02_Convolutional_Neural_Network.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# TensorFlow Tutorial #02\n",
    "# Convolutional Neural Network\n",
    "\n",
    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Introduction\n",
    "\n",
    "The previous tutorial showed that a simple linear model had about 91% classification accuracy for recognizing hand-written digits in the MNIST data-set.\n",
    "\n",
    "In this tutorial we will implement a simple Convolutional Neural Network in TensorFlow which has a classification accuracy of about 99%, or more if you make some of the suggested exercises.\n",
    "\n",
    "Convolutional Networks work by moving small filters across the input image. This means the filters are re-used for recognizing patterns throughout the entire input image. This makes the Convolutional Networks much more powerful than Fully-Connected networks with the same number of variables. This in turn makes the Convolutional Networks faster to train.\n",
    "\n",
    "You should be familiar with basic linear algebra, Python and the Jupyter Notebook editor. Beginners to TensorFlow may also want to study the first tutorial before proceeding to this one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Flowchart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The following chart shows roughly how the data flows in the Convolutional Neural Network that is implemented below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAB9EAAAJRCAYAAADyLkedAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAFIQAABSEB/vk4mAAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoAACAASURB\nVHic7N13mCRV9fDx7yYWWMIuOYOoKFnBQJCgoIKiGDAhGAkqYkQBE4oYfiKIERBUDAgSTCiiSAZB\nWCSJSs5pgSUtLLBh3j9O1Vt3eip0z/RMz8x+P8/Tz9R23bp1O8xOd517zgVJkiRJkiRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ\nkiRJksaiCb0egCRJkiRJ48jywLLZ9h3Agh6OpdVawGTgaeDeHo9lpKWvy53A/B6ORZIkSZI0yhlE\nlyRJkqRFy0RgE2BzYBVgaWAucD9wKXA9oyvwO9YcBXw8214NuK+HY2l1F7AG8A9g6x6PZaR9G/h0\ntr0W8VwMxguApbLt64kJCRp+ywPbAhsB07L7HifeyxfjpAhJkiRJkiRJkiQNwrLAV4AHgb6a273A\nEUSAXZ07iuK5XHWYz7UVsE92m9bQFiJw3AdcMpyDGqW+TfG6rDmEfi5J+lmvC+NStWWADwFnA/Oo\n/j/rDuB9vRmiJEmSJEmSJEmSxqotiEzzNPC0kAio3wLMYmBg6lFgu14MdowbySD6d5Nzrd5Ge4Po\nBtHHku0pD5o/DjxTcv8PejJKSZIkSePSxF4PQJIkSZI0rF4FnAusnP37DuBjRCBxReC5wEpEWfc3\nAL8mAuzLEqWrNX7sDrwa+ESvByJ14EFicsqriP+nliEqL7wQOJoIoAPshxnpkiRJkiRJkiRJarAK\n/TPQfw1MbeO4TYBriTLh6sxozkRflJmJPva8GPgksERDuy9TvCY3DvOYJEmSJC0iJvd6AJIkSZKk\nYXM4RQb6ucAeRJZ5k2uBLYG122i7BLAC8f3yIeCJzofZzxQi+Pws8ABFlulIm0A8rqWBp4jHNr9H\nY+mV4XgOliOqHDyQ9dmpxYj39IRsPIPpYzSZQFSCmEYsoTC7t8MBYEnidZ9IZIE/2YX+ViHKsD/U\nwXFXZbcmXyeqa8wAng+sBtzb4RglSZIkSZIkSZK0CFgHmEcEoecD63ax7wlEafBLknPkt+uAz1Cf\n8b42MDO7fTS7b1vgz9lY874eJtY5XqakjyWy888kMuzb8arkvHtXtFkTOI6Ba8g/BpwKbNxwjqZM\n9EOSMTRl2B6Vtbuo5f6PZvfnkwz6gGuSfvPbQS3HnZnd/9OG864OHAPcR//n4HHgdOBFDccfloxh\nMSKIeijFmux9wALgSuA1DX3NAN4N/Ib+jzfv4zrgi1m7OqMlE30y8ErgSOAGYrJI+pjuBU4ANq04\nfgpwPvHcnt7mObeneD0+VNFmEvB+4r2W/g4uAC4HPkD9koD5e3ImsBbx+38w8RjzvoY6wabOhcl5\nqp47SZIkSZIkSZIkLeI+QxFUOqOL/S4O/I7+gb+y21VE9mmZFyTtvk6UbF5Q09c1RPZyq1OTNu2s\n3/6bpP0LS/a/lgj01T2u+cC+NedoCqL/NNk/rWG8f8razWm5/+sNY8xvP2o5Lg9iX1Jzzh2IYHnT\nc/DRqg6AE5O2awP/qelrAfDBmr6OaPOx3kpkIVcZLUH0l9Pe43mW6vW9j0navbSNc56WtV1I+e/J\nasAVbYzpr8BSFedI35MvI37/W48fakZ7neuS86w1jOeRJEmSJEmSJEnSGPZHiqDSh7vY76+Tfu8m\nAqDPIzLddwX+ley/hgi6t0qD6JcRgdSbgf2JoODmRADxpqTdD0r62SXZ/7WGcc8A5mZtLy3Z/2Lg\n6aS/nxMBz3WJ7PNv0T/r/u0V5xmJIPrzgB3pP5nhndl96a11okBTEH0TiueoD/glsAXxHGwEfIP+\nmdPvrugnDaJfQgTdfw7sTLy22xEB/oVZm6eoDnweQWRn/wDYjXjvrEsEavcA/pac60oio7rMaAqi\nP0O8dvsCmxGPZ0PgjcB3sv192c+yygcvSsZwXMP5VqF4zc4r2T+d+N3L+/sT8DaK3+ldgAuS/b+t\nOE8aRP9n9vMC4vd4x+yxHdYw1sFam2ISzl1EpQxJkiRJkiRJkiRpgNvpLFu1HWnQ+jbKg8RTiWBd\n3u6QkjZpEL0P+AflGa6rAY9kbR5nYPnzyRQlx++kvtz0h5LzlWWSp5mzB1T08QaKYN1sYq3wViMR\nRM99N+lr9Ya+oDmInmYjf66izc4U5b4fJYKwrdIg+kIi+F3mm0m7L1S02YD6pQEgAs95P2+qaDNa\ngugrtnH+bSkC31VLFfwj2/8E5csd5A6m/0SLVifQ/JpPBH6WtHtDSZvW6ghH1Yyp236enPdLI3he\nSZIkSZIkSZIkjTFpWfK1u9TnX5M+d6lp9xyKbNr7iXWcU2kQfQH1pdjTAOnLS/YfnuzfsaafS7M2\ncxkY+N0m6eNS6jNZ02BiWYb/WA2ib5H0dQX1ExKOTdp+vGR/GkQ/saaf5SiCxWc1jL3O4kRAP68g\nUGa0BNHb9QuK135yyf49k3FUrXM+EbglazOLgZMR1qGorvCXhvEsTUwc6QPOLNmfBtGvrxjzcHhb\nct47KZ/YIkmSJEkdq/tSLEmSJEkamybTP7P7iS70uThRhhvgDuDPNW1vowjKrUyUn65yCXBDzf5r\nk+3nluw/Idl+b0UfLyCCxAC/JwKuqZ2S7aOJgFyVdJ3xnSpbjT3pYzmGyCCv0slz8NOafbOBe7Lt\nste2XU8TSwdAlOUfDy7Lfk6jfJLJqcBD2fY+FX3sSJRkh5hc8EzL/jdRBLuPbRjPExTB822oD5L/\nhKhWMNxeTExqgZiMswfd+b9OkiRJkkZsZrAkSZIkaeTMJwJmeeZpaxn0wdgo6e8i6gPNABcSa6RD\nrIN9RUW7ayvuz92dbC9bsv96YCbwEuAtwEcYGEhLg+snlPSxWbJ9YcN4riSyg5dqOW6s6+Q5uJai\nlHvTc9D0+t5DZETXlSSHWN/99cSEjFWISR3p+/p52c8VGvoZLWYQj+flwPOJag3p+3v5ZLvsMT1N\nBJA/QwSTX8rA37G9s599wI9L+tgq2X6QIuBe5eHs51LEUgt3VrSb2dBPN2xIVMaYRjy+fWh+30qS\nJElS2wyiS5IkSdL4NJuinPgMiozfwUqDelXBs9QdyXZdYLMpc/TpZLvqO+wJRBB9SaK8c5r9PJHI\nUIV4Dv5ecnw+vrwkdJ2FRFn09Yn1rceL9DW6o7JVyJ+n6cT7YiLVmetVpehzc7OfVa/tdCKz+S0N\n/eSWam7Sc58EvkpzOf9cVbtjgU8Tz/8+9A+ir0IxieU84KaS41dJti9ucyy55aj+XXm44v5uWY/4\nPc5//z5GfcUDSZIkSeqYQXRJkiRJGp9upgiibwz8e4j9LZlsP13ZqjA32V6yslVzRns7TgKOIDLl\n30v/gNoOFGtg/5Io+9wqz2h+lvoy5rn88U8FJlX0Odbkz8FC4nlokj8Hk4jnYW5Fu6G8vhOJdeG3\nzv59G3Aa8d6eAzyZ3QC+BrxsCOcaKZ8Ajsy2nwJ+C1xKPI6ngEeyfa8hsswBJlT0dQvwN6Kk/juJ\ngPrj2b73EdntUF2qPZ9w0MfAJQ6GYl4X+2q1LnAOxQSAzwI/GMbzSZIkSVpEGUSXJEmSpPHpImLt\nYrKfJw2xv8eS7abS29C/NPVjla26YzZwBrAb8VjXBW7N9qWl3H9ecXweeJya3VrXjm6VP7Y5DG8A\nfdIw9t0qfw4mEsHVpgzy/Dl4huoA+lDtRhFA/zmwF9VrbX9xmMbQTdOIDHSICQHb0n+5glTZOuhl\njiaC6EsBuxPr2U+kKOU+C/h9xbH572UfUZ69nckxvbQ2EUBfI/v3F4HDezccSZIkSePZxF4PQJIk\nSZI0LM5Mtndn6GWu02Dfem20T4OAdw3x3O04Ifs5AXhPtr0M8OZs+zLgfxXHpo+tKXi5BLBWtj2Y\nx5VmeZet8Z4ayXLxnTwHiwHPKTmu23bKfvYRWdlVAXRoXs97NHgFxe/hEdQ/d+0+nj9TlFXfJ/u5\nY3L8CVRXFsjL9k8ENmrzfL2yJnAusE7270OBw3o2GkmSJEnjnkF0SZIkSRqfLgH+mW0vy+ACTmmQ\n9yaKtY63AxZvOHanZPuyQZy7U38F7s+230ME099GUUr+hJpjL022X9Nwnh0pqroN5nGlZbPXqGwV\nEwCaAptpULnp9WiSPpam5+CVRCAd+j933bZa9nMO8GBNuw2pfy5Hi9WS7dsa2ja9BrkFwHHZ9ouB\nl1Jkofcl+8pckGy/tc3z9cLqRAA9nxjwTeCQ3g1HkiRJ0qLAILokSZIkjV8HUpQb358oh92OxYmy\n0O9I7usDTs22lwX2rTn+NcDLs+0rKEqrD6f5wK+y7ecQpbLzUu5zgd/UHHs6/Z+nqjXcJwKfS/5d\n12eV65Lt7WrafZIoLV9ndrK96iDGkvotxVrW+1FduWAi8Pnk34N5Dtr1VPZzaYo1sMscOoxj6Kan\nku3n1bR7O51lhh9P8dp9Edg12z6HWD++ym8p1mD/CEV1gdFkNSKAnj9fRwIH9244kiRJkiRJkiRJ\nGg8+SwTA+4CFwE+oztpdEngfcHvWfp+W/esRgcC+7OeuDLQZkTWcn/NNJW1ekOz/esP4t0na7t/Q\ndsOk7bnE4+2jvfXgT0iOPYOB674vRgQr8zaXE9nurY5K2pQFtlciymv3EUHwF7bsnwB8kAiK5v1U\nrU/++qTNr6kO/ufuytpeUrH/uKS/vwDTW/ZPIdbgztv8i/LJ+ScmbZoy5M/O2j1csu+gpJ+flZxr\nEpGV3JfcHqfct5M2azaMqc4lST8vAWa0cZuWHbtucuydlJfrf2X2GNLH9Po2xnVyyzF9xJryTfZP\n2t8MbFHTdmkiy/2TJfu+nvTTznIP7VgF+G/S78+B5Wh+vhcr60ySRpl3E3/DvlGxf5ts/zcplpGR\nxqvPEe/1D/d6IJIkSZIkSVq0fJrItE6D6VcTweVjiQDyWRQB8vy2e0lfH6AITufB1oOIrPdfAc8k\n+75fMZ7hCqJDBLdbg4mvbeO4ZYHrk2PuBQ4nHtehwA3JvlnA8yv6aQqiA/wgafMkEXT+PJFl+2+K\nAPV51AfRFyOCsWlf1wEzs9tBLe2bguhLZ8fn/d1HBJ8PBL5C/4DmQwycAJDrVhB9RSJTOu/rSiLT\nel8i6PCf7P6rgPMZ+SB6u7dTk+NPT+5/mFgb/SPEZJczid+tp+k/WaGdIPr2Lee8n5j00I4fJsct\nJMq8f5V43Q8kfg/OJio6VP1eD0cQfW86f67bnTwgadF1BHBKB7d2q/h06hSK/3fLfIri/7W6CU69\n9CLi78XlxN+d+cTnzQeJyYxfBp7bq8FpTLmbeK9f3OuBSJIkSZIkadGzLe0HAK+lPnC3B/2Dm623\nZ4mga1mmNgxvEH2/lrHcTWQst2MFiqBu1e2abPxV2gmiLwH8reYclxJlrP9EfRAdIhs6Df6ntx+1\ntG0KokNk+p5VM7Y+ItC+QU0f3QqiA7waeKxmLJcSa2b/mbERRF+emAxQ1XY2sAuxFEEnQfQJFJMK\n+qjObKzykezcTY9lLuW/hwbRJY0V6WSxdm6tf0u7ZSwH0dcD/kB7z98C4HfUf3bS8NoPuCW7bdjj\nsVQxiC5pVJrc6wFIkiRJkkbEhcDWwEuB1wFbEiXLFyMCYw8Q2ennEIHJOr8C/kqUIt0eWJn4fvlQ\nduxJwE01xz9Asab61Q3nuilpWxf8zZ1IsT50fvyCiratHiKCtq8B3khcaFyayNC/lcgUTtdPL3MK\nEcyECP6WmQvsBLwLeBtRXn8hkcV1OvH8PUtkrP+RyOyqMpNYP3uzbLxp0Po/LW0PIkqLP1DT3+xs\nbDsQpfg3ongObiMqD5zWMKafENnM0P+1KHMUEWR+pmL/2UTAfi+i1PlSWdt7iTW9T8vO8QPign7V\n+U4Dbsy2H6lo045vEe/3TtyWbD9M/O7tTrzH1qKYRHAe8AuiAsB6FO/769o4Rx/xHl2feC8d1+EY\nf0T87rydmHCzFkUZ+geJUu8XEq/HoyXH/55YBgLq31+duIjiOejElV06v6Tx71qa/07dORIDGUO2\nJ/7+zsj+vYConvMP4u/FfGJC3kuIvyeTic8TaxOfVTTyZhBLygBM7eVAJEmSJEmSJEkaSWsRgaA+\nYoKLJKlcmoleVTFmJIzFTPQXUyzt0UeUbK+rTLMicBhRJeZfwz46VfkCxWs2WicymIkuaVSa2OsB\nSJIkSZIkaUj2p6g2+J1eDkSSNC4tRVTLySvenERU7mmtepN6kAjgvoSiGowkSWOG5dwlSZIkSZLG\nrhcT651ClDI/q4djkaTx7CXAdGJZkYsa2m4FLAnMAS7r4himAttk2w8DV7VxzLLEcj4A9wD/HcR5\nPwg8J9v+N/AB6pd2Sd0I7NHQZjliKZmNgFWAWdl5/k48zjqbZcfPB87P7lsWeCuRKb8kcBexJE3T\nMkLbAVOIpV/y5UFWB94CPI9ISryZWELm9oa+UksR5e03IyogzM+OPxO4oYN+JhF/97ckXo/FieWA\n8iV3HkrarkUsD/Pc5L6XE89VahaxtEGZxbJjtiSWH5pELDtzNvDPDsa9FvB6imVnbgR+l/UlSZIk\nSZIkSVJXfIIoBXwmEczJy7Tu3MtBSdIYMJRy7hdmx93bRtv/ZW2rgraDLec+gcj+7iMCvUu2MZaD\nk77e1Ub7Mrckfew2yD7KLEFUUHk66T+9PQ0clbWrcg7F8wHwPmLyQll/v6Z+XfCHsnbnEwHjw4iA\nd2s/zwKHtvH4JgNfJEral41nIXAyAwPbZXaleF+V3Z7NHl/uEzVt09vpFefbg5h8UHXcJcALG8Y8\nGTiC8udwHvEcTsJy7pIkSZIkSZKkLjiJgReiv9LTEUnS2DDWg+gAH0v2va9hHBMpAuCzqA8gV3l+\ncr5HiEztblicCFanAeUriOfm8uzf+b6LqA6kp0H0D2fbC4CZRMb0TPoHcX9aM6Y0iH40RSD/4qyv\nfAJDfvt4TV9TiYz1vO18IvB8CpGF/VCy73pgRk1fX6b/8/EAUXnmFCID/eHs/nuSY96VPfZ7kuP+\nk92X3g4vOd9hLY/zuuxcpxJZ7/n9DwOb1Iz7hJbHf27Wz1kUkwC/jUF0SZIkSZIkSVIX7AUcS1zg\n/yzNmWCSpDAegujTKTKtL2kYx6uTfsqCpe14f9LHXwfZR5nDk36vBTZu2b8p/TOvj6roJw+izyOy\nsS8lAv+plxOB57yvTSv6ygPbT2U/fwOs0NLmbRSZ848DS1f0dVRyvj8B67TsX4LI0s7b/Kain72S\nNo8A7yAqEqQmAa+lfILAF5LjN6s4R2pP+r8uL23ZPxHYh3iu+4jlARYr6Wf3pJ+bGfj6rkOUzF9I\nvHYG0SVJkiRJkiRJkqQeSIPo1zAwKze9faPl2NESRAc4Ltm/Uc040vOs1zjqcock5/ruIPtotQpF\nJvKjxJrZZdYEZlMEydcoaZMH0fNM62kVfaXB4a9VtEmzw//IwGB17ttJu7eX7N+EInP8PKKseZVf\nUrxGra/l0kTgvI8IWr+8pp8qnQTRp1Fktd/FwAkEqY8n/b6vZd9Eioz1Z4AXVPSxUnI+g+iSRp2J\nvR6AJEmSJEmSJEkjbBNg85rbur0bWqNjku29K9qsRKyjDRHIvXGQ51o+2X50kH202p0ie/kI4M6K\ndncRAWuIQPSeDf0eCjxZse93RKAWqjPRUwcm7Vv9Ntku62t/igD8x4lS5lUOy35OYOB68+8mKg9A\nVKD5Z00/3bAHxfrshxCTCqocTQTAYeC4X0mRef9L4IaKPmbRvYkZktR1dTOgJEmSJEmSJEkaj84g\nynJXuWykBjIIVxLrhr+MCCwfBMxtafM+ikD1j4dwrnQd9WeH0E/qFcn2aQ1tT6PIHH9FXUPqy83P\nIYK+KxATDOrcSZQpr3JHsl3W147Zz5uJkuh1bgDuJ7LzWzPNX5Vsn9DQTze8OvvZR/+JAmXy0vm7\nMHDcWyXbv2vo57fAV9odoCSNJIPokiRJkiRJkqRFzb7Afb0exBAcQwTRZxCZwL9M9k0g1tKGyPZt\nDWRuQnWJbYALsuMgyonnlhnsYFvkpeWfpjpLOXczMUFgCerH/BT9x1pmFhFEryr5nrunYf8DyXZr\nX8tSZGEvQWSQN8knO6zccv/62c9niOUHhtsm2c95wP+10T5fe355ItaUZ9ynSwc0TSL4b3a+KW2O\nUZJGjEF0SZIkSZIkSZLGlpOJUugziJLuaRD9lRQBzhMYmEG+J3BATd+vogiipyW9Vx/kWFvNyH4+\nSvW68LmFRAb5GslxZdrJkm86V7t9VZV5h6IcOsTztU+b54SBkxTyvh6hviR8t+TnW4zOxj0BWIqi\n3P+yyb6miQ0LsuNW7OB8kjQiDKJLkiRJkiRJkjS2zCUC5J8EtiGylvMS5Pk66X3AcUM8z5XJ9suG\n2FduQfZzUpvtJ7YcN1ZcD/ypg/YPt/y70+epW2bT+fvmmWQ7fZ0mtjYsMdKPT5LaYhBdkiRJkiRJ\nkqT2tBPwayoX3i3HAp8gMoH3Bj5FZPS+Odt/DlEOvdWXgK/X9PtEsv1PIjN7MSK7fUMiODwUs4E1\ngelZv3WZ35OJcuEwMMg8GqVjvJ9Yr36wHiKepxnA4kT5++H0MPFcT2Jo406zz1cEHq9pO5X+meuS\nNGq0MwtIkiRJkiRJkqRF2Zzs5wrUr988jYFrWw+XG4Bzs+33EIHW9xCBSahej3suEeisuqWlw58C\nfpP8++NdGPd12c8pwIsa2m5K8Xiuq2s4SjwO3Jptv4xivfPBuCr7OZnBVwFIS89PaGh7dfZzWWDj\nQZ4P4N/JdtPruzFmoksapQyiS5IkSZIkSZJU797s50Rg3Zp2b6Q+yN5tx2Q/lwfeSlHK/X7gD106\nx7coSnR/ENixg2MnALu23Hdesv3uhuP3rDhuNPtz9nNpYlLDYJ2VbO87yD6eSranN7T9c7K93yDP\nB3Bhsv3OhrbvGsJ5JGlYWc5dkiRJkhYtuwLf6PUgJGmQDgTO6PUgJC2SLicCyADvp7zc9crA/43Y\niMIfiAD/asDhwKrZ/T8D5nXpHP8GDgW+Qkwi+D0RHG1a73st4ChgHfoH9E8BjiCCuvsCPwWuKTl+\nE+BD2fYTwImDGv3IO4J4XIsB3yZK4jdl0S9HTDhIy8H/jijH/zxgd+AvwK9q+pjEwHXj70q2NyBK\n/Fc5mXiN1yEmY5wNnN4w7qnE+/7O5L6ZwLXE6/dW4NVZX602AT7S0L8k9YxBdEmSJElatEwnsqcO\n7fVAJKlDh9CcRSdJw+VU4EiiXPsBwIPA0USm7xRgZyJgvDzwKCP3/9U84HhinfM8gL4wu6+bDqMI\nik4jJjSdA5xAlJSfRQSBVwVeQjwfexAl5q9q6WsOcDDx/E0F/gZ8OOtzHvF87pLsB/g89WtrjyZ3\nAJ8BvkuURr8Y+CLwC+K9kVsK2Jp4Tt8FvA64KNk/n5iwcR4RyzmBeA1+QP+g9XrA+4jn/MUtY7kk\n62cy8LnsvpsoSvbPIgLeEGvTv5d4XScTZfyPBL4H3J30OYV4jXclMu0PB77Tct4DgL8S74nTgI8C\nJ2XnnQi8Hjgua/sUsCSSJEmSJElSD70XeKzXg5CkQXiC/mV9JalT1xFrRPdRBJw78dHk+D7gGWA2\nEQTsI4LXewD/y/59dXk3nJK0L/Op5BxbtDGuNYjgc37MWfXNB20ikVm9gP7PQ93tKSJg3moC8KOS\ntrdQPJ/57Viq1/M+J2vzSBvjz1//6yv2P5TtP7+hn0nJ2H5d0+5LxGuct10A3E48xtuIgHL6OLep\n6OdtDHxOHsz6mZXcd0/F8UdS/fqUZZrvRkx0SNvdnZ2v7PX5ZMV5v9DS7uHs+Aco3v97ZX33EZMN\nJEmSJEmSpJ4wiC5prDKILmmohhpEhyhznQdb09v/iLLVMPJBdIj1rPNj3tLmMYO1IRE8foTq4Ozd\nwNeANRv6SicdtN5uJP7frwqgw+gOogO8gih7n05ySG/ziGzxjxNrqFdZnyjv3hp4T99/ZUsMQDx/\n7yXKwd/VMpaqcu3rAT9hYDA9veVl/teoGffuwH0lx94GvClrYxBd0qhU98dHkiRJkjT+vJcoybhs\nrwciSR16glg79Ze9HoikRd5iRBnudYig5n+JdaB7ZSJRontdImC5Nt1bD73OJGBTIlC+AsWa3tcS\nGced2JQIzi8FPAn8h4Fl4MeyZYmA+orE+2c+8ZpdQ2dl6pdL+lkI3A/cCtzQzcEmphLv9dWBJbL7\nbifG/UCbfUwBtgfWIkrG3whcQfUkEkmSJEmSJGnEmYkuaawyE12Syr2eIsP30B6PRZKkcWFirwcg\nSZIkSZIkSZIGZQJwYLY9F/hBD8ciSdK4MbnXA5AkSZIkSZIkSW2bRpQEXxr4DLBNdv8JwKwejUmS\npHHFILokSZIkSZIkSWPHCcBuLffdBxwy8kORJGl8spy7JEmSJEmSJElj1znAjsCDvR6IJEnjhZno\nkiRJkiRJkiSNHR+lWAf9PmItdEmS1EUG0SVJkiRJkiRJGjse6PUAJEka7yznLkmSJEmSJEmSJElS\nxiC6JEmSJEmSJEmSJEkZg+iSJEmSJEmSJEmSJGUMokuSJEmSJEmSJEmSlDGILkmSJEmSJEmSJElS\nxiC6JEmSJEmSJEmSJEmZyb0egCRJkiRJkiRJkjTGPQ/YCVgdWA54ErgHhftwRgAAIABJREFUuAC4\nEujr3dAkdcoguiRJkiRJYQVgmWz7DmBBy/6lgJWy7fuBp0ZoXGrPysC0bPvWYT7XqsASwELg9mE+\nlyRJvTQNODLbngV8sYdjkUarnYHDgM1q2twJfBX4GQO/ZwyXFYAPZtuXABeP0Hm7bT/iu9i9wC97\nPBZJkiRJkjROvRd4rNeDGEZrADt2eJuQHfsDIjukj7jg1GrPZP8bhu0R9M4MiufkRW20X47+z+OU\nNo7ZNmnf7Yn9v6F4fYbbX7PzPD4C51LhCeL3UJI0cpaj+Pt6Y4/HIo02U4CfU/yO9BEB8puBmcB/\ngadb9l9M/F6NhA2T835lhM45HO4hHsOlvR6IFi1mokuSJEmSxpNdgKM7PGYy3ckGWQl4f7Z9MZHt\nMZZMBf5GTCq4GXh+Q/t3A99L/r0V9Re2lgfOAyYSWSSrD3qk48vexIXUh4Cf9HgskiRJas8E4FRg\n1+zfzxJVG34E3JW0m058bj6U+My3NXAh8dnZCZnSKDax1wOQJEmSJGmcWBX4ZnbbocdjGYz7iWwZ\niPUc125o/8qWf7+qof12FNchzu1saOPaAcR75hO9HogkSZLa9imKAPqjxGfdg+kfQM/3/RDYFLgp\nu29DogqWpFHMTHRJkiRJ0nj1UyITpMnC7Oe3iDUKIS52LYouBDbItl9BrA1fZkK2H6JE5eLANg19\nb5tsXzTYAdY4mHgNJUmSpOG0MrEGem5P4LKGY+4GXgdcR3x23pOoQnTBcAywy6YCawLLAvcRVaWk\ncc8guiRJkiRpvLoPuLKD9ndmt0XZecCHsu1XASdWtNsIWDHbPobIot4GWIwoZVkmzVwfjkz0W4eh\nT0mS1D1LAC8DngOsQ6wn/SCxdvQ/gXkVx61AUSHnLmBWG+daFVgt274VeKSi3SRgc+AlWfsFxOfB\ns4h1mOtsQDymZ4B/Z/dNIwKlzweWJgKkZ7UxXo0t+xOBcIC/AH9q87ibge8CB2b/PoCBQfQXAadn\n218jJgZX2QH4cbb9KeAP2fYaWb+LtYx5j5bj+4gKVLlDidLzAC8nJs5+HXgH8X7OzQQ+B5xdM7Zr\ngKWISbrvr2k3FfhPtv074jnJ/Z34/2Ll7N8vBm4p6eM9jL2ltCRJkiRJ0ijzXuCxXg9iGH2IuBjU\nR//skG7YM+n7DSX7N032f2mQ51icuOi6OXFxecIg+8mtQlyIex7tLem2CpGZ3wfcWNNuv6zN41nf\n+ePeoqL9DOKidB/NF6Qh1k/fBNiYWDtyOEwFXkhMCFimw2P/SvH4W/tcj3jOV2w9qMINWV/XdTgG\ngJUonqeVGfr7ZbR7gvg9lCSNnOUo/s7XfTao8yoikPxU0lfr7VaK0tit1qP4fHJam+f8W9Z+HrB6\nyf4JwN7E55Ky8SwggpfL1pzjWvo/Lx8F5rT0c3Kb49XYkn9+6wN26fDYdSg+F8+nf3Aa4vN03ven\nGvp6Y9I2DZCvQ/XvWnpbSH8/SvZtQfXvR37sgVR7LGv3l4bHsHjS589b9qXPc91tx4ZzSJIkSZIk\nNTKIXu0rRGbDLUTQt1VVEH2t7Ji7kv2zk77y2w0V551IXPS6kMhkSi8I3Uuslz29ZtxvTs7xaiKj\n6gBizcW0r7Vq+kj9Nzmm7KIzwCn0vyh2Z/bvqgtp6QW+quz2lYAjiQyd1ovYlxGPs873KZ6HOutm\n43+y5Rx/A7bK2pya9XNxRR+tQfQVgOOJIG96YfFy4DUVffwxO8ezWftnGPieyV/T1KrZY53FwAuI\n84BLifdM2Xt4rDOILkkjrxtB9EOTPuYQwee/A/+iCLTlfzv3qujj7KzNsxRZqVXWpQhS/r5k/0Tg\nWPr/Db0hO8d59A+EX0P139Q0iP6F5JgHiL/hjxGfOTS+rEQxqWMeUY2gU9dTvF9aP+t1I4i+GDEp\n923J/uOy+1pvqTSIfgPxOI8F1id+D9YgllBKJ8S8qWJs3Qiib5iNMf/ce23FY+h0QqwkSZIkSdIA\nBtGr/SA5doWS/VVB9OfS/yJs1a2szPnSxEXkpmNvoX+pxdR7kna7AX+u6GPt2kdfODo5ZveS/ROA\n+7P9B2f3/Sr795kVfR6Z9Ll3yf7tiXXom56HY4hJAmV+k7SrsgP9A91lQeh3E+Ug+6gu758G0Z9L\nMYmg7LaA8sDvlW083tYLkxsTZW/bOW4Dxh+D6JI08roRRP8s8G1gs5J9k4j/2/PPAXOJtZdbvTUZ\nx0EN5/ta0vZ1JfsPTvZfVjKupYEfJm2qAuF5EP1JIqP4KuKzRl4ZZhLwgoaxauzZgeK9ce0g+/hl\n0kdroLwbQfTchsn+r7QxrjSI3keUbC/zauI9n39enlLSphtB9FyeEX9pQ1+SJEmSJEmDZhC92mCD\n6FOJDIh3JvuPZmCGROsF2knA+ckxFxPZ1isSmR6bEkHjPNPlf8S6gq3SIPrV2c9/EWsPbk5kV3+S\n9suLvyPp78cl+9dP9ueZ2/tQBJUnlxyTBoyf37JvM+KCeR+RjX1kNu4ZxOvwFoqL1H1Ul8pvCqKv\nTRFAz7NqNsrOswpRon4WcSE8ryrQFER/isgkegY4nFhPdd3sMR2TjOdRBpalXz97nHdkbW6mPLMm\nrULwj6TPY4h1ZVfJ+t6AKGV5BHAfBtElSd3RjSB6O9LAZFmwbzJFIO1mqpcwmUJU8ukDbmfg5Lu1\nKKrAXA0sWTOmNND5opL96eeT26ivHKTxI83u/vsg+/h20kfrd5bREkS/mvrloI5L2pZloxtElyRJ\nkiRJY8qiFET/MxHcrbul5RcHG0TPdbom+oFJ+6Opvkj1qYZ+0yB6H3AS1dna7UjXRS8rQf9hiqyr\nxbL7Xpic/2Ut7adTZKrc3bJvMjE5IM8827piTEsDM7N2z1KeodYURD8p2X9wRZsN6J+p3hREz8ez\nQ0W77yXt9qto0+6a6GslfR3b0HYyxWsznhhEl6SRN1JBdIjS6X3EJMMyX07GUrUG8puTNl8o2f9/\nyf5XNYwnrTb0zZL9aRD9fQ19afzYi+J1L1suoB1fTvr4fsu+0RJEP6Ch7dZJ26NL9htE15hXN4tE\nkiRJkqSx7HVEsLHutnSPxrY4xUWxm4CPE4HrMkcSmeUQgf86s4F9iRLig3U/RfB8PWC1lv3bZT8v\npShRfwORxQ1Rmj31Coqg/vkt+95GUeb0q0QZ9TJPEMF7iAyzD1QNvsKKREY7ROb4tyra/Ye4uN6J\nI4FzavblXtFhv63S9ekvb2g7n/LlAyRJGg2WJCq35FVZ8tvsbH9VCfTjib9xUL48DBSfleYDPy3Z\n/5rs5yxi/fM6txDZ7AAvr2nXB5zR0JfGjznJdl0lgzrpcXMqW/XWZQ37ZxJLIQG8eJjHIvVEWYk1\nSZIkSZI0vLYFVsq2j6c54Hk6USJ8daIc+k0V7X5HlFQfqvOJ7HKIoPlJ2fYEiiD6hUn7vuzfu2X7\n0yD19sn2BS3n2S37uYDm7OoriMzwtZIxtGsbiszsX1I/yeBnREC/Xb+o2Xc7cWF0KeA5HfRZ5qFk\n+y1Eps78iraSJI02M4D9iWVjXkB91ZwZFfffTQSr30yUj16JYhIfxNIteZD8DKKse2oSsEm2vQD4\nRhvjzj8/rFrT5kHg4Tb60vjwSLJdVr2qHelxj1S26q17GvY/Q7z3V2Pwz4M0qhlElyRJkiSNV0fS\nvC76oyMxkBJbJdtziOyrOmmJ8nWpDqLPHMqgEhcQpfEhguB5EH09otx73iZ1EREUfwVxvSEP8KYB\n7/Nbjtky+3k/sE52q/MAEUR/bkO7Vpsk2/+qbBXuycazSkM7iAvw/2tocxeRabdMG/3VuQn4b9bX\n64is+eOBc4GrGFr1AUmShtMrgN8SlWFS84ns8zwTd2VgGvVLkhxDBNEXI5YpOjzZ90GK6rs/Ljl2\nRrJ/VWJpnXbV/R1/ooN+NPalyxqsT1RJmlfRtsqLKvobTZ5po83T2c/Fh3MgUq8YRJckSZIkjVdz\nGb2ZHWmA9ocdHrtczb6HKu7fGTix5rhv0j97/HwicJ9mnkORVf4M8M+WPvLM9GWIko5XJNsQwek0\n+D+RuFgOkWHfyQSAqgy1KtOT7fvbaH8f7QXRn6K6DH8uvwA5lHXqcx8g1t5cmahIkJeen0OUlD+D\nqEYwu/RoSZJG3nTib9MKRKDx/4gKO7cRayanTqdYfqXK2cDNwPOItam/TXxmmUyx3MvtwN9Kjk2X\nt70xG1e76gLlTZ8FNL7cRlRFWIMIHm9J/wpNTVYENsq2+4CLhzCW4YzxtbPs1bLZz9bf5U4Yp9So\n5ZtTkiRJkqSRt1SyfSedleWeW7Ovqp8p1Aeel2j5d74u+guJkqurEoHlPKD+T4rMk9y1RGb/9Kzd\nFdSvh74kxcXsuVn/7Wo9d5M0gN3Ohe7RmtV9GbABcBCwC5H9BPF+2jW7fYfIrDu6FwOUJKnFuylK\nPX+U8gzxXDuT5PqIJWAOJyrkbE+sbf46YlIewHGU/72fTTFJ8CHi76k0GH8A9su2P0BnQfQ9ic/m\nAJcwcBJsmgE+nXorNewfihcS3weqrAgsn23fXrI/fxxNv9fD+RikITGILkmSJEnSyEvLyO8KXD3M\n57sG2Ldm/5Ul951P/3XRT6Z8PfTcQiKTZhfigva36b8e+vkt7Z8kMtKmAP8GXlYzvqFKs2OWr2xV\naC03O5rMBj6b3dYGdgJ2yG7LEVlDPyIe8697NEZJknIvzX72UV8VZyL9l1+p8zPgq0QW8N5EEH3v\nbN+8bH+Z+cTSKBsQlXKWoH5yolTle8CHifftHkRlqSvaOG4l4HPJv79b0iatKLRmQ3/bNux/Ntle\nsqFtq52IyQJVXpdsX16y/xHiM/VQHwMU5fJbJ/5Kw2picxNJkiRJktRldyTbG4/Q+X5ccysLoqdr\nnm9PlA9frWRfKg+u5xnodeuh9xFZ+BDZ7lMYPmkWzaYNbZcj1l0fC+4gsvHeTpQUPSTZ18k6r5Ik\nDZe8+k4f9dVgdqa9iW4ADwOnZNtvIQLiO2f//iP11W3Oyn4uAbynzfNJrW6kqKowiXg/NgWLlwRO\npXifX0L5kgJ3Uky43Z7qJYHWB97acM40IN/OUkWpPSmqO7SaDByQbfcBJ5W0uTb7uRrFxNxWiwOf\naWMsD2c/VyEqSUgjwiC6JEmSJEndkWZ6NGVJnJdsv20YxtINFxAXxSCC4dtn2/OASyuOuSj7uSyR\nVbJZ9u+7ifVLW52b/VyGyHYZLhdTPJbdGtruxshenOtWZs1c4FDg+uzfG+J1H0lS792b/ZxI/wo1\nqaWJCjadOCb7ORX4LUWgsa5cPMBRFGWmv0lkpTeZSnul5rVo+TRRTQlgHeAfxOfIss9fWxCfR/Os\n69lEBnvZEkJ9wBlJv4eW9Lkx8GeaJ6E+DNyVbe9K82TS1DTgdAZWaFqMWDIhX9f9N8SkglZnJNs/\nYuAa6zOA0yiWJ6pzTfZzZWCfNtpLkiRJkiR17L30L2093nyIuPDUBxzW4bE/SI5doWT/nsn+N5Ts\nXznZX1VGNDeBKPmYZ2XtXN+81nuS875pCP2U+S/FGP+ebf+jpv0UYE7W7uxkXL+saP+SrO8+Ivi7\nVEW7dvwmOV+Zc5P9b6loszxxoTFvd2dFu79m+x9vY1xXZW2r1pTMJys8TneC3ldm/T3Zhb5GmyeI\n30NJ0shZjuLv4gNEAKud2wuy43ei/9/VV7T0vyXF38rHaM5YT+XH5bdbaO9v6ceTY2YTa1q3Tmab\nQnxOOYzIbC/7rHZt1kdZAFGLhhWJUubp+/Ae4nPpscCviM+46f67KALQVTYgJkjmx/wbOJyYBHI2\nMQnzaeAbSZs9Kvr6ctJmAfF+viW7tU5y/VHS9sfZzweB7xBVjg4lPtPmbe6gek3zxVse+6ys/8OJ\n4PnD2f3p+H5e0dc2FN8Z8t/bW5Lb1hXHSZIkSZIktc0gerWhBtEhsq36iItC6zWcb1viAlgeQK3L\nkJ4O7EVceGo1nEH0Y+h/0a+PyNqqkwbP89sHa9qfkLS7mChLXmUDYg3Ksue/KYi+OcXzPYe4wD81\n2b8F8C/i4uJsRi6I/r1k3B+iOgv+TcD3qS6HCfB6Yr3XfBLDeGMQXZJGXhpE7+T2/uz4CQz8bHA3\nMJMI4OX3nUCUxO4kiL5vS78HdfC4vkb/oNx8Ihg3kwiKP9vSt0F0VVkS+ALxubDud2IecDQx8bYd\nb6F/ID29PQS8Fnhjcl9VEH0K8Zn+mZJ+Wn/X0iD6asRnz6rH8x+av+88D7i94vj5xPO2eHJfVRAd\n4rvQw5T3tWPDOKRBmdzrAUiSJEmSNI78FPg8ccH5P0TGxdxs33yKrCyI9cM/QQRRlybWSLyauNCc\nr/u3LJGhtRVROvFcRtb5xAXqVNV66LkLGXgh6/ya9h8h1lvfisgiuRk4k8ionp+1eT6RgZJfqKvL\nhq9yJfBhIqtmGpEd9C3iuZ5Ksebj54lg9FaUl9jstp8Tz8Ek4sLqN+i/fuWHiPfENOCj2e1KopLB\n7VmbpYlJAK8kMvCeBb44/EOXJKlRH/Bm4IdEkG8i8Tc3/7v7KPB14Ajg5A77PjE7bhoRoPxZB8d+\nnvh88nliYuMkYN2SdjcRn9Fmdjg2LTqeIibvfo+YbLET8f5ejqgMdA/x+fkMiuUN2vFb4rvDXsTn\n0ulEFvlFxKSTWUSg++1Z+8sr+plHfJ48EHgR1ZnjrRYA+wO/ICYxbkj8rt1KTCg9mWJphCo3Z8e9\nD3g18bw8RkxA+SmRYT8peQx31PR1fDaWjYky92nVievaekSSJEmSJEk1zESv1o1M9MWIIG1r9lIf\n/ddMT72ByHxqyuh6hCih2Go4M9FXZWDGyDINx2zXckxVNndqKvBdqrNt0tv1wMtL+mjKRM+9jvLn\n+36KjPmrs/uuruijm5noAO8mLoSWPd78NX0TRZZ53e0e4iLleGQmuiSNbc8Fdgc+A+xHZNJOrT2i\n3nOIQF8fkcU+WKsSf4s/SQQaDyAC/2sNoU9pLEoz0dvNmJfGLTPRJUmSJEnjyQUUmdNXdXjsSURW\nBES571aXJX1fW7IfIlC+D/BZYFPi4mu+xmZVadIziMzrnYgA7/oUgerZRGbF34DzKA/EX5qMqyro\nO1j3ERMvFs/+/QTNgeN/0j97/a42zvMMsT7pN4iL1jsCqxAX1hcSGS8zgT9SXTL1OOCcNs51JvAX\n4vXZkLg2cjuR3T6PKDu7Ztb2/oo+vgucnrVvchix1nrd5JUTiUkAGxNlL2ck+/LX9PdERtM22W0t\nIptnGhFA+C/x+H9H+ftXkqRey9cv7paPUGSjHjuEfu4j/hZLkiRJkiRJWkSN90x0aag2p8jA+VqP\nx6L+zESXJOWWI6r09BHLnEgaOjPRpcTE5iaSJEmSJEmLhIn0D5z/vlcDkSRJldYGfk2sEQ3wrR6O\nRZI0TlnOXZIkSZIkLSoOAR4FfkFkr6XWAH5ArM8KUT7/ipEbmiRJavAEsFTLfX8GTu3BWCRJ45xB\ndEmSJEmStKhYD9idyFibCdyT3b8a8DJgSvbv+4G9R3x0kiSpXQuBU4h10Rf2eCzSePE34PFs+8le\nDkQaDQyiS5IkSZKkRcWtwDPAVGCrkv0LgdOBTwN3jeC4JElSs22JpVfmAXcDs3s7HGnc+T0uZyT9\nfwbRJUmSJEnSouKLwDeB7YB1geWBJYA5RID9XCILXZIkjT5X9XoAkqRFh0F0SZIkSZK0KHkSOLPX\ng5AkSZIkjV4Tez0ASZIkSZIkSZIkSZJGC4PokiRJkiRJkiRJkiRlLOcuSZIkSZIkSZIkSaPPEsCK\nwCRgNvBYG8ccArwCWADsNHxDG98MokuSJEmSJEmSJElS700C3gG8EXglsFLL/nuBi4FTgT8BT5f0\nsQmwIzB/+IYpSZIkSdL48l7am7muoXkT8H5gQq8HMkqsD+xDZEP0wpLZ+fcYYj9bZf0sN+QRaTCe\nAPbs9SAkSZKkYbIt8B+gr83bncBbS/o5Pds/b/iHPH6ZiS5JkiRJUne9mLho8bPsljoS2Ljm2LuA\nD3RpHLsB+za0+SBx4WW4bQccnd0uHoHztVoWOJbI2vjVEPrpy/rZEPh4F8YlSZKk8W0zYmLtQuCq\nHo9Fo9vuwE+Bqdm/nwTOAP4KPEB8F1mZ+L75WuCFwJrAUcT3T0mSJEmSNARmog+vCcA/iJJ6a5bs\nv5D6TIL/dnEsn244Vx+RIT4SPpSd70cjdL5Wq2bnv6cLff2ZKIu4YRf6UmfMRJckSWPN08Tn0Cd6\nPRCNalsTWeP597QTGFjGvdWuwP+IiditzETvAjPRJUmSJEnqnlcCWwInU34xI/cu4PKS+4fjIsdP\nga9V7KsbYzf9Gvgb8PgInW84fRd4HfBZYlKKJEmSJA3WksBJFDHb7wKfaOO4PwDnAF8enmHJILok\nSZIkSd3z4eznTxra3QfcOsxjyT02gueq8jjjI4AO8HeiBP47gQOAB3s7HEmSJElj2F4UVcyuJSbr\ntmsO8Z1ksDYBdgbWAVYHlgBmA/8GzgSubLOfzYmJxpsD04AFwMPAQ8RyXn8HHqk4dgbwBmI9+LWz\n+57IxnEDUc7+OiKzXpIkSZKkYWM59+GzPFGu8QFgUkWbvJz7dh30+yXgFKIkeplXZvt/Aiye3J+X\ncz+yg3M12Ts712uI9fiOBK4BbiEujuxJlLRv9ersuL2S+2YAJ2b3v6jifAdn+/dvuX8C8Cbgd9m5\nbyEu8HyOWP+8VV059+nAocRr87+sr5nAL4G3VYzr8Ky/1nFpeFnOXZIkjTWWc1eTGynKuL+7S302\nlXPfDLiN5uW/zgJWqDnP4sCv2uhnPrBMyfGvJwLtTccf0viIJUmSJEkaIoPow+ftxBf839a0SYPo\nawPrUn9RAmADIsNgPgOD76sA92d97t6yLw2ir5iday3Kg9ztOjrr8+vA3cBTRPD6vxQXOL5fclzV\nmuj5GG8Elm7Z97Zs3ywiKyI3hSh32JfsvxZ4Jvv3rcRjTVUF0VcD7sj2PUoEz68iKgUsJCYIlHlD\ndsxfKvZreBhElyRJY023g+iTic/Gz2Hg5+deWYYYz5r0n9Q7WNOJz/Oj5fENpzUovtfMpTvPHzQH\n0XemCG5fRnxP+ybxXe5M4ppBPq7Lie9gZU5M2j1ELON1KPAF4Dii3PyT2f7Wyc4vpVgHfgExKfso\n4CBi0vLpwE3Z/q+2+bglSZIkSRo0g+jD50fEF/yDatrkQfQn6D+z/t/A+2uOe1/W7m4iIA4wkSiL\n1wccX3JMHqB+gggI5+d6EDiC8kyAJnkQ/VngXCKbPLcdEezvA97cclxVEH0CcEa278Tk/nWJoPZC\noixg6v8onrMtk/uXpriI84+WY6qC6N/K7j8ZWKpl33PpnzmfWoniua26oKTuM4guSZLGmqEG0Zck\nKjD9BLiLCDam3yNuJD7br1Fx/LrERNGZwNfaPOeHkmO2r2gzjagadW3LeJ4BziYqV9X5ftb/hdm/\nVwC+Q0xmzfu6oM3xjmX5xOE+4NIu9tsURH858X2xakL3UsBvkrGVVUVbm+J75gVUTwCYSnyfXaLl\n/rz/ecCOFccCbAzsULNfkiRJkqSuMIg+fC4hLgK8pabNhcRM/EuICxvnEmvF5Rcnflxz7M8psp8n\nEmXe82DykiXtP01c1LiOCFT/iShVnp/rPzRnwbfKg+hPEOXcW32c8iB2VRAdogz+ndn+vYDFgCuy\nf3+rpe0qxIW5J4HnlfQ1hXhcfcArkvurguh/yu7fvqSvJo9kx75wEMdqcAyiS5KksWaoQfQP01zq\nuo/4TrF1RR/5Z+s5NE+knQDcTDH5dmpJm82JgH7TmL5LfG8pcxbF87IhcG/J8RdWHDuefIzi8f6i\ni/02BdHbMYWi1PzMkv15da6ySdTt+B/dnzzQVZN7PQBJkiRJksaJPEN8dk2bg4GriSBwbirwGaLs\n3d7A+UQZvFb7AS8DdiLW634HUU49/9nqTOBUIkCdei1xgWZ9Iqj99prxVjmTWPu91QlE+fgtiCz1\nR9ro62HgXcTj/h7x+F5ClBX8fEvbXYgg+9nExb1W84h10tcnAuMXN5z77uznnsTFxSdr2paNezrx\nuv+vg+MkSVpUbQ6c1utBtOEi4D29HoSUeBT4PVGF6hris/+yxKTStwDvBJYjMns3ytqnjiYy2acR\na24fXXOuHYiKTBCTeJ9p2b8h8bl9KSIA+mviu8XNRMB8K+Iz/HpEgHgW9Rnwk4ilmlbJxn868R1i\nBrH00ni3XLI92ia7zwP+ABwAvIh4zeck+9P3RlqhrF2Tsp/LEO+dhYPoQ5IkSZKkrjETffjMIi4k\nbTbI44/Pjq8L/G5MXDTLZ/xXlRtvsgvF2nMrdXBcnol+WE2bPIvk5cl9dZnouYMoHtdsYJ2SNt/N\n9t8AnFJxuzJrk14crMpE35hiLfXHiQv7HwdeUDPOXH6eXdtoq+4wE12Sxratib+dC4ngzGi89RGT\n9aRuGWom+nNpzh7fj+Jz9AEl+5ckPl/3AVc19HUKxe/pei37JhJB/Hx5p6rPwctSfFaeB6xV0ibP\nRM9v720Y13j1FYrn4Htd7LeTTPTFgBcDbyUmZOyT3E5Nxtf6fliNopz7g8AHiYka7To56ftXxAQN\nSZIkSZJ6xiD68MnLHm47yOO3z46fW9NmKYqS7I/RP3OhExMpLqTt1MFxeRD9EzVt8gtr6bp17QTR\nt6W4iPLbijYnZPsfIp6HultaCr4qiA6RrXMa/Scn5CULX1Iz3ry04fY1bdRdBtElaWzLg+g/6/VA\nKqyNQXR131CD6O2YQCzxVLasUu47FJ9zX1bRZmWKCabnlux/Y9JH67JLrTalCLCWTcBNg+gnN/Q1\nnuXLYfURmf/d0k4Q/flEJYE59P8eVHUre98c39LmaeK1PZgIzNfZlIHfwW4Evg/sBizdcLwkSZIk\nSV1lEH34XEp88X/TII/fhCLro2r5tV9mbZ7Mfv6euGg2GHkw/q2ghtw+AAAgAElEQVQdHJMH0b9R\n0+aBrM1Lk/uagugrUKyrmD+2PUrafT/bd0QHY4b6IHpuGrAdkQ1yO0WwvipTP1/LfoMOx6LBM4gu\nSWObQXQtikYiiA5Rrr1uQu4LKILax1e0OZAimPnOkv0nJPvXaWNM/6G60lYaRH91G32NV++keB4u\n6mK/TUH0rYhKXGnw+yritforRZWvq5M2W5b0M4mofvBE0i693Ql8maiGUGZjYhmvsmOfBv5E/++V\nkiRJkiQNG4Pow+c4qksotuMd2fGzKvZ/MNt/N7H+YR4Er8sKr7I0RZbJ1h0clwfRT6/YvxxxcW4B\n/ctO1gXRJwB/zvafSGSwLyAuxLywpe0+WbsLOxgztBdETy0BXJ8ds3vJ/uWJx/kUsaa9RoZBdEka\n2wyia1HUrSD6WsBHiAD42cS65DOTWz6RtY+oXlXm79n+OQwsET+BorLWLMo/496Q7X8YWLeNW36+\nB0r6SoPoy1Y/7HFvHYrnYQ7Vk6k7VRdEn0LxXXIusXZ9VRn2zybjKwui55YgyvsfS1EVIb1dRf3r\nvAGRvX4WAwPy84B31RwrSRpnlqH4MLF4j8fSalViXGv3eiA9kL4uS/R4LJIkDReD6MNnT+JL/qmD\nOHYqcAVFILnVRkSG9jyKcvEvIS7KPUPns/PztfceobMgcB5Ef4rytQ0Pzvaf33J/XRD9M9m+GyhK\n9uXju4b+n8tWzs69kPqLOK06DaJDlDbsA/Yv2fc6qstcavgYRJeksc0guhZFQw2iTyZKp8+nvZLb\nfcAqFX3tlrT5cMu+HZN9VaXaq7KNm25lgdw8iP50xbkWJbdRPFdv7lKfdUH0HZLzfbmhnyOStp18\n/1oF2I/4/pUf3241sSnEd94/JMc+Dkzv4PySNOzWIy6yHgh8k7iQtT+wBbBYD8c1HuxH8QdgsGtm\nDpdziHE92OuB9MC+FK/LDg1t66wDbJ7dFuWZlL00g+I12LTHY5Gk0cYg+vBZjbi4dQ+x5nirjwIn\nEWuQr0VcEFsB2Bm4nCIToLU8+DSKrOjPt+z7WHb/LfS/qLAuUV7+A8D6RCB6SeJv4/EUpRw/1eFj\nzIPoc4F/EhnxEI/3nRQXCV/bclxVEH1L4Nmsv/Rv9iTgvOyYY1uO+RzF59V30n9S6hLALsAZxBp/\nuaog+slEJv+qyX0TiNfoceJ5ehEDfT3r7+CSfRo+BtElaWwziK5F0VCD6CdQXK98gPgsfyCwFxFw\n3TG7/S5pVxVEn0IR1LyqZd8p2f0L6f85OjeR4jvEPGB2h7fWJajyIPpwl7kfC/KJyH3Ed6xJXeiz\nLoj+0eR8TVXJLk7adhJEz62XjaGP+E7bqfxx9AGvH8TxktRVU4kLcXdQP3vsEaJc5Xq9GeaYN5JB\n9A2Jspf7EBd2mxhEH3oQ/WdJP7t0YVyqtzjxIeoLwG/pP3uzjygxJUkqGEQfXn8i/v5sV7Lv09R/\nxp5NlMBrlX+2+CsDg/MTiL9/fcBpyf3rNpxrIXAUna+nngfRDwFuIsqu30pRPnIhAwP9UB5EX47i\ne8e+JcesmvSbrsk4gViTfUG2bwGxnvqslsfXThD9muSYOcRkhMeSPg4qGVde6nIe7X2+VvcYRJek\nsc0guhZFQwmib0bxWfUcqsu0Q7Emel0QHSLzOG/3suy+lSmWevp7zbGPZm2uax56I4PohRnE9cv8\ndflKB8dOBb5Ycn9dEP2Tybl2rul7I4qJE4MNokNROr6TqmC5/HtkH+XLbA2rbtXWlzQ+PJdYi/AF\nLfc/SlzQm0ys/TeNyHLZC3gPsTbjr0ZumOrQq4DvJdv39nAs0nB4DhGwkCRpNPgxMblrT+CCln0n\nEReJtgHWoChdfj9R/vxnDJz8tQLwPyKY+zPiIkaqj8g2/2f279WIz3v3AG8lgvnrE5/fJxIl4WcS\nGS1Dufj1IFFO/mPE5MOliGz6HxIXxFpdlj2GNONlfSKo/gjxvLW6D3gjsD0RcJ9AcQHlYOI7yAeJ\nizlTiDLvFxIZ+KcRAfrcE9n557Sc413AK4kLiGsSr8n1wI1ENs7lJePakvju9Af8bC1JkqThk1Z3\n+gIDP8um1m2zz+OJSa+TiaSry4H3UVSeLftcnrsT2JioRrUEUU1KQ/cIEWf5I/Gd7UvE95IvEN9x\nqmwLHElMgvhqB+e7NdneDfhLSZvpxDJjdZOuNyEmPZeteZ9bjWIZsFuS+ycQ3yPrJm1AVEXO3VLZ\nSpKG2fr0z9y4n7ggtnZLu4nERaPvU8yiO3zkhjlujGQm+v7JuV7ZRvvNiBJAZdlT452Z6GPT+hTP\n99PEerLHUpQKMhNdkvozE314TSACxnOJQPl4k2eif6TXA+mh3xFl+10yZuSZiS5JY5uZ6FoUDSUT\n/fu0l10+nZgs205bKLKU52TH3kwRE6hbxjUdz9uah1/LTPSBPkx8z8if47uBbxPVyrYgluZ6FVHh\nLC2zfldJX/+PvfOOk6Ss8//7qQ6TZzbM7C67bAAEZFlAgkhQMZ4oIt5hPBRPPdEznOnU8zzDeSdG\nQBH1Z0Y5EUXPBCJyp6ggqOQcVjayeWdnZid1d1U9vz++1V1P9XSa3e6d2eX75lV0ddVTT33rqadn\nu+vzDbUi0btIakGfR5wwUogT9yuAB4hTsFeLRP8A8rv3asQJ4GikhFgK+S38D8Rzy5L8Hu9F2+5D\nspw9G3EGMEh51FOBbznH3s30s6gpiqI0hW7iP4oWid6YU/MIYTniKaQi+vSZzSL6ExkV0fdP5iOC\n0LEks+yMoyK6oihKJVREbz2nIBHjX5ppQ1rAE11Efypy/eV12pV9g4roiqIo+zcqoitPRPZGRP80\n8TPGZ9Ro919Ou0ZE9Oc7bd2a05+qc9wJxOm970fE2D1FRfTKvBiJ+LcNLtuA11fop5aIDuIE4aZq\nr7R8lWTq90oieqN2foOkCO5N49jNiEC/z9F07oqigPyxOypafxSpgzHWwHHrkJQyxzbQtg3xYmpD\n0pPsmr6ZCVJIXcMski6kEXtbxdxomQR2APkZtGWmmIOk2MwhY5Dby/56gAFkLDfuwfFpxHMtG9lz\nIHwZm4944Y0i11SeynVf047Y1IaI1HsrxrQhX/AnkfSw07m+ncB39vL8iqIoitJMbkW89ztn2hCl\n6XiI4+VPZ9oQRVEURVEUZb/CIM+QG2ECeUbmlhb6OPIsvvzZ8xuR5/vT4X+R8kVHAH8XbbPA1+sc\ndwdwBRJ1vBJJA34+sLZK+0XAm4B7kFJISn2uQe7PGxGh+3SmarmTSJms7yElrio9l30MuB0Iqpzn\nauSZ+X8hEe4uDyER8N9E6pDfHm0vLyfwI2RenwE8HQnWdCkAv0Wcy39etq8YmX4GEmR4RAUb1wJX\nIinrNUhKUZQZoQsR5JoRfVuJ5wG/Rv7hd72H/gp8EhEFq2GQeo23IV53IB5H3y/rbxL5g12t7stP\noj5upDHnocOd81arJTIX+AywhuR1jSNedM+qc456kej/4NhQXqO+nPc4bRc428+Otrmeaw87bYvL\nV8r6+0q0vV49kjmId+JfSY7BBHLP682lCxwbnoQ4RrwTeLCsv8eQVDa10rV0ImltvomkrwnK+ngE\nuAipc1mL2RKJbpAop/8E7iL2WC0ug8D/UHueXYWM7U2I4F2PQ4jvxydrtDsbmePlNt0PvK/OuYpz\n8jbEe9Yg8+AvZX0tbsDeRtBIdEVRlMpoJLqyNzzRI9GVmUUj0RVFUfZvNBJdeSJS/gytkeXfomMz\nyHNN95n6x5FnmO8Gbom2P46Ioo1GooM8U3bP+esGr6cL+JNzXB4RSD+EPOd7DyKa3kL8jPYNFfrR\nSPTG6EP0ihORTAAraH6A9EFR/8dT//l5LeYgzhUnIoGXbdM4Nhud+8RoOWgv7FAURWka5xD/g3df\nE/s1wCXU/0KwgeqpOIzT7mrEK268Qh9uWo9KQvonnDaNCJpu+puzK+w/FtjUwLV9iurCbz0R/YPO\n/uPr2Hux03aJs/31DdhomSqW/1+0fXuNcx6NRIjX6/tiqo/Bx5x2T0W80mr1VatswFsavNadiFdc\nNWaLiD6Xxq4nBP6jSh//4rQ7r4FzXljH5m7kC3E9m+6mugjuzsmXAtdW6aNZ9WNVRFcURanM61AR\nXdlz+pHv3L0zbYjyhERFdEVRlP0bFdGVJyJ7I6IDHEPt57CPRG2+4GxrRESfR/JZ+8umcU1tSMBS\nroFr2Ymkjy9HRXRl1qPp3BVFcWupXN/Efj8KvCtaH0Eian+BpPBYAfwr8o/nwYhoexySlr0aRyDp\nmncjovhvEC+3FcD7gZORLwdfBs4sO/Y7iChtkIfG19Q4j0f8UGYr8o+5y2JEdB6I3v8KiZJfj3hL\nnQP8OyI4fiC69gtrnK+VXI+M8UsR0R5EWL27rN3gNPtdiNyzhdH7GxCHgbWId+TZwIeRB6vvRsbg\nY3X6/DxwGjJHLkdKBbQh9/L90fp7kbSZN1fpYwhJC3QDco3jQAcyx14TLfOAHyBOAEONXe6MESLz\n/BokUnsLEq2/EHEE+FfEE/EjSGT3L8qO/zbyuWtHvEC/V+NcGeLaORuQVEwuaeCXxH8vbgIuRcbZ\nR35kvhX5sn1sZMtp1E7r/0Hkc3sfEtF2F/IZPQXJZqAoiqIoyuxkR7QoiqIoiqIoilKftyHP9KbD\nX5z1exGR/PVIOvcVyHPxtcgzuO8jz8x/gGSKBHkeW48hROA+GHnuOJ106znkWe0XgHORZ5UrkEjk\nIeT54sNI0NRvqfyM8FIk02a1mt2KoiiKMuP8mulFqzbCSkRYs0iU0zEV2hhELC2eu5LA50aiW2A1\nlSNcO5EvCMXI3EMqtLk52j+JCKnVeK5zvosq7P+hs/+rVfo4AfniYpEvAYdXaLMvItGLvMPZ/+w6\nfUH9SPTvOf19m8qR5sci994ic2FlhTYfI3l/q9XueZ3Tplrd68OoHw31Vqef91VpM1si0duoPGYu\nRxCXYritSpvLiT8XT67R17nE9n6kwv5/c/Z/k+pf/D/ktHtvhf3l2RF+hgj4rUIj0RVFUSqjkeiK\nouyvaCS6oijK/o1GoivK7OFviJ/RzVQQmKIoiqLMam4n/sfyeU3q84tOn++p0a4bScFuEe+58jQz\n5SJ6Lftc8fN1Ffa/ydlfq37jd5125eL/EkQUt0gKnc4a/bgi+CUV9u+vIvoi5F5ZJKV9d41+3uuc\n97IK+z/m7L+pRj+GOGXRmtqm1+XuqJ9bquyfLSJ6o7ji9rIK+09x9l9co5/riZ0+yudQJzIXLPAQ\ntUVvD/GOLTq9lOOK6INI2vpWoiK6oihKZVREVxRlf0VFdEVRlP0bFdEVZfZQDK7LsXd1sBXlgMWb\naQMURZlx5jjrzao/8oLoNYdErVZjFLgiWs9QW7TcjIi71XDruVeqi/5D4hTRlUR2gB6k7jrAHYgY\n6PI84jIYlyMCXTW+jkRgw9T08vszzyEWUa9A7mE1vkmcqqfeGPx3jX2WOBXRcvauFMmfo9enUL1W\n+/7En5z1EyrsvxW4M1o/H0ntXs4hxA4q1wKPl+1/FlL7FOBb1E6xFAJXR+uHUVnYL/JDYFeN/Yqi\nKIqiKIqiKIqiKIqiNJ+/J65TfiWSfl1RlDK0JrqiKGPOekcT+usFnhSt3039KKffE6fWPoHqdZvv\nQcTUarj/0FdK6z0M/AT5gnAycBTwYFmblwFd0frlFfpwRcrf17AFJM32A0ha8yOjfsdqHrF/MJ0x\nGEIcEU5CBNU+qs+He+r0tTF6NYizQzXxdSlSk/346JzzSUbLz49e26PtzXIcaRWdiAPCacic7Ueu\noegA4Iri/VTmK8DXouPOZepn7E3ETnVfq3D8ac76Jio7qbi4NZcOB9ZXaVctBb2iKIqiKIqiKIqi\nKIqiKM3lC8BBSKbRp0fbdgOfmDGLFGWWoyK6oiiDznozUiu7Al818cxlnbM+UKNdrYhniCOeoXq9\n5ssRER0kKveDZfuLEep54PsVjndFynUV9pezFhHRTXTsgSCi78kYnBStD1BdRK8nZk8665XubwZJ\nm/+WKvsrMdtF9FcgpREWNNi+q8r2K4HPIk4MF5AU0TNIinWQ+/mrCse7ZRauqLC/FvNr7NMU64qi\nKIqiKIqiKIqiKIqyb3ghEvBSZBQ4j8olGRVFQUV0RVHkH8kzovVjkWjtvcGtEz5RtVXlNrVqjNeK\nQm+U/0Mi1pcidfT+HQiifYcQ1ya/BokkL8eN1J+ssL8ct02ta9ufmK1j8A3EMQJgG5JS/F7EcWGM\nWCx/K/C3LbSjWbwYceTwkLIA1wA3ItexG3FGCIGViBcpVE9PPwZ8F3gH8AzgyUhtc4CXEIvk34j6\nLMeN5B+u0qYatdrWSguvKIqiKIqiKIqiKIqiKErz+AESrJMHHgWuQp6jKopSBRXRFUX5A/DGaP0Z\nTejPjTTua6C926Ze6ve9JUQiaf8NWILUYP91tO98YhHy8irHu2mqK6WML8e9tqGGrZw++/Jv+Wwc\ng+OJBfTfAOdQPXPBq1pkQ7O5CBHQdyOfy7urtGu0rvtXgLdH7S8A3hNtvyB69ZF655VwP5dPB+5r\n8JyKoiiKoiiKoiiKoiiKoswOPjzTBijK/oZXv4miKAc4NxBHhD6buJ75nrKdOLX6EQ20X+msb6ja\nqnl8hziqvZi+3RCLsFuB66ocu9FZb+TajopeJ6kc2V4LNz39nDptF06z771hT8cgj4xtK3iBs/5h\naqf+r1fPezZwKPHYfovqAnqxbSM8CPwuWj8fqaV+CPC8aNsvkHrnlXDT9h/T4PkURVEURVEURVEU\nRVEURVEUZb9FI9EVRdmEpG55LeJY8wUklfR00qf3EUer5oA7gVOAI4EVSF3sapzlrN86jXPuKY8A\ntwCnIWm9+4DjiMXI7yFRuZVw7XsB8OMa5zkGWBat38b0U1fvctaX1GjnIddSC/fc7dO0o5zyMbiq\nRtsnEztl3EnSMaCZLHbW19RoNw+Zl7Odg5z1tXXavnAa/X4FeBZSp/xc4GhiZ7qv1jjud876uUia\neUVRFEUpYa1NAzeMjo5mfL/a16jm4XkenZ2dpNPN+zk7OTnJ5GQjlWr2jq6uLjKZTNP6KxQKjI2N\nNa2/WjTTdt/3GR8fJwynUyVmz0in03R3d9dv2CD7s+0jIyP7xO5UKkV3dzfGmFFjzJktP6GiKIqi\nKIqiKEoLUBFdURSAjyMpsHuBFwGfBD5IfSHdi9r1Ah9wtv8QESsNkjr9gqmHAhKl/HfR+jbgt3tg\n+55wOSI8dwAvB04t21eN/wN2IiLkecg4VRNtP+Ss/2APbHRTZj8T+O8q7V4FHFynr0Fn/aCqrRrj\nRuReLQBeCXwCWF2l7d6OQaOMO+uHA5urtPt3kjXdZyvu9dTKDHEqcPY0+v0JsAWpgf5WYseRNUhG\nimrcgkSyH4U4npwO3DyN8yqKoigHPsb3/dP+9V8/mF23bhNdXT2JndmsxXMLkOTzsGMHlIt5nZ3Q\n1ZXcFoaJdhbYvns3/3nhhZx44olNMT4MQ771zW/yfz/+MT3t7dF5DGOpXgpeW6JtKizQHQzhWcf2\ndBo7bx7lVVa2bIENiTxL27noordz5plnYkyjFVlqc91113HZ5z7H4nnzwDpf3YeGwHEKsMbgH3wI\nNpu8HjM+RurBezDRsRYwfX2YJUugaKMx7BwZ4S3vfjdnnXUWzeD222/no+98JwO5HClne35gMf6c\nftyxtDZ5aQAmKNCxeQ1eboJEw1zSZ3MEOORv/obPXnIJntecRHx33nknH/7wR5k7dz6eF1sfhlPt\n9DxZ3Nvt+7BxY+L2kErBwoXStsj4+AhHHrmUSy65mFTKHaU9Y/fu3Zx/3nl0pVJk0unYWGuhUObv\nWyhMGUsABgagw/k6HYawbRuMj5cucsL3ySxdyhe+/GXmz58/MrUTRVEURVEURVGU/QMV0RVFARFB\nXw9cjQjjHwBWAe8HHqjQPoOI7R8HjgU+V7b/28D7EMH2TYgAd0lZm2WIqFcMZ/kcrYtULueHSMR9\nB/Bm4tTZtwP31jhuArgYEY47gZ8BZ5JMg20Qx4JXRu83Iinkp8udxILnPwBXIgK2y5lIdHE97nLW\n3wD8lKSwPh1yyL36DDJ+P0Mi0t007wb4F+A10fvNyJxoFX921j8FPB8oD8l6M/DOFtpQi25gbgPt\nAuRZ7wNISvpu5HP5LeCOsrYriT+vjVIAvoE4E7jZC74B1ApJCoH3AtdE5/sZMo9+XqV9G/AS4ATk\ns7Cv6GHq9xrjvJbfgwK1U/8riqIo02R8fIKXv/yNrFp1Ykmf8zzon2/JZp2GW7fCT3+aEN6wFlau\nhFWrkp1OTibEvAD40GWXNT1qfHh4mDeecAInHnYYWEuIx4PdT2NrdnFCGu8pDHHs0O/I2snY7nnz\nsM95riihEWEI3/kOXHRRcYulUPgxo6Our9zeMzY2xjGLFvH+V74yFkV9H373O3jssdL42lSaoX96\nH8FBS2I3WQOphx+k91VnQj4eY3PGGXjnnw/FqHNj+Pkf/sB4EyPeJyYmOHj7dj7U00OnMwd2PuNs\ndj3vpRgbj3qhAEHgHGwgM7yT5V/7MG2b/0rpn/swFM8Fx+niz2HItdu2Nc3uou3z5y/i/e//KG1t\ncZKnfH6qFt3ZCe3tSRF9eBguvhjWrpXPh7XS7lWvivVpY+DBB//Mww//oml2h2FIhzF86DWvYX5f\nX7wjl4OdO5ONd+wQpd/FWnjZyyD6jABywVddBQ89VLrIx0ZG+OKuXQSJm6YoiqIoiqIoirL/oSK6\noihF/gcRfr+LiKNnRcujSE3mQeRvxgDwDJJ1usufqA0hAuovEUHtYuAVwLWIcLUCieQuhin9CriI\nfccwIuD/PXCSs70RsfvTSO345yEp2x9AUsCvB7KIeFjscwK5zt17YKOPCMKfRxwNbkDG73bk/jwb\nifZ/HKnh/srK3QCSwv5mJIL4dET030ac5v2PSDr/RrkIeA4i4q8E7kfGYF1k64uBp0Vtc8hcGJpG\n/9PlF8DDSPmAUxGnje8gwv5BSMrzk5HrvhnJPrAvaTT9+T1IaYEc8EVEgO5EIsEvR7ITpJH7/lJk\nrC8F/nkatnwt6rf4lL+AiPT1uA5xjLgIycTwM8TR43+R7AwgGSmORTIn9AK/noZdzeCnyLysxFym\nOo5ci8xVRVEUpUkYk6KnZy5z5y50RHTLwICl3Q2A9n1RDeUgebUWenpgbpnP0+RkImTXB7JNTIde\nsh3o6+xkYXc3WEuAx6ae+Uy0LUyI6L2FFANBD+1hSo6yFtvTQ7hgASYV/7wOQwmqjyOLLcb00KQA\n9ITdnW1tLOzrhdAR0Ts6IJtNiOheXz/+3IWxiO5BqncLc4xJXKOXzZLu7aXk+WAMvZ2dNNN4A7R7\nHgvSabqKgxSG2K4e7JwFGBv7CU4R0YGMhYG2NjrSaTBeyc6SKh3Z2mfttDwOGyWbbWPevH6y2Thz\nQj4vS2lKA12d8VQv4nnQ1iY+CkVzs1no7U2K6F1dczGmudZn0mn6e3tZMGdOLITncmK4y+RkMuK8\nyJw50N+fFNG7uhKeAsO5HOkmRf0riqIoiqIoiqLMJCqiK4ri8iPgIeBC4jTRh0dLJf4atb28wr7f\nINHq30HSjZ/C1HrUFolQfiu1I2FbweWIiF4kj0R71yNAxuZriDjch9hfznpEQL9pL2z8IiJKvgH5\ne31OtBRZjaTXfkMDfb0WcZB4OuLYsNTZV6uOeCVCRMT9ChIl3wv8U4V2G6Pz3jjN/qdLAXFeuAaZ\nq0uRaGuX1Ug970bGajbwEWA5MkezTC2JkEPm3d1MT0TfgIxTcR79HMl40AiXIHPlUmSMj4+WSgSI\nc4WiKIryBCIMLUNDITt2hNhIZEunYf5cEpHoBoPJZpPKqCvoJTuFlPuz1SZzXjeTsTHsyAjGWiwp\nwrYCQTqZpL0QGMaCNgphKFdiLX4+w9iWCfBcEd2ye3eaIEhH2qIlDMtyfTeJgvUYD7JxBHZgaAsN\nqTB0UrKHBKGVIS+aYcGYNHbBIijEImrY2yfHOencW4HNZAn75hAWI/jDENOeJU0AJh6rgvXwfZMw\nwwvBeimZYNEdssbg981P5FQPcpOxyN5ETBhgJifwir9gDHhjebzxQsIvxJsIYcxinCpZ3m6PHjqY\nk/HENAudGciSlRRdVq4obQvUr641PQILE36asUI67tsPsUFb9F6MzwQp2srLLRTz6rv59a0VAb27\nO54nhQIM7mnSK0VRFEVRFEVRlNmDiuiKopRzHyJIHoFEW5+KpBSfiwh324G/IPXB/0TtJzu/AZ6M\nCIHPQ8T0NmAXElH9I+C2GsdbJA031Bd6R5y2jYh3/4ekmi8+VdtGHFFbj0ngfOAy4GWIkDg32r4R\niRq/EolEr8aNjr2PVmkTAm9Exum1xGnntyORwZcj13014vwA1SO+1yAZBI5GhHm3WOnjZW0vQWqY\n18qTmkME6S8jY3ACMC/avhGJUP4eyfre5fyCOBV+uQ3lfB8RjKFyCu5HgKcg9/RFSLS0j9zXa6Lj\ndyOp/IslCoYr9PN74vvyUIX9jfIdJIJ8OrhPG33ECeMKxFnjydH2IeBWxCHiEWAhsb2Nns/9LH11\nmjb+FJl75wLPQmq290b7BpFo+r8g83trheP/SGzvPdM8dz2K87ZR1jf5/IqiKE94RkZCLrxwjM7O\nYYpfEefMMXzm092sOiYdR6dnumk//gQ8vyz39e7dcJvz1dBawicfhV25siRk2zDAdpbVTW8GYYi9\n6ipsezvWWmymg+FzDmHHysNcPZfthT4eGzlDBF8AA9vXjPHTS/6AH1iKMd3WWrZtO5iRkeXRkR4w\nibXdTTXbYlg/uZDfDj4lHl9/kuNGfseSsdGSgGxTaUaGQnIdJL69Z+YcQvt3rsY4/qyZTIpMpxOd\nbowIpU21G/zjTiD3jndisnHEc3d3N72duygJ48DqkU62bij3Bg4AACAASURBVOtKiOhtE2kOXXQw\ntAelawzau1j/nNcRZNoAg+dZNj9wF+E6t/JPc8js3ELPn26gMxOlWDBgb7+D8J77EtkVUuOjeJPJ\npF3ZbCdvX/U8csf0i7huwWvPMJA9Gi8VidvGMJZay134TbV7aLKD36w9lL7e/tI8CANLYcKnuMEa\neNLgbZw0fAde+U+9iQmJWndF9Be8AJ797LjNunXw9a831W5FURRFURRFUZSZQEV0RVGq8Ui0fHkv\n+xkDvh4te8LXGmw3MY22IAL1N6ZvToI/k6zHPR3up/FI3euipRq30LiA2sh5r2mwLxAniFqOELW4\nPVoa4SbqR/WPI7Xuv1CjzR+jpRoPRsveciPNicD/VbRUYyvTm/cdxLXq/4o4k0yXHOIk0kjmhnIe\njpZWMJ15qyiKorQA34fVqwMkSYyIbP39HqNjliAwcYCwSUNvH4SOQGiMiOjDjo+btaW86KVjAx/S\nce3xZmI3b5b07IDNduKPjEl6bkdH9P00w4X5WCcCedOIx+2376JQcCN3LVL9qHiNHp7XmsRLE0Eb\nOwt9pTFKBVnyvic3pBiFbcEvWKnZ7VyPyXYSHHdSQqBOjQ3B4Ia4YTFNelMx2N4+wsOPImiPnSLa\nc8O05WN/yRAwYTu5XDIg3hQMYXs7dHaVdoRdvUwccRxBm/TneZAbHcduvKPJtoNXyJEe2knGqRvP\nxjXw6H3JhkNDMq8dUl1dHHLMEdCTj4Y4yufuLQUT99dlxjGmTMTeSwpBisGJTvLp7lhEDyGXjzMu\nWAMLC+1YP2BKsrAwlMWJ9mdgAFLOZzKfjzIEKIqiKIqiKIqi7N/oLxtFURRFeeLwGqA/Wv8C+76M\ngqIoinLAY3AToEtWcFMhI3iZOFisY13W0FRo2prk4nG/Jvp/0e7y8xmncWxy8ror99oay8uHzSBj\nXp6SvfJ9ECcBd7NpcgrxahTvbVEntqX/mSkNK15jeX8WsLbUn7FgrGXKBGoW5SnvPW+qoZ431QHB\n8yLnhiiXOyZad6/KYFswZ0rTgrjr4vvE+CbmdYR1nCpc3PTuiqIoiqIoiqIoBxAtKianKIqiKMos\nYzHw8Wh9G/DNGbRFURRFURRFURRFURRFURRFUWYtGomuKIqiKAcur0Bq1rcDZxDXL/8wtevVK4qi\nKEprcSN4XdyIVhtF6RqczOKti+iG+FSlOOCySPRKkdyRsbQs4nlPKEUHx7WrrY3jnotNgKlh3RVD\nlVs35tNFrsowdcwtxelRunctS1tQoeMpEdm2cpS2tcmluC3ZyKkK31zKTznl1RSnjpMmoGhL+aBW\nySChKIqiKIqiKIpyIKAiuqIoiqIcuBwNvLxs21eYXh11RVEURWkIYyCb9fC8FCCibTYbSYGuXpjP\nw9atUMgnxbd8HubMid9bS2F0N/kHHihtCsOAoKzGdDOwwOOpFTxqOgAI0m1sHO5g8+ZCUkfEAKlI\nzBfzwzCF5/WSSoUUhWdrLR0dHXR2xmm68/nK6dSbj4Hubpg3L66JnkrRlgnB5BOCebqQx6526p8D\nNj8B4zud7ozU9l6+vIk2WggDjJ/H89PFLUxMWMYmUpTGEYvN+3R7owmdP2Um2R7MY3fgR3PIUij0\nsGmzR5iVNp4HO3ZICe+m4/swNgbFmuhAvq2bwqJD4zltLdmuQTJjw8ljOzvl3vT1lTaFqQyjQQcW\n6c8Yw5ifxdrmTphUytLbC329tnTHwxAKk2FpXlggNdnOzuyiZGp/a+kdzZHdsSMp+qfTyZrog4MQ\nBE21W1EURVEURVEUZSZQEV1RFEVRDlxuAj4dre8A/gD8aebMURRFUQ5kUimPZcu66eqaC4jONmeO\nCOm+H+tu6Y2Pw2c/C8ND8cFhCK97HbzxjaVNFtj82c+y/qKLHCHbMtTXB297a1NtD0jzgf6v09F2\ncsn2bdeOM/6TrYlg4IGBDC95yXy6uuKf0rlcH11dZ04Ra084weO000TENsZyzz3tTbW5Kqk0nPFM\nSB9KURn1jOHQJTls++NxO2MIHn6IXeedB4VCaXO6txeWLU3W8t65E449tmkmGiCdG6Nj5+N0tbVF\nGy23PtLPPRsGSk4K2JCT5j7Gc+esxVX/B8ly8cRrWL+7Ey/a7O8yrPtEltAp3T0yAiec0DSzY3bu\nhFtvjcfIhmx86itY9/LPYYhF9EPSG1mW3hRvAzlm8WIoXjeQy3v89u4F5P1UKQnAw8Nj5MPbmmr2\n/Hlw1otCBgacKP4gwE5MEGcesNx3/9FcER6bTAyB5W//cDWH/e4GEp4YQZAU1QcHxelCURRFURRF\nURRlP0dFdEVRFEU5cLkhWhRFURRlH2DwPFlAdDXPS2ZutwChhXxBIs+LO8NQDkiX/UQNAsLRUYwr\n0nV1tcT6vNeO5/VEtltyfo7cZB5XMHS0ZgeDMZkpWa5TqThQ2RhIp/dRymuDCOmZDLHtRsRmN6ze\nQGhDzPgY5HLx9mxGLtQV0VsQWSzx+TaOdraW0IIfeM5YSpR52sRR/gApQnwy5GmnaKWPTKswjKeV\n67zRVKxNjokNsV6aMNuVENHJdEC6zHnC8+TeuHM99AhJESIR3QYI8ZpeuaA4DzPp4lmijXHwPxjw\n0h6+1z5FRLehlblRzHBQHIdiWnfQKHRFURRFURRFUQ4YVERXFEVRFEVRFEVRWoqJ6iyXSm3PwhLK\nSZNqKa+NG+9qizPLNJTk2WFwHUyp9vmUPfvK/P1inPaE4nVZKo6we91aD11RFEVRFGUmOBRojWdx\nc3kUmJxpIxRlb1ARXVEURVEURVEURWkK1iZrUIfFAGJXODdINLpbKL0YiZ4Qew3TEn/3ktBSSgVu\nrakYwWzLzC5uC0KwznVbnEty2rUC6ywk1iudsHx8K2y1Vi4mEbTeKuOdwYxut7VJ8bbifYi2hzZu\nWT6lWkvZRLehRNWXCfvGWIn+T+jS7rbitduK86q112KTr87JKs3z6kY5s8+UvVcURVGU6vw9cM5M\nG1EBr34TRZlxvgucPtNGNMBJwO0zbYSi7A0qoiuKoiiKoiiKoih7TTYLxx0HixbFOltnepLOO/+I\n/+igCKQAY8Ow6mjwndzo1hIsWUoYJJ9bTi46mrGTzo3TuRuLP7Km6bYbYzl1+WYWzV0bmWMZOcqQ\nKwub7+kLOfIIS5uToXvh/ALhyTsSIjrA8qO7OPTQ3qh/2LSp6WYDlt7xLSzfdltJtjRYcgthY2Yx\nxhZTdkNH0IPnZ+JDjYFUH9lDD3Xy1FvGepeyacmJWC9OLf64eZiDm2o1+CbNZLoLL91RsrFzbpYl\nvomDmy30pMMoRXh8H9pMnmMXbOGgjqGofrolH6ToyC4isMU69LB9u6TVbzZ+33zGj30apIrjaXlk\n7CBu/X0BimNu4aF0lgGvLyGipzKG+Yd1kO3MlsYCCws6R+Na8AYGu8ZZ5zVXjC74sGsXpDw5iQXS\nnkd3Ji1p6KPTZzs8+vun6uWZdCfkeyg2LISGPz86h81DcX33zWNbGWV7U+1WFEVRDkgsENZtpShK\nLb420wZU4XTg6Jk2QlGagYroiqIoiqIoiqIoyl7T1QVnnw3HHx+JbwbYOYz3lo+Ru1cCECxgjzoK\ne9HnYN68hEpX6FtALp8uCYnWWoZPOJftbS9xMkYH5H/4nqbbnjKWfz7jLp72pLEovNlgj3oy9A84\nrSwF47HTg6C0BbIT4/zTUXdjrFu32zK58FDGDhbB0fNgcLD5ma+NtRy8825Of+BrFKN/Ay/D3W2v\n48HOE+P63MDSSUM2UeMa2rMFDnvuc/FKUdWWje0ncl3fywk9EXkNcM99P2ZJU3PwG/KpDkbaF5Br\nizNRLlhhGFjhNLPQ+3gAWwuJwesmz6tX3YMtpTqwjNsOrs09n7xtK2UZf/hh2LatiWZH5JYdzs5X\nvZXd2U5Aypz/8vM+3/jWBNYW5y94pgtDZ+LYtjbDKad2MWduquRYMqerwIVvXkNPZxBFqxv8HTu5\n+8Hm1hefnDQ8tsawa8grJX7o7PJ40mGpUplzgO65cMQRJjFfrYXuLfNgbEnpXkzmPC76yUlcf+/C\nqJXB2sd4ykn3NdVuRVEU5YDk+8DrZ9qICiwH1s60EYrSIP/E7HRG+SIqoisHCCqiK4qiKIqiKIqi\nKE0hnYZMJhbRbRqsn4eJcSCSmP2CNEy7P0ctJpVKCKXGGPAykM44Qci+KJYtIJOydKRC8KJC5hkg\n67Yw5AA3sNkil9GZDqO43jhFdpi2THjSlee1zGw8LGnrO0YZLIaQVEJEt6ZCkm1jMKlUHAGNhVSa\nMNVOYLLFJoReKx4dGDAernpbng4di2NbwmyyXihp0aOr8m1IKhU/5CiOe0swHqSzkI4isD0IrCWX\n8xNZ3pN1DARrDAXf4Pux2O4HhpSBtFP6oFW2u6naS9nZy8bYmMoR/KbonVB0dDEehTDFeMGdHyls\nUx0uFEVRFEVRFEVRZgat8aEoiqIoiqIoiqIoiqIoiqIoiqIoiqIoESqiK4qiKIqiKIqiKMr+Snnh\nakVRFEVRFEVRFEVR9hpN564oiqIoiqIoiqI0AQt+HgqTUV1nwC9gUxlo7yq2IExlmSiAyRe3CKFv\nCUOnqriV/3mJ5NAtLPmXyG8NFAqQyyXbGCBdVqc6DAgwklncaRjafZTSujz3tpfChD5eMIkp2mDA\n+AbjNLMAQSEqVO+koQ8tvg+BibsPmluaW85kpV+37yn+ABbCwCITo6xAdxg62y2EARQmgCi1vQGC\nfPMNBzlXfiKevh6kbUhnNsCGTgp9rzwvu6WtDayVcS5ebxhG/7NhqSZ6WV74plCc3mHorlupLR9P\nFUmjH9pkJvopYw7GGrIZS5dT9j20U7LDK4qiKIqiKIqi7JeoiK4oiqIoiqIoiqLsNd7EGF3XXk3f\n3X8uqaG+Nax//uuZeN4FpXZbJ7r5xWVzGC+kcCRznvK0FCc/Mymk9vi7OG3xYKm2d0DI9e1jrbmA\nHTugq6skFgZ/+AN2aCihI3r9C5l/9suxXd1FlZ+czfKgtyqhHFqgI9VDZ6vVRGPgkEPgzDNLA5cK\nQg696xYW3nFdop5415MOwmvLJA5P5cYxE+PxoBvY8HiBa9aFTJZEXMPIiOXcc5tr+vbtcNNNkHFM\nGhqC0VG3leUMbyeneH8loeiGIQwPJxR4L+fTc/tPKfhitwd07d6GOXZhcw0HMvfdQd+H30G2WCve\nwCuzx/DUV61y/REID3sSdtnyhO2jo5Yrr8yxenVRRDcs6M1RWLsBuosOAAa2bm2698LkJKxdCz09\nkYgODLSPcXThEVKOF8j88ZD23VPP3bX6btixqTTX202at7/qSF7ataDUZsu2gD/eptkRFEVRFEVR\nFGU/pgd4DXA2MAAEwFbgB8CPgBZ5K88+VERXFEVRFEVRFEVR9hqTz5N97HY6N/y1JDDnO/uYOPfj\njCw5UqJbDax/MMePvruJXbvcSFvLaMqw5LBkn0fPGefQeTtLEqQPdKVb8HvdWhgbE2HWWqzvY2++\nGbt6tWMhmBUr6DrpeMzcuaVrDFNz2dxzHNYkf173G+hqvqVT6e+Ho48uCeGmkKf/9zfSf++tcRS0\nMeAfCe3tyWOthcCN8rbs2hlw7z2W8SAWQj3PNj1r/O7dsHp1UkTfvFl8GYoY4Mj+MZi7g4SIHgTS\nsFCI246P0/6na0nl85iodRaDWfny5hoOpDatp/2eO2hztj31xS/i1DM6neQKlvDkhYTHZXAr6W3e\nHHL55TnWrfNL11SYkycc3CXXU4xEHxlpeqp+34edOyXBQjESPdueI9W2kbQjoqd9ny7fn9rBto2w\nZUtpvmQyGZ51ziQcEx/76GOWex9qqtmKoiiKoiiKouw7ngN8Bzi4wr6XAP8KnAfcuy+NmilURFcU\nRVEURVEURVH2HmMk8rm4YMF4GAummOLayRBdrg+WDisSiYlR4ujE5lbY7r6a6LzWMdK4bU18PWBK\nkfKJLlthZy2KtkY2YTxZILLZeR8fRFke+giTuD+2BanpE1OlxjZRxIuyeJ2DAVN0JoDW3oQpArcX\nz/Nik7CxsYvnuImvt0VZDMqmujOWZY0qnb983E1UxsD1h2lhxQVFURRFURRFUVrKycA1QEf0/i/A\nzYhX8AuAI4FjgF8DTwPWz4CN+xQV0RVFURRFURRFUZTWsadaoJmyMvtondY5M+zja6k3dmXS+axi\n7+0yU99GvifN6b/GmU0ig39zO1QURVEURVEUZX8kDfw3sYD+DuAyZ78HXAS8C1gEfBV44b40cCZQ\nEV1RFEVRFEVRFEXZa6y15MOQiWKeaKAQhhT8PAV/Ik7J7ueBAuUhq2FYwPcnEtt8P8+EX6Ao9QVA\n2AqxzloKYchEEEQpzoPK57EWz/clL3apJrqP709iy6K8CwXI50Vf9DxLEFRIj90E/CBgolCQOuHF\nE4dhnK87spswjNsUCcNk3W1j8W0ATJK8PwUg1USrLWEY4PuTGGfciibG4npIIfTlvpSncy8uEbkw\nxEdS/hO1bm5Fcef0QM6xyAJhGOK7Y2ktoe9j8xMJ2/N5SxgWIktNdHyBnO8z4fulDAz5IGh61gVr\nQ4IgV/qcWQt+kGPS9xM10SnO8XKCIHmDwlDa5eMSCznfT2RwUBRFURRFURRlv+B84PBo/WqSAjrI\nD8R/AZ4OnAScCZwG/HFfGTgTqIiuKIqiKIqiKIqi7BXGGHqWLOHSxx+nc3Q02mqx4xNMXP1fhJm4\nenQuF7Jihc/SpUmhbXAwxVVXJX+itqcKtKfiutcWGM3n6epqbrXxeQMDfHHzZr65fXt8rrlz4bjj\nkg0zGcy118a1xoHApBj3rqQ8pjedlqVo+e7dw7zrXf/cVLt7enu5a80aLvj4x+ON1sLgIGSzycYb\nNzYUNr8r2MERq/5I6KQhN2aEvr63Nctsurq6aG9/nNtu+2fceuG+n9T0wXLF4Cg/S40lO4gcHdzo\n5zAMGT/qqISAmwOOW7asaXYXbd++ciXvHhpKZkAfHsb86ldJM3//B2xHOzhj6fuWtraQ446zFOdM\nOmV537WjpFO2JKKP5XIcduyxTbM7lUrR25vnllvej+fJxLQWMp7P9Znx5Ox1HTBcxseT4roxcNll\n0OZ8vgsFunt7Saf1cZOiKIqiKIqi7Ee82lm/qEqbAPg8ErEO8Pcc4CL6bM2MpiiKoiiKoiiKorSG\n1wGXAn3N6Mxam7HWju7YsSM7Pj7ejC5rkkqlGBgYoM0R7vaW4eFhhoeHWxpBa4yhv7+fzs7OpvU5\nNjbGzp07Wx7522zbJycn2bFjB0HQqlhxwRhDZ2cn/f39Teszl8uxffv2ltsO0NnZycDAQFP6CsOQ\nLVu2UCgU6jfeS9ra2hgYGCCVSo0YY5ryd8ZhN/BW4Iom96soiqLsG04HbgIuB14/s6ZUZDmwFvhf\n4Pkza4qiVOUm5LOUojy9V8yxwCeAZwMZ4C7gk8BPp3mu84D3AquQdFW/Bv4NeKTGMV8E3o5EK98+\nzfMpM0MPsBOZK1uRdO3V6IvappCa6Mtbbt0Moq7BiqLsCY8AzQ3pUBRFqcyPgNfMtBGKoihKbYwx\nTRP7ZoK+vj76+pqt9bWerq6upkfl7wva29s5+OCDZ9qMPaKtrW2/tN3zPBYvXjzTZiiKoiiKoiit\n52jgZqR+0BeBMeC1wE+QZ2zfa7CfdyDO1/cBHwPmAW8GngU8FVjTRJuVmWUlIqAD/LlO22HgQcSx\nYhkyLwZbZ9rMsq9F9H8Bii7g/wFM1GirNM5LgVOi9SuBe2bQFuWJQTvyj+31M22IoigHNG8HsnVb\nKYqiKIqiKIqiKIqiKIoCcAnQhWhGRUH0i4jw+XngZ8Bo5UNLDAAXAo8BTwOKKcd+BdwAfAp4ZVOt\nVmaSJzvrjzXQ/jFERC8ee8CmdN/XIvoFxIXpP80TU0T/KnAo8DjwD03q8/lIOjOAO1ARXdk33An8\ncKaNUBTlgOYlqIiuKIqiKIqiKIqiKIqiKI2wBHgecBvJiOJhJCjuX4CzgB/U6edcoBv4DLGADlLq\n4GEksHMOMNQUq5WZxq1/tbmB9luc9f03JV0DaDr3fc8pSD2KR2faEEVRFEVRFEVRlGZgrWXdunXs\n3r27yv543ZiaHSXfV2icTqdZvnx5U2uLb9u2ja1btzZu5x6ybNmypqaNHxoaYuPGjRVrojcwlNOi\nmbaPjY2xbt26xuuK72nNd2Po6+tj2bLmVaKatu3VsLbuTent7WXZsmWYJkzGIAhYvXo1uVw+sb0V\n87yjo4MVK1aQTusjJ0VRFEVRlH3MaYBBIsbLuQ4R0Z9OfRH9dOeYcn4FvBNJ6X7DnpmpzDJ6nPXx\nqq1ixqoce8Chv2gURVEURVEURVGUvSIIAj594YVsffBB+trbS9sLocfqXf2MFrIUtbp0GubOBc9L\n9tE7+jh9IxuTG9vboaOjJKJaYBNw4aWXctJJJzXF9jAM+f73r+JLX7oRz+vDWrHtsMNg3jxHvzVg\ncjlSWx4H36/b70h6HkPp2KF/dHQLH/nI23nRi17UFFEU4MYbb+Siiy5l0aLlzvXAunWwa1fczhhY\nvBgymeTxmWCSRbsfxeCI1N3dMDCQUFe3DQ7ylne9i7PPPrspdt9zzz185J3v5CBrSblj0dY21cih\nIShzzgitZaJQIHDEdYPUnHLfDxvDirPO4vOXXYZXPuH2kPvuu48Pv+tdLCqzfYcZYNDMT7Ttz44w\nL1PmWBKGMDgIhUK8LZWC/v7Eh2JoYoLFq1Zx6WWXkUql9tru8fFxLrjgvWze3IcxkujHWujp8Fm1\nbAjPAMVP6cQEjI9PcV7IDSwhaO9ObGtLB6RMGJ9nYgKTTnPZl75Ef38/iqIoiqIoyj7lsOh1Y4V9\nG8raNKMfFdEPDNxMoIWqrWJcz9y2Jtsyq1ARXVEURVEURVEURdlrgvFx3nvKKZyyYkUUZQtDk+18\n4MazuG/HgpKIPm8ePPOZoo8XsQZOvuubnHzHl0nIywcfDMX+gAB4x223kcvlmmr77t3jTE6+h/b2\nUwHRMv/xH+G5zxXNEwADqU2baP/e1zGjlSPuS9cD3NHzbP7Q9yLA4HmW2277HhMTk021e2JikpUr\nT+Ptb/+Pkp35PHz607B2bazJeh6cfrpo40Vd1BoYGF3Hq+9+PykbyAVaC8ccA+ecI94OAMZw9fXX\nMznZPNtzuRxHBAGfPPJIulyBeNEiEZOLWAu33AJ33JEQ9Qu+z+odO5hwhOgUsBQoytAe8EdjuGZw\nsGl2F20/NAz57HHH0Vmy3XJd+iX8b/pvMKUZbDlr4DaeNf/u5JyenITf/EaEdBONeUcHvPjF8qGI\nItRvXr2aK7dubZrdYRiyc+dccrnPkUr1R9tg2ZIh/t+bbiWbspRE9PXr4cEHp4jom198AaOHrMRE\nm42Bg3rG6MzG9+HRNWu48JJLCMMQRVEURVEUZZ/TG73urLBvZ1mbWhSjiyt9md4xjX6U/QM3+ry9\naquYDmd9rGqrA4DZJqJ3AQuj9W3AaLS+HHgmcBAwAtwK3A3Uyuk2D6nJAOItU/SMOAU4Jtq/Dbge\nCWaoxTJkrHJILfNaHIL88hwnWRdgMTL5ih4dGaQ2ejnudTcLD1gRre8Gtkfrc5F66gcj13Yv8PsK\nxxvgGcBK5A/jeuDXVP4DWonFwPHIvR0AJoB1wB8dWxrlyZEt85A/1ncBt0f72pCaHyD/IAzX6csD\nTo5s643s+ivwWxpLWaEoiqIoiqIoSoQxBg8S0bkScW0wJpUQEo0pSyNtwGBIlY4hTnftNLTFg1tz\nBYgUG9s45XTG4CHXWcsOi8UYuW7p1xEom0zxPK45lUybMuaAMR4ehsTdKTZ0BqBZkfOlU0RLyph4\nvlS439WIJP9SX26f5e9bgTEGz7Xdud9FEb04BzwDXi1LyidaNAbNHvOocxLz3ERzwBgSse6VJr91\nr7G425aNQ+zEoCiKoiiKoswIRU/GSl/L4i+v9XHycU3BK2uj7P+4mmR31VYxbgr3ZuuZs4rZJqK/\nCPhhtP73wP8CXwH+lqkf+luA84HVVfp6N/Dv0frRyI3/NiIEuwTAV4H3U91j4kZEHP8LIrrW4l7E\nGeBa4MXO9h8BpzrvVyCCbTmvBq6qc47p0uuc61vAPwH/BbyDqV4ltyLjXXQAODk65uiydmPImH25\nxnnfC7wKOJHKf2wD4CdI/Yx6jgyLgW8AL6yw707gH4BOZF4Uz31xlb4M8I/ARxAHgnJGgU8DnwLq\n52lUFEVRFEVRFAWwYKKFeLFWIl5LT2xsvDhHSl1vG0qIdJEwdELBnQ5aQGiTtolgX+HJkKGC0Fve\nSt6X6aL7BFeTdocqDOUai6ZaZLgJw2iF+MAyEX2fMM3z2Sqv5eJ6SyifxDaa5ySfKNrS/yocG4aS\nIqDUj3NzIJlivwWmJ9er2Fi2zUYfjOQux25jWjzwiqIoiqIoSh2KKbPmVthXrD1UL/iwvJ8tZfvm\nTaMfZf/ATdu/tIH2rq62oWqrA4DZJqK7zEEE0cOQHPxrEXfpZcjPslOB3wHHEaePqMbJiFCeBTYD\nDyEf9GOjPt+KRKefSesikHcDuxBBO4X8vq70RyZfYVszMYhI/7eRDWuibcuQ3/unIFHmJwFnAD9H\nhPZd0VKM8O8CvoREo1cT/d9NMjJ8LXGWvfnR+suA0xChvfyPcZGDkHv9pOi9j0SgjyKR6ccjc+Wd\nDVx/G3AF8HJn20Zga3SdRyEOF/+JzJu/Q4V0RVEURVEURanLRD7Ndfcv49FtR8bp11NZDlvVzsFO\nlbSujpCVh+bIZGIlzgIDwUHQfUay04ULZSk1tPDII0233Rh45tPyLF48CdaS9ixLR9aQ/vNQQp01\n27dh1qyB8bFI7LWMZuZwf/8ZWBPH8losd2xazG2PBlgMxsData1Jbz04CA89FAubhQLkctDVFevR\n6TQcfljA4sVgnQCUnlwH9J2BJbbNFnzsr34lkcfRpYgNNgAAIABJREFU4Nh774Vly5pr+NiYFG93\n6oDnBgcpdHc7DhcWb8MGvPHkz/SgrY3e5z+fzt7e0oUX8nDnfVnCQNp4wIOT2/C9FpTo6+uDo4+O\nU95jOXj9Lp76+C9LEeTWWvr6MwzOP9xJ8Q5eIUfPCbtJj42UblDBy/KwfyRBKPXgjTGszk3i23qP\nOqZHWxssXRqXnQ8tLFpoMUGQ1NH7+uCoo5IHW4vp7Uk6hFjYtiuDZ6N7aGDT9gyFQJV0RVEURVGU\nGeLR6LVS8GBxW7XA1PJ+TouOKddtiiJrI/0o+wcPOeuHN9D+iOg1AJr/A30WMZtF9P9CxMwPIAJ4\nUXBeClwOPAeJTv434D11+voicjPfAHw3WgeJrv4eIsQ/A/gEIvy2ghdEr3cj4v1fiSfavuQcRAj/\nGvBR4j+AyxAx/FTEoeA9wD8j4vJbkbT3ASJ8vwH4f8hziYuRKPtKQvMG4FLgp0z9IC0FPhb1tThq\n94oqNn+TWED/ORJFXkwDb5Bo929QPfLc5WJiAf03wPuAO5z9K4DLgLOAs5Ex+nAD/SqKoiiKoijK\nE5qRySyfveEEPHNKadv8frjieylOemos8nq+T2Z0OBn9DKRWrcTkyipetbdLvWiXm29uuu2pFFzw\n2nGedvKoRAj7Ptkf/Rrvd/eDcZKi7R7B3HWXFB4HwDLYs5IrT3kpgYmSfEX64V33W267vRBpkx7W\nhk0PorcWNmyAG2+MxzcMYXxcas/HIrrl6acHHHqoTYilhjkY82bcO2F/8AOC97xbOgE8YwisJXXO\nOc01fnBQam87kefjvs/uskHKhCGZsmwE5qCDWPSe95BetaqU5mBw2OMLn+mmWHbeGNiy7RYG0v/T\nXLtBare/4AWQjaq1eR7HXPVDVv7+y04qANh69AWsX/EK3GTpKXwOW3U4aS8XGQqTEymuv+EQJvKp\nUqKDtaMeOftAU83u6oJVq6CzM862sLwnxPg+pULnAAcdBCedVJYVwOJ5/aScYPMgMDz2eDsjUZyS\nMbB5Uzu5vCZ1VxRFURRFmSH+gOg4ZyGZeF3Oil5/20A/NwKvQ7It31a278VIMOqte2ylMtt4GNFg\n+5Dg0gwS3FyJpUgJboB7kDLJByyz+ZfNPOBNwGdIRmxvQMTW4rZXNtBXNyK8fptYQAe4H6kJXkwl\n/g4aS1WwPzMP+A7wZpIeROsRMboohn8SiZp/LvBL4nELgK8jKd5BosSfUeVcpyP3r5InygbgjYgT\nA0jEd6WxfzpxCvebgXNJ1lG3wPeB15Ksw1CJkxGHAJB/KF5IUkAHiZY/B7gpev9epI67oiiKoiiK\noig1sBgmCykmCunSkvPTpNKGbJbSkslAOgWZlCWTorR4aU8iezOZqFE6fnUXrzU/YzNp6MjES8oG\nmCDABL6zBFH68zglt7UQkCEwWVmQxQ9T5AuQzxvyeUMQ1LdhT7AWima52e/d0ubGQNorjrWJFkil\nPMi2YZwFz4NcDjM5KcvEBCbfgoRpxZTm7hIEEkrvLkFQlmc/ckvIZmVpa8PLtmGyWYJUJ77XRZDq\nwk91EXrZ1qQXN2bKvEwZSzYskLU+WeuTsT6esVgvnVjwomMyxfktS0C6tPg2TdiCxzXGyO31PHEc\nSXngeVH9dTfEvNSgbClLtW+MfO7D0GCtvIZWo9AVRVEURVFmkO2InnM88Exn+wKkhPImJBOxy1eA\ni8q2/QwYQoT0Pmf7WUj26B/RuqzOyr6nAFwTrXcBL63R9jXO+k9aZtEsYTaL6HcgUeOV2In8IQCJ\nYj6oTl+3AFdW2bcd+I9oPYWIsQcyOaSWeSXWk/Qq+iqV67YDuO78J1Vp00i+wkuj1xTwrAr7z3PW\nP0L11Or/A/y5zrneFb1a4C1UT50fEEefdyACv6IoiqIoiqIoTaVcbNtPxLd9VSO8qVSqFG6mbNnn\ntHAsZ+S6KlxPo3YYp+3+NsVce/c32xVFURRFUQ5A3o2U5r0OySh8EXA7IqS/jamRw/8InF+2bVfU\nzwrgTuCzSMbgHyNC/AdbY7oyg3zTWf8glTOZz0WCkUH0tStabdRMM5tF9F/U2b/GWa8nov+ogf3F\n3GVn1Gp4APAXYFuN/eud9Wuqtkq2qzf+RdqRD9ky4NBocUXxoyocc3r0OoKkEKnFz2rsM0jWAYB7\nqV+n4SYgSgTIaXXaKoqiKIqiKIpSRikrt6Pf2mipqiyWq3HWkizWbGl6TnT3fOVL+XaII6idyOjy\nQOk4YHrfKIqVTHSC5bHWRqPojJ2dskXMrTS+LVBGLWDDshD6Cuc2FRcr6ce94gZZN9h9J+La8rG0\nYAMpU1BciOe8u5R1BNjov6ldN9vkykvxc1a8jsoGVPro2mK/zrqiKIqiKIoyo/wVeCoieJ+FBCre\nj2Qd/mmF9j9CyuiWc3l0/BpEZH8eIpqeTJzdWTlw+C3wq2j9eKT88wJn/yGIZljUA7+CZHY+oJnN\nNdHX1tnvpiLvrtP2njr7B4GNSDrxlXXa7u+sq7PfFdjXV22VTKlea/yfingyPRtJ81HLcWNOhW2H\nR68PUD+y/f4a+5YC/dF6CvhAnb5APGnakWwHiqIoiqIoiqLUIJWCJUukjHmReX0+HZs3YB4ax1hD\nLNBVyG1eTN/tKMGFOQMU2uaWmoSEBE596aYRhvDb30qN7iituH/bbdiNGxMCskmnSR11FKaYUt6G\nZDoOZeFBHmGZunjKcRMcOW+ESNplzeZRjOmjuVgGB4d5+OG1sY3G0N3dz/z5XdLCSvbwNs+HQrIm\nOkGAGd2R6NHvmcfoC18JOSmB5xkY37yeviaq0xbIHbScwaetZMKLH0vsGA0YGk+qsEEewrJqfH53\nH9tunEP+vlh4H5sIeOTRMXJ5E90yw8jIBP39NJ1JP8X2sQ5GC9FkN4ZdqVUMzZuU1OiIMN29aZx5\nv/0JiZ/BmTQ7Vx3FYHdvadP4pEfB9wj8eLq1Iv1/V7vPcYeM0NuTlY8i0JvNs719KSnn9oa2m2C4\nJ+GQYLHsKKSZCGMh3YYhfYVB5qSKhejBeFtJV00gpyiKoiiKouwjHmNqdHk1Xl1j3y+Js0IrBz7n\nA39CBPNzgRcDjyI/aJ5M/MPmj8C/zoSB+5rZLKLn6ux3BdV6v+Z31NkPIgovpbKQeyBRb1zdn+q1\n2jbyk/4/gQ9R+f6MInUWDPGYZ8vatCEiNkj9jXoM1tg3z1k/GvhUA/0VafaTLkVRFEVRFEU54Mhm\n4fjjYcGCOBq1O5VjzoN/JL1uMxKKbqGtDRYtmlrbvFgT21HuJuhhd1+v84PCp2Bb8DM2DOHzny+9\ntUAhCPCtjQVDwFuxgo73vQ8zZ04p+rjdzOOoVKrM49fy5KcNs2rOOgnu9jy+98sdGLOkqWZbCxs2\nbGL16j9RVMezWY/zzjuFk07qKt0Hz4OuTB4mA9yfZ2Z8HPPII4nw4cmFK9j2H1/FGrk/xsDwL69u\nqogOhtGVp7D2bZ8k29ZV2rrpcdi23ZkCFnbtgqHhZDD87t0hV39xmB3b/dL1WEJgLIqqNoCHMSOs\nXNlIlbHpsTvfxuodc8lku6KrgbvazuL+5WfFPiAE/O29n+NvfnYhnjune/r501u+zdDSwzHRsOfz\nMJkDv0xEb3ZU90BvnpeeuoWBefnS/B0pdHL30IlYx8ZCHnKbp97v4WHxcymSMiFn9DzGwdnYF78v\nvYl2MznlWEVRFEVRFEVRZj3bgVOBy5Ayx23AKmd/AfgaEqT6hPjSP5tF9GbSyE/P8mSDyt7xauDf\no/V1SM2Mm4GtiNhdFOj7qC6Q+8h9MUCmgXPWauPO9fsQT5lGqRe9ryiKoiiKoihKRFH4k5dinvFI\nMC9Pk15OIid5lD7a0NgvumZQQygupbJOpPGu0ZcFz5ooQ3qtHPZ7i4n6j9/Xapt8O/VeyDUaTBRk\n0FrLPYx1nCmiLO2J81XYZiKhPKx43a3/SV+cl24Nc4MpGytPotKNlxxf48Xp953j3ddWW29cR4ro\nM5rYZirbUr49HvHy+6CPVRRFUfZz3o5EHH4X+PMM26IoiqLsW7YCL0eCjs+MXkMku8EvaSxo+YDh\niSKiz2+gTTHJ264aberVkDeIZ4YC745eh4FTSKbfd5lbZTtItPsuJIq8kbrrtcI6djrrDwBvbqA/\nRQHoRUoABMBIhf2dxJ/7IfbdI16lMfqQv90+sLvF5yr+PSsg2TYURVEURVEURVEURVH2N/LA26Jl\nHRJ1eAWwYSaNUhRFUfYpG4Cvz7QRM009UfhA4Sl19s8DlkXrD1TYPx69LqjTz2Iad0w4kF2z08CJ\n0fr1VBfQAY6t09ed0euR1BbcQcT6amwgFkBP48Aef2UqC4BDp7Esdo69GcmeUC17wRei/YNATwts\nn2nmEo9LI6UN3PaN5CvNOO0P3kMba/Ewcm9+0YK+y9kcnev7++BciqIoirJ/UIraLn+twGz6ht5o\nHu04aD55uLtzv2JmLa8YAT1tY2aJT6st/a9s+yyxrwbTN7HyJ0BRFEXZb/kGcBcSebgc+Cgipv8e\neB0H5vMvRVEURZnCEyUS/WXAxXX2F3+a/67C/i1IHe2DkIjTarXCX9yALRPRa28DbfdXuokdNOrV\nYH9lnf2/Bp6LRAK/Fri0SrseJMVENXzgN8BLEaHu+VHfyhODzyMlBhrlJuD/s3fe8XJUdf9/n9ly\na3oPJCGB0CGAVGmhgzRRHgFBsTcUC/b2oIIFHzv6KPoTELEBD6ACGloo0kNNCJAGgfR6781tW+b8\n/vjO7JS7u3fvze4t4ft+ZbKzM2dmvnNmdu/Z+XzL0VU69mwCR575SF2R4cRZwHXe/C+RlF7l+Dlw\nkTe/BcnyUa4Q5fHAv7z524Bz+mXljkU9cKY3v5TAmUhRFEV5czEXcc56fpDt6BOOA4mEzFsg4UCO\nBBmSFGqi2yTGJsAmItuabBYn2x1J525zWXBzIU2uBoWifVKpaJ32ri4pTB3GdaWAdXd3oaY0dOPk\nt+ESnI8BTF1XUEDaGCl4XQMSCUNdXYJwTfRkApJOHmuN1HJ3LORzkIvWRLe5PJl81Le+O2vo6gIb\nSjEeroNdLayVLgl3eSaTp7s7HxXSc5YUtlA/HCCFpakuz4jG8DDTIpnhg3TirmtqliLdGHA8o4yR\n65BMxtKdp1PY+npsqM/dunqyeYdsJrgS2az0hX+L+LeLU+2wB2vl3g3fv7kEjo3eFwlrcdyeQ/iE\nKx8BH4cc3RlLW+jadGSibRRFUZRhiQt8Ffin9z7tvR6FBCddA/wfcC1wL5K9UVEURVF2ON4sIvoR\nwNnA7UXWjQK+5s3nkdQ0cZ5EhNwk8K4SbSYQ1AAvx2rvdRwiLu2I9QNaEWeBBiQ6PImI2HGOpncR\n/Qbgcm9flyMDs0WxNg5wNXINyvEjRETHa38E0TTvxUh7++/qpZ2ilOI0JFod5GF4MUedocy9ofnj\nK2h/Qmh+DHAgsKBM++NC8/f1wa4dmTHA37z5q4FPDaItiqIoyuDxTeTv5GIkGuhPlM/wNOikUrD7\n7rDrroHObdw6ntv6VhblguG0k3VIt9RhnGhx5SkLbmanBX8B6ylwxpA65XTGnJkEV3boYkl11aBC\ni+PARRfB9OkFdde96SbyixcXpEULJDZuxF5/PW46XTjJNGmmOTcQEaeB0VNHwC5jCufC4sVw4IFV\nNdsY2H33qey772GFZcmE5fQjLPvsuqqghBtcmp97DnKdke23Zpu5c8NbsKEkda+treeJxY7f5QCs\nWQNf/GJVTWf9enjoIblvfJ599iWWLFmG35cJA++c0cq7dm4j3L+5ZIrjP7Qn3emmgoOFTSTonDQd\na8SZwXEML700kjVrqp+Ab0RTntkzc9SnMwAYLCObk+yxRzLwAbGGaZ1n0NG1t9RG92jpSnP3w9NY\n+UgguGezsGxZVHzetg0OOqjKhq9aBVdfDfX1vpE0TN6ZPc95H9bxnEAM2LYW3HU9fX+z7RncbODk\n0JmD6x9r5MV16UKbbZ0p8qOGYxYGRVEUJcY8YD0wObTMIIFOCeCdyHPdjUjwxR+AhQNroqIoiqLU\nljeLiN6FiLHvRSIdfWYgD6P8VO6/oHhtl78CX/bmf44MIOYhP9cd4ERv+RhELC7Xrw8hkZYJ5GHY\n94hGpq5n+NfSdZH+ORuJwv0lcBnBeSWA84FfAe2Uj8pfgzg5/Bjp30eA7wJ3e/vbC7gUEfceo3xK\n94cRT8mPeHYtAD6P3BNhkd8AewPvQGqnnwa80NtJK8OGK+g9oivs3HIZ4mxTrB76m4E3gBXATGBP\nxFmlVDT9LCRjB4hTUgI4hvIi+jGh+Qe3y9LifBiJ7B5uGQAURVEU5SvAo8h49wfAD5GsNr9Dxq+d\nJbccJBIJmDwZZswIRPRsNsnLHdPYlhHB0FpI5KGxG0xI27TG4qxsY8yTT4LrBTM5DqN2352Gto0F\ndTEHJHK9JbvqB8bAnDmw775iZCaDvf/+SFJqC9iODuxzz0UyjDuYnjVvDNTPmIHp3CNQStesqYHZ\nhgkTRrDffjsX+jzhuMyetopZE1oDO/MuLHwd2tpCodKWrtxEXtw2njxJaWvgpSVw//yooOu61U8A\n0N4OK1dCMvTrefHijSxcuBTrif9px/LO1Ab2nbyZSF73VANzDp4ohdmw8i+Zpm1WAzYhqrzjwLhx\n9fz979W1G6AubRk7xqUp7QfeWeoaHMZNJCKiJ1N7kE3tHtm2czMsu82w5JUg0jybhddfj/Z5Pi+3\nZFVpa4Mnngili7CkZs9m/DvOBCeUGSK/Aba91nP7zg4vXN4AltaswzOL9uDOJfWhRg6HHaYiuqIo\nyg5AHnHw/yiSmTWO7wY3AfgM8AXE+fMa5Hn7+gGwUVEURVFqyptFRL8MEblvBVYBryA/t/cjSDv+\nGEFEepxngd8gg4bRSOrhjYjgPsPbl4tEqV9P+X69DhlUTEFE5rNj6y8A/lLpiQ1hvgGcjESQfwTx\nTHwRee61LyKIZ5EU7LeV2IfPTxBHh88ggvv3vSnMPYi47keylnqydqm3j/ORa3cTIuQvQkTSsYjA\nrrV9dlweom+p/DXtv0Sjfwh5WnYcQZR0HD9SvQNJ+fUur/1PSrQfARzizW+kNulqB6IWuqIoiqLU\ngseBG5FxtP+Q8lhv6kb+Hl+HOKENmQLE1gaT/94AftC5MV6ybdOz1LUxYEJKrS0sLFYouwb4SrEr\nOatNTDUuZYEp0v0WIwppOB93jc6hWJ8DXvpz/5imeF86ku48HKvtmJ5pxGuRQb+YORKxHaRjl2UO\nYmHMdcEar2iQLLeuIe9Gm9UurbiJfeqMpM4PLStk+3ej193Y0GfAW+U4A3ebR+5La72DRvu8cL/0\nwIS8X2Ted46JtFEURVF2FP4DfKKCdv5Y1Xf+/BHyPO1a4O9ohk9FURRlmFL9vGZDk/nAqUhE5U6I\nqDOH4Pz/jEQbd5TZx6cQTzr/Z/h4JE3xWGAdUg/9lgps2YJES1/HjpnK3ecFpE/8yP5RSPr0oxEB\nfTlyHSpN3/xZJIL/QYI6O1kkwvXjwCkE9XkAWkrspxtxVHg34kwB0AQcimQUOIhAQH8dGfStrNBG\nRaklIymftaEvGORzWOnfgIdC8+VqxfvrniD4bB9Z5jh+uQf/GL09nk0idhfzgK4mo4l+n1SDRm8a\nCJqQfmoaoOMpiqIoteOnBA8lIUif2QhcBNyPZG76DrB7j62HIGX/2BdbWav65wPBYNs+LLXMKvfZ\nsOyDAaQm9+gw/swqiqIocRbR9yA8vzTmKcgz903A75F66vqXWVEURRlWDHQk+iEEYsrWIutvx0vI\nhkQHl8OvCwhQSVG8e5B0xCchkcYNSHrf+5FUxb2RRSLRf4AMAkYiguwK4C4g47XbGRkQZMvsayXw\nfm9+JPIgzKe38y7GFwjqsRfbvoWgX3vLffhV4Fuh7UqxuYJ93oekd54LHICcZxsyAHsIcUgwfbDt\nNm9yEFG+hcCpAcTb0Wd5L/v6MxLxvxdyX45HPg9Z5PosQlIQKcpvgV2A15Bo7Eo5FcmCMS207EeI\nI02Yp4EvFdneQTImXIxEnPnCcQfy2foZ8r1Wiss8G0AcULLAJcB7kHIFaeRzsmsF5zI/ND+3TLtj\nvdcHCFKzjwX2RzJ6xAnva36R9QATCZxo9ggtfxX5PvgB5evD/g0RlJ9H+qQUbwH+G3GmaUC+W5Yj\nEYD/g3hN3+W1vZee2TCKMQ34InAuQQ2xtUh0/NfomWK+AfHQDjsJnI2k0Y9zDtHSHzOR8hT+3zif\nbiSF2n3AHUj2DUVRFGX4sABx/CwmkPu/5SYR/B54Gvh/yDh380AYGMXiGIuREOhCBHrCCbJHg9S5\nTpCPPEW1WJykA42NhZroxhhsMkXeBlGybmHPNbDeGFwvAtcah1yijlyyOXI4x1o6cjZiQc4BWxfz\nGTRAuj5a8DvcCdW2PRSJjgWbdyGXD/RM15U03LlcJJ27cXMkE9Fg6VTKUlcXjeLOlvt1u502+8eR\nSGyHZDKB3+lJx5uLnGDsfAoh+Aa6Owq1va0DdHfXRNO11uJaQnXjLeRzmGwu3L1Yk8J1oo9drAup\nRI50MsjQ4FhLXcINB9bjuLmiWQ62C2Mkf34onTuOg81kguh0YyCfj9Rxj2xfiF6XzBHptKGpyZET\nxmCtU+uIeoOMzy+v6VEURVEUiD6z7iv+AK4Rebb1fsT583fe+guAM7fLutrgD+oGKhBCUfqDH/zz\nwKBaUZpKnjcryrBgoEX0cqIsiBCd6aWNTze9i65xcogIcldvDcuwHPjfMuuLOQeUoxp1ljsoH0Vv\n6Snc9Xdffd1nDhH6Sol9fbHNxy2xzfneawZ4poL9WCTF/It9PL7y5uJwpATBoj5uNxURZMO8pcJt\ndwL+D8mQEKcRyfJwBlJm4hKC7Axh9godfwoiJh8Qa1NpJPpK5LtvFrAP4nQSz6SxC1IiAWQA9xIi\n3k5ExPViIvqxofn5Rdafh/y4ai6ybhekxMMHgHdS+jvmGERcSJVYD/JD7hqifxMdYDdEWD8fyVbi\n92c50d5nLnINx8SWT0bqtJ/ktQkXm0zS856ZRtQRwyd8Pid6xypWhqLO2/5i4G2oiK4oijIc+Rfy\nd69clhTfAesgZNxyNeLQ9nskW1Z/HHX7TCpp2XNGB4fu2Yp1rVc1GQ7eJ4UbKoBuWlowzz2DyWQo\npOLGkr5wf9If/mtkn+vrd+G1+l0KYqLFpSUR//O6/VgMa8fsxcpJh2BdSz4H98/9Pa9O7ixkrzbA\n5s1ZHn5gE91defy60LvMTPHt700glQqUQ2shO8FgJzsiRhoDt95ak3zduRx0dgZactLmyD/8KNgX\nKCiyrgv33AOtrYENrsv43fbgk795DzYd2JVpz9KxuSuQb43hrnkdOE6xIVn/aWuDZcuieu6MGW9h\n7733KdhtbI49O/4Ky58k6s3gwBtvRBwT3GyWjkUvYfMyNHYMdHV2Yo85qqp2A7R3Jli5OkVdWmqB\nWwNj7r2FiQ/9s6CMWwurjjiXNW95GyY07HaynXzrpMdJHdlSuBY2myW3eGnBdjAsWPcq99VV8tO8\nD0ybBm9/O4wMElzlVq6k/SMfgVyu8JlNH3cc9R/4ACbs+GEtPPccbNxYsLsxleSqq3bna5Nm+VeM\nlSuXcOON/6qu3T0ZR+CMryiKogwPXCR4wBenDUMzS+5QtElR4vj3afUHuoqiRHiz1ERXhjcJ5A9D\nufiHjwGHefO3AJ21NkpReuF+pCb4aQSZJy6np9PGutj78UjNKV+QvgO4AVjqvT8IiajeA8mOsQ2J\nQC7HbxEB/THgj4i4PYLiEc6lmI+I6AYRv+PlK+Z6r93ecSySceKd3rqfxdo3Eq2H/kJs/YXIeRvE\n2ehqJAK8xdv2HKSUw0gksvsw+ldT/ThEqHcQp5+rvXNbg0R3vwd4L0Hmk0rYGRGrU0ga3nuQfpkO\nfBqJzN8F+DVyf/h0IvfMGMRBAkQ4+X2RY/hiyCgks8YIxJniWuBWJFK/C3m4eDhwMnBwH85BURRF\nGTo8Td9iaX2x/Uik1MovkL+pNyB11muGY6A+bWmsc0MFoQ1NaTcIuQWwOUhsg0TIJ9oCYybDlKmR\nSGnbNYGuTCPBkhyuqU1Edy5ZTzbViHUhZ2BrcxMbRwfmGAPrsxmWOOvoLPgwWpx0Gnf61IgQbS3Y\ncVmYkJUNHQdGj66J3fGa6K610N4B2RYiIvrWrdDSEhHRkx1tjB9vIWQ7Iy2MDsRcjOHZidUvLu66\nkMlERfR0upFRo4LAL0OOumwKWjOhWtwe2WzUKaG7G7t8CW42i0Ge1FsvqrrqtltDJmswXvS2NRZa\n20iuWxUI4xbc1jYyGUL3L6Tzlskj2hlR34r0L5DJwqgNga3GsL59Kwmnyo9s0mmYOFHuRS8Knc2b\ncd94AzKZgohuW1slK4QT6/PIe4vjwJSpdUyc1VT4lnKcBtLpmuoPFvl9dUMtD6IoiqIA8tyov5k6\nXeQ7O4Nk+bseeAQp93kZ8pzl/SW3HjxmIM9UquzJpihVxf8hNZVopt6hwneRwCdFGfaoiK4MB8Yj\nUazXIsLUImQAlkQiez8EfNBr2wF8exBsVIYPRyPCYylagX9X4TgrvGlKaNl8ek+z82sCAf2LwA9j\n6xcgPz7+ARwPfA4RxotFevsc6+3nS/Q/oeV8gsFPMRHdjyp/ksCJ5UFERD8aEanDg7ojiKYeCts1\nHekHg4jZxyOR7WEeQlLVzkc8ma9BxOK+4CBitW/buUhZEZ9liAD+DPCTPuz3AMRR4WDg5di6m5Br\nOBspDbKrdxwQEf8m5J7xRfSllI8efxvyHQlyL/w8tn458BTiHDCF4YeDZD/4Vm8NFUVRdmB2o3wU\nein833ojEcezS5AMKNfQ9+xZ/cTEXsstt8FoIFKnOTp0qWWWaD/nqI29LxzTSvpqiYoP5U7324RM\nNVamwnkNYH104/9XmAmvNFGvACgyOjQSWh2DFSWcAAAgAElEQVTaxmKq3vdhU0pSrtviO/BTjPtv\nt8e4Cihqe6R/6dFrJjxnvbbWXxO626xkR6j5WdjQ/Ru2saKMCYGzgA2N8gfwVlcURVFqz27I85K+\neEd1I2PXBUiZob/R9yykiqKUxx9xrWNoiujqhKLsMKiIrgwXJgNf8SaLPPgrVk/+QnqKbYoS5uu9\nrF+M1AwfDPYB3uHN30xPAd2nAxG0lyDRzpcgKcJL8STwZbavIuT80PyxRdaH66H7+HXRxyGpZcOR\n4nNL7BtEDPbzhV5M6c/0E8BVSMr1w5DI9idLtC3G8QT1w/9IVEAP81PEGaAvKZI+R08BHaANqeH4\nG+TJ41wCEb0/TA/N91aqZM12HGewSCCfx28OtiGKoijDHP/B5wzgSrS8h6IoiqIoitI7RyMO/705\ndGa8Ni8hQRE3A6tqa5qiKIqi1B4V0ZXhwDbgl4jYtDciPI2Jrb8NiVRcGt9YUYYR7yIIBPllL21f\nAx5G0pEf30vbX7P9XomvI2LvroggPg7Y5K2bjqQ+h0A4BxHNtwKjkc9vWEQvVw/9PO/1ZeDuXuz6\nIyKiA5xA30T0k0Lz1/XS9loqF9FbgL+WWf9MaH52yVaV0RaaPwFxrNiRyCPlAX462IYoiqIMIgch\npVv6mx857227EfgVEg10CJIRpfo4jkx+fnE/MjecBtpfFo92dZzIcovFOE4kE7y3g5qYbowpmOqb\n4psbbhMt4Skp63ucjgHjGElB7q00NaiH7tsXsREwxpHo8fBJlDp+sYju2Ha1sr2YGfFDGT/leW/b\nE6QX8udrZbXYaQq3tQSV9/yIFtqEXFkdvz+L9HNhvqa2R49ljIlE71u8u7uCzyjG4BiD64QyANT+\nVlEURVEGjndSWkD366V0Ic9ArkOeU2lOEkVRFGWHYUcW0a8jiIh8bRDtULafduCT3nwzUm94EhKh\nuAlJ754bHNOUYcjFyKC+FNmBMqQIR3qvFhFDx5RpC1KjCaRWeR1BPZw4T2y3ZcIDiIjuAMcgtbch\niCrPIvWtfFykr89ARHM/1XgDcKg3vwH5DPvsimSeAElR31sfbEY+/0n6VuMd4MCQnb310WN92O8L\nlP9OeiM0X660QCXMQ364JpCat3ORGun3ENRNH864iANHOacERVGUHZ1RyN/Yuj5ul0H+Zt+FpNK8\ny1sGIqJXnUw2y4uLF0uqqHBO51QqKqK3tsLSpVLT2sdaqdm9YUOkJvr6zGg2ZMPDgTytrZurbrvr\nuixd+iLJZBJrpTT1ypWwfn1U29yyJYvrbsYY3z/R0tWVYtGi10kmo+rh6lE53hiTAwzWGJavWME+\nc+ZU1W5rLW1tq1m16olClydslhe6ltORW0ekJnpXF+Ry4Y2hrQ2eekqukU82K219HIely5ez2377\nVdXubHYTbW1P4TgNheXpdLSEuSHPy1tWMLq1jR6yciIRUWzdTIaNeHXQkZt/CZCtQX7xrVs3s3Dh\nU6RS9XI+Bsa+vpxRra2hNPmW1a8vZd2LT2BCPjDJfDedW1+mMRtqm8vBmjWRmuiLN28mO25cVe1u\na2/n6SVLGNPcXFiWf/VVOn2HF4/Uli3ULVzYsyb6ihWwZYuXht5iE0m6X3ie/OYthauzatVKOjs7\nsZrXXVEUZbhzPEHAhI9Fnne4SJm/a4H7CQR1RVEURdmh2JFF9GVsX4pcZWiyDUkNpCnblf6yFqkT\nPRTxa1YbokJrJYyldLruDSWWfxX4YJl9vht4PPR+PkFd9LkEIrofVb4A+YyGeRAR0Y8hCG45nEAI\neICol/LU0Px5BFHplTC2D21BoukBWuldcF7dh/229LI+XBcoUbJVZSwFvgF8F/mb7vdZN3I97gD+\nSTQLgKIoijK8OIrK/15kkL8H9wK/Rf4GdNbIrgiO43DyKaewZMkS1jzySM8G4fBUP0o9zqZNsDkq\nkEs58ah4esIJhzJt2rQqWO2bZjjqqCN54IEHWbRobWH52LEwpog731FH2Yj5xsAbb/QMv32V6Gk7\n6TR77bVXVaO69913H84+ezn5/LzI8qUkWWZjFYr2LlKxyBiYP7/n8tj1sek0exfbvp/MnLkLH/7w\nAXR2PkikUniRrtlCPfM4s/edWos944zoImM4dd99q9rnM2bM4NhjD2b58geICPvjkpjTTosdP49d\ne0/cUFb7w9+C50MCdtsteG8M7uzZnLTnnlWzva6ujuPOPJMnWlownaGvhaYm7Je/HG3sOJiVK3vu\npLERGhoii+zyF2HF4kJPWGs57bRTaA4J9YqiKMqwwwDfJnhe46drX4yMMf+M1GJWFEVRdkwmIAFo\nBxIEuT2GZIR+U7Eji+iKoijDjZHbsW2qzLpMieVjkSj2UjTE3s8PzR9bZD5cD93HT+8+HkkD/wLl\n66HXqg+K4ackqyT7QKk+LMZAh918D4navwRJ6V6POCm81ZuuRDICfBB4ZYBtUxRFUbYPByk/Uu53\nWxYR2dcDvwNuYBC+7x3H4bzz+uL7tn1UUxQ1xnDMMcdw9NFHV22f5Y5VTfbbbz/23Xffqu6zFNUW\nor/4xS9UbX/lMFVORz99+nQuu+yyqu2vN5x4NHg/qa+v55Of+lRV9lUJ1bJbURRFGRTeTZAtcT3w\nB296YdAsUhRFUWrNKcAnEOG8mNf6/6IiuqIoijKItHqvmwmipGvJ/ZRPOx4PPwnXRd8PsbHRew/R\neug+fnR6MyKev0D5eujhKO7vA18pY9/24h9rJEEJzVL0llZ+sLnLm5qB45Afuycgg54EEsX4EFJX\nd9Ug2agoiqL0nVOBiUWW+ykz24BrkIeai4q0G1CGs2hWbaF1oFC7B57hbPtw/owqiqIoA8okJNr8\neqRcnKZrVxRF2fE5AjhrsI0YaqiIriiKMnR4HdgbiRCfSt9SiPeHO7ypL8wnqIt+NCLagvygKlZr\nPgc8ikTRHYuk/TrMW7ceeDHWPpzGvtZhVUsQsbkOmA28XKbtwIR4bT/bgH94E8BeSJ8fiYgwnwa+\nODimKYqiKP3g84iTl698+enaH0Cizm9jgNK1V8JA1kCutog5ULbXQnwdrrbr/VIZ1bR9uNqtKIqi\nDDg/HmwDFEVRlEGhAykL+ow3jUUC3d60qIiuKIpSW8KR3nUlWwkPIWlTAM4BflkTi7aP+QR11OcC\nTd78MwSR9HEeRET0YxCPtnpvebweOkj50JXAdCSiejSwdbutLs4jwPu8+TMpL6KfXSMbitGXe6Y3\nFgMXIv0KEomuKIqiDA/eifwtzCF/L19GHKP+BKwts92g4Lou8+bNY+Vrr/UsbB2vgW5M8eLXFVJf\nX8/JJ5/M5MmT+72PMNZann76aRY89VR0RQkbi+mQRZsWOe8TTzqJWbPKVdPpG0uWLOGBBx7AdWMJ\ndWzPQVYpuyu9FCeccAK77rpr7w0rYPXq1cybN49MJlYxp4jdULmNxc5x1qxZnHjiCVUTddesWcO8\nefPo6uqOLDelKvpsx3FnzJjBySefXBXbM5kMt9xyC62tbUXXV1PznjBhAqeeeioNDfHqUIqiKIqi\nKIqiDFF+DHyHaPaRN31kuoroiqIotWVLaH5SL23/BPw3Utv7C977LWW3GHjmh+aPJRDRi6Vy9/Fr\npU9A6qr43F+i/R+Ar3v7/hrSF7XgZuDniKj/eeA6YGORdnsA76+RDcVoJYg6rIY6sAEZ/CQY+Hrt\niqIoSv/5HFLi5Xrkb9Tzg2pNL7iuy19uvJGR1jJ9UmjI090Njz8OW0JDmhEjYM4cSKeDZdZCfb1M\nYZqaZPKPYy333nsvs2fPrqqIfve8eay++WamN0uSHWscuvY+iNyEKZG23d2wahXk84HZiURPs8Gl\n6cX/MPLZe8C6GOBFx2H8H/7AzJkzqyboLliwgFuvvZbj9t+/oCDnrGHR+kms2daMfxRr4eWXoasr\nuv2E8S7vfVcnyVCWb+skcdNp8LY2BhYuXMjYsWOrJqIvWbKEP19zDcfutRfpRMI/Mi9umMiKraML\nxwYYPRpGjoxun3Ly7NK8kfpEtrBsW3eCmx+YRCYXnEwm8yrHHPMkJ5xwfNX6fOnSpfzsZ39iypRj\ncRzvHjaWmZueZtqW54LjWAuzZ8PMmZHts9bhtbbxdOaD+z+RgEmTwHFkM2Ng9erXeOSRxzjxxBNJ\nFPqo/3R2dnLVVdfR1fVWEongM9XUBLvvHhXRm5th7NjoMgs0ZVtIuSHnAWPk4oQ+yxs3beLOO+/k\nyCOPVBFdURRFURRlcNkZeX6bAp71pv5wCJKlswt5njvknLqVqlAqQO5NjYroiqIotSX8wPs9wC1I\nWpRirACuBj4LzADuBC4giCKOUw+8CxFIb6yCrZXwBrAU2A3YnyC97AMlt4AnkEFWPfCO0PL5Jdr/\nGIkQ3xm4DPkD/n0gW6L9LsClwJXApvLmR9gC/AgR6ichdb4uQKK3fY5E6oBlCSLoa003Em24F1LX\nfDekz4txCbAO+DuS4rcYH0MEdJBroSiKogwPzkKysQybGpR1qRQXHH88h++3X6AEtrbChg2wYkXQ\ncOpUOOOMiDiOtSLGjR4d3emECTJ55PN5li9b1jPyentxXc7fZReO8IR5m0jS8o730LXXHEzIBa21\nFZ56SsR0n2RSzI4Kji6Tcoapj98Bbh4HuNlxsNW221oO2nVXLjv33IKInsk73LxoH55dOxHHsymf\nF/E/lwvsdF2YMjHPZz++lXToyYBN1ZFvbAYjUrYx8Le/3VTdVODWMnvyZC496yyaCgKs5faX9ua+\nFTMifbnLLrDzztEo84ZElqMnL2FUXQdipWV9S5pHXt6HbV2B4NzW9jCu+5fq2Y04XYwatSuHH/4Z\nkkkRia2BuUt+zxEr1oJxAmOPOgqOOy6yfVc+ycNrdmdzpqHgKpBKwX77yb0E0udPPfUf/vWvP1bV\n9kRiEpMnf4J0ejwg98DEiXDaadH7d/Jk2G23niL6hM5V1OXbKDg5GCOf59BnecmSJVxxxRVVtVtR\nFEVRFEXpM98ALid4dgvwf8BFVF4SbBRwE5Jh1CeLBCP9fPtNVJShj4roiqIotWUR8DSSRvskYBVS\nB9wX0p8GvhRq/yVgT+A04HBETL0VqSvuD3AmAwcCJyI1yS+v5QkUYT4i7PqDMBdJRV+KbuBxxPPR\n32Yd8FKJ9luAtwN3A2OAbwMfQPrB3yaJRIgfjnhDGuCqPp+J7PtQ5NrMQa7XYmANMBOYhTwzvIjA\nUaHKT7+LcgPwXaDRs+dFpIa8zzlI/fM5wIcRkeUe5H563bNxZyRN/ZHeNlsQJw1FURRleNAXx7Ch\ng+tiXDcQEf15X1QHmXddmcL4y2P7i1OrtCoWTxq0FmutZ6KJiOi+2f7phU8lHujsWhc3ZG3N0sFY\nG+tzr/a1DRKMF9W/DVgs1nUxoW62ruudj2wv0dGF3qm63aZwjcUWsdWEm/W8LSye3W7Q1pXettaE\nroWlNj0v94i1xrPRen0eSuvu3/dF7mnXWqxrwqZHPhLG1K5+edhu30zXlescfh+/py1F+lwMrYmd\niqIoiqIoSr/5EPLM819IVtB2JNPZl4BfAxdXuJ8bkGem3/C2G4uUGPsZsBrJ8qkoOzQqoiuKotSe\n9wB/RdLejAbeWqZtFok8+y7waSANnOdNxehCosMHkvnIYMznBXpPO/8gIqKH91HuidsC4DDgj4jI\nvQsSoV+K1ykdiV2ODCLY/w8SsW2Avb0JpG8/QTTSfiBS2/wI2AeJjE8iUf9hUt6rXz99NHCuNxXj\nVSRrwZqqWqkoiqIoISwSkWuNL1waMFZERRsTFntsXGL5QGI9Qdm3B1tUNo7XETeFc47szOsPB2P8\n+SqL0GWwgGtsIRLdGnEICAujIkQTHZFZsVUEdj/G2w5QPRivn2IHc63FmqidYqMhLuxbK9b6Phtu\njQz37Qz5LYQtC2y1PUV8fzsb2qTQw/7pmBpJ/zZmt9uzv8tsXdQmldAVRVEURVGGFA4ScNUGnA+0\neMu/jDyTvgi4AljSy34OQYJz7vDag5TBfBewEvgWKqIrbwJURFcU5c3AX4DnvPneBghxfgqMp3it\nbJA0OH6q7e4SbV5ERNAjvdcRoXWvFmmfA74I/BIR4I8CdkKikkHqzryCpHu/C4lIjnMLwblWmqKn\nUuYhAy+fhRVs8yfE69GnXA11nyVIpPmpwOnAwYjHYwIRv18FnkGuwQKKP8O7Aqmt/nqZ43QgQvkV\nSPr0KUifvoSI53kCUR2kxngxvu7ZtqyX8+oi6L/nSrTJIIPa/0YGuFNj6/1regnwK2Au4mwwDUlN\nX+e1eRb4JxLFX+r+VBRFUZSq0NGV4M/3TOKRxTM8Zc7QQAfn7D6XqXvuiSiDVgou19dLIWhfwTNG\n6qavWxeNWG9ri+ZOtxY6SlXG2Q4ch62Hn8KmvfbDteBah1X56bS+HJVpV6/u5o9/XEtbm59l3zCz\nuZVvHvg4dU5YSrU4u4/F+dFvMBgcY0k8+WjPcPVq0NICK1cW+jKZy3Pof/7NrCWbClHRNpXmkI9+\nk+yEnSI675iRhlRjXSTJo9PSSvLlV/AbGmNwViyD3WdX1+5kEhobQ/W0LW852GHqYTmCXrc0L3yc\npkefjvRdImFpHJOBVDD8G5l1+MJOC8m63skYWLT+JZY6/fGzLM+UcRlOPbKV+pTs2xrDlHwOs7o+\ncv92jphI15iZhO8iN5dnr22vk+/KiQMG4OSyjLnzWRw3i59Hf9SKpTjd1R3GT50KF14o5Qf8j14u\nJ2UKwtTl2xnbtbXH7Zp+fRlsaym8z9gE9zw6mpXd3s8bA+vXG7a2oCiKoiiKogwOByPPkW8mENB9\n/gocDZyNBBSV4+zQNmHWIcFRJwO7I8+oFWWHRUV0RVHeDPzdm/rD/+tl/V3e1BsWeNibKuU1Ak+/\nvlKpXf1hA/CDPm7zEqXTt5fDsn3n0pf05auRNEXFOD40/0yJNr0NPn26qbz/llFelLeIE0MljgyK\noiiKUlO6sw6PLx3BS6vHFSJtRzc0cdwpezB10gQKInoiIcKp40R30NEBm2JZ7JNJaGiIhstmqi+K\nWgydu+5L235HYK0hn4eNy2DL2qCNMbByZY7HHtvM1q1ZfyldY9ax08gHaEwE5euthY4Tz6LtrPNF\nhDYWJ5msjYje1SX95vWRk80ya9m/mbFoYeBi2FDPQXM/BfvsVEhPbwHHGhybiuzOdHWSeH1FEMZt\nDM6G9dUX0R0H6uoCEd1aZkxzmDElnKLdwpJXYPm9FMLqQeqOjxoJyVRh2wZjOHnMilA0t2FU92pe\nc8L+q9VhdHOefXfroqku5ASy0JXi5iERPVM/go6m8RjPS8ECiWwXU+qXk7atQTS92wEvPCwOIwbA\n0LBhA2bs2OraPVpKtE+YECzbuBHuuSfaLuV205RriXQ51sLmDbB5c+Ec8/kkLyzK8MwmU+gG8XsZ\nuKwLiqIoiqIoSoQDvNdHi6zzl82pYD9+m2L7eQQR0eegIrqygzPURHQHGIVE4LX30lapDWO8aQu9\np2euBWmkjm+G7UtRPQK5vwfjHBRF2bFoJkgl305lUfSKoiiK8qbEGFNId24R3VPSoxf+K7dxrAiz\n7bmsllgwGDlszCwi78NpxE1gowmcAoyfDN3Pce9Qu7zXsT4yxmCN0yNPt2vBxNJ396w4TyDshsTo\nAbsGIP0VNahH/xbe+3Z5r4botSmekL9a9JZzPWoN/rwJr4ndR07sHAeIeGlzU+rYhXvd7/fQpYg1\nURRFURRFUQaFKd5rsayqG2JtyuFnxSy2n42xNoqywzLURPQPAtcAFwN/iK0bD5yBpNTdGUmZuwi4\nCVjcy36bgQuR+rrNSGrexcCN9J52ty84SNrdY5H6vaOArcC9SCrd3kImDgfOAWZ67zd6297m2TwQ\nXIrUzPiW9zrQ7I1EeT4LHLgd+7kE+B5yz9xRBbsURdlxuRL5jn6qyLoZwLXALO/9/6NnKiRFURRF\nUYrRL9HYr8StKMpAUXlddEVRFEVRFGWI0+C9Fnt+6S9rqnA/Fmgtsm6r99pYZJ2i7FAMJRF9JPAd\nJN3vjbF1fwDejQjncS4Hfgt8EsgWWT8XqcVbzLvm68C36X+65DDHAn8ucZwPAyuAC4DHi6yvQ0Sa\nC4qs+zhSM/dsJLWzUhlXA58DrgL+jdSYVhRFKcbFwFcR56pngVVAPZL+6HCCv5XPe+0URVEURSlB\nOJjWDyTHdSEfSnWOIevGY3TBuAZjncJyi8VYR9KPeyJfntoFdGNdcHMSnu2Cm0+Qz4cjvOVUEgnJ\n2u3bmUgY8iZBLhJ+ayXK23qx3tZia6VUWhtMIMdxHGxdXdAmlcJ1TeHcfBwDODbUpxJDb3H8MwAc\n3EJC8irjujKBF7Xv9VM4c3uJTfORbeVmc/L5SHS6dd2aKMQWyOctOf/wBhIuOOFjWSv3fa4LbKj3\nct3YbFaKkfu25nKRa9gjNLyattvorosexnp9G0vnnnPBdYOw86x1MDZPwnYXTiVBVlV5RVEURVGU\nwaPbe20uss5f1lXhfgwiuLfF1vn1kirZj6IMa4aSiP55YBLwBXpGXR8IdCBC87+BdcA4JGr7w8BH\nkA/1pbHtxiPRhaOBhxDRfDEw0dvmUkS4Xwzcsp3274wI6I8A1yNCjAPsg4gus4B/ee9Xx7a9AhHQ\n2z0b/w50AkciIvAcJOL+0O20sRL+CawBFgzAsWrJNuAnwHeB9yOOFoqiKMVoBXYC9vKmOHmkVvqn\n0VIjiqIoilKSdBoOPhimTZP3FmjMZRj58hPw3HJ85W1DeifunvRuuhLRWtXNyW6a010Eyp2leVsT\nTRubA4HYuLRkqh/wYLCMevU5xjW6WNfSnXd4dN5ePLZsQrQUt0lz5JHTMEbUU2sNk5sm8ey+jSTD\nKrO1TBw1ginrV3jZ0Q31bethSrFnWduBtVJHuyUINDHWkpg7F+foowvL8k6SBcvGsm19VBdtanI4\n5MC6SBbxLfmJrMwfFNFzl7lr2KO6lkvx7GXLfI8EsJZs0yiy43fy0p0DrksyWU9qRHMknXsmk2HZ\nc8/R0dGBQe61JDAtkSh43htj6OjowL71rdW2nHWbUtz7xEjSKQn0sQb2XJpn140bI6nQ6+fdTvKF\npwn3usllSa5aBl2dvqHymk5H86C3t0sR8yqSycD69RGfFtrbYcyYwOnFAo2ta2HFw4RdVixw37r9\nWdmxX2Cmm2Pvtsc5OLuxsOyN7AZut5q4SVEURVEUZZDwU62PLbJunPe6oci6cvuJi+jjYm0UZYdl\nqIjoaeCjyIexmJh9FSLuxutbzwOWe+s/igjQ4fQS5yAC+lrgdIIP+wZEDBmHpHl/f4nj9oXFwNHA\nw7HljwH/QKLJJyPi/eWxNu/zXr+MRFD73IxE5r8AHALs583XkgUMfwHd5wbEQeFTqIiuKEpp9kUc\nnI4GpiH1fBqQvzkvAHcif2sURVEURSlDMgkzZ8Iee3jRrkC6PUvDU8th9UJ8IbGtPs9j28bTlhxf\n2NZaGDcOJkyIRrCO6zSMizyyydGRS1ffeGtp3LCSEavSYC3tWYclT+/Mwy9ERfSJE1P813+No7k5\nsLuuDl7beedoOXegvmE1s9pWSJ11x5DujD97qhK5HHR2BtG/jkNin31g0qRQdLphxavNbHoj0Gmt\nhbFjDXPmJAtR5gbY6o5iuR1VCEQ2xrLeTqi+iN7VBevWhUR0l9y2TrK5BAXR2VpMIkWqvj4ioufy\neVavXEnL5s0FET2NPOHzH3I4QLcxcNhh1baclvYki5bVk0iKQ4fFMmaty67btkWE8PTzC0g//XhU\nHM/lYM0ayIYS6aXTcPjhQV8YI/1TZfJ5aG2Vz6p/u2Sz0BRK6GkNpNe1wCsvEymvYC0L24/h2dwe\nON6ytO3gE523Mif/VKHdEncb86puuaIoiqIoilIhi7zXOUXW+cterHA/J3rbxDMk7x87lqLssAwV\nEf2dSHT49UjEeZwbymz7G+AHyG/mfYBHQ+sme69P09NbBuB+RESfGlrW6C0ziHDyRpHtDkXS/G5G\nhG7/GKVYj4j0l9CzzneKwHPnwSLbLkRE/wlIpHulIvrJSF32u5FzOBMRiFLIl9tf6OmUAPAWbwqL\n6TshTggAf6T4NToVmI7UmL83tm4ccBby5dronc9f+3AuPgY4BaltP8l7v9Wzcx4963O8gVzjE4Cj\n6OngoCiKApLU9AVq76SkKIqiKDs8sczigJGc4cbxhESLcQzGyNsg5lyaOSGx0Y+MjWRJp+f7qhE+\nmGMwjsFxiIjo4VT1/qtkHzeRCG9jrSRG94XfwvnXyO74q7VBqnMAvy+DTNxFN/XnDcF5G2MGps8x\nxa93kYTuppeJ2HwtMCboI2ukn3qcgONI/v8wjlM8XXt82xp1ekWH8W+WcPEEE3xGfXcGB+PdH0EZ\nBnFfcFEURVEURVEGhf8gWtgZiP4XLnP7du/1rgr28y8kEPXtSOZknwZEC1qDZGNWlB2aoSKin+O9\n9sdhuR35IkjRMw38Cu91MsXx65cvCy3rQKISL0VSs88lWmt9BvIFMjpkdyX4+czitbmziNg7zbPz\n+dj6eu9Y0LdIyI959n3Qm+I57C4H/ouewv0Z3rpvEYjoa4BzgZMQAfuDsW2ORqLtM976MJcA3yOo\nk+HzVeCXyBdxJb+wm4DbEO+nYtyNOA7EmYeI6O9ARXRFURRFURRFURRFURRFURRFUXZMuoGfA18D\nfoiUUc4D70L0ogeBx2PbZJFgxQmhZfOQ7MrvQUoN34VocL8ARgLfRj0nlTcBTu9Nao4BjvHm4x/e\nSjgV+fBuo6cAfSsiAB+ERJeHmYWk+bbAr2LrvgA8hQjP3wktTwF/BsYgX0S3V2ijQxDJ/WiR9f7x\nv4l8AfkkkCj7FFILfmmFxwtzJVKv/Swku91sJLJ/IiJ8T6tgHy7yZbkW+ABwUWjdeOBPiEPGp5HI\neZ9LkfT0DvAlJFPAToh4/wbwSeArFUY2amcAACAASURBVJ7HVxEB/QlEzJ+CRKPPAT4TO24Y/546\ntsLjKIqiKIqiKIoySERic2sZRtwP4oHDZdsi0cnDluFs+yBQ9tao+MaJttNLoCiKoiiKovSTbyMB\niZ9B9LGVSGbgl4F3V7gPFwmsfB3J2Pyqt68PIhmlf1JVixVliDIUItFnImJoG32vOdtM8GH9FRAv\nGtaORCHfgKQhvxSp3zASEVVbgIuRlN9hMsB5SIr2LwAPIJ42VwJHIBHaX+qDnV9CxN4twO+LrL/K\ns+lSYAki/OYQ0Xk35AvvA304XpjRSOr5l733W5BzHoGk4vgyEi3eG+sQR4S7gf8FngReAa5DRPo/\nA78LtR8PfN87j1OJRoHfDDyDCN9fQa5dsdTyYU7wXj9KNE3Ieno6T4Tx285BUo109nIcRVEURVEU\nRVH6gbEuDdkWmro3gl8TvXsriVxGUot7KayNdUmlIB37NdpMK2O7WoP00hZGdcCIVCAuWvIkstWv\nFQ1IcfPGRrExa6hrTNDUFE3n3tgozerrg7rvDU43I7asiomelvrcRshtkLfGSDHqvijxlZJIiFGh\nmugkEpE83QZDXR00hBZbK6W4u7ujGcezrVsxq9cU0sEbA2xZBbYS/+s+4Dhid6EmumVLa4KNKyno\nyQbDxHaHiel0pCa6yWZpdBzc0Dkmk0kSM2fheCdjANPZ2TOdehVIJOQeSHr3sDXQUTeaN+p2iaSf\nH2U2McLZGtk2a5K8bmbRSfj6pBiRmI5Jpgq2b0ikcKsc92CM2O53iQXI56jPbAtKKxhIZdsxuWyP\n+7Wx0TI6VIYhaQ0ddhwbMzsV2mzJtpDv2lBVuxVFURRFUZQ+kUEy854BHIcEaT6LlPhtL9L+Am+b\nOEsRbendSPbmLiRCvT8ZpZWhTz3w3tiyOaH5vYGPxNbfj2iaOyxDQUT3U6pvpBcH7hgOcC0SWf0i\nkn68GK8hou10pJb5od5yF4nEfqzEdsuBDyGpKq5HIqE/j9TdPg9Ji1EJRyGePwAfR+qox3ERkf4A\n4DSkfrnPYkRE31pku0r4PwIB3cciKdbfjtSjr0REB7gPuAKJmP8rUuf9dOTL9GOxtu9CROtbKZ5G\nfRkSyX8eEmF+Uy/H9r/cZ9G3WhstyB+ANOKs8WoftlUURVEURVEUpULSbhd7r7ufw+pfDRZ2dVK/\nbS10dRVE9HRDhqlTLR11gRjnAodueYzDXrtTREgpn07i1RyOmys0zFv428b4z5sqYAzsthvsvz9Y\ni5OD3RaP4uD6qIg+dizMng0NDfLeGhi54XUOv+ULJNxwdTFLOu1g6p1g/6+/DkceWX27R46EXXYJ\nBE9jYNQoUcj9Ztaw22xDR6JnTfSVKyNlyWl/4H7qfvF1bEb8jx0glemEM35WXdtHjIBZswI7jeUf\nD43mlp+HarUD75s1mvNmTA+eFhhDXWsrcxobcVtbg/riEybi/vFPmJFSDc0xUP/EU5iH7quu3UBz\ns9wHvv5vjGFhy9nctu34UP9azuy6hRO774hI4Zu6R/KZtZ9kYdf0wrI6x3Dc6Hrq6oKtV+eeIOXc\nUVW7k0kYPRrGjPEthMTWFmaun4+xFrzPXmLjCsyWLTER3XLw8Rlm7Bz63ObrePmV83m2JXjmun7z\nctqWXF1VuxVFURRFUZQ+YxH96x8VtL25zLpW4NdVsUgZ6owAflNm/bH0zPh8MSqi15zx3msxcbkU\nBomGPhdJC34GUss8TiNwL3A48B8kJflyYBwSVf0JREQ+GUnfHudmJEr6E8BvvWUfIVpDvRwHAn9H\n+tkXnovxHsQhoBP4InAP4tVzABL9fh1wJD29PCrhuRLLn0e+SCcBU4HVFe7v20j6/bmIF0o3IoS3\nxtr5zgoJStvtp66fXcFxbweOR7yl/oZ4Oz1CZSnuNyHOGhNQEV1RFEVRBoIkMvjuQrPADBZNiBNh\nO8U9ymtN2rMhQ3FP90oZhYxZ42NNZQhirEt9rp3GbOhyZbvAzQVinLUYY0klIZ0KmrlAo+lgdHYj\nJhRdTDYHuUBEz1lI5Gt0S9fViTruupCDdINDQ0NURK+vF83X132tgTonS3PbWhL5wE4Jw09BV528\nNwY6iv1krQKJhBgUFjwdJxqJbiQS3Y0FZVsL2WxURM+3t2M2vobpavcXYRyn+lH0jhPtTGNp60iw\nbl1URO+YmvDU6tD5pFI0+OfoNbbJJJ3TZsCocUEXvPYGJKpfxS4eRG8MdKdHsTE1KtTtLp35UeAm\nI7bn3TpWm6msYCa+Z0ADsDHhUJcwhfNuSYxjbI0i0ZPJIJNC0slTn2sXEd03PtcF+Xx0Y2tpqIfm\nEWC8WyGfN2QaxtDSFZzhtnQLrql+9L+iKIqiKIqiKMpAMxREdP8JSLpsqyg/RYTZtUia7xUl2n0O\nEdCfQwRY/1hLkAj0ViTC/DfAW0rs4zvesZJIJHYpITzOfojQOwZJa/6dEu3GAb9AxOYLEdHdZzHw\nEPAC8GFE1O9rqoxVJZZ3IdH/E5Ba6ZWK6Hkk6n+u9/43SNr7OBO817O8qRwje1kPUlt9POJkcCFB\njfsXPBt+7dlWDP/eGowHyIqiKIryZuS7SEmcoxBHxmLsChyMiKQbkew5feW/kLEWwP+j9Figv4wA\nDgJ2R/SBOyg9tirF7gTjpgXeNBD8GrjIm24coGOGORtxfPwb4nDZX36KnMMcJPuUMpQpKJ+mp+Aa\nVkVL78BrF2oUEkl7334wKWZnfNnQNL6HWYNopiF6yU3hvwqxBBHrNcic3xvhu9dS2nRTaFF8W3k/\nkCdgYgfv201gSswriqIoiqIoijJs2IhohX1he4ImhgVDQUTf6L1WenGuQmqHb0DSgL9Spu0Z3uu1\nFBdQ/xcR0Q+idDT21QT9dCzyEHR+LzbuidQOH4/UbP9KmbZHIw+P1xAV0H3eAP6JPDw8nb6L6CNK\nLDdITXmoPDU9SF2EcB6/i5GHm3FHBr+/f0jvjgdrKziui0TzX4U4ThyFXN/9kGt0EpKePk6C4OG6\nFmZTFEVRlNozCxmr3U1PAX0vZNxwMNGx3wL6LqKfgQi0Pn+geiL6lci4Yk+IhAGeTN9E9BQyDjrA\ne/8tBk5E31G4EhkHX0UwtleGKqFoc8JRrdZGaqL79dJ7SIT+wrAK5+9rIAgfy7fRSrR5ZFlkmwr2\nWWy+2oRtDyvQ4WN6Km2P7iUufAYS78AIooHqbQHXBl+8FuOtjvWdtWBdmXAi5z9wdvfExuZtsfvX\n2tgiG731bHE/lFpgTPhqF/LlR40J2wk9L4XfzATvFUVRFEVRFEUZdlhgy2AbMdQYCiL6a8jFmYBE\nDJeLFr4SiWrahIimi3rZ9yTvdWOJ9ZsIfmNPpKeI/nEk3furyAPfnyKRPAdQWpDdHYlYn4SIu5dV\naOOmMm18+yeVaVOKmSWWT0ayxmURob5SfgzsDzyIPAT+LJJi/ShvXz7LvdexVPdh8TYktfvtyL1w\nPOJ8cDbyQD6eln8K8gymA1hfRTsUpb+cjtyX11L9iMnhyN7AmUh0YSU1eqpNGvkeyyBOT/3lEKSE\nx+3AuirYpSjDmSuAOmTcFmdnRIgGKY/TRiAw94VRSKR1izdfbU5Gvp+2IRl3DkHGTX3lS8j51crO\ncvwVWAg8M8DHrTZLEWeJdyMOrQ8MrjlKOazjYEeOwo4bFyhp3d0weTImnS4og8mRoxmbbKPbCVI+\nW6DR6fJypwcSqDtyFLY++Pi5gF1VaRKtPtqfTOGm0uBaLJZx9e1Ma8xEgnJHNiRpqBtBXZ1TsDuV\nspDPyRRmxAiY4CXoMkbqwteCdFrqoodE5K25Zrpaw/1mWNfl0BVTl5NkmeRsCKKeDdC+mZwnmPpX\noy9p4yrBAnmTJJNsJJn0U95bRjd0s+uI9aGU/i719bA1MQ4Tlsbr6jCTZkNiTKQmemKA0ohb61UZ\nCKXBb85vZWe3NfAdwaV5lIOtm44NuQU4mRHsvCXN1jZ/e0M6DePGhTLbe7dLtZMX5PNSVWDbtkD4\ndjoT5OyoiIZelxxJU1NTREQ3QF3Slc+pt9h1YEwiQyLpFj62bqKNBG51DVfezKQR58yVRJ0n38y8\nDQkouQMZ6w00uwPnIEFNt27Hfi5CngX/DPRLQ1EURVGUoclQENHXAy8hkUn7U7w2OcDlSNT4VuTB\nZqla32HeAHZBxNViaSwPwfPPp6eQPAcRjLPABUj697cg9cuvR4SwuJP1rkgN9inANchAvzdHbD+a\naTfk4WpLkTZ+ffHXe9lXMc4HvgbEnujwDu/1USqvVfpOxLFgI/Igcz1Sq/1QgrStPncg6fTPRKLh\n2/pheyXch0S5nYz0Yfz+Odh7/Q89+0BRBpo9kB+ZNwO/i627kuCzXoxNyOe5GpwOfKaXNp8EXq7S\n8cpxIFLy4kYGT0T/PiKUbY+I3oFkNzkc+EAV7FKU4cok4FxEIH+wyPoXgVOQv9ebkYw21/XjOD9C\nsgh9HBHTq83XkbHhS4jD0yr6LqLv4+3n38h3xDnVNLAC/ulNOwLXIWPPS1ARfWjT0IB7yqnkDjs8\niHDOZkkefjimfVuh2YTOLs5/fb5XczlQCZ3ONZhtTZFdZo4/le4TTy3UYIY8uct6G8b0A2PIjJ1E\nZtI0XBdsPsdFe94N9csjSmZ25AQ2zD4dt16SelkDdeQwW7dCLuRTbC0cfTRc6FWhchy4447qq6LG\nwIwZcNJJEu0PZHJw+62NPLMwWajnbi2seDVBdyYwwbUwM7WWn068krTxfTsNnUuX0JILfNsdStdP\n2w7DaW8czxtTD6auLrjmZ+1/NxcmHwUjorMLLJ54HHeNuygaXT4e0p/9II5jC+dXV2c4sqGeVNVt\n7Ul3N2zaJLXFAayxHN16Bx/quDlwALAWzn43ubN+hgklNRmVh++vStHVHSyzFjpDv8qNgeefhyee\nqK7dbW3w1FMRnwtyubG0d51eSJ1vLew59hWOHN/YoyL7zEkd0LgkcNhwLXPGvopNBj/3l+TXsTi5\nDUWpEpcgGQ7jv4U/h4wNeuMNimct7AsHIc/4KuF8xAGwlvwX8D4kq+NgiOj7Ir+h/4/tE9Ebkeeu\nLcDvq2CXoiiKoihK1RkKIjqIELoXIiAVE9G/Bvw3Ivaej/yGH1OkXTvRSPbbkAjpjwP/Qh5i+kxH\napEDPEw0Wr0Jia6uRyKIHvOWfwI4DDgNiTD/n9A2u3jnsTNwCyL4jy5iYx6pxe7zAPIQeSwiqn2A\nQHB2vOO81Xvfn8HpdKT/vhVatgeSGh3glxXuZxfPPgu8n0D8Px+JcLoMuB+401t+H5L2fi7SHxfQ\nM9p+krevX9B77YQve/tZEls+DflBA8UFP1+UvK+X/SvKQPBT5DP01SLrDkBKVJSimiFXO/VyLBj4\niMnhziLEEeB9iJj+5KBaoyiDx/uRFOY3UtyRcBV9ryke5yRkvPR7ypfYmYhELoM46RQLAT0MGSut\nQcaDPv8u0rYvJJAa7TngY4jo319ORr6T5yFi/NsQB6R65LvnZoo7RB4KzAAeR6K3AGYjf2+6KV5G\nKHy8l4AXYut2RrL/TPPev+Ydv68lcxykPM++yHXKIynDnkLG3fFSQ/chfwfPRsaPmvFjyGIgkYRE\nCqwnj7uI0pgMfnqaZIKUsWBcYsnFoyKztRgngUnWhb5RchinykJ02H7jeKXMDUnHknDckE0WHBfH\nCaV499oWrV/tOMF5Ow4kahQlbYzsu6COg4tD3iaCROkW8q74LYSa4brguDkcEwQBOtaNiKcOtUqP\nbsAkZEIuccKBesfF95pwAccYXJPqYYObTBXyvlvAHQj13KNYuvUELmlyQcS8seQcg5uqI1IZxIFk\nClKhW8tayGajGflrdbv41RWC9waXZGCLAWscuWdjyEfPBjeEsd4U7DBpXK2LrlSLccA3gOeBm2Lr\ndkYCXXpjcxXsGFHhsbqrdLw3C9ciz/quQLIMqPeNoiiKoihDjqEiot+AeJe+HfhVkfWf914bEDG8\nFBcRjTj/JRI9fYS33bNImvFxyIPFBsTj8eOx/fwSqYH5b6JC+TbgPOTh3neBh5AHk3jLp3vz7/Sm\nYixE0i75tCHi/J+QyK25SNrQLuTB4qyQTY+U2Gc5rkccEE5HxO7RyMPXZiTNZyXpsFLAn71tf0I0\nqmkF8GFvP9chD2Z9se8CpA9PQlLiP+m1b0b66iAkCvQ39C6ifwbp8+eQa7gR8Vo9G/lBcwvF05W+\nHXkwWywTgaIMJIcApyLOMK+Waed/buLUIr3ZDcCnS6yrVfaIODchzjflSnkMF36GZCv5Gtsf7aAo\nw5Vzvde7arT/ZiQSaB2SAWdimbabkDHWXCRaPT7e2x8R4ZMEYnu1+Cwi0H+O8t/5lfAjZEx4KjIW\nOii2/pveumWx5Z9Cxsbh8fEG4AdIuZ9P0tOZ8h3ImGoTItT7GMQh80v0zOr8Q295pY6Zk71jvLXE\n+l8hvwvC5JG/je8HzgJ+W+GxlCFBucRcw01qG2729iTsoxBNnD9UqcxCX9sdTHoevoTtRewciPrn\nUP1ECIpSYz6NBNB8np6/h78GfKfEdilgMRKscm0V7PiPt69S/AoJMLmNgRHRP4mMMXt7jjbUySLj\nx/8BPoJEpSuKoiiKogwphoqI/jgijp6ARLbE05Y/iwilvREfrHZ5+/wC8F5E4D0g1PZmRGAOZ6c7\nCnlQ+bC3TXyg/izyUPKjyED+AiTKaC2V1f6OP+AEEbNXISk/jyaoFZpDBPWfA3+oYN/F+Ic3fRMZ\nlIKc+xUU/8GxGjmPNaFl70Z+hNyOeInGuQl5IHsiMpD3nR7WIg4Mn0bStR7nTb4Nd3rbhiPzO7zj\nvxI7xlXAGcgDV/8a5pBr9y2kj+IcimQ4uIP+pcJXlGryKe/1+l7abUOi8QaC7gE8Viky7BgCOsh3\n1wtIGYuZ1CLzqaIMbUYTRDnXqg7395HsOO9Cvr/Kieh54ELPlo8hGXN850HfmbAeGdv0x1GxFLOB\nbyPOg8XGJ/3lGuScT0IcCid5xzkRGesdRPFo+zBbkYe8DyHi/CME12omEj3vZx0Kj52uQLKobAK+\nh2RSyiFj1suBq5EyP/EosWL8DzKeewCJLlsF1CFZUt5GdFwY5jHPruNQEV1RlCGI6tOKUlVSwIeQ\nbDu3FFnfSenShO9ARO/NbF+6cZ8cpX83j0CeVcHApSRvZ/gL6D5/Qp4nfgwJ2hlkdyhFURRFUZQo\nQ0VEB/E4vB4Rer8RW3dcz+YV04k8YPw2ErlchwzKtpZo/zBBHe1S/JaeD++up3dxrBwPI1FEDkEa\n5Q56prPsD7d4Ux3SB22Urg/e33MrJq6DnMP3vGkkkt40nnY/zCsU7/8fE3ilNiFRUFspP8D2I87U\nm1UZbEYi0ZmtlM+m0Ve+ARyDiCD/XWT9Cchnsw1xCqplerSPILXZfoeIM5cj4spIJPLx10ikdtwx\n6XjgK0iq3u95y5oRx6ERiGB2b5HjXQ4cidRcjjsEnYv8CD8QeZ7ZijgBXUXf0khPQPrvJESsSiEP\nTxYjQlGx78WbkGwjF3s2KsqbicOQv/PPU53xS5xjkL/tf6cysRbEOfC9iOPebxFnl2VIxJCfdeiH\nVbTRQYToJJKpJ1++eZ9oQhwE/VTmy5Go7AWI0+D7kXISvfEE8t32Y8SR4C3I9foLQdahf4Ta7418\nT28DDida5/NZpM79P5Dv61voPXPKqV6btxMdjy8G7imz3dPe6xG97F8ZdCzgBhGvBiym95Q6xhQP\nkzVgTHhrS82Si3vHMgYwtrjdJmhXeI/UFy9qlXdOtoYhwBZwQ/1njR9lXkkio9h5evsIW1sby613\nOLfQl8bvSxNtJW+j5+J3ZzgdujHR26jULVUtTKFeQdReG2tkiKY7D9vp2+dnTo+fV7UJ0sW7kej3\n+PGMMd59Yf8/e2ceL1dZ3//3c86ZmbtvudkgG0kIBATCFiNLABes1dqqRbG0P6uiYsuvFXerttZW\nq9SfWgsCUqtWpBVxq6K4AiIoICRCICRkvUlIcpPce3O3Wc95fn98zzlzZubMXZKZe2+S5/16zZ25\nZ/2eZ87MPM/z+S7lK8rSGVQaahQwQ414JTAfEcEPT3Lft/jPd1LqYNiAZFCchZQVjCtt835k/Pc4\n1ee5orwBGb/2MHY/Jo7bkOyT70ScMD+COEUqpMTNpyntkwW8Dxlr3wj8zF92nr99DukT9pbt04iM\nXzuRMnP3RNa1An+DjOdPQ/qzW5C+4Weo7qwQx1okI9M5SJBUDumPP4Y4GZS30V7EsfLFSPao+yZx\nLoPBYDAYDIa6M5NE9K8jIswNSNTOZGsrToRR/zGT8ahfZGiW+kxqT5RqkUWTZSJet6chaZW/h6mH\nbph+1iKD1ocY/zPoICmGE4j4/RzV56L+E4lwfylSFzdanmEekr53LpLKt5qAvhQZSLtI/dvxohir\nsdy341lkUN6CCDWNiLD2WUT8uYbS2cb5/n7R+rbDiEB2JyJIr0IyWwRchTgNHERqkAc4yKRIcI5t\niGh+JjIp8AZkcP7MBK5nNjLQX4x8d21A3ru5vr1nEC+iP+g/vxwjohtOPOb5zwfrcOwmxElnmMpU\n3+PxE2RS8UOIaHw70kfYS3zWoaPhr5CsQv+CZFmqJbdSWQs8jUygfgWJzp+IiA7yPX05IsLfhrTF\nauR7r3zC+C+RydzbKRXQA36IOE6cjXxnj3fdo8jk9XyqO7XGEYwN5k9iH8MUk06P8q1v3cUjjzxS\nXOi6qIMHULlIFyifh/7+0sLMAENDMFg6ZHCzLoXnngt7Q0p57NixFVVjlVFrzbe//S0ef/xRERc9\nD/u5Taj+QyXbFRpaGN7Ug2f7lQ0UOAf20TY0WF5oGtavLxa2VoonNm7kT1aurLndT/z+9/zbTTeF\nCqnrwRNPJti91yoRwPv6oFDmSj1qD/Dvh57GDr4KlaJw6BCZSFFuBTxtWby2xm2+Y8dmvv71L+I4\nxSoRzbs30bh/R/H6UOxt6Wd/0+8q9necUg3XtuGpp0qanG3btpPJ1H4IvH//Fn7605uxLL8Qu4Kn\nnn+Cuft2lBilf3QPumdnyTLPk9s8ny91AsiVuZnv3bsdxznSrnk8hw/v5Re/+BKpVHOJPdFza+Bp\nDvCYtaPSgWL9emhsLM0/39cHmaKdB4eHGRid6dMuhmOAl/nPv57kficjDntQGRmeAe5AxPMLEKfr\nHZH1f4D04YapLANUjUCw/wqT71O+EBGb3wG8B8lk9mNkbH2xb+dfU1n28gxkTHpHZNkTSBahNyPz\nq68os+ffkHH0fZQ69i9EhPjTEGeF9UjQzZlIMNIf+eeayHzeKxHndRuZn/ghEsyzDMniOUi8o8FD\nyDj9ZRgR/UTjbCTb1Uyjw38uL2NlMMwkfBdM1lCfEqBHy7zxNzEYjg1mkoiukUm7f0AEp7h0TQbD\nRLkM6XzPxM6Y4cRjrf/8yJhbCd+i9Lt5P+JYdCOVGST2IALQPUia3yDC0kIG1HORiYNvEM9fIiny\nArJImYv3I97iR8JfIZHxr6UoeKz2bbwa8TK/dQLH+W8kC8nbEPtfhnQKlyJCjkaivaOR5R9BBPTn\nEMeBR/3lCWQy5D2IN/0qxu9g/jUioN+FtFPU+74LEcnj+B3ikHA+EjV6vKTZMxgmwmz/uR61IP8J\nidC5Hth9BPv/PSJuX4J8PoNU7+VROkfDEuS75jmq1+g8GqqlyA8itMtrpY9FkLJ9HTKpCTJxejWV\n2YLW+M82MvkaR6CWnMr4IvoPkYnpXyPOUj9EUrWPF2UW3FcpJGvTZKPSDHXGsiyuuuoqdu7swbLK\npLemhaX/BwJcXCHo8mWWVQzT9bn66tezZMmSozM4glKKF7/4xTz22GOl4vw5Z1XYk1SKRlVqD7MX\nok77ULztgaILrF60iLPPPrumDgDnnnsufX1lX7saXnSRRMeXU26iUrNxeEPJsqTWNJZteJFSnLNq\nFbVi+fLlXHPN68jny7q3885E6TNKFi1Gsai8zatQdqtwxhkrWLp0aU3bfNmyZVx77VXkcvmS5Uqv\nRumypGox9y+IDj0eS5acyimnLMGK2f9IaGho4G1vu4ahoWHK8wtU3BechKXKfJai4f5RFpZ+vudr\nzVlz5tDS0lITuw0nLMEY+tExt6rkTUif5Qni+04/REravBcZG16K1OY+CcmGZiGidpzjYDlnIBly\nPMSZ+0h5D+KA/U8UHeivRsTwzyGBIc9O4DjXI8L8lcj86if95W9ExtW9yDg5yJRkI3OvpyFj7usp\nOji2+8teicxFXDeB83/MP+ZbqKxFfxqwqMp+wTzJ2irrDccv5zG5McxU0zbdBhgMYxCUPn5oWq0w\nGAyGY5jvIJ3v1023IYbjkh5kgGWYGPdSFH2rcQ8i1P4G+fzeg0RSB1khf0h1x6d/8bd5DBEXPkoQ\nRCLRm+W8PbL+B0iKvMeQwbRGBPSlE704nxv9fUeJjxL8M3/9prLl1/jL76jYQ6LYn/TX/z3iBfyY\n/395+uVuRLDOIpGQ5ShEsNFIzd2AFn/ZUNn2/+0v/6OYY41Hj7/vhUewr6GUOyjNsGCY2fwtcu/f\nPYl93uTvUxliWOSFiBPRQxS9rQNOo/g92TDOudZEtr1tEjaCOOxoilFRcfwMmUS9PGbdt/39PzbJ\n8wI85e+7usr6TorXFY2W+Lq/7Joxjn1tZN+/qbLNpsg24z3eEtnvKn/ZN8uO145k8nAj++UR58c3\nUJ3odcb9thkmx5uooSOC1jqhtc5qg8FgKKUeDk9DSEYZw/GLhfT9NJPLQKMoZnIbK3NRAhl3ayRd\nuQ3c7///pUmc7zP+Pj8bb8MqrPf3f7jK+v/w15c7oX+F6vMLZyLj4jziPHoqEgHuUozQD3itf5zf\nEx9xO8ffN0MxMje6X3nw0yGkLzxen7ycoD8/mQxFhmObi5H3vA+5/2baY6Nv32/q1QAGQw1YR/Fz\ndGgGPjK+fefXqwEMhqliJkWiYBxEzwAAIABJREFUG2rLrYhw98R4GxoMhroTRGceGmObf6KY4ixA\nIV7w/454gF8H3BSz70cRD/qLEUH8SkTMfgPxJSzuQ0Ty7WXLz0cE/EVItPdLxrC3Gr9A0gKXczcy\nCbACOCXm3HGkkWt4DBHR1yBp9x6hMsvEyxFR5QFEcCpHI6nlLkYErh+Nc+4g0vXNiPg+mTIbh5C0\neHMmsY/BcDwQZJ/oqvFxr0AmN0+mMhopOkn3MDJx93fAT8u2cxBnn4A/Bf4ZSXtZK16CRHF/Jmbd\nMv/57cCrgM2Ic9FkqBbSFyz3qMxYMhadSAaPgLcj39PlvxvBMa9HIsbHYucEznsYmfT9IPDHwEVI\nmtDL/ccFSK3Pcmb5z8MxNhoMBoPBYDh+mIX0/WByGY4uQ1KhZ5BsN9XII2PvJ4B3A6f7+z6FOIVO\nhCRFZ47ytPGT5b+rLL8TeCuTG5c/jZR8+7J/3INItOKnKU3jDvAa//l/qMxEBBK5/lvEifSFSImk\nsdiBjAM+ivSzJ1pLPZgnaUeCAqazDKVhavk+Mucy01iM3M/VyiIaDDOBIPNlNzMznfu/YwLwDMcJ\nRkQ/fimfPDYYDNNH4LVdHu0cJU6YCLzOFyG1fN9GvIheQCYB1iG1z0AG/xuqnOu5KssfRyIHH0Fq\nki1D0sNPhm1VlueQCO3TEG/4iYjoIB7Af4VELb4C8U5/IzLxEeVM/3kZ1SMBgno8J03gvF9E2vs1\niED/G0Sg+1/GjpiFYmRf5wTOYzAcTwQC6kQ+Y0fCYv9RjXP95zgR/x8QZ6P1yPftdcjE4uVMTnge\njxRje1rP9x/2GNtUo1oKzKBNdjO5wfOX/X3vRn6nXoqUD7m2bLudSLrSRuR3olbsRX7jbkUizgJH\nsXcDn6AyGim4r3bU0AZDjfHKa5zXEaVUTVN0a63Rcenla8yxajfU1vZj1W6YWtuBmqVzh6n9jNbS\nbsMJR5BCOcfkBNUgI853GN8Jeoe//XcQh/URxIF7osLvqxCn6T7Ekf1oqDbmDlLKL0X6ShP9AP8n\n4oT658ACZBz70ZjtgtoZVyPj/ziCbSZS2/ZTSAaiv0OE/J8DDyJ13sdKRx+dJ2mntuWWDAaDwWAw\nGI4KI6IbDAZD/QlE1dYxt6rO3YiIfhbi8R7nJd6HeJnPQjzvj9SR5lEkMnMhIgRNVkQ/OM6605Ba\n4ZNhGzJhYCGptXbEbBO0bTNjp6LfxsTS125HBLmPI+L9S/zHR5EIhXdSve5Qu/9s0tEZTjR+h0w8\nnkpta1Z/yn/EcRrFSblG5PuvnBcjk3nDyOToTiQ1+sXAPwIfrpGdY6kF30ZSX/4jR5bSHaS8xFdj\nlr/af35gEse6HnES2o6I5o2Ig8FbkYwi0Yioe5HvwdciNUTroRx5iPPS+xFhfymV2ZSCEhkP1uH8\nhhrgui6f//zn2bhxM7YdHWZqbDxU9NZRCiwbVKQWswLlulAm8rnaoqBL/U5SKYu/+ZvrOe2002pi\nu9aa733ve9z74x/jRGqYk05DvsxvTympcR2KspqCZzOYb6z4cDQ2QlOTCktJ53I5rr32WlavXl0z\nUffhhx/ma7fdRqJQ6g/kxQi9lm1XnFdrjeu6JcuUbWMnSzP75j2PN193HS960YtqYvfGjRu5+Qtf\nQBcKpdW5c7nKNk+lIFmZaVjbTkl9bq0ra3u7boGzz34B119/fc3a/Nlnn+Xmm27Cc91S2+MMiKsh\nDnjKIlqXXGsYHS3urhR4Xp7zzlvJu971tzURpEdHR/nwh/+evr4Roj9ZWkPZLRDe5uXYdulyreXt\nKv3YuixdOpf3vvfdtLe3lx/CYJgIgQCeRLIOxfXvymlHMg3BxCPDdyLO2UmkP1nN2TyOt/rPd07Q\nvrGoFm0fRGhbSF9tpMp2cURruv+YSid0KGYzSlHdAXyv/xgrICDgW0hpuL9BsuO9xn98FkmXfy3x\n8wvBF4VmchngDAaDwWAwGOqOEdENBoOh/gQpjmeNuVV1goGkQlKWx4noNyFiUj8yAL6TI4+w7ENE\n9MmK3TC2h3pQz24ywloXci0Wcm2XAe+hMl1ycMzvUBlFeaRsR1L02YgH/h8iKYjPQtLBv4D4VNDB\n+2w86A0nGlkka8OLEcHz59NrDiARQncg3yF/haRRB4m4eRxJKX4fM8PW8QgmIqPRTquRSJ/J1Hlf\nhXyHBqlMD/uP/4NMst6KlNEIJl//E0mv/iJkEvR9VP62zAFeB9wyzrkbgb9EvtfLfwtOR1L2u0jm\nknLW+M+TcRYwTCFaa9avf4qFCy/glFPO8JeBo1zmW72kVCSYMJlCz51bqdL19qL2RavCaHZm5rM9\nMzciN3rcd98tHDx4sKYi+saNGzlr6VLOWLlSDC8U4OtfhyefLBVBk0mYNw+cYCit2Tw0l3f97vVk\nvaIAb1nw+tdbXHON5e+u+fnPf8GuXbtYvXp1TewG6Nmxg+wPfsDViUTJ9fQPDTGazRbbTSnmLl6M\nk0oVd1aKbCbDzp07SwT39iVLmLtmDSriUPBATw+7du2qmYje29tL7zPP8Oa1a2lwHEJPg/vug8ce\nK7GRl70MLr20RKDWToL8ouV4DY3hMs+DgYHSXZ99dgPr1z+J1rpmIvqBAwfYt307b73qKlKRdmdo\nCIaHS++XtjZoLfWj1ViMJDtxLSe0c3gYbr9d/DaC3YeHN5DLra9Z1Hs+n+fBBzfT0fFmEomiuJ1O\nw/ZIjiitxeSurtJL0RqWLi29nEIBnnoK9u8vbpvP72bHju9z/fUZI6IbjpQBpK/hIOPB5yewzxuR\nfsZ2pG83Hm1I1HQSGWeez8SdK09GspXB0adyh+pj6CALT57JlbK5HHH+HkXGsh9FsrU9UrZd8I35\nccZOfz8ZHvIfDpIC/mVIuu7LEafS86iMqA/GzwPEi/0Gg8FgMBgM04YR0Q0Gg6H+PI14Yp96hPuf\n5T+PEC9A/zkiSuxFBqrf48gjLBsp1u6dyGRFOedUWd6GCPMAz0zwWAqZlFgI3AX8CyLQfRKJRIxO\nAqz3n9dQe1wk+vwp4F+BXyHt+yoqBaNmZLKjwNgp6wyG45U7EBH9T6guTA9RWd/7fEojnN/K0U9K\nWsB/IQ48XwO+Hln3HJJR4g5/+Spgf2T9R4B/ijlmNMvHc8CKo7RxMvwPMvn4Q+R7dCFSgiMBfI7q\n2TGitCATxilEDI9+j/4UqRv/Qf9cFyFOW8NIFPqPgHf5r3+DCN2zkMjxS4FBxhfRk0jE+b8i3+M7\nkPthMeKo5CACf3lWk0bkd3TAv37DDCWRSLFy5fmcddaLQr3TocBSeyfNKjL/39iEt3gxOE4o8mpA\n7dyJ2l6sDKPRPDNyCo0jp2CFYp7L44/fXXPbLcvivLPP5qIXvlDU2HwefvlL2Lq1VOxvaIA5c4oi\nutK0JxZhq7XIx9FfrDSLFtlccYWDZYmwvWfPnpqmFQfpLC1KJHhxQ0O4zNOafSMjJWGDSikWt7SQ\namws2X/UtnmG0i/gWc3NLFm0CCsQ0ZXi4OgotbRcAXM7Orhs5UqagyhzpWDDBgl3DtpJKTj5ZDjn\nnFIRPZEkvfJ83OaW0K5CAQ4eLG5mWWBZCQ4c2FRDy4XZs2Zx2erVNEVt7+sTFT+wXWvo7obO0gBP\nT9kcbphH3kqGuw4MwPe/L2J6sMyykljWRLvNEyOZ7GDu3EtoaJgdttPQkIjgAVqL9t/dXRlEv2SJ\nLA/I5+UjMjRU3DaX24xt/7zm97rhhMJDSpOtQsbQExmXBpHhX2Fiac9v8499D+Kk/RjSB3qA8bO6\n/SUiTq/zH0fLOcgYPm45yHzCRL1p5gDfQOy7Hhmf/jvStzuP0kjvdYhD5kXUTkQPKFAU1L+AODec\ng8w1lEf8B/3pJ2tsg8FgMBgMBsNRY4pUGQwGQ/0JPOEvrLJ+rNq4DRSF8F9QOXhegYgWHhI1vQuJ\nsBxCJgFeWra9gjHnQD+AiCyjSO20yXIxxbrEUd6CzCw/Buyb4LH+L/DHSAr2tyNC+Xv94/w3xVrz\nAD9B0t2diYh39cKj6ATQEbP+fOT9fBwRlAyGE43/QSbn3kBUTSplB/K5HusxkZSRINEqwT7l349/\nhkyOPoRMIpbzDeBm5PvuY2Xr+idgY1wmimrs9/c5mhSVn0LSY56PfFf/GeJc9X5k8recA/45hyPL\n3o8I1d9EUrOX81EkGr0T+OvI8seQidf/QJyi3oCI8G9BBPSfIQJ7lGH//NGsHBlkInUzEpl0nX+c\n1yNZUN7p21jOq/3z/heTS2VqmAaCjNbBw9OgvdKF2vP8utKRxXE7R1KSFxdp6lNVwD92aLhXmZo7\nepGR15rAJq/kEbW9fLcaG17xf2BN1LKqu8c8yo9Z1wrg5e/7WOsi94XnaTyP8FFyz9X7fgnuzfL7\npeQ6NOjK5drz8LQus7P08ou218N0fVT34lhvl8FQQ37lP79wAtueDVyAfNV9dQLbvx0ZM+9CMo1t\nAt6BzJF+nWIGtTgUElkN8OUJnGsiXIc4DJaf5wb/9URrrgcOpCf5z19BMtZ9G1iC2BudCwjK9/wf\nYPlkjZ4EfRT7wG0x64P0LCbbkMFgMBgMM4tuZA7qfGS+/0iy1h7zmEh0g8FgqD8PIHWCVyOiUnmK\nsjcjYshXEI/7PUiN7/OROr7nIGmSP162XwoRrFqATyAiO4wdYdmNRBD+BxIFuBOJtD4dSYP+Z/52\nn2LiIlaUYcSL/WqkfrmN1Kb7pL/+YxM8znlIRGTOP1YQgX8zUpv8NcDtSBQmvq3vRdrwG4jjwV1I\n1IJCJkLWIpMj70NqN4/F3Ug73uk/Z/zjvMq3B+KjbC/yn++d0FUaDMcfaeRz+hEkYvmbMducFbPs\nSNlGMXtGOXf4j7GIE9dBruHmIzUqhr+q0XFuQhyn5iJOVnuQ34c43u0/ovy9/6hGAYkIj2MX8DZk\noncR8p14mGK9znJ+TOV7kwX+1n9tI9FSKURoHytN6duR34MvjLGNYYZQXgJaaQ2FHPL2+0XQLVtC\nWH2xN0C7rmwfPR4eFm5JOndVLxGdovYZ2h6jFOpEApyE2KQ8VMKhsQlwi9sFZdOz2WJ7FI6kyM4E\n8CybglOsGe5pjZdsRGs7bCmlqAwrBrSyKCSaw2vWQMFKkVcOVjhdoHCxau+BHxTjjkS8e5aD56RK\nItGV7aCURVQM18ryBepIMW8PcD1UsJkGPLcuGrqnIZdX2MV8+ZBXaDdS61xrdM7Cy5bmRNdKUXA0\nbvGycV1NghxJ31gFJMjX/F5XSpIoOJGZIMepvDWqBZFbeDjKF+EVaAsSjkUiocJ9PK/6/gbDJPgJ\n4jx4KTIuHIsgCv1njO/keDbweaTP80aK/Zj/RlKOvx0ZA74UGSeXcznSv8lQu+jtZuD7yHi8B3Fm\nvBEZWx5E+n8T4QNImvmNlPY9r0Umvl+DOKoH/akHkOt+I+K08LdIFP5h36bTEEfGP0LmJsa7hoeB\nLyFZg3Yj7dfhH3cx4kz/VMy+l/rP42UAMBgMBoPBUF9s5Hf5tUgfYHHZ+iDTzL8xcSe/Yx4johsM\nBkP9GQS+hXh4v5zKVLQKuMJ/xHEQifZ7vGz5Z5DB8K+pFKe/gYjNb0aE9D+gGIS0DEmNHoeLRCf+\nc7WLGYd/Q6LH1yOD7wRSxx0kvfyPJnCMVsQ5IIUI44+VrX8rIrL/KSLm3Oov/6p/rv+HpDb+HMU6\ncEEBUJf4mvLlzEPq+37Q/78f+c1sRaZi/znGLhCB3UPEfIPhROWTyPfdPyGRL3WSjU5YXI6s3EYt\nz7+9RsfZO+5WUh7gxUgK+K01OK+hjtg2zJ0LixYVdWc1NELqlm/A9ueKylpHB9bFF0O0PrcGUkmp\nOe6jgAXOflo6cihfmHTx+EEimmChdmSsJkbtVrTS4KZpGM3hHD5cTOeuNV57J/k3/Lmk6JaQbeam\nm/nYS1MUvKLMrBR4nuLWW4vHf/xxeM1ramuzVhZ7VlzBwy96YzGKGXC9HF6g/yiwvAILN3+PVLYf\nIkn0RzpO4TcLPoCHpNbXQMfcNrbPmo/yc+grpXi6pYVVNU3ojuQu37mzpL78jhUvY8/Cq8L3GwVd\n5y6mY9aCkl0tPNr7D5E63Btej84XcDbvQLuepBJX0LV1A8qtfYndrXsa+NJ3u0k6TeGy/GgXhdFc\neJ9rrdnfn+LgYLKkbEGqQbHm8gQdswgFfiszwtsS38BuzviXo3gqs411Kl1Tu+fM0VxztUtHZ0E+\npAp271YMDzt4kXQFDQ0VpdwBuHDODpbPHSRoc08rlv/5EgZ0W3iN+/bBr35Vua/BMEl+gjh8X4mU\nj6nmtJcCrvFfjxcZHpS1aUTGeeWlcN6FlAe7HHE6/IeYYwSC/Xc5ugxDUf4GEc23IXMHzUgJnAFk\nTNo3gWNcgjjdp5EMP9HMPQNIFqGH/PM8TNGp/C3I+PhNiBM6yDi+PbL/nglex9mI4H8TxTruwXEO\n+naVj8UXIdnsnmZipYkMBoPBYDDUj59RXZ8AmRu/zH/cjWTFzUyBXdOKEdENBoNhargNEZX+gkoR\n/VvIoPIypMZtkOKsF4kW/waVUeHtiJf9B/31cSLV/0W80C3Ec2w7MgBfi0wMnIl4uVvIYPtx/1hb\njugKhSHgRUi04iX+tRxAIt9/GbP9Ov8aogUfVyITIMNI7dxy+pGU7S9HJk0sig4CX0Tqyb0JGYyn\nkGs7gNT+/a7/OiDnn798MP8GJNXwZUj0fgMy+dCDRLbG1b47C8ka8CNkwsdgOFFJIym5r0WiVh4Z\ne3ODYUzWItk9PjXdhhjGx7KgpQU6ogVP3DzqyXXwxOMiLmqNmj1bii5H6ngDMH++1L+O1JRubxym\nvaFAINoVgEZ7Iv5wk6egkuStlHQqLJdk3oVMpkRE1wkHd9V5MHdeKIC25uDlMTkxHngAfvITea2U\n6MU1RykGZy9nx9mvLgbMa2naRKSohnKzeAcfhv5MJExYk0/Moeekq3BV0Xmhvw0yzaVlyfcnN9Y+\noDuXg/7+ooiuPfpPvoSeuZcSvN8KcE8GpyxxoO3l6BjeRMIdjRTjzpLct0Wi2xWgLJr6dqO8uGDS\no+PQ4QSPbGjBtouGZbOlmQc0sHVL5fve0gJt8+CkkwgjupvzOV7p/I4mNRReu+0cZIOaXVO7W1s0\n552rmd1dTN7f1WkxaxYlIrrjiD9LNKJco1nYOsCK9gOgxeVCK5s5i+eTjQju27fD78bL+WQwjI+L\nRDV/AhmbxY0LQSKd/85//b/jHHMR4midRdKdl5NGorVfiowv47LI/QSJ2q5l6vHfI+PI6xBncYWI\n3LcR73D4NSS73G8iyzqQUjybkOx25fwOyTa0DHFKCMggNd5vQpzhz0DG8PuQse99FFPrB6xDMrxF\nnSpHkfmFSxFH/5OQ9tvlX98dFLPLRbkGGc/fErPOYDAYDAbD1BJ4COeRDDH3Ak8i8+YrgVcgQW3K\nf85RdGY8bjEiusFgMEwNDyPi6uuQtGibIusGEHF3MmlQDjN+WrsRJHIviosI8w9O4lyTZZhiJPh4\nPEOpgA7wqP8Yi/X+I47nqR5pX04O+HTM8j1IZPtXJ3gckDTxHpLG2mA40fkm8ancDYbJ8rHpNsBQ\nA4JU4tEHlCl0ujIXfHHnKTJ0AkRThZeWRo8leqn1THGtyuxRlP0/TmlwVfY62upT2fqB3eX2xG5b\ncWuoyoauY6PHnapkmY6/1cv3LSZwL235uhQtiFYn0Bzlm6vCEgimNrqhTnwBEYbfhzhlx3lQ7UfE\n9okQN/YsZ9s4x/v6BM81WfYSH/kex/3+I0q5o34cv6BYAq6c3zF+ybOA7VS2kWZi7RulCYnCfw4p\n1WYwGAwGg2F6OYhk4/kSxdKwAb9FMq9ehWSQtZCysJ+lMnvucUXNy5oZDAaDoSrvQUTWD0+3IYaa\nsxLpONxJfJS6wWAwHCmfRzJmTCT1ucEw8xlPbZtpatxR2jPDroYJKf/HCMey9cek7foYtdtwLDMM\nfBRYgmQaMxxfvBMpo/Z3TKzkmsFgMBgMhvryaqQ0Y7mAHuVblJYxfXVdLZoBmEh0g8FgmDqeRby1\nZlGagtxw7NMA/BVwz3QbYjAYjjvGq+9pMMwcXBd698OunnCRHhhA2TY0F/ODZ1Mt9I604RUai9tp\naM010+o0Eq3ZbecK2NmyDLD52te4rsSCWV2wYEFJOnfmzCn+H+AWcIYqs9S2jObpzoqtSmkOFfqR\nUre1RSlJvx3WRNeSKT0XkSSUp/DaO8AZIdq+2urAdcErZtDH0i7Ndi68TAU0WDkUZen363AhaqQf\n6+D2kproKgFWpNesAaXzqNF94KWL11MoyEUHecktS5bVgaRdoKtpFMcuLsukkmTdRMl2I3v60Bwu\nUZ+btKIzk6Q1XYw4byoMoZoaS1XqkZGaR9J7QMGNNovC89P/l9REdwq0pMo1LU3CzUAmS9jmSmEd\n3I81NBr8i9X7fN3a3XBC8jWkTvhYk7mGY5N1SKa+yWTkMxgMBsPkOB8ROVNIlPGdSAbPyWAjwUPn\nAo3I3OeeGtpomDlMVKu4F3ir//qUOtkyY6iViH4aYxecNxiOZSaT1spgGI/vT7cBhrqwDhOBbjAY\nDIYTnYEB+Id/wGpr9xdotO2glp4Cf/iH4WabB+Zzw8+vZiDbEmpxngevvcrhqosT4TKtPbp/cw/d\nD/1vqZi4e3f9ryWRgve8B65/R8lilUxhd3UWc7opsIcPcPIPv1pRe7tzYw+XbNoGSrTRbw/uQ9Wh\nQkFrKyxcWBRBXRceflhqcQfNZlsOV7z5Ojpm54tCrQJ3b5L+mxO4/r4aWNZ+iD+Yv4Wk7RGkSM9v\n2w5qZW0NtywpvB3URFeK5ANfpnnjVkk/79OSgOaymQsLjaXc0nTkqRRcfnmxGLxScPCgKMQ1ZuXs\nQ7xv7aM0Jfxa8lozOGc5Q92nUDTKI3XzV0hu/CrKLc5HqWyCjmdOxdnTHO5rNTWQev3LoSFZtP2Z\nZ+RRQ/J5Rd+AjbKd0Omi4MGFFxYrKmgNi5z9vCC5qTTbu9Z0HtoCBw6HN5ZVKND24G14e/aGl30o\nm8Ge011Tuw0nNC5w93QbYagLv5xuAwwGg+E45/1IucsR4ACwGMmO+kfAryd4jCB9d9QT+GUYEf1E\nJzrwnQoP92mlViL6xcAtNTqWwTDT+DhGRDcYxuMfkI5ZeroNMRgMBoPBME24LmrXLkj6AYNaoxoa\n4PTToKsrDJXO5GaxZWA2B9OtoUjnerAvC5lkNE7ao5B14eABUJHo79wUZH1VFpx0MiTLCkd7GuUW\n/9cKLDdPon+/qNcRkkM9dGaeAyW1o7vyo3Ux1XFEiw5F0QJkszA4WBTRHdvCmz0PFkSuR2nwFAVP\n4RZkW0+DQ4HO5DAp2z+gUjQ72dobrpQI6WHIu8Ia7MXesxEVSTVv+48oFohYHs0S0NQkFx49fp0i\nohsTBRa0DdEUCPZoDnfnOHxS9P6FjpZe2thIyTyTTkLaAafVd2jQoFqho00yNgRqdltbZdaDo0Rr\nRcFV5AulEe4tLZFtgC47z7zkMFZ5AvfeUUinizdWPo+9eyf2tm3hMsfzUJ1tNbXbYDhOWYt8vQ1O\ntyEGg8FgOO54MfAp4EEkEv0w8ALEgenbwAp/2XjMAu5DtJELgFfVw1jDMceayOuN02bFFFHTdO7L\nly9n4cKFALiui2VZqBqnH5soWmu01lg1HnROBtd1se3y4f7Uof2Jh+l6D471e2D//v08U2PPf4Ph\nOCaNEdANBoPBYDAoVRTYApEUyupv63CzYKRglf0P8lqVH3Mq0XridcPjbFQWkauY0msI2zc4pYIw\nBD16Tdq3sJg93bc29p2oPxNto/ILnIZ7REfbxa8XXta0/jblqOJD+RsGtte5Tn202cY6VXHVOG0a\nPeA0vhcGwzGKEc8NBoPBUC8+hHTk3klRLN8AfBL4HHAt8P8mcJxovesbMSK6AboppnIvIE4ZxzU1\nFdETiQTXXXcdqVSKRx55hDPPPJP29vbxd6wDO3bs4PDhw7zgBS+YFiE7n8/z+OOPs2rVKhrqkD5u\nPLTW7N27l+HhYU499dQpF7I9z+ORRx7h9NNPp7Ozc0rPHbBr1y4OHDjAqlWrjkhI/973vmdEdIPB\nYDAYDAaDwWAwGAwGg8FgMBgMxwKNwGXAZqBc3PguIqK/gomJ6AZDOTcjGQpA0v1vn0ZbpoSaiugt\nLS28/OUvp7m5Gdd1ufTSS+nunp5aWBs2bKC3t5fLLrtsWkT0XC6HUoqXvvSlNDc3j79DHdiyZQv9\n/f1ceOGFU35urTWFQoGLLrqIuXPnTvn5ATZu3EhPTw8ve9nLjkhE37x5cx2sMhgMBoPBYDAYThDC\nUFctqcOD1NVaoz3w3GLAqufJI8hk7W8p2bU8DVZ9I3QhJnq7GpYfPOz/q1UQVTxNGbggrLse/K8B\nD0l7rpFIf60seR/CiGF5HQ2419qPno6mBajXdfmZw6Ih0dov7F6atyB+X6W9yEpd9igur9u7Un6z\nlKdSCCP/y/aLCwHXUbv9jHK1sbL0NMj9qqMZChTF/wNTqn0QJvohqXNEvcFgMBgMBoOhKqcCCeDJ\nmHU7gQHgjCm1yHC88D7g9f7rLcB7p9GWKaOmIno6nWbdunU0NjYyOir13qYrlXdDQwMtLS0opabF\nBqUUbW1t05rOPJlM0tTUFNozlWitaW9vJ5FITOs90NbWNql7IJPJsHv3bvr7+9m1a1edLTQYDAaD\nwWAwGI4fPK0ZzGY5FKlDXdAJthxazoha5meu1hyyOnnVn9jkVFHg9DScfz40NpamEXdOWQgXX1Ra\nE/3++2tuu9bQ0wOdnSLmKwWnLpNS7iUcOoT6xX2odLGKjervg40bQXslm2b37SM7PBwKjplcjlq7\nVys0Tb076F7/8/D0rqtZ1ZNmXm9emk2DZWkavjOM21Fqo5PtZNkpf4TnTw1oYK7Vi/rtb0H5dbyV\ngs2b4eyza2a3BryOTrwuMlpeAAAgAElEQVQzz6aQTIXLW3CYt3wlUUG5vb+PhsGBkv1zrsPv+haQ\nLhT3tRqSdLetxko6od07m56loPbWzO6QgwfhV7+CiMN+tuM5htvnRZxANE29e9BLFpfUeMe2IZUq\nEaIL2Owa6sZ1W1C+h8aekXZcXdvydHb/QZp+9r80t7SHjiBWzuaUvgYC2V4DXdke3NGN6DIPAK+3\nt7QmuudhFwpY0QyEhULNa7kbDAaDwWAwGCbMbP/5UJX1h4AlFAsLGQwT4U3Ap/zX/cBrOUFK09RU\nRNdak06nsSyLlStXTlsENsD8+fOZNWvWtNVEdxyHs846i1QqNf7GdWL27Nl0dnZOmxPBWWedRWtr\n65SfO2Du3Lm0t7dP6vo9zyObzZJOp8nn83W0zmAwGAwGg8FgOL5wtWb/yAg9FCO1hzMNfK3nYnYN\nrpZZGg2nnqr4yMegs8MjEs9NJqtIp0tF9NQLV8H5S0prRm/bVnPbtYb162FgQF7bNnR2VIroqmcn\n1gffB/v3F2edYqJuNTDiuvT5kdUWMKQUs2odoas17VueYMn3B0rCyZfueR49OBgROzXeQ/spuIWS\nCOeGRWdy0b++Am0HwjOc/Nh27DvvgEKuWDw7nYbXva6mpnsnL6TwB39IrqE4bzDrFa+iq6SwuEdy\nw5MkNkcyUSo4lG7krt9ewc7DHXKJGpIpuHCejVPU0Nky9GvyQ3fV1G5A7sEnnihZNKrLZioVtK5c\nCeWZ4TwPhoZEbPbbN6eSPHpwCdmG9jBZwOb+veS92mbVc/bspP3f/pEuKziuhlSK+XPnR0LfFd6+\nvRS2bysLrNcUtC7JwoBl0bB4Mdb8+cXtMhnCN8FgMBgMBoPBMNUEgtRIlfXDgI1og0YAMUyEtwG3\nIcOAAeAK4KlptWgKqenIprGxkTVr1oR10KdLwAZJLa+1nrYoaMuy6O7unrbzAzQ1NUl6vGlAKcXs\n2bOn9fqbm5tpamqalA1NTU2cccYZaK157LHH6midwWAwGAwGg8FwfBOMRDwsPO2EIrpGk0x4pJKl\ngnk+L/9H07krywLboiS5dZ3GGEFm8fABsfEZqlCAbDZiOZBIlG7jH1DrSDrxOtmt0FieW1zgeVja\nBe0SdVLI5XNQ7iicL0iqdys8GApdzK0fzfNeU/v9POKWI4/SpRE8lOPIfRA9v22jVQJXpfxU9eD5\nz6Wpyus0J6E1uG6pTa6L9rzS91vr8aOyw/vdQkciz+uRiF4BynWLR/avwy65V0B7rtznZfMJsRbF\npbU3GAwGg8FgMEwXQcqsjirrOxDx3AjohonwGuCLyFAgi6Rz//20WjTF1FREV0phWda0iudRplPA\nnQnnn24bjtXrD9K/zwT7DQaDwWAwGAwGw7FJpSA8VSeuwVlnylBIh39iUWXPU0odxoszpdkNBoPB\nYDAYDMcsQS2jWVXWzwKenyJbDMc2rwT+B9GRc8CfAj+bVoumAZNjy2AwGAwGg8FgMBgMNUFHHkEQ\nt9a6JKBZorwr5UKtiscoHi+Icq2vvBjYFQRgW1ZgSOl5g2srX1YesRtcd5CwPm6/mhJEPYcG6GJx\nd9+W8vMHdnkKpBB3xE7XK0Zaa4oNU1OTNUrpMQO1gwB4XXFXxLeoF91SgVdPV4a4NP4VEdlxbaZ9\nK/27Q8vryJKSz1HNbY7cFyU3fXA9Ssn9EuMkUG6TAvA8tPYiG3n1sNxgMBgMBoPBMDG2IinbX4hU\nlYp01DgHaAF+OQ12GY4t/gj4NpAAMsAfAz+dVoumCSOiGwwGg8FgMBgMBoPhqFG2TfvJJzMrlQqX\nNSS6WLK0gUR7URxc0DGMs34jNOYp5rLWJFq7aWydXaJ5JnZshp5Nxe2UgoMHa2+7gqVL4YwzfBFd\nebQNPw+bhks2Uj09lSnRlZIa0BHRUWlNqrGRdr8tFNCQz9cn1fXICOzfX2rP4sXQ2lpc5HnYDz0E\nw8MluzYkNAuf+RnYSUDk3TZ1mMyrXosV6KAKcs9toqGmtmu2bXO5444MjhNV0RUy1xdcisfs0Q66\n0qeW3BeDmSR7DjRwaKjoP9CazLBiz8M0JgrhkdwDG3gmkauh3T6LF8Mf/iHYxZrlyaymNa+KCfSV\nJplPSz35SNtlvASPuWsY9FpQSqHRKC/FAnZh0RvanmYPe4mk6a8FnZ1wwQXQ0OAb6XuMJBIlNlor\nVmCfdx6qzFHASiTQkWv2UGzPzmfEawmX9QwfJFvYUlu7DQaDwWAwGAwTJQ/8CEm7vRa4P7Lu9f7z\nd8v2WQG0Ak8jgqnhxOaVwN2IgJ4FXssJKqCDEdENBoPBYDAYDAaDwVAD7ESC2StXsnCWnzlQa9LJ\nVs5Z1cJJERF9dvoQiR/9L7gRQVdrUqtWkTr//NKDPvIA/OhHpXWlDxyoue2WJdriRRdJYC6uS/Lh\nTfDkzqK4qBRqxw5ULle5cypVKpBrTVNnJ01+W2ilaO3rq7ndaA2HD8PQUHGZ48CVV8KaNcWo40IB\nZ+9eEdsjdjYn4cwHbgOr6MyQXb2WoRs+DIlUeHnp73+7xiI6rFuX58EHhymWY9TIPE2kRrqClaef\nxMrTl5Tsm80pntlpMTpa3HOuM8yLUl+m0xkN9sY5dJDnTindtyaccw588pPQ2BguahpWdA9bEbHf\no/G7d8BP7i5p82HdwTcKV7GF5eFnosvLcKu+lzZyvuWKPJtZX+tSlfPnwzveAV1dxcjzbBb27SvZ\nzG5pwe7qqty/rQ2SyfDfbB7W/aKZrT2J8BIPHXqOkec+XVu7DQaDwWAwGAyT4RPAnwBfBq4GtiDC\n6HuA54A7y7a/FbgCOBN4JrJ8NbDKf32O//xKYKn/+oeY1PDHGy8GvgUkgQLwZ8CPp9WiacaI6AaD\nwWAwGAxThNbaAb6C6YMZDCcaBeD/KKWO6xzHCrAsCysUvLX/P9gRDVwpRNz1XKKR6OVRr8FyXLcy\nVXkdsG15iBioJGV43HknKCYrpUrEf1WPKPSAcjstSy4m6nxgWWJ7NGJeKWzt+jndZV+lEAE94Ucr\nW4Bd+58t15Ug7bGwLHA9C1eXnt+LlAcoXo7GwcXRBfDLAFjUKbV44DgREZRVwoaEXcyUjge2U5lM\nXik8laCgU1h+FL2Li6U0TpjQHax62B1kTXCc4j3juiUR9YD8X5ZdAa0lYj2RiBwPtJ3Es5Lhpp6V\nhHql0D/O0VrPBT4/3XYYDIYZxaNKqc9NtxEGg+GY40ngL4DbgUcjy58FXgNMNFXTa4APli17V+T1\nFRgR/XjiMuAHQCMyh/FG4DvTatEMwEzgGgyGuqO1fjVw6nTbYTAYppwdSqlvT7cRMwx7ZGTk9Q8+\n+GDSdWucorWGrFixglNPnfqv7UKhwLp16+jt7Z3yc0+Ejo4OLrjgAlKRVNVTxcDAAA8//DC6TuLh\n0bJ8+XJOO+20KT/vTL9nAObMmcOqVatyiUTiTZhCwWUYoc1gKP0YmM/ECU5roVC4+oEHHmBkZKTu\nJ+vo6GD16tU0BOn9j5LgN3nv3r01OV41bNtm7dq1tEZKVhwNWms2bNjAjh076t7PsiyLtWvX0tbW\nVpPjbdu2jaeffrrudiulOP3002s6Pti2bRvPPPMMnueNv/FRUGvbh4eHeeCBB5iKsdyiRYs455xz\nkoAR0Q0Gw5FwF3Av8BJgFlIr/VcQWy/oGkQ43V22/F8RIb4a9f3RN0w1Xwea/NdDwDv8x1hsAq6v\np1HTjRHRDQbDVPDm/fv3/8nvfve7MQOHxgvO0bp64NHRBvY4js25557L3Llzj+5AR8DIyAiPPvoo\no0E+yjjGuXhZU70R1Hhz9tUaUGs6OjtZs2YNdnmEyhSwa9cufv/7J8fcZtxrG4ujmGywHYdzzztv\neu+ZcSbXxppMGTcabry2GWf/5aeeymmnnfYzwIjoZTz//PN85jOf4QUveEEkWnNmoJRi06ZNXHHF\nFbz73e+ub9RkDJlMhi9+8Ys0NDTQ2Ng4YwRjpRSFQoHdu3dzyy23TMvnfsuWLXzuc5/j7LPPnvJz\nj8eWLVtYs2YNH/rQh6b83Ol0mptvvhnXdenu7p7y849HJpMhnU7zhS98gUQ0etNgMBgMhhgymQw3\n3ngjS5YsCcXtaH/RcZyScZnWuqS/NF7fKejbFQoFtm/fzi233MK8efNqZvutt95KMpmkqakJrTVD\nQ0Oh2JjP5ykUCiil0FqjlArtsSwLxylOUVqWFf5uKqVIJpPYto3neTz99NMsXbqUlStX1sRuz/O4\n88472bp1K0uXLh1/ByrbOdpn9jwP13XDbaLr1q1bx9KlSznjjDNqYDn89Kc/5Zvf/GboyJjNZhkZ\nGUFryQSTTCZL2ritra3EnvLXiUQivN+i1/jss89y+eWXc8MNN9TE7sD2e+65h5UrV4Z2BM+u6zI4\nOBgK7MF9HrXJtu0S+6PrGhoaSPqZQjZu3MiVV17Ju94VDZw8cvbu3ctnP/tZzj///JqM5QqFQmh7\ncI1KKXp6eli0aBE33njjUZ/DYDCc0AxSWf88jmpieJ//MJwYNEdedwIvncA+7XWyZcZgRHSDwTAl\nPPHEE/zzP9/GvHnxUWpNTTBvXrwupxQMDsIPfwiFQvzxbRtaWiozEUap5iistQs8xRe+8G5e8YpX\njH0hdWDfvn3c+OlPs2zZMhrjogAKBbjnntJalxG0bXNg5WWkuxdUxLdpoJVhZqkx+jtNTdDRUblc\nKQ4PDnKor49v3HnnlIvoWmvuu+8+/uu/7mHOnEWxmm6zHqLD66vuPpBOw86d8ccvFPB6e9H56rUm\nbcuqKiI+3tTE+26/fVrumb179/Lpv/97llsWjTGijPY8hrduJT8wEH8ApWhpaiI5hqCTGRmhkM/H\ntq1WiuSKFSRXrIg99oH+fk676CI+OA