Deep+Neural+Network+-+Application+v3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep Neural Network for Image Classification: Application\n",
    "\n",
    "When you finish this, you will have finished the last programming assignment of Week 4, and also the last programming assignment of this course! \n",
    "\n",
    "You will use use the functions you'd implemented in the previous assignment to build a deep network, and apply it to cat vs non-cat classification. Hopefully, you will see an improvement in accuracy relative to your previous logistic regression implementation.  \n",
    "\n",
    "**After this assignment you will be able to:**\n",
    "- Build and apply a deep neural network to supervised learning. \n",
    "\n",
    "Let's get started!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first import all the packages that you will need during this assignment. \n",
    "- [numpy](www.numpy.org) is the fundamental package for scientific computing with Python.\n",
    "- [matplotlib](http://matplotlib.org) is a library to plot graphs in Python.\n",
    "- [h5py](http://www.h5py.org) is a common package to interact with a dataset that is stored on an H5 file.\n",
    "- [PIL](http://www.pythonware.com/products/pil/) and [scipy](https://www.scipy.org/) are used here to test your model with your own picture at the end.\n",
    "- dnn_app_utils provides the functions implemented in the \"Building your Deep Neural Network: Step by Step\" assignment to this notebook.\n",
    "- np.random.seed(1) is used to keep all the random function calls consistent. It will help us grade your work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import time\n",
    "import numpy as np\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy\n",
    "from PIL import Image\n",
    "from scipy import ndimage\n",
    "from dnn_app_utils_v2 import *\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (5.0, 4.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Dataset\n",
    "\n",
    "You will use the same \"Cat vs non-Cat\" dataset as in \"Logistic Regression as a Neural Network\" (Assignment 2). The model you had built had 70% test accuracy on classifying cats vs non-cats images. Hopefully, your new model will perform a better!\n",
    "\n",
    "**Problem Statement**: You are given a dataset (\"data.h5\") containing:\n",
    "    - a training set of m_train images labelled as cat (1) or non-cat (0)\n",
    "    - a test set of m_test images labelled as cat and non-cat\n",
    "    - each image is of shape (num_px, num_px, 3) where 3 is for the 3 channels (RGB).\n",
    "\n",
    "Let's get more familiar with the dataset. Load the data by running the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_x_orig, train_y, test_x_orig, test_y, classes = load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code will show you an image in the dataset. Feel free to change the index and re-run the cell multiple times to see other images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y = 1. It's a cat picture.\n"
     ]
    },
    {
     "data": {
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UEFsxTOrG90BPmivLZd/+eOGxx///8KM/haOYoy3M7dL4HnZn5bvXfmaMMWYCvx1hjDEN\ncrTfE35I46f3nao+De+UdPOhDvzctZ/V7NzBP8PbrbbOOfdcnfuSc49yE40x5uhx3ec+o+uv+2wl\nQ8l+yNixHkd1EU4p3RkRD0m6UNJ3JSkitkk6X9JHD3Xsv7rkEj3zmc98/DPtdy7s31uULWepaVqw\n/9SlF7dpX5eMAvQiP+wD5qmXKKIXRWyiCF60X017imTEyF80T2TygD1Lwd409RvupVP6IUqHk5Gg\nP2ivF80gFOkLKCKJUUoe2Pen9ElMvfRGFNFsmO3t8v5vvf3w4ZAisNU7Np/itAeKkdDA/NCGPdti\nr1dCA05eRtek+dyK8rtBGkcXIuPRHO9lJpHceCRJo4mi1735zXrdm9+sH93zwONld97+Q73nD99V\nHEdsehGOiC2SztLBsXpORJwt6dGU0r2SPiTpvRFxu6S7JF0p6T5JV2/2WsYY82TncJ6EXybpb3Qg\nN6f0Z2vlfyXp36aUPhgR85I+LmmHpK9KemNKqZQTjTHmBOdw3hP+O20g6KWUrpB0xeE1yRhjThz8\ndoQxxjTI9ERR669WXnJvw++HlZVSxMpfXO+AMECpZbogKrRIYEJxAwSDbPOexCoSI8DDgILK4kK5\nmzMzV5o6ihfSA8QvUF5ISAto3CqkGspFSakUlGhcAtqGQmhZCw0iVJYLYtRWEnNhymDENGoviT1s\nGBoc8rPExo8hpU8CEwb1G5WVE6KsRREAyYQxhHZgv0Fqq3Y2d9FIQeMOAm+3BamRYFxQtM+aNgPR\n3E7adlJRtjQxLsvQpvXwk7AxxjSIF2FjjGkQL8LGGNMgXoSNMaZBpkaYS6kaNGl1UE+QyEUyCq5J\nZeRea0cpnqADqEaKH3LVCV1kkEKIok6BSEFuqlx4as+W7ad0LeSkardITKsX1SuHBD0SPOqmWaL0\nV+jqyo7Fc9UQ9NYFo7nVu0buciOnHYt1ZV9SPRKHSSDMy6hOAgvrcKX8jqIIR2MF/VHMQRoX6Fu6\nz9w5uh4k/nU61WO7cK75XimKn3ryUx7//56Ht9W6vuQnYWOMaRQvwsYY0yBehI0xpkG8CBtjTINM\njTDXilYlvdDSSpmWh9xreYocShFDYgGl1mm1QGRBixH87soOJUGPRAUSgMi9hq6uQSnkdNrVsl5v\nrrwminyluEZCVxvCPq6AQJMLHiRMkYuOyggSY4YYprHabxguEgTfugIv3ReJTiScDfq5Y47CjpYi\n3GqN/pakVqfsow6Ec8znagvmKTnhaO6ysFoUSTCPcjj5FYU6pe9yvTCb1N5cIByAkN1S2be7Tjn5\n8f8/tn178fP18JOwMcY0iBdhY4xpEC/CxhjTIF6EjTGmQaZGmFteXtLi4sLjnylsJQsSqJxVSCSI\nQT1yYdUNp5eLLLThT3niKM8ah1sEBxCMXhkesTx/D9rRoVCL6CQjJ+DG7aB+JFGL4Lxo9Zx1eXhE\nyndGx6ETDvPf1XN1kfCZOx4pb14fBDEaUw4VSs7Ljd181N907y28JjzX1QxPmkfU5O8tjTGUwQXI\n+UZ9lM9dEggFIv6OLVse//9WCDO7Hn4SNsaYBvEibIwxDeJF2BhjGsSLsDHGNMjUCHODQV/9/kFn\nCokPLXDZ5CJCbeEFt9tBfKBjKY/dalXcIKcTiWttcDXRvaNIAU3LwxCurJYCJ4UEnUExsOzvLtz7\nCok9Q5QzqtQMA0njwo42cD9lx2K4y5pN47lF7QBXHghsxX2hwAluvpqhIckZiX2UuUxJIOyA65Sc\nqCSU03cIRb3s+03hKEmApPMnuAdyLQrGqhNVV2EECcjQtxMDuPHrAoc6kzHGmCcML8LGGNMgXoSN\nMaZBvAgbY0yDTI0wl1KqiCboEIPfGYUwB2HtggQVOD+JNiTGUA6u7mzVhUah/yhEZSTa4CdqCF0q\nXX/UVupbDp9ZL1wkiSVFzj1yTVH4RXR0UY+A2EPuRhT/8vOD8EIuPRJH8ZrkfCsF0ohqv5EIR+Ad\n1XSlDfsgTmVQKEs6P4WWbbUgVGZNUS+fR1HDDSutI7GD4DakkJfkBCzyEtabV5P9kUYb9/Pj16td\n0xhjzFHHi7AxxjSIF2FjjGmQqdkT7q+uVCKnYQAo2H/L09zUiaq23rnqvmJN+0F56pQ8epe0zp4f\n7lnWi9bVpVQ12QvvtJ9Fphfcg4em0Yvx3W7ZjsI8gIYZuGTNiGkpD7klHtMyvdHGhg5JalFKLNjr\npT3h4aDcD6S92M5M9RojMmZgv0HUMGhvnf1wqew33sOlOVMvYhqlMsL7yspoXlF/CzWOshqlQaI+\nGmTj0CYtitKNTcx52n9eDz8JG2NMg3gRNsaYBvEibIwxDeJF2BhjGmR6hLlBX6sTkcdgLx/T/rR7\n1c17SiGEhgVI08PaEW3mw8v9mRDFwl9J3UhXmB6IDBbZ79VcuJQ4StZwUJZRv2EKITKvZKIKyRR1\nz1+3P+gq5TXqCaGUpUfQRxGleERR6uiquRCHkQOpIaCh1u03jEJWCHN4gVplJGhi22qIdZRurF1X\nrAPQYAGCdz4OJOh1YZ2ZjDBI6cLWw0/CxhjTIF6EjTGmQTa1CEfEeyLipojYGxG7I+J/RMTzod4H\nIuKBiFiMiC9ExFlHr8nGGPPkYbNPwq+S9J8knS/ptZK6kj4fEXMHKkTEuyVdJukdkl4uaUHS9RFR\nbugaY8wJzqaEuZTSmyY/R8TvSPqxpJdK+tpa8eWSrkwpXbtW51JJuyW9VdJV6527Fa2KQEDiETmF\n8s18TrlS0+1DV0TBY+P0MiQqcNSwumWQ6qXWvYKQ1oJhJx0B1CQUe9qlWKKUiyz1hD8Sjtg1Vo/8\nGhj5i65Jcw1ceiR85o4raZ35kAurpH3BXOhQGdwDlbEbbmMRiZyX5KLDoRpC9EC4ZCtL9YViJqVx\noghvFB2tpgieXwPNpDSeE/1YVyyUjnxPeIfGffWoJEXEmZJ2SfrSgQoppb2SbpR0wRFeyxhjnnQc\n9iIc418hH5L0tZTS99eKd2m8KO/Oqu9e+5kxxpgJjuQ94Y9JepGkf3GU2mKMMScch7UIR8RHJL1J\n0qtSSg9O/OghjXe2dqr6NLxT0s2HOuff/e1X1OsdfAE6KekFz3+eXvAzxcsXxhgzNXz66qt1zTVX\na3JTf9++vbWP3/QivLYA/7KkV6eU7pn8WUrpzoh4SNKFkr67Vn+bxm9TfPRQ5/3FC39RO3c+bfJk\nRZ08XOT4/Nlmfs1QiPVdWASFPszbAa46cmbVbAc5p+pEKqQwjVhEriBwJ5GIMyCxKxNtRhDesY7T\nbjPUGWcMDIm6H50LHJsdciSWZW0QlHIxN5GABS3rdEmQpTRW9H3ZeB5hyNja7saiqLYrVLlOSeEi\nQfQcwNyanS3Hqm6KrSJlGobFPNiOiy6+SBddfJFaE6Flb/3e9/SWN7+xPBDY1CIcER+T9BuS3iJp\nISJ2rv1oT0rpQDDgD0l6b0TcLukuSVdKuk/S1Zu5ljHGnAhs9kn4nRo/R/1tVv67kj4hSSmlD0bE\nvKSPa/z2xFclvTGltCpjjDEVNvuecK2/11NKV0i64jDaY4wxJxSOHWGMMQ0yNaEsQ1URgkS4DuRU\nm5mpbsCjKwauRzng6rpc6gl49XJMFbnYtE7IvQR5y0CkWF1eqXxeWNhfXnS4XBTNb5krymbmthRl\nvbmtRdnc7HxRFplg1WqVof9mumUZubwIcrRhaMVM3BnVdCiSMyuNSDDd+JpSKdxKpVhJc6ENc74L\n/UbutdphWLO+xPRoUEhjgM7OTr0wrHkI0IA/vCm3YB0n7TrVMOxoPh9wrm1YVt/l6SdhY4xpEC/C\nxhjTIF6EjTGmQbwIG2NMg0yNMDcaDjSccF51Zw4v/HDd7XDMeVbT2VMntCI798p6LRBeEuUoI9sO\nOIUeffgnlc933HZrUWfXU8thH5y8rShbhUs+9NPFouzMZz+vKHvec3+u8rkN4S5JTKKwgUR9YS4X\nWUpINAvIJ5dqir7kSovYWIAl9113pp5rkQQxEm6pHbmzrkODQJMXxLUEIUuHIGh2IEdbKRCCsIhi\nI+WOo1C48F2r4bCl8RSIr5MOP8xRuA5+EjbGmAbxImyMMQ3iRdgYYxrEi7AxxjTI1Ahz0WpXxAXK\ng0bOpkLcqCla0Ib86soStKyshznVis18qIIhKmuG1ASRbAi7/wv7q8LZcLhS1OnOle44cuQtrZQx\nl75/2+1lO0Zlv520peqie8YzX1jUqe2Ow0eFeo7EyOqhqFo3/CnIcDSPKOwopCUsyjpdCL8Ic6G/\nWo7LwuK+8gIgTnWhz3NhbnbLSUWd+S2lU7LbKcU1cgvWDVmaz4dhzXxyxAgcfkMQW9sQFnSYhRSN\nTj234ORUqBNm9gB+EjbGmAbxImyMMQ3iRdgYYxpkavaEO52uuhN7YrxnRC9bV/dvKAJUh/ZYMbtK\neXAf9omXlxY2bEeb0s1gFLjZoqxLe4Pw+3I0gvQvKd8DpYhsUDboF2ULi2W0tYUVekG/PPbhB2+r\nfD5p6ylFnaeeenpRxnuxpAVAO+q8yA/7tRTRi6Ko0f49vuxflIj3nbN52YW9U9rrve227xRlt999\nR1H2tG1lFLxTd5T7vf2l6l5pX2Wd577gxUXZKU87tSib6ZXzuQMmlNWVcm7lERHJsDUc1ot4N4SU\nWwM4low6SdX+oMh7NGfak0PsPWFjjDk+8CJsjDEN4kXYGGMaxIuwMcY0yNQIc0n5XnYpb/DL/dWj\nhsNSJBpCtK5cBJCkDmzSt9vlNbuw6Z8LRZxeBYwZIBS1AqJ6kdGjXe7+z/Sq90XpW4b9sv19uGYf\nAlG1uyC8gAjZX60KLw/e+89Fne3bS2EHDQskLmIOno2h/k6iVEYg1tU0etQVDfM5vrpSGmvu+OHN\nRdnDj9xdlN1174NF2dyzy/49ZVvZv+2Z6hx/4J77ijrz86WwOj9fprWi7wtFViNysYuiymlQ9uMK\n9HdEKUpS2wYgSOff3RasHyOIUNduT7aj/vz0k7AxxjSIF2FjjGkQL8LGGNMgXoSNMaZBpkaYi6hu\niFM0Jo6glG2ig0hEqU7IZYMR2Cj1TZ1fXaletCeMxgSHkgOPgrlt276j8nmUysYOIApXtMqTUXqc\nnkpRr4djVW3vww/dX9Q5dddDZdnOZxRlJHLQ+BH5PKK5UFdCGfQ3FnEkFlGHA3J1Vdvy4IN3FXUW\n9z9SlCUSQqOMaLawWrZ3dbUcv3bWR0OKqLdcOtwGMD/6g3JuzXbLqH1E3h/LcM35LaUYOAChvN8v\n20FO1NGwHJc6qdVozjiKmjHGHId4ETbGmAbxImyMMQ3iRdgYYxpkaoS5dqujTsXRQmJP+Tuj3c7r\nwS2B44pSCKGb6jDdWijYoK5IYRrLahQ6j+SBLSdV09DMzpVhCffv21OUPaVX9luvUzb4Wc84ubzm\nHIglmd1u308fK+rc+aPbirLtT9lZlFEoRIL6vOxLcriV56IxJgGIwmySk3EVnFmLC/srn++964dF\nnd4cOAiXy/CqASJZi0KWwj0Mh5kwB2JVp1cKt4HuuKJIqyAE9zCEa/V8dC5KUUSCG9UbDMp7pxcA\nchddUHqmDUKikgtzPfwkbIwxDeJF2BhjGsSLsDHGNIgXYWOMaZCpEeY67XYlVGUbwsfV8aCh6w02\n39HzUlP8IgEvF4UwFCKE9KOWkWiI7YB6vdmq4LHztDOKOnd8/8byuJmy307asa0o27K1LGtDO/Y8\nVhXi9uwt8/It3VuGTHzhz5bhHKXSDUb53oLmTNZtLMKVY9UHEWcAglWQmw/cWn0IH3r7P91a+bzv\npw8XdbqdUgidhfk8DyavXiFac0683AlIZsSt20qBt9uDi8JcIBmbHG35ofRdXoFwn7k7c3xsee8k\nEFLrcgmyNwshaEGUrH6b6z/f+knYGGMaZFOLcES8MyJuiYg9a/9uiIg3ZHU+EBEPRMRiRHwhIs46\nuk02xpgnD5t9Er5X0rslvUTSSyV9WdLVEfFCSYqId0u6TNI7JL1c0oKk6yNi44gYxhhzArKpRTil\n9JmU0nUppTtSSrenlN4rab+kV6xVuVzSlSmla1NKt0q6VNJpkt56VFttjDFPEg5bmIuxfe0SSfOS\nboiIMyW0ifcbAAAgAElEQVTtkvSlA3VSSnsj4kZJF0i66lDna7Vb1Y14FFAgDGGNkHER5W22wAlH\necDo/JhqrKhUHthugcMIVJAE90m/L6lteT6snaeXoSEfvPfOouyh3WVYyZPhmuTgWloq3WB79lSF\nuJ/s2V/UmRmWTrulxVLAI+GPQpa2UGyt1qN8deSkIsfcaEiToSzqQ7jIvXtKl+KP7qw65ObbEBpy\ncbEomwEV7nnPKcd5Pij0ZjkHl/ZX+7w3s7Wok4dIlVjkS9RvMC6dDo1ptWx2rswT1+uVIi3l9CPD\n2hDCbA7gMTSfWxSGlMKaToa95e8ws+lFOCJeLOkbkmYl7ZP0Kyml2yLiAo3Xot3ZIbs1XpyNMcZk\nHM6T8D9JOlvSdklvk/SJiPiFo9oqY4w5Qdj0IpxSGkj60drHmyPi5RrvBX9Q4z/Odqr6NLxTUpm3\nO+Nzn71OsxPvuIZCP3f2z+rss8/ebBONMeYJ49prPq3PXHtNxSuwb9/e2scfDbNGS1IvpXRnRDwk\n6UJJ35WkiNgm6XxJH93oJG980xt02mmnPf6Z0vkYY8y0cdHFb9FFF7+lsif8j7d+T7/ylotqHb+p\nRTgi/ljS5yTdI+kkSb8l6dWSXrdW5UOS3hsRt0u6S9KVku6TdPWGDenMqNs9uOmOYR+xTRu/4IHu\nNTg/OeHIlRbkwMu1Bzo/5R4rq62jNtYLqZmLFFu2luLG8198blH2vZtLF919DzxalM2AoDIEkWI5\ny2/W75e/VE/uluEMB31yzJUuPXK5jajfsrCSQwjlSOdCgQkcc7VsnJL27i2fjO5/6KeVzzt3lILb\n7GwpRG2fKcNK7pgvy7oJHGeLZf8+tqcq/p3x/BcVdSi3G4cOBUEaxMCAh6zC+UbuPghRSd8Nyt1I\nZRS2M3dLLoJYTCFoJx2Ew2MozD1N0l9JerqkPRo/8b4upfRlSUopfTAi5iV9XNIOSV+V9MaUEvkF\njTHmhGdTi3BK6e016lwh6YrDbI8xxpxQOHaEMcY0iBdhY4xpkKkJZTkcDjQYHhRzZrqlIEE2mDz3\nWiTYkK+ZT47cVCiIUUjD/LI1RQsMW0m5wWCjH9tLcQgzTjn11KLs7JddUJTde/ePirJHflKGW0yd\nUpjb9bSqmPaUHU8p6gxXy3uicIMDcDqhW4uEnNz9BLnYRiMQ62CM6fwdym8G11heKB2DkVV7ZE8p\nAHUgnCPlT+uAMJfgHh57pGzH3ElVL9XTn/Wsog4JkCRoslgHc7KG05WE1k6NMLLrXYDq0dzKc8wR\nvV4pKk8K9kNwYa6Hn4SNMaZBvAgbY0yDeBE2xpgGmZo94X5/VaurEy+S034Q7I/l0crQRzGE4zr1\n3rKnveNaThKoQodhGRlEoCJ1R7td3Rvs034q7IE+5ZQyjc62HWXkrFVIL0P7X91utkcJe4D333Nv\nUbYIUcO2ba+3v0Yv8rda+bFlQ1ZXaF+w3jVnYJ4OYV97eW8ZRe2sM55d+fzY0r6izqP7HyvK9i+U\nY9ABAwdtxT7lKTuLshedc17l8/aTy7nQ7ZTnJ02CNA5yv9KxtJdenAvSCiVIG0bppKhDKOVYHkWN\ntIBci5KqURgpIuN6+EnYGGMaxIuwMcY0iBdhY4xpEC/CxhjTIFMjzM3M9DRbeQEaNu7JiJBvkFMg\nrTaIBWDqIJWMNu4pAlSeuqhVNwwcbuDX39TPycXLVqs0vVDLSMjoQL/1TtpelOUplcbXrZYtLy2V\n55orX3gnUwpGOUNDS1kvj5I1hBfxSYSj85M4OiJjxkopLq7C/Z/2rDMqn8/aUUaLe/jh0hyzd08p\n1rUgddbJILCdDkaM7ZmRpkXqNhilaI636VhKJVYjUiD17XBYjh/NmVWIxpfgfC1QtzudXvYZlkny\nSU0IwwlSIq2Hn4SNMaZBvAgbY0yDeBE2xpgG8SJsjDENMjXCXBqNKsIKOm9IYGtXo1hxRKXyOJIF\nguqBMJcgRU7e3oCULgSmZ0IXHaVmgbZlAgqJLDMzZeQv6jeKaNYHwaPXhohS2elIiJnfUqZeIuGF\n2kH0QXTL72uwCo4/6EfqD5qT/X7ZtqWFUpjrzJf3Op+lntq56/Sizimn7irKMI0TiF9dEJS64KzL\nBVgS3DodyvlIrrdyDBKkWQpw4OUTn8aA0lPR+A1gXAarZdva0Ef5OrAys1zUobnQGR28J4rOth5+\nEjbGmAbxImyMMQ3iRdgYYxrEi7AxxjTI1AhzEVHZiK8rjORh8tC9RWIdpUECRawFofNIkEiZa4eC\n8nEqo8NPg9SBcJz5seQio3P1eqWzrg1uohUIZUkixGhUPXYAgs1MrxRnRiMK41mWUQoaKsvF3Ha3\nHE8KS0giDoZSBQYgFG1/aplSqjc/Xz0/iGbdgBCVlNqJ0vmAyw2+HoXo1umQQA2pjMpT4VixyF72\nZV6P7rNfU1gl5b2u4zEX9VaWS2GOzt+ZmFvLy6VDcj38JGyMMQ3iRdgYYxrEi7AxxjSIF2FjjGmQ\nqRHmFFHZ1CchYAjh4fIwcxhAEkU+yntFedzIUUNkghjloIIyEv7aIG5Q29j1V603aoGrDsMIlpB4\nQv2x2qfwgtVr0LnIvdWb2VqUkdg6WC6vSXkJ804ioZJCFVJuQXLbaQhzBtxgLXJmZX1JIlFnBoRE\nyn8H90UiZJH7T6UwR0J23dCeJMzh3K0xBen7QuR5FSWJDH59eOYkB14eqpbmLn37lpcOOiVXV0DM\nWwc/CRtjTIN4ETbGmAbxImyMMQ3iRdgYYxpkeoS5DBKASCypIzKh+45EBQj7GOREQkEiEzdUbuZT\nLixy+6CIg20DsubmjsL1CGgvGQ3bbcrTVbZ3abkazpFdU+VxnU4ZZpPcdhQysQOiUyF2kXZHgiwo\nO8NVGAM43yzkzqNx7mTCGc2PBA5CyuPWmSmvSQ6/FtxXK59J4OKkkKh18sRthvx8CdpB4WwpBO0A\nBDf6wlCOuXw+k9hIIUz7E4Jpn8TTdfCTsDHGNIgXYWOMaRAvwsYY0yBehI0xpkGmRpgbDocaTGxm\nz8zMF3U64ETKc8BROEp2oFErwOUG58M8dlE9lsQCaj+2IpEjqhQ8yMkTea47aD+GPaRbwkLKiVfW\nGwwygQ3MT0NoPzmYErjBMIwilKWs38gdRy46FF9BFEKX2yyEBSVBrAjDWlTBsJIkXuJcKE/HIR4z\n0Y2F5yMow5aUfT7Kx77mWI1GMGdANBwOKawrhbmt1qNQlsMNrjm0MGeMMccHR7QIR8QfRcQoIv48\nK/9ARDwQEYsR8YWIOOvImmmMMU9ODnsRjojzJL1D0i1Z+bslXbb2s5dLWpB0fUSUf0MZY8wJzmHt\nCUfEVkmflPR2Se/Lfny5pCtTSteu1b1U0m5Jb5V01fpnTZrcJ+KoTeVRuYGDDRGwIRkQvQyNDbQH\nVWN/Ft7iT3AujNzWoshttNdWJ5VMPQMKwfvEtL8OEbyy/qD9X9xLHlLqKNjfG5Tj3IY9xCK9EUaG\nq2doabehvZQ+CvpoBtJHpWyOdCHqGe1hz8yUzzN0TQpVhqmz2hvrCBgJDcaFDBaYcgzMO/l1yeBC\n+gC1ow9mngHs09J9dTpVIwb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sjDEN4kXYGGMaxIuwMcY0iBdhY4xpkOkR5nLIDEbpdjJx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb692e856d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example of a picture\n",
    "index = 50\n",
    "plt.imshow(train_x_orig[index])\n",
    "print (\"y = \" + str(train_y[0,index]) + \". It's a \" + classes[train_y[0,index]].decode(\"utf-8\") +  \" picture.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: 209\n",
      "Number of testing examples: 50\n",
      "Each image is of size: (64, 64, 3)\n",
      "train_x_orig shape: (209, 64, 64, 3)\n",
      "train_y shape: (1, 209)\n",
      "test_x_orig shape: (50, 64, 64, 3)\n",
      "test_y shape: (1, 50)\n"
     ]
    }
   ],
   "source": [
    "# Explore your dataset \n",
    "m_train = train_x_orig.shape[0]\n",
    "num_px = train_x_orig.shape[1]\n",
    "m_test = test_x_orig.shape[0]\n",
    "\n",
    "print (\"Number of training examples: \" + str(m_train))\n",
    "print (\"Number of testing examples: \" + str(m_test))\n",
    "print (\"Each image is of size: (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\n",
    "print (\"train_x_orig shape: \" + str(train_x_orig.shape))\n",
    "print (\"train_y shape: \" + str(train_y.shape))\n",
    "print (\"test_x_orig shape: \" + str(test_x_orig.shape))\n",
    "print (\"test_y shape: \" + str(test_y.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As usual, you reshape and standardize the images before feeding them to the network. The code is given in the cell below.\n",
    "\n",
    "<img src=\"images/imvectorkiank.png\" style=\"width:450px;height:300px;\">\n",
    "\n",
    "<caption><center> <u>Figure 1</u>: Image to vector conversion. <br> </center></caption>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_x's shape: (12288, 209)\n",
      "test_x's shape: (12288, 50)\n"
     ]
    }
   ],
   "source": [
    "# Reshape the training and test examples \n",
    "train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T   # The \"-1\" makes reshape flatten the remaining dimensions\n",
    "test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T\n",
    "\n",
    "# Standardize data to have feature values between 0 and 1.\n",
    "train_x = train_x_flatten/255.\n",
    "test_x = test_x_flatten/255.\n",
    "\n",
    "print (\"train_x's shape: \" + str(train_x.shape))\n",
    "print (\"test_x's shape: \" + str(test_x.shape))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$12,288$ equals $64 \\times 64 \\times 3$ which is the size of one reshaped image vector."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Architecture of your model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you are familiar with the dataset, it is time to build a deep neural network to distinguish cat images from non-cat images.\n",
    "\n",
    "You will build two different models:\n",
    "- A 2-layer neural network\n",
    "- An L-layer deep neural network\n",
    "\n",
    "You will then compare the performance of these models, and also try out different values for $L$. \n",
    "\n",
    "Let's look at the two architectures.\n",
    "\n",
    "### 3.1 - 2-layer neural network\n",
    "\n",
    "<img src=\"images/2layerNN_kiank.png\" style=\"width:650px;height:400px;\">\n",
    "<caption><center> <u>Figure 2</u>: 2-layer neural network. <br> The model can be summarized as: ***INPUT -> LINEAR -> RELU -> LINEAR -> SIGMOID -> OUTPUT***. </center></caption>\n",
    "\n",
    "<u>Detailed Architecture of figure 2</u>:\n",
    "- The input is a (64,64,3) image which is flattened to a vector of size $(12288,1)$. \n",
    "- The corresponding vector: $[x_0,x_1,...,x_{12287}]^T$ is then multiplied by the weight matrix $W^{[1]}$ of size $(n^{[1]}, 12288)$.\n",
    "- You then add a bias term and take its relu to get the following vector: $[a_0^{[1]}, a_1^{[1]},..., a_{n^{[1]}-1}^{[1]}]^T$.\n",
    "- You then repeat the same process.\n",
    "- You multiply the resulting vector by $W^{[2]}$ and add your intercept (bias). \n",
    "- Finally, you take the sigmoid of the result. If it is greater than 0.5, you classify it to be a cat.\n",
    "\n",
    "### 3.2 - L-layer deep neural network\n",
    "\n",
    "It is hard to represent an L-layer deep neural network with the above representation. However, here is a simplified network representation:\n",
    "\n",
    "<img src=\"images/LlayerNN_kiank.png\" style=\"width:650px;height:400px;\">\n",
    "<caption><center> <u>Figure 3</u>: L-layer neural network. <br> The model can be summarized as: ***[LINEAR -> RELU] $\\times$ (L-1) -> LINEAR -> SIGMOID***</center></caption>\n",
    "\n",
    "<u>Detailed Architecture of figure 3</u>:\n",
    "- The input is a (64,64,3) image which is flattened to a vector of size (12288,1).\n",
    "- The corresponding vector: $[x_0,x_1,...,x_{12287}]^T$ is then multiplied by the weight matrix $W^{[1]}$ and then you add the intercept $b^{[1]}$. The result is called the linear unit.\n",
    "- Next, you take the relu of the linear unit. This process could be repeated several times for each $(W^{[l]}, b^{[l]})$ depending on the model architecture.\n",
    "- Finally, you take the sigmoid of the final linear unit. If it is greater than 0.5, you classify it to be a cat.\n",
    "\n",
    "### 3.3 - General methodology\n",
    "\n",
    "As usual you will follow the Deep Learning methodology to build the model:\n",
    "    1. Initialize parameters / Define hyperparameters\n",
    "    2. Loop for num_iterations:\n",
    "        a. Forward propagation\n",
