Report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train the Agent if Necessary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the cell below to train the agent, or skip it to just used the saved model weights. If you train the agent, you will have to restart the kernel afterward because of https://github.com/Unity-Technologies/ml-agents/issues/1167\n",
    "\n",
    "The trainer uses standard DQN with a 64 x 64 x 4 dense network with ReLu activations. The hyperparameters are set as follows:\n",
    "\n",
    "* BUFFER_SIZE = int(1e5)  # replay buffer size\n",
    "* BATCH_SIZE = 64  # minibatch size\n",
    "* GAMMA = 0.99  # discount factor\n",
    "* TAU = 1e-3  # for soft update of target parameters\n",
    "* LR = 5e-4  # learning rate\n",
    "* UPDATE_EVERY = 4  # how often to update the network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#%run ./DqnTrainer.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Agent Visualization\n",
    "\n",
    "-----\n",
    "### Setup the Environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GPU :-)\n"
     ]
    }
   ],
   "source": [
    "from unityagents import UnityEnvironment\n",
    "import numpy as np\n",
    "import DqnTrainer\n",
    "import torch\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will start the environment!  **_Before running the code cell below_**, change the `file_name` parameter to match the location of the Unity environment that you downloaded.\n",
    "\n",
    "- **Mac**: `\"path/to/Banana.app\"`\n",
    "- **Windows** (x86): `\"path/to/Banana_Windows_x86/Banana.exe\"`\n",
    "- **Windows** (x86_64): `\"path/to/Banana_Windows_x86_64/Banana.exe\"`\n",
    "- **Linux** (x86): `\"path/to/Banana_Linux/Banana.x86\"`\n",
    "- **Linux** (x86_64): `\"path/to/Banana_Linux/Banana.x86_64\"`\n",
    "- **Linux** (x86, headless): `\"path/to/Banana_Linux_NoVis/Banana.x86\"`\n",
    "- **Linux** (x86_64, headless): `\"path/to/Banana_Linux_NoVis/Banana.x86_64\"`\n",
    "\n",
    "For instance, if you are using a Mac, then you downloaded `Banana.app`.  If this file is in the same folder as the notebook, then the line below should appear as follows:\n",
    "```\n",
    "env = UnityEnvironment(file_name=\"Banana.app\")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:unityagents:\n",
      "'Academy' started successfully!\n",
      "Unity Academy name: Academy\n",
      "        Number of Brains: 1\n",
      "        Number of External Brains : 1\n",
      "        Lesson number : 0\n",
      "        Reset Parameters :\n",
      "\t\t\n",
      "Unity brain name: BananaBrain\n",
      "        Number of Visual Observations (per agent): 0\n",
      "        Vector Observation space type: continuous\n",
      "        Vector Observation space size (per agent): 37\n",
      "        Number of stacked Vector Observation: 1\n",
      "        Vector Action space type: discrete\n",
      "        Vector Action space size (per agent): 4\n",
      "        Vector Action descriptions: , , , \n"
     ]
    }
   ],
   "source": [
    "env = UnityEnvironment(file_name=\"Banana_Linux/Banana.x86_64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "brain_name = env.brain_names[0]\n",
    "brain = env.brains[brain_name]\n",
    "\n",
    "env_info = env.reset(train_mode=True)[brain_name]\n",
    "action_size = brain.vector_action_space_size\n",
    "state = env_info.vector_observations[0]\n",
    "state_size = len(state)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the Saved Model and Visualize the Agent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot the training scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the 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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bAVUA0Hnz5/f1EbDBjKFCZVAAARAIMUQeFoE4BuzdtCqo/F3ADOk9aGjo1vZrdPPW8/S0L8Pis8ScmppO3UT7Tt/W9T78606VL14Tq+st8wwMrBVAPVsBAHoFahKJ8n2CNjboIz6gwB8EpTxDBAAARB4eggkJwE4Y0MojVl0RMAf2aYcdalb3HAhnvlqAx28ECG++61Hbarll8srC/bhb3tp/p4LNDgkkN5s6OeVPmUneH8nYwugV3eKk86wgZ4EZTwDBEAABJ4eAlIAvhdUmj5Mwl7Ar8/aQasVC2D6ND50bIy9BXDqmpM0adkxAX/Es2Wpa714R0p1RVp+sZ6OXo4UH83pXovqlsrtlQWTKeaGtA6k7g0gAL0CFRZA72CEAPQOR/QCAiAAAsmFwNMqANOl8aHjOgE4efkxmrL6pFiaYc+UpdfrGwvAZpPX0cmrd0S9n9+oSQ1K5/HKcrIDCjuiDH2mLL3h5NmePgjvb1gAPd07oh02kCV8aAwCIAACyY7AUysAU/vQ8bH2FsDxi4/Q9PWhYo0SssI1+XQtnb5+V9Sb1a0GNS6T1yvr+u7cPfS/fRdp+LNlqZsT66OnD8L7GwLQ070DAWiJHBqDAAiAQPIkkJwE4Ih/DtGszWfEQiV0D6/BxNV0/uY9Ue/HrtWpaUA+ryzuO3N206L9lxK8f+jpgyAAIQA93TsQgJbIoTEIgAAIJE8CT4sAVNO38UqkTZ2KTowNsVuUgfP309zt58VnCXni1h2/ii6GR4t633WuRi3KmYuk4WoH9Pp1Fy0+cDlBD2RXfTj7HgIQAtDTvQMBaIkcGoMACIBA8iRgJACjHz6iw5ciqHLh7OTjYxyi7EnTMCMA+/y2lxbsuSCGNqBVAL3lJBRLzbEr6WrkfVHv21erUsvyBQync/d+jLgrWLFwNnIWqk1t+NbPu2jpocsJxiD0lBsEIASgp3sHAtASOTQGARAAgeRJQBOATUvRhy3KiEnKmHuJkdHCU4pdZ26ntceuac3T+KSik+PsLYDSAseV+rcsQ70aG2fjqDZ6Bd24+0D0NfXlqtS6orEAlN7CX3aqTG0rF3I59B4/7aTlh68kmIXEZSdOKkAAQgB6uncgAC2RQ2MQAAEQSJ4EjASg/CxPlvS0Y3CzJDFxMwKw5887admhK2K8CYnXSiOXU/i9h6JeQuJOcmhSJg/N7FbTJYfus3fSyiNXaHy7CvRSTeM0dC47gQB0igiBoD3dPfACtkAOTUEABEAgeRJISADmzpyedg5JmgIwtU8qOqWzAEoLHK9U3+b+9G5QacNFKz98Gd25HyO++7xjJXq+SmHDeu4LwB208shVmtC+AnWsAQHo7V8MBKAFojAhW4CHpiAAAiCQDAkkJwEoLXC8TH2a+dP7zYwFYJkhS+h+zGOxmp++UIk6VEtYADYNyEs/dq3hcvVlsOqJHSrSi9WLuKzvTgW8v3EE7M5+caiLDWQJHxqDAAiAQLIj8LQcAXf5cTutOx5/B5B9U0LHt7Zbj+6zbRY4Lu8HlaY+TjKblBq0mGIex4p6CVnrJBuzAlAeUyckKj3dQHh/QwB6undEO2wgS/jQGARAAASSHQEpct5tWor6xjmByM+S0hGwGQGopotzltouNjaWSgxcrK3juOcr0Mu1jI9r3RWAr/24ndYfv0aTX6xE7aoaWxU93UB4f0MAerp3IAAtkUNjEAABEEieBJ4WASjFlVyFVKmITussgGqoGFXQqiv36HEslRwULwBHP1eeOtcuZri4kk1QQF76wcQRcOcfttGGE9fpi46V6bkqrr2G3dlREIAQgO7sF4e62ECW8KExCIAACCQ7AslJAKpWwnealKSPggMc1ut+zCMqM2Sp9vnINuWoS93iXhGAr8zYSptO3kjQs9jTDYT3NwSgp3sHFkBL5NAYBEAABJIngadVAPJqnPnE/g6gaiV8u3FJ+rilowCMehBDZYct0xZz6DNl6Y36JRIUgM0C89KMLq6dQF7+fittPnWDprxUhdpUKujVDQMBCAFoaUNhA1nCh8YgAAIgkOwIJCcBKI9geZF6NvKjga0CHU/Coh9SxRHLtc8Tyhks2TQLzEczulR3ufadvttCW0Nv0tcvV6FnKkIAugTmZgWEgXETmFodAtACPDQFARAAgWRI4GkRgKq4k8ugtwC+OmMbbTx5XXzdo6EfDQpxFIA37z6gqqNXaCuZUMo4dwXgi9O30PbTN2naK1UppIJxdhFPtxDe37AAerp3RDtsIEv40BgEQAAEkh0BKXJ6NylF/YJtqeCSohewGQEoj2B5Dm82KEGDW5d1WK+rkdFUc+wq7fOEMoZIDs3L5qPvX3NtAXzh282048ytBPMLe7qB8P6GAPR070AAWiKHxiAAAiCQPAkkJwH40ndbaUvoDbFQfK+P7/fpy6Xwe1Rn/Grt4w+b+xOHjDEq7grA9t9spl1nb9H0ztUouFx+r24YCEAIQEsbChvIEj40BgEQAIFkRUCNiWdsAUxHO4c0T3DOl8Oj6Z05u0UoFWehT5YevETT14fSlE5VqEjOTHb9/bUrjOZuP0ffvFqNOPews2LGAthx+hbadvqm6OLV2kXp7I0oyuWbjs7ciKK+LfypQek89MasHbTqqC1YNJdyBbNSujQ+InRLsVy+do93VwA+P20T7Tl3m77rXI1aQAB6/beCO4AWkEIAWoCHpiAAAiCQzAioMfE8FYAf/bGP/tgVJsjo7+RJXFJI1fbLSfN61DEUWR2rF6EJHSpaEoDyDh53kiV9GoqMy/crOw0dF0J+SgxA9WE1iuegP96qazi2FmXz0XcmjoCfm7qJ9p6/TTNeq07Nyubz6m7B+xsWQEsbChvIEj40BgEQAIFkRSDm0WMqNXiJmJOnArD3nN307/5LpgRgoewZadOApoYiK6RCfpr2SjVLAlDeweNOOFB0rC3bm1ZOjm2lzbd1xQK0KG7cXKFkHl9a1bexJQHY9uuNtC8snH7sWp2aBkAAevvHAgugBaIQgBbgoSkIgAAIJDMCD2Iek/8QawJwwF/7ad6O86YEYNYMaWj/iGCPBKDq4Ss70FscO3yzmXaeveV0lY6PaaXNt1ONItq4uYFfbl9a3c+aAHz2q4104EI4zexWg5qUyevV3YL3NyyAljYUNpAlfGgMAiAAAsmKQPTDRxQw1JYVQ82cEe8F7PoO4Kj/HaYfN502JQCN0rfJZ7myAJoRgO2mbaLd5247XaOjo1tq8+U7iz9vPavVLZHbl9Y4EYDB5fLR9M6uvYBbT9lAhy5G0OzXa1Ij/zxe3St4f0MAWtpQ2ECW8KExCIAACCQrAt4QgJ8tP0ZfrT4puPARa5rUPg6MpMjjL/RWO7MCUKZZUzvX9yWdMJwt0uFRwVoWkNfrldCEK9cvlisTrfuoiaF10qwAbPXlBjpyKYJ+fqOmcDjxZsH7GwLQ0n7CBrKED41BAARAIFkRUNOieWoB/GbtKZqw9Kjgsmdoc8rhm+4/E4Btp26ifeedWwAPjgym8sNtaeB6NvQTnsmyFMmZkTb0N76faFYAtvxiPR29HEm/vFGL6pfO7dW9gvc3BKClDYUNZAkfGoMACIBAsiJw536MJog8FYDfrjtFnyyxCcB1HzV2CKXCnz8pC6B0wnC2SPuGt6BKI21p4Njp5es1Nssll8I5MtLGj40FYMty+enbzs4dVGQfLT5fR8ev3KE53WtR3VIQgN7+scAJxAJRCEAL8NAUBEAABJIZgcjoh1QhLi+upwJw6pqTNGnZMUHmn971qGLh7A6U2OrGYpNLYh4BSycMZ8u0d1hzqjzKlgauTzN/+nzlca1qQh7KZgVg88nr6MTVOzT3zdpUp2Qur+4WvL9hAbS0obCBLOFDYxAAARBIVgTC7z3ULGK9Gpek/i0DxPzccQL5cuUJTUg5O/qsM34VXQqPtiQA1TRvchH0YvKZrzbQwQsRTtdo99DmWh5gTgEnhSs38IYADPpsLZ26dpd+61GbavlBAHr7xwILoAWiEIAW4KEpCIAACCQzArejHmgWMSMByFk0dg1NOBPI5OXHaEqcE8jUl6sSx9fTF3k0yp+fGhdCqX1SaVXMOoGYEYAhX26gw5ecC8Adg5tRjbErxbMHhQTQuMW2o2suBbJloC0Dg+yGLsfWqnx+kanEVWn66VoKvX6X/nirDtUontNVdbe+x/sbFkC3Noy+MjaQJXxoDAIgAALJisCtuw+oymjbkainAnDi0qM0be0p0cf4dhXopZpFHRjJHLn8xb5hLShbprRuC0A1z69srLcASi9cZ4u0fVAQ1Ry3SgjQga0CaMyiI1rV/Fkz0NZBxgLQVYga2UnjSWtE2rm/3q5D1YpBAHr7xwILoAWiEIAW4KEpCIAACCQzAjfu3KdqY2wWsbcbl6SPdUfAZiyA4xcf0bxpB7QKoLcalXSgpKZoW/9REyqaKz4fsFkLoBkBKL1wnS3T1oFBVHv8KkqbOhUNCgmkkf87rFXNmyU9bR/czK6p2bHJRg0nrqFzN1kA1qVqxXJ4dbfg/Q0LoKUNhQ1kCR8agwAIgECyInD9zn2qHicAWbixgOMihY8ZAagGglatiCqoF7/dQtvP3BQf/ftufSpfKJv2tVmRlZAA5Iwmyw9fphH/HKLrdx44XaMhrQOF1S9dGh/iPw9beEirmztzeto5xCYAOT7iisNX6N25e8TfW1coQFNfqepy7etPWE1ht+7Rgl51qUpRCECXwNysAAugm8DU6hCAFuChKQiAAAgkMwJXI6Op5thVYlY9G/nRwFaBbgvA4QsP0uwttowar9YuSmOeq+BASU3Rpg+R4g0B+MXK4/TFyhOmVydj2tQ05JlAGrzgoCIA47OeDFt4kH6Km5M7ArDeJ6vpwu17tPCdelSpiKM3tOkBGlTE+xsWQCv7h7CBLOFDYxAAARBIVgSuRERTrXE2AdijoZ84FuXijgVw8IID9Ou2c6LdSzWL0Ph2FR0YqXcAf+xanZoG5NPqmBWAnb7bQltDbVZEWeQdQBl+xezi+KZjAViWBs4/oDVRrZ2lBi2mmMex2ndmLYB1x6+ii+HR9L/e9alC4Xgrp9lxJVQP728IQEv7CBvIEj40BgEQAIFkReByeLS4E8ele/0SQhS5KwAH/LWf5u04L9q9WL0wTexQyYGRmqJt2itVKaRCvKewNwRg08/WUui1u6bXJkv6NMIC+PFf8QIwR6a0tGdYC7v5yw7Zs5k9nF2V2uNW0eWIaIdjblftzHyP9zcEoJl94rQONpAlfGgMAiAAAsmKwMXb96juJ6vFnDg37rBn3ReA/f7YR3/uChN9tK9amD570VEAPjd1E+2NS9H2ecdK9HyVwhpHswKw4/QttO20sQWw0aQ1dPZGlOm1yZYxLQ1uHUj9/9yvtcmeKS3ttSgAa45dSVcj79Pi9xpQ2YJZTY/HTEW8vyEAzewTCEBLlNAYBEAABFIGgbBbUVR/whox2a51i9OINuXEn6Uoy+mbjjh4ckKlz297acGeC6LK81UK0ecdKztUV1O0fdKuAnVSQsV4QwBK5wuzq8bWviGty1LfP/ZpTbJmSEP7RwTbzV9+adYCyA417Fiz9IMGFJAfAtDsepitBycQs6QM6uFfEBbgoSkIgAAIJDMC529GUYOJNgHYpU4xGtm2vNsCkD1l/7fvomjXtnJB+rJTFQdKbb7eSPvDwsXnI9uUoy51i2t1vCEA1UwjZpYod+Z0wgLY57d4AZglQxo64EQAPlOxAH1t4gi42ugVdOPuA1r2QUMqkz+LmaGYroP3NyyApjeLUUVsIEv40BgEQAAEkhWBczeiqOEkmwB8rU4xGqUTgGbCwPT6dRctPnBZ9OFMKKkp2jgAc08lVqA3BCBn97gWed/02uTJkl6EgXl/3l6tTeb0aejgSGMLoFkBWHX0Crp59wGt6NOQSueDADS9ICYrwgJoEhQEoAVQaAoCIAACKYDAmet3qfGna8VM1RAu7hwBv/nTThEzj4uzjBmtp2ygQxdtKTnKLHIAACAASURBVNo+bO5P7wWV1uiaFYBqMGnZWHoBS+FldsnyZU1Pg1uXpffi4vxxO/YMPjSqpehCjkn292ylgvTVS46WTf3zKo9aTrejHtLKDxtRqbyZzQ7HVD0YcGABNLVRnFXCBrKED41BAARAIFkROH39LjWJE4Av1ypK4563xfBzRwC+PmsHrT56VbQLLpePpneu7sBITdH2TpOS9FGwLeC0+ixX6dYSEoCVRi6n8HsPTa9NwWwZaFDrQOo9xxbomUumdKnp8KiWFBsbSyUGLrbry6wArDhiGUVEx9Dqvo3ILw8EoOkFMVkRFkCToIyqQQBagIemIAACIJDMCJy6doeCPlsnZqXG8HNHAL7243Zaf/ya6KN52Xz0/WuOAlBN0aaGm3FLACrZROQySAtg+eHL6M79GNOrUyh7RnEHsNevu7U2GdL60NHRrSjqQQyVHbbMIwFYYfgyirwfQ2v7NabiuX1Nj8dMRby/YQE0s0+c1sEGsoQPjUEABEAgWRE4eTWSmk1eL+bUsXoRmtDBFsTZHQH4yoyttOnkDdEuKCAv/dC1hgOj4M/X07ErkeJz9ahZtba5tAAmIAADhy6lew8fmV6bIjkz0uCQQHrrl3gByOnhjo9pRWpsRNlhm0oFaYqJI+Byw5bS3QePSJ/v2PTAEqiI9zcEoKV9hA1kCR8agwAIgECyInDiSiQ1/9wmAF+oVpgmvWCL4eeOAFTj8zUuk4dmdavpwKjF5+vo+JU7Ds959DiWSg6yHbdaEYD+g5fQg0ePTa9N8VyZaGBIIPX8eZfWJl1qHzo+thUduxxJwV/YmLgrAKUQ3dC/CRXJmcn0eMxUxPsbAtDMPnFaBxvIEj40BgEQAIFkRUAVOx2qFaZPdQJQzY7hbOJqnt8GpXPTz2/UcqjabPI6OnnVJgBVa9qDmMfkP2SJZQHoN3ARKZnbXK6RX25fIQDZgUWWND6p6OS4ENpx5ia98O0Wuz6chbfRPyhg6BKKfviYNn7chArngAB0uRBuVsAdQDeBqdUhAC3AQ1MQSGIENp+8Tu//tldc3Oe7VyiJT+DHjafp+w2hNPfN2nZ3vP7YeZ4+XX6MfuhSg8oXsp4Dduam0zR9XSj9+mYtKunCmWB/2G0hZD5uGUDtqsZn2DBD48ilCGIHDS7tqhaiyS/agjhLC6AZAahm+eC2BbJlIA6p8ig2lu4/fEztqxWmRfsv0qm4VG2qo0j0w0cUMHSpKQH4wrebaceZW3bTkncA9V67ruZeMo+vyHv8xux4AZjaJxWdGhdCq45csftc7SuwQFa6dfcBvRtUil6pVczhMSxmWdRuHtCUCmbP6GoYbn2P9zcsgG5tGH1lbCBL+NAYBJIUAfWlJ1+ESWqAyXAwknmzwLw0o0v8XTf5eZl8WWhZn4aWZ97puy20NfQmTexQkV6sXiTB/hpMXE3nb94TddzdB4cvRlDIFJsAVLN4uHMErMb4czZQtriFXrfl6m3kn4dmv247JlYdLlwdAXtTAPrny0wDWwVSt1k7tCH7pCIKHd+a/t1/0c472NmcjFiXHryYHj6Kpa0Dgyh/tgyW94HaAd7fEICWNhQ2kCV8aAwCSYoABOCTXw7JvEmZPDRTuesmP+fYbxwDzmqRYVP0adOM+q09bhVdjoj2SAAevBBOz3y1UbRVjzndsQCqHr7O5l0ity9xyBkutf1y0rwedcSfI6MfUoURy8WfXQlA9ahZPsdTC2BA/iw0oFUAdZ0ZLwClgF6494JdgGh3BCDfZ+R7jdsHBVHerBCAVn8H+vY4ArZAFALQAjw0BYEkRgAC8MkviCsB6C0LYL1PVtOF2/do7PPlDY8a1ZlbEYAHwsLp2a9tAlCNdeeOAGw+eR2diLvf52xFiuXKRGdvRImvqxTNTgt61RN/Do96SJVGPXkBWLZAViEAOYSNWlhQLtgTZpcizh0BWGLgIoqNJdoxuBlxthFvFry/YQG0tJ+wgSzhQ2MQSFIEIACf/HK4EoBsWVr6gfUjYBlPbnTbctS5TnzeXKMZWxGAfH+wzdebRLetKxagqXH5bt0RgE0/Xasd7zpbEb4XeCncZqVk8bX4/Qbiz3yfrsroFeLPT9ICWL5QVhrQMpBe/WGbgwD8c1cY9fsjPkewOwJQcts5pBnlzgwB6O1fKCyAFohCAFqAh6YgkMQIQAA++QWRzJsG5KUflXh38nNV3Hg6OjU0yvBny1K3eiUS7MqKANx7/jaxE4cQgBUK0NRXqoo/uyMAG05cQ+du2qx7zgpn2Yh6YIvT55fHl1b3bSz+fP3Ofao+ZqX4c6vy+embV6s57cObR8AVC2ejAS0D6OUZjgLw953nqf+f+10un/4OoBrTcPfQ5pTTN53LPtypgPc3LIDu7BeHuthAlvChMQgkKQLyuIkH5e7l/yQ1kadoMK4EIFuW/n3XZt3ytNyOekCVR9msYkNaB1L3Bn4JdlVr3Eq6EnFf1HF3H+w+d4vaTdvsIMDcEYDyuNrsfDkLx6YBTUX1qxHRVHPcKvHnFmXz0XcGWURkv+2/2Uy7znrHC7hykezCa/ql77faDZv5zdt+jgbMP+ByOnrWqnDfO6w5Zc8EAegSopsVYAF0E5haHQLQAjw0BYEkRkBeOPfkxZ/EpvLUDMeVAKxUOBst7F3f0nzO3rhLjSatFX0MCgmgHg1LJpoAZEHFwoqLGp7FHQGoClAzE+ejUT4i5XIp/B7VGb9a/FnvWa3v6/lpm2jPudsOgo0/cDcMTNWi2WlAq0Di/MJqYVH367azNHjBQZdT0QvAh48eU+nBtpiG+4a3oGwZ07rsw50KeH/DAujOfnGoiw1kCR8ag0CSIiBDTkAAPrllcSUA2bL09zs2BwdPi+qY0b9lGerVuFSiCcCdZ25Sh7igx2oeX3cEYPUxK+j6nQemp5slQxo6MCJY1GdHF7YgctEfq+s7bDt1E+077x0BWL1YDuEEIucun8Wi7uctZ2jowkMu56MXgPdjHlGZIbaYhgdGtKAsGSAAXUJ0swIsgG4CU6tDAFqAh6YgkMQIqOmv3D36S2JTeWqG40oAsmVpfpyHq6eT2njiuuac0K+FP/VuWjrRBKCa9UK1wLkjACuNXE7h9x6anq5MucYNzt+MogYT14i2anxAo87afL2R9oeF233laRiYmiVyiiNgaf1UBeDszWdo+D/uC0A1qPWhkcHkmz6NaSZmKuL9nYwtgOvXr6dJkybRrl276NKlS7RgwQJ67rnntH3RtWtXmj17tt0+CQ4OpqVLbf/iMFOwgcxQQh0QeDoIlBmyhO7H2PKfQgA+mTVzJQDZsvTn23UtDWbxgUvU69fdoo8+zfzp/WaJJwC3hd6gjt/Z7sGpFjg5z+yZ0tLeYS0SnE/54cvozv0Yt+YcOi6EfHxS0Znrd6nxp7bjbmdp5GTHRgGnPRWAHIuQBeDzcfcfVQHI2V5G/XvY5Xz0vzk1qPXhUcGUKR0EoEuIblZIthbAJUuW0KZNm6hatWrUrl07QwF45coVmjlzpoYsffr0lCNHDtMIIQBNo0JFEEjyBMoOW6p5VkIAPpnlksIoKCAv/WDgBcyWpd972oIce1rmbj9HA+OcEN4LKk0fNvdPsCsrTiBbTt3QHCHU4NbuCECZ/9ad+R4d3ZIypE1Np67doaDP1ommdUvmojlv1nbaTespG+jQxQi77z0VgPwsFoB8rKwW7m/GhlAas+iIy+nof3N378dQueHLRDs5P5eduFEB7+9kbAFU90GqVKkMBeDt27fp77//dmPL2FfFBvIYHRqCQJIjUG7YUrobF1oDAvDJLI8rAahmufB0RNPXnaLxS46K5u80KUkfBQck2FXNsSvpaqRnXsCbT12nl7+3hUJRj