2C2rGA53mcc845fOxjH5uWiObx+PKXv8zw8PD4G9YBrTWJ\nRIIPfehDzJkzZ1psqMbIyAg33HDDtAn7nudx6aWX8sEPfnDKnRvGQinF1772tbpHnVVDa43jOLz1\nrW/lggsumBYbxuLAgQN8/OMfn24zpgwNpF2XoaDTpTVZ5ZLLp8nliv2YXD7NSCGP60Y6dlpDLif1\nmaPk836RcsEF3Dp8DrWGTCbLyMiQ9DlcFyuXk/rnkZro5POVjmZxzo5ai92uGzo8Zj2vZFaiVuS1\npqSXqLX0H3O5ol2FgtgS1EgPiNgY7JvP58lkhtGu9APksrNIcEotcYEMxQ6s9peVpW73NK5b2l8L\nLiW4PK3B9UYZcV0/JboYnvG8uqR/cF2XkdHRkgjKdMYik1GRdO6adC6LFW1fYIQCBS+Np4ckHboG\n18swmsthWzlpBqXIRISVmtnteYxmswxlMsVc+JlM8XOnVDFte/lnEWTbyDXnC5DLWeTz2fASC4VR\ntK5vZOnxjNaaWbNmccMNN9Dd3R3+zlmWhdaapqamEqcsz/PGjeSNirlBH2J0dJTrr7++plHAga3v\nec97mDdvHp7n0dPTQzqdRinF0NAQmUwmFNEtywrHmI7j0NTUFK5zHCeMNLcsi/b2dpLJJK7r8slP\nfrLm0ctaa97whjfw2te+dkLbjieiZzKZkna3LAvP8/jHf/zHmtruui5r1qzhzW9+MyCZi3bvliBC\n27bp6OgIhV7HcVi6dGnY5lrrEiFaKUVLS0uFiK615vbbb695doRCocBLXvIS/vqv/zo8Z3CPZrNZ\ntm/fTt4fq2utS8RmEKG8vN0Duru7aW1tRSnF7bffXiJOHy2e53HuuefyiU98osTx40jJZrOho0nw\neVZKce+99/Lb3/627pH6BoPBYDBEeACYbKqfTfUwZCZhRHSDwTAluK7LmWe+gssuexPlEdNaw0kn\nwQtfWF0E37ED7r1X5gLjcBzo7pbShHFoLfNA8aU2M/T1fYJCYXpSK3uex+zubt733vcyZ/bsyg1G\nRuDXv4aBgVgvA89Osn7tNew/+yUVgqenYbHaw5nWBn/fsi20hvnzYcWKymMrxdatW7nxs589qus7\nGlzX49JLr+aFL/wDyseOGljg7mZ5fgNWtbFwb694X8S88XpkhEJPD3qMyYBEIoGqclN+SGvcal4d\ndUZrTXciwfsvvJDZzZXT8TqfZ/fzzzPc0xO7v2VZnNzSQktj9cnw/kOHSI+MxIvolkXbmWfS8hd/\nUblSKR7fuJH7Dx6c6OWc0My0SZGZFBk/09pmJjFTIvSBcLJ7uomLUJpuZkrbTClNTdyycSPfDJ24\nNK5yOLD/RrKJ5lArTXmj/GRkJ7Yu+x3dsQPuv7+0T7J/Pxw+HP7rATsbGmoe2d/QkOJLX7qFb33r\nm77pHurQIVQ6kilIKRgdhdZWKP8NjZtItyzpx/ki+h7L4rpIDe1akGho4NHOTt4b9Ra1LHjoIdi4\nsbhMa+jvrxTRczlp4wjug7+ksHM7WMU+0P79z3PdddfWzu5EgjlzdpJMfgIpuh5Q3l9VZDIWW7eW\n9sc8T/wCgjLvWsMwWT66dxtJVWyLQ/k8c2P6Skdr+7bt2/nQhz+ME/ndzBcU+UJERNeaxN4enAMH\nSoSbrB5gIzcyoltC24dsl4/8YB8Jq/jbd2hkhO4alslQStE3NMTHbrqJVPTz43kijkdxnJJ67yXL\nI9fsadjb6zCSjrRDfphZs3Izqk9xLBKIaVprhoeHw9+4TCZTItyV//YFUdDBPZdIJMKIXKVUKJrm\ncrm6/EZprUmn04z6TibDw8Ok02kABgcHw9fl2LYdRk0HTpWBmGhZFqlUCq01ruvWtY84UZE1EGSj\nbVgu6MZlCahHCvBCoUDG/wwPDg6yf//+0ElhcHAwtCtwxoiK6FbEady2bebMmRPeL83NzTIerqPz\nZqFQCLMTBNkHAiE9+jsfbe+oE0ZgW9C2QXsXCgXy+TxKqZLPQ605kuOW3zOjo6Ph++c4Do7jhHYb\nDAaDwTDFjO9NeAJiRHSDwTBlWJaD4zSidaWI7jgigFdz5E0mZYKs2hglWDfWGKbaegnCmN5JHqUU\nqWSSxrjJqny+OEMYcwEFpXCsBLbTiCqbB7E0JKwEjZZDbLr3oPETiVgRPVnnQfNEsO0Ett1I+Tyc\nBhwSNHoOVvmFg1xPMNEXM0HkWRZZxi5OmwLiJHRN6XTvdGApRdK2aYwR+V3PI0H1H3kLSCpF4xjv\n7ZBSVLlr0EDSsmiM+8AqRcK2j77GgsFgMBiOKWzb5t0f+AD9/f11P1cikQhT19YCy7K46qqrWLNm\nTc2OWY0VK1bUtG912WWXseCb35ySmrK1rIV71lln8V//dSOFKXBInDVrVk0F3TPPPJNPffrTYYRk\nPenq6qqZ7c3NzXzy058OxZp60tLSQnv7cZ/Zsa7Yth1GL69bt46BgQGUUoyMjJDNZkMhMZlMlqTs\nTqfTDA0NheuXLFkSpiiPRntnMpm63MOu67J161YGBgbwPI+NGzeGGY72798fXofWmtHRUYaHh1FK\nkc/nGR0dDYXnjo4OLr/88jDN+6pVq+jo6MDzvLHLoE0BgTNCNYKo6UBIT6fT4etsXIaHo0BrTV9f\nHzv97Gvr16/nrrvuolAo4Lou6XQ6FG1t22bBggUlInog1CqlaGxs5Morr2TWrFkopVi7di3Lli2r\nWQR3HCMjIxz0na8bGxvp6uoK0/cvWrSoJBo+EMmj90+w3nVdnn/++TDTQXDPBVHtDXEZ/6aJcueV\nZ555JszotGjRIhYuXBh+Jk44Z0yDwWAwGGYgRkQ3GAwGg8FgmAFkMpkwTaLruuEEYXTyJJgotW2b\nlpaWksiS6XZ4McwsymtIRuthltchPZGIpr0NJrmhKFaUp5c1TBylFKeffvp0m3HELFiwgAULFky3\nGZOmu7ub7u7u6TZj0rS1tXHhhRdOtxlHRFtb24wsHzEetm1z7rnnTrcZhglQ/vucTqcZGRlBKRVG\ncwdCYkNDA6lUqiRN++HDh8P1w8PDoejueV4Y6VsvBxatNblcjlwuF6Y1D+yNiuaBbYHgn81mGRoa\nCvstnucxMjISRifn83lc1y2JNp6plNfpDq6nXnZ7nkculwvbuK+vL6w/f/jw4bDf4zgOtm1XiOhB\nv7CpqYmBgYHwHgkiuevZ3tH68VFnsEBIDyiPNA/us/JI/+D/aFr0mXa/RCPqQcaAwbhvKpyzDAaD\nwWAwTA4johsMBoPBYDDMALLZLH19fWGKzd7eXqB0Qqi1tZWWlpZQTA/SGEbrGRoM0YnI6P1Tfo+c\niPdMNM1toVAII8ISiQSO44T1QY2IbjAYDIbpIlqiJPhND367omJs8BxNax04ggVCXeAkFqTLDl7X\nsw8QPYfjOKEom0wmSwT/fD4fRg4H/4P8VqdSKRKJRCiiT4XdhUKBnF8/LmhzqEzDXu5sZ1lWieDr\num7oRFAelV7rrCHRdgbpzzQ2NpJIJMIa4tH7pKGhoSS7RTQzRVRcnyqi93mhUGB0dDR8j8sdJoI2\nDO7tqIgefD6i90k975dy54hyp5Ro3zu4fwNyuVzooADFlPbR/QwGg8FgMMwcjIhuMBimDK3ja5IH\n6yZ6jPJx0ET2PS50gqoXr6umJNeMna58XGZAw411bbU4tqYyZfm4xz4WBrdKxV4b+Nc3xjXosT6s\npRvOiHvkeKF8oidu0ifYJjoZGrdvHGZS5sQhuB/i6nSeiML5ZCmPEDIYDAaDYSoJUpY3NDTgeR7b\nt29n7969FSKh1pquri5OOeWUcF/XdUsiiBcuXMjixYtDp8tAxB4ZGSmprV4rLMtizpw5dHd3o7Vm\n0aJFYX81m82GQrnWmv3797Nnzx601qRSKdrb28OsMI2NjSxZsiS81tbWVhzHoVAo0NLSUnO7Xddl\n48aNzJ49G601u3fvZvv27SilGBgYYNOmTeG2s2bN4tRTTw1tPfnkk7nyyitDIb2/v59f/vKXpNNp\nLMtiwYIFNDc3o7VmYGCgpnYrpVi4cGGYHWPFihVcfvnlocgbjWwOov8D8vk8v/jFL8I69cH9EGQL\nmIpyG9F07s8//zyPP/54mMFg69atoVMDUOJA4jgOl1xyCU1NTQCkUikuueQSTjrppPB+CsocBNvU\nkmw2y8DAAI7jcPjwYZ588smwraPifmDnySefDEib//jHP+bxxx8P+5kDAwOh00V3dzfnn38+lmXR\n0tJS01IkBoPBYDAYjgwjohsMhinDcTSppBdfE91WuG78ZLVS4LpFTS9OBxLvXxfLiheJtIZCwak4\nd7CuzmUkx0UXCuQOHiQXN2E/MkLC81BBXfQoSgEKK30Ya3BfRU10BVj2ACTSxMup4A0N4R06FHts\nt69PGn8acQoZEvnhivddAyo7TG50ECtO9lYKPTxMvlrduXweVUVUVIwjpM8AYcXzPAqjo+RjbPHy\neSgUsIh/1xXI/TTGoNxqbMRSKl6EtyxIpfCcROV6pdC2XRsvhxOMYDImSMW4c+dOtNYcPnw4jJZt\naGigoaGBRCJBR0dHOLESRKVXw3Ecurq6sG2bjo4OmpubAYk4aWtrm/FiYTTiKoiqgdJUjc3NzUd9\nHdGIl2Mh3Xk0LXuhUGBkZCSMcAoipoKo6vKJ92jUWnm9yWDiPajFaNt2yUT7sRKlHfeZcBynxPkk\n2k7B8omkc0+n0+Gkc/R8TU1NdREljgW01jz33HMMxNRE1zG/JhP9eMX9VCcSDitWrKipkLJnzx5f\nzCkuU1V+zCZ8PeXGK8Xy5cvp6uo6CktLOXToENu2bcPzxv/h9T/xFTZNlGXLljFr1qxJ2VeNwcFB\nnnvuOQqFyn5mhUk6vlsxoTYHumbNYvny5UdkZxxDQ0Ni+xSk3e3s6mL58uU1+T1yXZcNGzbE12Qu\nazf/lzD2OJXvT2WbN7e0sGLFChKJxBHZeqIT9HeCutaDg4NhH7GhoSEUa7XWOI5Da2trxW968Lqr\nqyusT29ZViiij1fX+0gJfgsD0XjevHmx9agDobqxsRGA5uZmTj755PC3OJFI0NnZWRKtCyJCRqO+\na4XWmv7+fp5//nkAtmzZwu9//3ssy6K3t5ff/va3YdsuXLiQ4eFhbNtGa83IyAiXXHJJ2AcbGhpi\n27ZtDA8P4zgODQ0NYR8zKgrXitbWVubOnQvAvHnzWLlyZex2hUKB3bt3h33nbDbLxo0bw5T60YwA\nlmXVPGq+mk1BNHxvby9PPPEEnucxPDzMunXrwnXlKfGTySSzZ8+mra0NkPvn8ssvp62tDc/zwtT1\nQZR+rR0CgmwDrusyNDTEzp07Q/E8m82GbZdIJDjvvPPC/bTW9PT08MQTT4T97cBWz/PIZrOkUils\n2zbfnwaDwWAwzBBOzBkeg8EwLaxYlGXtBSMQM8lX0A579zYRN1mjFOzfL3qfbcdPmBUKeTZt2gnE\nT2ZprVBqGUpVDri1huken4xs3syzb30rvWWGaMDSmjMPH6ahykSxAjrv/hTWdz9fsU6jaJvdCsvm\nx59Ya4b7+znY21spKCvFvkyGfGfnEVxRbVBas3T3g6xqPxwzuacYfGY9T/32PnDd2Gm+kXyezX19\nscdu8TyuzGRoo4reqxQqlYJqkzTT7BWeOXiQrXffzUDc5Jfn0XboEHOq7Kssi2RXF8yZEzvxqYD2\nCy+kdQxxwl37EkaXnoGitB2UBdnDGdi7bxJXY4DihJtlWfT19XH//fdTKBR46qmnwgiN6MRoVCCM\nCqJRgmXNzc1cfPHFNDc3s3r1apYtW4bWmtbWVs4555w6X9nRE504Gx4eDiOsMpkM2WwWxxFB7WjE\nS8/zyOfz4aTXsZIi37IsHMchk8mwY8eO0MkgmKxtbW0NJxhTqVQoIgdCcXnK2EBcCaKlXNeloaGB\n1tZWgnrhDQ0NM7ptAjujKWPjiEbqRUV0x3FiJ+ijk7f79u2jp6enZNLZcRyWLVt2wororuvymU99\nir5Nm+jyRRGAvGezsX8uQ7miiNLYCKecAuVN5bqVjo2DgzAwEO3/abTewU03fYLVq1fXxHbP87jr\nrrv4wQ8eoqVFBDIpJpEAACAASURBVG6FR3v+IA3eKMX+qSbtptiZmUPBK/7+NjRolp6iscpvt95e\n2LvX3xP2pNO881/+hVe96lU1+wzdf/8DvP/9N6F1qUjc0lLahVFKc/rcfhqdUiHBsx2yjZ0lHWzL\nqnxv9u3by9vffi1//Md/XBO7n3pqA9df/1FgMUrJybSGhd1p5nZmCNz4tNYMZBo5nGso6evZlmZO\nt0sy2nUuFGDPnpK+zaHRUeafey5fuPnmmkX0bdiwgY++850sTqexI+2WTqdJZ7ORuwVs/xFFWRYN\n7e3Y0X6/UpBKlbwPhzIZZl94ITffdltNxM6RkRE++IEPMKeri1QyWWynbBb27Stpt95cBzvTc0rs\n0RqWLIHW1shBtUfy8EHsXPFzMpzNku/o4Jb/+A+6u7uP2u4TnVqkpZ7K7CrR9OET2bba81RnUIpm\n7CkvgxNXDif6npS/Lt9mJvaZpqON4yjPwhXXftX2i2v3qaLa+1++TbV9455PUNYCd063ETE0T7cB\nBsMECNJsfISZGcZy4XQbYDDUihNzhsdgMEwLLU0e3R0uqkJE1wxlLAYGqkeZBAET1ea+tPYYHk7j\nuhmqxd46jhcrwGstE43TiTs8zPDmzRVfyhqwLQtv4cLqYq7nkdyzidToaGxa8sTIQmipPmno9vSQ\neeaZCjFVAVlAv+hFk7uYmqJpzhyiY2hP7NrR3p0MbdqELhRi3/VBYA+VKds1/H/23jxajuu+7/zc\nqt777fsCPAAkFgIQQVIUQVGKImohGUmUo7Fk6yQjxZYdSx7lWMczdiY5x5MzyXGc8ZIZjePI8XES\n20rkyIskW5IpWtLQEgmSEleQAIkdIPAetvfwGm/trbqq7vxRfW9X16t+2Lrfhvqc0+iH2vpXt253\n3bq/3+/7oxtwqssbSp6ryI01iGNZFC5cINlgfRc0XAdgJhKeFyNc2oH40BA0ypSTknLfAJVsR6gT\n3U1lGiUvRVwH/hp7qh6jyp5wHKeu1rWikcPXXx/TsixdH7FVdRlbibLVX6PS3zYqG+R6CAYdqAlT\n/4TiWphYvB6Uw9x1XZ215neiO45T115qeVhdVX/NT/++arv10iZhhNWQDTv/4N/+7YPHs227ri8F\nJfRvR2S5zP9y//08MDam7y9zVopff/HDHJvpR4iqo3QzfPaz3vjL32SlUm3Mpzh8GF57ze/Lc3jj\njV+tk6htBsViife//xfZtWs/UoIhHe6de4aB0jnfh0sulXr5kwuPsOB4Tl0pYWQYPvdZm3jM9zss\nJTz1FHzrW95/heDP33oLq5FCzk1SKpVZWHiATObX9DLDgKEh6OurtW/McPm1f/AyI5159E1aSuxk\nG1Nb3lHnLE0mIZutLTIM+Ku/+qumZlBWKhauu52tW/81punNVbsu/M/vvcCjb5/SwwhXwmuXRzk0\nNVA3jk+nJI+8p0R3l1t7gCgU4GtfA8vyjBeCH731Fn/tq/nbHNsrbCsU+LfZLGnfw8nFuTkuTk7W\nDYFSLB2PmckkI1u2kOzoqF0g0/SCG9XxhODHk5N8vVBomt1SSno6OvjXv/qrnqKA+uzJSfibv/F9\nGSU/yN3HH134INI3zpMSfuZnYM+e2qbCsel5/WmS0xO6w5y+coX/++DBdTXGWOv4FVKUwgzUgsb8\nTrywWt4rPbbxj7H8n+kfx4KXhax+y9X4VN1Lg/do/ziwFefhV/dR5+BX7onFYvpz/bXl1bZ+daTg\nWH6l2j3sOgdrioeNh1bbietvR78qkHrB0nML9vswJYZWEhwrNgoACGtbdR3CjnMbckf1FRERceOo\nYI9/s6pWRETcBkRO9IiIiBUmTChbNF6ltrju57pGGzY+wFoJ/G0ku+39cQ0jb+Ukltl3TTRNdRJ0\nibPX7wBrtGuD9WvivFrISp3fsn024oaQUrJp0yadsWXbNo8++ii2bfP6668zOTmJEILx8XEmJiaw\nLIurV6/qiRe/xHmxWMSyLCzL0nUP4/E4k5OTxONxtmzZQkdHB3657tWePLsW8/PzvPrqq1iWxWuv\nvcaVK1cQQnDhwgVdIzQej9dNIIehgg3uvPNOhoaGkFLS2dnJ0NAQhmGQTqf15PQ999zD4ODgmp/Y\nmpiY4OLFi1y5coWnnnqKUqmElJJisajlLMPkIP3X3D8R6c/AVpPde/bs4b3vfS+qRqm/9upaRE1m\nq5qelmXx8ssvMzc3x+zsLDNVufF0Ok1fXx+qHEAqlULV0VT1M13XxbIsTNNk165d9Pb2IqXk6NGj\nPPnkk2QyGcbGxnSd2U2bNrWkVut6wRCCTDxORzKp79sOSeKxLGasQzudYzFIpbxYrrr9jdptX5FI\neIpBtWU2htH8x1iv/6dJpTqqTnSbbClNh6zPDp6XKRLxLHEjq88nkXRpb6uQMAO/pamUd7JCIIUg\n1YLAPM/pkMAwOnzLPJ+sP5vcNBzakmk6UiqEEEBiJdMsZtq9KLgqYU70ZHKpHPMtWo5hxInFOuqc\n6KnELO3ptHbduhLSqQzJZEddv0ilXNoyJh0Z3+99MJtbCNLxOKIFztyYELSZJlmfE31WCNL4WxfS\neI50P6YQtBkGaX9/UOn/apkQpGOxpt+fTdMkm8nQkcl4nVcIyGS8L5rPiZ6Op4jF2pG+PHopvS6t\ndgUQjkVbKkU6ldRnnr1GiZmIa5NIJBgcHNQS3Z///Od1EEswEDCVSmlJdCklTz31FL/3e7+n5bgf\nffRRPvKRj2h1GnXPahWGYdDX18fQ0BBA3RjkxRdf5NixY4D32/X888/zzDPPIKVk27ZtfOITnyCZ\nTGpbH3jgAQzDwHEcLl68SKlUwnEcLl9uvuKVYRhs2bKFffv24bquVt0xDIOFhQWGh4e1g3x0dJR9\n+/bpdkwkElpNCmB6epozZ86Qz+dJJBLs2rVLO+Fb8d2oVCoUi0U9plefYds2uVxOj4tt22ZycrIu\nIGFsbEwHMqhgAeW8VuWfWsng4CB79uwBoL+/X5c7WVhYYO/evVrO3bZtZmZmtK2xWIz77ruPtrY2\nPXaLxWKUy2UdwBCr/oa2orZ7LBYjk8lgGAZdXV3s3LlzSSCruhbtPvkO13U5ffo0P/rRjwCvzVUt\nd9d1SSaTWJaFEEI/p21w/oa16QAcBr612kZERFyD+er7z7A2M9E/DTyy2kZERDSDyIkeERERERER\nEbEGSKfTWnYbYGRkRNcJ7OrqAtAOQf8EkaqvqBzq/uwYVbNZ1T10HIdyuYxlWVq+fK2jJr9mZmYo\nlUpMTExox/nZs2cZHx8H0G0AhGak+x3thUKB+fl5pJT09fXhOA6madLZ2anrEraiZmWzUdd4ZmaG\nXC7HhQsXKJVKuK5LoVDAcZy67DXHcfQ1DyoZqD4TrC3vui69vb3aKb/WAy4U/qz6SqVCLpdjenqa\nK1euMDU1BXg1RF3XxTRNyuWydkIkk0ndTuo7Y5pmnfx7Pp/n8uXLtLe3093drettRpmXeN419cJ7\n936fQjbV/9R2RdTPAkmWxtG1Ahn2CpGrkYjqy8OtLlvNMDJX1ldLMnyxh/XvgYVVRMB8FcggAsua\nTV1XodbujbYLLkOI6smGGBk8gZVAyiX2h52TDJ54dd/6xpBeqaVWdH7p6+WqX/g/S3jn4Vb7uT8o\nwP9aekx8W0bcCoZhkEgkdC3x0dHR69pP3fvGx8e1Ez2Xy+nxYDK5nE5Vc1ABeWGftbCwoANDAcbH\nxzlx4oR2OF69elUHtKkgNuVEX1xcpFAo6OWtsDuZTOpa7m1tbbS1telxdX9/v962r6+P3t7euixp\n5exV48xSqaTHZGp83qoxgj/DXI1T1PhZBR5AbUzjd6KnUqm6QAf/mLCVwRaKRCKhnfWVSoXh4WGk\nlGSzWQYHB+uc6EpZCzwndnt7uw56VGN8v7JUK8etqn0MwyAej5PJZLTz3J/tr8ouKVSg69zcnD6O\nagfXdXU/uY3UjaaBl1fbiBC2rLYBERHXgZK3+greI8laYz+REz1igxA50SMiIiIiIiIiVpnlJnj8\n8pDqb7/Uu38b/9/+CTXDMOrkAjfipExQUnG5c1RZ137pzrDXRmO5mo1+ydJGWerrrV3C6myGSYWq\nZep74j9P/3p/v/LvE/Z5tyuuMChkelloG6oukeQTSVLtCTp8iWBtWUk64ZAJiCSY8/PEc3N1yzpl\nF93d3XX+0BBxhVtGSEm7laO7fBEkCNchMTcN+Zl6qfOizebca+QdT7VBSslAMo4x2QMxnzdaSpib\ng0ql5tBtQTYcSAZjOXZkjtYWGYKu9hEyHR3at2kaYJjBVH9J2RIcP17v9sxkoNtXJl0IuHIFNm1q\nrt3txiLbk+eImdUsWhe6jUXPeVzbjHSq3h6ApLCQR96gEl+sGV8qEZ+YqLU5eIa3Qh1CSnCcOudx\noreX9q4ubb8Eko5DwnHqjDficYyRkbri4tIwKQ1tRfqcVmXbRNr5pprtSoElY5TduI5aETJGwjTr\nMtE7xRy73CN1cu5I6Cp1k8wna5u6NqVYFjszqM+xsOjgirVZDmmjEhzzrNV7dth9eLkx22qMP/wO\nWL8ke5jUvH9cHSblrf4fdKy2mrDxj3950DY/K/28EJTs97dt2PNNsCTP9draqhIA6j3ovPdv06iv\n+LcJBkKste9uRERERETE7UzkRI+IiIiIiIiIWAM0qq03MDBAMpnUGTKDg4NUKhWdSQ3Uybnn8/m6\nrHPwMk/m5+dxHIfBwUG6u7u1VOZaR0pJPB5nYGAAy7LYsWNH1aEmdBawlJLZ2VndBvPz87pN1ESW\nyjQ3DIOOjg4t214ul5mamiIej5NIJIjH48Riseuur76aSCmJxWI6i2d0dFSrDOTzeV1r1C8LqSZM\n/fVS1Xk7jsPVq1f18VVmkj+Tar0EYKisNSmlVl+wbVtL1AshKJVKujTAzMyMzrhSbQpe+9i2rbON\n+vr6kFJy5MgRzp49S29vL5s2bSKRSGCa5rppn1bhxFKc3/JuunbtR6WwlsqCofkkydnadqODDpt7\nF2jP+PJchUAe+zE8+5x2xkkpMUbeT/LBD9TleR/1+YubhUCy5+pz7J+87NnuOMSPHIIrk3UO0N5C\niX905j8j1W+ElJiD/ZhbPwmxgOPw1CmYn6/tX80GbSYGksc6f8S/3DqnnaC2EefV3Z9jfPQhRLXl\nDAGp9rgn263bXDJ5Kc5v/CdwfPkrPT2wbVutPDfAiRPw6U831/Y9ydP8cu9/I2NWpyWkJJ3cjbDv\nQHnGBbB5k0tHe32uv5jK4f7yv2D+9BF9PkJKuvDKCmgqFXjsseYaDp4DvVisXVsp6X/ve+l997vr\ns7IXFhDBuuaGgTky4knPeztjGwnOj74bx4x7ZyNg6vUf4zz79aaaXXFNrlrtYHVqMxNOnoF0ui54\n4T7zEHdZASVbKUlffIRYcrOvXIPB2Y69zPc+oK/PRPwUVvxHTbX7dkPdb9W9upFTVErJxMQEExMT\nev2RI0eYnZ3V93vTNBkYGEAIQTqdXnHnnBpDAJw4cYIf/ehH2obJyUm6urpwXZeRkRHuvvtunQku\nhNCy7ao9ksmkVtlpBalUSsuDb9q0STv5K5UK+XwtoKWzs5Ph4WFtx+XLl3n22We1bHg+n6dYLOrM\n5JGREe68805c16Wzs7PpdqtsZ9Vn1Pi/XC4zMzOj7VKlbvwO3Uqloq9PLBZj69atZDIZhBC6/JPa\nthX4gxVTqZQum5PNZtm7d6/OPFe2KoQQ7Nq1S4/XlHqD6iumaepxbCtUAFSbm6ZJOp1mZGQk1Inu\nOA4vvPCCzjyvVCqcP3++7hns3nvv5b777kNKyZYtW0Id7RERERERERGrR+REj4iIWFdIuXZqmEdE\nrHtuc2fPWsKfVaFQMtz++tPXM4GlHIZ+R+D8/Dzf/va3mZubY8uWLfT39+sJqvUwSZNKpbjjjjtw\nXZe2tjZd6/rSpUtMTk7iOA7nzp3Tk81nz57V8uPKcawmBpXMqJJWLBaL5HI5UqmUroutpLnXA0pC\nUkrJ7t27tZN4YWFBy+DncjmUpD+gHcuqv6XTaVKpFKVSiePHj+vl7e3tCCFYWFhYom6w1vuNqgOq\nJOwLhQLlcrlOcj2fz+uggUKhoIMN/JlOfrnOs2fP6pILzz77LAcPHmR0dJTdu3drKfjbXc5dCoFj\nJnHiGX2LcRwwY/XZ47EYxAyIGb7fNAG4FbAKdU70OBUSSRB+ufIWlVuOS4ukW65KW7tgW/VZzYBZ\nKdNWmatllUsJVhKsMjgBJ3qlsiL32rSw6DMXah9rxEnHba+WfHWZgUAYKgtdOdE9GfjFxXonejIJ\nhUJ9OzdbPVkAceHQaRTI+pzoiArai1wlFhMkEgHB/JgLszPIqSverlSd5/5i7uB1wJVASsxkEjOY\n9R728GIYkE5XAxqqmAmcdDuu6S0TAtxkuvkPPkLgYiAxfNLsS6Xvk8ImyUJgZwluCRx/vzZwjRh2\nLK2vjxNLItf4PWKtoxxxKiDQNM06p7j/Hnz8+HGeeuopwLtnvfbaa0xPT+v/m6bJ5s2bV0SaO+w8\nCoUClUoFwzB49dVXeeKJJ/T6rq4uBgYGcF2XO+64g3e/+936Pnv16lX+9m//Vktc9/f3k06ncRyn\nTh67WaiSNl1dXUgp6ezsZPv27QB1Dln/9opCocDTTz9NwRcw45d637ZtG/v27cNxnDpZ+Gah7FNO\n9GKxiJKVn5qa0s5nVe7H7xgvlUr6/7FYjL179+qAQbVNK4MolaqPaZpks1mGhjwlG8dxtENdqWr1\n9vYukZ5XuK7L7OysHr+bpkkymdQBp80uAaBk3NXzVmdnZ+jYeHFxkd///d/nmWee0YpHhUKB7u5u\nwGvf973vfbz//e8HPCf7eii3FRERERERcTsROdEjIiJWjkoFSiUImWAWBQtzthQ6zygExBcNujNZ\nyqYILTlZqVgU8gYVu9HkgEEq1XjStQXP4TeEoPrwG/LgZQixtE6iHykxUilMw1jSNBIgHsde5qHR\nte3QKp6rW93Th22HT0ILgXCcZW9kMaBR5T9fHlY4QniTm9VahEtY5YdbYRiYySSxMGlmQFgWYjln\nTizWWA9XSubtJFYpHdpGUkqseYNyrgLUf6mEgLk5J/LPN4HrnazyO0TV35VKBdM0cRxHZ9yqCSqV\nca0yvNcDahJYOcBTqZSe7Ozo6NATbcqJXiwW9aSgWmaaJh0dHdpJqvBnwKhjK6fpWkcFBySTSVzX\npb29XTvRDcPQE4lqUs+yLD2h6M88UuevsruUkzwej+t2UbUaWzFx3QqU/arf+DN+VBafP1srk8nU\n1Yv317L0Z6UvLi4C6Ox01f6pVEorRtzO3PjZ397tFRHCTX6HVn3c2oTvfvAIrTqfpv5Mibq34OKI\nmyRMErzR/cUvjw6Ejl9W+94ULJHiL6kTtp0irMzKSqHGAo1s9RNW4sW/bqVVapaTlve/B/8OHiMo\nU78a+Nsu6MwPXpfr+b40k+v5DH8JA39/9tvuD9y83uNGRERERESsEO3AKJAFZoBxYH1knDSR9TEL\nFhERsTE4cgS++91QR3D63Hk2/d2zYIdli0gGst38l3/yC7ixROj6K4tJvnRgJ9OL4Q5P04QHH0yQ\nySxd57rw/POrm+GeSSa5a2yMgRDnhJCSZLHY0GlrmCYDH/4wPWNj9TUk8TLCSm+8wflnnmmYCWVY\nFp1Shk5+zRB0ka4C5883zCLqunyZXctMSpSBfsIn8hJC0BaLYQrBkiNICfE44p3vhC1blu4sBLz0\n0nWeQGtIdXRw58MPM5AMCRNwHOIvv4wxORm+s2libNkCu3aF9gsH+L1XH+PlK1saToLOf6fEAqdD\n1gjy+Qk+8YnIi34jKMfuzWSxVioVjhw5wsKClzHmOA6O4xCPx3WmeaVSoa2tjVQqxZYtWxgdHdVZ\nHeuBeDyus2K6u7t1O6lzVU5hNSFVKBR0plKqGghTLpc5ffo0ruty5MgRzp8/rye1LMsik8lwxx13\n0NHRgWEYWs5zrdPX10dnZydSSvbs2QOggwdUu6gMa5WVDfUTq88++yzHjx8nn89z9OhRLXc+OjpK\nLBZj8+bNbN68WTvR18PkXiKRYHh4GPDaY2hoSGf1qeCSoOSpP+hAnWM8Hqe3t5dCocBv/dZvaRna\nfD6vywns2LGDbDargzBuZ9RXJhj3JwW41ThIKb3xCYYBdZnoS7Nh9f6tMznwQXLpKwzX9V5CeO8N\nt5e+l2htVrpUn+P9veQaqG0k1LVoA5Un//6ilaaHXXf//5XJ1/jZ0WfvOPVRs7JF6hBSetdefZbq\nB2rdUsuWLhO17QUSKbzvCuB1lxb81kq84y75nOBnNerTDZar/wkRfsYRt8b1jkfW4v05WH876AR1\nXVcHAAZZzoG6Utzs57ZaBv16CQtU8Dttw67JWiFYS/xa1yK47VoleF4RERERERFriPuATwAPAg8A\nHYH1ZeDHwB8CX+U2GfZHTvSIiIiVY34eJidDZ+HMibNk3ngx3IkuJZmBAd7+mSsNs4IvzGUZOJLC\njreFzrGZJmzbBkGFRfDm2t588wbPpcnETJOObJausAw/Kb2ai44TOsspDIPk8DDJ7dtDs7WtiQkK\ns7NLHOyKFJAmPIMkHrJ8RZHSUy9YCMpJAkKQKJdpX+bBMwW4hJ+DCcRUNkTYzqYJvb0wMhJ+8Exm\nVSMvYvE4bQMDtFclhOuwbdxlHDlSCIxsFjo7Q7+PEji9OMQrl0cxQk9RMjNzkfm5XMg6AyGKUSb6\nCiKlpFgs6lqNaiJSZRErZ6E/kzvpC75YixOuQVTGNdAwe94/CaXqgiupc/Bqcl69ehXHcXS2uV+q\n2zAMnVGssvbXA7FYLDQ7XLWHbdvYtq0DNfyZ+cpZ3NHRQTKZ1HKrUJOpVMoFKhN9vbSLP4Mc0H3e\nn1nmOA6WZXnqGtWa6VDvRE8kEgwODrK4uIjruszPz+usfZWJnkqlSKfT+jt3O2MYkq52m8Fuy7sP\nCKgslNhz8nsUzl3R23V3uJjzJdx4vRNdFIsI32BNSEmnMc+20nG9TOKSdhZbcwIjI96AUUpv3DU+\nDnNz9ff7eBzuvbf2fylx+wawdrzNGzv4mFgc4q3T2/VA5A3zRR5ssskSwUz3Nk7v+rD+3ruGSce2\nXnaPFHVNdIFLsrcDYilq6cMSZ84kl7tIxVYZcdDenqanpxvTrJ13o7i8W2J+3itwr9pNSs8p7VNQ\nElKS6J8l2zddv29+HvNDjyLeeX+1HUAgsNNtCOF9D4WQOBcvQKKRLtEt0NMDd91Vb/vevXXBl1JK\nTr5R5uyFQCCsIXDnOuqksGLCZueZvyUuqo5EAW2nj2BUyk0125yZJvO9v6atzZsPk0BsYQbefM07\nBxUx0dcHjz4aCGiQcOed0N2tx49CQldxkmR+Rm9WmD1HzGmy/v9tjhrPKUWUUqmk1x07doxnn322\nLrv14YcfBrw+uM1XFmilkVJy/PhxZmZmMAyDfD5Pd3e3tvW9730vjz32GADd3d2cPXtWj/PUuFaN\n1YaGhujo6MC27Tp1mVbZ7c8sD46Vc7kcExMTOujw6NGjXL16VasgdXR08OCDD5JMJkmn03VKQa0u\n+2JZFrOzs4AnM5/L5bSUuW3bnD17VttgmiYPPvigDgBMJpN1zwgr7egtl8tcuXJF9+NkMqnHuGps\nrv6vShf5bfSP/VZrPBZ04LuuSz6f1+NHIQRbt25lYGBAb9/T01PXxyLHekRERETEKvLTwL9cZn0S\neG/19Rng48D8Cti1qkRO9IiIiJVjmQwjLxvJrM9IUqgJNQxfekZwG1FLwGm0SWM19PXh8FvO0XWN\njKlVl7i8VZbJTFvmktdtd1OEZhQ1tmelkS3s1EJIDEEDJ7qoLl/ThQA2PGETW/7s4qC8YXB5cL/1\nTKMsmqAUp8p4CssA8cstbrT2CK4LSosGs8D89SeDtVfXO8F2uZ6JShWI4G8fqNXxDPvO3a6YhmSo\nt8KWoWpdcQFubJqhV34H55VXAe+ebAKJr9fvKwHe9z5E1aGiGIzlGCi+oP/vAJ3uDE1HCNi+3XOQ\nu65XTubYMbhypf6+n816zlIVpCElblcfhXe8xyv+7uPlnOSvXqkOgZGcNTPsb/Y9Uggmh+7hlQc+\nq2/9hiHZf9cimwcX0PdkKTFiffW3aAOsqyUuXjxJWfs8JcPDgwwPdxKP14ICWuJEv3LFC1TQHy1h\ndhYuXqzbLLVpE6nhofoBXToNn/sFz5mtLReUkh3IaoCkYUjsA8/BX/9F820fGYHHH6+VxpHSc6rv\n2OE7HclLb5p886QZTK7HsWtjWAl0uTm+OPuv6JDzqIvUMzeHsWtXU82OTV6g84//X7r9wVe2jfAH\nrLoufPKT8PM/v7QW1vy8p45VPSHDcRg8dAhytcBK68oUcbvYVLtvd/yS7YVCgZmZGX3fOXPmDAcP\nHgS8Prdnzx7e//7363v55s2bV/X+dPbsWS5cuIBhGBSLRV1aR0rJ/v37+dmf/VkMwyCXy3Hw4EEd\n0KaC/pQTva+vj76+Pq2u1Gr8Dtsgc3Nzdco9b731FgsLC5TLZaSUtLe3s3fvXjo7O4nH47S1tWkH\neqsdpLZts7i4iJSSfD7P3NycVt2xLItTp07ptk0mk3z0ox+ls7MT8AIzg8GqK+nQtSyLq1evokpO\n9ff362ug+oEK5lTKUypQFKCtrY1YLKbHsKtF8PmjVCqRz+f1uHF0dJR77rlHb6fa379/RERERETE\nKlIBXgCeBs4BF/Fy1AaBR/Ac7THgg8CfAh9dHTNXjsiJHhERERERERGxznAch0KhAKBrW6vJ1N7e\nXjKZDLZtUyx6k9hKklrJlMPGqrsXPAc1Aaik7IUQlMtlrl69im3bXLlyhampKcDLNO7u7qa7u5st\nW7bQ3d0NoDPY1yP+9gjWd4/H40gpKZfLut8cP36cAwcO1GVmx+NxHnjgATo7O7nrrrtIp9N6QnA9\n95lg26iMo85BfwAAIABJREFUK9UuarnqQwsLCzz99NMUi0UuXbpEsVhECMHAwAD9/f1s3bqVnTt3\n0tbWhmEYt72cu0AFpKgFnjNTSBfh2rVtwHsMD+6rMmEVUi4JzWpZDp8K9vS//MvDtvMvMwwQgUl7\n4fP7CknLiuQIA4QvC15IhCEwDJ8BQqgLVL9rzcfe+PCt/MqHXPMlnw/hEZGGEXDyCjB8JSeM6jat\nsr9R3/CZgzCQgesuqxL1Sq2hGsqFkLJeOaoFjhT1Pbvm56g+HXREhQRFL1WzihxArSQY8Bd2X1ZB\nXyvhtL0RwuS5g7XEr2d82upzutY4J1iHvtH2waDGVo6hlguevBVWuqZ7o3rtjdpuLT7PhNnkl6O/\nHml6tU9ERERERMQK8wfAbwCNpN/+BPgS8Hd4WemPA/cDr6yEcatF5ESPiIiIiIiIiFhnSCl1ZmxQ\ngjqdTtPZ2UmxWNRyn6Zp0tbWpqW519JEU7MIOkehVmvTX/+6Uqlo+Xs1Capk3Ds6OnR98UbZR+uF\nRpOKpmniuq7OlgKYmZlhYmICwzB0ewEMDQ3R29tLT0/Pum8PRbA9guelrr1yhi8uLjI+Pk65XGZx\ncVFnPPmDL7q7u2lra8NfdiAiYk1wU/PvG+/+sNa47lvwur1Xr1e71zbqnn3hwgXeeustfT+bmpqq\nc5p3dXWxb98+7fwcGhpaNZsBUqkU2WwWIQTbt28nnU5r2zZt2qS3c113SWmVrq4uAF1eptXKL47j\nYNs2UkpyuRwzMzN6uQqiAxgfH+fQoUP6mkxNTemyLkrOfWBggM7OTl12R2Wit0LO3e/UT6VS9FQV\nOtra2iiXy7pNi8Ui58+fp1LxSkwkk0k6Ozt1JnQw8HIl8CtFVSoV8vm8blc1VgdP/aenp0fLtavA\nTliquKWWBxWEmm23yugHr48IIbRkfqFQQAihJf57enp0wMjY2Bg7d+7Ux0mn03p86Q9Y3YjPaxER\nERERa55z17HN88AfA79Y/f/fJ3KiR0RERERERERErAXUJFC5XOby5cvakV4oFHS9w3w+j2maWJZV\nV/86lUqRSCS0zOFGnZiRUmoJcsuy9ATcwsKCzkS3bVtPwvX09HDnnXfquuCmaW7o9jEMA9u2tfSo\nEIL5+XndN1St0kQiwfDwMH19fXoS+3YgOGlZKBR4/fXXqVQqLC4uaif54OAge/fuZWhoiGQyWad+\nEBGxZlBa4TdElPnWaqLkwogbxS8N/dprr/HUU09hGAZSSo4cOaLHe67rsnXrVn76p3+6zvm4mvem\nzs5O7cT9wAc+oBVgpJRs3bpV2+Y4jg5Wk1KSyWTYvn27lvFOp9PEYjHtrGw2UkoqlYoOMjx27BjP\nPfcchmFQKBS4dOmSHiNMTk5y4sQJ7aRNJpNa8cl1XUZHR9mzZ492Zsfjca0c1Qonuiq/YxgGXV1d\n2ikupWT37t36+WFxcZFLly7pZ4ZEIsHY2BgdHR36WCvdV/zBBcVikVwup4M9x8fHtcPfMAz6+/vr\nap7fc889OuhROaj9ykKtdKKrQGYVAKA+K5/P8+STT+oSBmrdnXfeCXjPZA899BCPPvqotk0pQfm/\nsxCNKSMiIiIi1jQHfX93NNxqg9BUJ7qUEsuyKJfLulZNdNOPWE+EZfZFrCCqjnOj341rPvxIJOHX\nTSKqr/C91gSNzk8tD2ub6619bZqh211zzzXRNA3O0Sezqmpfhu5dlYINHFGvc0P3lP6NWNIQWoNz\n9Vju0yV47WIY4e0iBK5UPX/pkVy9aPlPESKsGULaK6JpqGyNYrHIyZMndY1DqE20zM7OLlkupaSt\nrU1Lcm904vE4hmFQLpeZnp7WTvSJiQls28ayLFKpFFJKRkdHeeihh0gmk7qW4kbEX+PccRxeeukl\nJiYmEEIwNTVFKpUinU6ze/duDMMgkUiwZ88eent7N2ybKFTb+DOAFLOzszzxxBNayUC1xY4dO3jk\nkUfIZDJ1/SZ69qH+xiBrdxrplzWl8d1CS7ovJzfdqt8xNa6qvqRapkyv3v9F4PP1cKyBma3+2V3y\n+dK/sKbX7gaNqTazWKLaXX+FWtbc+lVrKOFfqW1sNFr3nbAEqaTRV/M2V9dYUp/b9Qzzlw6pWnAi\n1zLEr+8f8pxRd72qCL2/qO668ccZq4nrujiOUyeL7kcIobO21wL+rFp/nfFr1a1W+6j7cyOZ72ba\n6cdfw9xxnDrnvfq/P4vab6//5T/2SlyT5TKYlaNd2aXadjXrhy9HMOjgZoIQVuN74P+OKnuD/cM/\ntvarQCnWyvc3IiJiQ/D3gHfhDdROAn8DWDd4jBTwD4GteNW1fgi81DQLI9Yjg76/T6+aFStEU2fE\nFhcX+f73v08mk+HOO+9k586dOso0ImI9sLi4yJtvvsmlS5c4evToapuzsZASzp+HauTwEqamvEkb\n0wxfXyjAt78NDSbyM6UEDx37EbPlRKjT0IgZvP3snaTbl/4mVRyLHy2cxVMfWSUcB/L58PMTAu6+\nu3Hbgdc+J06ErhJ9Ixif/edeKc4QJFAmpJ6hgPLcVWTu/PWcQWswDOTfew/ynnvCJ9qPHCHe2em1\n35KVArmwgHnkSOiEoZXq5tX7/hFOuif8s00T0bsXjD7CZmMnxeEbO5cmIwsF5Btv4MbjS60TArF9\nO2LPntDvQ5k43770Tk5d3k64E13y+tkyc7PjDWJaJKZp0Nc3RFglTNu+DEzc+ElFXJOw2nr+5cEJ\n1Ua1BDfqBHew7uS1WK6e6EbG3w/CJCPDJDE3Mtc6x0a1K/3tczu003WRz8Of/Al873s6+E+Uy2RS\nKeTevd42UiKSSYzhYYR/3CMl7N0LIyP1QYOOA667tPZ0Cygku1hM9yNdietIJt72SeZ7Fmt+RSDb\nZrD73iSJpC5mTSwWpz1WqtpV87jfsTnOu96VQFTLSC83lLtZBLB5yOLv7y9oe4Rj03nuDTh2SbeV\n5Qj+zQ8f5sJCe81CCZ1dWf7iL9J1TdqVG2fT6S8jZNVZIQTMvoTgg02zWwJX976HV9/3s6TiKW3P\n5rvaGNqW1ts5juTLfz7H1/5wof4ARgK+moF4zfnT2Sn45X8O6bR3LMOAiYnwYeItU6nA3BxUVSiQ\nEteqIM0Y/tDOBx8SjGxaunsiUQtckEBiTpL5jTzkF2v9u1hsviN961b41KegmqmqbK8L1pUSRkch\nmfRFV0ikhD//Xjcnj0tEdbEhJDuGHqIrW5uLvWC+RWGVx8kbBZVt3ugeHTaeWwv3I78NQWey3/bg\neQVrvfudvCtxXspWf3Dd9YyJwpYHndVh17JZXO8xg225XLuGjXlagb9tg9c7OM4K/j+sjf3t3Mox\nmv/zg/097ByWCwIJC2K4ncbhERERLcMA/gvwGcCuvlJ4zu/HgJnrPM4I8D1gL1AEEoAJ/D/ArzTX\n5Ih1QgdevwKYA767irasCE11oqdSKe69917a2tro6OjY8FkrERuPVCrF9u3bGRkZ4eDBg9feIeLG\nOHcOZhrcoy1reSd6sQhf+1rDQ2ek5L2uxGKpS08CRizG9jMfItHRvmTfkuvSv3BmdTOLlRM9LAo8\nHof77oNGcrqOAydPwoULS9dJifHQo8R+6nN1E3p1m+A50YMIAdbZk8j//O+u+zSajhDI970fHv/I\nElevlGC88AIxw/Am18O4dInY0aOh17aQ6eXFd/4SC707lu5X3dwwwx+6hZBcFt+6wZNpMvk88uRJ\nQueFk0nMn/95jH37QnetlEy+8l/fxXfe2NTQmSo5hZSThNW1FELS37+VgYEtoe1TKJyPHvhbhGVZ\nWJZFsVikUqno+nl+GXL/5I3KRPLXD9yI+CfL/KoyxWKRq1evIoRgcXFR14VUbSalJJVKaan7jdxv\nhfDqNJZKJRYWFsjlcuRyOYTw6jVCrd6kaZrE43Fisdiqy8CuFP7JysXFRebm5hBCcPHiRZ1NlM1m\n9fesvb2dTCZDMpm8LdrnuqlU4K23vODIKkJKYqYJ3d217TIZGBurOSDBu1f390M2W3/MctkbJ/pp\nUZs7ZhI7lsJ1wTFgtucOpn2+RSRY7S7uSBl8cZnCdUnYdvWWWQ1SQdKRNRkcRDvR/X7LpiGgLSMZ\n7ndq4x3bhmMzcPmyNt51DF55BU7kEhjV83Fd2HUX/OYHUnWXQrx2FvPoSe84AELQW7lEc1O8BeWu\nIXL7HiaRaAM883vvwstxqeJWXI7/+Tm+e3yC+jFJHG86ozZ27u8X/JOfh/b2mhM9n2/REN91vf6u\nkBKkWx3Lqz4AAwPQ1rZ093Ta99gjQOQkprS9NlcdrhXe/7Y2uP9+6Ourd54H525iMc9Af/AekjMX\nYhw8LnQfMk2IdWQY9D1iTYkCdlQ58JaYn5/n+eefp7u7G3+ZGiklx48fZ2pqSt97FhcXdSarlJKL\nFy/ywx/+8JqfUSqVyOfzTbfdsiyee+45ent7kVJy6NAh5ufnEULo8Zbi/PnznD17FoCrV6/y5ptv\n6ntuKpXS+/mlvCuVCufPNz/I27Ztjh49qtv85MmTuvZ8uVwml8sB3nhhZmZG17oGL5NYjSOllCST\nSX784x9rmXT/9Wu27a7rcvr0aX74wx9eczxSLBY5ceKEliFPJBIcOHCATCYTur0/OPfkyZMMDg6G\nbnezSCk5ffo0Tz/9NAC5XI4TJ06gVE4nJye1QqRhGMzMzOh5ZsMwcBynTt69p6dHl9eB2nP8yZMn\nGRsba6rtFy9e5JlnntFOe5VxXiwWOXXqlFbCklKSy+XqxtqHDh2q+x6o55bg9XvjjTe0nH1ERETE\nTfC/4jk6/6j6dwH4BeD38epZf+w6jiGA/wHsBj4NfBXPgfoHwP8GvFk9fsTGx8TLPv8A8GvANrzA\njF8GrqyiXStCU59s4vE4o6Ojuv5ORMR6Ix6P09fXB3Bb1f9cMdQMYqN117P/cqulE+om1tNYQjRw\nJIvrt6FViGVsuBG7gtuqiTFheBqXYbs0OtSya1cOUbV/SSa9kMu3zXW0m9cfQiTPRU29tXGPWf22\nqZM9vRF8k+eN55UbH9mbT1GR8WFO9psxKuJ6yOVyXL58mcXFRebn56lUKsRiMXp7e0mnvcw9lTGb\nTqfp7+/XkoGmL0hpIzn9wjJlFhYWkFIyPj6uJxUty+Ly5ctIKent7dWTuyMjI2zevFlLnW9k5ufn\neeutt5ibm+PAgQOcPn0aIYR2mKfTad7znveQSCQwDIP29va6Sb6NjAockFJy8OBB/uzP/gzDMJie\nnqZcLmMYBrt372ZgYAApJXfffXddfcuIKmqs5w8KDPNghumcr7EgHwHe8FEG7nRa5rrhXvpvJS2u\nRcdbeorC167UroVvjGkYAtOov08b1d10PKLwztmkOn6s7tuqcY+QojbGq7Z3YItlwkADW9af7rKP\nHiuBP+M/yBKldJeVM9Z1q4NAnxM9GJDaIEBVCK/PGL6uEewdG2eEsTrE43F27drV0ClqWVadQ3Bk\nZIQHH3wQQDtwv/71r1/XZ9199916/NgM4vE499xzDz/4wQ+07cphC0vHn8ePH9dZuKomuUIIwaVL\nl/T//dm62Wy2qfOOQgj27dvHc889x5NPPgl4ku3q/p5KpRgZGdHbDw4OsnPnzrrzCmYjP//886HX\nz1+zvBls376dQ4cO8Y1vfOOa2yrntLLbcRy+853vXNdzgW3b7N69+5bt9bNz506OHDnCN7/5TaAm\ng65sBeqSsxYXF+tsfeWVV5ZkeDf6zuzcubNpzz8dHR309vbyxBNP6GX+55FKpVJXZ76tra1u/enT\npzl37tw1P6dSqbB///41K7cfERGxponjOTpzwOep5U79J+DDeNLs9wKvXeM47wPei+dI/0p12Qzw\nT4GPAP8nnkN+bT3IRTSLXwH+fchyCTwF/Drw9IpatEpE4cEREREREREREWsYfxaI4zhYlkWlUqmb\njPE7gFW9QOU8V+s2kuPcT5icqapVqbL2hRBUKhWd3WQYhs5ETyQSup02KipT33EcCoUC+XyeYrFI\nqVRCCKHPPxaLkclkdE35jdxvoNYuwTIIpVKJq1evYhgG8/PzenkqlSKbzWonRSwW27DqDk2lURut\nxbYLdvdbjG0MHmMDf53WJGuwh3mERmiKtWWw8o5HrDipVIrf/M3fXJH7S7PlolOpFF/4whdWxPZm\njtsMw+DTn/40n/rUp5p2zGt9XrN47LHHePTRR5t2vOVo9pjw0Ucf5ZFHHmnqMRvRTNuHh4f50pe+\n1LTjLUck6R4REXGTPAR0A/+NpeKj3wAer76u5UT/sG8fPwvA9/Gy2e8GDt2KsRHrjnngGBAiibsx\niZzoERERERERERFrFCklhUJBSxleuXKF8+c9uXylmGKappaVBrRDNJvN0t3dXSfvvlHxS9a7rsuZ\nM2eYn59nfHycM2fOAPUZTmNjY+zZswfXdRkeHt6wk1MqYEA5ey9evMhXvvIVFhcXmZyc1Nk+w8PD\njIyM0NPTw5133qmlMDd6aSZ/9nk+n6dcLiOE4MSJExw4cADw2jAej5NMJtm3bx979+5FSsm2bdta\nWt90vSIBO5agEk+jPYKuS8yqIHzZ59IwsM0EmDWlAyklphHHMHwS0lVNbuFPKW6Vc0aCKORhcQbh\nAq7ALmYpl+N1zu9SHPIlsH1mGFKScStLnI6OZVIq1TKiW6bKqlLJVfawlJ4UuOP4pMElrmvjurZe\n5LrgOhKsqjSN9ISLhGXhViywPZUnKQRSSbs3z2gELqa0MGVcmy1cA9x6JYNkQtLVEch3lngy/25t\nWYcJZsnBjHkOaWGAUW5BXXEITXWXCFy3vhvIchGRDymcZEtPFJGqWFR+AVJJqKRrx/RLuzcJKcF2\nBJatPhiEdIlXinXbOY7EMRJL9o3FJKmU0GYZBsRjLjFDos7cNNzI/36LrGfH2Xq1PbJ75VnPtm/k\n56qIiIgNwd7q+6sh616pvu9pwnE+Vt0mcqJvTL4PfK76dxcwjCfnfjfwz4CfA34RL1hjQ7OxZ8Yi\nIiIiIiIiItY5tm1TLnsT8IVCgYWFBeLxON3d3ZimqWufK0dpIpEgHo/rWt+3yySPypp2HIeZmRly\nuRzT09PMzMwAaNlywzDo7Oxk06ZNuK5Le3v7NY68vlEBFFJKFhYWOHz4MPl8nnw+rzPF2tvbGRgY\noKenh+7ubu1E3+gy5UIIfY6VSoV8Po9hGORyOcbHx/X3aXh4mFgsxtDQENu2bcN1Xbq7u9f15G+r\nqCTbOPbODxPbtk/7LWNWnrte+ws65s+jJMcXujZxZM+nqCRq3z8pJcN3ZBi5s15aOD43TWJuut6Z\n2ET5YY3rkPqNf0W2w5PZLZHk+fT/zgvx9+j6z1JCKmXwg79LElNlVyUMp3J8dst3iYuaBLYU8Mbh\nt/HfX3677ieTk9CShMFyGWZna05024aJCTh9WrebdAymLxzj8mx33a598TLiexPEfPW5rVdeYfFb\n39ZefwMoFIu0/8zPNNXsHvsK95ReJO1WC8xLSXZxGDHbp7eJu/DpD8d4z+7++j6QL8J3vga5Gb08\nISrs/Y8niQkX5dCdmZvm8O7m1sIFIJWC4WF0MXkpWXDbWLjk38gl+7W/oPvJr9WZ7roSx7WReNdL\nACKbhS/8EmQyNYn1N9+EQ82dk5zPm7z4Roauzjad+N4xe5b7XvhDDFkLwrg09hDH3/Y/1ST9q5a+\n613wnvf4l7j0GTnSlHX0wJnMFV5+IarjGxERERERERGxSgxW33Mh66ar70NNOs5gyLqIjcEhwgMk\nfhL470AG+CPgKPDSCtq14kRO9IiIiIiIiIiINUqjGpJh21xr2Uak0Xku59xUNeNd19V/b0T85+Xv\nR4ZhaHUCtS7spdZtNCfxctdb9ZvgOfvbZaP3m1vFNeLMDexietPbddnwRHkO+9T3wJrG87JJ7Ewn\n0107KKe66upGt/WA3eOrJQ2YOGAX6x2orQjwkJLYqy8Rr15bW2SY3PQZTnd5TmRlYyIhmJszUUIN\nUsJ81sE1zoPhoKyXSGbf2sSpUzXTC4Xmmw14Geflci3julLxPmx+vlq8WiIdg1JhlkKhvn+XFguI\n8xMYvpkBOTFO5dw5pGXpeteOYTQ1o1sASVmi17lC1qlmPEsXKlkot+tGE0h2jgl2bkqje4YA5irw\nxlsQu1hr4HIZXnwRbEd/TrttI3YMN81ujWl6Dm+fE71CnEKhXjQhe/o0qed/UJ+x7roUSyUct5ax\nLQYHEV/899DToxUYcBw4cqSpZldswZWZGGWnmv0P2FMW8tAhr/09AymWNzPZ76kQ+L+jDz4Ig4O1\nriAkpGfLxMp51PVZnC1VM9MjbhZ1r2k1rVAqWinbmx3ot1J2Q3NtV2OTlcA/fmwG69l2pebUaja6\nmlhERETLSFXfF0LWqWXXE5WcwhsuLt7icSI2Ft/Ay0z/r3jaWv8H8A9X1aIWEznRIyIi1g7Xemi9\nxvqqCmUo+nFJiPDjrLaTQEpv4soN2iE9SUshqycRZrvvFVxfLWdoiBs/RSGq866rjAy816+UNQnT\n4AlWlzfsNdc5RxLWbuIm2rNVNGwXaGykXr7ct2bZlgdkaLNDLREu4taRUjIzM8PCgvd8UigUME0T\nKSXz8/Pa4Vcul/WE3JYtW+jt7SWRSGw4B6gfJVWusqwXF73nOtd1KZVKWJZVJ3sfi8UYHR3FMAxG\nR0fp7u7Wta03GqptACYnJ7l8+TJCCI4fP87s7CzlcplUKkU6nUZKyY4dO3j729+u66GrvrQR+0/Y\nObmuy+HDhzly5AiGYXD48GE9kZ7NZnnHO95BMplkbGyM/v5+pJRkMpmVNn1dEhDgDl1/I9uvKEIs\nucf5Fby1oxT/vXDpDv5jtOwrpQwK3v/1B9YNFut3JbDYZ6QIvDedJQOqoN3VZWq44h++6O3951X9\ne0UafSnBthSCUNn3hoNL/zNKi5x5YWNYoW3ytaMOYlgeqbdaM9/cdU+5XOaLX/wic3NzLb0PSykZ\nGhriM5/5DB0dHU05Zrlc5qtf/SrHjx9vme1qjPNLv/RLDA1dTxLdtXFdl29+85u88MILoQ7LZjvX\nv/CFLzA83JzgngMHDvDEE0/osVsw0C9o+41cF39goZSSD37wg3zgAx9ogtUeBw4c4Mknn7xu+651\nHfzn7j+O67o89thjTbN9amqK3/3d360LAFiuzcNY7jzVOtd1uf/++/n4xz9+C9ZGRETcpqg6PZ0h\n67qq79cT3lvEG+R1ALO3cJyIjcefAl/CC7R4eHVNaT2REz0iImLFcBcXcS1ryXIJiHQac2yssdfW\nsuD8+YYTSqbr0pvP4zTw3kkpyR8/TjGZXHKMMlCen29djc3rwBkcpvAPPsZiZqmscMUVfOXNTmaL\nDSLWpQu5DBQLLJnAki5WoUR+8twyTmODsNuBEDA7e4XZ2WbXwLwxxKWLcOpUrZaqwivMWK8pGcCc\nnqZdOdoDVGKDLLrtzM6GT/s5Dpw75zI/v3RfIeDCBbmaXQZSKcSOHZix2NJLaxjIY8dwzp9f2m6A\ntNNwZRfC2NZwytN1TRpOuAvJ5s2Ct7996X5CwNTU2gkyWO8oJ/r0tKeUVSgUMAwDx3GYm5sLnRjb\nvHmzdhxvdJSUveu6zM7O6owWy7KwLAvDMOju9qSLU6kUe/fuxTRNNm/eTE9Pzypb3xpUn1AZN5cv\nX+bll1/GMAyOHTvG7OwsjuMwPDxMKpXCdV127tzJ/v37dY3wjZrxojLr1eSkf2L49ddf58knn8Qw\nDCYmJvQ+bW1t7N+/n0QiwdatWxkYGMB13Q0ZYBARERERsfpYlsVzzz3HT/zETywZzwXvY2Es58Dz\nryuXy3zjG9/gp37qp5rmRLcsi6effpp3vvOdOlhxuQzgoOMx7P4aHNfYts03v/lNcrlc05zoUkqe\ne+45hBC84x3vWLLetm0KhULoeahrEhw7BccbQgiklHzjG98gl8s1zYl++PBhpqam+PCHP4wQgnw+\nr58b1JjY34ZdXV3aVhWE6r8O/nFgd3c3mUwGKSVPPfUUr7zySlOd6K+//jqXLl3ikUceQUqJaZrE\nYrE6J7L/73w+rzPAhRAkk0m93nEcJicnsarzTf5+9+KLLzIwMNA022dmZjh8+DCf+MQnEEJgWRbT\n09O4rovruszPz2PbtTmUWCxW1z/S6bQO4pVS6jZXZYRUIPShQ4c4cOAAH/vYx5pid0RExG3FZPU9\nbNKjt/p++QaPE3Si9wS2ibi9KONJ+m/CC7JIAEudPhuE28KJ7rouxWIxVCaoVCrpOqN+/DUSw9aF\nYRgGmUwmdOKx0YODEKJukBgRsZGR+TzuwsISh58EjKEhjC1bELEGP0ulEiwsNExxjdk2veWy5/kM\nwZWSS8ePU2GpS7ACWKtcE9cdGqX0c58n3xsoJSMgv2jzHx57mnNv1WQS67GBLMjFkPUu8kQR/u5s\ng0+WQBwIy8QUSDnF/v2r6ESXEnHxIuLkiXAn+sgIPPxww4wec26O9v7+UCd6MZ9h4UftzM6E716p\nwEsvuZw/X6ur6Ts4bW2rK1MpMhnEli0YqdSSddK2cV54ATk52cBJ3o7gH2OYSUToFhIpTaQ0CO9z\nkrExwbvfHZLNJOpKsEY0Af8EnGEYdeOT4PjB///bYWzhOA6u6+I4DgsLC1qKs1AoUCwWKZfLevyn\nJqlU7XQ1mblRUW1RLpcpFosYhqHbQ0pJLBYjkUjguq6e3NuoznNFmPNcTXg6joNt23oSU7WFaZp6\nQlMti2qhL49heC8l515rL5UtDQKBYYAwfMnFcqkKjkRiGNWFK/D7JqhPeA6/R9Zn8wplT0iK71I7\nW9hv/I3nby/hnZUQ6nyC33N1IWpZ7IYQdVs1Gg00A+m323VrHahuoxCFnTBZgKDzjRbmSKt+rWyV\nEmFU+7X+fKm6/BIbgldBQP2XRwhEi36TvfFEtVkFCMPXSkKA9PqA16/9+9W6mb59yur+/uuwwe8l\nK0FCfwuGAAAgAElEQVRPTw+PP/54qJP4Vn7//Jm6+Xye73//+zd9rEYkk0kef/xx7SS+Hqe/GpMt\nJ5Gt5tTK5TJvvvlm0+2OxWI88MADoVm/lUqloTKAGjMEx+f+8/YrJx06FFZa9OYRQnD33Xfz8Y9/\nHMMwmJubY3x8XI9xCoWCvuamaTI0NFTnRJ+ZmdFjZeWYVutHRkbo6OjAdV2uXr2qlbGaafvb3vY2\nfvInf7JubKrwO9Edx2F2drbOOZ3NZnW7VyoVTp8+TbFY1Oeq+szs7KzuY80aP2zbto2PfexjmKZJ\nPp9nfHxcP5dMTU1RqVT0tslksq5/dHR0kE6nl6yXUmoHu7oWBw8ebIq9ERERtx2Hq+/7Q9btD2yz\nHIeAD1X3ORNY96Bvm4jbjyTQV/17kQ3sQIfbxImez+d56aWXljjL1QD26NGjSwZSpmnS3d29xJGu\nBnZhDvZsNstDDz20RN5RCEEmkwmVfYzFYgwODhJXtdQiIjYyDTKCReC9IWH6g751y+0vhABf3cEl\nn78WHCnCBBHysywkrvQy0hvsSOPWq87cuWpaOGxfCJ/eC26zSlzrQfd6+kUDJ7tQevfL4DZo99Xu\nMsv2d/CCDhpORLle1Zqb/gQ1OXStY0Q0g1QqRVtbG+BNuqjJmhMnTlCpVBBCkEgktHM4kUgQqwYk\nbWRHn5InP3XqFKVSiS9/+ct64mx6ehrLsmhvb2dsbAwpJf39/VqWu62tbcM60FWW1sTEBOVymSef\nfJI//dM/RQhBoVBgZmaGdDrNvn37GB0d1Znog4ODev+NTDwe1+Nuy7L0hOypU6c4ePAgQgji8bhu\nm+3bt/PYY49hmiY9PT0bPtDgVrHtMqdPH6xmvnnLYlaeyoVztM9P6e0WiiZvHHmOSqKtzomey8Gl\nS7XjSSTx+Rni89O1iXTgymwwEeLWcaXkoBBYeHfAMi7ny29SyPfU3REtyyuDrR4HXQnJfI5nsm+R\nELVgTxfJySsZymU1TpPY9jGkvK+5drsu45cu8fQLL9QGJ44DZ87A5KQeA5VdQd45jBD1gaN5q8SB\nUxMkTB31gDU5SUFFNVStPyEES/Mzbx4JTM3N8ezRo6T9BeZnZmB8PHiSS0v35POeStX0dG15peK9\nquMfCRx2HKwW/N5fmZ3lwKFDpKq2SymZb5tiIfMWtTGUS+eFc7QFnkGklFiA6w8MsSySP/4xorNT\nO9EPHzmC5XPENIN8fo7jx58nk/EynCXQOXsWY+YqRrUmukRy8cIpjh192ns+0XZ7r6rAS3WhS3Lx\nKmalpBdNXL5MoVjcsPfZlcBxHB0EJ6VkYWFBO5gty6pzJJqmWTc/pYLC1G9mKpUiVQ26jcfjtLW1\ntfRe5jgOFy5coFKpeGpw+bx20gYD9vy2p9Np+vv7Q+fggLqgyJWqo61Q4+tGTnS/feA5dFWGtxCC\n9vZ2HbjYCttLpRKLi4vaYTwzMxMqba6ypP3BhPPz83VO9I6ODv0c4Q9qaNX32bIs7ehXzzD+QFf/\n5waDFfyBF5ZlMT4+zszMjA48VoEMU1NT9Pf3N912dXzbtrXjvFKpcPjwYRYWFnQ7J5NJYrFYXTCD\n/zxUkK+Ukm3btrF161aEEORyueh3NCIi4mZ5AZjCc4Bngbxv3Seq798O7LMNcIFzvmXfBv5FdZ8/\n8y3vBj4AnAKONM3qiPXET+NJuQO8spqGrAS3hRNdRV8Wi8W6Qa+Uklwux8WLF5cMhmOxGJZlhTrL\nY7GYHlT6KZVKulapHyVL5I+oVMv97xERERERERERfoKZLEpuOzjW8G+30Z2gfmzbplQqUSwWmZ2d\n1U70hYUFKpUKsVgMx3H0hGs8HtcBBxsdNYnun1j1Z6LH43GSyaRul9uhTRTBOplSSmzb1iUA1CSt\nUoxSGUK3UxvdDEII7r9/H0eOHOLMmdpcisDlrU4To2OTXuYKA/vSE14GtI8LF6A+6UqCKxHS9S9h\ndMeOpk6ICyHY/ba38d1PfpKj+nME7cY47xZ/uWT7oP/JEC5/LW2dzK3sLG85xiOjp6jluFcYG9vc\nNLsBtmzZQqqnh788cKB+hWXBYE3hSEp490ff4p0yEDxuwF+XnHon79AQ8hOfqNvOFoLNY2NNs3tg\nYICBvXv59uXL9fety5fDM5nDFIna26EaZKaXbd2qt5WAIwT79u9v6r1xYGCA4V27+NbJk3Xt5ooT\nSOHP25eIDhPxsY/Vt2/Y+RgGHDhQZ2fFcdh3zz1Nsz0ej3P//TuZmfkus7OGzkS/7Dqc3n1XnY2O\nUcS+/HXfuXhMTwcuT9WzLnzBuo7r8rb77tOO24gbx3Ec8vk88/PzSCk5ffq0lhOfnp7WjlApJZlM\npi6rVTklFQMDAzpQrqurq+VOdNu2OXz4sJZzP3PmDPl8Xjui4/G4tj2dTpPNZpFSMjo6ysMPPxx6\nr5VSaqd8sOb3ShCLxWhfRrlOyb0rKpUK58+fp1KpYJqmdopeK9v+ZikUCuRyOQCuXLnC+fPntdJQ\nT0+Pnr+0bZvJyZrqrpSSubk57URXzxlKanwlghXK5TILCwu6P2QymboMfv/vXzKZrGs//zrLsnjl\nlVc4f/583fOQEIKTJ0+ye/fuptqtSkqZplmXBV8oFPjLv/zLunnmdDqtr4EKXCiVSnXHUts+/vjj\nfOhDHwLg3LlzKx4wEhERsWGwgf8L+CLwx8Bn8RzpvwI8AnwDCMq6nMCTbPc/ZD0H/AD4SeCfAX+A\nJ+P+ZTwH6r9t2RlErAZjwPuB/8HymeWPAr/n+/8ftdKotcBt4USHxhJSjSacl5uIDlu+XG2q5ZZH\nRERERERERNwMQcd52NhkoxOUr/dLNQbbxZ91czu0TZDl+ktYttJGJBjAqs5bOdKDfeV2/E7dCoZh\n8PnPf74lDoIgSua0mcf7yEc+wqOPPtq0YzbCX8O1GTz44IPce++9TTvecjSzzXft2sVv/c7vNO14\ny6GCYprFjh07+M3f/u2mHW85lCRxM0in0/z6r/+bFfktMwwjcqI3Ef99Ovi3ym5WY6Dg/4N1x/33\nuFbd8/3Obn/2ddAJfiNO8dUenyz3+f4x6LW2bQVh7dmoTf19wL/tarZvcBx6I3O3weOovu+/Jivx\nm+fv747j4DhOTUHH97c/cFOhyiqpfaOEq4iIiCbxH4DdeA70n/Itfw74pzdwnE8BfwP8x+oLvJjQ\n38ZzpkdsHLrxgi6+CHwHeBU4jSfXbgL3Ah8F3u3b5++Ar6ysmSvPbeNEj4iIiIiIiIhYbwghtKyf\nmuAyDAPHcdi9e/eSyTohBOl0+rbJSK9UKpRKJSqVClu2bNH1BxcXF7Ftm97eXvbs2YOUks7OTjKZ\nDPF4PFRRaCPhui7T09PMzc1hWRZdXV16InFgYIBsNsvevXsZGxvDdV16e3tvC5lyIQRzc3M6+2dy\ncpLXXnuNSqXC1atXdWbzyMgIO3fuRErJ5s2byWQyGIax4fvNrSKEWNdOs0QisUQ5bD3QSCVtrWOa\nJtlsdrXNuCnWq+1CiNAScxFrDyEEpmlqCWj/2G5xcZFSqVSXie7Pkl5YWGB+fl7/v1Kp6NKGtm23\n3DGngpxSqRSqXrgak6pxiSKZTJJOp7XzcWJiQv8Om6apM+xVBrdyQAZLNbYC27a1bH5wXB0cE9i2\nTT6f122bz+eZmZnBsixisRhjY2NarrsV461YLKZL1WQyGbq7u3Ument7u87uD0rhB521hmGQyWT0\nNVDPHK2U0FfZ7wrVd6WUdX3FdV0sy6qTnu/t7dXnZpomqVRKf1dUGU41Nmn2c5HjOFrBSEpJd3c3\nmUyGbDbLXXfdRW9v7xI5d3Uek5OTzPrK0hQKBf3dNAwDy7IQQtQ53yMiIiJuAhf4HJ4z/X1AHHgN\neLq6LsiDeJWzglwEHsCTb38bUAL+P7zM9YiNSRfwj6uv5fhz4OcI708bivX3tB0RERERERERsc5R\nk3HXmkhbbuKns7NT/+2fEFVZDDcz6bJWnO9q8ni5iV5lq5pMy2az2oluGAa2bdPR0UFXl1f3tb29\nXdfDvtHMvrXSLnB9tgghqFQqevJRSacq6clsNktnZyddXV24rksqlaqTT72Rc72eftxq/N+nazkH\nXPf/Z+/Nw+S4zvvc91RVb9Pds88Ag43ESoIAQZkiSIkSY4uURNJKlFi2JcvWcuMb+ZEXhb6+yjWl\n2NJNtCSWrh07uvcq8SJZimyLjmXLiRNZMklJtkRxBUWCIEEAg20AzIbZu6e3qjr5o/qcqequHgyA\n7ukBpt7nGaC71u+cOtV16vzO930upVIJIQT5fJ7x8XFdV8pDOJPJMDAwgJSS3t5eYrGYDvF+Je1m\nrbSdiIiIiIhrAyV+9lQT0Mfjcd23S6VSjI+PaxF98+bNbNq0CfCeO0ePHuXEiRP6WP5IDJfqWzUD\nwzDYunUr/f39uK7LmTNnKBQKGIbBt7/9bV588UW9rf8ZuXnzZl5++WXi8ThSSjKZDPv379fPdn/u\n8dHR0ZaWQeWhn56e1jYqYVlNXOjp6dG25/N5jh49qnPRz83N8cQTT1AsFkmlUtx0001ks1kcxwkI\nxs0ik8kwODiIYRgMDg6yc+dOva42JHrt9VcTBQAtPvs9qNW1U+H0W2H7wMAApmkyOTnJc889h5SS\nYrHI8PCw7tsr+5RtsViM9773vWSqKT0cx2Hbtm16Ml9vb69ep9IaNZPFxUXGxsZ0GqB7771Xn+Md\n73hHYFv/uR3H4emnn+bVV1/Vy//+7/+e8+fPA969rtrdwsICnZ2dTbU7IiJiXXKE+tDtYRxaZp0D\nfKv6F3H98grw48Cb8cL+72Yp77liFC/E/xeAx1bVujaybkT0RiGKlusEhq27VFikK+lUqsHu653l\nBhGjwcX1gfD9+ZGAAUghoFFbaEIbMap/YcvXQgs0DDCM+t8Qb7x+OSsFl67dsHX+9WHHF4TX2Coj\nBNIwIGz2u1dp4e1DiKX1Ib/N3m8SCGTD3S9d5+1FCOHdN/UrvP+W29eXr7PRFo3bnZdE0ztNcH/v\nNm5/3ax1zp07x2OPPXZJ70Ep5YqEu9rBxasJy/jKK6/owdh2UCqV+O53vxuYJBCGEILh4WFOnjyJ\n4zhcuHBB96dKpRKO41AqlQKeOY8//rjObX25wm+pVCKfz19ZoZrE8PAw3/rWpd9bbdvm2LFj5PN5\nzp49q+1WnlPlcpmXXnqJiYkJpJSMjo7S29t7RTYJIThy5Mglr1crKRaLPP3008zOzl6yL67yVQrh\n5ZU9ceIEjuMwNTWl85lOTU1x+vRpnbfy8ccf13V3uSL67OwsxWJxXYTklFJy+PBhJicnW36uRCLB\nrbfe2rR2J6XkzJkznDx5sqXXSgjB/v37GRwcbNoxx8bGeOWVV1qeN1UIwb59+3Ru5atlZmaGw4cP\nBwSSViCEYHBwkP379zftmLOzsxw+fDjgJdkqlO3N6FvZts0zzzwTyN3cCoQQdHZ2cuDAgWsyusNa\nQE0MUxPc4vG4vsdVRB0losdiMS0cKhG0Nry76h+tVn5l5Rntuq4WcYUQlEqlgJe8//c2nU4zNzen\nRXTXdSkUClr49/dzV2P8zJ+/XHlkK2rrUXlN+/uhxWKRQqGg7101GaAV70m1fZTLue9q69Ivuqv+\nSyufiyrigmrrpVJJi+i5XC7wO+ufEKL696rMaqKomjiqvNLVts2ud390BOXBr+ounU4HJqf6UZGy\nVJQogGQyqcvj90CP8qFHRERERKwyZeAb1T9FN6AGi/LA+GobtRZYFyL6wsIC//AP/8DMzEzdDMzh\n4WHOnDlTt4/qBIV1tNRMw9pjJRIJxsbGQnPFpVKputBp6oVny5Yt183LpQptV1tvQgh27dqlw2T6\nicfj9Pb2tt2LKaL1vCwE3xIiVLIT5TLmxIRSjOspl6FQCBVDAXAcb12jSS7AtBChcWkcYLSNop83\nSD/L9773HTo7e+rWF4sOxeILCFFscAQXOAVM01hEj9FYLDXxHgdhIvokUFhROVqBKyWHTpwgZpr1\n11ZKOHMGzp5tPMlicRGOHw9dNVtMcPLMHLOlZKhMbNtQKrkIEdpigXM0rtPWIqVktlzmO5OTdId5\nM7gubrkcLrADRRwmeB4pYzQSyYUYARZC9xcCJiZOc/hwf93+QsDo6Kvs2BG99Ddiw4YN3HvvvZw7\nd67dpoSyZ88e7rzzzrZMhkgmk/z0T/80Z86cYW5u7pLbW5bF7t27Abj55pv18kY5KScmJq7Kvne/\n+91t8wi54YYbuOuuuzh9+vSKtu/o6CCVSnHfffdx77331q331838/HxgUPty2blzJ6997WuveP+r\nIZlM8q53vYszZ85w9uzZy9o3lUppuw8ePBhY56+fqxWF3/Wud+mB3OsZx3H4N//mt3n00UFM80a9\nPJ2S/NxPLrJ1yNcLK5VgbMzrv/kodm2g0D0UWJZ0F0m5SxNYXOB//v3f85uf/CR33313U2yXUvJn\nf/ZnnDgxzZYtNyKlN3fv6FG4eDHYzcjn4cQJr5/g7QvpNOzfX9uNlew2T3GLtRTp8NnRUf7xRz7C\nO97xjqb9xj7++Ld5+OE/o7v7ft1VMgy44Qbo6Ql2n86d87rU6tRSQjwOQ0NLy4SAM2ccnnvOZslB\nUWAYL/ClL72Nd77Tn1LxynnxhRf49+9+N2+dn0e/CUuJ/eA/wXnDPej+hZSYp45jnT0VvBDxBM6P\n3A6ZpTDWzM/i/M5vQz6vpzueFIL8O97B73/lK0173zx8+DAf+tCn2bjxrRiGZ70QcPKk1y31s2cP\n7Nix9F1KsCzv+vh/Fgxc+qw5DF+/89TICFPlMn/4R3/UUJC5HPL5PP/8n/8Gp04dBDpQ/djNmy1+\n9me7dfuVQEdxhs78GP6+rgS+/uw2hicygUthWcG2X6lMsnXrD/nzP//9pk4YWe8oATYsX3LtOkU7\nI6LU5ue+EjHWn65oNSejXW00p8tx3lmrtCu/u5/atuuPfhT2e+7f3t/eVrv+L3U+f+529b3deekj\nIiIiIiIaMFv9W9esGxH9u9/9LqOjo3WdkpmZGRYWwkWCK+EHP/hB6HLLskLF9Vgsxg033HDdiOiJ\nRIL+/v66Dq1hGLztbW/jlltuCSyXUpLNZunq6opE9OucLVu34rz//fxdoxcKw0BcanBo377l1y8z\nU1fSWO6UwA2pFFu2bl3++C2iq6uL17zmAIcPP4kQ9feBlJIHH7SXKZ4EevAmh4VhAPWThVaGZOfO\n1zVl4O5K2LVrF88/+yzfHBsL32BysqFIDngjlQ08FVwp6NpwgU7Z6GVVsnVr42aVTG5maxvbzIH7\n7+cHU1MNYwXIm25a5p4Q3MokN/NNGnuauyw3SSAWO8viYnj0g0zG4TWvqRftIjy6urr4wAc+sKYH\n1No1iBOLxXjb2962ZuumnaG5N2zYwC/+4i+u6bppB/F4fE23GVhf0TlcN4lh/ASW9TpACcwuP/2P\nZ/mRA2VQz9yFBThyBCqVgKK7sHkvc1tvWRpYBjorM3TaU3o7Bzhy4kQLrrngvvv+Gbff/nqk9ETy\nv/1bGB4OCsxTU3D6dND0ZDJcRL839gQPJP5OR1z6kxdfbDwh9Crs7ug4wNDQLwZE9Ntv94Ratcx1\n4fnnvfmFfhE9nYZbbw2W8amnbJ5+usSSA6CBYfxF003fadv8C8chrU7uOJTvvJvKL3wQpFJ0HeLf\n+y6xZ74fENFlJov7rncjBwdVgBw4P4L9u/8BUTXcAP5BCP6yyd583jvsjdx22wcwTW+ivBCQy8Gp\nU8H63bIF7rrLvy8kEvC614E/kIKJw/bEBUxRtVUIvv/cc/zJ3/5tU213nD7K5Z/Ccyjx3pC6u5O8\n611DWNbSfdczP8Lg1JFAL8+VkmNTB7lY6g+09WTSE9IVhcJxhDjZVLvXG34PcvVZ/eb5Pc+BuqhG\nKgS3+h0tlUo64oPyOm6luKhS8ihP9EwmQ6VSQQjB0NAQN954Y6Ccap/BwUH6+vq0R67Kqa68cZUX\nsl98bDa1Yr9fsPWPV9Wev1KpMDMzo0Oj53I5LYquRh9A5edWXswqZ7zruoEw7FJK7emtypHNZnXZ\nVEob9e5v27Y+rj/seyvxC8ulUkmXxR8ZALz7wJ9XPJ/PMz09rUOh9/b26pRYrQihXztBZbloCX4n\nLJUawO98lMlkyGa9CWEdHR36/m6F3RERERERERGXz7oQ0f2dmzAP6Wa+ODQ6VqPO8/WWK3G5elZ/\n/jpqNFM34vrjNa95Df/xD/6g3WY0pN3CyCc+8W/XrADQrroRQnDPPffwxje+cdXPvRLa3mY+9ak1\n22agvfWz1omefcsTtZ3GRHUTTlQva43adDDewLIhWVJ0/dGDav6XcimViHrKGUj9ZfkpXs3AaBjc\nyL+stsnVBczBReLF+xG02mZRrbdmIms+t6YEhhBLrUWLEYZPRPeufW3pJBJXgl4jQXotZRWsVqYt\nn/rIL6bX7xtcLnERsqYELevnybo/KY1qeZaWGlLWTJXUay5x/Oj3+GpxHIdcLsfc3Fydh+rmzZvZ\ntm1bYHu/CDo9Pc2hQ4f02Mv27duxbVtHT1SidKveIwzDYGhoiKEhL6pIT0+PFmAffPBBisXw6Gqx\nWIzOzk5d1vn5eZ588kkdEn7Dhg3a/lZFd3EcR9eVZVla6DRNM3DO2okL58+f54tf/KJOlZBMJtm6\ndasOL95KhxEpJVNTU5w6dQopJWNjYxw5cgQpJYVCgePHj+tJFCqdjxJ4E4kEP/MzP0NnZydSSlKp\nFG95y1vo6+vDdV3GxsaYn5/X7aq7u9Gk/eZgmqZ2Psrn85w4cYJcLqdtP3r0qK7jRCJBZ2cnPT1e\nJL9cLscjjzzC2NgY8Xic3/iN3+COO+4A4IUXXmh6P1HZqiZY2Latz6HCsSvS6bR2nDIMg71797J1\n61a9TSwW48KFC4CXwmNwcBAhBIVCgampqabaHREREREREXH5rAsRPSIiYm0QRRtoTCQAhBPVS2Oi\nuomIiIiIWGsoYTBMK78kYUqzOg6r88zz7JXajDD7vWXi0mWU3j/aA7BFNofZ4OnOMiAiS2TA7uB+\nYZO9g571Rquq33WDanNNZVYlXmRNDVa1dSR+oXf1JxcqcwVKlJQh16JBaGchl44hVisNjvT9L+uW\nBDaTVG0UelntPQ7h3yOujlpPdOWFLaUkHo8HPNFt29YitfJEzufzOgd3sVjU3sirkUtcedoqMVTl\nNAfo7e1ddj+/520sFgt49yrvdsdxVmVcQYnn6n+/cF6b3rFcLnPx4kXy+TxSSjKZDFu3bg3kg28l\ntm1rD/NcLsfFixeRUpLP5zl37hylUgkhBMVikVdffVWL6slkkosXL+rv6XRaTyKQUlKpVCiVSnU5\n4VuJP3d8qVSiUPBSy5VKJaanp3Uk0UQiwdTUlJ4UksvlmJycZGJiQk+2yGQygBe1qNn2q9DytW0B\nWDZaghCCVCoVaE/d3d06ekRnZ6dOLZpIJKL3/YiIiIiIiDVAJKJHREREREREREREREREXDWpFLz1\nrbBx45KQlonb9E0fhx/OosW4xUUvTrp/UFtKRMdGzBt9miqS8nPPM/f0o3ozF6icudI0Ncsh6SyO\n05sfQbqeiHWw32UH/sFwyfTGBAlrI8WKhRBLIdG3bAmKzlIKHHMLx603eKUWMN5ZYEeTvXSF8HJu\n33+/T9CVDrvtVxgYH18ScCX07dxOWcS1BVJArhjjyWcGkFJ45QHiiwv80n0jPjVUcHxsHCF20FS2\nb/diySsxwXXhlltq1FzBWW7gIi7CV3cx22DP8AU6Lk6iIxdMjiFWSeiJx72c81rzE3Bv3wu8vv8l\nX/uFPWaW7QvZpXkKEpAV5NeHWZCFpfIk48gHDkKiekAhvLQHTQ5Fb5oW3d2dGIbnUSqlpKfLpCdb\nImYu1W9H2QEzKFQKJPv3C9gQnFSRSgXDuU9PeykPItpHo5zcayGKVVju55Xkg15N22vzzftzVq/U\njtXOyX2l5/Db2OgY7Wg3frtWIiSHbdNKuxvV10rtbXQfLHcdIiIiIiIiItpDJKJHRERERERERERE\nREREXDUdHfDAA3Dbbcq7FsxihY3fPgwvn1tSx0slGBsLeiG7Lsbm3VgxAp7FpR98n4X/8P8gfB7d\n5cHBptsukPQunmdwoQdRFS43DpWRg07AUXvO6WTo1n5KNa/S9WPmAoftHJHbvW+G5NyJcbY3224B\ne/fCz/6sT2+1bbr+29MkXz60pOwLgXzgAS8Rt9bGJUfPpvnMF/qp2J6I7kr4pwfm+Dc/9QopywEE\nCMF/ffJc80X0m2+Ghx7ykoQrsgOeuq/r0+A4u3mG3TqIuASylTxbXvk7OuPTaBF9ZobKKuXtTSRg\ncHBJRHeRvGXjE+wf+iL+BiOsrYi5TYFlxcVFnv+bv2H24sWl4/V0Iw/8J+jsXDrJ7GzT3bpjsRj9\n/T1YVr9ntwsDfTaD3XkSprYaSg6YZs3ekoN3wY0SPYFECE9Ej/m0/5EReOSRppq9LvGn/6n1ZvaL\nbCpvtdpWeRIrwdc0Te25Ho/HV1XQvRwBWuX1VhQKBVzX1V69yht8NTy7w+rev8y27UBY+sXFRZ23\nXpU1lUqRTCZJpVI6x3gr7VXniMVipFIpHc0gmUwGwv13dnbqyAXKPtU+VB5627a1h3eYp3WryyGl\n1OVQXvKmaeo856qcruvq9u44Dh0dHWSzWRKJBLFYrOnpO2ttDYs00CiFqOubEGXbti6XECKwzjAM\n3c6jSI4RERERERFrg3UholuWRW9vbyBHDXgdesMwdG4aP0II3XGpxd+h9OO6rg6TFXa8Ri8+pVIp\n9HgqNFAtrQjD1ahT3KjTpl4Qwpbncrm6/QzDYGFhQYdeUqiXumimZURERERERERERMS1jxBBQVnU\nfVhuw5B3EoEnalffFwS0KF70kkCiFEKxtNRntic2r0RSEAQ3FCva68oIVGfV8MArnhD118Jnno6B\nMJ4AACAASURBVNpWUBUHBJi+hS0TUfyGa1f6usYSqLnaaxI4VptQbcIIM6NOVPEmbeA6S6Xwl12p\n06tenhWeTzUL3/+BW7kdpl9nmKZJJpOhszqpwj82VS6XdV5ogGPHjnH8+HHAu09feuklPfblui63\n3XYb733vewOiX6VSaTimc7UowVCNm/kFddM0GwrKp06d4q//+q/1mJpfeEwkEtxzzz1s2rSJSqVC\nf39/0+0GAmHbVU50hf838Mknn+QrX/mKrufJyUmmp6dxHAfXddm8eTP/8l/+SzZs2KAFdX+Y9GYi\nhGBgYIA9e/YgpWTnzp284Q1vALy84i+++CKlUgnwxuvUZ1XGe+65R+d7t22bkZERJicntd09PT0Y\nhkFHR0dT7Q4jk8mwe/duACqVCtu2bdNjn4Zh0N/fr0P+F4tFPv3pT3O6GvYimUzyoQ99iJ6eHoQQ\nHDx4UKcEME2z6WOoqVSK/v5+TNMM1KsKw67auUqpsLi4qAX1Y8eOceHCBd2m5ubm9L0yMDDA3r17\nEUJw9uxZRkdHm2r3GuR1wP/fbiNCyFb/rxcMIiLWDuqB+su0I6fRpTnQbgMiIprFuhDR+/v7+cAH\nPqA7LQopJWNjY7qD6CcWi7F58+ZATia1z9TUFDMzM3XnKZVKnDlzRs/q9HP+/HnOnz9fdx7HcTh1\n6lSoiJ5Op/VLkx/btnWOo2bQSKy3LItMJhMaYmhhYSHQ+Vb4Z8DWHiudTjM+Pl53rBtvvJGbbrqp\nrq4jIiIiIiKuR/L5PE8++WRof2EtIIRg586d7Ny5c9XPbds2L774IpOTk6t+7pXQ3d3Na17zGp3n\nczWZm5vj6aefDu0zthshBDt27GDXrl2rfm7btjl8+DATExOrfu6VMjg4yK233hrIf7meWPEby1oc\n+lkGX6boNUE0Jzni6lhLrTlCOWD480P7vYn9nt3KK1qN2ygPV0UsFiOTyWAYhnb8aLXtEB5Kezkv\ncsdxWFxcDIiR6rnpui6maRKPxxuOOTXLdr/HeSMqlQrz8/NUKhWEEOTz+YAnumEYZLNZuru7A5MB\nWuU84hf/a9tKOp0O9D/8YrhpmmSzWS2il8tl3aZU+VVe+NXwilZe2Mr2bDar6840TTZu3Kg90VX+\nedWeVTvv6ekB0PnEVxpe/XJR7VC1RX+78Y+x1ob0V/dguVzW9vnbhWEY2ot+nfQbb67+rVWigeqI\ntYxqn/+xrVZERKwD1sUT2bIsBgYGAuGWFI0GIhOJBJs2bQoV0S3Lquu0CyEoFovMzs4GQlD5jxcW\nBsl1XcrlcuhAejweD50tadt2Q+/1K8E0zboOsZSSeDxOpVIJXVcqlULrsxGWZZHL5cjlcnX5pQqF\nwjJ7RlwvTE5OcujQ86zVEVLLsjhw4AADAwOrfu7FxUWee+65gEfBWqK7u5uDBw+2JZzYuXPnOHLk\nyKqfdyW0u80cOnSIfD6/6udeKTt37myLoHYtcOHCBT75yU+ye/fuNRemTwjByZMneetb38qv/dqv\nrVr4RkWxWORzn/tcIPToWsG2bSYmJvj85z/Phg0bVv38x48f59Of/jR79+5d9XMvhxCCU6dOcc89\n9/CRj3xk1c9fKBT43d/9XfL5PL29vat+/kuhhI3Pfe5zoZNjr0ekXPpbVpfTGwUWAi5SqN9GzwNd\ntsmtVUKNW630Ulr7Pl+KVnqf16Kr8zK72y4SF1fvKtU/td7RTcary6UaDa4Jz+kc3CbsiIDwPVtb\nXP3KLF1vyBXVf22p9Wd/vbdIcKs9hdfOqY8GEXZ+Ud/yJRLpi2QgV3hvRFw9YaHGV7vv1ogrtcMf\ngruV4bivhlqh/XLK2s7r4w83vxLWSlu6FLXOUu1sN5c6b6Pw79dKXbeAbwGfbbcRIWwAvgKsfOA7\nImL1UbPifpW1Odj+U8A97TYiIqIZrAsRHcI7MrX5mRqtu9Rxltu+FaxG52q5jtzVnH8ddwzXPc8+\n+yzvec9ncN1thD/bLaCDRiNd2bTDj9+TI2Y1uMcqFS+35hXMrHeAV3M5/tW///c88OCDl73/1TI6\nOspv/uaniMW2EIul6tYL4eVcbDgRWUriC1MY5Qb9+9FRqIb4C6W3FzZvDo27uFAqMd/by5/+xV+s\nuqAlpeTb3/42f/mXf8XmzZvrx/EEOMePYz/3LDQIz2bEYiSWE1UaxZuU0ssDuWMH9PTUDyIKweHj\nx/n13/xNHmxDm7lw4QIfe+ghek+eDJ0aLYA0y0ybFgKjqwuxjDetPTuLGxJxRBE25K2YBu748Id5\n+KMfbbj/ekaF1vzYxz625jwMDMPgj//4j9s2wU3lQPz4xz/eFqF6OXK5HA899FDbBuVc1+VHf/RH\nefjhh9ty/kYIIfjyl79cF21otVBt5hd+4Re4/fbb19Rguwrx+tu//dvtNmXVEALicS9HshLnjFIJ\n+zvfpnTsRXQg7v5+Ym9+M6Kmb5E89SrWb//fSN0flMj5adx3vlNv4wKJY8eab7yUcOaM1+FyXRzX\n5eUnnmDy3LlA7zTR2c/+gw9gJdPoJ2GpBOfPB/oLEjjW/wYOD77ZC6UuwJcCu6kkzAqd8QLSrQpP\nRpkYFbAdMKo2OQ78z/8Z3FHA0JTNp8envfT0VbtviN1K/PZ/BvHqM0oIaPI9LoH5copjsxtIJpY8\nIydH4szmfQIDLr0Xj/HTmWH87wmWXaD7xe9BcU6L/Itugq+/+fOU3ZjWhF+depVS7GxTbQfoT+a4\ne9MpUjGvDUsBPTclEM6PBMPTOw7Mzwf2jRWL7DFNbF/7N5JJrGwWstUIskJAOt30uOi9vfAjPwLK\nGdWVsK10EvOXPwHViRRICQcPwtvfDr7JfgLY/uwLDE3MV80SSNum9PQTOGNLIYfzhQXMRGu9ndcz\nKlS6GltZWFjQEXyUV7Ty2FYe3EBgvGq1xq6klJTLZe0AUuuQ4rdDbef35k4mkwghiMfja2riqW3b\n5PN57QVdLpfp6OjQXuepVKotY1+145Iqn7y/7vzpLE3TDOTnrk1P6fcMX436r22jruvqtiOECETD\nLBaLlMtlSqWS9tpWedT9udVXEzW27Hd4UuHclbOV/7t/3NWyLN3ua73Xr3MuAI+224gQbqj+39w8\nABHrjW3AHwH/HfgzoNnh9lT7/By6E7em2E0kokdcJ6ytkduIiIjrFtt2mJ39MVz3Jwn3NkkDQzQS\n0Qc6y3ziV07RmWrQL1hYgMcfh1zusgebiq7LJ555BrvJebJWiuu6JJOD3H//x8hk6kUj04Q3vtEb\nRwtDOA7Z4eeJz4zXl10IePRReP75cMHYdWHXLnjTm0L3PTE9zW+dOHEVpbs6HMfhPe95Lw8++OMh\nzjAuxS98gfzffxcaRMaIZbP0VHPRhbKcgBmPw7vfDfv3162SQvDwZz/b9NxqK0VKSWcux8/PztId\nsl4AW4FGPo/SNIlv3ozR3x/qZSSBxYsXqczONnTgsgnvpRvAISF4PiQqS0SQ1RrAvBzWik1rcdLd\nWrFpLVwfP2vFS2w1J7ReDmvNntXAsrw/5YkusJFnTuG8/ApQFdZ37CDW2wudnQFvZ+u557CeeSYo\nQu7eDbfeqo9vA9bYWGuMn52FyUmQEte2mfjhDznz6qs+SR/6BgZ4zQ0b6chklrq0i3k48UrgmepK\nyZnSEHNJryKEgFbNT7KES9K0lwRz6aC8+AMuxydOeIK/7/css7DAm/PPe2pqlbgpMTd9EBIJbz/D\n8CYVNhVB0Y0xVUoTlxm99PwETEz4TZRsEpPsix8n8J7gFmHsjCdQV0X0Sqyfo/veTsHMVnO7w8jI\n90iYX22y7ZCOldmWnSUdr05ZFAL6YjA0FNzw4kWYmQnUuVku028Y3o2ivPwty6vvZNL7LgS0IN1Z\nRwfs2bN060kB/SenEV/4H+BWo+NJCV1d0NfnvYgopKSn9H2YPul5+wuQ5TKzz3yDksrJDaQA4667\nmm57hEcul2N+fl57tD7++ON86Utf0s/ALVu2cODAAf29u7s78M6y2l7rZ86cIZfLAZ6A64/2WCqV\ntJg4Pj5OsVjUucO7urq44447tJCbyWTWzHN+fHycJ554QqeMHBwc5A1veIPOwb1ly5a2pP4RQlAu\nl3WEu3K5TDqd1rZYlsWOHTv0RAbXdZmdndWir+M4gaiXXV1d9PX1ee+enZ0tn2QrpdRtVUXgVN+l\nlAwPD+s2WywWGR4e1hNI+vr62LlzJ7fccgvgtTVVjtVIheQP365CzYNXp0eOHGFqakr3mScmJpib\nm9Nl2blzpw79n8lkcF03IKZHRERcs8wAtwNvBn4Hb8LIHwB/AzT2WImIiFhzRCJ6RETEKmIAJo09\n0WPVbUIQkoRpkrAarLcsb5DHMC5bRHcBo83iiDdDPI5p1r9sm+bSgHT4vjZx0yJhmuEiupo13qiM\nhuGdJGTfmGG0PUOiZcWIxxPUvfsKiW2ayz7ILCFIhKTSWDpGA090WKoXrQQsIVcpL9xyqLsprPyi\nurxh3QhBXAhMw2gYKrRCWDDVII1EdPMS+0VERERErDP8oUupijhh4dxVv8Uvovs/r4adNQKTkEt+\n8dp2YVCdIbC0RhjUBegWQkfIXq7L0TTbw+qz9ntt/8UwfIHcqX5qcH2ajOctHgx4L2rqSvi2DPYu\nRHDj6mohJYb07SWXi53TrFL4TuFvsw13aV8vSV1W1a+uxi6o3ndVwdx1l9pJaEh33z2qPivPyVYa\nHwF4gqDjOPr9Zn5+nvHxce3F3dPTQzKZ1IJzbdSj1Z4U6E8FWJsfXK1TXsa1XtAql7s/9/RaoFKp\nkMvldGqt7u5uMpkM8XgcKSXpdLptky/9QrQSY1UbsCyLjo6OQK75qakpHdnAcZxAtE7TNPXEAJUb\nfTXLodq0+q5ytqt0mqVSSXunVyoVEokEmUxGl82fFqCV1E5IUR70yiu9UCgE0rAVi0UqlYoW1U3T\n1G1nLbXziIiIq2YB+D+AL+INk725+lfASxfwZeAHbbMuIiJixawrEf1ycv9c6jjLHat23XKzB1W4\nnrCO0nLHD8uvfqWEHcvfUW207kqIZlJGRERERESEU6lUdKg/NXAF9aEv1yP+cI6Li4vYth1YL4TQ\nIT8BYrGY/tzuCS/tQEqpPbn8A9aqz6kG+67nuvGXG8L7uxEREREREdcS6rmuRPTa/OHgPe+Ut7c/\nnLv/GKtlq98utUzZrvoqyhO9Uqno/p5/zE3tXxvqezVRIfT93/3jeEpsVoKzZVltC+fux9/X84ca\n93to1wq+ah9/vYcdu5U2N7K1tg0kk0lSKS8dXjKZbNivbWV7CQuhr8qhMAxD16kaz1X4++T+8ddo\n7DQi4rrhvwAfB7bjCekAGeBfAB8ERoDfr253ph0GRkREXJp1IaLHYjG2bt1aN+AK0N/fTz6fr+vg\nmqZJV1dX6AvHxo0bQ8MY2bbN3r1760IFSSm5ePEiF0OS4JXLZc6fP69zEPnp6+tjcHAwdJ9Tp041\nLSSRaZqhnc1kMkl/f3/ouq9//escPXq0brnruloAqCWdTtNTEwZQSkk2m40GNSMiIiIi1j3KqwW8\nwRYVerHRZLv1hF8Unp2d1V5L/ryZvb29us+y3utMSqkHox3H0R5JpmmSrObgrfVMu96oFdFXK2xt\nRBBZ90F9Dxkcrg09Hra80b4tQPr+/OHcNWGe3w2OI3zHWlWuwJM/fKsWiSdc2qwVnTnQPnx1vpoV\nLqonD/zMyPDP0t+6oGHrWIXfrKVrUHP/rbS9SFl3n0Q0D39IaPUsn5qa4vz58/qZNjs7i+M4+pm3\nc+dO3ve+9+l+Un9/f0CcU32lVj0TlVDu92pW55uZmdFe6QCvvvoqR44c0XapSZBeurMkQ0NDevKf\naZqUy+VAWZuNX8T3C9BTU1M89dRTervTp09zww036Bzit956K+9617tIV/OvJZNJnSN9NcRQvxCb\nSCR0fm0VHtwvPPvHJV3X5cKFC3qs1DRNhoaGdN70dDqNYRhaXG8Vqs8Wi8Xo7vaSlS0sLHDu3LnA\n2GsqldL9eyklH/vYx7Rd8Xicbdu26W1t2w5MDmgVhmFg2za5XE63m2w2qyeyOI7D9u3b6evr07ac\nOHEiMJlk+/btbNmyRZdD3QfXe189ImKdIIHfAz4D+EOPqht8K/CbwL/F80r/Q+Av8LzYIyIi1gjr\n4omcSCQCnanVxh+mqpZSqcTw8LAOQeRnaGiIzZs3h+7z0ksvNa0jGIvFQoXyjo4ONm/eXLdOCMGr\nr77K6dOn69ZVKhX9IlG7TyaTobe3N7A8EtEjPFR7Wa4dqFCNcl3GCVSRKkPfwUXN/9chjS9540J7\n44HrsLGsAOkfJA3fYGXHof4KRDV+5fhFvtrP653aeqmtm7Dl61k09Ze/UZu63uumtnzXe3nXDhJR\nKSHKBRXNHGGXsZNZ3OyA3sro6MG0EggzHtw90YFMZarXy+v4iFQaI5Ek8IRpZRQFJfhIiSUlcYLP\nOst1cYtFXP9EnVIJUanUPD8lZjlHsjgJGAghiZVzQLr5NjuOl+tcnV/Z4m/3QuDYtrfOt8yVEnr7\n8QX2RnZkcKRAqOK4oiVdKgMXy3AwxVK+5jgOCZylc+NiGu5SCH2FaUIq5ZVddZKtDsyYwDJV6P3W\nNZWKI5hbNClVqu1ACNzFGLIQTM2UkCkSsXLgWkjHYpEuHIlXJgmG20lPsYQRL+jjETJGcLUI6RBz\nFok73qQ9CViigpPtBtfxKs51EVYco1Cor0DHqesnimQSI5ulWhovhP51HOmkHfjHVwqFgs6JDuix\nJCXg9fT0cODAAb3eL8DD6jwPayPhqH5HsVhkfn5eb3fu3DleeuklADKZDFu3bsU0Te35nclktJCo\nxPPVEKZrIzIWCgVOnjypv8/NzdHV1aVF96GhIfbt20e2eh+0w4tYiej+8O2A9tQGb7xueno64N29\nsLCgRfR4PE4qldLCuxJzV6vfaBiGnuyp2rl/grGyCTzB/95776Wjo0PvXxu1oJW5xWvrpVKp4DiO\njkqgJkO7rkt3dzfxeFyL6KOjo7pupZR0d3czMOD1kWzb1gJ7FEUpIuK64Wt4Qnoj1AvR64GDeJ7p\njwJfAP4aCPdWjIhYfe4G/jtLL2WPAz/VPnNWj3UhokN7B878Hbmw0OiX26FbzQ55s8/lDzcWsf5I\npyFuhd2L1YEvt0IjUbS7o4woFvCNqAUplbzBprDc3gBS4hQKoeKg67pI38BCOxAiPO+5lF6RFhcb\n65rClaRLFRKlcn3ZhUDa9vIeJbaNUPVXa1S53H4hejEP83P1CbiFC8X6qCB6NSxVbIM2UZ9o3bfO\nMHHrcpxWf8+FQK7lF1ohEOk0VAcZ6lYbBnR0QDLZ+PpmMohKpeE0BWGY3nFqlwOizffTtYpt29rb\nQoW1FEJg27b2uvAPqCgvEagPVR3W71lpqpm1iBpMlVKyuLjI4uKiHohS/St/6Ex/nTUK7egXlP11\n6vfQWusDV7VhH/0h3IvFYp0XmD/EaKM+WW1IylpvrGvVM6ZVA5FhHu/rGaNcJPPC9+ldnNTerWXb\n4Idv+wjz91XbjYREd4otN2/GSgQjRrj9B3EP5gLPnu6hJL2bUvpRLKWD+8KLrSmA42iR0HAcbgFu\nJNg7FbOzLH7jGxSVNxxgOA4dhUI1/zZ6+e7j42xMfl0vsxYnEe/7RHNtlhJGR+HJJ5ee6a4LuRxk\nMroP5DoOF4eHsWdmArsb224k9eU/BXOpz1DpGuBiqQsqXr5yYQjmSwl6Mk01nK5EkZv7JulILDnb\n7J4+Q8W44Ou7STq7DOjcSuBKCAGve12grxOzY+w7m6IilzaxLDh/vpl2e/zwRIYP/387sIyUPtn0\nuW7mx/cETLzvx+L8ozcG+2O5eZf/8swUo8WyLlPvXIkvfv0xelK+SQ4XLjTsy10p3aUJ3jjyVfrS\nntiHlLgdaUY/+yWEUM9LSWZhgp