{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Neural Nets with Backpropagation from scratch\n",
    "\n",
    "This notebook shows how to implement neural nets from scratch to do multi-class classification of digits!\n",
    "\n",
    "![Handwritten digits grid](notebooks/nnets-files/handwritten_digits.png?raw=true \"Handwritten digits grid\")\n",
    "\n",
    "You can find this code in modules [here](https://github.com/arturomp/coursera-machine-learning-in-python/tree/master/mlclass-ex4-004/mlclass-ex4), along with the files used.\n",
    "\n",
    "This code was ported from Octave/Matlab to python and was part of the absolutely excellent [Machine Learning course](http://ml-class.org) by Stanford's [Andrew Ng](http://www.andrewng.org) on Coursera.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first import the appropriate libraries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.io\n",
    "import numpy as np\n",
    "from scipy.optimize import minimize\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.special import expit \n",
    "from decimal import Decimal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then set up the parameters for the simple neural net:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "input_layer_size  = 400  # 20x20 Input Images of Digits\n",
    "hidden_layer_size = 25   # 25 hidden units\n",
    "num_labels = 10          # 10 labels, from 0 to 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll have an architecture with three layers: the input layer (the training data), a hidden layer (which learns its own feature representations), and an output data (the labels). This is a rough illustration of our neural net:\n",
    "\n",
    "![Neural nets architecture with equations.png](notebooks/nnets-files/neural-net-architecture-equations.png?raw=true \"Neural nets architecture with equations\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization\n",
    "\n",
    "We can visualize 100 random data points (out of the 5,000 in the data set) with the [``displayData()``](https://github.com/arturomp/coursera-machine-learning-in-python/blob/master/mlclass-ex4-004/mlclass-ex4/displayData.py) function. If you look at [the full code files](https://github.com/arturomp/coursera-machine-learning-in-python/blob/master/mlclass-ex4-004/mlclass-ex4/displayData.py), you can see more comments on many lines' particular functions, for this or any section going forward. \n",
    "\n",
    "I found that applying ``flatten()`` to the labels ``y`` is crucial in getting good performance - otherwise the minimization isn't very effective. What it does is change the dimension of the labels from ``(m,1)`` to ``(m,)``, which in ``numpy`` can have significantly different behaviours in different functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def displayData(X, example_width=None):\n",
    "\n",
    "    plt.close()\n",
    "    plt.figure(figsize=(10, 10))\n",
    "    if X.ndim == 1:\n",
    "        X = np.reshape(X, (-1,X.shape[0]))\n",
    "    if not example_width or not 'example_width' in locals():\n",
    "        example_width = int(round(math.sqrt(X.shape[1])))\n",
    "\n",
    "    plt.set_cmap(\"gray\")\n",
    "\n",
    "    m, n = X.shape\n",
    "    example_height = n / example_width\n",
    "\n",
    "    display_rows = int(math.floor(math.sqrt(m)))\n",
    "    display_cols = int(math.ceil(m / display_rows))\n",
    "\n",
    "    pad = 1\n",
    "\n",
    "    display_array = -np.ones((pad + display_rows * (example_height + pad),  \\\n",
    "                              pad + display_cols * (example_width + pad)))\n",
    "\n",
    "    curr_ex = 1\n",
    "    for j in xrange(1,display_rows+1):\n",
    "        for i in xrange (1,display_cols+1):\n",
    "            if curr_ex > m:\n",
    "                break\n",
    "        \n",
    "            max_val = max(abs(X[curr_ex-1, :]))\n",
    "            rows = pad + (j - 1) * (example_height + pad) + np.array(range(example_height))\n",
    "            cols = pad + (i - 1) * (example_width  + pad) + np.array(range(example_width ))\n",
    "\n",
    "            # Basic (vs. advanced) indexing/slicing is necessary so that we look can assign\n",
    "            #   values directly to display_array and not to a copy of its subarray.\n",
    "            # Also notice the order=\"F\" parameter on the reshape call\n",
    "            display_array[rows[0]:rows[-1]+1 , cols[0]:cols[-1]+1] = \\\n",
    "                        np.reshape(X[curr_ex-1, :], (example_height, example_width), order=\"F\") / max_val\n",
    "            curr_ex += 1\n",
    "    \n",
    "        if curr_ex > m:\n",
    "            break\n",
    "\n",
    "    h = plt.imshow(display_array, vmin=-1, vmax=1)\n",
    "\n",
    "    plt.axis('off')\n",
    "\n",
    "    plt.show(block=False)\n",
    "\n",
    "    return h, display_array    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xgWMcachzF/PSvYNhrGFljEzuC0JFCgY5q4wLbs1qtfJIxWg08iwxwvKs7VtDRuhFfcYa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\nJNBms2nZbDbiwgU04rEghVZfmhqsB5bq4dfX1zYejyPpo/uevypsYt6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pXXjMNWSMeHJo\nxwLgiNMRSdxz2WzWisWiVatVu7i4sMvLSyuXy142YzKZ2GQysXQ6HXnu6iE81pz+1GAqPGwq4YIk\nZdO9FoGfyjzfojzloseq5DLGQj47O3NLLZ/PO8CiXxU9q1A0yLHXUYFgrVazZrNpFxcXVq/XLZ/P\ne1uUxWLhrZeSLnFjzGazVigUIpcwNfNSqZSD3fV6bcvl0ubzuS2XS187BVe78hh8y7P8mt95bnzH\nCivH/ZsWyAz/nMlkLJ/P+9niz3imNFGC/ysWi94EGpC8WCz2bszEhdABHoTPq9WqlUolBxaAqPV6\n7R40PDskfCiwNDtMAczw7+l02nVcpVLxGnHMJ5/PW6PRsGKxaIvFwubzuc3nc5vNZjadTm02m7nn\n6u7uLtYwPYT8qcEUElotYSuFtyBh3Nxsd8USv8vz8tRlhbLO5XKuVIrFor/K5bJVKhUrlUp+Edzd\n3bki+fjxoy0WiwiYOrYQnszlclav1+3du3d2cXFhtVrNcrmcK/fZbOb1aU5RstmsVatVa7VaVq/X\nrV6ve10ywBTAaTQa2Wg0svF47L2+ttXeOpSEIeav4d0lSW9sC5UDmtRIUWOlXC5buVy2QqHgHB0N\nmfF7ejbhS2EM0O/yUGvIfslms1apVNxQ4Xyx3wB9NLGez+c2mUy8+CThyWN5dHStSqWSF/tstVrW\nbDZ9rTBYMpnMF0Yk5V56vV6Ew3aMsOtJgqldb1ptq5FOpyNKju87dpx5G7n3JeMBIIavOEV+DEQf\nJ/tSTMewmvWZw6lR/hPhhUKh4N6NUqnkir5SqTiYQslT22U8HnvD6aTsTTPzCwgleXl5aa1Wy87O\nzjw0Qk22UyyLgM4oFouR+TWbTQdTm83Gm0ovFgsHzHBxaO4O7wPZ1Rl8yefoPgwLp8YBrG2gK47Y\n/Nrxb5O4/aaGoYIhABLgiTXA08TZCst1aPiPvczvzWYzP6+E1Q4lOnfAVLvdtsvLS3v37p3VajWb\nTqc2nU5tuVw68FMPnNkfVcszmYwtl8uj6388U/V63Y2SWq3mXl36/OXzeU9aeXh4cKMzl8vZZrOx\n5XJpZuaeuHBO+57PSYKpOPmWjCnlOHCRFQoFdyEqyS2J8eXnJDx4KAQsLC60xWLxhUJPsjyXGWX2\nZaPWY8lms4ko9UqlYuVyOdKiBkWHstOwA69cLhe57AD+anGfnZ1FiKaHmPe2sHGxWPS2Gefn5xGP\n1GQysW63a7e3t9bpdGw6ndr9/f0Xl3kShefKOWJ+V1dXEcAbhldWq5Vls1mr1+tWKBQsm81aOp12\nIvOhGk6Hz1fDyeyzdDrtRpZmUemLS/opr/4hRc8C3txisehzUlK5/p3zBYgKDU+MH9ZLDYHJZGLL\n5fKgelNLMmQyGSuVSt6aZrPZuGF1e3tr8/ncHQN4eYrFop2fn1ulUvEWN4PBIMK9PNQaqvcQw+vs\n7MybXRPGW6/XHkZXHWlmVi6XPXkllUpZv9+30WgUW1xz3/vz5MHUU7wnRDdgePAJsSgq7vf71uv1\nbDweO9BIkjzFe9rmweIgVSoVq9VqVq/XbTKZWK/XczfvsdyjXyPbQiKh9zAJ4VrGpMri/PzclRnC\nxZzP5x0UZbPZSBZfHNAgDKHW93q9dgV66LmaPYK8QqFgrVbL3r17Z+fn51av1y2dTnv/PBT+7e2t\nX9ZJ3neh5PN5q9Vq1mq17Pz83C4vLyOXMqGIxWLhgCqfz/u6q0fo/v7e5vP5Ttdsm/cm3EfsS/WA\nZrNZB+VwvQgbMRcIzk8BiUOuJ2esUCh4qIier3gvNMTHnwkDaoJA6MHXtXp4eLD1eu0XPaGyY5y3\nbDbrnt9CoeBgqtPp2K+//mrT6dTn0Gw2rdls+jpnMhkbDAaWTqe/4FAx10MIzzeXy/k6ET7lHp5O\np055qNVq1mg0rNlsRjyLeBExZMbj8cHD5ycDpp4CSi+9NEOOUC6X88XgABJznkwmEbf1Mayuba70\nMKZPmXsIrXqJU6+DDVipVFzBrFariBWaBHkuKUAVW/gzBJWQK/Wz9qkstl1geKQajYa12207Pz+P\n9PsLFbyGAgGGmrlnZm4dqwf1kGsYt05a4RzSebvd9jDK3d2drVYrD0NAGg0/L6mieuDs7Mw9IGR5\nQRFYr9c2mUys3+/7+qxWK6tUKlatVv1ir9frdn9/b8vl0iaTiROav8WgCZ8fKfIh2IYTZBYNMSug\nwsPJXEIODkR6yPTMT7344Zh2fd5YB/VG1Wo1v2whMPOstWQAoEg9awDH9Xod8QYrRxEgORgMbDgc\n2ng8djB1aFHPGSFKM/P9NJ1OHVCoPDw8OLEbrt/9/b0Nh0MbDoexNbMOce/x/OmGsFgs3Gs2nU5t\nPp9bLpdz42S1Wlm1WrVareZGKHc3XkP27KEMtZMBU3GiloNmaSDKCdIaMPzETQ/PAaBBSCVJCl43\nNi55DQnBn9HLKZPJuKK8vLy0q6srK5VKHoLQTKNjWFdPSQjwQkKpKkf9NzPzuWjWjQKrQwljpizA\n+fm5NZtNazQaVi6XfZ5hscCwQCeKP8wMm0wmrjQ1VHuMddT9Vq1W/VyRvQdHCIUJn+NUay5BDVDe\nzMPDgyvzbrdrnz59stFoFFH+ENRzuZxVq1X3Juj7zL49JMF+AmQAkAB9pVIpEjLWMDFGlqb9h+E9\nCMCsIZcwYZnZbHZQT37Iy7u8vPRwkDba5QyROYo+UCOF5094mueTSqWc8DwcDq3T6Xjk4pjJE3Ec\nNRXN5iPrcDab2f39vYPFVqtlxWLRPn36FLkXDklKhww/mUwi2XrcZxiLAKn5fG6j0SiS9ZfP561U\nKlm9XndjbTqdupFyCGdIosHUc5dCeJHqhcrvc2CUt4ICKZVKXssCJZNOp135J8VjE1p6oWuezvEg\nesAEl1u9XrfLy0v761//aoVCwfr9vvX7/cjlfCjvW2jtPBWy0zGpJaaVcdX6hoiI4lwsFv6Zx+CD\nqQe0VqtFwBQcB51v+Hv8n14EXGIoCgoIzufzCE/gGET7dDrtFxtgqtFo+P9DJkVBqtWY1DpT2/ar\npsuzF9Xz1ul07Pfff7derxe5pKfTqbVaLbu6urJ2u23pdNpGo5GHabgAvxVI6fgofNhut63dbnu2\nIXW9eOb8DmcoBPPheVTgcX19bdfX164v9cztU5S7hqHy4cMH+9vf/uZN5dWgQjfOZjPne2lWHvNh\nXxIyROcApq6vr63T6Vi/37fxePzFuA6tP5963/39fcTTMxqNbDqdWi6XcwdCsVj00BiZpuickDax\nr/ltNhvvgpBOpyOlDubzeYRMPp1ObTQaWTabtVarZbPZzNrttl1cXFij0bDNZuNebwAkYWn9vn3M\nI9FgaptgEWJ95PN5B0gKpszMUTbu0LB2CJkDZuaxYzad8qWOZTWrckRBttttz4wye8wGUosinU5b\nsVi0ZrNpl5eXntK7XC6t0+nY7e2t9ft952oci2MT/lm9M2GIQkmjXGC8j39Xlz2W5GQy8aQCs/2E\nHELRcCzJDeVyOeL1jHtpkT31QrE39cWccGnHySH2LWBIC3PikTIzv9DG47HXxJpOpw4aTsEjFYLd\nOC8iAB7dQeiLsB3hP/YGl8h4PN6ZV1GBEWAKkrxmoDEP9tBisYjw9FgXBfzqQeVslstlq9frvl9J\nJjgU/1LPO6U2zB45a1TN5mJWbpqeUbz9eDswrsPw4LFFaQ3qQOCM4ZHRe0vpAqvVygaDgf3222/W\nbDa9PlU+n7dWq2UPDw8e5eAz9nk3ANzm87k/Y9YuLvGLedzf39tsNnOaCtl+m83GwaLSdZ7z4u1C\nThZMNRoNq9frEdc1HCJ9cAqmcG3qZU24jEuJbADIhUnxTDF+UmH/8pe/fLHxVYGlUikPY15dXfkm\nnU6n1u127fPnz87nOJabOo5bYfbYfkTLBEBA1Ew35YSxtqz5arWym5sbv7RUMRwKYGh9mhBMqeWn\nYQc8N1wACpq4DLC2VOkcI0lCuVLsT8AUXAazx0rno9HIwRThoKR6pLZJSExWbhueAMC7chjNHp8D\nlwSeuclkEglHf+v+VKOIMCR1iNrtthucysPjcoWMHL7UO6z8pGq16vuacc9mM+v3+7ZarSJ7e5/n\nTYnynBUuUcLgys9bLBZ+T1BnisKQlCKBU0QJi6TtUaUEcJdRKmU4HPrZ4tmrB3K1Wvka4UVk/oQ2\nWctDefMBU3jWtVYUIB5R3YmXEZ4b8wBMTSYTX8NDyMmBKUBCo9Fwa4s4/za+ycPDQ4RHEPfioI1G\nI4/drtfriLI8hGzzcnDwsZwajYYrbsbK5lcwAk8D5dLr9azf79tgMHClpwrvUMTs8Dt5xmR1oLDh\nssH90oy3EExhmWDxk3VD5k3cd+96nspF4bJBOZMSH4aQlbsBHyC8CMLeVKw3ax7u0UOto9ljKwuy\nRev1upXLZfeAAOKZF+Ah6ZmjT0l4XlDyWgpBz6TZoweZS4MLj/MbhlZ2MUb9HAxL5UINh0PrdruR\nOmUKFFU/EA0gVAiAxnghQnAIL44CQoAUxjBniOKoEJgh06NLMKy5iJXQDG9MuZroGSW0mx0+eUKb\nAGsCEvOnnIBGKZQDx/MwMz+zm83Gs+Km06mXKNg1/SMMt/EzrA0V7kX9HV4Yk5DqWT/0LtmBGNT7\ndhqcFJhSjwskQbNHAhvKIk7RaYycDD4q3+KCh4sChygJ7l0uyUqlYufn59ZqtaxQKNjd3Z1700aj\nkZfSh7eChcX8SJntdDruydpnHPwpCT0a2kSUjKdareaWIlZi6NUJQ5t48NRyU5B9COG7tdwGGW2k\nYjPW0IMK6Oj3+zYcDh1MqSdKO6ebfXlhHkr0AiGzDdDLhYO3BnIpHpuk9hL8GgmtZyRws6CUAAAg\nAElEQVRcj9D7ys/FYhHxWOkz+db11O8mfEiZg8lk4p4MLq+7uzsbDofujY+bB5dQOp12nUm9LJ2j\nvg4p6/XagQFAQcN7hMgfHh489KllcPQeQAcRQuKuYT+TgYvRjQfoUARnvoPzpoCQECt6I2zTFDoY\n8Oz0ej2/XzD6uD8ALIeosxh+9lPfpXpTuWFwqeLqiXG+9jmPkwZTENCI+XNweK+6QtVaodaPbk74\nDrS3OFQ4aJuoizadTlu5XLbz83Nrt9sO/sh4GA6HvulxwzcaDbey4EZQ10fDD8cCUmaPHg0sQ8jL\ncMLUG8Wz0B5T2oOKz1YSrXIK9g2mVGkRPm40Gvbu3TtrtVp+CfGe8HfxWGBddrtdB1NcfKHb/dgh\nCD0/KGBCsXgoOFeEo+EhqrFyaqJKXEuRmH1peceFslH+q9Uqwkt6reh+wGLnkr29vfVLhv8npIzX\nMw4cKphqNBp2d3fn/ekUQIWtcfYNqnjOXJIYIoAhNTru7+8dSJF09P79e7u4uHBvG9mYgH/tt4cX\nCON0NBpZt9u14XDo+2Dfoh4ipUBQ24x1HgwGfheGJQEUhDw8PHjW6Xw+tw8fPlitVvNzXCqV3Lu1\nXq8PRo34FsGjPx6PnScY8myhevzpOVPETUNPBlYXyprLxyzqqs5kMu4GnkwmHh+vVCqRlHPiyKFn\nis/b9xzDPwOMyMiDKMg8tbAozwheFRc4Fgi/o1VxkX3NLW7jaiq2ZiPywiulNWK4lOGXcDHzHdQL\nq1arlk6nXbmpN/IQnhDdMxr6CttOhHwSBf7Uy3l4eHBFoAZDeFEdG5AQmiWcTHZQyMuB76WXML9/\nikIJBC3cqOtO2DmOZK9ALO7Ce80z4bkryMPw4tzxb7wIScaBKT4TvQv4YI3v7u5sPp9HvCGHLLPC\ndwEc4bkq34YyONQSvLi4sHa77aUpwow/vFr8HwY8Xn99mdnBGnWznroOGGhaskL5d2bRPRWCejzF\n9XrdvXHZbNZ1EBEQPmeX98Zzvx9niMS9R9dfaQ9h0eN9r9FJgCmzxwOtlWvNHvkYZG4Nh8NIeI5X\nmP4aZsApmEpCGAIvC9k4ZJhkMhmbzWZ2fX1t/X7fJpOJ3d3dubLjMm632+5xI3sKa+WYXjcFUc1m\n0wulanYmVoXZY60ULmO8Nv1+3w9KpVLxjEXN6FR3/6HLBijwZ7/GHWjWAqXYbDbdq8Vl3ev1rNPp\nuHV16AKdzwmeYsCUgkbtr6XFRZn3qYEqxkyNJTJm8Y7qRVcqlVxvaZaxpqwr6NnlM+CCUe+ClkPA\nCxMCKOXXcHETVoJawXk1Myd7E17CQ67Pa98SPj/9O3cGNAk8MLQ3IlqBMQ4HdblcuscnnU57shPl\nMKAgADYOsY/VyCb8qHwgdEMc1UVFn5MaqYPBwJ9bo9Fw75V+/7GjNdv+HnfnH5qmc3JgCo8Uhx6S\n8Wg0sl6vZ71ez98f53JPpVIRMKWhIgVaxwpD6AYBTF1cXPhhhhdwfX3thf4AU7iyaXOhlW1V0XGx\nH+NgoNja7bZdXV3Z1dWVNZvNSAhCibpY1RQGvLm5sc+fP9vt7e0XheeYM+unlZoPWeJCvU1hurlZ\nfDV2LdfRaDQ882s2m0VaLIRtH/RzwjHsY15x30VrlW1gSos8hp4YBVVxXKNjg6y4kKyZeV2cUqnk\n+4sQjGZvhjXQuMAw8LgIdznOMNwW16cs/N04faD8HDyPGD/aXJYeb+iYfdcOi/OybAupUpVfs6Ah\nJsO17XQ6HrYjMWe9XjvRnPdDTNfMP1L69yF6NnRO6AkFU1ozMHxOKgqyANmbzcYdEnjfKHfR7/f3\nMrdtEqdfnjMcdU5hhm24t/epVxIPpthIHAoIrgAqzWLSn+HD0irpSkwDlGlvpmOXCmCc2rIinU5H\nCs9pRpe6frXSMXH/ECAeQ/hezZ4B2A6HQ5+/NlFFqRGipdo3XcSr1apzrBqNhodVCIt1Oh0bDocR\nMHVI2cYp4f+2CeCK0EKr1XLATCsLwhCH3KuhJaj1hgDDnDO8glj87EUtYWFmrtCT4A3+GtHK2RpS\nojwE/MW7uzv3tHJ+CbcfIvTwLedduSaE3WnTQsYXXE2aVeuePIahppelPlfVKbofAYK3t7d2c3Pj\nhHKMFYxOdA96le86VJb3NkMJoKqVwb/WY63rhFNC6/hplw3NBD3G2sY5ANB/mgCjpHxdn0N48hMP\nphBAECnmYXpqnIQHDO8NGyZMm1TleEzhoFKjqFwue2hgPB77odf0V+2WDpgCaB2jd1ucsAaAqXQ6\n7aBQ6yaFLwihgKL7+3sHU1dXV97rLp/Pu7uadG+U5CHbI5hFvU8hkNr2Xh0fYCqfz7vbnj3PHtXU\n+2MoOK1Gr33f8LrgMSZ08vDw4BwytaYhQR/73L1UwrAKFw1eC7yRlUrFlX02m7XRaGSDwcD/Lc7r\nsA/5mr1ByBkOItmojUbD9SZeOS2rMBwOY7mmh5Y4T2JYPgFgRQi91+u5btF6fYApPMRhdtwhS+Yw\nF7PH+8HsMZsx1PEvHRfv47zSxxUPKzoIbt2h9SjfFXr3zR75clpTTUn5IUd133IyYEqVt8b9Neav\nB1kfOhtfOSwoONyd2z7rWKJjVtIoACm0jPDakVGlxO0QeB1zThr6wRoCKGqbEe1Sr2RZkg80869e\nr1smk3El3+127ebmxgvY4fo+JH9DG45mMhm3jp8Kx5hFiZPa9sjsD8BMmQFIz8ztkPt1s9n4+DBK\nAFJ6NgHCZo9cOZILzB4TBEajkaVSqYhXQL8LOcZ5fIkiVu+5AsxSqRQp2wH/RK38fchzz2mbt4Nx\nl8tla7Va3hKI1iNa5bzX67lHh3ZGcXpzV2v2rc9Kz+J4PLZut+sAidpn9NgLv0sLgmoJhGNFMdQj\nBmAn8qDE/6f4Uts+l3sC775ZtH6cmT0bLt6l6B2niVjKPdRMTbLXtTDyoeVkwJSGSyC0coFuCxOE\nll9c7SG1kI8NNsy+TF/VS7hQKHiBslqtZtls1g+4ZsZBZsYao9zDseeHl6zX60UOJqBAe2VpthMg\n1+yR7Ez/N0KgFOjr9XpOUJ/P5wf13ChYpLt8JpOx0Wj0RSsj5h3OjwtNy0WkUimvJk5dHG0oe2gg\npSGtpzzFAItareb/x97V8MtgMPBaR2TcHsJj862iYXg1yuK8FVpjCy+OJo4ce46Mm9pRhPNIegEs\n05sO7xr9PfEoJ3G9MJTJWuv1eu7BISFCMzFD4SLXEDZGLcbfoQBGaFyHtdy+FdjpHalJWnjhCKER\n9jyErmEfAeZKpZKXBSLxweyRW5tOpz0UjSMhzlDdt5wMmDKzyKIvl0sPE2kPrKck9FCB7NVVf0yl\nEAeksN6V4wUZNJvNOgBRMMVm4jklAUzxTPGSKU+K+T5Vq0aLYTYaDSeuVyoVz0b5/fffveAlDTsP\nBTRUKSmYWq1WnjShvQaxBrVL+2azcdd6pVLxjBqq+cLHKZfLXhLjGKJgqlwuRziMSiYHcGA1wsHB\nM4X3iqKJ7NtDtrL4GtH9qB4o5WconyWdTnt9u+vra+t2u9br9Ww0GkWyMo/pcVPuW61Ws/fv39v5\n+bl3WQiLyX78+NHB4GQy8c87phf/KdHSCZxJjUKYfen1Yi7KCQyTnghNHyq0yeeHYEpDk6+5t/h9\njQIQQisWi7ZYLCI67hCACqpDtVq1d+/e2d///nerVCr+HvTnZrNx0K866NCAKvFgikXjMNBvhwfY\n6/U87KFeiG2LHf671pjaRaPRXQhjIO241+t5DaVMJuM1QJR42Gq1rNVqOUGUtHrek4S5KWhSqzxu\nw+v/ae0eQnvn5+ce9kLRU3dLs6vMomu+r7BDyJHCbf7w8OCXrYJ4LErlaGw2GwfN2py51WpZOp32\ntklaY+YQJOZt8pSSQhGq4iX0hXWJslbeHJW6da8cmue2TbSnJ5wiWquoRwpdtVwufV/e3t46f097\n16kck2ekFxfhEi5RDLHQy0+dP/33YxCyX/Jebb0U7tun7goNqamnC8BxCL0ahu7QJSHd5VslJOzr\nvwPenuMnv0a2PT/NJOVF7USzxzVV2gFRDvWwhd0F9jWPxIMpM/OLR0mCVLuFHAiYei4dVxdOQ4Zh\nh/djCwoZDw6kay37r1aE1k6hkra27zh2fSkkDjAhceRRM4soepq2ttvtSPgEjxR1wr6WN/AaiVNE\nWPSkiYdhIBRzaD2pBw5Ox/39fcTyIsMGD9exPBtcKloriRcWrTYa5WIyi4aX8HBNp1P3coX1jw49\nxzgFD6inACR8IpIpEMqXjEYj7zrQ6XRiQ2LHPo/q8dC+mGrlm0U9BYSew8jAMcjYL31vyFV86jOf\nAi4aNTiWgRqe+V1HVFRPhR0kDgEc+Q7lWWI86jgYn44ZMAX/Vvlk+5ZEg6lww2iFWtCycmu+Fixo\nKCxMLT30JaVKi9dqtfJaUhCRyYbC6gd5az+l4XDoQGNfTVS/dk6hbANScWOk1oteYPV63S8swNR4\nPI6Al/CzdyXbFEq4hoSxnvs9fjcEI5SCoKM7RHsFU4QeDinKDVNyLhlQZhax5lXC0K2ZebNc0pop\nKXAIecne5GKBv1EulyNtjyimy3PB2Ot2u3Z7e+vhvbi9fWwwxRgU2LIG4X5WUj1ha61zxHvjgMu3\nznObB+9rjOZtY3nJfRHnBcKjfAiebegpCl/omV0CKZ4vADo8x4fSN+w3en5qqFUNTx0bxp0CqdAz\ntS9JNJgKJeSlhBl424RDE5cpFXoIkiB62IkJUzxtPB57yMvsMTOo3W6794o6S2GWx6kJXIVqtepF\nPqmf9fnzZ7u+vrabmxtPbT7EHOPCA3qxhMT5OK/VU5+rF5vWfEGhoWBCr88hhTMDb5GCuZlMxrl7\nceB4W2g0zLQNW0Aceo5qkWsigDaBxRMMZ0xDQITn1VuatPOn64GHnnB5KpXyPYaRSuPuQqHgxiee\n88Fg4PXflG7xWokLOWFYwbPTEJeGWVkLvS+UYxN6hZVbxd7T/m5m5q24aCz/LbWddikhsNqFvCT8\nuU/R+ajxQoYea7harSIJIGGRzriinftep5MCU0gIDl76kOKUtqL7JAnKGWRNdhphEL1IeS8xZqp+\na5+mQ4a9XitcoMyH9jjv3r2zVCrlXJSbmxsvgYAH6BgeRW3vEBerf+n+1IMfgilV8Noxflubmn2J\neqW4iEajkfX7fQd95XL5i997anyE/+LAFL976H2Ll4Y+l+122zMpqWWjter0ucD96vf7NhgMHOgf\nKyT7lKiHETBlZm6IsRZ4QzWUsl6vrdvtupcUYv2ujTe8EACbRqNhFxcX3ngZQIOnggtX+w1ShZ/y\nGxg7+hMQyJy1fpqZeW2qOO7bMQA/P3dVaT4pgF/rLAKctTsG91uxWHSQredrG6Dat5wcmFLvlP5b\n+B6zaPxVPVOAERYhSRWYQ3cqiglPk85B+Q7wiQiRhBmK4Wcfa05xot4b1imdTkfCKbTToRBkWJQz\nju+wK4nzqAB4CL9CkATE6mtb1hDCfJXsCvGyVqtZpVJxyyws7XEMCQEVa4Li03DfcyFR3hN6CPZp\nIW8LAekaVCoVz6g8Pz+38/NzJ2XT6BcQpR4RrGatdB6GLJMCqBSEU7SRsiVw2EiI0GrY/Fk/h2eB\nUYMnKC7U/y3z1+ryrVbLLi8v7fz83HU366feixBMcTbD0isatgNAZjIZP9eEcbWUAhyz0AN2CM9O\neK99jSd3294PQQh3Y/h9+5YwBP5U5XP1Juo4QyDMnahUoH2s08mBKbOvn7yGE8KinXCxdume3rWo\nsla3tQKPsGhbXDw5yaIHAeWNN4rmv4QVOp2OXV9fey+wQ1pUupe0eCh1UFDYWLBaGTquDpqZuRcK\ncjOhJQAa4STlpxyz7ZEKxgjZo5r5tU1C1zveHDLHlNS+j/0bKlP+DMG6WCw6gGo0Gr4OccVJVTSh\nJSTmJ1nYm1ShVxClYErLr+AdLRaL1m63LZ/P+16gJMQuiuXq2tCs+Pz83DmEIV82lUpFvE2qLzUD\nT71RCr5Wq5WDiUKh4L0IFXRS1DSTybjBpDXydrlnQ+eBhjAxWrTWGc/spWOIczbgAeSOOeQ+1nlC\nJJ/P5x5qJQrAXY6nlDGShEYiBcYP+3GfjoWTBFPfIts8U1TfDrudJ0X0MLERwhIQ2mKEfw9r3yRd\nUOgc5HK5bO122z58+GD1et0TD0ajkXU6Hfv8+XPEOjnkHPk+wkDtdtvev39v79+/98M/Ho/t5ubG\nzMy9UwrWdbxc4PADuLTI3OQS53e4AA6dTaQgRPflarWK8PTg7m1bk3C9ACAYNVrKYx/rGgekNpvH\nthS1Ws0uLy/tw4cP1mq1IkUbw+eglzhgQjODj7E/n5O4sDBgajabfcFbAeTX63Wr1+vWbDat1Wr5\nvmW/cvkBKsxeT0DnM+LAFCDnpR4U9WQomILnxv5TUMGZBIRRF83MPLTJd+t53BegYvx440L+77cA\nKb5DnQ0UajWzgxpuuoasyXw+j3SBgO6A8DwAstzv7M9CoeBA6juYsq+bfHig2Cg84Gw2a5vNxi0S\nFHgSLP04iXOVh39WsBgXCkqSMjf7co0YM6EVLE8sXu2jNZ1Ot1YePsQ8edaAKeXSoIQpAZDL5Tw0\nELe/AGV68MOfYYFZLr1QQRxC4vaiXkZY6E+FItUjRRkPyNpcxocOT6Og2X+EV2lh8VRIlXECJihJ\nkoQWTtskbh3jwtHKjyJcpvNi/8MfC4HnPsYdhqJCoBFyZ5AQdGkC093dnRUKBfdMYZCS2WhmXhIC\njzRtVrTl1T6Bs3pxp9Opl6lQLwy9PF+SlMXzNDP3PDYaDeci4WiYTqeRSvGHOI8YJmQ00+fS7DEL\nWKkUJESwNnBK4dWl02nXU09xrl8zt5MBU98i+pC45OA7qBWpoYUkS+jGVcUSl6l4bG7NS4S5YE3U\najW7uLiwy8tLq1ar7kofDAb2+fNn6/f7kcrfhwaJfB+ZXniSIIrzfzz/SqUS8VLEjZuKxlxEgDE+\nRz2MVKNGwe3LGv4aiSuUhydNQ806PoAUXrxer2f9ft+m0+kXXL99io4pl8tZpVLxyvoAWbOXZWPi\noaNwbAimkmbQhBI3PvXmUOsPzyHrBIcqLEq7awH0xHlKABpc+OjEuHmp7uQuQP8AkHgfnwNA1Ka/\nZDbSfP5bwmzf8gxoeI83Wz3apVIpklUZB37inA3FYtETLfAC6X5eLpeRe2dfol5TdN14PLZms/nF\nmi8Wi0jG7GKx8HngZWu1Wn6Gl8ulpVIpB767ljcNpsyilx/ZAXGeqaQQ0FXiNq0e1DjS4LaMqKRI\naBEoT6pUKlm9XneuFO+ZzWY2GAzs9vbWJpNJpHaT2XEAle6nQqEQyeoiVElquVrAcWMNG28reVvX\nWzPFqPofvucYAjAiVDefz31NzaJd7hEqxE+nUxsOh5HMt5ADcyghzFetVr1wpVZjDwXFz9qyNqTO\nJ6kI8NeIggINK9EjFAI2mZt4dbZ5hHYhykcjjKq1rTR099IMV92TWkGbz1O+FQYfjYDxmGOc70PX\nhl41xgWAK5fL1mw23UlAuNXMntx7/JsmvmBENJtNy+VynjU3m81sMplEOFr7nicCF1ONE+W7ab/L\n2Wxm8/ncO4WQ9U50gwQE+HxmjzXDdqU/3zSYUtBB/LtWq3ldGK0gnmSX/EuECz7MMEqyMMazszPP\n0mm3284TAjT0ej0PAek6HXOOaq0rYEJCfs1TeyuO36YXBHyAxWLhzYC5rJNAbsYwmc/ndnt765mY\nFJeFVK9hmel06sCj0+lE1tfsOGvLHLD61+u1X5rh5cxaEQrBSg5Bob4/6RIX+lAPDkT0er1u5+fn\ndnFxYaVSyQ2eyWRi4/HYExF2KVyet7e3kb6IIV+IsYdctThgomdM6R6aDa3PASOVUPZ0OnUD7ylP\n0K6Ez10ulzYajSI0AzNzjwy1v3AShPsQfaPJBc1m0yMBs9nMhsOht+cKi1kfSggrawY3z3q5XDqQ\nojQOHlMoEfV63cOWl5eXls/nbTwe23g89r06Go1suVzuZG5vGkyZRS9swBRWFETeYxdf24WodyrJ\nYb1QALrVatUuLy+t1WpZtVq1s7MzPzC3t7d+QSUhSUAVcVikM7TOX1Kh/CmOB5YolzxNnA/ZsX6b\nKFDcbDY2n8+t0+nYYrHw0ANZiZR34DmNRiMbDAau+IfDofMy+OxDSGgJLxYLm0wmVq1WI9Z9HMcI\nLw2JEQqmAP2nAKJUQg4ee5gSINTdOj8/t8vLy0hzakIy8Pl2IeqVnUwmzscDpMMVItrAHJ7aR3p2\n8bjBdcPzDRDhM5WcjfHNuhMG2yeY0s9kbFrCI5vNOiCCdgAVALDA50AnIHkAw6dYLNpms3EgNRgM\n/JkfYx/DcVIwlU6nfZ1Go5GNRiObTqe+nvDW4L7VajWvC9dut208HttgMLDBYGCfPn3y7OvvnqkX\nirpoS6VSpPK5ppkeO1zyGtHLWC2uJADEcAz8PazT1Gg03NKltIByaeIut2MJVhMXCERV5TvFVeFV\nUeUf1qoB5PMdKA8ae1Mvx+y4Xg8FGZDJIchOp1MrFos2nU6tUqnY2dmZ78vJZOLAcDabRXoq6nz2\nPTf1aEDuHw6HfslwiWrhVLPHsBPv15ZGFOkML/Rj71lkm07Aq808FUDk83mv+t5oNKzRaHgGH6EY\nLraw6fwuBKPi7u7OBoOBJwYA2DW7K050HBoq4owxhxBMIcpJxXMVFgLd9/2hwBLP2GAw8LUhPF2t\nVm2z2VipVHIOY+hhY01JfjEzB17sZ9Yy9FLuW8Izxpjow4c+JAwJWNRxUsICw5yXFtv91nIS2+RP\nA6ZQDFSNDomxSVF0rxVVEnHp88eepwIISiBQy4e+X8Ph0KbTqd3c3Fi/37fJZHLU8E+cYClzcPEe\naRYeLvQ4TkXI82CtsHqXy6Vn7eGSnkwmDlLISgEMJOG5hKBQL1otkovlqP2zkjAHsofYm6nUH9X2\nydbUTDX4YXD5rq+vI9XOkzCfl4jyZ7SuVHj5FIvFSOYqYGY8Hlu/3/dwi/YC3TWIBJwSXlsul5HW\nSl/zORqVYC9S1iSs48Zacm+oAa469tCG62q1sn6/b/f39w4qqtWqmZkDXzWwFeCrdw6eI/oGXXOI\n0OU2UUC1Wq0iXS40e1K7e+j7N5uNe7JooA53SvvW7tIT/ubBlCoLwFT4/6eg9F4qXGjKAzt2tlco\nHGxS0ev1uoMpyOZcToPBwGazmf9eOIdDWUohnwTLUK3T6XTqBR65eJ6q9aXEXq2crW0rCCPARYnz\nOB7jmcQJz8XMImGeuOyhuP879v7UNdVUaojXZubeCe2/d3t7a58/f/ZMt1ML7wEUyE4tlUoeRtNw\nLd46sqPMLNLaCTClHp1dPIcQDFAaZTQa+TifqmsWihbr1AzUp7K8tgGlY64zYIcwI8AA/YNRp+US\ndE/jVaeNUK/Xs9Fo9EWZh2PqE9ac9X4KsLJPWFOKWY9Go0ivSYAzHsVdze/NgymzaG0SejdB6NUC\ne0kIiX2NKDFWXdY6n2NayXHhPSVAUtOnXq9bOp32kMlgMLBerxeppWQWVVzHspR0ToQyyA7RcIFW\nicabgZuaz9E2FppZitdjOp16lopypMLncYy1jXseKuqBVKs9HPO2ORwiXML4wsuaiwavIzwqvbjx\nFrJft9WVSjKoUo8L/Bs4i1q3iBcAU704t7e31ul0bDQa+eWE7PK8qkGDrlNApDzR574rzrP0LV0F\n4rzC+1rvbeeNMU8mEw9Bkumn66eeKMAUPDcFVVowd99zemqe4Vzj7rJtuoPfw+Bhrrlczj3m8CN3\nSRt582Aq5KyECuGUwZTZ4yZDqWjY5NhgKm6cmolDP8FareakZIiPw+EwUkn52ONXUcUGSZVMsMlk\n4qURtP5OXEqxZgGG/cG0bhP/l0QJvXZxoOg5azIJomsKqZw1pe+ghvnwRhIe0WKASZnTSwS9mM1m\nrVwue3XxsGAsIU9qHKknA65U2IVhH2NFQt7T13xfqBufuqjD34sbyzFE9+tms/Hw5GQyifCC2LO8\nV0n3GG7cGftu4/QaeQ5Ahe8FNMG1Qwer0QSA/u6Z+grRuLFWkJ1Op+6WPlUwZfZI/qWHkVoX6iE4\nprsW0ZpSlUrFsy2Gw6HXGxoOh85HCq2GYx/yUImpV9DssXaLFlDd1qxTX1qLikPOv4fP4djPIJTn\nxpO08apsWxMumel0GqndBrAILx8FEUmeLxICAw3z4ZmCmKzGGgVWr6+v7fr62luwaCh0V+d12++q\nXtOMu6/R33GXc5y+ecl4Dilx+9XssRq4mUX2qnZjUMMbj5yWTkianvkWb7V6wDW7Lw4s77p/7ZsG\nU7o57u7ubDKZuEua1GxSP0+pUrEKm2SxWNhwOIyE+zgkSSnemUr9kZZbq9Ws2Wx6jyvqJ1ECQSuc\nJ13i3NIArBAMhRIXCtOLmc9Pwtr9WSTOk0Y4iJ8oag2ln9oaaWgTnTEcDp37RSKF8vog7pK5F1eY\n9FDPQUNsceG2r/kc/XlqEjd31T/KY+T/1HDjM/h5qs9hm6g3SmUfc33TYMrsEYGiDCgQOBwOvQDi\nqdeYUjAFEtfqrkmqO0V47/z83Ot9AaY6nY7H7ZPoldom26zFlwD05/bdqSv7U5TwWcdVrz9lfaGi\nPDEIvuPx2PlhYYkOylgoSRk59B49Bd1wCAn5VGEoK5SQi/RWdUw4rzju7S7lzYMpNhW1KiDrUadC\nsyDMTnNDEZagmitzSEKRSw4roRJaHlSrVe/1RQ0lwq6HInW+RuIu1tCrEWcRveTztim3JD6HtySh\n0kXCSyp8/6leRjpfziJ8Mfg2SlgOayptu4j3/RxO7TnvS0IdFLd/t7VB2vb3U3+2z3Gp9ilvHkyx\nmWazmRP0IPeG8eJTFoqrUUYAXseu2zp8i6TTaU+zrtVqXqEXD+FgMIi0Ldi3BbgdD4sAACAASURB\nVLFP2abgvub3vkuyJMkcml0K5w/+JXXBNPMtqYVIv8ujvGRNvq/b7uVNgyl1d2pBvlMhG8ZJHKfD\nzDyT7ynLOfycfYtar4VCwQvJUd5/vV5bv9/3FiSnGm7dtia7fMZJ3Y9/NnkL6xC3X+HWaOjuOc/T\nW3gWb12+r9Hh5Khg6u7uzjqdjv33f/+33d7e7vzzw5CLpi7vK3NhuVzap0+fbLFYWCaTsY8fP0Yy\nKvYhSl4OZde8o16vZ71ezx4eHmwwGNi//vWvrYRxxpTJZLxyslZR7vV