diff --git a/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb b/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb
index a6dc5f4..9f873ca 100644
--- a/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb	
+++ b/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb	
@@ -10,9 +10,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -35,24 +43,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
        "    }\n",
        "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
        "    }\n",
        "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
@@ -379,7 +387,7 @@
        "[49369 rows x 1 columns]"
       ]
      },
-     "execution_count": 185,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -392,7 +400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -429,40 +437,40 @@
        "Crocodylus novaeguineae          202\n",
        "Leopardus pardalis               201\n",
        "                                ... \n",
-       "Micrastur ruficollis               1\n",
-       "Hydnophora spp.                    1\n",
-       "Lycaste fulvescens                 1\n",
-       "Errinopora pourtalesii             1\n",
-       "Maihueniopsis darwinii             1\n",
-       "Porites divaricata                 1\n",
-       "Aloe trachyticola                  1\n",
-       "Polemaetus bellicosus              1\n",
-       "Sternbergia candida                1\n",
-       "Errinopora spp.                    1\n",
-       "Dracula tubeana                    1\n",
-       "Chinchilla lanigera                1\n",
-       "Peniocereus spp.                   1\n",
-       "Mesoplodon europaeus               1\n",
-       "Cypripedium yunnanense             1\n",
-       "Nectophrynoides minutus            1\n",
-       "Vidua paradisaea                   1\n",
-       "Bulbophyllum resupinatum           1\n",
-       "Turbinicarpus mandragora           1\n",
-       "Dalbergia retusa                   1\n",
-       "Pristis spp.                       1\n",
-       "Masdevallia andreettaeana          1\n",
-       "Dendrobium violaceum               1\n",
-       "Favites abdita                     1\n",
-       "Astrophytum myriostigma            1\n",
-       "Epiphyllum pumilum                 1\n",
-       "Pterostylis fischii                1\n",
-       "Colpophyllia amaranthus            1\n",
-       "Acineta chrysantha                 1\n",
-       "Anas spp.                          1\n",
+       "Duncanopsammia axifuga             1\n",
+       "Frailea mammifera                  1\n",
+       "Centroglossa spp.                  1\n",
+       "Aloe peckii                        1\n",
+       "Calumma furcifer                   1\n",
+       "Dendrobium subacaule               1\n",
+       "Leopardus jacobitus                1\n",
+       "Angraecum germinyanum              1\n",
+       "Euphorbia perrieri                 1\n",
+       "Pectinia spp.                      1\n",
+       "Euphorbia lamarckii                1\n",
+       "Manis crassicaudata                1\n",
+       "Euphorbia classenii                1\n",
+       "Treron calvus                      1\n",
+       "Masdevallia guerrieroi             1\n",
+       "Aerangis ellisii                   1\n",
+       "Euphorbia globosa                  1\n",
+       "Rhipsalis teres                    1\n",
+       "Weberocereus tonduzii              1\n",
+       "Cleistocactus roezlii              1\n",
+       "Encyclia fehlingii                 1\n",
+       "Grosourdya appendiculata           1\n",
+       "Euphorbia gorgonis                 1\n",
+       "Pleione speciosa                   1\n",
+       "Euphorbia bupleurifolia            1\n",
+       "Macrozamia miquelii                1\n",
+       "Zamia pumila                       1\n",
+       "Acropora pulchra                   1\n",
+       "Eria coronaria                     1\n",
+       "Paphiopedilum sangii               1\n",
        "Name: Taxon, Length: 3422, dtype: int64"
       ]
      },
-     "execution_count": 186,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -474,28 +482,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x12bfe1da0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff65a87d898>"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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Ia0diQQAAAMaKIQNYRNwh6Y78/wO2r5O07iAP2U3SKRHxmKS/2J4jacs8bU5E3CxJtk/JdQlgAABgqdJpDJjtSZI2l/SnXHSA7Vm2T7C9ei5bV9KtlYfNzWVN5QAAAEuV1gHM9sqSfibpwxFxv6RjJW0oaTOlHrIjRqJBtve1PcP2jHnz5o3ELAEAAEaVVgHM9nJK4euHEXG6JEXEXRHxREQ8Kek7WrCb8TZJ61cevl4uaypfSEQcFxFTImLKxIkTuy4PAADAqNfmKEhLOl7SdRHx1Ur5OpVqb5R0df7/LEm7217B9mRJG0m6TNJ0SRvZnmx7eaWB+meNzGIAAACMHW2OgnylpHdJmm17Zi77pKQ9bG8mKSTdIul9khQR19g+TWlw/eOS9o+IJyTJ9gGSzpe0rKQTIuKaEVwWAACAMaHNUZCXSHLNpPMGecznJX2+pvy8wR4HAACwNOBM+AAAAIURwAAAAAojgAEAABRGAAMAACiMAAYAAFAYAQwAAKAwAhgAAEBhBDAAAIDCCGAAAACFEcAAAAAKI4ABAAAURgADAAAojAAGAABQGAEMAACgMAIYAABAYQQwAACAwghgAAAAhRHAAAAACiOAAQAAFEYAAwAAKIwABgAAUBgBDAAAoDACGAAAQGEEMAAAgMIIYAAAAIURwAAAAAojgAEAABRGAAMAACiMAAYAAFAYAQwAAKAwAhgAAEBhBDAAAIDCCGAAAACFEcAAAAAKI4ABAAAURgADAAAojAAGAABQGAEMAACgMAIYAABAYQQwAACAwghgAAAAhRHAAAAACiOAAQAAFEYAAwAAKIwABgAAUBgBDAAAoDACGAAAQGEEMAAAgMIIYAAAAIURwAAAAAojgAEAABRGAAMAACiMAAYAAFAYAQwAAKAwAhgAAEBhBDAAAIDCCGAAAACFEcAAAAAKI4ABAAAURgADAAAojAAGAABQGAEMAACgMAIYAABAYUMGMNvr277I9rW2r7F9YC5fw/Y02zfmv6vnctv+uu05tmfZ3qIyrz1z/Rtt77n4FgsAAGD0atMD9rikj0bEJpK2krS/7U0kHSTpgojYSNIF+b4kvVbSRvm2r6RjpRTYJB0i6WWStpR0SC+0AQAALE2GDGARcUdEXJH/f0DSdZLWlbSbpJNytZMkvSH/v5uk70dyqaQJtteRtKOkaRFxT0TcK2mapJ1GdGkAAADGgE5jwGxPkrS5pD9JWisi7siT7pS0Vv5/XUm3Vh42N5c1lQMAACxVWgcw2ytL+pmkD0fE/dVpERGSYiQaZHtf2zNsz5g3b95IzBIAAGBUaRXAbC+nFL5+GBGn5+K78q5F5b935/LbJK1fefh6uaypfCERcVxETImIKRMnTuyyLAAAAGNCm6MgLel4SddFxFcrk86S1DuScU9JZ1bK352PhtxK0vy8q/J8STvYXj0Pvt8hlwEAACxVxrWo80pJ75I02/bMXPZJSYdLOs323pL+Kumtedp5knaWNEfSw5L2kqSIuMf2ZyVNz/UOi4h7RmQpAAAAxpAhA1hEXCLJDZO3q6kfkvZvmNcJkk7o0kAAAIB/N5wJHwAAoDACGAAAQGEEMAAAgMIIYAAAAIURwAAAAAojgAEAABRGAAMAACiMAAYAAFAYAQwAAKAwAhgAAEBhBDAAAIDCCGAAAACFEcAAAAAKI4ABAAAURgADAAAojAAGAABQGAEMAACgMAIYAABAYQQwAACAwghgAAAAhRHAAAAACiOAAQAAFEYAAwAAKIwABgAAUBgBDAAAoDACGAAAQGEEMAAAgMIIYAAAAIURwAAAAAojgAEAABRGAAMAACiMAAYAAFAYAQwAAKAwAhgAAEBhBDAAAIDCCGAAAACFEcAAAAAKI4ABAAAURgADAAAojAAGAABQGAEMAACgMAIYAABAYQQwAACAwghgAAAAhRHAAAAACiOAAQAAFEYAAwAAKIwABgAAUBgBDAAAoDACGAAAQGEEMAAAgMIIYAAAAIURwAAAAAojgAEAABRGAAMAACiMAAYAAFAYAQwAAKAwAhgAAEBhBDAAAIDCCGAAAACFEcAAAAAKI4ABAAAUNmQAs32C7bttX10pm2r7Ntsz823nyrSDbc+xfYPtHSvlO+WyObYPGvlFAQAAGBvGtajzPUnfkPT9vvIjI+Ir1QLbm0jaXdILJD1T0q9tPzdPPkbS9pLmSppu+6yIuHYR2t5s6moN5fMXy9MBAAB0MWQAi4iLbU9qOb/dJJ0SEY9J+ovtOZK2zNPmRMTNkmT7lFx38QQwAACAUWxRxoAdYHtW3kW5ei5bV9KtlTpzc1lTOQAAwFJnuAHsWEkbStpM0h2SjhipBtne1/YM2zPmzZs3UrMFAAAYNYYVwCLiroh4IiKelPQdLdjNeJuk9StV18tlTeV18z4uIqZExJSJEycOp3kAAACj2rACmO11KnffKKl3hORZkna3vYLtyZI2knSZpOmSNrI92fbySgP1zxp+swEAAMauIQfh2/6xpK0lPd32XEmHSNra9maSQtItkt4nSRFxje3TlAbXPy5p/4h4Is/nAEnnS1pW0gkRcc2ILw0AAMAY0OYoyD1qio8fpP7nJX2+pvw8Sed1ah0AAMC/Ic6EDwAAUBgBDAAAoDACGAAAQGEEMAAAgMIIYAAAAIURwAAAAAojgAEAABRGAAMAACiMAAYAAFAYAQwAAKAwAhgAAEBhBDAAAIDCCGAAAACFEcAAAAAKI4ABAAAURgADAAAojAAGAABQGAEMAACgMAIYAABAYQQwAACAwghgAAAAhRHAAAAACiOAAQAAFEYAAwAAKIwABgAAUNi4Jd2A0WDTkzYdUDZ7z9lLoCUAAGBpQA8YAABAYfSAdXTdxs8fUPb8669bAi0BAABjFT1gAAAAhRHAAAAACiOAAQAAFEYAAwAAKIwABgAAUBgBDAAAoDACGAAAQGEEMAAAgMIIYAAAAIURwAAAAAojgAEAABRGAAMAACiMAAYAAFAYAQwAAKAwAhgAAEBhBDAAAIDCCGAAAACFEcAAAAAKI4ABAAAURgADAAAojAAGAABQGAEMAACgMAIYAABAYQQwAACAwghgAAAAhRHAAAAACiOAAQAAFEYAAwAAKIwABgAAUBgBDAAAoDACGAAAQGEEMAAAgMIIYAAAAIURwAAAAAojgAEAABQ2ZACzfYLtu21fXSlbw/Y02zfmv6vnctv+uu05tmfZ3qLymD1z/Rtt77l4FgcAAGD0a9MD9j1JO/WVHSTpgojYSNIF+b4kvVbSRvm2r6RjpRTYJB0i6WWStpR0SC+0AQAALG2GDGARcbGke/qKd5N0Uv7/JElvqJR/P5JLJU2wvY6kHSVNi4h7IuJeSdM0MNQBAAAsFYY7BmytiLgj/3+npLXy/+tKurVSb24uayoHAABY6izyIPyICEkxAm2RJNne1/YM2zPmzZs3UrMFAAAYNYYbwO7KuxaV/96dy2+TtH6l3nq5rKl8gIg4LiKmRMSUiRMnDrN5AAAAo9dwA9hZknpHMu4p6cxK+bvz0ZBbSZqfd1WeL2kH26vnwfc75DIAAIClzrihKtj+saStJT3d9lyloxkPl3Sa7b0l/VXSW3P18yTtLGmOpIcl7SVJEXGP7c9Kmp7rHRYR/QP7AQAAlgpDBrCI2KNh0nY1dUPS/g3zOUHSCZ1aN8Yds9+FA8r2/9a2S6AlAABgNOFM+AAAAIURwAAAAAobchckyjjibbvWln/01HMKtwQAACxu9IABAAAURgADAAAojAAGAABQGAEMAACgMAIYAABAYQQwAACAwghgAAAAhRHAAAAACiOAAQAAFEYAAwAAKIwABgAAUBjXghyD5h70u9ry9Q5/deGWAACA4aAHDAAAoDACGAAAQGEEMAAAgMIIYAAAAIURwAAAAAojgAEAABRGAAMAACiMAAYAAFAYAQwAAKAwAhgAAEBhBDAAAIDCCGAAAACFEcAAAAAKI4ABAAAURgADAAAojAAGAABQGAEMAACgsHFLugFYvKZOndqpHAAALH70gAEAABRGAAMAACiMAAYAAFAYY8CwkAsu3HBA2Xbb3rQEWgIAwL8vesAAAAAKI4ABAAAURgADAAAojDFgGLa1L5o5oOzObTZbAi0BAGBsoQcMAACgMAIYAABAYQQwAACAwghgAAAAhRHAAAAACiOAAQAAFEYAAwAAKIzzgKGISQedO6DslsN3WQItAQBgyaMHDAAAoDACGAAAQGEEMAAAgMIIYAAAAIURwAAAAAojgAEAABRGAAMAACiMAAYAAFAYAQwAAKAwzoSPUafurPkSZ84HAPz7oAcMAACgMAIYAABAYQQwAACAwghgAAAAhS1SALN9i+3ZtmfanpHL1rA9zfaN+e/qudy2v257ju1ZtrcYiQUAAAAYa0biKMhtIuLvlfsHSbogIg63fVC+/wlJr5W0Ub69TNKx+S8wfFNXayifX7YdAAB0sDh2Qe4m6aT8/0mS3lAp/34kl0qaYHudxfD8AAAAo9qiBrCQ9Cvbl9veN5etFRF35P/vlLRW/n9dSbdWHjs3lwEAACxVFnUX5Ksi4jbbz5A0zfb11YkREbajywxzkNtXkp71rGctYvOABTY9adPa8tl7zi7cEgDA0m6ResAi4rb8925JP5e0paS7ersW89+7c/XbJK1fefh6uax/nsdFxJSImDJx4sRFaR4AAMCoNOwAZnsl26v0/pe0g6SrJZ0lac9cbU9JZ+b/z5L07nw05FaS5ld2VQIAACw1FmUX5FqSfm67N58fRcQvbU+XdJrtvSX9VdJbc/3zJO0saY6khyXttQjPDQAAMGYNO4BFxM2SXlxT/g9J29WUh6T9h/t8QEnXbfz82vLnX39d4ZYAAP4dcSZ8AACAwkbiRKzAUu+Y/S4cULb/t7ZdAi0BAIwF9IABAAAURgADAAAojAAGAABQGAEMAACgMAIYAABAYQQwAACAwghgAAAAhXEeMKCwI96264Cyj556zhJoCQBgSSGAAaPY3IN+V1u+3uGvLtwSAMBIYhckAABAYQQwAACAwghgAAAAhRHAAAAACiOAAQAAFMZRkMC/ialTp3YqBwAsOfSAAQAAFEYPGLAUuuDCDWvLt9v2psItAYClEz1gAAAAhdEDBmBQa180s7b8zm02qy2fdNC5A8puOXyXEW0TAIx19IABAAAURgADAAAojAAGAABQGGPAACwxncaLTV2toXz+CLYIAMqgBwwAAKAwesAA/NvZ9KRNa8tn7zm7cEsAoB49YAAAAIURwAAAAAojgAEAABTGGDAAS7XrNn5+bfnzr7+ucEsALE0IYADQ0jH7XVhbvv+3ti3cEgBjHbsgAQAACqMHDAAWkyPetuuAso+ees4SaAmA0YYeMAAAgMIIYAAAAIURwAAAAAojgAEAABTGIHwAGAXmHvS7AWXrHf7q2rpTp05tVSZJF1y4YW35dtveNKBs7Ytm1ta9c5vNassBDB8BDADQ2aSDzq0tv+XwXVrXb6qrqavVlM1v3TZgLCCAAQDGrE1P2nRA2ew9Z9fW7XLVgy4n3a073YjEKUcwOAIYAACF1O1qlhbf7ua6Xc0Su5tHAwbhAwAAFEYPGAAAaNRlvF/XsYFLMwIYAAAor+5gC6nxgItFHe9XN9ZP6jbebyQRwAAAAFoYyQMuGAMGAABQGAEMAACgMAIYAABAYQQwAACAwghgAAAAhRHAAAAACiOAAQAAFEYAAwAAKIwABgAAUBgBDAAAoDACGAAAQGEEMAAAgMIIYAAAAIURwAAAAAojgAEAABRGAAMAACiMAAYAAFBY8QBmeyfbN9ieY/ug0s8PAACwpBUNYLaXlXSMpNdK2kTSHrY3KdkGAACAJa10D9iWkuZExM0R8U9Jp0jarXAbAAAAlqjSAWxdSbdW7s/NZQAAAEsNR0S5J7PfLGmniNgn33+XpJdFxAGVOvtK2jfffZ6kG2pm9XRJf2/5tIur7mhpx1irO1raMRrqjpZ2jIa6o6Udo6HuaGnHWKs7WtoxGuqOlnaMhrql27FBRExs9eiIKHaT9HJJ51fuHyzp4GHMZ8aSrjta2jHW6o6WdoyGuqOlHaOh7mhpx2ioO1raMdbqjpZ2jIa6o6Udo6HuaGoQM54bAAAgAElEQVRH/630LsjpkjayPdn28pJ2l3RW4TYAAAAsUeNKPllEPG77AEnnS1pW0gkRcU3JNgAAACxpRQOYJEXEeZLOW8TZHDcK6o6Wdoy1uqOlHaOh7mhpx2ioO1raMRrqjpZ2jLW6o6Udo6HuaGnHaKg7mtqxkKKD8AEAAMCliAAAAIojgGW2l7G96kjUt72s7a+MXOuWTl3Xyb9rGzB8rD9gYXw/jR5jJoDZXt32lrb/o3drqLeh7RXy/1vb/pDtCQ11f2R7VdsrSbpa0rW2PzZIG1rVj4gnJL2qw7K9Ms9Ttt9p+6u2N2j7+EHme2Bur20fb/sK2zuUnq/tlWwvk/9/ru3X216uoW7rddJxXS+WNvQ9bnXbLxqiTuvXzvZ42/vb/qbtE3q3hroTbX/F9nm2L+zdhmjrkJ+nLsvX5TXOdb6cX4vlbF9ge57tdy7qvDu+h1p/9my/xfYq+f9P2z7d9hZNy5frPcP2s3q3Qeo9zfZnbH8n39/I9q6L2g7bK9h+n+2v2z6udxuszSPF9psGuzU85hk1Zc8b5Dm6bAO61G393uzC9kdqbnvb3mwE5r1cXqaf5tsH6z4jXb+fRqBda4/APDqtjy7bw67brRG1KOewKHWTtI+k2ZLulXSRpEckXdhQd6bSwQXPkfRnSf8n6bymuvnvOyQdIWk5SbMGaUfr+pKOVTrFxrskval3a6g7S5IlvVjSlZL2l/TbDq/PFQ3lV+W/O0o6XdILmuo2PP64kZivpMslPU3pqge3SPqJpB+OwGvcZV0vrjb8RtKqktaQ9BdJf5L01UFei9avXW7jZyXdJGlPSb+S9LWGur+StLek6yS9RtIJkr40Ap+n1svX5TXue53fKOl4Sav1Xp+C66/1Z683D6Uvr99I2kXSnxrqvl7SjZIeyq/bk5KuGeS1OFXSxyVdne8/rbcci9iOUyV9UdLN+f1xgaSvN7Wj5vFN783d8nttvqT7JT0g6f6+Oifm27n5vfazfLtH0jkN871B0lsr9z8q6drB3kPqsL3vUrfDe3MjST+VdG1+nW+WdHND3R/l5z4i327I7+Xpkj7eYn1MHWTadyWdJGnbfDtR0ncb6rb+furajpq65/bdPy3/nZ0/f73bbA2yrW+7PnK9LtvDLtuW1uu61Wsz3AeWvOUVM76yEjaWdHpD3Svy349J+mD+/8qGutcobZx/Iuk1uWywldq6vhZsfKq3E4Zo8/9K2rtatoivW29D/TVJbxzstWh4/EtGYr6V5ftgbyOj5i+XLq9xl3W9uNpwZf67j6RDq6/Por52lXn3HrOcpEsb6l7e/9ySpjfU7fJ5ar18XV7jPK0XNr6rdIWMNut6cb2HhvzsVV6LL0p6+xDr7ipJa1Yes42k4wd5LWb0z6/Fe65NO1q/h7rcJM2RtGnLur+StE7l/jqqnJC7r+46ks7O6+5iSd+WtPIg8x7ONqBN3S7vzUskbacUIjaQNFXSYQ11L64uj6SVJf1W0ooaJGhW6r9ukGkD2jdIm1t/P3VtR4vHrpP/blB3W9T1kad12R522ba0XtdtbsVPQzFMj0bEo7Zle4WIuH6Qbul/2d5DqcfgdbmsqTvx20qJ9ypJF+ddD/cP0o7W9SNir8EWqM8Dtg+W9E5J/5G7QwfbdbO20oXNQ+lNdWdD1ctt/0rSZEkH590WTw7WEKfxMhERD0TE5SM0X9t+uVKPxN65bNmGul3WSZd1vbjaMM72OpLeKulTDXWqurx2/8p/77P9Qkl3Shqwi6av7h22d5F0u1KvVZ0un6cuy9flNZakc2xfr9QD937bEyU9OgLz7rL+unz2brP9bUnbS/pS3p3VNIzjXxHxD6cxaMtExEW2j2qoK0n/tL2i0mdatjeU9NhItCP/vc/28yXdpeb3UJdty10RMXuQ5alaPyLuqD5WUu3u2Ii4w/Yvla6S8qSkgyLiwUHm3WUb0KVul/fmihFxgW1HxF8lTbV9uVKo7/cMLbxe/yVprYh4xHbT+n5KRJw9yOQnbG8YETdJku1nS3qiYT5dvp86tSPvDn+V0nvo9xFxRd9j77C9rKTvRcQ2LZ+yy/qQum0Pu2xbuqzroQ03uZW8Sfq5pAlKafNiSWequet4E0lfl7RHvj9Z0ic6PNe4jm2rrS9pvdzuu/PtZ5LWa6i7tqSPSHp1vv8sSe9uqLuPpL9J+p5Sd/Mtkt7TUHcZSVtImpDvrynpRQ11X6rUM3KLpL8qfXk19YC1nm+e/hql7u5P5PvPVrfdIE2vcet1vRjb8BalX0PHVub7s0Hm02Wd7CNpdUn/odTVfbek9zXU3VWpW/6FSrsVL5f0+hH4PPWW75tDLd9wXmOljeKy+f+nSVq78Prr8tl7mtKumo3y/XUk7dBQ99dKvRtHS/qxUo/nHwZp3/ZKPSHzJP0wfw63HoF2vC+/h7ZR2m78XdL+g7zf2m5bjsrtfIvS7tbXD/J++4bSybf/O99+IenoQV637+f356aSLpP0lUFety7bgE7fDR3em39Q+lyfLukApd1kNzTU/YykKyQdkm8zlL68V1Lfbq/8Hj87r7O7lT6nzx6kvdvl9feb/F66RdI2DXW7fD+1bkdeltmSDs23qyR9uqHuBZJW6/AZbrU+8vQu28PW25Yu67rNbcydB8z2a5Re2F9GxD+HOY93RsTJtj9SNz0ivroo9fNjpint7/9BLnqnpHdExPbDaXNlvjdIekVE/CPfX1Npwz6gB8MNA6sj4uKaurOUNsy/y/dfpfSlO2DQdZf5dmW79pdERBy2qPMeDW3ouE4mR8RfWpQtK+lDEXHkMNqzyJ+n4bL97rryiPj+MOfX+XPacr6rRsT9tmt/QUfEPTWPWUnp1/oySr+sV5N0cl3dymPWlLSV0pi0SyPi733TO7eji47blh/0l6UmRO06dRp0/+p89+KI+HlDvTdExBmV++OUrhf82W5Ls2i6vDdtv1RprNEEpTGbq0n6ckRc2jDvKZJeme/+PiJmNNS7VNIxSgFeSpfu+2BEvGyQdq8gqbe+boiI2l61Lt9PXdqR30MvjohH8/0VlXbn1b2HzpS0uaRpSmMlJUkR8aGaup22FbYnRsS8umlNbK+c59nY49p1XQ9lrOyC7H3JrKU0oFVKv1z/VlPvL8rd+FUR8ezK3ZXy31VaPn3X+pI0MSJOrNz/nu0P11W0/YAWtHl5pW7xByNitZrq/1Aa8NrzQC6rUz3ya7zSroXLlQZo9nuiF74kKSIusf34CMxXti9S/Tqpq/9Q5f/xSr9krmuYb5t1vbjb8Gyl3o2t8vz/KOl/IuLmuvrq9tr9TKm3rOqnkl7StwxP5F0rrQNYh8/Tiap/3d5TU7fLayylXtee8Uq/4K9Q6gEZzrw7f05bfvZ+pPQeuDzXdbUJSr+Y+/1vRHxCaTfaSfm5viTpE4M050mlHrDxkjax3R/MO7fD9mclHRER9+X7q0v6cEQcUvP8rbctEfGuQZajrv7pSr0GQ9U7o+/+40pfdLVsb6Q0Fm4Tpdet97i616L19kId3psRMT3/+6CkQXftOR0Je7dS79NTZREx4LMn6WkRUQ26J3vwo/SfptSbu0FEvNfpSNrnRcQ5NdVbfz91bMftSq9Xb/fgCpJua6jb6j2RtV4f2e9t36J0EMrpEXFv04xtb5rns0a663lKveADLpPYZV23MSZ6wGx/UKm79i4tGC8TDb0za1bujlfqIl8jIoa3j3bBfDv1Mti+QGlgY+9Xwx6S9oqI7YZ4nJWOMNoqIg6qmf59pa75M5U2JrtpwVEkg/7Kt72+pKMi4r9qph2lNBD0x3m+b1P6EJ2c53tF/2PazDdPrwaG8ZL+S9LjEfHxpnlWHruC0oDdrWumtV7Xi7ENnX+l9j1+wGtne2OloyO/rIUD26qSPhYRL6iZz5FK4eFULfxrcsB66/h5qq7T8Upd7rc3/Eod9mucHz9B0ikRsdNw572IvYGDfvY6zuuKiNiir2xW3Wucp+0j6UClXUMzlQL9HwcJr23bcWVEbD5U23L5kNsW2x+NiCNs125nIuIjlfldEhGv6gu5UgqOEREDzs+We8q+pDRWyoPV7T2H0nv5SKVxXXtJWqZhGzDs74Yh3pvPVfqcbqBKp0bdurM9WwteixWVdoPe0PCZ/pLS0aOnaME2eXWlozcH9HjaPlUpnL87Il6YA9kfImLAKS66fD91aYftM5TC0rRcd3ul3chzc90P9c17RUnPiogb+p93MIOtj0qdLZW2x29QOmrxlIg4uabeHyR9KiIuyve3lvSFiHhFpc7ZqgnvPRHx+i7tf2q+YySAzZH0sl7X+DAef3lEvKSm/MuSPqe0q+CXkl6k1HsxYCXl+pdFxJYtn3MDpfEfL1dacX9Q+mK+teXjB2w4c3ndL9enRMShg8zTSofCb1Iz7aLBZ9v8RTDYfAd5TKvXMv9inx4Rz2k539p1vbjaUPelavuqiHhxyzYMeO1s76a00Xi90tiEngeUNiJ/qJlP3fqrXW+L8nlyGqR+SXXjNET9Lp+Z5ZSOdmo871ObeXd5zob5LvTZ8xDn+qqGXNvvl/QBSRsqHSnYs4rSl+E7Gp5zttIX16URsVkO4V+IiDdV6rRuR+UxsyRNibx72fZ4pSMuX1hTd8hti/MuQtt7N9Q5frB5DCW/N18XEbU9zjX1L4+Il9ieHRGbVsu6PL5Fvcb3pu2rJH1LKfw8Neg9mg9iqj52C0kfiIh9aqb9peYhldkv3HNne0ZETKm+f5u2RV2+n7q0w/aeg9RVRJxUqfs6SV+RtHxETHY6F9phbcJMl22F7adL+qrSLtYBg+vrXqP+MqehGoMt12+HakedsbIL8lal880MqW8jtYykKWpezh0i4uO236g0YPFNSoOSawOYUrfmN9Sil0FpQONCbyTbr1Ralv42V09K2Gtz7REevYDlytGKDW2V7aO1ILUvI2kzpW7buvm2PRql03xz/eqYlWWUdqHV7V7t/4W4rKSJkmrHXnVZ14urDZJ+YfsgLfzr8Lze89X8Sh3ytYuIMyWdafvlEfHHhuddSJf1pw6fpxobqeEoui6vca5f/VW5jNJupNNGYN6tP6ctP3tHNC1Dbn815P5IaZD5FyVVe9Ee6H8v9GlzZGqXdvScImmaF5zA9z1Kg+cHzqDFtiUW7CI8OfrGF7lmbFrukbwmIjYepO1Vd7UNX9lj+UfBjbYPUNrdtXJdxY7bi9bvTaWe2GM7tPkpEXGF7dre8oiY3HF2XY6kbf391KUdEXGS7eWVTm0TSr17TWNLpyoNwfhNfuxMpyEdA3RcH7338BuVesA2VNrl2/Sj7Gbbn9HC4+EWGkIy3IA1lLESwG6W9Bvb56ryhor63W3VjdTjSmNc3tow397y7yLpJxExP3VINOp15Va/jJs2fEdr4PidujJpwSHRvTbfotT9P4DTAM4Tlce52J6vdKRS3a+t6uDOxyX9OCJ+3zDfNZW68nuHD1+i9Gukrpek9Xyz6piV3jqp/QWtNMalOu+7Io0DqdO/rm9R87peXG3oPd/7+sp3V/24nC6v3ZW291faHVkd31I3/qpu4Pl8pfPhzOwrb/158oLdR85/71TzOKYur7GUfv32PC7prxExdwTm3eVzOuRnr0u4jYj5kubbfjzSYepPsf2DaB47NTfvVjlDKTDdq3Q08rDaUXnMF3Iv2H/moi9HxLl1dTtuW/5k+72Rx8TkXtsva8EA8N7zP2H7BjePc+o3w2lX2hla+L3ZNFboQKUj4j6kNFZsW6XTTNTpsr3o8t482/YHlL7kq22uOzij+jntHRF9e91Mc2/lB7Rgm/w7Sd+KPMC9xiFKe3LWt/1DpYH+/91Qt/X3U5d22N5Z6TQwNyl9Vifbfl9E/KKmDf+q+c5tOiVPl/UhpaMvz1D6DhvqR+x7lI7YPF0Llm/ANlaS3GHMYRtjZRdkbdd43e4228+OvgHQrjlyLJcfrrSr5xGldDxB6QzNrcbvNLT15ZJeIenDWnhQ9KpKJ95stWtqkPl3OVpxJaVf1k/k+8tKWiEiHq6pO00L9/69Q+kw+P+sqdt6vsNYvg0lzY2Ix5z2xb9I0vcjDyJehPmO799g5J6GAb8QF1cburL9E0nXS3q7Uph4h6TrIuLAmro/UvpF3zs/z65KY3cmKf24+HKlbuvP0+KS3zO/Hk6oWJKczsfWv/GtO2hgoXFWTkfzzYoWu+nd4sjUtu3IdddTOmXFRfnLdNmIeKimXpdty2ZKZxc/X9Izlc4i/p66kGX7YqWj3S7Twj2SA3Y1OR300S/qfnQsTrYnS7ojFj6ab62IuKWmbt0uugG7CHPd6mevFwJ/1hBmTlMadtDbJr9d6fQ1bxmk3UMdSdv5+6lLO5zO1bVrRMzJ9zdUOhP+gB5Q28crnYriIKUxnR+StFxE7Ne0fG3ZdrQIN3k79KWI+H8t59t6zGGr+Y2FANZF/4YvlzXu58/d5vPzL7WnSVo1mk8+KKeTuvX3SBxWmf4aSVtL2k9pXEDPA5LOjogba+Y5WeksvJO08CDOug1Ul0G1l0r6z8iH1TodZvurqBm/Y/vq6BsX4sq4iuHON0/fX+kcN9UjsfaIiG/W1J2pFCQmSTpPaUDwCyJi50qd2tMM9DT05NS9L5petyHbUKm7nKT3K52rS0rd6d+OiH/11831X6nU9d4bsNsbZFy3sb4yIjZ3HmeWn+t3EbFVTd2LJe3ct07OlbSTUi9Y6/F5+fGdxxvlx71CA9/HTcHgAqXLnzTuDnXDNQMr8x7QM9KmN9AL7wqum2/dQQaHKH22N1F6X7xWaTzcmyt1Dpb0SaUB1r0fJJb0T6VLex3c9Jw58GwUESc6nWhy5YYfjkO2o1L3PUrnK1otIjZ0GjD+zYYfVq23LXna65QGcT+gdPqK2rFCbhg/EzW7dWyvGS3GJrrDoOhhbi9mKC1Tb+zc8kqnjHhpf922hvFlf23/57ahrMsYxeF8P7VqRy6fXn2NnLq3Lqt73fL37ack7aD0GTlf0merYdQdD+SwfVREfLjp/dHwnXpp3Ta1jhdxzGG/MbELMm+MPq6BwWfbSp3ekWOr9W20V60+psbGkiblX6g9TV8Y31Lq7t5G6ZIIb1b6VfeUvFH5re3vRd8uiEGcoXR9q7M1xJnq87y/rYWPVvxN70PY98U4PirnNImIB/Obvs6vbO+uBfvV36z0gajTZb6S9N6IOKZS/17b75U0IIBJejIiHs/r8OiIONr2lX11upxmYG2lX+cr2t5ceurQ/VWV1mWdNm3oOVbp6MPesrwrlw0YVJsdL+l/1Ddgt0GXM+G3PsN2w8ZpvtLu0W/nDWBvd814pTB6ldJr96Jc7+X9DXA6N9SGSkfx9ZYt1Hyo+IOSZjv1vjadB+h1Ax61QKj+MPYpqu8N3M92rzew9txLQ3iz8jUjI2Iv22upb7xoRHxR0hdtf3GwsNUvh6opSrvwTlR6T52sBeeL6tSOig8p9e7/Kbfvz6654HXWetuS622itLv3eUpjIY+MiG/3zzQifuv2Z9i/NP8AOlHSLwbpxejtknqT0ilUesu/h9LRvVVdTh/UM67a+xgR/8whbAC3PEdV/pFftz6bXGF7q8jnmHIaK1b3vm09NnCY309t2yGlXcjnKX2PhNKRptN738nVH0yR9ph8SoNcYSMiXpX/tl2HvXFcXxm01sKutH2W0uWvqtuhum1L6zGHbYyJAKY0aPRUpQ3pfkr7+PtPsva8PH2CFt5oPyDpvXUzHcYXxityT8SsSEcEHaE04LbOw7b/T4OExopHI+LrDfPp1+si7t+NtLkGjnN5yPYWvQ2n06H8jzTM971K3dI/UPqiXSY//n0a+Eujy3wlaVl7QZdw/iVYuzHTgsuFvFsNlwuJbrvKdlQaB7Ge0pEwPQ8o9VQMqw0VL42Fu+0vdDoqqsn8qB8PUee43Fv4aaWjIVdW8yUvfqg0LufMfP91kn7ktLv42r66NysdWNA7BP1tSq/HcyV9R9K7Iu8atH26pC0iX3YmB8GpDW2YImmTQb40+w15HqAY3iVT1lNqc6838BCl3sD/UAq+X47K0VgdPBIRT9p+3GmQ792S1m9o98F53W2khT//TScrfqPSZ/iKXO92p8tULVI7lLYt/3QeZ5M/e00DXbtsW26UtF9e13OcDuU/Smn8z0KcTrHxv5IuzM99tO3DIuKE/rpK78H/VBqD83Wn3V/fi4g/Vyv1es9sHxERUyqTzs69V9W6w9m1Ps/26yPirPw8uymdCb5Ol3NUzezwZf8SSX+w3dut+yxJNzgfJBR513AMbzf+d22/JRbeK3FKROw43HZk45UCcK/Xc55Sb/Dr1PeDyWnM4Sc1sMe86VQt1UscXRIRA34Ux4LxiptFxNf6Hn+g0hUC+o1XOt9d9f3d9OOuy5jDocUwT6Ff8qZuF9Z8eYf5Xqe8G7Zl/T/lv5cqjXtYQdKchrpdrsb+dqWN3suVBkFuofQFsqiv20uVBkP+TmlQ/Rw1XF5occ5X6XwxpyltmLbL/x/RUHfIy4VowQVTj851F7o1zPe/Oixfl8ubXCFpw8r9Z2uQC6lLOjy/HiO6rvO8pyhtIA5UOvVAU70Bn51emdIRa9Xya2rqDijL5T9R5aLLI3lTGhP1VaVf3jOUfvXXXsZEadzccpX7K0i6Pv9/ZV/diUq/ls9TCggXSrqwYb7fVPqBt59SALlS0okNdfdRuiTLvUqXQnmkab65/mW991P+u5KaL3repR1HKO09uE6p5/6nkr64ONbRIMt2g6Q1K/fXVIvLt+T23ibpPqUvzgHb9rxcz67cn6w0TrJaZzjbiw2VtvN/Uzoy8A+SntNyeScojd+rm3Ziza32IthquFi1BrlodYd1MuAi5HVli7Md+X3x+rzOBp2fOlziKNcfsA1uWr4leRsrPWBdLqzZ+sgxSVcrdV/fUTOtzjlORyr9n9IXbyjtiqyzZkQcb/vAWNDtO72h7qZKu662VeXEmKo5aiun+BOVeiy+o/QFflBE/Kq/bkRMz7tmq5emGGxs0syIeMj2O/N8j4qaQbVd5pt9Qukowffn+9PU8LpFxLW2/5+k5+belhsi4kt91XqHqbfejRQRP/MQ4/c6tqHnY5Iusn2z0q/7DTT4GZJ7B3hUf7U3resvKPXWVH+lfjQiPl2pU708zc2qHD5te42oP/XByq4cleZ0du5eN3r/oO/Ztr+rhQ/OmNXXzt4uzVUkXWv7Mi18NFjteX3c7YiiE5Q+r72j1t6l9DmoGyPWpTew17u+i5p713vt+kD+91tOF4xeNSJm1dVVCsG983ptkz8vX2ioK0mn5d16E/Lu+fcofb4XtR0fl7SvUig9UGlYwYBeKqndtsX2jyNij7xLvm5MTt14pNZn2HcaRP5OpfV7l9LY2LOUdnX+ROnLuup/lHaTVj9/+/bVGc724iZJW7nF5WlqPFTTzl7v46xof5LgcVp8BwM92bcN2EDNY+pat8Pdzq05L3IPYwvv0MKXODpcac/V5/qefw+lDo3JuaexZ1VJtaeBcRoXeazSkI0X2n6R0nUjP9dQt9VJd9sYE4Pwbe+q1NuyvtKvmFUlHVq38tztyLGLlD7Yrb4w+h67gtJYqNoBxM4D+2yfr/RL63ZJP42IDWvqzlHadTPktficTxBne0elL4xPS/pBLHzE1bYRcaEbBjBH/cDlWUq7IF6kdDHe70p6a0S8plKn83y7yh/w3oWArbTO94xFvM6kG8bvRcSAUxl0bYNbXn9tGG0eclC07XMiYlcPvMzKYIP7d1YagPvUoeJKh5n/Rmm83lGVuuO18EEGFytdeLw6UPap90idaDiHjrudxXxm9J3Ru66sMq3t9fZ6g2qfOqGu+wYS99VfVwM3vnXX8ZweES91Gs/0svzldU3UnPG88pjtVRmQHBHT+qYP68CItlpuW9aLiLlOR7fVteGmSt3e4PfNVHOG/Yj475o2/FlpGMSJ0XeaAdufqPshlD9/vaPsrh+Jz1+XH7puOEdV1F/JpMuJiVsfDNSV7Z0kHafUs2il63TuGxEDxv12aUfvM+l0bs1dlS6NdHHUH125ndKYvQs0xClH8nf1Gys/RicoXWJo2756Gyhtzwach0/pPTfgdEK2f6sUqr4dC05gO+CgtFw+7JPu1hkTPWCx4FpW85W+QAfznIh4i+3dIp0U7kdK4a3O1DbP3xQ48rSm4PE5+/9Td+bhkhTF2v/FDPu+iyg7giKCoCgIsqjoRUEFAUUERS/KIot4UT8VARERBDdQFGSGRUVkX1UGHPYdGWbYRRYBFa5eRVBBgfj+eLOms6szqzP7nINMPE8/51R1dHZ2VVZmZMQbb9iiwKfpGY2fyjRzO3JbP17SnfD3nWgXcofZAHnZpiiUkgIw52Lbz7q7m7AOxwbvXds4GaXdWk/H0Ygg957w2dURVml2lomNVhaiBr9X0oecMbpaakzYaIWiJ1tElWFKhZ+39bmtwt8assSLwz1pFq17IoMqNr4mAye62Nuzu3bv4XGSqfsdXZnf3S8zM3MBgg82s1tI49z+aWYbu/vVoe2NaOEObTRvYLF33VSS5f3IixZjRlOG+VBer7YEg2tah8ooiRGrAocx+Oytnmh/6NzSGEWxodUhDYbtt+HVyHkJ3UbW8IxXIGN83YISW07zTL2/EeeLj7r7t4MxuiTyyJ2KoCVtqeGoqiHzrkkGAmZ7yYfiDt39F8Ggb7L/9vMWZcWI/ajh1twVzUFz0x/5iXFiTbbyE8AdpoQdp1fiqP27HgIeMrO30cNKrh6+Z1amHwu4+42tfuZ4H0cm3U3JHGGAmdnJwL7eH4o52tNhxeLMMVd2zooo9ftSUybfQKkCRsvEuiF4x0qMxsWAu00hymGeuFvM7BJk5f8/E1C3L3PSe4V2v+yt1PCwSKbkSVMK/YeATUyZHm3w+yjtgnaSjYnlascAACAASURBVKdjc4KnI6M7t0d1wVxZW20AfE0GVCPNYv0PM1sOhUBeOoY+1BqjoxR0/zFwmfW4kXYlFHZOSYV3pp21tU4wGlNZWyua2Txe4J1FIaKYiuS5cC6Xul+TUbQHcHLY1ICwVR9p6bSLVTfSkMimDP6ajdJ7kYEw1MPi7tuEfw8Ou/dFUUgmKVZQA9FHS4w4CYVpjkJ0FbuSN0aGzi1Rf2NagLnQvPlMq78Ns/5rmr7mJDaSUgt2Zi4EGcS7oky7m9Fcc0nLiBtlvijZ6DZ9q2FJryEJrkkGapIdBuqJxm2b2StdVRYar2ZDAruCKSSZMgRr+nGhiQvsn8AeJgaDHHHs+j68lFDjub6FqIA5gT2/Q64E3hxshUuAm9BYSZUC+1PYqDTjbztasCTrVXkoJt0tEn8RANGGvagDDP43KhS6KdoBP46ydVK6u4Ub89tw/ArgsnHq8730gPiLD9HdNPXK6DbsyYuF4yWBtTO6KSDiLRndZZG7+M3heAVU1HVM7cbvAbMK+jEFhQg3C68TyINUby45F84fiAzd9yGj/A+Ic2ZMfagcE5MRHqLmM1uiBeQo4B0dekegkOnFiH7hAuD8jO4x0euE8JycmdE9JTwjB4bxsT+wf0Z3RuLcbR19Xh8ZXC9HC+fZqBB21/VYBGGexvyMjnD/fo64ubp0luh6dXzuPuBVhf2oSYxIPXu5Z6R4bkl8bjvgK5n3r0Leij3JJ04k50A65sJEH96NjPjfIaD2Ei2dmvliKpq/f4OgCwuTn7M2CM/IUwhD+Rzwt3EYb8XJQM09Rp6vGeH4lShMF+scH/5OT7xyySe1/VgCkf2CNp7LdlzjNcd6nTJtN8kse9NLwhiYn8L5VYBLEW/foyixbKWWzgNonnwg8bp/1H7OKRiw2xAr+1/C8RLAFZ4gCa1sdwaBI8d7sd8k+Wh4L0kB4Akgd9AvqsY+3mI9TrQjUWy7kUWAA7wDhzIR7ZpS1DdGGVi/QoP8a54ubDsvsFfQB03e3/M0Y/1dwLs8VD4IXriL3f1VqXa9F8qbF01UT2farenDvMioW4l+z1NuTIypUHROzOwetFhW419CqOyn7v5fifcOSnwET1ehmIZCFHHq/j7u/tbaPiXaHpqQEOle1v7O1Llwfmm0EVuJ/vuXKvd0FsJJtjEr+0Q6D8Dskkltcc+ULDGza9y9iCPKzH6KFvs4MWIhd98xods8e2cjD9yjwFGpZ2+sYgnMYvTe6shTtT0yxk7yBJ5qxO9dO7T9TpRk8GP0m3f2CCNYOV80dVrvd/e/mpIDXuaJZIfgefsA8va+HnmKVvcED5yJs+2rwHLuvqWZrYmyO8dUxDy0XYQ7DL9tQ+8uHzfhEu7HqsiIeYae13ftSOdn7r6D9dfnnS2ep6y4FRn83wQ+5vJgZtf28JkFEQY1W195vGWOCEEi7MN1JoC9od3WYbGCjcB2jFzmMUfOXHRgBYhi9mgB34pehk3qO28EbgyLxzdQ+Gi2AWaVLL8VUs2JNsHtFnGnmDBHU1yYo9T9aktJBlQj1xHqnAUj5Rkz+zXp2pxzAd9uxkzo17wJPRCe5QnkIi8xfoZiQEYcF/ejsMAoAORk1lboVw2H0u7Aj8PvA3gELUZJMQHlv8Bg2DQ1qW7p7p+PdP5iSiaIM0LnQ+NsqWCgxaS7L8t04zxkYF/KcGLc88MrK15fQLmRmhqIH0Eh2Sax6EqUxZWSTyEvxD5ozlyETJ27GjGzOCTYFLbOhqldYfwvopDSd4B1Q0jv85nfWNqPWxBNxYkIJN9cuxtskPS0Zr5oNl9rZyKPfeLu95nZZFd5tqnBAEgR8Z6EPD8N+ei9aC4YswFGIe7QhYs6FvG7/SdlYMOXkGaMb9WpNSj7oet/TjC+VkFevgGp3NzV1ufslDnCAwYQdgpNLPtX7n5n6/3kTr2RzI79SPTw7oJclXsCd7p7lpm39fl5UbbSZon3UtXYf+YjZkuMIma2oQ8vRPqiaTe0fTXwFi/DHDX3IJsBZT0m/B+hzNh4Uf6+p2uUjamE05D+piYB9xHTmKN2h3pnIt2arK2hVSgSnylK3Q9euwNQ6GQ21sgTDN2mLN31vT8h4eZ4d2/KXNsPcfQ9Su9e/w04wd2PpSXWkUk5FjGzTVLnPZ9JOzVx2tueuLAZOCVsUob1YTJwWOq+jlVMJNaNNDUNf+AJhvvIQ/UulGRworv/2oTFvM7dVxxDPwZq/w7RL8qYDM9II/OhSMktqXFvKgP2NgRbaOANH/F05l/jpZrtLZyIMWhD6oma2VFoU3q2/4eNAFNlhnhuKSnaXtr2Aj6kRnHKc2v5MnXV9Tm75EXtAbP+rKY/IpBt815fVlPlTr2RzyGM1izEU3UxeV6vlCyA8CspKa7GbmZvc/dLW+c+7Bm2biusGQc8bGbn0EvHvwolMyQzdMKitoJHAPSM1LZbw51yP/ISnU+/hyhVr21udN9m12E0s3YdxpgJ/2j6F+UcE35NqaVrrQBkHLVVxFodFs87UgZiRoZ6ZyKpydoqqUIB1O0kg9TwAA1NSHAxX3/bzPZ292MK273QzN7p7hfnFEYMg8Qh+tkLOGmwNV7I+O8ViRFBt4olvXRucfedK5o9Bnl4Pu/uszNXXWz/fWPDzOZrexPMbCnPZ+j9n5ntw2AIObXxKJkvms9v3frs8kQZwi3ZGW1kPom8bMsjWEJK/m4KZzaA7w2QB31M0p4vfHhiwCcQnvNZM3uagqhLiTET9NZm8H6kqCXejebk5RBWe0UUURqAslhBkkpLf0M05hZCCQbrAJ/wHodeLEOzzSNZy/trYE43sza3YLG8qD1gNhrH0VTSk+R4uN3jCXgyYtH+cmZnXVSNPeheCdwB/A8aMD9E4dFUcd2DCDXj3H31sIs8wxP4ERMm5yf06mN9CNjJ3bdI6G6NFuZ53H1lM3tt+G2p4qXF7Qb9Yu6UnCcz48H8IQq7NQvxzsBz7j5Qh9HMPuOqARifWzm1uJjZNcDe3l9q6Vh33zDSacbCXCh5434yOIZW20MLRUe654V+jNuOMLSbpItw9wcTusU8WTU7yfBeMQ9Q0N8SVVIAmOYJzqJSsV5411CI7l/0Mqj7JnYze6m7/8GUMT0gKY9d4vuWR8TG72ud/4y7H2mZ4uAZQ+IU4FXI4B62SfkeSrBpl75JcSjWzC0piMATyCt5UeK9IgnP1W7eqzv4PsTcn6LNwIRxu55BL+rA5rVmvkh81pCB0y5KXeyRDPrrIYN0LUQ/tDSwnaexZUsjEus2hUjSiJ/A+eJNaE1ayN07jRkzm4JoUe4gopZIrb9hXXgLcKm7rxs2Cx/yNDfjfcDW7p6F/LT0b0BQpfN9OLfXZxGkJt7cnd9eL4Luj9BaENfF3Mvds1CLLnlRe8CC8WUoC6Z0UF0Y/T8fCgP+PlYYcUcL/XHoZ4HHvEXsZqOlU2+K0uCbBfhL7n5aQg/qasYt4+5xaOMkM9svo3sw2qVfHtqdYXlqiZp2oYI7xXup6yVhrJo6jB9AyQOxnEnE7RXJfsAZZvZ7tEAvi1KYY6nFJDRSUii6kcUR982N9C+eQ4mCh0gNXURNFYqanSQU8ADF4qqhWVpHs1O8vLgv7t6kpE+ijucslkeQ0dSWapZ2erxakxhOa7IwGjsxaaaT9pbWzC0LI8PgzHC8bejTG0wceZ9uFE1YrIPpecCzG2gU0pliZpcjz8iSZLyGQeZz9078byTF80XLIG4A+QMUDTUeyaD/a1N4cA10HbqqiBRXaQhSPF9YRaIKArK/gzBm3P02y4TYURbzmpn32vJvd/+zmU0ys0nuPt3Mcl7Gx0qNr0bc/eHWGpzEeLr7EWEcvC2cOrRjc1dTF3OovKgNMNAvMrOLEJNyif5Z8bGZnYbSSmMZFdj3UrQLejK0vbCZrenuN0Q6NVXYG1kcGT+/RaGyFTs8aP8K16Qx8hZM6DTyJ1NZocaY25FMCRD0MLRJ83IevKJ2bQTuFBOn0amERd7M/oToMO5I9OM5M1vVAymkCWjZ95BZL3NzUesnTV2EaFcZixeUWmq8HmZ2qrfCMSZ8TC5EM7RQdKR7YKaNscpc8WLhSkTJFUev4cmq4i2jgAfIJi5RJf6ObYlAte5+bka12HCtWMAvCH+7rlP7M8Vwi/bYHCI1c8taiLLm2aB7LIF7CcEvPh3pnojGTJ8HPNPfWWZ2GJoDngQ28Xx4HOBUU+mmCxnOyzR0vogkNoifRUSvuazBYthEkDfQC9GtZwkOviA15eygYL6w0RJVio0ZlCy3prcw2hn5a9hoX4mSdx6nP9GNaM6uSVIBwWTeBLgp9LwvHQlzqJ7q3OiZ7SK7LUkcKJYXvQEW5Ndmtr67dw2+nLyCFhFrs6N194dMIO03oAt/kydApJEcR3/W3N/b57yOlK+R6xEtw5Swqz4CuIb+yb6R4ppx4b1j0A7GUUHZHNbkDjP7IPJivAJlTV07xnYbQszmyY1xMU6aGPN4xDM1HcBUFugE0teipA5jdeamDRKVdk2S7RTvyaS9ao0sQ3+m4r9R+O+fZtYHCHYRBdeMz1L5XzN7t/fTRSQxNl5RhSLsJGfSCxN27SRBO8nOydrdNw5/awhsG/zZUEZwU4huNXqbid3NbAt33yvRbI3hWrOAY0rOSHnjU6Dv4sQIqwsV1swtS6CF/G/heH7Eu/VsexwDTwTv5VAxsxNR0tLawOoIo3eMu38385F/odq8X6B3/XJzy9D5IvICrenuny3pMxUeybA5WxVFO+JqCqm5pcb73MwXK9JNLP4Jeokqt9CPiR2A0gSpMWZOQUbYHxkOyXgPImz9FKJSWZR+glron7P/gUp1zf7JZLzlyGP4bWRUPoo43VLPNGa2AxpDl4f+HmNmB7j7mW3dYDPEOMmlgIU9jcEeLj4BJGjj/UK1HZ9Fg3wmivfPzOg+iQZT87oXeF9G978Rad9J9Gr/fbSjHymiyVw/XoHc83fSK4uSJGxDwPf2uU06+rFFGDBHAVuM0zVeAKWp34QWj8OQe3882h5oJ9c2CeLO1LnovXnRZL02MG+H3oYV/R1KVIpSnJ8M4/Jv4f+myPDhHW0fiDwhB4XXzaj0zoLAj0cdnwhLchRKJPlV88roroqM/t8BDyMDerWM7iooXPonBJQ9D1hlnMbFXWgBvafguT615Fx03WYhtvzpaJLPXYu7CVjYcDwJ1Y5N6U5DRXqb4/cwfsTNr4teGyEaliMzug3B810IvjAFOCKjewLazH0qvK4K4+kiVE2krV80t6CF/P7Q/g8Rkewnwjj+RtBZL7y+FtrcMDq3Xqbd/Vr3Y1GUNZnrx/3AUhXXuXO+QPP1m8K1XTfub67PI4x5K9TdKvz+tcI4viUefwn9YmJxhBUr7fNSyLv9WJgDfoS8cynd+xAh7srIwF0RWDGhNxmYPh7Pzjjck9sQrKY5XprMmoPm7AuAe8PxcqjW7Ejf/WIH4a/s7g/YGMCvQ9q/B9UI/HM4XhK41jNhEVMJkMvpce7sCWzu7u9N6BYXGg76RTv2wt+VBPRG7Q4AeyeyXUsAsVPnwvlzkIESA/xf573SLrHuXshoibPudnT370U61SDnxPd0EZUe7gnCxSHtlRaKLh6fphIyp6NEjtl4Ee/YxVsBzs5EyfFdeh6iD6DJ+40J3Q2Q0foqYB40yf7d85lKxc91e7yYOPtmegJvEvAY6wPXuwoDvxL4qrsP1HQ1swsRiLYJKa+IQLYDJaZM5Up+jCZdQ8brLu5+X0J3K8R518Y9FYdMLUPaa3WJEdfRHyqcmyhU6COSMoe2Xg404+BGd3+49f70jo+7j5F6JXzHJcB7vSw7r2S+2A4ZtxsziMtL9rnSI3kGIif+Q/u9sYpVEIub2faIouJJUybqeqiSwVgLul/nUbLSEN3LgG1dJfuG6a6CPFoboHn8OlS/Mul5CqHWjzF4T1LJAH3XyERUe1vmus0g4CSjazz7OayVF3sIsgFJT/FCNm1TFfZfNTc1LJ6beRrX8WfktWik8WDkZHdEIvhFNAguI0/kV1xo2ApqeEW6JYtcDaC3abdkEqlq13ocXPOb2br04w1ytA4fRWVEGtfyVeSJI3fzKDThIufcDfhepDMKyLktWaJS4AsmPNzK7n6oKdvtpS4S3pzMh0qVTDWzpS2TjUnd+ByKF7FMQXAL2A5PY1YWcPeY8+lHZnZAQg8UwhhgBM/oguafR1yM3Zshr0RfKMZUn/TzaAw14S5DnrPjM+0+7e5PmxmmpIC7zSyHNVsYuMsEXHa0gN1swvPgEYDZhR3aoMRwRZQF26ISQEN3udbDS4K8cK9D3o+U1ISmikOFtQY0MlJWdffDzGx5M3udR5nNXki50upDDWAf9GzOCMZeJ/8dBfOFK+x0ppkd6O6HFnZ7KFWL9ZKzFgbuDOMtWfd3DJvGGmLxA939jBBOexvyUB5Hz6CO+15szAC3mtlPkJdoGFbrKWCWKas+xs6lft9P0Eaw2Yh/APhpqr9BTkXe7XegsOZO5MOmvzCzX9LbZL4fRRJSUoOTHCovdgNskpl9Hli9vWhAdsE4yN3PiXT+agI6pwyw+xBj8nlooL4HmNl8V7t9d38c3fgSqSk0vC+9HfvmzY49ozt0kfMKQG8kQyeREdqNObjia/kkGQ4uV7mpfUzA7+e9uyzE5GDgNg/DZLRwxO012YazSnd3liEqzah/F2XwvQV5PJ4K55IFqC1K9Udpz3Mjl/5GkU4z1pPjM9OPkkV5lILgPzezz6HJzgmTU2MweAvs7OWM4ABnAa83s9WQMXUemmhnZ+y5++HA4ZWexiJG8CBJj3QsIxquDwO3lxhfQWK85LOoPMtAOn6QmsSIbyAD5bLQ9mbA18PCcXlLt9iANoHu50bJI4ehBfT75Auvl0oxYD/IuaTn9pQMnS8aqTC+oAwsX5OcNeqm8YqwXs5vZlugCM0FGd3m2r4L1Ye8yMy+ktGtMWbmR4ZXCVbr7Mz5lNRsBEGQiu3N7D3ufnIwCq9KKbr7ASa6k2YOPj62IVpSg5McKi/2EOQaqI7ifujh7hNPc0MNuAM73LAHdX2/9ygRRuHqWR8N0sXQwrwownRcn9AtquEVdG9299dbf/ihj3/JzL7l7vu1DIm4v6m05KFhjVHaDZ97n7eyU3MSrtsUekbCEwj3lOIM+zraKf8gnPoE8LBHKfCR7nREJ3EmcLq7397Rh02jw06iUguhMetntr7NEyzY4b2hLuzScdlqdys0wSxPb1E+xNN8T0u7e1c6e6zbBS7t80xYBSN40G+u3WeAf7r7Me2xHOluhDCYfzd5HNdD5aI6YQg2hBE86MTA5fkR2P7J6P1PuPsPcvclcz/WR8/9FfR7AkpKbI2r2JBQYaQ3dG6JdKvGfUVfb/BEeHs8pGa+qGz3enffIHhRvoM2P2e6+6oJ3WIOvhH6MQkZ7W9HxvYvgR+mNgGm0PujCPO3HsJJ3pi6f809bsaFKYx9lbtvMMb+9nlMw7mtvJf4E58/AmE6443g4shzN7ARtBC+D3PSnmg+urHDk1rT7y2IrrG7Txu1rRe1B8zFyH5EuPGl/D83mzJ/GlfzXmg3lWq/MbAW0WHW21K9I/FexuZTZvYxRGL3t4x6zY79H6bMqxmmUkp/QF6aWJqdQs2uq8SDUtVu4zkAVmp7DyC7GJ0I7OnuV4U2NkaeolSM/bNoEt0jHE8jU8kgeBaXBXYAfhDu+enuPrDrC7vYZlzMFf5fov2QB/l32Ek3u+qliQghEzLUhZ1a0IeJV2QropT5B5HH8+zgdcy1W1PbcGcUtiphBAddux2Rp6XBXM2d0T0OWMdEAvlpdJ9PQSD02WKVjOBhF/txNNZXRd7a79PL5MTdfxD+1tyXw5A3dD4yXpZWPwbwaehezgqe91i3wcNsiMbadcCnPFOSJ2weuqgcGimZWxr5d1jwm3G8JN3jvlSmB0PpbPoN1zFhk4IUzxeVUuORrKEyWR1hOldieAUR3P155I0p8cjsgCgVjnJFiV5Kf5Z6LM3a8FcTTdAfaTELRH2eSnpzngpXnmBmuzSb4TAX7Ec/l2fcX9D9i+UDpLNejzdh/L6I+MsWIkPTYRUs+8GAvqoxusxsfjNbaVQD+kXtAYslGAXtGHQ7ZbVZ0A6kR6o2DYEL/57QfT1a3Id6W0bo709QKO85lJmyCNqxf33I5zp37GG3/jhaqD4VdL/nCSBwZX+LPSgVbY7iOahlU58HhfOcblLD+DOvQXi397v7wOJoZh9Hrvan0aLSVXlhJ7QbWw9lKm4HfNHdz8h89/+gZIstgMORC/snniidYwXFqkfxzobPvQFNXu9FmV8/DcbyCyam+q67o3qAp4XJbQd3PyKh23hcvgQ86gr55BI5ihnBrQC4bGbf6WojdY2tvkboRcigaoDrm6GN48qoIsWpkW5xYkSNhLnlMWQwds4tJqqWbVCocgpaIL/sCQLp4DHZg14JoCtQHdaBZ9UmqFZq1H7RfGFKuBjAJ3oA8Ed6kxGo/puF3z9Q9zHnObSKCiJBvwo/F/r+EvrnloFnxoRRPgtdg6nImPmSuw9EpUyhvEZmE6FnnpFVUETigygpZBdgKy8A5Y+nWAXLvpndjBKj/hWO50GJVCOF3ucIA8zMvo+ApJujHct2yJ2Yw0iUtjsTZUDF3pbveb6MzDRge+/Povmpu78joTvDlYG1E1qcP4fKzeTaXhwZP/HDMNaMlFpA67i3O8IE9S2EIziNnqv5aULx0/iahImxoWcwdP0+7Gm+p1eFtt6HgOynA2e1vQtB9zeItiJXf66t/0rkMTGU9t35IJe6sK2gWLWZbe3uF5jZh1Nt+BDcnonH5huolFSbM6hIrLuyhAP/h8rwnJf47Dz0cEZdC+IVwC9QNvEmaBOSy1S6EoV5SxjBb3D3N1ovzDIXCg/HRm5zbTdCeMDTw/H2wJ3uvnui3SNRiZVLUr8nof9LlFH5WDh+CfLw7QhcGRtzloZZjDn8F9oprQeLmb0abXQN/dZkWN/qSoY1+MFxl8r5YgYyLldCgOzzgFe7+zsTusls1UwfpgHHeD8H3z6eSDKzAAsp+nHSv5sEfs5DFnVLd2+Upf8Y/SWDRsrm6+jTJOBqd0/xODZevnMRJc42HtUKbelNRni1lehfI8cc0jezazxRbiujW2xAF7U3hxhgTey5+bsQ8HN3f3OkMwruqdbbkrr4OYzEHYj9+icorf2Kjp3OoQisfj/9D8NbIp1k2aTo9w08OCUPZM5zEummdi7FD3rQr5mgilPXTVmlH2wWi/Awn5aatEzp+KcDP3P337ffb+n+AqVHD01tjz6zDP3e2dROcjJaqEoLcl/tgYh0PMUUWt0GeU5WRRUKfpbbWRe011krkcAj5K3C4pUL4rJop3yTu19lZiug7OYBAkvrx/DNFk+EI4Oh9Fe0+94b4UXudPcvJHSvBzb2fkqHJBbGxNy/IAqj/Ru6aSjM7E6PKDXMerUH23OM1eNhXo08DIT+pqpKYHX1YE9y948MOxfOD8x7HXPh/cjbMqXQI1FcL7Fyvmg8rgegrNoufOI3kYF5Ov0G/8AG2vqpTECh4Z09sPO3dA9GG42hFUSCfjF+Lnh93pibs4NOZ4mnEuPHhOO+yN1Xi86117JlUPTpmdBuai27GG3E25vRarhGou1vI3zwUJb9GgO6RF7UGLBIGqv4H6YCsX9GZYFiGQX3dIUpoyH2tlxuKpiaeoCeM7MVmsU1LDg54+UHaGG5Dbgy6OYwYDugdO6uWmJN2aSGzTfmycr1oYSBusG1JXf3Y2g3lmtMWVNDJ6hS4yTI3PFO3d3vDYvigHghN02Q/4dY2m9gSGq7mb0bOBpNqI8jr+BdtBjyw+efM7PnzWxRL3OzHxS8B0OLVWc2Hk+g+/sDD6DfILehyebL7n7dsE7YEI46jypLZJp4yOQJbsvRwNvbCyKJSgKuCgDfiI5/R5o9HC9jBG/kcwi4PAvhSy4mjwtaHIXmmwVwoXAu1Ycq5n4071yI8EAgL//lJkjFX1u6xXgYUwb2nvQyBX9mZt/1iPsqkoMprwfb9sBNIp8BWVMCaJ3wO04MbU5BUYbc3FlTL7F4vqCHT/www/GJzaY8hsQ4CRohr6MyaTyvnRVEmvWKOvzcw2h+6JJmDK+B7m0DR9kaeZcHxPqL3DvCi7W5CEepo/vy8fbORbII5Sz7u6OySQ3G/GHyZeeGypziATsQ4ZLeirAPDpzgCVJTUyrwt4edC+eriALN7L9QuvwVaIC9Gfi4d5dbaT5rwGRvFe8O750F7OGJcFhCt9hrZ2ZfQwvP0Aeycndf3G7QnxBch5lNQbuhBru0E7rGH410cuGxbIkME0fP1QzutgbCeSacxluQZ2tdM9sc+JBnwuMmfNK6CJvYyX1jZj9CxarvoN8zmiIT/DZicI65bP4WfvMiHtUENMvWGU31N8lRN9Z7F9pOhdKSpIbWXwtyHrQYPuXuA1xZFgHr3X1VU2mt76d2qcHAedpD2Ct4KedNeT/NbFdkpExH42cT4ODUuKiVMD80NSlB7PVnld6njnZnIsxKU3t0IUTmm7rGTTZfnNnYztD9LDJaF6a3oWwW2xPdfQDIbWZvRdihvhJAHsqNdfR9UxRBWAxhhQ71Fh7N6khph84XkW4xPvE/LbXrWPjMiciwuoghWbqmkP67PKqBjLxauYLc4y7B63uZF4b0w2fWYtAzmty0jdCfEgN6eDtzggEWi5nNi8rYJK33lDGScx2P+P1LoUUIxNtVhBMa0ubrEcbgdjLkfJHuDIRbuyYcvwnh1l6b0C02fEx4ow0b93bwelzvadb1CQXKlkoYC3vRW7SuQtfimUinMzyW8trUjBfrpe7fBqzr7s9bNw1FMVbLzO5JXf9MuwOLjvXoTZKUJoXtFrPKj9B28YLY+pwhTrQN3P1zifdrGMGvB97W6+5JXgAAIABJREFUMlAu8TxmZVl6lA43+PjU5mzaHvDaeTcPXkmbs1AliQY0PC+qA5m6Ficib+vnEFZyH+Q12j3SMbT5OjzoAfLuDunHvPQXt2/Xi2z0GqzPrgjvcyrycr0ZjbvVW/o1FBBD54tRxQqTxArbeou7/8rSmbE5UlPMbBVvZcKmzoXzNYlR9wBrN9cpXMeZ8dwUeeGSktucl4qJYP1HKCu3JKR/EEpkWRN5tbdEWLTtErovRw6eBgd2FbCvJ6iHTNmuB9GfUPLlnD0yTOaUEORsCYNg4IEJ7uIPAitbYLEOsjC9kEGjmyRWjL4jGdu2HhfRhSYuos+b2VAuogI5GaXB9nlcMvIxYEoYCIawIMkFy+vCeV9DLMZ9u/txaBcToPirwHLuvmXYXW7o7ifWtNNqczLCiexEP8lru6+jFF7/uSkTss3mnMJe/DUs2lci1/TjRJ6tRH9OtnKg89Bi1ZEsZP3h8RXoEf92hbaHSTGrvJkt44OUCWt0/M490ILYeP+uor+KQVKCV+jcMMkOGGDUMYLPF+9i3f2pYPwMSDA+3oZqYX7ZzFYwszd4d9WDIrFBOoyX0aLDGFFORWS+DQ/fNvTA8G3ZG2XdPoM8T78E+mhawrV/ljxlwYBYrwTQzHC8uJl9zNNh0N8gD+PX3f3a6PyZZpbyuBRRQJTOF5H+A6SxxKlEo2SS2LDv6JBNUS3XgXJY5ENjIC9h2xA6g3RIv6FgKvHknALcaCoTB8qcPqmlc3TH55Ph2Er5BsoSLqosge7BOsCt7r5rWINyWd5T0XjfPhx/KJzbIqE7BTlKGhjAzkF3tA2pvwiKYY7HC7m1N0OcOJtGr/UQsWKs+wnvFdYceHV8x0xknKyD6hXuBVwxDn2/aYTPLAosOs7XcFnkWXgPsOw4tvvzMGBvC8dzoQcppbs9qi4P4nA5m3zh3qsRYLikDzWFrR9IvHKF1BdEu7K5EGZjHzKFaoP+1qj49APh+LXA+RndmmLV7wy/bzrC8DyEPAkLopppo967c1AI6GBkZJ4HXJzRvQeFaZrjTyNAe0p3Mq3i40P6sW302g5tGK7L6B6JKi3cjSbRc4DDMrrXxOMLLVa5do9DEIi7wvHiw55dBDBeoXl16M1AodVbo3PJZ2SEe/hGYP/wWn882qz8/hmJc7dmdBdKnBt5/LbaqZkvloxeL0P8VF/O6M5s/V0IwTdSunOHOeLM8NobeRlTuisXnnsl8lj+tvWcfAQlcqTaXgu4NcwTD6GEqld3XI/1EBRhX+Tpf6HH0JWolnKp/o3h7y3IKDfg7orxOXCuVrfkNcd5wHLi8kI9BGxog8zW8xPV1PPRiBUBnnV3N2U+fNfFRZTD+tQUO73KzA5HIMckpirntbPucihDxcxe6fJqNDunhiV7OTNbLtPfWlnK3X9mquuHqw5dLmRRXKMMYUquCR7PGE+VuhYHoImjr7A12tH0iVeQj3qPX+558p6FWA5mEOicowUZKP6dEhNY+a8IKN9kGt7jPeD9t1r6xbXdvFcE/eDgHV0U0UGkZDNEgLg94he6C/3WAXElJKxoZvN4d/JJI7E34FlkQL8no1sDrN8POMPMfo8m6WURfi4lb/TA/h5+w19MNBoDYhXJGUGKvXZWUBXAlOnayF1EpWPMbBFPgNqtgmanUmpKAKU8MfszOIZH4b8rni98MDvwW5ap5UtZklgjxyEjrPH+7RzODVByoGzQtkerqY8cyxoI2L4Y/c/Jk8BumX4cD+zvAYdnykg+gYgkthknprJjD4ZX814fMfUoYVOrqz16P0pK+TlllSVuNpGbn4CMsKeQcyYlfw7PUYOf3ZF8zd1/mtnG7n51+A0b0bv/1TLHGGA2JBMr0hvKbB3pHolc7P9Ei8raiFE656p8MhgRHwI2CQtfLjOmxpBo8EYx4L3tth2ljl+J7I+uV8qFPB6uY4C/B4OnmYA3IJ+BU1Oj7LfhNYnh16W28PpEyb/d/YlmoQ3SF3aOFsgi/I8Ld/ZdF97ptoKPFNV2s0pWeRfW7hcoi/R54HOZBbWRogUx9GOmF3LJeQUjuLvfZMK1xfikHJlvTdWDQ9Hz3Jec0dGVK6y8jl9JVYA76GWjLUdvLC2McFLLJ9pdyiOi0WBg5hjPV0IEm/8Kc9zawI9Shh2aW083ZZyDjOKcEZ/8usS5UeolFs8X1o9pmoQ4wXLr5YVhsf86iow4+bG3vvfjQ39lwo/G3/1KZKgv2jJoFiFa/xpx8eudZ2YbekFWc5AFPUqCcPfLbbAqx0+QYdfUKZ3dRQazMUcJmxbXHqUXiZiHgsoS7r5n+Pf7YU5axEMIPCEfRYbgN0Nfr0UYxJTsDpwSwt4gCFAS11sko7rOXsgX2h3MCj92OjKYfpXRLXblE1yHCBdxItrd39bRj2WRwfLmcLwCIk9M6d4a/h6OuGcg43afwOtWE86br+RcbbtBZz0U6nki/L0XgTpTuhciCo/70Y5u3q57UnANmtDLKcjlfjAKNf8aOOk/MJZPRFjFmWhDcQzK0Ou7BuHvA+E6lIRCj0JhCCvoQzM2m5DJ3Ahkn9I9j47QWUv30nCdFwNeg3AwR3XoF0MACCGFwn5shLJM742uX+66tUNCnyQfEtoJeakfQaWG7kEeo5TuzeHvbYTQSdc4Rov8bmgxOjP8n7yXiCgW5I35WHwuoft94N3R8dbAcRndW+J7jbx2uXZnhGu3KsJtfbMZt5nftkd0jT+BEgxK7+fvSnXH64XWmeY1DRlUaxR8bl46oCFo3lk1Ol6lfY2RZ3cq2iBOjV7fQRmtubaXRqH345FnfwrCvaV0z0EVY1YKry8C57zA17h5RmZG58ZtjUSh4zchPPMmwCZjbG8SAWKBjOFFxtrHOSIL0ioysayA2TrSvd3d1zJxLZ3p7r+w8WOULi52WtjeKAXBG+LajZGn7+uohMSAF87S2aM