4/+ROEf8qklJDPgy/CnwFk3v72QL84Oz2N6RMcI5rPtdBHCaPW\n7kaTRhuVb7XKHDYRb7l+9mp5nF8tYYJ4WF23qyyXqvdaGqXZvNT1ahWNztPOVAQRERFrgvPABFAf\nbjiIAFTH7z7gfuAiXk71LwNHWmVgRMQKyABfAXprlq0LmjoCVi6XOXbsGNlslt7eXvr6+tZ1KM2I\na49SqcTk5CS5XI7x8fF2m3NdIQR88P0p3nZ/F0KGCJf5Iky83NDVOuXk6Pjm34FsIM6ZJvT3Q03K\nAIWsVJj+4hdxZmbqRMGylBRct61icSoFe/ZANXqZRgjPYeiRR2BhIVwLjuHwPvkK+zhC3SQEIeD4\ncVzHCRc2pYSREcTjj4eL6IUCdHZeVdmuCtdFfPmPMb/z7RAXZ4n1yitYIekwFGZHB+zfH15xi4vw\n1FPeYGDteimRyRR5kaGS3VrfNgSUYy3wJLsMBF4sqETYukSC+K/8Cok77ghv11JiLCwEvdFqSP34\nj5NcZpJBuX8T5f6NdasMA2JHX4HZiRWVI2KJ8fFxnnrqKS0Oq/QohUJBi6HpdJp0Ok02m2Xfvn3a\ni6Gnp0d7ZvhDmfsHJjs6OnTIQb8HSqlUWvMDsaVSiTNnzmDbNk8++SSjo6MIISiXy3qyQSqV0uVQ\neRH94q8fv6CaSqXo6uqio6OD1772tdqjpaOjIzBRYa1iWRaWZZHL5Thx4oT2YJmYmKBUKtHZ2ak9\no+LxONlsVnv6qHbi9+5aXFwElkJ+2rZNMpkknU6j8k/eeOONa77NwNIECsMw6OzsbMn1LJfLzMzM\n6HtKhZ1drwjXJTY3RfziqLdASlyRYn7ojVy0Nuqw5h0p6OmEWM3bqJPagNu/9FiWQMcAyA1L20hp\nQzJFS/A9MwWQBToI9q7sSoX86Chq6qfES3Do1mwngczcHN2c0Mt6DaM1/c1iEaamlr67rjcZwD+R\nUAhKuRyVublAeHrLlXTc+QZELLlUxjKU8l6hhPDmLVac5noWCyBuOnQmyqQTvpqzZkCMBUR04v2Q\nCkY0IxaDHTu8WbpVjLKgc8HCrnZ5DSMwj6CpzOZivDicQRhpXZ6xMZfJyRiqJRgGbHU62d8f7EvP\nWC7PGYsMO6qfJRiqzGKP/ldI5PT+XLwIGzbQTGJuif7COQZF1XvTdSlkbuDsgdeBYXltWkDi8Pfh\nh99eimig2m0yGRT2hSC2aRNUnzMAsfFxxLlzTbV7vaEmxqlnsx9/Dmu1be2+qm+ktvdvo55XauJd\nK/CHcw/Lwe23Vf1f+6dS0vg96tX/rbRb1YtfiPWHkPdP4vT/2bat0zSGXZOw8jcL13WpVCoNJ44q\nVB+x1h7/9fFHLnAcpy5XfStst227Lk+4av/+eq+NbuCvcz+1KQVaUefqmkN9vfrLE5Z3XrUnVSbH\ncSiXywghAuta2dYjIiJWnRNcWkT3ozpbA8CvAv8XcApPTP9S9XNExGryG8B2IE9LQqutbZoqolcq\nFUZGRvTgY3d3dySiR1xT2LbNxMQEExMTzM7Ottuc647dO0z+0etjiLCXmPkCjMw3Hlicn4fD5wOe\nDwFSKRga8v4PQZbLlCcmsMfH60R0G3DbKRTjjXF2dkJXV/26chnOnvXG0cIGAeNAPj0P8Yt4/iA1\nLCw09LiWgCgUYGIi/ODlcmBwctWREuPUKYwarymFMT6OscyLpWGaXqWGDSgYhle+QiFURAeBIywq\nsY46AV8IiWs01zPochF4VzvsKStME3PPHsy77gq/pxwHRka8+6oBVjxeHxpBISXu5h04QzfU1Z1h\ngBFPwve+teKyRHiowZhaEb1cLusBvVgsRiwW0yEDVdjGWg8H/wATNPbGvpZQA1CVSkUPNKk0MhAU\nxv3ieVhftHZ9uVwmFotd014i/gFDNSCpBoP9g6O1g75q39qBbtUe1Z8aUG1lXslWs9x1vZLw7/7j\n+etyPYvoQFV1FUvim6hGKVGra/4PUD9nbXW5jGt3qS3bYrtqkw3K4b8OsLyN/kOoS9p8VnLQWquX\noQ0/3ctbJkPX++LC6G+r9rMRNmmWBu2ithGoezqipQghmJiY4N/9u3+nQ1v7mZ6e1iGuAU6ePKkn\nugG8+OKLPPzww3p9rZe0P2rP/Px8U/uIqm/2W7/1Wzon+NzcnO6rFYvFgOg5OTmpx13y+Ty5XE7b\nNzIywsc+9rHQfu7LL7/Mz//8zzfNbsUjjzzC008/Xfcsz+fznD59Wn/P5XJMTExou5577jk+/vGP\n6wmttfjr//nnn+f9739/02w2DINHH32U0dHRS24rpdS5u9X3hYUFLdoahkF3d7cuh2VZun0cO3aM\ne++9t2l2g9cHf/TRRxkZGanrO5XLZc6ePavbi4oY5X+3+e53v9swOpJf9H/11Vd54IEHmtY/E0Jw\n6NAhPvrRj2IYBo7jBKKJ+SeqAiwuLup+tJSS6elp8vm8Xv/yyy8HUgGo6zEyMsK2bduui/e4a5gf\nAxp7HkRErJyrEcjUTPDtwL/GEzO/eNUWRUSsnDcC/wpv3viHgc+315zVp6kiejqd5r777qOzszMa\nPLpMajvpKxm8bcYgXaOwU2pZo+NfyXlrB2zXIul0mttvv12/mEW0AEn4AJdcZp1af5UjeJcxBNcW\nGt0aegy6QfEFVFeErVy+xJesj7XwW77cdW+VfY3q81pDNgjjr5ZfajC04e+12j98VTujOlzL+D0x\nSqUS4+PjuK7LxMSE9g5OJBL678SJE3qARnm/SimZnZ0NDMwA9PT08JM/+ZN0d3eTSCT0gJM/9+Ba\nRoUnVx4fKiS5ZVk616M/36c/P6gTEoWjUCjoSQrZbJa+vj46OzspFot64HAt91f8LC4uUqlUmJub\nY2RkRHsMjY6OUiwWdXsBr/9W22YgfIDddV3OnTtHpVJhcHCQ7du3o/I2bt++fc339W3b1gPDUkqG\nh4f1MpWPtaOjg/7+fgzDYGhoiJ5qNJtYLKYnBfvbkJq0Al4bq1QqFAoFLly4oCe1DA4OisPWgAAA\nIABJREFU6vpev/g7dMHw5rWe2nXUPH5X/y5cst0z5cotWHXb/b9Zy/x++a+D9P3biNbe6iuYNdGo\nLxN2LP2bFpxT0E5kNa1BQDoPia6E3q5++xYY5DtPyFhA6LaXdZLQ40asjGQyycMPP1zXl1OspH+y\n0md0d3c33bWh0K6CZDLJL/3SLzE2NqaXXU1/qlE53va2t7Fly5YrPm4thmHwnve8JyCU13Kpcqy0\nzt/ylrewdevWyzFvWe6///6rqovacjUqx3333cdNN910xecJ44EHHmDbtm0N67aZdX7zzTdftn2N\n2LRpE7/+678e+o6xElZa5wDbtm2LnNPaSwEvFHdExNWyhasPfe3iifEjwA+BvVdrVETECkgCf4jn\nx/WfgCfba057aElCw7U+qNYO/Pk2/ZimGQg5qlDeZmEzDtU+/hBeV4rKjdnoPIVCoW6dml0cFlpI\nShkaKtOyLDo6OshkMnX7+MOvrhXWmj0REREREdc/yvNASkm5XGZqakoLmXNzc4D3PDVNE9M0OXbs\nmBbOVf5rgDNnzjA1NRV43m7evJlbb72VLVu20NnZqYXnWCx2TYjoygNdeaOowSTlma88axTFYlF7\nVdeGq5dSMjMzowen8/l8IJSi31NkrSOEoFQqkcvlmJ2dZXJyUpdhbGyMYrEY6GOqMkLQQ8fvwa/a\nhuu6nDp1inK5zI4dO8hkMi0LQ9oKXNcll8vpySnHjh3T6XrU4H53dze7du3CNE0MwyCRSCClpKOj\nIyCiqzL7+8S2bVMoFMjn80xNTWHbNpZl0dvbu65FdFsajOR66J8dBCmRQMVIUEhYyOoYsJQgpEPS\nKREzasIU5wvIxUJgmdnRSaWyFKpHyoYBdq4KCUyJfkbFJu+b4WBt3ochMgifma4BZkIGQs6bUmJU\n7y2FAESlEkydEpZCphkkk9DXtyR6Oo4Xwsh1lzyNpSS2fTuivz+wq7t1O5OTIjAykMt5u+uyCC/t\n+sBA80yWQLFscnE+xmJCvT8K0rFOUv0DgXDuTrYbJ1ETNcqysMsxpGGiBNtSRWCaaP3ZMLyMT60g\nrB0ahkEstvQuLASkUgaZTPCaVypgGCqmEIDANWIs9t9IPlVQiyi6MaTR3PZSMeLMJDdiJrNANQqJ\n1UO8MIMQpj635ZbqI3xJ6UUp8v0WSiFYkFnK7lK4/Rm3gCMj0edKsSyLN73pTe0244qwLIs77rij\n3WZcNkII9u/fz/79+9ttymWzfft2tm/f3m4zrohr1fZMJsODDz7YbjMiVoengLe024iI64KngDuv\nYD+VQaoI/BleOPfv43V+39006yIiGvMx4CbgHPDrwI72mtMeWiKiR9QTj8dDQzupvKZhA7Uqx2Ut\nqVSK2267rWm2NRKMp6ameOmll0JnV46Pj5PP5+v2TaVSoeGGLMvizjvv5E1velPd4KvfIy4iIiIi\nImI9UxtOfCURY2o/+5cpGuXNvFZplEPTvz5seVifp1E/6Fqrq+UmAPpFcv/nS633f78WqS2P8iT3\nh/L3L1MhY2vzcoZNNvUf73qoq2aRqyT4/SNvoO/CXdq5NhYXvPbuBN1VjU0Cll1ka2WYlPSl6REC\nTh6Fl19eElClJHfwTUxn7g345YakQL1qJILHrbdyKv66qlOz5JYPvJOBPjvgVJtMwo3bZDDjSbmM\nERZKd3zcU5/xxEbx1FPNN1wI2LoV3vSmJRG9XPZypJ8/vySiGwYDH/kIMpv1hX2Hi4UMf/UNC9d3\nuJMn4Qc/CIrEs7Nw663NNX10NsmjLwwQj3upg6SE2w/0ccvrnYAjdi5nkMsbgWWOA9MXLSq20Lnp\nATq7ljYyDC9Vdysi4UrptUP/bR+Pp+np6Qhst3274MCB4L4TE4JUKobf49xOdTP8Tx5iqmtp6bmX\nnsR+/q+aavdcYpB/2PqzdGX7dfCgXmuOf3TyCUx/9PbSImJHyBjZ3ByUStWCS2wsDsnbuFDZ6e0H\njNnHycvvNtXuiIiIiIiIiIhrFIEnQl4OZTzd7nHgC8B/wxPSIyJWk4N4YdwBfhFonBP0OidSLleJ\n5XLYXImArLxjWkksFqNUKunQqX5Ufs2wwcJ4PF4XbsiyLNLpdKgnugrJGhERERERsZ5JJpMMDAxo\n72IlAA8ODmrvYb8orJ7DUkoKhYL2EjYMQ+cMVM/jTZs2MTAwQF9fn/ZmrxUL1zIq7LxpmgwMDGhP\nX7/w6c8HqkKaq9zyKjz53Nyc9jpXIfLT6bQOeZ5KpXQf61qa4KeudUdHB7FYDNd16enp0V7pagKj\nbdv62vsjCsViMUzTxHEcHcXAdV1mZ2epVCraox2W79OuJWzbZnp6Gtu2qVQqjI6OsrCwwLlz5xgZ\nGQG8az81NYVpmoyOjtLf36+jKvk90SuVCqZpcuDAAQYHBwEv4sPZs2exbZu5uTlc1yUej7NlyxbS\n6XTbyt1uXGkwX0lhlNI6OnUcsP1zaKUXJt3CJqadK/D+twtQzAW2NSolXEnAG7w1CIqig7zIIvGE\n13JnJ06fzxwJbofEGJKY/p+IUglC3pmwbVhc1KlpRKve4WIxz2tYvWdZlueCXZO2xezuhkDoZokx\nn6JQEDi+TRcWYHo6KKJXfzKbiMBxDQplC6c6LOFKsC0Lam4hWQGnHAxw7giouFCxg+m6E9ZSOQyj\nNQJ6I4QwMIzAHBBiMagN1BaPK0900KUSJna6m0pa6Mj0dioLorkFcIVFycpQiHWC9OKClkUZyy5h\nCiXfS5COZ3wtgUoVIAzKJCiQ0tenKJMNssFHrJRSqbQqkV/8fcZm4Y/q0yqEECQSiabaXalUQse+\nmk2zbVf9nFaj0ig1s39s2za2ba/KxNVYLNY026WU+l2t1ViWFeqQFRERcU2xF+i65FZe+oAUcAT4\nI+BPgfGrPLcA/jFwd/XzceCrQHjOmMZ0Aj8L3IjXffwO8K2rtC1ibRPHa4cW8DXgb9prTnu5dkYH\nI9YMV9rZv9Y8uiLWIFfZhlR2vmtxSKdtd881fN9eVTZGeR3kcryaa3epfQN5OyOaRWdnJ7t379aC\n765duxBC0Nvbq0OuFwoFisUi5XKZ2dlZHZJ7cXFRf+7v72d4eFjnCxdC0N/fz65duxgcHGR+fp5i\n0ZvEfK3k2DNNk0wmg+M47N69Ww/OOo6jxeBisRiYYKD+r1QqemLC0aNH9WDjzMwMUkq6urro6Ogg\nnU7T3d1NX19fQGRe6xP9DMPANE0SiQS9vb16MDKZTOrJAmrCgApBroRhVV/ZbJZUKkUul+PQoUO6\n7BcuXKBSqbBx40adQ/1aEdGLxSJnzpyhXC5TLpd58cUXmZub4/jx45w4cQKozxGvylY7WcVxHFKp\nFB/96Ee5++67kVLy2GOP8dhjj5FMJtm4cSOGYZDJZDhw4AC9vb3hRq0TAtKgqIY193u4irCta777\nQnmvds/Nb79QJvjXNeoihD071bLV7k81Ol9YfvFqFftruar5L3PdmkfgHCxztWuagtpW1DaZaxmp\nJWzAm2zSynOBNzlF6Ir3tf6rqNDr4lq0kcXFRT74wQ/qiW8QjKxSS6OIRbXb16ZwURMQP/nJT9Jf\nk+bhamz/1Kc+xenTp0MFyzBba1PuNMJvdz6f57Of/Sw7wqIlXAGu6/K5z32O73//+6ET4VbSF1yp\n7XNzc/zO7/wOO3fuvHKDfXzta1/jT//0T1ec2/5yxuX82+ZyOX7iJ36C973vfZdtYyO+9rWv8cgj\nj5DJLKUJvpp+d6Oyzc/P8453vKNptp89e5aHHnqIzs7OUHvDUl/6aXRvqnXqL5/Pc/DgQT784Q83\nxe6IiIi28TNACQjL+WXj5feZAf4YL1z7i006rwX8OfATwAKeJ/sA8H8C9wJjKzzOduCx6v/jeLnd\nPwJ8EfjfiQYHr1c+CtyK1zZ/pc22tJ1IRI+IiFg1pOsiHSc8kaXrIsIG9iA4ElPjUVN7/IZJMn3L\na8+wFuTSRmOsemBQVhAy3KdDUAHpen/UiBsqnPEy5172NXUNjII1Cmet169k/7B2IWVtbYVus2YR\nAhrNpjdNJALHbdC6pddSRMOrL0LH2f24LjiuS20LalWu2vWAl0vV8zRQIrAS+fzhp03TJBaLEY/H\nAzmqlYiuhE7Lskgmk9rjReWGVue6lgRR5f1iGAaO4+hyKO9pWPqtEELoZf56VAKy3+tFeePH43Ht\niXWtTfpTbcQ0TZLJpC6bEn/9g3Eqb7eUMuD5o1LrWJal25SKamCaJpZl6fZ2rXjDmKZJKpXS9vf2\n9mJZFvPz8wHvIXUP+AUKf754QN9LyWRSr/Pfa6r+1USDiIiIiIiIK0VNgPvQhz5Eb28vUso6z2X/\nZ3/EGVj+3cnf9ysUCnzmM5/R0Y6aZfv4+Di//Mu/HCrMq/6Jwp9iJcxu/3e1XaVS4fOf/zyFQqFp\ndkspGRsb40d/9Ee5//779TPenyppuee7v18Qts7fv/i93/u9ptp+8eJF9u3bx/vf//7QlD21hEU4\n8Iv8fgFY9XkMw+DP//zPGRtbqd6yMiYmJti7dy8/93M/ByxNDA3jUhMwlL3+zypq11e/+lUmJyeb\nNjm2WCwyMDDAr/3ar9VNFnFdl1wuh23bDd8r/O92sBRBC9DRsYQQfOc73+HYsWOrEpUiIiKiZSSA\nXyYooNt43twS+Cs84fxbQLPDuHwUT0D/Xbxc1mU8Qf9Pqud86wqOYQCPAFvxPNr/B5AE/l88Af15\n4HNNtjui/dwO/Ovq519n5RMurlsiET0iImJ1kJKF//yfmfzLv6xfBSQ2bKD7rrsQjUTBSgUymYYi\neiWfZ/ILX8Bu8EIqpKS3WMQMmVleAjraHLa3XPZSZoaFyhSlRR4a/jDm5Fho2Q3psiV3FMeeCZVE\n5xcXuUhjsTw7MMDAnXfWv1AKAfPzUGxv2p3Zc+cYHx0NnfwQL5dJNXipFEDh4kVe+NKXQustHoux\nc8sWklu3hu4v40mcdBbHoU6HFqL9QnFi0ya2vv3tDIa0aduI8UP7IBPfG6y/7tKL8Hr7vh427Grc\nRx8dFywWGsvsf/3IHH/7nSO4br0XYS43zD/9p9eWELkWUN7AQJ1Ip+5P5XmtPoMX8vPQoUPk83k9\n0NrT00Nvby+7du3S4mo+n8d1XbLZ7Iq9VdYKKkx2bZ5z/2f/ILAabKpUKiwsLCCEYGJigkOHDlEq\nlVhYWNBCajqdZu/evaTTaSzLCoQgvRYE0WQySTweJ5vN0tPTU+eNryYNqGWqntSkBIALFy4wOzvL\nxYsXmZub02FBVUjzjRs3ctNNN+G6ro5usNbJZrO87nWv023k9a9/PY7jUCqVKBaLelJBuVzW0RxK\npRKAXg6QyWTYt2+fHmCfnp4GPG87Va9dXV068sO1lAZgtdEhrvFNWqh1dQ5xgRZCYAQWLeuvfJU2\nijqT/HONpARDbVPrum0YwX6q+rxartKXqktlY2DylARDIKgvd8gJWmB0uMd7WJfUHyYdvH5Y2Lb+\n7YT2rm5V3S9/XGVH7Xw17zfUYMm9fslD2G97K35r1XGDdd7A83y5ZXqd0Mdb+0+Ga4dkMskNN9zA\nwMCAji7jF91qRXR/32U5MbpWRI/X5hpoApZlceONNzI0NFS3rtZWNdFN2agm/6nvfvFQiauVSoWu\nrpVExb08DMNg48aN7Ny5M9AHV9Tej7Ue9P50h/7694vwrus2vQ8uhKCvr4/du3fr78tFLVDibq1t\n6rs/UpFfRO/t7W162HgVcUvVuZpQ3GgyQq2Y7H9HgqAHuJpcoiJyNTvFQFdXF7t27aqbXOpPh+S3\n3Y+/Dw5BEV1N2hRCcPToUR09KSIi4prlw0BP9XMJL0T2c8AfAH8BzLXovHE8j/NxvJzWKl/JV4Gf\nAn4SL9/1M5c4zpur230RT0AHz6P9Q8BPAw/jCerRAOD1Q4ylMO6PAX/YXnPWBtEoT8Rlcymv0Eb7\nXMm6iOsLe3iY8vHjhGmPxu7dyD17EI083Fw3PC9fFSklxVOnqMyF9z8EEMtmiYccQ0qJ2WZvTNf1\ntOqwcQyz5LA79wLp3OnwwSwpcaemkcViXa9F4PWUFgkf2JJ4s51lb2+4iG4YMDl5BSVqElJSWVyk\nJGV4uwFMlhm0K5WYPXMmdFUyk8HdtcubnBFGPAFWzPPGbhAhoJ2YqRSpPXtIhwwilV2TuZlezl9M\nhtaNZcHN8SRuT2Nv88VZWCjRoHIlx8/N8/3vz1M/HmEgRJ63vz36bb9clCfrcigPDf/9qr6r56ny\nVE8kEmQyGUzT1F7EauCsdsByraO8xWvx90n8g1hq0K9SqVCpVPT++Xxeh8NX2yiP5doB6msFvxdX\nmJe4GrBWHvoqvL3Kgw6wsLBAsVgkkUgEBrhV7vl4PE4qlcJ1XZ1aYK1jmibZbFZ/VwPufs8kf3h7\nVQfgDWYqcb2rq4t9+/bhOA5zc3PMzs7q48BSlAT1dy1MMGgtBSYmvszc3ON6iUrNrdKBSwkdsQqH\nuqeIGQ6BB83EBIwHU/+VxmYpPvesbyuXM2eOt6CuJS+88GVOn/621r9ffbW+m2BZ0NsrfTmt8XKf\nz8/XH3JhQS+XQnD47Fneed99TbYbnnr6aT71qU8tLXAceOkl8HsNCgGzs1BzD+dLFs+P9CGl0JtN\nTXmm+/sItv0y8M+aZrMQMDHxCt/61mcwTe/3XUrP7MHBYF+rWPQmnPpxXW/yae2kRtMM6rwXLpwl\nm21unmNPDDtGPv8ZhFj63bVtr+r9tn/nO/VNI5+HqSm3+mrjieiVCnz1qwbVn12EgLGxs3R0lJpq\n+9zcBb71rd8lkfBuSCmhQyzynHkaoXOi4xXEtus7vYWCt7yKi8Hp1DhzVq++R3O5KRxntql2rzeK\nxSLDw8NMT08jpeTEiRMsLi7qSYFzc3OB30C/13R3dzcbN24EvH7S5s2b2VqdNKwmWqrIPq0ai/Ef\nt3Zynt+r3l+GUqnEzMyM3l/1VRSdnZ0tjYbjui5jY2MMDw8jpWR0dJSRkRHA6wf19PTobTs7O9mw\nYUPAi14hpSQej7Nhw4ZAn301+gdCCMrlsk7jYxgG2Ww20L9d7l1D9WlqRXQV9agVuddVP9YfOWm5\nbWvtVTiOw+TkJOVyGSEEmUxGi9GtwHEc/T6h2qo6VzweD9Rzrfhf64k+PDys2/6GDRsYHBxEhXOP\nxksjIq5phoDfwOtcjeCJkf8FOLUK5349Xh7z/8qSgK74Op6Ifj+XFtGVt/rXa5YXgL8F3gkcAF64\nGmMj1hQfBl6Dd41/kWiCBBCJ6BF4L2i2Hfw9FUJw7tw5vvnNb2pvHT+jo6NAvQDe3d3N3XffrQdd\nFYZhsHnzZpLJZN0+tbNHI65fRM3/9Rs08IJQ65Y9uLdvo62ulTbW0OFDCBBGg3pY8noKWyt8f6Hn\nxPPUrzv2Gnthq7V/pVlSlyv3is57hU1y1Qi9Tl5SzUZeQWr5ckW41P26pB0s1+oiWonfG1sJg35P\n7Ub59a4nwjzSa5erQWJ/2HflXaS8XvxC9PWGvy4aoSZY+MPcQ32o8+uh/TRqM3785fW3C/8kBH/I\nd39I9/WMYRh84APv4a1vHaH2GRB2ewmxrX6hDMklEtI/PHDg55uWT9Y7heDBBx9gcPD5S5368qgp\nyybg9ttvb+q9dMcdd+jJIAE2b15RXSJhx531x63fdYg77njt1RtcZc+ePfzqr/5vdaLM5VZNWBH9\n3HbbEDfeeENT63z37t184hP/nGLx0mGwaz3oFXv3Bm1Xc1eFWApiIOVGtm3b1rTnUyqV4qGHfoFc\nLle3TnAZuaUDlS7YVFfAIfr7fywwkSni8iiXy0xMTFAqlXBdl2eeeYbp6WkMw+DYsWOMjo7qfoxl\nWVpcllKyadMmbrnlFv2cK5VK+lqk02k9qayV4pxf1PdHeonH46E5x8GLArOwsKDtNgxDR2hSomgr\n+yNSevnKJyYmkFJy7NgxDh06BHji/6ZNm/S2SkBvdG+mUikGBweXFX1bgZowubi4qNtGOp0OTV0T\nhppcqqgV0VsTHWMpNc6ltlsO13VZWFigUCgghNATQFuFSpuk2qqaSKkmIjSqR1XH/rYxPT3N+fPn\nAW9CbDabRQih75uIiIhrlgLwWeCbwBOsrhi5v/p/mLj9Qs02KzlOWJ72F/FE9H0NzhNx7bEP+Hj1\n8yeB4220ZU0RiegRFIvFus6ZEIKRkRG+8Y1vkM/n6/aZa+Dt29PTwz333EM6na4bvN+yZUtLO7ER\nERERERHXO2rimQrLLaWkUChw9uxZxsbGtNeC4ziYpklPTw+WZWnPo0QiQTwe157JzcoN2C783j3+\nAWTVB5mamuLZZ59FCMH09DS5XI5KpUJ/fz/ZbBbXddmzZw+33HKLzjN/veEfqFMDd2pwUS0/fvw4\nzzzzjB50VYOl+/fvJ5lMctNNN9Hf3183uHotokQH8AYq/SKEmlSq7hm1jfJaf+GFF/jhD3+IEILJ\nyUlM06Szs5M777yTZDJJLBb7X+ydeZyjR3nnv/XqlrrV5/Qx03OP57DH9vgAn2MbHBMSjLMbYucg\n7IYkkJAsJFkWCOwGErLZJZuwhGxgQwjJLleWTWCBcMUE28HG+MCe8TW25/JMT0/fp1qtW2/tH6+q\n5pX0qrtnRq3WdNf381FLrfeqt1TSW2/96vk9tNRyN1kHWJaVveuuuxoSNVXv3y0hBAcOHODqq6+u\n635rHaue7Ny5U1vhriT1LndfXx9vecsvNqy91LP8vb29/OIvvvmSK3swGOS+++5tdLmNCnQRuEVj\n9+QutwtR5XXeHdVbadddua9GnoN6vdh6lmXpyN7F7NNXqpyV/3tNqnPXaS0RfTUnZlaWudn7+fUs\nX2VUeyOo/D55TV6tfG+xyc6r8R01GAwrxizwwVU6dk/pedpj2WTpuXcZ+1HrTF3kfgyXBn8BhIAj\nwJ+uclmaCiOiGzRedmC1bnaW6sx53ZibDqDBYDAYDBeHuja7ox8KhQJzc3NMTU3pAVPbtrEsSwt7\nKhpDOcKsFWvAyr4LlPdBcrkcQ0NDWJbF7OwsuVyOQqFAOByms7MT27bp6uqir69PW5uuNSoH4dTg\noltAnpyc5MSJEzoPphp47e3tpaWlhQ0bNpRNkLyU+3SV9aEmBfh8vrJ8lCpqTw2E27bN8PAwx48f\n199BcESpgYEBHem1FidiLAchRB7HMtBgMBgMF4nqw4XDYWzbJhqNkslktICuhGYpJclkUk8Ck1IS\nCATo6urS/589e5Z43Pl57ujoIBaL4fP5yGQyK9bvUX1R1edU18zF+p+JRILnnntOn0tLSwsHDhzQ\n121ln10oFFas3G1tbfT09CClZGJigs7OTsD5PNzBJcPDw9pSXwhBOp1mcnJSn2dfXx8bN24kGAzq\nvrrbOarezMzMMDg4iBCCs2fPcujQIX0v4LbPtyyLjo6Osj7z6Oho2STCjRs36m327dtHX1+fp8tV\nvVB9UMuySCaTjJbSkORyOUZGRrRbifpOqH6ZZVns3btX97tyuRwvvvgiyWQSn8/HLbfcQltb24r3\nWb0E8cpI80pBvXJyw9jYGK+84rg7x+NxBgYGyvqaBoPBcAEoi+Bq+6Fz7y0nT1sIJ4K+OsLy/PZj\nuDS4pvTcD7xYYx33gMdtwInS6xeAe1aoXKuOEdENhrVLBHgzjm3MmVUui8FgMBjqgBqEyeVyetAr\nm83q/ItCCJ1vsqenh3g8rm0+3YM5l7IIuhySyaQWh0+dOgVAKpXSdosbN25k9+7d2LZdNji4HlCD\n72NjY3rQVEXp27ZNJBLRg/Bbtmyhvb2d7u5uYO21m8rP3B21p74vaiJGOp0mnU7rdePxONFolN7e\nXqLRKNFoVA+cGgwGg8FwMfh8PqLRKLFYDNu2aWlpIZ/P636MchQCxyVwbm5OX9OKxaK2TFeCnRKd\ne3t7aW9vx+/3k81mV0SMdovF7nRDSzEzM8MPfvAD3a/t7+/n1a9+tb6uqrQzxWJxRcptWRbd3d1s\n2bIFKSWzs7MMDQ0hhCCTyTA+Pq7rfGhoiOHh4TLno2effVaXa//+/bzpTW+ivb0dKSW5XK5s8ms9\nUYL/Sy+9hBCCJ598ks9//vMUCgVs2yaTyZQ58Ozdu7esTp944glyOSc1RSgU4pZbbqGjowPLsviV\nX/kV+vv7V7SffPbsWZ55xnEBHhoa4oknnsC2bRKJBI899hiZTAZw+mjt7e3aeSoQCPCWt7xFTxDJ\nZrM8/vjjzM7OEggEGBgYYO/evXrbelNrYoHbHWs52LbN4OAgzz//PAD9/f0UCgU9ccRgMBguEHXj\n2u6xrKP07CWMV5LCydPVBszU2E/qvEtnaHY6OPf5LkYEdE6o2ZUrzupjRHSDYe3ShmPDEQQeBj4D\nfAXvWWgNQQonD2PN2y+vPJjLWaaWq2MsVoali7mKSKRHCSUXV+6ltpfSOarwqt+l6r2B1Mj8XfPc\nxBLLJYBtg11jz/bF1nwDqPl9cR6LfZ1AUuvronJxqsfih648yFI1b7gY1GBNKpXiyJEjelBORSlZ\nlsXmzZvp7u7WEdbK8tPvXz/dvunpaXK5HKdOneKxxx7T0Rw+n49AIMD+/fu5/fbbsW2b7u7uslyF\naxVl+RoIBMjlchw/fpzZ2VmEEAwODjI1NYVlWcTjcW33ft1119HT06MF4rVWP+r7VGl7q+oJHHFC\nDTDPzMzo7Xp6eti+fTs9PT10dnaW5W01GAwGg6GeLGX1vFTEaiMnCno5Gqpr7FK4hcnK63PlvhtR\n9lq22u7oevV/oVDQUdPquXLf7ud6UmkT7jWJQU2oUC4GgE4PpYRqtdyrPa1kvbvL7y53Pp/XZfP5\nfDpdFaDvf9S27nNd7sSNelCveqll724wGAwXyEjpuctjWXfFOsvdT6WIrvY9fH5FMzQx/wtHGF+M\nLuBNpddDwLdKrwdXqExNwfoZTTXUpN6ds1qd1fUS4dVEjAL/FSf/yq3ADcBfAV+8TB3ZAAAgAElE\nQVQG/hZ4AGioP1S4v5+W9nZPZc+/73Lsm29BBkPe2ptdxJeqof8LgW9mhvbJSYpzc56rSNvm7Asv\nIEs3YW5yQGIV85YBhIsLbEkeoUuMVS0T2RTW/DSFRMJT0ZSA8LhRV1g4/jte33IJ+Ht6EK96FVTW\ngRAwNgaPPHI+p1J3orEY8UDAs1n4s1lEOu09AQAIBQJs6fLqM4K/qwvuej35rm7P5UXh50y+j+mX\nqGqTlgWzqz3HrliERKL6fSkRwk9nPE8m4P2523aBb3/7WRYWxvFeA2Zm+slmYzUP/8orgv7+Tdh2\n9fb5/DjOT5Ch3riv15UREO7BOZUT0Gs7te1aHphx21S6LU/dg4iqftbDQJV7cNJdN+r/WpE0q5Fb\nspFUfp+8XgM1I+lM3kqDwWAwNAK3OFp5za68Ni2Wd7mRuMXvCxkLqtU/aRTuY3vV+WL/L7a/lTqf\nWkK0imZ2v+8W0WuVsRH1Xtk+Ktt4ZR27Bf5aE0dqfR9Winruf7G86gaDwXCeHC493+SxTL33zDL2\n8wxwd2mb4xXLbj6P/RguDX57Gesc4JyI/gLwaytXnObBiOjrHCklCwsLJJPJqryiKrdWLQshy0N0\ntCyLQCBQZV/ktsU0NJQ/xvkBbOdcPpSfLT1mcKLTPwscaURhIps307Zrl6fgaV91gMIdr0WEqlOp\nSEDYNlYqUUPuA9/cHB3pNMzNeQrNxXyel156iZSHiF4E5lY5h2mkmGR74hn6ZEv1wkyWXGLKEdE9\nkELgkxKL6vkHEvDhJKjxFNGFINDXB7fd5i2inzoFTz99vqdTP4QgFo/THo2eK5MLe26OQjpds12E\ng0G2b9rkOWhkb9xE9o0/RW5gM8Lj/jSfh1ce9DF4vLpJCQHT0xdwPvWkUHDau8dvtGX52LApj7/T\ne9N0usAnP/kYjz32LFLWqr1XI2Wf5xIhoK9vFwMDOxCi8rddkkyOIcTzyz8Xw7LJ5XLkcjkWFhbI\nZDI6El1dZ1XEeSAQwO/34/P5dCT6Wrcsd+fbnJ6eZmFhgZmZGd2PEULovNXKftu27XWRw7pyINu2\nbaamppiYmEAIQSqV0oK6ikQPhUIEg0ECgcCatihX9aK+J+D0Z5WVpopAz+fzFItF3Z+NRqN0dHTQ\n2tpqBHSDwWAw1BWfz0drayvxeBwpJQMDAzq/89jYGJlMRl97otEoLS3n7iHb2tq0+5CyEp8tzf6N\nRCLaxl3Zptcbd1S22r974qJ7vYmJCUZGRhBCcOzYMYaGhsjlckgpdT82EAjo81Ci8ErZXKv+tJSS\nzs5Odu7cqfsCPT09ej1l7a76Tq2trWWC9WWXXab7l5WT7Vaiz9DS0kJvby8Ae/fu5XWve11ZNLe7\nrzMwMKA/j3w+TzKZJJVynHgjkQj79u2jvb0dy7JobW0tE+Hrjaq7jRs3Ami3JDVOGQ6HyyLRN2zY\nUFavfr9fW9EXCgXC4TCxWEzfC61k/8yyLH2fBU5dVorgCr/fr+vctm2GhoaYmprS646OjpIojfVI\nKWlpaUEIQThs0gwbDIYL5kfAWeDHcZxq3RFnP4sTVPe1im2uxRkid4viXwP+Y2mbz7ne7wXuwNET\njtax3AZDU2JE9HWOlJInn3ySY8eOVYnoR48eZXp6Wnda3aiB1cp9tbW1sWnTJlpbW6tmPUeVCGZo\nJBmc6PN34eio4Giq4Ni3vAt4L/Ac8NfA3wGTK1ISd7RW5TJl1Wb5QFQP1IvSn0VvgtSNaS3/6Utg\ngNsSTtR4FUsUvVYU9jI2LdVtaS0vpbgJ6u1iIvwEYNU4D2e/PhA1LoVL1fvqV80iqEGaGksF2LYk\nl5Ms3Uq8kCVDCctj+6aumEue8fFxhoaGSCQSnDp1Sg/YhEIhYrEYQgja2tro6uqira2NWCyGz+er\nikRea6KflFJbZxaLRe6//37OnDnDxMQEyaTjYhKJRLj22mvx+/3s3buXXbt2rWaRG0Ll561s7VOp\nFN/+9rc5cuQIlmWRTqcpFApEIhFuuukmfD4ffr+fnp4e2tu90qitDdTgsvreqByyuVxOTzAdGxvj\noYce0nlFlW37ZZddxmtf+9oy63eDwWAwGOpBMBhkYGCA7u5upJREIhFyuZwOWuju7tbX9unpaS2S\ngyMmKmERYGFhgZMnTwLOdW9ubo5gMEg2m13SBv5CyWazOhe3O6WQO7BCSsnzzz/PN7/5TYQQjIyM\n8Nhjj+lc3gsLCwSDQSKRCMVikUQioScAuM+vnvj9foLBIFJKdu7cyfbt28vKqzh58iSPPvqorr9i\nscjBgwf1et3d3bpPAU7/y+fzlUWB1wshBBs3buTKK6/Esiz279/P3XffXXN992eQyWTo7u4mkUjo\n+4mbb75Z53LfsGED+Xwey7JWbOLC5s2bue666wCn7R48eBAhBPl8npmZGR2Z7vf76e/v1+OQuVyO\nT3/60zo1UbFYpK2tjWg0is/n088qnVG9c9H7fD6CwSB+v59isUg6na4Zxd/S0qLrvVgs8sgjj/CD\nH/xAv3fo0CEmJib0tr29vfh8Pjo7O00wksFguFBs4MPAp4D/A/wGTg70fw8cxBHEj1Vs8zhOXusN\nrveeBL6BE43+n4C/BDqBT+Okj/39lToBg6GZMCK6gVQqxdzcXFXnLJlMLjrLtzIqSXVOVdRSJabz\nt2r8X+A9NZapPBdXAX8CfAz4Z5yL4TeA7IqXzmAwGAzLQkqpBw5zuRyFQkFHy4ZCIS2WqsgIZcWt\nImpWaqC0WXAPWKVSKZLJJOl0WkfqSyn1YFcwGNSDcO7IofWCivBJJBK6faj6C4fDWkR3R86sZZTF\nv+rbqjoBZ7BTReq77fADgQCRSEQ7QBgMBoPBUC/cKVXUOIvq26kIbXXtUf8rKoVaL1vyRjkTLTV5\n07Zt3ZdV4rk7hUqtlCkrdd11Tzp0RxlXUvkZVDr++P3+qiCVlZzIqsqr+jLn47Kk+ntqsoO7Pbn7\nOCtZ55XtVxEMBvX/Pp+PcDiso7O9+qfue6FG9M0u9PNU7d4dne7+XrrTKRkMBsNF8FfADhxN4KTr\n/W/jiOrL5ZdwUsP+YekBUADeD/z9RZfSYLgEMCK6oeZNiemwrRmewYlIjyyxnrJ7vxN4DY6A/gUc\nu/cfrljpDAaDwVATKaWOEgYYGhrihRde0NbSauCpt7eXaDSKlJL29nZCoRCBQKBsIHUtXtfVgFM+\nn+dHP/oR+Xwe27Z5/vnnGRoaolgs6nrp7OzkJ3/yJwmFQgwMDOhBW7cwutZQA4kqAmxwcJCHHnqI\nRCLB4OCgtu/s6emhtbWVzs5Orr/+egKBAJZlEQqFFtv9JUllbnM1cOu24HzhhRf41re+hRCCubk5\nFhYWsCyLnTt30tHRgZSSTZs26YHctdp+DAaDwdB8VIrglXmul8qZvtT79Srjco/lFsqXysndLLmi\nK6/7S01UqJVbvZnwqlv3BMJGT7xYrFzL2Ucj8Kor9/+17N2Bsjbv3r5Z24fBYLhk+V3gL4DbgQBO\nrvTDNdbdjRPBXskUjnX7q4D9OBrDg8BonctquDSY5dzkiWcWW3EtYUR0g2HtUwBOA3uXub76XQgC\nvwK8HSePyl/jCOqn6lw+g8FgMCxCPp/XVuVzc3OMj48D5wZkLMuipaVF58MMh8NlUSRqvbUs9Nm2\nzdmzZ8lkMti2zfj4OOPj4wSDQWKxGFJKotEoe/bsIRwO09bWVjZQtZbrxh1hnUgkOHz4MPPz88zN\nzel2FQwGaW1tpa2tjYGBAZ1HUonva4nKz90rT+vExASPP/64jozL5XL4/X7a2tro6+tDSkk8Htd5\nWg2GS5xeYE/peQp4oA77fB1O/sX7Kc/BaDAYloFyEFLXLHd0digU0vnRVdSzyqEMjn37zMxM2b4U\nsViMcDhMMBhc0cAJFRUthCAQCHjauYPjHKTyi6fTaXp6evT5dnZ2ksvldCoa5RqzGpMfs9mszmEN\njoV+sVjUdRsIBLT1tkpz6I6udjvcrFTZawmw7uMVCgVGR0f1evl8XjvrKGeraDSqU9coR6JGBNm4\n72vgXOS5ikQXQpBKpchmHbPEbDZLoVAoc5xqaWnBtm2daqdRwrS7P+l1f1E5KSCRSOh2r845Ho9j\n27ZOJ2AEdYPBUEeGcILkluKVJZY/WXoY1jengPtWuxCNZu2NjBkM9ed3gF9d7UJcJN0XuJ3y5d8M\nfAAn18mj1EjdbTAYDIb6U5nT2isaxCv6YSVtI5sJd924BdFaUU3rQThXVLYTtz2sl9VrZSTVWsTL\nfaDy+1PLDrRS1DCsawSwCyci43pgoPT+LwGpZWwfAv4tcC+wpbS/MRwB+zPAYH2L60kfTtqng673\nHgNuWmK7CBAG0jiRKF58FCdS5Wrg2YsrpsGwPlHpZqSUZLNZnQdcuQ8pYrEY0WhUX6/OnDnDs88+\n6xmZvmnTJnp7ewkEAmSz2RWbLBcOh4lEIkgpicViZWKhQkrJmTNn+P73vw9Af38/d9xxhxYRu7u7\nmZ2dLevbKdvxRqeamZyc5Dvf+Y7+350KSEpJV1cXr3nNa7SDjxBCW6q7XxeLxZoW8ReD+nyVoKyO\nqyYeqPfS6TRf//rXdVsSQtDb20tXVxdSSsLhMAMDA7S1ten9qj7TStW5u99ZWW+tra267Llcjpdf\nfpmFhQWdMz2ZTJLJOJchv9/Pjh07dKqd9vZ2LcCvVJ/N3a7VZ195/yWlrLKrf/nll3nwwQf15Ipr\nr72WzZs368kjpr9pMBgMBkNzYUT0dcT52nidb2dtDXfuikButQtxkdQjEa6FM1h2DNh63ltLiUAi\nkQjh0VaExBJyUXleWiCQOOOMHqWT0nnUOL4EvJY2TcutUX4pbV1+L8nnYsov1XEvVdRNN9V147wn\nQdoeS0FIG8ty2pVXJVrWuUfVts2ivdVq89JGCFmznM4y9Y2o9a2otczZXkqwpXebtGWzVNClj4qq\nnpubQwjB8PAwExMTBINBNmzYoCMturq69IBXV1cX8Xh8TUYRV6IGlVOpFIcPH9a5q8fHx5mfn6en\np4err75aD8bG43Ftdb+WcQ/YqfoRQjAxMcHx48dJpVKk02k9OLdp0yb27NlDPB4nGo3qtrPWJxr4\nfD5CoZCOlFPk83mdLx4gGo0SCATYsmULO3bsKBvkNKxLLJyI7XaPZW9bxvZ7gX/EEeHdXAbcihMt\n8umLKeAy+TMcAf2HONEp4zjntRTvB34PZ3LtH6xU4QwGQzluMVOJyVJKgsFgmageCoXKcnK7hTif\nz6fdilZKFHVPRFMibq1jFYtFLejatk0oFNIiusqHrc5T5SBfjb6Jbdu6nAp3OSzLIhwO10yD06jU\nSpXHqawvKSWZTEafi/psVJ9RtQ/VDywWi2WR4CtZZq/yuvPOW5ZFsVjUkxfcTgAKv9+v3ZTU5IGV\nFtArJ2fWenaj2r3bWUGl4XJ/V9bDZGiDwWAwGC4F1v7oqgFwbJtGRkY8I9eefvppnnrqqarO2fT0\ntM7B6kYIwc0338yuXbuqot527NhBf3+/zhHp3qbyvUuIPwc+tNqFuEiOAPsuYLsUTqTJQ8DfAP+v\n9N4FRMcI7h+OM5vtLQnhLqSEuXYsaSE8fpUkQFFgp2r/ZHUEQvzrDRvpaG/DUzDN5ej1+8l5LC0A\nsfM7mbpjR6LkL9tLtq2zemE2i3XgAGJiwnNbadukT52imEh47xvnQ6wlwAcCAYjFoFJwEwIiEW8V\nuZEUi+DxWwQggkF8XV3e20lJpnMjx6/9BbCqZ/ynAm089ZVWUkHvuikWbV54YZqpqYynGD08vLCq\n8w+KkRipPQdIerSZvC149nQHp494C/7ZrI+pqctwAuG8b8wHBvbQ0uLRHktcv0ty/a6TCI/tT48O\nOZNiDBeNbdsMDw9z9uxZhBCcPn2a0dFR4vE4W7duJRgMEgwG6evr06KeEosLhYK2PFyrZLNZEokE\niUSCH/7whyRKv4PDw8Ok02k2bdrEjTfeqK01u7q68Pv9ZZE1aw23HaYQgoWFBS0Ij46OcuTIER21\nowb+tm3bxg033EAoFKKlpWVFoqSaEb/fT2tra9XgfjabZWZmRkcW9ff3EwqF2LVrF/v379f2uYZ1\nTRvOxNIf4VgffmCZ220AvgdsBL6FI0YfwkmjtAV4I04apkZwW+n5X+NEwRsMhibGS5SrFNmWI7o1\n0wSwWrnFm80Zx6tO11o/shnqGS4uDVXl+GQzsFQ78XLNMhgMBoPB0FyY0Z91Qj6f58yZM3rWpkII\nwVNPPcX3v//9qgFEt0VV5TYHDx7kzjvv1LNSFW1tbWzatMkMLDYXfmD7eayfL21zEvhL4IvA8MUW\nQgL/70w7Xzm9Ca/oVuvZLkIPiJqRs7YtyGQCUCPCdddAlFv/ZBsdm7Oe61iZDJuDQbxikvNA6/mc\nzApgx1rJ77+WTHdf9cJshpabbsE3PYGX4GkXCqQSCVI1RPQIEK9xXAkEQiFoawOvyMzW1tUX0QsF\nKOXtrcQKh7E6OryVYlkk038Fh257J16zMyan4H/+pcXkRI3NpU0iMUo+P+11ZFpbveu7URRicZJX\n3UK4u7dMzBcCsll4/BHB4cPe51Yo+BkfvxLY4blvIWDHjn42bfKeXiIl/KurjnLPVS9SJbUJiyde\nPsU/z5sBgHqhrseVgyzqffe12B11tB5w5wl1R6eAUxcqUh/Q4vl6QZ2rmkxhWRa5XE7bQ7qj2FRU\n2noRz912/6oOMpkM+Xxe2+aqSQhKSA+FQjoiaj21I4MnNtAJzJb+V2mPlsN/wxHQvw3cg+N4BZDF\nEeX/e/2KuSghHDv3DEZANxiankwmoyfApdNpMplMmTCXz+f19SmZTOoIbkBHFquc1+p6vxrX/MnJ\nST3h0bZtpqamdOCGz+ejq6uLcDisJz9WRnu7rd1XmkKhoMuWTqdJJpN6WTgc1lbjUsqaEeirTaFQ\n0O0BIJlM6jzigLbbD4VC+jzc/ZzVmHRaK4JbSkkqlWJ+fh4hhD4Pd58sGo3S0tKiI7sbKUq778cq\n0xYMDQ2xsLAAOBM1Z2dny9bv7Oykv78f27ZpaWnR25n+psFgMBgMzYFROtcJ7oHC5S5brKPsztFj\naHquwRkoW4wCjp46D3wO+N84UTF1xZaihs2z9Ixmrdreru08buv3F9+P19JmmD8uKH0XvUqjv4vC\nWxEV5973sjRf6rhNj6hx3kv+/ojSCQoQNSYCLOMnzDmMV/TB6taeQE06qZ58stQ4R3mV1lpZiUte\nyxwfdwvhmYFB1GqrhvNGSsnY2BinTp1CCMHk5CTZbJaFhQXGxsbw+/2Ew2Fs29aDRYVCgXQ6vdpF\nbwizs7OcPn2aRCLBqVOnSCQSCCGIRCJEIhG2bt3KwYMHtVCsbBNh7VqVq8HPfGny0aFDh/RkyaNH\njzI2NoaUkiuuuIJ4PI5t2+zdu5f9+/dr0XgtE4lEtDuSOtdiscjf/M3fcP/99yOEIJfLEY870882\nbtzIm9/8Zvx+P9u2bdO5Og3rntmlV6miD3gzjgj/Ts4J6PUgCrwdJ5K9rfTeIZwJsU9VrPs/cEyY\nBM54wKdcyz4KHF3kOJ8Abiy9fiPOhADFIzj3EZVsAn67tF0EeBHHSr6yXG72Ab+Jk3Peh+OE9c3S\n+cx5rB8DfgPHDr+3tM0scBjHSetRj206gH8H3IVTfzaOtf0nWLwODIaGIqXk2LFjTE1NIYQgnU6T\nzWZ1P2ZmZobZ2XM/SYFAgHA4rPsDW7ZsYft2Z059e3s77e3t+Hw+gsFgQ4V0KSVf+MIX+NrXvqav\nv6dPn9Zl7+jo4Jd+6Zd0eqJCocDUlJNhwrIs4vE4wWCQfD6/4i6HUkpmZmYYGxvDsiyOHz/Oo48+\nqpddffXV3Hzzzbrv3UwuPu6+3NzcHF/96le1gFssFpmZmdECbTgc5oYbbqC9vb1seyXwusf8GjH2\nZ1mWtmOvJJvNcujQIUZGRnR5FhYWdLnC4TDXXnstGzdu1OfmnoS8kti2rdMkCSFoaWnRgUXz8/N8\n8IMf5IEHHtD3IdPT03pSjM/n47777uMnfuInsG0bn89nxlkNBoPBYGgyjIhuMKx9fh4np7uXkK7c\nzb8J/DXwTziCusGwLliehrY2hTbDpYNt2xQKBT2opQa+1MCQiiapNVFuLaPqRkWhq7oIh8N6IE5F\nNDXL4GYjcLsW5HI5kskklmWRyWR0Ham8kWoCxnoRh5VDgXqtviPz8/NMTEzoSDflXKAs35WrwVr/\nThlWlDcAAeBp4ETpvRCOgLuA0y+/ELbj9OEvw+nHD+KYEF0H/DJODvP/5lr/l0vHBGc84O2uZX/H\n4gLyr+JYz1Pa/3WuZRbVIvrVwMeALiCJI3ZfB/wsjo38Nz2O8VvAn+DUVR4YxRHiD+II6z9WUcZu\nHAF/D87EhDGceriytO7twKsrjnEL8GUcwR2cPPTdOKL9r+NMdviHGnVgMDQcd1RqZTCDu5/ozh2u\nUNc1d57o1cq17M4FDehIb3deaHd/xC2AVrrINAK3A1SlK5TKK6/yvjdr/6BYLOp69nKaVHnQofx8\noTrfdyNYKpjHSxhX26hzUU4MjWwri+VAz+fz2uVITXhWqL5mMBgsyz/frO3JYDAYDIb1yNoONTEY\nDFGcwS63gK4G6J7Cib7o4dwglhHQDQaD4RJlvUYtXEq5PxtFpQVkZb7U5WyzFqm0J3WnA4DyuqnM\nT7ke6sew4ijB+UUcUfcBIA1MAzM4ou2u89ynv7TdZcD3cfK07MSJev91nOjqP8YR8BUxzmUySlEy\nRCo9HlrieCHgD0uvf79i27d5rP8J4GvAttIx24Av4Ajkf071eMQv4ESpzwH/BmjByRXfAXwaxzr/\na5wT8gHehyOgf7W0fBOwFQgDN5SO52Ynzn3PBuC/4Fjzby6V79dw6uxzwP7Fq8JgaBxLXbvVtUw9\nKq9bzSDyekUEBwIBWlpaaG1t1Zbi7vNQ21XSiOtx5aSDxcrQDH0Et9ify+XI5XLk83ktoqtJp+p8\n3G2iVl+nEVHolRNEstmsfqRSKf1QE0Hd5VRpCSzL8pwsu5KfS+V+FztWZf0qJzH1UJNKKvupBoPB\nYDAYmgMTib7OqeeNlOnoNSUfxBl8yuHYGo4An8EZGDqxyHYGg8FgaAKEEHR0dNDf368HvQKBAK2t\nrVxxxRV6EGZgYICenh4d3aAGydYLwWCQN77xjdredPv27YTDYXbs2EF/f79eb73UicobqaLzg8Eg\nQgh6enq44YYb8Pl83HjjjXR0dGDbNlu2bFkXkehCCE6dOsXo6ChCCKanp3n55ZcpFAocPnxY52mN\nRqO0tbXp/KBtbW3a2cBguAj6Ss9bcCKnc8CDONHTrwbeBLwGJ3L6+WXu86eAa3Gir+/hnNV5Ecem\nvRf4g9LDK+p7pXkeZ0KvukmcxxH3fwJH8L+cc+caAD5Sev0W4Duu/SRwBO69OBHpb8KJmgcndRU4\n0fYjFcd/ovRw80EcMf9jwH90vV8A/gqnzj4MvBt467LO0mBYAdxiciwW03nPC4VCWU706elpTp48\nCTjX/87OTjZv3qz/b2lpYdu2bYAjWq9m2hYVFa8ict/whjdw441Ohoi2tjZmZ2eZn58HnGu2sm1X\nLjKLidr1RAhBPB4nFAphWRbz8/Nl0doAuVxOT1hQUcTuSG9VViVsq21XYszMPeng7NmzPPPMM9i2\nzezsLI8++iipVAqAUCjETTfdpG3og8EgxWKRbDbrOfnSPQljpcb6CoUC2WwWgNHRUZ555hmklGQy\nGU6dOqXTE9m2TSKR0FHclmVx4MAB3UaCwSDRaFSX030/5I78rifuNJeLTXTJ5XKk02nd9m+99Vb2\n7Nmjy7pjxw49IUC1ERONbjAYDAZD82BE9HWCbdukUqmqzqPqUHp15lVOUa9c6WrGpOqoK9bDAOwl\nxEbg3+NEuHwJ+BvgBywrC7TBYDAYVpLlWAyqKJFQKKSvx7FYTEfsdHR0EAgECIVCxGIxbVuezWa1\njeGFlqsZWE451ACl3+9nYGBADzDv3r2bWCxGX19fWV7QtTC5YDkDmcr6Xw3aKmvXcDhMd3c3gUCA\nvr4+Ojs7sW27LHfj+Q6uN0ubWU45hBCkUikmJiYQQjAyMsKRI0fI5/NMTk7qgdpisajr2efz6UH0\nSovc5ZRpreeYN5wXLaXng8CzOKL3adeybwK3AX+LYyu+HFSE+efxzhX+KRwB/TocEX/0vEt9cfxP\nqu89kjiW9j+GI6QrEf1GnIjwE5QL6AoJfBan/l7DORF9rPR8D45gvliueT+OAxfAX9RY53/jiOiv\nWWQ/BkNDcVuyux9KxFP9H7dgC2ircTUJTF3rm4VoNEpPTw9A2fiS25beyya7Ef0OldpFiZvL6WM0\nIv92LdzicSqV0mOAmUxG598Gpw2odlA5MaBSDG6kbX5l2dPpNLOzs+RyOV2vauxS/R8MBgmFHNNF\nNWHUvc9GlL+yfdZyLXA/IpGInqwJeKYLaoa+9SVGO+dcama58BQ5BoPBYDBU0Vw9aMOKkc1meeqp\np3SElpvJycmymauKtrY29uzZ42mLtH//fvbt21dl9aRuMAxNwTjOYNJDQGbxVVcbtxPkYtQeiBaU\n7Mhq3WwIUXPvyzlyI/AsfqlwzrnV2AinZrxqR5TeX+z8BNSsu0vi5m0xERJRs+6WPrXFaq856sUR\naMA9VrPY16CcWq3m3L5rLsNVt1W7XX27yEuB4eFhHn744SWvmYVCgeeee46JiQkAZmZmmJ2dZWZm\nRg+CqWiS1tZWPcCkBlPPFyEER48eZePGjRd0XvUgm83yyCOP0NHRseS6o6OjjI6OUigUOHnypB44\nLhaLhMNhhoaGSCaTZTksL5RMJqMjeVaLU6dO8eCDDy65njsS/eWXX+b06dNaQJ6cnMTv93P06NGy\nNjM4OKgH288HIQQvv/wy8Xj8Qk/roslmsxw+fJiFhYUl1x0eHmZ4eFhHoqUojAQAACAASURBVI+P\nj1MsFpmbm9Ofr6oHKSWBQIDHH38cy7KIRCLnHY0+NzenI6wM6x53f/w3OSeggyMs/waOuH49cAA4\nvIx97ik9v1xj+RgwhZOTfB+NF9GP1Xh/vPTc4nrvQOnZjyP+ezFQet7ieu+vgZ8Hfhf4t8A/Ag8D\n97uOo9iJY9texLGB90LgCPYDOB0lu8Z6BkNDWGrCZeU6qyGCXixeAR1ez43mQo7vtW4jxPXF2sJy\nz2M120stEfl82nOj2/5yjlE58aUyyt+4edaNbwC3lF6/AfjWKpbFYGgU6kcoQnP2V43uaFgzmMa8\nTlCzUHO56sl4Kq9QJWqwsHK2spo5qSLe3DRLNJIBcCwJvaI4VgmJM451DK9geNtOUiyGqPWz5LS1\n2oElqWyeHz4/xOB4wTPWXuZy5LNZzzD8IjC89AmsGFJKEok5Hn30YeLxzvKFAkQ+R/CVk1jzc3iJ\nt7JYZC6dJuOxVOIkr4wucnzf8DC+Bx9EeIh5Z8+eXVXRSErJM7kckVor2Lbz8N6axPQYLxx5AET1\nuc3NwcIC5PPeorOURWz7LI6DaCUCmGQ1jR0SiTkef/xh2tqqhcZ8HgYHYXra+9xs2yaXm2Ox+TUz\nM534/SE87+ul5LkTw7TKs1UyvBSCo2fOYJeiSgzV9PT08NrXvpazZ88ua/2Ojg4tKNey+Bsert+v\n2O7du3n1q1+9KtfzcDjMz/zMzzA4OMjk5OSS66s+CcCrXnUucNM98Hb06NG6le/ee+9dNbF4y5Yt\nvOpVr9KWrctBSklHRwc33XRT2XtQ3mdLp9O88sorF1y27du3c9111y294goQDoe57777GBwcZGSk\n0snZG2Xx39vby969e4HaA/iAvg6qCRnng5SS++67T9uNGtY1s6XnBRx3qEpeAIZwBOLliuhKhF5M\nHB/BEdFbF1lnpajViVSdN/cXqr30vBV4+xL7dXdtHwTuwIkeP1ja9u2lY3wbZ3LCYMUxfMs4hg+n\nG51eYj2Doe7kcjnGxsbI5/PYts3k5CSJRAIhhLY8V9ejhYUFPVlLSkk6nSaZTOr/p6enGR11fiL8\nfr92N8pms9qBpZ4Ui0VGRka0C6K7vzE3N1cWWTw/P68niqrxJTWJzbIsotGo3jYajeLz+cjlcvr8\n6omq58HBQf2/cqYZGxsjnXZ+Cpx79wQTExN6MmwqlSIYDFZNRKycxGnbtrarrxeqPOq+YnR0lOnp\naf1+Op3WkehSSmZnZ/UE3EAgwMjICNFo+WiBV19nbm6u7s6Tqoyq/zY+Ps7MzIzOjZ5MJnU7UpM+\nFZZlMTMzoz+XQCDA6OgoCwsLZZNChRDMzc3Vvf+eTCY5c+YMPp9Pl1fhznO+sLBAOp0uC1pKp9PM\nz8/rdjE6OlrWT1T3epOTk2XOEgaDwVDBrtJz/S+KBoOhjLqK6CrXSzabXbblkaExmM9heaiOuW3b\nK5Y3ab2yadMmfu7ntiBlBm/hcRBnjK9WW11crLQsyQ+HbXyj3utJgLvu8hRcJdAZibBx06ZFj7FS\nxONx9u7dzcMPP+gdASgl+CxEW3v1MpzyyzvvRNYQk5eMtA8GEV//uueiom2zf//+VXOY2L5zJ4/d\nfXdVgssyav6+SWzhJz/+NTwnH0g4eBBvkbi0vZQ2tSZ0hsPb2bRKbaa1tZX9+/fw5JMPeLYZKSEW\ngyuuqLUHyb59i0fl+v0WllW7bs/aNl99pejZtgq2zY/VPvi6p729nbe97W2rXYxFWa1+QyAQ4O67\n727qqIzVsufu6+vjHe94x6ocezmsVpsJBoNN32ZMP9xQ4qXS8wS1O7ZjOCL6cgVvJVJvWGQdtazZ\nB9hU+b4M/Mx5bvt9HCG9FycS7bXAz+FEoz2EMykhgTOBARzr+w5MmitDE6Luuz72sY/pgAblLgPl\nuZ6BMrdBIQTz8/OcOHFC7290dJSHH35Y/6/6MVJKotHoeTusLIZlWbS3t/PRj360LBhDlU+lU1Hv\nHTlyhDNnzmjRs3Ibd5/LnWN8bm6u7pPTurq6+OY3v8kjjzwClOcvz2QyZaLn888/z5kzZ8rKpmzF\n3XhFoE9NTdW17O3t7XzjG9/g1KlTSCnJ5/OkUilt9e8udy6X45FHHin7DJ588sll9W3n5ub46Z/+\n6bqVG6Czs5PvfOc7nDp1CkCXHdDCdKX7pUIIwdTUlD4Xy7L47ne/WzVuoZyH7r333rr1x0KhEGfO\nnOGDH/ygfs8tklsuZzbldrRz5079XiKR4LnnntPndPbsWc8JCvPz81x//fUmNdDy+AfgUOn1qVUs\nh8HQSNTsnWdXtRS12YQzkddguOSpq4ieTCb57ne/SzQaZefOnezevduzI2kwNCvJZJIjR44wPDzM\niy++uNrFWVNcc801fO5zn1ntYizKag1y9/X18ZGPfGRVjr1cVuPGTQjB7XfcwW23397wYy8X02Zq\nY5xJamPqZnFM/dTGDKJ5Y9qM4RLh0dJzP06Us1d42ebSc6UNeS2O4eQSv6zG8nZAWcPUy5ZDqRj1\n/tIdKT0vNx+8F2PAV0qPD+LkXt+OI6p/FSffegZow7HCf8l7NwbD6hGJRPj4xz++IlHilQSDQTo7\nO5decZlEo1He9773rbiTmRCCvr6+uu3P5/Px9re/nZ//+Z+v2z5rUe+y33PPPRw8eLBu+6uFEIK2\ntra67vOee+7htttua8hEyPZ274CEC2HTpk186lOfaki5W1paqtxBDZ782WoXwGBYBQZx7h2uoTnt\n3P8H8O9WuxAGQz2o65U4Fotx++23E4/HCQaDdbf6MRhWmlgsxlVXXcXll1/OSy+ZMZV6Yga4F8cI\nI96YdlMb02YMBoPBYLikeATHdqkfeBPwfyuW3wn04Yjrjyxzn98G3gL8AvCHVNun/yqO2P08jlV8\nPRgrPVfnk7k4vo8zeWAL8K9wRO+LYRp4Escevrf0Xhr4Jk79vwvH6r0edAA7cAYwD1UsawV2l14f\nonyQMwJcXnr9HFCde82w7hBC6LQjlxpCCLq7u1e7GBeEO33SpURrayutrauRrePiuVTLHggE2Lx5\n89IrGgwGg8FgWBPUdQTesixaWlpobW0lFAoZ4aOJUDMkV3KmpLK7amY7zaVQeeBVGzYYDAaDwWAw\nGAyGCq4Gfqz0uNX1/h2u9/dXbFMAPlR6/XHg5or9/XXp9eeAs8ssx5eBF3GE5y8B7pDS+3CEdXDy\nhdcLFdH+08BNpWN2UJ6n/EJIAx8ovf7fwC/iROy7uRL4C8BtU/S3OBMJKv2RbwFehxM5/5jr/d/D\nsY5/B/ARoDJRbi/wu8DvnEfZXwv8CO9c99eWlv0IRzR3s8O17NJUTQ0Gg8FgMBgMBoNhDWM8YdYY\nxWKxLBcPOLOBM5kMJ0+eJJPJVG2TSqWq8gZJKenq6uLWW28lHA5X5SHq6enBsixPwdxMnjAYDAaD\nwWAwGAxrmA8D93i8746e/jyOuOvm0zj5uX8DR3CdwIk8V96+TwC/fR7lyOFEVX8XuBs4gyOqq8ho\ncITivz+PfS7FQzii9I2cs6gHZxLA2y5y35/BsZ//zziTCf4ax4IdYBvnhPrvuLa5Gfgl4H/hTD4Y\nL+1DhQn+MfCMa/0XcSLd/y/wPuA9wGmcnOmbABVG+/GLPBeD4YKQUjI6OtowO/cNGzZUjQddKFJK\npqamGmbnXs987jMzM8zPz9dtf7Wod9nn5+eZmZmpy74WQ9m5x+OV844unPn5eWZnZxtm516vsufz\neUZHRxtm517PlAsGg8FgMBjOHyOirzFs26662RJCkM1mOXHihOfNTDqdxufzlYnfUko2bNjA7bff\nTiwWK1tfSklfX5+xEjYYDAaDwWAwGAzrkX8CRpdY5/Ea7/8mjg37rwB7gSDwMPAPwF/h5Ow+H17E\nidD+beCNnLNZ/zLwSeABj23ypWNdiH14AScS/CeAq3CEZ0G5Bf0/4AjsUzX28SCO9fwxj2X/Ffg6\nTqT47TjCeRYnWvsQzoSAH7rW/2UcK/xX4QjnHTiW84/jiPJuwV3xPRyL9XcAP4kTfR7FibL/J5yc\n6t+uUXYvXsGpTy/Vc6S0DJy6czPjWpY8j+MZ1jALCwu89a1vpb+/X7vjpVIpHSwRCATKRG+fz4dl\nWXo8p9Id0J0ey/1cKBQYHx/nk5/8ZN1ydC8sLPD+97+ffD5PJBJBSkk2m9Vlz2azFArnvgZ+v1+L\nyVJKisWiXuY+D8uyiMVi+P1+pJQcP36cT3ziE+zdu7cu5S4Wi/zpn/4pzz33HAMDA7rc6XRal3ty\ncrJsG/f4mc/nIxKJIIRASokQQo+xCSF02QGOHz/Opz/9afbt21eXsn/xi1/ki1/8Ijt27NDHVmN1\nQgjC4fCigS5LCcHqnF555RVe97rX8Vu/9Vt1KTfA3/3d3/GVr3yFHTt2eC6vLHdlWZdTdnDq/O67\n7+Zd73rXRZT2HKdOneJtb3sb+/btq/puSSnJ5XJlwU1Lpahzrx8IBHRq1JGREXbv3s1HPvKRupTb\nYDAYDIZlsAXnXmi5/C7wzytUlqbBiOhrFHcHrdbr5XCp27MbDAaDwdBspFIpnnrqqbKBwmZj27Zt\nbNu2reHHLRaLHDlyhKmpWrrL6hKPx9m/f78e3GokiUSCQ4cONWW/TAjBli1b2L59e8OPXSgUePHF\nF5u2zQB0dXWxb98+PYBuWBN88iK3/0bpUS9mcKziP7TUiiWywK9dxPFywNdKDy/+YInt/6b0qMUL\nwL9bZll+gLeN+lJM4US8/+cL2LaSp6ldn0cXWTa8yDLDOkVKSWdnJ+973/vo7u5GSsnIyIh2FYzH\n40QiEb1uOBwmHD6XzcC27TIBz7KssuuPGhNKpVK8+93vrnIyvNiyCyF473vfS29vL7ZtMzU1RS6X\nQwjB1NQUyeS5+SKxWExHBxeLxTLnRCmlFtz9fj9bt24lFotRLBb5kz/5k7r3owuFAm9605u45557\nkFIyOTnJ6OgolmUxMTHBE088oddVIrkqZyQSobe3V4vXlmURDoexLAvLstiyZQstLS1IKfnzP//z\nupa9UChw44038ta3vhXbtss+b7/fT1dX10UHwEgp+du//VtPZ8uLIZfLcccdd/Drv/7rWqx3s9jk\nEKBmPap2qLb9zGc+Q6FQ0O9fLLZtc+WVV/L7v//7uh2oshaLRWZnZ8nn8/qc/H5/2WdQeR5zc3Pk\ncs6ctng8TjweRwjBd7/7XZ5++um6fkcNBoPBYFiCMHDdeazfsfQqlz5mJMdgMDSEqakpnnvuucVX\nWkVhwOf3c/nll9PV1dXwY6fTaZ599llSqXTNdZa611us6i7qPlFK4m1tHDhwYFXcJ0ZGRnj55Zcb\nftzl4PP5Vr3NqOiIZmTr1q2rIqhdCgwPD/OhD32Iyy67rOlSoAghdKTJ7/zO7zS8fOl0mo997GPY\ntq2jr5oFNSj2yU9+kp6enoYf/9ixY3z4wx9m7969TdVupJScPn2agwcP8r73va/hx1dtJp1Or8rv\n8VJkMhls2+bjH/84ra2tq10cg8FgMFwCCCEIBoM6SjsQCGjBUEWqKoHOvR5UR3T7fL6y5eqeTgmu\nK4Hf79dR4z6fTwuNPp+vTND3+/1lUfWVoqn7HILBIKFQiGKxWDf7+UrUcWzbJhAIEAgEtChdGU3s\njkB2PyrfsyxLn7eUsu51LoTA7/cTDAb1/lUdBwIBIpHIopP4bNv27Fe6hWhVH9lstq5lB6cNqBSS\nlW23sr69UldWPqvvhZrAoOqn3pNQLcsiGAx6fp7q2KpM7v+9hHx3u1efpSp3M/X5DQaDwbDueAlY\nQszhTCMKstoYEd1gMDSEH/3oR3z8Pe9he1sbnrcBCwswMlJTDS5KyVyh0gGxHD9477uEr8ZyG3il\ntZX3/+Vf8uOvf/2ix1gJhoeHeec7P8SJExtxJny5kVgW7Nzpo5aeVCxKhobyzM97z1DetSnH9fsy\niFr3jbOzMOrtSJooFEht387nv/zlsgiHRiCl5Hvf+x5f+bM/Y6OX8CAlyWAn06E+pLCqPlspIeQv\n0tua8pxIULAF0+koBbv2QEah4OynenvJyMgR/vAPf5fXv/7Hz/fULprh4WE+9Hu/x0BvL2GvhiEl\nTE9DrWgBISAWg8Vy8Z09C8lFnEX7+pyHx76npqc5cMMNvPe97138RNYpxWKRK6+8kg984AN1zeVY\nD4QQfPazn9XREI1GDXq95z3vaTpBNJPJ8IEPfGDVokGKxSK33HIL/+E//Ad8Pl+VVavifN+/kG0q\nByK/+MUvMj4+fl7nUy/UAP073vEOrrvufCZMN4aJiQn+6I/+qCkdBAwGg8HQnKTTaY4ePUpHRwdS\nShKJhI6kdfdDpJRMT0+XiZuFQoFsNqvFxIGBAbZs2QI4uZzn5uYAp1+zEq5IuVyOBx98kPb2dqSU\nvPjiizr6PBqNVk2SVMJnNpslkUjo66XP56O1tVVPKIjH43oywUqUW+WiP3bsGFJK5ufnSSQSCCE4\ne/Yshw4d0n2hfD5PLpfTgmhl9D+cE0aDwSB33nkn/f39SCkZHh6ue9lDoRCtra26T6Qs3N0R87Wo\nJdI2SrxNJpOMjY0BzgT6w4cPY9s26XSaF198UbdtZbHv7k9Fo1EtTgcCAXbt2kUsFkMIwYEDB9i2\nbRtCCNLpdN0n5yYSCU6cOIHP52Nqaoof/vCH2pZ9ZmamLBJ9fn5euzFIKQmFQmX3gMlkknw+j23b\nvP71r+euu+5CCEEymTT9R4PBYDCsJl8F3r/ahWgGjIi+xqicAVvrvaVYzMbddOIMF0I+l+MNmzbx\nb666CsurDZ0+DYODjmrpQc62eTmZpChltVgKWEBL6bkW4RrLC8B/LxTI573SGK48tm0zN9fF9PT7\ngQ1Vy0MhuOYaPx0d3t/hfF7y1a8mmZ7OUTlNQAi48qZZPvL2USxZ4zfguefge9+rnsAgBCeSSf50\nBWacL5dCLscv7NnDT+zeXVU+KSWn267h0IbXID0uZxLoima57bKzWB6nnsr7eXqkn3Q+4CmySwnp\ntHeTtCzJl770AQqF1WszXR0d/Kd3v5sNXkJjsQhPPw3j495WBELA1q3Q1lbbxuAf/xFOnfLeXkq4\n8054zWuqlkshePrwYb6/lPPEOkcNcDWbiG5ZFoFAYNVEdDiXv7HRE3eWol4WkBdDIBCgpaVl1cvh\nRkq56u1YCEEoFCIWi61qObyYn59vqs/LYDAYDM1PoVBgampK358qVxP1Wr0vpSSZTGphHBwRW9lu\nSynp6OjQgm4ul9OiZKUgWS9s2+bkyZNa1D18+DCzs7MA9Pf309FxzvUzn8+XneP09LQeiwoGg2zY\nsAEhBJFIhIWFBS2gr9SExmQyyfT0NLZtk8lkSKVSCCFIJBKMjIzo9bLZbJktfS6XY3Z2Vten+zkc\nDrNlyxYtks7Pz9e93H6/n1AopPtkKj/7cljtPkoul2NhYQGAsbExDh8+TLFYJJFI8C//8i8sLCzo\niQrpdFp/9kII2tvb9cTSUCjEDTfcQHt7O5Zl0d/fT19fH0II8vl83UX0bDbLzMwMlmVx9uxZHn30\nUVKpFLZtl9m527bN+Ph4mYNcLBbT9zlSShYWFvQkmV27dnHrrbfq76sZfzUYDAaDYfUxIvoaI51O\nVw3WCSGYnZ0lk8l42i/5/f6qTrbK66QGsSs7bitln2VYwwhB0LJo8fsRXjcCqk3VuInzAUGcqHEv\nrNLyxVpmCG8R3bfEdo3BAiJUR6I7+HwhAgHhqXc6wk4BR0Cvrj+/L0RLMIioJaIHAk79e+w87POt\n+o112O8nFghUl0/ahAJBgoEYUniI6BKCQR+xUMhTRMfyEwpGKQrv3MZSOlq0l+OeEBLLWt1LqM+y\nCAeDxLwGBIpFCAadhxeWtfhyONcuaonogQB4iZyWRWgV8kUbDAaDwWAwGAxrCa880YrK972srd3P\ny9lnPfDa92L3k16TFCvHphopJC5W3175uVV9ern8eAWzNOJcalm0ex2/0o58te/9K8vgZZHvtX4t\nW/2VpNKxaSlr/8p1FW47d4PBYDAYDM2HEdHXELZt8+yzz/LQQw9VdYRnZmbKrJDc7N+/n97e3rL3\npJRcc801XHXVVbS0tFRtsxq5kQ2G9cvF3mib2cs1WaJqzMRvQyOxbdvTntLn86376647R2I2m/XM\nlxgKhfRrn2sC0FoflCoUCro+3AO6qg4WcyNa63XjjgRTET6VA55qYmjlAOZarxuDwWAwNC/q+qSu\nUZFIBHCuZ/F4nM7OTv3/xMQEg4ODelvbtikUCjoKdvPmzXpZPp/XkdaV/al6YVkWHR0dxONxpJRc\nddVVOjJ+69at9PT0lJVHRexOTExw6NAhisUiUkra29u57bbbdD7xDRs24Pf7y/JL15uWlha6urq0\nCK36BurYlXbuimKxSCaT0X2MbDbLyMgIhUKBQCDAFVdcQXd3N1JKDh06VPdyJ5NJJiYmAJienub0\n6dPYtl1lgV4sFpmZmdHR3MFgkNe97nVEo1FtBd/b26vzq2ezWQolW7aVdC5Q7TASibBt2zYdnZ1M\nJslkMgghKBQKjI6O6lQFUN7fDwQC2uXLsiz9mQD6HOpJKBSiq6tLH+vAgQNks1ls2yaZTJb1zWdm\nZspSLBw/fpxRVzq9YDBINBrFtm19Du6+vMFgMFwkrwX+FU5s2STwv4Bj57mPFuCa0iMCfAk4VbcS\nGgxNjhHR1xi2bZPP56tuKpR1VOVNkroxqIwsVx1o942bwWAwGAyGlSOfz2vbSCUaCyFobW0luM6j\n+4vForZIHB4e1raPSiC2LItNmzbpPktrayt+v1+vs5ZJp9M6Z6Lq51mWRTwex+/34/f7qwbhmsGa\nfqVx54110qbM6f6wGhz3+/1Eo1FtB68Gjdd63RgMBoOhuRFCEAgECAQC2hLcsiyklHR1dWkhWkrJ\ns88+y7Fjx/T/7jEclU9dkc1mGR8fp1gsksvlVkxE7+7u1vnct27dqsendu/ezcDAgF63UEqpZlkW\nx44dY3x8XFtYDwwMcO+99+prczqd1nbuKzFGJYSgra2Nnp4epJS0tLTQ1taml99xxx3L3tf8/DyP\nP/64Fk5bWloIBALYtl1mZ18PVM5tZTf/3HPP8dWvfpVisUihUCizmc/n8xw9elR/7i0tLXR3d9Pb\n26st9Nvb2/V9Rzqd1iK2W7yud/mV4B+Lxdi7d68W8FtbW/Uki2w2y5EjR/Q9gDpvdW7KYVNNqlX2\n7+q86z0BIBKJ0NPTg2VZRKNRbr75Zj1h023DLqUklUrp8ygWi3zhC1/gpZde0vvq7+8nFotpEV2d\nw2qnSzIYDGuC/4KT03oeR/S+HPht4E3Ad5a5j3/AEeHdF9+nMCL6eiAG3Ars4NwkjOc5/0kYlzxG\nRF+DeFlhmcFAw1pnbQcMi1ou98vevvaiS/234eI++VoO95pmr55m/vyauWxNjLleL47bRnOp9dZj\nXdayilzMJnWtUzl54HzWNxgMBoPhUqCWdXTlxDAvu+mVxitPuHuZ++G13aVI5TmttCV9ZZ9PTbhQ\nATNqUqFXBL/biafWWGIj2spiffwLrbtG9um82rX7tVf7NxgMhhXm9TgC+v3AvUAC2Ad8F/gisBOY\nWcZ+sqX1fwTchiPAG9YH7yw9KjkO/AHw+cYWZ/UwIrrBYGg8tW5mpFzUP9sqzYiu3FoClpRY+bx3\nvnVACoGIRhFe1m8qv/OqIoECUBkNIJESstkg2ax3TvRCQRIOQ1tbdU50ISDkyyMXFrxzogvh5M/2\nyqvt7Nx5rDaebUZFoHqL4RIQFth42/3ZEnyZJL6cVSPtt8Cf9yGKVlWjE0IiZP0jN84HWSxCMon0\nilC2bYSKLKnxfbMzGfDX6AZIiSgUnO9Tre9rsQi5XPVyISCfN17454mKwsjn8/p/NfBpBlvQtqMq\nqkTZMypLT8uyyGazenBwOU46amBNDTCqiI9LSURV7UZZuqdSKd1ustmstkB1W5arCP3l5F8MBoNV\nrkXq/2ZHWbiD42SwsLCg25ByefD7/dryVtlnKmrl3HS/VnWh6sn9vsFgMBgMF4qXsFwpxC2Wi1s5\nrtTax0qLuu7oYq+HQl1HmzUn9PnWkbvfXktIXYl6rzyWct1RfR7VHlQEurvPUvk5rQa16sztHqTK\nWdm21QSByvUbIVxXlruyLO71ak2oUOemHkZwNxgMdeTdpeffxBHQAV4E/gj4JPDLwEeXsZ83u14P\n1FzLsJ7YBXwOeAPwb4D86hZn5TEiusFgaByhEMRi1eKaENiBAHYmg3DlFlNIwIrF2HHnnTVFP5lM\nIh9/HEr2XlUEg4R+8zexururjp+xbUIPPLDKkbNpHDeUyYr3JYWCxbe/vY9AwFvoDgbh3nuj7NsX\nrapaKSS7nvke/N5Hsb1mdQPihhsQ73xn9cCFEDA0BF/+8oWe1MUjBMTj4PG5IW3iG1rYvkXUjBgP\n+AJMBjd6f7Szo+z7P3+AmBiv/uylBJ8P+7K9yFLOwbLFwHdnXwZ51wWdVj2QJ09SeNe7yAeD1fH4\ngQCBH/9xrB07PMVsmc8z/9nPkn3lFe+JJ0IQ7+0l1NLiiOVVO5Bw/Di4Bgc0lgUnTjgN07BshBAk\nEglOnDihc+BFo1F8Ph9tbW06F2Y9UAM06rWyF2xmFhYWeOqpp8hms5w4cYLZ2VnAsW1vaWnBsiwG\nBwe1uDszM0M+n6dYLJblIHTnUFTrdnV1sXXrVsLhMFdccQXhcFiv2+z1IoRgbm6OoaEhJicnuf/+\n+3X+yiNHjpBMJolGo7r9tLW1sWPHDvx+P7FYbFFB3bIsrr76ap3zcWBgACklgUCAtra2pq+bTCbD\nmTNndE7M733ve0xOTpLNZslms8A5609ltao+e7/fT6hicpmyyFXtJhKJEI1GaWtr48CBA3oChrte\nDQaDwWC4EHw+n77OQLmYdvbsWcbGxnTf5tChQxw+fFivt3//fu66yCkDegAAIABJREFU6y79f09P\nD1NTUwghyOVy2ro7m82uyPXK7/dz+eWXs2HDBn1NVdfOjo4Obc8upeTRRx/lgQce0JP/lD23suo+\nefIkfr+/TJRXE+PqjZROHm5lfz44OMjs7CxCCJLJJKdPnwbQ/8/MzOjPZcOGDdx4443afj8QCLB9\n+/aySZqWZWHbdplFfD0QQtDe3q5t8qPRKH19fboe3bnMbduuyol+/fXX636iyj2uUgBMTU2RTCYR\nQjA/P088Hq9r2cHpy/f19SGEIJPJ6PopFAps2LBBlzWTyVAsFpmbm9PbdnR06EmewWCQq6++mtbW\nVoQQ9Pb26v5qe3t73futgUCAlpYW3Wf0+/26rMVisazOBwcHSSQSWjDfvXs3qVRK7+vuu+9mWykX\n/OWXX05XV5eum2bvbxsMhqYlAtwOvIATNezm68AngJ9keSK6Yf1xGvh7HBeDIWAUR0d+Fc7kC+VG\n8HM4bga/sQplbChmhOcS5XwsjlZzRqnBUIbP50R8e7VHy0IWi8gaUc8WEN+4sWbEuJybI7vYIIBl\nEbr8csSmTVXHt4tFfM8+u9yzWCGKOBMDq2+SbNtiaEgJmdV1F4vBtm0BbrlFVGma0pK0vjILTz3l\n/TsgBOzb5zwqozaFgEikdpR6owgGIRz2ENElgWiA1lZqiuhC+Mj6op7L/EVBzyuHCJw95T2BwueD\n9gL4+6uqXQqI5OZYzUQCcn4eefQotlcpwmHkTTc5ded1vbBtcidOkH3ySe+dC0HLrbd6T3pRzM9D\nKfde5bZMTkJ///mcjoHqqAV35EVl5Ejl68X2qdatjB6uZd3YbKhBYhVtnc/ndcS+ek8Nqqo+Tz6f\n17k+lWAKaHtLFamu1lVROpdif0m1G3WumUyGQqHAwsICyWRS5w8FZ2A7nU7r6HR33vjK9mFZFrlc\nDsuyKBQKZfnFLxXcEVi5XE4L6MrJwD2w7xYSauWIVdFyqg35/X6dZ/1SbDsGg8FgaE6EEIRCIcLh\nMHAup7MQgomJCS1sAhw/fpzjx53xcdu22bNnD1dffbW+LhUKBebm5vS1vrM0QTiTyayIs4zP52Pb\ntm309/ejLMW9+prFYpGjR4/ypS99SQude/bs0f2zVCrFyMiILqOa3Kicd1aCTCZDMplESsmpU6d4\n6aWXEEIwOTnJE088odebnJxkcHBQ1/GuXbsIBAJ6Ml53dzdXXnklra2tVecci8XqXu5YLKZzuff1\n9XHgwIGa/fvK/op70mixWGRmZkYL73Nzc1r8TaVSKyKiR6NRurq6EEKQz+f1MWzb1uekjj80NEQ0\nGtVtec+ePTp/eygU4tWvfrUW4d1R6S0tLfreoV4EAgEikQiBQIBoNEp7eztQXb/u9qru6zZv3kw6\nndb1/lM/9VNcffXVVfdm6lwNBoPhAtgNBIAjHsvOArPAFQ0tkeFS4QSwHe8B72+VHv8W+AzgA34N\n+BTwTKMKuBoYEf0SJJPJ8Pzzz1cNYkr5/9l78yA5rvvO8/My6+6q6rvRjYsgQIAAL/GUeOiwKMmU\nxbXkpY8Jy8eGvJpwOGJmNsbejdFs2DNe2zOWx3I4vDMaj62Vx3J4JHsUtiTrMC3KFE1ZJiVRJwEe\nIEDcQKPvrqquKyvz7R9Z71VmVVajAVT1hfeJALorr/rly5edL9/v9/v+JF/+8pf59Kc/3THQajQa\nxGIxHckc5G1vexv33ntvh8zX1NSUHpAaDOtB318PpIyWjN80E9+dcuydy7u9DPtJwRHJ2vqpt1r7\niqh22Sas2q+E8DXfo15OheX/o0um+wa/0K7aK9ZgmxBiDW2zyhbd1qvl5oX/qlGOPpXh8vLLLyOE\n4OWXXyaZTOrMllgsRiKRYGxsTE9MplIpPelYqVS0bHW9qe6RSqW46667yGQy2gkI0bURNxtqAjaZ\nTCKEYHh4WMtu7927lz179uhtlcN9cHAQx3G0M1mhMoQqlYpum3w+Tz6fJ5VKkU6n9QSomrDezChn\nbjweJx6P635i27bOXlES+EIIFhYWePXVVzskyFUwArQm6m3bplwuk06n2b17t55QzWazfcno6TUq\nsMJ1XRqNBvV6nWq1Srlc1n3CsixWVlYQQrC0tBQpWa8COFS/URLxqVSKVCrF1NQUO3bsIJ1OY9s2\niUTCZKIbDAaDoacEZcLb61R3+719//VW2FnLGCr4vG13IAZtDioE9fM8otpSjbWD69RYWs3JRV2T\njWS1NrqSbVFtsNHqTO3y56ttsxna/mpQAcBb4Z3MYDBsKcabP+e7rJ/Hd5QKNjI7yLAZWUvt0k8A\nb8TPQLfwneq/3E+jNhozw7MFcV2XmZmZyCyZs2fPcvz48dAATL1wDAwMdNQHlVKya9cuDh061OFE\nHxwcNAM5g8FgMBjWCVXbWkm7v/jii1qGUU3SKUfvwMAABw8eJBaLIYRgcHBQO5YXFhYoFoshB/Lg\n4CCHDh3SGQ1qgmezO4kVQggd2Dc4OKidxTfffDO33367zpZRE1Eq48RxHJ1BA+j95ubmdKZRNpsl\nl8tpp6jaZqtI3VuWpZ3oSiLVtm2y2awOyKhUKgCUy2Xm5uY6zqlcLuuMapUtE4vFdADm8vIy6XQ6\nlF212QnWxnRdV2eiVyoVSqUSEJ4obi9zEJTkVPfg/Pw81WoVKSXJZFLLtT788MNa0nPqBlThqFar\n761Wq/9qPb4rmJnZC5R8bzf1gV4hhOh5Bt/KyooO6ugnvbY9GMTUT4QQZDKZnga1VKvVkLpJP+ll\naYj2gLJ+kslkiMfj/1EI8fS6fOE2pludaFjdUae277Z/sLzNenOles9B29vPo9s+/bAvqu2C399e\n9mc1m7cyG3Euq7V91LVZz/5xrUTdb5vVVoPBsG1QWZHdpFtW8LOIY9wA9awNfeHjtGTc37yRhqwH\nxom+RbmaCOO1RI1up4G+wWAwGAxbnfZsF2hlZAef9epzUGZa/a7+BY8R9T0bOZl6LURNUq7lc3sW\nV/Bnt0nPzd4uUfYrB7D6p5ap8+823gu2TVS222Zvi3bax8JRDoS19P2obKyoz+3feSNx4sSJ3b/9\n27/3jno9RlAbRVXxCRLd/SSppMQSbRtWqwTr1NQ8jze+5S188J//856pZV2+fJkPf/jDzeCRpgGe\ni4iSfbUilGssy/8XwJPgOCCluj89stkkv/u7v9MzGd9iscjv/M5/4syZi9h265XewiOGE1aakRLa\nggQkIGs1ZJsz28rnEYGL5nkesVSK3/3IR8hms9dtt5SSP/zDP+Rb3/oBsViwXJAkZdWxrpAIIwU4\nrk3NDfY1SQKHJC3ntgTcWIx/+cu/zN13333ddivbP/bHf8x3vvY1EoF+IIWgnswhRSBY3WtglRbB\n6wzOaP8rYQkRCnSXQCOV4l/8yq9w77339sT2b3/72/zX//qHJJOJkAWu235PSmzc8L0I/n3Y7oS3\nLL9sUIBKo8HPfeADPProo3/SE8NvYGKxGKOjo4yOjmp1HPUsf+6553jhhRf0tidOnGBhYUF/dhwn\ndL9Wq1VdxkRJTwfHkf0galwlhOCZZ57hxRdf1M/g559/nsXFRQCmpqZ47LHHdNCeZVnabqVElEgk\ncF1XB472EhWUqiTE4/E4w8PDCCGYmZkJKUGWy2UdvAmwa9cubrvtNh3MmMvldBCMUsYJjtF6TbC9\nlbpQ1LhPBRd1G6+4rsvCwoJud8uydF3xXgawdbNdCKGvreM4uiyREIJarcbAwIA+H9u22b9/v1bb\nVEGlKrhMtblSZeqn3Qr1XYVCQdvhOA7PPvssr732Wihwc3h4WB/HKBgZDIY+oJznQ13WDwN1jAPd\ncO28HPh9csOsWCfMk9pgMBgMBoNhE9DusFTy7KoOOKAz06vVqq5rrSbE1PbT09MsLi7qiTSA8fFx\nyuUy2WxW14jeSqjMasdxiMfjemJsaGiIWCyGlJJMJqMns0ZHR3Vmfz6fRwi/zqKaBF1aWtJtNDQ0\nxNDQEIlEokOxZ7MjpSSVSjEyMoJt29x99926xvvk5CSVSoVyuayzy4M14oN9YHZ2lmKxiOd5rKys\n6Gz2VCqlM9JVO2+ViT4h/HqySq1h9+7dZDIZSqUShUIBQKsNANTr9dDEq2of1Z7BjHZAB7D0O4N5\nK7C4uMj3vpdhz57/E8vyHaNSwt698IY3hP3OtRq0J0/bFrznHVVyOel7EIWA5WX4m7+B5rUCeObU\nKV546aWetvnKygqXLk3zO7/zn4jF4n4Fl/kFrKPf9z2MyngpIZ+Hdkfyjh0wFJibElCtCF74jkVp\nxd99ZWWBL3zhP/a0Hmu9XufVV0/x5jf/c/buPaQdoUNymVvdl7BEoI3KZTh/PnwhpKT4xS+y8txz\nrWXxOKMf/jDxu+/WntWlUonf/PjHe2a7lJJjx04wNPQ4Bw++WdsdFw3+l5HnSMdqgGh5djv+Jku+\nO7OXp88d0Ocjkdzvfpsf8p5qluGBuufxW9/8JrOzsz2xW9l+4sUX+ZFz53jL6Khe7tpJnn/7/0El\nO6b1MK0LJxj4vZ/Dmj0TsBxSQJqg+x+GhoYYDyhYVKXkdysVZn72Z3tm+/T0NPfddz8//uM/rptW\nSrh8GZaWAl1DSvYnLpKyqq2dhYD5efijP2rdvFLC1BTcfz+oZ4IQ/MGTT3Lh/Pme2X0jo8r2jI+P\n67Gf53lYlsWxY8f49Kc/rbcNKqkA1Go1rUYDhJ5VyWRSl3tZLye6cv4DfOUrX+HP/uzP9PeurKxQ\nLBaRUpJOp/mRH/kRXdN6aWmJf/iHf8DzPGzbZnJyUo9j++VEHxoaYnJyEs/zmJyc5MiRIwghuHz5\ncmjcpMbp6lyHh4e57bbb9DKlEKTWq1I5/XSiBxV0lJKSeo9Qv9u2TTqd7upE9zxPK1oJIRgfH9f9\nJZ1O99zuoP0QVp5yXZdSqaSfP41Gg2w2q8sR2bbNwYMHQ8FpjUZDj+XUfREsY9UPVP8OOsgXFxep\nVqsIIahWq3z1q1/lG9/4ht7u4Ycf5tZbb9XnbspoGgyGPnCh+XM8Yp0AxgLbGAzXQnAw1n95tA1m\na8yCGQwGg8FgCLGh2iFGuaQvCCG0w09JcyunnXKCOo5DvV5HSskrr7yi9w1m2Z44cYLLly+HJsr2\n7t3Lz/zMz5DNZimXy9oRGIvFdCbEZiYej7Njxw5c1yWfz+tMn3Q6rTOWghkywRrxikKhwOuvv069\nXufcuXNcvHgRz/PYtWsXN91005ZxDgeRUjIyMqIz1e644w6dBbO8vIzjONRqNe04r9frFAoFPamq\nJv6OHj3K+fPn9X7q2OVyGdd1dRkBKaWuTb/ZicVi5HI5PWH9xje+kVqtRqFQ0BL/1WqV5eVlnTWk\n7jM1CSuEoFwuU6vVdG31crms2y8YlLAVs/V7iW2nSSZ3YFn+JLuUkMn4fudgs1SrfpZ2cJltSSZG\nVxjKey0neizmO6yV004IBlOpvrRxLBZnYmKHzowWwiI+ONiRvc3QIGRz4WWjo/6/ACsVGByKY9kC\nIcC2Y76Dvof4zi+bfH6MwaEpPSgYlgl2uIPEgqXsYjHIhe2WUpJMJGjPK5zI5UgND/sXUAiSsRiJ\nHjurLMsikxkml5sKONEdxgeHyNnV8MbtpciQDFdGyAxMoVzREsmQO8ykl0E0nehV1yXVByebJQRD\n8ThTyVYWvRNLMpifIJHd4S8QYBWWGLBsgtYrJ3qGsBN91LKYCjx/ylKS6vHfEyEE2VyeiR2TSE8p\nJPi3lxDB+1Eykagy0O5E9zxIJlvXQ0pIp/0bPOBEH9giz4etQrtqjvpdPYMU7ZmwUcopUev6/dy6\nFglr27a1Izro4F9v5Zf2Nmv/zqi2i1KrCf7eLgHfa3uDqjvdttlKRNkb1Q+Cil3t262H2uZa7FT2\nbbVrYDAYtjSvA8vAQ/iy7cEXm/vwh6Tf2QC7DNuH+wK/X9wwK9aJrTdbaACufqJuNenOqHVG3t3Q\nDyS+xKVo71qiKSspBIguWYBX6O+yuY3sFk0vhD8vK2WnAzBq2ZZComdPZdvSQGN3O8NVW3YztIu6\nPt2um4zOSJP45yZlt3NcQxbAaue/CdpGWl2ET4OZc6v9/b/iF1zh3mhOsht6g23b2kGZSqW0A7Be\nr2sHcb1e19kswVq4jUZDP7vz+Tzlcpl4PK6zR4aamZIqg3urEZwkax8DdauJqJynKoOlWq1SKBS0\nY1k5kYMZQcHft0I7qXaIslW1mWVZekLatm0SiYTOqr7SGFApHcRiMeLxuM5i2ioo+1Vgigq2UBla\nQgh976hMN/Dvp3q9rts32H7q90QiQSqVIplM6uUqO93g4zt6vPBjovlMDi4S4Mteu15rjev6jjvP\n858zfR2rSep1D6nGE47EdQXCJfQ8lQ0BDT268H82JKIRtstxQHpqu36b7iGk2zq+5yJdFz1Pphyg\nXZ7X7WZJt7l/s91lo9F745sNIqSrDRC40Q3VkbHpb2OJ4BjG71RSWDoTXYo1jHGuFSHCEv7Cwn/L\n8HTPAImI2SG585afOmyZFAI3sMxby/jsWvA8aDSQql96gLSAtncnKcPtrvpQBM3c0c79DT2lvRTN\nWrdv/9wum70eqEzgbk77YAZ10Nao47SP8/pJVButpV2jPge336ix5fV870Y5f7tdg60wPldEvbO0\ny+xvtXPaxIzQqgG9gC9TbTDcyLjA3wA/B7wLeDKw7v3Nn3/dts8hIAccA9oiWw2GDv514Pd/2DAr\n1gnjRN/EtMtyKRzHYW5uTk8ABlESnO37WZZFOp3ukOtSWVzJZLJjHyWTZDD0iidn7mP25E+A7OxX\n6dgK4+//WUSUY1NCdijG4z87SjzRpa5vrUbiiSeQtVq43zYnDWWjgfjsZ6FYjDi+hIsX4f3v71y3\nbnj4Y5QoKS8LKBEOHGwhPI+hi2eYPFHsnLcSUJg5yTGiHckSGF5cZNfRo4j2AAQh4MIFX3t1o/A8\nvL/+a7wvf7lzQlFKaokMhfQg3dzkUoBrRa9NpBKM3PsG4o+8Kfq7hfCz4KLk1YTwU+w28G+ktW8f\nsV/8RZJt2WXgO0ovP/kklc9/PnJf4XkMnDlD1+qmQhA7cABuuy16vZS+xO7cXOS+LC3B5LYvidNz\nRkZGtCSh67q84Q1vQElABqWl1WdVr9B1XS5fvqw/nzlzhpmZGYaGhti/fz+WZZFKpbAsi4WFha5Z\nG5uZWCzG2NgYQMhRqWTagVBQgXKILi0tceLECYQQzM3N8clPflIHJSinalDau1ar6TbZapL30HL2\nCuHX9VST08GJOhVYcOLECUqlUmiCT0pJpVLRx5uYmMC2bfbu3cstt9yindBbgVgsxsjIiD734eFh\nHTShMviCQRZKth38gAvVNhcvXmRmZoZGo8Ho6Ki+d3bt2sXOnTvZtWsXO3bsYGBgAMuytqSiQS9I\nJmF4OJyo+uqrZ/jSl76nxyYCyb/+0TTve1MSK/inR0qyT54GL5CiXijA3/1deNy2vAxve1vPbT9+\nvMS73/11hPCvXZwsefsBrLbAzmJlhZoTnk/Kj6bJDrcynlWC7kMPWTppfWWlM6m9FyTcCrctfJ3D\nmXMop37swhliX/k8NJyWQaOjvux2ECmxGo3QZIBsNKj87u9Sb0rWC2DFdWn0+DkRFw4Pxb/FW5KO\ndrgKJJmleRCBv7vlMrz8MtTrul8I6XHHXY+w+30TITl3nAOcq/+i/ltWb9Qon7rcU7sBSKXgJ38S\nHnxQO5ZjCO735vFYam2Xq2L/h19D1MMZ3WJhAfvyZX3eUghmjx7la//4j3qsWpeSy83yJD1DSuxn\nnsZeKbay44XF5MNvY/TQHa1xsueR+txzMDMdHuM2GnDwYMtBLiWV3bewcMc7IZZQp0fxuRf9YAZD\nzzl//jyVSgXLsigWi6F5nbvuuos777xT9/83velNlMvl0LNdyUUH63Svl/Pu+PHj2p4LFy6EbNu5\ncycPPvggAEeOHGF6elor4ih1GDVuzWQyZLNZGo3Gushfl0olPRZYWloiHo9rB2gymdTlgqT0a6AP\nDAyEsuhVsOJ6By6o4EeFslvZ1e7UVXOI4JcCUOVthBBa/h+InDPsNcGyOX65lUv6/Uahrn1UUGe7\nc3q9CI4tq9Uqr7zyCouLiwghqNfrlEolHdhpWRY33XQT9957r7ZzaKhbyWLDVfA3wCPN3x8HvrSB\nthgMm4X/APwE8HHgA8AJ/PvjXwI/AP5n2/b/DXg7cDvwUmD524AHm7+rCdR/RisT+VPA2R7bbtgY\n0sD/CvwF3TPPYsBvAD/a/FwD/qT/pm0sN+YszxahWq2GJjIB/eLxkY98hEql0jEQn5+fp13eC3xZ\n04ceeqijdpSUkvvuu4+77rqr4/uDk+wGw/UiJVyojZIs3oSUnf0ql4eVfbcT1eWkhOEhiXdrI9rH\nDAjXRezc2X2WslqF//JfOmtCqi/YcEeSn8HS+YxSdjXoVmJESJdkZZF0cb4zQEEICrUiy3TPOE/X\n68ilpWgneqHQNftkXZASzp7tmjXt4rdKK98rjIf/NI889Pg48pEHYGKi+/e7bnTfEKIlX7lBiFwO\ncf/9WCMjHetktUrtL/6C0ve/H70vMEC4gE3U8dslaltfIP17qt4lwLuHdV9vJIL1EwFdEzI4IeQ4\nDo1GA9d1qVaroWe++lytVrEsi5GREfbt26czkZXDOVijcatgWVbkhGkwgykqo1zJl1uWxfLyMjMz\nM9RqNUZGRnSmf3DM1O5w3uxESYmqn6uN4RzHCWVpt9dyVMdIJBLEYrEtKeeusvAVUf0nqEQQzMyv\nVCq6FEK1WtUT0KrMgpSSTCZDLpcjm82STCZJJBId33kjYTeTblunL1lZqXLixCyep7IoJV5xgMl4\nOqxK5Hkwdzn8TCkW/UCtUqm1rFzuy3htZaXByy8vQtO9mEzGGRkdwrLCT8nFRY/ySmssJgSMjFgM\nDYWveT4Pd70B0hn/c7ehxPViSZess8hgPd0aLhYvw5nTLcez5/nP5PaxnJQIKcOKAFLivv66PpSg\nOca66abe2o1k2Cqww5oLOWVp1FufhfDHGfPz/s+A3Tl3mdyOOlhCn/diPcvl+pQ+n0ajiptq1cnt\nnfGWXwt8/37dpsLzGJq+BG7AzoSEQ7d07n/5sh9lEVAGuHzuHAvlcst2oNYHB6FYmEecO9u65pZF\n0imRDDaTB6Kw5N977X/nA3WHkRIvN0gtN4ZslkGwLGgkMj232+CPR37wgx8wMzODZVnMzMxo9RMp\nJU888QT/5t/8G2KxmB4HLi8vawdvKpXSQZrr/fyWUvLss89y9uxZLMvi6NGjLC0t6Qz1H/7hH+aD\nH/ygHqO+9NJLof2Dzt+hoSFGR0ep1+t9rc+tvnd+fp6TJ08ihF8mJxUoKTIyMsJNN92kr0EqlWJk\nZEQ7dl3XpVKp6HGFGnP1G9Wuaqyv1K26fbeUkoWFBe00dxyHarWqE3YGBgYYHx/H8zyy2Syl4DO5\nDziOQ7EZPLe0tMQrr7yivzMej3Po0CHdl9U90H4+G5HZ7bquDkYoFos8/fTTnD59Gsuy8DyP2dlZ\nXbLLtm3uu+8+nnjiCb3/VhhXGwyGLcmrwI8D/x34u8Dyb+I7wddax/rdwIfaln0w8Ps3ME707UIC\n+B/Af8JXL/gW8Bp+aYAR4E78a38ksM+/B06ur5nrj3Gib3KiIlaVtGtQZlLR7jxXqGjU9uwYJcu5\nlaQ5DVsXgV9LMOqVxsKXee+QegdfpVHi6w5GZLEDLcnPbg5fz/Nndywr2om+4RmH3V6cxBXWN1cJ\n0TyvTie63iRiVz1Rqvfv3Hcz0JLH7Fwe/Nlt3+gVAcnzbmxmJ5rqt1F9V03sdtn1qq7s1bbBJuo3\n24Vukn/BiaKgM/lKWRjrKYe50XRrl2BgQdQEXPD37Tax1U12tH2bduf6dmsHRdT9oPqNmvxsv9cU\n3aRpDQCi+TiICvRo70siMI7RG0Yv65OtwZGSEL5/1mr7uvbP0BpaKqSMHmr2xXQRGAWJwLKgUUFj\n1mBU8Op0Gzv2Atnt6FE2RtotAu8EzSANGTjVXhrbjgq+Dd3rbefTLUA3uFxl4Uu5Pu0eeT+JiEDU\niHtP2RtS+2rbr/2z4bro9rzpVs6mm1x6cFt1nPUmWF4maIv6fbVs7Y18pnZr8+DP9u27EZTx7idR\nNcOvdp/NRnu7rSXYdb3qoQe/r1tgK7TGlepe2I7vF5uATwPfbv5+aiMNMRg2GX8L7AUeBkbxnZ3f\n67Lt+/B9hYW25b+B71TtRoTkq2GLswv435v/uuECH2b1vrFtME70LciVXpAMBoPBYDBsH4LP/Hg8\njm3boYzper3OwsICS0u+lKzjODpzOJfLheo8NxoN0uk0mYyfLbYdsmbb61ZDa7JqYWGBb3zjGwgh\nKBaLOnjw3e9+N29+85vxPI89e/aEJnbbJ623E41GQ9eEn5+f11KTKysrWuHAtm3dTgcOHCCTybBn\nzx7y+Tye520ruXKVEQToSU2Aubk5Tp48iWVZnD9/nnPnzukgVpXpt3v3bh544AFGRkYYGxsjmfSz\nMbdT+xgMBoNhY2if73FdF9d18TyPdDrN8PCwDvRqz8oOBgiuVot8PVDOTBXQODo6ys0336zXjY+P\n60xpVUpFYVkW2WZ5iVgstm6JH8pmVaJFKdEMDAzodblcLlT+MOrZHxxXrKdSjRrvg5+A02g0QkEU\nwf7ieR6Li4uhMjfxeFzLvgdl6dejzJEaa0FLNUm1bSKRIJfLkcvl9PUJluWJCiLu57xpezBqUNEo\nn88zMjKi+8uhQ4e0LL1t24x2U3szXA9/sNEGGAybmDrwzBq26+YMrzT/GbY/FfzM8oeBh4B8xDaz\n+CU0/gB4cf1M21jMLI/BYDAYDAbDJiY4+aPUY5QMtZrUKpfLLC8v6wm6WCxGPB4nmUwSi8X0pJSa\ndFJ1DdtrI2412ttGoSayyuUyp0+f1pOzap/bbruNd73rXTpHeF/cAAAgAElEQVTbWE1yrXfdyvVG\nSkm9XsdxHFZWVnStz3q9rvtUUDp1fHycXC7HyMgIqVRqW/SZIN0cC/V6nbm5OSzLYn5+XkvPep6n\ngwyGh4fZu3cv+XyegYEBLfm+HQJTroX25FwhZPN3SVBL5opum25Z030lVKA9OtEYAucjOrYNbiNl\n64gytG8/uYYv6GJUe0Z0X65EVCNHZZx3a+ANRLQ3jFQp2G2p8KK5LtiCoeVE9vPwHdMP2ntneM3q\n17u1rxDNcgAbccveIAQd4PV6nXK5jGVZ7Nu3j2w2q8cue/fuDT3LVAkctV45gtUx12OcE3weqtI5\nnufx6KOP8r73vQ/wn7cHDx7U9s/PzzM9Pa3Ht7lcjiNHjuhjKSd2P20PKhVlMhmGh4cByGaz7Nu3\nLzRGCo4725//7WWI1NhJBSv2k1KpxMWLF/XvL774oh4Dx+NxDh48qG3wPI/p6WntRE8mkzzwwAPk\n8/6ceSqVolKpYFkWjuP0PfiiXq8zMzODKkugSuZIKUmn09x5550hB/TKykqoZnoymQwFg6prohzu\nvUa1h3Lmq++4++67OXDggO4X7XarOvMGg8FgMGwy6vjKA+DXOhtp/ss2lxXx5d1vOPk940Q3GAwG\ng8Fg2KJETSSuZYJru0uWQ3jyOUqOs91pHNxvO3M15xeVeXQjyJV36zcQLYcfXHejkkhALufXRleM\nj8fZty+nq44IIcnlol8/neVlZKWiPXFiZYWY6yLaHah9IJm02L8/gxDK+CTxuJKjD2KRycRoekER\nAnZMCMbGPD2NIIHcgMdQvEbWambCWWVs0YfJewQ1O0MlltXeT8tKkmi4iGYWom6zgbb64FIiJiaw\n9+0LHA8WFmwa9daJF6RLTSR7a7gQfl3wbLYlEe66fr3wRqA0Y63mbxcP1KaXEplKg2WHvLauFNSd\nlgO40ehPpSbPg5l5i7MXLVDHlwJm4+DaqmtgCUnabutDQuCsDFAv5vX1kkAxPkZm7x5d0soBYp7X\nU6+0BEoMMMdIwEkuyLgxkg2ntaHrguf6J6q/X+ISY8EbQrewlJQrWWZmBTKmT4+VlZ6ZbGiinkPB\nZ04sFtPZ2+pz+z7dMtHXS9GwWymYdDqtHbTqcywWCzmlg8/YYFb0epSYaW+nYHDqarXFowg61ddT\nTTKYFd1oNCiXy1SrVaSUxONx6vV6yIler9e1E11dh2BNdXXMfhL8nmAZnWAGv23bJBIJUqmU3lbV\nIe92zH73lyBBO5SdSvFoeHiYiYmJvttgMBgMBkMPcfGzzmc32pDNgHGib0GCL1DbfaLXYDAYDAZD\ni6CE++XLl3XmQ6PR0BNNuVyOVCpFPp8PTR4puWklCa+Wb7exhJSSixcvUq/XOXv2LNPT0zr7R7VJ\nKpUKZaCr7JHt6AQNZtdXq1VmZmao1WoUCoVQJrrrugghGBsbA/wJ45GREbLZLAMDA9s6w1pKSaVS\n0RKily9f5tSpUzozTk2kj42N6Yz8qakpRkdHyWQyOttpO95Pa2X/fnjiCWj5cgSJxM0MDOzRvjgJ\nTFx+CbF0Dl13GfBqNS7+5V/SaGbOAcRsm6mRERLKK68crX3g1lvz/P7vvw3bTiAEnDpl8YUv2DhO\neLtcbph0eii07KF7a9x+uNiqzy1AVKvEj30fUV4BIZizVvgHe7HndtftDC/t+CEqu24D6ZswNPtV\nDi/+v8QqTUlkz4Obb4a3vz3klBVA7h3vIBtY1pDw4Q+P8b2XE7qkutNYolD/1d4aHo/Dm94Ejz7a\ncqIvLcGv/RqcP9/abvdu+JVfgZGR1jIpcW8+gDs83gq4ABYKglOvt07RdSGgCt0zVsqC3/r9LPk/\nGQrUZAdky0YpIZ+Dt7zFj11QTxUBvH7S49hRqZ3onif5sTe/i3/1P39eH6/iODz1R3/UcwWAL/MY\nF3m//mxJyY8uFXjgQvN+BL+/FItQLoec6AuM8XvlD9CgFVBROG9x+nPxUGzL2bPwxjf22PAblKhA\nwKgAr27PneCyjXo2RWW9B21pr+Xe7dzWy4Hebl+/gg/WKwAgav1q/edK16BfdPuu4OdgX4k6p27n\nuB59JRhwoYJze2XHjTqmNBgMBoNhs2Gc6BtMt9pCUkq++tWv8oUvfKFj0rJYLHL+/Hld5yhIIpFg\namqq41hjY2P89E//tI7aDK7bs2dPD87EYFgb3VQY/eVy1XWuhEa7LGITIf3JIBF1AD17K1uTdVFf\nsqHISLMUQkSbrdZ16Ide7bd3a7sNbxdAiK6n5SurylXlJ6PWrems1LlH9ZlNICe6Fq4sy9llvyu9\nsG+Bc9/OSCmp1WqcPXtWO39V3UAhBKOjo0xMTHTIGKqMiEQiobfdjkgpOXXqFEtLSxw/fpwzZ84A\nfvDA7t27sW1bOz7Br7mopCC3Y4BiMKOrUqlw7tw5arUaCwsL2olerVZxXZd4PM6uXbt05szk5CTZ\nbJZ0Or2t+wz44+tCoYAQgrNnz3L06FEsy9IyprZts3v3bsbGxpBScvPNN7Nz507i8XgoMOVGxbJ8\n32gwITKVsshmE6Ht4nGboAMd8J/z9TperaYSeZGxWOfzt0/9z7YF2WyMWMzPvMtk/Mz6dhIJQTIZ\nnKyHTBpyGcKeUgFYLggXhCAuGgjRh+emAE/EcEVcZzV7IhY9yInFOtpPxGKIwAUTEurxHNVYCqu5\nqSNdpOhx3xbClyyIx1vjiVgM6nU/+1wIf3m93tpOIaVvs+gsKxEcmvSzdG+tLqhU2oOKWm0kJcST\nUHMh3hb3UXNtKo3W+MzzoGGliGcy2onuOA5W8Jx7gqBBjDpJ1MjQQuJRWpNcvkRQJxlyotdc/3IF\nk9b7FOdyQ1KtVjl16hTFYhHXdZmZmWF+fh7LslheXqZYbJUtnZ6e5sSJE3rM12g0dFAYEKorDa0M\n6WDwWC9pNBqcPn1aZwnPzs6ytLSkx6BOIEJJPUOFECwsLHD58mWdFR0sySOEIJvNYts2juOwtLTU\nc7uVtPmJEycAWFhYYHbWT77KZrOR6kXdiArKVAGdi4u9DaqSUrKwsMDrr78OQKFQ4NKlS4Avdz4/\nP6+vcywWY3p6Wkueq+uj2jyVSnH27FndvolEQpcDmJ+f1zXqe2n7/Pw8J0+eRAjB8vIyFy5cQJXW\nmZmZ0TXd0+k0r7/+um4/KSXFYjF0XZLJZOjdR9Wln5ubCykg9ILl5WVOnjyJbds0Gg0KhYKuQX/p\n0iXK5TLg33+nTp2iUChc9XdcvHhRXxuDwWAwGAwbh3GibwKiBthSSs6dO8fzzz/f4USv1WqsrKxE\nDqaSySQDbXJ9Ukry+TyHDh0ik8l0rDP1eAzrxdISXLzYzf/m8Oqr5a7+7VxWELPTxONW5ORgLl7n\nh3dfYDBe7VwJUKvhLi5CxMuLBGTUbOk6kkql2b//ALY91rFOCMH4eL5Dqk/va1UZPfM9mDtGh8tU\nCBIvv8yw+ti2r5SS5GuvUfvzP4+M7K4Xi8h+zkReCSEQQ0OIZDLSGZwqlxkqFLpmjzbw9WeisBwH\nceGCn23TzSmcSoW1aQN2UattrDNZCN9rEZEdKiyLOASmStvWA4l4vJXhF3Fsq1KB5eXuUS/VLvea\noa+4rqsnR1V9Sc/ziMVi2gGsJrva72nlBN2O2bJKslIFJy4uLjI/P8/y8rL++6CcoPF4XNcS3e4o\nSU/HcbSzvFKpUKvVqFarVKtVhBA0Gg1c19VypcppHIvFQlnW242gNHu5XGZ5eRkhBKVSSbdNrVYD\n/GdiJpMhl8vpydx4PN712WzwCfrBr/TEFG0/15Nrio2ToL3XoWXtKM96fxEdv6jPa/xuGbHr9Zl0\nfaxi95Xs2gx/rlYzoeu1Umz0+PIqNt0Mbb3diMfj3H777Xz2s5/Vz15Vj9rzPAYHB0MOwePHj/P6\n66/rba+kqhPc7p577umYP7pe2x944AE+85nPaAem4zhaDWl5eTnkTLxw4QIvvPCCtieoCiSE0A7t\nYHayegareuW9QAjBfffdxze+8Q0+/vGPd5RuUZnG14uUkkwmw0hQYeM6OXz4MK+99hp/9md/pr8j\nmKgTnDd0XZfXXnutwybF8vIyTz75ZGTWv+u6PPzwwz2zG+D222/n9OnT/Mmf/Emk7e0JR5/+9KdD\nNq1VQarRaPDggw/2bCw7ODjI0NCQtrvdlnZ1q1dfffWa+k+j0eD++++/4QM1DQaDwWDYaMyszyYg\naiAXrP/TPti61sF78CUguMxgWC9WC74tlRpcvFjs2ifT6RjVWhbbju7/UzmPh95xicF8KXICU9br\neIWCL1HYvg6QPY5MvlpSqTRjYzeTSEx2rLMsOHzY9+d2NI+AeMNl+LmX4OJXQXS2T3J5mUEpu87T\nxU6fpn76dOf34juh5UMPXe3p9BQxNITV5fok5+fJF4tdJxtrQOcV97EbDZie9gs4dlMwGBqCZEQt\nUCH8DKmNZBUnOpZFXAhSnWuauwqSiQSJ1TKdajX/pu32nNjo879BWV5eZmFhgUqlorOTLMtiYmJC\nO/NyuRyJREJPNII/dshms9s2Y9bzPGZmZvA8D9d1+dKXvsSpU6eo1Wo6+2ZiYoJf/dVfJZvNdgQQ\ndpOJ3A4sLy9rSfLp6WmOHz+uM9xWmgVslZPctm0OHjyIbdvYts3IyAipVGpb9hnwJ4Sr1Sqe53Hs\n2DFefvllhBC89tprHD9+HGipOCQSCQ4fPszhw4eRUrJ//35GRkZ0u93oyLaf3Zb5ntp2b60EZHg7\nKf0U11ZB9b6lFytfuC73LMCT/j8R2EYSVvDW+yp7lZ3Ilnexj15G/d3NxG3lz5eq3VSbdXuOCyCY\nIS+a74seyObQol/CO36TNPsCwrdFes1/zTaUq1xvi3AfEhCOVejf33KJ0P2hbYX+4UWtD24aujnC\n94QQq6tTXSvaZqUmr66/pQ1Bt3vooqv7U+IRuCfMNELfSKVSfOhDH4pULOw1lmWR6GEweSqV4gMf\n+EDfs2eFaJUp6gWWZfHEE0/w3ve+t2fH7EavbX/b297Wc+d2FCpQt5e89a1v5eGHH16Xecl4DxU+\nduzYwW/91m+ti90qoNVgMBgMBsPG0dMRULlc5p/+6Z/IZrPs2rWLPXv29HSgYjD0GyUZNjc3p+Ww\nDL3jSpLkvsNn9X27xZCo/fWHjvViY7NpekBk2+jJzVUmatc4E9ctY/mG4Epa+le7z3bApBdtSpRM\nYDALHfwsazXmCsq4BzNJlGN0OzqKwc/WUE70lZUVCoWCnsSVUmLbNhMTE5EKPNu1TYQQWu5fCIHj\nONTrdf1PSaoGJ+iUdKdlWaFM9O1IMNOsVqtRLpcRQoQkboPKDqlUikwm46u4JJMmCz1Awq2ScxaI\nSz98S0pJ0hIkq3bL6SYkF2aTFM5NhO851yF//zuwDy/oRZYF5FIQC0wcT09Hq8NcJ2KlhPjGc1ix\nuF8rfDHFwfgoDctqOdYlpGYuEF9p2ihB2BYD+w8g7V3Bo0HDRbz6Kiws+M/RSqUvBbrthkN+7iTD\ngYi5bG0W69BBcKq+LZ4HU1MwNxd6pksJ52tjzDhZfX0aEkbGbW67rbVprQYnT/bW7kYDXnkFcrmA\nNH45zsSBR7GHZ2l61IlPDDMaT3VMWHinT+OcORs+5mVwzwVlxR3kwoXeGg7Y0uFw5ZvskvVAEIMg\nkc+D1eybUmLbaezGERwnHM4Yi0EwLlRKmFkUfPHZZpkDoNbwODfdvZTRtSGZSi1yOHcxsERy9nWL\nucsZ7UMXSHZXbiEVHwsFjKzEBrlph4UrWu8GZ8/Ciy+GJdyNSFHv6KWTdb3ppVN+PVHS8luNWCy2\nZccjW9V2NSY0GAwGg8FwY9DT0YoQgnQ6reUNt+uEpGH7oiKxVR82GAwGg2GjaZcHdF23I8OnPYs6\nqm5juyzldkLJhar2UTKKqv4mEBqbRinzbMc2gVafUbU42yUmAS3frhSQlGyp2m67KhepMgAqOEUF\nG7iuG+oTapJ3u8ra94JcY4G9Ky+TCk6GOzGEEygsIiSf+d4evvK9/aEYrVgM/q9/exdjY61+ZksX\nUZ/F18TB944++yy8+mrPbRcXLxD/zX9PsmnUnsmdTL35bRCL0/IuWlSf/kucf3qutWMiQebg/4N8\n5y/pRRJgZgbrL/8Ccfw1PxrA86CH0r2KeK3Ege//NUfOjbbOJZXC/mc/GY46FQK+//2wEx145vWH\n+duLt+hq3lLA449bvHOqtWuxCB//eG/trlTgk58SPPUVP3VcSojHsjz23j9gICN1tvRgrMzbs98j\nRkD5xrJw/vZvWfnkp1qZ6hIqEupuM6kav9d4Ayngl+glKa/Mz8z+Nm+VNipHWNgWw3ffiaWdnpK5\n5F7+R/U/sCSnQgGpqRTs39/6LAR851Wb/+9TLWeMxCKVtvlAj//sPjx2nPfv+0cdlVt1Bf/2U/fx\nyedaBlkW/PRPPcHUZOvLpYRcXvDYjyR0DItlwdNPwyc+4V/P4P7b9HFhMBgMBoPBYDAYblB66kRP\np9Pcc8895PN5M8G0QXSTbN+uk5+9JpVKccsttyCl5Gtf+9pGm2MwGAyGGxwppa5/DjA3N8fp06e1\nY1Q5+oaHhxkYGEBKSaVSoVQqkUqldO1vlYW+3TKKVTZ+tVrla1/7mpbmnp6eplAosGvXLt785jcD\nMDo6qmvH1+t1Gg3fObddHeiVSgXXdbl06RLHjx9HCMHi4iILCws4jqMd6wA7d+5kdHRUq0kpxYJE\nIrHtHMdqXCyEoFAo8Oqrr9JoNPjmN7/Jt7/9bYQQFItFnaWfyWS45557SCQS3HzzzezcuRMpZaSi\nwY2MACw8Wq5FfCenkhXHz3x1PUGtEQ/5eD0BMhlHpAPHkw7CKhJyovcpu1FIiajV/H4uJXajji0a\nYAX6vfBw3Sqy1iohI2USIRsh57T6TTgNcOp+mZ1+aaIjsdwGMdcJLEtAPB7O2Pc8P/07eB9Lf1G9\nboVO07bDzZxI9EeUxnH8LHdtjhS4iRyNhDYP1wIZUaYIx4FioUPev13dnVQfVAuQJLwyKSlbTnQr\nxgBVbG2AZEXUERHXXKlqhYQYPCishK23epyYKYCYJUnarrZLSkHDgULJ0vZYFtRlkkbAGS6Bhgh3\nK8vyfw9WXFDnZ+gN6zl/0+tn/HrZvlXthq1rez/Gg1vV9q3cXwwGg8FgMFwdfdHNMQ/4teN5HuVy\nOdLxXSwWWV5e7mhPx3EiB2xCCMbHxzly5EjHsdTEaJTk0FaUT+o3pg8bDAaDYbOgHOYA1WqVUlMW\nOFgfL5lMkk6n8TyPSqWC4zjaAaqyjLdjzW8ppc4inp6eplwu67GVaoN9+/YhpWRwcFAHEah9tlt7\nKKSUuK5Lo9GgXC7r8WSpVKJWq4UCMKSUZDIZHYiRyWT02FD1n+2K4zgsLi7iOA6zs7NMT08jhNAl\nE8C/z8bGxkgkEuRyOS3nbhSLDAaDwdAPHMfhc5/7HMVise/jlJGREd75zneSyWR6cjzHcXj22Wc5\ne/ZspO29cjzats2P/uiPMjw83JPjSSl5/vnneeWVV3pyPOg+pySE4L3vfW/PbD969Cjf+ta39Ocr\ntfFq64NloKLW3XPPPbzhDW+4Rks7OXbsGC+88ELIpl46p4MKVPfee2/PbF9eXuZzn/tcKCA1aHeU\nGthaCe4rpeTgwYM88sgj12mxwWDYpqgHSe+lunrD+EYbYDD0CuM93WBc12Vubk5nQwU5f/48p0+f\n7liuJozbEUJw55138v73v79jkJbJZNi5c2fkhN92nUA2GAwGg2GrI6VkeXmZcrkM+JM2lUqFeDxO\nPp/XDvKg1HQikSCVSun6zUF57u1GqVSiUChQKpV48cUXKRaLernrumSzWe666y5du1CNobZj9nkQ\nz/NYWlqiWq1y6dIlTp8+rWt9Ly4uAv7Y0LZtpJRMTk6yZ88eUqkU6XRa95Xt2GdqtRq1ZtbxzMwM\nr7zyCvV6nUuXLlEoFHRfGR4eRkrJ2NgY+/fvJx6PMzw8bJzohrUjCadHGwwGwxqoVqt84hOf4Cd+\n4ifI5/OAn/igAt+ud/yi5oqq1Sp/+qd/yv33398zJ3q1WuXP//zP+aEf+iH9HIXWnFO5XKZeb5Vo\naDQaIWWgYLkiIQTxeFyf88DAALFYDNd1+cxnPsN9993XM0e053n81V/9FVJK3vjGNwKtMSb4warB\n73JdVyvWKPuCY6ZYLEYul9OlcYL/PvWpT/XU9meeeYavfOUrvPWtbwX8d4WLFy9Gbuu6LgsLC9pu\nIKQ4FIvFOHDgAOm0Lw0zMTGh++DTTz/NzMxMT53oTz/9NMeOHeM973kPQghqtRqFQkGP12tByRLQ\nQcFRn6WUOghUlRpVJZ3+7u/+jvn5ef1OcL1MT0/z2c9+lve///36vgzO0QYDl4OlgxTt87VBtTBV\nQkgIwbe+9S2++93v6j5pMBgMbaisils21AqD4QbAONE3Ad3k1oOZZ2vFtm2SyWTH8YLZaAbDVkXK\nDuXG0LrV4nslq6/faNS5dTs/r3l+UUHMskcztO2HFhHLNoRuF14If/mVou27HlYGP0Qfv9tL9mZw\nvgXlYYP2a9ncK9wTV5KXVeu7biNb9UhDi4UpiNlDpJTMz8+zsLCAEIL5+XlKpRIDAwPkcjni8TiW\nZZFIJLRDNNmsyzowMMDAwIB+9m9Hp/HS0hLnzp2jUCjw/PPPs7S0BKCDB4aGhnjkkUd0G6jJ2qhg\nxO2E53nMzc1RLBY5deqUzqxyXZdarUY8Hmd8fJxcLofnedx0000cOnSIWCymSwDA9uwzlUpFZ+Zf\nuHCBF154gVqtxunTp3WAweTkJBMTE3iex86dO7njjjuwbZuJiQmy2SywPdvm+pCEnjyRz6hVng1+\neeyQhLS0hP9MgdWfyb1Ayc4HB2TtzzLZ9IorkxCRkt3+tl6rDfr596a9naOe3RE2qke1p86piRpv\n6u36Y3WH2V2HczI8mmkKkUfa1W53fwT0O3q6v7y9b4pmmQIhQ+fl4cuoB8/Ik2qN0Mv6QnvfkPq/\nVYdt6tYL3oLq5xq6muEaGRoa4tFHH2V83E/iUuM95bC9nmeQ53lanebzn/98r0zWJJNJ3vGOdzA5\nOdnhRC8UClQqFb2t4zg6sC2oEiSl1ONbtf/g4CDxeBzXdfn2t7/dc7tjsRhHjhzhscce08GI8/Pz\ngF+6cnJyUp+H4zhUq1W9ryqdpGyPx+OMjo5q1SgVwKky3nuJEIJ9+/bpbOXZ2Vlee+21yLFuo9Hg\n0qVL+rpIKUOO6EQiwd13360d5/v27WNsbAwhBBcvXuy5hLkQgsOHD/Oe97wH27ZZWVlhdnYW8Mes\nKohYoYKGle3xeDx0LyjnM0AulyOVSmFZFufOnbvqudUrsWfPHh577DFs29Z9VxEMTJBSUqvVujrR\npZR6vhb8a5BIJHRfevbZZ3tqt8Fg2FbMAbfiO9M34wTHfwb+xUYbYTD0AuNE3wSYSTjDjUKxeIFq\n9cXIdfV6AylrkesAstkk733vBE3fUAgJ5D2XbHUeKsuR+4tGA2v3boioYapqYW4ko6Pw2GOR5mF5\nDfZcep5EoxLpMBdUSJZeZ3ZuPnKCuVSrsUh0MpQEhu+4g/y73oVoC7IRQHxpCXHp0jWdU08QAh54\nAG6JCqyUzMwk+P7ZTNdJu9QA7DkcPe8ea9SwLp+CRq1zAyHAdXGOHcNbWYk8ttucVNkoZDwOo2PI\n8U6FJFmvkX3kEVIDA9HPGM8jcfIkLC11d0qkUpBMdonckNQO3Ul9dJL2niUElI+/CrXFazgrw2qs\ndeIqOLmqfl/Pun0bRbfgQ5Xd0S6zuN2z0YNEnWt7H9mOcv9RXIu8bPs9Zehkrj7E94q3kIj5AzUp\nITdkMZa3W08JAReLOc6cIVQTPRFzSR4/xuBi1Y/PEmCVitjPfhlKxea+As6cgZtu6r3x4+PwUz/l\nF3mW0h+M7dsXrisuYM4ZY/aOn2oFGVo26V0PkTzV2kwC8Zk4U1M3kfCkb3ej0R9HejwO+/fD1FTr\nWT04CIcPQ7Bc18wMfP3roee9JeGhiZNM7RKhYcCBfIpsYIpgiWXStBxevSAWg7vu8od2wVuvWoVA\ngir1eIqXMkdI0fq7LpEM8W3GCbucc0A+sMwF+qEVIYAUkCUwU+m6LJ4+jbRt3Tec1AI/bH0UJ5lv\n9X8pcW/aj/uG21GF6KWAv1lO83XGaDnP60DEC8/1smsX3HGH7ovCheH9w+w+ER4Knj0L7UPceBwu\nX+4cMv7SL4Ud6889F763DddPcAynnLBX+xyqVCracR109pXL5Z47FpWdjuPoMoRBx6ZSTwqeV9DZ\n2G5PeyBotwSUXtldr9epVCpIKalWq9pRLoQIOc0dx9FtGjW+TCQSujyO53mUSiUajQZSyg7HcC9o\nNBo6a9t1XWKxmHaQZ7NZ3Y6O41AsFrVDV22jSCaTDA4OhhQQVNBFv9q9VquxsrKiFZNUu7quS6lU\n6mjfYF8IOquV/epcbdvWAQL9KOGk7qWgykDQzqCzv72sVvBeVucRTHi6Ed7bDAaDwWDYShgnusFg\nWDdWVi7jeceJzrAQgEU3V28mM8Cjj3pkMnbEeogVXDJfXoJiF8ed52FPTUGEbJqQEtFF8my9GBqC\nd77Td6YHkYBwXMb+6jvYhYVIh6eUDtPlcyw0MzCDCKAIzNNFUVQIkrfeSuIXfqFTqcKyiJ85g/jT\nP72mc+oJQsCdd8KDD3ZMQEsk85f38OLpg3iyc8ZOSn9e/Mjj0RN69vwM1n//PViImBUEpOvSOHmS\nxoULHW0nAC8q4mE9icWQw8PI0bHOdU6NgfvuQwwPRzvJHQfKZX/GOmq9lJBI+LOmUUhJ/eZDlO98\nIx1OdAtq6UHkt566+nMyRKIkI9UkUKPR0Jk7rutiWRaO4+hJMNu2SaVSN4TctOd5eqI2WPt9165d\npFIpJicn9bbtcprbHdVXVP1zJTWpgg3UOjU5GovFdDMhR80AACAASURBVB/azoEXaqJZTdYWi0Vq\ntRr1el1PKtu2zcDAAJ7nkclkSKVSejLWEM2yO8DJ6k5iti8BK4EdEpKZsJNtoer7dIPP5WTMI3Hh\ndQZqRfQzZX4evvgFmJtrbVivw86dvTd+cBAef7zleLYsSKfbno+SZXk7l24OBJtIf7PkdGAzAckl\nm/GRHSSo+wscJ3wevSIWg8lJ2LOn5Y0eHoZbb22dixB+MEBTkjhgJodvucThvW19Op1HkNBbJSk0\nz6N32LYfC3Hnna2hnevC8eN+vIHCSSY4K/eGnOESj/2MsBcRcqJngAFaI5IG/ZnoEEAC35Gu/kJ6\nUjI7M0PQ9RdPLXJP9vPEkonWQilh70OwP6dvACkkLx/dDewO7F3DDwHoocNHCP8l46abWk70BmQn\nM4yOaZ8+ALOz/r8gUsJLL7U+ex7cey/84i+iA5yFWD0203B1qOdyMiqC/Co5d+6cVqSp1+taotxx\nHEql0nUfvx0pJZVKhXK5jOd5DA0NaZntfD6vHbRXwnVdXaYHWtm9/QqCVCWUpqenkVKytLTE4uIi\nQgiSyWToO6vVasg2x3FYWVnR462BgQHtTHcch+9///ssLS0hpYws2Xi9lMtlnTXfaDQYGRkB/Gzs\nBx98UMuzVyoV/v7v/z6ynCT4GfcPPPCAluIvFotUq9W+BhAuLCxw6pQfjVav13WfdByHy5cv68AK\nz/OoVCqhAIBarabHq0qBSmWnHzlyhF27dmmZ+F7cS0E8z6Ner+uxYTBYJJ1Oh8aMAwMDV3XcXihO\nGAwGg8Fg6B3GiW4wGNaZ1V4Euq0Ta5iQidAYbGeTOwS6KmdLXzLUUlqnbXiB5VFnLgL/Ig7tL++S\nbbxpWFVS3EYQnfaizzty9yt0KpV1GPHdXdtsXeluv7jeidfgvdTlPFs9rlPBIHgIw/WhMlbURF2x\nWGRlZQXXdZmZmdEZPblcTk+IjY6Oks/ndRbEdp6EqVQqLC0tUSqVdNaPbdv82I/9GLt372bfvn2h\nrI9gHc7t2ibQmsAulUpUq1XdN5QD2XVdKpWKlqBUmUo3Qp8pl8vMzs5iWRYXL17k5MmTVKtVFhYW\ndH3TdDrN/v378TyPPXv2sHPnTizL6vkE7HZCCN8Rp7uN7Bx/6M8i/IwQoYWB548QYW+7ZfXv4bIG\nXWoBCGmFnLfK9NB27Rv0e7wQJTvfviyi3fyxTLvx7QGt/fs7EGVie7+IGr9e6fOVlveDdjsFgBAI\nERwjyUD7CrVkfYkc7xMyvtstFhWQauTc+8d2eQZ3U8FZ676boR2CSgCr2RMVfNiefbzeqHGdCpZX\nv3fLeg5uv1kducHrEAyM3YhA2SiVoutVdzLKRwaDwWAwbD6ME30d6TaYu5ZBXlRt8271zrdrJpHB\nYDAYDDcCKoNYZd+obGK1TGUTq8wMVZdR/b6dCbZH0AGcSqXIZDK6lmZwe9j+7QLdJU/bM/KDbXIj\ntUswK19l6wcnLpWEaVAO9EZoH8PaiVb4WW8rroMr9WfT3yNZqxP/2o7WZ1YJjjRsLpS0uQqCC2bc\nqkzV9u0VsVgspEYUDMZU6kaA/tkPlBqOymZWz0+lghNlu23boXFbMNNYCEEikcC27b46Si3L0lLo\niURCB88lEolQm6pSQcH9gqWDXNdleXmZeDxOo9HQajdq/NFr1Bg4+P3gZ3YvLi5qifRKpcLy8rK+\n9kIIcrlcyCldq9W0pH2tVqNWq2FZFo1GQysK9MN2oMPhH8zu9jyvoya6ulZq+1QqpeuJx+PxvgaG\nqntU9cdaoDxgvV4PfWej0Qhd93YZ+oGBgVDbKol3dXyDwWAwGDaQAeA9wLuBfcAgsARcBJ4F/ha4\nsFHGrRfGib4OSClZWVlhpa2urhCC2dlZPvrRjzIXIfH3UlAzLUA+n+d973tfh0yrEIK3vvWtPPDA\nAx0DLSVxajAYDAaDYWuhss3Bl4+cn58nFotRKpWwLIt4PE4+n9dZ1vl8Xk/UdAuw2y5kMhmdef/z\nP//zWtb98ccfZ2pqilQq1ZEdciNg2zY7d+5keHgYIYT+2Wg0qFarxONxbr31VnK5HFJKDhw40NFW\n25V4PE4mk0EIwdTUFA8//LCWD1X1To8cOcKDDz6I53kMDw8bJ/oa8B0DDYRoOWU8z//XLm7SkSgt\nXVxP4qgVQUdfYEOvq2TPddoONDwPS01wCxE2vLmV54GUSkY2fI5BPOniSs8/H6DRJ7uREtdrfQ9S\ntgxqP5eO82ka325X2wk1PK8P2dKy6UxyQpdZma8uvzIlnOks8aSkXYjYa/4LigD0w+0gm9/jBo4f\nXCaan+3m56bRrZ9t10Iim30q2InU0Xt3Bspx13BdbU/DozlWcNbUPdWtGfzc3tWMs6d31Go1pqen\ncRwHz/N45plnWFhYQAjB0tKSriENvjNc1XyWUrJ//37e9KY3Af6458knn+RLX/oS4KsV3XbbbcRi\nMe3c7TWu63L+/Hmq1Sqe5/GFL3yB6elphBC88sornDt3Ttter9e1Q3fHjh088sgjep4rnU5z+PBh\n7RB94IEHGBkZ0cGjvcayLHbv3s2RI0eAzmCF4Hza8vIyly5d0p+r1ap28AshmJ6e5kMf+hDFYpF4\nPM7999/P2NiYVpjqNZlMhpGREaSUzMzMcO7cOaSUlEolPvaxj2kHb71e56WXXtJO9kwmw2/8xm9o\n+XcpJc8++6w+bjCr+syZM+zfv7/ntudyOXbu3ImUUsvig9/+4+PjoeCRlZWVkJx7sGZ6IpHgnnvu\nYXBwEAg7qgcGBrpK2F8r1WqVubk5bNtmfn6er3/969RqNRqNBq+99lroPI4fP87y8jLgt+ng4CDZ\nbFYf64Mf/CC33347nucxMDBAJpPBsiwuXbrUl75uMBgMBsMa+RHgj4A9Xdb/HP7Ly7b3MW/7E9ws\nOI6joz8V6gXoO9/5Dhcj6jEX2mrXKRKJBEeOHOmQlFQvTDt27OgqJWUwGAwGg2FrEZzAcl2XWq2G\n4zi4rqszcyqViq532J7hs52JxWK6/vuBAwfwPA/LstizZw87duzYaPM2DCGElrYfGxvTk+Sq/8Ri\nMfbs2UM+n0dKST6fv2EcxSqzCfzJ44mJCRzHIZfL6Yy3yclJJiYm8DyPXC637YNRrhfLsjh37rt8\n+tP/DstqORnSaQiWvhUCvvtdv+51sJt5rsdHnjxLPhXIiqxW/Tregcnjc5bFzrYg4utFCMHs3By/\n+pu/ia2usxCtmuIBFhYFpZXw/RGLtclcC7BrFYZPfpdYtQRCUHVdLjQDN3pJqV7nD774RYYDE/Ek\nkzA2Fo5cWF6GZj3kEK+8Au11WhOJ1gkJQa1e59zsbI9td/nqV/87P/jBl0P+5eXlcECCbUMu196+\nkvzpo4xkMgSdzCvAMi0nugecaToheombSPCJbJa/D7xry+b3B90ctm2TWVrCCgaxSwkvvOAXHA+0\n57HTGbLZoeC3MDDwIrb9eM/stiyLz3zpSxwN9APXk3z3xTiLS/aa8uHb/TgvvQQf+1iou3D8+Hd5\n5JH/rWd23+gotRRVC7pcLiOEoFwuhxI0lBMdWmWAgpnGtVpNO24HBgb0+LGfWa5B1aSVlRWKxSKW\nZbG4uMj8/Lz+m1Kr1fQYJR6PUyqVtBPd8zxqtVpIianfBANXVyMWi4Wc6iozXTnRlYO3UCgQj8d1\nMES/2jsqE105pQuFgg4SdByHYrEYqjOusp7VviprPnheatt+EMw+d103pKYVzDT3PI94PK5tb89E\nV+8EqVQqdG7qO/qB6peqTJJ6RysUCrq2u+d5zM/Ps7i4qM8rqAihSjCpPqLUGtarzxsMBoPB0IVf\nAD5Gq4bnMvACsACMAzcBN2+MaeuPcaKvE1eqlXO1g7rVJDoNBoPBYDBsbtb6/G+vXdg+dgjWLlTb\nqAmvaxlfbBYn6lptD55nsG3UBGbU9tfKZnCkqvO60jUK9otgmyjZ+2CbQXgCcy3Hj/q+jabbNW+n\nvU3UvsHjtLdRcP3VjrU3Q9v0m7vvvptPfvK/4brhyd6oU+/WfGuqEN3M3lIT5L1gz549/Lc//uPe\nZ2O2nWg8kSCXy/Xs8IODg/z+f/7PfpB28Lu69be19tuI/WOxWM9styyLX//1f0ehULy2AwgQazwX\nq6nI0Sssy+L//vVfp/DLv3x9ygJtbRx1KNu2emr729/+dg4dOtSx/HqnDqK62+TkpFHA6wNRdZbb\nn2GbtZZy+/N2teev+tleYmWrslHj6uCY6FpsaN9+s1yDrTDfudo9Gly+WdrUYDAYDIYI3kXLgV4A\nfhn4BHQIgt0G/PT6mrYxGCe6wWBYR0TgXxC5yjq13sKywLKiX5wsq/myKET0jE635QHLNppu5ycs\n4ArnZuE/2aLOQ6yyTtJ8qbMsZLuDSE1yXOV59BwhfNs6Xpq9puPHl/bssmuzb3Sus5SjbZW+YRE9\npd+tPdcT32wlHNq+EhDWNd8Pa9lGCKuZfdRePgQsa6NbZ3MjhODo0aN85CMfuaJj1vM8Ll++rDOO\nFhYWmJ+fD5VpsW2b2dlZ7VgaHh7WEoHXMmn2gx/8gAcffPBqT6snCCGo1+t89KMfJZPJXHGyrFgs\nUigUdM1NNel68uRJBtoyLKWU1+woE0LgOA6lUmnDJr2EEDz33HP89m//9hVtUJlfrutSKpV0LVQp\nJY1GA8uyeO6553Tt0XQ6rWtIXu3EnpSSo0ePcuedd177yV0ntVqNT3ziEzz11FNX3HZlZUVfx+Xl\nZc6ePYvneVrdQUrJ3NwcZ86cAXwFqLGxsWtqG/V9wTqZ25FMJsPBgwc32oxrIh6Pc/PNWy+A3rZt\n9u7du9FmXBM7d+6kh/7hdWVqaoqpqamNNuOqyWaz3HrrrRtthuEqEELorGgpJdlsFsdx9HMonU5r\nR6nrujq7XCmoqGxdNc5U2dWpVIqRkRGdHX369Om+2B8MzkulUrqMSi6XI5/P62dpvV7XY7NcLkc6\nnSYejyOl1EpDKiMZWskk6+1M9TwvNIasVquhOvWu62pFKCEEyWRSZxur0kvJZBIpZd+CTIIBgCoI\nUPWhYPulUimd4ZxOp8lms2SzWT1GLJVK+lzT6XTfg0iD17PRaGjVBNd19RhffZ6bm9MZ3GpfRTKZ\npFQq6XMNBkL2Q6VL9UvbtkkmkwwODmo595GREa0c6nkeo6OjoYDoiYkJhoaG9DkE27k9CNZgMBgM\nhnUmgy/hbuE7zR8H/rHLti8Bv7ZOdm0oxoluMBjWCYllnQKeJ9otuboTvVxO8dRTiyQS0S+ddqXI\n0Is/wK6Uoh1/ngfT01Cvd6yvS8nr1SpvuboT6ilLSws89dQXyeWGOlc2HPI/+D5WpUhU+0jXZalU\notrlBbcKRO/pM3zhAseeegoR4US/NDtLsQ9129aKJyX/dOwYNceJcKJLXl8e49jMMaSMaJf/n703\nD5fjqu+8P6eqeu/bd1+kq8WWJVm2kbxgFuMFG0gMZpjXIcz7ZiYBnEC2dxgIkxlg7DhPYN6sEzKZ\nyYR5M5MnCSEvS3jCkLAEHAOGsbGNAS/I2NqsXbpX9+qufXutqvP+UX1On67uvpKs7rtI9Xkeqbur\na/nVqdO3Tp3vb5HQ27uMiJ6fI3fwAHZ+rvUKnke1VMJv0a4COCJEV+ptni9nz87wla98Rdd9a8Bz\nET98Gs5Mtt7Y84J0osVie6H80CHI59uGKZUXKpSPHSfcs4SAl146iG1H6efaMTY2xn333dcwCbQc\n27dvb/jc7ZIt27ZtY8+ePasycZNIJHj3u9/NiRMnLnpfrey/2EnX2267jZyZn3oFufLKK/mX//Jf\nntc5XMx5vpzrvnXr1lUT0VOplO4z52N7uG0u5Pf0ctpGSsmb3vSmjkZPR0RERERc2sRiMYaHhxkZ\nGUFKyR133KEdshKJhE55rlJaK9FN1cQ+cOCAFuHUvgCuu+463vnOd5JMJimXyxw6dKjjtgsRlJTJ\nZDL4vs+1117L+Pg4QgiGh4eZnp7W61arVS3Y9vf3s2vXLu04kEgkdEYGJVYqh7eVTnNdKBS0c50Q\nQVnGU6dO6THE4OAgr3zlKxvquRcKBWZnZ0mn04yOjrJjxw6klPzwhz/suH2O42jRNp1O09vbqwX7\nkZER3Xdc19X9RDlnvPa1r2VsbAwpJQsLC3zyk5/UNeyvvvpqRkdHtSDfjWcDM2357Owszz33nE5x\nfuDAAd0/isUijz76qK4tblkW119/vR5fpdNpYrGYLm85MDBAJpNBCEE+n+/4OCydTrNhwwYsy6K/\nv59cLqfTsd900026zX3fZ+/evbqMgRCCPXv2NDjCJZNJ3e/Vb8eyLJLJZCSknz+DgKp5eha4tD1Y\nIyIiIrrHuwHlZf7faC+gX1ZEIvoK0u3Bz3ITpi8nNWdERCe54YYb+PM/v7gMH0KUaCsFZyxKb7yz\nfXTwcr8P4O2xGNffcMNF2fdyGR0d5Zd+6RdqkYItBOuYpHjn7ctGPsff+lbibb7LASPnsKHY5u9D\nrreX97z2tXpCYCURQnD77bfjOE6rVgFgFHgzBV5WXHgyRulf/NSyWy4nQ/2M43DDKvaZ9773PSwu\nLlIsNrZO0NUl4rpr4bpr2+/kjW8894GWuW9IwKbY8rvt2zesmgi7Hujp6eFnfuZnVtuMNUksFuOu\nu+5abTOWZbX69dDQED/7sz+7Ksdey6yHPgNrJxVqRERERMTaR0W5qqjsTCajs8ek02ktmEJwHzSf\n1YrFop4bUvNAZiR6f38/qVSKUqnUtahoMxI9kUjoyPlMJkOpVNL3RFNEz2azpFIpbNtGSkk8HteR\n6GYJmtVARaKHI+jNet0qqw+go85VlgAlcvu+35U2V20E6GxVSkR3HEdHYqsoeSWiJxIJLdoC2knB\nrEX/cjPxvBx836dUKmkRfWFhQdtSKBSYmJhgZmYGCBwHrrjiCqrVqm7rYrGot69UKlqE7kbmAsuy\ntHMHBM93Kppf1UdX59TX16cdBSzLYmhoSDsuQJBRSdVwt21bOyyshVJS64i/B26tvX8r8NVVtCUi\nIiJiPfPLtVcf+K+rachaIhLRV4i5uTmOHj3aVB/n1KlT5PN5nbLIxHXDZQYCLMtiYGCgyZNSeS22\nIpq4i1htNm/ezLve9c7VNmNZVut3ksvluPfee1fl2OfLarXNjh07mqJw1xJRn4l4OUT35OWJ2qc9\nUdu0JmqX1aVcLnPq1KkViQzM5XI6vX4nqFarTExMdL4meohYLMamTZs6NiHu+z4TExNBTfQu02nb\nJycnyefzHdnXcti2zejoqE6x3AnOnDmjy2N0E8uyGB0dbftsf6EsLS0xOTnZ9RTYKtK4p6enq8eJ\nqHOu1ObtsvKsVlr0c9HKnrVg43L16M8VsLKSY5ROtlXY7m5dh3O13fm0X7j2+GqnQle/LVPAb/U5\nvE1424iIiIgOkCQQRV9HEH10APhT4NQF7mcr8K+BKwjE1UeAP6e5RnbE+mYYuL72/kfAYeO7BEGq\n93mCPnBZEYnoK4CUkkOHDvHII480DbzPnj3L6dOnmZ2dbbldK+LxOFdffXXLh2qVpisacEWsRaJ+\n2Z6obdoTtU1ronaJiIiIiFgL7N27l1/91fvJ53OYmWFs28JxzEw2ko3OFP32HMJYTyKYzW3Bs+N6\nqe8HFXhMKpUF3vjGG7n//o90LC3rxMQEv/6BDxArlVASsXRi+JmepmwsVqmAqJTry6WEmRlYWGhc\n17Kgvx9qkZ9V36eSTvPJz3ymY+Li4uIiv3n//cwdOULKMR7pXTco1WI+R6bTsGlTw/ZSBquVysaz\nKZKsyBMz5sJcz6NgWXzqM5/pSCkL3/f5o//0n3jxoYfImaK8ENDXF7SdwvNalpWRAwMwOorZ18pl\naPRJ9ymVZvnYxx7g1ltvpRNIKfmT//pf+dHjj9NjCvO+jzx5EswSLbEYYnAQwhGntq37hd48FsdL\nZHQX8n2f+flZHnjgP3D77Z0pNvXEE0/wnz/+cfr7+hrbc34+6AgGlf4RpNOc3yrsQ9Gqq+XzZ/jA\nB34pcvTsAmZU6rmeAarVKouLi1pIdF1XR6o7jrMiAqNZ1zkej5NIJHSa93AkurItmUxi27Y+T/Xe\nTCW+0lHRyjmsWq1SKpX0d2abqijoyclJHZU8PT2to7+llMRiMeLxOL7vdyW6OBx9rt47jkM8Hm8Q\naPv7+/X7dDpNoVDQKdLz+XyD3SqC3sxm0A3U9bRtm2QyqVO8m7Z7nqfriKt1e3t7taNUKpXSkeGr\n4XgRFvDNz7FYTGePaBVh3iqTwGo7AqxDPgc8VXt/eLkVIyIuIzLAN4FXA/sIxM93AO8F7qgtOx9u\nAr5BkOj0SYJkp/9XbV/3AN31Ro5YSW423n+P4IHrF4D3A3tqy33geeB/Af8FmFlJA1eLSERfIaSU\nVKvVpsGS67oNg/PzQT1EtEoDFaX7iYiIiIiIiIiIiIhYKYrFIvv2bWVh4f2Yj5c9PT2MjAzVxUEp\n+eDQJ/g/e76CkPWJYdeO88U3/S4LPeNaFq1U4Pjxui4pBBw58hAzM9/u6OR4pVIhXizy39/xDuK2\nDVLijWyk9KpbwXQAEJB44Uc4J17CUDvhM5+B555rFNFzObjrLhgcBGC6WOT+J57QwkQn8DyP6uws\nv7F7N9eMGEV7zp6FZ54JFE4hAht374bf+70GG6WEH/3IYv8hoRcLfG4XjzHMmeCEhWA2n+dDf/d3\nHbU9PzHBr54+zV2mI0QsBnfeCbWUwkDgnPD444FCbhp+513ID36wQdU9eBCefbZ+ip5X5nOf+w8t\ns729XKSU5Gdn+eXXvIY3XH990LZCQKmE/PCH4cyZ+srDw4g3vzlwpjBt7+nR/aK2kNKGq8jvvEHX\nEKpWi/zu736UpaWljtmez+f56Xvu4V/9zM8EdkPw+tBDjQ0nBBP3/irl0XGErJstBBjZwxECpqZg\n797A10Ft/uUv/x4LC93PMnC5YAprPT09DemgzXkf872Ukv379/OXf/mXWJaFlJLx8XH27NmDlJJt\n27aRyWR0avFuCbojIyO6NnUmk9F/Q8rlckPGxXK5rEX1arWqs1SodO7Dw8O6Hfr6+kilUriuq9Om\nd5OlpSXm5+cRQjAxMcF3v/tdbdvGjRt5xSteoUX+F198kfe85z06hXehUGB6elrXIN+1axevfvWr\n8X2ff/qnf+qonUII+vr62LJlixbGVYrzcrmM4zi6zZPJJLfffrsuE+C6Lg8//HDDNVhYWMBxHCzL\n4uqrr9Z958UXX2ybLfNicByHVCql+2pfXx9CCBYXF0kkEjrjipSSG264oeF3cMMNN2inOuWkYZYR\n6CaWZRGLxbSzh3KSc12Xubm5hsjzXbt2NdxHTac0dc2U+N/b20tvby9CCHK5XCSknz9/stoGRESs\nQf4jgYD+EeD3a8teD3wd+BTwGpavYgnBw92ngRjwWgJnFQH8P8D9wIdrx4m4NNhmvD8FfJnAUcLE\nAnbX/v088Gbgxyti3SoSiegrSHjwE3kWRkRERERERERERESsfwTBo2X98VIIByHi9WgsJI6wSQiB\nEI0CkG3HsayEFtEtq/4v2BdYVndq9wohiNs2CcsCJK5t4zpxCEXjxhwnENoNwRHLCl7N6HS1vGZ8\nvFu1fIUgZlk1u2soexobLhCpDaQEx1F1V9VSnxg2CWGjRPS4bWN12HYBOEKQMPer7Kw5MjSci7me\nlPiWBfE4GP3BcYJN66vKhj7WOeMFtmWRMA9mWXiYcfEga6KfMM9HysDIBkd4iec4OHZd7JHSw7JE\naI8Xa3bghB93HIQpoqt+WjsXKQSOHcOzE01TqmYQqhDBaYSTCQSCYsfMvqwJp6d2HEeLh+Y8Uqu0\nz8VikVOnTmFZFr7vMzg4SDabBdBinRnl3Q3bVfR5OIW1qvuslpkieqFQ0OKvEtHj8bg+31gspqOh\nux08oqLLVR30UqnE3Nyc/m5oaIhMJqPb0fM8nn/+ee38orZXZDIZ+vr68DyvK+Ku4zhaTE6lUjqa\n27Isent7tfidTqfZuXOntqFYLPKlL31JOwv4vo/rutpRI5PJ0N/fr2u+d6MURziKXkW+x2Ixcrmc\njvhX/cqM2L7iiiv0uah66qrdu1F73kQ5dyh7zEh9MxMDoH9/iljonqxq15tZC1T0fzRnHBER8TJJ\nE6RxPwL8gbH828BngXcDtwKPnmM/9wBXEziqqGwPEvhN4BeBfwP8DtA5j9uI1aTXeP/LwBiwBPxn\nghT+BeA64IPAtcBm4B+BVwLTK2noShOJ6BERERERERERK0ixWOTpp5/uaGRfp9m6dStbtmxZ8eN6\nnse+ffs4e/bsih/7fMjlclxzzTUrEgEVZnFxkWeffXZN1AYNI4Rg8+bNbN26dcWPvdb7DMDg4CA7\nd+7sairUtYFseC9lY6pnX6pyVaEvpK83ldRfpfE5vElXECI4jmmIYU/zQknTSZrLl1unW4SPp99L\nfX4ACNkcdiIJ4goMIbqDOm57GvqCrDsiNPcCELLhNMK7WTGtIXQ9w4cVLHPdTUPVZRLUI7+72eim\nU0LYQeEcm5mv4fcR3edC6p6b6aRNIW+t1EA3bTBtamfbatSHblf/vNX3ym4zc4Dv+w3OAybdvgbL\n1dpu9X2rNPmr1U/CdcHD34X7y1rp08th9g/zc0RERESXuY1ASP8SzUPnfyAQ0e/m3CL6TxjbmHjA\nV2v7eSVB6u+I9Y9ZO1oJ6HcC3zeWPw58BvgaQT/bAnwU+NcrY+LqcKnP5ERERKwRZmZmeP7551fb\njLbYts2uXbsYGBhY8WMXi0Wef/55napsrZHL5di9e/eqlIuYmJjgwIEDK37c8yHqM8uzZcuWVRHU\n1gMnT57kN37jN7jiiivWXHSBEIJjx47xlre8hV/7tV9bcfuKxSJ/9Ed/RLlcXhWhejk8zyOfz/OJ\nT3yCETN98gpx4MABHnzwQbZv377ix14OIQTHGDEcLwAAIABJREFUjx/nzjvv5MMf/vCKH79QKPDx\nj3+cfD6v63yuJUqlErZt88d//Mcdq4e9FkkmHWKxFEGmP0DCli1x9lwvsVT2cyRptnJKvqqhJrpn\nOQwN+vRklvRSt+ySnpzBlYGzkQBc+zRBCbYOo3LH16KGBTZOpYDE0/ZIQMTj0NeLlkx9HzZsgK1b\n6yG5UiJzvZTHtyEHh0BCubCIn3im83YLAclkUPNcTcqPjMDNNzeI6IXx7Zw+3DiGkwhkscRY2jWW\nSfbuz/GjvBWcoRAsFheYXerw32JRq3+u0tlKGUSWj49DNqtF5sWpAX5Q8iguGrXGpYR9w8ivnwKh\n6kNLSqUsUvbpTOWe10W/hXQ6SMuuImsTSeZvuhN/bgEl/du9WXpGxrB7Mg2GeLk+vMFRzI4lJcRP\nHNK/CVktYS0tcu7smuePlJLiCy+w+PDDDX0jeeoUcUNMlwhOnYbFYuvDm0OCQiEot6B2p6oHRPpQ\n9wmL0OEU6cViUUd8q2hkNaYKR8CulL2m8BkeW4YF57BIuhpjZXVs1Y7q1fy+VX12UzA1I8NX6jl6\nOYG5XTuq8pLqGqjo+m5lKlgO1dbKLhPlpKAizFW5gnbC+0qK1e2cQcz+cK62XGvPhBEREeuea2qv\nreqe7wutsxzXLrOfF439RCL6pUF4kvn3aBTQFUvAfQT9wiZI6/5vgXKLdS8JIhG9g5gDPhOVEqld\nTfSIiMuBp556it9997vZ0OI3AsFf3GTLbwJELIa9YUPbsAfPdZk/cwa/Wm35PUCmdpwwPnC0p4eP\nfOIT3H333ctY0R1OnTrF+973EV56KYaefG7AB04D7f5eCGCYRoexOqlUmlyut+V3AKO9Ja4aXWwZ\n95IvlSgnEnzq05/WEwErhZSShx/+Br//+58nlRprOSk35MyxOT6hJ+ibcF1oV1PS92F+vl4bMnx8\ny6a06waqg6Mt2+bo0X08+OBHVq3PPPDAg4yMjJJItL4utiWDSKhWSBnU7iwU2ocSlcvBLHQbFlIj\n5FPDtApTy+dnufvum/jQhz50rlO5LPE8j+uuu46PfOQjqzKZuRxCCP7mb/5m1cYnarLp/vvvZ7Ch\nbuzqUywWeeCBB1qO9VYC13W59dZb+fVf//U1N9H26U9/etUiwZVA8L73vY+bbrppVWxYjqmpKX77\nt3/7ko88GhhIMTY2jBApILi13v2Tkl/9VYltqzhuyfPPvIVHX/qJhj5sCZ+7XjFNT/KUXibzS8iJ\n/41U93ABX0/u51Fh1MzuFIuL8M1v1iK1Jc7uPdiveRWkUsZKEgZ6gn96kYRbbw0EVSOdu9vTx+yd\nP4XbPwzA/MIU1Ucf67zdtg3Dw4GQr+wZGgpEdOPecuaExRf+wWlwXADJHdsned3mKb284sPPf2Ib\nT+7N1h0f/BmSmc931m7HgV27YNOmuuIaj8Mb3hC0JYCAY/tt/s2f9XL8VGj0/tnD8L++g1pRCJ87\n7tjFO95xs96d67Yd3l0clgWjo3DllfoAvi84/KFPUHEdHSWfEiWujh3GptKwebVvjMLoVu0AAJL4\n/r30fevvEbVGL7geiYmjnY1Hl5Lpv/xLjn7yk/WRoeMwftttDF53nV7Nl4JHHxMcDyUh8LxgyGz+\nGevvh23b6tnphQhE9Yju0C41tZSShx56iH/8x3/Uf1f379/P6dOn9TobN27kp3/6p3XN6JUce0op\nqVQqOvtSJpNpeKZcWlrS44dqtUqlUtHb2bZNLBZrKVavBIuLixw/fhzLspientb1zpVtg4ODui2z\n2SylUkmnOx8bG+O+++4jl8uRTCZXNLuTqi2vxHGVVh+CmuhmO/q+z/T0NDMzMzpd+o033khPTw9C\niBV3Fi8Wi0xOTgJQqVRIJBI61bvjOGzdurWh//i+T6FQ0A4AnudpB4dkMkkqlUII0fX66MrR1vd9\nbUM4nbvpHFwsFnWpACkl2WyWTCaDKmNwruwMEREREeeBmkxp9ZCu0m4PXeR+1LLz2U/E+iBct+VT\ny6x7iCAq/TYgBdxU+3xJEonoHaRcLnP06FGq1WqTN+rjjz/O1772tSYRvVwu67pJERGXMtVKhVun\nprjX95tkPUkgcI8SZJFshcjliF99NaJNKtTS4iLPv/QSpYWFlt9bwFVAq1gaF/iDcplqpdLi2+7j\n+z6zs0mmpu4Fci3WqBJkzlmkWbCUBK4BbyDIoNLcups2bWXjxutohS/h7t2T/NKbDmCL0LZCcOjU\nKf7wq1+90FPqGJVKldHRd7Jp05ubRHRfSu7oeY5/MfgQsVaRaUIEIvFLL7XeeakUzAiWy81CspT4\nyTQT9/4C+VveQHPTSD7+8QeortJsoe/79PcPcv/9v8XQ0HDzChIScR/bavPgXa3CI4/AsWPtRfSp\nKWgT6S6BA+NvYt/47U01R4WAAwd+SLn87fM/ocsQ27ZJJpNrLr2zZVk4jrOqTn5qsqvbE14XiorS\nWU3MmpdrBdVnVrNt1MRvJtMFgfUiWVxcXPV+sxJYlsBxLH1P8H1IJCTZrI9j18/fTiaoxpINzm+W\n5RNzpok7Pnqc4/hgVUHUxmYC4qJL91wpg/uiZQXvPbfmhBZKMx4uAu37gVgdjxv3UomIx5HxBH48\ngZAgY4nu5L4WjbXXgUCgTqfrIroAGYdKRTSbICVx2w9yiROcbbnqkC/F6oH1fox4ugu2O05goxrc\nxWL1tpSBguvFHPIyw7wXEg89B0r1e5QQknI5HLHYpXTjqhh4Q010gZ/K4fkxHYnuoRxjDbukBMdB\nOjH9O5FIBAKrWtHXUXTJA0CWSviu2yCiy2pVO48oXBcqHg2/UdcNhssqE736yYS5DP7UrRrtIpl9\n3+fkyZM8+eST+l4zNTVFoVAAgvtjNptl27Ztq5JVTNmoRHQzKl4JhEqcVuKj+s4UIlfjPlqtVrVA\nWywW8TxPi56WZZFMJhsi/FXwjPp83XXXMTQ0hOM4TTWxu4mUsqG2vCnmtnLGKJVKFItFvU5fX592\nZF3psbjruiwtLWlB3LZtbVcsFqO3t1eP9aSUnD17tuG6mBHhtm031BbvpiCt2lw5LkD9N6vGqGr8\nrhxLzL4ei8W0s8Nq/U4jIiIuOdT0d6nFd8XQOssRo/aYcJH7iVgfHDfeLwFHz7H+8wQiOgT10SMR\nPeLceJ7H3NycfggwOX36NIcPH24aEPm+v2oiTETESuMACVon4owTRKK3ezwWQNqyEG288IVlEW+z\nb7V9vHb8MDatI9RXFgG0i0SHwMKGQpWh7xyCM2wW0YVIYFlthAUJjp0kHY/hNBV0FCRjsRUph7kc\nlpXAtjNNIrqQkpidIGM7xESbK68mOtt9pyagwwiBLywSsTjVeKapWYWQbSNCVgrLskgkkiQSra9t\nOl7FaSei23YwUR2LtZ/xdJzGSWIDiSQRixOPZwi7vggBsVh8uSD2iIiIiIjLhuZ7SNMS2fab0FZd\nrhV9Md8rDO1dO+CtgUCyC9GfhGjINt49WgkaZurhbh67Cwjj9Vw9uel9uNj4CgmG7YTJ8OJWNdEj\nVo92acWhnjbanHdaS9GsrbI3Lpe+PVxPeqVZLp18qxTeYdtNUbXbLBe9HO4z4ZTp7erUrwSm+N3u\nX9jGsJ3LrRNet9O2t1u+3PHalS6IiIiI6CAqYrNVvbOB0DrLUSAYsvbRHI2u9hOOXo5Yv+w13p9P\npKEpgq6taI8OE4noHWS5NFPtvlPLogFTxOXCcj1dsv4mzC4Z1m3jhyLFIuqcq1nOdd85531Jtu03\n0S0totOYk0sq0gNoqFFpRtqY6T8v9YiO86kFGZ5cDy+/FAlP1KuoLDMizuwfsVhMO0ddDv2m00gZ\n+tsvVR+U5qJaPw0rdDQLhyrcVe80fIAOo/bddCLLYCqLTSIogY+ZrL125bdmtu/yNi9/SmHxxNhr\nt5pd9Q2z3YXKIR681s+uVds1djZls9kaqz4UCfX/ps/1LxobuWuN3tguwnhf759BDgYjJwTUPofN\nUvXPw6ZHdIZwTe7Z2Vmd8XB6elpnOZFScuLEiYb7+fbt27n99tv1fq6//vqG8ZJat5vCnRKPfd9n\nZmZGR3Q/++yzzM7Oahvm5uaYmZkBIJfLceWVV+osTZZlkclkGubSzFrv3WZgYIBdu3YhhKBUKrFp\n0yYgaDfXdXnooYf0ebz44ouMjIzQ09ODlJKtW7dy1VVXMTIygmVZpBrKg3QHZUs6nWbjxo1IKSkU\nCrzwwgs6Tb5lWXz+85/XYxw1NlLtnMlk2Lp1K6Ojo3pfKrq+W84A5vWNxWKk02kdkT06OtowHqtU\nKvpcFOb3iURCt0M8HteZkizL6pr9Kmq+WCzqbASTk5MN0f0TExMNdvb19ZFO10vxqTr0Ct/3dc33\niIiIiJeJiihukbqSkdA659rPzbX9hEX0C9lPxPrgEDBFcL1zBGnaW6coDRgz3k+3XesSIBLRIyIi\nIiIiIiIi1jRqwlJKqdOUqvSapVIJIYSu3wgwOjpKPB7X6TYvZcyJdjM9pGov27Z1GkszjepqpUdd\nKXzfp1wu67aZmZmhUqmwtLTE0tKSTu+p6mUODg6Sy+VQdTcv9X7TaRKJoEZyPQ04pNMC15PU04VL\nUikYGGjUlC3p4T/9fapuvSSPlIJKZhiZDibyEYLiVB4pulB6x3FgbKxuVF9fPb27SanUmMNaSjh+\nHA4dMk5cQiqDzH0Tme0Nli3Nw9xMx82uejb7Z4bwErU2kpB1cmytePUMSwJsLHI5q6HNpQTXTnC2\nmtNKqithcMRhy5Z6Km/XpeOZZXzLJj+whZnRq1EOeb4dY+JIFi+R1A56p0+63DL6Etc4jcKH37uE\nP7TRCP2W3LilwsbpZ5G1ha5XIV2e7azhECjH8/MwPa1TrkvfYnpmM0Uvpn0/snEbd2MOP2b2FyhZ\naebmG//u9pAiY/Y/14UuCG5J26bHsurCuWXhnz1L/qWXtPrtI9jif5c0Q40iejxJfscNup9LCT29\nNuPjMSyjJnpPTxSt3knCIroa7xw8eJCJiQl9D5+cnGwQxrdu3co999yjxwM7d+7U35mR691Ob63+\nzc3NadH/qaee4oUXXtD2uq6rhdzNmzezdetWvQ8lPisxUb2aDgHdQghBX18ffX19DcsUP/zhD/nb\nv/1bXNdFCMHc3BxDQ0M6Jf34+HiDGN1tlFAspSSVSunjzszMMDs7q2u1e57Hk08+2dB+vb29WtDN\nZrNs3LiR8fFxvY5Kl96tdrcsSzvCqnEZBM6NGzdu1HXnq9Uqhw4dahCnVf9QbaBqqKvtVTr4bojo\nZh9XY07P86hWq0xPT+s2B/R36re3a9cuenp6mtrA3O9K9POIiIhLmh/WXu8E/iD03Z211x+c535+\nqrbNi23280MiLhU8gnqy7yFIe3sX0K7Gawx4fe295BLvB5GIHhERcUmxbgOqI9YW0QNrRMSaxIyo\nVq/LZfo5X6F4PU9StWoTM/1ou7a6lAV0k3Dkfato/Mu1bTrJwABcd129goqUMDomKJdsKoYWPTIS\naNQNTVwq4334d8gfPagXVTdewdk/+DTepu1AsP6U24M89J3OG9/TA7feWhfCe3thcRGKIaf7I0fg\nzJn6Zynh61+HRx9tOCEJyM99Fl8IBOBLCWOdF1IWykn+v+dvoP/ktfq418y7/MotJTKJ2t80AWnh\ncNX2cHkeQcEZ4kf5wQa7X/Eqi/HttTVE0ATf+15n7facBMd23s0LN92lh1uVCvzd38cpFISOQh+z\nJvid13yBfqvuXIH08V71Oio/8WYw6oqnHv0WmS//pb6GZc/jy7P7Oms4BB4FBw821HN3fYdnJ69l\n1ksG0d0ShofiXP/Kq4j3GWH9wMxJi/37zXTPgi29Ywy/7hb0E0y1Ct/9bsdNH0om2VqLygyOLZna\nu5cTzzzT0Dfe0vu/iceMAlhSwqZNyAf/OvCWgcDxIZbAy/brvm9Z8NRTkYjeLZa7l7W6Z4VTYYe3\nXUlapZpvNTY5X7tW8hyWS+duZq1Za+OGVtddsZ4jnNe6A2irMabpuHK+9l/obyIiIiKiBc8B+whE\n0HHgZG25DfwroAp8MbTNGwEX+Lax7O+AjwI/B/wZ9ZHtduC1wBPAsc6bH7GK/L/ALxA8nDwAfJ1A\nXA/zfwPqIfcxYGJFrFslIhE9IiJixRDxOJYQLRMZWr4fTBotVzvKjP4J47oIKdvWIVwfjx8q92cY\nC8tKEGRRaVUuwiaZVGloQ+0nJOm4S8qd09E5JlKCU8njFQvNk15C4BeLqy4oS6mDfZqWS2EFE5m0\nWEEIpG3jtek3wnWDSZxlaqYLJMJ363VN1baWSna5ivgeMr8IiRbRSgK8TBzirfqTAA+kdJDEaJcm\n1fEkVrXaZja0dlFE89eixbKI82clJkvW48SZGWVdLBbJ5/M6El1FpJgTnalUSkeil0qlZfetJnNV\nGkm1j3BqxbWKSk+uUkmqSXPXdfF9H8dxdBSPGYljTv62Sv1uYkbIADrKZy2j0muqaB6VtSCfz+v+\nY9u2To9r27aO3nccR0fsq32FUW1iWRaJmrCkoqjWQ7/pNEIEAropoltWs3OjZQXrNHQzGygWkIuL\n9WWFIr7l4DtJvX9pO91J0S1EEI2urpt5Eia+H0QJK+PV2DV8r5QS3ELj3XV4qONmS6DqO5S9mPG5\neSV1bZp+2cJqGMlIGTRDLNYYFN357izw7Ti+k9JN7HlQcaFUqV1rwHMEWadKn21kH5CSSlJSysQb\nRPRETBL3yiCDZb7vY3VrnOb7gcE6Fb2F50k8Dy2ie74Ay0bYoXTnonFMG6Sht8CJNS7scKMLwBIC\n27Ia6917HrJcblg37pVIWcY8mZQgSxCXwb8argMVYxip+llEdwiLcOcS2MLfmWOklRgHhgXz5QT/\nVkKjuY+XI7S/XMxSMMvVOzc/L2dTOPq5mynR1WsrIbfV+3bL2p3XSjyjmFkS1Kt5Pdo5Qprvw5+7\n1d+Xc8hsZ0sru1t9VsuiWukREREXyUeALwBfAv49QQ30XwOuBz5OXVhXfA2YozEF/AvAXxGIqn9O\nIKQPAH9U+/7+7pgesYp8H/g08LPA64DPA+8FVGo1Afwy8Ie1zz7w4ArbuOJEInoH8TyPubk5nWJU\nIaVkaWkJtybYmCyXoicWizVM4Kl9JZNJHMdpOYF5OU7aRawThGDgjjvYct11LSU7+/hxEo880jZn\npBSC8qOPtt2967r0FYtk2x0eyNL6j161zfKVJQFsAfqbvhEChobeh+O0+1shuO++IV7ximSz3m1J\n0t95hL7PPNhWDE+9VObod5cIuzcIIThZKlEdHGy53UpRLMLCQvNy3xeUto0j73oD2C1yEAhBad8+\nDn/yky37VSyRYMuOHSTapOwVsRhD9hx9c3ubS1gKyHYjTegFYB06QPz9v0IiFm/6zk+kOPXOf0fx\nFa9tWX7T9yxmvVdSSoV+j7UPUvrc8MLvMny4fUhR8u5r6d8jWzU7PT0QmpONOAdSyob7vilwdppq\ntaprQqvafWs90mFpaYmnn36aarXK97//fZ26dG5ujoWFBYQQpNNpPeGUTCb1xK05XlITcWYUVE9P\nD8PDw6RSKfbs2aNTQ15xxRX09vauyvleCDMzM7o+6nPPPafF87m5OarVKr29vfo8YrGYTnuvnASW\nmyRWNTJzuRwjIyNIKYnH4wwNDa35PlOtVpmYmMDzPEqlEl/96leZnJxkenqaM0Y0sTpvx3Ea0t6b\nDgatJr37+/vp7e1lcHCQO++8k0QiQSwWa0rRGRERERERcSEIIZiamuLBBx/UpUWWlpb02C2fzzc4\nCM7OzjIzM6PHQM8++yxTU1P6/pbL5cjlcg37h+D5eXFxsaPjTSEEnufxsY99jGQyiZSS+fl57bA2\nNTWlx21Qr50OQd3o48ePa3scx6Gvr69J1JVScvDgQe67776O2Q3B2PBTn/oUjzzyiD5O+NwUMzMz\nHD9+XNterVaZn5/X2/z4xz/m/vvvb6qFLqXkxz/+Me9617s6Zrdt23z961/npZdeAoJ5SVU3vFKp\ncOrUKV3XXDmjmjhGhopYLMbExEST3UIIDh8+zN13390xu9XxvvzlL3PwYJCJxnVdyrWHSDOdPwR9\nJZ/P69+BOnfzupiflVOjEIJDhw5xzz33dGzsalkWP/jBD/jgBz+IEALXdSkUCnrMuLCwoNtc2W72\np+985zu69jsEtedjsVhTnzt9+jRXXXVVNM8bERHxcvki8CvA7wMP15ZVgT8lENjPl/cRRHy9m0BM\nh6D+9TuBb3XE0oi1xq8A1wI3EqTzfytBdoNCbbnyEJfAvwMeWXkTV5bV140uIcrlMocOHWIhpPZI\nKZmYmNAD2fB37chkMgwMDDStPzQ0RDKZJJVKNW3vONEljVi7pMbHye3Zgwj3eyGCsJcnnghCXlrg\nuy6lU6dAtoqnDtyekrTOLwKBzhej9R89Sev475XFAXqAXNM3gTg0riMJwyQScPPNcPvtzTq5tHyc\n43M4019vK6LPTUtOHW5eLoA84N9yywWdSSeRMugSrYLJfR+8ZBY2b4ZWkS+WhTs1xdzkJLJFv0rk\ncvi9vUHa1hYHFo5DyqpAeYawEi2FIOYtH93adRbmsb//FHarX0QqQ+GO+5jbbLUW0X2LCX+UJae1\nRi5x2TVThJMnW0dDSYlTXCSebP49CgHxZl0/4jxRoua5JnnONxXg+ayz1sVQqNdEr1arlEolPXlc\nLBa186JZL9PzPD3hpER0UwxVIqnaJpvN6kkwFb28HiI/lJ2e5+mJx2q1iu/7lEolKpUKiURCT8L7\nvk88Hm8Q0dV+FGZ/UOt6nqcnALsVRdVplK0qUr9UKun+UigUGtaBxswD4Sh9M3pfYds2tm2TSqWo\nVqsN/Szi5RC1W8TLJ+o9EZcSqVSK3/zN32Rpaanrx+rt7aW/v9mJ++WSSqX4wAc+wOTkZMf22QrH\ncdi8eXPH9mdZFvfddx/Hjx/v+n38He94R0dtv+eee9i+fXvX7bYsi23btnV0n/fccw87duzo+thS\nCMFVV13Vsf2Nj4/z0Y9+tEEo7wZCCDZu3LguMkBFRESsWf4H8NcEYmgMeB4422bd1pPOUAR+nkB4\n3wWUgKeBZqEr4lIhT1AK4E8IUvnHgZtD6xwBPkBQQ/2SJ1JcO4zpSatoV4/qXLSKQlOTvauZYiki\n4mUjZf1fq+XnYLnefbn3fN8P/jU1LQJfBqku200xtqsj3y41/lpCgMqF2YzqV0Isn+K/Xd9rWN4k\nFa+hnOUt7BeBfe1MFKJ+fdu2TZD7s/V5rpVTv4RQ0TmnTp1CpSk/fPgwUkoqlYqOukin02QyGeLx\nOGNjY3pMkMvldBStisJIp9OMjIw0jSXMdNNKfF3reJ5HoVBoEIkBEomEjrI2I6rz+bwWfc3lqh3N\nMZmK7spmsywuLuqx1npoF4BSqcTCwgIzMzOcOHFCp71XqcnD9TpnZ4MsGuG2Uf+qNa8lIQRjY2M4\njkO1WmVgYKBhf2sdy7JIJpNBWmfLYsuWLWSzWXp7e3U0frFYZG5urkFwh0bnErOdzL43OzvL0tIS\n1WqV2dlZUqkUsVhs3fSbThOUXpEIIY3PQc3nhtup7+pyIHpbv4q0YmDUYZZ2rOn21i19QEqJ66vf\niAQpkGGvTAH4AiGNO6cUWMLGshvnm2Qth3rD+KpLYwZz7BcMeyS+L/F0W0l8KRGySmOjByVxpLQa\nTFPjSbXsPIfoHUEgsUTgnOcDAr9FTR+J9D2kV9Hp3BGBkVIYY5ZujtHCzzNSIvAR0qsfVgpc12ry\nDfa8+jUTovb3xZfge+jr02pA3wmzEXhY9e6LbCj1VO8yoYte+yw9rzGzk+UhvbJua2GBXCdOVmsd\n27a5ZRWdmC8G27bZvXs3u3fvXm1TLgghBDt37mTnzp2rbcoFs3nz5o6K8ivJerU9nU5z5513rrYZ\nEREREedLCXi8A/uZrP2LuDyYB95FkKr9bQT1zx1gCvgBQZ+6bBwpOiqie57H2bNnqVarpFKphtQ7\nlwuRiL2+8TyPpaUlKpXKinh+R0REREREKM6ePcvTTz+NEIKZmRm+9a1vIaVkcXGRarWKlJLh4WGG\nhobIZrPs3r2bWCyGZVls2LBBp82MxWLYts3g4CBDQ0NNYzEVQQvBuKW8xnPvCyEaalqrdOUQREon\nEgktnEO9jE6lUsH3fS0KmyK6qiEOUCgU8H2f3t5e8vm8jlL32pQXWUuo67ewsMDc3BynT5/W1zOR\nSDSlufQ8j3K5rJ0zlDhsRpmrdrQsi0qlotvYrBe+HjBFdNu2GR8fp7e3l0wmo1P/z8/P675QLpcb\nBHAz1azKWuC6rl6nWCzq9/Pz8zrqfz30m25w/HiRr33tLELUU5EsLibZuTOHZdUcEoRk4HvfoPfg\n0w0ipy999r/+vZRvFdQ0eOz+PvrGRkhmgs9CQCrVHW30TCHLn/7wFhwrEMOLbpzpah8+dSc8CaTc\nPpJewbBBcv2mTWy/7583iJG2rDLCEXpEOdhDqYRz4EDH7a5UJIcOVUgkVGYcSakAT7+QJl2rVCMF\nxGfPsPNHX27wf5PA6Q2v5OzwLq2eSglPPulx5Eg9G4rr+pRKnRV0pYR8HubmDD9I3+fe15xCeF5Q\nV1xAdvYY2a8+DktnGzZ2jp4g+eyzYFwdf2ycwk//rF5Wcau4i3/VUbup2cnERFA8vvb3MyYc3rTx\n+5ScbE0YhyU/xV//+S5KfmPJoKkplxMnVHqlwNY3D73AK7Y+VO9tnge1tMydQgrBc9t/iqFNrzHO\npcoVz36W4aP1clkScCyrqbi5NzfHzG//tl4ugVgsTiaTQwjlqAX2k4/Dts6lqY6IiIiIiIiIiIiI\nWFWOAv9ttY1YbTo6C1YoFHjsscdIpVLs2LGDXbt2kUgkzr1hRMQaoVAosHfvXk6ePKnrMkVERERE\nRKwkKgpWiRhmfWb1XqWeDv8z1znfVO35IMsVAAAgAElEQVTnkz5+LWBGS/u+r1O4q9fw963WNd+H\na4G/3MxBaw3zei53LqodWi1Xr+G+eK59rjXC/SLcJ1T0ubkcaDrf9ZLCfjU5c6bMwYPzmI+X/f29\nnDnTg20rER36n/wemUf/VgtvAK6TYOIXP8fiwNZAQJWQTAqGBgSqLKsqFdKNP1WzpTR/t2+3dgCY\nm4OXDgrC/hC9/aPketDarRDw9rffgPV6qaN4JZCmxGbrKXpEHhCUFxexZ2Y6bne1KjlypEoQABBY\n4DgO+4+kSaWEtmfs7CJ79v1TY8Q5MB8b5mRul3ZckBKef97n+efV2QQx4Vu2dF5ELxZhcbEe8Byz\nfN507RTpWKWWIknAS6cQh54PRGsjNN4+cAD7qSeoh1T7FN/5S5Tu+6WgkwFutYz3T1/vqN1AIKJP\nTzd0RMe2uXnni5BOqzPk0Nk+fufvt3FyPtngvDA35zE7W9HnLRBsHD0M2x/StiNlcIyOIji06fWk\n9/xckGkBsPwig8efYfvRRxuSPtitRPR8nvm//uv6/RLIxGL0ZrNYRltY1Sr4P9dh2y8/pJSUSqUV\nufcoh7NOjQOllJTL5RVxKOt00E6lUtGOl92mk7ZXq9WWpSO7QTweb1ti7uWwXm1Xzr0rgeM40bx6\nRERERETEKtNRET2TyfCGN7yBXC5HPB4nHhVFjVhnZDIZbrzxRnbv3h2J6BERERERK4oq46JEchVF\nOzc3R7FYRAjB7OysTh+tImyFEBw7dkxHZMfjcRzHYXR0VEcO27ZNX18ftm031b9e63X2VHT92NgY\nlUoFy7J0TWsl8KpJWyWGLi4u6shp13W1YKqyzMzNzel99PX1sWHDBrLZrE75LYTQdcTXOiqlve/7\njI+P68nIRCKBZVk4jqOjx33f1xkLzNTkqmRAtVplampKC/Hz8/M6nXs2m9XXYj0ghNAlDoQQ9PX1\n6RrvsVgMIQQDAwNkMhktWJhZC9Q+qtWqLg9w5MiRhnrqat+JRIJUKkU8Hr/ssnApgrTt9VTnQWZt\n5YRRW0kEXwjLqqfirq0spEBIq15qZIV9NSxRd56wBC2L4FhCYDWaXbNX6JV1C8j2ZXQ6Sf3Pudnu\nNLS5sETQ3oZGVltbn0N9X8q5RoT+dZYGG9URpEqXX7cQoKnRVf9RO/BryzG272bzKxu0Ei5qUeR1\nmwUi6C+h81SbSeM8hb5otfPsknAa/K50j8WSF3ZdRSjNu4DatTAv5Np3yFsPlMtlfuu3fov5+Xn9\ndyksSpui97kEcNPRMszw8DDvf//7O1YXvVwu82d/9mfs27evq/dD3/e5//772bRpU8f299nPfpbH\nHnus6+Mc13V54IEHOpbG/Bvf+AZf+MIXXtb864U40rquy1vf+lbe9ra3XfBx2vHNb36TL37xi22f\nRcKOm+fjyNmq9GWlUuFtb3tbx2yfnJzkYx/7WNfHfK7r8upXv5p3v/vdXT1ORERERERExPJ0VES3\nLItsNktPT08nd3vJ027g1crjUE2On290WcSFYVkWqVrIS+TtGRERERGxkijRz7Is4vE4vb29uK7L\nsWPHmJubA4JJG7PmtcKsc55Op0kmk2zevJm77rqLWCxGKpXixhtvJJ1ON9R+TqVSbN++feVP9gLJ\nZrPccMMN+L7PwMCAdhhoNxZqFVHueR5TU1P4vs+Pf/xjTpw4AcDQ0BCbNm0imUyyY8cOLTKrSKq1\nPt5SzhGDg4P4vq+dBhKJBEIIFhcXWVxcbNhGnZ/qB4uLixSLRQqFAlNTU/q8lWPH9u3b2bhx47qK\nyFZjOtUXrrzySlzXpVQqUSoF6a/NFP+FQoFKpYIQAs/zdP8plUocO3aMarXK5OQkJ0+eBNDOKRBc\ng2w2SywWWzfp7leVhmLha4NOmbTaeRou6vgreE1Wu51WlzXW+SPWHNVqlWeffZa7775bO7AdP35c\n37uU05ZyIkwmk22jyaWUDAwMMDAwAAT3RiUSF4tF/uIv/oJisdgxEb1arfLMM8/wlre8RR/TdHgM\nz2OpMWm77DhhhBC4rsunP/1p5ufnOyaiSyl57rnnGB0d5bbbbtOlXMy5OlPoNbPaqM/ne5y/+qu/\nYn5+vmMi+sGDB7Ftm3vvvRegqY1NBwxz3KNQfUkRvj7q3L7yla/wwgsvdFRE379/f4Pt4WxBZmYA\nM0OD6i/qXJTNqswVBM9DyrHgS1/6Evv37+9Y9q2FhQWmpqZ417ve1XI+17wG5jNJOBtWO9T23/ve\n93j22WfX1fg7IiIiIiLiUiSa5VllYrEYo6OjLb1d7777bu65556mAVYymeSKK65oOUm3XqKDIi5P\nJODJFtFFtSiYc/rxGvUHW+1b+stPysllwprkKoskAonT0j6JJQDZJiVeONImvAspkchzR4aso/S8\nJtL4v2lS8jzOqW2gknrQXW4fq91mQjSl29RYFuAjpbfMVG37b6Q8zwd12eoHvfpNs15R0edCCFKp\nFLlcDtd16e/v1xNG5XK5qdY3oNO3QyA4p1Ipenp6SCQSOhJZTTaZE2LrKTW3SmHvOE7DeGe5yTDT\n2cDzPPL5PJ7n6drqaiIrkUjoGuKq/vV6cVg0o81TqZSeUFRR0WYdb4WUUtd9F0KwtLTUlAof6pN4\nqn+ptlkv/cacuFfj5ng83nIC1szsoOqkQxAFpD6b523bts68pdrfcZx10We6QTiyGNTEcf2eUOtV\nOiK9tlL7MYqUoVu87NL9RSKkD6I2GY8IUlq3MUmIRrPDNkkaxxfd/LWoQOAgeFwGY2npNR687T1d\ngm/8bfDBsiSOrTYkGEN2/AzUdW0atF7cAOIiNz//49T+BuqOLY3sA+i+ipBBdw8FattWvV+Lxv+6\naTQgEfhIfaygX0ijr0uCZyKpfqeG4dK2Gy+ZsJb//UZcFNlslj179tDf34+Ukmw2q7PpqHEeoL/L\nZDItBV0pJWNjY4yNjQHBvUsFCywtLfHZz36247bH43FuueUWNmzYoMcbpogOdTExLKKfqwwNBPfl\nb3/72x2323EcrrnmGu644w5ttxJIw5mbWonoy9lujrsffvjhjtptWRbbtm3j9a9/vbZV2R0WzaWU\n2mFQfU6n08uK6Oo89+3bp7PxdAohhLZdCefKXjPLlPpcKBQazic8vjXT5Pf09OisUi+88ELHU/Vv\n2LCB2267raWIbj6XKdvN/m+OtduhHGG/973vddTuiIiIiIiIiAsnEtFXGcuy9CR3mJ07d3LHHXc0\neR1alkUul7tsJ+ki1i/fOn0NxRffBKH0gRK4Mrmbt/y7LcRF64k+aceoDm9uTP9pMDcPX/gSzM+3\nmMuR4Fgev7LrCQaTBcITVa7nYf3gB6s6CXR19gwP3vwp+mPppu+k57O4fx9+udJyis2yYPfHIDVA\n03ykFGAtzuPs3Nn22NbcHNXJyaaZRwFUm3e5oggBmzfDVVc1f+cj2XDkMcSv/wXg0WoCMjU3xzWJ\nRFBINURVSo5+97t4bYRoy7LYdPQoucHB5llZIWDfvlVVi8XoKNYb3oCVbu4zlrDYsvgiG79xjFbt\nIoGrYhk8K9563lb6DP2z18I/v6n1saVkeMtOeuyDNLq/SLAES/YxJi7zOLOXw9jYGLlcDggE39tu\nuw0pJcViUU/8qNqBrutSKBRaTsCMjo7S29ur08KrlNRTU1Pk83kdLbtehFAIJn3Ttb7ueV7LSbxw\nmnoIIqyPHTuGEILp6Wn++I//mEqlQjKZJBaLIaXkjjvu4Pbbb8dxHC2wt4rUWaukUimd0n1wcFC3\ng5rUM2t8mnXBl5aWtIj+yCOPcOLECe1coATlkZERkskkfX19q3Z+LxdV5xXqmZzCtc/N62z2n3K5\nTKFQQAjByZMn2bt3L9VqlcXFRd2u4+PjXHHFFWzcuJFNmzaRSqWwbfuydWgdGclw7bUbEKJeBqGn\nx+GJJwyxGUHudT/FhntfVUsoHWAJi5u3D+MZt2qrUiR39DnsShB5iYCeo89j+Y0T5p1gi3WSj2Tv\nJ2bZICXlV+xi7vd+DhIpvY4Q8Dd/A9/6lpmSOxhe9PQ0DgdidoKFoRuoxIKxyfzCWaqpL3Xc7v5+\niw99KMnYWH0cMHzi+7zqTz5GrJzXd+HUtk2I++5tHOdKyVVf/zLjf/U7erm0ba78xY+ydNVufUKL\n+SL/81OdjYCLOZKbdixy+40zdafFSoXkN74DpUAkRAhYWoJXvxrC9XIXF4MBvyFiF0lzZgrUY4Tr\nQi1ot6NI16W0dy8Fx0G3SixGpq8PK5vV9oykR/jIv72TYqgqSHLmDJmJA1p8FlIyNtCH2PBB9ICs\nWoXPfa7jtt/O/+btVBEE40Bh+fT/5Fbib/y3eh1fCI5vfwPldH89zT+Bc0XfQBWhHCcFxJ96EvFf\n/hDKRkNH0ZIdxXTmy2az2lGrp6eHVCrVIDybAqHKQAT1+5/aVq2rHMa6ge/7nDlzRh9fjS/UOZmi\nYzqd1pks0+k0o6OjDd+bEeqmAN+tubBqtaqjnRcXF5mdndXnYbaXcqQzHfXUNYF6NhxlqxojmMJ7\nJ6mXTxEN4xzP85ifn28Y4ySTSd3Gvu9z+vRp/b3v+w2ZirLZrD7PTgvoCnNsKqVcNrW7akNFeK5U\nlesJv+8Gvu9TqVSwbZuFhQWef/55XSppdna2QeA3rw8EWS/N9PupVEo/l1x55ZVs3bpVO8lGRERE\nRERErD7RHXkNE44GMpdHRKxHThb72Tu3CRmuwSfB2pTGv74CdqvJFwnxFHL7TUEURAgBlM/A4edg\neqpFJBQQt6q415Wx0guEVUPheYgjRy7izC6evliRNw4eYKyFQ43vVjm691u4bR5cBTD4g9Z/0CVg\nDQ9jt1Kha1iVSss4I9Fi2WqQzcLAQItILwGZk5OIJ55ojKQycDyP/jYPn0XP4+jkJKU2E36WZTFq\nWVCbBGqill57tRDpNFx3HVZNdG3A8+jZvw+mDrfZWEBfHyTa1XyWcN0rYHCwzdeSVDxOSiw0i/DC\nokfkz/c0IgxUNDQ0R4EolCDquq6OHlbrqAiH8fFxBgYGqFQqLC4uotIfTk9P43leg4C+XsYUZgSQ\nOTloEhbRVeT+wsICQghmZmZ48cUXKZfLDA8Pk8vl8H2fYrGoJxTXk3iusG1bi7et6rirSGponKiM\nx+NaRFd1z83ofBWhbzocrCfCk+ytJmVVxFO4fqZt2/r35DgOi4uLuh3VBHUqlaKvr49cLqdT6S5X\nd/ZSJ5l0GBxMYVlKuIFYDGZm6uMyX0LpxnHY3Y9587CAgd4E0jHGHksesRdnEUt1UdUpLnbFeS0t\niuxxfkzCCkK63cEkpVt8qGvoCAEPP1wXz9U/y2pOlGQ7Nm6mj0ptftz1PKTd+cfueByuvtrmiiuC\nfUugp7rA0EuP4+Tn9DI7cxOMjTUMkIWUZPMTZF94tH4pYg7DV87A67yayAszsx6f/4fOtrkloDfr\nMtJXCTqFAEplmJ0OBHJlp+cFA0Dfbxzc2zYUi/XP0scTNpVyPTmO63ZJz5USb2EBT8oGEZ35+cDe\nYCUSsTRX75C4fTSI0f3TZQYH5hDmCLu3D4aNlNTVKrQa210EAhhimq0crf/yBDA6ANlRYz1B8ZpX\nUsgO1zeu/ZaHtxjl6S2Jk88baRAiuoG6p0gp6evr00J5b2+vdiyEILV0Ph+MvdX9XUUXq/uVEkJd\n16VcLuv33bi3+77PxMSEdsr7zne+w9mzZxvOSdk2Pj7Oli1bkFIyMjLC2NhYwzi11T21W6V2VJS2\ncqY8deoU+/bt0221tLSk7U4mk/T29ja08fDwsLbXcRyGhoZ0hqNkMqmdNbsloqu2NUv7VKtV5ufn\n9bW2bVtnJVDnfPLkSd23PM9jZmZGb79p0yb6+vq0iJ5u4bzdCdS4U5UbUG1kCs0X2lfNqO9ujM1U\nVjDf95menuahhx6iUChQrVbZv39/Q3+JxWIN49CBgQHtPCKlZHh4WJfcuvvuu9mxY4ceg0ZERERE\nRKwwMeAVwI7av3Dd7jngALAfeAHovJf7GiS6I0dERKwYgmDirEXyxiCyQc1ItkJK8GXbYHEhg0lY\nQQtNj1qsbLv9L3fcFaWV9QAWsnG67QJ3e34P6q3abS0jg44T/Gv3YHwe17XdeQq177adbg200HJ9\nGtE2c0PAMuem08Au83uEZdpgDbTNOsecKDIFY9PBzkwdab6a/8yIW5NwWs31RtjucOrSsBNCOEV7\nOE252Zbh1JzrtY0U4X4S7h/m+Yf7iTnZfSnTymHV7Dft1r3QWqiXOq1uSeZtNBjlyJq62ZArWmf3\n1pvL8Mbd7oNqtCiDpNc+YIiwYTOWGx6sJFI2isWy3sp1WgmdepyAMYYSgYeiT739ZYuBe0cMxxhr\nGHaGr7kRbb5WaLrsYbtlsJaUNJSakgQ+A+p7jaTx/Lr2XNLmOSM8PpCAH1oz9HsIlq2da3K5YDp8\nmaKgeZ8OR7y2eg0v6xatxhvmd4p2ti9Ht2w/137NsWLY1nbvz2e/nabVNW5nQ7sxTLuxYbc41/Vf\ni2NR0952z2aK8LNKq2e4Vvtdi+cdEREREXHJcSNwL/CTtffNEX6tKQA/BP4R+CLw465YtwaIRPSI\niIiIiIiIiDWMOVHqOI4W9lR6akBHGKn1FxYWKBQKTE1NoVJ3lkolqtUqAwMDOm18vEWpg/VIq0lC\nIQSzs7M88cQTCCFYWFjQdc/Hx8fZuHEjvu8zPj6uayiqevOt9rteUSn9zZSo1WqViYkJHSVz4sQJ\nTp06pSOkVJrSO++8k6GhIcbGxi5JodjMcGBSLBY5ffo0QggmJiaYnZ3VbZNKpZBSsmXLFm6++WZ6\ne3sZGBjQv8F2aUgjIiIiIiLOFzV2q1arSCmZnJxsKDNijk8WFxdZXFzU9/nBwUG2bt2q9+N5HolE\nQkdUq9TqKm15pxFCkEwmdWSt4zh6LDI7O6uj5gHy+bxOmT44OIjrujoSPZFIsHnzZn2uiURCj9U6\nXd9aocq5qLI3ql2LxSITExN6PbO2PASp6GdmZvSYPZFIUCgUdASyGm97nteVjEeVSkWP6SqVCsVa\ntg7P8xrSs9u2Tblc1nZ6ntck7rquq1P+x2IxMpkM0L1nhnK5rDNnqSxaEGRSmp2dbXAINrMnCCHI\n5XL6XGzbZnR0lEQiofuPyqTUjf5SqVRYWFjAsiyKxaIuoVCpVPQ1UL9JM6uEEIJEIqGjzNV5x2Ix\nnU5fbVupVC7J8XeIVwEfX20jWtDZlDAREd2hVkuI97A2EomGuXa1DYhYlj7gPuAXabxWJ4DHgYPA\nEWA2tN0QcAWwE7gFuK3277cJBPX/CXwKWOqW4atBJKJ3GN/326YYbTURqx5qwtuspMdnRERERERE\nxNrFHD+Yaf3Ck3cqBXU+n6dYLLK0tMTs7Kwea1SrVT2RqkT0S0nwazXOWlpaYv/+/QghKJVKejJz\nYGBAi+gDAwO6Lc3JqktBQId6NLmJqpE5OzuLEIKzZ8/qvmKmNL/22msZHx9vSK16vlFi64F251Kt\nVvVk+NzcHEtLSzqKTjmvDA0NsW3bNtLpdEOt2ss1nfsFIULRzbWAaKlea8uaomZXqN+Z6a7V+ws9\n9IoF0GMEbJv2YgR4q5XUiq0i01vu0PjccZtD9lzkMQSi4aS73e6izfuWKzbYJBoXttt3t07gnNe+\nvhqhLtAUbN9+84gO4Hke+Xwex3HwfZ9HH32UM2fOIITg4MGD2tELAscvJYwD7NixgzvuuEPPKd10\n0036Hq7EViXcVSqVjttuWRYjIyOMjo7i+z6ZTEaLjXv37uWZZ55puPeq+2Ymk+HKK6/Uda83bNjA\ne9/7Xp0SfXR0lGQyqVOrdxopJbOzs0xMTCBlUMv9yJEjWJbF5OQkjz76qF63WCySz+d1u2azWbZt\n26bH4j09Pdx00026VNOb3vQmxsfH8X2/4Vp1yu65uTmOHz8OwPz8vK5zbqaVB3Sd7XD0tLkv1Z8s\nyyKXy2m7e3t7u1IXfWZmhpdeegkhBMeOHeOJJ57QNekfe+wxfUzf9/V4TJ3Lnj17dCmjdDrN29/+\ndkZHR5FSMjY2plPRLy0ttSx5dDEsLCxw6NAhLMtiaWmJ0dFRPM/TzhcTExO6nVXadwjuf0ooV2Qy\nGYQQup765OQkQgjm5uYuh3nh62r/IiIiLpyB2uv/WFUrItYbPcC/B94P9BIM7R8BvgB8GTh8gfu7\nGngb8NPAa4H/DnyMwEHqvwCdHfisEpGI3kHi8ThXXnllywF9oVAgm802LXcch9HRUWKxWNN3V111\nVcsalJfKxGVERERERETEy8cUNcNpuhXtUgG2SzV4KWGmbFefw6lFVWr88HaXA2Y6+7CYHG6nSyGt\n/YWi2sZso/Dv7FypOy8nXDdPsXgUIerOPbYNCwv1dSQwcSbP4RPFQPQ08BaXwLbr8mKhgHVmElEo\naOXuzNwcfhfauOz7HC0WiVkWSIk/P0/p5BFIprVaaFlB2WvXrQuJatnkZKgmes03ST3eLSzMUC53\nXjRxXZezZ0+SSqW1iL40O0Fc+pjuUXalQmJiolGYlRKUGGKmTJ+aguPHdR3y+YWFhon+TuB5HhNT\nUxw+caJ+7EolaEzzOdrzAhvD17xQCNZX+D5zCzNMnjqMkKK2aYViMd/x36QvJVPAMerZzYWUpAuF\neo+WkmpsnjMTx3GX8g3b52dPkp+aChLuK0G7VAo6Vo2S65IvFDoaTiSl5GyxyOG5uWahu1yurwec\nmjxOcTFfr+Uug77c4OtgSZzpM8Q8D2EEA8xKiVHdPeIiMe8xZnRwqVTSkaoQzDWZ4maxWNTieDho\nw/d9HQndjSh0henAZ44zPM+jUqlo280gFCGCuttKiC6VSg22muO2bt5vw+NEQDujKkxnBKhHeCvb\nE4kE1WpVOwB0OzV6eNymIrbNLETmeubnVvtSryrDTjedBJXN0Bg5XyqVWFpaahDRVX13COZSVdYA\nCPqc67o6un4l+ol5LOWooF7NdjR/d6pu/flcl8tkXPkNApFlrTEC/PlqGxERcQ7mgM3AR1ibkej/\nB/C61TYiooF/BnyCoN8sAX9U+3zoIva5r/bvDwmcov4N8PPA7wG/QBDp/p2L2P+aIBLRO0hPTw8/\n8RM/0XKg89a3vrVt2qZ2g9F4PN5SXIfLZ4I3IiIiIiIiojXnqn+pJl3VRI1KbxiPx/X7SzFqVkrJ\n9PQ0ruty8uRJTp48CQSTbyrV4s6dO3nlK1+J/P/Ze/M4Oc76zv/9VF/TPT33odE9Oi1LliXZBvkQ\nGNuY2ygBE3MEG4PZBMLml7CJd8lmsyFAwmYhCSFkE1gwECBA4EdsbMAOYDBgkA9syZata3RrNKPR\n3H13Vz37x9NVXVXTPZqje2Y0et6vV09P1/mtp57qrno+30NKVq5c6QyMLvbBKjuNazqdpre3l/Pn\nzyOEYHx8HNM0CYVCrFu3zom4jsVihMPhRZWxoBx25JXdD86cOeNEyp0/f94ZwO3s7HTSuS9dupTG\nxkbq6uqcMgtwad6jd3Z28vKXB8nlPuOZLoTSQd38xy8lP36izEb87SYBs+ARUAuWxUuuvbaq/bGh\noYENt9zCp/v7SxPzGeQX/mnCsqYJr3qVd1omAz/60cTtGoYraldK1q1bXdVUuHV1dVxzzRUcPnw/\nPT2l73FhFjDe9EZPu4lAAB59dOJGOjrgbW8rfRYC9uyBp592JkkpWbVmTdVsF0Jw5fbt/HzvXn61\nf39phpReYRyUN0IZJ3TicVi+3DNJZvux/v1vPdOWLWtg2bJlVbEbirbfcANPWxZ7/c5XtspsY+Ww\nvv+FCf1aSAth+S6KMpH4kY6Oqtq+YuVKfrBiBX/rjyJNpyfs2zrxBWSZ7zHP7YIAkc/D617rWaYQ\nCLBm/fpF/5sx15Rz2LIsyyPOllseKOss6F5uLpjM8cwvHNpZX9xOfOXE4Lm01S1O25TLJOmu++62\nfS4EXduGcsfgnm+3o/tZwf884Z9ea8qJyeUcGSoJz36nz7kSoMv1XbfN7v5Qbll3H5rK9bGIOQV8\nd76NKMPq+TZAo5kCtrvy/6bk37mQWIkW0RcKIeCvgT9APWn/X+BPgIEq72c/8Luo1O6fBN4CPAp8\nGPgoC7OfTgktolcRe8CxHO6UqxrNpUzF5wBZfKCo4DwnixE2M9p2aRdlI5jkgnTY8yEE0jAmNIEE\nmOUD1sJ/OJNIWa5nWKVzV+kYpCylCp2wVSq2K8V5k5s1z+0mL3DsFM9theOY9bCI3ba+LUlh27TQ\n+9XFizviwl0H3V1rz04JmMvlME0TwzCoq6vDMAyi0WjVUxouNE6dOkUikeDIkSMcPnwYIQTBYJAV\nK1YQDAa55ppreN3rXgcoYTlbjMSby0HD+SCXyzE0NEQqlaKnp4ezZ89iGIZT8zsajbJ9+3YCgQCh\nUIiGhoZF31dsEokEY2NjCCE4dOgQjz76KHYd1LGxMYLBIDt27GDFihVYlsXatWtpb28nGAw69V4v\nVdavX8+nPvWJOdlXIBDwlLWYLR0dHfzPD3+45vdCkz0nzoT6+nr+6I/+qKaRpDbVtF0IwT333DMn\ndgMVHdJnghCCu971Lt55551V2+ZkVNP2HTt28Hef+cyFF6wC1bw+L2UMw3BSgUspWbJkieOwlU6n\nPSm5/encV69ezcqVKx0RrqGhwXESsyzL+b2q1bmyo5ftCOb29nbHcXPjxo0UCoWy6cRjsRjLli1z\nornb29uJx+PO/a59zLW8T6urq3NqgLe1tbF8+XLsGtZbtmxxji+dTjt1vEF9J69cudKxvb6+nmXL\nlhEOhx2nRLvmdS0cWO007bazbCwWQ0rp3KPY8+x+ZdtQKBTo6+tzMhfYEdP2uSsnTlebUCjk3Gs2\nNjY65ZaamprYtGmT07ftFO/uiO7169c7Y611dXU0NjY6zo72uahVfwmHw05N9nA4TC6Xw7IsGhoa\n2Lx5M+3t7WXTuRuGQUdHB01NTV90GKsAACAASURBVM62urq6qK+vR0pJU1MToVAIuya9RqPRaDSz\npBn4DvAKoA94J/DDGu/zFPBbqBTvn0OJ6DuAtwPVTXM2R9TsCSeXy3miMuYaO2XVfN10SCnJ5/PO\nzc98YKcWmq8H2YXQB0zTrOqAlWbmSAknT3qzNLrnnT4c5PSRJoKGNaFOJhJkIES2NQOifH9KpQRP\nPRUhlTLKaoahgMF3Wq6gtT47YV7eynFkdA8vn9mhVQfLUuFMZQYVBdDy2tdiVRqgLxQIP/44sre3\nvDCazcLwcMVdx02Tla2tZeflTJPgPEaqCmmx+ddf4foTv5owzxKSrlgS4+Uvq6wIJxLwwgtlZ4Wi\nUZZfcw2F+vqyQrQQglgg4Au9cWbCL34xZ/VZy2IYEIlAMbLWjZXPM3jkCOme8hl5BFBfV0eo0u+D\nlIh9+9S2yw2YCIPsda8kd+3NCHztYwgGA+eQnJ7mAWmmils4d3+2B7dsUdhO5WgvEwqFMAxj0Yp9\n7pSKIyMjjIyMMDY25gxaBQIBotEowWCQUCjk3B/NlZgzX7gHPe06ou6XEMIZXDcMg5aWFkcUXqx9\nxcbdNplMhkQigWEYpFIppx5oPp93nFSi0agzyBmJRDwiwaWMPSh/MVJtcXsuuVgH1y9Wu+Hitf1i\nvkYvVSKRCKtXr6atrQ1Qwrj9m3Xq1CkGBgac3x47hbUtenZ3d7Njxw5nW3Ztb1DCX1NTE4ZhkMlk\navI7L4QgHo/T2NiIlJI3velNzrz3vOc9FSN0/QK5ZVkeJ8fx8XEKhULN7k8Mw6C7u5tt27YBcOWV\nV14w+ryS7fb23P/b6eyjZZ7dZoMt8tvtHY/HHQHXH0EfCAQcsR9gfHyc++67z6nBXV9fz+7du+no\n6HC2bZcRsO+zq01bWxvr1q0DVEnLl79cjcq4zz+o9s5msx4bbBHbXj6dTjvXQq3stVm2bBk33HCD\n8zzh3te73/1uz7J+O/xR/2NjY86xxmIxotEoQghaWlou+XtMjUaj0cyKNlQk+FbgF8DtKCF9rvg2\n8DRwP/AbwCPAq4HUZCstRGqirlqWxcGDB1mzZk3ZOuBzwblz50gkEqxbt25eRFzTNDl06BAbNmyY\ntwfWoaEh0uk0q1atmvN9Syk5ePAg3d3dNDQ0zPn+Ac6fP8/IyAgbN27UN54LhNOn1as8If5DNqAU\n9HLx1gaQKTPPxkDKyoOggUCAB5dtoqFhYnystNIcG2ub38BiKZXY7TdCSkQoRPOtt4LLW9lDNot5\n9Ciyt7f8/FwORkYq7ro+FCLW0lJWEE7kcgTnUUQR0mLj89/mWsqcNwTGrusxXvfWUgFSz8pC1fY8\nf77stkPt7Sz97d+Gzs7yQrFlIc6dU6kuy2376NFpH09VCQSgrk69fEghGDx+nOFnnil7xQigQwhi\nFTZtX3GVvjulMEg0bSDxsi6E71ZCCBgOnqIKse6aSXBHloO3nuDAwAB9fX3O4JEdvbN06VLC4TB1\ndXWLLuLarpMopapP+MADD3Ds2DHGXPV8Q6EQW7ZsIRwO0+pyHHKnCl1MbWJjOxUahsHAwACPP/44\n4+Pj7Nmzxxk0tZ0KYrEYu3fvdu5d7WisxYx9/nt6enjhhRcwDIPnn3+eU6dOOX0iGo0SiUS47LLL\n2LJlC1JKVq1a5QxuLsayCBqNRqOZP/yletzZhvwOXP703HY2Gfe2/NsuN73a9tvv7sxJ08Fdh9wW\n3mt9n+b/Ta+2WF8r+/1lnfxtNVkJKHfqcXd9+rnEFvzdzr7284tNuTTods15e3n/q5Yiut1XyvWR\n6QYy2de0+3mk3HnUaDQajWYa1AH/jhLQv4dKrT4f4vVx4EbgIWAX8DVUhHr5utcLlJqJ6EeOHGHJ\nkiXzJqKfP3+egYEB1q5dOy/7LxQK9PT00N3dPW8iuh2FtXLlyjm/8bIHIzs7O+dNRB8aGuLUqVOs\n13XZFgyTdUPLAtOstIBgqoLcZPtwHhZ90+VCEfvKGV/B5omLicmTZ0/eMBW3vxAe2oS0ELJM0v2p\n2iZExZTnAmaeln0BtM2FEJMd2yTzLtjfjGJ0c5lrUwgtn88F9sCNO/rCFgPd6cntZe2BKLu+9UK4\ntquNPbBmmiZjY2OOM6F7AK2uru6SqPFdDrtvpFIpkskk6XTacTCIx+POQGRTU5Nz73opicPZbJZU\nKoUQgkwm4wzc21mV7EhOO0VqJBLRaYuLDA8Ps/fZZykUCt4Z0/meKZv1ZOL6y5cv57LLLqta30yn\n0zz77LMky6VKqiLRaJSdO3dWrc/k83n27t3LSDknSX9bzvL7vq6ujp07d1YtCvv555+nr69MAESV\nxY5gKMTWrVudSN5qsH//fs6ePVu17VUiGAxW1faBgQH27d1b80I7Qgguv/zyqtZzv1SZTPwrN286\nzpHzVe96IW5vMVKpjabTdvPVzm7H1krzLzUuxWPWaDQaTVX4DEq0/gEqCjw/hXUCwL/OYF8DwO9N\nMn8EuBWVRn438BFUTfaLhqqO/FiWRTKZREpJOp0mlUoxPj5ezV1MmVQq5dQpmo+B0lwuRzqddtpj\nPkilUs45mOvBcjuVUiKRIBarFOtYW5LJJKlUyknNOV3cAoRGo9FoNAsVO/ra/n+yuoW1rGk4n9gC\nsS2i204F4E1/b6dyr3V0ykLCjtyxI4zsyHT38RuG4USj29Mvhfaxrx27XQqFAoZheKKcbAeU+SxR\ntNB58cUX+ei73sWmZBLnqUtKlemlu9u78NgYpFJeYVdKOHsWbBFeSojHYdcu9V7kTF8f3Rs28JGP\nfKRq6XD7+vq4996PMD6+FiECSAmt0TSXd54naLivAcmgbGVcep2D02mV8Md9OIEALF0K4bA6lHw+\nw9mzx/n2t7/pqYE6GxKJBP/7Yx+j7swZmt0ZaTIZGBrylAcyW9vJbL8Wv4vb0JA6HW7bR0chny9N\nM80s4XAP//Ef/0Zzc/Os7bYsi3/8zGc4f/gwS1tbS8J5oQBPP63sL+7clJJEoYDl+y6KGAbRQGBS\nhz1TSl5oaOB/fP7z3HTTTbO2G9T3xef++Z/pffZZlrrbQkrVpy2r5LgZDEJLi3p3LxeNQizmbfRU\nSp2IIgUpOXDmDB/6yEd