61u12bTwee/PQuIKHh1QI\nv/76q4/n5ubG/u///b/eSPTUZTab2fX1tfNffv/9968qdph0ub29tX6/b2Zm/X7ffvnlFxuPx0ce\n1W5ks9nY77//7pWur6+v7b/+67+sWCzu7PP5+RSY2tfZHI/Hdnt764lCv/76604+Nyny+fNnp5n0\nej37+eeffa+eutzd3dnHjx89TPzp0ycvzfAWZDAYWLfbjeWfqaSO5QlIpVIbGhFWq1XL5/M7/45t\n3If///vD8ezkO+/v771qdSqV8vntkwQegkZkHx4pyOIUhsPT9NS4UqlUpD8SLzgY1APZVp7ikJc9\nmUqr1crX7q0ohbu7O89kzWazPr+3Aqa08Wy1WrVarbYzsHFsgRg+Go3s/v7e57crI+0pXWm2/1An\nZRdGo5Fn/NIa5S2IFsZkbpSeOHWhRuB4PLbNZuN7U4sUn7LARx6Px9xRsZv+qGDKzPxiTVLG2WsE\nzgHEb+b3VkSLvtEA9q0cGjPzuT08PLhH8a3tzfV6HWng+1aEujJ3d3dvcm9qBh165a0AYagYd3d3\nlk6n9+7NP7RopwNthv4WREPEZuZn763tTelAETuxo+7WbDZrl5eXdnV15TWHTl3W67UXtEun03Z1\ndWVXV1dv5uCMx2O7vr62z58/W6PRsHfv3lmz2Tz2sHYmNzc3dn19bbPZzNfurXg3CA9dX19bLpfz\n+b0VpUdPx5ubG2u323Z1dWX1ev3Yw9qJbDYbX7vVauVrtw+P/jFkPp+7XimXy/bu3Tu7uLg49rB2\nJhQ67fV6dn5+bldXV2/G83Z/f+978+HhwffmWzHUJpOJz++pUN9RwVQmk7GLiwv7j//4Dzs/Pz/m\nUHYmZMEMh0PLZrP217/+1X766ac3s7Gur6/t7u7Obm5urNls2g8//GD/+Mc/jj2sncl//dd/eU/A\nq6sr++mnn6zRaBx7WDsRqsr3+32rVCr297//3X766ac3A6Z+++0357q1Wi375z//aR8+fDj2sHYi\nm83G/vM//9PX8P379/bTTz+9GT7fYDCwzWZj3W7XarWa/eMf/7B///d/P/awdib/+te/bLVa2WAw\nsPPzc/v3f/93u7q6OvawdiLr9dr+z//5PzYajezh4cE+fPhgP/3005sJY97e3tpms7FOp5NcMJVO\np61YLFqj0bB2u73zz39tCPNbLpnZbOad1Wke2m633wyYWiwWXmwzn89brVbby9ptk32vKRypdDpt\npVLJms2mtVqtV31nUuTs7MxKpZK3mWBvvhUwRbFJM7N8Pm/1ev3Ve/Nb9ts+nudms7FKpeKhS/Zm\nrVbb+XcdS0qlkof4qtXq3vXKIe+HXq9nuVzOM5t3sTeTIqvVyu+8+/t7K5fL1mq1jurR3+Xartdr\nKxQKz9I93k5Q+oXy0orT3yXZ8i3FML/Ld3mtbNt33/fZ6ct3nfK2Zd93/58GTG3LeFP5fkhOQ76m\nsrjZ93X9LrsRLRmgQt2m7/vsdOWlOuX7Wp+evPTuf+26vhkwtS21l4dDs9+w7lJYX+VrygwcUp5L\nXX4ulflYhTu/VZ5aT9YyroGzrmdcI9okVlcPL2kd46m3ejhGeZLXSpwOYA/F6RFtGsvf4+TUzqDZ\ny701z11ESZpnHChOp9OWyWQsk8lYsVj0wsKsOVXhKeWijeRVkjTPP6PE7Ve9J7bVltQz/LXGOvJm\nwJRK2JKEA5HJZL5Qhrz3/v7eX3wGv58k2dYn7Ckwpag7afN5iSjQVaXHSzuDa9V72gWFgCqJEo7f\nzDxGn+Rxv1TCfXsKVbTjxqz7j31HuYKniueqJHnOL5VT9/SH+zCTyVgul7N8Pm/tdtva7bbV63Vf\n79lsZv1+3/r9vk0mE9cvZsme559VwuLc6XT6i5ca2HHV/79WThpMxU049GBQ84LaHtQN4qLiUNEZ\nHYuDflRxn3soeUpJPTeO0BPz1OcnYU7PCWM8OzuzfD5v+Xze15WyE4AouqVjSYY9/8JxHGr+T81d\nwb3+27bfP4YC/xqQsA3cPzXuYxsw2+aHruCyzeVyrk9oDMw+U4MMOcU5b3tP3DiVmBuesTg9lBR9\nwz2QyWS8d2i5XLaLiwu7urqyVqtlmUzGstmsjUYjy2QyXmdvPp9HviMJOmQXknRg+DW8Nu57XqEB\njtzd3dlisXBQ9a3G90mDqVB4CDy4fD5v1WrVKpWKFYtFv4Q5IKlUykHTbDaz8Xhs4/HYZrOZTafT\nRDQJRtRboUXfQuSt3jZABQpAG5UmWeIuYuZL1XWqWxeLRc+SYT3v7u5suVxav9+3Xq/na5mk9VRR\npQ74V4C/rTL8qYhagyg2CmxKITx/f1K8iKwJoL1SqVitVrNKpWKFQsHy+bzd3d3ZYDCw4XBos9nM\ngRUShgNDIw1JypzNnqdMmD2GTkLDlL0aF2I/tDw1D85aqVSyYrHoVbvr9bq/yC5Mp9OWz+etUCi4\nvklq3cBteiKM1sT9PDWJo0eo55i7v1wuuyGkdyZ343w+t8FgEIlifIu8GTAVhoLy+bynaJ6fn3vb\nk1Kp5GBEL9/hcOi94nq9nlfjfc3D3dW8VFKplKe4h2gbr5t2eSe+D/JOOpjaxmfIZrOe7n5xcWHn\n5+dWrVa9LQOHiAt6NpvZx48fI/tC3bhJEFVuXNgAxIeHB5tMJpHmz0ka+0skvLxyuZyDkOVyabPZ\nbCu4OLagcLPZrHNo2u22nZ+fW6vVci/Gcrm0m5sbu7m5scFgYKlUKjIfNXYUOCZxLeM8t+FFpfNR\n618vp/BSSgJQVD0QGtv1et1arZa1221rtVpWKBTc+8h8V6tVxHhLWleEbV73bbQQDYEde21eI2F4\nnb1JeY2LiwtrNpt+hgFTZn8UMV6tVl4fC32kVJ+veTYnCabiLA4FFCDSWq1mjUbDHyYHRF19XNJm\njx4QM/NLbLVavegS3tWG3BYeIbxQKBQcRDAXtfhR5oBEvBv0ZJvP5+6lSpLViKhlwZxyuZxbkPV6\n3evr0DPv/v4+EgY8OzuzXC7nXgKs5OVymRgOlSo5LuxKpeIWMm01lstlZNyHHvNToXSzp0N7CPuz\nWCy69U8DYgBGkuaXSqXcklVPaLPZtGazaY1GwwqFgr/gzvA7YSFNvBuLxcL3pHodw7Hs+xk85bEJ\nPU5xHEV0jp5TNUwx4NCd6/U6Evrc9zzj5gegp7YhYJj1bDQa3jOP99FGBP1J1IJ1O2a4Mu6eCNdL\nx8ec1IvI/bCNK5QUkLXtTszlcl9EaLjP4b61Wi3vE5jJZHzeRC+Wy6XN53On97zGsDtJMIWEF1K5\nXLZKpWKtVsuazabV63V3z6ZSKX+AKAysZV71et29AvRnwzugTP9DoXm1AiuVirufOfz0NGQubCp4\nQyi25XJpnU7HstmsDQYDr/CdBAktpmw265cUnsRyuexri2cjl8vZarWy6XRqm83GvY1Yj4Rlzs/P\nfT1pxJm0sBlWVKvVcgt5tVq5xxSgwYV0bBD4UtATWop4d66urqzb7boH0cye7ch+SEmlUlYqlfys\nsS7lctmKxaIVCgUHEIQTKKRZr9edT6PzT6fT3ux2PB77uh7TKxeeAQVHGCScReUoxvFP1IhbLBY2\nnU5tMpnYZDKx8Xgc4RgdY47pdNr1PAZ2s9l044XsPb1w1+u1G6GEcgeDgY3H46Pqzzjgo+Fo1ow9\nmkql/N/Ozs58300mExsMBjYYDHztkix690L5qFQq7nHC8FZDiGKiq9XKJpOJGzTz+dzBFPsVo5Uz\n+WY5U6E1E3cJc3nS769er/v7FouFLZdLv3z1dwgLsijr9dqtx/V6HfkdlacIwt86v1CwNCqVil1c\nXNjl5aUjbjwzZo+Wh5k5WACBr1Yry2azkWabs9nsRXPahWz7HuVV6DwKhYJ7FdVirNVq/nt3d3c2\nnU6t1+vZcrn0Q9RsNv3irlarbmFOJpOIImFcxwYmZhapRk5fq8ViYcPh0PL5vAOrY4xt27899+zC\n8BBr0mq17C9/+YulUilvuYRn8VuV2K4llUpZsVi0ZrNpl5eXfu407IOg1PFgEToIvT3pdNov43w+\nb/f39zadTiOhv33OO05/hnNWTz1GCV7wSqXilxQecU3mUTA1m838kk6lUn6BHVrCMGMul3NvFL3/\n4NRks1nXRYR6FouF9ft9u76+tsFg4OCQe+EQ4992x8QBYWgCeLgJR6bTadefeFLX67V1u117eHhw\nL3HSjEydfxjG49y1221rNBqR5BB+AvZJGJhMJjYcDm04HNpoNIp4T9m7r/GangyYQkIeBp6ner1u\n5+fndn5+boVCwdbrtfV6PUehvCCHaniF8FGj0bBqteqejnw+H7FANR1aN96uORAobMIitVrN58bG\nWa1WtlwuI250tS42m42jc342Gg3/vfF4HAmPHVLUTRuGEdRDg6JTq5HfVysFwMhhYE+YWeRiwOvI\nczr2pW1mEQUelnIIX8cWgG4+n4+EC7DwtgE+vaD4HDwfAItjJwfofoLDBuEYPbCNJ8M+giKgF7N+\nZqVS8T/P53Mbj8eRkPyh5mn2SCDnrBQKBQeF6v0NARRnkAQCBYusqYbcASXoqUPRC5gn1I5KpeLe\nKF6VSsWN5+l06i8uWAwakgswyA8JOuIMGE3mwFOoYWc8VGbmURUoE2aP55j3wrPV70iKhPtVaS6s\nY7lcjoCuxWJhi8UikoykYVqwgGbf7kLHnhyYQoiPEv6CaH5xceGIU2uDEOtWpa21RS4vL22z2Vgu\nl9sKpojRxl18uxBVvvSGu7i4sPfv37trGqBIEbnZbOYHHaXFxmq32/b+/Xt3hQKmJpOJp3UfUvSg\nony1zAGeJbwBKHrWgM94eHiIxP/xUk2nUwdktVot4nHkBQfiUPyUl4iCKcAe80yStUgovdFoeDjk\n4eHBQ1bbAIHW/OLcsvYYBklYB0S5F5CRAVPheui49cKKWzdAWiaTsfF4bP1+33XSITmMeiHTGxX9\n0mg0PLmFuYSgXnlROncFyXiBMNwI52pCxT45U+qNgvpxdXVl7969cwMtl8v5PIbDod3e3trt7a2D\nJl6Aq7iyF4eS0HvPvYWRCKc0l8s5QOeeWK1WEQ8idAnoEEkmocd5o4g+aQISa4SzgAQXMvN5Aejj\nkiReKycHpjabjXuIcrmcVatVz7AhXX6xWLiy6na71u12I0gUQblxWZfLZatWq3Z3dxfhU2GJcwBx\nW3OZ7Hp+bB6yTdrttn+3mfn8JpOJcxPYOOv1OkIepVZKLpeL8Ik4QPu8qEOryiz6zCuVigM9nnWr\n1bKLiwtrt9sR0iCHRDMViXsvFgsvbVGtVt1LwiWIgud78S4mUYGwp5hn0jxThNIzmYxz0bDs44Q9\nFtZu4+ItFAq2WCwStRZxoRXGHYIkBb1K4NbP0n/HYwD/ajabHTyEa/ZIH8D7DXWAbDbGzrlTisBq\ntXLrnvlxfrV0C4YMHpB9eh/jAC7zU2ObcC3ew9lsZpPJxHq9nt3c3NinT58cTEHMDnW8ehv3OQ/9\nN91HcNkURGloC7DLPDhfnLlyuezeqyRnJSLsr3w+b7VazYExcwDgsye5CwnLTqdTdzaEmbZxP8M/\nv1ROCkxxqQA0cN1eXV1ZqVQyM7PBYOAWRq/Xc6+NxkNVUPAaU8VDAM+jXq+7e7FYLFqv1/MLIozL\n70LY+PAQ1uu1u5v582g0stls9oX1hEv37OwsEv7T17EuZjx+mijQarU8M5G6Lxx2wj+LxcKJoGTS\nQM7GBR8q6hCEJAGMxIkStAmRoAzVBX1sAQgomGJtxuPxk3V3wjIdeAbheEyn00QpdcA7+kDrTWGk\nsLe0zYhZtKYWvCIuOjOLXGoaPjsUuNeLWQFtsVj08g14NfDuExrBqlfrH10DnYDMqWOHqTEg4SJS\nSgUwyz7s9Xp2e3tr3W7XIxjquTA7btsjDXPxrPGyA15Zk+FwGDFwNLQKINHQltYfTIqeCUXJ5s1m\n00F/tVq16XRqo9Eo4nnSTNmwEDefp58d/ttr5CTBFAeFOlLv3r2zdDrtpMdOp2MfP3500LNtk4Bo\nzSwCprTcQKlUsoeHBwdu5XLZLZqnQNq3zi20ZBmjou1+v2+DwcA9NerFAGjGtVNRMHVIBaffQ6iI\ndSOEqam7vIh3j8dju729tZubG58zz2Q6nXoZAX1mpyCMNQRTXGaA+mOHJNmTeBPxGnJmCoVCLJhi\nvHFhBwVTzDsp3kJ4YJPJxK19QId6NTDCAPVmj2BKs+HUm87loJlHxwCSChABU3g17u7u3KIfjUY2\nHA5tMplEwBTryO+SKLJeryOJMYcEVAo8mJtGLiDRa2JRr9ezz58/W6/Xcw//Nu/IoSUutEeWM3yv\nUBcqV0gNaPjBWrxZARV6JgnnT0X1DrxhsjDH47F1u127vr72rFElk5t9Sdh/rffpKTkpMKWi1lUY\nr+fvcRyHOB6DEszVkmQT8pmpVMq9QLtG84qSUdTL5dLBncZ/cV+GPcHMHussEUZQS4zfVxLloTga\nSqonWYBDAYGQueP1m8/nNhqNvJhqp9PxsfN84NtoCrdy3RRUJskC0/XmQiuXy15bCq+jAmX9vUMK\n343Vu1wufV89RczW31fiP7wZzlZYE+cYEnLyAPFmj+0mlD/F+yApj0YjMzP3HlSrVd+XfH7cHI+1\nFzkXeH05L3inlGsymUwiNAn1ZrB+WsPPzBw4K5dzH3yjuOfHedJMRNZhsVj4xUsUg357nLenwj9x\nfz+EqEHMc02lUg5wCW+pfmSshAYJCaJj9rkurxX0A3cZdd7K5bI9PDx4uYper+eJZrR04vfDM7dt\n3f6UnilENxbomoyGRqPhtVxwR2tBLgUeCsYU8cMtMjP/s8Zjh8OhK5d9pHNr6j9eMJ2LHoDwwGua\nPXW2zCxSuDP0qO1TObCpIbRCYA7d7nphh6UPcMMPBoNIgTnWXgEkIBLvHJ+lzy3uoB1DlG8CXw9F\niZWpYz6GqGW3Xq9tMplYt9u1YrEYSQx4yefomTWzLwyVpMhms3F+BR6qXq/ngOHs7CxSCFaTH7jI\nKR6sfcH080PP8aGE58z3Uv15NBr5fry/v/+CPoDFr0ZYOp32MiZkx1HTb7VaOa8TL742Bt7Fem97\nbpq9V61WnVAPSOz1etbpdKzX63ndKNb7udDPsfYpBiZ32XK59KiFAlzV6VrMuVqteqLBZrPx+0yT\ncZIi6ApCmhT6bTQals1mbT6fW7fbtZubG+v1ep6ZHrd+h1yvkwRTZvaFpYtHguKb4/E4UldKlblZ\ntCou5GzAFJeyEivv7u6s3+9bp9Pxw7frruEaFuHwYIFoOG8b4lZei4IplP54PPYq6IcEFTxnCKl4\npni2Zl+WCICz0u123XokaywcMxccXAI8CGEV5kNnTT0nzIOxVyoVG41GHmKB+Hvs+kvKDwLg1mo1\n73ml7wsl9PiEJRKe8kwda504LwAp5T9pmI9ngpUPD6pSqVij0fBwX1hSRfXWIQt3hp7f+/t7z7RT\n76Je0uGahpyrEEyhK0lHVzC1zwtOxwlXilI3eGMAeN1u1z59+hTJ3FO98tT4DhkC1M9lTdQhoOcp\nHLvWDNO2OdVqNULMDrt7HFPHhALNRnsmUvLo+vraOp2ODQYD51yGyR8qh6ARnBSYUquK6te9Xs/T\n/onf12o1+/Dhg1UqlUjWG65AFk5bz1xcXHiV41Qq5Zli8LDgKY1GIz98+xYOjPKA9NCo14AQV6PR\ncMJlsVh0kiVWGEXnDoHclQeGm1nBjtasQanh/ZtOp56JqcUNUfrhJc1+QHEDojQkEYbMjiXwpAgt\naxV+M/MLGm/AscGfKmjlDhFGfY6XyHu0TyRlB0jPD8OFx14jRMGH2WOoU/+PcB7FEskeg5RNiQG8\ny3iINXx2iPWNe6bMTQGdGp1xgmGUz+edFNxoNNwbjPf+9vbWk0b2Keop00r0lJIxe/TM45HSsN5T\nRmUcLWTb9+9TFECF1A69E3RtSPQBWEIXCcNjT83tELLtGSsIJMKiPDCNeISfE87lEPrkJMEU7vdU\nKmX9ft8zv2hAitXcbredQMlPiKKbzR/FHcko0wJguECHw6Hd3Ny4UgCQaUx6XxIS5/QZhBbLZvNH\nfSxSnAFTm83GW1gABmmpcugwF2RjTZXGkoBAyRoBYBUEUz8r5JaZmYMprbmFFyp8JQFMIUpU5gUP\nAq9UqDSPKSgvwLByEsNx6jPGggZMLBYLT3UOW18kUbQsgoJ4s+gacv6U8KzFZgmTYpTR3uKYbYJ0\n3Z7zuvD/ABYKJ6I/2Q/oztvbW+d27nv8YbYbHRQIOwJiaUhNdOGpeYZ/VjnGWoWAF1EDk7XRvrSt\nVsvHO5/PnStGlCKJohn7ZNObPVI71LtN7cc4o+57mO8JwRo2Mw9dsZm0BgeKOqxkrGEJ6ho1m01v\nJaMp0cTWO51OpOhnnDv4tYsWKunwz3Hfo9lB1KNqt9veSoDsv06nEykhgBW3b3c7grVECBVrXetF\nqdUI8NO01qesP6xrLcgWcqXCYm3H5kyFnAZeOpekkOXNHhU150tLVyhI1T3JHFFyms7NuURp8v5D\nz3fbJaqioD38XW1d1G63vUYa/KGQrA7xGa/UIbw2cf8Wp2/ivNUKtFgjrU0FL6lQKDitQg3XuMr4\nrz1329YpzHgjykAyC0AWrxQewdDbHRptoRecP8c9x13dAy+dM+9Xvh7RCYqw0i9SSwiw/0Kge0id\nGBdC1rIi6u2l1qPSRdCP9N5TbrSuk85rn4bpyYEps2jGm7aHAQjRgqVUKnmTYDwj9XrdP4eibsVi\n0e7v763f70daCGgJekIu4cHb9zzjhA2hPCF6uuHO1WyVm5sbG4/HrjwOFVJQwECfL8BUJpPxsB5W\n7OfPnx30wet6SZX5EJhojS48IeGlnwTROL/yWeJKWBwL+OmlgiIrl8tmZn55ht4ViOVhdftarWaX\nl5futdGyAeGllSSJe/bss2q1ahcXF3ZxceEXF8YMQAoQD5G90+l4A/VDS/hsVZ+FZ0e5VWbma0pt\nP/qfwpPCA357e2vD4dB5pYfghCnY18xCjChAhGa86X7TUDbeDl4kHITUgWOfTRWyF+v1ul1eXtrl\n5aUT79GDWgWceR6zqruKghytoM9LPfetVsvy+XwEHCpA1AKdqkf3vU4nCabMHgEVF6Vm3dBShuJe\nvDgIcUJo6fb21juEU8NIfycJB8fMvGyDtkp4//69lctlD+1R1ff6+trBxCG5GYAcgCz8NKwM6tmg\ngD99+vTFM39uvBpu0W729JuCo6PlLF7yufsWwEMccTJUAscWxgqhtVwu+/MkVMXlElqVWJaVSiVC\nJAVkrVarSJPuJAs6R58FYOpvf/ubk521HAKXFYYeXRlms9mzHtddyzbKAHsQEMLZYdyAIeZLy6cP\nHz5EQAthNAVT+/QEhKEugA8vNdgAUhjGcV4lzqEStwlD39/f+++b2Rfej2PrEzxSl5eXXr8vm816\ntw5oEIB4wGcSzp16P7WMA5yvcrnsep0kj0aj4XObTqd+f+MICWkdenb3JScDpra54/XigRyq1gm9\n9iC4kt1mZp5iCnmZgp8gXdJPQ6LfoeYZJ+qtINuBrBUskdVq5aR5PGth7ZFDSVgJGmWNJ5E2MHCj\neOYvSVPe9j1aYyxsBH0MCUO3HGqsL8iVZEFpOQ+zKMH0WBKGPTSsgLd3vV775aPNpbWTOxw5bRWk\nn3to8Pjc94Ugl7XDy03iCrwOwpissxLOu92uG2l4uuO+Z19z1IQH5apppqIWjtUSCff395EGs7Va\nzXvbkTACqZnyK3qGDyHsSV5wabSgJc+dPazPRAEUoFibcePx0JIP6Jc43tku5x4CP/X8p1KPCRBE\nZLRt1v39vXOp4AoXi0WPXiShnZPqRSIuWi8w7AahRg0ZpWrENRqNSFV0rW/H7yO7mvvJgKltosAI\na3k6nTpviua26rHQcAqk0OFwaP1+33q9XqS7udnxvRihMH7NXKFAJ9k0vV7PFTe8jENfyGr5KtAh\njKDWkio6sy+z9uIk9ICpZ4qLWVuzJMktr70XlSRLwU68PUkYayjsPZQYbURSqZS75bUPoipC9iJW\naFiQ9BiA6mtE+V8XFxd2eXkZaQ4clkCYz+eeyt3tdj0bWA2bfQMpnicXDzWYyHbD+OSMcnlBoyDj\nkLUliWSz2TihGe8yBTC14foh5qcAXz1TYUFLDBT0BaCSOm8YpcxRuYyE+cgyZq7HCpWpbtXsPe6B\nsLgqRjeZ051Ox9vQJEXUM6VJLnEdITSrj/Wq1+uRVkFksbMH9rlOJw+mzKKAiksaK4IDgMWlIZXN\n5rG3FlVxqWRs9iURMUmiLlHlIWFNYj0BUrSdhdn+56Mk7xBMmX2ZgfdUmnKcVzK0LODpaF2f0DO1\nL6vxpaJeRQoeAqbMzPctHrq4aszHFOXQaEsnOFRU/q5UKg6Y4QpxwWL08H7AxyGA/nMgLW5/xHkV\nzf4Iq1AWgAbrZtHUbc7icDi06+trb8tCJvJT+3zX81SLX8NBeACU4KufQxPx9XrtnkbOFl4fCuti\nwC2XyyfntA8jVSkFAHmacXOmMNbUUw6IopwFGd08l7AX48PDgydOcB72fUk/N28Nf+HlRn8AnJgr\nSVZ4vnu93sHH/Bz/lbUBxGr/y/A5K1dOE1nMzFskIdr5Iy7My+d9q5w865jkEwAAIABJREFUmOIQ\n4YWi7xuWF4qdC3u1WvmD53er1arVajWrVCpWKpUi8Va+I0nCuCF93t7e2mazcSRfLpft8vLSzB5r\nOIVzOISHJsyuxPrlgGjD1G/xBKLoNLsIPg7hJOVJIEkIl6lLmtAzJTnC1ghJELx8k8nEbm9vHQDy\nf+rppbgqShu+Ch4dLmQtkwEQNrOD89rilPtTe5F/A1BsqwUWGhOHbpuDB5Hni07Ek/3w8ODEXd6r\nF5PWAdN1ImQCqRlDVLlzOgZ9ZocWLmYyLglFUyRXOX0YBoAwBb2a4IIRBF8HztgxhHM5Ho/t5uYm\nEoUhi3E+n3v183K5HKm7d+ywepxBQWjc7A8AVCgUPCkpjr9MIhkeRta1XC7bxcVFJHytpXN2fQee\nLJhSHgCKWWu8UCIAIS12MBhE3LmZTMZjzYApzQRIorAB1+u1gymq/gIoLy4uPOQ5GAwiXpFDeABQ\nzgqmQmsjBFNf8/lmjxwQwNT5+bnVajW/6OFzAKaODYrVy6RKmWKHq9XKld9z1v0hhecNmOp0Olvb\n9mCwaPsl/pzNZr32DZ4dLinA1KEs/LgLPxQNOYYeQrwSytkMf9fsSy7foULtegYB7ZRuqFQq7l3C\n87RcLn2MeK+URxWWusBIg/MImArbemx7jocSDYFhIMOpqdfrVqvV3DsMWIIaoGcQQ5VuGYTLBoPB\nURqs833sPQAxbYHosUiE5t27dxGvPiAsCXecAlYzc28uEZZMJuMGWdw9kclk3JvYbrctlUo5mNKe\np9Am8BjzDP8UnKnn3IGESkjXvbi4sKurKwdLZ2dnrtBpBUPVV/hVEPZwYZdKJb/Ykix4XqbTqXNV\nUJKZTMazHSjvAHDZd0XtOIIgnAx1vWMZhW0rvmZcWnOE3lPwdcyihO4keXk0JIESR4FrTax91QL7\nFlHPVCaTscVi4dwSLlDAq4IowgzL5dL3QD6fj5QaUcChYb9djTtONMymilyNDgVJ4U/2lvZPZOz6\nHVoWRDNL44yb1wCObRY/vLZGoxHpncc4NKUcgwcPIuFo5Q1pqFNDMngb8dJwxp9ah12DD+WqARII\nbZL0oFW16/W6X8KUEMAoYF3Vq8ict333MYT1wCs4nU79/7RnX6PR8PVQsHgIvbht7cNkASqZK0eK\ndnCAwm1gaj6f+96m/6vWmiTDHQOAMfypPVMccKwE2gbglbq4uDAzi6Qi09eN2lGEH7iwqEeFF2Uf\nqHWXwpiwDh8eHqzf77uXQFsIQEAfDoduiR7KM0U8W2u26IXDJRby2J4anypz7WnHi9CENgBNUpkB\nROcekpbDCyspovyQyWTi3iTGTrHRuFY+ZtszckMu4yHmzAWp3hf1NoWNsUOQRVYXhhhNjRWg4RVq\nNBo+R+09SRbYrsEjIAevO7rRzLyum3Iq4c9Uq9VIAWTmA8BQoEIPUMIojUbDuXF4g+fz+RfPbR91\n3tDVIZiC+kDmYa1W87ARXgvaIgEuAMhwjgqFwhe9FTFSaXj/rcbgLkTBPQYP/w4QzOVyDiArlYoT\n5/ddnf65MZO4gmewWCza3d2dZ7+GemSbF42Q7P39va8L89WsfqWamO1+rU4GTIXKFyuj0WjY+/fv\nPbzXarV8Mfr9vn3+/PmLYpDqpcGyVhfubDaLHNAkXWgqIcEQVzycgM1m45sMfsQhw3whmFJiuHoE\nvjb0oRcGXil4D6Hn61jcgG0SekOURxMWKE3KmBGttRMHfPQy0//Ti2YbB0lf+xb2JaRdvNJcRngk\nmI/OwexReQOm8DqZPYI05oKOwjuC145Q966BlJ47vDDtdtsuLy/doCRrudvtRuqDwY/Sz0APcpkB\njHl2gBTN6oNOocAGQLZro0bDawrqOUtQCyqVio8DAK1rjFeVBAFKtPBMlCenlewJPR37nggLHAMk\nSRJRKouCqUNHYHTtORvQM+Chffz4MVJ8c7FYPOml32w27vXN5XI2HA696XatVvO9GvaE3bWcDJhC\nOCDlctlqtZq1Wi3vwURHaVqSaN0TQBQHB0ClNYjCSyypIMosao3hBh2NRr6BqAxfKpWs2WzaeDyO\n9MMz2988uUh41lo7CUtQe9Ll83nvERWnbONCh1jGtExQV/1isYi060gKN0AvLb1wAcVa20yBxTH2\nYbhH9M9PXYih0aM/zcx5HNrI2SwKQg5x/s7OzpxwTMcE1oLwgpaoCPs7as0s9hscMg0bEs5VIje6\nCk8Vz21X4b1sNutZahgYJGPgnV+v15Hab/l83s7Pz+38/Nw5bZwlXS/GDb1CeVb6XMmOU08cRi7r\n/to6VCGQ0qwvxhzuKz2DPH8+Ay8OP1OplDfhxhv78PBg3W43Uk/rkEBqm3c3TtgHNN1mH4TlVw4p\numYUc4bfRLIYWZThHRcaW3Ehde4VPIp6B2mW9D7kpMCUugfDgnmpVMqLdFEHhPoSpMWyIGqBKok0\nzotxCoDK7I+wJoCJIpgoyWazad1uN1JQcJ9jUjDFJlYPGp4qrAX+zWz7RY0FSYwd0nlcb0V6/UGs\nTUJmpgIErXlmZpFGz9rqIml7T0Pfz3FH4v6fCw9FzvlDERIG1vfvC+hjkEERaLfbnml6f38f4RJp\nogSgAv6XeqnUo2Nmfomz1loscz6f72VuZo+XKD0CzSzS8JwMNE1Y0d6enKX7+3vvpEBFc4jqmgVH\n5XcuReUqEqaZTCb28eNHB1KhgfRa0QQOIhDw9PDah3xRDamTEa6ecvWca7kZLQOhBsExz2scLYXw\nHmtqZh7G5Pkcg0vKs9VMSwyR1WoViWI8V6FdDWz4iVA+CN1rcWjN7N61fkkkmNp22fNgi8WiN3Os\n1+tWKpVsPp/beDy2z58/O5gaj8dfhB30Egtj7MqxSZo8BYDYMPf393Z2dua8BTLFCPtRg2nfmYrK\nD0HBqXULINIQi15OcZc1G5+CbnDCSPWmvoo2qoYvdui2HU8Jz0a5YoR/4LC81mLfpYSW4HPjCr1R\noWcL74F6praBqfD3d/VM+D4tYHlxceHeiM1mE+H/6AXE2PWyJQWdPQ2xnufFnNQbCx9pV6IgF69t\nu912TpvW3ru7u/O51+t1T5vnBaGXPqW0iGH+cKTq9bqdn59H2q8ASAAq8FOhWXS73dh6QV8jcXsL\nD4SZeSiacLR6C8P9yN/xICq/08w8isGz6HQ6bqgr2fsYXuS4swkwNDNvLk8vO4C/gk2tZXdo0Uxe\nfSmYesobpZ+DoaJeLpIhCN+GYGrXkkgwpaIuWJQRpPNarWZmZuPx2Jvl3tzc+CWqykyzMqgrhfsz\nvPhVySdVwpALr5CAyYbVWj5hK4t9jlHd/BpWNXvsJ6V9+PDMKADWORSLRT8s5+fn1mg0vPYNlnen\n0/HmztrqIQnCfEKulO69JI13l4JH2Mwi5G6UodYhM9vPxcQZIZOtWCz6c1duHzwZMg+5VCHFMj6A\nBHxLQnpPffeuEyIUEGghX4jXGIqbzcYKhYJXMycbkzIX6XTaW91oCxza4GgB3OFw6M9jNpvZYDDw\nz9KimXiooFrsutm47hF4WfC3CFXisQjfz99ZL8almW5azBk+WEjcTso9wX2HsYrXENoHIV5tQn0s\nSgtnEI8R/FfOFKAq9FTrWLnPuUdwsHAnABoHg4H1ej3nw4VtunYliQdTZo9cE7IwAFP1et0mk4mN\nRiPrdDoOprS8v7oUtXs9ZHVc2uom3jUxdJcS8rq2gamwbYKCKSWD7nOOgCm9jBTIEa5VK5/nr61U\nyNpDWWthNspBcHAAU7e3t34xJCVsq2EgtbzCvZfUMN9LJW7cWM1aRiEOTKn3YNf7U/dBXCsjvJt4\njQDpSqDGOFBPhAJkzeiL+349o7sUDXcQOiGbCX2g543sKW29QogMfdrpdBxUQZoHbOAB5jLk4qZ+\nH+FMPHohmNqHAIbghimfSzsj8LzMoheqUibYHxD1qV4/Ho+/qhH7oUR5RZrlzJqolxEwdejiuOFY\nMWhGo5GXJNIMRPXexnEDSbLAsH737p2XAMnlcjYYDKzb7XpYFjC1jarw2ueQaDClDxBrkUJrlUrF\nisWib3oebi6XM7MvlZymR4Jiq9WqW09aF0cv4F1vtK+xyJ56L0qB8XEZoczU3Q4JO8wW26dweWqm\nzGAwcOWt1ZV5L/WLAF8KhNWSJjSI5QJfDCDV7/ed6KrhhCQoPuVjmEUBp4Ykkyi74rbwkz+rq39f\nmTYqeMjC8B3JEewr9AbrEdYw4rP4qWsbXgKchbCb/b7mp94oDSsT8tKyFtr2Zjgc+iXU6/UiFaOV\nawSYUK8qYIzzSZkZkiu0wfMu9as+683msbk086R2FgBS10i9UboXaI5MIhOgm0SZcOzH0C3bQl70\nqKtWq84bAkz1+33PQAS87HvscWE6nvlkMom09WHNarWaG9W8HyNTizXze3CnaUpNtiX7OM4rFTe+\n10iiwZSKZiZAeIQ/oyRCbXNBHDzMHIP0zEWM+5ZsqpCouE95CtyEXiizx9ACCh+lrx3dLy4urNFo\nWDqddmWGZbhtQ+1SAEcKpobDoXsdcM1yKHDvAo7V+leQTOYRl+56vXZi7c3Njd3c3DiQCoFJEoAU\nwrzCsOy+uWxJEd3zYagMa3QfQEM/FxCBMcblqxwu9WToZ7yE16VrrEBqX6BZPX8YF51Ox3mTXKqM\nV2sqAR4IA2mtqDhPqT5HMlH5XhJfeOmc1fOqz2rXgtdMC6muVitrNpuRsggYm2Rv4oXSwrPKL0pS\n4V+VbXw5pcHw0nIIx9SJqVTK1wlPKWA3l8vZ5eVlhAdFVIPQOx6parXqNcPUuwo/D68itJ99zvvk\nwBRcKWpGbDabL3omIZrCi0LBSgGt4zEhFq7k0n1LGKKL+3/9yZw0dMfPZrNp5+fnXqCPCugULoV8\nd8g2JerOJVMNC5HmsLjg6UIfggoFVEra5nNxxV9fX9tvv/0W6cGoyj8pEueVCuthJdUztQsJgZSS\nUMnqPMR64bnBazoajXxvET4IgZMCpKckNMTiwFRYc+k1cw73E5ln3W7X7u/vrVarRTx+GDgYORQ2\n1rYwIbAPdSLPAU9bSMrnJ4ZpXL2ufQlzo2wBHg4yGLXavtlj/bROp2O//vprpCyGctx07kmR8G7I\nZrOewVcul+3h4cE5XwBlff8xhOdHO7TVauVgCmcJDhOy8wBIGN/QfSiLBNjCiLi9vfU5UyZo3w6S\nkwFToWt+uVw6/wf3Nd4pVVIQfYmDj8dj93zAsaFCugINs90emm0xX4DgthiuKigOi4JDXtTKKZfL\nZmZ+gJT7oAXaDuHaDV26Zn/0SKRMA/wnunvri5Yw/D4XH2u3Xq/do0hdsdls5lb/NiB1bGtMQzBh\nssCfAUghCi4eHh6+KHa5q+cQgprQW0RICK7NaDRyRQ73h5CY8txeKugZeEN4v5WzuEvjDT05mUyc\ngzgajZyAbfb47PHIUEpFPdfKwQmfIxKecd6vnqy4Pb0rIycufKR/1wKbmqFHGj6lVJbLpfV6PTeo\nuV8U8MaNOSmhPTNzrw73AOsNpw39v40vdGgJaSDcC3g4tXhuuVz2aul6bz48PNhkMnHwy17nXoAy\nEsdB3cf8TwZMaVqncmbUuoVQyUWlFr/Gw9VyoXcdB+gQG01dsvCAwgwvgKGm6uKFAsVjzesrlfqj\n71a/33fy3e3t7VFaB6iyjTs02q2dOfHSulPwvrRmFf0WqfeiMfFDhWi/RUJvVAiozJJl+e5L2A8U\n0mX/H8Ja1pDYcrn0EP9wOIzUXdLK+vT61Et528WmZ5g9r5lg+8ooYjxY6PCgCLnp+7RtjrbOieMY\nPjU+nmXce/4/9t60uY1kydJ2EACJfSNISqUq3dvW1WNj3WMz8///yditHuurKo1EYt8XEsT7od7H\ndTKUICkKQCZZcjOaSioSzMiI8Dh+/LhHUoGBvn/WF1+r1crBVD6fjwTZdDTX4gj9vDTty/DZisWi\nF1XVajU7Ozuz1Wpl0+nUK5vTeN8sa5GLmrWnm+pr2XvsKS2CIAODLo9Un166fYy5exFgKpPJROhr\nIkUzc4DBywrvOWNy6D1E/lS7onK/Er/rWGPS1KRW1ZiZpy/DyyBV+6XMFAuGMfV6Pac7e71eRMx3\nSOcQxwSYfRHvIuBElEpFHixVuVyOdIVm80DzsmkQnNOEMBxfmhyfWtjTjOcOy+XT+vz7MvYz97eZ\n2cHFsHrI8sVz3N/fR5pq1ut1d8wEIew5FaU/9Dvu7/+8wmk4HFq32/XDmgNevz/87+eMCaPvFY1B\n4/ZDHBuqfuGpjO5TApdjrOVdfkfbOfBeNGCDodOrZOK0i2lkpLTdD/2k2u22X/gLSOz3+94KQec2\naR+jgTaVnoDb1WoVqdiGeAAgcX5AhtAXDkZRx3qsuUs1mFJHAXUNeuVeLE0PYBr5a9M1DmT+n6YZ\njm2hXoprGKhyC1kp/V4chP6bRl7QupTyalVb0htIDy9SIJPJ5CtmijJ1DjyYQ70uAmpX+6Wk3VQk\njF5PnfhrT/VhehE5Yu9wLx7zECYI00pe7YPT7/cjDQUfM+aR1IMy4HH+Zh8pr4d0l3HPFve7k/YP\n+7aQhdxu/9TKopnCbxJcP6aPSsP7ASAQiFPNRqsfvYg5rN5Lw/Nj+iycB+ib8A/hmQCQ0vOcWwrC\nBs3HDqpTDabUyO9D6REhxrWcV9EjLQEAIOoskzq4wt+vYCpMJxB9IKoOr7ZgYfH/tNOx3ritvzdJ\nI7IAGKluTFk4mEfNqzMejTaTbDz3LRYyFlQi0qldL6T+KxipKKpraSZ5jPGHwEP1Peon9EomxPFP\nueLCLJpyC/dj3KH2vanph/b2Q34uzXtmX4aOi4CMf1OL0yzuSl0m/c6QthQKBTs/P7erq6vIPYzj\n8dhubm6s3+/bZDKJtHNIq3FmE2hOp9Ov5C30TNOeheirzL4W4x/bUgmmQuo6ztGF7ebjxJDK2Gh6\nJY7OPjb1qaLqxWLhUS/ASoEi6bs4EIUWTBcYgGMXhX8si0tBMCeM3+xLhaIK0MPOt6oxCi+mfig9\nkUZTMHVycuJO/q/ATDE/sMtE2NxXt1wuj6ZbDE3XJqCO4AUg9dRnYw4B/LvY032NM0xzfc/PH+Pn\nDmmP+R3+HgrLASnh56TNeE6APhkagDuMFJVsnHnh+JJ69tA03WdmEbmGng2cj2EFtPbLSoKRwlIJ\nph4z7QP10EsLU2lmXze7THLDkKunSnE0Gnn/K01dZjKZSPfl8EsvbN4FNNJiu967imLv7u6+AlPq\nDNMsDH3MlLFQrRcpiNdsOk9aFk30CfvM9yYxr+HvZV2GB+9jpod2mkvr/yqmcxfHLr2EOdEzj4AT\nkoHMxXK5tF6vFyl0SBJgfIvFzU0Y4IRnOu8iDWfBiwVT2LfS12laVCoUHY1GjwLDp/49DQvrIdtF\nsevfH/u5tI7tMVNgeOzqyrQY4ljYKf4t/J6H/n7IZ1PTSJi/P+czj71ed2mo/sqWdr/4VNNgm+CT\na9VovzGbzV5EQU5oIasYl37V70uaaVNLNZjaRV2ro/iWRZJkNBJHPYd//x7n99DYkt5Iu35/OIdP\nndOXFlU+Ze5f2pieao/Nffhnkgde3DyFoGQfz3WsFOZrWUPPtdc2fsZDIEafxPAqHK0W1Z9Lq8Wd\n8085E9K2zlMNph6ytLzA77XXMo5vtUPqR16K/dXGG2dpfAeHAFA/7Ifty7Q/HfIQGCq9k9Hs5a/d\nl/T8iYKp7fbLzdHQ/S/dENGSq6YDrzbNe8mGzodcPa0NXotpqwX6k72WudMWDJQhIwJ/DUbDRTPz\n3mSvZW1S0q/VsNxr9hqMitbtdhtpmfJaTP3K96zNMP0FqEKsrSm9Y2UnYMa0B9RkMkntXYbfarPZ\nLHK33y5LFEzd3d3Zzc2NZbNZ++OPP5J8lL3Z7e2tffr0yZbLpZ2cnNjvv//u90K9BuOKmu12a4PB\nwP7zP//TBoNB0o+1N7u+vvbeXNfX13ZychK5iuMl22q1ss+fP3un/w8fPrwq3RZd/83M+v2+/eMf\n/7BOp5PwU+3Httutffr0yS+q/fTpk5mZ92N76Tafz+36+toLE/7rv/7rRZT0P9V6vZ4NBgO7v7+3\nTqdj/+f//B+fw+dYnBA71EYdC2hvNhv79OmTLRYLu7+/t48fP9r9/f2rCkI7nU7kdoA4yyQlUsxk\nMttMJhO5Lf41GFEjrfv1mpfXYFSerVYr78T+WjaNmfnYNpuNz91rAcKszdVq5fdZ0j3/NZhWNb22\ntQmLD7PI2kyTAPd7jKaaq9XKL5p+LUDRzCJ9ALVv2bfac8/rQ+5xWHx6eLH3Xsva5MyDndput7Ev\nM1EEk8l8uZ/u9PQ0yUfZm4FeaWd/dnZm5XL51SwsnDm9d7hg87VYJpNxzcHp6amVSqVXcyCTZliv\n135glcvlo4GpuIPgMW3Stzwb3cXX67Xl83krFotWKBSe97Aps+12G6nQYm2+liAU6QCtMgqFgl/a\n/hoM1ub29tbX5msBi4jd9dqlcrn8aoJQQNRjdxsmuhOz2axdXFzY+/fvrVarJfkoe7P1em0fPnyw\n33//3bLZrP3yyy/2yy+/vJqFNRgM7Pfff7f5fG6NRsPev39vl5eXST/W3uyPP/6w33//3SaTib15\n88Z++eWXV+PUF4uF/f777/bhwwcrFov2yy+/2Pv374/+HKHuQ+17euJ0u137/fffbTabWavVsvfv\n39v5+flenjlp2263Pner1crevn1r79+/fzVgcTab+diq1aq9f//e3r17l/Rj7c1ubm58fO12296/\nf2+NRiPpx9qLbTYb+/Dhg3348MHu7+/t3bt39ssvv7wagmQ8HtuHDx9c97bLEgdTl5eX9t//+3+3\nq6urJB9lb8bdf51Ox/L5vP3888/2v/7X/3o17MbHjx/9UG42m/av//qv9uuvvyb9WHuzXC5nw+HQ\nlsulXV1d2b//+79bq9VK+rH2YuPx2G5vb+3z589WLpft/fv39r//9/9OhJna1e39IRHtY/Zf//Vf\nNpvN7Pfff7fz83P7t3/7N/vb3/62n4dP2HhP/X7fttutvX371v7jP/7j1QShvV7PVquVffr0yWq1\nmv3973+3//E//kfSj7U3+8c//mGTycRubm7s4uLC/tt/+2+vBizSiqHb7dpms7F3797Z//yf//PV\nZCw+f/5sy+XSPn78mF4wpVHo9zr0fWm/vvc5wvG8lO6zT7W4cb2WsZl9fcXQMca3q//UQ7/3Oc8U\nztkhx7er9QVf6JlyuZz/fu7lCq9C0p9/yOIEuMca21P+//emMV/z3jvW3CVlYXDwmsa3a+5e0/j0\nz132OhLuD9i3dkj/YT8sCXuseetLXq86Hu7aKhaLVi6XrVQqmdmf41utVjaZTGwymbgO4yV08f7W\nxrsveS5/2A/7YfH2osHUY1esPAUd7+q4qv/2Uu2YF57u2773EE3LOHZZ3FoN75uMW5O7fl6/Pyl7\n6Dnpg3N6emr5fN6q1ao1m02rVqvOQtHDbDqdRn4+6XFhj/kbs29nHNI4jz/sT3tJPugQvv6xzzw2\nY78PO+Qzv2gwpRamBXhpaDDivjcuonytjuylM3RPYW5eyljiDLDBBaY6Fr0d/SUZ85TNZr16sFKp\nWLlctlarZa1Wy6rVaqQdRT6ffxBEJm0PCeb5Ci9Tx2iw+JJYtx+229LoU/cNgF4a2I/rvWV2nGd+\nFWAqbGCmziyTid4qbRZ1agqq0rxI9mG7AGfaxx1X/bWLQUz7WEJj05P+QkfEjfB0hDaz2EM4reNV\nh8a4isWi1et1Oz8/t/Pzc2u321atVj21t1wuU13qHzpqM4sAKEAjYBg/xM9sNpuv5vKHvTx7Slo3\niX35WJVs3H8/5TPTcGY8VT+pXeG/p5jlOZZez/WAxb3YXC7nzoz/zuVy/qUv9Pb21u7u7iJ/6n1H\ncb8njZRm3DPtiiQ4rDXqZ+Htcg5Jj1lBbjgGfTYOKh1LGgHHrrkpFApWKpWsWCza6empnZ6e2snJ\nia/P+Xxu8/n8q/WZFotLfxHEZLNZq1QqVqlUrF6vOyNVLpctl8v5lUTj8divu4k7DI5pu363gqRc\nLufAl698Ph8Bw7RD0TTmbDbza2+4Sw071jr9lnf7vUxHWvZenD31PYS+RMdEoE6anu/fxxr+lp+P\nY0h3PVOcj9z1mazlODLiEHv0oVR6eHbFrS3O/pOTkwhZcgzi5EWCKTWc29nZmRWLRW8AGn5p00y6\nmS6XS5vP536fnnY51c9/yaZaldPTU8tms76obm9vvXrq2PbQRgzBXSaT8YNKDyy+j7uhaNr4UtIn\nOKlyuWztdttarZYVCgXvHcQt8MPh0LbbP5vGZTKZnW0FkrBdzo9gplAo2Pn5uV1cXFiz2bRarWb1\net07Xg8GA+t2u9bpdGw4HNp0Ok0lYDT7c0z5fN5OT0/d1xQKBe8kf3Z2ZoVCwW90gGXDmXe7Xbu5\nubFMJmPz+dybqKbJx+wKrHYxAyFLF/c9L9HifBCmEhKtSFUmmYrUYz2rMqMACn1mwHucZGBXUJ7N\nZl3jqACFs/LQ43sIxIbAiC+