HW5_solutions.ipynb

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     "source": [
      "# Homework 5: Networks and Congress\n",
      "\n",
      "*Due Friday, November 15, 11:59pm*\n",
      "\n",
      "<img src=\"http://img.washingtonpost.com/rf/image_1024w/2010-2019/WashingtonPost/2011/08/05/National-Politics/Images/uscap.JPG\">\n",
      "\n",
      "<br>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "\n",
      "import json\n",
      "\n",
      "import numpy as np\n",
      "import networkx as nx\n",
      "import requests\n",
      "from pattern import web\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "# set some nicer defaults for matplotlib\n",
      "from matplotlib import rcParams\n",
      "\n",
      "#these colors come from colorbrewer2.org. Each is an RGB triplet\n",
      "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n",
      "                (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n",
      "                (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n",
      "                (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n",
      "                (0.4, 0.6509803921568628, 0.11764705882352941),\n",
      "                (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n",
      "                (0.6509803921568628, 0.4627450980392157, 0.11372549019607843),\n",
      "                (0.4, 0.4, 0.4)]\n",
      "\n",
      "rcParams['figure.figsize'] = (10, 6)\n",
      "rcParams['figure.dpi'] = 150\n",
      "rcParams['axes.color_cycle'] = dark2_colors\n",
      "rcParams['lines.linewidth'] = 2\n",
      "rcParams['axes.grid'] = False\n",
      "rcParams['axes.facecolor'] = 'white'\n",
      "rcParams['font.size'] = 14\n",
      "rcParams['patch.edgecolor'] = 'none'\n",
      "\n",
      "def remove_border(axes=None, top=False, right=False, left=True, bottom=True):\n",
      "    \"\"\"\n",
      "    Minimize chartjunk by stripping out unnecessary plot borders and axis ticks\n",
      "    \n",
      "    The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn\n",
      "    \"\"\"\n",
      "    ax = axes or plt.gca()\n",
      "    ax.spines['top'].set_visible(top)\n",
      "    ax.spines['right'].set_visible(right)\n",
      "    ax.spines['left'].set_visible(left)\n",
      "    ax.spines['bottom'].set_visible(bottom)\n",
      "    \n",
      "    #turn off all ticks\n",
      "    ax.yaxis.set_ticks_position('none')\n",
      "    ax.xaxis.set_ticks_position('none')\n",
      "    \n",
      "    #now re-enable visibles\n",
      "    if top:\n",
      "        ax.xaxis.tick_top()\n",
      "    if bottom:\n",
      "        ax.xaxis.tick_bottom()\n",
      "    if left:\n",
      "        ax.yaxis.tick_left()\n",
      "    if right:\n",
      "        ax.yaxis.tick_right()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The website [govtrack.us](http://www.govetrack.us) collects data on activities in the Senate and House of Representatives. It's a great source of information for making data-driven assessments about Congress."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Problem 1.\n",
      "\n",
      "The directories at http://www.govtrack.us/data/congress/113/votes/2013 contain JSON information about every vote cast for the current (113th)  Congress. Subdirectories beginning with \"S\" correspond to Senate votes, while subdirectories beginning with \"H\" correspond to House votes.\n",
      "\n",
      "Write two functions: one that downloads and parses a single Senate vote page given the vote number, and another that repeatedly calls this function to build a full collection of Senate votes from the 113th Congress."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "get_senate_vote\n",
      "\n",
      "Scrapes a single JSON page for a particular Senate vote, given by the vote number\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "vote : int\n",
      "   The vote number to fetch\n",
      "   \n",
      "Returns\n",
      "-------\n",
      "vote : dict\n",
      "   The JSON-decoded dictionary for that vote\n",
      "   \n",
      "Examples\n",
      "--------\n",
      ">>> get_senate_vote(11)['bill']\n",
      "{u'congress': 113,\n",
      " u'number': 325,\n",
      " u'title': u'A bill to ensure the complete and timely payment of the obligations of the United States Government until May 19, 2013, and for other purposes.',\n",
      " u'type': u'hr'}\n",
      "\"\"\"\n",
      "#your code here\n",
      "\n",
      "def get_senate_vote(vote):\n",
      "    url = 'http://www.govtrack.us/data/congress/113/votes/2013/s%i/data.json' % vote\n",
      "    page = requests.get(url).text\n",
      "    return json.loads(page)    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "get_all_votes\n",
      "\n",
      "Scrapes all the Senate votes from http://www.govtrack.us/data/congress/113/votes/2013,\n",
      "and returns a list of dicts\n",
      "\n",
      "Parameters\n",
      "-----------\n",
      "None\n",
      "\n",
      "Returns\n",
      "--------\n",
      "votes : list of dicts\n",
      "    List of JSON-parsed dicts for each senate vote\n",
      "\"\"\"\n",
      "#Your code here\n",
      "\n",
      "def get_all_votes():\n",
      "    page = requests.get('https://www.govtrack.us/data/congress/113/votes/2013/').text\n",
      "    dom = web.Element(page)\n",
      "    votes = [a.attr['href'] for a in dom.by_tag('a') \n",
      "             if a.attr.get('href', '').startswith('s')]\n",
      "    n_votes = len(votes)\n",
      "    return [get_senate_vote(i) for i in range(1, n_votes + 1)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#vote_data = get_all_votes()\n",
      "vote_data = json.load(open('vote_data.json'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Problem 2\n",
      "\n",
      "Now, turn these data into a NetworkX graph, according to the spec below. For details on using NetworkX, consult the lab materials for November 1, as well as the [NetworkX documentation](http://networkx.github.io/)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "vote_graph\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "data : list of dicts\n",
      "    The vote database returned from get_vote_data\n",
      "\n",
      "Returns\n",
      "-------\n",
      "graph : NetworkX Graph object, with the following properties\n",
      "    1. Each node in the graph is labeled using the `display_name` of a Senator (e.g., 'Lee (R-UT)')\n",
      "    2. Each node has a `color` attribute set to 'r' for Republicans, \n",
      "       'b' for Democrats, and 'k' for Independent/other parties.\n",
      "    3. The edges between two nodes are weighted by the number of \n",
      "       times two senators have cast the same Yea or Nay vote\n",
      "    4. Each edge also has a `difference` attribute, which is set to `1 / weight`.\n",
      "\n",
      "Examples\n",
      "--------\n",
      ">>> graph = vote_graph(vote_data)\n",
      ">>> graph.node['Lee (R-UT)']\n",
      "{'color': 'r'}  # attributes for this senator\n",
      ">>> len(graph['Lee (R-UT)']) # connections to other senators\n",
      "101\n",
      ">>> graph['Lee (R-UT)']['Baldwin (D-WI)']  # edge relationship between Lee and Baldwin\n",
      "{'difference': 0.02, 'weight': 50}\n",
      "\"\"\"\n",
      "#Your code here\n",
      "\n",
      "def _color(s):\n",
      "    if '(R' in s:\n",
      "        return 'r'\n",
      "    if '(D' in s:\n",
      "        return 'b'\n",
      "    return 'k'\n",
      "    \n",
      "def vote_graph(data):\n",
      "    \n",
      "    senators = set(x['display_name'] for d in data for vote_grp in d['votes'].values() for x in vote_grp)\n",
      "    weights = {s: {ss: 0 for ss in senators if ss != s} for s in senators}\n",
      "    \n",
      "    for d in data:\n",
      "        for grp in ['Yea', 'Nay']:\n",
      "            if grp not in d['votes']:\n",
      "                continue\n",
      "            vote_grp = d['votes'][grp]\n",
      "            for i in range(len(vote_grp)):\n",
      "                for j in range(i + 1, len(vote_grp)):\n",
      "                    sen1 = vote_grp[i]['display_name']\n",
      "                    sen2 = vote_grp[j]['display_name']                \n",
      "                    weights[min(sen1, sen2)][max(sen1, sen2)] += 1\n",
      "                    \n",
      "    g = nx.Graph()\n",
      "    for s in senators:\n",
      "        g.add_node(s)\n",
      "        g.node[s]['color'] = _color(s)\n",
      "    \n",
      "    for s1, neighbors in weights.items():\n",
      "        for s2, weight in neighbors.items():\n",
      "            if weight == 0:\n",
      "                continue\n",
      "            g.add_edge(s1, s2, weight= weight, difference = 1. / weight)\n",
      "            \n",
      "    return g\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "votes = vote_graph(vote_data)    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### How (and how not) to visualize networks\n",
      "\n",
      "Network plots often *look* impressive, but creating sensible network plots is tricky. From Ben Fry, the author of the Processing program:\n",
      "<blockquote>\n",
      "Usually a graph layout isn\u2019t the best option for data sets larger than a few dozen nodes. You\u2019re most likely to wind up with enormous spider webs or balls of string, and the mess seen so far is more often the case than not. Graphs can be a powerful way to represent relationships between data, but they are also a very abstract concept, which means that they run the danger of meaning something only to the creator of the graph. Often, simply showing the structure of the data says very little about what it actually means, even though it\u2019s a perfectly accurate means of representing the data. Everything looks like a graph, but almost nothing should ever be drawn as one.\n",
      "</blockquote>\n",
      "\n",
      "Let's look at bad and better ways of visualizing the senate vote network.\n",
      "\n",
      "First, consider the \"default\" plot from networkx."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#this makes sure draw_spring results are the same at each call\n",
      "np.random.seed(1)  \n",
      "\n",
      "color = [votes.node[senator]['color'] for senator in votes.nodes()]\n",
      "\n",
      "#determine position of each node using a spring layout\n",
      "pos = nx.spring_layout(votes, iterations=200)\n",
      "\n",
      "#plot the edges\n",
      "nx.draw_networkx_edges(votes, pos, alpha = .05)\n",
      "\n",
      "#plot the nodes\n",
      "nx.draw_networkx_nodes(votes, pos, node_color=color)\n",
      "\n",
      "#draw the labels\n",
      "lbls = nx.draw_networkx_labels(votes, pos, alpha=5, font_size=8)\n",
      "\n",
      "#coordinate information is meaningless here, so let's remove it\n",
      "plt.xticks([])\n",
      "plt.yticks([])\n",
      "remove_border(left=False, bottom=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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37emrBjB516+vAk4BjgN6EgplMGqUCYfDQWpqKtnZ2djtdqxWK1lZWQzq3RvL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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107006790>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The spring layout tries to group nodes with large edge-weights near to each other. In this context, that means it tries to organize the Senate into similarly-voting cliques. However, there's simply too much going on in this plot -- we should simplify the representation."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Problem 3\n",
      "\n",
      "Compute the `Minimum Spanning Tree` of this graph, using the `difference` edge attribute as the weight to minimize. A [Minimum Spanning Tree](http://en.wikipedia.org/wiki/Minimum_spanning_tree) is the subset of edges which trace at least one path through all nodes (\"spanning\"), with minimum total edge weight. You can think of it as a simplification of a network.\n",
      "\n",
      "Plot this new network, making modifications as necessary to prevent the graph from becoming too busy."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Your code here\n",
      "plt.figure(figsize=(15, 10))\n",
      "np.random.seed(5)\n",
      "mst = nx.minimum_spanning_tree(votes, weight='difference')\n",
      "pos = nx.spring_layout(mst, iterations=900, k=.008, weight='difference')\n",
      "\n",
      "mst_edges = list(nx.minimum_spanning_edges(votes, weight='difference'))\n",
      "\n",
      "nl = votes.nodes()\n",
      "c = [votes.node[n]['color'] for n in nl]\n",
      "nx.draw_networkx_edges(votes, pos, edgelist=mst_edges, alpha=.2)\n",
      "nx.draw_networkx_nodes(votes, pos, nodelist = nl, node_color = c, node_size=60)\n",
      "\n",
      "for p in pos.values():\n",
      "    p[1] += .02\n",
      "    \n",
      "nx.draw_networkx_labels(votes, pos, font_color='k', font_size=7)\n",
      "\n",
      "plt.title(\"MST of Vote Disagreement\", fontsize=18)\n",
      "plt.xticks([])\n",
      "plt.yticks([])\n",
      "remove_border(left=False, bottom=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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tW7bk5ZdfNrfdoUMHli1bxkcffUT+/Pl59tln6dq1K59//jkmkwmA6dOnU758\neV555RXzea1bt+aTTz4BIDAwkMmTJ3Px4kU2btzIsGHDcneCRERE/oGW+YmIPMYyv08rW7YswcHB\neHt7c+HCBXO5o6MjefPmJTU1FV9fX9566y08PT25cOECM2fOxNPTk0KFCvHzzz+bz4mLi8PW1tb8\neN68eTRs2JCmTZuaAymALl26sHv3brp27crChQuzjK1SpUqcPn0aPz8/fvjhh/vx9EVERO6Jdv+K\niAh58+YFwMnJiaSkJBwdHUlJSSEhIQGTyUTx4sVxd3fn+PHjmEwmkpKSMAyDkSNHWgROkLGM0NXV\nFYDU1FQaNmxImzZtCAoKonnz5kycOJGnnnqKoKAgJkyYAECTJk3o1q2bRTsHDhygU6dOFgGYiIjI\nw0TBlIiIZAlYWrRowejRoylSpIi5LE+ePBQqVIi//vqLq1ev0q9fP7p27YqnpyfPPfcc7dq1A8Db\n25vY2FjS09OJioqiePHiFChQgAYNGvDdd98xbdo0ANavX89XX32FnZ0d/v7+mEwmDhw4QP/+/YmP\nj+eVV17B09OTmJgYChcunLsTIiIicge0Z0pERO7K9evXiYiIuG1iinHjxtG4cWPy589PiRIl7qm/\nefPm8eyzz/L888/fUzsiIiI5TcGUiIjcteTkZCIiInBxcaFYsWKY9u6F0FCIjoYXXuBkzZqciYuj\nTp062Njc2/bcvXv38uyzz+bMwEVERHKQgikREbFKeno6p0+fxn3ePApMmWJRllKgAMb27Tj4+z+g\n0YmIiNx/CqZERMR6J05gPPkkplv9U/Lyy7BlS+6PSUREJJcoNbqIiFhv+fJbB1IAW7fCxYu5Ox4R\nEZFcpGBKRESsd+VK9mWGAdeu5d5YREREcpmCKRERsd5LL2VfVrw4lCyZa0MRERHJbdozJSIi1ktL\ng+rVYc+erGVLlkBAQK4PSf4vOTmZwYMHYxgGhmHw7LPPEhgYeNtzWrduzSeffHLPfS9ZsoTy5ctz\n4sQJ1qxZg5eXF2XLlmXEiBHmOkePHuW9994jf/78+Pr6MmTIEFatWsWWLVtwcnKiSJEidOnShTlz\n5vDf//73nsckIpLTdNNeERGxnq1tRpKJ4cMhLAyuXSPNzw/bUaPg75v4yoMTEhJC48aNadCgAQBp\naWkA+Pv707lzZxo2bMiqVauIi4ujUqVKdO3alUuXLjF69GgOHDjAwoULiY2NZdmyZURFRdG1a1d8\nfHwICAg1OwBoAAAgAElEQVSgatWqJCQkUKhQIX766ScmTZpE+fLlzX1v27aNwMBAwsPD6dmzJ40b\nN6Z9+/YW4/v666/p2LEjr7zyCgAxMTFs3ryZ0NBQAFJSUrC3tycyMpLk5GQcHBxyYdZERO6clvmJ\niMi9yZsX5s6Fy5c5d/w4f+3YoUDqIXHs2DGqVKmCYRgMGDCAPn36AFCkSBGGDh1KmTJlSEtLw9PT\nk9WrVwNgb2/P+PHj6dq1Kzt37sTBwYGkpCS8vb0JCwvDZDJRrlw5goODOX/+PH379mXYsGF8+eWX\n5n7j4uKwtbU1Pw4JCaFGjRrUrVvXYnxdunRh9+7ddO3alYULF/Lbb7/x1FNPmcvt7e0BKF26NPv2\n7btv8yQiYi0FUyIikjNsbXHw8CApKelBj0T+5u/vz549ezCZTEybNo1Lly4BkDdvXgA2b96Mn58f\n48aNIzU11aLMycmJpKQkZs6cyVtvvUX37t2Jj48HwN3dHQBHR0fc3Nywt7e3eN0TExNxdXU1P+7e\nvTtbt27ls88+IyoqigEDBrB48WLy5MnDhAkTWLRoERs2bKB06dIcOXLEfF5KSgoAefLk4fr16/dr\nmkRErKZlfiIikmOcnJy4evXqgx6G/K1bt24MHjyYjRs3YmdnR5UqVSzKn376ad5++23+/PNP0tPT\nb9lG7dq1CQ4OxtvbG5PJBGD+b6abH3t7exMbG2txzNnZmQYNGvDdd98xbdo0ANavX89XX32FnZ0d\n/v7+eHp6Ur9+fbp27YqTkxNFixZl2LBhnDp1itatW9/TXIiI3A9KQCEPRGhoKDNmzGD//v2kpKRQ\npkwZ5s6dS+PGjS3qxcbG8vbbb5uXmbz11lv4+fndc/87d+7kyJEjVKhQgSNHjtC7d29z2fbt27l4\n8SKFChVizJgxlClTBldXV2bMmJHl/DfeeINevXpRqVIlPD096d69O5MmTeKFF15g69at9O/fnwIF\nCtzzeEX+LRITE4mIiMDf3/9BD0Vyw7598NtvUK4c3LA8D2DcuHH06tWLggUL3lMXqamp9OzZk0WL\nFt1TOyIi94OuTMkDYTKZKF++PD/88AMXLlygevXqmEwmzpw5w+jRoylYsCAtWrRg/fr1DBw4kHLl\nygEZSz4WLFjA4cOHuXLlCtOnT2fmzJlcu3YNOzs7ypcvT0BAAE899RSBgYHs3buXDz/8kOPHjxMa\nGkpaWhrVqlXD19c327GFhYWxcOFCvvvuO1q3bk3v3r2zbJoGiIyMpEePHkydOpUiRYrQo0cP1q9f\nz+XLl3nppZcoUKAAixcvZtiwYfdtHkUeNo6OjiQnJz/oYcj99scf0Lo1/PDD/4/Vrg2rVsHfXyD1\n69eP7777jp49exIREYGjoyOBgYHMnj3bYgngP7l69SqjRo3Ktvz3339nzpw5TJ48+Y7aCw8PZ8OG\nDbRu3ZrmzZvz/PPPc/36dfN+sO3bt7N69Wq2b9/Oiy++SOHChTGZTBw6dIi1a9fy5ZdfcvHiRV55\n5RVlGRQRBVPy4LRs2ZI1a9YQHx9P/fr1MQyDuXPnMmbMGEqXLg3AzJkzzYEUZGxG3rJlC2vWrOHb\nb79lxYoVmEwmXn/9dapUqUL79u0JCAigaNGiDBw4kNmzZ3PgwAHmzJnDE088gWEYHDhw4LbBVExM\nDHZ2dhiGwZo1a9i4cWOWpTGGYfDhhx8yY8YMihQpAkBwcDA1a9Zk7969ADz55JOMHDkyp6dN5KFm\nMpnM+2ccHR0f9HDkfmnWDP7+W2e2fTu0bQvbtgHg4eHB1q1bGTt2LKtXr6Zjx47mql9//TVffPEF\niYmJtGzZkmPHjlGyZEliYmJwcHDg6aefzpJB8D//+Q/NmjXj6NGjLF68mFWrVrFt2zbc3NywtbXl\nypUrvPPOO0DGHqvx48ebsxa2aNGCJ554AoD58+czcuRIrl27Rr169Zg8eTJvvvkmV69exd3dndq1\na1O7dm0CAwOZMWMGLi4ujBs3jnz58rFr1y7zkkZvb29lGRQRJaCQB8fZ2RkAHx8fbGwy3oqGYVis\nvS9evDjh4eHmxzd/4525StXFxcXicea3npkf6lJSUujXrx9jxoz5x28vM8diMplo2bIlX3zxBQcP\nHuTKlSsMGDCADz74AJPJxIgRI9iyZYs5g1W+fPl48sknzR8gM9sRedw4OjoqCcWjbPfurIFUpm++\ngb8TSCQmJhIdHU1AQAAbNmywqDZz5kw8PT0pVKgQP//8M/3792fFihX8+OOPvPHGG+a/3TdmEKxQ\noQKDBg3C09OTCxcusGrVKhYuXEjbtm0BWL58OYmJiXh4eHD69GlSUlLMWQszAymAiIgIvLy8MAyD\n7du307p1a+Lj481JNW50406IPn36MHv2bItyZRkUEV2ZkgcqM7BZunQpAG+++SZjx47Fx8eHZs2a\nMXz4cIYOHYqTkxOpqan07duXevXq0b9/fy5fvszUqVOZPXt2tpuiMw0bNoy+ffvi7e1NiRIlLFLv\n3uzmbxhtbGzo1q0bH374oXnT9M6dO7GzsyMkJIQePXqQnp5Oo0aNLM6LiYmhcOHC1k2MyL+Yk5MT\niYmJt/xwKo+AEyf+ubxCBdasWUNUVBR9+/bl1KlTnDx50lzFMAxGjhxpTp+emJhIamqq+QuzWbNm\nMWTIENLT0xk7diyQ9UuyzL/Vmf9NT0+ncePGvPrqq+Z+MjMT3ujGL7pq167N5MmT6dixI3/88QcL\nFy4kNTWVd999F7D8N8XR0ZFXX32VlStX8tJLLwHKMigiCqbkAencuXO2jzNv1pgpJCTE4vHNCSjG\njBlj/n358uUAfPLJJwD06NHDXBYWFmZxXq1atSz+m6levXocOHCAWrVqmcuaNGmS5dzMshvHl9kv\nwOrVqwkICEDkcaMrU4+4v5dh/1P52rVr2bhxI46Ojhw+fJgPP/zQHJz069ePrl274unpyXPPPcf+\n/fsZNWoUZ8+eZebMmbfMIHiz1157jTFjxpCUlITJZKJDhw706dOHXbt2kZyczPTp0295nouLC+np\n6RbtDh06lMmTJ2d7DmQEVm+88QbTp0+ndu3aAMoyKCLK5iePuFOnYMIE2LQJ7O2hVSsYMQK8vbM9\nJSUlhfDw8HvORrZ3716effbZe2rjTtxpZsR7MWHCBAICAli0aBEnT54kPT2d+vXrExgYaK4zbtw4\nWrVqRVpaGsHBwSxYsIDBgwebExJMmDCBjz/+mLp16yrT2yMuLi6OqKgoi6VV8ggxDKhY0bycz0KN\nGhnLAHPb5cuweTOkpUH9+nCbDIJff/01169fp3nz5vfUpbIMighoz5Q8yk6dghdegI8+guhoiIyE\nmTOhZk34669sT7O3t8+RD/u5EUiBZWbEjRs3Ur16dSDjG9OOHTvSrVs3NmzYwJkzZ6hRowbvv/8+\nZ8+eZeTIkfTq1Yv169cD8NRTTzF16lTeeOMNEhMTze2npqYSERGBr6+vea9YWFgY27dvzzKW3bt3\nM2PGDEJDQ0lOTiYuLo4ZM2Ywb948vLy86NSpE/Pnz8+VeZEHJ/Nmr/KIMplg/fosqdCpUiUjm19u\nmz0bCheG9u2hY0coWhRuk2Hv5Zdfpnjx4vfc7T9lGRSRx4OCKXl0vfsuxMRkPX7qFMybl/vjuY8y\nMyN+/fXX1K9fH4AFCxYwYcIEQkJCWLZsGQD+/v7mPWjJycl4e3vz8ccfA5gzIFarVo0DBw6Y2z52\n7BhFixY1Pw4ODuaZZ57JslTTMAwmTZrEqFGjsLe3x9PTk6ZNm9K9e3f69OnD1atXyZcvH2fOnLnf\n0yEPmIODAykpKWjhwyOsVCk4dAh27CAtJITw0FDYswf+zm6aa775Bvr2hYSE/x9LToYxY2D16mxP\ne/rpp++5aw8PD0qUKHHP7YjIv5uCKXl0bd6cfdmmTbk3jlyQXWbETJl7AzI3Yy9btoymTZsyfPhw\nrl69CmTd3J0pISEBNzc38+O3336bLVu28OGHH3LixAkGDBjAZ599hslkYvHixfTv398cMLVr146F\nCxfy4osvsm7dOkBZDh8HN6ZHl0dcrVrYdu1KapUqxMfH537/N2XXu+MyEZEcogQU8ui6zX0/DAcH\nbr2l+d/r5syIPXr0YPTo0bi4uGS56XD16tWZP38+33333T/eC6hcuXJZkoL4+PhQuHBhEhISzBkO\nDx48iLe3N0uWLCEwMJCxY8cyd+5c3NzcuHjxIh988AGGYeDk5JRDz1geZplL/fR6Px7y5MnD1atX\nzbepyDUREdaViYjkECWgkEfXwIHw9wf9m50fPhybPn0oWLAgdnb6TuGfDBgwgODg4Hu+MeXmzZu5\nevUqbdq0yaGRycPq3LlzODo6UvA2iQDk0REbG8tff/1lvuF6rmnTBm7Iomqhdu2MZYAiIveR1tvI\no2v4cChbNuvxF1+kwLBhpKamcvToUc6dO5flZsCPtZ9/hiZNwMkJPD3hzTcZFBDAlStX7rlpHx8f\nBVKPicx7TcnjIU+ePFy7di1X+0xISOBMs2YY2aROp3//XB2PiDyedGVKHm2XL8P8+fDFFxnL/lq1\ngsDAjECBjEx1UVFRREdH4+7ujo+Pj3n/0WNpzx546SXLzdwA5ctnlOXJ80CGJf8+V65c4eLFi0qP\n/hg5evQopUqVuu9/Qw3D4MKFC0RFRVGkSBHyb9wIAwZk/L0HcHPLyOY3YMB9HYeICOjKlDzq8uWD\nt9+GXbtg2zbo1YvQlSt59dVX6d27N4MHD6Zw4cI89dRTuLi4cOrUKU6ePGlOynDjdw05+b1DbGws\nb7/9NgBlypThzTff5LXXXrP4Zvedd97h4MGDGIZBsWLFiIqKIjExkXbt2tGhQwcuXLhAcnIyr7/+\nOleuXKFv3773PrAxY7IGUgC//go37ZsSuR1dmXr8ZO6bup/i4+M5fvw4169fx8/Pj/z580NAAPzx\nB3z+OXz2WcbvCqREJJdos4g8dkwmEz179qRx48Z07NgRgC+//JIdO3YQHR3NqFGjmDlzJj/99BPV\nqlUjPDycEiVK8NRTT5GQkMC+ffu4du0ac+bMYcaMGZw9exZPT09GjBhBp06dKFasGDVq1KBGjRq8\n9dZb5M2bl4oVK9K9e3fzGEJDQ3n99deBjBS9c+fOZdKkSZw+fZqn/r53S+3atdmxYwc2NjY0a9aM\nb775Bm9vb2rWrMlrr73G0KFDKVGiBMOGDcPd3R1XV1ciIyMpXLiw9ZOzdWv2ZVu2ZKQgFrkDDg4O\npKamYhiGOZukPNrc3Ny4fPnyfdknZxgGkZGRxMTE4Ovri6enp2UFF5eM5ckiIrlMV6bksRQSEkK3\nbt3M/yDb2NhgGAYpKSns27cPX19fmjVrxmuvvUZsbCwtW7akRYsWQMaHxMjISPbv38+ZM2eoUqUK\nvXv3Jikpifj4eBo2bEjjxo1ZuXIlXbt2Zfbs2Wy9KUj5/vvvzUHTwYMH6dy5M9u2bTMfg4yMez/+\n+CPffPMNAwYMYN++fXzzzTfUqVOHQoUKUbt2beLj43nmmWcAqFSpEt9+++29TcztMq89zssfxSoO\nDg5Kj/4YuV9Xpq5fv86xY8dISkrCz88vayAlIvIAKZiSx1K3bt0ICQmhUKFCHDp0iPnz5zN58mTq\n169vvleKj48P5cqVI8/f+4QOHz5MWFgYEyZMoMrf91SZMWMGTzzxBIGBgRiGQVhYGNHR0fTp0+e2\nywLT0tLMWQQrVarERx99ROXKldm7dy8zZsxgwIAB5uAuIiKCUqVKkZyczLFjx3jyyScBKFWqFCVL\nljS3mSdPHq5fv35vE/P31bJbatv23tqWx46W+j1e7O3tsbW1zbHXPD09nfPnzxMREUGRIkUoVaqU\nsq+KyENHf5XksTR//ny++uor/vrrL/r27Yufnx/vvfcex48fp169esD/b3RrZ2dHqVKlsLGxwcvL\ni8GDB3P48GFq1KjB+++/z6VLl/Dy8iIuLo73338fW1tbKlSoQNu2bRkwYABr166lTp06Fv2XLFky\ny5K8oUOH0rdvX1auXGk+VrlyZc6ePQtAsWLFiImJyfY5hYeH8+KLL97bxIwfDzt2ZL0/S6tW0Lz5\nvbUtjx1HR0ddmXrMZF6dutf7i129epUzZ87g5uaGv78/tra2OTRCEZGcpWx+IncpJSWFixcvEhMT\nQ968efHx8bnrDw4nTpzgq6++ol+/fjk2roCAgCw317XK5ctcfPddvPbvx87dPeOKVKtWYKML2Y+S\n0NBQ1qxZQ7FixbC3t2f69Ok53kd0dDQJCQkUK1bstvViY2MJDg5m0qRJlClThvr163PhwgWWLl2K\nm5ubuV5ycjJly5ZlxYoVVKtWzfwcvLy8KFu2LCNGjGDmzJnUrVsXf3//HH8+j6rk5GQGDRpk/r19\n+/bUqlXrlnXT09MZP348rVq1uuUcx8TEcOXKFUqWLJllv9yECRMICAhg0aJFnDx5kvT0dOrXr09g\nYCAA7733HmfPnuXLL7+katWqtGnThqFDh/LBBx/QokUL2rVrx4oVK/Qai8hDRVemRO6Svb09vr6+\nFCpUiOjoaMLDw3F1dcXHxwdXV9eMSqdOZWTGW78eDAOaNYNx4+DvNNHlypW79yV5N0hOTmbw4ME5\n0laikxNRnTrhPXlyjrQnD6dbJWJZv349GzduJDExkbFjx2IYBuPGjcPJyYlXX32VwoULs379eo4d\nO0blypVJT08nNjaW2NhYKlasSExMDOfPn2fp0qUsW7aMn376iejoaJYtW8aECRO4du0adnZ2lC9f\nnoCAAPNY7iQhS+b4RowYwdKlS6lWrZrFc2jfvj0AnTp1YvTo0cyaNesf5+Cjjz4if/78NG7cmBMn\nTrBy5UrGjBmTg7N8az169GDBggU0bNiQMmXKcPbsWcaOHcvTTz9trlO3bl1effVV3nrrLYYPH07P\nnj1ZsmQJJ0+epECBAiQkJDBx4kRSUlKYM2cO//3vf60eT0hICE2aNKFBgwZAxhdGN