45StfWRXbn3ziCf7pD/+QNa7rxkJwrmkDI7FlrraUrI4OUGfkPOunC0H2\nnl2CJe1oZ2huVpe3e6jlwIHnee977+Ztb3tbVey+lLEjzu3f5Ww266mpHA6HnfubWCzmcdCJRqOk\nUqVAI8uynPmRSIT6+nrHOazWv2v++y23SGq/T2aDfcxSSkKhEIFAwKnZXWvc98zue233PHu+Hf0/\nX06q9v7dddDtNna3fz6f55e//KUzLZVKMTo66tQdt7M1dXV1ATjZq6B294V2H/BnhfL3DX+/ASb0\nYff67vu2uXR6tMfF3Y6FmUzG03+ampqcbE/+CHsdha7RaDSaKrAbeDdwElWHfCoCOqgHxrfMYH+H\nprBMClUnfS9wLyo6/ucz2Ne8UNU7iVOnTnH77bdjGAZjY2PU19fPW6SPfZMyX2ko7ZpNn/70p+ft\nxieXy9Wk5tJUGRsbm/BAN5dks1ny+Tyf+MQnZrR+2QgJjUaj0WjmCfcAojs1ZqFQ4NixY86A6blz\n5xgbGyMcDtPQ0OAMttqvxSQE2gNO2WyWRx55hEwmg2VZ9PT00NfXRygUorOzE4Curi7e/OY3E41G\naWlpcdrLXZtzMTI0NER/fz+GYXDgwAH27NlDOp0ml8s5Eefbt29n/fr1tLS0EAqFPKkxFzPj4+Mc\nP36cQqHAE088wVNPPYUQgpGREWegtqmpide85jWEQiE2bNhAZ2cnUspLItX9VLEsiyvHx/nTVIqI\n3WcsC664Al7/+pJgKAQcOgS9vV4R0TTh5ElIJtV0y4LWVnjf+0oivBD88Mc/5rFnnqm67abZwtq1\nH0WIEBLY1jXI7133JNGQO7Jess/cRo/lLZN17hwMD5cOR0qor4ebbipV4RkdHeSTn/zTqgoQUkoi\npsnvb93Kpvb2khg9OAjPPKPaFEBa5DdtZeC/fgyEAbKk8+7bB0eOeG0/dAjGx0vTcrkRTp/+UFVt\nF6bJu26+mRu3by/ZnUrB3r2qtFFx5znT5GQqRd5Xh7gtFKLzApnecsB/F2JidoRZIKUE0+SunTt5\nxdatJdstC86cKbU5KLF82zZvyR3bsWTFCm//7+2Fw4edaZl8nr/46leranu+UGB3YyNvXbHCscUU\nAX656V0cWPYKR0QXSHYveYK2yCglpwvJuUQ9H/vRdeQtu4QMbNoEd9yhnEVAmf+pT/01hcJFlZlx\nwWIYBrFYzMl+MjQ05Dj5B4NBWlpanGU7Ozvp6OhwfrcymQznzp1z5udyOeLxOEIIGhoaWLFiBYZh\nkEgkajpW404xb3+H2I58tq2TOai5hWshBE1NTQQCAfL5/JyMcbmFTdM0PY4JhUKBXC7nHEckEqGt\nrW3e7pvcNdH94qv7maGvr48PfehDJBIJQB3j4OCgc5yBQICtW7dy+eWXAziOhrV0PrWdRWwb3WJy\nnes71LIsx2HWxp1dyrIsMpmM0wb19fXOfW4sFnMyDNWaQqHAiRMnSCQSjgPrmTNnSCaTzrm56qqr\nnDrwbodO+7jDxS/WxZo9TKPRaDQ1JQb8PapS5zuB4WmsK1E1zKfCWsC+Ib1/iuucBn4X+Cbwj8BV\nQPUeempIVe+Yz50757lZ12g0Go1Go9FUB3d9c/eAimVZjI2NMVaMZEsmk2SzWSfy2l0/czGmMbcH\nn06ePEkqlXLqoyeTSeLxONFoFCklDQ0NrF+/nng8TiqVIpcrRdot5gGqTCbD6OgohmEwPDxMX18f\n2WzWE8nV0dFBd3c38Xh8xrVLL0ZyuRxDQ0MUCgX6+/vp7e1FCOEZaI1EInR3dxMMBmlubnYEjWql\ntl4sBICoEDiyhhBgGCoS1y2QBALq5e9j7s9CqFc47FHoahXlJ4SBYUQxjAgSCAbGiYVCxELufUnC\nRpigFcMugmMHHAeDXiE6GPSaHg6nauK8JISgLhCg3v29Hgio9nYEXsgEAoTD9QjhjuiDUGii7f7T\nEwhkPetVy+5wMEh9KFSyMxQqnffizgNCEMYbPy+BMGpkaLK+EJCSWvzaCSAUDFIfDHpFdHdDgjoe\n++UYL1WniES8y4bDpeOnGIFsGFUtjSOAkGEQc223IAKEA2GCwXqXiG4RCYXVuXFZEA2FCASiWCLo\nHEooNOESJRAIXgzVji4KykWilquXDKUoZJtcLjfBQdC9jl3Hea4Eupnuw1/X2l1ffS7vU8rVnrc/\nu9/nE3+fmKyuezKZdER0OwrafQx2+SdQfalQKNS8zd3n2t3e9jz3Mv51Ktnlj3CfS9zOIrb4n8/n\nHXv9GY/s93JOEBqNRqPRTJPfB1YBXwUem+a6JnDNFJYLACdQIroEPjeNffwbKq37K4E7gS9M08Z5\noVoi+h7gg1Xalkaz0PjlfBug0Wg0mksT92CKe+DQn+7QLZCHQiHC4TDRaNSJ2rGj1xcbdtmWRCLB\nwYMHHUcCW0xvaWlh165dSClpb29HCOGkMl/MA1Tu1K/nzp3j8OHDCCE4efIk4+PjTqaiQCCAlJIV\nK1awceNGIpHIom4XUKlM7QHh4eFhDh8+TC6X48yZMwwODgJKOI/H41iWRUNDA21tbQSDQeLxuBMV\npeuh+5DSk0IcKb0v/3LufmZZ6lVuHXcUe436pme34PrjTefuPxRnKTnxs/+Q5xS7Pe3/pazYfH47\nJSBF6cgtqG0dbekKjbfttgWUorAz//JUGWyb7f8r9c1yy/mX90+r1XewlCDd16hApSbAc5IFvg4s\n1Lruvu6+XuwlHWeM2livKTJVwbaS2LtQBF/3+3RYCPa7WWj22PgFaJtywrNbRHf/7+8vc3Ws/v34\n7ZmKHfN9XmZS091/nIv9flyj0WguYR4CngL+BThS5W2HgA+gxPA/q/K23bwGWF78/2fA4Wmu/yco\nEf0PuMRE9P3Fl0aj0dQQgb+eo3deZcqN47rnLXgMwxtZ5Js340OwG8U/oG3jS6E5LwhR+fgms88e\n3TMrDAHb61ZqV3doVrnutRA6TqWBW3sAmgtcGZUe8AEx2UN+8U+l62khNM1iw1/7zz+w4o5CCofD\nmKZJQ0MDS5YsccT1xSj6jYyM0Nvby+joKI8//jjDwypTlX3MXV1d3HHHHUgpqaurwzCMCfXAFyNS\nSpLJJLlcjp6eHvbs2YNhGPT39zM0NIQQgs7OTurr65FSsnnzZq677jonWmYxk8vlnJSavb297Nmz\nh0wmw4EDBzh9+jSg+s3KlSsd54tVq1Y56XPjrhrdGhctLbBqVSnqXEpYtw6WL/dEopsFiWxsdQmG\nQKFA4OxZRCJREh1bW+HgQTh/vrhcMRW8Wf1U0aEQLFlSMjNQH2b/QAfhgFlKaC0lydYojZ6S6BIj\nk6LFynh+T+uigoZghKhQ92d1RhaDGtxPWRYkEqVQYEAGgsirr8H59ZcWia7LOXjQ+5Mvparl3t7u\nut2xLGTiGbL9Q85vTDo/zlBuOhkAp0g4rFKduwXma6/15JIPpFLUHzpEwZeCN5TNUiimtbbJAWlK\n9zxZIFuL73nLUjn8T5zwRKKbJ06ouu420ShGLIZwHyOQPX2a1GHvGFP+/Hkyp087x501TZKDg1UV\noyWQW7KS9Matqo0kFDAYopW+s2AUHScMYKizmXAoiPsOMhGKEI0KQsVuLCVEzXHCJ84SDhbrdAsw\nRgYRcmUVLdf4KVfn2p12HFQUrB31Cure0U5rPR9ZVNz13PP5vKc2dF1dnef+1H8clu9Zby6FRfue\nyM5Qk81mJ7R/LdOczwa7nUzTJJFIOO1o1z/PZDKOg+769eudftHa2ko4HK4YKT0f+Guku9vbnd7d\nH1E/X04jUkrS6TTJZNKZFggEnBIEthOD+37bMAznHCxGp2eNRqPReAigBO4/A34N/DMqvflIFbZ9\nK0rc/g5wtArbq8R7XP/PRAR/EngcuB4V+f5UNYyqJYtvNFWj0SxYLCuNlONl50mZRTlKVSKLctAq\n/yBkGFFise0EAvVlBbxQCDZsgObmifNME4aGahf4MSVyOVVUs0yqZWkYDD/wAKZrkNSNsCzi6TSh\nrq4JgqlE1Z5015j0LiAxN28l/5rXe9OtohbPnx9A7t83w4OqAoaB2L0bsW7dBFFXAqKtDRoaKovB\n0Rjmu+4uLyRLC+PAQcSBA2XXtYwAQ12bycRXTJwpIBlsmt6xVJuREXj0UYjFJs4rFIgODFCggoge\nChG/6SZiq1eXv6KkhEcegZMnK4rwgXqoa5/Y9EKUv840M6dcFIn/c6VlLqWUgO5BZHfqynJtcam0\nyYXwp468lPqL/zj9g62V2uZSaZ9ps3Ur7N5duo+REtavh507XfcXklwGcnnfb4dZoH77NgJJVzHu\n4WH4zGfUDRqo6cmkqrFeZRoa4BWvUBm5Ac6ebeFvf3GtRxOVEt74RsHODcJ1nynpaD9NU+I0nl/b\nYBAjtgwC6r4tGBglLGpQizWfV2Ku7WggJXLbDvJ/8ieImHL2EEJy4jmD//OXEwflX/c6uP56l8Zr\nFVj1uT8h+uRPnfMwLCWnli+trt2GoZwkurq8Ivo//VPpJADh06dZ9clPqr5gIwS5Q4dI7tvnEafP\nAicpnYUCMOSuR14t8nn4xS/gueecSdI0yb74IjKXc4K6RV0ddRs3Iny128/39dHT2+uxfRA4LaVj\nuwmcbGioskeiYPQVu+l/8zucYPSCCc982eCnj7qD4A02b76C4Ubp2X0qIOhaVur7FrAkfZyWf/sS\nYZmzVyb6/NOwc1sV7dbYuDMS2Sm4bfyieTabZWxszPnc0tJCW1sbwKQ1yGuBlJKzZ8+SyWQAVWLG\nXU5n6dKlrFy50jmOrMtBxrIsp7yKbfNclpzJ5XJO2nO7zrzd/rFYjObmZkfUXUjCp7uvJJNJnnrq\nKUc0HxwcpLe31zmuhoYGPv/5z7N06VKklAQCAZYsWeI5R3OZQt/OrmXfj7lLDliWNaH/jo97x5bs\nczGfqfYLhQLHjx+nv7/fORdr1qxh2bJlzn1lKBRysmcBxONxXQddo9FoLh3eBxxERY3vAD4F/APw\nAEqQfoSZ1wnfXXz/xixtnIwu4Lbi/yMoB4CZ8HWUiL4bLaJrNBpNCSkzWNY4lI3GsSpMBzUklgH2\nUUloN4xmGhouJxisLzs/GFRjue3tE+cVCvDiixc0v7bkctDXN0HIBvXwN3ToEHnXAJszDxCGQWTV\nKuq6uspve3RURc1UiDQvrL+M7H96PxjenwTDgHzPYeQn/nJmx1QNDAPxm7+Jceut5aPOBwbUAHI5\npEQ2tWBed0P5Yz/XT+hvPoE41192vgxGOP/qqxjp3DBhnhBy/kX0sTH46U89A842wrKIDgyo/8us\nKsJh4q96FbEbb6wYzW8dOICcRESPxMBqA3+pVMNQIrp+9q8e7oHSbDbrRC7YAyz5fN4T2SOEqpu+\nGOuf+7EHjvO+SEW7FnwoFCIUCiGlJBgMTisV5MWMXSfeNE0KhQK5XA7DMJx+YkelxWIxLMtyBiwv\nhRSS9jHaThd2O7n7hGEYTn+xB2wvpVrxM6JSmmrD8EwTovjZ3ZSi2OcMV8Yhf1vX8JqdmNSlcuaj\nco5jAXsV9+qVP9YWASIQcO7phDHxd9qzuM9uQ5oEZcFZKYBkknxA1cWf0ty+5so1+gJDWJaTJUHY\nnyv12UJh0kxKEmqTCUoIhAggHWeyystNuddWSNakqT7+WtD2e7nU1+77HPs33Rbm5uPe0J3lxr5n\n8zsDlKs7Plkq8bn4Pbbts2tY2zWu3dMXKu42zOVyThR9Lpdz7g0Bp/RRW1ubp6/Y685HDXq3/X4H\nWPfnye5bJ7tG5oJCoeCpJW/fV7opd40uNIcMjUazKIijSjDvQt22HQH+huml3xbA24FbgKWoJFDH\ngC+jIqk10+MYcB9wFxABbM/f30CJ03ngX4EvAb9getWSbigu/3C1jC3DOyhpyt9EJQSbCd8vvt8w\na4vmAC2iazSaOUT43ivNrzRvGoM6/rXLjMG55y0IJjFEQNn02vb0SQ+h3MC2a54QIKRAJXF0IUEs\nlKGxWTz8qmMo9zAqSoOzk7VNmTZYIK0yqe2Iyc/ebI9BsICunUWMlJKxsTEnMqenp4eenh4nLaA9\nOBQKhZyBmrVr19LV1UUwGCQcDi/q6Ou+vj727t1LMpn0RCtt2rSJeDzO1Vdfzfbt2512siOhFjuW\nZdHX10cymeTFF1/k6aefRghBIpFgaGiIxsZGbr31Vi677DIsy2Lt2rVO+y3GfuLGjnQTQjA+Pu5E\nyKVSKUAdf0tLC1u2bMGyLFauXMmqVaswDMOph67RaDQaTS0pV4/a/uwWQUFFTOdyOedeZ7KyLPOZ\n7todUWz/XygUnN9kf1p6tzjqFxZrbbfb4Q5wHO5sytUO96dAn4/7KXcfAdW+6XSadFqNb6fTaU8J\nKPfzg213ue3V+ljcbWk7Ntr/u5fJ5XIT+ojbPn+7z1VEumVZzrNaJpNx+ostoNu22X3anWXBtm8+\no+c1Gs2ipRH4ObAFeBQYA34bJYLeBDw9hW0I4KvA24Be4AmU6Ps+4PeAd6IiijXT4/8C9/imBYqv\nMKpd34OKWP8s8DWg/wLbjACXodK4VyM1fDkE8F7X59nUM+9B9cmts7JojtAiukaj0Wg0Gs0Cxj2o\nmMvlSKfTngEZ+2VHjtiR6PMVPTJXuAeh7Kgm+xUMBp0o9FAohGEYWJZ1yYjogBM1VSgUnIG6QqHg\nDNRFIhGi0agTiX6p4B+odEeiu6+rYDDotI2ORJ8FUno8rqSQIKwJkeiyuChCAgKkLOs8WCsTp7qn\ncibJCWtfwLmxikgpsZz9F/u2dM8vvcqsrdrd93mukK6/5eeql5T+cGepMhzIMosXUdHztToLqs1F\ncX9SSqQQSMNw0rl7vAzdKetlsfL4hEwL3g+17EEXSvIgsSY2nUBlJ5Clj06jS1+H01QFd0puUJmI\n0uk0Qgh6e3sZdpU5OHXqlMexMhqN0lysreROH21v1xYm50qIDoVCjlPn8PAwo6Ojjq2JRIJjx44B\nEI1GaW9vd9Jxh8Nhli5d6hFI7RrYtbLdfY+QSCQ4c+YMQghSqZTzv5SSjo4O4vG4Y6tlWY4gDUr0\nj8VicxZVbN/zSSnJZrMkEgmEEJw5c4ZvfOMbTn8xTZM1a9Y4zxXxeJzW1laam5udY7ejqO3j8Nch\nr5XtAMPDwxw9qkq4JpNJnnvuOefe3TAMmpubnYhuwzDYtm0bkWLZjEAgQGdnp5MWXUrpPB+4xfha\nMDw8zOOPP04ulyOfz3P06FHnHAQCATZv3kx9fb3TjkNDQ47zJsC2bduIx+0yLPpeU6PRVI3/hRIo\nfxdVdxuUoP4rlDC+FRX1PBk3owT0p4EbgWRx+tWoKOlPA//G5PVZNRN5CiUgVyqGadeE2gR8DPgk\nKur/s6go9XK1cpegtN5a1kK/FiXUg0oXvGcW25KoqPxtKMeMBT1Yp0V0jUaj0Wg0mgWKlJK+vj6G\nh4cRQnDkyBEOHTrkEcdDoRAbNmwgHo97ohwW+yCMPcAaj8eJRCLccsstTtryW265hba2NlasWFE2\nLeilgH3c9fX1dHR0ONO7u7tpbGxk06ZNrFu3DiklTU3zXJ5iDgkGg47YsHz5cm6++Wby+Txbtmxh\nZEQ5bHd3d3PVVVchpaSlpcVJg7vYr6lZMTwMBw6UaqJbFkSjqlyNLSIICPzwJ4T27fekCJdGgFOr\nrqEQWVGsUyMIBnpZksgSKg78C8DK52si0tWFCqzvGCMUVCJTakBytAfSrsd4KeHkyShr1tS5TBCM\njrcSTrlFUUlYFFiT6aUuUByTSiQgPdMsd5VJixiP1V3L8egyx8hIYTltB8MYrlLcQ0OwbUKZasnq\nSD9tfUMlwdbKk8smGJXSmPP67QAAIABJREFUSU0+zswL8lWiYAoOnKon3tpsm4JhwPJAmGC4KNQK\nyIy1cDT4KvJ1JaFBSujcfp7lO3s9Ou/po/CL54oVAQCTAgPiySpbDgUjzPOdtxBdcpkjKBuYbN28\nnzpRqiGMZSHGx339VTIUXs+LTZ1IWbK+bxyODZW6kCkLnA8/RbWdAPbuhUjEa1IsBjff7O6+Jmv3\nPkDH3nOe/Q8Wmjg59CYKqGvEktAYj2GtWAVGsZ8LAUeOVNVmjTd62xY+M5kM4+Pjniw7tiBn/27Z\noqiNW8yd6/shwzAcZz1/BG4mk3EE23g87hGmbfHf3QZzFa1ri8m2gOuO5rbtdpeCsVPW27bOh3Oi\nO3rbjopOJBKcPn2a8+fPA+pctLW1Of2hvr6ecDjsOFnYgrY72nuu7LYjzUdGRpBSMj4+ztGjR0mn\n07jrtbtF8w0bSmXX3CUL3OdlLlK653I5zp075/SLZDLpnAO7xJS7rFQul3OuYcAptaTRaDRVpAG4\nGzhOSUAH2A/8CyqS/FXAQxfYjv0U8XlKAjooUf1nwCuBFcCJWVt8aSFRzgyvmcKy0eL7DlTt9L9B\nCen3AY+7lrMHdcaqZGM53u36/4tV2N5o8b0Z6KvC9mqGFtE1Go1Go9FoFihSSvr7+53ol6NHj3Lk\nyBEsyyKXyyGlpK6ujiVLljiRF5ZlOZEji51wOEx9fT0AN998M6AGnG+//Xa6urrm07R5xxZ93SJ6\nNBqls7OTxsZGLrvsMrq7uwE1+Oce/F3MhEIh6urqEEKwbNkybrrpJkzTZHR01BEh2traWLVqlROx\n769jqSnD4KBS6ezvHcuC1lbIZkvTDIPg/d8i8KUvIazSgLZZV8+RT+4hufwyRDGQOhpsoGE0Q8we\n+AdMw6iJiB4NmWxeNkwkGAQBpw9bHDwgGR2HkpAoueaaDtavL6X0l1KQSXeQzbU7IqQE4tYoXaef\npE4OK2ExlYJkkmqTMBp4KHo7TfWbnZ235AXb9grsLiulEk6vu86/tmRj4jSdZw6UHI0si77MGBnL\nco56lAuHp0yXvGnwzNEmxkSH0qElBIOwq1n5XdgMDXfwUPhNJF3TpISdO6HjNdJpcyHg6Hfh4VEI\n2N1PZhkaurfKlkMhEOGpFa/h/JpXglSuIJGAyeWvOkA4lisZNDwMDz6oHCic71WLc10382vejEXQ\naeNTp5X/idOHZJp84c+qWjNHStizx6txGwbccQe85jV2FgjANNn0ofuIHXZdy0iMQDeHut5ATpRE\n9Pb19VgvWw8hs3Tczz6ra/3UiMlqQ/udvPypuSvVi641k+2n3LGUe022Xi2ZShtf6JxMtu1a2ey3\n1c5oYD8TXMiZolzfmau2dv9vv9xZtfxZgS7U5v5t1VJId9tnC/eT9YXp9BeNRqOZIdejopm/W2be\ngygR/RYuLKKfLb7HysyrR/nbnrvANq5Aa5DlGEdF8E/V884ovkIoMftulGD+VeBzlB7bauXJ1wC8\ntfh/FvhyFbZp94tq+21XHd2BNRqNRqPRaOaYyQYI/cu4B7/KDS6WG3S0B5mmK6QvlGhb2/bJBrzc\nx2njrp9ZbvmZHttCaRdgSmk1LzQI7W+nmfQV/77mm6na4B+U9a9brr6m+/qrlV0XPZWOU4jSPPvd\nNL1iuGUBAlGUFYXrkzfre20HwEXpQzFazLN3e5YnO7eaXEag8rdHDfpBqbUM94QJuy1nDlJAWTNd\n3wuuV7VxuoTzp1ITTfxe8h+T57OzXYPaWK62Lgi4Esa7O4TPUN96aloAMenYllF1y6dyeboXnLC4\nKPYFu30liJq1rwaUQ2QymSQWiyGl9ERCZzIZcrlS5gN/mZJMJuOJmk6lUiRdjjz2dZ5MJmsScWzb\nnkgkkFKSSqU89aLt/8FbazwQCHhKFlmWRSKRcO577GO0y/hUG7vtxsdVhtRUKuWk0PfbbafXt22z\nI+btz3Y0tP8ewHaCrTa5XI5kMunJTCCEIJ1Oe9rYX3O8UCiQTCYZGysFrWWzWadf2CVt7H3Ugmw2\n69ieSqXIZDLOcbjPtb/tAoEAmUzG0+apVMrJWOC+D87lclXPDpDP50kkEgQCAZLJpNNH7DJKtt3u\na9Ju93Q67elP/nNgY6+j0Wg0M2Bj8b2nzLzDvmUm499R6cH/CHgMeBJ1g/6fgetQ6dwvlHLri1PY\nj2Z62A9JLcAHUPXp/3NxWqUU8bPldiBe/P+7wGAVttlSfK9l9HxV0CK6RqOZQ+yBvnIP65MNdgnf\ny4+cZJ5vS2UHGBf+INBkRzfbAU6BQJRpfsNYIG0jhDKm3CCPPa/cw6UQCKMoklQ675VGl4v1N4Uo\nto30r1uT8fBpM1mfuOAVVXFkvbiMmHx4VA1KTNyJEAuk3yxwTp48ycMPP3zBAZ1CocCLL77I4OAg\nQghOnDjB4OCgU+cPVGTtwYMHOXtWOQmPj49z+PBhDMNwUghOFSEE+/fvZ/Xq1TM/uFmSzWb50Y9+\nRGNj4wWXPXHiBCdPnvS0hxCCpqYmpx6ojT2INRu7kjWIJp0Ohw8f5qGHLuQsrvrN6dOnSafTHDhw\ngHPnlHN4JBIhnU4TjUb54Q9/SGdnJ8CsUncKIXjuuedob2+f0frVIJPJ8POf/5z+/v4LLmsPbgqh\napwODAw4A/72gGZDQwNHiiGboVCI1tZWYGYOAyMjI86AsEaj0Wg0F0IIVTv8Yx/7mJNlKJFIOEJo\nKpXyiIl+Rzg744pNXV2d57ONaZpkMpmqiou2LR/96Eepq6tz7r3cqegrOQAEAgGn3Aoop0E725Ab\ny7I4ceIE99xzT9XsBpXd6Bvf+AaPPfaYY2symUQI4UntDup+yl1Gyb7ndrdDuSw2UkqOHDlSVdvD\n4TA/+clPOHHihFOfPZfLOfc5w8PDTt+xLIuRkRGnjcfHx/mLv/gLJ0U6MKF+uL3s6dOnef3rX181\nu0H1zQceeIBjx4459dxtJ4Z8Ps/Q0JBjjxCCnp4ej2Pj888/7/RfIQTRaHRCf7afn3bv3l21Z9RA\nIMC+ffu49957EUKQzWbp7+93HBTS6bTT54UQnDp1ilisFMRpOwjY/ef73/++5xzY9Pf3s2nTpksi\nu5hGo6k6tjg5VGaeLX62TmE7aeAG4K+BJ4AEEEZFPf8h8PdT2MbXuHC0+qXIy1Ap2mf6JV9ARZ0f\nREWifw11njZVxbqJ3OX6/0tV2F4IWAucAmrjqVdFtIiu0WjmDCFeRIhHmKBIQnHaZNk7MqjSLZUG\n+WNkszEKhXjZufk87N8PDQ0T51lWjvPnT9YiQ+iUkFIyDDximrRUiBg8BxQmETtb02nqKj0UplIq\n6qvcfMvCPHmc3A8eAuF/4IS+vt55FY0sy+KJZ55RgyvlTtDoKAwMVEzvKuvjWIPD5Y99ZITA0R4Y\nGSk73wqE6Hv6MZKnT0+YJ4Skr+/k/IkiUjJsmjySStFc5qFaSsm4aZKr1Gcsi8YXXiBUyQFBSuTQ\nUEWBHSGQhw4iH3qwbL85dOjwnNbSu9jo6urK7t692xMlNBnd3d10d3cDsGPHjrLL+KOKbUHZHeUw\nVV7ykpdU3E+tqaur4x3veAdnzpwhkUhccPm2tjZH4HSTz+cZGBioun133XXXlMT9WtDd3c0rX/lK\np273hWhubqapqYmuri5uvPHGCfOFEFVro+3bt7NtYtHlOcHdZ2ZyPHb/aWtrc6b5I9MHB2fnZP2O\nd7yDWCw2/YvxYkI7CWgWELo7ai5m6uvr+fKXv+zUCq8loVCIlpaWCy84Rerr6/n4xz8+K6fFqSCE\n8Pxuz5ZAIMC99947J8+91bb97W9/O294wxuqtr1KCCGIx8uPtcyUt7/97dx2221z8kzdUG4gaIZ0\nd3fz9a9/fU7sjsViuqyQRqOZCfYXR7kfZPu5NDSF7QjgPago5L2oWuhh4LXAB4FfoyLUJ+Nviutp\nvHwVuHqa6+RR52QUJWR/Cdjnmv8csBNYBZysgo02lwP2oNJJ4HtV2OY2VMmBZ6uwrZqjf4k1Gs2c\nsH37Nj772fPFB41KHsCT/X6HUJliJiNHeSe7SdJZogbadux4A9u3z48A0NXVxV0f+Qjjo6MVrFdf\n1pN9YaeEIDXZTiZ7wDMMyIyWndXQEOHd734XodBU7q2qixCCXbt2YRgGQ5UE2YaG8p4RbsaGK8+7\n7bbKbSMEESGIVDgrd975G/MmGi3p6uJdf/7nJMbGKvYZpJzUnXFcpRqovMAHP3jhfjNWvt90dLRy\n5ZWv0BHpZRBCZKWU7Xfeeed8m7IgCYVCvPrVr55vMxYknZ2d3H333fNtxoIjHA5fNH1GCLF4vYtC\nIaiv99ZEj0TU74j9WyIlRKPIllbv70skRjBsEAxKVRNdQDAokcLrdlmroWrTgnTWwAwYICCbs/fk\n/Q0rFASZjNt0CekU4UzWs2iAJAUjQFZEAEFOFJA1Sn1dV1eqIy6lOg2Fgv/nWzI+Lr0/+dIiUzDI\ni3BpkrCQvkTotfoVNwxwBwsGAhCw8gSskuEBqSI5g0G3Uwvk8xajo96a6LmcIBIxPN2vypl7XfsH\n2z9NSpBBiUylccYjhYBMRh2k5/5ZEpQWUSuFdN3V14eDNDSEneORUpVSr7Ll1AXyxEMZ7LNqGBC2\nJIGc69qy8gjT9GV/khjSIh41yRnFaFYpCUckeRkkK+27TYEpdbRkFegXQtw9n9ldZoMQYkImoIuF\nhoaGqgqtc0UsFvNEOV9MXKy2B4NBlixZMpe7PDGXO9NoNIsC2yusXLS5PW0qd5xvAz4KfAF4L6XI\ntnZgD/AAsAGofgTD4ueGKS5noaIO7Trk91HZKeFhlIh+G/CZ2Rro4l2u/79M5QjH6XBb8f2RKmyr\n5mgRXaPRzAkrV67k7rvvuvCC88h8CX6NjY286c1vnpd9T5X5apuNGzeyYcOGedn3VNB9pjJaQK+M\nEGLBpyrSaDSaabFjB/zO73hFw3hcKYK2IGeaWPf8J6zdb/aIs5ZhcOW6TqxIMepSCDhuISLSKbAn\nUK6StRDSh8ZCfOXhdoJGGAS8cAAK5kQR9rnngh5xU8g8t/V9nl1D3/HYVWhu58W3/hGF9uUAjIWG\nGA0+WXW7GxqUL2AxUQkA/f3ws58pId3+GR4ayvP882OeGu+GIbnj9St49a5lpelWnpXBZprs40MN\nFlTbjTIQgK4uWLu25GMRFAXWJPYRTWeLe5a05mJs33w5adOb4nb//iT/8i/Dntr0GzY08KY3laJp\nTRO+//0qG45q13374Nix0rSoyPKBQ/dBcADH7aC+Hq6+uuThUOTK4SRd5/5VeYoUSV+5mcS2Xc7n\nXA4+//nq2377+md43ZYG7KtIAJ2ZAk1Pu8bApEnk/Gml4rtU/fYlo/z5e3uRYZUSXCLJmUGeyl8F\nBacQPScKP6Vb10qfFUKIcXTtUI1Go9FoNLPDjkIu5/HTVXw/PoXt3FF8/194hdPzwGeBj6Oi0r88\nfRMvaZagosUnI4OK1N6DStf+LWD8Aut8F/gz4K1UT0QPUUrlbqEcKmaLgepbFsoRY8GjRXSNRjMX\n/B3wbS2qVUa3TWV025TnImmXs/NtgEaj0WjmgNZWuOIKFX0OpZBdd9kKKWHDRuQWf91PSYvMI7Cw\nBVRzRDIaKI3UCKrj7l6ObN7gWG8dwogggLODSmZ0V0uRUlV/OXWqNE1ICScP0znwE8/2Uh3d9MgG\nEpEVCAnJfIS8mFjrdLaEQrB6NWzc6I08HxtTQizYpXkk+/blJojo11/fxEikHif428qx2gjjrpwc\nQRXaqyZCQCxW8rEACCJpKAwTLWQcHdqycrQ2m6SlN3HO6GiBX/0q5fhmCCFpbY2wenVpe6apdOxq\nIyUMD6tKSTYxLMxkDxi9pYnt7fDyl4MvTXZLoY+W4ZM47iBSYi3rxLy2FPWfycADD0yeLGi6CKC7\ncZirOk8jXPsml4dBl8eFZUEm5fXCsCzqjBzbN6YgUqqVfHq0gV8ca6NQjD4XQEpefBGlGo1Go9Fo\nNIsQ24P3ZuAvfPNuLr4/NYXtTBa1nvAto5k6v4vyEfc/JGZR6fKfAf4ZJZxXTEBahqeKr12oiPQ9\ns7YUXkfJGeOnwLFJlp0qrwcuAx6iumnna4YW0TUaTc0RQvx0vm3QaDQajUaj0dQIO6TYnQZayolK\noJQT1HDhTx4uhGfqXCAEGO7dV1jGPc9wLSx9C6ojKL1qRaUmd6c6L388AiFUSnSxUOqHC19rCeGp\nBGAvoo7Hf1BiQuWAWpWq9bepANV5jKL9/n7vNkTaawhngpQTba8F0t6vs/3i5wt1GPu8SOGKoJdq\nO67+c3H4dmo0Go1Go9FcEhxBpfy+AZVu/XBxehD4bZRY+x3fOu8BTLwZcQ6gBNndwP/xLb+7+P5i\ntYy+RKgHPkBJQLdQ7Z5G1Un/IvDELLb/CeDrwF9TqmM+G97j+r8a+bKCwF8V//9kFbY3J+iiVRqN\nRqPRaDQajUaj0UyDBaU9V6BWgmztkJ736eiy8yXiXnRNrNFoNBqNRqO5FLgXdTv9EPBbwGtQwvkV\nKPGy17f8P6Ayybr5O5S4+wmUKHsbKlX4A8CtKLH3h7Uxf9Hy56ia8jlUrfMHgbcAHcD7mZ2ADvBN\nVAT6y/EK4DNhGSpdP8Aw8O1Zbg/gj4EtqNTzj1Zhe3OCjkTXaDQajUaj0Wg0Gs3MkVLl0DZN57NV\nsCgUcKWFhnxO1Rv36p2SdAGkE8ErsRKCcasRs5gdUAApCsVo2tqYbwvOgQA0NamM1u75dXUQdD09\nG4ARDUNjo9eqWMybC75G2Bnz7dTtoJo/GMSV6hzCYYjHhS+dOxiGoFBwCe0WmNE4ZlNbaXvSwgpU\nechASgwzR9BMldqcAgVhkBdB7JT+eRkgkxVkfCp1Pu+P8ZcUCpJksuBsr1AoUCjUpgCAEKr9RDHo\nPCAEuVCMjBF3bCdUD4TAClKS2QVBESIYDJWmSQkBQwWv18TaMsa7KRSQmQxCFJO8S4mIRFSufVdN\ndGKxCVHq0hPVfjE6bGg0Go1Go9Esan6MErz/BvhGcVoCFQX8Z2WWH0FFqLvZD7wa+DRK/Pzj4vQC\nKtr591FR1Jqp827gWVRN+W8wvXTtU0GixPhfAn+PSu2/b4bbupOSfvyvqDrts+FGVHmBMeAPZrmt\nOUWL6BqNRqPRaDQajUajmTmjo9DTowp1A0jJQK6ZY+kuZFF4ExJOngkwMOjV8ixL8PxzQbKuIZv8\n+HJ6z32KPKr4tACGeZJd9FfddMuCZFIJo1LCpk1w992lQ7F54QU4edJte5A1Da+jPrbMEfcFIIIN\nRJd2YIXVUqaphPlqk8nAk09CryuGxLLgJS/xLpdOh9ixo3WCfhqPBzlwwDVBBmm468O0GMOAOpbR\n1DiZh+6rqt0BM8eGU49yVaSvuF8wA0F6Nt2EGY6q9OAC+gcCfOWbYUbHveufOhVDymUlsyU8+eQY\nZ84cdBwFpMyRSo1V1W5Q53HlSmhrK4nGIaOOn13+P2mMZFFp5SUiHCJidWEkS8MtUkJ36zkuX7nM\nKR2AlIiVq1X/KG7PFuirTmMjLFlS8rCwLAqPP47161+X3BECAYLvfz/GqlVeVTwSgdZWV0eWkI1i\nWmD6ygloNBqNRqPRaBYM3wL+f2AjEAJ6oPiANZGlFab/DNgONAKrUULqcSBfTUMvIZaiotBrya+B\n/4ZyoHgQuJaJmQemwm2oCHSAL83SpstQfTEIvA84OsvtzSlaRNdoNBqNRqPRaDQazczJ5yGRKIVq\nS0k2F2MoFfIogmfOwpkz3kBt04RnnjVIuYZzcrkYxzMvJeeK4hVkuJbBqpsupYo6t0X0pibYtQui\nUW8tbtNUwrVTPhpB/fLlBDuKqm+RoIwQzNURKEYXB2v0xG2aMDjo8VsgFoOlS73tWygYtLREJpTp\nHhpSvg+l6QbjL91BuA2n3nVybAjzMX+5xNlhSJOGZB9tY1HHmEIwwiHRQNZoxi55ft6EYydgeNgb\nFD02FkRKr1fC4OAog4NjlM5DnpaW6o/rCaHauKmp1DcMI8hA13bG64o2osqjRwHDlc3AktAalsj2\nlCuIXkJD3CmnXlNCIZVOoRSuD8PDyKOl8SsZCsH69bB9uzc9g53SoNT5IRhw6rk762sRXaPRaDQa\njWahYaFqm8+WMeC5KmznUqfWArrN36KcJ34X+BEqo8DJaW7jhirZcjnwMNAKfBj4WpW2O2fomuga\njUaj0Wg0Go1Go6keAkcMnTCrwrSJr/lV5C4GQdDdljOJXvavI8BTlrxmuu4Ew0uR/Pa7YEIG8Wkc\n41yo0pX3XHn6AutUvgb1fJqKOu47N/NVl16j0Wg0Go1Go9FM4AOoCPJNwC+AnfNgwyuBx4CVwCdQ\nNeEvOrSIrtFoNBqNRqPRaDQajUaj0Wg0Go1Go9FoNBc/JnA3Kip9BUrM/hAqtX+tiQEfR0WgtwH/\nHfjjOdhvTdAiukaj0Wg0Go1Go9FoZowQQuUQL76kYSAMA0NI92Sn3rP/VWGrZV61s99vj9+ucrYb\nwndwhkAIA0MIDOFepxa2C4QQE+zyt7W//S90LsrNr77luHaq/jeEQExrv+5+YZR51QZ1LmWF7AmT\nv4wyJ0EYdtoGe/u16S9lr1Emtpp/OadTBALedYWBMeE61qHoGo1Go9FoNBrNAkICHwTeiapj/5fA\ns8Abqc3NewB4O/A88F+B0eK+/rIG+5ozdE10jUaj0Wg0Go1Go9HMmN6BAX769NNEAqpWtQQGCi0c\nzxz3qKGnTsH5816B1LJgbAyy2dK0fF4ipeVL5b0P9dxfXXK5Mc6e/QlChJBSlX7+6U8hEvGWhH7m\nGejpUf9LCUJY1I/0MnxmAFeRa7IyzMH8CGlZhxCQSo2QSIxU1WYpJblcihMn9jA+3l+cpkpel2vf\nbNY7TUoYH4dk0js9HIbGxtLnZHKM0dHhqtqetyye7evDsBsSKATCHIg8Rj4cd1ry/HllYybjOXIK\nBVlM9e8e8xkG+l3TTGAQWeWc/KaZZ2RkL0IEnH0JofpFOFy0UKppkYi3Nr2UkG4cJnHmHEYxrbsE\naG5BHulxtpfLZTl7treqtltS8uLp0zy6b1+pp5om1vAwlhCeljT27cNIJr0p3IVQNdXtdQWcHwqx\nvyeGaZXaYWDgGJa1vGp2azQajUaj0Wg0mqrwFVRK979Didr3A3uLn78OZCqvOiXiKKH+D4ENqEed\nrwD3Amdnue15R4voGo1Go9FoNBqNRqOZEZ2dnTR3d/PggQMeMc7CwJTeiGDThHh84jZe+tKJJZgt\ny7uMEAV27ryGQFGorwYNDQ3ceONmzp//rjMtl4OHHpoYCZ3PK/vdPNNnsu+cV+yUgCkPIO0q2FKy\nY8d6wrbKWgUikQjXX7+NY8eeJJf7tTO9UIBUauLy5fRYW+x1c/iwX/iVbN1aPduFEGx7yUt47umn\nOe7eEYLCsYc9BlkWXHfdRNulLHc8VvFVIhbbwNKlS6tit237S196Jab5DEKc8MwzTb/Yrz5PcFwY\nszhwxtex7YhvZzlJW1tbVW1fsWIFP2hp4Ttnzngb74orYNMm78InTsDp0xM34usslgUF0ztt3bog\na9euqeo1qtFoNBqNRqPRaKrCMWA3cDMqMnwncB+qVvmDwEPAE8CJShtwIYC1wLXAG4DXAbY79o9Q\n6dv3VNH2eUXn29JoNDPhJPDXwD/MtyEajWZR8xUgDPzWfBui0Wg0i4y7gL8Hmma7ISnl+y3L+szs\nTbowKu16dR9hpZRVj1guh2FUN8X4XNkN1bX9YrUbLl7b59Lu4jX6DiHE12q4m3Hg/cC/1HAfGo1G\no6kdNwA/B76Iqte70FgNHAd+CNw6v6ZoNBX5OepaCuD3Jl0YfBr4AHAN8PQ826Ipz8uB9wK/CdS7\npg8BPajvwUFUSnaAZqAD6AbW4R1LGAW+CXwOeLKGNs8LOhJdo9FoNBqNRqPRaDQzptpi5VxSC2F+\nLtB2zz0Xq+0Xq90ajUaj0Wg0Go2mZjxWfEVRDkOvAl4KbANeUnxVIgM8jhLMvw88CuRqaex8okV0\njUaj0Wg0Go1Go9FoNBqNRjNXvBT4V1R2u7/1zTOALcAVwBIggoqE+ilweIrbbwReC6wA8qgUpr9A\nRVfVkseBTcAu4IUa7+tiYSmwH1UTdcsstvNe4L8V339cBbs088+bUNfKQsPWS0LzaoVGMznR4vv9\nqIpSC40r5tsAzZRJAw8UX6Duw7pQWTmagIbi9FFgBDjFIqhzPh20iK7RaDQajUajmS11wDJguPha\nDAhgDeqB9Ng827KQiKEeqFJA3yy2sxLVxierYZRm/jh16hT3338/+Xz+gstWyig91SDZLVu2cPPN\nNxMMVucxdmRkhO98598ZHvZ+bZWzU4iJdpavz+2tKw6q9vo73/lOIpHILC1WZDIZ7r//fk6f7i1r\np59y9c8r4T+ehoY4d975Turq6mZgqX/bku9973scOnCgjEFixqN/goknIhyJ8IbbbmP16tUz3KoX\nKSUPP/ww+/e/ODWbyrS3cP5M2LjnYygc5vWvfz1r1qyZtp3lOHLkCA888GCFlO7+aVOPWPf3cyEE\nt9xyC1u3bp22jZpLDoESzjtQ5Zvc3IgaxG30r4TqsN8B3gecq7DtEPA/gP+CumdxYwG3F7dRKxqB\nFlR6XY3CQLVJapbb+QbwV8AngatZmOmLNdOjkfLXukajuTD2A9Eb5tUKzWLEAnqLLw1aRNdoNBqN\nRqPRzJ6/Av4/YCfl6x+FgCtR9bCagQHgC1PcdgS4DbgFVXspiYpCerT4urByNzPqUHWgssX/NYpb\ngX9Hebz/xiy280FUn7kO2FMFuzTzxIkTJ/je9x7mFa+4hUCgFLCTy0Em4xUTjx6F/n7vNMOAq6+G\naNQ1TUhikQIBQ2KLei8ePMjx48d52cteVjURfWhoiC984ZssXfpqhFDbzGRgYAAs19C8BHZuHGbj\n8qQjMZoSfr6viX3fTl1oAAAgAElEQVRHG5zjkRIa6k3eeOMIrY0mIEhkMnz7oYe4/fbbqyaip1Ip\nPvvZb/LrX29GiC5nejwOq1d7xU0poVDwtrmU0NkJLS1e/fb4cUi5JA7TTFIofIu3vOX2qonoD3zr\nW9Tt38/G5mbHGDMQ4viam8iHYk77ptNw+DC4fTOkVMfY1OTZKitGnmPduV8iimvngQdTKS7btKmq\nIvq3vvUg3/mOQTC4EbtfSgnJpOovQpRs3LnT26cBlnfmWbc6j+HWqfv74cQJ52POsvjB8eNs2LCh\naiL6/v0v8Kd/+iSmuZ2SaC5Rgb3jznJCCFatWkM06tUd83k4c6bUV6SEyy+X3HGHpC7srMwPf/hD\nmpub2bx5c1Xs1ixqbgeuR90/DvjmNaM66P3AXqAfJcJeAdyFilxdi4pk998DCuA+4B2o+8V/LG4j\ngrqHfCPemp+ai4sxlPPFR4E7UfW0NRc3X2Rh10Sv1XOmRlMNksX3LSxMp6I/Rf0eazQXPVpE12g0\nGo1Go9HMho3A76GihvwC+haUWL4NNYBps4+piehXAl8HLi8z778Vpx+Ypr2ahcHHUek4Pwm8jIWZ\ngk4zRTZuvJx77nkvoVBJNUylYGzMu9zPfw4vvOAVdINBeNvblKBrEzAkLfV5AoFitxCCH/zgB/z4\nZz+rqt1SSmKxNq6//ncwjDBCwPCwEm9Ns7ScJeG2V5zm5h3nS0KtBVZgOb3j7Y4oKiV0tRX47d/o\nZfXSHCA4PzrKi8eqn8xCyjhC3EEwuNnZdzwOGzaoNnVstyCbnRgZvXGjEtzdwuivfgXnz5eWzeWG\nOHOmutmIQ0Jw2+rV3LxqlbPzfLCOPbveQyba7IjoIyMQCHhFfSmhowNWrHBNExZXn/guLwueQAjl\nPZCVkp5TpypEXs8cKUMEAq8lErkFW0S3LNXPTbMkoodCsGMHtLZ62/eqzTl2vTRNwB3B/eKL8Mtf\nOo2eMk1OJRKV0zbMyG7I53dRKLyV0lethfIT6y8ei8QwBO3tu2jyeimQTisR3cayYE235J57JA3x\nUscaGhrStdc1U+WDqM74+TLzHgHaALPMvC+iUrpvR6Vqf8A3/3dQA/ZnUBHtPb75/4VS+lvNxcl9\nwIdRfeiL82uKRqPRzCu2cH6AhSmiL5YMhRqNFtE1Go1Go9FoNLPiY6hI84+VmddBKVLo1yhv6ZdN\ncbsbgB8B7cD3gU+hItDjwGXAW9DRARcz/ajB899HRYbdP7/maGaHREoDKUvqoD/VuS0wll3bt6yU\nFkJKDP8KNRLopBSAMaluKSUYSISt6zvL+nJaI5TtxZVElYVcz56EcPZfjaaZaKr/2KqH+9wasijt\nSmNK3jSe9PRSCcDuvMn/j70zj7OjKvP+91TdpW9vSXdnTyCELaxBwAgqKgKigqigooOjgsvoqzMu\n44zvOL4zo+iM4464jsso47iMy4yi4wwgKooCQcAAARK2kJA96X25fW9VnfePp+pW1e3qdKdzb2fh\n+fIp7r2nTp166tS5nbrnd57naW6fQ1aU5un0v7Vy3c5ezGtej9vElvU5KovH1FTtOU6AE124iufK\n9DkdOBvJHV4vcoPk5ZyM25HnybORvONJEb0AXB2+/7NJ2p6q/b2RB85Ank+LyHPMXUydE/R4JFJT\nCcmR/jv2vnDwaUg6ofnIQoK14XmyjskhC1W9sJ5BIvycEL6/FVg/yXnOCOvcg4gvp4dt5YE/hOVT\nsRKJMtWGiCU3IvlS95V5SB8tRP7A9iMLbrNs34r8PrgQyaV96wzOpyiKoiiKMm1URFcUZaacDlx+\noI1QFOWw5kj2L+ey0nyWISG9HyA7jPs6ZFJsLRIW/QqmJ6Ib4DpkUu3LSO7LJPcCP5iZycpBxHWI\niP7nqIh+SFM/s2/D/2erBPVim8UYkwqJPvmJZidgwYTT2LrXUGy0WCxBandAQuHNSqTeQKIQ4jWr\nrM1YkAD1fT79xQyz0OU2FHJtPGJEFifs30RVkyX7hnWsTV9mkwwPLFh8auHcI4vrxoi1JtV/0+7L\nJtltzMQc5mJT8nyGwNoJHvyBletJhqy39TdCUabPn4SvP53h8dE85o668hcjwvNjyOLLRvIaJHLO\nkrryAPGMfnPGMS3AvwF/Svqv063I4sF6D73LgU8gv3/qWYcsHn2wrrwbEbx3A6uB74evERYJaf9O\nJnop/h5ZDHAk8DVElE7yw9D28Qx7zgjbPauufBjJR39NxjGT8X7g78lOnXQD8KKM8p+G9l6BiuiK\noiiKojQZFdEVRZkJZSRMmuY2URSl2fzwQBug7JU3I8+T/z7J/l1MzHU5HZ6HeNLsQkJvNpqzgIuR\nnJogYT9vQnKsZ4UPBVgMvBE4CfGSuQ2ZdBzJqGsQD/wXIpOTncgk5O+BbyGTjPUcgUwAb0Ymf5+G\n/Dt7JPLv7s+AHzFxErQbyS3ahywsOA7JGXpMWPcXyP3Zm9f+SuAViOeSi3j8/xj4416OyaKELKp4\nWng9FcRD607g10BvXf27kQUY5yOeWhv28XzKQYI7PkZuYDf5XJy1wQyP4/SOJaQDy/b1Je66K586\n1nEgCBxKpZprMXPmOFx2STtz58rPVWNgrOJiJwjw+4/vSzhuN3QuHhyE4eE4PDeIWF31Hci5oecz\nuMZyur0H1+uNK1roGPLovHk3dHiAkVjYe/Y03O6OtoDLLxpmfnfs9NfujHJkcTuuif9MDNLBIxyX\nCrNtrYStT4ZutxaOPRZOPTUuGx2FG25ouOlQKEAtx7rFcfIsHdmAV41TFc/fPYy/YS2VoXJsN9A5\nv0TXcGeiyy3Lgk2wdCm1wRYEcnENJm/HOWfsv1heXRePRTeHu3o1ptgi1ljo6C5w1slLaZ0Tj3Vr\nYencYUxvf9ygMZITffPm9GAbyfpnZeYYA+edl+eEE+Lvp+9b7r13ERs3ttXGhmMsLzytnyXzhoi/\nuJayn+PUkxdBGC4/CGDV8WXye3rjlOqOI1+cWVroohzSPC98XTODY1+KCLh7mBjK/Zzw9W7k2fTV\nwAVAK/Kc990ZnvPPkAWdFvg2IlRXEG/xC5DnnSyuAY5F8r6vQ54j3xfa+S9MdEg4FXmm/BDyPLQb\nWaz6FsTz/kbEU7z+WQrEC/+nyDPs3wCbkGexv0RSLq0DvjSJnd9DPMD/DvHePy6085XI8+Df1tVf\njTzTtQL/jTyb7gjL/wrJWV5BRPapuBz4p/CaPop4v1dDe56NPJ9mcUf4+rxJ9iuKoiiKojQMFdEV\nRZkJxx9oAxRFUZSDgpeFrzc3uN1Xha8/AUb3VnEf6UEWZpybse99SJ7u92fsewYiYs9PlL0ayb15\nDhMnNH+AiNL1/CnwAWQS+O66facik6r/i0zIfoH0s/rrgf9APLiSKsXS8Lh1yKTld5CJzYgrEFH9\nBUwU0h3gk8C7mBi/9+/Dfe/LuI4sjgB+iUwYZ/E1ZCK4nl8gCxNeGp5POQRxyyMUdm+jFCXjtpbS\n4CCdu3bW6lgsm+/p4de/bp+gs918c554CFqWLy9y+ukdrMgXABEBR8puU/Q5zxNBOfLS7e+XrV5E\nH/dcSXYdiei+z7n215xbvYlaUnSAgQC+PyYHRQcXizSauZ0B77yyn5OP2xP/Rdi5E+64HbxwLZAN\n2JI7iv8uHZc61lr43e9g7dr4uo2Bj34UTjst1kEHBuCe6QT03ReMEQG9LRbMXeDYgbvjlQwYgh1b\nOGnt56E3/vNqgdz8+eR2JRw1I/V/VcJw34cm5KEv2jFeOnId51hbW81kSi2suPD95Ht6xEJroaMD\nzn4etLel/1r39cK20Hk2GlybNsEjj6QH28BMoiFPjjFwxRVFXve6ViJxvFKBL36xnRtuiE/tmIDX\nPvcujl88UKuHBS9fYtvyBTURHaA0Nkph2yawQXySBtutHJa0IiI4iAf1VHwZGYx5RNw9B/E0v5yJ\nntyR4NqLhH0/o27/uxER/Cqmnw7oKODzoQ2vC49P8iUmz7F+AnAK6XDvNyELFC9DnimTC00/gwjZ\n9XwDeXa9DFm8+vGMOp3IwoILkWdBkEUDTwLXAm9jchG9CxHckws87wX+C1lA8AHiv2ROaE9raOtH\nEsf8HHluXwN8LDz/VPlwXxm+vpWJC6evIyt3hrAWuYcnIs/2jV+ppiiKoigKyDPY8cQRY4aAR5jo\n3HFY07yUW4qiKIqiKMrhTBewCvGwXtvgts8OX+9HPFF+hoT234qEdnwd+/4cW0A8Zs5FBOdLkAnM\nhchE6z+S7d2TQ7ydrkfE9AXA85EfDicCH8w4ZgTxrFmNiNw9SF99J/z8Y7LDVoJMZF6DiPknI55L\nV4Ztvhrx9M5iCTK5+5mwjYXApYgn0/OYGBIfZCL2Pcgk62uA5eH5XolM+v41MrE5HT6FCOj/iUwa\nF5D89U8L23lgkuMib6LnTvM8ysFIfcjyWhjzxFb/ecJWO3hW0ytnmZ5ZnnWgcUJhsW6LYmc7TnNz\nRVsj0xe10NpRPuuozyfPMR6ZGN2qpEf6PocgbwgTx4o1ZmImbxNfW9z/ZlYMtZmbEa90SxhvPpFT\nfIJJdZ2d7Pz6rdG224lbVF5/jdKf0bUYCU0fxMdF4d2bbbNyWLIYea4aAwanUf/Pwu0qREDfiTxf\nZT13zglfr0IW5/018lxyTPg+iqb3oX2w943I5PFPmSigR0yWY/1TTMyXfi+yiNJFnguTZD2Dgnwt\nPx++P28vtv4tsYAecV34ejLyXJbFB5kYIekniADegzwbRpwftrUeuQ/1/BHxbG9HnrOnIgrXMdmi\nhsmiQ1WQ/jJMHglAURRFUZSZcyYytzOIzMv9IdzWAwPAN5FnrKcE6omuKIqiKIqizIQzEaVgPdn5\nEveHZeHrauDTyATiBkS4vzDcXoJ4ZU93BexbkDDujyITsYmYuuxEQkhm4SL5FpP5Ln8NvAm4JbTh\nXaTlkjdktNOLeKIvQMJ/XkJ2XvdFSI7wLyTKrkMmMT+EeJb/V8ZxXYjnz/9LlP0YCff+9fC4axP7\nViFhPgcRkX1jYt+Pws93IJOr32DixGw9Ua77NxN7HlWRie69LbK4N3ytz6upKIqiKMrhRRTRZ7qe\nw6uRZ83FSCjzdyORbS5DFgpmPZvkkRzgX06UfRIRir8UtvExZAJ4KqIQ8TdN094kd05Svgl5hu7J\n2NeOLJY8FpiH9JcJy0EWYmbhA3dllA8iz2RdyCKDrBRLWXZaJL1QF/IcuTEsjxY8PgCcPokt0WKA\nUyfZn+RW5Hq/hixM/V/gPiYXz5PsQRaMLphGXUVRFEVRps8bkWiHk2nH7cic16uQ6JS/mCW7Dhjq\nia4oiqIoiqLMhEXha+MTz0pYShCPod8iXiYnIZOoL0cmBS9HJkKny+vC138mLaBPh3/MKPst4n00\nj+lP4FliAfxZk9TZQ3riNyLKTHzSXtr+aEb5/05y3BuQidl/JS2gR9yFhENdBDx9knMmiTzKTplG\n3STR+JmHLvA9tJmWV2rot2vS2wT/3oR7bNILuRnEnrkWay2BtZleuzbjP3HLDSZWPlBkG55pe4Al\nqPtvFg3FmoQ1JrvPLGAdJ7XVrsYG8YYlMNE1NXvETJPpeJdn3q8mjiFrJfx6uNnEWA+CMANB/akj\nWwy1UYMJxEl9Krd2RZlInLdjetyFiLzXAx9GhNmtwEXIgsMkkTf1OLJ4sJ6vI97oJeKIR1MRPes+\nNs36SbZPUh55rtc/8zwTWTD6LeAfkAhC5yOCe/Rs1Uo2fUy+oHWy883EziXh66XEHmn127vCOnMn\naTfJtUjY93nEOdH7kMhN509xbDLEvKIoiqIojeFM4CvE//5/D0kNuAxYgTiD/DLc14qkY1k8yzbO\nOjpRpSiKoiiKosyEeeHrVPkOZ0IVCXc+inhQJ8Nh/gSZaPsoEor809Noz7BvOTiTWOChScq3AUcD\nHcCOuv0XICHSj0F+cEQeR1E4zXlks4FsD5yoDzoz9gFsIdurajvird+O9EM06RgJ43ORUKlZRLko\njwF+P0mdiO8jXvA3Ix7wNyMC/hNTHBd5LDlIH9X3o3IIMHDnnTy2bRuFhFDY2dnJgkWLMLU44QEr\nWi/irLOPTR1rLfT1Ofi+hHG3FpYttszLD9NT00QMHQxhmiCOdrv9vGbuz8k78tO4urCd4bOOIDDp\nVKy7R1r58o+X1a7HEPCsZS9m1Z8fRy3YuzGSw/urX5X85MaIMjl/Pg3HWhgbg9HRdC7wrq44HzvQ\n3dfLeXd8bMKhpz7eT3/fUJz62hhKT76TxztX1kLXDw1BudxYs0e9PF958OncsFWCT1igxanw3qN/\nTFdhhCg0uymVyH3845Av1OyxwIOPtbDm/mSuccvm3aM8+v0hoovxrcf64VZe2ljT8RH3S4d4cUcO\nWBIEFBJ9zo4d8IlPSOLxZJz8Y46BVavSgnqxCMuXp3Oi904W1XlmWGvZ89WvsvnGG0X8BgI3zzln\nvY5V7zsvNsfCov4+2NGbstEdGaPnW9+Lx1VgMSeupPLKl2MKRQCMY/Dn9mhYd2UqIk/o7hkevxX4\nHPIM+FLSz4BPJF6zwoNXkUWDJzC5R3c90V+ayXJzN4oS8ST0PyILHJPC/UlIKqIDTfQFvx5JkbQ3\n1k+jvSrynP9JZGHEcxBv9z8Jty8C75jk2Oi5Osu7XlEURVGUmfE3xM89n0Oi+yTZCPwPMi93MRLp\n5s+BD8ySfQcEFdEVRVEURVGUmRBJK8UmtD2ICNO3IxOm9fwImUBdhnipb56ivQ7ivItTibr1VJg8\n36UXvtZ7wfwrkpMTJCzlBsT7fRgR3S9g8tyUk4UXjc41mUIx2XGRb2GUKDmaEI4mH68Mt73RPsV+\ngKuRH1vvQsJ6vSosvx/xrP8y2YsDkuOnwXKdMluUN2+md9262o9LCzhHHMH8009Pieg9BcvyFfNw\nEsPYWigUoFqNj+3p9mh3d9CW+IoVGW+KiN7mlDmj9CAFxxVj5vXAqlbIJX4qG7juF+38bt1c0Qgt\nOMay4kUnsuqsRdSUSWNg61YR0YeHY0HRn05k2n3EWum0SiVdViolvIcNrX19HLfp5gnHHrdnG5R3\n12y0xnDHwJ+wp28lxkrx8DB4Hg2l4ue4bfsK1g6KY6UFOpxR/k/7AF2FMMWttbBsGc4ll2B6EuuN\nLOz4NdyxJf5DZgzcu+VR7rz3XuIVAT5zuyb7EztzAmSVTzvx+fPW4tV7jw8Pw69+BQMDaRH93HPh\nhBMkGX1ELgfd3emxUmzwP6vWMnrH7fT//ne1eAOmpYWjn/1Mus87L1XP/GwM+hNj1xic3btpu+kn\n8WAIfDzvhYy/8UpMa4dUcyAoTeYkqyg1ooV9bchXqT4X93SIxOWFdeX3ha9tezk2ep6ZKkVNxBZE\nwG52zs/nI17et5NOyxNxdJPPP122hK8jiJdao7g73EDu0euAa4C3A/8O3FZXP0f8HLsFRVEUZW+c\njETBM8DDSEq6fQ1B5SIRQo4Kj/0NMsehHH4kIyZ+ZpI6PrKQ8eLw87ObatFBgIa9URRFURRFUWZC\n5PnR1YS2Hw5fJ5sYS4rmWTkl60mKszP1fpou5yMC+kbgaUju8YuR0PRvRTy2DwaisJ9vQyaH97Z9\nexrtVYG/Re7Hi4HPIl5TpwCfR/JdZhHdj3Gml59UOQgx1k7YMEYE9Lpw1lNGr44+G4PM9cyGZ6uD\nwWCMg8HBWFO3iRWOCTdHREOwENRdSOStu9ew9g1iuu0bZ8JWu9ZwAwemisbfIJMNFseYeHPAMHGs\nxPc/3Ex4PxKWGwsOdSH4m2d+vUXxIpH6i3SciduB9tJOfj+R5AppEotBkvdhwvW44Mh3Jm57dr6p\nyiHPELK4DibPqT0VZ4avm+rKf4p8/ZcAR2YcdySxB/r9GfuziFYgvXxfDJwBUbiSRyfZf0GTzz9d\nov44D/GebwZR7voo/dEZGXVOQRbHPs7k4egVRVEU+BTyb95XkBzXvwRuZHqL5CN6gFuR9HL/AnwV\neJDsRV/KoU+0KrZCdtq/iGTEmb0tYDwsUBFdURRFURRFmQkbw9fphsTcF6KQ64sm2Z/MuTQ4SZ0k\nyR8Ax83QpunywvD1W8DajP3HN/n80yVaqLAY8era2zadPo4oI2Hc341Mcr4I8aK/kuxcWcvC18f3\nyXpFURRFUQ5FfhO+rp5k/8VMLtBeTJwLvX5R4pNhmUG8o5KRN3OIN5VBPNazns+y+BoSSei5TMzB\nHrFkkvJ9IXomO4eJwsazkQWPBwO3AGuQKABfIY7yVM9zmN6i1ReSfa/zxM/LWWl+orFzyzTOoSiK\n8lTlbcBfAj9AHB8KwPuRRf9f3od2/g04C/l3MAcsQAT1DwOvbqC9ysFB9ExSYGLUnyTJBYuHfVQC\nFdEVRVEURVGUmfBHRFw9itiDplH8IHw9i+xJuChs1C6mL77+LHy9aq+19p9o0jYrRGkReEWTzz9d\nrg9fr6A5IfkjbgAeCd+vyNgfTYT+JmOf8hTCNtN9+EAwWxd0oL2bG8Hhdu/hMBzQCQ7na1Nmg+gZ\n74WT7P8g8nx3E/B14J/D17uRZ7lWZLHeNzOOfS8ipr8C+B0ywX814kF3GbLQ721M/6/OHuDPkMWA\nn0NE23eHbXwQ+C0Sbnx/+QPihb4cCen+3vC8XwZ+hXgOHgxYJFf5VuBPkYhDn0aElfcSRyH6DTBv\nkjaSfDJs6zrg75Br/gukX09HFnJm5V5/Ufj6g4x9iqIoisxJfAhZCHZV+FpF/k29CZkDOGka7Twb\nuAj4T+ALSBjvXUiUvTLwkUYbrhxwfpR4/6pJa8FrEu9vbJItBw2aE11RFEVRFEWZCT4yyXUxcDYS\nRjOLo4gXbi4IXwuk8zvuJu3tvAaZMDwP+CLwRmA03HcmceiwLzL9idDPIN7QlyMTfP9IOkf3CiS/\n+v6KuevC1zciHky94eciMhl61H623yi+D/w1EnL++8CbkPuQ5DRkkvSvp2irgPwgj0K7JVmF3Osq\n8armJGeHr7+ept3KQYjJ53Ha21MrtJ1iEes4iZzoEFiD76e/tNZCPh+niY4+e4Gh4sfhpP2gOWKx\ntRZbLmPdMCf62BiMjNTlRLfkqFIsip3WSlh310Th2xNhr0FyWre0SOUgSOfAbpzhsa1R0egodmws\nlRPdeh4USxOOdXJ5jOumQna7DuScABMe7jpBU/LQR1HBo8swjqFsiozSgvSlBYrkA4NJZGy0YS76\nlkJAmDEAgJxrkKmN5BhpwngxBretnZybq/VKrlDEdwt4biIHu1vAaW3D+H5sh7VQKE6wysfBM8Va\ntYrxCZrg6+AUCvKdjApaWjCeBwP9tTrWBpiqJ3nZk4szrIVSSXKiGwO+T5AvUqlSyywdFivKdPgN\n8qx0PuLFvbVu/0+ATrJDmG9FQtN+nvQzXMQWxAv6i4hI/4zEvrWI+H37Ptr7A+T56NOIR/pzE/tG\nkNzd+0sFeBnwH0ju2k8myr8Ybi9uwHkawWPA05HnvtcA76nbvx0RWrI8yOu5GRFyXl9XHiDj4J3A\nWN2+LuS3x+PIYgpFURRlIs9A5l6+i/xbleSHwAuAlwIPTNHOJYljkvQhf8MvRsT4qdpRDh2uRRbM\nnQZ8Iiz7F2pP/XQgEQ3+Ivz8cxqzoPCgRkV0RVEURVEUZab8G/LD6VImF9HvZmLe9BNI5318G/Jg\nnuRNwO+REGHnIl46XcjEXQFZ7fpP+2DrY8iK6e8iq7LfDNyJCPxLkZyLn2b/RfTvAx8AViKi8U2I\nIvT80P5rmDjheCDwkByf/4P8gN6ETGo/gXj/L0fE70GmFtEd5Jreg+TGehCZcD4SuXcFYs+yJCVk\nUrif2DNeOQTpftazOPmSS2iJlFFjcAYGcHbuTNQK2LluDg9vFgE6IpeDq66CrsRfCWNcHtixgPW7\nbNQcf3xiLl7QeHEx2LmTkWuvZTw6t+NQKBQmeHi/8K8+wvOufgPGig3WWubs7IctW0gJtpUKvPWt\nMD4ubYyOwi+b4ES4ezd84ANQiMXbcc9j9+goNuEp7J96FuMf+heMceNjbcD8G75L1x3/W1OzHeC0\nleP4x26D8Ir6Bgf4Ses4jcRxYOFC6OmJtX7XlPhC+99QcEV0tli63RwX7+ykvZw+/oSlA/z9lXti\nbRr46nfbue328xP5yccxpvEOEfm2Dk770Bd51upzZO0Ekld8o51DMshf3g1Y8vI3kHfjFQAWKA7u\nobT7STHdGKwNeLR9FWsWXVrLL171xni8tKuxSxeMYdGrXsXxZ50Vj9QgwP3Nb3C++pX0WH/GM2DO\nnLS3eXs7fP3riYUlls29c7nhRyW8oHYK1q6FY45ppOHKYcxnkXDgr0fE2CQfCbdFSMqXuchXaBMi\nnHpTtL0R8ZpbAhyLPINsYv9Cjf4K8Yw+Dnk+Anl2eYB4kWfEKVO09dpwq2ddeOyRyPPXOJLHdijc\nn7UyaOck5UkmS7vUMsVxZ+9l3zbgDcA7kMWteaQfdiLP20Fd/bp/KGu8Gwk1fAKSc7eILD7dzMRn\nxojXhvU+l3EeRVEURTg1fF2TsS8qm+rfq2Sdydq5OKyjIvrhwxiSXuYaZKHbtYhDyjbARZ7PDDAA\nfAmJJHPYL6VVEV1RFEVRFEWZKf+FeJy8AplIq/cWAQl9WZ/fsZ4nM8o2IsL2R4BXIj/QLPID7RvI\nw3x1H+29Pmzz/cBLkNCeICupf0g6dFUA/GKKc/wemZhNTqAOIYL5tYiA/GrkR8Uvgb8F2pAftffV\ntbU7PN89k5xrPNxf7y0+HJY/sRc7b0YUnnpd5gkknPo7kNXGZyKLFMaRCcyvIp5ASXaF50vmE60i\nk6AvQELwrwzL+4HbkL74zwy7Xop4m13LxBXyyiGE29JCYd48im4o1EaiXF9folaAj0O1Cm7CCxmg\nrQ06O+Oa1hrGx12qiZ/jFc9tThRpz8P29WGRL0j0RUnO9lugjVFau0lN2ef7AvAyvHY7O2Nv3kJB\nXOsbje9L/4Qy158AACAASURBVCa83K3n4deL6KNjeF0LJ4joQVuH2JZY+FDMAwW/1gFjeT+14KFR\nuG6do78xDDrdqXHhGqj6ae9ma6EtZ+np9Gv3JzCWtpKDMW2J2+BgTOOnOozj0NK9gNYlRxH4sU19\nfeAHsQ+9LYC/YC5ugdpfXWvA4sOeLckLp2qKDOe6ahK8RwHPKdBIT3oD5Do6KMybh4nGhu9DeQy2\nbk1FI2BsDFpb44OtlW3BAhnH1oIBr1JieMTgJb+jFRRlunwdeCvwV4iX9WBGne3hNlO2MtHLfX95\nmOyoOo1kU7gdCgyz/3nJA6YvvrQiz9IbkGgEiqIoSjaLwtc9Gfui+YTF02gnqrO/7SiHFsNIFMVO\nJKS7iyxsjAiA7yGpdaZa3HhYoCK6oiiKoiiKMlOqiPf2x5FVql/PqHPpfrS/HfEYfzMwB8m7tb9u\nkeuRsO4goag8ssX/cUQU3huT5Vd/AgnLCeJBNUjaWyZrwnHNFOfbM8n+x6c4DibPOwoiXn883HJI\nn/Ttpf7vM87nI6uTPxN+bkG8kobYO29D+vmzU9RTDjUi4a0OQ1pvnm4679lK+z3ZaQxgjUktQ5lU\n0K8X1JtFUvhsbMPEywhmL996/dnqx0qtMKtLZztFd8b5TOK1di2pvAXZxyWPnXWMibf68qyxWyuz\nE2zOakZR9kKARO35LvJMdu0BtUY5VHg18oz5LvZ9Ia2iKMpTiSiXU9YitYHwtTVjX1Y7luzf9fvS\njnLo4CDRXt4efh5Gohc+hvxsOQm4EFkM+Sbkee7js2/m7KIiuqIoiqIoirI/XIOI3FcD3yFbkG4E\nA1NX2WemEnkbQf/UVQ4aPPYuoE+XcrjtjRcgnvofRX6QKYc6WcJ58rOxWAuBjUVDy961utquJouk\nkehZ74GeOv2+CuK1i8heUHDQMYs2TjVUanWm2159/QPQ5VGO9uS1ZS4CyFgJkLS1WXabmoofC+Hy\nhQwkGkEyyXzyIupfgUm/uIqyb9yApI9RlOnyjXBTFEVR9k70W7wjY18U/2s68zZl5OG1nYmC/L60\noxw6fIRYQP8NEr2xPhLB0cB/I+lYPoZEJfjX2TLwQKAiuqIoiqIoirI/VJGchlcBq4A7Dqw5yiHC\nKiQE2GG/avkpQT4veZPdOGR4UKkSdC+ofbYE9PRYjh0awEmoi24OWls6KBQSYcmtpeB4tdDTxkAp\n7zHUBE9Xv1BiaOlJ5IzIjAWvTL7ci7HJ4BGGqlvAGycOz23BLVfJj41N9D6PwmEbIwJlbnZ+dpt8\nnlxXV0qidVpbsCO9YJx4lYANcL1xsS0Rzp2hIejtjQXVwUHwGhuhz3GguxsWL07rsPl8WsN1nYB8\ntUy+GleyQOD7DPtxZYulrVjlpMW9cZkdZ8Q2Npd7RBDEW2RrLpeKqo8xsGdP6uuABbp6q7SOjJAU\nsgtUmDMnXrzheY2P/m+BXSMlnujrrHW6CXy6OxbRcdRR6fHb3S050WsHW+jokIuspWuwBEZSMyTD\nufuHfTZERVEURVGUQ4Jd4WvWYrWe8HXnNNqJ6vQwUUTfl3aUQ4OFwHvD97uAl5PtZPEYkprvfqCA\nCO/f4jCOEqMiuqIoiqIoirK//E+4Kcp0+dSBNkBpIPPnwxlnSI5tAGsZL1uGh2K3cwu84tgNvHzz\nzRiTEMwdh9HjziNojR0lXDzm253kwhRrxhiGtvTR258UthvDyMJjufOqj+A6eSywsHc9z1h3HXk/\nIcJay+62ZezZZGJPdWtZ/NhOWp7YkFZ/29vhwgth7lwp6++HG25ouN1AWtgEij09LFy9GpsU7efM\nhXuuJ+Vjby3O4JMijiZZswbWrYs/j42JGtxAikV46Uth9epYRK9WpYtGRxNmF8ss6HuIueOJRNsW\ntnoLuH98WUIwt5x51Aauf8evwMj9Gfc9/vkXjU6FLPZ6nuT+jkR0Y6CnJy2Y9/fD9dfLmoTk0Djb\n7ObF3EfOhEK2tSw5pcS5555ZuzuVCtx6a8NN50f3reQPfecQjQPXBFx17nLOu3IH8ZfUioKfXBEA\nctPmzk0suIBxN8fOXaaWB90YGB5uvN2KoiiKoijKPnNf+Hpmxr6o7P5ptHM/khruDCSN3EzbUQ4N\nLkBEcYAfs/cohQ8jnuoXAIuBpwF3NtW6A4iK6IqiKIqiKIqiKMrMcV1oaYlFdEQcDxI/Ny3Q2QGt\nnaPiFR0SOA5PFixewvs2B7Tikyd0bTWGYq7xAjqAzRUo9xyJ6xSxQNUOQls7xk/8VLYBgZPH88HU\nnIgttuqL6plUSj1P+iLyRq9UJoqSjaIuEbXJ5chFXsORPcUCjI+kj7MWAk/sSnohl+uyMJTLDXcv\nNkbWGXR3x0J0pRJ7c0ep513Hkgsq5P1YRLeA9QLKfp5I+LVYWgoBR3UN18ZV2fdpKzTWg35v1K1l\nqAnKg4PpoVHO+ZBPhzPI49HWFhflco33RAfDcKXA7pE2aiK6E1AudcGCAGxiDGRFHsjn5QITIro1\nDr4fD4/J0qgriqIoiqIos85tiAB6MVAEkiGaLgtf/7vumC7kiTSZju7niGfyZcCPEuUdSHq2TcSC\nvXLoszTx/olp1N+YeL+Mw1hEb9KveUVRFEVRFEVRFEWpZ3ox2WdVj4tyWM/mOZvF/iiZE5J4N48Z\nmzmpiYfF3VMURVEURVGU/aWKRH7rAb4IlJCH5bcCLwH+F7in7pidiHdxkl8BtwOvAa4Iy9qBrwFt\nSD5sXUZ5+JBceT1n0loxcyc59rBDPdEVRVEURVEURVEURVEURVEURVEU5dDnY8BpwBuBPwE8xIP8\nPuCqabZhEfH8BuDbwJeQcN8twFfDz8rhw4bE+xcgCy8mWyRRAp6b+Ly+WUYdDKiIriiKoiiKoiiK\noswYYwwWCJKhxQ0YE0yoF0Q7QywGYyyYoPYr3YT/DyLv4rqw5Y2332JMUJshsEB98HiDwWDF1lot\nJlxPzdYw9HUzXTOstbGdkWt3vS1ZTKcvw3vaDLLGRlQOsU+5NYbAJEqsjAuDjePqWzmuOcH+szAY\nE2Te8vrPqW42YnmAIUj2rDGpvojHVyNJjtnEe4N8x5Ix56O47PVlydD/jnyX0+0piqIoiqIoBxEe\ncDlwDvB8IA/8EfgZUMmo/6LwmHoeB1YBLwNOAcrAjRzGobufwvwW2A3MQ3Kc/yUS0aAeE5YvCD/f\nxfTCvx+yqIiuKIqiKIqiKIqizJh169bxpS9/mXwiMXTVM4xXDanc1Ts2k+/fNUFEH7h/E36+pSai\nu/h0MISbkEYfevRRcnOTEeMaw9DQLm666YsYIz+NO0e28ccnH8ANqikhsd/+mOGH7q/ljzYEzNnx\nMO29m9KCY0sL9PfXcqIPj42xbfv2xtsNfKdaZVEyZ3l/P9x/f5yg21rJU//ooxMb6O+HoaG07Vu2\npBJyj1arbBkebqjd1eo4N974YzZseKBW5nmwdm2cXt5aaC9WKG/ZRmveozaGrKUvmMMOr4fkuOop\nb2dB+XFMWOYFARt27myo3WJ7hV/96ids3PgANpFHvFRKp70fHYV77oGxsXT3jjqPs9ldh5sQnv09\n/XibNtWux/crrF//IBdffGFDbe/ru5lqta92HsdYfvrLQR55dKRWBkii+vpY+7mcJLEPL8Ya2LrV\ncN99Lr4fH7t7951Yu6yhdiuKoiiKoij7xa3hNhU372VfGfiPcFMOX8rA3yMpAAA+CTwHiULwGPKj\n4STgLcjiDAAf+L+za+bsoyK6oiiKoiiKoiiKMiOWL1/ORS95Cb7vp7JS513IF+tccduOwGSIbHON\nU+e26wJzU+2t7OrixBNPJJ8QefeXnp4e3vjG19DfP0gkJBoWYYLLJmTYnuu4zDEJpRQHs+R4jD02\nXTHhhQ7Qns9z+atfTalUapjdra2tvPqtb2XHRRel7aw7d6q8HmsniqV19VqBy9vaGma7MYZLL305\nDz/8CI6TWEhh4bzz6upSwHGOpG4E0Y2hC2oitgjni3BYWKuXA1723Ody/PHHN8TuyPaXvewSHnpo\nPcY4dfvSdTs64MIMDdxwFA7pa3KNwU3dswIve9lLWLlyZcNsP/nkk3nPe55ICf9gcJ05GNM5vUaS\nUSaAJUfAy5ea1BBynAtYvfrpuInFNIqiKIqiKIqiHDJ8CVgMfABwkAgEL5ukbhl4O3tfgHFY0LyY\neIqiKIqiKIqiKMrByBuAa4E5+9uQtfbtvu9/Yf9NmhpjDE6WSLwfBEGArReTG8yhajc03vbZtDva\nGsVs2Q7gOE7DbLfWEgSzE/A+tPu1xpjvNPE0Q8iE3beaeA5FURSleTwb8Yz9JtPPzTybLAc2Ar9A\n8gIrysHIrch3yWU2MxtNn88Bfw48HQn3rRxaPAN4N/BCoLtu31bgeuAzpPOoH7aoJ7qiKIqiKIqi\nKIoyYw5lz9NGi9uzhdo9+xyqthtjDunvqKIoiqIoiqIos8oa4ArECfsIYh25jIjoTylURFcURVEU\nRVEURVEURVEURVEURVEURVEALLDpQBtxoFERXVEURVEURVEURZkRQRDg+/6snMtxnIZ61Fpr8X1/\nVkJ0NzKXO4DnebMWWryRtvu+P2uhxXO5XEPDuR+qts/md9R13UPWY19RFEVRFEVRFKUeFdEVRVEU\nRVEURVGUGfHggw/yzx/8IIVqlaTkZz0P63mpuqajA9Pamm7AWuyePVAn8tXLw+PAM174Qt729rdT\nLBYbYvv27dv58D/8A+M7dsS2Ow7k81AvYFYqUHc9ZIiF1nHx2jrBlZ/aQeDT0pLnmms+Q1tbW0Ps\nHhoa4iMf/jDbN24kn1hU4FmX0aAIpG2vvxRroVy2VCrp8kIBHMck6gXMnevwhS9cQ3t7+37bba3l\nE5/4JGvW3IfrtqTsay/5OCZx14MAxsbE2CT5PNTf/2qV5MVYoOK6vOd97+OMM87Yb7sj2z97zTXc\n89vfUkzed2vl/Ek7HSfqzHQjQSBbAi/fgldIjgtLEFR4z3veyerVqxti++23387nP/8FWlpKqXIT\n+BhbtyhgZGTidzEIsOPj6bKM84y6Llf9xV/wghdo+lhFURRFURRFUQ4PVERXFEVRFEVRFEVRZkRf\nXx/5++7jzQsWUEiotf727Yw//DAmFBetMRTf8hbcF70oLThWKvjvfS9sjVOrWaBCLNQZ4HZjePyI\nIxrqCTw6Osr2u+/m/+Zy5CLb58yBZcug3uP9oYdg8+a0Il0qpQVda/E6e9j24supdi8ECyMjA/zg\nB1+kWq02zO5KpcKm9et5xfLlrFiwIDo528rzuanvDKo2tt0YyGX86r/lFsvatTZVb9Uqhzlz4jqe\nN8TGjZ9rmO3WWh566HEqlXNYunR1bRgU8pbLL+ijrdWXm24M9PfDL38Jo6PJBuCEE+DpT4/LjIGH\nH4a1a2v3phoEfG7tWnbv3t0QuyPbH3/oIZ61YwfPmDcv3uH7sGGDiPjGiI1tbXDGGTI+kmN9ZAQG\nBpKtsnvx03ji9JfUlj143jg/+9lX2LNnT8Ns37VrFytWnMiLXvTi2BwLxdF+cpXheEwHAfzwh7Bj\nR2qc29FRRm+/PSWuB0BSajfAv+fzbLvssobZrSiKoiiKoiiKcqBREV1RFEVRFEVRFEWZMfMKBZ42\nZw6lhPBWGRhgLBIVQ0qLF1M48cS0sDg+znihkGrPAmViEd0BdgDbm2B7Wy7H0zo6KEa2d3XB4sXi\n8Zxkxw7Ysyctore1iVCasLwyt5uuY09lfMERYGFwcBdtbR0NtdkYQyGf56QlSzhp6dLauR8bW8y6\nlqdRsdKf1sZO0UmshfZ2H5FCTdimpa0tx9y5pnZ7qtVeyuXG2u44Obq7j2PRojNrtrQUAk47cTtz\n2hMi+p498MADMDQU97m1sHw5nHxysjNE/N2xo+b5Pe77dG/Y0NBQ7gA5x+HYjg7O7O4WW4wRYbml\nJfY6txZaW2H+fBkfSQYH67zTLVuWHEn+uDOJnPA9b4zOzh7qownsD8YYli1bzqpVZxAEcV+2Du+k\nWB5Ii+jz58P4eFxmDH4ux2BdXwZAMi6DAywwBg3kriiKoiiKoijK4YSK6IqiKIqiKIqiKMqMsRCL\niokyW1fHWiv1EiK6tXZCaOig7vgAMsNHN4zI9qRt9WHEk58ny0Vu5XqCwBJYMLXLbY71NtWX8r7+\nEuq6u3ac2JS+Q1KeDOfeFLPD89TbGL0JryUIQgNsbKYN4q0mMicuMixvZq54Wz8OssbLpB2fHMkm\nvF+W5K0Iavem4ZYTZIyN2ofJFhyEFestsiSXYEz8ziqKoiiKoiiKohwOqIiuKIqiKIqiKIqizBxj\nxHM76WUbhUNPeLTi+xPzR9d/Tra5t8+NxHXj9jPynAPizt3amvaKrr/m0PXbMQFuGOza4Gc01gAm\n9JnBGEvB9TBWbLJhubXuhEPjMO9hLSOR6ZPR6Y1pfLdH540c/a2Vz1XPMF6NTmbAdzGmAE6cOx1r\nCYI8QdlQk28NOJ6L4xRr96JqfQLTJJ9o15Ut6n9jZFzk8mKSBb/UxphfJKgWSYrmttqCrbSEphuw\nAeNVB9cfr3miB35lYp7y/cVa8DxMpRKbbS2MjcLocLpetSrf08S4thYqxU5sLoguEWN9jB0ncccm\npkBQFEVRlL1zPvDTA21EBlGYIf2HTTmYiZ7aP8bBuY7xWQfaAEVpFCqiK4qiKIqiKIqiKDNn8WJ4\nznNSIlq+VMJZv148igGsxd20CW67baKIXi6nmjOOQ1trKybRXovnNTw8NyDi+FFHxWpxqTRRSLcW\nLroIFi5Mu/KuXSv5uBPCer69k6W5XQShELqHflpIX19DcBwRbxMhwxe2elza8yBBIqj2tuEOfvH4\nMaTDgxvmz3dYudJJrXG49FLD0UfHtYaH4dvfbqzZuZykNT/zzLgrg8Bw+/pubJDI0e51Ulz0Gozv\npY7fubuTJ7/bVftsLSxqP4Mjjzi6do2eX2Fn6dHGGg4yvleskAsIx7V1HLwrXldbfWCAbbtyfOpf\nu9jd78a9bmB0YJzhvlGwcQj987wR3nT0/9TKyn6V7tEnafRcaMvD99F26w21Trd+gHvLzbDu/vRK\niUplwgKNkXw3N1z6TQKnII7/BpaVH+VZAz8nnwjqXnriieZ8RxVFUZTDlSPC7WBF/1FTDmYiEf2v\nDqgVivIUQEV0RVEURVEURVEUZea0tYmQnvA+d7q6cHK5WEQPAlFld+xIH1utgpcWSo0x5PN5TC7+\nueo2S5xzXejoiIXzfD7b/Xr5cjjllPhzEEBvL2zblqrvtJZoc8qAePiWGa15pTeUepduoNW1rGjp\nT3j/ixjq++lLshZKJYfu7nRzRx8NK1fGZYODotM3EseBnh5YskS60BioVg2PPtbCeJmaN7fjlCiV\nOkk6lBvgiX7Y8Ej6WoZXlCgsnV/TnT2/zFius7GGgxjb0SEXEI3rXA571lnYto6ag/nIo7BmELZs\nS6/HGByo0tdXqYXMNwZW7HmQ7tH7MWHZqOdT9EYaPmvvDvSS27YZE3cS3Hcv3HFHenAcdZQsJImw\nFq99Hk8efT5eTgaDBUoj95DfdR9FO17rG7e3t7kRIxRFUZTDjf8C3n+gjchgCfBLaFY4IUVpCKPh\n62UcnJ7obwEuOtBGKEojUBFdURRFURRFURRFUaZisnzYWfsPCNkCZlZpZH69uB69Rinim0V9KnFD\nwpaETfW2p+ol69i6z80k0TEmTARukn1lxQbH1Pn/1xtaK0uEp28WUWx+W/d5qpj9cdT81DUazYCu\nKIqi7D8DwPoDbUQGUQgh/YdOOZiJViH/BGhwLqCGcP6BNkBRGkWTEoUpiqIoiqIoiqIoTxmyFNcD\nLiork3IQOgzvz3CZVQfoxMn2xeRD8utga/+bTkVFURRFURRFUZTDCvVEVxRFURRFURRFUWaO60pO\n6Cj8uoFKZw/lxcdBELsct8yZR6GlZcKx5ogjsHPm1OrhuHidc8HN19rzB/on5ipvAGU/x4aBeeQd\nCUXf0paju6cN46RV2eHBFspPJs4fQE+1hc62trSCWyrJNfhhBFDfb4p66geGXSMltgy1hyWWXMGl\nrVjEMVHQbstoUKQ8XnewtXRWdnOk15/y/m7f5ZDviD3R80MDmPJYQ+22FsbHYWws7hbfh45Wj1I+\nUdEx5ItOKse2MTIExsbS7Q0Nwa5dcZnvyzkaTnSy3t7Y+FwORkfBiVIZQMGzHNnt01JND43hdstw\nV4AlDue+cG7UGSY23m9C9NiWFujsjHOiez47ikcw4PSljFy6cAnFzmJCE7dU8kvY02vwwuEfAD1e\nC7uchRRMtXbsqPN44+1WFEVRFEVRFEU5gKiIriiKoiiKoiiKosyczk445phYRAd2tJ3AuuVvIHJ5\nDqzllI4nObK0i6QbtLGWwgc/mGrOcwv0zzsGP1eUOgaGf38L9rE/Ntz0xwbn8bqbrsQxBayFlSfA\nG85yaGlJhBo3cMuvW7h/fSFON24CrnzmsVz0zHwsgEaVrZX87wAjI00RRQfLRa675zTmPXEiABbL\nggWGZz3bkI9T0/P4uMOjj9a5aduAV+74Hs/r+wHJezH3Gy3kW1wRUA2UqhWK/Xsaarfvw6ZNKT2X\nnGt53hmDlIpxJErfugzSiTVurcwY2LoVNmxIt/noo3DrrbEWbC309zfUbKFahbvvhkceqRlvCgXy\nx6+Erq5w9QEcUfb51KsG8CpB3L3WYjs68bt7EjnrDZ0PP4m552FqFYMABgYab/tJJ8EFF9RyuVc8\n+Ood5/PjP3pE60UcBz71jhzHrzS1tS8G2Lwlxw//T0ttMYa1cOyRxzB83l+Qz8UV7221HK/BDhVF\nURRFURTlcGA+8FJgNdCNpODYAPwn8OgBtGvWURFdURRFURRFURRFmTmOA4VCSkT32jsZnbeQmogO\neLlhcAegLpa46e4Wb/boc65IMO9Igpx4rRsHgu552I2NF+iqQY6tI10YU8RamFuB0RzYpFe0gb5R\n2L4z1j8dA6NeAVpb0yJ6RKQQB81JUehbQ3+5BcZaw/NBYVySeAYJ7/KKhUolITAjua3bvAGW+Fuk\nc6Md/S2p+5D3PEwTvOir1dhT3FqwOUtbKaC9xRejsVStwbOJa0GuwXXlepL53INAxPmk13c1dpBu\nLGNj4o0eGV8oYMbLUIld3/NBlSVzRsPFEzUVHbqKsDBRZoAdVRivxFEWfL/xY8YYiRTR3l5r23rQ\nVyjxpJOvndpxYKwb/IVgIxMMeGMwMAjlsjQVBDAwVqTfLZLPxacoO62NtVtRFEVRFEVRlNnGAa4G\n3gNkPeB/HPhquH9kFu06YKiIriiKoiiKoiiKojSUelnZ7HPy6MQBCa/YZmASbWedo7bPpJyIp9l4\nc6yObKm1nmFX0u76svhTQtBNXmB0YBNE9Og0SSG8zuJptZHVJsxi7vHaTZgw2sMxmyi3iS110zL6\nvFnUdYwJA8sne91EtibKat+/OjMzj1UURVEURVEU5VDFANcBfxp+tsAaYBNQAs4F2oG3AMcALwYq\ns27lLKOxthRFURRFURRFURRFURRFURRFURRFUZ6avINYQN+NiOZnA5cDlwBHAzeF+88DPjzL9h0Q\nVERXFEVRFEVRFEVRZs4Et+DUS+2DwWKNFbf01Ebaq3cSb9xmeRgHNr1ZE24kNithrJNb7KJbdz3U\nb43HRrZGdgeT94+1Ezf5ny9xu20A1p94gU0KRW8xE3rIQGIs2FT4+fi49PXWtrr7F9CsXg/tyBqf\nWeM33eGTNJa4edH7ZmCMxGt3HMlFYAzWmszbXX8ZWZcb3Yua2Xby8acoiqIoiqIoykFPAfjbxOdX\nAr+pq7MLeDnwcPj5ncARzTftwKLh3BVFURRFURRFUZSZMzQEmzcncqJbyoNL2LNnQRgwWvj1zh7c\nQYNJlLnG4yUL7qQzN1YrM+2d5J9/JG5rST4baboZka57SiNceOpduE4ea2HpUsMxAznyY+l6Tz9q\nKXPnzo9txDCan8PND6brOQ50dkSpxQ39w30MVwsNt7tUtDx91RjLFksaOmvBLToMD7dgwo4yRlJs\nH3103cHWsLNlNb9q9VL34rRTPbrmJOKNl8uwZk1D7XbxWeZvZKX3ADZUXa3vcOc9C8EthBnRoaUQ\ncMyy3bTmY3MATunxee1zvPTl7O7FbtteGyCerfDb3q0NtRvABgFDfX30jY3VJHFTKDDn3ntx58yp\nqch+ocTgshPxC6U4I7q1tNgybTt2pBeXlErwzGfGg9vzYPfuBhtu4e67obW1ZqNrDc9cehbulcfh\nJAzq74f77osFcWNgy5YAzxvG8+Lm5hQ9zjyiQqkQV3zi/uFweYSiKIqiKIqiKIcYZwOLw/e3ArdM\nUm8U+DTwJaAFeAPwkaZbdwBREV1RFEVRFEVRFEWZOf39sH59pBxjsYyWHbaNrIzFRgO//90S1q9f\nkhLDi2aMs4+8hs78FiK11CxcROm55xO0doEVYbpYbI7pS9sHuPqcGyk4LmAxrovT25JW7K1l3inn\n84yj5tVE5yAw/PLm+fz7bfNTVfN5OGo5FFvk89joLgbKpYbb3dYacPHzhjnp+IHISLbtKfLLe4p4\nfnq1wapV9Uc7bJxzIWu2X1ArMQYWvbqXrhWV0DXcyH3dsqWhdrt4rPQeYHU19hcf9Qr8082voLfa\nXhPRl8wd4+0XPk5HWzV1/HOWjPPsV9atcLj/frjttlqi7vHA0j/2aEPtBgiCgN6dO9luLZG/uJPL\n0X7LLbitrWGJpdqzjO0rL6I6d2HNAd0CPTsfoHXzg7GIbi0ceSScdVZ8kkpFVOxGc/PNcM89tfPm\nczle/pftXHLJcTUbgwCuuw7Wrk17oe/Z41Ot9uJ5tlY2v7XMC1b20RkNbcdw95r+pqZ0VxRFURRF\nUfaJucC7gecDeeCPwDXAhn1s51TE4/gUoAzcCFwLjDTMUuVg4PTE+8kE9IhfJd6/gsNcRNdw7oqi\nKIqigs9RrwAAIABJREFUKIqiKErDMNTk8FS5xRBgwnDe8ZaO2W1rjURR3mvtNkGgMwZcAzljyYXv\nI/tTmwHj1IW6Npk1Q+/7cAuF3WbgGHAdsdk1ZtJTZUUaN8bBkgMTbW4c6rsW9rvx0wVRtzlGJiMc\nwDE2HAJhn1mDtVk9K8flMje5fznAxTZPzJ0QF590HHNb+9+Ejo+/F3Uk6zWhzyfYHr6PxvTebndm\niPawzDGJjeZ8PxVFURRFUZQZsQC4G/g7YBzYAbweEdKftQ/tvAi4C8mJHYV6+kfgdmBOo4xVDgq6\nEu93TVF3Z+L9qUCTlrwfHKiIriiKoiiKoiiKosycfVDPskXEhlkyA/bh5NOMVD2rYmKjo2cfkGjc\njeiwQ0XBPVTsVBRFURRFUQ5hPg6sQITzC4FLgacDAfANphehugR8HRgCViEex88H/hzxSv9go41W\nDijJcF+tk9aauN8FVjbenIMHFdEVRVEURVEURVGUmVPnnWtTW9oBNpPAgg3iikH2AXttY3+pd3lP\nbWGZY8EE4SbXF4TmBlbCYQfBRGflptmddC2XglrXTbVl2lnfD7OIBSyBbCYIteZJbMlyrY86P3lh\nTbMzvWUYKPtsRv3kOMfKfwassVgzeYv7Z3T2IIj8/5NntXXVo8Mh9jo3psnfRUVRFEVRFGV/mAtc\nATwMfDtR/hDwHeB44PxptPMyYAkiuj+RKP8ysA14E4e5B/JTjE2J96dMUbc+YdiSBttyUKE50RVF\nURRFURRFUZSZ09oKS5bUcqIbLJ3eXI6tmlRO9Lvu8hkcDFI6bSkXEJxxJsxdgYTyttg5XZRNK8FY\nfGyl0hzT/eFhhtesoRIa5ToOpVwOU5cTvfroLsZ6bqnlRLeOyzHdz2TueaeRFD4DC+VxB2ulnuPU\nuqWxVKuwcWPNPoC28RZObJmPb6MTWknS3tlJUpC2FgoUKZUKiXthKe3ZAv4AtTD0Q0MwVpd/fH/x\nPHjsMbE/tNvxHZY+2EtnNU+UFH3eohK5c46GQt283OOPT8wZ3toKZ58dC+q+L/ncG4xxHDrnzaO7\nWIzHNeBs3ZqKhe6WPbq93fgmX7tGC7gtBXbNPyl5J9je383jv2irlVS8Mpu25horpRsDy5fDscfG\n6rfr4rSWoL8XE+VEt9DidlIq5VLf0YVzxnnrafdjvaDW3ClHuRQqRWrjyhi5p4qiKIqiKMqB5jlI\nDvSfZez7GfAWRES/YYp2zksckyQA/ht4M7AauHXGlioHE79B7q0DXAJ0A72T1L2y7nNH88w68KiI\nriiKoiiKoiiKosyczk44+mjIRT8vLT2mm9OcWER3HLj++ir9/V7Ki7WtZPGffz4sryAuuZbALTLq\ntOOPSB1joFxujverPzTEwF13kUOEzhLQwkQf6HHzc0bCvNYWMIUCq66+mgWXn0xSRB8dhVt/n2dw\nSHKU53KJbmkklQo88AD09tY6Zk5rK2cuWZpObt3WBitWJDzMJTpAe3sXnfMSIrq1tG1/DDZtoSai\nj47C8HBj7fY8uP9+eCJ2ZnF8n2PXP8qY59X6t3P5keRfeTWUFsQ33hg59tsJhxpr4RWvgMsvj+tF\n52gwjuPQtWQJC7u7MdG5fB9z110yQEPyIyMsrDxJvY/3rvYeniytrt0LY+D2OwzXX29wIod6m2fz\n5gIND/t+4onwnOek+tJtb8Pdtb1WxbfQmm+hrT2XOvuc4hh/+bzbyFsvvBSL09NNvrwSqm6tvaat\ndFEURVEURVH2hePD14cz9kVl0wm/HdV5JGPfI4k6KqIfHmwBrgdejoji3wReCdQ/5L8euLyubKrw\n74c0KqIriqIoiqIoiqIoM6c+tDZgjME4sRQou00YJjop0dkwTrRb0xuN49Q8vpuOtSK6snfZ0tS/\nN+Jxn3dNKgK364TXPrFLmkNiZYEBXJMw1trQVTp5gCxsSNoICVtteHwUz7sZF1AXbt1YiwkqOH61\ndh3Gemnj6o+vf+846fdN6nhjDE596P8oPn7CpsyzGwPGTVxXrTrR0clmGoox6T6KyjLqmcQuG74v\nOJZCMr57VmLAA5QKQFEURVEURUnRFb7uydi3u67OdNrZnbFvX9pRDh3eAzwX8UK/BFgLfA0J9V4C\nLkXC/ANsBI4K3zd45fXBhYroiqIoiqIoiqIoinLYouKmoiiKoiiKojxFiHI7ZeXaqdTVmaodC3h7\naUf1xcOLjcALgJ8Ay4ATgE/W1QmADwAnEYvojc+ldRCRtX5YURRFURRFURRFURRFURRFURRFUZRD\nh8grOMtLvCd8HZpmO2aSdrr3oR3l0OJuRDx/D/A74vG0FfgecC7wUeDoxDFZIf8PG3SliKIoiqIo\niqIoijJjqp7H4NgYlSj5t7UMO6OMOQOp/M+e5yGODLFntLWW4fIYA6NVKbcW3/EYNUN4brl27Ph4\nmVTc9AYRAGPErhgBMMhE3+1RoByWR1aMjo8zMJSeNxobg3LZoVw2od1DBIHfcLuttYxUKgxEeait\nBdeV3NxRTnRrJSH76GjdsTBazlMuB4nI4gHD42XylXGinOiDlQp+g+OLW2DUWgaCoBZavOr7lJH+\njcgHAUPlMtVyOd1AtTohdDrVqnR82F7F86h6WQ4z+2/7WBDQ7/upnOipEOkg9pXLKZvAMhKMMOYP\npMKej49LNoFa+HQ7RhBUaeRYt0C5WmWgXJZxHdkU2RjWCgLD+PgQlUolFc59vDrEUKVKjjgnOpWK\nHO/GOdErnjexLxRFURRFUZTZZmP4ujBj38K6OnvjceAZwAJg1yTtPL6PtimHBiPANeGWRQE4PXy/\nE3hiNow6UKiIriiKoiiKoiiKosyIfD7Puiee4P2f/SxuQhysmjzjtBBlhzYGNm70WbIkLbK5Dnzq\nW2XaSkFNNwyMQ8UtYU0cZbC3dxerVp2AaWDeZdd1GZs/n38aHKyJ5g4yI5Aloo8nCxyH0i9+QcvD\nD6fqeT709hk8z4QLByr4fi+O07ggcMYY/GKRT//hD3QUi8kLgmIxnZvadaG1NXW8BYbLeUYruVTp\nzyo7KATRUgERt3f5fkNtz3V28g3X5aeR+A8E1rJn4UL8hACby+f50fe/j5u8PoBdu6CtLV12333w\niU/UBFzfWjZt306hUGiY3cYY8h0dfLNa5ae9vfH4sBbmz08L+7kcfPObci8SjFOgbIskR1d/P+xO\nZJm01qdY3EyhcFnDbC8Wi/zk/vtZ8+ST6XFdLEI+H58bw7aBEhUvHd0zZyvcMf44TlLYz+fhzjtT\neesf27GDk1paGma3oiiKoiiKMiPuCF/PQzyGk5wXvq6ZRjtrgFeHx6yr23c+sv74DzO0UTm0eTGS\nIx3gxgNpyGygIrqiKIqiKIqiKIoyI04++WQ+9ulP4/uN97ZOYoxh3rx5DRVGFy9ezD9/4QuM1bxx\nm0NLSwtt9cLvftDR0cHfX301Q0ND2CZ7/haLRdrb2xvSluM4vOu972XPlVc2pL29kcvlOOaYYxrW\nnjGGd7zzney+4oqm93kul+Poo4+euuI0Ofvss5l3zTVNt9txHJYvX47rTifFpqIoiqIoitIkHgHu\nBJ4HrCD2Fi8Ar0XCc/+k7pg3I3nO/y1R9kNEhH898AVENAc4BViNiKc7G2++cpBjgPcmPn/5QBky\nW6iIriiKoiiKoiiK8v/bu/M4S6r67uOfU3frvr3PPgMD4wy7CMoqyqYQUTES9+jzKD7GxEiIKIlL\n0BAjidFsKuISfRkhJC5xX1ERRZRAUBRZFFl7YPa19+671Xn+OFW3qm7f29PdU909M/19+7re7qpT\nVb+qW7dfwLfOOTIrnZ2dnHLKKQtdxqwUCgVOPPHEhS5jxrLZLMcee+xClzEr69evTzUgnk/r1q1j\n3bp1C13GjPX29nLaaactdBkiIiIiMn+uBH4E3IILwkeBPwGOB94O7G5o/wlggGSI/gTwz8BVwHeB\n63FzoV+Fm4npXXNWvRzI3gacE/x8E27e9EOaQnQRERERERERERERERGRg9/PgBcD/wJ8Kli2HdeD\n+ENN2j8ODDVZfjUuML8CuChY9ivgNcA9KdYrB4bVuJD8P4F7G9YtA94HvDn4fRC4bP5KWzgK0UVE\nRERERGRWfN9nZGRkzoeKBsjn87S1taU2L7rv+4yOjuLH57OeA57n0dnZmVrd1lrGxsaoVqup7G8q\nadc+NjZGpVJJZV9TMcZQLBbJZtP7Tx7j4+OUY3O5z5W0a69UKoyNjaWyr31pb29PdcoFEREREZm1\n7wWvlUAO2Aq0moPrmBbLa8A1uN7sq3GB+s50y5QDSDtupIK34z7njbgRClYBJwBe0G4IeD7QP/8l\nzj+F6CIiIiIiIjIrd999N++56iqWL1mCFwta7a5d+Bs3Qixc99auxSxfntyB78NDD0GpFC3zPCgU\n3HtgoFrl5Be9iHdffTVtbW2p1L5p0ybe+uY3UxwYSNTeVLOHBAoFaAgMrfGodvZiM+5ftWu1ChMT\nY/zXf91Id3d3KnUPDg7y1iuuYGR4mGJ7e7SiUoHR0UStNpuj2tlD4uwseOVxvEqJhIkJ93kEqr7P\nYCbD5778ZXp6eva7bt/3efe7r+bWWx8mm4325xmf9R07yHmx/6ZXqcCuXVCLLbOW0e5VDC85EoIz\nskCHHaXbDkLwGfrWsqNc5ur3v5+zzz57v+t2h7Zc89738sBNN9ETuy/DuhK/+j6ViYlJy71ly8ge\ndli9TsB9XkNRpx/fWnZay1Uf+ADnnXdeKrXfeuutfOhd72JZLhfVCIx3raTcnvxcN2+GxucECpkq\nxy7bRcZE51OiwKDXiyW6FkNDW7jyyjfx0pe+NJW6RURERCQV21PYRxV4MoX9yIHND14esDx4NboZ\n+DPg4Xmsa0EpRBcREREREZFZKZVKHHPkkbzj8stpC0M6Y6h++cuUr7kGwt7SxpB/+cvJvuxliaCW\nchne9CbYujUKF3M5OPLIKKA2hlsGBrhrz55Ue7xXKhXyQ0N88OijyTcGo41qtclB+tq1sHJl9Lu1\n1No6GHjG+VQ7XHA9NLSbD33omlR7u9dqNaqVCm9985s59uijoxV79sC990bBs7VU+5YzcvJZmHiM\nbi1tmx+lsHNTdM2thY0bYWysvmxgfJy/uf32VGvfvXuMXO51LFlybv2wxUyJvzn58yzNj7hGxrgA\n/StfgeHhWNmWBw9/Hnde8B4wGbcMy4n+/Zzu34Ex7jMs1Wr87S23pNr72lrL6O7dvLZU4tzu7uhq\nWpt8AASolUrs3biRWqVSb2eB4skn03n55ZhMJmr8m9/A7bfXr/m47/PB3/6W8RRrHxsd5eJsllcc\ndli9Ht94PHz6a9i64exomQ8f/zhs2hQ9v+JbWNY9yD+c91Xas9F9tck7kp/mz6eK+44aAz/84b8y\nMjKaWt0iIiIiIjKv+nEjDlwEnBX83AnsAh4BvsTkYd4PeQrRRUREREREZNba29pYtmQJ7fEQvaOD\nCWMSIWKhWCTX15cMoycmIJt1qV0Y6HqeWxbrOduTyey7t/gs5DyPZW1tFPYVovt+MvwH6OiArq7E\n+dTaO/H6llHt7MUAmQxks+kOb22MIZPN0tfby/KlS6MV1kJ3d/TggoVKTw+FpcsnhejtY3toKw8n\nQ/Surig9NYaM55FPcTh0t1uPbLaXfD7q1FDITLC02MnyNutuFGNcD23PS4xGYK3PtnyRzq7lLkS3\nLkTv9ftY4XcS9k6f8H3astnUhqAPecbQk8mwovGa1JKjYlarVYwxxO8WC3QWCnT19SVD9O5uaGtL\nhOiFlGs3xtCVzbK8rQ0T3Ku+ybCrs4exnuX1O6NWc8+tZLOxr6KFfC7Lss5OitmqOxEsY14Pnfnl\niRC9UOgA0v+OioiIiIjIvNkB3Bi8BIXoIiIiIiIish9c73AbhcnGhZsQZG7196BNokd3s2UklxlD\nw9p0GeNeU/Vyn2pdPIgGlyMGu7Ph72mzU1y36JcmP4WmKGoOHlZIalZX7Fxs+H8N52ctFov1qZdv\nCe4/ayd/DnNZ+TSO0fhJ2HC7hnNqtU3q6t9TsMYG1zN5UIvrfe4lrm+0adSuWaVzWr2IiIiIiMi8\nU4guIiIiIiIis1apGobHMlRyQQ9b42FqebxCwXXFxuWbFZOn5ueSwaHvUyh2YLq6oiC7UIAlS1wP\n3XDjanVOwl1bq2EHBrCxoeRNsTj5WLXapPm5GRpyXXfjc5AXxvAffxi/2ImxYIcHYDy9obkTtRuD\nNV7iSQXj+4k6/ZERKo882LAhVLdtxN+1JTpvazHbt0fDuRuDPz7u5iZPWa2W3G3ZNwzQS9bLgjVg\nIJOt0tm3DC+Xjz4L3yff10F3N4kQvVDOQDkPwXDu1GqJHuypKhZd7/EwtPd9KqOjUe9/oFou49vJ\nMbNvMtSy+URPdL/q4w8O1nueV3wfv3FS8v1kgYlCD8Odq+r3im88JrwilQqEU51b37LK24GXKyee\nR1iTHcarVcHU6nu01sf33ccVtpvDZxdEREREREQWhEJ0ERERERERmbWte9r4nwd6yWZc6G0NrB1Z\nw4kbjsKzwaDWvs+WjiPZO76mHtoBmEqJo047i8KG7dHCjg44+2zo7AwaGfjlLxPzY6fFDg7i33cf\nlSA1NKtXkz3vPEzjkN0jIzA4mOzt/PDDbtjx+skYbK1GZfRaKsHQ7yXfx+9oT71ujEct104lX4wt\nypIdHcVUKvU6y/fdz44vXgk2ORT9kmoFW6smlnmVCp7vR9v6Pn5fX6plW+suY3xk/Hwuz835i+lq\n98OO0vSu2s1zX1mg048+c4Nl7VPPpPsskwjRO3d1w5Z1UYherUb3TppyOTj1VDj22Hpi7JfL7L72\nWmp79sTO0WKr1Umbl9q7YdlaTKbg2hlLac+PGfv+9+vjApStZbRQCEZ3SM/vjnohPz3r5dgg9bbA\nqNdNaWeska3xrp5P0lV9PPEQSbY9S2FsLWSC62t9/Ow4ZSzVoFn4nIuIiIiIiMihRCG6iIiIiIiI\nzFqlZhidyJANgmcfS9nP4RUKiRC95uUp2xzGRgGdwce2d0C5IwruOjqgt9fN0Q1ueVeXC7LTVqu5\nHuUE03H39ETHjPP9ZG94a12Avndvsm21it2xA1upRAOmr1+fft0mqNHz6sFz2DOa2PDmdnyMav+j\nrod6TI3Jw42bhmVA9BmkxNpkp35rwfMMI6bHDYEfXLSsqeH39gG5RJX53qIrqR6iGwpjGcgXYhN5\ne3PTE90Y1xO9qyvqdl0qUatWqZbLievnRSUGdYL1PPxsIRai+9SqlsrQUL1tFfBTr91QKnQz0rGy\nfoEtUC5BtRK7fX3LyuxuluW2Rg8kAGTawF8TnZD1o6Hgw0XqhS4iIiIiIocghegiIiIiIiIya+GU\n4vXfW7Xbn4PMQ0pnGt6nt1HjyZtwSvR5EQa34TFNQ/hvgnqabTeT39PS+GzCpN9Jnk/CARzUxu+d\nVmU2npMJ/m8+rn14bBv7fVIb4x5KaHH1kw1J3ldzMNOCiIiIiIjIglOILiIiIiIiIrNmMUFP6PD3\nIKyLT5RsbescPDGh8sJMrhwFjPagmeB5n8GlMfXPorFp49nt6/e0xG6HScvix7U0r6HV+SSWzmWg\n23hf2PpdM2WAHtsg+twM4Tdndg9wzICl4ZraJp+FpeX5RQ3CZXbS/kRERERERA41CtFFRERERERk\n1rKjAxS3PkS2Pkw15MtDsHIl9XTN9/E6i2QyJOdEx8CypdCGC32thbY2TLUKExNBIwPl8pzUXsu1\nMbjiGLJBsplZtopi70pMPpfotpvZvhMzNhaNaA1USyVqlUoiza5Wq9RiQ13PVbbo+240+ZGRKOf0\nBsu07diJKZeCug21iQnyRx+DaQhHs+NjeKVSomeyyecxmUy9jfF9KBRSrdvzLCuW1Fi+rFIfhj6X\ns/QVqhRzUSDdVR3FGx4BfyQ2hL6PKZfwYh2lDZbxSpbBkSImWFGqVpioZpocPQWFArS3Rxc9kyF7\nxBHQ3V0P0a01lCrJ/9RigbHcCipbPUxQmjWG0cFeBs2xseHcLSNmgnTjdEubHafLDiXmRM+3tVE1\n+XorYyHTXoDxtmg4d2vx24pMtC/Bepn6/vxcBz3FCr6JHlxoy9VSrFlERERERGThKUQXERERERGR\nWVtx11c549bPkTNRQNd+8Qsxf/teCENZa+nsPBzT7toYwmm7C3hvuRzf8wGDNRYGB/G+8Q3M4KDb\n1hh48kloa0u99oFVx/O1119Hxsthgb4+wwknZsnG/k3ZetD792+l866vJHoeb7aWnQ3hdAbos7Y+\nk3fj3ONpGR+HO++ETZuCBQaK9z/G4df+C97YYL3G4tnnsOGO/03OEW4t2TvvJHP/r5Pd2Y87Drq7\ng/0ZCkNDeJ/6VKp1dxZ93n3Zbs4/a7N7EgDA98lueRJTrdXPxezaReaW29xTArG6c7195C56bmzU\nA8vPdqzkmz9ZWT/Fmj/Bb7YvS7VuwN3Lxx4LZ5xRr92zlhVnnZX4jEfLOX6xaQXj1UwUhRu4594c\nt749H2tp2Lb5lfTnL4ktGyfv/R2Xptwn/cTavfxetcf1fLdgPUPlpNOpHrkhOpLvUdxzFGwrxh5c\nsEy0L+Hnp/wRvhfUbmFJ+zgvW7KNbPhEjGd4/N5BPDNXj42IiMghaDnwzIUuoolVC12AiIgcOBSi\ni4iIiIiIyKx5tQqZ0ij1mNYYjK1CPh+Ft9ZiMp6bQjxq5rK6XL7+b6YGsLnx+jZ1czW8uvHwc0Xw\nCljAz4HNulf90Ma6uaJrtUQdFvBJ9hn256bKpnw/yqEB/JrFL1cwpVK9PqzFK3ZQ7/4Mrkd3IY/J\n5ZIheqEQ9Tw3xv0cD99TYIB81lLM2+hi+YDnu1d4bOM3OUHf9aiP30S4nt9V36vff76fCeb2Tpkx\n7no0XBOTySTmojcmhy0UsV4u1ghqBiqV5C4rfo4SuSizxiNLur3oDeAZS9aE188t9T2LzZAI+k14\nfvXzseB5WC+PDUJ0C+CVyXmWrGfr26Z8q4iIyKHv4uAlIrM3sdAFtDBHw0KJzD+F6CIiIiIiIpK+\ng2Be8bhWsauZYt2BZr/qnI+HFur7b1wwjcr3OQn8AcQ2nFGYNZupL+1Cn+HB9Y0VEZGD1A7gxoUu\nYhruX+gCRKbwIG5CrAPd6EIXILK/FKKLiIiIiIjIoneQZf4iIiIiB6OHgdctdBEiB7k3LnQBIouF\nQnQRERERERHZDyb4n2PDn+M9hw1YAxib7O0aGzU6+t0yX31yrQXfgrHu5/po4fHhwg0YYxPnGA7j\n3thLPRgpu346c5nLh/VGv8e6O+M+FayddDXDWd2tG0e9vtzNl93QZXouniwIL/Cknu/xq2ax1gcb\nH87duuHIbbyb9zw++WBdndYGdUyl4faNTTE+rx3+Ewdq9nl68d9ti97+JrFp/JOKmix0H3oRERER\nEZH0KUQXERERERGRWdvUcTw3d59EJjb13fr8kZy+eQvGi1Lywq6HMcMN0/ZZsLVBfFuLltWqsHMn\nVKvud2NgeBja0h+xsD1f46lrh8lmXF2Do1luuaWI75vYPNXAxrNdyFyPoGHQeIwYk3gOoLvD8ooL\nJujtcTG7LZXI9venXncuazliTYX1R5brNZVKq9hy5v+DifF6PT3HHcvSsWAO8XpGbdg0cBg7d/mx\nwN3ydNNNX3s+Oki5DJmUpzP0fdi8GR55pJ7K+hi2+Suommy9Pj/fx/i6F+CPx+4XC+0dJ1Lc4cWy\nf8PAkJtrPJyTu1ZLTqWeFmsMY6aDYa+H8FEQg6WzNoJHdP9WSh7bt1pGSsntx8ehtzf5nMLgoKu3\nVciemhUrYMOG+s6t71O655eUfviD2Alacps2kpkYTxSU7axxmNmKH5vjvbM6jjc44J4+AXfxJw7U\nKTlFRERERERmRyG6iIiIiIiIzNqDfefw2TXvxZh2wPVxfkHHg5zy0I/xTJQIFm+7jeID94OJej9b\n36fy2GPUyuVoh11dZC64AHp6omV798KyZanX3tVe5eyn7iGfyYCBn/6inRuub2d0zMQ61xq2b38V\nQ/blxNNoYzJ4sZDZWli3tMYrrtzLymOqYA3ewAC5970v9brzOctTjy7z1OMm6sFof98G7h76JypV\nU69nxQpYO5TsJWyt4fbtR3PHk0fVz9EYWJnZSl/nuDtHY1wSnXaIXq3CQw/FUm5LzSvw0PFvYCLf\nU38godwG2592OtVqcrCC5T2wZqNJdHzeuRNKpSj39X0XTKfNYhjK9LI7sxw/fPjA1mifGMKj4hoZ\nKI8ZHnsM9o4kO2iXy7ByZXKfu3ZFz4qEUn8AwBhYtw5OOy3aebnMxGc+w9D110ed5o2h65xzyPX1\nRdtaS753mGPMw5CJQnQqFRiKhebGwKimvBQRERERkUOLQnQRERERERGZNRMM4O4FcZxvDR5gjIkS\n0HAIby+TSBbrqycNM+01DAc/l8NFRwO1Gwye1+zwNuh9HA05bsjEBniP7SkcI37OuhXXSwjKCQPd\n8PhRF3pD80tnDHjGxEJ0O08D6NP0s20cGt+N/G9ig82TOJ9mt0bj+1xwNZpEne6ADedjmtcZTheQ\n2Od8XPgm96Ix7l6Nh+imseiwQOMxaeKCeft+ioiIiIiILAxv301EREREREREWptWhHYwBW0HUalT\nOfBPY/IDFbPay4F/oiIiIiIiInKQUU90ERERERER2S/GRHNSR5214z23adob1oZzNBMbKN1aN+y0\nbdh2riTqsngGvIaOtp4HmUyiSjKZ2DmH7TKWqm8p1yxYQ6Vm56x030K1foktvg1HBYifG/h+43Du\nrS+nnfRDuixQs1AJe8tjqVoD1gcbjcEe9qqHhnA9vDfinaAtGD/6LKytBvPXz8UJWFdn/Xbxwa9F\ntRugVsPYanAO8ToNno2aAXjGI5f1EmPWN95TqdTc+LMNZnWPD9dvDLZFT/Rm94uJjzsfdrMXEREV\npfT+AAAaHUlEQVQRERE5hChEFxERERERkVk75hh4yUuiPM4CRw2X8fYMUU8bjaG6Ywf+li3Jja3F\nr1Tq2wEwMQG//jXk81G70VF4ylPSL75Wg6GhoHjLkcssb3qTpdowL/Ujj2TYti0ctttVumGDx5o1\nURtjoDTh88kbR5kYKwOGifIIj29Of4LukTHDd36Q554H2sC6ijpzEzz7iCfIGVe8BQZK7fzgB6sn\nbz/ipsmOajdkl/RQ7ul0w6YbQ8VmsNncpG33x0QtxxcePZW7Bs6sL8tnalzSfhedbTXCNNk3WY4q\nLsGaeMgLbY8+RPGmXyX22TtW4JihtnoQXfarDE38Brgk1dpNrUr3I79kaafB+u4eMNUK9rtfpjo6\nXG9XrGZ5wZ5llGvZRDju5wrU8sXEsoGjTmLbJc8lvKcq1TLf/mEZY1IOpHftgv7+KOiuVulav562\niy9OjIOfe+5zYenSRCBeybWztXgsNkz3DbRv/DXLvnYDXrUcHeOxx+A5z0m3bhERERERkQWkEF1E\nRERERERmbfVqOPtsyAV5qwU6Hqxhtown2vnDw9QGBibPfx5jAVOpwMaNyR6xYQ/ktPk+jI1BNgvW\nsqyrwIUXWGxDb+DDD/d44gkvUdKZZ8IJJ0R5o+dBf7/l458o8bvflXBpaYmnPCX9ukslw6/uz9G/\nOV8P0Y9bM85FF+6iI+dCe2vgd1u6+e/7VtE4WPrKlbB8efS7MZDp6qDWHi2rlSrYTLr/yaBcy3LX\n1qdw/+hJYQd6OnMTvO6YO1hdHIrqzOehd210U4VF7rwLfvL1aJm1rOjo4Jju7vqmJd/ytfKuVOsG\nMH6N9u39dPYX6vewLZWofOdb1PburT9eUfA8Tu7uwsR7eVuL7eyEnp7ovrYWzijDa55Zf4JkvFLm\n4cer6Q/DPzICO3fWb1ZjLe0rV9J+6qnJ79kZZzSE6Jaan2f36Gr8cDZAY+kZuJult90GE2PJ46g3\nuoiIiIiIHEIUoouIiIiIiMh+m5SfxYd4NmZawWC9TbNhpedKrCcumKbDnTcuC08tnuu7n8N9zH3t\n9UsUK98QLTDBIPnTuYyN5ztXlz6s2dXqgn53Dk1OJnYuwUL3MvEnHCwYDxNfZuycnoCJ7dsEdYeh\nenT/epPqNOGy+vbB9AA29j2xzYdOT6v2qBybfI8vb7zZG87NhveZ8ZJjzytAFxERERGRQ0zas22J\niIiIiIiIiIiIiIiIiIgctBSii4iIiIiISHqm6AQ8o97oC6WhgHjv6UlNTfOfW+5sXk2vFzo0dMZn\n8s/parXjJhf+QNekxgOy6sY693UzN65q8bOIiIiIiMihTMO5i4iIiIiIyKwNDQ3S3/8I2Wyhvqx9\n85Ps3rMnMZx7rVSixtQhXDDANVlrE+22ALXUK4fxcplHNm0iF8xfXcoNsXvQYL1Mot2WLbBjRzJn\nfPJJN3V3yBjYsqVKufwkUA7OZARrS6nWbK2lVqswPLwRz3NzhltgV2GERzY9SVsuHGPe0r+zk4GB\nyc/OZzJQa7ig/f0wOhqdy9DQIGNjY5O23R++X6Nc3sTExENRLdkyj+/ZyfD4CG54c+subC7n5qqP\nGxiASiU5dHipBOPj9V/L1jJUrWJTHl685vtsGRzkoV27ouOXy1SDCxnOiW6ATOPFtdbVXSol50Tf\nuxcef7zebKJSYWh4mDQrt8D2PXt4eNOm5DDuO3bAnj3Jm3rTJjd/euzalW2WJ8bL1L+RBvbu3EKl\nVsWLzWew21rWpli3iIiIiIjIQlOILiIiIiIiIrPS2dnJ6OguPv3pDxIf6MyrTOCNjyaCcLt0KZx5\n5rT3Hd92zBhOX78ez0tvMLW2tjY6ly7lA1/9av1YvvGoeflJvXLLZZeBxj3wQDJEB6hWLYcdVmHF\nijCE9FmzppNsYxi8H3K5HOvXL+Ohh/6DPXtcARYYGazRv7mCFyu9XPUYGM8RzpYeZqN79iSnswZ4\n8EEXrod8v0Yul0219g0bVvHEE9/C826pL/OMz3WPDpE1sQnmjYHf/GbyDsbHYW1DVGtM4mR8YHz5\ncnp7e1Or2xjDqrVr+XZ/Pz+69976cmstnHACxMJkADxv8sMiXjCHeDxE7++H666LareW8YmJVGvv\n6+vjjv5+7t22LVlTuQzVarLxli2TbgyLoWyzWBuG6JbM+BjZ44/DxOL+wUyGC1asSK1uERERERGR\nhaaRuERERERERBaXS4FrgZ793VG5XH7T3r17r/UbQ8Q5UCwW6erqwqQ0zHetVmNgYIBKYzqesmw2\ny5IlS1J7AMD3fQYHBymV0u3h3kyatVtrGRoaYjzWa3yueJ5Hb28v+canHGbJWsvw8HDqPfOb8TyP\nnp4eCoXCvhtPQ6lUYmBgIPWe+Y2MMXR3d9Pe3n6pMeYLc3ioYeAy4MY5PIaIiIiIiIhCdBERERER\nkUUmtRDduu6p+vdKEQlZY8xcJvYK0UVEREREZF5oOHcRERERERGZlSAsm9suriIiIiIiIiIi80wh\nuoiIiIiIiIiIiIiIiCwWrwOWAB9hbh4KLgIeMJLCvtYCzwCODPb7M+D2FPYrEQNcAewFbljgWkRE\nRERERERkgVwKDC50ESIiszAMvHahixARmWcZYD2wYqELmQdF4GnAqUDXAtdyKGrH3Uu9C13IAjsD\n8IGPNlnXBpwJXA5cD/wv8AvghdPc9+uA3+KCeQs8CVyJ+x7PxmuBSmx/Frh6lvvaH2tx38uVC3Ds\n+XIt7r44c6ELEREREREREZGFoRBdRA5WCtFFZDG6DBecvbrF+g7gHOBtwAeAa6axz+XAHwMfA74N\n3Ax8CngRrvfsVIrAnwJfDLa7GfgkcOE0jtvKS4AHSQaF5+/H/qS5bmA3cAeu5+1iZHC9uMeA1U3W\nf5vkfRi+pvPPHx8N2taA24BbgIlg2TeZeZDehftnnyrwZ8BJuIcg+ma4nzR8GHceb1uAY8+XNbj7\n4mcs3u+HiIiIiIiIyKKmEF1EDlYK0UVksekFdgL3MTncXhYsr5IM+8b2sc/3N9km/roDWNVi25OB\n/im2/Tqut/NMHA2Ug5q+APw18E7giBnuJw23AnuApy/AsefLX+E+q1ctdCEL5CLc+f97i/VfAe4E\nrsP9e9PDTC9E//2g3QhwVmz5UcCmYN0VM6z1jGC7H89wu7mwGEJ0cKMPWOB5C1yHiIiIiIiIiCwA\nhegicrBSiC4ii83f07oX+mFEod1PgS8xvRD9c7h/FvwQcAGuZ+vJwFtxAXIYpDeG9lmiQPGXwLlA\nATev9J8EdVhcb/iZCHva/9sMt5sLd+NqOW2hC5lDncAQ8CizH2L8YPY13Gf83Gm2/yXTC9HvCNpd\n1WTdK4J1W5jZNX9ZsN2XZ7DNXFksIfoFuPP86kIXIiIiIiIiIiLzTyG6iBysFKKLyGJSAHbg/rmt\nWe/uAm7+8DCUO53phejPpvV84+fh5gS2wNkN684OlldxAX6jK4L1m/Zx/Eb/yIETzi2GEB3gs7jz\nfPFCFzLPVuHmF9/KvqctCE0nRD+M6HvTbBSHDLA3WH/ONI55Mm6ahHuDbbYTTZ3w7Ya2eeDNuCHq\nR3Hfz0dxoffSJvv2gFfiHqbpJxpufgeuF/a6Jvu/GXgiaPe7WC0346Z2CPd7M/D9Kc7rU0GbxmN8\nOFj+NNzfme/hHsqpAC+NtesG3gPcE9RdAh4IlrUaAeNVwA9wf5d83N/T+4NaTmrS3gO2BcduNSKH\nLCLZhS5AREREREREREREREQSXo6bu/yzwHiT9SXccO4zdfsU634CPI7rnX4ibm7g0LLgfROwucm2\ndwTvy6dZx7NwgWIY1p+PC+wIavjvhvbn4q5JGGw9AtyAC/WaWYELiU8EVuKCzEdxPZHvamj7FFyw\nuDL4/VJcj9TQ14CHcEPMvxoXKH6+yTHbcA8TTAAfiS03wDuCnz+Im3v5UuAEoCf4eW+s7SXAC3HD\n+YMLcm/Ahb/NHBfUdQTQgQtTtwA34T6XWkP7/wRejwtAv9lin4ei5+AysTtwgWpanoH73DbjAthG\nNeBXwfFPwY0cMZUsbt7zjuD3HNE86BOxdl24UP1c3MMzv8WFz8fi7sNLgmP2x7ZpA76I+/vxKO5a\nZHDTKlyKG5b+ObgAP9SHe2gHoEhyTvZi8G6AC5l8r8U9ExeUdzYsPw33cM+rcd+TEeDBoF143CNw\nQfsxwADuelZx3+9rgrovxD1wGXo77iEdH/gFbpj+3mAffxycY/w8CdreAfwB7jo0+56LiIiIiIiI\nyCFKPdFF5GClnugispiEw7O/Zprtp9sTfV/uC/ZzacPypwbLx4mCrbiXB+sbQ6lW/prWc6vfFGtX\nxPWabdauCvxFk32fiwvzWu3/k7jQL/S8Kdpa4CVBu/OD33/U4pz6gvW7G5Z7sX09FxcCxvcf9uxf\nBdzWoobRWB1xl+F6zbaq/eQm2+Rxn2OZyYHmoeyTuGvyzhlsM52e6OEoDL+cos3ngzbXzuDYrwy2\n+dI+9vkj4PDY8jzwcZrfqwXgXUx+2KUd+BjRdA6N9jWce4boO9lK2LP+xIblPwuW14B/JnqYJqw3\niwvBLXAj7sGTUC/wnWDdx2LL87hpCyok56gPnUzznujgro8FPjHFuYiIiIiIiIjIIUghuogcrBSi\ni8hiYXDDK1tcr/DpSCNEPx7XE9MHNjRZ/83gGB/F9Y4NHQb8hpmF/m240PnTRMFmX/AKg10DfIso\noHwBLjTrBV5HNIf7Kxr2fT6uJ/uLcD1YPVxo+EfALiaHomFP318H654Tq6Uvdq7ns/8h+jbcHNcn\nA6txPfI7cA8LhCHj93E9c4u4YP3tuB7IE8Cpsf0egQvCR4A3EA3fvQQ4ExfYHt2i1tuDYz2/xfpD\n0U9x5/yyGWwznRA9fCDkh1O0uS5oc8MMjj1ViP50oqHelzVZn8P1TLdB2+nIAhuDbdY2rJuPEP12\nkg+3hP4wWP8rkn93QstwD6aM44Z8B/c3yeJGkJip8IGgfY0YICIiIiIiIiKHGIXoInKwUoguIovF\nelyIMzKDbfY3RM8DP2fqnq8duJ6gE8CTuDD5Z7he0luJ5keeiY8Gx3xLk3UXB+seIwrH4l5INE/z\ndIW9zu9ssm5fc6Kfz/6H6F+neVD4l8H6n9B8vu6/iG0felWw7PoW9UzlE8G2V89i24PVg0QPSUzX\ndEL0vwvafGOKNh8M2nxxBseeKkS/Olj371Ns/5GgzZ+3WJ/FheXPxg2FfiGuF7oFLmpoOx8h+hUt\ntgtHonhHi/Xg5j23uHMI6xkL6nkDzb9TrTyHmf9dkUOU5kQXERERERERERERETlwrAjeG8PYufRP\nuPB4G3B5izYlXBD2bNw84vEhpO/Ahd1p+r/B+0dxQzM3+m5wzGOCeh6fxj5vwYVrp+B6tVb2v8wZ\nCYPNRuG5foDm83V/HBfEXoALCGu4nvjg5lbP43qlT9eu4H3VlK0OLeGDGDN5OGU6wgdXeqdoE84j\nntaxjwvez8HNFd7MkcH76oblpwH/gAuLMy22bfbQylxr9f09Pnh/DfB7LdqcELyvCd5rwHtx35nP\n4IaJ/wluqojv4OavbyWcV71nijaySChEFxERERERERERERE5cITDM++dp+P9Na4n+ACud/f2Jm0M\n8DXcEOm/woVZ9+GGHH8+LpS7CTdk+vUp1RX2CD+B1vNYh4HzepIh3JG4nq2n43rbrmHyUNA9RGHy\nfGnWu7UAPC34+Wxaz9U8igs3VwJbcMNfb8Sd40bgq7hA9afs+wGM8N5qNhT4oWoAFyinHRCH99DS\nKdqE69K638IpD7JEAX2jIdzoCjtjy56Ne5Akh7tXvoN7GGMv7iGM9wDn0Tpcn0sDLZaH51qg9blu\nDV7xh23+ETf6wJ/iRpH4g+AF8F+4HvrN/saG4fl8/f2VA5hCdBERERERERERERGRA8d48N42D8d6\nO/A+XO/LF+AC8mZehQvQt+J6Q8cDpk8Am3Bzpn8YN6x1GgFUGPC+cRptO2M/Pws3r3gnLky+B/hF\nUJOP6/VdxPXenm/bmixbQjTc9FXT2EdX8D6GG57+I8H7ZcGrhhsi+y9x591MeG+Nt1h/KNqJ69W8\nJOX9hg9GHI77HJuNJHBkQ9v9FQbON+B6XE/Xe3Bh9J/j5mlv9DezrCccXcEEr2ajLcz24YXwXP8W\n+MIMt/1m8GoHzsUNU/964P/gPqvXNNkmvD92zLRQOfQoRBcREREREREREREROXCE4U3aYV+jt+B6\na47i5h9vNk94KJwjuVVA/i1cL9tlwJnA91Kobxw3RPZLcMPITyXee/7fcAH6W3DDoNdi6wwuQJsL\njT3dm2kWsIZBtgWezr6H/H4y9vNDuIcfVuNGBzg3+P083BD7z8EF6o3CntGLKSi8F3d9jkl5v7/A\nPdDQA5zM5AdR+ohGF/hJSse8GzdP+7NmuF1Yx3ebrMsRDYveKJzrvFUPdR93H7fjpqNoHM2iEzci\nxGzcjRuV4ixmHqKHxnEP1nwfuBE31/0lNH/o4djgfV9/c2QR8PbdRERERERERERERERE5kk/Lvhd\ngusxPRcuw/UaHwd+HzcE+FTCXuHN5iYPDQbvUw1rPROPBO8rcHOfT/UaDdquAU7E9V69jmSADnAY\n0DHLekrBe6shpdfPcr+DuF7SBvfQwL7Otdk87luB/8D12t8AfA7XifLKFscMA82HZ1nzwei24P2M\nlPc7ipvqANyDG40uwwXUdwCPpnTM/8Z9dy/APTDRStgzPFQO3lc2aftGWj+4szV4XzXFscJe9s3m\nLf8LZp9H3hC8v56pv2OZFj83egz3wEo7zUejCO+P25qsExEREREREZFD2KVE/4FTRORgMozrdSUi\nshj8HBf0nD3N9qcH7cem0faPiXqOPm+a+/9osP9WwdIaXGBtmVnv2HC/zcLHvwzW3c70A7i1wTZb\nWqx/R7De4mqO+16w/IIW2x4erB8iOXx86J+C9Y3zkXuxY7bymWD9Z6doMxPPC/b38xbrHw/WH5fS\n8Q4Gy3APQuyk9SjNJwHvjL02467TF2LL3tpku6NxYboPvB8XRncAVwATuO/GVGF3M68Mjv2lFuuv\nDNYPA28LasjhesSfhLvXH8Y9hBL692Cbe4h6pReBy4M6dwXr/7DhWBcFy7fhwvYLg1e8V/9fBW02\n4Ua2aMMNY/923LUZCNaf2LDvnwXLz21xngCfDtpsxv2z4Drc92o5rpf6+4EHYu2fGZzjnwXXJfy8\nj8A9gGBxc8I3yuLujxLRg0MiIiIiIiIiskgoRBeRg5VCdBFZTMJAtlVPYnC9qtcHrz8I2o/Hlq1n\ncq/wS3GBno8Lzta3eDX2SH0WURD8HpJDl68BfhSse5SZTSM7VYjeheuVb3Ehc1eTNmeRnBPaAHuC\nbRqHbb8YN1S6T/MQ/eOxY7Uamv13QZuPkAz2X4wLIWcbom/AhfPh9W3sIesBLyR5nS4B3sDknvUZ\n4Hpah/IriQJJ02T9oezzuHN/fov1byT6rFq9Wv271MtxD7E0tq/SPHjfl32F6OB6eI9PUes4yb8B\nq4keoAgD+HD7DxFdn8YQ3QO+3GT//xpr00708E/8VcJ9F+9l9iF6DjdyRnWKc90Ya//MJjUMx37/\nHc17tb8gWP/5KWqRRWSx/YEUERERERFZ7C4FPgH8yUIXIiIyQ5/G/e26caELERGZB0/DhU7/Azy7\nRZv/Zd9DU/8zrido6DbgnGkc/wO4nqWNy94Z/LwdF0QVcfMoF3Eh1QtpPgd3Kx/FhflXANc2Wf80\n4Du4HubDuN6mT+KGlV4XLH8QOD62zVXA3wc//w8uLF6LC9Y+BbwIF6AfRrLH+hlB7TlcWDccLH9t\nUAO4YPMLuGzlcVxwd3Swv6uBa3Ahfjy49IiGlZ8qk7kI10u2GxfE34/rGbw6OMZy4Ou4OeLB9TT+\nIO7BgIeAJ3APMJwabLMjOOfHG45zGfAxmn/Gh7pzcfOSf47JD1mA65k/VZgLbkj061us24AL4p+G\n+9wfxj3IcM8sal2P6+39OM17TYdW40LvM3H33W7cd+T2YLvRhvZLgDcBz8Dda0/g7ukf4UZh2ADc\nQvOh558RrA8fsrkPN0x9qBjs+/dw5/8Y7jt3D+4hgyW4MH5PbJsX477P3yIaNr6VY3DfwVNwD9Vs\nC+r/Ee5vW3yqg+ODOp5O9OBIP3An7ntWZrLPAa/GjRqg4dxFIbqIiIiIiMgi8xrcf8gQETkYvRH3\nH3pFRBaD23E9rY/DhaSNrgOO3cc+vkTyn/3+FRfw7csXcD2yG70U1xs6DLHAhdQ/xgXIzeqcyuW4\nHtUfwwXEzSzBheyvIArL9+IC8JuALwK/iLU3QfvX4a5dPMwMe9suxQWPjb3Gn4oL0Y4mCgrfi/ss\nQi/GBean4nq4/goX2t+Kmxt7GHed4vX8IPi52XzRcUfgHnp4cfCzxQ0vvQn4Bu5zCa/xCbiA/xxc\n4LoSFwxuA76PG+L6iSbHuBP3wMDRpDdH98Hku7hw+ngW5/lLcxuA3wI/xD0MJCIiIiIiIiIiIiIi\nIgegcIj2Dy90IXLIOBV3T31loQtZQMcxdW9yWZw+i+vJfvy+GoqIiIiIiIiIiIiIiMjC+j5uru3D\nF7oQOSR8FzcH9roFrmOhXUA0LL4IuIeWLlzoIkRERERERERERERERGTfno6bv1eBn+yvpbih769Y\n6EJERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERE\nRERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERE\nRERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERERE\nRERERERERERERERERERERERERBar/w89lh7LUZYtOQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('images/02_network_flowchart.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The input image is processed in the first convolutional layer using the filter-weights. This results in 16 new images, one for each filter in the convolutional layer. The images are also down-sampled so the image resolution is decreased from 28x28 to 14x14.\n",
    "\n",
    "These 16 smaller images are then processed in the second convolutional layer. We need filter-weights for each of these 16 channels, and we need filter-weights for each output channel of this layer. There are 36 output channels so there are a total of 16 x 36 = 576 filters in the second convolutional layer. The resulting images are down-sampled again to 7x7 pixels.\n",
    "\n",
    "The output of the second convolutional layer is 36 images of 7x7 pixels each. These are then flattened to a single vector of length 7 x 7 x 36 = 1764, which is used as the input to a fully-connected layer with 128 neurons (or elements). This feeds into another fully-connected layer with 10 neurons, one for each of the classes, which is used to determine the class of the image, that is, which number is depicted in the image.\n",
    "\n",
    "The convolutional filters are initially chosen at random, so the classification is done randomly. The error between the predicted and true class of the input image is measured as the so-called cross-entropy. The optimizer then automatically propagates this error back through the Convolutional Network using the chain-rule of differentiation and updates the filter-weights so as to improve the classification error. This is done iteratively thousands of times until the classification error is sufficiently low.\n",
    "\n",
    "These particular filter-weights and intermediate images are the results of one optimization run and may look different if you re-run this Notebook.\n",
    "\n",
    "Note that the computation in TensorFlow is actually done on a batch of images instead of a single image, which makes the computation more efficient. This means the flowchart actually has one more data-dimension when implemented in TensorFlow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Convolutional Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The following chart shows the basic idea of processing an image in the first convolutional layer. The input image depicts the number 7 and four copies of the image are shown here, so we can see more clearly how the filter is being moved to different positions of the image. For each position of the filter, the dot-product is being calculated between the filter and the image pixels under the filter, which results in a single pixel in the output image. So moving the filter across the entire input image results in a new image being generated.\n",
    "\n",
    "The red filter-weights means that the filter has a positive reaction to black pixels in the input image, while blue pixels means the filter has a negative reaction to black pixels.\n",
    "\n",
    "In this case it appears that the filter recognizes the horizontal line of the 7-digit, as can be seen from its stronger reaction to that line in the output image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5YAAAIgCAYAAAD+7/aiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('images/02_convolution.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The step-size for moving the filter across the input is called the stride. There is a stride for moving the filter horizontally (x-axis) and another stride for moving vertically (y-axis).\n",
    "\n",
    "In the source-code below, the stride is set to 1 in both directions, which means the filter starts in the upper left corner of the input image and is being moved 1 pixel to the right in each step. When the filter reaches the end of the image to the right, then the filter is moved back to the left side and 1 pixel down the image. This continues until the filter has reached the lower right corner of the input image and the entire output image has been generated.\n",
    "\n",
    "When the filter reaches the end of the right-side as well as the bottom of the input image, then it can be padded with zeroes (white pixels). This causes the output image to be of the exact same dimension as the input image.\n",
    "\n",
    "Furthermore, the output of the convolution may be passed through a so-called Rectified Linear Unit (ReLU), which merely ensures that the output is positive because negative values are set to zero. The output may also be down-sampled by so-called max-pooling, which considers small windows of 2x2 pixels and only keeps the largest of those pixels. This halves the resolution of the input image e.g. from 28x28 to 14x14 pixels.\n",
    "\n",
    "Note that the second convolutional layer is more complicated because it takes 16 input channels. We want a separate filter for each input channel, so we need 16 filters instead of just one. Furthermore, we want 36 output channels from the second convolutional layer, so in total we need 16 x 36 = 576 filters for the second convolutional layer. It can be a bit challenging to understand how this works."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import time\n",
    "from datetime import timedelta\n",
    "import math"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This was developed using Python 3.5.2 (Anaconda) and TensorFlow version:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.1.0'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Configuration of Neural Network\n",
    "\n",
    "The configuration of the Convolutional Neural Network is defined here for convenience, so you can easily find and change these numbers and re-run the Notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Convolutional Layer 1.\n",
    "filter_size1 = 5          # Convolution filters are 5 x 5 pixels.\n",
    "num_filters1 = 16         # There are 16 of these filters.\n",
    "\n",
    "# Convolutional Layer 2.\n",
    "filter_size2 = 5          # Convolution filters are 5 x 5 pixels.\n",
    "num_filters2 = 36         # There are 36 of these filters.\n",
    "\n",
    "# Fully-connected layer.\n",
    "fc_size = 128             # Number of neurons in fully-connected layer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The MNIST data-set is about 12 MB and will be downloaded automatically if it is not located in the given path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting data/MNIST/train-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST/train-labels-idx1-ubyte.gz\n",
      "Extracting data/MNIST/t10k-images-idx3-ubyte.gz\n",
      "Extracting data/MNIST/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "data = input_data.read_data_sets('data/MNIST/', one_hot=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The MNIST data-set has now been loaded and consists of 70,000 images and associated labels (i.e. classifications of the images). The data-set is split into 3 mutually exclusive sub-sets. We will only use the training and test-sets in this tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of:\n",
      "- Training-set:\t\t55000\n",
      "- Test-set:\t\t10000\n",
      "- Validation-set:\t5000\n"
     ]
    }
   ],
   "source": [
    "print(\"Size of:\")\n",
    "print(\"- Training-set:\\t\\t{}\".format(len(data.train.labels)))\n",
    "print(\"- Test-set:\\t\\t{}\".format(len(data.test.labels)))\n",
    "print(\"- Validation-set:\\t{}\".format(len(data.validation.labels)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The class-labels are One-Hot encoded, which means that each label is a vector with 10 elements, all of which are zero except for one element. The index of this one element is the class-number, that is, the digit shown in the associated image. We also need the class-numbers as integers for the test-set, so we calculate it now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "data.test.cls = np.argmax(data.test.labels, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Data Dimensions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The data dimensions are used in several places in the source-code below. They are defined once so we can use these variables instead of numbers throughout the source-code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# We know that MNIST images are 28 pixels in each dimension.\n",
    "img_size = 28\n",
    "\n",
    "# Images are stored in one-dimensional arrays of this length.\n",
    "img_size_flat = img_size * img_size\n",
    "\n",
    "# Tuple with height and width of images used to reshape arrays.\n",
    "img_shape = (img_size, img_size)\n",
    "\n",
    "# Number of colour channels for the images: 1 channel for gray-scale.\n",
    "num_channels = 1\n",
    "\n",
    "# Number of classes, one class for each of 10 digits.\n",
    "num_classes = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Helper-function for plotting images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Function used to plot 9 images in a 3x3 grid, and writing the true and predicted classes below each image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def plot_images(images, cls_true, cls_pred=None):\n",
    "    assert len(images) == len(cls_true) == 9\n",
    "    \n",
    "    # Create figure with 3x3 sub-plots.\n",
    "    fig, axes = plt.subplots(3, 3)\n",
    "    fig.subplots_adjust(hspace=0.3, wspace=0.3)\n",
    "\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        # Plot image.\n",
    "        ax.imshow(images[i].reshape(img_shape), cmap='binary')\n",
    "\n",
    "        # Show true and predicted classes.\n",
    "        if cls_pred is None:\n",
    "            xlabel = \"True: {0}\".format(cls_true[i])\n",
    "        else:\n",
    "            xlabel = \"True: {0}, Pred: {1}\".format(cls_true[i], cls_pred[i])\n",
    "\n",
    "        # Show the classes as the label on the x-axis.\n",
    "        ax.set_xlabel(xlabel)\n",
    "        \n",
    "        # Remove ticks from the plot.\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "    \n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Plot a few images to see if data is correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUMAAAD5CAYAAAC9FVegAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f210fe38c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the first images from the test-set.\n",
    "images = data.test.images[0:9]\n",
    "\n",
    "# Get the true classes for those images.\n",
    "cls_true = data.test.cls[0:9]\n",
    "\n",
    "# Plot the images and labels using our helper-function above.\n",
    "plot_images(images=images, cls_true=cls_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## TensorFlow Graph\n",
    "\n",
    "The entire purpose of TensorFlow is to have a so-called computational graph that can be executed much more efficiently than if the same calculations were to be performed directly in Python. TensorFlow can be more efficient than NumPy because TensorFlow knows the entire computation graph that must be executed, while NumPy only knows the computation of a single mathematical operation at a time.\n",
    "\n",
    "TensorFlow can also automatically calculate the gradients that are needed to optimize the variables of the graph so as to make the model perform better. This is because the graph is a combination of simple mathematical expressions so the gradient of the entire graph can be calculated using the chain-rule for derivatives.\n",
    "\n",
    "TensorFlow can also take advantage of multi-core CPUs as well as GPUs - and Google has even built special chips just for TensorFlow which are called TPUs (Tensor Processing Units) and are even faster than GPUs.\n",
    "\n",
    "A TensorFlow graph consists of the following parts which will be detailed below:\n",
    "\n",
    "* Placeholder variables used for inputting data to the graph.\n",
    "* Variables that are going to be optimized so as to make the convolutional network perform better.\n",
    "* The mathematical formulas for the convolutional network.\n",
    "* A cost measure that can be used to guide the optimization of the variables.\n",
    "* An optimization method which updates the variables.\n",
    "\n",
    "In addition, the TensorFlow graph may also contain various debugging statements e.g. for logging data to be displayed using TensorBoard, which is not covered in this tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Helper-functions for creating new variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Functions for creating new TensorFlow variables in the given shape and initializing them with random values. Note that the initialization is not actually done at this point, it is merely being defined in the TensorFlow graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def new_weights(shape):\n",
    "    return tf.Variable(tf.truncated_normal(shape, stddev=0.05))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def new_biases(length):\n",
    "    return tf.Variable(tf.constant(0.05, shape=[length]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Helper-function for creating a new Convolutional Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This function creates a new convolutional layer in the computational graph for TensorFlow. Nothing is actually calculated here, we are just adding the mathematical formulas to the TensorFlow graph.\n",
    "\n",
    "It is assumed that the input is a 4-dim tensor with the following dimensions:\n",
    "\n",
    "1. Image number.\n",
    "2. Y-axis of each image.\n",
    "3. X-axis of each image.\n",
    "4. Channels of each image.\n",
    "\n",
    "Note that the input channels may either be colour-channels, or it may be filter-channels if the input is produced from a previous convolutional layer.\n",
    "\n",
    "The output is another 4-dim tensor with the following dimensions:\n",
    "\n",
    "1. Image number, same as input.\n",
    "2. Y-axis of each image. If 2x2 pooling is used, then the height and width of the input images is divided by 2.\n",
    "3. X-axis of each image. Ditto.\n",
    "4. Channels produced by the convolutional filters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def new_conv_layer(input,              # The previous layer.\n",
    "                   num_input_channels, # Num. channels in prev. layer.\n",
    "                   filter_size,        # Width and height of each filter.\n",
    "                   num_filters,        # Number of filters.\n",
    "                   use_pooling=True):  # Use 2x2 max-pooling.\n",
    "\n",
    "    # Shape of the filter-weights for the convolution.\n",
    "    # This format is determined by the TensorFlow API.\n",
    "    shape = [filter_size, filter_size, num_input_channels, num_filters]\n",
    "\n",
    "    # Create new weights aka. filters with the given shape.\n",
    "    weights = new_weights(shape=shape)\n",
    "\n",
    "    # Create new biases, one for each filter.\n",
    "    biases = new_biases(length=num_filters)\n",
    "\n",
    "    # Create the TensorFlow operation for convolution.\n",
    "    # Note the strides are set to 1 in all dimensions.\n",
    "    # The first and last stride must always be 1,\n",
    "    # because the first is for the image-number and\n",
    "    # the last is for the input-channel.\n",
    "    # But e.g. strides=[1, 2, 2, 1] would mean that the filter\n",
    "    # is moved 2 pixels across the x- and y-axis of the image.\n",
    "    # The padding is set to 'SAME' which means the input image\n",
    "    # is padded with zeroes so the size of the output is the same.\n",
    "    layer = tf.nn.conv2d(input=input,\n",
    "                         filter=weights,\n",
    "                         strides=[1, 1, 1, 1],\n",
    "                         padding='SAME')\n",
    "\n",
    "    # Add the biases to the results of the convolution.\n",
    "    # A bias-value is added to each filter-channel.\n",
    "    layer += biases\n",
    "\n",
    "    # Use pooling to down-sample the image resolution?\n",
    "    if use_pooling:\n",
    "        # This is 2x2 max-pooling, which means that we\n",
    "        # consider 2x2 windows and select the largest value\n",
    "        # in each window. Then we move 2 pixels to the next window.\n",
    "        layer = tf.nn.max_pool(value=layer,\n",
    "                               ksize=[1, 2, 2, 1],\n",
    "                               strides=[1, 2, 2, 1],\n",
    "                               padding='SAME')\n",
    "\n",
    "    # Rectified Linear Unit (ReLU).\n",
    "    # It calculates max(x, 0) for each input pixel x.\n",
    "    # This adds some non-linearity to the formula and allows us\n",
    "    # to learn more complicated functions.\n",
    "    layer = tf.nn.relu(layer)\n",
    "\n",
    "    # Note that ReLU is normally executed before the pooling,\n",
    "    # but since relu(max_pool(x)) == max_pool(relu(x)) we can\n",
    "    # save 75% of the relu-operations by max-pooling first.\n",
    "\n",
    "    # We return both the resulting layer and the filter-weights\n",
    "    # because we will plot the weights later.\n",
    "    return layer, weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Helper-function for flattening a layer\n",
    "\n",
    "A convolutional layer produces an output tensor with 4 dimensions. We will add fully-connected layers after the convolution layers, so we need to reduce the 4-dim tensor to 2-dim which can be used as input to the fully-connected layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def flatten_layer(layer):\n",
    "    # Get the shape of the input layer.\n",
    "    layer_shape = layer.get_shape()\n",
    "\n",
    "    # The shape of the input layer is assumed to be:\n",
    "    # layer_shape == [num_images, img_height, img_width, num_channels]\n",
    "\n",
    "    # The number of features is: img_height * img_width * num_channels\n",
    "    # We can use a function from TensorFlow to calculate this.\n",
    "    num_features = layer_shape[1:4].num_elements()\n",
    "    \n",
    "    # Reshape the layer to [num_images, num_features].\n",
    "    # Note that we just set the size of the second dimension\n",
    "    # to num_features and the size of the first dimension to -1\n",
    "    # which means the size in that dimension is calculated\n",
    "    # so the total size of the tensor is unchanged from the reshaping.\n",
    "    layer_flat = tf.reshape(layer, [-1, num_features])\n",
    "\n",
    "    # The shape of the flattened layer is now:\n",
    "    # [num_images, img_height * img_width * num_channels]\n",
    "\n",
    "    # Return both the flattened layer and the number of features.\n",
    "    return layer_flat, num_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Helper-function for creating a new Fully-Connected Layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This function creates a new fully-connected layer in the computational graph for TensorFlow. Nothing is actually calculated here, we are just adding the mathematical formulas to the TensorFlow graph.\n",
    "\n",
    "It is assumed that the input is a 2-dim tensor of shape `[num_images, num_inputs]`. The output is a 2-dim tensor of shape `[num_images, num_outputs]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def new_fc_layer(input,          # The previous layer.\n",
    "                 num_inputs,     # Num. inputs from prev. layer.\n",
    "                 num_outputs,    # Num. outputs.\n",
    "                 use_relu=True): # Use Rectified Linear Unit (ReLU)?\n",
    "\n",
    "    # Create new weights and biases.\n",
    "    weights = new_weights(shape=[num_inputs, num_outputs])\n",
    "    biases = new_biases(length=num_outputs)\n",
    "\n",
    "    # Calculate the layer as the matrix multiplication of\n",
    "    # the input and weights, and then add the bias-values.\n",
    "    layer = tf.matmul(input, weights) + biases\n",
    "\n",
    "    # Use ReLU?\n",
    "    if use_relu:\n",
    "        layer = tf.nn.relu(layer)\n",
    "\n",
    "    return layer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Placeholder variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Placeholder variables serve as the input to the TensorFlow computational graph that we may change each time we execute the graph. We call this feeding the placeholder variables and it is demonstrated further below.\n",
    "\n",
    "First we define the placeholder variable for the input images. This allows us to change the images that are input to the TensorFlow graph. This is a so-called tensor, which just means that it is a multi-dimensional vector or matrix. The data-type is set to `float32` and the shape is set to `[None, img_size_flat]`, where `None` means that the tensor may hold an arbitrary number of images with each image being a vector of length `img_size_flat`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='x')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The convolutional layers expect `x` to be encoded as a 4-dim tensor so we have to reshape it so its shape is instead `[num_images, img_height, img_width, num_channels]`. Note that `img_height == img_width == img_size` and `num_images` can be inferred automatically by using -1 for the size of the first dimension. So the reshape operation is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x_image = tf.reshape(x, [-1, img_size, img_size, num_channels])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Next we have the placeholder variable for the true labels associated with the images that were input in the placeholder variable `x`. The shape of this placeholder variable is `[None, num_classes]` which means it may hold an arbitrary number of labels and each label is a vector of length `num_classes` which is 10 in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We could also have a placeholder variable for the class-number, but we will instead calculate it using argmax. Note that this is a TensorFlow operator so nothing is calculated at this point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y_true_cls = tf.argmax(y_true, dimension=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Convolutional Layer 1\n",
    "\n",
    "Create the first convolutional layer. It takes `x_image` as input and creates `num_filters1` different filters, each having width and height equal to `filter_size1`. Finally we wish to down-sample the image so it is half the size by using 2x2 max-pooling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "layer_conv1, weights_conv1 = \\\n",
    "    new_conv_layer(input=x_image,\n",
    "                   num_input_channels=num_channels,\n",
    "                   filter_size=filter_size1,\n",
    "                   num_filters=num_filters1,\n",
    "                   use_pooling=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Check the shape of the tensor that will be output by the convolutional layer. It is (?, 14, 14, 16) which means that there is an arbitrary number of images (this is the ?), each image is 14 pixels wide and 14 pixels high, and there are 16 different channels, one channel for each of the filters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Relu:0' shape=(?, 14, 14, 16) dtype=float32>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_conv1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Convolutional Layer 2\n",
    "\n",
    "Create the second convolutional layer, which takes as input the output from the first convolutional layer. The number of input channels corresponds to the number of filters in the first convolutional layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "layer_conv2, weights_conv2 = \\\n",
    "    new_conv_layer(input=layer_conv1,\n",
    "                   num_input_channels=num_filters1,\n",
    "                   filter_size=filter_size2,\n",
    "                   num_filters=num_filters2,\n",
    "                   use_pooling=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Check the shape of the tensor that will be output from this convolutional layer. The shape is (?, 7, 7, 36) where the ? again means that there is an arbitrary number of images, with each image having width and height of 7 pixels, and there are 36 channels, one for each filter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Relu_1:0' shape=(?, 7, 7, 36) dtype=float32>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_conv2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Flatten Layer\n",
    "\n",
    "The convolutional layers output 4-dim tensors. We now wish to use these as input in a fully-connected network, which requires for the tensors to be reshaped or flattened to 2-dim tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "layer_flat, num_features = flatten_layer(layer_conv2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Check that the tensors now have shape (?, 1764) which means there's an arbitrary number of images which have been flattened to vectors of length 1764 each. Note that 1764 = 7 x 7 x 36."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Reshape_1:0' shape=(?, 1764) dtype=float32>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1764"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Fully-Connected Layer 1\n",
    "\n",
    "Add a fully-connected layer to the network. The input is the flattened layer from the previous convolution. The number of neurons or nodes in the fully-connected layer is `fc_size`. ReLU is used so we can learn non-linear relations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "layer_fc1 = new_fc_layer(input=layer_flat,\n",
    "                         num_inputs=num_features,\n",
    "                         num_outputs=fc_size,\n",
    "                         use_relu=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Check that the output of the fully-connected layer is a tensor with shape (?, 128) where the ? means there is an arbitrary number of images and `fc_size` == 128."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'Relu_2:0' shape=(?, 128) dtype=float32>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_fc1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Fully-Connected Layer 2\n",
    "\n",
    "Add another fully-connected layer that outputs vectors of length 10 for determining which of the 10 classes the input image belongs to. Note that ReLU is not used in this layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "layer_fc2 = new_fc_layer(input=layer_fc1,\n",
    "                         num_inputs=fc_size,\n",
    "                         num_outputs=num_classes,\n",
    "                         use_relu=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor 'add_3:0' shape=(?, 10) dtype=float32>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layer_fc2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Predicted Class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The second fully-connected layer estimates how likely it is that the input image belongs to each of the 10 classes. However, these estimates are a bit rough and difficult to interpret because the numbers may be very small or large, so we want to normalize them so that each element is limited between zero and one and the 10 elements sum to one. This is calculated using the so-called softmax function and the result is stored in `y_pred`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y_pred = tf.nn.softmax(layer_fc2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The class-number is the index of the largest element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y_pred_cls = tf.argmax(y_pred, dimension=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Cost-function to be optimized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "To make the model better at classifying the input images, we must somehow change the variables for all the network layers. To do this we first need to know how well the model currently performs by comparing the predicted output of the model `y_pred` to the desired output `y_true`.\n",
    "\n",
    "The cross-entropy is a performance measure used in classification. The cross-entropy is a continuous function that is always positive and if the predicted output of the model exactly matches the desired output then the cross-entropy equals zero. The goal of optimization is therefore to minimize the cross-entropy so it gets as close to zero as possible by changing the variables of the network layers.\n",
    "\n",
    "TensorFlow has a built-in function for calculating the cross-entropy. Note that the function calculates the softmax internally so we must use the output of `layer_fc2` directly rather than `y_pred` which has already had the softmax applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2,\n",
    "                                                        labels=y_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We have now calculated the cross-entropy for each of the image classifications so we have a measure of how well the model performs on each image individually. But in order to use the cross-entropy to guide the optimization of the model's variables we need a single scalar value, so we simply take the average of the cross-entropy for all the image classifications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "cost = tf.reduce_mean(cross_entropy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Optimization Method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now that we have a cost measure that must be minimized, we can then create an optimizer. In this case it is the `AdamOptimizer` which is an advanced form of Gradient Descent.\n",
    "\n",
    "Note that optimization is not performed at this point. In fact, nothing is calculated at all, we just add the optimizer-object to the TensorFlow graph for later execution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "optimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Performance Measures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We need a few more performance measures to display the progress to the user.\n",
    "\n",
    "This is a vector of booleans whether the predicted class equals the true class of each image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(y_pred_cls, y_true_cls)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This calculates the classification accuracy by first type-casting the vector of booleans to floats, so that False becomes 0 and True becomes 1, and then calculating the average of these numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## TensorFlow Run"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Create TensorFlow session\n",
    "\n",
    "Once the TensorFlow graph has been created, we have to create a TensorFlow session which is used to execute the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "session = tf.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Initialize variables\n",
    "\n",
    "The variables for `weights` and `biases` must be initialized before we start optimizing them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "session.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Helper-function to perform optimization iterations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "There are 55,000 images in the training-set. It takes a long time to calculate the gradient of the model using all these images. We therefore only use a small batch of images in each iteration of the optimizer.\n",
    "\n",
    "If your computer crashes or becomes very slow because you run out of RAM, then you may try and lower this number, but you may then need to perform more optimization iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "train_batch_size = 64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Function for performing a number of optimization iterations so as to gradually improve the variables of the network layers. In each iteration, a new batch of data is selected from the training-set and then TensorFlow executes the optimizer using those training samples.  The progress is printed every 100 iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Counter for total number of iterations performed so far.\n",
    "total_iterations = 0\n",
    "\n",
    "def optimize(num_iterations):\n",
    "    # Ensure we update the global variable rather than a local copy.\n",
    "    global total_iterations\n",
    "\n",
    "    # Start-time used for printing time-usage below.\n",
    "    start_time = time.time()\n",
    "\n",
    "    for i in range(total_iterations,\n",
    "                   total_iterations + num_iterations):\n",
    "\n",
    "        # Get a batch of training examples.\n",
    "        # x_batch now holds a batch of images and\n",
    "        # y_true_batch are the true labels for those images.\n",
    "        x_batch, y_true_batch = data.train.next_batch(train_batch_size)\n",
    "\n",
    "        # Put the batch into a dict with the proper names\n",
    "        # for placeholder variables in the TensorFlow graph.\n",
    "        feed_dict_train = {x: x_batch,\n",
    "                           y_true: y_true_batch}\n",
    "\n",
    "        # Run the optimizer using this batch of training data.\n",
    "        # TensorFlow assigns the variables in feed_dict_train\n",
    "        # to the placeholder variables and then runs the optimizer.\n",
    "        session.run(optimizer, feed_dict=feed_dict_train)\n",
    "\n",
    "        # Print status every 100 iterations.\n",
    "        if i % 100 == 0:\n",
    "            # Calculate the accuracy on the training-set.\n",
    "            acc = session.run(accuracy, feed_dict=feed_dict_train)\n",
    "\n",
