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  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Neural Networks with Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div id='toc-container'><script type='text/javascript'>\n",
       "$(function() {\n",
       "    function regenTOC(){\n",
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       "\n",
       "\tvar toc = document.createElement(\"div\");\n",
       "\t$(toc).attr(\"class\", \"table-of-contents\");\n",
       "\n",
       "\tvar curLevel = 0;\n",
       "\tvar containerStack = [toc];\n",
       "\tvar levelOfTag = {\"h2\": 1, \"h3\": 2, \"h4\": 3, \"h5\": 4};\n",
       "\n",
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       "\t}\n",
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       "            var lastContainer = containerStack.pop();\n",
       "            $(lastContainer).appendTo(containerStack[containerStack.length - 1]);\n",
       "            curLevel--;\n",
       "\t}\n",
       "\t\n",
       "\t$(\".text_cell_render :header\").each(function (i, elem) {\n",
       "            var level = levelOfTag[ elem.tagName.toLowerCase() ];\n",
       "\n",
       "            if (level === undefined)\n",
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       "\n",
       "            while (curLevel < level)\n",
       "\t\tpushLevel();\n",
       "            while (curLevel > level)\n",
       "\t\tpopLevel();\n",
       "            \n",
       "            var listItem = document.createElement(\"li\");\n",
       "            var link = document.createElement(\"a\");\n",
       "            $(link)\n",
       "\t\t.text($(elem).contents().first().text()) // Remove the pilcrow sign\n",
       "\t\t.attr(\"href\", \"#\" + $(elem).attr(\"id\"))\n",
       "\t\t.appendTo(listItem);\n",
       "            $(listItem).appendTo(containerStack[containerStack.length - 1]);\n",
       "\t});\n",
       "\t\n",
       "\twhile (curLevel > 0)\n",
       "            popLevel();\n",
       "\n",
       "        $(\"<a class='btn-update' href='#'>Update</a>\")\n",
       "          .click(regenTOC).prependTo(toc);\n",
       "\n",
       "\t$(toc).prepend(\"<div class='title'>Contents</div>\")\n",
       "          .wrap(\"<div class='toc-headings'/>\");\n",
       "\n",
       "        $(element).empty();\n",
       "        $(element).append(toc);\n",
       "    }\n",
       "\n",
       "    if (typeof(IPython) !== 'undefined')\n",
       "        $([IPython.events]).on('notebook_loaded.Notebook', regenTOC);\n",
       "    regenTOC();\n",
       "});\n",
       "\n",
       "</script></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import addutils.toc ; addutils.toc.js(ipy_notebook=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/matteo/anaconda3/envs/addfor_tutorials/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "data": {
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       "}\n",
       "\n",
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       "    margin-bottom: 3px;\n",
       "}\n",
       "\n",
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       "    font-weight: 300;\n",
       "    font-size: 11pt;\n",
       "    color: rgb( 48, 48, 48 );\n",
       "    font-style: italic;\n",
       "    margin-bottom: .5em;\n",
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       "    font-family: Tahoma, sans-serif;\n",
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       "\n",
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       "    color: rgb( 240, 20, 20 );\n",
       "}\n",
       "\n",
       "/* Pandas tables */\n",
       "/*\n",
       ".rendered_html td {\n",
       "    text-align: right;\n",
       "}\n",
       "*/\n",
       "\n",
       "table.dataframe td {\n",
       "    text-align: right;\n",
       "}\n",
       "\n",
       ".output .table-of-contents {\n",
       "    border: 1px #cecece solid;\n",
       "    background-color: #fafafa;\n",
       "    padding-top: 10px;\n",
       "    padding-bottom: 5px;\n",
       "    padding-right: 15px;\n",
       "    padding-left: 0px;\n",
       "    margin-bottom: 20px;\n",
       "    display: inline-block;\n",
       "    position: relative;\n",
       "}\n",
       "\n",
       ".output .table-of-contents ul {\n",
       "    list-style-type: none;\n",
       "    padding-left: 20px;\n",
       "}\n",
       "\n",
       ".output .table-of-contents .title {\n",
       "    font-weight: bold;\n",
       "    font-height: 11pt;\n",
       "    padding-left: 20px; /* looks better if it's the same to the <ul> */\n",
       "}\n",
       "\n",
       ".output .table-of-contents .btn-update {\n",
       "    position: absolute;\n",
       "    float: right;\n",
       "    right: 11px;\n",
       "    top: 4px;\n",
       "    font-size: 9pt;\n",
       "}\n",
       "\n",
       "</style>\n",
       "<script>\n",
       "    MathJax.Hub.Config({\n",
       "                        TeX: {\n",
       "                           extensions: [\"AMSmath.js\"]\n",
       "                           },\n",
       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
       "                \"HTML-CSS\": {\n",
       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
       "                }\n",
       "        });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from time import time\n",
    "from IPython.display import Image\n",
    "import math\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "import functools\n",
    "import os\n",
    "from addutils import css_notebook\n",
    "css_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"b0fc727f-faea-4577-803f-53873d0b8ce5\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
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     "metadata": {},
     "output_type": "display_data"
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      "application/javascript": [
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       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
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       "\n",
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       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id !== undefined) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
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       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var element_id = msg.content.text.trim();\n",
       "            Bokeh.index[element_id].model.document.clear();\n",
       "            delete Bokeh.index[element_id];\n",
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       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
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       "      var script_attrs = bk_div.children[0].attributes;\n",
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       "        toinsert[0].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
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       "\n",
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       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[0]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
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       "\n",
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       "      /* Is output safe? */\n",
       "      safe: true,\n",
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       "\n",
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       "      register_renderer(events, OutputArea);\n",
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       "\n",
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       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
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       "\n",
       "\n",
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       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"b0fc727f-faea-4577-803f-53873d0b8ce5\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'b0fc727f-faea-4577-803f-53873d0b8ce5' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"b0fc727f-faea-4577-803f-53873d0b8ce5\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"b0fc727f-faea-4577-803f-53873d0b8ce5\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"b0fc727f-faea-4577-803f-53873d0b8ce5\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'b0fc727f-faea-4577-803f-53873d0b8ce5' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.12.13.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.13.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.12.13.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"b0fc727f-faea-4577-803f-53873d0b8ce5\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import bokeh.plotting as bk\n",
    "from bokeh.io import push_notebook\n",
    "from bokeh.layouts import gridplot\n",
    "from bokeh.models import ColumnDataSource\n",
    "bk.output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Neural Network Basics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is a neuron?\n",
    "\n",
    "A brain neuron has a structure formed by a cell body, an axon were it sends messages to other neuron and dendritic tree were it receives messages from other neuron. A neuron generates outgoing charge (throughout the axon) whenever enough charge has been received (activation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "image/jpeg": {
       "height": 400,
       "width": 400
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/neuron.jpeg', width=400, height=400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In neural networks we use an approximation of neurons that look like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/Simple Neural Network.png',  width=500, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In practice they learn how to approximate functions. For example, for a classifier, it maps an input to a category $y = f(X)$. They are called networks because they combine many function together. The graph depicts how the function are composed in layers. Each function in the most basic form combines the input with a series of weights. Think about the most basic linear regression $y = W^TX+b$. $w$ is called the **weights matrix** (if we are in higher dimensions) and $b$ is the **bias vector**. If we think about the neurons of the brain are activated by a stimuli, it is not suprising that an **activation function** can be applyed to the linear combination of weights, biases and inputs. \n",
    "\n",
    "One example of activation function is the sigmoid that we have seen in the previous notebook.\n",
    "\n",
    "In real life, neurons are much more complex than this, and there are many types of neural networks. So the analogy with the brain stops here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Architecture of Neural Networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/neural_net.jpeg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To define the number of layers in a network we count the number of layers that have weigths. This is a two layer network, that has one hidden layer.\n",
    "\n",
    "The reason we use layers is that allows us to implement computation more efficiently, with matrix multiplication.\n",
    "\n",
    "The result of all linear combinations and activation functions gives the output, and to quantify how the Neural Network is performing we define a **cost function** $C$. We have already seen two loss functions, MSE and Cross Entropy. In next section we will use MSE to understand how backpropagation work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Backpropagation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4.1 Gradient Descent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learning in this contex reduces to finding the best weights and biases that allow the neural network to approximate the function, that is to have a small cost for all training inputs $X$. \n",
    "\n",
    "For the moment we will use the MSE as the cost function, but the same reasoning can be applyed to all type of cost functions. \n",
    "$$\n",
    "C(W,b) = \\frac{1}{2n}\\sum_x \\|y-f(X)\\|^2\n",
    "$$\n",
    "Here $W$ and $b$ denotes all the weights and biases in the network, $n$ is the number of training samples in the data and $f(X)$ is the result of applying the forward pass of the network with input $X$. \n",
    "\n",
    "We see that $C(W,b)$ is non-negative, since every term in the sum is non-negative, furthermore, the cost becomes small, when $f(X)$ is approximately equal to the target, for all training inputs. By contrast it is not performing well when $C(W,b)$ is large, that is $f(X)$ is not close to the target for a large number of inputs. The purpose of  the training algorithm is to minimize the cost $C(W,b)$ as a function of the weights and biases. The algorithm that precisely does this is called **gradient descent**.\n",
    "\n",
    "Quadratic cost works well in this settings, because it is a smooth function, that is making small changes to the weights and biases changes the output accordingly. That makes easy to figure out the amount of change to apply to weights and biases. \n",
    "\n",
    "This is a well posed problem but at the moment we are ignoring all the structure of the network, the activation function, weights and biases and so on. It turns out that we can understand a tremendous amount by ignoring most of that structure, and just concentrating on the minimization aspect. So for now we're going to forget all about the specific form of the cost function, the connection to neural networks, and so on. Instead, we're going to minimize some function, $C(v)$. This could be any real-valued function of many variables, $v=v1,v2,\\ldots$. It could help to imagine $C$ as a function of just two variables: $v1$ and $v2$:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/valley.png',  width=500, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The objective of our algorithm is to find where $C$ achieves its global minimum. In this simple context it is easy, but in Neural Networks $C$ can be a complicated function of many variables. \n",
    "\n",
    "One way to solve the problem is to use calculus to try to find the minimum analytically, by computing derivatives and using them to find places where C is an extremum. This approach can be feasible when the function is of just two variables but it can be really hard when the function is composed of many variables (like that of Neural Networks), so using calculus in this case simply won't work.\n",
    "\n",
    "Luckily gradient descent doesn't require calculus and works pretty well in this context. If we think of our function as a kind of a valley and we imagine a ball rolling down the slope of the valley, the ball will eventually roll to the bottom of the valley. How can we use this analogy to devise a method for finding the minimum of $C$? Let's think about what happens when we move the ball a small amount $\\Delta v_1$ in the $v_1$ direction, and a small amount $\\Delta v_2$ in the $v_2$ direction. $C$ changes as follows: \n",
    "\n",
    "$$\\Delta C \\approx \\frac{\\partial C}{\\partial v_1} \\Delta v_1 + \\frac{\\partial C}{\\partial v_2} \\Delta v_2.$$\n",
    "\n",
    "We're going to find a way of choosing $\\Delta v_1$ and $\\Delta v_2$ so as to make $\\Delta C$ negative, so the ball is rolling down into the valley. \n",
    "\n",
    "To figure out how to make such a choice it helps to define $\\Delta v$ to be the vector of changes in $\\Delta v \\equiv (\\Delta v_1, \\Delta v_2)^T$. We'll also define the gradient of $C$ to be the vector of partial derivatives\n",
    "\n",
    "$$\n",
    "  \\nabla C \\equiv \\left( \\frac{\\partial C}{\\partial v_1}, \n",
    "  \\frac{\\partial C}{\\partial v_2} \\right)^T\n",
    "$$\n",
    "\n",
    "With these definitions, the expression for $\\Delta C$ can be rewritten as $\\Delta C \\approx \\nabla C \\cdot \\Delta v$, $\\Delta C$ relates changes in $v$ to changes in $C$ and it lets us see how to choose $\\Delta v$ so as to make $\\Delta C$ negative. In particular, suppose we choose \n",
    "\n",
    "$$\n",
    "  \\Delta v = -\\eta \\nabla C\n",
    "$$\n",
    "\n",
    "then $\\Delta C \\approx -\\eta \\nabla C \\cdot \\nabla C = -\\eta \\|\\nabla C\\|^2$. Because $\\|\\nabla C\\|^2 > 0$, this guarantees that C will always decrease, never increase, if we change $v$ according to equation above. That is exactly what we wanted (but of course remeber that is an approximation). We'll then use this equation to compute a value for $\\Delta v$, then move the ball's position $v$ by that amount:\n",
    "\n",
    "$$\n",
    "  v \\rightarrow v' = v -\\eta \\nabla C.\n",
    "$$\n",
    "\n",
    "Then we'll use this update rule again, to make another move. If we keep doing this, over and over, we'll keep decreasing C until - we hope - we reach a global minimum.\n",
    "\n",
    "Summing up, the way the gradient descent algorithm works is to repeatedly compute the gradient $\\nabla C$, and then to move in the opposite direction, \"falling down\" the slope of the valley. We can visualize it like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ly+FnMkKIKIqMMX6ZJ0b51yiEMBin7Pav3L23GWPpdJqPYQwGgx6PRxAEXdfreLhlprVr1QULitymcDvR0aNxTQtPn46iZFUbJ8/nysrq2bf7yUZqG3J+AU3mKn4ymVy+fPkvf/lLpLGagJIBjDv8PKjrOs9VmWc9flKLRCKqqvp8vqampswG/PHworVtW/q//zuZTg/rqs+dO4U77jg2Hh4ugJLwCyE9Ho/f7w8Gg8Fg0OfzeTweURRN09Q0LZ1OJ5PJVCqlqqphGDxd5bsrHu/4whsvUDLGjhw5cuedd+Kvr1ZgJQzGFzftX/wUmTX9a5y8rXzxxShjwqOPCv/yL65u7/iwrFmjHzpEX345+ulPY4Y+QF5C/m3IXW55aa9J24MwkslkKBQaJ+erOoAQBuNFVvtXZo0gq/3LcfqXIAh1/+ZS1+lrr8UJIe+/b15yiXfatJJHhQmCkE7Lhw6ZhJD77us+++zQhAk4yVQvvFRXFaH0LS9zGwMSiUQoFBrrQ4dyoRwJ9Y/HL03T+ACwrPavVCoVjUYNwwgGgw0NDfU6f9WNt95KJJMDFx/cc4/JWDmv0Lt2DXxVMknvvvvYiB0cwDjDM5nH4/H5fIFAIBgM+v1+r9criiIfUZZOpwkhhmGoqqpp2ltvvRWLxeLxeENDQ3nxes2aNddff/3cuXNDodDHPvax3/3ud21tbSP9Y8EQeJMK9Yy/leSz70nO8FVd11OpFGPM5/P5/f7Cp63xsBL20kuD10UeO8Y+/NB71llqqXeyZo1h//e6dYl16+LnnYc9JatR3T+f64yQs+WlZVn8+iFKaXt7+2WXXSYIwqxZswRB+O1vf7t06dKWlpamphJaAu6+++5Dhw7deOONCxYs2LZt21//+tdf/OIXb7755jnnnDNqP9Z4V/+vKzBuld3+5UhV1VQq1dzcXK8VnJ4e8+qr91nW4EcUhfz2t6LHY2bejC8r8vYUfn1W5mfjceHmm43MUu+ECfJdd80Khepwe81al0gkPB4PJuvWLsZYMpn0er2KohiGsXPnzk2bNr366qtvvvkm7+4nhMyePXvp0qUPPPCAz+creodHjhyZNm2a/c/Dhw/Pnj37s5/97KpVq0bxxxjfUI6EOmSP0uFDVjNn51iWFY/H+e7U4XA4FAq53H67XrOX7ZVXYpkJjBBiGOS550oLT7t3Zz9KfX3mX/7SPcxjA4Bc/C0QPzUpirJ48eLrrruupaXl85//fCwW2759+8qVKy+//HLDMNwkMEJIZgIjhMycObOlpQUVyVGFciTUlcy9t7Omf1FKVVVVVVUURXv6l/t7tmdY1GsaW73aYUbrmjXGRRd5m5vdduivWWPmfnD16ujHP96weHH9bzZQc+r1yTyuZP0S4/F4OByWJGnBggULFiy49tpry77n/v7+rVu3XnnllcM+RsgLK2FQJ+zpX7z7Pmv6l6qq0WhUVVW/39/Y2Fjf22+XYc8edf9+56R1773UZYd+KqV0dFi5H2eM3HHHUV0fF/sN1Ao0otQBx19iIpEIh0emC/Mb3/gGIeTmm28ekXsDRwhhUA/47Htd1/kCWGYHmGEYsVgslUopitLU1FS0AT8fO8+N5HFXjcyW/CxtbdaePa7ahnbtyruycuSI8fDDvWUeHADklzuiYkRC2M033/zss8/+4Q9/mDdv3vDvDfJBCIPaxi9y1HXdNM2s+GW3fwmC0NDQEAqFsKu0I9Nkr7xSaL/Ie+4xLKt4c9grrzjUIm2rVvXv21fytZYAkI/je0JejhzmPf/Hf/zHr371q1//+tff/va3h3lXUBhek6BW2dO/+BZsmfGLUsqnf1mWFQwGw+Fw5ljq8tTxSti77yajUYcyoi2dJq++WuQBTKWUI0cK3YllsTvuOEZpHT6AtQtF+ZqW2ZhvG/5K2K233nrbbbfdeuutP/vZz4Z1fOACQhjUHt7+ZQ9fHcv2r7oMYS+9FC16m6efNhIJD8n/sm3PaC2gtVVdtaq/1MMDAPcSiURDQ0PZX3777bffeuutt9xyyy9/+csRPCrIB1dHQo2xp38RQjIvfiRDp38FAoGRLT7W65pBNGq9+27SzS0ffJB8+9sk38PwyiuG8yeGevjhnvPOC02bhtlUFVaXbyfGm9yVMMbYcFbCfv/7399yyy2XXnrppZde+u6779of/8hHPjLMQ4V8EMKgZhQYvmpZViqVMgxDluWGhobhFx9z1Ws5cs2amGG4+qE2bzYPHfKedJLDRZTJpHLkiKsxFprG7rjj2K9+dWJpRwkATnLfHPJti8q7t+eff14QhBdeeOGFF17I/BaWVajTAIYD5UioAbz9y7H7nlKaTCZ5+1coFBqR9q9xpcB1kbn+/GeLUpHkhFE3tUjbli2p1asj7m8PAI5GfETFa6+9Zu8XbkMCG1UIYVDV7PYvXdftBbCs9i9N03j7V6nzV0tSlythBw5ou3eXcMViby997z2HDc4z94t04777uvv6Cl1KCWOjXovs44Tj7OgRnBMGYwAhDKqUPXzVjl+Z3fe6rkej0VQq5fF4hjP9a5wraRmMe+ghXVWHrDUmk0Wui8yVTNJ77ukq9VvDCKqztxPjVtZ5T9d1VVUbGxsrdTxQKoQwqEb28FW++WNm/dE0zXg8nkgkRFFsaGgIBoNjM/2r/lbCLIu9/HLJIcyyhCefHPKA79xZTvx9++34O+/Ey/hCAOByT0eJREJRFJc7RUI1QAiD6pK193Zu+1csFqOUVqT9SxCEegphGzakenrKqQmuW2d1dQ1e3vjyy3rmZ90/RHff3ZVMot0EoHy5G0eGQqFKHQyUASEMqoU9fJVP/8rcfttu/9J1fQzav8YJN+PB8lm+fGBDyURC6eoqc1PI3l7zL3/pLvsYAMa53Dc8fFw+zo01BCEMKq/w3tuZ7V+NjY0VbP+qp5Uwy7Kam0vrps906BDbvt1LCNmxY1iH8dJL0e7uxLDuAoYBr9Y1LbcxH135NQchDCqsCtu/xoNYLHbxxZbfX/49LF+um6b88svlJzlCyMkny5HIEVwDP/bq5u3EOJcbwkKhELJ1DcFLGlRMVvuXJEnV0/7lqG5Wwhhj0WhUFK0bbyy/gVfTyNNPyz09ZdYiubPOEk3TPHLkyHDuBGB8yleOrMjBQHkQwqACHNu/7E+l02ne/hUIBKqq/atuQhjfXYAQMn++MWeOVNZ9MELIvHns3HOHtQHRggUGISQej/f3Y09JgBI47t6NxvyagxAGY8pN+1c6nebtXz6fr0riV52JRu2WfPatbzkMX3UjEBAWLtS/8AUrHC7zNDJ7ttzYOFCI7Orq0jRXGx/BCMLfV51BT1jNQQiDseOm/UuSpMbGxups/6qPlTC+zbn9z+Zm/ZJLylnNOvdcRVFYIMCuvLLMGNfSMvgrppR2dnbWwcNbK/BQ1zrHlbBEIlH2xpFQEVX3Ogd1yW7/4ps/ZrV/JRKJzPYvSSqvQAauRKPRrBfgyy8nntJj2LJlA91gixcbixaVE+N4LdKmqmp3NyZWAJQgtxyJlbDaghAGoyur/Stz9Yu3f0UiEcMw7Pavyh5tYXWwEkYpjcWyp+TLsnnDDSV06AuCMHmyePLJg9XDK69kPl9pha1TT5UbGrIviuzt7U0mkyXdD8D4lG/3bvSE1RaEMBgt9t7bhdu/vF4v2r/GTCKRcJwHsWSJOX16CWeDCy6QMn9fDQ3WP/+zt6QjyaxFZurs7MTECgCXMCes1iGEwaiw27/4xY811/7lqA5WwjJa8rPQ732vhGXIs8/OHg+2bJk+Z47b5jBBIKef7jxgDBMrxhLe+dSufCth6AmrLbXx4gc1hK9y2e1fWZs/8vYvxlg4HK659q9aD2Gqqqqqmu+zU6fq55/vKkXNny9NmJA9HkwQyNVXE0Vx9aI+e7ZDLdKGiRVjoKafyUDyN+ZjJay2IITBiLHbv0zTLNz+1dDQoChlXlIHZcu/DDbgK18RBaH4a/N55zmH0UmTrEsvdbWcdtZZRc48mFgBUAY05tcchDAYAYXbvzRN4+1fPp+vptu/anolzLKsRKLILo1er3HddUV2MvJ4hMWL9XyfvfBCY8aMIqubgkBOPz3vPXB8YgWlwxrHD1DHclfCGGMoR9YchDAYLkoprz/ma/9KJpO8/SsQCNRK+1f9icfjbjLN+eebzc2FIvI55yheb94kKsvC1VcX+SXPmaOEw8WPRFXVnp6eojeDstXoeyGw5f4GcXVkzcErIpSPxy9N03KHr/J1l9pt/3JUuythfLNIN7cUBFp4Q8mPfKRIfjrxROuiiwpdKblkiZsDIQQTKwDyy9eY39jYOPYHA2VDCINyZA1fzW3/ikaj9dr+VYs5LJ1O63qRCqDt5JP1JUuct0ufMEE85RSt6ArKxRebkyc7Z+4C10U66uzsNE3T/e3BpVp8GkMmXnnI/IhlWfF4HOXI2oIQBqXJbf+yt9/m7V+RSIS3fzU1NdVu+5ej2v1ZXC6DHce+/nXnEHbBBYokFX8QFIVdc41zCJs711Ut0maa5tGjR93fHmD8yDojJZNJQRCCwWCljgfKgBAGbtl7b/P2r8zue0KIYRixWCyZTMqyzNu/ajey5GNnzUofSGkMwyi1qBcM6lde6VBSXLrU7SLW7Nnm+ec73ENLS8nPCkysAMiVeyLiDWHou60t+G2BKwX23ubtX/F4nBBSN+1f9YR35pX6VRdfTP1DL5ScM0eeMqWEyuBnP2s1Ng45w4giOe00t1XRTJhYMRrq723SuJJbjuRDwvBrrS0IYVCE3f7F41fm3tuMsVQqxdu/gsFg/bV/ZanFlTDGWO5mkW6IopXVoX/eeaWdLvx+etVVQ54P8+YpoVA5UycwsWLE1dbTGNyIx+O4NLLmIIRBXll7b9u9XySj/UtVVd7+5fV68Q6sCr30UqKzs8yvnT/fmDt3YFFTlsmZZ5a8iLVwodHSMji+9cwzy3+GqKq6YQMmVgAMyF0J4yEM5+HaghAGDuz2r9zhqySj/UtRlHpt/3JUcythjLG//jXy0EPOXfZu7uCGGwaWss46yxMIlLMQdcUV1O8XCCGiWHxGa6FDYcIf/9j9+uuRsu8BoM5gz6I6gBAG2Qq3f8Xjcbv9KxQKjav2r5oLYWvXpg4cMDZuNLZuLTS4q4DmZv2SSzzExXiwfMJh+sUvegghc+cqwWD59cS+PkXT2J//3Nnamir7TiDTOHnvVK9yT0QoR9YihDAY5Kb9yzTN8dD+VR8eeCBy/D+oYZT5inv55WTSJHHu3PL74pcu1efPV8q4LjLT5s2UEKLr9De/ORSJYHLYcNXQewnIlW/3bgwJqzkIYUDI8fYvvsqV2/6lqipv//L7/eO5/au2VsLWr09v3z6QnI4epatXl7kYJsvmT3/qleXyf+OCIHz5y+S000qY0ZqFMWHduoFSZm+v8bvfHTLN2vgtAIwZrITVIoSw8S6z/cs0TcMwctu/UqkUb//y+/3jM37VInsZjFu1yuzvL7N2/NRT5X8tpyjinj1lt6aR3l5F0wZT186dyfvvPzKc4wGoaY4rYfF4HD1hNQchbLwzTVPXdUopb/+yl3ns9i9BEBoaGsZb+5ejGloJ27VLe//9dOZHNI09+mg5FeRkUlm/3nj00fIjFCFk40byyCN6LOYpflMnvBaZafXqvpdf7hvOIQHUutyJ+QhhNQchbLzjjV/27kOMMUopb/+yLCsYDIbDYVke1gtwnamJELZihcNVhOvWGbt3lxyD3nqLEEK2bDE2bBgsaJb0IDAmvvSSRgi55x6L0pLPOYwJb7/t0JF2//1Hd+9Gk375sKpduxz/ALFxZC1CCBvv+PiJzI9Eo1He/tXY2Dhu278c1cpD0d6uv/GG8z5FDzxALKuEu6JUevbZgQD06KNWIlHOaujRo7KqMkJIdzd94YWSA31Pj2I4tZMZBv3Nbw729ZXfajae1cR7CcjHsRyZTCYRwmoOQhgMMAxDVVVCiKIoTU1NaP9yxBcLK30URaxcGZd0ZLIAACAASURBVM03W/7gQeuNN/zOn3OyZ49kB6BEgj711JCCpstnyNq1g7nvjTeMgwdLu0Rg06a8gy0iEfM3vzlkGJikD0BisRga82sOQhgMaf8ihPj9fmwBW7uOHTNfeilR4AaPPWbE4y4XtIQnnhiyzvTuu/qOHaVFKNOU3ntvyIzWe+/VNc3tehhj4jvvFJqOsXdv6t570aQP40u+ERXoCas5eK0d7/jegpZlhUIhv99PUKcoqPpXwh55JGoYhY4wlWJPPumqQ7+vTzl4MLt4+eCDlqqWcN7Yty878GkaWblSYMzVKlp3t+xYi8z06qv9L7zQ6/6QgMNSdz1hjGFOWC1CCBvv+MWPjY2NHo+HL4BVeciAAiIR69lni2/X/dprRltb8Ry2erVDma+/nz73XJHu/syn0EsvOWxVtGePuWGDq0sECtQiM/31r0e3b3dugwOoBpqmpdPp4rdzB8Na6wZCGBBZlvkfM94ZF1XlK2FPPhlLp4sfHmPkgQeEwj+Hacpr1jhv9fj660Zr60A9sfBzJplUDhxwvhDg8cf1aLRIDqO0SC3SZlns978/1NNT/t6U4001P43rj67re/fu9fl8I3WHubt3E8wJq00IYTD4OlpDc7AqpZpDWDpNH3886vLGra3WunWFXhI+/LDQyeHBBwXdReDZsqXQZ+++2yw8saK7WzZdb1AUjZr/9V+HNA1N+lBdDMPYsWNHIBAY1Xe5vByJEFZzEMJgEEJYTXvmmXgsVkIEeeQRK5XKdwYQHnus0BJUVxd96SUfIYQxlv8JI7zwQqE76e1lf/tboQ59l7VI2/796Xvu6SzpSwBGlWmaO3fuTKfTkyZNGsG7zV0JU1XVNE2UI2sOQhgMQggrqmpXwgyDPfyww4DWAqLRvN1dnZ1KNFrkx3zhBe3AAcGyLEop/386dDBGd7eSSBS5k7feMtranC+3dF+LzLR2beS553pK/arxCe0Ho82yrJ07dyaTSY/HM+JrVLkNYT6fT1HK2RUDKgghDIb8MVdtyIDC3n470d1dyhhWQgghL75odHQ4nLWfe654FdAwrJUrCWOiPe+Xb7dACOGB7K23DDfPpeXLnSdWdHfLJc2VtT300LEtW+LlfOV4gj/z0UYp3bVrVyKRIIRMmDBhZCNv7q8Ptcgahe1oYIiiIWycpzRBEGi+QaiVQymdPTs2c6Z4+HBpx2ZZ5C/PdV147fZes6/P6DeoHhADPqv5nY5TPMJcwhzepNlJSxCEjg7yxhuBiy5Kk4xlVL4PqWmKa9dq9kPFN8VyXHwxDLJiBbnhBkEQhjyvNm4s83G2LNbWdmzuXJmPXAEYe5TS3bt3x2IDlyqPbC2SEMIYy5rmyCe1YnWz5iCEwRBFM9Z4TmCkWjNoPB6n1PjpTz0//rHq8ksE2Qyf/27DeR8Yk/pfSTGLWYZl6lRXTS1tqsmvpHtjirTzIxM2XCNrA10mjBFKKWMD2YjvN/rss/oZZ0hTpw6sWdmX2R465BEE6/gCGWOM2F84GMmOZ7LWVuv99z3nnjtYfKRUfO+9kmuRnNcrnHhiuq2t7dRTT/V6SxstCzB8jLG9e/dGIgPtAV6vdwzWqBKJBMbl1yKUI6HkciTebFUVy7Ki0SghZNo0/ZOfdPW2KnBa6+z/fc8pX3p7ygyr2dvY7Gts9jY2+RoavQ0NnnDYE2rwhAKNlnbWS/uv+cax01bx+GVZJmM0a7NR0yQPPyzlPmVeftkghAgC4dvDS5IoSZIoSoIgCgJhjFcsLcvi7WTsySe1vr7BBrWurjJrkYSQhQsVWWamaba1tRlFJ72OY/hDHg2MsdbW1r6+PvsjEydOHI3vkvXr4/Mp8DutOQhhMISbEOY4omacqMKVsGg0aldIr79elKTC1WQ2+XNr5n/vmakzrCZvw2AC8zY0exsaPeFGb6jBEwp5giElEFIC3pB57II79/7TzaaUEgRBkqTcLa327DGzpl2k0/KePQ5dZYJARJFnMonflSDwtTJKKb3zTk3TBtLZ+vWuR1PkWLBg4D90XW9razPdT7kYN6rtOVw3GGP79+/v6RlyachohDCCPYvqBUIYDFGFIQMKME0zHh9sQvd6ze98p9D0r8mfX3PypZsavMEGT7jJ19Dsa2j2NjT7Gpp9jY3ecKM3HD6ewAKKP6D4A7LfL/tip65t/dQvBDlv+H7ySSMSGdyeaNs2VxldEARRFOxFslhMeP55mTFmmuTdd9ODq2SlPCF9PvGkkwbrmKqqHjhwwCp7VQ2gFAcOHOjq6sr8iM/nG40qYe4fBSa11iiEMCjn6shxuxhWbSE1EolkHc8FF1hTpjj/apoueP/ET28OKcGwJ9TgDTV6wk3eBnslrNHb0OAJNXhCISUQPB6/fLLXJ3u9kjc2Y33b+f8fv5/cX72qskcftSuhguNWRYXxwuX779MDBwI9PV5B4OtkAu/0zwxkhR9/XovM/EgqlTp48GAVXk4Bdaa9vf3o0aNZHxylWiTBSli9QAiDIaotZFSh6nl8dF3nF8BnEgR6000O07/kiX0zr3jdL/uCij/kCTR4QjyBTfA18mWwBk8o7AkGFH9QCQzEL8nrlTxeSfFIikCE7jn/6D/pzXwHs3mzsWGDlxDS2yv19ZWfeO67T9+4kfDm/czCJS+DZreSMUbIkF/HggUO3zoejx8+fLh6fnFQfw4fPtzZ6TAoeJRqkSQnhGElrEYhhMEQ7kNY7jXS40FVrf/19/c7fvykk4yPfjS7Q3/iZa94vZJX8vgkb0D2BZVA2BNs9IT5MliTtyHsCQaVQED2+yS++uXxSIoi8v/JiigTQtqX3snEvC1Wjz1mJRLie+8Na82puVn6+MeN5mYp84P8csqhzWT2ItlgJvN6yYknOl9TGYlEOjo6hnNg9aeqnsw1rbOz89ChQ7kf9/v9wWBwxL+d4ykaK2E1aty9iEKusoe18olQo3NQVap6NhVIp9PpdDrfZ2+4YUiIkSb1NLXsVkTZI8keyeOVvH7ZF/IEG7zhJl9Dk7ch5AkGlYBf9vpk7/HsJfP/yaIki7IkSoQQtfFQ38mv5/um8Th9+mnltdeGdUHismVyKMS++lVJUfI+tTIDGV8m48/b004TCTFN03RsJuvr68utFo1P1fAErhtHjx5tb293/NToLYMRp3Ik9iyqRQhhQAj28K41jDF7CpGjQMD45jcHR2T5ztgiioIoiKIgSoIoi5JHUrySJ6QEmr2NDd5wQPZ5JY+dvWRRkkVJFERRFEVBEokoCgPnit78IYwQMmUK+cIXnLdCckMQyOLFOiFk6lTzi190OeJrcJHszDMl+/pNxphlWXYg45msq6uru7u77MMDyNLd3d3W1pbvsyM+o5VzPD/H43HMCatFCGEwRKkhjDFmWePoWVQlITWZTGpakVmmF11kNTcPZGvP/J257VMCEURBVCRFFiRJkHhEE3jpjwjk+H8IvGv++FdFZ7zHJOe1LkkSPvpR8/zzrVNPlRxvUNS8eUpT08CVjAsX6hdeWOhKzyyBgHjiiVrmIpksy5IkZXb3m6Z5+PDhY8eOGYZR6nWXAFl6enpaW1vzfTYQCIzSng1ozK8n4+jlE9woI2RIEuvsHF9FycoqugzGiSL96U8HFpOkaR2MMMqYRanFqEktg5q6padNNaLGEkZKt3SDmhazKKP8fwOT7gljhGQ+HSwlpYWOOH7Hlha5ocEURfKNb0hyWZtxnH32kCfSJz6hzZvndl1t4UJFysl+dibjgYxnss7Ozt7e3lQqlUwm0+m0pmmmaY7DyyfHWy/ByPrww2O7d+ddAyOjXIskKEfWC4QwIGS45Ujm8Qh5miLqTTWshMXjcZcDSOfMMZYskYhHo0rKoKZODYMauqVrlqaaWtJMx7R4vxaNarG4kUybqmbpOjUMaprMMqlFGa/iUUoozfiR9UCP47e78MKB2/h85re/XfIagMcjzJ8/ZHlPkoQvfcmaONFVoHO8LjJTZnd/d3e3ZVkej4cQYpqmqqp2JtN13bIsLJJBAW+9deiDD9rF/BepkFGrRRKn8w9jDCthNQohDIYoL2RMmkQ7OqTxMJm84iGMUso3KXKH3XijTAIxgxqGZeiWrlq6amlpU0saqZieiOjxfjUa0WIxLZHUUykjnTY1zdINyzTsNMYsi1oWG5x3angd1uGmTZPmzBmMULNn6x/7WGnNYUuWKF5v9gPr97OvflX0eIqs2QSD4syZJew1yRg7fPiwZVl+vz8QCAQCAZ/PJ8syY0zX9XQ6nUwmU6mUqqqGYdRfJquzH2csMcZeemn/m28enjmzUM09FAr5fCUU04cvHo9jJawWIYTBEGWHjJYW+uCDeDqNumg0WtL893DYuPKzE1RLV01tYHNuI50wkjE9EdPiETUW0WL9WjSixWJ6ImGk0mY6baqqpamWrluGbumGZRjUNOhgxFZ0h3P9Jz4h2ftyE0IEgXzhC2zixBKeEkuXOj/rJk82r7qqSJ474wyHWmRhlmW1tbVp2kAbmSzLXq83EAgEg0G/3+/1ekVRpJRqmsYz2XguXAJnmnTVqj0bNx4jhDQ0FHrTOaq1SPSE1RO8agIhOX/PZYQwv58tWCC88Uadd5lUdiWsvd144YUS1nu4Ky7zaWkhbaopU00ZqYSRjOvJuJ6IavF+LdqvRvvVaFSPx/R4XE8k9FTSSKeMtGpqqqXplqFZhkENygaTh5KcnPUtPB5h6dLsA5Mk+r3vuV0Ma26WTjpJzffZ+fONiy4qtK5w2mnlBCPTNNevP3TkyJDvy7fIVBTF5/PZmQyFS1BV87HHdu7e3UcImTJFlqRCT7nRDmG5/XwIYTUKIQyGGE7IWLbMWrdO7Osb6WMCQgghjLHbbovcfbeVSpX2ZytJVpMwIWWmk0YqYaQSejKmJyJaPKLFIurAMlhEjUW1eFRLxPRE0kgljVTKTKdNla+KaebgNkSi5fUmTsj6FuedpwQCDs+Z5mbjmmtcFWWWLZNEsVCC/9jHtAULnCNdqbXITO+9p//nf7a2taXy3YBnMo/Hwwdv2oVLQkitFy7RmO9ePK4/+OD2gwdj/J8zZhT6GwyHw16vywErZcr63RmGkU6nUY6sRQhhQMjIzQn7+tfZT35S5niCmlDBlbCnnkquX69Fo+yxx0o4v/NxWR+b3JI00kkjFdeTMT0Z0+JRLdavxeyVsH4t2q/F+GJYXE8mjFTKSKeMdNpUVVPV6eBMiqaOc0XLS4Y+CBdckLc0c8455vz5StHjPPPMIh2FoihccYU1ZYpDk/6iRSXXIjlKpS1b9FjM/L//d9+uXdkbQOU5jIHCJc9kjoXLVCplFy6rM5NV51FVrd7e9MqV27q7B5N6OFyxWiRx+vUlk0lZlkdpIgaMKoQwyDac98fTptFLLxXuvbfOn1dj/xrW3W398Y8D/fjPPWccPFj8gkEevyiloiieP+Gj8eNVyJgWj/JlMC3Wr0b71GifGuX/jGrxmJ6I64mEnkwYqYSRShrplDmkWtfcfmHWNzr1VHnGjLyvSYLAvv51sfC6wCmnKBMnFr+sw+tlX/mK4PNlP7uKXheZT3e3zPu70mnrt7/dv3Gj+yseCMlfuBQEwS5cplIpXrg0TRPRpxZ1dMRXrtwWjQ4utU6dKotixWqRXNZZOhaLhUIhLG3Wojp/sYQyDHMP7y98wXrsMWnnzhE8oipSqdPcr38diccHfimUkj//WSr8O+Iz4nlKEARhtn/WRxqWxfVEVE9E9UREj0czuvL5elhEjfEa5fEolkwaqaSZyuwG80dnTdr/mazv9U//VOTgvV7zO98p9B79nHPcPqoTJ1pXX61k/hJCIXH69DJrkZs3D17ioOv0jjsOvPVW+dX0woVLVVWTyWQymazFwuW4tW9f/yOP7Einh7xDKFyLDAabeAfh6Ml95vDduxHCahFCGBAyjO0jc/n97LbbrOuvl4tNdK9hY/zyuWZNes2aIdtEbt1qrVvn3GvFF8D49uqZO6z/68zrk0aaFyKza5FqtF+LRbRoZgLjdUmTDrkSc8amrwtsyEkjHBYXLy7+mz75ZP2Tn3R+ZZJlYcEC3fFTjk491fj0pwd/9kWL5PL2kadU2rp1yJFbFrv33oMvvTQy+xplFS4DgYDX65UkKbdwifH91Wnr1u4nnthlGFmLXiwUyrtqq2mKKI76MlhuY34ikcCeRTUKIQyyDTOEEUJaWugFF5Bf/KIOm8PG/r1mPE5//ev+3I/ffz9Lp7MPJmsBLPNTswOzfnjyd2N6gtci+dJXX0YOi2ixiBaN8pSmx2N6InM2GCFkdsc/T2q7mBBCBjcxIhdeKCvFO74IIeSyy9jUqQ5PicWLFb+/tHrieedpZ545UOA8/fQya5FdXTJj2Q8gY+ShhzqefNJ5S4Cy8SkYuYVLURRN09Q0rSKFSyycFPDuux1/+1tr7u9h+nQlXy1S05SNG+m8eaMewkie+RT4hdYihDDINvwQ5veTb37TeuAB8cUX6/CkMPzHpyR/+EO0u9vhpN/TQ596anBBKN8CWKavzfjy5VM/G9UzCpFqtJ+vig3msFhEi6fN7GkRi8ML773433Lv86Mfdd5HMpco0u9+1yGvnXVWyQ+mIAif/7w5bZocDkszZpSwipZp06a86xnPPXdsxYrDlI7Wb9kuXPp8vsKFy1GagoFVtwIYYy+/3PbaawcdPzttmvMfl64rH3xgzJ3b7PGM+pvPfOXI0f6+MBoQwoCQES1Hcqefzm67jX3rW/LRo8O8p3Ft/Xrt6aeT+T67apXR2alkNuDnLoBluXXez/9l8nd1aiSNVOR49rKvjozrSdXSGMn+7X9ywoXLz/jvKc3CVVcNabBfskSZOLGEybENDcb/+B9DqqjhsDh7djl1a4+HffWrwrJlcnlv/imVtm0rFB/XrOm5++520xyLuayOhUtZlimlmVMwULgcA5ZFn3uudf1659OWILBg0CG7G4b8wQeGaZJFi6aM8gHaRzLkeR+Px1GOrFEIYZBtREKYKJLLLjNPOYXccINcZy8ZY7YSpmnsttv6C3wr02R3383y1R8dCUT44exvfzf8ywY5TAhhhFnM4vt2O95eEZRvzLz2D6f/2i/6CCEXX2xNmjR40rA3i3RvyRJj0aLBSzuXLStzugQhpLHRmj5dyKyNunfsWPHLS999N/LHPx7QtDGdj28XLjPH92cVLu1RsbjicmTpuvXEE7t27HDeGpUQMmOGJ7cWaZry+vWWYZCGBs/JJ4/FpK7cXzomtdYuhDDINlIh46STyI030pdfFv7617p6mo1ZCLvnnlh7e96SGWPUsujGjdYHH/jy1R8dPfIIa1t5+bOLnv7GjGublaZ8N/OJvs9PufT5pY/9+OQbxeMnCkGgP/6xh4e9yZPFefNKXsQSBPK1r4l+/0ByKjoerADLkh5+OP3WW54yctiHH7r6vlu2xH7zm33JZMV2Rc0qXAaDQZ/Pxy++G37hEi1EmVKp1Cuv7GprKzSm5IQTsh8xy5I++MDSNEYIOeOMyWPwkGLPojpT/O0gjAcjXo7kPv9569prhV/+Urr8cjZhAt6yl2D3bn3FinieTzJKKWN81US47z6hpUXI3fraUSSirF6tEUJe/dvkH3/5xh/N+u6W+PZ3Ix8cVA93qJ0Ws6Z5T5juPeGcprPOaTjLKzpM95oyRb/sMs9zz5kf/7gkiiXUIm0ej3Xjjd7f/ladOVOeOrXMji5CyMGDEmPk9df1UMi7ZEneLY9yUSrt2OG2lW3PnuSvfrXvf/2v2U1N7i5AGE2CIPDWMUIIY8yuRFuWZZoDSZFXpe3atGMswPpZlu7u7v3797e1FYpQokiCwSFPG8uS1q+nPIERQhYtyt7Oa/TkliMxLr9GIYTBADt7jWAI8/vJ175m/f3v8i23iHfeWSe9LGOwEmZZ7NZb+02H9RdGKWOMCQKRJJGvAHV10Wee8V59tasUsnz5QGz6+9/1c85RTjnFWBJetCS8qKTDu/xy9uabZNmy8vPT9OnGJZd4/f5hPYxvvDHwAP3tb1og4Js3z20OO3JEJsRtCCOEHDqUvv32vf/2b6dOmTK6e9GUhAcsexGUMcbTGKU0s0xpBzKXBevxhlLa1tbW1dWlaVIsVuhNxcyZiiAMPm0sS9y4kabTA4/zjBmhCRPGYmC948knkUhMmjRpDL47jLi6qhPBiBjZnXk+8Ql2/fX0vvvE9evxZHPr8OHkrl3ZEYcxZln0+PWPUmYN7sknza6u4m+ojhzxrF8/EFwoJcuXC045rzhRtG66yRMKDatZ6lOfoqeeWkISyqKqyoEDg1/++OPaoUOuNqkkhHz4YckLeF1d+hNPtCUSrrY2qogChUvDMLIKl5SOaaNb1VJVddu2bV1dXYSQnp4izYlTpw7+xVEqbtpEksnBk+QZZ4zRMhjKkXUGr4uQbcS3R/zhD61Fi8gPfiCIYj2svI72SpimaR5P/Mc/zlx0YRkDwMTc9QxdJ/fdV/SxFe68c0joOXjQ+sc/3AaXLE89Ze7aNayx4NGo9PTTUnkpkBCyY0f2g7BihdbdXXylyrLk3IDrxty5dO/evceOHSvja8ceL1xmju/PvOKS1y7tfcfH5xWXvb29W7ZsSSYHrj4+erRQNJckEggM/PlQKm7ZIsTjNOOzwoIFlVyIwoiK2oUQBgNGag/vXNOnk+uuszZuFO66i0llXwtXTUbvFcuyrEgkQgj55Cfp9On8F0EtixJiDwBzrii9+665cWOhCLJ3r9Lamv0ys2qV0dFRcqtTPK58+KG1YgWNx8s/gaxdS9vazL/9rbwUKLz2msM1AcuX69FokRx25Eg5z0BJIhMn6oyxjo6O/fv3m2WHx0rIveKSN5aJomhZVuYVl/a+45U+5NFFKT1w4MCePXssa+AvIp2W4vFCIezEExVBYIQQSsWtW0kkMuTGc+dO8PnG6B2m40pYMplET1iNQgiDbCMewggh3/sevegi9u//LnZ1CbWew0avsYYx1t/fz18CRZH94hcypRalTBQFUSze0HPvvczIU99jTLzjDofUYppk+XLBKrE697e/UUJILEYfecRT3vPEMKS1a3VCyLvv6h98UHIO6+9XMtchbJZF7rnHSKUKLdFt3FhOfmppUQRh4DtGIpHdu3enUqky7qcaCMflFi7tfcdHdVRsZWmatn379iNHhmyK0Ntb5KQ0ZQohhDAmbN8u9PdnP/cWLx67lnwudwNvrITVKIQwyDYaIUwUyc9/bjJGfvYzktlNXItGrxwZi8UMwyDHm6xnzTIuvFCSJFEQXD1cnZ0038LSxo1SV5fzMbe2Wq+8kjcGRaxI1kdUVXnttYFy3ubN+ltvldOMvH374E+0apXe3l5aZXPDhrxLNbrO7rnH0nXn5T3LkvfsKacR7fTTh/xT07Q9e/b09vaWcVfVJrNwGQgE7PH9WaNi62Pf8f7+/i1btuT29h05UuiNiCwTv99kTNi5U+rtzb5lMKicckreUS8jLvfxZ4xhJax21fBrIYys0StHcuefTy67jD74oLB2LRNFh8amcY7vHkiOJzDegG8P5XLp0UfN3P5iy5LuuqvQQK/HHjOy+vo/TG16uPfRf++47ReH/z0rh736Kn9iCPwJ8sQTemdnaYUYxoTnnx88HkrJypU0EnG7PkqpuG5doR8nmaTLl1PTdDiqzs5yVmFlmTQ3Z0c3Sml7e3t7e3s9Fe/4GyQ+vt8eFev1ekVRzNx3vBYLl4yxgwcP7tq1K7eUnE7LyWSRWiQhZPduqavLYRl14cJJojh2Z7N8jfmYmF+jEMIg2yiFMELIf/+3NX06+eEPBdMktXvB/GishBmGEYvFePziDfg8pwaD1g9/WMJYBFUlK1ZkLwK9/rqYzLv1Ef/u5P77pcyf6fXY2rcT7/SYvYSQXeoe++OmKT/77JBJEKZJ/vIXQStlaOuxY3IkMuQBTCToQw9JhuHq+dDRIRd96e/roytWEEqzI1d5tcizzsq7Z3Nvb+/u3bu1kn7+6uBqfwVBkCQpc99xx8IlH99fzYtkuq7v2LGjo6PD8bM9PUVeBydPFlpb5aNHnZ88YzkeLB/MCatdCGHgbDTOp6EQ+elPre3bhT/9iRFCar05bKRQSvv7+/mEJ0IIb8C3XyM/8xk6uZST/Nq15rZtg9U9XZfvv7/4AK3t2821aweLkvP98+z/3p0eDGHr1gmMZb94d3Zazz5bQlJcvdrhxezQIXPVKld38uabroLUkSPWI4+ImTnMNOW9e8upRZ52WqG8kk6nd+3axS+nqG+5V1xm7jtetYXLaDS6ZcuWWCzm+FnG2JEjhZ5RkkQOH6YdHc7PnKlTg1OmBEfgKF1jjGUFaEppIpFobGwcy8OAkYIQBgMyy5Gj1/Z044108WJ2220if1Nqj/+uISP74FBK+/r6dF3n59bcQq0osn//99JmhN5zD7FLLs88QyzL1QrTQw+Z/f0Dv475vsEQtkcbCGGWJT3+uHOee/11IzP5FZBOy5s3O7/mbdyov/12kSZ9TSuhqautzXzmGdFOjeXVIj0e0txcZKSFZVn79+8/fPhwlcSOokbkOLP2HXcsXPJ9x3nhcuwfHErZoUOHd+zYYeS7YoWQVEpJpfKurCqKMGuWr7Mzb0qryDJY7qWRjLFgcEyzIIwUhDBwNnpnzGeeMb1e8tOfDvxTVaV4vu15qtIIhjDLsvr6+lRV5fEr3/UKc+aY551XQlo9eJC++KKPEJJIKE8/7XYmVjrNVqwYiCkne072CAOhKmJGj5ldhJDNmyXd+c4EQsgDD1iRyOD09nzfZcOGQsfw/PP6vn2FwtzevaWdsnbsMFevHthccsOGcpbBzjpr8LrIwrq6uvbu3avneYzqW27hku87LgiCXbhMpVJjue94R0f8oYe2HTp0qPDNenvzPqN8PnHpUrHAGH1RrMB4MMfdu0OhEAoLNQohXBDgegAAIABJREFUDByM6jzSadPIzTdbzz4rvPwyI4SEQsKTT4p9faP03aoUYyydTvMEllV/dPSTn5T2p/rQQ1YkIq1cWVrf9IYN5nvv+QghsiDN8Z1qf3x3ejdj4oMPFiprJhJs5UrZsgo9bSgV/vGPIu1TDz5o5Z8XINgXZrr3wQf6m296DEPet6+chrD580u4cSKR2LVrV7y23lWMAnt8v2Phko/vH73CpaqaL7yw/4EHtnm9euHON8ZYvlWucFhaulQQRdbdnfcpN3t2UzA41juK5pYjeQir0RZbQAiDAaO0h7ej732Pnn8++/73Rb5qcN114pe+JNbIKPIRuHCBt+Hv2JHaupW6vEAhFLK+970S5jikUmzVKs8bb5S89rNihRmPS4SQed659gf3aq27d8uJRJEfeedO87XXCk2saG+Xi7awp9Ns5UpR0xwek1hMyR0Q4MYbb+gbNihlXMLm9QpNTaXFPtM0N25se/zxg6pazqGOmbF8zc4qXPLx/Y6Fy+GP79+6tfueezZ9+OExxsiUKUXuJ5XyqKrDG5WJE+UlS6iiWL29cr7xyKQS48EcYVx+TUMIAwdjcIJ++GGTUvKb3zBCiNdLfv5zcuml4uHDo/1tK4xSmkwm4/H4gQP0Bz9gf/yj7H5Q6mc/SydMcHvjcFj40pf0L3+55J2F4nH24IMKIWS+fzCE7Vb3PrDS1fbYzz6rt7c7V04ZI3//u6tQePSo9dRTHkqzX0E3bSpzIIIgkIUL9Suv9ChKaU/ss84amJNekgMHxNde67r11m3r11fpAm8Fe9fs8f1ZhUtRFE3T5OP7yytc9vamH3po+/PPtyaTBiHE5xM9niLPt+5uh+fDtGnKwoWmJDFCSEdH3r9Pn0+eM6fZ5bGNoNyVsHg8jpWw2oUQBg5GeyWMEDJhArn5ZuvOO8UDBwgh5NJLxcZG8pnPiG1to/ptR0B5K2GMMU3TotGoruvd3d7vf9/q7WW7d9N//MNtTpIkdsstbjv0//VfpcZGesUVbNKkkk/N69YZmzZ5T/ScGBQD/CNJK9kr5QZkh0fAsshf/iKoqsM3jceVtja3kXPLFmPt2iGLapSKb75Z5iSIOXM8oZB54on61VcrPl8JJ73588v5K9izxySERCLGffft/3//b8+xY67y6/iUte+4Y+Gy6Ph+w7Bef/3g8uWb29sHL4E86SS5cCyh1OG6yFmzPHPn6rw5M5mUE4m8z9iFCydJUmVeQLF7dz1BCIMBY1mO5L72NXrBBez73x/45yOPCO3t5JJLxN27R/s7D0sZIcyyrHg8nkwmFUWJxULf+pZqz6//859pJOI2J82fb55zTvH225YW+Z/+SSWEiCL9+c9Lu7KSu/9+mk5Jc3xz7I8IJ+91+bXd3fSJJxyS5TvvlPaMevFFfffuwYM/dkzJf4lbEWeeOfAfU6ca11wjhUKuWpi9XqGxseQWtEhE6usbfGnfuTN2++3bn3uuQ9drZq5pBTkWLnPH92cWLvft67/33s3r1nVk9SNOmlTk+ZZMejRtyC9l3jzPrFm6Xbbu7s77+uj1Ci0tlalF5p55+EpYRQ4Ghg8hDByMTQgjhNxxhxmLkeefZ4SQmTOFf/s30tFBLrlE3LJlDL75WGCMpVKpaDRKKQ2Hw5GI/+tfjx09Onjqj8fJffe5XQwTBHLTTUUChMdDvvtdy47UM2YYn/98yUXJ/n76+OOe+b7BiqQ4q9X9l+fuJm6a4iuvlLwg9PDDZnf3QOPz22+XGcG8XmH27MEltIkTza98RWhuLp7Dzj67nFrkwYPZ92ya7IUXjvyf/7N969YqmiVW/dWr3H3HswqXx45FHn54y4MPbu3tTWV1kvl8QtFaZE9P5ttOcsYZnunTBzM3pSTfbDCvV7jwwobJkyuTexwb87ESVrsQwsDBmIWwiRPJD35AH3hASKcJIeTmm8VwmHR3k89+VvzggzH4/uVwvxKm63o0GlVV1e/3NzY2dnWJ110X6ezMXhF5/nlz9263EygaG60bbigUqq69VjnhhCFFlq98hZQxTHvNGkPqWGD/UzhpP5FK6DR/6CEz8yLHPXskSkt+1dc0tmIFSadFw5C3bSszhC1e7FGUIb+shgbrmmvI1KlFHvN588r5E9i71/lqu54e7a67Wu++u7Wvr/bG61eDzMKl3x/Yvj22cuWefftihBDLskzTNAyDt5FZlnXSSZJjudxGKensHHhGSRJpaVEmTRqy6hmLeQzD4R4UhZx9tnTiiRNH7icbLozLr2kIYTBg7MuR3BVX0Jkz2f33M0KIx0Mee4wQQvr7yRe+IL755tgcwsijlMbj8UQiIYpiY2Oj3+8/epRef32ko8OxJiX88Y+i+134Lr+cNeXZL3j2bOlzn8tecJIk6+c/LzIE1dEz950QIsfHcHtUYdrgyKWiyyh86phpMkIIY+T558ucntXTYz32mFLqeLBMCxc6ZMdAgF51FeN7Ajry+4WGhjJqkXJmLTLX5s2R227bvnr10cKzPEZbrQyVddTREb///i1r1rSbJpNlWTlOkiRRFBljlmU1NuqGYRiGwTMZYyzrR04kFJ6xFEU46yy5oSE74h896vAQyTJZtkzx+dikSWM9Hoxz3DgS5ciahhAGgzKH5ueetkbPz35mtbcLra2EEPLJT4rnn08IIfE4+eIXxddeG5tDKEHhlTDGmKqqkUjENM1gMBgOhyVJOnbMuv766KFDeXPWzp30xRfdFg1lmd58s0OnlyiSH/yAOe5BcMopxkUXlTzQqOsYm5weHJMlzHLbFsa1tpqrV/sIIT09SldX+R1Rra2Gv9Dgi0ImTJCmTXNeefJ66RVXWHPmOD/s5dUi29uLn1E1ja5adfhXv9qxd+94HydWqng8vnXrngce2NbVlcr8ON/kQ5IknskaG72BwMDutJRSy7KM4+zZ/bwWGQiIS5cKwWB2bjZN8dix7FgmSWTZMkVRjMmTJ1d2MirKkfUEIQwcjHG/yKRJ5GMfox9+OPCa99BDA989nSbf+paYZ8+3amSaZiwWS6VSXq+3sbHR6/UKgtDdTa+7LtreXqSWd/fdLBZz+7CfcYbZ0pL9MvDP/6ycemremt2//ItQapS58ELlozNn2/8USwxhhJC//93Yt0959dVhTcxqafHMmqV985vlBLGWFrnAeDBFYZ/7nHHGGQ6Jdu7c8q6LdFsz7exM//73u//61zZVRXWyuEgksn379m3btu3fHy/63nDWLEUURZ7JPB6PoiiyLPPYRCk1TVPTjPb2ZDDIFi+2PB6HKRi9vXLWx0SRLFum8D6zqVOnjuCPVhLH934IYTUNIQwcDH8eaakuvZR2dJDWVkYIOeEE4T/+Y+DjR46Q//zP6uogdnxw+AAwvklwQ0NDMBjkexCZpnn0aPLAgeIpJBZjf/lLCR30P/vZkCWvKVPEa64p9HKuKNb//t8lFCUVhVxxhXGaL2MlbOYBIpfWmMUYeekl4eDBYV0YeO65FiFk5kztq18tOYedfnqRA5Yk8ulP62efPeSRCQTEcLjkWmR/vxyJlBY3DxyIbNu2df/+/alUqvitR1T1N+YTQhhjPT09W7Zs2blzJ//jOnKk+HNp4sQhf5u8wZ/vqsSlUr4JEzyLFlmiaPBmsqzCZWenNfQe2LJlitdrEELC4XAgEBjRn7IEjuXIRCKBnrDahRAGgzLLkWTMG0e+/GVr0yaBT1S/6Sax+fgcxLvvFrZuHcsDKSLrwbEHgGmaFggEGhoa7F3JVVWNx+MzZ5rXXuuq7/6ZZ6y9e9126Dc1mddfP1hhvPFGwecr8vs67TTD/R6Un/mMMnGiNVGeMFE+PiJWMoWZg2Pc8j07sl7cL7yQfP/7srecQRmEEDJzpjxjxkAemjtXu/zyEnLYrFlKY2PxrYoEgXz849oFFwzmsHPOkcub0VrqlyxaJDPGent7t2/fvnv37mg0Wuo9lKf6e8IopceOHdu0adPevXuTyST/oK6LhVvuCCGhkCjLeQM0L1x6PJ5Fi5jPJ9uLZJmFy2iU9vSoduFSENiyZV6fbyDNV3AZLB9MzK9pCGHgoCIhbMoU0tREIxFCCJFl8sQTAy/mlkV+8hOxOl81LMtKJBLJZFKW5cbGRp/PZz90iUQiza/5JORb33JVCmSMlNShf9VVJBgkhJBPfEJuaXG1cvOd74geT/GH0u8XLrts4A7n++bZHxdKGVRBCJk2TTr9dM3nM374w3KuDCCEnHfekHPUkiXapz7l9q6WLClhsefcc7WLLx6451NPLfnZxhjbu7fk6zenTRtccYnFYnv27Nm2bVtPTw91/ySoO6ZpdnR0bNy4cf/+/ao65CqTvr7ibyFOPlkpsMjHGOnv9wSDKh/Hai+SZRYu+/s95Hjh0jD0M88UFCVlWRalVJbliRMreV1kvpUwhLDahRAGDioSwgghH/84ff99xmdyXnCB8KlPDXz8nXfIgw9WS/WEPziU0nQ6HY1GLcsKhUK8AZ/fgHeGGRmjRT0edvvtrkqN27fT1atL6NC/5RZvOCx84xtuX/69Xuumm4rnwcsvl8PhgRww3z8YwlxPCxt45nz607wqS6ZO1b/2tZJzmM8nnHFGdo31/PON884rvrAmy8KcOaWVFBcv1j73OW84XM51kf39SjRaWi1y2jRJUbJ/cel0uq2tbcuWLUeOHDHNcnYcr126rre3t2/cuPHgwYOG02TezAF7+UyYkPe3QKm4d6+cTue9tlcQBELEo0fZ8bqlfO65gVDI5FdcmqYZDodVVR2lfcfdywxh/P0eypG1CyEMBlW2HEkIEQSydKm1YwdRVUYIWbFi8Fxzyy1Cf/8YH04hqqqm02mfz9fY2OjxeDI/Ho/Hc1cyzjuPnnaaqz+3u+9m8bjbxLl4sXnTTVJjYwkLJ2ee6dDUn6mpSfzUpwajz3zvPOH4HsbCtIPE67aRvLlZPOuswZWMlhb9/PNLGxt77rmKouT+aOzii80zzyxyV2ecoXi9Ja8nzZ+vf+5z3jKe+GXUIs84I++6jmEYhw8f3rx588GDB7Wie56Xpap6wtLp9L59+zZu3NjZ2Wnl2U5V06T+/iKptKFBkmXn22ia8sEHrLPTnDSp0LMikVD4GH1BEM4+29fYaNlTMGRZnjFjRta+4+l0WtM0Xrt097MOCxrz6w9CGDioVAgjhEyZQnTdMgzBNMnEicJ//dfAx7u7ya23Zr9sjP0RUkoTiQQhRBCEhoaGQCBgv5hllSCzCAL59a9d9WNFIiV06B87Jj//vMvb2tiPfiTz/YkdXXWV5PUOfjYshU9QjvfBiFQ4aR8hhJDiL+EXXyxlzcu4/HJ68sklXNt/zjn5XnTZ5ZfTefMKDd1YtKjM58aGDdbzz0um6bZ5jpRbi8yaqZuL90Vt2bKltbXV7ouqJ9Go9v77R556asfmzZu7uroK/zn39RV/5px0ksNtGCO9vZ533tHTadbQ4LD6mOno0YH/aGnxZF6cIQjChAkTwuGwve+4z+fj775M01RVNZVK8UxWYI/L4cs3JwwrYbULIQwGZf1tV2qxfckStmcP6+ggjJEbbxTtRtj77xc2bhxyy74+4fbbWV/fWBwnHwAWjUYNwxAEgb8ztj/b2WlklSBzTZtGv/pVVy/tq1ZZra3Fb6nr4o9+pH/wgfXaa6X1vfv95o9+5FwcnD5dOv/87HGv8zL2L1p4iast1gMB4dxzs5dwRJF++9tSIOBqDWbuXHnixLyPpyDQq68mM2c6P0oNDdLMmeUsIKVSYlubfvCgtWIFjUbdPqp9fZ5YrLRa5IwZkqK4rTb29/fv2LHj0Ud3v/JKR2fnCFxHWdnG/J6e9FtvdSxfvvVPf9r44osHJEl1czxurotsbs6+DaVia6uydavO3zbMmiUXWAK0LOHIEYMQcuaZntxtQzNb8gVB4G1kfr8/EAhk7Ttu73E5NoVLrITVNIQwcMCvIapcxwM58UTL4yGrVzNJIk89NXDSpJT8+MdD+tYnTiQeDznzTHLbbay3dxSP1jTNeDyeSqUURWlsbOSPD//Uhg3mddelLrww3d5e/H6+8x3BzXWCjJE//anItQiMCbfdxvh1DH/+M+3vL+1v+dxzrdNOc1g2uPpqMXcOZWZvfnLSrtNPLx4QL7pIdrxa0+Mx/+f/dBVuPvrRIllNFK2vf12cPNnhB29pkcWyzm2trQMDotJptnKlvnu3qz62traSS3sLF5Y27VPTxA0bIi++ePj3v996++0fPvlk244d/YZRS/37nZ2JNWsO3nXXprvu2vTqqwc7OxP84wW6uGyaJkWjRTJrU1N2LVLXlfXrh2wB2dhY6G1SX5/CGFm0yNPcnJ3APB5Ps33B9lC8uz9z33G/3+/1ekejcJm7cSTPedW5EsYY27Bhw6OPPvq73/3u3nvv/f/Z++7wKKr1//ecKVvSK0lIAUIgQZDepAmioKKIBfBey1fEr/VeUfxdpYgoKlgQBUEFATsgqFdREeEKXHoJhBYggQTSQ3qyu9mdcs7vj4mb3c3szmxQCXz38/j4hN0zM2fOzDnns2/5vPv27XP9dt26dW0/S/cvQICEBaCOy0jCACAyEhob5RtuQJs20X790O23N32emQmffOK2Bj37LGJZeOcd6NkT5s6llZV/cJ+VCtz19fVKBe7g4GCMsTI4+/ZJ995rnTDBtn27JAjw5pva26r+CP3jx8nWrb5afvcdPniwafeyWOhHH/lbpZs+95ynO69LF7Z3bxV3ahdjmjMsrFgoeeCxRt9UkuPQ0KFed7vISOHhhzXITVgY7tJFu+A3w0hTp3JhYZ7rmKY8mDccP+524JYtjq1bOUJ8PdnW+iL9s5yVlzfLh9bWCvv2XVy1KmfOnMyVK8/s2VNeU9NGFV8JoefP1/3yS/577x3++OPju3cXV1a6vWBhYbosglVV2rzf1RdJKa2u5vfsEWy2ZsYTH88yjC8CVFJCrrmGj4pSScuIjY3VGUWnaPdzHOd0XCp1x0HNcSlJKlKxmud3/afFYjEYDK5hqW0B9fX1M2bM6NSp08yZMwsKClJSUnieX7BgwYgRI7KysgDg3XffnT17dpsKTLxc8CPuIYCrHperfKQqkpJoVRX07o0OH6bLl6MffmjqzNy56PbbqbN0G8vCl1+iW26hFgu8+y6sWAEPP0yfegpiY/+A6S0Igs1mI4SYTCan/AQA7NsHH30kHzrk5hj673/J9u3s9ddr7ChDh5L0dHz6tPav4WXL6KBBKCRE5SmcPct+9JHbVrF7t7R3r2HwYD8245AQ8fHHDR980HzI5Mmev7MVmLApiU8sEAoBgAItoKefeabP/PleSdLQoUxwsK9xyMgQRo/mt271moQ4eDDLMLqYDc+Ljz7KL1niaGxsGqjERC4qqjWkpLKSraz07NLp01JJCbrrLj4oSL23VVW8xeLf5ZKSWNZP2dvz51VeGFEkp07VnjpVCwDx8eaMjPCMjPAOHYL17G1/6v4nimJBgeXkyeqcnBqbzdeddujAAmjz0ZISbaIWHt50HkJwXh5TVCR4RC62b+/L6OBwMHFxOCZG/Sm3Wh5M4WTO1GlCiCJIRggRhKZrYYwVpQzlD58SG56rgcViaWuFI3fv3n3//fcnJCRs2rQpPT3d+fmDDz6YlZV16623/u1vf1u4cOHUqVMvYyfbDgKWsADUcdlJGEIQFCRFREBeHrJaYfHips+rq2HOHLdF6rrrYMSIpr+Vlr16waxZVBBaX/5SlmWlAjfDMEoFbmVl3L5dmjDBOnUqHDyocuY33wRN5XP9Efo1NfSzz1R+4NpszPTpKrv+smWkocG/GT1ihNyxY9MhffqwaWleWZGrR/KMPSc1VbrpJvW7QAhpMlEAGDNGTkvzdgbo08cPkQiTSXjiCYMzQq9371Yua2fOqG9+9fX0k0+ECxfUrXd/gS/SbselpRoDUlpq++23kqVLs+fOPfLf/+YXFxdXV1dbrdaWyYZ/+LwWRbGurq6srCwvL+/kyZOHDh3avz9z3bozWVkXfTMw0OeLtNvZhgaNZpGRDMPIACAIbGYmLSryvC7LQlCQr87wPBcbq06mIyMj/yhTkx7Hpc1mczgcoigqarGuh7d0R9bX14eEhLQRk5IkSbNnzx4xYkTnzp23bNniysAU9OrV61//+tfChQsB4Prrr78MXWx7CJCwALzisjvsjUZAiNx9N7z9NtxxB0pMbPr888+Re3QBrFzptgbZbLB0KSxdSmRZVn506r8XSqkiACZJUnBwcHBwMEJIkiSHw1Ff3/ivfzVmZnrdD4qL6aefatfJjo8nkyfr4mHffCPn5blt2ISg55+X7XaVNbe2ln78sX9bBUL0+ed5AEAI7rnHl3HONTb/TGMOANx5J8TFqZCJAQPYqCjtnRUhOmUKCgtTuZFrr+VDQvzz1gUHC48/bkIIGAalpbXGDCbLcPKk102aUrRxo2PXLp4QtzWTEJqb67eoWGysfwJg5eV+uCysVpGQ2sLCwpycnOPHjx88eDAzM/PkyZPnzp0rLi6urKy0Wq2XokDWknIdOnQoOzs7Pz+/vLxcSU+pq+MkSXvGRUR4VZRwRWWlNmdNSmIopTU1/N69otWqcumUFN5HFYTych5A8EZl/iSVfG+OS4yxsuDYbDabzebbcdl25PIdDseIESNef/3122677aeffjJ50ad+8sknWZZFCI0cOfIv7mHbRMAdGUAz2pQ78vduEErxggXo73+nH34I48YBAFAKzz6Ld+4kzhDy6Gh45RWYM8ft2LffxnfeKbdv32wPQy5QvZwkSYrxwGAwcBynEDLn4QwDS5eyd93l6/f0qlVk3DiclKThbXziCfTdd6CpAEUILF7MLFokO/u7ciXKyfFKUH77TRo+3NC3rx8sJCJCfPBBw/nzJDHRVwxWZ2NnFjESlQHgolRRK9eGMWHPPcdPn+4ZQzZ6tF7+xHHy00/zr75qJ8TtcQwa1JrI5chIxyOPmPbskU0m7WCyligq4hobNcYtK0ssKcF33MHzfBPxqqrirVb/OF9Kyh/ji/SGoCBsNLrxQqU2YkNDg+snBoPBbDbzvDE3lyEECKG//weEUEqb/0kpVdL70tOxwWDxnQKsoLxcV1dTUnT5IktLtYlaSAjJz+cLCjxdkE7ExqqvZoTQggIDxhQh9UFWtAA1O3Dp0OO4VJqJoogxppSyLKu4I1thCbNYLK+88kpWVtaRI0eqqqpeeumll1566VL6P3PmzL17995www3ffPMN9p4Xw7JsRkaG3W6Pj4+/lMtdNQhYwgJQR5shYSCKEsfBnDlo5Uo0dmzT58eOwfLlbuvOk08iZ6CYAqsVXnzR7Q2nlCrr2oULUr9+woABwnXXCcOGCSNHCjfcIIwe3XjzzcK0aSzHGQBAqenrMQjdupEbb/Q1a/RH6M+bp8tqlZVFfvutqeXhw+yGDRob0tKlxGbzb0UeO5bcfLPGaXnEpRhSnP/MsecCQGio8Oijbk66a69lExL8sLKEhoqPPur2izk2lunQoZVh5vHxjv79/fP0OXHqlK63/eJFsnKlWF7edNet8EV26+a3L7KszA9jW9eunLNigQ8oFbfOnavfu7d8//7ygwcvZmZWHDlSefRo5fHjVSdOVGdn15w+XZuTU5ubW5eXV5+fXw/QoIeBEYIVoQdN6PFFNjayFotGs+ho7uhRUlDg9aJBQcjJm11BCOTm8ufPC3FxXkesXbt2l8XZ5+G4NJvNht/TYRwOR3V1dXJy8qhRo7755htKaV5enr/LdWVl5YoVK0RRnDBhAlxyjOC2bdsWLVqEEHrttdd8MDAFGRkZATOYEwESFoA62kiQAQBgDJIk9+gBw4cjhwOcoT+vvoqcyooAwDCwZo1nn3/4AW3bpnIj7dvDqFFw/jw9e5aeOUNPnqTHjpGjR9HRo/inn9j1631ZiF97TWMTVSL0Ne9r2DC9GvrLllGLBdXUsDNnarOTigr66af+yYadOMFs2MBqLuJdDM0eyRxHrvJHv35S377NHtgbb/SbuKemCuPGNXd4yJBWsigAIIT56ivbjh1GQvzrht2O9XsVZRnWr3ccPmyQZdQqX6R/ZrCyMv/8Fe3b+2E2Ky3VO80TEliMddHr6mpWlrXHPzqaYZhL9UWaTLhXLz48HFksvu66QweVgpKShE6eZEtLxYgIzPPqPUEIxcTEaHbyz4aigsFxnKJPFhQUxPP8Cy+8kJycvGfPnn379qWmprZr127cuHGvvPLKli1b9JyzQ4cONTU127Ztmz9//iV2r7a29sEHHwSAG264YcCAAZrtGxsbAwFhTgRIWADNaIPuSAUYU0mijz4KISHovvuoEmxQVwcvvui2sPbvD05TmRMvvIBVvX4vvoiVH5aUUhcdagQACxZAba3XzSkkhMyerSHipTNC//XXde2vVVX0yy/5Z58VKdW1Zf78s3TihHZomgJBYN56y37woLh9u0ZNSdfYfCcJA6APP4zMZgCAzp3ZTp38JiUAMGKEdO21LABwHLr22tarLVy4wEoS7N3r+PVXo5fiN+rIy2P9FW/as0fIzORiYvxjSB07skr8uH745YsMDsZGo16SRykqKtL7vDp00EuOdfoiVdXtPUAp9eaLRAhSU/n+/WlYmKO4WIPMtTS5iSKTlYWrqiQA6NhR/SFSSg2Gdhyndyr9BVAC8xFCYWFh//znPz/55JNHH3108uTJP/300+OPPy7L8rvvvjtt2jR/z3mJvZo1a1ZRURFCaNasWTrb33bbbZd40asGARIWgBtcy0e2HRIGAJTKggAffIB++w0vXUqVUNQ1a9DOnW7NPvzQk6acOwdLl6q85yYTLFnSXAbElYDW1sJbb/naISZPRnFxvnqrM0I/IYFMnKi9i8fGovHjpR49/DARLV6M7Poioz75hCok9bPPpOJiX33uaOjIoybHaI1Ue1GsPCuU/mDLl9NsAAAgAElEQVQ58I5lQ/n0ZSceWTT6pla+MAjRv/8dRUfjfv04o7HVUpZo69YmApeVJWzcaJAkvWaeEydaE6jepQvceKN4002GoCC9C6m/vsjGRqa83A9em57O6Z+2NTWcIOht7Fvm1AlZxmVlulqGh2s/6MZG3mpV4azR0dzAgUxyssAw1GLh7XZfp4qN9SS+jY1sZiZVvJwsCyEhKh2mlJ4/b0hK+lNC8v9AWCyWmJiYW2655eWXX960aVNVVdX27dv/yg4IgrBmzRoA6Nev3whnmrpPDBgwICgo6E/u1xWDAAkLQB0KCWs7PAxjoFQKCoKXX0YffYR+/BEiIwEApk/Hrple4eHwxhuexy5ahAoK3D5Rbu3mm1G3buqO1zVr4Ngxr/sly6IlSzQ2+FWrSGGh9vx68knkO/k9KgotWkTj48k//oEVg5MelJaSNWu01d4LCritW5t2IFGkS5diUfR6XxhwBJdQDnAOIAvg9otvTShdMKvqyy8bdmTDuTtMQ67JaL0Ri2Hkf/yDGzCglSKrAFBfz5WXN++1p08L33zDCYL2I6ipYfSEfnsgKAiFhgoAkJws3H039O5t1KPRHx395/oiExL8mLD6fZHt27MY6zLg1dRwenzBMTGsPl+kZw8NBtyzp6F7d8FkaupPebnGXSQluT2YhgYuM1N28rZOnVSyJimlFy4YGMYcHa17yv35UC0c6VGz6K/3n27durW2thYABg8e/Fde96pBgIQFoI62ExPmBMeBKJJJkyA4GO3bB7/8Au3aQXY2LF3q1tWpU1FCgtuBjY0wa1bzq+6S7YhXr/bmiYA5c7APF1WPHvimm/6ACH2DwVeEfkQEevddmpgoAwDPy/Pm+RHs9d134pkzvixbhOAFC9xoU0GBtHatG3Urlqp/tWa9W7Px0YsfjCietV44fwagGKAewE6b+UQEDh7QMKim5pIcNxjD/v2tz9c+cMDzaeXni2vXMjabxiqXk9OaKLQBAzjn5s1xtG9fx513cvHxvkYgNZXz1xeZl+dXlgNWDT9XBSGosPCP90WWlupigTrruHtotHbowA8cSCMjHc7ViRANBTWMITi4+UWtruYOHxZd5TNiY1UmeUGBIT9fGDQooeVXlx2+Sdhfj5MnTyp/ZGRkXMZuXLkIkLAA3ODqjoQ2IBXmAZOJVFXRJUvQvHnIbIZff4WkJJg/HxUXN7dhGPjqK08GuWkT2rwZXG17yg0mJ8Mjj6jPgqNH6bp1vjjBq68yvpmqzgj94cNJ584qfQgNRYsWQXJy87bdo4c0YoRemkIpWrwYCd53qB9/xC2rPG3eLBw50kz1coWSl6rXrKzfuqfxdK1s9Xaqv5lHrvlE/ugj7HC0nrvv2gX79wtbtmiEpqlCkth9+1TscCUl0ldf4YYGr1s+IXDiRGvi2Dp18qRT4eHSLbeII0cajUb1N0pnHoYTNhtTWemH5Sw9XVdepDIFqqt1SXkBAEI0NFRXNyQJ63SehoZqk0urlWtsbGJI4eHMwIFsx44Cw7j1ubpaI5gvObmZK5eV8ceOCa5LWkwM21KorLCQz8sT4uODUlL+CmUK/WibOmGFhYXKH926dbuM3bhyESBhAaijbZIwAAgKkoOD4bnn0J13QufOsGULxMXBjBlue3+vXuAsN/k76IwZoFS28YgAmzEDeZEVhDffhOpqXxH6c+dqUCKdEfpvvOF5nuBgeOcd6NjRc4eYPh3r1+4uKCDr16s7JWtquC++UPcefvQRqalp6s/15u5fxT3bkfMVGROCTaE7h1GKiovlL780+JuZqEAU2S1b7ACwbZtj926/eVhurtelrLJS/uIL5LwjD5SWcr4T61SRkMCoxr8jhFJTHffcg665RsVmGRPjny+ypMQ/E11cnB83UlKi9zElJfE6fZHV1ayeBaNdO13ZCRUVCAA4DnXvzvfsKZnNKryttFTjluPimtaxCxf406c9VcRSUjxfm6Ii/uxZAQAGDmxzZjA97si/HtHR0QCAENJjCausrLy9xdL8fxwBEhaAOtosCeN5wFh67DHAGM2eDcnJsGUL5OSg335za/b++66LFQWAggK8eLFKXTajET78UH23q62FN9/0RbPuvhvi433ZfnRG6Ldv7xahbzbDwoUoLU1l1zEa5blz/XBKrl8v5eV53gKl6I03vBICi4V8+GGzgaEj1+7zuGlDTF5X2DsMQ3/7sWkAMzPFX3/VjkVriUOHkPNd++knx5Ejfp0EO0PyVVFXJ3/2Gb14UYW9njrlz3V+h281MoOBDB4sTJjAR0c3j3znznrDqpy4cMGP9uHhjMGgl+RJEhQX622cnKx3m9Dpi9SXFwmlpVJyMj9oEIqJETBWmWUOB6OkN3qD0YgMBkERA8vP9zTR8TzyKGRUUmJQNEfCww3p6VHad9IGYLFYQkNDL2MHnMH4LQtktcSiRYuefPLJP7lHVxgCJCwAN7Rxd6SC8HAoLibLlqGFC9HBg9CuHWzeDGvWuLneQkPh3XcBgLo4aNCSJUx+vsoJR4+Gnj3VudTXX9OsLK97BsZ02TINY5i/EfpGI7z1FkpP97q79OsnDxig10Yiy7B4MfaoUrNvH5uX52vFPHlS/PnnZnNUGDYvi310SugNqIUcuQFxps3Xu36ycaN0/Lh/1ZMIYTZudEvmXL9eyM7WyzUrK7m6Og2LSGMj+eILqajIrWOCgM6c8TuZACEaH6/NYKKixNtvl4cONXIcAoD0dP/MWlarf77Irl21ld6cqKnh9Eh5AQDG+n2RTEWFrpZ6fJGSxPfogVNTBZb1+mQ1yxl17MgRgk6eZEtKVDqWmsq5huSXlfHOl2HAgHhV2nd54c0S1uoC3ps2bdqwYcPGjRsB4OTJkxs2bNiwYUNjo2cNDN8YNGgQz/OU0v379/tuWVtbe/To0TFjxrSut1crAiQsAHW0ZRIGAGlpxGCgU6fCLbeAwwEREfDuuygz063NffdBYmIzAwMAhwNmzFB55xGCFSvU54ISoe/jN156Ohk79o+J0H/5ZZ7n4Y03UPfuvncpOnOms7qJNs6dI99/32xYamxkFy/Wlq9Yv17My2umLBjQMxG3vxXzoAm58Zix3OBj//W0Wq1eTcrL/QjSP3GCaanE/tVXUn6+Lh62c6euvV8Q6Jo10rlzzefMz+daUUTxmmt4nfH1GEN6uuOee5i0ND4q6s/1RbZr55cvEryV9/FAcjKP0B/pi4yP17AIiiKXn88JAgQH+3o2lFJNebCQEDh6FHuzlkVHN3fj4kX+1Kmm33BGI3vttbG+z3wZ4Wbhp7ShoaHVVZWeeOKJiRMnPvzwwwih9evXT5w4cdKkSRUVFX6dxGAwXHfddQDgm4RRSmfOnPn888+3rqtXMQIkLAB1tHEShjG0by8/8ggymdCYMUAphITAkCHYXUMfrVmj6K82L1tbt6KfflLZfhIT4Ykn1KfD8eN0zRpf5q558/AfFKFP587FvXpp8wKzWZ492w+n5JdfSkVFTR1YtkyP3wBkmS5dCna725iMMff+Iu6ZRLbJU8MANvyoUn5EEOgHH4BmWqICSvF336mQQkLop59KxcUaRjVB4HwU3vaALNNvvhGys5tY48mT/vkHFfTo4V97s1nu3h3278cWix+P7Px5P+hhRIQfeZGyjPT7Ij30HXygpEQXC/RxQoeDP32a27lTLCoSPRyFLWGz8TabryuGhzPHjkn19eqPOD6+OS6tosJw8mTz6PXuHcvzrS/b8OdBdTW+FEtYfn6+a4VK5Y/k5GR/z7Ny5cqkpKQVK1acOXNGtYHD4XjkkUdGjRo1bNiw1nX1KkaAhAXgBg9bd5slYQAQHQ1VVdJrr6E9e9CrrzZ9aDajBQskp6ulRw88caLngbNmYdVg+f/3/5A3BcG33oKqKq88KziYvvzypUboOxzouefIpk16p+SQIXLPnnobiyIsXszIMj1zhtu/X+/uXl4uf/KJJwfqwiesjX/uOlM6AAxFfQoPKr/CPd+Tigr5k084PTL0eXmsxaL+mgkCXbWKVFT4MqqdOOGf24gQ+OEHR2amsb6eKSz0W5bMaEQREX5nU+blwdmz4rffOn79lamuNmjOKouFqa72g4R17eqHtEdlJaszeQJjqipk2hKiqMsXiRANCVG5L6vVkJXF7t4tKH7Djh15zTRP3/Jg6el8eDjjQ8Q1Kanp8MpK/sSJZpc0w6D+/dtoYWlv7sjLGxMGAB07dty2bZvRaLzxxhsLPCQZAUpLSx999NGnn3767rvvvizda+MIkLAA1KGkELZlEgYA118PJhMZOxZefRW2bgUACA9HXbqg669H1dVNbd55B3l47oqKYNEilTffYICPPlL/BVxfr5LA6Iq77rqkCP3iYua22+Tdu8mOHfL27ToDquicOZx+NbdTp+R//9v0xhv+hUDt2iXu3u3pbQzD5vdjHploHsJu9CWQffKktHGjRp4jpWjDBl+cprGRrFwJtbXe5Nzwb7/pqwzgji1bHAUFvH6XrhP9+7MttT19Q5LAWWKyrEz+8UfH99/jkhIDpV6XX/99kX5ptOptmZKi1xdZVaWLBbZvz2HcTIwoRXV1hgMH8P79DlfSGRurcTuEINUwLwDgeTRwIBcTI/iw9plMyGQSAaC6mj9+3O316949JjjYv4jGvxIeDEySJJvNdtlJGAB06tRp27ZtQ4YMGT169NixYxcsWPD9998///zzkydPfu211xYtWtTDXwPy/xkESFgAXtH2SRgAjBtH/ud/wGhEEydCbi4AwN13c2Fh0LEj/PILAEBwMCxd6nnU0qUoN9fzQwAYORL69FHnNRs20MxMXxH6H3zQmgh9SuGXX/CECUJtbdNQv/MOravTNTFDQqTnn9fr4eJ56N1b7tnTbzXU1aulixc9j2KBuatiojWrve9jf/1VyMz0ledYUsJVVmps8/X18qpVyGpVGfySEs5ub80ryvOQkWGfOpWPifGP7qSl+X25sjLeozpQfT3ZutXx9df03DmDLKs8kfx8P8xg0dEsx+n3ReLiYgEA9NB3f3yRurhaYmLTaFPKXLzI79kDmZkOD4mQ0FCG5zWMarW16iJnsbHswIHIZBJra3lR9PqkOnXiEIKaGv7YMbdxQwgGDmyjZjBQ80tYLBaMscmbxM5fi9TU1DVr1uTk5Lz33ntRUVE2m+2ZZ55Zu3bt+++/HxERcbl713YRIGEBuKHN1vD2BoaRr722/umnqcUCd94JdXUAAF99hQFg4kSYOhUEASZNQmlpbkcJgtcI/eXLWxmh37UrufVWXzt6ywh9UUQvvQRz5rhtOTU1dPFivVHtI0fKXbvqmsWPPcampkpPPolDQ/3z39ntdOlSxiOAnVL08ce6Nv7PP5cKC73ezrff6jpJZaX8ySeMR4AaAGzb1soyR8OG8RgTg0GcNAl69dI72rGx2GTy2xeZm6vuFHM46O7djjVrpOPHDQ5Hcx8sFra21g8S1qWLHzyyqkpXWSEAYBgIDtZ1s6LI+paKUKCI18syU1Ji+O9/5RMnBIdDpScdO2rfjqoWRkYGn5EhMQwBgKIiH45wGhkp19ZyR48KHstbp07hbapOUUt4WMIUpVasp2DWX4iuXbs+8sgj9957b5zvCrsBAECAhAXgA22chFFKbTZbXV1dZKQ4aZLUsyfKyYGHHgJZhvBwWL0aAcDXX0N6Opw9C19+6ck8tm9H33+vQkcSEuCf/1SfF9nZ9PPPfVmSXn7Zjwj9ykp8zz3yL7+o0Lpff5X37NHlE0GIzpunzSFuvJG96SYHAHCc/Morfut4nTsnffut26/to0dZp+muRZfc/imK9KOPoL5eZWetrOQLCvSGxhcXS198wQlC89mtVv78+VaSsO7dmzZpjMnw4dL48UY9Tsb+/f22I9rtTEGBLypDCBw54li3Tty7l7NaeQAoLvZvWY6N9SO9oLhY74zu2JFDSFesvaZUBABgjBITDefPM//9r3T6tMPHj5mICI3bEUWmosJtSA0GNGgQ366doLx7NhtTU+OVFCYn8xYLzsoSW65tbbNOkRMtV2OFhLXBEnMB6EeAhAXgFW2ZhAmCUFdXZ7fbTSZTWFhYRgaePZsCoE2bYO5cAIC770ZDhgAAVFZC377wyy8tNfThxRexxaJy8mefRd40qN95p0nIWxVBQWTePF0R+rt341tvFX2olr/1FrVYdE3P8HBp2jRfjK1DB/zoo82hYO3biw8+6DcP++GHZuEuQphVq/yIxKqpIStXsi3FIH780T8KlZcnrlvHO/fvw4f9VrpX0LkzazS67eIpKY4pU4yaNsLERL8534ULjM45lJsrfvONsHUrw/MoMlIv24uOZllWv0arRqVFV7Rvr3dr95EXybIoMZHv04e//nrEsjQ/X6TU12k1BSygifM1nyQ2lh0wALs+0PJyX6TQZGJUGVgbrFPkAUrpHygSFkAbQYCEBeCGtu+OJIQ0NDQowRBhYWEmk0np8+jR5KGHqCyjhQth7VoAgDVrmu1Ss2fD6dPo0UfB7OJtKCmBt95SmQI8DytWqK/jDQ2wYIGvDXLCBI3dKy4OHTjAPvOMyjbgispKunSpXjfZzTeTTp3UL2oywQsvEKM76br1VtmvfDoFS5cSpQjjrl3Y7mc0/Nmz0oYNbp1oaOBOnvRbpOvUKfHf/zZSCoQwu3b5rbOqYOhQlYceFCQ88ADTtavXMU9PZ1nWb0mLnBz/7jEoCKWkNN5wg3TbbezAgcbERN63r8kvDdiqKl35qgDAsqApEqHA4WCqqz1bGo24UyfDgAH89ddDRoYQFSUgRPToYuiJQisubn4Kv7sgmz+RZVRU5JVoxsVxubkO1UFog3WKWkKVhAUsYVc0AiQsAK9oaySMUmq322trayVJCgoKCgkJcVUsNZlg+nQSHU1lGR5/HDIzITISVqxoXp5On4awMDhyhE6bBk5D10cfIdXCNcOGwcCB6kvbt9/Sgwdbo6HfuzdesQKtXi2NHCmPHau9d/74o3zokC4ehhB97TV1Y9i0aWxioid1QIjOnMnqr0GpoK6OrFjBCQKzZk1rEhJ37nRLtNy6tZV2rMxMYdMmU34+q0ftrCWCg1FsrDp7w1i+6SbpppvUcx30C4I4UVurK1jKFc4i30ajlJxsHzxYuOMOPGKEoUsXg2pdcFfFUU24R0r52rn1+yKrqppf0ZAQtksXw3XXccOGkdRUR1iY4HTy1tbymvXCWdazjlBL2GycxSJDkwuSc7ogXfvj7cXIyOBNJqRaKuCKqFOk6o5sC6mRAVwKAiQsAK9oUyRMkqT6+nqbzWYwGMLCwgwGQ8vff6mpsHAhxRjsdpg0CUpLYdIkNGBAc4OFC1FZGcyZQ7Oy6AsvQEQESBK88IIKH0IIPvzQ6+yYMwf5UFrv0sUzQr9XL7x8Ofr0U2ngwKb9YfZsxmDQ/v36xhtIp+RpVJT06KOerGr8eHbIEHXCYTZLs2b57ZQ8fFjctct1k1NeD70/xNetE8+d4wHAbmf37PE7wt2J/fsFhqH+SkUouP56zseBCEF6uvDAA0aj0e2mOA4iI/02vOXl+bfA8jwKCxM8iiEwDImNdfTs6Rg3jtx0E9ezp9HprGzXzg9fpCji8nIRAPTMaX98kVJkJHvNNYZhw9hBg6SUFIcqkdJTVjIlxdejUXDxIgKAdu3YgQORag31oiJVCkZ79eJjYiRVZTiE6LBh7dtgnaKWaBmYH3BHXukIkLAA3NAG3ZGEEKvVWl9fDwChoaFBQUE+soEmTqQvvgiUQkkJTJ4MDgesX9/cWBThqaewJEFEBPzrXzQri770Es3JgQ0bVNbfdu1g+nT1C50+DStX+uqzM0K/Rw/84Yfos8+kQYPc9gaDgbz7rraVq6yMLF+u1yk5YQJNTGy+kfR05n/+xxdv6NZNuvlm/6xhw4Zxo0bZb73VD+V3V8gyrFhBqquZXbtad4ImDB7Mp6Q4nnjCxPlRG6kJXbpom47Cwx1TprCudab79+cw9m8uENIsD6YTfftyLIsxVikzDwAIQViY2KWLXXFWDhpk7NKFpZTVSYL1+yI5zrdFChHC2u2GmhpjVZXx2mtJ375SQoLDaPT6u0QU8cWL2mQxLk5jhClFxcViRgaXni656o05YbVyLSXyMYYBAwzh4UJ5OdtStwIhOnhw8DXXxGh277JDVaIixFv4agBXCJi5ShhzAAH8DvL7Ui1JkiiKl1GEhlIqCILFYpEkyWw2BwUFaVZMRAh69aIlJejYMSgthfp6GD+eduiAfvyxqUF5OXAcKDH7BgMMGgRTp8L586hDB8q28CL2749WraIONSaTlYXuvJN6+xXK8zQ+nrn7bvrMM3JSkvrWkpBAz55F589rbDynTtE+fZi4OO39EyE6cCD33XcyAISEoFdfJaGhGie/9lq0cye1WnXRi+Bg/OyzssFAu3aFvDxUXi670AXqSgV+jyBWIQeCAHV1XG6u3c8ywc3AGE2aJBsMxGiUe/UyHD0q66//2KsX16WLLmKEMenaFXi+KX9z1CjM8/65P0tL+TNn/AjkNxph2DCsvOAIIeKTMbEsCQuTDhygWVnS2bOospK1WjlR5ChlEcIIoZaU8fRprChyUUoppT6mUpcufGSkc0yRLLMOB1dXx5WV4fPnUXY2PXuWXLggFxdLiYmM7wqPCioq+IoKjdELCsLJybLv6CabjWvfngkPF701KyhgPUgYz6MBAzijUSQEsrNpS5do//7G7t3jrwgqIwgCy7KuD2779u2SJI0bN+4y9iqAS0SAhAXgCWdlQVmWRVE0Go2XJfBTlmWLxeJwODiOCw0N5ThOZzd4Hvr1o/v3o+JiyMyEMWNgzBjYskUpWgwAsH8/uvVWGvP7T1+Og/R0yM9H48YRiwWZTBAaCoqJhWGgd2+8fr0KRxEEuHgR3Xyz21dWK87ORmvXounT6Y4d8I9/ELPZF78ZOpT58kvtSo4nTuBbbyUtOWJLBAcThNijR8msWUxamh7pJjpoEPfjj7pYzJQpbFqaCAAI0b598a5dsiAgf0kYANx1Fxo7Fu/fT1oW7daDfv34nj2biBTPy71789nZVFVxqiVuv50xmfRyKYQgPl7u0MF48aLcq5fk7yTIysI1NXqvxXEwYgQbHk5/v7S2EbqhgcvOFgGAELBaSWWlXFws5edLubnymTMkNxdVV3M2GyeKHABLKS4rg+BgJjiYCQpCwcE4PJwLCWHCwtjQUCYsjAkPZ8LCcHg4GxHBRERgq5UpLWXOnoXsbHLuHLlwQS4tlaqrZau1mRxiDBkZupzCp06Bh1xtS3TtygUH+xqu2lo+OBg4zutLI4ooO9vtDEFBqH9/huMkAKiu5lsW/O7Xj4+JwampqW0/tp1SKoqiBwnbvHmz0Wi86aabLmPHArhEtAl/UwBtCoIgKG+Fw+GwWq0RERF/8QqlBOA3NjZijM1mM+9vADkAAGRnowkTUHEx9OoFO3bQ2lrUsWOzaaF3b9i6lXjYAubNI0uWNE2H9u1h8mQ0ahR07gwPPkgOHPCcJsoQrV0LPXrQnBzYvh02bCAVFW7NRo/G772nYXfZvRtPm6Ztm7n3XuaJJ3TFJBGCv/2W3H67H46wnTvZxYs1Yu27deNmzGh0fRMqK+msWRKlWLGsuFAuSgjBGKuSsK5d2X/8ww4AFgs/f769FTxs2jQcE+N2mCiyq1fLmraWqCj88MN+cykAuHDBGBrqCA31I8Te4UDr1sk63X8MA4MG4ZQUxmBwLelDJZ8mvlOnDKdO6c2QGDjQkJDQ9P4o1Zo5L65cWUbbtqlYjFqiY0dej1nRZmP37dMeuhEjGNckR1c4HMy5c1gQSM+eko+1qKyMP326uT+RkUz37lTxWlJKjxxh6+rczt+3ryE01JGamhobG6vZvcsOSqnVajUYDK4P7tlnn01OTn7xxRcvY8cCuEQEYsIC8IRzmVP++ItpuiiKdXV1jY2NRqMxLCysdQwMALp2pR9/DGYzZGXBhx9CdDR9//3m5fvIEXjvPc/V/IUXsFPhubgYFi6kt95Ku3alHIfy8qjqf337opEjyaRJ8gcfyB4MDAC2biWbN2tELQ0ZQocM0c6UXLtWPnVKVwBURQXOz/ePaAwdKvmWIeU4NGWK5/4XHi4/95xXV7W3t2b8+KYvgoOF6dON/mp9X3MN78HAAIDjpIcfxklJGqbCkSPZVjAwSunevY516+QjR4yiqPf4wkK9AVgYQ79+ODwc8bzbkCnFW70dRQjKy/ODZ7dr55JM4XNGV1WpVwRqicREXZe+eFH7GcfEsKoMjBAoKjLs2yeXl4sdOzI+BoRSWlDQTPUSEtgePYgzbqyhgfdgYH36GEJDHUajMSbmCogGc0JVMf9ydSaAPwQBEhaAV/zFJIwQYrFYGhoaMMahoaFms/lSLHAMA337kiVLgFJ45RVUWAgPPgi9ezc3eOMNdPq02yEcB+vWqfChnTvh668ZloWW/xmNdNUqXxRq3jyoqvJ9F/S11zRD3YBSeP115KoXr4qqKvahhxybN8vbt/sRO48QTJvGBAV5bXDHHVxcnIoxo3NncdIkP1Is+/ThkpOb7XmRkcK0af5laA4bpm4sYRj5vvugSxevPBVj6NChNe7P8nK+qkqSZbp/v33DBlxcrGtgz5zRZTZDCHr3xtHRyGhELf16Pl6Lqire4dCr8ZGYyDKMXjNeUZGu+W4wILNZezwpRUVF2s1SUlR2oro6/uBBrMh6MQyEhvo6T0MDb7M1DUinTnxamuQ6ngUFbo179TKEhTkAICkpqe07IhWorsNWqzVAwq50BEhYAF7xl5Ewxf9YV1cniqIiAMbqCYDSgtkMY8eSGTOgoQGmT0cAsHFj84LrcMBTT3nWgszIgMceU1mUX30VSkrUF+uePeGuu7xuljU1dP58DQtWUBCZP1/b4Hf+PPnsM1/NamvZhx4SFKfbPV8AACAASURBVKL2/vuktNQPGU+el19+Wd2slZDA3HKL1yj6G26QBgzQZaLDGMaN86QCcXHCY4/pzfzo0IFLSvJq/sGY3H038VYIctAg3pu3yzdOnmz+u65O3rjR8dtvBtVq4k7U1zMVFbpIT48eOC4OAYDJpJoO6dUYduGCH7MyLU3vm+BwMBcv6jKwpaXxvwuU+EJdHacZDYYxeET3CwJz6hR3+LDgyqt8B585Iz67deOTk93Ewxob2YqKZgLXs6chIsIBAGazOSqqrWuDOaGswy3FWgMk7EpHgIQF4Im/2B0pSVJDQ4PNZuM4zpsAWKsRFgb/+AcdPx5++AH+/W8IC3NTozh0CJYu9bzWiy/i6GjP89TXw/PPe50s8+bh4GCvfd60ifznP5qeMtq/v/ZO+fnncm6u+qkaGtiHHhJstqZ/2mz0zTdZvyKuUlLEe+9VMfNMnQo+lSDolCkoPl57JbnuOi42VqVDnTo57r9flz1sxAiNVxEheuutZPBglbvo3bs12rA2G87J8Yy7yslxrF1LsrON3hyO+fm6SE9GBk5KanptOE791lSNYaLI+BCFb3EGFBam9z3wXfDHFbGxusazrEy7TXIy5/QbEgLFxYa9e+WyMrc+t2vn63KCgMvKBADapw8fG+s5Mq61OHv0MDj13q4gM5gTHh2ur68PkLArHQESFoBX/NkkTKnAXV9fTwgJCQkJDg72IQDWagQH0/ffh27d4IEHUF0djBkDf/tb87evv45yc93acxx8/bVKN7Ztg6+/Vt+iDAb68ce+aNa8eai2VsMp+cYbjOaOQAi8/jpuGZlkszEPPyw0NLh9eOaM/OWX/jn77riDpKa63eOoUdrB1xjLzz+vEbzHcWjsWK/n6dFDuOsuja7GxbGdO+uJQ6ejRomjR7udLTGRCQ5ujTZsTg6jyrREkf73v/bvvuMqKjxvmxDIydG+VmoqclaaCgrC3qxKqsawsjKWEL2zsksXXqf2vUdYlQ9ERjI+shSdkCSFG2kgIaHpBhsa+EOHmJwcz7JCUVFNGY7eUFHBMgwaNMgQGup5OVfCes01hujoJgYWFBQUGRmp2be2g5brsBKqHxbWputdBqCJAAkLwCv+VBLmUYHbW67WpYNhICSErF8PRiPceitQCh98gK65pulbux2eegp7LPrdu6MpU1QI0csvQ3m5OlHq35/edptXK0JFBX3zTY0bDAkhr76q7ZQ8e5asWePWzG5nHn5YrK5Wabx+vZSV5UdmA8Z09mzOaXwJC8MTJ2qnZCKEzGZxxgwn71EZolGj2PBwX97AQYPEsWN9hVuNGIH0a5oPHCjcfnuzl3PECD88s04QAseO+aIaFRXiN98Iu3YZ7Hbs8iFntWqQnuRk5CxPBABGn/yzpTEsL88Pv2pyst75a7HwSkUgTXTqpCtaoLpaOzvBZEJGoyAI+PRp7tAhwWpV6UDHjr4eHyFQXi4PHOhZlF1BaWlTAfVu3XjXclVJSUk67qDNIaCYf/UhQMIC8MSf7Y6UZVmpwM0wjGsF7j8PPA+JiXTDBjh8GK1dSxGCrVtRp05N3+7fDx9+6NmBV17BLX8n19XBjBlep8yCBVg1skfB99+TnTs1tq4bb6Q9e2rThdWrZafDSxCYxx6TKirUW1IKb71Fa2v9mObBwdKMGU305f77meBg/dHf4pQp6izKbEajR2tYRCilo0ZJQ4eqU8awMHzNNf4VrOzRwzF5sgkAeB4SElpjBisq4hoatEnJiROOdevg7FmDMlPOndNon5CAundvfiIMgxjG1yB7GMOsVq6qSq97MTgYm0x67720VNc0RIhGROgymHmpIOSGlBS+pITft4+UlqrfFM+j4GBf9ysIfK9ewPMqXZJlVFAgAkBGhqFdu+ZxCAkJiYiI0Oxbm4LqOmyxWAK1I690BEhYAL7wx1YuopQ2NjbW1dVJkhQcHBwcHKydFvgHgefpkCHw7rvwz39im42azbBxIyQkNH07bx7Ky/NoD2vXqsyOX3+F775T77PRSFes8EWzXnoJNTT42ucQom+/rT0gkgQLFjCyjESReeopyXc6W00Nffddzq9n2LOnOHo0f+213KBB/vGewYOl669XMfiNGcPqk0ilt99OevdWOcOIEWwr3pTUVMdDDxlHjOBUS9xo4vhxvUc1NpKtWx0//shXVHC+lSNiYlDPntj1R0dQkDb1cZ0mRUV+LNoZGZzOHziyDKp1FVsiKYnHWPtp2u1sba0vrsayqFs3Q3k5OXNG8KFX3KmTr4KSFgvH89Rbfy5e5ESRdu3Kx8W5GXSvRDNYy8B8SmmAhF0FCJCwADzxJ5WPFEWxvr6+sbHRYDCEh4fzPP8XR8UiRP73f2H8eHj4YSSKNCEBNmwAxdzV2AhPPYU9brRXL3T//So9nDMHKirUez54ML3lFq9kobycvv22hlMyPJy89JK2AzE7m6xfzz/zjJyXp/10DhyQf/jBv2qPU6bA5MmtySW8917o1MltBMLC8NCh+skcmTyZdu3qNkomE+7d2+/i2QoSEgSEGP36Xk40NLDnz/tnPysuFmtr0R138Glp6k85IgL69vWMezQYtJ+g0xhGCOTl+ZFt4SoP5oTqjK6p4UVRF+l0JhP4xsWLXieCyYR69jQMG4ZCQ6XaWo3b8ZYBQAgtKuKLiijG6mcgBC5ckNLSeA87aGho6BUaR+WxYFqtVkJIwB15pSNAwgLwhT+EhCkVuBsaGuD3CtyXJSkJISCEfPABFBWhI0dAliE9HTZsACW7aM8eWLHCs1evv45bLtc1NTBzpteJ88Yb2GDwencbNpC9ezWckrfeSrt105iYgwcz48ZJiYl65++qVXJenh+qHzt34g0b1GPSVeF8oBiTZ59lXMuNjhvH6OEZLqciDz0ErsWzhw7leL41piwAKC1l//OfxrVr2bo6/0RPTp1qzSuakiKZzY7Bg8XJk/nevQ2ufCs0FPr397T88ryKPJgqlCNravjGRr3kOC6O9R3P7oriYl3d0HQOKqAUVOXBwsOZ/v0NgwdDVJQDIVJWpmHejItTVzgTRXzyJJ+bK3To4HUK1NTwCQlsYqInk74SzWDgpXp3UFDQH6LmE8BlRICEBeALl0jCKKUOh6Ours7hcJjN5tDQ0Mu7ZCAElJJ//xtefLHJrtC3L/z7302R0S+/jPLz3dobDOpOyU2b4Pvv1Tdps5l++KGve5wzB9lsGk7Jd97xdYapU9m33hLDwuBf/2IiI3VxBVGEBQuwzprZJSXcsmWOzEzp229bU7vdYJBnzWoyvLVrxwwY4Helbozlxx5jYmIYAGBZ1L9/K81gALB/PwWA8nLps89Ifr7eXFFJQidO+B1G1r07x7JNjIHnhR49HPfeyw4dauB5ZDbDgAFMy+QTPb5IBYoxzEN01De6dNHrwf1d4kEbnTrpkgdraODtdjfeHBfHDh7M9+kjh4Q4lDMQgjWFNlRFXOvruQMHaGWlGBHBqAbjQ1ORcpSc7EkEw8PDr1D/3e/1WJthsViCg4OvOJWNADwQIGEBeOKPckcqAfhWq5XjuPDw8MtVCNwDHAfBwfTtt2HJEgAASYL+/WHHDuB5sFrhn//0dEr26YMmT1bp9osv4pZ1ihQMH05HjfI6s0pK6DvvaDglo6LICy+otAkNRYsWMVOmiEqeIMeR997T62csKiLLl2uzEEFgZs9u8h5+95148KB/IhcKYmOlp54yAMDtt6PWRf2xrPT002xoKB44kA8Kao1jFABqalhnMUGHg37zjWP3bqNmuXQAuHCBa2z02/bWo4fnJwwjderkmDQJ3347r5q0wbJ+2QiZggK91BBjiIjQ67isqGB1zvK4OP/kwRCiHTrww4Zx3bpJHikCVVWc72dhNCKPQwiBwkI+M1NUBGA95FRcgZAxOtrhwRcJgcTEK9IMpopAauTVgQAJC8AXWkfCFAGwurq6P1UArNUwGGi3bqh/f1RZCZSCLENGBpw8CcnJsHMnrFzptlkiBG++iVsKItbUwKxZXjnlu+8yHOf127VrycGDGhbBO++Ezp3dBq1rV2bVKjpwoJt3JjFRfvppvSIUmzdLO3f6Im2Uorfekuvrmz9ZtkwuLGxN+c6ePaX/+R/Dtdf6F9rvCp4Xn32W69+/NYmNCg4e9HwEe/c6vv2Wt1o13sZjx/yo1a0gKgqHhKh0tbYWamupySRHR0NkJHZ9K8xmrNMXqaCiApeV6W3fuTPvIx3B4+dQQYEumhsaig0GbWIny6ikRGAYSE/nhw9nO3USVEXFNC+amuqWVSCKzPHj7NmzTYNsMiFv2m8XL/JyC35HCGAcHxJypbKWlpYwhYS1hV+2AVwK2tDWGEAbRCtImFKB2263KxW4/zwBsEuBJJFhw1BZGQIAWQZCoF07OHAAJkygc+eiCxfcGhuN8NVXKjPl55+ZTZvUZ1BQEF261CvNolSXU/K995rPcNtt7AcfiAkJKs9i/Hjaq5feibx4sa9AnF9+YY4ccdu9BIG+/TbU17fGnFVaSktLW1l/XYHNhn76iXE4WrNMWSw4K0uFAl64IH7+OSop8dqxqiq2pMTvKpMDBnAt/XQXL6JFi/j165XRoyxLIiJoVFRT1KDJH2ev1Upff10+etSz0JY3pKToPzNbV6eLdKam6prLDQ189+6G4cNxQoLgrVSU1crV1/u6KEI0Orr52Lo6/sABUl3dfEiXLnxL+kEpLSw02GzgEUlGCJw7Z+rZs72e/rdZtHRHXqGu1QBcESBhAXii1e5I1wrcYWFhl1iB+0+F0QjV1aRPH9ixAwBAkgAAzGa4/3544w06e7bnvBgwAN1zj8q9zJjhVQr/hhvosGFe51dBAV2yRGNLi40l06ZxHAczZzIzZohGo/qFEILXXuO8fesBq5W+/bZ6OaPCQm7lSpXoq8pKsmQJJ/lpGyopYTdvFt5/HyoqWs3C0ddfC+fOiZ98wmnarlri8GGviQUWC1m7Vjx82KiqO5+d7fdLizG0b+9plSkqQu+9x1VXw/HjyFnZEAAYhoSF0agoLMtAqa5r2e104UJaVUVsNsjL0z7EZMJBQXp5pGZ0vBNRUb4IoCThykrDqVN8SIgUHe3wLdNfXKxxrfbtm4QwCIELF/jDhwXXGpQsC+HhnjdICJw9azh3zpGcTDw+z87mMzLizOa2+INQJ1quwwF35NWBAAkLwBd0krCWFbj/MgGwViMqCkpL6fDhaONGAACFlwwbhnbsgNmzqWvZZgBACN5+GwcFeZ6kshLmzPE6iZYsYRjG65b5+eckK0vDKTluHH77bdyy7rUHzGby9tt6g8Oys+W1az0jvRyO5lAwtUOkL79Usdt4ezcIQUuWCABQX0/efx/V1bXmZSgu5hWPVVGRuGoVU1/vR0qH3Y4OHfIVzk8I/Pab48cfjXa72wNyONCpU37nAfTp4ymdde4ceu89zunYXb/e8yWRJKirI5WVtK4O2e1Ylr2+J4JA339fLipqapCTgzQTLNLTfWlruYIQuHBBl8M3IYFTTVR0OJjSUkNWFrdjB8nKcsgy0SyULklY09aoCGEIAnPsGNtSeq2ls5UQfPIkW1gopKbyGEsun8OpU7wss337tvN9xTYO1cD8QOHIqwABEhaAClxF8zVJmCRJ9fX1NpuN5/k/vAL3n4r4eLh4ETIy0ObNTcFhBgP8/e/w//4fJCXBDz+AazVGkwm++EJlvnz7LWzdqj6PQkLo4sVe+QelMHs28iABTggC/uILPGqUtHYto8cW2a2bNHmy3h/6a9dKx483++MoRQsWEKvV1yG//ipu3+7Gw3w85e3bUWVl0x5ZVUWWLmWtVn95GF67tpkMXbwor1iBqqr08rATJzhXw4k35OQIX37JVFY2j1teHieKfgdBZmS4HXLiBF66lLO7cNozZ1BhodshNlvTIYJAGxpIdTWtqUE2G5Ykt1EVRbp8uVxYiJzJhrKsbatLSNCbVVBbywuCrsYeYhBWK1dQYDhwgNm5Uz550lFZKSpvqQ/NCCc08wCCg7HBINTU8Pv3k5oaT+aHEPXQPxNF5sgRVFEhYQzt2zd/RQicOcOXlAg339xRf82rKwUNDQ0BEnYVIEDCAvAF35WLlAqy9fX1ABASEhIUFNSmAvD1ICmJhoVBdjbatQtkGSiFkSMRxnDHHahXL9SjB505k1ZVNTW+7jo0YYLKUv78816dkmPGwKBBXsckP59+8IEnc5Ik9O23eNgw+Z13ZEph1y6yfr12ZBVCaOpUpGcLBABK4c03oa6uqc8bNzLHj2u7G1evFnNytO1tFgu7dq2bnaOkRP7gA9av0K6zZ9mKCreNtrZWXrECysq0h0KS0N69eq1ZNTXyF1/Ip04ZAYBSevSo39FgCQmMydR8uYMH8ccfqzh8161rTryVJCxJnnNKkqjVSmpqaGUlWCxYELAkwerV8tmzSJaRLDe3Ly5GzneyJaKjGZ7XexclJboYJ8ui4GCREKit5c+e5XfuxHv3ijk5jvp6t2dkNGI9KmKFhRqmspQU5vx5PitLaDlKANChg8HV7mi3s4cO0bo6GQDS0ppNkpTCmTN8UZHQvXt0SsoVHzulGpgfIGFXAa6wLTOAvwaa5SMppYIg1NbWOhwOk8kUGhraNgPw9SAujt53H3rtNTh6tCk4bPVqlJUFd98Ny5ej5cuha1f6+OO0uBgQgkWL8O/x1M0LYnk5zJunPpUQgmXLGB+WwVWryIkTTVYiWUabNuHhw8mrr8quu/jixSQnR9uSxDB04UKVaGVVVFWRxYsNlEJeHvfZZ7ooiyTBokXUtzmKUli+XIXPnT8vf/wx72Hm8X4SvG6dSpesVvLxx3JBgQYRPHWK1Syh7QpJoj/95Ni61VhczFdV+Z0X2b9/84Ds2IE/+4xVjZ3Py2tO+PDtT6QUGhtJVRV58015717GbseiiDw24OPHPbVUnOjaVa+9UBSRplswJASnpPBpaYbTp9kdO9ChQ8L584LDoT68aWkq2QkeaGjgVat0OxEbyxYUEB/lChITmy/R0MAdOCAreiIYQ3y8JwPjeebGG3UnKbRVtKxZBAF35NWCAAkLQBseJEyWZYvFYrFYWJb9aypw/6lgGDCbyaJFePJkyMkBSYKoKFiwAM6ehenT0ZQpAADr10PPnvTee2lpKaxerTJr1q2D335Tn01hYXTRIq/7IiEwaxbT2Ii2bWNGjSKzZsn2FqFZoggvvogbG7UHOTJSfvllvcFhe/dKP/1knDPHDxWJ+nqyaBHjcLj1hBBCCKGUUkqzs9lTp9S32FOnpE8/1SXTdeIE29Cgvs07HHT1aik316t6GSGwd6/f1iwAyMpy8Dzu1cu/+k4GA4qNdQAApfDzz8yGDb4I0Jo1mFKgFHkImbYEIfDVV+T4cbDbicVCjEYSFUWjo1FUFI6IYEJCsCTh6mq2pd0ZIYiK0ssjKyt519QEhoGYGCYtje/TxzB0qGHUKHbMGHzddSQ9XairI8XFgqs1ThXR0dqX9iHNbzKh/v35hATso256fDzHsk3Pt7qaP3RIdFrLunZtNoPl5hoKCwUAGD48MSTkklJ02ywCJOzqADN37tzL3YcA2hyUPVX5QxAEg8Gg+BmVAHyLxQIAQUFBZrP5ivM/qsJkgsZGmpyMnngCbrsNwsOhb1+0Zg0UFcH586h9e1pTAwCQlwcffww5OXDbbRAZCVVV1JWO7N+PJk2iBrVNvEsX2LULSkvdth+EoFs3PH48mjiR1tejJ5+UBO8R0rW1UF3NDh+uTWGSk2lREc7P1zYFderETJ0qORxMTo4faqi1tbSy0tCvn6j4R5RXRYkdFAQ8b5799zxKT+MNAJSVkYYGQ/fukg/STgizbJnDB1cjBI4dk6OiDO3aqWz5585xmZmtIWGpqUyfPo6kJDk93VhcTBsbdfnpBg3iY2NFQuCbb9j//EfDWllfj7p2pWYz9h2vRils2ED27WtqExqKWbbpaSJEMaYsSzmO2u30gQege3e2Y0e2fXsuKooxGpnYWBwZKROCZRkp/1f+kyRECJYkLElIEIAQhhCmvh7FxjIdO7JpaUxaGqSmQnw8iYiQg4JknpcZhihmLYeDzc7WHtKkJC4mRoOEiSJz6pRKG4RoRoaha1diMMinT/siqT16MMpolJTwrlUNWBZ160aVdITcXMOFCw4AiI42jR+fehVEg1FKRVHkOM51vV23bl23bt369et3GTsWwKUjUHYqABWouiNFUbTZbLIsG43GK9361RIdOkCfPvT662H8ePj1V4iPh7Vr0XXX0epqsNsxpcR5u0ePgtWKfvmFmM1w5gw9cAAdOAAHDkBxMbz2GvPGGyr0ASH46COmf38CAMnJ6Lrr0KBBMGAAiYxsbvzII8yKFb7I0A8/kAEDuBtv1NgOEYLnn2eOHJGrq33t9ImJ+NVXpZAQ8uCD+OxZxpv5ShW7d4vJyeabb7YQQhBCzkzYNWuQS6lsSojc1KMmPoaUY81m0/jxXh1yBw5gH2S06dQU1q8XbDbToEGe59m7t5Xy+sOHN21vISGOiRPxuXPGrVvtmtUz09KILMMXX7CHDun6NbJmDX7sMeJj6lAKP/4o79rV9E+EvFb4tlph82aYMEEymcBkgshISEmB7GzD5s3Ut09QFGWMaXIy26uXLjd0WRkDoG3iSk7WXhDKy1lKPR9QfDyblgYMIwBAQwPno6R3RARjNIqUQn4+7+Gv7NqVx9hBKT13znj+fNN9jRnTgWGuhl+JqgjEhF0dCJCwAHxB2TwVATBBEFiWvez1H/889OwJDzyADhygd9wBmzdDt25w//3w+edgswHGSJKo877PnoWZM/F778kZGZCRQR98EACgqAgOHULnzuHqaqoIwMpykxKs8vdzz7Fjx0qJieob++OP4+3bSW6ur+1z/nzarRtu316DGijljO6/36ufsV07PH8+iYggAIAQmTOHffxxUlurNyuQUvrll/Z27bg+fUSFgVFKS0u5HTvsCGGEgBD5d0sYpRQoJb87tBFCsGWLYDabbrxRhYdJEvvDD3rdoz/95LDZTCNH2py/BwoL2eLi1hSajI9nIiJcDySpqY7kZH7nTjhzxislTE1lAYQVK9iTJ/Xu9IWFzKFDpH9/9W8pha1b5S1bmj8xmRCA18e9bx8aOJAmJDT9U5KwYgHSglLzR1efCYELF7TNYMHB2FsZR9dTFRa6ncpkQj16cEFBzQd65JB6oHNnRpblnByutNTtWhwHMTEipTQvz5if3zQC6emRqanhmj2/IuAtJiwg1noV4Kr9lRDAHwir1SqKotlsDgkJuVoZmILhw+ns2ej0abjzTmhshDffRIp7kRBECHIVLP36a/T11267Y2Ii3HEH7dQJZs8m994r//3v8gMPyA89JD/8sPzII/Jjj