVJL/PvwQChcOaVFkCOa1jZ8h1JzhDqkDhBlO5frAbpFAQJwHFy/CE0/AWWuzwNKUkfQ0e/llt4hP8+FnCWSU+4EE5daxzs/SNEdeZpSYyiZ1UjmJjgXJrpYsa8m2Dmm4FA2ld8NBe21/DBk0+AP+sNda+SEe4i7usixSmT34DM9wEItDLNLYqTSlGc5wc9WpSfg6QGhZZ74QZC6JfI6lBZFdkI7f0vlbg7sloJ0H7taPP7kLiI3v5EulUSN46il/ikTTDoIEnDQXg8C+rS2lpeaLvMhudtui4xRya1rThS7eyakozESA8DjHjY4WdishnZKFMp0TeaDEPjiXQ5nEZcT7foAf2tmi0+vP/3Fr3cyWacj8RukQIrMt7UIVqhjzMR0DlWfQsc1Dl5ATDbuSTt+mDXTuHLqyyCicaxs/o2g6Y51TBwgzlr69nvbQoUMsWLCAdevWcezYMWOttBS98cYbady4MUWKFNHSclACAXEe/PsvDBjgDNeRkTBxom4z6ow0XU8lI9wP5KIsM9rEieAGyE1uPudz36w4qZxEx8I//MObvOl7b7evWFUDblIc8JbW2c5lg2wXlE9tuq1Uqh2caD2UnKTMe5HWUKZBBwhNi8rsQidaDyXtlS8ffPKJ2Z31umBJQDsPgiX54O8bEBt/6VIYOdKZw+qXijNydBkVJ81Flx3NJzvSCrM7DlSa+dzJ3AJJ1vLZsURFYSYChItZzAd8YI5RN6+aeSe0nOkMh7lPwkkLdnEauzbc2YWny7a2zI/Ms3QqqCezIWXGpmNtRlWeQcsSCD+EWbNg3DhnzlW2LLzzjjO0NBX/SUDb+P6TraYcPAnoAGHwZO+anfft28cLL7zAzz//bMwaTAsiIyO55557GD58OKVLl3YN76HMSECcB8uXw7sOtk8cPx5yOJC1F8qKyyC8Z4T7wSlOIe0X3QKZyMQ3fOO70stJ5SQ6FpaylJE45GgMpkDn3AHN5zjHwd4SUGK/Mr0KVGAo1mc7bWGLuXk1JjkbwQhKUcrkavA6g8SEM8r8RgornXz+FbZXRZkzBz43Ef83Qz8qypOvo+cQmpFW8NZo50HwZB/snQNi4zv5UomO9rxUNISVBEL0c+mIDvawhxd4wRFaThDJRz6j7bxXUFGYCZtMxgfIGIGQh687Q5evnTuGdB2JTtv3ZWaTmvtb0a94VzNLU6xZyUpj7rxT8AVfkItczpBTeQad2TmkqUyeDPKPE1CgAIwZ4wQlTcOfEtA2vj+lq2kHSwI6QBgsybtk30WLFnHXXXdx6tQpEhK8V9FERESQK1cupk2bRoMGDVxygtBlIyDOAydLFkTUkhqVU22eVuhqKmNynhHuByc5yeM87hoFSxaoBAglUOi4AyE9gomOhSUs4X3ed40slBmZ1RxaODhHcXcpKLVXmZ1ylGMYwyzjb2Yzr/KqZbz0ECRrXbLXTYOuIDQtKrMLZ8+GL74wu9r7OinonzRJBwidkab/qGjngf9k63bKAbHxnSxZkJfKN9+4XayaP4sSyAi2fHoi2cUuo8WoW0C6OEg3B8ftexMBwhnMYAIT3CIKdT6+7ALdvlTHT415MTNkjlGmV+NAC/oXe9gy/nKW+64mtUBVus9IFxpHICO/NGwI8Pvv4YcfbBBIhpo/P4wd6wwtTcV/EtA2vv9kqykHTwI6QBg82Qd95/3793PDDTdwUmZYAC1atOCRRx6hTp06l9uJHjx4kBUrVjBu3DhmzJhhrMuTJw/r16+nePHiQT9DKDMQEOeBTEqWiclOgJQqiPMgk4/ghRN7aRoBk0BGvgfEEmu0GHUL5CAH4xnvYSdQikl0LKxlLW/xlltEoc7H8tpQd7k6fmrM0zkhp/oMV9XZIPvZz7M869g5xjKW/OR3jJ4rCQXqN6N4+MWL4QOHOnzlzu1cNaLicTSaCQlo54EJIYXpkoDY+IsWwahRzkgwTx747DNnaGkqrpGAyz+LfpXTUY7yJE/6dQ8rxEtQwl6nDhVlJtr4C1nIR3xkhV13rp3aGtpMdYa37GfhrL2k5wa7HqB3mbst87OBDQxkoGW89BCkxWgUUc7QU3nOnNk5pKmIm3SCQzH4UqVgxIiQFkeGYF7b+BlCzRnukDpAmOFUfuXAvXr1YvTo0Uj70PHjx9PZxzTcSZMm0aVLF6PS8KmnnmKUU5fSDKqDgDgPzpyBHj0gRj077rJ6rrkG3gqDAEIGfd7SO3ZGvwf0pz/b2OaKp6ImNelHPw8vgVJMovPgNKd5gieIwYF3RTCleTY7FDsApx3IpK2yFtZWs3Wa+7iPDnSwTEOC1+LYkipXu1CIQnzIh74rU+1uFGz8QP1mFM956BD06gU+mjWYol6rFvTta2qpXhRECWjnQRCFH+StA2LjO/lSqV0bXnRPtVWQ1Rc227v8s+hXOccTTy96cZjDft3HLPGmNDXsbGVQUWaije/0XGvlM9hF/K8IlNoDsdF2KcHtc2FuM1t0Hl01muY1C1mmcY5zxnzMS1yyjJsaQXWUQbobqzxntk8R+gS2boWXX3bmHLffDt3dMz7VmUOFIRVt44ehUvWR0AHCDPwQVKhQgZ07d9KjRw8+/vhjU5Lo2bMnY8eOpVy5cmzb5g6nuinGXbgoIM4DObfodsEC+xJ4+mlo2NA+HU3BVRLI6PeA+cxnDO5o9C/BQQkSGhAoxSRrTSTZxZJlHPLQaxR81Mv+McY8AU/4mBfjZRdpFTua0RSggBIv3/ItP/GTEm5yJKmSvRvrGc62Nw40gUD9Zmyca+hQWL3aBoFE1FdegWr2Ytf2mdAUfEpAOw98iihsFwTMxnfqpfLqq1C1atjqI6MeLAQ+i35Vzc/8bLTudwO8zduUp7w6KyrKTGbjS5cQ6RYS8tD+O5jc3v4xfroH7vlFmU6W2Bx8MnU/2e+ZrURDOnv8xm9KuMmRetKTxjS2TecyAZXnzLndQ5aSJP/16wc7dtg/wjvvQNmy9uloCv6VgLbx/StfTT04EtABwuDI3RW7ZsuWjUuXLjFv3jyaNGliiqf58+dz2223kSVLFs6fP28KRy9KWwIBcx44kdKUK5dnWnLmzFqdYSaBjH4PuMhFI6P3LOptYTEfiQAAIABJREFUJJ14JApTmFGMulLlFSjFJHMeODH3LgtZEJnagWxk4zw2vi8brocbNthhgagcF4k9UBBynVGmU4c69KGPMv4RjvAUT5GA9/nA3jaQlkMSAM9DHmU+QgYxUL8ZGwJZtQreftsGAaBoUXj/fd3t254UA4OtnQeBkbMbdwmYjf/XXzDM+pzbFDIrVgxGjtQvFTc+SDZ5CoHPos0TekeXLgxi48cR59d9fBGvRCUGM9jXMu9/V1FmMhvfibl3tu1zIDOZ7VXOLWgETewlPmctcoILewtClPpzcce2J3h81RgwMQMyLcVuZ/uVrjGKT4aMpviETwyZOgYqz5ljm4c2od9+sz878NprYdCg0JZDRuFe2/gZRdMZ65w6QJix9J3itDJDMGnGYM2aiVUrPuSxatUqatWqRdGiRZEZhhrUJRAw54GwOHgwrFmjzuwDD8DdGaACRV1CIYup7wEYVVpSraUKNaiBtOjcylZVEvSgB7dx2xX8QCkm2cVWAlGSYbyOdcrnkJaaM5mJtM9RgWiiuZ/7bWV8l6EMRe7/g+U/llJhwcC5q/05Fn5ZjpNZDynRiCSSN3kTcQrZAQnuSZWrKtzJnTzCI6rooYUXqN+MDanEx3taEG3frk6kZ09o7GCyuDonGtOXBLTzwJeEwvfvAbPx5aXSv7+9sgX9UgnbBzEEPot+l/04xjGLWcr7NKQhW9jCAQ4o0+hLX2pRSxnfQFRRZjIbX4Kk0qlkF7uU+ehEJ37gB+VxBNnJTgta8CM/KvNw43+3E9tkDhs3RijTaPfEEaaOKseFaLUkwCyx2Rk2dzXFz1yjHCAU5qWqdBWrlM/RkY7cy73K+GkiqjxnznIQstQuXoQ+feDgQfUjSBWiSbes+iYa0xEJaBvfETFqIi6TgA4QukwhgWSnTZs2TJ8+nXHjxtG1a1dTW3/55Zc8/PDDtG7dmilTppjC0YvSlkDAnAeyvcwifO012LfPujoaNPAMTopQN8Stb6oxAiUBfQ8AmVMi1XtLWGJZ7KUoZQSCZI7EK7yCVH1ZBQniPMzDRJDsNxYoxaTKfD3DGV7lVfZjPQFEnChS8bae9UamtNWMbTn/czzHzdzM13zNVKZaFSX5yGfsneN0UQY2asyO1fks06hTB55/HrYXWM7Axo25FGW9mlHmBzbBXGW+NwbluRrEIDaxyfI5qlAFmbEpVYQZAgL1m7EpzGPHQFqEHj1qnVDLltCtm3U8jREcCWjnQXDk7oZdA2rj23mptGoFJu+AbpCr5sGaBELks2jtUBZXy0znoQxVSn67lmt5jdcM217+LcmAVsGxII6KMlPZ+HIOuasc57jVY3AXd/EQD7GMZYxkpOXuFpI4J3vfwA1G+32VkQbFT13LW78tIe5IfqQrskogRua7Pf44rCk6m7cbtCI+k7UqwoiETLy4+BdqHbjLI0PFCkJBle41r/M6e9hjWR/1qU9vejs/X1zlObPMffgiiKtNXG7ierMKOh/fqsSCu17b+MGVv97dPxLQAUL/yDUkqEpr0WbNmnH99dezYsUKsmfP7pXvc+fOGdWDmzZtYtasWdxxxx0hcU63MhlQ54EI4cQJTxsiK7MjmzaFxx6DqAziYHbrw2KTrwxj69s4aGzEJT69qQcLyk0wLe1KR+vSd/EU8lwsbOAcbvcxQxjCPswH4uXC/SAPXn3Bs3EW0wdI52IrjgPJat2B+UEKt3M7j/Io4gAQkDknIxhhulWoVA4+zdPUo56BnzD5fr674TV+qmy+JVOx05Xo/8dMip6tYNA4fRpkjsMmC7E1yYd48kmIjvZIcWOBxQy/pS2ns5iL5kTGR9H9r09psvPhK2qw4TwQIlKNKc6YNZivApdMdXEcZCWrpcchpBcH6jfjgJAOHYIhQ8BKI4a2baFTJ90F0AHxB4yEdh4ETNSu2yjgNr5+qbjuGXADQyH0WfSruC5wwUgEXMlK0/tUo5qRtCZVbwISxJFAo5VEQJkB3Za2KRMATXOQaqFDyvwvxzaGtrzVUkWkVKl1oMPlcyxlKTK3PIYYU6eR1qQv8AJV8cw5lQTCL/iCecwzhS+Lyh2vSb9F08h3oZiBI0lWMoJ1927TJLjzTk+SVaZMHpxVRWcwsl57LkaZGzOROTYbz/z5DbX3tzW3qQn7X9rgDmOYpS40MnOwO939kwDo0HNmTkDhuUqeSXk2rSQCdu4Md92l8/FD6YnQNn4oaUvzalYCOkBoVlJhuu6NN95A/pHA36effkr16tXTPOmaNWvo3r07K1euZMCAAbz++uthKpHAHSvgzgM5mvQ+mDkT5s6Fw4fTP+w110CLFlC/vrZUAvdI+G2nDGPr2zyotNhcXOobZlYaxZYCf6arj8JnytFs25PcufVpMsdnu7Ku3WQjmDOd6cal11uGrmTQtqJV+i2HbJ7F9MOUzuVVnCnSKnQuc706QyS7WtoFSWAvRQUkGM6HKUxhEYvSnUso1W2C24Y2SGvQy5B4/tVFZzLtmvdYVyR9J0KeC0W4bfvjtN78PDljUlYMXroEc+Z4/vnvv/SlUr6855V3661XB2AOZ9/FlGuHs7DMV5yPTjt7XAKDtffdQ5tNL1LxeO2UG5lwEPjSl2TA/8qvRpssbwHo0pRGKlKb0tT5rGJfTAb774H6zTh0zrNnYfp0mDfPk7+THtx4I0iRz003ObSxJhMwCWjnQcBE7bqNgmLjW3mptG6t+5i57qlxnqEQ+yw6L4BkFKVbyG/8ZthRu0k/qlSCEoYdJW3/U3dgOMUpw66V1u/pVROKLVyd6kbF3Y3c6NyZHFTmmXbjmcY0w66UAFV6IJ0oWtMaGaWQGqRV6f/xf0ZFodioaYHMJW9AAyNIWpSiKZbInUsCjXLX8NYloyAFuYM7aPHTZ2SNy5GCxvnzMGOGx47yFoy5/nqQDgzSISR1Q6R9uTYaNv6i0pOIibyQ5jmi47JQf09Hw8YvdeoG8zo1af9LtxB5Lucwh0OkP9pAxhbInesWbnEm6JzWSRx8zswLKvxWil0/dSrMn59+NaE8izVqeAKDN1h4rMJPWqF5Im3jh6beNNfeJaADhBn4CXnzzTeN00+bNs0I/EVERFClShVq165N4cKFjf9OmlG4bp1nJpUEEluJp8oL6OChuYcqKM6DJNZkZsnff8OiRXD8uCdwKBWkxYqBVA2WK2fuEHpVSEggw9j6Dh50e96/+K3cOA7k2sz5qFNkictB/vMlaLD7Aar91zzt4Euyi6BcliVTWS7OJzhhtCCVYfIlKWk4HeTfXsHBs3jdx8flVRwqMh9D2q9KwPMiF41s6uIUN4JQZSnr8zcgLXR+53f+4R/DoSJOgZzk5DquM2jkJvfVNFKdf3/OTfxa/nN251nL2egTZI7LRp6LRai79z7q7LubqITMXvmQV558xv74w+NEkFdetmxQtCg0aQIVK/o8BuejTvNH6YlGW6LTmY8QlymWnJfycc3R+jTd8ejlrOarKJl0EPjmAEN2G9hgyFOy2CWQK5nZhSiEZBRLwDZ1oNYM3bBYE6jfjMPCio2FFStg2TI4eRIkqJ0jB5QqBbfdBiVKOLyhJhcwCWjnQcBE7bqNgmrj65eK656HYDEUop9Fv4pL7CgJSC1gAYc5bHS6kG4LEohqRCMqU9mnHSU2vdj3K1iBBA3F5hfbuBzlDBu/CEWcP4OTyky0S4Xv5SznT/40AoVJdxVJNpNziK3vC+T8EnjdyEZkTIHYoLnIZQRHRZ5y9/EFO9lp0JDxBpJoKYFFGRkgbTRrUtNz5/Jy/rg4+OsvWLLEY0cluTVKlvS4NUqX9sUBnIk+xoKyX7K+8HzOZPZ0DclxKT+VDzcyuoLkulTAN5HUKyza/3Ln+pu/WcxijnHMuHOJjV+MYsZ9qTzlrfNgFcPJ58zq3mG4Xmz6pUs9dr50tpHPs9j44moTG7+wpwmRhhCUgLbxQ1BpmmWfEtABQp8iCt8FmTJlMoKASZCQkJDiv5Of3NvfUksoTqw0DT4lEFTngU/u9IJwkkCGsfWDfVCLF0Gvz1igzuIkz07+aAJ1fid5To+WW2UciLMHco9wemYCKTe9l98koJ0HfhOt6wlrG9/1KsoQDOrPYhip2UllhqJd6uT5A/VYaDkHStJ6Hy2BgEtA2/gBF7neMAAS0AHCAAjZrVtIgNAfEC+lGhp8SkA7D3yKSC9wSAKheKdSOnqwD+rkRTBQZ3GSZyWlpYMUqPM7yXN6tNwq40CcPZB7hNMzE0i56b38JgHtPPCbaF1PWNv4rldRhmBQfxbDSM1OKjMU7VInzx+ox0LLOVCS1vtoCQRcAtrGD7jI9YYBkIAOEAZAyHoLLYG0JKCdB/q5CJQEQvFOpSSbYB/UyYtgoM7iJM9KStMBQifFlqFpBeo3k6GFrA9vRQLaeWBFWuG1Vtv44aXPUD2N/iyGqubS4NtJZbrV9vemLifPH6jHQss5UJLW+2gJBFwC2sYPuMj1hgGQgA4QBkDIegstAR0g1M9AICQQincny3Jx8yGdvAgG6pxO8mxZmV4QAnV+J3lOj5ZbZRyIs7thj3B6ltwgT82DaQlo54FpUYXdQh0gDDuVhuSB9OcvJNWWNtMqygwn+1Pl/G5VfyjqJZzk79bnQvMVUhLQNn5IqUsza1ICOkBoUlB6mZaA0xLQzgOnJarpZQjbPQCHjImBP/+E336DAwfg3DnImhXy54dbboHGjSFnzjSet2QXvl3s4ku+ZBObiCWWBBLIRCZykpMmNKEd7chM5vQf2snt2L4d5syBDRvgzBnP0ly5oHJlaN4cypZ14JkPwCX1T/7kO77jAAeIJ96QRSSRFKIQ93CPIY+rIJmepWv1qlUwbx7s3u3RR3Q05M0Ldep4hryLbrzB2bPw+++waBEcPQoXLkD27FC0qEef9epBZi/qENqrV8M338DevSCjdhMSIDLSs3ebNnDHHZBm5+5EGcvZ17KWecxjBzs4y1miiCIPeahFLe7gDgpS0AGl+peE6G8965nLXLaxzTiHPNu5yU0NatCMZhSlqH+ZsEI9AO8MK+z4Y+22bTB7Nmzc6HlXyHhpeUfdcIPnXVGmjD92dZ7mzp2ed9769Z5zyG9MznHddZ5zVKjg/J7+pKidB/6Urrtpaxvf3frJKNxlgM+fZVVeOBvJ4m9K8/uXZTi0IwfnT0eTNWcshcqcpeFDu7i1826y5Yr1Snczm/mKr9jOduKIu2zjiz3XnOa0oY1h36UH8m3buKggc8eWZ8uyApw5nplMkQnkLniRas3/o9mT2yh+baLhn0RERZl+tvHFrv2d3/mZnznMYUMWEUQYNmExitGBDtSlrldZxl6KYPnPJfjti3Ls25ibcyejyZI9lnzFL3BLx900fngnuQteAi/nP3nSc2dbtgyOH4eLFz02fsmSnjtCrVoQlb46DP6WLIHvv4eDBz02voDY+IULw333QcOG3h+1uIhY/io2lV/Lf8ae3Os5F32S6Pis5L1QlJv33s9t2x8n78UiHiJ+1ovlH0UyBNHp3/xt3FXkLis2fjTR5DlRmtr77+b27d0pcL6knS00rgUJyLvi339h7lzYutVjG8tdM3duqFYNmjWD4sUtEAzSUjnHli0eG3/TpivnEBu/ShWPjV+qVJCYU9xW2/iKgtNorpaADhC6Wj2auXCWgHYehLN2g3M2lbtjcDi1sasfDymBwZ9/9hivp06lz6MEpxo0gE6dPEGqy9BuMhvYwEd8xBGOeD2kXJ6rUY0XefEqJ8LqmUWZ3PtW4yLgDa69Ftq1g6pVbcjTj5fUqUzlB37gPOe9MiiB0la0ohOdrqyb3A4JDM6cCdOnwxEv4pQLfO3a8MADnoBfchA9SlDvjz/g0qX02ZDA6+23e5wAqQOFEpicOBEkyOgN5LkQGg8/nHJVQrvvjWCayOMgB9MlIU6Vm7jJkEMp3HdLksDgQhbyC7+wj31eZVGd6nSkI+Upb+PhdAjVj+8MhzhUJvPXX/DDDyABQl/vivbtPZdwN8K6dR7HnDgNvIEECO+/H266yY2nuJon7TwIDT35g0tt4/tDqpqmVQmE8efPqig4fzqKyQMrG4GocyfTzwjLliuGRt120uHN9eTIG5NinxWs4DM+4wQnvO4vSXA3czO96GUEy5LDku9K8tPg69m9LvkF4mpyVW47SIdB/3BNvWOeP6oo0482/jd8w3SmcwkvxjWQjWzcz/3cxV0pDimBwV+GXcfs0RU5eTBruvKMzhJH/Y576FSv7FXJgHI3mDTJExiM9RLTzZcP7rzTk8wnd4bk8Msv8NNPnsRBbyCJooIvNkhyiCeOade8x4xKH3Ase/q2cWR8NHX33scD64ZQuNVKy8+vvxEkMDiHOUxjGoc4lO52meIjqbW/LZ3+GUyJ09f5m60MTV+SWsUvsWePdzGIH6BDB6hUyZ3iWr4cfvwRduzwzp8kQMtdRf4dCqBt/FDQkubRqgR0gNCqxMJ0/a+//sqECRNYunQp//33HxcuXGDt2rVUTvaGXrhwIf/88w+5c+emc+fOYSqJwB1LOw8CJ+uMspPK3THkZOOnQ0pG3vDhniw9s1CwILz8sidDVeC3dk/yCZ8YmcRmIS95eZd3jeorgakjruHrPtXMohuVQt26QYsWplFSLvST8+BDPuQP/rDEVBWq8AqvGM6USxPb8dFHnku/WZAsxL59PdVGAvv3w5AhcCj9e+5VpK+5xkNDMjMFvvjCU5llBSpWhLfe8mR4xkbE8On9zVjAAtMkxJnyPM8bAWS3gDgOJjCBWcwyzZJkHD/DM9Shjmkcvyz00zvDL7xaICpOLXGMmQV5Vzz6qCfb2E0gv69x4zzVgmZBkgHuvtvs6uCt086D4Mk+2DtrGz/YGtD7iwTC9PNnWbnH9mfl7VYN2Pl3PtO4JSuf5OWZf1CwtCfJTarkJChmBaSbwju8Q1ayGt+4if2qMOUd80GVyOh4npqwnAYP7FFTph9sfLEHBzOYdayzIgpu5VYjYCpw7lQUI+6tz7pfEyvqTFCSbh39+1/piCBdVoYOBakeNAs1asBzz3m6wgiMGOHpGGMFJEHppZc8GBcjz/FB3U6sLDHFNIncFwrxUtZnqYR7ojnS6eZjPmYRi0yfI/ulPPRZ8jM3Hk6jC4xpKnphWhKQBFlJTJ061bx8pEL26aehfn3zOP5eKe88CQxKAqBZkLtz9+7QtKlZjOCt0zZ+8GSvd/afBHSA0H+yDQnK586do2vXrvwkqVOIg8bjoYmIiGDdunUpAoSLFy/m1ltvNf62ceNGKrk1TSUkJC9t6vZSKrGWfs+YMZQsUCBEONdsulUCGcIR4IdDSmXZoEG+q1fS0rtUEA4eDNur/ciIW1KllZp8UCQ4KBezeaMqM+GZGiaxUi4TY1qq1yyDH5wHYxnLb/xmmRVBqElN+sb147367ZCMQ6sgl/433vAE+F55BY4lJl5boSNVSgMGeC41//d/VjCvrJXqzjcHJTCm1qMsKDfeMhEJrr3Ga1yHeUeS5U0sIEhwcAYzLGB4lkqwtx/9kIrCoIEf3hlBO0vixtOmwVdfqXHxxBPuuXhLS7CxY9XO0bUrtGqlhhsoLO08CJSk3bePtvHdp5OMyFEYfv4sq/HsiWheu6UJezfksYxbtOJp3lr6G0sKTmEc4yzjC4K02hzJSL5/tQo/DbZeGhMRkcALPy2hTswt1vf3g40/lKGsZrV1XoDbuI1HLj7JkDtvZf2CwpZpiG0vCXjSAvTVV3139khrA2nLKAG+MWM83UVUoG5deLZPLMPr38Oq4tMsk5BEwEEMojSlLeM6jSABX0nqXMxiy6Qzx2Zj4IIFVDwe5ERAy5y7G0GCgyr3T0kEfPFFT0tdN4BUP0oXHxXo1QtuvVUFM3A42sYPnKz1ToGTgA4QBk7WrtypdevWzJw50wgM1qlTh4YNG/Luu++mGSCUA1StWpX169czePBg+vXr58ozhQpT2nkQKpoKHT4zhCPAD4eU6pVZ5gujrnogypaPY9fmrCREep9Z4u1JKvdnR3bWm0RCQoTSAycZd2+/rTCX0GHnwUpWGtnSdqD28O9Y0be9MgnJMpZ8C5l1oAqSISztG+1Azcf+YtVn6re0XORiFKPIQQ47bNjGXcIS3ud9ZTriCPmAD5Bq2aCAH94ZQTlH4qbShvP1161V3CXnV94Vw4YFfy6hzBOVal3JlFYBcYS8+SZIMN6toJ0HbtWM//nSNr7/Zax38C2BMPv8+T5wGivea3czy35Qb9teucUu/p1RzlJ3kNRs3DD9Rda3VreNM2eLZeS7URQqZFEEDtv4kigmCWN2oHaf31kxwsdAPy8bSG61tBOVGfGqIPPLVZIQk+9X640prHy9rSoLFKawYVt7m1WpTNwC4kxmMh7riYxJW+Q9X5RRM7eQNS6nhV310vQksGKFp6ORKmTJAu+/77kHBxP++cdjo6uCtAKWCl83z1fUNr6qdjWemyWgA4Ru1o6fefvxxx9p166dEQz85JNPeOyxx4wdM2XKlG6AcODAgbz55ps0b97cCCxqUJeAdh6oy05jpi2BDOEIcPiQ0lq0Rw+Q+YO24Lcm0MR8G8mr9ur4DXzX0RYLjRrBU09ZJOGw8+A5nvM5n84rhzFRRJTdRcL+EJi47kPUEXlOkXDCesZ6crLd6EZLWlpUqrPL+9OfbfgYcudjy/a0N2bQBAUcfmcE5QzJNlVpiZWaZ2ndI5WEwQTJ3p8/3x4HksX/wgv2aPgTWzsP/Cldd9PWNr679ZNRuAuzz59ltR3cnoPeFVsoJ99d