    "            # Message for printing.\n",
    "            msg = \"Optimization Iteration: {0:>6}, Training Accuracy: {1:>6.1%}\"\n",
    "\n",
    "            # Print it.\n",
    "            print(msg.format(i + 1, acc))\n",
    "\n",
    "    # Update the total number of iterations performed.\n",
    "    total_iterations += num_iterations\n",
    "\n",
    "    # Ending time.\n",
    "    end_time = time.time()\n",
    "\n",
    "    # Difference between start and end-times.\n",
    "    time_dif = end_time - start_time\n",
    "\n",
    "    # Print the time-usage.\n",
    "    print(\"Time usage: \" + str(timedelta(seconds=int(round(time_dif)))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Helper-function to plot example errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Function for plotting examples of images from the test-set that have been mis-classified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def plot_example_errors(cls_pred, correct):\n",
    "    # This function is called from print_test_accuracy() below.\n",
    "\n",
    "    # cls_pred is an array of the predicted class-number for\n",
    "    # all images in the test-set.\n",
    "\n",
    "    # correct is a boolean array whether the predicted class\n",
    "    # is equal to the true class for each image in the test-set.\n",
    "\n",
    "    # Negate the boolean array.\n",
    "    incorrect = (correct == False)\n",
    "    \n",
    "    # Get the images from the test-set that have been\n",
    "    # incorrectly classified.\n",
    "    images = data.test.images[incorrect]\n",
    "    \n",
    "    # Get the predicted classes for those images.\n",
    "    cls_pred = cls_pred[incorrect]\n",
    "\n",
    "    # Get the true classes for those images.\n",
    "    cls_true = data.test.cls[incorrect]\n",
    "    \n",
    "    # Plot the first 9 images.\n",
    "    plot_images(images=images[0:9],\n",
    "                cls_true=cls_true[0:9],\n",
    "                cls_pred=cls_pred[0:9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Helper-function to plot confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cls_pred):\n",
    "    # This is called from print_test_accuracy() below.\n",
    "\n",
    "    # cls_pred is an array of the predicted class-number for\n",
    "    # all images in the test-set.\n",
    "\n",
    "    # Get the true classifications for the test-set.\n",
    "    cls_true = data.test.cls\n",
    "    \n",
    "    # Get the confusion matrix using sklearn.\n",
    "    cm = confusion_matrix(y_true=cls_true,\n",
    "                          y_pred=cls_pred)\n",
    "\n",
    "    # Print the confusion matrix as text.\n",
    "    print(cm)\n",
    "\n",
    "    # Plot the confusion matrix as an image.\n",
    "    plt.matshow(cm)\n",
    "\n",
    "    # Make various adjustments to the plot.\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(num_classes)\n",
    "    plt.xticks(tick_marks, range(num_classes))\n",
    "    plt.yticks(tick_marks, range(num_classes))\n",
    "    plt.xlabel('Predicted')\n",
    "    plt.ylabel('True')\n",
    "\n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Helper-function for showing the performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Function for printing the classification accuracy on the test-set.\n",
    "\n",
    "It takes a while to compute the classification for all the images in the test-set, that's why the results are re-used by calling the above functions directly from this function, so the classifications don't have to be recalculated by each function.\n",
    "\n",
    "Note that this function can use a lot of computer memory, which is why the test-set is split into smaller batches. If you have little RAM in your computer and it crashes, then you can try and lower the batch-size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Split the test-set into smaller batches of this size.\n",
    "test_batch_size = 256\n",
    "\n",
    "def print_test_accuracy(show_example_errors=False,\n",
    "                        show_confusion_matrix=False):\n",
    "\n",
    "    # Number of images in the test-set.\n",
    "    num_test = len(data.test.images)\n",
    "\n",
    "    # Allocate an array for the predicted classes which\n",
    "    # will be calculated in batches and filled into this array.\n",
    "    cls_pred = np.zeros(shape=num_test, dtype=np.int)\n",
    "\n",
    "    # Now calculate the predicted classes for the batches.\n",
    "    # We will just iterate through all the batches.\n",
    "    # There might be a more clever and Pythonic way of doing this.\n",
    "\n",
    "    # The starting index for the next batch is denoted i.\n",
    "    i = 0\n",
    "\n",
    "    while i < num_test:\n",
    "        # The ending index for the next batch is denoted j.\n",
    "        j = min(i + test_batch_size, num_test)\n",
    "\n",
    "        # Get the images from the test-set between index i and j.\n",
    "        images = data.test.images[i:j, :]\n",
    "\n",
    "        # Get the associated labels.\n",
    "        labels = data.test.labels[i:j, :]\n",
    "\n",
    "        # Create a feed-dict with these images and labels.\n",
    "        feed_dict = {x: images,\n",
    "                     y_true: labels}\n",
    "\n",
    "        # Calculate the predicted class using TensorFlow.\n",
    "        cls_pred[i:j] = session.run(y_pred_cls, feed_dict=feed_dict)\n",
    "\n",
    "        # Set the start-index for the next batch to the\n",
    "        # end-index of the current batch.\n",
    "        i = j\n",
    "\n",
    "    # Convenience variable for the true class-numbers of the test-set.\n",
    "    cls_true = data.test.cls\n",
    "\n",
    "    # Create a boolean array whether each image is correctly classified.\n",
    "    correct = (cls_true == cls_pred)\n",
    "\n",
    "    # Calculate the number of correctly classified images.\n",
    "    # When summing a boolean array, False means 0 and True means 1.\n",
    "    correct_sum = correct.sum()\n",
    "\n",
    "    # Classification accuracy is the number of correctly classified\n",
    "    # images divided by the total number of images in the test-set.\n",
    "    acc = float(correct_sum) / num_test\n",
    "\n",
    "    # Print the accuracy.\n",
    "    msg = \"Accuracy on Test-Set: {0:.1%} ({1} / {2})\"\n",
    "    print(msg.format(acc, correct_sum, num_test))\n",
    "\n",
    "    # Plot some examples of mis-classifications, if desired.\n",
    "    if show_example_errors:\n",
    "        print(\"Example errors:\")\n",
    "        plot_example_errors(cls_pred=cls_pred, correct=correct)\n",
    "\n",
    "    # Plot the confusion matrix, if desired.\n",
    "    if show_confusion_matrix:\n",
    "        print(\"Confusion Matrix:\")\n",
    "        plot_confusion_matrix(cls_pred=cls_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Performance before any optimization\n",
    "\n",
    "The accuracy on the test-set is very low because the model variables have only been initialized and not optimized at all, so it just classifies the images randomly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test-Set: 10.1% (1009 / 10000)\n"
     ]
    }
   ],
   "source": [
    "print_test_accuracy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Performance after 1 optimization iteration\n",
    "\n",
    "The classification accuracy does not improve much from just 1 optimization iteration, because the learning-rate for the optimizer is set very low."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization Iteration:      1, Training Accuracy:   7.8%\n",
      "Time usage: 0:00:00\n"
     ]
    }
   ],
   "source": [
    "optimize(num_iterations=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test-Set: 8.3% (834 / 10000)\n"
     ]
    }
   ],
   "source": [
    "print_test_accuracy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Performance after 100 optimization iterations\n",
    "\n",
    "After 100 optimization iterations, the model has significantly improved its classification accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time usage: 0:00:00\n"
     ]
    }
   ],
   "source": [
    "optimize(num_iterations=99) # We already performed 1 iteration above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test-Set: 70.2% (7019 / 10000)\n",
      "Example errors:\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU0AAAD5CAYAAACj3GcTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njdGXqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f210fe38d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_test_accuracy(show_example_errors=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Performance after 1000 optimization iterations\n",
    "\n",
    "After 1000 optimization iterations, the model has greatly increased its accuracy on the test-set to more than 90%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization Iteration:    101, Training Accuracy:  76.6%\n",
      "Optimization Iteration:    201, Training Accuracy:  81.2%\n",
      "Optimization Iteration:    301, Training Accuracy:  82.8%\n",
      "Optimization Iteration:    401, Training Accuracy:  87.5%\n",
      "Optimization Iteration:    501, Training Accuracy:  89.1%\n",
      "Optimization Iteration:    601, Training Accuracy:  84.4%\n",
      "Optimization Iteration:    701, Training Accuracy:  90.6%\n",
      "Optimization Iteration:    801, Training Accuracy:  98.4%\n",
      "Optimization Iteration:    901, Training Accuracy:  89.1%\n",
      "Time usage: 0:00:03\n"
     ]
    }
   ],
   "source": [
    "optimize(num_iterations=900) # We performed 100 iterations above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test-Set: 93.0% (9303 / 10000)\n",
      "Example errors:\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU0AAAD5CAYAAACj3GcTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNEVEUlChKSKSggpNEZEUVGiKiKTw/wFYhGeufg/ivgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f210fe38400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_test_accuracy(show_example_errors=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Performance after 10,000 optimization iterations\n",
    "\n",
    "After 10,000 optimization iterations, the model has a classification accuracy on the test-set of about 99%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization Iteration:   1001, Training Accuracy:  93.8%\n",
      "Optimization Iteration:   1101, Training Accuracy:  92.2%\n",
      "Optimization Iteration:   1201, Training Accuracy:  93.8%\n",
      "Optimization Iteration:   1301, Training Accuracy:  92.2%\n",
      "Optimization Iteration:   1401, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   1501, Training Accuracy:  93.8%\n",
      "Optimization Iteration:   1601, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   1701, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   1801, Training Accuracy:  93.8%\n",
      "Optimization Iteration:   1901, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   2001, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   2101, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   2201, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   2301, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   2401, Training Accuracy:  92.2%\n",
      "Optimization Iteration:   2501, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   2601, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   2701, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   2801, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   2901, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   3001, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   3101, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   3201, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   3301, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   3401, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   3501, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   3601, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   3701, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   3801, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   3901, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   4001, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   4101, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   4201, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   4301, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   4401, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   4501, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   4601, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   4701, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   4801, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   4901, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   5001, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   5101, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   5201, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   5301, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   5401, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   5501, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   5601, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   5701, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   5801, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   5901, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   6001, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   6101, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   6201, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   6301, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   6401, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   6501, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   6601, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   6701, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   6801, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   6901, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   7001, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   7101, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   7201, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   7301, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   7401, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   7501, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   7601, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   7701, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   7801, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   7901, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   8001, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   8101, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   8201, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   8301, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   8401, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   8501, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   8601, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   8701, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   8801, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   8901, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   9001, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   9101, Training Accuracy:  95.3%\n",
      "Optimization Iteration:   9201, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   9301, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   9401, Training Accuracy:  98.4%\n",
      "Optimization Iteration:   9501, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   9601, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   9701, Training Accuracy:  96.9%\n",
      "Optimization Iteration:   9801, Training Accuracy: 100.0%\n",
      "Optimization Iteration:   9901, Training Accuracy:  96.9%\n",
      "Time usage: 0:00:25\n"
     ]
    }
   ],
   "source": [
    "optimize(num_iterations=9000) # We performed 1000 iterations above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on Test-Set: 98.6% (9859 / 10000)\n",
      "Example errors:\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAU0AAAD5CAYAAACj3GcTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f210fe38cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[ 975    0    0    0    0    1    1    1    2    0]\n",
      " [   0 1128    1    2    1    0    1    1    1    0]\n",
      " [   5    5 1002    7    1    0    0    6    5    1]\n",
      " [   1    0    0 1008    0    0    0    0    1    0]\n",
      " [   0    0    1    0  981    0    0    0    0    0]\n",
      " [   1    0    0   17    0  870    1    1    1    1]\n",
      " [   7    2    0    0    4    4  940    0    1    0]\n",
      " [   1    2    3    3    0    0    0 1017    1    1]\n",
      " [   3    1    1    6    1    1    0    2  957    2]\n",
      " [   2    4    0    5    9    2    0    4    2  981]]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARIAAAD3CAYAAADRydumAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f210d4ea710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_test_accuracy(show_example_errors=True,\n",
    "                    show_confusion_matrix=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Visualization of Weights and Layers\n",
    "\n",
    "In trying to understand why the convolutional neural network can recognize handwritten digits, we will now visualize the weights of the convolutional filters and the resulting output images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Helper-function for plotting convolutional weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def plot_conv_weights(weights, input_channel=0):\n",
    "    # Assume weights are TensorFlow ops for 4-dim variables\n",
    "    # e.g. weights_conv1 or weights_conv2.\n",
    "    \n",
    "    # Retrieve the values of the weight-variables from TensorFlow.\n",
    "    # A feed-dict is not necessary because nothing is calculated.\n",
    "    w = session.run(weights)\n",
    "\n",
    "    # Get the lowest and highest values for the weights.\n",
    "    # This is used to correct the colour intensity across\n",
    "    # the images so they can be compared with each other.\n",
    "    w_min = np.min(w)\n",
    "    w_max = np.max(w)\n",
    "\n",
    "    # Number of filters used in the conv. layer.\n",
    "    num_filters = w.shape[3]\n",
    "\n",
    "    # Number of grids to plot.\n",
    "    # Rounded-up, square-root of the number of filters.\n",
    "    num_grids = math.ceil(math.sqrt(num_filters))\n",
    "    \n",
    "    # Create figure with a grid of sub-plots.\n",
    "    fig, axes = plt.subplots(num_grids, num_grids)\n",
    "\n",
    "    # Plot all the filter-weights.\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        # Only plot the valid filter-weights.\n",
    "        if i<num_filters:\n",
    "            # Get the weights for the i'th filter of the input channel.\n",
    "            # See new_conv_layer() for details on the format\n",
    "            # of this 4-dim tensor.\n",
    "            img = w[:, :, input_channel, i]\n",
    "\n",
    "            # Plot image.\n",
    "            ax.imshow(img, vmin=w_min, vmax=w_max,\n",
    "                      interpolation='nearest', cmap='seismic')\n",
    "        \n",
    "        # Remove ticks from the plot.\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "    \n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Helper-function for plotting the output of a convolutional layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def plot_conv_layer(layer, image):\n",
    "    # Assume layer is a TensorFlow op that outputs a 4-dim tensor\n",
    "    # which is the output of a convolutional layer,\n",
    "    # e.g. layer_conv1 or layer_conv2.\n",
    "\n",
    "    # Create a feed-dict containing just one image.\n",
    "    # Note that we don't need to feed y_true because it is\n",
    "    # not used in this calculation.\n",
    "    feed_dict = {x: [image]}\n",
    "\n",
    "    # Calculate and retrieve the output values of the layer\n",
    "    # when inputting that image.\n",
    "    values = session.run(layer, feed_dict=feed_dict)\n",
    "\n",
    "    # Number of filters used in the conv. layer.\n",
    "    num_filters = values.shape[3]\n",
    "\n",
    "    # Number of grids to plot.\n",
    "    # Rounded-up, square-root of the number of filters.\n",
    "    num_grids = math.ceil(math.sqrt(num_filters))\n",
    "    \n",
    "    # Create figure with a grid of sub-plots.\n",
    "    fig, axes = plt.subplots(num_grids, num_grids)\n",
    "\n",
    "    # Plot the output images of all the filters.\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        # Only plot the images for valid filters.\n",
    "        if i<num_filters:\n",
    "            # Get the output image of using the i'th filter.\n",
    "            # See new_conv_layer() for details on the format\n",
    "            # of this 4-dim tensor.\n",
    "            img = values[0, :, :, i]\n",
    "\n",
    "            # Plot image.\n",
    "            ax.imshow(img, interpolation='nearest', cmap='binary')\n",
    "        \n",
    "        # Remove ticks from the plot.\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "    \n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Input Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Helper-function for plotting an image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def plot_image(image):\n",
    "    plt.imshow(image.reshape(img_shape),\n",
    "               interpolation='nearest',\n",
    "               cmap='binary')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Plot an image from the test-set which will be used as an example below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfiAowg8ERfiBoAg/EBThB4L6f6yMEem39pFEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2101cde400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image1 = data.test.images[0]\n",
    "plot_image(image1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Plot another example image from the test-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAD8CAYAAAC4nHJkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21019c4828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image2 = data.test.images[13]\n",
    "plot_image(image2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Convolution Layer 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now plot the filter-weights for the first convolutional layer.\n",
    "\n",
    "Note that positive weights are red and negative weights are blue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUoAAADuCAYAAABf005JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAEBRJREFUeJzt3X1wVfWdx/HvCTc3DxBIwkUS8nSWh0BFMGhGGFGGYdDt\ndlndoTCrU2qf0NUdbZniCN1l1FK3E2zrWIvSDnRZKXa0WG0XRrvoIk/t2CHBqF2NoBDCUySXECCE\nmATO/uHfzOd7Z0jPZXm//v7M73tz7r2fnDvzO+cEURQZAODScuJ+AQCQ7ShKABAoSgAQKEoAEChK\nABAoSgAQKEoAEChKABAoSgAQEpmEUyNHRmF1tQ6ePSsjFz75xDVzSGGhzFzs6ZGZNjNLR1HgGhqD\n1NChUVhSInMDR4/KzBDnzKCuTma6z+vV2ttbrasrnb3HNj8/CocN08HA8Sd4r2SrrJSRQ0d9X790\nuikdRdEo3+C/viAYGZlVyVxenv57r6vtc83se/99mUkm9LzWCxcsffGifOMzKsqwutoad+zQwe3b\nZeTMnXe6Zg6fPFlmuvfskZlZrmnxCUtKrPHb35a5zmXLZEb/a/lcvuO93Nk8XGbuu6/eOTEe4bBh\n1njHHTro+GLZwIBvaEODjNy34hrXUmvXBod8Q+NSZWb/I1PV1SNlpnFLm2tiW02Nnuc48ag/dco1\nj5/eACBQlAAgUJQAIFCUACBQlAAgUJQAIFCUACBktI/STp8227xZxv60aJHM3Hzrra6R6V27ZCYV\nhjKTc+yYa15ceo4etWbHHknPfza91flzP12j90h++qlex7kVLTanT56019avl7kveRZ7913XzDF1\neo/k9de7lvp/4+WXHSHnQWlxZDo7OmRGX6ryOc4oAUCgKAFAoCgBQKAoAUCgKAFAoCgBQKAoAUCg\nKAFAyGjDee/Bg7bPsZk86Vls3TrXzNcmTpSZqa2tMuPdWBqXvLobbeyORpk7MULfhbt040bXzGWj\ntupQc7OM7Mxtd82LS5H5btx8wJEZm5/vmnn8uL7XbiKhbz57JUgmE1ZRoW/KO7X1v2Smr6vLNTN0\nZC7nd54zSgAQKEoAEChKABAoSgAQKEoAEChKABAoSgAQKEoAEChKABAyujInf/hwq505Uwd//GOd\nKS52zbznq1/VoT/8QUYKs/x5BW1tZvffr3O/LiuTGc+jOMzMbs7N1aGKCp1x3HI/Tr3me3RA6Mgc\nG1brmul5Lx1PMDEzs+XLfbm4TKnqssYnX9HBB5fIyEXnzCOOzJxf/lJmCleudM3jjBIABIoSAASK\nEgAEihIABIoSAASKEgAEihIABIoSAISMNpx3jZpgryx+Tebe/k+91uOP+2a+/fUNMjMn9V290Asv\n+AbGJAjMXE8Z+OMfZSQcN841c2d/v8zM6u3VC130bhOOR2FRkdXX18tcy1tvyUyP86kXa/69U2Ye\nfbrUt1i2SyZdu+fPHD4sM8MXLnSNrN60SWZWdXxTZtoHnnPN44wSAASKEgAEihIABIoSAASKEgAE\nihIABIoSAASKEgAEihIAhCCKIn84CDrM7NDgvZxBVRNF0ai4X8SlcGwHzxV+bM04voPJdWwzKkoA\nuBrx0xsABIoSAASKEgAEihIABIoSAASKEgAEihIABIoSAISMnpmTKi6OwvJymesJhsqM9zErww5/\nqENlZTLS2tFh6TNnAt/Uv76SIIjGOHIF06bJTNM7bc6po2WiurpAZk6ebLXu7nTWHttUMhmFBfrv\nsMrKyzbzfw/oeXl5vrVOn25KZ/OVOaVBEFU5crmOzDHnzGJH5rwjkzazs1EkP7sZFWVYXm6Nzz8v\nc3sTN8lMd7dv5qyH9Vr28MMyUv+97/kGxmSMmb3kyF23e7fMBEMfck5dIhPLlk2RmVWr9IO74hQW\nFFjjzJk62NBw2WZ+4Z+mysyECb61Nm8OsvrywCoz+29H7hpHZqVz5jxH5gNH5jHnPH56A4BAUQKA\nQFECgEBRAoBAUQKAQFECgEBRAoCQ0T7KqKXFBqZPl7kbfvITvdjatb6hjg3u9p3v6Ew67ZsXk4Jx\n4+y6p56SuWDo7x2rLXDNfPddvUdy6ur7ZOY/erJ6m5990D/Bph55TeYajui1Fi3yzexs75OZIK/f\nt1iWO2tm2xy5Wkfm8bvv9g0t1lvOr12zRmae9k3jjBIAFIoSAASKEgAEihIABIoSAASKEgAEihIA\nBIoSAISMNpwHJSWWmDtX5lqXLpWZMD/fNXNdS4vMLJ40SS/U1eWaF5sRI8y++EVH8MsyMW3aZtfI\nqY/Pl5kTr74qMwOuafGZMMHs9dd1rqJCbwCfNs1zn26zA0eSMvPRRzpjZjZxoisWmxwz83yb67//\nfZm5uOJR38zlj8hM4apVep1nnvHNc6UA4CpGUQKAQFECgEBRAoBAUQKAQFECgEBRAoBAUQKAQFEC\ngJDRlTlWUWHW0CBjYXOzzGzcv9818i5P6NlndeaBB1zz4tK01yzIu+hI6tvb791yzDXzSIW+6qbS\ncQVV4rPPXPPikrv/Axvz99NkLgr11VsH3ml1zRzbcK/M7PU+DiXLFdfV2fwdO2TuC9OHy0zLY45L\nqMzMTH+fH3rob2TmcP9vXNM4owQAgaIEAIGiBACBogQAgaIEAIGiBACBogQAgaIEACGzDefHj5s9\n8YTOtbfLSK1z5HZH5kuz5+hQUZFzYjzq6gLbsUNv7v7d7yplpqsicM3U75JZqrdXZiLXtBj197s+\nk+scmcW5vkdBbHVsJu9xrZT9jp8YYj9c7dhM3rJdZoqK/s41c/x4nUmldCbhbEDOKAFAoCgBQKAo\nAUCgKAFAoCgBQKAoAUCgKAFAoCgBQKAoAUAIosh/XUUQBB1mdmjwXs6gqomiaFTcL+JSOLaD5wo/\ntmYc38HkOrYZFSUAXI346Q0AAkUJAAJFCQACRQkAAkUJAAJFCQACRQkAQkaPgkiVlkZhpX4UQZSb\nlJn9+30zRzm22Z47pzOnT7fa+fNp3zMSYpBKJqOwoEDmPjtzRmbynDMvODJDRoyQmdaeHkv39WXt\nsR0RBFGZI1dUWKhD5eWumUe6i2VmyBDXUnbsWFM6mzecjwyCqMqRSzg+395nMwycPSszF667UWaO\nHm21zk7dCxkVZVhZaY1btshcX1m1zMyb55v5rW/pTFOTzmzYUO8bGJOwoMAaZ86UuY9ff11mHI8T\nMTOzLkemeNYsmanfudM5MR5lZvYLR2725Mk6tHy5a+Yjb8+XmWHDXEvZY48FWX3VS5WZbXPkSi/X\ng27MrPOtt2Sm6/eNMnPnnb5e4Kc3AAgUJQAIFCUACBQlAAgUJQAIFCUACBQlAAgUJQAIGW04t/Z2\ns4YGGdsy9zmZeeMNzxZgsxUr/llmfvSj9x0rnXfNi01vr9mHH8rYm46lTjhH6mt8zMo2b5aZHue8\nuBQlEjbbsZF57549MpMYrzeSm5k9eYt+F4LRB1xrZbuEmenrkMxsxgydeeMN18zSe+/VmYf1e5V3\n5BPXPM4oAUCgKAFAoCgBQKAoAUCgKAFAoCgBQKAoAUCgKAFAyGzDeUmJ2YIFMlbvuJFx9MltrpFj\n5+rMhQtTZOammxy3oY9R/6Qp9ulWfUfm+599VC9WV+cbumSJjLQfPiwzub5p8bqgH3xxw+rVep0F\nta5xd0za50hl+UUQTj1mtteRK1u7Vmbec84sdqzleLCH+2IJzigBQKAoAUCgKAFAoCgBQKAoAUCg\nKAFAoCgBQKAoAUCgKAFAyOzKnJMnzTZulLHqd5bqtX7hexTExwfHyUzO4m/ohVpbXfPicuCA2cKF\nOrdr1z/KTFXVDa6ZM2boW+X/5q5HZCZ3wwbXvNgMDJh1dOhcruMao8pK18hJk3QmP7/GtdamTa5Y\nbAoLCqx+4kQdXLVKRvYlbnfNrHM8VeJNx3NT+r5b75rHGSUACBQlAAgUJQAIFCUACBQlAAgUJQAI\nFCUACBQlAAgZbTg/cfKkPbN+vczd5Fjr2PTprpnzw1Bmtjpe0xnXtPh0dw/Yrl0nHEn9l3R1+WZu\n2nRKZl568UmZOfXbbb6BcRk3zuxJ/XfYV76iMy+95Br5oONpHN5rILJ9w/nBgmvtnin6MSZ1f9Fr\nzZzpm+nZ0O85vitX+uZxRgkAAkUJAAJFCQACRQkAAkUJAAJFCQACRQkAAkUJAAJFCQBCEEWRPxwE\nHWZ2aPBezqCqiaJoVNwv4lI4toPnCj+2ZhzfweQ6thkVJQBcjfjpDQACRQkAAkUJAAJFCQACRQkA\nAkUJAAJFCQBCRo+CSOXkRGGOo1urq3Xm3Dnf0IoKGWl6J3AsdMiiKO0JxmLEiFQ0enQoc8OLHPte\nA+ef2damMwn9EWnt6rJ0T0/WHttUcXEUlpfrYGenzhQV+Ybm5clIW0eBa6mOjqZ0Nm84TyWTUZif\n7wimZKTv4EHXzGRhocz09/TIzGEz64wi+dnNqCjDnBxr9HxQfvADnWlq8g194gkZCYbmOhaa4ZsX\nk9GjQ1u9Wj935PbZfXoxR7mZmdmDD+qM48Ndv26db15MwvJya3z+eR3cuFFn5s51Dg1l5IE1U11L\n/fznQVZf9RLm51vjDMf362tfk5G2RYtcM6snT5aZ9j17ZOZvXdP46Q0AEkUJAAJFCQACRQkAAkUJ\nAAJFCQACRQkAQkb7KG3IELPiYp378591Zt4838wXX5SRjz76pszMn5+1+6HNzGz4+U/t9r88pYOp\n2Trz9NOumY2/+pXMVDrW6XdNi1Fnp+tzNPCzn8lMYvt218jn7n9PZl54wbVU1hsYW2udL26VubaR\n+jt4xjnziGOPpOOyF/PetpwzSgAQKEoAEChKABAoSgAQKEoAEChKABAoSgAQKEoAEDLacH6ur8/e\nbm2VuRnjx+vFHBtGzcze+4d/k5mpt1wjM/mnTrnmxaa722z3bhlrXrpUZv7kHOnZ8p90ZLJ7K7+Z\nnT9v1twsY4lbb9Vree7kbWYff6wz3nsAv/qqLxeXxL4PrHS2vglxqeOm3y+fPeuaueDuu3VoyRIZ\nSTpuJmzGGSUASBQlAAgUJQAIFCUACBQlAAgUJQAIFCUACBQlAAgUJQAIGV2ZkzSz0BNcsUJnFi92\nzRxzvb7uY59jnV7XtBh1dbkuwagLQ5kpdFw9ZWY23JE54cgMuKbFKJEwS6VkLL1p02UbWXyLznif\nhpLtV+YM1F5rndsaZa50Rq3MTHJembNt8a9lZs5ux6NVurtd8zijBACBogQAgaIEAIGiBACBogQA\ngaIEAIGiBACBogQAIaMN5zlm5roR/pYtMvJKepZr5vxevVW8bc0ambnomhafjuob7blletPuv9zV\nKTO1CefbWlMjI8W33SYz+W++6ZsXk9OnTtkWx2bymx1rlS5c6Jr5aO+/ysy28IeutbJdorfbSlv0\nA0g2rNCXhtxzywHf0MVzdGbdOp1Zv941jjNKABAoSgAQKEoAEChKABAoSgAQKEoAEChKABAoSgAQ\nKEoAEIIoivzhIOgws0OD93IGVU0URaPifhGXwrEdPFf4sTXj+A4m17HNqCgB4GrET28AEChKABAo\nSgAQKEoAEChKABAoSgAQKEoAEChKABAoSgAQ/g+YUoJEnkFtxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2101d870f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_weights(weights=weights_conv1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Applying each of these convolutional filters to the first input image gives the following output images, which are then used as input to the second convolutional layer. Note that these images are down-sampled to 14 x 14 pixels which is half the resolution of the original input image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUoAAADuCAYAAABf005JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYiAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f210889c2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_layer(layer=layer_conv1, image=image1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The following images are the results of applying the convolutional filters to the second image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUoAAADuCAYAAABf005JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnWmQVeXxxntEkG1kmWGHmWEH2WUVSAAFAgoIplQwwUii\nMQb94LeUxrKouKQiSSylClKJMQlRspVxAYEAIoQdZAAR2WHYl2FflM35f/iX7dOdufedudzLnXvm\n+X16Dn3mzsu5Z7pO9+m3O6ukpEQIIYTE5qZ0L4AQQio6dJSEEBKAjpIQQgLQURJCSAA6SkIICUBH\nSQghAegoCSEkAB0lIYQEoKMkhJAAN5fn5Nzc3JKCgoIULSW17N27V4qLi7PSvY5Y5ObmluTn56d7\nGQlRVFRU4a9tXl5eupeRMIWFhcUlJSUN0r2OWFSGe7dcjrKgoEDWrVuX+KrSSK9evdK9hLjk5+fL\nqlWr0r2MhOjXr1+6lxCXvLw8Wbp0abqXkTDZ2dlF6V5DPPLz82XlypXpXkZC3HHHHWU6r1yOMhnE\n21uelVVhH0oygsuXL5vj/fv3qz558qTqunXrmvPatm2b2oVVInbt2mWOq1Wrprp+/frGVqtWrRuy\npkzk2rVrqvE+xn8XsfdyvXr1jO2mm5KXWWSOkhBCAtBREkJIADpKQggJcENylBcuXFB96dIlYztx\n4oTqPXv2xDxv4MCBqn0uojKD12zevHnGdvbsWdVVq1ZVfe7cOXNez549VY8bN87Yrly5kpR1Zjq1\na9eOafvyyy9VHz582NiWL1+u2uckn3zyySStLvNZuHBhzGO8vj6/3r59e9Xf+c53jM3nhK8HPlES\nQkgAOkpCCAmQktDbl6msX79e9datW41t1qxZqhcvXqy6evXq5rzHHntM9Q9/+ENj6969e+KLzTBO\nnTpljlevXq36+PHjxnbrrbeqxus5Y8YMc97bb7+t2oc2mPKobMQLt/GextCwZcuW5rxnn31W9cWL\nF43tkUceKdPviipvvfWW6ueff97YbrnlFtWdO3dWjeVWIiJffPGF6lSm5PhESQghAegoCSEkAB0l\nIYQESEmOctu2beZ4yZIlqjGnJmLLT26//XbVPp9z4MAB1efPnzc2LCXC3EYU2b59uznG0qsmTZoY\nW3Z2tuoaNWqoHjRokDnvz3/+s+pf/epXxjZkyBDVUS8VqlmzZkzbxx9/bI5fe+011XfeeadqX/KD\ne4kXLVpkbHjdJ0+eXK61ZiL+3v3lL3+pGrcpiohMnDhRNeZ533//fXMe5iwxVywicvXqVdX4t5AI\nfKIkhJAAdJSEEBIgaaE3dvVYsWKFsR08eFC1353Qu3dv1RMmTFA9bdo0cx7u4PElRvhY3a1bt/Is\nOyPAkPerr74yNtwJ4sMLTEO0adNGNYY8IiJbtmxRjSkOb4t6l6F43WZ+8IMfmGMM0325GoL3Me6A\nEhGZOXOm6p/+9KfGFsVOWu+884453r17t+oOHToY21/+8hfVeP9jCklEZM2aNap9idUDDzyg2pcs\nlhc+URJCSAA6SkIICUBHSQghAZKWozx9+rTqvXv3GhvmB/wWLyyfwBzY/fffb8578803VfsylXg5\nvGR2OU4XWCqF5UAitiuQ7x6P842w9MrnvzCXM3fuXGPbuXOn6qjnKD043sB3sxo6dKjqAQMGqPal\na5g3q1OnjrFt3rxZNXZ6Ku3cTAVLdPwYGcyhv/rqqzE/A+9rP9IFy4V8eRDe1/5vo7w54Mz3IoQQ\nkmLoKAkhJEDSQu9jx46pxjBcRKRZs2aqfej9rW99q9TPa9SokTmuUqWKaiwVEhE5c+aMal8G4LsQ\nZSIYNuB1FrE7Grp06WJsOKIVUxA+ddG8eXPVPuTDa1vZePnll1UfPXrU2B5++GHVmP6IR+vWrc0x\nhqJRDb2x2xWWCYrY7la+6W6sXWBNmzY1x9g9yH8+cr3lVnyiJISQAHSUhBASIGmhN76Z9Q0t8LEX\nw0GR2KExPlKLiNx8c+yl4szqKIJpBx+SYKMB3+wiPz+/1M/zb2bx2jZu3NjY8Lvzb36j3oAE7ysf\nXt92223l/jw/8xt3qeF3HCWKi4tV+5ncfjcO4t9Sf43/HjAN55vCJBM+URJCSAA6SkIICUBHSQgh\nAZKWo8QGmj4HVlRUpNrntTDvhfkHX5aCpRW+dAJ3P0ShHMiDXYFwjrGIbZI8e/ZsYxs/frxqvM6+\nsTIOFMNBTiK2tCuKHW3igbvG/Lzu+fPnqx47dqxq3x0L8YPh+vfvr9oPdYsKmJds0aKFscXb6YVl\nfuhbcLeUiM3tpnLIIJ8oCSEkAB0lIYQESFrojY/VfocINsn47LPPjA1LWBo2bKjal/zgTh1fftSn\nT5/yLzhDwQYMIjbc8A2N//jHP6rGsh+/cwptOTk5xobfq5+pHHVeeeUV1Zs2bTI2nP2E34lvHvvC\nCy+oxvtbxDaqjjevJ5PB+6dv377GhmH5jh07jA3TTdhM2jf0xtI2P6/oepv1InyiJISQAHSUhBAS\ngI6SEEICJC1HieUNDz30kLG98cYbqg8dOmRs//73v1Vj+YRv/pubm6vaN+/0ebUo4xsTT506VfWU\nKVOMbfny5apHjx6t2pcYYRcbvM4i/9vFKcr4bbM4yArneIuIFBYWlnqe7wKEpXFYaiViy4OiCpby\n3XPPPca2dOlS1Xg9RUQ6duyo+sMPP4z5+fiZvjMZc5SEEHIDoaMkhJAASQu9EZwhLSIyceJE1Rs2\nbDA27P6BnUb8DgcsAcKdEJUN31WlXbt2qrHRrIidm4xlFPjvInbHjd+ZE8WdTrHw3W2wDM2nK/AY\ny7J8CNm1a1fVPXr0MDZsXFsZwHBaxM648c24MQ2CJXD9+vUz5w0fPly1//6SCZ8oCSEkAB0lIYQE\noKMkhJAAKclR+lnanTp1Uu1LJDA3gfk3363Ybw0j/w9eM9+dBcumMP/bqlUrc179+vVVR71reXnA\nUizfEQvBPC7mjEVsKRuWEVVGvF/AWfPnzp0zNjyO1xUomSVA8eATJSGEBKCjJISQAFmxhviUenJW\n1nERKQqeWDHJLykpaZDuRcSC1zZ1ZPi1FeH1TSVlurblcpSEEFIZYehNCCEB6CgJISQAHSUhhASg\noySEkAB0lIQQEoCOkhBCAtBREkJIADpKQggJUK6mGLm5uSUFBQUpWkpq2bt3rxQXF2eFz0wP9evX\nL2nevHm6l5EQBw4ckJMnT1bYa5uTk1PiG4ZkEhs3biyuyDtzcnJySvLy8tK9jITYt2+fnDhxInjv\nlstRFhQUmEFUmYQfSFbRaN68ucyZMyfdy0gIPzSqotGiRQv56KOP0r2MhMnJyanQ2wPz8vJkyZIl\n6V5GQgwaNKhM56WkzVp5wDZrW7ZsUe23VmJ7Nv90UK1atRStLrPwrcBWrlyp+uDBg6r9qI6BAwem\ndmERwF/b1atXq968ebNqHG8gInLHHXeo9u3CKtsoiEyGOUpCCAlAR0kIIQHoKAkhJMANyVHi6Ml/\n/vOfxvbee++pPnDggGpswy9i82pTpkwxNt9+vzLxn//8R/XUqVONDd+i16tXT7XPlWHO9+TJk8Z2\n4cKFpKwzE1mwYIHqN99809gwR4nX6MqVK+a8Bx98UDWO4xARue+++5KyziiQjHytHwvh88XXA58o\nCSEkAB0lIYQESEno7cPmmTNnqn722WeNDcNynLyIYbg/xnBTRCQ/P1911KcI7tixwxw/+uijMc99\n5ZVXVI8aNapMn48TGUUqV+i9fft2c7xx40bVvjxo6NChqvE7OH78uDkvOztbtZ/CePr0adV169ZN\nYMWZTbxwG9MeL7/8smr8WxcRee6551T7Sa04HfN64RMlIYQEoKMkhJAAdJSEEBIgJTlKn+t5/fXX\nVfsSCSztefLJJ1WvWbPGnIe5Ob9vd8SIEar99ryoMW3atJi2VatWmeO+ffte9+/D7aG+/CJqbNq0\nyRxjvvvxxx83tokTJ5bpM1esWKF6w4YNxnb06FHVFX2/fDKI9/5g9uzZ5hhzj3jdOnbsaM7D3hN3\n3323sVWtWlX1pUuXyrdYB58oCSEkAB0lIYQESEnovXDhQnO8a9cu1f4VPpYL4U6FHj16mPNmzJhR\n6ueJ2JApiqE3lugUFhYaG/5/faiNoTK2wfKtpbBTkw+PGjVqpHr//v3lWXZGcO7cOdW+XKVhw4aq\nx44da2x4zbDMB3dAiYhcu3ZNdefOnY0Ny4V8Sd1NN0XvGSZe6O1TbS1btlT93e9+V/XVq1fNeXiM\n34OI7UyGn5cI0fs2CCEkydBREkJIADpKQggJkLQcJeZi1q9fb2w5OTmqceuXiMiQIUNUY/6yVatW\n5jwsU/Fbwc6cOVPqOkREqlSpElx7RWfPnj2q/baseCMOMC+Zm5urGktW/PEzzzyT8DozkVOnTqnG\n+1REpFu3bmX6OcwF++5LmOM9duyYsWF+1Jev+Hs8CvjyMuwM7/OL7du3V/3YY4+p9lt4cQt06Pdd\nD3yiJISQAHSUhBASIGmhNz4CHz582Niw1OGuu+4ytlhdU7DriohtwomhtogNW3zj1CiE3ng9/WA1\n7LjkwRIWDL2PHDlizvMdbyoTWVnfTCr1oTeme7Zt22ZsWAaE5/kyn5tv/uZPzJfGYWiI50UVTDWI\n2PsTS7FERPr06aMa0xA+JYd+x6crsDzI+4w6deqUddkiwidKQggJQkdJCCEBkva8f/HiRdV+JnfN\nmjVV+9A4VnNNvzMBGwj4Cn8MmTCUigq4+8Dv/EB8aJOXl1fqeTjjW0Rk9+7dMT/z7Nmzqn3zWh9K\nZiKY4vH3LTaL9m9lMczD6+B3L+G96tMm2LTB7ziJChj++jf7eO39rqWePXuW+nl4zURsquOTTz4x\nNmyY0bZt2zKuuHT4REkIIQHoKAkhJAAdJSGEBEhJTULv3r3NMeYo165da2zYbBN3MfgmtJiL8M07\nu3btqjqKZRaYv4mVdxT535IqPMYORL75r8/7IMuXL1ftBztFASxP84PVdu7cqXrfvn3GhtezU6dO\nqn1nq9atW6v25UeYv/NlRVEoaxOxuV2/Yw9LgLp06WJsmNvFa+PLejDvuXLlSmPDvC/6oETgEyUh\nhASgoySEkABJi1MxbPG7b7BRhQ9h3njjDdUYQv/mN7+J+RljxowxtgYNGqiOSsiCYFmJ38EQDwwr\nceYLllqJiMyfPz/mZ2C5lQ/tow6G134H2axZs1TjHCjcpSMi8tJLL8X8fAwN46U/MhkMeX152eLF\ni1Xfe++9xlbW0rNDhw6p9ukLDOdr1apVps+LBZ8oCSEkAB0lIYQEoKMkhJAASctRYlmO346EHT78\nzG/sXINNaH0ZADby9A1Vy9sJJJPxjWHjdQ/CrYqY18WZySI2N+zzlVHYplhWfIcZvM+wdE1E5IMP\nPlCN23f9vO8OHTrE/PzKAN6feJ+JiHz++eeq586da2xYAnjbbbep/t3vfmfOKyoqUu1z6Fh+dL3w\niZIQQgLQURJCSICUbGPBJrEidkZ3QUGBseEOHNyp4Od6Dxw4UHXjxo2NLYolQbHwXWawPKJp06bG\n1q5du1K1Z9myZap9eUsUd+PEws9bwl0l/p6bPn16mT4Tvx/fnSiKc3Hi4VNmmIrAEFpEZM6cOap/\n/etfq/bXEH3NqFGjjM3vtLoe+ERJCCEB6CgJISQAHSUhhAS4Ia12ML/j85eYs6xsOZtkgHk13127\nLD8jYq97ZcpJhsAtcb4sKxY4BM9T2e9v39lr8ODBqjGXK2JLh3ArYrNmzcx52Ln81ltvTcYyS4VP\nlIQQEoCOkhBCAmT51+1xT87KOi4iRcETKyb5JSUlDcKnpQde29SR4ddWhNc3lZTp2pbLURJCSGWE\noTchhASgoySEkAB0lIQQEoCOkhBCAtBREkJIADpKQggJQEdJCCEByrXXOycnpwRHp2YS+/fvlxMn\nTmSFz0wPOTk5JXl5eeleRkLs27evwl/bTL1vRUQ2btxYXJELznNzc0sytUdAUVGRFBcXB+/dcjnK\nFi1ayIIFCxJfVRoZNmxYupcQl7y8PFmyZEm6l5EQgwYNSvcS4tKiRQtZtGhRupeRMLm5uRV610t+\nfr6sWLEi3ctIiP79+5fpPIbehBAS4Ia0WUPOnz9vjtetW6d65cqVqnEshIidxDZ06FBjy9SQlUQP\nnHwpInLTTd88i/gWg1WrVr0hayLXD58oCSEkAB0lIYQEoKMkhJAANyRHuXr1atUzZ840to8//lj1\n8ePHVfv2b927d1d94cIFY3vqqaeSscyMJxWt8M+ePZv0z8xEcnJyYtpw7GpxcbGx4dvg5s2bG9vo\n0aOTtLrM55ZbbolpO3DggOpLly4ZG45oxnxwsuETJSGEBKCjJISQACkJvffs2WOOd+7cqbpatWrG\n9u1vf1v1xIkTVW/fvt2ct379etVFRbb+Fie2dezYMYEVZy61atWKaVuzZo3qJ554IuZ5CxcuVF2v\nXr3kLCwCxAu3sQwIJwP69MeLL76oumfPnsY2cuRI1X5CYWUAw+21a9ca2/PPP6967ty5qrOzs815\nuJHkkUceMbbhw4cnY5kiwidKQggJQkdJCCEB6CgJISRAShIjhw4dMsd16tRR/aMf/cjYBg8eXKbP\nfO+991T/9a9/NTZsJlHZcpTXrl1TvXHjRmN79913VX/66aeqr1y5Ys7D/OXf/vY3Y8OcW9RLhWrW\nrBnThjlyEZGpU6eq7t27t+qnn37anIdbb3G7rojInDlzVN97773lW2wG4st3Nm/erPq3v/2tse3a\ntUs1llX5nDyWEfp3F1988YXqGjVqJLDib+ATJSGEBKCjJISQAEkLvbErUKNGjYwNw2GspBcROXr0\naKmf5z8DQxNfSoCP2D6sjFqHliNHjpjjy5cvq/admVq1aqX673//u+p//OMf5jzc7YC7qERE+vbt\nm/hiM4x44dn48eNjnhvvGr3zzjuq27RpY2y///3vVVeG0Nunbj766CPVp0+fNja8d3/84x+rxt17\nIiKffPJJTBv2zh0zZkwCK/4GPlESQkgAOkpCCAlAR0kIIQGSlqPEjj6+zKJ9+/aq9+7da2y4pRHz\niT7f0KDBN7OV/JyL//73v6p9ni4KW/IwD3ny5Eljq1KliuqrV68aG3Z+v/3221VjuZaILdOIWk73\nepg9e7Zqn/vGkpURI0ao9p35cehW7dq1jQ1LtnxHrHhbUzMJLN/x3d9xq7O/77DkCmcy4fsIEZHq\n1aur9u87Tp06pfqrr74ytvJ2GuITJSGEBKCjJISQAEkLvbH7iZ+hjKHd1q1bjW3AgAHl/l3169c3\nx/j47Rv+RgEM5zCcEBEpKChQ7TszIWjzJVrHjh1TjaF8ZQc7//iyrOeee061HxoWC0xBidjuTtj8\nVyQ6oTemjfw1xO5BTZo0MTbcsYfXwt+f6Av27dtnbPh340uT6tatG1q6gU+UhBASgI6SEEICJC30\nxrfLvnkpvonG2d0iIi1btlSNb2l9KILhtZ/jjQ1R/ZvFKIAhhH9ziG9Vu3XrZmw+TP8aH77g8blz\n54wNGzng7xKJP+ckCmCFhn9r6mfLl4WsrCxzjH8n/q16VMC31JjiEbHpurZt2xqbv5e/xr+txqYw\nvlIG72s/a6e88ImSEEIC0FESQkgAOkpCCAmQtBwl5g58LgZf4ftcAebAsMwCG3eK2Nf5vgSoQ4cO\nqqM+pGnZsmXmePfu3apfeuklY8O8LubAfJcczA/53T0+V1yZ6Ny5s2ocwCYiUlhYqHrgwIGq43Ug\nwu9DxF73VMxkrwjEKw/Cv1W/MydWmZ+/vuh3fHkc7kDzQ8nKC58oCSEkAB0lIYQEuCFxart27VT7\nWck4/2bWrFmqfQlQ69atVfsSAXzkLu9m90wAy3J8QwvcjYBNHERERo0apRpDFh/mYWrEl29hA+Wo\nlwN5cI4LzpwXEXn//fdV9+vXT7VvCDNt2jTVvgTogQceUB2F5i2l0bBhQ9V+x96GDRtU+1AbU0rd\nu3ePed727dtV+/u6a9euquPNQyoL0fMqhBCSZOgoCSEkAB0lIYQESEmO0jeXxRzY5MmTjQ23Ne3c\nuVP1uHHjzHk4mMnPDY96SRCWNkyYMMHYMAfmZ3Jj/gaHY7311lvmvIsXL6rGUisRO5c66vgBV1ge\nhAOuROy2UsyNxcuh+fz8kCFDEl9sBnLnnXea4x07dqjGJr7+GEvWMHcpYnPv+H2JiPTq1SvxxTr4\nREkIIQHoKAkhJEBKYlbs6CFiwxQsFxARmT59epk+Extv+lDb7wSKMj40njRpkmofei9ZskQ1Non1\nZSpYfoTdnESi00C2LPj7FrsvPfHEE8aGZVk4WxrDSRFbUjV27Fhj89c66vjyne9973uqfWmbny//\nNTjvW8TOLvKhvU91XA98oiSEkAB0lIQQEoCOkhBCAtyQuhrsDu27ECdCZcpJevwWTSyB8MOrsEM3\ndpr2g5WaNWumujLlJEPgfevndeN1ws43fnBbjx49VGM+jdhr5cuvhg0bphpzxb5DEF5Tv703mfCJ\nkhBCAtBREkJIgKzyzMHOyso6LiJFqVtOSskvKSlpkO5FxILXNnVk+LUV4fVNJWW6tuVylIQQUhlh\n6E0IIQHoKAkhJAAdJSGEBKCjJISQAHSUhBASgI6SEEIC0FESQkiAcu31zs3NLcHehZlEUVGRFBcX\nV9hN4rm5uSUFBQXpXkZC7N27t8JfWz/+OJMoLCwsrsgF55Xh3i2Xo8zPz4/ZULOi07dv33QvIS4F\nBQWydu3adC8jIXr37p3uJcQlLy9Pli5dmu5lJEx2dnaF3vVSUFAg69atS/cyEqKsc3Vu+FQu7GIj\nInLw4EHVOESodu3a5rzq1aur9h1EfOfkyorv0I1d4bGrOQ57E4n+cLZU8/nnn5eqRWxHGz+orUmT\nJqldWAaDvgDva+wYfyNhjpIQQgLQURJCSAA6SkIICXBDklP4kmLLli3GNm/ePNU7d+5U7XOZ2PHY\nv5gZP358UtaZiWCOF6cBiohJsONLOMz/iIiMGzdO9UMPPWRsPp9ZWfGd5efMmaMav4Pt27eb8zZv\n3qx65MiRxvbUU08lc4kZx+XLl1X7v3fszr9161bVFy5cMOdhnnfEiBHGlsxJCHyiJISQAHSUhBAS\nICWhNw6HF7Eh4bFjx4ytSpUqqjds2KAay4FERJYtW6bah0E4+Lxhw4YJrDhzOHfunDn+4IMPVL/+\n+usxz8XrjiGPZ+DAgea4MofeWKKG4Z+IyI4dO1S3bNlStS/RmjVrlmpfhnXPPfeobtWq1fUtNgPA\nYW0iIsXFxaqnTJlibO+++67qM2fOqPZ/+zhQzw9v69KlS+KLdfCJkhBCAtBREkJIADpKQggJkJIc\nZbx9tfXq1TPHmAP7xS9+oXr37t3mPCyzOHnypLFhqcakSZPKt9gMY+XKleZ4xowZqs+fP29szZo1\nU40D5jH/IyLy5Zdfql6wYIGxtWjRQnVOTk4CK84c4m2Pe+2118wxlq90795d9YQJE8x5//rXv1QX\nFdkt23PnzlU9efLkcq01E/H59RdeeEH1H/7wB2PDfGaHDh1Unz592pyHeU5/X+PPVa1aNYEVfwOf\nKAkhJAAdJSGEBEha6I1hny+RwB6W06ZNMzYM5x599NFS/11E5Gc/+5nqmjVrGhuGow8//LCxYflR\npoI7aZYsWWJsmIZo0MC2LPz5z3+uevjw4ap9+mP+/PmqMZQRETly5IjqqIfePjz79NNPVWMZlohI\n27ZtVWOI5/nTn/6keujQocaGO6f8bqkodnRas2aNOcaUme8WNnbsWNVPP/20ah9eL1q0SPXixYuN\nDXecde7cOYEVfwOfKAkhJAAdJSGEBKCjJISQAElLhBw+fFh1bm6usWEXD7+NCbcgYd7H5xYxv/PZ\nZ58ZG5Zq4HYnkWhswTtx4oTqTZs2GVvdunVV33XXXcaGXZYw/9avXz9zHubf8HsUsZ3R/Xfnt5NF\njVdffVU1XgcRke9///uqMTfsS7Tw/vZ5SOyW5UtnfB45U8H/s+9uhR2D/Eyjn/zkJ6o7deqk2r//\nwK2lBw4cMDbMrzNHSQghKYaOkhBCAiQt9MbdHSUlJca2YsWKmD83evRo1TVq1FCN4aa3+fIg7Ibj\nG3tGIfTGzj/Hjx83NkxzjBkzxtgwJMTvBAdeiYhcunRJ9alTp4wtXtlX1EPvt99+W7UfBIZpjrI2\niPU7f/Da+rA8KuD95AevYXrhwQcfNDYMtzFt5DsExRssiKH39aaNon2nE0JIEqCjJISQAEkLvfHx\n2L/5w43sviGvf9v1NYcOHTLHuCvEh58YEkZhJ44n3myRRo0aqfY7Z3wK5Gv894Nz0mvVqmVsyZw7\nkmlgOileuqKs+PC6cePGqn06KSrgveb/bvH/3Lt3b2Pz9+HX+BAavyMfTqONoTchhKQYOkpCCAlA\nR0kIIQGSlqPEDirbtm0zNswVNG3a1NiwGw52uPGNewsKClT7PB3+7qjsaECys7NV+w5BWA7ldz5g\n/hfLq/zuHsxztmnTxtjatWun+nqbn2YaAwYMUO3LzlatWqX6vvvuU+1z8NghyA++u/vuu2P+XFTA\nPDnuIhMROXr0qOrCwkJjQ1+A+J138cqqWrdurfp6S9n4REkIIQHoKAkhJEBKuoMOGzbMHGNDTT/z\nYvXq1apxN44vv8A5yhgqithdEhhiRgUMof0OBpyF7nc+YKiH1wx/RsResx49ehhbFHY2JQo2mX7x\nxReNbf369aoxHeJDRmye7Hfm3H///aqjWNYmYnfO9OrVy9gwneHTQZhG6tixo2o/MwpLA7EJjIhI\nt27dVDP0JoSQFENHSQghAegoCSEkQEpylL6jBw4Nmz59urFh+cTFixdVd+3a1ZyHuU1fZtGzZ8/E\nF5sBYOnIyJEjjQ23ZvnBYAsXLlSN5Va+QxCWXvnrHtXcWWn4sjOc1/34448b2759+1TjYCxfGofl\nVpgzE/nfUrkogt2t/HA13Jrry36w/GrevHmqP/zwQ3Me/m1MmjTJ2GJtg0wEPlESQkgAOkpCCAmQ\nktDbV8sEfemOAAABUElEQVRjyPHMM88Y2/79+1XjY7TvfINV/K1atTK2KO7GiUWzZs3MMYbie/bs\nMTacIYLX1qcusOTI22J1IIoivjExpoJ82IgzdLCsDWe4iIgMHjxYdZ8+fYytMqU1RP53bg2mOtau\nXWts2D0M0xy+i9P48eNV9+/f39h8Odb1wCdKQggJQEdJCCEB6CgJISRASnKUHsz9+BwbHmOJgO/C\njTO/sRyjsuE7juP2RuyYLWLzaGUtlahMOckQWHrl70cEc40+D4cdlypbTtLjtxFizhY7/YjYwWD4\nzsNvqcWtucnMSXr4REkIIQHoKAkhJEBWeUKtrKys4yJSlLrlpJT8kpKSBuHT0gOvberI8Gsrwuub\nSsp0bcvlKAkhpDLC0JsQQgLQURJCSAA6SkIICUBHSQghAegoCSEkAB0lIYQEoKMkhJAAdJSEEBKA\njpIQQgL8Hy5wLN51ZjFMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f210b123438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_layer(layer=layer_conv1, image=image2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "It is difficult to see from these images what the purpose of the convolutional filters might be. It appears that they have merely created several variations of the input image, as if light was shining from different angles and casting shadows in the image."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Convolution Layer 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now plot the filter-weights for the second convolutional layer.\n",
    "\n",
    "There are 16 output channels from the first conv-layer, which means there are 16 input channels to the second conv-layer. The second conv-layer has a set of filter-weights for each of its input channels. We start by plotting the filter-weigths for the first channel.\n",
    "\n",
    "Note again that positive weights are red and negative weights are blue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVMAAADuCAYAAACEaORrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzRQAwACaKQCAATRTAAADaKYAAAb+D6eqlk+kAJCNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2101c042b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_weights(weights=weights_conv2, input_channel=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "There are 16 input channels to the second convolutional layer, so we can make another 15 plots of filter-weights like this. We just make one more with the filter-weights for the second channel. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVMAAADuCAYAAACEaORrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKQCAAWimAAAGoJkCABiAZgoAYMD/AM6smdqQb8ZdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2101cbfeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_weights(weights=weights_conv2, input_channel=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "It can be difficult to understand and keep track of how these filters are applied because of the high dimensionality.\n",
    "\n",
    "Applying these convolutional filters to the images that were ouput from the first conv-layer gives the following images.\n",
    "\n",
    "Note that these are down-sampled yet again to 7 x 7 pixels which is half the resolution of the images from the first conv-layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVMAAADuCAYAAACEaORrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsQtzBuwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21014f8160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_layer(layer=layer_conv2, image=image1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "And these are the results of applying the filter-weights to the second image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVMAAADuCAYAAACEaORrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f21019781d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_conv_layer(layer=layer_conv2, image=image2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "From these images, it looks like the second convolutional layer might detect lines and patterns in the input images, which are less sensitive to local variations in the original input images.\n",
    "\n",
    "These images are then flattened and input to the fully-connected layer, but that is not shown here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Close TensorFlow Session"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We are now done using TensorFlow, so we close the session to release its resources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# This has been commented out in case you want to modify and experiment\n",
    "# with the Notebook without having to restart it.\n",
    "# session.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "We have seen that a Convolutional Neural Network works much better at recognizing hand-written digits than the simple linear model in Tutorial #01. The Convolutional Network gets a classification accuracy of about 99%, or even more if you make some adjustments, compared to only 91% for the simple linear model.\n",
    "\n",
    "However, the Convolutional Network is also much more complicated to implement, and it is not obvious from looking at the filter-weights why it works and why it sometimes fails.\n",
    "\n",
    "So we would like an easier way to program Convolutional Neural Networks and we would also like a better way of visualizing their inner workings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Exercises\n",
    "\n",
    "These are a few suggestions for exercises that may help improve your skills with TensorFlow. It is important to get hands-on experience with TensorFlow in order to learn how to use it properly.\n",
    "\n",
    "You may want to backup this Notebook before making any changes.\n",
    "\n",
    "* Do you get the exact same results if you run the Notebook multiple times without changing any parameters? What are the sources of randomness?\n",
    "* Run another 10,000 optimization iterations. Are the results better?\n",
    "* Change the learning-rate for the optimizer.\n",
    "* Change the configuration of the layers, such as the number of convolutional filters, the size of those filters, the number of neurons in the fully-connected layer, etc.\n",
    "* Add a so-called drop-out layer after the fully-connected layer. Note that the drop-out probability should be zero when calculating the classification accuracy, so you will need a placeholder variable for this probability.\n",
    "* Change the order of ReLU and max-pooling in the convolutional layer. Does it calculate the same thing? What is the fastest way of computing it? How many calculations are saved? Does it also work for Sigmoid-functions and average-pooling?\n",
    "* Add one or more convolutional and fully-connected layers. Does it help performance?\n",
    "* What is the smallest possible configuration that still gives good results?\n",
    "* Try using ReLU in the last fully-connected layer. Does the performance change? Why?\n",
    "* Try not using pooling in the convolutional layers. Does it change the classification accuracy and training time?\n",
    "* Try using a 2x2 stride in the convolution instead of max-pooling? What is the difference?\n",
    "* Remake the program yourself without looking too much at this source-code.\n",
    "* Explain to a friend how the program works."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## License (MIT)\n",
    "\n",
    "Copyright (c) 2016 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "\n",
    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}