78Xdu3axbZt\n23juuefM5wUHB/PEE0+QJ08evvjiCxITE2natCl79+4lPDyc5557jv5/3+spNTWViIgIfH19MZlM\njBgxgnLlyhEUFGQOpvr06cOZM2c4d+4cy5cvx9bWluXLl7Ny5UqaNm1q7vduXmMRkftNwZSIlWxt\nbfHx8cHb25uYmBhOnz6Nvb09hVJScK9bF6Ki/l95xYqMTHk//wzFiwOYE0fkBAcHBypUqJAjbV2+\nfJl8+fLlSFvycAsJCeGzzz4zb+gPCwvj008/5ezZs8ycOROTycSECRMoXrw4bdq0YfXq1ZQsWZK+\nffsybNgwJk6cSNu2bfHz8yMwMJA1a9bQo0cPLl++jMlkwtnZmT///JP9+/djMpl4/fXXqVKlCu3b\nt7cIpr7//ntzav/MhCyRkZHm2wdkWrduHaGhoezevdt8G4GQkBDee+89goKCAMiXLx9nzpy567nI\nXKSRkpJC9+7dyZcvH56enowePZp58+YRHh5OXFwc48ePZ+DAgaxatYrjx4+zatUqmjdvzpIlS0hL\nS6NatWr4+voydepUatasyZ9//snUqVPN/ezfv5/y5csDGdnvZs2axQ8//MDu3bstgilPT08OHjxI\nXFyc+ZjJZGLkyJH4+fkRHh7Ou+++y5QpU4iMjCQ5ORkHB4e7ft4Ax44do+0NeyLt7e0t3guzZs2i\nQoUKNGrUiHbt2jF27FhGjRpF3759qVOnDq+++ipVqlQxPz+TycTLL79Mp06dLPooWrSo+XFwcDAH\nDhxg2rRppKWlce7cOa5du0aJEiVwcXExL+uzsbEhKCiIBQsWmM+19jUWEbkftG5H5B6ZTCby589P\nhQoVKFiwIKnBwZaBVKboaPjgg9wf4F2KjY3Fw8PjQQ9DcsHNiVhux2QykZaWRr9+/Zg6dar5g3ue\nPHlwcHDA3d0d+H8Gv9WrV/P+++/j5+dnDnxcXFyArPdsu5OELOfOnePIkSP079+f6OhoVqxYAUD3\n7t3ZunUrn332mbk9m7tYkjp37lx69erFuHHjANiyZQsvvfQS06ZN48yZM1y9epWwsDA8PDzw8PBg\n3759+Pn58euvv7JixQreeOMNpkyZgpeXFwUKFODAgQMA1KxZkyFDhnDhwgWL/nbv3k3lypWBjCx1\nffr0ISgoiHbt2mUZ25AhQ5g0aZLFscy5K1u2LH/++ScApUuXZt++fXf8nG/m7+/P3r17zY+Tk5Nv\n2Wfma2wymShVqhRHjx41l48cOZJ33nmHkSNH4uzsnCVRREJCgsWSzbfffpstW7Ywb948vvjiCyZM\nmMDJkyfNCX9u1KBBA3bv3m2RKfBuXmMRkftJV6ZEcpCHhwfc8KEkiy1bcm8wVkhOTiY5OdniQ488\num5OxNKhQwd69uxJfHw877zzDoZhMHr0aFxcXGjfvj1z584lLi6OOXPmUL16dSDjg3Xmz40KFSrE\n5MmTOX78OCkpKea6t3InCVnGjx9PSEgIVatWJTU1lZYtW9KyZUsMw8DZ2ZkGDRqwbt06mjdvfld7\nGHv37k2jRo3My/xuDvQMw6BIkSK888475mPly5cnNDSU33//nSeeeIKUlBT69etnvqK7c+fObAPH\n+Ph483JgV1dXZs+ezcaNG1m6dCnlypVj69atdOnSBQA/Pz+uXr1KbGxslnGfOHHCPF/3msmzW7du\nDBw4kM8//5y0tDTatm1r8V4YPXo033//vcXrFxQUxI8//sisWbPo168fXbt2xdPTk+eeew5HR8cs\nGf3KlStnsaczNTWV+Ph43NzcKFCgAIsWLbrtGAcPHswLL7wAZMzpvSa4EBHJMYaI5KxnnjGMjJ1S\nWX7SnnrqQY/uti5evGj8/vvvD3oY8gg5deqUERsbe9s6v/76qzFjxowc6W/Tpk3GqlWr7qhuaGio\nsXHjRvMYxo0bZyQlJRmBgYHGoEGDjHHjxhmGYRjvv/++0bdvX6NPnz7Gvn37DMMwjDp16hizZs0y\nDMMw9u3bZ3To0MEYNGiQMWvWLGPHjh3GnDlzDMMwjLZt21r0uWHDBuPjjz82DMMwWrVqZT7eqFEj\nIzEx0fw4s+yPP/4w3N3djd9//90YO3as0b59e6Nfv35Gt27djL/++sswDMPo37+/ceHChbubqPso\nMTHROHToUJbjb731lpGUlGTExMQYBw8eNM6fP2+kp6ffdft38xo/LDLfa2fPnjVat25tREZGGkOG\nDLGok5aWZvGeuFFycrLx5ptvGoZhGFWqVDF69+5tNGnSxDh37pxFvczzb26nW7duOfVUROQmCqZE\nclpwcLbB1J/9+xsnTpz4xw+XD8qvv/5qxMXFPehhyKNi927jerNmRlLlyobRoYNh/PhjtlX37t2b\nI11mBjsPhYQEw/j8c8NYvdowLl40DMMwUlJSjH79+uVYFykpKUaXLl1yrL0cERFhXAwIMNKaNjWM\nwYMNIzzcMAzD+O2334w9e/YYR48eNa5fv2518w/Va3yHQkNDjcmTJxtt27Y1Ll++bBjG/wOe6tWr\nG8HBwcaePXuMVq1aGREREUbXrl2NK1eumM9fu3atsXbtWovzVqxYYWzYsMGin+yCqdmzZxs/3ub/\nPxGxnpb5ieS0Pn0ysvh9/73l8WrV8J4wAce/U6ufP3+eAgUKkD9//ofiHiopKSkkJibecs+CyF0L\nCYEePXDJXOZ24AAsXw6hofB39sAb5VRClhuTODxQn34KPXtC5r3hHBxg4EDsJk60SL5xr65evcqo\nUaNyrL17tmkTtGxJwRuX+c2cyZVFi7hSsSK+vr74+Phku+TzTjw0r/FdMAyDKVOmsG7dOvLmzWtR\n5urqytChQwE4deoUEydOZO7cudjb25vr7N69mz59+gBw7tw5evTowS+//MIPP/xwR/0/88wzbN++\nnapVq+bQMxKRTNrBKZLTXFzgm29g0SJo0gQaN874YPnNN5jc3PDw8KBcuXKUKlWKhIQEjhw5wtmz\nZ7PsMchtly9fJm/evPf0IUcEgCtXYODAjOuxN0pPh/79ISHhwYwrtxw5Au3a/T+QAkhOhkmTYMGC\nHA0GPDw8KFGiRI61d0+SkyEoCG7+W5acjGvfvpQtVoxChQo9ln9jTCYTM2fOZMqUKezfv9+i7Mbg\nysvLi4SEBP766y+LOpn7ywCKFi3KggULCAwMZOPGjYSFhTFgwAAiIyOz7f9e99WJSPZ0ZUrkfnB0\nhC5dMn6y4eLiQokSJUhNTTXfr8rZ2ZmCBQtm+eYyN1y+fJkCBQrker/yCPryS/g7g18WsbFcWbuW\n5L/vaXTjB+u7+T23z7urNubOxZSayi3NmIHRvbvFIeMWSS/utex+1b1dmd3XX+N68SK3YhsXh/PO\nndC8+S3LHweurq6EhYXRqVMnhg4desug0tPTk1mzZtG7d2+mTZuGt7c3kJGMJPMeY5m6d+9O8+bN\n2bRpk/lecZltnj17ll69egHQr18/Tpw4QcWKFe/3UxR5LJmMm/8yisgDYRgGsbGxREVFkZqamqtL\nANPS0jh8+DAVK1Z8bFIO33jD1ky///47c+bMYfJ9vGHxvdyw9ddff8XDw4NKlSoRFBTEoEGDHs4b\nl378MXTokG1x9Pz5JDRsmO2H9Mzf/6n8Tn7P7fMASvfqhftPP3Erhq0t+/fsAbJmN8wuSMutuvfa\njuumTfj060d20j/6CJsb7j0ldy42NpYpU6YwYcIEq85/8803mTJlCs7Ozjk8MhHRlSmRh4TJZMLT\n0xNPT0/i4+OJioriyJEjeHh4ULBgwfuaCvjy5cu4u7s/NoFUpt9//52OHTvi5OTEq6++SqVKlfj5\n558ZNmwY586dY/ny5WzatIkdO3YQHR3N1KlTWb9+Pd9++y2lSpXCxsaGESNGULNmTVq2bMkvv/zC\nBx98wPbt29m5cyd58+ZlwoQJ5nsy3esNW0ePHo2/v7/5mKura5aU4g9aWlra/9i787CqqvWB49/D\nLGVwSfgAACAASURBVIMCgoAoIiIgYmqppVbaZBJajpkjyFUTFQ1NUwPHTNNyFktuCVo55XRFy8yb\nljnkzykRQVCUGQEB4TCfs39/HNlxGLoqCKjr8zw+yd7rrL3OCWG/e631vqR7eNBMXx+deynRtRgb\nYz1sGDzJhaGffRaqCaYUbdvWasHuBmXIEJgxA6r4/y7p6nK1WTMMY2OxsLDA3Ny8QewVbfDOnoU1\na7CIiGBQ48Zw8KBm6fgD+te//iUCKUF4RJ6uOydBeEyULQFs3749+vr6XLt2jZiYGIKDg/Hy8sLP\nz+++n1CWbWwuU1JSwuTJkwHo1q0bU6ZMYejQoVrr6b/55hv2798PQM+ePTlz78Zw4MCBfPDBB0RE\nRAAwZswY0tPTmVBh2dLjQJIkQkND+eSTTwgJCeHbb78FNMVQP/vsM2xsbEhNTUVHRwdJkigpKeGX\nX35BoVDg6elJYGCg/Dk0btyYgIAAhg8fzvHjx7lx4wYdO3Zk6tSpciAFNS/YunjxYvz8/OSCtR07\nduS33357JJ/Pg1KpVCQnJxMREUGxhQXSzJlVNwwKerIDKQA/P9Cr5lnlP8zcPPbs7ODez5aKFO+/\nT7s+fbC0tCQnJ4fLly8TGxtLZmYmKpWqjgf6mNizB3r00Mz0XrrEs7//rtmH+xCzU88999wjGKAg\nCCBmpgShQdPT08POzg5bW1uysrK4e/cuXl5eDBkyhKZNmwLQoUMHxo4dy7lz5/j666/ZsmUL8fHx\n/PDDD/z444/ExcVp9RkeHs7rr78OQKtWrVi7di3Lly/n+vXrdOjQAYBXXnmFtWvX0rt3b7p06cKx\nY8do1qwZbdq0YdGiRfj4+PDyyy8zZMgQrK2t6dixI2fOnHnsMkWVX5pVtmypbL+akZERRUVFfPnl\nl+zbt48tW7aQn58PUKkga1kRVn19fYqKiggMDOTSpUvMnDmTxYsX4+zsDNS8YGvFmSkzMzPSqtmj\nUldKS0tJS0sjIyMDCwsL2rVrpwkglyyBdu1g3Tq4fh1127YkDByI7bRpGNbriOtAhw7w7beaoOre\n/z9JXx/FBx9ojj3JVq6EFi0oXbUKvaQkaNEC/P3hww/R0dGRZ9/VajU5OTlkZWWRkJCAqakp5ubm\nmJubo1ddIPo0KS3VBN5V7b1buBDGjgV7+7oflyAIlYiZKUF4DJQtAbSzs+M///kPAQEBBAYGEh8f\nj729PdOnT6d79+5cvHiRCRMm8Oyzz+Ln50fr1q0r9VV+diQhIQFfX1++/fZb3nrrLblN69atiYuL\n49ixYwwaNIjbt2/z3//+l9deew1jY2P8/Pw4duwYb7/9NvB32t3HSUlJCRMmTCAoKIiJEycyYsQI\noPJeEHd3dz799FOOHDkiH6sqEUF5ISEhfPfdd+jq6mJlZaXVV2xsrFbbfv36cfToUV5//XVWrVqF\nh4eHfG7u3LmEh4fLX5fNTH3xxRcA9bqpvLS0lKSkJK5cuYJKpaJdu3Y4ODhozcQxapRmuVtGBjqn\nTtHIx4f4+Ph6Ge+DKC4uxt/fH39/f95//32OHz/O2LFjtWZvU1JSWL16dfWdDBsGiYmwZw9Jy5dT\ncPUqLF+u1SQrK4vZs2cD4OzszKRJkxg0aBB5FZJ36Onpcf78eQB5FnPo0KEABAYGkp6eXuP3XGsU\nCpgxg7hff+Xu7duQkACzZkGFJcQ6OjpYWFjg5OTEM888Q9OmTbl79y4RERHExMSQkZFBaXVJPJ4G\nZ85AUlLV50pLNeU3BEFoEMTjH0F4zPj7++Pl5UVJSQkZGRmo1WpiYmIoLS2lqKiIv/76i6NHj7Jh\nw4YqX18xxe68efPYtWsX4eHhKJVKzp8/z8yZM7GysiI8PJz169dz6NAhjh49yqZNmwBwcnLSCtQe\nt7S7v/32GwcPHmTLli2Vliguv3fDu3TpUgA+/fTTavv5/vvvAdi1axcAb97LUFcdT09PZsyYwYgR\nI+TXABw8eFCrXdm55s2by/um5s+fX6m/y5cvM2PGjH+8Zm2rdibqPlhbW5ORkUF2djbmDXipX0hI\nCP369ZP/f5aUlBAaGspnn31GdHQ048aNw8XFhaSkJFQqFXPnzqW4uBiVSsXnn3+Ot7c3Dg4O9OzZ\nk64vvMCnv/xC/iefMGDAAN555x35OqGhoQwbNgzQ1E4KDg5m2bJlxMXFybPEAG+88QYrV66Ul6KW\nN3z4cL755hs++uijR/ypPJiCggIa3WfK9rLAysLCArVazd27d8nKyiIxMRETExN5j9VTNWP1v5Y+\niqWRgtBgiJkpQXjMBAcH4+fnx/z587Gzs6NJkyY0bdqUnJwcbty4gY+PD8bGxgQFBVW5BKwsxW6Z\nu3fv4u/vz7///W9Gjx7NqlWraN68Ob179+bWrVsYGhrSrVs3eSlOVR63tLsvv/wy+/bto3HjxrXf\neWIifPQR9Oyp2Si+Y4dcb0lPT6/WCrYWFxfz4Ycf1kpf96P8TJRarcbd3b3yTNT/oFAocHBwICEh\nAbVa/QhHWzORkZF06dJF/rqseKqfnx+bNm1i9+7d8rkjR45w69YtLCwsUCqVpKamkp+fj6enJ15e\nXujq6qJWq7G0tOS7777Tus7JkyfloOnSpUt4e3tz9OhRrUAKNMlKXnrpJQ4dOlRprO3atbvvwq11\nRaVSoVartYrO3i8dHR3Mzc1p3bo1zzzzDNbW1uTm5hIREcG1a9dIT09/omesiouLSUlJIcLEhFJL\ny6ob6eg8VBIKQRAejafoMY8gPP68vb3x9vbWOlY2izF//nyUSiW9e/fm7t27WFpa0qRJE60ZEIBR\no0bxxdKl9FAq+cbHhxQDA4yNjSvdqI0aNYpR99JbDx48mMGDB8vnWrVqpZU+/OjRo/LSs6falSvQ\nq5d2sdZDh+DHHyE0FKDWCrYaGBhoLQl8VEpLS0lNTSUzMxNLS0vc3d0f6ia5jKmpKWZmZqSkpGDf\nQPd8tG/fnnPnztGnTx9Ac4MLmv10Ojo6FBUVyW3VajU9e/bE399fPrZ161YOHz7MlClTaNu2LV5e\nXjg7OxMQEKB1HZVKJc+2dOzYkbCwMGbOnMm5c+c4ceIEN2/elGdIx40bx7vvvlspA15DzMBZUFBQ\nK5njygIrc3NzJEmS91glJyfTqFEjecaqJt+PDUHZe8vIyECpVGJpaUkbd3f0Pv9cU6uwYgWbadPA\nyal+BisIQiUN76ewIAgPzcTEhNatW+Pu7o6urq6cBbB8im2LsDAGBQdDnz6Y9etHm9dfh3L7ch6G\nSLt7z4wZ2oFUmbAwOHaszodTE6WlpSQmJnLlyhUkScLd3Z2WLVvKN66hoaH079+fyZMn88EHHwBU\n2ldUHXt7e/z8/Lhz547W8aoyTfbv35/ExEStdm5ubuzYsQOAdevW8dJLLwGaoN7X15cPPviAmfey\nCe7du5effvrpgd77+PHjOXDgAP7+/kyaNInTp09X2/bNN9/kr7/+YtasWUyYMIHU1FQCAwM5efIk\nHh4e9OjRg+3btxMSEoKhoXbqjdatW5OcnKx1bNasWaxYsYJp06axatUquSSCrq4uo0aN4sKFC8Df\n+/UyMzMbVGp8qL1gqjyFQqE1Y2VjY4NSqSQyMlKesSq5l5L9/PnztGjRgsLCwvvqe8iQIVX+vTq1\nsdcNNBk7z5w5w+rVq/nhhx+wtLTkmWeeoWXLlprPb+xYTQHsN9+E5s3hhRc0D2VWrryv9yUIQh2R\nBEF4YqnVaikzM1O6evWqFBERIWVv2iRJmuec2n8MDCQpIqK+h/t4y8mRJIWi6s8XJGnSpPoe4X0p\nLi6WEhISpIsXL0rx8fFScXFxle1CQ0Ol8PBwSZIkadSoUZIkSZKPj4+Ul5cn+fj4SEqlUiooKJB8\nfHykmzdvSi+99JL0+eefS2PHjpVyc3MlDw8PaeLEiVJEue+7PXv2SHv27JEkSZKGDBkiSZIkbdu2\nTfrPf/6jde3BgwdLkydPliRJkiZMmCANHTpUkiRJeuedd+Q2YWFh0s6dOyWVSiV5e3vXwifz8Equ\nX5cSZs+WpA0bJOnWLfl4VFSUtGbNmgfub+nSpdLcuXMlSZKk4OBg6cyZM7U21toQHx8vpaWl1cm1\n1Gq1lJ2dLcXFxUkXL16UoqKipPHjx0tffvmltGXLFmn37t3Szp07JUmSJG9vbyk/P1/y9/eXpk+f\nLk2dOlVKTEyU3N3dpYULF0onTpyQ3N3dpSVLlkg5OTnStGnTpGnTpkmBgYFa11y5cqV0/vx5SZL+\n/j5dunSp9Ndff2m169u3rzRy5EhJkiTpvffek9tnZmZK0dHRkq+vr3TmzBkpPz+/3r9HBUF4eGJm\nShCeYGVZAN3c3HB0dMQwOLjqhsXFsH593Q7uSVNaWnk5Tnn3loo1VCUlJSQkJBAZGQlQaSaqKiEh\nIYwfPx7LavZ2lM926OHhwYwZM7C0tCQvL48uXbowbtw4raV+FTNNvv/++6xYsaJSYo+yvVd79uyh\nS5cuSJJEeno6LVu2lNt069aNK1euoKOjo5Vevs7NnYte27a0WLZMU4PJyQkCAwFwdXXlxRdffOAu\nZ8+ezZIlSwDN++zWrVutDrmmHsXMVHUUCgVNmjTB0dGRZ555BgsLC27fvk2XLl347rvv6NGjB/v3\n7+fmzZvY2tpy48YNLC0t+eKLL2jatCnZ2dm4u7szb948evbsibu7O3PnzuW7776jsLAQCwsL4uLi\n5FkveLi9biqVioSEBFJSUpgyZQrLli3jwoUL2Nra0qhRo/r9HhUEoUbEnilBeEqYmJhAhbTcWq5c\nqbvBPIksLaFbN/jzz6rPl0s935CUlJSQmprKnTt3aNq06QPtiRo/fjxeXl4sW7aMv/76Sz5uaGhI\nSUkJBQUF8rGKtbh0dHRo0aIFCQkJNG7cGB0dnUqZJr/66iu5Flf5TJMAI0eOpHv37ly5coWff/4Z\nKysrEhIS5OudPXtWrsllYGBAaWlp3WeD27kT7u15kqlUmhpcnTvD4ME8++yzNbpEQyzGWpfBVHkK\nhYIjR46gVCoJCQkhOTmZqKgo7Ozs8PHx4fPPP6e4uFirtIEkSVpBf9nfJUnCy8uL/v37V7rO/e51\nkySJAQMGMHr0aBQKBXp6etjY2MhZQOfMmSP3WW/fo4Ig1Jj4VysITxNHR4iIqPpcq1Z1OpQn0rJl\n0Ldv5VmoXr3gXk2uhqJiENW+ffsHvpH78ssvOXz4MHfu3NFKwDBw4ECCgoKwt7evthZX9+7dWbRo\nEf3798fS0hJ7e3s506S1tbXcbsKECQwYMIBDhw4xevRo+bi9vT1xcXFyQgaFQsGUKVPw9fWlcePG\n6Ovry0lSym5k69yXX/7zuXJJXZ4UZZn26iso2LNnD+Hh4RgaGnL58mW+++47pk+fzvjx47G2tiY7\nO5uYmBh5b56Hhwft27dn5syZTJs2DX19febPn8+MGTOYMmUKv//+O8XFxVo1xcr2upXfqzZr1iz8\n/f3Zvn07SqWStLQ0cnJyUCqVeHt7s2DBAuzs7KpNGFJv36OCINSYQpL+aV2KIAhPlOBgzVKjCiSF\nAsXvv2vSeQs1c/q0Zjbi99+RLCxI69MHi6VLMWwgdZXKB1FWVlbY2NjU601cSUkJkZGRuLVuTf6v\nv/LFrl188vXXUCFZw8OKjIzk4MGD8oxWnWrbtvrZ4LZt4dq1uh1PBaGhoezatQsHBwf69u1LdnY2\nVlZWeFVIu33z5k02bNiglcGzOvHx8XzyySds2rQJZ2dn+vTpQ2pqKlu2bNEqrVBcXIyLiwvbtm2j\ne/fu7NmzRy6M/fvvvxMUFISBgQGNGjWib9++D/0eU1NT5UCpbEloXl4eWVlZZGdnY2BgIGcFrJgg\nRJaWBt9+q/lvly5Eu7tz+L//ZerUqXITlUpFZmYmGRkZSJKElZUVTZs2va9/W/X6PSoIQo2JxyCC\n8DTx89Ms59u4Ud7fIxkakjJ9OtbduvF4JxhuIF54AfbvB0ABkJpKclYWres5mCopKSElJYWsrCys\nrKweaibqUdDX16f18ePovvIKFllZDAL4+WdNxrJ7qflrQkdHhylTptS4n4fi4VF9MNUA6rIpFAom\nTZokB09hYWGA5ub+22+/5fbt24wbNw47OzsAzp07x5YtW1i8eDHz5s0DNAW7Fy9eLPcZGhrKgAED\ngH8uRLx//37mzp3Lli1b6N69O4MGDWLQoEGcPXsWgGHDhqFWq/H19a1RMGVra8vWrVu13rOZmRlm\nZmY4ODiQm5tLVlYW0dHR6Ovry8WD5cBq924YORLKpcN3dXNDuWoVALm5uWRkZJCTk4O5uTkODg7V\n1uOrTr1+jwqCUGMiAYUgPE0UCtiwQXODt2EDfP01isREdKdNIzY2tkEXUn1cNWvWjNzcXPLz8+vl\n+sXFxcTHxxMZGYmuri7t27fH3t6+QQRSAPz3vzT+4AP07m3AfxYgPR28veH332vcvZubW/2l7Q8I\n0BRYrUhHB+6lk69vZUXAy+95MzAwoKioCBsbGzkQOX36NNu2bWPNmjX/mJzhzJkzci21f0rOsHfv\nXnx8fFAqlXJK8eTkZJYvX87atWsB6iR5SFlQVZaSvKSkhOjoaK5evcrtiAikCoEUAFFRuH/+ORER\nESQkJGBiYkKHDh1wdHR84EAK6vl7VBCEGmsgv00FQahTTk4waZL8pQ1QWFhIXFwcbdq0qb9xPYF0\ndHSws7MjOTkZZ2fnOrtucXExqampDW4mqpJVq6rOgqhWw+rVcK+G1GPp5ZdhyxZN/bG0NM0xW1vN\nrNtDZPF7FCZPnsxb95KjlNWwWrt2LTNnzkStVrNgwQIA7OzsuH37Nkql8h+TM5SUlGBmZgZUn5xh\n+vTpREREMG3aNNLT09m2bRujR4/G39+f4OBgrQQodZmYwdTUFFNTU1q2bEleXh4lK1agqBhI3WP4\n6684NWqEsfh5KQhPvQb4m1UQhPrg4OBATEwMSUlJWumqhZqzsrIiLS2NvLy8h3py/SDKB1HW1tYN\nN4gqcy8Ve5WehAyTI0fCu+/CqVOameEXXoD7zJZYF6raNv3KK6/w2WefYWNjIycQadWqFZMnT+b9\n998nODi42uQMNjY23LlzR+v7vHxyBoDFixcTEhLC888/T2lpKYMHD+bGjRtkZmbKwZuXlxf9+vWr\nt8QMpqam/1jOQKFWY1xPs82CIDQsIgGFIAiy0tJSOZVw06ZN63s4T5Q7d+6Qnp6Oq6vrI+m/uLiY\nlJQUsrOzsba2xsbGRs5016C9+ir8+mvV5/r0gcOH63Y8wsM5dgzVb79xIiWFSw4OTC2X9vth1Xti\nhj17qs+4aG4OyckglucJwlNP7JkSBEGmp6eHs7MzSUlJ8j6Gp11oaCheXl74+fmxf/9+Zs2a9T9f\nExYWxsGDB7WOWVpaolKpyMnJ0Tpe8XnW+++/D4Cnpyf+/v6888478vKrMq+99po8GzBr1iz++OMP\npk6dytSpU9m4cSP/+c9/UKlUWunKGyw/v4c7JzQMOTma5YyvvILu/Pn0+vJLXly4EHbtqnHX9Z6Y\n4e234V6tsoqyfXyQjIzqeECCIDREDXjthyAI9cHIyAhHR0du3LiBm5sbBgYG9T2kelUx49m3335L\nSUkJY8aMwcHBgZ49e9K7d2/8/f2xtramV69eAOzcuZOffvoJGxsbAgMD2bhxI5cvXyY1NZV169bx\n8ccf07p1azp06MCgQYMAzZ4VNzc3QLPMaN26dZw6dYoTJ07Im/pBE5idP3+ey5cvk5mZib6+PjY2\nNvj7+8uFakFTKLdiPZwGZ+hQCAqCTz/VFLQF0NPTHLuXFU5owKZPr5Qo5NmiIs3yxhdegJYtH7rr\nsn8L9UZPD375BSZOhAMHNPv4LCxQT5tG5siR3I6JoU2bNo/HDLAgCI+MmJkSBKGSxo0bY2dnJzL8\n3VMx41lRURH5+fl4enri5eXFd999x5gxY/j888/lTfl9+/Zl3bp1REREoFQq2bp1K7a2tjRp0oTj\nx4+jUCiYMGGCHEgBnDhxgk6dOgGgVCrlIrTDhw+X2xQVFaFUKhkwYACbNm2Sa0Xp6OiwePFi/Pz8\n2LZtG6BJAPDbb7/V1cf08BYtgps3NYVsv/oKbt2Ce6m3hQZMqYTvv6/6XEkJhIbW6XAeCVtb2LcP\nUlI0e/iSktCZP582zs4YGxsTFRVF8T/srRIE4cknZqYEQaiStbU1BQUF3Lhxo06z0DVE5TOegWbW\naOvWrRw+fJgpU6bQoUMHeaN+mcaNGwOamS1JkrC3t2fevHnk5uZy69Ytjhw5Ircpk5+fj4mJCaCZ\nVVq/fj3h4eFs2bIFJycnDhw4wFtvvYWuri7vvPMO//3vf0lMTJRfHxQUpDUzZWZmRlpZFrmGrkUL\nuLfEUXhM3LkDhYXVn09KqruxPGrNmmn+lNOiRQsMDQ2JioqiTZs28r9dQRCeLmJmShCEarVs2RJJ\nkrRu2J9GFfc1paamEhgYyMmTJ/Hw8GDkyJFs3bqV2bNny3ulygdXpqamdOvWjalTpzJ37lyuX79O\nYRU3oe7u7sRWKPL6xhtvEB4ejr29PZ988gkDBgzAyMgIXV1d5s6dS3h4uNy2bGbqiy++ACA6Oppn\nGkBxWOEJZWsLNjbVn783y/oks7a2xtHRkdjY2EdeE0sQhIZJZPMTBOEfqVQqoqKisLGxwcrKqr6H\n80TIz8/n+rVrtI+PRyc+Htq1g1dfpVSlYsaMGaxZs4bCwkJSU1PJycnBxsYGa2vrB96b4ePjQ+iT\nsNRKaLiWLYOqMvc1bw7R0fCISwE0FAUFBcTGxmJtbY2trW19D0cQhDokgilBEP6noqIioqOjad26\ntVyQU6iBK1co8fREPyHh72MdO0J4OKcTE7GxsSE3N5dmzZrRrFkzdHQefBFBcXEx165dw8PDoxYH\nLggVSBIsWKApvpybqzn2wguweTPUdwKJOlZSUkJsbCzGxsY4ODhUWvorCMKTSQRTgiDcl9zcXOLi\n4nB1dcXQ0LC+h/P4UqmgbVuIi6t0qrBLF659/XWNgihBqBe5uRARAU2bgotLfY+m3qjVauLi4lCp\nVCLTnyA8JUQwJQjCfcvIyCAtLQ03Nzdxk/CwDh2Ce2nWq6K+eBGdjh3rcECCINS2xMREcnJyaNu2\n7VNfXkIQnnTisacgCPfNysqKJk2acOPGjUpJGYT7U3L9+j+e1ym/9E8QhMdSixYtaNasGVFRUSiV\nyvoejiAIj5AIpgRBeCAtWrRAoVCQIG7670tBQQHp6encuHGDv/76i4R/2pCvUGiSUQiC8NgTmf4E\n4ekglvkJgvDA1Go1UVFRWFtbY21tXd/DaTAkSSI/P5/c3Fzy8vLIy8tDX18fU1NTzMzMMDU11Sz5\n6dIFzp2r9PoiT08MDx2qh5ELgvCoiEx/gvBkE8GUIAgPpbi4mKioKBwdHSsVn31aqNVqOWjKy8sj\nPz8fIyMjTE1N5T96elXURk9OhpEj4dgxzdcKBeq33yZ6zhyatGxJ8+bN6/R9CILwaIlMf4Lw5BLB\nlCAID02pVHL9+nVcXFwwMjKq7+E8cqWlpXLglJubS1FREcbGxlrB0wNl4Lt6FW7e1KSQbt2a0tJS\nrRsuQRCeHGWZ/tRqNU5OTiKJjyA8IUQwJQhCjdy5c4fk5GTc3NyqnoV5jBUXF8uBU15eHiUlJXLQ\nZGZmhrGxca0/YVar1Vy/fh1dXV1at24tnmALwhNGZPoThCeLSEAhCEKNWFpaYmFh8URk+CssLCQ9\nPZ24uDguX75MVFQU2dnZGBsb4+TkRKdOnXB2dsbW1hYTE5NHEujo6Ojg7OwMQGxsLGq1utavIQhC\n7QoNDaVz586AZklfq1atOHjwYKV2WVlZLF68mFWrVjFixAj+7//+r1Kbr776ihs3bvzj9bKyspg9\nezYAzs7OTJo0iUGDBpGXl6fVrri4GEdHR06dOgXAnj178PPzw8/PDw8PD3bs2MHevXv56aefHup9\nC4IAT9ZjZEEQHkpxcTEffvghkiQhSRLPPfccY8eOve/X29vbc/36deLj42nVqhVjx45l/fr1mJiY\n3Hcfv/76K2lpadjZ2TF//nycnZ0xMTFhzZo1cpvjx48TERHB5MmTSUtLo3379ly6dAl7e3v27t1L\no0aN6Nu3731dryxZRPk9T7q6upiZmdG4cWOaN29eb8WJFQoFTk5O3Lp1i2vXruHs7PzEzfoJwpNE\noVDg5ubGqVOnSE1NpUePHigUCm7dukVQUBDNmjVj4MCB7N+/n+nTp+Pq6kpOTg4xMTFs2rSJ6Oho\n8vLy2LBhA6mpqRQUFLBgwQLy8vLQ09PDzc0NHx8f+XqhoaEMGzYMgM6dOxMcHMyyZcuIi4ujQ4cO\ncrv9+/czd+5ctmzZQvfu3Rk0aBCDBg3i7NmzAAwbNgy1Wo2vr+99/+wUBEGb+O0sCAIhISF4eXnx\n5ptvAqBSqQBo37493t7eeHp6smPHDnJycujYsSPe3t6MGTMGBwcHevbsSePGjfn6668xMjJi6tSp\ncr9Hjhzh4MGDFBYWMnjwYH766SdmzJhB06ZNGT9+PFu2bJHbbt26lU2bNvHHH38wdOhQJk+ezIgR\nI6od85YtW1ixYgWbN28mMDCQd9555x9vCNRqNUqlUg6clEolhoaGmJqaYmlpiYODA/r6+rXxcdaa\nVq1akZSURHR0NC4uLg1ufIIg/G3w4MHs3r2b/Px8+vTpgyRJBAcHM3/+fNq0aQPA2rVrcXV1BaBJ\nkya0b9+eX375heLiYpKSkrhw4YLcn0KhYNiwYXTt2pURI0ZoBVMnT57E398fgEuXLuHt7U1ycrI8\nW1Vm7969hIaGcuLECfLy8jA1NSU5OZnly5fz/fffA5rZcJG6XRAenljmJwgCkZGRdO3aFUmSCAgI\nYMqUKYBmxmnWrFk4OzujUqmwtLRk586dFBUVkZ+fj6enJ15eXiQmJuLk5MT06dMBzUwXaG4cdnNf\n1AAAIABJREFULC0tsbOz4+zZs0yaNImvvvqKHTt2MHz4cK0xZGZmoqenhyRJ7N69G09PT3m5W1VO\nnz7N2LFjOX/+PFD5hkClUpGTk0NSUhJRUVFcunSJ5ORk1Go1zZo1o0OHDrRr146WLVtiYWHRYAMV\ne3t7rKysiIqKorCwsL6HIwhCNRo1agSAra2tnIhGkiSt5cCtWrXi2rVr8te6urr88ccfTJo0CRcX\nl0oFfo2NjeV+ylOpVPJsdceOHQkLC6NTp06cO3eONWvWEBAQQEJCAhEREUybNo309HS2bdtGYWEh\n/v7+rF+/XutnnoGBAaWlpbX4aQjC00PMTAmCQPv27fnzzz/p27cvq1atYujQoYDmySnAjz/+iLu7\nO6NHj+bVV1/F1NSUrVu3cvjwYaZMmcLGjRuJiYlhzZo19OvXj7y8PAoKCpAkiY8//lgra1VKSgpR\nUVHs2LFDawxlNx8KhYLBgwfj5+fHwIEDuXv3LvPnz8fe3p6uXbsC8Mcff5CUlISfnx/p6ekcOXKE\n3r17A3Djxg0KCwspLi7GxMQEU1NTWrRogbGx8YNl2mtAbGxs0NPTk5f8ld1gCYLQsKxYsQJAnnWf\nNGkSCxYswNbWlnfeeYc5c+Ywa9YsjIyMKC0txd/fH3t7ew4cOMDFixd57rnntPZJVrcvs3Xr1iQn\nJ2uVUZg1axb+/v5s374dgMWLFxMSEsLzzz9PaWkpgwcP5saNG2RmZrJgwQIAvLy86NevHwqFQiwl\nFoSHJLL5CYJASUkJH374ofy0s3nz5syaNYuhQ4eya9cu4uLimD17Ns899xyHDh1i+/btfPrpp+jq\n6soJGU6fPk1qairz589n3rx5TJs2jTt37rBz504sLS3p0qULw4cPZ/v27aSlpTFt2jStMQwbNowd\nO3Zo7YsKDw8nJiaGgIAA4O89U5cuXWLOnDmYmZmRkJDA/PnzWbx4MXPmzCE0NBRTU9MnMuDIycnh\n5s2bODk5YWZmVt/DEQShliUkJJCbm4uzs7Mm058kQWQkqFTg4QH3HghFR0dz+PBhrWXVDysyMpKD\nBw8yc+bMGvclCE8jEUwJgvBIpKSkkJOTg6urq/x09Y8//mDt2rV88803lZJThISE0LVrVzp16lRl\nfxWTRSgUCszMzORU5Tdu3Hgqbgjy8vK4ceMGDg4OmJub1/dwBEGoZenp6aSkpOBy8yZGs2ZBdLTm\nhKMjfP45DB4MwPnz53n22WdrfL2oqChatWolL1METYKLNWvWcOHCBUpKSnB2diY4OBgvL69q+1Gr\n1QwbNoxdu3bVaDwLFy5kyJAhtG/fHoADBw5gYWHBiy++KLfZvHkzbm5uREdHs3v3bpo2bYqLiwtz\n587V6mvPnj0sWbKEc+fOAcgPCL///nuuXLmCv78/wcHBLFq0qEZjFp5uj+eaF0EQGjw7OzsMDQ2J\nv3ABFiyALl3oGRDAjmefxaSKdN8+Pj7yGn5JksjLyyM1NZXY2FguXrzIzZs3KSwsxNzcHDc3Nzp0\n6ICjoyNWVlYYGRmho6Mj7/V6kpmamtK2bVvi4+PJyMio7+EIglDLrK2taZ2bi8HQoX8HUqAp8D1s\nGPz+O0CtBFIAbm5uWoEUaGcnDA8Pp0ePHgCEhYXJKd/L9r327NmT5cuXywHLjRs3GD9+PLm5uYwd\nO5aAgAAWL15MRkYGH330EXFxcXKgNHr0aKKionjvvfeYPXs2kZGR8hg+++wz9uzZw507d8jJydEa\n39GjR+nevTsKhYKJEycSGhpKREREpfe2b98+Ro4cye/3PjOADRs2kJKSwpIlS7C1tSU5OVne5ysI\nD0MEU4IgPDKOJibYDRoECxfCuXNw9izMng29ekFurtxOpVJRUFCAubk50dHRXLp0icTEREpLS7Gy\nssLDwwN3d3ccHBywtLSsstBlVTcET6pGjRrh6upKamoqqamp9T0cQRBqmdm//41OVTf4KpVmdqoO\nlGUnPHLkCH369Km2nYmJCbNmzaJr167ExMSwdOlSgoODOX78OL1792bVqlXcunULQ0NDbt++zbFj\nx3jppZe4ePEizZo14/bt21haWjJixAjc3d0BCAwMpGvXrgwaNKjS9XJycrT24YaEhNCzZ09ee+01\nrXZJSUmYm5vj7e0t72HLy8tj586dciZEgDZt2siJjAThYYjdhoIgPDKKFSswuHWr8okLF8hfuZJM\nX1/y8vIoKirC2NgYU1NTmjdvjomJyWObLKKuGBoa4urqSkxMDKWlpbRo0aK+hyQIQm35p5v7ezNA\nj1pV2QkNDQ3lrH9lmQfLEhUBNG3alIKCAu7cuVNln+bm5pw+fZqJEyeyYMECfH19efnll3FycuLf\n//43Fy9eBMDJyYkrV67w6quvVuqjsLBQa5n4hAkTeOWVV3j33Xfp378/S5cupUOHDqSkpJCYmEhg\nYCCnT5/m7t27mJqasmDBAsaMGUNYWBiGhoaYmZlVyqIoCA9C3K0IgvDo7NlT7SmdvXsxMDDAwcGB\njh074uLiQvPmzTEzM3vqAqnQ0FD69++Pj4+PnA2sKpIkERoaKi+z0dfXx9XVFaVSyc2bNyulT75f\n165d4/PPP+fWrVt07tyZ999/n1GjRlXq7+jRo/j6+vLBBx/Ie9MWLFjAlStXADh8+DBhYWGkpaUx\nb968hxqLIAiAnd3DnatlK1asICgoCNAs/evVqxc//PADq1evrrT0DjTB1Lp16wgICOCZZ57h+PHj\nfPjhhzg4OGBmZsaLL75IXl4enTp14uTJk7z88sscO3aMVatWER8fL5fD8PX1xdjYmHXr1lW6ho2N\nTaW6WI0aNeLNN9/kjz/+YNWqVfj6+nLy5En27NnDxo0b+fTTT/nuu+9QKBQ8//zzzJgxg9GjR1NY\nWEhsbCweHh6P4NMTnhYiAYUgCI+OoyNUNTMF0KWLZtmfQFhYGFZWVnh5eTFkyBBmzpxJcHAwoEmt\nXFBQwOeff07Pnj2JiYmhqKiIESNGsGvXLlq1akVkZCTu7u6kpKRgYmLCqlWrWL9+PdevX0dPT48V\nK1bg4+ODi4sLSUlJDBgwgDfeeEO+/vTp0/n444/Jy8tj/fr1rFixgkmTJrFs2TIaN24MaAK5gQMH\nsm/fPkCT+rlRo0ZERkbKm8V/+ukn0tLS8Pb2Zty4cQQHB1e5JFMQhP/hhx/gXomKirKWLsX8o4+q\nTZv+NFi4cCF+fn40a9asRv2UlpYyceJE/v3vf9fSyISn0dP1+FcQhLr19tsPd+4pFBISwoQJE/D2\n9mbdunWEhISwadMmNmzYgEKhoEePHsyZM4devXoxcuRIvLy8UCgU+Pn5ERQURG5uLnPmzOHq1auo\nVCrUajUmJiacOnWK9PR0FAoF48eP57PPPmP37t1a175+/TpNmzZFkiR+/fVXhg4dSn5+vhxIAWRk\nZNCyZUv5627duskzUosXL8bPz4/169fLN3hiH4Ig1MCQIXCvCHp5ah8fsoYM4erVqxQUFNTDwB6B\n1FT44ANwcICWLWHyZEhI+MeXTJ06lfz8/BpfOjc3l8DAwBr3IzzdxJ4pQRAembt+fhjt2IHB7dva\nJ1xcNL8wBdmECRN46623AOSim4C81K5sX4KOjo7W8jszMzOys7Np0qQJrVu3RldXlz///JMLFy6w\nefNmfH195ZuOsr1oRUVFWtcuv6zylVdeYcWKFYwePZqkpCQ2bdpEaWkpn3zyCQnlbnDOnj2Lu7s7\nV69eZd68ebi7u3P48GE5IYbYhyAINfTFFzBuHOzdq0k80b8/Op064QTcuXOHa9euYWNjg62tbX2P\n9OGlp0OPHhAX9/ex4GDYtw/+/BPs7at8mYWFBRYWFjW+fG31IzzdRDAlCMIjkZ2dTXxJCW3/+APW\nrYMDB0BXV1Mj5cMPwdKyvofYoJQPkKZMmcLEiRMBmDx5slbw07FjR5YsWUJpaak8C6RQKOS/Gxsb\n4+DgQFpaGp999hkxMTH/89rGxsao1WqtZUOzZs1ixYoVrF69Wmtcvr6+NG7cGH19fZYvX86iRYu0\nxl7WR2xsLEOrWaYkCMJ9atdO86cCS0tLTE1NuXnzJjk5OTg6OmJoaFgPA6yhNWu0A6kyycmwYgWU\n+/kjCA2V2DMlCEKty8rKIiEhAWdnZ4yNjet7OE+l9PR0UlNTcXZ21mTliomB//s/aNYMXnkFys1G\nHTlyBKVSyYABA2rl2mIfgiDUndu3b5OSkoK9vT1WVlb1PZwH07kz3MvgV0mbNhAbW7fjEYSHIIIp\nQRBqVVkg1bZt26em7lNDlZWVRWJMDG6ffYb+3r1Q9uO+dWvYuVOTBOSeCxcu0Llz51q7btnTckEQ\nHr3CwkLi4uIwMDCgVatW6Ok9JguPunSpPtW7qytERdXteAThIYgEFIIg1Jo7d+6QkJCAi4uLCKQa\nAAsLC1y/+gr9PXv+DqRAs6zG0xPy8uRD1QVSYWFhcir26OhoFi5cWGW7IUOGaF23LJA6fvw4GzZs\nqHaM77//PgCenp74+/vzzjvvcOHCBa025ZcLzpkzh7/++gs/Pz+6du3Ke++9x5QpU7hw4QJffvll\ntdcRhCeZkZGRXLg8MjKS7Ozs+h7SfSn08qr+5ODBdTcQQaiBx+TRhSAIDV1mZibJycm4uLhgZGRU\n38MRAHJyMNi2repzGRnw/fcwYcJ9d1e2kKGgoIAlS5aQk5NDx44d6d69O1evXmXRokXMmDGDgIAA\nGjduTIcOHXB0dOTnn38mLi6OoqIirboxFy5cwM3NDQBTU1PWrVvHqVOnOHHixD/Okpmbm7Nx40YW\nLlzI0KFDcXd3B+CLL76Q95oJwtNGoVDQvHlzmjRpQlxcHDk5ObRs2bJB1u1TqVQkJCSQ/9ZbuO7f\nj+6lS9oN2rWDGTPqZ3CC8IBEMCUIQo1lZGSQkpKCi4vL47kJ+kmVmAj/kD659OrV+/olEBwcTHh4\nONnZ2bRr1w6FQoFKpcLS0pKdO3cybtw43N3dmTdvHocOHaJbt26MGzcO0MxMvfDCC8yZM4f33ntP\nq98TJ07QqVMnAJRKJVOmTOHo0aP8/vvv9/0Wy69UNzExITU19fHObiYINWRiYoK7uzuJiYlERkbi\n6OiIqalpfQ9LdvfuXW7duoW5uTluXbuic+IEfPWVJmuhWq0pm+HnB/cymApCQyeCKUEQaqQs0YEI\npBqgFi3AyAgKC6s8nWZmRsalS5iYmGBqaoqJiYmcPr28yZMn89ZbbxEdHc327ds5dOgQ7u7ujB49\nmldffRVAKxNgxdeX1auqWGQ0Pz8fExMTQHMDuH79esLDw9myZQuurq788ssv/Otf/0JXV5fi4mIM\nDAy4fft2tamMRTp2QdDQ0dHBwcGBnJwcbty4QdOmTWnevHm9FvpVqVQkJiaSm5uLo6MjZmZmmhOm\npppZKDETJTymRDAlCMJDu337Nrdv38bV1RUDA4P6Ho5QUZMm4O2teepbUdOm2M+cSTMjI5RKJXl5\neSQnJ5Ofn4+RkZEcXJWWllIxT1Hnzp2ZPXs2KSkpqNVqAGxsbJg7dy5z585lxowZXLt2DQ8PD1q2\nbFntDZy7uzuxsbF069ZNPtavXz+8vLyYPHkyXvf2U0yZMoVx48Zhbm6ufRNWQUpKCq1bt36YT0oQ\nnkhNmjTB3d2dW7duERUVhaOjY73sZy2bjSobT0NceigID0tk8xME4aGkpaWRnp6Oi4uLCKQasoIC\nGDMGfvjh72OtWsGuXdC1a6XmkiSRn58vB1h595JUlM1emZqaYmxsXCtPuEtLS5kxYwZr1qypcV+Z\nmZksWbKElStX1rgvQXgSZWZmkpiYiK2tLTY2NnVyTbVaTWJiInfv3qVVq1bVPggRhMeZCKYEQXhg\nqampZGZm4uLigr6+fn0PR7gf0dEUnjhBukJBS29vTQHl+1RcXCwHV0qlkoKCAho1aiTPXpmamt7f\n90FBgSbpxc8/g7ExDB/OBWvrWknJnpCQgJGREdbW1jXuSxAeR8XFxcy4t1SuuLiYESNG0KtXL602\nX3/9NaWlpWRnZ+Pq6oquri4WFha8+OKL932dX3/9lbS0NOzs7Jg/fz7Ozs6YmJhoPRQ5duwY7733\nHocOHcLKyor33nuPkSNH4uHhQVBQEO3bt6dRo0asXLkSf39/vvjiC/FQTnhsiWV+giA8kJSUFO7c\nuSMCqceNqyu6Tk5kXb1KywcIpAAMDAwwMDCQ9yqp1Wry8/PJy8vjzp07xMfHo6OjoxVcNWrUSHv2\n6s4dTbHgv/76+1hoKJ3HjoWvv4YaznS1bNmyRq8XhMddSEgI/fr148033wSgpKSE/fv3Ex4eTmFh\nIQsWLEBPTw9bW1uKiopISEhAR0cHHR0dwsLCOH78OE5OTujo6DBz5kzGjBmDg4MDPXv25O2335av\ns3XrVjZt2sQff/zB0KFDmTx5MiNGjJDPq9Vqbt++TefOnbl48SKdO3emVatW8vlhw4YxefJk+eu+\nffuyd+9ehg0bVgefkiDUPrFoVRAquN+6OmW1b8rXwKlKxcnf+6mr89prr7F69WpAU1fn5s2b9OvX\nTz5/4MABNm7cSGBgIOnp6Q/w7momOTmZrKwsXF1dRSD1GNLX10elUsn7nB5WWeBka2tLmzZt6Nix\nIy4uLjRu3JjCwkJu3rzJpUuXuHbtGklJSeTk5KAOCtIOpMps3gzh4TUajyAIEBkZSZdyhbj19fXZ\nunUrISEhLFmyRKsOW5MmTbC3tycnJ4fU1FRUKhWenp4EBgYSERFBUVER+fn5eHp6ynsXy2RmZqKn\np4ckSezevRtPT0+cnZ0ByM3NJTIyEkmSeOuttzh37hzbtm1j+PDh8ut37NiBn5+f/Lv12Wef5ddf\nf32UH40gPFIimBKEf1AWCKlUKj766CMCAgKYOnVqlTejGzduJCAgAF9fX5KSkvDx8WHhwoXs3btX\nblNVXZ3Zs2dz4sQJrb4sLS25dOkSOTk5gCYLWo8ePTh58iQA27dvZ9SoUQwfPpxvvvnmkbz3ipKS\nksjOzsbFxQU9PTGp/bgyMjKisJrsfjVhaGhI06ZNcXBwwN3dnWeeeQY7Ozt0dHRIT09H/e231b/4\nn84JgnBf2rdvz7lz5+Svi4uLtc5XfLBnaGhI8+bN0dfXJzk5WT4vSRKmpqZs3bqV9PR0pkyZovW6\nsuQRCoWCwYMHc/DgQS5evEhkZCSTJk1i37592NraoqOjQ6tWrcjPz6dJuTTnw4YNY+PGjcyfPx/Q\n/C4UWTiFx5m4I3pCFRYWEhAQIM8ktG/fnpkzZ+Lk5PRIr/v+++/z1VdfyU+q4uPjWbBggdaeiLJz\nCQkJLF68GFdXV1xcXNi2bRvdu3fnwoULnDlzpl6Lb5avq+Pm5saRI0e4deuWnBUpKSlJq71SqWTr\n1q307dsXHR0dzp8/j0KhYMKECdjZ2cntHqSuzsyZM1m2bJn8tY+PD4sWLcLZ2ZlGjRphZmZGu3bt\n+Pjjjx/Rp/C3snS2IpB6/BkaGlJUVISxsfEjvY6Ojg5mZmZ/bzjPz6++cW7uIx2LIDwNxo8fz/Tp\n0zlw4AAqlYr33nuPUaNGMXHiRPLz8wkKCpIfyJVRKBQ0bdqUgoICMjIyiI+PR5IkUlNT+fTTT9HV\n1cXDw0PrNRX3NuXn5/Pmm2+ybds2QkND0dXV5fjx4wDMmjULtVqt9Ttux44dREREAJrftdeuXaND\nhw6P4iMRhDoh7oqeUEZGRmzcuJHjx48TERHB6dOnWbp0Kf3798fT05MJEyZgbm6OpaUlQUFB9OrV\ni169ehEdHU3v3r05e/Ysr7/+OsOGDWPu3LkUFxejUqmYPXs2K1asYNWqVaxcuZIePXrwwgsvAFXP\nupw6dYoTJ05oBVPlzx05coTo6GjmzJnDli1b6N69O507d+aLL76o12CqYl0dtVpNz5498ff3r7K9\nJEnY29szb948+diePXvk+jpl7reuDmjSRufm5pKVlSVXts/JyWHDhg34+PgAlev5PAoJCQkolUpc\nXFzQfcC9NkLDUxZM1bneveGXX6o+98ordToUQXgS6evrs27dukrHBwwYIP+9bdu21b5epVKRkJBA\nYGAgZmZmrF279u+Tly+DUgmdO/P6669z8eJFXnrpJZycnIiLi2PUqFFas09l9xSg+T1V/uvffvtN\n67r79u2Tl78LwuNILPN7wkmSxIIFC/jkk08ICQnh22+/5eeff6Z3796sWrWKW7dukZubi5GREYsW\nLaJnz560bNmSb775hv3798szMhYWFiiVSlQqFcXFxWRlZXH27Fk5kIKqZ118fX211kqXnQsICGDz\n5s2MGDGCPXv2MHbsWDlbGGgCjdTU1Lr7oCoovxxCoVDw5ptv8tdffzFr1iwmTJhAUVGR1uZ6U1NT\nunXrxtSpU/H396+0B6pMWV2d8vr168fRo0d5/fXXWbVqldZTwLlz53LgwAH563fffZc9e/bImZcy\nMzNp3rx5rbznqsTHx6NUKmnbtq0IpJ4Qj2qZ3/80bx5Utc+uVSsYN67uxyMIghZdXV0cHR1p0aIF\n169f1yz9O3kSPDzgmWege3do0QIfpZKSkhIiIyNRqVS4u7trBVIPasCAAbRo0aIW34kg1C0xM/UU\nqBgYVJUNv2wGxdDQUF6Wo1arq5yRGT9+PMOHD2fUqFFafdzvrIuJiQmrVq0CNLMeERERTJs2jfT0\ndLZt28b48eMxMzOrtzXU3t7e8t9dXV3l2aaQkBCtdjt37gRg165dgGZZXnmbN2+u1LenpyczZsxg\nxIgR8usAOeFFmbJzzZs35+7du/LxgQMHMnDgQK0xlM1S1bZbt25RWFiIi4uLKLD4BDE0NCQjI6Pu\nL/zSS5qU6PPmwe+/g4EBDBkCS5fCvSyBgiDUP3Nzc0xNTUk8dQr1W2+he+8hJwAZGegHBGCzdClN\n/PxqFESVee6552rchyDUJxFMPeEUCgXz588nKCgIY2NjRowYQZ8+fZg4cSKXL1/GwcGhUhG9shmX\nshmZiRMnMmvWLLKzs1m/fj2dOnWioKCgUhrTslmXbt26ycf69euHl5cXkydPrpQRCCA0NJSQkBCe\nf/55SktLGTx4MOPHjyclJYXWrVs/gk+kjkkSfPMNfPUVJCSg16EDPu+8U2vdd+vW7ZH8Irp58ybF\nxcW0bdtWBFJPmHpb5geapX6//QaFhaCnp/kjCEKDo6enh+OPP0L5QKqcltu3o5g9u45HJQgNkyja\nKzywOaNG4XD3Ln59+sC770KzZgCUlpYyY8YMrcJ9DyMzM5MlS5awcuXK2hhu/Zo6FSquYVcoNHV1\nxo6tnzH9Dzdv3qSkpIQ2bdqIQKqehIWFYWVlVeUDiPIWLlzIkCFDaN++/QP1f+HCBTp27Kj1/zcr\nK4vPPvuMZcuW4ezsTJ8+fUhNTWXLli2YmprK7S5dusTq1asxNTVFpVIxbdo0XF1dOX/+PG+//TbX\nr1/H0NCQtWvX8tprrz3w2ARBaCC8vODQoarPKRSgUtW4PpwgPAlEMCXcv/x8GDhQs1SnjIEBfPml\nHBhcuHBBK9nEw0hISMDIyAhra+sa9VPvYmPBxUUzO1WRtTUkJmo+vwZCkiRu3rxJaWmpCKTqWVkw\nlZOTw7FjxzA3N+eTTz5h586dXLhwgby8PDZs2MCSJUsYMmQI4eHhtG3blsLCQq32mzdv5vLly9y9\ne5fVq1ezdu1a8vLyyMnJoUuXLlqbvletWkXv3r3p3LkzQ4cOZdeuXSxbtgwvLy+tTFtDhgxh27Zt\ncp0xlUqFrq4uH3zwAR4eHhgaGjJ69Giys7MJCgqqckO8IAiPgQkToMLydlnz5lAhq60gPK3E3ZJw\n/z7+WDuQAigu1mwev3oVoMaBFEDLli0f/0AKNE/0qntWkZ4OZ87U7Xj+gSRJxMXFoVKpcHZ2FoFU\nA3Hjxg06derE1KlTMTAwQKFQYGBgQFJSkpzkJDAwkK5duzJo0KBK7X/++WfWr1/PuHHj2LZtGwqF\ngmHDhjFnzhyOHDmida2TJ0/KQdOlS5fw9vbm6NGjlVIW6+rqoq+vz5kzZ/jXv/7Fvn37KCwsJD09\nHR8fH/7zn/8Amn0Xt27dqoNPSRCER2L8+OpnniZMqNuxCEIDJu6YhPtTWgpVJFQAQK3W7AsStP2v\n7He1uF8kLCysUhKLMkOHDv3H10qSxGuvvcZXX31FmzZttLIUlrd582ZOnTpFaGgo/fv3x8fHh08/\n/bRSu9WrVzN58mQmTpxIaGio1hi+//57Pv74Y1JTU7XSyAtVCwwMpGfPnsycOZPY2Fh27tzJ0qVL\n6datG/n36jY5OTlx5cqVKtuXV7YIwdjYGENDQ1QqldZ5lUol1xDr2LEjYWFhdOrUiXPnzrFmzRoC\nAgIoLCyUM3o+//zzjBkzhtTUVHbv3s3t27fx9/cnNjaWmJgYoG5S9wuC8Ih07Qpr11b6XVXcvz/M\nnVtPgxKEhkfs/hXuj1IJOTnVnxfT/ZUNGAAffKAJRCtq2RLKJeqoLWFhYRw/fhwnJyd0dHSYO3cu\nGRkZBAUFcfHiRTZt2oRSqWThwoUYGRnRr18/bt++TWJiIuPHj+eXX37h4MGDFBYWMnjwYN544w25\n76NHjzJ27FiuXbvGxIkT8fLyYsSIEVrXv3LlCmlpaWzYsAHQFHHu06cPABs2bKCwsJAlS5YAkJyc\nTHFxcaUCkMLfQkJCiImJQVdXl6ZNm2JnZ8eKFSv4888/5Zotvr6+nD59mnXr1mFkZKTV/vXXX2fa\ntGlkZ2ezcuVK1q9fj0KhwMjIqFIw1bp1a5KTk7VS7c+aNQt/f3+2b98uH5s3bx4TJkygSZMmFBQU\n8K9//Yvly5cTHh6OoaEhly9f5uuvv2bZsmUYGRnVzQclCMKjMWUKDBoEP/wASiV53btzy9oadz09\nxG4pQdAQe6aE+yNJ0KYNxMVVeTo3MBDjBQtELaKKPvkEgoK0Dqn19CjcsgXjCvW3aqLUiN78AAAg\nAElEQVRsj01GRgbGxsYMHTqUESNG8P3339OnTx9+/vln9u/fT0FBAefOnWPKlCk4ODjg5eXF+vXr\n+eijj9i1axf9+/ena9euSJKEvr4+c+89fczJyWHq1KmEhYURFhbG3r17SU9Px9fXVy4yDJqU7jo6\nOgwePBiAL7/8Ejc3N5YvX45SqeTIkSNy8LR06VJeeeUVrVplQt3Iy8sjKSkJV1dX+Vh0dDSHDx9m\n6tSptXKNH3/8kdzcXN59991a6U8QhIYhJiYGc3PzJ2M5viDUArEGQ7g/CgVUqKNURrKyImvgQCIi\nIkhKSqKkpKSOB9eABQZq9k69/TZ07gxjxlB07Bix7dtTUFDwSC5pbGwM/L2sq6wOiJGREUVFRUiS\nhFqtJjY2Fh0dHZycnOTXSpLExx9/zPz58+VACqCwsFCuIQYwYcIEfvnlF/bt28ft27cJCAjgm2++\noV27dpw/f15ud/HiRdq2bYuJiQnLly9nzJgxclru+qwl9lSLj6fRRx/h2KePphjnokVw9y6urq5y\nMejaYGtrKwIpQXgCtWjRgpSUlEqz24LwtBLL/IT75+cHd+/CsmWQna059uyzKL75BoeOHbEtLiYt\nLY3IyEjMzc2xtbXF0NCwfsfcEHh6av7c0whomZVFbOz/s3fe8THffxx/XvbeUxIhoiIxajRKzLao\nUVvtik1DSKKoTdEqQYjVqMZotUptpfzQolaREiGJETtDRPa4XO73x7lvc3IxQ1Q/z8cjD3ff9fl8\nL3F378/7/X69LuPl5SWpopUVxX3CtDFkyBCCgoIwNTVlyJAhGvsCAwMZPHgwNjY21K9fn14Ps2eO\njo6kpaVpHGtsbEzr1q05evSoZMIMYGdnR0BAAAqFAl9fX1xcXABo0KABISEh9OvXj7Vr13L58uUn\n9nMJyphr16BhQ3STkpByyNOmwdatcPgwdevWLbOhykKMRiAQvH4YGxtjaWlJYmKi9P4uEPyXEWV+\ngmcnJwfOnQNLS6hevcRuhUJBcnIyKSkpmJqa4uTkpJHVEKhITEwkLS2NatWqvbJG/aKiIuLj4zEy\nMsLd3f3xB8vlsGcP3L0LdesyY9cuRowYgcNDX7EXobCwkOHDh7Nq1aoXvpbgGRgwAB6KgpRg8WIY\nNeqVTkcgEPw7kcvlxMTEUL16ddH3KvjPI4IpwUujqKiI1NRUkpKSMDAwwNHRUSo5E6i4fv06crkc\nT0/Plz6WQqHg8uXLGBsbU7FixccffPKkqum4mLBIWpMmpIeHU6lWrReeS1paGunp6VSqVOmFryV4\nBmxt4f597fvefx/273+18xEIBP9a7t69S35+vngfF/znET1TgpeGjo4Ou3fv5vr169jb23Pnzh1i\nYmKIiopi7NixL3VstRlpmzZtGDVqFB07dpR8eQAOHDhAWFgYAH369OGnn34CoGvXriQkJPDZZ59R\nUFDAqJe8Ul+xYkWUSiU3b958qeMoFAri4+MxMTF5ciCVna1yvn9EodH68GEqaZFCfx6sra3FB3B5\nUJpnzJP2CQQCwSM4OjqSkZEh2TQIBP9VRDAleOkkJCQQGBjIggULOHfuHA8ePOD3339n1KhRUk+O\n2rMoICCAffv2cfHiRXr27MmECROIiYnh+PHj9O/fn/79+3PixAkOHTpEx44dmTdvHsHBwRrjnT17\nFi8vLwDMzMxYsmQJEyZM4MiRI9IxjRo14vjx4wA4ODhw9uxZ8vLyMDIyknqNDAwMMDU15c6dOy/t\ntZHJZHh4eJCZmUlycvJLGUOhUBAXF4eZmRlubm5PPmHjRrh3T/u+TZsgKalsJyh4dXTqVPq+zp1f\n3TwEAsG/Hh0dHSpUqMCtW7fKeyoCQbkiginBS0WpVBIZGcmsWbOIiIjgl19+oXLlytSsWZOgoCB0\ndXWJiopCqVQyZMgQ5s6dy+bNm0lJScHGxobevXvj7e1NeHg4ERERfPPNNyxduhSZTEbjxo357LPP\nSExM1BjzyJEjvP322wBkZ2czcuRIBg4cKAVuoFK2Kygo4OzZs7z99tvI5XKOHj2Kn5+fxrVq167N\nH3/88VJfI11dXTw9PUlKSuKBWtijjCgsLCQuLg4LCwtcXV2f7qSEhNL3KRTwkrNogpfI1KlQzEdK\nTUGdOqp+KoFAIHgG7OzsKCwsJP1xPpQCwRuOCKYEL53ibXnqrI+trS0eHh44OzuTk5PDgwcPuH//\nPkVFReTn59O0aVM+//xztmzZwtq1azWuoX78qAS4mpycHEnwwtTUlPDwcObNm8fatWvZtWsXQUFB\nREdHU7t2bRYtWkTz5s1xcXFh7dq1vPfeexrXelXy3QYGBlSpUoXr16+XWcmEOpCytLR8asUlhUJB\nurNz6QcYGkLlymUyP0E5ULEinDoFY8eCjw/Uq4dizhxily0jU5u5tEAgEDwBV1dXbt26VeKz+FlZ\ns2YNu3bteq5zDx06JJnFg6pne+XKlVy9evWZr1W8TSAwMJCAgACmPOIXCWhVo23YsCEbNmwAICkp\nialTpz7z+IJ/H0IaXfBSkcvlDB06lClTpmBiYkLv3r2Bf4IqPT09XFxcsLKyQk9Pj9jYWDIzM9mz\nZw/79u0jLS2NVq1aUbVqVYYPHw5AQEAA+fn5pUp/e3t7c/nyZXx9faVt7du3p127dgQEBNCuXTsA\nHjx4wPr163F3d6dp06YsWrSINWvWcP36denasbGxNG3a9KW9PsUxMTGhUqVKXLlyhWrVqr2QQpJc\nLicuLg4bGxucHxccPSQvL4+UlBTu37+PRfPmmLu5oaMtA9Wvn0rEQPDvpUIFmDdP9QPoApUyM7l2\n7RrVq1cvc6l+gUDwZmNhYYGBgQH37t0rUyPfP//8k+3bt5OUlMTkyZO5efMmCxcupHHjxty9e5cF\nCxawcOFCbty4QXp6OvXq1WPNmjXs37+f+vXr8+DBA3Jzczl79iyRkZEoFAoaNmyIq6sr0dHRBAQE\n0KtXLyn4gZJtAosXLwYgIiKC7du306FDh1Lne+rUKTp27MiOHTvo1asXjo6O3Llzh4KCAqF4+IYj\nMlOCl8Yff/zBrl276NmzJ2vXrmXFihV07NgRd3d3vv76awC+/PJL3N3dWbNmDVWqVKFevXosW7YM\nFxcXhg8fTlhYGA0bNqRhw4asXr2a1atX8+6779KsWTM+/fRTAI03QlCtJp04cQKAn3/+Wdq+a9cu\nDd+rxo0bExcXB0D9+vW5ceMGgMb8zp8/zzvvvPNM9x0ZGclHH30k9YFpozR/JUtLSxwdHbl8+bJk\niHjw4EGNFTdtyOVyAgICAHjnnXfo378/QUFBJUwV1ePOnz+f8PBwTpw4QYcOHRg0aBARERF4e3vz\n5YIF6Pz2GxRX7dPRgd69VfLZgjcOc3NzHBwcuHr16guvLgsEgv8eL8PIV19fH7lcjomJCb/88ovW\n8v7Dhw+zcOFCPvzwQ+m8tm3bMnr0aOn5ggULsLW1xd7enqioqMeOWbxNoDjvvPMOsbGxjz13zZo1\n+Pv74+zsLGXEqlSpomFkL3gzEZkpwUujadOmz5zV0dHRwcHBAXt7e+7fv8/NmzeRyWQ4OTlhbW2t\nOkguhwUL4NtvVWII9evDhAnQsiWgynb5+/u/8PwLCgqeS3VQJpMxfPhw2rVrJ2XiAgMD0dfXp7Cw\nkLCwMPLz85k+fTqxsbFMnDgRCwsLQkNDAdWbr6+vL76+vnTv3p3CwkKOHTuGlZUViYmJ3LhxA2tr\na6ZPny6NuXPnTj744AMKCgqwtrZm4cKFHDx4kLNnz5bolZo0aRJubm40adKE9evX069fP7p16yZl\n42rXrs2J9HQa/P23SiL97l14+214ki+V4JVRUFBASEiI9Lh3795ERkYSHh7+XJ5uCQkJLF26lOHD\nh3P79m1cXV3p3r07P//8M+PGjZMWF56GWbNm4e/vz6pVq4iPj6eoqIhWrVox4JGerKlTp5KamopM\nJqNSpUrS/7VevXpRu3ZtJkyYQH5+PmPHjmXJkiXPfE8CgeDVoTbyTUpKooKWvszn4euvv2bDhg0c\nPXqUQ4cOASXL+9UZn+KZHwsLC43ryOVyAgMDsbKyAuD48eMUPixrfrSMv3ibQHFOnjyJl5cXYWFh\nJCQk8OWXX5Y47/fff0ehUJCamsq3337L7NmzX1mrgKB8EcGU4LVEJpNha2uLra0t6enpJCYmcvv2\nbRwdHbEbOhTZ1q3/HHzgABw6BBs2wMcfA1CnTp0XnoOBgQE1atR4rnMjIiKYM2cOAwcO5MKFC9jY\n2DB9+nRmzpzJhQsXkMvlTJo0ifT0dCZNmoS1tTUmJiYYGxsTHR1Np06dqFq1Kr169SIhIQEbGxv6\n9OlDYGAgvr6+GqtwoFpNGzp0KHFxcaSmpjJ16lT++usvjh07Jh2Tn5/PpUuXuHfvHqtXr8bBwYGJ\nEycyd+5c9u7di6+vL0OHDqVu3bocPHiQBg0aQLFSScHrQ0REBO3bt6d169aA6stCZGQkc+fOJTY2\nlsGDB9O4cWNmz55Neno6tWvXZvDgwTRr1oxmzZoRGxtL8+bNOXXqFB988AF+fn4cP34cfX19Ll26\npGGmfO3aNQCGDh2KpaUlNWvWxNfXl+nTp1OpUiU++eQTvL29AVWf3pUrV3B1dUUmkzFx4kSqVavG\nwIEDNYKpXbt24eLiwsyZMwGk1ey7d+/i6OgorR4bGhpKippl9QVNIBC8HCpUqEBMTAx2dnbPXda2\nbNkydu7ciYeHB82aNWPatGlkZ2djY2MDUKK838/Pj6+++oorV65IGaVHjxk/fjyjRo3C0dGRSpUq\nMWjQIJYsWcKiRYukihQ1xdsEsrOzCQwMRKFQYGtry9ChQzWOPXv2LCNGjABUvVKTJ0+mR48eAHTs\n2JGioiIuX75caiWK4M1BBFOC1x5LS0ssLS3Jzs7mwc6dmoGUmqIi+Pxz6NZNVZJWzgwdOpQWLVrw\n8ccf06BBA+nNXSaTlSijUm/r27cvNWvWBFRmvq6uruTk5JCWliadExYWxsmTJxkwYAA//PAD5ubm\nAGRmZpKUlET16tXx8PBg5cqVhIeHs3PnTu7du8exY8fo06cP7u7uDBw4kK+//pply5Yhk8mYNWsW\noOorGzJkCGZmZmIl7TUnJiaGnj17Ss/VfU4jRozAxMSE8ePH06RJExQKBTY2NmzcuJHBgwdjZGTE\nzJkzWbx4MW5ubowYMYIePXrg5+dH1apVmTNnDnPmzGHXrl0lynWSkpLo3Lkz7733HidOnMDW1lZS\n2yw+r+Ly+3PnziUqKoqFCxeWmL9a7GX69OlcvHiRn376iTVr1tCnTx+OHTvGwYMHadGihaSoWfx+\nBQLB64e+vr7kKfk8PoJq+5PH0axZM+Cf8v7HeUFOmzZNerxu3TqUSti7F6ZMAU/P72ndGsaMGaNx\nTps2bQgJCaF3797s3r37sXO5fPlyqfu2bdtGYWEhWVlZODo6PvY6gn8/IpgS/GswNTXF9Ny50g+4\nepXMv/5Cx8cHXV1d9PT00NMrvz9xY2NjWrduTXx8PPfu3WPcuHHk5eVRo0YN9PX1mTVrFvHx8VKZ\n38SJE3F2dsbc3Jz+/fsjk8kkyfRVq1ZhampKUlIS9+7dw9bWVip3yM/Px9bWlqysLKn5t6ioiM6d\nO9OrVy9WrlxJ165dsbGxYeXKlXTp0gVDQ0OGDRvGhx9+yP79+9HT08PHxweZTEZcXBy1ivdLCV47\nfHx8OH36NK1atQJUpX6gWnjQ0dEhPz+f3bt34+3tTb9+/aTARV3+YmhoKAXiRUVFGtc2NDTE3t6e\n7OxsjX0//fQTBw4cwN/fnw0bNlC5cmW+/fZboqKi+OSTTwDIzc3FzMxMOmfChAnY2NgQHByMq6sr\nK1asoFmzZvj4+HDq1Cnq1avH9OnTpZXbLVu2SIqWZ86coUWLFpibm5MkvM0Egn8FTk5OREdHk5OT\nI31GvQ6oveh///2fbTNnwvTpUCzmKrM2AVAtck6ePLlMriV4vRHBlOBfQ05ODoqCAswfc0xqZiZ5\nN2+iUCgoLCxEoVCgo6ODnp6eRoD1pMe6urqlqgU+ieIrayNHjgSg8yOGqNu3by9x3vr16zWez3uo\nttagQQPmzJmDh7s7ZgcOwJ074OYG8fHkVapEfHw8gwYNIjIykvfff58FCxZw/vx5LCws+PXXXzXq\nv9WCHO3atZNUDbt06aIx7v/+9z+pf0vwejJkyBCCg4PZsWMHCoVCa9amTp06TJgwgbt375YImKBk\nKUxcXByTJk0iKSmJ4OBgwsPDuX79OqAq3wsJCcHY2Jhq1apx6NAhduzYQVpamlRqCFCtWjUiIyM1\nruvk5ESFChXIzc3VyFBNnTqVTz/9FENDQzw8PDh8+DBdunRh/PjxAPTr148HDx4QFxdHkyZNnvu1\nEggEr47iRr5vvfVWeU9HYsYMzUBKzfTp8N57UPwtpizaBACsra3/6fUWvNHIlEK66Y1BW1O6OiWu\nZs2aNdjZ2UlfpB+HUql8bEBx8OBBkpKScHZ2Ztq0aXh6emJqakpYWJjGcRs2bODgwYMYGhri4uLC\nhAkTGDBgAOHh4Rw/fpyNGzeyZMkSQkJCNBrNi4qKyMjIID09nfT0dHR1dbFNTsbpES8oiXr14K+/\nSmwuHlgV//dJjx8XhD0uGHveIOxxZNy8iV7btphER2tsTxw9Gv0pU9DX1+fgwYNUrlwZOzs77O3t\nn1vi+vTp09SrV68spi34F6NUKrl06ZLq7+nCBThyBKysoEcPeIz8cVBQEHPnzi0zKWB/f/8SAZpA\nIHi9iYmJwcXFBUtLy/KeCqB6y7p3T/u+AQNg9epXOx/Bm4XITL1BaGtK37ZtGzt37iQvL09Sf1MH\nNw4ODowbN05S7Vq5ciVeXl4cPHiQtLQ06tSpw+bNm2nevDnR0dEEBwdLPT2gqkH+5ptvOHr0KN27\ndycgIEBSr1OTmprKb7/9xnfffQfAzJkzOXXqFACbN2/m3LlzrFy5ElCV8V29ehVTU1PS09PJycnB\nzMwMS0tLnJ2dVV/OfHxUyn1ffaV58+bmUIril66uLrq6us/8eioUCq3BlkKhQC6Xk5ubq3W/jo7O\nM2XBniYIs/jyS3gkkAJwCgvjSsOGFNSqRePGjbGxsXnhYE4EUgJQZa487O2Rt2mjuUgxdiysWgV9\n+2o9LyQkhIyMDOzs7F54Ds+rqCkQCMoXFxcXbt269VoEU0VFpQdSACkpr24ugjcTEUy9QWhrSl+3\nbh2bNm3ixo0bLFmyhBo1atC6dWv69etH9+7dtXrKyGQyevbsScOGDdm8eTMhISFER0ezc+dOjWAq\nNTUVPT09lEolmzdvZufOnSU8ma5evaqhiOfr68uFCxcAWLhwIQcOHJCyTzY2NmzatImePXvi6OiI\nubk5OtrEJL78Epo21ZRGHzUKPDxe9CXUQB2EPesKe1FRUanZLrlcTl5entb9pQVhukolzmvXlmoK\n53bgAAYPFYQEgrLEcMoUDB/N9ubnq5ZyGzaEKlVKnPOoFP+L8CKKmgKBoPywtLQkOTmZe/fulcnC\nyvMgl8tJS0sjLS0NHx83LlzQ3sMlRGsFL0r5y54Jygx1U7oadVO6GnXgpP5XncFQByzFFdzUjepG\nRkaAqikzPz9f43rq82QyGV27dmXXrl38/fffZGRkEBQURGhoKFWqVCG6WEblr7/+4q233iIvL4+Z\nM2fSuXNnLl26hL6+PlWqVMHW1paKFStKjfSl0qYNbNoEhw/DwoVlHki9CDo6OhgYGGBiYoK5uTnW\n1tbY29vj5OSEq6sr7u7uVKlShWrVquHt7U2tWrWoW7cutWrVolq1alSqVEny1TIxMUGvsBCdx6jr\nKVNSSvyuBYIXJj8f1q3Tvq+wUNTFCARvKGvWrGHXrl0AxMbGMmPGjOe6jqurK3fu3NHaswkwbNgw\nQKWgFxgYSEBAAFOmTClxnJ6enmR826tXLwD+/vtv/P39GTlyJEOHDkUulzN06FAKCwtJSUkhLi6O\nmJgYcnJycHZ25osvjLXOwd4eHlE8FwieGZGZeoPQ1pTet29fhg8fTk5ODlOnTuXo0aPs3buXv//+\nm3feeQeZTIaLiwuhoaEcOXJEKvF6tFRMW+nYoxkbHR0dhgwZwrfffqvRaN6yZUsGDBiATCbDysoK\nU1NT5HI5derUISIigpCQEL777juuX7/+n240VwdhJbCzgxo1tJb5AWT6+HDn0iVkMhlmZmaYm5tj\nZmYmBcICwXORnq6SwCqNO3de3VwEAkG5oF58zc3NLeFb9/nnnwOqgGvQoEFYW1uzfft2kpKSmDx5\nMjdv3mT27Nm8++67ZGdns2DBAum6Z8+excvLCwAzMzMWL14MqNoVtm/fTocOHaRjW7ZsyYIFCzRE\nmr744gs2bNiAvr4+BQUFPHjwAGdnZ3788UcaN26Mo6MjFhYW0neXzp1h/XqVLPpD6zyaNoWlS0Eo\nlwteFBFMvUHo6+trCDio6dSpk/TY09OzhI+D+g1OLV5RXLRCrf5WzdGRaR9+CNevg7s7AB988AFR\nUVGSESiovIpA1W+kFo7w8fGhTp06kl+UqakpmzZtksZQK9udO3eO4ODgF3sR3lQmTwZtPjsuLtiF\nhGBnZUV+fj5ZWVlkZmaSmJiIQqHAzMxM+jExMXmmfqrHCZo8SZzkScjlcsaMGcPSpUvx9fXF19eX\n69evs3z5co0yMXU/n6+vLw0bNiQ/Px9vb28CAwNZvHgx77//Pj4+Ps89D8FjsLMDV1e4dUv7/jJS\nvBIIBK8favPcBw8eUL16dWQymYZv3ccff0xycjLffvst8+fPB1QLrHK5HBMTE3755Rd8fX15//33\nadOmDV9++aXG9Y8cOSKZ7BbnnXfeYd++fRrbzMzMaNKkiYbvk46ODunp6aSlpZGdnY2lpSV+fn6c\nPn26VI+rPn2gVy+4cgVMTUH4gAvKChFMCR5PYSGMGwcrV0JODshk8MEH8N13+Pv7ExcXJx2am5sr\nBVC5ubmYm5tjaWmJq6vrE5XlRKP5E+jRg8LsbBRTpmB45w5KmYwcPz9Mv/tOpbCGyh/I0NAQW1tb\nQBWwZGVlkZWVxY0bN8jPz8fExETKXpmamj62lFKboIm/vz+VK1emZs2a5ObmcubMGbKysli6dCmz\nZs0iMzMTc3NzqlSpQs+ePRk6dChWVlbY2NholG/s3LmTDz74AAB3d3fCw8P58ccfOXv2rNaeG3d3\nd0klctKkSZw7d45PPvmEKVOmaF1AEJQBOjoqsYlHTC0BcHKCJ5hrCl4ekZGRbN68mYoVK6Kvr8+i\nRYte6niPLn48urChJiEhgRo1ahAfH4+zszO9e/fGxcUFa2trbt68yb59+2jZsiUdO3Zk165dhIaG\nlpnqo6BsCQgIoG3btsTGxvLjjz9q9a0rbkYPKpPuDRs2cPToUQ4dOgSAubk59vb25OTkaFw/JydH\nw7ZDzcmTJ/Hy8iIsLIyEhAQpCBs8eDDdu3enqKiI+Ph47t+/z/3793FwcKBy5cro6emRnZ3N4cOH\nH3tfOjpQteoLvTQCQQlEMCV4POPHq3qS1CiVsG8ftGqFblQUrq6u3Lhxg/T0dGQymaS8Z25u/kyZ\nC9Fo/mQSW7VC1rIlLoWFYGrKlcREqrq4oL0SXJWpLO5zoVAoyM7OJisrizt37pCbm4uRkZFG9qq4\nybE2QROZTMbQoUNxdnbm+++/x8DAgNu3b3P27Fmpd65Ro0b06NEDGxsbmjdvTv/+/Rk8eLAUaIFq\nVVLtwXXz5k2GDRvGX3/9xbFjx574OrzzzjvExsZSq1YtyQdJ8JIYPVpV6jdvHjx4oNr27rsq8ZfX\nQKXrv4pMJmP48OG0a9eOfv36AbB7924OHTpESkoKCxYsYNu2bdjb29OuXTt69erFhg0bmDdvHjdv\n3sTa2poZM2aUUHL966+/uHHjBtbW1pL6K5Rc/Hh0YUNt8i2TyWjbti3r1q1jwIAB0nvGxIkTAVWm\nefny5YDq/WjLli30EOI5ryWPilM96ltnYWGBg4MDEyZMICYmhvr169OsWTOmTZtGdnY2NjY2gOpv\nwsnJSVLANTZWfWJ5e3tz+fJlfH19yc7OJjAwEIVCga2tLUOLNTEpFAoKCgq4du0ajRs3ZvHixdjZ\n2fH1118zZ84czM3NkcvlLFmyRPpcEAheNSKYEpRORgasWKF9X0wM18PDKWzfHktLSxwdHTE0NHy1\n8/sPUVRURGpqKtWrVwcDA2SAvUJBcnIy7g/LLp+Erq4uFhYWkriIUqmUgqt79+6RkJCAvr6+FFhV\nq1aN06dP06pVK+AfQRO11O3GjRvZtm0bM2fOlFYd5XK59O/jLOzUsvcAbm5urFy5kvDwcHbu3El2\ndjZnzpzhs88+03ruqVOnpCDvsSIlgrJh4kRVdur8ebC2htfIiPO/TEREBFu3bpW+tOro6KBUKpHL\n5ezfv7/EYlZ2djbR0dGsWbOm1Gtev34dX19fPvzwQ43txRc/ilN8YUONu7s7N27cYO3atfTt25ff\nfvtN61h169ZlxowZIph6DSneClCtWjWmTZsGwE8//QTAuHHjANXv/9SpUzg5OeHn51fC1xL+aRtY\nu3Ytf/6ZxOHDlUhNhXffbcOxYyH07t1bo3wP/mkTuH//PllZWSxcuBBra2vGjBkjtQJYW1uX8J8T\nhvOC8kIEU4LSiYtTlfaVQqW0NGQiX/5KSE1NxdzcXKMkxt7engsXLuDq6vpcPlpqwQp1UAOqUs2s\nrCwyMjLw8/MjNDSU77//Hh0dnRIeYs7OzsybN4+TJ09KH5gbN27kp59+okuXLrRq1Yrhw4dz/vx5\nKlasKGWlQLUqGR8fj30x89ehQ4fSqVMndu/eLa22q78Q3rhxg9GjR0ulRTVr1kSpVAqRjVeFiQk0\naFDml42MjCQsLIyzZ88il8vx9PRk2bJljzUV19avpzYB11Y29ChpaWnMmTOHvAhoKMMAACAASURB\nVLw81q5dS8WKFXF3d6dPnz6SUpiayZMnc//+fZRKJY0bN2b37t0cPnyYihUrYmxszPr161m6dCkz\nZ87UOtawYcNYuXIlbdq0oWrVqlLPyRdffKFxnJGREb///jsNGjRg4sSJxMfH8/PPP+Pp6Um7du3I\nycnhvffeo1evXkyePBlHR0eGDBlCu3bt+Oqrrzh37hwrVqxg69atrF27lpycHAwNDSksLARUgZS2\n1029GJGVlQVAWFgYJ0+eZMCAAfzwww/S/9niix/FOXXqFD169GDatGnI5XJJna1x48b88MMPdOvW\nrdRgyszMTENBVvDvo3PnznTu3Pmpjt240Y7AQDvUwn7h4Xp4ePiTlKQSgCgqKuLBgwekpaVJVQw2\nNjZ4eHg89aLZoEGDpMyXQPAqEcGUoHScnFQ9UqVkGGQuLq94Qv9dtGWg9PT0sLS0JCUlBScnpzIZ\nx9jYGGNjY+zt7alcuTLfffed1HeVlZXFmDFjuHPnDmZmZsyfPx9TU1Mpg3To0CECAgLw9vaWrrda\nLZ994gR89hkUFECbNvTt04fQBQto1KiRJHJiYGBQYoVy48aND08/UWKue/bsoUuXLmVy34LyQSaT\n4eXlxbFjx0hMTKRRo0aASprZzs5Oo0TNz8+Pjh070qJFC/r37y8F6uqSs7lz5xIbGyuVlCoUCrp3\n746/vz/Lly+XvmRFRkaiq6tL+/btSUxM5Oeff0Yul9O2bVv+/PNPsrKyWLBgATNnzuTYsWP06tUL\nJycn9u3bh6OjIz4+PjRv3hwzMzMuXbrEmjVrMDY2lsrr1DyLWtn777/Ppk2bqFu3LllZWVLQU6dO\nHen+BgwYQMuWLenVqxczZszgt99+Y+/evdy/f59Ro0bh7e3NnDlzuHjxIi1btqRZs2aMGzeOa9eu\nkZ6ejpmZGd7e3gQHB2NlZcXUqVM1lFzr16/P119/zb1797C1tcXE5B9PnuKLH48ubNSqVUvKTKnL\nbnv27En37t25VZp4CRAXF6fhWyh4c4mLg8BAGY8qpF+9WocRIwqYP/8WGRkZkpVIpUqVnmuBUBjO\nC8oLEUwJSsfVVeXn9MgXXADMzbWrywnKnMzMTCmL9CgODg5cuXKlzIKpR3lU1KKwsFAKrG7dukVe\nXp4kahEUFKQ9MzBihGa56OLFWLduTZdiPRnPg5OTE23atHmhawjKn65du7J582ZycnKkklJtmJqa\nSuVFLi4uBAYGsnXrVkkNdMSIEZiYmDB+/HiWLVuGv78/77zzDk5OThqr1X/++Sd2dnbUr1+fVatW\nAap+QA8PDx48eIBSqWT//v3cuXOHjz76iNGjR9OlSxd++eUXTp48ybRp0zRKWL29vWnRooUULKl5\nFrUydeDy448/8tFHH/HNN9+UOK927dpcu3aN+vXrU1BQwI4dOzT2z5kzp8Q56x76hI15KCKifv3U\naFNyzciAbdvgm2+geXOoXh369u1LaGgojRo10rqwocbd3Z158+YBqrLi4s/hH3VYgK1bt0qZLMGb\nzdq1lAik1Ozcqc+CBVbUrOn+XAGUQPA6IBoOBI9n1Sp4dPXQ3FxlmCsa0F8JycnJODg4aN1nYmKC\ngYEBaWlpr2Quenp6WFlZ4erqipeXF7Vq1ZICucTERM6fP8/Fixe5efMmaWlpKH7+WXvf3d691P3f\n/15oLnWENPcbgTrQcXJyksp5Hi1Rg3969QBpX3GzagsLCwwNDcnPz0dHR4e6desycuRIRowYoTGe\nQqGgZs2aJQzOf/31VxYtWkRKSgqbN2/GxcWFxMRE4B/JZ23Kc+bm5lIZXXGeRq0sKCiIvLw8QBVU\nhoaGSkIPjxIVFUWVKlVeyJLgSfz4I7i4wCefwKefgrc39O0L5ubWZZ4F7tSpk1blTsGbx717pe+T\ny2Xo6dmIQErwr0ZkpgSPx9kZoqJU2akzZ1TGDB9/DA9FDATPz+N8nNSovaNK880AcHR0JCkpSVLt\nK41Zs2bh7+/PqlWriI+Pp6ioiFatWjFgwABAtbL9qHzx+vXrmT9/PhkZGYSFhbFw4UJCQkIkOXId\nHZ0SohY5OTlkZWVx//59dJYupdSQOzISJk164uskePNRZy/Wrl2LTCYrUaL2KKmpqUyaNImEhAQi\nIiLYu3dviSCjR48eHDp0qER5bOXKlWnXrh3z589n37591KpVi4oVK1K3bl1WrlyJg4ODFNDs3r2b\ngIAA0tLS6NOnDx4eHiXGSUlJ0apE+rRqZWreffddTp8+jUwmk8aIiopi9OjR5OTk8OGHH2JjY0Nq\naioVXoJBzqVL0K+fyg2jON9/D56eMH163TIdT5Rk/Xdo2FDlrqKNihWF35PgDUApEAjKhfDwcOWe\nPXuk5wUFBcqtW7cqBw8erOzbt6/y8uXLytDQUGWXLl2UixYtUvr7+ytnz56t/PTTT5W//fabcvPm\nzcqNGzcqlUqlskOHDsp79+4pa9SooQwNDVX27t1bmZubK11bLpcr/f39lUqlUjl9+nRldHS0Ui6X\nK/v161diXt26dZMe37lzR9mnTx9l9+7dlRkZGUqlUqkcP3688vbt2093kw0bKpWqrrsSPworK6VC\noXiqy3z33XfK9u3bK/v376+cPXu2UqlUKqdOnapxj9q4du2acuzYsaXuHzNmjDInJ0fZv39/5bBh\nw5RdunRR/vrrrxrH+Pv7K7OyspT9+/dXDh8+XBkYGKgcNWqUMj8/X3nmzBnl8uXLn+oeBGVH9+7d\nH7v/7t27yr59+ypPnTpVYt+lS5eUYWFhZTIPuVyuHDRokPQ8I0Op3LFDqdy9W6nMzJQrAwMDy2Sc\n4ixbtkx54sSJMr9ucHCp/1WVDg5KZVFRmQ8p+I+Qm6tUVq2q/W9rxYrynp1A8OKIMj+BoJxQe3Oo\n0dfXZ926dURERDB79myWL19OdnY2nTp1YvTo0QAMGTKEuXPnsnnzZjp16sSOHTtISEjA3d2dzMxM\n3NzcCA4OpmHDhkRFRWmM5ebmJj2fO3cudevW1ZDA1YazszNKpRIfHx9J2at27dr88ccfT3eTDwUF\ntJFTuzbnzp0jLi6OxMREcnNzSz1W7asTGRlJdHQ0oFL4UygUfP7554SEhEg9K5999hnBwcEsWrQI\nmUzGqVOnGD9+fAk1wpSUFGQyGcbGxshkMkJDQwkPDy/Rz1J8DqGhoYSFhfHRRx+xYsUK6tSpw5Ej\nR57utRCUGWphktJITXVCR2cd3bvX5+23ITRUpX0CKqnnxo0bl8k8MjMzmTx5MqCy46tQAT76CNq2\nBXd3Pezt/ctknOL4+vri6+tb5td9nGVbcjI8rEYUCJ4ZIyM4eBA6dQJdXVU5rJsbLF8Oom1O8CYg\ngimBoJzw8fEp0bdRnJycHIyMjCQfGVA14evp6ZXoCxkzZgzp6elSI7u+vj75+fnSebm5uRoCFhMm\nTOC3337j22+/JTY2lqCgILZt21Zijr/99hs1a9bkxo0b3Lx5E/inR+SpGDkSrKxKbtfTw2zWLGrV\nqoWjoyNyuZyrV69y7tw5EhISuH//vtQXoyYiIgI/P78SPSW3b9+mUaNGDBgwgJiYGAwNDVmwYAFj\nxoxBqVTy1ltvMXfuXBwdHaUeGIDjx49rlGd99tlntGjRgsGDB5d6O8qHfTFqfx1Q/U6KX1dQvpw8\nqVJxX7sWEhLg779h7Fjo0AEUCtUxdeuWTcmaWnlsyxYIDoaHCuMA3L8P06bV4fjxMhlK4mWVxxUT\n4SxB5cogFKcFL4KLC2zZAnfuKNi16yLXrsHw4eU9K4GgbBDBlEBQTgwZMoQdO3YwatQoPv30U44f\nP07fvn0ZPnw4kydPpn379lhYWDy24bxHjx7o6enh4eGBtbV1iYBMTbVq1bh27ZrGNicnJypUqEBu\nbi4LFy6kY8eOGvsfPHhAREQE48ePZ+7cuXz++ecAz+YyX6kSHDgATZv+s61WLdi+HRo3RkdHB0tL\nS9zc3PDx8cHLywszMzPS0tKIjo7m4sWL3Llzh7y8PIYMGcL+/fvZsmWLxhCrV6/G1tZWq5GvTCaT\nhAuMjIw0AsxHvXPmz5/Pxo0bWblyJceOHSMoKKjUDNyJEydUBso8Y3ApeOmMHw/afh1798LOnS9n\nzEWLtG8vKoKH7YWvPUOGgBbBUACCgl7tXARvLvb2ujg75yH0JgRvEkKAQiAoJ/T19SUhh+J06tSJ\njIwMbt++Tdu2baXt3333ncbjxMRExo0bJ5UZOTg4MGvWLJT5+Qzr1UtDJMTKygojIyMKCgokN3tQ\nBRCPopYvtrKykh7b2dmxfv16AM6fPy8JZzwVderA779DUhLI5SrJ/VIwMDDAzs4OOzs7lEol2dnZ\nZGRkkJaWRkFBAV5eXvj5+fHzzz9LQeakSZMoKiqiSpUqeHt7k5uby7hx43Bzc6NDhw6lBqPe3t4l\n5KVr1arFvXv38PDwYOHChYDm6z527Fj09fWRyWTSa3f37l0qV6789K+H4KWRmQmHDpW+f/t2eGTN\noEx4mKTUyqVLZT/ey8DNDXbsKKJvXzm3bxsCqmxUSAiMGlXOkxO8MchkshLKlwLBvx2ZUvxVCwSv\nHZcvX8ba2lryd3oqkpPJGD4c819/RZaXp8oATZsGDyWNb926hZGREXZ2ds89r4KCAuLi4rSql71s\nCgsLycjIkH50dXUlJUFzc3ONjFSppKRAdLRKpdLLi08//ZRly5Y995xSU1OZPXu2hlnri/I0Ko9l\ngVwuZ8yYMSxduhRfX18aNmwoGbEGBgZKxyUkJFCjRg3i4+Nxdnamd+/euLi4MG/ePAoKCnjrrbfY\nsGEDDRs25OzZs5w4cYLh5VS/k5WlWkMo7VNt8GCIiCj7cRs2pNRyvm7doJi90mtNYmIi2dm5JCZW\nJjMT3n1Xe5WuQPAinD17ltq1az/de7ZA8C9AZKYEgteM/Px8srOzqVKlytOflJcHLVpgERPzz7Zz\n51Tf5DZuhG7dysTTxcDAoFwCKVB5XNnY2Eg9ZLm5uWRkZJCUlMS1a9cwMTHBwsICS0tLDZNWQKU+\nMHo0rF79jxKBnx+B06eTm5tb8vinJCcnRyp/LCsiIiJo3749rVu3BlRBz7Zt29i5cyd5eXlMnz6d\n8PBwZsyYQUhICM2bN8fIyAg9PT309fU5dOgQKSkpLFiwgG3btvHHH3/g4eGBjo4OEydOlMbZuXOn\n1H/m7u5OWFgYoMr0nTt3TirllMlktG3blnXr1jFgwAApMwewbds2Jk6cyNq1a2nYsCF16tQhNDS0\n3IIpMzP44AMoRUOEzp1fzrgjR5YeTH366csZs6xRKBQkJSXh5eXFs7z1CATPio6ODkVFRSKYErwx\niL9kgeA1Izk5GTs7u2cz5/zpJygeSKlRKmHGjLKb3GuEsbExjo6OvPXWW08Wshg7VmUeXLyn7OhR\nvEaMwFjv+deU3NzcsLe3L4O7+YcnqTyuWLGCJk2a8Mcff2Bpacnff//NH3/8QdOmTdHR0UGpVCKX\ny9m/fz8ymYw2bdowefJkSQVRzZEjR3j77bdLjF9cXEONu7s7N27cYO3atfTt21cq09myZQv+/v5k\nZ2eT9VB9obwFOb7+Wruf+AcfZPPhhy9nzD59YOzYPPT0/kmJGRur+qVatHg5Y5Y1SUlJWFlZYWho\nWN5TEbzh6OrqUlRUVN7TEAjKDBFMCQSvEUVFRdy/f//Zv6D//nvp+6KjH29