From 068b47656397e2539e957aaf8b33dfb1439a383e Mon Sep 17 00:00:00 2001
From: James Timothy Meech <james.t.meech@gmail.com>
Date: Mon, 19 Apr 2021 17:30:15 +0100
Subject: [PATCH 1/7] Fix for issue-#3

---
 SenseGenModel.ipynb | 1134 +++----------------------------------------
 TestRNNModel.ipynb  | 1061 ++--------------------------------------
 model.py            |    6 +-
 model_utils.py      |    3 +-
 4 files changed, 98 insertions(+), 2106 deletions(-)

diff --git a/SenseGenModel.ipynb b/SenseGenModel.ipynb
index 58d708f..b231227 100644
--- a/SenseGenModel.ipynb
+++ b/SenseGenModel.ipynb
@@ -2,9 +2,20 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nAuthor: Moustafa Alzantot (malzantot@ucla.edu)\\nAll rights reserved Networked and Embedded Systems Lab (NESL), UCLA.\\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\\n'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "Author: Moustafa Alzantot (malzantot@ucla.edu)\n",
@@ -17,11 +28,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n"
@@ -29,10 +47,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 31,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import data_utils\n",
@@ -42,10 +58,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 32,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -54,22 +68,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 33,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import tensorflow as tf\n",
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior() \n",
     "import numpy as np"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 34,
+   "metadata": {},
    "outputs": [],
    "source": [
     "data = data_utils.load_training_data()"
@@ -84,10 +95,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 35,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# To get reasonable outputs, should use something bigger than 1000 !\n",
@@ -97,1034 +106,45 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
       "Tensor(\"mdn_model/add_1:0\", shape=(40, 72), dtype=float32)\n",
       "Tensor(\"mdn_model/strided_slice:0\", shape=(40, 24), dtype=float32)\n",
       "Tensor(\"mdn_model/Exp:0\", shape=(40, 24), dtype=float32)\n",
       "Tensor(\"mdn_model/y:0\", shape=(4, 10, 1), dtype=float32)\n",
+      "WARNING:tensorflow:From /Users/james/Desktop/sensegen/model.py:171: Normal.__init__ (from tensorflow.python.ops.distributions.normal) is deprecated and will be removed after 2019-01-01.\n",
+      "Instructions for updating:\n",
+      "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n",
+      "WARNING:tensorflow:From /usr/local/lib/python3.8/site-packages/tensorflow/python/ops/distributions/normal.py:153: Distribution.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\n",
+      "Instructions for updating:\n",
+      "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n",
+      "WARNING:tensorflow:From /usr/local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:201: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "keep_dims is deprecated, use keepdims instead\n",
+      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
       "Tensor(\"mdn_model_1/add_1:0\", shape=(1, 72), dtype=float32)\n",
       "Tensor(\"mdn_model_1/strided_slice:0\", shape=(1, 24), dtype=float32)\n",
       "Tensor(\"mdn_model_1/Exp:0\", shape=(1, 24), dtype=float32)\n",
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-      "915   -1.70063\n",
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-      "917   -1.7774\n",
-      "918   -1.75612\n",
-      "919   -1.72889\n",
-      "920   -1.75656\n",
-      "921   -1.72852\n",
-      "922   -1.76788\n",
-      "923   -1.75318\n",
-      "924   -1.7599\n",
-      "925   -1.74186\n",
-      "926   -1.69443\n",
-      "927   -1.7704\n",
-      "928   -1.68314\n",
-      "929   -1.7162\n",
-      "930   -1.75969\n",
-      "931   -1.71512\n",
-      "932   -1.75012\n",
-      "933   -1.7304\n",
-      "934   -1.76883\n",
-      "935   -1.67868\n",
-      "936   -1.78476\n",
-      "937   -1.72472\n",
-      "938   -1.74392\n",
-      "939   -1.80336\n",
-      "940   -1.79781\n",
-      "941   -1.75887\n",
-      "942   -1.75583\n",
-      "943   -1.78798\n",
-      "944   -1.75244\n",
-      "945   -1.80076\n",
