{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face Generation\n",
    "In this project, you'll use generative adversarial networks to generate new images of faces.\n",
    "### Get the Data\n",
    "You'll be using two datasets in this project:\n",
    "- MNIST\n",
    "- CelebA\n",
    "\n",
    "Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA.  Running the GANs on MNIST will allow you to see how well your model trains sooner.\n",
    "\n",
    "If you're using [FloydHub](https://www.floydhub.com/), set `data_dir` to \"/input\" and use the [FloydHub data ID](http://docs.floydhub.com/home/using_datasets/) \"R5KrjnANiKVhLWAkpXhNBe\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found mnist Data\n",
      "Found celeba Data\n"
     ]
    }
   ],
   "source": [
    "#data_dir = './data'\n",
    "data_dir = '/input'\n",
    "\n",
    "# FloydHub - Use with data ID \"R5KrjnANiKVhLWAkpXhNBe\"\n",
    "#data_dir = '/input'\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import helper\n",
    "\n",
    "helper.download_extract('mnist', data_dir)\n",
    "helper.download_extract('celeba', data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Data\n",
    "### MNIST\n",
    "As you're aware, the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset contains images of handwritten digits. You can view the first number of examples by changing `show_n_images`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd45b2809b0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WaVOTftixyY0RCgIlpfF6vZy8lxLxSGuPLDmfSqWYQDz66KOYP3++qX9WYmoH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\nFmBaZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd45b1bc518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_n_images = 25\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "%matplotlib inline\n",
    "import os\n",
    "from glob import glob\n",
    "from matplotlib import pyplot\n",
    "\n",
    "mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')\n",
    "pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CelebA\n",
    "The [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset contains over 200,000 celebrity images with annotations.  Since you're going to be generating faces, you won't need the annotations.  You can view the first number of examples by changing `show_n_images`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd45b17db70>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8Bkf3iEfR1IAtPHuThqm8+SnxJWa80S2apPGswuY6RYhv5xWubXLJ9aMCmk21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\niQG32V5TNX7uRQrm7TJtFbRE3HiN2gR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xvwTg0Dn3XcbYVz7t+y9a0aeCOzOb4vNXdvHV\nn/ksAGCvn2NMLDABhUf7PpSumIg05n6QDJMJemkA5eZwwq/WmilMyUTk8ZNj9HKSQqPutHsfvI9H\nTz2QNeoV6NHnnWkLTeF6oSS2uv7vtKmCubNYdy7SDJb4rrpqMXeEWvd6SKkrL1iwHU1maKjxZ1QU\n2CAtA+sMFku/u+cEnL32yh3MCFwsIXBO58GWZ8jJj1KkeUyPEgJUE6MD8xmJEGgo9GVKoGlXlOHR\nwO9iL5HS9Dg3cNZ/h0k6KLZ8dKNMjRlFDWlbx/p/oHYvF6eYVv7fe90Uw8QzT+vGwMFHP5cHDDe2\n/bm+c+LTi7Jtojr2xYoRqzimILZkliMlsG57y39uaSy+c9fv0KeLZaSK746HeO2q93K4fXUPI4pu\nXBr8SGssSJbsydMzvEP304PDk5gW9pIEY5r/MfEjOsxiRGkqBENOaczlIscDEn44KUlk5qwGZ/6c\nJQc+Q6zI8dYORhSFVcbi4Ny//mzho8LtjRbXKK1UhVqpYC8rnM/IQ3MyQ033WQBJ+4MuLpFuxHjQ\nQUJNVU05j5WmRAmMBiGzf7bxvAazv84Y+xqADB5T+A0AQ8aYpGjhCoD9n/RB3SzBz925jq989mXs\nblFulSYwRCl99/4+fnTgL+Loyi08JAOT2bG/Od64fgk7r14DAIyHKRIyCKkrh0Pi/reK484Nz2v/\n4V3v7PPe04eRDptIiSEZnh49PYQkm/ieUriUU+szC8IcWBGowCCpBKhkgu2RD32HeRoR8GO6aYwQ\nyCmoenL4BP3uDQAAk25VLqQQcTZbYkadmvuzBu/d885L13scl0n8Nc9z5OQVKYKJzGSKsg1l1nTl\nFekQS3KDjKNHZbH9c/+gfPOjc8yoEvHBg8d47bbHc375zWvYJFNYXWp0BgGE8XM8ny/QEO/+3Ap8\n87s/AgC8ffcBdnf9oridi1UNkyzn62ZFVEuSJJZvGw7UVLVYaoOCKh+3rnlzmulsincf+oWlv72D\nx9RFea4dCvKSbM1DvEFKXcWWf6Cr5QL7+/5+eXQ4xTkJ4xxphwf7fm5H400ktV+cX9v0c/wzt24g\n7/vfTd1gp+8f7i/eeQX1XS87X1HVprEiSqtzZmJ5+WS+wPnMpyvn0yoa1gYtzfn5FFsD/x1bgxFY\nqDSVFWYTv8gIlWP/qT/vQ3IOu7o7QDv388Mv72C44ee7yLNYjWtb7Vu6P8X4U6cPzrl/zzl3xTl3\nA8BfBfC7zrm/BuAfA/gr9LK/jrUV/Xqsxz9T4/8PnsLfBvD3GWP/MYDvAfjvftIbenmKX/78bez1\nEuRBBk3yqLMnuMHGwIeDd27dxJRs0+TS7+BFmsEGolBrvCoJADiDakH28lUNXfswd5f6/G9uDcGZ\n3/n2NkcoKTz7YN+gpS6fpqlQ7/sdlJN0m2MrmW1nGZKACncUNmiHqc/m6JG/45wqCk4qtEELwTSR\nustbg+WEkHiKFCQkBgSSFVsd3Nz2O17KHRSBYIK3UKSmUZFBDnMmEl62xxsQxv+9NRpj0gHcGA3Q\np1r5lM4pK1KMyFynnC9w5+YNAJ7TYQJEpBtoQudl4Ee0bdQg0G2K8dgf58bjJ7hOIji6bZFQOJ5T\nFHe8rCPNWQgRI4W5vABiWo2MorSrGz4K6NzcwxlFUDzJwEp/X2xubGD3kj9mYVssKcoM1R6pFsgp\nhO+2Dlfq4HOZgy1DNesSlide2v8ScWRu3LmBgq7vyf5TZESKunR9hN7+e/6zg/+kW1U1AA5Qmjtb\nzNGh5j2VcZD0JjKqDJV1icnE35vNdhNTqSxRUKEJigEvXfbHdPWSh+l2Nobocx+BpACEW1kfBG5N\n0zSeX/Ipxp/JouCc+z0Av0e/fwTgy38Wn7se67EeP/3xYjAaOZDngFAWCTG0ciXQI5Wiz790Fbtk\n1VsvDvH5W37FrIJ8WG+Vywtr0VD92zr//wGgJxSSikBF8vb6yuduoaadNFUpZlRa44yjoZ3LCAdN\nOZ67wBY1kQugURMwdD5bIk/8DnzzxhhFn1hqxKAUSYKSLNicLGITTL2so+WXpIwu63bQRYhAelGl\nyNQ1mopwktZFirGmVu+2LIGcOAHLWWRKtgYoCmrlThU6VO4N83ZjJ41ScJ+78xZSYh4WikFpcn4+\nb1HPaG6DBZtxSAmgvdTtoEdg15t7A3DnX9tq4PGB/767ZNO3f/IYhr7QXmDg5ZwBWRBHdQiGCSSy\nhasbI/zqz34BAPDBOx/g2lWPJfXGQ1we+6ii380BS14HFHklRY5N4hPs7FzC3Q89vsDKEm9due7n\n1ja4+cU3AACffeNlfzwJx+LYR6Zl1cRrnWYKfWrhT1Uw7dFo487O/LkA6CUCg8zP18bONjiFmfWS\nVMVbDUU4UDOZxfLzsMhQ0om3zmCLSu0F4RpcSnQk+TtwE7kujJkoE8AYW0ndPeN4IRYFBwdrWxgr\nwejhUBDoUN95N+NRd7C0DnZED4j2wIpSDkF8mDcaIMqsYBJdQp/LWmMkAtHDn3abdFBbMkXRFr0g\nw805SkLqDV9p5oVeBThAE4BnjcGS6sp3HzyGe+M1AEBfCSjtb0zpqC69YGgnVDnpjcFokTGljr31\n0TnYNLG2rZrFygJea3TI8boEx5IWQE1aELpcYHniVZRn1dNIeFFcIaUHFowjpUVmizo8084gOhAZ\nrACzpi4xJ2MU0xhUQZ+Aqg9NVYNN/GI7GCUY9KijtJtFCjZzEhnZsr+646/ZN997gtKsQtzA6x/z\nDJyqQyNusT8lrUQKr9Mdh88S6Hh7exNVYBJ3MqRED15MzqBL6h6leROMIaHOzsHeJjrEe5ieL2Ho\ngcxToEdds4IuTr2YoglmP4slkq5PVy7fuAr7T8gpOugrcgeJ0D/CkNN8DjMR9SuGqYgSa6czoknD\nRnEep1tomvs0U7hCCtStsZECL2XQnuCQtNAJsZIINMbG1MwY8wkN0GcZ6y7J9ViP9fjEeCEiBTjA\nagdt3Iq2CwVGh1ckKsp/JY2OCrcihuUCXIQa/AKsDf4OFkU0ADEoEv/ZHQKLmtbTfwFg2syjko6U\nHGhCI81KnyEw28AYQvzAGEMbmo4WNaql/zxXiMimZNEPMIUKmv1ZJ36eYCKy0YJVXlqomBKYxSyy\nH1Mu0IRQsyqj8lKQroMzsET35Rf6YBgDalKyapsWjIDUjGjCWdYHC14HuoamHnxXV+ioUFqtIUOw\nQSCoMRacJNZkWyJo1jGVQGbEp9BAj1Sddroh5BaoL3SPhvLkTm+IRShVWg1OaOz83B/P/HACQVJp\nw0EBTikRTxOUBCrPZ5PYSWsp9UvAo7KWns4wHHrx32HRjxaBglk0U98FWZ+TyvfkPEYprXV46YZP\nV/qDHII4DSboslkbAdhEiOhynQAxErBti5oaqaYUYfW6vRjlyEQiaKlZ5i7Yy6xUv4N4bqI4xAXp\nNk0gdlPraK5jjImiuM86XoxFAQAcg9E6qgSbtl09/EwhpXq8yr3UNrCyhpdSwpEgXsVqWCLhKGtB\nlofInImU1z7VnVFbzOhBaXQdhVO4zJCIkB6UMPh/o7cu5t8XJd9022JO4a7ppxiPyBil50PVg5Mz\npJQSpXkKRb83rYWmduH5OdFvpzOMxz5UzXKFpqaw3Zp4wa3WYNSkESS3ZJ5DUepTMQOQmYq0BtXS\nPzRl1WJOC0tN5qlp20AQ0s+kg8gIAe8M8GjfI/IwAh3KZwvCHIxlWMx8zj1SWZQac8ZBCGqDr8q4\nQAZTWVwQCBEXpMRmvTGOaS4eHOyjpNc8PfLy+tf7Z0gJtxFJiowWeq4y6Ma/RpsSivvji7J5cNC0\neNWzGbo90nOUEpz6FXS1RE1emOFnOV9gdurvkVo7nC98apYuOXLqRk0pLVWsjmke44gVFeM4LFG+\nTeti6UrQQpB2+8h7fpFKixytoQXZtrBEu2ZCQCg/n4L6RLjksdphjIWhdKzV+hPpw2KxFllZj/VY\nj+cYL0ak4Lw6rmktGgrF6sYiDZ1lgkESktjt9qMMQUO73LJZRGGO5WyBNCDAUqLToWaebIFD8tTb\nGHqwK80lckKTBVdgFEZKxmM455ZllP+6yAsLwJhUMobl0BoVNdoopdDr+EiBFnu41sRqhxcy8Wty\ntViioW44RzX6bj9DRUh/lkikVIlZ1OUqNGSrbrgoPpqmWDQrl+FQcWirEopAqdZaLAgcDdRZOBcB\nWLAcQSrg+HQSbdry3ig2II2IjwCmMJn5MPjw8BR7BIzleQ5OO6WryqhsXAZZZrAYYVlrY7TwYF7i\niFD5s8aiJcDv3Y896/BafxM9Umgu8gyGaM5G66iBoLiMNHQZrNSMgQtWalWDeuojAc41JKWsZr6A\nOaVIjxiIi/M55sHxuqzxh//X7wAA/uVf/0XM64DwE+hqBVwAVy3gqJq1KGuMyfUuTdNoCLSge6Xo\ndFCQuBADB4hjobVFHVJQwZDkIZIlUJLZSIk2xqIOFai2/USkYP88eArPOxyAyvqD0UEUAw4mhOVW\nx9yfmzY+kCXdPIu6ikayaZKCEenJyhySUgbIU2haRBZE9AGXaIgOWjkejVSZ5FEsw9Qi0lVtICw5\nF1MNXHANSi+QcJwDJJXDND1VSZbhmIhX9x7cj8SauqqQRLVmqoAkKhqEaKsj/mCtjdqUjTGeZI+V\nXiVXK2OZi7kkYyz6dDKlsAyYCWELbduCBYVj08YuSaPb2BGqtcbTpz6VOKfS6nwyiUrTCgyaOjun\nukRK2E1bzaM0/DktCknegaAwWWsdj/nRbIoH1AW6BItt4E+I7vvek6cYkelL1lEgNjqcTmIKyZiM\nqWWQx25Kg3Lu7yGhMrg2mNnUaMNCNV1ELGFOxzCbz2PPfGU0DolkVvMcHz3xHbFhXvMsj9WOJBVR\nSbtxLgr7FEURN5nw4GZJAt0EM58WmshpUl0wKGoacLpvJeEzsDY6bjVNE6932zYX7kP3ifTsWcY6\nfViP9ViPT4wXIlIwDpgYIOUMjNyJrauhEcLdGi7Ywi01XPAXpD74PO9Fw5X5/Czq4R3Oj7GgXbo1\nQJeo0vOAQjufpgDAedPgaOl3uZY5cNrZnBIrGS+2WnED0Ki18dp+ADpJsiI1mZV93ZJYTw1zSMiK\nvoGJbKjB9kZMD2xAkJmBQHCiFjBRydfG6IdZjYZ2mKjfh5U+Qds0aIlP4evZFG0wDmIKx/ShbRu0\nMw/U1YsZBDXRSC7QITouFynawF9Y+J20SAQUgcC5MTCl39GrxqCU5PvALJYEMB4QCU0mCRjz8y3E\nyhPxeD7BGTUlaesiN6SmEH0pBZ4SLblfdmBK0uDMB+B0D0DKyN94eO4bppbTORx1sOYXPBTqpkRL\n6tHsdI4FRSRzAmWdFODUEHZwdoRzukcmVY0lBWIN5SWLZoll6b+3U6ToheoEWIz60jRFGxStKZ17\n+vRpTGGGo0HUsUxUioz4DXYxRx0ihZrEcJSAps/SZnWt27aNKcNFrYpnHS/EoqCNxclkga1iA0wE\n45R0RbpgDpYeoGWjYcIJE/KsEo6Ebqok30JJZSx9doqe8hOZVAYzCo8r8locJDk0eTCWpsGMVJ+M\nNRCg9yUCzgZiiT8cjhXFvdQGJV2YMcujidQCDUpKzENOXjV1DGu7RY6CFIaYSpGRd2NdBvlvhqYN\nsu8WjOqLmtmYVimu0FT00NOCNTMNBAvEnRaGUimVZVhSCvLBo1Pc2vRVhJIelFkzh6XP4tZ6dSIA\nUmjk1F+hlEJC1YfgVqvrEi1hH6ZpsKQbt1EMhuTVc2lQUp4cHqRlVceFoNvtoiRM6HRZRZclawXA\nqWoRW4E50pQYgcohpXlR0oGR4EjaSdGSCmA5pyrKYIyUziPpJtHCXjKHio7ToELF/DVjRMKSCqgI\nX3j34Agbu76Fe2tnBEWS+OE2VUqAwd+/o9EQVgfDHI6WHs75YoGKyHVh02DgMQ2YzWYwdE00DJSl\nvhvG4n0/n1HnZK8T+xqsc5G8pJ1FS7qMkvNYwnzWsU4f1mM91uMT48WIFKzB6WyOemsYQRgmREwJ\nAESePByHC8AW/XtVlmgpOrBJjpJi43aq0RDxZHJ2giOiHWfEH8i2k6iyu9RznBGIxJz1Et3wYbcg\nMM5SWuLg/R0BoHUyruBSyJWCs3MRHA1mMpN5ienC/63XdegX/n15B6hib4ffBRqjkVHIubnRi8rH\nQsmI6jMuwCh8DmFk0zSQBGo1TEWQSQgBSfM1WVYodejsow5O7uK8np4v0c7IdVskqCmEVR2DYTCJ\noc9dHp+jIhLO1JRYkK9klmfIB5R2KImGdq6g59g0TQxxrbWRQ9I2VXR21swhSulTCed4Ngd7yWs9\niKwHS5yF09M5RqS9kKkUgtD8lIR6XNtAOX9swtaYk2jNfDaLLK9knIJZfw90iVI8q2p8cOTP7+B8\ngoIo9qNCIA+Eo+D5qXWMIJeLOdKg86lrlJRWCJ3ESC/koFJKWOIYaG1QVSFycQCpm7emifZ9kZtw\nITpwjMVnRBsd1bFVUUTZ+Wcd60hhPdZjPT4xXohIoWkNHh6d4ZUrW2gJtLHMRdNVBg5GpTfLOara\nr6SHxLQ7fXIEpYnCnAx9Lgqgmc3gCEeYVFOcwf/+0hXfZZn3OpjQzr1/MsXZjHAELlYRywXlZhME\nEBiLpUxUNtq7ZcnK0FVbDhnwEeIGK6FwcnJAn6vQH3lmpZUCCwKw6gB21gvYnMqFA4mO9K91HNBU\nilVSRc+CUP9vLZCSb4IYbse8PUkSLOmzmTM4XQauA3VDdjpQtONxl+JtUrX6oC5xnai9404fU5qD\nAEQi78GQPNzjR/cwPfQg4Bsv7WE88Lu1ZgZnBEwuqJZ+0QCmaS6U0C6UnC1cNLrdHvg5PJqf4/HM\nX7NRYzGiBqX67AzvfecdPy/LFtnAMwS7W1fo2jUwM89Tcc5iwqkxbXeEMeErppmAyI2Run44L/Ht\nD7xSl3QOr1CH7pP338WcwCIRnKbb5gIYzUHiT5hMz7Es/XeM+/0IgpZzfz2cMFEZuugVaLQ/Z6Ul\nBHVgcsXjfZ13yIeDuWhI7Ji74E/CkVAELIVciX8843ghFgXjHCZVg8oYlG0QBcljOMQvlFm1dWiJ\n5z8c+IlWmqM58xOccwVBctlL5mDoweJTgx5NVNC4m5dLPCVO/Q8+vBcBwSxT0XCFWRNNVNgFkZVw\nAVpjIMPzkWZoCc0vaxN9FwORZtwrcJlalcdFggE9YEXOoYb+oW8IRHMuB7UlQCQ2ysFbtgKoTNti\nSfyMOTkjd0ZdVBQAjq+8hLL83wH4EDXSo53DfVpQq5tDmleDIg0IeIrrO56cJJ+cISMRkfFoiN6W\nfyhCRMpNi/KRX0Da6RJb5OK9sZ1HQHC+qDCnhXxKD4RzFhmpNjdNg4ZUi/sqQUMoumHAiBbUN696\nzcgTw/Cd97zq9qVRisHIH3+v24UgHUc8PYuOz0tSsIYx6IQFuyyxc8UvZL2tPjhdwOOnM0iqeB0v\n/ML7T979EIdnPqV7aXMDv/rzXirkG//r/4GHlFYEh2cpBFLqOjXlEps9f+ynsyWOideRpxkSuqFH\nJME26o7Qy/15yFQhpy7RvJNCpHTdYbAIQDkB4lzI+N3GmAhgWuei0xqzLi4QzzrW6cN6rMd6fGK8\nEJGCsw5VpaGcQ0XU0GXaokgIqONpZKZpa5CSZFuwDOuoDWgKxevarSzWnAORFCHSDnq0+wd7rZN5\nhR/cewIA+HD/EIwagkSSewoxvOFGG7wDQnH/AsDDHJAFNmLaQa39Ont2XmJJ1GXZod0fFl3aSYbD\nAikx07hyUBTRMGpgclUTgShuGRBq0LqNdFbDEIE9TuXZohhgTqKkNz7zJpaUJmhtYs1aCY57Tzyz\n8v6Z//fdoUKfdjYrObo9f8z9Y4vqga/1z5cO+R2/6wT7N31+guk7b/tjm5xieHOTjk2jNIHGrXFG\n2gGHBPwa6yKj7yKjcTNjmM5X/ombZJjy+LHflSfO4Wziz/mjw8sY0m6bXd6CJNEd7hwUMb37XUq7\nbIuq9DwMudkH3/Tn4ViFZkElwGoRrQPvf+yjn4cHJyiIQ/L61gb4U/8Zo24PmfRCqhRIwTqGmiIi\nYzVmJYnjnsyQZ/59Is9wdcPP0ZiEZgd5B0ngnggRacxCqcjobGwTweRQvhVpGuetbpoIbKdJiiTw\nTBQDD6zeZxzsT0Nu+LMejDEHLiCYwOs/9+sAgFe/9DUsKNydL6ZoiHbbzEswotLqs4cAALk8xkaX\nQuYux4ByrtmyXuXaTYMZcdWXlrrM0i1sX/88AGDz0nVcue3Vnh8fn+GPfvRd//vTx7EtOczVb/8P\n/2Y8dm9rTlTaqkFDaPHTx4/xJ3/0hwCA7//x9wAAdd3EjslGNyipxbltdQyfecyVGAx9r5Qi/t02\nOubiSqloXpvSwtMfFpGLP500+A//g7/t36cNvvH13wcA/Gf/5X+Dsxl5dtL7r4w6uLyxkgKfUJi/\nrBuErmwlFSQlDsMOeSOmCThxIQb9DP0+VXayXkx5qsUM9+lhejj1rz2bz7BJepa/9rWvYmvTw8og\nQwAAIABJREFU96P0vsDxvW/5Poff+9++j2ZJVR5aFJMkifwVo20kb1lrIy6RZSl293ZoPolefX6O\n01PqotQ6vpZzjvGI8v3RKFKWL1265M+v08XkzN97B0cHMbSXSiF95I/z4LHvnDSsQJ96Qz738svo\nEF9kOj2DpIWlyDuoaeM7OPD377KcICfXryuX97AxpoUuU5HgND2f4ejgjD7DXycrOCZTfw/l3RFA\n6uHLusLRkV+w5otJVHL6hz/8wXedc2/hJ4x1+rAe67EenxgvRPrghwMXCoORX+HLZY2GAJXlbIJy\n6lf5op5jw/hQsp/7n5tjhcvbfqeRTMce+mUmcbbwO8LZRIC1nroqyGzEmnMsH3vt/pNqEq3Rt66+\njF/9uV8BAPzuN76OBwcEpAVRkAuOVs662O0nJIMi+LpulqiIrsvFqklGEKiVCIUFhYHWAglxCALj\nUViLpCBbsTyL0cjp+SSKabhGg4nACvTn3x90MDkn4xjIFStUOJyc+JShqRtImqM+qSy/dmmE61uE\n5DcNzsj4ZloK1BTCZipBTse/0fMv2NsYYdAlr4vtEXau+EpFZ7AV5+XoySN867t/4j/7A+/ZYViB\nGRmdHB2eYJvUqlu9xGPydVjMKugm6C+stBdCVAXn7dkAD7RFenfboiYANvAGGGMYUURQliWmJDEn\npYyVn3K5RI+8IufkFyKFjNem1iVa2pkZT9A89g1RAHEiVIGMFLF122BOWpp13UQdita4aAUXrvXu\nuIfb1zyQ+sUvfSnS8bM8i76obTPFwWOf6r79g/cBAB88OEWWb9G/t0DwUHU2Xve2XXEnnnW8MIsC\ng0OS9qBILnxydogpLQTzusSo9Q/YdRzjet9P6mVybhrnDIM+VRlYjobclmpp0BCR5wAW+9xf3AWV\ndhh3yKS/cNadYX7ow8HzpIPxjl+cvvZLv4x//O1vAwDe2felKWNdDD/btoWhh1RKHkVijg8PcUat\n2kHqfdztI0mClTtgidAysy0yClsLwhl6qcQGuTh1u11UVNX4mFtMA61YOyQkuNEhwQ/XIJqcgq9y\ndc54JFkBFqHR7vqW/47PXN3EgDCaRSOQEKGnoxhaWhRSITCgMHdvx9+4e5e3MdjyN/xweweDbS8/\nLrM+FPU+XLpyBaDja6yfS/noFHcf+Zv85OQsKlm1dY1zMjtpahOf6kHfh8zDXi+K1fbzNOJLp5M5\nzoNepbGYnZMNAFWahBAY08PW2dmK12+5XGJGRCaVKnTIHj60nC8WS5wc+0UKYtX5ymULS9TtNJg1\nOgcg+ECWMJSibW9uQYTQfjmDo3Rri67vzjjD5970QrEvvXQd3U1/76V5Dkv3vdVT7Oz611+/4RfQ\nb/3BO/iDP/zIf3W2jSVtaolcEeosGBb1qlv2WcZzpQ+MsSFj7B8wxt5ljL3DGPs5xtiYMfaPGGMf\n0M/R83zHeqzHevx0x/NGCr8B4P92zv0VxlgCoADw7wP4Hefc32WM/R0AfwfeIOYnjiTrY0Kr67Qp\nwSnc3+swvNbxK9+1eoa9wq+I1zd8VLEzLlDkZP6hAN0S+YN14mp+PjM4mdGucu4BTH6Bb3BuFrjX\n+tr9/v4SE+tBx8s3XsNXf+GXAADz3/tHAHx4HULVxXIJhKYjJTClPvwnj/YBChNv7vra/kanGz0Y\nZSIgyB364ePD6CF5bc+nAbubAwy71GWoEpyR0Ifi2zgnOnZZG1iqq4eEZnY2j7ZxZVnFSMEBOD/z\nkYvkDh2qVty55neljUEPjJqEuDbIAhqerxpx8kRiRFTbfkgZxiNsUHSQ9YYQ0u+wSdqBpO7JrOjh\nzS94fGtOdO4PHvxW9O501kSUnRlELcU0S2OIff2KJyH18hw7G56Y1EkkHEVp80WJCaUjZV1hQYBv\noGNvbG5ie3OLziONepRt06Kiao6TIgJ7IcSfTCaoqFHsbDJFQ6irkDraBwSn6sY0qEk27+TUYpMM\nYLhMoiRa05ToEX9jl8R+djYK7Fzy10F2OkgJrFTdPkzjoyazEEjoewZ0v/1zX3oZmrgz3/jufbjc\nz0trzQV6u8SStCufdTyPwewAwC8B+NcAwDnXAGgYY38ZwFfoZX8P3iTmJy8KTECkfUyJNGJEjmuF\nn7yf2wGulh7hfetqhqtbPmTud4NoJWBJ7FJ0ckBS9yE4QsvdeFbj+oLyrA0qETYMU7pJz2qH/sLn\nocPmHG3izUpPD+6iuOxDu6+89UX//raNi0Jd15BBVogZHB/6haVezLFBIegeIfI7gyGG9PvJ2RH2\n+v7B6vCdKCobfCI3Bhl6HeqQSxJkksL5fBcntCicTheYLIL6lD8EJSSmxJSzeiXvXesaR0dHdJgG\n1/b8A3Lrql+wkkyhJb10DkBQtSNVPD5AUjgIoulJFRYjBx47W3NwTgtZ0oEiAo01Bv2Rv2Fv3L4N\nANgc/lM46t5rqwUspV25yuIxN22Jy9v++F65dcvP4cYoCqSYpoXifj5HfYvLW+TPwRw0VUlSEq0p\n8iJ+rm6a2OKOLIVM/LHxoohkoCU9xKdFBwdEUlqUFarW3yNO61j5aaiKpKyB8FAFisEIir67Nhrz\nOvRdNNgm0djNMWl4Dgt0qaWeKQlB1TPe7cE1Qf+zhKhpbikNzjOOL7zuF8v9/WP8iMq2WaeHhK5T\nIjnkp/Sif5704SaAIwD/PWPse4yx/5Yx1gGw45x7Qq95CmDnx735ohX9cxzDeqzHevwZj+dJHySA\nLwL4W865bzHGfgM+VYjDOecY+/GWtxet6BkXjvMUaXeMoCE+zix+6aYH6F61D3G98LvDjT5HgbAy\nk4iFk3DSr546yWFJww/ORU6DSGtICikd9c/XrYtSYpmwGMIDTjzT0M5XHBKu8OTIk3defvVNAECn\nM43SV56AQ+BSuQKRbF2tgC1S4b20tRvdgHOVYnvD79Z7WxxDIukUJFUvbAvehl4LDU6hauoEeqGj\nMM9Wkl8EMknGIWlHZBc4KFVVRkCtUAqvXPch/1Y/qC+3URE6EcrrkgNomY2RAhfGy9/Dg7QAYJ1G\nS2lHzhGly5hkwEXSDF3XrR2/82+Negi6OTOD6LothIodk1mWYHvLh9hjAuWyi25HnK3s7IWKtvSM\n89VxXoDeQ1cmg4jzCSB+n1BJfL2ifg7BBXYJdD44PsaSdBXhHDRd65Cuoaog6G9yYzPStau2iRFG\nTxqMerTTU3QoE+V7fQAkCQPaIK/PV6mp5HB0rjr0hrAEXZLMv3plgG++44FwLmWU7uTMQf60XKcB\nPALwyDn3Lfr//wB+kThgjO0BAP08fI7vWI/1WI+f8vhTRwrOuaeMsYeMsVecc+8B+CqAH9F/fx3A\n38UzWtEzxiGTAvloO/oivLqp8fqI6rizBbYE1XYNIIWPICztPjYv4KiUKZIETgUZaONJAABgRAQd\nWQDnnInOz9oIcJJWKliN8tyzzfo727DE3qMKKLI0jTtbLThK6n9fLpexy9G2OpYiA5utalpoalpx\nELEhSHGAFM+g21CXF1H9yFQWhtiYrmnJYxHIlcSC+v4Ju0IjJFJB3xfMKuFLfcGBe2dUYG/bRzGc\ndhEDgEVqLIejW8Pb+QX2H4vNZGE/0Y3BfOpz2bw/QkY5OWM8lg7hXGww61ED08svv4T27bfp4MTK\n71BaqHSl2ZCTNBmnyMTpJro5MxhwivRkkq4cu0UaS78hOvAYgv+dCw4TPoN5+jngdQ8CGzREHd1u\nF5f2fHTzwccfY0bX15gaS4oWg1yfYgwybNHOoayDp0gbd+vxMMWQIoVA8+bSoSQF7g1pwbSP6Ny8\niveZrUtokrQLWAbjEoqO8/LeONrUnZ2dYkCmQg42dhg/63je6sPfAvA/UuXhIwD/Ovzd8puMsb8B\n4D6Af+UnfQjnAkV3iKIzQIeIPm/upthinqcwEHU0Z5EiASfgxNBPkaRw9HCgbeONjlaDB4lsx2Ep\nJtaa1JwZA6dWbWcdMuomM8Z4kBJAp9sFiNoaW4HbWVRPTpRCVQelZRe57xvjDXQIMLIU9h1PzsEv\nPGAh9Le2hiGEO9iwJ0pCIViyO5SkR2mMA5ehTTxDQwIomrpLdeuQ0gIqeRvDSK3b2D+x0c+RJUHq\nLk5FlBVzgoEFNRRmEB4mo2MLClLiR8AygEA77licN2sd3AWSTpgDSXyM2y/fwfmRT9Hq0xYs0LtF\njZwAVmcMHALFPPSdWDD6G2M2/jucBg8Pi9PQJrr10HyvzFngGgTjWmt1BI2t4XGRifMiFQpK7brd\nDk5Jzt4uq2hWFEL8TMq4CZ2cneCMqj3D/gCKSF9bwz4yknE7pc7WZTkDYz6VNM6gDV6oaKJsnLUO\nhibfxlqTBSf+w+bGEJeoBfz7Hx+i36NNkvMIuj7reK5FwTn3xwB+HJf6q8/zueuxHuvx5zdeCEaj\nEBK9wRY451Ctr+kMZReq9pFCZhegKAkiTaP4Cr/wM1CQuWNB6xNoHVxNZaqqhaHmGSGDyq6LFvBC\nMQgKu81So5E+9L9y5w6q3g0AQC1WNNqw6zDGMOj5MqLkHJYihYHM0MxXNFcAWBodQ3/uXNxVODTg\nPhkpcFZCsRAmcixJ7IFZgBOdNSkKDEgSzJJQqZMKE0pn9Pk0MgLbto2/9zophAsRVNhJORSlOa2t\nV+K4DtG0hsPFUCEKhrYaNkRbMPGaOGcAFqzRL5TEaFcejsYYUGlOLSbR/o5xix4JqnCh4SiqcxQJ\nQaZYWXpyhDTGWICZIGKqgx1GFI5xcPH6N227CsvdyvYPjEXvCEuRhDMa1pEzDmthTOiCXNGqw2Cc\noaLo6GwyQUFR0XjQhSSG6Oa4G9OVJJR12waK/B2cY1Ep3FgHkCaHhV35StLfHOMwoXRcZLj9ki+j\nv33/cVSuTpVahXfPOF6IRYFxAZUPcb5oIUhNt6wy1JSzKcGRBLpq2gEoxwehtwYSlmi0vDOAC6Yu\nQoLlNIEWYITmK5pUpp2XWgcgoeEqMlnRAgtH2o2MIaioyFD7TZLIrbfWom0D5dmgT8IvdlGhovxT\n0MNmrEVdBfNXAxUj3JVqTnjYmGvig8u5iAIwkrOIDnPjIn1WqaB2bFAUPrQvijymD2maRk3BIuHI\nVKA/B40/CRdTTxffJwRHmoT6OEORB4MbSlEEB0IIDxvDfAe7CssvOhQFdD/NUNbBnCX9hM36aEw1\ne2HQIT62iGmCAeNhseHgdC39PITuyZUte+hTYYzDGEpRbBIfTEDEOWArFfx4HsYZ1E2QrZ/BIVyT\nVedmQVqacIgqyqxxGFDaKZhBRgv53u4Imjp3U+IgKGmR0KLgry5VJeTKbBbGRpMj14aW+wSa2tMl\nGrx001dJht9J4QgzUSrz1aRPMdZdkuuxHuvxifFCRAqOcTiZotIGU1qpn0w0bhF6zjoMKjjtFhkQ\nQkJquGH5CLLwoahVCgbkE+g0lAjhl0FNQh/B9r3WGrPghtxYmDZ4E1hMaWO6xBI0JNpSUtfbzi5Q\nUAcjYFFRhMEY0FZ+VX6wWGJOWoo9ElZprY2RHGcshsEAi9p+NgJ1NuogGoco7WY5iwh5ay0kgVYp\n8TQGUqGlD9YG0dvQcAZLu+N4OEROCD+7YC0XqMbO2tVOCwtFEVInSX3aA+rKA5B10wiGGV2jIcpw\nnnVjCMu4jNBY6OqUSYKaGJT90TD6SAgB9PuBsZpjl8CzwL2wTELwIKvH4+cJqeIcMjA48pNzmu4V\nDjhqhJM8iTwLxxAt7ZxrYChlCwAmsxopzXGnSIELqUbAs8NPbTUUhRoqEehS5SQTDNsbPh3d3R1j\nSjR7mCCWwpCR76g/j3BsLkZWMkljasNZEB9yYIK8RZYlBpR2DbopplOKFGQWVcGfdbwQiwLAfG5k\nK1QUIj06rVCO6Aa0AjMK5x8dV1HFJzgl7V5S2NomsVI1h6RQrZq1OCf58Y/3J7j/yGMUJ2fU0ixF\nbF/e7ozRp7C7ZkBLxKhy2sCIGLsDAKazKbI0hK0u5tSCGzTExT89PcbDex/7t9FZpmmCMVUk+kkS\nCSvW2kgr5hTqMckgWAj7GBzF9gpuFa0bRPfa0JGY5gm6JGa6MdiEozRncXoMSSSrokgR4uSAaE/n\nczQ6LAQu5q8OFk2gIEPCNKFNfIWvTI79vDKWYEDpd74o4YJRS1ZgTNqOLOA2dnUeW5t9sGC5DocO\nlWr7RR85+W1qmsXaKdgyVIyWCMWCXhco0rBQr0JgHnANcSGNsQaWgKfGAi1Vj5izYIQDhDKyMy16\ndM0u7+zg3n1PY7eNWRkQ0XcxAIrC/X6eYkgGPwksLu35fL/T30DdkNjL2YTmp79SR3ImCvUwwSP+\nxWW+KlGTsOtiKXBy6MuXtjZoWv/aQbeDyREpXMkaRf7pFoV1+rAe67EenxgvRKTgzVU8qMNoNT9b\n1FiO/Eq7P5vjwQe+b/zxdIE6hOBEGb65/QBffuMOAGBnO4WkbrG7783x/ke+DeOdB4dYZn6lvbLn\nBS02VYGEjN4eLDV6tCvtdTaw2yG7elji7wKK3I6fPvljdIPibirACHyqlkucHfk+/mE/wzGt0NMp\nybcbDQTJsPEGKCqFsTYq8c5Jdq7VbWzasUCs4/fzHNsjf2x53onGL5ak0MElcqJVD/pjLEiq/cHX\nv46EQE5tWhAehsNznxJ9/PgEZ9RI1bQagsCuXpJga+h3ympQI6eIpUPNPtWsxfmRB+Lu3jvBsvIy\ndp1Ojuu3vGnL7pXrkHSrDTf93M8nE2xt+KrNZ27fxnukqm0sgyRSGucSoGsyIdD2/v0PUZU+cnGw\ncJRiDvs9jIc+QtoaDdAjElxCZjG2sZiXfm5niyVmlNpNlnUkarnGxgaqnV0/x6NhAUmy2klaBBY3\nxlkKJBQW0TVNOMMWRRXb/T6EDZGuw/2HPjr47W+8jTwj0h1FBHvdPHJITOMVwgGf8gaC09HxAkeP\nPe9hQveYtjYC09Ozc5SkI5IlOVrjv487IGWfTs35xVgUHNBoTyQJKPxiXkbNubPzSVTN+dwX3gSj\nqsODB175Zn5wjI/evQsA2Oy/Ck3trY+fLHB46i/+a9e2sHXdt6SOBsQ0LC3mZ4ToHpeYBEvyjkBK\nzEJplugOw41JRqLLBRSh96nKwegiltMZ6qDpP51hn6TPZ8GjUQCc2ndrbZCGcqLVqKnktiTcYlG2\nmAShWJEgJYZe3QBa+5tjVGuMSXEqmJIyLqKkfJKmaBnpQJ6d4EqXNBjtqpTHKFcfDHsw9AA9OVlJ\n3/ekhlBU3pIsuhQllGrJNIde+nl7fDbDgrCGV8ab2B77Y7uytxN1AiMRqq7w6p3rAIDqyT4cCx4Z\nFgVVl1TCY8omqbqyt9XFfE6kn9pG2fqTo2OcUofqZNTD1V2PxG9u+bDdcYcnj3069/joCHM65kVl\nYuheCIUQaevS+3O4a1dweY+8IyyLi+GXbt/G7z7ygjEh3E44i2a8gjnv8ARA5TlExy9Y3/nhA0xm\nvr1+qPw99otf/gxmZ/4hTvM8Svgv5kt89zteGezbf/I+uPILzmuvvurn9fJlcMJzOlmK4yde1aqp\nc8g0SL9XWCVVzzbW6cN6rMd6fGK8IJGCg7YO3DJkhAS/tj3EZu53q8EwR4cEKx4fnuLDE7ILJy3C\nWznHTthdmgXI7BddvsBbb/odwzXHMK0P137rn/pU5HCpsKH87qlUht0hKRTb4yhSkUgXgZq337nn\n/9YVkRQjBKed10vHK+5f28v7uHndh8/3D72OgUxE1Bg4PjtDjxSDhRKQdlV7BoByWuGQLNItU9ga\n+0ipyBJw4g0wqSLxRhOgyCWDo12VgUOSpkNn2MdwSrJjTEQeQpCcb9sWjw/8DnZ0MsfhxM/VmQQk\nod17/R3kBJ7dft3vVpNFg69/1/cwfHQ4Ja9uH0mM7vv+kd7WJnYp0gsVF8Es+mQcc+/JEVzPRxVw\nDTqkLbCx2YGiO7RD2hNdDuiKwNO5RrskEpYFOgHtVxIFRWEJzadlDmOyAZjPppC0H0oBtASk9jsK\nuxv+uzdJ16Iz6kbPUuYctof+fvnsy7fxB7/tVboDdb1IEshA55YMc+rQPa8bzAkkPJvMolFLn0Ro\n3n/vHna2fBR77da16AP54ON9nD7x90BebOKIotBv/uBH/ni+9z5S56/Nq1c38eXP+WtyOl3gw3s+\n0jk4n0ZOyrOOdaSwHuuxHp8YL0SkAMbgmAB3Ej3tyzQ37QKbVN4Zb27g47uk8AuO3V2fn41e9zvt\nbaHRRwB9ajS0e+RsietX/Q6UdK/BggRUd3yeuX+wxOSEZLsShztbVG5zKQ5Ck09aREXdknLBYsBW\nsmqCx8Ymxh0x/Lzy8SXCDzRtj0trYKivnguHMQGpugLOg5cilV5nlYkOzZw7jAb+PKqmxAPKnau6\nA+v88ffIC6Do8Kj8bJ2IVnkbOzv4+EOvAjwU3eh/6agMyQ3DjV1/vL3eCI/Jp2FZLdEtQrOSAaet\ne+vKVX/+ZYmrNMcbgx42d/3cvvULP4vtSz4XzzoKKTlCBwYiaxeYkh2b2NpA0xKYax0kYRiv3rmB\nhIXGK8IiuIMiILXfy5CnXfpcjYTUqfJkFb0Z8mh0ksE5quPn/ahqNdAMc8J88pRBEPU4IVVmlXVi\nk9Swo7B51Z9fT8nYuZmIgCMAOXECpKmxQY1U6cZlvPz6GwCAjz/6ANcuey2LDpW6D+6+hwXJ7XWG\nPSR1iDYL7Oz579gd7GJC5kLvfP+PAADXX76BfsdHmK/cuoQrex5gl49OsElR2OMjC8b+WeQpOM/m\nZLbCnCilU5sgJdBmZzPFYHADACBzBUHGL5KIN2yywJIetqwYQBOKzuoGjlDmzpXteINcNf5zd6WE\nvkSS3LZFlzjuJ5MWLU1kknagqCsxyMwzziOBSAgRxULSNI1+kxXn6JKn5dVNX6M/ns6grV9YbmyO\n8IVb/sF6+vgAFRFautRzMcg7MWzlUkJShUPkChnV+h1azEjZOSUF5yLrRfktLhgYVSUWVYuGeiJy\nKX0fA1Yy490iR4+qOuPEYFv6B302T+CI39BVKWSgGNOx7e3u4Vf+4j/vv2MyR5fESbb2xuhu+9RN\nKAUeeP5EfsrSDI8nvjJktwYRqdfMQhE/Y9wdQFfU2UopjOooXL3q57WuGzgCNk3boiXaNHMWlmr5\n1tADwRgAkmbrjSIpaJSlEATipkohy4OcHD3cSYaE5uja3iWkKQn4zBukdF8EtbMkVVGQJpMMn33V\ny91fv/MKOn2fjnz59s8jDXJrzF/T/bHC3hW/2PR39mJFIe8McJnu5WkJaFL0fvOqT8XyIsWQ0plO\nJ4n9M52uQK/v56hpDXQdmoGebazTh/VYj/X4xHghIgXmDFQ7Ba8rVLUHw+5PEkiSd8yERdKlhhkn\no5eiIHBNaAHJiNGYdZGQVsCgm0KTGUwzb5CSdJWVPnpIM4uWB9quQz3xK+15kyAlheKGpR5MBDA/\n9eAN5zvgPGgo2OhZUHQKgMqaQohYRjTEjtzsZ8gG/jhfvzpAl757lGcoKB3p0g61MxxhQi7DSZZA\nkc6EyjLkQZGl5kgpYslCt5wQSMIOJhks8Ri2XrqN9Hvexs6ZFox4eAEYc5xFz4pGl5EG3O1lyDK/\nO3aSBLFjCFTG62/h8uu+e35ydLDqPky7K4quTKM4S6PJHbzbxYDSi/1WIPpYOwttA2O1QBsaFHkA\ndlm0ZFdyJZgDmcGpYJxSQwRpNgRxX4UsDeI8CoqujRBsJccmRPSnCE1sgjsYoiNXTQNGPI1ESVTB\nJCe4OmdZFG/ZHPXxxiseaH7ljdtQ0T6eQUc6dijPbkYhXCELGAKPs75DTpTvYtlAxmu2S/PKAjsc\nzjSoKCqWrkVKAHPFUzwmod9nHS/MoiCqM6CuYWjSD8sMsxmp+3RKgGi8PEl9+zQARmSkejJFIklp\nxmZRGbiuGiwPfDrS2RwgH1CFYhC+eQkxI7rr1MFM/IWZnTKUYz813bSIAifBqEUIEZWBrbXICE12\nUsERn//0Qh9eECTJOxm6wv++OerAEWElTRV6tFjMjX9oNtUQsqRas2uRqdBa7CApZE65jCSdPuXs\neZoEMyXUTYWWqijXP/sG7v6JV0TW5TGcCdTs1aJgKuL9Vw6MWiISlaBDaZCDjlRbSfRhyVRUmNLZ\nKWzATEwLGPrdrbgcoTTkJMeSHrxpbeBoPp1u0Rp/E4teF6Kl6gFJIzLH4twrVQCxG9LAsaDmjFg9\nCtotXDIUJDzS1iKmQRwcjBZtLgQYC3wP/z6jFzAm6Hwm6A9IDMUuMdz1PIvpxJOJZpphRA/jzuYG\nLl32D2/W7yIhFe+WWTDCcQxVGVQiYCgVFlyipfbrtjXIUkpHMhVVs4N0vhTSA1IA9LyGJnJaM1+g\nou94MFlA8U+XEKzTh/VYj/X4xHghIgUHhVbsoSlSuA2/m803B3hQ+fr3a9NTiNDBt82geh5lNeR5\nUNYOfTLWcEkfNdmzWyGg4VfVe+8f45WhB2gSsphDw1ATS7FaMiyI5VY2Lnor6ONT9Pb8Ku9YsKYT\nK1t3JZATb0CjgaY6vsoTVAQCBnSa6wp5XPml57QCYE4hI2t3NfUrf2Ur/xoAcCICcdz5/wBfFy/o\nuyWxEV2Sow5IvWuQEArKhn3svubr2A++9Q04QueDMAnXFhxBcIbHYzMOUdciZQ4iqEcHyjS3aCls\nta6NLYNcyChEo9sKLGBdlBpoZ3FC3aeaqciFMDZFUMlxgxKsIvGRYGwBCU7RpOAi6hswNLBBWUWk\nELSrRmUIziNDlhtAECjnDIOLTt8cnOr+wVfUooGm+UyEgxpRGtvj2L7lKwqT7/8BAKA/3sGIqNuD\nQYIk6EtmCimlLikAzfw1npIIDwOihqVhPKZdrm2g6fqoJIueFKHzF1JFdXBrbOSqVIsFlhSFzIxG\nE5i6zzheiEVB9DYx+oV/AwtToNelsli6wOnCo9PT5j42KF90iwqcSk+zj31qcO9hDUO03cO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uCTn/gEAODFF17EBlWEk1AgYWV/wR28C2JISpvNLi58dRpBCBPb1VWJCMrZvPO9f+8//hsY9imG\nsjlCw4q6qgokznOhN8LhuX398MwWwIw2Xq4rkNJXuKUAYhYlaxY4OwMM2YM+2BnhYMfOy/7eFO8/\nYKX64QnK5glVaQBKAwtGSqUJEWmHmlxjKOp2DUd2jMtIBl6zAabz7Ly2Mw40hziJEPN7XEhqAG83\np4RBQRhzB4trsOetPULS2eM1jfKpzxe+8AV88YtfBAB874/9pMdeSMBHE65XIcSTBUrjcQVSA2/8\nhjWi+W/+zt/FzV2rZfCv//gPAQC2hoEP/YU2tskP6z/ROh2CpkFJUtExod1v3r+P19+zZbF7R0dY\nME1VEPjyf2+jnp//BYtX+K/+3hIFkbBRHPhr0nYGZ2cX/rwDCuZsDWxkk6UxHvD7isYg5BwN0hDM\nFLCdRxhmdg6dX8ij8wrzJY+9rHBlyz4vg9hgyEhvMCzwgz9m09d/+T/44LeNMZ/HR4znAuYcSoHN\nfoIbV67gGj33xqPMg3RUB8x54Y6WK9x5YC/SG79njTL0qsKElOWXbt3Ai9et9uFWP8eASkZ51kPI\n/HRIuC/iDBUXgjSOUdBhZz6fI2FdIurFWLBy3rJFKozCoGdDyq6pvLDIsN9DSkGSs0WNc1aAndNT\nHEe+HtC2T7YLjfM8QUSr96KoUBPmPFsW6BO/HokUeWovchxcYMmH0LU6szjCiJ8RdxJn5/YYgidg\nzEEg0SdceUgQz5WNITJ+x8VyhZqwcm0AyZt72M/Qd8a6zCnapvUgpFlV46yy87ZsNUoqtVRVhbZ1\nvcz1ObtU6/Hjx3jzTRsmf/7P/KSvfguBJ0BN7l/jodtiTYyGbjXu3bX3xfb2Nv6VH//TAICdLdrB\n6xKqc+1e5VM+I5WHMYedWEOzpRPmDRDACbSuQVhGCKwYxn/9d+y5FUrB0IQljmJoR2s2ARzxW2aR\nt7A/IbV+P5S4sWEXiEezCiUt0M7nBerKic9oL8ozYMc/DEIMqIlZyhjzBXUs+wFaap3WdYIv/7/n\n+DjjkiV5OS7H5XhqPBeRQj/v4Qc+92lMhkOv5Qch0HA3OitXuHPf7gK/9+abuPuBDZ9jypztbU7w\n8oFVzn31xgvYY2h/dZJ76exAdAgTx613PoOZdXGGnYiYu0M/TVCWdgcWpsKqsBV1xwwM0EFwh26a\nCr3EHvPVvW0cndGvcLFCx53Chbut6rzFuf2F/ScMIsSB01i0L+ZhjpYRSGACRImNDh6enqPh63Gc\nIefGfXZBb8Sy8orESZIhouSZEMbrSgZhhC1CbL/nwHo73tzd8mFrEiboiFJqm8YLpyRpjJgpVJ+R\n2c50w//d3QeH+NLX7I7/+sNjPOL510J40pg76SiK0FGu7fHjx/ilX/olAMC/+e9+0TP8DOD1ItZT\npr0OpHmCXdlUDU4I2PlTX/gM9rdZdOzYRVLKR56ma6Dc610D7eT1tYHgLhw53EgUeJ/HUAgvcGI6\ng7OH9u/uP2AXSRuMnAaGFJgRF5DFIYaRvbdWxqBxboAsqq9WNbYG9ueR1N6ar40MLiiW8uC8hO5o\nGMTiuRYS5ws6V4exx6o8PFthwBtjdlFj5cKUZxzPxaIQBgGmkwkCSDS8I0rT4WxhH7DZcomvfNW6\n8dx/cA8ROwM589rdQQ8HGzYluDbOsEXas1yeIHV4jQ6Q2iHemJaclz7H7UmJlLlapUJfIW61Ro/A\noTFTjSROfG4dJhGu7llQVFnVWDA3TNMYGQFAjqWmVOsfjlAGCOFuoAB90iA3Rva78jTy9uxN3aFc\n2bqE0sp/R9NpDEZ2sdiZ2jlZrEpUtQMbKc8A1FqtH24YTAmQ+cJLtjU3zTOkbD0O8x4SCr72ehk0\n/64FEDBnHvYpU44OioCkvUEfV8j3+N9//Sv4f96w/p5tXaPgdXWpVJKk65QCAp/61KfgxrqO8PvR\nTcLAn4cVZ7HvmZ3P0ZGh+cKNlyC44DifjaBtIcjF0KpFy5RQqxba3SNK+LQiYrGlF4cIeb+0deU7\nXxIGb/y2vT/nM6YlHZAnrO10Cu70ytUCWyOKvlQlBKnmPX5XqrWXDBj2IlxcOB+KEE4Ttio1Lvh0\nJ7wv9rYyjEm5f3Q8w5DpH8IQS3Yl0ijCo9Nn7EVyXKYPl+NyXI6nxnMRKQCAMUCtNEqGrY9Xc0x2\nbGj7u998C48eWkiogfSAnJg99qnscIvY8pFeoE8DENkJxM4zUCYQNHtxZipaSGt3DqBsNeYr+90S\ngXd6apRG6GzeuUvGUYKa/Pg4CrDiqnz46GgtDQ6FvW3bMVisyHorlVfWDQH0KScuAAyJWdjesNHM\ndCOH0k4nUQBkcxaFwrK0u/y7HzzCGTsbw7E9tvEoQ92wmKkkHp2xT28MAicjHzbYdt2Tvo1MJkmK\nmLtgHocYbZLJNxhBspLdAl5tO+IOLVXjuRERNG6yJ/5jr93EKY9tVVao6aDtip1VVfmfu07h9PT0\nqd+7IT7079ov275qGHkcPX6EIc8lS0NIsmq1U49WNTTTgLopoMiiNF2LzqHM9LqQGDHny6MIfSfU\nohqAYN1enOH3fpcYgQs7P2mooBjuizBBxvlcVjVK4lO2xz3s0cU8oMN4ZjqkxFDUSqNmhKkajXGP\njlx5gq51zFx7ON2qwCYFXupBjjn1PQe9AB2pAFWjIaVTL3i2POIyUrgcl+NyPDWei0jBGLsjz6sK\np1zBr7z0Atz+8O4H9+D6d7ITPscbsbe/N4gxCmiZhQYpEXFJkiIjnDdJBxAsYoZ0kUYYQGmHTDTo\nsXd/dHqGamW/Y36+QG/TRiyJEwoynWcLqjbC/ftWYSd8QpEpDDR2qez7kO3UURZjxAJQLA1yRhVK\nd+gTCdgnG3Cax8jYbpoXFc5mNtqQpkJA1aDt7SEeU9WpZeQiRYCIbNA4lj6Hl4FARNj0IA5xa89C\nbfvZmmWXUhk6SzNEbK3KfADJXDUOAoS5rd0ocvtlsYSrDFZoEDpLvskE3/PSDQDAB7MC5495nL6o\npz0uJAgE7ty5A8DiEJ6MFhyOYh0piPVrQnjF6McP72O6ZaObAMr7S0jtsAktwMgLuoNwrUXdeRCE\n0mtiWcC6RS+KPF4mlhJLyrhFQYT5zEYIZWvPLYngGZ5dozDgvTCZJCj4d+ViYS3eAOxt0OjFdD7i\nuViUGBhXEJb+/ARqDENX07JzbIxBcWHvvf3pxBY1ABTLyjuld6ZD+2w8KD+ei0WhVQqPzs7QZSlu\n/AkrUpH1B7jzpjVEbbtuDYmF8V2CEcPaXig8/jzolKckh4gQMT2ANF4sI6Z5SZz1sGyouddU6DtD\n13GOiCCiqm1RsYq+v2eLaHVbQ/CBXhXNurf9hPZhv5dAsPCVkvY86sXY3ybGQGgPFuqMRtnS85Id\nlaaqPDAnThKMCHTpVOcl14d5gDTd4nG6nvgaott1ytO60zjGkMzIV6/v4IAFwcBRwMMA5gnhmMDT\ns1MI4jqEEDChE4khyCoIAS42Ik4QsmAWBhc42LR/d3tnhPdObJX8zMOu1wxOrTW+9a1v8buBJ+mT\n7kcvBm3Euvxo1vJuFyenuHHdYlxMW6MjWzNgcRFawfBnadSaum3WACjzpLa1k1YPDLbItNwcDDGj\nzNliOcfKUNE6JPchSCFJ8Z+dnWOXKe3eMEbJRfZ8pdBwAV8t7fFMRqlf/fYHkceArDTQOPNaCA++\nIpIaurXpBgAUF3NssphpjPTds+EwRdF9vFXhMn24HJfjcjw1notIQUQhoukWrt+8AcFWWNtqXwTs\ntIHDtMlAePu2TRaWhmmCmKlBLCUi93MUeqSglB2Eh7k6oU7bOgSAtqs9AUk1rZfz6g+GCCK7SxfU\n9Ne6BRG8aLXwHgOtamFc2zKIsCQcO+NBTAY5djYt7LpezjEZuNQmxQnJKk3jxFo1igWlyKoOEedl\nMBx5W/eiqrFkGO92OyEDdA523GkE3GniIPK8+q1BD31GWd5GXcMj94xRazMUrT0Ks+s6BJyXyLXp\noD0pK5ChLYrChuFj7mi3d0b48h37GWcUBbGtUuN/XjG8lh8SWfhwoREG6wjDaJQUOTVViQFRmlAr\nGLYkHQZBqxYdozjTtj7lMZ3xxcVAGAjn1cD0Q+gam0z5rk6nOCQK8WR5gSULgimvbwvj8R0wQI9s\nrRS1F9EZZQnOeVmdZkeVdBiRCNfLBbLQfsZ5rXwRe9lKaO7hpfMr1evrW5fK29sN0hBdbb9vfrpA\nSnHeZx3PxaIQJgm2XngRnZSQzgkJwpuTNBQCAYBQAJsEd9zctezF6cYYIxpy9Ho9xLzhpTQQFL0I\nO+H1Gg1NbFu0CDhhQQDo2BUNlAfFKKUR5Y46bI8nSwLE1Isf5n0IR3ttGiQ8Zt0qX/vYGNswems8\nQsqLGCapd2HKwh6ub9tw3mkfLtvOs/BqpbCsiZ1vGyxWNADpAEhX+V4bqbbMPYuy9PG3NtoaogDQ\njYIm5daH0dr4B0V1Gg3Tscy0EKR1R2Hg82/JsDXo1gAjIYRP8sJA+vO/vr2BfXYz3mMODKw7DUII\nX1/4WKGrEJjPbYcjCICusQ9sVZ0hoOal4XmaroNxx2wEhKOiG+3vOd0pv3A4Lk2nWkhS4zdHKbYG\n9n6ZF0vMVvR/dB2nrsOci3QkBMYsQg0irOnsbYVYOIq6/Xe2UEhY28rCDv2YaaM0aDiHpwY44WMw\ncwzOnvRaodACgtD0MAK2R/aeK5rQLyzPOi7Th8txOS7HU+O5iBQMBJSIaIflqr/rQlTXdX7HS6MA\nG4wUpqzijnsZ+rndiYb9kdfsL8uSzsSAjEJIroEuAjFGI+BurVUN0zrpNmP9tgCkaQ9nK+fvyEJP\nmiPvOXKNwAXNPZTp0I/tTqLbGkNGHjd3bHFxkMXIuOvIXrK2LtMtEmInQqIKR4MQDXewi6JAwYKa\nSnNoV+1v4FFzGaFvUZqgMQ4RqJ+o5Auf2rSqRcmoqXDkGhMgpP6gUNqnWKZYeWkyHUpIdigcCaoz\nxtu1KaW8Y7LSGu0T+IUXKcTytQ8s53/RrUvHQkjEDP3dxufGhy0LhIUx8hg0Lpa2gNmpCqdH9wEA\nuSkQCQfTdrJq0v9dEEpP8jJCwPu5d8Jb2bkCXt0qKOIbslBjg3iSB2cRVudESzL0T4VBS8m3UGhQ\nFwjDOPIkrl4gIajj2PA+rJTAY7qfD3sCGY8nDSKYyM5nHmov4DJvGAm2QNpzxkCRDREAGBmgLIh7\naGqU1ceLFJ6LRQEQEFrACLOWGRdAQyCIahrvmpOnMTbYIgoIJFGthnBKMyZAyBu3aRWkZDstGXgQ\njmDVXxuNjhOtqgq1ExVVCobV91VRQbOiXPLhByRa5nVl3XijkziMbY4KoKlaXDmw6c2rV21qEMkA\nK+akYZJ6g5MoECCrGyOq/CCSaHhseRTgaG5