B/wbwOCGLi8eOUVRa\nbdfly7B166ud7BN4ksqjUqmkadOmrF69mqpVq0p/M9bW1qxYsYJ58+bRqlUrcnJyACSFx0cruh8V\n4FBz6tQpqlWrxrRp0zQyWY0bN+b333/nrbfeAuDmzZtER0czevRoUlJS2LBhA1D+ghxvvw1//aXK\nCPn4gJ8fLF+uJCzsNrdv33wpYyqVSvz9rxATk0VkJKxfD7dvqzJW/wYKCwtJSUnB2dm5vKci+A+g\nzkwJBG8KosxPIHiNSE1NxdzcHAMDg2c7UdtSvBp9fXj4hfq/goaQRVqaqoesNM6fh+7dX93knsCQ\nIUMIDg5mx44dKBQKevbsKak85uTkMHXqVOzs7Lh8+TJfffUVeXl5UqDk7e3NnDlzuHjxolTC9zgB\njvj4eOzt7blx4wajR4+WeqZq1aollfldf2hA1LNnT7p3786tW7cAiIyMJCIiggYNGlBYWEjXrl0Z\nMmTIayHI4ekJS5cW3yJDoahCbGwsSUlJODo6lul4KSkpGBoa4ulpTtWqZXrpV0JSUhLW1tbP/r4j\nEDwHIpgSvGkIAQqB4DXiwoULuLu7a80YPJbTp6FYaZgGH3+sKgP8r3L3rkpBsLQP72++UelCv4Fc\nuHCBOXPmYGdnh5ubG2PHjqVbt25s2rSJtLQ0QkNDmTVrlnR89+7dJQXH5+FpBDleVPhi5MiRhIeH\nM3v2bEJCQrQqYj5u7EuXLuHq6oq1tfVz32dxFAoFFy5c4K233sLIyKhMrvkqkcvlxMTE4O3tjb6+\nfnlPR/AfIC4ujgoVKjz755xA8JoiyvwEgteEjIwMdHR0nu8Dpl49mDKl5PYqVaAMleb+lTg7qxxb\ntWFlBQ/9qd5E9u3bR79+/QgNDUNPbyxVqhxh27aLeHvP4fDhPO7evcuIESMkw+bY2FimTJlC7969\nycnJYd++fYwZM4bhw4ezb98+IiMjGThwILNmzWLOnDkAzJgxg6CgIAYOHEh6ejp3794F4K233uL2\n7duMHj2atLQ0aU7ahC9WrFhBUlIS586dk44rLnyRkpKiIXwBquyjqakpd+7ceerXQ19fH09PT27e\nvCn1eL0od+7cwdraukwDqby8PEaMGEHHjh1p2rQpI0aM4OrVq1qPnT59OhcuXHjs9R5dMw0KCiI3\nNxd/f38GDhzI4sWLCQkJKVFS6uXlxU8PF2KWLFlCkyZN2LRpEyNGjMDT05MRI0awePFiFi9e/MQ5\nCARqRGZK8KYhgimB4DUhOTkZBweH57/AzJkqSbGAAOjdG5Yvh6golfX8f52ICGjQQHObra2qX8rc\nvHzm9AoYNGgQR44cwdNzMEFB33D1amMKC725eHEiXbro8uCBDY6Ojnz//feAyk/siy++oEuXLmzf\nvp0lS5ZgY2NDhQoVOHnyZAlBi8zMTK5fv87ChQtp3rw5Fy9exMnJiaioKJo3b86hQ4ekfi41ZSF8\noaZ27dqlGiuXhrGxMZUrV+bq1avkPa788ynIz8/n/v37Zd5rZGRkxPLlywkODuaDDz7AwMCARYsW\nERYWxqVLl+jZsycTJkwgJiaGo0ePsmzZMo4ePcpPP/1EUFAQgwcP5u+//2b69OmMHj2aNWvWSNdO\nSUlBJpNhbGyMUqkkICCAlStX8tFHH7FixQqNedSoUYPDhw8DEB0djbOzM926dWP58uXUqVOH5cuX\nExgYyCeffFLiXIGgNEQwJXjTEMGUQPAakJ+fT05OjiT7/dw0aADh4fD99zB8uEorWgB2dqpA8+BB\nmD9f9frcvAkvwb/pdcLc3JyWLWdx/foqYDugBFTZHYViPWfOdODzzz8nMzNT6/lKpZJJkyYxdepU\nJk2aBJQuaKHe1rhxY2bOnElQUBD79u0r0Z/0osIXj97f84hdmJub4+rqSnx8PHK5/JnPV3Pr1i2c\nnJzQewFFyMehVCpZvHgxJiYm2NraEh0dTXJyMjY2NvTu3Rtvb28aN25MQEAAfn5+hIeHY21tjaOj\noxT89uzZE39/f+max48fp2bNmoDKXsDOzg49PT2twaxMJqNixYr88ssv1K9fv1SzVSsrK6m3TiB4\nEiKYErxpCAEKgeA14Lnk0AXPTvPmqp//CNu2bWP69L2o3up9UAVS+sBUoC0JCSuYMuWoJId97949\nJk+eTEJCAhEREdjY2DB48GBsbGwkufbif6Pm5ua4u7szduxY0tLSWLlyJWlpaYwZM4bq1auTkZFB\nl4em0f7+/ixcuJDKlSvj5eVFSkoK8fHx1KlThwYNGmgVvlAqlSWEL4oTGxtL06ZNtd77rFmz8Pf3\nZ9WqVZLQRm5uLl9++aXkVyaXy2nevDkdOnRg/PjxbN++nYCAAG7eVKn+BQcHU1hYyOLFiwEYOnQo\n33zzDQCZmZnk5ubi4eHxYr+kJ1BUVETv3r2lAAjAw8ODVatWERUVJcnhgyrQnTp1qnTcjBkzsLCw\n0LheTk4Opqam5Ofnk5+fL2XDT5w4gZeXF+vWrePMmTN89tlnAPTp04eGDRty4cIFfvvtt1LnKTyD\nBE+LCKYEbxoimBIIyhmFQsH9+/fx9vYu76kI3jA6duzI8eMdiYoqvvX7Yo/fpVOnm1StOojc3FyN\nniWAVq1a0apVK63XVkuhT5s2TWO7vb29FIz88ssv0vZmzZrxxx9/4OrqioeHBydPnmT06NHY29sT\nFRXF1atX+eqrr5gwYQLt27fHz8+PDz/8EB8fH3x8fEhKSuLBgwfs37+f9u3bM2DAALZs2cLUqVNZ\ntGgR3377rdS3VFhYyJUrV3B1dUUmkzFp0iS8vb2Ji4tj9uzZhIaGAuDo6IiVlRXnz59HqVSyZ88e\ndHR0yM7ORk9Pj19++YX33nuPnJwcTExMqF27NidOnKBBgwbcunWLoqIixo0bx7x586T7jIuLY/v2\n7XTv3p1OnTrRrFkzMjIy6N27t9QrBirJ+5CQEOmxj48PFy9e5K+//qJKlSrY2dnx9ttv4+fnx9y5\nc3F2dsbc3JymTZuyY8cO0tLSaN26NQ4ODsybN4+BAwfSt29fhg0bhrGxMe3atdP6e/P29mbHjh3c\nvXsXIyMjxo8fL/WjzZ8/H319ffr16ycd7+LiwrVr19DV1dV6PVBl0P6N4huC8kEEU4I3DaHmJxCU\nM8nJyWRnZ5e7nLTgzeRxQo/16hWxZ899srOzyc7OpqCgABMTE0xNTTE1NcXMzOypSthiYlQGtefP\ng5sbDBtWMgGo7q0yNjamadOmREVFERcXx+LFi1m4cCGFhYXs2bOHkydP0q5dO3bs2IGOjg6tWrWi\nT58+nDx5EoVCwbFjx6QszdWrV6lWrRo1a9akXr16REZGMn36dLp27YqBgQH169cnJiaGoqIili1b\nhrGxMa1ataJ169ZUqVKFTp06Ub9+fRo1akRKSgrNmzdn2bJljBw5EktLSyIjI6lRowaxsbH8+uuv\nHDx4kIiICFxcXOjQoQMNGjQgPDxcI5gKDg5m0qRJZGVlaezr0KED27dvl45bunQpnp6etG7dGlCp\n6unr6zNjxgy6d+8uLa707duX9evXP+2v+7EolaBQQEDAMAICAqhRo0aZZJR+/fVXMjMz+fjjj8tg\nloI3nTt37qCjo4OTk1N5T0UgKBNEXl4gKGdeWHhCIHgM9eqpgptHMTWFxYt1sLOzw93dHW9vb2rW\nrImTkxM6Ojrcu3ePCxcuEB0dzbVr10hOTiYnJ6dE38yePVC3LqxYAUePwo8/QosWqta04ri7u5OQ\nkEBeXh5t2rQhKSmJ9PR04uPjkclkzJw5U+qlMjExkb7kWz70UDty5AhXr16lefPmrF+/Hn19fakf\nS19fX0OJzs3NjU6dOnHr1i0aNGjAoEGD2LNnD7NmzcLS0pLU1FRWrVpFbGwsFhYWbNiwgfPnz/Pu\nu+9iZWXFxIkTOXz4MA8ePJDK5H7//XcsLS3Jzc3F2NiY//3vf1pf7ytXrmBra1vidfLw8CAlJUV6\nHhMTI5VOqu9BTfFzTU1NSUxM1DrW05KcrFL/NzNT2c4dPfop58/blVlpnpOTkwikBE+NyEwJ3jRE\nmZ9AUI6kp6ejq6uLqalpeU9F8AazYoUqwPnuO0hJAV9fGDMGqlXTPE5XVxcLCwuNPpu8vDwpc5Wa\nmkpeXp6UvTIxMWPECEvy80v2+k2cCH36qJTp1Tg4OGBrawuoeq+8vLzw9PTk77//JjQ0lHv37pV6\nD5MnT2bHjh1ERUXRt29fKfgwNDREoVCQm5srHevq6sq1a9cwNDTEwMCA9evXk5GRQVZWFsuWLcPP\nzw9QZcv09PQwMTFh+PDhGBsbU1RUxPXr13F1dcXQ0JCIiAi6devGtWvXOHz4MB07dqRJkyZ8++23\nWuepLUBRKpVcu3YNpVJJUFAQNWvWxMfHh9OnT0tllOrM1KM8r8iGmuxslc7KpUv/bLtwoTb9+ytx\ndIRilYfPTZ06dV78IoL/DDo6Oi8k/CIQvG6IYEogKEdEVkrwqujRQ/XzrBgZGWFkZCQFQUVFRVJw\ndfBgFgkJVlrPk8thyxb49NN/tqnFGwAWLlwoPd60aROA1ENU3Dj4559/Zs2aNaxdu5bKlStTVFTE\nypUrGTlyJOvXr+fIkSOsXLmLnTvzyciQcfeuSv5cLpejUCiYNm0ap0+fZv/+/fTq1YuJEyeydetW\nzM3N6d+/Px0eepDp6elRqVIlBg4cyIIFCwgMDOSLL74AYMiQISQmJqJQKDh9+jT5+fmlisWYmJhI\nq+7/+9//GDNmDBkZGQQGBuLg4CDdt1wuJzg4mB07dqBQKOjZs6dWMY27d+++UAnw+vWagZQahULG\ntGllE0wJBM+CyEwJ3jREz5RAUE7k5eURFxdHzZo1hYqf4F/JH388Xl1+4UJVBuxlkZOj8mMuXnGn\nqwthYdCx4y2MjIyws7N7xmvmEB8fj6enZ4mM8fbt23nnnXce6yu1b98+srOz6dSp0zONq43U1FRm\nz57Nghcw3u7RAzZuLH1/bi4I7QjBq+T+/fukp6drXSSIjIwkLCyMs2fPIpfL8fT0ZNmyZaUKqjyO\n7t27ayzMnD59mitXrjyxJHXYsGGsXLmSNm3aULVqVRQKBTY2NtLiCsDUqVPp2rUrtWrVwt3dnb/+\n+gsLCwsGDBhAQUEBP/zwA4aGhpw/f57vv/+eChUq8P777+Pj4/PM9yF4/RE9UwJBOZGSkiLk0AX/\nanx9Vd7HpdGmzcsdf8oUzUAKVAILo0ZBaqrrMwdSoMosVa5cmcuXrxAeJegkZwAAIABJREFULuft\nt8HaGho1UnDtWu0SvlmP0rJlS9zd3Z95XG3k5OTw+eefv9A1Hmc1Z2gIL8kiS/CMrFmzhl27dgEq\nyf8ZM2ZoPW7cuHEAdOvWDYCEhARJxr6sGfaw2bJNmzYEBgYSEBDAlClTShynp6fHmTNnAOjVq1ep\n52zZskVSzCwtM6Uu/z127Bg7d+6kUaNGgKrHcOLEiQwePJjjx4+TkJBA06ZNCQ0NZeDAgQDs3buX\ngQMHEhISQnJyMgUFBUyfPp1OnTpx/vx5srKySElJ4ffff6djx47MmzeP4OBgjfHPnj2Ll5cXAGZm\nZixevJilS5dSsWJFDQGZFi1acOjQIaKjo+nYsSMHDhzg2LFj+Pn50bVrVzZv3gzAd999x+DBg4Wx\n9RuOCKYEgnJALYdub29f3lMRCJ4bIyOYNUv7vsGDS/ZklSVFRbB6tfZ9SiWU0tL0VFhYWLBkSVVG\njdLn77/hwQM4dkyXMWPcWb78yR+bZdVD5Obm9sLvEb17l77v449FMPU6oi4YUigUjB8/nqCgIAID\nAykqKuLatWtcuHCBixcvMnPmTKmf7vTp04wePZqMjAwmT57MqFGjWLVqFaCyJZg6dSo9evRg+fLl\nDBw4kB9++IHr16/j5+fH4sWLCQgI0BA+edqgAlQLCI9mT7Wd07FjR3788ccnlvmpg5F9+/ZJPYUG\nBgbk5+fj6OjIunXrkMlk1KhRg5CQEGxsbEhMTOSbb75h9erVhIaG4uDgQGFhIVOnTmXGjBlSoKqm\ncePGfPbZZyXEXY4cOcLbb79dYk6Pmlo3atSI48ePc+DAAYKCgjhz5gwHDhzg/fffp2vXrmzZsgW5\nXM7169fx9PQUxtZvOCKYEgjKgdTUVCwsLLQ2nAsE/yaGD1f1RjVuDBYWULVqPl9/ncvKlS933Lw8\nVZBTGpcuPSAmJobY2FiuXLlCQkICt27dIjExkZSUFNLS0sjIyCAnJ4f8/HwUCoV07tWrsHq1sdbr\nTpmiKo37t9CoUS4ff1xS2MPTE776qhwmJCiVZcuWMWLECCkrtW/fPq5fv461tTXZ2dncvn0bAB8f\nH7y9vZk6dSqmpqYcP36cDRs2EBYWhr6+vlSWtvFhfaeRkREzZ87Ez88PNzc3Vq9ezbZt2wCV71hg\nYCBVq1blxIkT0lyeNqgAVeDUpEkTdu/erfW+1Ofo6OiQlpb2xGDK2Fj1f0+tLAqwePFixowZw9Ch\nQ8nJyQGQynD19fW19jGqVUENDQ3Jz88vsQ8oobqpNrV+lJMnT+Ll5UVYWBhBQUEolUrkcjlXrlzB\nw8ODgoICYmJiqF69OoaGhjg7O7NgwQI6d+4sXUMYW7+5iDUpgaAcSE5OFr5SgjeGTp1UPwDJyenk\n5OSgo1PppY5pYgJeXtrFFQCaNTPHw8ODwsJCCgsLUSgU0r8FBQXS8+L7ioqK0NXVZdMme5TKClqv\nm5YGf/4J77//Em+ujMjLyyM+Pp4VK9wYPBi+/x4yMuC996B/fzA3L+8ZCooTEBBA27ZtiY2N5ccf\nf6SoqAg/Pz9GjRpV4tjigYOzs7PkV7h37168vb3p168f7733HoCkzmlo+H/2zjssiuv7w+9Slt4R\nRUUsSIjGGBODMbb4jcbexY6iooC9oqKxRDSxYC8oGlHTSH7WqNGgMUYTS4waIxoQpIkivZdld+f3\nx7oTlmLvzvs8PLs7c+fOnWHLPfec8zlGWNz9p2uNGaVSCVBOXe9BjIq4uDg+//xzALy9venXr1+F\nxZ3Pnj3Lm2++CWg8TGq1+r4CFNr6bDt27AA0YXVLliyhatWqlYbGe3t7M3r0aGxtbUUxm7L36l7b\nQGNcRkdH4+7uTn5+PhMmTEClUmFnZ8fo0aN12r7zzjskJCQAUKtWLdLT08V9w4cPp0OHDmIBc6mw\n9auNZExJSDxjsrOzMTAwkOTQJR6K0NBQdu3aRa1atTA0NGTVqlWP3JcgCDqTicmTJ7N48WL8/Pww\nMTFBLpcjCALLly9HLpeL7X755Rd27NiBmZkZxsbGBAUFoVAocHV15dtvv6V58+bExcWxf/9+Fi5c\n+NTzAWfMgOHDy2+3t4fRo/UxNi4/sbsfSqWSP/64ty6TkdFDd/vMKS4u5vr169SsWRMbGxvat4f2\n7Z/3qCTuRWkviUwmo0OHDvj6+uLv709WVhZr164V91etWpVZs2YxZMgQateuzZgxY/Dx8WHGjBkE\nBgZy+/btCg2Wsp/J69evM3v2bJKTk3Vyrx7GqABNWYUhQ4aIOV1lj9HmX8lkMtGgqohhw4ZV+rq0\nlwf+M7i0Bp2zszMdO3YU92vFJ9544w3mzZsHaEIeAd58sw3Hj8O8ed/q9NmpUyemTp3KoEGDKvW0\naZkzZ474vGzuVZMmTUhJSRFfHz58mN69e9+zP4mXF0nNT0LiGXP9+nXs7OywtbV93kOReInYvn07\n9vb2dOnSBU9PT3bu3Mm+ffs4cOAARUVFzJ8/n02bNtG/f38OHjzIRx99xO3btzlz5gy5ubmMHz+e\nPXv2kJmZSZMmTfDy8gI0Qiiff/45K1asYPjw4axfvx5TU1PCw8O5du0aEyZMADQTvV69erF3717g\nv7pIP/zwA5mZmVy8eJGNGzcC0L17d3bu3CkW3H2arF2rydvSzlvc3SEkBN5++9H7TEkBJycoVQdY\npGZNiIvTqAa+qCgUCiIjI6levbooaS8hURqVCmJi4tmyZT1Lly4tt1+pVDJ16lRWr179xM559epV\nDh48yMSJE4mMjKRRo0ZPrO8HRaHQCNRs26Yp3wCaEOWdO6F2bc3rixcvPvHaaU+jT4kXBymAU0Li\nGVJUVERhYSE2NjbPeygSLyEhISGMGjVKNMR37txJSEgIixYtIjg4mIULFzJnzhxUKhWtW7dm3bp1\n2NjYULVqVc6dO4dMJmPAgAGiIQVw5swZnUmNdn2tbG5EamoqTk5O4mttvt+ePXvw8vIiPz+fvLw8\nAKytrcvlVTwtxo+HxES4cAGuX4ezZx/PkAJwcIAlS8pvNzSE9etfbEOqpKSEqKgoqlWrJhlSEuW4\ndUsT4mluDm+84czvvy/l6NHy7QwMDHS+J54Eenp6jBs3Dn19/edWZ2ryZNi8+T9DCuDUKejQAe5G\nPD4Vo0cypF5tJGNKQuIZkpKSQpUqVSQ5dIlHYtSoUYSEhODo6Mjly5d19gmCQG5uLoaGhqJRY2pq\nyty5c1m0aBGjRo0C/suf0FJZbsTZs2dxc3Nj586dTJ48GaVSKcb/g2bSnpiYyJUrV5g4cSKpqal8\n+60mZMbe3p47d+6US+5+Wsjl0KSJRlThSTFpEvzyi5oOHTJp3lxg1Cg4f15T1+pFRWtIOTg4SEqh\nEuXIzdXUhduxQyPgApr8v06dypcYgCdvALi5uWFiYvLcivZmZlauABoVBT/++GzHI/HqIOVMSUg8\nI1QqFZmZmTRo0OB5D0XiJSU4OJgjR46QkZHB+PHjGTJkCL6+vhQWFjJnzhz8/f0JDg5m9+7d7Nu3\njyFDhuDj44OJiUmlRS8bNGjAj6VmEdOmTcPQ0BCZTMby5csxNDTE09MTgPHjx+Pl5YWFhQXGxsZY\nWloSEhJCs2bNUCqV9OnTh1GjRnHnzh1cXV3Jzs7G2tr6mdybp8F77+WxZk0qrq4vvidZqVQSFRWF\nnZ0dDg4Oz3s4Ei8goaEQHV1+u1IJCxY8O1EVmUz2zBZaSnP9+n9GZEVcvgxl0rIkJB4IKWdKQuIZ\noVVbklT8JF4EFAqIiNDImQcFjWHDhg1PpN/09HQWLVpEQEAAubm5L/X7/ebNm+jr6+Po6Pi8h3JP\nVCoVkZGR2NjYvPBjlXh+9OkDu3dXvr+k5NnVHbt48SKNGzd+ZnLhhYWFXL2ajbt7VdTqiiNDtm6F\nu/V/JSQeCinMT0LiLhEREQwePJiJEyeKKkH3435V6Ldt28bp06cJDQ2lX79+fPrppyxevPi+5w0L\nC2PkyJGMHTuWxYsXc+fOHebOnfuYVyghoWH9eo3AwrvvakLj/vhjAn/++WSKJxUUFDBr1iysra3J\nzs5+brkRT4Lc3NxyYZGgEQPRFgGNjIwU6wKVRatsVhlqtZpNmzZx48aNcvuGDx8uFmQtS1RUFMuX\nLyc+Pp4mTZowfPhwPvvsMyIiInTahYaG0rRpUwRB4N9//2XBggXEx8djbm7O7du3ARg0aBDTp09H\noVBUKMEt8epwLyl8ExM12dkZfPnll3Tr1o1JkyYxcuRIYmNjn9j5te/p4cOHU1xcfM/vhsDAQG7e\nvMn8+fMZPHgwkyZNwsfHh4yMDJ12Hh4eAHz88ceiwumsWbOIj48nLCyMb775hoiICKKjo6lWTUXH\njqpy5wKwsdEUsZaQeBSkMD8JibuEh4fj6empI606ffp0VCoVtWrVYuzYsYwePRpra2tsbW3p3bs3\n165dY8GCBbi4uPDHH3+wYcMGxowZIx5/7Ngxhg8fzqVLlxgwYAC+vr4MGjTonudNT0/np59+IjQ0\nFPhPNe3WrVsoFAodqWoJiYdl+3YYN053299/u9GlC1y9CgcP/qcaqK13M3r0aMLCwpg0adJ9+y8t\nUmFubk52dnaFgivbtm3Dzc2NyMhIdu3aRb169cjNzWXOnDk63qzSCoObNm3Czc2NuLg47O3tqV69\nOmfPnsXX1/fRb0glqFQqiouLxeKelaEN7lCpVAQEBIg1rFatWiVORFu2bEmfPn04f/48y5cv58iR\nIxw7doymTZuSlZVFYWEhFy5cYPXq1djb2+Pt7Q3AkiVLiIyMxNvbm/aldM2Dg4OZPXs2OTk5vPvu\nuyxYsICaNWvSvXt32rVrJ7aTyWQ0atSIr776Cnd3d3F7586d2blzJ8OHDxdDOuVyOWZmZty6dYvq\n1SuusSXxcjN4sObzXxF9+ijIysrk5s2bdO/enZ49e6JWq/H392fLli06v32ffvpphe/p3377jbp1\n66Knp0dAQAAbN24kKiqK7OxsFi5cqHM+PT09Bg8eTFhYGBcuXCA8PJwZM2YAmpDVmJgYatasiUwm\nY/bs2TRo0ICoqCgWLVpEUFBQufHb2try999/k5KSQl5eHtHR0bi5uREYGEiPHj3EvNBt26BLF03+\noxY7O43Hztz8ydxnidcPyTMlIXGXkSNHcurUKby9vdm8eTPXrl3DyMiIFStWMGnSJI4cOcJHH33E\nypUriY+Pp1atWjRo0IB58+bRsmVLPvzwQx1DKjs7WyxgmJOTw/79+2nRogUflwlML3veGzdu6Kir\naVXT6tWrx4ULF57BnZB4lblbkqUcqamaMJfSaA0FhUJBUlIS8fHxtGjRgqVLl3L+/Hk8PT0ZNWoU\n+/fvJz4+ntatWxMUFMSIu7Eyp0+fxtfXF09PT2JiYnT6PnbsGM2bN0cmk+Hn58eqVatYtmwZ8+fP\nLze2yqLRmzRpwqlTpx7uBjwgubm5mJubVyoWs2HDBvz8/ESvVHh4OPHx8djY2JCfn09SUpLY1tLS\nksmTJzNw4EBOnDiBTCajc+fOTJw4UWyzcuVKNm3aRFBQkFjg1M/Pj82bN7Nr1y6dc8fExGBjY8ON\nGzcwMDCgZs2aANStW5fU1FSdtn379hXl87U4OzuTkJDAjh07GDJkiHh/GzduzG+//faot0ziBad9\ne6ho3cHNDYKCjKlXrx5OTk5YWlqSn59PcnIy6enphIWF0aJFC/G3T+uxLfue7tSpE3PmzOHKlSvk\n5+ezc+dObGxssLGxKffbpaenx8cff8zRo0fZunWrKJADGgn10osy2venq6ur6FEtjUKhoKioiD59\n+jBr1izUajWOjo40btwYhUKhI7Dj4ADnzsHRo7BsmaaQdWIitG79uHdX4nVGMqYknjl9+vShuLgY\ngH/++YeZM2eK4TDasLmyzx+EsqEvPj4+OhMFqDgcT6FQULt2ba5cuUJgYCBjx45l7dq1CIJw33hu\n7USroglXUVERZmZmFBUVUVJSwtixYzl69Ch79+4lJSWFyZMn8+WXX2JhYUFgYCBbtmxh//791KtX\njytXroj9aCvTW1hYVBr2IyHxIOTnw70Uy//6S/NY1lAoTcOGDfH39ycsLIzAwEBCQkL46quvAHjr\nrbeYOnUqtra2JCcns3fvXmbNmsXChQsJDg4W+yi90AD/TZZsbW3F93tpJk2ahJ+fH9988025fWZm\nZiQnJz/Q9T8MOTk5WNwjLmrs2LFs3LhRNP7UajUtWrRg7ty5bN26VWcyqJ3MGRoait99ZcMHKzIY\nraysMDIyEo/RoqenR3R0NEZGRmI/giAQGxuLIAjid4uW8ePHl6sX1LJlS06cOIGrq6u4TfqOefXZ\nuBHCwzXFrj08IDhY46XRapbo6elhbm5O7dq1qVmzJlZWVpSUlJCcnCwaSXl5eRW+p7VeXEEQEASB\nGjVqMHfuXIKCgujWrZvOOPT19enXrx+bN29GT09Pp+5iYWEh5hW4iSIjI3F0dBRrVZ04cYLc3Fyu\nXbuGWq2mTZs2mJiYkJWVJR4vl8tRajXP7yKTacQ2pk2DQYPAxOTJ3FuJ1xcpzE/imdOnTx927drF\noEGD2LZtG35+fgQEBJCUlMS1a9f47LPP+Pjjj7l27RqLFy9m3LhxYr6QhYUFCxcupGHDhgwbNoxe\nvXpRv3594L/Ql7y8PNq1a8eyZcsYM2aMTt5DRUbPvn37CAgIYMGCBdStWxcDAwPUajUNGjSgsLAQ\nf39/nJyc8PHxwdfXl3/++YdatWphYWFB1apVCQgIYNasWURHR7Nq1SoxFKpq1apkZmaSkpKChYUF\nMpkMExMTOnTowO+//87KlSvF8x85cgQDAwMaNmyIra0tn3zyCd7e3hgbG+Pk5MSMGTOIjo4W48Ml\nJB4FY2OwsoLs7Ir3y+VppKWlMWzYMHr37k1MTAzfffedThttId7Sk3/t56rsBEsmk2FhYUFOTo5O\ne+1CQ1nS09ORy+WcPn2a77//nl53pbVWr14thvmV5UEMgNDQUHbt2oWdnR2urq4EBASUa+Pv769T\nvPTYsWNkZWUxefJkcZs2NPHkyZNcvnyZn3/+mZs3b+Lk5ESHDh3w9fXF39+fvXv30rFjR2QyGZs2\nbSI1NZU+ffowfPhwABITEzlw4AAxMTGk3K02PHnyZMaMGUOVKlVEz15FCIKASqUSPVLHjh1j0qRJ\n5OTkMGHCBBwcHMTvlu3btyOTyWjZsiUrVqygtrYqKTBgwAA8PDy4efOm+P+LjIyktbRE/8rTrp3m\nrzKCg4MJDw8nNzeXxYsX4+joiK+vL1u2bKFmzZpkZWWRnZ1NfHw8+fn5CIKATCbT+X01NzfH3d2d\nCRMmIAgCI0aM0Nmvp6eHgYEBdnZ2YlirljfeeEMMcwdYvHgx9vb25OfnM2PGDARBEA09ExMT3n77\nbUxNTbGwsCAgIAA3Nzc+v+uCl8lkGDwrVQ2J1xdBQuIZU1RUJPTt21dQKBRC7969BUEQhL59++o8\nln6+YcMGwcfHR5g/f74wePBgQaFQCO3bty/Xb/fu3QVBEITY2FjhvffeE/r27SsMGzZMp01cXJww\nbdo0nW0DBw4UiouLBU9PTyE3N1cQBEEYPXq0cPv27ce+1rlz5wq//PKLUFJS8lj9lJSUCCNHjnzs\n8UhITJ4sCFD+TyYThLNni4Q1a9YIISEhwsWLF4WDBw8KU6ZMEf755x9h6tSpOp+fqKgowdPTU/Dx\n8RH27t0rxMXFCdOnTxcEQRBmzpwpxMXFCXv27BG8vLyEnj17CtevX9cZx4ABAwRBEITQ0FCha9eu\nwsSJE4URI0YIcXFxOu28vLyEvLw8QRAEITg4WPj111+F0NBQ4eDBg4IgCMKgQYMElUp1z2sODQ0V\nDhw4IAiC5vMuCIIQGBgoTJo0SfD29hZyc3PF75sVK1YIEyZMEHr06CGsW7dOp5/BgweL/WnPn56e\nLgwdOrTcmH18fISYmBhxzF9//bXw9ddf3/0fTBauX78uZGZmCuPGjbvn2EujVquF6OhoYfv27cKe\nPXse+LgHpez3pYREZRQXFwt37twRIiMjhYsXLwoxMTFCenq6oFQqH+j4P/64ISxbFiyMHz++wv2T\nJk0SiouLBaVSKaSlpQlRUVHCxYsXhRs3bgiZmZmCWq2+7zkiIiKEpUuXPtR1SUg8CpK5LvHMMTIy\nwtHRkRUrVogrz1pKr1xpnwuCQJcuXXTCBLSr46UpHZLXtm1bli1bhqenJ0lJSWzevJmSkhJ8fHx0\njqmo6OioUaMeL9zl7FlYtQquXsXPzo5ECwsM2rZ9tL7uok3Ml5B4XAID4e+/4Zdf/tumrw+rV4O7\nuxHu7hpFN0EQqF+/Pk2aNCEnJ4chQ4ZQXFzM1KlTKSgooH79+uzYsUOnb61nR7sq7OzsTPfu3bl8\n+XI5iXQ3NzdSUlIYNmwYw4YNq3S827ZtE59rP79t2rQBNJ6sqlWrPpC8ckhICIsXL2bEiBH8+++/\nnDx5kg8//BCFQsG1a9fEdidPnmTLli18/fXXOsc/bGjijBkzCAwMFIUf+vTpw5AhQ/Dw8CA+Ph6X\nuxWG4+PjKxzvzz/DokWaoqq2tjBsGAwdGoeJCXh6enLp0qX7XvPDoFAomDZt2hPtU+LVRS6X4+Dg\ngIODA0qlkuzsbDIzM0lISMDMzAxra2usrKzKCSb98gvMmAHnz9cBfGjVCv78E95//782KpWKESNG\ncOnSJeRyOZaWltjb21OvXr2HklLX09NjXFm1HQmJp4BkTEk8F4YPH84nn3zCzZs3gf8Mp4YNGzJ9\n+nQmTpyIoaEh8+bNY+rUqYwbN46TJ0+iUChE+dOymJqaolardQwyf39/li1bJh4THx/P0aNH8fPz\nA6B69eoVFh29ffv2o9XH+f57TRC2SiO/Wg2odvy4poDHrFkP399dtEm8EhKPi6kpHDsGx4/Dr79q\n5JIHDIC7GgYiMpkMMzMzzMzMcHR0RK1Wk5ubS25uLnFxcZSUlGBhYYGlpSUWFhYYGRlVeL5bt/RY\nurQux45pPpfdu8OcOTBhwgSyK4s3fEC0MuwPwujRo2nbti39+vWjefPmNGzYsMJyA3K5XAwNzsvL\nE7c/bGiig4MDdevW5ezZs7i5uVW6iFTR5HDfPujdG7TK0SkpmmT548ercfq0MTKZjCZNmjzQdT8o\ncrmct95664n2KfF6oA3Xs7OzQ61Wk5OTQ1ZWFrdu3UIul2NtbY21tTURESZ06qSpcafl5ElN/tL5\n82qqVMkmIyOD3NxcLCwsqFGjBtbW1o9ci8rNze0JXaGExL2RivZKvPykp0NsLOGxseQbGtKzZ8/H\n7E5TdHTFihUPd2BJiaZ4z5075fcZGkJCAlSr9lhjk5B4USgpKSE3N5ecnBxycnLQ09PTMa4MDAy4\nfRvc3eHumomIo6NGUausAfe02L59O1WqVKFz586sW7eOGjVq8Mcff6BWqyksLCQgIIApU6bw/fff\ns3btWmJiYsjJyaFp06Y6Cp0DBw7k22+/Zfv27fzf//2fKOc+d+5cnJ2dxXZaOXfQ5H98/fXXtG7d\nmosXL9KhQwcSExMxMjJCEAQGDhxYLi+tYUONTH1F7N0LPXo8+XskIfE0yMvLIysri6ysLKZMcSI8\nvHxUCUD//qkEBmrKKFhbW+t4gSUkXnQkY0ri5aWgAMaPh6++0ix1GRhwsV07moSFQQWFNh+UxMRE\njI2NqVKlygO1FwQBpVKJ+vhxjDp0qLzh5s1QSv5VQuJVoqioiJycHNF7ZWRkxJo1NQkOrlgRb9Ik\nuKuT8EJRXFxMVFSUTnkCLQsWLMDHx49q1RyeyLl++ukncnNz6VeqWuitW1CjRuXHjBmjKbosIfGy\n4egokJxccamBd94RuHix4n0SEi86UpifxMuLlxf88MN/r5VKmhw+DP37w08/PXR3arUapVKJvb09\nSqWSzMxMVCoVSqVSfCz9XPsImjAHi5s3qX2v/ktKpFoEEvdk+/b/CuY+CHFxcaxfv55ly5Y9UPvA\nwEC8vLzYsmUL169fp0qVKhQWFvL555+L0sQLFy6kffv2fPDBByiVSjp16oSLiwu3bt0iMzOTt956\ni0mTJrF27VrWrl0r9m1sbIyxsTEODg4IgkBBQQHHj1deYPrQoRfTmNKGGJXl6FE4fHgC8+dnY2kJ\nnp6wcCE8TvRttWrV6NSpk842IyMBmQwEoeKJpSTjLPGyYm8vo7IqBmZmhaSnF2JlZSWp70m8dEjv\nWImXk5gY+L//q3jf4cOU/PUXJW++WaERVJFBpFQq0dPTQ19fHwMDA/Gx9HNjY+MK94s5WvXrw/Tp\nkJFRbkiCvj7/1quHeUIC9vb2Yj0OiRefiIgIUZq3Zs2a5eqU3Q8PDw9+KG30l0G4KytcEdu2bePL\nL7+kVq1aKBQKFixYwFdffcXvv/9Ou3btGDp0KH/++ScuLi4YGBjQtGlTDh48yMCBA2nevDnr1q3j\n7NmzACiVSmJiYqhZsyYymYzZs2fToEEDoqKiWLRoEUFBQQB4eXmxePFiPvjgAw4cOED//v3x9vbm\nxIkTXLlyhbFjxwIaGfRbt25RvXr1cuNWKpUUFRXdc1JkYKACXrxQntKlFLT89BN06wYqlQ1gQ06O\nxjv0++9w5gxUki52X0rnPRUXF5Oamkp6ejofflif33+v+Dti4MBHO5eExPNm2DDNT2RFDBmiJjs7\nm8TERExNTcU8q7ICFhISLyKSMSXxcnLxokbRuRLuHDpErqlpOYPI0NAQExOTCg2myia0D4yxMen+\n/tjNnFlul2zqVFzatiU9PZ0bN26gr6+Pvb09tra2Umz4C054eDienp507NhR3LZw4ULS09OpVq0a\nM2fOpGvXrrRt25YrV64wZcoUqlatyoQJE3BxcRFFVjZu3EhUVBTZ2dksXLiQ2bNnU6dOHRo1asTh\nw4exsrIiOzubHqUSYhYuXEi/fv0oLi7m/PnzyOVyiouLadasGdvJy7QJAAAgAElEQVS3b2fo0KHY\n2dnRqlUr8vLyGDRoEOnp6fTu3Zs9e/bQp08fzp49S7Nmzbh69apOIVlthLerqyu3b98Wtzs5OZGR\nkUFhYSFhYWFs2bKlwvvSuHFjfvvtNwYMGIBarSYvL0/Mn9KKU/TsaUxERMWWRtu26UREpGJvb4+d\nnd0Lsxqdk5NDjTJxdnPmiJoyOly6pNGc8fR8tHMJgkBWVhZpaWkUFhZiZ2fHm2++SUiInI8+0ghP\nlGbKFHjvvUc7l4TE82bCBDh6tIQjRwx1tnt6go+POTKZOYIgiAIWycnJGBgYiIZVRYuQpWvIaQWs\n4N6LVPfj+PHj3LlzB0dHR+bNm8c777xDdnY2EyZMKCf8smLFCk6dOsXu3bsBcHd3x93dnfj4eDZu\n3IiDgwNTp07V8eJLvHq8GL9eEhIPy32EHGq+/z68+eYzGoyGuLg4FL16YdOoEXqrV6P8+2+oXRuD\nceNgyBDkgKOjI46OjuTm5pKWlkZSUhLW1tbY29tXWPFd4vkzcuRIlixZwv/93//h7u7OqFGjEAQB\nS0tLdu/ezcyZM5HJZEydOpUrV65w4MABzM3N8fPzo02bNvz222/k5+ezc+dOOnbsiJ6eHhcuXEAm\nkzF69GgcHR3Zvn07vXr1IikpSefcBQUFLFq0iMTERNavX8+aNWuYPn06arWaH3/8kVu3bhEXF8fy\n5cvZvHkzxcXFvPnmm2zevJmqVavSqlUrjh8/TrNmzSgsLKzwPRYZGYmjoyMHDx7k6NGjjBw5Eg8P\nD1atWoWVlVWFCnagUX+Lj4/n+vXr5OfnY2pqiqWlJbVr1xYnPTNnwuHD8Ndfusc2bgyLFjlgYGBG\nWloaV65cEeWPy3qFniWFhYUYGBjorIZnZcGFC5Ufc/TowxtTCoWCtDRNgWRtfqa1tbU4+XvzTbh8\nGTZt0kij29lpVvU/+eRRrkpC4sXAwEDNsmWR+PrW5dgxU/T1oU8faNXqvzYymQwrKyux/El+fj5Z\nWVnExsaiVqtFw8rc3FwsFOzr60uXLl3o27cv27dv5+jRozRt2pRLly6xceNGsrKyWLx4MRMnTmT+\n/PkYGxvTrVs3GjduzKBBg+jRowcjRozA3t4egJ07d7J582Z+//13PDw8GDt2LAqFgoEDB7Jr1y6d\nazp79iwuLi4kJCRQq1YtnJ2dWbduHd999x0XL16kW7du9/TiS7waSMaUxMtJixaaGUep+jAizs7Q\nvv0zHY5WKtrFxQU9V1fo3JnE2FisrKzEXJTSWFhYYGFhgVKpJCMjg4SEBARBEOVlDQ0NKziLxPPA\nwsKCwMBAALp27UrTpk2RyWR89tlnnDp1CtDkCwEYGhpSXFyMubm5+D/UqrbVqFFDR4p79+7douEQ\nFhbGL7/8wpYtW7Czs+PAgQPUrVuXevXq4e3tjb6+PsnJybRs2ZJu3brx3nvvUb9+fXbu3EleXp4o\n468NVbWzsxOP09ZLe+ONNwgNDRXPrw1dLCwsZMmSJdjY2Ii5Wq6urtSuXVtcbQVN6F5+fj6xsbHk\n5ORw9uxZWrdujYODAxYWFhXKF5ubw4kTsHUr7NmjcSb36KHRYdHYdRrp9Zo1a5KRkUFSUhIJCQnY\n2dlhb2//zD8HFeVLGRmBgQHcTY8sx8OsgWRnZ5Oamkp+fj52dna4urqK752yVK0KFSi3S0i8tCQn\nJ2NubkbPnqY8qOiutjxDjRo1KCoqIisri6SkJIqLi7G0tCQ/P5/du3ezb98+vLy8SE9Pp3Pnzgwc\nOJDjx4/z1Vdfcfv2bXx8fAgODiYwMBBnZ2f69etH48aNadiwIf7+/jrnTE9PL+cpl8vl5co/nDlz\nhqZNm9KhQwe2bt3KggULSExMxMfHh/Pnz3P69GlA14sv8WoiGVMSLycymUZ8okMHKL2a7+CgyaV6\nhqFzsbGxKJVKXFxcdMIKDAwMUFUUG1QKAwMDsfBhfn4+aWlpXL16FXNzc+zt7SssTizxbNm3bx9H\njhzBwMCAhg0b4uLiwt9//01QUBBpaWkVHtO/f3/mzJnDuXPnyM7OxtzcHHd3dyZMmIAgCIwYMQLQ\nrMIqlUqmTp2KiYkJHTt2ZP78+WI/ly5dYuvWrSQmJrJu3TpmzpzJzFJhpO+99x4LFiwANIVy4+Pj\niYiIwMXFhcaNG7Nr1y7efvttAKytrTE2NkahUDBv3rx7XrNcLufmzZvk5eVx8+ZNcnJysLe3x8PD\nA0tLS2rUqEFKSgpdu3a97/0zM9OE90yYUHkbfX19qlSpQpUqVSgoKHhun4Pc3Nxyix8mJhoDsMyC\ntMj9cphKSkpEL5RcLqdKlSrUq1fv8cOKJSReIrQ5gQ0aNHjkPoyNjalWrRrVqlWjpKSE7Oxs8vLy\naNeuHZ06dcLa2pp9+/aJHqa2bduKRb8bNmxIafFq7eevou+WihaGiouLKS4uJjIykuDgYNq0acNP\nP/1EQUEBN27c4Ny5c8yfPx8nJyc2bdrEunXrOHDgAL1798bCwoI7FZVMkXhlkKTRJV5uioo0s5zI\nSKhXDzw8NFVJnxGxsbGoVKoKJ0e3bt1CT0+Pag9ZW0qtVpOZmUlaWhoKhUL0VlVWFFXi5UWtVpOY\nmIiFhUWFHszw8HDy8/MfuXZanz5jgCCuXDGhenXo0+cmAwYYi5ONshQUFIh5TwUFBWLonqWlpU6+\ngkKhICoq6qkWeS37OUhLS2Pz5s04ODhUKATyIMqGoaGhVKlSpVK1xEuXLlGjRg2CgoL44osvcHFx\noUuXLsTHZ/Drr5+QnV06nu8Ebdpc4ddfx1JcXIyvry/btm0TBUe8vLz49NNPycvLw9bWFnt7e0wk\nKT6J15Tr169jZWWFg8OTKSugZfv27dja2tKyZUuysrL46quvqFKlCj179sTa2prVq1fj5uZGjx49\nuH79OgsXLsTU1JROnTrxzjvvsG7dunLfGf379ycsLIwTJ07o5ExNnDiRd955B9B8Vw4bNkwUF1q7\ndi0uLi5s27aN77//HoVCQc+ePTl06BBBQUG0bt2a999//4leu8SLg2RMSUg8AoIgiDHcla0y37lz\nh5KSEmo+RmXSoqIi0tLSyMjIwNhYMwm2sbF5oVe1H1beuzRRUVHs378fDw8Pevbsibu7u5hvpL3m\nspPmAQMG0LZtWy5dukR4eDjt27enZcuWXLt2jYkTJz5wvbBnzdatW1m4cCHx8fHo6+vTtWtX1q5d\nqyMSAXDx4sVySc8Pwr590KfPX6hUuooFpes7lZSUiMZTTk4OhoaGOoV3K1qhfR4UFRXxxRdf4ODg\nQPv27bG3t8fa2hp/f39UKhW1atWiV69eDBs2jGbNmpGYmMg333zDoUOH+PXXX0lNTWXFihX4+/tT\nUFDAoEGDiIiIICcnh6+//ppr166hUqlISEjg8OHDfPTRRzRp0gQPDw/Wr19PamoqgYFLefPNIM6f\nt8faGho3PoGxsUbdsKioCD8/P0JCQujVqxeff/45YWFhtGzZkvbt278w91FC4nmQmZnJ7du3efPN\nN5/6b5cgCGKh4H379hEeHs6GDRuwtrauNP9Ti0oFZ8/C7t0h9O37Ph988M4TGZOXl5dOiLXEq4cU\n5ich8ZA8iCEFmtCloqKixzqXsbExNWvWpEaNGmRlZZGenk5iYqLOSrdCoWDq1KmAxmMwaNAg2rRp\nI471YX68Jk+ezOLFi/Hz88PY2JjU1FRGjRolKtlNnTqVO3fuEBERQfPmzRk+fDhffPEFO3fu5Pz5\n85w4cQIrKysOHz4sGlPh4eEcPHiQoqIi+vTpg6urqyjDXa9ePSZOnCiePzg4mNmzZ4uhG8uWLWPM\nmDE6ctVlr0cmk+Hj4wNoZMg3btwIaCTNv/zyS2bMmPHQ9/1ps23bNry9vcXXKpWKffv28c8///DP\nP//oeIEexZBSqzVGU1lDCmD1aoGePZOxt89AqVSKnqeaNWu+sLl6xsbGTJ06lS+++ILPPvsMV1dX\nGjVqRHFxMUuXLsXY2Ji4uDhcXV1ZsmQJkydPJjk5GT09PQRBoKSkhKNHj9KyZUuqVKlC586d6dKl\nC6tWrSIoKAhjY2OSk5OxsLDgjz/+YPz48YAmtCclJYU33niDZs2a0KJFLHPnarx6v/4qMHduGFeu\nXKGoqIi8vDwiIiJQq9XUrVuXzp07c+LECTrcq5C3hMQrjlqt5ubNm9SpU+eZLALKZDIxJ3ncuHGM\nGDGCrKws4uPjUSqVWFlZYW1tjaWlpc54Dh4EPz9ITATwIiQkis8/1xTJfhwUCgXTpk17vE4kXngk\nY0pC4iEQBIEbN24A3Dfv4UFyph4UmUyGjY0NNjY2KBQK0tPTiY6OxtDQkH379tGpUyc6d+4MaLwN\nXl5eouz2t99+yw8//MC///5LWFgYH374Ib/99hunT59m2LBheN6VIktNTUUmk2FiYoJMJiMoKIic\nnByWL18uGlNBQUHEx8frhEbMmzePyZMnk5GRQVhYGDt37iQjIwOFQoFcLmfNmjVieMO5c+c4duwY\npqammJiYcOXKFZ3rjImJwc7OjtzcXI4fP46HhwdmZmaPpPDWoEEDZs+e/cj3/GkhCAKLFi2qcN+N\nGzf45ptvdAwt7TFqtbrCx4q2XbokIy7OupLzyzh2zIyZM61eqnpnFhYW4n3r2rUr3bp1Q09Pj6io\nKIyMjCgqKhLfJ8bGxhQXFxMcHMzevXvZsWMHBQUFonEF8PPPP5Obm0vv3r0BTb6Ug4MDKpUKAwMD\n0tPTKSoqwtXVFUNDQy5duoSnpyeTJ0+mRo0avPvuu3Tu3Jlu3bpRUlLCkiVLeOuttzA1NcXU1BRz\nc3NR/ENC4nXl1q1bWFhYPDe1Wu3nsXr16igUCrKysrhz5w6xsbFYWlpibW1NYqIVvXvro1BojzIk\nJ6chY8dC9eo8sFhGRcjl8qcaDi3xYiAZUxISD0hpQ6pu3br3XWXT19d/YsZUaeRyuSixnpOTw5Ur\nV2jWrBlxcXGixHpp2e1vv/0W+M+j0759e+rUqcOtW7dEQwo0ykSlv/SnT5/OL7/8wp49e3TOXzYy\n+O233yYiIoLRo0eL4UzVqlXjwoULfPDBBwiCwOzZs8V6Wv7+/nh6etKoUaNy11Y6HKpt27YsW7YM\nT09PkpKS2Lx5MyUlJcycOVMUfiguLq40hOpFDYVMSUkhJiam0v2HDh3C3d1dx0DS09NDJpOJj6Wf\nV/SYn28EVGxMAZibWz5UamFoaCg//PADtWrVomPHjjq1sAA2bdpE+/btqVu37n37qSxn6ccff8TG\nxoaWLVtWeOy+ffv46aef+OOPP+jUqROjR49GpVJx/PhxnJ2dmTp1KmlpaSQmJqJUKtm2bRvx8fE0\na9YMmUzGmDFjkMvleHh4sGPHDqZNm8Ybb7yBr68vq1atIj8/HwsLC+rUqSN6mG7cuMH06dMpKCig\nY8eO2NnZERgYSGpqKkePHkWhUFC7dm309fUxMTHRqRkXFRUlin9ISLyOFBUVkZGR8ViiE08SuVwu\nCj4plUqys7PJzMxk4UI1CkXFeaTLlz+eMSXxeiAZUxISD4AgCMTExKCnp/fA4Qr6+vooK9NTfkJY\nWlrSvHlzCgsLMTU1JSEhAYVCQWFhoRgfrjU28vLyAM0KfEBAAF9++aVOXwUFBTqrh8uXLyc6OppN\nmzbRv39/vv/+e3r16oWzs7POcSEhIfj4+HDgwAF69eoFwPnz51m0aBGtW7dmwoQJeHt7Y2trS9Om\nTRk3bhwBAQE4OjpiYWGhIxduamqKWq3Wub/+/v4sW7aMVatWiducnZ0ZP348OTk5YkhWWdLT01/I\nuh4WFhbI5XIU/y2D6lCnTh0aNGigYyA9LM7Omr/4+PL7ZLKHnxxojRGtERQWFsaZM2fIzc1l/Pjx\nJCcnU1hYyPz588nLy8PAwAA3Nze6du3KmDFjqFu3Ll27duX333+noKAA0ExsSod/ZmRkoKenx/bt\n2zlx4gR169ZFT0+PgIAAAHr06IFaraZDhw706tVLFHoAmD17NjY2NoSEhJCWlkbz5s05fPgw4eHh\n2NraolKpMDQ0ZNKkSaxZs4bCwkLi4uLIysri008/paSkBBMTE/T09BgwYAA7duxgwYIFREdHA5ow\nzIyMDK5evYogCFSpUoXhw4frGE/az5N2TMeOHRPDWSUkXkcSEhJwdHR8YQpyl8bAwEAUd7p9u3Lp\ngDLBExISFfLivcMlJF4wHsWQgqfnmSrLqFGjmDJlCocOHUKlUtGjRw9UKhVXr17FwcGBVq1aMXv2\nbEpKSjAzM2P+/PkYGhqyZMkSunTpwgcffABowuJ+/PFHnb7ffvtt0tLSqFu3LivvqhbEl5qhx8bG\ncubMGbZu3Yq7uzsBAQGsXbuWixcvMmvWLKpWrQrAJ2WqjX711VcVXouXlxf79++nZ8+eYhhho0aN\ndAwpQEc+vDTaiSzA999/j5eX133u3rPH1NSUfv36VXgPZDIZI0aMeOzJh56eRmTCw0OTVF2a8ePB\n1fXh+9ywYQMHDhzAz8+P9evX065dO0xNTTl37pzO+Pv378/777/PoEGDaNWqFQYGBvTs2ZMPPviA\nmJgYMWepW7duOuGfpYVaOnXqhIeHB4MGDdIZw6lTpxg3bly5sb3//vtERkby9ttv4+joiEqlolWr\nVuTk5IiFsc3NzUlNTcXLy4uBAwfSpYsnn39uzZYt8Xz7rSlt2jgxc2YuRkZG9OjRA2NjYwoKCkhN\nTSUrKwtLS0ucnJzK1aGqjJEjR0rqfRKvLRkZGajV6hdWAKg0Tk6V/6bXqvUMByLx0iIZUxISpQgN\nDWXXrl1iQc1Zs2aVM6QWLFhA3759adiwIQC3b98mLCyMSZMm6fRVOmeqpKSESZMmsX79etzd3XF3\ndyc+Pp6NGzfqTCLd3d157733kMlkrF27lgEDBugYCKNHj2bz5s065zE0NGTt2rU62zp06IBarSYj\nI4N27dpRUlIiFkKVy+XlLzwujkbh4Wzcvx+6dhVrc0B5w8fZ2Vk0dOrUqcPWrVsBTVHYtWvXolQq\nycvLEw2ph6G9sTEXFy3SFCWqUUNT3XX4cI075SHR3ssXkRUrVvD333/zzz//iNtkMhkrVqwQ31eP\nS69ecPIkrFgBFy/+dzuHDHm0/saOHSvm5ZmYmOh4FLW1rgAxD0sQBOrVq8eaNWvYtWsXP//8M3Xq\n1BHDRMuGf27fvr3CPkpT1nuq5c8//6R///7MmzePkpISBg8ezDfffIOnpydKpZLk5GS2bNlCbGws\n3t7eREZG06zZdaKj6wN6FBXps3u3KeHhKn75pR6uroWiwp+9vT0NGzZ8aAP3RX3vSUg8bVQqFTdv\n3sTFxeV5D+WB8PWFsLCK9/n5PduxSLycSMaUhEQpZDIZvr6+dOnShUGDBhEdHc3EiRM5dOgQmzZt\nws3NDYCNGzeiVqtp3rw5rVu3JikpiZKSEkaPHo21tTW2trZ8+umnojF14MAB2rVrB2iMkXXr1vHd\nd99x8eJFHWPK2dlZVKOriMaNG3P27FmaNWt232vR09PD3t4ee3t7UWL92rVrmJqaitLSMpkMFi6E\n+fNBrWYCUNi4MSZDh8K2bRoXx0OSm5vLnDlzHvo4/u//YMAAmmhdKYmJcOYM/Pkn3OOeVMaLPJmt\nUqUKf/31F7t37+bkyZPY2toyZMgQXB/FZXQPmjfX1LZ+EpQ2bIYMGYKPjw8mJibl8p9Ke26vXLnC\n1q1bKSoqon379ri4uLBo0SKUSmW58M/K+ihNgwYNuH79OlWqVCEhIYGJEydSXFxMgwYNePvtt3Vy\nlBwcHPD19cXAwIDGjRtz7tw59u/fT25uLgsWxLB9+1bgC8BYPCY3V585cxSsX59NjRo1Hkn4RELi\ndefWrVtYW1u/NAI3H32kWXSaMQNKSjTbZDKNIeXr+1yHJvGSINWZkpAoxfbt29mzZw+pqal07dqV\nAQMG4O/vzw8//CAaU7/++iv/+9//aNWqFX369GHFihWsW7eOjz76iLS0NIYNG4a3tzcrV64kJiaG\nt99+m+nTpzNu3Djq1KnDBx98QOPGjTl//jynT5/W8RQ1a9aMd999FycnJwICAnTyQgBOnz7NiRMn\nmDlz5iNdnyAIZGVlkZaWRkFBAY5XruBQmati1SooJVv+VFGpoG5dSEioeP+VK/CEPDYSLy4pKXD6\nNFhZQatWUColCdDUqwkKCiIwMPCxztOunYpjx/SBn4BcoJ+4z9xcIDf3xRQvkZB40SkoKCA6OpqG\nDRvq5BS+DNy5A3v3gkIBnTrBS+JYk3gBkDxTEhJl8Pb2xtnZmUmTJjFr1ixRwKG0zLF2DaL0CnpF\n6xJaEYrS4UlOTk5s2rSJdevWceDAAfLz87lw4QLTp0+nVq1a9/RMWVhYPJbcclmJddU9pMPVmzZR\nPHq0TlhW6edlHx90W0X7DC5fxr4yQwpg/37JmLoHpQslR0ZG8t133zFv3rxK26vVavr3769jqPv7\n+7N06dJ7nmfbtm24ubkRGRnJrl27qFevnuiJrFOnjthu+PDhrF+/HlNTU1asWMGpU6fYvXs3AOPH\njycoKEhnEUEQYNo0WLcOUZ64Vi3Yvl2zaqzFxsZGlDK/FyUlJSgUCoqLi8s9avbVAyyBakAnnWPl\ncsmQkpB4VBISEqhRo8ZLZ0gBVK0Kd0sWSkg8FJIxJSFRCrVaze3bt3nrrbfo1asXe/bsoUaNGgQF\nBXHq1CkxdCwsLIywsDB63pVFk8lkfPLJJ/j6+vLPP/9Qq1YtLCwsRBGK0uFJWkaPHk3Pnj05dOiQ\njkR5aRISEvC7G7Q9YcIEMcn+SSCXyyEzs9L9QlIScXFx4vVpH0s/f5B9D9L+vsGEL+EP8/NCa6QW\nFhayaNEisrOzady4Md7e3rRo0YIePXrQtm1bQFPX6vPPP2fFihXExsYC0LJlS/r06cP58+dZvnw5\njo6OYt/Hjh1j+PDhREVF4efnR+fOncnIyGDy5Mk6OU+lOXv2LC4uLiQkJIjS6nv27KF///5im+XL\nNWE2pUlIgG7d4N9/NfleWt59912USmWlxpJCoUBfXx+5XI6RkRFyuRwzMzNsbGzE1yNGyDh5EqB8\nQeS+fR/hpktISJCWloZMJsPOzu55D0VC4tkiSEhICIIgCCqVSoiMjBTi4uKeWJ+RkZFCbm6ukJGR\nIcyePfux+/Pz8xMKCgqewMjuMmaMIGgcA+X/2rZ9cue5H2q1INStW/E4ZDJBiIx8dmN5CQkNDRU6\nd+4s+Pr6CgMGDBAWLFggFBYWCjNnzhTmzp0rtG/fXhAEQXwUBEF45513BG9vb0GhUAiCIAh9+/YV\nBEEQOnXqJAiCIPz444/Ct99+K7bPysoShg4dKp7vwIED4r6BAwfqjMfLy0vIy8sTTp8+LSxdulT4\n+++/hblz5wqCIAi3bt0SfHx8xLZqtSDUqFH523D69FwhISFBiI6OFiIiIoSLFy8Kly5dEq5evSrE\nxMQIiYmJQkpKipCVlSUUFhYKKpXqvvdLoRCE9u3Ln6t2bUFISnrw+y4hIQiCsG3bNqFr167CmDFj\nhIkTJz7wcdOnTy+3LTY2Vpg2bVqlx0yaNEkoKCgQhg0bJvj6+goTJkwQxo8fLxQXF5dr+8EHHwjf\nfPONIAiCcPz4cWHdunVCZmamMGDAAOHatWvC7NmzhZSUlAce770oKSkR/v777yf7+yQh8ZIgeaYk\nJNB4pK5fv46xsXG5OkqPREwMzJ2Ly+7dyABZt270LrUS/6g8cbnl8eM1QhOFhTqbBZkMpk3jmQU8\nyWSwZo1Ggk6bAaxl6tRH0/J+zdCq7WnD/A4dOkSDBg3w9PTkf//7HwBWVlZiezs7OwoLC8nIyNBR\nXtTWJzM0NKS4uFjcXlRUJO4rTXp6OnK5nNOnT4u1yLRs27aNgoICbty4wdmzZ5k/fz7m5uY6oao5\nOQJJSZW/0yIjZRgZGWFhYSF6lh43hMjQEA4cgKVL73D0qC0lJYZ07AhjxoC0qC7xsJQWLtJGGYSH\nh+vUUatZsyafffYZrq6unDt3jp9++kn0Bk+fPh2VSkWtWrXo1asXf/75JzNmzCAxMZFvvvlGPE9q\naioymQwTExNkMhlBQUGYmpoSHh5OcHAwEyZMENv++eef9OjRgx9//JGBAwcik8m4desWPj4+rFix\ngho1ajBw4EC+/PJLZsyY8dj3ICkpCRsbG6kcgMRriWRMSbz2aA0pExMTaj2JohI3b0KLFnDnzn/h\naz/8wLvHjsE770C9eo/c9RNXqHNz08wqx4yByEjNturVSZsyhWxXV+oJwiMVjX0kunTRqPetWqXR\n8q5eXaPlLcVdPRBCmZy9Jk2aMHPmTG7fvo1arS7X3tbWlrVr1zJ27FhWrlx53/9z1apVySwVFhoc\nHEx4ePhddbwFODs707x5c0BTYkBb6Fabl7V27VoOHz6Mra0tLi4uJCUlkZeXR35+IXZ2b5GeXvHP\nUcOGZjg4lDfiHhdBKKZ79zvMnu3wKMr7EhI6hISEsHfvXmxtbQFYs2aNTh21I0eOsHTpUqpXr06H\nDh3E465du4aRkZEoqhIXF4erqytLlixh8uTJJCcnU61aNQDOnDlDo0aNxGO1n/n333+fvXv36oxn\n+/btzJkzh2XLlnHjxg0EQWDr1q2sXr2aGnfjZhs0aMDse+TNPij5+flkZ2c/sbIOEhIvG5IxJfFa\no1KpiI6OfnKGFGiSP+7cKb89IwOWLYPg4CdznifF//4H167B5cua7P8mTbDX1yfnxg1iY2OpW7fu\nsxvLu+/Cjh3P7nyvCMOGDROfv/HGG6L4RNjd4in+/v5A+aLGgLjyrX2tbVN6wqfFzc2NlJQUhg0b\npnPOsnz55ZdiX4WFheTn59OtWzfy8vJYs2YNnp6e6OvrU716dczMzBg7Vo/PPivfj1wO3t4Pdg8e\nloyMDGxsbJ7dYoHEK82oUaPo0qULX3zxBZcvXy5XR23atMc6KBYAACAASURBVGmVChfplSpBIZPJ\nRA+ysbGxjne4oKCgQu/w2bNncXNzY+fOnaKY0YkTJ1CpVKSnp7N161Y++eQTAgIC+Pnnn7GysqJj\nx45P7L2fkJBAzZo1X0rRCQmJJ4FkTEm8tqhUKq5fv46ZmRlOTk5PruPw8Efb9zyRyaBx4/9eAnXr\n1iU6Opr4+PgnE/oo8dKQmQkhIfDLL2BhAYMHQ48eGhGU7OzsSo9Tq9Xk5+eTl5d31+uUj6GhIebm\n5pibm1OtWjV8fHzKeVg//RTi4mDnTk32EoC1NYSGahTznwbp6enUewwvsYREaYKDgzly5AgZGRmM\nHz++XB01b29vZs6cSf369XUKTzdo0IDCwkL8/f1xcnKie/fu96yz9uOPP4qvp02bhqGhITKZjOXL\nl2NoaIinpyc7d+5kzpw5oshL9+7d+fjjj9HX1yckJAQfHx/UajXNmjWjevXqj3Xdqamp6Ovrix45\nCYnXEanOlMRryVMzpACaNYNz5yre99Zb8M8/T/Z8TxFtCKSZmZlOcWGJV5fERE2Np/h43e2enhqp\n8tLzPIVCoWM4FRcXY2pqipmZGebm5piZmWFg8OBrdtevw4kTmjpTXbrA06r5mZeXR0JCAg0aNHg6\nJ5CQKENmZiarVq0iIyODjz/+WFSCfVjGjBnDhg0bnsiYNm7cyHvvvYe7u/sjHa9UKomIiOCNN97A\n2Nj4/gdISLyiSMaUxGuH1pAyNzd/OgbCypUwZUrF+wID4QnEqD9LVCoVUVFRWFtb68hkS7yaDBkC\nX39d8b5du4po0SJb9D4BotfJ3NxcTIx/0YmLi8PU1BQHB4fnPRQJiQopKoJvvoEff9RUhujVC/r1\ng5iYf3F2dn4iQg9//fXXY+XhxsXFYWhoKOZgSUi8rkjGlMRrhdYwsLCweHqelqIi6NhRs8RemmbN\n4OhRKBXi8bKgVCqJjIzE3t5eR/lN4tVCrdZ4g0qlaejQvXsW69blip6n0oV3XxbUajWXL1/mrbfe\neiivmYTEsyIvD9q31+jxlKZ9e41e0IvwscvLyyM2NpaGDRvq5HxJSLyOSJ8AiadOREQEgwcPZuLE\niSxbtuyhj9+0aRM3btx4rDFERUWxdOlSjh8/joeHBwsXLmTIkCHlFNDu3LmDvb09SUlJACxYsICI\niAhx/5o1a3ReV4ixMfz8sybho0cPTeXRkBD49deX0pACMDAwwNXVldTUVNLS0p73cCSeEipV5YYU\ngFxujZOTE7a2ti+lIQWacCsLCwvJkJJ4YQkKKm9IgSblNiTk2Y+nLIIgkJCQgJOTk2RISUggCVBI\nPAPCw8Px9PSkY8eO4rZFixaRlpZGXl4eK1euZMqUKVhZWdGoUSPc3d2ZP38+tWvXZujQoSQnJ1NY\nWMiZM2fYuHEjoIkbLywsZOXKlbRs2ZLbt2+zYsUKRo8eLfYzdOhQ8XwbNmygf//+6Onp0alTJ5Yt\nW8aYMWPIzc3F0tJSbLdjxw6WLVvGtm3bmDNnTrlrGTp0KJ9++ilr166990XL5TBsmObvFcHQ0JD6\n9esTFRWFvr4+NjY2z3tIEk8YQ0P46CON3V8Rn3zyLEfzdEhPT5fC+yReaL77rvJ9334LY8c+u7FU\nREpKCnK5HGtr6+c7EAmJFwRpSUHiqTNy5EhOnTqFt7c3mzdv5t9//+XkyZPY2Nggl8u5du0ad+7c\n4X//+x/9+/cnJSUFOzs7Bg0apJMgvm7dOkJCQti8eTPr169HJpPRsmVLpk+fTnJyMoBOP1qUSiWX\nL1/G2dmZqlWrit6pgoICHUMKNHU8hg8fzoULFyq8Fmtra+LLZua/RhgZGVG/fn0SExPvqeom8fKy\ncGHFYUSNGmlU/Z41oaGhNGnSBICSkhKcnZ05ePDgPY+pLHpdoVBQWFioU7y4NCUlJYy9O1N1d3dn\n4sSJ+Pr6smbNGp12Xl5eZGZmkp2dTbVq1VCr1cTGxjLlbq7kwIED+eKLLwAoLi5m/PjxD37BEq89\npWpaP9S+Z0FJSQnJyclPXrhJQuIlRjKmJJ46FhYWBAYGsmXLFvbv349araZhw4bMnTuX9evX8/77\n7xMWFoYgCHh5edG6dWtmzpzJ7t272VGq5lDpCZL2uelduS/t69L9gMaQioqKwsjISJSAbdu2LT/8\n8AMqlYqkpCTmzZtHQEAAv//+O0lJSfj5+ZGamkp4JTLmr3pYQ2hoKN26dcPLy6vCsExjY2NcXFyI\nj48nNzcXAA8PD502ZSel48aNo1u3bty8eVOnnfa45cuX89lnn9G/f38EQSA6Oppx48Zx8eJFgl+0\nulyvOC1bwvHjmrQ/uRzs7GD8eI236mmp690LmUyGm5sbp0+f5sCBA3z44YeApiip1qgaOHAgAC1a\ntGDp0qWcP39efJ6QkMDs2bPx8/Pj66+/JikpieXLlwMwZcoUnffkgQMHaNeuHQDOzs6sXr2a4OBg\n7ty5w+XLl8V2bdq04bfffuPEiRP069ePc+fO8euvv/Lxxx+TnJxM1apVuXTpEqBZgDAzM+PWrVtP\n/2ZJvBLcfQtWSLNmOSiVymc3mDIkJiZSpUoVjIyMntsYJCReNKQwP4mnzr59+zhy5Aj/z96dh8d4\ntQ8c/072ySQiiyyIJIIQa7W0anu7qCpFbRVLG1WRICGWKKWWootGSgWlraC11FYUVW0t1Ve1CCqC\nJL8kIrtskswkk2V+f0znaUYSbzGycD7X5ZJ51jNPlnnu55xz3yYmJrRt2xZvb2+MjIyYMWMGKpWK\nuXPn8sEHHyCXy/Hy8uL48eMcOHCAnJwcXnrpJeLj4wGYMmUK/v7+AEyePJni4mK9zGGlpaXMmDFD\nOo4ukLK1tcXOzo7y8nK97UNCQli+fDmffvopAH5+fuzZs4fGjRuTlZVFcHAwnp6evP/++9ja2tK1\na1d8fX0f+RSwMpkMf39/+vfvz7Bhwzh06BDHjx8nMzOTFStWsG/fPho1akTv3r0ZPnw4u3btqnSM\nO29KV69ezfbt24mMjKyU+GPevHm0bdsWHx8fjhw5wscff8ylS5f4/PPPsbKyIjQ0VPq+CzXj2Wfh\n8OHabsU/hg4dyu7du1Eqlbx0l7GGCoWCkJAQEhISaNu2LSEhIWRkZKBWq3FycmL79u3s3buX7du3\nk5eXx+3bt/V+Hk+dOsWUKVMqHbdLly5cu3aNDh06APD8888TFhaGXC4nJCSELVu2cP36dVatWkV4\neDijR4/m9OnTHDt2jOeee46OHTty8uRJRo4cafiLIzxyZs+GPXvgzs5/JycN48cXEBUVj4ODA87O\nzjVaKPf27dsolUo8PDxq7JyCUB+IYEp46AYNGsSgQYP0lt3Z4xEeHq73+j//+Y/0te5JNEC3bt30\ntuvduzcA26ZNg6VLCXd1heHDKWnWjGvXrmFnZ4eLiwu+vr7s37+fwYMHS+du3769FEgBrF+/Xvra\n3t5er1dM5/DhwwwZMuTfvO16bcOGDezbtw9fX1+MjY3RaDSUlJTw008/SQGpro5QXFwcZWVlevtX\nvClNSkpi4sSJnD17ltOnT+ttFxcXR0lJCUuWLAGgb9++bN26FR8fH6mwpUKhIC0tDWdn54f9toU6\nSpcG2tnZWeoZNjc3l57QF/499qni8D3d119//TUDBw7E29ubn3/+GUtLS4YMGcLIkSN5944yBUql\nUq+gqs6ff/7J66+/zoIFCygtLWXp0qUkJCTg4eFB06ZNSU9P5/bt21hbW7N3714SExNRKpWcP3+e\n5557Dmtra9LT0w1/YYRHkpcX/PorLFigzd5nZKRNjf7++zJatGhMSUkjUlNTuXz5Mk5OTjg6Oj70\nERMajYakpCRcXV3rRfkDQahJIpgS6reSEvDxgd27pUWauXPJmTQJ+3nzpBvwPn36EBkZ+cCnc3Z2\npl+/fg98nLrOz8+PV155BYDBgwfz3XffsXnzZpRKpd5NrFqtxtXVlYKCAoqLi6WhHxVvSl1dXfn8\n889ZvXo133//PYWFhZw/f55Zs2bh6enJ2LFjCQgIYM2aNchkMpo3b6735NPa2lq6WRYeX7qHIJs3\nb0Ymk9G7d29CQkKIj4+vNH+v4s3es88+y7p163BycpJ+JgcMGMAnn3xCjx499Pbz9vYmJiaGRo0a\ncePGDaZOnUpxcTHe3t506NBB6pkCcHR0xN7eXjqfl5cXv/76K0OGDGH27NkAjB07ltzcXK5fv07P\nnj0Nf1GER1b79treKY1Gv1A2aJMBNWvWDCcnJ1JSUrh8+TLOzs40atTooQU66enpWFhYVDvfUBAe\nZyKYEuq30FC9QApAptHgGB6ufZRXoTdDN4n9QRjiGPVBxflp3t7eLFu2jOjoaPr06VPpJtbW1ha5\nXM7169fx8vLCzMxM76ZUx8/Pj8GDB3Po0CHGjh0rLR84cCDGxsZMnDixyvlRqampYliJgUVERLBy\n5UoiIyMpKSmhRYsWrFmzhvLycmxtbYmLi8PBwYH+/ftXe4zhw4ezc+dOvWWbNm3S2+/cuXPExcUx\nYsQIaZtjx46Rnp6Oi4sLCxYsoFOnTuTl5REUFKT3+7Vw4UIuXLjAd999x61bt7C1teW3334jPj6e\nH3/8kR9//BFLS0veeOMNfHx8eO+996T2uLm5ScHXM888Q9euXfnrr79YsmQJxcXFBAUFERISUuk9\njRkzhtDQUJ599lnOnDlz12tYsSc7LCxM+rpi0LRlyxYALl26JCWnEIR7cWdsFBERwe7du2nWrBmm\npqZ8+umnqFQqUlJSpN8re3v7ew6qEhISCA8PrzRqZOPGjXh6enL8+HHOnDlDy5Ytyc/PZ968eXp/\nlxMTE1m0aBEKhYKioiKWLl3KX3/9xTfffEODBg0wNTVl+fLl7N27F7lcrpfdVxDqOxFMCfXb3Ypu\nfPEFvPBCzbXlEfHmHencly1bVmkb3U3itGnTAO28uIyMDGJiYvDy8mJMp06EzpvHswMGsPPvyf5m\nZmYcOnRI7zi6m9/+/ftLN+ALFiyQ1mdlZeHk5PTIJ/2oaRWTOqSlpUlDabOzs/WudUREBEqlkszM\nTIYNGybNbdu2bRsAt27dYtKkSTRv3pwBAwZU2q9t27ZkZmbqnXvLli2sX7+e3377jeHDhzN58mTU\najU+Pj7srvBgRCaTYWVlRXJyMt9++63UUyqTyXj33Xfx9vbm+vXrLF26lNDQUFJSUlCr1VXWv8rN\nzUWhUGBqagpoa9eVl2ufw+zYoa2z3bcv+PraGnwYr1qtZubMmQY9pvD4qjinVfdQ6tSpUxw8eJD8\n/Hx69uzJzZs3iY6Opk2bNhgZGTF37lx69OjB0KFDOXv2LJ988glqtZrQ0FAAPD09cXBw4L///S9r\n1qxh0qRJ0vl+/vlnevXqRcOGDZk8eTKvvPIK2dnZBAcHs2nTJmm7hQsXEhoaip2dHaBNQvTZZ5/x\n3XffAdoe5Z07dzJ06FDeeustEUwJjxRxhyLUb3+nRK9SamrNtUPA0dEROxsbCoYOxbZXL4YcOwYz\nZkDz5rB06X0dU6lUMmfOHAO3VIB/kjocPXq0yqQO4eHhmJmZSTdWVT3pzsvLw8TEhMGDB9OjRw80\nGg3h4eGYmprq3ZBVlJWVValgrpmZWZXZwcaMGcPmzZu5evUqXl5e0nJdz2mrVq1I/fv33NPTs9qS\nBllZWdKQPNAWJx4xAoYNg5074cABmDIFnn4a3Nw6V3mM+2VmZka7du0Mekzh8bZhwwYmTJggBS6r\nVq3Czs4ONzc3kpOTcXBwoGvXrgwZMkT6nWjQoAHBwcH4+Phw4sQJ1q5di0KhwN7ensuXL9OjRw+e\nffZZvd/bvLw8ysrKKCoqokGDBtLvnZ2dHSUlJXptUiqVUntA+wCjYvr0rl27EhUVhZGRETk5OQ/t\n2ghCbRDBlFC/db7Ljc+TT9ZcOwQAXHbtouH+/QBI35nycpg3D37++Z6P5+rqqjdUUDCcqpI6VOTp\n6cnly5cBbbKHkpISNBoNKpVKb5tVq1Zx8eJFFi9ejEwmw9PTk6ioqGrPW9W5iouLKS4u5tq1awQH\nB0tPs11dXbl48SJdu3at8ljXrl2TSh5UN7eupKQEpVKpV2D0228rjQ4GICoKFi+utumCUCdMmDCB\nDRs24OLiwqVLl9BoNLz77ru89957vPvuu8jlclq0aEGTJk1QqVRcvXpVelhhampKcXEx5eXljBo1\nigULFrBhw4Yqfy+VSiUajYZmzZrpPUzJysrCzMyM06dPExwczMmTJ7G0tCQ7O1vapkGDBiQlJUmv\n//zzT9q2bQtoHzDUZnp3QTA0McxPqN9mz0bz22/I7izSaWVV+2XiH0cV5pFUuU4Mu6xTKiZ1uNPL\nL7+MkZERc+bMYeTIkaxatUq6GdK5fPkyX375JUVFRbz44osUFBTo7VfVUJ6Kw/B27txJTEwMeXl5\nzJ8/Hy8vL2n+0cWLFwHYunUrRkZGLFq0SNpv2bJlODg4oFKp+OijjwCIjY2tVO8MtDd+tra2ejeD\n27dXf022bYOVK6tfLwi1bd26dRw5coTs7GwCAwMJCgri7bffxs7OjqeeegrQ9iTb2NhgY2ODk5MT\nSqWS69evU1RUBGhLjcydOxcXFxesra2ZM2cOsbGxfPrpp9LwbY1GQ2FhIdbW1tJ5jx49Sn5+PosW\nLcLNzU3KsOvm5saMGTOwsrJCrVbz/vvvM2XKFN566y29OVO6tt3ZOy0I9ZlMU12peEGoB9LT0ynb\nuBGX1auRJSdrF3boAGvWQPfutdu4x1HDhpWLo+h07w6nTtVse4Q6Z8OGDXTp0oVOnToZ7JilpaX4\n+/vzxRdfVFoXFRWFu7s7CoVCWvbii9V3lFpagkgeKTyKsrKySElJQS6X06RJE6l3uqIbN8DcHBo2\nLObq1avs3r2bKVOm4OjoaJA2XLlyhYMHDzJr1iyDHE8Q6gIxzE+ot3Jzc8nIyKDRjBnIEhIgMhKi\no+HiRRFI1Za73SAb8OZZqPs2bIAnngBbW20RYF3iP19fXykRhKHosovdSTfsr2IgBdpgqjp9+hi0\naYJQZ9jb29OuXTtsbGyIiYkhPj6e4uJiQFvPqm1bcHPTJsHt0aOcrCxXgoODUSqVBmuDkZFRlYWx\nBaE+E8GUUC8plUoSExPx9PTU3piZmGhv1lu3ru2mPd6qe9ool0NgYM22Rag106eDnx9cuAC5uXD6\ntDbhQ1iYds7GncMFH5StrS3u7u6Vlt+ZeELHzw9cXSvP2ZDLYf58gzZNEOoUmUxGo0aNaNeuHXK5\nnKtXr7J9exqDB2u4cuWf7c6elTNsmC0FBVX/bt2rTZs2cfDgQRQKBW+++SapqalVlif4X3Jycnjn\nnXcAKg3rXbhwIYMHDwa0mUYbNmxIVFQUCxcuZOTIkQQEBLBu3TrUajWB4vNIMCARTAn1jlqtJi4u\nDnd3dywtLWu7OUJF/fvDxo169b3w8tI+9qyQjU14dCUmVj/naOFCKCiomXZoNBpycnKqDKYgm40b\nY3nzzXIUCjA2hldegRMnRN4a4fFgZGSEs7Mz7dq1Y926hpSVVc7WmZMjY/Vqw50zOjqakJAQKXlG\nfHw8AD169CAsLIzRo0eTmppKdHQ0Pj4+LFiwgH79+ukdIyIigtdff73K41csp7Blyxa9cgrz589n\n7dq1+Pv7Y2ZmhkKhICUlxXBvTnisiRmAQr1SVlZGbGwsTk5OohJ7XeXrC6NHa7slzMygY8fabpFQ\ng378UZvAsSq3b2unzdVEiZnc3FwsLS0rDSksLCwkKSmJ7t29eOEFIyIiQKOpXBxVEB4HxsbGnD9v\nXO36//63HEM8d9doNISGhrJ3795Kn926tO3ff/89J06c4OzZs3z88cc0btyYvn373tGe/961V0lX\nTiEhIUGvnML777+Pra0tvXr1wsfHh44dO3Ly5ElGjhz5wO9NEEQwJdQr8fHxWFlZGWwyrPCQmJpC\nly613QqhFlRRM/ee1htKVUP8dL3aHh4eWFhYSMtFICU8zhwcID+/6nWmpvlcvJiAXC7H0tJS+t/C\nwqLK2nPVkclkrFq1itDQUObOncsTTzwhrdPNadSlbYd/asndeY6ysjK9TIDXrl1j3bp19O7dG9CW\nU4iIiKBPnz7cvHlT2m7+/Pl6w4utra1JT0//1+0XhLsRw/yEeuPGjRsANGvWrJZbIghCdQYMgApx\nih4HhxLc3ZMqFfw0tJKSEgoLC/VqS5WXlxMbG4uzszMNGjR4qOcXDCMiIkK66S4pKcHNzY2DBw9W\n2i4nJ4eJEycSGBiIn58fVypO/qnGnfNtdPN3dMurSrM/ceJEAPr160dQUBCTJ09mfhWT7CwsLDhz\n5gwAc+fOlY61bds2/Pz8CAwM5MMPPwS0BXfvVpftYSstLWXQoOqL6E6bZkPbtm1xdnbG1NSU27dv\nEx8fz4ULF4iOjiYhIYGMjAzy8/MpKyu767kUCgVbtmzhgw8+4OzZs9UGY2+//TbvvPMOixcvxsrK\nSm+dh4eH3vA8XTkF3Vwp0JZTGD9+vN5+77//PgEBAYSGhgLaIKxDhw53ba8g/FuiZ0qoFzIyMigo\nKKC1SDAhCHWavT2EhsKUKdrhczqmprBmjRHm5kZcuXIFOzs76QbN0LKzs2nYsKFeIVLRq13/yGQy\nWrduzenTp0lLS+PZZ59FJpORmJjI/PnzcXR05LXXXmPfvn1Mnz5dGtZVUlLCN998w/nz5ykoKCA8\nPJyVK1dy48YN7OzsWLBgAaDtvQwJCWHJkiXS/J3qREZGSp8/VlZWrFq1CtCm+t+/fz8DBw6Utn3h\nhRfYtWsXnTt3pqCgAJlMRnZ2Nj/++CMbN24EYPHixfz555+88cYbzJ8/n88++8zg1+9/yc7O5ubN\nm0yZYk9UVEOOHtUPbt56q5CBAxWACQ0aNNB7CKEr4K1UKlGpVOTk5KBSqTA2Nq7Ui2Vubs6bb74p\n7fvtt9/q/b/z71SfuiF9OTk5tGzZkuzsbL39APz8/Ni1axdBQUHSfjq67+udr6tKePPXX38xY8aM\nf32tBOFuRDAl1Hl5eXmkp6fj5eVVZZV2QRDqlkmToE0bNf7+M7h9G2xs1MyaNYrhw3sDTXByciIt\nLY0rV65gb2+Ps7OzNHRn2LBh7Nq161+fKyEhgfDwcKZMmcLq1atZvnw5WVlZNGvWjOvXr7N//356\n9OjBW2+9RZ8+fcjPz2fUqFG8WCE/+sCBA9m/fz8HDhzgq6++Yu/evWzcuBF7e3tefvllWrVqxbZt\n2+jWrRuRkZGcOXMGf39/Q182oQpDhw5l9+7dKJVKXnrpJTQaDWvWrGHBggV4enoC2t6divNjdAG6\nmZkZKSkpREZGkpiYSNeuXaVC0llZWUyfPp1Vq1b9q/m3p06dqrI2WpcuXTh69KjeMl1ipO3bt/Pq\nq6+yfv164uLiaNeunbRN165diYqKokuXLiQmJt7jVXkwJSUlJCYmolaradGiBZaWlhw5AkeOwOHD\n2p7lIUNKkMvjKCz0rFRaALSBrqWlZaUkUGq1WgqwsrOzSU5OprS0FLlcrhdkyeXyaj/PbW1t9Yp0\nA1y6dInw8HCio6OxtramS5cuUsHge6VWq5k5c+Z97SsIVRHBlFCnKZVKEhISaNmyJWY1NdlCEIQH\nduXKBlatGiA9bS4pKeHQoUMcP36czMxMVqxYwZEjR/jxxx9p1KgRVlZW+Pj4EB0dzaJFi2jRogUN\nGzYkIyODs2fPsmjRIj744AOmTp3KJ598AoCnp6fe8B7Q/s0oLy/HysqKdevWERAQQEJCAv369ZOG\n+AwcOFAvmLK3tycnJ4fTp0/TvHlzSktL+fXXXwkLC2Pfvn3MnTuXzZs3061bN5544glCQ0NFMFVD\ndIVlnZ2dpZtvjUajN0TMzc2N69ev06pVK0B7s/ztt9+yb98+Fi9ejFKpZOXKlfzxxx+MGzeOrVu3\nolAoMDY2Ji0t7V8FU0qlssqg4o8//qB169asXLmShIQEPvjgA0AbBPr7+xMZGcn69evx9PRkzZo1\n0n5nz56Vfjdq8iHhrVu3SE5OxtHREU9PT+k6ymTaxDD/JIcxJTe3GfHx8Xh7e//rNpqZmWFmZqY3\nxLasrEzqxSosLCQzM5OioiLMzMz0erDkcnmVPdXfffcdI0aM0BsefPjwYdauXSsNvbwXZmZmeoGt\nIDwoEUwJdVZJSQlxcXG4ubmJFOiCUM9cuXJFL1OWqakpRkZGaDQaSkpK+OmnnzAxMWHYsGEMHjyY\nYcOGAdo5EfPmzSMnJ4fly5cD2jkoJ06coFevXqxZswaFQoGFhQWXL1+uFExlZWXh4OAAaOdFqFQq\nmjVrpncz2Lx5czIzM2nUqBEA//nPfzh58iQqlYpevXpx5swZcnNzsbGxYe/evURERHDq1CkKCgqw\nsrJCoVCQlpaGc8USAMJDo/s52Lx5MwCTJk1i4cKFODs7M2jQIObMmUNISAgWFhaUlpYSGBiIi4sL\ny5cv548//qB37958/PHH3Lp1C3t7eymBwtq1a/Hz82Pu3LkVgoqq5/F4e3sTGxtL165dKSwsJCgo\niLKyMuzt7fHz86u0/TPPPMO5c+eQyWTIZDLs7Ozo06cPfn5+mJub06RJE7p06YJGo9FLhvKwFBcX\nk5iYSHl5OV5eXv/qnA0bNuT27dvcuHHjgWpNGRsbY2VlpTf/SaPRUFRUJAVZ6enpKJVKZDJZpeDK\n39+/0jxLjUZDcHAwI0aMwNbW9r7bJgiGIIIpoU7STRZ3dHTUe8IlCEL90LZtW86dO8dLL70EaHsL\n1q1bx3fffcfmzZtRKpUAUvpyS0tL2rRpA8Dly5dxdHQkPT0dR0dHXF1d2bp1K1988QW//fYbY8eO\npX379gB6Q6Q0Gg3Z2dm0adOG4uJiVCoVHh4eZGVl6W0THx8v3Yy1b9+eF198kblz59K6dWt69uzJ\nzJkzadWqFUlJSVy+fJmpU6eSmZnJtm3bmDBhAtbWvcUqgQAAIABJREFU1hQWFtbUpXxs3TlfpuLr\niIgIvXUbNmzQe71u3ToAZv1dSFyX7U1HN99m06ZNgHb+TmIizJ//LSUlVJqP069fP2bMmMGoUaM4\ndOjQXdut29fY2Fg6NsCoUaMYNWqU3rY//PADQ4YMuevxHlR6ejppaWm4uLjc85zBpk2bEh0dTU5O\njkGDFl3QJJfLsbOzk5aXlJRIwwRzc3M5duxYtVn3VCoV33//PWPHjjVYuwThfohgSqiT4uPjsbS0\nxMnJqbabIgjCfZgwYQLTp0/nwIEDlJWVMXLkSLy9vVm2bBnR0dHSMLuKPQJmZmZ4eHiwdetWfH19\nUavVNGrUiN69e7NlyxZsbW2ZMmUKc+fOxcXFBWtra70b7OLiYiwtLTE2Nubq1avY2tqiUCjIysri\n559/Ztq0ady+fZugoCAcHR0JCwuT9j179ix+fn44ODhw8eJFRo0aRUREBBs2bODpp5+mtLSUoUOH\nMmHCBFJTU/Hw8KjZCyo8NJcvg78//Pab9rWLC7z3nnaZjomJCb6+vgY/t7Ozc6XCtIaiUqlITEzE\n2NiYNm3a3NdQeSMjIzw8PIiNjUWhUDz04fampqbY2NhIQy+vXr161+2LiooeansE4d+QaTQV8y0J\nQu27efMmKpWKFi1a3FMdC0EQHi3FxcWkpqZy+/ZtHB0dcXR0vOvcjdjYWOzs7MjKysLCwoKrV69S\nWFhYaSjgg8jKymLp0qWsWLHCYMcUas+tW9C2LWRkVF63eTPUx04PjUZDWloaGRkZNGnSRBr2+iDS\n09PJy8uT5qXVlNu3b9OkSRMKCgoqrTMyMiI+Pl6USxFqnUiNJtQpmZmZ5OXl0bx5cxFICcJjztzc\nHHd3d1q1aoVKpeLy5cukp6dTXl4OgFIJH34IHTqAp6eG2bNt+esvFTKZDFdXV/r06YObm5tB26RU\nKpkzZ45BjynUni+/rDqQAu3PVn2jVCqJjo5GqVTi7e1tkEAKkEaJpKWlGeR4/1aDBg147733qlwX\nGBgoAimhThA9U0Kdcfv2bRISEmjdurXI3CcIQiVFRUWkpKRQUFCAnZ0zPj6NOHVK/6FLgwZlnDwJ\nHTsa11Irhfpk+HC4WyZ+pRL+TihYp5WXl5OSkkJ2djaurq4PJSlDSUkJ0dHRUjr1mrRt2zbCwsKI\njo7Gw8ODSZMmMXHiRPHQVagTRDAl1AkqlYqYmBg8PauuaSEIgqCjUqkID7/NrFlVz6l87TXYs6eG\nGyXUS1OmQHh41etsbCA7G+p6ecP8/HwSExNRKBS4urpKNdsehpycHFJSUmjTpo2o+ygIfxO/CYIk\nKiqK0aNH69Vx0aUrrs6JEycIDw+X/v+3dLUh+vXrx+TJk3nllVfIycmpFEj5+Pjw4d9jLRISEqTM\nTKAdw11d978gCI8uuVzO779Xn5zmwAH4eyTgIy0iIoKnnnoKjUbD1atXKxU61Rk+fPgDn2vJkiXc\nvHmThQsXMnr0aKZNm8bEiRPJzs7W265fv34EBAQwadIkMjIyKp07MDAQtVr9wO0xlLfe0tZYqsq4\ncXU7kCorK+PGjRskJCTg6uqKh4fHQw2kQFtQ18rKiqSkpId6HkGoT0Q2P0Fy9OhRxo4dK1WIB4iL\ni2Pu3LlcuXKF7du3Ex0dTUREBGVlZXTr1o2mTZtK2x46dIjk5GSpuvhHH31EWFgYK1as4Nlnn+WZ\nZ54BIDIyktatWwOgUCgIDAwkNjaWS5cu6aWvTU1NxcnJiQsXLlTZXicnJ1JSUlCr1WJYoCA8Zu52\nkyuTVX+D/CiRyWS0b9+er7/+mq5duwLaG+y5c+eiVqspKyvj008/BbRDtN544w2aNWtG9+7d6d69\nO9OmTcPGxoYOHTrg5+dH+/btGTduHOfOnePLL7+UahGVlpYSFxdH06ZNkclkvPvuu3h7e3P9+nWW\nLl0qFUMGsLKyYu3atdW2+eWXX2bv3r28/vrrD/HK/HudO8Onn0JwsH4A3qNHCUuWVC4gW1fk5eVx\n48YNbGxs8Pb2ltKw1wRXV1euXLlCbm6uKF0iCIieKaGC8ePHc+rUKd5++23Wr18PQKNGjVi2bBnP\nPfccFy5cYMWKFdjb29OoUaNKQc7TTz/NsmXLKC4uBrR1ZXJycvjzzz+lQArg1KlTdOrUCdBWY1+2\nbBmzZs3Cx8dH73ibNm1i9OjRPPvssxw7dqzKsdGenp6cP3/eoNdBEIS6724J+gYNejyCKdCOHvj+\n+++lFNFHjx4lMTERW1tbCgsLSU5OBrSZEZVKJf369aN///5s376dt99+m9WrV/PTTz8B2pvk6dOn\n061bN72/71euXMHV1VV6rZsd0KpVK1JTU/XaU1hYSEBAADNmzKiyvZ07d+bYsWOGuwAGEBQE//d/\n2oQTc+fCgQMq1qy5ilxe97o3S0tLiY+PJykpCXd3d5o1a3ZfgZSvry85OTnk5eXh7OxMeXk58fHx\nTJ8+vdp9dN93Xbr0GzduVCqmW52NGzdy+vRpIiIiePXVV5k2bRrjx48nPj5e2mbLli1s375dej1w\n4EDKy8uZPn06QUFB0vKqiiQLQm0SPVOCxNramiVLlgAwYMAAJkyYINV6sLCwoLi4mJKSEoKCgqSn\nUSdOnKj2eBMmTMDHx4cxY8boLVcqlSgUCpKTk7GwsGDTpk0cPHiQzZs34+XlxU8//cT48ePZu3cv\niYmJKJVKzp8/X+UQFlE8UxAeT8OHw1dfwc8/6y93cIC//4w9NgIDA1m5ciVubm6Ul5fTvXt3AgMD\n9baxsrJiy5YtHDlyhClTptC2bdtKx9ENszY1NZUeioF2jpqVlVWl7a9du4aLiwsHDx6U/m4rFIq7\n9kxZWVnVyb/Zbm4we7bulZz4eCvS09NxcXGpzWbpycnJISkpCTs7O7y9vR9ozlLv3r05efIkMpmM\nESNG8McffxAdHc3zzz/PkiVLuHXrFs7OzrzzzjsMGDCA7t2706VLF4KDg6Xey6VLl3Lo0CF++eUX\nvdEqH3/8MT179iQkJERq488//8y4ceO4fv06AQEBvPLKK2RnZxMcHCwVTh42bBhvvPEGI0eO5MKF\nC3Ts2JHS0lKysrIwNjZGqVRiaWlJx44dOXPmDE8//bRBrqsgPCgRTAmSffv2ceTIEUxMTGjbtm2V\nPUGzZ88mMDAQJycn3N3dad++vbTuzJkzzJkzB7lcjrOzM87OzqhUqkrDOby9vYmMjEShUGBlZYVM\nJmPAgAH079+fyZMn079/f3799VeGDBnC7L8/3caMGUNOTg4//fQTAQEBAKxevZrY2FiDzAcQBKF+\nMTWFgwdh3TrYvFlNYaGMl182JThYe2P8uJDJZPTo0YMVK1bg7u5O37598ff3JyQkhNzcXD777DNA\nm9J62bJlGBsb065dO0aOHElwcDB79uzh+eefv+s5vLy8iIiIkF4vW7YMBwcHVCoVH330Eba2tvTv\n37/KfXU9VQCjR49GLpfrfW7UVU2aNCE6OhoHBwdMTWt3uF9JSQk3btyguLjYYEmann/+ecLCwpDL\n5YSEhLBlyxauX7/OqlWrOH/+PA0aNGDPnj288847aDQaZs+ejZGRkdR7uXr1atLS0vjqq6/w8vJC\noVBw4cIFmjZtSvfu3XnnnXekc+Xl5en1nul6uOzs7PR6tuRyOY6OjiQlJbFx40amTZvGnj17GDBg\nAObm5uzYsYNx48ZJvZsimBLqChFMCZJBgwYxaNAgvWU7d+4E/kkYAdqu+Ip085wqzncCmDNnDiNH\njqz0QdSjRw+mTJmCr68vuyrkpD148KD0dc+ePenZs6f0+uuvvwa08610SktLKSgokOpfCILweDE3\nh6lTYcSILDQaDY0bN67tJtWoN998U/p6T4X0hRs2bNDbTvd3fNWqVXrLN2/eXGm7yEhIS5tIejrY\n2UH79tCwYUMsLCxQq9UsWLDgrm3SnUvn0KFDeq/nz5+v93lSV5mZmdGoUSOSk5Nxd3evtXbcunWL\n5ORkHB0dDVp/0c3NjYSEBDw8PGjatKlUlDcmJgaZTMbixYs5deoUAJaWllIP0529l+bm5gwcOJDO\nnTtjaWnJiRMnpBEtOkVFRVUGgFlZWZiZmXH69Gm+/fZbXnvtNcaNG8eaNWtITU3Fw8ODqVOn4ujo\niEwmIzExkXHjxtXZ3k3h8SWCKeGh+eCDD+DQIejdGyIjoXFjSnx9Serbl0mTJmFubv5Ax8/Pz2fe\nvHkGaq0gCPVVeXl5jU7AfxRpNDB+PGzc+M+yBQtg8mRYvRpmzJjB7du3H7gI7ODBg/USF9Vlzs7O\nXL58WRpeVpPUajWJiYmUlZXRqlUr5A+h2JWjoyP29vaAtoezdevWtGjRgosXLxIaGsqtW7cq7XNn\nMDdnzhw+/PBD5HI5Tz75JB06dKi0j5OTEzk5OdLrdevWcfToUfLz81m0aBFubm5069ZNWj9x4kRm\nzZpFYmIirq6uUqbgmTNnEh0dzfXr16s8jyDUFlFnSnh4Nm8GX1/tp3QFxUOHYn63KomCIAj3ICkp\nCXNzcxwdHWu7KfXWhg1Q3bz+rVvhjvxAj41bt26RlZWFl5dXjZ0zIyOD1NRUnJ2d683Ii4SEBIyM\njCgpacbJk9oaXf36/VPweNGiRQQEBBjkd3TSpEmEhoY+lABTEO6HCKaEh6O0VDtxISWl6vXnzmlz\n0gqCIDygGzduYGlp+cC9Jo+zZ56BM2eqXvf885UTfTxOoqOjcXFxeehpwIuKikhMTEQmk+Hm5vbA\nozdqUnFxGa+/fpv9+xui0Wh7r+zstD2dAwciZQ40xJDJc+fO8eSTTz7wcQTBUERqdOHhuHix+kAK\n4PDhmmuLIAiPtPLycoPNJXlcpaVVvy49vebaURc1bdqUmzdv8rCePWs0GlJTU7l27Rr29va0atWq\nXgVSAB98YMy+fbZSIAWQnQ0jRkB8vLbYr6HmnolASqhrRDAlPBz/64NAFNkVBMFAysvLHyhNtAB3\nuz993O9dra2tkcvlZGRkGPzYSqWSq1evUlhYiLe3d73sXS0vh+qy4RcXa4eQCsKjTHz6CA9Hu3bQ\npk3V64yMYNiwmm2PIAiPLI1GI4KpBzRrFphUkZLK3ByCg2u+PXVN06ZNSUtLo7S01CDH02g0JCcn\nExsbi5OTEy1atKj1FOz3q7AQ7hZn/t//1VxbBKE2iGx+wsOzdi3l/fphpFLpL1+wADw8aqdNgiA8\nckTP1IN75hnYuxemT4eYmE2AA23a9GflSujUSZtgIDw8nOXLlz+0NkycOJHPP/+cfv360bJlS8rK\nyrCzs+P999/X287f3x9zc3PUajVLlixhxowZyOVyzMzM0Gg0fPLJJ0RFRXHmzBn8/f0BbXa8GTNm\nSF+PGjWqUjmPuzE3N8fe3p6UlBSaNWtWaX1OTg4fffQRH374IS1atKB///4olUqef/55fCpk7zh+\n/Djz58/H3d0dlUrF7NmzsbOzw9PTk5deegmAuXPnsnfvXl544YUqiyvXNVZW0KQJJCdXvb5165pt\njyDUNBFMCQ9Nprc3+Xv30vzQITh/Hho3hgkT4MUXa7tpgiA8QsScKcMYMAD694cPPoD4+NOoVNv4\n9ls5KtWrdOzYkT///JPZs2eTlJTE1q1b8fX1pVWrViQnJ0spzxctWoS7uztvvPEGt2/fZu3f478m\nTZqESqUiLCyMHj16kJqayooVK6RzR0ZG0vrvu24rKyupJtaGDRvYv38/AwcOBCA7O5u8vDy2bdsm\n7SuTyQgNDcXS0pKjR4+ybt06goKCCA0NlYKpDRs2MGDAAPr27QtoC+Hu27eP77//nqKiIhYuXIhG\no2HRokVYWFjw6qva9zx27FgGDRpEVFQUGzZswM/PDysrK5o3b87UqVOlNkREREgF6p944glWrlwJ\nwLhx4+jTpw8ODg6Ul5eTmZlJr169mDVrFpaWlvj4+LB79246d+4sXSuAN954g/nz50tFl+symQwC\nA6FCnV6JQgFvv13zbRKEmiSCKeGhKC4uJiUlhdb/+Q/8/eElCILwMIieKcORyaBxYw1bt37H7t27\n8fLyYsSIEXTs2JFWrVrx0UcfERwcTFpaGjKZjAkTJiCXy5k5cyajRo3Czs6OUaNG4e3tzZgxY/jq\nq6/QaDRMmDCB8ePH06NHD2bNmsWoUaP0znvq1Ck6depUqT1dunTh6NGj0ms7OzsGDhyIn58fZmZm\n2nqGICWH6NKlC9999x2gLTCblpaGs7MzV65cYeTIkdJxTE1N2bJlC7t27eLGjRusWrUKmUzGkiVL\ncHNzk95zu3btmDFjBjNnziQzM5P8/HyeeOIJvYLJAP/9738JDAys1P6OHTsSHx+PmZkZiYmJlJeX\n4+zsLGUG1CWaiIyMJCAgAIDly5fTsGFDEhMT7+2bV4tmzdImMQkPh5IS7bImTbQVUupJWTFBuG/i\n00d4KBISEmjcuHG9y0gkCEL9I+ZMGZ5MJkOtVktfA9jY2ABgYWFBcXExoA1YTExMKC4uplevXsyZ\nM4e9e/eyefNmvex3uq91xW/vzIynVCpRKBSV2vHHH3/QunVrVq5cSXBwMEVFRfj4+LB+/Xp69uzJ\n3r179bY/c+YMbf6er2ttbU1hYSEAbdu25dy5c9J2uvd2t/eve3+gDb6Ki4v55ptvUCgUDLtj3m9Z\nWRkmVUw6i4yMxMTEhMmTJ7N//36cnZ2ln9Xi4mLpOj7xxBOsXbuWtWvXYmVlBVCvfqaNjCAsDJKS\nYPdu+OknSEzUptUXhEed6JkSDC4tLQ0jIyMaNWpU200RBKEKmzZtwsHBgf79+3P16lV27NjBggUL\npPXDhw9n586dlfYbN24cq1evZsqUKaxevVrv5jc4OJhly5YREBBQaf6KWYXsnZ6ennzyySe89tpr\n+Pj4sG3bNnx9fUlJSWHq1Kk0a9aMrl27cuvWLb788stq542cO3eOuLg4RowYcdeeqX87D8fCwoIT\nJ07w9NNPM3fuXGJiYti5c6d0Lfz8/Fi/fv09X+v6qKSkhNGjR/P+++9LPU1AtUMpdcuPHz/OgQMH\nyMnJ4aWXXqJly5bSMLvJkydTXFxc7TG8vb2JjY2la9euFBYWEhQURFlZGfb29vhVqCack5PDlClT\nsLKyIiMjg48//phjx44xc+ZMTE1NkclkfPLJJwCkpqbi8ff83AkTJjB9+nQOHDhAWVkZI0eOZMyY\nMfj7+6NUKnnvvffQaDTMnz8fS0vLat/zvHnzUKlUODg4oNFopPUeHh6kpKTQuHFjLly4wNSpU8nN\nzcXb25tGjRqxadMmjIyMOHHiBDt37iQmJoa8vDzmz58P6PdMTZs2jVatWmFhYXGP37na5+QEQ4bU\ndisEoWaJYEowKJVKRXp6Ot7e3rXdFEEQ/gXdzWBmZiaBgYG0aNGCmzdvArB69Wri4uIwMTG5a+KB\nzMxMZDIZcrm82vkrOp06dWL79u3SHBhdG0pLS1Gr1XzzzTf069ePX375hejoaNavX09YWBh79uxh\n6dKlNGzYkNGjR+Pv709mZiYnTpxg4cKF9O3bl4yMjPuahwPwwgsvsGvXLjp37kxBQUGlm+iOHTty\n5swZnn766fu91PXCyZMnOXjwIKGhoYwePRpXV1dp3ccffwwgDa3buHGjtE739X/+8x+943Xr1k3v\ntS7pQ8U5TwD9+vVjxowZjBo1ikOHDlXbPltbW7744gu9ZRXboZOVlYWTk5MUZJuamlY5/2jw4MF6\nrzdv3qz3+s73/NFHHwEQExNDTEwWv//ugFoNAwb4sWvXLoKCgrhy5QpJSUmoVCrc3d31Hjr07t2b\n48ePV2pHbGys3uvDhw8zREQlglAv1J8+ZKHO02g0xMfH07Rp03qb4lUQHlc7duwgICCAJUuWSL+/\n5eXlKBQKTp8+TWZmZrX7/v7777Rv3156XXH+yrVr1/S2NTIy4q233uLzzz/XW/7kk09y/PhxlEol\nNjY2rF27FhcXF0pLS/njjz8YMWIE3377LceOHeP5O8YOderUiVmzZpF2R+XZu83DubNduuFn27dv\n59VXX600DK1z584cO3as2mtwPzZt2sTBgwcBuHr1KosWLdJbP3z48Cr3GzduHIWFhdL/FQUHB6NS\nqfD19SUgIICpU6cSFBRUaVjbL7/8gq+vL5MnT5ay3H366afs2LEDZ2dnjhw5glqtZt68eXf93t+r\nX34Bf38YNw527Phnfo2JiQm+vr4GO49SqWTOnDkGO96dDh92p2NHO958U5tXqU8fL86c6UFWVhbR\n0dFYWFjg7e1d5dDFf8PZ2ZkRI0YYuNWCIDwMomdKMJiUlBQpfawgCHWXra2tdIOcnp6OnZ0dgBRE\nmZubk52dzcWLF/nyyy956623UCqV1R6vuvkuZ86coXXr1mzZsoXz588za9YsAPr27cuoUaPIz8+X\ntm3ZsiWhoaGsWbOGr7/+Go1Gw7vvvsv58+d599130Wg0fP7551y6dIlly5Zx8eJFaV9zc3OMjIzu\nax5OQkKC1OswdOhQ/P39iYyMrDSkz8rKqlLgYkg12UOo0WhYtWqVlKihpKSEqKgo0tPTCQ8PB2D8\n+PF4e3vj4+PDV199xezZsx/o/Wk0MH48VOxEioiAZ5+FI0e06bWfeOKJBzpHRRV71Aztt99g2jRT\nKv64lZXB1q2d8fJKZdaslsjl8gc6hyGvhSAID5cIpgSDKCwsJCsrSwzvE4R6oG/fvkycOJGLFy9y\n69YtVq5cSVlZGfPmzeOPP/4gLy+Phg0bolQqCQ0NJSYmBqh+zoy3tzcHDhyQXt85f8XU1JSxY8fq\n7TNz5kyeeeYZvWWenp7s2rWLy5cvM3/+fN5++22io6PZtm0bPj4+uLu7k5qaKmVCq6iqtv3beTg6\nzzzzDOfOnUMmk1U63vXr1+nQoUM1V9RwdD2EvXv35uTJk8A/PYTHjx+/7x5CXeAE2qCrYrBhamrK\nlStXeOqpp6RlTz75JDExMfTu3Zt33333gd/Xnj36gZTOf/8Ly5Zp/9UXa9fCHXG7ZO9eZ957T6Tp\nF4THiQimhAdWXl5OQkICzZo1qzKbkSAIdYu5uTkRERGVluuG3k2bNg34Z16LbhjYV199BVSeo9K+\nfXupRk5V81cq0iW26Ny5szT0TLfPnQGOqakp/fr1k4Y7ffjhh9K63r1707t3b8rLy2nQoIFee3/9\nFS5dAheXfpw+/b/n4VRsl7GxMQDffvut3vKff/6Z0NDQux7jXtVmD2FSUpK0vqSkhDZt2rBt2zaG\nDh0KwKVLlxg2bBilpaUGea9bttx9XX0KphISNEDVAVN8vAikBOFxI+58hQeWnJyMQqGo8mmxIAiP\nNrUadu6EzMwggoJUvPmmnCefNMyxnZ2d6dev3123qZgWPSMDBg6EM2d0a01wdPRl3Djo3PnB2jJ+\n/PgHHrp1p9rsIQwMDMTX1xdra2ssLCxYvnw5Dg4OTJ48mbKyMrp27Yqrqyupqak0btz4gd9rbu79\nrasrSktLycvLIzc3F0fHhkDVw9n/zsouCMJjRKa5c5C5INyD/Px8EhIS8Pb2lp7oCoLweMjKghde\ngArTlwCYOxeWLq2ZNpSUlHD16lXat29P//5QVQeUiwvEx8PjUPZu0qRJrFmzxiDHSkhIYNeuXfTq\n1YuuXbs+0LHmz4clS6pe98or8HcejjqlqKiI3Nxc8vLyKCoqokGDBtjY2JCQYMMzzxhLyTMq+uab\nEkaNEgmYBOFxIrL5CfetrKyMhIQE3N3dRSAlCI+huXMrB1KgHbL1228104by8nJkMhnx8XD4cNXb\npKZChSlDj6Q//4Rhw2DXriDat1cRFkaVN/v3wszMjPbt2z9wIAUQEKDB3r7ykEFTUzDAlCyD0Gg0\n5Ofnc/PmTS5fvkxsbCwlJSU0btyYDh064OHhgZ2dHZ07G7NjBzg7/7OvtTUsXlxIu3bRqFSq2nsT\ngiDUOBFM1WNqtZrAwEACAwOZOHEiJ06c+J/7LFq0iKioKIOcf8WKFcTFxbF7925effVVfH19WXbH\nwPeIiAgpK1FJSQlubm4cPHiQqKgoRo8ezdSpU6UCi1VNCBcEoW4qK4Nvvql+/R3leh4aXcHeGzeq\nTwoAkJhYM+2pDT/9BD16wO7dkJnZmsuX5UyfDtVkVv/XzM3N8fLyeuD2aTQaVKr/Y8eONF59FXT1\nlZ95Bn74QZvRr7aUlZWRnZ1NfHw8ly5dIjk5GRMTEzw9PWnXrh2urq5YW1tXGlr52mtw4wb8/LM2\niE9JgfnzFTRr1oyYmBgKCgpq6R0JglDTxJypemzDhg0MGDCAvn37AtpgZd++fXz//fcUFRWxcOFC\nTExMmD9/Po6Ojrz22msArF+/nvz8fHr16oWvry9t27blzTffpF+/fuzYsYO8vDw6duzI22+/TY8e\nPRg6dChnz57lk08+wcXFBYDc3FxOnDjBjBkz2LJlC/7+/vTv31+qGq8jk8lo3bo1p0+fJi0tjWf/\n/tQ8evQoY8eO5eWXX5a2fVyKYgrCo0CthrtlCs/JqZl26OZMeXmBiQlUly/hUU40GhKi/X7cad8+\nbV2nO8py/Wvm5ubcunXrwRqHdrggwPPPN+GFF7Q/NyUlUFvTbIuLi6X5T0qlEmtra2xsbO65RqKp\naeVr27BhQ4yMjIiLi8PDw0NKjiIIwqNL9EzVY3emsjU1NWXLli1s2LCBpUuXsnbtWtasWcOCBQv4\n5JNP6N69OwAjR47kq6++4ocffgCgSZMmhISE0KJFC8rKyrCzs5MyWTVo0IDg4GB8fHyknq/S0lKi\noqKwsbGRJn5v2LCB7t2788ILL1Rq59ChQ9m9ezdHjx7lpZdeArSTuU+dOsXbb78t1XN5GEUxBUF4\nOORy7ppookePmmmHbpifszP4+FS9TevW2nk5j6LkZIiMrH79/v33f2xzc3OKi4vv/wBAYmIipaWl\nNG/eXOrdUShqPpAqLCwkOTmZK1eucO3aNVSWF/3SAAAgAElEQVQqFU5OTnTo0AFPT08cHBwMVmy+\nQYMGtGjRgoSEBHJq6qmCIAi1RgRT9Vjbtm05d+6c9PrOCvc6dw5P0D0p0y23sbEB4PDhw3h7e7No\n0SIpHa4uza6pqan0oZqYmIhCoZD2A+0QvZ9++onvvvuOjIwMgoODpTTKugxYzs7OUvBlbW3NkiVL\n+OKLL9i/fz8ajeahF8UUBMGw3nsPqkos17ixmt694ykrK3vobdAN8wNYt04bUBkZ/TPer1s37VAy\no0f00+5/TVd9kOmspqamlJWVUV5efl/7JyUlUVRUhKenZ7UZCB+W8vJycnNzSUxM5NKlS9y4cQOZ\nTIa7uzsdOnTAzc1N74GgoSkUClq2bMnNmzcN0rsnCELdJYb51WMTJkxg+vTpHDhwgLKyMkaOHMmY\nMWPw9/dHqVTy3nvvYWJiwsKFC3F2dmbQoEHAP0HUnR9uTzzxBO+88w6pqanVfnhmZ2dTXFxMx44d\nKz1xk8vl9O3bl99++42wsDAANm3aBMDy5csB2Pz3RIp9+/Zx5MgRTExMaNu2LTKZrMaKYgqCYBgD\nB2rToi9YAFFR2hv3gQNhxQpTTExMiY6OxsPDo8raR4ZSMZiytIRvvtEwduwVSktb4+ZmzKP+J8XZ\nWRswnj5d9fpBg0qA++9xMTMzQ61WY2FhcU/7paSkUFBQQKtWrR5awHKnkpISafheQUGBVLLDxcUF\nMzOzGmlDRXK5nFatWhETE0NZWRlOTk413gZBEB4+kRpd+NfUajXR0dG0atUKuVzOokWLCAgIwNHR\n0SDHnzRpEqGhoQav5SIIwsOXkaEd+mdt/c+yvLw8EhMTcXR0xLli6jMDysnJITc3Fw8PD0BbriEl\nJcUgiRPqizNn4MUX4c6cByNHKpkzJ5YmTZpgb191XaT/JTY2lkaNGumNRPhf0tLSyM7OplWrVg+9\nkLtKpSI3N5fc3FzUajU2NjbY2NjQoEGDOpNltqSkhJiYGGxsbGjSpEltN0cQBAMTwZRQvStXYONG\nSE+Hp54irkcPFI0bSzdFOTk55OXl4e7ubpDTnTt3jicNVe1TEIQ6oaSkhPj4eGmIlaHmpehkZWWR\nn58v/R1KTk5GJpMZpNBsfRITA2Fh2h4qe3sYNw5GjYKiIhXx8fGYm5vj5uZ2z8FNUlIS5ubm//qh\nWWZmJunp6Xh5eRn8ew3/pC/X1X8yMjLCxsaGhg0bYmVlZfDzGUpZWRkxMTFYWlrSrFmz2m6OIAgG\nJIIpoWrr1sHkyVBhuF+JoyOmv/4KrVrVYsMEQaiPUlNTyczMxN3d3aAZzjIzM1GpVNIN6tWrV2na\ntGmdvrGuaRqNhpSUFLKysqS5Qv9WRkYGarWapk2b/s9ts7KypF5BQw6rKy0tJS8vj7y8PG7fvo1c\nLqdhw4bY2Njc8/DD2lReXk5sbCympqa4u7vX+DwyQRAejkd0Sq7wQJKSYMoUvUAKwDQjA0QtKEEQ\n7oOLiwvNmzcnMTGR5ORkDPUcT5caHbRP/4uKih7qHK26JCIigv79+xMQEMC+ffuk5XdeW5lMRpMm\nTWjevDlJSUkkJibyyy+/EB4e/j/PYWZmVmVGv+DgYFQqFb6+vgQEBDBx4kSCgoJwd3fXC6Rat27N\njh07APjss8/o2bMnAJ6enuzduxcAn7/TMK5atUqqg1hUVER6ejrXrl3j8uXL5OXlYWNjQ7t27fDy\n8sLJyaleBVIARkZGtGzZkvLycuLi4u47sYcgCHWLSEAhVLZtm7YiZ1VOnNAGW66uNdsmQRDqPSsr\nK9q0aUNiYiLXrl2jefPmD9yDUTEBxe3bt7GysnpsnvjLZDImTZpE//79AejevTuDBg3iueee44cf\nfiArKwtnZ2feeecdBgwYwHPPPcdff/3FqFGjSE5OpqioiMOHD/P7778zfvx4qYC6p6cngwcPZuzY\nsbzyyiv88ccf7NmzRzpvZmYmMpkMuVyOTCbjvffeIzMzkxs3brBx40aCgoKkbdu1a8evv/7K66+/\nzuXLl6VahZ06dWL79u0MHDhQ2nbIkCHMnz+fmTNnUl5eLiWPqKpobn0lk8nw9PQkISGBmJgYWrRo\nUWfmdgmCcH9Ez5RQWW7ug60XBEGohomJCZ6entjZ2XH16tUHrsOjqzMF2uQTj1uR1DVr1hAQEMCl\nS5dQKBSEhITw1FNPodFoaNCggRQEyWQyZsyYwcyZMzl//jxOTk7s2rWLU6dOsXDhQsLDw1EoFNjb\n23P58mVAGwiFhIRgZWVFWlqadM7ff/+d9u3bA9o5cYmJibRo0YIePXpw7do1vfbJZDKaNWvGnj17\npHaBtpfmzTffJCwsjMLCQi5evEhBQQHJyck0b96c9u3b4+rqSoMGDR6ZQKoid3d3LC0tuX79ulSK\nRBCE+kn0TAmV9ewJH3xQ9TonJ3iMsmQJgvBwODo6YmVlRXx8PPn5+TRt2vS+UmiXl5dLiQ5u375t\nsOyi9cXkyZN55e+KxLq5UOfPn0cmk7F48WJOnToFIA2JMzExobi4GIVCQZs2bYiPj+fSpUuo1Wre\neustKUjS1RM0MjLC1NRUrwagUqlEoVBQWFhIfn4+Hh4eWFpa8uuvv9K6dWu2bNnC+fPnmTVrFgCj\nR4+mW7duREVF8cMPP5CZmUlBQQGNGzdm7dq1FBcX4+3tjampKZaWlo9NRldXV1dSU1O5du0aLVu2\nrJX07YIgPDjRMyVU9vLL2sIlVZk7F8QffKEeUqvVBAUFERgYyJQpU4iIiAAgJCQEgOHDh+v9r1s+\nbNiwKo93r3N+Fi5cKM0H0blz3snUqVMJCgqqVID7zvklOTk5vP7662g0GmJjY5kyZQqRkZGsW7fu\nntpU2ywtLWnTpg3l5eVcvXqVoqKiez6Gbs6UWq2mvLy83s2jeVBV/Ry2bNmSixcvEhoaWqlgbMVe\nnieffJKZM2cSFhbGyy+/zMKFC5k5cyaLFy/W28fY2FjvZ9Lb25vo6Gji4uKwtrZm4cKFBAUFcejQ\nIfz9/Rk7dixhYWFSRsWGDRvy22+/kZSUREFBAUqlEnNzczp06MCiRYs4fvw4pqamaDSax+775+Li\nQqNGjbh27dp9/fwLglD7RDY/oUqa3Fyy/fywO3QIWWEhNG8O77wDEybUdtME4b6Eh4fTokUL+vbt\nC2iTFRgbGzN8+HB27txZ7f8dOnRg0KBBxMXF8cUXXzBp0iQ8PDxo3749KpWK8+fPU1BQQHh4OEuW\nLKGgoAATExNat26Nr68vEyZMwNHRkdOnT7N69Wq8vb0B7byTDz74gBUrVjBu3DjCw8OxtLTk6NGj\nREdH6807GTp0KCYmJmzdupUxY8awbds2jhw5woULF7h06RKff/45VlZWjBkzhq+//rpWrm9FUVFR\nLFu2DAcHB5o2bSr1UOgkJCQQHh4uFfMGbSa4mzdv0qRJExwcHP71uRITE7GyskKj0VBQUKBXquH/\n2bvvuKrq/4Hjr3vhArKHsoeACuIqV1qaVlrmrly4ceUCQQRz5cqVC1fuEi1njswyU7/OtPyZo1RA\nHKCADJGNrHvv748rJ66AEwXl83w8eMi959xzPvegeN/n8/6830eOHCEhIQE7OzumTp3KG2+8QVpa\nGn5+frz55pvSftOmTeOff/5h165dHDhwgPj4eM6fP8+QIUOoW7cu/fv3Z+HChUyaNIk1a9Y8+4Wp\nwArT9QoKCqhevbpWUHPz5k3MzMywtLQENMUhBgwYwOrVqzE3Ny92LJVKpVW+XFdXVypf/qjiIPv3\n7ycjI4MePXqU/Rus4JKTk4mNjaVGjRoYGhqW93AEQXgKIs1PKNHd/HzSZs/G6vvvNZ0gLSzgNcxb\nFyqPK1eu4O3tjVqtZuzYseTk5LBy5crHvq5q1arMnDmTH3/8kb179yKTyRg2bBh2dnb88MMP6Onp\nERsby/nz55HJZPTs2ZMmTZrQu3dvGjdujIODA9OmTWPkyJFaswhF153AfzMMTZo0Yc+ePVpjkMvl\nDBo0iNWrV0vPffTRR2zevBlvb2+pDLiRkRHx8fEvrEHukzp48CD9+vWjXbt20nNBQUEolUqcnZ35\n5JNP+L//+z/Gjx/P7du32bx5M+PGjWPhwoXcunWL4cOH8/XXXzNo0CDeeust7t+/j52dHX/99Rdz\n586lSpUqLFy4ENDMenz22WcMGjSIjh074uvrKwVjmzZtYs2aNfzxxx90796dUaNGkZeXh7e3Nzt3\n7pTGJpPJMDc358SJE9Jzs2fPZuDAgbz77rt069aNatWq0aBBA/766y/eeuutl3QlXx6FQkGNGjW4\ne/cuERER2NnZUbWqNcuWwdKlTsTE6FC7Nowenc9bb0USEBCAvr6+9Pr8/HypfHlGRgaGhoaYm5tj\na2urtd+j2Nra8vHHH7+ot1ihWVlZoaOjw7Vr13BzcxOl/QXhFSLS/IRi1Go18fHxmhQNPT2wtBSB\nlPDKq1OnDmfOnEEmk7F48eJi6U9Po3Btyvbt25kzZw5NmzYlOzsbQLqrXBgcFa6DePgDZeG6k4f9\n9ddf0rqTgIAA4uLiAE3wdPLkSTIyMqR93dzccHV1lR6bmJhorW0pL4MHD+bkyZMMGTKENWvWEBYW\nhr6+PosWLcLf3x+1Wk2tWrWYN28eNjY2UnEDAwMDPD09kcvl0ofKefPmERMTg6+vL+PHj+e3335j\n5cqVGBoaYmVlRUREhNQQePLkyVqzWsnJycWa1Orp6ZX44X706NEsX75cemxoaMiIESM4evSoVHGu\nYcOGHDly5EVcsgqjatWqeHp6kpKSQt++qfj7w40buuTlybh4EYYOVbBtmxvNmjUDID4+nvDwcK5c\nuUJGRgaWlpbUq1ePWrVqYW1t/cSBFKA1W/i6yMvLw9fXF19fXz7//HOOHTtW6r5mZma4urpy48YN\n0tLSHnvsojdnUlJS+OKLLwCoUaMGY8aMYejQoWzZskXrNUePHqV169b4+/vj4+PD+fPnAU0q8YgR\nIxgxYgS3b9/WKlMvCMKjiZkpoZi7d+9SpUoVkWogvFaGDh3KuHHj2LdvH7q6ujRp0gT4bw1JaX/e\nvXuXyZMnExUVxZo1azhw4IB0TDs7O+bPn8+ZM2do1apVsdfXrVuXdevWsWTJEi5cuKC1XsXLy4uf\nf/5Zejxu3DgUCgUymYwFCxagUCjo16+f1nsYN26c9CG2JHfu3NEKrsqLiYkJX331FQAdO3akRYsW\nWsUlZDKZFJAaGBiQm5uLvr4++fn5FBQUYGRkhK2tLSqVioSEBPT19TE2NpZ6HqlUKvr160e9evW4\ndu0aKSkpmJmZSYUoCpVU0CI3N5fc3FwiIiJYtWqV9HPT19enU6dObN26ldatWwMVN1h90fT19VEo\nPNi2reRVACEhVejY8QrGxpry5Q4ODpWqJP3TWLt2LR07dpTSi/Pz8/n11185evQoSUlJLFq0iJ9+\n+onDhw/TuHFjUlNTSU5OJj8/n/r16zNs2DAmTpxIXl4eSqWSkJAQWrZsSefOnXn//fel32MbNmyg\nZ8+egCYoXbJkCQA+Pj60bdtWuskgk8lKnKVt2LCh1kx9//79mTJlCsuWLXuZl0sQXkkimBK0FM5K\nubu7l/dQhEoqLy+PwMBA6fvevXtLH3ifh0KhYMmSJXz33Xd4enoSERFBp06dcHd3Z/DgwcybNw/Q\nzDaBZmZi+PDh1KxZk9atW9OmTRuaN2+On58fhoaGJCQkYG1tTVBQkLQmqOg4x47dzA8/QK9eITRr\nBmPGjNEaT7169aQPL999990jx75jxw5AMzNStBDA1KlTpe+Tk5OxsbF5pop4Ze2nn37iwIED6Orq\nUqdOHby8vLh//z7BwcE4OTnRuXPnYh+8P/nkE6ZMmYKDg4MUbFlZWZGamkpGRoZW+ejRo0czceJE\n7OzsyM3NpXv37iVWQiv63I4dO4iMjCQtLY0pU6bg4eHB4sWLAbh48SIymYw+ffoQEhLCe++9V+L7\nioiIoH79+mVxiSoMlUqFUqks9rVnjy4qlUmJr8nKkpOQUINmzUQxose5cuUKvXr1kh4rFArkcjlq\ntZr8/HwOHTqETCajffv2eHt7M336dHr16kXDhg3p2rUrlpaWREdH4+XlJTW8NjIyYvz48VrnOXXq\nFL6+vsXO36BBA27evFniOsSis7Tnz59nxIgRAMyfPx9zc3Oio6PL8lIIwmtLBFOClqSkJAwNDcWs\nlFBunuRO7v79+zl27BhmZmZ89dVX7N69mz///JOMjAx8fX0JCQlh5cqVpKamMnv2bJYuXSod//Dh\nw/j4+HD16lVGjBhB+/btuXfvHgEBAYSGhkr7yWQyFi5cSHp6OnPmzMHc3JwuXbrw888/4+3tjY2N\nDXFxceTl5Wl9aI+Ph27d4I8//ntPjRvDzp3g7Kz9Xv38/Lh//36ZlILOzs5mwoQJz32cstClSxe6\ndOmi9VzRYhMAX3/9NQBzHrRhcHFxkX7mhQrXRRWmCtaqVYtGjRoBSIU2wsPDyc3NlYLhotq0acOF\nCxdo1aoVR48eLXW8RYPSs2fPSt+7uLhojfvw4cPSmCqCkoKgJ/0qDKJkMhk6OjrFvmQyU6DkYArA\nzEwEUk+iTp06/P3333z44YeA5gbRqlWr2LNnDxs3bpTSg4v2R8vPz8fAwAADAwOSk5OpV68ekyZN\nkrYXzuoWpVQqi6W0Aly4cIF+/foREBCAg4ODNJMF/83SgmY26+E1pBXhxowgvApEMCVIVCoV8fHx\n1KxZs7yHIlRij7qTW1BQwKFDh7hx4wYNGjSgc+fO6OnpsXz5ctq2bYuhoSFnzpyhf//+fP/999y5\nc4fhw4dLx0pLS0NHR0d6XLjmwNLSkvz8/GJjGT9+PDk5OYwbN47ly5czefJk5s+fz40bN3Bzc8Pd\n3Z1z585ppd717KkdSAGcPQtdu8K5c9rPe3p6Ps+l0uLk5FRmx6po7O3tMTExISoqCktLS+zt7bl4\nUcby5XD2rDPW1tlMnGjCg+w8ycCBA7l69WqZjWPw4MFlEviq1epnDn6KPpbL5SUGQkW/9PT0Hrm9\nND4+MHkyPPisr8XBAVq0eO7LUCkMHTqUsWPH8vPPP6NUKunVqxdeXl7Mnj2bsLAw2rRpA2iXrN++\nfTvbtm2jW7dudOvWjb59+zJixAiUSmWpaXeurq7ExcVhb2/PhQsXGDNmDNnZ2bRr1w4rKytpFvbY\nsWPFZmlBe2bK39+fWrVqVboy9YLwrEQwJUiSkpIwNjauNA0ThYrpSe7kTp48mYsXLxIUFMTMmTMx\nNDTkyy+/1DpOYepcYSly0JR0LqnoQ3JyMnp6epw+fZrt27fzySefAJrZE0NDQ7Kzszl27BhKpZLk\n5GTWr1/PrFmziq2huXgRjh8v+X2dPw8nTmh6YgtPz8TEhNq1az8oqx5HQIA9BQUywBAw5OBBWLoU\nimY6KRQK6tSpU2ZjaNSoUalpcY8KfB7+UqvVjw2CFAoFBgYGpW5/0bMG5uaa6zlsGKhU/z2vp6di\nzRo5JUyCCCVQKBTFAqB33333ka8ZNWqU1u+tbdu2ce3aNQwMDNDX15fSfosaNmwYP/74I35+fkRG\nRpZ67NJmaa9du6b1eP/+/Xz66aePHKcgCBqiz1QFNnDgQBYvXoxcLsfDw4O4uDiio6NZunQppqam\nTJgwodido8LeOI+jVqu17oQNGzaM0aNHM27cODw8PLh16xbTpk3Tqq704Ycf0rZtW4KCgti7dy+j\nRo3i9u3bhISEEBERgY6ODt7e3hgaGvLXX39pzQgIwpPKz89n7NixANKd3N9++w1jY2PpTm5eXh6R\nkZHExcWxbNky9u3bx8mTJ6lSpQodOnSgbdu2zJs3D09Pz2LpZt7e3mzZsoXQ0FB+/PFH3N3dycjI\n4Msvv8TFxUXaz8fHh+XLl2NkZMSmTZvQ09OTFnh36dKF3bt3M3bsWCZMmICNjQ0AP/4ID3r+lmj9\nehg0qIwvWCWTnw9OTkoSEorPqujrQ2wsWFmV/NpnCXwe/iotLe5Jv+Ry+SuVPnX2LKxaBVFRULs2\ntG8fRcOGVaS/88LLoVKpuH79Ojo6Otjbu7Jjh4z9+zUFd7t1gw4d4Pz5czRs2LBMznf+/PnXsrqi\nILwI4t5SBdaqVSuOHz+OTCajR48enDlzhrCwMD744AN27tyJUqnkwIEDbNu2DUtLS4KDgwG4f/8+\nY8eOZcyYMRw5coSrV6+SlpbGzJkzmTRpktRwtPCu0/nz53F0dMTExAQzMzOWLVvG6dOnOXnypNYv\nUzMzM2lB6m+//UbTpk0BOHToEHv27NHK1164cKEIpoRn8ix3cvv166dV+W7fvn1cuXKl2CJt0KTW\nJSYmMmDAAAYMGFDqMYsWhXi4qt5PP/1EQUEBmZmZWh8qH5chKzJon98ff1BiIAWQmwvr1yfx6adp\nJQZNT5IWp1AotAKf4muJKlfFusaNYd26/x7n5dkTFhaGubn5U5U9F56PXC6nRo0a/PtvFM2a5XDx\n4n8ZJKGhmvTizZvLJpCC17NMvSC8KK/O7bFK6P333+fIkSOcPn2a4OBgjhw5wvHjx7Uqhq1Zs4Zv\nv/2WBQsWYG1tTV5eHp9//jmTJk3CycmJTZs2YWFhgYWFBefOnZMajhadvj9x4gT29vbY2dmRlZXF\n6NGjGTRoEN7e3sXG9M477/Djjz9ibW0t3V398ssv8fPzY9CgQVy5cgX4r3moIJSHjh07EhoaSmys\nZibIxERzB7dTJ2jVyk9a9P08MjIymDx5stZzDRpAaXHfm29WnBS/DRs20KlTJwYOHMjs2bOf6DXd\nHzXlhqbAhJ+fHzdv3iz1tY87RtFEifz8fEaNGgVA06ZNGTNmDMOHD2fz5kUPvWogkAKkAbbI5QZk\nZWWxbNkyAgMD8fDwoF69esTGxnL69Gm2bdtGtWrV8PDwoEaNGri6uuLs7IyDgwO2trZUq1YNS0tL\nzMzMMDExwdDQEH19fXR1dStdIFUSPT097O3tiYqKKu+hVDoymYytW121AqlC27bB1q3lMChBEMTM\nVEXm4uJCVFQUrq6uODo6kpCQQHp6OiYm/1VYevg/d4VCIZVSNTMzw8HBQWstya5du7SqBgEkJibS\nuHFjDAwMMDIyYvny5ezbt4+NGzfi4eHBoUOHGDx4MKApX1y7dm0OHjwoVQ5r2rQpTZs2JT4+nokT\nJ/Ltt99Wmn4sQsWVnKxZJF/0M9++fXDkiAV//GHx3McvvEnxsG3b4LPP4NSp/55r1Ah27XruU5YZ\nmUzG8OHD6dChA7179wY0lQUVCgUFBQUsWbKEjRs3curUKQwNDaWKdsnJyQQHB/PVV1/x7bffcvfu\nXTIzM5k6dSqbN2+mQ4cOWFlZ4e/vD2jWOc2cObPY+WfNmiW9dvHixfTq1Yt33nmHJk2aSAvy9+3b\nJ33v6OjIF198QXJyMjNnzsXA4G9ycho9OFor4DggA3rg7n6Zc+fCaNeuHf/88w9nz57l7bffZuvW\nraxatYpbt27x7bffljhrKTyZatWqkZKSQmJiItbW1uU9nErlhx8eve3BP2dBEF4iEUxVcNbW1lg9\nWAAgk8nw8PDQ2j5kyBCGDRuGpaUlgYGByOVyFi9ejJ+fH6AJdPz8/FCr1QwqYbGGUqnE2tq6WLf1\njh070qFDB0aNGkWHDh2k5w0MDLh27ZpWukthxbPU1FT69u0LVJzmoULlVbjO42FZWfDVV/AESwuf\nia2tJhXt7Fn43/9ieOutqrRqVfGqYq1du5bZs2czaNAgLl++jKWlJdOmTWPGjBlcvnyZPXv2sKtI\nBJicnMzYsWNZunQpcXFxnDhxgrfffpu8vDzu3LnDG2+8wYQJE9i4cSM5OTnY2dkRGRlZrEpieHi4\n1mvDwsJQq9WMHz9eay3RkSNH8Pb25vLly2RmZnLnzh3y8/N5//2W6Old5fvvC4Op94HFQBUGDw4m\nPHwTV69eZdmyZTRs2JAZM2ZQo0YNqlSpIhWxKFpmWng2Li4uhIeHY2ZmJtL9XqL09GfbJgjCiyOC\nqQpuzZo10veFpU3hv/Uc7dq1o127dtLzhQ1HC9ecvPPOO1rHe7g5aGJiIu3bt2fZsmUMGDBAq3jF\nL7/8orVv4bbCcrqF53q4v0tFah4qVF4HD5a+7fffX/z5GzcGY+NMnJ0tX/zJnsGwYcN477336NGj\nB2+99ZZ0c0Qmk1FSXSIjIyN0dHSIj49HrVZTp06dYhUUQZOq16FDBzp16lTieVUqVbHXGhoaIpfL\nyczMJCUlhdTUVBISEjA0NJSqi1pYWGBnZ8fevXsJCupJbu5UTp8uIDNzFipVFLVrV2X58qp88UUC\naWlpGBsbY2xsTFpaGitWrGDgwIGA6J1TVvT19bGzsyMqKqrYTT7hxWndGvbsKX2bIAgvn/hfpbK5\nfh1Gj4Y33kDdqhUFa9bgZG8vfdAoCxWpeahQeZVQAf2JtpUluVyOqmhd6QqmSpUqfPTRR0RGRnL3\n7l2Cg4NJSkqibt26dO7cmdGjRxMcHIxSqaRKlSqsXLmS2bNnS8UZAgMDGTlyJDExMYAmEOvbty87\nduwgODhYSvcrmo7s5eUlvXbEiBGEhYWRnZ3NP//8w+3bt1EoFLi5uVGvXj0uXLiAXC4nJSWFkJAQ\n/P39sbGxoX79+mzfPp3bt2eRkgI9e1rTunVVYmJikMlkWv27evTowa5du2jxoDFScnIy9vb2L/Eq\nv76sra2RyWQkJiaW91AqjcmToaT2T1Wr5jN4cM7LH5AgCKI0eqVy4YLm1tVDKX10765Z6CEWVwuv\nkU2boH//krf5+SlZsqT0hqVl5dq1a1hbWxdbp1iZ3b+vIiMjnezsVNLS0jAwMMDc3BwLCwt0dHRI\nTEyUPpxv2bKFuXPnPtFx1Wo1V65cwdHRETMzs1L3W7lyJY0aNZKqkQrPJzc3l/DwcDw9PUW630ty\n6pQmqDpyBHR1oUsX+OKLVPT0blGzZj4/IPUAACAASURBVE3RK1IQXjIRTFUmbdrA4cMlbztwAB40\nSRWE14FSqSkE8dNP2s97ehawZk0Eb7zhrFXM5UW4fv06VlZWmJubv9DzVHRKpZJjxzKZNk3ByZNV\nkMuhbds85s2TU7++AqVSKQVRZmZm2NnZoa+vz7lzT9c3Jz09ndu3b+Pl5VVq5b2///6bRo0albhN\neDaJiYmkpKSIdL+XLCcHdHRAodA8Tk1N5datW7i7u5fYnFwQhBdDBFOVRVoaWFhAaT/uzz/XrNgX\nhNeIUqmpordtm+aDR/v2MGAAqFQZ3Lx5E1tb2xdajezmzZuYmZlhaVkx1029SAUFBaSlpZGSksLF\ni/kMGOBBVpZ2ZrmVlZr9+xPR04vH3NwcW1vb557duH79OsbGxqKp7EsWERGBhYWFqO5XztLT04mK\nisLNzQ1jY+PyHo4gVAqiAEVloVaXHkiB5lOnILxmdHQ0WazF2xuZ4OnpyfXr18nOzsbZ2fmFFCao\n6Gumylp+fj6pqamkpKSQnZ2NqakpVlZW7NljTlZW8Zmi5GQZq1dXYcWKsksRc3R0JDw8HEtLSxSF\nt+yFF6569eqiul8FYGpqiqurKzdu3KB69eoixVgQXgJRgKKyMDfXNN0pTefOL28sglAB6OnpSWlJ\nERER5OXllfk5KkMwlZubS0JCAhEREVy5coWsrCxsbGxo0KABbm5uWFhYcPRo6esx//7btEw/fOvr\n61O1alViY2PL7JjC4xVW94uOji7voVR6JiYmuLu7ExUVRWpqankPRxBeeyKYqkzmzYOSFqZ++CEU\n6SUlCJWFXC6nevXqWFlZER4eTkZGRpkf/3UMpnJycrhz5w5hYWFERESQm5uLnZ0d9evXp3r16piZ\nmWmtWXrUzfEXsWzNzs6OjIwM0Tj8JbO2tkatVpOUlFTeQ6n0jIyMqFGjBrdu3eLevXvlPRxBeK2J\nYKoyefttOH0avL3B0RHq1YOvv4a9e0H0XhFKsWHDBt58801Ak8bl4uJSrAdZWZ3naY8bEBDA/fv3\nGThwICNGjGDMmDH4+fkVm2Vyd3dn9+7dAHh7ewPg4+NDVlYWS5cuJSkpCVdXV27evFmmZZ7lcnmJ\nPZteRdnZ2cTGxnL58mUiIyNRKpU4OTlRv359nJ2dMTU1LbXoQ58+pR/3UduelVwux8HBgdu3b5f9\nwYVHql69OnFxceTm5pb3UCo9Q0NDatWqRWxsLHfv3i3v4QjCa0usmapsGjSAzZvLexTCK6Swb8/p\n06eJj4/n7bffBiA6OpqFCxcCmmCla9eu9OvXjy5dunD58mW+/fZbDh48yC+//EJOTg6fffYZsbGx\nHD9+HDc3N+RyORMnTuSLL74ANKl2Q4YM4fz582zYsAGlUknz5s2xtrbm+PHjnD59mgEDBtCvXz8A\nkpKSkMlkVKlSBZlMxsKFCzE0NOTgwYOsWrUKPz8/6T288cYbbN26lc4PpbPKZDL69+/PlClTWLZs\nmdY6KhcXl1KDgycll8spKCh4rmOUp8zMTGkNlFwux9zcHFdXVwwNDZ/qOAEBmoKhx49rP9+xIwwe\nXIYDLsLS0pLExESSk5OxsrJ6MScRitHX18fW1pbo6Ghq1apV3sOp9AwMDKhVqxZXr15FpVKJAiGC\n8AKI6QhBEB7rs88+Y+fOnRw8eJAPH5TQX7lyJUZGRlhZWXHp0iUA6tatS2BgIJaWlsTHx7N06VIs\nLS2xt7fnzJkzyGQyPv74YyZPnsylS5dIT08nISGBuXPn0rx5cwAWL16MlZUV1apV48KFC7Rt25YB\nAwbg4uIiBVIAf/75J/Xq1ZMeF84ANWnShIiICK3xy+VyBg0axOrVq4u9N3Nzc2mdR+E6KrVaTURE\nBPn5+c913V61ND+1Wk16ejq3bt2Smujq6OhQs2ZN6tSpg4ODw1MHUqDJLj50CLZs0UyM9+2rqbK4\nZ4+mT86L4uzsTGxs7Cv1M3gd2NjYiHS/CkRfXx8PDw8SExOJj48v7+EIwmtHzEwJgvBYhU0gbW1t\npap3KpWKfv36SQFNdHS01NtEoVCQm5uLWq1m0qRJ6OhoGuSGhoZKH8YLgx89PT0AqQhBXl4efn5+\nUm+mjIwMJk2axPr167XGlJ2dXWIvlb/++gtPT082bdrEuXPnCAoKAuCjjz6id+/eJa6LKlrJTy6X\n4+rqSkJCAmFhYY8tMbxhwwaWLFnC+fPnyc/Pp0aNGnzzzTd06NCh1GDqyJEjXLlyhVGjRpV63NKk\npKQwb9485s6dS40aNejQoQPZ2dm8//77UgojwNGjR/H29iY6Oho9PT3efvtt+vTpw6hRo0hISKBO\nnTpcvHgRe3t7fvjhB5RKJW+88YbURNfDw6NMC0MoFNCrl+brZTE0NMTMzIw7d+7g4ODw8k4saFX3\nK/w3LpSfwhtFkZGRqFQq7O3ty3tIgvDaEMGUIAhPZP78+QBs3LgRgNGjRzNx4kTs7OwwMTFhwIAB\nxV7j5+fHkCFDsLS0pHHjxgAPFSYwxc7OjkWLFvHHH39Qs2ZNxo8fj6+vLzY2NlSvXp2bN2+iq6vL\nvHnz6NChA82aNQPAy8uLn3/+WTrWuHHjUCgUyGQyFixYgEKh0JrJKtyn8PUFBQXo6emhVqsxMDAo\nNnYbGxuqVKnCjRs3sLOzo1q1aiVel9LSIK9cucLatWuJi4sjICAAW1tb+vTpQ+fOnXF3dwdg//79\n/PnnnwwePJgFCxYAmpRJJycnlEol3bt3Z+DAgaxcuVIKaDds2EDPnj0BePPNN1myZAmgWQPWtm1b\nqlatKo2rVatW7Ny5E09PT620xdDQUKZNm8bChQvp378/jRs3Ztq0afTu3fu1Kyfu4ODA5cuXqVq1\nqijZ/RIVpvtFRUWJdL8KQqFQUKtWLa01j4IgPD/RtFcQhFfWyJEj+eabb576devXr+evv/5izZo1\n7N+/n4yMDHr06FHivrm5uVy/fh0jIyOcnZ2LraMKDQ3FyMiIP//8k+zsbJo0aYK1tTUeHh6EhISg\nUqmQyWQEBwcza9Ys1qxZw9GjR5k1axZt27YlODiY8ePHI5fLMTAwICYmhtWrVzNw4EBmzJjBqlWr\nmDt3rnS+7t27s2XLFnR1denevTs7duwAICQkhHfeeYcmTZoAcOzYMS5dusSVK1cwMjLi7bff5urV\nq3z22WcMGzaM1atX4+/vz549e9DV1aVLly789NNPT30tXwUJCQlkZGRQo0aN8h5KpRMREYGlpWWp\nNyOEshMaGkrVqlWpX78+gYGBLFmyBDs7O619goODmTNnDteuXcPAwAAXF5fHHvdJZsOzsrLw8fFh\n+/btACxbtowaNWpgY2ND586duX79Ovr6+ixdupQPPviAOnXqlP0FEIRyItZMCYJQIeTkwOrV0K4d\nfPQRLF8O2dmPfo2fnx/3799/6nMNHjyYNWvWAJrUxdICKdDcYff09ESpVJa6jqqkNMilS5cycuRI\nevToQfaDN2JmZgZoZo2cnZ25ceMGBQUFqNVqevfuzdSpU1m7di1yuZyGDRsyevRoRowYoXUupVKJ\nbgkLjS5cuICbmxsBAQHSLJdSqcTKyoq4uDju3r3L/fv3uXr1KpmZmSxcuJC0tDSOHDkCaNKAXuVi\nGY9ibW1Nbm4u6enp5T2USsfFxYW4uLgX0sdNKC4sLIzg4GDWrl2LnZ0dM2fOxN/fX7ohc/PmTXR0\ndBg7diwrV66kW7du/Pvvv6SlpeHv74+/vz9TpkzROmZJs+Fr167l999/l6oEGhkZYWlpSUxMDAAH\nDx6kXbt20