5eovidqPPzIW8DEY34/kCKPQ2y91/Y6pR9hpPpB+XhExshMLWo0r4/uU9v9Nqft8X6C+Ye6j383SN8t0N8edzaKx11TMrLlRrdazy73X3c6PjuYD/5+6HjuW3hbZqyC5rGMGLWdqDflHVAzO7FIGVD0dEtI8j70cyu7JGrK4qwED2p+WpPoppdqyfqPQZd/+OjSHb3PppRvreAuZ396T3ySrqJU5giLVYrIKSo9KjhZldixJZ2uP+rITu4sAhKCPUw+cO8URdWDO7zFsULu1zNgJpa5hb3oY8uH9EtUc/klojg7f5MwzCJpIRGhNtxfsRl+VzPfXkvLUywuKtRP8YSune7O6vz/3OWplTQpDFmVjUufIvDJP1P4E9wk3OLoSZmHWSi4i6YqclMorLfWg4z5QZ+DJ0vdal5+5fBGX2jNRuW1z8YkkG7pYsRfiNpkw+ULgsJUv7kJDJKAaWFWBsIt3LgXejZ+kW4HFTaYtchu0/gC+ECcJ9jDQDUbs1oemmUO2B9ArVprAtkClgm+nDua3jZ1EobjykmOySOqLgoSGhljyGFqu50DOzXsoIRF6MfyJDcCfkXc/SEpi4yg5nkLcohQ98P/Kifszd/xiek1yN2T+Y2efop/p4LKUYNqDr0YNC7Od5mp1nTVi/nZGhCS04hlVU76gcv7Gcgbx8PyRP7trI0PliFEOiRtz9snCvh1JyALub2V0tg/Foz9O0LODubdLTXD/+Qi/7OCkmrOgCwFLhu+O14WUt9WrSVjR2JiOv86dQWPx9Gd2GcHcrhhPugsbkGh3XNpZzUWTiAoazEFxqqufb3ggmqxMMlbG60F6IF3WZWMWu/KC/BMEdjgZbNvsPGQaroVDWZLQDPTyju0Lq9QJft6HhPDSQpyOMSOx2Px+V4xmp3TH0eRa9jL/fIMB1LpOnOGRS2Yc42/VWOrJd6YXz/hvtICGTqRjeWz/8tgfpVUp43Tj02RDG6MBwvDzwhnG6JysirqzmGVm49f7V4e+TiJyzeT0J/O0FHO/rhdfXkEGyYXQuF3ofGhKK3jsUJalcHj0nOSjEykQhfJQItFJH369GnrWZ4XofTCbrrvKaLIUyN2eF13cR1iunvzhKnOgM26AQ8/cQ6XDze7+QGDfZ1zjd81tqdIfNF2TC4gzJkK/ow17AYq3rvWdGdyAclzoXvfcV4J2F/ZiW6McvWzr7ovD9M/TDIG4DPplp98p4fkCG2ZXjdZ/pD1dmM5BR5v1ARm1G94aKfjyQeCXhDUXtjfXCvNAvBPZ7NwmcBvWp7bVYpuKYNXWGRA1Wa3UUJrgEgR5/RX4RWAClI78iHL8UlX5J6b6v4roVtzsO93s94IeZ9/4LUS+cinb4DwHvGIfvrMHYzAq//xJCij/dBthMAoYwHG/cpV/R52KKBGQwfxDhRb7UvDK6uwE3Ab8Nx69Aobdxv9fjcA2md7xyz8hbwxi6HIXeHgQ2z+jeQzmNwc2xLvKYdy0YzQIzq30uoRsbuk8jz88T43D9irG2L5YXMlT3DM/gEs0rozsh80Vou9RwraHkuA1YPDpegg5qkjAung/3rXMDlPrOjn7sXXEd7kGVJJrjeZGXL9Zp1sbkK9Pu9eHvL1HkZd1mTsronwXchxwF32leGd0PIgN76IZtvF9zRAjSzE71UErFQyaWmZ1KqwaT16e212QqgtKM5wFmmDIo/4A8bgPig7iL9dBEMdZ+1Ljca8J5F5rZBxmMg6fCJjXtVoX02uLuvzaz5P3wupBJjdRku34ZTQpXuzLqVkEGd06ec/ermgN3v9rMBspTAZgK9z7i7s+Y0sTXBk7xKIwSSTFFAvIgP4E8AsNc9HsRWOVDu79ph21CX/syJkvE6rOQO8UDQbBlGMET3z8pfHdpSOh25PEdWjIMcQ/OnoNc2YJd2VvPhP78xlS/8VF6RLp94lG4zqxXFSClG3BPnmjj7Qn1felhbTcPeLevtto7zd13DOMs1W4KM1pDN1ArHw5/O+slhr4NnS9sNKxtslwX6fB4MSUHyky/zszOQB7u7VDyR1K8Loz7vFXUNTazfeixv1+O6sumcLwp0tY2Pc8otSCLCHcjOZ9eGHSYvAbZEm8hKvlGhgHAVH1mJfrXyZFKHM0pIPw+MHgYtLPcfc2E7inoQR/K9WI9oP7hob2fdAFJwyB9DD0wn0K4ju95qz5Zx+/IlUMp7oeF2mel30cvFX0+FCa4xxNlaUypus2iHAM4B+gpatoN+jNROG9tRIT6Q5RNsmlCN8ZgTELG2pI+RqBswPftxuCDk6oDtyzaFd3k7lcFI3OzUR+yVtvfQuGouAD80wSMjvdzv81A6dkrIS6r8xBZ4jsT7d6AMn5uCobY0qikTmoM3e7uaxX2tyap5Ty0W/5dYdszXEk126BJeX8UrhhTEkx7vgjnks9N1/Oe0K0pGTYNOMbdzw/H7wH28UQ9yvD++gjnuRgKdS6C2OCvL+xbbr6INy/zIYzNM56u2diUrpqBDPpnrFXGysxe7u6PhM3BgHgouN1q92YSdAPu/v9KftsLKWa2tbtfYHUlw2qSxL6OQp8xJcfD7v7pTH9ejRj2Qd7IzqoYVk7XVJNwUZuosl5oD/Q839rV54mSsOFpaC26Er/uA9YscdoEx8+qyNCOwf2deLqcvKg9YMEL0QDq48Kv/0KDJyU13FCPmrhptgCOMNW4GvBoWS/jY08XyPFplEHS1feUIfH7sfQjyAVmtifCxXWWyan0wr3c3YuIPyvbhX4C22O9g8CW/nv2LCoWO5DFM4Kch8DTlzLEc+gqUfSN6Ph3aGc3IGY2lfROOQeUbYyLg1rn12Vw1/W8i7R2G7SYH9N4uBLyHTQmXmJmh6Hd8hczutea2WvcfVbm/ViusPKklsWBO0zFzDszJoM088+7gDN8kB9ttpiKw+9Bfw2278eTqlXyJwW5zMzeB5zdeCU6pKZk2O6oNNWxaM56GBkeOXnOxZn2FHkOIgBav63hqEomD7n7Da1TVwRjPSWPmPiszgWmmdlfUJgubu+R8Pe3wchfH43bm909C4p29/vMbLKrZuTUMI7HbIAlxsXl5L0zQ8XdLwh/awqs1ySJfRYZXXuE42nkSYJx9zvM7H8J4zf2WrWlxhNXGT2oTVRZAIU+p5rZ0ma2srs/kOhvnPk6DzLyxsUzaooYNBVPDFjezD6cMkap82y/Hhlr4+K5mlM8YIdPxG7JCikPzOxOhI9oOJz6VglPp8LHC2zD3H2WJ+gGSvsRdAcGsrqQzJYakA4v3PFokS9ZlIvbDe8Vp81PlDTelkLd4mzXsHg3Mh/ilPv9qDuiVts3IAbwLwBbu/sDXd4r61EkgHbLOYqEO1EySQOwbSgrUl6tSYhVPqbN+GFqAjKzAY8m5AlczexrKETxTxTmXAxxSaVoUobuwoOB/16EEY3DD08iuoG4tmDzmYa+41lkxHTRdwwUPB8mVlZrD1OVgWVR4tDp7n57h+7U6LCZW07wVh3OoBv/jkmIRf04bxW1TnxuU0LVg5RnwMx2ReH3xoOyMcIRpjxExXQDtVLrnSlo7wK6MzdT3s5z0Ny2HzJ2/oIwygOe6sq+JKspeD7SUOyJq+zHrxF9x2/D8SqItikVbj4IGSlruPvqpsoAZ7j7Rm3d1udmh9LdPVXftbbPtyD+zXvC8erAaRkv+OUoOnMTwz3bZyBP9h/G2keYQwwwADN7GRqAcfgoZc2WtBWXWBiQtjfJzLZDi9DG6CZZv3qai2SEfi1Dv+u4KJTT0V5xOK9yUa4KE1pBSG/Eia8YI2WiybjW3S/OfUekO3LIJBgsV/v48D2tiTwp17n7aSa+mh3c/YiM/nr0eH2uSW0Mgt6KqfM+9qLy1RKewSdc+M0FELnhQJF0S/Dzpc6F81X8SRV9/QZ6NrIlw1r672KQt6iLimJZRF/zfuS1O93dO+ldCvr8MD24wLPo+T4kNopz82DU5wHvupndA2zceL2CN+xqdx/w/NgYoRtdUjMuwnuTUZmseB35XfR+s4nYFhnEDR5xR+Axd+/CHWUNV6ug5Ih/BzLo+qopuHuu/N3QEPIoYnXcZTOQN//XHkLiluGdy3zXyFxyrXYGvjPXj5KNY7Q+LYwocW5kiLFWIi/qEGQjYaf8AVqkaijldRRpl1joM6hoATjd/UzgTDM70AtJJa2OILC901kBgdpTWK0al3tNOG/Lrt8zhnZLQ3pHVXx/I2cBrzez1VBI+jx0b1M7z32Bz5vZv4DmWiU9HeGNUUMmr0AFmMdDVkWhgedDnx5AIbABMbMvoWzas9B4nmpmZ2QW8BT3WJKPzJRAcTC9zU9jmKdA7duG/i0T9LLepKC/S/R//FYq3Pucma3a2oXnQsn3mcKmKzEc77cN8hY+EY4XQ5uDc9u6lJUMa9r9PgrFbI48P9uR50Nq+vdH4DvBG/YZlJ06+/5ZBhgefT7ldV2lPTeYcHyxxPPgCsiDY8gj+TsgVRv1/1D90Ub+Gs7F37OCu/8uMuyHQjdGkOJxYWZ7o9D/Y/SDrWM+sibJ62jvJ9y8IGzM2m2WlusaBXj+b3f/s5lNMrNJ7j7dhCHNydAQcq1YfaLKv9zdzaxJMmiXN4rbLg6lm9m+yAh8Ej1P66JSZwNRoiA3B+9ozH+X5NDsuGexjLI+DZU5wgMWdltrd9z0F52E3cv3GQS135LRLdrpjLfLPdH+uHrhQpupWH8upLc1cFFjdAxpN2bjftoDRmqsO6iakEn025r6aH9E7O9jxq2Z2Y9QavRZqKRIV6bpPcA6HkLcpiL0MzIeiQcRT1i80P4RLUy7xWPU6ljl70Oh0mToM6F/THQ4Hwqf/trdt0vo1uzCaxjBB0LT4zSGmmoRzd+FgJ+7+5sz+q9Cnq/tgD8hosezPAorWg8YvhEibD09HG8P3OnuuyfaralwcQIqR3NxON4SeK+7fyLSaYy89YC10GLvKPR7u7vvGunO/h4zO8vdcySbI0vluLgPeYaG1oA1s7uAd3nIpg3e54vd/VUJ3arkk1KxMVRTyHniEnpD5/ua58FEUvoKhGc+HNVZ/Im7H5PQrQml3+bu65jZOxCG7kDg1NQ4DvrzogzujcOpq4Dvdl2LEgnj4A+tefYl7v7gKO3NER4w9HDNzfCU+cYVXprtlqJH+NY4PUjPuvtxhbo1O51iQOQYvXAroqysOAOqOkwYzhenzaNF6FtmNtToQMWRd0Sp6E0R2BxdRPMbZ3sO3f3CjOrOaEc2lKHZKxm8Ex7MATB51PaHTBieHYGTwq5yKsIytD1Wv0cTabODnBdRGaRkGsJw/DL06e3o901F5JoxBquGVf6xUuMLwN33jo/D7v2nGd0aBvFiRnDSyS7jMS/+M/z9hwkH82fEVZWTKei3v93dk8k6HvBVZrYHCv89G46/jxaY2RIW1pei5InX0PPyd1W42MDdd4u+7+cmqpBYlg5/Hw6vecNxqrh27NYswqjWSuW4eBhlepfIp4DLzSw27D6e0S1OPrE6So6iagomxvrdEYRkFnDiMK9OyXwfSXGiirsfZUrW+RvKQPySu0/L6HYmm7S7HP6+Exled5hlMnYkOyBjbnbkxcy2QiTiY5EzULZ5I8+Fc1XY0Nl9mkM8YGeh7LHL6I+7pjhZana/xfQII/T5YDSwh2Yr1ux0rA4QOa5eOMvEyqN2S1y5TVtddB+N0bErMviSRodVYKRMYez1UUkLQvs3ewLXVeOFC/rbEtVU83T4qtGt9mCa2ZJBbz80Sa6GSAWPsV5YaoXw+xrepy1QfcdUKny2PqD1qCGa8bQDWiTOJoN7ikIJmyLczLkt3bNzv63Vh7mRFyXltdsLkSzHZVl2dPfvJXRr8H5TUPjsu+HUXojI8yMlfe5o90C00L41tO0oeSFb2snK0+bvATZs5pJwLa6Pr5sJJP9RhFe5ld4C9iQw1d3PSLT7SzR3xmGbTXxECpiWByzpdRtVrD98NSCpMWdmJyJD7SL6x2eyvFDwojS8dnfnDLvcvJiaD60QX2oKbV7qgduuS8zsdASruApBSR5y932HfKYm6lJcZzboL4uSahzhfgcwnUHvZGBfLyi1FLxlL0Ph8HXQnHS5ZyiZzOyvyKO2Y7MpHI8xmPGYj1w/ek4xwGo4WWqy3ZoQ1peAR130CENvUqHbtjhb0RQnbzKwmp3Oj1Ou8kqXew1n2M3u/vrwYK7r7s/nBtaIBkojTax/U3ffsOMzWaOj5DsT7c0EXtv0OUxwt3oalFkT+vte6Ntp4dT7EUPzXhn9GjD5u5ERuhrCRZ3s7o+bwOp3uvtKuWejkcwzcgnazDTepvcjg+2/6PGIDYyn/mZ7XtRWKCGlm6TkaHlUJ6Gw2s88kQWVmfhy3FfNgvEMWpi6MhsXROGMt4VT04CvuPvf27qjSljI5/OAM8vobIru8YOhv8sDH/Y0h9OuCJc3PehuAhycudc7uPvPCvu5BMJINd7ZKxFgP7VpXA/4HIPe9Ziv8TnkETLEffeP5i06FvDCvlaPOevPTI+VB3BpCU/15RTSW5gItXdMzQHRPDsbEN4xji9D5eA6vXbxhsqE77uxYA0rnu9rxESF8SVUocXQpuzL7j4loTvwuzuuxSS0mbjfVVt5SeBl7j4z049bUeLcqejZOKOj7RqcaxW/31DxF4BufzxeyF27VngNlCGK9GrqYV2BgNX3op37JLpLPbwbsZz/HWUTPU+mvNAEX4t5kddubaKyDwm9gykv03EpYt4+BhkT30ZehJTujxDX2pHAKwv6OzV6nYBoFZbpuMbnIFf6AY0eCps82NJ9ABmifa9MuzPj3x6uRVfJoEUQ1uB6xKXzcVo1EIPe3dCrNRrG0F0d7dbUHjyZfEmTt45h/CwV7vOt4XUsCi3NA6zW0l0l8fmBc+H8RiXnovc2jV4bIS66nO6s1nVuwM8v5HM3NypgfGZ47U1mLqKi5l94/xaUut8cr06iFBE942xZFKJ6D931az+JMktB3vAbxzJ2onbvIZQjQ8kiq8bjek5/oWjIychL9JYwdyVLogX9dVEFkweRYdxVK3EeZGwficKLyTq6KKnod4j+KFtOpz1/5OaTlk4z3x/L8PneKKwzG8bFktHxkrRKEUXvFZdaIirxxJByT/E1QHPdNASiz5U5uht5DpcJ/V0y/g0t3VXRmtCE4K8dy7ifUzxgm9EiVSO/O2x2v0Oz3ayS8bzGbRv010K7+thbNtC2FWSPjehynxAvXNAvChPWSnBLn5i5t29198ui4yWjt+dDYOQl3P1Lic/uiIo0x16Dz7n76W3dVvudXjgzuxDYy0Omlynl/lh33zrRZJUHs0RshPT2yvZrWOWLAd8j9KOWQbyUEXxplHHYpotI4SSLw8c1HrvwXk3afJZzL9euCee3F/JOTInvn5l9y933swzG09NYpmt8CLfTCyFm9hJULmk5d9/SBEvY0N1PTOjW3OuhnmoTxnbH8GoSJ/7H3Vfs6O+KFFJylEZ+Ik8j9Hsbh3l+S6MuxyFnw1vc/VXh2brEE5x4JgjQZh7A7qaw+uWehtPsgojW+0otufupCd2Y/Hk+FOK8JXXvgv5F7v6u8P8ktL5+2t1TROs3eIJ7sEuskN9vaDtziAFWTKpW2e6CKHvuudDmK1GmUg57UROmOwjYDBlgFyML+2pPZ3gNzR4bNcwzkVIaJrSKWP849CkbdjUR3DaTxo2exya8B/gI3aG/ZrFaNLR5Yzh+Y2h7s44+zksHaNj6s0aB2RmWKcM8O9lDmtsrhBdTC20cVmxY5Y+kv87eIsAB3l+eZkMETN0P+GZLd5vU8xE+V0xbESbRT9Ajmp2GPBIDlAOWYQTPLLSXEBZOhCf8MPC/ngDxlyzK0flZKHPbw/FktAPPkWhOQYtcjL+anHpGwvN0rLvflGor1T9TUs9V7n5W2xA0s9e5+y1Wh2V6O/KAtXG5pfX3xkXM7OdoQ/OF8DvnQtCCFNl0zb0eirU1s+cR7upjjQFlZve2d0O1AAAgAElEQVRnNrhZBvshv29+YIVm7RtPCcZrPB8mmeCtB9WZPW46xv0pqLbieWiOeQ+9Qtt4C29nlaWWos8tjxLmxpxZa8IHd+JcI91F6Q/TX4FCrKXJHX0yp2RBzh0PQHe/1xSjT4qVZ7tdCby5segRyer70eSXkr8Gy/dKVGbkcaKsl5Zsh8CCt7r7rmGw54oMD80e87qMkdkynl64SLeNTXpDbKCg8FYsa3tEjuoqFN2O/beNjj7J9CP2rDTYsq4xPQntVOcCVjez1VNeEbSwfLP9nrv/w3ollEbihbEemHxmOF7czD7mEZjcKzIrUwZWgfxP9H9TH7BdEHwNxF20GL0MUxCIe7eW7jwonDEX/Rxxf0PPQU6OpJC2woXdOy68hsnQotKRLOnCfu4bDI0rzCxn2NRwkf0CON1UYgxkPKYyBRvZA3momsSiq1BGakreCOxkZg/Rw1h5ylsG3GZmF6OQ5ufD/NX3nHlIykkZWh2yE4JALEw/p9YLaoABS7n7z0xl63CV7srdk5p7fQAw3fqzINtz8LYIUD/dVEv3p0E3JeeiLHuskJLDhLU9Cj1fK5vZa9FiPxLpZ6vtHVDI9PLQ52PM7AAX52Vb/h02EM1mYmnypbiaUoCNnBf+Juc0ryi11JJHUCZpUkIfP8vg2pfymDXer5j3zUkX456CShftEI53RhuA0aoN+Isg3j7sFX70D5FHaTOEI5qS0f0a2pV9NLymAYdndJs48d7AZ8L/yXh8eG9BtIjPhXZP+5CPFd8Y/t6CPAGGMmlinW3D69toZ7ZjdG7bTLsvQZiAn4fjNdEOLKV7EAq5PRYGyR/RLi6lex/wqsL7UYVNoi7WfyjCrS0crtseaNJJ6U6PXtPCuFgjo3sECmFfhGoZXgCcn9MtORe9tyLwtvD//CSwYpHujMS5Wzv0N0YhShCeYeWM3gZoA/EUCr8/h+qxlT5jN2bOb1jRxoqlukH/mgKdWfR20AOvzGduaq41ASNJBi+GjDRQiaV3ISzPb3NjG2FyLkc73weBzTO6k8LYbfBin0AereLrM2S8DbwyupNRuGaJaAytm9HdKDxH96IQ+QPkMZVJXM8L/Qr3Ykl6c/kGwBVjvddBrxRruyCCslyADOLjEJ1IrHNr6v8hv+0W5GGPP3v7OF2324hwuAgDmsOi7YQM60eAwxDOa/sh7S8ELDREpxhTjTb1DQ7uWOBq4EcdbV+CQPh3IYzpFDrm8Irrlpq/B84VtzceN3OiX+FB2B+5CM9GsfPkA4Em5knRceP6T+neirLdrgdeHc51gfBXRtlMzfH8wEoZ3e8h78HuYZDditK/Y52pHa+cgflzZH3fFo7nyvUZLV6TIt2XANMyukMXwzHcv10Q0PFQlCRxN7BzRndgEshNDJV9uCc3ZhK6AyDWjjG0GzJ8fhuOXwFc1tF2MZgcGdAXAPeG4+Vy9wmxPK8WxtlktFvPbTyWiF5LAe8gD5RdGuE0jg+T2JT22EShAEJfz2+/Oq7F0I0HGWODbqPjnPDsHYy81echEs2U7lZokVsLGfK3AO/u6HPRojzC+GwbP9mEkugzyyD6kRVQmCqn9wEUogPhZ1+X0asBI59CZrPzQr6QV+kaxO91Tbh+a4/1XlOZRNHS+zitOYBoTqEAJB/0GoMxNsCyiUOV121W63hYAtorwzX5JB0b9XBtb0UM/A+Fa/zqjO5tYYzdGo43R/jflO6Ho9dOdCT3BP1b2teLsDFL6C6KKrXcHF5HA4tmdK9DHHzN8UYI3jDSfZgjMGA1YqIb2Mx7HDlLoDBkrgbUp9GidkQIKeznmULKJg6XN3k/wPAab4ERzcxQRtfD4XgllImUTJmt/H1Nva84Hp+k3jCzG939DQFDtzkKH93loWxG0CnmcBolTBh9tijWbwJxfhe58x0tznt5BOK0/lqUqX4M8PoErMj23gGaNBFc7okyXWJQ7MLoPn8o8ZkZyMNwQ3Q/ugqTF4PJraKumtWltz9AD1PW1Af8srtfndAdyqs3CoYofG5qWn38sIFWyAg+pI1REmBegXj92iGQJCGp1VUcKC7SbGbHosSBTVwA6iWAX7bnrKBbDEYOGLfV0XMS144dN66vUgm4rzVCH7L8aZVtjmuFBBuBksPEW3YZovt4H4q4zO2Jigcj9OfraBMR0+fM9DQebgO0SXwyHC+CjLAbErrXImN/ejjeDBUET4HwJ4QKI7R9vbtvYOK2+w4iqj7T3VdN6J6Fwopxcs06nuZQXAdtPpoqLn9BCYEjre0vagyYjZbhdThwqwloPDvbLfP5K1AooTm+nx4GIyVzxZO4u/8rGGHtdj3gLl4Tjh/saLPBknwbuc8dWdn7uWr/teXvJvC7h89uQJ7d+WYTu/gJaGJ/KrQdS4zv+Qfw9vinII9j87sWDt95KCrPcyrMzqLpYvnGy2P9H0TX4tvh+OpwLpZijJT1iEr/AcwwcevkyHx/ghImvkb/mHnSE1xIQZ4J46D5vrnoGLMIl/AJFJ6CACbP6BbXVUOM6/Og33gkuj8plnfcPVXbLydDWeV9NAwRXoFrtAIGcRuNEXxlBEFYiX4+qxhn0zwjy6Bkg8vQuN8cpaGniGanIg/mN4PermTuR5CaigOHormiLxs7o/smDwBqEBF0e86KsJTTw8I8FIyMiKNfLPIGevdvPTPD0zjXknvdyGQzMw8eioCBGpjrS8XdJ4/wsb0RZc8zyFD6Jbr3YxZ3P8B6BNIAx7v7ORn14wj4tSBPJc41sqBHGd3ufnnHvFWMqTax2B/KIFdXbtP/FRNg/tNo3lgEbXBSsqr3Y/IOCZvfAXH324B1ghGKu/8t02aRvKg9YNbL8GoI7Zr01A+hi580rGxItpuNkHYdPldMwmZ1mUrXI69Psxv5AKotNrAbDZPlMcjVezsKEW3XtsAn2AtXnA0W3ivesY+3WDdRqccTdbSAvwKFsk/0UO6lo/0jEZP6LmjC3BNlSn5hHPpeU1dtRcrT28eVVX7EjRKmzOPjUC21tcxsbRQSGiggbgUM4jY6I/iJyGCbDSxOGW6mLLoPu/sfwvFLgZM8wRRvIRvX+kkyuzJ0azKxarKxb0Awi5uDIbYkwXCLdKa3PxeJeybVP3x2Cfo9fMkyShMlZnYq8ljPoOc5dE9XSam511W0J3OymNlSwJ8bYzPxfsobmPPEn4O4DuO1+nXuvk1Cd0HErD+JHhXGj1KbXRNTwLYoTDquRouZXYcyu68OxxsBR3kHUfi4ffeL2QBrJOX6tQ5+ITN7GT1LGejnABpDyGRVVMpmOWSBPwzsklnk7kY78aGZSqnBPMSgKXK5Wx1fULEXzgrChO3fQnnZi5cjA7PhGLoKUVg8Eul8xt2PjLxbfZKZfPd19293nRtxAZ+EwJ5vR/fjl4gewVt6oxopW8Rte6auWtBdOrT1v0P6PK6s8jYCFUb43BUo2+wH3gux3u7uayV0h4ZYbTRG8Jqw210eFWMO9/4OTxdovhZ5F85ErOCPAl/zRJmloJ8ygpLGj/VKl30NYWgGSpeZ2VyujMBdgG2Q0ToF4UcPcfdkzc1SMbN3Ie/ey1Gdy+WA33gEb3ghxFQ0e82SRbnyXhfTnoy35BwDjeQcBIVtb4DGzf8hj9KpCAc6Ca1lA5m6ZnY2SnZoMpD3RMknA17QsJk7hP4i2Ae7+18Sukd4y7ueOhfOT0cJXqXVV4q9nabs0pORAWjo2nwkeLsmVOYUA2wGWuCvCcdvQrv7FO7pCBTPvoMoPXosgzbxHUNJ2HKLUmoxCn3+Cz2D5v0IzPn18Jn/a+m/icGBlXK5T5QXbiVkrDVG0tXIWHsw03bNjn0aCgXGO6id3H2LSGdrd78g593ydEmWFEnoeCzgs7nkwvFkBM7+R0tvJCNlmARP50EIHDsJZuO6jnH3gcK94TNVHFUTJVaHZ7wSlQv6Icrm/QOaJGNizL573LVJi3Q+iLyMlzDc83Rs0I1xM/d5q6h40F0fZWAthha6RYEj3f36rv6UiBWQaFp/HcZXo2tnaBN0e6bdr4Y+xp7RT7v7FxO6M5Bn9pKwqdoC1WFtU5RMqJjZGSgK8YcC3eJ7HfTnQRtdZ5ywZSWScww0knMQFLZ9M0qqWRQl1mzp7tebqFpOy2zClkE4qmYzcCma75O8YRV9Sc3JOc/a+ug5uoKyOp7F3s7oM+MSVqyROcUAex3avTUW6l+Aj2YmyXvQ4pIsnNrSrY0rNzu/NpNycqEL+qPWjYw+0gPuWp3LfcK8cDVidcXGU96Z4vqeifZ2RBiyjdFurJGFgec9Ch+PuIBfjygongrHC6FFKekNrOx7SYWE/ZG37uMevJXBm3kcAp5/M9HuuLLKWz45o/N5MiVGfBI4wxUe2w5RqmyZ0B0aYrXRGMEPR6Db39K/YcsxbG8LvDkcXul53MyEig0peJzzaA5pszjS0NpUvdbdfbzmi8K+Nl6ihVGNwBvpX5hTno7ie20V1VcmSoKh/U/vr187sLmrbHP2XJrw6I4lyaAY1mPdyU7XuvsAD6cp/P8UgwbVIZn+DPV22gjJXOFzRdyaJTJHGGCNmEB1eHdR26HZbpFuVVzZzL6PahJujnbi2yEvSSqUNiG4p0qX+4R44awgTNhquybWfxmhrFE4tSPiwYoNpWIXfbgGKyPjrw9Yj7w+z0a6oyzgVQajFYDJI92SCgm3Alu4+59a55cmeCcSn5kQVvlaCYbi8QjY/heUjblTPD5tRAbxij7ch56nkTIkE+11EpGmDIMRvmNowWMzewSl1uf6kcoUnok2Rs+E4/kRdiyVXXkZYjk/AgGcH0fUABuM4acVyyheopp7bRNUfaVGJmJz1/KMFm04S+Z7q4D1hHV8cRJzcmpNCJ9JQhM6fudQb6f1irOvgTDjzbO7NVrXU1nvB1FY4aZIfBw4RSb6RQFPBz2itrOQVf0DOgqYhs9MJ+IMK+jHzNbfhVB5j5RuDcfJAsAXUSYKaOBsldE9A3hp5fUbyheEFr/c6/6W7jSU1TVXeH2EDL9Y0C8mNkWG6vnA/6JJ/dx2n+kVcG54pLYOr58gBvtUu/sQkcGO49i8BlgvOn4dHbww1PF1lRCVZokZu96r+H2z0E5vRjh+JXD2WMdbS39BMuS19PMnnTUB9+9cMoXhE7pPInb/v6EQ4ADZbRi3v0bYtk3oLzi+6Tj1eWjBYxSi/RIKTw+8Mu1+FsEJPhZeVxMIqhO6C4dnf+6guz+w9Hjfn45rsBrp4u8bkymOXHmvB/i2Uucm+DeOK+ln+PxzYfw+iaAKf4uO/535TNV8X9GXU0vOhfNH0iK3HdL24Yg49gp6ZN2/yuheGc8/YWxfmdEt5tYseb2oaSgiKaH/vzn8vYXBchg5b8lngItNYOChcWXkxQGl/C+HwKc5+oV/u/ufzWySmU1y9+mmemwpmRr63exsHkWG1uwSSi2X+51mVuJyT3rhUAi1T7yOmmBpd58aHZ9kZvt16G+BJvdYtkycw+X96PQSeNhNmdnR7h6Xj7ggYBxSsgxwk6nG2xQEaB8P9+9+wBlm9nvkjVgWeQ+z4u73mdlkl8dpavBixdl8zbi+2ZQYkOVmQ6z3Oel7z8x+5u47WCYhwNOJAE+7+9NmhpnN6+53m1kOSF483oL+ksgg2BhwM7saeXJi7qu4tEuSQ2uMshhwt6kkTefz5FGJKDMz5AFqe3yWReO9CX1fhDwnd3R1wsy2RyHjJ83siyjF/yuexif9GS2YjTwZzsXyB++ARqTExYV4G8KLARzq7r/M6MbfP1D4+gWQbxE9M5E8Ed7bOvFe8b1Gz94P6a/NmZtbJkr+bmbrNWPABMX555DPdIqPRodRPN+bMggPZhDWk3p2++YFE+4252HcA/gfM8smA7Vke2AVL/Nsv4T+ufJf4VxK/unCMD9rwow9jsLTI8mcYoAN5enwALy2TLZbpt3DUFx5Pso4Xi408Wp9He1ynTyHU03dyFXd/f0mvBKumoPtmmKj1B4s5gsy1XHcH3ksPm4iklzD03U0/2xmH6I/TJgijJwd6w/hjUYWRvxJqX6cTHnh7gXNbBUXfxumzJck54y7f9HMDkQZhbsCx5rZz5BX8repz5SIu99kArDGxbW7wLolfF3F3GyIkyYFGjUijEKQ5jnYqqN/bXkkjPlzgWlm9heEKUxJDT8VKNx9JSKZBC1yp9MzAKDfUJwIvMRBw1UGJRjv54aQxOei88+hmo+/MBVd3xG43MwOcfdjO5o80N3PMLON0e//OsLxpXAs9wE3mFlfweMG0xI2kLmahEkx4YsudffN6a5Z2ei/B2XTNRnhQ/Gz4ywvcfdZ7ZPuPsuUJJSSmntdU5tzoqR6czdBUjTfBzmRBKFwLKa6nZ8H5o/mLkOGz/Gpz3hFfdwgtyODuyRR4BTgRhOFRrOxOimjW8KtWSxzBAbMKng6UnFsy6fYV8WVW5+dF5UlSuLRrCBTKdK9FuFxrnGBkVdFu+Y3RDqroUnnmtZnN0a73QEjwuqyD09HA2oXFyfTAggQmcpIWxGFfDdEC8C1KBPpdy29UWL9KSBw7v79F3pg44K5H3f3S1Jth8+sgwyw/0Ju6Q2QC/kzuc90iako/B5Exd8RrUKOGmQomHyixMy+i3jErhmqnP78pnSwyteMt6A/8PxZizrFRmAQnyixfkb8pvj7pu15KMwN70IL1UrIIz/F3R/taPvWYLQejjCpP+kY952GhLsfYmZL5J6xjj5chkpBZTG2ke59wDYpI+iFEDP7jbu/IvPefe6+2hjangyc4gkw+AstYX4p3dxNVB+K5vugW0P1cbhHPH4F+p3JQC3dyxHTf4m3ExO/5pvR77vK3W8t6M9KjJFbc07xgO0BnBwWdBBg9yOxgvWy3Va2fiDswojXIyUXm9nbuxbs1nfMJrB092fMbAEz29MjAsvMIjdAi9CSg9Cuc3kz+zECO36kpTOKy328vXCE94aGCYPeE8ATZvast4D/Znaqu++c+NgkM1vcA2+MiegxOU7d/RfBU9dwD93tmezX4AXdBfgT8loe4O7/NgHSf4PC0aPIcQgH04yBncO5/259/wru/rvoOjyN+HKyUukNLJF7gaNMBKI/Q0Z+cqKxEVjlqRtvAJeY2QdCX0BJLX0hrxFDJhMl8TP2LMqQe0+sYGanIJLkixHfVpLyISGPmtkPUPjyiGDE5SoZHBK+K0uHU2t8BXkKmGWigpl93zxdmu2x/5TxFeRmM9vN3U+IT5oSFG5pnbva3Te2wWzdpBHv7s+Z2YpmNk9hCGtcxUS58LC7/zHMUeshL/FDZnbwiPd2ZCmZ762imoKZvdLd70bevQHQv6fZDZLJQPSoMdpS69l+DmVXOlGWZUqsxTNqZpvkDMFhMkd4wBqxDp4Oq8h2iz4zlGSypT+UwDIs9B9A2LDORa7VzpJoUBkqwtrOarvJE/XbwntJwtXx9sJFulWGQdsraYr1z3T3NRO6uyD39Bmh39sBh7n7qQndYu+TmR2CvBCpDNBXeUemYZekPDyZc3H20VneH1LPtV3sDazs84pojH4AeZVOQ/f63khnFFLaVLbrwHiLFkJDz18z4U0CnnohvVqlErwi+3iC1qOl9zw942XoYh99bgHklZ3l7r8JRvJrUptDUxr8qaiYOmhTsYsPwZkNE6vj1fsWqsLRxid2ZoGOl5jZS1DR9X/RM7hejzzL23iLlmOE9k9Bmcrn02+MZjNLx0tMONW3ucpGbYJC9Xsjuo1X+agZd6P3Z2lgNwa5Jz8a6UzvaMI9ypo2s+NdMJfUZ/p0o8/MQpmK17v7a02wj696ol5j5jdsjKp97JV4b1/0+85Cz+k2KCEuVXGk4Rm9k34aqJGym+cIA8zEffXJxjUeFpApni4BtA/KpBhg3h2HfhQTWBYucp0cU61dQ7HLPeOFG/bbtkCZmGui1N2NENHl5QndIsPAolg//aGjf6EBnnQ/m9maaGfjwHTPF+7+IfI+NQvEzsBz7v7fkU7Kk9NZXqhGwmS5vYcQsIla4UzvCIOXGlGmUN5mLW/gFSljewz9XxclJawde5usgpTWRgiPvxjEKkNNForbT1BfVkid93SYp7jgcWUf5kPPCYhg9ukO3YENkbrru4ylD7Viwhk2Yew73P1XBZ8p4WZMelA8wzs1nhJv4MJc/r/ufnA4HpkTcQz9uRZtxNqF4s96AfvQkDbPAN7oikDdkVp7o8+si6Ji26Ns/rM8gcM04ZM3dPe/h+MFUSZ7ii+zmGe0ROaUEOTVCHS6P/AylOKdq8k1UdluoDDh6SFUAOJSSgJWg6flCBROaBa5LyHqgUaO7vgup9+9WuxypyLUFPV3WrhmjRdu37YXLpKiMKG7Hw4cbpWxfmRUWfR/TtZveZp+FYyWWE6m35OzJj0w+njIAcjtHuPQdk3ojQImPxq4zsT2PdsbOIa+ArMNqi3R5uCtyHN4cEttthfRVdKmq8lRwuNNX95N5MH0dNLHhIjXh5quMbHhn06/VyTJpF4pF9HzCs6HvPn3kM4grSl4PFTCePgqqjX6UOjD8mY2FRl6Ax5lT8MHXnAJ16HL+zJbrC4rfMINrQ6ZbKGUFHo+Px69959YsxfwRHmglFgrsxmt3e3M5kY3lfl7aGatKkoGMvG17Rhef0LPqrmSS7Ldpj9h4DnIJrHcj9akcTHA5ggPGMzeTU9HF3XdLhezabVost1ej4yQMWW7hXZrCCxTi9xp7n7eiN9d7XIfby9c9JmiMKGFWH/uOzJt17iDh3qfajw5tRLGwwbofsRA2YGH00YEk5d6Awv729AjvBMxh/8UOK/Z+WX62/QxS0prI4THw3tfQ2GFH4dTOyLizxpjfUxSE2qqCZmMQ7/WA/aMvbnRe8UFjwu/65sIK/spD/QSJrjHUSjtft9I99PufrSZJUNx7t7JLv6fFKurSTsNzS0xzOKnnii8PgH9/AJ6Rv+EuPTWc3cPnuaT3X2jzgbGvz9fQQlZFxfoTkMY0Ji+YzN3f1tCd6a7rx3W9q+gzN8v+XAG+yYZ6OftzYEJAnAVqqhxXzh3v6dpMJrP7A98GK2voKotJ7n7AG2UmZ0FrANcRn/oPYWTHCpzhAFmZjsDByLLem3gHYgdPVss08Y5262irzWL3Gfc/cjw//bufkb03lfd/fOJz1S73MPncqGm4th9q72hhoGNFuuvcQe/FXGo9XmfYu+AjVBeqEZKw4ljaH8d5CFqsnNGLhBrZr9CZLVn+TiG6G3EjLRwr1/r/aVWbk3d64mS/2SoaZjkjFfrL3jsaME5ZNR7ama/AVZvRwrC/bg7vrdm9l53P9fMBowWAHf/T3CCFYnVZYUXF6yfoL5ugHDEl0Rz4erAQuPkca3pSzFW2goym6PzNZm/A0lbmXPvRQ6HjVB06qfISdLJc2niWJvN9J/xwmEVOMkSmVMMsHMRvcDj4fgNCGydulHtbLdzPcp2+//tnXm0HHWVxz/fgBhWQYQogsii4gIOiGIg4+DC6IwRBsboBIIexJ0JiagoehBQB8edxeXIjCIMiHNUIBEUUZAAoiwBJYCoLMoiiHIgcFgGIXf+uL/y1etX3a+ruruq+/X9nNPndS1dfd973VW/+t17v18z2y7ttxY+gNmh9Rgd4ngOXuTf6gOV92rs+iKnCrYQZej3LFzLsfs2MGg57io8tfhoWp6Ne921m0V5Mh1mn8rM5FSM93N4N86ZrRewXikzG9gkks7AVaaL0uN7mVmhdlEagO1pqatLnsq+qM4BWC6W9ayNx56kRWZ2mtp4xxXNllV4//yxZ+HpmE07zbhIWr/oxq7Ce//WzJ5bdlvavh7+PepJILQOVM6TdiWeWbgtLW8NnNXPm7eZSJoZvYLJnc0vM7MPFOx7Di46vhf+eX8Ez1AUDYhbr49r4YO2KY1cafv6eIfyQnyi4FT8/1eoeJCON4fJTQaFFmhyHcfsO9GbNIjVaK3QzwewTpv1xwBbt9n2/JblZXRhlZLb/1J8IHMtPttyNJ7frvo7XFP0vGi55HH3wme77sZTK/vjdSNF+x6ee76gZduxbV6zBBe6Owb4OF7cvrhDPAtIVg94of+Z+B1o0b6H4TZOR6fHL4GlbfY9BNg4t7wJnrap83P4IN7F91cmLD0e6NOxr83/3/C70FrtULqMcw6uDXQRXmPzedwC5OfA0zu8biFex/FNvFbvVuDNNcc+F+9oui0tvxjXZcvv867086iiR5/iyB/zo3jqZnabfXefLuaS73023kXZun4RsLzNa3bB7bTuSI+V7b7Tw/JI359Z+EX2rbjI6qZt9n0dcBue5j0tfU5f2/TvUPPfa4fc/3rKo81rsvPh4+mxJq2bcl7ELfj2A56Tlp9Bi90QXluat03KjnUvbSzcCmLaBK+ju6DN9sX4hM316Zy7qt15FveB/EM6v12czlmvqPw3bvqf3OUfcEs8P5v5A34P2LJln9m4cvCX8Dqttbs47sXpn3kBPlBZ3u6Ek/ZfmX6ual1X8fe6uuh50XLJ416I61BN631YJQZKDgyY8M6ch1+kXw9c3mH/XdLJ8dBOJ3WKvdIqD1yH7ZFOBLNzy7Pzn71he+B+p4vT41VdvuYZuMbQ3nQYrA0w5stxK5H8zVDPHpqjFDPe2HQ5UwfQVwDPbPOaXwGvzC3vSfLHG7YHFXwj0/an4a4R84GnNf17NPB3y7yJf1rwKPRV7PK4Va7VXQ22KsZzE20G4gX7rsQdYrLl5/YyBhiVLsiT8bTegrS8KK3bK7dPlW63I0vG8X9ZKlPSv+PTpxuUPEaezEZGTLVlaLWR6RorVxSsNs+LlvPru+0aybaDD7xOMrNzU2Hn5INOTgt3U+ewliRZ+iak13djKdVXNLhOvpPx7t98cejQ1thYlx1pmtqUcUf6uYWkLazmGhczu12TuzwnNdVIOmGa11cqwE3Hzjxe2x27UF9oupjLYKgkad0AABTYSURBVK7Qv5ukVzHREfgDM7ugw8vW2NROzI4Clg1SqUvXvAu8tq7cYcPM3pl+duognIKmV6wvfa02syMGeJ69Hf8sdMOTzOw3ubh+K9ejrMSoDMC6MQN9gU10u30dv3vriJmtkHcXZh1cV1iqM2vDEnza9FDc9+5V+FR2JWw4VL6tzfOi5YyyA4OuVL7NZQF+o6Qa30XsXcuCDApN7eRbImkP60Mnn5l9QW6pMS+tOsi6EPUdATL5ldl4J++v8AH8Trjh8RSLsQFyu6TdAUsn0iW4NEGevMzLMVT0j2xDFY/XbmIujXlDT1dNPbi/5ZfxzmrDxSkvlLRTOlZle5YBUMU3MsiRPm/PZnKN1KkF+3WjWF/6Wp0K9V/G5PPs7lbQqFaBW/DP87lM7mwsqu3sq0n7qBThX4Bf9PNmoAdZToi1ShG7pDfhra8X4ReAv8ctar7b399geFFneYTZZlY4uk+zGNnAoKN3lsqpfF8M7Ix/KfOyAFNmAlRCFmRQDLqTL91NbsXkE1+tM0SDQtKZeA3VqrT8IuBoq1HpW9LTgONxA2zhIsSHWgmv0j7F8Qbg3OxzNM2+pWIeBJIu6bDZzOwVHbbXigboGzkOyEV3t8MHVHn19ykzv+pCsb7itXpg51mV6IROkweHkLv2AV+2ipZVozIA25qpZqCLzez23D6lu93k7ch72UR35Wa4RkyrjUxHe412aYKZSJnuUVVQoZdrvEzB2vgQpo6U5+Gfi9rNagfZySfpE7gn6M1MzEZayRTz0KICJeuidQOOYQ+bquA/ZV1uW19lTHLHPQ0/v30Pd/m4seTrl1qBblHQU5du151xMxlJv8ZnraYdLKgLxfqK1+qBd0yrg7dqbp8DcWWFB3Pr5ldNh45ECtIKzEBTCvK43D5V0nmzWlKO91JsgDsXzxOfgRerdpQFn8mUTBN2nevXhJ3Nipb184C72rxmz/Qev4e/qXe/1Soao1bkU8A1cq0z4TUKH+78kq55E14kXLshcE1cWzCdX3fq6kS86WO6dQPFzBbJxU8X4iUWRpr1z5/sO3AYufPhoJFUmPoxs2PriqEES4GzJB1AgYh10QskLcZTzX9iwqvU8DT5uHEd8HTanIdbmFaxvuK1emDnWbV4q0rq5K16IvB+SQttwj/441SsFRyJGbAiJN1mZoX+aSWO8Vn8C5WlNt+Mp8kOb9lvLbx+aWHa/1z8xNiT+e2o0m2aUOX8BM8Bjmit1ZC0Iz6FPaVQVq7Vs39WFCkXKjzDzF7Sy+9XlpRSzdcR9mQEnDvu94D3TFOXOLKkGdK8mfrFwFetgwdhH997Li7nsBTIG2xvhOs/vTi3b2YeDl4D2pWLQcW4NsU9TZfidV3bAyfYNNpvkm43s636Fcd0SMpb08zGG2yuN7MiG66hQCVErCXdhM/gTLHQGRc00SCyIW4EfgWTa6Q6Zn40oVh/Xj9uInPnWcO1Ift1nu3aW1XSNcDB+IDtaDP7Ti9lCSMxA9aGnmehzOyDkvZjIp97kpmdVbDfE3hx93kpB7wQL9o7xgrMPceAbrtHy/gJVimU7WtHShlUTydfdtd3HSVOfCPGj9Ojo/nzAFgH72BeG7/AZDyAi0f+DTPLbx8I8g6vg/AB16m4eOU9qX7yBvzOuxO13kmb2afzy5I+Tc0NMGWxEr6RlOuMm6l03SDSptyksGykB+Yy4f6wNhPWQb1SxlvVzOzqNLg8Q9JuTPZ3LsUoD8B6PuFI+rS5yeiZBeta930yfpe3EO8GOYH+fQBGggppwkxmA5gktVE0c7Bxh7det836vnaklKSMkXpVTsEN3VcxkQYZeVTB/LnfpM/wCknfNLM/qIMSfk38K/DF1vS5mT2sZPvTMhOXJ6ujaZIn451vM4UynXEzkg51t7Pw62CeKjJQXSPpK/jgLstWvUvSa8zskD4c/hZJRzLZW/WWNvveBS5RIum1+Pn5RW32nZahTkFOd8Ixs54GkEUFtUoGoS3rTsX/yD/ADVmv6+V9R5UqacISxy5dKNumI+UrVmCGPYqog8n1KKMS5s81xDIXl1DZwMyeJbfYepeZvbeuGEYJSWun2exrmDg3r4UL6h47UxoBNMQeoXWRvpOH4EK9y/GZ6kOAD+Ciu/vk9u263KRiLDfiTjaZ5uMsPI38/D4cO++tCn4dOdr66Jfb9r2HeQA2KCS9B3gvsC3eYZaxIfAzM1vUsv8aJmqd8n+wvteADDOdBgRqY7ha4thz8BnFxygolG3N96e6vFPN7ICq79kv1KVGToXjfgG/+17O5LvwkZahUAnz5xpiuRxPOS7P6jhUYCg8wPdvd5MJwLCdW7KbVknb5VY/Dtw9U258AkfSMuA+XMfr1cDm+DVviZn9smXfvnsZtxz/HOCQ1JCXKSN8qZeb/opxbAZ8iKl+0JUyHqOcguyFbwE/xGts8p0UD1qBlo6ZFXVGjiNV0oRdYWZ/AnZvKZQ9t12hrHk35taS1mmyS1BtNHLwOp5eyQo7X55b16/0ZpNY6+ArrXwidf/VHUzfVOUrvPeG8DfJkbvwNIjwdPoz6oqjBAIws5un23GUSRfaw3FngJ4vtCPKtrlZrf/GP5/PalOrWabcpAobAr+WlIm2vhQvQVkO1epiVU1e6nTgf/FypHfjQux/LvveGWM5ADOz1XiB5UIASZvjX7INJG1gY6j10iVXSXpHmzThyjavKUXJQtlbgJ+lL1K+G7POOo1d6VIjpwIHm9mkWgRJ2w7gfermBklvaZ0llLQIKKV/1QcGoipfgb1tsv7gV+U6hR9rIJZObCbpsHYbZ1CNVHahnU8fLrQjSr6J6glJd7RrlLHBu7oM4ntQRV5qUzP7uqQluTrSK6sGMJYDsAy5+vQXgC1wk++t8ZNvbUKQI0ZpPZ0Bc3N6zGJyJ1udlNHIKct3mapH9R2gVpmNAXAIcKaktzH5c7Qu9X+O3o2ryj8T93Y9P8VXNw+l79W38VnOheRuKoaItfDu0ZmuhdjXC+2IMuhZra6x8raB3fB0JuSl9qc7ealsUHqXpNcDfyTph1VhLGvAMtId5qtw9fudU/prkZkd3HBoQ41K6OnMVNSjRs40x94Bvwn4DPDB3KaNcKusGXGDoMnmzzdYZ/PnGU2SWjke2COtuhRYama/byikQvpd2zOsSPqFmb1c0o/wjvc/At81s+2meWkwADRg20BNyEt9FmgrLyVpPl6kvxUuDbNR2r9jOrPt+475AOwqM9s1DcR2NrM1kn7VkgoIhhRJPwYWmNn9aXkTvEv1tTW8d6FlUka7Fu4uj70PbnC+N16An/Eg/vtdVvXYwWQkfQb4JPAIrmO1E96deVrHF44pGpAX5rDR7wtt0Bvq0jawwnFb5aWW41Zgd/YWcXeMdQoSuF/u/3QxcLqkexjOaf+gmM2ywReAmd2X6vnq4E5cE63VR7CtdVK3mNkyYJmkuWb2816OFUzLP5rZ4ZL2xS2t9sPPB7UOwCRtiV/osxmwS/Buszvav6oRXj39LqOPTXj7rQZe2WQsAdC9bWDXtMhLHdONvJSkbYDFTO16r5TxGPcB2D7Ao8D78K6jp+C+TsFo8IRynpSpNbmuKd3jgCMK1q9O2/rRHn2vpAvwgd6LJO2EF2t/sg/HDpzsHPh64DtmtlqdHRsGxcl4d/aCtLworduriWDaUdQlPhPp94U26JnzUjo4bxv4gx6PuQifcFkCHJr73neqcTsb1w38Pn0Qxx7rFGQw2kh6HXASsIKJuoB3mtmPanjvgWmi5Y6zAq8B+1oTGlXjgKT/xNO9jwAvw6VWzjGz3WqO45dm9nfTrQvqIaW8vk6LC0UvpQVBb2iybeAlVmAbWEMMl/fz3DDWM2BtRBBX43Y272+VAAiGCzM7T+7JmOlkLTWzv9T09gPTRMuxnpld0TIj83ifjh0AZvbhVAe2OrXaP4zPjNfNvUmGI7vDX4inWYJmeNTMTmg6iGASl+EafWuApjpSj5e7JJxPH8Sxx3oAhqeK7sCn/gX8Gy6qeTXwDWDPxiILuiINuM6Zdsf+M3BNNOAvSXE8s994I4ORuxhbJK3EZzrOAO4zs4dopg70bXgN2Bfx//dluDl30Ax9vdAGvZHOqx8DLsSv1SdK+riZfaPmUHYEDsTVE7KZ0cri2GOdgizqeMym/aMbMuiESlonVXyPbfEU6+64JcitwAGZHUfQO3KD+YPwmpKr8Lqr8wckrBuMCJI+hV9obyZ3oR0zJfyhQdJvgN3N7N60vClwmZk9r+Y4bsKFt/vivjLuM2APJ32RTEvkjXhRPtRXzB2MIGWtk8oiN5vd1cxeI2l9vAvowX4cO5jAzG4CPirpSFz1/Bt4c8fJwPF1FZ1LOgXvesxLqnzezN5Wx/sHU1iAW/E0ZnMWTOJeXIYn40GaSdFfh5ef9CoCC8QM2La4+OFcfMD1C7wj8k7gJWZ2aYPhBV0gN3Cew+ROpRlhJZXp1DUdx0wndZceBPwz8CPchmYecGBdRfBF+lrjork1jEg6G2/o6cuFNuiNJBmxI7AMv1bvA1ybHrVZYEm6CNcKvJI+CG+P9QxYKrJvJxcQg68hR9Ji4CjgT0zOx+/UWFD95SeSPoB70uW9LsdCCqAOUg3Y/Xgd2IfNLDupXi5pj/av7DuzJG1iZveluJ7KmJ+fG2Zj4MZkP9QXh4ugJzLbuYxl6WfdFnRH9fNgYzkDJulwM/uMpBMpSDWa2aENhBWUJOXjd8vqAmYakm4tWG1mNhMMuYcCSdsOQ7ezpLcAH8G9PoWXQ/yHmf1Po4GNKe2cLkKGolkkbYSfA4eiHCMJby80s0r+seN6h/Xr9POqRqMIeuV2XDZkRmJm2zQdwxhwp6T9mSq4Wasgs5mdKukqvJvKgP3M7IY6YwgmiIHWcCFpV7xBZsO0vBp4m5n1q+O8TCw74+bdC/DGqO9VPdZYDsDM7Pvp5ylNxxL0xC3ARZLOZXKaoJZ6gEGRzdCm5wvM7Du5bcea2Ueai27GsQwfxK8k9xlqiCfhs1/Z86BmJF1qZvMKNCI7qaMHg+cbwHvN7BL428zTydRUbiLpubg230LgL3hZiMysJ5uqcU1Bfp8OXY6R5x8Nkk7PFMzsmLpj6SeSrjazXVqfFy0HvTEszgKSlgDvwO+mBewLnGRmJzYaWBAMAW2aVGo7F0pag/uzHpw6p5F0S6/lIGM5AwZ8rukAgt4Z9YFWB9TmedFy0BuXSdrRzFY1HMfBeD3jQwCSPg38HBdnDRpC0ubA7Gx5pnRYjyArJH0NF0w2XLfvouSEUodA7n64UPtPJZ0HfJs+nIvHcgAW+f2ZgaTNgMOBFzL5JDnqYonW5nnRclABSavwv+XawEGSbsFTkFmqqe5OWuE2KxlPEIPtxpC0N/B5YAtc82lrvHb4hU3GNcZkouitWY+d6UGJvlvM7Gzg7KTJuA+wFNhc0leBs8zs/CrHHcsBWO7kO2UTzZx8g2qcjufi5wPvBt4K/LnRiPrDiyU9gH8e103PScuz278sKMH8pgNo4WRc+iIzGP4XXBojaIZP4B6zPzGznZPg8qKGYxpbeq216hdphvpbwLeSWPIC4EO4ZVVpxrUGbOtO28PqZTSQtNLMXiLp2mzQLOlKM3tp07EFw42k2figfXtgFfB1M2vU6DylU+alxUvM7Jom4xlnMhFkSb8CdjazNWFPVz+SFpnZaZIOK9o+6g1XYzkD1m6AlWl6AJU0PYLa+Wv6eZek1wN/BJ7aYDzB6HAK/vm5BPgn4AXAkiYCSW4O15vZDkCYPQ8H90vaALgYOF3SPTRj0j7urJ9+1i24WgtjOQOWp0DT48zoPBoNJM3HL6Bb4cXKGwHHmNnyRgMLhh5Jq8xsx/R8beCKJrtLJS0DFkeR93CQan0eAWYBBwBPAU6fqaLPQTOM5QzYoDQ9gnoxs3PS09VA/O+CMmSzp5jZ41Lj9e6bANdLuoLJtlMhiVMjkrYH5pjZz9KqNcApKTuyMc0YQI8tkk7otH3UXWvGcgAG3IjPnMzPaXq8r9mQgrJI2gZYzFQV87hoBdORNTrA5GaHpgQ3j6z5/YJijgOOKFi/Om1r5x0cDIa80v0x9NmLsWnGdQA2EE2PoHbOxjvFvs+EGXcQTIuZrdV0DDBpxmVFy/p5wF3NRDXWzCnShDOzVZKeXX84403erUbS0pnmXjOWA7BBaXoEtfOomXWcog6CISdmXIaLjTtsW7e2KIIiZlzB+qymA2gSM3vIzL5lZm8AtgSuwTU9gtHgeElHSZoraZfs0XRQQVCCtjMueGo9qJerJL2jdaWktzM5HRYEPTP2XZDB6CLpU8CBwM1MpCBtBijhB2OCpN+Z2XPabLvJzLavO6ZxRtIc4CzgMSYGXLsC6wD7mtndTcU2jrSYoq8HPJxtYgaYo8cALBhZJN0EvMDMHms6liCogqQzgAvN7L9a1r8d2MvM3txMZONNUr7PTNqvN7MLm4wnmJnEACwYWSSdDbzTzO5pOpYgqELMuATB+BIDsGBkkXQRsBNwJW6kDIQMRTB6xIxLEIwfMQALRhZJ/1C0vrWlPwiCIAiGjRiABUEQBEEQ1MxY6oAFo42kS81sXkuHDMyQzpggCIJg5hMzYEEQBEEQBDUTM2DByCNpc2B2tmxmtzUYThAEQRBMy1gr4QejjaS9Jf0OuBVYAfwe+GGjQQVBEARBF8QALBhlPgG8HPitmW0DvBr4RbMhBUEQBMH0xAAsGGX+amb3ArMkzTKzn+IilkEQBEEw1EQNWDDK3C9pA+Bi4HRJ9wAPNRxTEARBEExLdEEGI4uk9YFH8JncA4CnAKenWbEgCIIgGFpiABaMHJK2B+aY2c9a1s8D7jKzm5uJLAiCIAi6I2rAglHkOOCBgvWr07YgCIIgGGpiABaMInPMbFXryrTu2fWHEwRBEATliAFYMIps3GHburVFEQRBEAQViQFYMIpcJekdrSslvR1Y2UA8QRAEQVCKKMIPRg5Jc4CzgMeYGHDtCqwD7GtmdzcVWxAEQRB0QwzAgpFF0iuBF6XF683swibjCYIgCIJuiQFYEARBEARBzUQNWBAEQRAEQc3EACwIgiAIgqBmYgAWBEEQBEFQMzEAC4IgCIIgqJkYgAVBEARBENTM/wP8A6C78utGrgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -511,7 +509,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -529,7 +527,7 @@
        "       'Martes flavigula'], dtype=object)"
       ]
      },
-     "execution_count": 188,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -550,14 +548,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Lxodonta afkicana\n"
+      "LoxooJta africana\n"
      ]
     }
    ],
@@ -603,22 +601,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loxodonta aLricana\n",
-      "Looxdonta africana\n",
+      "oLxodonta africana\n",
       "loxodonta africana\n",
-      "Loxodnta afrTcana\n",
+      "Loxodonta africaan\n",
+      "Lxoodonta africana\n",
+      "Lokodonta africana\n",
+      "loxodonta africana\n",
+      "Loxodonta afrcani\n",
+      "Loxodsta africana\n",
       "loxodonta africana\n",
-      "Loxodonta africMna\n",
-      "Loxodontaafricana\n",
-      "Loxodonta afrianaZ\n",
-      "Loxodonta africaq\n",
       "loxodonta africana\n"
      ]
     }
@@ -639,24 +637,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
        "    }\n",
        "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
        "    }\n",
        "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
        "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
@@ -670,92 +668,92 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>Equus przeawlskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>Equs przewalskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>Equusprzewalskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>Equus przealskiis</td>\n",
+       "      <td>EqNus przwalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>Equus prUewalskii</td>\n",
+       "      <td>Euus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>Equus prlewalskii</td>\n",
+       "      <td>Equus przwalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>equus przewalskii</td>\n",
+       "      <td>qEuus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>Equus przewlskii</td>\n",
+       "      <td>Equus przewaslkii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>EquusPprzewalskii</td>\n",
+       "      <td>Equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>Eqlus przewalskii</td>\n",
+       "      <td>Equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>Equus pzrewalskii</td>\n",
+       "      <td>Equus przewalski</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>Equus przeawlskii</td>\n",
+       "      <td>Equus prewalakii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>Equusprzewalskii</td>\n",
+       "      <td>Equus przIwalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>Equus przewlskii</td>\n",
+       "      <td>Equu sprzewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>Equus przewalskji</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>Equs przewalskii</td>\n",
+       "      <td>Equus przewalsiki</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>Equus przewlskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>Equus pzrewalskii</td>\n",
+       "      <td>Equus przewalkii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -765,57 +763,57 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>Equus przewalsNii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>Equus przewalsiki</td>\n",
+       "      <td>Equus rpzewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
-       "      <td>Equus przealskiu</td>\n",
+       "      <td>Equus prewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
-       "      <td>equus przewalskii</td>\n",
+       "      <td>Equus rpzewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
-       "      <td>Equus przeawlskii</td>\n",
+       "      <td>Equsu przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
-       "      <td>Equus przealWkii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
-       "      <td>equus przewalskii</td>\n",
+       "      <td>Equus przwealskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
-       "      <td>Equusp rzewalskii</td>\n",
+       "      <td>Eyuus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
-       "      <td>Equus prEewalskii</td>\n",
+       "      <td>Euqus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
-       "      <td>equus przewalskii</td>\n",
+       "      <td>Equsu przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
-       "      <td>squus przewalskii</td>\n",
+       "      <td>mquus prewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -830,32 +828,32 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342171</th>\n",
-       "      <td>Martes fwavgula</td>\n",
+       "      <td>Martes Ylavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342172</th>\n",
-       "      <td>Martek flvigula</td>\n",
+       "      <td>Martesflavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342173</th>\n",
-       "      <td>Martes fwvigula</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342174</th>\n",
-       "      <td>Martes flaigula</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342175</th>\n",
-       "      <td>Martes flavigul</td>\n",
+       "      <td>Martes flavigual</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342176</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -865,42 +863,42 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342178</th>\n",
-       "      <td>Martes flaJigula</td>\n",
+       "      <td>Mares flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342179</th>\n",
-       "      <td>partes flavigula</td>\n",
+       "      <td>Martes flavigla</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342180</th>\n",
-       "      <td>Martes lfavigula</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342181</th>\n",
-       "      <td>MarteP flavigula</td>\n",
+       "      <td>Martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342182</th>\n",
-       "      <td>Martes flvibula</td>\n",
+       "      <td>Mrates flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342183</th>\n",
-       "      <td>Martse flavigula</td>\n",
+       "      <td>Martes flaviglua</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342184</th>\n",
-       "      <td>Martes flaviula</td>\n",
+       "      <td>artes flavigulaB</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342185</th>\n",
-       "      <td>Martes flavigual</td>\n",
+       "      <td>MartQs flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -910,67 +908,67 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342187</th>\n",
-       "      <td>Marts flavigula</td>\n",
+       "      <td>uartes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342188</th>\n",
-       "      <td>Martesflavigula</td>\n",
+       "      <td>Martes favigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342189</th>\n",
-       "      <td>Martes flNvigula</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342190</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Martes flavigua</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342191</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Martes flaviula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342192</th>\n",
-       "      <td>Maxtes flavigula</td>\n",
+       "      <td>aMrtes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342193</th>\n",
-       "      <td>Martes flavigulaa</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342194</th>\n",
-       "      <td>Martes flavigXla</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342195</th>\n",
-       "      <td>aMrtes flavigula</td>\n",
+       "      <td>Martes flavigla</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342196</th>\n",
-       "      <td>MartesLflavigula</td>\n",
+       "      <td>Martes lavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342197</th>\n",
-       "      <td>Marets flavigula</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342198</th>\n",
-       "      <td>Martes flaviula</td>\n",
+       "      <td>Marte flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342199</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Marted flavgula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -980,72 +978,72 @@
       ],
       "text/plain": [
        "                    Input             Target\n",
-       "0       Equus przeawlskii  Equus przewalskii\n",
-       "1        Equs przewalskii  Equus przewalskii\n",
-       "2        Equusprzewalskii  Equus przewalskii\n",
-       "3       Equus przealskiis  Equus przewalskii\n",
-       "4       Equus prUewalskii  Equus przewalskii\n",
-       "5       Equus prlewalskii  Equus przewalskii\n",
-       "6       equus przewalskii  Equus przewalskii\n",
-       "7        Equus przewlskii  Equus przewalskii\n",
-       "8       EquusPprzewalskii  Equus przewalskii\n",
-       "9       Eqlus przewalskii  Equus przewalskii\n",
-       "10      Equus pzrewalskii  Equus przewalskii\n",
-       "11      Equus przeawlskii  Equus przewalskii\n",
-       "12       Equusprzewalskii  Equus przewalskii\n",
-       "13       Equus przewlskii  Equus przewalskii\n",
-       "14      Equus przewalskji  Equus przewalskii\n",