v/sfnc8QMfS2YiIfE95bLpQeAyTD0xjBKad+y2pwM\nkJK70AbrfNjVDuIwRsAFVNfK5+dKCoQ5FwPegDKKIAUXXqUQuHpOnCLkuXSrChusn0wZfhez5bqf\nZDT6hE1/GNi8Fn/ne8WT7zE4PbHpyL1796AP7Xnf3hnBefOekrJ8Pl+goGR+KwxCsmfDIMCQUOHN\nwQARhX4b3k+tbuEMNGMJz8adjoagQr9v+alQgBkBemLt2hVL6VPeThlf2zki2GjRGO9OhtKgY41C\nhQKB2ywSjSFb0TF9VVsF1FyE53WFwvFAghCdpmJTqNFzosco8SzjMn24HJfjcjw1no9IwRho3UEI\n7SW9FQwe3LdsyK5p0GN43M9z1DRquXvKYpAOkU0IJW4U9qme3C46LEu7jJ9XpyDJDCHBH+PBAL0e\nWYRG+9C1qVpoTo3qFGbUTayYajRti5bQWGlCv8sLZSBZoNvbGOITL1jm5pQhZ97rQ1H0pVEtOuoN\nRNJ4Tb0+dRqyQc9HHY9Pz9ZEHKXwgIAl0wKCO4mku3IeSoQs/JVaIGS60mmBKd2jb2xNkBD6qqhL\n2cYhWoKUVJIhSUgeyweAY1rKtdiJxw0IAU0tB2lidEwZOhFAEB7cQfro5gqxC4hYsyQAACAASURB\nVI8uVn7HVzCe5KSfCBXMEzqOPtkQa61JtBoLCtksispb22/2EvRj+33v3LOqzQ+Pjr3yc9iLkQ9t\nSjcZbEAWjLKKU+RkmPac4IwIPNYjkiEGTO92Bjk0u2ArRo1dB18wzQODkNEU4j5SXh/EGqxboxdT\nI2S+8IVtoRSkdFgP4btnJg6QM7oZO4dxKbw/5KxQOGLh8+6yQ8X0LpYdQvwLmT64SrTxeZ9SCo8f\nWnlraO0txVdV7TXtj9nye/+khyNWfW9M+thlV6Kqljg6tTnsWw+PcHJh8fUxc/3rV3Y9P+G1m1d9\nzSEUERqmCqHsQ9X2swti1Y0xnhYdh9IbuupWIafIxvd98gauMY/uO/PYXh/aG7tKb9Ueo0PC84v4\nMOpwffNfTRNEBB4Ne30MhzZk/p137mLG8255I42DFBkZl6Y2EMy7gs4g5+uJjFCVNhQ9oefity7m\nSHgHjoYTjEa2RjPOM2yzjToYD9DjwtE5AM5qhVN6GD46PcNpZRfnk8UCZxd8YM9OIfiA7GzZzxqe\nLDFbkr0ngNh9Lp4ca2r0OtVY61Lq1qCgIOy7dx9CsHazvQGkFJrRXCBNGK3FaOMUGZGZaZah79Cb\nwnimZcUFJhbSd22EbH0Ld9jLcNGzf1ezM1S160UBYYRTphXH5zWaas1UlGxFZNyFNnoxMjjmpPAL\nYSUkzvgZs65D5ajffC0MEkxyewz9sEZApa6jpoaiUMtLwxCvvWRTt5//xQs8y7hMHy7H5bgcT43n\nIlIwWEcKrkKu6hozRgIBBBpq4w3SPq5csfp7it2Ck9kc1duWu5+/fAMB+/RX9rfwzV/+NQDA4cND\nKGcTv21VdqP+BpbcMb/51tu4vmc/VxqJJV2Z2yTEzraNPJrHa7xEQGBKFAsPDjk7PsZ4YrUVtGnw\n/mN6RUqq6QYz9Dnjm3mAQUqZsyDAkn31hloJlRY4ptT38Xzl+9xdvUSfxbqNjS28dWgVpheUst/d\nGGCbnIm66pAzwihKjTB0lusSD45ttPXNd+9xLpXHG3RB6rH4V/oxvuelAwDAi/v72Nm25+eYqifH\nJ/jGe+8BAL569x7endsdsQgiaIbVYVMh4GdndOLu9XIfKQRSona+mb9PM9RJrz0BY3JzoQ1WBACd\nzQv0GEG8euMGermdgxH/PZtXOJ3Z+bx/coityn7I9obGMLXfPRpk6LEj4EVm9FphPBBrHctRL8Nd\nXpSE91tZAiHfG4UJLmg1P182WDb29aUOvNv2mHHRjbzFK9v29+M0gOIcLAuN09K+ftZFOGcnpWC0\nmZgVNi7sOW31AoyHNiLYyhWcAdYVofD513jT/SKeaVxGCpfjclyOp8ZHRgpCiH8A4F8DcGSM+SRf\n2wDwjwDcAPA+gD9vjDkXFljwX8A6TxcA/pIx5nee6UgEAEg4G8qyKLG6sPkudOd9CvIowvzY1gmC\nyK3KIUKuruONMTIWmeIwwMGB9eU7nc3wgEKqr79hyTff+NY9/NCftIo/o1GC0zObG/fHY6Rc/c8X\n55hu2ghij/oOQgI588nxMEfLPj0kMGAh8aJR+IAkoA/ObW1Ea4FrYxs17AwFtjfsOV2fjDDMbT58\n/9Ce26OzJR4RifboooZhBW47B6bcEcfDMVpld/qTBWHOqLA5tHWSze0xrp/a6OY9NUPKCKOTBppb\nyd6OjYJkEOOcvg8P5gWmW/Z4XjrYxpDEnvPVDJuB3elDRiBnH8zRpyHLKzdfxOLuI37GCruUwLoy\n2Yei98Uh6wz9LETC9qUJJC7Ycn06UnB79HoYY1yHEK3WOGe00e9P8P1fsM4CL93aRaZs5DTK7e65\nOVE4YUf5YraEuHC+oWeQbLnuTwa4QeHgfuBcxwPfqg3DGFlMshkCnJ/bXXqc2wg0bzVi3gtX4gaD\nob1OhYjx/rGNaLSAbxkPByzmRg2Uk7AOAs9s7YyAoDK3UK1Xx05YRNxOAxxwjvNAIUupVj5KcIW2\neS9vtTi49c8f5vzfAfi7AP7hE6/9TQC/ZIz5aSHE3+R/fxHATwC4zf99L6wF/fd+9FcYuiutQ6um\nrtdahBHQoyx2rFtMN+2N7GTS1LJARmDRwc4UCavFQivcum5dpNqqxXRmb8iobx+aTgSoF3YhQH+K\niJV86A5XKIt2fDHH6YkVeIl4g40Ga9ek8XiEe/ft73tJiM2BfdA3JyPIwP48ok73qJ/hGn0sRVth\nUdgFIE4j5Oxj77DAtzvdw0FleAyV5wSoZoUxjT6CsMONfTsX7xzZh6CQCkvqUm5sDPBnv8cqev/P\nv/nrmO6weJjl2M7sDVkxxH00W2B79yYA4OU0x/bEnuvVnsCUTf/z83Pvf2kcakobbO/bOZ5mOXZf\nvA0AeHByhsWJPb/q/AgHZJj2CTg71RKLc7sQiCjDg1Obamm9lqKX0kMEnug++EYFWtVhwYV+nA3x\n2q0bAIC9zRCLI1uM7fHB25Qh9l1HSQjsbtvNIo0DFEubpvYDAQc0iNk5ydPUF4RV1XlKtTYCAfOY\nHgvXfdNBObp0rjFi1yk1KYbX7WZQS4mKC6Rh6hN0re80RWHqsRldW3isw34ucZMcjIjdM9F1GJAn\nkScxwA5PGgKvjOxCtf3CEG26TnufZXxk+mCM+WUAH7aW+UlYm3ngabv5nwTwD40dvwHrK7n3sY7o\nclyOy/HHOr7TQuOOMYb9QjwGsMOfrwC498T7nBX9I3xoPGlFP9ncgjACWhq4KtLh0X1UtCgbJAn2\n2WO/Nd3EiKgyZ5Um+z1M6HB8ML2OEDRyaQts0e8vvZ0gu2NdqjXxAaNxjuTA7o5luQI7doBW2Egc\nAq+HQyIBW6Y2qimQjuzvz87OoMmefHF7jOsMtfcHIfZ7Y54rPSTiCClVfUUyxHLlWqQRIorEZBmF\nQAJgk62wzSRB5PC+0QBhRGHWZoZXr9rI41esszyarvOouvl8iU++bKOiK5NN7G44ZKVAnzvaBu3Q\nr+xuIxjYz+pPNpGzZSeqFYpzu+tWVYMqpHiJtMfbNspDlLe2JrhKMd39foCLEZGXFxnc/tPSbGWY\nJEg54cum9QKsTwqsaL2GJOgn6oxGO0HbGopSdy/f2McVpjxdcehJTgOeRxB3kGQqDqMYaWwjiDyN\n0duzc9RPU0TEADhiWxwGaEobjZRdAaGdPkWAHeKYD6Z0175Y+MhmIw/RI7s2CgR6oY2wYgGwxonZ\nBaHmZYcUFLgJ+j4CyUUNE7MI2g+wMXKiv0yxu8QfTxBGqDnHkyTCFiO9s4sKp18+wscZ33X3wRhj\nhBAfLhk/y995K/prt16wj53RiIgzx2KGnA/jTr+Pl3fsw31re+JFKELYm6rTAlf3LVBob+saJJWH\ngAAh5ddH2318gtXZVWGZdV3XeGzCcJxDU9euLpaeZZfH0ncEHHBnnA+hmOvdv3+MEXEIiQww4kM/\nCCUiUpyd8rOBAlG0WLQKTeUYeUDDRcHhXZAFSMnb6MkYktyHRsErNHdFi8TxOfhQ1V3n7d4HWYKv\nvm0XwqjTyIRT7JFeK9L1ueM8R0yocS9LfD9+oQzaxknVB4DL8h3YRkhUpD3rqsKAgiRXBgNsOlnz\nOMTZzD5YTmZddi0SHsOsrRFG7pp9SKfR/fiEQ4zrROi6QcYuyBc+9Rpiip6Uq4UnhSROhSpaq39P\nkgTuQmRxiJw1gTRNEDozVrcRtA26xoGJDHjLQUPihRfIA2GHQKkOEWtfcRph2KMIkBSQTEGMMki0\no9IzvRwH6HG++8M1d2e77ZA3XFikRuBxLeTlSAO3k7Vh4kFUiHI8ZFp4tDiHPPqjEVk5dGkB/3VL\n0QMA155436UV/eW4HP+Cje80Uvg5WJv5n8bTdvM/B+BvCCF+BrbAOH8izfhDhwGghYCERsKwbzsI\nMWXhZKfXw8HUFtSubI8d9weC7tFniwIj9r+DqAfNgmFdrVCzNz0/OcfOvu0iuMq6ltoz5Gq1RLWw\nhbHIxJ5oIoRBS9Sg39kb4IxQ46YRWJF1dFF0KEqnWhxAcJfS7EcL3UFRm6AoWpQO0acjJJHdpefU\nG4g6BRGzYh0AIKtRCXgfyPmiwYxdh9bhO2AwI+lqM89x/+hdAMBnb96AdLblRkKT8GUISw5FgJQM\nT1NrFMRpCNUiZoQRhglCpx3AaxMGkde7qMsKGRGZQIwodnDcFWpnDOM1EzvcumHxDydvvOP1IKSU\nT2s0uh9cnVELLwZTzhe4ecUWDG/ub0JUhH83lddFAO+FOJJIeDy96dgXldMoRETbOGMUOsLUW0qY\naV3CEN4uTGc1He1v8G/9+7bA+vpv2ULzr/18gnfft78f3w6xndjPSNGDodu2ltrLwmVMUVQkkBD7\nHGaRlxMctyli6msEEPbiAwhddCgNBO89FQYoCdd+/6zCG0v72KW9GLF5Gif6UeNZWpL/E4AfAbAl\nhLgP6zL90wD+sRDirwK4C+DP8+3/FLYdeQe2JfmXn/1QDAS0zy13h31c50RdHfYwTB3rK0LOh+3w\nsc3JW9WiIlWt6loIrhrtqvCmHvfv3cfJka2XvvpZqwOZbuUQTpWmEuiYO6qmWHPzdAW9tIFQIOz3\nnq9W0A6TDoWisBdu1etjxbbRcnHhTUzz1D7woZHQTJIXsxnq0qkDaUTUZnQ019WywNh2lZD0BygI\n3iqKFbp23bY9XtIzkItCFMTQvHmqpsaIrdMf/+HP4/4jex41DFxA2TYOVy58BVzGkYczm67GRUF1\nI0TQ0nFFHK1dYsUHaRcSCY16GmOgKPohtfQpiAtxkyhBnzWO7ht3cGXXPtziQ8pL0mWmwqVg8IWG\npirwyRfsg5mUZ9DsIkDVENo9yO4hBwQfwiySiJkeouygnEmMadEZ1w50kOIWwpn+Ntob0FaqxfSq\nnaMfntpWdTVb4eFDu7GUbQ3T2dA/CCKn0A+0Ap1LTajuFMgQMZm9Ik4QadcBabCkv6nUHRJeE8Fr\nEEXSGwYJbdDwWgahwJjmSIlosDflzfoWnml85KJgjPkLf8ivfvQPeK8B8Nef7asvx+W4HM/jeC5g\nzjAW3mwEoBgabY5HuLVrU4JhnGDI7kKc9nDMHvSCodW8rPC1160NeXlxigll3Y/u3cXm2IKJrh3c\nxltvvGG/7iv2vS9/8gVkuwQkofXyZ52Gl35PpMDtPXYRGIHEUeAdoa25mbMc78DNEUVZeo9Fp/uQ\nBgEkC5+rZeGLg3mS+uJRxp12WbUwbCOYRqFjCqPKEjWLdbUATleOVORMbwQSgrrKYoWW0crRvPLM\nT5jOF02NK6wJYWl+AGJhEDDlOZ/PUVVu112nDUNCqaPDh77wOT8/w7hHeTsZQJTcpVWDJcP5hXJp\nUIjHjyzUOs1zfOoznwbw+9WcfUHTMRyNQeRMUVbniFt7L5ydH8FQDyONEkxY/C2Zap2dnXnBlqDf\ng+B5d01tNRth5fEd2UoZp1nQeuOfsixRMlUq6gqGDNSYUeyLrw6xf80WsZuqRcN7KA5LRMLJwilI\nkp+ceF0UZcgY0UXBWiIvTVNEJLSFWnltDDc/RoYWzAErBlMyzXlwtsSVHRt5DPsGN1/4MATs24/n\nY1EAfQAg0BCthWyI4dR2OuOqguSNfnhyhiMuCmM+8Ct0uChtmPVbb3wVr9x6EQDw8iuveAed/nAD\nGzSIffvN1/mdFa6RAZkPU9RsLTZKeQHVQAi8dN1yJQR1+6QAQodyEwIJgUB1o9A4CXOZehUfwYcq\nTDNoLiwnF0u0TpBFRGhIB1eaWotRD2ALzagOHQVnmrZBw/y7gkDBdCOUTqxWYDyyx5MKjdc/sACh\nf/Czv4Af+BOv2nnZHUE5K3nnE9kpxJWztW+heOzLRQWh3XG03j7eXQ+lgdUF06BGY8gFOY9jOHXb\nqq2xJGBHs3txXq7wxjtWKWn7+k3I0C2gTzeyvO28cO24Dk7V5u7dB5jdsx3wpF2gPLF5dNC1mJAa\nnVHwdnY+8/wLtT1Gn+AzqBaic0K+kUdUOn+KpmtRsyPWNI1XYWpVCy0dgIvt3T2NFz/BlvodiYIy\n80kILwgr0QGh876gI1c+RMLdJG4qKOWUs0L0eG8p3Xi+DQJXG+rQKfsZhQJKLjYGEZbn9l6ehi1y\ngu6edVxyHy7H5bgcT43nJlIQlFPouDsu4gGWrMhHqxUen9si0uHhMTYGNkIICZrZ3d/FFneuZVkD\nqV0Z+zt7GG7YSEEGBlsvWizDXWr5ff2d96G42u9f20YnHT5defx5FETIaR6T9W3xRsIg5vclcYjQ\noZ60huvjx1nPy4s3rPrXbQsE9r2NEfhgZouEv/nmHdwgByFmYfTalV2E3vFn7qv3bQB07P+3KwNX\nMczYFcjSdXcl6jQWH9jveP3+I2zSafr2zggtI4Wqc/gHhdipZiuATQk0dY3joznnEJizK/H+vQ8A\nAGfncxh6bFa6RUL/yJ3h0BcJK936OQh4fVdl4zUo4zT1xb7g6TrjWrORr3dCoeG8fOt4iaxnr2k/\nD3D3oT3O6vwxPjixx9knFD5PMgyJoTg+X2KV2HONxPohCLB2BXfFQKU1GqYXTdv40N6mu/yZqdt0\nP8SP/IQtnn7lSyWO3rKpS9YICLC7IOHP27BTc7gsIVlQvDrJYRgJPjq7QMNIMJGxd8R2IjoGGg3v\ni0oJSPIyXnohwJUxjX9uZfjc97tI4cPA5D94XEYKl+NyXI6nxvMTKfD/XO5YhhGW3IHEssKKCjsm\nChFM6GfIAk+W9zAiM/D2cIrEqf12wIq5eCAaSPajb3ziZQDA622NN+6RwRgLjAdczZMAhsXBWBgE\nDmXJVmiWD6Ccx9+qRMNaQxoCijURpQOPCmyYT5/WKwxYULr94gvQd60WwrJdoers7n5wxe58SRKj\nbu05L7sOFNJBF8i1H4SWPoKYkCQl4wrbdDUul4VXfpaQOCMhTGnhFZk6SnVVbW19HQC0QYjZkloA\nqwJhj7JqwkCEDtdBX4jJCA2t1FTX4uHJGd8rvPJxq/RamZvszDAyeOUlCzFPB3384J/6Efv6h4Ra\ntXT2boE/D6dEXQYxPv+9lvB1YzdHp+1xrB4+Qj2313VINaLxcICIUdrF6RlOSMYKpUbgCoaBXJOx\nfG3OeDUoLYTXtTBaInB9aXfMMbB9y0a3L60CHH3AuW0qJGxnF7rDWWt/vqCWx7xe+Pm8c77yhjrn\nFyViQlz7aYCd1H75DmsLWvawhI0wVipDTFTsZz/Tx60D+54XXhbIxh8PcPxcLAoCgHQhkXHFtwAB\n6Z81OkyGZPUp4O6xLRhGhKduFAm2FoTRXiwxINT0ZLWEJGClEQrG2PeHIeHDvREaQ9afEL74ZhQQ\nMESXwmCxsDf6aGRD/DCKnpICdwVDYYAFF4BGV4j5kNWuV94AKfUMd8fb2KYsfZYl3ia9Y8FpKVsv\nprHqNCRDykR0qBmCny4qLwXn/DWDQPjCWdsq1Fyw+knsFZOLqgFYnXfSX6VpETk79EWBGRfhCLG3\nYg/iGIIMPSd6EyiN2tj3Hs5OsVD2+6q2REm8RNE0a+/N1qYXqlMIyOE4enwfP/uzFv/2F/7tv4In\nh2OHrrMK6aHpO3s7uHHbFoH3hxFu37Bg2pNWoYntZ/dYlMuzxDssqUGCsrY/F0XjAUlx0CGOHVDL\nMTWFr/BDrqXgbAfg6VzHCHhXs5uvJvjmgZ23428YGALRThuFxwveD0wphBHg7YaqqfyGlPVSVEy7\nHtcFllwsnLrlXi9G2bFb1bXYspANvPaaxGBApuzOGF3w8RKCy/ThclyOy/HUeC4iBcBGCE+h2aTA\naMOG1ItegoMbdhmcLRq899Du3G981UK0vvfTr2DKIpoahYi27c9b/StwNKYHJ6f4tX9m9V7ufWBD\nyx/4wp/EdNe2PYeTPgS9+Ky3ANdLoZGP6BTNnbaua5wy/Cwa7Vf2clnj3SO7Ex5s99DLKNRBCa+6\n7XByYn8fqRliRhtFGCAhfqFwbbNQoAKLYbHEmGmHVsDx0kYmb7x/jIhIuDnbrdPNFOeHxHEo7V2l\nX7hxBRMqKb99/z6mY6t7kFFottIKESHIVbPCikjJ4niJnMSs8XSKlGxVVwAsyhr3L+z1uHvvXdyi\nqM04iyH42SvVQBFNeDK34W5re5kAgFsHL+CN92wq9WErekc79PbzUviQ+sXbL2JAe7Q47rzOwN33\n70LNLQZiEFOQZpAjIoaiMq33AAmN8Z5u0lTQwomo8OuxZmVqs7bIc//+vsFUJExb3P6cPbYHdzpI\nuIJu4LhW6DO93Epz5K4TH0kvMFsrgZW0UcWJ0igZORZMY1daYk417nl9jE/uUCBmFGI4tfeFigSE\n+HiEqOdmURBPOEIB1jVpOrHpw70khQtqtra3kEX2Id0QdtKNAd58aPHn4ugE5u07AIBBmnj2WbWo\nIBlq/cSPWjDmzetbaJc27YijDppVZiEARQn389ML5BMLcAoTezxFUWJBJmKlgH5mP7dc1Xj9jpUU\n3+xHSF+0D4iDsEaZ9KzGTAsMEvuADeRaJt6xKC/KC6/KnGQRBMP8R3WI33jL9uYfLzvPich4k+9P\ntyEdq9MA167aY9jf2URBcM9stcD0of3u/Lrlg+RJ4OPGwXiI3S37+gNzFxV73pGGl2p34B/ddsjJ\nBT7Y3Ma1AxvOh3noFYYqo3FMluQFMRud1uAzjLZYYTj49r30NXNaIOYiuzvdheaEdVIiop/o1sFN\nYGnvkfOHdrF5/+gUtdNdNDUcZmt7a4KU0ulohVeIMk5MBcZ3JLQRHv6s/kA+gQEEOzgmx/VX7FEP\nr1aYfcCFBRrgQ9p3wkFlAa2I+5DwGJG6MwiY/o76AUzzdG1gJQY45P2rRYDTu/b77mxfwfe/8Al7\nSvJ9hN39P3Re/6BxmT5cjstxOZ4az02k4MY6WhDoyGRTcYrjhQ3Xr46GyPrcFa/YjsP54ak329i7\nto+YFefRYIRE2l16dbzAxtTuJFcO7M6/LE9RExFm6g5ZYHcr1bVYSFsw+9U37uJI2te/MLGdgSAI\n0TkrcyE9KtcYjfnS7gJfvfMI0y1qQFCOTbRLKO60hQmhWcxrICCdLbvzaUhjBIFzhm5suA3grQ+W\neON9SwS7aLXXENgkAWZ7awcPl64b0OH7fvCHAQBhcYxqZglRrdB4+75ltA+op3B7e4CMBcow7SHm\n7jm9uotVZOd+GKYY9uyc14xQ4kTgIiVBR2qvlVZ1GitGU6fLGt96wEjOX1+FmH36pqnx2U98hvP5\npMfDOqXsXP5gAkjqZ2Z5iIp28CoOMBzZ+d7YvYZRahmYV6/YyOXs5BAnDuvy+D7OTu3xjPopJpmN\nKurO+GspXQqjjS8utrpFw1RCwUATn6AlPUh1B8HukzY9RCO7W1//bIs3vvlP+Xmd12+YUG9hZGLE\nlZ3DXAXQhETPTQ3NAvtRtEJAsxfV2O+4v1SYU4hodxgjIyr2+K7AvbnVHj3Y+2G05Zc5o+7fbz+E\n+X2S2n/048XbO+bv/Jd/Eb/1q4/x3tv2JAUS638IYLkskJBpOBqNkLC74CCwSinU1HM8m81wdmbz\n6/l8gc5Zp0cxlPNYdJV6KT2sNgxjSNYGgiDE4AnsfMW2ptPte/Orv+zFOF65dQ2ffvUGAGBnewM5\nATK7uzt46WWbtx89tjdgIgKsGEbfv/vIi7wW9RyLmWXXTXKn1tODZAvt8PSxrwdUVYs5BW2v3zjA\n9Ru2rZew3nF8coqarL7lssCXvvS/AQC++eZ9zC7oSBVn65CY1yBOEwzY7ZmMB5CsL5yczXE6s4tX\n12rvWJRzIehnMUa5M7KJcU4B1tniAi3p2QhTxFzgd5gSJqlEyPZkEgsUrV1A/smv3INbFuwhMt/n\ndZICHsSTxBGG1LbczxN89roFDr203cP1bTuPg4HjzIQ+BUOrsWDK87V7C/yfX7Hp5rvHZ6jYiXC1\nmlgGngKexamHns+LAv/1P/khAEBKhqvWls4OAO+8fYrf+RWbSr7/tRIBxVLG45FXkSrIy6ibGrpx\n6UhnUwxY1zLDZyCQEsORnbsV+S6rqlpfR63X93UQ+Z+frNG8/s57v22M+Tw+YlymD5fjclyOp8Zz\nkj5IQMdoG4mKsM6u7bws1dl8gSQjU61pPEPPyb7PZjPPO1+WBSrqFIRx4nUBIKy0FmAZjIBlxWn2\nfovVCimjEUCgJCsxiEIfSkcs/AVhgJCN5clk4F2LsyjxXpD7+wde+t2ZsEgtUVFp+dbBTURUNn58\nXCELbei7OSCBqzfGiISv6I5ElHL3HEkfjaRphprQ5P7YdlwGgyGa2obJSZLi4SMbVSwK7WXZZSA9\nMctV1uumQY/z0xUFKhLMVFEi8UVQiZzy+VsjOjUPc2yNyd1PU6wYCTw4PMTjuY36LpoGNQtzKxZ7\n0URw3Dfd6bU+hXna99CH847hKNdaDkkQYMiI7cWdDdzatse0v51jtGExJRnZtUmSrpWh2xJpZI/t\nk9dS3D20P5+uCmjiOhyILs9zGOJMokAiJsGqUg3OWju3gXZaFyVWc3tsJycrSE3GqNAYEW5vhEbF\ne6sunMhO5y3kZABELKRmWbYugponFK2Vs7aTHkpvpe9dhKV9lA0AQfDxWJKXkcLluByX46nxXEQK\nQgBhGMKYzudLVWWgHAlIK7SkNZtAIFPs2bOglvb6MCQohUmKM22xAEVRYekkz0yHhn1et+Nr1UGR\nnNJ1BiFrDmVZoabL9Wg8RkJSjdu2QqExJXZhZzJBjzBgIwJs05JuMBxjNreFvRFVkldnC+yz8NUf\nDjCj9NrOdAcxlZsjab93urXrCTfD8QR9wphboxHFdmfL+yMY1h0qKhr1+wM/h11X4d4jW5TUJkLI\nmkmrFBrmsJJIw36aeAg2WoXMCdqO+ug7Q5WNTcScuykxJNd3t9BnBKJhMCey9Hh3E/doPPv2g0d4\n5CDWhPNWRmJJq7i8FyGKiAqFBJ6COlOpySE3swQNSVx5HOL2no0IXt3bDVoXtQAAIABJREFUxPUt\nKnBPJ+gPbZTVc5FCmPrds+sqGEZ6O0mHF7bteT8838Ab9yy+AdS16MUhJIvDT5bf8qyH86bkEdr7\ncLkyOHxgsRfNMsHqwvAYxn6ey2IBoez1ubVvo4ftzRGmGzbCGk+GGG/YY9/c2vSt5rIocX5q6zWv\nv251Qb789bdxcmE/qzXw/hRar+tmxhh/3z/reC4WBRgB6BBRlEIQJtrpxnPaW9WiYnpQFguUF3Zy\nIsJEgzDw1elKdShZ9W4a5VMQQPvioAtJgyCCuwG7Tnsn6SxLEYTOir71vokdjyeNQlynucnWZOB7\n2r1BH9s79vW6KiBZCA3InEyiBJub9vdN1yJjbz6NUv/ApsQu9PtDlHS5HozHGFJSvqgqBIa6g2m2\ntnt3NbRWoUeQTttodIzRoyjxUPKmVR6661KjNAoQdnbe9iZj7NO0JosDHBA+vDndxoBYgI0hUyZp\nIPmwFWXp07i9rTE26IbVSyN037R4gYYcgDBOUTF8LqsSAQuRtni8xquwwO8Xo04pX2jcHWZ49apd\nFG5d3cDmhNiL0QgZF7KERcAoShDzOpgug6KbUrdc4sYV+xmPVy0OuXjVlDzLs9gLoCxXJcLYKTjX\nOOd95oR1VssAp7Sabg4FLs7J89AZNFOzfgx87tO2M3D7Bdsh2Z5uYYdq5ZPNDaT8viAKIJlOtbVC\nQT7KztS+tzUBfuW3ftd+X9t5QRpjNLonVrCP20y4TB8ux+W4HE+N5yJSUErj9KRAsRBefLJu67XK\njxEQnesPC5Ts3wuyxZoWXh2n60oE3F6SMEDCoo0RZv15nnylfB88CCTSlAW8JPNFnaZpvJpSzRbS\nZDzE/g51GkINxRV6a3sKQ/Wbplwz3JxvwObGFAm9ILSqvalNL+2hXNnP7jHV0IFEw7ZZr9/zUU4c\nRAjoVp1kOWq3C5D0onWLkN8rhEAUsV3WAZrYgk5rvwVrtnKzQYzr2zYKeOXaHvr8jK2NCa7Tjm1j\nZwc9mqskJAzpcgVTEglqOr+Lp2kAQwxF3bY4PrM/v/OA6Uwce2i37koPD/7wWF8r+99d1/n+/svb\nY9xgyjAepejT2KaX95HxWjo/iSCKEYSOgAQI9vqjTmBny879jfMC93ftdT0qmY4NBt5LcjAIPamq\nKGs8fEi1KKZ8TSXRzO1cHH+wQD23v+9FGgHhyp96+Rpee9FGXgfXLSx5Z/8KhhP7vUmW+mspAE/W\nkrL1aczBDZuCfu7Tn8Ab37Jq3UUzWxdSjYFmWiyl9KnEs47nYlGo6xbvv/sY9+8tETn/xVHgK+uL\n2RxDXuQ8C5ESHiokQR6V9jh7gdTLWSkor+BrhPGdBqczGIShxy6EQQiXv2qt/F3YlIU/Dpeb7e70\n0aO/Yl0rZAzt82Efy5WtZyQysJhVwFvV570+WrcIJRkS9vqV0hiMKbLi0gHRQdFfN81zCKc4rAyS\nyPkc5l6mrea/Go0PvqMoQsUHVsoAgqAYbdYgHWfesjnqY5/grs1RHxnD+en2tpe962d9SNY+DBdh\nAUA5sZFQQPI6oO2QshYzHA6wsWnn6M4DC5pqW+npyQJAFK0r5E/C3QU/26WBQgjsMbW5tjnCBr0i\nszRFygUwC3t+jkIeg4xjBCEllU0Awy5KZgw2uNhf28rx0oGFd4tjskvHG17n8fHRsV8U0yzD29+w\n+JPA0HBGpmge23mZHy4RVnQqMyVGW3YO93e2sLNtw/89ytMPJxsIXZdBig+1XMjWDIHQeVf27Tnd\nvLqHG9fsAnFyUXrOhBQCNUFdUkqE4cd7zC/Th8txOS7HU+M7taL/zwD8WQANgHcA/GVjzIy/+ykA\nfxWWaPbvGWP+r4/8DliJs6YpUDkBy1ii4Y4fRhEONuxq/LlrfUzp4Vcy1XgwUzgsySxDiK51KYhC\nQZJT3RlIJ2bMSLXTEobphdadhxLXXYeOYbVNI5yyhj226WTkV9/WSAydMGbbQpFp2ctyCBYuR4Qg\nd8YA/L4kibwIaBwlSHv2Pa4hr9qlF2eJpUBDReEgiBA5TwatvQp05URgtUFAplGSpSi4Y2RZ5q3i\ngs4gpZSdQwRujDLsTm0FfGOj73hWSHoZUlbwbYfIPDkVUJ0VEAUACO2lByCNRwUOsgw5U4X+wO6I\nJ6sK0ilQBwEaRjrOd9NOhfbsSMXvHfdy7LE6vzEZIuLxyzj0xcooTBA5bAnnKkgz763RKeMt3hFn\n0JTb296aYINpzibNW7Z293B2anEfjw5P4PbRSABjwtd1a7+rq+B1KNpl56HrMmixz0hpc9zH5rYt\nNsfEt5gwQMditdDKM3SFkd5hW0oBQXKfi8Am0zFevX0LAHDn/YcoGQmHYQgRFPw7icx5mzzj+E6t\n6H8RwE8ZY5QQ4j8F8FMAviiEeA3AvwHgEwD2AfzfQoiXjPn2FjUyEMjzEJvTHg7PbRtvWSjUXCAm\nqcDnrtkJ/L5bOaZUXlqt7A1/PFc4Iy5cG+Nv2FYbzCgGsqwBwfZOxbB+URkrkw1LyZ3T3enBeYFD\ngox0p9Dyho24EORxYrUgYR19UlIci4u5h/y2ZY0+L7pTfl42NSKqRWltvDloL8s9N7gjvHgxnyFl\nq880DVq2EHvpwD9MnVK+1hK4ha7V/qqGYeAfJo11mypLYmww5QlY3R5kMTYJQsrSiLZUQBQGCF0K\nBrNeUXm3GkgI5gGRDCF4IEoH6BLX1gsw5sPbZ2fkeDHz7eBACLSto/dKD1+XwnitTBdSTwYJ9sZO\nvjxEzNpGFBiE5ETI0EBy8XVheRinCNhmhOhgKABjghgNnbF6+QDSmchIdi1cWA+7IDs9ztEgx/Vr\n9pjP5wTFzRIYPpi60aCmD7I0xi5bjqN+D3HiAHWuXtI90YQVHo5tu14UmQkCCKZujpeRj4e4fYse\nqpsT3D2b89iF37SEEIjiCB9nfEdW9MaYXzBr6NlvwHpGAtaK/meMMbUx5j1Yp6gvfKwjuhyX43L8\nsY5/HoXGvwLgH/HnK7CLhBvOiv7bDiEkkiRDnJRwofqyLP2qOxjFuEpb880sQD+zK+2QhbpJsnIO\nbRBSrnv2nYGQNgwsigIis1DgLrXhZ751BSayu+P5bIZHx7ZI+NVvvY9fpYXc0dkKbrUOWMlWGt4S\nrZ9qaO4uVWWwSc+FclFjb4uiLK2zLusQtGvJt42xDSNhFDTTjiXFTVTbIGUHoKgrZDH77nHid9VO\nKRgHvlKuEKkB/j4UkY8q4jDEqGfnaxDBKxtXhbPKaxGzEJeFOYQDCKgOT4JkXfrQ+cKfRBh4K2Zv\nDKP1mmwlA4NB3362t6PTZg2wwROe80J4fIcMAnT0RmD2gb2NDHubvP55gNRBguOe96MMJCAZavuc\nUQS+c6CNXv8M4V3Ks16OEVmjiwcWWFYrYEaSVwfzlFnMS8PX7Ht7tqNyGnU4FvR+NIBh1CeDAIpR\noQgShExzQuF0JwH5ZEDkrfIEtIMoy2CdukmXRuSYTm3X4uDqDh5QSVup1pvnqLZFU/8Rdh+EEP8R\nAAXgf/gO/vavAfhrADAe91GuDN65cw/aOTMlPW/ymiYxElKKhaqxnFsWpLM1N/UCsdMGTwboyDUY\n9HvrfD8IEA9tCwi5rfQn4w0goWJTucTRY8tqi1DgAZFiRd1iWZCey0Wq7Tr/cx/GPuGwobRzU5Iy\n9DfQjCg/BCEEeRcbW9uecdi0DRYre05VxZpEr4+uJmNPxh7vX5bVGkzVtv7hdAYpQhvUpU19pNEI\nGHKmcYycYWQedpgyfThqaMB7PsMRvTbHvZEPOS+KpV/UkijyHAQH9AqDAHAW9+j89YOQ8EFxoJFl\nTL2Y31qJdON/flLHSLoHwXS+Uzlg2L496iMnuKcTEUrYzxtFG+gI/JJpiogchSByaEvhu04isOhT\nwFLfA352FCXYZr1ifvKm/fdojpLIyzgf4owKV2ko8Lm9PwcAWLQWBXksHuOd0M7nSTeDEXbelssl\nvvFNy8TsZRk2tuxmkDGlCgLhxXUAASGdPmTovUe16jzCV/tcI/Ddoyt7W4i/bpXIqqpCzNStKRsU\n3dN8ko8a33H3QQjxl2ALkH/RrCFTz2xFb4z5b40xnzfGfL6ff7xCyOW4HJfj/7/xHUUKQoh/FcB/\nCOBfMoZSvnb8HID/UQjxn8MWGm/jGZQdqqrG22/fxYMHJ+gPrWZiEkdonO9g08DBkbU2yBmu9sjI\na+seWur9QRok3Eny/ggZq/rB+BqCnu0Pa+4eLRTgJNCjAKO+ff3l/QmGiTU7sbuWfU9B5mCjYggW\ntSIhsKT8WRyN16FmDfjp5XaX5X0PmTVt4wUyqmWBquFuxJ10OS/w8K6NXC7OL3x1PgoMxuTV9/O+\nTw889MdoXMxtGGkGud/ZEwmM3G4tgA1XEC1yzmGJt+68xw8R2Nmy0ZQxGkenVuth2Ha+a+G2q1Vd\n+11et43vjyttvN17G8ZerbhPZquU0vf/+3nkQ2MhJDq/FRovZ58wHYjDGPOl43a00Ec2CrvzYI6X\nX7L6FTevpohdwMI2ipCh5wYYrbyvZKUlSkqhLSp4GX/TOT2JpZdx65vQd4S2NzcgFdMxbffBMN1E\nht/k9BgvlZeEEpvEVhSLBb7+NQtNFsba+F0/uIYwWXeUKkZ6i8USJ6f2vi6LChXxMs5iT4YxUuYd\n/SxFj9fm5OICPUoEJqHEOdmqzzq+Uyv6nwKQAPhFAk1+wxjz7xhj3hBC/GMA34BNK/76R3UeLsfl\nuBzP1/hOrej//rd5/98G8Lc/zkHIQCLNM7Q6wIIMvygyqNhLrmsNV+7q4g3Ivt3xwwF9EzYDa9wJ\nWEMZIttkGHnT2Kiee38G11VTbYWW8mK9ySYi9qs3RikOBnZr+zXVIevbnTkobBTQaoE+t+B+EqFg\na3Q0BJaVjSaOD8+8/NfeVRv9LMqVX+0HsxOv+hQj9PDnx/et0vS3vvktNFTx2d6e4spVKyGXSImz\nU5vXzpo5xoTHllQuKoolyoXdXUJpILkmZ6HBtjOMqReY9LjzEsX48JHC+bndUX739W8g584VpqE3\neDm4cg1X921dpkeD1qPjI6yo9HR+do4VCVF5mmBCwpcJtXd2durRMBqtg6abEJErDKJ+oj0nva+F\ncNBfpbBFKPjtq3tIqT9xWissV/b63P1gBaPtfLljiJMcjbC756IocXhi5/D+4zPPMJW6hGEheHNi\n/+4bjy9QsX3ZnJ55qHwv7aElS7Ij83NVLNfnFEj8f+2daYxk13Xff/dt9Wqv6n2Zpac5Qw453EUp\nkqxYymJIMgQZCfxBhoE4iIF8CRAnCBBY0Kd8DLI6geMkyAYEgp1E1gYFiWBTsiAjsRZaIjkkh5qF\nPWtP77W+qrfefLjnvZ5RJGgYs2cayDtAY6qrpuu+e999557lf/7Hk9jI2vIiF84bhqy5drdIjY77\nxsoJhiNqVl65G9LfN5bZzvYWvV7OueDgCtq1JgF2ZXtMR8YqDMNpEWhNU2nwC9QtRf9gzLuRYwFz\ndl2X1ROLOPaVwxqGLCIW/sRR4nC9J9VnjFF7UtbbNf+eWWpwWmjdK9U6Yd8s6mQ8YWNbHqDUIsvM\npsk3aLOa4dcNLmI1vcBMR8pX5zo8t2YUz1e+e4O+cN91pRzXcZ2CCCPLdIHk0Wlc1FqcWl0pAqXT\noTRIGYUkEsD0l5fwZXPbtkUgbsfkwESyF9s+tpicjWYTL+8jWHGZWzCmfThJsCSwl4iyGQ/7pDJu\nFE4KoheAhTlRIPtR0Y8wf7hnum1SwSZU/AYTwWGMR1OsuyYSb2eKrrguFTFVR6Mx79w0bs7u3j6R\nAMDarSYTUciJTrEzszFtKZFOkriAmGdZipOb7T9G9Z8bmkK7Sce1aWLGDg8CXMso0xNLiyDNbKJx\nn4Mtc011Je6oBdNQqOL2ekTCpTlb85g/YwJ/Nc9mJK7StetGOb/82o0ic5AmSeHmjMdjIm2UqGPn\nFac2FT9vOGThSZYgilLeeNu4ZkvzAWdOm+pIWyDoOzsHrPiyFzwbT9y8mU6NrgDHgsQikkPNlqB6\nZ7ZObVnwD80Gr79pxnjjxu2izf3c7Aw3N3d5N1LCnEsppZT75FhYChXP4vRqkyfPtbj0jjFV45gC\nSedZmjkJAtYrDrfH5vR47ZapELt6JeT9a0ZjPnn+HNW2ORGD/T30yHxff3PEBAm+SI/DTme+6GBt\nJ2FRXek2T/D0C2Zpnr/Y408vbciVSnqvUsHNI2foArE4DcaMBR7t2T4Iw/TOjjlRak4dLSSg4Sig\n3TQnfrPbIRB8wowQfA6ylFu75mTb2Dug1zPzaFR91lZNYKtZrR62i8v7FCQxmQTJ4nBaoAbD6ZRM\nzNzu7CxxnjeXoOvKyTlqLXNazS+fZn/PmLaj3hYqlYDodFKkQHN052g0IozMeDPzSzS7xsLCUrjS\n3+CgNyCQQrG8Q7NtH+bdFQo757rgUJRSRRfqBUEEohTvbBur49VpD7dlXp9LK5xaNNZiu9YhneQp\nXuNWdWZmSGWN3SygJg1utFfHkpTkeBoyEpiykrbbaZoVgU/PcYqiuH5/wB+9+d/N+1rSn7HPQNri\n2bbCES6Lg/1eEYw+dfpJNoTIpVETmj5LsbBsiqNqnSaTwFxPHMTcumks2ev7I/ripu5tmb9fX1vl\n2acMzLni+cwK0tdzFKkERDutVoEifVA5FkqhWq3w/NNn0H814/P/9Y8BePtaH1vMsvmGw0pTzHU9\nKXjrxmMB3ng2g54xkQ4OutROG1BJuzlHbd+8PxNOaKw+DcBr14yJ+Nr1hJcEi2/bKaEgWivNkyzO\nGPjoxz7SZyqKZUs6M7meU2ATUFaxkdMwYjwSkg4nobpoHrInf/5DAFx+4wpv3DEm3jPVE7TFfHYr\nFbJRbhoKPFVXOHnWzKNz6hRf+OLvA3D9ravEgXnYnr9wjlrVbITMzrn8UgJZl0zH2G4Od4XbN01T\nkPXH1lh//DwAw6F5aC5feYe9q1fNtX/vFZoC8Dox02SmIW5FtV7kyAv0seXQbpuH0fbrXBdXYnNr\nmzl5kG0s2kLnnufmT59eY3zlqnzJYYm0URF5nt6mI+bzusRllA0/uCEsyTsDArmej1bq9ITf8+xs\ng7aYz0MpSW8GAX25N7Fd41s/MKQvb93YY17iNU07ozIRH94z1zvTbnB3XzJbuEX5cpom9CrmgVWx\nuAxpHVU3C7O8MEu4Z8ZuNdtFReTP/fxH+fY3vgHAjRtmL6yfWSkqfr2KhycKaxpq9g6M6/ns83+O\nK9dNdv/aZXMfb1y5Q12U6ZNPnuXUSbNGvucwkQyG61jUhRnsQaV0H0oppZT75FhYCpay8CtVPvDi\nE6RTo6f+yb/8KtuSx2/6c9g5WQgx51eMRvREpzVUSMMXshDXhYkx4Ry/Sa1t6uO9YURNaIlfeNz8\nvW0rTp45Kd+lcJU0bfFcXDnZnlw7wd1T5sSfpuZEuBPH1Ny8R4RLTkvsaEW3Zk7N3fGExZZxY1aW\nzCmxu9VDj00gsVlvMBHm4HgyLaLeTcmAEIDbMFaAoyxOLxm0+JJf5cySOcW7nQaO4H+TQGi7okNr\nRVkw2zHzOLk8x/KsmZ9f94gNkIKDnlmr6XREtyGReq/OirSNq3k2riX9IhxFEgtRi1SwVv0acXpI\n87Y6b4J2vluhJ926x71tmqsmazG7aNbi5Cjj5l1jBlsqxs7LK/VhPw+dZrhyEi61jSX0+Mo8a4+Z\ne7a9u08gFbHr51c4tWxKcNTggFhQqEivxfFgwETch37ocfmWab13a3eEVRX0JhH7d83pXxc371Mf\nfprvXDRoxI1be4UlN7cwT71h7nUgbpmONevnzTxfeOkZ/vBL3wTg/Nl1moILGWxt8PwzBk9hPW8y\nEp2ZBhUx8S0NvgR/V9dPg8r7TyScWzbjdT/+EXMNtTqrpxblO9qEYry2KlWGgqyN4xjPvdcp+9ly\nLJQCAErjVz3WheXn7Ppp7vzwdQDatRUW2lLJ5vm4tvEjTz5lNlgwGtCwBQhjaZRsXLtRY2XNAET8\nRodMzPWaRIgbjRq2mG1pqkmFE9GatrAlRXhq7SQvfcBw6uXw2i/9ySWakhaaDIOinbvteqwJMetp\nt4ojD3i8ayLZz55ZwTptFEUSjAjG0skpTonjvMGJAIyqFWKh8mY65MMvPgNAFI5peQLoUVkBq55O\n8wyIi2PnHI2w3BTSmorF0pzZVH7FBoFVr0nTlLX5p1EC/W3WO9TEfFZZhO2a+U3DgKbEUuqy0Waa\nNdycntxyySRWcWK2RX9qlFeWrGBLKnIopKXXr17C9w7rD1ThPugC+58T+gK0pFPSfNfndN538mQH\nLW6TXa/SkjYAUaoYSpoxzwxkcVKYxb4T80sffZ/5xW7QbYkLFveKNGEqYDK/ovjIBeO3f/e1q3zr\n1csAbAYD/vzaLwCwsWPgxeNgm/knzP1/oX2Om69cBODptRVOCMGsU3NwxSXyJE4W65RaVWIcWVZQ\nss/NztESGP5wNEBLduXsmnF36vUWVXENlOWwsmRc3m67w2hiXGSyDIt3pxRK96GUUkq5T46JpaDR\npGit2dw0QRTH87ClSrDqV2jIyexXdYERiKUJR6ProGKJ6sdxAYnVtoMvefV5v0oyMqa7JacVaUIo\nVYJUKtjSVVo5Lpkr2nz2BE+8ZMy1mpySJ2+P2d82VFyu6zIJpMuz8gr6+M78Aq5AlqtSnON4HmTm\n81u9flGspLUqAFWOnBje3DyhnMDadfBzevmsih0JzHcSFNRzeau4NMuYmzMnSTAdMyuBPU+popBq\naW6Rua45gXLroFGbIUnyVmNu0ULPcWuMp8bstp06VYGY+zLPar1WVIy2mk3SKC/a0cx2cvBSylAK\nr3Y3TIAvCQe06mZdBuMJlvAimCYsOaQ9oe4IXVwjb7gDTcEs+J5TZA4ylZKK9UMcYufUc2LaW3hU\nBTRUsTM6Rd1WjEqMCxVbGb5gVWzbrI/OQnzbfO/7H18uuk5/+X+/ytmO6RW5VDXrvbN/FTc0611J\ndUEbWPNtTp40boXyHTIl2YUccJfERQFamsQFTsPxHKpCztLsNIqskpXzMLgeTl4oqBVdwbU06w2U\nNpZCFqd49rt7zI+JUgCFRuuMjhB9bN7dRIoEGYwDqvW804+NlYoJKxs+jAOGgiR0ul1sMc+0gjQV\n31LZeMLzqAXrHmcaJea62+7gNI0/rN0apMbfH+xug5jo4cC812w22N8Sl8G2iPOW62FMJA+FZzsF\nY1GuxGxU0b0qmiTUxb1QWIWCyI23Wr2Nk3MfAq6Vp8gSQkF9xiEEkp4NJnl1pU9NypT90GVe+lWm\n0YQwzensD/12VbB9UtC343hFlL0/OmAoc9JRRLOdV53K5m+0Gcq6BOMJXene5ToOmSicxEsLv9vJ\n40AVl2leJanTe3KR2WF5dhZTy4ltZP6OBs86JGyxCoYln5z0cePmBokgPC0lTYOjmKyI29RoyHVG\nqWYi7mbTdUglPpIT94aJJsx9ckvTEtfz8eVFfOGB9IQdoDPXoSdxEt+q0JSHNLEBKfd3G3XIhJtz\nbPasTg4JhNMsLbp2ZWlKWpGMl+tjadnXee9b28aSfpxZPMURN29uto37Tk7kmxUcow8qpftQSiml\n3CfHxFIw7oOyXB5bM2bWh15a520hunAsG1tM6UZnHkcisjkRyOTgLvWuicI2ltZxWkZzR9GUbGi+\nI0kyPDHN7ZrR4K5jo3L+wUqNVIradRIXUOHe7jYdYc+qCU33dDREJ9I/cDItKgOv7fZYnDFR+8VJ\nSM0/5FUEY/ZNhR5OaWhIhsPFLoBTsXwXWVq4IkmUFe6FjkMyCSomkSYSyPZE4MCteoOOdFq26xXW\n540ZfH1zRJpDotOMWMyweqUpc1bFdzUbraJ2/+7mXbK8SY5fIczrRsS9sByfVI75vc27BD3jaswu\nzFKX2pQ0g8kkb4Zi7t18u82u5NLRuqha1EoXvRvREZG4iHnQ1dMenrhxnl/Fl2yP6/vsCcnIZBwx\nO2OsvoawZFtJymRkXMWDNMIXgFujO4crLpgVTckkwDieDmXcyWFXqywt6P5//sWn6cc35VYJdiFK\n0VIZmXg+J8+ZLEOwv0smmTSreUjhnogbmCXBYV9NpQvgURIlIJ27napbNPApaCrUYR1PFEYk0syn\nu1ArnpdJmhLnhJsPKKWlUEoppdwnx8JS0AjhqqbwZZ98fJ2TCyb9E2eKkfj1La3Ico6EPPBiWTj5\ne7MrZILyu3x1g1kBc9Ucm9HAnCSdeRMYsl2/6MWYJppEcvfKyhgH5vWkv09VKgqvD0xwsbd/UHQM\nDqchfeFy2N3e4eZtg7Y7vXiiSJEJixvaVkWgyqp4+MKaU2nUUJIODUWrj6cTPMnRO76LLRDCaJoW\nWIFJMCGQ68iJe2zbIpMTpdcbcXLFpG3HwQgnt0YSzVQCk9WcB8zKUMLZ0L9zk50dsbBGYzLx/a/f\nuU1f4g6qkqfTNBs3zYkZ9geEEhDsT8bMdY3PnFqaSOaXybXPzs+zL2ullDqMbSgKp1mTMRBrYl+Q\nl8lcq8A02JZGloj+wR6bd0zqV6cZjZpgOeaM1RiOhlTFt+4P97klOIWFTDG/YnAPaaQIBEGo82Kt\nSUwi8OI4mJJGxoKoVVocTAwvwiAQaHOyxKq3Jmsc0ZSg5ZU33qQvcRevnWJ7ebxCyIZRJLL2xEnB\nwzCJElx5HhzHOaTnkPuoE0ikUjOcBIWF0aj5BYv1aDIt0rIPKsdCKYAxg5JME4uZ2e8NCrqyK1t9\ntkdmoVamfWyJ1E6F2mx3Z48Q82CutFaxpfHsYPMGjgTd5h47V5SZZkkexbXI9GE3npwlN4um9IXW\n2yPl+nUTPPq9L3/FjHF2nUjcAN91CwKRWq3Cjdsme3LnxBnD0gxFX0rlOhzsm++dTMKiN+V4PGac\nB53ENqw1QuIsrzOwccRED8Npgecfj8ckYvLb4j4Mhn1+cNHgOxoTmolYAAAPoElEQVRzXdpCttFs\ntQp6tEmq6eVs1JYoQttjOjDfO+j3icKcS1JzbWPDvB9PefOKeX1jRyjxNNy8ZuDKpxcW6QgzcjAd\nsSfXXK1VmYjyGgvdnO25JBJQU/fwMmZZVvBOWpbNUGj8b0gdyPmlDl15gOLQMbhnIA5D6gJC2rrT\n40ei1Hel9DiKJlScPEKnmRUI9nQ0YLhjlH3Ft4tGxvlDGk3DojQ+CEJy231hcRZXKjs9x8zDt2wq\nqVnvMBjTnTWKaWu/z40bBhTVnJuj6sl9zXtUHuyxsykKLQqLOphWp42TdxTTSYEBKQz8JCUKzTMQ\nhRNiWVtH6YJPdBIlNAWU9qBSug+llFLKfXI8LAVpJ6CzQwQbtksq0acbe1M2dsxJ+uxqu7AQLm4Y\n7fonr99he99oyZXL+9ICDqLJqOhvEMeKE1KglJ/yNMApUoEaJSmr6XiEJad0tVLhzo45pV69bcbt\nrkxoSNDSsm18QaBlvs/2rskPv3X9HWqS9iLPwaOKMUY7B1wNDBLOca0CztuWFvduprDkxAx6B+i8\nA3ecFqduzkcAhxwRw8mYodDGNdUcb2wYiG4axlSEC3OCJpE1cKRpCI5d0KMNx2N8CUAORmMCSdMl\nVgUkDXzxisEbTIbDwj3ypwGZuFJexWWwb3Ah7bhBIjn5kZi4o+khEhRtCGfBIPpy98GyFBPpd3Fr\n29yDg/6ImZb0ZLAyrDBnytZF38XVlUW0nKqZYAUWluZJo9wKsAsocRwlBANjvSVTi1AstrGs8TCY\nMBarYTiNcKrmnl6/vccT7qfNXF1D3ecnVdxIMDRAR7AgtWab1y+ae7165gSWWAg1QVIG0wnbm6bY\nqVXxqUh/EmxVtBa0bItU3Aqdp5bjpLAU0igijnOEbFyQ32ZkzMyY63hQORZKQWMi4jYaR6Km8+0q\nWrIB09RlczdnmJlnJGbS118xG/7S7QAkArwX3kXJ5ohTjWObzXTr7ogLTxq46gsvmOrDuTgiqx/m\n6acS4e4NDugJCUcyHDHtGfdhTqC2tUaTpvTzmwY9skyi6FnMVKCoO/0+u7vmofDEh6xXa0XXpNXl\nVbwchFOvUXNz2u88DJ2g8vRykjLIQVaZVSgZp+qY7rqASnOfPKMlNPN1v8JlYfFxUs1yLYcu20VG\nYSCb/yAI2JI1TjU0G9KjMhgzSsyDkmTQlxLgnH8xdV1SYYi8Ox2zv2+up1Wt4sg1deKYhijnRGI4\nYRgVD79S3FNGnZGb6BXfxxLFcftASF9ixWRkrsf1XBQSa8gUI8FvoKHZkgdS7pPn+kX3qUjHjIRF\ny7IUqSieMHKYyncHY6lLiaKiXLw31eCbefzg2ja/XDWxiK6Al+L+gFhaAyjl4MkB9+RTj/Ptl78N\nwMbVW1jS4akh7E7NVo2qK3zHaYwrLp/bqGH7ec0PROL25i5jkoRkSc4bGhJIamhnd0QYmve7s3PU\n8u5jDyil+1BKKaXcJ8fCUjDhNU2aptgSR19dXqAunZ230glXpIX5drBKLBgBS1q1v+/MCnVBfi12\nmjTFTN4fBPSl6MhCcfeOiXbvnTYR6e78IpZY4HEYEgs91mQYMJXqQSsK8VKjdWfrwmtY8Q6blwCO\nnPLTaMJB3m7u1h0+/Nz7AUhic51b+5t0JCperzbw8lOs6hc8BZG8iIIxE+E7jOIQ288Lg9qEUl2Z\n6aQwwdPc3fF9amKiZ1nGvtC8tSqVAnmZWYcM1UPJywdhzJsbJkgaRVlR8FX1PCxx4zzbZbkrHI1S\ntZrECZsHxpIa9gOmwl/Qy/aLE+fc+hn8vCeindPYZaRFoDHvumgCvjkfo2VZRXfrPaGxG8UGiQqQ\nOV7RSfzGxm12Ns0pfeLEqUOuxL4EjL0QJejWabiNFniw51q05OS2HZdULK9M3DVLqaLHxXZvyB2h\n+utFmkrFnPSubVw+J6sRSZA7IStcpmeeO8+1t4z7cOn1N6lKxeTCZK4Yy7ZkvX2PZJJ3VU+I9ETW\n6BBHkjNKp2lUNP6J04ye0P5t3Nw0ndOBdqvBNMpb8j2YHBOlYNpvaw7NyHarylzH3KxrNw549ZpJ\nkb21NeJ010Rkl9s5ln2MLTe868+gtFmEbqtKtZG3J3fYlcj//rbZxOmZdWyJwoejIeOBeYAG2zuM\n+iZaPFOrsiwNYjt18/e24xQ9DtM0LRqPTKIBd4Rl6dZ2wI1ts4FWT5gqy3g85dqlt+U628xKVLhW\nrRLnMYO85bxOCGVT1btN5jsmtYjjMJFVy4CpgKxyQIxrqyJeUFuqk1MXVapVhqJk2llGUx6Ecb7B\nsAjFTO602lQEJhz1hzgCK656Nm35u4qUkafaYizbKAximsJ+FFmau9LbENszddxQkJ3GcVx0tSrQ\nOPI6Z9xK07RQvvuBuZ6t4ZT1BXM97dRCCQ9ipTtLpW6AY40Lz3FaCEc8AXIpyyWVkvLenbtF6vjK\nOz+iWzMK59RsoyBbzSsVVaIY5XGNg4DLd8w9naaH6fNE1iLOXLRAnzPXZSxNZOYW5njqwhMAfOvl\nP+LuTeNujHbMQZeOp6TF4aVN91qgtTSLJS5rvd1kYVmaGeUeZhoXRLJRCleuG6V+d+8AxzlM2/ZG\n747ivXQfSimllPvkmFgK2lgIWhear17z+fD7nwPg2q2AzR2job//5i7z7zPaeP20RPeTBCsxp3nN\ntU0jP6DdbOIK1NZzKpw6ZYI5dwRLEPV3sSSwOTzYYyd3L7ZuU5FuIkuLp8gbVfo1Y60opYjCsHid\nMyZv7w/YH0oOPU3541f+FIATp4y7cnJ+nv1bJic+PujRkG7FluvhCegntXNwE3jCf1BfnkE1haIr\nSoo2bqNgzEQi4w2pBh329nHkBPP8CqfW1sz/3d1nb99gC1qNWdq+sBxL6mAcRCxJy7QLj61xpmOu\n52Cnz/aWAHacOvNSgZnl1ZnRlHbXjPeEWqG9YuDFl3fuMBCr1alUC/dmb8+sYRRFpmEK99sJruse\ntsVLk6Kj9ViKsq7c2uJ5ue+TKKLbNJbLYrNGJTQndjOzGYjFpuR+pFmCLdbYJAjxpTfnY6fX8S2B\nIGcTdJp3fBY3Lkm4LViHq9t9DoS8pdWooHVuuh+2B7SERh63wUiK1GbbLhdeeB6AwXjInVsm07C+\naEhhXCzmF6WVoeugxFLIKg6T/HnwfZRgUbICE36I6RgGE1594xJgOK2rUuindVIEhx9UjoVS0BqS\nNEFplXdkx3YyPvgBU5r6yhtX+ZZwLX7/8jYXzphN8fxZ45OpYIROjaJozs6hhB/fb1bxxNz1vDau\nmISdlpiUOuVgy6Q1b9/dYXvLuAzD/X0unDe49db8EtsHedcnAd7YFIonTjTiRrI3nhbIvCyNee1H\nph/hM5fOArDw4rMsr5mNMNrsgTT+jJVNjoByJErvLdTwhDXJb9eIZQNOxuMiJTscjfEF3FJvSW/I\n3U2qomBcxyv6CN7Y2ceV6rzaVo920yjRimRGpmlEIlkEZSv8plEWy/4CVaGiD4KYkeDrA4lF2DbU\npUS6WrPw5ZqzHZu8Q+Q0Ghd+8LbcR6/iFJ8rbRXrpizLlHFiuB3z9Fsm7ser1+/wkQtmDec6MYGg\nWm0F2ZZBKeo4prZmMk1+VSpfLdDiglSDgKy/JWsf0GpL9WHsEqYmpRqKLz8cR/zwunEb7+7v40oq\nM8s0V773QwDOnTN8l76bYflmXa00IplIGnYa01mSmp6/8DH+17cMI1NP3IfZRp2BPPxVt4En/U+r\nrSZz4v5U/MPsiZ23qs8yIjmQrlx7hxvXDedjs96mIlWl4WRSVkmWUkopfzZRh71hH+FFKLUDjIF3\n17XivZO5cuxy7P8Pxj6ttZ7/Wf/pWCgFAKXU97XWL5Vjl2OXYz9aKd2HUkop5T4plUIppZRynxwn\npfBvy7HLscuxH70cm5hCKaWUcjzkOFkKpZRSyjGQR64UlFKfUEq9rZS6opT6zSMe66RS6ptKqTeV\nUm8opX5D3p9RSv2BUuqy/Ns9wmuwlVI/UEp9TX4/o5T6jsz/vyiVQ+KOZOyOUuoLSqlLSqm3lFIf\nelhzV0r9XVnzi0qp31VK+Uc1d6XUf1BKbSulLt7z3k+cpzLyL+QaXlNKvXgEY/8jWfPXlFJfUkp1\n7vnsszL220qpj/9Zxn6v5JEqBWU6kfw28EngKeBXlFJPHeGQCfD3tNZPAR8E/paM95vAy1rrc8DL\n8vtRyW8Ab93z+z8E/pnW+ixwAPz6EY79W8D/1FqfB56T6zjyuSulVoG/DbyktX4aQyn5GY5u7v8J\n+MSPvffT5vlJ4Jz8/E3gd45g7D8AntZaPwv8CPgsgOy9zwAX5G/+lcq78zxK0Vo/sh/gQ8DX7/n9\ns8BnH+L4XwF+AXgbWJb3loG3j2i8E5gN+ReBr2EqhXcB5yetx3s8dht4B4kj3fP+kc8dWAVuAjMY\naP3XgI8f5dyBNeDiz5on8G+AX/lJ/++9GvvHPvsrwOfl9X37Hfg68KGjuP/v5udRuw/5Zsnllrx3\n5KKUWgNeAL4DLGqtN+Wju8DiEQ37z4G/D1JkALNAT2ud1xAf5fzPADvAfxT35d8ppeo8hLlrrW8D\n/xi4AWwCfeAVHt7c4afP82Hvwb8B/I9HNPYDyaNWCo9ElFIN4PeBv6O1Htz7mTYq+z1PySilPgVs\na61fea+/+wHFAV4Efkdr/QIGVn6fq3CEc+8Cv4RRTCtAnf/bxH5oclTz/FmilPocxoX9/MMe+93I\no1YKt4GT9/x+Qt47MlFKuRiF8Hmt9Rfl7S2l1LJ8vgxsH8HQPwd8Wim1AfwexoX4LaCj8vK3o53/\nLeCW1vo78vsXMEriYcz9LwPvaK13tKk3/iJmPR7W3OGnz/Oh7EGl1F8HPgX8qiilhzb2u5VHrRS+\nB5yTKLSHCbp89agGU6aL578H3tJa/9N7Pvoq8Gvy+tcwsYb3VLTWn9Van9Bar2Hm+Q2t9a8C3wR+\n+SjHlvHvAjeVUk/IW38JeJOHMHeM2/BBpVRN7kE+9kOZu8hPm+dXgb8mWYgPAv173Iz3RJRSn8C4\njZ/WWgc/dk2fUUpVlFJnMMHO776XY/8/yaMOagC/iInIXgU+d8RjfQRjNr4G/FB+fhHj278MXAb+\nEJg54uv4GPA1eb2O2QhXgP8GVI5w3OeB78v8vwx0H9bcgX8AXAIuAv8ZqBzV3IHfxcQuYoyF9Os/\nbZ6YYO9vy/57HZMhea/HvoKJHeR77l/f8/8/J2O/DXzyKPfdg/6UiMZSSinlPnnU7kMppZRyzKRU\nCqWUUsp9UiqFUkop5T4plUIppZRyn5RKoZRSSrlPSqVQSiml3CelUiillFLuk1IplFJKKffJ/wGA\nP7E6eb8E6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd484624c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_n_images = 25\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')\n",