8lLlhCPaCpXMAxdsYL1bV60WmHOHHWpVQ/4KGcUGYlff524ypHzvPzWWwadylKUUkIIpbB0KS0t5ZRPZBkWL3bb/hFSLGAYY4wxgxB2WlUJId99Z9+5k1PCyFyP2rEDaRqfXLFtm+Pnn83OcyjluluBkSNxS+sRx4mjRon33GMKDlZ/KunpeNkyTj8DAwC7nX7/Pfb2BPfsIYpqnRNms6+zEQLff98coV9erlGN0eW06kWWWqK6WpeGRefOnKZlvK6uuao3QrRbN8PAgeDKwBobmfJyry+3yYSMRvnYMba01LNNRgaPMTl/3piX1/QSchy+6aYOmt2+suBKwiilgZiwqwMBEhaACpTZLkmS1WoFAIZhwsLC2kgF7j8bt91Gn34aDh6ESZOA42D16qbPWRaJInLdPmfOZPPyPA9nGFi7Vj0dsrERpk3DLePunYiI0JbRP3GCLF+uK9B4/Hh67bWeEzw0FM2fTxMSPPfqyEhp7lyNMjqUUlmWnS5Iux0WLsRWK6aUbt+OKipkSomTV3nk7rkSMowxQnjtWjkzkyOEKOckhNhszK+/NvpbqnTfPsc33xglCSoqmgsL+oWwMBQX59WAFB1tv+8+ZuhQz1QAlkVffEHOnfNjRhCCHA5aU4MOHVL59vBhsm6d281zHMJYYzjy8uDo0aY+nDunNyQ/PZ3VTGNUUFCgzcAwhshIP0Ly4+O5YcO4du0cHry/pMRXUF1iIp+ZCS1lwwwGFBUlnT9vOHu2+SEOGdI+PNy/HIu2DFWRoIAl7OpAgIQFoAKlAnd9fT0hBACcgfn/F2Aywf/+LwweDL/9BlOnwpgxyBn5yvNIFJEgIGVJtFjQ44+rWDWiouDjj9XthWfO0Dff9GVKHDaMjh+vEd/9+eeyZv1vAEAI5s93K2cUFIReew2Sk9X3y+7dpfvu87pvKRYsAFCYlPJhWRlZupSrq+PWrZNcbV2/H6Kkd7QMUVKixPDnn0NurgnjpgN//lmSZSLLCitraSbziqNHhbVrjarMRg9GjdIQVkBI7tHD8dBDhqSkpmFvbITTp/miIv8u5LSD/vCDZ9xbdjb59FPPPgQHIz1U6aefkCBAXR3vodrlAx5ip97Q2Mi6lmv0hg4dPAsGtITDwVRUiGYzHjCAT08XW4rvCwIuKvLKodu358+fF1W1LTIy+MJCLjfX4dLYfN11CS1bXrlQdUcGYsKuDvxf2VkD8AuNjY12u91kMoWFhbWihveVjqQkmD4dQkNhwwaYNQs+/9yZpgAcB5IEotjEw44ehfnzVTjT6NFw993qk+uLL+jWrb5o1osv4pgYX92jFM2di6qrtW0wruWMDAb08suoc2dfe+odd5CBAz3pnYcBzMMncuaMWFCgKEkqm4RCuRDGSInEpxQopaqETJZh+XJ64YIBIWS1Gg4eJAzTxPAIIS6EzJmt6xUMQ0aPJmlpfvvKjUbUoYMu+5nRKIwbR8aPNwkCOnSItdn88ZsCACBnxmV9Pdq/v/mLs2fp8uWeN4jQ/2fvvcOkqLL38XNuhU6TM2mGnAWJUd9MugAAIABJREFUwoBEFURQEBRx1dU1sbjqGtZl1Y/rukFcXVERc1jT14iioqKyBqLkJAoMmSFPnulUVfee3x/VU1PTXd097Mfn50fo95mHh+mpcLvCrbfec857QFFatIuaGlixIrrrfHxQSYmStLOQiSNHWtTnoE2b5PPD0aNSz57q4MFkjz/aceyY7BiJliQYMEBNS0PHqKjHg/X1sGNHEwNr1UqZNq2zLJ/6j7aUEnZq4NS/UlP4L+D1ei0DsNOQhAHA+PFw9dWECPPnw4cfwn33RT6XJJQkMAzQNDCz455+Gr76yoEPzZnDWrVy5kl/+hMeORKXQikKvfJKkoBjRQX97W9Ky9sZKQrcfz/26pW0spJuv521atU0LTgKYKZIZSpYv/0t691bu+MOhXPOOTftKsw/McYYQ8bQKo+0EzKTk2maeOopOnJE+fBDbtI4xlCSmCRJFiEzx8AjcBbJJkwgWeZTptC558a1EHPEqFEKYssLKqltW61dO8XjkVqYQmdB09BuyvXxxywcBgA4cICeflrE8g+fzyFNzRF5edCvH1RXtzQU26FDixbjHA8cSL7NzEzJ5UpyXTU0qMXForBQi3fQOMd9+xw2kpUllZbKPp++e7fzSNLS1J07mxhYYaE8blzrzMyYJq+/cMQqYeFwWNO0FAk7BZAiYSk4wO47cHqSMAD4xz9w6lRChD/8AYqKoLCx/6+iIABwjpMmQUkJEMEtt7Bjx6JXVxR45x1nIaG2lu68U0qQQ92mjbj//iSizooV4u23W9TO6Npr8a67pDPPbJF7liyLBx9UVTWuAGa/GCZNUgcONCRJ6tZNzJplSm5IRBZlMhUsW0IYWlFLaORkfj9fsUKSpGZXWQwhkySJWbW6dpGMiAYNkjMyNABApP79tWuu8agts+eUJOjW7SRMxWRZVhTlgguQiGpqmKadxPwZZTzm9+OKFXD0KD35pHDM0/d4WnTTZWXBDTcIlws6doSWpAykp0s+X4u+cmWl6tg1KAqdOycyIj5xQl2zRvL7ETGRBFtR4bCvTp3UM88UsmwcOeLQ0JMx6NPHfeJEEzkrKJBLS33t2rVKOuZfImJLI1VVdTnaEqbwi0KKhKWQBKctCQOAl16CceMAEWbOxEmTmj43p76PP8bnnoOxY6miAm65RYoVM4qL4aGHnB9Ra9fS008nolmTJ8Pw4Uluz3nzxPbtSQJGnOOjj8LHHzObg1cSpKcbf/2rGk8Asxbr2lW+9NKQydWIaMAAuvZaX4QuMQnRFLGEjZDxxqilRcgQETMypPPPD116KZWWykKQo9DVyMlYlEgmBHEuRo7UDMOwVLL8/NCsWWqbNslDacOHK7HJSfGgKIr5ZpKWxvr2FZpGNTVUVYXhMLP3sHKEECyWRqxbx158UTgWaqgqIiaPRWZkwA03iKwsCATI7YaSFrTn6d5dbuHtvH9/coFQljEry4HSGQY7dMi1ahXbulULBHhiE1chYO/eZhuRZRg0SC0u1hBJ13HPnuhdqCqWlqpHj3LLxD8/X+7fH7t06XhKFg/FnrKGhoa0tLSfZTAp/LRIkbAUHGCfyE5nEsYYvvoqjR0LAPDcczB6dJPblaJAfT0+8gi88AL84Q+0fDnNn+/w1J8+HUaNcr7LnnyS1q2LSxQQ4V//knwJ4yqGAf/zPywQiPvUqa+Xrr2Wf/KJvmmT8fzzLX1pFkJ06KDPmuWKJ4ABQFoa+93vDMaEXSobNsyYNs1l5t0zxuyEDLFZVNEkZACAiNOnyz4fSBJceCGNH+9CjChkNkKWSCQbO9aVlsbNq9TaviyHL7uMhg5NIoidcUaLApGIaK9NCQZx6lTJ9FA1DKitpYoKCAYZUdwTEcu0iovp5pvFiBHOq8QzxbDD64XrrhP5+aDrEV+PggLIyUm0CmPUks5CAOD3K7W1yZfs0EGJIovhsLxnj7piBe3YETZbTHbqlCRtv6ZGNZs/msjJkUpL5bS0iMR15IgSJZKlp0tDh0qBAFleFfn5ct++1KFDidt9csHoXwpiu3fX1dWlpaWdkozzdEOKhKWQBKczCQMAjweffZZGjQIA3LoVJ02KpJZLEjIGH36IX38Ns2bBq6/C88/TunXRcyIiPPMMc3xlFQLuuANrauJOo243vfRSEseKAwfokUecl9m5U5o2TbP6F334obF4cZJHlCVrMcbOOw/PO0+FGAHMxMyZkJ2tmUvaK2fHjePjx9upjxWJlCRJNiUsk2YJIYTgXbtCnz7+RkIGY8eKadN8TSpZhJBFOFksIZMkOPtsHtm6bG3fvGKN0tLQtGkSkeGY2N+/v6yqyQNzkiSptugmEQaDIiMD7fxJCKivp4oK8PuZENEn1FzF/kn37uK224THA8OHY9u20XtkDKw+RfHgcsG114pWrQCaM7wOHSBBKLZzZzVxWNDCoUMterq3atV0VBsalB9/VFasMPbta9ZlslWrJN9l376mBbp1U/v2FZY8aRgsSiTLzZUGDCDG+I8/RtbKy5P79hX5+dn5ietZTi3U19enEsJODaRIWApJcJqTMAAoKMDHHqPhw+H4caysxKlTweRhqgoAOHs2hkJQWgoffghPPYW1tdGrezzwzjvOkccjR+ieexIFJbt3p1mzkiSHffKJ+PzzZjyMCBYuZNdfHw6Fmp24efP4tm1xWZ1ZhWjLAKPrr4eOHR0exhMmsL59Q7HFkubOL75YDB8eby92jUxyuaTp0wVE0rwiGDAgeOWVrkYSZg9cNiNkJicbP15WVSN26yYhkySpQwc+a5Y7Oxs454Zh2KOWgwYlv6oVRYlyJw4GI4Wxkyczr7fZFojA76fKSqivZ5w3Ta2Wp4mJAQP4TTeRSZUQ8bLLMCqXKy0tSUq+osA114jiYoBG7zHrT7IMHTvGXdFcJSkMgx06lJye5uXJqqoTQXW1snGjvGaNHmuj2q6dIsuJaF9dXURyU1UcMkRt3Vqzf/fyctnO59q2Vfr0IcbE4cNqQwMHgNxc6cwzyeWSO7Sw3OCXiVglzAxHppSwUwApEpaCA1LhyCh07IgPP0yDB8Py5diuHV5xBVg8bN8+fOwxBICiInj8cfjkE4d7qlcvuOMO58jjl1+KN95IRLOuvx569Egy1c6ZQ4cORfar6/iXv8DcuQ7VZIZBDzyAJ05Ej8QugNllLUT+wANKenqzvXfqBJdcEopa0g5EuuoqOPPMpG4ROHGiWlhIjXn3klUL2b176NprpcaopdlbySJkrLHUEmQZ+vfXRCOiVDKLxGVkiOuvV/v1c1uGZEKITp3A5QpYhMzpW6CqqjHfsUnT8njQjFPHHEwIBqmykmprmWEwaJ6SP2oUv+aaZhn0xcVs8OBmRzhes0gTkgRXXSU6dYr8GhvozMyEoiKHFXNzJZerRRWUJ04oduoTDyUl7Ngxdc0aaeNGPdZD1URS2mf2KcrPl4cOZR5Ps+HpumRvENmli9q1q4EodF0ye0nl5Ej9+gFjonPnzqd2Jw9ImYSdukiRsBScYd3zKRJmolcv/Oc/qU8feOYZnDIFbr0VODcjR/Dkk1BWBgAgyzBlCuzd63Bb/e53eMYZzlxqzhzavj3uncgYPPusLCXMMg8E4N57JU2D6mrpqquMr7+Oqz3U1Ii//EUOheyhtCgBDMAWgvR4xD//6bY+9HjEzTdzlytWAGsGRPHb32KXLokG3aqVNGpUsHF5jBKxOncWt93mQSTOhWFwzpsULIuQTZmiKoqIGrMjIZMkcf75YsoUr8n2ZFkeOVK2GZJxu0IGAIwxVVVjv2Mo1Kyr0qRJUkZG3FsjHKaqKgoEmNeL5sYmTuSXXAKxR27KFLQC1m53opR8xuDyy0X37tD4laOLLk0UFzv0O+ratel0JD59+/Yl0q5cLuzSRT3rLNe2bfq2bZqjgaqJvDw5sXtFMCgfP6737Onq1cuIzRs7eLCp2KVPH7Vdu4hItmuXxDllZ0v9+wMiLyoqyszMTLCXUwCxM3B9fX0qMf/UQIqEpZAEKRJmoX9/nDuX2rSB//kfdtNN8OCDpOvAGBgG/vGPTSGnoiKI7RHJGLz+uuTIpcJhuO025vfH3W9mJj35ZJLksH37aOVKZepU7fDhJCerrIw/9pjLjOs5CmBWyrz5qC4sNGbPdpkE5aab5IKCxJuPgDFxxx1Su3ZxZ5gZMyCeeGGSrDZt+J/+lO7zyZIUqbW0Eya3W/TqFbbHLC2hC5oTskZqRd27azfe6PZ6sU0blp2tRWWSWdtnjCmK49HGKINWWcbzz08iUrrdoKoiJ4f++EeaMYN5PA7Lezx4wQWRz32+RBucNo369Gn6VdOcW20iQufOzfQ2WYbs7BY5U9TXq468KjNTOuMMdcQI5eyzqaRECwRA05JcaUl7hJ84wUpL1cLCcCwnDIcl06WMMRg8WM3L06zhlZdrWVnSgAGAyH0+X3ELg6y/ZDiGI1NK2KkB6f777/+5x5DC/0XwRhsrzrmu66dJ48ikaN0a+veHt97C48fh97+nrl3ho48QAMrLsaSEevWKLGaWT0bB7YaBA9mCBQ6PzepqqK6Wx4yJK4G0awcVFfjDDw6PPUWBGTPYnDm8Rw+RkyOvXJm84m/fPsEY9uplxPMAi2IzrVpxw5C6dVPOPfckmjMyRkOHKmvWiEAg+k/DhikjRsRvotkIj4cPHuxav14YBtqIFgLAtGlSbq5md9PH5oAYvYeIXC5jwAA5P19KS2uWSWYxObfb7cyUAcJhjI39deyIK1aIYND51nC70e0WbjfMnMnPOIMkiVQVfD50uxljyHnT2027dvDDD9TQAD5fXGYzeTINGdLsrw0NcfudKwrIMtTURH7t3l3Nzo58ZSGEUzJfBHv2SPX1kUuIMSgqkrt1k7t3x7ZteVoalyQzhw+2baNY0w070tNZSYmRYM7QdTk7W8TLGNu7V6mt5YoCQ4YoXq/eOHLYsgXdbhw4EBnjjLEePXqoLTSF+yVD13VTxLU++eqrrxBxwoQJP+OoUvhJkFLCUnCGPRwJcTrInoZAxH794M03aeFC/PJLvOgiWLAAJAkQ4Yknmh7SQoCmNbEBC8OGwZVXOt9077wjPvssUfzuT3+KtuBHhIsuYgsWiFtv1bOyCAAuvJAmTkyimZkS0Suv0Nq17gQCmLWk+RY+caIybFhLLbUsqKpx331qVNgkLQ0vvLClZM7n0++6S83NtcaJiCwvT+7VS0SJWFF59w6J/QAAEAyKBQuCq1ermtYsZMkYc7lcCd40/H6rONO+Fl54YdxVfD5IT4dbb+VWABEAAEiShNcrcnMhN5dlZDBZRkScPh0zM+Om5I8fT8OGNfuTYWBiGlRYCFlZkf+3bduiDki6Lh0+rCkKdOyoDBniGj2a9eplZGdrUW5qNTVq0q5NXbsq8Y6lrks7dqi6LsXrVRAKSeXlms/Hhg5V7AHNY8dcQohBg5AxgwhKSjp5Y8OupyIcfcJSStipgRQJSyEJUiQsCrKMvXvDu+/S3/7Gjh2D4cPh889BVWHvXly8GDweMqNshgGcO+gx99/P2rVzfjrdey8ePBj3iS7L9O9/NxGsMWPYW2/RPffoRUX2U0N33on27J8o2NOqHnoIDhyQwZYBFkW/zFwxxlgopNx5p/bQQ6yi4qTTnz0e44EH3HZn76lTJZ+v5Z2CwO02brtNbtu26UtNnaoARBL240UV4xGy9esVXcdVq7Q335TLy1Xza8YPQUag68g52Io0mwjZ8OGsqCi2STkoCuTn89tvNxJ4qDImXC6RnU15edi7tzx6NBI5zMmjR9OYMdHbD4djF4xGSQmFw1RUJCtKklgk51JDg6LryvDh6siR2LGjnpYWjpedZjeVcITLhWYPgygIAYcPq6tWiWCQe71xv8D+/VJ2tjRoEMpy07B1nR06xAcPZogGEeTmFhcV5SUexqmBqHvTRIqEnTJIkbAUkiBFwmIhSXjGGfDYY+L++5kkQZ8+sGwZeL1wyy1YVweqSiYV0zQwDxsi7t8fOYCyDO+950xl6uvpzjslxyY2JoqKxN//Lg8axP79b5gzR+/QweFZyBjNmyfHun2SrQukeU4DAbr/flZXF2GKjgIYIuq6dMcdWnU1VVaKf/yD1da2qKmzHenp+l/+4pYkAoCuXeWBA4MnuwVF4bNmSWaHnNatWbt2DluwEzJZjs67NzlZdTVu3Giaa7DaWvrgA75woWfbNndiBgYAtohqs5ineX4vvtj8D9l+oHNn+MMfRGFhi4L4iIRIZ51FoZCorMTaWtbQwEIhSdfZoEE4fjxFqUrxUvLtEIIWLxarVoni4mbzPBGGQnJ1tXr4sGvnTteGDcrXX7MlS/jatYbHE3a5tHhqnIm6OqWmJokm2rWrGtsmsq5OXbdO2r5dMwzq1i3uVRQMykKIvn1FVKr+0aNq//5gamCK0qpbtzaJx3BqI5WYf8ogRcJScEYqHJkYsoxduoRnzKDXXkPGoH17WL0acnNx5kwGAIgRKma5qB8/jjfeSEeOEAAUFdGTTzrzsI0bad48B0JABDU1bOlS/PZbuuUW0bNnIiXJ4xFR/vgWqYryXDh0SMyZo5qKHcQIYIwxzqXZs42jRyNn/8gR8dBDciBw0vNGbq5+331eWYbLLuP/XXKhJPFrr8U+fZSLL5Za0tnaTsgskWz5cqHrumHohsENQ+zbJ73+uvy3v8lVVVKC1kOGgXGS0CNcbOBAuaSEACwnM2jThs+erZ9U0V4wCB4PTprEhABNo2CQ6utFURE/+2x+4gRUVEBNDTY0sFAIdR11nckyKEqin2++gc2bIRSCBQvCR46oe/e6tm5VV6yQliyBb7811qzRtm4N790bPnFC1zQBAN27Ky3pSn7gQJIFGIPc3GZvEpombd+urlunmeZeeXlylBuFHYYh9+jBo0ai60qbNobJwEKh3AEDWtCh6VRBPCUsZdZ6aiBFwlJoEVIkLBYZGdSpUyA/n3btAkTIy4NVq2DPHly9umm6ZIyIIBiEM89Ew4ARI+D554lzmjgRzj/f+e579lmxYkVEJwiH8Ycf2JNP4ogR4uyz9ZtuMhYvFrffzqqqkty5bdrwBx90gZMAFoX16/lLLzUtaQlgiMg53ncftzz3Tezdyx95RNW0kyZSrVtrt93mKSg4iYbZUWBMnHceS6oAxcLklDU1rp07hSTJjEk1NWztWvfu3QrnFArBK6/wqiowDNZM5mpEIACWvhUP06dL5oMSEdq3Fw88AF5vU9TSseuAHZxHeN6QIdi+feTDTp3oqqvIpM2cQzhMfr+oraXqalIUkZUF8X4yM+mHH/jWrTwri9xu3LSJv/hiaMeO0OHDWn29IYTDSBiDwsLkMWLTVCLxMp07q4xFrhkhoLzctXIlP3y4iXV16+Z89RJBOOxOTw9FHWrOmdtNjBkAUFmZWVrakbHTrkgoRcJOVZziBncp/O+RUsLiARELCnQiPHKE2rcHxsDng6++guuuw9deI8t/ARFUFcJhuOMO/OADcccd8I9/4I030vTprKHBtDCI1LIpSuT/e/aQEPj002LrVofn4rFj9Mc/yvPn66qa6KQMGyYuv5y9/roRWx8QhQULeEmJdM45mp1/CIFz5tCPPzoM4Mcfjccfd912W+ikDDJDIfnxxwPDh3smTw78dw9RIlq0SOzapZ97rnfIkMDJCmorVgAi6Dru2qUeOSIDgBVP/PJLaexYo6AAfT70+SK8xzwOnJteDPadWYe96cMePVjXrsbOndizp5g5kzIz0fqrde9EecnaxxYMNn1+2WX48MPUpg1dc41wrNT0+TCBZEVE69eLTz+NLBAIEACUl5PHA717xz1kXbq4GEueZVZenvy9vVWryDVTXa3s3Cn8/mabLSyUHT1jdZ1t3y717Bkd6AyFZABZkkIAcOhQ2jnndHK7T6/HluPcm2rgfcrg9LqaU2g5osKRKcRD9+4YDEJdHRUWUjiMsgwvv4z/+Q+OG2c3CwVZhg4d8PLL8dVXqbKS/vEPUFW68EKcP193PMB+P9u7N64ysXGjePhh5Z574sZ0TOnluuuwrExZuzaRwmEu+fjj0K6d0qMHb+Tc+PjjtG5d3NSfDRuM557zzJwZjGObH7sX9vjjhq7D119rgYD3ssuC/4XD+datSllZCAC++CJ84ID7oos0t7tFRX8AcPSoUlYWOnJE2rVL0XV7QwhzePjBB2zmTAgEKBSC9HR0uyPaVSCARM0cVmMadZsnGqdPZ4sXG1ddBdnZzKwbaNwF2tZ1IGRCoL3BVOvWbNw446yzRLxENY8n0TfdupUWLoxsTdMY55GRlJWR2y0amxpFX3MtKZ/Udam8PElZa0mJIkl6OCzt2sWOHXPQzDp3drjca2vVLVv0rl2ZJDVjbPX16okT0LFjCAD27vWOHt0xK6ulfehPMURdRanekacMUuHIFJIgpYTFAzba2Pbrh+XlcOQIKkqkEfWoUWzZsmYLqyowBg89hDk5kU80Dd5/n26+WXE8tD6feOedRNniH3wg3n3X2SHJiirKMpszRy4oiHubW2EyztkDD0hVVRIAEMHzz8PSpUmSr5ct0197LSEdsOHTT9nu3ZENrl6tvfyy+2QDmsEgLlrUxAC2b9efe046dqylHlEffyw2bHD9+KNqZ2B2fPcd27+fAFAIrK2FmhrGOQoBwaCg5u3DzZij9QOAAKjrtHo179lTMEYuV1xCg83R+NVA2HawZw//5htqaHDegteLjMW9GbdvF++807R3UwazsHUrHTrksG5JiWKvQ4yHw4elpNNA69Zw8KBr5UruyMBat4720BcC9u1zrV2ryTIWFjZjeBUVrs2b9Q4dOADs2uUeOLC4VStf0kGeeoinhKWqI08NpEhYCs6wv3hhyjTfCXZ6OmQI27sXVq4EK05UUsLuuAM022PF7QZJwrlzmXVohYC33qKJE2Wzg14UiovFE08k4mGPPCLWrm0mKMVmgCmKeO45JTaqZc8AM5PWq6vpL3+Rw2F880322WctytxavFh/773kPGz/fuXDD5v5nG7Zoj/zjCsYPIn5Z8kSqaGhGbmprhYvvMA3b04ygHAYnn/e9eGHUk1N4t3h2283LRAOU2UlGIYky002Y1Htw80cfESoqaGHH+ZffsnefVeSJNaoriVPBUNEABYKNd1uW7fyxx/nfj+9+qoZBo1ePYEx1p494vXXmw6Ro5HYhg1UURG9YocOyQkx57h/f6KrQpahfXvXpk28rCwc70t36tRsR+GwtGmTsmtXGAD69JGbt+52rV8fHjhQReS7drm7d2/dtWt20kGekkgl5p/aSJGwFJIjRcJagpEjWSiE//43mE++wkIcOJANHw5HjjQt43bD+PEY1bB5zRoYO1Z+5hkWa04xZgxdf33cuB3nMHs2HjoUYVjxSiCzs/m8ec2COBY5iMrWP3iQNm9W3n//JGzxFyzQFy9ORINCIfnhhx0cJcrK9HnzlLq6FhlelJfLq1Y5OOwbBixcqC1a5NF156ls82Z2773qokXQkuv3hx+YvSeBLIOqiuxsyslhqmo6jaHd+dU8jNu2Gffey3ftAiIKBtny5U1x/CjjD8ebyOzjiYgA+N134sUXwSyNOHYMFy8GIYhIEEU6L7lcaOW8R+HAAfHyy83+FNuoAAA4h7Vrsba2aSQFBc5JWlE4dkwxDOeD6POxfv1cZ5/NQiFhNTiPRbt2it2urKpKXbVKVFXpANC6teLzRcZABGVlrm3bwj17utzu8K5dalFRzqBBhUlHePqAc+73+1Mk7NRAioSlkBwpEuaI2EDt+PFYWAj33gv19QQAF1+MvXvjgAHw5ZfWKuB2w0svob3VChHU1OCcOWzSJGn9+mhN4pZbsLQ07n1aW0u33878fnAsgTQf/IjYu7e47TYVnAQwC+3asXnz6Kyz9LlzWxpkNPHKK/ry5W7HPxHhvHmGFucRf/Cg8dhjUlVVkuwwzmHhwkSx0fXrtZdfVqqrm6mGVVUwf7782GPKoUMsHnuIxZtvNkXcMjLQ1GYkSWRmUk4Oc7tNwhQhWES4aJF45BEIBCLOFC4Xff45HTvmsDtHQkYEpvU8EXzxBX/rrWb9l1aulMrKzBOK5kpeL1ln0M7qDh8WL7wg7Pco5xgOO39rw4BVq4QVqezaNflTgAgdu3rn5clnnaUOHiyys8OBgHT0aCKprEMHa2ywa5e6YYNmnhfGoFs3sob9/ffqnj3h7GypXTt9925XXl7GOeec+t0hEyBWCWtoaGCMnSbdAk55pEhYCs5IhSP/CyDi1Kmsf3+44QaoqSEAmDcPW7WCq66Ce+8Fs80fY9C6Nc6a1YxsCUGhEO7YgdOmSXffzWpr7dukJ5+U43l+EkFZmbj7biSKJlWW2YT56+TJdM45zFEAA4ChQ6UnnjDateMAUFxsPPTQyfGwp582Nmxw4GGff87KyhLxpxMn+KOP4tGjiaKua9Yohw8nSVA7csR49lkqK/MAAOfwxRfSPfeo69ZJANDQcBKX7sGDuG4dAYDLhbLcTNRhTKSlUW4uer0MAOrqaO5cvnCh1XoSETEtDQHwpZcY54mcKazzEg6jEMQ5vfee8cknInbhN9/EurrIHlSVmUmHYIt1CiGOHePPPcejmkjGa2dpQpahuNhwuURGBrMkqASorFRDoWY7KC5Whg1T+vQxfD7NvJT27Em0hfbtI2lnwaC8fr28b1/TTnv2VM22SLrONmyQDx/WGIN+/diePXJmpnv8+M6p2qCoI2A6tbIWFsWk8H8bqbOYQnKkSJgjHEsWEPHqq9mwYTBjBtTWkizj/PlMUeDFF2HcOKirAwCQZbjnHoxqBGkYFA4jALz5Jnv11WY3pqqKd991zusiEgCwfLn0wguu5p9TjCQmZs/Gjh2l2Ln7yivlP/9Zs7eO7tbNuPdeZ3HLEULAY4/xH35oFvQsL1fffz+560FtrZg7l/bvd06xr61STPvTAAAgAElEQVRln3+evNU3AITD9M472o4dPpfL8+23khnmC4WQ85O7dN9+WzIMiJf0jEher+Ac5s6FH35o9ieXCxkjRDh4kG3YEK17ORKyYBAMA195hS9bFs2uzIVDIXzrrUiX7rQ0k+01dcNExKoqeuopoWnN5DGzniDeF/R66Ve/4gUFokMHo2fPFvGbvXsjJFiSoHt316hRcufOuj3Fvr5eOXEirgyGSKaZ7fHj6nff8bq6Jkrt9bLCQgMAQiF59Wo0o5P9+rnLy8HnkydO7JFiYLGXTX19fXp6eurInBpIkbAUkiNFwhIg9shIEt5+O+vXDy64APx+6t8ff/1rxjl8/z306QObNgEAuN348ssYRYc0jcy0sEcfxcrKZjNsTo549dUmmkJkZgtRowYDzz8vlixRIEYAA1vwS1Hw6adVe0cjjwfvv59deWU49qV64EB+660nwcN0nR55ROzZExlkOCw//LBTUpITAgF64glRVubgPvDJJ/Hc6pvB7YazzpKuv97VujX4/ezSSyNlniclg5moqMBNm1CS4pKYtWvFvffyYFAUFGBmZhOntds2vf46CwQcaiHtHEvTsKFBPPOMsXkzRC1mwlxs925YuhQYA0sGg0Ytra4Onn6aOEe7tElEgQDEhixNeDwwY4bIyyMAkGVwu4Oc88R3d22tWl/PPR7s29c1YgRr3Tps+qbasXt3oi107OgC4Nu3q1u2aFGcuE8fGVHU1ysrV3K/nwNAq1ZKQ4PuduNFF/VJ8QxovKPtn6RMwk4lpEhYCs5IhSOTIsETQlXxwQdZhw4wYgQEg/TnP2PnzigEhMNw/vnw1FNABMOG4XnnRW8hHAbOAQB///voe7NvX3HPPRGaZQpgiPZHL9x/P23fHp0TZs8AQ0SfTzzzTIQntW3LnniCSkvjChijR/NrrjkJW6ZgkObMofJyGYDNn68HT6ZFpKbRU0/xLVua0b4dO+Tvv0+ipaWnw6hR8vXXu4YNUzweBABNg86dsbiYhULo6A6fFIsWMcMp/qnr8Mor/NlnrbuBXC6RlwfZ2cztbsbbdB3fey/6DEYxrWPHxGOP6Tt3RrOlWEL22WcQCGBzCkf19TR/PrfaeNtWYsEg2Tmf2YqKiFSVZszgVsf39HQGQGZjzQQ3eE0NDhqkDhlCubnOLb3r6tSqqrjxYsYgKwvXrGGxHmNFRUpamlZRoa5ebZjlLKqKXq8hyzh5cr8UA7PgSMJSx+fUQIqEpZAcKRLmCMdwpAWfD194ATMyoH9/qKujTz4BlytSo/fXv8IVV0AoBK++ilHJtWaPIyJYvhy/+y56kp0xgyZMaCaAWWsBQDAIf/iDVFUVnf0dpa8UF4u//lU1k8BKSpJ0qrnoIjF1aku9uACgvp4efJAtW8a2b0/eAycKhkEvvKB/910kHS0cxo8+SpTonZuL48cr117r7t9fVpSoHDs8/3yQ5f/GatjtRr8fVq6M/ry6mh54gEfZv5lQFCHLWF/PwmFmWbl+9x3u3x93L9XV7J//5EePMktJs7OlKELGGPt//w/sxbN+Pz39tOH3O4Q4w2FmUTJ74FJR6JJLjKIisw7AZDxWcyERy8NCIenwYdexY+6SknB6uhbvQBLRrl1xVUNZhrZt3evXa6bKZQcide8Ohw651q9vksdyc7nLhVOnDkglPFmIF478WQaTwk+O1IWeQlxYD7AUCfvvkJvL3n4bGYO+fSEQwPffRyt7+quvYOBAOH4cX3gh+h4kimRV//a3DqYVDz2kXHmlLElNApj9zBw+TH/8o6JpECWA2bfAOQaDMGECpaW16JxeeSWcd16ixPkoTJnChg0TAweexCoWiOCNN7QlSzwA8O23UnW1M5Nr0wanTFF+/WtXz55SvIf1yJHs7LNFXh74fCc3y/l8AACvvSaFbRrc99+L2bOF3W3EDiHQ7xfhMNXXU2Ul1NayUIgJwV54wVlRq66mP/+ZV1VFfrUIk/1k2QmZz4fV1fjxxxF2FQrBc88Jq3qjmT5G4Pc3o0SNDAwuvRTat2+i4z4fEHFrF0IIXdc5p7o6Ze9e16pV0rJlfNu2cE5OEjJdU+OqqXGWwTp2VIcOlY8e1Rz1yG7dXPv2MUvpFEIUFISzsti0aYNSDCwKUbewmZj/cw0mhZ8W0v333/9zjyGF/6PgnFv/0XXdk7hbymmJUCikqqrk2OEPAACys3H4cHjpJXjuObjxRmzXDpYujfTACQbhhRfgggvw2DGIMmslAiI0DECEoUOjEv/h7LPZyJEsFKI9e0DEaBBHjoiKChgxIjozzNzsunXst781vvhCrFgBffpIhYUtavszcCAeOIDl5ckXnj5dnjAhhEj9+6PfL+/bd9J6GADs2MHT091LlwZ487URoXNnNm6cMmSIkp2d6Dnt86HbTR074pIlQpbB42G67nCsYuH1ostlthBAt1t06wacw3vv8TfeSOQ05vejnWwJAZoGwSDU1KAksa5dwe5xf+QIPfQQnTjhPBrH5LCMDAKg8nJo21akp9Pzz4uKCuclNa1Zg3PT0F+SaNo06tRJ2HeRlYVWB08hQNMwEIDqamPHDuPwYcNk/x06qPn5icRIIti2DcPh6O+SkyMNGCDn5uoHDyrHjztQNEVBIungwUiAsqZGS0sLtWnjvvTSISkGFgVd1xFRtvX5WrlyZXV19dSpU3/GUaXwUyFFwlKIC/MtGRpJmNvtTmUh2EFESUkYALRujYMH01tvwcsv0+230+HDsGcPcA6MASIsWgQ9e8KRIxRlpiUEIOK6dTh9OsVGHnJzcfBgnDSJMQa7dlEjAzBPF+3YwXJz5V69mh7GRLBrF7v1Vv722zwUAgDgHJYtg0GDWE5Ocm6CCKWl0o8/gqMDloWJE5VLLw1Zq5xxBgAoO3eeNA9LT8crr+SlpUpZmTAz6yUJevWSLrhAOfNMOT09yUXIGGRlASKkpWF9vdi1CxHB4wFZZklz/DMzm67xH3/E/v35/Pli/fpEq3COpi1cLIjghx+oe3chyygEA8A9e8Tjj4vaWmyJdZnJltLSJFWN0KlDh7CyUt+5s1m8EmzUrb4+WrSWJJg8WVhGXCbS0iRVJSGYpkmBADY0QDgMhkGaBosX8717uccjXC4680xE5PYdRaG6Wo2y0VdV7NfPVVKiS5IIBuUtWxw4XEGB4vXKR45oABAM8t27GwoKtJ49fdOnl6YYWCw0TZMkyU7CvvnmG13XJ06c+DOOKoWfCqkrPoXkSJz8dNqi5YdlzBhpyRJQFLzoIjznHMjPBwDw+0nTgAi+/hpvvhlHjoSox1w4TIYBv/sdxu6BMfJ6IS8PbrqJXXMNq62FUIjMxBpEBoCPPAKmSxYAHD3Kfv97cc01+r59zTYUCMDs2ay8vEWe9YyJP/9Z7tgx7owxerRy+eVR5ZA0aZL41a9OosTSxG23MVk23G792muloiL3rl2unBzXeeclUb8sZGai1Tzq4oslrzfyf5eL8vKYmbzvCJ+PWSu63TR9Ovf7Yd++JLtzNKa3QIRvvMEMgwIB8dFHxp//LBoa0OWC9HTmdjdpUQnQKEBju3YwcyaNHy+1aeMctdQ0k9vZUvQZTJpEPXqYxrAkSeDxYHo6I4KqKqysFPX1XNPMIg/gHD/8kA4dgupqWLWKHzwINTUa59wwDF3XdV03DMN6MQMAIaCsrIlhI1K3bq5hwzAjIxJh3LnT4dI94wxXmzbs8OGwELRvn3/z5uquXXnfvmmXXJJiYHGRCkeewkgpYSnEhTXhCiE0TXO5XKlZMgqhUEhRFPtLajy0bs0mT6Y338TPP4ehQ+HoURACTU8KM0r48stw9tmwdCmEbK5YnMOJE9i3L1lu4xZM9Y1z6NMH339fHD+OoRDqOgoB5llauRJHjsR58+jBB414yUyhEKxcyUaMILtJWDwwRqNHK8uX89je0medJc+cGXKkFO3bizZtXOvWJXFbtXD55UpRUWjlSmnBAuX555U9e6RwWP7+exwxQtjNNeJBVcGe6+ZyIaLYutXKbgSXC1wuZtLfKGRmRnjwwIH89ttFnz6Qn481NZQgv94wMKkLRk0NZmeLzZvFokUEAJmZqChCUcjlAq8XvF70epnbjYqCZqqffWBuNzPbgXftSr/+Nfd6QZaxZ0/csQOCwehYZH09GUZEJGMM3G522WV41lmQlsbS0jAtDT0ecrnMJUVjnlZkdc5h4UKxZ09k34whAG3eLPx+bN1adruZvdDSnBlOnFAOHTIAABELCuQBA+SsLM1isVVV6u7dzWQwjweHDlW9Xr5mjXHihLZlS20goA8ezPr0ybj44lJZbtHLwGkIUwmzy+2LFi3Ky8sbNWrUzzeoFH4ypJ6pKcSFPTEfUkpYDE42ONu1q7R2LRQU4H/+gzfcAB06gFnTp+vg99PEiTBoEGzeDJdfDtZ8aybp33wzCzn5lbrdYNK/xx9n5iq6DoEANjRgTg5OnEiBAJSVJYk2Hj9Os2fLyZpbR6Cq/NFHXVlZzb54797yTTeFE4Rk+/fX77wzeUJhOAyKIn/6KV1/veepp1yrV8ume62qUlaW9NZbLZTBoj+ZMEEybbEsyDLl5poGDU1IT0dEKigQd9xh3HIL5eREPp86lSXo0ZdYBjPhdhMirV9PABDlZAEAiMSYkGXhdou0NJGdTfn5mJ/PcnNZVhZTVdR11qMHXnYZMcY0jWkaUxQ2fbrscknmr7ou6brEuezzSbm5Ul4ey8uD3Fy6/HJ94EBNlg3GOIBotDXBYNBqhWT2ZSLO6aOPxK5dTSpXWhpDJCFg40b+zDPat98KzmVFURRFkSQJEYWAHTvCuq7LMu/Xj/XoEWYsbK3OOf74YzPa3batWlrKVDX8ww/a1q01mzfXqCoMHoz9+2dMnpxiYHFhFTjbP6yvr081jjxlkFLCUogLSwkjonA4nDT56TREKBSSZVlRWloJmJ7OrrySFi3CxYvp5ZepshL37YskjIdC+N13cNVVNGkS/OpXsGMHHjwYKX4MhzEchlGjHEiwqoKmQUEBHjpE+/ah1ws+H6gqBAKwaRMuWICSJPn9qOskSdHhTgu1tbBpkzx6NNntQONBUWjMGGXxYm4monXuLN11l2YmsydAbq7o18+1fLluOTiYIIJAACsqpPJy+dgxWdexogKjlgEARKioYP37i8SF+enpqKoO3rnp6WLt2uhtKgp4PMwwgHNAhJwcmDTJmDWL2rRptpiqYk4OOKaFtUQGa91a3HKL6NoVO3aEtWshMxPtSfrxgEiMEQDW1YnevcWkSYZhUDjc9INIhYWwbp3w+ykUolCI0tJQlgVjZq4hTpoEZ54ZbfIJAIbBGhrIan+JiETw6ae0fbuw7d3kspHHvxBw8KCoqeEdO3IAYIxJklRZ6Tl+XPTq5e7RQ6iqLmwgooMH1WPHDHPvjEHfvkphYUMw6D92LLhsWXD/fj03V+rblwYPzjz//FJFSS4kn87QdV2WZfvc+9Zbb/Xp06d///4/46hS+KmQImEpxIU9/6MlGeinIU6WhAGA282uuoqWLcP58+H99yk9HVeujPCwI0dQ12H4cMjOhunTobQUvv8e/X5AhI0bcfx4yslBIhQChQDOIz+MgdcL552HS5YIsy2SHfX1oOusqkpqaJAMA/PzKScn0nDajspK+vFHeeRI0YLIKrhcNHq0+umnvE0b6e679ZaEMgEgI0MMGeJetszgHLxeqKrCY8fk8nK5okLy+xnnmJuLia+vujoYMwbCcdxbJclBBjPRrh1u2CBqahy4ndsNqsr69KG77tIHD3YeQOvWsG8fHT8eOx5MXHE5dCi/8UYys3cyMzE3F/fubVE5qgm/H0aMEBMnCkf2nJEBrVrhtm0kBHi9kailiXHjaOBA0cixmqXw19UB56LRiYyEoC++gO+/J3utZUaGpCjNxllSgueeawBws0ZH0/RAQHTvjpmZXJKYJEl2f41AgG3apBmGQaTn5Og9eoQRazVNC4fFl1+KHTt4YaHUqxeUlmace+6wFANLDCLSdV1RFHsqyKuvvlpaWtq7d++fcWAp/FRI+T+lEBdmTi4AEFF1dbXP53O5TsI//XRATU2NqqreKMfVFoBzfs01sGGDWLGCVqyAX/0KTH95xujdd2nECJBls3UgzJ8PTz+NmgYdOtBrr3HTxcqC+XhWFPB6Ydkyfv31ccv0amqACPLyxAcfCEWBw4epvBwOHaKDB+HQITp0CDQNSkvZffdp5q6TYs8eWVUDubnJix/tD3jDYEePktuNs2frGzY0kYv0dLQy6BPgn//UO3bEqiqKZT+5udEtt+3YupX/7W8On2dl0WWXiW7dyDSnsFKaolBZSffdJ+z8T9fR7NHuCJeLpk/ngwY1+zA/H996y1i7tkWHlwiHDeNDhiQhbVu2wIIFIje3qRZh7FgqLY04ska59Woaq6kRVhWtEPDll2LjRnMXaMpjjGF+PpgymIm2bWHSJMN60TAMZAwZiwQ3NY19+63QNDAMMAzSdTp6lDU08OxsmjxZ5OQQgDkYXLKEbdlitGold+lCI0emjRw5XFX/GzO50wpCiEAg4PF47C/Ao0ePfuCBByZMmPAzDiyFnwopEpZCXESRMK/X63afdKXbqY3a2lpZln1RzKhlIKJ77hEbNogPPqCDB2nsWDxxAgCgbVv6/HMqLCRr1t29G+6+G1etgvvv5xddZAkbzbamquDxwC23GF984bw7IaC2Fojgqqv4738ffdcTwfHjUF5OLhcOHaoDiMQzQ0OD9Mc/al26SNddF4xSjzAGMfvCqio6elRcfbUw/ahUFbOzWzQRtW4t5s3jjGF1dTNTD7cbMjOTbGHOHH3jxiY5gTE691xx8cVkGBgIRJLZvV70eJyp2Bdf8Hfeafq1ujquzURRkbjhBmHWwFrwejEtTWgazZ0rjh1L8jVVFc4/nzp1apG7x/bt7MsvIwlYI0fCiBEc4vRLqKqyj5m++grWro0sbP2bmcncbmE5X7RpgxdeaKgqEIGuY0MDCEG5uZGNaBp8+CGvrgZovCDDYamykufns8svJ58vMn4haOVK6bvvtMJC7NABxo71DR48yO12WxJaS77m6QnOeTAYtJMwIho8ePALL7wwfPjwn3dsKfwkSJGwFOLCImEAUFVV5fF4Un6tUfhfkrBgMPjyy/Djj8q//kWhEEyYEMk9mjyZnniCfD6yHk9EsGABPPwwvPsu9/mcs7tcLqiqEhMninjZ4pxDbS3IMr39No8tt7Tgdkd6UR85wn/7W2F6xpo/JjQN9+4VQgAipKdD27ZCksDjofnzuSy3qFiBCKurae5c4+OPARHy8+Pmq8XiD3/Qhw0DAGxoiPTnbtxCkqnswAFx113CTDjr3FlcfbUoLgbDwMrKaDtcjwc9HpKkZp9zTn//uzhwwDwCrLbWWaMaPJjPmEGxUd38fDQbLx49SnPnitheCBbS0uCKK8DrbWk9aV4erlkjPvmESkthzBiOCKJRJ7Tzm0YZzAQtXQqrVkWTPMYgLw9MCgcARUUweTJXVQyHmd8PJoErKGCSJIhI1+Gjj3hFhf0o4YkTWFiIl18urJ5IRLRli/LNN1pBARYXw8SJeQMG9AMAq3E4IprVf1E9A1KARhLm9Xrt7a169uz5ySef9O3b9+cdWwo/CVI5YSkkgjWbh0IhSZJOKvnpdICmaYioqifRXdGEYRj19fW6rg8erNTXS8uXw9Ch8OtfQ0UFbdyI27djSQl17oyKEmEniNCzJ0ybBps2YefO5NgMh3PIyEBFoVWrnHfKGCgKBIO4fz8lMHo0N64okJ7OjhwRa9aA3w9+PzQ0QEMDVFTAoUPAOQqBnGMwiFVVLBzGqipWVCS6dGnR1zeJTu/euHCh8PnYSaUabtqEEyeSLIOqgqpiKBT51klXzMzEigpeUQFXXMGvvJKysgAAamoc8rp0HYJB4JxJUlMePWPYvj0uX05EUFvr8PaqqnTZZfyCCyBW2fH50CIlaWmYkQHbtjkPMi8Prr+eezzQwtbjaWlMUahtW/R6aehQjtgkgNkZGBHU1Vk9A2jFCli50kFmy8qSZDmSH1ZYyC66SHDOqqspGORmJpnPx9xuAQCc46JFVFkJjUIbIqLfz1q3ZpdeqttPx44dbPlykZdHxcXskktKBg8eYLq6qKoqy7LJuszmlaYhmUnOzKZbKUJmHhlFUeyH4qGHHrr11luzs7N/xoGl8FMhpQOn0CKkZsOfCkQUCATq6uoQMTMz0+PxXHGFrKrw2WeACP/6F77wArndcN99ePAgBALN7C6zs2H8ePzmG1y/HrhTqCoUghkzWAKVS5YhPR1Wr5aWLEk0yEAgkqN23XWSZdYAAPX1EJv7r+sRKjN3ruz3J/v+TaCCAvY//yO3hD81Hxv7+OPI1aiqlJvrUBHpCM7pjDPw73/no0ZFeFIwyHQ97rqhEFVVUW0t0/XIPFlSgmPHQjjMYg9+QYH4wx/4kCEO20GEqNqFwYNxwACHG6pdO5g5k6eno663KH8fETweAoCtW+HTT+mbbyJiVayepGmsMRBJa9bA8uUOV48kNTHF7Gxp9GiqqzO1RmRMYoxJEktLIyGEptFHH+nHjjV5tyIiESsuli6+2FDVpl2XlcF331FGBm/TBq+8stuZZ/a2D4wxpiiKy+Xyer0+n8/tdpvvM5qmhUIhv98fCATC4bCu66IlPadORcRaVBBRyqLiVEKKhKXQImCqh7cTTvaw6LpeW1sbCoU8Hk9GRoaZ50FE112H1dW0bBkAwNSpuGwZpafjrbeiYYDfH13JOGYMzp0LF18Mb75JsaWCQuDNNye6rxUF0tLg0UeZSbPiwe+HUAjS09msWREH0epqiLeKYUBNDYbD+NxzJzGlGAb06wedO7d8jQhef12yuGB9PVRWQiiUaL+aRsuXG7Nm8SeewLVrIx8KgQ0NyR/t4TBVV1N1NdM0BgAXXcTMnHQ7Bgzgs2eLoiLnLaSloT3PHQAQcdo0jEoa69YNrruO+3yQ1PbCtmUGQCtW0HvvcSKxYgW8/XYTZbRABI3flNavh6+/dk41y8hgAKBpjEgqLdWIePMLD7OzJUlCIunzz+HEiYjNmBCCcxEMUm4ujBsXMgmxKWLt2cM2bEC3mxcXyzfc0KdLl/aGYWiaZspdUbwKEU15zOPx+Hw+j8djukMbhhEOhwOBgN/vD4VCmqZZcczTE4FAQAiRcsw/ZZAiYSnEhf31K0XC/pcwBbD6+npLADMPqZl4J4S48kpZ02DNGgKALl3wu+8oNxfmzQMhMEoPc7vx0UdZRQU88ACMGkXPPUf19c32NWwYKy1NpFyqKtTVsZdeSqJumi0FL7hA6tIFKyshQRoTAHAONTX4wQdSeXkLdSmsqSEivOgi5vefrM6KL73EACAQQE0jIqiro+pqNIzo7fj9YtEi49pr+bx5aFpUvP66VFUFAFBf36KW3iZ0nWpqqLISJUn61a+apk1FoenTjauvpniBekusioLLhb/+NbNSx/r3p6uu4i4XNLYeSg5EVFX69FPxxRfcMmLdv589+yxWVzc7DpZ0t3kzLFnizMBkGYWAyko0DDr/fC02y1FV0eUSnMPnn4ujR8kMdzImAci1tXJWlnTeebqiCM65SbDKynDZMgAQxcXuO+4Y2qVLByv+CI1RNpOQmde/nZOZWWKKorjdbp/PZ5YEybJstu4IBoN+vz8YDIbDYcMwTu15KVYJa2ho8Hq9qcyQUwapnLAUEoE3xl00TQOAlEVFFHRdJ6Kkh0XXdTMDzHzLN59DJgMTQlgFYp06sdpaOn6ccnJAVeGCC6CsLJJ1LgTaZ928PPR6YfVqqK2FZcvgjTegvh66dEHr2dmhA378cSKSIcvw/fc4ZgzF89ZqHDmoKgKIL74gq8VNPJi+smVlbNIkZ2srC0JgTU3EZqKoCDds4AcOMJfrJNLz9+1jAwY0K2MUAoJBIGKKQohQWSnefZc/8ghs2YKc27eLu3fDWWeB33/SD29ZBp+POnXCmhqoqGAFBTRzptGrV6JBZ2RgPMuP9HT0+WDbNhg9mi68UDAGRJEK1pbA45Hef9/YtMlqsM0afShw7Vpo1YqZNYxWBtv338Nnn0UzMJeLZWSwjAwkYvX13OeDKVMMx0siN5cR0eef06FDkfERgd8vVVWJ3Fzp0ksNjweszPqdO+Uvv+Q+n1Fc7L755gE5ORlEZLq8mr6jZhq+9WpnkjArGwxi3gAZY6Yhn5lPxhgjIiuNzDAMc10rPa1FR/CXAHOKsGedHj169J133rnzzjtPpa95OiNFwlJIBIuEtZBtnG4ws1USOHeYJZCBQECSpPT0dFVVzQeP9cgxm8CYC3POfb5Afb2+caPSti2oKvbuDZoGGzdCcTFw3oyH9e0LmzbR0aOo6xAOw/r18PrrcPQodOqEWVmQl4c//AB791KC8n/GsKrK2YjfhNsNGRkgy9CxI1u6lB8/nnzSJ4JDh7B7d+rQIe5mTdMye1pVnz744Yfk95v9E5PuJLKRlSvpnHNEVMPKcJjWrqXXXqN//5vKypwHXFmJui46dmzRjuzIzo7k6XftCrqu/+53ok0b5nYzxsCxVIIxSJy606YNZGbyESPIvAQ0jQWDLVLnAgFcuNDYs4dDJPbHmj+Rcds2IMLiYgqHWThM27fDxx8bjaNCr9fkXuB2kyQREdbWks+HU6Zws14hCmlpTFXFkiV08KBVqcOqqiAYFJmZeNVVwuNpGvb337u+/FLPz4fevbPuuKM0Pd3TaPGqaZpp4kqm1iXLssnJrCQ2q0OlPV4ZS8hMncyE+QJjNRq3CFnjkfllMxVTJrSTsP3793/22We33nrrL/2rpWAiFWNKIRHCjTlHfr/fMIzMxLLJ6YfEh0XXdb/fL4TweDxut9ucNK3uLvbsabMxVDgcZox5vd5Dh+Dtt+k3vwGXC4SADz6gjz+m+0fRM18AACAASURBVO7DkpJmsa3jx2nCBFFVBXV1TealjMH558ONN2LHjjR8uEBMxMMAYO5cfsYZ0ZOAqoLXC3afhYMHacwYPTbe5wifTyxeLLKzHfiEycBiI5tPPGEsWgQQcW0VyVQ3qqvDcJhmzdLPOSeypKbRqlXi/ffF4cMIAC4XZmSQI6ULBDAY5P/4By8qOonHmM+HPp8AAL9fvP228HrF5Mlkfw5yjuEwBAJk8cvMTLQb2UdB0+Ctt/i2bXTLLSI/H4igqgo4Tz4hV1bCwoUUCESaZyd4GHfoAMOHU3m5WLCAKwp6vUxVgTERlaNWU8MkiaZM4Tk5DntnDPLy2H/+w/ftM+siWU0NhELCPCa/+Q1kZETonRBs3Tpp6VKtRw92/vkFkyYNtHrbm7zKpErWq50ljMmybCdhJosySZidTpl8K56vmLmiFQ9tHDyTGvFLZC2hUEgIYbeDXrp06d1337158+afcVQp/IRIKWEpJII1l5lTZ8qsNQrxDouZAeYogJkFZVECmEnmzDIxxlhmJuvWjWbPhj59ICsLzjgDy8th5kyhKNC3b5Me5vNh+/b4ySfkcqGmRcJYRFBWBm++CVu2QL9+sHEjqGqiMN/+/ThhQtOj1yyf9HqjfRYyM1FRYMWKFr2z6ToePy6GD3dwjqitRcfcsn798OOPSdNA08AwWOIxh0IRe9Uff8Rx40Q4DJ9/zufOpaVLob7eOqoQCABik82H9XldHQmBBw7A8OEO3RUdwRhkZoIQ8N13/JFHaP9+2LMHO3Qge3K96QDi9YLXi4rCAKKLIu04fpwee0zs2wdC4MaNeOaZBMBMZpMY5eXw0ksGokBsCkHGw+HDDBE4x2AQ3G6SZQcfWk2TOKcpU7hlwRqFzExp2TJj714iwvp6VlUlzKw1rxemT2d5eREGFg5Lq1fjihXh8eOVSy5pO3bsQLvJuyV9qarqcrlM7mWGFHVdt0og7SJZbNTSLiG3PGppiWT2XcAvJGppOjXaM8C2bdu2adOm3/zmNz/foFL4KZEiYSkkgvXOaqZfpMxao2DWeUWRMDMDzDxcVgaYXQCz+y6Gw+FgMIiIPp/P5Grmn7xeNnYsXXsttWqF7drB2WejEPDPf9Knn9KwYSwvL7KvTp3w2DHatg1cLoyqlDxwALZtwxkzREkJpKWBaccfi4oK7N8fWrcmAEhLA58P4gUEzzwTly6lpG7vJnbtwv79RW4us/tHmPKV4/KShG43maWLnEM4jKqKjpKHYUBdHQGAJEGrVqKhwfjXv2jDBgiFHBbWNAiHm0U56+sjocPKSszLEyUlLXoSZ2bi0aPG3Ll86dKmTLtNm3DIEIp9MUEEWSYh0GzcaXqqNfYFAiJYu1Y88wxZAzYM3LgR27UTSd9xdu6kl182vF6mKKYPRbO/EgERGgYzDAyH0TBw0iQxZozRuTP17g2GgSdOxEY+MBCgCy/k+flxz8vGjXzXLgoGpYoKYZ0+rxcmToT27c35AevqpPXrafduMWOGes45XaOsKGKOT4QtqaqqqqqiKCZd+/8hamkWBPyCopa6rkNzErZhw4bdu3dfccUVP9+gUvgpkQpHppAImll7BhAKhQKBQHZ29v/Z2epnQTAYDIVClmuiKYCFw2HTRt9yoLALYNa6nHOz2tzlcrlcLscDaxj83HPFuHH4u9+By0V/+pN44w3QNLj7brjppghH8fvpwgtNTQXq6qJvaI+H3n2Xd+gAmkbLluF//gOrV0drUS4XLVwoCgqcg3d2rFnDZ82iqqoWTRtnnMHnzhU+H6anCwCor8dgMNFqRHTttUZ5edNxyMxEt5uaLwOaRu3bG2edRSNHos8HQtCtt/IjR5IMxuvFtDTSdbQ73bvdNGcOz85OckkHArRsmfHttw5/at1a3HWXQ2kk5+h4lAIBeucdKisDSQJZJkkCM8ksFEIAftNNFK/5EhGsWyc+/JBLEubnm0QEOQchkPNI30Z7pSBjcPXV2KdPszNdUwOrVsEPPwhrMcOQxo7VCwuddyoEbN8ubd9u1NZCONx03DweGDeOevRAxjjnrKICDhyAYFAMGiSXlvYtLm7ruLWW4CeJWtpjl05HkqyQpbULK2r5f62NUiAQYIzZX/NefPHFlStXvmNvoZXCLxkpEpZCIlg2ieFw2O/3p0hYFILBYDAYzMnJAVsGmNfrtUhV0gwwj8cjx/a4sUEIceGFwuOh+fMxLY1+8xuxbBkQQefOMH8+dusGALBlC11yiTCMSKwtagt9+9K//83dbsjIIABoaIBly+Crr/Crr7CuDkIhIIKRI/njj1NmZlwZzMLs2fp//oOVlS2aOe69Vx81CrxelCRqSTXipk3irruaheQ8HkxPJ0QoKRGDB4viYuzSRUQ9JXftEvfcIxzda+1QFExPh/r6Zkv268d///u4qxgGLVnCV65Ew4gbKBw+XFx2GUXdFrW1Dprfvn3ixRfF/8feecdXUaX//zkzt7f0hEBCKKEXadI7IogdxYIirMqu9WtZy7LLDxHLul/Lquvq+lVcCypFXXdXF1bp0pGOKM2EmhiS3NxeZs55fn+cy2Ryc9MoSQjn/eLlK97MnTkzd3LuZ57zPJ/H74/fg8VCnE4AgPR0vP9+rK47EWHDBrpsGSOERCJSNMoUpbbrbzTCtGnQu3fixkelpbBxI+zfz4xGacgQ2qpV4lNjDLZskQ4epIFAlQ0sFjJhAuvYkVitNBSSCwpQUYjNFs3MtF5xxcCMjLQah9VANLWkj1rpSyy1vymmQ3svnDatrV2QaeG06m2UeM5A08541UXYq6++WlhYOH/+/CYcleAcIpYjBbWhOWJzhx4tu1zA4QsoFoulQRlgwWBQURSeASbXpXoIIVOnSl99hX/6E44eTW65haxYgeXl4HbD++/D+vXYrx/p2JH4/bB9O8gymEzx65K//EIMBuzbl/tcEEkirVvDkCFwww1ot6PHgxUV8PPPUo8eLCsLjMYEXXf0dO9O/vlPxvsF1anD9u0jV1+N6enEZIJotG5frlatyN69tLiYnD53yMhgI0bAk0/Sm2/Gzp2Jw5HA/CI1lXi97NChOnaelkaMRma1ErNZUtXYYIqLpdxc1rp1grv6p5/Yq6/Sffskg6G2cR89Slq3xuzsylciERKnOBnDlSvZRx9VaTqukZREJIkRgqEQHDyIffvGkrf4P0T89ltlzRomywSAeDx1yE2bDW65hV1ySY0uITYbdOkCeXkkEoHsbJbQcMpikX78Ufr+ezUcxqqvw/jxmJ0NBoP0/ffGpUtVACknR3G57NdfPzQt7Vw20mnMVcuEbZR4GlnTrlpGo1E+PO2Vb7/91mAwTJw4sZFHIjhPiEiYoDa0SBjPc0pKSqpTNFxU8AChJEnnPABWnYcfVj/+GN94g/TvjzfcwEpLAQAoBUrxoYdg+nQyfDgrK0NZJgYDmEyotdwmBIxGXLuWJiVxS9X4P3pVha++wj174KabMDcXMjOhdifI//s/df58RCTl5Vi7IDAY4OWX1XHj+Pcf8fuBJ9TXwi+/sHvuoTk5OHAgjh0LvPBUkiA5mXi9rKb5ilJ88EFaUlLjbpOTCW96yEEESiW/H6NRdDjwT3+iDkfl92tFBS5aRLdsAUmCjAyIqyWsDiE4dy7jiXqnixwrf+v344IFdP/+xO+1WmPLtRrt2+OddyJ3F1MU/Mc/1B9+iH39V1RI+mXB6qSkwE030Q4daqvKBIBoFBYtgr17VQBwOCAvj+TkQEYGJCUxl0uyWmHLFrZuHfh8VT5dkwkGDoRwGDweUljIGMPBg0lenpqdnXLNNUOs1sbzr6l91ZLrMDgXq5bagfiLjb9qGQgEjEaj3qLiySefTEtLe+aZZxrh6IJGQIgwQW1oc5yqql6v1+VyNVQ6tGB4EzdVVeuZARYKhSiltWSA1cnzz6svvIB33w3XXgt33sl4Zjdj4PNhly4wdSrOmxdTRWYzsVqrrJH174/vv08lCRgDzSg1jqefpl9+iW3awIQJMHAgtmsHWVnE6SQGQ5XRhkLslltocTEAgNtdo5O+2QwuF1os+I9/qNpySjRKKiqqOKyWlmJxMRYV4bFjeOwYFBbCpZeyGTOqzEtGI6Snx0Zek6H8Tz+xOXMS9xi0WonLlTgyRKkUCOCll6p33w0AoKq4Zg1bsgT5SaWmEqOxXsZdaWk4ezYzmSAQqBIGKyhg772XYAmSQwikp0P1isWuXfH225misAUL6LFjMQUWiUgVFbUNJjsbpkyhycmQ0GlCIxiExYvpgQNV7DBkGVwu2elknTuTHj3oiRNyWZlqNAJvlG40oqqSdetUjwesVkMgoBqNZORIbNOGdemSPXp0/yacFhpt1VITZHGrlppF7fk4O7/fzwOB2iv3339/z549n3jiifNxOEHjI0SYoDY0EUYp9Xg8TqdTtMvgaBlgAKAFCM9HACyODRvorbeyDh3g1lvx6acrvct9PgSA7GxWXh6rEzSZiM1WRYfNmUNvvjnmol5RkSCIhYhXXklPngQASEoCszm2/7ZtoX9/6NEDOnSAVq1IUhJZt47OmhX7bUUFVF9ls9vBZottMHUqnTmTx1PB58OiIrJyJT18GI8cgWPHEjpW4AsvUJ7uBgAGA2RkVCqVSIR4PCzhvPXWW+qKFfEv8mhWdaGjhzHy2GPU71c/+IAVFcUumdkMyckNmB7792fTpoHbjaf3iatWsa++qm0PycmVPbP1IOKYMfToUXr8eOw+QiRlZbW5iHXoAJMnqwYDZGRIslyjVquogIUL1dJSWYtymUzE6ZRMJgaAZjO58kqWkYElJVWO5fORpUtVSZJkmZSVUYeDjB7NcnNJz555Awb0bj4pCtrio9YNDHTuGHq3ME1XaRkXcYZktQS69IJM03OaIDuHhmSIGAgEzGazftadPn36+PHj77333nNyCEGTI0SYoDY0EcYYq6iocDgc+meyixN9CaTJZAoGg8nJyTyPpJYAmMlkOlcZdV4vvfVW9sMPMG4cLltWqcP8fmQMHniARSK4cCHx+cBoJHa7XofhN9/QNm1i23s8WN3n/fBhvPlmyl93OhP3PQSA9HR84gnapg0gAmNw4gSUlXEnT2AM2rYlXIExBowhY3DggLpkCdG6bnPL1rh8Iz3Z2ezPf2ZmM0gSZGbGSggrTwOJz4fVay0VBR94gJaVxQ2V1J7UBQC9erHLL6cffYQnT2qVDZiZSWqXbtUZNw6HD2dQ1xIkx2gkKSlYfa0TEYcPpxMmQCAAixfjyZMIAIGAVEu78d69ceJEKkngcEgOR42bFRXBkiVqNCqVljJEsNkku53IMuV3iMtFrrqKJSWx8vIqxv2lpWT1auZwgKrCL7+w1FQyejRt107u06drly6d6nNZmgruQ9agVUsNvnH9U/urr1pq/z2b8VcXYZMnT54+fbqwqGgxCBEmqA0+fwEAIrrdbrvdfpF3LoorgVRV1efzOZ1OLsLiAmDRaDQcDp+TAFgciPjKK/T559nQofjjjxgKxQ7q9yOl+Oc/s8svhw8/hPnzSUUF2O2VufY5OfjVV5RP6Tx+Vj2I9dZb7O23Y9/BdnuNjqNt2+KHH1J+Wojg80EggJIEaWnxmgkAystx5kyqbzSOCKEQeDw1zj/XXkt/9SvMzARZTryNqpKKCowLDu3ezZ55pjJO5nJx//3EyDIMHcomTmS5uQQADh9m//u/jFISDhNE1OeQ1YdolHi97OGHmSTVtgSpkZ5OJCn+EBYLmzKFdesWyx9XFFy6FLduhZrKUQmBYcNw+HDevwhqEo6IeOgQfPUVBYCKCslkIlYr6o+emUkmTaJWK4ZCcnl5ZYz05Elp505qMDBKpaIizM4ml11G27QxDBjQJyenTf0uTLOgoauWXF3pg2T1EWR61379qqUmyBr0GMYYCwaDvHm59uLll18+a9asa6655iwuhqAZIaojBbWhJbQCQDgc5j7UTTukpgITmeDzolE+NWsmRqArgTSZTPUpgWwohJChQ6VRo3D+fJg2DZOSoKCAAIDJRFQVVq4kQ4fihAnwq19BmzZw8CDx+2NJ+l4viURgyJBY2r7JRBiDuHXJSy8lK1cyHk9SFEAkCaOfHg+RZejTJ7YrsxkkibhcCRQYAFitJCMD163TnwIYjWC1SuFwYnlx4ACZMqW2BUFJ4s7+UjRauU1WFikuZkeOAMQW2uLNIzg2G0yYQO+/H4cNg6Sk2BapqcRuxz17UJLA60VVlQghkpR4D3EggtcLZjOkpbH//pdVVNSxvc0WnzuPiHl57M47WW5uZRWeLJMuXUg0Kh08mGAFVpZhwgQ2cGBsPykpUsJm4YiwfTt8+y2lVAKQrFY0mZheq7VvL02apJrNQKlUWqqNCk+dMu7eHZUkZIyUlEDHjuTKK2mbNqahQwe1apVV90VpTsTVWppMJr5uWFOtZXVxFldrmdCyn+gaA/C3a7WWvNxSW8GsT60lIiqKojnN8lf+9re/XXfdde3btz9fV0rQuIhImKA2tEgYALjdbovFcnGa5tfkAaaqaiAQ0B6U+bTLGDtXGWB1cuoUu/NOeuWVrFUr9swzhJs7BIPocuGiRax169hmW7bAsmWwfDnhemvRIrVnz9ivECEYxFCoym5LSvDqq6lmdaF5jFXno4/U9u2BMQgEIBBArcFidRDxmWeoXodpAygvB72Q4qSmki5d1JdfZnUqWMaIx4PaHsJhfOAB6vFARkYCRZiRgRMnshEjIKE9PSK++y5dtoxoRYiEgNksWSy84LTG2TIUIp06qTfeiC4X+Hz42musvLzGARMSX3SJiKNG0fHjIa4GAgAiEcnjYYWFuGRJleiayQTXXss6dmSn/5ckzMenFL79lmzZwkIhJsuQlhafHterFxk6VJEkggilpVI0ygDAapWOHCE7d0YBAJGcOkV69ICxYzEpyTx06FCHw1HjuV2ANGjVUp/af/arllq5ZUJNxpMZrFar9hSHiJdccslnn33Wv3//c30ZBE2DEGGC2mCMKacTpysqKnhcp2mH1MhgXSb4vD+dehrtD4pHDXl47LxmLqsq++MfaWoqmzKFvfYa+fBDYIyEQtiuHX78MbPbY5tJErjd8PHH8PnnxGTClSupXk6HQhBnbbVkCXvuuUo5xUsdq59HRgZ75x1GaWW5pdkMNcXDPB68+27q8cS/jgh+P/j9lW9JTo4Fin79a/Wqq+p1HaJRUlERCxd9/z17912MSwXr2BGvuIL171+HIa3bDQ89pCSUUCaTZLGA2YxxZ5eTAyNGqJ10+VEVFfj66zXGw+Ly8W02dtNNrHPnBN0M9YYXfj8uXIjHjiEA2O1w440sO7tyJ3H5+JQSRZG8Xly8WD1yhIdeoFUrSb8EiQjDhpHevRV+XJ9P9nqp0UiSkqStW+nBgxRiuYPy0KHQpw9NT88aMqRfyy7NOftVSy7FzmDVUu9/wQ+hqmo4HOb9ZLX3dujQYcOGDZ07d26U6yE47wgRJqgNvQjzeDxciDTtkBqTBpng8wwwQojJZOKyTFt30JqunL9S9mXL6N696p13sgMHYO5csmcPCYdh4ED21luV/vKEgNMJHg9+/DGUlMDTT1dxbQiHq8ggALj7bvr995WvGI2QnFxFhykKBAIwYID6xz9WeaMsQ3IyJFwaW7eOzpuXeM6JRkl5OUMEl4tYrZWLYu++q2Zm1uuiIRK/H4NBtNvJZ59Fv/469nq/fmzSJKzP15aqkvJyduQIe/55VpP1BgAYjcRiISYTpqSwa69lnTolqFssK8PXX2f6NLjT74WUFOBhMERs357dcgtqS6JxBAKS3rCeMVy6FA8dwptuovqFWp6PjwiqKkWjJBRiqoo+H3z+OS0ri22Wni6bzZULz5IE48ZBfn7slWhUKitjKSmywYArVtCTJ7UkBMPll2NWFnTu3K1v3w41X7kWCFdLWpCs9lpLzVcsYa0lANTZRklfa8kFGQAoihInwjIzMwsKCrL17sCCCxkhwgS1wbUF/9nr9UqS1MJWImqizgBYfUog+QIEn8T5GoTe2vucC7LCQvbvfyuTJzOjET79FP76V1JeTiZPprNnV/6NEwIOBxiNGAjAqVPM5cKkpMoATDRapetRIIATJ1bJpjcYIDkZJQkohUCgcg3x8ceVSZOqnAsXfAmLK194QV25MvEp8BieToEBAHTowOqzKKkRjRJJYorCZs9WO3XCCRNYq1b11HA87IQAsGYN++CD2hLzCYFLLsFbbmFt20qMoaKgooCiVDnfX35hf/0rxmXo6/LxccwYOm4cyHLi4SVsQFlYiBs3qkOGYGZmLBvPaCRWqxSNYjhcmTdWVgZLlqiBQOx/7XYpObnydEwmuOIKbN1as8sigQCx2yEcxqVLqVYtYTIZx45lkmS94or+2dlJtVyNi4GaVi31QTI4d6uW+rQzWZZ/+OEHp9OZm5ublZUVCATOYEVCVdU5c+b8+9//Pn78eL9+/Z544okJEyac2aUQnENEYr6gDrTpJhKJAMDFUB3J2wOoqmqz2bTHUH0ATP9gGo1Gg8EgIUQfLePw2ZMbXpvNZj5N8+BiNBqNRCINytKtk+Rk0quXtHIltG2L3bvjuHFQUgJLlxKbDXv3rtwsGgVZJlYrJCXBH/+Ib7yBJhOmpYHNBgYDMRorux6ZTKR9e/jmmyreqgCE21vo0/m3b5fGjWNOZ5VTiESAMYl79+u55BKyfHl8FhqHR+NkuUqCudtNkpJYPZdfGINffsENG+jy5cRmY3fdhXGjqgWfj2gqql07cuoUO3Ys8ZatWuGdd7Jx49DlInY7MxjQZAKrlbujEatVMpuJLBOHg3TqhDt3VvrZ2u3EbGaI6HDgtGns0ktBkmocntdL9BcZETZvxv/+l4VC5NAhSVGk/HySno6Uyj4f1XvYHj8Oixer3C4OAAwGkpYG2qfgdJJrr8XMzJgCU1XJYJBMJubzkX/9q1K3ZWUZe/WiycnZt98+xOW6GDNB49An3ZvNZi68AIBn3EejUZ53D1WftXi0LE6f1dlGyWg0Go1GXjdgMBgYY/fdd9/s2bPfe++9tLQ0vmVmZmaDUk5vuOGGTz755LHHHvvNb35z+PDh2bNn9+rVq1u3bufwEgnOABEJE9SGPhLm8/kQ0eVyNe2QziuMsVAodJYBsDrhCxDaUzV/MW7WPpuz2LlTyc1VIxEAwE8/JW+9BfPmseHDq2xjt4PZjNEo/uY3dPt2AICMDJg2DcaMIZmZRB8Pe+IJ+u23aLWCxQIGAxISc5eIyyHr3Zu++mqCpDGDAZKT420mtmypEp+D02lh2j55YaPuXXUvSgYCuH8/LlyImzdXJqhNmYIzZlCjse5ZLhwmXm+V0Jeq4rx59PjxKptZLHD55WzcOCQk5mFWp5dYcTH85S/o9YKigNGIANihA7v5ZuZyxX/O+v9FJNEoyjKRZZAkDIfh88+VgoL4Y+Xny506qRkZeNrTFQ4cIF9/rejn9aws2WCIqbn0dDJpEuW2I5GI7PNhcjIxGGhJibRsWZRSvuBOMjKMqansxht79++fU+elu8g5f6uWvGaTLz5UVFRs3rx57dq1CxYsiEajgUDAZDL1799/2LBhc+fOrTNLZPPmzUOGDHnvvfdmzJjBXxkzZsypU6f27t17Pq6JoP4IESaog8jpwIjf72eMtWARdgYZYGdfApkwEVhb49DbXjSI8nLVYFBCIaAUy8vhlVfgmWeQ+20qCjeeAKsVrFZ0u3HaNKoP+TidMHkyycjA4cMJN0q95x519+74QygK6BsQAcB996lTpiQYDCFV/Pc5r7yiLlsW+5kx8HggEomfixwOYrXGEtfat2evvELjVu4QobSU7d6Ny5fjoUMkoQHsvfcqo0ZJDgck9KbnnF74S5DXNXcu5cEhQqBPH7zxRuZ0xn6bkVG3DSznxAn21ltYUkKCQTpkCJs0CWsJgEHVUkdEPH4cPvlEjevLDgCIpLQUGMP8fBgxAtPT2fffk7VrqzjwJifLdntMgbVtS8aPV41GEgxKPh9TVUxPly0WWlBAVq9WAEhyMklNNRKCTqf1/vsHpqRcXFU454RzuGqpKIqqqnqBtXv37qlTpx46dGjv3r3r169fv379vn37duzYUacl7H333ffhhx+eOnVKK29///3377zzzi1btgwYMOCcXwRB/REiTFAHmggLBAKqqiYltcDUkMYJgNUJP6I2g+udL/TTdz1RFOrxKAAx74YDBzAvDzMytGMBY4QQUFX84QecPp0Gg4BY+Q8AUlLw+uvxqqvAbodf/YpVX0Dk4klbCDMa8Z131Ly8xBfEbid6M/dgEH/9a1pSApSC2w01dYSUZeJyIQ9laZWSioLHjuHmzezbb8Hn44dDr5ck7A5uMLCnnqJt24LRKDkcUN2Cldv3x6VzaezZw157jWVm4k03sfz8ytf5QmTCtyTkwAF87TXlmmtY16513DCEQEYG4QWYlOK6dbhmTeI26R6PFA7HxmA0wqRJrLgYi4uxpCT2oZvNRPOk6NaNDBtGQyHJ54uFYbg+27ULjhzBdu1IVpYUDGJBARkxou1113VvPs2ILlxq+otOaEKmNRrXgmT8LXyS4X/7GzZsePTRR/fu3dvQT2fixInFxcU7d+7UXtm2bdull166aNGiKQmfnASNxUVqvCmoP4QQLWbeIiU7T+qqJQAWlwHGA2B2u/2ce4BpLpGgS9Hlzhd8UVibvnlef+17Mxrl9HT52LGoqlKzGTt3JocOkVdeoffcA3l5hJDYEqHRCB07knvuIc8+Gy8pTp2CTz8ln30GADB5Mvbti4WFUFhICgvh2DFCKUgSJCdDIEB4ByFFIc89J7/5Jkt4YQIBVBSSlBRzr7DZyG9/Sx55BN3u2m4rStHtBodD6tSJ7t5N2rdXvvmGbNzIZWLl95DfLwWDiSWRqkqvvgrz5lGHg7ndYDAQh4NYLOvMVwAAIABJREFULJWVoYEAUZQa5VRWFhk5kl13Heqvt8kEDVJgqopHjtDsbGjTpu7vzpSUmJGE34+LFtETJxJfnXC4UoFlZsLVV9PkZOQyERHKyuSTJ8FolH75RT11Ci65xNClS/TkSaSUqSooimS3ywC0rAzatcPu3aGiAn7+mXm9pvvv79O5c0bCIwoaStxftD5IxqvOa1q1ZIyFw2HGGPcd1GZgj8djs9nOQB+fOHEiK6uKv25GRgZ//ZycqeCMESJMUF9angjjXUGi0ajRaHQ6nU0VAEuINn3zZp3652lt+tZHyGoaT26u6dQpum+fkpvL8vPh9tulO+5gubnsD38gXbsSXl7ncsGMGfLBg7hoUZXPlwe67HZiteIXXxhGjlRnzoz1OlQUPH4cCguhoACOHCF795K9exERDh6UFixgp9NO4olGoawMkpMJj2x162bo2DGydWuVkUsS2O2YkoKZmdimDeblQceOkJsLBgNxuSRVlQ8fpohV3hIMklr6KgJAWZn01lv46KNMlkFVsaICJYk4HMRmY4pCalJvhw/jsmV02zYAkCwWNmkSnv5ouMdEvUBEt5v97W+4bx8QIr32Gt52G7ZvX+Pfkc1GTCaGiIWFsHChWpNNBqWSxxMbdp8+MGaMqteIhJD0dOzcWTabWTBo+PprumZN9JtvkLHYdbNYyCWXsFGjaHIy+v3y0aPsxx+hY8e0WbP62WwXe3PY8wRfYdR67+oFWfh0DYV8mmg0Sik1m83anz+frN54443CwsKmOgXB+aClfa0KzjnRaJTfJOFwOBgMpqSktIx1Cp7cCgA2m433IIJ6eIDZbLYmb9x0Bs4XiPj11xEA1q8flJbiHXcwtxvz82H2bNK/P+Hf34oCN96obt2aYEIwm4nDgZKEixfT3NzEM4aikI0b2c6dePAgDB5MeZyMZ6/zf7qfSdu2hDFWWsqCQXzrLTUjA1q3xrw86NAB8vKglgtsMpHycjZ3rhqJxM4xHCYVFfUKSl19tXrjjVUGb7EQlwvCYQgGKxPCKIVdu9jXX7OCgipvnzSJTZyIAJCWRmpJL9MTibA1a+g//iFVNWDDyy7DkSOxeihTliE9HSiFVavoxo01HgIR3G6iKGixwIQJLD8/wZZms2Q2kxMn8J//VMrLq5y1yUQmTZJ69QpHo4ayMnS7Ydcu8qtfdZ04sWN9Tkpwzkm4asmfsr7++usePXp07dr1xIkTt912W0pKyvvvv9+mTYO7dk6cOLGoqGjXrl3aK2I5spkgRJigDhRF4XmjkUgkEAi0ABGmD4BpjR1rCoDxjRstANZQEmYBaxGyuELLQ4fUDz+MXnMNuFw4ZQorK0OjETIzYfZsMnIkkWVQFBwzhv78c4I5gedmJSfjF1+oWmZ6NYjHg5EInDzJZsxQE5pQAIDDQRwOBACvF8Nh6NCBvvACrb/zCSFk40ZcsEAlJObvWt93Aj70kNqvX+x/jEaSllZZzkkp8flgxQr1n/+ssePQ1Veza6+FpKS6j8gY7t9PP/mEeb1ynAbidOqEU6awOLOnjAwpEKALF9Li4tqm5UBA8vtZ69Zw1VXU6ayypdUq2WxElqG0lO3bh8uXK1r0i+NwkNtvJ2lptLwcQiFWUCD98ovpuecubdcuuc6TEpxvotFoKBQihJjNZsaY1+vt0qWLoijJyckA0Lt376effnrw4MGWhC23auWBBx54//33S0tLtffyxPytW7eKDkhNixBhgjrQRFg0GvX7/cnJyQ1KD29uXLgBsDqpj/OFqrJZsyKpqeyGG+DWW9mJE8iLpZxOePJJcsUVJBrFMWMS5yFxo9cePdjf/05raV0TChGfDzdtor/7HY1rDW42k+TkKh2NeInl8OHqo4+yBunbN95ge/ag252grXUtmExs3jyanQ2yTDIyKt0lgkHcsIF9+SWGw8AYiUZ5Blv8rmWZPPIIHTSoDhFWVsYWLlQPHyYAxO0mNdUcOJ14++1MC2q4XNKxY3TJEpXVuntFkdxuNnAgDh1K+R+iLBO7XbJYQJYZACKSoiLyzTd0//74dP6cHHLzzaiq4PFQRYGdO6U+fXIfeKCHyXSOG8wLGgoihsNh/mRotVq1Wcjtdr/++usbN270eDyHDx8OBAJGo7F///433HDDY489Vv/9b926ddCgQX//+9+nT5/ODzd27NiysrLd1cueBY2LEGGCOtBCLNzCNCkpSa6/eXlzQh8As9vtWqH4hRgAqxO980WcIJNlee1a/PhjZcoUeP55tm8fOp0xhyqjER55BAYOhKlTWVFR4pnBaiXXXEOffZbWcklUlbjd+Mkn6ptvstOHJikpUJNfl98PN9wQveGGBsxFDgf85S/Ktm0kFAKfrwHTWFYWmzeP5uXFyg/Lyti337IVK6D6HhAJ78ukNfPOyJAkic2YQQcMSHy8SASXL1dXrEBCCACppVyAQwheeSUOGoSEkG3b6LZtiasg9UMKh2HCBNq2LbNYJJuNnG4rro2HHDokLV4c9fmqjFCS4NJL4dJLMRhExrC8nPj9yfff36tduxZY7HzBoc02FotFezIEgGAw+OCDD37//feff/557969VVXdvXv3hg0b1q9fn5OT8+KLLzboKJMnT/7mm2+effbZzp07f/zxx0uWLFm8ePF11113Hk5I0ACECBPUgSbCVFX1er0ul6v5x4Sq04IDYHWSMOMEkSxaJDEGX3+N69ah01klE2vkSKYosGwZqmqCHRoM5Le/pTNn1qYYEElFBf6//6csX47JycRiqWOeoRQffVTt0aNea4upqcRsxkCA/f739MQJAqetVmuPIWnccAObOZMeO0a//BL37KnPOwg3V9NqKu+6i/btG1fEgPv2sU8+YeFwzAM9GpXqmaw2fjwFwP37E13rqmRmyldeqSYng8HAdMIrBiJs2iT/619hfd0oAKSnS5Mng8nE/H6KCG63ZciQzpdfnlefsQnON6qqBoNBAIibbQoKCm677bY2bdosWLAgpf6VIDVDKZ0zZ86//vUv3rboySefvPzyy89+t4KzRIgwQR1oIoxS6vF4nE6nsZa1qObHRRUAq5M454vSUvzkE1JQAF9+CWYz6BOzxozBOXPowoXw5Zdw8CCJK9MjBF57jU6aVJvIQIRAAF96SVm9ul5jczjwlVcoIbSWOUmSSFpaZWvw48fZ739Pg8HYxxSJEK+X0ZrFoSzjiBEwaJBqMtEPPmjA1KcohC99ShIxGonBAHffzQYOZLKMkoSnTuGnn6qFhchr4ACAMVJWVvfk6nLBhAnqqFHEYKAHD8K6dagPQDJGGCOUAqUEEa65Bvr1i1bXXqdHCF9+KW3fHtW/aLNJgwfLgwdHKipIMMgQ5YyMnOuv72axXEh/wi0YngQmy3Jcl+6VK1feeeed99xzz9y5cy/QlQdBPREiTFAHWoIRY6yiosLhcGhV1s2fMwuAWa3WC0tonjE8QrZmTfSf/6QlJbBhAxgMWvMcMngw/vnPNBxGrxeWLYP//Ifs20e0joQA+NFHOGRIvORBhGiURCIYiXBLWPzDH5TDh+s1ng4d2MsvU78fQqEE8o73QNSnlAHA1q30f/+3yjSmKMTrZXrVaLdjp06sXz82aZLkcsX2vHYt/fDDes1+lJLy8uphNrz1VtajBy5dSrdvR6ORGI3EaAQuyyoqiNbdPCEGAwwezEaPpsnJksvFGAPGiKrCjz+SVavw2DGmqshbiQNAUhJMn07at6/BrwKgrIwsWsSOHYt9FoSAyyWnpMDo0Sw3l5aUEEVh6enJw4b1yskRCfjNgpqSwBhjr7766p///Od33333uuuua3nPgYI4hAgT1IEmwhDR7Xbb7fYLoof3mQXA4ibEi4dgkC1eHEpJUZYvJ19+CYoCBgMYDNCzJ/7f/1EAqKiIOemvXQv/+hfZvZv4/WAy4aefst69kRBkDCIREolgNBq/86NH2axZtCZL+jjGjaOPPMIUBcrLK1UIAFgsJDk5QW9KAFiyRF20KP5FxojLxdq2pYMHs86deeskyWaroqTqo8MYI3Ej4SQn49ChjDF1+XLGWJUW7BkZRN+APA5E6NsXxo+nLhcyBhUVqKqoV3iIeOAAfPddLCrWpQu54QaakYEJY2CMwZ49sHIlKylhEMu9k8xmzM6G0aNVi4UcOQKUWsaM6dCnT94FXVLTktAmHKvVqn+m9fv999577759+7744gvRWvsiQYgwQR3EiTCbzXYGBdKNjAiAnRn790ePHAlnZ9N33pEXLEBFAVmG3Fz84AOakQGhEMoy0Xy/Dh2CFSvI0aPwzDOUEND3/K7O2rX02WeZw4H1SbG791565ZXcMh54U22Hg8R5MehBxJdeUjdvJgDgcOAll2CfPtirFzocQCnxejEYZA6H5HQmiK6tXUs/+qjGWRARysvjWyrl5ODo0WzAAF6KCAcO4Kefgt8f+21qKjGba1ylbd0arr6a5ubyXoGkvBxqCZj98AMeOMAmTMCcnAQZYAAQDpNly+iRI1BSgiYTSUoiRiMDwH79SM+e6vHjpm3bYNCgVnfd1cNiueBTG1sMNSWBHTp0aOrUqfn5+R988EGL7A4nSIgQYYI6YIwpp5d23G63xWLRWsA2Q2oKgPG+bNUDYKFQSFXVizYAVh1K2ebNkdzciKLgSy9Jn34KqgpJSfj667RbN3A6sboCP3WK/ec/NCVFDodZZibk5hK7PcGV/Pvf6XvvMYcDHI7EAS09L72kduvGmydCOIxWa22BpXAYi4vZ8uXqoEHYsSNUT6EJhQgimkwsYSSoJh3Ge0pqIkmWoWtXdtllrEOHWAsaOG2D7vPhhx/izz9jUhKJC7Zp2Gxw+eWsXz+qjcHtruw7VP3QP/2Ey5ZRRYGMDHA4sGNHkpsLmZnodCIPs508Sb76SgmHSSgkWa1oNqPZDEYj5OVBKMT27SPdu6c++mj37GxXTZdO0Mhoj3zVk8D++9//zpw58+GHH/7DH/4gApYXFUKECepAL8IqKipMJpMtzmKy2dCgAJiiKNwXUQTAqlNaqrrdobQ01eeDefPIl18SoxH//GfauzeYzeB0YpzQOX6cPfooLS6GYBA8HkxPh/x86NCBtG9PcnOhbVvidBIA+P3v1e++Y7JMkpLAbK49ZQrfe091OsHjQUUBq5XY7ShJGA4zrxdKS7GoCI8ehYMHoaAAeAlnejo++yytbiQbDEJFBSIiIcRsJhYLmEwsbvwJdZjHQ7hIsligf382fjxLSQEe/UJEQngVZOUdtWUL2bhRrV5PKkkwcCCOHUtttsoDeL1SIJBYgZWUwNKl9PhxJkkkK4sQErcZ5ueTkSNZaalkMJCUFGo0MpMJJAlOnCCrVimhELRu7bjjji4DB2bXcoUFjQwihkIhRVHiin4YYy+++OIbb7zxwQcfXHnlleJR8GJDiDBBHehFmMfjMRgMdru9aYdUHREAOx/88kvEYAhJEno88Oyz8M03MHcuGzQo5tqqlxQAUFLCHnmEHjsGPM9Jl78PAJCWBh07Qrt2sG0bdbmAMUhJkXJzmSwjpcBY/L9olIRCOH58tKgIi4qgqAiOHkWfj9hsYDTWuNiXlobPPktdp0M/iOjzEZ8vwfYmk2S1gtmMvIs5VNNhPh8JBllKCg4bxkaNQl3HP0SE0wlglXeO1UqSktjx47hgASstrTxQu3Zw5ZVqdnaVaxUISHyZNY5gENauZdwtjBCSlUV4J28NSSK9e0vduyuKglYrSUuLJYp5vWT1alpUxEwm8003tb3uuk6yLKIpzQjGWCAQYIzFJYF5vd5f//rXBQUFX3zxRadOnZpwhIKmQogwQR3wEDr/2ev1SpLkcDiadkhxiADY+UNVKX98B4CKCnjrLezdG3nnH6MRnM4q/qtuNz76qHroEABANApuN1Y37jIY8Ikn6NChCACSBElJYLdXmYIQIRgkPh9DhGPHcNYs1eutsgezmTidUFPeVUoKPvccTUriWjBxlaUeo5FYLMRiQYMBNR0WDJKUFDp6NOvXr3LlNG4JsupOIDU15r8fDuOSJbhrF7pcMGEC7d07vhNAOCy73fElpYzBrl347beV5QtZWbIsxzZTVSkcJq1awcCBUe4YlZQkJSUxAIxGybZtbOdODIWk4cMz/+d/uicnN/eUzYsNLQnMbrfrnwZ/+umnqVOn9u7de/78+c6ae4EJWjZChAnqQC/CfD4fADSf+UIEwBoHRaFebwQxiojl5WzRIgyFcNgwqX17sNmq5Hh5vfjEE+q+fQAAiODzoZaxrkEI/va3dPTo2MxjMoFmph8OE6+3SiniyZP45JOqxxO/E5OJOJ1gsSTQWC4XPvMMRYRotP5tJUGWidVKvvuOHjqk9uyptmun/6UWAKuyBHn6jZCeXtkBCQAUBf/zHzp0aHxjRwCIRqXy8vhWS8eOwX/+Q0tLK0ebni4bjSwSkcJhCIWY0wkjR7L27dnp30o2G2UMDh+GFStYWRlp184+a1aP7t3T6n++gkZASwIzGAxWq1WfBPbVV1/de++9v/vd7x577DGRBHYxI0SYoA70Iszv9zPGXK5mkeorAmCNDKWspCSCGEHElSvZH/5AnU4YO5aMHStdeilonvjBIM6ape7YEXuXqoLbjUq8xRU+8ACdODH2FkIgIwNCIUjY5KekBB9/XHW7EwzJYCBOJ1itVaJNqkqiUfb440pevT3hy8th3z7cuRNPnoShQ9UpU9BgAEpBUSAaxXCYnjahgDgFJkmQnl65aBgK4Y4duGwZC4dBlnH8eOjfn2nZb6oqlZZWUWA+HyxfzvbtowBgNBKTiRiNRFGIorBIBAHAaIQBA6B/f0WS+HGxVSvZZKKnTpGvvsIjR5jdbnzggc7XXddWPFQ0NxAxGAyqqmo2m81ms/YBUUqfe+65+fPnf/zxx+PHjxcf3EWOEGGCuolEIvyHQCCgqmqTl0+LAFgTgohHjkTC4Qil9PHH6fr1DBEyMsj110u33w5t2gAhEIngnDl048bKuSUU4tnxVfZ0zz106lS0WivXNFWVeDxY3bWhtBSfeELV51rpkWXicIDdjoRgNEp4Gr7FgrNmxQW0qkApFBbi3r24bRseP86FILd4gNRUdv/9NDMzdr4AACBx83pVhWgUIxG+0ooZGZIsMwAoL8fvvmMbNsRPqITg6NEwaBCzWODUKWQMCAGjkUgS2bcPVq1SAUCSUAukeb1auhh27kxGjFC1DExJguxsKRTCpUvJzp2KLMN11+U8/HA3m008VDQ7KKXBYJAxZrPZ9E99brf77rvvLi4u/vzzzzt06NCEIxQ0E4QIE9RNNBrl9wmXPsnJTWm6HYlENJcdEQBrQg4cCPv94SNH6Ny59MQJjEaBEMjLIzNnwqhRJC0Nnn+erlqlb8IDHg83GwO7nfCk+Nmz1eHD46cgbu4VDld53e3Gxx9XS0pqHI/BQNLTIRBALQ/MbMZZs9T27ats5vXC/v24axdu315ZPSDLkJkZ58WPt9yiDh6MkgQJhTsi8XoJpXj4MCxfzgoKGIDWbCD+B6MRR4yA4cMxOZkC4MmT8K9/Ubc7PhmuoiJWMpmRAaNG0datKzcwGCApSd6wAdasCbdrZx03rtUtt3RMSRHpX80RRVGCwaAkSTabTf9A+MMPP9x6662DBw9+++23z215k9/vnzdv3s6dO3fs2FFWVvbUU0899dRT53D/gvOHEGGCutFEWCgUCofD56Sb7BnAK4wURREBsObDsWPR8vLg3/8eXbYMGQNKgVIwmcBqhauugv37saQEi4rgdAsjQASHA/Vy5/HH1XHjEsxClILfD8Gg3tYBH39cLSpKMAyXi7u58o2JqpJwGINBZjLhk0+qHTrA8ePwww9s2zas3kDJZIpP6uJJYADQuzdMm0ar++IhkuJiWLeOrlxJq+erxWG3S8nJXBeya68Fu13dtAl/+aVK1QJjUF5OwmG0WmHwYOzZU9XuWKuVyDL56Sdp795o166OSZNyhwxpK4ofmyeIGIlEIpGIwWCw2Wz6B8IvvvjioYcemjNnzv/8z/+c8ySwwsLCvn379unTp1OnTu++++7cuXPnzJlzbg8hOE8IESaoG70IC4VCqampjTwAnpemVRhpNd4iANZMKC5Wvv468NZbES0NnzEIBCAQwLFj6cyZ7JdfYN8+cugQHD9OSksJpcRkqrSHePDBmEV+dfh+/P7KhLPHHlOPH6/cwGIhKSmgt3IwGiErCzMzMTOTpKURl4uuXausWZN4orPZICUlLiLFxVwsBcxmYw88wHJz9VqQfPklW7FCrZbolgCXS3I6mdGIffvCwIFqerq28IplZaS4GIqK4Phx+PFHVFXs2ROGDFFMJmCM2GwGs5kwhqEQFBaq+fnJw4d37Ngxve5DCpqImpLAVFWdO3fuxx9/vHDhwtGjR5/XB8KysrKMjAwhwi4ghAgT1I2iKLx9cTgcDgaDKSkpjRlY0gJg3Ce2/gEwi8Uiyo4ak8LC6JIlwa1bo/v2MVUlEPObALOZPvEE7dWrckvG4NAhKCqSwmEsLcWjR8lll7Grr453c9Dg+/F6ERFCIfbEE/ToUTAYIDub5ObSrCzMyoLMTGzVCrKyMCUFqn/smzfD3/5GS0qY3kw1KYk4HKzqgaooMI1rrqFjx7LycvjmG1y2jFZvJZmQ1FSpVSs2aBDr25fWZPqvqlJxMe7ezbZupSYTkWUCgJKERiPk5Rmys1mPHhnDhuWnpjZTh2QBp6YksLKyshkzZvh8vs8++6xt27bnexilpaWZmZlChF1AiIZiggbQyIt6+gCYw+FIGADTV31rAbC4eVDQOLRrZ3r8cVMkwn76ybd5c/j773HLFiCEIMrPPSdNnEhvu43xj0WSoHNn6Nw55kfvcKDRCAUFsGAB9XjQaARJinUQNxhAloks8x/A64WiIpaZCSNGsBtvVJOSNNuIOhg0CPLzpfnz4aefIBwGn485naSqw0WlDZi+BNJoxM6d0WzG4mJcu5Z8841K402+EkAI9OkjjRqldO1Kq7dR0vB65VWr6L59FJHYbIQQBMCkJOjWTcrJMeTntxk0qJ3JJGbp5k40Gg2FQtxDUf9MuGvXrqlTp44dO/aNN95ozt3eBE2IiIQJ6kaLhEWjUb/fn5yc3AgRJhEAu6BBxKKiYCDg37GDrl8vbdgAPh/Jz8fp05WcnATbm0zgdEI0yt5+W123rsZJiTEIhYiioM2G112n3HRTfAOi2gUZpbh0KfviCywvB0VBo5EYjVzeMVmukoOfno49emD37qxjR8bFfzAoeTysogJXrMDt29npiuF4JAk6doSbbsJ27WpcraSUlJdLP/4ImzZFdelxmJsr9e6NHTs6cnLade/eSiQyNn8QMRwO80ptfe4pIi5cuPCxxx577rnn7r333kb7KEUk7IJDiDBB3aiqSikFAEVRfD5fUlKSXMvT/VlzBhlg4XAYAEQGWDPE4wkHg95IRNmzB9atk7duhRkzWNeuNGE8yWgEux03b6bvvEODwcrXufYKhTASqZyxDAbSti0+9RRt25by2yFuNqv+zYcIfj9Zt46++y49nb7G34SEEJOJdOhALrkEBwygOTnMaKx0jvD5JH37o3AYV6/GTZtYIFC5c5MJevTAsWNZly7IGz4iEsaIqhJVBVUFRYFIBPfvh/37md9PtZ3LMnTrBn37mrp2TcnLa5eS0ix8+AR1wr1yKKUWi0Wr1AYARVFmz579+eefL168eNiwYY0ppoUIu+AQIkxQN5oIU1XV6/W6XC6D4XwtkYgAWIskElGKi4OBQFiS1AMHCADk52MwyBLmtssyeDwwf3503z4Mh0kwiNy5NCGE4A034C23MIcDzObKrLLqM1s4LGl2/F4v/vWv9MABRESrFdq3h549WZ8+GNcQ02AgDgdxOpFS0O+P/6yquG0bWbuWqSoOGoS9e3PTL2AMFQUUBVUVdUeHH36AHTtUq5Vo7S8tFtKrl2XYsKSuXVvn5KSK0NcFBKVUM4vWz4clJSXTp09XVXXx4sVt2rRp5FEJEXbBIbINBA2Af0mcJ+F+BhlgPAAmMsCaP2azMS8vCSApFKIVFX63O+R2K5s2wb59aDRinz4kL4+0agVOJ0gSSBJkZsLzzxs+/RQ/+EDV2ikmBJF89hnZvp3cdx9t1Uqy2cBqBYuF6QWNooDHg5EIBQDGwOuF8nIcMwYopUOGoL5BpB5JImlphAu76vIeEaNRSE1l7dpRvx8liRw4IEkSTUlhcTdjeTns2IE7dqipqbLLhQAYjUqpqabhw5Mvuyy7a9c0ob0uOHgSmCzL2oMiZ9u2bVOnTr3qqqteffVVs9nchCMUXCgIESaoG+1L4vyJMBEAu0iwWuXBg5MAkhSFWa2Brl2De/dGFixgJ08yiwVcLuzRA7p3Jx06SNnZcOONZNAgw6uv0qNHIRQCn4/VlBT/88/kD3+Q77iDjRjB/H4ghFitxGwGRFZYSH76SS0pweJiPHmSFRWBbifSzz/j/v0wZgzLzq5yVycnSy4Xq2oeBgCgqlhSAvv3sy1bWEUFQqxyU1q/njHGyyqlrCySmws5OdiqFR46hEVFNDtbnjrVRAgxmx3Jyc5u3dIHDkwT9+2FSC1JYAsWLPjd73734osv3nXXXUJYC+qJWI4U1A2lVFVVAEBEt9ttt9vP4UOeyAC7yKGU/fhjcPPmwOLF4d27qckENhuYzbHolMUCHTtiRgb06cOMRoxGpfJycuIEDQSQUtDsYfkPjEGPHqy0FA8fZpEI+HxoMDBEQCRaSlYoFFslPJ1GFhtGly7ssstYfj5YrSQtDQyGyokREQMBOHoUd+9mu3dXaf6oKKS8HCKRynQxlwvatoVOnViXLszh4IWWssNhT05O6dmzVVKS8Li/gKkpCSwajf7ud7/7+uuvP/vss0GDBjXJ2JYuXRoIBHw+31133TVlypQpU6YAwJVXXin/3DwUAAAgAElEQVSqMps5QoQJ6kYTYQBQXl5us9kslnPzXSICYAINRNy9O/i3v/m/+y5cXk4NBr6wiPxzNpnwppvY6NGMp20xRsJhiOtuZDCAy0UsFjx0iH7zDduzJ35yOz3dEb0sC4crZdmAAXDTTUq7dihJhFI4dQoOHmRbtrCysvhdMUa8XuL1UgCwWkl+vtSjB3TvTtPTmSyDqgKAxWRKys5O79QpVdyrLQBVVbWGafoksKKiojvuuMNgMCxatKhVq1ZNNbz27dsfOXIEgJvCxMxWCgoKGsGcTHA2CBEmqBvGmHI6g9rtdlsslrN/umpQAAwAotGoCIBdPKgq3b07smlTeO/eSEWFWlGhut0sHAYAHD8er76apaczAGCMqCoEg6Cq6HKBwVDFefXECVy+nG3axFS1cg094TqRqpJQCBwOSE3FtDTs0IFlZdFNm9jJk6y8HBGBkJh9KyFgMIDNBoqCGRmkSxfs1Inm5FAeOSPEYDY7HY7Utm0zRdCrxcAnq3A4HJcEhoibN2+eNm3ajTfe+NJLL4l5SXAGCBEmqBu9CKuoqOBRq7Pc4RkEwAwGg9VqFUGFi5NTp8KlpaEdOyLbtys//aRKkjpwoNqnD+rNUgwGYjKB0Rj7ZzAgIejx4IoVdM0aFgjEFJjdDmlpkJGBLhcxmcBgYElJanp6fPY9t65QVSwowCNHoLQUGYNIBHNyMC8P27ZlLhexWIxWq9FuNzudlpSUlIwMEfRqaSBiKBTik5XFYtGnRrz33ntz5sx57bXXpk2bJpLABGeGEGGCuuEPgvxnj8djMBjsdvvZ7Kr+ATBugg8APAnjrE5D0IJgjBUXhw8fDhQWRg8fDhcWRn/5hQaDTJKIwQCSBLIMsgxWK7Hb0W4Ho5GcPMn69KE9e7LUVAyHIRCospRZnUgEPB4IhcBoJLIMhBBKZUKkjAxTdralTRtbVpbNbDbJsizLsvgObpFoSWBWq1U//4TD4ccee2zlypWfffbZgAEDmnCEggsdIcIEdaMXYV6vl3fnOIP9NDQAFg6HFUURATBBfWCM/fxzeMuWwK5d4Z9+CpeUqAYDk2U0GsFolCSJZ/ojIUgp5uZCfj60aYNpaczhYIQQVZWiUSkclgMBye+XvF5JVQ2ZmcZ+/Wxt2zpcrsovYH7Hqqqq+ecBgMFgkGWZ/1cIspaBlgRmt9v1U9Px48dvv/12p9O5cOHCjIyMphugoCUgRJigbvQizOfzAYDT6TyDPZxZAMxoNIpvNUGD0AIYZrPZbDafv/uHCzJNk/EXNTVmMBjErXshoiWBxT0BIuKGDRumTZt2++23v/DCC+fPs1pw8SDuIUHd6L9ICCG8j2T9oZQGg0ERABM0DpqCt9vt5/trkhBiMBgMBoPZbEZExhhXY9FolD/f8sVKrsnEzXxBoCWBxSl4xtjbb7/97LPPvvnmm7fccouQ14JzghBhgoah1T/XhwaZ4IPu65OXQIppTtAgNCPNJlHwhBAuuTRBxiNkiqLwQLIkSVqETAiy5gl/YmSMxfXhCAaDDz300ObNm1etWtWnT58mHKGghSFEmKBe6L1n6inCeG81VVVFAEzQCGhfn3FGmk2CJsj4g4cWIeNBMgCQJImrMb5kKZ43mgOKogSDQZ7zqp+djhw5MnXq1KysrC1btqSmpjbhCAUtDyHCBA2jPiIMESORSDAYJISIAJjgfMP7KIRCoepfn80ESZJMJpMmyLQcMu78wtc0tQiZuP8bHz5lRSIRg8Fgs9n0PhSrV6+eMWPGr3/963nz5jXDW0twoSNEmKBh1CnCRABM0JggYjAY5Peb3sap2cKfQPhSFyJqVZaaINMiZEKQNQ7aLVQ9Cewvf/nLiy+++M4770yePFl8FoLzgRBhgnqhX44EAESsPiVpATAekKhnAAwRRQBMcGZwEwFEjMvguVAghBiNRk2QaREy3hwChPPF+aemJLBAIHDfffft2bNn7dq1PXv2bMIRClo2QoQJGkZNIkwLgJnNZi2mJQJggvOEtn4U10nmwkUrtASdIKOU8tOE084XQpCdQ2pKAjt8+PDUqVPbt2+/adOm5OTkJhyhoMUjRJigXsRN+voVSX0AzOl0ak+TIgAmOE80mg1YUxEnyPTOF5og0+f1N/V4Lzw0EW80Gq1Wqz4J7Ntvv7377rsffPDB2bNniyQwwflGiDBBw6glAKYltNYUANMMeEQATHDGNKYNWHOguvOFlkOmd74QVmT1R2tHG1dIyxh7+eWXX3/99ffff/+qq64S6lbQCLT8KUxwbtFklgiACRqZprUBaw5oggxO/w3W4nxxEV6f+sCfG6GaiPd6vffee++BAwc2bNjQpUuXphug4OJCiDBBvdA0E/+BZ7OKAJig0WhWNmDNAe4upne+4BGyOOcLLUImrhgARKPRUChUPY/wwIEDt956a48ePTZu3OhyuZpwhIKLDdE7UlAv+OTOf/B4PAAgSZLdbk8YAKveBVIEwARnjNbIT5Ikm80m0nTqRLMi430tQThf6MKo1ZPA/vOf//zmN795/PHHn3zySfGIKGhkhAgT1AttTucZYAaDwel0igCY4Hyjpe9cKDZgzQ2+ZKn9/fIXtQjZRVJoqVVyxIVRKaUvvPDC22+/vWDBggkTJlwMl0LQ3BDLkYL6Eg6HeQYYAGgTmcgAE5w/uA0YAFygNmDNgYRWZJRSzYqsxTtfaHdRXBJYRUXFzJkzjx8/vnnz5o4dOzbdAAUXNUKECeoFV2A8A6yiooLHvUQATHCeaHk2YM2B6lZkXJPpnS80e9gWIMi0hezqd9GPP/54yy23XHrppYsWLXI4HE04SMFFjhBhgnphsVgQkT9PE0J40okIgAnOBy3eBqw5oAmyOOeLaDTKc1QudOcL7WkwbiEbEb/88ktuA/bwww9fiKcmaEkIESaoF/pudwCgKAoiao2JQATABOcIXr9GCLlIbMCaA3HOF1pef5wVmdZivKnHWzeajrdarfppSlXVefPmffTRR0uWLBk7dqzQ94ImR8xxggbA07+MRiNfwohGo/x5GgB4VbywDxCcMZqOj6tfEzQmmiCr7nzBBVnzd77QksDimhGVl5f/6le/crvdmzdvbteuXZONTyDQIUSYoF54vV4AkGUZES0WC5yuuuKPy1pvb27k3WxnZ0GzRbMBEwvZzQpJkrRIkhYhi7Mi0yJkTf6p1ZIEtnv37qlTp44cOfKf//ynzWZrwkEKBHougMCyoDnwl7/8JS8v7+abb37zzTd3795NKSWErF69euzYsadOneIJ+0ajkRsK+P1+n88XDAaj0ShjrKnHLmjW8Bx8v98PAA6HQ0RSmy08J8FqtTqdTpfLZbPZTCYTYywcDvM/+UAgEIlEVFVtEucjHkkNh8Mmk8lut2sKDBEXL158xRVXPProo++9955QYIJmhfAJE9QLSunhw4dXrVq1evXqNWvWhMPhtm3b7tq1a8iQIfPnz8/Ly9O21LdS4QpMW7+4UBJKBI2GsAFrAWjOF3FWZFqtZSN8rPpIqj4JTFGUOXPmLF68eNGiRSNGjBA3mKC5IUSYoMEsX758+vTppaWll19++aZNmyRJGjFixMiRI0eOHJmfn6+XWfr1Cy7ILrgMX8H5Q2vFzZcgm3o4gnOA3vmC99iA8+98oSgKtzCMa6hw6tSpGTNmhMPhJUuW5OTknPPjCgRnjxBhggYQiUQeffTRN998c/To0fPnz+/QoQNjbM+ePatWrVq1atW6dessFsuIESNGjRo1cuTIdu3a6SfcmgTZhVsDLzhjRCvuiwHN+YJHyPh3Dc/6P1d/9ZqfnMFg0JrYcnbs2DF16tQJEya8/vrrPI1VIGiGCBEmaACIOHny5HHjxt13333VJ1BVVXfu3Ll69WouyFJSUrQIWU5OTpwg06bmOEHWMlwiBbVAKQ2FQtV7yAhaMHrnCy1p7Czj4ogYDAZVVY3zk0PETz755IknnvjTn/40c+ZMcYMJmjNChAkaBiLWOakhoqIo27dvX7ly5erVqzds2NCqVauRI0fyIFlWVlZCQRY3NQtB1iLhNmCSJFmtVmEDdtGS8DFMa6BECKnzD19LAotraRWNRmfNmvXvf/97yZIlQ4YMOb+nIRCcNUKECc4vvGh869atK1asWLNmzaZNm/Ly8rggGzFiRHp6eu2CrIX1UbmYETZggoRUT1So0/mipiSw4uLiO+64gxCyaNGi1q1bN+ppCARnhBBhgsaDZwJt3Lhx5cqVa9as+f777/Pz8/l65bBhw1JSUvQLCtUXL1p8p+EWjKqqoVCoevGaQKBHq63m2f1w2jxWX1udMAkMEbdu3Xr77bdff/31L7/8srjHBBcKQoQJmgZEDAQC69ev57YXO3fu7NatmybInE5nnCCrnt4rBNkFgd4/02q16uMWAkEtJHS+IITwhmlms1nvBPb+++/Pnj37lVdemTFjhpgQBBcQQoQJmh5E9Hq933333apVq9asWbN3797evXvzpP6hQ4fGPe/qlyz5i0KQNVs0GzDRiltwNvA0U25owtmzZ8/TTz89dOjQwYMH//vf/161atWSJUsGDhzYhIMUCM4AIcIEzQtEdLvda9as4YLswIED/fr14xn9AwcO1Pt5JhRkmhoTgqzJ0b41bTabyMEXnA1aPYfdbieEMMa2bdv24osvbty40e12E0L69u07btw4LbGhqccrENQXIcIEzRdELC0t5Z4Xq1evLiwsHDhwII+QDRgwQO9ukNAiUm/TLwRZYyJswATnCu1eiqvnQMQNGzZMmzbt+uuv79mz5/r169euXXvixAlCyIMPPvjaa6817bAFgnoiRJjgwgARi4uLNUF28uTJwYMH8whZ37599bWTNTVREYKscdC8A4QNmOAsYYwFg8HqlnKMsXfeeWfevHlvvPHG1KlT+euIWFhYuHbt2latWk2YMKFJBy4Q1BchwgQXHoh47NgxrsZWr15dVlY2dOhQntTfu3dv/cpXdUEWV2wlJMI5REvcqe4dIBA0FFVVg8EgVFvODoVCjzzyyLp16z7//PO+ffs23QAFgnOAEGGCCxtELCgo4K6wa9asCQQCw4YN44Kse/fueh2QUJDp+yYJQXY2CBswwTmEJ4HJsmyz2fTL2UePHr3tttvS09M/+eSTtLS0JhyhQHBOECJM0HJgjB04cIBHyNasWUMpHT58+MiRI0eNGtW5c2f9VM7tiLgmixNkorP4GcCDFogobMAEZ4mm5k0mU1whztq1a6dPn37nnXc+++yzotRD0DIQIkzQMmGM7du3j3cWX7t2rdFoHD58OO8s3qFDh+qCrLphtxBk9UHroFw9aCEQNBQtCSxOzTPG3nzzzRdeeOHtt9++8cYbRZxV0GIQIkzQ8lFVdc+ePTyp/7vvvnM4HFpn8by8PP2EXl2Q6VvaCYURh/aVKWzABGdPTUlggUDggQce2Llz52effdarV6+mG6BAcO4RIkxwcaEoyo4dO/iS5fr169PT03kXy1GjRrVu3TqukWVcSzutszjPIWu6k2gWCBswwblC31YhLp5aUFAwderU3Nzcjz76SBiACVoeQoQJ6oXf7583b97OnTt37NhRVlb21FNPPfXUU009qLOCl/J9//33PKl/48aNbdq04Z3FR44cmZmZWb2zONdkQpAJGzDBOaSWJLDly5ffdddd999//5w5c0SxraBFIkSYoF4UFhb27du3T58+/7+9Ow+Lqmz/AH7OyDbDIiqipqgggriQikosMmJKLuWairjgUi6UaZq7aL6S5hJp2WtdKobLFTpEi5rmFQ4DiLIoiuJuhrgjIjvMcp7fH8/buc4PjBSBB4bv5w8uO8PF3KMTfHme+zx3586dd+3a9emnn65evZp1UTWGNjalpKTExsZqNJrk5GRHR0e6X+nr69uiRYvKgUw6WVwMZNLjyowVjgGDGiQIQnFxceXJ7oIghIeHb9u2LSIiYsSIEXibgbFCCIOXk5ub27JlSyMLYVL09/KkpCS6ZXn27FlXV1e6PObj42Nra1t1IKMTk4wykIl7RjgGDGoE3dHmeb7C26mwsHDu3LlXrlz56aefunTpwrBCgNqGTg54OUaf2umPhEGDBg0aNIgQUlRUlJiYqFarv/jii6lTp3br1o1uWfr4+FhZWclkMjMzMzMzMzrIku5X6nQ6rVbL/T1ZnH5s6IFMHMVdYc8IoBrEm2pNTEwUCoX07XTjxo2goCBXV9fk5GQbGxuGRQLUAYQwgH/E87y1tfXQoUOHDh1KCMnPz4+Pj1er1WFhYZcvX+7ZsyddIfPy8pLL5XQNTAxktIdMq9WKK2RiD1mDSzDS29ZMTU1ZlwMNGyGkpKREr9dXuKmWEHL8+PFZs2YtXLhw+fLl6DWExgAhDOCF8Dxva2s7YsSIESNGEEJyc3M1Gk1cXNyKFStu3rzp4eFBT4Xt27evubk5DWQcx4mBTK/Xa7Xa8vJyrkEFMhwDBjVL7CmsEOgNBsOmTZt27Nixb9++oUOH1vP/LwBqCkJYYxcXFzdw4MAqPuH8+fPu7u51Vk+DwPO8nZ3d2LFjx44dSwh5/PgxPYRswYIFWVlZnp6e9MwLDw8PU1NTGsjMzc2lgYwmG47jxP3KehjIcAwY1CydTldSUiKTyaysrKRNYPn5+bNmzcrKyjp9+nTnzp1r/Hn1ev3q1asPHz589+7d3r17L1myBBO+oZ5ACGvsunTpsmvXrio+wcHBoc6KaYh4nm/VqtWECRMmTJhACLl//z49pn/OnDkPHz708vKiW5a9evWiSUsMZAaDgfaQSQOZ+DnMEw/9ecnzvKWlJY4Bg1dURRPY1atXJ06c2KtXr6SkJCsrq9p49nHjxp04ceKzzz5zdXXdt2/f22+/fejQodGjR9fGcwG8FNwdCS/nyZMn9vb2Rnx3ZE0hhNy5c0ecLP7s2TNvb2967EWPHj2eO1mcfqQXpXOT6jiQYRQ31KwqmsAOHz4cEhKyfPnyRYsW1dJmd3JyspeXV0RExLRp0+gVf3//nJycS5cu1cbTVXbhwoXFixfn5OR4e3tv3LhRDJrp6emBgYHBwcErVqyom0qgHsIvuAC1guf5Dh06TJ8+ffr06YSQW7du0UD29ddfl5aWisf0u7m5iceMcZJAptfry8rK6NeRzk2q7UgktuzI5XJTU1MkMHhF9B1FCKncBBYWFrZnz56DBw8OGjSo9t5pkZGRCoViwoQJ4hU6BTwtLa1Pnz619KSi3NzcDz744JdffikoKOjUqVPnzp0XLFhAHzp27NiNGze++uorhLDGDCEMXtSxY8eKi4sLCws5jsvMzIyOjuY4bvjw4XK5nHVp9R3P887Ozs7OzrNmzRIE4dq1azSQbdq0ieM4X19f2tTfuXPnfw1kYg9ZjQcy6TFgFVp2AKpHq9WWlpbSd5R0oSsvL2/GjBk5OTn0YORareHPP/90dnaWfpuiAyhv375dByFs69ata9asadGixfXr1zmOS0pKEkPYvHnzIiIi6HdUaLQQwuBFhYSEZGVlcRzH87xKpVKpVDzP3759u3379qxLa0hkMpmbm5ubm9sHH3wgCMKlS5foqbDr1q0zNzenDWRKpdLR0ZFGLjGQifuVOp2O+zuQiXOTXjGQ4RgwqFnibKvKm9qZmZmBgYE+Pj4xMTGWlpa1Xcm9e/datWolvdKyZUt6vbafuqCgQKPRrFu3juO448ePcxwnvQvK2tp64cKFkZGRtV0G1GcIYfCibt++zboEYyOTydzd3d3d3efPn6/X6y9cuBAXF3fs2LGVK1c2bdqUBjI/Pz8HBwee501NTeluDg1kNJPREdpiIKMrZC9bBo4Bg5ol3ldbYbYVISQmJmb+/Pmffvrphx9+2BhOPPnvf//LcZwgCN9//71CoQgKCpI++tprr/Xr149RaVAvIIQB1AsmJiYeHh4eHh6LFi3S6XTnzp07efLkjz/+uGjRInt7e3pMv1KpbN269XMDmbhCJpPJpD1kVT8pjgGDGidm+gr31er1+jVr1vzwww8xMTFKpbLOVlvbtm374MED6ZWcnBx6vbaf2sbGpnv37hzHxcbGZmdnT5w4scIMgLS0tOHDh9M/nzt3buXKleXl5VqtdsiQIStWrMD/j40BQhhAvWNqaurp6enp6bls2TKtVpuWlhYbG3vgwIEPP/zQwcHB7292dnbSQCbOTZIGMmkPWYVnoQtplZcrAKqNNoFVzvRPnjyZPn16UVFRSkpKHTcwODs7JyYmlpWVWVhY0CsXL17kOM7JyanOaoiKiuI4bsyYMRWup6SkhIaGchx36dKlFStW7N+/387O7urVq56enunp6VFRUViZNno4ogKgYaBNNmfOnKE9ZKmpqZ06daJblr6+vs2bN688WZxmMkEQuL8DmdhDJnZMy+VyHAMGr66KJrDz588HBQW9+eab27dvr/v7eFJTUz09Pffs2RMcHEzrHDhwYG5ubkZGRp3V4OLicuvWrcePH7do0UK8mJaWtm/fvm3btnEcN2rUqA0bNri5udGHNm3atGzZMpVKNXbs2DorEphACANoeOjBS6dOnaKBLD093c3NjQYyHx8fGxubyoGMZjIayChsQUJNEZvA5HK5mZmZeJ0QEhUV9cknn6xfv37OnDmsVlvHjBlz4sSJsLAwFxeXAwcOqFSqQ4cOjRo1qs4KkMvlFhYWeXl50ouBgYFhYWHOzs4cx9nb23fq1CkpKYn+FZ06dap///4hISHbt2+vsyKBCfwGDNDw0IPsAwICAgICCCGFhYUJCQlqtXrjxo0XL17s0aMHDWTe3t6WlpYymczMzIz+aLx165adnR39CgaDobCwkB7QTxfJsCMJ1fBPTWA6nW7lypUxMTFHjhzx9vZm+O5SqVSrV6/evXs3HVt05MiRgICAuiygXbt22dnZxcXF4t2g4eHh/v7+NIFxHOfk5HT79m29Xk/3HxUKBcdxT548qcsigQmshAEYD0JIXl5efHy8Wq3WaDRXr17t1atX//79BwwY0KdPn++++27dunXffffdu+++K5PJpE399PuAOMUSgQxehHi2XOVV1cePH0+dOtVgMBw6dKgOWuDruS1btixZsmTz5s0ff/xxWVnZgQMHTp8+HRERIX6CTqfTarViRNu7d++0adO++OKLjz/+mFHJUEcQwgCMEyHkyZMnGo1GrVb/8ccfWVlZ5eXlY8aMmTlzppeXV4WDA6Q9ZNJAVj8ni0N9II63qny2XFpa2qRJk955550vv/zS3NycYZH1x5EjR44cOXLhwoXmzZu/++67kyZNku7bVuDn53f9+vWrV6/a2trWZZFQ9xDCAIzc8ePHg4ODCSGzZs3KycmJi4u7e/fuG2+8QbcsPTw8pOteYiCjmQyBDJ5LEITi4mI63qpCE9i+ffuWL1++efPmmTNn4t1SDUePHh01atRPP/309ttvs64Fah1CGIAxCwsLCw0NHTJkSGRkpL29PcdxhJC7d+/Sjn6NRpOTk+Pl5UXPvHj99delPT3SQCZOFkcgA2kTmHS8VXl5+dKlS48fP65SqTw9PdkV2IA9evSob9++GzZsmDRpEutaoC4ghAEYs99+++3atWvz589/7l2QhJC//vqLDrLUaDSFhYXe3t5KpdLPz69bt27Sn6/PDWRiGkMgayTEJjATExO5XC59Uz148GDy5Mnm5uZRUVGtW7dmWGTDVV5ePnjw4FmzZk2ePJl1LVBHEMIAgOM4ThCEmzdvqtVq2tSv1Wr79+9Pm/pdXV2lP27FyeL0I70onZuEQGaU6MEoer3e3Nzc3NxcuoV95syZKVOmjB8/fvPmzThftNpmzpzp7+8vJrC9e/dOnTqVbUlQ2xDCAKAiQRCuXLlCA1l8fHyTJk18fX3plqWzs/NzAxnNZBzH8TwvnZuEQGYcDAZDSUmJIAgVBowSQnbv3r1mzZqvvvpq8uTJ+Oeuti1bttjY2MyaNYv+Z3FxcWhoaHh4ONuqoLYhhAFAVQRByMjIoD1kCQkJCoVCnCzesWNH6Q9dBDJjpdPpSkpKZDKZQqGQblKXlZUtXLhQo9FER0d7eHgwrLChO3r06Pvvv//mm2+KP5Hv378/bty4uXPnsi0MahtCGAC8KL1en56eHhcXp1arT5061axZMzpW3M/Pr23btv8ayKRzkxDIGgRxxLuJiYlCoZD+q2VnZ0+ePLlp06ZRUVH0BGCoNltb28LCQkII/Rumf/jjjz/8/f1Zlwa1CyEMAF4aIUSn0509e5Y29Z8+fbp169Z+fn40k7Vq1apCIBPTWIVA9tzJ4lBPVNEElpCQEBwcHBwcvH79esweBag2hDAAeCX0jrmUlBQayJKTkzt06EADWf/+/e3s7J4byMTJ4ghk9dM/NYEJgrBjx44NGzbs2LFj/PjxWNEEeBUIYQBQY+gp6qdPn6a3WKamprq4uIiTxZs1a1Zhsri4ZUkDmUwmk/aQsXsdjZ1Wqy0tLa3cBFZSUvLRRx+lpqZGR0e//vrrDCsEMA4IYQBQKwghxcXFiYmJNJClp6d37dpVqVT279/fx8fH2tr6XwOZtIeM3etoXAghZWVlWq3W1NRULpdL/43++uuvSZMmtWnTZv/+/c2bN2dYJIDRQAgDgFpHCMnPz09ISKCBLDMz093dnTaQeXl5Vej4ls5NqhDIMFm8VgmCUFJSYjAYLCwsKkwXVavV06dPnz179tq1a6VrYwDwKhDCAKBOEUKePn2q0Wji4uLi4uKuX7/u4eFBA1m/fv0qjIKWHtNPv1khkNUSg8FQXFzMcZxCoZD22guCsG3btvDw8J07d44ePRp/5wA1CCEMAJghhNCZ4vTYi6ysrH79+tEesj59+kgXY7jnBTI6MQmB7NXRJrAmTZooFArp5m9RUVFISEhmZmZ0dHS3bt0YVghglBDCAKBeIIQ8ePCApjGNRnP//n0vLy8ayHr16iWNWXSQpdhDJgthggsAAA4iSURBVAYyTBavhiqawG7evBkUFOTs7BwZGdm0aVOGRQIYK4QwAKh3CCHZ2dniZPGnT596eXn5+fkplUp3d/d/mixuMBgQyF5KFU1gv//++/vvv79gwYKVK1fixgiAWoIQBgD1GiHkzz//FANZSUmJj48PDWRdu3atMMgSgezF6fX6kpIS7nlNYJs3b96+fXtkZOTw4cPxlwZQexDCAIyBXq9fvXr14cOH796927t37yVLlrz11lusi6p5giBcv36dThbXaDSCINDJ4kql0sXF5Z8CmV6vpxdpFEMgo+frlpWVVW4CKygomD179q1bt2JiYlxcXBgWCdAYIIQBGIPRo0efOHHis88+c3V13bdvn0qlOnTo0OjRo1nXVYsEQcjMzKSTxTUajZmZmThZ3MnJqUIgoxOTKgSyxjlZnB6oq9PpzMzMKtyLeu3atYkTJ7q7u+/evdva2pphkQCNBEIYQIOXnJzs5eUVERExbdo0esXf3z8nJ+fSpUtM66o7er0+IyODNvUnJiZaWVmJk8Xbt29f9WRxrjEFMrEJTC6Xm5mZidcJIb/99tvs2bOXLl26ePFiNIEB1A2EMIAGLyQkZO/evTk5OXK5nF75/vvvZ8yYkZKS0qdPH7a11T2dTnfu3Dm6QpaUlGRnZ0enWCqVytdee63qQMbzvHRukpEFMrEJzNLSUnpzg8Fg2LBhw86dO/fv3x8QEGBkrxqgPkMIA2jwhgwZ8vDhw/Pnz4tXzp4927dv34MHD44bN45hYWwRQnQ6XVpaWmxsbFxc3JkzZ9q2bUsni/v5+dnb2/9rIBN7yBp6IBObwExMTORyuXSh69mzZ++99979+/djYmKcnJwYFgnQCJn8+6cAQP127969Vq1aSa+0bNmSXmdUUb3A87yZmZm3t7e3t/eqVavKy8uTk5NPnjwZERExZ84cJycnmsZ8fX1btGhBIxe9SZAQQqOYXq/X6XTc34FMPBW2YQUysQnM3Nzc3NxcWvzly5cnTpzo6empUqksLS0ZFgnQOCGEAYDx43newsJCqVQqlUoaSk6dOqVWq7/55psZM2a4urrS/Upvb29bW1ue501NTU1NTbm/AxnNZKWlpdz/D2T1v3fKYDCUlJQIgqBQKOgrogghP//887x580JDQ+fPn1//XwiAUUIIA2jw2rZt++DBA+mVnJwcep1RRfUaz/MKhWLw4MGDBw8mhBQVFdHJ4ps3b87IyOjWrRu9xdLb29vKyuq5gUxcIZPJZNIeMtavrCKdTldSUiKTyaysrKRNYHq9fu3atfv371epVAMHDmxYC3sAxgQhDKDBc3Z2TkxMLCsrs7CwoFcuXrzIcRxafP4Vz/PW1tbDhg0bNmwYIeTZs2fx8fFqtfo///nPlStXevbsSbcsvby86EgfMZBJ5yaJgUzaQ8b2dRFCysvLy8vLTUxMFAqFNGbl5uZOnz49Pz8/JSWlQ4cODIsEADTmAzR4qampnp6ee/bsCQ4O5jiOEDJw4MDc3NyMjAzWpTVUhJDc3FyNRkPvsrx161afPn3olmXfvn0rdFbRU2FpJhMEgfs7kNFMVveBjBBSUlKi1+srN4FlZGQEBQUplcpvvvlGoVDUcWEAUAFCGIAxGDNmzIkTJ8LCwlxcXA4cOEAPax01ahTruowBIeTRo0dxf7tz546npyfdsvTw8DA1Na0cyGgmqxDIpDPIa08VTWCHDh1auHBhWFjY3Llzma/VAQCHEAZgHAwGw+rVq3/99Vc6tmjp0qUBAQGsizJChJB79+6Jx/Q/evTIy8uLrpD17NlTOoGRkwQyvV4vDrIUe8hqI5CJTWAKhULaBKbT6UJDQ2k09/X1RRMYQD2BEAYAUB2EkKysLHGyeH5+vre3N10h69GjhzQD0UGWYg+ZdLI4/fjqqUhsAjM1NaXta+JDOTk5wcHB5eXlKpWqXbt2r/hEAFCDEMIAAF4VIeTWrVsnT56kk8XLy8t9fX3pCpmbm9tzJ4vTTCYNZNWeLC4IQmlpqV6vt7CwMDMzk36Fc+fOBQUFDR06dNu2beJ9GwBQTyCEAQDUJEEQrl69qlar1Wp1fHw8x3G+vr50kGXnzp2fG8hoJqteIDMYDMXFxRzHKRQK6X4oIeTAgQNLly7dtGnTe++9hy1IgHoIIQwAoLYIgnDx4kXaQ5aQkGBubi5OFnd0dKwwN0naQ0YvivuV/xTItFptaWlpkyZNFAqFNN5ptdply5YdOXIkOjr6jTfeqO2XCQDVgxAGAFAX9Hr9+fPn4+Li1Gp1YmKira0tPYTMz8/PwcGh8iBLsYeMXhSXx2ggI4SUlZVptdrKTWAPHz6cMmVKkyZNDh482KZNm7p+nQDwwhDCAADqmk6nO3fuHJ0snpSU1KpVKzpZXKlUtm7d+rmTxelHerFJkyaCIBBCKpwERghJSUmZPHny2LFjt2zZYmZmxuC1AcALQwgDAGCGEKLValNTU+ldlmfOnHFwcFAqlXSRzM7OrnIg02q19Ix+juN4nk9NTb18+fKAAQO6du0aGRkZGhr65ZdfBgcHowkMoP5DCAOojgsXLixevDgnJ8fb23vjxo1WVlb0enp6emBgYHBw8IoVK9hWCA0O3WE8ffo0vcUyJSXF2dmZLo/5+Pg0b96cELJz586jR4/u3bvX2tqazrLcunXr559/rtPpmjVrVl5e/tFHH02bNs3FxQUhDKD+QwgDeGm5ubkjR4785ZdfCgoKOnXqFB4evmDBAvrQ+vXrV61aZW9v//DhQ7ZFQoNGRw8lJibSQJaenu7q6tq0adOEhIRp06aFh4ebm5uLn3zz5s1ly5bl5eUZDIb09HS9Xt+mTRt/f//AwMB33nmH4asAgKphcgXAS9u6deuaNWtatGhBk1ZSUpL40Lx585ycnPC7DbwinuctLS3feuutzz//PCkp6dy5c6WlpUlJSYMGDfrhhx8CAgJWrVp14sSJoqKiU6dOBQQEdO3aNSEhISUlJS8v79ixY1OmTLlx48aZM2dYvw4AqIrJv38KAEgUFBRoNJp169ZxHHf8+HGO4wYOHCg+am1tvXDhwsjISGb1gdHRaDTjx4+Xy+XJycm9e/fOy8uLj48/efLkmjVrrly5wvP8nj17Jk6cSPcfrayshgwZMmTIEI7j8MsAQD2H7UiAl1NQUHDnzp3u3bsLguDo6Jibm3v//n0bGxvxE37++efY2Nivv/6aYZFgNARB8PT0tLGxiYqKatmypfQhQkh2dnZ6evrIkSNZlQcArwLbkQAvx8bGpnv37hzHxcbGZmdnjxw5UprAOI5LS0sbPny49EpycvLo0aPrtEowFjKZ7OjRo7///nuFBMZxHM/z7du3RwIDaLgQwgCqKSoqiuO4MWPGVLiekpLi7+9P/5yfn//jjz8OGzYsPz+/rusDY2Fvby+dRwQARgMhDKCaEhISeJ4fMGCA9GJaWpqbmxu9c+3s2bMzZsy4ePGivb09mxIBAKAeQ08YQDXJ5XILC4u8vDzpxcDAwLCwMGdnZ+nFAQMGyGSykydP1m2BAABQr2ElDKCa2rVrV1paWlxcLF4JDw/39/evkMAAAACeCyEMoJpmz56t1Wq//fZbQRBKSkp27tx56dKl2bNns64LAAAaBoQwgGr65JNPfv311xs3bvj4+IwbN87ExOTbb79lXRQAADQY6AkDqHXoCQMAgMqwEgYAAADAAEIYAAAAAAMIYQAAAAAMIIQBADReRUVFS5YsCQgIaNmypUwmW7t2LeuKABoRhDCA2iUIQkFBwdOnTw0GA+taACp68uTJzp07dTodHW/K8zzrigAaEcwjA6gtd+7cmTJlyp07d7Kysnied3R07NixY2RkpKOjI+vSAP6nY8eOdOpDbm7url27WJcD0LgghAHUlvbt22s0GtZVALwQHFcEUPewHQkAwB56swAaIYQwAAD20JsF0AghhAEAsEd7s9Rq9YYNG2rkC8bFxcmqlJGRUSNPBADVhp4wAIB6pKZ6s7p06VJ1o72Dg0ONPBEAVBtCGACAEWrduvWMGTNYVwEAVcF2JAAAAAADWAkDAGjUjh07VlxcXFhYyHFcZmZmdHQ0x3HDhw+Xy+WsSwMwcghhAACNWkhISFZWFsdxPM+rVCqVSsXz/O3bt9u3b8+6NAAjhxAGANCo3b59m3UJAI0UesIAAAAAGMBKGABAvYDeLIDGhse8MACA+sDR0VHszaLfmdGbBWDcEMIAAAAAGEBPGAAAAAADCGEAAAAADCCEAQAAADCAEAYAAADAAEIYAAAAAAMIYQAAAAAMIIQBAAAAMIAQBgAAAMAAQhgAAAAAAwhhAAAAAAwghAEAAAAwgBAGAAAAwABCGAAAAAADCGEAAAAADCCEAQAAADCAEAYAAADAAEIYAAAAAAMIYQAAAAAMIIQBAAAAMIAQBgAAAMAAQhgAAAAAAwhhAAAAAAwghAEAAAAwgBAGAAAAwABCGAAAAAADCGEAAAAADCCEAQAAADCAEAYAAADAAEIYAAAAAAMIYQAAAAAMIIQBAAAAMIAQBgAAAMAAQhgAAAAAAwhhAAAAAAwghAEAAAAwgBAGAAAAwABCGAAAAAADCGEAAAAADCCEAQAAADCAEAYAAADAAEIYAAAAAAMIYQAAAAAMIIQBAAAAMIAQBgAAAMAAQhgAAAAAAwhhAAAAAAwghAEAAAAwgBAGAAAAwABCGAAAAAADCGEAAAAADCCEAQAAADCAEAYAAADAAEIYAAAAAAMIYQAAAAAMIIQBAAAAMIAQBgAAAMAAQhgAAAAAAwhhAAAAAAwghAEAAAAwgBAGAAAAwABCGAAAAAADCGEAAAAADPwfLNcILHTGLzwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/valley_with_ball.png',  width=500, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make gradient descent work correctly, we need to choose the learning rate $\\eta$ to be small enough that equation $\\Delta C \\approx \\nabla C \\cdot \\Delta v$ is a good approximation. In practice a lower value of $\\eta$ will make small changes in $\\Delta v$, while an higher value could even end us up in a worse position $\\Delta C > 0$. Setting the right value of $\\eta$ is tricky and in certain situations can vary over time. \n",
    "\n",
    "You can think of this update rule as defining the gradient descent algorithm. It gives us a way of repeatedly changing the position $v$ in order to find a minimum of the function $C$. \n",
    "\n",
    "How can we apply gradient descent to learn in a neural network? Position $v$ now has components $w_k$ and $b_l$, and the gradient vector $\\nabla C$ has corresponding components $\\partial C / \\partial w_k$ and $\\partial C/ \\partial b_l$. Writing out the gradient descent update rule in terms of components, we have:\n",
    "\n",
    "$$\n",
    "\\begin{eqnarray}\n",
    "  w_k & \\rightarrow & w_k' = w_k-\\eta \\frac{\\partial C}{\\partial w_k} \\\\\n",
    "  b_l & \\rightarrow & b_l' = b_l-\\eta \\frac{\\partial C}{\\partial b_l}\n",
    "\\end{eqnarray}\n",
    "$$\n",
    "\n",
    "By repeatedly applying this update rule we can \"roll down the hill\", and hopefully find a minimum of the cost function. In other words, this is a rule which can be used to learn in a neural network.\n",
    "\n",
    "Notice that the MSE cost function has the form $C = \\frac{1}{n} \\sum_x C_x$, that is, it's an average over costs $C_x \\equiv \\frac{\\|y - f(x)\\|^2}{2}$ for individual training examples. In practice, to compute the gradient $\\nabla C$ we need to compute the gradients $\\nabla C_x$ separately for each training input, x, and then average them, $\\nabla C = \\frac{1}{n}\n",
    "\\sum_x \\nabla C_x$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4.2 Backpropagation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In previous section we talked about gradient descent,but so far we applyed it (in previous notebook to a simple case where the parameters to optimize where only one weight vector and a bias term. What happens in a more complicated setting, for example that of a neural network? How does gradient descent work in this setting? \n",
    "\n",
    "To train a Neural Network in practice we use an algorithm called **backpropagation** that calculate the exact contribute of each weight of the network (that is its gradient) to the final output and update each weight (and bias) accordingly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 500
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/gradients.png',  width=500, height=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a neuron in a computational graph of a Neural Network. Suppose we are calculating the output given a particular set of inputs $x$ and $y$. The output $z$ is passed **forward** until it reaches the output and the loss is calculated. Now we need a way to propagate the error **backward** until we can find the gradient for $x$ and $y$. Suppose that we have already the partial derivative of $z$ w.r.t. the loss $L$. What is the partial derivative of $x$ w.r.t. to $L$? Well it depdends on $f$ for sure, but since it is a composite function we can decompose its derivative with the **chain rule** that is \n",
    "\n",
    "$$ \\frac{\\partial L}{\\partial x} = \\frac{\\partial L}{\\partial z} \\frac{\\partial z}{\\partial x}$$\n",
    "\n",
    "The important thing to notice is that the local derivatives $\\frac{\\partial z}{\\partial x}$ and $\\frac{\\partial z}{\\partial y}$ can be computed right away when we recive the inputs $x$ and $y$. Then the whole process can be repeated recursively.\n",
    "\n",
    "Then when we have all the gradients calcluated for the whole graph we can update the weights. Tensorflow does that automatically, because it knows how to compute the derivative of $f$ for each function that can be used as activation!\n",
    "\n",