ix4YZDVv39/Vq1a9bIuhyC8FCLNTxCEcpedDW3bwqlT/z33++/w3Xdw5EjpfYo8PT2f\n+9yFZd8fRS6X4+bmRnx8POHh4bi5uRVbr/VwGuR7773HkiVLMDAwKLEqYKNGjWjWrBmjRo1i0qRJ\nWimTX375JT179uTo0aPF7hy7uroSFxeHvb09Fy5cYMyYMWRnZ9OuXTusrKyYN28eKSkp/P7779y5\nc4fPP/8cU1NTLl68SG5uLrt372b37t3Y29uTnJxMQEAAbdu2RSaTlRikvQ5kMhmOjo7cvn0bLy+v\n567SKDw5AwMDke73kqjVahYuXMju3bsxMzNDrVajVqsxNTVl165dUuVU0PxOmzNnDvv372fTpk24\nurqSk5ODnZ0dkZGR5OfnSym/p06dwtfXt9j5GjRowM2bN6XUYh8fHzZs2ED79u1p2LAheXl53L17\nl4EDB+Lt7U2/fv20Cv4Iwuvi9fyfUxCEV8rKldqBVKFz5yAkBL788sWcd8OGDezcuRNnZ2cUCgUh\nISHF9ilMnenQoQO2trYYGhryySefsHnzZqpWrVpsrVjdunW5fv06PXr04KOPPiI6OpratWsD/wVc\nrVq1YvPmzYwcOZJbt26xYMECzMzMMDAw4MsvvyQ+Pp7g4GCMjavyxRcX0dOrz6pVLvz771kGDBhA\np06d8PPzIyQkBHt7e06ePEnr1q0JCwsjPz8fMzMzunTpQt++faXA4b333uO9997TGquVlRUbN27k\nypUrUnrg68rMzIykpCQSExOxsbEp7+FUKjY2NqSkpHD37l3pg7dQ9mQyGUuXLmXhwoVMnDhRSjGe\nMWMGJ0+e1Nq38CaPm5sbv//+O4mJiXz88cd06dKl2HEfNRver18/AgICcHR0JDAwkNmzZxMfH09Q\nUBA//vgjiYmJ+Pr6cu3aNSIjI6lZs6ZWwR9BeB2Iv9GCIJS7bduebdvzkslkDB8+nBUrVpCcnAzA\nr7/+SnBwMD4+PqSkpEj7hoeH06tXLxYsWIBMJuPIkSPMnDmT//3vf3To0AGVSoWPjw+ZmZkkJSVx\n7Ngxevbsyfr16xk7dqzWec+fPy/NqhkbG7N06VJWrFiBs7Mze/fuxdbWli5dNrF9ex/mzTvKzJmX\nSErqwttv/48bN5Jp06YNubm5JCcno6enx4EDB8jPz5ea6Lq4uDyyie7D5HI5o0ePLqOrWnE5OTkR\nHx//2qYzVmTVq1cnNjZWpPu9YEZGRmzatIk5c+aQlZXFxYsXWbhwYalNe2UyGZaWlnz66ad89913\nBAUF4e/vr7VP4Ww4IM2GDx06VJoNX7x4MYGBgQC0bt2a6OhoXFxc2LVrF/v27WPlypVs3LhRqsha\nUsEfQXiViQIUgiCUu/r14d9/S97m6JjLoUNR6OvrS18GBgbo6+tLJdefVWhoKLt376ZatWoYGhqy\nZMkSfvvtNw4fPsydO3fo0qUL2dnZVK1alSNHjuDr64ujoyMffvghBw4coE+fPtja2pKfn0+/fv04\nc+YMb7zxBpcuXaJu3bqcOnWKTp06MXv2bDYXaZa9bNky6taty3vvvadVROLChQscPHiQPn2CcHWF\nvLxcYCDQDOgErKJWLTlff92cS5cu4eTkxGeffcbYsWOZPn06tra2z3U9KoOYmBiUSuUTLbwXylZC\nQgLp6enUrFmzvIcilODWrVtkZ2dTs2ZNVCodfv8dkpLAxiaCyMgDWo3Qn9XjCv4IwqtIzEwJglDu\n2rUrfVvHjro4ODhgYmKCWq0mNTWV6Oho/v33Xy5evEh4eDg3b97kzp073Lt3j6ysrKeaeRg6dKi0\nWPuff/5h1apVzJ8/nw8//FAqHFFIoVAgl8vR1dVFV1cXY2NjcnNz8fDwYPbs2cXS6IyMjFCpVDx8\nz6q0HllnzpzB09OTzz9fQl5eAKAG8oHrgBuQR2RkJE2bfoijoyNWVlYYGRlhYmJCVlbWE7/nyszO\nzo60tLRiP1vhxbOxsUGpVJY6SyKUL2dnZ4yNjdmy5TbVq6vp2BF8fKB9ew/27m1BTs7zn+NxBX8E\n4VUk1kwJglDuAgLghx/gQSaJpFo1GD9eB2Nj4xIb5xYUFJCbm0tubi45OTmkpaVJj9VqtdYsVtGv\nor2UVq1axYEDB7h37x6+vr54eXkxe/ZswsLCaNOmjbTfkCFDmDVrFm5ublL6nJeXF/fu3aNr167M\nmDEDBwcHrdTA0pr2enl5ce3aNZo2bUpWVhZ+fn5S5b2hQ4eyb18uUJgK8wZw68H3zqjVyWRna6oH\nFo7jzp07uLq6Pt1Fr6R0dHRwcHDg9u3beHh4lPdwKp3q1asTERGBqampaOZbARkaOjJqlIr0dO0U\n4cOHGxIUBMuWPd/xn6TgjyC8akSanyAIFUJ0NMyYAbt3g0oFnTtrCk88a2sgpVIpBVmFAVbhl0ql\nKhZgFQZdz9q0Nicnh+vXr2NqaoqjoyNqtYxfflHz2293aNLEnm7doDAeLCgokPrAAKhUKpKSkrh1\n6xZxcXEcPuzEsmUNSzxPtWoQEwOFn0OTk5OZNWsWixYteqZxV1bh4eFYW1tjaWlZ3kOpdOLj48nI\nyBDpfhXQkiXw0JIpiZERJCRo/hQE4T8imBIEodIpDLSKfhUGXUqlssQgS19f/7F30pVKJVFRUcTG\nwsiRbly+/N/dXQsLTaDYqpXm8dmzZ7G2tub27dvEx8ejr6+Pg4MDTk5OGBpaUqeOnKio4ueYNQsm\nTvzv8e3btzEwMBBNUZ9SVlYWN27coE6dOqK6WDkIDw+natWqorpfBePrq+nxV5pr18Dd/eWNRxBe\nBSKYEgRBKEKlUpUYZOXm5lJQUICenl6J6YN6enpS2t277+Zy4oR+sWNbWKg5duwmd+9Gk5SUhKmp\nKU5OTjg5OWH6UDOtGzdgyBBNny0AMzPNHeOpU0G0SSobUVFR6OnpYW9vX95DqXRycnKIiIigdu3a\nIt2vAlm+XBNQlcTEBOLjwdDw5Y5JECo6EUwJgvBKKdr36Ul169aNH3/88Yn2PXbsGJcuXWLUqFHF\ntqnVaq0gKycnh7y8PHJzc6Uml0lJxrz/fvUHr/gKTTW+dUAkUI0GDeJYty6YunXrSiWCP/jgAzp1\n6oS/vz8TJkxg+PDhbNiwgW7dunHy5FXy8qowaFA7kV5TxvLz87ly5Qqenp7o6xcPfoUXS6T7VTxp\naeDmBvfuFd82diwsXPjyxyQIFZ0oQCEIwivt1KlT7N27l4SEBCZPnkxMTIwUDHl7e7NgwQLCwsKY\nMWMGgwcPZsOGDdy9e5fMzEwWL15Mr169eO+997h06ZLUD+rXX3+VeuIsWLCAlStXcvXqVdLS0pg5\ncyaTJk3C1dWVevXqcfbsWdRqNREREfTv358qVQqLVhSgqcLnCMiASYAX779/lS1bVrOwyKcSS0tL\nLl68SFpaWrH3N3RoFwYNGoSv7yNKHgrPRKFQYGNjQ0xMDO4id+mls7W1JTU1leTkZKysrMp7OAKa\nGfDffoNevTSz4wByOfTtC3PmlO/YBKGiEonigiC80hQKBfn5+RgaGrJr165i2x0cHPDy8uLLL78k\nPT2dEydOYGFhgZ6eHmFhYchkMgIDAwkMDOSXX34B4K233mL27Nnk5uYSExPDpk2bsLCwwMLCgnPn\nziGTyRg2bBht2rQhISGBOXPm0LhxY9LT0zE2jkFPTwVcAZyKjESTBNCmTS3u3LlTbJxBQUHMnTu3\n2PNyuVyrQqCgMXDgQFJSUkhLS8PW1haVSsXNmzeLNUh+WPfu3bUe29jYcP/+fdLT00t9zXfffcfp\n06fZsGGDNIM4ePBgbt68qbVfXl4evr6+BAQE4OPjw//93/8BUKNGDUaOHMmnn35KZmYmCQkJfPnl\nl8/4zl8vLi4uxMTEkJ+fX95DER5o0gQiIzUpxjt2aIKq0ND/it4IgqBNzEwJgvBK+/rrr9myZQt/\n/PEHR48eRV9fX+ozVdh7qXAtk0qlok6dOlofZAtT7XR1dcnNzS3xHA4ODlqv2bVrF2ZmZty7d4/s\n7Gz+/fdfUlJSsLW1xcysgE8/TWXr1vuAdjn3+vXB1TUCOzs7fvnlFw4dOsTgwYMBTbn0jIyMEgMn\nPT09CgoK0NUVv7ILtWrViuPHjyOTyejRowdnzpwhLCyM999/H29vb7Zs2cK5c+c4ePAgzZs3Z+3a\ntVI6mVKpZOLEieTl5aFUKpk+fTqtW7emb9++/P333yxYsAA7OzvpXIcPH8bHx4erV68yYsQI2rdv\nz7179wgICCA0NFTab926dXTs2JGPPvoIpVLJZ599xp49e3jzzTf55ptvmDt3LlFRUdStW5e4uDjy\n8vIq/XqhKlWqYGNjQ3R0NDWetXSnUObkcmjdurxHIQivBjEzJQjCK+ebb75hxIgRzJ8/n1atWjF1\n6lR2796NTCajQYMGnDlzhpCQEG7d0vRnqlOnDkFBQZiZmSGXywkMDGTkyJHExMRIx5QVqerw119/\nMWHCBKpUqYKjoyNNmzbFz8+P0aNHc/z4cTIzM/nnn3/IzMykWrVqhIaG8s8//wBQtWpVQkMt6NnT\nDh2dB3kyqKlWbQb16n3O4sULmTx5Mh06dGDx4sXUrVtXOu/EiRPZt28foFnPUxjoyWQyEUg95P33\n3+fIkSOcPn2a4OBgjhw5wvHjx2nVqhUffvghhw4dYv369QwZMoRVq1axfv16hg8fDsDBgweJjo7G\nwsKCrKwsMjMzMTExoW/fvnh7e3Ps2DHpPGlpaejo6EiPC5cZW1paFptNuXz5Mo0bNwY0/awMDQ1R\nqVRcvHiRAQMGcPjwYenn7e7uzrlz517oNSpLoaGh/PLLL9y+fZsePXpw586dYrN8hXx8fJ6qibSN\njQ2jRo0iOTn5qcd15MgRtm7dyrFjx2jdujX+/v74+Phw/vx5rf2mTZtG165dAbh79y7m5uZcvnyZ\nadOm8emnnwLw22+/ERoaKmYOBUF4KuJ/Z0EQXikDBgxgwIABj9znhx9+AMD/QcOUqVOnStvmz5+v\nte+OHTsA8PDwkPZrVVi/HM1s1pAhQzh+PIukpPuYmsKKFSswNzdHT0+PZs2aceTIESwsLOjVqxeG\nhobcv3+fiRNTMTc3wNs7DxeXadjaaqqXlVTsoHAM9vb2pKWlsWfPHiIjI6levTqXL1+mSZMmz3Kp\nXmsuLi5ERUXh6uqKo6MjCQkJpKWlYWJiQp8+fejbty82NjZYWVkhl8vR0dGRZoFUKhXvvPMOvkXK\nlllZWXHnzh3kcrnWDGVOTg5GJVT+SE5ORk9Pj9OnT7N9+3Y++eQTvLy8+Pvvv/nwww8pKCggOzsb\nuVxOgwYNCA0NJSgoiL///ptGjRphYmLyVAFHRRAWFsb333/P2rVrMTMzk56fPn06qamppKWlsXr1\nagDmzp3L7du36devH7q6ulrrGLds2cLGjRs5deoUhoaGzJ8/n5ycHCZMmEBsbCzr1q0jJSWF77//\nnsTERIYMGYKtrS19+vShSxfNGsLCkuqbNm1izZo1/PHHH3Tv3p1Ro0aRl5eHt7c3O3fulMYok8kw\nNjYmNjaW7du30759e+l5c3NzTpw4Id1QsbGxETOHgiA8MRFMCYIgPKSgoIDU1FRSU1M5eVLNzJku\n3LhhAYCpKUyYAOPGFRAVFUW9evXo0KGD1ofL2NhY7OzsmDx5HAYG6Q8++BlgZ2dHVFQUHh4ejzx/\n165dpbvoOjo6jB49+oW911eZtbW1VLhAJpPh6ekJaNIiraysGDJkCACff/45wcHB2NraIpPJ+Oij\njxg+fDjBwcGkpqaybNkydHR0sLKykmYYC9nY2GilXq5atYqDBw+SkZHB9OnTcXFxoXnz5gA0a9aM\nwMBAfv31V9LS0phYtCEYEBwcjK+vL1u3buXatWulzuxURGq1moULF7J7926tv+sZGRlER0fz7bff\nsnHjRn7//XcAhg0bhr29Pb1792bkyJHFjrdnzx6tNY76+vpMnz6dn376iWPHjtG4cWNyc3OxsbFh\n06ZNBAcHU6dOHYKDg7WOk5ycXGzWtrB9wcP69u3Lxo0bi/0bHD16NPPmzWPQoEHSc4Uzh82aNXvK\nKyUIQmUjgilBEJ7Lhg0b2LlzJ87OzigUCkJCQqRtarVaK33uWX333Xd4enoSERHBzp07cXd3JyMj\ng8mTJ+Pq6irtl5eXR2BgILq6uqSmpjJy5EiaNGlC9+7dpdmfwjvjkydPZsyYMVKz27y8PCmAys7O\nxszMjJycqvj5mZGR8d97SE/XBFM5OfEMHaoo1vQ1IyODnJwc3N3di713a2trUlNTiY+Px9bW9one\ne2GAIBS3Zs0a6fvFixdL32/cuBGFQkGDBg0AaNmyJS1bttR67dq1a7Ue79ixA6VSSYMGDdDRqcmq\nVWBgAJ07a34GiYmJj50V1dPTY9myZcWeL/y7V61aNbZu3UpBQQGZmZnY2Ng8/ZsuJzKZjKVLl7Jw\n4UImTpzIm2++WeI+hWmQhX/KZLIS1zE+zMzMDFtbW3R0dEhOTmbp0qUEBQWhUqmYNm2atM/DSmq4\nXNi+ICIiglWrVkkzzU5OTmzYsIG2bdtqpfjq6+vTqVMntm7dSusHC4VexZlDQRDKh1gzJQjCc5HJ\nZAwfPpwVK1ZIax4GDhzI9OnT2b17N9OnTycgIIBBgwaRn5+Pj48PALVq1SI2NpYxY8aQkpJCixYt\nWLx4MX369ClW7e7w4cM0b94cmUzGiBEjCAkJYf78+dKHrEKFBQAWL17MunXrmDVrVqnj9vb2ZvXq\n1cTHxxMWFkZYWBj379/HxsaGBg0a4Orqyvbt5lqBVFGbN9vj4OBQ7MNcbGwsDg4OpQaR1atXJyEh\ngfv37z/yugrPrn///ixduvSpXyeX6xASUpN69QwYMQJ8fMDBAYyN/cjOzi6z8RXeCHjVGBkZsWnT\nJubMmcPZs2eRyWSYmJjg4uLCuHHjOHr0KB999BEAq1evZsiQIQwdOrTEdYydO3fmgw8+kCodXrhw\nAZlMJs0EtmzZknnz5rFx40atf0tRUVEEBQVJj4um4e3YsQN/f3+GDx/OlClT8PDwYPHixXTt2pW7\nd+/y3XffMWfOHJYvX87+/fuZPHky169fRyaT0adPH/79919OnjxJ48aNiYyMlGbLoqOjCQoKkqo1\nCoIgFCVmpgRBeG5r165lz549GPR1rQAAIABJREFUWFpaAkilw42Njdm3b59WCpC5uTkXLlygdevW\nHD16lHv37mFhYYGpqSkBAQHs27ePY8eO0atXL+DpCwD07NkT0C4AcP78eUaMGAHA9evXiY2NRaVS\n8b///Y8BAwbg6OiIiYlJsff177+lv+fISDl5edrlggvTwSwsLEp9nZ6eHk5OTty8eZPatWuXycyd\nUDZWrIDvvqui9VxODgQFWfDOOxZUr1425ykss/8qKTojt337dq0/i65JBM1M8sMeXsc4cOBAZDKZ\n1IC7X79+gObfd1hYGGfPnmX48IWcO6fmxo3pTJ/+FV27dpL+vfz9999s3LiRd955hz59+lCtWjVa\ntmzJzJkzqVOnDr///jtGRkZSBUeFQsGECRPIzMykTZs20trJzp074+XlBcDZs2cJDQ0lLy+PCxcu\nFEtP1NPTw8jIiLi4OOzt7Z/jagqC8DoRM1OCIDy3oUOHsnbtWuzs7KQ1J6amplLgU0itVtOiRQtm\nzJhBQEAABw8elFKdChf5KxSKpy4AEBAQwPHjx6UCAIBWAYB69eoxceJERo4cSdWqVZHJZLi7u2Nq\naoqTk1OJgRSAk1OJTwNgY6MdSKnVamlW6nEsLS0xMDAgNjb2sfsKL88335T8vFoNq1a93LG8Kh6u\n8hcfH/9Ur1+7di1Dhw6VbsRs2rSJfv0GEx7+Dh9/PJFJk97j2DEFmzcXMH36cgBOnz7Nli1bsLCw\nYN26deTn56Ovr8+8efO4ePEiDg4O1KhRQ7ohA7Bv3z5UKlWx30lubm4kJSVpPde+fXtMTEzIyckp\nNt4GDRpw/Pjxp3qPgiC83sTMlCAIz23VqlUcOHCAe/fuSWkwMpkMU1NTKQUoJSWF1atXk5KSgr+/\nP7Vr1yY9PV0qS1yaZykA8Msvv3D37l18fHy4ePEi9+/fR6FQULNmTczMzLC3tyc5Ofmxd5eHDtXM\nVqhUxbd9/rn247t372JgYFBqYPYwZ2dnrly5grm5OcbGxo9/gfDCRUeXvi0q6qUN45VTtMof/Df7\nZGJiIs0U9e/fH0tLS3766SfeeecdkpKScHZ2ZuDAgezatQt3d3fpRkxg4PdEREwGzgIewFRyc+04\nf96NpCRN1cvExET09fUxMDCgffv2pKSk0K1bNxQKBWZmZuzZs4c+ffpw4sQJWrZsiZOTE99++600\ncw2aGyA3b95ErVYTEBBAvXr10NHRwdjYmC+++IIlS5bg4uKi9V5NTExISEh4GZdVEIRXhAimBEF4\nLiUtyi+a5vNwClC1atW4ffs2gFY1r8JF+oVrLop6kgIAKpWKrKwsxo4dS1paGoaGhpibm2Nubs5v\nv/0m7bdlyxZAk6I0cODAR763evVg3ToYOVKT7lWoe3couuRFpVJx584dKaXoSejq6krlvb28vEpc\nSC+8XF5ecPZs6duE4h6u8rdy5UpycnKws7MjMjKS/Px8HBwcGD9+PMeOHePtt99mwoQJXL9+ncDA\nQKKjo3F2dubSpUv4+vrSvXtfvL39gQKgM3AVmAIYola7sXOnZt3hyJEj6dy5M1OnTmXWrFkolUqq\nVKmCWq3m/v372NnZMWDAAL744gtatmyJjY0Np06domfPnhw+fBh/f3/S09Px8/PD2tpaKmASGhqK\nTCajRYsWLFq0iOrVq5OXlydVB4yIiODdd98tp6stCEJFJIIpQRAqnD/+gLlz4dQpqFoVevb0IyUl\nDWtr7f2KljDPzMzE2NgYc3NznJycHtvktmnTpjRq1OixY/Hx0VR027kTMjPhgw/gQZE4SUJCAqam\nplSpUqXkg5TCzMyMtLQ0bt++XewOuPDyBQRAnz7Fn1coVHz8cRQ5OfZSI2VB4+Eqf2q1mg4dOtCp\nUydpn6JV+Aq/d3d3x9raGn19fbZt2yZtf/vtrqjVocBG4BiQCkwHcoHe3LhxH3v7fC5cuMBnn32G\np6cntWrV0qrQ5+TUnOPH/6Z378nExv5Jeno6Pj4+TJ48merVqz+yWXLRmzW7du0iPj4eX19fAgMD\nAfj333+l7wVBEEAEU4IgVDC//QadOsGDSsrcuwczZ1rwf/9nwa+/Qn5+8RLmVlZWuLm5PdXszpME\nUoWsrGDYsJK3FRQUkJiYSO3atZ/4eEU5Ojpy5coVUlNTMTc3f6ZjCGWjd2+Ii4MZMyAjQ/OcgwOs\nXCmnWTMTIiIisLGxeeKy9pVFYZW//v37M3r0aNatW8eJEyfIy8vTapXwsA8++KDYGitra1Ao9Piv\ntswOIBJIA6bg7GzC8OGjMTU1lYKizZs3I5fLmTBhOkOGqPnzz1PALwCYm//M9Ok/MHFiD5ydnZ/6\nvdna2kqz5nl5eYwbN+6pjyEIwutNpn54NaYgCEI5atAAHuqbKlm37haNG6dI6XumpqblXg3v9u3b\nyGQyHB0dn/kYWVlZXL9+HS8vr8fOqAkvXmYm7N2b/KDPlBWFP5K8vDyio6MpKCigevXqTz0TKfzn\njz/+YOnSpXz77bdaBWYiIqBr17WEhzcB3tB6jYUFREQo0dVNJy0tjfT0dHR0dDAzM8PU1JRRo0z4\n/vvivw/MzWHKlJW0aNGIpk2bvui3JghCJSOCKUEQKoyYmEdX0Bs+PI+VK/VK3+Ely83NJTw8nDp1\n6jx3EBQbGys1+xXKX2GvMzs7u2LbkpOTiYmJwdraGltb23IP6F8HajWMGqWpmqhW56NZK+UFaK6t\nuzts3gwPx0LZ2dmkp6cTFZVBy5buFBSUPDv9xRd/M2fOk89GC4IgPCmx4lkQhApDoXj0dmPjihNI\nAcTFxWFjY1Mms0n29vbk5eVJjY+F8iWTyYqV0S5kZWWFl5cXWVlZhIeHl2lD38pq7VpYuVITVIEC\nqENhILVgAURGFg+kAAwNDbG1tcXIqGapgRSAWi0CKUEQXgwRTAmCUGHY2ECLFqVv79bt5Y3lcbKz\ns8nMzMT64aoYz0gmk+Hq6kpMTAx5eXllckzh2T0qmAJNP7QaNWpgY2PDtWvXiIuLe+T+wqOtXl36\ntpMn4XGTfy4uYGhY+nZRjVEQhBdFBFOCIFQoISFQpPiX5PPP4a23Xv54ShMTE4OdnV2ZljQ3MDDA\n1taWmzdvltkxhWfzuGCqkKWlJV5eXuTk5BAWFkZWVtZLGN3r50EhvhI96KTwSKamMHhwydscHKBH\nj2cblyAIwuOIYEoQhAqlUSM4f15TprpZM+jQAXbs0KylqCjS09PJz8+natWqZX5sGxsbZDKZaAxa\nzmQyGeHh4fTp04cxY8awYMECALqVMD2qq6uLm5sbdnZ2bNu2ja+++qpMZ6k+f9Ah+uOPP8bPz49R\no0YxZcqUEsdRWOHO29sbgO7du0vbJ0yYQHR0NLt379bqvVYR1K//bNuKWrAABg2Colm39evD77+D\nqGgvCMKLIspGCYJQ4bi6wqJF5T2K0sXGxuLg4PDY/TZs2MDOnTtxd3cnIyODyZMn4+rqWur+KpWK\nnj178sMPPxAWFoapqSmHDh3CwsKCFo/If8zPz8ff358VK1bQtGlTmjdvTm5uLl5eXvj5+Un7RUVF\nsWLFCkaPHs3y5cuZP38+oOmTtWLFCmbMmPEUV+H1JpPJOH78OP369aNdu3bS89evX2fixIlcuXKF\nrVu3cu7cOfbu3UtCQoLUx2jLli1cu3YNAwMDVq1axbZt2/jzzz/JyMjA19cXlUrFhg0bUCqVNG/e\nHEdHRxYtWkSLFi24c+cOi4r85T9//jyenp4AGBsbs3TpUgDWrl3L3r176dy5s7Rv27ZtWbRoEd9/\n//0j31eXLl0YNGiQ1vsqb4GBcPhw4Zqp/+jpwZgxT3YMPT1Yv15T2v7CBU3acOPGZT9WQRCEosTM\nlCAIwlO4d+8ecrn8iXpCyWQyRowYQUhICPPnz2fatGlER0cTFBQE/DdTMHDgQKZPn86ePXvIzc1l\n9uzZTJ8+nQMHDpCcnExaWhqhoaEMGjSIr776itmzZ2udZ9++fbRp0wYAF5f/b+/eg6us7zyOvw/h\nkgQCxBgShTbUG2woCCoUxcqgbKlSWaYVvCxyK5doIEIZA0YBXRV2ixQCyUihVnTq2m29dGzRiloE\n4khhV9GKSKFCwCBMYJIAgUAuZ/+InBITFR8DIcn79Y9zntvve8484+TD73m+vxSys7NZtmwZ+/fv\n5/1T+sx/Ude5pKQk9u7d67tapwiFQowcOZK8vDwmTJjA8uXLAUhMTGTevHkMGjSIzZs306pVK8rL\ny4mNjeWFF14gKiqKQYMGsXjxYvLz89mzZw85OTnEx8eTlJTExo0bWbRoEQkJCSQmJrJ582YArr32\nWu69995a6y7l5eXRu3fvWvX17duXbdu21djWrl07vv/97/Pyyy9HtpWWlnLXXXdx11138eqrrwLQ\nokULioqK6vX3+qZ++MPqIHTqK4hdu8ILL9ReJPurdO5cPaNtkJJ0NjgzJUmnKRwOs3fvXrp27fq1\nzoHqd2vK/7kSaQ2hUIhJkyZxwQUXsGLFCu6//35KSkrIyMigX79+JCYmAtWPeY0YMYI77rijxvl5\neXlMmTKl1nVP/sHd6zSek7r44ot555136N+//2l/t6YsFArRtm1bHnnkEQB+9KMfMXHiRDp89kJf\ndHQ0x48fJycnh2effZa33nqLN998E4AOHTrQsWNHOnToQGVlJVVVVUyfPp24uDgA3njjDTIyMiKB\nfO3atcR+1j3h848HHj16tMY6TCdt3LiR7t27k52dza5du5g/fz4AEyZMYOTIkURFRQHVC+o+/vjj\nQHV4P6l169ZUVFScU+uajRsH//7vsGlTdWfPq66CenwlUZLOiHPn/6KSdI4rLCwkJiaGdu3afe1z\nDx48SOvWrWnTpg0VFRUANZoVdKij60a7du04cuQIx44dA/jSP7jrqmnTpk3ceuutzJ07l/Ly8si7\nN3WJi4uzecIpQqEQq1ev5r333qNly5b06NGjzpm9gQMHMnfuXEpLSznvvPMi5578b9euXRk9ejTj\nx4+nffv2jBw5kpkzZzJ16lSSkpLo2rUrPXv2/MJZw9TUVHbs2EG/fv0oLS0lIyODyspKEhISmDRp\nUq3jo6KiGDVqFJmZmV/5/c6lIHVS69YwYEBDVyFJp89FeyXpNFRWVrJlyxYuu+wyok/zbfannnqK\n5557LvLO1Jw5c0hJSWH8+PH07NmTl156iZUrV/Lggw+Sm5tLbGwsw4YNo0+fPmzfvp2srCzWr19P\nOBwmJiaGpKQkbrrpJm6//XaeffbZyDhLly7lyiuv5JprruF73/se/fv3r/Odqfz8fHJzc0lPT2f4\n8OGRWaicnBxmzJjBfffdR1JSUv3+cI1UcXExBw8erLdFlCsrK/nkk084dOgQKSkptG/f/rTOq6io\nYMaMGWRnZ9dLHQAffvghq1atijxuKkkKzjAlSadh7969lJeXk5KSctbHzs/PB2DHjhQ2bap+r+SW\nW6rbQQMUFRWxcOHCyCNpX1dFRQVpaWn86le/qq+SG72SkhIKCwu55JJL6vW6hw8fJj8/n7i4OLp0\n6UJUVBQff1z9vtDu3fDd71Z3pPvsyU6guglFnz596q2Gjz76iJSUFGJiYurtmpLUXBmmJOkrlJeX\n8+GHH5KamkqrVq3O+vgHDlRxww1lvP/+P1cljYuD//kfuPHG6s/vvPMOV1xxRaDrFxUVUVJS8rXe\nBWvqDh06xP79+7n00kvr/dpVVVUUFBRQXFzMu+9exIQJbTn1dbr4eHj1Vejbt96HliTVM8OUJH2F\n3bt3ExUVdVrt0M+E226rDk6fFxsL+flwBpa7avYOHz7Mp59+ymWXXXbGxti79wiXXhrD0aNRtfb9\ny7/Ahx+esaElSfXEPjmS9CXKysooKioiOTm5QcY/eBCef77ufUePwjPPnN16motQKFSvC+/W5bXX\n2tUZpAC2boX//d8zOrwkqR4YpiTpS+zdu5fk5ORIq+mzbf9++Kz5X50++eTs1dKcnI0wVVz8zfZL\nkhqeYUpSo7Zy5Upuvvlmxo4dW2sx29MxYsSIGp///ve/89hjj5Gfn8/ll1/OnDlzmDVrFq+//vpX\njjtu3DhKS0tZsmQJW7ZsCf6lTtG16z8bTdTl6y5o2hSsXLky0pDhZFOQVatWfek5a9asITc3N/J5\n165dX9rNrkWLFnWGqZPt5W+88UYyMjJIT09n9uzZtY577733GDduHNOnTyc9PZ1jx47Vumeuuw5g\nCVD7XomNddFZSWoMzr1FJiTpawiFQqSlpTF06NDIYraPPvooBw4c4MiRIyxatIhVq1axYcMGDh8+\nzNSpUykpKWHFihV1NhdYtmwZ999/P0eOHOGqq67i5z//OQkJCQwbNozBgwd/6bgnt48ePZrZs2ez\ndOnSb/z9YmMhPR0+W5O1hq5d4XNZsFkIhUJ0796dt99+m3379nHNNdcA1V0PFy5cCFQvQjx8+HDu\nuOMOhg0bFmlx/sorr7BhwwbGjRsHVLcsz8rK4sSJE1RWVjJr1iwWLFjA/PnzefLJJ/nxj38caSH/\n7rvv0r17d6B6DbAlS5YAsGLFCl566SWGDRsWqfHhhx/mt7/9LS1btmTdunXk5OTQqVOnGvdMVhbc\ndNNoXn55NlDzXpk2DT5b01eSdA5zZkpSo7dixQoGDBjADTfcwEcffcT69euJj4+ndevWbN26ldzc\nXOLj40lKSmLjxo0sW7aMJ554grS0tFrX+sc//kFCQgIlJSWEw2ESEhIAuOiiiygsLKxz3FNDFkDH\njh0j7czrw8MPw4wZcGon62uvhddfhzZt6m2YRuUnP/kJzz//PK+99ho/+MEPAHj88cdp27YtCQkJ\nfPDBBwD06NGDmTNncv755/OHP/yBv/3tbzz00EORRXJfe+018vPziY+Pp7S0lMrKSk6cOEFxcTEf\nfPBBJEgB5OXl0bt371q19O3bl23bttXYFhUVFVkUt1+/fpGZylPvVYDnn+/IxRfnR4JT587w2GMQ\nsMu9JOksc2ZKUqM3adIkBg0axMiRI7n66qvp0aMHc+bMieyPiYmp8Xnt2rVERUXRunXrWtdq0aL6\n35j2799Pu3btAAiHw+zcuZNwOMz06dPp2bMnUVFRNcYdP358ndepD1FR1X9gP/AAfPBB9TpTZ7DJ\nXKNwco2k5OTkyG9dVVXFnXfeSc+ePYHqmaoOHToA1bNZ3/72t/n444+prKyMXKeqqooBAwYwderU\nyLaJEycyevRoBg0aVGPMo0eP0rZt21q1bNy4ke7du5Odnc2uXbuYP38+lZWVlJeX06pVKzZt2kRq\naipQ81796U9/SnQ0fPe7LXjuOTh0qHo2qh5vHUnSGWaYktQkxMTEMGTIELZt20aLFi2YMWMGx44d\nIysri1GjRjF58mRiYmIYOnQokydPJjMzk+Tk5MgMxUmxsbEUFhYSFRVFXl4e06ZN49ChQ2RkZNCp\nUycWLVoEwFNPPVVj3BdffDFyrXA4THR0dL1/x44dq2ekVG3BggUAPP300wBMmTKFrKwsLrjgAuLi\n4hgzZkyN46+88kr69+/P3XffTWZmJgBDhgwhLS2NzMxMiouLycnJoXfv3pSVlTFkyJAa56emprJj\nxw769etHaWkpGRkZVFZWkpCQwKRJk2oc+8ADDzBx4kTi4+M5fvw4Cxcu5He/+x3hcLjGPTN8+HCi\no6Np2RLOO+9M/VKSpDPFdaYk6RSrV69m69atTJgwoc5ZiNPxyiuvcPjwYUaOHFnP1elsuO++++jc\nuTPXXXcdvXr1imyvqKhgxowZZGdn19tY3iuS1Lj5MIGkZq2iAh5/HK65Bnr0gP/+72uJibk0cJCC\n6kfP/OO48erefT5PPpnO1Vf34IorYOXK6u0tW7Zk7Nix9TqW94okNW7OTElqtqqqYPhw+OMfa27v\n0CHM2rWhZtl2vLl78EF46KHa22fPhv/4j7NejiTpHGeYktRs/fGPcEo36xp++EN45ZWzW48a1oED\n0KULHD9ee1+bNrBnDyQmnv26JEnnLh/zk9RsvfjiF+979VU4evTs1aKG95e/1B2koHr7X/5yduuR\nJJ37DFOSmq2vmpd33r55+ao1u5rrml6SpC9mmJLUbN188xfvu/56+AY9KNQI/eu/Elk89/M6dIDP\n1gaWJCnCMCWp2fq3f6v7D+R27eA///Ps16OGFRsLOTnViySfKioKli6t3i9J0qlsQCGpWTtw4DD/\n9V9HeOONZIqLQwwcCPfeC6mpDV2ZGsr//R/k5sL27XDJJZCeDldd1dBVSZLORYYpSc3aRx99RFJS\nEvHx8Q1diiRJamR8zE9Ss1VUVARgkJIkSYEYpiQ1S+FwmIKCAjp37tzQpUiSpEbKMCWpWSosLCQ6\nOpq4uLiGLkWSJDVShilJzU5VVRX79u1zVkqSJH0jhilJzc6+ffto3749MTExDV2KJElqxAxTkpqV\n8vJyCgsLufDCCxu6FEmS1MgZpiQ1K59++innn38+rVu3buhSJElSI2eYktRslJWVUVRURHJyckOX\nIkmSmgDDlKRmo6CggOTkZKKiohq6FEmS1AQYpiQ1C0eOHOHYsWN06tSpoUuRJElNhGFKUrNQUFDA\nhRdeSCgUauhSJElSE2GYktTkFRcXU1VVxXnnndfQpUiSpCbEMCWp3o0dO5aioiJKSkpITk6mqqqK\nnTt38rOf/YyHHnqILVu21Dg+MzMTgFtuueULrzlixIhAtYTDYQoKCr50gd7y8nLS09MB6NevH/fc\ncw9paWksWbKkxnG7du2iT58+TJ48mVGjRhEOh1m5ciWrVq3i3XffZdmyZYFqlCRJjZNhSlK9Gzhw\nIOvWrWPt2rWMHDmSjRs38uabb3L99dcDsHz5csaPH8/KlSsB2LlzJ2+99RZbt25l3rx5HDp0iGnT\npjFt2jRmz54NwI4dO8jKymL48OGUlZWxYcMGxowZw5gxY/jrX//K2rVryc3NBeD2228HYNKkSUyd\nOpWXX36Ztm3bMnPmTKZPn05GRgZVVVWRev/0pz8xePBgAFJSUsjOzmbZsmXs37+f999/P3JcKBRi\n8ODB/PKXv6R9+/YcPnw48thgnz59yMvLO7M/rCRJOqcYpiTVu+uvv541a9bw9ttvk5mZyZo1a1i3\nbh0DBw4E4LbbbuPXv/41f/7znyPnDBgwgNTUVLKysnjmmWcoKysjPj6enTt3Ul5eTmJiIvPmzWPQ\noEFs3ryZnJwcVqxYwfLlyyMh6vP27dtHamoqEyZMYPXq1eTn5xMfH09paSkFBQWR4/Ly8ujdu3et\n8/v27cu2bdsin8PhMGvWrGHEiBEcPXqU9u3b1zi+bdu27Nu37xv9dpIkqfEwTEmqdykpKezatYuy\nsjK6dOnC/v37OXToEHFxcQCREPL5ZhAnP4fDYYYOHcrcuXP5zW9+Q6tWrejQoQMA0dHRHD9+nHA4\nHDkvHA7Tpk0bKioqACgtLQUgOzub6Oho7r77bsLhMAMGDGDOnDk88cQTfOtb34qcf/ToUdq1a1fr\ne2zatIlu3boxd+5csrKyCIVCDBo0iN///vdUVlbWCGQAcXFxkbElSVLT17KhC5DUNHXq1ImEhASg\nOiR169Ytsu9kaPp8mGrVqhVz585lxowZTJkyhfXr13PixAkWL15c6/pTpkwhLS0NgPT0dC6//HKW\nLl3K4sWL2b17N2VlZcyaNYsLL7yQbt26MWTIENLS0sjMzKS4uJilS5fSpk0bAFJTU9m+fTuJiYns\n3r2be+65h+PHj5OamkqvXr3o1asXAPn5+ZHxMzMzWbBgAX369Il8j08//ZTvfOc79fUTSpKkc1wo\nfOo/70pSE7Fnzx5CoRBdunT5ymOLiopYuHAhjzzySODxDh48yKOPPsovfvGLwNeQJEmNi2FKUpOw\naRPk5sL27XDxxZUMGfIPbr31Ilq2PL0J+HfeeYcrrrgi8Ph79uwhOjqaxMTEwNeQJEmNi2FKUqP3\n9NMwbhyc0qCPFi3CPPVUiFGjGq4uSZLUtBmmJDVqpaXQuTOUlNTe17EjFBRAbOzZr0uSJDV9dvOT\n1KitXl13kAIoLq7eL0mSdCYYpiQ1aidOfLP9kiRJQfmYn6RGrbAQunSpOzS1aQOffALnn3/265Ik\nSU2fM1OSGrXERJg1q+59s2YZpCRJ0pnjzJSkJuHpp2HJEtixAy69FDIy4M47G7oqSZLUlBmmJEmS\nJCkAH/OTJEmSpAAMU5IkSZIUgGFKkiRJkgIwTEmSJElSAIYpSZIkSQrAMCVJkiRJARimJEmSJCkA\nw5QkSZIkBWCYkiRJkqQADFOSJEmSFIBhSpIkSZICMExJkiRJUgCGKUmSJEkKwDAlSZIkSQEYpiRJ\nkiQpAMOUJEmSJAVgmJIkSZKkAAxTkiRJkhSAYUqSJEmSAjBMSZIkSVIAhilJkiRJCsAwJUmSJEkB\nGKYkSZIkKQDDlCRJkiQFYJiSJEmSpAAMU5IkSZIUgGFKkiRJkgIwTEmSJElSAIYpSZIkSQrAMCVJ\nkiRJARimJEmSJCkAw5QkSZIkBWCYkiRJkqQADFOSJEmSFIBhSpIkSZICMExJkiRJUgCGKUmSJEkK\nwDAlSZIkSQEYpiRJkiQpAMOUJEmSJAVgmJIkSZKkAAxTkiRJkhSAYUqSJEmSAjBMSZIkSVIAhilJ\nkiRJCsAwJUmSJEkBGKYkSZIkKQDDlCRJkiQFYJiSJEmSpAAMU5IkSZIUgGFKkiRJkgIwTEmSJElS\nAIYpSZIkSQrAMCVJkiRJARimJEmSJCkAw5QkSZIkBWCYkiRJkqQADFOSJEmSFIBhSpIkSZICMExJ\nkiRJUgCGKUmSJEkKwDAlSZIkSQEYpiRJkiQpAMOUJEmSJAVgmJIkSZKkAAxTkiRJkhSAYUqSJEmS\nAjBMSZIkSVIAhilJkiRJCsAwJUmSJEkBGKYkSZIkKQDDlCRJkiQFYJiSJEmSpAAMU5IkSZIUgGFK\nkiRJkgIwTEmSJElSAIbbZyTYAAAAM0lEQVQpSZIkSQrAMCVJkiRJARimJEmSJCkAw5QkSZIkBWCY\nkiRJkqQADFOSJEmSFMD/A3UX1ZzM/jLrAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x107325610>"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Problem 4\n",
      "\n",
      "While this graph has less information, the remaining information is easier to digest. What does the Minimum Spanning Tree mean in this context? How does this graph relate to partisanship in the Senate? Which nodes in this graph are the most and least bi-partisan?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*The edges of a minimum spanning tree trace a path of low resistance through the network. In the present context, this has the effect of moving bipartisan Senators like Hagan towards the center of the graph -- it is much easier to connect Hagan to a Republican node than, say, a partisan Democrat like Al Franken. Partisan Senators are pushed away from the center of the graph and deeper into the party cliques. *\n",
      "\n",
      "*This scheme also moves outlier senators to the outside of the graph. For example, John Kerry cast very few votes before becoming Secretary of State. Most of the edges connected to John Kerry have large difference values, so the fewest possible number of edges (1) remain in the MST.*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Problem 5\n",
      "\n",
      "(For this problem, use the full graph for centrality computation, and not the Minimum Spanning Tree)\n",
      "\n",
      "Networkx can easily compute [centrality](https://en.wikipedia.org/wiki/Centrality) measurements. \n",
      "\n",
      "Briefly discuss what ``closeness_centrality`` means, both mathematically and in the context of the present graph -- how does the centrality relate to partisanship? Choose a way to visualize the `closeness_centrality` score for each member of the Senate, using edge `difference` as the distance measurement. Determine the 5 Senators with the highest and lowest centralities. \n",
      "\n",
      "Comment on your results. In particular, note the outliers John Kerry (who recently resigned his Senate seat when he became Secretary of State), Mo Cowan (Kerry's interim replacement) and Ed Markey (Kerry's permanent replacement) have low centrality scores -- why?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*The closeness centrality measures the average difference between a Senator and all other Senators. Bipartisan voters will have more shared votes with the members of the opposite party, which tends to increase their centrality. However, these senators also vote less often with their own party, which can decrease centrality*\n",
      "\n",
      "*Centrality scores are also small for people who haven't cast many votes (like John Kerry, Mo Cowan, and Ed Markey). This says nothing about bipartisanship*"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Your code here\n",
      "\n",
      "bet = nx.closeness_centrality(votes, distance='difference')\n",
      "bipartisans = sorted(bet, key=lambda x: -bet[x])\n",
      "\n",
      "print \"Highest closeness\"\n",
      "for senator in bipartisans[:5]:\n",
      "    print \"%20.20s\\t%0.3f\" % (senator, bet[senator])\n",
      "print\n",