    "        b. Compute cost function\n",
    "        c. Backward propagation\n",
    "        d. Update parameters (using parameters, and grads from backprop) \n",
    "    4. Use trained parameters to predict labels\n",
    "\n",
    "Let's now implement those two models!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 - Two-layer neural network\n",
    "\n",
    "**Question**:  Use the helper functions you have implemented in the previous assignment to build a 2-layer neural network with the following structure: *LINEAR -> RELU -> LINEAR -> SIGMOID*. The functions you may need and their inputs are:\n",
    "```python\n",
    "def initialize_parameters(n_x, n_h, n_y):\n",
    "    ...\n",
    "    return parameters \n",
    "def linear_activation_forward(A_prev, W, b, activation):\n",
    "    ...\n",
    "    return A, cache\n",
    "def compute_cost(AL, Y):\n",
    "    ...\n",
    "    return cost\n",
    "def linear_activation_backward(dA, cache, activation):\n",
    "    ...\n",
    "    return dA_prev, dW, db\n",
    "def update_parameters(parameters, grads, learning_rate):\n",
    "    ...\n",
    "    return parameters\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### CONSTANTS DEFINING THE MODEL ####\n",
    "n_x = 12288     # num_px * num_px * 3\n",
    "n_h = 7\n",
    "n_y = 1\n",
    "layers_dims = (n_x, n_h, n_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: two_layer_model\n",
    "\n",
    "def two_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):\n",
    "    \"\"\"\n",
    "    Implements a two-layer neural network: LINEAR->RELU->LINEAR->SIGMOID.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- input data, of shape (n_x, number of examples)\n",
    "    Y -- true \"label\" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples)\n",
    "    layers_dims -- dimensions of the layers (n_x, n_h, n_y)\n",
    "    num_iterations -- number of iterations of the optimization loop\n",
    "    learning_rate -- learning rate of the gradient descent update rule\n",
    "    print_cost -- If set to True, this will print the cost every 100 iterations \n",
    "    \n",
    "    Returns:\n",
    "    parameters -- a dictionary containing W1, W2, b1, and b2\n",
    "    \"\"\"\n",
    "    \n",
    "    np.random.seed(1)\n",
    "    grads = {}\n",
    "    costs = []                              # to keep track of the cost\n",
    "    m = X.shape[1]                           # number of examples\n",
    "    (n_x, n_h, n_y) = layers_dims\n",
    "    \n",
    "    # Initialize parameters dictionary, by calling one of the functions you'd previously implemented\n",
    "    ### START CODE HERE ### (≈ 1 line of code)\n",
    "    parameters = initialize_parameters(n_x, n_h, n_y)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Get W1, b1, W2 and b2 from the dictionary parameters.\n",
    "    W1 = parameters[\"W1\"]\n",
    "    b1 = parameters[\"b1\"]\n",
    "    W2 = parameters[\"W2\"]\n",
    "    b2 = parameters[\"b2\"]\n",
    "    \n",
    "    # Loop (gradient descent)\n",
    "\n",
    "    for i in range(0, num_iterations):\n",
    "\n",
    "        # Forward propagation: LINEAR -> RELU -> LINEAR -> SIGMOID. Inputs: \"X, W1, b1\". Output: \"A1, cache1, A2, cache2\".\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        A1, cache1 = linear_activation_forward(X, W1, b1, activation=\"relu\")\n",
    "        A2, cache2 = linear_activation_forward(A1, W2, b2, activation=\"sigmoid\")\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Compute cost\n",
    "        ### START CODE HERE ### (≈ 1 line of code)\n",
    "        cost = compute_cost(A2, Y)\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Initializing backward propagation\n",
    "        dA2 = - (np.divide(Y, A2) - np.divide(1 - Y, 1 - A2))\n",
    "        \n",
    "        # Backward propagation. Inputs: \"dA2, cache2, cache1\". Outputs: \"dA1, dW2, db2; also dA0 (not used), dW1, db1\".\n",
    "        ### START CODE HERE ### (≈ 2 lines of code)\n",
    "        dA1, dW2, db2 = linear_activation_backward(dA2, cache2, activation=\"sigmoid\")\n",
    "        dA0, dW1, db1 = linear_activation_backward(dA1, cache1, activation=\"relu\")\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Set grads['dWl'] to dW1, grads['db1'] to db1, grads['dW2'] to dW2, grads['db2'] to db2\n",
    "        grads['dW1'] = dW1\n",
    "        grads['db1'] = db1\n",
    "        grads['dW2'] = dW2\n",
    "        grads['db2'] = db2\n",
    "        \n",
    "        # Update parameters.\n",
    "        ### START CODE HERE ### (approx. 1 line of code)\n",
    "        parameters = update_parameters(parameters, grads, learning_rate)\n",
    "        ### END CODE HERE ###\n",
    "\n",
    "        # Retrieve W1, b1, W2, b2 from parameters\n",
    "        W1 = parameters[\"W1\"]\n",
    "        b1 = parameters[\"b1\"]\n",
    "        W2 = parameters[\"W2\"]\n",
    "        b2 = parameters[\"b2\"]\n",
    "        \n",
    "        # Print the cost every 100 training example\n",
    "        if print_cost and i % 100 == 0:\n",
    "            print(\"Cost after iteration {}: {}\".format(i, np.squeeze(cost)))\n",
    "        if print_cost and i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "       \n",
    "    # plot the cost\n",
    "\n",
    "    plt.plot(np.squeeze(costs))\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('iterations (per tens)')\n",
    "    plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "    plt.show()\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the cell below to train your parameters. See if your model runs. The cost should be decreasing. It may take up to 5 minutes to run 2500 iterations. Check if the \"Cost after iteration 0\" matches the expected output below, if not click on the square (⬛) on the upper bar of the notebook to stop the cell and try to find your error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.693049735659989\n",
      "Cost after iteration 100: 0.6464320953428849\n",
      "Cost after iteration 200: 0.6325140647912678\n",
      "Cost after iteration 300: 0.6015024920354665\n",
      "Cost after iteration 400: 0.5601966311605748\n",
      "Cost after iteration 500: 0.515830477276473\n",
      "Cost after iteration 600: 0.4754901313943325\n",
      "Cost after iteration 700: 0.43391631512257495\n",
      "Cost after iteration 800: 0.4007977536203886\n",
      "Cost after iteration 900: 0.35807050113237987\n",
      "Cost after iteration 1000: 0.3394281538366413\n",
      "Cost after iteration 1100: 0.30527536361962654\n",
      "Cost after iteration 1200: 0.2749137728213015\n",
      "Cost after iteration 1300: 0.24681768210614827\n",
      "Cost after iteration 1400: 0.1985073503746611\n",
      "Cost after iteration 1500: 0.17448318112556593\n",
      "Cost after iteration 1600: 0.1708076297809661\n",
      "Cost after iteration 1700: 0.11306524562164737\n",
      "Cost after iteration 1800: 0.09629426845937163\n",
      "Cost after iteration 1900: 0.08342617959726878\n",
      "Cost after iteration 2000: 0.0743907870431909\n",
      "Cost after iteration 2100: 0.06630748132267938\n",
      "Cost after iteration 2200: 0.05919329501038176\n",
      "Cost after iteration 2300: 0.05336140348560564\n",
      "Cost after iteration 2400: 0.048554785628770226\n"
     ]
    },
    {
     "data": {
      "image/png": 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ccglcdBE89FDc0YhINksrgZrZADNbLUV5UzMbUPuwROrPsGFw2GFwzDFhKTQR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\nvzPXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb692f86588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters = two_layer_model(train_x, train_y, layers_dims = (n_x, n_h, n_y), num_iterations = 2500, print_cost=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table> \n",
    "    <tr>\n",
    "        <td> **Cost after iteration 0**</td>\n",
    "        <td> 0.6930497356599888 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> **Cost after iteration 100**</td>\n",
    "        <td> 0.6464320953428849 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> **...**</td>\n",
    "        <td> ... </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> **Cost after iteration 2400**</td>\n",
    "        <td> 0.048554785628770206 </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good thing you built a vectorized implementation! Otherwise it might have taken 10 times longer to train this.\n",
    "\n",
    "Now, you can use the trained parameters to classify images from the dataset. To see your predictions on the training and test sets, run the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "predictions_train = predict(train_x, train_y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table> \n",
    "    <tr>\n",
    "        <td> **Accuracy**</td>\n",
    "        <td> 1.0 </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.72\n"
     ]
    }
   ],
   "source": [
    "predictions_test = predict(test_x, test_y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "        <td> **Accuracy**</td>\n",
    "        <td> 0.72 </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note**: You may notice that running the model on fewer iterations (say 1500) gives better accuracy on the test set. This is called \"early stopping\" and we will talk about it in the next course. Early stopping is a way to prevent overfitting. \n",
    "\n",
    "Congratulations! It seems that your 2-layer neural network has better performance (72%) than the logistic regression implementation (70%, assignment week 2). Let's see if you can do even better with an $L$-layer model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 - L-layer Neural Network\n",
    "\n",
    "**Question**: Use the helper functions you have implemented previously to build an $L$-layer neural network with the following structure: *[LINEAR -> RELU]$\\times$(L-1) -> LINEAR -> SIGMOID*. The functions you may need and their inputs are:\n",
    "```python\n",
    "def initialize_parameters_deep(layer_dims):\n",
    "    ...\n",
    "    return parameters \n",
    "def L_model_forward(X, parameters):\n",
    "    ...\n",
    "    return AL, caches\n",
    "def compute_cost(AL, Y):\n",
    "    ...\n",
    "    return cost\n",
    "def L_model_backward(AL, Y, caches):\n",
    "    ...\n",
    "    return grads\n",
    "def update_parameters(parameters, grads, learning_rate):\n",
    "    ...\n",
    "    return parameters\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### CONSTANTS ###\n",
    "layers_dims = [12288, 20, 7, 5, 1] #  5-layer model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# GRADED FUNCTION: L_layer_model\n",
    "\n",
    "def L_layer_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 3000, print_cost=False):#lr was 0.009\n",
    "    \"\"\"\n",
    "    Implements a L-layer neural network: [LINEAR->RELU]*(L-1)->LINEAR->SIGMOID.\n",
    "    \n",
    "    Arguments:\n",
    "    X -- data, numpy array of shape (number of examples, num_px * num_px * 3)\n",
    "    Y -- true \"label\" vector (containing 0 if cat, 1 if non-cat), of shape (1, number of examples)\n",
    "    layers_dims -- list containing the input size and each layer size, of length (number of layers + 1).\n",
    "    learning_rate -- learning rate of the gradient descent update rule\n",
    "    num_iterations -- number of iterations of the optimization loop\n",
    "    print_cost -- if True, it prints the cost every 100 steps\n",
    "    \n",
    "    Returns:\n",
    "    parameters -- parameters learnt by the model. They can then be used to predict.\n",
    "    \"\"\"\n",
    "\n",
    "    np.random.seed(1)\n",
    "    costs = []                         # keep track of cost\n",
    "    \n",
    "    # Parameters initialization.\n",
    "    ### START CODE HERE ###\n",
    "    parameters = initialize_parameters_deep(layers_dims)\n",
    "    ### END CODE HERE ###\n",
    "    \n",
    "    # Loop (gradient descent)\n",
    "    for i in range(0, num_iterations):\n",
    "\n",
    "        # Forward propagation: [LINEAR -> RELU]*(L-1) -> LINEAR -> SIGMOID.\n",
    "        ### START CODE HERE ### (≈ 1 line of code)\n",
    "        AL, caches = L_model_forward(X, parameters)\n",
    "        ### END CODE HERE ###\n",
    "        \n",
    "        # Compute cost.\n",
    "        ### START CODE HERE ### (≈ 1 line of code)\n",
    "        cost = compute_cost(AL, Y)\n",
    "        ### END CODE HERE ###\n",
    "    \n",
    "        # Backward propagation.\n",
    "        ### START CODE HERE ### (≈ 1 line of code)\n",
    "        grads = L_model_backward(AL, Y, caches)\n",
    "        ### END CODE HERE ###\n",
    " \n",
    "        # Update parameters.\n",
    "        ### START CODE HERE ### (≈ 1 line of code)\n",
    "        parameters = update_parameters(parameters, grads, learning_rate)\n",
    "        ### END CODE HERE ###\n",
    "                \n",
    "        # Print the cost every 100 training example\n",
    "        if print_cost and i % 100 == 0:\n",
    "            print (\"Cost after iteration %i: %f\" %(i, cost))\n",
    "        if print_cost and i % 100 == 0:\n",
    "            costs.append(cost)\n",
    "            \n",
    "    # plot the cost\n",
    "    plt.plot(np.squeeze(costs))\n",
    "    plt.ylabel('cost')\n",
    "    plt.xlabel('iterations (per tens)')\n",
    "    plt.title(\"Learning rate =\" + str(learning_rate))\n",
    "    plt.show()\n",
    "    \n",
    "    return parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will now train the model as a 5-layer neural network. \n",
    "\n",
    "Run the cell below to train your model. The cost should decrease on every iteration. It may take up to 5 minutes to run 2500 iterations. Check if the \"Cost after iteration 0\" matches the expected output below, if not click on the square (⬛) on the upper bar of the notebook to stop the cell and try to find your error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cost after iteration 0: 0.771749\n",
      "Cost after iteration 100: 0.672053\n",
      "Cost after iteration 200: 0.648263\n",
      "Cost after iteration 300: 0.611507\n",
      "Cost after iteration 400: 0.567047\n",
      "Cost after iteration 500: 0.540138\n",
      "Cost after iteration 600: 0.527930\n",
      "Cost after iteration 700: 0.465477\n",
      "Cost after iteration 800: 0.369126\n",
      "Cost after iteration 900: 0.391747\n",
      "Cost after iteration 1000: 0.315187\n",
      "Cost after iteration 1100: 0.272700\n",
      "Cost after iteration 1200: 0.237419\n",
      "Cost after iteration 1300: 0.199601\n",
      "Cost after iteration 1400: 0.189263\n",
      "Cost after iteration 1500: 0.161189\n",
      "Cost after iteration 1600: 0.148214\n",
      "Cost after iteration 1700: 0.137775\n",
      "Cost after iteration 1800: 0.129740\n",
      "Cost after iteration 1900: 0.121225\n",
      "Cost after iteration 2000: 0.113821\n",
      "Cost after iteration 2100: 0.107839\n",
      "Cost after iteration 2200: 0.102855\n",
      "Cost after iteration 2300: 0.100897\n",
      "Cost after iteration 2400: 0.092878\n"
     ]
    },
    {
     "data": {
      "image/png": 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D4eefNZhIRESqF3sCNbOWQBEwLVHm7g5MBfrW8DTDganu/mldYuncGQ47TCsTiYhI9WJP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\n6lMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb69024fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "parameters = L_layer_model(train_x, train_y, layers_dims, num_iterations = 2500, print_cost = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "<table> \n",
    "    <tr>\n",
    "        <td> **Cost after iteration 0**</td>\n",
    "        <td> 0.771749 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> **Cost after iteration 100**</td>\n",
    "        <td> 0.672053 </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> **...**</td>\n",
    "        <td> ... </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "        <td> **Cost after iteration 2400**</td>\n",
    "        <td> 0.092878 </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.985645933014\n"
     ]
    }
   ],
   "source": [
    "pred_train = predict(train_x, train_y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table>\n",
    "    <tr>\n",
    "    <td>\n",
    "    **Train Accuracy**\n",
    "    </td>\n",
    "    <td>\n",
    "    0.985645933014\n",
    "    </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8\n"
     ]
    }
   ],
   "source": [
    "pred_test = predict(test_x, test_y, parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected Output**:\n",
    "\n",
    "<table> \n",
    "    <tr>\n",
    "        <td> **Test Accuracy**</td>\n",
    "        <td> 0.8 </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Congrats! It seems that your 5-layer neural network has better performance (80%) than your 2-layer neural network (72%) on the same test set. \n",
    "\n",
    "This is good performance for this task. Nice job! \n",
    "\n",
    "Though in the next course on \"Improving deep neural networks\" you will learn how to obtain even higher accuracy by systematically searching for better hyperparameters (learning_rate, layers_dims, num_iterations, and others you'll also learn in the next course). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  6) Results Analysis\n",
    "\n",