a2RaLRe84f3t761+HtBcDpnZFvyyiQqFgtVrNGo2Gdz6my2yhUIiA\nKQ6p2Wxm4/HYxuOxLxQOrF2HxL4dxPcsyMeeSRmd09NTK5fLls/n3bkDPo4JpuLGq84qzoEDporF\nos/p2dmZbyjumlMHlgag8ZDp3JTLZbu4uLB37975GO/v7z39td1ufb1qijptTKr+G2uuVCpZq9Wy\nn376ydrttlfwDQYDv+fx8+fPdn197UBKAXGaDmR0X6VSyer1utXrdatUKu5rAFjFYvGrQ/b+/t6K\nxaJtNhtbLpd2e3sb8TNJjzNu3z3EWOtzs3/DsaQ5KN3lHx5jRpQZB2xwdQrrVi/9PfTz6rPBjgLu\nkQqY/UkgLJdLJwt41rizDtP1DlDjdojHuoF/69geeg7GBxEQyh2Ws6UVAAAgAElEQVT4+Uzmz5Yr\n1WrVGo2Gzedzm81mtlgsHORyKfQh7MWAKQzErAChUql4xFuv1z16xKGpqNXMLJ/Pe2qlXC5bvV63\n8Xhso9HIxuOxI+9DRxePLZ6nfu+uz87lcs7YNZtNazabdnZ25ih9OBy6c08KfIS/l7klwgJE1Wo1\nq1arzq4pvb5cLv2uK5hGAFbanLjZl0iSNVir1fxQXi6XNh6PbbFYuDNYrVZWKBTs8vLSx75er206\nnfoVI2kQqGsajLRes9m0VqtllUrFTk5ObDqd2mg0sn6/b51Ox7rdro1GI7+mKE3MojrpTCZjpVLJ\n91G9XrdarebACX/D+gyZDNYzDLFKD9JgChQ0dcmX3uDAPKEBY/3xZ1rmb5c9BJjMooEOTDhBOXd1\nkuplPvVuwdFo5OzUIeeYdXVycmLVatX9iN4pytg40+bzuU2nU78wG6aK90DgmsvlrFarWavVsmaz\n6fuSFPV0Ot0rq7qLZWIsescta46UOXeN0nKl1WrZmzdvbDgc+j4Lz7m/fJoPJkKjXoSsjUbDarWa\n1Wo1X2D6xc+bmRUKBbu/v7e7uzunAkejkZVKJTs7O7PRaGSj0egrHdWxnN8u2vxbcvyAKYDIxcWF\nvX371orFojNzZn9e45CUhblvnAMODLBbrVZda5PNZn1jAyyWy6Xl83mf67u7O1sul/6ZaTJdwwiy\nYThYezc3NzYajdwBctdVs9l0h75YLOzm5sadhEaaSZlqF8vlsp2fn9vl5aU1m02rVCpmZjYajWw4\nHFq327Vut2uDwSBSzZdG42AtFot2fn5ub968cXAPK6FgJNSYhDorTZukCfDzjCqZ0DQmttlsXCKw\nXC4jMomQLU3L2HZZnE/Fj+RyOSuVSlYqlRys1Gq1iCYXAL1arWw6ndpkMrG7uzsHG2b7ewfhsxJ0\nnp6eWr1et8vLSzs/P3epgGr2kEFMp1PrdruWyWQigArGR8fdbDbtzZs39ubNG79kezQa2XQ6PThI\nPDk5cQa4Wq267jKTyXjqrtPpWKfTsbu7O99PgKmffvrJszDIQHgXf0lmahd4QItRqVSs2Wza5eWl\nNRoNL7uOy60qJaj0O8gcB8dG4aUDtuKo7+dMyq70CMBA896ah4dNohpoV2pMny2fz/s7Aozg/Deb\nzVcR9KFs1zxymGixgEb4bKJareaMAOBhvV7b6empFQoFy+fz7tTn83lk/tJgcZEwzFS1WvULa4n8\ner2e9Xo9n3Nd2zh1IrLxeBypEjv2uJW9Icgh5d5sNt25m/0ZHQ+HQ/v8+bP1+30bDAaextx1AKdh\nDlWXCbDHuZ+enkZ8g34xJq0u5rA69sXnD6VyVAsGkILxVzDFZ2gQSmDG3pvP57Zer509iPv9Sczp\nLr8bFhXwLgCVzLNmPrRqky8Y4sVi8V1+dZcvj9OjEXCSSr+4uLDz83P3oaFOmMAMfwGgQrsHEwer\njNax3W7bbDaz6XTq4+NZ9zmXSpScnZ35MzSbTQf3ZhYpHiOlynMUi0V//vl87ufDMfZbqsFUaGFe\ntNVqWaPRsHq9HtED8b0KllS0q2CFhV8qlfyzmczhcOgsFQtu3xQ2nwnTBhtTKpUc9Gw2GxsOhzYc\nDm02m0Ucdtxi1vdUq9Vcq0J6aDwe283NjQ0GA2c19GePZSraZcw4AqXWC4WCV35Np1M/hAFY0LjH\nfv7nmgIP5nm5XNrd3Z0Nh0OvbCOVslqtPPqHDTEzFzuT0kwCSKmpI261Wp4GW6/XNp/PbTQa2fX1\ntV1fX9tsNousvTTPm2pkYCM4UHalVpk7PbC1ajhp08OHoAXfg+YUrSnfC+uk2pxSqeTXAHHg8jWZ\nTMwsffpFPSMATxzC1WrVNXC8A/VDxWLRfS978/b21lPX/X7fJpPJXnSomvJS+QP/DxClqedCoWCb\nzcZ1QpjOHdkbAuvb21sHhaVSyS4uLuzi4sL7wOVyOa8qhs3a9xom+Dg9PXUCgBYq1WrVq37ZT9ls\n1jNTZ2dnDt5LpZLfg4semH13aHuRYEojX8CU3p6ukaK2PlDgEvaDKZfLzhQQnSFy1rL0fToGBWen\np6cRBqbVavlCX6/Xlsvl/FB9bCEzpkKhYPV63S4uLnzjoMm5ubmx4XDoC/TYhxk5caK+RqPhmi5t\nc8E8wc6Nx2P7/Pmzdbtdu7u7c6eehgPqW0zBFKlK0s3j8TgCmjWVst1uXQ+hKb9jMx1q7MtcLufp\nPSr3isWizedz10hdX1/bzc1NJDhJM5DC9ODV6uDw+RV4oCHi3zSlooye/tyhTYGEpkXa7bbrwDiQ\nCGBgoRQg8jkcbFowwWXMuobTZrx/shz1et3a7bZdXFxEwCWMubKLWhlHem8wGFiv17Nut2uLxcKZ\nn++dV2XN0NpxbtVqNbu8vLS3b9964UMul/NARZlBwFI2m/UUdT6ft/V6bZPJxNd1rVazq6sr+/nn\nn61SqXjgsNlsHEwdopIPMJXL5axarXrKst1uW7FYtF6vZ+Px2As50JsCABHY8w4Au6zfY8ggXgSY\ngk2CteDwbTQazkjBTigDBe26WCzckd3d3UWiDnWO2hfHzBzhkyNWXcf3TopuZhY4SJzFDpDTjanO\nd9eC1sONaIpqE/LHpImOqVPRqIJNw3hPT0+9wlB1RWFUPBqNbD6fRxhH6F6ciB5gabIwIsZBApi0\nBxFj4HuJjlmHOA8qw44BJnetNwBtvV53UExF4nQ6dY1Uv9+PRLbhYRM3Z4857UPPM6kPgKv2kTL7\n0p1ehdi3t7feZkUF2couHyK6j7O490cK6+zszJrNpoMIUsmAdQ5RAANrTtlE1iV+tNFouEhYW5bE\npfwOPXdx6XV+LwCFyi+CWHyRmUXaxnDYU/GNXgwGjgIm9u++5pZn1XQsa7BarfqeI+Ccz+c2HA5d\ni6jZmLD34nq9dn0jaV1Sa5VKxbLZrN+XORgMvAIXH73vFB/jVbkH5xYB2e3tbSRbwfOT3QAPsAc5\n/49RsZ56MKWLAeYIIAUjlclkPGpSKh1NyWQycfCwXq+dzkaDgviXRUYaZbPZeKM9/k5KZV9jwqk1\nGg2nV9kwq9XKAWGc830oxaeghWoT6HjQ+jGbBipAQlfTbDbt6urKKpWKzWYz74LNPOl4lFq/vb11\ndtHM/B3hzPYVFe7b9HlUkIy4czKZuIhX9Ris+1qt5joHquKm02kEQB57zDwflT+kHczMGTWEosPh\n0IWgGumn0XS9op2hQAW21Mw8Ytd+dVSTKojgoAMwh+n1YzF0HFYwwmhi2u12pBcdawkf2u123Zfq\n/iI1hh8lLcTYhsOhBzxJmmYrtJ1Os9m0t2/fWr1e9/YWpKUBRZwtgGYFU2jF8FsKYPY5nwoAAbDV\natXlETzzZDLxVDoZFc4afp7AAIBYqVQ8kG80Gi63WC6XXjByc3MTqbw9JNvI2iL4R/5wfX1ti8XC\n1ykgGJZNCzzQ87HfwvPkEJZKMLVLsEtlGmic3K9ZVBRJnnSxWDiiBlAtFouIqPDu7i5SVaOo2My8\n/JzJCTVZ2GMTtGtMOOpGo+FOjY0LiGIxPJVF0moUFpeCTaWpd6Up9mFxYya9R6UGNO58PrfxeOyN\nG2ez2VcpEFK8CGRpikg+nwq4zWaTaNrrIYsDuzhh1hlrg8gLPUelUrG7uzubTCYRbdUhmz/uEu7q\nWKhKRMNYq9XciZH66Ha7HpTses7nOOh9p91DC8FUKBFg7TEfk8nEAS5gisOVfbdr7x3DwnQsX61W\ny/+/Cug5UG9ublwTRCl6JpNxRgfGH5YAMEVAekzbtSY0NanXG11eXnqgQrDCfDJffBGIsmf571D7\nt89DW7MSKg+AECiVSnZ3d2er1cqGw6F1Oh37+PFjpLiDM0GrpAGPZEXevn1rjUbD39N6vfaCEZhl\nnctDr13A1GazsfF4bL1ezyaTibNs2+3Wz4Gw2k+bcs/n86OwoakEU3EW0u04NUATKJS+PLxM/i2M\nHPhvM/MUYa1Wi/SkUjaMqJK0zPfQuLopiApqtZqzbAALgOBgMHCa/SHdFs6Zd6X59TQwAJnMnxUo\nlPlns1kHuHHjNPu61w9p2Gaz6VV8RIiqjUuj6VjC1h3qhGFISfc2Gg3vD8aBPRqNbDKZfKVhOfTz\nh+OAXa1Wq1YsFu329tb1DThg2Iy4z3qK7Vq/h46OzcyBR6vVciZcq5nQnHQ6HQdSs9nMgxb1EzDm\nx5wzntMseuE0/fWoSMQHwr5zCHU6HRsMBs7UmFkkUCHFDvNPcMqaGI1G7lOT2Jc6dgJLKsPQBFGl\nBlAidadaWW2TEwKsQwekzAm/j0IP9e08Vxgom32pmlbGTDW1ZHnIYDD3vV7Pbm5urNfr+Zo+xBj5\nPMZBloEWDRp8a1Aym82s3+87k4qcBWB57PMg1WAqzHHHgSlEcdCRpBIAT7rA+CKyWiwW/tnKmGjJ\nPi0YSEOdnp76BvqebqqAKdUNFYtFy2QyNpvN7Pr62nq9no+JcewCcbpgtLxXy3TDZ00CYAGmGo2G\nN9lcrVaRqJdNHzJ/AA3eGakUmlyq1ihtFq7lOCClKYizszM7Pz+3q6srT0FrJSZ0+7E0bwqk1CFr\nm5JCoeCH0WAwsE6n4zqHOM3CrhSzHk67Iv1jgBHAf6VSiVQnouPAsU8mE0+DERyoflOfPUmgH84X\nVdAKppg/2nNQ0QxrynvB4sCUNlhEEHzs8Ye/B19eLpe9X1SpVLLtdusBHGCYdG14tVi4B+J81L5N\nARzgvVKpuK9QoETmQZv4agHPLjB1dXXllcGbzcYmk4n1ej1vqkuxzyFTtXFgqlQq2f39vZ8Z9Xrd\nmXmIEq3Gh11FL6X3Xx6DUEg1mMI4RCnFRfAIzT6bzWw0GrkDgHWig7nS1jg4og00ENqsTu8jIorT\n37vdbr9rYTGxgMNKpeJOx8xcpzAYDLy6i5/TP0Njo/DcWv3xUNrr0OmGUHhN2kQv8tVCgfB5AdJK\nUZfLZU+NAabCaCyNFgemNOVHR22cw/n5uffWClOh6sz37SzigKx+vopizcyfkQNpMBjYcDj08nj9\nLP15jZjD7wkj67jU+iEOZ9WYEGBplRuHU3jtjc6nMqshaDwUi6HPr/8dJ+4FWMC0oTFVIHxzc+PX\ncWjKMm5+9GAHtG232wiQOqTFrQP8DSJlWBh8CJVvYUsHpCIcxA+l8Hb9+VwLAwZMC27C6vTQv8YF\navhDmDltQlqr1fxzyRLASFEwEfeMh2CnSC2Px2Or1+veRZ6qU85tqtrJQHEe6zpG8hEXnB3CUgum\nwo2qDoBI3cycaSINRxND7ecS56i1+sHsy8WsMB/VatUrCKn+A6DAfnxrHjY8+HA8yh5pZ3YV0+ti\n2AV++Hyt1gEUJpnqCw9h3jMbI3wuzfPDGKKVo1KMVC13K6ooMo3MlFk0Dau3m+u1OZqKqNfrdnZ2\n5pVC/X7fxuNxJGI+xlj1eVV8jc3nc7u+vnamWMXYu3RI9Bijhw2BD79DAx9lBPjzUJVwmhbSva+B\nifomLnKG4VCRuRa9JHGZeGj4GtaY3t8Gw0ZA2uv1bDAYRIBFmDLJZDKR4gN6NMGiKiA5ZlqTZ6Ns\nHhau3W77XOFjCVJub2/d96s+isB8V/HPsXyNnhlaMcoZocwbwT/n4GazcdE2l6pfXV35nJ2cnPi6\nRWze6XScsTvGGPX8o9gKIA/ZcHV1ZblczsGU9gFT4TkpShqpHmuOUgumMBwAjk2jKbMozayN4uKi\naUwpRRy/mUUa8kF/hxfsnp6eRi5Dfs5EhUBKDyila9UZhdVP4e/WwzUs5Y47AJMydegAx3CucLwA\naaodqfyjpxhgajQavYjmj2bRaFlZKdY3kSJMZT6ft7u7O294yeF8DOo6FOzikHXuYBZpV4H4Uw9e\nTS/whV6Q+wgp7sApavVUqDPSzzvUAa1tRXQvhYfo2dmZtVotK5VKznJQIKDMXFrAVHjTAEJe0idU\nXqJhfCggNTNPFSmYYn7iKoaPaYCpVqtll5eXdnV1ZVdXV94LC30ta5c9qD6Y5rnaIiHpoFTbM+j5\nQJZDK/xIV2rVbbvdtnfv3nnlJX3uSO2R3jt01V7c+GCWwlYxBJe5XM7TgHp3H+sZLMD8HrOKNDVg\natekhfS5sjg42xB4hAfzLlqdTQPLASAjBUWjS62K0+7HT3n+p5g6aKoWG41G5M6hMF0ZRuxxlK+O\nNU6gmIRYmzYGk8nEGYparebRn25gDu9KpeJtI9B33N/fRy4fRTyaZlNtgwpaeQcKgmFN1+u1i7jD\nVMuxomMF/7CeqgkKBbmwjWFvGy1fpoyeuyJhsRRM8XmqgaR66tBMhwY7+qemTsy+gC7GRTNB/kTL\neXJyEtl7YaqM33nI8aj4GnBISpIUD0yvCs4VBDOvHF5U8zWbTU+dacolDox9rz32WYBF7lK8urry\n+1thbFQGcnJy4leKqSzi7u7OBoOBMzcAGN5J+H4Pafr+mSuCGPYOGtxWq2Xr9doKhYJ/D/tN54ob\nMbTpqPaCi2u3cohxhmcyfoVK0lKpFEnPNptNvzYMkTpjCQvRjtlHMTVgSi1MYYSbWfVPChDCQ0Y/\n4zEjXUjln7YiCBumKaB7TqQSgh9N/9DAE0qTVIgCRq0i2fW5+h7NzFkDTTscC0zp76D6qdfruYah\nUqn4ptButTCC6B0oX2au0LIQQeIc02xxoJbmiQBoKmpgN2A4dHzHjI51TXGQcgWFjkn3oF4CS6QM\nu4sOUUXrAKQwBaxd4emAjF7i0HMd+p2QQeVPBYl6NVKj0bDBYOBNIIm0ARvHnEPAFAcSfoXAhAMa\nzRvBTeiDVfMY9icqFove6HOXHzqGEYDR9uDt27d+SbhWn8Jc0SKHcRLEEegwbvrA6Ts9pvH7bm9v\nPfAHLAFuAbOcI1SXIl3hfkEKqabTqbcdQCMVzv2xjTOeuzw5d6mmpY8dBrtGg21ltI45jlSCKbWQ\nmSIy1EZqcZUzu0DVro2NgFbBFIsyjK4VTPGZz5kwQKE+Pz0zzCxylUEYnQP29FAhyopjLDQFozoG\ns6hY+5CRMtoMOsoj/lcwpdoMHDaRNAwUwkKoXxWyA4CPFfE/1XAQMFM6Tg7hfD4fYQZ6vZ4336NS\n6qH09SGfXZkpijDYh8pAqHAbTQNAGO0DrA0MDuBewRR7gwgbHRXNLo/VPTxOr6X/n/cCWGGey+Wy\nd2uGvTP7UmmkPx9+3r6N+WOdqQZM2UXYeYo51PeqD6Qal/vT6Byu/X/UP4eM+T4tZOIzmUwknXVx\ncWGXl5dWrVb9+2ET8UGlUsmDy7u7Owf6ZtH73VQDeIz9t+t3KJgC/FarVQeB2+3W72VFVA8zRSsM\nUmLT6dQ6nY5r5Mbj8YMZjmNZJvNnw9jRaGQnJydWrVbdF9RqNb9CTLXRvBO0VmGByKEttWAKx0IU\nW6vVXC+FI9hut5FN/q26oDANhniNDsZUEoRpihBMPWds/L7ZbGaDwcAjJhwSN2RD3SqrFDJM2qg0\nm81atVr1/hwI9THSiPV63WnQQ6f9OGAymYyDVjOzbrfroImFr7oO6Hoz85YXYZoStgpRIlFV2lok\n6KZmXDi1TCbj+oZut+stBUajkbd8iGNdj/XcVBnq/uPw0SspSK0AfhmjfrFPAfX0ymK+9KJrPo+0\nOykJ0uwaQO3bSC8SuQ+HQ1uv15E9pXOqmkZAJodaq9VyRo+9wH4Nnf0hNTmqxQwrXwEYCLDZQ+Fc\nwLzBdGi7CGW5YFbDqzz2Ob4wiGTtNBoNe/PmjV1dXTnIw2/i47Uztorl7+7u7Pz83O7v731caOfY\nq0kyNmZfAN5sNrNut2tm5reC0LiSdgLsXQXRZuZnT7/ft5ubm68KW/T3JWWkY2k9wlVy2nqDTAX3\nQuIzVSv1l2amFBkDJkDVCHPJcyvI+R6RNeAG/QYHN0yUAirt7fEtE6XsBALN6XRq/X7f0yFE4USR\nUNA4Qe2+i9hOn9nMXIsCOFOjo3aj0XDwoaLmQ9GijB0wBQXL5ZSh2J9Di0hrNBrZp0+fHOBCY1Px\nhigR/VsS6bCHLDy49JAyM1973W7XPn786Hdr4eAOecg+9sw4ZG0Pwv5br9deQcRF3a1WK1L8wMHM\n+AFTqlsxs8j1Sux1gBtrBQ0L63Tf4J/PBFDk83nXPaGjZE0qwNW2AKxPxsOVF4BmGCDVeB7SlBEN\nq+x4Xua4Wq1G2EbaxfD/CNRI27KOtYAGwEJ/qmN06AeIl0olv6bq3bt3HvziawHHNL3VwFTT77rO\nASNJgymMueEiacAhY0CEzp6l3xRngQYmg8HArq+v/d+SCtrU+N2cFexDfAD3SGoF32g08q+QWT2W\npRJMqWklEYAGZkgpaAU4ZrsP0ZBW59/QfejG0h4VoQj1uabPSI8sKta4E5D/VnZGqXMFVfRn0ooo\nNr82zNOeWTB+2luFaJn3so/NFKZWFVBp2gOHTZUeDVkBuDT1/Pz5s7NvHNTcy0QlHJ8fRsPhMx3a\nwkOe3wtQgGmFrlYmhEaJ4YF9jEM37pkB+lx+qqJsWFSukqGbsrI32qeIzyUQWC6XPjbejZl5A1v2\nftie4NDpMdgyet50Oh2/ASEEU2GPJcrUAYMI1CeTiQdMWhEcvu9DGABRb4WYTqd+bZaZWalUsvPz\nc7+kervdesWUVoppSbquS31nNFIOWYJ9mgan/F3XJeAVH8lhy8XE2lNKNanVatUmk4lX9uGftPqR\nd3osf6K+lC8yC8vlMnJOUjilwVrIavEnjHjYSihpwKgSgu126y2PmA98kZ5vPHtSwWeqwZQevlr9\nFDpTNV7ic14mgEUnUZ9F//we03GR2yXSn06nHgnisPRyVTaJNjFVZwCKh7bXHDp5dTrJ9vt963a7\nDj6e+96eM3ZSc8qI3d3deWUb5bo4fy7ZVGaKyATn0Wg0Iula/Z1JWQjGq9Wq6zgajUbkYNV1rpU0\nST0/641Um/bgIQCgG/3V1ZWDqO1269pDqo60g7ZZtEsx76dcLpuZ+d1uaWAXb29vvfUGh6k6bgVT\nuj8R+sKow2w8VBF8KOP9wUaoRk8r+8rlsrfi0J/V9WtmER+pQazqrmCA9BJufZZ9j43frx3cGTP+\nAD0ifgXdqbbh0OazgGGtGta2HUkbZwiMjWZUQmAUAkBlyGF/01bAw3MzPvppQTScn59HMhrVatWD\nhO+V4jzHUg2mzL4WaYdOVg+b70GlIZDS9gOHSnsBpojaF4uFDYdDT5sgwMYZA6oAS/pcoRAYHQEs\nGo5Ax3N2dhbpQH4MMMXYlaEiIry9vXVaOpfLOWs2HA4dTEFF63uo1WqWy+Ws1Wp5/jwNFrIXPOvl\n5aW9efPGK6CYMxXtQs0nASRCAEi6mQuMEcLDTLRaLXvz5o1Vq9UIANZKRNVkhAESvwtwWa/XE+tN\nFBpgajKZxALbODB1dnZml5eXDjq1Z1h4Z+ahTQ9SGHcz82eIA33a5FclBjAZ6o91DIi06bUFu3qs\nVCbBGdd+KStFQ+dd91mq/4RZ4z64kBnl+5MEHgpgYalCMLXLl+veBkwpMDNLBztl9mWcjA8NpwYt\ngF0E95yTx2apUgmmwmhDUTdRPF+agiBPHAeEHlr4TBBMD59Djlw74bJYn6OXinsOpWzNvnZI6BO0\nORlf6vR0g4DWNWpUJgBnqBcLH1LXsCu1yu+j8zAaKPLg6MmGw6FX6ulhjNOjAlJF3TAkx9xMcWmy\nTCbj4m3Se0S3XHsAyNAUdlKOjPcF+Asjc9YpvaK22y83t9/d3dl4PPZO7RxeHGiwULwbxkvaCL0V\nJdBm0f3AdUP7rBCL8wvh52laOu574yJ9QJXu04d+xz4tLgUKqDEzF/VmMhkvJ0cPR0qSz9FiD9hx\nDezUv1BMo/v1sefbpwGmuP6EKli+WD+75pxxk9YjWFCRugKOJI09CkCnurLZbFqpVPJKTQXC+H58\nJMxyu9129vWYTS7jLO6s4L/1vbPvWN+MpdlsessIBZfhZx/CUgmm1LRBGWBKdRsgUpxCuVyO7cJs\nFj9ROGVK84nSqIbjGfQmdaKzfZiyNEz8crl0UKQAgf8OnRmgkoobhKIqCoW61kVGlRIpmGNHWvrs\nVODgCPSeMCqpFBQBcmezmacttLOz9io69riU2clk/ryEs91uW7vd9jVFlKU6NdUEJW3aoDNMN2cy\nXxpA8u7NzBaLhfX7ffv48aODKFhXwLqmwvhstFbaAJJARtNGet/Wvg+00E/Esb67DJaZsZFiJxBS\nXVJSpsEcV8fA4miQFqcLYt4IeLSSmkOOlifdbtf6/b7rN8Pfv08LAzPWBQGKXi0Wdx1O+H609QPB\nOb4Tvx+2lUnC+N08K1fEXF5e2sXFha85Mh+0C5jNZnZ/f++9wWCBIQe0iCltpgwwZyFpZ2XJSemu\nVisbDofOrB7DUgOmws3GJoaZIrpdLBYuPKMctlKpeMO5er3ukUnYKTnMH/N7OdArlYpX3gCm0AHw\nu7UZ2PeOU50BGz5MYcb1uUJPpWBKK274uzJTCp44mPSdHruSQw/Ws7Mzq9fr9ubNG2++xuYnxRK+\nP6LmxWLhIm5lBrQKLIxwwnnYx1hCU+dcqVSs1WrZxcWFA32ajK7X66+0NJoCSoJu53eG97hpaxAt\nuVZHBpsIQIeNCtcyQIPI+OLiwur1uv87TpCu3KQLAVP6np8zvjhTETlf+CD2ZriWQqaNijIYbmV6\n4tbhoS1kxBkPjWHDYoLw6indo9vt1vtm8Z60Sz3NH2mJsM+9tmuP8RxmX0CvpoR2SULCz1G/SjBa\nLBY9gNaO2ofwIY9ZeGZtt1tnh7kQvd1uW6vV8jkDSClTvN1uvcIUITeVgWkA/buMudPmuJpyZY1W\nKhVrNBru+7XIxeww/h9LDZgKjcEijgYs9ft9j4q0/JhO2Nls1u+Vomt0KKzjs5WKb7VaEadOZR15\ndwCIsjgPbdDvGbMaEZdZVFOzWq0iTo90DMJrohbEehx0gEPk/zoAACAASURBVBMiU+3bdOyDO0yN\nFAqFiHYsLKtWWlffjTaM1DscKT8/tvPDOdOskrTVZrNxLcn9/b1XvikToIdzUqk+jfTjUgRU3AHm\nee9mZq1Wy9NIMFOMBSeoaU+9mJW0EwcY+42DAGZq32PlizQ/GicYGNJEqtuM64umXabPz8+dIYgT\nmyc1txj7SCURsGvKqtJTijG1Wi0vS7+/v3e/PBgM/NDWCr5DrGOVNIRgXlscqPBdAaOCLIIFGpE2\nGg2r1WpWKpVsu/2zEnUwGNhoNNp5cfexjXFwtcqbN2/8AnjeA1q/brdr3W7X/41eZ+pDFWinzUKW\nnz5nMKQwkOqn0CyCG/Ti6kNaqsEUFDKAZjweu6MjioCdogIMAGFmvtm1z40yUwAOomOlSYnESCXR\naXY+n0fEffsGUiFzxmJXEWcIpMy+bLDZbOYAk8/LZrOeNqM8GApcf8+xTIED7AcpH6rABoNB5OoN\nTN+Pgksz87XAYU3EfEwwxe8ATL1588bXLGDq5ubGRdwwoIcA599jCqaUmcnn81atViPd91Wnd35+\nbmZmlUrF08oKVhCyUx1Ic1pSegQtBEQcYuj9DqHpYB3S4oE0I/NCgYa2TNEiAe30zmEMSNQeTLrH\n02B6kIZsn16h1Wg07PLy0lOw+F0i/36/741mAVNajbrvZ1ZtKDrEk5MTb8TJnlefqSyr6m9g9Fut\nll1dXdmbN298vvTSXNZhksZc6ZU+PDc6X3q4sYe63a5dX1874Gfu4jJBaTaVHXBVkNkXTSUZFtYE\nfRRVs3novm6pBVNm0T4i3JtHg0vKVvkiOuHKESppiHLjnDBtArj1nGsRSGVAF08mk8g1M3FO8TkT\ntIuJiksFmpk7gDgQpHQ3zBMARSM5olF0X+EBnkRKKYw+YN4eq0rhwOeAU7q3VCp5/xX9/kOOT9Nj\nFEY0m007OTmJrXLStEFcCtrs+PMRsn50sx6Px5F0JMyEmXmlUyaT8aCmVCpZpVJxppRUOiADRkEb\neJLWHY1GrpVT3cuuVM/3Ggct83V1deXgEHZCr5gKRdkcyNo/LK6ZJWs5ac2NWhwroVo5fGOr1fLU\nl7JAXJDb7Xa97UCc4HdfqT7AFGm5er1u7Xbbcrmcs5kcrqvVKpJi1jkNwVSj0fDiB2QGBJ0EBcps\nHEsKoYaPpOcXzDZFO6Rd6alF9sHsS6+4er3uoAuZwb51wIcwLYghi6EgChZb2W40fhRD7Dv1HFqq\nwRTGAYuATqvZtJEg7QJqtZqZ/blRAESqeWCDgWL5wglyqOlBMpvNjr6Z9PfsAgJxTA2iXa5WoTyb\nK2RUs8NnH9OUdVRKPmSsQio6HD+bn/4yjBfHMZ1Oj1aCro5eO9hzefN8PvcDZ71eux4n7jOe0k/t\nkKZpvu12600rN5uNO29t08EXaSH6F7HW9HMZExElrTnQpehFpUSToVZpXxayHICpy8vLSEpI+2sB\n3tlr/CxyAe38rkBKLyhPS0VYaNpXjODy4uLC2u22NRoNP7BXq5V3Eb+5ubGbmxsXnR+y/5LuMRoQ\nw5oVi0VnMPUOUyxsA8D718MZmcF4PLZut+v31SnYSGI/arCJTqrdbtv5+bmnXM3M9b3aMDWfzztI\n1BRmLpfzlDrVl7vkMGkwZUvZW/gObcS62Wys1Wo5sULvqcViYWaHDahTCaYUIKh2ipcSVp6USqXI\ni67Val4lpJV9yl6wMNFIAM6oAuOgB+WHTQeTeCcPGY5GG+cBLvQKGYCU9hbZBVYObbxnrZIx+6Jv\nAHDoV8jaAcYAUzhNUrVhiuXQlsl8ESIDpuh03+l0vBUFonkdi76TpBkqM3M2F+Gq3kave0efD4Yn\n1LPxeUSTXNKKEyRgoaJI04jYId6BppoVTMX9vrDfHYcrwvw4U4CsDSLTwk7BVJt92XekO9++feuV\nX9VqNSK7GI1Gdn19bTc3N9bpdCId+w9pgKkQ/KKZJbDiC9O+dHwO/w5oxpeQiueS8UOmLZ8yXjOL\ngClkKcwLxTacV5PJxIbDoW02G69Qp5pYrwvCLw0GAycLkjgHdlmYtdAzPpvNRsDUcDi0wWDgcgFt\nKgwhopmuQ1gqwZTZbu3Qdrv1BUCUC1oPo2RlCMKuvUwOESWHrjaxDEWVYdR1KOf+VNONpnS93sHF\nM2sErZHzQ2m0Q1rIqMHebDYbrzbZbrceEer8q+m/hUAricNKD06YCISTet1NeNGxlnAnBaTC9xcy\ntIDys7Mzv4aiVCr5ftKfD8fDHCv7xBcRsuqiHgNR+3onum903sIebvxOwIdqckIWirHqdR2r1cqu\nr6+/YgGOdUDvkg7oYUVqlmIcmA/0fuoXYaTQEqkmNe537nMcrEntsA9rRnYCfZD+HOuUcyBsR0PK\nqNvtRi5pDvsbHds0takCbHrrsYZUP0yQrAUfpNRhghknAd73Vske0pRZ1Lt5kU5wh59Z9FxU+Yv+\nv0NYasFUnHFAsqE3m01EVKfVa5onDw9jfcnoo1R/Q9Vgr9ezXq/nk8XBmLaFZhYFJyocDquOWIxJ\nddeOM1KT2jPq/Pw84uz4wkKwrSmkJHUpuo44TM3Mo30z86opBVMwiodsnvocA+jRIb/f71sm82fV\nXaVS8ZQBhxnvnLErK4z+UK/0YMwcWsdgo0LTSJ3nZkxhzyV9prCFBX9Sno+jh3mjI7xKBpLSKuoz\nm/05FtJHpPZarZbPKz2pEDXDSKElO4ZplgJgjgZRK/sQW4e+OjwH6I2lDWbRRymzz+9Oak+GYArQ\nS0odfwE5wM0KZhYhF7bbrQvqIQpoY6Li7LT4HrMva1T9JecXxMdkMnEWPSlphFnKwVRcNLXdbt0B\n393d+c3YvGCE6HHRcpzpxiJCGQ6H1ul0Isg9idYBj1noyFXrEnbtNbPYfkHH7ni76/ChTw0O4uzs\nzKNO0kNm9lWUqGmWbDbrESepw2MCKn3/2ugVQW+j0XBgkclkvqoaJRJOyzrTw4t3CbhAUE81ogIP\nGEW9NFx7EaEb07Wpv/PQ7IYa60/T4xRvcDDrc4VfmKb/SBUhpOfwgtnR/nfHmuc4RtfsS2oPRoN7\nI1utljMc9NkbDod2c3MT0RPFAd9DjYnPhfk7OTlxgJfJZLxnkvoDfk5ZRwAzQTljQQf2EKg/9Jw9\nNE8Kpiji0HcOQ0qmRo3ghXsLO52O925DT6TjDP87aUOCo9cxqRYRjZTuWWUgj7HfUg2mdplS6ZPJ\nxMWxOGgFDeRX1XB8ek0MwsXFYuGMFBR2WvQNTzEOMipbiLa0MzOpTXoAJXV48zsRfXJ3nV4NdHV1\n5XlvdGtEUUSg2suJqBIB5iEFseFYVB+juhIY02KxaO122yqVip2cnFiz2fR+WIh6d6VMkjbGx3PS\nS0r3mB44muZTkAHTGDcvSY0X8ItW5v7+3h0zzll9io5ZtTbhvYT0uiOtGZfCTNoQcqsOhwIDmIzx\neGyDwcA6nY7ro8I2AcdMRbO+VquVjUYjMzO/Eoeu/CE7RcCixSowpbS8oY1OGvSKZlFgtUumgUEg\nsB45D3VNkmKnShG/81JMgxjS0s1mMwKgW62W3++KPpg0pn7OIexFganQWeuVAdqwjdJINlbIUOHY\n6V3FwuKLCIXDOI2VN6HpuyFlomCKxogKpvTqkiTZEPov4eTMzNsb1Ot1//9o12A8GAslsPSpIoo+\nJpjClJlBlIsGpV6vO5DSe+9IdzG+pKqGdpmmTnlW9lQcG7FL16ZgM07nFvf3Q5oezNvt1iaTiWsy\nqe5CcwIDHhasqH6Htg6j0cjZKEAUjGOagJSZ+e0Dmt7jKiokFb1ezxmpbrcbuXcvqfQkfhkNGsC3\nUCj4wUp6KwRTmmLWVDSgXy0t+1D78QGmQrYslHrgB2kxoo1vka28hLMNUyBF1Sk94Zh7zn4CP87A\n8DqyQ9iLAlNmXwuXlTkCJGlXZSJKfYlK8yqYYpHptTFxvz+tpmkZGuqNRiOvKMtkMl9VziX5rJrq\n47lwGkQeVGrSqJU/EXEDDDkMtS+Mah74nccak/Y1UW0ewnPt4TQcDr28N3zmpCwuhWxmkRYW+mdc\nNL8LKD0llXDMfYb/oNnjarWK9K8LgRSpTfSY+AyCMHwKDF4IHtOQSuH30kj1/Pzcy+bpsRf2kRqN\nRt4mIHz2Y+wtDF9vZl60sFwunTWEldIigpCx4UtTeg/NTZJ+X1lvlREoQIcJVjE9KVCkKrQcITUW\nN7a0nm/KtHE+m1mkpxsgk3Ocpr9aif8DTAUWahVgMyj1JKKEfYHuxVhMWtmg2o5daYiXYmww9F+A\nKDZcmC4zS96p4yC5LHe5XEYOLmht7j6DVaOqg7sGicJIl8U5yUOPh/dPqbLZn2uOlhR6xch2u3Xq\nnXV47Gf+Fgsd70PRXhqA0q7fH8eekYLjQNKuywArZXXRZnAwkyoCyMcVEyQ9doz1BbPbbDa9MhPG\nnzR1t9u1wWCQqhR06DdU+6bsBd+rrKiyULtAbtpM0+SsMR2X3rOqlbKAKNUvwnynRZv5kPF83ERC\nypm1yp5kPrU6mHUbgqm/fJovjJQxHBbVHZlM5iuhWlzZNhsPnZWWVcYJTPUZ0mz6fkDn6tjNzA/t\nJEX1cYcZYAoQqGW93HtGJQt5cVKA0+nUPn/+7DT2QznyY0XRqsmbzWae2oN54/m1WvHYwuSnWAg+\nDg0ODj32Xak2FdrrPCjTwZ/aB01ZgTDqZzxp8yU80+npqVWrVWs0Gt6IFS3S58+fvWpvOBymIjUb\nZwqKdG4fe96nMqRpSM2GrBPMDH5TWVGtSkQ6Ee7fNK7J0MJs0na79SIlWs3UajUvVuK9UDzGe1B9\n3w9mSixOm8G/gbgBCVwpE77AuChFPz/uhadtoe2yMNUEqBgMBpEmjEmX4e/S2GgKlzSrNlCdTCau\nlSIaAXwRgTzUL+VYh/+u9JhGz7oGteIkTRZ38Lx027X2wv/H31VXpZ3ptalsOI9p9yE8IyxUt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XnH885PjUb56envpYyuWygyUAlDJS+BN8PL6EP5VVnk6nR7kebNc88ZzVatWazaZdXFxYrVaz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OKh0W+K7BrAlK2DEsUQH+ICKf5MbNsqAJcEuYsUGYWDYhWZiKgV2+/3tQZYsVj09ZRCmdPS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110669d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mat = scipy.io.loadmat('notebooks/nnets-files/ex4data1.mat')\n",
    "X = mat[\"X\"]\n",
    "y = mat[\"y\"]\n",
    "y=y.flatten() \n",
    "\n",
    "m = X.shape[0]\n",
    "\n",
    "rand_indices = np.random.permutation(m)\n",
    "sel = X[rand_indices[:100],:]\n",
    "\n",
    "_, _ = displayData(sel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cost function\n",
    "\n",
    "Now we load some pretrained weights to easily implement the first few parts of the network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mat = scipy.io.loadmat('notebooks/nnets-files/ex4weights.mat')\n",
    "Theta1 = mat[\"Theta1\"]\n",
    "Theta2 = mat[\"Theta2\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we unroll these parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nn_params = np.concatenate((Theta1.reshape(Theta1.size, order='F'), Theta2.reshape(Theta2.size, order='F')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a first step of the neural net, we implement the feedforward component that only returns the cost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    \"\"\"Computes the sigmoid of z.\n",
    "    \"\"\"\n",
    "    g = np.zeros(z.shape)\n",
    "    g = expit(z)\n",
    "    return g\n",
    "\n",
    "def nnCostFunction(nn_params, input_layer_size, hidden_layer_size, \\\n",
    "    num_labels, X, y, lambda_reg):\n",
    "    \"\"\"Implements the neural network cost function for a two layer\n",
    "    neural network which performs classification\n",
    "    \"\"\"\n",
    "    \n",
    "    Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)], \\\n",
    "                     (hidden_layer_size, input_layer_size + 1), order='F')\n",
    "    Theta2 = np.reshape(nn_params[hidden_layer_size * (input_layer_size + 1):], \\\n",
    "                     (num_labels, hidden_layer_size + 1), order='F')\n",
    "\n",
    "    m = len(X)\n",
    "    J = 0;\n",
    "    Theta1_grad = np.zeros( Theta1.shape )\n",
    "    Theta2_grad = np.zeros( Theta2.shape )\n",
    "    \n",
    "    X = np.column_stack((np.ones((m,1)), X)) # = a1\n",
    "    a2 = sigmoid( np.dot(X,Theta1.T) )\n",
    "    a2 = np.column_stack((np.ones((a2.shape[0],1)), a2))\n",
    "    a3 = sigmoid( np.dot(a2,Theta2.T) )\n",
    "    labels = y    \n",
    "    y = np.zeros((m,num_labels))\n",
    "    \n",
    "    for i in xrange(m):\n",
    "        y[i, labels[i]-1] = 1\n",
    "        \n",
    "    cost = 0\n",
    "    for i in xrange(m):\n",
    "        cost += np.sum( y[i] * np.log( a3[i] ) + (1 - y[i]) * np.log( 1 - a3[i] ) )\n",
    "\n",
    "    J = -(1.0/m)*cost\n",
    "\n",
    "    grad = np.concatenate((Theta1_grad.reshape(Theta1_grad.size, order='F'), \n",
    "                           Theta2_grad.reshape(Theta2_grad.size, order='F')))\n",
    "\n",
    "    return J, grad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we test the ``nnCostFunction()`` function to ensure we get the expected value of 0.287629:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Accuracy: 0.287629\n"
     ]
    }
   ],
   "source": [
    "lambda_reg = 0\n",
    "J, _ = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, \\\n",
    "                   num_labels, X, y, lambda_reg)\n",
    "\n",
    "print('Training Set Accuracy: {:f}'.format(J))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Regularization\n",
    "\n",
    "Now, to the cost function above, we add regularization. Regularization helps prevent overfitting! We now expect a cost, again using the pretrained weights, of 0.383770."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def nnCostFunction(nn_params, input_layer_size, hidden_layer_size, \\\n",
    "    num_labels, X, y, lambda_reg):\n",
    "    \"\"\"Implements the neural network cost function with regularization\n",
    "    for a two layer neural network which performs classification\n",
    "    \"\"\"\n",
    "    \n",
    "    Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)], \\\n",
    "                     (hidden_layer_size, input_layer_size + 1), order='F')\n",
    "    Theta2 = np.reshape(nn_params[hidden_layer_size * (input_layer_size + 1):], \\\n",
    "                     (num_labels, hidden_layer_size + 1), order='F')\n",
    "\n",
    "    m = len(X)\n",
    "    J = 0;\n",
    "    Theta1_grad = np.zeros( Theta1.shape )\n",
    "    Theta2_grad = np.zeros( Theta2.shape )\n",
    "    \n",
    "    X = np.column_stack((np.ones((m,1)), X)) # = a1\n",
    "    a2 = sigmoid( np.dot(X,Theta1.T) )\n",
    "    a2 = np.column_stack((np.ones((a2.shape[0],1)), a2))\n",