3nB9ZahsYQZBak4bL4DfG1nmPzmCzL+zPOHFYRu/O919zl/0eshTuYgosZ+EM8ENJkmCkZW25mmdKaLUHhJ226sAfJZnqU/wekJK9aC0fT3MYVvPZveVn3D7ju62aGhkjwQkAXDjRnvSuOce6NTJHg272JK0vGqVPSrNm3tGI7gVtI3vVs1ovuxIQAcI7UgvxHHbtGnDtGnTeOihh/jyyy8vn8ZbgHDq1Km0bduW0qVLs3PnzhCXQHDZ186D4Mo/VHfP6Jd9pweq2GnVl+IZun8yTFasevuvCJTaA7HRth7L6GhPu75cuSyQcdB5cIQj9KSnhc3TWPrjvXD/j/ZouAn7/+6CNtbbDyUdoTjFjdZUEahVltoVxVa28jIv2yVDPvIxmtHByZQOo5emtMzt2VO96i5JkZkze94VMrczGOBUYoZUQ378MUjVsBtBOw/cqJXA8KRt/MDIWe+iNpouo8jtfy9Zm/mXrlye/hA+7K0mNgku3rheDTcZltj2klgj32/T4KCNv5nNvMqrprdOc+HonvD0aHs0XIOdAGuqQtV/lDm6nut5gzeU8e0i/sVfDGOYXTKUOVGVd+b+HbS7iu0DuITArl2eFqF2IU8ej20sfoFgwKFDIC1C7Qbhs2Xz3FXk324EbeO7USuaJ7sS0AFCuxIMYfzixYtz8OBBJOjXsuWVCgVvAcKVK1carUizZcvG2bNnQ/j0wWddOw+Cr4NQ5CCMfN1q4ndYAM8+C/v3q7GSAkvai+4rAUUOWSc2tB+8PNQ6XhoYXbpA69YWSDnoPJBWOdKO0hY0mw1zm9ki4Srk6qthdU1bLA1kIJWxPrfG1qaJyHbmSabe/0VepDa1nWDLGg2H3xnWNnd2tczF/O47Z2g+/DC0aOEMLatUpAHFePWOVim2a98e7g9Scaqvc2vngS8Jhe/ftY0fvrp128nC6BPnqGjjYiPoXvQuTh/NYp9urlNwsAhku2Cd1rMj4YNnreOlgdG7NzRoYIGUgzb+AAbwLzaqKIXtan/D2moWDuDypc1nwix7XT4kCbAEJYJyUAkOSpDQCRj86zIqHavrBKkMS+OLL2D2bGeOL/6N+kEqTv32W/jpJ2fO0b073H67M7ScpqJtfKclqum5QQI6QOgGLQSJhyxZshAbG8tff/1F9erVL3PhLUC4atUqatWqheCeP38+SJyHx7baeRAeegz0KTK8I8BBAVy6pNCux5vCVduMdvgWvu/gyKNkuc2og84D2+1FRQKFD8Lhwo7IwhVE8h6H4/bKm4LZZtSJ9qJJegham1EH3xnBfqacaC+adIYmTeDJJ4NzIifaiyZx7uY2o9p5EJznyw27ahvfDVrIGDyE0SfOUYUd3pWdp8q2co6mapvRxvPh98aO8GG5zaiDNr7t9qJxmSA6BhIyOSILVxAptQt2l7XFyjM8wy3cYouGKvKTPMlRjqqip8B7/K+x3LG9hyO0MiqR114DmTPuBASzzagT7UWTZNCsGSROwXJCLI7S0Da+o+LUxFwiAR0gdIkigsFG4cKFOXr0qKUKwqS5hSVKlGDPnj3BYDts9tTOg7BRZUAPkuEdAQ4K4MQJkMw0x+Cne+CeX6yTaz4L5jS3jpcGRq1a0LevBVIOOg8cuWhmvggxVvonWThrMJZmOwfnctjauR2e/wsG9KY3//GfI1u3ohVd6eoILUtEHHxnWNrXD4sHDYJ165whXKcO9OnjDC2rVN59F5Yvt4qV9voqVUCcKm4E7Txwo1YCw5O28QMjZ72LbjGa3jOw8+889K3hYEeKxfWh/lLrj5x0klhzJRHbOoErGFJJY+ne4qCNL8lq5zinzv7xvJD/uDq+GzELHIYj9pIaH+MxmuHgc2pBTmKTn8eZhP8H1r7N3ZtesrC7XppaAjJT2yn3ajADa04GOqViWiqn3QjaxnejVjRPdiWgA4R2JRjC+A0aNGDp0qW89dZb9O/f//JJvFUQyrzCiRMn0rp1a6ZMmRLCpw8+69p5EHwdhCIHYeTrVhO/gwKQOViPPKLGRppY01tCy5nWCbb5P5jaxjpeGhj16sFzz1kg5aDz4Gme5hAKLVaTsyttnM5YGaJo4azBWJrjNJzJbWvnB3iAu7nbFg1V5Bd4gT04kwwkZ5CzBBwcfGcEnPdUGw4dCqtqbxaJAAAgAElEQVRXO8PFLbfAM884Q8sqlQ8+gMWLrWKlvb5GDUhmwjpD1CEq2nngkCBDkIy28UNQaSHKchh94hzVwL6NuXju+judo7nyJrhplXV6dZfBcmdaL955p8V7i4M2/iM8whnOWD9/Esa5bJDDRoBRfWf/YRY9AAeK26Ivs9sb40yFqVVGHuVRTnPaKlqa67v+PZJWW5xppesIQyFI5KWXYMcOZxhv1Qq6BiEnU7h/8034R300ZwoBNG7smb3uRtA2vhu1onmyKwEdILQrwRDGHzp0KK+88gpFixZl+/btZM2a1ThNegHCP/74g6ZNmxIfH8/YsWN5/PHHQ/j0wWddOw+Cr4NQ5CDDOwIcFEB8vKfFaGysQ0/CilpQS2GWw+OfwufOvE+D6TzoRz+2s92eMCtugW0V7dFwE3aR/+C/YrY4eoInaEpTWzRUkd/gDdazXhU9BV4XutAaKwMyHdk2rMorPv4YFixwRi4yerpbN2doWaUyYQLMmGEVK+312nngjBw1FWcloG18Z+WpqaUvAQfN4rAS85lj0TxSwMHkqt2loNRe6zK6awpMu8s6XhoYHTrAffdZIOVggNB2EmACkOs0nM1p4QAuX3rtRth4vS0m5e5UE3uzylUZeJ7n2YvCM53Ghr2XTaTBniAkAaoe3oV4TiYBPvAA3O3g68+KuN5/H5YssYKR/tq2beHBB52h5TQVHSB0WqKanhskoAOEbtBCkHg4deoU5cuX5/jx47Ro0YKvvvqK/PnzXxUglDmF48ePp0+fPpw5c4ZSpUqxZcsWoqOjg8R5eGyrnQfhoUd/nUJf+NORrMOCee89WLbMAS2W3gXby0NkvHViUnnYerp1vDQwpK2HtNwzDQ46D2Yzmy/4wvTWaS7sMxxGBKnvoT3O08buNg7GP6pMOZJIxjCGvORVpmEHcQYzmMAEOyQu445iFEUp6ggtS0QcfmdY2tvhxStWwPDhzhAdOBAqV3aGllUq69fDG29YxUp7/YsvQu3aztBymop2Hjgt0dChp2380NGVmzgNo8+VK8Q6oGFj/v2jkH1ebloJK2pDhAKpLx6Bx2zaxonbSnvu0qUVeEiNomD7f8VXTGOavc07fw0TO9uj4Sbsga/DgEHKHGW/lIex0/aRNS7ZKAIF3agy8A3f8DM/q6JfxouOy8rYaXvJdamAbVoZmcC8efDpp85IQPwbJUs6Q8sqFQkOSpDQCRg8GCpVcoKS8zS0je+8TDXF4EtABwiDr4OgcvDrr7/SsmVLJAgoFYSNGjVi1qxZREREGEHDS5cusXLlSk6ePElCQoKxZsGCBdSR4TUabElAOw9siS/skbWTIB0VOywYaYEhrTBsw+CX4eWhamTiMkHFrbCznBp+Ilbx4jByJERYcWA4eBGNJ56HeIgYYtTPsbUCVNqqju8mzIh4OFQICh5T5qo+9XmW4LXsOctZpILxIheVzyCI1anOy7xsi4YyssPvDGU+HECMi4Onn4ajR+0RE6fBiBEW3xX2tkyBnZAAzz8P+/bZI1qgAHz0EURG2qPjL2ztPPCXZN1PV9v47teRGzkMo8+VK8S7+NtSfNDpZvu8SJDvkfFqdKS1ZvH9cNJeotf11zuXWIOC7X+BC8Yc6QSkFFARltSDWxwqLVJkwTG0qBg4mx0yq7ehabn5GbqtSRVJUdCN6pkOcxipDLWlU6Dxjm70XKn4+1BlPgzxLlyAHj3gvM2xkDfcAAMGBE9A0pnpySfh5El7PJQrB2+/Hby7ii/utY3vS0L676EoAR0gDEWtOczz4sWL6dy5M7t27TIoS3AwOUhgUEAqB7///nvq1nWmj77Dxwg5ctp5EHIqCyjD2kmQjrgdFowTjupMUfHE7y0GRdTn70UPe5WYfupZqCItaRcobQMtgcMX0Q/4gMXYGy6W484/ODu7gaVjpF4cFWWvdWy2bPYvaDlu3M7ZdRVsnWMgA6lMkMq8Ejn/hE/4lV9tnaMvfalFLVs0lJEdfmco8+EQ4k8/wbff2iMms1elHXEwYdYsGDfOHgcdO8K999qj4U9s7Tzwp3TdTVvb+O7Wj1u5C7PPVdDFHHspgidLtebkIc8YFRXInPcsl/YVguzqXvusz43hwvtPqGx/GUdmBsvsYEdA0fYXm3QDG9RZSIAcNTZzdo16SVCSmyrRPaXES44ccPasEuplpBxNl3H213q2iIyc9S8lTl+XkoaiblQZeYd3WMlKVXQDb8i8P6l4XBcP2BJiIrLYxWIf2wFJwLvZgbwIOzzIPUXuK3ZAgqW33WaHgn9xtY3vX/lq6sGRgA4QBkfurttVKgi//fZbpkyZYlQMHjp0iLi4OAoUKECNGjVo06YNXbt2JXPmzK7jPVQZ0s6DUNVcYPjWToJ05OwHwaxdC0OGgMwkVAFxUi8ZU43dedeqoBs4j51+jpl1B7Lv39xKNMqUgbfegixZLKI7fBE9wxmj4uwSlywy4lkuLTX7rpnIe3U7cFGxaE0uE1JN+fXXSiwYFZhyuRozBs6dU6ORKRO8+NEuPnq0Omczn1AiUo96RvVghFJPK6Ut00Q6whH605+TqKWCSvWgzFjJRCbnmLJCyQ/vDCvbO71WnsmXX4b9+9UoS0buoEEQbHPu0iWQlsg7dqidQ37j8t7Onl0NPxBY2nkQCCm7cw9t47tTL27nKsw+V64Q96JJpRj1oLq3vPtnK/jxsRYcRb10/5mDQ/hfrWc4ulftgyXtwOV76Vi1vKLtLxVnvelNHHFKus1CFp5Z8DMj7mhOXKyaTSgzGGNiYMoUJRaQBMI+fTxdFISOChg0vl7NiHb1iYm8oEKCO7f04pG/R12Nq6gbJSbAmEH4Cq9wHrUAeMOdXXh6xZeq22u8VBI4fhz69QP5twrceCO8+irIPTSYcOYM9O8PBw+qcVGxoqdi2s0TrbSNr6ZbjeVuCegAobv1o7kLYwlo50EYK9eBo2knQTpC9JNgfvsNPvkErGakSjCqe3eMC2Kv+4pzHOsWfVva8iAPcnhXdl5v0MSyA6FQIY/DP39+hQfPDxfRXewyAkJWHQgSCHuVV6lCFVb1b4fMWpE2JVagZk3PxV+cKOPHq2VhPvooNG/uudRIoFDFgfDCCyDF9hsKLmRok2aWW3RK1aC05MyMO5JytrGNN3nTsgOhAhV4ndfJRjYranR2rZ/eGc4yaY3aoUPw+utwzGL32iJFPBdupXeFNRZNrRYHiJzDqgMhXz7PO69wYVPbBG2Rdh4ETfRB31jb+EFXQUgyEIafK1fo4eeh1/HNy1YGdHvYvvfVDXQctB5JfpNWjOewnjXWjW60pCW71+VmYOPGnDlmLZNPZg7KzOCcOR0UpQ3bfx3reIu3LLellATAYQyjNKVZ+HVpRnerQ0K8lZkI0KgR9OzpuauNGgUy68wKSNCkd2+oXx+2bYNXXrGeHCo0RB/XXQfLi//CyHrtiMtk7bJSZ+89PLfseyIToq5m34ZurMgi+dq1rDV0Y3VERFWq0u+HFUQluOOuonp+t+Ht3Omx1a1WuZYt62ktKhWyboADBzw2vtVWo5IAKOfPk8cNp0ifB23ju1s/mjs1CegAoZrcNJaWgG0JaOeBbRGGNQHtJEhHvX4UzLJlMHo0pirXpMpMWtu1b3+lN/6ldhONSqs97DH9bHamM21oc3n9sX1ZGdamATtW5TNFQzLs+vaFvKqjTfx0Ed3HPiPAZTYjNZpoBjCAa7jGc+7J7diwwZPhe/q0KVHQtCk89hhGdrCAOBCkvcn335sL/EqWosxMaJCsu+nhw/DSSyCZkGZAApOyvnr1K6u3tXsbaeFjNngscwd70tM1wcGkk+xmt+FAkAxyM1CTmkYFZFbUW3uZ2cfnGj++M3zu7ccFR47AsGGQ2B3e506VKnneFW67cJ84AcOHw5YtPo9gLJBqafmNFSxobn0wV2nnQTClH9y9tY0fXPmH6u5h+rlyhTrmfVqOL56uSVyM79KayKh4Hhqxhpa9r8zEliChtEuXrgpmQJLexJZrRKPLy/dvzsmw1g04sCWXGRJUrQrPPecHh79N238zm3mDN0wHlCRJbAhDKEGJy+deOaUYox6sy4Uz0aZk0bYtdOp0pTJKur589RXMmGEKHRkdILJMbp+L/STVVmY7lkjnBQlcVEg2PWBd4V95r147zmY2lyDafOtTdPv7/bSDg3IUm7oxJ42rV4lOhzPcdLeQhjQ0OsZETe6kuqXG8yIBmdEt8/fMJtBVq+Z5vt3WVUMSGuWusseka0RmrUqiby5zr8igPkPaxg+q+PXmfpKADhD6SbDhRPbixYucOHGCQoUKkSnY9ephJFjtPHC3MifTLjAMBukiEJjDhd4uJw9l4bcvyjF3bHmO7L46BS9b7hgad9tJsye3UeK6tCNXi1jEZCZzgANpCkCyaCV40oUuFKHIVWviYiNYOaU4cz6uwLpfr/67IMhFoFkzuOkmm21E/Pj8xRLLT/zEHOZwilNpyiI72WlCEzrQIWUgKdFLJu0Uf/8d5swBuSylBgnqyUwWqfhLfmFPvk5aMc6dCwsWpJ2NKdVUt9/u+SetQKtUMU77f/bOO06qYtnjX5YlZ0FEJIsSFJAgoAIikswBMV/0iYqoiAFQLwYURcR81YuCAXMWr2IAUQQEVJAcBCRIliyZBXbfp84QFnZ35oQ+c87MVN8/8L3tqq76VZ+e6q7uqhGRQwgJZuTWCheGVq3g6qtz2Zx1/oRd7ELmxUhGIi8sj2zppNOc5rSnPbWoFXha0by+XEkdO5GJlh7yqvDIJmlEm9KUDnSwaicGnR415gqU4KexMjenTIGRI2HOnNy1lcMwWSvkdW1YXTg56Js6NfKdT5+eux6SNkn0aNLk0CWAmPYNuIMeHgRsgACHVx8/QPATYOgE/+lJAIRzF3H98iKMHlKDH4bW4J+/c15eKlFuN2ffuJi23RZTvlrO14KZZPI93/MlX+Z5WUouvIk/9y/+RWly3t7bszuNX3p3sn63FyzIKWe+tCyaXLiKDrf+yclnrw3t77b4tR/xEWMYk+fLypKUtPzaS7kU8XOPbFvWFWTMm9UZNfh41i3NZc9VJOJby29/5cq521SCfILl+PG5B/okc4LQt26de9BB/Ci5SDh6dN6XAeVFluwROnfOPT37tgKbGFvtLUYd/19Wl8h526nQ3qK0WHYN7Rd1p/rmhtG/Hx/3ZbE+XLnUOY5x1r4tt8uuMrel/IH4+Cewv46kLmaxYHX9d0nFL5eXZX7ndolOfHrxiWUPLD7ygfqcrgf0iVC+sd9+i+gxb14ua14+aNgw8p3KniWse5UjJVcf36cJo2wDRUADhIHCH+zg27ZtY9y4cZYQrVq1ovgRuSvWr19Pt27dGDFiBFKjUP5+4403MmDAAAo5LnQVrK5hHF0PD8JolUMyaYAw3PbxW7rMfTBz9DGsWVicHVsKULjYXspU3EXDc1dTuJi92hvy0koOEaRuyW52U5zi1gs52VjltlHOTadV84szb/zRbNsYSd9Souxu6mw/lWOPNYRAnDaic5jDWMZaN1OzyKIEJZCXco1pnLsiR2w45TXgH3/AsmWRIJ/c4pWXULKhsJt2SW4IT5sWScu4a1ckkCcHB3JD225dFznM+eGHSLqUffsihw2nngqnnRbFHtkwFt0XspClLGU72615UIpSNKCB9W8itcUs5k/+tPSQoLccBEm6oaNwk+s2IM2T6GBDAuiy8ZbXrnJIIHNT6hZVqBAQti6HXbMG6/WwvByW716+b7lRfNyhhwcuOcefTA8P4o95WEZUHz8slginHEn00xNOgGNItTcjH9NHVrCCUju3plO4+F7KVdnBKR3XULCwvYLkEkD5lm+t7BCSmlF8fEmRL5feYtZc3j8BJJWgHPyLXyuH4iXaTKbe2X9Troq7enBBGWMKU5jEJLay1boYJv6svJw8iZNsiSR7rtljyrPqgzOt2t9yzCSpxMXHl5d/dprQiY8vF/nE3xcfv1KliB9kN+AwYwb8/POhlIglS0YuIYocdpoEkOcdPY4VJeeyvcBmCu4rQuldFWi45hyK7bGZ6iVO+7Jo+sheRV4UyoVG8fElMCg+vtQUl38Pa7qY2ZkanvscuVbI3KxXD8qW9cw6rgzkJeH8+ZG9inyX4uNLcDPsJQNyA0l9/LhOHR0sTghogDBOQIdxmLfeeov/+7//o1KlSixduvSw14GZmZk0a9aMqVOnkpWtKFe+fPm4+OKL+eyzz8KoUkLJpIcH4TaXBgjDbZ+Uls7kZiwEG9FcbWlSx6AnS1gxDhqXMIyfTPMsDHiqDIchoIcHqTsh1MdPXdvb0Vx/euyglMR98poAqe4v6ocRWIpR11+b2sw1dEqY2Aioj5/Y9lPpc0dAA4QpPDOuvvpqPvzwQ3r06MELL7xwGBIffPAB11xzDRIQbNiwIWeeeSZjx461Aoby//v666/p2LFjCqPnXXU9PPCOoZ8cNEDoJ7rK2xMCJjdjYT2MMKmjJ7ANEIcVYwOqJTyLZJpnCW+M5FNADw+Sz6Z2NVIf3y5SqdlPf3pS0+4HtdYAYe4TQD8MDRCm+NKg6icOAurjJ46tVFL7CGiA0D5WSdezfv36zJkzh/fff58rrrjiMP3OOeccRo4cSZMmTZg4cSLp6ens2bOHli1bMnnyZC6//HIkiKjNPQJ6eOAeu3hQaoAwHijrGK4QMLmBDmvwyqSOrkA2SBRWjA2qmLCskmmeJawRkldwPTxIXtvG0kx9/FgIpfbf9acnte2PBgg1QJjXJ5BoewZdzFJ8MUtd9dXHT13bJ7PmGiBMZuvG0K1ChQqsW7eOSZMm0bRp04O9JRBYunRpdu3axRtvvMF111138G/Dhg3jhhtuoHr16ixatCiF0fOuuh4eeMfQTw6pGiDMIIM1rLFqDkgNDak1cAzHxK6n4acxAuK9jW2sZS1StL0QhazaavGur7Z5Rwbfjs5gzYoCFgoVq+zhvK1XUTy9sBlUbG5EpY6i1FmRWopFKUp5ylOMYmZkyI2LTxvOTZtgw4ZIfRKpayI1D+zWMBQxMzNBaqRJfTT572LFsOpBFoiYJ/dmE2P/wFTOeSLg0zxTxBUBQUAPD1J3HqiPn7q2t6N5UD89u9hl+fg72HGwBrL4c1I3LtXaFrawnvWWj1+YwpSlLKWxWSfOK1j7J8D6jC18t2wOazfvJj0tjeNa/8l5ZxehcHq61xFs00u9OamZLjXCpZai+PgVqGBhEvfm8cNYv/7wGoRSZ1xqEdptUlt89epIfTRpsj+QOs5xNIe+ILRrLO2nCASMgPr4ARtAh/cFAQ0Q+gJrYjAtWLAg+/bt4/fff+eUU045KLS8GGzRooWVSnTVqlUcI97V/nbgb0WKFGG7VNTW5hoBPTxwDV2ehHEL6pkXPSfHOAcVVrGKUYxiLGOt4GD2JhvF9rSnNa0pTvF4aB/YGLJRnsc8vuM7JjOZfew7TJY61KEDHWhKU+twxa82ZvI2ht1zMjsnNILM/IcPk7aPoqfNoOvVRWh5dB1vIkSZZ3JQ8Cu/MpKRzGf+YePkJz/NaW5hUYta5g+XPB4SZBd2716YMgVGjoQ5c46AMg1OPRU6dICTToJ8eZyRbdkCP/0Eo0bB2rWH85ADhLPOgnbtIgcJjlqcv3NHsqVCZ4PzLBXgUh2dIaCHB87wSqbe6uMnkzWj65IIPyNLWWr5+OMZb130yt6qUMXy8VvSkiIUSWrDZZLJDGZYfu00piE+f/Z2CqdYWDSikZmLkXlMji+XT+XjdwuRMb0OZKUdjnn6Hkq2/4Vbn11Eo1r+XcSTYPEEJljzYglLDpOhIAVpQQsLixrUiN+ccPExZWTApEkR/3zhwiOgTIfTT4f27eGEE/L28TduhB9+gNGjQS4SZm+lS0PbtnD22VC2bBygSLR9gQubxQFFHUIR8B0B9fF9h1gHCAABDRAGAHpYhpRXglu3buXbb7+lvXhO+9vAgQP597//zQknnMD8+YcfCk+bNo3GjRujAULvVtTDA+8YHslBA4TOMZVbxK/wCr/wS0ziAhSgE524hEvMB4Riju5/hxWs4AVe4C/+ijlYGcrQjW7WIYLJtnnbXm5t3oi9c06EmDe6syhw8gJevq8ypQs6uCKbXeA8NqK/8RtDGWrdKI7V5PCgJz05lmNjdbX/d0MbzhkzYPBgkM1/rFa5MvTsCVWqHOoprwQ//RT+9z/YsycWBzjjDOjWDQrbvXidaAcBsSFIrB6G5lliKa3SxgsBPTyIF9LhG0d9/PDZxC+JwvwzIj7cS7xkBcViNQkOXsM1VlAoGduf/MmLvMhqVsdUT15V3s7t1KZ2zL5ROxwxOf7avpZ7H8ogc3klG3yzKNJqCq/+8KfxF4VjGMPbvJ3jQmhuQp3ESfSgR3wyqDj8mCZOhNdfj2T1iNUkQCg+vmQOOdDkAuE770SCi/J6MFpLS4sECq+/3ucXhYm2L3Bos1h20r8rAomCgPr4iWIpldMJAhogdIJWkvWVQN/06dO59957GTBgwEHtmjVrxpQpU6xUokOHDj1M61GjRtGxY0eqVq3KkiWH3zZLMnh8V0cPD8xDrAFCZ5hKep3+9LcVEMvOWV4S3sItZm7XOhPZt94LWMATPGFrs3xACEnJJEHCNrQxItfK9bu468TzYNNRzviVXc9Lz6VTvrCL1Ei5bETlNvHrvJ7jZnU0oUpQgr70NXfT2MCGc9w4+O9/I6lA7TZJO3rffVCnToTupZfg55/tUkf61agBDzxgM3Vpoh0EOIMi/L0NzLPwK6kSBoWAHh4EhXzw46qPH7wN4iVBWH9GJEW++Ph/87cjKC7kQitQmExpR6cznad5GimjYLfJpUi5/CYZQ1y3bJNj1j9/0b9nOdjh7FVgWrW/GDb/FwoXPOKloUuhPuVTPuZjR9SSfvUhHjJ7ETA3CRx8TF9/DW+95UgNSpWK+OdVq4K8PHz6aZg+3RmPevWgTx8oVMgZne3eibYvcGAz2xhoR0UgARBQHz8BjKQiOkZAA4SOIUsegvvuu49BgwZRsmRJPvjgA1q2bMmbb75Jz549rfSiw4cP58ILLzxM4aeffpo+ffpw2mmnMWHChOQBIwBN9PDAPOgaILSPqWySH+ERFnJEPhabLC7gAv7Fv2z2Dnc3uU38AA+wFRtXUI9QRQ5QetObJjTxpOSujEyuq3omWWuc5qiMDJtWaSXDBh3j/JbxERtReUn6LM+60qUUpXicx636hJ6bxw2nbPgHDnQWHDwgs9QV7N8/cqP4u+/caSIBRjmEiFqbUFgn2kGAOzjCS+VxnoVXMZUsDAjo4UEYrBCMDOrjB4N7EKOG8WdEamg/yIOsZKUrSK7lWiRQmAxtEYvoR78cqVXt6CZBQsHR9UvCbLUGb701C7aUsjNsjj4F6s/lvRlH5Mh3wUlSq8oFQDdN6tE/xmOIr+9bs/kxjR8PL77oTooyZSI+/nvvRVKTumlNmkCvXiCvCo23RNsX2LSZcZyUoSIQMALq4wdsAB3eFwQ0QOgLrInBdPXq1dSpU8dKM5q9ZWVlUbduXWbNmmUFCrO3s846i3HjxtG9e3dekqcV2lwjoIcHrqHLk1ADhPYx/ZzP+ZAP7RPk0lOCQSdwgiceYSCWG9azmOVaFKnLOJjBFML9ddK7binCylfPdy2DEFa//ieePLe1Mx7ZNqI72Wm9DJV/3baGNOR+7ndLfojOw4ZTUoHeeiv8Ezs7ap5ySrrR5cu9qXHttXDEHZucDBPtIMAbJOGj9jDPwqeMShQ2BPTwIGwWiZ886uPHD+ugRwrjz8gbvGHV0nbb5PKbpNyXGuSJ3KTGYC96sRz3Dp0ExgSLNFxEg/ZPjhuH/MqW0c08Qdn0P+/Sq4f7fcYGNlhpU4+sre5EKMkgcyu3OiFx1tfGx7RtG3TvDrsPL6XpaJyaNeHPPx2R5Oh8++3QqpU3HrlSJ9q+wIbNfEBJWSoCgSOgPn7gJlABfEBAA4Q+gJpILMePH8+VV16JBAsPtBo1ajBixAhq1z487/6iRYuoVasWEkD87LPPuPjiixNJ1dDJqocH5k2iAUJ7mMrm8DZuYyM2CrNFYdmKVtZmM5Gb1B28m7s9q9Cd7pzFWa75XF78HNhe3DW9RVjqHz4e6vBmb7aNqKQWfY3XPMlg7FDJw4ZTUoL+5z+e1DBCLHVORI6oN4wT7SDACDIhYuJhnoVICxUlpAjo4UFIDRMHsdTHjwPIIRkibD8jcslL0t/vYpcnhJIhU8gc5ljZUry2+7jPXc3xTzqza28GXbrkg70FPImRVnkFHy5z+eQNrLSikl7US5MXlVK3XsoK+NJsfEwjRsDbb/syuiOmUtPw8ccdkdjrnGj7Ahs2s6e49lIEEgsB9fETy14qrT0ENEBoD6ek7pWRkWGlC12zZg3HHnssLVq0ID09PYfOP//8Mz/88IP1/+/duzdFixZNalz8Vk4PD8wjrAFCe5j+xm9WLQ6vLZ10a6NYkpJeWQVG7/WW9QHBj+d4q4ahmzbk3R2M/td1bkhz0Fw84FeurunglvL+jaiJW9YHhJG0VJKeylPzsOF88EGYP9/T6MaI778fGjaMwi7RDgKMIRMSRh7mWUg0UDFCjIAeHoTYOD6Lpj6+zwCHiH3YfkZMXPYSeCU7hvj4BSkYIrSdiSIp8yV1vtfWiEZIkNBx+6Qz/caMYe5g9xcID42ZRc9pb3HGKc5qGAr9XvZaF0M3scmxCkcSSHkJCR770mJ8TFIb/K67INu9dl/EsMtUShlI3XGjLdH2BWFbAI0aQ5kpAnkjoD6+zo5kREADhMloVdUpIRDQwwN3ZkqqIGA0CHzcIEhwUIKEJtqN3Eh72ptgFXceEhS7gRvYznYjY8tBRCUqOeZ1baM6ZEw72TFdbgSFGs/mnXsd8No/z5aylD70MSKD1CcZylB7vAxvLNeuBUn7E5Z2+ulw5wlz1jwAACAASURBVJ0upPHx+3chTeqRGJ6XqQegaiwI6OFB6s4D9fET0/bJsPRLzbz5mLkldQ/30AwHl85CZHZ5Qfl//J+nlJoH1JHsGOLXOr4Q+UlnrrxnBZnLne8NcoOy1EU/MfSLdY5Rns50BjDAMV1uBFWpylM85Y2Xyw9twYJIfe+wtHPPheuvj5M0Yd0XuLRlnFDTYRQB3xBQH983aJVxgAhogDBA8HXo1EZADw/c2V8DhO5wy04lt2AXs9g7I+BiLuZqrjbCK95MJA3TdZh5uSey96UvDWjgWI0rKrYga/WxjulyI0irspwPn65sn9f+DecUpjCIQfbpYvR8n/eRF6Yxm+GN5dy50K9fzFHj1qFWLejf38VwYT0IcKFKQpIYnpcJiYEK7RkBPTzwDGHCMlAfPzFNlwxLv9SS9lpC4ID1rud6zuXchDTmGtZwB3cYk1185GpUc8bvk85c3nUrbDWTkrNgo9m8+/s8ZzIAoxnNEIY4psuNoBjFeJM3vfFy+aFNnAjPP+9taJPUTZtCr14mOUbhFdZ9gUtbxgk1HUYR8A0B9fF9g1YZB4iABggDBF+HTm0E9PDAnf01QOgOt+xUd3Inq1jlnRHQkY7WK7xEbJJqR+q0mGpub1pfXrYNbCxrRIx8x/zNRy8eY5/X/g3nz/zMfzBXuE9St0p6qpjN8MZy6lSQlD9haVWrwlNuLlqH9SAgLMD6LYfheem3uMo/nAjo4UE47RIPqdTHjwfK5sdIhqVfgno72GEEnCu4gk50MsIr3kyWsIR7udfYsI/yKLWp7YyfBAi77IRdRZzR5dE7f+2FfDBvumNeIxjB25gp3Jef/HzAB45lOIzA5Yf244/wyivehjZJ3aAB9O1rkmMUXmHdF7i0ZZxQ02EUAd8QUB/fN2iVcYAIaIAwQPB16NRGQA8P3NlfA4TucMtOdT/3s4hF3hkl+AtCST/UhS5GcBAmD/AA9anvmN8Vx7Uga5WhF4RVl/PhU/qC0LERfCKoXRsefdQF87AeBLhQJSFJ9MAjIc0WNqH18CBsFomfPOrjxw9rkyMlw9Lfne5sYIMRWPQF4SEYJa2mpNd01KwXhFtgq5la7QUbz+LdKX84EkE6/8APvMqrjulyI5DLf3IJ0FNz+aFNmgTPPedpZKPEzZrBPfcYZZk3s7DuC1zaMk6o6TCKgG8IqI/vG7TKOEAENEAYIPg6dGojoIcH7uyvAUJ3uGWneo7nmMQk74zAeoF3Nmcb4RVvJlKD8CZuYgtbjAwtL/AqUMExr381PZHdk52nJs1toCLNZvDWPQ547d9wLmc58gLSRCtLWQYz2B4rwxvL9evh1lvtDR2PXq1auayJGNaDgHiAFoYxDM/LMKikMsQfAT08iD/mYRlRffywWMKZHMmw9PejH3OZ60zxPHpLbeomNDHCK95MMsiwahDuYY/nodNI4zVes5cZI/toUoOwzzIyl1bxLIMwOOqyH3jlk42Oec1mNvIC0kQ7nuN5gie8sXL5oS1eDPfd521ok9QXXgjXXmuSYxReYd0XuLRlnFDTYRQB3xBQH983aJVxgAhogDBA8HXo1EZADw/c2V8DhO5wy041jWneN3dAIQpZN1KLUtS7UAFxeJd3+ZIvPY8uaYfcbr7f/992vrjYTJX7a56eykVVGtnXJ9uG09TLUklHJWmpbDUfNpZS82/WLFuj+97poYfg5JNdDBPWgwAXqiQkiQ/zMiFxUKE9IaCHB57gS2hi9fET03zJsPSPYYz9S1pRzFSKUhYfW/WkQ2rul3iJcYzzLF0zmrm7RPdJZwZO+ompz7X2LANkcd8fb9GoVjHHvDLJpAc9WMc6x7RHEkhZCSkv4am5/NCysiI1/5Yv9zS6MeJnnoHKDpK2eBo4rPsCl7b0hIUSKwIhQEB9/BAYQUUwjoAGCI1DqgwVAXsI6OFBdJxSJhCYFww+bgRko3gHd7CWtfYmax695OWgyRp+noRxSbyGNfSkJ/Ka0EsTPFvQwjWLy0u3h39Kuaa3CMtu4OPBDmsZZptnJg6V5Jb1y7yMvCI82OK8efz1V5BNe9DtuOPg2WchXz4Xkrj5/k3i7GZ8F2qGlsQklqFVUgXzGwE9PPAb4fDyVx8/vLYRyZJ5id/Nbm7hFraz3ZMRLuVSruRKTzyCJl7AAiv9v9f2EA9xMm5ue8HezEyuLnIxZBTyJEZajSV8uGiKax7DGe65dqCxi6EePsBRo+C111zDYBGKXy7BRi+tbl3o188LB0O0QfvrHmxpCAFlowgEgoD6+IHAroP6jIAGCH0GWNkrAnkhoIcH0eeGBgg/8fXjMVGwfhCDqEY1X+WMB/OBDGQqU10PVYYyyC3lAhRwzePee/Kz5NlLXdMLYZ3uY3jkrLOc8ci2sZRDpVu5la1sdcYjW+/mNOdu7j6cPs6bx337oEcPkHSjblvNmvDnn26pI3Rdu0KHDi55uNnwm8TZzfguVQ0lmUksQ6mgChUPBPTwIB4oh3MM9fHDaZcDUiX7Eu81O0Z+8vMiL1KOcuE2ZAzp5PLfv/m3p7rrlanM0zxNPtzc9ooIeMvlZdj4SVtPWJ417C26X+c+Y8s//MNt3IakXnXbOtCBrnR1S36IzsMHuHNnpJTAdg/x75NOgjlzvKlx993QvLk3Hkaog/bXPdjSiP7KRBEICAH18QMCXof1FQENEPoKrzJXBPJGQA8Pos8ODRD6GyDcy14kMDaTma4+U7lVLLeLk6FtYAN96ctGnNf1kEMUuZ18Eid5gkJuGF97YlMyF1V3xSf9xEW8/Wh10tPSnNEfsbGcwQwr/ay8MnXajuZoHudxSlP6cNIANo/z58Ojj8IeF6VnSpWCxx+HMWPgs8+cohDp36gR9O4N+fO7o8fNht8kzm7Gd6lqKMlMYhlKBVWoeCCghwfxQDmcY6iPH067HJAq2Zf4XexCahEuZrErQyRyffEjFV7BCh7kQVcvKgtT2Cof4PUy5LZde+la9Wyy1pZ3ZY8iLSfz1rilrmizE/3Mz0i9dDdNAqX96W+mrITHD/D332HQIHevAI85Bh57DD76CEaPdoMEnHlmJEjpKkOIuyHzpgraX/doS9NwKD9FIF4IqI8fL6R1nHgioAHCeKKtYykC2RDQw4Po00EDhP4GCAX9HezgSZ5kHvMcfZsXciHXcI2n27SOBoxD52Uss4Jbm9hkezR5MSipRaU2iYm2edteutU+k6yVFR2xS6u8gteeLEfx9MKO6KzOuWwsJzDBehG5j322+UlKUQmUHsdxOWkC2jzKAYKk+HQSJJTgYN++UK1a5ODhzTfhu+9sw2B1lJqDffpAYRfmODiSmw2/SZzdjO8MpnD3NolluDVV6XxEQA8PfAQ35KzVxw+3gVJhid/MZsuv/Yu/HBnjWq5F/PxkavOZb12KdJJ2tQhF6E1v16lFj8TvrzW76F33fNhUxhG0BerP5a1ps5xfAMxjlO/4jjd501FpBfHtxcc/rHyAIy2O6GzgAxw7FgYPhkwH9xnLl4cHHoAKFUCyjbz0EkyY4EyRZs2gZ09IT3dG51vvoP11A7b0DRtlrAj4iID6+D6Cq6wDQ0ADhIFBrwOnOgJ6eBB9BmiA0P8AoVhAUs28zdv8wA8xA0IlKGHVI2lHu6T8fNeznsEMZhazYuonm2W5YV2b2jH7Oumwa+9eurWpyc7xTaRKRgzSLIq2mMYrt9ansNudah4byznMYQhDWM3qmOI3oAHd6c5RHJV73wA3jwsXwiuvwPLlMdVA6onIjWA5QDjQJEgoAcKPP46dzkhM0LYtdOli4ODAzYbfJM5uxo8NceL0MIll4mitkhpGQA8PDAOaQOzUxw+3sVJliZeLgK/xGnLxK1atbUmXfx3XcTqnh9t4LqVbyUr+y39ZyMKYHGpQw/Jrq1I1Zl8nHeQi4G1nNGDPzDqxffx8mRzVaQwvfbTeWHDwgKyTmcwbvIFkUInWJK2qXIK8mZspTnEnqsbY5Hc2wmvmTBg6FP7+OzY7yexxyy1QOluSEwkufv45fPkl7NoVnUehQnD++dC5MzhN1hJbOg89gvbXU2Ux9WAiJU1OBNTHT067prpWGiBM9Rmg+geGgB4exNg7YGbzEJiBvQ4cZ4dfbhr/yI+MZjQSKMveTuAEpO6E1JcrSEGvmoWefjnLGcUoxjGOnew8KG8aaZzKqRYWklLUSz2SWCAsWLaTZ+6uxKavzoCMQod3L7SLsu2m0rtTDWoUqxCLVfS/R5lnkmZ0NrMZyUimMOWwwyW5Wd2a1lawuBKV4nIQ4FZRCfLNmwcjR8Jvv0VuDR9osuFv2TJSK7BqlHOgjAyYODHCY9GiwyU5+mho3x6k/GPJkm6lPILOzfdvcpPuZnxDqoeCjUksQ6GQChEEAnp4EATq4RhTffxw2CEvKVJtiRe/Xvx7uQwoteiyN/Fnxa9tQhPSCcuzKP/mz5/8afn4EjTdw6E89KL7aZxmYSH7Hj99/KlDGvHyRxvZOu4U2HtE/fKi2zn2qrHcP2gTFY7yb88lWUKk/rpgIeUFsje5ENqGNpaPXx53aVGjWtDgByhBvhkzIv75tGmHpx0tVgxat4Z27aBilOQsO3bA+PEwalTOC4XHHRfx8SWtaFH3JSD9m9BB++sGbekfSMpZETCPgPr45jFVjsEjoAHC4G2gEqQoAnp4EN3w+oIwPi8Ij7SC3DCWw4NtbEPq68km0eit0QT63qVOowROJUhYiEKUopT1bzyb1CacOX8Xi5dGcujUqJGP+icUMX6bOJZOUs9mC1vYzW6r/ohgkeMgKQE2ibt3wz//gPxbpEjkJrHTx5fbtsHWrZFAY/HiIGlJQ1GHJJYR/f570IcUJvVLgLlsUl3l5Q8CenjgD66JwFV9/OCtpMt4ThvIxS/xa+VlofhwJSlppqZc8OZ2LIFkUBG/Vnx8qTUofm28L0GKjz959g6WLYOChaBmjTTqHR//KJRgIFgIJsUoZtUSl0uRvjWfPk55BbhlS8THl2Ce+PhOaoHLhULx78XPlyY+fokSIffxw+x7+2Rn3+alMlYEHCCgPr4DsLRrwiCgAcKEMZUKmmwI6OFBdItqgDCYAGGyfWeqTxwR0I1gHMEO4VBhPqRwCpfOZaeIaf9cENDDg9SdFurjB297XcaDt4FKoAjkioB+nOYmRph9b7WzOTsrp9AhoD5+6EyiAhlAQAOEBkBUFoqAGwT08EADhFERCLPD72bCK03yI6AbweS3cTQNk2nN0rmc2nPZkPZ6eGAIyARkoz5+8EbTZTx4G6gEioAGCH2eA2H2vXUR9tn4yj5IBNTHDxJ9HdsvBDRA6BeyylcRiIGAHh5ogFADhLpMJBUCuhFMKnM6VibMhxROldG57BQx7Z8LAnp4kLrTQn384G2vy3jwNlAJFAENEPo8B8Lse+si7LPxlX2QCKiPHyT6OrZfCGiA0C9kla8ioAFCT3NAU4xqilG3E0jqKM5jHn/wh1VLUepoSC3F+tSnOtXdsnVMt571TGYym9jEHvZYdV4qU5kmNMlZvy8X7qLHQhYyhzmWHtJEj5M4iZrUJB/5HMvkK0GUjeDixTBzZqSuh9T4KFYM6taFWrVCXtvDV8CSjHmYDymcQq2HGk4RM9b/zz9h9uxDa4XUAKpTJzHXCj08MDYtEo6RBgiDN5ku48HbwA8JpI7iTGayiEVsZ7tVL11qKTaiEcdxnB9D5spzDWuYwhSrbrvULJfafTWowSmcYqt+n/j44t8vYMHBvYro0YAGVKVq3PTwOpD49PMnlGXuuKPZvqkg+fJlUbxsBvXb/k2NxptzZ68fp1fYD9GH2fdWO5uzcxJwkrVi3jz444+Ij5+WFqnzWb8+1KiReAqqj594NlOJYyOgAcLYGGkPRcAXBPTwIDqsGiDUAKHTD28HOxjLWEYxipWszJX8BE6gAx04jdMoQAGnQ8TsLxv+WcxiJCOtgwP5v49spSjF2ZxNW9pSjnI5/r6b3YxnvMXjL/7KdUwJdIoeLWhBQQrGlCsuHY7YCGZkwMSJMHIkLFqUuwSVK0OHDtCqFRQuHBcpdRC/EAjzIYVTnfVQwylinvrLWvHzzzBqFMhlgtxalSrQvn1irRV6eOBpWiQ0sfr4wZtPl/HgbWBSgq1s5Ud+5Hu+Zy1rc2V9MifTnvY0pamtIJ1T+SQ4+Tu/W/uMGczIlbwsZWlHO8vPF3//yCZBzZ/4yeKxmtW58qhNbUuP5jS3daHQqR4m+u/alp9x71Rl5H9rsnx2Tj1ljONP3UiHW//k9CuXU7Bw5qFh9eM0YYIIjzD73mpnc3ZOYE47d8LYsREff8WK3BU54YSIj3/66VDA/PGML+ipj+8LrMo0YAQ0QBiwAXT41EVADw+i214DhBogdLI6SCBtIAPZwAZbZHI7937u5yiOstXfTqcMMniZl5nEJDvdrcDe7dxuHQAcaKtYZekhN5PttIpUtPQ4hmPsdPe3T7aN4Lp1MHAgLF9ub8ijj4b774dKlez1115JgkBYDzb0UCNuE2ztWnjiCViZ+52OHHKULx9ZK46L30MR11jo4YFr6BKeUH38+JlQl+v4YR3USJIR5CmeQoKEdlod6tCb3hSnuJ3utvrIJcRnedZ6vWinSdaQe7iHetQ72H0xi3mSJ63MInba8RzPvdxLaUrb6R63PivmluCJ81qybmkxW2NWPvkf7v96POWq7Iz014/WFm62OoXVj1Y72zJfsndatixyHrB+vT1Nq1aF++6DsmXt9Q+yl/r4QaKvY/uFgAYI/UJW+SoCMRDQw4PoAGmAUAOEdhcR2XA/wiPsZP/G0yah3PJ9jMeQf702SSH6BE8wm9mOWEma0O50pzWtWcEKHuZh2wcgBwaSG8r96U8FKjga23jn/Rt+OfB/8EHYZO/846AYknb0kUdAXgppSxEEwnqwoYdXcZmAf/8dWSs255GFLC8hZK149FGQF8hhbnp4EGbr+Cub+vj+4puduy7X8cM6iJHErxb/WvxsJ60SlXiUR40ECSU4KPuMJSxxIoKV/lQClZL+VFKJiq8uWUKcNLkAKHRhCRL+NbMU/c5szfbNzrKXlKm4k/4TfqR8tR0aIHQyAWL1DasfLXLr4hzLekn996VLoV8/2LHDmZoSHOzfH8rlTLLkjJHPvdXH9xlgZR8IAhogDAR2HVQRkCf2K6i8/3Rr+eDBVEqEqzJxNJwGCDVAaGe6bWQj93Efm3F4wryfubwkfJzHPafpfImXGMc4OyLn6CMHCHLL+E3eZB3rXPGQ4KC8PJQby4G1Tzqza1fkdY/d10BHyirLoNw0LJV7tqLAVNOBfUIgrAcbeqjhk8EPsZWUQ7JWrFrlbig5OJC1omRJd/TxoNLDg3igHM4x1MePn110uY4f1vEeSbJqSJYMpxcAD8hZl7o8xEOe0o1KWlHxr6cz3ZX6hShk+fgv8qLjC4AHBpTahhIk9KM0ghOl/llbiHsbtWXjSnd7jePqbOGJ30ZT+NtLnQyrfaMhEFY/WmTWxTll565c/Lv3XueXhQ8AJkekAwZAoULhhVB9/PDaRiVzj4AGCN1jp5SKgCcE9PAgOnwaINQAoZ0PbBjD+IZv7HTNs8+N3GjV+nDblrKUPvRxS27RSapTCXZ6aVdzNRdzsRcW3mg/6czXX8Nbb3ljc/HFcPXV3ngodYIgENaDDT3U8H0CffklvPuut2EuvRSuvNIbDz+p9fDAT3TDzVt9/PjZR5fr+GEd75Fe4AUmMMHTsL3oZdUkdNuk1qBcJPTSjuZo1xcAD4x7K7da2UaCbO/ffzJfDKzjSYTrn5/GuRUbeuKhxNkQCKsfLSLq4pyyU/Wdd+Crr7ypf8MN0LGjNx5+UquP7ye6yjsoBDRAGBTyOm7KI6CHB9GngAYINUAYa5HYxS5u4RYk9Y+XVoUqVm0TSffppg1hCKMZ7YbUKI0cQMgN5TTSjPK1yyzzo87cdResXm2XIvd+8iJo8ODEKVLuTdsUpw7rwYYeavg6MTMzoWdPkBSjXpq8NJa1Ij3dCxf/aPXwwD9sw85Zffz4WUiX6/hhHc+RJDOIpODfxz5Pw9anPg/wgGsegxjEFKa4pjdFKPUIJdVqUG3P7jRuqXQ+W9d7e9Jz7Ilbeb5/CfK523IFpX54xw2rHy2I6eIc3nnjo2QZGXDLLbBtm7dBKlWCZ54htGuF+vje7KvU4URAA4ThtItKlQII6OFBdCNrgFADhLGWgR/5kVd4JVY3W3+X2iJ1cH4rVoKT3ejmuKaILaFcdLqXe2lMYxeU3klmPtiZxx7zzkc49OgBLVua4aVcQoxAWA829FDD10kzfXokdZCJJoHGM84wwck8Dz08MI9ponBUHz9+ltLlOn5Yx3Okz/mcD/nQyJDP8zwVqeiY13rWcxu3kUWWY1o/CAYwgJrU9IN1TJ7j36vCi9c2i9nPTocHHoD69e301D4xEQirHy2C6+Ic03zJ2OGnn+C//zWj2cMPw0knmeFlmov6+KYRVX5hQEADhGGwgsqQkgjo4UF0s2uAUAOEsRaGp3ma3/gtVjdbf5fUnJKi02mTW8VyuzgsTVKlSspUX1seGz5JLSopRk00OfCXg39HLV6bZN3wOjKL8c7xsLPa2LjZsjN84w347jszQ7RqBbffboaXaS56eGAa0cThpz5+8LbSZTx4G3iR4EEeZD7zvbA4SHsd13Ee5znmJdlBJEtIWFpnIv8Lor1wdTMmfFDFyNDnnQfXXWeElTKJh0/sFmVdhN0il9B0zz0HkyaZUeGCC+Bf/zLDyzQX9fFNI6r8woCABgjDYAWVISUR0MOD6GbXAKEGCGMtDA/zMPOYF6ubrb+3oY2VrtRpM/mK0enYufVvTnPu5m4TrPLmkceG78UXYfx4M0PLzWK5YeyoxWuTrBteR2Yx3jkedlYbGzdbdobPPw8TJ5oZomFDuP9+M7xMc9HDA9OIJg4/9fGDt5Uu48HbwIsEPenJajzmrN8vwKVcypU4L1hr8hWjFywO0HakIzdwgwlWjnn0b9eKWaOPcUyXG0GYL/YYUTCeTOLhE7vVRxdht8glNN0jj8CcOWZUaN0abr3VDC/TXNTHN42o8gsDAhogDIMVVIaUREAPD6KbXQOEGiCMtTD0pS8LWRirm62/t6IVt+P8GcooRvEar9kaIx6dmtCEPvTxd6g8NnwmD/3r1oV+/RyqEa9Nsm54HRrGcPd42FltbNhoh7OTmiK//mpmiHr14MEHzfAyzUUPD0wjmjj81McP3la6jAdvAy8SiE++lrVeWBykvZALuZZrHfP6hMj/wtLa0pabuTkQcfq1PpO5Y8sbGfv00+HOO42wUibx8IndoqyLsFvkEpruoYfgjz/MqNCiBdxxhxleprmoj28aUeUXBgQ0QBgGK6gMKYmAHh5EN7sGCMOzIQ3rB/oETzCNaUbEO5dzuZ7rHfOayESktklYWmtacys+X7XLY8M3dCh8/70ZJE49FXr3dsgrXptk3fA6NIzh7vGws9rYsNEOZ/fKK/Djj2aGaN4c7vb50bRbSfXwwC1yiU+nPn7wNtRlPHgbeJFAamovYYkXFgdpr+IqLuESx7y+4RuGMcwxnV8EF3ER13CNX+yj8n3qktOZ/MVxRsZu1w5uuskIK2USD5/YLcq6CLtFLqHpnnwSfv/djAodOkDXrmZ4meaiPr5pRJVfGBDQAGEYrKAypCQCengQ3ewaINQAYayF4WM+5lM+jdXN1t970IOWtLTVN3unNazhDsJzta0rXelAB8d6OCLIY8MnB/5y8G+iXXUVXOL0LCdem2Td8JowsXse8bCz2ti9fWxQjh4NQwyVdbrmGrjoIhuDBtBFDw8CAD0kQ6qPH7whdBkP3gZeJJDaf1ID0ESTeob1qOeY1QIW8ABO8907HsY2gZQQkFICQbTPB9Tmw77OMcxN1ltugTZtgtAiCceMh0/sFjZdhN0il9B0n34KH39sRoXbboMzzzTDyzQX9fFNI6r8woCABgjDYAWVISUR0MOD6GbXAKEGCGMtDOtZz23cRhZZsbpG/XsJSjCYwRSkoCs+j/EYM5npitYkUSEK8SqvUpSiJtnm5JXHhm/XLpBN/44d3obPnx8GD4bSpR3yidcmWTe8Dg1juHs87Kw2Nmy0w9nt3BlZK+RfLy09PXIpoWRJL1z8o9XDA/+wDTtn9fGDt5Au48HbwIsE8npQXhF6bcdyLM/xHGmkOWYl+wtJ2/8XfzmmNU1QhjK8zMukk26atS1+m9cUonvl89m31zmO2QcoWiqDV14sSOHCtobVTrEQiIdPHEuGvP6ui7Bb5BKabuPGSN3AzExvahQrBq++CgXdHc94G9wGtfr4NkDSLgmHgAYIE85kKnCyIKCHB9EtqQFCDRDa+daf4ikmM9lO1zz7eE3Z8xu/8TRPe5JBgpMZZHji0Y523EQccvZE2fANGwbffONJDVzXJonXJlk3vN4M7JU6HnZWG3u1Ukz611+HkSNjdovaoWVL6NHDGw8/qfXwwE90w81bffzg7aPLePA28CqBvN6TV3xeWhe6cD7nu2bxPd8zlKGu6YWwMIXZxS5PPC7jMi7nck88vBI/f2UzJn5UxRObc+9cwPWnn+iJhxJnQyAePrFbwHURdotcwtM9+yz88os3Nc4/H7p08cbDT2r18f1EV3kHhYAGCINCXsdNeQT08CD6FNAAoQYI7