    "pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the Data\n",
    "Since the project's main focus is on building the GANs, we'll preprocess the data for you.  The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images.  The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.\n",
    "\n",
    "The MNIST images are black and white images with a single [color channel](https://en.wikipedia.org/wiki/Channel_(digital_image%29) while the CelebA images have [3 color channels (RGB color channel)](https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images).\n",
    "## Build the Neural Network\n",
    "You'll build the components necessary to build a GANs by implementing the following functions below:\n",
    "- `model_inputs`\n",
    "- `discriminator`\n",
    "- `generator`\n",
    "- `model_loss`\n",
    "- `model_opt`\n",
    "- `train`\n",
    "\n",
    "### Check the Version of TensorFlow and Access to GPU\n",
    "This will check to make sure you have the correct version of TensorFlow and access to a GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow Version: 1.2.1\n",
      "Default GPU Device: /gpu:0\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "from distutils.version import LooseVersion\n",
    "import warnings\n",
    "import tensorflow as tf\n",
    "\n",
    "# Check TensorFlow Version\n",
    "assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)\n",
    "print('TensorFlow Version: {}'.format(tf.__version__))\n",
    "\n",
    "# Check for a GPU\n",
    "if not tf.test.gpu_device_name():\n",
    "    warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
    "else:\n",
    "    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input\n",
    "Implement the `model_inputs` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n",
    "- Real input images placeholder with rank 4 using `image_width`, `image_height`, and `image_channels`.\n",
    "- Z input placeholder with rank 2 using `z_dim`.\n",
    "- Learning rate placeholder with rank 0.\n",
    "\n",
    "Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR:tensorflow:==================================\n",
      "Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):\n",
      "<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>\n",
      "If you want to mark it as used call its \"mark_used()\" method.\n",
      "It was originally created here:\n",
      "['File \"/usr/local/lib/python3.5/runpy.py\", line 193, in _run_module_as_main\\n    \"__main__\", mod_spec)', 'File \"/usr/local/lib/python3.5/runpy.py\", line 85, in _run_code\\n    exec(code, run_globals)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in <module>\\n    app.launch_new_instance()', 'File \"/usr/local/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\\n    app.start()', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 477, in start\\n    ioloop.IOLoop.instance().start()', 'File \"/usr/local/lib/python3.5/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\\n    super(ZMQIOLoop, self).start()', 'File \"/usr/local/lib/python3.5/site-packages/tornado/ioloop.py\", line 888, in start\\n    handler_func(fd_obj, events)', 'File \"/usr/local/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n    return fn(*args, **kwargs)', 'File \"/usr/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\\n    self._handle_recv()', 'File \"/usr/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\\n    self._run_callback(callback, msg)', 'File \"/usr/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\\n    callback(*args, **kwargs)', 'File \"/usr/local/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n    return fn(*args, **kwargs)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\\n    return self.dispatch_shell(stream, msg)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\\n    handler(stream, idents, msg)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\\n    user_expressions, allow_stdin)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File \"/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2698, in run_cell\\n    interactivity=interactivity, compiler=compiler, result=result)', 'File \"/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2808, in run_ast_nodes\\n    if self.run_code(code, result):', 'File \"/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2862, in run_code\\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File \"<ipython-input-6-60e8f0e9d266>\", line 22, in <module>\\n    tests.test_model_inputs(model_inputs)', 'File \"/output/problem_unittests.py\", line 12, in func_wrapper\\n    result = func(*args)', 'File \"/output/problem_unittests.py\", line 68, in test_model_inputs\\n    _check_input(learn_rate, [], \\'Learning Rate\\')', 'File \"/output/problem_unittests.py\", line 34, in _check_input\\n    _assert_tensor_shape(tensor, shape, \\'Real Input\\')', 'File \"/output/problem_unittests.py\", line 20, in _assert_tensor_shape\\n    assert tf.assert_rank(tensor, len(shape), message=\\'{} has wrong rank\\'.format(display_name))', 'File \"/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py\", line 617, in assert_rank\\n    dynamic_condition, data, summarize)', 'File \"/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py\", line 571, in _assert_rank_condition\\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File \"/usr/local/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py\", line 170, in wrapped\\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File \"/usr/local/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py\", line 139, in _add_should_use_warning\\n    wrapped = TFShouldUseWarningWrapper(x)', 'File \"/usr/local/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py\", line 96, in __init__\\n    stack = [s.strip() for s in traceback.format_stack()]']\n",
      "==================================\n",
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "import problem_unittests as tests\n",
    "\n",
    "def model_inputs(image_width, image_height, image_channels, z_dim):\n",
    "    \"\"\"\n",
    "    Create the model inputs\n",
    "    :param image_width: The input image width\n",
    "    :param image_height: The input image height\n",
    "    :param image_channels: The number of image channels\n",
    "    :param z_dim: The dimension of Z\n",
    "    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)\n",
    "    \"\"\"\n",
    "    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='inputs_real')\n",
    "    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='inputs_z')\n",
    "    learn_r = tf.placeholder(tf.float32, name='lr')\n",
    "\n",
    "    return inputs_real, inputs_z, learn_r\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_model_inputs(model_inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alpha = 0.1 # for leaky relu\n",
    "print_every = 10\n",
    "show_every = 100 #change to 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discriminator\n",
    "Implement `discriminator` to create a discriminator neural network that discriminates on `images`.  This function should be able to reuse the variables in the neural network.  Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of \"discriminator\" to allow the variables to be reused.  The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def discriminator(images, reuse=False):\n",
    "    \"\"\"\n",
    "    Create the discriminator network\n",
    "    :param images: Tensor of input image(s)\n",
    "    :param reuse: Boolean if the weights should be reused\n",
    "    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)\n",
    "    \"\"\"\n",
    "    with tf.variable_scope('discriminator', reuse=reuse):\n",
    "        \n",
    "        # Input layer is 28x28x3\n",
    "        x1 = tf.layers.conv2d(images, 64, 5, strides=1, padding='valid')\n",
    "        relu1 = tf.maximum(alpha * x1, x1)\n",
    "        #print(relu1)\n",
    "        # 24x24x64\n",
    "        \n",
    "        x2 = tf.layers.conv2d(relu1, 128, 5, strides=1, padding='valid')\n",
    "        bn2 = tf.layers.batch_normalization(x2, training=True)\n",
    "        relu2 = tf.maximum(alpha * bn2, bn2)\n",
    "        #print(relu2)\n",
    "        # 20x20x128\n",
    "        \n",
    "        x3 = tf.layers.conv2d(relu2, 256, 5, strides=1, padding='valid')\n",
    "        bn3 = tf.layers.batch_normalization(x3, training=True)\n",
    "        relu3 = tf.maximum(alpha * bn3, bn3)\n",
    "        #print(relu3)\n",
    "        # 16x16x256\n",
    "        \n",
    "        x4 = tf.layers.conv2d(relu3, 512, 5, strides=2, padding='valid')\n",
    "        bn4 = tf.layers.batch_normalization(x4, training=True)\n",
    "        relu4 = tf.maximum(alpha * bn4, bn4)\n",
    "        #print(relu4)\n",
    "        # 6x6x512\n",
    "\n",
    "        # Flatten it\n",
    "        flat = tf.reshape(relu4, (-1, 6*6*512))\n",
    "        logits = tf.layers.dense(flat, 1)\n",
    "        #print(flat)\n",
    "        out = tf.sigmoid(logits)\n",
    "       \n",
    "        \n",
    "\n",
    "    return out, logits\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_discriminator(discriminator, tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generator\n",
    "Implement `generator` to generate an image using `z`. This function should be able to reuse the variables in the neural network.  Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of \"generator\" to allow the variables to be reused. The function should return the generated 28 x 28 x `out_channel_dim` images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def generator(z, out_channel_dim, is_train=True):\n",
    "    \"\"\"\n",
    "    Create the generator network\n",
    "    :param z: Input z\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :param is_train: Boolean if generator is being used for training\n",
    "    :return: The tensor output of the generator\n",
    "    \"\"\"\n",
    " \n",
    "    with tf.variable_scope('generator', reuse=not is_train):\n",
    "      \n",
    "        # First fully connected layer\n",
    "        fc = tf.layers.dense(z, 7*7*1024)\n",
    "        fc = tf.reshape(fc, (-1, 7, 7, 1024))\n",
    "        fc = tf.maximum(alpha * fc, fc)\n",
    "        #print(fc)\n",
    "        # 7x7x1024 now\n",
    "        \n",
    "        # Reshape it to start the convolutional stack\n",
    "        #x1 = tf.layers.batch_normalization(x1, training=is_train)\n",
    "        \n",
    "        \n",
    "        \n",
    "        # Deconv1\n",
    "        dconv1 = tf.layers.conv2d_transpose(fc, 512, 3, strides=2, padding='same')\n",
    "        dconv1 = tf.layers.batch_normalization(dconv1, training=is_train)\n",
    "        dconv1 = tf.maximum(alpha * dconv1, dconv1)\n",
    "        #print(dconv1)\n",
    "        # 14x14x512 now\n",
    "        \n",
    "        dconv2 = tf.layers.conv2d_transpose(dconv1, 256, 3, strides=2, padding='same')\n",
    "        dconv2 = tf.layers.batch_normalization(dconv2, training=is_train)\n",
    "        dconv2 = tf.maximum(alpha * dconv2, dconv2)\n",
    "        #print(dconv2)\n",
    "        # 28x28x256 now\n",
    "        \n",
    "        dconv3 = tf.layers.conv2d_transpose(dconv2, 128, 5, strides=1, padding='same')\n",
    "        dconv3 = tf.layers.batch_normalization(dconv3, training=is_train)\n",
    "        dconv3 = tf.maximum(alpha * dconv3, dconv3)\n",
    "        #print(dconv3)\n",
    "        # 28x28x128 now\n",
    "        \n",
    "        # Output layer\n",
    "        logits = tf.layers.conv2d_transpose(dconv3, out_channel_dim, 5, strides=1, padding='same')\n",
    "        #print(logits)\n",
    "        # 28x28x(channels) now\n",
    "        \n",
    "        out = tf.tanh(logits)\n",
    "    \n",
    "    return out\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_generator(generator, tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loss\n",
    "Implement `model_loss` to build the GANs for training and calculate the loss.  The function should return a tuple of (discriminator loss, generator loss).  Use the following functions you implemented:\n",
    "- `discriminator(images, reuse=False)`\n",
    "- `generator(z, out_channel_dim, is_train=True)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def model_loss(input_real, input_z, out_channel_dim):\n",
    "    \"\"\"\n",
    "    Get the loss for the discriminator and generator\n",
    "    :param input_real: Images from the real dataset\n",
    "    :param input_z: Z input\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :return: A tuple of (discriminator loss, generator loss)\n",
    "    \"\"\"\n",
    "    g_model = generator(input_z, out_channel_dim, True)\n",
    "    d_model_real, d_logits_real = discriminator(input_real)\n",
    "    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)\n",
    "\n",
    "    d_loss_real = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))\n",
    "    d_loss_fake = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))\n",
    "    g_loss = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))\n",
    "\n",
    "    d_loss = d_loss_real + d_loss_fake\n",
    "    \n",
    "    return d_loss, g_loss\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_model_loss(model_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization\n",
    "Implement `model_opt` to create the optimization operations for the GANs. Use [`tf.trainable_variables`](https://www.tensorflow.org/api_docs/python/tf/trainable_variables) to get all the trainable variables.  Filter the variables with names that are in the discriminator and generator scope names.  The function should return a tuple of (discriminator training operation, generator training operation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def model_opt(d_loss, g_loss, learning_rate, beta1):\n",
    "    \"\"\"\n",
    "    Get optimization operations\n",
    "    :param d_loss: Discriminator loss Tensor\n",
    "    :param g_loss: Generator loss Tensor\n",
    "    :param learning_rate: Learning Rate Placeholder\n",
    "    :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
    "    :return: A tuple of (discriminator training operation, generator training operation)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Get weights and bias to update\n",
    "    t_vars = tf.trainable_variables()\n",
    "    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n",
    "    g_vars = [var for var in t_vars if var.name.startswith('generator')]\n",
    "\n",
    "    # Optimize\n",
    "    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
    "        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)\n",
    "        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)\n",
    "\n",
    "    \n",
    "    return d_train_opt, g_train_opt\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_model_opt(model_opt, tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network Training\n",
    "### Show Output\n",
    "Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "\n",
    "def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):\n",
    "    \"\"\"\n",
    "    Show example output for the generator\n",
    "    :param sess: TensorFlow session\n",
    "    :param n_images: Number of Images to display\n",
    "    :param input_z: Input Z Tensor\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :param image_mode: The mode to use for images (\"RGB\" or \"L\")\n",
    "    \"\"\"\n",
    "    cmap = None if image_mode == 'RGB' else 'gray'\n",
    "    z_dim = input_z.get_shape().as_list()[-1]\n",
    "    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])\n",
    "\n",
    "    samples = sess.run(\n",
    "        generator(input_z, out_channel_dim, False),\n",
    "        feed_dict={input_z: example_z})\n",
    "\n",
    "    images_grid = helper.images_square_grid(samples, image_mode)\n",
    "    pyplot.imshow(images_grid, cmap=cmap)\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train\n",
    "Implement `train` to build and train the GANs.  Use the following functions you implemented:\n",
    "- `model_inputs(image_width, image_height, image_channels, z_dim)`\n",
    "- `model_loss(input_real, input_z, out_channel_dim)`\n",
    "- `model_opt(d_loss, g_loss, learning_rate, beta1)`\n",
    "\n",
    "Use the `show_generator_output` to show `generator` output while you train. Running `show_generator_output` for every batch will drastically increase training time and increase the size of the notebook.  It's recommended to print the `generator` output every 100 batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.core.debugger import Tracer\n",
    "\n",
    "def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):\n",
    "    \"\"\"\n",
    "    Train the GAN\n",
    "    :param epoch_count: Number of epochs\n",
    "    :param batch_size: Batch Size\n",
    "    :param z_dim: Z dimension\n",
    "    :param learning_rate: Learning Rate\n",
    "    :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
    "    :param get_batches: Function to get batches\n",
    "    :param data_shape: Shape of the data\n",
    "    :param data_image_mode: The image mode to use for images (\"RGB\" or \"L\")\n",
    "    \"\"\"\n",
    "    \n",
    "    # create input placeholders\n",
    "    #print(data_shape)\n",
    "    image_shape = data_shape[1:]\n",
    "    #print(image_shape)\n",
    "    out_channel_dim = image_shape[-1]\n",
    "    #print(out_channel_dim)\n",
    "    \n",
    "    inputs_real, inputs_z, lr_rate = model_inputs(*image_shape, z_dim)\n",
    "    #print(inputs_real)\n",
    "    d_loss, g_loss = model_loss(inputs_real, inputs_z, out_channel_dim)\n",
    "    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr_rate, beta1)\n",
    "    #print(inputs_real)\n",
    "    \n",
    "    #t_vars = tf.trainable_variables()\n",
    "    #g_vars = [var for var in t_vars if var.name.startswith('generator')]\n",
    "    #saver = tf.train.Saver(var_list=g_vars)\n",
    "    \n",
    "    def scale(x, target_range=(-1,1)):\n",
    "        min, max = target_range\n",
    "        \n",
    "        # One liner, but too expensive \n",
    "        #x = (x - x.min()) * (max - min) / (x.max() - x.min()) + min\n",
    "        \n",
    "        # First scale to [0,1]\n",
    "        x = ((x - x.min())/(x.max() - x.min()))\n",
    "    \n",
    "        # Then scale to target_range\n",
    "        min, max = target_range\n",
    "        x = x * (max - min) + min\n",
    "        return x\n",
    "    \n",
    "    \n",
    "    steps = 0\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        for epoch_i in range(epoch_count):\n",
    "            for batch_images in get_batches(batch_size):\n",
    "                steps += 1\n",
    "                #print(\"Step {} ...\".format(steps))\n",
    "                \n",
    "                #1 Scale real images to [-1,1] range since that is also the generator output scale\n",
    "                batch_images = scale(batch_images, target_range=(-1,1))\n",
    "                \n",
    "                \n",
    "                #3 Sample random noise for generator\n",
    "                batch_z = np.random.uniform(-1,1, size=(batch_size, z_dim))\n",
    "                \n",
    "                \n",
    "                #3 Run optimizers\n",
    "                #print(batch_images.shape)\n",
    "                            \n",
    "                \n",
    "                # Optimize discriminator\n",
    "                _ = sess.run(d_train_opt, feed_dict={inputs_real: batch_images,\n",
    "                                                     inputs_z: batch_z,\n",
    "                                                     lr_rate: learning_rate})\n",
    "                \n",
    "                \n",
    "                \n",
    "                \n",
    "                # Optimize generator (Run twice to avoid faster convergence of D)\n",
    "                _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z,\n",
    "                                                     inputs_real: batch_images,\n",
    "                                                     lr_rate: learning_rate})\n",
    "                _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z,\n",
    "                                                     inputs_real: batch_images,\n",
    "                                                     lr_rate: learning_rate})\n",
    "                \n",
    "                \n",
    "                #Tracer()() #this one triggers the debugger\n",
    "                if steps % print_every == 0:\n",
    "                    \n",
    "                    d_train_loss = d_loss.eval({inputs_real: batch_images,\n",
    "                                                inputs_z: batch_z,\n",
    "                                                lr_rate: learning_rate})\n",
    "                \n",
    "                    # Optimize generator\n",
    "                    g_train_loss = g_loss.eval({inputs_z: batch_z,\n",
    "                                                inputs_real: batch_images,\n",
    "                                                lr_rate: learning_rate})\n",
    "                    \n",
    "                    #Tracer()() #this one triggers the debugger\n",
    "                    print(\"Epoch {}/{}-Step {}...\".format(epoch_i+1, epoch_count,steps),\n",
    "                          \"Discriminator Loss: {:.4f}...\".format(d_train_loss),\n",
    "                          \"Generator Loss: {:.4f}\".format(g_train_loss))\n",
    "                    \n",
    "                if steps % show_every == 0:\n",
    "                        show_generator_output(sess, 9, inputs_z, out_channel_dim, data_image_mode)\n",
    "                \n",
    "                \n",
    "                \n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MNIST\n",
    "Test your GANs architecture on MNIST.  After 2 epochs, the GANs should be able to generate images that look like handwritten digits.  Make sure the loss of the generator is lower than the loss of the discriminator or close to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 10... Discriminator Loss: 0.9267... Generator Loss: 3.0303\n",
      "Epoch 1/2-Step 20... Discriminator Loss: 0.0128... Generator Loss: 5.2754\n",
      "Epoch 1/2-Step 30... Discriminator Loss: 1.0608... Generator Loss: 2.6022\n",
      "Epoch 1/2-Step 40... Discriminator Loss: 1.6446... Generator Loss: 1.1610\n",
      "Epoch 1/2-Step 50... Discriminator Loss: 1.0090... Generator Loss: 1.2422\n",
      "Epoch 1/2-Step 60... Discriminator Loss: 1.1629... Generator Loss: 1.4662\n",
      "Epoch 1/2-Step 70... Discriminator Loss: 1.5492... Generator Loss: 1.4174\n",
      "Epoch 1/2-Step 80... Discriminator Loss: 1.3179... Generator Loss: 0.9317\n",
      "Epoch 1/2-Step 90... Discriminator Loss: 1.7052... Generator Loss: 1.2884\n",
      "Epoch 1/2-Step 100... Discriminator Loss: 2.0016... Generator Loss: 0.2221\n"
     ]
    },
    {
     "data": {
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Cei/+6Z/+CWhX9fvxVFTDrvLA69P4qkuyboNZJgs23sWLFxc0Cxyq8l/wxm57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\n8MpCswEOOeSQom1zVE3SDJx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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e886f6c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 110... Discriminator Loss: 1.0928... Generator Loss: 3.6356\n",
      "Epoch 1/2-Step 120... Discriminator Loss: 1.8290... Generator Loss: 0.5105\n",
      "Epoch 1/2-Step 130... Discriminator Loss: 1.8758... Generator Loss: 0.6536\n",
      "Epoch 1/2-Step 140... Discriminator Loss: 0.9872... Generator Loss: 1.5308\n",
      "Epoch 1/2-Step 150... Discriminator Loss: 0.8386... Generator Loss: 3.2762\n",
      "Epoch 1/2-Step 160... Discriminator Loss: 0.4610... Generator Loss: 1.8612\n",
      "Epoch 1/2-Step 170... Discriminator Loss: 0.8992... Generator Loss: 3.7001\n",
      "Epoch 1/2-Step 180... Discriminator Loss: 0.3389... Generator Loss: 3.2347\n",
      "Epoch 1/2-Step 190... Discriminator Loss: 1.2121... Generator Loss: 0.9055\n",
      "Epoch 1/2-Step 200... Discriminator Loss: 3.2045... Generator Loss: 0.0909\n"
     ]
    },
    {
     "data": {
      "image/png": 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pjH7IIYcAcM4556S66dOn92tv2dx5P/lJxXDz+OOPT2ULv90qJrm0qnSzcR/s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\nylshlzByMGhVHH/xxReB6r1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e7b097470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 210... Discriminator Loss: 1.3834... Generator Loss: 3.7705\n",
      "Epoch 1/2-Step 220... Discriminator Loss: 0.8444... Generator Loss: 1.6745\n",
      "Epoch 1/2-Step 230... Discriminator Loss: 0.3921... Generator Loss: 1.8625\n",
      "Epoch 1/2-Step 240... Discriminator Loss: 0.6757... Generator Loss: 1.7159\n",
      "Epoch 1/2-Step 250... Discriminator Loss: 0.8184... Generator Loss: 1.3769\n",
      "Epoch 1/2-Step 260... Discriminator Loss: 0.3848... Generator Loss: 2.3387\n",
      "Epoch 1/2-Step 270... Discriminator Loss: 0.4555... Generator Loss: 2.0942\n",
      "Epoch 1/2-Step 280... Discriminator Loss: 2.1406... Generator Loss: 0.5748\n",
      "Epoch 1/2-Step 290... Discriminator Loss: 0.2285... Generator Loss: 2.4981\n",
      "Epoch 1/2-Step 300... Discriminator Loss: 1.9429... Generator Loss: 0.4845\n"
     ]
    },
    {
     "data": {
      "image/png": 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cx6mblsbjDx48OKjnlHp/tWr/GngD8JGPfAQoT2V9zz33xHYr1mDterF62UGp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\n3cY990RkLeCbwNYhhA8Agyn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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e8d36d2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 310... Discriminator Loss: 1.4466... Generator Loss: 1.9327\n",
      "Epoch 1/2-Step 320... Discriminator Loss: 0.9744... Generator Loss: 0.8699\n",
      "Epoch 1/2-Step 330... Discriminator Loss: 1.4074... Generator Loss: 1.3701\n",
      "Epoch 1/2-Step 340... Discriminator Loss: 1.0074... Generator Loss: 1.0729\n",
      "Epoch 1/2-Step 350... Discriminator Loss: 0.4718... Generator Loss: 2.2176\n",
      "Epoch 1/2-Step 360... Discriminator Loss: 1.0672... Generator Loss: 0.8503\n",
      "Epoch 1/2-Step 370... Discriminator Loss: 1.8639... Generator Loss: 0.4736\n",
      "Epoch 1/2-Step 380... Discriminator Loss: 1.0121... Generator Loss: 0.9073\n",
      "Epoch 1/2-Step 390... Discriminator Loss: 0.3627... Generator Loss: 4.4332\n",
      "Epoch 1/2-Step 400... Discriminator Loss: 2.8513... Generator Loss: 0.1151\n"
     ]
    },
    {
     "data": {
      "image/png": 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8Nlx9/vnn3Y6pVVEZryzcZhutMiOf12p77DtjhjwxBAOstdZa3b5HGyEl8Kpa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\nG0YHYje+YXQg/x/BbNlvYuKhKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e92c2f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 410... Discriminator Loss: 1.6384... Generator Loss: 0.4555\n",
      "Epoch 1/2-Step 420... Discriminator Loss: 1.0845... Generator Loss: 0.9582\n",
      "Epoch 1/2-Step 430... Discriminator Loss: 2.6283... Generator Loss: 0.2061\n",
      "Epoch 1/2-Step 440... Discriminator Loss: 2.0905... Generator Loss: 0.2952\n",
      "Epoch 1/2-Step 450... Discriminator Loss: 3.3657... Generator Loss: 0.1126\n",
      "Epoch 1/2-Step 460... Discriminator Loss: 0.8958... Generator Loss: 0.8718\n",
      "Epoch 1/2-Step 470... Discriminator Loss: 3.1746... Generator Loss: 0.0618\n",