    "    a3 = sigmoid( np.dot(a2,Theta2.T) )\n",
    "    labels = y    \n",
    "    y = np.zeros((m,num_labels))\n",
    "    \n",
    "    # one-hot encoding\n",
    "    for i in xrange(m):\n",
    "        y[i, labels[i]-1] = 1\n",
    "        \n",
    "    cost = 0\n",
    "    for i in xrange(m):\n",
    "        cost += np.sum( y[i] * np.log( a3[i] ) + (1 - y[i]) * np.log( 1 - a3[i] ) )\n",
    "\n",
    "    J = -(1.0/m)*cost\n",
    "    \n",
    "    sumOfTheta1 = np.sum(np.sum(Theta1[:,1:]**2))\n",
    "    sumOfTheta2 = np.sum(np.sum(Theta2[:,1:]**2))\n",
    "\n",
    "    J = J + ( (lambda_reg/(2.0*m))*(sumOfTheta1+sumOfTheta2) )\n",
    "\n",
    "    grad = np.concatenate((Theta1_grad.reshape(Theta1_grad.size, order='F'), \n",
    "                           Theta2_grad.reshape(Theta2_grad.size, order='F')))\n",
    "\n",
    "    return J, grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost at parameters (loaded from ex4weights): 0.383770\n"
     ]
    }
   ],
   "source": [
    "lambda_reg = 1\n",
    "J, _ = nnCostFunction(nn_params, input_layer_size, hidden_layer_size, \\\n",
    "                   num_labels, X, y, lambda_reg)\n",
    "\n",
    "print('Cost at parameters (loaded from ex4weights): {:f}'.format(J))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backpropagation\n",
    "\n",
    "The backpropagation algorithm is on of the main reasons why neural networks are so powerful- by backpropagating the error from the output layer back to the hidden layer(s), a neural net learns its own features and can create better representations of the data than manual feature engineering can.\n",
    "\n",
    "We start with defining and demonstrating a function to calculate the sigmoid gradient:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sigmoid gradient evaluated at [1, -0.5, 0, 0.5, 1]:\n",
      "[ 0.19661193  0.23500371  0.25        0.23500371  0.19661193]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def sigmoidGradient(z):\n",
    "    \"\"\"Returns the gradient of the sigmoid function evaluated at z\n",
    "    \"\"\"\n",
    "    g = 1.0 / (1.0 + np.exp(-z))\n",
    "    g = g*(1-g)\n",
    "    return g\n",
    "\n",
    "g = sigmoidGradient( np.array([1, -0.5, 0, 0.5, 1]) )\n",
    "print('Sigmoid gradient evaluated at [1, -0.5, 0, 0.5, 1]:')\n",
    "print('{:s}\\n'.format(g))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important part of a neural network with our architecture is \"symmetry breaking\": a random intialization of the weights. Otherwise, all the weights are modified in exactly the same way, which leads to very little learning. The function ``randInitializeWeights()`` serves this purpose:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def randInitializeWeights(L_in, L_out):\n",
    "    \"\"\"Randomly initialize the weights of a layer with L_in\n",
    "    incoming connections and L_out outgoing connections\n",
    "    \"\"\"\n",
    "    epsilon_init = 0.12\n",
    "    W = np.random.rand(L_out, 1 + L_in)*(2*epsilon_init) - epsilon_init\n",
    "    return W"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "initial_Theta1 = randInitializeWeights(input_layer_size, hidden_layer_size)\n",
    "initial_Theta2 = randInitializeWeights(hidden_layer_size, num_labels)\n",
    "\n",
    "# Unroll parameters\n",
    "initial_nn_params = np.concatenate((initial_Theta1.reshape(initial_Theta1.size, order='F'), \n",
    "                                    initial_Theta2.reshape(initial_Theta2.size, order='F')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we implement the backpropagation algorithm. Backpropagation happens after the feedforward pass where, with our initial random weights, a cost is calculated. The backpropagation of this cost means that for each node in each layer, we can measure how much that node contributed to the error, or cost, from the forward pass.\n",
    "\n",
    "![Neural nets architecture with equations.png](notebooks/nnets-files/backprop.png?raw=true \"Backpropagation architecture with equations\")\n",
    "\n",
    "To implement this we build on our ``nnCostFunctionX()`` function by adding proper calculations for the ``grad`` return values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def nnCostFunction(nn_params, input_layer_size, hidden_layer_size, \\\n",
    "    num_labels, X, y, lambda_reg):\n",
    "    \"\"\"Implements the neural network cost function with regularization\n",
    "    plus backpropagation for a two layer neural network which \n",
    "    performs classification\n",
    "    \"\"\"\n",
    "    \n",
    "    Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)], \\\n",
    "                     (hidden_layer_size, input_layer_size + 1), order='F')\n",
    "    Theta2 = np.reshape(nn_params[hidden_layer_size * (input_layer_size + 1):], \\\n",
    "                     (num_labels, hidden_layer_size + 1), order='F')\n",
    "\n",
    "    m = len(X)\n",
    "    J = 0;\n",
    "    Theta1_grad = np.zeros( Theta1.shape )\n",
    "    Theta2_grad = np.zeros( Theta2.shape )\n",
    "    \n",
    "    X = np.column_stack((np.ones((m,1)), X)) # = a1\n",
    "    a2 = sigmoid( np.dot(X,Theta1.T) )\n",
    "    a2 = np.column_stack((np.ones((a2.shape[0],1)), a2))\n",
    "    a3 = sigmoid( np.dot(a2,Theta2.T) )\n",
    "    labels = y    \n",
    "    y = np.zeros((m,num_labels))\n",
    "    \n",
    "    # one-hot encoding\n",
    "    for i in xrange(m):\n",
    "        y[i, labels[i]-1] = 1\n",
    "        \n",
    "    cost = 0\n",
    "    for i in xrange(m):\n",
    "        cost += np.sum( y[i] * np.log( a3[i] ) + (1 - y[i]) * np.log( 1 - a3[i] ) )\n",
    "\n",
    "    J = -(1.0/m)*cost\n",
    "    \n",
    "    sumOfTheta1 = np.sum(np.sum(Theta1[:,1:]**2))\n",
    "    sumOfTheta2 = np.sum(np.sum(Theta2[:,1:]**2))\n",
    "\n",
    "    J = J + ( (lambda_reg/(2.0*m))*(sumOfTheta1+sumOfTheta2) )\n",
    "\n",
    "    bigDelta1 = 0\n",
    "    bigDelta2 = 0\n",
    "    \n",
    "    # iterate over training examples\n",
    "    for t in xrange(m):\n",
    "        ## forward pass\n",
    "        # t-th row of X\n",
    "        x = X[t]\n",
    "        \n",
    "        # sigmoid( z2 ) where z2 = Theta1 * a1\n",
    "        a2 = sigmoid( np.dot(x,Theta1.T) )\n",
    "\n",
    "        # bias unit\n",
    "        a2 = np.concatenate((np.array([1]), a2))\n",
    "        # sigmoid ( z3 ) where z3 = Theta2 * a2\n",
    "        a3 = sigmoid( np.dot(a2,Theta2.T) )\n",
    "\n",
    "        ## for each output unit k in layer 3, set delta_{k}^{(3)}\n",
    "        delta3 = np.zeros((num_labels))\n",
    "\n",
    "        # whether current training example belongs to class k (y_k = 1), \n",
    "        # or if it belongs to a different class (y_k = 0)\n",
    "        for k in xrange(num_labels):\n",
    "            y_k = y[t, k]\n",
    "            delta3[k] = a3[k] - y_k\n",
    "\n",
    "        ## delta2 = Theta2' * delta3 .* sigmoidGradient(z2), skipping bias unit gradients\n",
    "        delta2 = (np.dot(Theta2[:,1:].T, delta3).T) * sigmoidGradient( np.dot(x, Theta1.T) )\n",
    "\n",
    "        ## step 4: accumulate gradient from this example\n",
    "        bigDelta1 += np.outer(delta2, x)\n",
    "        bigDelta2 += np.outer(delta3, a2)\n",
    "\n",
    "    # step 5: obtain gradient for neural net cost function by dividing the accumulated gradients by m\n",
    "    Theta1_grad = bigDelta1 / m\n",
    "    Theta2_grad = bigDelta2 / m\n",
    "\n",
    "    grad = np.concatenate((Theta1_grad.reshape(Theta1_grad.size, order='F'), \n",
    "                           Theta2_grad.reshape(Theta2_grad.size, order='F')))\n",
    "\n",
    "    return J, grad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's easy to get backpropagation details wrong. To verify that it's properly implemented, we do gradient checking. This is a numerical verification layer that serves to verify any function that computes a cost and a gradient.\n",
    "\n",
    "Note that ``checkNNGradients()`` actually creates a small neural network with a small number of input and hidden units that keeps the number of parameters low. This is because calculating the numerical gradients can be expensive, and the only purpose of running this function with a mock neural is to ensure the cost function is doing backprop properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def debugInitializeWeights(fan_out, fan_in):\n",
    "    \"\"\"Initialize the weights of a layer with fan_in\n",
    "    incoming connections and fan_out outgoing connections using a fixed\n",
    "    strategy\n",
    "    \"\"\"\n",
    "    W = np.zeros((fan_out, 1 + fan_in))\n",
    "    W = np.reshape(np.sin(range(W.size)), W.shape) / 10\n",
    "\n",
    "    return W\n",
    "\n",
    "def computeNumericalGradient(J, theta):\n",
    "    \"\"\"Computes the the numerical gradient of the function J \n",
    "    around theta using 'finite differences'.\n",
    "    Returns a numerical estimate of the gradient.\n",
    "    \"\"\"\n",
    "\n",
    "    numgrad = np.zeros( theta.shape )\n",
    "    perturb = np.zeros( theta.shape )\n",
    "    e = 1e-4\n",
    "\n",
    "    for p in xrange(theta.size):\n",
    "        # Set perturbation vector\n",
    "        perturb.reshape(perturb.size, order=\"F\")[p] = e\n",
    "        loss1, _ = J(theta - perturb)\n",
    "        loss2, _ = J(theta + perturb)\n",
    "        # Compute Numerical Gradient\n",
    "        numgrad.reshape(numgrad.size, order=\"F\")[p] = (loss2 - loss1) / (2*e)\n",
    "        perturb.reshape(perturb.size, order=\"F\")[p] = 0\n",
    "\n",
    "    return numgrad\n",
    "\n",
    "def checkNNGradients(lambda_reg=0):\n",
    "    \"\"\"Creates a small neural network to check the backpropagation gradients.\n",
    "    Return  the analytical gradients produced by the backprop code in the \n",
    "    cost function and the numerical gradients.\n",
    "    These two gradient computations should result in very similar values.\n",
    "    \"\"\"\n",
    "\n",
    "    input_layer_size = 3\n",
    "    hidden_layer_size = 5\n",
    "    num_labels = 3\n",
    "    m = 5\n",
    "\n",
    "    # 'random' test data\n",
    "    Theta1 = debugInitializeWeights(hidden_layer_size, input_layer_size)\n",
    "    Theta2 = debugInitializeWeights(num_labels, hidden_layer_size)\n",
    "    # generate X\n",
    "    X  = debugInitializeWeights(m, input_layer_size - 1)\n",
    "    y  = 1 + np.mod(range(m), num_labels).T\n",
    "\n",
    "    nn_params = np.concatenate((Theta1.reshape(Theta1.size, order='F'), Theta2.reshape(Theta2.size, order='F')))\n",
    "\n",
    "    # Shorthand for cost function\n",
    "    def costFunc(p):\n",
    "        return nnCostFunction(p, input_layer_size, hidden_layer_size, \\\n",
    "                   num_labels, X, y, lambda_reg)\n",
    "\n",
    "    _, grad = costFunc(nn_params)\n",
    "    numgrad = computeNumericalGradient(costFunc, nn_params)\n",
    "\n",
    "    fmt = '{:<25}{}'\n",
    "    print(fmt.format('Numerical Gradient', 'Analytical Gradient'))\n",
    "    for numerical, analytical in zip(numgrad, grad):\n",
    "        print(fmt.format(numerical, analytical))\n",
    "\n",
    "    diff = Decimal(np.linalg.norm(numgrad-grad))/Decimal(np.linalg.norm(numgrad+grad))\n",
    "\n",
    "    print('Relative Difference: {:.10E}'.format(diff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical Gradient       Analytical Gradient\n",
      "0.00690654083257         0.00690654084016\n",
      "0.0121251042606          0.0121251042698\n",
      "0.00619847901451         0.00619847901705\n",
      "-0.00542355683253        -0.00542355683402\n",
      "-0.0120761028533         -0.0120761028647\n",
      "-0.000110852709234       -0.000110852709719\n",
      "-4.29822000214e-05       -4.2982201756e-05\n",
      "6.41872688334e-05        6.41872657506e-05\n",
      "0.000112549716214        0.000112549717833\n",
      "5.7200055803e-05         5.72000570968e-05\n",
      "0.000143851837109        0.000143851834164\n",
      "0.000151666188497        0.00015166618726\n",
      "2.0301222925e-05         2.03012234636e-05\n",
      "-0.000130018475986       -0.000130018475479\n",
      "-0.000160500355229       -0.000160500356082\n",
      "0.000266299664542        0.000266299665123\n",
      "0.000206873380648        0.000206873383154\n",
      "-4.22496704289e-05       -4.22496700519e-05\n",
      "-0.000253048282328       -0.000253048282047\n",
      "-0.000230637482446       -0.000230637482064\n",
      "0.102286353136           0.102286353137\n",
      "0.0995931722958          0.0995931722974\n",
      "0.296932421506           0.296932421503\n",
      "0.0509594910136          0.0509594910137\n",
      "0.0502316727413          0.0502316727388\n",
      "0.148332514744           0.148332514746\n",
      "0.0496506203529          0.0496506203509\n",
      "0.0478387282654          0.0478387282666\n",
      "0.142655641524           0.142655641521\n",
      "0.0532772700002          0.0532772700025\n",
      "0.0519209996308          0.051920999632\n",
      "0.15619265977            0.156192659771\n",
      "0.0498448914232          0.0498448914238\n",
      "0.0489765403033          0.048976540304\n",
      "0.144170900542           0.144170900545\n",
      "0.0507054723209          0.0507054723203\n",
      "0.0487430344798          0.048743034478\n",
      "0.146349533869           0.146349533867\n",
      "Relative Difference: 1.9655399731E-11\n"
     ]
    }
   ],
   "source": [
    "checkNNGradients()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If backpropagation is implemented properly, the relative difference should be less than 1e-9. In this case, it's 1.97e-11!\n",
    "\n",
    "## Backpropagation and regularization\n",
    "\n",
    "Now we add the last component of our cost function - regularization after backpropagation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def nnCostFunction(nn_params, input_layer_size, hidden_layer_size, \\\n",
    "    num_labels, X, y, lambda_reg):\n",
    "    \"\"\"Implements the neural network cost function and backpropagation\n",
    "    with regularization for a two layer neural network which \n",
    "    performs classification\n",
    "    \"\"\"\n",
    "    \n",
    "    Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)], \\\n",
    "                     (hidden_layer_size, input_layer_size + 1), order='F')\n",
    "    Theta2 = np.reshape(nn_params[hidden_layer_size * (input_layer_size + 1):], \\\n",
    "                     (num_labels, hidden_layer_size + 1), order='F')\n",
    "\n",
    "    m = len(X)\n",
    "    J = 0;\n",
    "    Theta1_grad = np.zeros( Theta1.shape )\n",
    "    Theta2_grad = np.zeros( Theta2.shape )\n",
    "    \n",
    "    X = np.column_stack((np.ones((m,1)), X)) # = a1\n",
    "    a2 = sigmoid( np.dot(X,Theta1.T) )\n",
    "    a2 = np.column_stack((np.ones((a2.shape[0],1)), a2))\n",
    "    a3 = sigmoid( np.dot(a2,Theta2.T) )\n",
    "    labels = y    \n",
    "    y = np.zeros((m,num_labels))\n",
    "    \n",
    "    for i in xrange(m):\n",
    "        y[i, labels[i]-1] = 1\n",
    "        \n",
    "    cost = 0\n",
    "    for i in xrange(m):\n",
    "        cost += np.sum( y[i] * np.log( a3[i] ) + (1 - y[i]) * np.log( 1 - a3[i] ) )\n",
    "\n",
    "    J = -(1.0/m)*cost\n",
    "    \n",
    "    sumOfTheta1 = np.sum(np.sum(Theta1[:,1:]**2))\n",
    "    sumOfTheta2 = np.sum(np.sum(Theta2[:,1:]**2))\n",
    "\n",
    "    J = J + ( (lambda_reg/(2.0*m))*(sumOfTheta1+sumOfTheta2) )\n",
    "\n",
    "    bigDelta1 = 0\n",
    "    bigDelta2 = 0\n",
    "    \n",
    "    for t in xrange(m):\n",
    "        x = X[t]\n",
    "        a2 = sigmoid( np.dot(x,Theta1.T) )\n",
    "        a2 = np.concatenate((np.array([1]), a2))\n",
    "        a3 = sigmoid( np.dot(a2,Theta2.T) )\n",
    "        delta3 = np.zeros((num_labels))\n",
    "        \n",
    "        for k in xrange(num_labels):\n",
    "            y_k = y[t, k]\n",
    "            delta3[k] = a3[k] - y_k\n",
    "\n",
    "        delta2 = (np.dot(Theta2[:,1:].T, delta3).T) * sigmoidGradient( np.dot(x, Theta1.T) )\n",
    "\n",
    "        bigDelta1 += np.outer(delta2, x)\n",
    "        bigDelta2 += np.outer(delta3, a2)\n",
    "\n",
    "\n",
    "    Theta1_grad = bigDelta1 / m\n",
    "    Theta2_grad = bigDelta2 / m\n",
    "\n",
    "    Theta1_grad_unregularized = np.copy(Theta1_grad)\n",
    "    Theta2_grad_unregularized = np.copy(Theta2_grad)\n",
    "    Theta1_grad += (float(lambda_reg)/m)*Theta1\n",
    "    Theta2_grad += (float(lambda_reg)/m)*Theta2\n",
    "    Theta1_grad[:,0] = Theta1_grad_unregularized[:,0]\n",
    "    Theta2_grad[:,0] = Theta2_grad_unregularized[:,0]\n",
    "\n",
    "    grad = np.concatenate((Theta1_grad.reshape(Theta1_grad.size, order='F'), \n",
    "                           Theta2_grad.reshape(Theta2_grad.size, order='F')))\n",
    "\n",
    "    return J, grad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we check that backpropagation is working properly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numerical Gradient       Analytical Gradient\n",
      "0.00690654083257         0.00690654084016\n",
      "0.0121251042606          0.0121251042698\n",
      "0.00619847901451         0.00619847901705\n",
      "-0.00542355683253        -0.00542355683402\n",
      "-0.0120761028533         -0.0120761028647\n",
      "0.0503774063798          0.0503774063788\n",
      "-0.0575784386792         -0.0575784386815\n",
      "0.0247912963847          0.0247912963803\n",
      "0.025322571926           0.0253225719274\n",
      "-0.0576266494567         -0.0576266494557\n",
      "0.0547016974473          0.0547016974437\n",
      "-0.0166132637047         -0.0166132637047\n",
      "-0.0326209654311         -0.0326209654299\n",
      "0.059306422866           0.0593064228662\n",
      "-0.0452197351608         -0.0452197351624\n",
      "0.00873350014752         0.00873350014872\n",
      "0.0396260693036          0.0396260693063\n",
      "-0.0600416620644         -0.0600416620631\n",
      "0.0387642221278          0.0387642221274\n",
      "0.00876199509836         0.00876199509771\n",
      "0.102286353136           0.102286353137\n",
      "0.0995931722958          0.0995931722974\n",
      "0.296932421506           0.296932421503\n",
      "0.101447750103           0.101447750102\n",
      "0.0896508686643          0.0896508686619\n",
      "0.173542536954           0.173542536956\n",
      "0.104208465963           0.10420846596\n",
      "0.107200223063           0.107200223064\n",
      "0.202092082866           0.202092082863\n",
      "0.0617444704831          0.0617444704861\n",
      "0.0766481087466          0.0766481087465\n",
      "0.195209930181           0.195209930181\n",
      "0.00443674170292         0.00443674170534\n",
      "0.0163352736493          0.0163352736507\n",
      "0.126896701542           0.126896701545\n",
      "-0.00682998415824        -0.00682998415944\n",
      "-0.0112563779142         -0.011256377915\n",
      "0.0886656843568          0.0886656843541\n",
      "Relative Difference: 1.7885626810E-11\n"
     ]
    }
   ],
   "source": [
    "lambda_reg = 3\n",
    "checkNNGradients(lambda_reg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we make sure that the cost with our debugging parameter of ``lambda = 3`` is the expected 0.576051:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost at (fixed) debugging parameters (w/ lambda_reg = 3): 0.576051\n"
     ]
    }
   ],
   "source": [
    "debug_J, _  = nnCostFunction(nn_params, input_layer_size, \\\n",
    "                          hidden_layer_size, num_labels, X, y, lambda_reg)\n",
    "\n",
    "print('Cost at (fixed) debugging parameters (w/ lambda_reg = 3): {:f}'.format(debug_J))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Success!\n",
    "\n",
    "## Neural net training\n",
    "\n",
    "Now we use ``scipy.optimize``'s [``minimize()``](https://docs.scipy.org/doc/scipy-0.19.0/reference/generated/scipy.optimize.minimize.html) to learn a good set of parameters by numerically minimizing the cost function defined above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "maxiter = 20\n",
    "lambda_reg = 0.1\n",
    "myargs = (input_layer_size, hidden_layer_size, num_labels, X, y, lambda_reg)\n",
    "results = minimize(nnCostFunction, x0=nn_params, args=myargs, \n",
    "                   options={'disp': True, 'maxiter':maxiter}, method=\"L-BFGS-B\", jac=True)\n",
    "\n",
    "nn_params = results[\"x\"]\n",
    "\n",
    "# Obtain Theta1 and Theta2 back from nn_params\n",
    "Theta1 = np.reshape(nn_params[:hidden_layer_size * (input_layer_size + 1)], \\\n",
    "                 (hidden_layer_size, input_layer_size + 1), order='F')\n",
    "\n",
    "Theta2 = np.reshape(nn_params[hidden_layer_size * (input_layer_size + 1):], \\\n",
    "                 (num_labels, hidden_layer_size + 1), order='F')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the hidden units\n",
    "\n",