2DdOQJW76KanfO+YS0oW6a0dPJqJDWbvF40c8VOZkdRn3F6fAjx+9JdJxC2NvYPDtCCYMs++Xek8k9oPvrfnJrV5NiYlpQ+TWqzOEzVw/s7hQtAFn/p0qUTVr+mTZvSmDFjKFeuXE43z/3794n/JwtvoCJFilB4eDhlzZrV1KZDJRAAgaRJQAYL5tF5IgC3ht4QR3CdahZNmhMkoj92nqeC2TNSvVK5DcfImSTm7jhHbSoVFPUSu6gCkAMnc7aHBqXzaAKkXqlc9Gv32jRn2znitHBsEXS3TFx6lKatPSWavdWopHBWSKhYEYCbTl6nV+Ji4bEo+v616vTzlrM0drHNAmbmCFgNR2R2rtsGBVG+rBno2OVICv7CJgC5dKtXnIIC8lH90rnF3lx26DJ1rlNMHKcapZyTR8nuCsCG/nmof3AZLQ2eKgC/WXuKJiy1CfCEivzN8ZF9Gp9UVMsvF/F9SC4nxraitKl9XHXh1vcQgClYAM6bN48yZcpEJUqUoFOnTtGgQYMoc+bMtGXLFkqd2vhfGiNGjKCRI0c6bDIIQLd+d6gMAkmSQMURyygi2nb3yhMBKF+af71dh6oVc1+oJDaUQxfDqfUUW5oyZ/N76+ddtPTQZVJjyyXmuCQzv9y+WvYLHpv8nEVU7yaltHt1nqzLkL8P0C9bz4lp9GjoR4NCAhOckhUBuOHENer8gy0dWv1SuYmdWKasPqk9z5UAfPw4lvwGLXYb+fqPmlDRXJno8MUICpmywa49h0/hMCrSsti1bnEa0aYctfh8HR2/cseu7smxrShNah+3LYCNy7AADHB4Nq/X1DUnadKyYy7nxHXZ+UWKPha1teJiGp4aF0KpfVK57MOdChCAKVgA6jdKaGgolSxZklauXElBQUGG+wgWQHd+XqgLAk8XAdX70hOhIUXLl50qU9vKhZLc5JcevERv/WJzhnA2v8ChS+neQ1uGCU8YuDtpySxL+jQUGef4oApAtiy1rpCfPv7LFkjYkzG9O3cP/W/fRdH+9XolaNizZRNNAK4/fk3Lh8v34rhsPnXDtAB8EPOY/IfYYt+5U9b0a0wlcvvSwQvhDlY4yU2ylun1mk1eRyd1OYePj2klhBbHxHSn8B3Oj1qWoZZf2ItPXq8pq07Q5BXHXXbHda9ERGuib22/xppDizyadtmJGxUgACEA7bZLnjx5xDFwz549TW0jbCBTmFAJBJ4KAlVGLadbUbbwG54IjaQuABfuvUDvz9ub4PyetCOM6hxxO449v+xLDLQJELYsBZfLrzlxeLIuXX7cTuuOXxP9SetXQhvSigVw7bGr1HXmDtE938HzSZXKLQF478EjChxmPhKFnMeqvo2oZJ7MpOYiVufIR7vSslgmXxZa1qchNf1sLYVeu2uHgp0tuAQMdW8MzQLzEcdYbPF5/PGz/B19vuI4fbnqhMv/BvDaXou8TzXG2lLZrejTkJrH9efJurt6IN7fEIDaHgkLC6OiRYsKp5A2bdq42jvie2wgU5hQCQSeCgJVR6+gm3dtAXg9eeFIMTPlpSriDl1SK3/uCqN+f+xLkgIwl286uhHHnq1Q0grGoVSCAvNqmSQ8WRfOzcs5erl0rl2MRj9XPsGlYQHCQsSTfbDm6FXqNssmAGuVsAnALaHmLYAR0Q+p4gjbvTe18Onn41jnw2axVDpfFtpz7pZDKBZuxcJOirrSeTPTig8bUdNP12rH7rJnDrfCz+FQNO4UznryUXAZzQFFtuX1mrz8mN0xuLN+ua6ay3jxew3EkXKqVESnx7d2Zzim6uL9nYwF4J07d+jkSdvdiypVqtDkyZOpSZMmlDNnTvE/vsvXvn17yp8/v7gD2L9/f4qMjKQDBw4Qh4MxU7CBzFBCHRB4OgioGRg8ERpSAH71UhV6NgkKQDUnq7P5/VcWQHb+kKKLRUjZYTYBwpYltgIO+duWSsyTdVEtXS/VLErj21VIcENWH7NSCBFPnrf66BV6fdZO0bZm8Zzk40O0NfSm9jxXdwBv3X1AVUbb8harJX0aHy1GpdHgl37QgALyZ6VdZ29S+2/s07FxffYSrjTKJixL5vGlVX0bU+NJa+jMjSi77g6ODKaHMY8Nx5AQtFbl81O/4DJaCBpZl9dr0rKjNHWNzQknoaK3AHIGGBbvfCTNdwC9XfD+TsYCcO3atULw6UuXLl3om2++EUGh9+zZQxwKpmDBgtSiRQsaPXo05cuXz/Q+wwYyjQoVQSDJE7By9MeTkwJw6stViT1ak1pRU3I5E1JPOhSOZJY/awa6HBEtkKlihS1L7EwhU4l5IgBVYd+xehGa0KFigktjRQCuPHyFuv9kE4AcxJrFy7bT5gWgegSqDlK9I2k0+EXv1adyBbPR9tM3HfLxcv3tg4KoZpxDBTvcrO7XmBpOXEPnbtoLwP0jWhBn4Kg5dpVb27d1hQJCADaJC0ItG/Nx/oSlx+jbdeYE4NWIaG2cv/WoLZx/0qZORSfGQgC6tSAmKyfbQNAm52+pGgSgJXxoDAJJioCVAMCqAJz2SlUKqZD0BOAPG0/T6LiMDElNABbMloEuhtsE4M4hzYhFGBe2LNUpmYuGxeWSdVcAxsbGinyyDx7ZMrx0qFaYPn2hUqIJwOWHLlOPn3eJ/tkDmD1qWZTJ4soCeDk8mmqPdxRfOX3TadcTjAb/v971qULhbMShiDrFZSJR67GXcMNJthRxxXNlorUfNaH6E1ZT2K17dt2x+L7zIEbLJ2z2B8oWb06z12iSLQuJLOxVPHHZMfpufajLrnht1fn//EZN4VGdLo0P8bUAbxe8v5OxBdDbm8WoP2ygJ0EZzwCBJ0OgzvhVdClOhLgrNMSLdcAiMdBvXqlKrZKgAGQrzCdxAZGdeVX+VxbAAtkyaOy3DGxKdcavFizZslS9eA4a+T9bKjF310XvVNGuSiGa3LFyghvKigWQ4+z1jBOAlYtkJ87Tu/1MvACUIVmcDSDsVhTVn2ATamrJlzU9XYmIj0Gr/37hO/WoUpHstPnkdXo5Lg6hWkd1qCiSMyNt6N9UiLwLt+0F4J6hzYnvIeqFnKtfYNvKLADLaHmIZX0O4Dxp6TGasfG0qy7E2l68fY/qfmJb+xmvVRfW1AxpfejoaAhAlwA9qAALoAfQZBMIQAvw0BQEkhgB9YXortBQBeC3r1alluWTngXwq1Un6LO4cBzO4qqpeWg9YeDukkrRrAqcdR811gTIMxULUJWiOVxaLp09Vw0rwnXYOYeddBIqVgSgGmqnUuFswnq148wt7XGuBODZG3cNxRfHZdSLNe6UAybHPI6l+b3qUtWiOUiNQ6jO8d9362vhYWSMR/UfPLLuriHN6FbUAwdnDlfr+nyVQuIImH9DauH7nBwDcOamM666EAJQFcBsSecczhnTpqYjcd7JLjtxowLe37AAurFdHKtiA1nCh8YgkKQIeE8AVqOW5fMnqbnxYDgWG8dk4+Iss8J/JQBzZ06vOV4s+6Chls2CBVvFwtm0XLLuitLjVyLtQpPw3Uy+o5lYApCzWLBo4cLjZgvgzrPmBaCay1cdI8f4O33dPmQLf8/WseiHj+nPt+pQ9eI5SQ1Do7bn4OTSOYSP2zcPDKLa41Zp9y5l3R2DmwlnHH0waVebuX3VwtQv2F+z3Mr6B0a0oE+XHaPZW8666kIIwPM3ozQr4hcdK9MHv+0l33Sp6dAoW3gabxa8vyEALe0nbCBL+NAYBJIUgQYTV9P5m7YjMXeFBreR1qzpnauJ2HVJrfDxr7yM7yy3qtV0eO7OWTJTw8BI70/u67nKBYVzg0yl5u667Dxzkzp8G+8Vy3cKv3m1mgsBuIKu3/EsHNCi/ZfonTk2AVi+UFbKkCa1WwJQL1jlQP3zZXbI2sHfZU6fhu7cjyF2mODUaWoYGnWSv3avpaWoY4ebrewUouQ8lnXZWYSdcdp8vcmtpXyhGgvAMloQZ9mY7xROWn5Uy8SSUKe8tqoFdGL7itT/r/3EDjAHRga7NR4zlfH+hgA0s0+c1sEGsoQPjUEgSRFoNGkNnY0Li+Gu0FBTeHH+1+ZlzUcTeFIQ2AGEHUG4HBnVkjKmc0x5+V8JwByZ0mpBuOe+WZte+n6rGGe7qoWIAxePj7u76O66rDpyhd6YbfPK5cLrwuuTULESDogzjnDmES5lC2QVjHe5YQGU6frk0a4cJ/d1+FKEw7D5SJnTpzEzdpZRvZDVyj92ra6Fp8mbJT1tH2xztJHhbmTdrQOD6MLtKMNQMgkxY+/qvsH+Dt7DfKTM1w44l7OrwsGqz96M0jyJR7ctJ7y/s2ZIQ/tHQAC64ufJ97gD6Am1uDYQgBbgoSkIPAEC7AXKR2RGYkf/eA5hIY/Z3BUaMY8eU6nBthRefHm9mQUByI4LZsbrLr7hCw9qR3GHRgaTb/o0Dl08CQHIYUb4aNTHJ5VmNZVChgfETjRvxx2jstduqbyZHZxXVEbcH8fJS8URg3VlwZ4w6vObLfg1F05Z9kPXGoboOA0bd8F34/QWwPsxj8RnfHwqn8Oin72LM6SNF9JqthVOuZYpXWrafc4WhJpLQncAHz2OFYGc2WLJ7aIe2FLyceH7hPvCwh3GLS2nbOGrVyo3qc9XK6tM+bidPa2rjV6hBd+WdTf0b0JnbtzV8hmb3WMcX7FvC3/Ne1u22z44iCYvP07zdpx32RUfF1+NvK/FEhzSOlAc/bvynHbZsZMKeH/DAujp3hHtsIEs4UNjEEh0An1+20sL9lyglR82EkIioaIGDHY396iaw/WHLtUpKNAzCyBbSgYtOECTX6xE7aoW9iqfgfMP0NztNksMv2yzZEjr0H+FEcsoMjpGfO6uCDYz2Bt37otjwgalc9PMbjU1AZglQxrtuWo/fLRYIo8vTVx6THzMzitsyZy1+QyxY0MO33TC8YDjBU7v7GjZm7XpNI2I8yDm9hxUela3mg5DZTHX5LO1dP/hY2Kxp6YE5LXlfxywE4bqRPLit1to59mbtHtoc8qeKZ3oUxVgbLlkIS+zkPD3CQnAll+sp6OXI0U/LHpkajz+e5Wi2WmPIiTlBKTzzE+v1xSC8a1fbCFo9OXzjpU0IcyicdfQ5qSmPpT1WQDHJpBxxNkav1KLBWAZ4mw6amGL4mfLj9Efu8LMbA8a/mxZzeObM4uwAwmHwGHG3i54f0MAWtpT2ECW8KExCCQ6AXnHzEwGiKDP1tKpuNyoHL+MY7iZLWyFkqm2+LitaYBnAlCOl5/rbQHGaeA4HRwXvpuVLdOTF4AzN522C+ki5yvvsul5swWQHSBYCHBR08RxlpCyBbJoacaMeLHTCzu/SGcJFp4/v1HLYVnDox5qmTLUL/WhSaQDBdeRY2dnheeqFBLNVIsjp1zLlD4N7YtLQ8ffJyQA1bVXM6Nwu2rFctgdJcsxSu/gmd1q0Nu/7BLWbqPySbsKNGD+AfGVtKhVGrlcHB97o7xWpxh92NyfKo+yF4CbBjQVAnD+7gtuP+b9oNIih7AUrG534KIB3t8QgJb2FDaQJXxoDAKJTsAdAdh88jo6cfWOGBPnTlWP9lwNNOpBjJa+bGbXGtQkIK+rJobfJ6YAfH/eHlq496J4Lsd7Y+uZviS2BVANRcPiSs6XQ33cexh/5CnHxXcAS+Ty1cLXqDlt+buC2TLS12tsKT+NBKC89ygDTdctmYvmvFnbYd7OcvByn2z5k+FNVI9UIwE4f3cYffi77ciZLc58zO6JANSHfalRPIddOBk5gaI5M4lsHvyPjt5z9tgdG6uTHNmmHA3/55D4SN6pU9fao82qNOpatzj1ae5PLCrVwgGoP195XFjh3S1vNy5J36w9RfLI2t32rurj/Q0B6GqPJPg9NpAlfGgMAolOwB0BGPz5ejp2xXYE5+yOnLMBR0Y/pAojbC8/tsY0KZP0BGCvX3fR4gOXxRj5Dhi/WPWl4ohlFJGIR8DjFx+h6XFZIVQB6CzXLceXK57LV4gILmqeYBYdvulTa3lmjQTgR3/sE8eP0omiVomc9FvPOg7zvh31wMF6xZX0oUn4M2kdlnvry06VqW1lmwWQLaxsaeXil8dXeLCqd/fMWgA5W4eap5fHraaUkxPgtG6h1+8Kx5YPf9tLkfdtx/f6MigkgMYtPio+5uP2AyOCSb3vafWH2K1ecWEBlL8B2d+afo3p8xXH6Z99tn94uFPeqF9COC1JpxV32pqpi/c3BKCZfeK0DjaQJXxoDAKJTsAdAajewdo3vIU4rjNb+ChNWj9mv16TGvnnMdvUrl5iWgC7z95BK49cFc/jy/l5s2RwGGNiC0C+3yg9QlUByE4hMl2bOijOMMECkI8CuRwcGUwcq5DLu01LETtOTFtryzNrJAB7/LSTlh++IvIJbzx5XeTn/fPtug7zvnn3gcP9NdnnuRtRWho1/ozvo/G9NCMB+PvO89T/z/2ifz66ZrG1X3HecObRqnqRc1t92Jc6frloS+gNh3HLet++Wo0+/mu/0yNdFmd8FM6Fj9uZo5r1xaPNqjTqXr+EsACWi1sb+RXfvWXxzuFx3C2v1i4qwsfIsDXutndVH+9vCEBXeyTB77GBLOFDYxBIdALuCMBWX26gI3GhNjh8RS4DC5mzAasWJL6Q3zAJCsBXZ2wTIojLtkFBlC+rowBU74V5+w6iEG1z9xCHSpHiSq6PPuyJ5MxOF8Vz+2oBrHldqsXlCWar1s27D7XYhkbj7fTdFtoaepM4Vy0/l50pFvSq57CMHPy4xlhb/mG16GPT8Xdr+zUWYzIUgDvOi9h1XIrlykRZM6SlAxfivXedCcCHjx5T6Tgvcm6rD/siBax+fIEFsoo9y1kzhvx90Gm+4HealNQspexhfHhUSwocutTw2N2TH2WPhn7Up5k/BQ5batecg3p/sfI4LTloszy7U16sXph+3xkmPK85cLW3C97fEICW9hQ2kCV8aAwCiU7AUwHIAXHzGggkZwNm71YpTDiJfYPSSc8C+MK3m7V7ZJsHNKWC2TM6TCexBWC3mdtpzbFrDgLQmfcpp4JjS9pXq233/Fb1baSFCRn7fHli65x6pKyfkBT1XeoUEyFwOJzKwt71dur5EQAAIABJREFUHeZ9NSKaao5b5fA5C0AODcRewLLIvLtyb3FqORaqXOZtP6c5W3DOXbYiH7wQH7/PmQBUnYi4H33YF3Ze2XDCJt7VUqFQNiEwv365Co3455AWvkZfjy10Mh+vzK1bZsgSuh9j7DTi7g+zZyObAJSOULL94vca0JerjtOyQ1fc7ZLY+st3VmXqOrc7cNEA728IQEt7ChvIEj40BoFEJyBf0i/XKkrjnq+Q4PPUO4DOBJKzDlQLUlK1ALb5eqN2HLnx4yZUOEemJy4AVRGqHgE749q6gk0ASkcPNaXZ+HYVhDj7TrlTqO9Hpvf7oFlp+mLlCZGd4993Gzg87nJ4NNUebywA9enZpMCXe+url6oICyMXGcaH/8zChT1uD110LQDv3o+xOz6tWjS7Fj+QxXHD0nlo3XGbcFZL5SLZRZgZvoc4dtEREUfPqHSuXYx+3mpLx8b5iYU39eAlhsfunvwoezUuSR808yf/IbZYmLJwqB7mvvKI+wKQs7aw5bBwjoy08eOmngwrwTZ4f0MAWtpU2ECW8KExCCQ6AXcEoOoFzAFxi+R0FEjOBqxakJLqHUBV4DqbX+VRy7X4c4lxBKzeszQjAFkE8HEre4Ny4SDb3X+yZfZgC+AZdoDYYMtuYjRe6egwqm05GrbwEHFw5qUfNHRYRtXTV/2S+zx59Q41m7xO+5itbUKYDlwsPlMF4K/bztLgBQfF53x0yfEB1QweziyAei9k1evXJxXHL8xLq4/a7m+qRdbjOH8cK/FSeLThFuV4ijIWX9rUqejE2BAqNWgxxTz2IOifwRN6NylFLLJlMHRZhdP6cSgeo7G7+vFz0O5VR68Sezqv79/EVXW3v8f7GwLQ7U2jNsAGsoQPjUEg0Qm4IwDVOIDsvciWJ7PlUvg9qjN+tag+q1sN8cL2pCSmE4ia6k7eY9OPMbEFYP0JqynsVny+ZXW+Rrw4wDMLACnyJnWoSB/FOVmMec4mAOXRpl4AsoNIyUE2kcbHtO/N3SOcK5b3aeTwqPM3o6jBxDUOn3OfJ65EUvPP12vfsSWZ76dJsaMKQLayDf3bJgALxAlAea+UP5MeuPoH6b2QVa9fFoBNA/JqDjxq29p+OcUdx09fqCS8bVnIGhU+opaeuNxf6PjWgg0z8kZ5rykLQH/yi+Mt+/zr7bpCABpZL42eq14FkMfe7BG99iMIQG+sk74PpIKzQBUC0AI8NAWBJ0DAHQGopoJb+WFDKpU3i+kRqhakpBoGpva4VXQ5wmYhWt23EfnlccyMktgCUN+/KwHIuXv5CHDmpjNi3INDAmns4iPiz6OfK0+nr92lHzcZWwBVUcXHtp1/2E4l8/jSqr6NHdZV7+krK7AAPHY5koK/iBeAH7cMIA57Iu+7sUXwmYq2I+CftpwRlkYunKUjp296zbEoIQHIOXk5N68s9Urlok0nbV6/qX1SCQG44rDjMap0DpnYoSJ9tfoEnb9pLABZSKv38HheJQYu8ijrh9GPgq1/HLhZWkVlnT/eqiMEoNH9RaN+WJxKTVqzeE7afuYmcaib1f0c18z0j9NJRby/YQG0tIewgSzhQ2MQSHQC7ghA1UK29IMGFJA/q+nxqRakpBoIWk395Sw1nlonMY6A1WNHM0fAnO2jYPYM9NMW2/011Zt1dNtyIgaeFIf68UpRx0Gmf3qjJr3w7RZyZk3SO3qoAvDo5Qhq+cUGbS9wgGIOQVN2mC0czdSXq1LrigXEn2dvPqMFXOZsHpzFQqZ34++dHQHrnVBUpw/2kGYOSw85etJyuCG2rnGmj2/XnbKLHcjPkzmFOQXe2jjnG/6cUx3qxZrpzW5QkcPMvBdUWvOMllXm9agthKkUs66ewWJXWiUrFckugmg7E+2u+nL1Pd7fEICu9kiC32MDWcKHxiCQ6ATcEYANJq7WLCh8eb18oWymx3f2xl1qNMnmKZpUU8GpYT+W92lI/vkcLZycy5Vj4nFJDAGoP+J2ZQHke2D5smXQYgeyM4+MI8jZLVi4cV5go/EeCAunZ7/eKOLITX2lKrX/ZjOxZ+6G/o4OBXpHD1UAHr4YQSFT4gUgj2FgqwAt6LEqANVUd7kzp6Ncvum14OLcp7MjYPUKAddTBRsLwBbl8mlBvNVN2SzQdjTM9yE5aHJoXCpDWYfHcP3OA5JHxfLz0HEhDse1pje7QcV+Lfypd1NHAfhr91pCAPIxtZmiCkC+r8nimVPqrfjQ8djeTH8J1cH7GwLQ0h7CBrKED41BINEJuCMApccoD4ovr7OHpdmiWpDYUaFZWeu5gDm2Wv/gMm7lJHY23qUHL9Fbv+zWvnZm4VQFIGfa+Ci4jEhn5o0S8+ixnZOAGStUkzJ5RMDq33aeF0MIqZBfE0Ijni0rBCCHd9ELQBZiW07dEEGg+d7fpA6VqO3UTaIez4kDd7Mo4pzNm09dp2/XhdJ6Ay9bFsGHLoZT6ykbNQQcmmbscxW03MHsgHEgLIJK5vXVHEC4MgeLZgF2/IotvaAs7DDRt4U/peILb3El7FYU1Z8QfwdRCjv+mgVgcPn8hsGU5dEuW0PZSipTGcp+WfDysTDHP9xz7rb2PLbW8dGst0r/lmWoV+NSDhZA9oj/evVJcZRrpqjxIDmTCgtaZ/c2zfSXUB28vyEALe0hbCBL+NAYBBKdgDsCsM74VZoXJYcbqVYsp+nxqRYkTsvFd9c8KXqLGDscsMXJalHFLffF8dnKFnQ84lYFINd7q1FJGtAqwOrjRXs1Wwr//dS4EM1Jw9kD+IiTU9b9tTtMVFHvxg1/tqwQCDK8ibRYhkc91MQZt+GQKqPalqdnvooXcfJ5ro6h+fuDF8Lt2rJ17rMXKmlxH/VBm2XfHAKG05jpBSB/r78nqlqQ+fsWZfMJ8cqFvXZblS9gmE6NvZEXHbhEbA2du/2c3XEzt5VWNPn/XllIg054j/Be0e9fvg4xdc1J2nn2lkMro+DfPNeHj2yOKXz3kx2GnHluW50L3t8QgJb2EDaQJXxoDAKJTkC+kF6pVZTGuogDWHPsSi2O2m89alMtv1ymx6d6in7XuRq1KJffdFu1ov4F6i0BFjB0CUU/jA/66+yIu9roFXQj7giYx8VhWL55tZpHc9E30nu6nhjbyi77hdFD+C4c36P7e68te4hqyRr2TFkKvX5HpAvjIgWgPqYfe9QOf7ac3TGuOwJwf9htavO1zXrIhbO8fPpCRao51jFuoDoHDgLNAlBvleM6E9pXoI414oV96LU71PSz+FAzMgYe1+U0eWz5lAzUZ8gMJ8yCw7yoHsdcTx79ckxCZx7C3lhcds55s6GfgwBka/jUtSftrI/yeWzdfaO+H70+ewc9iAtIraYEZHYc15CznSx53zF2o9Vx4/0NAWhpD2EDWcKHxiCQ6ATcEYDshcnemFzmdK9FdUvlNj0+1VN0eudqFOwlAcgZHIY8U9b0OIwq3o95RGWG2FJ0+aZLTXcfPKJ/etejioUdj7j1ApAtTHx/zhtFn2/36OiWDpkj9M9hL1e2pP0bl0tWtWQNaR0ojoB/3WYTgHykzMeqF2/fo7qf2ELycGGr4bBnytl58srvzFgA2RFBHh9zOx4Th10xChytjp/v+3G6PY4jqC8cxPqlmvEC8OTVSGo2Od7TmI+Z5ZxZFPHf5++5YNcNnyC3rVRQCENm8eeuMAcLoBSSnP/3zv0YbyyjYR/8/O4NHAUg5yj+Zu1J2qfkQ5Yd8P3OH7rWoKafrhXOPFxUAcgCmq3G5QpmpUXvQQAmxuIhDIwFqhCAFuChKQg8AQLuCEBV/LibzUN1FOCXXsvy3rEA8j28EW3KWSIls5SwYCiQNQNdDI+mBb3qUpWiORz61QtAtjBxnDtvFDVdHvd3aGSwXfYLo2eweMuSPq3mAcsxAc/djBJVWXSwcJBOIXykzE4E+vt0bLFjC5kazNkdAbjn3C16ftpmbXh1S+YiDrui3tkzGjuLLg4Fc0rnmMF1OYbhq7WLac30nsYyDZoURW0qFxQCTy08V643f/cF4ZTy+87zDs9ikclHw4ldmO/r9Us4WAA5RA57J6vp8ORY+JoEX5dwJgDZe/vew0fE6e7+965j+j6rc8L7GxZAS3sIG8gSPjQGgUQn4I4AVGPUuRvKRb0n9s0rValVBVtYEHeL/giYU3hxvDsrRd5PZIsUW9PYKYAD9FYr5loAPl+lEH3esbKVx2tt9bHu9g1vQZx7OKFSxy+XcEKRqcQ4tAoLWi587MhHwHO32xxE+Eg5bWofERy6sZK7l2PoDX2mrF0+X/lMMxbAXWdvCQ9iWfhImZ1KGk5yDBytzoVDsOTPlsHBM5frcGaS1+oU16rrHU3aVSmkWfw4ddtzlQvS7zvtBSDfoXuuSiEhDDk2IQs9KY5lx5yibVpcFhWvLKKTTvgOYpe6xR0EIKeom74u1C4biuyCHVimd65OjSet0cLX8FzlcbCMCegsf7PV+eD9DQFoaQ9hA1nCh8YgkOgE3BGAFUcso4ho2zGZu44cMuQIt532SlUK8ZIANJPD2BXE3eduUbtpm8WlehYNZ25E0Z9v1aHqxR2dXKqPWSHChsjSvmph+uzFSq4eYep7NV8yN9g9tDmx00lChcUWC6k1cTHssqRPQ5FxR5mDQgKEuJq3wyYA+Ug5Q9rUpD9OZUvT0NZlDQWbOQF4k9p/s0UbJgcontChoqGgVOeSIa0PFcyWUTveVL9jB5Zu9UpoH6n7hz/sUK2wZvFLn8aH2lUtpAld2YiPS1mgs4c0ezb/uvWssO6qpU8zf5q+/hRFPXhkao08rcReyJ3rOApAdpb5fkOow9E0P4fvNU57pRqp4Zd4rvfj7gPKsXA8wIXv1PN0aE7b4f0NAWhpU2EDWcKHxiCQ6ATcEYAybywP6ttXq1LL8uateHvP36bn4sKMqHHh3J2g3gLIKccmdrAmwNYeu0pdZ+4g9laNfvhICBJnTi7qPUgeO+eQnfSCtedLBvpgx9sHB7l0pGCxlT6tj5ZJQo0Tx56nnAlEhog5PCqYMqVLQ/q4fSw0BrcuS+wJrS9mBOCOMzdFEGlZqhfLQZ+0r2B3Z89ondmaxc4XfE9RX9gi+Ub9eAGoP2buVKOIJmxZFLWvVlg76tYEIH9etbCw/HEgZg4DI++wyjp9m/vTnO3nnOYINrs/1RRtRm3kkbZ+/05sX1EIQCNHGA6ezb8V1UPdSACy48+CXhCAZtfKnXq4A+gOLV1dCEAL8NAUBJ4AAXcEYNlhSzVLiZrf1cwwpZWN66qpwcy0VevoX6Bs+Zn8orUj2IV7L9D78/YSH6deu3NfOCXMfbM21Snp6OWsF4AsRD5pX9HdaRjWvxIRTbXGxXvObhnYVMuf7OwBLLZYSG0+ZUuLphY+9mTvWfZ+5XJgRAvKkiEt6b12+R4jHxcbOW2YEYDbQm9Qx++2ao9mQfJJu4qGTiXq+NhCx1lM2OKqL9JrVn6+66y9lVENeM2WRLYISm9n2Ub9nNOwzd5yhm5HPbR7FFsG/9l70S4YtSeLqQpvo/YyXJFRGKMZG0MNj8Hl/dK641dplkuj0DB8VYGvLHi74P0NC6ClPYUNZAkfGoNAohNwRwCqoVK+6FhZ3K8yW9QXuLviMSEByJf8v+xkzQmD4+QN/fsg8Z0rtkZxXDpnXs56AeiNI2g5P314lg39m1CDiQnfo+MYfmlS+9D2046BhDn4MB8BS+cIvlPInqN6McXHpANDAgytjWYE4NbQG9RJEYB8JDn++QqGYWXUtWQxw8fuRgJQxs2T9Xl+L06PtzLy3U8Z35CFXsfqRbSA17INO0m8UL2wsPy917SUyATCHt5q4eesPnLVdCBmZ/tdjc/njgDko+EfN50xtILyvcYvOlUhNUe1Ud81iuegP96CADT73yJ36sEC6A4tXV0IQAvw0BQEngABKQBfrV2UxjxXIcEn+g9Zol1A5zAfbHUxW9QX+JSXqlCbSgXNNrWrp7egyGMyjzqLa8SBeCctO0Z8nLw/LFzcx/r5jZrUoHQeh25rjF2pOVnwl95wQpEP0YdnWduvsZ2zhtEc2drmkyoVsSOGvrB1ix1c2AuWy56hzSmHbzrSCzZex0EhgYb3Dc0IQM4U8vL327THVyycjdjiZRRYWh0jOzEUyZmJzhpYAHns7zQppVXnrCUvfR9vZWTvb5nijoVexxpFtL/LRhzS54Xqts85R/L3G05r+1fWYUvjttM3RLo4K0UNz+KOAOS7jpyrWe+cwn2wo8vkjpVJv+f0/dcskZN+71nHyvAN2+L9DQugpU2FDWQJHxqDQKITcEcAlhq0mGIe27IQ6AP1uhqoKjrY87FtZfPWQ7VvvQCUnpKunp/Q9+MXH6Hp60OJYwryUerhSxE0+/WaxFk29EX/Mu5SpxiNbGvNC1k+gwMRq/fwVn7YyDA0izomtrZx4Vh8+sL5ZznEyoK4+Hg7hzQTWUM2nLhGnX/YrlXnY+yBrQLtsoPIL10JQI4tyMxemREvADkuHQcVl3c+E2Kvhq1R68ncufKzjSeu06s/xD+D7weyRY8LC0AO5/LjJtvfZeEwMywMuR4HDGdnj1jb9tUKh8rh9ZYi2dN9ZHQ3T+2LcxG