-       "15       Equs przewalskii  Equus przewalskii\n",
-       "16       Equus przewlskii  Equus przewalskii\n",
-       "17      Equus pzrewalskii  Equus przewalskii\n",
+       "0       equus przewalskii  Equus przewalskii\n",
+       "1       equus przewalskii  Equus przewalskii\n",
+       "2       equus przewalskii  Equus przewalskii\n",
+       "3        EqNus przwalskii  Equus przewalskii\n",
+       "4        Euus przewalskii  Equus przewalskii\n",
+       "5        Equus przwalskii  Equus przewalskii\n",
+       "6       qEuus przewalskii  Equus przewalskii\n",
+       "7       Equus przewaslkii  Equus przewalskii\n",
+       "8       Equus przewalskii  Equus przewalskii\n",
+       "9       Equus przewalskii  Equus przewalskii\n",
+       "10       Equus przewalski  Equus przewalskii\n",
+       "11       Equus prewalakii  Equus przewalskii\n",
+       "12      Equus przIwalskii  Equus przewalskii\n",
+       "13      Equu sprzewalskii  Equus przewalskii\n",
+       "14      equus przewalskii  Equus przewalskii\n",
+       "15      Equus przewalsiki  Equus przewalskii\n",
+       "16      equus przewalskii  Equus przewalskii\n",
+       "17       Equus przewalkii  Equus przewalskii\n",
        "18      equus przewalskii  Equus przewalskii\n",
-       "19      Equus przewalsNii  Equus przewalskii\n",
-       "20      Equus przewalsiki  Equus przewalskii\n",
-       "21       Equus przealskiu  Equus przewalskii\n",
-       "22      equus przewalskii  Equus przewalskii\n",
-       "23      Equus przeawlskii  Equus przewalskii\n",
-       "24       Equus przealWkii  Equus przewalskii\n",
-       "25      equus przewalskii  Equus przewalskii\n",
-       "26      Equusp rzewalskii  Equus przewalskii\n",
-       "27      Equus prEewalskii  Equus przewalskii\n",
-       "28      equus przewalskii  Equus przewalskii\n",
-       "29      squus przewalskii  Equus przewalskii\n",
+       "19      equus przewalskii  Equus przewalskii\n",
+       "20      Equus rpzewalskii  Equus przewalskii\n",
+       "21       Equus prewalskii  Equus przewalskii\n",
+       "22      Equus rpzewalskii  Equus przewalskii\n",
+       "23      Equsu przewalskii  Equus przewalskii\n",
+       "24      equus przewalskii  Equus przewalskii\n",
+       "25      Equus przwealskii  Equus przewalskii\n",
+       "26      Eyuus przewalskii  Equus przewalskii\n",
+       "27      Euqus przewalskii  Equus przewalskii\n",
+       "28      Equsu przewalskii  Equus przewalskii\n",
+       "29       mquus prewalskii  Equus przewalskii\n",
        "...                   ...                ...\n",
        "342170   martes flavigula   Martes flavigula\n",
-       "342171    Martes fwavgula   Martes flavigula\n",
-       "342172    Martek flvigula   Martes flavigula\n",
-       "342173    Martes fwvigula   Martes flavigula\n",
-       "342174    Martes flaigula   Martes flavigula\n",
-       "342175    Martes flavigul   Martes flavigula\n",
-       "342176   martes flavigula   Martes flavigula\n",
+       "342171   Martes Ylavigula   Martes flavigula\n",
+       "342172    Martesflavigula   Martes flavigula\n",
+       "342173   martes flavigula   Martes flavigula\n",
+       "342174   martes flavigula   Martes flavigula\n",
+       "342175   Martes flavigual   Martes flavigula\n",
+       "342176   Martes flavigula   Martes flavigula\n",
        "342177   martes flavigula   Martes flavigula\n",
-       "342178   Martes flaJigula   Martes flavigula\n",
-       "342179   partes flavigula   Martes flavigula\n",
-       "342180   Martes lfavigula   Martes flavigula\n",
-       "342181   MarteP flavigula   Martes flavigula\n",
-       "342182    Martes flvibula   Martes flavigula\n",
-       "342183   Martse flavigula   Martes flavigula\n",
-       "342184    Martes flaviula   Martes flavigula\n",
-       "342185   Martes flavigual   Martes flavigula\n",
+       "342178    Mares flavigula   Martes flavigula\n",
+       "342179    Martes flavigla   Martes flavigula\n",
+       "342180   martes flavigula   Martes flavigula\n",
+       "342181   Martes flavigula   Martes flavigula\n",
+       "342182   Mrates flavigula   Martes flavigula\n",
+       "342183   Martes flaviglua   Martes flavigula\n",
+       "342184   artes flavigulaB   Martes flavigula\n",
+       "342185   MartQs flavigula   Martes flavigula\n",
        "342186   martes flavigula   Martes flavigula\n",
-       "342187    Marts flavigula   Martes flavigula\n",
-       "342188    Martesflavigula   Martes flavigula\n",
-       "342189   Martes flNvigula   Martes flavigula\n",
-       "342190   martes flavigula   Martes flavigula\n",
-       "342191   martes flavigula   Martes flavigula\n",
-       "342192   Maxtes flavigula   Martes flavigula\n",
-       "342193  Martes flavigulaa   Martes flavigula\n",
-       "342194   Martes flavigXla   Martes flavigula\n",
-       "342195   aMrtes flavigula   Martes flavigula\n",
-       "342196   MartesLflavigula   Martes flavigula\n",
-       "342197   Marets flavigula   Martes flavigula\n",
-       "342198    Martes flaviula   Martes flavigula\n",
-       "342199   martes flavigula   Martes flavigula\n",
+       "342187   uartes flavigula   Martes flavigula\n",
+       "342188    Martes favigula   Martes flavigula\n",
+       "342189   martes flavigula   Martes flavigula\n",
+       "342190    Martes flavigua   Martes flavigula\n",
+       "342191    Martes flaviula   Martes flavigula\n",
+       "342192   aMrtes flavigula   Martes flavigula\n",
+       "342193   martes flavigula   Martes flavigula\n",
+       "342194   martes flavigula   Martes flavigula\n",
+       "342195    Martes flavigla   Martes flavigula\n",
+       "342196    Martes lavigula   Martes flavigula\n",
+       "342197   martes flavigula   Martes flavigula\n",
+       "342198    Marte flavigula   Martes flavigula\n",
+       "342199    Marted flavgula   Martes flavigula\n",
        "\n",
        "[342200 rows x 2 columns]"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1078,8 +1076,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "input_texts = []\n",
@@ -1094,8 +1094,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Takes in the input and target texts and adds their characters to the list of input and target characters\n",
@@ -1111,8 +1113,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "for index, row in corpus.iterrows():\n",
@@ -1126,7 +1130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1136,7 +1140,7 @@
       "Number of samples: 342200\n",
       "Number of unique input tokens: 55\n",
       "Number of unique output tokens: 56\n",
-      "Max sequence length for inputs: 36\n",
+      "Max sequence length for inputs: 37\n",
       "Max sequence length for outputs: 38\n"
      ]
     },
@@ -1201,7 +1205,7 @@
        " 'z']"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1232,7 +1236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1254,8 +1258,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# encoder_input_data is a 3D array of shape (num_pairs, max input seq length, num input characters)\n",
@@ -1277,8 +1283,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# i = training examples\n",
@@ -1299,7 +1307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1316,7 +1324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1336,8 +1344,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
-   "metadata": {},
+   "execution_count": 21,
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "# Define the model that will turn\n",
@@ -1347,7 +1357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1356,48 +1366,219 @@
      "text": [
       "Train on 273760 samples, validate on 68440 samples\n",
       "Epoch 1/100\n",
-      "273760/273760 [==============================] - 743s 3ms/step - loss: 0.2597 - val_loss: 1.3135\n",
+      "273760/273760 [==============================] - 467s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
       "Epoch 2/100\n",
-      "273760/273760 [==============================] - 702s 3ms/step - loss: 0.0587 - val_loss: 1.5200\n",
+      "273760/273760 [==============================] - 459s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
       "Epoch 3/100\n",
-      "273760/273760 [==============================] - 658s 2ms/step - loss: 0.0232 - val_loss: 1.6334\n",
+      "273760/273760 [==============================] - 451s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
       "Epoch 4/100\n",
-      "273760/273760 [==============================] - 655s 2ms/step - loss: 0.0141 - val_loss: 1.7563\n",
+      "273760/273760 [==============================] - 451s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
       "Epoch 5/100\n",
-      "273760/273760 [==============================] - 648s 2ms/step - loss: 0.0101 - val_loss: 1.7978\n",
+      "273760/273760 [==============================] - 453s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
       "Epoch 6/100\n",
-      "273760/273760 [==============================] - 648s 2ms/step - loss: 0.0078 - val_loss: 1.8374\n",
+      "273760/273760 [==============================] - 454s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
       "Epoch 7/100\n",
-      "273760/273760 [==============================] - 645s 2ms/step - loss: 0.0062 - val_loss: 1.8761\n",
+      "273760/273760 [==============================] - 454s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
       "Epoch 8/100\n",
-      "273760/273760 [==============================] - 658s 2ms/step - loss: 0.0052 - val_loss: 1.9187\n",
+      "273760/273760 [==============================] - 452s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
       "Epoch 9/100\n",
-      "273760/273760 [==============================] - 779s 3ms/step - loss: 0.0045 - val_loss: 1.9649\n",
+      "273760/273760 [==============================] - 454s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
       "Epoch 10/100\n",
-      "273760/273760 [==============================] - 706s 3ms/step - loss: 0.0039 - val_loss: 1.9650\n",
+      "273760/273760 [==============================] - 456s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
       "Epoch 11/100\n",
-      "273760/273760 [==============================] - 640s 2ms/step - loss: 0.0035 - val_loss: 1.9923\n",
+      "273760/273760 [==============================] - 457s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
       "Epoch 12/100\n",
-      "242432/273760 [=========================>....] - ETA: 1:06 - loss: 0.0032"
+      "273760/273760 [==============================] - 456s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 13/100\n",
+      "273760/273760 [==============================] - 457s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 14/100\n",
+      "273760/273760 [==============================] - 457s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 15/100\n",
+      "273760/273760 [==============================] - 458s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 16/100\n",
+      "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 17/100\n",
+      "273760/273760 [==============================] - 459s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 18/100\n",
+      "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 19/100\n",
+      "273760/273760 [==============================] - 459s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 20/100\n",
+      "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 21/100\n",
+      "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 22/100\n",
+      "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 23/100\n",
+      "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 24/100\n",