    "Remeber that $x$ and $y$ in neural networks are tensors and weights are matrices."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4.3 Optimization functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Gradient descent** is a way to minimize an objective function $C(\\theta)$ parameterized by a model's parameters $\\theta \\in R^d$ by updating the parameters in the opposite direction of the gradient of the objective function $\\nabla_\\theta C(\\theta)$ w.r.t. to the parameters. \n",
    "\n",
    "The learning rate $\\eta$ determines the size of the steps we take to reach a (local) minimum. The standard way to perform gradient descent is to calculate gradient for the entire training set. When the number of training inputs is very large gradient descent can take a long time or even don't fit in memory, and learning thus occurs slowly. \n",
    "\n",
    "**Stochastic gradient descent** in contrast perform a parameter update for each single training example. It can be faster but the result could be erratic and while it can discover new local minima, because of its fluctuations, it is usually harder to converge to such local minima.\n",
    "\n",
    "**Mini-batch gradient descent** can be used to speed up learning. The idea is to estimate the gradient by computing them for a small sample of randomly chosen training inputs. By averaging over this small sample it turns out that we can quickly get a good estimate of the true gradient, and this helps speed up gradient descent, and thus learning.\n",
    "\n",
    "To make these ideas more precise, mini batch gradient descent works by randomly picking out a small number m of randomly chosen training inputs. We refer to them as a *mini-batch*. Provided the sample size is large enough we expect that the average value of the gradient will be roughly equal to the average overall. Mini-batch gradient descent is typically the algorithm of choice when training a neural network and the term SGD usually is employed also when mini-batches are used\n",
    "\n",
    "There are many optimization functions, most of them tries to improve simple stochastic gradient descent by adding momentum or other *derived* function, in order to reach (local) minima quickly or to overcome being stuck too early in bad local minima.\n",
    "\n",
    "SGD has trouble navigating ravines, i.e. areas where the surface curves much more steeply in one dimension than in another, for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/without_momentum.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/without_momentum_3d.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Momentum** accelerates SGD in the relevant direction and dampens oscillations. It adds a fraction of the update vector of the past time step to the current update vector:\n",
    "\n",
    "$$\n",
    "v_t = \\gamma v_{t-1} + \\eta \\nabla_\\theta C( \\theta) \\\\\n",
    "\\theta = \\theta - v_t\n",
    "$$\n",
    "\n",
    "The momentum term $\\gamma$ is usually set to 0.9 or a similar value. The momentum term increases for dimensions whose gradients point in the same directions and reduces updates for dimensions whose gradients change directions. As a result, we gain faster convergence and reduced oscillation.\n",
    "\n",
    "**Nesterov** is another popular gradient descent upgrade. While momentum first computes the current gradient (small blue vector) and then takes a big jump in the direction of the updated accumulated gradient (big blue vector), Nesterov first makes a big jump in the direction of the previous accumulated gradient (brown vector), measures the gradient and\n",
    "then makes a correction (green vector). \n",
    "$$\n",
    "v_t = \\gamma v_{t-1} + \\eta \\nabla_\\theta C( \\theta - \\gamma v_{t-1} ) \\\\  \n",
    "\\theta = \\theta - v_t\n",
    "$$\n",
    "\n",
    "This anticipatory update increases responsiveness and prevent the update from going too fast in the wrong direction. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/nesterov.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Adagrad** adapts the learning rate to the parameters, performing larger updates for infrequent parameters, and smaller updates for frequent parameters. It uses a different learning rate for every parameter at every time step, based on sum of the squares of past gradients. \n",
    "$$\n",
    "g_{t, i} = \\nabla_\\theta C( \\theta_{t, i} )\\\\\n",
    "\\theta_{t+1, i} = \\theta_{t, i} - \\dfrac{\\eta}{\\sqrt{G_{t, ii} + \\epsilon}} \\cdot g_{t, i}\n",
    "$$\n",
    "\n",
    "$G_t \\in \\mathbb{R}^{d \\times d}$ is a diagonal matrix where each diagonal element $i,i$ is the sum of squares of the gradients of each parameter $i$ up to time step $t$. One key advantage with this algorithm is that eliminates the need for learning rate scheduling. It has a weak point however, every added term is positive and the accumulated sum keeps growing, this causes the learning rate to shrink and eventually become infinitesimally small.\n",
    "\n",
    "**Adadelta** is an extension of Adagrad that seeks to reduce its aggressive, monotonically decreasing learning rate. Instead of accumulating all past squared gradients, it restricts the window of accumulated past gradients to some fixed size. The windowed accumulation forbid the denominator of Adagrad to become infinity, instead it becomes a local estimate using only recent gradients. Instead of inefficiently storing previous squared gradients, the sum of gradients is recursively defined as a decaying average of all past squared gradients. At time $t$ the accumulation becomes:\n",
    "\n",
    "$$E[g^2]_t = \\rho E[g^2]_{t-1} + (1 - \\rho) g^2_t$$\n",
    "\n",
    "As in Adagrad we require the square root of $E[g^2]_t$ in the parameter updates and this effectively becomes the RMS of past squared gradients over time:\n",
    "\n",
    "$$\n",
    "\\Delta \\theta_t = - \\dfrac{\\eta}{RMS[g]_{t}} g_t\\\\\n",
    "\\theta_{t+1} = \\theta_t + \\Delta \\theta_t \n",
    "$$\n",
    "\n",
    "The authors note that the units in this update (as well as in SGD, Momentum, or Adagrad) do not match, i.e. the update should have the same hypothetical units as the parameter. To realize this, they first define another exponentially decaying average, this time not of squared gradients but of squared parameter updates:\n",
    "\n",
    "$$\n",
    "E[\\Delta \\theta^2]_t = \\rho E[\\Delta \\theta^2]_{t-1} + (1 - \\rho) \\Delta \\theta^2_t\n",
    "$$\n",
    "\n",
    "Also in this case we use $RMS[\\Delta \\theta]_{t-1}$ and make the update proportional to:\n",
    "\n",
    "$$\n",
    "\\Delta \\theta_t = - \\dfrac{RMS[\\Delta \\theta]_{t-1}}{RMS[g]_{t}} g_{t} \\\\\n",
    "\\theta_{t+1} = \\theta_t + \\Delta \\theta_t \n",
    "$$\n",
    "\n",
    "with $\\Delta \\theta_0 = 0$\n",
    "\n",
    "**RMSprop** divides the learning rate by a decaying average of squared gradients as well as Adagrad and Adadelta. \n",
    "\n",
    "$$\n",
    "E[g^2]_t = 0.9 E[g^2]_{t-1} + 0.1 g^2_t \\\\  \n",
    "\\theta_{t+1} = \\theta_{t} - \\dfrac{\\eta}{\\sqrt{E[g^2]_t + \\epsilon}} g_{t}\n",
    "$$\n",
    "\n",
    "It was first proposed on a series of online lectures by Geoff Hinton. Hinton suggests decay to be set to 0.9, while a good default value for the learning rate is 0.001.\n",
    "\n",
    "**Adaptive Moment Estimation (Adam)** is another method that computes adaptive learning rates for each parameter. It is similar to Adadelta and RMSprop in that it stores an exponentially decaying average of past squared gradients:\n",
    "$$\n",
    "m_t = \\beta_1 m_{t-1} + (1 - \\beta_1) g_t\n",
    "$$\n",
    "that serves as an estimate of the first momentum (the mean) of the gradients. In addition it calculates also an estimate of the second momentum (the uncentered variance):\n",
    "$$\n",
    "v_t = \\beta_2 v_{t-1} + (1 - \\beta_2) g_t^2  \n",
    "$$\n",
    "Since the two vectors are initlized to zero, to avoid a bias toward zero, the algorithm calculates a bias-corrected version\n",
    "$$\n",
    "\\hat{m}_t = \\dfrac{m_t}{1 - \\beta^t_1} \\\\\n",
    "\\hat{v}_t = \\dfrac{v_t}{1 - \\beta^t_2}\n",
    "$$\n",
    "The update rule is similar to other methods:\n",
    "$$\n",
    "\\theta_{t+1} = \\theta_{t} - \\dfrac{\\eta}{\\sqrt{\\hat{v}_t} + \\epsilon} \\hat{m}_t\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1.4.4 Optimizers in TensorFlow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We introduced all base operations of TensorFlow in previous notebook, now we talk further about optimization operators and how they are used. For example we used optimization at the end of previous notebook to classify digits:\n",
    "\n",
    "```python\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(cost)\n",
    "_, l = sess.run([optimizer, loss], feed_dict={x: x_batch, y:y_batch})\n",
    "```\n",
    "\n",
    "In a session the optimizer operation looks at all **trainable** variables that cost depends on and update them.\n",
    "\n",
    "```python\n",
    "tf.Variable(initial_value=None, trainable=True, ...)\n",
    "```\n",
    "\n",
    "Specify if a variable should be trained or not. By default, all variables are trainable. For example the global step of the training procedure should not be trained or some layer of the network could be freezed (a technique we will use in later notebooks).\n",
    "\n",
    "\n",
    "There are many available optimizers in TensorFlow: \n",
    "- tf.train.GradientDescentOptimizer\n",
    "- tf.train.AdagradOptimizer\n",
    "- tf.train.MomentumOptimizer\n",
    "- tf.train.AdamOptimizer\n",
    "- tf.train.RMSPropOptimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Activation functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are many activation functions, perhaps two of the most popular ad the sigmoid function: \n",
    "$$\n",
    "\\sigma(x) = \\frac{1}{1+e^{-z}}\n",
    "$$\n",
    "\n",
    "and the hyperbolic tangent: \n",
    "\n",
    "$$\n",
    "tanh(x)\n",
    "$$\n",
    "\n",
    "Recently (2012) another activation function has become quite popular, Rectifier Linear Unit (ReLU): \n",
    "\n",
    "$$\n",
    "max(0, x)\n",
    "$$\n",
    "\n",
    "and it is the current default choice in many modern network architectures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "image/png": {
       "height": 800,
       "width": 800
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/activation_functions.png',  width=800, height=800)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.5 Additional Resources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [TensorFlow playground](http://playground.tensorflow.org/)\n",
    "* [TensorFlow examples](https://github.com/aymericdamien/TensorFlow-Examples)\n",
    "* [Colah's Blog](http://colah.github.io/)\n",
    "* [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com)\n",
    "* [distill.pub](https://distill.pub/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Classification with a Multi Layer Perceptron"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets('example_data/MNIST_data', one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# The MNIST dataset has 10 classes, representing the digits 0 through 9.\n",
    "NUM_CLASSES = 10\n",
    "\n",
    "# The MNIST images are always 28x28 pixels.\n",
    "IMAGE_SIZE = 28\n",
    "IMAGE_PIXELS = IMAGE_SIZE * IMAGE_SIZE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 The Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will build the network step-by-step, we will discuss its (simple) architecture and how to built it with TensorFlow.\n",
    "\n",
    "Our simple MLP looks like this: input -> hidden layer -> output classification\n",
    "\n",
    "Defining a `tf.placeholder` with `None` as its first dimension indicates that the first dimension, corresponding to the batch size, can be any size. These placeholder will contain input images and labels and will be fed to the program during the training phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_placeholder = tf.placeholder(tf.float32, shape=[None, IMAGE_PIXELS])\n",
    "label_placeholder = tf.placeholder(tf.int32, shape=[None, NUM_CLASSES])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to provide a dictionary when feeding the placeholder, we use a small helper function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_feed_dict(batch, place_1, place_2):\n",
    "    return {place_1: batch[0], place_2: batch[1]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hidden layer has a number of units that can be specified with the parameter `hidden1_units` and it computes a transformation of the linear combination of inputs, weights and biases. There are a lot of transformation available in TensorFlow, but for this example we will use ReLU.\n",
    "\n",
    "The weight matrix is initialized to random values, while the bias vector is initialized to a costant values (a smallquantity, it can be zero); it is also good practice to initialize weights of a ReLU with a slightly positive initial bias to avoid \"dead neurons\". A reasonable-sounding idea then might be to set all the initial weights to zero, which we expect to be the \"best guess\" in expectation. This turns out to be a mistake, because if every neuron in the network computes the same output, then they will also all compute the same gradients during backpropagation and undergo the exact same parameter updates. In other words, there is no source of asymmetry between neurons if their weights are initialized to be the same. It is possible and common to initialize the biases to be zero, since the asymmetry breaking is provided by the small random numbers in the weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "hidden1_units = 32\n",
    "\n",
    "with tf.name_scope('hidden1'):\n",
    "    weights = tf.Variable(\n",
    "        tf.truncated_normal([IMAGE_PIXELS, hidden1_units], \n",
    "                            stddev=0.1 / math.sqrt(float(IMAGE_PIXELS))),\n",
    "        name='weights')\n",
    "    biases = tf.Variable(tf.zeros([hidden1_units]),\n",
    "                         name='biases')\n",
    "\n",
    "    hidden1 = tf.nn.relu(tf.matmul(image_placeholder, weights) + biases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the use of `tf.name_scope` to define a prefix to the variables. \n",
    "\n",
    "The last stage compute a **softmax** transformation of the hidden layer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with tf.name_scope('softmax_linear'):\n",
    "    weights = tf.Variable(\n",
    "        tf.truncated_normal([hidden1_units, NUM_CLASSES],\n",
    "                            stddev=1.0 / math.sqrt(float(hidden1_units))),\n",
    "        name='weights')\n",
    "    biases = tf.Variable(tf.zeros([NUM_CLASSES]),\n",
    "                         name='biases')\n",
    "    logits = tf.matmul(hidden1, weights) + biases"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is possible to use TensorFlow automatic differentiation to find the gradients of the cost with respect to each variable. For this example we use basic steepest gradient descent as optimizer.  **Gradient Descent** is a simple procedure, where TensorFlow simply shifts each variable a little bit in the direction that reduces the cost. This *little bit* is the **learning rate** and it is multiplied by the negative of the gradient. For this examples the learning rate is fixed at 0.05. Actually we are using **Stochastic Gradient Descent**. Stochastic means that we are not using all the data to perform a single update of the weights but we are using a subsample at a time. This subsample is called **mini batch** and it is random. Each time we exahaust the training set we can recompute another subsample and so on. Each iteration (that is each time the algorithm consumes all the dataset) is an epoch. At each epoch the function gets better.\n",
    "\n",
    "Next we define the cost function, cross entropy in this case. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learning_rate = 0.01\n",
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits,\n",
    "                                                           labels=label_placeholder,\n",
    "                                                           name='xentropy')\n",
    "loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')\n",
    "optimizer = tf.train.GradientDescentOptimizer(learning_rate)\n",
    "train_op = optimizer.minimize(loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate the model we first compute the position of the maximum entry in both predicted and real output. This corresponds to the class to which the example belongs. Then we compare the two vector to determine what fraction of the predicted output is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(tf.nn.softmax(logits),1), \n",
    "                              tf.argmax(label_placeholder,1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After we constructed the graph, we need to start a session to run it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next thing to do is to initialize the variable in the session."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok, now we are ready to launch the training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy at step 500: 0.8828125\n",
      "Accuracy at step 1000: 0.9296875\n",
      "Accuracy at step 1500: 0.90625\n",
      "Accuracy at step 2000: 0.90625\n",
      "Accuracy at step 2500: 0.90625\n",
      "Accuracy at step 3000: 0.9140625\n",
      "Accuracy at step 3500: 0.8671875\n",
      "Accuracy at step 4000: 0.890625\n",
      "Accuracy at step 4500: 0.953125\n",
      "Accuracy at step 5000: 0.9296875\n"
     ]
    }
   ],
   "source": [
    "MAX_STEPS = 5000\n",
    "BATCH_SIZE = 128\n",
    "\n",
    "for i in range(MAX_STEPS):\n",
    "    feed_dict = get_feed_dict(mnist.train.next_batch(BATCH_SIZE), \n",
    "                              image_placeholder, \n",
    "                              label_placeholder)\n",
    "    _ = sess.run(train_op, feed_dict=feed_dict)\n",
    "    if (i+1) % 500 == 0:  \n",
    "        feed_dict = get_feed_dict(mnist.validation.next_batch(BATCH_SIZE), \n",
    "                                  image_placeholder, \n",
    "                                  label_placeholder)\n",
    "        acc = sess.run(accuracy, feed_dict=feed_dict)\n",
    "        print('Accuracy at step %s: %s' % (i+1, acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**REMARK** Epochs are calculated directly by the mnist object. Whenever the number of batches requested is greather than the number of examples it automatically updates the number of epochs. The object shuffles also the training set for better learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9169\n"
     ]
    }
   ],
   "source": [
    "print(accuracy.eval(session=sess, feed_dict={image_placeholder: mnist.test.images, \n",
    "                                             label_placeholder: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Structure and Logging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Managing experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.1 Saving and Restoring Model Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A good practice is to periodically save the model’s parameters after a certain number of steps so that we can restore/retrain our model from that step if need be. The `tf.train.Saver()` class allows us to do so by saving the graph’s variables in binary files. It does not save the graph! This class has basically two methods: \n",
    "\n",
    "```python\n",
    "tf.train.Saver.save(sess, save_path, global_step=...)\n",
    "```\n",
    "\n",
    ">This method runs the ops added by the constructor for saving variables.\n",
    "It requires a session in which the graph was launched.  The variables to\n",
    "save must also have been initialized.\n",
    "\n",
    "and \n",
    "\n",
    "```python\n",
    "tf.train.Saver.restore(sess, save_path)\n",
    "```\n",
    "\n",
    ">This method runs the ops added by the constructor for restoring variables.\n",
    "It requires a session in which the graph was launched.  The variables to\n",
    "restore do not have to have been initialized, as restoring is itself a way\n",
    "to initialize variables.\n",
    "\n",
    "It is good practice to save parameters every $n$ number of steps.\n",
    "\n",
    "```python\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    for step in range(training_steps): \n",
    "        sess.run([optimizer])\n",
    "        \n",
    "        if (step + 1) % 1000 == 0:\n",
    "            saver.save(sess, \n",
    "            'checkpoint_directory/model_name',\n",
    "            global_step=step)\n",
    "```\n",
    "\n",
    "In TensorFlow lingo, the step at which you save your graph’s variables is called a checkpoint. Since we will be creating many checkpoints, it’s helpful to append the number of training steps our model has gone through in a variable called global_step. It’s a very common variable to see in TensorFlow program. We first need to create it, initialize it to 0 and set it to be not trainable, since we don’t want to TensorFlow to optimize it.\n",
    "\n",
    "```python\n",
    "global_step = tf.Variable(0, dtype=tf.int32, trainable=False, name='global_step')\n",
    "optimizer = tf.train.AdamOptimizer(lr).minimize(loss, global_step=global_step)\n",
    "```\n",
    "\n",
    "It is needed to tell optimizer to increment global step. This can also help your optimizer know when to decay learning rate\n",
    "\n",
    "To restore the model from a specific checkpoint: \n",
    "\n",
    "```python\n",
    "saver.restore(sess, 'checkpoints/name_of_the_checkpoint')\n",
    "```\n",
    "\n",
    "Note that you still need to construct the graph first. It is also possible to automatically restore the latest checkpoint: \n",
    "\n",