SwS85nPQzzk+hWhbNqf5VnKUc7OcLn22cc+etObFaxwzeMCLkBqnQgvN60ABRjEII7DTH0QXG7q1qyBXr0gw2WsMy0NHn8cjj/eBQph3SS7xNIFAkqSGwJu5oXazPe5tHp1ZK3Ys8fdUPLSeMAAqF7dHX08qPTwIB4oh3MM9fGDt4su48HbwKsEU5hi+bZuWylKWa8Hi1PcLQsrsHcnd7KRja55SP3D4Qx3TS+ZQUQPeUUYZFs0pQx9m7chc5+7V4QFi+zlgi1tYwAAIABJREFUmdmjOOb3c4NUI7nGduPjBo2ALs5BW8D38RcuhAcegCyXSZ4KFYJnnoHy5X0X1fUA6uO7hk4JQ4yABghDbBwVLbkR0MOD6PbVAKEGCO2uAN/xHW/wht3uB/tJuiFJX9SQho5pjySQWoRy03kLWxzzktqHt3M74xnPS7zkmF4I7uIuTuM0V7S5EnnYvE2eDE8/7W5T0K0bnH22SzXCukn2gKVLJJQsOwJu5oXaLC5z6NdfQW4ZuzlAkPRFrVvHRUzXg+jhgWvoEp5QffzgTajLePA2MCHBR3zEZ3zmmJVk5niYhzmBExzTHkmwlKXWZUQ3rwDl9aK8YvyKr3iHdxzLkp/89KUvJ3OyY1o/CH54rTqv3tTEMet8aVn0Hj6BJheuxu0lRMeDpgKBGx83aFx0cQ7aAnEZ//vvYaiLx9f58kGfPtC4cVzEdD2I+viuoVPCECOgAcIQG0dFS24E9PAgun01QKgBQicrwLd8yzCG2X5JKAcHPenJqZzqZJiofeUF4QAGILUR7bbWtOZmbj5YU+QnfrLqCNp9SSgHB1KHsQUt7A5pr5/HzZukFXnxRfuvg+Tl4I03Qtu29sTLtVdYN8kesfSAiJIKAm7mhdosbnNn4kR46SXYu9fekLJW3HSTh4sE9oYx0ksPD4zAmJBM1McP3my6jAdvAxMSSB3AT/iET/nUNrtiFLMye9Slrm2aWB0XspCBDGQrW2N1Pfj3C7mQq7naqn8oekiQ8F3etU0vtcXv5m4jFxltD2qj4+gh1RnavTFZmfls9IYChfbR491faX7Zykh//Tht4Warkxsf1xZjHzup/X0EN1yspZTAG2/YvwhYoAD07AlNm4ZLj9ykUR8//DZSCZ0joAFC55gphSJgBAE9PIgOowYINUDo9EObzWwrhc8sZuVJKpv0pjTlUi6lGtWcDhGz/2Y28zmfM5ax7GRnnv2rUIXzOA8JEObj8A221FsRHtOYlmfAU2ga09jSoyY1Y8rluIOBzdvixfD55yAvCqO9EDrlFLjkEqhTx7GUhxOEdZNsAEuPyKQ2uZt5oTaL65xZtAg++wx+/z3vtUJuFB9YK2rXjqt4rgfTwwPX0CU8ofr4wZtQl/HgbWBSAkk3+j/+h5QWyKtJun3JpiH1+ipQweTwFq+1rLUClROYwB7yzo8tfrkEB5vTPIcMM5jBF3zBHObkKZ9c/mtGMzrRicpUNq6HCYZzx5Vj+IA6zBiZN87yavDUi1Zy6QPzqNFo86Fh9eM0YYIIDzc+rrnR3XFS+7vDLUGp5s6F4cNhxoy8FZDLf6eeCp06QTXzxzO+IKc+vi+wKtOAEdAAYcAG0OFTFwE9PIhuew0QaoDQ7eqwilWMZrR1iLCNbdbNXak/Up/6nM3ZHMVRblnbppM0RJIy9Bd+QYKGGWQgN5orUYm2tKUWtXIEBo9kLgcR3/M985hn6SGtBCWsG9HC42iOti2P444GN2/r18MPP8Ds2bBtG2RmQvHiIIf87dpBBVNnOGHdJBvE0rEdlcDd4YnaLJCZI2vF6NEwZw5s3RoJFspaIZcH5HWxsbUiTtrp4UGcgA7hMOrjB28UXcaDt4EfEki6T/HxF7GI7Wy3MnCIb9yIRpzFWZSkpB/DHsZTfHK5CChBy3/4h73stXx8uXjYjnbUoEZMGSTriOghlwJFD9mriB4NaGDtVUpTOiaPMHRY82cxvn/1eP74uRzbNhYkLS2L4mUzOLnNWtrevJiylXK5LKkfpznThXXvE01Dtb85+ycQJ6k9LmlH58+PnAdIUFB8/Hr1Ij7+Uf4fzxhFS318o3Aqs5AgoAHCkBhCxUg9BPTwILrNNUCoAcLUWxVCpHEibt7CuklORCxDNBU9i+JmXqjNPMOuDEAPD1J3FqiPH7ztdRkP3gYqgSKQKwL6cZqbGG58XHOju+Ok9neHm1KFCgH18UNlDhXGEAIaIDQEpLJRBJwioIcHThGL9E+ZwGEiOvzuTJo8VCY3PEHb36Qupi0cNDYm9Qkzzib1VF6KQAoioIcHKWj0/Sqrjx+87fXnNXgbqARxQCDaRA/aX9aPMA4TIMoQQds/L9F0XgQ7L3R0Iwioj28ERmUSMgQ0QBgyg6g4qYOAHh64s7UGCN3hplRxQMDkhifoTZ1JXUxDHzQ2JvUJM84m9VReikAKIqCHBylodA0Qhsbo+vMaGlOoIH4ioAFCP9FNbN5h3S/p4pzY80qltxBQH18nQjIioAHCZLSq6pQQCGiA0J2ZNEDoDjeligMCJjc8QW/qTOpiGvqgsTGpT5hxNqmn8lIEUhABPTxIQaNrgDA0Rtef19CYQgXxEwENEPqJbmLzDut+SRfnxJ5XKr0GCHUOJC0CGiBMWtMmpmJTpkzhm2++4eeff2bu3LmsW7eOAgUKULFiRc444wy6du1KixYtPCvXr18/HnnkEVt8xowZQ+vWrW31ddJJA4RO0DrUN4wBwsWLYfRoWLIEtm+H/PmhVClo3BjOOitSgNlxc+jUb2ELP/ETv/M78t/72EcxilGDGrSlLdWp7liERCVYxSq+53sWsIBtbCMf+ShBCepRz8LiKKJXwc4kk5nMZAxjWM1qdrKTQhSy6M7gDE7jNApSMCc82TY8O3fC+PHw66+weTNkZEDRolC5Mpx9NtSuDfnyRUHYof2N2yrMmzcX2OzYvY/3P83gl/ers3N5OTK3FyF/ye0Ur/k3Z//fMi7pUJT0/NEMYhzhCMMAcN6yBcaMgalTQf573z4oVgyOPz5SJL5aNZ90VbaKQIohoAHCww2uPn6KfQABq2vi5zUrC+ZPKMvooTVYPrsUO/4pQMHC+yhdYRfNOq2g5bXLKFJir++abmQjP/IjM5jBVraSRZbl45/IibSjHcdxnO8yhGWApSxlNKNZxCK2s5385KckJWlEI87iLOu/ozXZH8leaSxjWctadrGLwhSmHOVoRStO5VTSSY/KY9umAvw0rBpT/leRf9YWZm9GGsXLZFCt4WbadVtEjcab4weXBgj5Z8923p7zO9N+Lsbu9SXJ3FmQ/CV2UKrGRs49sxjnVGxAWtRNV/zM5fdIGzdGziRmzYKtbCErMx/Fj8rgxNM3WHOzYq1tfosQm7+JxTn2KNpDEfAVAfXxfYVXmQeEgAYIAwJeh82JQKtWrRgvJ+oxWpcuXRg6dCgFC+ZyQB+LeP/fNUBoE6gQdgtTgFCCP19+CQsX5g1UgQJwxhlw2WVQvrwDQG0GQdawhk/5lElMYg978hzgBE7gIi6iKU0dCJFYXWczm+EMZxaz8hQ8jTRr89+JTlTj8EiIBAa/4zu+5Vv+5u88eUiwUQ4hLuES64DmYPuksxUM/OwzGDcOJEiYV5NA4XnnRQLIue5ZbdrfNwuFefPmAJsNW/bw7BOF+PO1M8laXy7veVH9LxrdPoE7eqRRuEB+32DNwTiOOK9eHZmbkybBnryXCmrVgosugiZN4geDjqQIJCMCenhwyKrq45dNxikeap28/LxKYHDsW1UZ8eyJLJtVOk89CxffQ6suf3HZg3MpXWG3cTyWsczy8Scz2br4l1c7mZMtn1QuwiVrm8IU/sf/mM/8PFUsQAHrEt9lXEYFKhzWT/ZIIxjBKEaxgQ158ihDGesy4YVcaF0OzN7WLi3Kp4/WZcIHVdizK29f8fhTN3JRnz9oftlK/82RwgHClTvX8/wPc/jri1NgS6k8sU6vtYjTLl1J9wYtSE9L898mAYywdGnEx588GTIz8xag3tl/c8m/53Fym3UBSLl/SC+Lc3BS68iKwGEIqI+vEyIZEdAAYTJaNUF1qlmzJosWLbJeC3bu3JmWLVtSpUoV9u3bx6RJk3jmmWdYuTLiaF911VW8//77rjXNHiCcJVesorTq1atTTJ5XGG76gtAdoGEIEIrj/cEH8L//2dehRAno0wfr8N1WsxEEmcc8BjHIukFrt8kBwpVcab2qS6b2Dd/wFm9Zt6rtNHkB2JOeVrBQ2m528wIvIAcQdpvc2P43/+ZojrZIVjzXmQEDYP16uxxAHifffDOkH3lZ2Yb97Y/iomeYN282sVm0chcPndOEPbPq2AagRMcJPPXxXxxVooBtGk8d44Tz3Lnw1FORF852W6dOcPnlMV662mWm/RSBFERADw8OGV19fA0QxnsJcPvzundPPobe0pgxb9jPvFG20g7u/3Y8VU7eYkxNeeX2PM9b/qmdJn59F7pwHufZ6Z4wfcSv/5iP+YzPbMssl/d605u61LVoJJvIUzyF7JvsNrlYeS/3HnyRuOCXo3jyghZsXX940DAavwt6zeeaJ2fia0wqRQOE0zYv5skB6WQurWLXpBx90QSeurIJRfPbt6Ft5gF2lKDgCy9EstXYafnyZXH9C9M5p8efdrqb7+N2cTYviXJUBFwjoD6+a+iUMMQIaIAwxMZJNdHOP/985HVgp06dyC85Go9o69evt9KMLliwwPrL2LFjkRvJblr2AGGWXBMNoGmA0B3oYQgQvvtu5OWg01aoEEhm2xo1bFDGCIL8yZ88wiO2Dw6yj3gxF3M1V9sQIjG6yKu/N3jDsbDymlA2/3LjWgKt05numEd5yvMYj7Fr0XE80OhcK2Wj0yZZk3v0cBCIsRkgcyrHYf3DvHmzof/K9bvpdfpp7Ft4vGMYirX5hcHf/kXhgnG4ZRwHnOUnU9adaK8G8wJJgoRXXOEYQiVQBBQBQA8PDk0D9fE1QBjvRcHNz6tsCV++/lTGve0813aJcrt5fNIPVKjp4CZOHqBIKtGBDIz6ajAvPK/nes7l3HjD7dt4H/GRo+DgAUHkNeHDPEwVqvAojyL7JqetKlWtvdbf0yrycKvW7Nrm/PLY+XfPp8szM50OnRz93XyENjT/Y9sKHv53OllrDn8laoOUo86bxEv/apY0LwmlXMCgQdFfDeaFyw0vTaXjbYvswGa2j0/zwqyQyk0RiI6A+vg6Q5IRAQ0QJqNVk1inESNGcMEFF1ga9ujRg//85z+utNUAoSvYQkEUdIBw4kR4/nn3UJQpE6EvUiQGjyhBkB3ssF6//cM/rgW5h3toRjPX9GEhlFRDD/GQ7ZeDR8otdUek5oikHHLbau89mW0NfmbF3LzT28Ti3aULnH9+rF77/24jQGaTU97dwrx5s6H/DR2OY9uo013DUO3OLxj0XJQ8nK45H0HoM87btsGdd0ZqDbpt8vJZ0426RU/pUhkBPTxwZn318Z3hpb2jI+Dm5/XbF2vy5h0NXUN7XO0tPDVzFOkF3F8+lfSXd3O3Vf/aTZOXhP3oRx3sZ09wM048aCSrh1zgc9ukHqFcApzABLcsOHXbWSyqPYKNK4u65nHH+7/Q4qrlrukTltDNRxhD2b2ZmVw/YD4ZM93P7wZ3/ETfFq0TFtYDgkvGmrvugt32Hhnn0DdfWhaPjh9DrdPzTrnrC0g+zAtf5FSmikAUBNTH1+mRjAhogDAZrZrEOm3fvp3ixYtbGp577rl8/fXXrrTVAKEr2EJBFGSAUG4W33svSJ5/L61rV+jQIQaHKEEQSac5jGFeRKAmNRnAAE88wkAsKYOkNouXlp/8rm5pHxzzi4vgki+8iIAEjl9+OZdUo7lxtREg8ySMEId58xZD//FTt/Ni4+u9QVBkBy+uHM4xZdzXurUlgM84jxgBb79tS5I8O0la5P79vfFQakUgFRHQwwNnVlcf3xle2js6Ak5/XvftzcftNc5lw3L3gSCR6J7PJtLsUve15z7gA6uetpfWmMZWhoxEbw/wAAuIZA5y2yRgarf8QK5jvHoz3PKq2+Etuqr1NzNo+ve51xz3xDnkxE4/QhvqfLZ0Ch/18VYkO1/5dbz5QsmETzXqNqNRdpibXrKCXp9PsoG8wS4+zAuD0ikrRcAWAurj24JJOyUYAhogTDCDpbq4GzdupGzZSJoeeUn4pZs8j4AGCBN3JgUZIJRUfQ884B27ypXh6adjpJTMIwgim9y7uItVrPIsyBM8wfE4T8HoeWBDDNazntu4zdvG34Qs7UfC9+09c7rnHmhm51GnBgijYt3zxmKsft17eq0mz75Pn7ucp5NyNBF83CRLrVR5PbhmjSOJcu0s6YuqOc+45n1g5aAIJDACenjgzHjq4zvDS3tHR8Dpz+tvX1Tk6UvO8AzryW3+5qEfxrnis4c9dKc7W/Dw7B+sOuMv8iKSBj9R2xKWBB/klIegp0yHmQ08w/jozz9S+4w4v9TyLLVHBk4/QhvD3TjkN7aMbmqjZ/QuHfpNoGtd79+7Z0FcMpB6g927w9atLhnsJ5NXhC8v/Zpyld29WHY1ug/zwpUcSqQIeEBAfXwP4ClpaBHQAGFoTaOC5YbA8OHDufTSS60/9enThyeffNIVUNkDhO3atWP69Ols3ryZ0qVLU7duXTp27Ei3bt0oI896fGpag9AdsEEGCOWF19ix7uQ+kurRR6F27Si88ggCzWGOVQ/DRGtDG27hFhOsAuHxMR/zKZ8GMvbBQRfVgJpm6jfUr28zAK0BwjxtvmHLHrpXuBh2ensBIAPkP3ERH8yf6u/88nGTPHMmPPaYGfHbtoWbbzbDS7koAqmCgB4eOLO0+vjO8NLe0RFw+vM64JwWTP/uWCOwPj//WyqeuM0xr4lM5Hk81DHINuKlXMqVXOlYhrAQDGEIoxkdrDiTmsPpZl5Xtbz2L3q881uw+sR7dKcfYQz5FmxbxQNdK0CW9xrhRZrP4K27vQd+4w3pgfHGj4cXXzQz+mUPzeHyR+aaYWaHi+F5YWdI7aMImEZAfXzTiCq/MCCgAcIwWEFlsIVAZmYmp512Gr/9FnGup0yZQuPGjW3RHtkpe4AwLwYSLBw2bBgXXXSRqzEkABitrV69mqZNIzfglg8eTKX9LyNdDZZCREEGCO++G2KY1bYlYtacyyMI9CVf8i7v2h4nWseqVEVSdCZqkxeQ05gWrPgfd4YrPjYiQ9GiMMxO5lgNEOaJ9w+/bOPV0/7PiD2EyeB/3qdsSR9fEfq4SR4+HD74wAwU1auDy/s4ZgRQLopAAiKghwf2jaY+vn2stKc9BJz+vHYtdyFbNxSyxzxGrzve+4UWVzuvOfc2bzOCEUZkqE99JEVnojZJkSqvCANt/+kBPf9jRISKtbbw/B8jjfBKGCZOP8IYir2/6Fe+uN9OqhUbCBXeycdvF7HRMZxdZL/4zTdmZGt4zmru/+ZnM8zscDE8L+wMqX0UAdMIqI9vGlHlFwYENEAYBiuoDLYQeOaZZ+jVq5fVV14RfvbZZ7bocuskAcLPP/+ciy++2ArSVaxYkT179jB//nzee+89Ro0aZZHlz5+fr776inPOOcfxWPny5bNNowFC21BF7eh38LBbN9i0yYysnTrBFVdE4ZVHEOh93ucLvNW7OzBqWcoymMFmFAqAS1/6spCFAYycbchXukH3V4zJ8OGHkOb2YqzJwGGYN29R9Pz02+18fK7H+oPZrPno0repXdXHAwQfcTZRm+QAFOXLw0svGZvmykgRSAkE9PDAvpnVx7ePlfa0h4CTn1epMX5l+mVkZdrfu0WT4oaXptLxNufZJcQnH8MYewrG6CUlBOQiXaK227mdtawNVvxHHoJ+ZrK2lCq/i6F/fxVfffL6CBJ0v/DKrJ/5sX8LYxi+sfMDihdO98bPyULjbaTDqMUnH+cuk3EOKU5ovoHHJ/1oUDoPrALC04PESpqiCKiPn6KGT3K1NUCY5AZOFvXGjh1L27Zt2bt3L+XLl2fWrFnWv27bgXSiedG/+uqr3HJLJPWiBA8XLVpE4cKFHQ2nAUJHcBnp7HeA8NZbYf16I6JawUEJEubZ8ti8mUyreQzHWDVKErU9zMPMY16w4r9+A9z4uhEZ8ueH99+PUZsy2kgJuuF3DF4UPb8YvY3325l7QTho1TtUO9bZ2u9IHx83wvJ6UF4RmmgVK8LzZrKemRBHeSgCCYGAHh7YM5P6+PZw0l7OEHD683pN4UvZszu/s0Hy6H3zkCm0vcn56zeTaTVrUYv+9DeiTxBMetKT1awOYuhDYw64H/oOMCJD2Uo7GLz8ayO8bDNJsgDha3MnMKqfubqB7+/9mPT8Hi8FOF1obBsvesdXXoEfDcX06rRaxyNjfzIkmUc2AeHpUWolT0EE1MdPQaOngMoaIEwBIye6inPmzKFly5Zs2rTJCtKNHDmSVq1a+a7WjTfeyOuvRw7+3333Xa655hpHY2qKUUdwGensd4Dwvvtg8WIjonLjjdC+fRReeQRBvuM73uANI0KcwAk8zuNGeAXB5Gme5jcCrufx5QVw0ZdG1C9dGoYM8cBKA4RMnLGd508x9IIwXyZv7PjI++3iaCb1cSP89dfw1lse5lM20lq1oH/injOaAUG5KAIOEdDDg9iAqY8fGyPt4Q4Bpz+v3Sufx4YV3usXi7S9Pp9A00tWORb8Qz7kcz53TJcbQROa0Ic+RngFweRBHmQ+84MY+tCYQ2+Em4cakaHaKZsYNC3ONRWTLEA4fNnvfNDLXXmZHEYss5GPN/7g3bZOFxrvI1oc5ELpF2YSCtH00hX0+sxMrU3P6gWEp2e5lUHKIaA+fsqZPCUU1gBhSpg5cZVcsmQJLVq0YNWqVVa6T0kr6rYmoFMUJk+efLBG4E033cQQTyf3OUeXAGLlypWtP2iKUafWyb2/3wHCjz+GTz/1Lqtkn5XC4lEfweYR7FnDGu7gDu9CAJdzOZdxmRFeQTD5kR95BXPpPV3p8E9JqLgKdhRzRZ6dqE0b2P9w2R0vDRCSsTeTLjXOIHN5JXcYZqMq3nYSb3wfvZas50F83AivWgV33ulZQovBlVdKam8zvJSLIpAqCOjhQXRLq4+fKl9CMHo6/Xkd0q0Ro4cc71nYgkX28srKERQvs8cxrwUsMFY38GZupi1tHcsQFgIJlErANNC2rDJUXwKZ3l+WXvLveVz1+Oz4qpNkAcLNGdu4+dZ9sKWUZxyPvmYUL7/7j2c+OF1ovI9ocZg3Dx5+2AyzW16bTJuuS80w88olIDy9iq30qYeA+vipZ/NU0FgDhKlg5QTVUYKC8nJw8eLFSLrOYcOG0aVLl7hps337dooXL26Nd+655/K1PMcw2DRAaBDM/az8DhBu2AC33QaZmd5kb9QI5DVi1BYl2COv/mYww5MQ+cnPf/kvZSjjiU+QxLvZTTe6sYMdQYoB3V6BId08yzBwINSo4YGNBggt8Po/nsmsB6IV+LSH8UXDh3HNxd4Dv1FH83kjLK/+Zs2yp29evST1raQyKuX9PMabIEqtCCQYAnp4kLfB1MdPsMmcgOI6/XldOr0UfRpGS+1hD4Q2XRdzy2u/2+t8RK8ssriXe1mKt8P6IhThVV6lMD6mSHeloX2izWymO93Zxz77RH70vHg4/O9iT5zzpWXx8pKvKVdlpyc+jomTLEAo+vf6aizL3jnTMRRHEtw8cRhtTzPg4ztdaDxLHmEgdVN79YLly70xLFY6w7rQUKhowN/ZATUCwtMbikqdigioj5+KVk9+nTVAmPw2TkgN169fz5lnnsncuXMt+V966SVuk8hMHNuOHTsoViziOGqAMI7AexjK7wChiPb00/Cbx6yW998PDRvGUDRKsGcKUxjEIA9IwWmcxl3c5YlHGIiHMYxv+MaTKCUowVa2uuaRb3pDshpOdU0vhCecAI97zfaqAULLBsv+3k2vypfDnoKubZJWeQVvL55AwfQ01zxsEfq8EZa1StYsL61FC7jDzKNlL2IorSKQcAjo4UHuJlMfP+GmckIK7Obn9cEzzmL+xHKe9B34+/fUaLTZNY/RjEZqEXpp53AO/4e5esxeZPFC+wIvMIEJXlhQiELIhUK3rcCo89jTYYRbcouuyUUr6fPFRE88XBEnYYDw902LeLKbt5e+BU+Zw9tT55AmKX28NjcLjdcx99OPGgWvveaN2Xl3LeC6Z71dOvYmwRHUAeJpVA9llvQIqI+f9CZOSQU1QJiSZg+30v/88w9t2rRh6tTIgfvAgQO599574y70lClTOPXUU61xpR7h0KFmahAcUERfEJo3aTwChMuWQd++sNvlXrN+ffj3vyEtVtwhSrAnk0we4zFm4y5VjdwslleIlfCehtG8FZ1x3MhG7uM+5Kaxm1aVqlzERfyH/7ght2gu5EI2X/cC496u5oqHvNB68EGoW9cV+SEiDRAexOKhh+CP/p1dA9rxg7e44UoztYiiCuHzRlheOz/6KOy/a+MYj6JFYcAAqFjRMakSKAIpj4AeHuScAurjp/xnETcA3Py8/vFzWR5p05p9e2I56bmr0eKav7jjXW+3CDPIsNKMun1FWIpSDGQgZSkbN6z9GmgVq7if+9mJu5d3dalLc5p7qt1+bWYXZp83kOnfHetKzULF9vLYxB+pWt9AOkunEiRhgFAguOvzcaz8sJVTNCL98+/l6m/f5eJ2Bl4PCj83C407yXNQZWREziT++ssdw9IVdvLk1NGUOXaXOwZ+UAWIpx/qKM/kRUB9/OS1bSprpgHCVLZ+CHWXV3vt27dnwoTIbcG+ffvy2GOPBSKp1B18bf+1rHfeeYdrr73WqBwaIDQKp8UsHgFCGWfaNBg0CPY5zMZRtSo88gjIoXvMFiPYs53tPMzDLGNZTFbZO6STbgXU6lPfEV2YOy9mMY/wiOMDhHKUoz/9rUOUr/iKd3jHsZpy8HAnd7JvdzpPNOnEbIcxW7m82r07tG7teOicBMkUIPSoy97MTHp2KcO695ynC6v32Ec82Nfd4aBjK8ZhI7xtG0jAdIXDcooFCoC8dj75ZMdaKYEioAgAenhw+DRQH18/i3gi4Pbnddy7VXi5S1Oyspy9Lqp75lr6jhxPgUIe6xAAcvntQR5kHescQSYpRWVvcDzeXlg5GtTnznIZ8gmeYA/OajpWprK1NyhOccu/Fz/faWtPe7rSlZ1bCvBI69YsmeasLEP+9Ex6fzGBRueY5yILAAAgAElEQVStcTq0mf7JFCDMti/YtWcft11Yma3fneEYp5ZD3qHHTXFKvet2EXKglZQ/eeAB8TccEAFFikRqGOYobeFx/+VMilx6xwEzzzIqA0VAfXydA0mKgAYIk9SwiahWRkYGF1xwAaMkXwLQs2dPnn/+eceqSK3C//u/SFqVhx9+mH79+h3GY9asWRQpUoSaNWvmyXvIkCF06xapKVahQgX+/PPPg+lGHQuUB4EGCE0heYhPvAKEMuKcOZHUfdu329OjXj24+27Yn7U2NpENB30b23iGZ5jDnNj8wNok96IXcqM22dpf/GXdmN6AvR1SNapZgdKjOOogFJLW6XVet13vRA4OJIWT1HOUlvFeZ15+GSZNsoduwYJw++3QvLm9/jF72ZgzMXkc6BD0Bs2ALnv3ZfHvewqy9AWbtWPy76X58x9x9+3uU5PaxjfOOEuQ8KmnYN48exKWKAG9e0Pt2vb6ay9FQBHIiYAGCA9hoj5+4r/mSrRv3Isb89vwivznmmZk7Ey3pXbzy5Zz+9u/UbCI9+DggQElSPgkT7KEJbZkEH9W/Frxb5Ot/cEfPMVTtssB1KEOvelt7XukSW3HL/iCD/nQ+m87rROduJzLyUckULzjn3Seu/w0ZoyqYIecoqUyuOezSdQ7e62t/r50StIAoWWP3fvoc2