      "Epoch 1/2-Step 480... Discriminator Loss: 1.5734... Generator Loss: 0.5139\n",
      "Epoch 1/2-Step 490... Discriminator Loss: 0.7358... Generator Loss: 2.6876\n",
      "Epoch 1/2-Step 500... Discriminator Loss: 1.1104... Generator Loss: 2.3262\n"
     ]
    },
    {
     "data": {
      "image/png": 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6k2OrVRcliT0RZ3YpSs74zrmVgGOBDb33o4Du5Orr/xE4x3u/OvAR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e81125048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 510... Discriminator Loss: 1.3891... Generator Loss: 0.5434\n",
      "Epoch 1/2-Step 520... Discriminator Loss: 1.4872... Generator Loss: 0.8000\n",
      "Epoch 1/2-Step 530... Discriminator Loss: 0.7497... Generator Loss: 1.9807\n",
      "Epoch 1/2-Step 540... Discriminator Loss: 1.5252... Generator Loss: 1.0185\n",
      "Epoch 1/2-Step 550... Discriminator Loss: 1.4869... Generator Loss: 0.4923\n",
      "Epoch 1/2-Step 560... Discriminator Loss: 0.9228... Generator Loss: 2.2592\n",
      "Epoch 1/2-Step 570... Discriminator Loss: 0.8381... Generator Loss: 1.1396\n",
      "Epoch 1/2-Step 580... Discriminator Loss: 0.5408... Generator Loss: 1.6971\n",
      "Epoch 1/2-Step 590... Discriminator Loss: 3.1695... Generator Loss: 4.3885\n",
      "Epoch 1/2-Step 600... Discriminator Loss: 1.1400... Generator Loss: 1.2354\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e79abfd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 610... Discriminator Loss: 1.2808... Generator Loss: 0.7663\n",
      "Epoch 1/2-Step 620... Discriminator Loss: 1.1544... Generator Loss: 1.6539\n",
      "Epoch 1/2-Step 630... Discriminator Loss: 2.0728... Generator Loss: 0.2722\n",
      "Epoch 1/2-Step 640... Discriminator Loss: 2.4404... Generator Loss: 2.0143\n",
      "Epoch 1/2-Step 650... Discriminator Loss: 1.0015... Generator Loss: 1.1314\n",
      "Epoch 1/2-Step 660... Discriminator Loss: 2.2646... Generator Loss: 0.2215\n",
      "Epoch 1/2-Step 670... Discriminator Loss: 0.8644... Generator Loss: 1.3619\n",
      "Epoch 1/2-Step 680... Discriminator Loss: 2.0751... Generator Loss: 0.4561\n",
      "Epoch 1/2-Step 690... Discriminator Loss: 1.0712... Generator Loss: 1.1022\n",
      "Epoch 1/2-Step 700... Discriminator Loss: 0.8807... Generator Loss: 3.1525\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e8111a0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 710... Discriminator Loss: 1.4505... Generator Loss: 1.3298\n",
      "Epoch 1/2-Step 720... Discriminator Loss: 1.6401... Generator Loss: 1.3325\n",
      "Epoch 1/2-Step 730... Discriminator Loss: 2.1060... Generator Loss: 3.4476\n",
      "Epoch 1/2-Step 740... Discriminator Loss: 1.3121... Generator Loss: 0.8670\n",
      "Epoch 1/2-Step 750... Discriminator Loss: 1.2358... Generator Loss: 2.5686\n",
      "Epoch 1/2-Step 760... Discriminator Loss: 2.6190... Generator Loss: 0.1099\n",
      "Epoch 1/2-Step 770... Discriminator Loss: 1.5482... Generator Loss: 2.8853\n",
      "Epoch 1/2-Step 780... Discriminator Loss: 2.5531... Generator Loss: 0.1868\n",
      "Epoch 1/2-Step 790... Discriminator Loss: 1.1788... Generator Loss: 0.8387\n",
      "Epoch 1/2-Step 800... Discriminator Loss: 0.6670... Generator Loss: 1.3919\n"
     ]
    },
    {
     "data": {
      "image/png": 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ldiSiouvxabagoWJU4tWqn6uqqPqGYSx62INvGC1IXVX9Tp065Vn1GyWnfL0p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\nCG1tFK0l0k+99OjItOW6H7L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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e796fe320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 810... Discriminator Loss: 1.8112... Generator Loss: 0.5763\n",
      "Epoch 1/2-Step 820... Discriminator Loss: 1.4400... Generator Loss: 0.8454\n",
      "Epoch 1/2-Step 830... Discriminator Loss: 2.1204... Generator Loss: 0.1975\n",
      "Epoch 1/2-Step 840... Discriminator Loss: 0.9309... Generator Loss: 0.8856\n",
      "Epoch 1/2-Step 850... Discriminator Loss: 2.2666... Generator Loss: 0.2093\n",
      "Epoch 1/2-Step 860... Discriminator Loss: 2.7204... Generator Loss: 0.2036\n",
      "Epoch 1/2-Step 870... Discriminator Loss: 1.4407... Generator Loss: 0.7119\n",
      "Epoch 1/2-Step 880... Discriminator Loss: 1.8585... Generator Loss: 0.3937\n",
      "Epoch 1/2-Step 890... Discriminator Loss: 1.1191... Generator Loss: 1.4012\n",
      "Epoch 1/2-Step 900... Discriminator Loss: 1.9723... Generator Loss: 2.9803\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e79525cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 910... Discriminator Loss: 1.3118... Generator Loss: 2.4486\n",
      "Epoch 1/2-Step 920... Discriminator Loss: 1.2882... Generator Loss: 0.5410\n",
      "Epoch 1/2-Step 930... Discriminator Loss: 3.1985... Generator Loss: 0.1271\n",
      "Epoch 1/2-Step 940... Discriminator Loss: 2.2658... Generator Loss: 0.2157\n",
      "Epoch 1/2-Step 950... Discriminator Loss: 1.4683... Generator Loss: 0.6122\n",
      "Epoch 1/2-Step 960... Discriminator Loss: 1.3805... Generator Loss: 0.5894\n",
      "Epoch 1/2-Step 970... Discriminator Loss: 0.9084... Generator Loss: 1.3491\n",
      "Epoch 1/2-Step 980... Discriminator Loss: 1.1829... Generator Loss: 0.8846\n",
      "Epoch 1/2-Step 990... Discriminator Loss: 2.6936... Generator Loss: 0.1252\n",
      "Epoch 1/2-Step 1000... Discriminator Loss: 1.0920... Generator Loss: 3.3635\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e791edcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1010... Discriminator Loss: 0.4717... Generator Loss: 2.0444\n",
      "Epoch 1/2-Step 1020... Discriminator Loss: 2.2474... Generator Loss: 3.6244\n",
      "Epoch 1/2-Step 1030... Discriminator Loss: 1.1826... Generator Loss: 1.1267\n",
      "Epoch 1/2-Step 1040... Discriminator Loss: 0.8570... Generator Loss: 1.7126\n",
      "Epoch 1/2-Step 1050... Discriminator Loss: 1.2136... Generator Loss: 0.6905\n",
      "Epoch 1/2-Step 1060... Discriminator Loss: 1.0117... Generator Loss: 0.8767\n",
      "Epoch 1/2-Step 1070... Discriminator Loss: 1.6421... Generator Loss: 0.4355\n",
      "Epoch 1/2-Step 1080... Discriminator Loss: 3.1265... Generator Loss: 0.1189\n",
      "Epoch 1/2-Step 1090... Discriminator Loss: 1.5463... Generator Loss: 0.5217\n",
      "Epoch 1/2-Step 1100... Discriminator Loss: 1.9496... Generator Loss: 0.3557\n"
     ]
    },
    {
     "data": {
      "image/png": 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S98oujZbp5aTO5SBFM/USRdT2Dz/8MMh0nQAJ9tLX5OCDDwaK/RcaWWeh0qWQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e79666710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1110... Discriminator Loss: 1.0777... Generator Loss: 0.8982\n",
      "Epoch 1/2-Step 1120... Discriminator Loss: 0.8473... Generator Loss: 2.6787\n",
      "Epoch 1/2-Step 1130... Discriminator Loss: 2.1421... Generator Loss: 0.4104\n",
      "Epoch 1/2-Step 1140... Discriminator Loss: 0.5819... Generator Loss: 1.3670\n",
      "Epoch 1/2-Step 1150... Discriminator Loss: 0.4012... Generator Loss: 3.8247\n",
      "Epoch 1/2-Step 1160... Discriminator Loss: 1.8693... Generator Loss: 0.3963\n",
      "Epoch 1/2-Step 1170... Discriminator Loss: 1.1938... Generator Loss: 0.7962\n",
      "Epoch 1/2-Step 1180... Discriminator Loss: 0.6501... Generator Loss: 1.2007\n",
      "Epoch 1/2-Step 1190... Discriminator Loss: 2.0899... Generator Loss: 0.3304\n",
      "Epoch 1/2-Step 1200... Discriminator Loss: 1.8512... Generator Loss: 0.6061\n"
     ]
    },
    {
     "data": {
      "image/png": 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fLYeRT9ajIeOGqmPRn332WSBa8UWfr2Q+uuKKK4JM1um14TBbPH8xSJ567V5b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\nKXgfWiXT5bglCWOl+kenjJY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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e7928ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1210... Discriminator Loss: 2.4600... Generator Loss: 0.2065\n",
      "Epoch 1/2-Step 1220... Discriminator Loss: 1.1053... Generator Loss: 1.4613\n",
      "Epoch 1/2-Step 1230... Discriminator Loss: 3.1921... Generator Loss: 0.1285\n",
      "Epoch 1/2-Step 1240... Discriminator Loss: 2.4057... Generator Loss: 0.2731\n",
      "Epoch 1/2-Step 1250... Discriminator Loss: 0.9394... Generator Loss: 2.9006\n",
      "Epoch 1/2-Step 1260... Discriminator Loss: 0.8145... Generator Loss: 1.2383\n",
      "Epoch 1/2-Step 1270... Discriminator Loss: 0.3363... Generator Loss: 1.9907\n",
      "Epoch 1/2-Step 1280... Discriminator Loss: 2.1134... Generator Loss: 0.2527\n",
      "Epoch 1/2-Step 1290... Discriminator Loss: 4.0813... Generator Loss: 0.0666\n",
      "Epoch 1/2-Step 1300... Discriminator Loss: 0.5599... Generator Loss: 2.2968\n"
     ]
    },
    {
     "data": {
      "image/png": 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4FJhIRrVf5L0XvXQO0CvPVyQQdVSrV0K9qU/VIjYWxSAuyjoBpATU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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e7928e860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1310... Discriminator Loss: 2.8760... Generator Loss: 0.2637\n",
      "Epoch 1/2-Step 1320... Discriminator Loss: 1.3657... Generator Loss: 1.6037\n",
      "Epoch 1/2-Step 1330... Discriminator Loss: 1.3713... Generator Loss: 0.7570\n",
      "Epoch 1/2-Step 1340... Discriminator Loss: 2.7794... Generator Loss: 0.2296\n",
      "Epoch 1/2-Step 1350... Discriminator Loss: 1.6856... Generator Loss: 0.4993\n",
      "Epoch 1/2-Step 1360... Discriminator Loss: 0.5796... Generator Loss: 1.5256\n",
      "Epoch 1/2-Step 1370... Discriminator Loss: 1.6392... Generator Loss: 1.6606\n",
      "Epoch 1/2-Step 1380... Discriminator Loss: 0.8350... Generator Loss: 1.6210\n",
      "Epoch 1/2-Step 1390... Discriminator Loss: 2.5282... Generator Loss: 0.2095\n",
      "Epoch 1/2-Step 1400... Discriminator Loss: 1.0859... Generator Loss: 1.1357\n"
     ]
    },
    {
     "data": {
      "image/png": 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JL/Dhhx829NhTpkwBCss9jV5m6Nj8tOoqiIqvd6PkPpJ7aPr06cydO9dUfcMw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\njif+42ndugAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e79546710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1410... Discriminator Loss: 1.1798... Generator Loss: 0.9080\n",
      "Epoch 1/2-Step 1420... Discriminator Loss: 1.3249... Generator Loss: 0.7105\n",
      "Epoch 1/2-Step 1430... Discriminator Loss: 0.8106... Generator Loss: 1.0409\n",
      "Epoch 1/2-Step 1440... Discriminator Loss: 3.8837... Generator Loss: 0.0626\n",
      "Epoch 1/2-Step 1450... Discriminator Loss: 0.9882... Generator Loss: 0.8990\n",
      "Epoch 1/2-Step 1460... Discriminator Loss: 2.3555... Generator Loss: 0.2031\n",
      "Epoch 1/2-Step 1470... Discriminator Loss: 1.1362... Generator Loss: 0.7481\n",
      "Epoch 1/2-Step 1480... Discriminator Loss: 3.1776... Generator Loss: 0.1075\n",
      "Epoch 1/2-Step 1490... Discriminator Loss: 1.3520... Generator Loss: 1.5787\n",
      "Epoch 1/2-Step 1500... Discriminator Loss: 1.0190... Generator Loss: 1.8918\n"
     ]
    },
    {
     "data": {
      "image/png": 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8EjWs1RBVVKukMVUrFmyiy3bryjWivjYi00w1+0mqOpbat4xNzPBY6jii6sfG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\nwiLEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e797f5da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1510... Discriminator Loss: 1.3130... Generator Loss: 3.2561\n",
      "Epoch 1/2-Step 1520... Discriminator Loss: 1.3796... Generator Loss: 0.5629\n",
      "Epoch 1/2-Step 1530... Discriminator Loss: 1.6151... Generator Loss: 0.6132\n",
      "Epoch 1/2-Step 1540... Discriminator Loss: 1.0203... Generator Loss: 1.5598\n",
      "Epoch 1/2-Step 1550... Discriminator Loss: 0.7105... Generator Loss: 1.3498\n",
      "Epoch 1/2-Step 1560... Discriminator Loss: 1.7961... Generator Loss: 0.4888\n",
      "Epoch 1/2-Step 1570... Discriminator Loss: 1.2594... Generator Loss: 2.2816\n",
      "Epoch 1/2-Step 1580... Discriminator Loss: 1.1150... Generator Loss: 0.7396\n",
      "Epoch 1/2-Step 1590... Discriminator Loss: 1.3450... Generator Loss: 0.7354\n",
      "Epoch 1/2-Step 1600... Discriminator Loss: 1.4369... Generator Loss: 0.6458\n"
     ]
    },
    {
     "data": {
      "image/png": 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PDu1mMAzLPZR2LOa5ZxhtiD34htGGZBqk45wLqndMJWmE+i/rsrqYpcTb65RK\nWVn1RTXT6nJM1dcqbVavRaJOl/IrkLYery5gWi9icyQprPTx9OtbLa+QOmVWv379ivbX+TjNgB6b\n+BjINUn7+moS3zDakEwlfkdHRzAQScCHlvz1kmz6F1Gyxeyyyy6hTySJNkxlZbASjUeX7dZpm0UL\nee2110KfaAGNXhuW+ZfsMFBIpglwzz33AMUSXzSTeo6tsxSDwhzpa/v222+HthxfS+dqxiRzkPU6\nfBrk3PW9I22Zn7Saq0l8w2hD7ME3jDYk07z6PXv29FK9RjKQaKNbzF1VvwqIqhMLmtCGOq0KCXqf\nog7qNfPYK4dWG6Udmy+tfupXBmnrPsnp36tXr9C3/vrrh7b0v/TSS6FP1H5t8NOqaMz4FAva0PMm\nrrg677vMm1ax9XGkrcchbT1/enu5scXmCgrzoedI2nps+nVIgmH0eOVaaYNX7JWgGuNd7D5IepaS\nAmlkuw6S0tdMtss9BIXcCfL/nDlzeOeddyyvvmEYXcnUuLd8+XLmz58PFH6htaSQX+NSv5zSH5N8\njVhW0sR+reWXWUurUtJfEOmiDVNaW3n++eeB4so+SRIptpQlbd0Xq/4SMwZVEwJb6juxOYp9R2+X\neyI2R1LDEIrnSM4nprklzUs1xM5X9yVtF7QGI3NQamzyfX3/y/UTDS6tUdIkvmG0IfbgG0Ybkqmq\nv3LlymDME2+pJDW2WdZTY+qXqJeVqMbyHa3i6Zh3mR/96pL2Fahe6P2lPbdS1yltfoGY2q896uSV\nQRuD9fbY8RtpuE4y8gr6FSb22hXz0Ewatzb4yRyIYbYRyTYNw1hNsAffMNqQTFV9731QRZLU13Ik\nrVHH1KvYenS9VMFK9hNT52L+BLH16O5OxCnqpF77j9WGj1nRk4h9R6utsv/YKlAlx2kEMi8DBgwI\nfVtvnctCp+fqscceC+3FixcDpVddyqE/J/Mhvgpm1TcMoyRpK+n0Bi4HtiWXavsoYC4VVtJxznVZ\nc671l1okvTaQaanQWcOoxzFrIeY1GPNBqEZqNoKYF2Wjk4PKtdLXUeaoWeZFIwFg1113XegTrfPG\nG28MfXfeeWdo15K1JybVReLXu3beJcCd3vuPkUvDNRurpGMYLUuaLLu9gM8AVwB475d775dhlXQM\no2VJo+pvAbwKXOWc2wF4BDiVGivp1KKm6aKOX/3qV4Fi19N77703tKVQYSXHi8WDi+qm8/NXQ8xw\npddyG1myq4drAAAPeklEQVQxphq0oVTmRQe9NDI7jVZpZY6aRb3XTJo0CSg25MnYZRs0JpGqHKfS\nV640qn4P4BPAb7z3OwLv0Emt97mjlayk45yb4Zyb0YwXzTDakTQSfyGw0Hs/Pf/3n8k9+K845/qr\nSjpLYl/O19n7HcAaa6zhazEGifQ54YQTQt/YsWMBePLJJ0OfTpdcjRFFpJiWbJI5aPDgwaFPvMh0\nsEhaShmpulOy7bHHHkBxNRkd4nzFFVcA8O1vfzv0ZZWiPBZO251oY7KW9IJobk899VQm46nUWJ4o\n8b33i4EFzjkpj7U3MAurpGMYLUtaB56TgT8659YEngfGkPvRsEo6htGCZJqBxznnxUBUjcomqv7T\nTz8d+rbYYgugOCnkzJkzaxlmFFHdhg4dGvomT54MwN57FwoK6bGlRauN4qGW1XXRxjvJ+rPppptG\nPyuvIbfdVlDuDjvsMKDxwVSxBKndyfHHHx/av/71r7tsnzt3LgDbbrtt6Gvka4p+rrz3loHHMIyu\nZC7xa/nlHj58OADTp08PfWJc0iWvG1nLbN111w3tV199teh/gCFDhoR22uWb7iyTvfPOO4f2v/71\nL6B4SVEbLkUz0TnhxowZAxR7rTWCrGsuxtDxIJIzEgpFODTnnHMOAOedd17jB0axRmQS3zCMKPbg\nG0YbkrmqX4vKJsanzTbbLPTNmTMHgG222Sb0NfKctFouoZW6Es5f//rX0D744NxCR9JrjVYhs1qn\nFrV92bJloU/W7E8//fTQ95vf/Ca0Bw0aBBSHl0pCTG0QbBYDXL0ZOHBgaEv9RSis4+v7TsJy582b\nl8nY9HNlqr5hGFHswTeMNiTTDDxQuRqu15n79+/fZR/iXprVK4s+zgMPPADAiBEjQt/+++8f2mIx\nf/DBBzMZWyXI2rN+dZkyZQoAl112WejTAUSitkpgFMANN9wAZFd0tDvRqxkx9Hm//vrrjR5OTZjE\nN4w2JHOJXyk6KEaMYFoKTZgwIdPx6Dp32jAm6KCWCy+8ECjWCLpzHVqHM3/ta18Dio2JhxxyCJAs\nscWoCYXzjVXKWd3YZJNNQjupPqO+R5uR1f9qGYbRBXvwDaMNaXpV/7jjjgttWavULrKzZ8/OdDx6\nzV5cg2NluwE+9rGPAcmGL729kev4t9xyS5djaiOUlJ2OxZdrJG4fCnPQiOwyzcZHPvKR0E6qmtPs\nBk6T+IbRhjSlxNe/nDqIRIwnOvRVl1LOAr38JcEZpSS+LP8klU9upGHs0EMPDe0999wztGUut9tu\nuy7f+dznPhfa4i0JBS/JH/zgB6FvxowZRfuD4vmIFTRpVUSDK4W+juLlKGXPATbaaKPQPuKIIwC4\n7777Qt9DDz0EFM9lLPhM55es1lhsEt8w2hB78A2jDUlU9fO59m5SXVsC44A/UGElnbRodVgMThCv\nK5cVosZ99KMfDX0SLFQqJbYYzpLUsXqp+noct956K1DsSai3X3755QAsWbKky/bDDz889G211Vah\nLbH52vj305/+tMs4tAFU9llNQtJmQV7v9GtTUhp0yRmh8zckef7J2v9zzz0X+rTR9Gc/+xkA119/\nfXR7JaRJtjnXez/Mez8MGA68C9yKVdIxjJalUlGzN/Cc9/4lrJKOYbQslVr1DwVuyLdrqqRTDr2W\nfcYZZ4S2WPi1atyzZ08A3nzzzXodPoq4ux544IGhT1T9Uqr6/PnzgezcdMUNF+CAAw7osl2nJDv/\n/POB4rH36dMHKHYx1i7Koq7rVy39KibINQF49913U4+/WTnyyFwW+c033zz1d7R7dFrklUKvVOnX\nrgULFgBxX49K81yklvj51NoHAH/qvC1tJZ20xzIMo7FUIvH3AR713r+S/7viSjrOuYpF37///e/Q\nFomj1/avuuoqoDipoc6OIhKnGqmrjVRSsUf/AouRq5ShR6Sp3h4bR60agez/lFNOKbtvXVNQgky0\n8W7UqFFAsZTXYxePSS3NJk6cCMCGG24Y+rTBsFXX79dZZ53QlnsryTinkXnXmqieC5ljPb9Sl1Gu\nA6Q3ilZac7GSd/zDKKj5YJV0DKNlSfXgO+fWA0YCt6juC4CRzrlngf/N/20YRguQSnfx3r8DbNip\n73VyVv6GolUYMRppVVOMWNrNVKtUkiteq0+x4Bq9Ni2JKHUVlH322adoW+exxdBx650pVTSzGkQt\n1QkvZX1XZ/859thjQ1tyCWiX0NGjRwPF56WTRR500EEAPP7446FProl2ZY4Z9PT2Zo9VB/jiF78Y\n2nqOyqHvOzEEvvzyy6FPx/CfddZZAPz3v/8NfZLYVLvkNgrz3DOMNqQpg3Q0WjpIiO5f/vKX0Cee\nUXpZSkusXXfdFYCvfOUroU8CI4YNGxb69LKUGFS0hJQAjUqMKJdeeimQbOCqxgCmxyEazP333x/6\nRAPafvvtQ9/48eND+//+7/+67PPmm28GisuMX3zxxaFdLtuO9iCLaTCNrG5UT3r16gXAySefHPrS\nGvWk7qH+jjZ6XnBB4W34S1/6Upc+Lf0rpeJcllUfyTCMlsUefMNoQzKvpFPL90XFvPvuu0OfZIPR\nhpOYOh5Tp/W5a28oOU4l67aCjl+XjC1Jqrw+Ti0qsaQf1+ModQ5yvtqQJEa7G2+8MfRptV+qBOnC\noOKdKOXKO9OdyUXTog275557LgAnnnhi6NP+HI1EroUOfPrFL34R2mKQjc2pVdIxDCMRe/ANow1p\nKVVf0CmkxHIuRQqhOhW9FsTVEopzr+v+cjSiaObf/vY3oLA2D+lXJPQ9EfuOdiMVFT9WY6BV0P4a\nkyZNAuI17xuNzLt+XdQBUwsXLgTir46m6huGkUhLSnwthaQ89rhx40KfToMsJZ31r6R44WnpXMoP\noDN6P9/5zncAuO6660KfTv2d9hxiXm31ui464EZ73JUyxnVGn++YMWMA+OMf/xjd3khqKa8eQ2tZ\n2sfj97//PVB8TWLocYinotbwYj4P2nD7xhtvAIXagwBDhw4F4JJLLgl92vNS5lrvU8Yh57NixQpW\nrVplEt8wjK7Yg28YbUjmqr6oKfVSEWV/OkZfB5bEEl6K8U/cM6F4rVZeAXSfuAa/+OKLoW/WrFld\n9l3puKFYrRTX10ZW1IFCsNHIkSNDn7j5Llq0KPRpl11ZZ+6Otfl63zf62urCq7FAJUEfW6voZ599\nNlAclBVLghnLy6DnMu056ntH7mV5tXjvvfdYuXKlqfqGYXQlc4kvRoh6S7RWqlsGpY17Mi+tEtSS\nFfW6b2La3k03FbLH77XXXiW/++STT4b2brvtFtrdmVdQ5kU0OJP4hmGUxB58w2hDUrm4OedOA44h\nl0n3KWAM0B+4kVxmnkeAI7z3ialVGvVq0QrqfSm02t/K59FIYoavau4lyVak/Ri23HLLst8RT0Vd\nHrxZ0obLfMj/dUuv7ZzbFDgF2Ml7vy3QQS6//oXAz7z3WwFLgaOrGLdhGN1AWlW/B7COc64HsC6w\nCNgL+HN+u1XSMYwWIlHV996/7Jy7CJgPvAfcTU61X+a9F9PzQmDTErswEtDuo7H143q7q7YKei5k\nnbrUaoe8CiQlMZXv6/3olQJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e7913c320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1610... Discriminator Loss: 1.7995... Generator Loss: 0.3567\n",
      "Epoch 1/2-Step 1620... Discriminator Loss: 1.0228... Generator Loss: 0.8237\n",
      "Epoch 1/2-Step 1630... Discriminator Loss: 2.8634... Generator Loss: 0.1942\n",
      "Epoch 1/2-Step 1640... Discriminator Loss: 2.6316... Generator Loss: 0.2240\n",
      "Epoch 1/2-Step 1650... Discriminator Loss: 1.7795... Generator Loss: 4.5748\n",
      "Epoch 1/2-Step 1660... Discriminator Loss: 1.5611... Generator Loss: 0.6124\n",
      "Epoch 1/2-Step 1670... Discriminator Loss: 0.7054... Generator Loss: 1.4339\n",
      "Epoch 1/2-Step 1680... Discriminator Loss: 2.1514... Generator Loss: 0.3458\n",
      "Epoch 1/2-Step 1690... Discriminator Loss: 1.7364... Generator Loss: 0.4061\n",
      "Epoch 1/2-Step 1700... Discriminator Loss: 3.0349... Generator Loss: 0.1355\n"
     ]
    },
    {
     "data": {
      "image/png": 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NZIzEYL1s2TJWrFhhGXgMw2iJPfiGkUBqquq3adPGr7oeq9W5SqnYej1VcuMf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\noLPBVItYAAoUH2yxOtKaX0M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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e8935d518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1710... Discriminator Loss: 0.9496... Generator Loss: 0.8696\n",
      "Epoch 1/2-Step 1720... Discriminator Loss: 1.4395... Generator Loss: 0.6739\n",
      "Epoch 1/2-Step 1730... Discriminator Loss: 0.6664... Generator Loss: 1.6997\n",
      "Epoch 1/2-Step 1740... Discriminator Loss: 2.6954... Generator Loss: 0.2849\n",
      "Epoch 1/2-Step 1750... Discriminator Loss: 1.3603... Generator Loss: 0.9009\n",
      "Epoch 1/2-Step 1760... Discriminator Loss: 1.0816... Generator Loss: 0.9107\n",
      "Epoch 1/2-Step 1770... Discriminator Loss: 0.8256... Generator Loss: 1.0552\n",
      "Epoch 1/2-Step 1780... Discriminator Loss: 0.4205... Generator Loss: 1.9499\n",
      "Epoch 1/2-Step 1790... Discriminator Loss: 1.6130... Generator Loss: 0.3562\n",
      "Epoch 1/2-Step 1800... Discriminator Loss: 2.5861... Generator Loss: 0.1800\n"
     ]
    },
    {
     "data": {
      "image/png": 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vFo5TEv1Hq2GSAUYXd5SkhZBRw/R5ikqr1W2tvor6q5cMotrF9tmbc7mNja18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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e7954e2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1810... Discriminator Loss: 1.5904... Generator Loss: 0.5657\n",
      "Epoch 1/2-Step 1820... Discriminator Loss: 2.0948... Generator Loss: 0.2882\n",
      "Epoch 1/2-Step 1830... Discriminator Loss: 1.0161... Generator Loss: 1.1578\n",
      "Epoch 1/2-Step 1840... Discriminator Loss: 1.3440... Generator Loss: 0.5831\n",
      "Epoch 1/2-Step 1850... Discriminator Loss: 1.0551... Generator Loss: 0.9924\n",
      "Epoch 1/2-Step 1860... Discriminator Loss: 1.3557... Generator Loss: 0.5949\n",
      "Epoch 1/2-Step 1870... Discriminator Loss: 2.1658... Generator Loss: 0.7761\n",
      "Epoch 2/2-Step 1880... Discriminator Loss: 1.7988... Generator Loss: 0.6089\n",
      "Epoch 2/2-Step 1890... Discriminator Loss: 1.3895... Generator Loss: 1.2412\n",
      "Epoch 2/2-Step 1900... Discriminator Loss: 1.1182... Generator Loss: 1.1952\n"
     ]
    },
    {
     "data": {
      "image/png": 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f68985mu4IbZJnHMHAYu899Nq3Zcy0QnYDviT935bMq7heWp9g92f\nknJdFqKaL/4CoJf6udU8ffWKc24lMi/9Hd778VnxQudcj+znPYBFtepfkewGDHPOvUumMMreZNbI\nXbNp1KGx7tF8YL7PZIyCTNao7Wjc+xNyXXrvvwbycl1mf6fd96eaL/4UoH/WKrkyGUPFw1U8f0lk\n8w2OBWZ47y9THz1MJucgNFDuQe/9Wd77nt773mTuxV+990fRoLkUvfcfAvOcc5tmRZIbsiHvD5XO\ndVllg8VQ4C1gDnB2rQ0oRfZ9dzJq4mvAP7P/hpJZF08CZgFPAWvXuq/tuLbBwIRse2PgH8Bs4F6g\nc637V8R1DASmZu/Rg8BajXx/gN8CbwKvA7cBnct1f8xzzzBSiBn3DCOF2ItvGCnEXnzDSCH24htG\nCrEX3zBSiL34hpFC7MU3jBRiL75hpJD/D1+dxhMowVfhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e7925cd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 1910... Discriminator Loss: 1.4968... Generator Loss: 0.5684\n",
      "Epoch 2/2-Step 1920... Discriminator Loss: 1.4977... Generator Loss: 0.6296\n",
      "Epoch 2/2-Step 1930... Discriminator Loss: 1.9247... Generator Loss: 0.2941\n",
      "Epoch 2/2-Step 1940... Discriminator Loss: 0.8135... Generator Loss: 1.0944\n",
      "Epoch 2/2-Step 1950... Discriminator Loss: 2.9539... Generator Loss: 0.1693\n",
      "Epoch 2/2-Step 1960... Discriminator Loss: 1.6268... Generator Loss: 0.4130\n",
      "Epoch 2/2-Step 1970... Discriminator Loss: 2.4348... Generator Loss: 0.1656\n",
      "Epoch 2/2-Step 1980... Discriminator Loss: 1.0910... Generator Loss: 1.1037\n",
      "Epoch 2/2-Step 1990... Discriminator Loss: 2.4345... Generator Loss: 0.2697\n",
      "Epoch 2/2-Step 2000... Discriminator Loss: 2.8119... Generator Loss: 0.1547\n"
     ]
    },
    {
     "data": {
      "image/png": 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CXWVG0zNXvtDXWpvpY091jcyg+bSAtddeO7TF00vP+DJG5TB66u3I9ssRgpxv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e79a53c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2010... Discriminator Loss: 2.9430... Generator Loss: 0.1119\n",
      "Epoch 2/2-Step 2020... Discriminator Loss: 2.4929... Generator Loss: 0.2289\n",
      "Epoch 2/2-Step 2030... Discriminator Loss: 1.3928... Generator Loss: 0.6620\n",
      "Epoch 2/2-Step 2040... Discriminator Loss: 0.9953... Generator Loss: 0.9853\n",
      "Epoch 2/2-Step 2050... Discriminator Loss: 2.0209... Generator Loss: 0.3261\n",
      "Epoch 2/2-Step 2060... Discriminator Loss: 1.0119... Generator Loss: 1.4060\n",
      "Epoch 2/2-Step 2070... Discriminator Loss: 2.3089... Generator Loss: 0.2537\n",
      "Epoch 2/2-Step 2080... Discriminator Loss: 1.8246... Generator Loss: 3.4251\n",
      "Epoch 2/2-Step 2090... Discriminator Loss: 3.6070... Generator Loss: 0.2074\n",
      "Epoch 2/2-Step 2100... Discriminator Loss: 1.7052... Generator Loss: 1.4942\n"
     ]
    },
    {
     "data": {
      "image/png": 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67/3OwEe0Met97uwlK+k4555zzj1XUc8Mw6gbSTT+XGCu935S/u/7yD34naqk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e8110c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2110... Discriminator Loss: 1.7078... Generator Loss: 0.4842\n",
      "Epoch 2/2-Step 2120... Discriminator Loss: 1.8781... Generator Loss: 0.3792\n",
      "Epoch 2/2-Step 2130... Discriminator Loss: 0.9712... Generator Loss: 1.0053\n",
      "Epoch 2/2-Step 2140... Discriminator Loss: 2.0136... Generator Loss: 0.3907\n",
      "Epoch 2/2-Step 2150... Discriminator Loss: 2.0800... Generator Loss: 0.4026\n",
      "Epoch 2/2-Step 2160... Discriminator Loss: 1.2040... Generator Loss: 0.6960\n",
      "Epoch 2/2-Step 2170... Discriminator Loss: 0.4719... Generator Loss: 3.4201\n",
      "Epoch 2/2-Step 2180... Discriminator Loss: 1.2094... Generator Loss: 0.7431\n",
      "Epoch 2/2-Step 2190... Discriminator Loss: 1.9730... Generator Loss: 0.5210\n",
      "Epoch 2/2-Step 2200... Discriminator Loss: 1.3208... Generator Loss: 0.9190\n"
     ]
    },
    {
     "data": {
      "image/png": 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LqENznaPZwGyfqRgFmapRA2ne8xNqXXrvvwfyal1m39Pu81PPG/9V\nYMusV3JVMo6Kx+r4/RWRrTd4KzDFe/979dJjZGoOQhPVHvTen+e97+m970XmXPzNe38UTVpL0Xs/\nH5jlnPtRViS1IZvy/FDrWpd1dliMBD4EPgL+T6MdKGXu+1AyZuLbwJvZfyPJPBdPAqYCzwBdG72v\n7Ti23YEJ2fHmwCvANOB+YLVG718ZxzEAeC17jh4BujTz+QEuAt4H3gXuBFar1vmxyD3DSCHm3DOM\nFGI3vmGkELvxDSOF2I1vGCnEbnzDSCF24xtGCrEb3zBSiN34hpFC/j8jlrynGEasuAAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e830072b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2210... Discriminator Loss: 0.9230... Generator Loss: 1.0733\n",
      "Epoch 2/2-Step 2220... Discriminator Loss: 1.6415... Generator Loss: 0.4990\n",
      "Epoch 2/2-Step 2230... Discriminator Loss: 1.2177... Generator Loss: 0.7985\n",
      "Epoch 2/2-Step 2240... Discriminator Loss: 1.7188... Generator Loss: 0.4603\n",
      "Epoch 2/2-Step 2250... Discriminator Loss: 1.0832... Generator Loss: 0.9234\n",
      "Epoch 2/2-Step 2260... Discriminator Loss: 2.4330... Generator Loss: 0.2552\n",
      "Epoch 2/2-Step 2270... Discriminator Loss: 1.0972... Generator Loss: 1.8646\n",
      "Epoch 2/2-Step 2280... Discriminator Loss: 4.2175... Generator Loss: 0.0387\n",
      "Epoch 2/2-Step 2290... Discriminator Loss: 1.4911... Generator Loss: 0.6146\n",
      "Epoch 2/2-Step 2300... Discriminator Loss: 1.0731... Generator Loss: 1.9281\n"
     ]
    },
    {
     "data": {
      "image/png": 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MmqWYKrtrANsBYwG89z9675dhnXQMo2YpxtTvBSwFbnfODQCmAKNpZCedWqJa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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e8306f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2310... Discriminator Loss: 2.6048... Generator Loss: 0.1825\n",
      "Epoch 2/2-Step 2320... Discriminator Loss: 1.3122... Generator Loss: 0.5521\n",
      "Epoch 2/2-Step 2330... Discriminator Loss: 1.3775... Generator Loss: 0.6418\n",
      "Epoch 2/2-Step 2340... Discriminator Loss: 1.1761... Generator Loss: 0.7925\n",
      "Epoch 2/2-Step 2350... Discriminator Loss: 1.1322... Generator Loss: 0.7320\n",
      "Epoch 2/2-Step 2360... Discriminator Loss: 0.1161... Generator Loss: 3.6323\n",
      "Epoch 2/2-Step 2370... Discriminator Loss: 1.0060... Generator Loss: 1.2201\n",
      "Epoch 2/2-Step 2380... Discriminator Loss: 0.6671... Generator Loss: 1.1994\n",
      "Epoch 2/2-Step 2390... Discriminator Loss: 3.2159... Generator Loss: 0.2751\n",
      "Epoch 2/2-Step 2400... Discriminator Loss: 3.4933... Generator Loss: 0.0844\n"
     ]
    },
    {
     "data": {
      "image/png": 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FV2PjlqTe62sU453O7KRdya+44gog7htRLYOeJu2L/wPv/ULn3FrAI865mfpD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\nPwAxMmrNoiPP/mmR6jLa4Nq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e83075f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2410... Discriminator Loss: 0.9275... Generator Loss: 2.6940\n",
      "Epoch 2/2-Step 2420... Discriminator Loss: 1.1644... Generator Loss: 0.7646\n",
      "Epoch 2/2-Step 2430... Discriminator Loss: 1.8302... Generator Loss: 0.3799\n",
      "Epoch 2/2-Step 2440... Discriminator Loss: 1.7304... Generator Loss: 0.3922\n",
      "Epoch 2/2-Step 2450... Discriminator Loss: 2.1595... Generator Loss: 0.3598\n",
      "Epoch 2/2-Step 2460... Discriminator Loss: 1.7138... Generator Loss: 1.4030\n",
      "Epoch 2/2-Step 2470... Discriminator Loss: 1.2481... Generator Loss: 0.6815\n",