/UquYgxcwC1AWqEZBqKWDkd7xSD9GDmY9rwcEoKdrl1A7WAAtUIUAtAAPTUHgCRBwRwD6DVxEcfqP5AvN7BBVK5G7x8fqM/QCkHPCzuhSw+wwDOsNnL9feJCyQ8Cyw5dFTDZ9KjLZUM2Gwp+9Xq8EDXvWWiBq2bc+Pt+yDxq6vEfH1rZHj2Pp0MUIh7nxfNi54J99tiwh2wcFUd6sGWjN0avUbdYOrT5ngeGj0AojHEPOuBKAHFuQrXOqOOPMFOz0oIaGcbZAzjJwsHfu+81Ka83WHb9GXX6MF62cB/q79aHie/aC5jmwhU8tHNaHhSHXUwWjWoctcGx9YyuclSJj8jnrw5kA5HuaP205Y+iEIh2c9OkH9c/guItz3qxtZfiGbfH+hgXQ0qbCBrKED41BINEJmBWAsbGxVGLgYm08+jhtzgZ67kaUuOi/NfSmJhI+71iJnq9i/vg4IQHI6bJmdqspqjx+HCte5sVyZRIZL8yWXr/uosUHLot8sZxTl4+Bf+hSnYICHfMV6wXgmw1KCA9ab5TzN6Ps7vwteq8+tZ7imJ9XfVb5Qlkp5lGsYRgR9nzlDCycC5fL1oFBIu7eskOXqefPu7Ru2Ir5casAKjtsmcM0XB1Dr+rbiC7cukevKeKsTL4sNOb58naewc74FMxmC7ytL+8FlRaeu7LoRevbjUvSN3Hx+1gA8lE8W3HVkpUFYK2iIs4eX3HQO4lwXd7HN+48oC9XnbC0hDKDjLsCkC2dfJfxSoQtdqNapIORGn/TqH8OBv5rdwhASwvopDEsgBaoQgBagIemIPAECJgVgCyu/AbFC0B9mA6joS4/dJl6/LxLHKWyBUaKhMkvVqJ2Vb0jADkX7s9v1BKPHzj/gAj5IWOumcX36oxttPHkdWJhOnvzWeKQNc7iHOoFYM+GfjQwJNDsoxKsx2JZDZ7M6ejUHLtGjTl0zYNHjw3TqX3QrLTIfbvs0BXRdNOApiLsyqL9l+idObu17nht2GFEpsNzdzL6bBql82amUW3L293Zc9Zn/qwZ6HKEowDs3aQU9QsuozVbcfgKvfnTTu3v/P3Xa06Kv7P44hh7nFFDLRzU++WaRUWgZw7XI72h1Tq85rfuPqRR/x52d9p29dX4i0YdOQsDw2vEwlQfnob7YK6cEk+Nv2nUt/obsDQJXWO8v2EBtLSfsIEs4UNjEEh0AmYFYMyjx1Rq8BJtPPowHUYD7Th9C22L806d1a2GiLXHhYPfctw2T4r+CJhDt8ztYbN+yO/4xb93WAvT3bN3KTupcIDqGRtCafe52+QsX3GtcSvtrDVsieJjPG+UszfuUqNJa7Wu5veqKwJU64vqAcu5fzkwsFEsPQ59whlYVh21OTiwVzE7Xfy95wJ98NterVsWsex0oa6vlfmUzOMrBKB6L9BZf2rmErUOZ+jor3BdevAyvfVLvNWSLYRT4qx2fNevS91iNHWNvQDMkSmtSCf31eqTwulIHoXznTm/PJmJU+99/XJV+m3HeRry90GXU2bL5rErkYb18mZJT1fjMrDICrw3t4TawvM4E4DvNi0lcjVzHmh9kY5ZavxNo4dzKj9Ozejtgvc3BKClPYUNZAkfGoNAohMwKwDvxzyysxDpw3QYDVQVgD92rU6vz7JZcNz1IFb71gtADoT8+1u2C/DyOz762z8i2DS7Dt9spp1nb4ng1jM2nBZ/dpauTi8A9ZYq0w81qKhP0fbHW3UcjlFl7uNNJ68LgcUC8O6DGJG+Tl9YJLFzCN+f48LHucVz+9IfnBnjz/1adRZbLADVI34r8yiR21ccp6vHws76y+Wbjm4YiJ+ejfyEY4osiw9col6/xlst+Xh48orj4msWgN3qFRdCTy05fdOJo2E+3m1RNh8tP3yF2PM4dHxru3rztp+jAfMPuJwy34fs9N0WcZ1BX9jqqQ/mrN6f5HSFPBb9/uV/QHCu5vB79vEJuf/X6hQTQlqNv2k0yMZl8tCsuGsQLifhRgW8vyEA3dgujlWxgSzhQ2MQSHQCZgUgZ8gIGLpUGw8fGfZqHB+mw2ig6styxmvVqXvcEd7EDhXpxepFPJqb/gVauUh2+jsuDZb8jo/jDow0LwDbTdskrH7fda5GMzaeFtZAZ9lK9DHZ9HfVPJpUXCO24jVRcvTOebOWXXgVriadTqRTjX++zHQnOsb4Hl3TUmJefLzNZXXfRsLyxYJj0IJ4wcMx8j5sUcZBnHg6F76DOeLZcnaOJs76YmutPjgz19XfrWTr3Xtz92jdsGDl0D1ceL1fr1/C4R4fC0AWzCwU+RoCC2H21j02ppXdcH7feZ76K4LY2VhZ0L38/VbDoNscjFkfiseMAGRnFr62EBmXYlF9thT7avxNo7FxLucfu1pzhDLqF+9vCEBP/xsg2mEDWcKHxiCQ6ASkaHIVzy7qQYydk4A+TIcrAchHqtLxgNNfvVjDOwKQ78Atfr+BeLycCzsFHB7V0jQ7DlfC9/5YpHJWBrbwOItVqBeAfIfrg2bxzgqmH2pQkWP2BX22TvuGYxGq4Vr4Cw5VM+SZslosv1J5M1PEvYcOx49cl62TO8/e1CxWKz9sSKXyZhFep8MWHtKeIz1u9eLa07kUyZmRhj9TThP8CfXDnrpG4kfvXa0/tuZj9wlLj9oEYIY01L2+H32+0mYRlIUF4Ov1itOny48TH/vyuhr94+CvXWHU9499LqfLgk7eF9VXZhG2Ou6oXX5nRgDyPH/bcc4hQDX3IRmo8TeNBukNT3ijfvH+hgB0+aNIqAI2kCV8aAwCiU7ArACMjH5oFyZEH6bDlQDk49W3frEd4bkbQ1DtWy9S/PL40uq+jUUV+R1nhjg62t7KkxDINl9vFJ6/fEzNMeM2nbxBzmIV6o+AOdTKu0Hx4UqsLBinoGs2OV4AqvcmZb/S6UQG1ub5swXN6A4ZBz/mejvO2IJEc1iZMvmziHuOYxYd0YYqgy5bFYAyHRo7mnBoHNXT2BkXPr69cz/G4Wt5/Cm/0Iu0QSEBNG5xvADs0cCPPos7EpZt+A7gmw39aOLSY8QBs/ecu0382R7d/VC9uHQ2VhZ0fKy9Pu5IXa3HWTv+3msLtyOLnQBsW044qugZ8zzZAhn98LHDY6XYLz14MT18pAtgqNRuFpiPZnSpbmXrGbbF+xsC0NKmwgayhA+NQSDRCZgVgHxHqdLI+DhxZo4+1SNgdrCQd7g+aVeBOtUs6tHc9C9QFhvs3aoKQM6Ne1x3zJfQw575aoOI/ceCiwXghhM2j2CjUDX6QNBmjsLNTvTk1UhqNnm9Vl09NpcfckBjvn+588xN6vDtFuL7duzMEGFwhMh3+9gJgYUPl8XvNaCyBbMKb9lPltjEExd5n9OqAGTuD2IeU4FsGYi9xNU7e84YOIufJz1gZbvfd5yn/n/F31vk/kfHee6yBZC5yCNh2YaPl99uVJLGLzlKHJuQPaLZWWP74GZ2w/nfvov0rnK87GysLOi6ztxOa4/Z7lSqhUPpzN5y1u4zVQDKsEl6xpxK8M+dYcKTW1+k2C85aLGI9eis8P3G716DADT7O3OnHsLAuENLVxcC0AI8NAWBJ0DArAC8HfWAKo9aoY2IvRf7togP02E0VFUAcv5f+ZLl0Bb8gvek6F+gnNmCM1xwkd9xXtYTY0NMd9/qyw1CHLAnJd8BZAuPM0eVaqNX2DktDGwVQD0blTT9rIQqnrgSSc0/jxeA375azc7zldtK71iZz5fv212PvG94hMiiiO8KsnWTy7/v1qfyhbLRV6tO2FnLOBtG9wZ+lu8Aylh4LLJYoJkRVc4yaHB6Ol4DWfieHIf5kWXEs2VpxP9soVvY6eftxqW0I2FZh/Me8z5laydbSjkvsvoPBllP72DibI1Y0L0xa4fmVa3WY2ur3gvZjADkHMYce1Jm2FH7lI4wJQYucshgotZrWS4/fdu5mlf2oNoJ3t+wAFraVNhAlvChMQgkOgGzApCtTNXGrNTGow/T4UoA8pHq+/NsoUfczSIi+9bHIuTP2fpzIM7jV86FjyI5Q4XZ0vKL9SKQ8i9v1KIfNobSmmPXyJmjSpVRy+lWVLzHpplwOGbHwUGbg7+IF4Cq1VT2IYX37nO3RIgYTqV2JSJahILRFxYQG45fF6nOuHBcwYqFswunCBlChT9nMdW1XgnLAlDe52NRPvSZQG29E5o/i3Wj400+Uv2iUxWt6S9bz9qFauFYj0Pj7jGy0OP9yJY+tfDnbKlmS6HMOFI8VyZa+1ETu3r6EDMJCcDus3fSyiO2uIpqUe8kys/NCEBO9zZ/T5ihwJPWXleW2ZAK+WnaKxCAZn9n7tSDBdAdWrq6EIAW4KGH+Q6BAAAgAElEQVQpCDwBAmYF4LXI+yIpvSz6MB1GQ1UtgJz+TcaekzHR3J3ew0ePqbQSi5Dbp0vtQ8fH2u77yblwEpDTulAfCT2rxefr6PiVO8Retz9sOC0sPM7uKfIxuBqyg++i9WjoHQvg0csR1PKLDdpQVdEsP5RH7+y0ws4rhXNkpMvh0cYWpIZ+tObYVTE3Lgt61aUqRXMIS5nMosGfOwtR4u768P06Fscc2mVw60D68HfXjhUclsXodLN1hQI09ZWq2hD0jivjnq+geTKrlj51zGwZ5HAxbClkUcrBltlphnMsq2Xl4SumHFZY0PX4aacIJ6MvzHCoLpagKgA5LE6Xuo53ANtWLkgLdXcHZd8yxqQrAdi6YgHhte7tgvc3LICW9hQ2kCV8aAwCiU7ArAC8GhFNNcet0sZjJgWaKgA5+LP0tJSCw93J6UPRyPah40LIxyeVnQWLX75mS9Bna+nUtbs0r0dtEQeQLTwsMPh+lr7og/J68wiYj6H5OFoWzpiiF1HS63h/2G2RJcRZKjXug0OM8Fz46JPLX2/XoWrFctLYRYft8ubKI3lXQsMVTxnUme/eDQoJNBVaxVmf+nttP248bZetgwX6x3/ZjoRVS59eALKDC1sKZaYOvgu4JM5rXNbVp5lzNibeU2//souWHLzsUEX9B4780owAbFU+v2F/3IdZAfhspYLEVyy8XfD+hgC0tKewgSzhQ2MQSFQCan5fV2FgLoXfozrjV2vj4fRhfM8roaIGgp7UoaIWfNjdVG3yGXfvx1C54Y75ao+ObkkZ0qb2WAA2/XQthV6/S7/3rCOOgDl1mjMrZblhS+3u2/HRH7+ovVEOXQy3y/2rMpP9S69jzvDxzFcbhVODPgOFrMtepCuOXKGzN6LERxxYukbxnDTin0M0a/MZbcjyuNuqAGTnj0vh0eJOHgtAM8GVnXFTczxzHb3nssqGBSdnPRkZdydQ9slH0uzgMnjBQWEpZkeLioWz0T+969s9luMDdlFyGSckAN/5dbeWW1mtx7mj35gdn6qOvzMjADmEy8ojtkwt+iKvWbhaF85ywmGLvF3w/oYAtLSnsIEs4UNjEEhUAuxZyB6GXPRhN/QPvnD7HtX7JF4AyiC1ZgUgx/6TXpzSI9LdyUVEP6SKI+I9kWX7fcNaULZMaT0WgI0nraEzN6KEhYwtgGzhcSZSA4cupXsPH2lD96YXsBR1snP2ltaLKBmyRYpFeexqxJJFOt9v47XjwhbO2n65aPCCAyL9mCwyN7MroeFqvfg4OuzWPWFtGxBiE16eFn1+2+nrTtnd8WMv7T6/2Y6YmUGf5v52sQ35cxaALERV5xEO2PzX23XthiWzqrgaKwu63nN207/7LzlU/a1Hber43Va7z80IQJ4ne50bFXYs+Sg4wOXdTP19SVfzMPs93t8QgGb3imE9bCBL+NA4mRHgy+jsKclZF5wVvgc2aekx+rCFP5UrmM0UAW7Dsc74vhN7ebKw6/fHPhH77LU6xZ32oeb3ZQHYL7gMffTHPmpbuRCFVChg1+78zShqMHGN9pkrwcgVnaXNkvehDoSF0xcrj9PHrQLIP18W0Tff0+IXdqcaRSgoMB/N3nxGBGlmj1AOeFxldLwnshzMtkFBlC9rBrsXJb8UuQ1bx4YtPCiC6tYtlduQRYOJq0UqNc69y3cAFx24ROydyWNhJnxsKIs+KK8UZHwPj4VV13rFqUHpPKI68+Vj7+rFc4o0YLJ8vuK4GFf4vQdUJEcmkUKsbwt/4QjAVj1Z2Aqpz1ErBefhixEUMmWDSINmFEeP++A5s4fr5Yho0eWc7rUEg/5/7qPfd4Zpz5ExD60KQHawYCHtjVKrRE76ractxR+XqWtO2oV5Ue9HsgDk35T+Dh4LUbZSq+Fj9P1y31tO3aCXvrcXb0ZzYEH3/rw9hnf2Fr1X3856y+1VAeiJo01CAlBaNPk57aoUoskdK3sDu10feH9DAFraVNhAlvChcTIiEHYriupPsAmok2NbUZrUPoazqzxquQjsy0LxkMlsFvUnrBaWF+n9ql5qT+guHMdsY0HDhQUdH6N+tz5U/F3f7uyNu9Ro0v/ZuwqwKaq2/YiUICDdndL10t2iGKjgJwYodgLGB9KCKCgWdoBiIQgYdHd3d4M0SEjqf91n9pl95uyZndndV/z1m3NdXvLOnJozZ/fc+8R9z7Tn3K56Aep/W7mob8gNAPZqVZo61C6srI8Aq7kypqWF3RqrvgBARy6zwIk8QKEkAutNVZGJzIPPeaEh5c+SLsJSgizaEUv22Fq4bmsByyasZD8+UVvRwIAXjgvciysFcbBOyssAUBIE8zhSvoyvmTKZMRbcpl93rEGthoQBIIByr5/Cih2oxzGHnDDC3HumFwF93J9XHVBAFgXKIgCnnUespNEr9tlNWPauar8pdOT0hbg/dUWzp1exlMlRdEudTl0z5O5KNGX9QQXGkImdLs3VERZHAMBeN5dRP4a43FklHw0S9DK4zqTaXvPGO+w0YiWNEWvHbdb1ae4ITyibNyP98lRd4pABVmGJBrJ1MmnWmR40aWMExQz6B3clih9KJq9nM90Pzu8AAMazb+w2wQZKaPmCxv+iFZAkvyApxsFtKvKA8JvIUKTrODuTEm0mrj1gq27sGNCSrkJarKGcv3SZSna39H0BAGFhYzUDfWxdp1Yn6jX17wYAe95UWmm3mp61w9DFioYFRQJAuPxqF83mSEThMWc/35AKZI0EgEgUAKBlUOK2nrUGTFNauj8/WUdJwelZmbKdXGuMzzF5NQdMU/FvPG/8X2ruch8SdOtrBgB6y3vz7MuS7JgvMu3M5oOnqNmbsxXodyMJhpv+x5X7bNqaLx6opjRxdTcmwHXzMrmUJbH954tp6S5LOSTWUjJnBtp08JSvZqzN61ZZj9WDpfitqVvs6h+0q0xNS+ekrYdPE8YF0Nfd5bCOvnxrGdtVjMYcLyrHZU5FvuaWmYt32Pn7lTR6eRg8cxvcQ5ws3sfJsxfVDxL8oIKCztHTF6hQtvSqajQAuKlfCwIVEJJ7UBjY4UcD1ELk88GijCQlZKRXLZjZ9Qelr5fhUik4vwMAmMj+CbSAE1q9oPG/aQUkx5vpEOJnjQcAlu89yVaCwEE0Z8thW0N2fd/mlC51SuNSyqxaAEBYHmG1kiCGG+oyZXCRvnZH+aivyA0AAtggPs30rA99uVRZdngOXAcuPyQw1BJxiDz4rOcbUMGs6SMOV8TQfTxnu50F6wYAWd4NRMlQAtEtPLKdfoDD7Q5qFpkdzPWh8cqZqnzt9wuX6fqeFujWy9gnaitqFy5S7oyvMWmzrhpi6g/qFLD0sdbu0PZJ1LBUDnp0+DKauC6cyYoEBrjbUV4ctZpGLN0T10cPuszMOejVAYieR4Usvaa6erbuG5M30bvTt9pVP763CjUrk8v+W1cKwQ0AQHBOMv+kKQEE9ZhSB/+GjvQTDYtFqIrwfpQWajlvvz/WogFAZLNf/vNPm+pIWvY465vHxLvtc0tZr2VO6H4AAAMAGGyghFYgaBysgLUCMsNzQ98WdE3qq41LEw8AZBcmH1JLdh6jOz9coPpf0LUR5c50jXEsCUYAAI+euUDjQgHu+oGmq1ToSg2mAdwAoEl5gseTNBs4EIuEklRAc1Ex/3WOOEQec8ZzDZQkmn64+gWALO8GehAkgUCZwXS4y6xpvs+ayFKtgZ9l5NI9duYzX9M1leU44Om77f359iW4l3V5M3afbzt8mhq/EdYNNq0/3unIpXvtpBVIyzUpnTNCzQISeA1K5lBddBuzRlku4ykAWKw64tW+TdV8jjhEvb7O1zdw4kZ6f+Y2u5oErbgIMCldvbiGMIpXby9vK5IkFcpMIx91JoCgHmJR2fUO0Phko2IOqTweFO/QDSAnBwBEHzIu9+lGxex4YT1ByE8Sltc78LofAMAAAHrtkaj3gw2U0PIFjf9FKyAPmbV9mivrhKnEAwCZyBj94RBZuP0o3RXKSJz0bD0qmctKsNCLpFUBWECs2Pg1lmVIP9B0lQo/gefxAMCnv11hWyFhKS3Vw7KWIZ6vTJ6MjjhEfp5pXepT0ezXxg0AWd4NawW6EY5BlAc//i0PZ77HvHym9/bDsr029yGvpy6px/2AEHnUY7WUugcXWBeh2iELJ9DoLnnT+72nRgH6fklYZ5ZdvTJeEe2ggFKnuJUgg0SK4QudmrZ+P4ZIOmLdYa82UMD4ZfV+o4IJ2uqKHQMmbKCPZlnxqShDOyRRwxBoxd+jl++N4EwEAES8H2sS1ymWjb7qWD1iavLHGTKHQSkD+Ti94B12Hb2avl0caSFNLgAoY0Sl3rbOEYkEn56totMweb0Dr/vB+R0AQK89EvV+sIESWr6g8b9oBaSbaXXvZpQxbSrj08UDAFu/P4+W7z6h+sNBNH/rEbr700Xqb3DbVSucxTgWYr7Khnj1AAAP/Xbedg3qBxpnnXJHfqgn3AAgq2eYnlXGWIHepUJfi/YFurglcl5LjQxWLyg7wGKkWwD9xgCyvNuUTvWUC/i7Jc4DPlr8Hh/SpmcZu2KfrX7CfQBkmxJZ0qZKQV93rE63f2BZblEARN6eFo57wzUm0d555Aw1eD2clGN6wUjUgYYuK218eE9lalE2N/3n44W0YPtRu8m3D9WgmkWzqr91jsBYPoKwsC3Z6S9+EPtn+sZDduiCPo6u2auTV3NCC7eTa83X4M4d3KairacMzr1P70+KeCT54wak0gD1Oqcgf7Z0Ch3uLLkAoLQySwDIMZ88Hjgeu3vwcMby7kx1g/M7AIAJ7aFgAyW0fEHjf9EKLNt1nG7/wLLuMG+d6fHiAYB6BurszYcJ11DY7Wf8ghe8eogpQhIDy1zpB5rugvKjPuAGADmT1fSskqIE9C7VQ+ojAC+w8jV9M6yVy88E4FY8Z4YIAAg1DyR1sBKG2yHN8m6wJMIFDNAkC7czKZGwm870LDILGNrESBCAbi8/kxwDWcCft0+iO0Kue9wDDch7M8JuT1xjHeXdR89SvUFhWh7ZFyeGIFFHPgtn+9754XwHUGOCaAUwf1mvQHA8pUaRLLRw+zFfTW8qn1tZqt2yjkFwvfilJnZffX5eR0PnhcmrmdKGK8i15mvXpLqaEDv68PBl6pIuL8f1ZDylopRpWsLWGdb3gZuFNLkAIMbjvYQfAOA3RNFjPqHyAo7Dv7IE53cAABPaX8EGSmj5gsb/ohWQcXnLezSlLOlTG58uHgAo1QngNoV1p8PQJap/SLDdXiWfcSxkEAL8oAAAggqFVQn0A026sKMdpnIgqQQir0OdAUL3pmeVMWjI7mWQA6UDWAClVi73yW5u3QKIeDm4M70AICdwIJYQLmBJkowxeC3OXrhEpXs6lUg4UJ/HTpMyBW3qZ2kTI57yiW+Wq39z4o+kA5Jrku3a1PTBPVXs2E3cg96ydHviGsu26byMsi8AH5BVg/JEurOxhlCNQKbxqj2WxRgF/IeVC2RW/35l/AabCijWjx9crHO3mkmN9b4ggQZ3MXMU6vcBxFYI+p1eP66lLxaEXdNMas3t4E5+8psVjm5gVQXoZYUOt7CF7YdP25Zl6Bh3aVbS1hmWHWIfuFlI/woAyOEFmIOcI++NrjcEADDWPRpr/av+hE02KHGtQAAA41q2oNG/cAUk2ezS7k2UOL2pxAMAu3y/yk5cWPxSYxXUzoceJw2YxpLxaACAe47/rtxyKPqBJl3YuN+iTC768N4qUd+UGwBk+TTTs8oDFpY9tvhBrQKxjDe+E+bJ48EnPluXSuXKGGEBBNAEfYYXAGSuNmQTw/r1pQAaci2ky5zH1sl6ASCW9Wiqbk9Yc4Ae+9oCgGt6N6MMaVORzqfI/UDT9822FR1qEtBb/mSO0xoHRZU2SfkVWJfKLPJFwI0JcK/zykGv9tZKeenGd+bQuv0WhxzKT0/WpvL5rlP/fnXCRvpwltPq6Pfj6EXtIvuB1u+GX39TBNymAg6/NX2a27e6j11DXy0MW2ZHPVpTEWxzAeE1x/rxNQBAgGr+MfSfavlpQOvIzHX5TqBn/Hyzkg7yaO4PnwndEinv+VmnaFnA/JnjOhIA6vuGZeL8jBlvneD8/hdbAGfPnk2DBg2iZcuW0YEDB2jMmDF066232nsFuLdXr170ySef0IkTJ6h27dr0wQcfUPHixX3vp2AD+V6qoOK/fAWkW3Zxt8aUI2Na4xMnCgBBOAvAw24veYjoAx4/c8FW1gAAhIoDdFFRdAC4fPdxR4ICDvCP76sa9a25uYBZzcL0rP1+Wa/ImFFkVizi+UANwhxpcuDxT9el0nkiASAydH9atc+TB5Dl3UAoDQAodXLlWpik6HAQw2LEknoFs6ajWc83VNObtO5XeiTkfmSrr1v2LpIekLHKyTtoj0xPfS7QwL2zan7FOSe1meV6AMQcPnWebiyf287qxn2WfJNJQ7jO64d/m0iH/X40G5fKQdNCPyC82qDujqNnbHCu15eWVNzTky+k1RL3IXn36FeWq5cL+vjkvqp2OIRb5qy0yubMmIbwA6Xz92HyaO4Pnwk3F/lfYQHkDHOMr1t8mSTaa50TuR+c3/9iADhhwgSaN28eValShVq3bh0BAF977TUaMGAAffHFF1S4cGHq0aMHrVmzhtavX09p05oPL32zBRsokY9f0PbftALTNx6kB4ZZYvELuzamXJmSDwBKdQLonB767ZxteYqWLXj09HmqElLWAADcfuSMrUuqH2hLdx5zxKe5BdTLd+ZmAWT1DBMAlNmeoCdpH3JlQxYNWcCSJoXHAn8fJPB068pjDYoqEMYWQDdSbJZ3m/ffRsoFLGPNJAA0ZfBijEfqFaGKfS2JOsxx3NN11b8lKGHQrwfz8zPAvd3n5rIOSTKQ/eoZuezSd4slRH9IoICFsHmZnDRpncWpiMLgsdHrM9W75jK5Uz1big9Zx+9oiSd+P4f4UcAxpF5tYC2EfJ4bcTSr2nA/unwdSLMr5LesligSbPM1AMDP7k+iez6zEqLgUje5TSWYzp0pLcFyzNyB8jnwmXBzkf8VAJA5JjEH3eIrKWK81jre+8H5/S8GgHJTQClAWgBh/cuTJw916dKFnnvuOVX15MmTlDNnTho2bBjdddddvvZUsIF8LVNQ6X9gBSQYmP/fRpTnOjM3XzwWQEmdArLfMxcu2fFQoNt4o00F4wrLjFRkAQMocQyXfqDpclkNS2anoR2qRX1zbgDwuWYl6MlGxY0xgNIChZg1PBsKXNngmZNZsjy4GwAE+J25+ZANAKG0kCZlJP8iy7sBmAMAsgWS++e1OHbmAlXWtIgRywjXIsvksQQY2kq35NwXG1K+zOlIz6bmMQAcX7rxerr7EwusoEDpQefkYzcuQH61UIKM/hLAiQiamEalctgufdSBFbVtUgFi6UBuxzQ6+PvtqVvozalO6hm/H8+W5XLZNEJebRAvePzsBYcrWm8DHsgU4MeB4ooIc8Df/M65zeR1v9pWb74GtR38iOA1lVm1ciy5lgDPSK7g2E1ZD/tAp6PR94jXc8fiApYAEGC5xoBpdvcyQcRrzHjvB+f3/ygA3L59OxUtWpRWrFhBFSuGRabr16+v/n777bd97algA/lapqBSMq8AuLQW7Tim3IKIh/r/UGSQOmvXQjIKcVAQqMePMN3K5teqgMOKCZyR9YjCFgzIZcENZiqHTp2jav2tQwXWJrgn52+z6EH0sSW3IO7XK5GdvnwgOgB0cwGzfJo8DJHwgRg7rBMT/sLq133sWjUfSKABALYN8RvK5+l7SxklZaZn14IGBQkxbAFEkgesnojFAzgAUEJhTWJY6QD+WA9ZHu6gCoELmAm2+R6sfzeVz2MTCUsFC2gKPxUCsDOfa6DkwPRkGu4HHHrPNStJ7UL0PbhuIkvG+72lYl7l4gWBtamwJFvd4tlsiy7qISsaoLLGK9McyRespII6Q6ZvodcnxwcAkRkudZSjfe6QMXzu4h9KhcOtgDB9xe7jVD7/ddR9zBpbphD1QdqNtebiBgCHP1DN3jNsedbHk5ZwAEAo1ejuZP5MvDZxI30gCKnlHvHzPRMLAOTPCfqVn1X8Ld3DfsaNp05wfv+PAsD58+ermL/9+/dT7ty57b3Tpk0bdVCNGDHCuJ/Onz9P+I8LNlD+/PmV9TBjxvCHNZ7NGLQJVsDvCrACAw54HPr/H8qYFXttTVLWrmVLDHRuc2ZM67D+8IHjZ+6Pf73MtrwADIFkmmOYAC5HPFLT2I20fAAsAQAyjYcOACW3IDoDuBj+YCSprhzIDQCyZcN0GEqJMKmEgX8DJEkLmdfaoC/Q78AaZirsEuZ5LHmpiaKN0TNvwdtYvreVLa0X0HHUK57ddjOWypWBJj5bT1WDDi8DcaaqAaAxubHxnp5qVNzuB+1bV84boTsLRRQALQla9DmVy5uJ1uw7STotCwD1PTUKEhNfczu2TuJvgBuAnHjKbZXyRsjoufUDcLvzyFlavNOdNgZuznemb1U/kBAzK8GldFtjDCMAvDoFffNQdTt0gSUI9TlJ1z4AIMi2O35phWvIgs/E65M20ZAZYUk6vu/3x1osAJAt5RhDf98SHMbzrvy0CQBgAABjAoC9e/emPn36ROytAAD6+bgFdZJrBe79bJFrLFtyjRFrP8hGfWHUatWMrUF8GCB2qnD29BHAw++h8sjwpXasFw4NgMnnQ2NBPg0as6Yi3UqwDG09eNo+kPWx52454gAntYpmpW8eqhF1GdwAIFsvTIch+mUrJKxrH8221B/QpkrBzI45eL0DcM0t33Wc9p88Z6wKbj54GAt3Ha/uI1EDLmApOYbrIJpuMtgsu4ZM3UoFMtsZqMVzXEtTOtdX/UnQz4kWkg5ITgqAGmDy3s8s/kaUWyrmoR9XWtrMXKCI0rJcbpIJPPrDVS5wnSIG15U58OPgvpqFFPk3rK1cZEzqx7O30Svj4wOAJsBqWnhYcmE9RtYuv2tTvevSpVL61Cjg8Bu35oBdjcm/+YIbAPz24Ro2/yYDYH0smdyDbOz+rcvZmcOyLj4TgydvUqBUL34/q24AkF376JfrSIul/r7drJmmdYz3WgAA/0cBYLwu4MACGO9HLWiXnCsglQ78fjEn5/imvr5etIteGmO5M3XpMnCiFciaLm4A2PGLpTR1gxXsD0ACVYwXf1ij/pYxafq8ZPA7SIORoACLGYq+bjKLGfejWRZ5HLcYQJN8GrdBbOGMTVYmMmLrWHYLWY9JhbPQ/SGCaz/vC+5vAEBoHJsKYgJTpkhhZ/Cu7AkAuCPCwjO9S32jAgn6hCJD0RzXUtfR1noXyZ6epnexrM5Sn