+      "273760/273760 [==============================] - 468s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 25/100\n",
+      "273760/273760 [==============================] - 467s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 26/100\n",
+      "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 27/100\n",
+      "273760/273760 [==============================] - 467s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 28/100\n",
+      "273760/273760 [==============================] - 467s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 29/100\n",
+      "273760/273760 [==============================] - 466s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 30/100\n",
+      "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 31/100\n",
+      "273760/273760 [==============================] - 458s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 32/100\n",
+      "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 33/100\n",
+      "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 34/100\n",
+      "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 35/100\n",
+      "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 36/100\n",
+      "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 37/100\n",
+      "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 38/100\n",
+      "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 39/100\n",
+      "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 40/100\n",
+      "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 41/100\n",
+      "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 42/100\n",
+      "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 43/100\n",
+      "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 44/100\n",
+      "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 45/100\n",
+      "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 46/100\n",
+      "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 47/100\n",
+      "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 48/100\n",
+      "273760/273760 [==============================] - 459s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 49/100\n",
+      "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 50/100\n",
+      "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 51/100\n",
+      "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 52/100\n",
+      "273760/273760 [==============================] - 466s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 53/100\n",
+      "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 54/100\n",
+      "273760/273760 [==============================] - 466s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 55/100\n",
+      "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 56/100\n",
+      "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 57/100\n",
+      "273760/273760 [==============================] - 467s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 58/100\n",
+      "273760/273760 [==============================] - 498s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 59/100\n",
+      "273760/273760 [==============================] - 501s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 60/100\n",
+      "273760/273760 [==============================] - 480s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 61/100\n",
+      "273760/273760 [==============================] - 464s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 62/100\n",
+      "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 63/100\n",
+      "273760/273760 [==============================] - 466s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 64/100\n",
+      "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 65/100\n",
+      "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 66/100\n",
+      "273760/273760 [==============================] - 464s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 67/100\n",
+      "273760/273760 [==============================] - 466s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 68/100\n",
+      "273760/273760 [==============================] - 469s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 69/100\n",
+      "273760/273760 [==============================] - 495s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 70/100\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-206-614d9c150bbf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m           \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m           \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m           validation_split=0.2)\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;31m# Save model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m's2s.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1703\u001b[0m                               \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1704\u001b[0m                               \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1705\u001b[0;31m                               validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1707\u001b[0m     def evaluate(self, x=None, y=None,\n",
-      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m   1233\u001b[0m                         \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1235\u001b[0;31m                     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1236\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1237\u001b[0m                         \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2476\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2477\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[0;32m-> 2478\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2479\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.6/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    903\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    904\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 905\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    906\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    907\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/python3.6/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   1135\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1136\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1137\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1138\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1139\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/python3.6/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   1353\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   1354\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1355\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1356\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1357\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.6/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   1359\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   1360\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1361\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   1362\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   1363\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/python3.6/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   1338\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1339\u001b[0m           return tf_session.TF_Run(session, options, feed_dict, fetch_list,\n\u001b[0;32m-> 1340\u001b[0;31m                                    target_list, status, run_metadata)\n\u001b[0m\u001b[1;32m   1341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1342\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: "
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "273760/273760 [==============================] - 475s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 71/100\n",
+      "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 72/100\n",
+      "273760/273760 [==============================] - 484s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 73/100\n",
+      "273760/273760 [==============================] - 486s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 74/100\n",
+      "273760/273760 [==============================] - 489s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 75/100\n",
+      "273760/273760 [==============================] - 491s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 76/100\n",
+      "273760/273760 [==============================] - 498s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 77/100\n",
+      "273760/273760 [==============================] - 509s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 78/100\n",
+      "273760/273760 [==============================] - 508s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 79/100\n",
+      "273760/273760 [==============================] - 507s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 80/100\n",
+      "273760/273760 [==============================] - 496s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 81/100\n",
+      "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 82/100\n",
+      "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 83/100\n",
+      "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 84/100\n",
+      "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 85/100\n",
+      "273760/273760 [==============================] - 464s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 86/100\n",
+      "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 87/100\n",
+      "273760/273760 [==============================] - 486s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 88/100\n",
+      "273760/273760 [==============================] - 518s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 89/100\n",
+      "273760/273760 [==============================] - 546s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 90/100\n",
+      "273760/273760 [==============================] - 487s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 91/100\n",
+      "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 92/100\n",
+      "273760/273760 [==============================] - 472s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 93/100\n",
+      "273760/273760 [==============================] - 484s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 94/100\n",
+      "273760/273760 [==============================] - 464s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 95/100\n",
+      "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 96/100\n",
+      "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 97/100\n",
+      "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 98/100\n",
+      "273760/273760 [==============================] - 464s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 99/100\n",
+      "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n",
+      "Epoch 100/100\n",
+      "273760/273760 [==============================] - 489s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/paperspace/anaconda3/lib/python3.6/site-packages/keras/engine/topology.py:2368: UserWarning: Layer lstm_4 was passed non-serializable keyword arguments: {'initial_state': [<tf.Tensor 'lstm_2/while/Exit_2:0' shape=(?, 256) dtype=float32>, <tf.Tensor 'lstm_2/while/Exit_3:0' shape=(?, 256) dtype=float32>]}. They will not be included in the serialized model (and thus will be missing at deserialization time).\n",
+      "  str(node.arguments) + '. They will not be included '\n"
      ]
     }
    ],
@@ -1418,7 +1599,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": []
   }
@@ -1439,7 +1622,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,
diff --git a/s2s.h5 b/s2s.h5
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