      "Epoch 2/2-Step 2480... Discriminator Loss: 1.4053... Generator Loss: 0.7218\n",
      "Epoch 2/2-Step 2490... Discriminator Loss: 3.3293... Generator Loss: 0.1335\n",
      "Epoch 2/2-Step 2500... Discriminator Loss: 1.8164... Generator Loss: 0.5824\n"
     ]
    },
    {
     "data": {
      "image/png": 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UVT0+ovJqNTafgazeMtYUg1b1W74iQjzoRY9lWwE9hRgBY69aulS7rNknNVqW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e8307ce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2510... Discriminator Loss: 2.6035... Generator Loss: 0.2349\n",
      "Epoch 2/2-Step 2520... Discriminator Loss: 0.8650... Generator Loss: 4.0960\n",
      "Epoch 2/2-Step 2530... Discriminator Loss: 1.5684... Generator Loss: 0.5153\n",
      "Epoch 2/2-Step 2540... Discriminator Loss: 0.7783... Generator Loss: 1.2478\n",
      "Epoch 2/2-Step 2550... Discriminator Loss: 2.4847... Generator Loss: 0.3230\n",
      "Epoch 2/2-Step 2560... Discriminator Loss: 0.8980... Generator Loss: 4.6620\n",
      "Epoch 2/2-Step 2570... Discriminator Loss: 1.3185... Generator Loss: 1.2826\n",
      "Epoch 2/2-Step 2580... Discriminator Loss: 3.2025... Generator Loss: 0.2373\n",
      "Epoch 2/2-Step 2590... Discriminator Loss: 2.0644... Generator Loss: 0.4936\n",
      "Epoch 2/2-Step 2600... Discriminator Loss: 0.9851... Generator Loss: 1.2821\n"
     ]
    },
    {
     "data": {
      "image/png": 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PwE+ALYERzrkty7X/ItAA/NJ7vyUwCDglffxjgOne+02B6em/q4nR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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e78bfcc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2610... Discriminator Loss: 1.7005... Generator Loss: 0.7132\n",
      "Epoch 2/2-Step 2620... Discriminator Loss: 1.7881... Generator Loss: 0.4661\n",
      "Epoch 2/2-Step 2630... Discriminator Loss: 1.1388... Generator Loss: 0.9582\n",
      "Epoch 2/2-Step 2640... Discriminator Loss: 1.0748... Generator Loss: 2.2080\n",
      "Epoch 2/2-Step 2650... Discriminator Loss: 1.4443... Generator Loss: 0.7525\n",
      "Epoch 2/2-Step 2660... Discriminator Loss: 2.8320... Generator Loss: 0.1446\n",
      "Epoch 2/2-Step 2670... Discriminator Loss: 1.6948... Generator Loss: 0.9054\n",
      "Epoch 2/2-Step 2680... Discriminator Loss: 0.5985... Generator Loss: 1.3193\n",
      "Epoch 2/2-Step 2690... Discriminator Loss: 1.9464... Generator Loss: 2.4039\n",
      "Epoch 2/2-Step 2700... Discriminator Loss: 1.1410... Generator Loss: 0.8293\n"
     ]
    },
    {
     "data": {
      "image/png": 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LpWqux7aVHgMpNS5FMjR6m00bQCUPnfbcq+f2mh4j2fLU10kbQtslw0+tkTGS8Um7RWoz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\n3zAyiD34hpFB7ME3jAxiD75hZJD/B+wiWUg3DIwgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e78f5bba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2710... Discriminator Loss: 0.9968... Generator Loss: 0.9076\n",
      "Epoch 2/2-Step 2720... Discriminator Loss: 1.7824... Generator Loss: 0.5328\n",
      "Epoch 2/2-Step 2730... Discriminator Loss: 0.2981... Generator Loss: 2.5057\n",
      "Epoch 2/2-Step 2740... Discriminator Loss: 1.0728... Generator Loss: 0.9905\n",
      "Epoch 2/2-Step 2750... Discriminator Loss: 1.5693... Generator Loss: 0.5073\n",
      "Epoch 2/2-Step 2760... Discriminator Loss: 1.0690... Generator Loss: 3.7526\n",
      "Epoch 2/2-Step 2770... Discriminator Loss: 2.4759... Generator Loss: 0.2352\n",
      "Epoch 2/2-Step 2780... Discriminator Loss: 0.9367... Generator Loss: 2.4314\n",
      "Epoch 2/2-Step 2790... Discriminator Loss: 1.5539... Generator Loss: 2.2785\n",
      "Epoch 2/2-Step 2800... Discriminator Loss: 1.4832... Generator Loss: 3.2962\n"
     ]
    },
    {
     "data": {
      "image/png": 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V4AHn3Cv6Q++9z5dkwzl3OHB47DPDMBpDyRl4nHOnAf8GDgO2997Pdc71BCZ6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\nyHRJ8noQq/lX8Pu1PBnDMJo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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e78ccbc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2810... Discriminator Loss: 1.1598... Generator Loss: 0.6267\n",
      "Epoch 2/2-Step 2820... Discriminator Loss: 1.3759... Generator Loss: 0.5195\n",
      "Epoch 2/2-Step 2830... Discriminator Loss: 1.0197... Generator Loss: 0.9089\n",
      "Epoch 2/2-Step 2840... Discriminator Loss: 0.5584... Generator Loss: 1.6436\n",
      "Epoch 2/2-Step 2850... Discriminator Loss: 1.8377... Generator Loss: 0.3658\n",
      "Epoch 2/2-Step 2860... Discriminator Loss: 1.2147... Generator Loss: 0.6694\n",
      "Epoch 2/2-Step 2870... Discriminator Loss: 1.1408... Generator Loss: 0.8225\n",
      "Epoch 2/2-Step 2880... Discriminator Loss: 0.8431... Generator Loss: 1.3163\n",
      "Epoch 2/2-Step 2890... Discriminator Loss: 2.2990... Generator Loss: 0.2128\n",
      "Epoch 2/2-Step 2900... Discriminator Loss: 2.0544... Generator Loss: 0.4308\n"
     ]
    },
    {
     "data": {
      "image/png": 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sxjeMFsRufMNoQVJV9Xv16hViviVjjs4bHtvD1iqZtCtdCsTKHmuSFnUsltWn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\n4xtGC/L/Ab6QwcUfR7NJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e789c6080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2910... Discriminator Loss: 2.3895... Generator Loss: 0.2125\n",
      "Epoch 2/2-Step 2920... Discriminator Loss: 0.8310... Generator Loss: 1.0606\n",
      "Epoch 2/2-Step 2930... Discriminator Loss: 2.2490... Generator Loss: 4.4028\n",
      "Epoch 2/2-Step 2940... Discriminator Loss: 1.2407... Generator Loss: 1.2902\n",
      "Epoch 2/2-Step 2950... Discriminator Loss: 2.6363... Generator Loss: 0.2990\n",
      "Epoch 2/2-Step 2960... Discriminator Loss: 2.1573... Generator Loss: 0.2985\n",
      "Epoch 2/2-Step 2970... Discriminator Loss: 1.2197... Generator Loss: 0.8472\n",
      "Epoch 2/2-Step 2980... Discriminator Loss: 1.1789... Generator Loss: 2.3603\n",
      "Epoch 2/2-Step 2990... Discriminator Loss: 1.4132... Generator Loss: 1.0563\n",
      "Epoch 2/2-Step 3000... Discriminator Loss: 1.4316... Generator Loss: 2.1424\n"
     ]
    },
    {
     "data": {
      "image/png": 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dyNTCOxM41Tk3jcyS3rV17KdhGDUkVZfdbt26eVFJxOjTDPHnjUCv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\nvILUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e789c6630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3010... Discriminator Loss: 2.1096... Generator Loss: 0.3688\n",
      "Epoch 2/2-Step 3020... Discriminator Loss: 2.7113... Generator Loss: 0.1992\n",
      "Epoch 2/2-Step 3030... Discriminator Loss: 0.7655... Generator Loss: 1.4882\n",
      "Epoch 2/2-Step 3040... Discriminator Loss: 0.8179... Generator Loss: 1.1351\n",
      "Epoch 2/2-Step 3050... Discriminator Loss: 2.7765... Generator Loss: 0.1775\n",
      "Epoch 2/2-Step 3060... Discriminator Loss: 1.6212... Generator Loss: 2.3479\n",
      "Epoch 2/2-Step 3070... Discriminator Loss: 1.7499... Generator Loss: 0.4827\n",
      "Epoch 2/2-Step 3080... Discriminator Loss: 1.0921... Generator Loss: 1.2112\n",
      "Epoch 2/2-Step 3090... Discriminator Loss: 2.1500... Generator Loss: 2.5354\n",
      "Epoch 2/2-Step 3100... Discriminator Loss: 1.4504... Generator Loss: 0.6992\n"
     ]
    },
    {
     "data": {
      "image/png": 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+CvzSe78usBA4rPRRDMNoJ9Kq+r2BZZxzvYFlgXnASOB3+c+tko5h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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e78a1dda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3110... Discriminator Loss: 0.4549... Generator Loss: 2.0005\n",
      "Epoch 2/2-Step 3120... Discriminator Loss: 2.3352... Generator Loss: 0.1726\n",
      "Epoch 2/2-Step 3130... Discriminator Loss: 1.8152... Generator Loss: 0.3370\n",
      "Epoch 2/2-Step 3140... Discriminator Loss: 1.2448... Generator Loss: 0.6605\n",
      "Epoch 2/2-Step 3150... Discriminator Loss: 1.3393... Generator Loss: 0.6704\n",
      "Epoch 2/2-Step 3160... Discriminator Loss: 0.3832... Generator Loss: 1.5856\n",
      "Epoch 2/2-Step 3170... Discriminator Loss: 1.4028... Generator Loss: 0.6451\n",
      "Epoch 2/2-Step 3180... Discriminator Loss: 0.6644... Generator Loss: 1.8764\n",
      "Epoch 2/2-Step 3190... Discriminator Loss: 0.9284... Generator Loss: 0.8118\n",
      "Epoch 2/2-Step 3200... Discriminator Loss: 0.9369... Generator Loss: 1.0325\n"
     ]
    },
    {
     "data": {
      "image/png": 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PE/GC0r+88guu0d5oW2yxBZBfFRNI/kLrbC617JFnzTPPPAPAuHHjgkyMVFAat7xCRSsh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\nHefcWsCngGXAJECcj62SjmF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e785cea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3210... Discriminator Loss: 3.6743... Generator Loss: 0.0663\n",
      "Epoch 2/2-Step 3220... Discriminator Loss: 1.6144... Generator Loss: 0.5525\n",
      "Epoch 2/2-Step 3230... Discriminator Loss: 0.6816... Generator Loss: 1.5077\n",
      "Epoch 2/2-Step 3240... Discriminator Loss: 3.3266... Generator Loss: 0.0811\n",
      "Epoch 2/2-Step 3250... Discriminator Loss: 1.7431... Generator Loss: 0.5593\n",
      "Epoch 2/2-Step 3260... Discriminator Loss: 1.4966... Generator Loss: 0.5404\n",
      "Epoch 2/2-Step 3270... Discriminator Loss: 1.6944... Generator Loss: 0.7823\n",
      "Epoch 2/2-Step 3280... Discriminator Loss: 1.5925... Generator Loss: 0.6031\n",
      "Epoch 2/2-Step 3290... Discriminator Loss: 1.2572... Generator Loss: 0.9174\n",
      "Epoch 2/2-Step 3300... Discriminator Loss: 1.7342... Generator Loss: 0.5187\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e788973c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3310... Discriminator Loss: 0.1825... Generator Loss: 4.5827\n",
      "Epoch 2/2-Step 3320... Discriminator Loss: 1.2775... Generator Loss: 0.7300\n",
      "Epoch 2/2-Step 3330... Discriminator Loss: 0.8732... Generator Loss: 1.2389\n",
      "Epoch 2/2-Step 3340... Discriminator Loss: 1.8792... Generator Loss: 0.4560\n",
      "Epoch 2/2-Step 3350... Discriminator Loss: 0.8879... Generator Loss: 0.9273\n",
      "Epoch 2/2-Step 3360... Discriminator Loss: 0.4954... Generator Loss: 1.6400\n",
      "Epoch 2/2-Step 3370... Discriminator Loss: 0.6375... Generator Loss: 1.5675\n",
      "Epoch 2/2-Step 3380... Discriminator Loss: 1.5641... Generator Loss: 0.5521\n",
      "Epoch 2/2-Step 3390... Discriminator Loss: 0.4753... Generator Loss: 1.8465\n",
      "Epoch 2/2-Step 3400... Discriminator Loss: 0.4872... Generator Loss: 1.5438\n"
     ]
    },
    {
     "data": {
      "image/png": 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lRlqrrGLkknVpSAZQiEuvNlhlVaGaTdU/4ogjQnuzzTYLbYkHF5dmKPgtaMOX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\nBxVNXpov1iXVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e781a3048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3410... Discriminator Loss: 2.7610... Generator Loss: 0.2020\n",
      "Epoch 2/2-Step 3420... Discriminator Loss: 1.1922... Generator Loss: 1.7685\n",
      "Epoch 2/2-Step 3430... Discriminator Loss: 0.8931... Generator Loss: 1.5826\n",
      "Epoch 2/2-Step 3440... Discriminator Loss: 2.0843... Generator Loss: 0.4748\n",
      "Epoch 2/2-Step 3450... Discriminator Loss: 1.3589... Generator Loss: 0.6113\n",
      "Epoch 2/2-Step 3460... Discriminator Loss: 0.7527... Generator Loss: 1.3879\n",
      "Epoch 2/2-Step 3470... Discriminator Loss: 1.2510... Generator Loss: 0.7047\n",
      "Epoch 2/2-Step 3480... Discriminator Loss: 1.1979... Generator Loss: 0.8418\n",
      "Epoch 2/2-Step 3490... Discriminator Loss: 1.6452... Generator Loss: 5.2290\n",
      "Epoch 2/2-Step 3500... Discriminator Loss: 0.5588... Generator Loss: 1.8905\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e78212fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3510... Discriminator Loss: 1.0099... Generator Loss: 1.4121\n",
      "Epoch 2/2-Step 3520... Discriminator Loss: 2.7485... Generator Loss: 0.2938\n",
      "Epoch 2/2-Step 3530... Discriminator Loss: 1.3084... Generator Loss: 0.5821\n",
      "Epoch 2/2-Step 3540... Discriminator Loss: 1.4313... Generator Loss: 3.7524\n",
      "Epoch 2/2-Step 3550... Discriminator Loss: 0.9619... Generator Loss: 2.8347\n",
      "Epoch 2/2-Step 3560... Discriminator Loss: 0.8457... Generator Loss: 1.6197\n",
      "Epoch 2/2-Step 3570... Discriminator Loss: 1.3874... Generator Loss: 0.6073\n",
      "Epoch 2/2-Step 3580... Discriminator Loss: 1.4953... Generator Loss: 0.6685\n",
      "Epoch 2/2-Step 3590... Discriminator Loss: 1.2233... Generator Loss: 0.6353\n",
      "Epoch 2/2-Step 3600... Discriminator Loss: 1.0592... Generator Loss: 1.0551\n"
     ]
    },
    {
     "data": {
      "image/png": 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MowmoRNXfjUySDbL/tx9sG4bRkKTNpPMWmTRZHvi39/5y59wM73237HYHTJe/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\nG0YLYi++YbQg9uIbRgvy/8f+7mt6VRiyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e77f44c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3610... Discriminator Loss: 1.9337... Generator Loss: 0.3964\n",
      "Epoch 2/2-Step 3620... Discriminator Loss: 0.7413... Generator Loss: 1.2235\n",
      "Epoch 2/2-Step 3630... Discriminator Loss: 1.1959... Generator Loss: 0.9519\n",
      "Epoch 2/2-Step 3640... Discriminator Loss: 1.0010... Generator Loss: 1.5190\n",
      "Epoch 2/2-Step 3650... Discriminator Loss: 2.8820... Generator Loss: 0.1989\n",
      "Epoch 2/2-Step 3660... Discriminator Loss: 3.1258... Generator Loss: 0.2029\n",
      "Epoch 2/2-Step 3670... Discriminator Loss: 1.7464... Generator Loss: 0.5479\n",
      "Epoch 2/2-Step 3680... Discriminator Loss: 1.9657... Generator Loss: 0.3596\n",
      "Epoch 2/2-Step 3690... Discriminator Loss: 2.0325... Generator Loss: 0.4481\n",
      "Epoch 2/2-Step 3700... Discriminator Loss: 0.7904... Generator Loss: 1.4904\n"
     ]
    },
    {
     "data": {
      "image/png": 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pwDfALOBn7/1azrkFgduAPsBUYHfv/RfF9lFLJBNMvm9Acpa6/PLLM+1Hr9MP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\nMiEWEqnRsh13zLkkbLXVVp2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e781ae3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3710... Discriminator Loss: 3.2823... Generator Loss: 0.0943\n",
      "Epoch 2/2-Step 3720... Discriminator Loss: 3.5929... Generator Loss: 0.1268\n",
      "Epoch 2/2-Step 3730... Discriminator Loss: 2.5083... Generator Loss: 0.2654\n",
      "Epoch 2/2-Step 3740... Discriminator Loss: 1.2404... Generator Loss: 0.8706\n",
      "Epoch 2/2-Step 3750... Discriminator Loss: 0.4249... Generator Loss: 3.3969\n"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "z_dim = 100\n",
    "learning_rate = 0.001\n",
    "beta1 = 0.5\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "epochs = 2\n",
    "\n",
    "mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))\n",
    "with tf.Graph().as_default():\n",
    "    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,\n",
    "          mnist_dataset.shape, mnist_dataset.image_mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CelebA\n",
    "Run your GANs on CelebA.  It will take around 20 minutes on the average GPU to run one epoch.  You can run the whole epoch or stop when it starts to generate realistic faces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 10... Discriminator Loss: 0.0727... Generator Loss: 9.3794\n",
      "Epoch 1/1-Step 20... Discriminator Loss: 0.0076... Generator Loss: 12.2277\n",
      "Epoch 1/1-Step 30... Discriminator Loss: 0.0632... Generator Loss: 3.0810\n",
      "Epoch 1/1-Step 40... Discriminator Loss: 0.0050... Generator Loss: 6.2162\n",
      "Epoch 1/1-Step 50... Discriminator Loss: 0.0349... Generator Loss: 5.5600\n",
      "Epoch 1/1-Step 60... Discriminator Loss: 0.0111... Generator Loss: 4.7894\n",
      "Epoch 1/1-Step 70... Discriminator Loss: 0.0235... Generator Loss: 5.5622\n",
      "Epoch 1/1-Step 80... Discriminator Loss: 0.1791... Generator Loss: 1.9836\n",
      "Epoch 1/1-Step 90... Discriminator Loss: 0.0956... Generator Loss: 6.7089\n",
      "Epoch 1/1-Step 100... Discriminator Loss: 0.6838... Generator Loss: 8.5993\n"
     ]
    },
    {
     "data": {
      "image/png": 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yRNcd+N2p8hfcES7B2QoLn8J5OB3cQXz1Dw/49N9YBYqoLjD28DtQb2YFJ9Ke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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38733be80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 110... Discriminator Loss: 0.5457... Generator Loss: 2.6397\n",
      "Epoch 1/1-Step 120... Discriminator Loss: 0.8652... Generator Loss: 0.9929\n",
      "Epoch 1/1-Step 130... Discriminator Loss: 2.4754... Generator Loss: 8.4404\n",
      "Epoch 1/1-Step 140... Discriminator Loss: 4.9178... Generator Loss: 0.0960\n",
      "Epoch 1/1-Step 150... Discriminator Loss: 4.8412... Generator Loss: 0.0560\n",
      "Epoch 1/1-Step 160... Discriminator Loss: 3.6360... Generator Loss: 0.1029\n",
      "Epoch 1/1-Step 170... Discriminator Loss: 2.6070... Generator Loss: 0.1986\n",
      "Epoch 1/1-Step 180... Discriminator Loss: 2.5153... Generator Loss: 0.2089\n",
      "Epoch 1/1-Step 190... Discriminator Loss: 2.1722... Generator Loss: 0.5444\n",
      "Epoch 1/1-Step 200... Discriminator Loss: 2.5551... Generator Loss: 0.2539\n"
     ]
    },
    {
     "data": {
      "image/png": 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pQ0HPgRiDYsjxTxPusxMp1J/M1GCVyhpb0n7iitcqhIQZvwGlxAX2e20oNppK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd388fce0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 210... Discriminator Loss: 2.0813... Generator Loss: 0.3078\n",
      "Epoch 1/1-Step 220... Discriminator Loss: 1.9055... Generator Loss: 0.4443\n",
      "Epoch 1/1-Step 230... Discriminator Loss: 2.5085... Generator Loss: 0.3532\n",
      "Epoch 1/1-Step 240... Discriminator Loss: 2.2245... Generator Loss: 0.2461\n",
      "Epoch 1/1-Step 250... Discriminator Loss: 2.1935... Generator Loss: 0.4254\n",
      "Epoch 1/1-Step 260... Discriminator Loss: 1.9623... Generator Loss: 0.3481\n",
      "Epoch 1/1-Step 270... Discriminator Loss: 1.9798... Generator Loss: 0.3965\n",
      "Epoch 1/1-Step 280... Discriminator Loss: 1.8648... Generator Loss: 0.3960\n",
      "Epoch 1/1-Step 290... Discriminator Loss: 2.0754... Generator Loss: 0.4496\n",
      "Epoch 1/1-Step 300... Discriminator Loss: 1.7719... Generator Loss: 0.5716\n"
     ]
    },
    {
     "data": {
      "image/png": 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T0X9sjHknVdV0KqnkbSun4dx7RESP5HhsjPkCEe3QV1NNp7REUs2llBLTeyqx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\nLeq+bLuyUTp/alzml/PWsFSU+Q2pak8+l3rPKmaoizFqC+VpLo3dWsy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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd389f2ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 310... Discriminator Loss: 1.6204... Generator Loss: 0.5335\n",
      "Epoch 1/1-Step 320... Discriminator Loss: 2.0243... Generator Loss: 0.3130\n",
      "Epoch 1/1-Step 330... Discriminator Loss: 1.6476... Generator Loss: 0.8068\n",
      "Epoch 1/1-Step 340... Discriminator Loss: 1.7978... Generator Loss: 0.4309\n",
      "Epoch 1/1-Step 350... Discriminator Loss: 2.0922... Generator Loss: 0.3991\n",
      "Epoch 1/1-Step 360... Discriminator Loss: 1.6118... Generator Loss: 0.6267\n",
      "Epoch 1/1-Step 370... Discriminator Loss: 1.6625... Generator Loss: 0.6424\n",
      "Epoch 1/1-Step 380... Discriminator Loss: 1.7148... Generator Loss: 0.4611\n",
      "Epoch 1/1-Step 390... Discriminator Loss: 1.5929... Generator Loss: 0.5055\n",
      "Epoch 1/1-Step 400... Discriminator Loss: 1.6716... Generator Loss: 0.5413\n"
     ]
    },
    {
     "data": {
      "image/png": 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9fSLal+2xMeY5IrpE30A1HWsNFTlDR1eKQrotQNpcItMqNUVR9AF1Kk3pbg2i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\nMz5NpvLuunveNPGUo+fGMz4007qFWLRdr2s80GXKkh3k5O15YsWMiAV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4QYIcQgkPfpAg\nh1Cc32NZ3VtyMec2IMFrmzf67QGSJYTx3KnyShoLsLfxnPTeL9/gN/v74AOAc+4b3vs37utFX0YJ\n47lz5ZU0FuDWjidA/SBBDqGEBz9IkEMot+PB/4vbcM2XU8J47lx5JY0FuIXj2fc9fpAgQW6/BKgf\nJMghlH198J1z73bOPemce8Y597H9vPYPK865E865rzjnHnPOfc8592FtX3DO/Ztz7mn9v3Gjc91J\n4pyLnXPfds49on+fds59Vdfoc865/I3OcaeIc27eOfcF59wTzrnHnXNvPcjr45z7iN5r33XOfdY5\nV7xV67NvD75zLgbwZwDeA+AMgA84587s1/VvgUwB/Lb3/gyAtwD4Ne3/xwA86r1/FYBH9e+DJB8G\n8Dj9fZC5FD8F4F+8968FcD9kXAdyfV52rkvv/b78A/BWAP9Kf38cwMf36/ovw3j+EcC7ADwJ4Ki2\nHQXw5O3u202M4TjkYXgHgEcgSQObAJIXWrM7+R+AOQDnoHYraj+Q6wOhsrsIYAGSU/MIgJ+6Veuz\nn1B/NpCZ7Imn704U59wpAA8C+CqAI977WU7vNQBHXuSwO1H+FMDvwDKIF/EDcCneIXIawhf0V7p1\n+UvnXAUHdH2895cBzLgurwJo4QfkunwhCca9mxTnXBXA3wH4Te99m7/z8ho+EG4S59z7AKx77795\nu/tyiyQB8BCAP/fePwgJDb8O1h+w9fmhuC5vJPv54F8GcIL+flGevjtVnHM5yEP/N977L2rzmnNC\neKb/r7/Y8XeYvA3A+51zz0EKo7wDskeeVxp14GCt0SUAl7wwRgHCGvUQDu767HJdeu8nAK7jutTf\n/MDrs58P/tcBvEqtknmIoeJL+3j9H0qUb/DTAB733v8xffUlCOcgcIC4B733H/feH/fen4KsxX94\n738BB5RL0Xt/DcBF59yseuqMG/JArg9ebq7LfTZYvBfAUwCeBfC7t9uAcpN9/3EITPxfAN/Rf++F\n7IsfBfA0gH8HsHC7+/oDjO3tAB7Rz/cA+BqAZwB8HkDhdvfvJsbxAIBv6Br9A4DGQV4fAL8P4AkA\n3wXw15AKbLdkfULkXpAgh1CCcS9IkEMo4cEPEuQQSnjwgwQ5hBIe/CBBDqGEBz9IkEMo4cEPEuQQ\nSnjwgwQ5hBIe/CBBDqH8PwueoMUDTDh0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3858ae828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 410... Discriminator Loss: 1.5165... Generator Loss: 0.5024\n",
      "Epoch 1/1-Step 420... Discriminator Loss: 1.6313... Generator Loss: 0.5201\n",
      "Epoch 1/1-Step 430... Discriminator Loss: 1.6951... Generator Loss: 0.3886\n",
      "Epoch 1/1-Step 440... Discriminator Loss: 2.0725... Generator Loss: 0.3885\n",
      "Epoch 1/1-Step 450... Discriminator Loss: 1.7706... Generator Loss: 0.4545\n",
      "Epoch 1/1-Step 460... Discriminator Loss: 1.6135... Generator Loss: 0.4660\n",
      "Epoch 1/1-Step 470... Discriminator Loss: 1.7225... Generator Loss: 0.5455\n",
      "Epoch 1/1-Step 480... Discriminator Loss: 1.8447... Generator Loss: 0.5592\n",
      "Epoch 1/1-Step 490... Discriminator Loss: 1.5571... Generator Loss: 0.6380\n",
      "Epoch 1/1-Step 500... Discriminator Loss: 1.6766... Generator Loss: 0.4717\n"
     ]
    },
    {
     "data": {
      "image/png": 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iqsd7X1xtSjO9Z1ev8f33toDwE23sJYjFupBfYqTwykLz1IUsDTMB3PaUTTM/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\n6ShCMaJCFEHGXL7Q46QytgK8c3W5CWR3xjGEPJ0v+UMiqsC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JugVt7wkx1IULupy51FVj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\nYMztTFUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39ba142e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 510... Discriminator Loss: 1.6623... Generator Loss: 0.5098\n",
      "Epoch 1/1-Step 520... Discriminator Loss: 1.7112... Generator Loss: 0.5697\n",
      "Epoch 1/1-Step 530... Discriminator Loss: 1.7006... Generator Loss: 0.3925\n",
      "Epoch 1/1-Step 540... Discriminator Loss: 1.5537... Generator Loss: 0.4265\n",
      "Epoch 1/1-Step 550... Discriminator Loss: 1.7764... Generator Loss: 0.3655\n",
      "Epoch 1/1-Step 560... Discriminator Loss: 1.3598... Generator Loss: 0.5995\n",
      "Epoch 1/1-Step 570... Discriminator Loss: 1.4859... Generator Loss: 0.6381\n",
      "Epoch 1/1-Step 580... Discriminator Loss: 1.6684... Generator Loss: 0.4805\n",
      "Epoch 1/1-Step 590... Discriminator Loss: 1.7357... Generator Loss: 0.3859\n",
      "Epoch 1/1-Step 600... Discriminator Loss: 1.7320... Generator Loss: 0.5614\n"
     ]
    },
    {
     "data": {
      "image/png": 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ttg+snUdzb4+I9uTz1BjzEhFdo6+hm47rejQYMqHTDRlOtxsK9YuhZNQ9AhHM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\nOdF72vgIdxbqNz+s48n4POty7yMoc38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s6PZBSpsS/txe6Bi6ItpZgNT4fzVXou9vljyH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\nASGqR6d8zs+fvKznFoSUxyC9Duo2E4kY5dC/bmPOA+5CJmc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+p1LirAnG2s+hQNK0yvdI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\nNPfiO3M2gfb/jZzdSxOCqVgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd388de5e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 610... Discriminator Loss: 1.7415... Generator Loss: 0.3289\n",
      "Epoch 1/1-Step 620... Discriminator Loss: 1.7709... Generator Loss: 0.5224\n",
      "Epoch 1/1-Step 630... Discriminator Loss: 1.6991... Generator Loss: 0.4420\n",
      "Epoch 1/1-Step 640... Discriminator Loss: 1.6305... Generator Loss: 0.4226\n",
      "Epoch 1/1-Step 650... Discriminator Loss: 1.3594... Generator Loss: 0.6459\n",
      "Epoch 1/1-Step 660... Discriminator Loss: 1.5848... Generator Loss: 0.6701\n",
      "Epoch 1/1-Step 670... Discriminator Loss: 1.5340... Generator Loss: 0.5317\n",
      "Epoch 1/1-Step 680... Discriminator Loss: 1.4880... Generator Loss: 0.7570\n",
      "Epoch 1/1-Step 690... Discriminator Loss: 1.7384... Generator Loss: 0.4051\n",
      "Epoch 1/1-Step 700... Discriminator Loss: 1.6310... Generator Loss: 0.3631\n"
     ]
    },
    {
     "data": {