5aTFiQdkJwTsmLvr1XIzljFPT2LF9dYzQPhA6ySoj8bW/5gjdz46yn7NlMCccwj34DlE5nDKADAJhk0P+sNi+uYFfvoMkuPuDRi2h5dkk6vDiqYUyGgqie08I8obmMCgKmuvkpp/9763jxVjZNg9HGkJGKujGlp0J3lHUCc6+Mz8eaUzRHqLKbPi9t6mQAgEomQeczFBAAlZyfqcSa9n/cSb50AAP6PAkBOAkECCBJBULAZcuTIESSBxPtpCtpdsRVo89ECQtJCLF/Mf/Xkvpi/k3r9tE4NA6qWYjnCyhWwbuTLck3cAPCBYUvsYP+2VfNT+fyZbLCJeLBJnSyXpF5kZuFdSflVRiZrueoAcMamQw6rCGS/cLhGK20+XGB08Znk07gfxBYCbKJIqw8yOAE6OZPaz/tCXwCA0tol263v21wBQGQBo6zq1UzRwMgsWFgIAejcZNeQaALqkAETLKuZDDv4fskem0/uh8dqUpWCWUi3pPJ88COgbVJ+O+sZ18G1OHGdpc3M5eN7q1CzMrlUPKKbW5rXENQyoPbh0v3G66lD7cI24OXrK3o0pcwhYvJh83ZQ75/X+1neiDpw6wIAXrwcnTqX3eEdv1hiE4+bBmRCa9xrcn1O+0cO/uY4Wm7nBgBHP1bbjs9kImx9LKnwgnf5VttKjmxsro/PBPaGrs+M+35/aJoAoE7VZAKAOgclk6nH9aJ8NgoA4L8YAJ4+fZq2brVM2ZUqVaLBgwdTw4YNKUuWLFSgQAECDcyrr77qoIFZvXp1QAPj88MTVPv7VgCSa26WrL9rVtKywvFL/EWPmK4816WNGwC2H7qYZoasZgANtYtltaWspEVKf3bJfwbguPHgKVshQj/QJI0N+oE7FpQz0Yp8D7KeST6N70sXsFSWANCCXu1DhtgstzlAAxkA8JKLRQqxfamvTkGlekxUXazt01xZwN6aGtbfBR0JLICc5auP1aF2IQJQYbdxgSzpaPYLFg/gd4t3039DlkHW25256ZAD5HF/2ANwoTJpsQn04BpL+506d5HKucQlMliCNUsqbSCRBjGA1/e0npcLk1Tj7+ELdhpl0Px8brCHxqzcRxcu/RG1OrtvH/tqGU1Y6wS4siHezYXLVl96RjMoexCvx8UEAFOmuIp+fLK2TR7O4Fmf3MXLf1Dxl6wfAZCge69d5YhkH9zDZ8ItSSYRAMjE6Dwv/l6QVr7fL1x2vDfW0/bzXuKtEwDAfzEAnDlzpgJ8ern//vuVlY+JoD/++GNFBF2nTh16//33qUQJS5vQTwk2kJ9VCuok9wpIqSu/X8zJPQe9PxlczxmM/EUPxYacmeIHgDLmEQAKMYVsxcmf5Rqa80Ij4+NJcllYb+AuXL33pKqrr9vU9QcdgfHRYgt5MF1yjK/r8mlycrAsLtlpuaE5mQH/Bk1NraLZjNmZbu8O7u+1+8KKF3o9xPxBLYLl3WARhAtYWnjgRpzWuYGr7i7IhS/98YetUgFQAnCCIt3+wx+sRnWLZ6dpGw7aKi1yPohXBGVPh2GWhB+KdIfztc/bV6VGpXKSdFvqz8WWU1YE4fuwGgGkVdKobJBte01qix5HzjnWzwRc9mNX7JXlmMUAACAASURBVFcydNEKAHWR7NeqDOloWcOwvjJ211VGoBONWFcubgDw56fq0A1vz1HVvnigGqEfvYA1oEg3Kw4UCj0f31fFQXrO9fGZeH/mVho4cVNEH36/Z0wWQFhmO9YtYvfJdSQw1HWo9Taxvis/9YPz+18MAP1sgETrBBso0RUM2sezAje9O8c++P1+McczTixtpOto3NN1qEyeMHExLD+IwdKzT/3Ovd2nC2neVou+RZElV8prx3GB2HZB18bGqe4+etYGNojf2vjrb67rphPtIpD/pyfrRF2CVu/OVdmoemEVA9NhCHJf1qmFqsaukAsT4KJOsexGfja3Scj2pjqQzUuXOqVKikCBXi9cwIMmhQ94WKFgsWJNYr0fAEDEGDKQkestrWkM3EyExegToA37gCX8cE2nccE18No1KJmDdIuQnBdn44LqRlrjkDgAkCn55NBuc78bFC0Oyoglu+340Vj2N+oikejHFfvozIXoAJDj9zqPWEmjV+zzNYyuMyzjFtGBCQDCeosfW83enK3G0PWD5cC8F6HJ/Hn7JKPiDD6PkMmDXJ5e/H5WTXu+d6vS1L52YbtLEwCUVkpUdMto9rWYPisF53cAAH1uFXO1YAMltHxB4zhXAL/4NxywLD9+v5jjHMp3M5k9+POTdahcvjAABPjKem1qBwC86iqiHQNu9NW/zLaFxQ+cfpzJiQNtaXdLmzbi0Dpyxo5tAzDAmq13WbeJaw/Qo19ZurYoUvHCbZIt355j9yfr6Pq58h76ZZ3azOlS0fFQFijWCLFtTAztZ2Fke1P9BV0bUfo0Ke1YOgAh0MBICw/UJEDr0vD1mcYhYZkESGUJPcSQLerWRNWV8XQf3VtFcRWaNGtRFyTKrSvlc1hZpTucB0f2LNZBtwjJyZn4A3Ef1CE3V8wT4c6WhMtMoeRnffU6oBL6aeV+O3HDrQ+mQXpx1GoasXSPr6FqFsmqOB25sLQe/+0GACc9W8/O4Ia0IDK2TYVBF7Scv3ywmlFzGt8lblnSfr9nTADw5VvLqs8sF64j4/yklRL1oml8+1pQH5WC8zsAgD62iXuVYAMltHxB4zhXQGqd+v1ijnMo381gNYD1AIUzQvmLHm6569KncgBAXQor2kAy6SVj2pT0WINiNpcb3IBIbjAV8OMxsGldKa8Ca5w1qq+bDlwk4bHb3Jq/Odso9cVC9qbDEJJomw+eVl1KFyAoURqWzEHPjljpe81le1MjJBJkSJvSlnHb2h8AcIfDwgMXMaTdGr9hpoHBwQ0rJxMaw4W4tLsFAD+fu4P6/mIlVAy5u5IijP5p1X4jiEXs5u1V8jliHBHDyMlMPP+vHqxOdYpnU5Y9Tl7Rn+2eGgVsl7S8ByqdFmVzUfO3LIsYF/muRy/fa9TB9bPoWAs8HzJWoxVO4Og+do1xnqa2+lpgT0uSdxMAxPuf1qWBvcd18mg5Du9F/Gj47uGaEWuEulgntyxpv98zpj3/autydFe1AvZ0uE7XG0rRI/WLRlzHBXAVImv8ryzB+R0AwIT2V7CBElq+oHGcK9D4jZm07bBF/uv3iznOoXw36/fLeltijC0R/EUPCpaM16R0AEAEsG99paWv/u/4YD4tDdG3wHLYuUkJemOKpeaQLvXVtL5vC2M/IH5mYAO34dp9J2nLIQt86esmVS1wP1p2MQ8G7rytof7kBCCfBuuG6TBkGTN9wnCRNiyVg54bucrXmvipBFdkxmtS2fJusITBBdx//Aa7ORI8fnqyNjUNuRH1fgG2wGXHaiNZ0qdWfIIoEiwwz5sbwALNCaywDw9fZg9RtWBm+73yRRAbIxby0uU/qFgocUGfExJmPp+3I2IJkH0NDeebh1i0KFzku5bk1X7WUNaBnjQAIFtt3dpzAkfvn9bRsPk7fQ2DpCNO7EIDJOyA8JyLCQDis4B3zAk8HHtoGpD3IrgHRz1ay8j7iHWSoN5tDaM9kGnPD7yjPLWpmt9u5gYAi3QdZ8dEvnxLGbq3ZgAAfW2eBCpd9SeyIYIS1woEADCuZQsaJbgCDQbNsOkv/r8AwF4/rqUvFuxST8aUIPxFDxABV6SMAUTs2eb+N/haidven2fTt6ABuOlYzzZaPwBnTHCMRBRYstyAMwMDHKr4RgTXIGLjopVGr8+k7QYVDlC6dL3heiMARBIF6Gn0Ak6/ptfntGlVfC2MRyXMHwd+1X6WpJrJwgMAPebx2kaLENog7g3g48hpi2tQWlw/mrXNpofhQ14Sgsvp4fkAAKUEmYyH5LocxwauPUjYmQoANlub5X3EXtYvmT0iw1V+RqRkYaxrjHhItOe1cGsP13vuTNdQ/3Hr6ZM5kUDV1A5JR1I2TiauoL4JAOI6rI11B85QXeqZw3IcCQDHPl7bSPuDdXKjyfH7PWMCgIPbVKDWlfPZ0+E6eqav5G90I7WO9Z1Fqx+c34EFMKH9FGyghJYvaBznCkixe79fzHEO5bsZiIK/Xbxb1R/5aE1KKpTFBkCII0MWpgMApkyhgvP9lFuGzKVVoexd1NcVGVjyTO9ry8FTtmXr5gp5lAWQAZu+bqxqwdQcRbKlp+keMnv1B82wkzjk2FD46NrSDAARQ3fwt7CcJLdDRiz475hw2c+6eNVBfBhcftVemabczdsHRFp4YGXC++JMUr1PWG9/WLbXpiuBS3lN7+aqmswYZR1e7AHTM4C65Y4qeR1xlqYs5u8fqUlwh8IuwQom+pxYQk2/DsLh2kWzRaipyHc9Yc0BeuzrcKyn1xrK+6DE+WX1AaVTHK1wBu/AiRsjVFfc2ulrARLvNCmtzGUUt+QagL7ar05XdeCah4veVBh0AcD/8lQdGzTKulgnN5ocv98zJgDI+s48FtcBbc9D9cLZwSW7T6DzIYqdAa3LEfbeX1mC8zsAgAntr2ADJbR8QeM4V6DmgGl0ICT/5feLOc6hfDd7fuQqGrlsr6o/4uEaVL1IVhsAwnKSJlUKBwBE7NnGl/0BQD3bVqcPQWxbyqutLE9ZoPzBGZLgoVuz94Sr5RQgp8vIVcqlfPbCZQLJ8MznI2mkZP84eE3WPEiedXMBgHChHjModyALtHnZXNRjrKWmkhwF2dhZ06dRWbGge9nSvyVJwm6MkT711UpL+aZ35xqH1PVvUX9dyOX+3oytdkYxx2wNX7jL+AxQAkGiiwRfiLPkZCYenK3H+NsEJnAdcoCvT7ZCAGSB5TWpYJYInVv5GXEDUn7W+8E6hZUF0ATgZXtkX+fIkNZVVcM0Fvgs2c2O+5DxQ5wsF7d5z3o+7ALW3cZyHF5LAPhxT9U1Zn1jnb5auIu6G/ag3+8Z0ztjeT+ejxsALN1zovrsobx2ezlqmxQAQD/7MpE6gQs4gdULAGACixc09b0CY1fsU5qxkMkqmDU9Ves/lQ6FrBBeX8ywpCDuKku61PTaHeV9jxmtIoLg7/tsEQFUMb/Xs9+toLEhXVdweI1YsseOt8PBibNMusMQe4asyg2//kav31mBIPeWL/M1yqIGd2L5fNfZU9CzbXXXoe4u44agfWnxlsWRBh46cADuPmYpR+jrxtmhSDL57dwlQrZxxfyZCUcwLBhXwTesFQnETWBEp71BHSn/JdtABQQauKymkhwvqv9tZem1CRvV84AGBRZX3cIDID7ykVq2moQ+rg62Gbj3/Xm9Iw4PlC7bDp121SUG0TEAIPRxuUBXmGMy+drox2tR5VAmq4wJk/OC69Ck5wuZQLyzJ75xWvjku3bjKfSz3gg9QLKQm/Yy98GWODdSZdNYOqm1btV2cwFDdafJYCvpZUv/GyiV4YcQ7tkAME1KRR7dKJT0IxOJsE7fLNpN3cZYsn+yeH3PcF0TAOQMcb2OzvUnNZz1uEE/7yfWOsH5HVgAY90zjvrBBkpo+YLGPleAv1ThRvv0/qpU5eUptv6r1xfz9sOn7S97SYfhc2hjtUGTNtJ7M6yMXx4fh+641WExe9kQFjFkdcqAeLa0oR7cVkdOh91qoKpYFko0wP0Wb812aL7q1hIoXmRMmypirrAusWsTSRaIsWKLnb5u7LrkucDSxXxvOicbDySBuN/1BIA6dzFSSaJygetUFi1n1frtz289Bm66hQeWQSQFgNTaVPREDY65dLPOuc2nQcnsCgA++c0Kuwrc7HoM5dgnahPi4VCKdRtvVDkBr5xJzg3JIXCldv5+laLxAd0O9IJBc8NFl/zzu36oh32MPW6y+sp+mMJFKtF4jcM/PFBPJtpwO9ZG1pOI4CoGcEKMLSTvTD9U0Ae/L7j8l/VoQuV6TaZ0aa5WdCudRqyiZxoXp05NS7jyJHp9z/A8v1ywk3r+uE7x+OEzhTjcVT2bUaZ04c8nx/TO/28jyiPUTsr3nqR+rKDgRyH2y19ZgvM7AIAJ7a9gAyW0fEFjnyvAX944RId1qEYV+06mEyH+OK8vZgkA9bgin8NHVOvz8zoaOs/KbuTxH/5yKU1ef9DYJVxzUHb4aqEVI4giAZbeiOPV+HrTwbMcliI9kWJZ9yaU1RD7tG7/SZvvDDx0K3efsK03+rpx8DuTK0uAqh9UPC8JxP2uJdx6SHBAljAyVlftOalczyCeRpxiv3EbCJQwfW8pS5DAY+1i7h9JHbOeb0iIAz0VOiz9jM2uWz1GD4ZNSN61fn++3Q1ARrMyOZXLXo9N4+xtLwAI7V9o0w5fsEuBWihU4ECHOgYXyMqxRZavMYck/i7x0gQ79lA+IyybL42JdJUj1KBkrgwqBhFJJ2+2rUhpU6ZwhAdAh/m+zxf7WbKIOtjHAIB7j1tJPJBek1nN3GBlz6Z0XbrU6s9oknZyAKwrJP3Ag1ijSFabuFrWwWcIllyWdePPH0izU6QgR8ygPnl+X5w1L9sAXDJAc0vi8fqekeNxfyB3xg8/gFNZsP/PXrhEGbQfbZX6TrYzrPXEkbhemEej4PwOAGBC+yrYQAktX9DY5wrwl/eN5XPTe3dXpnK9JtlktF5fzLuOnrFpIqLFCPmciqomM355/AeGLaHpGw8ZuwEv3tHTFxykuG6uUO5APhfT3jB4goUQ6hRcdNksvo6kD45tAw8dLICsHauvG2e0wmIErkCpMsHEvvrDSSAey/qh7rv/qaRc6HO2HKZ7P1usLFXIkgVFCzgLB7etSCatYSa+lolAfsbGeq/p09xo4UHiBbgWucDCCrAGwmhkQ8OKA5c9JNCYwNsLAIL7ESEHTAsDF/GdVfM7OAJNGdFIUCibN5OaCngATbq7cA++MGq1PV+eG5KNAF77/LxeufyH3F05Ymnmbz1Cd3+6yM+SRdRB9jHogtgCCOCMd6QXncPPa61ke+kCd5uk7M/r8899cBus1YaXzbRJqDtq2V4jFZHfceJa2FCjqv2m2BnWTCuUSH9ebYPzOwCAXnsk6v1gAyW0fEFjnyvAX953JeWnV28vT9f3mGjrkXp9MUs9XGmZ8Dm0sZokuOXxpV6v3gjZmQd/O0ejl4dlsRCMHs2CJZ8LZM4gdWarHNxYp89briIUJt7Vx12z96Qd2wYeuuW7T9gZnPq6vT11C705dTNVKnCdsrrJ2CjQqQAI6aVc70kxWeFkew6MX7j9KN318UIqmj29Akgg1AYQfKNNBZL8h9w2R4Y0tPilJuSmQuL2XuFiXN27OZmUML7uWJ3aCVCE2DyQNmMuDNJk8gri09wydHl80McgMxjxqyC3BgAEqHzmuzDRtSkjWpIZl+oxweguh3tQ8iXy3BBTmj9LOsca6uvB6x3P/scPmR9XhgEgrJWthkQmz6zp3cxh3YoFAIKTUca/muaZCAD0Sr7ibHh9XK/vmXjWU2+T1H+q/fnUM4eTo3+9j+D8DgBgQvsq2EAJLV/Q2McKSDoMJFMgtka6xry+mGUcEmcn+hg2ahUEiSNYHIXHl3JtemPQoiBwnvVkcR90FNEUFeRz1Rs4Q7kK+aBndxmPM+O5Bsryo5fVe0/YpMDNSgMAHrctDPq6MWWHrsmKPsc/XZdK58kY0b/MWox1TT+8p4pSrVi26xjd/sECguu5bVJ+ZXWD1NnAOypQmw8X0OKdxxxd58mUluZ3bUz/+XihQzrMa3zQwazo2UxRusDlLMvQ9knUYdgS+xJIsEG1M2DCRmJrq7TWIevajaSZO4E1Dm5s5lesXSwr3Vklv0PpBNZMnVMP1DVw46K4re8bd1ZwPAOSh+CWhZ5yrozXKCDPAFRflyU7j0XwBHqtHd8Hz+CYFftsC+DEZ+vaSUayj3V9mjvcnrEAQNbRjjanRAAgJwO59e9GlO31PeN3DaPVq/HKNNtCzxby5OjXrY/g/A4AYEL7K9hACS1f0NjHCiDup0yvSaomlA46Ny2hCHIRR4Pi9cUsLYBusWw+puGo8t8fVtN3SyyNUx7f5K7kRsiehNtswtpf7X4Qy8ZxjKbx5XMx3UruTGlt+hvZZnKnelQipwUaZIHL99ZQcgMSaAAAmYJFz7J8+Zf1SiUDsWNTtFhGN41VyVsW6xp+el9ValI6JzFIBbBrV6OgolVhS2/bjxbQoh1OAAiwM/fFRvTI8KU0aZ0VcynjFd3mwYk1JgsPsjQfEQodcEcDACLTlmM1ZcYuYklLdp8Y9ZGRkNGzVWlbGg66v3dWzacSDriY9sCUTvWoeOhdyqxQORjiw5DowYXl9QCcEQv6wcxtxOPrk0wEAD7VqJgC0JwFPK1LfaOE3saXW1DaVGEOv1gAoNtels+READ0IGDXFXF4XK/vmVj3v6m+pFVCqAtCXv7KEpzfAQBMaH8FGyih5Qsa+1iBAyd/p5oDLKLX55uXpCcaFnPwo3l9McsYQHCGgUYm0fLCqFX0/VKL84/H18ma5Rg4jHcfO0NTN4RjBN348EwHTq0B09ShC26+nUctGhdZ3KwmK3Yfp9tCyQ1IuFiy87htddQzotmtjQQMuPlkYXUKfVypXBDrmsLqBum39ft/o5bvzKHsGdIQtGYHT9msCHBBhGuyqsJSiCQQ+Q7c1EXknFjD12ThgY6vzM5FBi34/5CQwq7wCvky2WTc6/s2p9I9rR8lboW5EBlQ1CiSRcmBSeBmigMFqCqa3XK3u7nYER8mNZOZFgguZmSDQyYOYQcvtigVMb1EACB+gMGFzhycUoVDDqQnW8UCAOXzu61tIgCQ+SDd+kaSi06jIz/nse7zWOrXHTid9hyzEmw+aFeZbigXAMBY1i+eugEPYDyrFmoTAMAEFi9o6msFNv16ypbp6tK0BD3VuHhMABCxc4ihQwFnWLEckZYyXxMRlRB/hWBxeTCAbkUn9eUmyM4E3QcyMLnEAgCrvzJVke/CNbnp4KmI6UrqEHkT2qocpA8iYrhTOe5QJ9rlZ4L1ja2b3NewDknUoGSOiHHdeOr8rOcXD1RTmbFbD51SPG6whmGd3pq6hSCd1+/WcnT3JwuVDq8sSNCY3qUBSe1lpjyJNi7i7RZ1a6Lc8DITF210i1q5vJlUJjIAIBcAuIXbLWskkhwq9Jkc9TEZgDGgAM8hYhxl7B4nb8iOpDsfY5jCBBAfJmMJeW5InoHizNeLdtOzTYrTs01KRMxx6c5jdMeH4YQXP++K66DP7xbvsd2UkHzjH2eyH52PLxYA6OdHWiIA0EuD200pxeuHZizr6FZXKutwiERy9OvWR3B+BxbAhPZXsIESWr6gsY8VWLzjmJ2hyVxdsRwA2w6ftt1UMsDex9CuVTqPWEmjV1gJHXww6FQtsjEsW5iHBDOxJIFAyxY8gdIKJfsf9WhNqlooS8R8Ob4ON0BoDAsgJ4/ohzRAEcARrJWwIMmiE9niXjSpMj9ri8SL2sWy0c4jZ5QuKxJbHqhTmN6ZtoU4fu6eTxfR3K1HHN2xRvG707bQG1MsNQzE183b6gSK+hzgPl/QtbGiMdEtPFBdePGHMPkv1vnminkJbnEuIHLmLG8QHbO+sNuzwl3apVlJRZwM8udqhQAA89HzInuXZfdkHxIAuWVZ6wAQ4H7axkMKtIIIGT9OYP0DCNWLFwBkLWjTc3VqUoK+WbzLVgJBTG21/tMiquo/LmIBgHNfbEj5MqeLuoVi+fxzR9xGp1jSB3JTHLkSAFBqa4NiB9KIf2UJzu8AACa0v4INlNDyBY19rMDU9QdtaSs+VGM5ANjChKH8ZBj6mBJJ1Q8+GBoMmmF0z6I/BORDlxcAjIvJ+iPHlgdO5ZenqNg9gAg9KQJtvnmoOtUqmi1i6tLdBw5FgGmWmtLddMxjiED/ITO2OvoCnx04+mS5dPkPz0SIaGv57UM1qGbRrCo2ErFPCM5/uG4RNTYsgb1vLkOmzGrWKJaSboiVciPh5jnATQzd2IlrDzj0eHH/5VvKUI8f19nTRSZ0K42UWo7hZvmSz8sWOB4vqVBmZQGU9C0y05rbyoxufu/6OuJ9PC34BEH5Ao1e/B8F/wbBcYfahSNegRcAhIv04mUrvlYvsMBD6o5VeMA/WaXfVFWNKYrwbz2+NBYAuLBrY8qVKW3Uj2Esn3/uiNswjY/bAIh/fejLpRG3rwQAbDJ4lqIcQuEY2agLkeDN4PwOAGBCWyjYQAktX9DYxwrIrE3QULzQolRMLmCphwvesioFM/sYNXoVtpahFh8Mbrq4qIPszA0HTikePi7RDlrZL/7NliBQiczZ4rSI4T7Ic+uVyB4xaWk9xf3FO47atCJ6oP79ny+mWZsPqzhLJGLIMuiO8gq8yHL+0mXPRIhoqwjuvWqFs9Ch385RtVemqVg78My9LxIYQFgs3eboL3+Wa2jOC41sehVcg8tYkmybxuV2JgsPJLmkuxd7pFX53A61jTur5LO1nt1i3+S40Ot9slFxmrj2V3r0q2Vq34Eb8IUfwvx9pnkCpAKsokheOFlXj1lE8gdiUqH2cuHyHyqJBxQ0+OGhFy8A6KbUgn7wTF8s2GVTlYBWqWLfKWoIuZ91sBQLAHRTnZHPkQgA1D9b+vq4SeVdCQDY/M3ZdojH5+2rUqNSORP+rorWQXB+BwAwoQ0WbKCEli9o7GMFhs7boYhtUaBE0PWG62MCgFIP1y2Zwcc0HFWe+Ho5jVtjyb7xwRBNFg0HNGS58J/fIg8cTgZAIodMJOG+Pru/KjW+PvKwkJxvAI+Lth+zlSWQyJAudVihgBMuet5UOkKO7eVby6oEDVmgZOCVCBHtWX94rCZVKZiFjp+5QJVetkAEEic+nr2dkDXd/abSxKBU9sOuXGnJg2X43elOq6U+NiePmCw8cJe+NnGj3QTWOsjSSV1iuKW/XLBL1Znepb4tL+j2jOyCZQ1bSN0hCeS/oyN1ZmUfsC7mzmQBQMkLJ+sgQ1S6se+vWVABM5B9n714WYFmUMWAy1AvXgAwWkY1fhxAAYdlCyFxVqGvFQsp3dmJAEDIuWVOb6mIuJW/EgDO2HjIQQnEc7gSAFBKPg7tkEQNDXG3fr8//NQLzu8AAPrZJ651gg2U0PIFjQ0rgKxQcN6BIw7lrambVWIACoBB3RLZFTDw+8XMWaao/9WD1alO8UhXqelFQKEiZYoUykWJAH4Q7HJ57KtlNqULHwxu7jq0AbExVDlMCRxumwD9Is7u59UHbHefm6tTcuohyYMTNhZsO0r/+WShGkK66PA3VFH2n/idkGRzbdqU1GGoxYOnx8PhGovWoz7iGBFrdu7iZSrXO3oiRLQNzokrUiqMgQx4E7u2vJ46DF1MMzaFE2fQH2fzsmUN115qeb1SEIlW2HU8feNBemCY08WH2DZw53HB+8ZaQ9OVC8Dpp3O2E9iHwNXX/K3ZUcfDnB6qV0RZ4+BShL4veA4h0xatLO7WmHJktFygnPyj1weJNuIK7f3YoKiifkF8JCyzyCSFlRAgVi+JAECA2s/mbre5C6UFUCrHJAIA3XSt5XP8pQBw0yH7syDHvBIA8MZ35tg/Et2s+lE3T4w3g/M7AIAxbhln9WADJbR8QWPDCvCX+49P1CbQWzA/HaqaKEq8vpilHBpTj3gt/ImzF2zXFurq8lFS95fHd+NsQ3tQiqzac0JlAvst6Fe3VoGbTqqJcF8gjUX8F6tTsBstmuwX1BpMAE6PL8MYTL/DYvX4+57qBW3rj99nkvVY8xZAslQPi1OPM5DhCoZW8IPDlqjkBlmY0FmSXCPeja3EbnOB0si0Lg3IpIShxz3WLJKVoJ0s4wJhZfxw1jYVHwe5NpbYcxuPY/DYpYi9DBcwSMSjFekCrTlgmpH38cN7KjviGE0AePiD1ahu8ciwAJkVb5oHXNXIHjcVvJNPZm+3ZQglHY4kNk8EAG7o20JlMkcrVwoAMsF26dwZafwzdePZ5jG1kVRSsfxYjWkQUTk4vwMAGO/eUe2CDZTQ8gWNDSvAX+4cdyZ1d5HIMFOzCHkBQCmH5jezTnIP8hTlOB2/WEpTN1gkxLgOUmqQU7sVAFeQMDPHl58Xj37fmLzJ4doEP963iy0FElnebFuBmpbORQChKEwmPG/rEYfEmWzjRmWC4POOWhA8E3DzuwFIwFpyAgD3q0vURXtO5i6U2cQM8DnWs+MXSyJc3qzpi76/XLCT8mdOpyzG0l1rGhdEzlM61yckr8BaBjcm3KYosDh+NHu73Qz7DMTZ3ceuta8h/g1u5vOX/iAQYzO/otszIrHk3pqFiC2O5fNlUhbAl8aE+zS1lXKFzP+IegDHACT1S+RQVmnEFXKBVvP3S/fQ0TPn1SWogTzesKjKCDYV7CG4ehGTCpeuLMgwhqqIHgeKOt1alqIPZ223ycSxR8Gr+OefRK9P3qTaoSQCADf3u0ElBEUrVwoAgjbq51UHVCxlzpBVNurEErzZ7tOFdjb7Nx2rU61i/rwV8Q4bnN8BAIx376h2wQZKaPmCxoYV0AGg1N1lugvZzAsAwvJ2S0gNwy+7PicmuI0jLVMY/+TZi1GtYbDOLd153OZP8/Pi0S9IkUGLwgXZscPmOw9s3Bt4e3mqXTybyqZFYQAIN/a92YsiawAAIABJREFUn4Xd5XJc6b6T13VdXNxjlyy/G9CkfHJ/1QgKED2WLtpzSsmzEt0n0IVLf9gqJJztDdeprkqSJmUK2tTvBkfXyEztIcCaaVyoe0x8tp7jFkvs6esK3WQkzUiwBjf4G5M3Kw3qkY/W9JRT4ySMGSGXIrgF2yTl95ynlFGTiUVwj/e5payav57IsrxHUyUTGGsx7VvWaDYlbmAN3puxlY6fvRgB9Oq8Nj1ZAKBOUG56pisFABEmgR81V6o8/vUyGr/GUgviLPm/cuzg/A4AYEL7K9hACS1f0DgKAITgPZQNuo5eTd8utmTXcCjrWaFeAFCqYfgVWP/15DmqMcDJbybHaT90sW2JxPXdR89SvUEzXN8n3InIyNV1X6NtAPQr4x9RF65RuCH10v+2slS5QGYCGTUKXJRl82ZSa4VMWlMBaEDcol5M1i2mZeGDFy6xz9snRaxR1xtKKf1cP0WScnOSC2c5s8VRyr1xn4hlBM+cLF8v2uVpWbs+d0YCD6QszLvWrnoBRZ7MBZQ3NYpkdbhrYdF7beImxaMI68zdny6K+pgA5QB8MzcdovZDlxDIqmHFk25lUweSnkcqQ3SoXYh6tSqjmnBiCbeXVkM/a891ZPwlX+MfSW4AEDQ9LGEoPxPJAQC9KFp4jlcKAPpxR8ey3l51pcTkiIdrUPUiWb2aJHQ/OL8DABhsoIRWIGic3CvAX+4MAKXkF2KzFmx3Ev56AUCphmGiMzHNH8kOtULWNL4vx5H0JLgu4wxN/cGihISM385d8r1c6BfWP1gBuYAI+21hEeTEjt6tSlOp3Bnpro+thA+mu2HwYRrUjczYlOAAGpsBrcvb2dfQngUArPOaE/TCRQj9XD8FmbRFQpJnnO3K8WfMoffo8GU0cV1YP9n0LnANLk2v5AoAsHFPOwEgk3czjQr3j78BqGXGLgBdv3Hr1TuEiolMRDI9L2fhMggHaL6rWn5HYompnbSASWUIzoxGG8mNib/9JE6YxpI623yfk0dMABAZ4u9M3/KXAUAvmbYrDQB1qiQ/+zqROgPGb7BDEWBlTjKQuyfSv942AIABAExoPwUbKKHlCxobVkB3AXf5fhX9sNySXataMDMt1QLUvQCgJEOGvizi6LzK3uNnI8CNHEcqVOB6tFg7jIV4svnbjtgkzF7j4z76HTJ9C70+OQwAX2hRkgZODHP0pU99NZ25cFll6RbIko4eHm7FhXH8ELsfTeO5qTiYOO4gMTa4bUUbACKjdliHahFWTz/ZuDwXxK0VyGplVrP1CFY6yOmx5J90icln0N/590v2ePLrIQbvpyfrOJaCaTdurZiHxgr9Y1DelM2b0aEOAutx75/WKfenKU5SX2O2NrMbHi5oWAB7hyiN3PaAfDZIGCJpA4Xd8Pi3zlUXr6vy9wuX6fqeVgIOl2gAED803py6xZank3OV7up4YwDBQbjxZad737ROV8oC6Cce0c9n2W8duNc59pJpkvy2jadecH4HADCefWO3CTZQQssXNDasgA4ApeoGi95zMy9dT9RbtP0otQ1ZxvreUobuq1nIc933HDtLdQc6rVvyUPvPxwttSySuu+mH8kCIXZy95bCrwoJpQuhXHgio0+Om0g55sqzpU6uMTADD7NemsWXGONvZRHnCY4FuBATMekEWKixysoBg+L12lW0ACFLlLx+obmssc12dUDnaQkvJr8ZvzKRth89Q4WzpFeDhrGNw3ZkUPnSAMXLpHofEmmlc7B1klsty07tzaO2+31TGL8de4T6sbSVyZnCASmTeIiYQ6/1Bu8r0mKBhMY3HQIp/HAAAIgkkWrayvp8bvTGTth+2ACBnRuPf+nvVOR09N3iogszA5jbRAGCfm8uoZA/Wk05uAIh4O4BZr3KlAKAuaec1r0Tvy1jW0Y/XUlbov7IE53cAABPaX8EGSmj5gsaGFdBdwFJ1A2609QfCZMogn93cP7rFQHLhAUA9WCdSHkufxq6jZ6j+oJmOy/Kwa/vRAlq045i6j+vfLd4dleDXlL3s9fLRL+L9XhUxdYj1k4kJUIxARmjnpiUofZqUNjgEWGlRNneEpUiOCcktPc4R93EAczYx1weA/ax9kg0AQcY8/MHqBOkqWWIBgJLwuOXbc9R7zZkxjdKZBaB9vEExevKb5UrWTC86AJRqMW7rCiLm0Y87ASCSg5AkpBNsP9GwKBXMmt4h2wZiXsi4HT51nkxUOfq4AIk3lMtNTMUDt/ldSQUiSLZlOz3BhYEx6mBOzzcvparrZMXxuiqReIMEHFlAKdSqQh4H2TrfRxzkwElmABhvDKBUELkuXSpa2bOZ10cjJiJ47swvaJRWc13SznNiCVZARvUz361UvTBPZoJdRm0enN8BAExofwUbKKHlCxobVkAHgFJ1A1QeW0JamWjqJ2ZIcuGBxwyWFK9i4kqToKPNhwtsTV5c/3j2tqixb7WKZlUEyrEUU7+Ii3xu5Cq7myLZ0ysLEbJmr7rqKjtjmN2Pbrqm6GD+fxtFxDni+tb+N1DJHhMVtQ2XOsWy0Vcdq9sHL8iYkS2skyHrFspozwsXdI4MFuFx6/fn0fLdJyhj2pQqxg7JJI/UL6oIsH9atT+iGx0Ajl2xj54dYR2cbgXhA6Meq+W4ffsH8xXnnZ5cBBd07uuucaw19JY7j1ilMrkHt6lAnb8PvwfTmGxJ4x8g2Lt3VSvgsODq7eDSX9e3hX2ZYxRx4elGxahzs5Lqnh7bGa+r0qTnHA0A9ru1LL02YSOdOm/FsiZHEgh4HTmrONu1qWlp96aeHxO/YE525LeNBIBe4SWeE42xgnyvyaVbHm0KwfkdAMAYt6izerCBElq+oLFhBXQXsMwEZRchN/PjAp675Qjd85mVscn6rF4Lv+3waWr8htO6JQ8DBg58CA6atJHemxGZncvjwPoEgBNLwXhQnpAatTicYRHlwhZRgFq485giBmoebZMKRGSLyvHhgtWTOPh5Srw0wZaMwzVIo418tJYNAGGp+aZjDWr5jpV1zMUPITPXXda9CWW9No36k13qbA1iFQ3p/pfj6AeztJy4rXG1Qlno+0drOm6zJVdPLkIyS9b0aaiLANtIrAEghcXVpJaij/tW24p0a6W8NvE0iKgRfyrfp94GAHh177ALVGrDcmIM2kCzWSahALSndOH8i7bn/vjjTyqi8VfCuoksaFMSCCzQr4zboOJOea9w//FaAJlsGf3AAryoWxPPj4lfMCc78tvm7wSA4Apt/f58NW3O5PdcjAQqBOd3AAAT2D4BD2BCixc0jlgBSQrMGbuSdJldntwwxVVE2wfcGHUlJRUK04t4Lf3WQ6eoyeCw1BeIaWFl4XLb+/NoRQjQAYyAgw7xO24FGaix6ADz4frZ3B0OixHIlznRA3UqFbhOzQNubWjqjl6xT02BYx2lXJo+t1nPN4hwc/O4JbtPUITHXDiBgg9RxGpBV1lXw0CSgFeSA/cpqUskrQ7us/RcpxEraUzomeT8dQD486r9DmBseg81imSh7x52AkAm3tXVLxDrliFtSoeVDwcyyJdBdgxtZC/eQc5iB/1Pm48WEKy1/0kqEFWyDlx+oOfhIrVh4ebH/kXR6X3idVXKzxuPGQ0AIomq3y/rkxUAIjZy46+n1PD4fM/7byOvj+cVcwFfaQvg1kOn7bAKJkr3XIwEKgQAMACACWyfAAAmtHhB44gVkC4pPkAfGLaEpofkwHJkSEOHTllqB1y8vqSlW4UVJryWfvPBU9TszTAA1A9mjh1jwOTmquRxoNGKL/dYCgLQhy/Y6QBU0AeVvH5suQJJMIAJy6axBW3i2gMOyTA5Psiim4pnlOtZuudER8Yy4tcmd6pvH7yIVRvxSE26NUSwzW0BnLwUObiupC7R+f5YRq3z9yuN0nf6Ox+/5oBDG9e0znDDf/NQDcctpvMBwF2996R9DxY+gP5OI8Ju3qmd69ODXyyhXUfPkh9LJ1thOQsd1mvQ6YAmR9dl5oGzZ0hDSMLhwrGR+JsTY/BvneDb6zMQbd/plr5oABDPhCSWs8loAYR1eclOS3oOmeyzX2jo+THxa82THflt83daABFfyglYAQD03AbJUuGqP/EzKChxrUDwCyKuZfvHN4LrKAXMb8lcZFbiwDvKU5uq+ZWrCy4vFBkvxEObrB9yfjJgXnKp6c/Af+P/mw+dohZvhd2bAJ6LxcHc6t25tGafBRhw+LILE6ABgfV6QdZsLDJwaI/AfiSXSIsarG7M9Yc6DUtmpxmbDivXIrgIeU5sLYoGjHDA3PjO3Ii54nnK9Zpkx3mhAuY/pVN9W7MXAOb7R2oSXOGywPLY88d1vnaFzFx95rsV9KOgYQGQvL9WIRWDN2qZRQEkiw54ogFdbmcCgPzjQlqhUB/uW5ASc0A+roEeB3sRes5+6G4QL3dPjYK0bNcxuv2DBVQoazrlln9t4kYFLk37BMk1C7o2th+Vs5RxASorjzWw4lcl7ZAfK3i0F6IDQI4fNbmAwYXY86e1dO6itceTIwaQ9zD6A73Q9OcaeO4fv2BOduS3zd8JAM9fukwlu1u0PPh8VSucxXMtEqkQnN+BBTCR/RNIwSW0ev/MxoiBAgCCekKXUFB6cjwJtEy7j1nriD2DhBpUB+ZuPaKGgA4sB6DzmDpVw+q9Jwg8fS+0KKUOYJ0zDZYUgLzP5+1QmXbI9gR4uvuThZQjY1qCDFz3m0o7MkD14HRpmZFWL85i1dcDlh38uo+lsHtXtgE3GMAElxvK5qIJayOJknEfyhFVC2YhUKmYCihRWCJP3sehXqHPZJvrje/pViuTHBqyRL2ULrg/mbkqyb5xHy5WcPE9P3IVjfQBAHVlDNPzmlzALDUH4AFgxwUZvBcu/+EAgEhaufuTRcqSiz1k0sqV47IbXhKR8/1rUl2tJOX0gni4uS+GXaDyhwbiEh+uZwFAmdjkJxEq2r6LBQAiLKPHj8kLAPEZ50xv1mv2+pz4BXOyH79t/k4AiPnyPJF13rBkDq+lSOh+AAADABhsoIRW4H+vcecRK+1Ys0RcT/rKublRZYA+XI8yNg19bOl/g0P0XlJnYH6mTFgmlGZ3lwy2N71RWB5XCHoKGZslLRgtyuQyKldwdiv6znRNqghw5XcXITPw5iHz7OqQyjNZyLiCnjQixxn1aE2640MLTIJPEG6QVuVzK73ZSn0n25mZbnOTcmiIl3u1dXk6fvYCdffQ5OX+5HvTYyiRbNCuekF6cdRqGrHUkgEsmTMD7T/5O8GydkvFvI5p6coYpjnDmgKriiy85/TQgs/bV6XT5y+rpA8uK3o0pbYfL6DNB08TEjLemhrWaJZ9Qs4OIHFSp3qUMW0qklKEXA/rxVx6DpCSNR3NfD7sApWxphwXifogFQcYRdHjU/3uJa4ngRFcsFCCuSb11QTr8Ys/rCZYR9lFC3WTbmPW2J/BeCyACKcAuGdFG7jFWerRpNdseh4AfliH37qrIjUqldPXI+N7ACEFsO42vt69DaxwkFTEfvvgniq++k7OSqA+giUf7yFtqquTs+uIvgIAGADAhDZYsIESWr5/ZGPJzZacANCk+4oFkpYwuLvAToJD5NiZC2r9dAoMSZ6L+U1a9ys9ElLI4AUvlzeT+pLlTE3w2UWL0dOzM5u9OUsBARTO8H2/XWVCvNfQeTvt9/rZ/VXpwS+WEgNXzHvpS02UhU23ZPrZDOOfruvIvH24XhH6ePZ2u6lOlK3z1SHIHpYnrB1oTRhEQH4Mhd36VftNsXWLYTmDkomevQqy6Q7DlhDi58Y+Xlu19aPJy5OVkmf9x62nT+bssJ+DFVukDvQDtQur5BBT6EE0wmvulDOZ5Tp3H7uGvlq4O8IlC3ALwmeZcb2qVzNC1jASFp5sWIygiWsqoNfJlTGtPc+Ve05ExErComzShUam8LQuYReoJByHDNsDIQ7LhduP2qEAsCZueDlMHeNnH8k6DACRpDK1U33H+sJS/sGsbba1E/Q3kMdj93U8ABDvHdQ+TNvzUN3C9rvH5/Lnp5xqLW7PE08Yit82fuvFutZ+61+p8YPzOwCAfveksV6wgRJavn9kYwnUkhMAylg/xwGVNR3tPHrWsVbS1aqT4OoA0BQfxnyCrNcq25heis7PJgEjDm2oWHz7UA2auuEgIXMXBRQ1IE+WdB1Mc2FysfrZDFM713NkJyMuDDFlXHQLJOK5ZBwbLDxnL1xS4IP1bE1UOtX6T7WTbWDRalE2l4OAGuOBbPrRr5bbFDG49s2i3cpC5FUQX7dDZG+/PmmTA1Ah1qxNUn6l7wudXxQZv6n3H03zmOuaeADd6Hvgav/15HmH+3xN72YKdCGb+5H6ReijWWHgLeezqFtjypnR4jdEQUiCtNriWp5MaWn/yXMRywSrEyyHXGR2NMdF4p5Ut9H3ptfa6/clAJwuwCfX+2DmNnuP4QcTyLDhHkeJBwCijcza7tSkBL051ZI7rJj/OhWWEZQrswLB+R0AwIR2WrCBElq+f2RjmZWbnADwro8X0MLtlrqGLCaXqaSD2dC3hXJZcWn0+kw7ngvzMyVCAAjtPnaWONFEtjG9FF2jVNZnaw6SKkBIzJYstPn0viSbgxD9Ms1F5Zen2BbMWDbBzOcaUIPXwwolOh/dfTUL0pcLwnQ0OLAlQTIyUeF6PHL6vK1nq6tPYD41B0yjAyGAUr9EdoIU3As/rHZMlftmkmjcBFgDaPMqOuh8d9oWemNKWPOYKYBeGrOGvl5kAUBYO7u1vN7YtU6LYqpkUgL5aNY2GiCUVrgdKF+wPx4Xcm9IWgEARLYwrJGIITUVZPEi5pPLmr0nqdUQZ7INEkL0HzWoD17H8c/UtdvKH1scF4mbTC2DfyMudo0P+TS3d2IDQJcEDElyjh8UcL1evGzlTcYLAKV0olSPMYF0r70U3I9/BYLzOwCA8e8eCmhgElq8f2jjez9bRHO2WEkZyQkAZbyT19IUzJpO0XGgrOvTXMmgcWn4+kylJ8vz+2X1fnrym3AsF66zBZFdjQ0GzTAeyExMrEvOyfoAM5f++JNArAwuQLYMIc7ro3uq0N2fWrFaKABgM55rQNLF6vWs8j440mq/Ot2+pPMCguj69clhIKUrVsDNd/rcJWXdYz1bkwUJYyDZBwUxjjeVz+MgRcZ1Bp8sE4drXpJ4PHEddOpKKmyZlbGBsLp1vcEMACXZt9t6mqxLbvMFRQ5CAqTeLyzN0JSGdBxi2Nx4HxErmDl9ansaSDDS+RJ1RRuuzHyL/LeMi+W4SNxjahn8Gz+Q4J6Ot3gBQMlFiZACxABjv+uff79E0PjOkEk78kdM9cJZFL1QUK7MCgQAMACACe20YAMltHz/yMa6Dm5yPYTMrPXqU2ZtwjWXIW0qu4kOABFvJIP5+dA8+ftFlVCATOH6g2bYgFKOnS711YrzDBmwyDbmUm/gDGUhkgW8dnCX4T8UxPshLlDStnCWo3Sxej2rvL+4W2Oq9so0+5JM5MDFV1uXc2gSA0hJNQuMDwsg5Mw4PtAEIOoOnG7T1kAnF9qw0pWMsZjyBdbB99pVVnMasWQ3vfiDtwUQ67peSJ59uWCngz6GYzN7/rjWtmhC7QRSfqYikyLc1hPxkch8lsWNJgeUL3D1gviZy6Z+LRTdDxRdZOKCPh7AGNaUy7r9JyPodtyIwRHvOkboFcvsaLxbSMmhMLUM/q0nKMWyn1CXASD/ONHbD5u3w6Yigrwd9gHLBMZrAZTUTO/dXdl2tdculpW+7ujkaoz1eYL6/lcgOL8DAOh/txhqBhsooeX7RzZm3VZMPjktgF5xeHKxpAVFP3CldQ7zM8mEwT0LLjOm6pCAR44js3cl36C0kKE+klO29m9Jb0zZZEvCwco45O7KdGco0xb12MUnXayxbAJYlyq9PMVuAnJixCOiIK7u7bsqOcAuXKnPjwq7bpFl+dvvF1X8GesKIwN4mVCfQF9yDZuVzkk3V8wTYUV9oUVJGjhxE7WulJcGt62o5gAqH8SIeRXdbakDR+ai6/3TOlveLhqJt0yKcBtbt66hnuTTk+0AtFfsOeFIHoLc2n8+WagyYqNlX6/t05yglMJlw4HfVFapLCaKH9zXE1WkBZTDFVBPUsuY3p/X+sv7XgAQZORM7QOw9tS3y1Uilv75j8UCKF32HIuK/qDJDLLzoFyZFQjO7wAAJrTTgg2U0PL9IxvrJMjJ9RA6qIrW7/W5MxIOVpRVPZtRpnRhi4u05gEAjl6+1yHpxWAJ9O+cWSkPLzkuDldkg6JIvkEdwLEVbfDkTfTOdCs7FPF+sLJJsuQK+TLRj0/WUW5cdrHGsn6wdpbrPdlusrBrY6oxwLIIAgB+eE8VB2gBaJCADAAUlk+MDfc34vV0kmv0JcE4uAZvqZgnQlGEEyFgDRvQuryaw8ilexyA0+3ZdKsjYidlrCLoa2B1lAAQmbfPNS9p7FLGxLmNWTZvRvrlqXB8HeqZ4vN4Ty3acdQhu4fsVQDARTuOqfWQxNVyTD0paeOvvzlIxVEXrk70oxedqxCya5+GkorYLY42UjNWVw+JZT+hrhcAlIk9CBt4/JvlxNIJ8VoAJY+h5LZsVCoHfd4+KdZHCOrHuQLB+R0AwDi3jtUs2EAJLd8/srHkwEtOC2DVflNVcoKfwjQuqKvHXOkAEDx5CFw3FeZWcwNkoPOAuxRF0s1Uf2UqHfwtPFeWsHpzymab3wwuNcTg3RYSd0cfHOTuZnH0enaAi1I9LKUAFCQcsHQUrJA4PNsPXWLf15NEAIIAAKFKwi5cZKTOF+oTaCyznOHivbVSXgJpsixQH0HSR/tahaj3zWXUrWhrLdvqViuZFIB6cJ23LJeb+v683k62eLpRMersQjwuXaJua6gnWKDe7qNnqd6gGRFNsM6Ic5XPDAswaHMWbD9KLcvlovFrzATcOi2RLiuIwZBZzXG0cnCZUIPrMkv5zbYV6LZK+VR1yS3ImeVee8ftvhcAlNZZ/MCQbvF4AaDMYgbfXfO3LNnFpqVz0if3VY33UYJ2Ma5AcH4HADDGLeOsHmyghJbvH9lYJ1pOroco13uSkRzX1D8C+sGvhrKsexPKem0461LG5+GAiuaW7HpDKXqkflFXixyUGaCxiyKzjXWwytald6ZtocGhbFa4qd9oU8FBAcIWHrekE6+1hBWyaLfxdrXlPZoSMopRAAChdStjDvWYQFggT/x+UcU7cvYlZN7mvBBWn0Bfkhgblji4ecH5J8uN5XPTuNUHFCUKJ2f8sGxvRLKI6Zl0q5WMCUN9UMy0KJubpAXsmcbFqVPTEsYlkhYxtzU0kQyfOHuBKvYNu9S5LcAe9KfB4chru33AjUphBqo0ACogFjYVXZlmy8FTEZrLSJxh3WbZBzKu4RLlIrOj2S2Oe0hEYRUXXT7Oaw/p970AoLTqfnSv08IcLwCUgF0mNiHc4OMAAMb6CuOuH5zfAQCMe/OgYbCBElq+f2Rjab1KTgtgiZcmOGTgoi1OlYKZlRsMriiddkMHgNESEyDp9UTDYg7aEzkurHicUSxju6q8PMV2DaM+B68Pmb7FzsKFxQkuWJkBCsvP8AerO1yssWwCrLdUboD7u0JfyyUMAPjDY7UcFsdXbivn4OVD7NnxMxdUxjNzCJqC/xGzxi72WyvmodaV89F9ny92TJWtWE83Lk7QHkYxudtNzwfL6sJuYc1b6RJEfQCN5mVykSSIBl/cM02KG5fLRLasVzQBQCQzSEDNbbDOklyaaWuwBohfk+ov+ji6NjWyiTlOk+u6SfjJjGrUldnRSMBANjaK5BZkaqFY9pGs6wUAx6zYS51GWBZ0WOekVTReACjfF2J4wYuJAsLxT+8PLIDxvstY2wXndwAAY90zjvrBBkpo+f6RjWu8Ms12iyYXAATzfRFh2fJaGATLIxsTBzgC9qHhy0UHgNG46bo0LUFPNS5O8pnk2G7JJrpUGlyC77erQu/N2GqrJsDa9urt5R0JABzj1HTwLNpyyFISiaXoABCgtGyvSaoLAMCfnqzjAJygDnlpzFp7CLigoQIC3Vs8O7j3iuW4lpBMIstN786htfusGMvWlfPSHZXzOehscB1JFeDEYxCNa3osn9uz6aBFd+F+el9ValI6Jw0Yv4E+Cimd8Lsy9ekWyyfrlsh5LU3u5HxO3C/Xa1KEKgvWWVolmQaow9DFNGPTYYKrlvWp9fnon4lth09T4zesRB0ubjGEugXsi/k7qddP61QzxN/dUC63+rd8XpMFN5Y95QUAZRIVK9tw//ECQKbGQXY9+BVLdrfCGpBx/un9QQxgLO8vkbrB+R0AwET2T2ABTGj1/v7Gn8/doQAAsu/8Fslh5xcADp23g7Jdm0Zxq+HwK5L9WjXcn3/+qaTMSubK4Ihd85oLguiRCQk+MiQhwJp1XTqLe023UEaTJ4Om67NNSpAbLQuseOtDySZfPVhdWR1BSIz6v527ZE/zrqT8Cux9OGsbvRoiFgbYeqV1OWr2phXfhNK8TE766N6qDher17PK+1jvwl3H2UH4cEtf39M6PE0WQJAHI5OUC/Rwj54+r5RLEFOHhBWTZezmIXMVuEO5s0o+urNqfmrzkaUbrBepUWvKuDa14ZhJvqdz5UGLFxqvWEusKQo4Dp9sZLYAmrj29HFNQBd1TPGfCgBuOkQdQvGUTAT+4LAlynWLdUTiianonwlYkEFNJItbFjH/kOC6kqeQraK4J58XnJizhH5wLPsJdRkAgpxa6hBzP5JHE+/lgWHhWNB4ASBnRoMOaG3v5vaPvyAJJNa3l1j9AAAGADChHRRsoISW729tLAOx/QI5TLh870k2+PHTTlJWoL3U1dWD//0uSM0iWRUAZEkqSKB9eK8l3K7TUYCwV4IgOQYnFiCR4vCpyAQUtnLJNohFA3CWWr4sUyZddoj3A89gk8FhAIi4OVBpSBer32dGPay3zEAGNQnUhLvFAAAgAElEQVSIpgFGACoQIyeJoiEfxhYktMecjp6+oKyP4NUDuDJlx0pS7rYAf0n5HdnMcs7MpYhr0QAgcyqinu521uPkhnVIogYlcygJMuZVlJZGfc1MVCt6HV1nl+/LpCa+hnWetfmwLePHvIVwfyL2j/Wf/QDAnUfOONRb0Obu6gWUbJ5eEG+JDGgu0qIK9ytiD1EktyA4Mac/F9YPjmU/oa4XAJRSikM7JBHiPH9ZfcBOaOLxotHAQFMZ8m/Mc/jryXMqe51/CPAc4Fof2iGggYn1HcZbPzi/AwAY795R7YINlNDy/a2NZbyWHyDHk72+x0T6/eJl9aefdlL3Ux6w+LeMmYtlMRBzt3TncTp/ydIklWS4+kGkkwzLcZhbzk2ZA8kmiLdi3jO0xSEFCg8QRHNhkuJP52ynfuM2qMsNSmZXNDONhPvvtkp56c22FUm6WGN5bqw3rD/Qvk0qlIWqF8mqYvoASlqWz6345yRo6HFTaXr5l/X2EFi3I6cu0KaDp+jBOoWVbrGJILnNhwto8U7LwoVsX1g4OelAny/LtuG6iXQb8X6gbzlw4ndb7k13x+pWMnDBwSots2Bh5X2sQVHjcm369ZSdSeq2nm5Ex7cMmUurQtZOzAvUPaVyZVSxfhz3iHWFu/3R4cto4rpfbfe3aSz9M2HKNEbm9LD5OyOa8/7gG/IHEltFcW/9/t+o5TsWt6AbsPW7r7wAoFTtQIIK4m9BoN30+pwOxZNoAPD0+UsRbbBnkcBSNm8mG4TqSTB+nyGoF98KBOd3AADj2zmhVsEGSmj5/tbG8QLAYt3GG6Wg3B4mGgB8e+oWWwg+lsVAAgIAIAPR69KlopU9LTks/SBiJYNqhbLYoIbH4gxWPamD78PSg1g4tjTiOsaGxY3BJ64xkIRlsG8IcMHdC+3a+oPC7j+4UwfdWYEk6Ijluf0A7lPnLtpcgchyllq3mDssnRt/PWXLmeFAR/KILO0+XUjzth5Vl+6pAQBYwBFbKJVYAJhurmAlJ5jeNfcvraM6JcueY2ep7sAwHQvc7XWKZ6M3Jm+id0O8ipyxbVovU6atXs/NVQqeRliTUWSiyZwth+nez6zEF7ZaP/H1chq35oBym2MNTUV/R/qzoQ3CCBD6oBdYcUHQzUUmorBVFPekxdMtttHvvvICgNM2HLSzoRmYm/qOBgC95sJzCIigvVYqee8H53cAABPaUcEGSmj5/tbG8QJAmYXqB5CYuOG4neTNi2UxYClYuvMYnQlZ4SSxsIzpwjgMykyB+w/VLUwv3VhaUakgOUIviONbu/+kUg3hAjcqAMPFyyE5BCJikmIpmwVQBLelBDZw/SEzNxbdYzknP+v9+4XLdlwgxh80aZPdBayS4C8EgIBV77sle1Q82/ea/ur9ny9WLlCU+2sWpP9UL2CTGYNwGpbRFbstGh4Zm2bSXWZ1C2kd1a2O+0/8TrWExvE3D1WnWkWzkSTWfqnl9fRQvSLGbWJKtNAr6nGHfP+ujxfQwu2WtVMmmkiVEP6B8eQ3y5X7E1Y3xFH6AYB7j5+lOq85uQbxwwP7Uu4h9MWxpNyvnMPwB6tR3eJWrK60eJpiOGP5LHkBQBkLycD8rwKAnCUfy/yDuvGvQHB+BwAw/t0TuIATWru/u/GVAoDSKsbPzEBGWnhiWQ+4YWEB5Di8aACQgYeJuqND7ULUq1UZ0rN6eS6wGiLeioEmrgPQLN1lUdBwYSoU6W5uUzUfPdPEGZMHMNXnlrIqno6tTrE8tx8AeP7SZTurEkkub03dYg+BIPuDv51TOre3V85HPyzfS7WKZlX8gbJ0/GIpTd1g8dzBXQkrIMcyghKldrFsNkCEWxCAHMWkrYuEnRGP1LSBOK/hyEfDVsdDv51zaBx/93ANqlEkK8kfCDLZRF8zU6KFXgecjnNfdPIdos69ny2ySZllnKGkpmHi6me+W6EUQGBNBJeiqejvCKorMi4TbRAygL0iwwhwvV31AtT/tnJ2tzI7+uuO1dW6o0hyaajiTHjGqXASy57yAoDSEvpNx+pUKzQHfYzksAAGADCWN5d43QAABgAwoV0UbKCElu9vbXylAOBbUzc7QAgemg/JgRM30vszrSzPWAroIhCHdyqUiSsTS3QL4Cezt1P/8RsIFBuTNfJeBmTgIYNChl4AXmAtkxm/oHfhmDGuz9nEXy3cRd1DWbf31iioOAZZqg11OVlExtjF8tx+AOCly39QsZcmqG6falTMdqHib/CsAQCu2XdSuW0Rs2c6dB/7ahlNWGspXSBW8J4aBe1M1jQpUyiOPrRFYbCGf5uSehhgSusokni+fTgMOpGZXKXfVHspYJGEZVKGCCCeEXMxlV1Hzzhc7aY6bnx5DwxbokifUWSc4fxtR5TyBwoy2Jd2b0KdR6yk0Sv2KZk/Nyk//R0dOPk71Rww3TElxDJ+vXCXY1+hAu9Hriyzfb99qAbVLJpV3ZIub1MSTyx7igGgm4tcAmG2zJr6Tw4AyHyascw/qBv/CgTndwAA4989gQUwobX7uxtfKQDY5+d1NHSeM+CdD0lJ8xHLeiAbEnF4DNoypElJa/o0V13UGjCN9p+05Nswzkeztqk4OFaukOPAstXv1nKOzGZ5H0AFWq7Hz4bBYcmcGVQShSwgQoYVUOqmAuw9XB+UMZZWLwoni0i3YyzP7QcAglqncFdLLYQzfXkMxCUiAxMAlsmITZmXT3+7wgZ4iFcDmGVXNjJiwQ341UIri3XM47WoUoHM6t8yY5THZIApraN6rNfJsxdtQmu0gz5slYJZSCqr9GpVmjrUNgNAU5ydvq4myTvUefjLpfYPAxlnuGDbUaX9i8J6yZAUREgDlExMWeO85+TYnPEqryFmFAo1R047ww4eqF2YerYqbVeVQI9BMW5uPXTKtsgiUx38j/EWBoBuLvKF24/a6jIS7OvjJQcANFmj432uoJ33CgQAMACA3rskSo1gAyW0fH9rYynb5QdY8GRjjQHs8v0q5WqUhcd7ZfwGYzC818IAyAAAMjDjLE20kzQpGAc0IqATQYblmBX7HF2DQ3BA6/JGMmBURNwgAKA8qHFQ7j7mdP+x61CqjjzRsCjhQJeWLaadkUkWXs9qWjevNkW6jlOZywChn87dYVcHz9z+E+eUjB6sqFM3HDLqr3b+fiWNXm6tFUDk/bUK2lYsgO37ahWk92ZYltvxT9el0nkyqn9PXPurQysW1xjsSeuorniBLFEmtEab0Y/XosoFMjuyxEFpc3+tQsZHN7lZ9Yq6+gjff+Kb5UrSDkW6mSVNEsutvTBqFX2/dK/KOpc/CqK9I929jbqIGQWQZJ1pbg+wjcQhLjKDmEEx7smYR1MWt9f+kPe9ACBibe/40OKAlCBUHyM5ACBLJcYy/6Bu/CsQnN8BAIx/9wQWwITW7u9uLAHg9ldaUgowCXsUXbHDD3CU8WTcPbeTWq9eY8v7ADKLth+z5diiAUBW50AG7shlTiCKOL2Bd1RQ4AMgRC+wXiHeCokTXHJmTOP4G9dfaFGSHm9QzKE7jIQCuE4rhbR6UY+TDGTcWSzP7We90V/xl8arBAOdbgRWUNCxQEUFwAxUJ7AEfnCPxaHI5b8/rFYJIiiwVrWvXci2ZCIh4rH6Re3s4uld6tvE3pPW/UqPDF/m6IstjNI6KnkbUfncxctUqodFaI0y9onaKtFEKqu8fEsZuremGQCa3Kz6uuK9LerWJGK5n/1uBY1dabmzpZsZPzCY/Jrdx11Hr6ZvF+8hgGDJAyk71d/RoVPnHFZg1IVrfuzKfbTnmKUzzQWuYbihuUjwyKAY97YfPm3TCyFTffTjtSOey+8FLwAodZZHPlpT0Q+ZSnIAQI4X9Tv3oF5iKxAAwP9xANi7d2/q06ePYxeVLFmSNm7c6GtnBRvI1zL9v6wkXcBb+t9Aqa5O4TlPmWGKyn4AiSnejduZ3MOekyBS7lwAwCOnLWCWPvXVtK5vC/VvXaru3WlbFP8crH0jluxxcPohEeKNNhWodM+JEQH56AvJDXDDsUsZ15BwoscLsutQZjx3a1mK2iYVsHVO0ZZjzGSWrZ/n5Tp+1ht1S3afoGhqkFTwtSAcRtwfrGVIQIF7e8H2o3RT+dw05O7Kjml0H7vGdvECrMD1ikxpFFi/8Bz/Hb1G/T3vv41UTByK5IzjDlneS1pH9TFl3CLa/fxkHSqXLxO9P3MrDZxoZTFLwml9zUxWNr0Ox/Hp158fucr+YdC7VWlqH3IzL9l5jO4MWb44geSlMWvUeiIOUtIAyT71d4Q9WlXEN6IuLMHIJoYknyxY6y7NStqXpGscVD2g1EGRSS/IVB+l0fjEsqe8AOCqPSdsDkhphdTH0GNv45kDkq6+f7RmLE2DugmsQHB+BwCQRo0aRVOnhgOwU6ZMSdmyWdlmXiXYQF4r9P/3vgSAG19uQWlTXe052RNnL1DFvhYQQPEDSHSlBeh/bnulpWrf68e19MWCXZ7j6hWgmAAX3aGQegcrNaCeDgA5kQAULKOW7nVw+t1aMQ+9dVclkuTWcixYr6Casfd42FKTOmUKuhAioOa6TFEyZsVe6jRilboMl+XtVfI5XJvsYpSJB7E8vJ/1Rn9lek5UmctQ8Rix1LLkocANDlqSJTuPq2xm/J/XQM5DAnMonzxQp7ANZGEB7H9rOYLrFAXJEQBXKFPXH6SOX4alwnCN9W0R8/bCqNWqXutKeWlw24r2kDJuERd/eaqOIgiW0nqgz8E7NBXE40HNJVrhTF69TtfRawh60Sh9bylD94WsjNL1yQkSPX9cS18u2KVk9yQ5uOxTf0d6ggvqIl500tpfI2JJOZmI+5OWURl/J5NeEgVNXgBQJqJIK6S+jskBAJkyKJbPRFA3/hUIzu8AANLYsWNp5cqVce2iYAPFtWz/LxpJAAhB9nSpU3rOCxmk1V8JJzXsGNCSrgIxXJQiY/K4Grucu41ZY5TE8poIQAusV+yalQCw+itT7es4jJlKBIkMiEWU1BssvVWqxwQH1x+Pj1i1rYdPu1J+cD0GdlIK7dXW5eiWinltTj7U5Tg2k1vc65lx3y8ALNd7ksqQZqoX7ht/7zl+VsVPcjYzW0Hl+DI2EwkuyL4t02uSqgIL6JttK9iasGt6N6MMaVOpe5I0mPtjd6+0jrLrXY5ZtNt4uhxCVaA1Ab2JJI8e0LqcUiUxFXA4soXSbR2zpE9Ny3s0jbgtf4RAOxn7BEVKGLKKSO+f1hkVPGSn+juCUosMA0BdAD0oYYCORxZd71iGXEgOPhkbmGjcHAPA/FmuoTkvRNLkSNJpmfCjL2RyuIATtWb6+QwFdcIrEJzfAQCkQYMGUaZMmSht2rRUs2ZNGjBgABUoYP6i1T88wQa68l8nv527SNemTukrZs80OxwqO4+eoblbj1DPH9epKqt7N6OMoUOc25jGkfxjqAdLHix6ssBtlfGalPTb75coU7pUtjVK1gHgvPTHn9T35/UqGD7WAgsSAOCBULbvNamuphU9mypuvgavz3AAQCYTBsUGKDyYOgZjwj0J92f5PpMjrHrW/Zy0/chp2u5C+svz5gxVKYX2VtuK1KJsLkdsG1uxZOZpLM/uFwBW7DuZTpy9qGTrZmyyCJ1REAcJAAjiY6YygZXwtTvKO6YhJdgASjrWLWI/BwDg++0qU7tPLYoUGT4glSu4wxvL5ab32lUmaR3V+e5Qt0T3CfY7mPRsPSqZKwMxhQ/uv3Z7OeVSNxXdMm2qI9Vi5H1I5UESD0VaGWXsW5Hs6Wl6lwZqv34+L5xUYxpHf0emuUFxBATLSMaRxSR3xwBtaPskalgqh6ous54TzZz1AoDyM//jE7WVdKCpJAcANKnSxPL5COrGtgLB+f0/DgAnTJhAp0+fJsT9HThwQMUD7tu3j9auXUsZMmSI2E3nz58n/McFGyh//vx08uRJypjRygQMyl+3Ahz8DULfz9snxTWQDPDnDlb2bErXpUtt98cWBmldgHUGsWUAblz02EFJGYE6ELaHELxeYN2BZcHEqefnoSCZBZoO5mKDWxZWwMuX/yT8+2hI1QOH8euTNtGQGVtVQgQsdHr2ZrR4LrgvEW8FN3C0wpY9ZJOya/SDdpWpSemcVDzEyYf2A+8oT22q5ifJs+fnebmOXwDopm0MpQlkMM/fZsm8obA6iZyHJGBGgsvDdYvY3ILgXPzywep063vzVBNpBZ6x8RB1GLbE8UhsZZXWUbyL3jeXcdSTcZhTOtWj4jkzkFQP4bUzrRdiMsHlKAtcvrwPcN2NCFpSEcFqe1fIyrhi93G67f35qsviOa6lKZ3rU/9x6+mTObEBQNPcYFUFAGQ1FZ63KdPZBoAdkqhhyUgAmCh5shcA3HroNDUZPEtN8acna1P5fGYAeMPbc9RnGsXvPuXn5jkk+iyxfJaCukQBAPwfB4D6h+DEiRNUsGBBGjx4MD344IMRnxFT0ggqBQDwynydSGtFrF+yPMMb35kT4Xpa1r0JZQ3FcaGeJODlcaTGLPelxw7qChfwDrNiRjT9VD+rJ/uCCxEgRsbmmfrA3JlsGpQssNBx4oifMeG+hLXUTfeV++AMVUmE/Hn7qtSgRA4q0s3i5EOBVfDWSnkVKIZubizl7bsqKpeyn1Kt/1Q7PlLWB9gDuIf1l8t9NQtS31vKOrqV2bdIcAE9CXMLZkibklb1bKZi/XJlSqusZlykbBhfYwujtI6yBJ8clN3WuDatS30qmv1ah3oINHIB/E1Fp5EBkIDL+OEvlxFcm1DtQLJPmTyZIppLNZqBt5enNkn5VR2Z/ADux0md6tHgKZsVN6EscKEjo5fd1/rnEpb08r2d4BRWVbiAmVAclEH4QfN1xxp0TWpnLC7c8Wv2niRIwaUMJWpJ2ptE9XO9AODOI2eoweuWnjXHZpreAZKlsK/h3m5RNrefbWrXwY+DD2dtpw/vqUwFs6aPqW1QOf4VCABgAAAjdk9SUhI1adJEuYL1ElgA4/+wJUdLab2LFwDi1zx+1cuyuFtjypExrX1JEvDyOCZX1oa+LRwH1k3vzqG1+5xxTeiUaVrcsm39rA0kyNj6CEsWAKDOx6f3g7mzhQeceMi81LnXoo0NuhmABz1WK/XVKSh9mqtta2L/28pSu+oFHTx4LN0leROH3F2Jbiqfx1aU8PPcqDOsQxI1CFl//LQxxV2iHeLbAGjnbAkDQJ18GPWYPBv/5vhGfg4AwDW9LdJtvUA/GBnOsjDYk9ZRne4E9aUe84znGhDi7obO20F9fl6vuhvcpgK1rmwGgGcvXKLSPa0YxVhj4uRelyBz9d4TdPMQy8rJcmvSIonrrOxSrNt4e2/qn0sdnKIdeCMnrD1gf1b8xNLKNZXaydB3Htahmp9tYazjBQClu3nc03WMIDruwYOGf+sKBAAwAICODQh3MOL/YOl7+umnPTdnsIE8lyhZK0jLUbwAUMbq8OQWdG1EuTNZVB4oQ6Zvodcnb1b/5nFMdBbr+jSn9GnCySNNB88yuktZhUEe8rEujMy+hSULElU7XfRYuW/MfcD4DfTR7O3KigUQ4ibhZZoP6GZwAK7ee9JxO9u1qZVlk12MnKAAq85DoSxYpsxgUmZ08NG9VZSM2oujVjuyc73WAtafusUtvV0/xfSO0Q5xkDuOnlX8f1x08mFcl/rNzI1nA0ChuqLPBf3epwFATmyQ1lHWTpbtkcXL6hqznm+gLEFfzN9JvX6y4lTZemp6fpktGyuXnKSakSATVrdWQ+aq4crkyUjjnq5LI5fuoedDmcy4zmvHvIvy88LzPHP+kp1Aw9cAAPFjJF6XqeQ91Em1/ewPWccLAEproyT9jnWcoP7/vxUIzu//cQD43HPPUatWrZTbd//+/dSrVy+VEbx+/XrKnt37wAk20JX9UEv6kHgBoDxoefZzX2xI+TKnsx9GugB5HJOklZ48Unfg9AhyW3QK9+/EZ+s5VDpiXTnE+HEGLyTc5m09quLzohXMneO2HqlfRFnoYNHzWxC/BgCoB+sjKQBJLuxO5gQFmQXL7jIJDuAWblQqJzGfnN95fNOxOtUq5o+aCX3WHzTD+JwdahdSazZTJIaYrHHDF+ykHqEEIebGY6AgSbf1+c/Zcpju/cxpAWRqFakSwoTYsr20Ws55oSHlz5KOpHxcNBc4aHmQRIISKwCUVj0JMiX9Sbm8mejnp/6vveuAlqJoukUQkJxzzjln+MhIMOcsZv1QfzOfoCAoiIqiYgJExSygiIkkOSNJcs5IRiRK9j+359VQb97uzuzO7tuFV32Ox8dup7ndu323uupWc3IKXXNqv4rPj7PlhZyfS6d2JuYIv8ofl+y0ZWDC/SzLzyIClYZ1qe91K6Wox+sazEdSRv6Pf+I/VLmw+npHDHaCNdTzO40TwFtuuYVmzJhBBw4cMISvefPm1K9fPypXrpynraobyBNMUat085C5NH/zX6a/cA8NnkTN3hNSJKFniwvXCUQAoR/X/LWpyTTQ4AuGSF8uwXzPGpbJa9JItRow1dVqFwwsmX0BfmvwY3OLzsXVWt9fV5soTxAdHOBubeT4V9cuSn8e/IcWbj2YbFrIUoFrONYhHHBDTbqxfgmSQRAcyMCizOiALXle5ETkgCMebEyNyubzvI/avDEthcgwGuPKEuLDU9bstftyig/jjW9/32YLPbN/IxMFKbrtnNCs9fvpjo+t6GAuTNykdfS5TpVNijlZpNWSxaW/mLeVeo5ZYaoNurUOQcg6UJFC0uESwOGzN1PvpGtmOcbKnYfo8kGWBZDTrTmDnOC792yHyhSKADqznKA/PD8y8XBwUbifZSl8zTqLnjeHo6IbAZQaixydHelY2i6xENDzO40TQL/bUTeQXwTDa3/lu7No+Z/WdWS4hwaPJAkJvybTeeE1eS3G47D4bJZL0tuaeUt6tqc82c5HDyMS05klA/21r1qIPrqrPjlFocN5epmBA1GkIIBOX0Znf5Cp6fvrKvp09hbCYT1p1d4U4ruh5gDhZFyBQTdPFmQIQY5g1iF888ZaRvRZ+sCxFUv6PX7zQGNqUi6fp2hSOd53Dzeh+kFScAWafyA/T9R7qEVZk0cWOYC5OMWH8brU7GP/RiYKUnPROfbsDftteRh+j+VLpHVU5tzlevLHAbskfDV/Kz3/g0UA2X8y0PNKvTz+seF1bwUbY9XOw9R50EzTTZ2SuemHrs3MlS2iXblwbmcpYeP8XJ48c5YqvXA+zR3aIrAGAt38YyTcz7JMLxcolZ/XZ0c9NwIoNRaVAIaDbOLX1fNbCaCvXaobyBd8YTdu8+a0iA8NDObMuMATmPRUCypf8Lzsj8zAwIcTJ6BHEABr6cksEOgrmKAyiw1f/d4sO/Ix3IeXsh4IXJi1YR+t2xNanmVd306EKMrhc7aY/KuTV++lVUlSFV7Gv65uMdr19wmjOSgLroaRKYJ1CCGMfG2d4iSvQDmwRuYZZr9Ajkz2MgfUCZWBIVAfwYg2rG5YR1jjuDjFh/G6lGxh/0YmCtBcXP2ylXbPWeCXeVuSPiC/xynMpHVUZtzgem3fnEYbk/QWGTuZPxjag51rBI8u5fmFSwBHLthO3b63MpTIMSTZY3066Q+H+siS8mT7inbqPbzmJHPyepqfFWkC8WzswxouAZRWOdZZ9LqXnPXcCKCUsWGB7kjH0naJhYCe30oAfe1I3UC+4Au7sTPLRbgdBLqOQh/OX/YyCpQjFFkQFhkVDh4/ZYIgfn++LRXMcT56WEa8yrlxpGmgvMBen6FAjsx2kACuMmEBdJNngUwNCCDSdyHwYNravSkCOkKND9kR+FtJ2RTURxQyImk5oISvOaUFkK/H5ZU7C+kGkhMJNY9QAryB2nV+Z2ZAogsr6Po9R2miIIAISHikdflk3Yxdvou6fmWlemNpFF5bWIDXvNwp4HQhZXLrR/OSvcc/LiQ2gdK6dXhrhm2dXfB8O8J6y6toSISEkhexCWCY+WRlRpzBd9Qz4t0osPB2fNuy9nG6NWdEL/T8sK+kVd1J5px5jtEfUgd+Pm+L7S8bLgGU6eUC5XL2+plCPcYNwuC4encW+cwaBRwOsolfV89vJYC+dqluIF/whd24xosT6MjJM6ZduIcG2gQSpcXrzi92mYEBVjRE4PKVGA5mXAtB92x+j7ZUSMjHBCOAyHzweLsKdOfH85NJkIQDQOGcWWwJF1xlzli/346iDNYPopT7j1tNX87bZvTJQEKc4ruh5gC9QVzzop0suIKGFY0JIASvYRWUuXBZI7HOSxNtuRiOopRR1l4wCKW/Fqi9dBWQ7+PKElbT8St32y/zNaasJ/31WBrFCwF0+sihT/6RIK2j7DMpx5RCwqxLKa1zHEEdDK9ICSD0GFmsfOid9eiyahYBlBkw2K8QFvTyz4+zNf+YPMtc0s7PJT4nSHMnC67A4ZbA+yfcz7K8lmWhbS/7KFAdNwIog1jC3YeRzknbpQ4Cen4rAfS103QD+YIv7MZluv9qCyuHe2hgMOk7JAf/+dHmVKP4eZFcGRnJUi8si1EkVxYT/Xr67L8057k2VDS3JR8j/bCcD8ap0iLNgYv+OHUZ/sZVJiRH3K5zkae2/7g15roN1hqQkAVbkgd0hFqEWxuWMARQBk2gPgjo2BW7bAvO+7fVJUjGSKkTtpzKrBwcGCKv2L1sgnCv3pClwxm5jHFwZbl+7xEau/w8AUSeX0i9yCIFnfl6m4kCMqes7RvYAjh/0wG6eWhyCyATYXk9HEjSRWpIcmaakQu3U7ck2RVJzkIRGbbWecEVdcav2EUPf2lZOz/uUp/aVilk/oawcfu3Zpi/m5TNR9882Nj8LQk9p26Tfp7Oz2Ugtwvgjc8YuxCE+1mW+YURGIPglUiLGwGUPozO74lIx9R2iYGAnt9KAH3tRN1AvuALu7G0sIUrHovBpKirHHzMI80Ika1cEDWLrCMofJXJqXc/NWMAACAASURBVLEgFwEfpJNnzpGUjwmU8YD7Y301pEmDFl8kpWTerLbwM64yYZULJDot+8bcYQH8dsF2gq8brnKRB9drgd7g3sMnadLq8z5zaAur2Y9Ld9pSK3w9KX3n+FCXsjvTnmlFpfNnM1HJjK+XuUx8sgVVLJQyNWOwts6MLFwPlth1e48kWwOQ3P7XJc8FLIM5+Hqb9x6swbAKByoIlrlpyNxkbzEO8nqYCbOsKK2WS1+8jBD0I4NRht1V36TWC1Z4fg1K56FRDzf1AqupI4NTZL7dDXuPULuBFgFsXj4/fXl/I/O3lNhBMMdDLctRKAKINk7LeK8rqhJ+BHAUebgEUIqyX1O7KL19S+wIoLRghuuK4HkRtGJcENDzWwmgr42nG8gXfGE1dvoSIcI1Q/p0YfUhDzXZkIMT+DVJUNgfa8GWv+jGwXOpdL6sxir2z+mzNOPZ1lQyn6UfGIxc4j0+vJ8a+QeNXvxnWHPmysgMwbp/COgAAXQKNDs7XtyzPb06bjWNXLjDZF8ACXH684WaDPQG9x85lezKFPXRF0SB2YmfryclYeFDvfErk+2raybMUt+Ox8dacjox55w4NZpX4IL5WsIKCukRmYYu0BWitOTx9bZNADOkp3X9AhNA3iNynoyD7DPQda4MEFrRp4PJHiP981hDMdoEUPomyowrHPSE8WSO2qvem2XvO45mrtZrPB07ddZMLRCZcxJAWMQhtbT/6KmgbUKttXTlQKT6WzfX9ro1UtRzswBKC6bzh2LEg2rDhEBAz28lgL42YlraQO9OXm+yXtzbvIwvzNwa42DANdv9/ymbrKr0+8Eba/t2pMwZrbyhkG25JH16eqBF8jbOsaS4rXwPpO7dW+va18BDZ2ykV8auMVVYkoMtOOUKZDMEEM7hsLa8eWNtQwLv/2xBMnkR2f+oh5tQg9J5qfvo5fTN79vcIAj4PsblKFE43uPgRr7WUAXk9bXxa4wlCdd18FFz+vOFag+9wQNHT9Gvy5NbLXHw41oZmnookLiB1A2eDc8oiUCzV6fYvl4c3Srr8fgy04lzTmw59ArcrUPnpYhcRltYQUEAf/zjfB7iQJkkFm87SNd9MMfaW0nRt0wULsmQjtb36xxwKou2/kXXfxjYAoio6RsGW+9JSxt3JK+tV73UgbJmykg/LNlBT45YarW5pwG1DpEOL1IL4JyN++m2jyztQplxZdO+o9Tmzenmdcj+fHavlW7tjmHz7R8RsOTh+0BGenshgH2uqkbvTF5vfGnlXvG6vtLafl2dYjQwhgQQc2Jsw41G9/o8Wi8+CKSl8zsYwun+xU8cLREhkFY2kJR/iMTy5hVcKfDKwRfc1mlh40NSRgQ6c/M6x1209SDhejBY4cNL6gCylY9FfpHVA3iwFEz5gtkJmSoavjI5aL9MIvv8vNI4v0dSKhXKYUeJ4ipz2rq9rgEdIFyvjl9jrI6Q3sAVpdTAc5sHgj2Q7k1azNAGQTP/980Sm5CydUpGjjKW134w254nr48zpRj6lELXznmxpqDbfPl9SVJkG1gu4ds2RhBAmf+W68o0aBwZyyQAOZk3vBKMACbfX7mzXkJ/9LrMdCv3XqDUdhIn9huUV+ogYCBiwQrPr36pPPTdf71fAUurJedvxhiwNrd+Y5oZrk3lgvTJ3Q3M3498tdj+QQAi16VpaRowYQ29P3Ujta5UgD4NkJfXaQGEDA5+WIAESnLpdX2PnDhNNXpPNNUhVTTwpsgtgLiyx+ciUHYWng/P3yn75HW+Wi8xEUgr53co9JUA+tibaWUDSYdwaXnzAV3ApvLaiQ9Brijfw2uchk2SU746CzavQDptsi6TFlg73/zNygXM14+QULn70wUmL+qOg/8kE3yGkDRbS2R/8B9DTlf2L3x13Brj+xRJqV4sp+3zh8Nq2rp9hlSEKiCer49fSz8s+ZNgtcNhP2Flcn8+CBtD6+7xb/9I0RVSp/19/LRpz+WHrk2pTsk8JMWWpXUKwReIWC6cy5LHQSotWJmqFslFlQpbfnyS2CCTw/86VTbEHGOhgLgOm7WZhs7YZP4tg228YId8vDLfL7cBAdy49yiNTnoeBCPc07Q0pXe4Ekgiy8EXXgigtBzCfxLrlC97ZjM8+5Di728fbEyNHZlNpN8i//j5aelOQ7RR3PIhR0oAsV6wPqJ8/UAjalrOSrm3Zf8xapVEANtVKUjDulgEsPvoZfTN79vN35wlBe4ZIFHYF5dmsqzysjgJINrd2rCkaVO7ZG5j7QynSGkW1tgMp72si1zFsKRDPzFjhvQBu9l24Lix+FctqmngIsU5EdullfM7FPZKAH3szLSygSQBZMubD9iCNpUHL0ffcmX5njlQk7JwSMsgol5zZDmfms05kBTjDTQJJoBv/bbOWCdQOP8nO8vXKp6Lth/8x76+Qh1EqULGI3/2zHZ+XHOIvnp5smFkv+HiBxLJka0gMngWZ4o2Z5/wuRswYa258gTZWbz1YIrr3Gbl89FX9zdO4aiPvhAhC1L2/eIdpmsZAHHZW9NtIerP721ILUJYp5zzQiAMAmJQOBVb7Zcm2gQQuE1cuZse/GKRqeOU23HDTuaMlnVxDY7sKfw8wYJLUAcEF4UjY5nEwFcRVvBARZI8Z+7eZTv+pqves4iW0+cUr904eI4dob2hXydDRqREy5f3NaLmFYLnQ46UAEq3CJlyD6SnxYCpZr4y3RqCioZMt4g5Z0lxWw8nAex7TXW6o3Ept2ZB3z9+6gxV7TXBvA+tSlhxtSgC4SKQVs7vULgoAQx314j6aWUDyeAJtrz5gC1oU3kYOceR13LogIMzJAF05uZ1DiQlLwJNggnbGxPW0ntTN5gqrP2FPLoPfbGI6pbMbaJx2YEddeAcDiuKlGrB604CKK+Ww8UP8h6/b7EieEFkEJnrZgHE9fWAiWsNkYDjPTQAYVWSpVWlAjT8noYBCeAD/yljLJ0IIkGRWTBktg03cuJ8Vknu4M+I4AwnAZyyZg/dO3yhacpr7RWzYHI7yEELC+CoRdbzTH2mFSG4xlkk+WHrJpMYGAs39U9O7Lm9tKY5c/fKvf3To82oZvHzUedoz1eR+HvTK52NVVISZbgZNC0ffQIo9f7YVxVzkJ+rjtUK0+A765nHlHmyX72uBt3SsKTrsjgJoFfiGKxjqc13Y73iNEAJoOsaaIWUCKSV8zvU2isB9PHJSCsbSBJAZ/5bH/ClaCoPUOc48noNDed1b2uuGeVBxQK6weY0Zsmf9MSIlFedXJ8Jm7yqZcdvzg4BIrb5wDE7KwfawnIC/TcZqIHXnQTwk1mb6aUkeZlwcWtaLh/N2WilZIP8BsSM3USdETwBAggiAX8tXHXx9SeP365KIRrWpX5AAgi9Pzjc85Uf/PSW9+lgmkoC6EZOnM8qLbEsku0kgFKLL9w999AXC1NcdTNuyD+LPLQowXwLd/79DzV9dYqpw9ZNLwQQ+F6ddJ3qzN27cuchunzQLNMnC2JLXG4ZOteW6OF9I3UVOY9ysH0TqQVQBntIy6T8XHWuUZg+uN0igF/O20ovjLHyE79+Q026qX4J163sJICBMqG4diIqyIw+ECt//Qa1AIaDn9a1EEgr53eo9VYC6OPTkFY2kLQShGuNCQde6ZDudLh2iuzy4S19lZyp2Zxjj1iwjf73vRWlGqjwwdv3l1XGBw1l5ENNjH8Q+61BFBcO8rsPn7C7gIN+l09+p6pFciYTZ3YSwK/mb6Xnf7AOz3ALnOU5gheptBCZG0jsWPYL/8U3J641wsfwu4JsDFu/uB4f7oGymEBw+ujJ0yaTCAq06aBRhyLTrQXyaQv1fBxQgzrsfB+KALIunlfMun61KJnYM7cDbpv2H7UJbbCrZZlrlq2bjE+6dESbg1gApZXambtXujA4c09jfrd9NM8m+LxvpMVaXs8GwoHnx3l7vWIliR77d6LtjoPHqflr1hWwTLcmr6UDBdCEmhu/x/mVvc7RWU8SQKQlfPX65DqOkfar7dIWAmnl/A61qkoAfez5tLKBZGJ4trz5gC1oU0kMWDKEK0txXrzG0iDSX8ttboH05+Rk+ODt/dNKGj7HitblyEjWZIMmGq4Rdx46TwARKAB/tTolcyezyjkJoNTJc4IQSgcPdeGIzxG88OfDVa6bDAwyb7w5cZ2xFsLvClYoWPPkWJxJIRAB7NqqHMFJ/rO5W810kQcZ2oKGFLw70w5KYZLsdU/IlGmcTsxJAKU+nVtwj3NcpDZzRi6jDgJhEEzEFs1gFmMpNMzBFxKfYMLF8prXmbtX/ogKJGtz+7B5NHuDZeHl/tntAK/J69lQJCtcAohcz437WxHsUuhYWkFltg25LpwlxW3dnXvrtetr0M0N3K+Og/V76sw5qvjCOPN2ICFvt/no+4oAEEgr57cSwBjt97SygeTBJrNfRBtWmZXAGfkprwQx7qSnWhIkWNbuPkId3rYyFrjNTeb4DTR3Pnh7jllBX8yzSA+L43JaLvjMrd9z1Na2Qx2IBYN0NC6bN1mmDSdRkNYT5/g5smS0pWUCza1T9cI0boWVwgz+fLjOXrrjUMglQADLwInraOKqPYRrNxB5PJcci6MoAxHAR1uXp2OnztjSNciDDAswisxc8d3DTah+6byet4OUROF0Yk4CiCheRPOiuMn7OAd+4tslyaRe+H0Q57W7D9s+jcH8WUF6q71oBRnw+odLAJ1iz/KHyuzn2hh/UVlknmjeNzIncaDAEdk+UguglFGSvom7Dv1DTfpb1+Ay24Z003AGugTbAM699fr1NemmBu5Xx8H6O332HFV4Xgmg5w+cVgyIQFo5v0Mtv1oAfXw40soGkl/6059tZaRNYlHYzw59O/2z5GGI9yc80cLIikjfKpmZI9D8pLxLoPf54JWCzU6RY/jMwZqDQBAuuAp7ZtRSAjmctnaf/bqTADqfQc6hUM7MRmA6WEHGCrZqwZ8P1sTlf4YmgPA1G/jbOhMwAof9NbuPGMumHIuv0AIRQKR8Q8aTj2Za1+GQd5nXo635W2auCFcgV0bEcjqxUATQqQnptveCZVyBcPGKnYfsbCzBiKXM/8oah14IoNyLvG94rtLXzmndRp1ABHDSqj10/+dWIMz3/21KsO65kSwEKY3u2swNIvt9mVVDXgFLy6DU2pPP4fRzdJsbv+/VdzBYfzIrEOR28ONGiyIQLgJp5fwOhYsSwHB3jaifVjaQzGLAljcfsAVtKlNfOYmmJIfoAGLE1YrmMteg7HgfLKrTJmoiujfQJJiwdftuqW0l+vD2utSpRhFjOYNlEBGR8OfiNGjoB9ercIzvUK1QsuADJwGUVi3n+MhGIvt0vo9DmNPIvXxNdfr29220cufhkMuACGZIz0xes5dgdQFxhW+jTCt3Z+NShP4CEcDH21agE2fO2rIfsFrBemUI4Puz7SvocFNkSZcCTifmJIAz1++jOz+2LIDhio+DjIMgOwssp9gvLATNcivOeufO/Utle4w1LzOR80IAV+08TJ0HzTTtWD6G+5bEKVBQC3xI2ceT942MhJbkLNCi8/zCJYAyolZaGaHf2ChJ3FxKrUiLIafJc/sucO6tATfUpBs9BI8E61euz+2NSlI/JYBuS6DvB0AgrZzfSgBjtP3TygaSPltseYsFpDJFmDP/qxQPxth8XSWvEwM518t59vt1lW3NCkUApQWJr7mGz95MvX9eRZfXLEKrdx6206ChH1wtvvzLKoKvlJRZcRJAZyCLnIPM9BFobjfXL2FHr0JG46t525IFnARqA5+utyeto6lr9xEO3Q37jhoyBzFrJo8Qe37xymoBCSAidGEN+2CaJV5dMm9WmtGttflbZq4IJGsSan/IqHJgB71BJwGU/qCb+3emdIi+8FieHrnU1vqTTWA5RaDRL8us1Hah+mXSwle5XgigDPRwpnuTzxxIr/KeT38364TC+0a6PUj/vGgSQHmdKn059x45QQ37Wb6BMtJW1vd6leskgG/eWIuur1fc42qmrCbz8yJfdd9r1AIYMZhpuGFaOb+VAMZok6eVDSQDMNjyFgtImWShbwQwVChkZY5AcQZQ8LUjsglAQw3FjZz2+nEFfZ4U0BBo/kwIHv92iZ0vlg+rYTM3Ud9fV9PVtYsa8gSfLi7dOlYyGTegSSajbJ0EUF6lO8evWTyXidINVnDQcTQurnNxlYsr3VAFGL0zab2xLOE5EAGLlF1SU/DBFmWpR+cqAQkgcufC4X7QFEsTEZZDWFlRZOYK1kr0uie2HjhGLQdYacZglbunWUoCKPdcsKCLYOM9OeKPZNlLuB5SkOHHDKKiUUL1y6SFLcBeCKD0R2XfQR5bvufMcoM69w1fYCy1cl6cfQav/fxocztXdaDn5vkhEOmHMK6AJZmSUjMyEtoZaMFjMXl3W3cnARx4Uy26rm7kBBDjcZ9swXabg76vCDgRSCvnd6iV1ytgH5+L1NpAyJGLhM2FclrptaJR8MW/bs9RKpUvK2W5JGX6JjmGjPyDtQdyJ2hbpUgO2zKD/tbuOUJl82c3GSPCKSAZC7f+ZUgeX3NyBg7uB1eez40+L+HCUZEymT3IKfo69y9RnRK5U6T44qvdSzKko9NnU6bARlToip2HjdWMNfZY7HbI9I3Uf9wak3sUQTF4fi7sn4fDiINHAhEMZzYTiZEkZYGwQ15ejkyG5eWT2ZtdCSAsheNX7KaZ6/cTIjY37z9Og5Lyr/J1I6ReIJAc6AoYEbqw+Lw9ycqKAp3DyU9bBPCGD+fYmUgC6dqFWn+Zvg+k7K4mpVNYAP0QQEng5TwghQMsEBQTaH1kXcaD/dy8EEAZ6evMjiKvhwNdaUvxaiam0mXAjWRHSgAlmZK5gPcfPUn1+04ykDj97Hgs1nB0+6w795bX6OFQ/XKfdzUpRS9dXd1tCvq+IpACgdQ6vxMZeiWAPlYnNTaQdHgOZDmIdPosMdGoTF4a8VCTkN1IXyRYlb6ev82QNfbfQuNRC7fTs98to7aVC9LHSYnjvc5N6u5xG6el8Yu5W6jnjyvtLjlvqfQVg3DxkKT8sYGkJpgY5MySkQ6fOJNieoGscJzvlDMg4DoMlrpA1rf7m5cx5AIBIoEyRkA/sHVSflXn4Eil5sxfCyINQosCSx3nxsV17rCZmw3h9lpwlY0cxkgNh2tsiEOjQOqlW8fKVP3FCSbfqSyI0MX+47zIFQtlp4lPtjRVbho8185M4iTrbnOS14vwPwRxdl4BS2tpuBbAYDIw8NWECLXT0hZovkwwnEEg0g/S2U6mTHRmR5FRwIGe58HPF6YgpnJvu5HsaBBAGc3917FTVPfl38wjOv3seCzoKj7Qoqzbcqf4ceE1etgLAezSpBT1UQLougZaISUCqXF+JzruSgB9rFBqbKAjJ05Tjd4TzSzdZE7CeZRAwrPB2svUXbC83TjYunKVwsAyM0S4B3ag1F1OvzJnFg3WZ5N+UpJE8dWmfCbOEQur59YD56N4uU7mjOnpZBLh4tcQOXpv8zLmKvWtSetMEnsEEqzalTIA45HW5ejaOsWo/9g1hBRntUokT/cltdW4f0jZ4GoV0bj3fWZFfHKB9QlyLxgfPnHsi4crtA+nbaT14hpaksVA64jUZNWL5qR+v642V653fDzfVONcvIhghWQM5sIi2IjQRYHlE6VKkZwm7zHKzUPm0vzNVmo653W92z48eOwU1UkiF8EIICzKPX5YYfwO/9uqnFuXyd5HnmEmuFI/ERbRiSv3pAi2CNT5p7M3myjrN26oZSzJiFzGHoC1VLomyLbSz8+ZHQXP8+JPK02+aOwNZ5HZS/jzI62gwB34BytMypAzGkE54RRYt/HjBALN7Gsp18hJALEn8QPyzZtqUdZMGV2HcloAo0kAYRnvfVU11zloBUXAiUBqnN+JjroSQB8rlBobCKm4aiYRQDeZk3AeBblrOZOEG2FzpqS69aN5ZijIiczvYenC+SGAUgKDn8EZ9chXsPw+52iVUhnSgoegidduSJ4hgK8tnYLNoXBjmZKBE9caXzhcOSEt3Yo/UxJA5LQNdLhz/zKCkl9DirevH2hs/uk8KKXjP6J535lsXcW+fXNtenfKetq475g99SK5stAuIU7tfCYp2SEzKXAuXq4vI7Fh4QGxBHFBqV4sJ/3ymEUAZeqycCPDYWmExREFgRldmqa8Ag5nLzvr/vfLRbZmIvY24wqCg2hyXAOjuO37cOcgrXzhZkdxzhljS/cGN/9WPwQw0HNKMWy/fnbOfe3MkxwuzvKzogQwEvS0DRBIjfM70ZFWAuhjhVJjA8lf4s7IWB9Tpw5vzbCvEN0OQilgjKstth7JqFA/BFBeJ/IzOYVvZRJ61Bl2V31qV7WQ8XF7+MtFplnxPJeaa04UCCd/eIeVv5RL+4HTjdVMWoXcMORMFa+PX2MscIiaXbz1YEAR5h6dK9ODLYJbq6TAMI+LzCJf3NfI/NN5UEq/r/emrKc3Jq4z9WBBARlEXlsuuJ6VfonO5+JgBrwuIzmfaFeBnmhX0a4uo61xxZ89c0bb97JW8Vz046PNTV1pQZ7ydEsqWyC7G5T2+5KABosC9txZgIpOaxrjCn/OH//YSXM3Jc+44Wcs2VZKvYSbHeWRrxab9H4o/Hmcu/EA8Y8tNytrtAngoeOnqdZL1s2DXz875772Kh8Tal24z3ublaFeV1aN1hJqP2kIgdQ4vxMdTiWAPlYoNTaQjMYL19cq1KP95/UptP0viyy5EcAfluygJ0csNXVhebvn0wXm7woFs9NvT1k+YX4IoBQV5jk7c59KCxjqDL6jHnWsXph+WbaTHv16iWmWLVMGOnbqrPlbWta4z0avTDJiy85o3VA4QQvvyfYVqf/Y1ca/8IH/lKEFWw4GzMPL1qxg/Ul/Tq7TulIB+vSehuafzoNSXvtJCyisebiu3bT/PAHE9SD09YIVxgvvSx01p9VSkn1cf+fJdom99lJj7o5h82nWBsuSFq44+Nlz/1K5JJ099iNz+gD6+FjSA58vJIhu895mXOEX+v3iPwmR4172fbhzkD6e4WZHkdfW/HmUskFuVlZ+RrgdwHLst8ibB79+ds59/f5tdY0fqp/CfUJCCD8itCgC4SKQGud3uHNK7fpKAH0gnhobKFiuTh/TNk3r9/2N9h895ekg5AAPVIbljbMT1CiWi35+zLII+SGAsi0/Fwd58L8HTFhjJEy48CHi1Afk9+V1Jb9Wuec4OnH6HD3UsqwtbuyGI/z6nu1Q2ej8fTxrMyFq9vfNB2jxtr9TNOWI4VB9gviAAHFBZpFhXeqbfzoPyolPtqCKSVI4GBtzQPng9roEi6QUjsZahMoMgnzFl1UrbI/LY7GFk9+Qgtu9r6xKBXNmoa5fLTZvNyidh0Y93NT8La/tnVlb3DCVzwqfOmAaTQJ4/2cL7LzJ8goYGShGLNhO0I5Ecfvh4+U5ZJ0t+49Rq6QgH7fMHc6+ZeAKzwuahexv62ZljTYBlL7Hfq9Znfsa+7dzDSWA4e4vrR9dBFLj/I7ujKPfmxJAH5imxgbacfA4NX9tqplluNdKoR6taq/xdDzJWuZ2EEoJFiS5f/hLixDUL5WHvvuvRQj8EEBExsJ6IoszivKVsavtKFjUY0fy7xftoKdHWdZJWUrkvZRmdrOyVqDI9F7w6+PABrfl52CS3j+tNDIsyI87f/MBYwV0Fi/yFhJ3tJdX1c6DUl75yyhorAHmLwNZ4PyPQI5A8jYYh6/Mec48FjQMu7Yqbz/K+BW77PWFRAuu1e8dbgWnyIhx5OnlqOVIgpOcBLRWn4mEtGQobvvRbc042If74rEQPf3l/G12BhO/4zjnIfUN3TJ3ONtK6Rqel8zAM+2ZVlQ6f/AUjNEmgNJPM9oEULojuK1lsPf5eWGRf/5ytQBGimNabpca53ei46sE0McKpcYGkoeKU1vMx9STWZvcDsIv5201qc5QYEXp9t0y83ez8vnoq/utAAY/BLBJ/8kpAhicQrp9fl5Jn87eYj8yCzSPXLCdun1vzUcWGaGM1/kqHQklXruuZsA2gfDkTBkvjFluhJhxJQwfMr5GlG28HGyQ1oDEBpcrahah926ra/7pJIDy0JdZUmDNe/nXVfYVPtrCOgf/xr+PWyTKWVjOhF/nsdgCx6/LiG9E6JbNn41uH2ZFDMv1vvvT3+28x3O7t6EiuS4NOG6wF3n8p9tXpMcQMR1FAijnJi2AyNk8fM5mO4DHbd+H9UBEtO3AcWoxwPqx5pa5w9m3FK/mecksN27X7NEmgNJflT8D4eLh3Gv8b/yA6Vg9OhbAQNH+kc5T26UtBFLj/E50RJUA+lih1NhAMrLQmWDex9TDIoCfzdliR4KCMLyaJAvSvHx++vJ+K4DBDwF0kiL05yQsTMD4mTkNFTQJe/xwXiCa3wfR29ivsy0GvXHfUWr75nTKkSUjwbLFPo1uGHKqqee+X0bfLthOICyzN+6neZssPzJZnHMO1LeT7F5Tuyi9fUsdU9VJAOXVqsyEAmseInMhqMylcdm8JgCGg2CcY3PUtPNQdgauTF69x5ajgWwKUtTdkCT7IwNWZOqy+T3ahi1Szs/KQSjRvAKW19OSAEI+56OZm21fyWgTwO1/Haf/vG4RQLfMHc71kekHeV5Lth2kaz+YY6q6XbNHmwDKHMF+Ay2c+5rT67l99kK9z30qAfSDYtpumxrnd6IjrATQxwqlxgaSKaSklIePaacgG24HofQ/k2LLyF4x8mFLRNoPAazWa7wdvMHP5SS7TMD4/VeurWEyFDgFoiUuy3pfRjmzXGJegnTLdR/MMVeaED7+v2+swBG3An2+V6+vSc+MWmrEr3FlCn222RusSFJZZCaFYP22GjA1me/e9XWLGz01FOdBOfu5NgThYRTp6wii2XNMcgIIMo7sDcHSw312b0NqWbGAPS0eS4p5402pqwjZlOpFc9GVyghPqwAAIABJREFU780y7VpVKkDDkwJWZOqyBc+3owI5MrtBmex9Hp91CKNJAGWAiiSAuKKHfiJHS7vt+7AeiIiku4Zb5g5n3zJ/Mc9LimG7XbPbBFBEaoc7f1lfRmr7DbRw7munP2ok8+Q+4c/bvVOVSLrQNmkcgdQ4vxMdYiWAPlYoNTYQ/LouH2QdwH6TqPOjyvyfeM3tIBw6YyO9MtYSA76hXnFDhFCkLIgkgJxT1yu05XuMpTMiMALtZNQq/i0tJPg3Z+iAYG+fn63gCBRY/i5Jn55OnT2XzGrCxAZp7EA6/psU2OA2R6R+G3hTbeIrOkStzli/z9aSk+2d0jWB+nYGvEi9QudBOee5NlQ0iQDK4Axcj/cYvZx2Ct0/kLMjJ87YAQ7OsVk4m1/nsZz5XGXaP0TN1i2Zh9q/NcM0k1leZKAFUujlyx4ZAeRMJNEkgE6Rc35Wp3yO27532xvO92WKO7fMHc62z45aaueR5nlBfPqq92abqnIvBJpXLAkgMty84CPS1rmvo3GTwX1yKsNw10rrKwKpcX4nOspKAH2sUGpsIHkI4Eru9kalfMzYaurUo3M7CD+YtoFeH7/WJgGcSgvXgxOebGFel8RmXd9OnvMBB5JGQX9OqQhY7H5autN+9hevrGoyWgybuYn6/rrafh1XvJdekoH2HjlJMp0cW9CalM1ntPwe/MLSDnQrV9UqShCu5ShNSKNMW7cvRdo29OPF6uOUvJF5VkMRQOmbB19Q+GHuPnzCnj60DU+d/TfgvFDJaZ3ksRDpe3ezMnY/yDjCOo/w92xcJp/t13ZZ1UI09C4rYllaAJf0bE95smVygzLZ+04LTjQJoMxSIi2AIIADf1tnB8+47fuwHoiIZKaXcCWb/vfdMhqxcLsZkueFnNNXvGv9+JvXvS0VzhU8F3i0CSBSEFZ8YZwZ22+ghXNff9ylPrWtUihceAPuH2SJQcpCLYpAuAikxvkd7pxSu74SQB+Ip8YG4qtLTJPTkvmYsmkqD6pAOWud/Q+avN4cnCiINuUMIqXzZaVpz7Y2r0sCuLz3ZZQj6erVba6BxJHRxpktQGZKwPt8dYkrvdfGW9ZJFFzxZrkkA8F3UkrJ8FVxx2qF6aYGxe3IVrf5cZRu168W0djlu43/IPLJTl27L0VTL4e+U/Raiuw6D0oZXCGvZkHmYBGFpiEXkLOMGdKZOQYqTlkdHgvPc1eT0nYTmX0CQRPw+2v0ymTzvoxYlpG2S1+8zKQFDKc4ozijSQAlxpIAYk+9Nm6N7TsZbQK469A/1KT/FAODlPDxgot0cQhEAH/v0dZI8gQrjCey4fyUJNbtZdxgdaRYuF8/O+e+9uIr6zZ37pMtyG719X1FwIlAapzfiY66EkAfK5QaG0hqgeGXrte8qNCMgx9Pv2trmCeEc/8nszfTgBtqEUReO749037yja90pgxggkEKyB9IIAqyf2z7y8qji/Rjc7u3NX/LzCILX2hncp7KAlHbtyetN1IW+46cMBbCCgVzmDRgnHRe1ke6M1x/vvTLSsqWKaPJ0cvEE/UQjJIvWyZ6NikimdviivfSTBnMVai8RuZMIjfVL06X1yxKXT753dPKZ7kkvbkGPXD0lMmcAivslNV7ia2gshMvjv/OtHcywjIUAZSWOZC5J779w1g5uXSuUdjgNCrpet75cM7UZDwW5+Ll+vM2HaBbhlqp/uAz17pSQar90m/WGlcrREPutCyAkgCGQ/h5HB6fAwyiSQA55R/GkgQQPrTIhcwp86JNAKVm56SnWlD5gjk87TFU6j56OSHSm+eM/6/aeZg6D7I+p78/35YK5kg9Aigt83797Jz7+tO7G1DrygU9YxOoIvfJOp2+OtPGaRKB1Di/Ex1YJYA+Vig1NpBMB+VM2xVs6sdPnaGqvaxcq0t7XUa5sl5iBxgg88QjrcvbkZ2os+bljsZqFqxIEeacWTLS4RNnTNW82TLR4p7tzd9Sy08GLzgPfOcY8G1q+uoUypQhPV2SIZ0dDALrE0ijk9CgDrTuIGD8y7JdKbJfIDAlW+YMxkLHkcIYk3UEcZ3VqlJBW9ok3OWHX9yElXtoypq9KZp6IUKsJ8iNpXXlug9mJxOYXtu3I2XOaK2LzAoBMofsJwj64PL1/Y0oS6YMJtAlUHFqSNbvO8m0d+aXlj84cGXaoVphqtxzvOkS18zDujQwf8so4JV9OlC2zBnDgrLTOzPN2rFcyudzt1CvH1cSB92E1ZmjMmsZ8nxZexHRyrDePjd6ufFlxR6LZtl7+AQ1TLKWhpu2kSVfpID5mt2H7R9qgX5Uybk/+PlCmrhqD0UjzRr6ldla/PrZpSCA9zQwPyz8FGcQkZ++tG3aRCA1zu9ER1YJoI8VSo0NNHP9PrrzY8ta5fWLWF5Fse8Qf2HCQvb85VWSEaAVfTqYnK/BSv9xq+3MGRnTp7MDNrJmykCrXupomkl5k0CHn/MQ4LGQ7gxkIEfmjDS3R1u6Zehco9MG8jZp9R5zqMmCekdOniGQ4VELdySTQkE9aNUVyJ6Zxvyx074mxutSxqVBmby2lSvc5QdpQPq5aeIKGHOa8kwrT5GwsKx8MW+rHbgifZhw7QYrEvwY//2XkvnVSU24UQ83oYe/WEQHkvQEpUQIIlFhwXPKwThTk0HmA8LLTr8yOQ6uTK+oUYTKJqVtQxQxoolRpNae2w+IQBhDmBvajMXzZDVvIzAJluUSebLa0j3hro2sD02+ormzUMYM6Y3PK4SNCyVdoUJbM1rjyDH3HjlBDftZ1+VTn2lFZUIINwd6NqwdrHywjqNIAogfWvjBFayAsP158B8qmc/C02+RgWJ+/eycn31nRHokc+U+/69NeXrqskqRdKFt0jgCqXF+JzrESgB9rFBqbCDp++VVkFUeHCwgy1+YlQvnMFImnN0Bj+/mxN/3l1U0bNbmFEjh2hjXxyhSyy9QBGQwAojDANex+bNnooUvtLfzuEKCZMySP2l+Ut5WHhxyIyAOiOSFMDQOdlkQDYtr6s/nbjV1nk46HNiHED5vIMGsbRfu8uNqevSSP5MFW0CqBVZPr0WuqZxjqPbLdxyy5VgQbfzA54tsQWnnVWbz16akIIBeU5NJ6RGWHeK1k/mVJQGUlkqvGFyM9WTebjfhZi/PLz/Hf/RqT7mzhhdo42WMUHWi5Wfn/OxHQ9BeCaDf1dX2qXF+JzrKSgB9rFBqbKBJq/bYuXdlxGioaSNLxU1D5poq7IzOX5gVCmanp9pXTCaD4uZf5Ly2lGNv6NfJWFmkll+gNFjBCCAsatDYYxIFy9b4lbsJvmkQecY1oSzsg4ir06EzNqWAAbl1qxTJQe9O2UAyif3tw+YZ7T4QuFL5stoCu+EuP67YRi7cnkwGBnOa0c0KhvFSkEINqdRQvF7rAwdYSlFGd21qonAPJmX9cBLAxq9MThYhjDZeU5PJqHPO2cpr17BMXpOOEAWkHZIxKOv7daJLMlhWq7RccKWOq3UUN+FmLzhJAhhJoI2XMULViZafnfOz75QkimSe3Ccy8zzZvmIkXWibNI5AapzfiQ6xEkAfK5QaG2j8it308JeWZAlr0rlN+bdVe4wlDYUDE/gLs1yBbPRom/LJMmEE8tmTY/Qcs8JcWwYq7P8ltfycAQdoF4wAwhoJiZmyBbLRlKdb0SNfLaZfl++iPldVMwRPZrtAP5CeQTDGjfWKBwx4QDBEnRJ5qN/Y1SSzbFzx7kxztQwHdFgRWV7DDUvn+0hjhZRwszbst9/CVR+u/LyWORv2021J6dWeuawiPdqmgmtTmREGfnMgkMFy59bv+xvtP3o+3Rw6/+nRZlSzeG7XcaT0CKfs4rWrWzI3je7azPQhcwFveuV8xhXXAS7iCgeOnqR6SQTQTbjZCwyS9HvxL/XSZzh1eN2R//qZDpFfszo/+8483+HMietyn15/QEUyhra5uBFIjfM70RFUAuhjhVJjA/26bBc98vViM8vLaxYx+nhu5ftFO+jpUUtNNfiLNSid1yZgICvI5gFHeC5u11XdRy+jb363NMqcBSLAkAAp/7ylGYbizOOL14IRQCSaHz5nC1UpkpPgDyj19t76bZ3x95MF4tNLdxwyAQmTVqcMxIBuH7JiID9wm8oF6ZO7raCFFq9PNT5muD7NmimjbU1zw9L5PkRsIT49Z+P5TCAg1ZOf9k4AZaSt18humRMapB4WTQ7GcVoAa/aeYL/H8/eiUYi6knRwyi5eOykxIqOZwxX+DhfzC6U+cjxzRLvbjyovzyTXws1P10t/4daJVqCF87OPgKWm5fOHO51k9ZUA+oJPGxNRapzfiQ60EkAfK5QaG0imAMP15rAulgxHqPLJrM300i9Wdgz+tc1fmLiuRGon5JLl4iZZIbMUOMdFFC8IYLUXrahjFGeuz2Biz6h7eY0ixuIHfcExjzSzM25071SZ+iflHJZjNiqT1/gF1imZm5Zs+zsFDEit1r5qQXr4y8VUr1Qegu8bSq0+E43FDM+KAAvObuHsIFumDCnS0sk6sCAOmbExWS5gXKv/9lRLt2Wx35eRtsgs8kCLsq5tpXYjyNytQ+fZ5NhJAKv0HE//nD6brE8pih1qsHV7jtBlSZk/OGMD7x0m6WjvzLfr+gBpoMLfx0/ZkjlumTu8wCGvgFe91MH8cEnNEi0/uxQE8IFG1LRcdAgg3Fn+r627BT01cdOxLgwEUuP8TnQklAD6WKHU2ECjF++gp0Za1jyI8n5xXyPXGb89aZ3R3ENh1X3+EoavXZempezUbqgDyxsO92DlqRF/mMCHQAVXnyCAUsvPKeJ88NgpqvOypSXnLA1K56EFWw4SiN2Ih5oQ50RFxPPg6RtT1EckKnzPIEK95YClRygLZESuql2UbvtoPjExO3fuXyr//FhCtjkI6iJwpM2b0wPOp2COzMn09ZyV4MD+3tQNBD9LLgisGf+ElRHFS5GRtl7FvWWEKYJsbhw8xyaqTgJY4fmxRipHFrc15rob9h6hdgOt1G/OvSOJrhLAlCt96PhpqvXSRPNGNAigtABGEmntZS+GqmMTwLYVjN9wpMVJAL95oDE1KZcv0u5MO+7z6fYV6TElgL6wTKuNU+P8TnRslQD6WKHU2EAIOEDaLxRo3I182HLCD1X6/LzSRMiiOB35i+bKQrc0LGln9kAdN/8wZxo2OTayX4AAcgYEvDfghpp0Y/0SdrUt+49RqzemBZwyAjK2HjhOLDHS7bulNHLhDqMH9+2ClNfOyOSBIBFIpSD3rbPc2bgU3dyghPHxA5n7/fl2Rvi6Zm/rYMZBuvfwSTu9mbM98JE5dp3v4/rqrUnrDGnlgqjisY//x21Z7PeXbv+brn7fyvHKOY3dGkvrEgjgDYPn0PFTlpXPSQADXbdPeKIFVSrsLky8ad9RmxyzYC/3J30d7xg23/aDjLagshsWifo+LMywNKPILC6RzlcSwHhEWkcr0MK5H0c82JgalY0OAfTqQxvpGmi7ixeB1Di/Ex09JYA+Vig1NhAiYXv8YPnrwf/tRw9pnpAmbPRiy2I38KZadF3d4vYv5kI5MxsR3PennreuuUmEcGBGIKhwbQsCCCFoLs7sEjKy1NkH8vbiupLzzHJGBPzbqQGItgjsgMZfsAKpHGSX+M/rUwlZPNa83Imgr9b8talGXw15ivnfgfpgmZlg/ePwGjBhLS3cep4A1iiWi35+rLnnnSQDLV65tgYhututwGpZPemaHVfANw6ea1/zeiGAvz3ZgioUcieA0teQfTn5AEeavVn/s+RuOKo6EAF1e5aL9X1JAN1y93rBQGYCiUekdbT87JwE0ClK7gULZx3uE4LwELbXogiEi0BqnN/hzim16ysB9IF4amwgzmGLaXq9arz/s4VGRBkFenq3NixpE0CkaAOJkrp+gaJ2JSwPfbHQZL8IVECIkGlEppbjPL1cX4pZB4P7ylpFTRaDF8YsN1G2fDUMwipz3t7asETQgBT0jQCXrq3L25YYWE4QQXv5oFkm+nfB8+1ICmU755Mn6yW2vEqguSKIBOnEFgv/Q6/EnPuTB7vMVhJqK0I4udILVkYOkO6bh8w16fECEbBAFsBJT7Wk8gWzu+727X8dN+QZhfXauL/CObPQvB5W6j8lgCmhlFfAyDrCwtOuoAepIPeJW7rGSMcI1Y7X3a+fnXM/cmCanzkrAfSDnrYFAqlxfic60koAfaxQamwgRJz2+dkK6PAqN3LT4Ln0+xbLR633lVXp7mZlbAKIbAIIvJCyLm6yDPd/tiBgxC2TBFgA+UoTrzl/lSNzBlKXhSqcmuvFH1fQZ3O3EiJrN+47RpAekWQLFj6+3g7UH3KDPt2+EpV7fqwJ9oDG4ca9x+jWj+aZPhGtK3O2OvsIdrXM9UC+cMUuA1AQkPJDkjyKl+0kAy3evLEWXV+vuGsz+DFyRg5YbPE8p8IggFOebkllC7gTQMjuNHt1ipmPM4CIxbrx3m0fzbMjofUK2Fo+SQDha1owKfOI6+J6IIDxkNqJlp9dLC2AXqPoI10DbXfxIpAa53eio6cE0McKxXIDwW/t901/0bq9R4xOHkqRXFkIadYgaAxrDuREKhbKTvVK5U32FB3fnkFrdh8xryGa9qGW5WwCmDvrJdS+SqFkGnqv31DTpOI6gygJIkqfLh01LpuP4Kt27NQZev6HFSlQSpcO6buIQMhgWeM5oiKCBd6/vS4h2rVuyTyGLPE1djC472hckvpeU4Ne+nkVfTJ7s0l7BVmNtpULmoALloPp2qocfTAtZXAI98u6YCyFAqFppLmDliLr2O05fIIaJeVsdc7HLQoY16/Pj1lhsOFSv1Qe+i4p2tjLdpKafsi3e3XtYl6a2WsICwoIGAd6eLkCnvZMKyrtITWZJMcs18EHOIg+BIlREIU8d5MlhaME0Fo+6acZbQIYD6kdXne/fnaxJIDPdapsUmRqUQTCRSCW53e4c4lXfSWAPpCP5QZiKx781tjSg+tJELNxK3Ynm7UzT6jMy/tku4r0eLsKNnmAhQuJ2H9aet6PDindkEs0nIKrZGQ+cCvww3usTQXjNxeqPNSyLHXvVIX6/bqKPpq5mdKnIxO1e12dYrRo20ETKIKCX/yvjV8TtCu2PsIncfP+Y6Zezyuq0su/rKLWlQrQp/c0TGapcXbUomIBO80baw7KOoim/d/3y2jZjkP2y7c3Kkn9rq3hBoX9PubFPpOcbs1LYz5IEXgDUo5oYpBbaMTJ0nLAVBsvfn3Gs6095YmV0cYcrVnx+XF06uw5kqnglACmXDHkHGY5pKW9LjOuEX6KvI6PB8mO1jVrLINAwvkB5WcttO3Fh0Asz+8LBS0lgD5WKpYbKJAfV8b06WwrnZy2M/euTMsGixlIUZnuY00TWLiaV8gf0KcPliyQQWf+3UAQwXcNYsuy9Lu2ekBrIXwQv/l9G0FIGBZBBBOAUCB3KhLYZ8uc0aREK5UvG/Uft5qGTD+f4g1C0SAe3y/eYdoWzX2pEYsOVnp0rkwPtihHMhsKj3917aL0zi11TNPhszcbC9quQyeofdVCJkDk56U76anLKtKX87ZSgeyZjVjtFYNmJvMJxFXq/327xGQVQUG6uac7VKKcWbwf9vJg52wbXrYhBL7hv4jMIfj/h9M20l1NSqfw7UMgB0jy2OXnfyg4fyQEG09ms2Df0PV7jhiXATjbs1/bLUPn2lqI8SAnXvCKR53P524hXNfD7SIaZcj0jQSr/c0N3AOFojGe7CNWBDAaMjBIkblg61/0vw6VKT1+LWpRBMJEIJbnd5hTiVt1JYA+oI/lBgpEAINNVX6hnj57jiqIrBwQfcY1MGfqgEWuUZl8dh5X2SfyxcKiFEwkWdaFztm9wxfYV82QT5nTva2xtH08a3OyqUK/cOb6/Z7y3r4+fk2yK16IvEoNMmgAIg9tsCJ19Zq/NoV2HPzH6Cdi/LualKKXrq4e1oqPX7HLiEpzwXMjyGZVUo7iSMiP9LMbdld9ale1UFhz8lJZXkeivtcoUqnZGCo4CEEo/EMhEgy8PIPWiS8C/B2EdI1dW0UeaRsLIej4IqOjXwwIxPL8vlDwUQLoY6ViuYHCIYAy84ZMR4VHw/XkC5dXpSq9rAhSWLrgCzdv03khY4YA/oUggNI/Ltj1MFLA3fvZQtsXrmz+bDTlmVb0zqT1RidPFhZt9iJ6PHDiWho0ZYPd3BlR/Mf2v+maJA29QEsnJWg6vzPTEDUeP5KcppNX76H7PrPyKqPguW8fNt8mvpGQH+ln9+k9DcyVfLSLvI6EgWRT/8s9DSGlTEJZapQAeoLzgq7E30F+Ay1SEMAopIK7oIHVyScEArE8vxPiAT1MQgmgB5CCVYnlBgqHAErhZelfhnkjurbXlVVtIWRcI9conitgGrWFL7QzBLByT4ssogTLuAG/M1gAOSMGS9TgarV3UtQy98Ek0kvEq8xigvZOUWkpVBxoXV69roYRukZhPzUe32vaNdmv0+KI577hwzm+CKD0s/vivob0nwoFfOzCwE2lbEwgP8FgA0q9wa9DpOy6achce+0jIcFRf2DtMOoI8HeQ30AL53fZV/c3omY+cwFH/WG1wzSHQCzP7wsFTCWAPlYqlhsoHAIorWQyywQeDfp6kIKp13eSeVJE71YunJOQZQDEAAc+F4gkX5IhndGbg48eCl+fOmHa0K8T3TN8gblaRalVIjf9+EgzkqnrnG04t2woyN+dvJ7e/O28BdGZV1j6qAXqR5JMp37ha9fXCNuXas7G/SatHBdcpV4+aCat23PUvBQJ+ZFW2lgdhlI2hjOieNnq/5w6a1uLQxJAITUUCQZe5qJ14osAfwexkkCks3F+l7nJTkU6jrZTBMJBIJbndzjziGddJYA+0I/VBjpz9pzts+dletJPbsa6fXSX8JFDRg1ci8pr3ZJ5s9K2v44ThH13Hz5hhkBGjtUvdzR/1+87yY7w5QAKOQ9Y1CBM+8DnC02wBUrDMnkJCv/OK1PZzksGgA+mbUgmKeP0Q3P6ODrxkVGBnFaO64QTcMFtIGWDrBso/NztBk434tKREkCpF8dSK17WOdw6fPDCijvt2daemp84fda2AIeam9SaVALoCdoLrhLvHw6sivQBnAQwVlbvSOen7dImArE6vy8kNJUA+litWG0gpx+f2xQRKdv7qmqmmlN0GbIm/a6pbmd3QB1EFf59/LSJHmUig4wb83u0M320eWMabUqSUIEDuNT4w/tZM2WgVS91pEe/Xky/LNtl2mAcZI6QhMk5b8iXwPoYqiDqsf+48zIvzghntA1lHf3w9rrUqUYRM0TfX1Yly3gSCdmSPof83