    "First, let's take a look at some images the L-layer model labeled incorrectly. This will show a few mislabeled images. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Hdqeokp2ezEGezSl2NbMyw3YNyfwH\nlAIoeP9G3pG23pbQnN9Yd1Qw9nQ+jQ1PL8/Z/pPfob5meykabNKnGLeg0L/bx47p9dnfOkWSkvUq\n0czMTpeoVxxiwztUR0QNzWnuLSkM57A8aAjU2KdzT5VuyHpuCGiSRhEac9zBtWVjwGuPa1cexRLs\nqzzybdzhJc2FRYpzOKNcREn5FjOznAa+ojIr2pvNjg9AniYnID86wL16Tmv0/gG+b2a2zGj/R3Fe\nh+TlBNtxfcjrPpbP6/7aCGNuM7N3P9gH+eUXL4B8Oka9Hzx8BHK/j3VMjtE+ok10bt2eq0OxwDqq\nIiOZv8CG8p7Vp/F3Y3bXMVU8iSkX6VMM7QUdkikf1tvA8kJsd1K68+J4imuTF6AObuqS/efqmLob\n90DOGuYF5x653VHX3aedK9g0eJichbTlezNnMWfzc9buVh1Wf++83xRotendVqYToNSrn7McNejE\nP/E7dL7gnGpxO9t05DZTHHfrkavjv3ib4uryp5vY4RyymdmbB+ggf/fPcY7+t9co9h/x4ZCtltnm\nw5ZxMGuwwdWPeWw4deO8z93gzJmnGBe2F67kHG9hzczuP74D8mxxDHJMa1SaY0xTmXsWkNW4lo/n\nGC8Mh2gsHVqjjueHIOcl1lHXGOsdnzx2dBiPMXc/W+C6enEd1+KwxjiqrLGdHJexYexsX3F0eOby\nCyDfuIFyWsxAPqT4cjTcBjlJsE2L+SnIk6l7VvTZV74E8sHJQ5DvPXwP5IDzjJRzCGntH3Qwnijm\nqKOZ2/dbQ9y/7+7geVNF+4vHp0cgZwme02xQed0IdTIzu/sQbSQtcXwz2n+EJF/a2QF5fR1juze+\n8ybIf/Hn/9rR4dorz4O8NqR4kM7ADmi/MDnB8X7wLo5ddYT9lH77+44O33vnNsiPjvCMOJtj3/7T\n//q/c8o4q+Q05uMZ9uf7T56AHPfQjq5voA2YmW2T/Y9OKR9290cgb23gOdUH7+LzZ9ZwLkR0VnuF\n9mZmZp/+zLMg3z6Zgvxnb2Ad9y6g7X7jDrb7ncfoR6YJ7hEWcyzfzOzFa9g3v/O5l0Hev3UX5HgN\n904vbOAcznnxp6U8S911Zz5Df1rmqHdC59Yz2mtdungJ5E6EwSO3++gU6zMzm9OZcp/Oc6+vow6P\nyDfOaG2bTdAHjChneyPGOW9mtl6gXVd0nspnQUVMa2FI90IC9OmjjWsg52to02Zmh6foV2Z0lj7o\n8dk55b4pJnj9r/4E5P4G3lXZNIxbzMz6GcYRIeV3TpbnN2nXCfncf3Us7ITKTdtFGhPnrJVyLPw+\nnzEFpGO3izFYp+uu4yHFBgEl1iPKRXa7lJOh894w5BwPb6wdFVrb6eaaKBdFeynOr3Aesszc8wmG\n+5bHO6A5XVHus6D7OznlhXK+32NmJd3jKKjMnPxvSvdCFkv0CcsFxh4L8o0Lik3M3PuFrIPv4Rwf\nruP7/R3SmaoocrQXCrnNzKxLG8+qj3bc62D+k45eLapw7fNz9FvjU/Rt0xPcT5mZ7X3qGayji2v8\n6bG7TpwnPrh7D+SHj7CP7j/E559+8dMg7+26sV1R8PrQlkPmM6LVuVmPbHMywfiirNw865RiFr7K\nHLL/cs4h0Z7Zh/O9AL4fwfdtzJrP7KiQv1U8qpBTNM79bzNLE5zni0PcB/M6YRTHsU+eL/H5Ykn3\nWsk3cr+bmW1sYwzjrD3ccXRnvK3f3Stu3kr5//v1o/Bd5ySjvEhE622H1+zVSUDf5znWcO+I2snX\na/6m97FcrYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIc4Y+uMIIYQQQgghhBBCCCGEEEII\nIYQQQgghhBBCCCGEEEKcafTHEUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEONOEn7QCZmbD\nwQjkNMtAzssC5LKuQI5CtxleUKNMfwcSBT2Q/cIDOatS/N7H8ooKdQo89+9MuE7+W5TQwzoNq3Ao\nqc6yzEGOI7cffK+7Uk9WIQo7IK8P1kHOCuyXJEN50Os7OlSG45WT3mlJ411jOyM/Rp19f6XsB+5Y\nVPwD9TV1g3k1/lKVJeqYYxsce2j4syPf8J08x3YvEuzLinTsdNFmwwDHKvAD/N5ptFmaoA6PvjsD\neX2Cz4e/loC8+QyOL8+9sMaG933XqKczbKfvYRm9MHK+Od+grdU+DZyHY+JVU6eEOn+A70R7VAba\nTl3jGNjyFsrhAMSgh+V5fbSD08nC0alTYh0Z23uOc6r20Fa6XbSDHslJiuVlNEczFD/8JsO+7eRo\nr1lOOtNaVNM87/hYiV9jGyJysMuW8szM8gJ/G4zQhy9pDp/MUecowDrZx4foJmyW0DoSus4rrbGd\nUYxzNsmpXyKsZLrEOsKA10LyGzTWeYMzm5OPvnhxC+VnnqUv2Muzb2pZgJnafb9u+O1nmaxAe0/Y\ntuMhyCGtJ01rGFPxQknjzM99H5+X5DeCAG23rtluzMoKv3HiB3/13x17NCnb7Kaq3Y7wGmLOlWVQ\nZ/qkY0U6+B73o6uDo7fTVdxO8vn0OcusY1M/uT+t7ksug+P0hhrwfXaoDTrUrYaLH7S5DdaZbdjM\nHS9uiFOGR/Ei2Rhbl0dRdJPOzm+kkv8xbfYs49o2/pDR0n+8cMe0yvGbfg/lgAY5CsjuyHctaZ/b\nj3E8tnpu0FRSnDZLsI6wR2v3aBvkJME9xuDyDZB3rz+H5dE+x8ys4M4i2+V1o1iOQT649Q7IVWcD\ni4uwjcu5G89uBBjDjDo8wCiG7BrJ9DOar+uYBrEOF2BmFY1f3OG1jJVAkbdjLNP2zSIuz8zylON0\n1KE3wucxbt+tZBMlOSSlJrk7LwKaSwPqh5jmQYJhiPkUIwfcbTQNGraxxgNesIm6ap8bujSo3Q7m\nIoZdju957WhYB2j/xzkXDqlKWmNPaA86W3C8Q/43WToqLJeY76go91jQHpZ9fJpgP3AdixR1PDxx\n9/IcOgwHWObuNubkpgvUaYscyeYmvt/roX+NPDdWufcQ/evFXdxbHR2dgDyeYzvHJ8dYB82Vno/v\nexn2u5lZSn1Xk+OoOC5z/C06Pze2RLluiPycOdySP6193KuXPo5dWVHMXWIBh0dPHB0e7x+AnFAO\nIaB8WZ1QXzpxH+U8UuznonBjgCCISMYyXnnusvPNuWJ1Kr9hA9jy/lOU6bhIlgN+TnstXtSeRicq\n0+N9BvlQpw4uk48kuDx+zm1qKpPr5G9obXfed8Zu9b7pvfv4/L/6E9dP3J49RaLipwzv57/6A1zL\nfvmb2Pn/4O9jO/2Y2slpeY5P+XlTzMTdxGPXEPN+lLaQqmX76dqfWfvm+2nmzTkijjD3H3d4DZuD\nXOW43jx88n2nzDRF28soIM8Mc9h81rWgvDzn7EI6+4o77lnoDsVRD8ZHID+hvfKLl6+DvFyegry7\neRPkjM4Eg45rKMN1zHfOEyzz3sO3QX7n1rdB/vwrvwHyzg6uu7nh2Di+z8xOE4wxjt6ns6Max/Po\n7l2Qh7u4vx+tYbw5PcR+7QauU79y8RL+UON4HUwwLk5ybFdAk5LPqXmSvvHdNx0dfu9//32Qn/s0\nnhfEPdzjbG1iDHy3+gDkjM5vv/VX3wI5P8SY2Mwsu4VljGm4ZjPKQ1DeOSJ/1qHY7sWtTZAvVBbu\nugAAIABJREFUdtyz1Sdj7Ou1gs62mw6zzwkZJR+mFAt3YvQZnS7a8v7MHVM/Qt+VJti/L/hfADmk\nfMr83m2Q3+zhfO1cxbPYnw93HB2e21kDOe7i/Pq/v/8+yH/yHu0xKOfHPr6gOxxN6e7tTfQTvD+L\nj7FdX9zFOexTHYsZ+sqQzwZK9yw1jtDeX7iGfXdA+++DI8wbcmxQ0DlnRfOR16UPf8POefT4Pshv\n/PAHIPdp3Tg5wb0251oubKOfOj195OgwK7BdHNfzkW8npjPlDto07wdtD+3jAvlKM7Pf+K3fAfnP\nfu+foQ7JPZDnFDPUA1zrXv7UL4DcH6H9nD7Bdc3MbPL2n4I8pJyu/3HPgM8QvSHe2ajpXJTPtfiu\nWMAXB8wspjxgSLYZRijHMdpRp8vfU54h5BxOU46G9A4o9iMfX9F8dMJ7amfg3C9r2KSSDlwm+wD2\nK+RGnHZWfN+M8pBmZiUloCs6a01TvttCd/mWuPbNZ3hus1igzHfgzNycLZ+9u2fMq/uWz2aPjzG2\nTFI3b9i2nSMTtYrOOBanWCd1m9FSaHnacG7Xx5em4wnKE6wz9FCpqEJfFgxw7uZdXLeWxb6jQ5f0\n4rO/xTk/i3XOW8le9/cfgzydoH1fvHDBKXM6xXGsybc4dzJI5rskvN50aD26T3upIHTv1z58iPHE\nlPZO27QHMNors49175WQbyN/yP7RzKx07txQ3vzjXRsx98Iu+WAnAMUCooYrpa997udB3uyTzhHl\n8guc07PpnGS0jXSJeze+nzFaw1yAmdlghGPF/tG56uTA9kU5YU7AOQmzpgzb6rsqJZ0XLMknR9SP\nIcWOzlrXEpeYmfk+xYY0r/ibH+cs9nx7RyGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCnHv0\nxxFCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhDjT6I8jhBBCCCGEEEIIIYQQQgghhBBCCCGE\nEEIIIYQQQghxpgk/aQXMzPr9GH/wAhD9PEeZnhdV4ZQZUsvKCssocvymE3RA7kWoE+voBR7IeZ45\nOhRlCXJapCBXJekd1CDWVYXvV/i8yPG5kWhmFsXYV6GPsnn49zHdEPuhJB3myyV+Tt/3Ol1Hh0WO\n3xSG7S5rlH0fy2SdPY/6yVAuqZ8+fIfkCsfG83A8q5r6usDxXWYJ1lmifUVOjWYB1VlkKNcpyjze\nWY/aPcDy/Q7aqKuBWcF9k+D4jt/Hdu0HRyBv//oI5IvXL4L8cPwQ5LB2//4qLPG3coY69Id955vz\njEf+rA6pzyq0vbo4dcuopvhO+pDeoDKzOcqLE5TXr2L5a8+AHF+4hp8n33R0Kkv0d2WB83yW4PN+\nLwK5KngOolx56OSzFOdgmuN8+lAnlOc12h65Hos81Dkg3zVeYht8dCO22euB7FF9WeGuXQWtC76h\nT67IJ2fUqN0h1rngfiG/s0zQvvodNyxYpKhTL8KOGsQ4dj75zz4/D9DmS3p/NMB1ZJ65/ZTRNxee\nvQ7ypZsvO9+shnwjrTNWO6uIU4JHizCvTT9rhD7a0sZwDWS3f3AC1U6fm/kBrR9k/yHZFsdhHLNw\nne5z149EIfkqmpPkBpx2cLzBVBQrBP7fPFTnuIr73tUJZf6+8RtnuHg8Vz52fmjrpw/fIRVaKuHx\nZbkmH806NNmk+xMpxUquFtteb/zFD9Ama1vdDq4joHnjxsxkL01atdTR1HfnhTjEtvVi3q9hZ5ws\nUY4bNnAbI3yHQ8MwwjGLaF+aZLQ/66MfGXZoH9ywXlU5/kbbcSsK1CH2ce32fdyXnD7YB9nrYLx/\n4cZzjg7xcAhyGGGMMz+8C/L4wR2QJycYM+cB+z6MsRpCINvo4jeRY9socw5iQXuh9U0sYEDl05by\nQz3ZN/F2niZcQQEpmYfRMmN1iTr47tJnHSozXkOZUwDsJziuLyl1skzx/cnC1WFAnd+jvktxG2sn\ntN1JqF1hSGPh0167wW1x13jULw3L5bmhG+OcjWnC+DXn23gtasiP0HzhMKwssYw0xb3XbEl5QvJD\nnKObTskozGyxwFxVQN9wfG8+1snfLxe4L5/TXsvjfJyZXb6wjlWQIW2NsO+/8vkXQZ4usQ7qNssL\n1Hl9E32pmdlvfvnT+A2tC7vbG6gjzYbT8QzkEe3/0hm+77MjMvdf7ik5DisoP0rvB5xGIZPznSDM\nHQsHzhOSnNJeO88oX0p+a5mjc7tz975T5Qf3H4F8fMr5n9XxKceF3A9ZxvPG7Qfe71zcxjzgb/7y\nzznfnCvYGNt8Oz1vSIM6/s3dBHCZ9AOtUXx+4H5PcpO5c4DA7eBv+H1+zjpyXMUyt9HMbZejQ8v7\nLe3mGt94Hz/4x3+I5b01duP0reHqwRsnFJ/kP/190CxDvX/3T1GHn/8Sdsz1NfLJ3M88dm321vRN\nmz0x1PU1xRE8p+rWffS//df/vxB6u2jPSZxl0gTXrHiAg5ZmGHfVdP56fHjslBmEGC8kFIB3aQ/Y\nG+yAfHqK6ySvWXmKA9+JcH0yM+v18JsLJb7zcDIG+Qd33gL5F1/7ZZBHQ4zT7j/5Dur8/nuODoMB\nHtoN+3h+ltC54izBGPXtu6jTWySHMe6199bpbN3MKhqvfoRreyfCuPntb2Md8wWN/5VdkNdiaiPJ\nZmbzU7Sx//P/+CrI0+kE5Ne+gLn8k0Mcq8cPn4CczrDfkpkb6/cT7Ov4Pcwh3J6ijj/MMK7utriB\nPm1oruxsOe8kBc6DyZziRT7zp/15TvKMztJPDvD8tuDNgJll5eozi/N8hrF/ivuzis7j+j2cP70e\n5omaYuXxHO0qKtA3vf4HfwTyxf4myHEf58tphXsp89E3nmTu3smjM8QBnWO+dBFt8fYx7jvqmhND\nq++m8H0KM7MPKM93j84lLxrOp50ezpcj8oUZ7TFruqMT8obPzCKKJ0/uoE7Xrl3BOiiHwPvzuI/n\nVwn1fdUQ9HBeKKU7NNNDPKufkn+ePUSd+2Rysyna8J/ccxOHx2PWE+H8VhCij+/G2K5f+sXPgzw+\nxX7ZabiD04lx/Nd38Y5B+cE9kPt9zPnuff4rIN946bMgc43pM1eNeWuCdp48fht1GOw535wXLlNe\nnZevKELfFlDeic/szcwCSnI75z/0ftvZG49izWd3TXki1pN9ckVl0GPnXNM59qd7WKl71y+lWKKq\nOVeZkEw5Olp3lov5SjnnQxhz85uc56t4nedtsnN2R/fwaKzZXszMIsrzhZQ3Yl/YdvaaUL/yfUS+\nI/dhmSTTc+cO0IzyZ1PUqSL76dABR2/g3k3pDHEsKp/so6A7bnSZz+d1ZA396+D5Z0Gezt5xdBjP\nD0BeJjheydKNic8Xq+8msO1MZxhnLZbuwROfZTln7DRuzjEHDSv72AHd1z06xrV9tIaxn5nZ4RHG\n+Pcf4Bq3uYm249yvpX2K4+dpjgZ0V9rng04z59CBfSjPqbbkp/OU80COAu17lt0t7JcXrmJMHJDv\n4vu7yQLtY/8x7kHnc4yh+H7F1u4FR6eIYtqKTxlr3qsRjsHxHGhLiDX0W0tamdeRhHIzHVr7Bj26\nz83rtxNDN40l3VVpWbOfxh6Yc3x8K4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKInwX0xxFC\nCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhDjT6I8jhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII\nIYQQQghxpgk/aQXMzMII1YgK/JsNj/6GIwgCkOOg75RZlgXIdTYHOa8SfO6VKNdY5zxLQR721kDu\nRl1HB9/PQC4q1KmqKpQD1MHqGp+TXBT0PZVnZtbtdED2zCMZ21lTHctsCXJaYBt8H8cir3NHh4J/\nw08sjOgH6gbzalv1AqlsJX9vZnVd0TtUhtFz6ssixzbkBbWJZK9knc3qEvu+rlD2a5wHntH4L1Hn\nIsCxsQDHsuu3/+0TW0xVo07Hd3DevPP/3EUdPoP28MgOQF6/tO5WWqJNlkucW5FF/zZ1zynk70Ka\nD9mCZPRdZmZ1jr6GJ4HHtkDjbOSb6mKCMs1hz8PyoorqN7Msw9+WKZaR0Zzqx6hTFKAdnJyiLYbs\n2+j9PMM2mZmVJVp8lmM/DQZYRk3ryIxsNc3xeUH9nhc4pzvky+a167OH5A9r8kUJ1elRGTsj7JcH\nGfbzkvqFvQS3oekbj0KHLjnhiHxRFLLvw/eDFlc1y12dtikOePnnfwHk/vrl1YX+jXF9PC9G7MNr\n55uGMs4RHE/4AdpBSeMahmhXpefOj5pjGM9z3sHnrFNFz/l7HpPV5T+NDgz3C8PxSfVjmAnXwTpy\nLNgGx4r/ppYVkplPvwQ00VknV+d2vXwfX6oqtg+j51wHtaGl35pot6HWAlY/pjb6njsWPFNYJ2d/\nQFVyP/pOzMBj3aQz+z98p6gagvNzwtoAZW57lmB/9kPsi1Hs2kxl7D+xjJDGbJpT7EBjOOri+0OS\neY9iZlbRviSn+GN2cAJyp1uTjHvjMsU47uD9t7G+bObosH35KshxD/f82ewU5SnGr70B6lBl2Pez\n40OQB1133elEtG7UHK9iu3Ma/24Px2ZjiDpk2erYwcws8HnSosjbTlpOrchWr20+PV42TNewj9/0\ne7RPJaUq2vB75Ic4BphMsb5BQ5aKbSygOCHllEPEe2N8HlFfU7hqDe7WaBpYSevItDq///ZIj/Ze\nIcVUvIaWvDrl7vxy1jSK73n9Gs8oh2eok0/fZzM0ivHY9TPZHHMqPcM9bEh2FpBtcuwxm+Pe/WSB\n5Q17saNDQOv2IqUypjhfXrp5E7/v9kC+92Af5A7Z5dbGrqPDwcETkN+9/QDkosC+ffbqJZCPT3ES\nn5ziGrFBe/1OwwaQ1zZ2h068ws7OQx39AO0jonUpCN2xoBSrpeRYJnPMBxxN0X6WJerQjYcgVz7m\nGzJ2KmZ2eopr24QcZE65n4hy0Sxz/OuTEVelm8Pd20C9f+MXPwfyqy8+73xzrqC13YnX2Xx5/WgK\nlfkbXnydtZ6fr66jbtOR0oxmZhZSjN9WBuvEZdJ+3zlx4jY26cS/cRktdXrOcz5Xwff/8h7KL1zD\nRv8XL7pKXr+wet/6xn384X/5Ovrwbz/COffj7Pc/Lu8+QV/zL/81tvM/fxHf98OWseZxabJ5/q3N\nJvl9nldcPs8JznM35uxayuBzG+c86nxx5/7rIK+v46BM5xg3+RzrlQ0Bu4+2ltN5aifC81TPw3Ux\n8rdQh5MxFt/B+ZMWuJ8zM+sYroN7Wxj3ZJTjfjzDOr719l+DfDC5iBXUWGenQwkBMwsqjEHefP3r\nIM8TbEftYwDy6P4xyLd/cBvkZ/YugPzSay85OixoTpySPD7FvfM3v/4tkK9QomP/4h7IR2Pc36dT\nlM3MPLKZ+Rj7+tkhxrD7X0MdjhYY+/dDnLRDOpeu+EzNzML1EcgnFNslKdpoQmffS3IDHLl1ejgP\nHo/dfjiZYiyXUR3O4TYvDC15x/pp/B95UT6j+HhZ4rPF/UcPQeb7Es9cQdvmnGrB42VmFPLbMZ3X\nepQH3B/gfHpmhH5qzccz9CrAfUp36N5/eesQ59M37h+B/KnL6CcK0nGtQzm8DrYzKfhs1+2Huw9x\nz/h+FzvmB1TG/hDbPTrFNhjl8Ooe9iPfhzEzqw5xfnXuPgJ5efsxyOt9rKPYQR/POe/A2Vs1zBaa\ncrM5+tfvfPPPQD6Zoh96kfbnRYF+iWfo9uUXHBW8AeYQbt+7A/JySef3ZOcDygF3t66DHPRxfW6y\nh+MJ2tiFF74E8ptjbNfuHvb9YO85kI/GWF5APr5sGIv+C7+GZYTXQH7hRcylnCe6Xcp/hGi7ndjN\nf3wUPrM3c8+l3DNGxD1TQrHge1VPcR5YlnyvBO2iIh+dLPE5x6Ipr/sJvr9c0r0rM0tTfKekOnO6\n68Lnu0xbv4Whe0cqjPC3fo/zQPicy3TPYrH8quS7Lu4cTxK6L5jRuUxKdyK5TL5PU7BM95qe4pyV\nbcY5x65xXvR6GHtu7WGceOEyrtdV0HAfq8YyBh1cby9d3AB5eYLtSCZoLws6kPDXUedi6vbDbIJ2\nzPu2PHH1PlesTh83/vJRmuYozxE+s3DvSKxOLnS7OCeH5IOnC9RhexfjUTOzBY3jnQ/ugPzsDVzj\n1tfR9vg+teMHqD6P9lYBH4KYmUd3dPns5uPe2eE7GnzboeYkH7/fkNO+2seY6MI6xlmcF1+S7zqi\nPevtuxjv5mQ/ow3McezuuffTar6P7dzZoPWVmuncK3LyZ/x9u/90t5D8A99DpzvktF5G1K8x2Tzf\nLTXfjes5UcjtZrlquGfZxvk9vRVCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghxM8E+uMIIYQQ\nQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKcafTHEUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBC\nCCGEONOEn7QCZmaDfp9+KUDKfA9kj+QwiJwyszygbyqQF8UE5GW2wPerGuQ8z0A+TcYgR37s6BB4\n2L2+h3p3og7IZZCAXNU1yVh+UWKbqrx0dOiG2Ddlhd/4pGNVYt/nBba7KFH2fOznpMgdHbIav7EA\nGxLSeGbLFOuMsA4/wL/pqWpsU006mpmVBfZNWZJM/VKxTO9XBfZTSIPjVe7fHdUtsmerf6BuszJH\nHYoS+z7u8rxyy1wWaPdFjfZSVWjXd989Bfn9tx6CHG1jP73wd19wVFjb2gQ5pHYtZ+74nWdqD23N\nSPZqmlPLpVvGnPxXQK69R/4pCEhGe/WM6iQdKvJlPcM5a2a2oHk/y1AOAyyjoDl6PEH7XmTYL0GG\n73c6KAehu7zV7NfJX51OqJ0VlrnZwzIHtPTcPcI27p/guNTkqyKPvYBZSL/xK4sE+8WnSR1Rvwbk\nX8dLml+sE5mGmdlGD9eqeYr9NOzic1LB5llBz2lNr1f/nSavnWZmN29eBvnVX/ktLNNHnVppdcg/\nTiF/2++fLRYJ2v9wMFr5Ps+PsnJjGo/6rKQ4iHHXdq6T5hvFRF6D7fE3bXAZHF847/s4H6LIjXFd\nvb2Vz93vnV9I5PIadG6ZMyX5W1fnBmezogL2x2ZmPsWg3Lc+9SXblBNXUQwc8Fra4BicGJR04LFh\nnSoO7o37jdvUMLZsp9ROHm+nXzjGpfd53p1vz/XxWRugHR6ekp8qUF7rYf+HDfO1pl4OA9rH5jjm\nJa2ja0O03V6AY8yxRdBx12E/pHU0wrV/Mp2BfKN3AeTNTfT5VYLlnTx5DHK2cOPbxeQQf6jWUSRb\nL43nC45N3MWGT0rce/e67hyP+Cfqqor8Qklx1/YmjyU+z5f0vGHdqchZVSHlFEinlqXR6pJsNEe5\n23d16A9QZvdYF9T38S7IFdn05OQA5IJ0Wqf6zMwi6oc0o76ndvci8l287NDc5FnQtJTWtD5OKKz2\nP2aMcKagfFkVcI/ReljTWtSwhtZkexHZ/5TyQtMFzZcOGiLHGnmKA5Qn7p41S9APBB76Ot6b5ZQH\nev8+9sv9I5SNfMKao4GZ30P5+ecvgcz5gvHshErACdPxUceYxipLXH/bG+IEuf7SNZC95RTko8c4\nh+cJjvd7tEZ0SYebV9BHmDXly2gOO/EuxVjsmHyKoQPMiXgRdbyZeV4X5MkJ5ru+96N7+DxFHQcj\nzHX5IdpgTM500bD2jceYa84ytNuAcjmdLrar2xuCXJNz5DxJHLqLxq984VMg//znXwG513Xz3+cJ\nZ2/F7o7XkzbZzF1kyDfwc4/fp9drdqkt8UqzTqwDB4gtZbbpwDJvvhp08gLue97IfLwyaz5PIjfx\nj36V5nBMMVGvIU7nVBPp9MLzJO/hC//ZP0MlH8/dfe7fNgVt8L76TZz3//HfRx13N2hNJ3fq8WbC\nTVk49uXRGl07W20yGK6j7Z93425k+/6wVqqDnjrz6MdKDJ4ZKsM16uAQ15sFxWHsGwPfzbt3O3Re\n0MM9YVZgPME9POht0HMyLjqf6PVcR7I5uAHyMp2DvD7CtZjPgPePcQ96fIpyxLFd7MYTcXQM8vyU\n4qgH+LykXD3nbBandA69wLF56wHurc3MHp7i2bdH+S6eYnvULtzdm53cfQByleFYdBr2QRn1bYcO\nHfbpvCBPsB+WdFaUzLDOZc7nOK4OfKaQ0TeZc5bNvmvlY/Mo5uXzfDM3pvWcQlbXwfgUJIScB2nQ\ngfN6H9fFnmU6tGdMU5w/NeWVhjSnk9yNlWdkm/HeHshf/nU8p7r8DJ5jTY/xjL30MLj4/g+/D3Jl\nrg5vHaLtPbOG8XmU0j6jwnb7tBD3yZ/u9XBfc+cJ70HdOzPTJfrbz13GPd+P9tGfPl+hsccp+oTK\nR51T2rubmQ1pXxLEKA9ocR8uUOfHlCM4PkJ/uqCzeM4HmLl5iB713XMvvAbyow9wT/mY8hZ1hDMy\njvGex9bIXXdGXfytnKKNHVU4fnS1xEJqV5lhuze6OLYPyf7MzH7va3dA3llHnbprnwX5zSPs+/vf\nfwJyXWG/bK5jTOHs/83saIzfLMotkK/WDXdmzgm9PvWPkz/hc1LKiTbkozn2K+iOR0lraEFyQvmO\nhHJRJa3J/NzM9dlZjnJF+Y6cY4OSz8lW7wf8hviWcygB3UkcsB+inA33Lcd5rFPTOWhBc7Sgdi4W\nOEdzGivuFy6Pz1X5vLDpHU6cO/EOtdPJszhnuVi81xCd1Hx/hp53OzgWozU8S+oN8YudK5jD7e9Q\n/Gy4NzEz2+jgvbd+B9e6NMO1ju9hhrRfCnuYhzyaYh5y6wLmZ83M5ku6PzW+BTLnus8bbmxNz9vu\n+zTd+zD2mfi8Yl9B77O1RnR+N6e8elmiXWxubjs6rVGSYzzBPeHDfYxZ1tbRXoNwtS/iRjp3GRr6\nyZmo3G8cs3KV1JE1+5qa7sDRXS/ORR0d7Tsq/vW/+irIv/OP/hOQ966/SjpiP83mdFYU8H1v1OGl\nlz6N5V/EuN/M7MkTPEepKrYfukfEBbRcInbmgDPW7l7C3ZPy3RSnVJAyyi/MluhPQ1o7Q7pv0Ghf\n7mUWfEzNCPmO7VNwnve9QgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYT4GUB/HCGEEEIIIYQQ\nQgghhBBCCCGEEEIIIYQQQgghhBBCiDON/jhCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBn\nmvCTVsDMzPM9kKMwAjkIUK7qAuQ8R9nMrPbx7z7CMMY6Imx6RH8nUtYl6lAFIBd1jnJZOTr0gwHK\nMcpR1AE5j7DOzMN21RU+r3KUF/O5o0NVo14e/TkMl1nXNepAdZQVPvd9LD+nfmn8jerwFjj+foqy\n55GOPvZLSeXlpLOZWZHhN1WJ31QVtqNmucAyfeqHwEMb9bijzawbos3FHZTrGr9ZphnIRYH9yPOg\nNpoHntsPNdn5dDEFOc+wjmF3CHLooc6LU/w+e3eB7wc4b8zMXv67r6CafSyzLF0bOl+g7Vi1pMc4\n7laQnJFs5swpI59qPj3nYQloKQjZftG2uqNNVKl0fXBCtjRN8Z21GJXwPZxz0yXZAfn0vMQ2LqYp\nyIOe65ML+mlOOvke9lMUojzooA57632Qxwscm5MZ6rQ7Qj+xID9iZlYY+VSa9zXZT7+LOg16uK5s\nDfD7e0c4R7sh9mPou3N2c4RlDmO0l8kC27lMeA6Tzh2soyR/y+OwPcR+MzPbXMf1tDvYcN5B2B68\nlaKzTvEc81z7cuug9Zfnvjv85wqfbIm7mGM/JmK/ZGbcaWGEtlFTzBPRustrvU9+paRYLgjctZzr\n4DLacN+nmKdGmU2vCc/juGm17Brf6jbUtesX2pVabe8e9QPHn9zPThPM7cu2seEi/IDXIeo3+p7t\nx8yNc7ivHLV5uQ74MY8Nv9CwblCcENA+qs1GA1KirV8bB8PRe/X8Pk9Ml+RHaM+xFdOeguab5zXN\nL+zPJMMySrKz9R6t5UG9Unb23pGrQxRjrGcPD7BOioHiAcYKg01cl6uE914YK3SHVJ+Z9dewjCzB\nvW5N8W3QpXZU2M4yx7nSG+IaETTEs+w/K7LtJdWxuYY6dUIcu3ROY0Nj3TS9csd5kY5OiMLjj8+X\nNBYlVToaukrEMX5T8DaT5Gp6BHJK+/1lgu8P+tioIHD9bTdCHdIMy+SwgUJkc9I19JyG0qrS7YeE\nylgW+M4wOr/B3Wx8CnIcd0Ee9FH2yc/kubvHnywop+Z0Hw6q53PegPJjNIeTBA2trNz8iNXso9n/\noo4ZbSg/2Md+OVliHR3yr1ujnqPCtStrIB/SBKm53dMJyOMxzjcrML+Q0fx7r2Esbry4DfL+Pvbl\nZ59/BuR+hPu/v/ruOyBP5qjDDrX7xqUdRwfeGtROTEw+nXJ6fofin5BsknLC5rl7zGWK43vn4QnI\nr7/9ARZBZV7cw/JKyvFxjHVwgGurmVlC48Nx2mCA++CQ8opegOtxTc4v7qLOn3/xkqPDL30Oc3aD\nPn5TNeR9zxM1h84cJrU9bwrt+JuWnJ2z/aL3WUcnnGzT2cwNIBwdW8oMuA0t7/P2viHm4diuVSc+\n42iJmQynh232OVdK/dw0ltyOiPeQ+Pi5l1B+fg9feHy7YW36CfPDB+jj//Dr2DH/6Q18329p89PY\nfM1BLn/DawDZPJtr27a5cTvKBuKkZn62cnYFnb9w2qDfw4GnkMgyDAXMzD37Mjq72tu6CnJIxtXp\n4hhdfeYmyB6tP+MJxmFmZsMYz7I8ys2OT3Fgn33+OXz/zrsgnxzj2VdC+/3TBJ+bmdXVGMvk/VqO\nsd516vyEYtx6hGt/UqAOkznm+s3MCsrN5xQPpLShy6jOO9MZfY/PC4rDmmJ9zvO5B9OcB1ydF/xJ\nzEnPdQQo8vrLvoza5DecCfOS3zVea9CBVXQXIiEdR7RW9mcYr3rOZr0h13jO/dtHuf75Y4E2AAAg\nAElEQVTsNZDv370H8mSG+5aLu1v4/v5Dp0yPkiz3Ht4H+Y//9I9AvrR7AeQnB09Avnsf9xh5hXP8\npefdeP3uCfqZz2+tg9wJcE7ubWAO72SCvmu7h77zizeug/zoyPV18Sbut3ox2u4LZKtfpTI+u4l5\nwGKBvmxJ+/kZ7YPNzGYJjt96iotTSXV4S3zfD3gvxfc8MI/oN5wVsdsoKVjsxCOQdwfYb3PKVe5t\noQ6HR+iPjx+7a1+HJvWVTbShi2u4jtBSZrcfow5/9Id/AHKZ/gLIf+e3v+jo8Owl3DNeu4A2uUln\nvLfuPQI5jmm/Pv8OiEmKm+3R9qccHXYG2A/7+3dAvv0A+/48MT45BjlJcf7kFLgVtG7zczOzlOZT\nyrkqOivjO0xch3vex2ck7vwKQ84Lov/l899ujL6O7+EF4eqzOyf2sKbcI8ocA83n6Ou4HwqK41gu\ni4Y7j5Tr53WdQy4nBnNiMhT5LN/5vgH3fN9bIbn5sKqlTU13MDyug3P5dLdzPscYaYBLvPW36D4A\n3Suq5pTkMzMvR5uaLjEnO85xTY8jXIeG65h/9SknEZboC7fWMYYwM7NDnO9pgjnZyjWhcwXbgWtt\nlOvn+z4NCTL3DsXqOxX8Pu8RQsqZjCnuWl/fBXlIuV4zs8EIY7P5AmO/23dug3z9Gsa8POfcc3+s\nj7vgae6+eB7PY7rzS369onm9TLBNyRLPf0fbaP/sN3ojmtRmtnEJk3Adyg9w4oN3ThnFZQuKTzsh\n+oBXX/0cyEWDfZ2eYuzmF7ieFmRfWeOdgY/Q4qP5rguPS3MRqw3CnXdIyv1GMfpowPdx3Dtg7hXF\n1efcTX3dhv7nCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCnGn0xxFCCCGEEEIIIYQQQggh\nhBBCCCGEEEIIIYQQQgghhDjT6I8jhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghxpgk/aQXM\nzIoa/0Yj7AQgl0WFz2sP5LrC52ZmQdQB2fNKLNPLQc7rBAsgnczDOsMAu67jxY4OvbAHcreL73Sj\nLshVhTrUdY06V9iGito9O506Oszn+FuvP8AySupbH9tdoQrm0fPCUnzfsJ/MnK6zYInjawusJKoi\nfL+g7+mHosY2FEXm6FAU2Hd1gXXWJTWU+jagjugFqONOH8fy5u7I0eHVm9sgP3NhHeTNnTWQ3zlC\nG/3f/uX3QD5dHINc5DgW88Wpo4Pv4fgliwXIi9kSZW8OctdHu6/n2C9Fgv329p994OhgNF4v/srL\nIHcGbt+dK2rscy+/h89z7PO6RNv1eEKZmR+RK++QHNK4sXsLyKeSfXuGOsSjDZDLBp1OU5yniwzn\n5XqMPno8p/dpjnpku8MeyjXZ1SKlOW1mOS0V6wNs59EEx2ayxDl4YQN1Xh/2Ue6hb3s8xjaVFT7P\n2e+Y2XKJ3/QjWh9p7GryTSGN5doAfVMnwPczWl83+9gnZmYbA1y7Hh7juvJkjH4jDlGHyEc5ydCe\nAlpX+M82t6kNZmbz6QzkbPYE5O7GTfqCbNyo72m99Woem9Xvf/gR9qVTx0qNzh8xxTxFgbbtBzgf\nOJQLAreHSpozbDo1xYccgPCweS2j0ORvm4Yen7PvYh3oOdlJVaLfCUJ3Tjr2TGVyfNiG7/NY4PdN\n/eBacIu9kw8vKf6cLXBOx130twHpaPbx+7qidcKjxbDk2K+x3Qh3Na9FTt85Rsh7Gm4Tv94QA9Ce\npK1MnibOPqqt3Q2TgPuaabah80FCsfBaF221Q2ug77MPoI2Omc0SLCMt0Q+MKN6IurRPpX0ux/88\nf/PS/fcSemu4Twm6RyDvbmEMtHNhE7+n2KH0cZ8bhZewQh/nvJk5E6zG8MHyHNvpkb/sdjB+qJcY\n53VzLLCTNvy7EVRnRn5jc5PGf4ByitszM55uXGXD2sfj5cwn8qc+zcecTKykSvs41NYfuDo4vo7k\nMsM6E9oHzKcUn2KaxMyj9b2hG1iHitrZoX7x2J9SoSEtKxwTzBqW0mmJ79BUtIaQ9dxwNKG8UobG\nneQ45yuasCUnlszMpz1qGOEcDmmQOLbm9W25xD3JdIaxxWwycXSICmxHTXPQC3C+LDM0jJI2LmxX\nSU4+oXAN6/5j1HNRoG/rxdgv9+5hnuf4FP3r7gjfH1B+9f2HWJ+Z2Zz0qii2uHUHy0ym2Nc56cx+\napqgPSwTN2fX76ENeQHlP3m5JF/mR+hYwj7m18IOrluV56bDDx/jnvK9uwcg7x9h3/se9m1C7RrQ\nWjqf4TxKM7cfIlq74i72/fYmlhkPsF0zyukVJXbcJco7fvkLrzo6rA8xbxxQX3ueG5efK1aHTe5z\niu0a/xkqXtgoL2PcpS118JrmnO60ldf0TlsZIbfhY5bnxDwNOjntbvmG4ybeOrMO/Nwpj8YlaghI\n2uqgTwYYIttr1/CFr992q/hJw2vT//D76G9/7kvYMS/9HK1d1Ad1w+liq5tomVdOz3MY4eyz6XlD\n3NGahOOxbcn9nHWGAxy4JKEzPtoHeZTz7MXuwG9t4frRobxORTH/6QTPto7nuIYFXXx/fYhna37D\nIP32l14EeYPiizfevQXynSNc6ye7uE6ubeK6PB9j3DU+wT2mmVmRYlzEuaiKzhEP+EyDzgYWS4wX\n8iUOTpa5OQU+8+V8V0nnTZwX4pydm6uiPIezsbXWOVfTWkbm0ZDjpQIoKdyUw3PSo+ycPIpxfT4T\nW31HoKTyuqU7Fl2aTD3K+5YhzpMkQpvlc+uc5mLgLJY0ec0a8nh8TnJ+c3a/9uv/Hsh37uDC+8M3\n3wb53mP0S3GfkxdmsxnO++US/cJ3v/ttkN/wOZbG8nh+DgboS09OThwdsiWeIfv1Gr1BfifF919+\nFnNyn93FPcazI7TDV6/sODqc0B7y4mAL5MiwXzjnklM8MqF9DDuFaEhBlZlldP40pjmYHqKP78Z0\nJ8cJJ9AH+BHuteLe0NGhQzrUNc7x+T7a2NUp5lcfkw/vk4HMfazz6MANHpcZ5jo86ruMEoM5+fhH\nx/h8STq98iqurXwdwcwsyH4Acu/0PuqU4Rq+XfwQ6zy6AHKZ38Hv6fpT5OF9GTOzUYx3LcIU5+q8\n/5C++HtOGWeVN77zLZB5zEuaG875Yenmqjh+YYYj9Dt98l29Ptounw/6tJfjM0ozM5/XXVpTCzob\nyHOMmZZ0FsBn1Px+WTTFVJzfJP/KZ28t54O+z+ekWGfjisw5b36r5QzSjQJaziQbEvPu+R/r9PHO\nRTnHy36rCeeslD7h+4ExJe7X9vCDoIdjmxe4BsyOcO00M4ti9LdJPQZ5Uj8AOfNwDU/mj0DeHj4L\n8mBI84bzTWZWVLRvqyjOaDpnO1e03PdxthBP82/Frz5/470OP+c7RaMejsHRCY773oVnsPyGM0Gf\n5uH1a9dBfuuHuO4+2sc6NtcxtnPP6GnOOufYrk9mP85+gf0f7/9Zhd4Q15EF7aMDukfEOYmdHTc2\nvPnlz4K8sYZrUxhiLMh588NTzP3P6bzpuRdeAvnq9Rsgn56iTzAzi+nA6HSGfqSkM4yAxqLiu/Ee\n9zvW57jThoXFvSeHIs8aZ7/ojD0+5n4L6Rxw0MNx+VBN7Ce+A8Y6/jinE/qfI4QQQgghhBBCCCGE\nEEIIIYQQQgghhBBCCCGEEEIIcabRH0cIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEOJMoz+O\nEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHEmUZ/HCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ\nQgghhBBCiDNN+EkrYGbWiTr0C/7NRlWmINdVCXI37jpl+mEM8mJ2AvJyMgc5DLArqgDryGuUq7pA\nHQJug5kX0jc+fhOQjnVZoVxjeWWFz6sKX6gWuaPD8gTb2Yt7IK8NIqyzwDrShHQIPJALFM333L+3\n8RP8LZvieBq1w68DkEOqJPBRrgx1LrCbzcysLqkzqS89ekxVWJ/a9Wwf5ZduDEEebA8cHSY5tvuv\nbz0CeXs8A/mLn70O8i994SrIf/jnpyAHc2x4scCxN3NtynK0mTLFMvISn+cedkxYkAup8Pli6trk\nD77+Hsiz5QTkm6992vnmPFEvvgOyN7+PL2Q0P8h2a+NBNDOal16HxiXEOeXVaP9G9u55vDRgnd3+\nGsi9AfoyM7P0YAzyIkVbINdkoxh1jGgSLgv0pwmVl1GT0sLtJ9/Heet1yTeVWMfpIgP54jr6y4D9\nH/miZYbzabrED2oeBzOjKWaHU9SBHtuCGs467azh2Gx18YX7E9SRfV+Tngm1i/vaI0fD60Y3RLlD\ny0aOw2CdLtqGmdliiv5ytv8jkEdXPosfeK5PRtheGuZZy/PaeDzbyjjfRCH6kYIW58Dn+YdzOvRw\nvpmZFeXq2Kui+DCgGVM5vg919HkCNY0zleF5rn3ic573NF+oHwLDdnsNcZVTBtXBvo7jx+l8CvJo\nMMLvnfJcx8A6sHdyHlNfJukC5CDAfuSh8Brn0+o5xv7U91ePFfdbQ4nuNx7bGI0N7Wlqp1228jlT\nN7xQcynULWwfPDgejS/HGY69NWpGcUhLO88TgxD9Uifk2Bj7LwxQTjO3d4oS7WaLlzBnXcU57tgl\njw+NadiwzkZ93Nt4PVRitI57ym6MfqRLsagXoI/3/HWQFzN337JIcCOaU0xc1FhH2EWd+pub+HxO\n/UC23803HB2KA9RrtI5jszGk/RfthXh/T67OGasicu0hpPHLMuwHSlsYpSksXaIcYTdZv48y20dT\nmRVt8QoMV226QJ171O6a3K1PfmeRNniNaLW/9R3fh3Icsm8jnWiuznJ3TchKLPRCjOObnePQrz/E\nOcuhQVZybELreuT2ZxjRHKacHC8eZcn5MJTzBA1zNsF4ZzHBvIOZ2XZMuaaAcpE0p+cp2R3vL2k/\n0KV9+L3Hrg4PnqCc0b532KOcHRnvWozPd+n97RHmSzPyGWZmDx9gX50ucdJ79ROSySlw/myIa0bQ\nwT1pWrpzfED5UavYueH41rRXqEN0blEP1wAvwvfz1E0cHp9gPzw+wpzbYkm5mnq1XymoDUv6vtNx\nc9kDcsr+AOULFy5gGX1sd1U+Brmk/dML1/D7nQ2c22buXPRIbsr7nivYPLm5HDaxQ2zqHv6trQ5H\npg84ZdeqY4NObe84myl+3vJ9q44Naz0v5vwOl9kmc0ph9fGTWdhSn5kZT1vuR5K9GNv0youUV/8a\nxYLVTz+YeH8ffdW/+ANsxD/+DOncpTW/qZ94eHks2tzI6nSDkyt3ymvoRicd1JbCc1O254rxKe61\neI2qqI97Axxo3kOYmS1TjBcmtP/q0Z5yEG6BnNMZcEkJ4jTDgb52+TlHh+eu4vnZiPa60xlujiYp\n1nlnQuez5H8Hm7juDtfcPWREMcd8gefSHNO6myPKq1Pw5tGEOD6gDZ+ZZRlu0Cqukt7v0xlxv4c6\nnR5gDDujg53pGHN8ZmbLBfZtkqBOObWrrPj8vS3nx2Pl5jUK3o9TviYOKZ4kx89742WIC0lNzifk\nsTWzguqc0V2IHu9bKe5Kas6DUL+RPQQN3easuJyza+nrs8zlC3sg9/q4kO/u4vPXv/U6yPuPaLNm\nZnmO8TXvz6IO28nqvGrYQTtcW8ez1/Ep+hAzM48m9cEEz8ouD7CM3YuXQH5pF/cA1+m+Q56jX/ny\nHub8zMy+W2JfegvUacF3DarVe6eTMbbT93EuXL6C9yXMzC59Cu8S/MX9eyD3qM4+jf8lukPxo7ff\nAHm5xH64fAn70cysR/nTF569AfKrN3Fdqh89BPnmDPegX/urb4BcGK6dR2761E5PeQ3HvfPjI1qH\nitX3A5xznBn263byvzo6/MP1N0HufvsOyD6daaQ/h2tC+IDuaO2gjuE++vh86y1Hh3Id/WPnNs7F\n9It4N8XsnzhlnFWWOfqALMH5x3EdH0I1n70hfMbUpVxEj3IXyzkaa55zHEBy7t4nKik/VtAdDs4L\n1nz3q+Vs9mloOzNsOydtPzddfTbXVCLHSM5ZKudT2f86B4hcgbtXd39hP7L6bL2m+CUMOJ/6FDbY\nsofk+4Mbu/h8tEU6UcydU853yfcZzSz18e7TzH8AchljHjHx8S5g7mGucpijkvmSzidO3Djk9OgQ\n5Cigu0uhe7fifNFmKy1ztumw2uM5ws+pDOeMHZ+HEfrgu/dx7X/p5VdBLgvX/41P0ZZe/vRLIN/5\n4A7It27dxvc/9SnU0eM5R/dA6Cynyfdxu917brSX4rvM9HYnxrOA/hDjTb4LHdK+ahC6hxz33/8A\n5I2dHSqTcvV0Lj2bolzTveXPfO5LqAMlRmZT9+zHuXOzjvOe1y6+y+SxffJCQmP7NEudc22E77I4\n95D4/dVrHa87swXmC6IGPxXT+X7tXDThxc0popVzfqIhhBBCCCGEEEIIIYQQQgghhBBCCCGEEEII\nIYQQQojzjv44QgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQZxr9cYQQQgghhBBCCCGEEEII\nIYQQQgghhBBCCCGEEEIIIc404SetgJmZ7wcg50UBchDh8zqoQK5q9288umEN8uPpIcjTkznInagL\ncm/YQZ2iGciFZaQT6vzhOynIfo061QHWUdUlPq/w/bLCdtdUXuC5w+kv6Yd5AmIvxjonBZWZYwFe\nB/u6E0ZYHzbZzMyKE/yxSrEOI9GjHzz6G56wRnuwyiPZ1aH0sAwvwDrIBG2QYT/tBthPfhSD/Gc/\nfAzywfEdRwcaLos7OP6eh+345rewjItXtlEH6pe6wPEPSqrQzMoSO8evsU6f+q5kG6SxqSw3+gGh\noTEzq0ivW289Anm6j31v/41bxpnm5F2UaUy4D7kLa6/BwMm+LSJfEOE8rUv0Vx4bJ/sikntrOyBf\nuH7FUenW/QMsg2wnL7DMimwxJTupuJtIni6xvDCgSW1mFX2UZqxTQTLad5e6uROizn3yjwWNbUHz\nKclRZzPX/1E32TDGdo1T1LnMUR500RbWYpSTQ1ok2BbM9QPctyV94/vYL4HjB/CH0Md+S6lfGtyI\nzWktu/3m6yBffO03sYy4RyVw3MDzivuB54jbT57xvGkpo2mxOkdw+531h8bdJz/G87XpG36Hy3Di\nJP6efFtFtu757jizRbIOTp00X3itd+zEa7L41Tq4tobwnB32hyAnFPOEpEOngzGymZnHcZWjN8ew\n5Mzo+34X46qa3veDpr/nxjpdnfj9uuU5ld7SpqYy+BvnOY8dl+f4U263q7RXr25301xapRPPk6ey\n0ZrXbIoz/H8ntpw/GSimMgo/2CbKCvt3kbp9E4U4ZhzSBAHFJ/S8pDWuzLG8kN7vxH1Hh/4Gxnov\nfwFjSSsWWGaNcuRjmd0hlldV2IYkcTeRAe2VLMN2jC69CHJOMbZHfqO/vgEymza5wg/1jDGHsL2J\nOYCC9rVlgeMdUazIMVPGoWCDO/drXuvoE/pmvqA5je7VRtgNFlGgtkwalCA9a8oZLHMsIyuw77ts\nYrz/oSqDho5YptxweoFdU4sP9+n5gnIKx4nr6zY6WIaz5XK3H+eG4WAEskd2U7IvJKPxvYY1lNcb\neu7EWPy9419pr5fifPVrN2fHORUuw6NEUUZ2wits1EVfeWV3HeTbD44dHRa0n4s7aFgx7ed86om/\n8/nnQT54dIQ6kr/9hS+94ujw7R9+AHLUxTqenEywTIo9LuxsgbyxhvbSHWDsWUdrjg5ehI6C97Wz\nlPfqtLYVU9QxGoAcUO7ydILrlpnZyRTzxJM5ypwv4BjJS3BvXda8T0D7Wd/YdHTY3tkFOSZHs76B\nfc0zYzDAfW9Ac/HGlQsgN+VN/ADr9HyKARrm83mCjxg43Wa8R+SFuWmPwa+0hPg1r2EcLjo6fcz6\nzG2n0242DZZZJ5L5e+foJmiwo7Z2kylah97nOngby+/T2ZFFLWNrZkbhqTPeXCc9f+Yi/hBHKM/4\nzOSnQEW+7Pf/Av3Gb//72PFf/FXa6zUV2jZ2bWmPtudsj09xHuH8xHE4hwnnOK4zc3M0Hh0KlZSb\nTWmPMIjdDhrPMfYaDnGd45yNm4fHeKOmPcNGH9ew4RDjLDOzMETjq2gtrqjdZYlxUkm5DO6njPYk\nfN5rZra3/QzIkyNsVxhjGRTSWMGHAY6944TqjdycTyfDvk6W+M5ghM5qtI3ObUBn43vXcCw5x7BY\nuPv5LKVzEYrNS9rfT47GIB8/xthuSrFbSjmELHdjfT5G41zjWkj9RN9ParInym1FbB+hmz8tac+z\nSWtPlxzYgNaFhOLHRUsOIWrKXXL+tGaPeH5jux6NWUD72jW6F3LtP8Dc1eEB7q3Mms4C2M/UK5+z\nn+I9Rb+P+6KLV19wdHjr/dsgP/tLuMfb2sW90PUffQPkzQvoWP7yDta5s4nnvdEA78eYmc1o3m9f\nwzn43vQiyNdGnwJ5ScHBeIp7qcEAdTqYuXP8pc98FuQrV3CvdOeDJyDHXRzvm9dRx0uXcC+2WKJX\niHlfZGazKfqqAdnYm+/dATlbYDsnC9xzejHunTeHWN4F2s+ZmSUp6pmS3tentJ8vV9swB03Xbr4G\n8iJ82dFhdA3tNN1A3zcy1KGzQx73AvkhzsntUv6nj2uKmdn7YxyL45jq2Mc6MJNytvncr10Def/W\nKcj33kZfVnKs0QCfx21fRNvs9nBOTidYx8FjlPnOG9N8Vkc5uJZzUCc17JytEXxvr0FHjgUDOlgJ\nw9Xnvxx7tp3VNW1k+A7Fay/eAPnaLvq+jMb39XdugXxwjPbx48BniE4Ogn7grXXg5JXxedNYc26Z\n+7pDZyBreK3Oot7qTSNdibQicefJtIvrSrFGcX6H7p92aL+Toe+anOL3Cd1fHZ+6eeQxxcTJkuJT\nznOcN1pScG2tb07ZrS6U7dFvOVPvUG7p+BTHdXMb443DQzfePBmfUB3oa27euAHym2+9BfL6Gu6V\nnfjUmaN0Z6chH+bMy2q1j/Vozrr3HRCOgZOE4jDKM+2NOEFn9tyv/iqqSHdPMoqZSspJFDnmB3p9\njKmfuXoV5DzDeLhpHbl0Ddfo5Daew8wXdEbBe7WPu/Z5PNZN+0OqYmUNbhnOnGmBcxxOm80sorMb\n3jN5TqL54/s6/c8RQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQ40+iPI4QQQgghhBBCCCGE\nEEIIIYQQQgghhBBCCCGEEEIIcabRH0cIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEOJME37S\nCpiZBR7KdRCgTFpWRU2yW+Z8uQC5H49A3tu5AvJyNkOdpqhU1O+AXI5S0rl0dKhD/K3wllhGiQ2r\nixzkqq7wfWpoVWM/xB3U0cws8LAv0zHq/SRDOeN2eqij72G/eCnqmB4ljg75AvvBqz3nnY9CzTIz\n5wcqD+XA3PJDH9vl1diXw+Uc5C6NxfspVjI7PMX3Q+ynju9OrZL05L4s6W+Vvv/eIci3H01A9kJs\nE5fX1M0VyQG5AN+nv5eq8IuaBqemsal9so8GD8Ndw3Y+m+A8OW94Kc45C6hDyP+x+TfOHh43LiMc\nUCHUxwXO27qieVzj+0G8B/LNX/ktR6V3334P5P1TnGNhQLbD/q5Euaqw5Rk1OaImD2L3b/+6ERsf\n1pFlGepInd+hz0NyPusxvrDRRR1ItKLhzxMLchRxhO32qZ/6pBOvI77FIA966Ddyqi8K3Ulb0Vj4\n5D93+hHII+qHUQ/lNMc1YZmizh479QYCDzvvnbfeBfmLk32Qu93LLSXyRGP7RNlzvKmZ1TSgNFbu\n4tbezrNMRfPL9ym2c9YT+t4NBsyjdc5Y5jIbyvgo8znGft0OzhenPnPXLCPf5NM66LSzRSeus/n1\nlrWZvuHnHC90oy7IywT9tc/rlJkFPrebNKTxT3PyrwF+UDnBPD7nNcHMrCrxmyCM6A3aL1TUD6z0\nj0FR0H6A7MPpOx4Lel5VtJ9wxr/dJivy0R5/43GcwX3LleL3VYP/47EocuyXTrfnfHNe4H2Nx/1H\ndjbL0U7j2O1PZ79FcshDSHUkOcX3Na6zAa1xgxHuk83Moj7GjnGOsWA2naKOGe5bAu8iFhhiHdkU\n9zVpQvGxue32KGmwcf1lkA+XuPYfPLpD36OvG9C+N+JAzcw293C8vBD7MlvgN/kSxzMKsa9Lmk9+\nj9bGomF+0U8R2diCuq6iflvfxDq6XfIZ5HaChnWnpHgxL7CONMd+oK61mnx4x6dG0ZqSV66vqzP8\nLWAfT59E1JCC5mpJ7X4yxzaMOu5YbMQ8v1F0lqFzhMf7Sx4izk04AUxDvsxZv3gNXL0RdmNFitep\nvNBzx9QzbBf7Hbb9JMcyCrLti3tbKO9ugpw3bCEeHaI/fe7KNsihYQx1MsE47dOvvgRyZ/AE5MMj\nLP/l115zdPjRB49Bvrw2BPniRdz/33lwBPILz+Jei2OsJOMJiv7YzMzvreM3Ba47t/fJxx+iDr0+\n6nxljs6xT+va6czNXR6c4Nq0SLHvS7I53icb7XMrD7/vxX2UB6izmVmHchZDWqNpG2we/TDo4X5m\nbx1jsGtXcH0OO+5YeBQrej4HHud7H+vxQsjhgc+xNT2n7moqg1MHHpfJdXIdbTIP2VPoxFsEJyDg\nwxunX6hOfs5bygadnO09v9NSp/E6HLWNFf1AcZtTXtNv3C6WfSxzl0LktR7tFdKGheKnzIMj3Nv9\nk/8Rdfyn11C+8ukGn+CMXYvfaLLRj+LkZqi8ti2t2ceeN01pv/PExe1XQZ7O74O8jPDsK0lwHzQe\no2xmVlLOpRNhzJImOIHWe3yWSbkH2junKa6zs8XY0SGk5L1XUp6cbCkK2HnRPiehXAedS9elezB9\nfIJ9yTlkjxykRzHxcoLxA+fNOzE+H200nQnj+h4uUc+tHYwXIuq3gGPijPKQFFwNkaUAACAASURB\nVLY3dIOtr2FcXNJ5U0l738vXMQbO5ljJYo7trkM6K28ItCfHmPd9fA9txpthO8c5yknFeyAcu4JT\nfg05O3Z/ww7+0AvJJp0tENkL9VuP6sRo8998E1IZnPv4d2Dt+UnxjT/+VyCXlGApKSHSoTsWAcfB\nZtahjT+fBfAekfOENSX1ApKzFOfv2qu/7uhw/9HXQB7sXMI6wzdBvrr+BupM/fDH30MdPv/83wP5\n3Xt41mtm9r35CeqJU9jKCe3vqg2QH9x6B+Tv0Fhd2Ma9dHf7qqPDK69eB9mvHoH8+rd+CHKa4Xi/\nf2kX5NHG90D+5T2cK6+/f+DocEi+Z7SLZf7Jn/4pyHnOZ6Grc8BtZ0lP8w0/53Mblpkl7RnfbLjC\ncZV89LPPY9D7yhXcz3ce4/vzGus4SdHBDmK00fuHbr+89Zc43v/XV3E8Azr7/of/5X/vlHFW2bmG\nuYV4gP05p3s3WYJzIV24cd3aNuYSXvz8MyAfPMA1dTnFdZlNl+NEDu/Zl5q1b4u5Emc+8ff8nP03\nBzhmVlFeMKC4LeA7jaRTELBOtCbw9Gu4BPTiTfR//+A//G2Q9x/h/NpZxzzSV77yZZB/93/+5yBz\nLuxpcj7clxEFPE6I7dyR47Na6peGfuB7IRzXx32UeyNaj+k8ospw7KopxcO1my9LPOyrMMCca5dy\nCmT2Npui75vNMR+bj/F+63KO+ykzs8kU52uyxH4JG840zhWOffI6yndPVt9L/bCE1WU6d0/ocUAG\nX2Q4jindMx2O1kA+PMSzVTOzGd1fmdFd5hvXb4L83q1bIL/97tsgl3QY1uvhroH9oZMLNjOfDuBq\nmlPciz55cZ4PPvVzt0t3VZZ0f3FxDHIe8iphtiixDD4vSBIcmzylOUfn1H6Ae4OQYokkwTm9d4GS\nfmZWsq+K0Wcvl7hG1y33hNzYEMe2fop7djxYbVfYnDnCU6S1Pjpj4/uyZragu0mjAZ2J8BrwY9zx\n0f8cIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIM43+OEIIIYQQQgghhBBCCCGEEEIIIYQQ\nQgghhBBCCCGEEGca/XGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHONOEnrYCZWbfTwR+K\nEmXPA7GsKnzdCrdQL8A6+j2Qr157DuTj40OQ09MpyFGCf0dS5SnW1yOdzSzsop79Lurkzccg5wss\ng9tZlg3t/AjdMHJ+86nvirxGuUC5zjPUMUSdbYk6ZQvsB25DE7V5rW98lKoknT3UoaqwzhpfNzOz\noMpB3k1xfMMK+3a/wPHejlEeRdiGpES5SYdOhOMTRWj3BbXjpMY6Uxq7Xhfr9BvqZOoKv6lIjgzH\nO6O5xVX4PupYkwl6HVcpP+Txx3dKnv/njJrmpBeSG2Z/SF1YF2jLZmaeMzA4ELU/wPd5DlY07yuc\n13U1wec0By++8uuOTl/5nbsgH/5P/xzkbJ6AnGRoa2wFUYC2meb4xjRFOWuwo5sX1qhM8us0B0PD\ndsbkDqsCdY7Jtq+s4dguSaWtPhVoZgdTfKkXoo5dknd7McjTGY5dl3z4oIPyGq1Lax33byZ7ZKJ9\nKrPs0/Mu2t+ICuhG2MaUxp59flm6Y9nrYpmHB7ieLsYHWOcujuXH/ttQx5W5vq12rJbf4cn8FE77\nHOH7tP7U2F8BzfGmmMen2K6mOMlvKSMMyBY7OH8i8se8LpuZed7qNczzaF2sq5XP2/AabC0veR1A\nnQIf28E612R7Aa9LPVwzqqphLedvSM+8+H/Ze/NYy7LrvG+d6c7vvnmsqq65eia7ySbZpNIkxSbZ\nIiWRdiTLtizAsI3AMOQYigMDgRE4NmIkfwRIgCiOGEgJCMW2LJmSJVukSE1sqdlNsps9VvVQ3TV3\nTa/qjXe+Z8wflBF+3z55tylZadbT9/vvu2faZ++111p77fOqMK7UKlVsQ87jj23m6cH5xncv4qQD\nJfd9EOw9du6UJJstsUnO7TjHde2B7YWfSLkd51klfiOg3M0n/8bt5n7xfbo+4Hm2t72YmXkFBYqI\n38ONd/uFCE3bcur/cYw6oFyBfZ+Zm58H5D8Lst3BmPqb8vc6xd1qBc+vzh9x2uB7NCd57euh7Sc0\nPXY3tkHfvtgHffrsm6CXWq5dLS3Ngg4aU6ip7zodzC1//euXQbcpP/nEhw6Anmq5YxGRLacx+XSa\nkjVa+7DLLwKeb3i8JM13nBMtWy0psI0t7DarN/f2dfwOUYm7JZM0cvEW0k3qde437hfOCfB+ZfUC\nn306zYM6l5Qo9aO033YSWs/T+Qearr/le9bI9ZWEy30DZ9KMR3ZYkPF7Jbl3wXd17IDmm3E+g76N\nY6ZHuUbkmpUTVzNah+SUe47IByRkFHNTWHf06R1mmph7mpnVq2i8Rw4ugN7YwFplPUFHcfnqOmiP\n6oJTM3OgL16+5rQhpBw4ohz57sOroGfmlkAvzuMzfPIJoxG22a+0nDZs7g5AX7l2E/Rzp8+C3u3g\n+e023jOhetr01DTo7S7GJTOz9dtUFx6js+O1B8fjjGzOz7ANrSbGsXoV7cXMrFrFvh/TeMcUKBpU\nD+C1+H3H7gLdrOHivSwP4UCRpZgDeP4PxFbCnx8Uoxz3xb6EjpekylbwPTg/mHB8ouZhDMihOuug\nsmsmaH5POl6UPGJPSvKNif3AbWJTnNBmq0zQHCjKTJ23Xmgt4L4X3nPhII7N2iIev75T8sz/n+Fc\n8Rsvot/5J/8DdsI/+cfu4N/1AOV+3G+sv7+tInesOVEpy8n4GZwEc/lnn/+TcouzPwp6YbYHejy+\nAXpz5xzo7c23nHvGCe4fZCnHE8qzajgoq0unQPcHWOstPFzvdXfxuJlZSnubbhkI7XnKxz3CabKl\nQUz70jEZSonz29nBiVyt43vHI1zQdTvY5jimWN9AZ5TRJuB4UFKrquA19QY6K879xtSmeoMdIO3P\nJlwncidMbpg/NNq0tzPGe4S0EI1I15p4fW9EeVrJWERUi2w0sd65fRZtdjzCe+yOOSegGh29t19S\ns5uixfM2OcQO1V4aEb7nkBIL38f7tWgsmyXBK5jFZwbUl37JftB+oTfANQOneVWaK0b7UGVr/IzG\nJKD6tEfxJeSaNieL9Ixqley2TkUeM/vcjzwB+va5b4D2q6dB7/bRr3jkCx9cw34Y7FwAvdLEtZSZ\n2dVbt0A/ds8nQJ98+GHQv/XM10CPd3FdGlDRZjBGnx92cb/PzMxPD4Keaa6A/rHPncR7kj3cvHEd\n9NmzGNs6GAptqsmJn1mac90dB/Suu3A9NhrRe9Ec3trawjZ0eG/e9XU5rVOPHDninPO91Ou4DuX9\nV25Di2LjcSexM0sCvMfFC9i329cugV45Mo/nJ2jn250h6GNLGFMuD3FtbWYW0H57n2oA0zW3HrNf\n6OyiXdVqaKv3fQjtsN/FuTAeUqHXzJYO4BhNz+H3Fbx16qdoy90e5mmdHZwrAflG3j82K1l6k1Nm\nF83zo2xLcS8q/E2cmeUhrWv4eD6pYorwa3qUrHLN3Mzs0KFjoJ97Hf3vi6++BvroKs6nf/T3fgb0\nj3/6h0H/X7/670FnJcEv531NOt6oY2eHzvc3VE/jGjCHxtL1Ie8n4E14n8Upd+XYpqXqGuhPff4n\nqY1uze7JM78D+gqtR7wMHzqgD4P62zgP0t4m6KSLxxNee5hZlmBfcs78/dr9HY/zkZzzocCeh8tO\ncR7h1BJQ8h7i7jaOa5ryfODnuw3IqP67Q7Z24vgJ1MfQT3zr2efwmRk+dGGBv1XgPemSvRyP5yCf\ngDJ3N3tI7v19TbWC+cZMiLHt5jp+i2hmFlDOevc87rP0u7hv3edvnamfPOqH4RDj7QLdv4x2E/cs\nWqQ7HazF5Lkbk78X/r5mkoE634Wa2/fO4EyYRubt/d0dr835eQVvcptZb4B7Nfz9TY32Ub7f763c\nVgohhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQtxh6I8jhBBCCCGEEEIIIYQQQgghhBBCCCGE\nEEIIIYQQQghxR6M/jhBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghxB1N+G43wMwsLQrQBWnf\nUHsBNjuM3Hv6Of44TMega9UK6JmFJdCjZh1vGCd4/74Heux3nDYEFWx3u4bHva0cdDJCnWWo0yzF\n6w3bUC3rCCKnexr1rfUzaiS+d57jM7OE7ke3K/+xmCDxhyzHZ3jURLaXMMd+MjM7WfRBN2areI/G\nHOjHDqI9tBoB6J0RPrM6MwN6ZqrhtGF2YRavabVBr9/aAf3auaugX790C/SNjS3QTj8V7mB4Po5f\nUODfR0U+vmfg4/Gc56aH98t9Ol7B49/9ESW3MhnTAO8zilEM2qM+tyjaWwclfcrz2sNrPGPnQ73u\n9+h+MWn0b0U+osdNOU06/MhnQB/9+u+BPvfaZdBxhm2aqqOfz8m+O2Oc5/0h+qruwO2nmQb2y6F5\n9PPtGhpnr4/3DKnfkjEej8i25xoYZ4b0jjyfzMx6I/JfdErFmaP4nl3qh+km3q9dxX49NYe2sUC+\nzswsd2YpPjMh+/Oon6IQO6ZVx36JE2zjiOKt5W4/FTkHAo6nXbqC7mnue+L9UHqT4piZecbxkM/h\neVoaMPcNozH6iVoNba3gdIPmT14y7oPxLuhKhHO44vPf/Jb4y+9tA9mNOyfd6wMf5xDHXoYPBxNM\nj3O7vCSnCXz0ZZwHOXCeRX2bO7aLMi/cd4wTjmXYbn6GT2OTTWozNaLsdOe9SXPf5Sn6AY/aFITo\nm3yfx8LNT9iGPLIP9ofcZs/b20Yn5tDfvcuep2Q0FgHnHZOgNpa1mN8jctYk+9ffhbSaHqTYvznl\nZD7ZxDh25xfPFw9N09grhCHeoxWxXdL89HEdVJk77rQhHeC6JKHYHMd4z0FniM+M8PyXL2JM+NK3\nroN+ZM21rE/O43psefEY6CDC9VZvjO/96jVs06lVWjtPtbDNHrbRzF2fe0PUoc9zGq9PnbiE1/vk\nE8rS/IRcD6dJtRZe1MJlqXk05T0yuZR8RJa58zWlpUGR4Dl1mvKhjw+p0lJkSGvpUYr9FJb4jCEZ\nfj3C9yYztyCgvJ367fYInzlb27tGYWZWq1JcoJOq+/ifHuFaVEAJjRPPHFt2O4d9XUG1pqJwc6C9\n4PtVQmxjEbrxj4eZY2ZMidyIbH+2jX4ozXDCXr6+Dfq+u484bTi4ugx6MELftbGD+W+cYT+9dWkd\n9Djm3AMDVavl1g03d/GZYQUnbRrj8bUFrGVxXlepUJyhYNnruvXTq9dugH7jHNYLbmxgX45oLZ6T\ns1vfxDjWJSeytY39ama2fnsDdMI1NnqGRzbq5Hk0Eer1JujpWaxDmpmFEQb9Xg/bOY4xVqUJrodO\nHUR7WllaoDZSEMgnOy7OHYuJ+esdDsV2J7hTl/E6tjxZ5meQZvfEuzV8fJL2OPdzG8X5wcQ2cS33\n+32nP00/TboHa8oNjN0d9yvl2M75ZdOD8ouJbaCOmsKtAbvvKN7gO2+VPPNdhmPjV59B/7vzj93t\nxZ/7Bzh4970fj88uY7yk5cnkHctJ9rZ3mcbMStZIXKPb565uOOT6COY0QXAC9NoSrhmX2+9x7nn9\n0pP4jBTj6lQN9w9aFZyErQDj6oFljGkHDhwCPd3E9ZyZWeCkOfiedx3EHGZl8V7QDz9wCvQoRVvt\nDjEn2h24+erNLTzn+i3cw7u1hXlQJUS9vYs5DNeiuDTV67ltWD6A+UFGOUtOi8yE9iMimoPDAfYj\np+l+xXWYHo1nMsSJOj2F+ebcNI4n14WHA1yUUlnSOj23ZudxzY6+K/CopsDFFo+dC8dTCqZRSd2w\nSrXGpIoOb5b6LqdnZgEer2aYC05T/W2q4S5kq/di36brVPsYvQOneYfS7Q9At+rY/x4F/5BrwSU1\ncQ4yIY1R2a7RXjomP5PQ2jseu7WqQwvog19+5jdBj7yboLeG2MbpaWzDcBfXKc+ffQH0T/3Qw04b\n3vvRj4AuqF+q47dBn1xE//vqDvft3ongjZu3nTYcv4W/3ffoo6DH5EeaI/Q7tGy1pWUsqJ0/j7XL\njR1ck5qZ3by1CXpx7QDoR6lNjQb6nU4HY8Brr70G+pVXXnGeOYn7778fNK9TNzYwPnMbd3YwDm3Q\nOnm6wYmbWUxrqCHNveVldNqdBN+7n6F9hLsvg77l4fU38qNOG7xdfOb01DToY5zH7yPSIe3fNFHP\nreAcb85QwlRSC25QvSsMcdxrVAyevwtjzfo6xr8xfW/BdcR3UlLl/J33Xnk/zylVkm5RTL774EHn\nmdtUk7u5i3ndyPk+Aq939jWdcgP1g+8uhB794GOg+x38/uHpb38L9Auvom/8na9/A/SnP/U46P/7\nS78DutPFb+rM3G/3fCqE8B5ZxFvOE/bRmbJ9U/6Jv2HjeMzrgCBFm3zi/Z8C/f4HPgq6H6NPMTPb\nGZ4HPX4Na5mXb2CNtj/AZ0YF5uiFTzGftM/1KTMLQtrjqtJ8LrlmP+F+xTHJe6DtFe9gob935mbm\n0y81+h5y/SbaQRjS2oz2D9ISH8y+Y2eXchCqX5w6cRL0W+fOgb51G/MV1z+yL3L71al7T9pO471T\n2vvJaaOS7x/RovRtml+tmUWnjYfWsIZQ0LewI1rP90nHtPlar2EsXFjAOnulgvOvbG+10cAY3G5j\nfrKxgWOTpuQHqKODAP0K+2eHsm+GSbOHdr+XoljnFqL3ls7nPO485O8Fuz2MRRGtq8OSeDmJfbx9\nK4QQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEKIvwjojyOEEEIIIYQQQgghhBBCCCGEEEIIIYQQ\nQgghhBBCCHFHoz+OEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHEHU34bjfAzGw46oP2PGpW\n4YHMSAdBxbmnF+I5lbwJOknGdAX+nUit3sCjjQKPt+ugh/k1pw2NRgK6GeIzBzneM09RZ1mKbc4y\nbJMfgI6CyGlDnud7as/wmfznMkVGbaS+Lwq6nuR3f5pwjXMR6iKn8ffxeCXHfnkw4rE1u+/BI6D9\nBtrD4cU50O3pFuh6owo6iLCj0gTb4PnYZjMzr4K/edROfxbfq38A27A7jkFv7OyCLhLqt7I20Ph6\nOf4Q0dwLfDxeFHu/p0djW7hNcMaf7SNO0Eb3G1ubW6BbSRs0WpoZd6EXBMYUUZXOobv4pMkPmE8+\nNEPb9LIuPi/fpka5oSQdboKOB+jng4Bsx7BNlzd6oOsR2yZeXyVbHaauHW12hqCXp9BnzlSxb7d3\n8R5pij458vD6jAarEXGsQr07wvuZmU3X8D1HMc45NohBjG1sVPhvHvGCRh3bfGgOY11/gH7GzOzy\nNvbblV20j16M79EIsQ1rs/iMo4vof418QkhjmaTUB2a2O8A2tNt4Tn/jBl6QjVDzHCH7s4I1+S0+\nbmaecTv5HI6XJQFzH7Hb3QHt+7OgPSNfRn7EzRXM2q0Z0CnnRTQ/MrqHR8fDEOeDT7aXZa7t+XST\nnG9K5BQ3A0q0OC8LyMd7vuvzGe4rj9vEqR6/J+VRfH0ldPPLOEFfkWf4HpUKxpUiRz9RZJgjFx7O\nyZxy5CAomy975wvct0mKz8xpHhcj9Cvs80ejgfMMnxKrSgXfg23M1Wj3PDYFzRNu83fheEp5VYzv\nXa3iM0J6Bx5LfqbHyeSftBSuIZsKSvKE/cIgwXdLc1qf0fQZp3i8Frr9GfrY52NKqNnXNUKK3d7e\nYxZUMA5HU6tOG+LeTdBJgnaUUJ5Vn8JYX2+TvnUddJpgXM5zdz1feHiP6eXjoP2wtqc+soB2uTRN\n/pXs1JwcyswK/M0PeLGM/RCTqyJXZlG4d1zyPdfXDTENs7CO79Gep/UcdoNl5AuzPs1XWkMmqdsP\nCZ3DOW9EtZeA3oNcm+X0g09GHfBaxcxa9F4cHalkYAHNm9t91BG1caZG8bhkLc1LJjahkmXavqHf\nQ0Os1THecdzn7uPYZFYST/gUp9SEHc5mwvGKpyvbrZlZTg+t0JozSdmn4zMWF9CfjqguFFTwfnOz\nuPY3M1taXgJ989YG3pNc/HaX4noN9fkL6G+5n44eP+i0YauDPnl2lnwb5RLDEeb5KXVuu4X+u0rr\n4u5ux23DNv62sYX1rjGtOVPKVwZjfIdtesZgjGMzGrl1w5jHj2qsQUh5Ga/VCZ+cQhCRE2HnaGZj\neo+CBnA0wrlYoZzh2CGM6c062ijXz511g7l5nB+gHXslsWpf4QRn21vz0qtseegU9v5s2lnucXrC\nS+2y9J1/4xjG2jnfCbwlD/keKFco3ZHiZ5LvME4XuazDy1Y+n4+z5ueXtpHfg96br6F+8en4qWOU\nE9HtOZf8QYDX6t98OXHOee3n8Jzjx7BzP/Mp0j+G1x++h/wxhpXJc+Yd5GQFdy7b6A9g3/+nZDTE\nBMOP9q5NcL4eRmvOPY+e/EugH1rDGso992IexPWyg2u4P+fR/PEoufN8d82QDDFHGfUwH4g8qn9V\n0Fjqtb33xgIf36FkyWAFLRr6hnt8Q5oyXD89d+UK6G9+51XQr751GXQYla1j3Z++Fx5/j3K58QDz\nrpDrFvTiydh9oE/tataxX6YauMDr9zEH4npbPMKxq1FeNQ5o4WxmHcpxUwqQl3N0Lo0a2X2G9pBz\njZb6Ic/cfpj1sS/rtOc/oDrGbQpeBeeTGe1nVfD6VsngR0OqA1ONzp/dvwvZLOP6GI4Z134bNUwu\nxom7f8d1U9674jUABymOoxyOxvQdwGiAPsLMrLF4DHQQYr2s2D2Nz6Cazk6BPvzKFdzvffAYrsVq\n4StOG86ffh10WqDtDjLUF3ZxPmaGtpsX6BxTWpMsLLt1wyjEc7Yy3JMeDnHNNyY/k9H6LyF7GAxx\nT3u76+4NhDTe7AbOnj0Len19HTTXUjY2sB7AlO2ZMU899RToI0eOgH755ZdBv/DCC6Df8573gF5c\nxvg8aFx1nlmfQd/UIHu4Tt+BjQuMz0WOY5XM4PW1Bvr4AxnGSjMzo/rM8sc+APoDkTuf9wszbcw1\nKjXaS6CcanqW6gQle5CDEc7J7W38ZmM4xv68eekWHu/imEaUczklwNLvy7gwSPtgE2oTXBcM6T3f\nfwh96ace+4Rzj7vIT1y4+Qbo/+3pp0H3Ev62D++3OIt73AvzmFtyDdDM7NRdd4E+f/o50E0f+3qq\ninH+xZcxl/zkow+AZj9WPhZITieNRrTXSmv1KuWF822MAXVqc7XiLsZrdE6NzvHrFEc8jG3TBX6j\nsFpD3/b00y+B/rU//G2nDfV5jI9LjSnQ8S5+A5al6Mtm5pZBjyOcVxVqc1Tit7q9vb+j5G8M9h17\nf5LhFMw8KmaVxVHOD33ec6fjfIdKhBP99ibmbo3WIWoDXl/2LUpA3w50dvGeQ9o0XFxcBH3ixAnQ\nm9toW/x9Juevfsm+NW8y5E4xEiV/h1xwMZP7lXK/JMF3rLdxDv/wJx53mnjlPOXAdDyj+dLrYW7X\n6WC+cuT4YdDNJsZPXmuY862YWa1GfmCGvg2l9cdoRHvnE/bZ2J44NJZ9VcLfubnzgq/aew7wWDsx\n/h0EfW7DkPZIBkMcq1Ydv9d/J+h/jhBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghxB2N/jhC\nCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBB3NPrjCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ\nQgghhBBC3NGE73YDzMzi8RB0rToFOqO/4QjDCLXn3jP38Ue/1gQ99gp8RhLj9XkO2vPomSF23Zwd\ndNow1b0Juhlgmy4PMmxDjm1IMtYptiHAfvH9kr91KfA9S7qKzt/zcsuoX5wTym7p3JOv2fuhhY86\nTLENd1fGoO99rzsWm7t9vMe1TdDR9augt2baoFsVHO/VA8ugZ47chW2uo72ZmQ0GXdC3LuMzL7xx\nDvQLV3ZA3xhVQacZ9ovv4fjnnjs2nscWgOcEXgC6Qjo1tFmP5plHFsZjZ2ZWUBM8Hm+njfuLZ184\nA3p1ZRH04UMHQE/PToP2W65tWbOFmvyV0TiyLsh2jLRHvsnyLbw+cNuUJzjngpBsicY9y8i2HMeB\nx0N6hxoFgrDEHzbIB4+H+F7TVbon3SJPMvoB9XCAvmg8Jh9Ptl0N3TYmCZ4z36qArpMvGo4xLkzV\ncexrVbx+qol+ZHMb4+8r19HvmJm9tjkCPaJ5Pj2F45+M8J79m+j7Ol2833wT32l7gO80IJ9vZjZN\nQzHfxr6/du5N0EcexffyApozBcc2fuaEuGVmBftHc9s96R77idnpBdBxMqQz0P4Dmg9BQH7L3PyB\nY1qWYZ/7dJxzu0ljEJT4kZxsIyc/wLHYic10Pb/TJG3mvvekvCp3+m3vv43mfDKj/NPMrLNzG/TG\n1i7oU6fuxXuS3/B9HF9+J+76OEa/YWZ2+/Y66LevXAT9+qsYby9dwuPrNzFPH/Yxbg1HGCPiOHHa\nwCkL222b/ON9d2O+GEQ10FENfdORw8dAr6xijmBmtnrwMOjZ2TnQ9Ro+wxl/egcef48SNzeXLLPB\nvzh/f+95GGcjykeSnHwbdV9UlivTPboY4mytibboO36D/GdOPqE6AzqsoTZz/Wcao6+LKmhX9elZ\n0HEX11qH5jDWP34f5reV3C1LNOtoy+3Fk3hChNcsLOMa8GMP4nxrVfH8uIN+q76M6z8zc3PigH06\nnk4lAsscf0wGQOu5rCR18Cr4zNo0rdcaeH4yRJ+djikGsEvPaaxT1yYpTXeWFgXFV6cf6JbjFN+p\nGeIDkthtQz3Cdsb0Hrz03aK0o0M59sEmdTYNDblOMytZp9JDO/H+Xcf2B9ihHAtCHwfdi2i9WZZz\nFZxD4T2DAO+Zs62SYcUjXouhDkqqYZUI105+wLklvsdUqw660UBD2V7HDQ/enQAAIABJREFUNceI\n1oObW+5aa3llFXRecJ0P12/dAeYn2S285/w0OoWUHMuV65j/mJnVKrSGJP+6s9MDPaacqNXCOJKT\n04gp/+10cH1Yds96Hd/D75IN0lprPKTxJycxjvEd/JK1RrWO4xuneA83f907p2KLi8kms9TNLdMM\nc97hEPNTn977wAL2/fLcPOiIcs2M17mcmJiZT7HK59DlrKn2GdwlnNaSLpzjJbGA8z02v4COT7rn\n3qZX8kOJD55Ue52U3nPg5ds578jXlzyTz3Ge+X1qfoeQ20zrar5dWRsj1nRSxfaGrj90F+15BJzv\n/ODXjcq2hnY66D+ffwnjwsun8fwv/SbGnZ/5G2gMn/8r+JD5g1R/q/4p+onti+fhD37X/5lojTHe\ndD2MgezbPErwy9YtnQzPefk6xqj5Gbzpex7CGFWt07qWFltegHaVp26daNS5hdc4DaU9X4qLvLfF\n+amfk6Py3XwiyDG+N2kBlga4Fl6YwXXvwYUV0I+ePAH65QtY2/rjF19x2nCrtw2aw3+ljmMVUe6X\nUw40P4u1rSLH41s7tHdkZosLuL6OaC09pH0Z7vtxgv2YUe4/pLyqNe3WFAqjPGiEbViaweM+5f5+\njOc7e2hUP0t5vW9mF8kG70uxDlErsN3TtM/XpbVzlOIz2tSGZknNt7JM6wPqqmChZPG7T+D1WkR7\nlDzHK7SXVpQkLLwOTVKMSRVOIGiMymr938uI1rVRdtE5JxnfDXpmCevwRz98P+jx+G3Qv/Fbfwj6\n8IOfBv34sd8H/dD97v7v62/9Z6C/eRbb/dy3vgJ6dQ37/sDxQ6DXDqFhBhXsx1bF9bcHDOPAhctf\nBz300Y94LbxH5yrOjdkp3M8aDAagb93AGGNmVm9gcsdLpcOHsW7PHDyItczf/d3fBT15H8hlbW0N\n9IkTGEcuXkSbmpnBeN3t4np9fgG/2bp544b70BrF6B1s9yDHvu0EmHd0PWwDp9jzLdpbL/n+5Vgd\nx8u4rhyj/91P5JTH3trogM4obs/O0nd4XLg1s24H+3N3HWsTm1exxhLvoA8IKY8LaVD5mVlesvhy\nmrW3/Wf0LUFIdZ8j8/g9zn/+iU+Afs+D6FvNzA6SH/jwE4+APkff8r30NtbcPv7Rj6J+34exTffg\n/t/6Fu55mpkNb6PthlQ3+rH3Hsc2tzAH+/LFa6AHPZzjSeru/zKcpzEZ+SaOr60G1jbvO44+oUp1\n5Erk5nW1Gi/G8Rn9Am1yK0WbreVo93GIPqIZ4fXdbbd+uh2jnW+EOP592tOotfAZc/NHQO9u4T57\nPsY2U2nUzMxGlHfw1nkYTSpK3Nk4ma5TuOGFLK8HS2yZ7uHWe/l7SKTI0U/0aEN36QDaHq+9ysqI\nOfnQIdW9e32017U1XEMeO4q+5dXXXwXt1KS5bOm7czD3aC+HN0f5JnScvxt1riY/srWB/nB1Hn3b\nzhbuQZuZdToY/1anML8Yx1x3xznc6eFYzs7hHA5DNx9F3DjF37bPUd7VbqN99MhHZ7Sv6Xz/zbki\n5UheSR3faeWkeirPs0np6IS6dNnlfElK7e6SzXP99J3wF+fLFSGEEEIIIYQQQgghhBBCCCGEEEII\nIYQQQgghhBBC7Ev0xxFCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhLij0R9HCCGEEEIIIYQQ\nQgghhBBCCCGEEEIIIYQQQgghhBDijiZ8txtgZlZveKAjH3VaVEB7Hh73i9y5Z5ZloEMfX7WoVPGe\nQYDX5wWeT9rz6JnhlNOG3TG2Yf2t86CvXh+ATpKMdAI6pXeqh/QOTgvMiqIo+fV7wb+P4fPzPN/z\nuJEufR7/VGBLnUvouEfju2rYb6vHF0H/0embThPOX9wGHfr43u0m9uXyHPb1wdU26IcX0F5ObNwC\nffvWjtOGc2+8Bfrb53ZBd2JsU9JAm6pOYxtthP3AeKUWgfjOKfhe1QDn3ihP6QY0eDQt/NI/v+Lx\nJxv6gfBKf3589Q9Pg16Ya4F+4NQB0PffewT0wUN43MyssYrjZlXqU5rnnhcZnUDH6QEFzgcvI9vz\n0JbNzLYvvwI6HqM/4zlYIWOMAtTNCr7jTANtsxej8eVuWLDAwxcdDGPQcw00vofXmqCXmthvEdly\nktD8oEYEAb5zWuIuM7omirBNIelqQWNL/To9jfbF/vbNW33QZ7bGTptSHpuI/N/xNdCbV6/jDQZo\nL12KjcsteqcQn9dLXF/G9xjGaF9XL14BPd5GXV1dBu0+Ye9Y57GzMzPjXIQ1T6yJ8fnOplqh+RKi\n3eScb5DOUhxTMzOP8p5iwjj5NB8olTPfozbR/MtytDMzs4yshXPSNKO8iZ2RYyb4A9+vLK/ic7Is\noePY906ctb1zN9Yp+zYz+1f/+ldAf+1rvw/6v/un/wz0Bz/wQWwz9cO1a5dAf/tbfwz6m9942mnD\n2TdeB725uQV60Eff46aw+EOzRvZAU7Yfu56iEnDOyn2HNnTjCq4FlpYWQD/y4feDfvLrXwb9yitn\nnTasrKA/O3QQffKxU/eDfu9Dj4A+eOgI6PkFvB/PE89zbZJtim10ckZ6B8O5MBEUOH8Ciqm5k3SZ\ndUc0xykPG9L8malyjEI9GqMvbNWnsU2VutOGsIZrn1oL51dYx/yi2kCdjjC/8MeboN9/agV0pYZt\nMjObO3gcdHvlPtDx8DboteNo636C67/ty2/g8YDiTImv8w37PqdAkvk4P8IAB4dzmiSh6ynMZIVr\nD81pfEa9gce5jhEE1Gb28WQ/cYrPHLupoPmUf+Zj7Ks62yAtTQZ9fEbFp1iZsQ2XtIHWlVGA1wzJ\nR28l2Oa1KeyXKrUxxKWFpZk7tym1sZzWrcXIuWTfMNXCdVEYcV5HhpXx+tO9Z8B1vYCLB5wT7b3e\nGw1wANIY13oco83MhkO8JqJB5vmR0KS9fB19463NLuilefSNQUnMIDfhxOGZOfR1UXAZL6Aazd2n\njoIejnBSX7iB9zMzO7w2D9ov0D9ubvVAVys4YXZ2qf5FY1Whse313ckypvVcQGuHahXXATHlWGmK\nz+z3h6BDanM1IGdqZqMRtotzxYAcEa81mIwS3tGY7p+7652CxnNMdb+lOYzP9584BjriuJPSPOB1\nREnhJAypfu4k0c4l+wv2V3u7Jvd4WW7IxVfOp5170g90z4JimAV8/wltNDOP70nP9OgaJ0Vx3nuC\nZlPjdyjDXcDv3Sh+pvOMvc/3uB5NZfjSZ/A1E/8ZMmzD7Bw9MsLjcVnh8I6E1smUA7/xFvrDf/4/\n4glPP43x+Wf/Sxzc93wYz49aZfPwnbTze9gvXf//wckMa/nPZTXQ7APYb21vbhoTUuz2pjAP+r0z\nOM4Nsvf3PMhtwDYWIeUTXXf/LadY63PNxnGXvFbizS18J6cOyX7KzPFVkWFOUqdrdjPMs31ax7Ri\ntP8PHDkE+tSxg04Tnn8Da0d/+NyLoHOP9p3HmC/Ua/jMkPpxSHlYrepu6OUp5jQ7XdROak5FuPGI\ncqIYn1nQ/nwtdNuwTHu6UYHvNRqgTX1nC3WNv1ugPCuksS4LbSEFwHob27RSwWfe9rE+c73Pz8B+\naZFRTzVxHpmZebu0jqJvJfLL+3chG9I6JKA5zfk971n2EndvIJ2058dBzkMdBNj/nBby/evFq04b\nzr2G/m/52CnQH3z886D9HNcUX/3OBujNdayXFYfQJoJi3WlDb/g86MsB+qbtu7CGd3QWv49YWsT3\nHOQ4NlzzWaA6pJnZiRGeE1/EWmR3Cdd8XoxrwnAXz/cqeH5Bey5x7BbMItq35j3iI0eOgH7ooYdA\nX7hwgZ6B/pjXYryeM3PXyj/8wz8Mensb66OPPIJ7A/ffj/XUl19+GXRCdZEwx/0MM7Mu7cs0aK6d\nnMd5cmlARbiMPOj1N0F6M/i9zLDltuHWiOJEG+szcYJ1jf3E25dxjo4HaLsJxfkt+i6gjJji8HAT\n79G5hX4lIt+Wc1Sk/CenuM/1EzMzLs1SKdJZ9h6jfbIff/zjoD/zcdQPfOTDoMOwZMFw9Ryes7QE\n+qf/9t8BffKPvg7683/tp0H7HYohMfbzSujmlucL9D2PfuwxvOfOSdBnn38B9OwU5QYex62S7x0Y\nXko7roj3oKnmy/UzykVS8hmRszg3833M4xKn2VT3yzFuhB7m3Lc2MBZagt/6PXDPjNOGcxvoVzpD\nHJsgwr6enVsFPTOPe7fDAa4TkhjfOyupB/Q6nJvQN7G0f7Hf8IzXZ7wHwXUjLvyU3HNCqYkv4W+3\nshTXrTF911Sto+15tHnG3yWbmY0pHxjRpt3uLtoib81MNfGZDdpU3O1gm3d2MFdYXKBilZn5ExxB\nTn6APSrv7fDamn3R9Wu4B3L3sY+Avnz5ktPGjGr7Pk2IzR38jnini/3A3z+0pzD/CGhdXDgF2sm0\nqE6ytIjfOm9u4N4N55Y+tZHX1TwOZaWtSZ+sFRO+8XCmkTNn9v4qhGNE+Uno28b07Xy3PzmXYfQ/\nRwghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ4o5GfxwhhBBCCCGEEEIIIYQQQgghhBBCCCGE\nEEIIIYQQQog7Gv1xhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh7mjCd7sBZmZegH+jURQe\nnZGDygrURVFyU7pHQdfUQnxmVqD2fWpTjtebl4EMw8BpQjC/BHqU4PH41mvYhrwLOknwgpza4Hvc\nb25H5Dn+RpeY51Ff0y34nh4dp14pbUNh+Awe3cJ5KN51ajzE43WUf/DKTdBbXepoM2vVa6AzeuT6\nMEV9dQf02Vs90NfiKuj3P3IU9JsvbThtuHhuAHqQV0B3xtiGVoA2Nu6PQXNXe+7gOW1wwdHwyR5q\nAb7nOItRe9TXIT7Tj0psEl/LsY8ieCftvnO5vN4Hvb45Qn0L/cC165ugH/0A2qKZ2X0R2lKjtQLa\ni+gCH+eD51EoYH/H5GiLWX/dOeW1Z58Dvb6J7z0YoS159MypCtpFq4ZtrETks8doWNsDbKOZ2a3O\n3j51ro5+/IHlFugGtSGK8HxuY3+Eczrz8Z1ydkRmVqd7jskvmOHxSoA6IN1oosO8sYH28+ZtHJes\n5E8mWxW8J3vYt69eB+2HbdAz003Q9QT968octnGBumWj5/r0K9s4vrsDPOfq27dA3z5/BvSh5QdB\nexyZ2H9SXHL0OzqHYjb5bI6Ndzoe2XuWYn9EIc6XOInpOPo1M7OcAkgUoXNLUpwvPB/GMdqNT8/I\nadyznOefWURxMaAclv1KQf3Q7+Mc7Oxuo+6g7vVwjpqZpfSebDvVKvr4egPn4PTMDOipqWnQjTqe\nPxxi/mJmdubMq6AvXr4M+vkXngfNafKzT/8x6N/76ldAX796DfQ4decc5w9Ozkvnc9xIM87t8Xi7\niY0OS3KaOMbfajVKUukReYrnD4do9+kQc4BWFd9iZX7KaUOrgedsbmJe3LiONrt+6wboN87geuTe\nB94L+vFPPgH67nvRf5qZNRoYL31/73XYfiJN914zcm4d4nBYwYsrM5uJcI53U7RFN15gG8YJnu9X\n0VfWZnGNWnBybmZhaxl/yM+jjCnvuol2V2tTDkVrsbiDfuXAKdeuDj38SdC+z/4Xdb01B/rg+z4N\nuqAJObx9Ae9X4YTZLBhjb6eUKHnO+NH5NOeLHI8PyL0GVTcRa9IcD3yMn0WKY5GiWzEKUzamOklC\n75gn7nyNaF1ao66qUMimMGUpPbPqU30npnnillZ4pWs5za3bI3zRgCbKVIXWqU5dBHVQVimja9IC\nG5qXxKr9QrPZwB8cP4+HkzHm5lnm5lS1OsZMz+P5hednFLfTDO1yNMJ1dRLjZAh819/6NEFoylpA\nhrC+sQV6OMT3nJvCd5proZ9qVV3Dmmpj3mXVWZBHDh8Cvb2NuWJBfbu8sgB6QI6mUnP9TK2C7dra\n2QWdUo4VUVwf0jO2dzCfWZjFXDMtWQePE8rjydfVG9i3MTmawQDHfzjGvL/m4XyNk47Thl6/S7+Q\nv0xxvHm946wpyabHVNscDt3aTuDhe4Vkt3etYXxeW8GY7huvwfB+XOsOQzf2OZkGranYf+472Few\nM5q0cC87zr+R7TjP5D5m7dyPNAfBkrjK1/B+gbM147SJ28zvxP3GNRa3owrKYVKuH1MoCcl9+vye\nFadwvncbeb0XlgwmvbfbTxTLuN/In1ZaVNMLnQvcNvwFYDTG9/7q76NxvPUW9tvf/1nst8/+VTfO\ntBapXsD7D26y+Q5aeudyMsG9q2/5mD9kNGHGY7fOzvghrvnGtGbMaP58+UWMq7UC60AnPoD1j5Ry\nu1EP68vfhfeN994/dfZG+W68R+LEfneO5mQ7nC/UPcwHOuTbujn2S5LhYqs1xHeYarpt+KH34Pp6\nZQ778huncd9ml+p+jSbmsIMhjn9M+zBZyXzp7GJuNh5w3YHWiFS3iCk35DZUq1TzHVAQMbOEcnXf\nw3scWsT66AymrGaGba5QfOUwEpYkSfxLPcJ1VUB7xN4OBrcwpnp6hu/QpEDTmqWik5kFp3D8R9/G\nvbx8112r7Rdi+saiTsULrtlxbTmquOu3POZ1C9eb0W44vIScW/NxSmgCo+8jzOzs638E+ujDWMv1\nfVpTUE7zvvuxfjZ7L+4/zNF6bbjrrlvqVDuaS/AeqYd7hC9dnwd96CA+Y3qW+pq+wZlqYIwxM5vP\n8BkVH2Nb9DbOl+05fO/bGbZ5uou6UcHRqVRL6oZUU+D19xe+8AXQGdkHa/4uiCn7Bodj9Be/+EXQ\nvJfE93j66af3bMPHPvEYaLeKbBaOcCyiCtmHh2vtzghj/gzVNdIm7ePRrjTXaszMDi3h+BYFnlNt\nYRv3E70d7K9kRKNE9bSZNtrykD9gM7OCarVc8+S9tYR8YzrBlnkhxXuzf3ISKI6zXN944v3vAf2X\nH/sh0AeWMB5GHuYO7veJZt6xe0Ent94GfWQRfdsXXn8L9MmLGHOPF1QX2sHvK751+mWnDf/qqZdA\n3/Pg/aB/5KMfBr320EOgH6R99DfOnQPdpXqaG7nc/T6G62GsnW/B6Lskj2qAmbO4N4sN36NPNhcn\neI9GhHle4GEc2eng2Nwavw46abnrn8YY86xejO2s0R7Y9Mwi6GYLk82ANkFGlOfnuRv7Bn0cL98n\n/xi6OfG+gnJfzuX2/srYLfGVXTNhy88qEe3xdXFdGoWY74f0HV+aTd438cgncqy/eRP3+QdD9FW8\nzuV+6vUwtzt/AfdK7zp00GlTvc72SH1PHcf7sbxOLpy6Odr25m3cc+Yc+vp1zCXMzGansI0JrSk7\nO7gfEPC3e/y9Yhvjhk/5h1+UZUUIj0Wtin5kaRH9xLXrmK+MRjj2vE/jfMY+4XvuPzmLNO2dTjrf\n+eh8wve9XCMpPWnve3Ce0B9x7JrMft/SEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCHEPkd/\nHCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCiDsa/XGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ\nQgghhBBCCCHuaPTHEUIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBCCCGEuKMJ3+0GmJl5RQQ6iQeo\nMzweVlAXJX/jkaU5ai/Da8ICNN8hy/D6PE1Qe3i9R9rMzAsroNvLC6CPvu8e0G888xLouJPiM3N6\npnmgC7cJZhPOwaOTyQvsF6P7FeWN2PMc1pV4jBckMcg3B3h+M0B9eBbtw8xsvYfj34uxbwM6f3Wm\nDvpHP/cR0A985BHQ3c4I23D8lNOGr/27J0G/duZN0PU6PrPWQB1n+A48mIXt3a9/chLioQX4pD0f\nXUTN0KYTw3lhNBZeyZ9feWRCfIr3/RrlHcZghLY3It80ItvsDbGPE/JtZmbVWg30ffNLoMPaIbzA\nb6D2mqiLTdL0zBztPRsOnTZdvXIL9NZmB3RAhrA9xHk+Xcd5PFVBSwlD1BtdbNO5TdRmZjvYtZbT\na13t4wmdGE+Ya1dB330X9uNciuOw00NfNqC4wvPNzCygnwqek3ScY1WL/EiFbONWZwN0j/yreewN\nzaIA+5qmufV62Nfh9CLoZkD9WMVntBrYrwm9U5lL6AxprAb4HlvbfdC3rl0DfSgnYyBf5wZUJ9g5\nbSqMfXTmnINMOn6HU3CetLctBzQGZbGgcKyB7jkxL0Jb5jalKdpFmpGdmNnu9hboq1cugT595mXQ\nFy9cAH3pIp6/sX4TdLfbAx3HNEfNLCPnFVK3hAHOsVoNY3eriT5/Zg7n7Oqho6CH7CfM7Myrr4Me\njfCcX/3VXwP9737tV0BvXnsb9JimQ05jGfiuQVSigM7B4wEbERlE6OMFKcXXJMHrq/wAM8voHn6I\n/iyitUAlwjYkI1zzXHjzPOjcw1jYnmo5bfAKtNOM4mm1wGcsLy+DfuMl9Jd/8JXfAP313/0K6Pc+\n8qjThk8+/gToDz/2MdCtqbZzzX4hJruJaE1I09E4na9y4Dc3JLXJblqks4xzJHxoY2UN9NTBB0FX\nWugDzMz8ygzo6PKroLdurIMeDNDOGv1pvB/N6Xp7FvTsMdeuGvPoi9LOLujxDrYhG6D/DNtToGcO\nHAcdUEJTD918NhtiHsX5a57SupX8c5Hjeycj8t8Rjl192l3H+pRH+QEaUTqm41Qz4KV1F6e8FRme\nX3WbYMkYz+HVSFHF9xiMqG9pHiTUphq6TktLUqSkwL7sk4/e6KM+ME31H1730viPUzzepOWSmVlc\n4HvS8sSyfP8uZJ1aA+XrowHOn63NHdDVOg2ymXkcQ33OFSfUskiPhyM6zvUSF5ouVgnQAU9TznTq\nENb0xrR2Hya01h9jTL52G/NIM7NDPfSfMxWMmbPT6MtOHL8LdG8XfWNrCvs68LGeMOi7dtqhNsS0\nDg4DzGfqNaof+LQ26+J7nr2Ia7HleYwBZu74Nhrof4sJeRuXSTIKuCPKubjWZWaWFxzDyUapwOVx\nfur4GXR+KdWVb9/GmomZWb2G7ZptY+537NBB0LUK9pNHNszrgjDiuejaQ8GFkhz7Mitz0vsIp47p\n1GjoOJ1flKxbnLWtTwbv+D++597PcNrMRRu3zGMF56B8zoQ2OpqvJ10k2Mg3zrpt+vIZfOYb25Tz\n0vkrM3j84w/iMx57DNvYINflBJJJfWDm2INHne+WIOgXcj0ea/eJwtw9jnOXMO780/8ex+HKFTeh\n/Vs/S7XJA3vvYe3ewNFYOTmxmXcUh/Iu6BbVv7o5GmdAcTiKsP5sZpbR/sGI8okKrVO3yY/8h9N4\n/CfmML+cW8KFTEFx1cydk045l2ZZxce4mFJ9JScdFVxXdPOJlGvO1AjHZSe4Z/Lkty+BvnAB64af\n+chHQd8funlVQnWgw6uYPxjtR33r7GnQ022sEeQpjvf5ty/TE926YV7gb5xHcS7X7+P5Ba06FxZx\ngcZ7Q4OBW8OtUl7FKcw07fW0yGZvUu4+TTlsHKMNRqFbN8xp3VSt4jPzHPPsKyl9K0HxOvRxHjQo\n12t6mBuameXPoT0UN7AjgnbJ4nefwPtvvEZI6XhCewO1Ko6PmVma4D04neAUKadnJlSkqZFNFLRO\nGfTd/eCUcvr2DNbw0hj9pxfiM0+u4Nq5Qt+ifPs5fO/OjXudNhzzHwD9Uy20/2dfeRZ0N8N1a9pH\nv9Qfo59hd35l143b7z2Ca6XFNfRduzexH7o9igF17OuE9r2bVMdYWVlx2pBQgSuntdR4jMd5/4nr\nHn8aOE8ajfA9uE2sJxH66JdOrh52ztmYwvHt3cT3vopph2UB17Jp7jWwDtJqofbMzfVmGzgPxtsY\nX7fC/buOHfUo5qZoE/PLuBBaWEF94zL2lZnZ6ir2eXTXHOjb17Hud+0t1L1dbFNG9WeumedlVTtn\neuAPUQXn6I0A49mru7hXkNP3EQtXcb8jbLoxNJs/AtqPKY5cuoT3nMaa3rmrV0GvLaHfevb1c6B/\n8Q+ec9rw9jrugbx+FWts33n5NdB/72/+FOi/8lN/DfSvfunfgm7U8b3HsZtjZ/ne84druhwMc8qA\nY9rPSmOKx1wEMbOYfFef9lnGpGv0rd/2EHOoMzeeBz2MboMepVxAMBumvAmNkveD6+TLfKqvjpOE\nNPZzNXTbEPg0XmOcv37ofh+1r+CaDB3m9d47CbP8bcmka0KquW1s4ZqhWqeYRWvpwEfDKf+kk/bj\nyP6vX78Bep38xFRr7/ye1ygXL10Efe89dzvXHD1yBLRP78WDwd95TGJA+wlD2u8dDjHXGPbd2NWq\nYd92ac+jUsHjOz3MFbMc32mKvm3wvb2/SykNZbzHRSdNt/EZy0v4DcfuDr5nTnlewYuRgvcz3EZl\njtHRHNj78Pedv3Iby75j9nj/0fn2Gc9332Ey+p8jhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQ\nQghxR6M/jhBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghxB2N/jhCCCGEEEIIIYQQQgghhBBC\nCCGEEEIIIYQQQgghhBB3NOG73QAzs9CrgfajIZ1R4PlBBDqJM+eeaZbSHXLQGUrz/QB0jo+0lH6o\n1augPY9uaGaFt3e7lw6vgd64tg76+voNvB+32bDNXsnfulR8usjzQKZ0TUF9nQekqR+5TXz9n1xE\n7cQ2eBmOXxSPQG+kePxAqwL63rvaoJ+7tOM0YZxiI2o+tuHEAo7nT/7Eh0AfOXkA9K//y98E/eL5\nTdCPfOhupw0z2Eybq6PuBziezQa2qb/TB+3TcHPPl4yEGff93ofNJ3up+GjDEdlg7ONTi9RthZfi\nPevkhioeju9+Iy+wT9g2U+qzJMX58OLr6BfMzFpNtJXVo4dBL8yeAu1Fs6CLoIk39HBcLY1RZwOQ\nQYh2YWZWb+A4hmRbbHsF9UtE9p0k6AeubeB8WN/FuDH2XH/ohfRZu1axAAAgAElEQVRbjn0fZ3j8\nzR187+9c3gZ9eAUn9cwUTupWFds4GuD94sSNXU6raaJTWDGPnHClgmNRqZEfGWFs5C6pvIO/mYyp\n33hsR9vXQHc8fM8ja2hvOVnDYIxtHMeozcyaVfQbyRDP6Y+xr3MO6uwhnWDGMZ1iHwc2M/MKHk/S\nhWP1zj32E9znPMf5OPvGwHMiFKcwVhQ8LngPPj8I0L4H/Q7ol156DvSFc685bXj1lRdBP/PMC6C3\nOz1sgxN3qc3F3jHA7YWyeI+/cOwOeuizNzYwT/IvXgH9zDefBx2X+IWYE2niwrmLoCuUT1adNuP1\nIb157roBS1LXh8I96aYexQWP7CFw4g47XLcfwgr6fc712QgLiq8h5U23d/CdqpQS1SO333d3u6AT\nH9vUffEN0KtvYx7RruF75QvToJtT6LNrAa/TzP6P//1/Bv2Np58E/Q//0X+LbVhZdu5xpxLQGFYC\nnuM45tUgJV1yUwqsNYovRc7+Eo9XplqgGysP4f1WHgXtVWmRYmYBNWzqCK6Nxhn6y5CmY5aMQVeb\nuO5dPHACdHPxuNOG8RBtu/cW+t+t534bdOUw2lXt7ntRt+awjfMroL2ds04b/FoDz8nJr4zJeSU4\nP/IcOyZOaGzbtNaquLlB4KQP5AfIHycZXrDVwTnuUx5XCSlel7j3Xox5V72B1+z28aIqrykzegb5\nRi/nPMyNfjnFx6s7+F4Ncr+1Cq05q7zGwvOrER5Pc9fns4v3KabPzezff3skTRLQ/R7mOxcpl+j1\ncQ27sDjv3JNrcAGte4IQ7S6jGl+aoo5j9DucY3meW/7k3IBzqimKgSuri9gmMqT+ENcgPr3TTg/X\nh2Zmb771FujjJ/Ca8YjuSTFhqoX11GRM6+IBPrNT0obNbfK3tG5dWsT3zimfaVMbhgNsQ6eL+hY9\nz8ysID/gUzxtNNAfj2Lu+xEd5/UgxWfPTS5pOWIJrSmjCJ2AYz/swul4luE82tnG+oKZWdLAvrz7\nMNYiVxcwllWruN73Q0weOYaEVHdM0pJ+oNw/pHsWBc61fQeHIHbtfJxrXVy0KbsHDwzXdwNeh/Az\nvs828mKr9Bp6Jhd6KOd17smPoNj/4mk8/F//jttPb3ZxnuYTaib8yC+dxpf6W5fR7/9X/wXlBqt0\nfx6X0jydNE5ZZ/w5pfECXqPS8ZJHisnsdNB2vvCLiXPOaIiD93d/Du0lof2KX/oFPP4LX/iztPAH\nj/kc4+ZyjvnBRkzxhPxS5AQ9syjkvAp1TMPi0b7leoFj9NvP4AU/8hDW8NpYuviPd8U2UDu52dUK\n5hdV2pcaxLhf26gtgc4K19ayGPcJx7R3/dyZS6C/9crboP/D118CfY32jF94HmuV/9M//PtOG5Zb\n2A8J5T2rC7g+fx/tUczM4Xo9DBZAf+OZXwbth24/zOMlFtN6Ih/jeOdU11g8hA6Wc5iU9tTabRzL\nsmt6PcrtaL3Boa7bxXkxN4d7alzbzjK3TlmhNjj789SG27Q/5flcR0RNW8xW3XZzu4zy4oL6rthx\n63z7hWoF53SScc0O+2tAa4rpyN33DH32MzjuvKfB9emM8u88QhuoVnFUpxY+6bTh0dpl0EmM6/PR\nAP1GHmH+vtvF93zuKZyfN6+jHa5M4d6CmdlGFfcAN8nJV/voC1uNLdA17y683wj7IaCauDfCNpuZ\n3djGPY54nZ4xwL4/QPva/ngXdL+NdYucYkR74BbMOl18b4/3AijwhDTn77nnHtBnzpzZ8/p3g8U5\nDLiPPHyvc87tDYzRv/Tsb4A+cep+0C36YCajeZTx3mET58VcG2u6ZmYvncX4eOnCOWzDcfxuYj+x\nvIq22qxiDF09jLad0Fqt3aRvQMxsfhnHiL8P8/k7Eppvl8dUR+xTLHLWQe5KyKcNO+dbPqrzrG+i\n3+nRtwZ98uktignjbbdetnEN9yPCA7iHcfQE5kzHr2Be9yv/9l+C/jrVfN6+gXt1N7dxLpmZefxB\nGO03XLx0CfT/+ouYp+10MUYcP/kA6P/mH+Acv3YdY4iZ2Ze+/DXQux1sZ6OOfcv10KJAPYypTp+j\nfXiem1PVa5Snj7hWjXEioJpG1EKfXxRYmwxpjyQbum3IU/7GZm8fzzlAQvOis4txKab7z7Xp+y0z\nG44wpvf6+F7+cJ/X7Hi9N7GmMrnK4nx7xd8ScOEmp7r3LvqOehP3Kfl+/CEy7/d+9yJeW9O3VEOq\ntW/cBl2p4HqP2xBFOJ+6tNdz9q03nSatreI9G80WncF7MfQdiF9WZPt/iQeY13GsunkT33FnE7WZ\n2ZuUR33qc7h+P3fxEuhOF/1AtYY+eqqFsZB9m2MbJbg1Ceyneh2fubyE9nPjJvrkmHLugmJCzk0q\n2Wv1uN46YY9s8mvufYJ7tOz7ngl963wc9f3nyPt391YIIYQQQgghhBBCCCGEEEIIIYQQQgghhBBC\nCCGEEH8h0B9HCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhBDijkZ/HCGEEEIIIYQQQgghhBBC\nCCGEEEIIIYQQQgghhBBCiDua8N1ugJlZFOHfaBQ+NqvwAtBJnoEOwrLXKEDFaQza9zw8u8Dzm9Uq\n6EGR4+3p+qhScVrgBXhPz/CaLMN7Lh09CDp89VW84WgE0veo3+idzczoEWZFCjKv4DVFSP2So84T\nfgAddx5o5nGzCmx3LR6DHuZ4j5UG9u3nH78X9O+/cAn0Vh/f0cysis20h5ZwfP/6X/4A6KW75kD/\n+he/DPqp05ugo0YL9NGDJ5w2zNfwvee2roH+rfM4voPtHug8d8cXobEsO7/YU5aMFdlwgR3p859X\n0fVBHhjT8hqg62EN7/mD4Zb+3GjV8P3Wd3BSpR76t4zmYLGDdmJm9vwZtKWH3ncZ9PzJbdBe7QBq\nv4k39HFMiqyL548HeLrv+r/ZOZwTOfnQNMH39Mh4dvvYL4Mh6hu76NMjMrUwiJw2VTJ8RpZSm8iv\nj6nv39jA976xjfrEgTboahXH2u9jm73CnaP8E89JjlUMx1Pfx3eIM+z3akDHx67/zHO8J3WjzZFN\nW4b3iBPUs020l2GMx3tD7CceJzOzqTreIwyxjXGKjUwozlg2RB2gXyooVrJ9GucEZb9RrmIUs62g\n4/sMttWAAkZOx8MAJ3FR0sc+3YPP4dxu/eZV0M88/STol198Ds9fvw76wlsXnDZsbWFsZvvlOUhN\nMqPYPBo7kXjv680sY1/GKSrZFvc135LndEYP5evN3BzD47yazo8pX4zpnk4OS5LH1syMpr1VyJ9F\n9GLNEDtqmKAufM5Z8AEBBxozi8j9BdTwWoD20ayh75qdwvjb72OML1KMZQdorWBmNjM7Bfry2zdB\nV6mjdnsYu8Yj9I/ve999oA+fOgX6+ts3nDZcb2Neffo7T4O+8NbroO+55x7nHncqoZEd0XHPQxuo\nRXg+25CZuQk2hxeP8osG5m3B1DLo4QBzqPzN38PzPdffRm20tQrpmRN7r88Hndugqw3MkRaPfwS0\nH6ANmZkNO2hrg/EtPH4b8998jXLP3XW8IY1FWKVcMd5w2lCklC/kNEcTyi/INw5j9EvVKdTNNuow\ncsciHeNveYya88nhkOIQWSUv36KI8l83FbR6DX+sV/Ge4zHeteqkhng+mazllHOnXHMws216BnWD\nzdbwh5BcNrl8q9DaPPDpnUrStGYDbxIneI9WVBK09wm727ugr1y5AvrlV8+DrlA9LSnJ58MQ52CV\nampVspM4QcOIaZDylOqEVEcMAjeO83xwrqHcIAyollWtg240cU3BOVS97vq69U1ca186/xbowRjX\nRptbHdDTU9iG0bCP1/dQ7/TIb5nZdgdzg5ubmO+OUnzvVarpzbTwvZOE82Psxy7lImZmPvVtZjy/\n0CCaDXzmpo82ykl0QuvgsvpaRudw/ulTrbpSwfdqNDAnG8UYMwIK6NWKa5PNGs6Lu48cAl2jucVr\n0CLDecL5bWbs5N1+iCr4DJ9qKynV1/cdtKYo9l4imE06XnYOF3qobuPec9Lx779NvLwq+BncxrKF\n6fceJtPa3cTz/8XTeL+zXTfQTqp6Tzq/R8nBLz+Dz3j/gziHP/15Wns7fqHknTl3p70fPu50W0hx\ngcqxVbe8Kv4UDEZu3vHL/xqN1PfRtw1SHKxf+1X0db/whf9EjfsBoTLCWH+qjvnF6RzXb+OY+8+d\nHxwX3Togad7LquC4XezgGP3B6WnQn34Y15xmZjMtfEazsgg6zfE90hRjdb2Ca8pmMAs68DDvSlPs\nRzOzZoTPfOr0GdD/7F98BTTXy3Y7eM+M1tqvnMVc8StPPem04Wc+9TjosIP5QTKF/XRwaQ10TnXx\ni5u4t3T83gdB19qYl5mZNaYoGJHJ+NSXGdlHtYn5SBChPXi0H+WHbp4dci2e9p1H514CvX3jbdAr\nK1hbmZ2dAd3ZpT2zkkDWosUvr5PiGANHjXLiJuXRFSpR1OiZVae2aZZTbucFtL9Ysk7aL1R5XUpr\nSt53SqjInqVuvhLSmKQJ+0ccwyTF4yPyp2Nag9QpkatNHXHaUKdiYjw8B3qH1u9VD231YBsN6Z7P\nos+4fg3rcetfx/ubmb14DdeVN+njkXsP4zM3h+hPK1WcX+kY97SrdTTutKRWtZVhGz78xCOgr3z7\nKdCdTfyuozfCZ7zYRXt5u4Nz5diRY04bXjrzGuhWC9eEp6iuvrGBtcflZeyHfh/X750OxudajQol\nZjYc4ni2WhjL+J5bW1ugZ2bQt/G+HqfIufOhkNm1G1iDTUbYpuU2PmN1Ed87cvwQBg0+XJSk6Qtt\n7PtN0nGZEe0TmjM45pyDvX0dx3x6Dvtm5ciCc8+tbbSbnPylN8BcuV6hvTnnWwV6AKXrZZ8+uN9D\n7P1vPmfkj69fvQR6a4C+8aNHV/E42a2Z2Ze/9Tzoc7tfA/13/+ZfB334HvyGrffvcR/m9ddxf6NN\nuUWl5i4IQ1rb1Gq4iNzuor+9cQuf8Qtf/Degf+yTj4H+2z/9k6CTsVs3bDbQP/6f/+bXQR9YWwLd\n62Ff5pSDDWL6jjPhPW03/o5pf6HbxdpiRnO8lWCsrFY4/yFflmGuGZbsTwS29/cAbNjxGOdRTHtF\ngw6OXZrgvOoP3Popf1+V0abHuOz7lX2Eu9dFNbwJpa2yOs+Ecpfz/UNnB2N5TPY8tUCFHsL5HqLk\n+ZN2mUL6Ppr3Pfi7PI/W7/Nz86ArlF9cuYLf15iZ3VzHWH/sGMYe/naZOz/3aW+c9qVHffTRMdX4\nrl3Db3iufPtJp41XN3Dd2hnhfDly4ijozQ3MDes12utp4Fj6tNbKaa3O64DvQnUPWvdGtM6dm8Pv\nlJeX0b92drGfuMbhk/ss/RqNxsb9ZtHbQ022z5KviibeoZiQGEz6DumdoP85QgghhBBCCCGEEEII\nIYQQQgghhBBCCCGEEEIIIYQQdzT64wghhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQtzR6I8j\nhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQghxRxO+2w0wM/Mr+DcaeeGBTizF870IdJbjcTOz\ngv7sIwoCukeBJ3h4vChyfEaW4ek5np/j4f94Fag0S/AZdE2tPQV65ehB0J3tLrbBw36iNzIzszjA\njvAjHPKC2sh9meMjjIbGPJtwQknLfOrbsMB+mYrwHp97/EHQb+90QF++tgs6KumJR5YroP/Gjz4M\n+sB9J0A/9Zt/APprZzZA76TYxuUq2sNU023DxYvXsJ11tOOjB1ugL6zje2Yp9lvBJkxdn+d4/nd/\nQ13QeOU0L2ioLDNqAz0zCPCHtjWcNjSDNl7jk03yi+0zlmbRFm9sjUCn/PrOnHLH9dbWAPSZMxdA\nv+exLdC1qTHewMc2WVCnJ5CvGQ3petcHV8n3ZGR8V3exDdtDvMc0+YG1Fs6XNMeOSuj+3YTe0cwy\neo8GxQWPfEdMXT+kOXh5owf6+MFp0K062naF41KJu0wzmmPU5qiC96yFqL2C5zD5eLp/RHPWKwlm\n/WzvObk9pGemeI+1JrZxoYX2do36sTfAmLDQrjrPbLfRRocJ2dc22ujWbfTh+RjjRlBD++L4XThx\npaRPCr6G8gZDe3Oc+D4jpbwppPmWZTjnvQDHIEnQDszMArL39fWroL/1zSdB/+Hv/g7oM6++Bfrh\nh+4HPT2FMSt2m2CFh/brV1An5HtC8o88Bz2PcmB6Xpy6Pr+ge+TkD1nTFLVsgu1xPuHavzn2O8ma\nnRk0YUqxe8xK8kteL+R8VYR9O2B/GuLxlGJhUUHfE9QoVppZFNK8pliUGV6T0TPGIzSy29sYz43W\nDn7hxrYDh5ZBz8/iemI46INmP3/i5GHQh09iTtzZvA16dQljnZlZ98gK6K2NbdBJ4trxfsH3iz11\njezQ2Bc61u6uZXJap/J6Lmgsgk68Gbxh7xbIYoh5oRe4JYHs+jl8Zn0edG3tAdBhDe0uaqHv43wm\nHmOcjodop2Zmw85N0KNsB/XSAmiPYkTRRdv1q3g8DCjnitx8I6A5ng9x/vg0vnGf7CHC2Naew/PD\nkHLosqU05WG0dLZhQr6MawwhXU/38+mZ9ar772dUArymP8JzWpETaPB6dp/0CF7/DDgJN7NbfRyL\nlRaOX7PKPp6aRJ1brZBNJtiIesONbD51VkBxpPA5n9w/nD93HvQbb14CffnqJuhGA+dTFLl9055G\nvzEzg3WCqEpxmZKHLKXaFeWenNDEsRuLeP1X+HiP7R2swXEeV6WaTjLGNrGN9Eex04aC1hDr6+j7\n1jfQ9/WH6AR6U7guGowwVxiP8ZmjsWvbuz28JvApVtElBWWsOx1cW6WU54fkn6ema04bBn3MgXLK\nkdkfpwN8L15bpOQMJ+WeZURVygWpX6p0PGTHM8I2RBSHlhZnnWeePLQG+vAa5nkVp85Cay6qr+U8\ndjwNOPE3szTBvqXUxnzKZfYdHAZZB1wIpQ4q6x7nGjrOqdjENny/57sGz/smE+/J+wFkS71dPOGX\nn8LjT97gWtefP7tjnB9f/Cq28YMfwpeePU43KGvkpIZzYsV9T/06fxCPryzjCTcwvIo/A70B2uAv\n/TKtV2ioxuP9u4Y1MysyjP1Hx5jr2Q4ev7CJ67eE1nNmZq0ZjFn1OuZyUYh5U1TFfKBex7haqaF+\ncYQ5TyWac9rw2YdxP63mYTtrEe6/5QXlFwme79FaPBljXlaNaO1tZv0R1rm//NTLoDe28XizSbXI\nsmLk97aB4vRTp19zzvns4x8B3e5Rnsz7D1RnzwvMy/oJrcXn8PyE6gFmZnkD85xWC/tqisZ/uY73\nDGdwfOuUdyUB2tcgd9cbzv451exub66Dbg8wp63UsY2c+9XrOHa8VjAzC8i5VLmOSLndZ2kf+/e3\n8R1O4FaiNShnDsu+U6C1WEE1eOM27SO47l6p4LuPMlqf0XiVzUdOn5OE9oCo1pQkGG8GY6rtkhzQ\ntwehW6Kx9U2s8/W2nsE2FFTfSrDmtkCvlWW47j11COu851ddX/dbL+C+jH8IbfeJu/H89zbeh+e3\nD4C+eh2v92ho/LykI6YPgVz52I+B7vSxc2899XXQuY8+Px+g79vZQXtpzGD91czMIqyTnziFL/6Z\nz3wK9Ne+9jXQx49jAvq5z30O9LPPPgt6bQ3Xi2Zmp0+fBv3EE0+A7vdx/H/+539+z/Onp2mfu4W+\n7pvPnnHasNvFZ4xj7NtOB21s2CdnRnMvJ831uGrNrSmMKLcJQ5wHmfPxxf5h/Rr2L/sujk+bt9HW\nF5YwPzIz69Aee5vi8soqfsO2c4vsgsaQY7JTiiipifsUI524TkO6deMS6N4ivnefaug7bcxV5lfd\nvYF+H/O2N86h7/vn/8sXQP/kj38W9Cc//jjoM2fPgp5bxTm9sIT5tJnZt5/6Y9Af/+BDoL/zne+A\nfvX8ZdC7O7gX9NQ3vw16eQF9/Mc/8gGnDU988mOgn3vtTdCf+Us/ic948knQL38Hfdluh2qAVHjk\nWpeZ2XiEhZPBEOc813hT2kTxfZoXtGc5FaA9LDXdmt1ohAv0Pt2Dv10a9DC3HI/RnvpUT42pbnx7\njLmqmdl4RN8cUBsK/rhzn8G5nVvWZF8zwfeYTfzosqDvZ29ex9p9UdA6t+Z+H/m9cJ3cK0lx3HZi\nGxu0Tpmfw/1b3ovhfpiZwXl/8uRJ0M+/gH7FzOzNs2+AXlvFfLHRwPUcz0kul4W0JxLHOKcHPVyD\n7ryOhcbmDTcf8ca4v/T6G7jP/eCD+F3QeIx+odnE66u0P+X7E+yPv0E3txYfBE5xHttA/bi2vAp6\nfR39QrxB36DnHH+dJpnvfF9DLeKir2OkNK8mFEv5+4DyjXC+htuE2i+bOBPQ/xwhhBBCCCGEEEII\nIYQQQgghhBBCCCGEEEIIIYQQQog7Gv1xhBBCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQggh7mj0\nxxFCCCGEEEIIIYQQQgghhBBCCCGEEEIIIYQQQgghhLijCd/tBpiZbW8PQHteDjrOY9BTjRboIvWc\ne8YJXhMGeI5HfxfiBxHoMV3vefSMothLmplZ4OEzvALfq+B7ZBno1SMHQV89f2XPNnv1wGmDT+9d\nUN+mhjqJsQ15gpr7LcjxHQLuJzPjrgktBV3z8Yyf+NBx0FMr06D/H/beM8i27LrvWyfffPt27tf9\ncp74JmAGYQDMAAOCgsAkgrJFUKItVlkussosu+Syy9YHu2yXy6H8Qa5isiGapknKlCASFACCICmA\nCIM0Ob7cL3W/Tre7b74n+8P4A/7/fdw9AESDr7F+3/73nLPPDmuvvfbap9/7N19+GeuETZDzTfNv\nfj75kYdALz3+COirL70B+g+/eQf0FpqDJDR2XhX7/rWvfNGow6s3BvhMqQq6XCG7D7GfqKuNfjX6\nvsAm+Te22yTB8U5z1LmDdbQDLKDko0sppxWjCrZgX/E8yFIa0APGoRn0X68vd0HHMfYHWoGIXTDH\nUuqzlZVN0P32OuhgtoMFuJMgLduj6ziu1hB9dppyLUWS8QjvoWHtk33XfZy3Ryd80JMVrFMnwX5a\n64WgPbObpOljmUmGlerxnKNJl5Nr2eyNQcc0f3wXKxF4WICfmpM0po5KaN0IPIc0jo1tYZlxiHUc\nhjG9D99fd8w6jUMqk/1IjHWcpLF8YAFtvtdD29jYQc0m7rrm2uaTTWbk3GzBMre3d0GnMdqLU8Ln\nrdy0aSAv8FP0m0X+U4SfKXLSBwkcSMfBMUtpftk23t/pbBslXr70Gug/+7PPgv76V58DzbFfyUdb\n6uyiXayP0LeNQx5DEbeE61rMk4janSY4B/Mc52BC8ycnv8R+SEQko1gsylDzmpAL15H63ogf+PkC\nh2pxXL3XG8wfCkrc83mrYL7kOZYSp9T37Kto/C3yI7US9iP7/KIpS8MnQSkA3Syzb8Lx3+mhtuuz\noKeaaG/dtStGHfKb6K/GEZbZnMA4+szZ46AXFg+Bzmg/0uujP7325ptGHdpbbdCTUxOgqzWMeQ8S\nFu1jahQb83ykLaZ4HsVcIhKRXdl0j1+ZAp2706AdC23dcekdGe3vCiakQ/FAMtwC3b/xHdAu2a7b\nWsTnyZdtrV4G7XsYo4mIRAO0q9HWGuhximu5bN9F7WE/ORb1Y5P6bendRh3SmxinW+M+6Jz2zkKx\nX71GY8E5CcoXxGMzvogj8vl0zyjG62Uf+zrCpU08qqNLXR8mZtw1onay1fJ+foRuSCzKOpEJSpph\nndZG5rxoVvEev4QVrwZoDxnFYWWqQ057CcelfgwK4vQI68BLdGfHjBsOClcuXwd9++4O6MEY+z+h\nQe72ekaZYUj7N1qXbZtyT6Q5dkxp/cppj+o7uEaLiOQUnyfks3c66APCGI2b98EZBR9+QLZc4HC3\nu5gn6nRx3R0NMZakUFEiWvc3NrHOtUoJy6P9oIhIf4hjMdWqg56bboFu1fH6Vhvj9mGIix3HbJ5j\n9sPSwgJomqIyIhsbk+acrUf75ITGJuOOFBGLnRNNco7zXF5faQ/pUCxpk69sNXGfLCLy0LnTdA/2\nPfdcmpBNUn7NopjA9bENVsFeI6e5lSW03hYFDgeInJZBi/cEpPl+MZdREc6z8D37vGNfTWucRWu9\nsa8REfGoTnwP15Gmx/JNfMevfRWvf3oZHxgW2Nr/33zzOs77L34JG/nvHKE6lgsKIX9mpHmsfdpJ\nY9Wcw/ufeT8OxCsXaT7+DejHg8J4fHDjtnfCKKC46zXMpx1xcD16IcT93nhoxnajECeERznshPJj\npXJtT11rYF5hPMB4o7uDe04Rkaky7vHe/xDOmTDBuCvPKWYRXCczdn45roluiuWJiCzfwnOYl966\nic9wzEvJJt/H+GI8xn5jbt5aNX57eRn32wsz2C8l8mUlB2MYj+pwooH90O6jvrRr5nC31vEsO80o\nTqKYhTMC5Qrmw2pl1BnF9rlnnkMGZTp/JRsrh7i/90vlPTXv343z/YKPBNKIvlPgFCzFqKsR5Zho\nn/oohY+8w/G8gkWfz/Yox2RVzH3SQYHHJAjQ0qII7ZL3DLz/EzH3pSHtQ1M6Y7RpUzCivRKHEjYF\nl2lCSR0R2djG892vvoz54paD9b52G9v1njPHsA40f3or+H1Ev2TmdS85aIzzJ46A/kaG8+t0G/vp\nkUX8/uWxJ/8B6GoT14BSCfPbIiK1SeyrbcG+CqdxnYhPfxC0bX0LdP867sU5VHirh3trEZGFD3wc\n9EaE/vAvfv3XQbNNfuMb3wB9/Djm7ScmsB+eew7XaxGRRqMB+vnnnwfNe2XWr7/+OuitLcwBP/vs\nR0C/8vpFow7DEZ2BpXQ+UcMcAm8pLZoJ7E55f9+kfbKIyPoufheR8nmtXbQpOhgMBnTuTzmeRy48\nCDqhxO3yjWWjzEoJ14qGhX6g1cRYcbiEtt8Z0Hy8Tfk1yjMJxmgAACAASURBVDUXHblz3q9cRb/T\nmsT4tFLC+6sLWMeU8mErQ7z/1hWzHw5P4Lwv03cm65t4fvE7f/jHoHkOH16YB338EJ7NBVXT15VK\n2O4kx7E5s4S+LqcEwCtXsV0razhXfucP/wT0+hqewYiI/NTH0A9MTuJ3RdNzWIdqDevMMZRL3yGF\nCa6NY1orRUTCgt+gTIfOfy1an/nQbYx+5L5zf5tfaLzjaucroPPY3AvAKyiO36XvYQY9XCsjWniy\n3Cw/4YQpOcyUcxYHHvLtRvP3/XrB/N6BdES2sNXGOGyyhfPc5W94uQ77fK9ZVAcmz3gPib7Jo2+r\n3Az9n5fgfr6W4PcyrZHpB+5+7SXQ31zGOKpeom8uHOy3Cvnw2hSe38Z3Mf5YSjAe9e6ugE4SM06v\ndbFdp586Azqgb12nAuynEuloC+sw5u9aLd5nmefcbgnXEZvOExzaK/BefHoa++kQrSO9LvoRPiN7\nZ2l8/k5ub5vlMo3U6D7fwOXv4Bu5fb86/z5c3cGNBBVFURRFURRFURRFURRFURRFURRFURRFURRF\nURRFURRFURRF+ZFA/zhCURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFUZR7Gv3jCEVR\nFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVR7mncH3YFREQky0FGaQw6lQh0noT4vBUY\nRfJffWR5Btpz8ZncdvB5B0twMryexFhHxzX/zsR3sHuTBNspVop1oOdr9RroqUNTWF43wTqUzOHM\nMqznaDyiOmEZdo5lWDm2O4+xlo6N2rWwn0VExtywBOv0yAK289wHHgX9B7//F6A3BljnmmeB/uij\nS0Ydpi88BLp96w7oz/7pd0DfGWA7yh6Ob+BjPz37DNa5eveqUYcrJRzvuWOLoN+8eBN0lmO78hw7\nMqd5k1l4v2FQIiI8PPRIkuINcU527mCh5bpH5WE/RTv4vIhImJLd09zMMtOGDhKLcw3Q1dIa6HGE\n9m0MUm4ObEq20OuNQe+srYOeOrFBr6BxZOOxyA9QnfIx+WQRySLy42RbFfKZx1s+6LkG+miLfPJU\nFefgnQ62ebqEdRYRsWys93aEtjhKUMcJ1nmSuqk7wrEKQ1qr+P00RwPPXDeSFO/xaU6xL3JdvD8k\n++m0d0APh9hPjoX9WMdhePseB/uBTXSC+vr+efTprTJef+N2FzTb7/xECd9P41ZEvYwVb1RQ9/u4\n9mXxEHSe49hZpMXwrwV+yvgNOyrnkIvu37+V9zbGmpXhfLt48Q3Qv/lr/9QoY3NzE/TUTBN0o1rB\nB+gdOc2njY1t0DH5Ldc146oRrc2ZjY6B3KXEY6yDw2aSo53QlJbIMf1EFGMhtKxKTt7HIuuyLPJO\ne8v/D+PkdWLvGMSiH3KL60Sa65QX/D0314vKyCimHcRYRu7gOuPT4JVsGqycOrrgHdUy2gOZh3jk\nFyo2+po0Qv9oxfjO+Vm0eRGRlGwyCND/PfHeJ0GffeAc6OuXr4Hura2CPnryNOiNuxRDiEi7cxf0\nw0/eD/rEyVPGMweFMm1DHd4LGaZLsXJsrieuh7boViZBW+VpfIBjQ1pfHF5+Up5/RhXEcsmZWWSr\n8S7o8XoH79/F+NZrzuHzfh10Z8zxrxjOzbUpJmpivJA5GOMktI8JApw/XoB1cBxzfuWN41jmLsbU\nwxH2S6WFk94j+7DJPqIx6vHQHIw0wXYPx3iPxwsLrbfkEoS3WqMRluf45l7DJSOpuBwz07qzj39O\nU7y/3adcS8HCM90oYz1d2kPSXOK1LiW753WoHFB5RSZJa1FM4aLnH9x/e2RrG9enccxxLuWFyI8V\n7WF9irN8D43VdSnGIrtwyCdwHiEjO7MK9l68r03omTjFdq5vYuzI/rZcQb9kU2DH7xMR2d7pge6P\n0HdVaV/Dfmc4wr14TPvuNOe9nGncHvl8jttta2/NcV4UYhtsG8e636c1Q0RKJey7VgvznxHlYKsU\n9/OeMijj9RH109ZW26jDaLR33zEcv7JNVsrYphLZ4OnDh4wyD83iGs9b4ZTiUZcWedvhPAjqNKL1\nvKCNfoD1tG2Oof9mHCX8tcHNI9eRU17UvL8gsHL3eYb13sNo3k97jpxy9cbzIuZeil0k2XeIKRX5\n7a+ivt5H/YlzFM9SHd7cMNeFVzfRHoepec8PwphyfF96AefTz3wCO8FvFRSyj30U5uLhOvlTihV/\nlurw2S/i4F5epuBDUb5Pbq3jvn+wimdlsyWcL3mAxpqlZjyRJpRztikuovPXhPLwKe0pogjr0N7a\nwuuhOeF+fYPOQju4/i8uYD64Xq+C9l2MH6KI4w+My3LH9PlffeVl0JvbGPdUKIlg07oRc5zNZ4LU\n7PaOGVd96UtfAb00i+dRlRq2s1zFOk1MTKBuoT5ewzitfuSCUYd2iGUOYzyjiFLcX0ThAHRCZ8gj\nimHTEMc2SczYrpPufe54hM54G+TEfY/2I5x3zDg/a86LMcVeGcVVFuUmrw7x/qbN84TyHJTr5tiw\nqJ5CuWbLPrj7WHPM8Lrn8Tcc2N9RWpCzE84/o92MaA77FDxk9PyQbMSlb1HYhkRE0gx/e9d9mHO7\n/xx+e3Bz/RboL7+IeqGJ86ffwzq/cKfgrNWi87d1zNm8von9sLp9CfT73v+zoB84/xHjHUBRjJXg\nNxU1C/3I1tpt0L0xXrfIl6UO5rfHY1zXqh6uISIiT92P/nX1MpYRkq+amsJ3Li8vg+Zvdi5fvgx6\nY8PMyx87dgx0u43jubKyApp9Qq2G57nXr18HHfg4/o8/bOb5OUe0tYk+n1fLwEd7sXmuujhWThU3\nI13jwyORMcUhgY82ujSPfX+QCCj3EA8o/plDn8A59jttjAtFRGoVXAPLtMfc2cazWqlhTDVxfAav\nU6yxfh3za9HIXENdsr2JScyXnL7/AawjrW+txbOgwwFuWn0Hzwa+9QLORxGRlS2cLxMTeFbge3hW\n0O7i/Ot0Md65vYJ9/cYlnOOOa37zmHJMXcF3ND0a/wD7vjGB82enjT5hbQPL+/rzrxp1ePAczvtr\nV66A/r3f/i3QG3fxHbUq+plnP4g+fzTAfvrCl/HbQBExchRPP/VhLGOEZdxcwZh81MH4d6L1BOhB\njvPk1obZD7tDtCGOAThQjymXORruHe9mGeeRTWxn79hm35zEvY6x/aIGU4fwdyGWZca9xlEW6dEQ\nc/ejEa439hz6P4s/FKE8Op+jFO2t+fvJiL7xXb+Da/W3PvcvQbtD3DtvXn0L9K0exjSDL2MdzrvY\nZhGRI2W01yaGYRJUcd2o0PmdR7abkMtNBnT2RiOxSR+6dgv24nNTOBZPWPSSrz8P8sdiPMeOBjg2\n138D79+g63dDjJlHHsY3IiLlSTzfP3zyKOijp/EbjOYi+ttKGc+tjyweBr1J3zpFvP8s/KZt7++I\n8v3mmfkRD76TvwkyKmCOnfnL3nmQ78fVHdxdr6IoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIo\niqIoiqIoPxLoH0coiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoinJPo38coSiKoiiK\noiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoijKPY37w66AiIjne6DzMAOdpCHoMOqDDjzzbzxs\n10Lt4Dtc1wcdxwnoNMM6RHFEb8iNdxp1sLFelVIJ9HA0wgccqrOFzzdaTdC7o23QSRYbdYgirHcc\nYjvtBN/pumgStuVgFQO8/0wL71/0U6MOL97eBZ1n2Hcf/ui7Qb90ZRX0a9e3QKc5PX8c++Xsex83\n6hDScK1dugr6jS20sczCdkYJ2kPJx3ZXPOzXnQ6NrYjcWcN+GDlog9wuG7teMqNrsY45PV9oodQu\ni7VNZdI7c7wstVoN9LCH/djp4VwVEUkSqifXO99/bt3LLM63QE830A7aXbQlsz9oEEQkTXGg2h3s\n96sXr4M+/uA50K5DxpaTLyFD4BpkCftHkZh8j0tuuhXQDznOMW61S7Y5WSOfTsUNxmiLIiIWrXhR\ngu1KSVvU9xZVarOP/dTexn5vVLBfeX45tmnrZR/vSekWbmdGY7PbwXZvbqHfWd8dgj4+GaCenTDq\nNAqxnWnKvgbrPBjj/S/fwn7h5yfKODBs81bB33E2qlhvfqZCMcDdu7iOtG+8DvpQrYzlObheWzmH\nS5kwRr3ZgVrsUA+2r3NdtP/hsAf6z//is6B/97c/BfrObYwFREROnFwCffdmB/Tu9g5oh+KwRrMB\nOrHQjzgOjkk1QH8sImLFOI4u+d+Y3plXKnidnrcsnJMh+SG+X0QkMX5Ce7RoTtqy95xlDMv8vmwV\n+8GIN4TnOdWJpn2RH0jJqef8ThuvW3T/OKOYN0Z7sF3s18A2x8LNKX7M0XeQeRhxuUNrXynrYnnk\nTy2yYRERi/YwS4ensY60xK/euo3XA/R/uzexDusrXwVdDtD/iogcP/8I6J/5e78E2vPxHQcKGsOY\n1gvLRbtKI1pTc9O2vTLubZzyJJZJ73Q8fEdGPiEejbH8AO3UCHBEJB7jXoa2hMY7PYodkwTX/v7t\nTdCDMdYxK5m2LR6t9Rm2Iyhj39Um0Park3OgyzWMwd2gCtqxTduWhSdBdlaWQffjAdaJNmy83c8o\nx5CEFIuOzfiii0udxBSmN6roPxPa35cC2mPa5Nt8vF6wfZMSxz28LpD0cXsjtP2TMdVxM0QDm62a\n/eA7+FvFoxwR9YtPsWBI7/QCjsvx+Tgz18qM5l6aYL0bOFUPFMOYYg2L+w87MKAcXxyZMRWlhYz9\nYxCQIREO+VfOv6UUF6SmWRk5N4cMwaPrQQP9RquJcR7vq8eUf+v0zTzROMR2D0dozOyht3fR72x3\n0TdWSujLeM/LuS0RkWoZ+9qj+RPTM76H90+36qCHQ5yfETmBccFe/SbF/r7PdcK+Tdy9bS5NsO9j\nWn8dTriJiEftYpsKKAbyaZ0KAtr/DHBsD89PgT57bNGog082GMfYV65D+1LKIzvsgCmJxzkO3zHn\nGXu/3MgjGo8cLKiLjVBtP+0U7J04ncDa4+vUyWSuFo17zu80tFklI/+7zztddH/y7z+L97cm8Z11\nTi1R8Z2eGQP/yXP42//0Vcp1hgWO/Afg1ZtY57V1vH7kcMFD+40dzw+Osx3e9+L14xewzb/yj/CF\n/+S/MfekvYH5m6Lsx/JL3wEdjTGeyHvXQD84h2dAt2qYnxMR8WlfUi/h2rs9QltNeA2zcd20Mox5\nLMrJjCjPKCLi0/r+yudfBd14CPfaJz70AOjAxnVxY+MW6FYd99J3OytGHdY3MBcfUUzCe+mU4iw+\n5+E9JEeHSWLG2d0tPDcOae8U97CfdlLs+02Kszi35Vcwx+NOvmbU4egzvwp60sP9ehziRjezsM6u\nR/lRqkREubAoNM+n+BsA3s9V+1iH0TouBOzCua9dD330zg7mpUVEMtr0JPSMS/Ng++XPgd4Y4Aa9\nTrHcA9QvfkHONz+G85dzHxbHlwcI3rfmvLui6/xdSG6b8UrOZwF8xkgbz9je+9xzNML5Waazsrjg\n7HVpFv3IAp05X7yF5w0vX8d3XLqD+5IJC/3tZILtPhyYZ4bPnMJA6WIHy7izeRP0kUXyha65J9wL\n/jZBRGQU4Y9T5Lta27hO3OxjzrsueN2mHESa4P59VtBPiYh84CT2/VdW995T8j53YgL7lm12Y2MD\ndNH3E4cP41g89thjoBcWFkB/5jOf2bMOJfqWKYxwrE4eRfsRMff8LuVSKrRu2LRe12uYW+kKnZnV\nsB+cEva7iIg9wjoEJcopeAfX15GbkVoT4532Ntpuxjm62Nww1ptHQM8fR7sq0xgk+Vl6Hr9LWSld\nxDpTjqe/hWMuIhJSTsWh7wFdymlv370Bun0V19DYw7myVZoF3c3Ns4Fc0DZ/6eR9+IyDa/9vfutr\noHsjrEMS8zccGINbDm8wReaWzoBub66BHpfwme0djEU7dLiQURIjF7SHwdDMXYYUZ81THnBl+TLo\nHq1tjSYmzfttnNPHF+ZBl31zLJpTOF5/72f+XSyzh3b+G7+P/RCGFA9baOOZi+dT2yNz/e3Tfsan\n8bIpgRTSXIvGvBbunVcsymXzOpDQNzdZ0UMHCJvznBQfZMa3B6wLMH7EZ/o9nOd8plGuou3w9wwW\n1Tke0z63j/NBRKRz9S3QQQ9jmDGd3/45f29J+eWzTbz+D06gnbxrEes4UXBUykcKoxjfMeL8f47r\n8O4Q58/aLuULbFqb6H0RLROr2+Zonp2hdeMK5j3mS/iOk7hNMlJ+/D1NTCFxl8LZW32zTp+/jDmD\nf/H8K6Anm9hPj5/EffNDD+B3ndXF+0Efmkb763TRXpOBmS9wyF54n7yvFzG+5dvz8r7fmhb+ypOb\n5tH3802x/s8RiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqLc0+gfRyiKoiiKoiiK\noiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKck+jfxyhKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqi\nKIqiKIqiKIqiKMo9jfvDroCISJ4loB0H/2bDzbGa4yTE605slOkHAeg0t0Bb9HchSYx1GI/G9HxG\nb0hB2VZu1MGy8Jk0wnekKdab3xEEHuip2WnQ7TuboMOx2Q95jGW6qYPaxnf4DvabZVG/Wdju7RDL\nP1RtGHU447exjPk50M0Hz4L++v/4B6AHOdb5wUm0h498/GnQ8+fOGHXYfvMt0J/79k3QHeo6n2zQ\npn44f+4I6EM2FnB7Y8eoQzfCvjo2XQedkI1GSR+0JXg9z9nmaKyM62L8ORQ1SyybyshQZzbZqFcC\nvTMcgh6MIqMKxlT6EWNxHufx0bka6OU17MMk5Q4z/6aNbaE3wH5/7Y1roJ/84DroVg3rQKYkkqLv\nyh28wQ58o07lSnnPIqcr6Hs8B9swCsk/Cl6vlfGdJ2eqoF9a6Rp16o/Rf5GUOMV3VGmFTLAbZJf6\n+eqdbdBnDuEcjxJsk+uaY1kuYbv6Q1zv4pjmlI1lpDRnX7uN/XC3h+WdaOE4LLRw3ERE2rtog6vt\nEeiNPnZMP8H72YIrPvp09lQRtaFUCYSxyVcl9E6PbHR7twd67cYt0IfO3w/astlZosyLHFlGBpVj\nv1gUN5gtP1h0Ozgf/vkf/A7oL/3ln4PudXdBz81MmIXGGJv1uziuOS1qswsYb7QmyN9eXwGd0ZCk\ntunbkhzHMbbInh2cUxnFuK6FfiCycc6FKc7RpGjNNFwHOitHyGdTnTk8sAxbzEmxBzduMeIJXuxz\n6ieOURybNBWYFEwXD4uU2KnQHUaQg9rB8R2leL8TUwxU5LNtHC8/Rx9tZTg2IWnPpUbQeut7WMfh\nAP2viMjswizok/fdB/rSm5dBz89Nga618PkBrTu3b2HM8J6nf8yowz/8xC+Cnpk7DJrjy4OEndFe\ni2w5pj1GkqGPqDQxlhYRKdeaoC2yXdtBu3FozeL5FY+wEuwD/JK5zjoe7ZVDtO2c9r4+xYJWzL6S\n5sYY9znj3S2jDn4LbbVxaBF0UEP/WZnA+8tVXEf8Eq4BLu1jbNf0+WItoJzEfWuydht0r4frlEVr\nADv1nGLPeGg6u40ujkXZw3vGEc6vZgWvewFe98h50vZNksj0dY6P7QjJPwrNcdpKy2iAeq2Pdai6\n2C8lx1z8vBwrmtPe2qG9hFAsmOa8v6cYmvIcSYHbikJ8pk55CatgnTgoeNRfEa3TvIdxyU/Fxp5W\nJCK/0uuhXwgqOEd9j3JV9t6+0SVDzAri9yyjMaOYqVzDd05Nt/B2iv95Dd3pod2GYUHOjqo1HlPs\n6KKdsQ/v0550HGKdUnogywuMm/rSofiDY8XNbcx32dQIziNmtFba7CREZEy5yza9I02xXVGE7d7p\noaPhvh6NeS0sCC6pa7jv6w1cV9jmbPIjM5OYH73/9DHQjRrmC4oqYdnkZ3ifamMdMlpXuJ08L3if\n8HaZbHNUJsWrBw6PNJur8z1eFxHx2LiwTy16Z85lOHyd1zy+39r7etE7qE7i0tpOIeuxCXJedP9+\nbWrNmHX6edy+y80d7NzffJFyTT9gSmWtiwW8dhGvH3nsHRSy3/i7PBY09vw8xXE/8Umcbxcvs4GK\n/O+/y/uPH/HEu/KO+HIH82k+mepQcN2s3f4O6CMVzDOIiCS0JlmUw5ui/didMu57xxVcZ5MK3p9j\nmCWxvWTUoTyFv21dx3PJV9rLoBszp0CffhfqUYLxReTiHjRKzbOvqRncQ47D10FzCJJTnBVREoHj\nKCOGKYjtchvzY9Wpo6Ad2r9FY8zJSopjl1OuK4swxh3uYn5VREQon390CccmymisxviO2RruDXwL\ndUr9khbEJxz/57Rw9K+hPbR3Mf6M6fkO5aGrVTyP2trEXLiIyOQk7h98n/Y4FG/6UQf0uIdluh7a\nYMmi8ibN/I71MczRZbSm54XJ54MBnxXwPsUmv8WxdVGoEdO+hDW/M0n5zIjtEnVMh5C9oWlXVQt/\n29rF7x0+9Tksczl/F+hsHnPBa8uvgP5JD2395049bNThSop+4FvPfxv0wjz6kV/+h0+CPnEC61Cw\nK0EKBqPi48IQuFjvfgPnX335KuhyBfPscY7zxyF7iHLz7LTsYs1dwXZvbuJ3PBsbG0YZ383yMvol\nXgOKjmmuXsV2tdu49u3u7u6pn3vuOdDbO+gLr1zB7wtWV+8YdRhSLiSkvEariX6o2UL/GdRwvbXI\nt21uoL35Y3MsVldxXvQ7OBbVkvnMQSHawLUjptjg0Z/FjU2/i/e3O+YaeuQIztlqFb9tGdD6xLmK\n6SZ+RxVFlLOJUbcLvumIKE8+NYtnAzOz+A4Z45hXmhhr3gnx/k4XY48+zQ0RkY9Mou1+8jDGVBfX\n0PY/HaBv4+8duB84t8Xfo4mIDLrY106Gttyo0Eaa8jwZf+ND9sHfVxStfYGPecKf+8jToK8s4zcX\nf/bN50G/66kPgn76kYdAL7/6KuiZeex3EZGP/sQnQE/M0DriYEx17uEP4TvIv44pTxiH2E+dHTrQ\nEBE7xP2JQ2cBOeWZ+R3RkL5loe8DvID3BebqyL9Z/G3KQc/Z0T6G7dW2KI/KedaCOcakdKbX66F/\n8ihvXirhmuYLrlkViuXufOk10Pf17xp1yDZw7zOijyRucbvKGLN8/DDa2i/S57MLdcrd0/s7BYdj\nI/LJu0O0vbtdLGWbzihyyjtmAcVZJcptdencpUPnfZFZx3qGPnm9TecJdAS8RJ821/gslXJ4/Mna\nNOXwTrXMOj2Cx9byueuoP3Ub6/hH30Z7eOvKKuj/8JGvg36YzsU7Y1yvr8W4FoqIZDY1ZL9psU8O\nw1w4jA+R9rnfxPh2yZi73/tZ7ME9vVUURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEU\n5UcC/eMIRVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVHuafSPIxRFURRFURRFURRF\nURRFURRFURRFURRFURRFURRFURRFURRFuadxf9gVEBHJLQ90mkWgsxT/hiOTDLWF97/9DJYZRvhM\n4lAdbPzBckhnKWjHwa7zPCpQRPI8Bx2nWAdLLNQWttPx8B3VRhW0a+P9yRDLFxFxLayXQ+10HY+u\nY5lGG2J8Rzseg+5XsU0iIvP1EuhzH3wU9PW1HdArm13QVRfr9BMPHAZ96txprHNk2sO3vvYy6Nc2\nQ9BkHmJZ2O5aNQD9wfecB93Me6Bvh6Y9pGxD9LdJpRK+oz/ogw4qWKdRH3WW0Attcyws+onH17JR\n5xZ2TG6jHofYj8MB9n1umuSPPHNzk6BPHZ0C/crVbdDrnRh0muEYiYjwLy6NPf8VXBriOFn7DhS9\ngXyTW0ffJCIyc2gGSyDbq5fR9/gOvmOzg76lO8J+8Es4n07O1PAFtvm3f2+u4ZwakD9zySfPV7CM\nQ1Vs97Vt7MfX1rDOPq0LFWqzY5ljmSXYziTGduYpzrmYRtd18R0Z9UOUkt+gsd/Ywj4SEdmgschp\nrZogH9+gZiUpOqdxhG2yqd8PTVZAz03R2IpIFGKZjs3rKfk28r/bG1ugs0EHy2tQeETmZOVYnohI\nznMzT/bRB9tB/vf/7T8B/eLzz4M+efoU6PNncW1/65XXjTI319v0C/a5S4tcg2xz5dYq6O0hrXk0\nX8o2zjcREY/sP6F4kP2KRYtzIhSfkt2kFsYClmvGE4yTo9+QhOuN/ZILx6PUD3z/O7JV9rn8TtQ8\nZ23SHq0ZGS8iIlKi8XIEfXLo1akO5Bc4JqLYfxCzrzJ9dr2GNlavkg8mnx6m+FLfx3c2JzAmaJOv\nmp3F6yIi5x96GLQXoA8VG2305tVroB1/BfShI8dAH7vvKdA/9rd+2qjD/KGjoC3q3CwzfeZBISOf\nMI5pL0Vzw6thzFSdnjbK9Gv4m5XRWt7DvdJoQGsi7Slc0mmCczoOcZ1/+5ky1oF8nU1jbDkUj9B8\nLJXx+ZjcVJqSHxORaHcdtLMwB7pC8yUoY7zgUJ1syjk4LtbJcnyjDpZHMc8S7gGTDVzbsuEG6HhA\ncVeMmrZe0jOXHQls9sHY9xTWS0phE+dBxhH6HTInqdjmWAxDfKfLWSTyp0mO7xiRC+gl2K/zZbRh\n3qOKiHgu9SV1S7mClegN8R0pzaOGTzEELTy7HXPdCapYRqWG+i4O/4GC16tBRGNG94/IMP2CeKZH\n+Y727i7oiakJ0EGAviyhNXY8xnfGpCslsw487qUy+r7mRBN0v4/+ktvQHeAk3u0OsY6huR4Ohljm\ndg91QutIo4qxR6WM7xhFOBpdytHYjrlPLtM6sd0ZgZ6cxH7Y3sGxCikvVKI1pFpDnfEEFpGS4PiE\nlNcbDbGdlpG7xPLiBPua970FbkbKVG827OFwIHvdwGr+fQAAIABJREFUUKvgOnP6NO5vDi8ugLYp\nH/t2iVxPztE5dJ0KoJjLojgwpb7nHPDbz1DfZry/MR45WHA4wF3ErmS/6yIiXr7nPblDaw6vs5Qv\nM6/T8/vVUUQoLJKcnzHqTO/w935nzubNOeqCfJhHU/Bj78Fn/vlrqHeigon8PTCimPiLX8PyPvKT\nZsf5dZoAnIuifjP7AWXOwSBdr8zg9V/5z8345PYKvuRzX6TcfJHDU37kufE9mkWa4vqS9naMeziv\nXqF5Pj/GuKnSxXzHvyHHxDGQ42MMNL9o7ltWbuK5Sn2IMUonxut/8du/C7ovH8U6TFOe3sIYeMPH\nmElExM6wb0oe56x5P4fv4DiJ7+d8Wskzj/ijMdZrRDFMmc4o+Hze9nAxdF3K8cWUV+dzSTHPn1q0\n/3I4b2Th+K6PMT6doXWnTn3vBrSIiIhLcZBHZ+MbVcwzx3XcTA8oHp3hnBzNoxMnMTcmIuK4fP7O\n3x3geJcq2I4K5SCWyOzLN3AT6j+Ke6i330HnS+tUSHhw14mphSXQvo82kcS0pwxRc15JRCRNKNdP\n/pHnLNtJnqBdVXcxKWO7aPtxynsQkZnDOF8mJo+BPrdM53l9vC5BA2RUxjpNRGhXeRPvFxE5QYHb\nB554F+i2jfu3/qgFOuZzzxz9NXejmXUQcX3KdzqzoJvkN9wjeDa/5WKecY7G9rSsgfaa80Yd3hji\nnBsFeC5+eAltMKZ3GPayD56RkBNx6EyD7bpUQns5dvQIaD7WbDRw/z81hW0q2g46Hv56aAlt5vxD\nZ0G7E1jHuILzIIvRPx9u4lhVA9MmZxcxn95Zxb5emMN2HCTsmOJ/G9eety69CXo8xvnZ6/O5q8i1\nyy+Anp7AMdjZvA3aKeGZ1EBwTMOYvs/oUxI8MzfSp+57CPX5C6BLFbTV6UPHQPsN7IfBbbS7nPaD\n76qbZ28/fwrP3ip0qHGOck/vpvznH1t4FtSoYz8FAfqArfamUYdwiH2ZljFOs41vFunMg75nsClG\nL5fRl475sEFEXnjlDdAPHsL55lN88+BjT4D+1X/8n4F26HuJly5eBH3q3ANGHZ56+kNYBuXwOluY\nq6w30D+36Fu/tXX8XuDl72A/99bN/U7NxjNn2hrIiOKIUQfnWkg5Wkk4zuBvW4wqSEbfIKQJPpMU\nxOUHCf6+1rjOqad97i96xhHa+yY4jtN1nINHXdwbOZuo/QF+R/pkC+1ked08L9iic8QVMpWnD2Ol\n/z5uGeQ+DHmEtmLG92JXuljea3fNuOvmJv7mU8fNNrCSrRqd59H9HQfjE8/D+HUU0T6YPn04WXDW\nc6aGfclpi1euo75Bnzg+eRTrPFvDOrB35E9X3IJ4tUXv+Lvn8aFLQ6zzFzD8lBV0bTLYxXXoPZN3\nQc8EWMDnN83vON/McE0f1zCGFhfHhl0Re67MmGfcD3z9e4t/337C2lO/E/R/jlAURVEURVEURVEU\nRVEURVEURVEURVEURVEURVEURVEURVEU5Z5G/zhCURRFURRFURRFURRFURRFURRFURRFURRFURRF\nURRFURRFUZR7Gv3jCEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVR7mn0jyMURVEU\nRVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURbmncX/YFRARqdaqoAf9DHQUjUFbtgM6y1Oj\nzDjP6BcLVJLiM66NfyfiSg46t/H5wPdBO575dyZZhnVwHSwjTugdQu2w8brtYbtt16PbI6MOtuWS\ntox7vpsspzplWKeM+83HOvUGI6PMD7UmQJ954gHQn/r9z+M7Euy3B5rY1+9/7wXQ5YVZrMOrLxp1\nePNWG3QQYN+Jj/2SUjtPnVoE/fDRFugbX/gK6O2BORa+jzYSR9jO4SgE7ZawTgunAtArN/D+fjsB\nbRWMte3Sby7ZvYvjWbHxnQnNtU5/CDoakw0XmBtXi3We7W2j9zoT01Ogz51G2zr+xiro7X4HdJLh\nHBURiVP8zXFwHBfm0F4r9QYW4KIPFvapLs1rKl9Ss065hbbFvsdz8HotQD0uoT2vd2PQ24MB6N4Q\nr0/XS0adPnY/+opccA7m1I4mmr9kIb5jlep0p4/9doLmQ7WE/VZk690BzutxjHW0HdSjGOtcomYf\nbuEP19s4Zwch9vNOge8qldAHN5s1vIHW5NEYy1jd6oJuVbG8MpU/1UJ7jBNzjR+NsZ+mWxXQi1Oo\nx2Mcq8tvXQP97vYW6Hod1y3hmCI3bd7K+B6q9zso4yDx8ne+AXp+YQn04gL6QodipKOnTxlldl94\nCXRM84NjllvXcJzHMfqZ48fPgL5zawW0bcSSIuKhvboJxX8Z2hr7wjhHnbAZkG8UywzV7RznrRuh\nP0wtnJOWxbEclmdYomGb+9tqbjyD7eCYxCO/EbE/tDlYoHVHRGwX+8bJMbYrZ9gvI6+JBfCcpVdm\n1PfD2PSPGdWrxHuaDsafFZf9ALXLQp9t0XpbrZH/FRHfw3pGEdZz/tAC6JduLGMBAT7/93/5F0Gf\nOPMgaNcp2D7S+Ge8jzKfODBYFJclGdp+5uN6NDGJvs+yymahMfoRt4xlWA2M48Ih2npMe4okwknv\n0JjnuTmmOe3HPB/vSVPyQ7RPzWyOFVEHVAfLQt8qIhKN8R35COeT55/FOtD+3KV9jE22a9ncbtPn\nW7Q2BXUcv9IsrlXdq9ug45D8Ro7ldXHoJIzM2LBRptwImoeUAxorahbvtTZ2UbsR9nNC22QRkVGM\nhfgBaovyFhHNg9UQfVvVpoZTHUulAq9BfedaNPdSfGefti81nEbi0hqSkB+zbbMOtSa+oztCux72\nce4dKCieCSk+j2LyM7SOOwW5ibV1nNN1GqTxCAfRJz/EeSOH/A5adnHobdMzpTL65DHt/9a2cAJF\nCV4fDHHOr2/1sc4FCZKZFr5zh+xop4f7t+4Aywg5Hqa5UqKc3WDEPSPSG+I7ee++fHsD9NwkxiMh\njX+U4NidOIq+03PMfhiF2I5qBe3hzure/cI5vCii/JpLOVzHXHcsjqEpLI8pFsxT1AHtISeq6Pt4\nbxGNMdctIpLTehonOF5+hv0SkE2lRtSFdXTI99l+QQxAMbLFeRPOXR80uEs43c/rJG9Tiv4ZKi6T\ntUPjZryT1l26n7cUZvlmlYxneFtC78wpb879kPM2lvvJ26eNbz8F6thpvLpYx+s77X+7u4yvvIq2\nfuUNs+PuX6B+4LHgOIzjCe6H/VLgFO9MHTPb/I9+FTv7+RexEmubFDwqivDqYLo2SolLlWKDyQLb\nnSJ7rdCaxDPqKOW2rlIO5jrH+zGu7Ss3XjHqYNt10At0PjBMcZ73Oz3Qz3/j26APncL93S6d79m+\n6cwaKcZ2CxO0N9qhOUl5RD6+yyluqtK+t1Excwo3N7BdcXoV9H1HsZ84VOdYPqe9s+dgLBAX+PSd\nu3dBDw7Pgz7SQiubrGEhAwo3LncoL5Jhnc4cmjbq4FEOwKHgLirfwToHWKcK5WhzHhvaYKSxGV/a\nnHegeNCKcR5MT8+AfuyT/wHoZ7ewXx/4vf8btHO5wOc3qQ4n8azQ8goChQPChQ8/BbpURp/w6svo\nR0oUB/u+mSAJOYa38JnZucOgW9N4Jrl89TJeH+G+pjWBe4rtyMxVXdo8Anq8gbnfYBLt6r1LOP/q\nFcovxzi/ZpuPgj7zwaeNOlSr+MzD5MOTlL9XQH1r5Sbor33906CbE5jHP3cMz3FERNojPHf8yu03\nQW8F6B+zmfeBfu4r+F1HdQp9wNxZfL4/etmow7/+V6+BnizhHDa+udlHM3zW/4H3vM+45/QM2thz\nbz0Pepe+ufITXE9X+5i3WDp6FPT5xz+A+sJ9Rh2OHD8BOqA8BH9TxTkB43sWOs/i/HvJK/C35KQT\n8q9F58wHBb81B7o1gXOeUlPS79N3dwX9OT2L57k//bc/AXrQxTP1jV30ZZ/5zK/j9Tt49trrYN4o\nND8vE5d88ObaDdD9Dp7FRUPM2S1NnARdJt93bogv/YWjeA4mItLifXCK62wlwaj679TQF36Jvhup\nTOMa8fGf/jugv/U8nquLiDz/3F+A5m//HIe+F6QcDn9HyX7n/MnjoFfXN4063Opw3IX+skPnWRfe\n9W7Qk5MYpzkU5z/61NOgX3wJ10oRkZzi+N0dtMHV29exTmQvo+011LuY67x1/QrWMTfzp1YV+zqh\nuG5IMUKnM6Dr6H9T45svLC9JzRiAv4tI6Z6s4JmDhMVJlP3+KXju4sIcDOXSA3xols7f3j+L9p6u\n47coiwHazjNnsLxrq/i+b26ZY3aR6v2Th3Bd/S8fwet1n8og871EZ4J/sowd9+oO6qptrpmPTeI9\n902jdig/1qOjspAGI0hpcaLvRFI6Y6zS2E0W5KdHXXwHbXukP8Ayn1/H+1+h5z9+BPWJOazEYfrk\nMi8wsCFVk1Px78LQUb5IdRpRmSF9G+iRPkJnt+8pY25AROTNt3DNdpp4TufPYyw48rEj2WL5LMn8\nnJX7pSj+5T3p3t/wfD9fnuj/HKEoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoyj2N\n/nGEoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoij3NPrHEYqiKIqiKIqiKIqiKIqi\nKIqiKIqiKIqiKIqiKIqiKIqiKIqi3NO4P+wKiIhUKiXQaZKCHvT7oJM4Ax25eL+ISMW1QJcCH5+J\nE9BZhmXYtgPad1EHpQC0Q+8TERn1I9DcLrGwHS69w/WwTD/B4fLKWIehNzLqYFG1bDHr+d3kGdYp\nTbHOeY7XM+rHu7H59zYXM/ztqDSxjG4M+lAF2/WJx46Cnv/A+0EnE7OgX7n+x0YdNhKsgxtQP2So\nbcsD/cH3Pwr6/NFFfOcYi+tnZj836tiuhx45Dfry+l3Q26M26OlHJ0BXz6O9tO/i+Ptlc3rPn5rC\nOh2qgA7qNG98nDc7l3ZAf+7/eB5fYA1Bur7ZDy6ZSJ7jPVmSG88cJIKJSdCnTp8A/eiDaAc3N7BP\nNzs4X0REIuqzOENdq+E4e+UyFmC3qEQyaG+AmvxEnpp16vex3inViX1TqYRzzh9gHRolNJyY5lhv\niP62M0AtIlIKcM60qjgny+RzA7LNaIRlsi3znxv61Mg0QX85Dk1b7/TxnlTwHo+mdUL9miY4FtNV\nXF+PtXBO98b4vmapYD21yT9SncolLHM4QF9UL2O/N8p4P6+nXP7dja5Rp4zW06km2jQtXbLTx36x\nNrZBd3fQt9WOYAGWhf0ktBb+vzfxD3tr25ODjE39MT1ZA91eXwVdaqBvnJpivyRy7BT6y+0tXCdX\n76yBrlXR92UZ+qWVa5dAxxHFPD7OHxGRwKWxZ2PL0X7HgvYeku/KbSzPkr19pYiIF+OccB2cYzmV\nwebKRbInMj1TUey491rNV10LHWRm4R2OEbDi/HAcc75YFEfbHvqSNKKYJMF+y7wG1pn8p2Vhvya5\nGeN2KNYfhOgrYnpm2O+BLlXQd/V72Kb7HrmAL8zIF4nI+p0boGsNbNeNa3jdqc+A/uBH/y7o46cf\nxPsdXHiyzBz7jIzM5vHOzbXl4EB2QbbamMTxcIM66gI/k8VoVyNaoyx6h+PiGCUOPh+NaY8Yom1X\nGugrRUQSwWeyjPbCdfTpaRQK/YB1pvJdB3+JC2wki3FOJn3sh2xMvrCB64ZFvpF9Rk5xWW6Zc9ym\nvrV99DO1BdzPDbdwbcuiG6DjENs5ojo0K2Y/pBSPUrOkXKI4jeK23R7FoyOcwxV6Pi/y+fTOMYfZ\nNvbd3QHqcIxxfT1gn4F1olSMiJjLrU021OlTvclV1aqUx6B+Go+xzs0Jc92hJV66u/hDYB3cfWxi\n5Iko1tin6f1haPx2dx3j8ZkZjAV3u7hm+pTTo6khno9jFpEtZwWxi0MTite4YYTGHhp5RCzP97A8\nz0O76g7MfTPnKg/N4jqxS7HGTpf8M3U+L9PDEb6Tc6EiInkPH5qYqILe2OyAbtIaUK3gOsK5z8Cn\nXKagLxURGQzxHb0Bdq5PG+HZScyPtXd2QTvklxKK8yan5ow6BCWMy3Z2cK8R07pUDtDmKmXUG9ub\noJt17CebHbqIOCHlgT0s06UYICKH6XEMbVPMTfcXRfkWO2GKiQ/6Pla4eRwe7He9YA2zKI+Tc7qW\nNZe53/P73F9YJ/otp3ONHF2TiEvOhfrBonzafnXIC80I31GawDKbFWpouyAv8wOwRb7wjz5vln/y\nSdSlOt3g0PrI04knHY/dPrGEZZvXH3of+vWf+QS+9H/7LfKH6b/dflPuTe4n26PpJlXSPs1Pzt0W\nwXdwLFamVeiJHNfZOznGCzHt16wC52bnOB+ujzEmcUq4Z5xxMG9UaW+BDqINfJ58W+Ow6cyGFO7N\nBdiOjZT8LcXVE97e63CZYgPfOKAQGY5w/9XuYcWX2/RMgv1QpjO9SkD7YuqIMMDYUEQkuX4LdBRh\nxzzznkdAL05gboTPI06SUb56B2PiUoZjJyJy/hie4VKRcmOI7bgaYSx4vso2RjlbfmGpwL/SpoVd\n8GafziAE48XOVToT26aYtky578ScF8kcjQ/ngKyDuy4MEjrPI9vNXdrfhXj/6tq6USav5XGCe93c\nRVtuzc2DjugsdbONe4ZM0CYqNp3likhC32Hc2MB8WWRhIDezgDbQGeGcL5Fvy3zcD754C32hiMiR\nOfwuY4H28xklcfpdfGezjvuxp9/7LOjxJr6z1MbnRUTm7nsYdKO1APqzX/4M6B7l5NZ38CypatF8\nojWlNVUQwNpYr/YO+t9r166D5n3pfvj0jcbwoQvGPbdHmB+NI/SPm7uomxTX90JqQwf31hF9L7O4\ndMSoQ5NyAgHl7Dxa2/gsKIxxHmV0BpLSGuI4vFkxzyNSOofjb8sOEkNqW075NIf28ImF/R2HZp7o\n+PGToJ957xOgu/QdwJee+za+k8cjonPQDOMj/n5DRGSTzr18+nalefwsllHFc5jjS8dAT/i4efvo\nAG1/oWd+V5K30RdlZKvpAOffefrW5aE6vnNrEteEn/rZnwI9fQLPwEVEXvr2V0AP+phPjcb43Z0l\nOJ689sV0HrHVwW+AXN9cdx5770+APn5yGnRtiP51+hDWifOpHL8eP4n2dqdt7jUGWyugKzZ9uzTA\nc5lwgOtrSL4xopzvaIz2kIRmLns4pDMP8ukR9W1EcUVM+dLYyK/TXC7cc+19os/nLAeNom8m6A7S\n76Q/8J5kDW3pTIbjOEf+7OwcPv/IAuo761inP30D7eBawfL040fQ1n75Ibxe9fEd9HmZ/Ovr+M5/\ntYzXyxQL/PgS6rN1s6Mr1Pm1Gu3vy7Svoc1XL0LdpTONVfqmOKDvec/SluaYXdBxCf4WOFjm48fo\nfsqFfnoF6/Q/XMHbf4Hcwk+cQj1RNu3No37jOz52HH/ZGeP9v3YFr3/6Jj7/OIa/Mk3noCda5lge\nqeFYfWkN17ILPja8MXcc9LqF3xzv910RuyU+1ysshaS133d37wD9nyMURVEURVEURVEURVEURVEU\nRVEURVEURVEURVEURVEURVEURbmn0T+OUBRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRF\nURTlnkb/OEJRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRlHsa94ddARER285AV6se\n6Cyug97u9rCAPDfKzLIU3+FaoH0f35ELlhHHeL/tYPm+jz/YtlkHO8e/PUkkNe75birlgN6JZYah\nD9oro84C829drAzbkWRUzxz73srx/jRN8HYrxjo0sR+yiZpRh7sV1H/y2c+CrjRKoKdbDdCn3/9u\n0P7SYSxw4w7Iq3c2jDp4dSxzEpstYYztWjg0D/rDzzwOev2FL4J+rY39mts4NiIicYz3bO3sgD7z\n4ydAZ/UzoEs1tNlaHfvN8xzSOJYiIj7ds7uBc+nupXXQb75yFfSNi9jXg8EYtEXzzPNNm/RsvCdP\nySb3nib3PHYd/dn04UXQ73niAdDXbm2D/sarOEYiIjH14SDEeRtGqDOa97lFDs6qonbKqG2cL/ko\nMurU7w/xneR7yAwkTnDgO2PU4xjrXCfbsi28PopMn7zdw3oOR9gvLnVD4GAlm/ROi9rQYh9M/TwK\naZ1h2xeRYYztjshXBTQ/PB+X8ZCedxxs40wF/cg63W+sESJiU0PTDCvlUsfNTKGNl3r4fLs7An2j\njbpewjYNBmhvIiJzE7heJim2Y2sX7a/dD7FOFXzH2vVl0POnToF2ajS2pnuV3MW+tewS3YHvzB1c\nHAuKvKdZmJ8FvXrzBuhzD18AncZoB3du3jbKPHriJOipqQnQO1voL3mS+iXs826vA9rxMVbILRxT\nEREnR1tzLZwzqYXrfxTRmkdxliUUh5Fz9GOMFd6+Ce0/pTpR6Gdcz8naeKXO2RgL4mzDAeZ8Ga87\n1C5ybeJQHG3RfHIK4qqc9g8uNSTzce3yoz7oNMEYKHLQd+UxrhkWV1pEejH6lsvX74Kem8B6j0L0\nZz758Gmy6RMnj4PeZhsXkc7GKuiVWyugz114EvSzH/950IeWMP70HLJ7GmrLMWO7jIzGtvGePD+4\nf4+fUNvLdRxz28W9UZqiITmZuca5tCd0aT3JU3wmHWE87lL8ktOOPw7RbrubqEVEvAq2IyhjHTKq\nd216GrSVox3FY6xjOEKfP+jgui0iMh5R31C8kfZxPrhL94O22SfEWIdUsN1egPNPxFybXR/9hFPC\ndtfmMX6IfBzLTruL96eb9L6CsfBw7SpRrsQPaN1J8XpKPpyGSoIKxv0O+wARySw0oozHN8WxcW6h\nL2wN26BtG9cljwLcMMGxEhHxyAkPKF/T7mEdJ+u0RuBQSJiggdCyI0HJdPpbO/hMRvuVAvd4YIgS\nWqc5DiB43S+KJXqUS9jewXW51x2AbtTYVrHDHSMWp/0i10lExrQu2wHunRLy2dUSzrce+d9eH/VE\nA2MRxzVTsJaD88eysN6tBvqA0RjrzPtojvPG4d5tevudlC+gfWqZ2t3exbGam8QYeukQOpq5qSbo\ncEQbbxHZ7dO6MKYYmYbP97FODcqzxCk+0B/i2IyGGBeK7G8zcUh5D49ythG+Y6eLY3NzZQ3fx3kY\nEZlqTYIOKCaIaGwC2r9blPMTti9KbvM+QUQkpzywbZTxN+Io4a8PXgZ5mLj5fN0cVsl/wDIsl3LO\n/Lyxl9o7FhQRMcJz3m6x5n7hMqmOwjlpTqmY4Yaxp3QopRIY2xRqJxfwPZJQruv/+oI5P+YXseGf\n/I/wenmRxoLGJrd4PaUXGEvV/m0K6ljPX/pPsJBr17Dj/vIv0ZelBblJ5eBTJWPzKFbjaCF8B7Zo\nmDPnEow5i5zIcf05SY7iEjkeq+Df/eM6DOk84SI5tx06M7QzXKv5zMQJsbzeXdNPUNgkjRK261AV\nnVtnROe3FNv5tA67FK9EBbFdntFabtN5e457XzfEd+6s4flqUsI4vNnCs9OFlrn4vfmX/wL0VzOM\ni19/Hc98n3niQdDHFudATzQwnixRm77wjdeNOqQRxs0PnD4K+s4Qy7gzxrj7kWm0OV7aMhorh/b/\nIiI5zaaI8hw3b2Ff343QgN41g2fhkyHmFHKP9v/vxbEREZEu5Tcpt5I/hPHnQYKWdul2MAdTraFd\nbvQwNzUxOWOUWa/gOGcJPuOUcV/S7+HeKSjjO2cOLYFu1NHWu6vol0RELi1fAr1DdmX5OGfznGzA\nxnxaRnvG+DDOx3LVPP+Nczzb2abzN4/y7kkXz7WbKd4fzB4B7Tax7y99+dNGHeYTrHf1GObRHzj3\nNOjXXnsR9MIM+pn7LzwM+tod9Cu15q5RB5fOjENORv6A8Wm1imN56sHzxj2XvvMt0E8+jv516+vf\nAd2ib022N2gNoJzD9Ys3QXcewe9EREQWJ+7DHxIsIxzj/nuzjXnDrTbvlSnf7qN/PryA80ZEJKB7\nLN5wHOCY16MzyXCIc3Y3xTNGzhvUm+Y6EA8xR3P5lZdBr+3imLbXboA+d+YRrNMY19TOFub88oKz\nN5/yfkeOngX96Hs+ArqeY07mfffhudeogvnpmQxjslEdfYKISPvVi6Bf/9Ovgq5som+7NsZ2Xc7R\n50/TfrAsOFeK8oY25QkHA/Svu9tYh1IJ28V+ZHsXfdl2B8fS88yz2N4Az9JnH/g4aJ/KrAdog6Me\nXs/oO82JCfT5c0sFBiFboKYmW6DPnMR1ZGMb37m+hW2gz/aMb18GoXlOs9unc7mU9wK838cyM9pz\n5fRO/pZKLNNvWfwOI+d+cH2diBibTCPdT+e1xvcOBcQUH969eAX0IxM4Tg/NYR9fWMDyOvRd1L98\nDe9/YYz6qQVzX/uPMcySuTLW4WYX3/HbF1Gv9fAdP3UUrz86i+8M6Cg2KMgjNmto7+UKfR9Dnd1N\nUNdKeP8atWEHp6i0aOwen8H3FxzfiUvzIaZv80I6x3ziCM1Jyij81i18yf95A+9frOP9T6Mberue\n9M13Z4zPlCif2iRzqFJseZ2ONJZ3sY7TmOKQOn+uJiI/dgzfeWkHy3j1FsYAT7jXQR+aQx9+NzXP\n1r+bnHOjBV/Fcb6o4OulffT+HODjW0VRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVR\nfhTQP45QFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFOWeRv84QlEURVEURVEURVEU\nRVEURVEURVEURVEURVEURVEURVEURVGUexr3h10BEZHVtTXQ5aAE2nYt0I16BXScRUaZlp2idnPQ\njoV/F5Jl+HzgeFgHB+vg+aijKDTqkGVYh1yoTvSnKZaDP4yjIeiUKpljk8R2zb91yVK8KbfpIWyG\nBCU0iVKjAbo6UwZdmcGxqtbwuohI9+YW6NX95EwcAAAgAElEQVTr10G3Gi3QXoCVshp10HlpAvT2\n1S+AfmUN+01EJMux7zzbAR1Usd7PfPhDoE8sToH+qz9eBn1lJwEdJmRQIhJUA9CdHaxnbRCDnlzE\nvi9V8HkHm2BiTgtZvXEV9J//sxdAX39jE3S1hS9p1LBOlkVjZdukzTrwM0I2GdB8P2jkQRW028JO\nOnHuLOhn3tcGfetuxyhzdXMEejxGW7qxgmWMez3QpbSPdXSb+AIb57mVkR/JTXtn/2SRs6mWyMfS\n9cMzNdArG1jnMc2xkou+y7HQ34qI2GRaMbWDLS+h6yH507KH82M2QJ0mWAeekoOEOklEIqp2TpWi\nKghPl4husGOzH76bxYmAfjHrZNO8djwcuyqtydEY18OtbRw718Gxqvj4vpTaYFmmfbkWjc0Ye3cw\nRp/sUBu4Y1eWb4G+r7MLulzGNhY5N8up0g8l0tRQrtMBY3rhMOg4wjHaXLkBujdAuzl84pRRZmdn\nB3R3F/3hifNnQN+5fhPv76Cvcyi282mdTWJzIeWlt1xBX5UM0fbSFJ+wMvTPQvGIFW2jtsw5aVEA\nkMZUBvkFi/xhTj6bfWNGDtzhG0TEIl9RrmE/jEN8B9cpy8ivUCzAvo3bLGL6IovibJfmWOLQnAzR\nHko+1jkSmvcx3i8iEudoI2sbOL55imXMT2FMe+joAuiZKYxxd7bRF9WaeF1E5ObVK6A3tgagf+F9\nHwO9dBjnFvv4nNcBkmwfIiIZrZe58J7FfOag4FUpRqJ9ivh43VjXeRMqIlaCduT6uH74FbSrvITX\nOR5PKC4c7aJd9dqoRUTCPtp2HKKPrtRxzo/auIeoTGAs6dLe2aEYqtBCbN5vY51G23dBpxHavl3B\nPWbOPt2wfXMsuA62i+12g2ksI8M1oDS9iOUFtO/1aX/nUqwgIrU59BM8nbwy1olNqplTXyfYD34J\n62CsUyISNGZAN44/i3VongB9tr0KerS9Atrx0IaT4TrotVf/zKjD6OY38Z47ON6cewlKNA8yHEsK\nV6U1hdfj2LSHXpdyQg7ek+wddt/T2LQO+wHt5Wiu1GivF6dmf/ZHOAjrmxjnbe9gnNeood1MNNH2\nSxWMxccUJxT92zAOtYv3rOydKGUnLcp/tSnHk0TY7lqZ6ySyvYvxxTBEQyr5uHfirXjFxzIDmtO8\nT2p3MXcgIpLQ/itNsQ4L0+jTE3I0g8EY9NQE5o2mJ3FtbG+bk8V1OffEeykcG7YfL8CxqKJ5yChE\n3zYOsc4iIn4J35nE+ExA1wOyc8+lfTLFx/0R9v3yHfSNIiKeR2s+rZe+h31pWXuvp4ZF896jYF+b\nxNg3dkL32H8jjhL++qBpalFzc24+b1OKtvl8D2sqk9/J91vkc406saspGrL9nuFU0X5lcrs5pNmn\nzSJiGKxNKZYyuYW/7h1GZ2j6qv/5U6iHCTbk3/tPsSMaS3ufBRmT9PtpFZWxeBLj0f/6f8XBa/xX\nODif/wzePyxot3LweMHHdXOKrrdoX+OlqO2CvZNHGxWLNr/sBjyL87+oj9B8uCoci5u5Kv7NEYwX\nohTjy16IcdLVXWzn69s4x+ebWKelgpx1NsKJvhlhHHXuwQugd1YxHtje2gCdk4Md0plIHJn7t5z6\nisvoDTBX71MOezhxHHSVzjUrlLvqF/iuddp+90J85/L1G6DHNBYn5jCOqtC55OoAx2btJp45i4jc\nfv150P/df/Efgz5VxX453sB2NqroLz3aS6dd3L/4dcz5iYi4dZxryQ7G/g+2cf/wXEp2zTnalMa7\nif2QfwjzByIi+V18p3Wji/oP8OxQnjSKuGfxPD4Twj1ls4n9PTkxCbpEeSUR8yi0RN+SDOlcakx2\nU2+gT/ADHNOJFtYhKti/dWkvNKJzlbKFdarbmD+Zps84XltBO7w5xu8I4p2CvOFNPMNw6DuOMMMy\nqxT7PVzCeGP71m3QXgn7aeb0SaMO3sIc6M0rl0DXqljGA8fx7Gix+rOgj85g3x/K0W+9vGmeT4Q5\n5rPEWJt+sO8deD94c/mScU+ZcgJJgnWqlrGM48cOge66aD8JBeElyjHw9zUiIu0NzAN2evhd0JXL\nr+E727jWRV200aVFHNvpWcy/3hig/xURWTx0DH+wsN4xTk0ROcw/3LNMzx0D7dhoEw75qbUVPDed\namB/i4jsrGHe/Qu/9xug4xL6x6yO63QyQt8XxejLeiP8NjAamGexVTpnsVw6v01wUHs7eM6fbuL1\noI51HpUpt9XF50VEXspwPv0vu2irpRzruEUb6a0M55e1jba/u47lW1HBN4+GG+Fvs/CdzYlZ0EMa\ni206V88p0ejTeZaIyNWLL4JOop8DHYU4J3vUTncX31GqoH9ObVxDOpsUm4hIOEI/M1k9Atqjes+2\nsMz1Ji5+m5sYI9i0F8lzc1+cURyWUf5UqIz9PwOhc3TjbNaMsc2f9j6bPWiYnyvwfKDLRn+Z6/LW\nJtpr4KLv+NBpvP/hJSxjMMaX/rOX8fk/3sRKXGji/b/yoGkorQB/e2UNx/XPb+D95+jTvk+exjpW\nqGM4JdecRFv23ILvAEiPYiyzH9GccrHM1V2s06u3UG9s4f1/6wJeL/N8yQu+1SLt0/mdRzd4Nfzh\ncQyRpBfj9U/dwet/tIL99Oi8USUh1yNlD58Z0DvKlI9NyJGcr2GbpvhTloTKM4+jZL6GdXh2Efv+\nT27iOycprl+qYA7jr0a4d+8k+FLOT7yjL+QKvo/CQr/3GPtgf5mnKIqiKIqiKIqiKIqiKIqiKIqi\nKIqiKIqiKIqiKIqiKIqiKMqBR/84QlEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURVGU\nexr94whFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFUe5p3B92BUREXDsA3e2N8IYs\nAemXHNJmM/I8B+35+HcgWYY6z2PQlo3X3ZKPz1t4/2AUGnUQKwOZJPhM4GG7MwvrPA6xzDRNQcdh\nhK9zLKMKpSbWuzpVRt0qgfaoTraL/WBn2KbpGOs4sU1jJyJXU3xHOol6Z9AHXZmrgP6z578J+kp/\nDXT3hZdBD3Nsg4hIqeqBjmLsu2Yd3/nU+x8DnW1dAn29g/3iBdiv0xP4PhGRhROHQU+caIGuLmId\nLA/tfjjGOpd9bGcgeP2NK98y6rA9xnZ0aG4tPoh1OH8O61xLJkH/1V9dBD3qYnkZ2YuISIxmLjb/\njZZDNxwwLKeGP5RxHMtzOI8feexB0K+8ccMoc7ezDDrNsA8vXcc5c+fGHdDNI8epjjhHRdCec2uM\n7yPfJCLS3u6BjmP0f46N7axV8B1lD+tQIl90Yx3L3+ihLzK9oYhL74wztjW83qzgWlNyUdsePm/l\naO+7Q5wPrRKWPzanh5g9iSRURzelMlOskx1hiRNl7MfJGq4Ro9CsQW7hO6oVtFm/hP6v2WyA9jwc\n23CM9pNTv91Z3wE9NpcV8Wi96w1w/Ach9n25hHWol1HvbndA99tb+Pw0+j4JmmaleG5bFfOe776c\njfe8fq/z5FPPgr7YnAD94je/BjrntYHWJxGRtbvou/o0x07fdx70brsNemN9F/TkDI7jqI/355np\nSewptIWc/MJ4TL7IcDM4B0sWGnhOa3nOdiUiNs1Jh+ZDnmFczPeLze1iX4bXbaMRIjV6ZyWgeR5h\nO7KcfQvHm/gOn0L7rCDGZUo+joWXom8JyX/aOd1vD/GdLj6/ExXENAm2M6T1sDvAte/Cw0dBHz6+\nCPrWteugF2gtHPRNh8h++/DpC6CXlo7hA9SVPLo5xW4W2YtlmX9bbxvjQ33FE/wAEdSq+EMJfZ1F\n63KW4HhZnrlnsGyKN2gMHLILi/o7HmKMxPsUl/ZaSWqOT28T10EO6cfWAK+HaJthH/d3Hu2lXfKd\nSUEA5Dh4j0t9NdxBnz3exXjXDepYXoDxiWHLBfuWPKG1yKG9sY/vsFzUWYxjwXMhitDZeWnBupNh\n5zgB+k/H5fHDOsYjHJv+Fo6tXUcbzjNzMLIQx7u+hOPr0NhMHDqLevF+0EmE63E0wD2nPzVv1OHm\nt3FuDZf/H/beLNbS7LrvW9985vGOde+tujV0dVV39chuNltqkTQlWZRkDTEjRoYiI0oQW34QhABO\nHgIjiIMEQZAgQIA8KA+WLAlBJFiOQsUyJ0XiIIrdZA/V3VVd3TXfunXn6cznfHMeaAT6//dJFUlb\naNbl+r397/nO/va399prr7X2d6r+BO9RRH/reuTDexTXU5zP+dPhoTkXRaoB8djbuNSOFQ2yk5T8\nRu6gLfsujl+S4tiJiCQxztEBxeNr9zZBl8iPFCmeL5bQtw0LD47FRUQc6jf7hYzyFHJdQmVDWZxF\nH7C+RbZO8ZGISEgFkjGNy3CCY0fhjRQC3kcoX6T1GRTMGDseUm2RPufcy3OxTc73Wk1cr8Y+5ph7\nX6WCY0fDICnFpxH5y6Mu+lvLxrltNLH+1u+TfxaRziHuKxbtQ1GE+dtMYxH07MwMaNdCf7q1v0t9\nQJsXESkVcI+vFDEXKBaxTddD++B1lSVUyxGKMThmE5GU1qvr4li6/jH/d5Zc9DU577N8BME1zGk1\nTYc1XYO3FEpTxDK+T5qWFH+f2/9u2jC+w8/N1z90XMizTDuRItOyi9hGufjh5xTdEa7B/+W3sU+7\nHRyY3/xvcKBnT6MPtv4GauCc/q8+hmv6n/6v2KdXfgon4/d+Cz9/5w3sM/sZ5dHECnFPO6C9eo98\nf8nFHMSMJkRiipt8qjY4pBt0djpLQc6AznwXaf9p1qb5W7T30RhjnIT6wHmpQ2vSy/DzzhA/H6Zm\nAjC/jGc5Fy5gjabZPAG63bgH+ublL4Ee0blzJ8LRH3POKiLFAp+rOPQ5zufREcasXTrPTSiu2ruP\nfc4zPksSGRVXsY3RGujDLdTFEo7lO2O0UbeM/nfcxbht59ZVow9P/exP4z2KuLk1T8yCbtA53dlT\naHOOT/XXI4wn87FZs/MWMT4MT+BzBM9iztLqYu3bErZJmu8B7Su/e93og9C5s/0a1k6s0fH16+t3\nboAe0bsIlSrG2k9fugR6ODLf+/jgJtrusI+2eHSE9RPOleIEbdsjf3v+cfQh1ZIZyL34ONru29dw\nTbbrOKel6BC0FaFtB1QGssjPREdm3hJZmK+v3+qBrrbwuQJMEcV6+RdA33wL312oDPZAd/iwQERq\nI5zPMcWS9778BdCnH0d//Is//yugPYqhzkS4p6z65jsXn+9RH/J/u1yJc68S1QDTHo6ziEiNfNfB\nwQ7ok0tzoJ85g/azsoo2+ua1u6Bn2niG1qiatZZ+B+959+43QL/16rv4BTocP72CvjLtbIF+/e23\nQYcc8IrIp/42zu/8CaxFdnrTkqLjwfqtK6ApxBLLQrsaDaiuP0E7FhGZ//hPgp45+Qzoiy/+KOi3\nruL6+NP/549BD1L0fcV53Pe9oTk/c1U8O6tTXWd/ex20Q++sXb1xC/TeJu5/507hWpgrmXZ1/Tq+\nB7U1RH+aURKbkW2mGb27MMJY4b238R23ZhvPL0TMmpxN5xPNFp5ZF4sUB1Lcx/vStHd8mBa9DzO5\nd4uuwOecP3MGtBWj7xrQ2dEH7/0V6EN6j0lEZK6OdcO1m7RX0Tj5Pj7niTm0n17viDT2cTwlrkvo\nXaeMzlFyql0ax6L0Bz6751cSph2r5nyeT1Vc65j/2+jTzqcffL3xF+Maj/baj59F2/nICs77dQz9\n5HPXcU7+cA2d8DwFGL92EZ9hjo6YRUS+vI5t3tpB/TPnsI2ZEp914vUFj/Ng7GOPXlmi12tERCQk\n+6bXH8T36d0CCt3e3kB9dQ3Xz2KT4lOqx/bwFQ7xpyyQItU6OXq0bYqRKb+br6H+sRXUIzoT+zyN\n020zZJZn6dVll9c5rekz9ArajIPj9Fgdrz/ZpLml9nlfEhGZofT9cXoN7tIB1W7Ifhop+s+TRYwr\n3u3jQzj0zgO/yy9imKyYb68g1pR3lx7G8faOiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIo\niqIoiqIce/THEYqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiPNLojyMURVEURVEU\nRVEURVEURVEURVEURVEURVEURVEURVEURVEURXmkcT/sDoiInFyeAT0ZjUGPxyHoMIrw+nhitOmX\nfNC5lYJ2HAd0luHvRFzfA10o4FD1u338forti4i4DrbpU5t8jyTG50yMNnNQTgH1yrNtow+FRgVb\nSCxsw8JxSDO8ZzjBPq36eL0dZaDfn2D7IiKJh88ZJdhmy01AT/wC6L/o4PzeuHId9Pa1bdB3NkdG\nHwqFEt6zhuPyykeeA31qBsf23X/xedBfu0n3sHGuT58+ZfTh0//eZ0G/E30L9CjDNvtH2IfAJ5ue\ndEHf6bwOupPvGX2ozJPNnMexLjhN/IKN1+8eHYAOY5w7i35vlVumPYhFv8miS5Jp3zlW4HoQOwBp\nlfDz1gra/wvPPWa0ePnqfdCDIc7LcIA+9b0rN0Cff/oCaN+vUx/R/1m47CUeDY0+HRz0QNfJh9ar\n+Jw+raFeH/ucJHhTF10Rm5GUA3N7ixL0b66N3/I9bHSpXQUdxzHoYox9Gk5wb0rIZ4fk0sPUtPWU\nniRJye/TV+IYP5/QPTxaw4tl9CMzNfQBY2pPRGRIfl4y1MUitjk3t4B98HAuuh30Xbu7R9Q+ykqB\n1oyIWGQvAxrc7hjXQEZ+xXfp+0O0t8HhIegZsj+rgM/8b/4IMufpjTFukAnew6qZTT7K/J2f+wzo\nV175BOh/5uM+3N1fB33nxvtGm50e7pPPvvQy6CTBeS9QnFWqYizAcRaZtoQ07yIiRfrbxtY+6DRE\n27Ic9PEO+R0vR7uIrCJ+X8jZienvMvpDbuyzeEEuHANz++gHMsOYjWUq/cEAdIzuUGzaR4TWIMfM\nGV0f2Di3IqbPtmzUBWqzQEOZ0NwEOdpDnOH+W3ZN/9gje+BxyTLs9+4++ZaZBuqFedDb93F/tzy0\nYRGRn//sr4J+6rmPg260MD+wyB5sskmLcgOe/twcBrHpjzlZaZaZedJxIfUXQfuUx/Caz0Lcb1LK\na0VELLLFPMLv8B7oFdEuXOpDnmL8YtPaKDUo7hORaIz2P+jQGid/a5F/9Qrk++ieEcUWpbK5r0aU\nh3LsR25E4t4W6Hz2DPbRTkk/JJgUkTzFfuYZjmXGDtShuSDXx3tCFu+AjkJ8ZhER18FGPIpxbZ9y\nCVrjKbWZhmhzgxDn2p7iZ6RP19x+F7RzgPZRbmMu7AbYZm8X8/lwF/fSZGjmsV4F89RWC5+75uJz\nRTGOQxihLpbRb/UHaA+DPs61iAibaUr7rW2GDceG2Rkc/1GI42PRWsgyylEGZs0upe+MJ9jmvY1d\n0HXyM/UaaZqgagNzOa5tiYgkMe1PtH5C+twR1BHVQ7jW0W6gPz7qoj8XEbFdvGdvjPHuJCLfRfco\nFnEtxJSj0pZh5NUiXGkUwx9aOflCuvzE4hzoVgPjm14PawMB+S0Rkfm5WdB7h+hXXA9j5JTK2fuH\nHdCDPtYoWi2sO3tT+tDtYF7a62LeeuIE5rmnVk+DjiLs850NjOPyFG0w9sw4//72JuhKGXMmN6A6\nCmmeb9vFzzOKCTiP+E4btHnxhB/3mp2H69ZIx7jUxNqZEixzG6ypbsNt5pyH8D19+ty435Q+eRS/\nUxsWt2k890M+f9i4TesTtWkX0dZqtHdz3jqlxb9xRiH6x9//I1xjnT4++D/+73BgVp6gOuWUnPPf\nFl6yrRbe8zO/is/w4sexz7/1P+D++i9+j87lxsc4ADrG8BLdIzvZphz+fI4xT2vKXnBI+8euh7Zz\nRLFfSOe1cxbaf4dukeYYR+1SmVXErPUznAvHVE+2qQCSJfhMKdXyT57FuqSIyHPPY/1z94ByazpW\nnJ3BGGeTLogGdDZOOarDibGINGoYuxc8jHt8yoUHYxzbSYLr3Lb4zJhq+UHL6ENQxXuUmlg7ifZu\ngj649TboShlj+ZRimMEQx6lQwOtFRJ48fRK0TWeXtQrmE1cEbfZigLGdJDiXWQdj3Dwxa198/m7R\nOcrBhSXQg9cwDrl3D2sGw308n7XoMMi9hjGxiIhcpe8YFxzf2M4hb9c9RNutUT2tUcL6WL+HsbmI\nyN27t0GHlL9xncj30Y/k5E9jh86+qMYahuYaf/MDzHU65A9TsjuJ0AfUV1dAny7hM/D58FHfzOeT\nEvZreA3Ham0D/cheE237zJN4z811PBsKKpgH8TiLiFgd7MPB1l3QlSHmc+7da3j90Sugbcq1bzs4\nzssd0x5eyTEn/Epo5vzfCz7tnTmVjdfXzHrZ05eWQTcTzK2DefSPM3NoYzffwvOKjTu3QJ84heMS\nOGbs59n3QJ9uYMfHFzCXPjrCz106b+1SrWWfajNhZPrbty9/APoi5effeAfn5sf/9qeNNh5Z6J23\noIJznlAdvhZQvSw262VLJy+BfukTPwG6vYyxxuGY3h+is1d+v8Km2MS2zfcARn3cZ2++9yboRh0P\n1Y/Ix7+6h2cFd7dwj5xvYgz264/jWhIR2b2Bz7VQwz1zNEHftNdBv5FE6D9PX0CfsX7jKujz/qrR\nBwpfJUtxfeTG2RvqUqkMulxG3evjJsJzJSJy8gy+RxTR/N5ew1p/cxH9kOvjPa0SvTcSoQ1++8Yd\now/nn/sk6PADjJFqRWyzKDj2tSra/flTJ0BP+H3U0DyTjinWS1P2h0YBjSTVNFhTbSjPTX/L7+Jx\nFJfzedUxw+bCJ5HzWXXOY27Gvc/Qu3eTbdyj/uAWnlF85Tqu+2uHuGZ8mpW//xj2+XEso8vn1owu\nyVfWsd+//iR+HpDtXN9E7fpoB4fk5u8MsI+vU3jRM0M/MTwDje3Ls9jmShX1W/dw/RzR+d3jdEx9\nA18Bltvk6lpctxSReoBtLjXxmgq6fXGoNlkMUDeKOHefXsXPr1AM/joec4qIyEU8kjB8ekBhOx0n\nyWmqhd7G8FZ6NDEz9P04NcepQGPHr+Lx+4hrR/iHmTZdUMEG+PyK16HNucp3rgKVGf7vwe+hfDfo\n/xyhKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKMojjf44QlEURVEURVEURVEURVEU\nRVEURVEURVEURVEURVEURVEURVGURxr9cYSiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqiKIqi\nKIqiKI807ofdARGRgmeBLtZLoEtBBjrOPNCjBLWIyDiNQbse/Q4kRe3kDmoamTgOQU/CBLRl4zOI\niKR5Tn3ARj0f79nv9fHzAvYxLUxAn/rEArZfMKczT7BfSQ/HMprgOCUD1IGDffBprm4PfNBhFhl9\nGEx6+Afsgrg0dE/4I9DXI+zDlX3U+fw8aLswNPpwuINj2zs4BO03cGzf/8L/CfoPv3AH9Dp+XVwX\n57IxXzf6cOfuNdCjlTHoBLsgrotj2+t2Qb/7rbdBeye3QTdPm+ui2SiCti/iPXZvYyfefgPv2eum\noMc4VWLT760sx1wXtvDfUKdTvnOssPD5csF5spwAtFObAf3Y46eNJk/Mvw56bb0D2iX3t7mxC7qz\nvQN6roVrSvw23RGfYXxIa1xEDg4GoAu8RsoF0P0hroedQ/z+cIK2N4jRkcxU0JanuGTJM/TJbIuL\nTexTtYxtRmP6Pu1NFQ/72CFn15mg7kXYnoiIjcNk7CN5hm045EAH5PMDG7/fLKO91av4jE5IDlpE\nxgn69TTGfcKiPhaKuIdXmy3QR0c0twN0JAGNge+Ze5vt4t8GHWzDJT+S0lwnNI4etecFuA6F/HEu\naCsiIhb7vwj9pwxx3eVH+/j5nNHkI40laBezs+hXfuM3/nPQ+3sboP+rf4Kfi4h40QHoeIL7/bCP\nY97v4572xDOXQN9+H/flXoi2bvtkByKytYN96PQwHhQL15hDPr9koa3mOa1Zirtyy1yTvI8kCf/W\nGcfesijm5X2YHKZFrmnarpzQNVmK/bSoj5ZDC9um56T147IvnNKJzPDp6IMzGhYrw89zHieX4qaQ\ncgnbnAvaemQUY0c5X7h+Yx10rYL+cn62AXprBwPOSy88afThRz7+adAe2e14iDGw7eHgeh7Ghg5t\nRDxO07DJxrIUx9qyj+/v8VNyAWmEduMU0EiCWg0/nxKwcN5q0fpKxxgzuRToOX4FdEadjIYYJ2YR\nJSEiUmpgG9EEfdegj/5yQuunOoO2zFWHlPrUOzLzN4eey/Uz0hwfYJsZ1QMcv4qX5+zspuTSGbaZ\n52jb8Rj3nTzBcbJd7MNkhNdHQxz7ctnM3yz2PbRvpBHWKSwqZLA/tn28x3iAcZmTY59FTB8/vHYZ\n2xhiLlJfPgfaLeHYH66tgY6PMJcoN83916lhv1tNfE47RnuhJoVCPbHJL3Xo+hLF9SIiMf0poLUa\npub8HRfm5mZB79MAd7u4hicR+ojRBO1UxNxfODcbjfE765TDtshPlZYwby5VyqBdNgIR6VMeEnNs\nQXYyCbFPozE95xh9RoXySYf2YBGR8RjXHLsm1i7FitUqxhLDIfapUuIc18xjbq1jfJuT2xlQHy2K\nFbKYNkOa23oT6wndDu5DIiJFiqmqJfQD97cxd7IptqxW0c9MIuzTgOMhy5wLz8M1fPoM1l4uPH4e\ndClAmxoN0D5KJbTB4RDHJUx43EQOqe53a+0WaM6NSwUcJ8vGccgtvIdNBhVMyXdsyn2ZzHSPxwuK\njYVNhV0J1zCdKbEz/43a5Nppzvfg7YXay13Oa/h6s0viUf7mUR9Z+3RP6pPlkuPgPrsPHoPvQG0U\n0dhqNV4zP3jGGFGt8nP/Gv3n2n2cnM/8Cuqf+Ls4Toun8Bndafb1PcLZh0d1w9XT2Of/7L/FyUoz\n9BF/+NvmWVCaTqljKD9QfIviiT7tD4wgojsAACAASURBVDyDh2Q4lSkpfkb5WByh/RbpnkVyBHvk\nC7tUf06n1NEZrsFxvu2RrpCvK5Gv2htin5Mc9TP1JbMTFuXSIcYwJ+YwdsuyJujcw1itvYA1BK+P\n+VsyJb4slbEPlQrm5709jKtHlEtlZA98FhCGdM/IjO0sB89TeWsq9PZAJ33s05AC0koV46ofef4l\n0M+/9KNGH3784x/DP1Au/V6KvXqP4uaJi7WXsk8xwgks5meH5jjklE/4tLpWTqI9JAH28fLrmGsf\n2niOxyU7PqeZds0PE5/++M+AvnrtHdDPnX0c9IVVjPcrtpk7faP4BmjPpzWd0r6YkS9LjUnDz+l+\nA8pZRUTu3aN3AWh9fHQVazKnzuFzeeQ3bIoFjvbQb60srhh92NrFHLKygO+reFTrr3E91ME+L80t\ng/7i6zfw+1XT121cwfUx6uD6ePm5p/Hz+1iXv/K1vwBdoZrc3Tf+FLT99n2jD6fpLPT6LI61Ne1g\n5a9/TrrVRH+9euYs6Gk1hbyC/U6LeB67egLnd3Mb89Z7t6guQnWNeID2MNjBMzURkWj7G6C5BtQ/\nxHVw2MXnGHWwPurRubfnYd5qrCMRuXMHc/6dzm1q4xjX7BZxzbfnFkHv76HPyOnMan/btG3Px5jf\nb9T4ApBBEd+L8jOcs94hzk/cpXr0lBpNQjXqaB733dXnPwJ6OLwK+vIaxRqUTlbpfcQv36M99jud\nAHmqhf3cpLOCXS6Y0OdJjLbe38d7fvXLGD+JiIRUkLYctOWMYuqYzgoKAe5lzTrWjbg2Gk45K9rq\n4d8OLYyJbt/fBH3rT/4c9PYezsVMDWOw/W18P+BoZOaYfcq1BwPsU6eDtWkvwdr1uSX0rycWsNYd\nUx1xEppnJDHVPyO6JqN3T/gc3Ci4UgzA71Vw7CoiYj9kY5nylWMFn3U9DH4Hw5+yj5YrOI9vHqH9\nFTzci/dLuC9Gh2i/pxp4jxq9NvrHazjP/+x9s37yaUoz+/QeyO9exTbWRqgP6X2xPvmyMd2Sz0Sm\njjL9sUjm/QwN7Y0dvMkd6mOtgl+4jKGl/Amd1x3Rklzgl4xFpEpb/S+exWuemcM+tCjU96g+UAxQ\ne1Sz+OQ8fv7mkdElodevjXGzKVFOHlJefa2HDVzdwz3iRzGknjqZ9EqHVHy8KKJ1c3+Me/78GOP4\nAzr/5WWak+/L2BfKlPeK+AJ2j99Hknt831RRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVR\nFEVRFOWHAv1xhKIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIojzT64whFURRFURRF\nURRFURRFURRFURRFURRFURRFURRFURRFURRFUR5p3A+7AyIik5D/koAahagz+tzzfaNNt0R/s/B3\nIFmM2vcdvNzJQHcOh9i+g0OX53i9iEhu5aAdzyNt4RcKI5B2PQI9THCg3ID6nJi/dXHoGqeZgs6G\nMeh6uQK6GmMfYgv7XKJxy6k9EZHCBPtl4VckyvHzO/SbneVyEfTV+13QmeA427WC0YdmrYR/IJt6\n4/0PQL9zdxP0V9/De/ZDvOfps3Og24sNow9b3jboYXECuuRhv+N4DPr2F+6A3rjaAd16GfvUXDDt\noVQog7bPoB33RgPQmYM2l9D0ZmxzNtqHG6DNi4g4LtmtjW3Y9Pmxg3yFRWtKLPRdllcH3VpcNJo8\ntzoL+ugA/VW7gfMuGdpKZ28f9GyI3xe/hX2ycT3l/AwiYuXoa+KENflxaiJPcZyyDL8/WwlAF1xs\nYLuLvktEZBxhGzM1HOuCj+vBSrGPtTJe36rjOFi0Znd2e6D376OP78Y4DyIiXorPYZF/S8h+clqT\nkY3jsj3CZ57QVlWivXKSmuM2nOBNZhq8l2EbxUoVdJziM3jkF5qGfWKfPc/0CVGCD1LNcNzKVWwz\n8GnPJz9Tq2OfC2XUlkN9tEzfJkkfZD7YRb11H/ToHurKk2aTjzKei2PE1l4s4t6+cvIs6H/0G/+F\n0ebbb34D9Fe/9K9A9/s4B/PLp0Bn5PtaLdyrgxLGQJs7h0YfDnu4T6Zk37SlSc3D68s+2uooxnHy\n6PM4M/1EntFCttFXsZ+wKB5NKe4Sas6xMtJmH1K6R0prkPc2y8HntB3a6xzyExb6gXBKyhJmtI5d\nbCOnfahA93Bs9rekc4p5p8Q0xSJeU03Z5+Ln7E9v3sF4s1yugX725U+B/tlf+HtGH8oVjBMy2j9T\nMffoB8JhCY1DbqxmkZT89mSCe8k0P35cCEq471q0PpMQ432L1o5dMPNYydHeHbrGpnhlfIS+Kk2P\n8J4+5hgZxTfpBPsoIuLSPfwA2/BDnPPRCHOGUQ/9caGM4zQZoY2Mx2YOyTlBkpC/DbDfhQHmRlmE\n8axVaqO2sf0sNfuQZRyP4tjxcvCKtB4jnItogH3iPYSfUUQkovWU22gPXIXIYrxHQvcIqfiSUOiX\n8B4zpY1oiPMbjvCeGYY4kjq459/75tugXdoj2udWjD6UaRyKLu19GbaRUq5RLaNNpzmtKxoIxzXX\nZsGj+aEak8sJzTGi2UTbrpQxD9rdQ1vvDzAvShPTrni0PJ/3esrvdnGN1ysboMs+3qNVQ7uzcnN9\ncWwYhmgHQYB2wDESP1e3h88dJ/hMnmvGM1GEa9Km5zZ3UMp7CvicR0e4Hvt0/amFptFiGqNPv7eD\na7w/xnGxHbT98Rjz3F4P+zAJcZ+qUa4mItJqoY3ZLtWmaCTsAG3Q83Gf6XbxGUZj3DPm57GGJyIy\nOzsDuljCsS1Q/dO2eE9AeypVMNfwCtjneILjJiJiC87FcIw29cHt26AziutXlpawDyUca9/F6x1r\n2r+ZRL6N5tu2fyCOEv7mcAPS5K14UT5MfxfX5HwPHmJOQyiv4Tq78U9hTSlfGH/z8wdr6pNFPld4\n2+Q+cB9d0ycL5YBWgLpeR79g0fnQtDzlwyahfeb1yxhvvnMVB+qP/g8cqP/4N1D/+N8199NajWLk\nh/bqexunlHL7wYTz7u+pOeUHhCOK+bn+LDTvIU00ldj/v1b+Oj7tMSHVSwY21bjZlrgWxe54SujN\npQc6GpUC+b4a1eDKFazJ1Auo+5S3FvgMWkRsC/fuhM90yf9NRtjmwvwC6PYsxm4O1VvX1teNPuQW\nnYXG2Id9ipOSnAaKYt6Yagh8njclhZTxwT3QntBLAPQl9uFsDt0enrN0Xn8V9J0tcxzefu8y6Kef\nehb7+JFPYh9PYW2ajS6jODvfxRxIxsaLDpIJnQ/M43wmPs5Vs4Ab7n5/B7RdonM7Xibfh08+zm78\noyvnQD+1uAz6YA3P3O0u5i2LjRNGm8uz2ObtLTz3z2jN+w76hHINz53abaxVFSl+z0rmDC0tYsw/\nN4Pnw+df+DjoUhNzH65Vpl2001F/DfSd9btGH2yq81TbeI+XLuF6i2P0I7Nz86Ddj+C5zDfu4fVb\nN183+rC9jv1cPHMRdHMG71Gwcewv/+HvgK63MX+LbtwAnYZm3XCJ3uMpNqjI9pAFxvn8Ky89Dfrp\nF18AfeUGniWIiHQi7NfHXsR3COZr6G+/9oWroBtNHKfyLuac0QTXRXf9K0Yfkn30+Xx+VSf3eHUd\nc+F6Hce+XsV3Eooh7n0HU86x+zn+rU71mJ0B5e/HiNvvvQV67YN3QBv7OL2HUKCzWhGRMdWPMzoj\n4q0/pyxkto126Lq4/sIIfaMfmDFVo46+7eQ59L/nn3wO9GiMvbr5Pq7hwQCfqVJGf7vrmDaSufid\neg/fF1tbx/URURMuxW0R7evOPNYf9o4w3hExY+QCvRdp5VzTxlgjF/x8bhbX/IDi28PDA6MPH1x9\nDfQnP/ky6J/9mU+Dfu0K+qqrm2gfnR7VmagWVq2Y/taNcb+VaAskn+/u0btP7TLGuwtkXyuLqEcj\ntFERkcEAfVeHzsBC8sccM/PrpDnF3BzH2VPev8rpOfkd1R/Eusi/S8whefB5jG1zHd0s2sXkj+7T\nGcTZxx4DvXoB48NKG9dUo4j7z+/Q+0Hbu+g3hrnpe97vY7+/8hZec39IdUHKvT0P+5DwPfj9CSok\nLTqmHTk01q0qfmmOvvNGl85daPKaJdS3emjLdyl2CGkf2s6mzD195+51vOb8Fvb5fIDj8soyXn9u\njsaZ1hcf798bm33q0pkvHWHwEaOMaI++T+9ad1K04T+7j+3PFHEcT7fMueR70qt+MqILdhN8P+B+\nsAp6TGetlpAvNPyWWVDiOILhkf1+fJ3+zxGKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiK\noiiKojzS6I8jFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEV5pNEfRyiKoiiKoiiK\noiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiK8kjjftgdEBHpdrug240qaMfyQCdRBNoLCkabjo86\ns3PQsWT0DQvUaDTBe8YpXY7tibAWcT387YlXwnuOCvugQ3+EfYxjvEOGfcypz7aFWkQkSxLQ6Rif\nw6Lfx1j0XPUyfj6sFUE3M2y/POX3NqMUzSwN8R6VBWzT9/D6VZx+sfYC0J3BEO/Xo7kSkbiLf4sH\n2Icrgz7oqIDPkZwug6472Kmshn2+Mrpt9KF1sQ7aK+F8ZTmO5cZfbYLevoz2ktL871/DcQl80ybr\nNLb1FXyu2cfwuRdu4LiN90LQCc2NU8L2C0Xsk4iI4zigsxz7mWW8No8Z+UN8CU+bjeujUGsaTZ45\nvQz6rcv3QJdoHpaX5kD7ZM95jLZo87q2KyDL7bbRpxNzaO9rN7dB9wfoxyseznujiHZSDbAPjou2\n5vuoXY+MU0TmEhx7z8d7WDb5VJqLmRY+0+lzJ0EP9zdAj0e4XrrRAK9HF/+dPtDeZJFBcJ8CF/vs\nejh3Ma2vxMJntn3cLMtVc82urqANVmo10ItLaH889raDc2eTOfFcjic4T5MR2oqISEJuwvfxng7d\npFLCzy0ah5m5WWzPw7giz3AcrRjnUkQkHx+h3sB1OLx9F/TB7gH20Wjx0cYmXx9R7MZ2kaU4xh/9\n6MeMNp9/9lnQm/dxn/zWN78OeuXkCuhBB8d8N8Q+nTv/OOj1rW8ZfUhTXqO4Bovky2oF8ivkT31a\nw1aKfbIsM64iVyZZzvEg+oGH/haa1ktGsV2em/tyQDFuGlNMIxyT0j1IJxnHAviQiWWmLFFGY0lx\nc8XDNnKKswI0UYnIBvmpA9uMs4MS+orFJdwPxyOMUY8G2OrzL30U9Gc++2ugLz35FOhCoWT0wRhq\nY6jQr+e0L9g0/znFYRY77dyML3OKZapVjC+Pc2xXrGHeyuMb9zHHCHu4f+Sxue9aNWyDlrgkIa2P\niOYkxbVgkVH4FbSjLMIcVEQkDTGG8SmWDBK8R0pBzag3xj7G2N6APvcLZtzmUpx2cID9TLIO9qmE\nfayu9PDzOvkE9jupGZix++Pcmn1V7mOMlA5x34kmeI/JBNub5vPDIY5dSrWRjGoEyQSvD9lecozr\nxvTYcWT6umGXcgMf+1mbOwXaLeK6SKlPEcdVtHdmFtZiRESccJv+wvsl9ttxyclTvjNE9yxlyp25\nLiIiEpCZZmQgvJcdJxoNzINm2g3QG1t7oKNDXH/TYgmOBTmfm5DdcAy2toH1Ec/Gz08uYJ8Lvuln\n+kOq+9EUzga4poMAc6cx9dFzKT6iPgdsRCLSqGEskeXYpzDC9TcOcdH2+BnontEI/e1r72DOIiIy\nV8d9weH6KdVDy5S/VSu47zdbaB/ffvMd0OE9s2b37FMYh3t0D65hVKgusrzQAt2ooq/rDXCcTiwt\nGX3Y2toC7XroR4q0V21uoF+ahDhXzSb2KU3JHxfNuG4ywrhhMsa9b0w1hSi7A7pPNYj59gzoOcp7\nSxXylWLuRTElH0mK9zh2OBSbObQv8pBx7D0t9TK+Q21yG4Z+yPXcR4+c2bTTH3ZHHrdBn3ON2egD\nfc7PzMWsaX0iPy4FtL1Kk2oKFvq3R3EbjmJ85jfeRH3zN9GgPvOquWb/4X+Jf1taofqpEdpR3EX6\naIT6934L2/vSv8R96DjnescZm+vN9HnKxxU5n0uaC46PJn26JjRyHT4bQ1viEoxNfsSdUqOhrVso\nbZECLQif8pa4MA+6KBj7Ndu4d9dKdAgtIgdHGAePI+zEAcXJnSOMPyp1rLNnk0PQn/jUS6At23So\nG/d38R57GKsfTR58th1QXF7wKd+nPHjEQbSYtcWE5saie/C5DOd3HJ90U2y/c9c8j71+9y7oL/3Z\nF0G//JsYh/+d5gJor0LPRQbmnFrEz90pQQD5yAk912sWxtE9ylkeX8K6R2tC5yTmEYWJxev3h4fO\n7Wugayt4VjB75izo66+/Afp3vopaRKRP/jAc0Rl6H+P3NEH92AW0m8UF7INDNfdi2/QzP/HJT4Ke\no7yjPoO+bExHBdfX1kEn27dAHw3Q7gYD9FsiIhM6C4vHmG/15k+ALrcxDykEGHM3ZzB/s33UgyOs\nr4mIeFTHcwuYv7dmcE3X27i+bnzpT0AHI+yTQwGxlZq1qklG7y9VWnSFceAP6tzyGdBPLOPcuTF+\nv3dkLvqY4snutTXQ4yr6uvg+1lLOP/E0fp/yf2eM73BlYubzxRblUCmOy4KFOeQv/zjOlUc1uzt7\nqN+5gvPP9SIRkWoLT1iLRXzun7qI9nCcCEoYnwQ+PrvroR/xSNca5nsnM7RmfZ/Pe+lsgGpR7UWs\nFc8uUz2ti7HK/XU8TxcR6dC6j0KMkfi9uJxP9Fy0o0IZbWSGbMZOzDV+cxfrPrMtHNuPnMPnTD30\nXcsL6PPPn17Fe8Z4z/e3zZqdeRxHz0lnp66P9pAmGDuUaRxOzGPdqNPFPUBExM4w944PsRa1vk/v\npvTpfZwCnaFRzS9w8PrVk2h/IiKLS+g3ogn2M55QPZVqWVtUu16do3ed6GzpxJy5Lu5vY635LsXc\nMb28wvlRZsS3lA897J0xEcmptphSDnXc4zyP3kHiM76U38Ojs7Ny2aypbG7ugLYdXOd+QDEK5V9z\nixhf8rxX2ugH5jp4vyvvvGv06Z0O7nMx5VsO3cSj3KpZxj7PBrhGZx30C7MZ+vSna2aunZAziihE\nvbOBYx9Sn9tVHPu/tYyfFyidq5Gru0pp0HDKuwr8vsNWjPfcOcLn+grFyF+mcPM/vYjXP0dh3uuY\nqst6aI7bDoUszQKdS9NXOtSnQ3oXhg3szUNcE/4tnIdfLplxG5cxNjO0n04dbfbCuWdAZ/QOyKSD\nh63GeyVG2fDhnorfv2Jp84sT3wX6P0coiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIo\nivJIoz+OUBRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURRFURTlkUZ/HKEoiqIoiqIoiqIo\niqIoiqIoiqIoiqIoiqIoiqIoiqIoiqIoyiON+2F3QETk5t3roEfzC6DrjRroNEtAx2lqtOla+Gjl\nchF0UsDrjw66+PkkBm05Oejcxj7klaHRh7A6Aj1yQ7zAylBO8DmyHD/3/AC0a3ug0xD7LCIyHkeg\nnRzHxbbwej/FNpIS/n5mlE7wCzk2YBWpQREpuD59BccyinBcHLxc0tky6LMt1FueA9q3qQER8UIc\nu719nO/xGOfTcvC5bbInx8LPXcF7OgXsk4iIbeNcRMMx6JtfvAt665tHoLMU7SESsskdvOe914wu\nSBChjS1ewD4V5/A5aj6uG5/sPiL78AsPti8RkSjE545oraUhPtexI49IkzMiv8Bu2i7WhDl1egl0\nUERb2N5DW2pWsc2TSw3QC8NVbK9JfXJKIL3KjNGndqsCeo3sNSR/VXXRlkq0jC0H/Z3t4DOmCdr2\nfJ3GVUQsF7/THaLvmcTYRkJrbhzjM5TrLexThs/k7qKtj9ID0EUxbb1Aa6ZEvqhGroU+loGNbXpF\nHLcT89jn6uwy9nmCa1xEpDKPY1mp1UE3WtimRXMT0dwMh7g3xvQ5z63NDykilQKvG7zGEpy7JMVx\ncWg/LZSqeD3tnXlvH+/nTgmf9vdAxptboLtHuO9MpsQux4mcYhjbxjlySEuGc2Tb5j6a2jjuP/vz\nnwU9GPZBr9++SffAMW/NzoG+c3sNdKdrxnYW7//0GBWf4oecfB/5GZfHxSVfZ/RAJOZtRGisMl7H\n2EqW83phW6RnkCmbOT1XklE8SM+Vc8xikQ+ne8Q0V67De+N3vgXfifG5HY98ekZxNfnLjNak0acp\na/bkXBP0ix99HvTXv/4t0LMNHJdnn3kS9KVLT4F2KYbOMnMc+G/m2qGxp62H16o52zROmbl35dSo\nZaUP/PxYQQNmrOEaxkPpBGOPcd/0MxxAV9oYj1fmcB+O+jjng80NvOd4ADqjPTIOTduOaa92A/bZ\n2Ealhn3MyI+Mqb2Q4o1a24xvXR+fi3OhYpXy+xDz1HjQwQYpbxULdT5ljec59jOJMbZLU3TIvD4m\nI5zvcEjtpdiHow7VC0Sk3MHnKtkYw6QJzWfMfcDnilL0K5GLfRiPzFhw/wj3V8dDO+/H2IfJAPfT\nMeW9fH3RQvuQe+8bfags4nMEZfZNeP04pn0pRG0nVCcp0n7sTfF1tDdZ5ABsY/89PlQq6MuaTdz/\n2jOo2WaSkbl/2eTr2DclZNvsbwdjzL3ubmLOm1BcUC6a8XtE9yA3IyntkU3ydROyZfYigz7afrtl\n9qFaxjU9Ip+8c4g+fEI1nfUtzFMKgU8a1+vdDcxJRUQSik8LPvYzNuJ0nIxaDX14qYj1gjOrWK/4\n82+8afQhfRvvsbqC31leRl3yKP4pYZ8bNawbFkpow/0B2qiIGa+USzg3B5TvDUfoR5rtNuiActbx\nGO1hMqH6qogc9vCaIECbO3VyBbTn4XOPyXWt76F99KkP02LLFuX3pQLOp+ubNddjBdXajcKmy8Ef\n6WkJHC99bsPjz0nzPYzv8+dkCP6UTvE+x204D/mc7/G9PuO0ceJ6qId+vFjCuNmxeqCTY5hzdHs4\nJr//2+Yzbm/iYP7j/xHX6LnzOPicm+92cD/9/d9C3/a7/zPqyZQ9XXkE4RyePjZKdkYDZuWA0isZ\nW1wXQs1uhf2Cy76PzhT5YxERn/6WUx9CwX3Vc3HPq1E+PxjiXt1uzIJeWTLPRK6vYYzRob19eQY/\n//RPPAG6mGNM+/5VzK3SzjroEwUzB9mNcP+PyAcnVBcs0lmSY5Rk0U8UC9jgJKRCpYikHD9SmxYZ\njE1z5TkPPo/NyIajxBwHtuOZFsZqy7TnL9I5ZUJnEn4VYyLzaGfKPkT1UpsSjhdsvOcXc9Sejbmy\nzwlLzjW/72Iv5Gt48R4j8hrO+d42rp/Zs4+DjsqYQxQt852LaIBnPGGIOspwPVRqOL53PrgCml8l\nyEcY35w+87TRh7kWnueeXJnHNmtoq50O9nG+huurexdzzrBPe7+LeZGISMmnGg059WtX3wJdW1gF\nfbCPeem77+E5zv6Y8jmjByLtKvrwCfm2m9s4ludKWE8NaL6tnM6OqL7dtM31VWnhPmA18bzJpbG7\n8NgpvIeL55LvXMUzxZeewvZqU5Z4ceEi6N7eJuj+HawTn5k5Dfr0DOaY1ytco6X6QOmM0Yfxwbug\nC+wPKTewctxfowHtWwO08Y0e1nh39tFmRUQWqS58ZhHHdrnNCcnx4annXgYd0ns4IdUBEjpgTGLT\n1yUj9BtvffvroPujQ9B7u2i7/O7Xiy88B3p1Bvv8p3/6OaMPl6++h22G5KvozMPY9+dwD7BS9CRz\n9M7TaB+fSURkRLXFmSa2eXEG32l0yQdU62jL7Tr6ofGIzoamvFiV8VkpvRdXKNF7lAmuBdvF5+b3\nbQoBfu57Zu1yY2sXdMXHeOTaDXzXc+MAJyNNzoLmnDIdbIMe9dB/i4iks7gf8rgcHGDNbkR2v9FH\nmz48iXvI8gL680rJ3HnqFXo/isaK3x/gd1W+V6ZFdXyee6zPXqdQq+Leze+bxZQT8HlOITDPAO/c\n26F7oC04tGb4nJFngLXtYY3Go1zqybqZx9ynVs5VOBfC65fm0S/UCjguP34B7blA70G5PWwwMF/R\nkQnlRpdx2OQyLjnjPZGfPYefv7yCtrxI5eYNTIslxzBe3hub/jLmd8ponEK2D5rL6yP8wu/fxoHY\nohrd1+iYZZSbA/flLWxzM6JaPtnwX9BzDund5xl6V3pMtZmbHvqpL03wehGRcII2eP4F3JP//Z/C\nGkW5gXvflWvXQA/ew/06HdM6I9/IZ24iZopqfM7ld2uKkT4E/Z8jFEVRFEVRFEVRFEVRFEVRFEVR\nFEVRFEVRFEVRFEVRFEVRFEV5pNEfRyiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiK\n8kijP45QFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFEVRFOWRRn8coSiKoiiKoiiKoiiK\noiiKoiiKoiiKoiiKoiiKoiiKoiiKoijKI437YXdARKRcK4LePtwB3Rv3QZ9dWQLtFwpGm57r4T2K\nJWxzNASd5/h9x7dAZ+4Y9LCMfYyD0OhDTqPrioOfJxnoJMVOFIoBaFt8bDDB66NRbPRBcnyOOE2x\nTzb2qWbj5138WHL6PY1bwYf0adxERFKb+lXCe+QptpFGEegwxbFdoj5MaFz2RuZcjIY90ImfYJfq\nZdAB2Y9r4T3CGJ/Bp8lOBZ9BRCTt4D3f+4M10HtXO6BpKiTN8A+jCJ/T9rAPtoXrSkTkzlsT0Lt3\ncB2cOoXrZHyIz+HGaLNpgnM7GaPOpphkNiYbiT26wLShY0VG9mmzrdA659+weSVhZhcXQC/MNUDf\nubOLXchoHlO0zXSAPlditE1xathD3/TB7Rm8xrFxXschGkfoo/06Nj53Qn0mly0J+dNwPBGmPYvj\nwm12B+jnJcO78D1y9tnVJuj5WbeYVwAAIABJREFUJRzXM3OHoA/30C+JiBRzvEfRxnsUaXl0yafv\nkXl94uIi6OXTZ0CXm3OgK7b5m0nbw73IcXFjsB3a2yxsI6dxdix8iCLtdY6D33c88hEiIjQOvDdZ\ndA/bw3VVrlRAFyhG6PcG2Mf9PdC+ZY5TuI1xwaCH62gU41pPaN0dN9ivRBHqIMB5HwxxzEslnCMR\nEZf25hdf/BjoxROzoP+n//6/Bn3tnXdBnzl/DvT2zj7omPZ6EZGCi3Pvs4tmX0f7ok327ZIxew4+\no8XOTkSiDNdckqN2clqD9P3M4uAOn9O20FZTWsMiIjb/jdaEzfegeNMzMhDsZeCgTsVcLyl1oehi\nvz2bfQ/fkuaXnsljXzclPKmUcP/r7B2BdlP8khVgm8snz+I9yVdZFvs20/dYtG9YbDT8HUp6jGHh\npIgumDYObCHRGPdT1+fY5hhB4+XSGhcbfV2xhbHI+PDAbDKNSKP9J2OM33mPc0qY1/S3cA8LKJ73\nS+Y+69iYR8TjEWiL4gXuo1eugu4cdPH7tL6qDTO+zTNsszWD+4JFjqRUx3taFq7xjMbVpmcUyzTu\nJMJ4Mgpxr+K8NQ4xEJtQbNnt4DgOJviMk4npb0v7ON8xLcqMSjpxwvk/6sEI+3zz9gbonS2MV0VE\nul3st0t+xyW/49K6cCiepceWJqUS/ozRBbFP4EU5PTeFGUZdIgzxgqqL+3Oa4zrwDM8mYpMDDGNc\nB+ExzmNr9TrouVmMuVa6GPf2+2gz9zcxHxURCaMH1xJy3n/Y79BaGE5wze93cf2OxlP2UNojh5Sj\ndgbYxsoC5rghGR7nhxk9RG9oxjOVAtVx6PMoMuPRv85ojGu6EOBaccluywXT5/Nzs60H1OZME8eh\nUUP7SCjvWT25Avq5jpkH31zbMf721+mRjcUJjsv6ffRle0forzke6vQp9xeRQhGf8+AA92iObxcX\nMdcu0v57dIRxYaeL/nUyMmsWrot7+tIS1r9nZ9qgM9p/OQ8e9HHc9rs49v3RDaMP51ZPg16cnwdd\nOu7/zhLVN4RjO46t2fVPiSeMRIQDarrlwzV/n/rk0ufelD55FPPzdzCEfXib/Dn3mdOBaVsm5zEJ\n2ncmU2pDP2TEsVkg+OK/Rp87prr7P/gnOPjbe9jG//X7uAd8+y+wvVH/wfuQ8qjCtkQ1HRvjE87/\nptWq+E9FyvlaNdwnY6q7H/UwZ+Syk0u+b9rBdtHDfmYe5oi9BPtUoOcME65lYn7HtcqlBbN22Rvi\nd+6v4d775MUnQD+2eAF0rYFx1snmOuj3NrZAXyhgPCIiMo5wLNfexVjc93EcOP6IuIZN9bNWk84Q\nORkTkYiOlxKK3Tzab6t0xm/TRmHRXI3GOM5pPCXOrmLOP5mgv1vqYxtuxvU16gPn4jRO+ZRxsKlu\n6HHNrYN92KHn2nzzCujB3En8vo8b9nezNr+fKx5VWnNYXLh7B+PxwR6up/MrJ0D/1CVcryIi3/r6\nn4G+n6JdlStod40Kju9aH/OU/gHmDNEEP1+MzHcNnAjrRP19tN2M+hSNMPd5/iyeCb756jbomxPy\npYFpIxEZ28Ee5nMbu/hc53L0G1zX39q4DTp20H+fqZvvO7iHOFZrPt7jiXM4/4GF+ZdTa2GfdtDf\nlmgPaBfNWDRfPgU6pDrwz/zcz4H+kZeeAb13A23w9j6exU9SnOs79+8afWhFmG+XI/T5ToZnXs5J\n3De8Op5rv7WDNvfUEu0By3gmJyJizeO5887b/wrvQWcF3THuATGlxlU6B/yFn8a1eOsO1rpFRPb2\ncOO5s4E2ubqCcchxYmPtJug4pAHlui3HeVNyszzENfzBVbzHO1ffAD2hYm8eYDxz9d3L+P0B2mmY\nmX6mVOMaHNXdJ1h7zGk/K1Vwzn06N+3deB9vSO8OiojECY5VRjHzQhvPeiYFvKfloy1n9E5HTLHD\naGzWqjh3brWwLlQI+ByTtMtRM70/4eDnHufyItKl9x3+8ltvgf75n/5J0M119GWDG/jcW13UxTL6\n70Uxz8ws2h+5BufRuyQ52VR3gPbSo9p1vkDnGca4iQQ0ny7lOxw7mlVe87zhQUw9aeB7UGx5fKO6\n7/DC88+D3t5BX3LYobOtJCR5z2jz4AjX/mOPYX7GZxJZ9uAzdoZz61Idz1V6FhfgRH71PPqGV5bI\nPkv4nWqDzpDpDDijc8uUArGMz7mnPBS/4rl+hN8Z0znkahvXxydO4fUVH/XTJ1E/toTtLZ9B/c/f\nNZ3V+wPMa7v03lxO71NztYv1B3SEsR9h+3YFfdd8BeNXEZHXqSDab2I+x2fCRzH6v09efBz08y98\nFDS/79iis4NmE+NdERE6wpJyFfdbdiRRjOPGZz07O5hLjNbpXIZze36HSES+V//4/XDMTzQURVEU\nRVEURVEURVEURVEURVEURVEURVEURVEURVEURVEURTnu6I8jFEVRFEVRFEVRFEVRFEVRFEVRFEVR\nFEVRFEVRFEVRFEVRFEV5pNEfRyiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiKoiiK8kjj\nftgdEBGplCzQ93Y2QId7KehiyQP95OOzRpuRFYN2HPwdiO9hG4GPQxFZQ9Cxf4Q38LBP9pTfmVj0\np6rjg+4mE2wjwC/YtgPazXCcep0R6Cwx+5BnCfbJykDXwxx02MB7hDaOowg+t5Vje55XNvoQODi2\nVhHbqJZxLnLssoTdCPT7hzgXfo6fJ8s4ziIipXoB+2Dhc/pegF9I8LlcB7Xv0PU5tpdP8JlERN7/\nqx3QnXW0McGpkDTDe0YxDkwc4jgGNo5zyUX7EREZdHE+h0chduEIbcrLsVPpAHWCX5eMHsLKzHFw\nBefHEuxnTnNz7IjRXsVGPyBsW+xb7IIw5VYb9MryHOgbt3ZB07Qa/tCmObAStIs8R20H5ro/e+kx\n0O99+33QSYr2i9Yu4pJPlhBtN03It9EzSW76Q99DWyuXcKyjFJ97EmEf2zEa/OYWjuvy0jzo1kwL\n9MtPrYL+/Dc/MPp4d38AepXMYULPuUn+cmkV+/Dcs0+CLlSwTzn5Lss2wwKLNrMkxtnKUrRpy8U2\nXNp/a3W0lzRFm87pfjy1IiJJkpLGPlkOznUhQL9jk5sJx+iP+x3sg2fTnpDh/UVEDje3QU8Mm6W4\n4Zj/PDWn+MD3aQ7ILkrlCmjXMW0xJb/h0j63snIO9Gd++T8B/Xvj/w304dEB6KBUA332VNHogxX3\nQY9HaP9RhItyTOulyPskrReO0xJqT0TEytExeBSr5RQ/iIU+vujj2IfUR5viTXHNuUgovmRfwmuM\nt/Ysw3FwaBeoUPjgmaGdiGCjZbrItdBnk0uXCcXArrEmsf1Wq270YDzB/fDenXugeSrGQ9zzd3e6\n2AeXYmLesL8POOYVWpsO+Ut2usb2mvOOLWLRWAXFEugk5Xzi+JCMKZ+jPcyh/cct4dgEsTk28QD9\nTDjsgU5T9E0O5TG2j9p1cX6GR9heHJmxZUYxTzRB7RXQVtMULaWA7lTcIvbZojVvT9ntU/JN5Ro2\n2uvi3m3Z7IfG2F5EuRfl2nlu7u0pjcO4j2s2HuM94hGO7XiE9jGO8JkGA2x/Mjb7sLONuW9CQzWh\nnPCwhza128E+rO9SDaGH9man5hoPaH48h9Y8bRNkckbMUy/iBdUitj/TMPcdl/Yu3meiiG5Cvq2c\nU95rca6Mz51Oq+9YlH/7FBNPyX2PC6USxu9zC5hv9shvjSdod3Fi+rqtbYzDuP7heWgHRo5Ke49D\nRsFeheMAEbNmd9THficp5z0U31IfaWlIoYR7QJyYnegNcGyimOyM7sE+wKYFViqiT2/XcU/o98gX\nish+F30Zj51HQdJME/2xTfHrJMT2LAfXxulTJ4w+DIcUU9MewGt2exftZ31rH/tEucQkRB8Qx2aM\nnSbYh3q9AZrHtlHDz1OywS7tGQnds0l1HBGRRqOJ19QxR4rpOca0D6Up3uOog2uzWa+Cth3Tb23u\n49hGtMefmMP1f+zgPNThmhxvcpw7Talp8kbIWxA7j4d+zn16yP24j9Puwf3mrZj7wG3y52xa/Pm0\nEynOQyjPHYzxudJ/B7nScYDj8K/+OfqJq1cwlhwO0b+OBrjv6LD+cOBQLsT7QcplBDKMbKqd4Jdm\nqhg/XjyBNevtIe5hwyHWxPmcyaF4xHLNPSwh//jKU1gX71Ot9p17WMsdjmivpr1/cWYJ9L0NzK1E\nRAYUV104g3t5nUqNX/36FdCFziHqDNdw/dwl0CsXnzD6sLF3A/RoTDklxbROgjFrRIEzx+Ecf7If\nEhERi/NtHHtuc5lqbnyPUYrtBT76umbR3FjKJRzs3gi/U6U681N0zlygzSyn65P7aD9W1TwjkyLG\n4sa5B9VgQ8oXJpRX2VxoJPIpW75F6zc3ov3jex476aHtL5w6C3r3g8ugWzWMxV98DtebiEizgHP4\n9ctvgT6coB39/V/8JdCf++LnQX/j+nXQFsVk21ubRh+27t4CfeniBdDzc5gDlJv4XHc+2AP95h3M\npQ45jx2Zeew+1ZI2t7GN3MX651MVzH1qc7geT53EPPX20UnQrm36mYBy3XIV87Mnz6DP/vz//ceg\nNyhXmqV87SKdPVXL5lnRtQCfK6jjPU+dxe/sHuA4tSi3frpNhyB9zM3GEb1fICJphv61T3ltNUCb\nKjXR33a+dRX0nIt9WF7A/XtE9R8RkVOncC8qZbind97/E9ArK2dAv/f+bdABvWu0sIB76aXTpr+9\nfxfHansT7XZtzRy744JNNc2Aaqj8voVDOW+lhOMrIlII0P4zdwb0ucfRP/Y6WBM/6FGcR58X6PCg\nXUc/JSJSrGPtqFTCuCym9+JmKE6L0Mxkfx3tbLfbAd2cxfcnRETyPXyOA6pdVT06z6X3Je5dx/PC\nfT4roncyukOzZsfncRUaB4fa8HyqI0X0HhLB9dRCwO8picQxxrxfe/UN0C88jT7Azbimi8+dxTiu\n9Tmc/8aieRab0P4YTHAcgiL6Bc9HPzRIuXaJvpPPrO0pdRSP3jPidxQYi98HMD5/sJ7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6qg1+sLoOdLDdB+Xgbdm+agoxkjGVoN9GIUgC6m2O8sG4EeRBlob5yAXqhinxYXmk4f/CgC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\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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb690290ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_mislabeled_images(classes, test_x, test_y, pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A few type of images the model tends to do poorly on include:** \n",
    "- Cat body in an unusual position\n",
    "- Cat appears against a background of a similar color\n",
    "- Unusual cat color and species\n",
    "- Camera Angle\n",
    "- Brightness of the picture\n",
    "- Scale variation (cat is very large or small in image) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7) Test with your own image (optional/ungraded exercise) ##\n",
    "\n",
    "Congratulations on finishing this assignment. You can use your own image and see the output of your model. To do that:\n",
    "    1. Click on \"File\" in the upper bar of this notebook, then click \"Open\" to go on your Coursera Hub.\n",
    "    2. Add your image to this Jupyter Notebook's directory, in the \"images\" folder\n",
    "    3. Change your image's name in the following code\n",
    "    4. Run the code and check if the algorithm is right (1 = cat, 0 = non-cat)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "## START CODE HERE ##\n",
    "my_image = \"my_image.jpg\" # change this to the name of your image file \n",
    "my_label_y = [1] # the true class of your image (1 -> cat, 0 -> non-cat)\n",
    "## END CODE HERE ##\n",
    "\n",
    "fname = \"images/\" + my_image\n",
    "image = np.array(ndimage.imread(fname, flatten=False))\n",
    "my_image = scipy.misc.imresize(image, size=(num_px,num_px)).reshape((num_px*num_px*3,1))\n",
    "my_predicted_image = predict(my_image, my_label_y, parameters)\n",
    "\n",
    "plt.imshow(image)\n",
    "print (\"y = \" + str(np.squeeze(my_predicted_image)) + \", your L-layer model predicts a \\\"\" + classes[int(np.squeeze(my_predicted_image)),].decode(\"utf-8\") +  \"\\\" picture.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**References**:\n",
    "\n",
    "- for auto-reloading external module: http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "neural-networks-deep-learning",
   "graded_item_id": "TSPse",
   "launcher_item_id": "24mxX"
  },
  "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}