      "image/png": 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NY8yvGWO+Lf8O3+kYzpw5+96ys0D9v0VEP/6GbT9HRL9urb1NRL8u/+/MmbOPiL0r1LfW\n/rYx5uYbNv8kEf2IfP4FIvpNIvrLZzrjGzg9lKNuRPwe2gEBSA8KPkoprOjA8iATgigbK+kzJYVx\nkcDbDYDOgcTVK1/j/Y2W7uMJi2VSXXJklo/vAdkyEFFKu4JCGCjsaQ8YDnbg/Tqb8P7YYMKHv2/1\nGIIPO0ouDSWTcQIS1gtYKuQCNSeBEmi14GiyAKi/0HNu1dmJQ52DZovhtg8EZrmCIh0h7fIcyDtp\nWpGlOn8JfC7r78JSq5zzUqpKUVpdP9fKkTHA4EiyH0OQQe92QURzT6B+UwtcwoDnvwBC8HTM8/J4\nX0UwH410GZLfe8L7LHQuX/gE19QPd6+utwVATJbEY8fGKTW5WsvIExGV2BxGbku7qfNvBa4vRkrI\nnqyU9px7nLPy4OHd9bbHtTBnOZGxnK1M5/2Se5estU/k8z4RXXqfx3HmzNl3wT4wuWettQbraN9g\n2EnHmTNn3xv2fn/4B8aYPWvtE2PMHhEdvt0XsZOO53l23V9M3hVYw9zsCOwJFB6NAHKFEqt9dKJs\n/HHK7fvyUOPIly4p1/jsHqc89rpQVCEx5xyUG0/S0frzwxO+nIcHKiBZx90j6N7SEHb7FLrrtEEO\n6mMtXmb4hTLAxz5DsgLYdIM1/iJ+2YJYbiCdffylHscDAUkjMDizeu6VyFstVlr8coC98x7xddy4\nquz1LREk7Q6hI08DjjnlYy0Tvd4TaVKwmukypFgqS17X/UNznXW9fgH+ogcFMGNZKSQLXVL4EkGJ\nYJl3daj39LM3bhIR0RCKugIpXFlCB6JKlnmDtj4jpa9LgftHfI3fuPOaXk/B0PtjEPvf2tbimTqd\nOIBnIxFpLtR0WMx1/ovkiIiIOn2d66U8O+NSxUqPl/qgjA5eIiKif/ZQ4f/sIYt5XqpT4Ku39cFP\n2fuF+r9CRH9RPv9FIvo/3+dxnDlz9l2wd/X4xpi/S0zkbRljHhLRf0FE/y0R/ZIx5meI6B4R/dRZ\nTmaMgRi7FM/4OoTQ4zfmQQLZWaAqE4iqySEkjuXH/BY9SZSsaT3SJr4PLvMbtYKimI+VdcGHEl/F\nRN/GMynI8aGv34bkCfgg3V1I7Nos1J3V3WGJiB4csRcsoTSzltdOoLCnAde4J91Smj7E9iUWb0F+\nZg7Zgs0Ge75uT/MOwh32OEmm87s5U08yPTqSf7U0+UmdbQbZenFLj5nJvTqGPIv5hL374bEqGNlC\n5zoXNrcH8uV1V94ZlLGewPxPBUWApuRa7ailoGctqU1E1JGusthddjSVohfI5Lxy4zYREW1uKgG5\nC3O5t2DvXszVq6Yi/vng7kMdD5C8vQGTjOlSz31wwLLXDx+8st62v9BzXtrg/Z+/sbfe1r5ynf8t\noK/i5Gj9+dXXuDXl/Ze0E3214LlsN3kMFRCZ72RnYfX//Nv86YtnOoMzZ86+58yl7DpzdgHt3FN2\nfdGaNxKzb3UUrrWF3AtLiMOD2GYsTSgPAVr7IqIZDxWSXm0pwRNKmusBwNyrCcO5JXQpqRsjEhFt\nSVHGtWe0C3hZ8jhXViFpJd1YLvX1Go5mCrVOklT2hcagQr4EEKMOY7wN3hv+JaJa2x4000PoNuRL\nsdH0QJcM904FwjcVNjb7Gve+ss0132OYFwp4rmI4dgh5FqsxX/sM8glmQr4+xNjzVOdoIYKZgz60\n/Zb5TUB9JgKVoqYUw8Aw1q3RkQxud5XgbPeYxO0AuTqreExZBk0+l3y9376vS0Ool6LWDpN2uyDe\n6tfzHuoa00uBmCz5+KegnDMS8i6Fe+sZfUatEI5TyGVoSary7kDhv22qstQffYuJvOhUB7yQnIu0\nUxddfWfJPWfOnH2E7XzLcj1DoXjw2vOZBEpfU/YOG9Apx0CRzmIuHrTQfRbyhrveVtWSZ5/9pJ5U\niJkCeqGdCsFWQpeYqwMNDQ0MZ2hNSvXkLx0ysTOa6LmvbXBYcHdbVVKG20psPTxkD/DaE/WG68pM\nKOhoQ7lnR1SIPAsKMKKF5wXQU430rX86YYT07QdK1N0b13qASrpd1ymiqiclngbChjIHfgS6diF0\nzYl4rkMIW1nR9DupNPT5CDxoKSHY6VTJsqb0oNvd1jlfQM9BK2TbSa5zWfcHbGP/P5DAJukbGG6p\nh/QbPP9f+4aG5v7ZfSbdDqDc28JxPn2L/z2JoEV3yNd7+5KG8NpAvraafK9mpPd5PhVC8L6GU19+\n8Ej/LqHBwY6igM/cYDTyEz+iJeYfe+H71p9/4scY9RwDf3dwn+e9v8ceHztPvZM5j+/M2QU098N3\n5uwC2rlCfc8YJWlE+aURK3RriShiHCpBEQCJsrHFsKiCGuVBn8sEXriuRNz2Fc1Gmyz5fKuZQt5U\nRCkjIGu2NxUi9nuiKnOq0G3X4xjrsKfH2d2SYp+lbhuSLlN6Is7YaCuxNZoyLE9XChV7Xf27L6ox\nfqTbUoG8VQkY2n9z15ZhT0mhQkhPv6FQ/tpNLanYEqiazbHxJJN/ENamCkjPunZ8ANcTiGjlc7u6\nlGq2FSZvCdm2LBXq39zk/fuFzv+juc7hfsTLqW5Tlzstqb33oVnoaqnLrtGcYfKmpzA5qou9miDE\n2hCcHOj8VaAO1JSszjaIkHaI/7410AaYzS2YgyEfK/IUtldGGrxu6Jw/B9mjxzOG+teh9fbI4+ft\nEDoI3Qj1nj9/jTMr/8zt59fbftvc5bF1een2e/BcvJM5j+/M2QU098N35uwC2vlCfd+jnnR48SWG\njiHstsQkE4jpdgGGXbvMEL7XVBb9uW0pLAHGNYY04GomveVTkD0SwUUf+rh7UMdspI670VZodrvF\nrGlZdWEfgZodhcsbLRBKlIzg+UuaQtxvcgHQxCpMBaUw6ks8NoailbqiJwXprShUaN2RrjD9vsLK\nuQh8hoGmJTdAk6ApactJAAKcUky0ApHRcg4NNKWoqQtj29xkqDqEOP0c4vhZLdMVa1w8avL1nB5o\nOqqdau7GUJhpLZtSxbbdgU5WAI1QF7IMCkAk87p0V9r4nM7b993g770+gs49Vp+xrtzKRlPnt9uU\npplQ/x/2INVcZNu24d7/iRdeJCKiCeSKTCH+XqdxbOxqqKXb5bkeXNZn2Yd8gr7Iam33dDlz/Srf\nqy3RB4j8s/ly5/GdObuAdq4ePzAebQl51ejymzmw8BaUDKk8gQ42XSUrLkl76+1NkD4W8igtQZ0G\nyh8PjjlDyyz1PEWDyR6/ACLEQFcdEVLcgJhxIQUl1tdzZzmPswKVmjCHOLMgmGFDPW1bvGVZwT5Q\nRJKKCKPnq9f1ZlK0AiI1UEdDXUEokVHkkYgwZgrS0QGIThpBOLhPJCpD1QzabUNhkJV9POgp2JT4\negdKZJdAzi4lZyJfoOS2oD0QrNzaUhRH4vEbkCE4kU5IHci9uLGtKGO7zd69AX9vSL/DLsiS7/b5\n71dOoODJ6L0IpEQ6gA43ldwfA6jQz/TcNRvqQYennas3+F/w+LQLalJ1lyWURJciK8+8ubyaiCiv\nmMDsQYHQM4IyTkTByJ7RlTuP78zZBTT3w3fm7ALa+UJ936fhgMmKsi2FJSdK8JxKbT30MKS9hv5P\nLRnuhdDwUIp9sokWmyznCtPSER8ztwqzRgdSJz9QkqTTUxJls8ew0ocW0zOpsy8hXTUwDNOKQkmd\nEqBxIkuA0QgKe0R9BqOtmwB5Q7leAxruhSj8RBXMBTTaqYTk8qAWvScKPhUIQBJ0cklqTXtQ//Hq\ntGaQy1lBR59ScGQIUN6XovmwqZA0aiBpKvenpec+nTBtZxKdqwL6FYSSfmth7KHkHdwEMqzXBAHV\ngvMAohRSjGVZiVmsgRTzNDZAIwE6HdWNK1eJko1Zzp9LC008gWitRE/Bj/QaW7VuA6RZB01sRirF\nWoE+Y5XkNeShXpfNNL+hkHs2jPV5OhICenQiYztbjY7z+M6cXUQ73yKd0KNICjNMi71tBrpr+TG/\ntSaZvm2fgAzLVIjAcK7efS9ir9sY6ps1Bd2xlajkFFDYM8/5bZwXGjBKMyV4huIVBjuazReIp06g\nqIhSDgl50AlnaUGOWjzBCuWoRVYbOwhB522qanYmAGJR2lJX4BVpBZlnUk5rwBPHteINZO6lFrvd\n8DELuO6mMIYZQfvqClpe14cvdZuV+wft8iiExn4NCTVmUC6bStk18FZUwLwt5PzGgxCrZAhe6mim\n2xZ0VIpr5AIkr5cLqdfEx1y8cwwddwAJVTXaQblvqdv1gV31e4BGOvy8BNDNieR5SQGBGCgPjsTT\nR4D2rITpoP6Hlkudl3LOoWDb0efSJPwM1ipD+Cy+k52lk841Y8xvGGO+aYz5I2PMz8p2103HmbOP\nqJ0F6hdE9J9Yaz9BRD9IRP+BMeYT5LrpOHP2kbWzaO49IaIn8nlmjHmJiK7Q++imY8ijiBjidDuc\ncRePNH77rRUrjMyXCvUtQK7OfYYxL0LTxq0+w55BQ7OqSkgHDGR7XIHCjkgwh0DGLCHOfzzl+Hxn\nQ/dprDP3AHoJ2TJG9A8dVmZ19x2E05IvkEH9OYpxVgKdk1wzy6THJCVLve4Klg9xXI8d2mjXQpYZ\nLjOAtJOlzxzUgZaSTubD8stC0YcvzFEKMe5EiM0+xLB9kM4pJNuvXOkxM1nuLCDmnuRAfMn9TSGD\nM5NzDvqwLoKipdWESchlR4kvkvbYBSwZYrkGC8vBItE5msm8LkcqEW4tP48W5LUH0OSzrhmzcN2l\nkH/JWDUSkrGe59Itfm69WAurfFFL8iD7zvOQ/OPrSUmXB025tlDWi+bDgvpo0krr+4jod+mM3XSM\nMV8yxnzZGPPlFNRknTlz9t2zM5N7xpgOEf3vRPQfWWun+GZ5p2462FBj0O7axV1+ky567GkSIKRK\nYTVKeJPPcn2z1mTQaqmeqyasGhCzMSGQQsI6reAdd5Lz5wjaNOdQJttM2eOvwOvWzFYJ5FEp4Rcf\nW3RD++pS5LkrQBZxm5EDUEtUQHhyKY0pfJhfT5oyrCBW0/IgC2zJ48S8+6pk778yQAKSzlsu4cIV\noBEr52lBxmKrDY07Qt6eQ0vsOhswBRRmS828XMq8+thrTk75lBQ0MJx1SK2N/QEFjeQgid4MdI4C\nYk8/mStaLGRsDWzmkUgGpjlebxvl+rxlohgUokS4ZFGWgJ6GPZ0XqhEZEHmntXz5gWr7Hd5Xee5i\nwh5/5zm9xv5Vvlcm1mw+u1A4mUhr77LC3oQ83kIyI+2H2VDDGBMS/+j/jrX278vmA+miQ+/WTceZ\nM2ffW3YWVt8Q0d8gopestf89/Ml103Hm7CNqZ4H6P0xE/w4Rfd0Y81XZ9p/T++imk5Yl3ZEMu+Rr\nr8oIoLBElGayDGDjXKHOozHv+6lNBcodiXdPJwqpJg/0cyVk3AyWB5OFtBSGYogh9KJri/jiMUhP\nG+n04gFB5tXQGV6fRaRwMBPCaZVgiSefs/QAzkHgNpHjV0BwZqcMIVOQ184BBnsx77861byEpuQb\nVJAvUIGqT51d1+3oUiAR2GhAbLOKFVqHsjTyMM7vCRG3hEA+pBUWsmQpKr2PdacdAwo8yxnAV7ln\nGcD/us0hykdXuLywsjwBAm41k/kHyfMy5Ey4kpDU1OOEotoDDZOoFHWg6VKh/Alk4VkpA88Weu5J\nwc9LDKpI7R29f9OKj+lPVIAzkoKbxlAj4yHkn3RN3bsQsxylsE1+Oxaez3eys7D6v0Pa1f6N5rrp\nOHP2ETSXsuvM2QW0c03ZtZ6lrCk139LlhBrXdTANTlk0IXR3wVeToLOyrRszicGejh+vt50ulVW+\nJCKE220twqkhvgdNJiuIq3sCRR8dqEBkIGmdgybU7ddRCE/h2CqD+LzEhP0mxl1FIx/Y9GOo857N\nF3JMXYYs6/rrBWi5AyO+CHm8IaT5VtLa2S8VswYQHw4jyWWAAu5Qavg9aDmewQ04mDETjo1M4wZH\nKRqwXImh/Xidt7CAaMi8XtIBKs2hf8Iy43N6ED1YyTiXc11GeNBBsxSlpjLRRzqqITjmHcgwwgBq\n6xuaBtySqEsUQ7HVgmF5CnkUB8ca58/qlOlKl4u9vqjoNBTqY1HS0WO+P/ZIn/XegCMBBp7vCqI7\necz3PD+Bq1hxAAAgAElEQVTReZlLens25ntjIS/jncx5fGfOLqCdb5GO0Zi3FXWUEMsbpWBhAbFI\ni7FpGW0yUBWc1wU52IcQvyX1Cm1RydnqgGJKJOo/DfCQ8Lb2RR8ubEDCUSwKJ1B2m4uKSgVfi56S\nxqmz/RSBtOR6MGMuhEKkusNQE8pyIxmbB3HrDPIWPImbF0BWhhEToAkUoDQJY+lCuoECUiblqTbT\nfSZAph084TlOVqCP12GEEkF2XAsk061kSeYFZvPxMTMYWwlZeoEo4mQpQALJghxnOpfJEryukMDZ\nAFR9unxMLJ4JZN5LIMGyVL1uW7x64Olx5rXKEyC3HnQ/Kit+tqyvpPNEimvuH35lve1bX9GW2XVx\nzSee//h623VJAcQ8lSkUrN0/YvL26IF2LTo+5TmYiQx6VZ6N3HMe35mzC2juh+/M2QW0822TXVry\nBJJ1RN1m2daiisIwrDFQm11CJ526eCc61VTa4YCPswIlmYdP9O9HCybotiKFR61YtACgsOcIUiP9\nkM//zA2Np966zimW0EeTSKBzBcKYxkDxjAgttiBldy6x8oYHOgQART2JbUe+zkFPYumzBggzrgDS\nLXlJ0QciqIhEgQdi5U9g3ronPOYcBTgl5t+DluOXhkqKhi1Wv8lyVU1anXARSgGpshUQZ1TJsiqA\n/Aep529BdmkblJbmC772JjQ6zSSivIKCGkwdjmVesSW5lY48oadLQ+Px35ewnFmAuGsoS4DsUI/z\n2uucF3KyULK38SntZjO8fJPH1ldZ7FXOZNv0dV1GPHisBTuhpHN/5nm4j5ISnGf6rM6O9dk6OuLk\n2MfQuWl2wp/notJUVg7qO3Pm7G3s3HvnNSXEMp9Je9+Ovm39q9I44ljdagaho1y09J7c1dDdM01p\n3QwZW/jOM0K2jcE7tKSE9Pq2evThDoRIpFglCKHARcgygtCQqUuGgfBbQa5TrfOXLUCFRXT4ZqDm\nEnrq7SJir56DZzsKGRWdngDhB6/s7U2+jmigiKApTKhXqJfPsdRXrhGqjKnTrLPW9BqxsURLZKTD\nFLL9lnweH/rpNaHoyBeysiTIaIz4pMUKiFIgDKuEzz+CYq2WhCpDuCcZNPvIBGmtFlBYdcqTtDNU\naGGkH58H5Fw3xPbj7DkXICEeN3icPjTwmBZa5JN3+e/DXVXGiYnDeN2lHjteAUKRw9+8pqizHtMc\nhJbGqRLV2UxCtKfq8bOSr7cSpGPth1ik48yZsz9e5n74zpxdQDtneW2PNgdMHNUdddpXNPYZSleW\nfUjpOgVY0xVI3OuBCGYiRBQUlmxArXRT1EqwXnworZCbsb73Op5CyLkUS4RwzHQkcWKUWBYiJQX1\nmGKFnYF4vAOQ6c6krj9u6zIjCHQOFkLaWcieq/UJMLvQgHrNQopQCigGipoMnQtAfmWJMuDyAeLv\nKyG8qkQJ1/IRLJykGGgG8fWyxdtMDmpGmJEnteN5qdA6EzRuQWR8ECgBF0vWXAgCnU0hYn24oAja\naHs5f9eC/kBdPn86hqKWibQHBzIYuwnVug11FiMRUSiZfS3gLFcJEMN/yPPVKJ+st1U+z3XU13MP\ntvWcoXRkmsCyaXbIz/oIiNIFFF4l0hbcwPMWSZemSERnff9sP2nn8Z05u4DmfvjOnF1AO1eov7M9\npL/07//rRETU6zPUHVca+xzd+zYREX25c3W97cFrX9O/n3BM9JsAf77yUGrvIe01mSkTmgibX0sU\nEanufgVpolC2Tp4w8yUUtVS19Bawpr7sHwC88kONSNSS6SHE130rEBAKNhoQN//CX/kSERFtbui8\neE0p9JjpPsdTpeOnkkcwO9Jl0cN7d4mI6B6wz4vHGkFJ73BM+qWDb663jSVPAhTJKITuPQuJCoCK\n1jodNoSmoyWkAVu5dh8b+si8WehKFIMuQ92sdIDNRjcZWnch+N8H7G2JofO3n9xfb7t7wJGj5UKf\nh9WaWQemH+6PkeWmZ/Teh1JAFMFFWCgqsrI0zSBEUj8vOUR8Spi4+nkL4DyBHDOA9u0hpGlH9VyC\nFv+2zFFH2pUvocX5O5nz+M6cXUA7V4/vk6G+kXisvOm22xrbnPb586ChmWFjaNOxWvK+K+hmEwdM\njgRQGmtSlGrm8xSQHTeMRAUH3nsWXL4nJbFxpF5sNmWvMQXyzhOFmG5HPbYHmVNG6ogtqOUsJZNu\nCl6mB2owV0VosdXTUtFCCpq++ViVhR5/7ffXn6ciiLlMNcvuq/f/iIiIkvtaGJKONItsIrHy44lm\no5V1rgPkFaA0dV7VsWIg9yq+Jx58rwBU1BCPhd7SCOJKAKXFgDK6kqTQ7eocVNKn0DOKatobSgj2\npAvN/akW7gRy7iaQtJ5IboOjpQa0nW4JSRajcpE8O72Onm/YU8L2aMRI6ehE53JZt8EGZJFD3gLJ\nHEXwXNbPWwQl2QayFyN5brtdJTVj6c3nCVJ5W8mcN9hZNPcaxpj/zxjzNemk81/J9lvGmN81xrxq\njPlFY0z0bsdy5szZ94adBeqnRPSj1trPENFniejHjTE/SET/HRH9D9ba54hoREQ/850bpjNnzj5M\nO4vmniWimhUK5T9LRD9KRP+2bP8FIvovieivvdOxTBBSvLlDRESpQL9yU4m8QGLkR3/wu+ttD4+g\nseUJkzUliHGG0jXHAxLFgKLNTo//3u8oPBq0mBDxIAWzhKnwpWBkMVFY/8hKDBUhoMD6ZKGkWdtT\n2N5q83Fa0NzxwTqsq3C6t6cQ0l5/jo8JQpOFdMg5ChVKJs9r/5JHrzwgIqInd19dbztYvkZERCcP\n7q23rWYKk+uOP8BvrrUAqEAVFxDJrGEp5AbkBV9QAdgZBTy70rSzhDyKQpqNhjD/MSwfat2AbqDz\nWnT4+DFoKLSh7r/VkGVIU8fuyRIqBOIwEkLx8lDn/IUbO3pM6RzkhyDOKsRwBYReyyjUH0hKbw+U\nfOYZPzsJCHDmkc5l/ezEQN7V3XAi/61JT1N3MoJ077oXgqk77oD60TvZWXX1fVHYPSSiXyOi14ho\nbO26jO4hcVutt9p33UlnPJm91VecOXN2znYmcs9aWxLRZ40xAyL6ZSL6+LvsgvuuO+m88PxtS00m\noBqxsHZN9ap1JtZjUNiZQb+xI1EZiSF7ayMQMgb65W3f1LfxzR1+mz9/S7XP6qIYH4iTAN7g3S57\n2PvQBeXrL3MXlNORFoEcite8N9ZMK2xrXAmxGEEBS2dXMhaNequ9oWaBBbmQZQFkJ2ZMWD0z1D6D\nL7UURWyE/N3D5j9eb9t9yO/h6LK+bCeHOkfrK48UCc1WjAjyqV5PDkSdlfCmfUrJx9YXq+OFLMid\n6xyWnE7Ue+dT2R862ARA/sUdRgk+SnvXPfyASUqhBXhDvGQbEFlbMv9C8KB7l/m5+8GrWlb7/bdv\nrz8vDZO4c5DSzgTV2Ern76TUEGFPCpW2mkryltK78GSmz8sUHF/dWy+E617W8wHxVHisKRc/Xb6F\nzHrTO5unr+09hfOstWMi+g0i+iEiGhizLua+SkSP3nZHZ86cfU/ZWVj9bfH0ZIxpEtGPEdFLxC+A\nf1O+5jrpOHP2EbKzQP09IvoFY4xP/KL4JWvtrxpjvklEf88Y818T0VeI22y9o3keUaMt2UddkVBu\nKUnVeCzqKCPNNnvwSD8X0q2lgKDvDel88rEdhdPP3VRI/LEdhryX9pTAMQ2pKwc5aix6KVL+3NYV\nB7UEVj6E8XxFOvY8HGvseJkq1E/qLidWCcqe1Ly3bm7o+SAvYWOLxxQAGRn6LEHeB9i4N1E4/roQ\nipdS7W/S2xIln71/fr0tLzU/orfL5x/0FDu//hUuMvn6SLMl7zxRNZiFkEqHR9BRZsGqMBZEU/t9\nvRfbssyJUh37aMbzfwIcYgSw/8mYv7sC8m8157l8/lm9tzFoMNRaDZMTnZea492AFt4f32EVoc99\nWimprYFC9ERaUfdXuo8kjJKp1E9GqHQjy828reOpydP5li5HplNdno1OeelzAsX3J6LB4EF+QwHL\ni7LOf8Cu0xLbn8izVp6xaeZZWP0/JG6N/cbtrxPRF850FmfOnH1PmUvZdebsAtr5dtIpiPJTKWgo\nGJLFE2Vu74x/j4iIZq890J0MSmYxrLk5UPj0xc8wZLt+U6HbtR0tcOlKFCFoK3vqS0qkXyIDD4U0\n8u/Q6HHMNkPwBtRpJ0uGXI9PQN5qpsxtnd47SUFW6hHD7SVo//vABlcZz0cb68GltjsLIZph9Zgd\niUP/0M3ndLxTZuhDeLcHPrDoojlQN4QkIvr0Ld7/R0+eXW872L+z/vzNe7xkefRQox37Igw5OtIl\nQa3ZT0S0K+mwYUfvczXnc88h/XmZ67XtiyDraIU5EQx/o4UukcxQ5/DVV0WIEppvNiXuvb2jUZPn\nb1zm47V0W5Xp/as70ZQAp00msX2IDoRGYbsvHW58rG6SFHID0aJmpj+39oD38UjPs5B7X2H/BID6\nsaQerwzmrPDf6zRfz5wtZ9d5fGfOLqCdb5FO6NFwj9+U+xIHnZN6yG++9GUiInpyRz1KBP3Kbg15\nuD/4SSV4fuizn+bvtdU7b/X0bR72mXixRr2HqZVmSvUOPpRUWiFMfFBpyQWh7GxrdtblCcfPP3mo\n5F0GgoqFFE6AOA2lS77u/DU938GmknuDHVFUAXWgSPr19WNlG0tQp6Fc8hvgPGaTEVAAuQoEEuK2\nHMm/SiI2ZLytUInQfls9yFCEUec/fGu9bb7P2YTTU71nrx9rzH4lHYECaPect9jDjjNFbhbabNck\n4irTbalksM2hgCjbV7LyicTILcj/dNr8vNzYBQTY4vtXAtpIoLKl/mQBtXRlDtJK56oLpdhLubYy\nUeRQc7wGpt+A7LgnOQq9WEnEy7JtCrkIK0itNOLVm1BiXk/RukjnjOY8vjNnF9DcD9+Zswto50vu\nWY+SVIQupUHg40xjxpO7HA+3UCQSQ8z+2mWGwZ+/oYU9gw2OcfsxKOOAKGUgHWs8X0kWKxAe6xmC\nQuOpnjQ/tAHouueyZACBk0tD/l57qHCto2F+WsnYp0DQVNJdh6DNNQGxVYnYpgHoZiNeHngA8QqI\ni0dCYhnoRlO3vzYVHAdII6oFKCEk7EVSIx4q2WihH0GdSR1aJdW61/jar2zoEqjfUdHJQ+n0UkIO\nwtGK9+/oYZ5qqlmnrq4gVh5Jamq7rXMdk45z0eblRcfqfb60xcTu9W1dGnZi3ge7SVeQNluk0s0G\nUpA9UzfS1H0K7N4gH9GLRtI5KE0y+JruU9Wp0NhFSZaY2HI8hMKg3MpvB+5pKToR79WDO4/vzNkF\ntPNtk+1ZituiTCLFHZdWWjyz1+QCCmP1fVQAudHsMxkU9FVpppgLwRNomKdqQPcR+ehDsYknKjdB\noB7SA52zQkpIoYUfeZLZh6GWlWRJLYCYSp/q6MP7lCAXXq7VeEDWegodciTk5qGiUMHHMZV+z0JP\nPF8KNKCFH5k65AMhMYLCEluHniCrkOp+fNAJh0o9aCkdfewIwlq1g43V+7aaesw8465HYaFEak/I\nyhxKVhcgKV0I4rPYlUhQ0e3bGrbNTpQcvCQk4yGEszolo5D+EIp5OkLc5nq/VwV4ZXGwfqT7mIrn\nOk11ztFMIV4XwmyBoLACMhpR9ccT1m8CWXorQYFJic8LFlbJ30G7byXlzF5dZn22xD3n8Z05u4jm\nfvjOnF1AO1eoX+UFLR4zNC8lCy95TVsHPwz4b8Bn0N4ljZ1+8hbHY3dA6DAXhi4GOG2AHDFC5AG/\nRkEtUwwB9gzItuWCIW2xgJbM6ZubMo6ky0+60H1DIF5iwXZNKDY5TmsBTtACgJbYyZyPb4B5tBKz\nzyqNjxNpxl1e18SncJGypLCwPDAwR5XA06pQAU671trUOa+WsGy6x7H6+Vxj9l6HY/7Nli6/vESv\nJ5W6diQ4cxlTDl2JloWep1ir9QBhK89EuASlpIE+By1pNtpuQ3ZiR+A2NKGMG3xPUoD3GRQIWVm+\nWaj7t0K0roDww27poeBrA2RkvUws4DglZvbJmqLb1CPldZvsFIp9gPgtyrqNtv697gLUjESJymXu\nOXPm7O3M/fCdObuAdq5Qn4xHJH3m05w7nrze0IKcg32O/1qAMs2msquBpMMmkBq53eAYbSOAHuew\nTy1g6DeUdQ4ixl8WoJdXqJClFeicVcpoL6VoA2WPBgOGadtdPd/9Az3OQqAhcsG1pnwFUB87uVTr\n4hzo8x5KCvISliYJwH5pmkmZwn8S4cenjl3oPibnayuOFWJXsizy+wDVU43JTw75Xs2ODtfblhEv\nzwa97fW29qbC/mtDjraMD5TpvycM/gKKjioQ41wvg6CTkQmlP0Kkj6yZ6HPSlWIgmyi2bsphYqP3\npxCGHqW+QoguzBOejyUIWpaSc7LyQCYOojK+PBPtpp671RSZLLglT1CGS5ZAJczBsCHLFShMG8d6\nntGMz7PVxOvh/Zuyr3/G1F3n8Z05u4B2vuSeLWkpwpFzKUE8mapHMaLskjf1fXRpCD3KJIPq4Yl6\njxVxocxWoF5m2AF2ULKhQiCsfCnUqCwU1EA2WrpkwitJNZ6dE48t9JSMuTRkL9frqQcsQQ65KZ4A\nRSPrdt0rIM0semXJyCtDbSFkcinsmSoRlwE5VStx+9Bquu6KUxHOH3iKFV9PttBUw7LOUIv1e8tc\nmbGjhD31JFVvOJkzOjiB697Rqaa5ZK7NoVdg3Q79WkMfvyncsjp3A+PecZ1bkAHZCIVKcxmTBbHT\neJPvT7+rz4YR9crTRJ+hAu5P7d3TUq97NGYUV0G6X6up+8Qyjnyh+xSSgzCa6X3aP9X7NxKSt9/S\n52lQy4VHIDsOLbH3hnzOFNSillJ45QmCMR82uScS218xxvyq/L/rpOPM2UfU3gvU/1likc3aXCcd\nZ84+onYmqG+MuUpEf5aI/hsi+o8N44n33EnHGkulqJiYBRNFx3dUlfv0kP/WSJT4CqHgxkjsfwZN\nMeePOKY8Gmtdf/tQ47ubW0wuXdtVmDYQjXZrFCquILV1X4QbsxWkyAq5N58pRDyUdt0LqPXfGSjw\nSaci6gka+lNJ78VcgxyIPlvxeSzEuOsClgRqyOcgSJpKoUYy1uMcHXEnnXuPlTzdv6dLkv1lXdyh\nsP1yk5dAP/C8FkFRW2H/nSWTg7/5WM997558TiFHAGrMa3h8Cven1ur3oDkkzsGaAIWadyOx7Qam\nKkA7dJJ9CiAJvZoMzhRiPz7k6/3yK9pM9B7q3UuXpghgvReJdj2M5/KOKvCEXWnmCsuMxYLv1SmM\nMYOCnZq3DKGjki/1+gZSldNUr6cWnqpTiImIFrI8s+vC/7Pl7J7V4/+PRPSfkvZh2KT300ln7Drp\nOHP2vWDv6vGNMX+OiA6ttb9vjPmR93oC7KRz+9ZVe/roLhERHYzYU8++pioqaYffI1eALujuqppO\nJEU846kOOxrw3+dd9aoxFEuMxSt3O+q5BtJSGdq1UQIhqkQy8pZQBnu4z6Tk/tHj9bbHI9623ddz\n97vaAeeRZLhNgZDKyzpzDzwThK2m4slROSeSLK/kRHXtDr+l40hFxag4hpbjU57Xaa4eZYmqMUIU\n7k/1Zfywy5+3r+n1+BAe+8YDHtvd++pBjxd8bWWm3jtYIcklGY9QvFR3BTdACPrggyoppYaEx3Ub\n89EBEJxAIjZDHrMF1aSFEHSv3H243vboiMnT1/ZVEv0YdBLrctkAimviBnvgQUtPOJ0BKRdwqHgG\nUtlPRlP5nt6TDDpA1S2vU9BerD9db4O0OiCupkiu20T36UhhVVa3dD8juXcWqP/DRPQTxph/hYga\nRNQjor9K0klHvL7rpOPM2UfI3hXqW2v/M2vtVWvtTSL6aSL6x9bav0Cuk44zZx9Z+yBx/L9M77GT\nTl6VdCRZd4tTJlfyPYU1nxowlB9i/7+VDvH/+K1vEBHRvAD4GjNs//5tJaR+7HMqMx11pFgFMq1K\nXwpUKojdQ4qVTWZyHn0v3hszKbQPfFImnVXmj0ENpwFFFaKukowhZp+JrDJ0PImhKikWOG5SHY/p\n8PIhCXSfFIQbC2lJM3uibbLvSeeZsgPKOJcvrz9vXWJ4GgEM7m3xeJ/bu7nelsOy6YnElJ+AmOaw\ny7kDk1TnCvMS/IKvYwWdf0KRiQ470MFmBASckGAWOtfEwupBDRR1gMga9kUNKYDMPYnPL2dKYB4c\nH8l4IYcDm+KIlLnxQR9AiN0eyHQ/e+2aXqNk3z08UNJzIvH7EsRBUxB4WMk15nBBC2ksagdKAt6+\nohmRoSwVepBLshD9hkiyRM9K2r2nH7619jeJ6Dfls+uk48zZR9Rcyq4zZxfQzjVlt8wzmj7izizl\nimFLY0PfPdcChqJ9aBiZnijUfDxiWNO0GkO1A4ZC3aFqwRcAAW39eQX17RIjL6AOu1gq1CQR7swT\n0LaXDpobANFbQ16aFCtliBsDzA1gOLmAvIRMcGUABUJtYIuzqeQJBJqya1ccHdha6XHybYWAR1Kz\nPe9CPwGflys7lzXKuoD3vE0Y8m6qRD796G0WLr3x6U+vt41GqpfwohTC7ECTyUcHDOVfWyg0ngJ7\nfUPShE9AnsyTmvg+6EQd70MxlkRV5lCLPhxImjX0P1jBvYhafMxuE+C46Ok32wqNvQ4vBW6RLgmO\noM/mQmL6h2ONmpQ5j/35Zz+23vapz35y/XmyYIj+tfv319sqSUtGSbceyHmRpN1+fEPv8/6Y7zPW\n1Mex7h82+TkpgNVvSfHS3Lw3H+48vjNnF9DOt5OO8agr7aY9KaP9DMhnBwG/rZ/raLGJhYy7L1zh\nN/jJQvfZHAhKuKzttlHxplqKB10pIVVJO2ICj+9D4cP1HX4L39zT4o7Py1v/FLKvxjNR4Kn0e1Wh\n3nsspNvRUiOddUJYgRLi8P6NTS39rbHloGByrzFUoqgf6j5z6aPXHqu3Wwp5FR9rfgKBBHkYiuf7\n9Avrbdcus6fv9tSj++BVq10+5qUNRTXbLUZKgxlkCO5rvkEx52N98rpmU+5dZ4HVJaiD3vtDRRaJ\nFMo8Wqlni0QY1WuBr4KYfZRINiB4WE/aoG8OdV6+0OJna7HUuTweqct/NWSvHfWVvIslR+Dms59Z\nb7u8p0iqM+Mink9cVVI5Nnw9Jte5qpr6ue9JO+6hevzdXZ6jGJDQNvR8PJK8hPlCn+UTydosI1Ga\nOmObbOfxnTm7gOZ++M6cXUA7V6ifFzkdHHGsc0tUXvJM0xyTuvY7V6i50dA4dKfHy4ObfV0KBH3+\nbmMD6u2BXDqR4hED+bmm7j4CbaOjEhRvUoGIl+C9mDNc7HcUoh9Jpx3Uh59BH4CNLo99t6vkXymx\n8AUKJsLSZrli6Bw09HpaGUO7CqB6BETfbpNzGRrbOpedRNon93SfZqiE1tYGf/Y6ClmHO7yk8DHr\nE1JtY2kXHfQVOgcf42VO61jv09Vthc6TKV97HEOKa5vPczjVJQH5GgNvSLPLDVgOkVdrysOyKNLr\nCQImfEMQr/QkzdcH4qsjKd7tJkBiqMefeLx0vBbqT6M75G3P39DeDY0mEMwVX+/uQJd8PXkOLKgz\n+dAmuynio+FQ732R8/FD0EBIc4X1LSEMT4wuA1tSpLOsb9oZe2c6j+/M2QW0c1bgMTTL+JXUPOU3\nWVLqG82T0ssR1Pl1CMI8G/xmbva1F5rf4Devh11XoKTVkw4uFlRNKnmLltDS2i9BqSaXfaBFd+DX\nCjzQPjli71LrpxERreYaFqwJNtT2zpvsufwVkHOAGMIOX5sHHXvKWk4cZJfLJWTCSRHPpT31xDvS\nL64F6jNN6NlsS0YrBYSYQqmeqRYgPT0F2WtBUlGuaORSj889gN6Ec9CMqyRTczrWTMRAykrHlaKs\nqtRzruZMyGaAaiLpFnQy0eOEU9BmFM+3F+r1lLJPASXXVuaf2i3YV+/FpxpMEvuReuKudAnaAO8d\nQR+8QkKWA9Dcy2Y8R1tNPQ4cktoDvj+1JiQR0XwqZPEYUlch88+KOlMMWYfHUs5c89lnbZbtPL4z\nZxfQ3A/fmbMLaOebuVcWNB0zoRNEolCyVOjWloyl2VxhWNFVqFlnNGG74rDB+MkCeZRNlCiaTRh6\ngzYi5RKnNtBhpQI1nko6mlSBnjsUMseDwhHT4HFmESj1gJZ2X8Q2G9BGOx4xHC9bCtVzqJNviG5A\n2FbYbkWfwEIXnxJkpkOp3Y+sxoRj0SdodhTq+xATLkQtxiygGKjkScKCpmIOn2Wp0Y5A+0DEIj2I\nyXuJTvZKLr3wlZAqYp6vy0azLU92lOi7eypzACRXHPO8n46VKEWlmg3JB9loK8loZPUwX+q5G1K3\nbiIQRYWcCCOwPsSuOBWfM4EKreqSEtB1PkHc1vlv9Hg5mWZL2AaNRWXZ1YCllteSYisQMy0gpm+F\nMJxDU1lfMH65lgt38trOnDl7G3M/fGfOLqCdK9S3VUWZwJipiD0uRhAXl1i7zRWa9QYaL+2JVnyQ\nKHzym1JbD7Xqqww+C/taJgpPW9IR0WvoksJ2QWZrzAUsQaZMc2uLYakPUN4IW5xCmuRkqfs8EJYW\nggy0K3BtahT2HRsYh9ST59BP3mQc3y2gmMebKOQtpIe9VwJtLKmrBmLHQaTv+UIYfJAXoEDuSQFd\neqZQR18LYi6BVfYSHlMJ/ebLUiMbZT2HoJsfNuV6u1A7f6Qx8pZEcsYRNLO0IrQK/QgqyM2w0jB0\nawtzKupB6Llb0kzUz/QaV1AwVZqFfE/PPUl5DsqZzt/2DLT6pWHnyUSPOa5r6yFVPC00GtWR3gUe\nND8t6kamRqH+HFJ+6yiHTfU3M5PGr3U9vv2QxTadOXP2x8jOKq99l4hmxO/Owlr7A8aYDSL6RSK6\nSUR3ieinrLWjtzsGEfdHSyb8VjPXpCAB/j6TLL6VBy2roTR2WUtTQ5Zd0eAilIanpA50mKZuj0my\nwLmmoFEAACAASURBVFNv6Ek2oA9vxyaU07bqWQHCL5G4ObZCnoonWAEZs0pAEHPEyGF0gmWUHCc+\nATIsKZXkqg9fWuiKY/l656AYNAJv16/Y43ggELk45Fsx8nQ8vQ0VAjWSq3DwUItjclvHhKFk9b7e\n0kRIyKCtXiySgpo8BenvifYPXNVtuMFLrcb8eRHpNSSgkuOL/Hk31KfDSrx7MlIPWQLKawuplYKI\n6VxQkw+9qpcB7x+A0OcEWqT7a9FK/WnMZ3y9udFr3Ia8hVyubTbTuT4QlaIQOjSZQMc+jrnsdwXH\nsVKynZfwbAD5Wsr8Vw0Q4JTMSk+KmD70TjpE9C9Yaz9rrf0B+f+fI6Jft9beJqJfl/935szZR8A+\nCNT/SeJGGiT//qsffDjOnDk7DzsruWeJ6B8ZYywR/U+ilX/JWlt3vNwnoktvu7dYRUQrSS8NJGXV\nh5TEXGrexyuFawuII09Ew90D3f1GxbHTLMe4K3TiEYgfQi101BBY1ED9coV7oZBTJ8dQuCMKPq0W\npN8W0qklAqHIXKFZKm2RtzStgB52eGkyTfQ4Rzt3dOxNJv28BujUD7g2vDrUY/vQnWfV4HmBWhOa\nC/xcgWhkvlBY3xG2LVmCqo+0BR8DcXgInYMSKSwa7irh2u/xHGZThf9zgOOZEcIwg7RZkiXSDNSK\nUt0/lCVAAORrKMkbpwtIrS4gNVi6DFWwZAh7IuoJadYr0blv96BRaanPmCfP4ALUfwKZF9vW5+54\npDoH81PeZ7nUJU4g96/bUhHYCmD/RPoaTDx9xrI5n6cENnIx133GQhJHsExpi9hp+d5qdM78w/9T\n1tpHxpgdIvo1Y8y38I/WWisvhTeZMeZLRPQlIlU9debM2XfXzvTDt9Y+kn8PjTG/TKyue2CM2bPW\nPjHG7BHR4dvsu+6ks9FvW19kkmPpaWeAYPNeYY9UZJAFBm2EyWNvWPjqPQrxNEUCveZAp4yk916y\ngj5t07pTC4Te7t1bf77zbX6vWegHd/PGTf4XPL6RbLJsBGziQr1lXTg07ujYlil7jRGQlq1Q9fMi\nKRjxjGbupaKxdjyGNstWOxDdaNzg8bb1mJ5IME+W6gMyCFHVRFHnFhCLQmilJ+qFel09Zu+APdt0\notsel+zlPEhkm4Y6b770OUT1mbzgseWJhm1Xns5hJdmYMWTcpUJcNhrqPJolXK88Rmml12jqIiyQ\nPF+JRw9TRRMR3j5xnT70fvYlW7OYg/rSHEqPpXx4q6+k2/YlDvcVCz3QE+hadCCFSB0o1jISYk1B\nbh0bECVCPFYGStBJiF/5XvVhZe4ZY9rGcNDZGNMmon+JiL5BRL9C3EiDyDXUcObsI2Vn8fiXiOiX\nJUwQENH/Yq39h8aY3yOiXzLG/AwR3SOin/rODdOZM2cfpr3rD18aZ3zmLbafENEX38vJ4jCgW7tM\nxl3uMUEUGI2/N3oMw9LTg/W2FrRc7vZEcBEKG44lAysAkcturFlghWR/jaAePJfONnaqkPT+IxWL\nvHOHlxzdCGreLcO0weCZ9ba62WW8gI48J7oMyU94/3tjhfpHUrSxKhSSbQN0tgL3cqtwe3HM4xwd\n6zUcjpV0u3aLr3e+QPLpd4mIaP9VXTbtbuhcb2wzxB8MNZusMgyx/Z4uVwahEnmTikVDH72i96cQ\nYnH4jHbp8WPF/aUsywzE+eMWw+BjSOdbTvTvHVkq5JHC+qDg6xhCtl+Okt0rIcYwz0IkvyPUYpAC\nFy+DbkxwnEIy4NodOLcsHX0LuR4tfcZ2ZFrTlT6XI1kKHC+V2T04UPKvzg3wd3SZVy9dcKW6CnUc\niSxnm9BkNZlIbot0PMI8k3cyl7nnzNkFNPfDd+bsAtr5FulYS1ZiwR1p+pikCqdbHUkZBRgchPq5\nIYgM02/HqfSgtwr146ZCNxsyJGtBobwVmNaCJojRnkJaE4kEVfFkve3aFn/XC/TcDcMpmrGv1Out\ntkLWfyopsAlIZtUqW1hg8ulKGfpqwe/ilQ/pqtJs8WEPqOYjhYD7FUNIO9JtBw/5en2j8XHbVChq\nZKkFNUWU5Rw1mI8Unp4c6ZLi6//vbxMR0f1H2jHmsgiFfgGo8dYt1aRf1vAYYuALKbJa7EM66lOd\nK3kO2vh01hEHECmFVAfyJBYfoISXLM+mSxD6bPBnmH6ywKLX9e0tSP1dnPAyIs112WTa0Hsg5c8n\nS13mfesBz9H+Y+3IYyEnpTnkqMJ1aBpb56csT/U3sYJcha5oQpSgCdGTSVrIssl4rh7fmTNnb2Pn\n6vGN8SiUNsT3XmJvOWspMXZ0xG9ULId9/FBJro0W//36tnquQYcTBu2JviVtCa9zQRR1MQkRUS4E\nWtTQt2NzpWTN3gZ/Rpnu7ra0lQ702GMp3KnamsKQZ4oIYinEyaHM0gZ87vAb+qb/27f+wvrzg/+L\n52O+Um/Y22UC7vlQ5+UIsto6kk+w7OgcDMLbRES0WarHjqDLTLzB+0e5op6OlIUSqNysII/i5vd/\nnIiI+p7OQZ1ENtxT9ZmNa7u6v+QgjOd6zIVkNGI1lU0gK1HKfhcFdDqS0uftniIzLOxJBGmlkPWW\nyLl70CVpVRcGGcjUhGIfk/D2JWSH1udGwdY01eeyKd1wuuBtN0R4E5w8rXo6jts7/Jzs9LS8N88Y\noRyjFDz45mzJ52yCx59KqXpDPP5ZPbnz+M6cXUBzP3xnzi6gnSvU96yhOGcIVAlsahZKrFyVriIR\nxHzzRKHz0YOaGISGkgLhMwOE31whV2vIsc98Bp1rYobE6b6Sai0QotyXsvXFVOuOshGfMwKFzlgK\nWO7fVQLt4KsKaa+PGYq+AsTV1i2+/n/rud9db9sgbbb427/58zyeF3Q83ibD282hwsIJKWl0JGH1\n2UJhfWcuRTggfJnvKbw1B6/z9zYUls8WTO5lHb0ef6nXsyF8lnlW03wD6c6DHW5Wh6BEk9dtpyHd\nWBSOCIirTciRXcljaQDWe5JP4IM6U6MLpJ2ITYaQ9xFKPb8PtfUmkyWWHoYqSBuvpH026t3nQjCH\noDLU8oFZPOVnIsp12bopdfbJri4h+1AU1u3zvUwgpXokjTwrgpT1hl7jJam5L6GAKJK24LEn8xO+\nSmcx5/GdObuAZqw9m0bXh2Hb2xv2X/vJP0NERGHIHvruE/VC+wdcnjqbKFkWQCFNQ2SQMyjiKaX8\ntIDwC3TJplJCYQVUO1Ty3RK08rJKx1GHlgx0WDGixlNAN5pAtg2bmhF3/bZ67x/74p8jIqLnP/cx\nHdDlTxAR0Sv/RMmsk+M/WH9+sc9hoEZPEYwhzu4ak6KNEyh9ffytbxIRUXqiCGYlRGDkq3fevqmf\nN1r82cRKUuUt9lIeqOVUh1CaLGW9SaHEotfgMR0/1LHdefLK+vM9yVZbgo5fXEqBUE+92Qa0st7b\nE31Dq94yifie/9bv/BMdLygOpVI2nUDIrRBE8XTraP6M0twYAFu3PoRdPCHtQvDyPoQAA/HEEUh2\n+3JUiEyThdLxSkhI3FbaN48tAC35OlO0ARmNAyF5+9s8V1/9o1dotli+a0zPeXxnzi6guR++M2cX\n0M43jm+JglrQcZuhXXOmpM/8sSjarJQkaQB86kkOgEU1TYkp+xC/xZi9lUssoCV2KTjOh/feAgQX\nV0IuVvhelFgw1rRnuRQAgYrK9ko/JwJlb2wqZJ2GfO7RJYXLM2j+2O0yjNu+psotfsCk0PyRxunv\nf/ur689HM84wXC0UNnZCvp5mTwtDWi0l8rav8fxbT4t0ygXnVkwgrj0zei+WJOo1VpV8jMT5t64q\ncXW/1GurhHks4Z4FApNLaCleQVw9HvIx4wZkU4paTwZKPgXg8Xpr9dTSlY/vwW30ZHkWQveiEGTH\nA2k3XUEOhyff7W9ott7OlhbXVLJMnIPoaq0PYCDnIZlBMZd8t/CwQKiSfXVefFDbqbNYW9gKXH4T\n5oyy2uv93tO3nTlz9sfC3A/fmbMLaOfbNJMMzSqGsqevc8z5zqsvr/9+8pDlryykQ4Z9EFyUxogl\nNmgU5jYG6DZs6Pus2WQIaqGIZ5GI/BLAsLlV+LQ/5+0pFF0EAkVz4Evnklq6nClce+lVjaP+i0Me\nZ+fWrfU2KyxsMNR9nr3xJ9efe0++TEREjY5CcCtM9dETjd1/6+XX15+fyPYYmj9eH3Ck4fIzqqV/\n7aYe86qwwDO4oHtTLs55+cmj9bZkqpECWjJzfxX08HcChrx7L95Yb7t0Qz//o+LXiYjoziuqd1Av\ny3zoMX90qPkCecbPxI1bGmy/JNA6LQDqw/KsqvvEewp5Q4H1HvRHqKW7rm/vrbdd29OIwqDkY+KS\nzpP4e+uyfm/D07TxU8mfGIHg6FIWHxYI9hxU5CrJX1mt9BpOJQW5RJ3/ABh+WYZgzX035DlqefW1\nuk46zpw5exs7ayedARH9z0T0InGE898jopfpvXbSsZaWQpwVdcYXxM/rbCnTBPlmTzOxrJBLHrwR\nayKv29RLGYBKS1vaZ7cgJj9u8hgaEItdGoiNSqnjA1DOEc1I2kxRuFGQwUoRyGKlBNxv/A4jmE99\nXj3XzhUez15Tx/jMll5v9YCvEfMr6lbLwGtR1NXrvVyyJ9q9pPkEL954gf92Q0tkN7r6eSGe5PBY\nPe0oZaK1DBQdzUHQcseX9uLb+vebLzCauX7p+nqbgQw3X5RhXt1WYebpCZ9zPtfOMy/tKwk5EUWc\nEfSi25L24ZCuQRUgBiPx7hYo1nSFeOxDCe0LzzFp+ic/8afX2z7/jM7LKhOyGbo1ndhaKluPfVLo\no/7kFc4/eRxpOfNccgySWAm9Eno6ZhmPzQxB9j3k663btBMRGbjGZSVkpa8ooBDS1K7zSz5cj/9X\niegfWms/TizD9RK5TjrOnH1k7Swqu30i+ueI6G8QEVlrM2vtmFwnHWfOPrJ2Fqh/i4iOiOhvGmM+\nQ0S/T0Q/S++jk44tKypFmHImzSUjEKrM17Be4c2kAL38lUAdwHtDKfbxYB9UaTEFQzYkeEKBRd2W\nQuwIuqmYNhNwAw+04KXQYwptmgcip3OUKxFUAfn07d/6+0REdPdPf7/us/lnZRB6vgcA7T4unYbS\nhar/3L/DxNir9/5QrwEERz/+IsfnP/3Mp9fbhjv892mpt/gYmjoujmXsCxXObEgtehuKTaJAYXLL\n4+scptAS+5Tnd9mGwpFIIfpeRwqLLmsOwXKTx3ZyokuTAuLVd44Z9ldLqK2fSrNVSGe1FtJZZbMH\nuvvXNzj19wvfd3O97Yc++VkiIvrEM5paPexr8VMZiR4+PA/bIpyZQdpxG9pfd68xUdiKdWxzGfvh\nSvNU0onOWyFEaQ4x+2wg6kDQjnsE67tKBEdnkKrsy4WX0oUHU9ffyc4C9QMi+n4i+mvW2u8jogW9\nAdZbXpC+bScdY8yXjTFfzkBgw5kzZ989O4vHf0hED621dR3p/0b8w3/PnXQ6rZY9usveay7kkl2p\nRw8kTATdiMmv9O1Wisf3QemkIR1aekAChlh8I2/MZQBeWTK1fBCcs1B8EwnRtwmhu5F01RlU6plK\nUVRJVkp2LeHltpJCmr/1//zqetu/cZlJN/tZDXl9rFKPf7jPWXF+qdczk3BdD8pYr93Qgpvbu6yM\ns7Wj4bqJhMxOTx6ut42BSPUS9tSLkaIAI/Lbl69q2Ko6gkzFmRTcQGnyKuHzLA411JiABiHNpFV1\nW9FVq+RtSyBkr1/R8Frd6XlVgWqSFGuhVh495f35c+ArEroi4cvPf+7z622fusphzM5QkUHdjYaI\nyAqZCWI71Ig6cmwYD1SCbdbTDopBp9ISuxFp1uas1HPOZP90oftMRUUng5O3gOQ1UjKM2YvrVu81\nov2w2mRba/eJ6IEx5nnZ9EUi+ia5TjrOnH1k7awJPH+JiP6OMSYioteJ6N8lfmm4TjrOnH0E7axN\nM79KRD/wFn96T510SmtpJOTXcsJQqABhTL/FkLkRQ11zDpAsr+PvkJknoCUC6JXBMWsIv4Cii4YQ\ndQUIZxoghSL5ewPj5rVgJjTNvBWxyKIBSPow0Ti+32HoHIe6bSrqP88pal/DciKilRQoFY+VdEtF\ninzv8s31tt0thZAtw/B2lWmN/lQkvU8ea3y8zBTSGhH97AN27tXE5FLnwgt0bGP5rpfr0mY04uPn\nS1Dtgbr1UIqSorkuD5Kc56DbAUUbkC03A/784BTyI+TwFo5tkZCVfzc7CvVfuH2TiIhuX9aCp6Yo\nLYXQxcfCs1Pl/DzVkuZERC3R8c6sHruEZUYiz9vGJb0eL2CCuLEA7QhMxDCSkwKZdpsy/1NA68BJ\nUzLm78YgtrmSnJaOCMd6Z+P2XOaeM2cX0c69oUbdAtsTEgK5mliIlQq8jAVyr474RFBGGYsaSQSh\nFB88cCGEVgi6a62YPVbVgKzASvf3It7uZ+C9Ja8/Ao3AXos9Uw/ytnvQ044iDs+Ur+uxp6+yR09u\n6dt/D0i7g0MJc3aBuJLag35bI6YbPSXgrGSWrRbQjnvO3t8jyIyEFtKhZCCapnrvVNpXo3bcg1KP\nmY14PvoREH4xz/+kUM9GiX5udvg65hM9ZmX57wNo8OGFQJxJP7lRqnUCafHmzLQAesg1ZBxXrytJ\n+LlnXiQios4ASmxlqivId7fwvJRCIqLCji/lsA2o58gBMZTiqUPwo6EQl/ERNDQxitIOj3kgATR6\nmUfSGy8DfwzXOJbn0pQAF8Xjl7Wiz4ecuefMmbM/RuZ++M6cXUA7X6hPlvKaNBGCogGwpYYwC4iF\n+1CQY6V9cgxwuxKCpwR55gqKGLyKoWwAxRtDEbKsYoW5M1BHSWTJ0egpFB2KauK9iUKpyQnD+scL\nJc0miY49qHh7lOl5/Imo1yyf1fHOAe5JpiEtgIxMeDzdvkLoFFq0+D2JCcNrfCbKRqcQpy8TzSLb\n2uRlgyl1fmcpz0E1VmJxnigkXglBZ58Se2QoG3c0Cy/VP9O0LkuFpYAnmZWTUmEw1AKty1Ox7HYt\no4NdZgAG14o6Xcg0bIgIZoCKNrK4hI7XVIHgpa0LxUC2p6BalQdYN9gnkvMUXb3PgbRgj6D7UROk\nsusCogVMVqsuu20AUZ3A8sLy/M9AoSqTst44rPMcHNR35szZ25j74TtzdgHtXKE+VZY8KQQp5MxP\nARNhkCsoeokBzrViht4bwERHkUB5FFmE/sm5YMgy0qVAIl1ZCOLAJwCfKtEKaAUKs+pGhVGpsfJD\nGW/dGJKIyC7fLOo5Hqv6zOpl1sC3P/rietsCmlTOpaFka0NZ+1giErmn4x0tlfEeEH93OoM4/pg/\nj2e6DIkMqBAJivagy89E2mSXoD7TWQHUlzr5OUQH9ic8pq1U2X8DAqm1nkLZ1ZTdqaQyH440x2AO\nkY26K8ywBanQZR2nhsIcKOxp9zhufmULcgwKPs+1id7HQth2H1YRVQBCn4aXLMiO59KTARtlGgzJ\ni8YEasAmskQywP4HEPuvSt4ewzIkqkU0oYvPOFEth1qZJ4d8AE9yELx6zl2bbGfOnL2dna/HN9pq\n2Jc4fgp98nwhTzrw0grg3RTU3snoq3UhWVNNT9/qSOa0OuwBNhtK1Fkh9Y5BI208R3JPtmcobcz/\neqAB2FryOaeFkmY5lBHXyYItIJKOJ+ypk7F6uzTU2P+pZNeZlV7PZluKRKCDUAFx6KV41WKl3sHK\nvHbgexkQj1nFY/a66l1iyZko5urZlnM95zDix8UDnb5kwsc8hu81e0By+dKncAXacxXPdQ4lzD5k\nW/Z6fD0BePdkwccJIKvTYqm13POO1QzA7RaX2FaVjocE+YUBFHUh0yeoqgKJdk/i6gHChKdS5EQO\nHMi/bsb7rzw9d1jps9Od8d9nKz13Z8ljWqTQGxJUf1oNHlsv0mcjk8KgtsyVy9xz5szZ25r74Ttz\ndgHtfDvpGKI6E7KWCvaAqPNF8aZhlOgpsamgEBdjUNippChmf6LwdKuvUOhKDU+huCNo8jFzzcgl\nC7HnStoQl/CFUuBcHwjBjTZDs30gIBF+kqQLr4A4vPPtrxMR0dX9H1xvu7TU88xOeAkQAllZawn4\n3lCvIYAuM5KW2/AU5m61OH7fzXWfqQ9di0qGk6bU+Hssy7AACKIM1l3dNl9nm6CgRu7jChRpphMV\nnZxJ2+pOAxptCtyOuxCvBlJ0LrkeHah/ryQluxUg1AfpaRlzd0OXdB1ZIlUNWJqk0sUHCMxcV2dk\n66UEwPo6vyT0MFcE7rnoD+TQKjyXpcQK7uMMCsVO5LMlPWYZSoGWbqIBLDevbPL1pEASrk5FVlzG\nbRy558yZs7ezc/X4njHUlGKMqG75C0UkK9GeW6xALQdiJLHHbjn01XskJDpkI/X4o7mGtfZP2Ms9\nGiqB1uyw906hZHK8gpbLkrUVwrmNEGvFAop9xBPsbCrCmEFIp5JxWCDY5nPOijv51t31Nq8LMt8S\nGoyXGuI7ndSS23qcRqSoKMnYRYzHOgd37nMI8WSqx5mcQqaceKKwrd47lOzGTgwhs0g9yL7IjTeN\nkk8r8T6zuW4bjxVZtCVEdX1PkceVqyJZA957AVqGowkfqwFo0Eojls1NzcyLwOtu9PtybG0gUklJ\n9mhf7/1yxfM2Xeh4J6Cl5wnR1+roM7YlBGivodp8rZ6Oo5Dn5QSO+fAJZ2ieHCmJW8uKExHNEiZ5\nswJCwTLVIYTzZoBMAsmyDCCzdSnPRCDosjpj23vn8Z05u4DmfvjOnF1Ae1eoL1p7vwibniGiv0JE\nf5veYycdY4lCQaiBwEkot6e4yRDHg8wwUBKmSLKgKlgKxCLauewqNGtDkX+tpHI4UwItlrr1HLri\nhBDn3xQImjzF00neAZw77PAXrrYUXh6lChsPZ29WGRo/5m0TWI5cbelSoZQLhhA3BfWcLaG7C+Qd\nVAmP7eVD1Tt9uM+fl2M9zykIO1oZU6up421K8U2xqbB8EwpPjo4Yqp5AUdIq58EZq4/S9kBh8O4u\nk4fdvi4pBtJzMIFYei/WzyfSmcisQGiywcff7eqxt67oOPf6LD663dN7kS0ZehegD5BJhuAs1SXM\nkwmo5Mz5Ed4Ekmy2zRD/ypZ+bxfI4LqO6fGd++tt377H/QcnKci+l3rPammduAWxfVm6hGN9VpNK\nz1kr8+RQQNQR8ropsX3vwyL3rLUvW2s/a639LBF9joiWRPTL5DrpOHP2kbX3CvW/SESvWWvvkeuk\n48zZR9beK6v/00T0d+Xze+6kQ4aIBFHnEn8vM4U/Delgk0OqJjYQTKUoxofijWitgalQ0QR6WVbS\nfHOA28WccXSzrXjtuY+pLFI14b/ffwBMsyw/KkgNjkRj/9mBdolZ3FBm9/DxIxkDFHyIrFW+0lh3\nf+u2jl1knrCsOhIWN+joeA3kGDx5wp8f3df21nNZFqUQWy6gIKeUNVYMxTFtka/a7Oqyp7+rcf4j\nYe6rse4TSM6DhZbjARRJZVKMMtnX8Z5s8Rw+88L2etvlywrb79zlyMfpCOrOU77RL96+st7W6UOk\nQPQAsLCqI3XtI5AKe3SXG5n+wasKy79+qmz7UJ63H/7E8+ttA5JW6yDyijoGmSzbDo90qZVLdCYD\nebGjJ7pEOjhiVr8V6z3tW95nG+5zAPJw/piXZZ1A/74MJYejltf/sFN2RVr7J4jof33j387aSQfb\nEjlz5uy7Z+/F4//LRPQH1tpanuV9dNKJbSBVBIUQGT6QOg1pZZ1gpcH/3961xdh1neVv7X3ut5lz\n5uKZeJzYblyc0EJbQpUqIEpoRIiAh4oHKtQHBG9INAVRGvFQIV5aCYWLhCohCg9VBYg2gipCJCEN\nfUzbtAnk0twa1/Z4xp45M5459+vi4f/3+b+0cT1OxmNPzvoky2f2OXvvtfbae69//ZfvI4WbsTpE\nUiR5XVanXpFYYUY0u3d2tdSXsp1inZFmqZjkPSUrgz2nUtcpYvIpaSnvIrV3pSrbVlZM1WbYsxnn\nue/J/tt9criottmICkMq3hRwxj0tWx7b7NDMiUOrSixCt1BMOVLJ5i6ZCXFeZsAmeQnra2YRdJRI\nNFO0/hxdkGtw2x0mG72wYExBHY2vV1JccKO3EF2r+WWjs66pU6550RR9uip3niMnbGXGnHZbMzID\nXySnW0fnqPm8OQkXSfOupvtnqSIqmzi8cmahpLVo672nrSw6d9nOM6EJJxnyZL5KSnYBwOeoEEnL\nZSskz94eamEV0YbXy9aOsead8AO4luRc0P1/gvIFanNiIfVJmzCzrSSb1zFz7xMwMx8ISjoBAYcW\ne3rwnXNFAPcBeIQ2fx7Afc65VwF8TP8OCAg4BNirkk4LwNyPbKvjGpV0oiiapEJ2NFYZkVMorc6T\nEjk8hsQ2Uq6KKbWYt9hnRZlXxsSDPiTizAta+MMGUKUgfy3WzFT0JImdkK/HxNozr1dqjurXi2kx\n9ypZMxVPUh33fEnMtHbb0hti1QSoUWrl1qYx9ERKKNqjJU4Ss1+kWHk5Y0uTlSVlDKJz78bStm7L\n+lXoWSptpyyOsWLFlhlFZck5XTOn27GFWyafeyfFCbnVNEdd1BWTdURzyMKC7V9Qp1uraN+PxuIM\nGw1JEJWWNjMDWVIsRTbOawON4x+xfs9QWm1BnXK+S/z+ensfqZq5vfQz7wcA7LbNVO/smgm+pss8\nKvvHTF6uf4FSiNNpK5JK/InzVXPyVrQAabdL6c805gt55VCgpVhWl3ynlu2+TFGa9plLStS6Ze1t\n9OX7nBYK+ZCyGxAQcCUcaJFODIeKlrfm5+Sd0yW2l+TlRhM+asR4c6Imb+502mbilk4armdv+iwX\ns2Sli7UUzUgzCaebzR5lKoyoVmWm9pTdNer+uCJPS0tJt5vkpSKZ5jmd7TaogKigmVadVbMCznpz\n4MRe+pajkNisFtKkKcNvkCW2nb60faZiMy1U9aYVURZkxWaSO1R7r7hgjrhkrliZMwnvIjn/X4JW\nJAAAEVtJREFUqvNy/PTYLIe4rBbD2NpTqtJ11fEb5ah4pi7tvbxlx1kfkWrRSK6Rz9hM3c3IcYZN\nG5P8vF3rtHLPjSkbc5hN9AHtODl1wJVoSu9nbUxrWgyUofLqljIGgajgM5SRV9FQcp2cnpmEN3DB\n2rg0a+3YUe7JmHT7cpFsK9DYb9XXJ583ld48nbLCn1LiVNU2uP2SyQ4ICHj3ITz4AQFTiINl4Ikj\npGfE9IlUjrjdMPqTgcawOYw/SyZ6HmKGFSmzzGusN0WOrwE5OBKnXLlkjpmiFuTERFedptruk7Ni\nBt+Rtfj8RrOpx7Z9+kr62dogpxnFjHt67qUi1ZWrtZ66YA7IF3bMufdeNedTaTPZhkqcGZNTrU/E\njQm9c5/r/jPSxwJt++n8STvPspjjYyrCyY7EPC1kbUnRalOBi2b7dYhKe0YdnIWKtadGBJFZlfDe\nGdmSYTMnfa/v2jWY2TBnWU8t4mjTTOMXz8qY3rViY1umOP+MMuakYOf2KtEdxeSI07bFlBHHgp25\nUVLXbtffXU4o4e365yg7tKvZi/kBO9akb/MFu4dSM7QUU3ahEYhXoSFx/AbxGbCYa0of1xlik+om\nrFXqGI+DqR8QEHAlhAc/IGAKcaCm/sh7tFQ8MQlT9ynFsKkx/TyZpx3yovfVNM9RgcScpuyOyTua\nFAABgFOrtJA389UpTZEj8U3vzfs9XxLzLLtkXvBSS2LPDdJ5T7gFdrA22fZs27zbBRVojEq2NMlr\nyKI9tMKQFrUj1nTkojOTNaPe4pgYQdO8tEnLdWk0KDVVr2G2aueez1lRS0a1BXZbJqoJJ9GHTtvO\n0/SWHgpV0hmMrI9rXTFLj6WtyMl3KVKjYqZtileva15Ca4t0Aqi4pjYnxxrN2rkHLdlWv2Qe7QIR\njsZlVUwic3wxWQ69STZHrlVMnvwMmcdeU7KZ88FBOQ1adpyR3RpIq8JTlijhekoYm8lQ7Twtq2KN\nbvVJkidS1ach32MUocok6lORjU8pI9/39R7ZY41OmPEDAqYRBzvjj8aob8tbPq+vL/ITodmWt1+D\naHdGRVJyGcpbssRFfpoRls9wth8V9gxk/z7po41i1TUj+p+ths042VhmmqNFmy2zRYm1u4y91Zuq\n7nJuYJexsW3nudTT0uMOEXkmLCtdc1ztLlosPZN7AwDQoQy0kc4+Wzs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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3844060b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 710... Discriminator Loss: 1.5873... Generator Loss: 0.5126\n",
      "Epoch 1/1-Step 720... Discriminator Loss: 1.5325... Generator Loss: 0.5958\n",
      "Epoch 1/1-Step 730... Discriminator Loss: 1.3896... Generator Loss: 0.6622\n",
      "Epoch 1/1-Step 740... Discriminator Loss: 1.5005... Generator Loss: 0.7440\n",
      "Epoch 1/1-Step 750... Discriminator Loss: 1.7124... Generator Loss: 0.4646\n",
      "Epoch 1/1-Step 760... Discriminator Loss: 1.6152... Generator Loss: 0.6327\n",
      "Epoch 1/1-Step 770... Discriminator Loss: 1.6164... Generator Loss: 0.5734\n",
      "Epoch 1/1-Step 780... Discriminator Loss: 1.7138... Generator Loss: 0.4487\n",
      "Epoch 1/1-Step 790... Discriminator Loss: 1.5637... Generator Loss: 0.5049\n",
      "Epoch 1/1-Step 800... Discriminator Loss: 1.6402... Generator Loss: 0.5617\n"
     ]
    },
    {
     "data": {