      "print \"Lowest closeness\"\n",
      "for senator in bipartisans[-5:]:\n",
      "    print \"%20.20s\\t%0.3f\" % (senator, bet[senator])\n",
      "    \n",
      "\n",
      "plt.figure(figsize=(15, 4))\n",
      "x = np.arange(len(nl))\n",
      "y = np.array([bet[n] for n in nl])\n",
      "c = np.array([votes.node[n]['color'] for n in nl])\n",
      "\n",
      "ind = np.argsort(y)\n",
      "y = y[ind]\n",
      "c = c[ind]\n",
      "\n",
      "plt.bar(x, y, color=c, align='center', width=.8)\n",
      "\n",
      "remove_border(left=None, bottom=None)\n",
      "ticks = plt.xticks(x, [nl[i] for i in x[ind]], \n",
      "                   rotation='vertical', fontsize=7)\n",
      "limits = plt.xlim(-1, x[-1] + 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Highest closeness\n",
        "      Collins (R-ME)\t98.685\n",
        "      Manchin (D-WV)\t95.837\n",
        "        Pryor (D-AR)\t95.008\n",
        "     Donnelly (D-IN)\t93.965\n",
        "        Hagan (D-NC)\t93.940\n",
        "\n",
        "Lowest closeness\n",
        "       Markey (D-MA)\t42.925\n",
        "       Chiesa (R-NJ)\t39.446\n",
        "   Lautenberg (D-NJ)\t25.260\n",
        "       Booker (D-NJ)\t11.866\n",
        "        Kerry (D-MA)\t3.945\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ECK/tT09PlySlp6dr8ODBju3z/P169OjhWP+IESM0Y8YM5c6dW9OnT/da/tCh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\nd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       "text": [
        "<matplotlib.figure.Figure at 0x1130ab410>"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Problem 6\n",
      "\n",
      "Centrality isn't a perfect proxy for bipartisanship, since it gauges how centralized a node is to the network as a whole, and not how similar a Democrat node is to the Republican sub-network (and vice versa).\n",
      "\n",
      "Can you come up with another measure that better captures bipartisanship than closeness centrality? Develop your own metric -- how does it differ from the closeness centrality? Use visualizations to support your points."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "\n",
      "\"\"\"\n",
      "Here, we compute the mean weight for the edges that connect a Senator\n",
      "to a node in the other party (we consider Independents to be Democrats\n",
      "for this analysis).\n",
      "\n",
      "This only considers how similarly a Senator votes with the other party.\n",
      "\n",
      "The scatter plot shows that the betweenness centrality and bipartisan score\n",
      "correlate with each other. However, the betweenness centrality judges Democrats\n",
      "to be more bipartisan as a whole. Part of this is a bias due to the fact\n",
      "that Democrats are the majority party in the Senate right now, so their\n",
      "votes are considered more \"central\" due to their bigger numbers.\n",
      "\"\"\"\n",
      "def bipartisan_score(graph, node):\n",
      "    party = graph.node[node]['color']\n",
      "    other = 'r' if party != 'r' else 'b'\n",
      "    return np.mean([v['weight'] for k, v in graph[node].items() if graph.node[k]['color'] == other])\n",
      "\n",
      "bp_score = {node: bipartisan_score(votes, node) for node in votes.nodes()}\n",
      "bp2 = sorted(bp_score, key=lambda x: -1 * bp_score[x])\n",
      "\n",
      "print \"Most Bipartisan\"\n",
      "for senator in bp2[:5]:\n",
      "    print \"%20.20s\\t%0.3f\" % (senator, bp_score[senator])\n",
      "\n",
      "print\n",
      "print \"Least Bipartisan\"\n",
      "for senator in bp2[-5:]:\n",
      "    print \"%20.20s\\t%0.3f\" % (senator, bp_score[senator])\n",
      "\n",
      "    \n",
      "senators = bp_score.keys()\n",
      "x = [bet[s] for s in senators]\n",
      "y = [bp_score[s] for s in senators]\n",
      "c = [votes.node[s]['color'] for s in senators]\n",
      "\n",
      "plt.scatter(x, y, 80, color=c, \n",
      "            alpha=.5, edgecolor='white')\n",
      "plt.xlabel(\"Betweenness Centrality\")\n",
      "plt.ylabel(\"Bipartisan Score\")\n",
      "remove_border()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Most Bipartisan\n",
        "      Collins (R-ME)\t153.089\n",
        "    Murkowski (R-AK)\t132.875\n",
        "      Manchin (D-WV)\t118.500\n",
        "        Pryor (D-AR)\t112.913\n",
        "       McCain (R-AZ)\t112.393\n",
        "\n",
        "Least Bipartisan\n",
        "       Chiesa (R-NJ)\t39.038\n",
        "       Markey (D-MA)\t23.435\n",
        "   Lautenberg (D-NJ)\t9.289\n",
        "       Booker (D-NJ)\t3.659\n",
        "        Kerry (D-MA)\t2.235\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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k11+3XrtTpywzFhXZCPDixdbT16+fLfj97ncJeTg/gh4AAOezdav0/vuWvioc\nO2bz8264wbY1uwCOI+3caUOzO3ZYFZZbbrHMmJtrU/xKS210OCjItj0rKJA+/VS6804bEWbmFHxB\n0AMAoCZFRafHT8PCqgY9yeqgfP65DdV+W2PWF+vWSW++Ka1ebW9dWGiLLr7zHSuWvHGjzdsLDLSK\nLQUFtnA3ONh6+WJjfRoZBgh6AACcIz/fxlI3bbLFFWVl0tChVjLliy+q1s9LT7fevbi4875tWZm0\nebP0wgtW6LikxIJeePjpMint29v0v5MnLVeWlNjIcE6O1Lev5UkWYMBXFxz0srOzVV5eXuXYmSVX\nAABoVk6csGAXGGilT4qLbc/aV16xRRYFBVbn5M03pTvusMD32WenX19aanP4fLB7t2XHzZvtbePj\nLegNHGiLdFu1kq6+WrrySunrr23E+MgRm5dXWipdc42NErtrrYILnOZT0Nu7d6/uvfdeLV++XMXF\nxVXOuVwulVVs8QIAQHNx8qSNnX7xhaWtqChLVD172jLY/fvtuoAAexQVSfPmWW2T2FgLf5K9zodx\n1OJiC3mZmfbWLpe9zX33WW589lkbHe7Z085dcYXVZF6yxN7+wQctDDJkiwvhU9C7++67lZ2drblz\n5youLk4ul6u+2wUAQP05dcqWsb7++ul5dwEB1jNXXm6JrKLbLCjIEtjJkzb2+umn0sSJltjcbuuO\ni4o670fm5lrACw622nkREbbt2caNNs2vdWsbwg0IsKHaL76wdR49etji3qAgm78HXAifgt6aNWu0\nevVqXXbZZfXdHgAA6t+OHTYUe+biirIymxCXmWmLLCpSVUXNPMex648ds4AXEWEhr3//aj8iO9uG\nZ4OCLLi53TZC3KqV9datX289ev/zP9ah6DgW/FwuG+LdudPy5YQJ9rNdO+vhAy6ET0GvU6dOKioq\nqu+2AABQ/0pKpK++qn6T2GPHLLgVFVnwCwiw44GBlrQiImzZa9eu0oABltQqrvlWXp60c2eRDhzI\nU1FRoIqLo9S2rU3ti4iQPvpImjTJeveKimwOXmCghbjOna2GXliYBb7166XbbrPjHk8D3Bv4HZ+C\n3rPPPqtHH31Uf/rTn9StW7f6bhMAAPXn1CnraqvON99I48db2Dt8uOo5t1tq00YaNcrCXjXy8oq1\nYsUOffTRBh05ckIhIUHq3bu7IiL6a8uW9goIsGz48cdS797WIdi7t/X6de5stZgPHLCP6dDBFmt0\n7mw/gYvtIyP+AAAgAElEQVThU9CbPHmyioqK1KNHD4WEhCgw8PTLXC6XcnNz662BAABcsuPHT/fg\ntWplPXIhIdaldibHkf75T+mHP5T++lcbQ60QGWmT5gYPrvYjysrKtHLlGj377HIVFp4uv7J//zHl\n5x9U377X68sv2+v6623+3aJFFuh69LCFvzt3WraMibHOw6QkWxsSE1PXNwMtiU9B749//GN9twMA\ngLpXWGjjnxs22EQ4l8uSU3KyhbbXXz/3NSdOWBD82c9sUcbOnRbyBg2SunWrcdnrkSNH9I9/fFEZ\n8tq1i1Dv3l3k8XSQ2+2W212syy6zsil9+1q5lKAg6dprpaefts5El8tGjGNi7GOuvtp2ywAulstx\nHKexG1HfXC6XWsDXBACcKTPT6pb87W+WoDweW1QRFGTz9K680hZlrFhhPX7Bwda9dvPNdu4Ci9Wt\nW7dOTz/9ng4ckGJjozRs2FX69FO3Nm/OU0CAozZtWuvyyy/TNdcEads2W2n72WeWHSdOtNp627bZ\nMO3NN9sUwN69rQMSuFgXXDA5IyPjnFp6HdhVGQDQlGzZYjtavP22bUEh2U+v13rzgoNt54uRIy1p\nlZfbaoikJDt/ERWJi4uL1bq15Ha7NGTIIL31Vrn27cuXZNmyrKxUmzaV6dSpIA0cKI0YYR+7dauV\n5xs6VLrxRmvSZZdJbdtaPgUuhU9BLycnR9OnT9eiRYtUUlJSpXeMgskAgCYlK0v65BPrnUtOtoUT\np07ZfLsDB2xMNCHBjkVH26qHsjI7fgndZ+3bt1e7doHq2TNKWVkR2rcvp/JcSYnUtm24IiOD9a9/\n2Q4XmzZZr93VV9vakNhYm/7Xt6/V0wPqgk9B7+GHH9aXX36pt99+W9/97nc1d+5cHTx4UM8884zm\nzJlT320EAMB3hw/bhrEHDlgtk4wMm2M3cKBNiFu/3gJeaKh1mbVtWycf6/HEKy4uSa1bO/ryS5uD\n53Zb9ZXw8AB17OhVmzZuhYRYaZUJE2x9SFiYhbz4eKljR3rxULd8Cnp///vftWDBAo0YMUIBAQFK\nSUnRLbfcori4OL344ou66aab6rudAAD4prTUuss+/9xC3vHj9ti3z8LeyJHSoUPW41dHW00UFEjr\n14crPv46HTy4URERAYqKstAWHd1KXbt2kNfbVm63jRT36WO7XZSV2bBumzYW+IC65lPQy87OVqdO\nnSRJUVFRysrKUteuXTV06FDdc8899dk+AAAuTGmpbVNWUGBF6E6etLFTyVbfXnmlBbzLL6+zrSa2\nbZNWrpSCgmLVtesIXX99iQ4daq/AwECFh4fL6w2T221ddQEBNgeP1bRoCD7NNu3SpYt2f1tLqGfP\nnvrb3/4mx3H01ltvqR0b7wEAmpL0dAt2hw5JOTk2ES4+3hZghIRY795119k4aR0oKLD8WF5uZfk2\nbw5VYWFr9ekTr9xcjw4eDNfJk6fHY/v0sSmCQEPwqUfvrrvu0pdffqnU1FTNmjVL48eP13PPPafy\n8nI988wz9d1GAAB8s3+/zc3zeCzo7dwptW5tCy66drUevNhYC4J1VLfk1Klzd1PbudNGiWNibLe1\nVq3sY/v2tU03WGyBhnJRdfT27dunf/3rX+rWrZv69etXH+2qU9TRAwA/VVRkgW73bktPq1fbQou/\n/tVWQpw6JeXm2kS45GSbNDd4sPXo9e5dJ03IzbWPy8ysetzttqAXFWVDtR062HOgIV1wHT1J6tix\no+Lj4xUUFFTX7QEAwDcFBVJamiWssDALfZ99Jl11ldSli/Svf0nFxVJgoM3b27nTitUNGSJ9O++8\nLrRubZnx7KBXXm7HysttqJaQh8bg0xy9Z555Rm+++Wbl73fffbdatWql7t27a/v27fXWOAAAarRz\np21xdvy47U+7ZYuFuwULLHmlptp4aVmZ7WE7cKD07/8upaTU+RLXyy6zkeGzhYdbzTyms6Ox+DR0\n26VLF82dO1fXXHONVqxYofHjx+vll1/W4sWLlZ+fryVLljREWy8aQ7cA0AyVlkrHjp0uZnxml1hh\nobRunW0Qu2GDJaqhQ20MdfVqq5t3++1Wy6RiU9nAQCutEhlZL83NzrbNNr7+2j4yPt7m5HXoQG08\nNB6fhm4PHTqkzp07S5Lee+89fe9739Mtt9yifv366aqrrqrXBgIAWqD9+6UvvrCfpaUW8gYMsJUM\noaHS3r3Se+9J775rY6MBAbZZ7JAhtrJ2+XJp6VJbJREaateMGlVvIU+yWniDBlkTy8qsGQQ8NDaf\ngl7r1q2VmZmppKQkffTRR3r44YftxYGBKiwsrNcGAgBamIMHbY/a48dPHysosOBWXGyFjt9/33bA\nCAqyMJeTY1ufFRZK118v7dhhc/aKiizo9e9vjwYQFGQPoCnwKeiNHTtW//Ef/6HLL79cu3bt0rhx\n4yRJW7ZsUXJycr02EADQgjiOtHFj1ZBXITrajqelWehr3dqGdkNCrMcvN9de27On9MADthK3Tx+b\nQJeQUGfFkYHmxKfFGM8995yuuuoqHTt2TG+++aaio6MlSevWrdNtt91Wrw0EALQgubk2LHu2Vq1s\n/9oPP5SOHrUh2PJy26e2rMyGboODbZh3zRqbLDdggDRp0un6eUAL5FOPXlRUlP74xz+ec/yXv/xl\nnTcIANCClZdbr97ZYmNt0UX37hbqvvjCruvY0ebxhYXZ0tZDh6znLyTEiiYHBDT8dwCakBqD3vHj\nxyu3NzteXRf6GdgGDQBQJyIjpbg4G5I9U8UuFkeOWBHk6GjrtQsMtJ67jAzr0UtMtDl6HTuyEgJQ\nLeVV3G63MjIy5PF45HbXPMLrcrlUVlZWbw2sC5RXAYBmZPdu6c03LdBV6N7d5udt3Wo7XERGSq+9\nZitty8okr9eOjxplQfHQIemWW+q0MDLQHNXYo7ds2TK1bdu28jkAAA0iOVkaP952ucjMtCAXGmo9\nd/v2WUmVDh2sTt6hQ1Yo+dQpq0zcp4/06qsW8rzexv4mQKO7qL1umxt69ACgGTp50kqmlJbaXLtF\ni6R586yUyqlTVkQ5JUUaN07Ky7N5e9/5js3XGz+ePccA+bjqNiAgQEeOHDnn+LFjxxTARFcAQH2I\njLSh165dbSVuXp7UubMFvOBgKT/fwt2CBVatuG9f69Ej5AGVfFp1W1NvWHFxsYKDg+u0QQAAP+Y4\nVh7l+PHT5VFiY6Va5oLr5Elp+3br1evWTSopsf3GiovteUaGhcCRIy0IEvKASrUGvT/84Q+Vz//8\n5z8r8oytY8rKyrRixQr16NGj/loHAPAfhYXS2rW2R212tq2kTU6WkpKsFEpkpBQTUzX0FRdbMDxy\nRDpxwoZlL7vM5u6dOGFBLypK6tVLuvJKyqkAZ6k16P3xj3+U69vl6f/zP/9TZZg2ODhYnTp10gsv\nvFC/LQQA+IevvrI9aMvKbFeLjh1tu7ODB61nr21bW2SRkGB7iJWUSKtX2xBtfr69PiLCXteli513\nHAt/XbsS8oBq1Br09n5bnXzkyJFavHhx5SpcAAAuSG6utH69hTy324ZYMzOtFt66dbbg4rbbpPR0\n68ELD7ch227dLPQNHCh9/bU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Hqn///nrhhRcqj3Xv3l033XSTfvOb31Qeo2By48jLs5p2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\nhBCijZJCTwghhBCijZJCTwghhBCijfo/0YKWRZ2HdfkAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a04b650>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Leadership in the Senate\n",
      "\n",
      "There are many metrics to quantify the leadership in the Senate.\n",
      "\n",
      "  1. Senate leaders sponsor and co-sponsor lots of bills\n",
      "  2. Leaders sit on many committees, as well as more important committees\n",
      "  3. Leaders usually have been in office for a long time\n",
      "  \n",
      "Another approach uses the philosophy behind how Google ranks search results. The core idea behind Google's PageRank algorithm is:\n",
      "\n",
      "1. A \"good\" website (i.e. one to rank highly in search results) is linked to by many other websites\n",
      "2. A link found on a \"good\" website is more important than a link found on a \"bad\" website\n",
      "\n",
      "The PageRank algorithm thus assigns scores to nodes in a graph based on how many neighbors a node has, as well as the score of those neighbors.\n",
      "\n",
      "This technique can be adapted to rank Senate leadership. Here, nodes correspond to Senators, and edges correspond to a senator co-sponsoring a bill sponsored by another Senator. The weight of each edge from node A to B is the number of times Senator A has co-sponsored a bill whose primary sponsor is Senator B. If you interpret the PageRank scores of such a network to indicate Senate leadership, you are then assuming:\n",
      "\n",
      "1. Leaders sponsor more bills\n",
      "1. Leaders attract co-sponsorship from other leaders\n",
      "\n",
      "### Problem 7\n",
      "\n",
      "Govtrack stores information about each Senate bill in the current congress at http://www.govtrack.us/data/congress/113/bills/s/. As in problem 1, write two functions to scrape these data -- the first function downloads a single bill, and the second function calls the first to loop over all bills."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "get_senate_bill\n",
      "\n",
      "Scrape the bill data from a single JSON page, given the bill number\n",
      "\n",
      "Parameters\n",
      "-----------\n",
      "bill : int\n",
      "   Bill number to fetch\n",
      "   \n",
      "Returns\n",
      "-------\n",
      "A dict, parsed from the JSON\n",
      "\n",
      "Examples\n",
      "--------\n",
      ">>> bill = get_senate_bill(10)\n",
      ">>> bill['sponsor']\n",
      "{u'district': None,\n",
      " u'name': u'Reid, Harry',\n",
      " u'state': u'NV',\n",
      " u'thomas_id': u'00952',\n",
      " u'title': u'Sen',\n",
      " u'type': u'person'}\n",
      ">>> bill['short_title']\n",
      "u'Agriculture Reform, Food, and Jobs Act of 2013'\n",
      "\"\"\"\n",
      "#your code here\n",
      "def get_senate_bill(bill):\n",
      "    url = 'http://www.govtrack.us/data/congress/113/bills/s/s%i/data.json' % bill\n",
      "    page = requests.get(url).text\n",
      "    return json.loads(page)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "get_all_bills\n",
      "\n",
      "Scrape all Senate bills at http://www.govtrack.us/data/congress/113/bills/s\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "None\n",
      "\n",
      "Returns\n",
      "-------\n",
      "A list of dicts, one for each bill\n",
      "\"\"\"\n",
      "#your code here\n",
      "def get_all_bills():\n",
      "    page = requests.get('http://www.govtrack.us/data/congress/113/bills/s/').text\n",
      "    dom = web.Element(page)\n",
      "    links = [a.attr['href'] for a in dom.by_tag('a') \n",
      "             if a.attr.get('href', '').startswith('s')]\n",
      "    return [get_senate_bill(i) for i in range(1, len(links) + 1)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#bill_list = get_all_bills()\n",
      "bill_list = json.load(open('bill_list.json'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Problem 8\n",
      "\n",
      "Write a function to builded a Directed Graph (DiGraph) from these data, according to the following spec:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"\n",
      "Function\n",
      "--------\n",
      "bill_graph\n",
      "\n",
      "Turn the bill graph data into a NetworkX Digraph\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "data : list of dicts\n",
      "    The data returned from get_all_bills\n",
      "\n",
      "Returns\n",
      "-------\n",
      "graph : A NetworkX DiGraph, with the following properties\n",
      "    * Each node is a senator. For a label, use the 'name' field \n",
      "      from the 'sponsor' and 'cosponsors' dict items\n",
      "    * Each edge from A to B is assigned a weight equal to how many \n",
      "      bills are sponsored by B and co-sponsored by A\n",
      "\"\"\"\n",
      "#Your code here\n",
      "\n",
      "def bill_graph(data):\n",
      "    \n",
      "    sp = nx.DiGraph()\n",
      "\n",
      "    for bill in data:\n",
      "        sponsor = bill['sponsor']['name']\n",
      "        sponsor_data = bill['sponsor']\n",
      "    \n",
      "        cosponsors = [cs['name'] for cs in bill['cosponsors']]\n",
      "    \n",
      "        if sponsor not in sp:\n",
      "            sp.add_node(sponsor, **sponsor_data)\n",
      "        \n",
      "        for cosponsor in bill['cosponsors']:\n",
      "            if cosponsor['name'] not in sp:\n",
      "                sp.add_node(cosponsor['name'], **cosponsor)            \n",
      "            cosponsor = cosponsor['name']\n",
      "        \n",
      "            try:\n",
      "                w = sp[cosponsor][sponsor]['weight'] + 1\n",
      "            except KeyError:\n",
      "                w = + 1\n",
      "            sp.add_edge(cosponsor, sponsor, weight=w)    \n",
      "\n",
      "    return sp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bills = bill_graph(bill_list)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Problem 9\n",
      "\n",
      "Using `nx.pagerank_numpy`, compute the PageRank score for each senator in this graph. Visualize the results. Determine the 5 Senators with the highest\n",
      "PageRank scores. How effective is this approach at identifying leaders? How does the PageRank rating compare to the degree of each node?\n",
      "\n",
      "Note: you can read about individual Senators by searching for them on the [govtrack website](https://www.govtrack.us/)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Your code here\n",
      "\n",
      "pagerank = nx.pagerank_numpy(bills)\n",
      "names = np.array(pagerank.keys())\n",
      "vals = np.array([pagerank[n] for n in names])\n",
      "\n",
      "ind = np.argsort(vals)\n",
      "names = names[ind]\n",
      "vals = vals[ind]\n",
      "\n",
      "print \"Highest Scores\"\n",
      "for n, v in zip(names, vals)[-5:][::-1]:\n",
      "    print \"%20.20s\\t%0.3f\" % (n, v)\n",
      "\n",
      "print\n",
      "print \"Lowest Scores\" \n",
      "for n, v in zip(names, vals)[:5]:\n",
      "    print \"%20.20s\\t%0.3f\" % (n, v)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Highest Scores\n",
        "      Brown, Sherrod\t0.030\n",
        "         Harkin, Tom\t0.030\n",
        "         Reid, Harry\t0.027\n",
        "Lautenberg, Frank R.\t0.026\n",
        "    Menendez, Robert\t0.026\n",
        "\n",
        "Lowest Scores\n",
        "        Chiesa, Jeff\t0.001\n",
        "      Kerry, John F.\t0.001\n",
        "   Cowan, William M.\t0.001\n",
        "      Sessions, Jeff\t0.001\n",
        "          Scott, Tim\t0.001\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Your code here\n",
      "\n",
      "deg = nx.degree(bills)\n",
      "\n",
      "plt.scatter([deg[n] for n in bills.nodes()],\n",
      "            [pagerank[n] for n in bills.nodes()], 80, alpha=.8, \n",
      "            color='k', edgecolor='white')\n",
      "\n",
      "labels = ['Reid, Harry', 'Lautenberg, Frank R.', 'Menendez, Robert', 'Harkin, Tom']\n",
      "for lbl in labels:\n",
      "    plt.annotate(lbl, (deg[lbl], pagerank[lbl] + .002), fontsize=10, rotation=10)\n",
      "    \n",
      "plt.xlabel(\"Degree\")\n",
      "plt.ylabel(\"PageRank\")\n",
      "remove_border()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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x5MgRfHx86Nu3L8bGxko7rm+Lubk5vr6+bNu2jR07dvDXX38xduxYHn30Uc6e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\nIiLSCxNIIiIiItLL/wGbR3WXyNHNzwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x110167290>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*The PageRank approach does seem to be effective at identifying influential Senators like Tom Harkin and Harry Reid (the Majority Leader). We see in particular that Harry Reid's PageRank score is relatively higher than his degree -- he seems to sponsor fewer bills overall, but those bills appear to be more important. This makes sense, since he is the figurehead of the Democratic party in the Senate, and thus probably focuses on the highest-profile legislation.*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Interactive Visualization\n",
      "\n",
      "Producing a good node link layout is not quite so simple. Nevertheless, we will give it a try. \n",
      "\n",
      "We will use [Gephi](https://gephi.org/) for interactive graph visualization. Gephi supports a wide variety of graph file formats, and NetworkX exports to several of them. We'll use the Graph Exchange XML Format (GEXF)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nx.write_gexf(votes, 'votes.gexf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Problem 10: Analysis with Gephi\n",
      "\n",
      "Download and install [Gephi](https://gephi.org/). See the [lab](http://goo.gl/SzHioP) for a brief introduction. Load the exported votes file. Try to produce a layout that clearly separates Democrats from Republicans (hint: filter on edge weight and re-layout once you filtered). Run PageRank and some other statistics and try encoding them with node color and node size. Run the \"Modularity\" statistic and encode the results in color.\n",
      "\n",
      "Include a screenshot of your \"best\" visualization and embed the image here with `IPython.display.Image`. Make sure to include this image in your submission.\n",
      "\n",
      "Explain your observations. Is the network visualization very helpful? Try to visualize your LinkedIn network (see the lab) or the one provided in the lab. Which dataset is more suitable for visualization and why is there a difference?\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*David Chouinard did a very nice job with this -- go look at [his page](http://static.davidchouinard.com/congress/). Notice how it is generally difficult to study the details of very complex networks -- we can see bipartisan structure, but not much else. The LinkedIn graphs are easier to digest and interpret*"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### How to Submit\n",
      "\n",
      "To submit your homework, create a folder named lastname_firstinitial_hw5 and place this notebook file in the folder. Double check that this file is still called HW5.ipynb, and that it contains your solutions. Also include any Gephi screenshots. Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. If we cannot access your work because these directions are not followed correctly, we will not grade your work."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "---\n",
      "*css tweaks in this cell*\n",
      "<style>\n",
      "div.text_cell_render {\n",
      "    line-height: 150%;\n",
      "    font-size: 110%;\n",
      "    width: 800px;\n",
      "    margin-left:50px;\n",
      "    margin-right:auto;\n",
      "    }\n",
      "</style>"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}