    "```python\n",
    "ckpt = tf.train.get_checkpoint_state(os.path.dirname('checkpoints/checkpoint'))\n",
    "\n",
    "if ckpt and ckpt.model_checkpoint_path:\n",
    "     saver.restore(sess, ckpt.model_checkpoint_path)\n",
    "```\n",
    "Checkpoint file keeps track of the latest checkpoint, restore checkpoints only when there is a valid checkpoint path."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.2 Log useful information with `tf.summary`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to visualize summary statistics during training, tensorflow has several operations:\n",
    " - tf.summary.scalar\n",
    " - tf.summary.histogram\n",
    " - tf.summary.image\n",
    " \n",
    "First you need to create the summaries:\n",
    "```python\n",
    "with tf.name_scope(\"summaries\"):\n",
    "    tf.summary.scalar(\"loss\", loss)\n",
    "    tf.summary.scalar(\"accuracy\", accuracy)            \n",
    "    tf.summary.histogram(\"histogram loss\", loss)\n",
    "    summary_op = tf.summary.merge_all()\n",
    "```\n",
    "the last operation concentrate all summary in a single operation for clarity. Now you can run `summary_op` during training in a session like any other operation. Remember to write the summaries to file with a file writer. Remember to add a global step variable so the model knows what summary corresponds to what step. In the next section we will see all this in action!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Model structuring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A possible method for structuring models is to pass around TensorFlow objects such as training operation. Another method is to incapsulate models in objects. In next section we will see how to structure a model into objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def lazy_property(function):\n",
    "    attribute = '_lazy_' + function.__name__\n",
    "    \n",
    "    @property\n",
    "    @functools.wraps(function)\n",
    "    def wrapper(self):\n",
    "        if not hasattr(self, attribute):\n",
    "            setattr(self, attribute, function(self))\n",
    "        return getattr(self, attribute)\n",
    "    return wrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class MLP:\n",
    "    def __init__(self,\n",
    "                 layers,\n",
    "                 input_dim,\n",
    "                 output_dim,\n",
    "                 learning_rate,\n",
    "                 batch_size=128, \n",
    "                 base_dir='temp/mnist_fc', \n",
    "                 run_dir='0', \n",
    "                 act='relu', \n",
    "                 restore=False, \n",
    "                 verbose=True, \n",
    "                 save=True):\n",
    "        activations = {'relu': tf.nn.relu, \n",
    "                       'sigmoid': tf.nn.sigmoid, \n",
    "                       'tanh': tf.nn.tanh}\n",
    "        self.act = activations[act]\n",
    "        self.mnist = input_data.read_data_sets('example_data/MNIST_data')\n",
    "        self.x = tf.placeholder(tf.float32, shape=[None, input_dim])\n",
    "        self.y = tf.placeholder(tf.int64, shape=[None])\n",
    "        self.input_dim = input_dim\n",
    "        self.output_dim = output_dim\n",
    "        self.layers = layers\n",
    "        self.learning_rate = learning_rate\n",
    "        self.batch_size = batch_size\n",
    "        self.verbose = verbose\n",
    "        self.save = save\n",
    "        self.model_dir = os.path.join(base_dir, run_dir)\n",
    "        self.global_step = tf.get_variable('global_step', \n",
    "                                           dtype=tf.int32, \n",
    "                                           trainable=False,\n",
    "                                           initializer=0)\n",
    "        self.prediction\n",
    "        self.optimize\n",
    "        self.accuracy\n",
    "        self.saver = tf.train.Saver()\n",
    "        self.sess = tf.Session()\n",
    "        self.merged = tf.summary.merge_all()\n",
    "        self.train_writer = tf.summary.FileWriter(os.path.join(self.model_dir, 'train'),\n",
    "                                                  self.sess.graph)\n",
    "        self.test_writer = tf.summary.FileWriter(os.path.join(self.model_dir, 'test'))\n",
    "        if restore:\n",
    "            checkpoint = tf.train.get_checkpoint_state(self.model_dir)\n",
    "            if checkpoint and checkpoint.model_checkpoint_path:\n",
    "                path = checkpoint.model_checkpoint_path\n",
    "                self.saver.restore(self.sess, path)\n",
    "            else:\n",
    "                self.sess.run(tf.global_variables_initializer())\n",
    "        else:\n",
    "            self.sess.run(tf.global_variables_initializer())\n",
    "\n",
    "    @lazy_property\n",
    "    def prediction(self):\n",
    "        return tf.nn.softmax(self.forward)\n",
    "\n",
    "    @lazy_property\n",
    "    def forward(self):\n",
    "        layer = self.x\n",
    "        # write few images from the input\n",
    "        x_image = tf.reshape(self.x, [-1, 28, 28, 1])\n",
    "        tf.summary.image('input_images', x_image, 3)\n",
    "        res = [self.input_dim] + self.layers\n",
    "        for i, (input_dim, output_dim) in enumerate(zip(res[:-1], res[1:])):\n",
    "            layer = self._nn_layer(layer, input_dim, output_dim, \n",
    "                                   'layer'+str(i), act=self.act)\n",
    "        y = self._nn_layer(layer,\n",
    "                           self.layers[-1],\n",
    "                           self.output_dim,\n",
    "                           'output_layer',\n",
    "                           act=tf.identity)\n",
    "        return y\n",
    "    \n",
    "    @lazy_property\n",
    "    def cost(self):\n",
    "        with tf.name_scope('cross_entropy'):\n",
    "            cross_entropy = tf.losses.sparse_softmax_cross_entropy(labels=self.y,\n",
    "                                                                   logits=self.forward)\n",
    "            tf.summary.scalar('cross_entropy', cross_entropy)\n",
    "        return cross_entropy\n",
    "\n",
    "    @lazy_property\n",
    "    def optimize(self):\n",
    "        with tf.name_scope('train_op'):\n",
    "            optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)\n",
    "            train_op = optimizer.minimize(self.cost, global_step=self.global_step)\n",
    "        return train_op\n",
    "\n",
    "    @lazy_property\n",
    "    def accuracy(self):\n",
    "        with tf.name_scope('accuracy'):\n",
    "            correct_prediction = tf.equal(tf.argmax(self.prediction, 1), self.y)\n",
    "            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "            tf.summary.scalar('accuracy', accuracy)\n",
    "        return accuracy\n",
    "\n",
    "    def close(self):\n",
    "        self.sess.close()\n",
    "    \n",
    "    def feed_dict(self, dataset):\n",
    "        \"\"\"Make a TensorFlow feed_dict: maps data onto Tensor placeholders.\"\"\"\n",
    "        if dataset=='train':\n",
    "            xs, ys = self.mnist.train.next_batch(self.batch_size)\n",
    "        elif dataset=='test':\n",
    "            xs, ys = self.mnist.test.images, self.mnist.test.labels\n",
    "        elif dataset=='validation':\n",
    "            xs, ys = self.mnist.validation.images, self.mnist.validation.labels\n",
    "        return {self.x: xs, self.y: ys}\n",
    "    \n",
    "    @staticmethod\n",
    "    def weight_variable(layer_name, shape):\n",
    "        initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "        with tf.variable_scope(layer_name, initializer=initial, reuse=tf.AUTO_REUSE):\n",
    "            return tf.get_variable('weights')\n",
    "\n",
    "    @staticmethod\n",
    "    def bias_variable(layer_name, shape):\n",
    "        initial = tf.constant(0.1, shape=shape)\n",
    "        with tf.variable_scope(layer_name, initializer=initial, reuse=tf.AUTO_REUSE):\n",
    "            return tf.get_variable('biases')\n",
    "\n",
    "    def _nn_layer(self,\n",
    "                 input_tensor,\n",
    "                 input_dim,\n",
    "                 output_dim,\n",
    "                 layer_name,\n",
    "                 act=tf.nn.relu):\n",
    "        with tf.name_scope(layer_name):\n",
    "            weights = self.weight_variable(layer_name, [input_dim, output_dim])\n",
    "            biases = self.bias_variable(layer_name, [output_dim])\n",
    "            preactivate = tf.matmul(input_tensor, weights) + biases\n",
    "            activations = act(preactivate, name='activation')\n",
    "            tf.summary.histogram('weights_hist', weights)\n",
    "            tf.summary.histogram('biases_hist', biases)\n",
    "            tf.summary.histogram('pre_activations', preactivate)\n",
    "            tf.summary.histogram('activations', activations)\n",
    "            return activations\n",
    "\n",
    "    def fit(self, steps):\n",
    "        for _ in range(steps):\n",
    "            i = tf.train.global_step(self.sess, self.global_step)\n",
    "            if i % 10 == 0:  # Record summaries and test-set accuracy\n",
    "                summary, acc = self.sess.run([self.merged, self.accuracy],\n",
    "                                             feed_dict=self.feed_dict('test'))\n",
    "                self.test_writer.add_summary(summary, i)\n",
    "                if i % 100 == 0:\n",
    "                    print('Accuracy at step %s: %s' % (i, acc)) if self.verbose else None\n",
    "            if i % 100 == 99:  # Record execution stats\n",
    "                run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)\n",
    "                run_metadata = tf.RunMetadata()\n",
    "                summary, _ = self.sess.run([self.merged, self.optimize],\n",
    "                                           feed_dict=self.feed_dict('train'),\n",
    "                                           options=run_options,\n",
    "                                           run_metadata=run_metadata)\n",
    "                self.train_writer.add_run_metadata(run_metadata, 'step%d' % i)\n",
    "                self.train_writer.add_summary(summary, i)\n",
    "                #print('Adding run metadata for', i)\n",
    "                self.saver.save(self.sess, \n",
    "                                os.path.join(self.model_dir, 'model'), \n",
    "                                global_step=self.global_step) if self.save else None\n",
    "            else:  # Record a summary\n",
    "                summary, _ = self.sess.run([self.merged, self.optimize],\n",
    "                                           feed_dict=self.feed_dict('train'))\n",
    "                self.train_writer.add_summary(summary, i)\n",
    "                    \n",
    "    def predict(self):\n",
    "        acc, y_pred = self.sess.run([self.accuracy, self.prediction],\n",
    "                                    feed_dict=self.feed_dict('validation'))\n",
    "        return acc, np.argmax(y_pred, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy at step 0: 0.0743\n",
      "Accuracy at step 100: 0.3019\n",
      "Accuracy at step 200: 0.5238\n",
      "Accuracy at step 300: 0.6444\n",
      "Accuracy at step 400: 0.7136\n",
      "Accuracy at step 500: 0.7565\n",
      "Accuracy at step 600: 0.7803\n",
      "Accuracy at step 700: 0.8037\n",
      "Accuracy at step 800: 0.8186\n",
      "Accuracy at step 900: 0.8318\n",
      "Accuracy on validation set: 0.8425999879837036\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model = MLP([32], IMAGE_PIXELS, NUM_CLASSES, 0.01)\n",
    "model.fit(1000)\n",
    "acc, _ = model.predict()\n",
    "print('Accuracy on validation set: {}'.format(acc))\n",
    "model.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now it is possible to launch tensorboard with: `tensorboard --logdir=temp/mnist_fc/0`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "INFO:tensorflow:Restoring parameters from temp/mnist_fc/0/model-1000\n",
      "Accuracy at step 1000: 0.8416\n",
      "Accuracy at step 1100: 0.8498\n",
      "Accuracy at step 1200: 0.8566\n",
      "Accuracy at step 1300: 0.8618\n",
      "Accuracy at step 1400: 0.8661\n",
      "Accuracy at step 1500: 0.872\n",
      "Accuracy at step 1600: 0.8743\n",
      "Accuracy at step 1700: 0.8787\n",
      "Accuracy at step 1800: 0.8816\n",
      "Accuracy at step 1900: 0.8852\n",
      "Accuracy on validation set: 0.8894000053405762\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "model = MLP([32], IMAGE_PIXELS, NUM_CLASSES, 0.01, restore=True)\n",
    "model.fit(1000)\n",
    "acc, _ = model.predict()\n",
    "print('Accuracy on validation set: {}'.format(acc))\n",
    "model.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Control again with tensorboard you should see more steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now training model: 0.5_relu_[64]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.9666000008583069\n",
      "Now training model: 0.5_relu_[32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.9581999778747559\n",
      "Now training model: 0.5_relu_[64, 32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.9634000062942505\n",
      "Now training model: 0.01_relu_[64]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.8547999858856201\n",
      "Now training model: 0.01_relu_[32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.8388000130653381\n",
      "Now training model: 0.01_relu_[64, 32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.8324000239372253\n",
      "Now training model: 0.001_relu_[64]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.5059999823570251\n",
      "Now training model: 0.001_relu_[32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.32659998536109924\n",
      "Now training model: 0.001_relu_[64, 32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.28760001063346863\n",
      "Now training model: 0.5_sigmoid_[64]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.9366000294685364\n",
      "Now training model: 0.5_sigmoid_[32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.9323999881744385\n",
      "Now training model: 0.5_sigmoid_[64, 32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.9211999773979187\n",
      "Now training model: 0.01_sigmoid_[64]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.6746000051498413\n",
      "Now training model: 0.01_sigmoid_[32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.5863999724388123\n",
      "Now training model: 0.01_sigmoid_[64, 32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.11959999799728394\n",
      "Now training model: 0.001_sigmoid_[64]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.17720000445842743\n",
      "Now training model: 0.001_sigmoid_[32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.09759999811649323\n",
      "Now training model: 0.001_sigmoid_[64, 32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.13019999861717224\n",
      "Now training model: 0.5_tanh_[64]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.9603999853134155\n",
      "Now training model: 0.5_tanh_[32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.953000009059906\n",
      "Now training model: 0.5_tanh_[64, 32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.9599999785423279\n",
      "Now training model: 0.01_tanh_[64]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.8410000205039978\n",
      "Now training model: 0.01_tanh_[32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.8321999907493591\n",
      "Now training model: 0.01_tanh_[64, 32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.8014000058174133\n",
      "Now training model: 0.001_tanh_[64]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on validation set: 0.5526000261306763\n",
      "Now training model: 0.001_tanh_[32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.36399999260902405\n",
      "Now training model: 0.001_tanh_[64, 32]\n",
      "Extracting example_data/MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting example_data/MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "Accuracy on validation set: 0.34700000286102295\n"
     ]
    }
   ],
   "source": [
    "from itertools import product\n",
    "\n",
    "act_list = ['relu', 'sigmoid', 'tanh']\n",
    "lr_list = [0.5, 0.01, 0.001]\n",
    "layers_list = [[64], [32], [64, 32]]\n",
    "for act, lr, layers in (product(act_list, lr_list, layers_list)):\n",
    "    print('Now training model: {}'.format('_'.join([str(lr), str(act), str(layers)])))\n",
    "    tf.reset_default_graph()\n",
    "    model = MLP(layers, IMAGE_PIXELS, NUM_CLASSES, learning_rate=lr, act=act,\n",
    "                run_dir='_'.join([str(lr), str(act), str(layers)]),\n",
    "                save=False, verbose=False)\n",
    "    model.fit(1000)\n",
    "    acc, _ = model.predict()\n",
    "    print('Accuracy on validation set: {}'.format(acc))\n",
    "    model.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Considerations on simple MLP models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The performance of the simple MLP is pretty good, but what if we want to improve it? Increasing the number of layers does not improve the accuracy, but why? This is known as the vanishing or exploding gradient problems. That is, the learning procedure based on gradients is unstable if we have many layers, resulting in the last layers to learn slowly. For this reason in order to train deeper network we must use another kind of network, the **Convolutional Neural Network**. In practice to avoid the vanishing gradient problem we use a specialized network with fewer parameters, that takes advantage of spatial locality of the images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Simplify TensorFlow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Estimators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimators are an high-level TensorFlow API that greatly simplifies machine learning programming. Estimators encapsulate the following actions:\n",
    "- training\n",
    "- evaluation\n",
    "- prediction\n",
    "\n",
    "If is possible to use pre-made Estimators or write custom estimators based on the tf.estimator.Estimator class. See [tensorflow estimators](https://www.tensorflow.org/programmers_guide/estimators) for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2: contrib.learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The module **learn** in the contrib section of the repository of TensorFlow was formerly known as skflow and it is meant to provide ease of use and modularity, for beginners and for integrating TensorFlow models in existing code. It has an interface similar to scikit-learn and it is possible to create a Neural Network with few line of code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.contrib import learn\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = learn.DNNClassifier(hidden_units=[hidden1_units, 20, 10],\n",
    "                                 n_classes=NUM_CLASSES,\n",
    "                                 feature_columns=learn.infer_real_valued_columns_from_input(mnist.train.images.astype(np.float32)),\n",
    "                                 optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.05)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With just one line of code we are able to create the same neural network that took us many line of code to compute, and with just one line we can fit it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DNNClassifier(params={'head': <tensorflow.contrib.learn.python.learn.estimators.head._MultiClassHead object at 0x7f19607ea978>, 'hidden_units': [32, 20, 10], 'feature_columns': (_RealValuedColumn(column_name='', dimension=784, default_value=None, dtype=tf.float32, normalizer=None),), 'optimizer': <tensorflow.python.training.gradient_descent.GradientDescentOptimizer object at 0x7f19607eadd8>, 'activation_fn': <function relu at 0x7f1975f49598>, 'dropout': None, 'gradient_clip_norm': None, 'embedding_lr_multipliers': None, 'input_layer_min_slice_size': None})"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.fit(mnist.train.images, \n",
    "               np.argmax(mnist.train.labels, axis=1).astype(np.int64), \n",
    "               batch_size=BATCH_SIZE, \n",
    "               steps=3000) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy is similar to the example above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.954500\n"
     ]
    }
   ],
   "source": [
    "accuracy_score = classifier.evaluate(x=mnist.test.images.astype(np.float32), \n",
    "                                     y=np.argmax(mnist.test.labels, axis=1).astype(np.int64))[\"accuracy\"]\n",
    "print('Accuracy: {0:f}'.format(accuracy_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Learn has many models, for example it is possible to create a linear classification model or even a Random Forest! It is also possible to create an highly customized model by providing a model function to an `Estimator` object. However if the model is highly complex maybe it is best to use plain TensorFlow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---\n",
    "\n",
    "Visit [www.add-for.com](<http://www.add-for.com/IT>) for more tutorials and updates.\n",
    "\n",
    "This work is licensed under a <a rel=\"license\" href=\"http://creativecommons.org/licenses/by-sa/4.0/\">Creative Commons Attribution-ShareAlike 4.0 International License</a>."
   ]
  }
 ],
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