      "image/png": 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nyXXTcebsQ2un0dx7SEQP5fPEGPMCEV2ir6CbThj4dHGVYV4hIo/eAtRnhIBL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\nP0ItAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3842c6dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 810... Discriminator Loss: 1.7563... Generator Loss: 0.3923\n",
      "Epoch 1/1-Step 820... Discriminator Loss: 1.7762... Generator Loss: 0.4678\n",
      "Epoch 1/1-Step 830... Discriminator Loss: 1.5962... Generator Loss: 0.5257\n",
      "Epoch 1/1-Step 840... Discriminator Loss: 1.6072... Generator Loss: 0.6522\n",
      "Epoch 1/1-Step 850... Discriminator Loss: 1.6226... Generator Loss: 0.5555\n",
      "Epoch 1/1-Step 860... Discriminator Loss: 1.5246... Generator Loss: 0.6241\n",
      "Epoch 1/1-Step 870... Discriminator Loss: 1.6424... Generator Loss: 0.5019\n",
      "Epoch 1/1-Step 880... Discriminator Loss: 1.5898... Generator Loss: 0.4438\n",
      "Epoch 1/1-Step 890... Discriminator Loss: 1.6457... Generator Loss: 0.5456\n",
      "Epoch 1/1-Step 900... Discriminator Loss: 1.6340... Generator Loss: 0.5472\n"
     ]
    },
    {
     "data": {
      "image/png": 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pRF3mEG2WSLLVHApcepDYEwR8LNPUJZ0RwiqB6jpz6GdXREo7TQ0RzDp8jW9P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\nCAKSHimHZgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd385721f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 910... Discriminator Loss: 1.5515... Generator Loss: 0.5978\n",
      "Epoch 1/1-Step 920... Discriminator Loss: 1.6087... Generator Loss: 0.5024\n",
      "Epoch 1/1-Step 930... Discriminator Loss: 1.6616... Generator Loss: 0.4458\n",
      "Epoch 1/1-Step 940... Discriminator Loss: 1.9298... Generator Loss: 0.3957\n",
      "Epoch 1/1-Step 950... Discriminator Loss: 1.5688... Generator Loss: 0.6162\n",
      "Epoch 1/1-Step 960... Discriminator Loss: 1.5625... Generator Loss: 0.5942\n",
      "Epoch 1/1-Step 970... Discriminator Loss: 1.6850... Generator Loss: 0.3905\n",
      "Epoch 1/1-Step 980... Discriminator Loss: 1.5903... Generator Loss: 0.5228\n",
      "Epoch 1/1-Step 990... Discriminator Loss: 1.5929... Generator Loss: 0.5685\n",
      "Epoch 1/1-Step 1000... Discriminator Loss: 1.7475... Generator Loss: 0.3601\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ftd8KcZkFMG5OWcdA3b5mpmPkZLOLAnIPhJg8PtcxSA6V7Lwp6akRqAflgtgw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\nvgIA2CBVpZm2z1Wo75uaTrVguQE5JYsPKDmgo/cwE8Q7NBdTZT8X+Z5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3841e09e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1010... Discriminator Loss: 1.6496... Generator Loss: 0.4886\n",
      "Epoch 1/1-Step 1020... Discriminator Loss: 1.6259... Generator Loss: 0.4527\n",
      "Epoch 1/1-Step 1030... Discriminator Loss: 1.6950... Generator Loss: 0.4469\n",
      "Epoch 1/1-Step 1040... Discriminator Loss: 1.5717... Generator Loss: 0.6022\n",
      "Epoch 1/1-Step 1050... Discriminator Loss: 1.7170... Generator Loss: 0.5175\n",
      "Epoch 1/1-Step 1060... Discriminator Loss: 1.9889... Generator Loss: 0.4170\n",
      "Epoch 1/1-Step 1070... Discriminator Loss: 1.7334... Generator Loss: 0.4863\n",
      "Epoch 1/1-Step 1080... Discriminator Loss: 1.4844... Generator Loss: 0.6532\n",
      "Epoch 1/1-Step 1090... Discriminator Loss: 1.5442... Generator Loss: 0.5522\n",
      "Epoch 1/1-Step 1100... Discriminator Loss: 1.6279... Generator Loss: 0.5473\n"
     ]
    },
    {
     "data": {
      "image/png": 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YKxETth7pOArJE8ACrBEs3+pYvHlMdXkOdf2TCqSpJWfCgxYPVmB7memxYyE7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\nBMwgwoMfEDCDCA9+QMAM4v8BKTr0nJiO53gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3842cd080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1110... Discriminator Loss: 1.4117... Generator Loss: 0.6320\n",
      "Epoch 1/1-Step 1120... Discriminator Loss: 1.5478... Generator Loss: 0.5325\n",
      "Epoch 1/1-Step 1130... Discriminator Loss: 1.4858... Generator Loss: 0.6128\n",
      "Epoch 1/1-Step 1140... Discriminator Loss: 1.7873... Generator Loss: 0.4608\n",
      "Epoch 1/1-Step 1150... Discriminator Loss: 1.5082... Generator Loss: 0.5438\n",
      "Epoch 1/1-Step 1160... Discriminator Loss: 1.6175... Generator Loss: 0.5056\n",
      "Epoch 1/1-Step 1170... Discriminator Loss: 1.7110... Generator Loss: 0.5155\n",
      "Epoch 1/1-Step 1180... Discriminator Loss: 1.8326... Generator Loss: 0.4454\n",
      "Epoch 1/1-Step 1190... Discriminator Loss: 1.5819... Generator Loss: 0.5531\n",
      "Epoch 1/1-Step 1200... Discriminator Loss: 1.5146... Generator Loss: 0.5342\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pi8jfNcb8NRH5HXFttt7WrLVSlA6axCDdwkihSZq491DOMPeCikwq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\nbdVwboM44kdErCAWXEnHHVZemSkx1j8wUqeuI+Q6RR8V5LqodHSaUKn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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3856ce160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1210... Discriminator Loss: 1.6864... Generator Loss: 0.6659\n",
      "Epoch 1/1-Step 1220... Discriminator Loss: 1.6513... Generator Loss: 0.4822\n",
      "Epoch 1/1-Step 1230... Discriminator Loss: 1.6701... Generator Loss: 0.5620\n",
      "Epoch 1/1-Step 1240... Discriminator Loss: 1.6497... Generator Loss: 0.3909\n",
      "Epoch 1/1-Step 1250... Discriminator Loss: 1.5540... Generator Loss: 0.6141\n",
      "Epoch 1/1-Step 1260... Discriminator Loss: 1.6308... Generator Loss: 0.4989\n",
      "Epoch 1/1-Step 1270... Discriminator Loss: 1.6109... Generator Loss: 0.4821\n",
      "Epoch 1/1-Step 1280... Discriminator Loss: 1.6181... Generator Loss: 0.4695\n",
      "Epoch 1/1-Step 1290... Discriminator Loss: 1.6461... Generator Loss: 0.5510\n",
      "Epoch 1/1-Step 1300... Discriminator Loss: 1.5410... Generator Loss: 0.5102\n"
     ]
    },
    {
     "data": {
      "image/png": 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Lxd2CNzT3FbDIKxOkUKxk5ln0uglkwonnLJchZGbZsyWJIhSXxTeCUlwPPAVVpXACinRc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\nFe0LgEwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383f66e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1310... Discriminator Loss: 1.6111... Generator Loss: 0.4807\n",
      "Epoch 1/1-Step 1320... Discriminator Loss: 1.7205... Generator Loss: 0.5266\n",
      "Epoch 1/1-Step 1330... Discriminator Loss: 1.6384... Generator Loss: 0.5218\n",
      "Epoch 1/1-Step 1340... Discriminator Loss: 1.6635... Generator Loss: 0.6340\n",
      "Epoch 1/1-Step 1350... Discriminator Loss: 1.6056... Generator Loss: 0.5998\n",
      "Epoch 1/1-Step 1360... Discriminator Loss: 1.5510... Generator Loss: 0.6269\n",
      "Epoch 1/1-Step 1370... Discriminator Loss: 1.5337... Generator Loss: 0.5658\n",
      "Epoch 1/1-Step 1380... Discriminator Loss: 1.5314... Generator Loss: 0.5803\n",
      "Epoch 1/1-Step 1390... Discriminator Loss: 1.5945... Generator Loss: 0.5902\n",
      "Epoch 1/1-Step 1400... Discriminator Loss: 1.6117... Generator Loss: 0.5127\n"
     ]
    },
    {
     "data": {
      "image/png": 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+iYPWlPHYBfnXIUIvzSkMDfI1IylyK+56FkbDu+dLvZ6mdd8PuR+fR1dU3ms5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\n/nbTSKIj4iVHxqYhFx8F6+XYbKsiTqPB16tr/DG26nyNXm+rsau0quU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3856c8da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1410... Discriminator Loss: 1.6758... Generator Loss: 0.4837\n",
      "Epoch 1/1-Step 1420... Discriminator Loss: 1.6267... Generator Loss: 0.5655\n",
      "Epoch 1/1-Step 1430... Discriminator Loss: 1.5508... Generator Loss: 0.5399\n",
      "Epoch 1/1-Step 1440... Discriminator Loss: 1.6216... Generator Loss: 0.5033\n",
      "Epoch 1/1-Step 1450... Discriminator Loss: 1.5129... Generator Loss: 0.5440\n",
      "Epoch 1/1-Step 1460... Discriminator Loss: 1.5355... Generator Loss: 0.5692\n",
      "Epoch 1/1-Step 1470... Discriminator Loss: 1.7166... Generator Loss: 0.4328\n",
      "Epoch 1/1-Step 1480... Discriminator Loss: 1.5201... Generator Loss: 0.6278\n",
      "Epoch 1/1-Step 1490... Discriminator Loss: 1.5664... Generator Loss: 0.5372\n",
      "Epoch 1/1-Step 1500... Discriminator Loss: 1.5953... Generator Loss: 0.4794\n"
     ]
    },
    {
     "data": {
      "image/png": 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j7fYIWmcLcZnM9DjpEFpmr0nj0AKWjhNekvihxvYr67rkcLkks4E+Q4ksL3Jf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\nMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383a09940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1510... Discriminator Loss: 1.5750... Generator Loss: 0.5716\n",
      "Epoch 1/1-Step 1520... Discriminator Loss: 1.5316... Generator Loss: 0.6153\n",
      "Epoch 1/1-Step 1530... Discriminator Loss: 1.5014... Generator Loss: 0.5747\n",
      "Epoch 1/1-Step 1540... Discriminator Loss: 1.6495... Generator Loss: 0.4497\n",
      "Epoch 1/1-Step 1550... Discriminator Loss: 1.6587... Generator Loss: 0.4627\n",
      "Epoch 1/1-Step 1560... Discriminator Loss: 1.5300... Generator Loss: 0.5422\n",
      "Epoch 1/1-Step 1570... Discriminator Loss: 1.5563... Generator Loss: 0.5813\n",
      "Epoch 1/1-Step 1580... Discriminator Loss: 1.6175... Generator Loss: 0.6056\n",
      "Epoch 1/1-Step 1590... Discriminator Loss: 1.5851... Generator Loss: 0.5327\n",
      "Epoch 1/1-Step 1600... Discriminator Loss: 1.6242... Generator Loss: 0.4894\n"
     ]
    },
    {
     "data": {
      "image/png": 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vaaba5rouM+oVviUZQOyi00u3BjkPoF6Tilb3QQqtpmOG3gtNaCYKrZ2PEh7z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\n0rhBRJXapmHfikg213zNVVyxsUxSi6CUsXomuWzP+WhIjcaARUAzjRp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8OneZMZaFhgAA\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3837ff9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1610... Discriminator Loss: 1.5834... Generator Loss: 0.5294\n",
      "Epoch 1/1-Step 1620... Discriminator Loss: 1.6298... Generator Loss: 0.5103\n",
      "Epoch 1/1-Step 1630... Discriminator Loss: 1.6286... Generator Loss: 0.5374\n",
      "Epoch 1/1-Step 1640... Discriminator Loss: 1.5931... Generator Loss: 0.5947\n",
      "Epoch 1/1-Step 1650... Discriminator Loss: 1.4749... Generator Loss: 0.5701\n",
      "Epoch 1/1-Step 1660... Discriminator Loss: 1.7635... Generator Loss: 0.4730\n",
      "Epoch 1/1-Step 1670... Discriminator Loss: 1.5748... Generator Loss: 0.4961\n",
      "Epoch 1/1-Step 1680... Discriminator Loss: 1.4538... Generator Loss: 0.5815\n",
      "Epoch 1/1-Step 1690... Discriminator Loss: 1.5649... Generator Loss: 0.5636\n",
      "Epoch 1/1-Step 1700... Discriminator Loss: 1.6351... Generator Loss: 0.5656\n"
     ]
    },
    {
     "data": {
      "image/png": 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jzOuiCtxQY9jLrQEoskZ6ZjJTwKmNtMumRZzySL2Hp0g1xFzaQGFmulLv3RCL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\nlhZ8RpMyxXFnVPlUJQ+pVsXegbWkF93GKbZ+rOqFxHzeumxGCTNfOm7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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383b4b400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1710... Discriminator Loss: 1.5923... Generator Loss: 0.5038\n",
      "Epoch 1/1-Step 1720... Discriminator Loss: 1.5411... Generator Loss: 0.5850\n",
      "Epoch 1/1-Step 1730... Discriminator Loss: 1.5569... Generator Loss: 0.5380\n",
      "Epoch 1/1-Step 1740... Discriminator Loss: 1.6375... Generator Loss: 0.5668\n",
      "Epoch 1/1-Step 1750... Discriminator Loss: 1.5744... Generator Loss: 0.5069\n",
      "Epoch 1/1-Step 1760... Discriminator Loss: 1.4704... Generator Loss: 0.5417\n",
      "Epoch 1/1-Step 1770... Discriminator Loss: 1.5687... Generator Loss: 0.5396\n",
      "Epoch 1/1-Step 1780... Discriminator Loss: 1.5869... Generator Loss: 0.5047\n",
      "Epoch 1/1-Step 1790... Discriminator Loss: 1.7663... Generator Loss: 0.4312\n",
      "Epoch 1/1-Step 1800... Discriminator Loss: 1.4238... Generator Loss: 0.6334\n"
     ]
    },
    {
     "data": {
      "image/png": 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Y6YLJkxn4zWsNJmZCSBqyAted35+IaA4wLJeouVZL4Wkq0Dmf6XalCXEJUYu3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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383a0d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1810... Discriminator Loss: 1.5628... Generator Loss: 0.5775\n",
      "Epoch 1/1-Step 1820... Discriminator Loss: 1.6301... Generator Loss: 0.5084\n",
      "Epoch 1/1-Step 1830... Discriminator Loss: 1.5955... Generator Loss: 0.5984\n",
      "Epoch 1/1-Step 1840... Discriminator Loss: 1.5123... Generator Loss: 0.6310\n",
      "Epoch 1/1-Step 1850... Discriminator Loss: 1.4927... Generator Loss: 0.5799\n",
      "Epoch 1/1-Step 1860... Discriminator Loss: 1.5843... Generator Loss: 0.6180\n",
      "Epoch 1/1-Step 1870... Discriminator Loss: 1.5226... Generator Loss: 0.5901\n",
      "Epoch 1/1-Step 1880... Discriminator Loss: 1.7258... Generator Loss: 0.4123\n",
      "Epoch 1/1-Step 1890... Discriminator Loss: 1.4861... Generator Loss: 0.5732\n",
      "Epoch 1/1-Step 1900... Discriminator Loss: 1.6749... Generator Loss: 0.5128\n"
     ]
    },
    {
     "data": {
      "image/png": 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aV+pJpiN+qw1A7eWBqJpMATisgSzzzmP2Cg/vqEd/9w435HjtvVvLfRlkwm1K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\n/ZwS2UztBMBAV5cxhRfb6qA8vWlWyyniynufhhDd4tU2hopfCSrrpO6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38383ea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1910... Discriminator Loss: 1.5475... Generator Loss: 0.6490\n",
      "Epoch 1/1-Step 1920... Discriminator Loss: 1.5889... Generator Loss: 0.5187\n",
      "Epoch 1/1-Step 1930... Discriminator Loss: 1.4351... Generator Loss: 0.5514\n",
      "Epoch 1/1-Step 1940... Discriminator Loss: 1.6251... Generator Loss: 0.5055\n",
      "Epoch 1/1-Step 1950... Discriminator Loss: 1.6869... Generator Loss: 0.5026\n",
      "Epoch 1/1-Step 1960... Discriminator Loss: 1.6051... Generator Loss: 0.5311\n",
      "Epoch 1/1-Step 1970... Discriminator Loss: 1.5428... Generator Loss: 0.6195\n",
      "Epoch 1/1-Step 1980... Discriminator Loss: 1.5524... Generator Loss: 0.7520\n",
      "Epoch 1/1-Step 1990... Discriminator Loss: 1.5038... Generator Loss: 0.6006\n",
      "Epoch 1/1-Step 2000... Discriminator Loss: 1.4429... Generator Loss: 0.5845\n"
     ]
    },
    {
     "data": {
      "image/png": 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TrfGllNYLRPTHdMpqOsaYzxHR54hOhq568+btz85O/Us0xjSI6P8hor9lre2Z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\nEXuEEt2JjGGaowgm1CsQGIwKR4nEXpTRfTDV69ne52VQGa6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383568390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2010... Discriminator Loss: 1.6835... Generator Loss: 0.5229\n",
      "Epoch 1/1-Step 2020... Discriminator Loss: 1.6322... Generator Loss: 0.4383\n",
      "Epoch 1/1-Step 2030... Discriminator Loss: 1.5226... Generator Loss: 0.6373\n",
      "Epoch 1/1-Step 2040... Discriminator Loss: 1.5326... Generator Loss: 0.5994\n",
      "Epoch 1/1-Step 2050... Discriminator Loss: 1.6601... Generator Loss: 0.4407\n",
      "Epoch 1/1-Step 2060... Discriminator Loss: 1.4935... Generator Loss: 0.6701\n",
      "Epoch 1/1-Step 2070... Discriminator Loss: 1.4603... Generator Loss: 0.6488\n",
      "Epoch 1/1-Step 2080... Discriminator Loss: 1.5661... Generator Loss: 0.6325\n",
      "Epoch 1/1-Step 2090... Discriminator Loss: 1.5542... Generator Loss: 0.5592\n",
      "Epoch 1/1-Step 2100... Discriminator Loss: 1.4832... Generator Loss: 0.5962\n"
     ]
    },
    {
     "data": {
      "image/png": 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03k9En6djVtMxxnyaiD5NRFQpH12zefPm7eTt2D98Y0ydiP4FEf2n1touKn1Y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\nP/geHmMI/+B7eIwh/h8AZ9+T4iO/DQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383a0d358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2110... Discriminator Loss: 1.6266... Generator Loss: 0.4967\n",
      "Epoch 1/1-Step 2120... Discriminator Loss: 1.4847... Generator Loss: 0.6371\n",
      "Epoch 1/1-Step 2130... Discriminator Loss: 1.6838... Generator Loss: 0.6783\n",
      "Epoch 1/1-Step 2140... Discriminator Loss: 1.4585... Generator Loss: 0.6055\n",
      "Epoch 1/1-Step 2150... Discriminator Loss: 1.5668... Generator Loss: 0.6090\n",
      "Epoch 1/1-Step 2160... Discriminator Loss: 1.5711... Generator Loss: 0.5704\n",
      "Epoch 1/1-Step 2170... Discriminator Loss: 1.6091... Generator Loss: 0.4876\n",
      "Epoch 1/1-Step 2180... Discriminator Loss: 1.5204... Generator Loss: 0.5957\n",
      "Epoch 1/1-Step 2190... Discriminator Loss: 1.5270... Generator Loss: 0.6346\n",
      "Epoch 1/1-Step 2200... Discriminator Loss: 1.4999... Generator Loss: 0.4813\n"
     ]
    },
    {
     "data": {
      "image/png": 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3Uk0nDKpEhbkQGcMRwhb+O4KYLEzdpJkQeUD6DGe8vdgEcq4O5J/U2SsKhXO7\nD5hUyoBQakAUWSHxBK0mEC8dhvXlgRJfWZ87PAR5ZtNT6NyWNNoZ4FMHkkFchnJIzYwlai6GZJ9E\nohyHADVLUOPJCx7LUU3PMxOloCAGBZiJ9j0WJaQEovRq0taACLVRpOOWD3g86pBCG0jUG5Y2xyg9\nkgSjAiSjrZTuDnJdukTATN4RdaZ7sCB6faLpwdU1ACSuS9JSH/zeDyWJqr+oS7pcjo2wPIxAwl3i\nALBuXyTPTgoxHCaC1GN5Hi2MWzTn/qQtgPqwvJjI0mYK/S0kSQ3TtBsQ35AKrB+ekpZ71vLYVf/e\n6QvW2v+PHs0Z+mo63rw9huZDdr15u4B2vpV0wpAaInw4E19xCQk5heTEj0BM8PWRsugNwR39OZTJ\nFmo3gUQLgn0mApkt+L17EiragOSYGiquiJrMcK7Q2hHdsYEccMMwerCv/ui1XYWsa1IyWxcCRKCn\nojb7cLUZSPJGHRRtGjH3dwhhpEGs/6lJPyx4IfZyF2KsY9VMwbsgOd8R1iQXx0we6vIAy37XZAm1\ntbZZtdlSwmuh/kFOuqRo1Xjpk0EMbEuWd8vgoz4A784LkhQzAEeR3b9FREQJfVH7BsecCEu+AwO8\nP+Jr3x3BPUm5DcN9W6BuaXOG5tOZ9i1usZeoBqXLH2wrlz2RWgu2rZ9HsoycZTqWBjwXpVt6wjXO\nLY9l2IfQX4jDyCTHH6U2q73fwu6/k/kZ35u3C2jnOuMTEbmX/Fg2hvDuSSWBJ4HXkQWfMkVMRM0g\nmmw8lgo3BSRiwBvTzd4EhIlzZ8ehEi+m1GMOJKXy7i6k2IpfdhFSed1Z1nRSpKTQ2f9fF//wf0/v\nYOHPVZu14jPcNIX4BelGu6HXNY6VGIuFKpxAzcFSLtJA8hGO286IY69KiG+YSfibsTDjgGpMo81o\nLQXfvzFCqo01oSmCG5gKQRoHqpBkCkYm3wIo4bdLvX89mdmOkYTmQ0REVBS/WDVhqfFSmGEk5XZ6\nPLv3N0D9R56HKUyNNUAO5QFfxxCi54YSsTc8ggpDe3DMjK9j+IYirpU6j1ttDCnBQL6mUj8wNFDS\nXZBJd64YcR3uhcNRp07qZ6uOXZmf8b15u4Dmf/jevF1AO18Fnrygcc/BIYadHVCNMRJyalHBBXKy\nl5YlbBPEE1986Q4REd1/U6WPkxaUXF5j1ZkWhH9GLX7fZR2FrA9hqXD/DS4Dvd1Tcm9doNnaskLs\nxkBywKcBC2gbAAAgAElEQVQaFnt1WftWcNeO+exPtX/7u6rN2ef+UyIiatYVBrdcqO5M4XT/6F61\nbReu8V+o7nIwOCQiojtAJ9pMx3p0yMfa2wXNAanu0oElUAugfi4JUzbXY7Y7rAEQdtUX3l3UMTIC\n9QNYft1ocT8eAGg9gHgNEBuvtmj9R/k8wX+mx0bQK32D20h3d/j+3VvV+/jcFU4pwYSnWalnnEus\nxJt3Dqu2e7tMug3n+j1TQkivhDUb+Hwc8HN+BAlny2s6rk/cYOFTA+KgmSzV0hAqM0EYNpJ6J8y4\n8R++3bcq8zO+N28X0M6X3LNERlwfNSFZWlC/7kgi2EpSwsNCSuXuHr+FQbiFlkWmOASUUEKBBJKg\nuLAFJJeQbtuvazQYlPCjWGaaa2uabluX9NJGrP2NCj54FyKyyp72/WtOfQVmgioosfj3te3HdDP/\nPSHgILljcUE06GY6qy6BNPjWFiOhZhuIx22JNisgqrAOs3LCM18MUXjUYTJsGXQQI3ArTmWaODiC\ntGcpJhJ3lLxLQSnIiJupnmp0YiDagb0u1JKD6zWrjMTsG9+tfVvkG9QEWfEcElyszGEGIvKMiU60\n5ZJ8U9RVrSiGBK0w4TFaK1WfcElStqeZjvkCSLg7lJENdbbtH0nNwABqOiba35qg1tlM+9a0PP5f\nAhLx6+VpSAhN+uSey3Jy6rfean7G9+btApr/4XvzdgHtfKG+IZL8DJrNeKMGktGZ5EKjL3YMkU8z\n8a8beF+NiCGi7VjYR485E6i5s6+Qy4rfdoQijABvuy3eXgdovCLwv5ErpO30+JyNewAvP6TQ+fv/\nvb/LG3/jp/SCXBL51s9r2yeg0otUFios5PXPeUnSAKhoQLnIjt0SCSSfxbFrQPZ6nivc7qbc3tpU\nSEuiphPnulwpIfkjTRjCB4lC0YkkphSQHB+D79rs8HejIyVA7SZD2vUf+k49zoOv6ef3ZXly669V\nbcF/xaW7b/7uRtU2g8SqqZPNBpWbBfGlT6ze27sP3NoPkpdakKQjyU/xFR3/WiKRe1O9xhYsgUgI\nuvFMyeKxcIOTI0h4mkNJ7EIq9uwpND+aiNJPCvB/SZcUdlcIbID/FMj9+1t/k/9+5mfpLOZnfG/e\nLqD5H743bxfQzLvN430/trayZP+tP8+ZvA3JbT6AMtmNNstNbe/sVm0HB5oMMZ6IzBMwu5fWucwz\nFDahdEEZ20tXOLnj1o3nqrbYQf0BMN4Q/nl4yPBr+96dqq1MeJ9aTWGhFRmnN+68VrXdu6f+9dz5\nYAFqbu/eJyKiwVxh6rUra9X2c89zPzuQNHT73l3uV1/90Ts9XbocHTIEzDNYCohMUwwimMYCqyzJ\nUS3QlE9CV4ZZB7M/Uy45k2PWoIx5KtC6gJDbAOWiZAwy0KGfSgjsBI8NS4XMhVfDTVlfY8j78aur\nVVu7A2HYHYbjt67rfV7vsFemt6exCq/fv839BW9SL1IvxJe//IdERLS3rR4fV4T1yvqNqm2486Da\nLmWJdHNLS44/+6GPERHRxrqep7uqsH117SoRES1sqMRaIvcCVp0UgtfFusQfKNe99wY/G26p8xf/\ng5+hF168/Y7Sm37G9+btAtq5+/Ed19QQCWssK32QMQrYm+gbbQAptjNJUcyBEGyKkk+Y6YyxsqiC\nv7bGs/80wkvlV2qtDfXp4POdfZ7p2x19G2/d4Dd0F/zVLpZqranS0esLeu4/uv0CERG9+oaigJ5c\nj4HzpYAiZn2eDWeLqCLE4/HCa3CcI4jQkhmyBinOTmEmhIi4OqSiLsmsfR1mpKYkSaECzBu7ep6H\nR9z3CdyTQqrqdBqggKQa1pUAKNavK4SEHABKmAJaCST1OYKy1C5tehvKSlsQbb62wfdgqQNzmeGE\nqTRSUu3SU3xP70U6+772UI/5+j6jwPkIlIckGCQotqu21kyPuSoVnq609ZiXBVE8df0J3WcNFIca\n/FwG8BzUJC09wJgGQLdWxjCCWJK8ZLQ4HPP4heHZftJn0dxLjTF/YIz5ilTS+S+l/YYx5rPGmFeM\nMb9kXJqWN2/evuntLFB/RkSfttZ+hIg+SkQ/aIz5diL6O0T031lrnySiQyL6iQ+um968eftG2lk0\n9yxp5H8s/ywRfZqI/qK0/wIR/RdE9PNv3R+tKAs6HDFB1ZkxoWITJVZevM9Exb1t8PmCQGRddNgb\nLfWXOp88ljBebwJUEjWfwZ4mXUSXWEHm2VtPV21JCuGsVxnWW1DgKQXWt0GFJZNEmNqG+sKvtqE4\n54gh+v1Xbldt49IpBkH8Aui+v/7GG9wHKLXzykMmkg4OtD8F6rELrK9B1aFE2roQjnqtq337xLOs\ng//8zetVW1vIpclMx/zBrt6Lr73GUPf1BzqWM8vjv9aFvH2oLTCSMGwD2TO7AqMnEHs9KZFklqVG\nDjEcQuwa0LNfAl3+W0LAZXP1i+8ect8XV0C7oMUE3Ne39Xtf/BdaMan3gPeJYE5sy7MVh7oEenJD\n4fYT3WU5D5B3q1JJp6v9bXZ0n0CSz4pc++GetxKKuQZQpYnk2QvgWa83XewLf4bhyW9nZ9XVD0Vh\nd4eIfpOIXiWinrUVTXyXuKzWaftWlXSmM19Jx5u3bwY7ExNgrS2I6KPGmAUi+lUieuasJ8BKOovd\nth1LeeZ7D9lNN9XJg+7fZldX1sMoO33ru4mkCXV5l8T1sdHVt+klmBWcXkk2VtddOWWXkAEXU9jQ\n7Y0NJl5qpESezTjaLI10xp+Ip3E80P7WlhWNNGdPEhHR/ps6o3zudb5GG4NLDAie2zIur4M6zY6k\nfWKlnBDq18XihsPjrAjZ9uyGEo+fvK5Rbx//KN/CjXUlI0ORqy4gY+l5kDr/DqkN98rX/7Bq+/pt\nIRwh7Tmt6+zfFyA1Gan7cqkt9elILdjW2X/gkpoABZSCGDaXlHC9sa5u21aD57C9sT5Qo31Jxhoo\nankl5+0vvKRE6eCN+9W2FTnrekOfoUtt3v7Egt7bD29qP64/wfe5Du68lQ1Ou8XKShbk3MuZaCHO\nFMXZiK+xLFUnsSignHfckDZwg4oMUSL3Lji1hvZJe1fuPGttj4h+m4i+g4gWjEt/IrpMRPceuaM3\nb96+qewsrP6qzPRkjKkT0Q8Q0deIXwD/pnzNV9Lx5u0xsrNA/U0i+gVjTEj8ovhla+2vG2NeIKJ/\nZIz5W0T0JeIyW29reZ7T3j5HRA17ImqYQUJInyFOE3y+pSJN6oh/eDlRqHN1leHP5WUlVtYWFUY3\nulLNJoREGslRv/vCl6q2DKqypCJQ2akrTGtJiekQiEUS5ZZaHWAuJLhsbfCa5Ns+ohFfr27zdd8d\n6xJmMlS4t9djqDqZKukzy5yPVvvYakDOvDs3hC9eW2Ti67u/Rc/9oSvXq+31RSYkG1DgksRvHjeV\n6Awht37lMo//wqYuq7auv0RERG+8drdqsxB61gilAGmh15uKrHkj1bFMANLe3j0pS24lAOTGuu5z\neUMf391XXiciojd3FSb/YY/vz8uHeu43D/jzMcDlBJZd3SW+9qtQAedGk7/7JKjpPLWi47J2kyF+\ne0vHOhQZ+QAKZR4LPJDqR2SgfrvoUFhQES2nSrSWkRMh1fHNM74XIcm4lGeLxD0Lq/+HxKWx39p+\nm4i+7Uxn8ebN2zeV+ZBdb94uoJ2v2GZZ0mwsFXTEV2sCyOMWn3ABbHsCJOWG1FW/tapM9Y1FZujX\nFpSBb9RBoFAEKhOCWuwCP2cg3D4cALQWyNtd14SQKOTtSabsNEk9edBLPCYmENYZQt586lbV9rH7\nzCrPXtQEoASq80xE+z5HBl+Y2hAZW4CqkXTgEoiMftsTHKtw69KVqm11Tcet3hSRTIijCKXaUK2h\nUN5AfrsViNlZuannFqmqJmjt9/pQ6UiSsNJCx9fV+6zDsftt9WI8ED380VzHwD0mESwDo1Kh847E\nGwz7+vlAWP0+tGVSO4Cgik+S6lgudrhPlyBc+GNdHqvnrqhP/dIz16vttMvXHsH9cRJzsDo75sWw\n4rnIoABsIKHB+L0cxsDKsiCp67jVY352SonXMFhk9m3Mz/jevF1AO+dKOpas+GPTlN+OEZBuVmbL\nOdQTiyBCzaXjrjZABUeizWJIYyWQSw5kVq9B1FWUT+XcevlRE/zicsw6HCd7yAkfOaS2hpFTBFKb\nQ7TfuM+Re/Mx7COVeCxcY4alkt05ITU2kkgtPA+q7aTy1r8MfuYrK+yfb8LMFQDpFojfPQA/fejO\nAATl8RxRN4sp+ZfEjCKaqQZXlIXGNcwvM1KaTXRc9vd4XBJITlpe1Hu6fsQzWzbQcYskIu0I4hsO\nptr3HZf9BRGE5Ugkt3u6T+DGHe5tAYRYJim2S5B5cnWTr239piKddEHRUyCoxikhEREZQZgm1KhM\nE+r1lIJWQojcy8dMfOcw/jNAfoUQpCHIwluRP8+FELSln/G9efP2CPM/fG/eLqCdcz6+raBINhWo\nVQP2w5VpBhHMGrya1iWfudFRiBg4Zs3ApcDyIRLirAYkllN2KSGpBcvdlOJjn+RIujnCCspkB7xT\n0FA4jdrn4ykfZw66+g2JB4hihdDTucLBQOD2aYDNArxHccu65HEvdRUu1yVRqZbquCCsdznvYQCf\nyxhaIA4pA6JOPg9jvd5QkpuCFBJQhgqt6ylD4jLVOIudKXvoO1bPswzJTZclmWgeQL6+ZH1vH2p/\nupD335CkmJFRCN6/z0lFQygMmgvphuRpNNPjuDLly6C7v3KFE5qa3SX4HpCiQqyFoGZk6rIMhKKY\nJQjHkiw3LbRlIyYoJxDjUUKIeDnjZcEMCHETSRUfVwbeeqjvzZu3R9i5l8l2kUWjmcxyoLxCksSA\nBEUDosg2tqT6SwqJDzJz4eQdlKAzJyQW1q8LZeYzUHEnBiWgQEi/MFESJQ34nBGcKBRFoAISicZj\nJWsKOc8UqgGZyEVYaX8szHzOI4RSiHnl3oS02wSknmVWX4G00EQIoBKQUACJPS590wAKsCJFfiz6\nq4DZsKpMo8dMGjwLxm1NjrFHSu6VE57xak0dy7ogkwzJU3CDNkQdKJ0AwpGBmYCrNysA2UnK6g64\nEnf2mGybz5Eg42MaGOAQxmCjzcjlmRsahbeyxi7RAInSGGb8Fo+7AbLSzanlVPtTThUJlULUlRN4\nXgRhzgHNOfc3EVFY6SiCOpDUMUzl3p9VQ9PP+N68XUDzP3xv3i6gnbvYZiDE0VzIFQNEnINhM4iq\nQhLMVXKJoYpJTfKdMdkkArnkUuBxEaMII/8JLMBc4LOm7nMg3WI5N5J3ucC4pEQ1HIXGU4lZyIEg\nc1xks6P9jafaN5dXjVDUiVMiisMgvob4+euwLCLJzc8gKpCOCY7KNmoXuL4D8YWKLtZdZwkJT6KC\nk7Y1rz+qq1jq/C4n79SB4FwSgvPBkabhAIKnpiTILMF57FxIub7C5SMImTwQGPzlV3TJsbfHMDmH\nYwdG9oGEpi5Ewn3PLfbVf8utJ6u2BZdbX4dlU02XLi660YKOgZNeJyzsCc91IaReDqXLy1xy6kn7\nY+b67MxlqRAOlTBMn+QIzVCeb/NB5ON78+btT4f5H743bxfQzhXqG0MUBo6FFwgO0CQnV6lFoXwd\ndMVnA4Y6OYTs5lKlZl4i+4yYWDTa4R0XyvICoxtLoNndMmQyAkZV4Ncc5JMSw0uBVRD3LEFzfi4J\nGGNgdm3B2y3IPjqaQGhq5V0A74GLO4D+pjXwSMj15BMNGZ0Jm05t2AkyRko5j4Xim27YAnSRGISq\nruAkHFN2ihsKfdurWhno6O6bvC9Ib/UNQ/wI8sonpJ6RqM79TDMIFxYd+xIWW1PwBNx+wO0PD3Ss\nJ/J5Csu8tsSNxOAf/8Q1TWT6+MefJSKixUuaoBVLkVCD9QIssPWSWEUxepN4CRXAsjTo6BLUuGvD\npZiMbwnVpWyJnhh+zgrwhNlsIvvw9XtW35s3b4+0c/fju9nWzSoGA/dkRqoBqZMAM/Ngl1NsZ+Bb\nbqcyE0DE0sqi+rNXltjP3G1rJFZQyRfr2zGfK9E0OuJkiQHUqkvbLFRZa2n0Vi6JFgcDRQbTvhJb\nLgJxXujs0JO6Z3MgDucgPZ3IrBHCzOY+DQAldAEJNURsE+vXWcvHL8F3PwJUNBVkkkwUoaRyphqI\nZR4jBKVSjwUxyFwIUuCgKJtBVOESz5yDvvr2B3P3DOg15DBG/aEo8KiLm1we086RzoaH8PkDIf+m\n4AOviYQ5Zk3b0iUa6VjMIc6iN+fxOOxr1RyT8LPTKDU60daxlLicodTriWuSNBPpPZlBXMLRIZ8H\n/fSFyI3PIKmLAijXLXEC80xrS1LGxynHB3J9GLHyaDvzjC8S218yxvy6/N9X0vHm7TG1dwP1f4pY\nZNOZr6TjzdtjameC+saYy0T0Q0T0s0T01ww7C991JR1jTFUsMhGMPweI7qCOBWWWDCBZIbnH4aIm\nYnQXGYIbcMQ3sfSz8+/DcVwuvAVCL4SEkEhUTFaWdHlgpMBmDhA8E1KnD6GYoyNdHriCJxYq5Qx6\nDO0ODnRpMZyh4KKcDziaWPpTB1HI5ZZuN0V4E6TtqZBlyP6h+rV39vWcTue+DVVZrq7w9S5BcUcD\nRF424TEcTRSWHx3y9QwHOi4xJOw0hNDKIdzVxQ4sQ7nt7UPVvt+XfPwJhBv3hbzaH+t5DiEU90iI\nPCyoWikWAfwPqhgO3ff2PS15/ev/TO5pT0N2P/k8F768fBViRaaQwy83azrV5cxIKkYNRjpWd+4r\nrLeGx+DSssY/tBMhaWfoi9fnsi5VeUILRVZHkqNfk9/O2XJ0zjzjf4aI/hPSCPNleg+VdLLijL3y\n5s3bB2rvOOMbY/48Ee1Ya79gjPned3sCrKTTqie2FJ+U0xKbgGtiLG0FkFDbI50Np68xqfGl2/pm\nbcRcpebZa5tV23O3tDRxXvJbNurrTNGVBJYIIr/GkLo5kdTNw309z92XuNrKnX2d0Vcl8WQN3EUN\nIIpIqrEcHijhtyf7DyZ6XSH0w0XsoY5fS+qsdSDCrAmzey4JT6MjHcuDFm/3oIKQzWAfIZUejvV6\nenf5cfjEpz5StdWhLPhgwN+9L2pERES9Qz7oENRwwrae6GrMFWfqTah0tMKIbTjQc+/u62y4M5RZ\nEpCfy9TGuSMAFBeJ+zMHYjGS+9JMtW81ScAqYc7DxJ7plAnkJukALzeZ0F1aUwWeBrgDd7a5lswf\nf/mlqu1FqZfYA2lv21TNvvoyj8H2it6fpy+J5DmU9c7AFZwIubvagWQs6acRUviMgXtngvrfRUR/\nwRjzrxJRSkQdIvo5kko6Muv7SjrevD1G9o5Q31r7N6y1l62114noR4no/7XW/iXylXS8eXts7f34\n8f86vctKOsaoYk4kxE0GpZJHM4bJASiMjAYKew77TMKETSVEylKgM/g+S6tRZJek0k7LgLxz5xqf\nGxJHRkP9fL/P53zlVYW0r0mZ7T5AwOmhRKO1te3mJohOyrJmCATPocDYKWDW5YbCRpezVIPy1ouS\nHNLAJQWqDEke/Q4sQ15+yLBzCuF+Nze0aOb1Sxxd167pWCdSctmCHz9uKZEaSR7+ZKRQ/ve++GUi\nInrhxTeqtpU1vT/fM+Zl11PXte3aDZYb335T95lBFN5gxMuufApJL5KshUlSLYhEdJxfXkJilRDI\n/ak+G7lExeWoQwD4OBT2b2es/fn9F14jIqKbH/9E1dbu6LjMtjkRaQTRloeiUjSNUGxTf25pnWH/\nP/+jV6q2I1EM+taP6FK1BpWb0kV+tgLQjsiOZIkkf8sz8mjv6odvrf0dIvod2faVdLx5e0zNh+x6\n83YB7XxDdo2hUPz4YyHRh5CIXQqjXa8pdL66okxorckMc9BUyCquz2MhrCkk0kzlRDYGrf30pOAl\nwZIjENxYSxXy3rzMrPQBaOCHwrhGKYg+tjRBpZXydwdw7PxVTlqZQ272BPzRdbmgBuSLp9LWSiDZ\nBEJp3dkNtF1dkTxt8JVvQgWia4uu2KKy9q40QRhof+JUYxnaIpzZONKEm1yg8TNPaKLL0zc0l/3q\nJt+/BoxRp86s9GxBmf61VfVN3xaGH+G/yzcnC7Ac9BRcztII6ifkspSbwvi7/dGTgkUz05jHwyXE\nEBHNSqndkOpYGYhLCKXy0MrWVtV2Sfz08yVdqs4LSEqS7QDkuvYk3NbUwAPyhFZh6nZE1DNTT4CR\nZydIHKvv8/G9efP2CDvXGT/PS9qVqiZjUSCZzYCukQirSx2dZT79yQ9X2/Umv5nV40tEhmflFpQj\nrtf1LZrLDD2GFNu0xjNOmugbOOugICa/rQtIBnICoJdRMUgSPpZBXrsNhJMj49KxEjwN8cHGQ5wJ\nQE5ciBsDQpSlqBZ1E72uzQU958oCj9f65uWqbWlZUBGoxjQAFdUFJ8zGUJ9OUkTjQMfSzk9+vtVS\nAvOHvv3j3G+YNdt17Vsq/u4mlPUuekxChlA6OwHSNJeEmwiiMUeG0RWKokbgx58K4ZjD7G6E3Etg\nFgwkLTyFFNoazLorEh26AVGbz0vabre9rPuEer1X1q4SEdFqS5Hotav8nA9BLWewD0hpLmpSf/aj\nVduW1OZbAmnvZgwRpSKyGUCsQi7l1AtRnTLmbHO5n/G9ebuA5n/43rxdQDtfqF+UtHfAEMhKaKsB\nuJZI7ngHoOJmW2HnyjLD5AI0zUl8ywnsE4OG+8EhEyFHsZ6n6cpBQ5UZE2kYZEtKPq+CoOJU4PYY\nfP+Ob0pBmSUEiD6Z8fLiCHL0ndJPCARmDssdI5shaAWE8n6OQF1lIdV39oeuMaG4tqAQPJWY3iIB\npR4g+gLZ7g9gOSP+86RAAk3jG4zkz68saI74SveabGkb+t8LyfuPwcddSBnzYFuh/hCUcwIhfMdA\n5OXynLQgviED/3wmMb3ox57LGC7Cku7pRQ6LvbR1tWpbWFEIf/OSFMhs6bitiG5+A1R3YqxRILn3\naR3Hn58dC3oS9jrccymqGUJp8/oSk6cGi5bC+JdHHEsSwvKrJr8PJ6p6RqTvZ3xv3i6ineuMX5aW\nRqIF5ziwBLJNWpKU0QRCiqAsdSIzHpa3TiTKzNR0xp9DmmsoMsdLDX2zOjecgRLQEbyZE0EEUUNJ\nlkySeKbgfrSuPDJEi+VzTdEd9nimHx5CCq6o9aC6D759O9LNBiACVyI6gRTaNhCYdXFrtQB5xG6A\ngWw0oKXnkj8gWVYlp6HUN1bSMbmk/xodA+eeLVAfD8bSVd8J5kpsUSFRmyChEwPBuSzXMQYSKxb3\nWx3IvSKDqEPp5iw4SeQtQpr2E4t8xU8uKPpZWlP3WST6esFQ+1uXWRW8u0QllMSe83XEQPiRPBIG\noiCDpj5PhegNony5Q7/zARx7poliTqq7gPRf4x4D0OY7i/kZ35u3C2j+h+/N2wW0c4X6lizlLh9f\nfOB2qtCsFIhtgaEYgyhllW8yg0SEGsPKosCcat0nFPWaBkRqBSIpnYGkcwbwNHUJFlDQ0FUxMeBv\ndmWPjQWf/FTjBYaixlPAcmUqhTYzexLGEqm2JSbkRKLwUgBBmUawdJHxQh2DusDOEKrrzI2Oy0wS\