    "As mentioned before, one of the powerful aspects of a neural net is its ability to learn features. By visualizing the hidden units - the units between the data and the labels - we get a glimpse at the representations it learns from the handwritten digits. As mentioned in the accompanying [pdf](https://github.com/arturomp/coursera-machine-learning-in-python/blob/master/mlclass-ex4-004/ex4.pdf) to the exercise, \"the hidden units correspond roughly to detectors that look for strokes and other patterns in the input.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iq7x44NTmSbf7BOmxgrmqcsk/DPG3L4Ece7z7TvdfPN4rbPRTy+X7ukGOQ48/\n+PnzZ/38+bN+/PjRTf8xoU0gL/GXyYLzSO1RdwiSq9rPqaqqNfCma3lccL44r3alTwOmGBioUCQy\njpkSAw7L/DQUD2wORkQJSLEvnMZgRqYqFNG2nL6XiT0QeHXDb2H1sSZQIOXhOBS4vOydeJ8yrk0o\nZQqtayZArHO5nwBJC+wSSMlA2Z4DbDob1yftpzHxePJV1yD4TmNOTsD5MQasbguEydtkb7SZBDzJ\ne+cN1zEl2bs9kGc+HaFKL7Nq2g3XSanSwd89KLC6xBtETk9PuwXvsjU9R84Bs/rbAuQpC94WQLm8\n+Mn2+8B5aod6R92jvflap9ls1u0T0KZrS8ZpelCAV1VBAReCdVUkWdlmOw6Q/YGb7i95vK+38Uq4\ndDA9Db0PVOxC9EvJBydfSf/m1TX2V5+KA3rkQ1WtVAhpSxwjCxMOWPlsNyZFLV0nr30dluJmy2c4\nKEpyk223NtrttrL8FGAqORlXMu2TeVozROakRZHJ+TMrI2NdEavyMyz0u4CU3zmnthmg3bhThufV\nLTkHTge6YbH6xYBOxe1Tnl2dvYh8pgz1H6+VMgY3IvKcvzMoa3MgVbX6eomkR+xvyqY9SFSt3y2U\nQEUaPx0k26JTafHFAW7L8W5CDqTYB9cvfsphi2cir3YkXSPgSiCL1SOvYAlM+dSejmfVQ3fpqW1e\nSwFbxASoqiJQSDzyqTDa/ZjAuCnRfvib83GoDfehqU+s9vC/5BtTguNgirKVvCQjBeTJZHXhOZMq\nbyeBKlbh9d/Z2dnKsarOMLFxwEyQ9qsAVVX7bl63RwIL+jodK/113kmW6r+DKQEqB0W0ed0xSf8o\nufnxHAN1RHLyKi8r3yS3pRZgVxXR/VWr0DAUExMdJJhKQUb7HvSS8xAz+ZsHRQZjLz+2wFXV+ktf\nhZy5iJSZaeuBcAyYDBJc2+HlVDppLrpVVuVKzn6If+Kh2hxyBu7YdwFUNOI0hZA+vTTr+y5/d4AO\nfHwcNEC248enSkYLTLkDJjBJbbsz4Fikx9JTb9NpX8BXfUjtE4R6BYbnuoPra5sBmcFYxzLgch0F\nNzl0Tjup0qFFtA8PD/X09BSn1dJUpFcupU/8zXk9VJlqbdvQWMA0xP8EfugXyQfJgrLyoO6+NNmk\nJ5KSlwK5eCk5aHrPgbsDKT1kM1WmFou/18PqeAEq3tXpvKXNOZDyxCnxdZOg3LI57rcAlVda/Hgm\nKqwSK9F5W0l+AAAgAElEQVSnvHy6W0BXSYVkwDXCmkVRBUvVXfr6VKV1MOVgTNdJ/GXbbCuBa07T\nLpfLtc9tZSY6SDAlagWLviyMwEe/q8QnoTMYufJ5YPT2HEwxsKhPnhGIkhNRVsBFr3o+jo+biqXK\nVFV16zl8nYkcOvvPcahPfc6A124FjzHEdlPG68Zftfo8EG+DOkDZqX1mOUNgyseTgG8qHfeBKZ7D\nYOOVpVZQ9aTAS9CtsciJtcBWHw0FYu9nqr6IUmW3ZaMcd5rmY6WZUwneFx3j1Q5VprSQ9vHxMS6K\nVztcLE7wwDU4BA0MwgRmfevudgVWY0HUJpR0nXqnY+SzCF4UOLlOjECatuNghBVFBe+np6euAs/A\nzGk+9ofASFNWLjdO8/nxrRjg+2wzJbybyGJT+Y3RG+l0y6+6XYonVbViZ7wZQzLRTM9kMllZp6Rz\nJUf5Hq1vdLCddNx9diog0L8kgOj669XJlOj6NKj64m2OoYMFU30Ope8/V5aWED2LSsDMr5cyFbWh\n4yl4n/5jKZvoWY5E2TNvUSUoomIRTDFbd8NiZaoV1NLUCnk01sEPkTsPggu2z3135N6eyzplOHQC\naWwEKWwjAaMEPN3x+nn8L+kWdYTHszIlUNzSTR+Lj3dX4nUcRHgZXsHPx0KHyVvk+8CUzpedeNZN\nSqCXlSmBqZ8/fzYf+CkwdX19XTc3N1X1vn6EgdQdvPMnAc2WPbXGM4a2yaD7QLPrO0l+JC3WPz09\nXblbkfKl7B1IpcqUgvjZ2dlKMJacCH7pRzn165Upn+YjkHIw5TzivoOpVjI6JJNNgNSQfrgtpsqU\n4iL9IWXiANorU1qnKDlUvVcNdT55In3QtCL76OPxWCSeinSdlEAz2WK1jTqharUqzg6QnZfb2FTV\ngYGpvqCtQboTErp0p05B6TOhdTKOYMUrUMnRpOArASawlhZHspzJO/T8jpnT09O6ubmpm5ub7inQ\nl5eXNZlMVqY1ZCgeeJwX2rjoMgHLsfJpUVJK8ps8ZPut7HDoWilD4b4Cvr7LwYhc1t4Ws9G+cWoc\nPm0geTPrp6NJVRD1y8eTgGiiBCJbxyUnv41z4ZgS+doF8cGTksVisRJEq96fF5Om+Rgs1G8BMd2h\n9PDw0K3FeXt7655srrWNPrWu/tCp87q6lvsAOXAHUcvl+9rNVgBuJRd9lJIN32/JMQUfX2civqf1\nKAzUqgz5seStVx5aPo9VeD2nK1UVHVyxMkU+s4JCX+N2x4fIkihjn+qj3+jjdZ9M+/ylznObSole\nquJQfyeT9xujUsFBwJP9ZOVJsymMlZQrExUl+tIL1zHavKbzOJWvMYoX7o+S3/fjUmxhPBgLgvvo\noMDUEDkIEnkVxwOYM1WffWCqqlYCn2fWDMZE+FIgR89V62+V59ywytg6Lt1Gzhe4TqfTbhrCMzC1\nlwClf7aeGruPitQYZ+6ZEysr21wrGZMDIhmRG2ifcSUH6mPwcfFTbfHcVOFJma8ClRxXH6Dal2MY\nS85j9UVbAhP6PQVD/ff29tatt/AkicGA00qqRNHe9Klqymw2q8fHx3p9fe2eRaS+X1xcrCxYFs81\nJk5r+DjFC6+ypIqUgpEHeqcx9pf+T0nTEDB2MEEw5TLwficgSX1OdkNdSQCK1UI+iPPy8nKlL1y7\nw8Dbt0BcoEDH6ruuo/1k063E2q8xhrz9lp9O5xAgtXydnyc+EUgRzPCaPv2pNrV+yvvin1x/KLlo\nrVqKf+qfYiCfwah+9W0JUCYgxXGmbRf6FGAqZWlkkGe4WlDm0wxsI1VsXDlpMGmOXI6Bc/BaDJ4o\noWf1S05anz4FcXZ21gEoVqaqai34Sjk59lbG06pMja1mjKEhJ8FrClTpd376OX3tu6N3uaZMZ8wU\nUpo6lN74uQT1rSpZX3WFYIpAyqts6t8uMhpy7Pw9/efOKGXQfWDK7Zbl+NQ3Ol+99466Q6C5WLzf\ntq3pPgZZkb+/z8Eqg1HilVelBKacWmCqz5n32WDLFtzXpCArIpjiIn7pnXRT02dekaF9sKqRgqf7\n7qr1Z1e1ntHFBegOslmZYoLrstbUk/7ntJ8S4tbSgD4ep4rIGGJxINkXP10fE4hoVaaYhKQ4xHGo\nqkq/loCxdIKVQu7z2oqLHCPlon5NJpM1IMbEqJUsu647j/vktw/6FGBKlKomntn72g1X9FQxcjCl\noK7/vFrgqJrlbV8cyU8fB/d9ysezMn9ZKl9NkcCUrkvwlzItvtfMp/l2AVHJEaQsIF2LckgVl75r\n9hkZs+xWVSpVUarya0qof+w3x8Pzl8v355wx86PupgqYO00f2y4gyomOPfF1iOdsx/nB6mOqTLEK\nRErBoqq67FovQOaaCYGCqnegIDD1+PgYfYK/cobXcjDlY06AgmDKx5RebaN2dqXk+1LlhH1yIOWL\ndqmDabqOlSnqvvtLTyJcH9LDXFWVYmXKNw/QnvwwoWSfWEUhD/zF22lfny0b2CZQJ51qgYOqWhm7\n20fqe8u3uc/hDU2ixWKx8vgKLUr3KiLX7/JcVfsod42T9iW7HqpMOc/69Nxj2RD42oY+FZiqWg+0\nybBk+BREKvslcCHjoKAZQL1qoADpZeeEllt95ZyyOxCWuPmdC9B9Uac7OK69cePy1wgMOQ5+DlHL\nKaRKmQMp8mvX6yRA5c6QwZ56RZ3wyhT7WrX6vKoWmNCUgtaUUG9a08qeEGi/z5Hvg8a01wJSzg/v\nbwtMeZCteg/0qiiozQSmpO+a7hO/uLj54eFhBUjpmj7FRz11kOzycCDF9VJOk8lk7REorYC5aUKT\nbIBAJ1WmBHoIJtQ/TteozZav8aoTdV6fDuTI/02m+dxGHRAkAN6yEflBjZ13I7aSPPrvqvyMrU3I\n+5aSJlIrxtA38NjEZ07b+npeT7Al9+fn5+6dfbpZyhN8reVl/H17e+uqilzCQrvi9VJlKgGq1vhd\nDh67Em939Z+fEkz1gSgquAfQFHSr3pUtBVLPunxzgJacmDYqEDMiv6PIF+/x6enJsTCA00iqVtcP\npKyEa6a8UrKpI0+yShkElT8BWoJZ/ebnJ0NIchXRUTt4TNlbq0rlx3ibrX5Th3hHmMbKMfdVEFr8\n3BZU9cnY5ZfOS3bn5MFc0+MCOdLRli17lSjppic9nj0rwJyenna316sSJmevYKK79y4uLrrpHvI+\nAXX+zspUCsACLy0gNUZ+Lbm1ZJYChgfvVPns60/qg3hAf8hAWVUrfs2f50XS0gbdZVn1XmVM42qB\nyFaApX31VTM4Tq+Eta7f4rPzbMi/ur/0tlqAgm2z76qsMgZ5305PT+v5+bm7O1Ob3hzAp8UnX6l+\ny5aq/tYvLwq0wCr57HxIYJxJbWtNoyfQzps+3o+lTw2m0sJzLy9S2ZlRunMeAhLJ2TDLY59UZUjX\nl0JLodLmDyWUs1fA1S3DfM2FgroChfou5ee0FLe00N3nyhMvx8ip5dSHAr//T3CcnEdqj8CSYKdV\nidM51DH+JtnymqqMMDAoUFJfUuVQ+pHIHXpLt1v8GiujPmIgHAMoxVt3rqz48A5FbVrMSvtoba1E\nh0891wJzvVfs4eGhu8bZ2VldX1+vTE3I1pbL93eU6bb8k5OTFafvupH4zOqjj4c8SpWtTZ33kOy0\n759JT1Im36poqM+tIKpNNuhteXWdz4+iH5xMJnV1dbXycOKq9ef1qS/OawfwqTJWVWv6qjFSxgSH\n9Pv6z0HVNjJrtenxqgUGCOZ1rMtDj5rQuAU+KB/5LL5GRlVhHqf44fGL74wV8KWsdL00lta4Wjwj\nIHQZDlVkeU5KDDalTwemqmrFaOjAXdBEqq7sUpjlcrkWBBIqbmU5KbBwoR2VWoKXwk2n0zWnwlvA\nHTho7AJNmtpgm5eXl931FWj0jr+hQOjl300VrHV8cjbulFqUHH8rGyP4agUCbglI+bVFbEfX8fUc\ndPra0vSD9n1M7Ad5mYIW+cAA+SsBVQIKaTGqgI7aI8Dn1Dllx3ZT0PVKRXrSuR7Qqee6nZ+f1/X1\ndTc26oj6xWe26ZyUtCVeMJg5j7gux6ttKQDv6uBbSQIp2ZFf32WhIKWEsU8vq1Zf+Ox+ihX2lFwu\nFotuv+p9OQOfyK0tLYVw25OOeZ8J+CiXNDaf6UgVtcSHbWVH+ahPTC7TcTrfwYRAPBMXPjPKx+kv\nBW+BKU/Eua6QibyAlfSAsuC4h3yxjiEvNDbaMmeE3M48VuyDPgWYEtOr1tEnA38CU2xDTNYng5qE\nUPVuXCnbSkFL57PCQ8WmcugYVZboSOQMZNS+PkvXURCfTqddUCdAk6Pjs3X4uAbyqA9M0cCSU2g5\nevLegZQ+mWl5++Svg1r/3+fKqSOUB8fVqkyl66ttgigRA4HWChDcyAG17oJJRJ6nakxf4Grp5qbk\n8uPvCUy1Fg6ndQ8KwAT6mlZLFQY9EuTq6qqqVm+ZlmxUseACc4Gph4eH7v/z8/O6urqqs7OzFb0h\nCNLzcyQ3TrEnwEhnTB9Dnp2evj9Akr6hNa2W5LErqBoDpAjOE5DyjH+oWlr17q8InuT3kg/kJrmJ\n9LgGvWeRDzmWjlBXZIPpDk2OUTd5SL4OLJ3SNKbze1v78/MpI9lO0hUHUtJDJiv63cea5EwgRH+V\nwJTbAnWElWgm/v5kdI69lUikhFN90qd445XJZO8JWJPnm9KnAFNVFY28D0xVvT9mgMylE+baFUfs\nDiRSVcozOVdSGSn7f3JysgJ8VJ2is2E2T5StNliZqqpOOXk7M+9e8vdZ8bNVsUmVqWTAfZScjTtx\nb6cPsDooS9XCqloxdPHagaJXpniNBNCkQxqX+E45TqfT7lzJvgWm+qb53Nk7mEp9Fj/2AaY0Rjqv\n5Ohcf/xWdk7X0LkTTOlRBY+PjyvvR/NqTlV1QIRgihW+VJl6eHjo+MGF1P6gW/ZN7XIxsq9rVDu+\n3of6In8jYMm799J6qeRTtpFb32+e3LhP43EJULlvawU9JkPyT3ysi39eXl6uganZbLYiI4FvPcVe\n2+PjY3358qVub29Xqh8CU6yutKYENTa/8SBRCsK7AN4kG4Km1qxI4jl1ihVyvuaFS0LIDyZBtBFe\nL4Gp5Bv40FTe0HB+fl7T6bSz2z4w5eNL/GrxXHJMoIpVulaSug0dFJjaJAh4haVPubnvSN+VM2Xe\n3HQMS67MVlO5lPPFBFRyMA6mdL4AXwpsnhXLKShQK/NLt6t6xaOvIpVkRBk4f/vk1ddeq8LCalNq\n0zc5cDp/voLCK1PeFw90acxsQ/yVLDnt5NOlnrl5iXmIt33A1sfgvE3/D5EHYbeN9FsCV16hYjLD\nd+eRZ/oUb+nUaR8KmJPJpB4fH1cqFvqeMm/1g2CM2b/zinYn2fnvQ9VDAqwUKHcFwfQTLsOxNko5\nyrc5yEmgi3ZHH3d9fd1tNzc3dX19vZJ8aOM6UfrV+XzeJSW8m5N+VYFbQDXpu45P/GVFYwhE+fdW\nkrGpLBOQSjqUvutYjVPVtnSzlIMKXpM2KzBFnlLP6f9ayQDtxAGZrqPH+zivGIN8BoNb8kkaD8ep\ngol4keS2Kx0UmCK5Mvl/NBAxUQY9mUxWlKh1u3MKxiIpjJA0jdkdHxVMxylQ8K6ii4uLlek6Tu0x\nK5hMJl2FyQEOs2Y9xyYBQw/grcXBXqJ10OifuyhdCr7JSVDG/M3BYNV6dWq5XK5UD1qApgVS2A7v\nvEtG7KBUfFPWx0qEpi2YZfu1E1DxQNwKunSouwblJLf0yWu7PbLSkx45kLJrz5BPT09X7MOnDH3j\nOim9MJxrBak3XrHg+h9m7j79pO+pYpVsSYkS114RrJF/adtVZi631kabF2CsqpUx89NfC6LzaHOX\nl5crIEqvwiJPtS9782lwnz5SfzV1eHNzU7PZbAW0aapPlQnKg7bLdlvP2xtKZuibdMw+AbH7yla8\nSkBMtqT+LZfLlVmKqlWQwzaku768JN1Rzmk87XPqX8dfXV3V169f6+vXr/Xt27f6/v173dzcNGd9\nks/z+Et79Xjp/GM1jDMDrQrrpnSwYKoqA6pWRkewIWb5fLnP/3pwJUl59N6uqloRgCsghU2wwzUh\nl5eXXXAlmPI7+eQsknJIaQmmWg6YWWZyEiljT4q1L+TujsHl6aRjmOFwPPqP+lBVK8CU05YOqlLA\nEak9Ap/WWLhJdnJE6pNK3AziyUG0MiU/NjlXP26foGook6MMCKQ0dtphX0XYK1qsrrKayEXsCui6\ng09gSguVUzD0ZMt9AgNG665b39zWWLlsPQIgBYi+KsRYWZGvLrfWxvUyOic9687X4ghA+3o5gR0B\nKYEpr27omq4b7BP9kyoaNzc33c01XLuoz/T8P1aE6R8dTHmVw/maZEKbdNrUDpOfZpxr+WNexwHy\nZPL+kGbGyzQWVp08VvFzOp2uJSZ8PhmTSIGpb9++ddvNzc2aLnnMZtymv2ffPOHm+N1XerVtCDiP\npYMEU32Kl1Cro/HJZP0hbpwzp/I4A7kvhaqqNSejz+Vyff0FHbwAlbInATI6Wne4+t8zPY2fU0nu\nnMk/AqXWAnQHAwlYJt5sS54JS44EA/7p/dZ45BxpYOKdO0Yfb8t4eF1OIyTeubNTZfL8/HyltJ7A\nPR1EWkeVsk7XfT/GAdW+KTket0VVeLwK5FWHtEaHsnUw41WDxeJ9AbuAk9ZKeWUq8aK1DoWAjlWl\nNN3lm09xcp/9T7JKQGpf1ank43xL4EHVJb/rzqeKtLDYged0Ou1AFMGUxkZyMMWKCLezs7MugFOn\nEu8Jpqg3yf+lGwycj9p34Kvf9wGEXX6UD33hUPsEyPJLqjYR1KfzmVDoU0tHvCqltbqahdG+QBT9\n3fX1dVeZEqi6ubnpzuEn+axY53qnChj9Pseu8SU5Jt+za3w7SDA1RAwm+qRzV/AikPJ59arhaT5O\njfktpDTO2WzW3Y1EYOXTfFIItu/PV3Ew5WCAla/5fL5SeUlOkw6m5UQT0BCfnbZVNL+miM4tAQR9\nemVKhs4shUblQLQFgtg3XUt8ZhWQsqBTS0D07e2tq2hKBp59sbJCwNZyxn0Bt+U8WvvbUstWWoBK\n/7UqUx6MEphSAGc2XfVemeJzpR4eHrr1UqxM9fHOeUx798rUEKBiQGYlamgtInnXqhRsIyvXjRaQ\nckDF5EAB0+9Y9b76HXtaQygQ9eXLl7q5uelu0vD1PD5N5JUp2j35lngp8iSo6r3Kzd9b03zkZWo/\nxaF92VmKUa2Zgxa532P10MFUqkwJSOm419fXtVf7TKfTztaU2MjfCXjL793c3NTd3d3KNN/19XV3\n7nw+r7Ozs5VYqtmB2Wy2lowtFosurp2evr9BwkFRy+ZaOGBbOngwNZTF+V0l2tICPF8zxQDtSspA\ny0Dvxq3pGr9LKVWoCOTSFIBnEb6JB5x+0rE8R8QAxfUGVLIEpni+Kxz/24acvyKCYg92fj6BUtIJ\nD2A+xr4AI0DGypRkzGmmvqCkgOrB2StRAtsu21aFIm0tR79v8uCcwBSDCxd2twCU2qV80poMBjr1\nw8HU/f19t/hcyQ3vYuK1qWvqA/sj2+Rz2wimWqCK/2s/6Qd5k2S+azWjr1LR2qryy4b9NSFaMKz+\ni3gTho7j9B7BVKpEpKqtJy3SjzT1l3y9+xiXPXWPIKOvStGySSZC+5Cd95ExK9lfS96eVDIWVNUa\nOGH/yQ/FwLRu6uLiYuVBt9JxX0t1c3NTt7e3dXt7W3d3d3V7e1vX19cr6wmr3p9xJX6oMuXTfW9v\n748uOT8/X6kuOw8pH84itOJb67c+OngwlcidohuIfmuV8VPVyUvxEhorCh5MpDR8V5GyYoEr3Z13\ncXFRd3d3K4sw1QcpjALscrns1gKwkpWcnVde+jbyz3m5CyJPxOBLhSV5lYfK6w6KVaKq6kq8CRhx\nzO48pAdccMzzdY3Wuh5WTwie9WyiyWQS1+l5GzqX42vpLOWUwAB5/hHkgSVlvC5DykBVnqr3KVgu\nQOZC1hS8pedVtQJCFYj91nfauc4TOZDjp+4s8+ceuX+gPrkuyUknfeb6ubQ0YF+AiuNrBYQkrwQW\nGTC5WNzBfgrwPgWvTY9tUSVDj7QQEFbg9ADPoOiBk0Gbvts3rplysMW+tyocY/m5j8SGQCol/n3A\nqnV98ZTj8lkU3vDhiSvjknhEm6Z/9Y02rDt41aY2ziB58UPjJ2/StKyPT/sJOO6TPh2YcmeTgJQH\nYTLPFSSh9pOTk87w5LxVcnRlfX197ZwAs+KqvwUvJ1RVdXd3V1++fFkBU0L8dBC+xkttESD4AnUP\nbmnzvlPpPMjsW9FagZ5Vmb6Mi9NH4pVnqARXDj5dZxzgERwzA3K+sG2CKcmNfdWnX4sVFtdRVi0Y\niBOo2oTn+wBabKMPUPm+1tMQWPnCZoEpD95KRpjxLpfLCKQoM5dTAs4+JgI49tHXPXkWLQAtW1a7\nBM20cVatGeg9yUn7m8gp6S79o8soTa+mCpwnAQr2rq/u17Sv4M1njDGgpioEidVjfbZAmy9klt8Q\nTxykebXJ9b0lC08oEt83JcpH373S7v7Sx+FtpYRNT5AXkPIEgneZy+YcTKk6xLhIvhO8Ckw/Pj6u\nxFfac6qW0X/qOqwotsZOHekDUrsWFT4FmPJBMsuryiVsD1B0clwsSUG44nM6jS9M7cuy5AzomLTP\nypQybilAKyho80oLAVXKMtPmYFCBYCib2ZborFyGVHxmYCkQ6LsMWEbKAEA58lO8oi5QH1x/6AA8\nOHubBFMqRauvvvn0sOThG8ebeJASCM+wt3UIrWCR5OYOvQWkJpP3pyQrEKsCxXU4WkuTps7oAHnz\nhQMqAteq/M5NOnXnE6tSBBJucw4cpCfSM/XBq8FcK5emtlq83jfRx0kPCXT5MGFO62if+iobYJsp\ncUk+U2Dq/v5+ZbrPn7zttuDtua/U/1yvQ8Dashu2T9+kjUHcycHNLgDKfSYTzmR3+o+fkqn3l/5K\nm6/ZbU1fS1coF1af1X8VIwiitO5UdvD8/FyPj4+xeti3LIC2K9/i69z8vD5wr7b2QZ8CTImo4EMZ\nuhsb11bxTh0++kDXYBty3FqD4Qvb9T/fF/X6+tpVn/R8jaurq1iZkjOSg+HdDCnDT1sKaCmIaEwp\n2H+E0yYlQFWVp/p4vGSijYub5dg9e3NAqUyUupCmbSnzvkXSch506g7wdbx/qg0PSgRTclL87kCq\npbMtPu9KrWx7CFQl8DKZTLpsWGBqPp/XYrFYy4ZPT09XslaBkZTNJvCbpivoeEl9lSmvXDNoiZiZ\nM0GQjDywe4UqJYW7yqzVHve9MiVwq1dTefLGqitBGakVvBxM/fz5s378+LEWRPsCKoFpAtGsjvjm\nyZvzIiWW1OOU4PCYfciN1xUl/5auw+Q7EWMaE39WX9OaKD2fysdPkMnYo7cZkB/yw5K//IAXPXyh\nOXnsviWBqZbufXSs+zRgykFU1eo0lW8pUFEAunWXDx3zoOaVKVWn/OGLvi0Wi25a4+LioruL4fb2\ndq0ypfHQwQjFp7VcdOpeVRMvvALiAcADc3IS+8qKqeAJKPH6+mQwZJ/dKfMOSzdo5xOBiQKy94nA\ntq+CpXZZafAbArw6RmfIknjKsL1KRZlx/MxYydfE8zE0JG937glEubMjvygT2tTl5WV3g4aDn8lk\n0q1H47pCVqa8ksg+ClD5lqhVmfLkRHx2een1HClIsyrj/oJZfF/g3gclfaBfFMDVa148KeMyCBHt\nVd9TNUD7AsO6A/PHjx/N6hJtgTbqFb40vgRaq1b9RSL2wZOGBLZSUrYLuR27PSeAPJQg6zgBUMWa\n2Wy2Akq0+eMPFK88QafuEJS7vZBXsmGvnnnSLJBMWXhc82n35FOTPyVP9kWfBkyRPEi0jILBT0GO\nAVYbMyy2J8GkjFK/SUhC3frUnSxfvnzp3hulO1r0SgXeZsyKl9pJmTQzXN0J5kbHNQf6nUClT7Ec\nxPSBKjeCXSiB4ZZhcIGpOzsaOJ0wgwGrShy7SNM1fMaYg1ryicYvB0EwQX57hu5T0A5KNI7krD+i\nAjWGUtBzfojSeFiZ07GcbqLDJP/UhgCOZ6LScXegqdql9kitl+6mNYq+6J1+gnfwEgT2JX9ufy2e\nt75vSukarFrIZ3rVoqrWKgisDLmPIc9ld3xobVWt2ZT6R8AkXjqA8FkH2h3HKp2aTCZxUb3rXUpI\n5RfkVznePnkOybElHxYQnBzUORiVnNz+EjhlcqA2NeZUIXSA47rAZFTH+Do1/edJg4+HPoJ9dn9P\nXqd+0kf52HnuLv70U4GpTYKHA6mqWnEKNBoxXPsuFFcQLz+qXdHJyUl3CyiBFMGUXnuQAImm/zzL\nr1p1eLpWAlNpkSCDjhsU98cA1U2JTqGvTb82gUq6OyQBEDpPtenZaAI5VavTNmrz7e1tZf2I9IaL\nxT379vMTMPe7S9xJaKuqtQAxhn+70pATV5851hYPkvMSqNJ3gikPiqrm6cnWfju1An+q7vk0FfWC\n/eHUf3ozgfYnk0k3TcLAwGl/ViLJk5aMU5Xvo8mvoYoB93lXnz7dHypoyyYFbpbL5Ro4UbtMGnmT\nAY9XBUU39Mj3iWh3Xo2mvKU3mnryh5DyERYuBwdpDqRoA7+CCGJSRYegJ9kcga8SE/1HUJWmV1v9\nWSwWa9VWB3YOptQ++UlKNwCxnw6qGLPJkz5/RBtN19iUDhJMtTIzovWh46ve1+MInPgdOZz+cgXU\nNfsCIIEKDVmLzQmoBKT4JGCBKVdaKZ4rEisa5IVnYf5cK/WRQKqlQLuCqW3AV8qeON5WdTAFoFT5\naIErdxjMztyB8llHBHE6j7qi80W6FkGgphxazmIoyLod7NuhJ+DL/VTdrFq904bjITHR0fdUgdN5\nkif5zISDvyUw5RWGxFOBqfTUc69kMAlT9YRTT3Tuctjkn4Mo2eavAlEkXo9AR2tpeHOAEk4HUtJ5\n2TPYxsEAACAASURBVAr542NdLBY1n8/XwBSrv+K9KmTqGx/CKXLwIP8rfZFO0Pb8tTN8EOhQbCF4\nHwK+HyVLjxcOotL/6m9KsN3WmeD1+XP9x5kSTd070GFMSxWnqjwtzv55Iub+0cfrIDPp5D7pIMGU\nUytD7lNWR54EU63KVHJoNESV9DmXSwNWlnN5edkBqVZVSlN9Cf3LuabAIDClTDgZs6YaWEpNZVHy\nytE6eTwWIG0CpFoggv8ToPgUii9wZGDyZ564LjCA0bAJMplJLxaLlUw3GXDLAVFHNAbP1t3x+G+e\nVbWIzmYXcueagJXG7Oe0MtoEkPS7Vw19DAn4yDlXvb9nLy3q17WSDTio8qkfVmX4QErJhBViAn7Z\nTLI9l22fXSae98lrLLX0g8Co6m8/JJ+nsXDqh3YpPpOfrBgRTNE3yXd69ev8/Lx7cKPOUZDWmOkj\nNC7ZqapdDjJOT0/XbjTgU769bd/UpuRNmxsCV7vIzfuW+unAkudKfl4EqFp/v6n7MQcz+k16LqCr\nG7Wenp5WQK2qyLpWqsi77+oDUvT5id99fBGlWLepTJw+BZgStQBU+t2DogwpVaY4xULjpIKlaRkq\nJW8pvr6+Xpvmu729raurq5W3mnOaj6QgznK6iOsVWpUJKXeqTLWyf/++KZDahvoyE/1P/nP9hBxt\nCkzn5+cr69mqVp+LRCNLGzNs8c0rG26IBH4pQ/RzCfxUZREPHBy2qkCJWo58Uzm2nLc+1Uf2jcGL\nv7nDS7bZulbV+vS8plp1vCqV1HGCKXe8LQDrD6pkZWo6na4sylb1ZjabdePmjQ367s/pcX3ltNiQ\nLDb9b4iocwQInLpzP6c1pi3/xDa5lknjoy+VbMVvPj1drykRkNJrStxH0V/TplTt8iCtahs33gzC\nT/p7rnnT91YwH0PbyK0P5BEwMCl0IKjfqIe+LmpsZUptSaaakn18fFxZb1q1Ptsiu23Fo1ZlysGU\ny6APaHpxhVVG0rbJ6EGBqRbK9O8M9EOOhpucP6eMZPTugJPCVq0HgLOzs5WKE1+f4OullAmxdE0H\n1leqJS/8Pw9CUm7eCsxAwykTgkp3Pikj2YX62kxjJZDiHTkCVNIXjplTLnT4NCR9tsDU2EzPp3xT\n9pPGRIfH49VHB4eJP2PJ9WTTc1ub+knnxjUqsrdUyWvZGNtMIIfrlui8/WYOl+VQRYjARsdRn/jI\nAwfBBGBM1pi0pTsJpT+cFqOM9p3EpOSJciHIoZ62XvXSAlMkJSG00+TzOE3IfvJNBOov+U5/reqH\ng4ikS/5uObaVkioHi30JQvq+qf9MFSCS+/r0yWMZ/1JlJx1LmTlI0//UESW4fE+t9F8PrXbeJXBE\nfUw3qbSW6jiw8nE4qPZP8nVbuR0UmOojR9ZE3xSwB1g62FQ1EODw4OlBUU7UhXd5edmBJW4CVpzW\nk7CXy+XKbd1SQAUHPm291Sft0/hbwV1gikDGKyVcC6RjyMt9UVJQB8cat6+T8tvJCaLm83kX2B4f\nH1f4Lt57dcn5xyyc/WUQ9gWydPhVq2tkksF6MEv8dz6IR/rcBhRtcqxv5I1XfLwSocqFeMEFrlXv\nTwx3wKvffconvfdOU+mcpuGduT5uOl53wq3pfmXNvJ2eFQ+1q0esKHj4UoIErpyvvgaSn+RdojEO\n3/1jApACDw566bMIuNI0XyIlPWpPa7J8ay10l29UQqTALHmcn5+vTfnrPNoVt77qaQssJeC/b8Cr\nvrT2W0lYstPU76TzXu3TPmOBbNWPUft8ej1vGJBMOUPC2KPzE5hi/yRj9cvt13Xa+6jrOJjcFCwN\n0acBU1VZeVkm5HHM/FiWdVQvR5DQPwPrZDJZy0blSO/u7la229vbbsqPm1dZ/BlWfD9RynSSIaeF\n2VQcffKuqWRIPH6TaaVtKGVelC3BoT/YkJv4ySrV2dlZPTw8rEzLKPv0Ba66vjuhvqoFP9kPBr7k\nHCgz7feBKQUyX7jp7XwUuYN2wJ4C/dvbW/esJTk+f/0I7Y5yVUC8vr7u+HB+fl5XV1drctNUDdc3\npedHsR/eRgqyyrAFzpXwKHDrTkJN2SnrFoBLz7NKwI0+iTdSOP+dvy0aAlv0Y9I3r5z51BCrQfqN\nlSjfkk7StiXzs7OzlUfLcB1hquC5/9ZaKOmWZOIVLy1eV1sEFPRvY4GUPlNi+5GUwFULLHmykxIj\nJjfiYRq3V/la1UcVBrj4fD6fd7JitVPXZaxJsYr+UOf7HZdeaeZ+4pX724+gTwWmRGRQyxDcWIWs\nU1boAvHgTiH63P7NzU19/fq1vn37Vt++fauvX7/W3d1dfMeYHpQmxVPWxYcQypl7MKdy0JgViPgW\n9qr2s3UYvFug6SOQe2rLASLHR0edgJQDGVZxfE2E3/Ku76qQ8HwBMl+n40HRF+KyWuPZH8fJLQE1\nBp/kKBP43Del66bg6X14e3urq6urlSod76ZykOxvDuC0AG3N3xV2fn5eT09Pa8+E0noljUFOk1Ul\ngi+CidPT064ComD88PBQ8/m8O3c6na49Wy69wJn9TIGCiYDf/ffR1Q71VYGUU2YOpliN6KtMtcCU\n9FXy1liTHbcSl9ZG8LRYLLogzkq1g3n2yYO7xji2MvWRckqJNPvhCVpKerzPnHarWp0yS0TQwVkd\ntatP8p6bkhPpOqd8VZ1NlSj9ltbnkR9MDty+yCv3tR9Vlar6hGDKgVQLUHkgVrWHDl2INxmrGxwz\nUVU9tND8+/fv9ccff9T379/r+/fv9e3bt7WArGxWD597fn6unz9/dk88JxjilAE/k0FrjMwMlstl\nXV5eVlWtKLD4pbZSxcCNdd9KlxxFyiJSZcqn+QQ8WeFYLBZrUwaqIGpTxSqBnMlkEl/w2tIRr2p5\nZsVplDSdmCpTkj+d4K8GUtx3EJTW91W93/hAMKX1TdQvVhSZVKj6wyk/Vqa4OZDyCgQBCoGOzkvT\ncYvFop6enroA8fDwUKenp93aGr7wXO1ynD4V6Vm/9EO66neHJlnsi3h9BlIBTI2JstbY1JdNK1PS\ne1WeVLHwt0dwdoD99IX/5Kv7LV1L04gK6G6XqbLKgN7yfwlEfaQdJn5UvVdbef0EqGQD9Gs6X+15\nZap1bcm+ZfOpMnV2dhbfmeljciClLS3+T2DOgZSPw317y9b2QZ8WTHHfhcPKFI1W5zAwpKxXysP2\npXgycC0yv7u7q69fv65Up759+xZLkHK+ypweHx/r8fFxzbksFosOlStApGyjqlYWZCsw0WEysDmY\n8kBZtf78IDfYJIex5HJiu27QLYV30Kx1LZwiTVU53kE5m83q6upqDaCKT6xmMaMlr3z6zQ1W8tad\nZ2mqjGBLm2dsY3gy9v9EKctOgM0BPJ02gZKvHxMQ9OyaNsgnXEsPaW9pDZKDVS/1M7MmmPI2uenW\ne+mU1t3otRusojloU399nZePu6pWFp33TQX3yXNMdp38o+yf/dX7ST0gTybvVVrvnwOQVtWC+2rD\np3e9oqh92R+rlamSslwuVx6dQP/NKlaq9qbrOlBjEsDPMbz3fcqv79z0v/s++ZUECFvX4TSvbKKV\nPHtlvarWbJ5x1iv1DmTdr/WBKfpMJpNJ1xKYcr9EPnGcfXLcNMZ9OjBVlbMEDz6t7EmGpvffJSeo\nZ5xw/ZHfIs3FzXpvkZROmZYL/v7+vqtGKfA74KNSqvytgKFx8pMvgE1AyMGSA5q+oOlKuw/qAwra\ntGiRBqks2u/oU0WDCxF9jQodLNdYCRCRFHgdSHtml/jm/OPYHFQvl++v6mA5nVVTOqlWGZ/X2Aep\nHYI7BwTipYCHg6XEB3eacuRMWlRBVCXQZcdr+4uOaQPqM6uyLeDiPPdpINmzkh8BjxQUNM4E+OnU\nFfhZTU2gdhe7cyDFfU5vcv2gB8DWGjC278DDgUvika79+vrarTlz/VaFOYE5D5LiqSpU0iNNFTNh\npk6pasY7gL0Km9ZsejXa+TuWWjbD/1s6Rn+XErV0/snJSVed58usFQt9SUlKTHgd8VF6pAT09fV1\nZRZAG5/nlXQ7AXr3eeQbp/EdTAqA+fSf65H2+du2dvdpwJQE58aUsq+q94XZdJJUAh1LoMQ1FXLO\nVe9rI6SAUg6+Z0/rI+Ts0/bw8FAPDw8RTNFovY/sqwclGrpnQ66cyeETtaesLSlcn4yGZOhZin7X\nmAWm6Ky4zoJOTq+7cLCZMiAGMn1XYPDj0oJJ8pXyaemp84ptiOjAdLw7Ro036b2D4eScx8jFj03H\ntxxOC2z1gUoGcgZcn4ZzvpD36WXHSW4OqlvAsCq/woJg6unpqav2pnU9KdtWu74GUFUursFq8W1X\naoEZyoEyVv85FemL6d3OBEx8PaP7sapqrqPzjQDIpxsTT5n4Ckz5TQcOpjg1ncAUk1zXs03sa4iS\nr01giomCzzAk/+By53IHrSlNVaOqVTCla4nf8tnyo0x2F4vFCogimHIe+5ilMy0wlXx76ruAlMca\n8rvF+23t7+DBlAeMqnUEy2NFDESp/EinKaWSsShbkrBloARTfPQB36hdVStGyPVQmtZ7fHxsgine\nkefGkdbtpKw6Zd5SLvGJWQ35loxyF0CVHIOMU99Z5aC8eSzHqH2ti6ABtfrGQKx9NzbPvqpWKzRu\ncAnwtkCEG30f4HMgRee9iaG7LIacvsaU+sZjyGfnhbfnjp2OmdeTQ3YwlUB+Va0BKfGJYyVYbQEq\n+g+fCtIYBNwfHx+rqjqQ0VofRb3S+V6BZt+H9HZT8nNc51iZIpjSseq7prNb06v8ndVCyYby8P75\nlqpArIa5T0sJKCtTPlZOuYtkX5J9C0w5kErTWGPJ9a8vEekDDKnK6jbi/onr/7j5UgMe75Up8Zw+\nl1Vm+SqCKM3gnJ6ersU42h9tzoGU/DV9RmvqUDHbt75YlmLbpqDq4MFU1Xq2z6zCp7ZECUixssGM\nXw7u8vKyO5YkpaIi+jSfV6a4homLbPW4/SEwlfrfWt+VSqE0LP7Haowj9gTg9p0pu4Izs3Vgpz5q\n/ZhnYFrQqr77NI2PQ22kKVQCvFTh8GmFBBJ1bApmvBYraeRtytIJElpJBa/D/7wPLWoBv1bVjAu9\n3SGyX6l/XqGjjM/Ozlbu3uOUkjvX1jRfCzikYEG+Uz84TSJ5zOfz7tiXl5eVO0R5PHWJlQ8CqNbU\nZMvWdrG9JE8CKd4c4/0nmGIVLi2LUPWOd3MJqIwZA3l0enq68kiK1nQ9eUp7JLhNQZWJi/rIRM2n\n+Rywe0LRok3klnx1AgipIjp0PVaafIpPlamUOLlvZNJEnyqdYDz2KT6tUdUSCx+rgxkHUqkylRIk\nfTJZTglhAlbpv03oU4CpqnVAJUbTYEmtqTZmT3JmzDrUbnIiqmD5NB8dEjNZPVr/8fGxA1BcxNoC\nU+5wdVx6qJ0rN4l8YlVG/HQDGgJQreDtysrjXYYOLLwf3Li2ImUQfJs85ehO0YGQ+uJOSZktjdcB\nEoG8B24/Lo2Z2bwDBJcXtz57SNTKfl3eLqvEFzo/B5QCwN6mA1r2gxVS8UMgrVWZYoATuCE4ke2w\nzxxDn1MV9U3zSc80RafHJLB6Tb4RDHNaT0lUqpaTxgTqsQ4/ASWvTDnYkh15wqGArPWj+nx5eamn\np6fuvWyTyaS5sDxVV/h4DP5OPVA70gU+WiP5a/dvBPNsh4kYY0Dr2XZ9SUySU59MXJ60mVSBoq/h\nAnLvR+rTZDJZmd7jNF8fmNKn24XipJJdPulcd2ASTDnvW2Db/SATbfKFCTB5uly+33hAvU1V5ySf\nBLLG0KcBU1XrK/CdKfzOzMLnv7negkGR1CppCyAJ1EhwLEmfnJysPA1WzlOKICRP41bAdADQAj/u\nGJxcIVk1k7MUcPCFzi3U3gJNffIaCvit8ai/zLx0jigBH623SkDF9cQDrgCzXkotJzCdTiMw7wOA\nPjUhecioWfXwbDgFWHeYKVDsixwALpfLbpFyAsTMADU+3Q338PDQW9Xz4JACp1cKfLqFbSXQpPPV\nJqcpSEywWqDb19+oTYIn6pP7HYEpji0F6F3lJ1+TAL8DJw+W4g0rxQzy8h2qHnnlhgFQ54o4TpdJ\nuokgkeRQ9X5jCtuvqk7Gvsm3JL47oKF9qn9JRvuyPb++y5F+0QFyAl4tPy4go+dyeeWUN+ioCu1V\nYrZ3cnLSxTPJRhVm+TndTc+4qNiY+Jh8tesxbdBlkDBC+r+PNpXrpwJTLUrZcmtaj2BqPp+vVHdI\nCUydnp7W09PTyrOHWMXivLyX9VVdIZqWggrgyGDcGYmSc3Dyaa5URaGjcafxEc69r/8JFJA3+q1q\n/RbYdNednlPkATxtCUxdXFysvAZIgMqn3ag3HrQYoKqyk5xM3p+Flqb1vIqW+JeAw6bkMnYnLZDj\n/3EqhtOBcvYCU8pavVrItUb+EM1UkaL9epVA102Vzqr34EG5J575HXauj6zksPJBMOWgij6H1TQH\n90NJx6bU0k0HUa6rzrfUT69YyKe0gK5TAtcpoataT5jpP123vA8+fslRfe4DUuwfZwxaFaN9USoM\nUB5KbqpWH3PgIFnHcyzkv+JRayZE7TqgSUk+45mSWd6UJTC1WCzWlrwITI1JDpMea6zOwxZvx9C2\nMv30YIoGQMNKwc8XhQtMJfIpGe0LTHGqxgMoKyY+VcMALtK0YNW4ylRryoL7DqTUBokBKznNVBnZ\nlnh+Ak+s2NBABDoYBPSZXo2g1/a4Y2yBIZ8eEJjinZqpMqUStfO2lSEyiDuYItjvq0xtogO7yoqg\nycemB/I5gJHjl7PWM8+c729vb90DOavepxNai409WPbdvZcCHANKqyo1mUzWKlMMnOKFV6DZzxaQ\nSpWpPntyW9lWhpRlCkIOqpw8udJ3B1Maa6syxb44r7wym5JIHxen8UR8YLH6wvGz4skp9pbf8yRT\nMqx6Txio/7vSkP/VWLyym0CxZOkxkRVB2oLfsSjb9mv59UUOaiaTycpz1haL1Yepcu2wrpX8Wqq4\nJR0m//p4q33+vk/6tGDKy5buzFLwa2WJqW05Td1V4usMmJH1CZhtOiCQET4/P3ffvfQvagVT/t8H\nqPRbS7Fa7exK3p47ef/NjZLj9du0WV1SuV8O1TcPxJJ7qkzx5oK+ab6xYJMle06deDbtVSn1Pcm9\nNaW1C/FarcCrvvnaFukuM1GO0cc6nU6rqrp1Up5hk3fJflmZIhhPfPEqSIuGpvlYTfMKQPI38g9e\nDU9TUKwMsRoxRmYtPXRZ+nfXfZ6jTRUMglEmjWqvVZlK4xG/aIvJN6XqVNW6zaoPsnPpnesG7U4V\nnjFgimPT9Z3Xu5CP2QFlqgzpewL5qiTT9pTUpETT14a9vLysjJE+wAFon69gTBNIS9N86XzqpwNX\nv+ZYgNQX9/ZBBw2mhgK7K39rXUVfZcrBlNrjnS4CVAzoEqxn6BRy+s7gL6WfzWYrc9JyBAwW4oc7\n4ITIyRcCqTT91coAWvLg5yZy3OSTRpiCGe9kVEVKj67wRa/6ZEDTftWqYz45+fshf34Xyhgw5Q6x\n5aA5Tk8AWG3pA9MOYvZNvI70kromvnsfVEEUv9/e3jrgk7Jf3b2n9liZku2nqs+YyhTb8Aw9VT4m\nk/fKFG94IB9otw7WNgVTBCQEUvzchwxdb1pAqgWm3B9xbNw8iU1TqpSH88Wv3UeyeT6QU0BKd11K\nhsm/ecIiGTrISHrnYJXAfxfq8x/833Xe9ZIPYSVoVxucukzJnLZ0hyfboG86OXl/nRLjmtuvvz6K\nYMqrnVxaQPtI4I2+1Xn6UcAp0UGDqVblhA4uKR7PF5P5v5/vjxvgu9gIhORYNC2n83lcytgTiNJG\nUMcA0ReoveLk2QLHLlCi43xzR+6VId/nbymLHJJnK/NKsmudy6lLtcOMzB141d+3XvPp9nLA7mhd\n7l4BdGCUgFvSSTd26ZJPbzgvEpB2OTjPdgnGKUHh79wX35gJu6Oj3TCYpopTWoyqrNaTJMlOFUmt\na0t2kJKsZJ9qU5UngTu/A8qfy8R9l1eSo/O5ZT+ttreVKSuHXsUXr10PXVaSkYMrJatcJ8rAz09P\nagRS6D+lOwSlOkZ6xgpT4l3LhzhPWXXxY90eUrvbysT7y/44z0T0R9JtHq/fqmoNILVmbSgnyqDq\nfX2VQGialpXfpP9x/VE7qeKb/GyLZymGJFl5/Bw6Zh8JzEGDqUQSQp9RuMIxQ5IAVdnQNIM/GbZV\nXZKyshSanteSyqm+Dktg6uHhoUPpaR6fROeWsgUFOXcAaY2Azu0DRfuqfCQwlJxFcmAOSrRGo+q9\nlC3DasktbS0Ql4w2VZESL/vG787as3gfays4t4AUdX4buRHAELCSLxwLHelk8v4eN8800/oityU9\nM4xTEYvFolufxWRDwULTsrItBXAPHA7YWlPzGoPAIa/hlRCNhZ9e5fQEi58OjreVWYvcpiUrr7TI\nBykYeuXadV5670llq9rBNnx6j1VGgVefqhoDIgW4fWovAVi2Sf30KprzMPGWbbeo7/8Uw/qmEKWf\nXuFTEsBZEvoX+si0iWdJP6tWq2HuD3Wux2D3naxmMQlyW2A7SR4aF4/z+OHHD4Ep8XPIxw7RpwVT\n2k+ClIAYvEUSJEGHPxFW6zlamaGUWC88pUK6ItPBeHldGRcX46XKlCgBEv7nn+5IHZykcxToCHY2\nUawxgIIZZ8reE6ChwutcBzxqw6dl0gLN09PTtcDdynRalZQ+4/SNVQE5vwSikqNuVTvU110CcmrP\nq578L4Ft8Vn7aU2LBwB9V/lfi9VTZcRvvWc1Ui/Avbi4WHmeE9dVsWqiSojrh9pjMPc1egSLSfap\nQp0AlfTZaRt7S224X6BtVK1WLASoUhLnlScdL9/D8XmQlZz9