G3fnGZEqiMlgEdOnKYaSbmJQ1nZ3Nbd7X3GqUqRnAT5Gi9FEuxQ1skbB8+x8yErAfSC7IVXh/dPJK4T8mmdn1fOMHPhIaIzvpgQiNX5fSFhpATQx2rFagNt2X+MWoncum5ThFbewJtrm2pfzd+aTIqlUZm89Or1NZPl6mWNPVzbsqAxBKUnPtnS9CEtXEjP5pRdgR7hkl6X0dMjlxp5FhRIqeCKV2a5cM57bvc2VCTXpSEfZ9jMTdT319V2ndnPtaFiuZO3CUUA5TWz8zoZIs7Vi+VygzPZ+zJvLz+31BiMhPzIa9ZoSGIEeyDGqV6pPITsIV6KvDoOdVUHP0jOhxwJBl7monXiiwDvH2cgVrizcn5eP7u3IbWsGH2/13DnpfXTNgKxOr8vJFSVAPpYrVhtIKcfn9sUkS3j47sbmGrQhoMGHFv5apfITcj0cdlbM1J007x8fpq1wfLhk9ksJAHEFewtQ+cla8s5YZHd4+v528x7V9QsQu/dVjdkmrWVfToYzb9QRaa+Qz0EXcBXUZZQBHD4PQ2oVVJU7WdzttCLP620m87s1ppK5M3qBmey9yWh5eeWQsuRkB8ZoOGWhzmsyToqM07w4/zivkaeu+J2SgA9Q3ZRVuR9ACF1yElFWpyf11hFvkc6P22XNhGI1fl9IaGZ5gng+++/TwMGDKDdu3dTrVq16N1336WGDRt6WsNYbaCZ6/fRnR8n17rj1GuBJtagdB4a9bBl4Xl13BoaPH2jSce2fu9RwvXfGzfWpMsHzUrRtGO1wjR+pSUWLEmkJIC4Ouz0zszkBCzJp4zTtuFNzuUrrVuyEaKPEUCRDg8SokBIt9ePFmmDzx2CTZxtQhFAeW05Zsmf9MSIP+zRkMYM6czCKTLqmH3pWrw+1fhQokRCAKWP54gHG1OjsvnCmZLnuowT/ECH3lU/7HahfLWu/3COyUQSKQaeJ6MV44YA7x8v8k2hJqkEMG5LqAOHQCBW5/eFBHqaJoAjRoygu+66iwYPHkyNGjWit99+m0aNGkVr166lggXdtdlitYGcfnzYUPmzZ6L9R08F3Fvy+patcs3K5zM5g6HPh+vhQNp5IG3fLbKucK+tU4zeSrpGlgQQV7DNXp2SbNxKhXLQhCdbGEsjLI4onA4NOYU560jOLBnp8Akryhi5fZGNwq0gC8cLY6zcw3zl6mwTigDKQJOpa/aaSGUU8M6N/TqHnTVAZu3g52aB6UjJj8TIS2CMG2bB3mecrqldlN5OyoDipS9uF8pX67oPZtPibVY+5EhIsJd5aJ34IsD74MUrq9I9PjKbpCCAdzeg1pXdv1/j+/Q6+sWOQKzO7wsJtzRNAEH6GjRoQO+9955Zs3PnzlGJEiXoscceo+eee851HWO1gZx+fJgIW/QCTYqvJvHeI18vpl+X7SIc+mP+2Gn85xAZe0NSJKtsj+CR4XO2mJdkIEn7gdON9RAFV7DVX5yQbFikdPvp0eYkNfvubVbG6A2i8Bd+ibyX0va//jGvlcmfjaY+08oVU6SM6z56ualXKl9Wmh4gejUUARzdtalJGYeyaOtfdP2HVgQvyOiy3slz5rpOhijZlTY/NwgxsnmgREp++BlSgwAikrv/dd6AnfdSAAAc2ElEQVRzFfPcQvlqKQH0snsu7Dq8DyAj5Se1nfPz+nGX+tS2SvSz31zYaOvsUxuBWJ3fqf0cfsZLswTw1KlTlDVrVvruu+/ommuusTHs0qUL/f333/Tjjz+mwPXkyZOE/7hgA4EwHjp0iHLmDB3dGs4isR+fbINgjmA5eiHh8uX9DU3KMujknfuX6MEWZWnoDCunbjAtP2TGeG+qlXVDSslIAri5f2fbosfz4StnOc//tipHyBggCWD1YjntnLmsE+iGw8iF26nbd1aOYSZczjahCKAM9Niw9wi1G2j5PoKMzuzWxm34FO9L3UEOpmjafzLtPGTJ5/glgKMebkINSucNe15eGjBO8N+CH5fX4oUAXvvBbFtMPFIMvM5H68UHAd4Hfa6qRl2alo54Es7Pa6zSH0Y8QW2YJhFQAkiUZgngzp07qVixYjRnzhxq0qSJ/QHo1q0bTZ8+nebPPy/+y2/27t2b+vTpk+LDEm0CyH58cqCHW5aj4XM204nTlkCzs4CA8XUs0r2NeqgJXR0iZVqNYrlMjt37PrOuSBFEwinJkN3j5qFz6en2FenRNhXoqvdm0bIdhyhThvRGIPqhFmWpe+cqJAM2nmxXkR5vV8H09eyopfTj0p30/cNN6fZh88w18P3Ny9ALHkjI94t20NOjlpp+EKTy5f0pgxd+WrqT/u+bJSZLCCyGfBWJNlJqBpp2rQZMM1qHMlI6nG87mRqNtQ6nrd1Ld3+6wODzWFvrmcMtHd+eQbheXtSzPWW5JEO4zT3Vf2rkH/TDkj9pxrPhBb9A6BoW4CU92wcN2oFLASRyUJQAelqOC67SHcPmmyCxBc+3owI5Mkc8/4G/raNBk9eb9hCahytIjizh+eJGPLg2VASCIKAEUAlgWAQwtSyACKSAnp4UdEYkb+caRQjk49Gvl6TY0jfXL0EjFm6ne5qVNsQOX7BOTT5Y1AbfUY/O/fuvEYHOmCE9HTx2yvjH5c6aKVmfmMOlmSxiAmkQEL9//yUCISqcK4t5HRHA8DlEkemi4OMGoor26AdtoDPoFgCCfmTgxuU1itD7t9cN+PHl+WFu3y3eYVsNkc+4XIHsdhuQQOgqFsmVxdP4zsFkUEvjsnnp2wetHwsSn0i+Yc+e+5fOnDtHmTPGhvxhTliHk2fOhU0web1DEVP8uGAJISWAkeyAxG8T6f4J9GT4vGTOaP2AjNUPnsRHVGeYSAgoAUzDBDCSK2Dn5o3lBtpx8Dg1f22qPeTAm2rRdXWLm8hLRGCi4Or3n9Nnzd/tqhSkSav3mqs+lmzYeuAYtRwwze4jHD04Lx9Uaa3z6yjO4/28dKetO3hrwxLU/7qarlOBzyN8H1Fg7SqZLzypl1ADSGFkBNZ8dX9j1/mkhQpXvzeLlu44ZB5VCWBaWHF9RkXg4kIgluf3hYJUmr0CxgIhCASSL5B+QUEQSMmSJenRRx+NaxAI5rLvyElq0M/K34vCKc6W7zhEV75nSbrky5aJDhyzIoOh94crOVyL3li/hHnt7+OnqPZLv9l9SAtWNDaojFZ+5doadFujkr67Hbt8F3X9yiJzD7UsS907VXHtc/yKXcb/EcWL2LRrh6KCjNgNV08vnHEutLpKAC+0FdP5KgKKgERACWAatgBiI0AGBkEfQ4YMMUQQMjAjR46kNWvWUKFC7lFqsdxAMl0Y5vr+bXXp8ppFaPWuw7YuHyJ8ORqVI26H3lmPLqtW2OxzXDOW6zHW3vPRJjCTVu2h+z9faPpnC6Xfr5jxK3bTw18uMt0826ESPdK6vGuXE1fupge/sNr49VcKNBg7sbeqVICG3+NNI9J10hd4BfYLxWOoBfACX0ydviKQBhGI5fl9ocCZpi2AWCRIwLAQdO3atWnQoEHGMuilxHIDnTpzjiq+MM6eBnz3OlYvbHL3QqcPBdIquCo+ffZfk5/3+Kmz5MwsUePFCXTkpKXF16ZyQfokKWOIl+dzqyMFq5mgurVxe1+Syr7XVKc7Gpdya0ITVu6mh5II4NJel1GurNF1MGcCGG38XB8sgStc+e4sWv6nXgEn8BLp1BQBRSAEArE8vy8U4NM8AfSzULHcQLh6LNtjrAm8QGHpBOnXB23AHQf/sf0AUQ+ZO5D9g4vUrAs3I4QbNogWvmmIpbMXLWkHKd486NY6dFWtom7TIHkF7CXdnGuHjgpMANtVKUTDunjPqBHuOBdSfSWAF9Jq6VwVAUXAiUAsz+8LBW0lgD5WKtYbqHLPcbbsC+e43fn3P9Q0KTNH1SI5jQWQs23gUZC5A1fDXJDGDdfGKLhChqUuWkXmLA6VNiyc8aav20ddPrHS4HlNGi/9Btf17USQwYlmYQLYoVohGnKnEkBge8W7M22NR70CjuZu074UAUUgNRCI9fmdGs/gdwwlgD4QjPUGqv3SRPr7+Gkzwy/va0TNK+SnvUdOUMN+k81rtYrnMhZADgTBa8jckT1zRvupbh4y1xaQDjclmBs0a3cfoQ5vW0LL0RI0nr1hP90+zNJg/KFrU6qTlNUj1FxkMAqEq73Izbg9m3yfCWDnGoXpg9vrhdP0oq0LrcCVO60fFkoAL9pl1gdTBC5aBGJ9fl8IwCkB9LFKsd5AjV6ZRHsOW5lHvnmgMTUply9ZZC8if3cfOmGEjlEypE9HG/p1SkaAHvx8IU1ctce8f2O94jTgxlo+njh50y37j1GrNyyZmZ8ebUY1i+f23ffcjQfo1o/mmX6mPN2SygpNv2CdszB0rMgIE8BoW1B9gxXHDpQAxhF8HVoRUAR8IxDr89v3BFOhAyWAPkCO9QZq8fpU2vbXcTNDtrAdO3mGqiXl5q1TMreRi4EVECVP1ktoSa/Lkj3RM6OW0neLdpjXws0J6wbNrkP/UJP+U0y1CU+0oEqFc7g1cX1f+hUufKEd5c/unoFAikfHwhrFBBD+iPBL1ELU+Z2ZtCrJtSAWmCvGioAioAjEEoFYn9+xnHu0+lYC6APJWG8gmZN3dNemVLdkHjp55ixVemG8mTUI4KHjp2nT/mPm36XyZaXpz7ZO9kQv/7KKPp612bzWpUkp6nN1dR9PnLwpMmzUfdnSGfRqrXMbfNHWv+j6D63AEq/+fKMX76CnRlrp42JBRpgAXlunGL11c223R0gT70vf0lhgniZA1IdUBBSBuCEQ6/M7bg8WxsBKAMMAy1k11htIRlryFSvSdCE6mAkgLILr9hw1/0aqt58ebZ5smu9MWk9vTVpnXvOaj9crJMdPnaGqvSaY6lOfaWVkafwWiFkjzyyynKx+uaOn7mDhhKUz1gTw+rrF6c2boneF7unhErSSEsAEXRidliKgCHhCINbnt6dJxLmSEkAfCxDrDXTnx/Np5vr9KQgWW6RgAYReIDvjB0pV9u3v2+i50Va+XuQI/r+2FXw8cfKmUmja63Wt2+Ab9x2ltm9Op9L5stI0hzUzWFt5bRwLaxTj/dLV1eiuJqXdHiFNvN/7p5U0fM4Wygi/01c6p4ln1odUBBSBiweBWJ/fFwJSSgB9rFKsN9Ca3YcJ+XZL589Gtzc6L4gsCeC5f4kgx4KCfMDDujRI9kSwEOIKGDltuzQt7cmnLhxIINx84sxZuqKmu16f136/nr+NKhTKTg1K5/XahL75fRuVK5CdGpbx3sZr56t2HqY5G/fT3U1LU8YM0ZWY8TqHRKsH6++X87bSZVULm/2pRRFQBBSBCwmBWJ/fFwIWSgB9rFK8NpAkgLDALNhy0DzF5TWK0Pu3R0/nzwc02lQRUAQUAUVAEUhYBOJ1ficSIEoAfaxGvDaQJIBZMmaguZsOmKe4rm4xGniTBin4WFJtqggoAoqAIpAGEIjX+Z1I0CoB9LEa8dpAkgDmyHIJzVi3zzxFtGVefECjTRUBRUARUAQUgYRFIF7ndyIBogTQx2rEawNJApg3ayaavGaveQr4qPW+qpqPJ9KmioAioAgoAorAxY9AvM7vREJWCaCP1YjXBpIEsFCOLDR+5W7zFA+1LEvdO1Xx8UTaVBFQBBQBRUARuPgRiNf5nUjIKgH0sRrx2kCSABbPk5V+XrrTPMXjbSvQk+0r+ngibaoIKAKKgCKgCFz8CMTr/E4kZJUA+liNeG0gSQDL5MtGo5f8aZ6iW8dK1LVVeR9PpE0VAUVAEVAEFIGLH4F4nd+JhKwSQB+rEa8NJAlgxYI5aMTC7eYpel5Rle5rXsbHE2lTRUARUAQUAUXg4kcgXud3IiGrBNDHasRrA0kCWLVITvpq/jbzFH2vqU53ND4vGO3j0bSpIqAIKAKKgCJw0SIQr/M7kQBVAuhjNeK1gSQBrFU8t0nJhfLGjbXohnrFfTyRNlUEFAFFQBFQBC5+BOJ1ficSskoAfaxGvDaQJID1S+Whj2ZuNk/x7q116Mpa0UvJ5gMabaoIKAKKgCKgCCQsAvE6vxMJECWAPlYjXhuICWDD0nmpfuk89MG0jeYpht5Zjy6rVtjHE2lTRUARUAQUAUXg4kcgXud3IiGrBNDHasRrA305byu9N2UDfXFfQ/p52S4aNHm9eYrP7m1ILSsW8PFE2lQRUAQUAUVAEbj4EYjX+Z1IyCoB9LEaibCB3puynt6YuM48xYgHG1Ojsvl8PJE2VQQUAUVAEVAELn4EEuH8jjfKSgB9rEAibKDB0zfSq+PWmKcY80gzql0it48n0qaKgCKgCCgCisDFj0AinN/xRlkJoI8VSIQN9PGszfTyL6vMU4x7/D9UpUhOH0+kTRUBRUARUAQUgYsfgUQ4v+ONshJAHyuQCBvo87lbqNePK81TTH2mFZXJn83HE2lTRUARUAQUAUXg4kcgEc7veKOsBNDHCiTCBvrm923UffRy8xRznmtDRXNf6uOJtKkioAgoAoqAInDxI5AI53e8UVYC6GMFEmEDjVywnbp9v8w8xaIX2lG+7Jl9PJE2VQQUAUVAEVAELn4EEuH8jjfKSgB9rEAibKARC7bR/763LIAr+nSg7Jkz+ngibaoIKAKKgCKgCFz8CCTC+R1vlJUA+liBRNhAX8/fRj1+sAjghn6dKGOG9D6eSJsqAoqAIqAIKAIXPwKJcH7HG2UlgD5WIBE20BfztlLPMSvMU2x59XIfT6NNFQFFQBFQBBSBtIFAIpzf8UZaCaCPFUiEDfTZnC304k9WFLASQB+LqU0VAUVAEVAE0gwCiXB+xxtsJYA+ViARNtCnszdTn58tHUAlgD4WU5sqAoqAIqAIpBkEEuH8jjfYSgB9rEAibKBhMzdR319XKwH0sY7aVBFQBBQBRSBtIZAI53e8EVcC6GMFEmEDLdjyF904eK4SQB/rqE0VAUVAEVAE0hYCiXB+xxtxJYA+ViBRNtCUNXuoTP7smgXEx1pqU0VAEVAEFIG0g0CinN/xRFwJoA/0dQP5AE+bKgKKgCKgCCgCcUJAz28iJYA+Np9uIB/gaVNFQBFQBBQBRSBOCOj5rQTQ19bTDeQLPm2sCCgCioAioAjEBQE9v5UA+tp4uoF8waeNFQFFQBFQBBSBuCCg57cSQF8bTzeQL/i0sSKgCCgCioAiEBcE9PxWAuhr4+kG8gWfNlYEFAFFQBFQBOKCgJ7fSgB9bTzdQL7g08aKgCKgCCgCikBcENDzWwmgr42nG8gXfNpYEVAEFAFFQBGICwJ6fisB9LXxdAP5gk8bKwKKgCKgCCgCcUFAz28lgL42nm4gX/BpY0VAEVAEFAFFIC4I6PmtBNDXxtMN5As+bawIKAKKgCKgCMQFAT2/lQD62ni6gXzBp40VAUVAEVAEFIG4IKDntxJAXxtPN5Av+LSxIqAIKAKKgCIQFwT0/FYC6Gvj6QbyBZ82VgQUAUVAEVAE4oKAnt9KAH1tvEOHDlHu3Llp+/btlDNnTl99aWNFQBFQBBQBRUARSB0EQABLlChBf//9N+XKlSt1Bk2wUdL9+++//ybYnC6Y6ezYscNsIC2KgCKgCCgCioAicOEhAANO8eLFL7yJR2HGSgB9gHju3DnauXMn5ciRg9KlS+ejp5RN+deJWhejCisprtHFU/am2Cq2sUMgNj3rno0Nrug10bGF7evIkSNUtGhRSp8+feyASOCelQAm6OKof0JsFkZxjQ2u/IWPqxS4RqhLRHRx1n0bXTy5N8U1Nrjq90HscI1mz0oAo4lmFPvSL6Yogim6Ulxjg6t+4ccOV8U2dtjq94FiGzsEEr9nJYAJukb6xRSbhVFcY4OrkpTY4arYxg5b/T5QbGOHQOL3rAQwQdfo5MmT1L9/f+revTtlzpw5QWd54U1LcY3dmim2im3sEIhNz7pnY4MrelVsY4dttHpWAhgtJLUfRUARUAQUAUVAEVAELhAElABeIAul01QEFAFFQBFQBBQBRSBaCCgBjBaS2o8ioAgoAoqAIqAIKAIXCAJKAC+QhdJpKgKKgCKgCCgCioAiEC0ElABGC0ntRxFQBBQBRUARUAQUgQsEASWACbhQ77//Pg0YMIB2795NtWrVonfffZcaNmyYgDNNnCnNmDHDYLZo0SLatWsX/fDDD3TNNdfYE4Tq+4svvkgfffSRyf3YrFkz+vDDD6lChQp2nb/++osee+wx+vnnn40y/PXXX0/vvPMOZc+ePXEeNJVngkj00aNH05o1a+jSSy+lpk2b0muvvUaVKlWyZ3LixAl6+umn6dtvvzWRfx06dKAPPviAChUqZNfZtm0b/fe//6WpU6caPLt06WKi3DNmzJjKT5Q4w2H/4b8tW7aYSVWrVo169epFnTp1Mv9WXKOzVq+++qpRU3j88cfp7bffVmx9wNq7d2/q06dPsh7wXYDvB92zPoCNU1MlgHECPtiwI0aMoLvuuosGDx5MjRo1Ml9Yo0aNorVr11LBggUTbLaJM51x48bR7NmzqV69enTdddelIIAgLSAcn332GZUpU4Z69uxJy5cvp1WrVlGWLFnMg+DgBXkcMmQInT59mu655x5q0KABff3114nzoKk8k44dO9Itt9xicDhz5gz16NGDVqxYYXDLli2bmQ2I3a+//krDhw83SdUfffRRQ6CxHihnz56l2rVrU+HChQ1JB8bY4w888AC98sorqfxEiTMcfmhkyJDB/AjBDxTsTeCzZMkSQwYVV/9rtWDBArrppptMZprWrVvbBFCxjQxbEMDvvvuOJk2aZHeAH3H58+fX74LIII1rKyWAcYU/5eAgfThs33vvPfMm8g2XKFHCWKaee+65BJttYk4HeZmlBRCHK/I9wkr1zDPPmEkjXRksVCAtIDirV6+mqlWrEg6M+vXrmzrjx4+nzp07044dO0x7LUT79u0zP0SmT59OLVq0MDgWKFDAkOQbbrjBQARrQJUqVWju3LnUuHFjAjm/4oorTN5stgriB87//vc/01+mTJkU2iQE8ubNa0ggsFRc/W2Lo0ePUt26dY01um/fvuZHCH5Q656NHFcQwDFjxtAff/yRohPFNXJc49VSCWC8kA8w7qlTpyhr1qzmF5a8vsR1Ga4tf/zxxwSabeJOxUkAN23aROXKlTOWFRwCXFq2bGn+jWveTz75xBDEgwcP2u/D4gXrICyw1157beI+cCrObMOGDcZiBetp9erVacqUKdS2bVuDW+7cue2ZlCpVip544gl68sknzbXmTz/9lOzQ2Lx5M5UtW5YWL15MderUScUnSMyhYCXFPsNnHfsU7h+Kq7+1ApYg1G+99Ra1atXKJoC6ZyPHFQQQP1Bg6cd3Y5MmTczNSsmSJfW7IHJY49ZSCWDcoE85MCwkxYoVozlz5pgPFpdu3boZi8v8+fMTaLaJOxUnAQSe8PkDvkWKFLEnjqsh1MW1O64icQWHq3ZZYO2CzwuujNJ6gTX6qquuMj9GZs2aZeCA5Q9X5fD9kwU+q7hyw9X7gw8+SFu3bqUJEybYVY4fP26ukMeOHWv7vKVFfEGk8VmHvx98I4EnrM6Kq7/dAH/Ufv36GYs+iIokgIpt5NjCmg/LKvz+4MqB78Y///zTuIXApUG/CyLHNh4tlQDGA/UgYyoBjM5iKAGMDo7OXkCCcQCA/BUvXlwJYBRghtUfATK4PoPlf9iwYebHHq7Y9DCNDODt27cbN47ffvuNatasaTpRAhgZlm6t8GMQ1v6BAweaIDHds26IJdb7SgATaD30Cjg6i6FXwNHBUfaCwA64ICDaGkE0XPQ6LbpYt2vXzrgr3HzzzXoFHCG08FGDywYCbLjgih3fCwhOgiUaOKvbQoQAO5rBZx14tm/fXvdsdCBNtV6UAKYa1N4GQhAIrs8g/YKCazf4V+AA1iAQbxgGCwJBAAj8/FAOHz5sghmcQSALFy40kcQoEydOJETBpuUgEATQIAAJQTXTpk1LJpsDjNjx+5tvvjGyOSi4Rq9cuXKKIBBcGXEk+9ChQ+nZZ5+lvXv3UubMmb0tbBqo1aZNG/N5h18qgkAU1/AX/ciRI8blQBZYprAnEXiEoDrFNnxcA7XAdTD2K3wD4XOpuEYH19TqRQlgaiHtcRz4o+GDBCkSEEFErY0cOdJEVkpdNY/dpZlq+CJCgAIKggpwJQEfNDiB4wsKvmjQA5MyMMuWLUshA7Nnzx4jwcMyMLhKSssyMF27djXPD+uf1P6DEziufFBwNQxfPpBpyG2AMKLA9xKFZWAQSf3666+bAIc777yT7r///jQtAwNtOkgPYX+CtABn7FNYqGBNUVyj9/Ulr4B1z0aOK35EX3nllebaFy5L0FaFuwJkoUD+dM9Gjm08WioBjAfqLmNCAoaFoBGlOmjQIKMJqCU4ArBOgfA5C8g0iAkLQcPyBL+V5s2bG3mIihUr2k0gBA1LqxSCBvZpWQga1tRA5dNPP6W7777bvMWCxbBWSSFo6P5xgUUGhwPWCcEfWBcQ8rQsBH3ffffR5MmTjTM9CDX81WChAvlTXKP7beckgLpnI8MXkllwAzlw4IAhfPgeRbAN3BZ0z0aGaTxbKQGMJ/o6tiKgCCgCioAioAgoAnFAQAlgHEDXIRUBRUARUAQUAUVAEYgnAkoA44m+jq0IKAKKgCKgCCgCikAcEFACGAfQdUhFQBFQBBQBRUARUATiiYASwHiir2MrAoqAIqAIKAKKgCIQBwSUAMYBdB1SEVAEFAFFQBFQBBSBeCKgBDCe6OvYioAioAgoAoqAIqAIxAEBJYBxAF2HVAQUAUVAEVAEFAFFIJ4IKAGMJ/o6tiKgCCgCioAioAgoAnFAQAlgHEDXIRUBRUARUAQUAUVAEYgnAkoA44m+jq0IKAKKgCKgCCgCikAcEFACGAfQdUhFQBFQBBQBRUARUATiiYASwHiir2MrAoqAIqAIKAKKgCIQBwSUAMYBdB1SEVAEFAFFQBFQBBSBeCKgBDCe6OvYioAioAgoAoqAIqAIxAEBJYBxAF2HVAQUAUVAEVAEFAFFIJ4IKAGMJ/o6tiKgCCgCioAioAgoAnFAQAlgHEDXIRUBRUARUAQUAUVAEYgnAkoA44m+jq0IKAKKgCKgCCgCikAcEFACGAfQdUhFQBFQBBQBRUARUATiiYASwHiir2MrAoqAIqAIKAKKgCIQBwSUAMYBdB1SEVAEFAFFQBFQBBSBeCKgBDCe6OvYioAioAgoAoqAIqAIxAEBJYBxAF2HVAQUAUVAEVAEFAFFIJ4IKAGMJ/o6tiKgCCgCioAioAgoAnFAQAlgHEDXIRUBRUARUAQUAUVAEYgnAkoA44m+jq0IKAKKgCKgCCgCikAcEFACGAfQdUhFQBFQBBQBRUARUATiiYASwHiir2MrAoqAIqAIKAKKgCIQBwSUAMYBdB1SEVAEFAFFQBFQBBSBeCKgBDCe6OvYioAioAgoAoqAIqAIxAEBJYBxAF2HVAQUAUVAEVAEFAFFIJ4I/D9Le03t1OE+iAAAAABJRU5ErkJggg==\" width=\"640\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f751dfb80f0>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scores = pickle.load(open(\"banana_scores.pkl\", \"rb\"))\n",
    "plt.plot(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualize the agent's policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = DqnTrainer.Agent(state_size=state_size, action_size=action_size, seed=42)\n",
    "agent.qnetwork_local.load_state_dict(torch.load(\"banana.torch\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 9.0\n"
     ]
    }
   ],
   "source": [
    "# get the default brain\n",
    "brain_name = env.brain_names[0]\n",
    "brain = env.brains[brain_name]\n",
    "\n",
    "env_info = env.reset(train_mode=False)[brain_name] # reset the environment\n",
    "state = env_info.vector_observations[0]            # get the current state\n",
    "score = 0                                          # initialize the score\n",
    "while True:\n",
    "    action = agent.act(state)\n",
    "    env_info = env.step(action)[brain_name]        # send the action to the environment\n",
    "    next_state = env_info.vector_observations[0]   # get the next state\n",
    "    reward = env_info.rewards[0]                   # get the reward\n",
    "    done = env_info.local_done[0]                  # see if episode has finished\n",
    "    score += reward                                # update the score\n",
    "    state = next_state                             # roll over the state to next time step\n",
    "    if done:                                       # exit loop if episode finished\n",
    "        break\n",
    "    \n",
    "print(\"Score: {}\".format(score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When finished, you can close the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "env.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Future work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Future work could include using double DQN, dueling DQN, and/or prioritized experience replay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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