nh8oClgJChs3moGI5hmKiUvAzjvWYmSRHFQBJQ+ibkWu3IS4z+O8E8uDTHshrC1GXQ3TjWPp5ratw\n+eBQSTBTOBFN7W9TxnURpMg3JSFqsQ6VdFLtx1j0CXo9vY/PPc+RiMmSJhoRjKUrNhpAVZxYknMs\nSCkZUHwy8uxZUBmai/bE/oOvV20lqCY1XbxIsaf9yCTprSMEJUo3vY35Gd+btwto/ofvzdsFtHPX\n1XdgxyGSY6XY5T10BPD+cA7SReI7NVgRRvYvIAQ2Bd913YVjQvhnLJ6EKchfFSAMH8QMafOxsquH\nu5ybv9hRXf3QHRMY6RDgabfF5y6Abd+WQpF9YG4T6G/LCuMNd8bVHphOwN8PmelG9AsKyL03Tnkz\nQP+69nMi1XvmIM01FM2BZqrHrg+1osyyJCqlXY15yA64BkEBYxWDq6CqYWD0OPMZ6yqYfYXql+Be\nuHr2XwCffC75RQYSd/DZcZsG4x9kOdWGsXT6DKtLyrbXGlhSSRKvOqCHcIWTb0KQDyNCX7sTA4CY\nBxn+HJK67ECFT+O2+PlhuZnLM+jqOhARjQ91CZXnklwGWv2FiH42a67mgU/S8ebN2yPsrPLarxPR\ngFgAJbfWftIYs0REv0RE14nodSL6EWvt4aOOUR3LRXC5P5BAkRVOQll94btQFnkkUXMBiBGSEHkx\noIA00VluOOAZDUtVZ6JkkoMyzhRmrJpMWfjuLGT26Y/0DeyEghLwa0exzjgzcea+uadv8JHEA2SA\nDBLYZyKzsmnoO7kryUlTiPC7+1CH+nKLZy9EI7koa5oJSDpPAfUUfPzDfY0qfO2lV4mIaG1RCbS1\nhka1kdQsLMCX7mIhykLb5nD/XL5PDFGSVuIRjvo6aw6HICopsRLgxifj1H8GigYTmN1XJAloF1DR\nTGb8o0LbhpIYtGT13Nu3obT2Ect8r4KS0nDEz1B67/WqDUnEQMhQm52srjObAGpBdSDxyZdAxmU5\n3yvILKYZEMfbu/wcLYEqT22FCUcXpXhWHet3M+P/OWvtR621n5T//zQR/Za19hYR/Zb835s3b4+B\nvR+o/8PEhTRI/v5r77873rx5Ow87K7lniej/McZYIvofRSt/3VrrSpA8JKL1R+4N5uCzK5NtgcAp\nhSTDMN4R+JRHGUOhOAPfs4RttpaUeAnhsjIJ3+2BfvzgBRaInAHZZbsKaRui+pMuKgHUDTkRZgKk\nXCZhnQZ8uqheY+W9un0ARTVdvjisIxJQzplJiLGFL9TEH22hTIqFpc2oCidWiJ33Gb4mkJiTtlRB\nxpGQBkKDu5eZgGs39bqbXR3X2JGVsDxzIanhEpSv7gOJJVVhTADJJmNeStSPtD8lLGN+QyA6BA6T\nlQm3wGwAACAASURBVLGe5brPCuTUZ1L+up9DHQYhAndmen++ti2xFZEeZ7+ny7dSxnJrotc4eMiP\n+fBQl2wtSJJKJXEoByK12eX6MmEdyNXJKWME8RrZkNuKmd5HA+HGYYOXHwaSjmZCnvalolEBy5q3\ns7P+8L/bWnvPGLNGRL9pjPk6fmittfJSOGHGmJ8kop8843m8efN2DnamH7619p783THG/Cqxuu62\nMWbTWvvAGLNJRDuP2LeqpBOGgY1dxQ86Wb/O8UNj0jf0w32tqfb6XX7zbi7r56tL7FqCIjE0nuhc\n4UqcTSFCbU9kjAuIENxaVr20mSjAlKG+eUsh8KZA0DgSq4z1Glo1JcbmxLPtAEifudTRK5GFQYJT\nfFQH4J6sJ9x2ZVVJneUVnbEy0Q4cgH8rEvIohijHCRBa46FEPILMdGeDZ/wWRPvZSPffv3OHzzeA\nmtiGxyCI9bqhAE6ln1hminqSfe5ba/+1qu0OuFtdZdZjM0n5HBER5ZHWbZkHGNknhCDs4vKLdqHe\n3j97g8nMEnTtLncV4Tz3JCO/m08+X7W1rrACz+FQr+FgeF+vR+oYrl5Vtah05TpvzHSfEgjBiShR\nHT3Qn83BDsuNZ4AGm1vqBo3FhTg3iPz493Qg1ZryM8prv+Ma3xjTNMa03TYR/UtE9EdE9GvEhTSI\nfEENb94eKzvLjL9ORL8qbreIiP43a+1vGGM+R0S/bIz5CSK6Q0Q/8sF105s3b99Ie8cfvhTO+Mgp\n7ftE9H3v9cROQDIHzDsZSGnnPYWSyxsKOw+ErKlDTvyyXMLOthJK/Z5uGyFe4lBh8nqdiboC6ko3\njkAUMePlxcxgnj33qSiVEGyI7zgFooegmGK/YDgHLu6K1MSCjwEQdY7UGwDZdUfUaeCyqQFEUluK\ngBrI966JFPQQio5uv6KVazKJ3JvAEqjTZfKosQCJSAfQzxoTdHmoBGcx25fjaanp5Y7C00CEbuI+\nkFRTrjzz1Hy3avsdvbTTfdHdv0tERNOH/0bVtAtLl74QgkOI7HNjicuqI4llaIMv/FueV1jfFI2F\nWabPXTgXhZ2uiplGbb3G5oJE1EEFp2Ii0ZQz/YkNNGSCBtNA+q0Px0xCNGsNPXdc6uc24Xs6m4Py\nkEiRu/Ls5RnJPR+5583bBTT/w/fm7QLauSbpGDJVqKPLHQ8h/NNBsoNtZULfhHzlzUWGvKtXFFKZ\nBQ5Tne0pO/pwT/OVXXHONFCGPgkZSpWpwsLhjsLGbM6MbQzZJp0Wb9dA8z+IXG1yhfdHmS4PXpOw\n2jGIIzq/ugE/PaD+KvHEAkNfCgTMge7FSjB10euqNZSdTtpLcl2vV233H6qHJJHw3mBBK//MxAsx\na0FIxpLGNwTSj2CqxURTEYtshOpJabV0jIxLZvnaC7rPikifjXTMdaHwCPvxT/Pf/xrENGHcchmP\nLIeqOTKGIchbpZK0demaVvtZf+JGtd27zcuhL7/6ctW2/wdfISKiCJZFT0ENg+WrLDha1sGdIfoN\nCyATdwCSZtkyexXKTPH/wjJnIi1A0dGs1CVSr8f3bwyFZCciAbZ/yMumHK//bczP+N68XUA71xk/\nCAJqCXFhRYY6g7fxTIQ4sUpJB96YTs0lg1TdTGbYFGbA9XWtaFILGB3U6uq3bQoBV0B9tBwUcUj6\n1OoCySLdyFB5xaVRgvrPq3fVz/zgTY4XaMA1rEuCUAkSyimQe3nJfatB2m1LfOFYJWUC2pVHkt56\nWNMItJllIihq6zW2FjWd9mgkKatNEPDcuklERFtPf6hqS6Bij3WJReZS1RbOefY3E0ABc0Vk5es8\ncxqIeCzuMSLbBlHVGZZHWpTrPPhvtO3P8p/aZ5TUTOA5mcTcbiDqzT1joMhNqSDOyUDHd/9A+9aQ\nmoPPrus11uu8XQdRziVAQqFTO2ro505hJx/pjN7cvVtt7w34Xh0cKApIJAnKGr3GMofkJ0GOE0i2\n6k/Efy+KQdaTe968eXuU+R++N28X0M4V6idJRFcucQLMeMLweAbVUmZNhil10I9PF0CLXN5ToyOF\ncw8tw6daBOKVkHhSStL8wWBbzyMxpY26Qt/mivpljZBkEYhb5hPnSwfffskQe3CkENvONBllbYEh\nb2E1T34s5EsPQmkbqULjZiSQbQyCl3INqNs+gMKWr/VlCVTXfRYlxQXHpbakOebdpkB9aEuFoJsN\nbldtUVOJplAqDJlSlymFhO+GE/CfA24P96VGwcuQEPU838e1z/ystv3lv6rXeyhj8/G/om3fyn+2\ntrRs+izTJd+OaDWksDwjSc5pQnnx5TbD6aOJQvB7DzS+4YktvlfNBX0eGqs87mkHagfEOtahK8GO\n06irqBSDzsCShlk35RnPIN++FMJ3r6ck7CGEbk/7PK75TJcmMyHJi5Y8Y8HZftJ+xvfm7QKasfbU\npLoPxOq12D5xmd/YRpJeMqxM44g+ILGSms7+TpuuhH3GE0l6gZRVgmSIRFi5GqS+5pKqO4F0TXSf\nOfdPmeksNZfkB0yXLWWfHPZFme5SJJ9jYJdq0rcmuH7WVpQU2hvLGxyOMxjzrDGHtgxchHNRFzo2\nBtLPEKITw0i3a212HS1fuVW13bz5bfw9IB4P9l+ttkeSGjs60lns4OGL0h/V7rOQXuOq2SDBVpOy\nN61UZ+cntzar7ac2pIw51KJbe5oJts/+3uertmZHx7CQCDgsBT4bcX9RTzEXYrjb0jHfugxEnqgZ\nEYxVW1K2O8uKfppQIWcqyANlxZtShSkG91oCEZ5dQRSNtqKAQBLAZkeKTnFm7ksU5t62Esg7PVEH\nqvNxfubv/A/06p277yi852d8b94uoPkfvjdvF9DOldwrSktHY4YrUciwNIGwNSPLjlkOOe9QDDAU\n2e0cSlW7pQJKLWM0oEtoyMBnbAQy5+DznEH1nUL81RhR56r7WCjKWEp/A2hLQFTSLQXwc4eCc5BB\nNhCd6Mp6l1B9x0VjzQDqY99dYgYKl5qquo6euoQikyRk3GioX9gtmDwyUPJ6WlcYfLjPJNhgoEo0\nMxkrC8kkBOSfFaWEGNRykpRjA6BmKU1iJbGOJFbiIRQGNUu8RMTqOnamY+RKla+3lbClFp9gMoA4\niZkU/gRJcwtaAV2J1ty8CrEgXT53F1SaOm0lnV0OfEG49JAYApBeT2AJWubOz6+ft+TZSUEfAMvI\nhxJS0SQlOBc6vO2e+ST25J43b94eYf6H783bBbRzLppJVAg8Thw0AWicCxSNULM8gKo5wpJjJR0n\ndIg6TSUmuAgMM9DmwgQwuDEGtj4VeNWGvjlmPoN35USg9yw7PUyydIKiFklWV0JIId4IQpCnUxHw\nBIhXOO8BMMTHGXw2rLVObtwATxuIAwjqDFttU5NNBhJq21rQuIKcFNKWixLqvK959BRwm7XqWy5L\nGA+5zjlc70TGAEctu6/Lh10JZ53fUV2Fg4Rx7rV1jYm4vKyMeDfkYy4CHC8L9rVvg/TZVO5J3IDY\nCXietpZ5qXDz1rNVWy1lD0gYgLcC3BSBhNhGzQW9IPm4BPFPO4bkGon9KCbqDYkk2SsCj08By14j\n19No6X12haJc8anwjFO5n/G9ebuAdtZKOgtE9D8R0YeIp6y/TEQv0ruupGPJSu6tkVMbeHNamTkb\nUAknhrpzmfjVrdF94tD5vXUGzEChpJTZJQSffECuppq+9zqQjPLcGs8GH76m0VsdqS6TATL4+gO+\n3Bff0Blwu69vaKeig5LHjhysxXqNrZpuu/p3x4LAKjiDJZfBZBYzGJMhiABTUuOWkkKdTRaQTDo6\nS40fsn94/55GRmY9TXcuJUW0zCERRs5pQSyTTokNwTiJqcRRzGE2m/RBaWbbzcqKIhav8xh3IHpu\nEdJ/O6kbV/3cSatjXIFu67m7HfXpr25yim2zo8ghkVqKJRxnBslAhVTnqdUUWdRFdDWp6XM1JZ3d\niyHP+LOeJunElvse54pGMOkmkOcpAvn5UH4LdnKS4H07O+uM/3NE9BvW2meIZbi+Rr6Sjjdvj62d\nRWW3S0TfQ0R/n4jIWju31vbIV9Lx5u2xtbNA/RtEtEtE/7Mx5iNE9AUi+il6L5V0rJJtDvoVQAQ5\n2B6BXnoIPvC47oQzlfyouUoj4C/NwSc/F/14kMOnhQZf9lMbSg59x5NKcj31BMO9tXWoItN059T+\nfOqQof5Ld96s2j7/VdWSeeUuQ7s3oZLOvvieB0D4lUOFgLm7Dli6lFUNAjTw2UORaGehC9kF6cqo\nVHg633mJiIgmd79StY1FNabAij2A4N3Rj1X0EZ+9Aa33AJYXtlK6PLlMMceqKEE/xRedJgqTG3Id\nOZZNh5taSrxHDP2o1HggZLchobjdtt7bG08/U20vrV2R48H9EfhcIEkLz6CVRChU/4lk2RolCtvT\nlv7cQomzGIOOhCmZvJtDIhjBEtWcUlt+LvB/Ls88Lqnezs4C9SMi+jgR/by19mNENKK3wHrLC71H\nVtIxxnzeGPP58hzzArx58/ZoO8uMf5eI7lprPyv//z+If/jvupJOHIWVmLBzO9SgdpgjJjDQLYB3\nU71K4oEUXJmSGnV9c0YR1CiTCWKxqQTa936co7I+9iHVXVvZUt21eo2RwLE3tBCOFmbiliiurLY0\n0up6V2epf/oFrjxz+AWdaQ/GvH8JCCWDNGRXjhsrkjm+5vhr057YCjByT2blEsuDj2Bcil3pB0QA\nuhdzqGOFRF2V0AVtkagLGbhpBtxepZO7BpTgSprjrBMAMqnLjN+GL3Rlth0NlAyzmc7ay+tMxIYw\nUx/N+NpXQHkolhm/2YEU5ZYSnG4mRo/kTNyBmNCWABpx+okzmPFn/YMT+7QX9DxxJHXwoMR6KSnd\neaak5nio6cO5yC41lUOkWNy1meP7zsbtvfOMb619SERvGmOelqbvI6IXyFfS8ebtsbWzBvD8R0T0\nD40xCRHdJqJ/l/il4SvpePP2GNpZi2Z+mYg+ecpH76qSThAYqtX4lIkr/Zzh5/K3pt2qQzJFW/zd\nM6gOY2eMe2IglDoAK1clUeNjt1RG+lufYVi/tqp+7QTOGUu+eJRAsoQom1gsiS3LjHpdCZy1VYVz\nz15iuPe5P1Yof+dAim+ifxbDrU6JyHNrH6xHjL5pdfMjIcjHDCA+rszKE/vgMV18g4Ukm7I8uQ/i\nybyKyNPvIdoMToGebrXUgKpDSOi66kr9oarc9LZ5abK+BIpCEN3YkRLec4iijCVnvgY5+qkkRHU7\nSuzW4BmbTpmILSERLJEKRSbETDDdTKQAZ1TTpKKB+Ol7O1pcEwVhUyEXQ1Bfmo0HJ86dFxA7IHri\nAXwexjz+hbR9I8k9b968/Smz8y2oYQzVZWaNzMl3TuFi2+GtPQMyLRCiCN+ITXnr1+FFB4rRtC6l\npW9eV4WXhUWe6SPQYgvhbR6kUBjhrdcA3wsdKQfqPo1cY8nXNvgNvrKg7r75fW6bwuwbwvU6gs4c\no/JOuvNOe7NjToCLK49hykXyLxKCDUFAnrl6htoWwDHd8TFlWMkrnPFPpgejJ8xF+WVAcGKpcEes\n5YA8+lJ8o3VFZ+p6UxFZJG7dDBBKQ8p926kih8hJq4Pk+XQGGoJTqZEI0aEdOWatoYTeBPycsci0\n1xuK9mpCMqKeH0b+XZFcgDhVgrKM2D08maj7dzyC6MYx7z8nJf8iV6K94l2/QWWyvXnz9qfP/A/f\nm7cLaOdcO48oEOgXSXIOpmsWc1GsgV5BFWEqBM9EgBsTy8eZIiFS18831hh6Ly0qkRdJeWsTKVSk\nEk7qCBUg3awklGB6r0l5/wCYniiHmnZNhqV1gIhOrLOA48yg3l5oHJw+mVpcnsLnEakPHVdPiZCd\nTUhaqYM6S02UZgJI253L+NfrmqBCRvtOxNd7MNQU2vGcYelsrv5mJBSdf76EiLq5QPwMIu8s1AV0\ntzeBSkfBnOF6p6HKOM0mpA9LijSmcS8siP9+DtcgD5SBxKgZRAOG1T3Xa3DLLnNseQXrSbk0XFY5\nwjcBwdA7fZBel9rpLRDtrIuU+XRfx9LC89QXKfNaCMlAjZqcW3t7FvMzvjdvF9D8D9+btwto56/A\nUzg1GUnSAUg7nUq+PYR81gCeOjhXgB7+0IWmgmb5laaGaF7Z4qSLpQ3NIap1RTEFLv+YIKZ4Co4l\nqBhRmgmBNTWyZCgULkdYbEWc5DF4DyKB4Miwn7Z9jMG3ru1kmC7+B0FeLB6HtY5CyY+tqr7A9TVO\nSrp6+emqbWWLk5OuXXuiaqtBzvtEtPz3QaDTTnj7zstafeerL/1htf211z7H+0411Pb2ES8VDqYa\nyoxLGzcGIcDthoxlM4WlSw1Di3mfDBj6UJYxWFPBLcumcz13PoUy2k1h2QM9tvNSIF+eoM6B6DqM\nx5psNXXqTLCERMWhnhS+rNXhOBIvkCzo8iDsgUKP6ExAoShqia6DWx59o/PxvXnz9qfIznXGJ0tV\ngocje7Jc3/ROfQZfrQW+J8XBnIGscuBmCriSdl3f1mubTAY1IEovdNF3OG1CVBVJ9R7U9jOSuIJo\npHI4l+D3nyuB0xTi5XJLPw8dQgF/MyrnOCLpmCK389HSOxgmz8iMtNbQGf/bn7lSbX/kIx8jIqLN\n65qoVBcZ6QRm+QDIp0IUeDbB/V5IOuzVZ5RAe+oN9U3fe3mLiIgmUCL6//rC7xMR0e+++lrVhj59\nR3CivHldIvuSBiR1wT3P5jzTT8aalBRG8rxAeKKV88wzJE8RcQkii/T+DMasTJQCmbh+eUuPKfEG\nOw+V9Ly7y+nZA4g+JJAqf7DHiCMFJaa2kNIhqPYQyJZ3O4xkg0KFrkYDUX+SWoqY3vx25md8b94u\noPkfvjdvF9DOFeoHgaGaSAdHgSjjAAwLa+JjhZzqAOCrUziBOpp0uc0Q55NXlRD51g9rIciljRU5\nt77j8ty4DumBkLMbD+Xc4E+1DMmOSVSLLxzDTaHYDUUiuPgtT2mC0JXPcwjn7lS/GIHop5XxOJ4b\ncxLkn5YIE8H6oBm7ZCi9xTugRPPw8CF/rwMEmSj05EfwWAQK+weytHl4X8uCj3YZ5mYQXpuuarLK\npeu81Kpf0vtTF8HM3SMd39cPdLuT8OdzuM82cKSc+twfPNDikXMpHW0LIMskIGTcV2KxlHtvYr0u\nEypEd/EaEbC0RkjcpS7k08MStCNrnzFoBdx/9atERJSB735lCwqhSsWf1wq9JwuScNN77aWqrd/f\nq7bXZQk2ARLx4IgTwRKR9s5RSOBtzM/43rxdQDtfd561VMxdWWueIQogI5zbCiWhsUacle82Ycrf\nXOIZ/7lnr1Vtl5+4Xm2Xhs8z6utMkcisXYL6yWgIs9gBlyl25bSJiKzh87SWdfauS223CEINcdZN\nhNxbu6bRZmsdRgnxzvzUfdz1HtO9O7FBx17ZkUiQr4JE+CduMtJ5ck2ThnojdWH9zme/SEREb97W\nJJIPXWMX30pdE2FmQL7e3eHZ5f6OkkumzYRXd+Nm1ba5CNe7zjO9zXV881DO01VksH2kny9LsYtB\noPdnnvPMORpogsqgpzP1kajTXFlX0q1V4/1TmL0bEsFZNhS5bR/quR8KQdep61iudfkaGvDcNUD2\nPQ15pl6oAVG9z+m491/SBK0JII9rcqw0VtfpQG7wV174atU2Heo9M1JMpB4qikikoEkoyT7mlOS3\n08zP+N68XUDzP3xv3i6gvSPUF629X4KmJ4jobxLR/0rvspKOtZZy8cGnNVETAfg0njoJZZBNBgWS\nlpA+i22FYYmQheOpwvLtXZW4Hk6ZEKlD9NbWFsNBA+KI91++U22/+RqTRv2hnnskedzr1xWiPys5\n/qsNzHPX6zUdSdQAZZZbawwbv3BX4RqW9X54Su69ObFxPNKwJvvX4eT7RwwRDw8VKiaxwltXKnzU\nV+i81mYoefnD6tvPBqqhOp/wsaZTWBbJcqm3o9WExgCtn8tZ7agd6v1pSUTd5ppGU/6LV9XPvyT3\nv93U/rZSXjbt7eu5xyOFzo74bScatdkX3QAL4qFRxtfw0leVQNsHeXOS5/NSB2SxRaFnizTysQb3\n1CV1Tfq69Bgc8vYUKu6Uud7zr3z5D3jXz32+amu3uZ8BEL+LDY2J6PWYdG4DedraZFKvTPm5CqOz\nrd7PIrb5orX2o9bajxLRJ4hoTES/Sr6Sjjdvj629W6j/fUT0qrX2DvlKOt68Pbb2bln9HyWiX5Tt\nd19Jh1R+KIgl8QTY0VwgTgihsguQ4OJI06MjhY07CcOnz35Vfbqf/arCxs1VZqg//d2qFXq1y3Cw\nBP1yAhb39ZH4U6fat9cOGFZG27o8OHzIEPE7n9f89cuXdRiCkI+ZlHrsK3WGuSs18N0fE9vkP++U\nbIHyVq68+HYPxCmHDJcxgrMDocyp+MUPIh3LTyUMGxee0soy9ZF6BY5G3M+v7yg0fmOH4ecrL36p\nahvNv1xtL6Z87R+9pN6Qv/SDnyYion/5U99ftX3xS1/X63FLsEIfz45oGsxhEZRBlRlXccZAjv9O\nn5dlDw7UF35/nyF+Ty+bAtAsaEhyU5yrf31zmZ/LuK4QO4blppFk+LSmy4POMns2vvDV16u2l3a+\nVm33iJcxsKolKfBEf+5T31W1NTtQuHWVn7O0DVk6Nb7uMJJzf6NZfZHW/gtE9L+/9bOzVtIpyrPF\nEXvz5u2DtXcz4/8rRPRFa+22/P9dV9JJk8i6yLRMUjxRb1FUrakBCTPqUVb5506qiSPPSLotquDs\n93VGGs0kUSPAtyTPYmFNI7E2ntSX0hNSh2z/oYoePv8cv3nbXU10aRPPKGWhUWeh1VkhlMi+KNF3\nYt3wBUeosIPZKFU1IUgsqUrpQOqq7kGlKMMgClhqM6nZgNTVeqzjlouopClQfUai2lKQNF9Uou9S\nwLPKZRjfxj7v//xTmgQ1nENaqSjzzEcqM51IOeinnn+uantiTff/53c5tsAO9OHoiPrM1qJeD6Yz\ntyVmYqkLkuhCjA1Lvfddw/enDspCUaAkYlum3eugmnRFnrGlJX1eMIMokHFtdjV+4cmnP0pERA8f\nKgF590DHrZbzPqNcr6ec8OfPPfOxqm1hUxN7skxiGXKsJiTpyiIv/g2f8Ynox0hhPpGvpOPN22Nr\nZ/rhG2OaRPQDRPQr0Py3iegHjDEvE9H3y/+9efP2GNhZK+mMiGj5LW379C4r6ViylQ57KX5k1BqP\nJPc4BoImBh/3rU2Gr594RsNzL10RnzwQgvt7CoV6RwwRF9tKLoWSuxyCX3ttA8pfG4ZcBwu6ekli\nKaQJxTcbiwzDmg0o4glhwEbKJwegDtR0qjxAd2THlH74LxYWLk/RSj+mtiPk4GKqfbvSlPoFUCFo\nOlbyzy0lsChpOC6kD3ocFIvcuMFQ9zv/jPbnnhCp4x6QlQuaALN8hUN5Vze+vWpbkjDfyUR93KsQ\nz+GqDJUgbrl9xPdkca7nGUI5dCPPSQLX87Eb1/nvM0q67Ux4+fCwjzEAuk9LtPifALWiK11eMrRa\nEA8Q6pIikASuOjwbT3+YoX4t0fF7+SWNHbARP08P5nonNxf5u89+5CPat5EuN4cZP6+tVI85HXNc\nQ2ndffYKPN68eXuEna8CT0mUi25YItp1OKPnorCDStcbXX2LfvgyzzjPbgF5lAqxBeW24y6kzg55\nxm+BVp6rMhMZUH1JlcxZ3WR31tqWuuac/l6Zg+SzJLBEMNOaQGfVUlJFDdT6W5QElEWYiXsG80/l\njz3VSXKqJYJ2nujqzPbJy0w09UF2vNSgNipFOWd/RyPhQkl5DYAgMiAuFCdC7j2jM9LlGx8iIqKs\nD/X2QBI6DHj8ozqglhEn+wxuv1o1vQ6RfxNBg+jlnIvf6+gQUAtApaVFnoHbid6LRRnClQUdl2c3\neHsWKWkJ3a2iLJtt3aeQcZlO1P1rsOy3PDslJGvVJJX36VuKDFYXUL9QZNYB8W5d4uSlFMqu724r\nIdi/zwg0CZVktHK9hSNxfZKON2/eHmX+h+/N2wW084X6ZCthyUJgPb55XKnrBqjGLEDSwSVJ2qiB\nj3s+YyiJecuDPYWvkfg5E4D1pnCECfqEsRCkq5qjOLcsGMoGJcA9B9EhkehYNGDG/bBQ+NAJheZw\nDZk9qZoSgo/6ZFlKohCrtohPeSVVzLoiy49lgLlJW7H+G/c56DJLFTqvLjN/G1hU09TxN9L3EGSG\njMhdRxBbgWyllQjCcgICkXfYT//m7/2eHgcKWzbctYGvvCWRlUgGN5E0le82M11y1EQVKJ5rW0MK\nW3ZARNSAxHUkWgQl6T0byzMGWkWU95V0C6TCUAnxAJGo6bQWNBKl1VZlqLkklRU5Lhn4ONlYl4bF\nQMelU5Vl17gE635HkkQWenltb968Pcr8D9+btwto51s00xhKXO64QP52XbvgJLU2EoVMqwl0URjq\n6Uxh1kTEFfeHCrFHQ4V2myvsv6+D1JIpBH4a8AOTsvWlhIeWYxBpNADhxZyYJkSEEkE8ARUM3eZW\n9719yMc8wAKYcI1V4UXwYecusQl8tAjpEqG/Id+Jsv+/vWuLkbMsw887/5x2dmd3Z7fb7UJbeobW\nEqEShSAEUCIS9coLjfHC6J0GRKNAvCBemhgPF8bESDQxRo2IQHoBKnLhBSkCUuhRWqhtt91TO3uY\nf84znxfv+8/7Vrbutmxnd5jvSTb7zz//6fu/+f/v+d7D8woFDIw345TRKTgywZHXwyaMNxhgWloz\n1DhoGit6U7a1mvTTbKEn45kgKFVtip+53tA+C+c5fPfkxGRrXc3IreXEQ1M2NexJpjExWO+M9Ybw\nNc3XNLkmJ9OHRqiW8UgqLJ3UKRAZr0zD8baFGQ0xLpb4mAnSfcqmcGszyfclnVXff3Q95Xmd4qQS\nun9C+tLmz0d3tVrR663MmP3rvG3DZPaEMuVIS6iyo+V5g/yI7+HRhWh7mezIgNcnZ84ZA82YCWUu\nMQAADgpJREFUlK/eZnz3/eaNWJaR2gQ7oSFqL/kFHaUqJqKrTyK0pk6r6CHVeVsyFUuSaZNcI/7Y\neklHu3qd36wJwxxclkcfm9SChhmF5NpDo4xzUCIJJ0t6vYNmtFzMf7/YO9xuF8lvk4mJKEvlmdAY\nPY/8Z6K1PJ3n9oSko8ubL3Odux1bVQAy6NXkmWRaDEimokzpPO9fPa218zJm4APxeSimI3Vzgftn\n6qIxwhq6skEoVN4kVgX9Uruwpn1i4ycC6RdDRlCNEpBSeu6YVHCylYoaRiWnIAKeF6f1XjXkOEGg\najglE5tBohy1MaXBrYEYPfNnNUU8bXzsPTkxtJoqPpCkouac/jZioWFfYmAm1UdFSURMg30cl+As\n4/w/8CO+h0cXwj/4Hh5diLZT/YiIDIuxZkNWL2FA/PfpHqNfbnyWUb54zfC5WlTE0ul2+YtqSDp8\nlENBX35dKdc9e9mfun6L5jrnNqmPe3Qj59ynM5rYEyvx8eumKKYTjXebBx8zhslGq5iiKsC8dYH3\nn6+orzttqGijlVtvOOsiiTu2vPiCGPImqqaYqGjFX5hXY9d563texN17UWIUqkaVJ5Y1U6Ah8fOb\n0NTYJG87l1cFnUpRKXwiK4lXfbpPRYx3JTPsZIwxMwrINuI0qEpxyUZZDaVZM8XKDbOhNR7X+18R\nXf56xcZZ8LpiQTUU5gtqQJue4b6yfVoqcRsroao8uYpOE8MYTwsmzum97pfaDVPvHG6tGzY+/c27\ndwMAUn26LiFh1GUjWOOM6k/jEE+n+szUkSRUOd5YHsWP4Ed8D48uRHsj90hHmkge2plKLfNivBho\n6OidNTXKQomQqxiJ5f51bEkayOmbfr6qb+NR0VMLjPslvo5Hhw27NFFjZLum+iZk1I5VjAuvl9+o\n9YKRm66y4TAI9U0fj6l7rCaj08HDKt39joz4xaphLSYkr+neWyY7UpoxTUDTuPNCGcWOTav7MS8G\np5iJDLOJJU4MTZFKEAD0i8JMM2fKZBvVmSBKMrH1AaUmXvpGjUoLz7/dWk7U+R6ki8pGyqI315PQ\nYydryooKUr/uDPS+hqKBd5sxXtly6A1JgTbyhmgE3Pc2/TcsszGyZGrWzcyoIW9Ajtk/urm1riSp\nsRWTvgvz0yhWeJ/QMIuTU8wwFy5cbK2r1E1E3slTAIChDabyj6hFleN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383568b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2210... Discriminator Loss: 1.5486... Generator Loss: 0.5724\n",
      "Epoch 1/1-Step 2220... Discriminator Loss: 1.5582... Generator Loss: 0.5687\n",
      "Epoch 1/1-Step 2230... Discriminator Loss: 1.6047... Generator Loss: 0.4993\n",
      "Epoch 1/1-Step 2240... Discriminator Loss: 1.4981... Generator Loss: 0.5811\n",
      "Epoch 1/1-Step 2250... Discriminator Loss: 1.5765... Generator Loss: 0.5936\n",
      "Epoch 1/1-Step 2260... Discriminator Loss: 1.4636... Generator Loss: 0.5365\n",
      "Epoch 1/1-Step 2270... Discriminator Loss: 1.5911... Generator Loss: 0.5017\n",
      "Epoch 1/1-Step 2280... Discriminator Loss: 1.5097... Generator Loss: 0.5504\n",
      "Epoch 1/1-Step 2290... Discriminator Loss: 1.5114... Generator Loss: 0.6006\n",
      "Epoch 1/1-Step 2300... Discriminator Loss: 1.5466... Generator Loss: 0.4722\n"
     ]
    },
    {
     "data": {
      "image/png": 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PbjFjMYBwnoQiT5aaL+96AXaBeHwwVA+bLhmtrLc1H/7Tr3ySiIg+/5aqz9w5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\nhI4fEDCB+Bv3/jY5JEgm5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3838c4630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2310... Discriminator Loss: 1.5663... Generator Loss: 0.5642\n",
      "Epoch 1/1-Step 2320... Discriminator Loss: 1.5616... Generator Loss: 0.5305\n",
      "Epoch 1/1-Step 2330... Discriminator Loss: 1.5452... Generator Loss: 0.4717\n",
      "Epoch 1/1-Step 2340... Discriminator Loss: 1.5097... Generator Loss: 0.5591\n",
      "Epoch 1/1-Step 2350... Discriminator Loss: 1.5448... Generator Loss: 0.6524\n",
      "Epoch 1/1-Step 2360... Discriminator Loss: 1.5724... Generator Loss: 0.5009\n",
      "Epoch 1/1-Step 2370... Discriminator Loss: 1.5217... Generator Loss: 0.6050\n",
      "Epoch 1/1-Step 2380... Discriminator Loss: 1.4962... Generator Loss: 0.5783\n",
      "Epoch 1/1-Step 2390... Discriminator Loss: 1.5700... Generator Loss: 0.6142\n",
      "Epoch 1/1-Step 2400... Discriminator Loss: 1.4578... Generator Loss: 0.5684\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZuviXiejntdb/kFc/4C469F7ddJw5c/atZadh9RUR/V0i+prW+r+Dj1w3HWfO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\nEOdUclNTTJmOT+wz4W2AieZ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q193fFUWhvUNzbQsouOna7kYgeb4O3XdWL5qy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\nwj3Sctj/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3831b4ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2410... Discriminator Loss: 1.5131... Generator Loss: 0.5926\n",
      "Epoch 1/1-Step 2420... Discriminator Loss: 1.5050... Generator Loss: 0.7034\n",
      "Epoch 1/1-Step 2430... Discriminator Loss: 1.5339... Generator Loss: 0.5644\n",
      "Epoch 1/1-Step 2440... Discriminator Loss: 1.4892... Generator Loss: 0.6411\n",
      "Epoch 1/1-Step 2450... Discriminator Loss: 1.4928... Generator Loss: 0.5780\n",
      "Epoch 1/1-Step 2460... Discriminator Loss: 1.4603... Generator Loss: 0.5994\n",
      "Epoch 1/1-Step 2470... Discriminator Loss: 1.5285... Generator Loss: 0.6405\n",
      "Epoch 1/1-Step 2480... Discriminator Loss: 1.4590... Generator Loss: 0.6900\n",
      "Epoch 1/1-Step 2490... Discriminator Loss: 1.5542... Generator Loss: 0.6006\n",
      "Epoch 1/1-Step 2500... Discriminator Loss: 1.4520... Generator Loss: 0.7350\n"
     ]
    },
    {
     "data": {
      "image/png": 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VomKi1zgcMvS+dfuOjhcI20qIwnyhsL4j+RyuWIqIaGdDQXEi3Z4mkMcykr4T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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383098780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2510... Discriminator Loss: 1.5680... Generator Loss: 0.5199\n",
      "Epoch 1/1-Step 2520... Discriminator Loss: 1.4815... Generator Loss: 0.5623\n",
      "Epoch 1/1-Step 2530... Discriminator Loss: 1.4943... Generator Loss: 0.6459\n",
      "Epoch 1/1-Step 2540... Discriminator Loss: 1.4227... Generator Loss: 0.6303\n",
      "Epoch 1/1-Step 2550... Discriminator Loss: 1.4773... Generator Loss: 0.6954\n",
      "Epoch 1/1-Step 2560... Discriminator Loss: 1.5832... Generator Loss: 0.6401\n",
      "Epoch 1/1-Step 2570... Discriminator Loss: 1.5105... Generator Loss: 0.6780\n",
      "Epoch 1/1-Step 2580... Discriminator Loss: 1.5102... Generator Loss: 0.5282\n",
      "Epoch 1/1-Step 2590... Discriminator Loss: 1.5095... Generator Loss: 0.5546\n",
      "Epoch 1/1-Step 2600... Discriminator Loss: 1.4809... Generator Loss: 0.5799\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZJNAwhONMpNvQeKKwcNxU2B/IdSSlLk1KkmIUmL5yOcf/ISKiR1B0tPuIP4NS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\nJzO3d+w9+TwlM1OrcPps5PXzUqLEY0YjnTi5M73XKREqk7f7kuaX3a5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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd382b7f978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2610... Discriminator Loss: 1.4600... Generator Loss: 0.6504\n",
      "Epoch 1/1-Step 2620... Discriminator Loss: 1.5427... Generator Loss: 0.6289\n",
      "Epoch 1/1-Step 2630... Discriminator Loss: 1.6130... Generator Loss: 0.5409\n",
      "Epoch 1/1-Step 2640... Discriminator Loss: 1.5036... Generator Loss: 0.5403\n",
      "Epoch 1/1-Step 2650... Discriminator Loss: 1.5142... Generator Loss: 0.6227\n",
      "Epoch 1/1-Step 2660... Discriminator Loss: 1.4610... Generator Loss: 0.6740\n",
      "Epoch 1/1-Step 2670... Discriminator Loss: 1.5189... Generator Loss: 0.6304\n",
      "Epoch 1/1-Step 2680... Discriminator Loss: 1.4857... Generator Loss: 0.5588\n",
      "Epoch 1/1-Step 2690... Discriminator Loss: 1.4780... Generator Loss: 0.6343\n",
      "Epoch 1/1-Step 2700... Discriminator Loss: 1.4853... Generator Loss: 0.6399\n"
     ]
    },
    {
     "data": {
      "image/png": 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E5F+e6rEbuXq+qIZqD+v0eSKZsuN8W/CakuD5R1DM3d+ez/R6Jr6xyr4+L622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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd382deb3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2710... Discriminator Loss: 1.4660... Generator Loss: 0.5470\n",
      "Epoch 1/1-Step 2720... Discriminator Loss: 1.6547... Generator Loss: 0.5747\n",
      "Epoch 1/1-Step 2730... Discriminator Loss: 1.6056... Generator Loss: 0.5382\n",
      "Epoch 1/1-Step 2740... Discriminator Loss: 1.5344... Generator Loss: 0.6217\n",
      "Epoch 1/1-Step 2750... Discriminator Loss: 1.4596... Generator Loss: 0.6441\n",
      "Epoch 1/1-Step 2760... Discriminator Loss: 1.5443... Generator Loss: 0.5064\n",
      "Epoch 1/1-Step 2770... Discriminator Loss: 1.5773... Generator Loss: 0.5066\n",
      "Epoch 1/1-Step 2780... Discriminator Loss: 1.5259... Generator Loss: 0.6386\n",
      "Epoch 1/1-Step 2790... Discriminator Loss: 1.5320... Generator Loss: 0.5977\n",
      "Epoch 1/1-Step 2800... Discriminator Loss: 1.5102... Generator Loss: 0.6136\n"
     ]
    },
    {
     "data": {
      "image/png": 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c3ebnYDCFIqCCYYewbNobwbJLiOERyIWXBZWgMo0TxDQotw7HzCQ334b6PCRS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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd382dc10b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2810... Discriminator Loss: 1.4854... Generator Loss: 0.6670\n",
      "Epoch 1/1-Step 2820... Discriminator Loss: 1.5372... Generator Loss: 0.6835\n",
      "Epoch 1/1-Step 2830... Discriminator Loss: 1.5328... Generator Loss: 0.7039\n",
      "Epoch 1/1-Step 2840... Discriminator Loss: 1.6758... Generator Loss: 0.5030\n",
      "Epoch 1/1-Step 2850... Discriminator Loss: 1.3977... Generator Loss: 0.6095\n",
      "Epoch 1/1-Step 2860... Discriminator Loss: 1.6221... Generator Loss: 0.5926\n",
      "Epoch 1/1-Step 2870... Discriminator Loss: 1.5251... Generator Loss: 0.6481\n",
      "Epoch 1/1-Step 2880... Discriminator Loss: 1.4604... Generator Loss: 0.5213\n",
      "Epoch 1/1-Step 2890... Discriminator Loss: 1.5202... Generator Loss: 0.5007\n",
      "Epoch 1/1-Step 2900... Discriminator Loss: 1.4630... Generator Loss: 0.6140\n"
     ]
    },
    {
     "data": {
      "image/png": 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9yk+8wMTbv/o9KpvdDPg8hvTcu3eZ7NrZgYg4iEuwIlS5vKSE3/ZVBkY3bmnu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\nU6QJB9tE0KQ3LHqurtFQzboRJlPNxMhW7zSzL2+uFrPbNcvhwkWfDFE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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd382b66f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2910... Discriminator Loss: 1.3858... Generator Loss: 0.6565\n",
      "Epoch 1/1-Step 2920... Discriminator Loss: 1.4337... Generator Loss: 0.7014\n",
      "Epoch 1/1-Step 2930... Discriminator Loss: 1.5349... Generator Loss: 0.5818\n",
      "Epoch 1/1-Step 2940... Discriminator Loss: 1.3969... Generator Loss: 0.6059\n",
      "Epoch 1/1-Step 2950... Discriminator Loss: 1.5284... Generator Loss: 0.6069\n",
      "Epoch 1/1-Step 2960... Discriminator Loss: 1.4804... Generator Loss: 0.5743\n",
      "Epoch 1/1-Step 2970... Discriminator Loss: 1.4891... Generator Loss: 0.7033\n",
      "Epoch 1/1-Step 2980... Discriminator Loss: 1.5484... Generator Loss: 0.5540\n",
      "Epoch 1/1-Step 2990... Discriminator Loss: 1.5461... Generator Loss: 0.6244\n",
      "Epoch 1/1-Step 3000... Discriminator Loss: 1.4820... Generator Loss: 0.6587\n"
     ]
    },
    {
     "data": {
      "image/png": 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YBJQEpAwWXIG40izkI8TiZksxUg0jtISMQ4KzK4jgLtSkuwbRdWnI59y6oDLR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\niBnE/wH62H+kwvGIKAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd382663278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3010... Discriminator Loss: 1.5879... Generator Loss: 0.5517\n",
      "Epoch 1/1-Step 3020... Discriminator Loss: 1.4577... Generator Loss: 0.6005\n",
      "Epoch 1/1-Step 3030... Discriminator Loss: 1.4562... Generator Loss: 0.6765\n",
      "Epoch 1/1-Step 3040... Discriminator Loss: 1.5195... Generator Loss: 0.6498\n",
      "Epoch 1/1-Step 3050... Discriminator Loss: 1.4982... Generator Loss: 0.6116\n",
      "Epoch 1/1-Step 3060... Discriminator Loss: 1.4935... Generator Loss: 0.6510\n",
      "Epoch 1/1-Step 3070... Discriminator Loss: 1.5386... Generator Loss: 0.5516\n",
      "Epoch 1/1-Step 3080... Discriminator Loss: 1.4903... Generator Loss: 0.6672\n",
      "Epoch 1/1-Step 3090... Discriminator Loss: 1.5832... Generator Loss: 0.5869\n",
      "Epoch 1/1-Step 3100... Discriminator Loss: 1.4703... Generator Loss: 0.6179\n"
     ]
    },
    {
     "data": {
      "image/png": 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YPzbGvGmMecMY8yvSnlXTySyzj6kdBuqHRPQfW2ufIqLPEdHfN8Y8RVk1ncwy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\nEHAA8X8dn481LhCDrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd382d80b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3110... Discriminator Loss: 1.5633... Generator Loss: 0.5450\n",
      "Epoch 1/1-Step 3120... Discriminator Loss: 1.5169... Generator Loss: 0.5910\n",
      "Epoch 1/1-Step 3130... Discriminator Loss: 1.5457... Generator Loss: 0.6699\n",
      "Epoch 1/1-Step 3140... Discriminator Loss: 1.4988... Generator Loss: 0.5929\n",
      "Epoch 1/1-Step 3150... Discriminator Loss: 1.4436... Generator Loss: 0.6491\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-94-9e4afd78e3c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,\n\u001b[0;32m---> 15\u001b[0;31m           celeba_dataset.shape, celeba_dataset.image_mode)\n\u001b[0m",
      "\u001b[0;32m<ipython-input-13-10fc281759f1>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)\u001b[0m\n\u001b[1;32m     77\u001b[0m                 _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z,\n\u001b[1;32m     78\u001b[0m                                                      \u001b[0minputs_real\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_images\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m                                                      lr_rate: learning_rate})\n\u001b[0m\u001b[1;32m     80\u001b[0m                 _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z,\n\u001b[1;32m     81\u001b[0m                                                      \u001b[0minputs_real\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_images\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    787\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 789\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    790\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    995\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    996\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 997\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    998\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    999\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1131\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1132\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1133\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1134\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1137\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1138\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1139\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1140\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1141\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1119\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1120\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1121\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "batch_size = 64\n",
    "z_dim = 100\n",
    "learning_rate = 0.0002\n",
    "beta1 = 0.5\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "epochs = 1\n",
    "\n",
    "celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))\n",
    "with tf.Graph().as_default():\n",
    "    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,\n",
    "          celeba_dataset.shape, celeba_dataset.image_mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submitting This Project\n",
    "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_face_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
   ]
  }
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