LjFfgqF9BVdVj9XfPjDlOsKgLz47\nb0UtOY0FVKRWtaol41bw56yAzxhon4mJ9Fq2muSeprd9TDrPx6++KnF1gKT/U/xtATddR49NYGwm\npUqV+2JiAY5jDIhq+Vrf34Q+HZiqWl0f4Q7LAwFBBwNt1ftCQoIorZFJgIMBgOVutc0AoiCRStnu\nhPjcKyL4lBGlMSWio9FxfWPi9QioNqE+h6N2vX90mOyPAz+e2yLKl+sHuLCZMvLyt/efTsOzPC6A\ndr6m6pja41i9KuVBL2VKY2UyBli1gCydbsoMndcOXh2QcLxKQtyW5vP5iuwpA1YwOC2layv46kYO\nBgACAl1HAIK3fivrZ6WaLzCnPMkngvFWZSoFbuc/+boNKE7kwEi/kR++ZorntipTY64nGXDBsYAu\ng7s2vvCYfiFdw6/HqpjO5RpUPz4Ff1X6aQd+zZRI+P6QD2yRAxr3jTomVaacF9xPFShvN1VOKXuv\noPM66hf5KDm0/JsSLCaXvtHmKIdUHPExpfb6rrMv+lRgKgUUOjJXHC70FNEpipkEUfpMqNorU462\nmeEpSPB1Mn59BhRuGhc/tU+FahkHjU0K66Cp1b7v70vZCNQoN+eHZxcpE/KScQpaXP+mcjI3TW+o\nPO4Bxw0xVaZSn5g9MkAlGSVnJ3InMYa32wbiZE/SSw8S5Ls/qI/PjPIbBXxqybPrBPalL+mGEp9C\nPD09Xbk1XouaeV0uAle/NH45eD2yQe/eTFN6rpfiU2uKzytWnjDtG0h5e/Rd9Ivii3wXdUH66dOC\ntBW3RT//9fW1eyq63gIxm81iUEvraVJlwvmmPvAuTIJe2iiJAIDJVQK4vu/t9H3vI+cbfTR/c/9I\n23HQS6CawHRfRU42red3SS4qDPjSBoIeLySwXwnMMSYncO3+L41luVxd88opxtbmfNlWdok+FZiq\naiuvC8oXenMdBM89OTlZyUT1OguvVrSQvc9FM9P29/J5hpumqpKwtS9gJIecjIFKOoaPYwJ2OmZb\nxaNzZzbWF0TIB89yWmNxJ9QCXQyGKZi5ESYARJk5YKIj8GSgJW/n8xCvxxwzhhzIklwX3RknB0UZ\nsFLFaRwCrSSvqlqTucuTgIUAIoGwlFGrT3rfHl8ZpQQpZdjih/rZSgxSn51X+05enGh3TAxYIXe9\nFUjxzfXefRETWMqF4yT12W0rufJKEsGCwD0DtI5xuXiS4J/evyTbREO+MgHQtJ9AFI+hLPuq5iJO\nyffZ52Ty/jogJSesgrGPDlgT+OFUpMcTB1dV61OOHK/6wLiY+Jvi6Ca0qV/9dGAqOfnEtMlk9flD\nDJrusP2lnUTXQuVaH+UO5+TkpMt2+UBPXyfAxYG+yDAF6eSw0vQB13Ikp8S+JrDgx/jxv4JSv4eM\nTr+RJw5eBGzlEDhd5ME1XU80ZIwMjh4AWqCvKoOpPp5vCrL2Te4gOZWm26m5WJt3eSmQaRqNgKzq\n/U5AVkK4PqkqgxVW0fiiXb4HU8FFPkGP0fB1kgJQumNPNqWxE5R5QEn651UbHduSm+vHRxEDnECs\n+yTx1nmpRw/wOIFZTqvrSdjSCb3DbzabRd9GIMv3M3pVz8GZxkNg7FNO1C3K1IGIg22Nj35W4/0d\n9qf+uJ8WoOD0VwtI6Jy0FIJT9JzNkA37AzYTIOM6SZ1LWad44311INUCwPStOs6TvgRanRf7kuWn\nA1OixDRHxzJwZiZpIXIqx8uZcK1FKzCmRbcJHEmBdZ4CiSN4L3tqP00hsOJGoJGUpPUbqQVkPooS\ngGv1gRUsOlCv0FWtvjLB133QgdJ5Jv5V7VbiJ6BKYKoVdFs84m8tINzqSx9tejz7Tgf98vLSASlV\neLlIVovGU3sEUR44WyBK5y2Xy5XAr6qw3nLABEvVaIEnPpxVwZwL09U/6VRa8JsCfN8UU+L/R9mb\ng3QHw1W1EojVHwdTAlSt6SECIslYIGo6nXYve0/+jVPynJp32fsn+0vwrakq2Tx9iIOpyWT12VNp\nSoogwX3NryC/lttCn+64Dqr/Ph1PQMXlJg6mGDPJD1a35HPlq72y3BpbigdpPCKCPq8Yj/XbvaCG\nKwAAIABJREFU+6JPCaYccbccloTLUiAFToeZGCxj091G6c6DFNQSEJhMJivTGyxNemWKwZ/ZlWdp\nLM8zgx8CJkNg4VeAKL+e95vAif979dEBFQ2Lwd4XkDMT9XEnRzkUEFtG7zLhsWOmdRNIagGnj5JZ\nSlhaWa9PrVGvJ5NJVzmgzhMUpY36rn1OZ+h6Hvhns1lXUVb/FABUmdLaqJubm5W7yfSsKY6VupMC\nuwMWB8pDgOkjARWJ/Nb3NFVCMKWq1Hw+X3sMjHyqAKqqfJPJZAVMXV1ddZVCl7FXtriuzkFrqzLl\nG+8YdvuhTKsqJlc8nomrg/xfST5Gr8onHaQfVBs+rec3jgiAMsnReke1oU+vHHrMdX1LY2pVvFKs\novwUA9mWjiMf/P/E013pU4Ip0ZBjF1ASCheg8cWynilxXlbzxXpyMa/BPnhQTLdYeznag2dr/ltb\nAlNV78+3aTkVBwncbymdePirnEULRHHzDLs1tqpac4q+QDOVn5NT6stw2AeS62KrvO064+PjNXy8\nzod0/EdQqqLSUSYwRb7KDgWCPIh5hVdVD42Ld+FxKj5VpLSlaq6Dqdvb27XHaGiqqGr9tTbyLfQx\nLpdUmRprU/uUYdJP+jifLuHvXpXicgcBU586FTg9Pf37gacEYwJTvqWZAvonr6w4KHIZsw3aG/nB\nqqZP8zGOsJLD3z7aNw6B7lSRrxouNpBPrWk+xgdVppiYeLxx+/JqmYN39oc2xPG1bMUrUzqHoDGN\nm/qU/Oeu8jxYMNXHREfc/p/Ol6HTUDV/r1I0s1yuqaFwWOZM/fAsS2sAdB1H0xyjAksK4l6lktOQ\nIsvZe/brFY0EOngMA3kLxQ+BirHUAkPeR89sWu34uKrWp8+86seKSF9ffOwJbPqn6yH1k4AwGXyf\ngff1L/GW1AeYNyG3Owf8zmuf3tanVxzoBL2CKLvV8QpqnEJoTeESzOk8bVwjJUCVsmP2j9dJTtr9\nkbJml3Gf0+6T4bayc5DutsFx8nOxWH1sizaBWh3HKSMBKi3e15qp5+fnury8XHm9kPtbnzpUH9jX\nxA/6L1/+QB3U8gudw+lar6S6P5UOeqLnfB7a34b6fEBr47kOehQXE4jidKb6zjjEqW9fJ+XLUNwu\nUhWe40qxyT8TX6kbY4GU20QLb2wiu4MFUy1yBlLxyURnQprX5Zut/XxXNt3h4O3KKBmol8vlygJX\n7QtgcatafYieHDZfy5Dmq7Xv6xS4GL2qv6qRFGxTOWxDCTxxTAQbcoQ819vxfQ/mycG0xuuO3J+q\n3Vr/5H1mezTc1GeXQ+JHuqb3eyxtIzsHUr4pw9eCfyUgqgx51aFqvTLH6T8HVHLkvvZC2TWTGiYd\nekI3t7Ozs7q5uVlZbJ6miDUevhSd04uagkxVEW0EYA7gKTd37vugBNLT/wRPLmP3Q6+vryvrUBn8\nXO58nEKqQrb00JMNT1QZdLUvgM21UlqiwTEkuQlMJb1ugWEHp5vSvmVNftBfUsaTyaSbyk6ASXyo\nWn9RtMAzbxJg3JHtkueu8+7r1L6DMfd56TwCaPf9+nTA/dH06cBU1TpocieQFN+F5rdpe3s8zkGX\n2tZ5yfh8cevV1VV8Dk/V6tu9Caa48S4VB1PaeDciqQ9Q6fc+1D6WxpyTgBSzP353YJz4nwAGwa33\nyY0z/V+1Kteq9feW9YEjHZ/6nI7163uG3sfXfTvlFiWbS9W+qnd9ns/nazLt2whmWoCKMkhVCDro\n8/Pz+Fb609PTziaVgDiQY0WaQEr9SXJ00Cbw51NIlF2fbn+0PPVJvvvUbevp5Q7+1YZ8l/icbtlv\nTae5X3A5qC23Dx0rIMV1P56QegCmvXssafmfvqRmE9qXnB1IpQRSxynutMCUvjNxpI0peWehgNVj\nTbk7eHbf530nkEoVYpeTAyrRmMLKR9GnA1NDGTIdKo2OQiOQUlbLUrIM0ytTvi6CgdOrZFzYygcA\nejm16n09D4OHHiwo5VeG7Rmw7nzhe8NYknb6KCDF9ob+S47Ts6qUUYx1ZnKuzLRaxuntJd1KTrbV\nJnVPToQOPPHB23IgRRl9lGPoa9cdVMvupL8JQPrGZzvp04HUyclJBFJemapaXW+hqaXpdLr2/Crt\nc6pfYIrrZzgNlaozyWmndZJV68/JUj/d7rT/0eSyTgDWkzqvnrufnUzeFyvzbQ++VtF1xinZE31i\nqgbKbwhIaZ9LNyS31jX77KsFpg6F3I/6miX+z8X9XEZBAEVb4Hm0m9ar11rPu6pafV2OKE0VcnbC\nwXrLF2sc+hxT/Wz9t62P/XRgqqoNqGisdK6O3FlxEpjyilQ6zoXsyJ7Cu7m5qS9fvtTt7W19+fKl\nvnz5sjYFoD56GVuldN05k+aktc/+pbuQ1D9ScgZ9ytWibZwKHVLLKbau0cpuRMxUUlaWZOdghk5F\nshAg62uLBl/1fstuq99+nu/zu49tm2xrVxCWMsIEpBIIdZpM/p5uUHWo6j37TYDK7YPBjYugZQ9y\n+K3HGAhwsUKcKiEEEum9cu5z0q3mQ4F6KIH5yMya7WosHGN6YGea7mYCpGkeJhKerLaCYcu3eoXQ\n/WgKxgSEPj3rQFjX9s+U7GzL348m+QsmF6mCl6bSvDKVihI+zce3hbgt8JOgjH31PqdpvpRQ0gcm\nPUp+aptEdBvZfTow1cqOvbojgRBUDVWmeG5aMyWQ1hIyha07hO7u7uru7q5ub29XFtK6IlMJBaaG\ngBQzDV/g24fg+2gIVKVMYGy73HfQMFTibTk5NxTKKPWhrz0aadXqq0jGgB6CWL8OeaDP1lRX6uc2\nstw3teyO++lOreTsrq6uurtjNV3ttqDxa/qG16Vty65ad+YmZ5rA/GSy/mw53srv6xq9WsOERt+p\ni0kfGBx+JblMXH4CIKzKJUDiiQQfV8Gxuvz7Kgeu91yHlZ511Aeo0k0JDu54TQcfbu8tXvbx+aOJ\nfSc5jzapeHMqV3FFSZCDKekI/aVX9NlP31yGqa+MabJT9Vv7aRZhiG/7lM9BgakxKJKM5W/8z0ET\nhUSDcGcrwcq5S3B09m5sbIel6y9fvqwsctUiPe+zMi8pFhWDhp6mLNlfByO8RuJznwzG0iZZG7MT\nB8QcP9vryw4dTLFdVg7Y19ZUnY+HOsTgnYCUg+IWjxK/nGd9FYpWm86DjyIHi6enp4OVBQeo7lwV\nfBUoqyoGx/l8XpeXlzWbzbrMOMkhgb2WP3FZnpy8v8kgLTgfA6Zop6qqkS+8durTr6K+6zpPE59l\ns3r+nq/BUTu8VqtyIBklMKSpPa3B0bFch6NrtdarpappCriSm/upMfbYR35+SxcSOdBO/kW/p0Qr\nAVfJrwXA1Ccdqziqm6VYyR0CmWzf/S19LcdBW/ZPr24mWfL/McnnrvIlHRSYGkNJCWjkMkxVbbyy\nw4yHWaza4VQBgZSmI4am6YTor6+v6/r6ulukp7sBSR7UPThLkdQnjleUEL0fNwY8/SrH7o5BMtB/\nrDokMMXP1G4yNv6vff6eHB4BFY8ZC+haYL81llaFYl/Gvks7HlzT/33TLg5wVD1UYNSCZcqeyQLX\nN2lLU+YtZzo0Jl2TU0lpnVACVV4N1ycdvdvkPm1tTBLT93uratCqZKj/qlq5X3X9b333/eSvWSUk\nmEoBNIEpP9bPSfpB+Wyb7LCtXcn7x6StBUz92ASi0qY+kweyZ8UvyqgPKPb56hboZn9ZKZYOtoBU\n4sUYILVvOkgwNSZz8uk7N7Tk1N3olU1SgJyK08P9uLDWp6TkWNzxaj2I7hjioxXcmbQUu+pdmdND\nPtXnNEXmmVvi6+8CUiIBKXcIKSsWjQkefYbkY6YM1L42OWRScrAcT9/xLXDobfH3X+kM+igF1RR8\nVVHgVLmDDlZtuGDZ75Kr+lv/uUidN1q4jVe1s3GOQ58uizTN1VeVSuNy8ChbZIa+L4DsYxqiIV1K\n8mxNDQlMMfC2KgfJHr0vCQzpOAdDXA9Fvqebe3itPtt3EJF495HJTouSz1Q/OEOT+Jx0v29zGes3\nTfGdnJys2F5r7Jv6bF2vBfwY31NFinz6XUCq6kDB1BBJOMw+XLE8a3U0LWFwbpggLS2EVoB1MMUF\nms/Pz/X29rb2LI70CAYi61aWICeRsns6Aq+WDTmHRL9C+ZKhOwhOBr4tOTDh1ucMJFt/DpmPI7Xr\n/6vNlozZDmX20Y56LBHYUOf0n6a1GNh4F57AR6pwCEw54CD/BKRUMeZ6Qq4ZZLLicnYQxXFp36eE\nGLRbFaoEqBxMkW+/CxyPvT6DF/0KbZFy074AVrpZwH0dr8VrEoAzOeRn1TsAZwXRz+Ui5patkS/0\nN5vy9aPIZZaAXyshc3Ch/ZQQUa6ur6wQs3roftl5nHxdq4/8nb5PGwFk37qoFqD6VTb36cCUg4QW\no9wI6RR0HoXEczzj7fu+XC5XXrWgRZJ87pP2q9af6OuG4X0gGndHxTuVvIrjypwUN31+JPX1oVWR\n2hRMjTXm5Gx4XgKlfcbqmdKQc2mNief3Ab5daJs2W+PRuAWcfMpL+ycnJ3FR8mKx6KpAVfmJ137n\nXbqDlTdeuE5vwnuXp6aaEphqASkCKvbpo+Q5RAzKfQmEB1j3nwxuurFAFarWOrI0NZfkkSpTLnf6\nUK2z4/sCHUy1qmtp7C6rPj6m8z/Kf6Z2U4LQOtcBrftXyldgi+NiUUGVqQSm2I8E1DxR6tvYnscw\n16cWDU3zfwR9OjAlGlKkVjCWELhOJ7XbQsnJKXsFjFNsVavTWalc6c4mKVdfMEgZQosnCclvQqn9\nTZXWAUMr46KTG+qz88A3rzq4jNS2Aydvmw7Vx8FjU3+8b32y2CfYHasfm7RD56j/3DmzgusJifOL\nbXHf+e3JhQf5xK8+MJX43rLJvvYccBwqJb6TfEzyZ1xq0AdKE1/Ex1ZlM8lX/3GKp6+fLb+Y5NNn\nd31jG+Kpt5P2XZfG2jV9U/rN/U4r+ataXeidfGCSMduV7SW/OjSGsfFO1+T1h8BUH59/BX1aMDWW\nyFAPoG64IimJFCVlOr5myp/DwvZVhh6rSC3EnqYgCOhagELfW8q1rdLtQ1mTk+Dvff1uUXLUaZpU\n16GzdzDVZ5TOXwYL9sP7lHjAMSuA6DMd2/r+K4m6R9DLAMb1i1w/lfrttqHPVLVoTemwbwno9VHL\n7tQGlxL4ON/e3mJV5FCIeqnvIrcxjpV+KwEX7bt8W2umEphLvmoymazxU//7GlL6Y//0jWP3cbXG\ndsjU8p+kxGtvg3GwBaYIxpjM+M1XSc88JvbZmutIAtv8bIE+nvMr6CDB1EcpMJVmuVyuOTxeV0qS\nDDLN4beeZcI2WmCK/eN+QvJUZJ8m7Av6+wZS+yR3CDTIviCQqKU7yQj1u66RqlWta7YywtZnq3/J\n6Amk3Pl9lLw2tTk/3qsOrEj4beqp0lBVa7qu/RQY0zSOg++xYLbVhwScCNyYnbPylvp0CEE58YW/\n6z/K0Kd5eIMOP1uBjv/5tYYSA5cz+6W7myWTpBsESQ6m3P8mmZEORYaiMT7J/0ufbMf9je9z6ppx\nra9a5HaU1jwl3eB39tWXATjw69Ptj6aDBFPbUl8gFVEADJ7pM80t+6faTJsLOQWLVv9dyRxM6fs2\nzjoFi33QLm06kGo5hU3b4/e0fqAql737sh2Rg29vd+h89i05HYISv96hUMpc2Wfn+1ASkZys26Lb\n4ZANJFCV9MPtLfXBnTdtMlVSkh7tmza1uTEJiSeZAi0Mln2Bqw88tRIID5YESuQhAZ2qVGltlP+W\n/LX60KpKfSSI2pf/dZ+Z/FSfP01JbPpkzCSfnRjreI0+wM1+JHJbo+ySL/gd9H8CTPUhcSrYkLL0\nfWo/OfDkiBO5k04ZofZbjsfBlE8ffibq41NyCkPn+f8p6/HPltPocxaioQzQ/09yZT/dOVStT02n\ntn4Xub728apPFul/BzL6bNll6tdQ3xP/+vrgPkVZOacjWw80/ChAte+ATHIgknxMS0ZjdIEBsbWE\nIVUftc/nebV8cxpDCsp+nUOkMSC4ZYdDYyKo5fFDNkc+p7Vr7tsSaB5LOt7XHzPJSctdfhUdBJiS\nAenOkNlsNvrcMdnomDbGOLuUabYceV+mmwJ66kMfoGJGxYxY5W8HXX3l1TE0m81WXhtA4+NTrJ+e\nnlYeMLovGtvXFs/72nHjcyfcFxySPNmmOxZ+ukxSNXNI38aQZOd3WCXZjaWhfozp5xDQ7ZPV2Iy2\n79otXSF/WuNxW2Jw5qMhEghIvGmNQXcH+0Mr3V9uIrsWP7b51L6Prw+U9PGOSyWq1pdbJHsS+TRV\nqz9c0yVf5jdK7Lp+6u3tbU126rMe7fD8/Lyz3ETbgvUxvnGozbRWrWq9yuy2tWkMSrM/6qP7a/2e\nPlvjF3ms86nLFh0EmHp9fa2np6f68eNHF4gvLy+7/33Am1YL+qiFtvuO30RILUDUCrzJaXPfNzoA\nOoEUqFugbogvVX879f/85z91f39fT09P3a3sLy8v9fT0VH/99VdXJeMUwVC7rbFuS85nb7d1DfYr\nTe+OuV5fm63+JXm0wPVYp+D///z5s/7f//t/dX9/X7PZrNOF+XxeDw8P9d///rdOTv5+AvjYNvto\nk2P7ZDTE030AKn32ySNdP9lUa+HzNkFZ1/7vf/9b//3vf+vh4aGen5+7YDyfz+v+/r57GPDj4+Ma\nf1ptjuXJmN+0n6pBouTTEqBKj31JU3Z91+f1Eoh1n9iqTI2RW19cent7q//85z/1119/1ePjY/dM\nrhTr9KqWRGPkuWslxschfo5t0/Xdq5gtQJVsrA8st+JZa7p/U4Cp63msky4O0cGAqdlsVvf39913\nPVOE5EJu0TYKNVZ5NhHU2OyuZbBJmVxB6WjoDFoAiueOARai5+fnur+/7wKynjPz8vJSj4+PHZDS\n06zHtNnHs12oxecx5LJITtQd+lg+tgIUtzEAqi/jT/89PT3V/f19/fz5s+bzeRewnp+f6+HhoZsu\nSRXhsTbntI28dwFHu4Ap7Y+xD8ret7RGJ+nTJv37+fNn3d/f1+PjYz0/P3dAYzab1c+fP7vKzf39\n/UYgbewxQzJp+bC+sSZ/1Fd1SIAokdtIi/d9AblvrdsmslssFvXjx48uICtRSbGuVckfK899g6lN\n+0G+OeBt+Tn/39tr6ZqDMh3fktmY/julWDeGDgZMqdypSoc/gbxF2yjPtu1sK6Ah5zTUbl+W2MoI\nk8LuAjLe3t7q6empnp6eVhTs+fm5Hh8fa7lcdvv7ksm2tEtQruqvFPKYBHa9jTH9ozySLuzKT9nU\n09NTBFOLxaKrdIylfcq4L0h/FLVklz77bNKPa1U10mdff0Sz2aybxpvP57VcvlemJpNJB6xS8rkt\nbSKPBKb4OXQNfXri1xcoU1stH5r600pKW9faFqDIVwpMKdncNtb10T7ssS95GLp24t3YYsIm/Uvx\njP3s07+xdifA67FuiCYf7bSaF55MugtrrQ+fcDxWOX514N71eptmAbs6sT7F3UT2y+X787T4hGO+\nzkPyOyTaBUz5/ibXGgum+n7bl25zbZRkt1wu1+S2iVP/aDD10TRkV9u2MSYz3gRM+VPF9RR5l13f\nVPQ+aCxfNtWLvsTC29y1QuoJ0Fj5bUtuc1pHt22s66PfCaZ43Bjd3tbeh0DYmAraUN/0m8uNU33L\n5TJe6CDAFH5b+TzSYVFLmXfN4o708TQku6PcDpeOsvuc1Bf8j7I7bBooQkShHUQZIb1r66MzrSNt\nRovFYuWFzkLuLjctiP1VtG0y8L/kxN7e3rq7+SS7xWKx9gLhQ6sq7ot2rU7+TnKbe3l5qZOTkzWb\n28dU0ZH2R6pu+Pa/GuvSkpZDpVasG6KD8J5nZ2c1nU7r+vq6rq+v6+rq6pc59t8t3F9VGexbbzCG\ntK7t8fGxWyP19vZW5+fnK3K7vr7eiaeb9vNXBMrPoiOthZtayyb5qWR9eXm5Irerq6u996mvrx9F\nQ+uhhvqxzXRBq/2x57fO1Vo3yU+vt5LsJDfe/bwv+gjf9LttaVcay5Plctn5SslPYOrq6qqzuevr\n698KhFs+Y5PzE7VscMyU9660yXjS9bWWjbHuU4Gpq6urur29rbu7u7q7u+u9XXSf9LuN+7OAqefn\n5/rx48fKXXvz+bwDU5Lb3d3dTpnWpsbZ+r5J20PHfRYdaTlGPbpCd37N5/Oq+vuW7Ovr6/r69Wvd\n3d3V7e3t3vvU19ePorQuprXQf8x0y1h9+AgwdX9/Xz9+/OgWm2vB8nQ6rS9fvnQ2d319Peram9AR\nTK3TWJ4sFov666+/uqKA7nJW4UCx7uvXr3u9eWBT+hVg6lcvC9kVTM3n87VYN4YODkz98ccf9eef\nf3aZ1qYLecfQoRr0vp3XLotBnebzefdyUT0OYTKZdAH57u6u/vzzz/rzzz83BlNj+zlUcegz2rHX\nGhM4t9Wffch3zBj9WN1Czzv4lstlXV5e1pcvX+rr16/1559/1vfv3/fSp0SbgJVN+eR3D3E/3bnl\n5/XdKbYtsN5ETn3nX15e1mQy6e4w0rOsBKa+f/9ef/75Z3358mWj9reh/2v+dlfq44duzpHsdAef\nYt3d3d1arPtVtGtM3dQGfvdav7E3Bolms1kX6+Qvx9DBgKnpdFp3d3f1/fv3+te//tVNOeyaBSY6\nVOP+nZng0LUfHx+7YPz4+NhlXOfn551z+PPPP+vf//73VmXrMf1sBcv02QeCNgFU+65S7SrjsVNV\nPO6vv/7qKlJ8rpSA8Ldv3+of//hH/fOf/9xbnxJtAky3bbMPRHETgNr07rtN9XST89L5qibyuVIn\nJycdEP7+/Xv985//rK9fv45ufxs65Erk76I+nmha6PX1tR4fH7u1pPKXKhww1v1K2qQ6Ovb81F7y\ny78STG1ji/6on7FLjg4CTE0m7w+54y2/v+rau/wv2uZ2zV1vEf3/7J3nduRG0rSz6c3MSLur3fu/\nw0+aIZue/f3QG+CD6KgC2g1JSXkODtoAhapKF5ll8DOJS7D9JajcgV3825amnGtykOl7cohzlNgB\nWypvF5lolXPI1DdfN+TPId9aOnfIjOm2IHpu2QlIOZjyzFT63HvuJvXz8qfu83f96f5ddG5XR/oP\nTRN1K+0IL/5pEvpHoDlTJ3YJentD6x8JbKcXls+678D1+pSUmDzl5NPY85RR3lfW6GeV8d7UyzT4\nW8RJDqTceTpQSzvH656pd/WxHP/ckiv/zOftiz6KseoZ0ing0ho20G/kbdqJWXKiZyUw1dL3FphP\ndei14R96o3+yXR+HpqZM+HVT+uG/6zPtW8tWf1b6kGDqPTs0GdXk2Hogyp0hP0+NIzslA+3P/DsZ\n7haIar0403ng87lSdiu92iK9f8rLaZXJuiS5mpsl+ezUMpxTQMbfLj8l+8x0MzslPqbMlPiZgFUr\nu+V8pl4mu/APvdGU0+7RXMD63rrz3s/flFp2i9/Z964fvK6ll15O8q2frd9EHxJMvRe1HB1/qxoL\nkr67UU1HQub+XKeWseb/vfv+ipQAFV9aKmK/pGGSlOniC1f54lV/mbTPC0tgKmW2UvaEZ9JnNSpT\n1NKrVnvnGO3VajUCRJoyIJ0RDyUjnmV0MMV6Ubb85byklo7+Q5mmHO/UfVP60bIF/9AbLRaL5jtc\nq/qgqpWtbQW0fi/PmwynfVT6B0z9HyWD3gNEomQI5gIqf44/vwW+/H//TeX81Yx6UvSUSarKc2fk\nMOVUWQbL0esD+CoBASnNUWnxvao9TOgvUeUb1h0IkO9/BV7O0a90fZL9lE3U56q3t9gL+BJI8ZwA\nVdJXypacD8vyevZ09h9qZ3FFc/otBaNTz/zszvpnUQK5czJTU2BKn11H/FmflU8fCkwlo7ptOXOM\nmBv4FDE7am5lgVyQWm+P5z29unt5dPitaOFn0KEFvdWWXvTqvEqUQIzKVb/qf4Iy/2+xWAzvtuP9\n/OwZDIEllyc5+95BQOVtPgTff7bzbxnRFLQoa8hMEftbmSjvO35m2elIQ7lp2Jd1eXl5WdNbbxu/\nv7fj6Dm7HrWuawGV3vU9Pef/rXJ7gWSr7nPavQk/PgpQbvmkOdTyLwlI+WeXeWZvWa8pP8uAck7/\nT13zXmDsQ4Gpn0ktBut7bwhmjvB6dOyAzB1FSygZGRPRu9D3jPhfhVKEU1WDMhJopmwFh+g0TOfX\nyTG6odCzWxmtdHbDQjlw+aCc+GeXQ5fdz8TvXsYmRb9VNQy3MmOYwM3JyclIBlImsBWwyZhrJY/O\nrm+t7CXrlGSxBRJamTnSewUwree3vk/93stksL+SHnsQMRXcpt82qeeUjPbk+DMQ653sl1/Tup86\n4aMDVe2pM9RH9mWvT1v+873Ak9NfEkzNNQ4tRuu3NBSQ7k+ffX4Nl1nynCJu/87PypxUVRT691Tu\nnyHUNHTkSctxyUD4MF1rLyyBKZXFzIN+0/cWn1KUx2yHeO8ywt98iErfq2qt7vsy6oeSnTkZBDpN\nzzqobwVanp6eRlkhHaenp6NslG8JwX51Gal6A9xarn52djYC1aqn9lvTe7sItCkDvDdF7R6kpf56\nT0fRA1FzPycHmMDUYvHnDu/Ok6l66HvSf782AaqW7ff7+PmzAiqvZ+ozB0OtMngP55bqc6ufU0Dp\nC0NSf/ZAWeL1e9BfEkzNoR5z+PscQJUOOm19TlmH1Wo1inJ98vPLy0sdHR2tCWlVjQzRXwFQbRIh\ntwysGwUpK/mhM+cpifTeOgIkGvlkRJg5aUV5aQgvASg/lHFhOwXO2F8fzajPkYOUfUhRKsEUXzzq\nevP8/DwCqnoBMIdV9ZtH0ZIV7bd1fn5e5+fno+FgkXjy8PAw+l+yo2v0O2VS1zDD+BEm3/aen/Ru\nTmDZso0p2KiqkRMn6PTAaaqvEuCZahMP8sNl1MHenOzNR6CWf3B72csspSCV/ou66PciyPNgAAAg\nAElEQVTSL7r/Y9luxxKfPupCnb8dmJpyyOkgmk4CkjJO2kiPh2dG+O6fnqPgs+jYaZxc2FP7DtmX\nu9Jcw5OiRwfCKZvnm1PSyab+pQPnKjAaHg7xiG9pqEfgx+dIJfBEwOfz49yQbAKgfpZh78nEVB0c\nSOk3gtqnp6d6fHwc9bnOZ2dnIyD1/Pw8ZKv0+8nJSZ2dnY30y3mkrNTFxcWQBWS7BM4lPwRJqq8H\nPWyDfuMwRy8z957UA0n83LvO7aPk2ofnFTR6xsrL9gwT9cBtoH5rlcFyPGPSA1Jzde8jAKoEpFJ/\n+eEy2AJetIPSKZH7SS7mEc8pBylIFHkAQvoIevOhwJQrwaHIDUDLOLSu8yNNJNaO4XLe6bMcpkDU\n4+NjnZycjEBVmufhSu3GhNdt49zeUyCn6pRArPOJGQI6KQIaZQq9nOPj41GU9fz8XCcnJ6NJ554p\nIRAmGPPMlMuJ+E8A5jrAthJcJdDBz5v09z50LslMqz4tWdU95LUyPqmv/ftqtRoN0cm40znQkBP0\n+FwrXafXgPAgbwXwVCadgnjmfc220jH39M5twM8g8sHPLfuZyvDDh1nZD5SZ5FQdcKd+c6DTks1W\n3fjdn8N6zgVUh6ZWHRJwap39SETZd2DFYEdgKoFq6YnXk/1MvfQzZSfx/T3pQ4GpQ1NLafifU2Iu\nh4dcUDwD4iBKxl7HarUagSuBqtPT08FJHB8f19PTUx0dHQ1OwwEDlcAd7qY0F+HvYkjcaCYHy+9p\neCwN0zngkXKrTR51Og+rasQ/ZbhoPBVh6XlTQ4LeTv9tqj97TqxHP8vQsx5TdSN/aJBpIB1MaX4S\no19f1aeyGS1Ll6STul+fCcoeHx9HkTKBkwdKelYP+BJctYw/+2TK+R+avH6bACjxKcl1Gt4mMYjx\njAd11OtaVTFD5PMKXQdo+/3MfmjZo33RPnk8BZL8c7q3an3RlerpwJK2b6pOXp8Epj3ASHVx/0xb\nznrq86H5l+hvAaYSQk5C45QEIk2UowH1oT0HVGdnZ8OhdzKlIUGBKH8/kIAUhwhalAzJXJoLqLah\nOU4ltcv72eez+P3KaDw+PlZVje5tGdmqMZjiZOKqN5DG59GJu3N3GdrE0LGv5gIpl81DUyuCdMOW\n5hBx0qr3Mw24Z6IEqFJGz7OGHGI/OTlZm3v1+Pg4HAJT7GvNj9L97OeejK5Wq7UMmOtsciapb9+b\n2B89GSQw9rmCBJepPF6X5u7wGTrPseu8x9vTctTvEYRsQy056v2enjmVKNB3LzdlkEiJf+Qtg1qf\n0pDml7r8pIA61Zd9dUj6kGBqk4bPcUI6kwH83CrDFTdNitR3Omofyktg6vz8vM7Ozqqq1q6Rkfco\nWQKseVStOs/pvzmKvA2gmnp2C7S2HAtBrPqZ857SXCb209PTUz08PAzRr4bWWB83xHS8LJ8glvVr\nRdTswzlAqgV+U1Q2ly9zdWkbY9MDUKnObCczSMw0JeM8B0iJWmBKQ7WemXp8fKyHh4d6eHiIYEp1\n9eG+xDMCKfar6pT4RtlNmdb3Jgc8XjcGeWyrAg6unvSscgtI6ZwcuD7zrOdUjYfTRS4jDhJc5n4W\nmNqFvI7sfw8ukk8gH5NPTOA5AbOeTCfeeX28njpzlEf1JU8pO2xP4uHP4ueHBFP7IAc7OrsCtyYa\nshyW547RjcGczJRWCp2fn9disRiMvibNKiuVhhw0gc/T5XNAFOs8FyhtAqi2BVI6JyXkfwQ7aVUW\njS+H+QRMdV+a2E3FTKCLEy2TcXEnMredySi2+iwZuN6zDklzgBTlNhlcn2fBYMXBFOdM6VoHu84H\n6RH1j3PgCKbu7+/XHIQbZg/AEq/cwCcQznITMNsmiNkH+XM9WHSH5naS/a+hdYEjOcjVajVsrsqy\nE6CS7DhY5rPJG26BoSxiS9cSaPe+8Gs/CqV+96DOgVQKIP0zp6qkDJUHllP9kvqecz9dZnj20QPq\niftw/cZrPSv2M2ziXxZMOSUgJeVrOTVX2J4RnZuZIqC6uLioxWIxippl7JPTlANIEaLXvdcPmxrr\nfaD8ucCtZSxFviKvta8JjbrmzXAOVC9alUHXMGxVDXzxIVc3YgQE3vYe4G05Zn7ugSm2u1XmrgZl\nKnJv1TMBKWamxCPvO52pG73MFA29Fm9ovqGAlK/A1BCfMlNuJ1gXz5i0nDSH83zOVeoLfnag1aIW\nONiH02gB+Cm7w7mDXCXJQzaXTtCHcXy+VGv43MutGu8VpjolQJEoBW9T9pJOfg65PuwC1Fhft0Et\nUJV0kwE7p1C4DSJ/UwbL6+b2sSq/aitlqR04ua71wFTV+ivDNuUT+3cu/SXB1Gr1lq7vpTBFSaBb\nE9t0dudJQWNmielKH+JTZiptQOg7K1NoOcm96m2jSd/fo0dT1yRnnfpiLvUMUg9gtEDVFLBgH2nJ\n/Ovra93f39fr6+sAsJKjoBHScXd3V/f398Px8PAw2riRK8c4x4Z7WvkeK62tEVorP32bjV5Gww3t\nPpxsD1TTUDt/0tLptLWBD4URTPmk86r1/Zl8Lt3r6+tomFcGW7zT8J74eHR0NDyD7eJ9Akd+b2sF\nE+fo9aJ6D/TcyW3Cm11pCjzweQ6AHOTxf2+LD9lTL3yXe+pjep6CVJ6r8hydZFccdLkzZ73nArS5\nvJtDqd/Zvw5OUqAov+jg3rfukV3kc1kWM4icR1j1NmSY6iOQy0P24enpaWiP+7tkM1ln1pP91JKV\nViAk6vmdHv3lwFSKEl14WkLuhtDvFWPSrtccWpCQuGF0QdJ1ifHeFm+PhEm/p+0A5vQR292jHniZ\nS1PRNq/rHVNASv3EoR2C1qenp7q7u4sGqdVWOU8eymoQKOjZHM51Z0Hj4L+1wJMbEzdYrH+aIM/v\nu1ILVKWgg8ayNZGc2aYEptLqvaSfrqvih7JOnEfl+7opAPIonv0pULVYLAYQRTDl84AcTKlNjLyT\n00tzVvbllOdSkhM65eQsE5DSmU6S/FOQqWyHHDQzFK1MC38TgPKV0m6nU11Znu9P50fLZqh/pj5v\nQ70gNOl3smUiB62twM6fw7owk+j1bE2dqarBB3KUpqqGBULqf12bDgdUDvxSQJxkppXB38U+fkgw\ntU80T+a3JqulZ6aMgcCSGMO5G3IKvuqHz6IAyEnyuVQAfzZBmWemnHoC0UPcvX7fl1Gnk5oruC0Q\n0IqevI9krHlwOIK/V62vrjw+Ph6t+qIT9cwUs5CXl5d1dXU1bP7oAMoBgaJEj7x8/pwPI7lDc5na\nF4hSeb3PSad81dzDw8MaHwiSdC/BjDs69k1LlwScde/j42Pd398Pw8PUXTpxzzZwGEL9njZsTbz1\nviGYmhMkJHB5SEryw3o4oEjDSsnBcwiXYMoXkzifW1kp/04QpTIFqJm5XyzWX2Sus9uIBG69X0Rs\n16H4xb5nPXpBp/POfRBtCw9vI+WAwEbkYMfrrWs82852yR6zvlNZKZ8S0wNTki2/hgGC9+km9CHB\n1LbUivJ8Ut0UkKDQcbhF8wAEorRJo8/R4HPIFEfYPuZLgfehQBpoClVyYjRc3j8pS9FS/tSf6bpN\nyQ1277oekGrViYZaCnN/f18vLy9rmSUukVeqmX2vzFIalvLhKy4OEJj68uVLXV9fNzNRbgg86tK1\nqb00EFVv++6w/1pZg23IgXjr7PzwVXPiRQKDLcPoDrTqbZhIw+dVtQaSpK96h14rw1BVA+h2YJfq\n6o43DbFzGJKOwduThpXS5xZP9gWUWWaLEqhIGRH+ryE2z0xx/mNyfD0Q5X1HO62znkv9YQafGVzq\nkg85twCV+qP3eVdb2SsnOX/ngbchTSNIbZwqj1MkKNuygf5st206VD59qNenB6gSmJJtTDq7WCxG\n8/kcoHp7N9GtvxSYcnJGpKERv573yVBrHF5DOmKWTwin4ialT0wnQNL9p6endXV1VVdXV1VVIwTP\n9kgYVe854ChFiuns5baMybaUjG6rrs6vlsMRsU/VR5rg/PDwUDc3N3V7e1t3d3eDc+fy+KurqyGr\npMxSymKl31art7kBl5eX9fXr1/ry5ctaWt37U+eUfmfanP3TmnhPvk05u2355uW1yhaY4aq5u7u7\nEchjuQlMJadaNQZTFxcXtVq97UklUJQmQvM51F3fMZ3Pbm0YmoYe+ZnyKJ6mrBizYe6U96Vzu5Dz\npGqcdeqBdl3j7VB/aLjH+dQboiH4EZjyg9lEJ7dvDpIFvj0b7AcDgJ9BBDqqe+o7Xcu6E4j43DJd\nrzP7nIDD+6Il16nvPFAUj8QnrbpOIC0NSRJM+X1JdhK/nZI9m2szPxSYmhuJ9T57Z/TGSB08kfk+\nHttySC5wTCl72jq1MaVYZQAICI6Pj0fDSYxmWS4Fy5/H/pDAeT+wvJ4QbWM8WkbW+yk5T01K5VFV\na5kizxr5kJ5nojjRlYYxKazzS9cQTF1cXNTl5WVdX1/Xly9f6uvXr/X169c4V6oFrhMfvd8EcD3a\nct7vixJw6oE1fhdQvbu7q7u7u1oul6PgQnVttT052KSHKkd9rGslB2k4lofP4VLfplcGObVARtX6\nMGVv/o47rn2TB15zrm8N4bCdDhx72QkGKl++fKkvX76s2YLEa31PQ/ZpGxqCYB+WJRhKmV+nVn+l\n4JPtTuW0fBh/8/qwvvruAbqyf2m0w4+puiQbrP9Vvng8N3HgYEjzrjiHiu/U9KFbDkt6v0/xpzUS\nkPRhrm44fSgwtQkl8ORnouSqikpDx+nZAM5N4co6CaEEJDkSN5pMG+u5UghH+gIPLjwOFgg+PEJK\nERXrkCJhdwg6zwG52/CLjs6NoxtQZez4mp3Varw7dmtysztCglsB58ViMcxNu7q6quvr67q+vh6y\nVK1MlGcYlI36+vVrffv2rb59+1Zfv36NoDmB6ar15cMtIqh0GTiEI+ZzWkcyrMpGLZfLWi6XdXt7\nO/A0zdmQztGB9J5F4OM85bDa09PTkIVMk5WT06WMCYC/vLxEfop/cvj87DKTZPzQvJtTLq9pDa+4\nA1Ld5Rz5XwpSFWTwTD66HVB5tBkeMKXFPQJTbgc0B7LqDQi09LPlfEUOoub2c4tc3x2MSG9U5xRk\neL/52X1Sen4CsYmfLf1M7aGv1SEgzGzZ6+vraESIbwzhNBomMaYAUupPfZ8CZHPpU4IpZzh/02ei\nYw6/JAeWJoZ7WVRMRgdkHAU6RaMt5WQKUt8TqOsZZREVLxkG1cXJhSv1965GPjlDN3St/WkIpmgs\n6eTo7Ah6HISy31QuI1sHU1dXV2tZCy4+4FnRtgCVQBWNj6e96bAoZ3pOkjfyg/05J8pO1Ls28a0X\nQDjIVFZKQOrm5qaq1if6U15TsNKqi56lSNkzgBrifXx8rOVyWXd3d8OcOOmzghU/VqvVSL5Ulk9+\n9Sw2I/okN7qO5znZg0OQ67c7cNpI2UbXTc+GkA/kxcnJyUg3dChzyCxgart0nlMsnp+fRw5XnxOY\n4tYZBLotMOVtavUfnbLTXF2cAlLSBfVt0gl+96Am2V7a+3RmuewPBvqttnhWS+3whRoEUrIdzk/Z\n5qRnSW8SwG39tg8gVfVBwdSmgtcSAhotCZ+YQaGUI3NH3UvPq54aanEhTWc9rxfpVNWg3J7S9LSr\nK4nq1BJaAbJd+n2T6xIlIOWZpR6YOjs7G66Tg9R8HIIpPzjE4/wgT05PTwcQJUB1eXk5CaZ0OJhS\ndiqB6LQyRf2hdhFse4TKPqVxbA0P7kqJLy1gQ0DCzJTAlPpahnK1Wq3tx5TamdrMzBQdDz/LCd/d\n3dWPHz/q4uJiZOQ1XOuZTAJ2ydjr69vrZaRbPvwlPfO+IDiuagMY/ZaCxW2oJQ+tcj0gY2aK99L2\npCDVg4XT09NBLwimNGdGhwILDx6k8z5X1ffwOzs7q9Vqtaa3ykiJR9K5Hpjy/qMzn9KzKRC2CR+Y\n+evdU1Vrekg+eb+2wJP/7z6FE9hVVw/a6QOr3rZmYB8zM0UwxU2uT09P1/iSMrqpLs67Kf+7LX1I\nMDWXEiLXmUCK6LgqT6IjOj47OxuGT5iVamWmyNw0BERHl7IH3oa02svb7O0VOZjS0SuHpPpwGHCO\n0ZhDKWKSwtOQpnZKeR8fHwdAJcPqK/RYNod4fHi0qkZO4vj4z1WUAlEOpvxIQ1rX19ejIQyCKfZx\nVa1lxDSEqY1B5VS5yoVZAPWNsjJpeGJfNAWkdHgmUJtmClARTDmgqVp/MW4r8nUZ8gCEUaxA2ePj\n4wCmHEilHdLFY9/WgbLDMtzWqD98FSjBsdshH8JR3/O8T57q3HJCDqRSZkr19eHAlPE/PT0dASnp\nB/VYPExZB2bt+bu/puv8/HwkG9J7lSu+pGHmBFxEPVC6rb71gJSD2t51/Kz2qfzkD/ndD28rn0Ew\n5SBHnz0jKH/q1wlMKYEh3fFRiLOzs7X+VV17sst2tIBUspXb6NqnBVMJSLnR8THVqvV3A7WICsf9\nhQiW/H4fkhCRaW48WXcHMHo+DaszvYXG3am6kW7VNfUzz7uQG+CUQeIcIJ6ZsaHBTRtpevaOEWrV\nmzGQEabSCkwpM6U5U2kelj9DmSnep0g5RX90VlwiLt5JBphFc74nQ3aoyKvHT+etR8bOZ2V0dQ2B\njxwZHVoyhCk48ABJ5fjBQMZB/f39/WjYkRu0CkwxG53aznJ9gQQDJmZvVIaD5Tl6OodX6q+W3XQ7\nkoA5eaTvzM754fsK8VVaFxcXoxe+exBEG+tBGG3lVBbC2+b90gMzPTqkbvlz0tCjnxOQcJl0ndE5\ngSn2C8Es5dvr6EN/Xi5lkGWpDAJvn3SeyuPzU39JrwQw5/j+belDgqleQ1NH9ISgdR0F4vn5bTM+\nAih31D6cx2fSOLYMawI7vfIkAHp21Xo2LEVUbjRd8HVfS4lYl9Sv2whicjitoUq1U0pAPvHVIIvF\nYvRqEDm8ZCSUGVRkLWPv8yzOz8/r8vJydGhrBD+SI9U9Z2dng8NJ9768vAwRtOZ8KAvik7WVeWKU\nz+iPwGwOwNqW3IALoFMmyEN9ZiR7cXExAGbOX+EQpQCFshDeBh9u4JltVL+cnZ3V1dVVffv2rVar\n1VpGUDLBnfH1Wb9z6G+1+nPfsuVyOfBQgDnJhM+BYzTOs2dDJOf7MvwerLnOtYArdVa8Iyhlfdn/\n5J2DLD2PQZXXhyMCnllO4NztHvte9wos+6uAPGvNYWO2zakF0ryfe8R6+++9ALml0+IZA7UWYPZ7\nWjZfsqMsuX5LtklDtAwE6V/dD4i/tGW9vqRPTfJblXdQ99GKFjBjP21CHxJMtcg7rncQnVeNh6+c\nmWKilI4ZD+5BRMHwSK0FkrxOPUOVEL+MRdqDQ0bZV9l4nzFDx/pL6OkQvX+2AVTpvxaQagkzeaaz\njKE+V9XanCn93iIO5zFSJphS1KzIWUMGSRnZtqoaHPf5+fkITKVVh2mrCxl7zjF6eXkZOV6BDN6n\n/jo0kKoagyjyiMS+kZxq+NwBYNWb43QQtVqNX2IruSWA8sP1U3t+qdyLi4u1tglAEUjJCSUQ7fM5\nuFcS+yBlW46OjkYbw7KvOPeq5eA2paS7/tmHkDwTorpX1Zrj9LluPoTqQ3+0keJ7ylKIF3wnpnjk\nfe2BacqMToEpn6KRAEwiB1Qt+9e6z79Tn1qAyof+XO4IYntZKfad9yOBFIGvsv/qU9+OQvLgQTLL\n8PmrtGWpr+f4Dz4r9VOS0W11KtGHAlPJafeu4ffUoRrW8uscuIiBihoXi8WgcFRkGbsUEWtYgZGX\ntyMpiIw1BcTH+Tne707y9fVtMqyPEbeAEDMAqQ9bQrqr4PUUosVztkVDa8pQCXxwtRV5yCMNQRBA\n8XCApazDnIhIjtszU1yWr8NfkFxVayvfBKa4Qmm1+nOythugBCxaICPxpvfd+SHQQzlukWem1CeU\nB7WFUaQcgcrQ2fWuN8fw6Oho4J9A9JcvX+LeZAK4bH/iu/rfhyKSc/R75Wyenp7q8vJyDVS27t8H\nkEogKtlKOm5eK55Rf6Qz0ksCGJXhgCqBtBQYvL6+LRrQRrsPDw+R715PleeZJ89i98AUh4hUp0Q9\n578NJT/htr/Vdj6XoyQqr8X/9F8KGLnwRnPOuKVBylSzH8QTBpN+fYvcf3hgS11Jtq9XLvt+it8t\n+lBgStQDUvzcA1V+TwIGnrHhWdkBnqvWJw3TuXDHdJEzJkUxblAEFpiV4jwhKpTqrO+pz9RO1okR\niEcsc4651FLQFqBqkQ8z6Dd/T15rZY7ADbNROpSBuri4iC9M1VwmtoPE/qMMEEyJhwJKd3d3MRrz\nzS0FpjyDlQxeD0DNzUz1+JDAwhwjRd3QfWkivxtBZqj4FoAWkEpgSpkpZYOkU+pjBUm+R5nAFbPE\npFQH72fPVOgsICy+ewCme6eGIuZST4fpfPhs70sHdQJT2oBztXoLbOjgUxDD/1tgSv8TTP3xxx/D\nfLaplxrr8Mn/PgeWAY0P86kvCDh+FqWAkPLGI9lW8bSV6UyjAmyfAymW7WCPmXvaMm+PgC4zhL64\nJsme2zvnsfsSBmW9jLWX7/XdlD4kmJqiHogiw2WQGUU7Q6ryDq4CUQRUi8VilKmoept7pOdJ0VuM\nc0PfGgYQaJAj1gtaXZlkjN0YuXC4kDM7xWhin1mpFpDtZaVaQpx4lIbPNIbvfSWHquyI5jalybA+\naZYrmHqk/lWmQvyTo9E8m9vb21oul6N+lcJz5ZsOZq7YRy1wkyLaJIub8NCfk35vyZ5klNsJyKlV\nvQ3xOXBROZ6xmANg+GxFzvz/5uZmcETaEfv19XU0DKR3CKZ+bR2sk2eKdT47Oxtl6xjV63lpKGMX\nmgJTIg/WvD9lRwWmLi4u6vr6erCzkg1lkRMAIEDjfBkf+qHTvb29re/fv4/2CJMTF2hjRl/fE5hi\nFrK1MjcBjZ7e+H8tfiWwP0cfe6CKNpy+L/HW/Z78Qo9ot/nd/1c9CV6YFSKgos1mMOEy1/MdDqxS\nhtszev59Ds21l58OTDlapuAn9OyAisMLAi6JQT6mLiNHJN2K+CWsLUqKx3bJIKSJr0yTpwhP1yUB\naj2fdXBA5f/NNeqte5OjlXGeCwT5O++TE0qgU4BJkbQmlgscc4hWTk6OjnMAWorMyZY+p2S1Wo2A\nuYYjBSg4TCTnoWv4HGbd1N7kvN3QpT7dhH/p7Gl2n1NEnnDOE/vFDwdTLsPkKaNK6aaeq+e1Ivuq\nGvH3/Px8yEI5b+WUUt8kku1RfZIeevudh3NA4qaUQCbbwmyP+sT3ENLZd6PuOTtmTmh3Zaf0mcBH\nh4a5qQ/MONE5e39yErQvAGAGym2d8yHZzk10aVcQzHLYx4vF256BLdvkOkieT9ln12+/R2fZXNon\nPY8ASX3p+0l5cMQ+F2/dtzMAbfW3yzdtC2W2FZDy+1z6dGCqqj3HJ4GpqvGE2aq3LIc6LhnRtNJD\njNa8ilQvB2xTICRFta2IihEH79VzNU9BRsyHAacMqgNU/2/KMbdAogM07w+1Kc01S8rgz9CzOTTk\nB7NReneeZ6E4pOST031OhdrkwwYCvAINympxQQPneNGZVtXaZHpGXJwPpv7yLE0CUnOoBbB1dv65\nvrjxVZk05Oqb19fXNUChzROT00iAitkNryPb5MBTfUXg8PT0NMpysG3cRDZliQj0HfwQSPL5Lm9p\nzlcCVPuiBNDSELhn83Q+Pz8fwBRtSHLoPHSd9Ic21VfsPT09DSta7+7u6vHxcZSx4jMFer0vU51S\nBizZI/JgW0C1LyDF8pJ/S+10UOG+gvf5Of3W8wNun1ar1TDVRfrHQIiT1Xt9zfaSkr61/GwCTeSf\n2wv3rZvQpwNTLYPeA1NV4/2lqtbHh90x+ARFznHw+71eVFI3JrqW9aKQKaLyuRsyPJ6JUXmqu9qj\nuvCVFz1BSXV1p7EJoJoLpDyr4oLeiiBEah/7xp308fHxMJzHl6zKITCi9eEXORZlB5lxkLPlvDoB\nBR7KOKkMZqbceGiTS27wSGNFfqh9rX7zz+l7Ir+/ZWg9MzUV6ZPnGorx3d89Za++bg0Vub7xmV4P\nAWWduZu+97Xrkz4zG+B95o6Lz3UwleQuZaV2zUy17vOymZni/MGUmUpgymXEMwpuH12WmYHXoXmD\nWsVHMEUZTFkNz9LSFvlcG7dH5FsL1PYA1b5BVKsd3q9uvx2Y0zcm/XHe8Xlu990+KDtc9RbgMPAR\nPxRMqf4+DJjAOeudALNsIeub6qhn8PeUYNhW1z4dmKrKjjp1vFPKxLgB0Oc0QdERchLkl5eXQWCS\nY+C9VTUaUuISax/X19gylZ+CSOOVHIsEzw2Ct8WBX+r7dP9cPiWeePbAjQSVweuhexxAOphiVopg\nyh0XU9GcnO6pZwdT2tnbwZSewYiYmU43asqKMjPFZ7I/JCsOJhOvtqGWfKfgoxcZp+gzgSnPBOvZ\nKTOVnAqNeAqePDIWYE4ruQigmAkR31uyyLPqws++AnAqK9Uqdxtyp8WDAYTmQp2fn8fnt4b5UgDp\nADsBGs6hod3VYgwf5mMZLltT/Ufw4JlU9o341XOuDKz2TW6bdWY/9/o+9UnV26vKVB5l3TPvrXZ5\nQOaBhwAU97fiELf7sEQEzFzNzuA5AebkP/Q894Vz+38ufTow5cbdlTeBKXdCCcl7ROppZyJv1SPV\nQWDKV4hIONiGqj+ROidTqr6emXp6ehqtAtJ1bBsNBf+n83Hy+1vX9aKTKX6xj/wZNFx8zlR9WHfe\np/b6UFsa5kvRDOdMcRNPGmIOpxJMff/+vZ6fn+NmjElWGMnJaKhMribTc13O1AcOpFKfbQqsekDK\nQQf/c96mrJImBxNYaO4YV4PRYRJIcT4MZV7GNGVxOWdDvBGQ8oyYeMFJ8cm5TPWxZET3+jCfsmRT\nmaldomXWhQ7Fwa0PbSuQSICEbWAwMAdIVeV91/xl0tw4Wb+lYWS2LwVGbCPlyhCWYF0AACAASURB\nVOuXsiMJmKV+/RmkvpPdID8TmPKMjwAOs4IObumrdF+SP7f/riPKFlJ/PFAWpTl1bBd9seRNbXH7\n7cO73kYG3im5INqWp58OTDm5ovKzgys6X5EjdxccF6Y0T0XPTSjfnZA7n+Pj4zXDcnx8PGxOqSGq\no6Oj0WozCpIbABo8biyYDKm3LwGdFiUD1LqudU0L4OlMZ+igLEW84pFnp6reVkZqZ2vO0RJxzy4a\nYO45xs1cORRMB+/DeAkcymELRGiJt2ewVFe1Re32ybbbgKgWXygPHoykfiefvIy0ySMBi9qbVlq9\nvLys7VKflsJLl/x9XlxZKaDKZ3F+I/VGK3YFzgi8aE94JKdAUM6sD+vWis73SS4brrsJiHibKA9u\nQ7iPms5yhpRlvueQB+XA5YEjA63n+/cEUpmdcXucQOOUfUu+ZN/EZ7SCJddLgQWunlX9COi9P1me\nZL8l46xP6vvUZ/pPK6t9BIc+c05ftMCQ+4iUYPCEi/7bhT41mJoCUqSe8SBKFVJO0bDu92jHAZWe\n747HM14CTh6h0VloiOri4mItonWisnjaXpNJ0z2HjK42KZ8Konu8nS0jwr7m8Al59/z8POxkzx3l\nqfzcI0z0+vq6tiM+wZRPFG/Nq3Fi9kpnGgAddPKvr287DDNL4wAnAattyHnX6nNe73NYfA6Zt9OD\nEB928/lsjD5ZHwYQaTNNPWuxWKzxTnKn+kpfmAUjWHb7ILDvGbvj47dXFrk++ryjQ1BLHlpBZXKs\nzqMkZym7VDWerqDsI9+jybdLeIAkQOXzm1LfpyxUukZyQDlrZbqcGNwxyDgEtXjF/knBJW0g/YED\nqQSE/HktYOQ+NwEp8oPX0zdRdrhhrgcromSLWB8/qIdT2TYH09vQhwRTcxrk6DiBKj/IZAlYy9lT\nGTnOrPqlzk+C7RED09fazZcGSBGsDwdQITxKSOAwrUhr9cmhABWNTe8ZyfG3ImaPktJcF88eCky9\nvLwM8y9a0StXMPE5/p5GX5nnczm8Xqn+CWRUrQ9DCOS5POkeH2LbxcD35MANVXLUaSjLAY7S9Uln\n0xyslj77QblXHRSFuk6Sb4zQZejVFtdfdwrMgPpQiYCg9wGzZ63A6FCkrAWp51zojFL2yLOoPKr+\nfK+hJvrLrnmGVxsiuw3Tc6lHSc4S2PZAmMO+nLPI4Mf7pAc+W7/tk/z5riOuK5RRtdnnLfm8ymSX\nqU/kB+tE3U3+x0GY14FDcnrfqtrmlMBc6pcEpjwodflS+a1+34Q+JJiaS8mY8+zCRgfmwMQBEpVR\nk2X9GSLPTC0Wi9GzyVilw7kRqECUQJX2RJKx0PcUkXg9PDOlidTa/Tk56rmofBP0LuVIfTvFz6SU\nKdKUYfQoM4EkGWYaIckD5/JoxV4CU+nl156ZImDWmfN+2Baf1+VzAGjU3GguFovufjn7MvRuEAlK\nWsGKZ0UdRAhAtIC8l+tL5n3+oc7JeVa9ZUd86IjDs6y/7AMdkTsgB4inp6drC0Y0t86HOH3+2CEC\nGacW0G4FKrzP5zilYbo0hWGxWIzuEZjihrSyg64PDIA4KZr1dt1J/PfPkif9LlDlfZX6jbLgfXQo\nHjq/HNy2AJX6RBkg92nsw1ZCgD6SYCr1hQOVVG7V24Ir6UzV2+iDbOec4H7K1rFPHEwJRLu/1+dW\nmVP0KcFUD0DpnIYO2GH67EM9OivSVHSZon8HM6xTihZklDRv5/b2tqpqiNwEquR8BaguLy/ry5cv\na5EfIzVvQ5pQ6pGkRx892sZYEFCxDHfOSXBd+dNR9eYoBXpbwEUGnxNaU3bo/Pw8gilOhuXZV3uK\nx4y6NXeKdffIuuVYFWU6WFK2ylc47QtQtYyKPycZVa6G5Dw/Hp495DAM5eT19TXOV0vUAtFyoDLW\nfFWTO2oHYqoHdYRgkWeCC+kzy/T2UmcPSa3IPbXP2yq7pflO/r5SbgnicsesO/nO/aP0WbaKAFy6\nl4I/DxpbNsIP6ROdtgIz9pfOPqzm16j/DkmsD/nh8/48yNHmmLQZHOGgnLvPpG9IoyEeRCVARdmm\nTNA26D/ZzZ7eTfVROhxMcaRJ8iT+q33b0ocCU1MZjBRV+X895+yM7h0pLehCm+riwuPGi86fxp6A\nT8rOvW+8H9yRzYk6U7/2+mufxH7kUINn2VptYKZNByNfZgW8HIIcRtZUeAGpi4uLkSEXEOJLivWZ\ne0ZVvTlZjxaTfBE4if8tvnI4gs5fYEztOjs7Gz17X3xr/cazeCtwodWT6k9mpQimpo6qquVyOfBJ\nfcXn6nPKXqofFM2nbDSDq9QuB0Np6M4Prj5ifXqZqFbd9sFDb5PbTnfSj4+PA5D1OU7pVVtJ3gSm\nGDQsFov43lMNrzMz6M7NA1UGut422lZdy2y2Lz5IQYM75RYgZcA4Rf6sBGx7903Vy8ts3efP5PA2\n2+RBpYM2r6NIfU6dk30gsGJGkjqShvmpR0l+vU8c4LeuYdt20bcPBabmUjIyCVUmJO7Gr2XU/VkC\nV8louCFvRXpy0lVjYWNWQkaNWSy+J8zn4shAuKBMHS5svb7dF9GxqC/nzPehIvJdeovFYi0ToOEc\nn8TsGaTX17fJwVdXV3V9fV3X19d1eXk5Mv6r1VtWyocnaIyPjo5G71xTRvDq6ioa+1aU50MlcgJa\nUUbeu1NX231+iZ69K++mwMfx8fFoWb329HJ94zCf1006S+fLOTm8N2WT/SBIZ5TqeqohObaPdZlj\nM15f3/b+oi3wDKRk3/XQbcg+SeWlTDSBFN+XeHx8POgUV67ys44WeE/A0IcIBbjkzCgjzKKTj+IX\n68o5arLXLZDUs4G8dspeHpqmQBCDUmZYPCvk80rdFxDEihepHsx0MQhuBeu8TkGrv06GbWEGkb+7\njjO48uckP5L8WgKfu9CnA1NueHsRDIVLDKBBJGP8IIhKzyal4ZvWkI4YxwjQ2yFjIcPl74dLEQlT\n4A6oWkrkZewzGk58o/Iza1A1Xn2ZQIAip4uLi2HTzaOjo9Gwm4ZaHh4equptvoWySA6oBNCurq7q\n27dv9euvvw5z09jPmuemoVkNTSQHrsyMg162S2ef0KtMZJrIyzJ1D8GJniuDt0/Dn7IaDkaqahiq\nIZDSfl5piDYZM+mKMllyqNIVApJWxogAyLMT7G/WnU6IEbDaxOE8LRRwflKHqPtpkYHXRfK4bx0k\n7+igUraBq6kEOB00EUz55rJObqdoj1zuPWMh3rMcftbzPEPFc7KD3n7va352IJWAg8vPPsl9WstW\nS275P8HUlB9QWyifLd45L9inrTboXj1Dtozto94xM8nfdR3tT8o8JUDlwUMCqLvay08HpkQ95vF/\nKaQY4BGmgyl9dxDlwIodn6LX1qRbOUQ5/ZSh8HT7w8PDaCyZdVCU64ZxU0DlbT1EBEZwWzVe5kuj\n5P0r3ikzdXV1VV++fKnj4+N6eHio09PTenh4GGWTxCv1ta9GUp+dnp7W5eVlffv2rf7973/X5eXl\naEhD0brA1HK5rJubm7q9vR2GBvV6DQ1fuYImY/T6+jpySDJQ4pUvMU/DfJIrzblTBJ/4uit5BOkR\nMPnjmSnXLd3fSuVzzpWGvF2PaHhbQ29cAUmdEmhw/ngg5HO/tCu4Mh/MHkreVLcEdgnECE5SZoyO\nelcn7eW0MlP6LJlXvTg/R9uLeHbK5Y1Bo+tecnrcv0tgmhk82gyVT9AnECXe6Mxni+9pMYD3M5/N\nwwHMLvyZq5/u9EkOpHR9mkbAebZskycSaEPpO7iAp+oNpPXqTf1T9l7yQplhZiqBKb+W7U/9NAWo\neD150ernOfRpwZTIO1NnN/RS0rSCyg29R4gp6uJzVB4n2mriLc8qS9GdNo/0LIaDqfv7+1Gd+JlA\nyj9PHUlwkqHdJ594pmIzCk3PlIHVjuRfvnwZ9bn4KUcmZ0AwxYhYfaVhPoGpq6ururm5qZubmwGc\ncJhPr4358ePHMIFSyq/Jsy57zmMdWs0pZ1L1NreHw7v63+tPWZNjYx/uGxCnaJgG2OdLKTPVklsC\nQz6DZTHbyB3lHUARsHBPp6RPAqjsK8lgWpCgXfPVLg2pEkxQpjW8KKfm+0xVvb34mkEQ+5j9sQu/\nWt9dx2UTCOrohJkV9SE6DvPRBkv3mD3mRraUC8kB7amcqIbsvK7sKwJcASvqERed+H5fdN7kQSuL\nw6kgPpx2iOxUyqKwnuSn/vd6tQJq+koGIcnmVNUaL1oJBg/k5V/Pz89HNkzPdzBFu9LyWZ4lawWS\nrlOpX73+6fsUfUow5ULfiuDIII86fV4CGVNVa4zzCECGsmo9M+VbEuiQQdYOyNpVmcKp53mGwgGf\nPpMY3XtEKQPTAlRzBWdTY8EsV3LGdD48p4NgKr2oWNGYhuUSQBMPq2rk/JXt0hwnDSkxuvb+U3kO\nJFzWVCdfNs8MAGXQsyiUSxoff38kZanFs2RY5vDQARTbTiPooEbg0g9mQuZEjIlSRO2ZIGXwHIj5\nvCm1k23xjTYJptKcFNoI/U4wxd3bCU5YVsuW7UJuL1MGiZ/dMXkGzoEU5zTxmWonV7dq404Gtgls\nO2+8rm5XqDsibmWiRSNVNQzlp75pBXvM4uwjUNnk/uT42U8pY+PDfOKRZ21kb3U/wZT7JfKkBUaS\nLKmOtH06C0i7PXVA63xg8qDVTw6oNuHHNvz9VGAqOQVXAv/fDW7VGDj10pQiN+zMOCiSIrBxp6j/\n6ZCrxuPEzjyVx0wFHbQMNaNHtdlXUHCOCgVRvyUDugu5wRZvPCJdrVbDXDD95llDnfk6Dn+tiBtg\nN6I+/KY+uLq6qsvLy2HoxuvHcjVfSwp8cvLn/l/X19fDHC6B5AR4lXkSr/xVNA7UWkaBcsjPbmgT\nTzblnzssGUWCKWZyWScdAi3uKMk3Gm31jT4fHR0N89TkjNM8DZVH40xZZ0bQFyfooJNI/eaRbyvQ\nYh/Q5vAZnmVkVrkFIDYlljUVmbuzciDFg+A9OULxm/M8eb1n8ZUdZpZIfUVeqd9VtgMytjPpSOIj\nQbXu7wG45G9SQLAPcnlyu+x+KB3SI/Vnqqfrq7KE7HOvS68M2g7ng4/M6DvnjKb2+TOZmUyy3aIW\nD/fBv08FpqrWo2QagpZAJcDDVGAv9ekOQ/8xu+WZLgdTei53ylZb1AafE6XfZYiqajSBXfezHfrP\nHboMlMgF3JH/Joi+x6ce31yQOe/BJxMruue+RdyLhm0lmLq/vx/t/u50fX09ek9aC0wtFosRcBOw\nOjs7G2Ub9cqflsy4MSGY6gEq8jaBrGTEknPYFlC5vgmYpv5ifRwkuBwkw69M3PPzc93f39disRit\noJQOkQhcmHlyuXZA5aCqNzfD2+Tt8YyUPnsApwyB85v80/Xst30Q+5l1Yv84eGEfsg0MAgiYlaXV\n5H9m2HWPAJTPr9OmnlU1GlbyaRCcKyj97mXd2f4EHFx+W6Cj9fs+KdWXvyffQRlk0kB9wHlqDLJ1\nJDlwnrvNadXd/XECU8pOeoKBgTz7mbqlcpL9mKIWT/dFnw5MVfUNcrrOo2cKhRsRnZnqV6akB9Ro\nqCiUrJdHckTerC+NqoyXCzTL9ShSAI6gpBXBtCLUfaF2N1Cu8PxN7UhDfJ6V0uGg8ejoaIh6OJya\n2s5sElcP+bXKTC0WbxkpOQXPmBFY6+DwQAtMJceaMlQtcMXf9mUgKIvMSrlBT7rnYMr5ncAYMxii\n19fXtXkvdA7ebsmBgymfj5gyVQRTrmPephR00L6kfmTEn0BL4tscJ7YNJZDpQ9DsZx4ORCXf0g/p\nBfnJzDqH63VQf8R31s0DXc1nkw77MHrK4rTa7sHGlH/5GaBK9RQl+XAfxMyg+OT89AUb9AEu2wlM\nt+SwB6R4pv2resvYuy45UbcYoCS707KZrb7bFy8/HZhKzjml/JLzlsEm0zkviStsuMRdk8d9fN/L\nJsNdURV1p7Q42+ZgStdT6HWv2u5gUEOLBIQ+wZ5RSctJ7CpkbEurz1QHZt1oFDhxsQWmHDRyU0Gt\ntONzVab2lZqTmeKyYZFPVmYZ5KeGYNXPnAjtEX6KrN0g+GcHFnz2rgbDQSWNGX93+adceWTpETXb\n4pOdKdPu5HUPwcjJyUk9Pz8PmZGqdcfcGvLjUDDb789xx+JBUTLebnNaYCrxy3m7LSUesR/cBvpc\nKH1O/Uh91fwy2TzZUzlyn6t4fX09GtrnM7yfqt62TuA2KdxcVDrWcqTOC/WNn5N9dx/DMvcJsHq8\n9jpw2FMH5VRD5woAmWX3zJSCCslGzxbRX6nOHkC5/MjfVr29Psb9gdvQJIMejG1CiX8sf1s9+1Bg\nKjmFRL2oIV3nQy5V44wUV5zo0PyYqvFcAH734b0WoBJxPJrXE6QRNHl/sC0yYIwo6Zh98qwPnen5\n/iwpTitLw9965KDQnXJPERLo49Ae28aslDJyGnLj5H9ep75gZor89bqmBQwEp8kIeHskc+IV3+uX\nsgCpP3t9r/t6fNnWeHh/+O+pPDpD3udlse+q3ray4OFAhoCfZ+kDnUBVH0y1ymV/pUyGZ8da/HG9\not1xoOD96+3bhRKf2CYuWOG2IIk8E86METNPq9Wqzs/Ph21LCKY4vHd9fT2Sj6oaAWnyizaTgGqx\neMtskm8tYhu8j3SmndS554j1zEOBqpY9TYtApEfSReqS7udcWpeHqlrTi15/Uk57IFY80mefI+t6\nlNqsz616TAWgm9Am930oMDWX3LAn59uKHvSbULOYSeOwWo1XAKW9bDyScmDnTvf4+HjYWNKzIMnA\np/KUZeJcAUV/+k9ZKf3vAiohduDGvvV+3pWSoqUMCvvEo0EvT+1gGeojDSFcX1+PJiwngKb+UZRE\nY8Iom6AqASd3mDweHx/r5uam7u7uhg1EGRHrOQQX5HdVjXiuOmnyO1eIJqe+CQjehKfOS2bg5AgV\nmHCfJpF4IJ4pg8EtI1pDnZ5l5FwdynxL3jTEob7XEIjKWCwWo0UDep8mQXdydGnokkOMHPrisBSz\nOz5csyuQYv1EKbih3MlGeP8nx+o66rqZAJbPfSTQlN54PylrwoBEL0nWnDoeCjAFfHsZUbbF+4rt\nb+nPlJPfhlpAyomA0AMZB7opQ59sbQqyyVPqnEAuVwzSX3L7mjSFw+1psqXeLn1mYMp3paYgKR2q\nA+1DSmTMoU8HplpoteWMRekeASEaEHVcAlI+dJbmUum5PkwlMMXyaJRpPGRYHYylpdqvr69DVkaC\nJjBF4KbnUDncWKpveN6VehGK+t+VuwcIWkZdbZTyCmxQwT2y0So+gikpKctyR8ksZ4rqfFjq8fFx\neLGrFD4ZOQfs3JfIwdT5+fkwTKkVibo2GYJ9GnnykEbPgZT2SNNQjIwx52twmwHJrspXOf5c8dt1\nzIe0KScuS25E9TvlT46d+1LxOs+spcP12mWDjoEr1PScXqTPNm1Dbrc4PCQQnzJQ3qfuCD1gI5ha\nLBajrLFsVG/eFsHoavW2qSj34PN3/fH1W9S1BKT0n/fxHCDrNuUQRF12nlWNNzIV4BTQUJ9RTluZ\n9VR/13OWRTnVtbrefZZsretsGqUgsPYhedcl353fN5FNoMrPq9VqsEe7BDAfEkzNEWCRG64Wc/xe\ndVrVeJUcwRTRtGcL+H/PuFJofBdn1o8OyKM6Ca5nphTlp1VJvcyUDhoa759dDUPLMFHZaJB9CDKV\nwd89Uqp6S8sLTLXmm1GBZBAEpgSsVJ4yRnoOD/a5+pPzoXjIwKfMFOdikc9yKovF24ag5L3225Jz\nogN0kLNvcoPHLA6BlNrBbJ8AFIMDRrjsS26ISdlMmSnKkBt4UQ/8uAGmE6eOUXZ6QZeGHd3QpyEs\nPTMBKfZ50ol9Ayo+g0MyVev773kZlA0GltqN/ujoaJBXz0yJ7xxmdOd3fHw8eq2NeO2ZKc2dot3x\nYJr96n3SymS0+v1QOuafHUTR+Ut/9F/KTDmISf5Sz3PA4fa6x3P6LNksX+msw/uQ/pDA2veocl0i\n0HIQxXa0ggLafvbBXPqQYKpHSWgTiOkpjP6TkCWlaxlJBzUtAUmHrk+ZKQqPlIKZmx6YkiOS4Vf9\nW5kpgpCkKN5PuxIjPwc0NOJqYzJi7hz9dyqltiwgWE4KpudKBrQ5KvtfPPO+4XcqJ4GEDzlw0rlA\nrw/tqAw6W4Ip7qivOSd0TgQCNK5TvNw0EmN7uYJKQ87UFwE6ZigEqLgZJod31Ifij0fQaUgsGWnK\nG/mWMswEw+pDB8Vy0ilT6FMAUhkCCSnjojpSFlqOfBtKgKIFpNxRqo50NrqOZ11PIC++V9WwGlZ9\np/4XGBfv7+7umnO2uDpQYEIAijq3WuXtO7xt9AVT/cw+9H46BLnN9IAi2VQPlllPD/Q5GtACVCl4\n5T1+DcvnSAqz0K6j3gbZAS4O0xxTnj0YaQGplI1KfbOLvn06MCUiI8VAppZdyV0Qq2pNGPUfhwxa\ngIpgqmXMPVvVy0xReDTcIQPkKVM6UwEBHlW1pigitdWH+bxv90EeWdFYOYBjn1Mx2SaWxXq7ggpU\nEBClVWKqE/uQMsIhl+SU2Y8yXpzLoRci39/fx2yER+4Ckj7HRmDKnbe/M05zf7jc2I3/vviqehKA\naAGAg5+q9dcBSWdPT0+H1wOtVm/LufX6GI+meyDK9T4ZRl3DiFlnATgCWjl2DdMul8t6fn4evV7m\n8vKyXl7eFn0oC6M5jCpXzp7vJnP59uBiX0CKvCO5I/FpCwzQCJB0L8ugbKTMlGx0ykz5ZGmBKZen\nxWIxyAgzNOxfnZmNoaz0+sSB1T7B7LbEOqgPxS/PpjpgcBub+qPVL3o2y1ZZCUjRplGGGUBR3hiM\nev2ZXPCtUQiuGIgk8OcAqwcOdw1gPiWYcudA48oOaCFultGKWhy9E1C5EDAC0jlFCz9+/Kjv37/X\nzc3NsJuzL/VuRd1p0h4jMz2r1fZeBOlKwu+7kkcdIgdzSXjdKSvzofQ+30XHZ6VhthSpJP4zW8X+\n9npqSEYGpCdrbAszLcxiCEypHXyeAGLaAZ6Avhdd7pM8CCBITQ6IsiwQQzDjqXUCNL7AmhnCFPRU\nrW/A6wZZWYtkbD2jyCFZATANHzNjqfqm/hA44MRY1zc5spRRPyS5DZQtYcagFYx5oOhZcy3aECA6\nOjoaQKQ7Up/bxDq15JnzQwWwaEc9wEoBrjv11D89neY13j8eSG5C7j/YbiYOBPoIPjgUndrb6ksH\nyQ5C9HvqR082HB0djTKPHtTrmaJW1ojDd9RhTjhXOa3+atm/Hm+3tZefEkyRWpGV/uN503IlMHR6\njIY8u6H7qOw8397e1o8fP+rm5qZub29HKX+V43OkepGErnfDQGWsyordE5h9gigKs2cOpbQ9kMM6\nCUzd3d0NRsUnKcqx+XYRLYXyPuUqLzrqVuq4ZaxT1uT1df0luL4yNCk3wRTnGBFkTPHsUNE1AUlr\nObvk1+dIyfFyoqwDn7u7uxEPlK2lnqgcZrZkkBOYWi6Xg2yoLgJObqyV5ZNT0NA6ZaeqRtkmOnrV\nQUBK9+rMTEgrm3xIctvFDEUCUpJhDzgJopSxOz4+HobO1U6u+FTfSI/VPyqzVUd3tlU16t+UwfQA\nlP+pTHfErYznFMDaN7nNduBDwMF5hmmhFMtsZbFan90Xyh/yGbJJsle0aymDlIJtZo48Q+WvCktD\nwT3/73LbCmDm+EmnTwWmWsJLA5DARKuMqeuoiI6wieir1hExDbI7B5596a6MSAJSCVR5xipF6Elp\netmgXZ2u398qz1PUjJDSITCls7JQvulqGtJz4ETnxcxjVY0yUzIKDthU3xbQdd5QNvQ5ZT09w6ly\nCEJ0sE2M+qZ4nGgbx5CiSPJDnxOYkvO9vLwcMrRVb/NhOFSq65m1EAA5OTkZDR1qTpOcawJSy+Wy\nTk5Ohq1KdKT6Sx48M+0yyzNlgLrHbIL3NwOpQznrOQEUAzN3NJQlXcPMKufxXV1drdlLtr9q/DJ3\nB5sEU6yf6sF7ZYtZNwZFLVvJ/5xPAo2pHw5JrWck0KFrqTOUWwUYHnSrjDQMxoA22SIFL9y/kMPx\nAljMQKfRHCYj2BavXys7pU2ZBc69jFY2svV/kvFNAdWnAlOkhDb5uYV6N30GswdUcKak3Vi+vLwM\nzOZcC0YOnIiseqbMlDva5LDpEBS5O5jwcWMn/rYvo9Fy5m5QU7YngR8NxaiOz8/PaxO95Uz9oEP0\n6ErDdTLA2q9JfavXx+g4OjoaMg+9zCF5lPo2geRk8Blhct5d4t3PIvKWxjFNvucQkE+e99WNdAwK\nOiiz6iMFHzLa2iZEQKpqvLKQQ3wCU0kGfPUQeaC5PgJivr9NKwgg+e86e9YkRdU9Xsy5pleWOxpm\nXdOzCPTpYLnKlPdyOM8zUw6GmCFO9puOVt89g9JyqKwz57u6kyeI2BZI7aKX3nb6GicPGB4eHtYC\nDpXJuqnf3VcIoNBeeiaY8960UMTtIO9jZorPo71nn1NemJ2iHicw5XJJIMdrUsCwq9/7lGAqGSIJ\nTisa3FSwPR3ICZTuRBzhPz8/183NzTCkp89cEcNy2B4ab89OJYetunEi7WKxGEDb0dHRkPYV6Zne\nN1NZvU2ohe71bBoHP1zguTKKyvX4+Fi3t7d1e3tby+Wybm9vh1U8DqR9eCllepQtUh04fMG+V38K\nULWMtg8l+OEO1EE00+cEU3T+KaJUm1N2ap/Ay4EUJ1uTLy0gdX19vQamuOxdAYkDKYEpGXyt3lQd\n9N4vZabkbASkbm9vo265jGlulACUJsqfnZ3V7e3toENcxu/kcpAMuGdKCaaq+oBqG3564JSeRZvq\n2QLaWgZznpkSmOJwK50fbXRaxq9npqCMAMyHvdw5t4AUh8rJc/KuBZDnOt5d9C212XV5tXp7XQxf\no6W6Sz/YHt2rflMQwePo6Gi0Ai+BKe1v18uuezDMjKJGGagX4qHblZSZirsqWQAAIABJREFU0mIQ\n16Ojo6PBnqvdtMGeqXR92wYvVH1SMFWVx0UpZIyuXNlaUR+FtTWk56ngNJQm48CoWBmTZERd+I6P\nj0cb2/mrUbglg2cvlFVpRcWeCeNxqAxHC0i1AKn6nsNCynpUvW2mSCep+Wh3d3ej9rLdDmJFzHbw\nO50EjZAi19TPKWLn7ynidXDlBt/nJMjApf7tGYL0+zYRWc+xqH9o/MQf7dKudnBVoiYsa2GGdNeH\nYqiLHgS4XvX6l3qd9DtlShiRJ9CW+lm8JEihg+E5BUwtWzWXT06tIEfXUw/kjBlwcL4e582wz3uU\nAEJVre0blKZQqE4toJQcuMuDt4dgKq0e5DNbnw9lO3t9STuWMjhHR0ejN24InKY+YtZdB4M10fHx\n8dp17Dvnm/ydnpNAm48a6DmeAU18rcqLmhxY+6hEAn/JZmxKnw5MueNKUVXVG6ByB5MAhEcuOqre\nVuroXs8GJFCS5od4dJAiJmYn/BUcFxcXMUORshs0iowipWjsI48+98kn9jkzcE6e6ZPR45vlGX3R\nCNAxCsSqvexvKp+uSwDKQQzn6zAlTblygEbnW/U2nOfGvZUtmlJqf3aa+7BtdNXiD+vmutQCAjSi\n9/f3dXNzMwL03p6zs7P68ePH4Ayurq6G/mPKnhGrG2a1XXUSYNPLcDk5nf3HNumZq9VqBBp8oYDq\nLhuRHK7IbUvKTqVh32Tot+EfdXCu3CkjzL4h2CcwrXrLQHFncr7igyDY+9+HWJXpa4HiNATVyv45\nX31ahACBn1v6swsvNqVkC1Lw5D5OAF+Z+ufn51GgSXIb9vr6OkxvIEAieFP2+OzsbG3Im1MxyDPq\nLBeKaCsRTl3Q/bKjKVMl8KtrRZIPt+Ppc8tmbQOsPh2YqloXZkbppBQBJcPUGqqRUDIF7U5L9zNK\nkxA4M2mM3LDyuwSZq2P0st4eCPMoV+RRrvojOd19R1ibAiml2QWmrq6u6suXL0O0RCDy8PAwahdB\nkiuyOz/x1h2FA1ZOQKfyJiClQ8qsdrPerJPLkmcuW4bUn52ypPvg4xxnkoIQl8mXl5d6eHiom5ub\n4XMakry6uhomox8fH9fl5eUwB5DE4TgCKvGW/JQuaWIuhxPdsLNtohaYkhxwCIuy5pkNBko+5MAg\nrGeLdnHePSDFgIuH2sd+Ud09KGRGkiv45Fw1lEveOR85Z0fz3hxktvSU17j9I7XAlGSOqwt7YMX1\nMoHVXShl41QvXkMbxusll1rkJHDr9oK853O46MaBFucvnZycrG2sSV1iuRyxIY/5DF2b7Gi6P8m0\nywhXQvvhftPrvAl9SjBFStFH6gQCrwTG3CG4k3eHr/NisRiGgajIKbtBxNwy0rpPIIpgKkUkyfAS\nrdNI0yGr/vqcIp+fQSnDIgAiMPXt27dhMrCU5/X1dbRDtow5nalnp6reUsG6tpeZYlaRDpN1cHCj\nZ2toR7KUwJFP+kz9npS6B6haDmAbvvS+u/NNwQHBlN6VpgxVmjz89evXUXlXV1exn/yQESdAlq5x\no1BmOzT0Ll46mFKft3SVYEOROtstmWKZzOokeWgFWrsCKedhK3jyOjqvBUQoVzwrG8J2C8QmsJT4\nyd/EQ5XFOrqOpr7z+q1WqyaYIqDS5xSskJ/sn33ZTA88W77N+z3pKwGLiH2sc5qjeX5+vjacLj3x\n/d88KNEzPWBgZokT3dmvBMLiv3jeA1Pklewv/a/mPaaRnV4Au4nefVow1Wv41G9EoFU5M8UxYAqA\ngymWpeXqaZ6BFD4hZBdmzc3wIylCimYZPTJqpANW3R1I7ZtHc6I1rxfB1PX1dX379m1wloyAZQip\nUFQwj7oc9HB8X9c578Q/vr6CdXZQU5U3kU2yygUCraxUq6/82Sk7tS0lR+lEXUrZVUZ80iGBl6oa\nbbiq4+Hhoa6uroah3cvLyzo7OxuAjzbpS+l+AV3yU46TjkG/awI5h/EpB2xbWgxCp6YyxG85A8o/\nM1MOplxmesN8+yIHRN5eRe38Xf2ZMkxVNXKQKpvDsM47X9Xs9k22S/3D4JFA2SdBc/6NZ1Vkpznc\nw/YJxGmoT4f3vYObfVJL3/x5KaDTNQJTaT6VbyXj83E1J9CDMc1vchl1IKxAyYEKA172K+VPc7V4\nv/TKwZT0No0WpZEhn3dKPWTfUuc2oU8Jphy5J5A0dWZnCZ0TWUvYxEAKAJlX9WaMZGz4+hJmJRLI\n8knG+sz3rikzlfbAcYPrfcTMCIeqDmWkE6882iIlIKV+9WjYHZq3Oc1/Se1MEWcCJz4EQUPEPV18\nTojLYw/4OyhXvVmG/8d+S/XeNSs1xc9Ux5QZ0JGCkqq3jTDFV7VBQ3xnZ2dDdkqGWK8doVMWH5QF\no94RUMkZ09GKny4vki8OIRG0J0DHMvg59VEK9nogahsdbfE/Zaacl8y+EcDQRnomQn3K79xDqpfh\nd/sqPfLMGfup5Sh1pECDYMqH+ZIv0eHgIvFjKmjclCgb1PEkD6ne4kV6vyTP9DVaFesZPYGz5EOd\nn8kGMqjnteKneKYgWW1ie56fn0ejBdLpBOzTkJ6PAjE4Tn3pn6foU4IpOmcayFaEnIyUjqrx/Anf\nC4qHz2+R4b+6uhodQvmaAyLBTcbaIyqPuHgQ4eugkZdx99Rs1fgt4vucV5PIo6cE8vSZfJRSPD4+\n1nK5rB8/fgzRw/Pz87Dhot53R564HHi/sj7sD06W5e9afntzc1OXl5fDMn8trV8ul8PwBTcLZT1c\nzpwSIFK/9LIUdNatclpl78pXBgepvzn8ot3NPS3PssRvLenm9gkaSpDcVr2l/mWcxcPlcjlkWqmn\nuldOVGUwM6K9sKrGm+WqPVV/2ojlcjnU448//qjb29u6u7sb5JDgQxlmGvIpcO2AmXKyD0oOsvcM\n8ljt8qwjr/Vgp2ocuBCU8R5luzxoJfBhNiENf3rwwSAyBSdTmQfvjzlO9VBAiuW77acOKqvDbQF6\n9dTZ56JKbzR5XTrKOUZTto1yQzDlbSNg4rOTbjBLqfZycjozU74De0v35vb9HPqUYMrJI6renCQ3\nCFW1BqISkPKXKspIKIrW3ipC+CkV7vcmAMDslh9V6/NlKGgURF0r8rocGlCRN/45oX7Vv6oG5yrl\nlUKn96bR0fYcfOp7Dc/c398PfSTe393d1e3t7ZAhkbPnQb4yIjs5+XMVlM/3IHldnBcuzzQGDqZY\nXq/MXfko5+SAilkLyq6GCzScSV6rHALa5XJZ5+fnA4/V9wlM8f6Hh4dRmUnX6OxPTk4Gnb6/vx8N\nF/tQgJyystSaPO/v2Hx5edv0VXX05ffJoDtfE0DW923Jy5gLtClrBCgtMOV2jDJJ5yt5lvN328Qh\noBQcpWyzAymCOR4qc8qhsl3kT+LJIW2o19HtP9vM/pwCUzzUr3qO/AiBlPtP8sGDPYJv6V6yWc4D\nBtRJPwjG1F7Xc/HYX7nlMrspUJpDfxkwxbF+n5fEjk3Rla9ESECKK4FcEPn2eG1kloykj/e2VqwQ\nULEdrkh0FFVvc4BSZCvFSHNqDgWqWkDKBZpGSQ5ruVzWYvG267kcmo/3MxMnOfBoTeCGCkdDod+4\nWSRB7MnJyWjnXR3iHY3C8fGfEyWZqm4dLadGuVEbWlFacr4OlPcFrFpASm32OQrMRrnBJEgWeOXq\nVc2R0qqilJlyo88+pVy50V+tVkO2mLZhsVisDQ1IBiUbkkNtSsrMFJ+nqJnRsTtxDoHMde7bUgJn\nc+SBQafLJ8ueAlMczmaWQuc05Mfnq4/cOao+OlNnKKve73OyU26zvM2tft4XtQCF+lV654F44m2y\nEzrSMDanu7g9IphK85CoN5J5z2CxPylfnIpCeyN91/VqK/tdh+ux7IUD8NTfu9CnBVMEEYx4PDLm\n2ScbCkG3wJMfCfEfHx+vzW+6uLiIY7bKgnDbf0YXbgxShEwD5RmvnoGUoDK75tfu2xDQmOm3FI2o\nTfrOlVncK8XnWXBSq8pvDfNxHk3V+sRUTub34+joaDRHSp/dmMiBegTVSoW3+JbkQL9733ofJmO5\nT+DcygQITFEHffKuR56r1TgzdXd3V5eXl4NuPDw8jOZFVK3v4qyhIZ9ELqJd4MRqZsI4L8oXjaxW\nq9FLkP3dYA6mPLBrRccJUKnuyfFtSy1Z8HOL16muLd11nVGw6UAqAUjatJRdoa13YKr/PTsyBabm\nOE/eU7X+Opc5/bgtJeDH6SUpM+VBmVOSB91P2+iHyqRd5VCbT0dhhirx23mnukkGxDvVj+1ie1v9\n5osReoGL99O2Qc2nBVNVNVIWolV/gzkBjjvKxWIRHSV/I5iqGne+MlPpecxWyUlozo3m3Shy90xW\ncuoOpDifxMGViHVNmal9ZCxaZbSElIYsDckIZApIKWvgpDlLc4b59CyCNn3nlgctYjZMR5rbxiyZ\nDznouR5lukH2e9OcL+/fHqDy+zal1C/umDwzTNDL+nE+jL5rpeTp6emgK9yfiM90p+KreFzGqOda\n2HFyclLL5XLYkZ3RKzNK3On86elpGPrVfDkuQEjZM5VNgJECC2ZZSPt00HMCrkSeNWc9XebYTtor\n6qN/5+/OT88k06GnYZukcyw3gam5TjMFgeyDQ5DXV9kZDp2y73p22G0O657apgDFN9j0+b0nJyej\nSevSOfoYt/mskweFCUwRUOk+1SEFIQxiNwVSqd/m0qcGU1VjpygB8LQ+U8tiAsfQOfHRnaHQvp7l\nTHCmqg4UOCm3r/DziKDHPG+np8T5vRWZ+ARPz2xt0/eb0pRwMlPEz3yezno3nzIEfMkn+dzK1rSM\ntitcVY2Gf/WZoCE9R7+nFZRHR0driwWSzFI+2P7WM/X/oQy8KEWZlGkupNBvnJR+fHy8BkTThNH0\nLPad5F7y4teyPxV80IgzulZEyyEG56UHLHyO11v/++dtI99diDLUe7bLkGc6XDaZ9U0ZH/W7PnMS\nM8tTH8veuk7SXvscWHfG0jf+NgUkUzZmKjDxsuYElJtQ4gX5wNGK1jMTaNcoie+9RkrbKBAwE1Tx\npceeiVX9FUC5ve1Rz75IVlpluI55+5JP35U+PZiqqpFBpbInhWV0pIwH98lglEtnxrRxVU47E9F7\nivH4+HiIYAmmSMnYthy0gyIeDqbkGLhipmUwDkWbOBAqm+8bxUNgarlcDmDKh998WJOHL23nyi8q\nb1UeCnYwVbUOerW6xqN2GRZlbiinvJay62AqOYp98JBgY055buwEpFQ3gRXfH823A2Emh7rhTpxZ\nrdXqbajQAQ5BDoMbRrlyLv6GAnfQCXyLks2ZI+9zr9snpWe5Q6GtYeSvM4EU+8TtTdUbmBK//bpE\nKWignCdd0vVy2um1Wa1nLRaL0XNaAZjXbZu+3pTc9lNumfHmM51XvF48mAKMae4ws4pMEvC1Z57p\nlSykEYhNgz+3MQRkCXBO6dc+gVTVJwVTKSKg461aFyQZUK60krFwgMGoVcLAoRYXWJ5l4F3hmZmS\nALB8UkLRblgSqGLmy+smQ5OyUu4c9smbbe5XGcxIJaemicsCUxoaOjn58xUHXDabDMbr6+vIaGhT\nSM9sLhaL0fCvPnNYj+DDMybasNXH8Vkfz254lioZjcS/fQPinrFJ0R+NrRtL7ytvK/eCSRke6pJA\nqPRegYoHJMxi+PzDqrfMMveh6mWmXJ5YLwdUqQ9b/XkoQDVVrtqQwJQ7Oc8OOKDSvTy3nH56hjvA\nVr/TtrPuzAS3HKWAk+ogINbKUvcAFdtxKErAVu0g6OfvvYyOruH13m7ZRbd5AlPkv8AUF+x4QCTf\n4zrFNvLca4PLU1phn2T2Z4CqTwemUhQlx8Vo0sEUlZOA6ujoaC1jQ0OQIrMphX95eVmbE6DntDJT\nPYVMRj2BKA53eJ2rKoKpJIC78GYutSLkqpxyZ331WfNYOMynuUxcUp+MohSce0rd3d2t7SqvM3fh\n5tYMTDM7aBWQ0qawcubKVrEfkqPi8yn3vchS/bgrP6eyUi19YNRIGXeDlvjvhriVDfHhOs27YpZP\nxI1BlX30+klelDXh/UkWaSPYRjm3ZLgPBZY2Jeer87Eqr/zTNT7MR12ifRKlTGvSZ8/ucejPM+ms\nN8nrkWRObWCGrKrWntEKUvapY1PEAK1q/J5E9U/SrwRA1OYU5HOaiM4CUdo6RHaLNkn3e2ZZq/nc\n77Jd3r45fek+k7wUPwm4W2D6UPTpwBSJzPCJrglMVb0ZRxlUKjdRblLuJKweGetoRaoO1lyYHAip\nLa7kaXhPzoKONxkMnltRKGnfAtgDUmpfekWBH5qgzr2nBGDIjxaYYgSmhQFpvpvAFJ9zf38/2vWa\niu78oLEhH10+UoajlZnqgUTv0134NAdQeR8ockyGl0PiDlDSnkwq27MhzChwfofLlnTd30wg48s5\nU70+9fo6wKDjmoqEvc9+BtBye9IiD2hSlO9ASmXSBnv/+m7j1A8Hyy4jKfhrBRHkbfIBBFIOVDw7\n09KnferYFKXyXWYSgEpgarVaReDjdpYrbLkiVYGmA2pfLdvasmBuX7WAYdX4fZcE45TvVIaXfwj6\n1GDKO4UImJPRdR1BDoUiGU4ODSTGtIwsHWlK+feMNeuflF/lcg8Qgg4anZQF8OirlZU6tHH356VI\nmf/RqPpeXf4ql3Qt3/3lu8DrmRya4koVRm/Mirhz9/3MEiDQ2Y1cAu1qO3mfjuT0NjHwU0Zujiw4\n6PEy2Y/sZ5dFZXZeXt7e4yeddZ5oZSyzkp6NYKTKuiobxaE9OhXvX9/ipJXxZB3pbFp8nsObuXya\nS0m/pupAEDHlHBPAaf2ewInXlfpCIKjvrfvT86sqyoh+b+lUz163yMHONtQCUvys7xz60u+yW8oq\nVb1lfzks528A4QR+luk+TZ85PK/Pqn+L30m21e8Cba63tNeShQSWpNMtgOn92Ov/TXTu04GpFmIV\nqQPkPNXxNNYqp1XeYvG26Zc6U8LKDIG+u0OXQNBotgAEoyzPWrmD9R3a+QJRzzZ5+1TXqeGhQ9JU\nlOfCzwhYRtDBEuegqQzyX+nqlM2T0nHOjL/ny4coCHS48/319XVdX18PL11l/R0M+ATNHpBS+3yi\nPB3/NtHfrtTSI32m3PcyvARf4h13G7+9vY1AxN9UoAnHCbC4PInfcgQXFxdRr1QmvxNIuS6l938l\nQNWzX6JNovg517B/N6UEfJM+eXBGGWYmKGWbVU9eW5WH33TtXGDowazO/D0FmrquBwQPRaxfehZt\nB68hkKJPUXkOpLSyjy9yl056IMLEAp9Pm6bP3k8OmPRZ/9GG638CxFbSowVwk1/ZBlBtQp8KTLkC\nVK2P9essB1RVI4VOwMWHII6P3/bJkGDSmTsAStkhGXw9X/enNrmxqspzDRg90PC7gfO+SWDKhXkf\nNOUckjH37xL619e3XbIdlHqfp0iHwz/auZrghP0s5dfzfC8VV0LRxcXF8D7G6+vr+vLly7AqjIen\nwlO5/gyCckWFCUylzFTq10OSR8se1baAhJP4qJV55KsbwKSHBEt+j/jAYUJf2aShDc6LE7jzFZ/U\nN33mkEgC4nOHATfl3SZOYVNAlYCRZ3B62W4fhmmBMLaD7UnlukN0mXBHyna02jYHTPk9P4OS3fS2\nM2hggMJrq/7UQ2amdH54eFjLpCfASNDLZ3mQyCREkgX6Kf7n7WUbnB/83LN9yb4eClB9KjBF6kV3\njngFbFJUJCOYXt1S9bbah0x3oUjGgSvR9HsyAinqEyp34+97f2j1Wc/guIN2B9Tq27k82IRaBi05\nTEYimmfB/uwN2TmY6kU1cqoEVb4CLIGp1WqcmRKg4jsUdW4ZnCQPNGbMrrayUsmBbUO7OodkpGTk\n09CAy6cMpr/SScECefb6mifScmK5wBONKa9l/+vQLuzL5XIom5lnyRP1np8dSDE7lbKPyXYdChTT\nIbcAVXLY+j3ZqQQoXR8VRHIup9tP2SEGvCkAVLlJlloZikQeRKZAawpMed8egpIspOG8ZDer1oOY\n4+PjCKYkpykpwWf5anmdadMk/6yzylGSwVdp839SAoc9AJV+dzCV+i31+zZ69ynB1BxjlLJBMow+\nRMZ9MrglPjMKLDfNUUpZDzlEgQFHyBKSZFjoHKREaWiDm1s6MHFF8+taxvOQNCWo3jc0DGxDGl7g\nMzjMl+ogkrFRCpvZDZ/srHvFYx/m+/LlS9zHhQaHRod97kaK7ayqNSCVhvm8bYcm1z9+d6Puzi8N\n3WllJfcPWy6XMWjxV1jou57n0bnz1nlxcnJSd3d3dXNzMziW1Wo1mqMo/WsFUb3MlLc/UYt3hwBU\nmxCDUtq6lKFy2+KgRGAp2cvFYn2YL2WLdC15OidQbDnkBAwTMHsPe+nPTYFnVY3mGAlw+miLz+3k\nK9bc7iT/QJDkv7eG+XSmD2MdCdST/6LMtUBzC0g7kDpURkr0KcFUVR9dpk6vqpGx1lmOiQ6ZczK4\nMaOU3TMjNBSJcZ45IUNTlmu1WkW0768zIZhqCVLKqCRFUf134UWPkkFo/d/6ToPHlYvs+6r197f5\nMIKI2YpkfOgMk6PgXCkdmuzJulZVzH4mSkOaVesGvzWX5D2oZ6SoD4kXClq4AlD/kc8ENeS97mHA\nwuP4+Hi0pYXexacMIp/bOwjC6IB4+ET7ufOktomEE5B1amVRpC9znkmZlw0kYElAivVxPeT9rWdJ\nxmnHvUzXV28X2+eAKNmi9Hvrvjn2csre7cOZ93xgyoan7LjmTHk2laMx6mv5Jm+Xl9+agK77Bcxd\nNihHBEJ6vstsCrx1uM4xID4UEP6UYCoZJjcsHhHRCfWW3DtKdqfoxl2M80PP5pnCkiI8zgGi41Uq\nlu8rc6fiEVkSJjdKuwCobcn7JJ3TMEBrOFXtIZAlYPG0M8mzFK15LgLR7L/j4+MhG3V9fT0M9TEz\nRX4ko+aGOslSCgxamYCeczw0n3vPliGlXoonnO+hYYiLi4vRdRz646t80k7L0tOXl5fRazCYqdR7\n+ZjZuri4GFYI6tCO+px4rrq6EVdbesOYuwQrhyI6KA+6WtSSxZZddhutfmPmwbN21A0vy7MtKTPl\nNpFgLrXfn6c6vnegQurJgfdza54mh/M0UpMy3MzWq6xkb1ar1ahsnasqXuv6oes8M+hgKumVfmM2\nU3xLi4YObQM/HZhyxUrKysjWgVTKLKUo36MuGmmm/KtqNOdGwtuL3FKUzaOqhmELZcY0UZBDlJ4d\n85T5IQz7PqgV9Tnw7YEpAU8fOkr7niQwJSPgS4U5LMN+Yr/qGQJTBFQaEvY2etbCV9t4RMbDgWYr\nG/Ae1Hs+jWXit/qSRvbs7Gz0n+SeGWXNeeI2FARTnHuheYXcT+zq6mp4ETlfTO77lnHvMh9OTgHK\nnAnnm+rdz9BTf0bSSyf+15qY7AFuq/ykb5SRqjfQzYwUHbg7egd5zHS1MjleN9Z7Ssa3oU3unfJ7\n7HPaqDS1gLri/s/lWaCqBU6r8gIu5wVlhQmHqhr9J11LQCqtjlVdBcj1rKSDh6ZPB6aq2oLF3yn8\nYmjKRKWxf1dIGlLPTC0Wby9H1TJrAaDk/FUPOQbOx9C5qoY31vPZPlcqrS5qCRMBRzJ2P4Nnrcgm\nRZQ9MEU+qg2+BYGP4bs8VL1li5ilcOBFWVI/6r6rq6sBSBFM+XNahsFBvIyT7uFZ5aX+em9AlWgO\nCFRGoeotk8p35GkvKG0eeH9/H4fdZFyrak0fFovFAKTu7u7q9vZ2mOvG4+rqatBLPxS9e2ZKfBXN\nBVFun96LWI8EqJLO8D/vZ9odZkCcvO3puwMpBwkOqNJQD4G164kDN51VjhwzAdimPNsXj3uZFc/S\n6LMDKW2YqnrxSMECgQoBWMvXONChjPDeBJyrxqs8ua2Qj/awLyRjrTr97MTBpwRTVRmN83cqgDMq\nrYryiYcpM0UwxR2XNWFdjvni4iICJA05aO7W3d3d2r42utYFfLVaDdG5AyoHHVV5DogiBp0/QnYq\nRTspYkpAitkcgpwUkXnEuVqthoyUhnkuLy9jZomZEmWznp6eBjDFzJTm4lS1QT+HDv1gHb2+Oqch\nvvegXrRO+SJfxb+qGoZEdQ+HFQSknp+f45CeR6syqspCcT6hdI3763Bo9urqatj00+1D2l/KgRQd\nfQpcXA4+Ek2BhLmAiu1lho7X6nPqB5bHZ7nz9KBJYMqfkeTPy/P26z4OQ9KfvAel+vZAFbNJbgcZ\nwAlszslMcU5juif5Gv5HPzo1zMcV2Cn4p8758+hHPHghHYqXnxZMJSKTGOW3AFLr8KwU5yb5Xk8a\ns9XzT05OBnBFNK56yNhzTxtfYVi1Pmm9qtZAlANBOmQHUBSgKaOehC1Fqm6E5pLf532egGyLT2pr\na46Aj+HzuVzdIhAsMOV1olHRLtjKaDCzlZbcJ8Vu/e8gLvGAWSwHWz+TejKUAJXkU6/scRmnM2Zb\nqT903g5O9T8zTBzio2PhXlIKUqrWV4/5fmasqwMEH4Jw/u6SnWrJCb9vS4mPrt/+ewo2yA8BX5dl\n9iH7hYFgytLSnqVVaq16SSZ6AISymoBU69455Hxp2YM51LqW/e4yODW1gEG7lydbJ71pBYAJ8HjW\nUve2dEN9xcBL4I9n10FOE0hgfF86Moc+JZjyKIbGlGCKSszOlwL62KzK5mRVGunVarWWRdLwngRO\ngiRjzmySXntxd3c3movhhprt43CiT4ZNiqp2eIScIgL25zYR86b3uIPlEIk7TY8QVX+lrM/Pz+v1\n9XWUbeC8J5+/onLZZv/PjbjqqHozCtJvyjKqHM5LSM6Unx0EJ55W1RpoZl++J1He9N0B62q1ioDe\nMxfM/lJ3fYg89ZEHCnIC+s8dr+RKk9QZ7brjp0yobF7Hg0NOLV37rJT6WCBG7UwLORKlbEsKCj07\n0gJTAuaUDQ88UoBCW5QCtVZQ8x6U9IpAMYG0o6Oj0QpX9m1rDiBqEm7TAAAgAElEQVSDQR9SdSKA\nagWJDrKphwk0sx5chOUT6BmkeZ3EV55/Bn06MOVASr8549IcB0frHFagUnHclr/JeTI7pCiMzuHl\n5WWIeDlx1rdlcGDkgIqgrqrie+g80vO2ehSQouRdeLEp+XPpjJPBYL05zKb2+wRyLQbw9qdnk+/J\ncfK+1JfMglT9KSdU9ikDk7Jv6dn8/1DGfRtg3AJT6fCoMq3CY58QYLX21BLxu/RRnwmCHVwra1U1\nfmdbqz1uK3pgat+69rMp1dkdL/tZPPV5TC3Z9+cwS0Iw5brsDt5BuetVC3y4o21lx98bTBHwJaBY\nlbNd0h0frZnK7ru95cFnsTz9zucnch62wJSPMvj7Tmlz2C98dg9QzQFY2+jshwRTvYa4ICUgJUZP\nZaY4fJOGCqrGb6WX0aeB9/ur3vaz8qGENNmcEbe3UwqhZ/uLfVuZqWSEWqnVfVNPSJOj8oyUzmko\nh0om3qbdfNnWlmPz/2hIdY0bIu9Hgeuq9RdrzzmYhdHZ680sSisb+V7GnmCKbfLskhsyDgO5zrmx\nV7/OzUyxPsycuExUvW2fQN65/rjM8v4EGOngD61rqf3b0tx6sj91X2tYqWeD/LkvL3++SJzb1lTl\nDHsCVFXrG4Qm/UqZqZTFagGpOaBh3+SAqgUQWadk6zkUlmwOs3/uQ6bAlNfXAU9Vrfle9816zvPz\n8wiYJ/Cc+p7gif3mSYop2pavHxJMTRGjiaoc+ScgRYa9vr4OTPVok2P3yj4psvYJeG50X19f15ZY\nC1SlyXseAXm0xH2kOH+D9yUF7zlx78t9GYVtBFUKzvuljA5sOLQg4p5SAlN8Tq8/WkNNlAcacu9D\ngSlG0b3neVluFHVfK4vSMl5zaN+Ay420zgSkbjDVNukc+5lDfGyrLxaZO8zHYYmkH9RlZafS8JED\nAfJO5ehzAlJJt/bJi0MD6VQ+dUftTxmkBK58uIj8f35+Hpyp7G3SHTplzgViX7eAEe0s2+fXJ2D2\ns8BTIpc3JhSq1u0dVzrrPg63u8118ON9TvBMPU82LAXD5J18bwtMpW1kWsPnreCy9f8h6dOBKReC\nFmDoOSkKgQy7O9XWWLQrHR2oDPTT09MwP0pzpO7v79fqntqjuqosAikHY62oycGjAz5H7tv0/y7E\nerAfadhUZ/LBh4qqajSmzj2lvI/dwXmfON95vSZAel+6MUngXt9ThJ1AQXI+jAJdpg8BklpluoFK\nRk2/qS+oIw6mXOck8z53Zu6cKQc8dBDJ2Dtw8+EFHWoX+ZICgGT0PzqR3636et+5bLeCBTpGXwzi\ndRCA0qFsfLLtydky8KI+9hw+2+9AKl37EYhto32cCrIcfJFa/PND1zLYZX28bqkO9L8qz4GU21oP\nLqd0a592cRMd/lBgqhXJObWQJ5XPlYfPYKaJht1BhhsQV0oKDyeKr1ar0XwoPZd1YJ11Zrum0Dcd\nUerHNFzRUpT3JtaBQMOzC654VTVyejoSWPW2u5Im0Ow8cCDTUlqXG5XHtvYAtZ7D1LobLC9jW/J2\n98rs/ceIkH3M9sjoJzDL8h2EekaSmbBk9P1I/UsA5WBqanjBdabFy2378tDk9U8y7oGByPug1+9H\nR0cjkMShXa8Hga3PjWsFmir76OhoNAXC9+EjIE/AMQEsPsv1fBeb6XKxjRx4nVIWjUN6rblGojn+\nIQHVli9sfU7zHt1W+G+9LO+Ufu1DxzYp40OBqU2oBaj4vRWJ0Fk72m0BKi9PZVW9Zau0cmK1Wq3t\nS8Ositc1RU0twXGhbzG7BaZ6ivJe5M9vpZyPjo5GS2WrauQAfXjHeZXAVMpQJafiGZGk4K1+TOC8\nd/ayWkDe7/koxH7hUBD7wfvPZZ4ywOwQs3VTstwCiW7sdfZhqdbcn7l2h9kSr8++aJ9luszzd9kv\nDvd4nyeQxQxrypA7oHL5JlAQaeoFeU/Q5FvH0JGz33p950CqZ2vfg1gXAl/yUBkiZnoS9QAx/9ez\nki3qHapvD1C1wFSrTqkf/Pe5gc2+6NOCqar1VK1+S45LRMZRUXqZqSlFUqREgeNQnAtLMsJJAFvX\n+xCRf07C6EZwypj8LCIPRK5YHPZTJoFgyo116zkpAqOTFLlTEX9bgIrfnZIh5rDmHHDkzvtQQGpX\nh+E8JKDwtjo/qtYzA+54j46OhmEA/daT4ymd9WNqWCFlpqZ0mX1zKIO+Dz2mnDqgYv+0QFSrXml4\n2510DxCnwCa12/c+8sUMvL+npz2n/RHsZVVb5lTvNFWhNzTW4of/x2e1bFeyZQ6ofO7nFHhK9XJb\n0eqT1n+b9O9c+tRgStTrLDdoVeOsjTPFAVUqz7/TubvAqJy5makp58o2eBt57dxs1EcwEARUrtie\nrnZlTA6v9YypdLYbDPKvNV8pgak5ytuK8Lz+/O3Q0dU+y3c5TTLdAkKUAQIvXwWUrk3GndeRUn3m\nZHT9nmQf3E54fT8iqb6cj0P5d4c8B8Sm/kv96gCW0zBoU1s2knPfGPikbJfbih7IaPF5H7RLeWy7\n26IEjvi7k1/vv7We2wJOvXYl/tHO0961ZM3BNs9Tv82lbXjzYcHUJsI7p9OcGYkhvc/JaPIzlZfp\nZN3bcvLuuPWZz9PvU4Z9yoixHz6aUWd9pFievfE+0n29drL8VqYukc+ZcmOegADBeUsOW3LTcsAt\nQPDR+CdK/U5DP6XTiX+r1Wpt+Lb1HM9EtJxM7/lJnvy+ZJ/o2BIvEwDv1ePQ4DkRAZXqoYPZ9xY4\nTTaL5IBKZ5/36PxsvQXB+e1ZKNc3gvxW0KM6utzukyf7KGfK1ojmyls68/8p4OQAr1deev4m9UzP\n5Nk//wz6EGCKK+Bubm7q+/fvw3Jl0lwHlWiKUQllp/vmABd31FMR3Nxx6BTd9erkdZvbF4n0ktj7\n+/t6fHwcjJX26tH/379/jyt2ErUMcuvcimrYzlb5rb7r1Ytnd9BTPGOdvX76nMpQ/RipbwKGk/z/\n+PGjlstl3d/fD1ttrFZvu7cvl8u6ubkZvVdwinq8m/t50/Knfm+BKd7X67/Eo0QO7n04P9mBOeUm\nurm5qeVyObwxQc8j7378+DG7vClq9VPPyVZlIJV4nfQwgSnNk5sDpHpZq0Rp6DYFbC3QMIeen59H\n9pLb7WjrHNlLbfo7RZvo/DY010fMBTCb2K05z2td16vPNrRcLgfeaY+7OfQhwNTz83Pd3d3Vjx8/\nhvTu+fn55H2p03YBU737W9cm0LKp45tyxglMuVH2+k8Z8Cln6PTw8FC///57/fjxY3DKVX++L3C5\nXNb3799Hkx0TbeJE5oAqljlHWVt916unO4veUEMC5L16tgDz1Dyduf2oZ9/e3tbvv/9eNzc3o/3O\nHh8f6/b2dniFhBv1KSM29dy5353mRLGt5/LozbFpgal0TtQCwi05m1Nmot9//73++OOPur29rYeH\nhwFc3N/f183NzbCB7e3t7Vpf9Nrq17b6JtV7qv/9+U6pjzjhn1sozMlA9QKa3rOpWz0b7G2bQ6+v\nr/X//t//qz/++KOWy+XoJffu63pBzKbywjrPpTm+oVV+L1DaB5DysjcJ4tJvc9pGX3d3d/c5wZSW\nPz89PQ3v1pqifaHyTcrqRVz6zPOc8pLS+m8tgDQFILdRFi/76empbm5u6ubmpu7u7oYo+enpqZbL\n5TDHgu8629YZpzZNGeq5Cpv41CMHU3N41apjeqaX0TL2vTKmSBnfm5ubenh4GMDUw8ND3d7e1mLx\n5xDO3d3dRuX2aJNAp0fbyE3Poc4B0ZsY9wSmVMYmQKRFt7e3dXNzU7e3t/X4+Fir1Wp4XdXNzU1V\n/ambh8hOpe+bZCymnsE+8vlSsiGtpfYJNM8Bc63gd45Ot8pNdvj19XVkLzXSohEY93UtILIrzalv\nL7CYKnOTwGhum8iPTal1nwcMPXp8fBx4x8TBFH0YMKVNLZXpSENF+wROiTYtv5d12Pa5Uwo89Yyk\nIPtQTBlw7ehOMHV3dzcAqZubm42UplX/3u+bOkn/f25WqlWHKR7NdSYtw99yynPqmZ799PQ04p1n\npqR/379/n6x37/lz2r2Jju0SlScwtU3Gq9fOxL85AGqTdvENChxev7+/r6qxc94HbepYE82Vf55b\nK88SaJ0LfKaezzpMlbONbyDvmJmir7u9vd1pWsQ2ddu0/BYdWpe3pV37Q5lf8W4umFocGqA0H7xY\nDA9O+7v8zM4n9SKaXl/ts7775Mk+IxxffqwVdnPem7SvOmxD29QnZZC2qVMv+muVsWsmyon7u3C/\nHd9pfa5R/+i0D/3ZZKhjl3KmyPXNeUceHpo2ydhtWlYrIO2Bml2yn/vib+9+3+OK73L9CL7uH8rU\n8nX4PzLsQ4ApRiStoY1/6H3JU+0+6XZqa4J/6P0o8W21Wo14dkgQ/A9tT2nhg2dw/uHdx6Q0xyvx\n7R/efSyir/Mtcf7v/8iwDzHMd3JyUmdnZ3V2dlbn5+d1dnYWnfIh0pyHFuS5GY1D12PXvtOw0MPD\nQz0+Ptbj42M9PT0NfOPxWYzD3Hq+V8Ah2nZITfT8/DzwTDxcrVYjnp2fnzfnKe76/LllbkKbPn+T\nzOBHIvJNx9HR0RrvWsN8u2RqPnK/bEs/S5ZlL6lzj4+Pg6+Tn2v5ul2f/568+2gjE5uS5pOSf2l3\nAacPA6YuLy/r6uqqrq+v6/r6OhqHvwKYSmnqfQ/rJNq177TU9/b2tpbL5bA8+/T0tC4vLwe+XV9f\n/+XB1M+eK7CrsdREcy351e7QZ2dnI75dXl4e5Plzy9yE/i5garlcDnyr+nOO1NHRUZ2fn494l1Y/\n7yqnH7lftqWfCaZkL29vb4ctEeTrrq+vB3+363y3j8i7Q/iAn9UmLapy3k3RhwFTFxcX9e3bt/rl\nl1/ql19+2SnD8bPmNvXKbzng3qTj9HnXemxKLYD3+PhYv//++7AL+cPDQ1VVnZ6e1tXV1cC3X375\nZdawwzaKsWuZ+3Tgm0yy3ZR2mXid7l8ul/X777/X0dHRaAKsHPIvv/xSv/76a339+vUgOpfqtG/q\nzXXs1WHTicv7ormy/P379/rjjz+q6m2y+fHxcV1cXNSXL1/q119/rV9++aWurq5mPXfOHL6pez8T\nHaKdc8p8eXmpP/74Y1i1p21HBKa+fv066N3cleubPN/pkLz7aPObezRH7x4fH+uPP/4YVqjL103R\nhwFTErB//etf9dtvv9XFxUVVHXYlyb6J5fbAlBvwOSuAdq3PHOoBvPv7+5FwaUm9wNS3b9/qP//5\nT/3nP/8ZJjL/zEzioScEbwqkdn3uJten+vD+Hz9+1GKxGO1PxMzUr7/+Wr/99lv961//2qquP2sy\n9rbPT5Obt10Ftk+aky3THkSKlheLP/dkOj8/H8DUb7/9Vl+/fp183i5TC3r9swtA24S2fc6+2rlJ\nmS8vLyMgJXupxMHXr1/r3//+d/3222+z9lTc5NlOP0O2fzagOqQPka/TCva5mcMPA6YUaf3rX/+q\n//73v7MjrV1pH8517hCCgFQy4JyI+DOG/Tal5XI5ZDVubm4GwKRhPgHh//3vf3tbpn1o2tTIbup4\nW+X/bH6en58PWQ1tFvjy8lJnZ2cDEP73v/9d//vf/35qvXbph22G+fx57wmkNiFtF6P5NZ6Z+u23\n3+rXX39du+8Q7WqtoPsINsrpPev0/Pw82g9M2Sf6ul9//bX++9//NofX/6H3IW4Xc3Nz87nA1Eei\nTedW+Ez/XnmeuUoRsxv9uZmYj+4QPhLNNbLOB/GamY3Ew6nntORozm9/F5ri0RwetsCT/y+dm6NT\nf2ee9LKz+xgR2PcUiJ+VMduGPmKd9kXvOTr0nvQPmPo/csNaNW1Mw5LJbpkewel7Ovx+lu8OvUUf\nbR7IZ3qu84GGmZ9bvEv1Ss4iATT9PofHfxWak43dFARTb1Lf6popPm3Cg78qv5Kdmzv0PSdDy6w9\ndULXJT5N1dft5kdz4B+tPrvQJuA62bWP1hfb1OcfMFXtzFALVLXmP/Fz62XHDqiSM073MjOSnul1\n031/F0CVnr1t2xNPvO91XdozpgXMW/xL3/+uQKqX6Zu6t3Wf+pQvPtY1vtdP4kePFy2n8FfiXQ9I\ntQBnb+ivZQsTn9wWzq0nv390UPUZyPnUom2nQqTrP1tw/g+Y+j9yMNMCJemcHGVVDeXMBVTukN05\nt56T6K9kzHuUnO+U0Zybhp6KiLXZJY8eIHdn0ePn34V/ohaI2jRjlXimzfdaWUXxrqfTKXOY6pMy\nmJ+ZpoBUL0vFfmvpJ6+RbpBfVet2dKqe/J7sgdv2Q9Ffgf9VbR2cKw/87rras9WfDfz+bcGUG7sE\naKpy5Nkyur5baoq4RW5gWhkOz470HLEbr0NkOH6WIdqGWg540+xB4kvitf7nK1l6YIq7ILeAlb22\nYA+98rGplZVyQNQDVa37WjrHrAdfrpv0KoHd5Nhbzvsz8zABJH7vOVEHrU4JSDmgSgBskzrreyuI\nbf22T/rM/J+iFl+SvvTK8Gt+No/2RX8rMNUyyD1UnJTc39lDp6jzycnJsDSWTlbPS86ATlqOOtVp\nql26tifsU/d/RuplEFrfEzGLQaPei7zEM/HaX6/DiNujb31WOd6WOXWeG7XPLe/Q1MompcCidf+c\nY7XK79nyV+ko+9EKUpzSNen7lDy+B21ShxZI6umBn3sBoq6jXfXy3HamZ7f4NQeQ72r/5oAFfu/V\n6T1oHzI5pTsE106twEhnAu90T688r+Mm1AoEWvS3AlOJep3lWYOXl5dhyavODqj0+fT0dG3sv2VI\nqrLQrVbj96elKDkJGsucysL4dZvQR3EMKcrVf/w+V8k2UVrO6fBMB7NV/rLal5eXAUiJfCjqr0g9\nECUw6kOnifx9kLw2gSnX2xbwSo4gBUEMnPyzf2dZH51awcdcEMXPBMetzLu+Pz8/j8Bzy26meiV9\nlz1oBaYO0Ob0xV+dWnI/5R8SkEry0pMh54fLh8vXtj7rkPQhwdTP6KQpBXKjKCf4/Pw8HE9PT4OD\n5FFVIyAloeAbwqng/qyqGoEnGoKk4FOAqkUOtvbR7+9pgFpGdQpMOYmHyWEk/vG/o6OjOjk5Gd4I\nf3JyMoApOnKvjxv/KVC1b2NyaJ3rOVt3uuo7ASuS+oT/63MrUKHOao+tRK3MUnohdCvT2Mo4su6t\nzMl7UpK1VlDSouQQPbigc/R+1Ss7+Lzey4BbTtx5p2tbctdq95xAdKo/PhL1bEoL7KQ+8HZNgSl9\nbgUfKbDh9IreXNSPRB8STB2aWtExyaNMAqmnp6fh0HvOdI2/3V2GxIfvGEnLuCuKXq1WI0fiStAC\nVFPUMpj7AlTvBaRo/HrZgeTQUznp/uQEvGw6jpOTkzo9Pa2Tk5PhNS4669l06nOc1WeNlOcAKfat\n+k+gNMm+X6N+Ztm6nnp7fHxcz8/PzYwGv6ssB24CSp5t1Fn/ixgk0YHo+3s6Bpep1NcJpDi1eOlZ\n2rRgwzOQ/qw5Ntr1VUO5VW+BaavOXqYDiF1B1XuSt29OkJZkwAM//tYDtPw/BR3OY5cJldHKUH8k\nUPW3BFOiHiMcSBFQPT09DW+TVraBgEpl05icnp6uZTLkKHyY0DMjPRCwC4ji549i3HchKq/PYasa\nO/JehJb44XNr9Godj9iYkTo9PR347g4+OYIpx5HqOpdXH8ERTAGpBKY077BqzF8BVZ7TliKr1WoI\nfOjQyV+XE56VaXTQ5kP9/5+9M11uHNmZNuR9affMnDfm/m/wizndbcuWN+n7MZH0o1RWkVrcbc8Z\nRDBESWSxCkABCdRCAajZbLYWHLFtH7nPtYK1loMUeaaADtEzhwSlCVQlp8uyU30dSPHT6+jy0H/J\nHrRAVQuUfYT+RerpU9JFt0VeTvJFrftbOpPmHFMmDqT4WrKxQPgj0P8UmHJjWdWOmlPE6cCJHV/K\noag3GQxPbaZJrxwm8ExLckZeb1EacnBjyMnVU+mQinyIslqRtXiXosrkNPW5Wq1ixiHNz2FWshf5\namiJ5aYIrRdlT+HdR4igXUf9N36SUh9w3iaw7M+qWh+mlTxptBXESJ69RQY+NDVWX96fQPGhyPvy\nNjJ3m9cDc4n3CYwke5eGRwmueM42tfRedRVYpe3tOXLqA/sy655448/18235zjbuen+ifXXM75df\nagXuqR+3bGnL7+gaZhDZDxmcuJx6dZvSzqk8T8FDj/6xYKoXaaVPkgTJg0i6qgZDQIOtazWk4xMq\nBcZOT083EDuHCFttSYbJDQMNjoPBVhQ+ZlDfgw71rNRB9JvPSWsZWn4S9HjmwR3I2dlZnZ+fbxxn\nZ2drh4aWfCK0Dw/3gFZqbwIu+v6zAJU/143fGOBPEazk8fr6Osyj0e/8Pxlqn+TfMogMZlJQ4wGM\nHIA+OQ+LQVQ6pvBtKo/3oanOsQV6UzCXwJSAEoPBqk07xixuDwSR2E+Xy+Uwb7XVJl7fCkZbQDC1\nnfbS5ftRMyaJxkBj61qXO/93IJQSDs/Pz2vlJblUZVCW7Nw2AO+96R8FppxpLpCqbMDTfcnRMvWs\n9H/Vm0Ko03JlCh3D2dnZhhNQZMyyW22i4WJ0R8clx8B6qH4EBu9BU8ttdeRdyGXXcgQpe0Cn69lF\nP9Lzzs7O6uLios7Pz+vi4qIuLi7q8fFxA1CdnJxEcOugwOvI8ynONxn896RkyHpOyY1t1Xp2gd85\nz0xtEnlAwN99TuNyudwYZuLwAT89kuYzXHeSjmwDkFo89LYeoq8mUDR2pCCtd43PixKPGZAogKAN\n41BuymC4Q+T2CQpcNYTr+kc5toISnXNYWYtGpgIO72+/ElT5s6fYgFZbvbwxHeF3TzBIVvzfAxYn\nB1L6THJ0YNXCAu9JHxJM7dLwFiDyaNadVmsYoQW49Mmom6tQZDTcQPsQj8phdqvXdj5XRqg1B0HG\n3ic8uwL757Z0CFDUin6nUGoHyxmLeCSX5Bi5wOD5+XnDwa5Wqzo/P6/Hx8e6uLgY5tCdn5/X09PT\nkLUSmErAyeXuxp/XpKjskLRtn0tRPp2kZyi8zgSTbLPAfxpaY129v8jYPj4+rh3L5TJmClO9vS6M\nsD1L3QJTnjlOwVriof++jTymXuugN9U3/dYauuNnGrpbrVZDv3h6eloLID0zldrCkQD2A2UBJYen\np6cNkCwepgDGn3N0dDSMFui7ynLeOah3+VJ2P8OBpzr6b6kePbub9LIFmlJGUn3JA0jPMiffS3Kb\nOMXGe51dXl5+av+ucvuQYGpbSgxLYMiH7FIHSx2EBpuHOpyMs56hTAa/Kx3tZcmQJGVODoOKe3p6\nGo2YnJIPM5IPbNuujvnQxmIXINYyFN7hGRVXrQNfz2Y8PT1tnPuQ3HK5rPPz87q8vKzHx8fhOgdV\n5+fng4ynguY0HNvjE3UyAcv3Iu8PdKpjWSCPNNkvWyCjqj3vUTJ+eHhYO5bLZV1eXtbl5WVV1Vp9\nUhDioKnqLSvlYNvBlLe7ZaCTk/LrpspwVyDVOjw4c/vSu8+P5XJZDw8Pa9lyr4MWDzBbIfIFOuSN\n5CIwdXp6WlWb7/Lzfs7FAfqczWZ1cXEx3Cfb6kGM21I/T4FpTz7v1UcT8Ev/67wVeLbAioNqgmKd\nc7qEDs8Wyiarrs5X6oR8WgpOkh1qBVw9uzuFdz369GBqCpCic/L5ML1hvyQ0GkqPvikIGmNGZG6Q\n2GFb4McVqGfAZMTYdimyR5fJULQATUL9qdONUQKNY8/dlnpDTYyMyW/9l7IPcpyekXh6elobvvB7\nzs/P6/n5eTD0Y0aA9UgRbys6TIaEZe3Kxxb1IlbqpoZM0n2Sg87FO69365lJj5+fn+vh4aHu7+9r\nPp/X/f39Wpbp+Ph4GGpP5fWiY0XWj4+PEUi9vr5urD5MfTnxzdune3qgaxdKsmrZEB50kr5fVA94\nydZqnowcKO/xlV0ebFZtzn0kAFdfowwla9pWB8r87uCOv1MOLCeBPJd5j3Zx1vsEvrzfQaJozC/Q\n7lAHkr4ku7RcLuvk5GRDB9zf+iezxLTlLXvI9uo6l9OucuvRpwZTifkUgCs+HR6zUyyjap35PBj1\nUMkYYZ2fn9dqtVozOr5HjerHlX+uqKl9AkX81P9s32q12gCLVDpF5y0H4vz4GdRznuk3N3QtsOmd\nXvOXuM0Fh/PkMP14enqqx8fHwfEzW+Egyo09M1OkZJjS4Tqkc/LEHbcPEZJv+xjkMQBN46o+4e1O\n0TuDDye1NWWSlDXUNiXKSC0Wi2GY7/T0tB4fH4dPbW/QGo5kOyRnArXFYhEzlZI1Dxr/Fg/17FZ0\nvg8lZ9MCS9rKg1tOcEsInfeeQz18eXmpx8fHenh4qLu7u7q9va37+/uN8vW2CM8KJuetsj04JojS\nELs/I4FgluXZx5Zzb8lzW7lMpRQcsYyeDd+m7v6cZFvkLxmQMFhXX0rD4743o9czHckWMmPvdsh5\nQtkmnNDiW49nLfrUYIrkQkhRSEo9Uik8AlG5LIcIWd8Fps7OzjaACx0ljSvBlF+rNnDJcNU6mFLk\np7KkZFRwX4FIoMZIzjsdIxg+/1BymhoFJLmkT6cUJXPIQLL09L/A0GKxGI6zs7M6PT2t+/v74Z6q\n2tAnlcMhDW4UmQBFmrSbhlXo8JglYFki11PpxBiImmLcx66h3tOJJR0jgCAve2X60KHa9/T0VFW1\nJrvHx8daLBa1Wq3vScWNVB1we9CjPrZa/T33Z7FY1N3d3aALfhCoc2jDgyV3Dg6m6GjG+D4FHDug\nIoiSPvm8Muk95ajz5CglV31yCO7h4aHm83n9+PGjbm9vo56nNrCOGgoUUW9oR3XtxcXFkAk5PT1d\nC3ZagZEHWW4v2JdacnXANyaXMUpAKtnPlr1O4Lz1XAcvvfqkMhOg5qEspY8I+bXMCrK9ngxg2/lc\n1xP2qQQwPTB3Hk6lTwumknNKQMojSDd2rmCuDLyHw3t8Pjx4gw8AACAASURBVNPD+t4ymg6mBIT8\nSBEgQZTOVWd29AQmWS/VP2XvkuPbNzoeoxQR+TNbMnKis6ADTs9K+vD8/LzmTDQvTcZAjtuNLych\n6z8BKdcXnTs4csflYIr/q04uq5ThSYZuW0rRXyIf5uM7Kt1Yep1bYIp6Kx4ww8xhVzlKfc5msyEr\npd8Iptg3xXfqkOSpzNTd3V3d3d2t8SEFMOyj1EHPbrPPs68SFO9DLnvplGfQlFXn6lQCKn4639Pi\nGgdTd3d39ePHj/r+/fsGuGO9eKgfnp+fD9dQZ1qZKbWlanM14dPT02Ajlf3y8jT/zefWSm89q0e5\n9vzSPpTsh+Sbnstnt/7fhvSc5DPSc5PeMpOfMkXug5xkV/i/+4QWkEqAv9XOBPKm0KcFU6QkjASi\nWmDKh1lYrndYGj19Z+bBI63Wp+o3FUhV1ZqB9qwTy3aeUPFcyd1QJIe2b0ckbRvxsN697yw/DWGk\n6CkBb4IpDtVw1ZD4pCwhjXXV21YZjFRTOxntM4tDx6FzZlZ0H/WQ2TbyRDq6j1NOQKoVjTJ65Bwi\n8piOVHV0EJj6jco9OzsbMgsCSAJQXDygIVcO8zEzRTAj0FT1tqCEQxYEUz9+/Ni433VBR2seFXno\nQVZL3/eRn9sXB+8CLZqsf3l5WRcXFxtZPQ2X+SINbiFC26thvvl8Xt+/f6///ve/Q71oVzyQECC6\nuroaZHF2dramM7JbXFyi6zSh3O2+ZKHAiM6T/Vh93kEjQbdnyzhacQgA5XyaCqL8Hp6nYecxUnuq\nKvpVB0GewaO9JUBtAU8HPqy7jxJ53/B+4mU6b1Qv953+/Cn06cGUM7wFpDzKYCQlcgPOshit+LCN\nDw1w0q3X1cEenUQPSFXVRlZKbZhCyfnJgRBE9ZSPvDkUJdDH35PzaYEp/ebZHp846xEOD00Y50rJ\n2Ww2DPNwiMkzUz3ZpSiRDur09HSQgQy2zlUPOT0BvdZwS+LhFIc8ZqCn3E8wm+bD+NB6KzOl/9Nw\nuhw6Qc79/X09Pj5uzIlx58fskxv7qloDPhx21xCiwFSad5TAFA0/HRmfn+ZstaL7bYmydzDFeVIC\nIBcXF3V1dVXX19d1eXm59r/OfT6hnkO70cpMffv2bSODtVwu17YS0acDKQcqY5kpOV/aetlNyZM6\nqGuPjo425lFyyP719XXIljlY2BZE8V4nt4et/xK1sisK4MfuZQaVfGf2Tp/uUzm87X4tjZi4DSaY\nYt1bIKwXXKdynK+t67elTwmmEvNbQMmFR0NZVdGgJSPmz9KeGYpI/dmtTAjr4MaTmQX9TyPsdUgG\nmPemclI07aSyW9HDPtSLRnTuYLUHBFKGgVGQOqa/w80zI/pkhoEGQ05GK/hklAiGXB4J7PE/z5yx\nTQIOPoeF2SxlwNiGpFM9mSXetnidDA/1qgUKUkY4pftZBh2+b4Lqw6A0+BzGkvPU9hQKGpTV8kOO\nXENd5+fntVgs6v/9v/9Xf/31V/348aPu7++HfcS8fu5AKAM6af6u8+Pjt1W4qd9NkduU6xOo4twp\n7xeyD8rkHB8fr80pXCwW9fDwECd3i1eyhwJsydGzH1HXFfAJ/Mxms7XNWM/Ozury8rKurq6G4/r6\nuq6vr4d7aZeram37ksfHxyE4pd2oqrUMJydOU78SYNlFbsmhOzEwSve0yvRgMwWk/pw0YsI6MSs1\npe0JyIjcbjObzfKTfkqm3h4C5BRovgd9OjCVjGDPSLtAqtajXn1SoEkwVW8KxN9Ter81fMeyWA/9\nzohdhoBRhEdQPYM61pGYdWG9CG62NdjbEp9FebFj9Tq/t4VzTzwiVfkyhJQJeXR8fLyxJcLz83Nd\nXFxsrERJRseNnTuxBGp17qCCQzB0MMqazWZvGximrBR5vGu0lWSW2kb+u5w8ovUtBZi9YQbOV5c5\nmCRoId+c57qn6g0UuL2QQ/Vs4dPTU3379q2+f/9e379/HwCCIm0B3svLyyZ4ZnabPBk7DkVJHx2w\nJvmtVm+rgkncx4tgyoPKxWIxgCnJ4eLiItoUn8PFIXaBKdWLq6DPzs6qqtaAlD4lb5/WITClfqW5\njh6EOZhS1lqBjAfokuvPoJY99P/dxiRdSHpHHdGn22i119vcC8bSJ+sq8sylwDODJ+mVJy90f0qi\nJD4eij4dmKrazGbQUMvhefSjz6SECUilZ1a9ASr+ltL7PsQ0tU06l3NNUVwLTDlY9Dbpf2Y+vDPx\nue8BpNzwpOyhg0iXi8uOEbai/6QPHD6j46X8BaY8M3V5ebkW3TJz553VwRPBBh2X84P3qz1c5u1g\nSvd7xJp4fWhyo9sbrnIgJcfkmSnppL/r0PsSZe267UO8DhD0TN9k9enpaUM+x8fH9fz8XPP5fDju\n7+/r5eVlmL+j7MjV1dVG8MZzApLUrxlhvyeQSsMvPhROR+lByevra93f3w9DqzocXIjHDqYEOt3m\nsj6uS6vV2xYYVTX0Z/H+5OQkZqZ0LYHzarUaXvmkjCLfcEC5JTAlHSNPDg1+SS37kMARqQWWeb/O\nHbgwsOHBoMDBVAJU7lsStQJOD4TlU1NyIflu9yetJMYh6dOBqSlASp2D1ELtCXj49Xw2J9zp3DNj\ny+VySAMrIk5lUyH9U50hRT5SoDRE521kW1Pmgzx1B9By0ruSt8Ofq4OAztuS2ignrKE2yoWrurh9\nBQ22D3u2MlMEUicnJxubeeq5Hgn6/C19pqFole+ZKZ/zQ91gtjTxPEWe+8rRdStl51y2Gh7nsAkB\nKmWkYdWLi4uNsh30sD/JwYpvzmc9z4eqtI2C2wPN+/E5Qup7GuYT2PYsSNXby5HFO+9j3tcO2efY\nlgSoEvAk0Cf4lcwcXM7n82iLxDsNcbLv8UjZxAToaNdVjlbtEUzpUB14LJdvrxxSZoqZSuqIt11g\nSvMb3Un/DECVwDF/17lslK8O9nKqKvaPpB+yc2kYmzRmi5wc4HO0h4kD6onK64EpxwnOo0PTpwNT\nVXmiOQGVlq87k1sOQJScNUkCoQOTwfEhAxkO1oNGglGXt0mKkzqrG+aUjUjt1X96toMpBzKufIek\nFohiBMJ29pwL2yKDIfkLTD08PAxO1gGRG400FMWhPQI3X4ZPvrmzSJOgWX7Vm9OV/H3COQ2jMmOc\n7Jl04ZBRcytKdnCToscEUj2bId5q2ExDNnyWyIf5GE1zMrX0gdkw6QR3Svfsigdr1E85FtVDmSk6\nXY+IdS79TrxNw7+HoJb9c5Cf5o0yINH+XXd3dzWfz9c+6dRUhobK9J+CHc94tDL3tOvUl5Sl9KyU\nMlOcyyW5LBaLtcyUstWUecpKaaiRdn4sM3MockDVAlP6nUPWnGfY47NsiT4Z/EnnNQnffYpTskXp\nO+tLubJO7rP57BSIVNWGX+HzeH5I+jRgipFuVZ64zGv1yc7taeTecAsFpGs9UyODwXQ2VzckQNQi\nb0sCih5ZUDESv1pRaMoiHDIiTmWId67U5OXYvayv2uSTkzXMp2GbqpzyZSf0CDk5leSIVDdu9smo\nTuUK/MioydG7oeY1BAspcvOjx3vVs/U7+9Qu8ndw7EPQPpzGdxl6ZqpXPmVHQEb5OL/YxxlwEVyl\n+VMEUc4/j4zZT1tZJ1IKFBhkUTcPRckhtQIuZt3THl6aJ8WhvtVqtWFb2XY6fh+y1pYg3kclE9Z9\nuVxuDMU6iG8FELTnriPM7hL8O6BjprPlf96LXKfYb1yG7vMUiJEXIg7b6f7UnyRjAqxWmS177j6u\nqtZAmz61EpeZKq8PZc7ntYBny563/tuWPgWYaqHcBKLo6DzSo2IlMMVyGSWrQ+kZHrnJsMj4JCPq\nBtLPPTPFrIOUneVJuccAlSt/ikCT8vTAzS7k4KmVQXOjIJ6ovpQf98chmJLxTyuUVK4bwRYvWtkz\nGVhtmcDhDjf2yixV1RBFf/nyZQ1gCFxos0TPvDiYS2CPRF3ehraN2hL4d97REetgxsD7CZ2ZgpQ0\nb0dbIaSsSAIHiqy1L5Keq77udXDALb5ILnL40oEEyByM0dGprDEZ7eOox/pYCxjKmaXXLfGQbFer\n1VrWVHxNz1Xf5cpJyojzTjnEpPr1+in1pmp9Hz0PGjyo9HIcJKfvPdDs/N9WbqoPibopP+G2Qb+z\nnbL/vYCF+pquY1myvZJzAjgJ/PE5+j3N7ZKeeIaK8vLRncT3FJy0QFWr3VPpQ4KphHCdXFDJ0biA\nHVRw7ol3DC3PbAE2PU/RiqJlDfd4PfndO0qKZAWkvCPquyK05DTJC0YlXCHjhq7H6ynymEIOqFhX\nd1i6hh2PHZlAivvTzGazYaUOV70lp9FzmOlaB1QvLy9DpH5/f193d3drTkCHtlaoqmES7tevXzfe\n+8eJsV53PxKgatEuwKploPwaBgACUtTh4+PjNRCloSKBIBpw8tznQfoGig8PD0M5lF8LlCtrrDKZ\ndZJesS1sh/OcCzc8K5kOdyg+N8RtTSsY25da/b5nd3xolt8Jkh0stYKTqlobitXwnICuDwNzC4Ie\ngFHdWTfZyTTcI33ugSkHVARRXhfyUWUfmnq2nsBBMmAb5QeSLXcg3fIHKldgWH0lrcKUPFhuq2+0\nMu7qK7q3FRAkP0p+kC/erkMCqw8JpkRTgBQNMVORaXiEc1cIqryjtgRHg61PjcPLsRIJTzGALUCV\nhktaaNs7GZXJwZSnxVsGz/k/xWkn4j2sb1LgnpHXtTLEDqJ0zGazYdfltIcT+evG1cGUrveolBNU\nPTP1/Py8MadjuVwOekIwtVgshk0QJRNOOPe6JxDVc1rbOONdnbUDqqr13f0554aAyjMFzm85Vzl0\nz4ioLO79445R5JkpgSk9l2CKzsR1j8NC0mNmppJjdcdB3kyZBnAIEDVVV5JDbQEpz0zR1pJn6ZAd\nFpj68uXL2lAigabLIWWwyFtmH90WuzyngKkWCEgAL/F9V3l5eclP6DuH4WRbWSfxnGCKdUtyT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TbRJw4FBKMigEU7puX2fcohagosNzMOWAKum561/rs6rWjDb7vQMp9QlmuY6OjjZW9WpSubbA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z5tvfCRERzHi5Xy1LAGU6gCQnd3dwPY4w7qPGtPK45V+7CeMiHMgl1cXNTJyUnMeLkxS9FD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\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TzD49Pz+PZEP7XXnU7Y5omwN4T5Ku8JNOWueIkn7q0wGJvitbSDnxXeS10MMz6mn/p6q8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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110c3ea10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, _ = displayData(Theta1[:, 1:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction accuracy\n",
    "\n",
    "Lastly, we use the neural network learned parameters to predict labels. We also report the neural net's accuracy, which should be at least around 95%:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def predict(Theta1, Theta2, X):\n",
    "    \"\"\"Predict the label of an input given a trained neural network\n",
    "    \"\"\"\n",
    "    if X.ndim == 1:\n",
    "        X = np.reshape(X, (-1,X.shape[0]))\n",
    "\n",
    "    m = X.shape[0]\n",
    "    num_labels = Theta2.shape[0]\n",
    "\n",
    "    p = np.zeros((m,1))\n",
    "    h1 = sigmoid( np.dot( np.column_stack( ( np.ones((m,1)), X ) ) , Theta1.T ) )\n",
    "    h2 = sigmoid( np.dot( np.column_stack( ( np.ones((m,1)), h1) ) , Theta2.T ) )\n",
    "    p = np.argmax(h2, axis=1)\n",
    "\n",
    "    return p + 1 # offsets python's zero notation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Set Accuracy: 99.760000\n"
     ]
    }
   ],
   "source": [
    "pred = predict(Theta1, Theta2, X)\n",
    "print('Training Set Accuracy: {:f}'.format( ( np.mean(pred == y)*100 ) ) )"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