MZ1r7bwR50hXbR4c2P6HqVnVu69ljG7OVlEYrJ/FCHjRsl4xYstVm6tGxZuO8+kIvLOZqB/ZcD0XN2jRNmnmRUYkVAA4Q6B5IUAQ0QJqlhE1GtTp068fnnn1uiS4pRCQ7mi5IbvmDBgpx44ok5VI0VIJS/S8rQs846i3POOYd69epRtmxZ9u7dyx9//MF77713MEiZP39+hg8fbgUuTTcNEJpGNH4vCA9IvmUL/PgjfP89rMvjkm+DBtCuHTRpYiOtaHZIbDrokm5UahKOZCSzmJUrqEdzNO1oRxvaUJKS5oEPCccd7GAc4ywsVrIyV6lO5EQksCc1GAtQIEefNaxhFKMYwxjkleaRTV5gyqvBDnSgFrUO//Mnna2i8bNmgdxzmDw5UkT+yFaqFJx9dmReyCbNWLM5Z2yNF/QGzaAuP/66nc//W4G1H7WG3bncGi61mWrX/8RV3f+hYa04HhyIIeKIs6QblTkpc1PmaG6tfHlo3x7OOgskSKhNEVAE3COgAcJD2KmPb/LH3v2cTCVKrz+vG1YUYfSQGvwwtDqb1xTJAV2+tCwaX7CKDrcuon67vzFRzuzIQfayl1/4xfJr5zM/V/Mdx3GWX3smZ1KUOPswcZxQUitcfHPx0deSe9CtHvUsLE7lVNLImQlCsq4IvewVdpEzrWJBCtKCFpaPX53qObQTP+r3ryoyavDxzBiZe6CwXJXttO22mLNvXEKp8i7rUZjCNYkDhAJRZlYWX/+0na//W5WNw1vBvpwB/XxHr6Nm13H865bt1K6a8zs2BXWufLwuQg6E27MnckFVfPwFC3InrFQp4uO3ahUlm5HB/ZcD8Q91jSNmruRTIkVgPwLq4+tUSEYENECYjFZNUJ2iBQNzU6lq1aoszeWqlJ0A4YEXhtGgkqDh66+/zkUXXeQLohogNA9rPF8QZpdeNozTp8OSJZEXhenpULIkNG4Mx7orWQEuHHSp1TGVqWxhC3KoUIxiVpohSSea20bZvAXCwVFuB89jHnLjWIJ8cvu3BCVoQAPbN6t3s5vJTEYwldonhShkvTZsRjOkvoudjeD69ZGAzObNIHUi5PWobM4kWCxzxHhzMWfylCHoDZpJXfYruXL9bj77DNatKETG9gIULplBpZo7ufySgpQq5odBbFg4IJxXroykSpZLDnv3Rubm8ceD1EiNWR/VhlraRRFQBDTFaPY5oD6+BgjjvSaY+nndm5GP30dUZPnskmzfXJCCRfZR6phdNL1kJeUqu6uN5wYLqUk4k5mWjy+XA+V1nFxUk6wgB165ueGbaDSiu+CwiEWWjy8X9+TyY0MaIsFSO00uFP7Gb1agUXx8Sc0qlynFx5e9k5225s9iTPmqIlvWFmJvRhpFS++hRqNNnNJxDWmRxCLBtyQPEGYHeNHKXfxveBqb1hQiY0c6RUplUL3uTjpfWIiihQIyiKlFyOFMkvOImTNh69bIRVXx8SUjSJ06xL7I4MP+y5H4AWHmSEbtrAjoC0KdA0mKgAYIk9SwiahWvA4P1q5dy4gRI5g0aRLTpk3j77//ZsOGDWRlZXHUUUfRoEEDOnbsyPXXX09JifL41DRAaB7YoAKE5jXBVYDQFzmUqX0Egt7UmNzUJZMu9i0Y/55B4xx/jXVERSBlENDbxYdMrT6+Bgjj/eHrz2u8EdfxQodACgUIQ4e9CJSIi5DJvaQboyQiZm70VJqER0B9/IQ3oSqQCwIaINRpoQgEhIAGCM0DrwFC85gqRwcIBL2pMbmpSyZdHJgw7l2DxjnuCuuAikDqIKCHB6lj6yM1VR8/eNvrz2vwNlAJAkZAA4TBGiARFyGTe0k36CciZm70VJqER0B9/IQ3oSqgAUKdA4pAeBDQwwPztlCf0jymytEBAmGdgG42e0Hr4kZmB6YKTdegcQ4NECqIIpB8COjhQfLZ1K5G6uPbRcq/fvrz6h+2yjnBEQjrx5Fsvn/QOOeFpxu54mUbN7Il+Oeo4icmAurjJ6bdVOroCOgLQp0hikBACOjhgXngg/QppSbGZjYjdS3yk9+qh2G3joV5JJSj2GMFK1jHOqv+YgUqcCxuC0LaxDPICRhNRDebuqB1cSOzTTOFqlvQOIcKDBVGEUguBPTwILns6UQb9fGdoOVPX5M/r/vYd9DHL0ABqxZ1EYr4I7hyjYmA1FkXH3896xF7VKSiVUNQm00ETH4cNoe01S3ZfP+gcdYAoa1pp50UATcIqI/vBjWlCTsCGiAMu4VUvqRFQA8PzJs2CD98IxsZzWh+4Ac2sekwpepQhw50oClNSSfdvMLKMQcCq1nNW7zFDGYgBzrZW0EK0pzm/It/WYc7xlsQE9COEm423EHr4kZmO1iErU/QOIcND5VHEUgiBPTwIImM6VAV9fEdAuZDdxM/r2tYY/n4YxjDVrYeJmUDGlg+fiMaWRfRtPmPwFKWWj7+XOaSRdZhAxamMGdyJldxFUUp6r8wiTyCiY/DD/2TzfcPGmcNEPoxS5WnImAhoD6+ToRkREADhMloVdUpIRDQwwPzZoqnH76LXbzO64xnPPJaLVqTYJQEpVrRyrzSytFCQOzRj34sZrEtRBrTmN70NnuoE88JaEvL/Z3cbLiD1sWNzE4wCUvfoHEOCw4qhyKQhAjo4UESGtWmSurj2wTKx25efl4lGPgqrzKZyTkCUUeKXI5ydKUr4ldq8weBLWzhQR5ELgHGavnIR2ta053usbqm7t+9fBx+opZsvn/QOGuA0M/ZqrxTHAH18VN8AiSp+hogTFLDqlrhR0APD8zbKF5+uGxUBzDAdjDqgKZXcAWd6GRe8RTnKPa4i7ty3O6OBctxHMdTPGXudWe8JmAsxY78u5sNd9C6uJHZKS5h6B80zmHAQGVQBJIUAT08SFLD2lBLfXwbIPncxe3Pq6St7E9/W8GoAypIUEqChO1p77NWqcf+b/62LvTJRUAnrRa1eIRHzF4EdCJAmPu6/Tj81inZfP+gcdYAod8zVvmnMALq46ew8ZNYdQ0QJrFxVbVwI6CHB+btEw8/PIMMa8O5kIWuFLiBG+hIR1e0SpQTAalDchu35Ujvaher4/l/9s4DbIvi6t8/ehGkSBfsihHRKDawIgp2jVhi+Szxr9EogkSKFWJDjViwgIqxYBR77IoFUSxR1CiKFUEUCCK99/81S8AXeMuzu/Pszu7c813fpfHdOXPOfc7uc3bOzszWGqABhV5e/nVJBGAUTaO8cKdtSxSdo7BJu0/anNO2n/EhkGMCTB7k2LkVmEaOn77vo/y8ztf8YKXaZE2OZID5WK292kfqS6cNCZhz3f+ivwTnu0dpZlVnH/WJ0jXffaLcHEkQyVvunzZnCoRJRC1jeEqAHN9Tx+fcbAqEOXcw5rlLgMkD+75JIg9/TI/pKT0VWXlzTsmtulXN1CyyDDr+RuBO3alRGhULibWibRIBGMvSUjon8TIehUsSetlmGUVeFDZRxqEPBCCQOAEmDxJH7syA5PjpuyLKz+s9uic4czBqq6Eaukt3qa7qRhVBvxIEzErOsRobi0lf9Q3OiaSVIBDl5ogC0JdcPgobV/uUFRtJ+TKp2HSVP3plhgA5fmZchaIhCFAgDAGLSyFgkwCTBzZprpZV7JxymZYFX7LO0ZxYyh+pI4MzCWnxCJizHw1H45c4bRNtosEaHEdEMgEYX8MNJSTxwhflxkxCr2LwDCszCpuwY3A9BCCQCgEmD1LB7sSg5PjpuyHsz6tZPXiuzpXZKSROM3mpyfNp8QgYPxiWq7QqlqDNtJlu0k2xZOSuc9ibIyoAX3L5qHxc7EeB0EWvoJODBMjxHXQKKsUmQIEwNkIEQCAaASYPonErr1ex33dGa7QGaVBsxeuojoZoiKqremxZPgt4Ta/pXt1rBcGNulFbaIt4soodgPG0K713Ei/vUbgkoVcxeIaVGYVN2DG4HgIQSIUAkwepYHdiUHL89N0Q9uf1Rb2oB/VgbMWbqqlu022cfReT5D/1Tz2rZ2NKWd3drAytr/pWZOVCSNibI6rRvuTyUfm42I8CoYteQScHCZDjO+gUVIpNgAJhbIQIgEA0AkweRONWXq9iv+9cq2v1mT6zojjnlMTH2EM9NEVT4guSgu2HzDZEsVqxAzCWcmV0TuLlPQqXJPQqBs+wMqOwCTsG10MAAqkQYPIgFexODEqOn74bwv689lIv/agfrSjeX/21g3awIstXIefoHM3WbCvmH6yDdbbOtiIrF0LC3hxRjfYll4/Kx8V+FAhd9Ao6OUiAHN9Bp6BSbAIUCGMjRAAEohFg8iAat/J6Fft950JdqP/qv1YUP0Wn6GgdbUWWr0LO1JlaoAVWzG+lVhqogfFkFTsA42lXeu8kXt6jcElCr2LwDCszCpuwY3A9BCCQCgEmD1LB7sSg5PjpuyHsz+vpOl2LtMiK4uY4ggN0gBVZvgox70lxjxBYw66N2qif+vmKckO7w94cUcn5kstH5eNiPwqELnoFnRwkQI7voFNQKTYBCoSxESIAAtEIMHkQjVt5vYr9vmO+Po17/uAa/Y/Vsfqj/mgfgkcSzdkkS7TEisVN1ER36I54soodgPG0K713Ei/vUbgkoVcxeIaVGYVN2DG4HgIQSIUAkwepYHdiUHL89N0Q5ufVnHN3ok60pvSf9CcdokOsyfNRkHlHMmeN22hba2sN0AAbovIhI8zNEcdiX3L5OIxc60uB0DWPoI+jBMjxHXUMasUiQIEwFj46QyA6ASYPorMrq2ex33e6qZumaZoVxU/WyTpGx1iR5asQmysIW6qlbtbN8VAWOwCjascLelRy6fVzNZbSI8LIEMgcASYPMucyawqT41tDGVlQ2J/R03SaFmtx5PFKdmQFYXyM5j1puZbHFyQF272abV8z3cIGtG1jeZewTRR5pRFIO87xCgQKJECOXyAoLssUAQqEmXIXyuaJAJMH9r1Z7JzyKl2lL/SFFcXNdqX7aB8rsnwVYnPL1520ky7X5fFQFjsAo2rHS31Ucun1czWW0iPCyBDIHAEmDzLnMmsKk+NbQxlZUNif0Z7qqZ/1c+TxSna8QleordpakeWrkP+n/6e5mmvF/I7qqPN0nhVZqQkJG9C2FeVdwjZR5JVGIO04xysQKJAAOX6BoLgsUwQoEGbKXSibJwJMHtj3ZrFzypEaqcEaHFvxWqqlu3W3aqpmbFk+C3hez2uYhllBYIq/22v7eLKKHYBRteOlPiq59Pq5GkvpEWFkCGSOAJMHmXOZNYXJ8a2hjCwo7M/ov/QvPaJHIo+3puMm2iTYsr6KqsSW5bOA+3SfXtWrVhDcpbvUSI2syEpNSNiAtq0o7xK2iSKvNAJpxzlegUCBBMjxCwTFZZkiQIEwU+5C2TwRYPLAvjeLnVOa8+7O1blaoAWxlDfnkpjzSWjxCJizSU7VqbG3IKqv+rpH98RTxvQudgBG1ZCX+qjk0uvnaiylR4SRIZA5AkweZM5l1hQmx7eGMrKgsD+j5oxxs8os7raW5uw8c844LR6BhVooc5SAOR8yTmuhFrpVt8YR4UbfsAFtW2veJWwTRV5pBNKOc7wCgQIJkOMXCIrLMkWAAmGm3IWyeSLA5IF9byaRU5oVa2blWtRWSZU0UANlzryjxSdgzg38QB/EEnSSTtIf9IdYMoLOSQRgFC15qY9CLd0+rsZSulQYHQKZIsDkQabcZVVZcnyrOCMJi/Izalb+va23I41nOlVTNd2pO2U+PKPFJ2C2av1G38QS1EM91EEdYslwonOUgLapOO8SNmkiqywCacc5noFAgQTI8QsExWWZIkCBMFPuQtk8EWDywL43k8gpzRetV+pKTdKkSAacqBPVVV0j9aXThgQWa3HwxXfUVZ3my2JTZKysyvHxJhGAUbTkpT4KtXT7uBpL6VJhdAhkigCTB5lyl1VlyfGt4owkLMrP6GzN1mW6TNM1PdKY5+gcHaSDIvWl04YEjD8u0AVaqqWR8LRWa12tqyP1da5TlIC2aQTvEjZpIqssAmnHOZ6BQIEEyPELBMVlmSJAgTBT7kLZPBFg8iA5b9rONWdoRvDCOUVTQhlxmA7T6TpdZhUhzR6BX/WreqqnTLEwTGuohhqkQaqu6mG6lX2t7UALqxUv72GJuXt92rHkLhk0g0BmCDB5kBlXWVeUHN860tACo/6MTtbkIMefqZmhxrS2G0WoUfN/8Y/6UZfoktBbv5qdWm7STXY+AEwKc9SgDasf7wthiXF9UgSSugeSsodxckuAHD+3rvXaMAqEXrsf49MkwORBcvSLkWvO13yZrYg+0ScVGlJDNWQmDg7VoRQHK6QV7QLjj77qq1/0S0ECttN26q/+qqqqBV1f0EXFCLSCBv7fRbzwh6Hl9rVpx5LbdNAOApkgwORBJtxUFCXJ8YuCNZTQOD+j5kNA8wHZV/qqwjE30kbBWXn7ab8Kr+WCaATMik6zstOsKCyk7aJd1Ed9slUcNIbFCdpCwKy5hveFMLS4NkkCSd0DSdrEWLkkQI6fS7d6bxQFQu9DAABpEWDyIDnyxcw1f9JPGqERwZkli7RoHaPM16td1EX7al/VVu3kDPZ4JDOZ87Ae1vf6Xqu0ah0SZhvRnbRTsIpzU21qn1IxA60QbXnhL4RSNq5JO5ayQQktIeA0ASYPnHZPUZUjxy8q3oKE2/gZHa/xQY4/WqO1TMvWGXcrbRXk+OZ8O/MhIK34BD7Wxxqu4TKrCtdv5oO/dmoX5PiN1Kj4yhRjBBtBW4hevC8UQolr0iCQ1D2Qhm2MmSsC5Pi5cifG/I8ABUJCAQIpEWDyIDnwSeSaS7RE0zRNZiWbeUndWBurqZqyYjA5N68zktludKzGBltEmcKgmSxoq7Z2Vwyub1sSgVYeT174U4q2IgybdiwVwSREQsA3Akwe+Obx3+wlx0/f9zZ/Rs0HgCbHN+eQm23p66meGqtx+kZ6qoF51/pSX8qs9DTF2SZqojZqk70Vg2m9R/C+4OmdkwGzbT64M2AuKmaXADl+dn2H5mUToEBIdEAgJQJMHiQHnlwzOdZej5R2oPHCn5/wSzuW8kMSSyCQGgEmD1JDn/rA5PipuyCx3RrTtxQNckMgqdyP94XchEzuDEnqHsgdOAxKmgA5ftLEGS8JAhQIk6DMGBAohQCTB8mFBblmcqy9HintQOOFPz/hl3Ys5YcklkAgNQJMHqSGPvWByfFTdwEFwvRdgAZhCSSV+/G+ENYzXJ8UgaTugaTsYZzcEiDHz61rvTaMAqHX7sf4NAkweZAcfXLN5Fh7PVLagcYLf37CL+1Yyg9JLIFAagSYPEgNfeoDk+On7gIKhOm7AA3CEkgq9+N9IaxnuD4pAkndA0nZwzi5JUCOn1vXem0YBUKv3Y/xaRLIxeTBL79IX3whzZsnrVol1akjtW4ttWqVJtoNxs5CrrlMy/Qf/UfTNV3m/LyaqhmcqbGzdlY1VXOKZxaUmaRJ+lbfBmdCVlIl1VVd7agdA6aJt6QCkBf+cl1rzgn9VJ8GZ9aYf6+lWmqmZtpJO6mKqiQeFuUOmFTMuGU12kAgVwSYPMiVO0MZk4scf/Jk6auvpPnzpUqVVuf4bdpIzZqFYpHWxS79jJq83uT4JfOP5moenI3tXP6RlsMKHHeVVukH/aDxGq8FWhDwM+e+m/elBmpQoBRHL0sqaHP4vmDOBzU5/izN0lItVW3VVku11A7aIftnUzoarkVRK6l7oCjKI9QnAuT4PnnbH1spEPrjayx1jEBmJw9WrpT+8x/p1VdX/9MUBtdvv/ud1KWLtMceUtWqqZN3Odf8Rb/odb2uN/SG5mneBqzqqZ4O1IE6WAerkRqlztJlBZZruT7QBxqhEfpaX2+gqikU7qJd1EVdgomEyqqcjDlJBWAOX/htOGiKpgQxMUqjgsmk9VtDNdRBOkid1MmdyaWkYsYGYGRAAAKlEmDywN/AyGyOv3y5NGbM6hz/yy9Ld+DOO6/O8XfdVaqcUB4VIZRc+BmdrMlB/vGW3tIiLdrAik20ydr8o77qR7DSny7mw6539a5e1auaoAkbGG4KhXtojyDH/51+F3wcmLmWVNDm6H3hR/0YxMQ7eif4+G/91lRNg3fojuoYfCxKc5xAUveA4xhQz30C5Pju+wgNwxOgQBieGT0gYIVAJicP5s6VBg5c/UVxIc2sJOzTR2qSwqqtEvq5mmu+olf0oB7UCq2okKZ58T1TZ6qzOld4rY8XTNM0Xa/rZSZjCmnmi9KLdbHqqE4hl8e7JqkAzNELfzzgq3ubr8yf1tN6XI8H/15RMyt1z9N52kf7VHRp8f+eVMwU3xJGgIC3BJg88Nb1ymSO/+uv0g03SD/+WJjjtt1W6t1bqlevsOsTvirNn1GTczyhJ/SUnioo/6iu6vqL/qIO6pAwpWwMZ4pAN+gG/apfC1J4N+2mC3VhsBtLplpSQZuD94WVWqmH9JBe0ksFudjsGtJDPYIPRWkOE0jqHnAYAaplgwA5fjb8hJbhCFAgDMeLqyFgjUDmJg/mzJGuvFKaOjUcAzNxcNVVUvPm4fpZvNrFXNMULZ7Uk6GtPFEnqqu6hu6X5w6mKNhP/TRXc0OZuak21d/0t2BroqK2pAIwBy/8tvxgJufu1/0yRfiw7SydFXyBnmpLKmZSNZLBIZBvAkwe5Nu/5VmXuRx/2rTVOf6sWeGc1rTp6hy/gXtbO6b1M2ryj6Eaqtf0WjiWks7ROcGKQtpvBL7X97paV5e6ArM8TttqW12hK7JVJEwqaDP+vmCKg4M0SO/pvVC3itk5xhSOKcSHwpbsxUndA8laxWg5JECOn0OnYpIoEBIEEEiJQKYmD5Ytk/r1k77/PhotM4EwYMDq80tSaK7lmiM1UoM1ODKJC3SB9tN+kfvnqaPZlrWv+gZnN0Zp22k79Vd/VVURt8JNKgAz/sIfxX9l9Xlez2uYhkUSabal6q3eaqd2kfpb6ZRUzFhRFiEQgEBpBJg88DcuMpXjL1woXXqpNGVKNIdtuaV09dVS9erR+hepV1o/o//Sv/SIHolklck/TE7LKqfV+Exuf4kuCf0B4Br4u2pX9VGf7Gw3mlTQZvx94WE9rOf0XKR7zLzvmY9KW6t1pP50KjKBpO6BIpuB+PwTIMfPv499tJACoY9ex2YnCGRq8uCNN6S7747HrWtX6cQT48mI2NulXNMcnH6uztV8zY9ojYIzFIZoiMyWiL43MwljJmPiNLOt0wE6II6I8vsmFYAZf+G35QBTNDb32DItiyzSnFlym25L7pzK9TVNKmYiE6IjBCBQEQEmDyoilN+/ZyrHf+op6bHH4jnjT3+SDjkkngzLvdP4GTU7WZj8w5yJHbU1V3PdolvSyz+iKl6EfnfpruD8xjjNFBgzU3BNKmgz/L5gjpQwqwALOTqgrLgxq0uv1bVxwoq+xSKQ1D1QLP2R6w0BcnxvXO2VoRQIvXI3xrpEIDOTB6tWrT5HcOLEePjq15fuukuqWsSVWmVo6FKuaV50zQtv3NZN3bSv9o0rJtP9TQHITMSYglCctrW21gANiCMiet8owZnhF/vooArvGWf1YMlRLtWl+r1+X/jASV0ZJWaS0o1xIACBtQSYPPA3GDKT469YIV1wgTRjRjxntWy5+ozySpXiybHYO42fyjirB0uafrku107aySKN7IkyH1L+WX+O9bGXsdrsBmFWETrVkgrOHL4vxFk9WDIGzLn1W2krp8ICZcohkNQ9gxMgUCABcvwCQXFZpghQIMyUu1A2TwQyM3nw7bfS5ZfbQd+jh9Shgx1ZIaS4lFOaL1nHa3wI7Uu/1GyNYs7k8Lm9o3d0u263guA6XadttI0VWaGERAnOHL7wh2JWzsXmXJLu6i7zhXHc5uSkkjEqSszEhUF/CEAgNAEmD0Ijy02HzOT4H34o3XSTHe79+0s77GBHlgUpSf9UmvzDfLwXdcv7kibvoT10sS62QCG7Il7QC3pID8U2wGzbat4VmqhJbFnWBCQVnDl7X7CxC88aHx6oA4OPTGkZIZDUPZMRHKiZPgFy/PR9gAb2CVAgtM8UiRAoiEBmJg/++U/p2WcLsqnCi/beW+revcLLbF/gSk45UzOtvowM1VBtrI1t48qMvJt1sz7QB1b0PVbH6o/6oxVZoYRECc6cvfCH4lXBxZM0ydqkWmVVDs4xdG4r3ygxYxMysiAAgYIIMHlQEKZcXpSZHP/OO6VRo+z44LDDpDPOsCPLgpSkfyonaIK1lWpVVEVmpZT5p6/NnBP3lb6yYv6ZOlOH6lArsqwISVIfAOoAACAASURBVCo4c/a+8Lk+1zW6xooLzHEd9+k+K7IQkgCBpO6ZBExhiHwQIMfPhx+xYl0CFAiJCAikRCAzkwdmW9C34p3/sBZx27bSFVckTtyVnHKiJqq3eluzf6AGqpVaWZOXNUE2Jw86qVOwlVHiLUpw5uyF3ybzsRprdWXtPbpH9VXfporxZUWJmfijIgECEAhJgMmDkMBydHlmcvwBA6RPP7VDfp99pAsvtCPLgpSkfyr/o//I7EZhq/n+EWAP9dAUTbGCM7WPAMvSPqngzNn7wmiN1iANshITRshwDeesT2s0iywoqXumyGYgPj8EyPHz40ss+Y0ABUKiAQIpEcjM5MFtt0nvvmuH0vbbS1ddZUdWCCmu5JTf6TtdpstCaF7+pb6fn2BYGqY22n7aTxfoAhuiwsmIEpw5e+EPB6z8qz/RJzL3ha12p+5UYzW2Jc6OnCgxY2dkpEAAAiEIMHkQAlbOLs1Mjm9y8i++sEN/zz2lv/7VjiwLUpL+qRyjMbpRN1rQfLWIIRqihmpoTV7WBJ2v861s12rsPkpH6VSd6g6CpIIzZ+8LIzVSgzXYmh/NKt3qqm5NHoKKSCCpe6aIJiA6XwTI8fPlT6xZTYACIZEAgZQIZGbyYOhQacQIO5R23VXq29eOrBBSXMkpzZew5otYW82cqdFUTW2Jy5yca3WtPtNnVvQ2Ww+ZLYgSb1GCM2cv/DaZf6NvdIXsrVJ+QA+otmrbVDG+rCgxE39UJEAAAiEJMHkQEliOLs9Mjm/OHzTnENpoBx4onevOmV5J/1SO0zj1V38bJAMZ5vy9mqppTV7WBJkdV8zOKzaaOULArCJ0piUVnDl7X/hQH+om2Tkz1Rwf8E/905mQQJEKCCR1z+AICBRIgBy/QFBclikCFAgz5S6UzROBzEwevP66dM89dtAfe6z0x+TPeXMlp1ymZTpbZ2uhFsbmuZE20r26V1VVNbasrAowL3bPys75mOagenNgfeKtrODM2Ut9Ulzna77O0TlaruWxhzQrB+/QHaqkSrFlWRXgygPNqlEIg0D+CDB5kD+fFmpRZnL8J5+UHn+8ULPKv+7MM6VDi3fOm+s/fXM1N9iqfoVWxOZpPv4zWyk6l3/EtqxwAXfpLr0lO0dc9FVf7apdCx+82FcmFcw5e5f4r/6rC2VnG+NttI3VLYGLHTLey0/qnvEeNAAKJUCOXygprssSAQqEWfIWuuaKQGYmDxYtkv78Z2nx4nj8K1WSbr9datIknpwIvV3KKe/X/XpZL0ewYt0uR+gInabTYsvJsgBbL4q1VEt36+50vtSmQGg9BM2kmjmnJG47SSfpD/pDXDH2+7v0QLNvHRIhkBsCTB7kxpWhDclMjj9jhnT++dLKlaFtXKdD9erSkCFSnTrx5JTTOws/fbfqVr2n92IzMNthmm0xfW7f6ltdrstjI2ikRsHHXpVVObYsawKSCuacFQgN/6t1tcx543Fbah+GxlXc1/5J3TO+8sXu0ATI8UMjo0MGCFAgzICTUDGfBDIzeWDw29hmNKXtRY36LuWUkzVZF+mi2EFtiiDN1Cy2nKwLsLHNaGrbi5YXnDl8qU8q1mxsM2pW5ppzTuqpXlJqFz6OSw+0wrXmSgh4R4DJA+9cvtbgTOX4NrYZTWB70Sz89NnYZtRsfWjyj421sb83kKRVWqU+6hN7m1EnP/ZKKphz+C5hY5tRswuPOeOzhmp4fY9lyvik7plMQUHZNAmQ46dJn7GLRYACYbHIIhcCFRDI1OTB5MnSxRdLK2Jsm9Ovn9SmTSpx4VpOeYNu0Mf6ODKLPbSHLtbFkfvnqePn+lzX6JrIJplC0M26Ob1iKysII/uurI5mUulKXSlTKIzazHaz5utiJ5trDzQnIaEUBNInwORB+j5IS4NM5fjffCNdeaW0alU0XFWqSDfcIG22WbT+BfbKwk+fyT8u02X6Xt8XaNWGlx2sg4PjCGgKdoMwH0RGbeYM6dt0m3sfeyUVzDksEJotfHuqp6ZqatSwCM6jNOdS0jJEIKl7JkNIUDVdAuT46fJn9OIQoEBYHK5IhUCFBDI1eWCseeed1VuERmknnywdc0yUnlb6uJZTztO8YNucKC83m2rTYHuVOireNk5WoCco5Ck9pcf0WKQRe6iHOqhDpL5WOlEgtIJxfSEzNTOYpJuhGaHlm3NJ+qmfu18Wu/ZAC02YDhDwgwCTB374uTQrM5fjv/CC9NBD0Rx23nlSx47R+obolZWfvl/1a5B/zNKsENatvnRbbRvkH9VVPXTfPHYwBVdzNMMreiW0eVVURZfoEu2knUL3LXqHpII5hwVC4xuzG495j16gBaFdtbN2Dlammg9EaRkikNQ9kyEkqJouAXL8dPkzenEIUCAsDlekQqBCApmbPDAWvfmmdM894c4qOeEEqWtXyZxBmFJzMac0EwgDNEA/6aeCqWyuzYOX3YZqWHAfHy40EwhP6Ak9qScLNtdMHPxZf9YBOqDgPkW5kAJhUbAaoVM0JbjHpmlawWO0Vmv1Vm/VVd2C+yR+oYsPtMQhMCAE3CfA5IH7PiqWhpnL8c3qwWeflR55pHAkJq8/6yypc+fC+8S4Mks/faaAcZ2u03RNL9ji7bV9kH/wAeC6yFZqpf6hf2iERhTM0hRYzQeAu2m3gvskemFSwZzTAqHx1Q/6Icjx52hOwa7bRbsEx3zUVM2C+3ChIwSSumccMRc13CdAju++j9AwPAEKhOGZ0QMCVghkbvJgjdXjxkmPPy6Zf5bXttxydWFwjz2s8IojxNWccqEW6nE9rpEaqUVaVKaJZoucjuqo43W8zL/TSifwgT7Q03q6wvNK2qiNTtAJ+p1+B8qcE5iruRqu4XpH72iJlpRprSkIHqSD1FVd3f9y39UHWs5jCfMgEJYAkwdhieXn+szm+B9/LD35pDR+fPnO2H576fjjpbZtrTotTz9vszU7yD/MNplLtbTc/MNsK2q2PWTlYOmYzIeAb+ktPafngtVjZbVKqiRTBDpRJ2pLbWk1Nq0KsxnoOS4CVsTcfGz7qB7V+3pfy7W8zMsbqIEO0SE6SkfJfCBKyyABm/dMBs1HZfcIkOO75xM0ik+AAmF8hkiAQCQCmZ08WGPtTz9JI0ZIY8dK8+atPrukTh2pdevVXxNvs02qqwZLOsX1nHKxFgcTCKM0Kvja2PzvWqqlRmqk/bW/9tE+fO1Y4F1mJhG+03fBl8bmDDqz/YyZMDAFILPNUGd1Vku1LFAal+WFgIkDc3+Z+8xsO2qKheYea6ZmQfF9L+2VnYk51x9oeQka7IBATAJMHsQEmOHumc/xv/9eeu016auvpPnzV+fzJsffccfVOf7mmxfFO3n8eZuv+WvzD7P9+Zr8o7mar80/qqlaUXjmTajJ8b/Ul0GOb1aQmdzObBVpcvxdtWuQ4zdRE/fNthnoHhcI1zjarCI0H9uaQqEpzJt7zHxQa47l6KROwUpSthR1/7YoV0Ob90zGUaC+GwTI8d3wA1rYJUCB0C5PpEGgYAKZnzwo2NL0LySnTN8HaAABCFgiwAPNEkjEQKC4BJg8KC5fl6WT40fzDj9v0bjRK2MEbAY6BcKMOR91IxGwec9EUoBOEFiXADk+EZFHAhQI8+hVbMoEASYPknMTOWVyrBkJAhAoMgEeaEUGjHgI2CHA5IEdjlmUQo4fzWv8vEXjRq+MEbAZ6BQIM+Z81I1EwOY9E0kBOkGAAiExkH8CFAjz72MsdJQAkwfJOYacMjnWjAQBCBSZAA+0IgNGPATsEKBAaIdjFqWQ40fzGj9v0bjRK2MEbAY6BcKMOR91IxGwec9EUoBOEKBASAzknwAFwvz7GAsdJcDkQXKOIadMjjUjQQACRSbAA63IgBEPATsEKBDa4ZhFKeT40bzGz1s0bvTKGAGbgU6BMGPOR91IBGzeM5EUoBMEKBASA/knQIEw/z7GQkcJMHmQnGPIKZNjzUj5IrB8xSo98eJCjX6shRZMrq8VC2uoWr2Farj9dB171ix1+H3tRAyeoil6Xa9rkiZpoRaqmqqpnuppL+2lPbSHqqpqIno4MQgPNCfcgBIQqIgABcKKCOX37+T40XzLz1s0bvTKGAGbgR6jQLh46Uo9+sxi/fupllo8bWOtWFw9yPGb7PRfnXj2XO3SOpkcf6Im6k29qcmaHOT4NVRDDdVQHdRBu2pXVVbljDkYda0TsHnPWFcOgT4SIMf30ev5t5kCYf59jIWOEmDyIDnHkFMmx5qR8kFgzoLlun1QJX0xZG+tnNSqTKNqtv9EB/YYq9OOr6XKlSpZN/4TfaIX9aLGamyZsk2hsJM66QgdoTqqY12HzAjkQZcZV6GoHwSYPPDDz6VZSY4v8ZPkb/xjeUQCUW6aCAXCabOW6o6bq+vbe/fTqmlNy1R2o04f6PC/fq3jDt0ookFld1ulVXpf7+slvaRv9W2ZFzZSIx2sg3WoDlVN1bSuBwIzTiDKPZNxk1HfDQLk+G74AS3sEqBAaJcn0iBQMAEmDwpGFftCcsfYCBHgEYGJUxfr8iN+r6WftC3Y6k3PeUE33LlA1ava+crXTBwM13A9o2cK1qG5musSXaJmalZwn1xdyIMuV+7EmOwTYPIg+z6MagE5PgXCqLFDP48JRMnjQhYIx45fqOsO3Ucrvtu6YNDbXfqkrrpmpbUPAVdohYZqqN7QGwXrsKW2VF/1VQM1KLgPF3pAIMo94wEWTCw+AXL84jNmhOQJUCBMnjkjQiAgwORBcoFA7pgca0bKNoGpM5bqr3vvqeXfbBPakCanvaJBD8y1MoEwTMP0vJ4PrYOZOLhW18p8cexd40Hnncsx2G0CTB647Z9iakeOT4GwmPGF7JwSiJLHhSgQfvvTIl2x10FaNaVFaIBbX/y0Bvx9Reh+63cwHwDeqTv1tt4OLauFWugaXeP3biGhqeW8Q5R7JudIMC8ZAuT4yXBmlGQJUCBMljejQWAtASYPkgsGcsfkWDNSdgmsXLVK/69zK81/vX1kI9rd9Ij6/LVa5P6mo5k0uEN3RJaxuTbX9bpeVVQlsoxMduRBl0m3oXR+CTB5kF/fVmQZOT4FwopihL9DYAMCUfK4AguES5ev1Jm7tdWyz3aIDP7gYQ/q7FPjnUtoPv4zHwFGbW3VVpfrclWS/WMNoupEvxQJRLlnUlSXofNDgBw/P77Ekt8IUCAkGiCQEgEmD5IDT+6YHGtGyi6BNz6Yr7vbnxnLgEpNftH9k95U7RrRinPmy+Ie6qGpmhpLj17qpd21eywZmevMgy5zLkPhfBNg8iDf/i3POnJ8CoT+Rj+WRyYQJY8rsED4z38t0LN/OCOyaqZj1dbf6+GvPom8U8gyLdN5Ok9zNTeWHmankG21bSwZdM4JgSj3TE5Mx4x0CZDjp8uf0YtDgAJhcbgiFQIVEmDyoEJE1i4gd7SGEkE5JnDBaRvrl2FdYlvY5ZEHddZJ0b4wHquxulpXx9ZhZ+2sy3RZbDmZEsCDLlPuQtn8E2DyIP8+LstCcnwKhP5GP5ZHJhAljyuwQPinzi01/7XoO4Sssen/3nhARx64USQTR2u0BmlQpL4lO+2n/XSBLogtBwE5IBDlnsmB2ZiQPgFy/PR9gAb2CVAgtM8UiRAoiACTBwVhKvpF5JVFR8wAGSBgzh7s3uI4aWmN2NrW2meMHnxnQiQ5N+tmfaAPIvVdv5OZhGimZlZkZUIID7NMuAkl/SHA5IE/vl7fUp9yfH56/I1zLLdMIMrNVECBcOz3i3T1tqdZUbbhcW9qyBMzIsm6Ulfqa30dqW/JTtVUTUM0RHVVN7YsBGScQJR7JuMmo74bBMjx3fADWtglQIHQLk+kQaBgAj5NHhQMJYULyStTgM6QzhH412sL9EjneFsPrTWqynINX/Z0pC2I/qw/a5ZmWeFjvi42Xxl703iYeeNqDM0GASYPsuGnYmjpU47PT08xIgiZXhKIcjMVUCC8Z9givX6anQJhpeZT9diU0aHds1IrdbJOlvmnjWbOIdxJO9kQhYwsE4hyz2TZXnR3hgA5vjOuQBGLBCgQWoSJKAiEIeDT5EEYLklfS16ZNHHGc5HA/Y8t1Mt/PN2aaoPnPKJNNq4WWt4pOkXmjBIb7QydocN0mA1R2ZDBwywbfkJLbwgweeCNqzcw1Kccn58ef+Mcyy0TiHIzFVAgvOm2Zfqwx8l2lK25SI8veiG0rIVaKJOX22rmvPIO6mBLHHKySiDKPZNVW9HbKQLk+E65A2UsEaBAaAkkYiAQloBPkwdh2SR5PXllkrQZy1UCDz6xUC+eYK9AeM+8R1W/TtXQ5p6qU7VUS0P3K63Dn/QnHaJDrMjKhBAeZplwE0r6Q4DJA398vb6lPuX4/PT4G+dYbplAlJupgALhzXcs1QfdTrGjbO0FenzBS6FlLdIinS577xk91VN7aa/QetAhZwSi3DM5Q4A56RAgx0+HO6MWlwAFwuLyRToEyiTg0+SBy2FAXumyd9AtKQLPj5yvYQeeaWe4Gos1fNFzkbYYPU/naYainW2yvvLd1V17a287NmVBCg+zLHgJHT0iwOSBR85ez1Sfcnx+evyNcyy3TCDKzVRAgfAfwxfqlZPsFOcqt/pZwye9H9rwVVols0vIci0P3be0DuY8wx21oxVZCMkwgSj3TIbNRXV3CJDju+MLNLFHgAKhPZZIgkAoAj5NHoQCk/DF5JUJA2c4JwnMWbBcZ7c4TJpbL7Z+Gx/+joa+8N9IcoZoiN7Um5H6luxURVU0WINVX/Vjy8qMAB5mmXEVivpBgMkDP/xcmpU+5fj89Pgb51humUCUm6mAAuHEqYvVe7M/SsvDb/2/voXNz3pJtw1dEMnw63W9PtEnkfqW7FRLtXS37lZN1YwtCwEZJxDlnsm4yajvBgFyfDf8gBZ2CVAgtMsTaRAomIBPkwcFQ0nhQvLKFKAzpJMELr6wuibdfnRs3U546QEdd+hGkeT8oB/UV30j9S3Zqb3a6yJdFFtOLgTwkMuFGzEiewSYPMiez2xpnMccn58SW9GBHAgkT+DcExpq5hOdYg/c7eMHtO+u0XJ8Uxw0RcK47VAdqjNladeTuMrQP10C/DCly9/j0cnxPXZ+jk2nQJhj52Ka2wTyOHngNvHStSOvzKLX0LkYBD7+aqFu2CHeFkSVt/xRD3//gapWrhxZxUt1qb7X95H7m4791V87aIdYMnLTmYdcblyJIdkiwORBtvxlU9s85vj8lNiMEGRBIFkCL761QA92PCPWoDX3/I8e+uC7yDJWaqUu1IX6Rb9ElmE63qJbtKk2jSWDzjkhwA9TThyZPTPI8bPnMzSumAAFwooZcQUEikIgj5MHRQFVZKHklUUGjPhMEeh2Rl1Ne/CQyDof8uiD+tMfa0fubzp+rs91ra6VOa8kSvu9fq9LdIkqqVKU7vnrw0Mufz7FokwQYPIgE24qipJ5zPH5KSlKqCAUAokQWLlqlc4+dFPNezXi2dyVV+jkVx/SMQfViaXvaI3WIA2KLGN/7a/zdX7k/nTMGQF+mHLm0OyYQ46fHV+haeEEKBAWzoorIWCVQB4nD6wCSkgYeWVCoBkmEwQWLlmhv3TZSgtH7RFa3zZ/e1z9rrRTlBuhERqqoaF12Fyb62/6m2orXpEy9MAud+Ah57J30C3HBJg8yLFzKzAtjzk+PyX+xjOW54PAr3OWqfv+v9eyz8LvsLH34IfV/dwaVkA8odX/F7aZnUEu02WqpvhnKYYdm+sdJcAPk6OOyb9a5Pj597GPFlIg9NHr2OwEgTxOHjgBNqQS5JUhgXF57gnMXbhcF5/SXLP/dUBhtlZaqd0GDlfvi+y+sI/SKA3REK3QioL0aKM2ulgXayNFOxuloEGyeBEPuSx6DZ1zQIDJgxw4MaIJeczx+SmJGAx0g4BDBKbNWqo+x26rhW8V+CFg1WXqdN+j+vNptaxZYXYIeVEvapiGFbxbyO7aPdietIbsFCmtGYOgdAnww5Quf49HJ8f32Pk5Np0CYY6di2luE8jj5IHbxEvXjrwyi15D52ITWL5ypR57drFev7O1FryxV+nD1Vyk5ieP1IndpqvD74uzYm+Kpuhlvay39bYWaVGperRWa3VWZ3VQB1VRlWKjyZ58HnLZ8xka54IAkwe5cGMkI/KY4/NTEikU6AQB5wgsXb5SDz22RG/ftaMWv7dr6frVnavNTn9Lp1wwS7u0Lk6OP0ET9Ipekdl2dJmWlapHW7VVF3XRbtpNlRX9fHPnnIBCdgjww2SHI1JCEyDHD42MDhkgQIEwA05CxXwSyOPkQRY9RV6ZRa+hc5IEPv1moZ57vKbmTKmtpQurqWa9JWrRep5OPqmSmjWsnogqpjj4nt7TJE3SAi1QdVVXPdXTntpTW2iLRHTI7CA85DLrOhTPNgEmD7Ltvzjau5Dj8+iP40H6QsAPAu/9Z6Feebq25v1SS8sWVVWt+ku0edu5OvnEKmpY1+7OIGURna/5QZHQfBS4UAuDVYIN1CD48K+FWvjhCKyMRoAfumjc6BWbADl+bIQIcJAABUIHnYJKfhBwYfLAD9LlW0leSRRAAAK5JsBDLtfuxTh3CTB54K5viq2ZCzk+j/5iexn5EIAABCCQKgF+6FLF7/Pg5Pg+ez+/tlMgzK9vscxxAi5MHjiOKBH1yCsTwcwgEIBAWgR4yKVFnnE9J8Dkgb8B4EKOz6Pf3/jDcghAAAJeEOCHzgs3u2gkOb6LXkGnuAQoEMYlSH8IRCTgwuRBRNVz1Y28MlfuxBgIQGB9AjzkiAkIpEKAyYNUsDsxqAs5Po9+J0IBJSAAAQhAoFgE+KErFlnkVkCAHJ8QySMBCoR59Co2ZYKAC5MHmQBVZCXJK4sMGPEQgEC6BHjIpcuf0b0lwOSBt66XCzk+j35/4w/LIQABCHhBgB86L9zsopHk+C56BZ3iEqBAGJcg/SEQkYALkwcRVc9VN/LKXLkTYyAAgfUJ8JAjJiCQCgEmD1LB7sSgLuT4PPqdCAWUgAAEIACBYhHgh65YZJFbAQFyfEIkjwQoEObRq9iUCQIuTB5kApQFJckdLUBEBAQgkE0CPACz6Te0zjwBJg8y78LIBkTN8XlcR0ZORwhAAAIQgMBvBPhBJRqKSIAcv4hwEZ0aAQqEqaFnYN8JRJ088J1bFPvJD6NQow8EIJALAjwAc+FGjMgeASYPsuczWxpHzfF5XNvyAHIgAAEIQMBrAvygeu3+YhtPjl9swshPgwAFwjSoMyYEJCfOJ/HFEeSHvngaOyEAgQ0I8AAkKCCQCgEmD1LB7sSgFAidcANKQAACEICArwR4//HV84nYTY6fCGYGSZgABcKEgTMcBNYQiDp5AMHwBMgPwzOjBwQgkBMCPABz4kjMyBoBJg+y5jF7+kbN8Xlc2/MBkiAAAQhAwGMC/KB67Pzim06OX3zGjJA8AQqEyTNnRAgEBKJOHoAvPAHyw/DM6AEBCOSEAA/AnDgSM7JGgMmDrHnMnr5Rc3we1/Z8gCQIQAACEPCYAD+oHju/+KaT4xefMSMkT4ACYfLMGRECFAgTjgHyw4SBMxwEIOAOAR6A7vgCTbwiwOSBV+5ex1gKhP76HsshAAEIQMABArz/OOCE/KpAjp9f3/psGQVCn72P7akSiDp5kKrSGR2c/DCjjkNtCEAgPgEegPEZIgECEQgweRABWk66RM3xeVznJAAwAwIQgAAE0iXAD2q6/HM+Ojl+zh3sqXkUCD11PGanTyDq5EH6mmdPA/LD7PkMjSEAgQQI8HBMADJD+EqAyQNfPb/uMQKDfxqsTVpu4i8MLIcABCAAAQgkTYB3nKSJezUeOb5X7vbGWAqE3rgaQ10jQIEwOY+QHybHmpEgAIEMEeDhmCFnoWrWCDB5kDWP2dO3ZI5PgdAeVyRBAAIQgAAECiLAO05BmLgoGgFy/Gjc6OU2AQqEbvsH7XJMgAJhcs4lP0yONSNBAAIZIsDDMUPOQtWsEWDyIGses6cvBUJ7LJEEAQhAAAIQCE2Ad5zQyOhQOAFy/MJZcWV2CFAgzI6v0DRnBCgQJudQ8sPkWDMSBCCQIQI8HDPkLFTNGgEmD7LmMXv6UiC0xxJJEIAABCAAgdAEeMcJjYwOhRMgxy+cFVdmhwAFwuz4Ck1zRoACYXIOJT9MjjUjQQACGSLAwzFDzkLVrBFg8iBrHrOnLwVCeyyRBAEIQAACEAhNgHec0MjoUDgBcvzCWXFldghQIMyOr9A0ZwQoECbnUPLD5FgzEgQgkCECPBwz5CxUzRoBJg+y5jF7+lIgtMcSSRCAAAQgAIHQBHjHCY2MDoUTIMcvnBVXZocABcLs+ApNc0aAAqF9h5IH2meKRAhAIMcEeGjm2LmYljYBJg/S9kB641MgTI89I0MAAhCAAATEOw5BUEQC5PhFhIvo1AhQIEwNPQP7ToACof0IIA+0zxSJEIBAjgnw0MyxczEtbQJMHqTtgfTGp0CYHntGhgAEIAABCFAgJAaKSYAcv5h0kZ0WAQqEaZFnXO8JUCC0HwLMddtnikQIQCDHBHho5ti5mJY2ASYP0vZAeuNTIEyPPSNDAAIQgAAEKBASA8UkQI5fTLrITosABcK0yDOu9wQoENoPAea67TNFIgQgkGMCPDRz7FxMS5sAkwdpeyC98SkQpseekSEAAQhAAAIUCImBYhIgxy8mXWSnRYACYVrkGdd7AhQI7YcAc932mSIRAhDIMQEemjl2LqalTYDJg7Q9kN74FAjTY8/IEIAABCAAAQqExEAxCZDjF5MustMiQIEwLfKM6z0BCoT2Q4C5bvtMkQgBCOSYAA/NHDsX09ImwORB2h5Ib3wKhOmxZ2QIQAACEIAABUJioJgEyPGLSRfZaRGgQJgWecb1ngAFQvshwFy3faZIhAAEckyAh2aOnYtpaRNg8iBtD6Q3PgXC9NgzMgQgAAEIQKBMArz7EBwWW1c5ZAAAIABJREFUCJDjW4CICOcIUCB0ziUo5AsBCoT2PU2+Z58pEiEAgRwT4KGZY+diWtoEmDxI2wPpjU+BMD32jAwBCEAAAhCgQEgMFJMAOX4x6SI7LQIUCNMiz7jeE6BAaD8EmOu2zxSJEIBAjgnw0MyxczEtbQJMHqTtgfTGp0CYHntGhgAEIAABCFAgJAaKSYAcv5h0kZ0WAQqEaZFn3FIJjBkzRi+99JJGjx6tcePGafr06apWrZpatGihvffeW2eddZb22Wcfq/QeffRR3X///fr88881e/ZsNW3aVPvuu6/OP/98tW/f3upYJYVRILSPNstz3Su1Ut/qW03XdC3WYtVSLTVWY22rbVVZle3DQiIEIACBLD808R4EHCfA5MG6DvI1xx/802Bt0nITx6MV9YpJYIVW6Gt9rRmaoSVaEuT4zdVcW2krVVKlYg6NbAhAAAIQKEmAdx/iwQIBcnwLEBHhHAEKhM65xF+F9ttvP73zzjsVAjjttNN07733qnr16hVeW94FixYt0nHHHRcUJEtrlStX1pVXXql+/frFGqeszhQI7WPNYr43T/P0lt7SCI3QNE3bAIqZQDhYB+sAHaA6qmMfGhIhAAF/CWTxoemvt7A8YwSYPPjNYT7n+BQIM3bjWlR3lmbpDb2h1/W6ZmrmBpI302bqrM7aV/sGRUMaBCAAAQgUmQDvPkUG7Id4cnw//OyblRQIffO4w/Zus802Gj9+fLBa8Pjjjw9W8W222WZasWKF3n//fQ0cOFCTJ08OLDjppJP0yCOPxLLGyBg+fHggo2PHjurevXsw9tixY3XdddcFuph2991365xzzok1VmmdKRBaR6qs5XujNVp36+7ga+KKWg3V0Hk6Tx3UoaJL+TsEIACBwghk7aFZmFVcBQEnCDB58JsbfM7xKRA6cTsmrsQrekUP6SEt1/IKxzYfAHZXd+2snSu8lgsgAAEIQCAGAd59YsCj6xoC5PjEQh4JUCDMo1czatMRRxwhszqwa9euqlKlygZW/Prrr8E2o99++23wt1GjRsl8kRylvfnmm+rUqVPQ9cgjj9QzzzyzzphmrHbt2mnSpEmqX7++fvjhBzVo0CDKUGX2oUBoFWcgLEv53st6Wffr/tAQztJZ6qIuofvRAQIQgEDBBLL0MC3YKC6EQLIEmDz4jbfPOT4FwmTvOxdGG67helpPh1Kliqqom7rxIWAoalwMAQhAICQB3nFCAuPy0giQ4xMXeSRAgTCPXs2xTS+88EJQ0DOtW7duGjRoUCRrDzvsML388suqWrWqJkyYoJYtW24gx6wuNKsMTbvxxhvVq1evSGOV1YkCoVWcgbCs5Hv/1r81UAMjATBnlVysi7W7do/Un04QgAAEKiSQlYdphYZwAQTSI8DkQTj2ec3xKRCGi4OsX/2aXtO9ujeSGVVVVVfqSm2v7SP1pxMEIAABCFRAgHccQsQCAXJ8CxAR4RwBCoTOuQSFyiOwYMEC1amz+hw2U+R78cUXQwObN2+eGjVqpKVLl+qQQw4JCoWlNfP3xo0ba+7cuWrfvr3ee++90GOV14ECoVWcgbAs5HtmqyGzVegczYkMoIEa6E7dKTORQIMABCBgnUAWHqbWjUYgBOwSYPIgHM+85vgUCMPFQZavXqAFOlfnFnR0QFl2bq7NdaNulPkgkAYBCEAAApYJ8I5jGaif4sjx/fR73q2mQJh3D+fMvpkzZ2qTTTYJrDIrCZ977rnQFpbcXnTAgAHq27dvmTK6dOmiESNGBCsNFy5cqGrVqoUer6wOFAitoVwrKAv53rt6V7fpttjG91RP7aW9YstBAAQgAIENCGThYYrbIOA4ASYPwjkorzk+BcJwcZDlq1/SS3pAD8Q24SpdxSrC2BQRAAEIQKAUArzjEBYWCJDjW4CICOcIUCB0ziUoVB4Bc1bgscceG1zSu3dv3XDDDaGB3XHHHcH2pKYZecccc0yZMrp37752G9Mvv/xSO+ywQ+jxyupAgdAayrWCspDv9VM/faWvYhu/o3YMtiGiQQACELBOIAsPU+tGIxACdgkweRCOZ15zfAqE4eIgq1ev0ir1UA9N1dTYJuytvdVd3WPLQQAEIAABCKxHgHccQsICAXJ8CxAR4RwBCoTOuQSFyiKwcuXKYKvPDz/8MLhkzJgxateuXWhgZsXgmsLiRx99pN12261MGTfddNPaswdfeeUVmRWFhTZTACyvTZ06VXvssUdwyU+DB6vl/1ZGFiqf6zYk4Hq+N1uzdY7Osea6+3Sf6qquNXkIggAEIBAQcP1hipsgkAECTB4U7qQ85/gUCAuPgyxfOUmTgjPCbbRqqqZhGqbKqmxDHDIgAAEIQGANAd5xiAULBMjxLUBEhHMEKBA65xIUKovAwIEDdfHFq1+8zCrCp556KhKs888/X3fddVfQ96uvvtL225d9EPzgwYP1l7/8Jbj2ySefVNeuXQses1Klws+OoEBYMNZyL3Q935uoieqt3naMlXSzblZLtbQmD0EQgAAEAgKuP0xxEwQyQIDJg8KdlOccnwJh4XGQ5Ss/02e6VtdaM4GPAK2hRBAEIACB3wjwjkM0WCBAjm8BIiKcI0CB0DmXoFBpBEaNGqWDDjpIy5cvV5MmTTR27Njgn1HaWWedpX/84x9B1/Hjx2urrbYqU4y5zlxv2rBhw3TqqacWPCQFwoJRhbowyzndd/pOl+myUPaWd/H1ul5bqez4tTYQgiAAAb8IZPlB65ensNZhAkweFOacvOf4FAgLi4OsXzVGY3SjbrRmxmAN1ibaxJo8BEEAAhCAAB9BEgN2CJDj2+GIFLcIUCB0yx9oUwoBc/bfvvvuq1mzZqlmzZp69dVXtd9++0VmldQKQrYYjeyicjtmed56sibrIl1kDcwgDVIzNbMmD0EQgAAEAgJZftDiQgg4QoDJg4od4UOOT4Gw4jjIwxXjNE791d+aKQ/pIdVUTWvyEAQBCEAAArzjEAN2CJDj2+GIFLcIUCB0yx9osx6BCRMmaJ999tGUKVNUpUqVYFvRo48+OhanpM4grEhJU0Bs1apVcBlbjFZE67e/Z3neeqmW6iydpSVaUrjBZVxZS7U0VENlzimhQQACELBKIMsPWqsgEAaB6ASYPCifnS85PgXC6PdQlnqac8b/rD9rlVbFVtusHLxLd6mSCj+uIvagCIAABCDgAwHecXzwctFtJMcvOmIGSIEABcIUoDNkYQRMUdCsHPzhhx9ktut84IEHdNpppxXWuZyr7rjjDnXr1i244plnntExxxxT5tXdu3fXoEGDgr+br5x32GGH2OOvEUCBMBrKrOd09+geva7XoxlfolcXdQmKjTQIQAAC1glk/UFrHQgCIRCeAJMHZTPzKcenQBj+3slqD7PFqNlqNG47QSfoOB0XVwz9IQABCEBgfQK84xATFgiQ41uAiAjnCFAgdM4lKGQI/Prrr9p///01bty4AIgp6pmtQW20N998U506dQpEDRgwQGZFYVmtS5cuGjFihKpWraqFCxeqWjV7q7UoEEbzZtZzuomaqN7qHc34Er0GaqBaafUKVBoEIAABqwSy/qC1CgNhEIhGgMmD0rn5luNTIIx2/2Sx12f6TNfq2liqV1GVYPVgAzWIJYfOEIAABCBQCgHecQgLCwTI8S1ARIRzBCgQOucSFJozZ44OPPBAffLJJwGM66+/Xn369LEGZt68eWrUqJGWLl2qQw45RC+//HKpss3fGzdurLlz56p9+/Z67733rOlgBFEgjIYzDzndVbpKX+iLaAAk7ayddZkui9yfjhCAAATKJZCHBy0uhkDKBJg82NABPub4FAhTvhETHH6lVqqXeukn/RR51P20ny7QBZH70xECEIAABMohwDsO4WGBADm+BYiIcI4ABULnXOK3QmaVXufOnfXuu+8GIC677DJdc8011qEcdthhQWHQrAw0Z6C0bNlygzGGDx+uk046KfjvN954o3r16mVVDwqE0XDmIaebqZlBgW+GZoSG0FiNg6+T66t+6L50gAAEIFAQgTw8aAsylIsgUDwCTB6sy9bXHJ8CYfHuMRclT9EUXa7LNV/zQ6u3mTaT+YiwtmqH7ksHCEAAAhAogADvOAVA4pKKCJDjV0SIv2eRAAXCLHotpzqbFXtHHnlksKWnaeb8v1tvvTW0teaswjPPPDPo169fP/Xv338DGSW3GT3qqKP09NNPq0qVKmuvM9sftWvXTpMmTVL9+vWDcxAbNLC71QsFwtCuDTrkJaczEwim0Ddd0wsG0VRNdakuVXM1L7gPF0IAAhAITSAvD9rQhtMBAvYIMHnwG0ufc3wKhPbuqaxIGq/xul7Xa47mFKzy5tpcl+gSNVTDgvtwIQQgAAEIhCTAO05IYFxeGgFyfOIijwQoEObRqxm1qWvXrkGhzjSzxagpDlaqVKlMa6pXr67ttttug78XUiA0nczqQLNK0LSOHTuqR48eatGihcaOHatrr71W48ePD/52991365xzzrFOlQJhNKR5yulma7Ye0kN6X+9rhVaUCaSqqqqDOuj/9H+qp3rRwNELAhCAQKEE8vSgLdRmroOAZQJMHvwG1OccnwKh5RsrI+J+0S96UA9qjMZolVaVqXUN1ZDZVvQUncLKwYz4FjUhAIEME+AdJ8POc0d1cnx3fIEm9ghQILTHEkkxCZRXDCxN9Oabb66JEydu8KdCC4SLFi3Scccdp5deeqlUzStXrqwrrrii1BWIMU0NulMgjEYxjzmdKRSO1EiN0qhgReEyLVM1VVMTNdH+2l8d1ZHCYLRwoRcEIBCFQB4ftFE40AcCMQgwefAbPJ9zfAqEMW6iHHT9Vb/qdb2u0Rotc8TAci1XdVUPdgMx+b3J8zfSRjmwFBMgAAEIZIAA7zgZcJL7KpLju+8jNAxPgAJheGb0KBKBpCcP1pjxyCOPyBQVP/vsM82ePVtNmzbVvvvuqwsuuEDt27cvkrUUCKOC9SGnM5MHZtUgDQIQgEAqBHx40KYClkF9IsDkQXoFQpdyfAqEPt315dtqVhKu1EpV0W/HWkAHAhCAAAQSJMA7ToKw8zsUOX5+feuzZRQIffY+tqdKgBWE0fCT00XjRi8IQAACBRPgQVswKi6EQFkEmDzwNzZK5vgUCP2NAyyHAAQgAAHHCPCO45hDsqkOOX42/YbW5ROgQEiEQCAlAhQIywdP7pZSYDIsBCAAgfII8HAmPiBQEAEmDwrClMuLKBDm0q0YBQEIQAACWSHA+0pWPJVJPcnxM+k2lK6AAAVCQgQCKRGgQEiBMKXQY1gIQAAC0Qnwwh2dHT29IsDkgVfuXsdYCoT++h7LIQABCEDAAQK8rzjghPyqQI6fX9/6bBkFQp+9j+2pEqBASIEw1QBkcAhAAAJRCPDCHYUafTwkwOSBh07/n8kUCP31PZZDAAIQgIADBHhfccAJ+VWBHD+/vvXZMgqEPnsf21MlQIGQAmGqAcjgEIAABKIQ4IU7CjX6eEiAyQMPnU6B0F+nYzkEIAABCLhDgPcVd3yRQ03I8XPoVEwSBUKCAAIpEaBASIEwpdBjWAhAAALRCfDCHZ0dPb0iwOSBV+5ex1hWEPrreyyHAAQgAAEHCPC+4oAT8qsCOX5+feuzZRQIffY+tqdKgAIhBcJUA5DBIQABCEQhwAt3FGr08ZAAkwceOv1/JlMg9Nf3WA4BCEAAAg4Q4H3FASfkVwVy/Pz61mfLKBD67H1sT5UABUIKhKkGIINDAAIQiEKAF+4o1OjjIQEmDzx0OgVCf52O5RCAAAQg4D4B3mPc91EGNCTHz4CTUDE0AQqEoZHRAQJ2CFAgpEBoJ5KQAgEIQCBBArxYJwibobJMgMmDLHsvnu6sIIzHj94QgAAEIACBohDgPaYoWH0TSo7vm8f9sJcCoR9+xkoHCVAgpEDoYFiiEgQgAAEezsQABCwQYPLAAsSMiqBAmFHHoTYEIAABCOSbAAXCfPs3IevI8RMCzTCJEqBAmChuBoPAbwQoEDIHzf0AAQhAIHMEeLHOnMtQOB0CTB6kw92FUSkQuuAFdIAABCAAAQisR4D3GELCAgFyfAsQEeEcAQqEzrkEhXwhMHHiRG255ZaBuR9ed52aN2jgi+kF2fnCEQVdxkUQgAAEIJAkAR7OSdJmrAwTmDVrqi69dI/AggkTJmiLLbbIsDWoHoZAyRz/ug+vU4Pm5Phh+HEtBCAAAQhAoCgEeI8pClbfhJLj++ZxP+ylQOiHn7HSQQIfffSR9thj9cQRDQIQgAAEIAABCEAgnwQ+/PBD7b777vk0Dqs2IECOT1BAAAIQgAAEIACB/BMgx8+/j32xkAKhL57GTucIMHngnEtQCAIQgAAEIAABCFgn8Pzzz+uII9gawTpYRwWS4zvqGNSCAAQgAAEIQAACFgmQ41uEiahUCVAgTBU/g/tMYPHixRo7dmyAoHHjxqpatarPOLBd0tSpU9euKjVfIjVv3hwuEHCCALHphBtQohQCxCZh4SqBn376SR06dAjU++6777TNNtu4qip6WSZAjm8ZaA7E8VuVAyfm1ARiM6eOzYFZxGYOnJhTE8jxc+pYz82iQOh5AGA+BCDgDoGff/5ZrVq1ChQySUfLli3dUQ5NvCZAbHrtfqeNJzaddo/XyhGbXrsf4yGwDgGeBwSEqwSITVc9g17EJjHgKgFi01XPoFccAhQI49CjLwQgAAGLBEg0LMJElFUCxKZVnAizSIDYtAgTUVYJEJtWcSIMApkmwPMg0+7LtfLEZq7dm2njiM1Muy/XyhObuXavt8ZRIPTW9RgOAQi4RoBEwzWPoM8aAsQmseAqAWLTVc+gF7FJDEAAAuRRxIDrBPitct1D/upHbPrre9ctJzZd9xD6RSFAgTAKNfpAAAIQKAIBEo0iQEWkFQLEphWMCCkCAWKzCFARaYUAsWkFI0IgkAsCPA9y4cZcGkFs5tKtuTCK2MyFG3NpBLGZS7d6bxQFQu9DAAAQgIArBEg0XPEEeqxPgNgkJlwlQGy66hn0IjaJAQhAYA0BngfEgqsEiE1XPYNexCYx4CoBYtNVz6BXHAIUCOPQoy8EIAABiwRINCzCRJRVAsSmVZwIs0iA2LQIE1FWCRCbVnEiDAKZJsDzINPuy7XyxGau3Ztp44jNTLsv18oTm7l2r7fGUSD01vUYDgEIuEaARMM1j6DPGgLEJrHgKgFi01XPoBexSQxAAALkUcSA6wT4rXLdQ/7qR2z663vXLSc2XfcQ+kUhQIEwCjX6QAACECgCARKNIkBFpBUCxKYVjAgpAgFiswhQEWmFALFpBSNCIJALAjwPcuHGXBpBbObSrbkwitjMhRtzaQSxmUu3em8UBULvQwAAEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACPhGgQOiTt7EVAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEDAewIUCL0PAQBAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQj4RIACoU/exlYIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAHvCVAg9D4EAAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIOATAQqEPnkbWyEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABLwnQIHQ+xAAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAgE8EKBD65G1shQAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQ8J4ABULvQwAAEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACPhGgQOiTt7EVAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEDAewIUCL0PAQBAAAJpEqhUqVJBw++///566623CrqWiyBgm8CkSZN033336cUXX9SPP/6oefPmqXHjxtpiiy3UsWNHnXDCCdpxxx1tD4s8CGxA4IADDtCoUaNCkRk5cqRMPxoEkiCwdOlSPfTQQ3riiSf0+eefa+bMmapWrZo23XRTdejQQWeffXbwTxoEIJBvAuT4+fZvXqwjx8+LJ7NvBzl+9n2YdwvI8fPuYb/to0Dot/+xHgIQSJkAkwcpO4DhKyRw++2365JLLtGCBQvKvLZ79+669dZbK5TFBRCISyDs5EHlypVlJr9McYYGgWITMB9QHH744fryyy/LHapbt2667bbbVGgOUGy9kQ8BCNgnUOj9zUeA9tkjsTAC5PiFceKqZAiQ4yfDmVGiESDHj8aNXtkhQIEwO75CUwhAIIcE1kwenHfeefrLX/5SpoUbbbSRttxyyxwSwCSXCVxzzTW64oorAhW32267YOXL7rvvrnr16mnGjBn69NNP9cwzz2jPPffUzTff7LIp6JYTAhMmTCi3WG3MHDdunE488cTA4oMPPlgjRozIifWY4TKBZcuWaZdddllbHNxpp53Us2dPtW7dOlh1PXr0aA0cOHBt/A4YMEB9+/Z12SR0gwAEYhAgx48Bj65FJ0COX3TEDBCSADl+SGBcnhgBcvzEUDNQigQoEKYIn6EhAAEIrJk86Nevn/r37w8QCDhD4I033tBBBx0U6HPaaadp6NChwTZ5pTWz3Ub16tWd0R1F/CbQp08f3XjjjQGEYcOG6dRTT/UbCNYnQuDJJ5/U8ccfH4zVvn17vfPOO6pSpco6Y3/88cfB38xEQ/369TV9+nRVrVo1Ef0YBAIQSJYAOX6yvBmtcALk+IWz4kq3CJDju+UPX7Qhx/fF037bSYHQb/9jPQQgkDIBJg9SdgDDl0pg5cqV2n777fXdd99p55131pgxY5jEJlYyQcDE7mabbabJkyerTp06mjZtmmrXrp0J3VEy2wTMasFbbrklMOK5557TkUceWapBxx57bLDy2jRzRmHbtm2zbTjaQwACpRIgxycwXCRAju+iV9CpEALk+IVQ4ppiECDHLwZVZLpGgAKhax5BHwhAwCsCTB545e7MGPvKK6/o0EMPDfR95JFHdNJJJ2VGdxT1m8Brr72mzp07BxDOOOMM3X///X4DwfrECFxwwQW68847g/G++OILtWnTptSxe/XqpZtuuin4m/n4ol27donpyEAQgEByBMjxk2PNSIUTIMcvnBVXukWAHN8tf/ikDTm+T97211YKhP76HsshAAEHCDB54IATUGEDAmeddZb+8Y9/yMTnnDlzVLdu3eCamTNnBmcPbrLJJmrYsCHkIOAcgf/7v//Tww8/HOj15ptvqmPHjs7piEL5JHD77bfrwgsvDIwrZAWheb7Onj1bG2+8cT6BYBUEPCdAju95ADhqPjm+o45BrQoJkONXiIgLikSAHL9IYBHrFAEKhE65A2UgAAHfCKyZPNhhhx20atUqTZw4MTizqFmzZurQoUOwAoYJbt+iIn17zcqXcePGacstt9QPP/wQrCIcMGBAsCpmTdtuu+109tlnq1u3bqpRo0b6SqOB9wTmz58fPDsXLFigzTffXBMmTAiK3DQIJEHg119/1dZbb625c+dq77331qhRozY4g/DTTz/VXnvtJXNu6ymnnLK2mJ2EfowBAQgkS4AcP1nejFYYAXL8wjhxlVsEyPHd8odv2pDj++ZxP+2lQOin37EaAhBwhEAhk9fHHHOMHnjgAdWrV88RrVEjzwTM+Q7VqlWT+efuu++u9u3ba9CgQWWabArZL774ourXr59nLNiWAQIPPvhg8FGFaZdddpmuueaaDGiNinkiYFYOmi2ZFy5cqF122UU9evSQ+ZjCTGy9++67GjhwoObNm6ddd91VL730kpo2bZon87EFAhAoQYAcn3BwjQA5vmseQZ9CCZDjF0qK64pFgBy/WGSR6woBCoSueAI9IAABLwlstNFGOuqoo9SpUydtv/32qlOnjqZPnx6sPBgyZEiwnaNp+++/v8y++6ZwQ4NAMQnMmjVr7fahNWvW1OLFi9W8eXP9/e9/12GHHSbz3z766CP16dNHH3zwQaDKH/7wBz399NPFVAvZEKiQgHmOmm1FTfvmm2+CwgwNAkkT+Prrr4NC4H333RfsDFCymYLgJZdcEqy+rl27dtKqMR4EIJAgAXL8BGEzVEEEyPELwsRFDhIgx3fQKR6qRI7vodM9MpkCoUfOxlQIQMA9Aub8obJWXk2bNk2HHnqozJZkpt12221rzzdyzxI0yguBn3/+Wa1atVprjpnE/uSTT9S6det1TFy0aFGwuvCzzz4L/rspFu655555wYAdGSNg4tZsK2q+jjdbOL7//vsZswB180DAbB3av39/DR06NPjYp7S222676Yorrgg+DqJBAAL5JUCOn1/fZtUycvyses5vvcnx/fa/K9aT47viCfQoFgEKhMUii1wIQAACFgiY89/MysJly5Zpm2220XfffWdBKiIgUDYBs8d+48aN115w4YUXBsXp0prZWvSII44I/nTRRRfp5ptvBi0EUiFw/fXXByuzTBs8eLDOPffcVPRgUH8JmLMvzUc977zzTnD24F//+ledeeaZ2mqrrYKV2P/+97911VVXafTo0cHZmDfddJN69uzpLzAsh4DnBMjxPQ+AFMwnx08BOkPGJkCOHxshAmISIMePCZDumSBAgTATbkJJCEDAZwKHH354cFaRaZMnT1aLFi18xoHtRSawZMmSYBvRNe2FF16QicHSmpn0rlu3rpYvX6599tknmBinQSANAm3atNG4ceNUo0YNTZ06VQ0aNEhDDcb0mECvXr2Cop9p5tzg008/fQMa5lnZuXNnjRw5UpUrVw5WZ++8884eU8N0CPhNgBzfb/8nbT05ftLEGc8GAXJ8GxSREYcAOX4cevTNCgEKhFnxFHpCAALeEiiZkHz44YfafffdvWWB4ckQaNKkydrt8cwWojvttFOZA5vzCf/73/+XNKynAAAeX0lEQVQGW5CafflpEEiawJgxY9Y+F7t27aonn3wyaRUYz3MC5qzBRo0aaebMmcHZl+YMzLLau+++G3xQYVqPHj10yy23eE4P8yHgLwFyfH99n5bl5PhpkWfcKATI8aNQo49NAuT4Nmkiy2UCFAhd9g66QQACEJDUu3dv/f3vfw9YUCAkJJIg0LFjR7311lvBUGaFyy677FLmsGsmGszXnV988UUS6jEGBNYhYLbBvf3224P/9txzz+nII4+EEAQSJWA+kjAfS5h24oknavjw4WWOb1Ze16pVK/j7IYccopdffjlRXRkMAhBwhwA5vju+8EUTcnxfPJ0PO8nx8+HHLFtBjp9l76F7GAIUCMPQ4loIQAACKRAwZ7yZs95MM4d0b7rppilowZA+EejXr19wVpZpZjWWWZVVWps7d67q168v82Wd2Tbv1Vdf9QkTtjpAwJzPap6J06dPD87OnDJliqpWreqAZqjgE4GS5zpVtIp13rx52njjjQM85vf9+eef9wkVtkIAAiUIkOMTDkkTIMdPmjjjRSVAjh+VHP1sEiDHt0kTWS4ToEDosnfQDQIQ8J7AhAkTtP3222vp0qXaeuut9f3333vPBADFJ/D555+vPRfrlFNO0cMPP1zqoA8++KDOOOOM4G9XX321Lr/88uIrxwgQKEHArBg8+uijg//SvXt33XrrrfCBQOIEVq5cGZx7aT6aMOcE//jjj2UWqs25rmtWuXbr1k2DBg1KXF8GhAAE0idAjp++D3zUgBzfR69n02Zy/Gz6LW9ak+PnzaPYUxYBCoTEBgQgAIGUCJhVA4ceemiZk4jTpk0L/v7pp58GGg4cOFA9e/ZMSVuG9Y3AYYcdFmx9V7lyZY0YMUKdOnVaB4HZbsOch2lWtVavXl0//PADq1t9CxIH7D3uuOP01FNPBZp8/PHH2nXXXR3QChV8JHDyySfr0UcfDUzv37+/zCqN9dusWbOC8wfHjRsX/Mmsujarr2kQgEC+CJDj58ufebOGHD9vHs2nPeT4+fRrFq0ix8+i19A5LAEKhGGJcT0EIAABSwS22GILma0zzHZk7du3l/nf5lwis42BOf/t7rvvDv7dNDOh+Prrr6tGjRqWRkcMBMon8O2332rPPffU7NmzVbNmTfXo0UNmQsHEqDkLc8CAAUFx0LQbbrghOCuTBoEkCZhiizn3bcmSJdpxxx01duzYJIdnLAisQ+Drr79Wu3bttHDhwuC/m1WCp59+urbaaiuZcwc/+OCDYIXrpEmTgr+bjy7M7zoNAhDIHwFy/Pz5NE8WkePnyZv5tIUcP59+zapV5PhZ9Rx6hyFAgTAMLa6FAAQgYJGAmTww25BV1EwBcejQocFZbzQIJElg9OjRMl9vmtWspbVKlSrpsssuC7YXpUEgaQJDhgzReeedFwx74403qlevXkmrwHgQWIeAKfiddNJJaz/uKQvPgQceGJzvarYlpUEAAvkjQI6fP5/mzSJy/Lx5NF/2kOPny595sIYcPw9exIbyCFAgJD4gAAEIpERg1KhRMv///vvvB9szmtWC5vyiOnXqqFWrVurQoUOw+sCsLqRBIC0CM2bM0O23365//etfMuflmPMwzaqtAw44QOb8rF122SUt1RjXcwJ777233nvvPVWpUiVYlWXOfqNBIG0C5pl53333BVs0f/nll8Eq7KpVq6pZs2bBtsxmm6KjjjpK5gMLGgQgkE8C5Pj59GverCLHz5tH82MPOX5+fJknS8jx8+RNbFmfAAVCYgICEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACHhGgQOiRszEVAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhQIiQEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIeESAAqFHzsZUCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCFAgJAYgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg4BEBCoQeORtTIQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIECBkBiAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAgEcEKBB65GxMhQAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAFQmIAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAh4RoEDokbMxFQIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIUCIkBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCHhEgAKhR87GVAhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhQICQGIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIOARAQqEHjkbUyEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCBAgZAYgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgIBHBCgQeuRsTIUABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABUJiAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIeEaBA6JGzMRUCEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACFAiJAQhAAAIQgICDBN566y117Ngx0GzkyJE64IADQmt5xhln6MEHH9Tmm2+uiRMnhu6f5w5bbLGFfvzxR51++ul64IEHEjN1//3319tvv62///3vuvjiixMb1+ZATzzxhE444QRtu+22+vLLL1WtWjWb4pEFAQhAAAIQgAAEIAABCEAAAhCAAAQgkAABCoQJQGYICEAAAhCAQFgCFAjDEgt3fRoFwjWFtcaNG2vChAnaaKONwintyNUrV65U27ZtNW7cOA0cOFA9e/Z0RDPUgAAEIAABCEAAAhCAAAQgAAEIQAACECiUAAXCQklxHQQgAAEIQCBBAhQIw8MOs2Iy6QKhKaq1adNGX3/9ta6//nr16dMnvIEO9XjkkUd0yimnKOvFToeQogoEIAABCEAAAhCAAAQgAAEIQAACEEiUAAXCRHEzGAQgAAEIQKAwAhQIC+NU8iqXC4SPP/64TjzxRNWqVUvTpk1T3bp1wxvoUI8VK1YEW9dOnjyZVYQO+QVVIAABCEAAAhCAAAQgAAEIQAACEIBAoQQoEBZKiusgAAEIQAACCRKgQBgetssFwr333lvvvfdeUCQcPnx4eOMc7PHXv/5VN998s7bcckt9//33qly5soNaohIEIAABCEAAAhCAAAQgAAEIQAACEIBAaQQoEBIXEIAABCAAAQcJUCAM7xRXC4RmW9Hf/e53gUHPPfecjjzyyPDGOdjjk08+Ubt27QLNXn/9dXXq1MlBLVEJAhCAAAQgAAEIQAACEIAABCAAAQhAoDQCFAiJCwhAAAIQcI7AlClTNGjQII0YMULjx4/XwoUL1bBhQzVp0kQ77rijunTpomOPPVYbb7xxqbrPmTNHd911l1544QV9++23Mv97k0020W677abTTz9dXbt2VaVKlUrtu+a/9+vXT/379w8KH0aXjz76SLNmzVKLFi102GGH6ZJLLtGmm25aJrsffvhBzzzzjEyhb+zYscG2kqYZG/baay+deeaZOuSQQ8rsn2SB0CYvw8msKnvnnXc0ffp0NWrUSAceeKAuvfTStUWysow2fh44cKCeeOKJwO81a9YM+vzpT38KeI0aNUodO3YMuo8cOVIHHHBA8O/GT3/7298qjONVq1atvWb9Mwi/+eYb3XTTTXrttdc0depU1a9fXx06dAjOCjT+itOuuuoqmXjaaKONghiqVq1aheJeeuklmXP+zKpDEztmS8/mzZtr1113DQqMJ5xwgmrXrr1WzgMPPBAwMm3ChAlBnN5xxx2BjO+++05VqlTRTjvtpF69eunwww9f22/evHkaPHiwHn300YC5WQW4++67q2/fvgUV/LbaaqtgvLPOOktDhw6t0C4ugAAEIAABCEAAAhCAAAQgAAEIQAACEHCDAAVCN/yAFhCAAAQg8D8CprB0xBFHaO7cueUyef7554Pr1m9vvPFGsI3jjBkzyuxvCnyPPfaY6tSps8E1JQuE5t9N8am0Vq9ePRkd9t133w3+bAompnBSUTv11FN1//33q2rVqhtcmlSB0CYvU/zs3r27li9fvoE9ppj18ssva7/99isVy88//xwUEk0xq7RmfH3hhReqc+fOwZ9tFgiPPvpoGV+YAuX6zRTW/vnPfwYxFbWZoqbxp4mVt99+u1wxJm7NWMYv5TUTN2bF5JpWskD42Wef6ZxzztG///3vUkWYAu5FF12kSZMmBcXuL7/8stT7YNiwYTrllFPK1eOkk04Ktkw1xXLjQxoEIAABCEAAAhCAAAQgAAEIQAACEIBANghQIMyGn9ASAhCAgBcElixZEhTWzArCunXr6rzzzgtWjJnC09KlS4OVSmZFlVmZN2TIkA0KhO+++25w/bJly9S0aVN169ZNO++8c7Caysg0RcGHH344YGlWID711FOlFkbMfzSrDceMGaPWrVurd+/eweors9LOrG679957tXLlymAF4xdffKFWrVqtI8ecx9amTZtgpePBBx+sHXbYIVgBOXPmzGBF45133rm2KHPllVeWuvotiQKhTV5mld2HH36otm3bBkVC889FixYFvrrtttsCXptttllQAKxevfo6vIy/DO/PP/88+O9mhdvZZ5+tli1bBkWne+65R2ZF3Z577rm26FWyQPjLL7/I/P/ll1+uZ599NvD3q6++uoFvzerTNW3NCkKzIm/cuHHB6jxzpp7Rw6w0NP2vv/56LV68OPCz8Wnjxo1D34fGNlNMNix69uwZrJAsq5kCpeFoVpyaZrbvNIU+o3eNGjX0008/BQVGE8fXXnttmQVCw+njjz8O+v7hD39QgwYN9J///Ecm1sx9YFYJmiKiKTAa242/zGpWs8LRxIRZ7Whi3dyDxm5z/5XVbrnllsAu04xvt9lmm9CM6AABCEAAAhCAAAQgAAEIQAACEIAABCCQPAEKhMkzZ0QIQAACECiDwJtvvrl2W8OyVgiarmaFmimmlNxi1BRitttuO02cODEodpjiX8ktGNcMaYp7pnBimtnC1BTwSraSW4+a4pHZ1nL9lYZmZdVpp50WdDv++OP1+OOPryNjwYIFwQpIU3QqrZkClNk206z6MkWZyZMnB0Wkkq3YBcJi8DKr0UxBcP0CoClmmeKdaU8//XRQtCrZTAGxR48ewX8y/zRFp/WbKfaaLTPXtJIFwjX/LcoZhKavKcSZ2Ft/y1qzctCsLDRtzaq7sDevKZqagp1pJm7WyCtNjim0rbH9/PPP1+23317qVrimWG62KjVF8DWt5ApCE8OG8zHHHLPOMKYAu8suuwTFWlPsNDFq4nuNfmsuNsXYNduQVmS3KVjuv//+QVezkjDOSsuwbLkeAhCAAAQgAAEIQAACEIAABCAAAQhAIDoBCoTR2dETAhCAAAQsEzDnpa3Z0tCsYCrrjMHShl1TtDPn1pmtE8tb7WUKIqZwc/LJJwfbR5ZsJQuEZgWhKR6V1kwxzGyZabYHNSu7mjVrFoqGWU1oVmaZs+WefPLJ4FzEkq3YBULbvAz3H3/8sdTVZuacO3MWoSlsma0tTdGpZDPnDH799dfBikGzYs2sllu/mRV4ZnWaWQFnms0CoVlNZ1aIrt9MIdfoZMb8/+3dV4gVdxTH8T/BikZQIthjjbuCFSsWjL1ExYoKKiqiD4oRWyIYXZWIomJvKMu+ZLGBCmtDQVTs5UFRsSuixi7WRNHwO2SW2Zu5d4t3d/ZmvwdCjHd2Zu5nZvPy45yjUFOhW25LP+M9W3UleiNSI8/z8uVLG9Op4FvvnMaDarxpTssfECqkU1gXVArzvDGn2q+oLsmg8joss/veem56fqrff//ddnNSCCCAAAIIIIAAAggggAACCCCAAAKFX4CAsPA/I+4QAQQQKDICCn20h061fPlyG32Y01Inn4I2jfXct29fzB+bPn26W7JkiatVq5a7detWlmO9gFAjMr2Rl0En07V0TVV6erobOnRo1GuqW+/PP/90CsoUCHrVuXNnG405e/ZsN2/evCw/n98BYby9+vTp43bv3h3VQGMytetO+/527tyZeZy6JxXCqYLCQ/8J1V2obkNVvALC7J6zdh9mZGTYqFqN6cxtbdiwwU2YMMF+TKF0ixYtAk/hDxLT0tIyO1Rzej1/QKguzsjuQe882uOozkTVhQsXXJMmTQIv0bdvX9uxmd331vvrdTLq3N7zyel9cxwCCCCAAAIIIIAAAggggAACCCCAQDgCBIThuHNVBBBAAIEAAQVpSUlJmaGdwhR1MHXo0MGClcjRlf5TKOzTeNHcVOnSpa1jy19eQKgRoJs3b456OnUpfv/99/b5rFmzbCecv/RdtDtPnXoKYtQ9F620a3Ht2rVZPs7vgDDeXgpdFy9eHPU76hkePXrUAuBDhw5lHqeuOo2EVWk/pNdBGnQifT5ixAj7KF4BYdCIWP+1NUpWz1C7MW/evJmb18uOXbhwob0fqlg7+hQSL1iwwI5TaK3nk5vyB4QKYrX3Mqi0h3D+/Pn2kUbhBo3h1Wc5/d56z73fy7Fjx7pNmzbl5rY5FgEEEEAAAQQQQAABBBBAAAEEEEAgJAECwpDguSwCCCCAQLDA5cuX3aBBg9yVK1eyHKAwTyGTgguNUIwcv6hdfpFhX06MNUbSX15A+Msvv1i4E63++usvp7GaKnWIrVu3LvNQjQ/VKMlz587l5BacduelpqZmOTa/A8J4e82ZM8fNnTs36vft2LGj7bvTiEt9N680CnPYsGH2n7FGcHqfe2FivALCUaNG2S7IaJWbvYZB59BOQe0WVKkjVR2LQaWQeP369faRxql671aOXiDn7DuMHj3aDr99+7bTiNCg0jNKSUmxjyLfff/xOf3e2mPo7c+M/D3I6b1zHAIIIIAAAggggAACCCCAAAIIIIBAwQsQEBa8OVdEAAEEEMhGQGM4Nd5Q/2hfmvbS+Us72vbs2ZNl350CFYV2PXv2jNnJFnlpjb70lxcQapeadqpFq1gBobrc1O2m0qhHdSNqx512Duo+vWvUqFHD9hcGhVT5HRDG24uAMPhN8Xc96pkqIA2qRA0I1bXrdTtmF6rzPz4EEEAAAQQQQAABBBBAAAEEEEAAgcIjQEBYeJ4Fd4IAAgggEEXg4cOHtldwzZo1mV15Ct60a82rqlWrugcPHjjt9Tt48GCeLb92xKg6qr777jun0Ysal+kFhUE39O2337o3b96EEhDG2yuvAWHYI0bzu4PQ//1i7QaM54jRguwg1PjcZs2a2eutMbkKOikEEEAAAQQQQAABBBBAAAEEEEAAgcIvQEBY+J8Rd4gAAggg8K+AQrfWrVu78+fPu2LFijmFcRo9qvrpp59cRkaGjTt8/PhxzH2FsUC9gFCjIDUSMlrt2LHDRqGq/vjjj8wxmRor2rx5c/v73bt3uz59+gSe4urVqy45Odk+C6ODMN5eeQ0I79+/76pXr24OU6ZMccuWLYtq/vPPP7sVK1bY50EjRjViU6M2tRsyu32UGsF59+7dQHv/DeR01Ga0m1ZorTBWtWjRIjdjxozAQxUeDhgwwD7Td9A7kZsKa8Toli1b3NChQ+1WNUJWY4ApBBBAAAEEEEAAAQQQQAABBBBAAIHCL0BAWPifEXeIAAIIIOAT0D437XVTKXypXLmy/Xnjxo1u/Pjx9mftcvP+nFs8LyDUzymIbNq0aeApevfubWNOtQtRY0K9+zh16pSFmKr09PTM8CTyJP6wK4yAMN5eeQ0I5VK/fn137do1V61aNRsnW7Jkyf+Yf/jwwdWpU8eeuSooIPTGdFaqVMmp6zRWFVRAqHvQfd+6dcsC5W3btgXe1suXL+37v3371mmErt6jyD2bsb5PWAHhtGnT3NKlS13x4sWddm+WLVs2t79yHI8AAggggAACCCCAAAIIIIAAAgggEIIAAWEI6FwSAQQQQCBY4OjRoxa01a1bN/CAv//+27Vp08aCOwURL168sE5ClXYC1qtXz8I6faZuwljdTMeOHXPadRi5E84fEKoTUHvjypQpk+V+1DGo8aGqgQMHuu3bt2d+/uzZM1exYkX35csX16NHDwsR/efUgdqt2L9/f7u+KoyAMN5eXxMQqmtw6tSpZqHg1AuA/eiTJk1yq1evzvyroIBw3rx5TvfxzTffOAVuGuEarQoyIPS6ELVzUl2L0UoGXgflxIkT3cqVK//z7uhn1Umrd187Lb0KKyDU75h+b/VvdRBSCCCAAAIIIIAAAggggAACCCCAAAKJIUBAmBjPibtEAAEEioTA3Llz3fz581379u2dOvQaNWpkYdv79++tw0ydgadPnzaLyZMnu+XLl2dxOXnypOvYsaOFheq+0uhD7SqsVauW+/z5s3WVaQSoxjlevHjRrVq1yimI8ZcX5ikcPHv2rEtKSnIzZ850Gjn66tUrCwM3bNhg51MApTGkCpv85Y3v1N916dLF9rJp7KVGn2o0qcKc2rVrW4j15MmTUAJC3Vs8vb4mIFTwq665S5cuGaP8xo0bZx11GkGqbkcFvi1btsx8/gpuI8Nd7Z7s2rWrnWP48OFOoaL2QXrlD54LMiD0jw+9fv161AD83bt31n2qd1MlE3XC6t0rUaKEWSiMU2fqggULnIJHr8IICF+/fm0hpbo7Feoq3KUQQAABBBBAAAEEEEAAAQQQQAABBBJDgIAwMZ4Td4kAAggUCQEFhCkpKdl+1379+llI4u0f9P+AQq8hQ4ZYJ2F2lZaW5kaOHJnlMC8gVOClinY/5cqVsx2DkSGVfkbXbteunbt3717gLaiTbO/eva5Xr15R9+ApAPvxxx/t54O65bL7bvo8J/vz4uml5xetFNyqw0xe+m6RJatOnTq5mzdvBp6iW7dutqOwZ8+e9rnuu1WrVlmOVWjbtm1b+yyo1NXpVUEGhJ8+fbI9i48ePbL36bfffovq9PTpU+tKPXLkSMxHnJqaGnpAqN8fvWPq4tU7r9GuFAIIIIAAAggggAACCCCAAAIIIIBAYggQECbGc+IuEUAAgSIh8ObNG3fgwAGnTrALFy7Yvjl13akUPqiDTIGeugtjlToI1VGlUZ46j0IXjZ1UN2JycrKFVAphtPsusvwBoQKv/fv3W6ehugk11rFKlSoW7P3666/W4RatNGp00aJFbteuXRYClipVyjoN1dGo7sfy5cvbf+uzMEaM+u87nl7RPLILCPVz2r+nfXba06egULsI1cGpZ65OOgWyGs2qunLlin0WWepqW7x4sT17nUPn9ILBsAJC3eOsWbPcwoUL7Z27evVqtr/P6jrUKFuFneoy1Xupd09dhXqH9P76dzWG0UHYvXt3+30dPHiw27p1a7bfiQMQQAABBBBAAAEEEEAAAQQQQAABBAqPAAFh4XkW3AkCCCCAQCEQiAwIC8EtcQv/Cmis5uzZs61jTUGgQtdEKXVI/vDDDzb+Vvsv1emYyKVgu06dOrZH88SJEzYalUIAAQQQQAABBBBAAAEEEEAAAQQQSBwBAsLEeVbcKQIIIIBAAQgQEBYAch4uoe4/7aTUnkLthzxz5kwezhLuj0ydOtUtW7bMqfNu37594d7MV15dHZ3aDdmjRw8bl0shgAACCCCAAAIIIIAAAggggAACCCSWAAFhYj0v7hYBBBBAIJ8FCAjzGTjK6e/cuWMjW9UdGFTqHFQHoUojXydOnBjOjX7FVZ8/f+5q167tXr165U6dOmUjcxOxtG+wbt261j2oEb4NGzZMxK/BPSOAAAIIIIAAAggggAACCCCAAAJFWoCAsEg/fr48AggggECkAAFhOO+E9j2mpqa64cOH2/hN7dv7+PGj7RpMS0tzhw8fthtr0KCBO3/+fJb9e+Hccd6uqt2I586ds3BQuywTsY4fP267BxV2aj8khQACCCCAAAIIIIAAAggggAACCCCQeAIEhIn3zLhjBBBAAIF8FCAgzEfcGKdWQJiSkhLz4klJSTbOsmbNmuHcJFdFAAEEEEAAAQQQQAABBBBAAAEEEEDgfyJAQPg/eZB8DQQQQACB+AgQEMbHMbdn0djK7du3W2fajRs33JMnT9y7d+9chQoVXOPGjV3//v3dmDFjXIkSJXJ7ao5HAAEEEEAAAQQQQAABBBBAAAEEEEAAgQgBAkJeCQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSKkMA/AcUWOb6amdgAAAAASUVORK5CYII=\" width=\"900\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# Create color maps for 3-class classification problem, as with iris\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris.data[:, :2]  # we only take the first two features. We could avoid this by using a 2D dataset\n",
    "y = iris.target\n",
    "\n",
    "number_of_neighbors = [1, 3]\n",
    "\n",
    "plt.figure(figsize=(9, 4))\n",
    "for idx, neighbor_number in enumerate(number_of_neighbors):\n",
    "    knn = neighbors.KNeighborsClassifier(n_neighbors=neighbor_number)\n",
    "    knn.fit(X, y)\n",
    "\n",
    "    x_min, x_max = X[:, 0].min() - .1, X[:, 0].max() + .1\n",
    "    y_min, y_max = X[:, 1].min() - .1, X[:, 1].max() + .1\n",
    "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100),\n",
    "                            np.linspace(y_min, y_max, 100))\n",
    "    Z = knn.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    plt.subplot(1,2,idx+1)\n",
    "    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
    "\n",
    "    if idx == 0:\n",
    "        plt.xlabel('sepal length (cm)')\n",
    "        plt.ylabel('sepal width (cm)')\n",
    "        plt.axis('tight')\n",
    "\n",
    "    plt.title('k = {0}'.format(neighbor_number))\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimator interface summary\n",
    "\n",
    "http://www.scipy-lectures.org/packages/scikit-learn/index.html#a-recap-on-scikit-learn-s-estimator-interface"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regularization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train errors Suppose you are using a 1-nearest neighbor estimator. How many errors do you expect on your train set?\n",
    "\n",
    " - Train set error is not a good measurement of prediction performance. You need to leave out a test set.\n",
    " - In general, we should accept errors on the train set.\n",
    "\n",
    "**An example of regularization** The core idea behind regularization is that we are going to prefer models that are simpler, for a certain definition of ‘’simpler’‘, even if they lead to more errors on the train set.\n",
    "\n",
    "As an example, let’s generate with a 9th order polynomial, with noise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
       "        fig.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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x112282400>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "# Create color maps for 3-class classification problem, as with iris\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "\n",
    "rng = np.random.RandomState(0)\n",
    "x = 2*rng.rand(100) - 1\n",
    "\n",
    "f = lambda t: 1.2 * t**2 + .1 * t**3 - .4 * t **5 - .5 * t ** 9\n",
    "y = f(x) + .4 * rng.normal(size=100)\n",
    "\n",
    "x_test = np.linspace(-1, 1, 100)\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "plt.scatter(x, y, s=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
       "        fig.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\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\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 overridden (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",
       "        event.shiftKey = false;\n",
       "        // Send a \"J\" for go to next cell\n",
       "        event.which = 74;\n",
       "        event.keyCode = 74;\n",
       "        manager.command_mode();\n",
       "        manager.handle_keydown(event);\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"
    },
    {
     "data": {
      "text/html": [
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\" width=\"600\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 4))\n",
    "plt.scatter(x, y, s=4)\n",
    "\n",
    "X = np.array([x**i for i in range(5)]).T\n",
    "X_test = np.array([x_test**i for i in range(5)]).T\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, y)\n",
    "plt.plot(x_test, regr.predict(X_test), label='4th order')\n",
    "\n",
    "X = np.array([x**i for i in range(10)]).T\n",
    "X_test = np.array([x_test**i for i in range(10)]).T\n",
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(X, y)\n",
    "plt.plot(x_test, regr.predict(X_test), label='9th order')\n",
    "\n",
    "plt.plot(x_test, f(x_test), label=\"truth\")\n",
    "plt.legend(loc='best')\n",
    "plt.axis('tight')\n",
    "plt.title('Fitting a 4th and a 9th order polynomial')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar example for classification: http://www.scipy-lectures.org/packages/scikit-learn/index.html#simple-versus-complex-models-for-classification"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}