-      "946   -1.82768\n",
-      "947   -1.70025\n",
-      "948   -1.74566\n",
-      "949   -1.75812\n",
-      "950   -1.78949\n",
-      "951   -1.79442\n",
-      "952   -1.81262\n",
-      "953   -1.7307\n",
-      "954   -1.80108\n",
-      "955   -1.75149\n",
-      "956   -1.74458\n",
-      "957   -1.76611\n",
-      "958   -1.7385\n",
-      "959   -1.77024\n",
-      "960   -1.78371\n",
-      "961   -1.66448\n",
-      "962   -1.79503\n",
-      "963   -1.76339\n",
-      "964   -1.79786\n",
-      "965   -1.77586\n",
-      "966   -1.782\n",
-      "967   -1.73044\n",
-      "968   -1.7482\n",
-      "969   -1.82103\n",
-      "970   -1.76213\n",
-      "971   -1.74923\n",
-      "972   -1.76435\n",
-      "973   -1.70612\n",
-      "974   -1.77428\n",
-      "975   -1.77256\n",
-      "976   -1.81739\n",
-      "977   -1.81118\n",
-      "978   -1.8018\n",
-      "979   -1.83559\n",
-      "980   -1.71298\n",
-      "981   -1.83131\n",
-      "982   -1.6946\n",
-      "983   -1.77274\n",
-      "984   -1.78746\n",
-      "985   -1.788\n",
-      "986   -1.70328\n",
-      "987   -1.8035\n",
-      "988   -1.79989\n",
-      "989   -1.76743\n",
-      "990   -1.71508\n",
-      "991   -1.79559\n",
-      "992   -1.76411\n",
-      "993   -1.80683\n",
-      "994   -1.81438\n",
-      "995   -1.78\n",
-      "996   -1.78637\n",
-      "997   -1.73508\n",
-      "998   -1.77904\n",
-      "999   -1.84538\n"
+      "0   0.40117803\n",
+      "1   0.23122519\n",
+      "2   0.18913844\n",
+      "3   0.14960684\n",
+      "4   0.1319322\n",
+      "5   0.12631807\n",
+      "6   0.124129064\n",
+      "7   0.12099192\n"
      ]
     }
    ],
@@ -1161,41 +181,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tensor(\"mdn_model/add_1:0\", shape=(1, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model/strided_slice:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/Exp:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/y:0\", shape=(1, 1, 1), dtype=float32)\n",
-      "INFO:tensorflow:Restoring parameters from models/mdnmodel.ckpt-999\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.text.Text at 0x7f65466a3390>"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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llBNUG7C6rqx7IjLQbiBBEIQYlBQuyNRNCUjwiRBx6SxhDEgz1u/E5npxEcvMJm9/fGuJ\nsGsXLFfidSevIXM0mUTZ941Qj9XbW9DU0RN2NQgiVFTaTsqGs1ZSKisdSPCJICp1ZC+E0em/N2Y2\nznlgku/rqBTcYNX2ZuHXVOfuShd6B4QILnpkKq58ckbY1SCIUFBxI0ml9UMpQ4JPBFGvO7sj7PrP\nWLcTPam0p9/KFtoKFXftM3PEl2f4TKZXBBFt1taKTUoc9thNEFGmV+MTajVKHhJ8iNgzc3199vP8\nTQ343tOz8dCENb6uqcLOTUJgHcK/G0KH5kRCNRQY7gjCFSpu3PWGs6ZRPkxI8Ikg6nVnd8jUmrR1\nJXH1mFnZv2ubuwBkQipHEeO7p8VIvFBxoiYIgHaoieihonChj/HUn8KFBJ8IEZfOkpZ4H8lUbmH6\nX361JWE5JxpLFanxiUnTIggiAF7TIjySaE5EDRWsM3QUqkpJQ4IPEWvMuz5pTWDxOgDRIEoEhYo7\nlASxrakDO1u7wq4GQbhD4eE0LpvYUYUEnwgRl4VumH1eH3C8mhXJ1/TYlyeyPcSkacUCeheEShi1\n5rReIwjvZIMbUE8KFRJ8IkRcdgnCjGHvV+Ojo4LmR6SpmyXh32JJEpNuThAEQRiJ2Zy6vakTa3e0\nhF0N15SHXQGCkIkuc0XFx8dcjLHWZQoIX4Q4KLgBoSJx2XATQTKVRnkZ7RdHCRVH1bj0qdPv/QQA\nUHXfyJBr4g7qwYR0wuz0uoq5ele7p9+HrenJeXQBVIUyShMEQVjzwZKtYVeBiDDZqG4h16PUIcGH\nkE6Y9q36un7h5kas3h49Fa2RwE3dCIIoeWiY6aUnRUvWqKDim2K9Tj5EiJDg44JlNU1hVyFDxGei\nMJUKxlDaNY3utT5ha0TW7mhFW1cSAJAIoBmErdEiCIJQFlqwEj6g4AZqQIKPCxZvaQy7CrFApuxg\nLkuU4BKWgHD1mFm44cV5mTooab1MEEScIOtXIko0dfSgYlQlxi7dFnZV8tDXDdSnwoUEnxLmsn99\nhtveXSq93FBN3Qyf/QgO0oIbWHw3c309gGAUf2FrtAiCIAjCK4urMxvUXck0ALUMZPS60CwbLiT4\nuCBua8LF1Y14adZm6eWGGtzAZ+EqmYIJ9fGxuJY6d0oQRFgoNOQRRFFaNVNwFcmausVtMRkxSPCJ\nEHHpKmHeRzrgwieu2IGmjp5Ay9CFL1qQEARByIN8Mwg/0JytBiT4ECUFz7V1E8qO5k5c/8I8/PKV\nBWIvbIPQQZR2oJSBXgWhEtQeiSgRBdki7C41ZU1dyDUIFxJ8CPnIDG5g+jsd4Cze2ZMCAFTVtwm7\nZqHqUjhrgiAIQhScc2yu95bjTiZT1tRh6RZFouy6Qo3gBpUlno+KBB9COkEKH8UIsmTZUdaElmbl\n40OCVSjQYydUwtgew16wEcHyzPSNOOeBSeqk7rDhh8/OwdefmO7oXJXG05temh92FQiQ4OOKQmN+\na1cS3VoUEUJhApy5sxFbAl4cZMdxlUZ0giBiSaHx7I251UgF7ThJSGNe1S4AQHWD+lqfKFLb0gUg\nfFO3UocEH0F8/i8f4eoxs8KuRiRQJbhBUGKDrF3RIOr/9oKaAK5KEEQc+eNbS/DSrE1hV0MapPEi\nhEANKVRI8BHI/E27pJSzsa5VSjlBEWYoxyDLTkra+RSh6Gnq6MGkVbV5379YQosYVaE5kVAVq7Fn\nV3u3/IqUCMtqmrB8q9pmZ4R7aIgPFxJ8IkhzZzBx6jt7Unh7wZbABZMwO32wPj7iCSp86i9fWYAf\nPzcXtc2dtueQIR1BEEaspoZSFtQXbt6Fj5dvD+z6lz4+HSMfc+bLIpISfqVECVAedgUIFwQ8wzz4\n0Wo8PX0j9hzQF+cdtU+gZYWF8RGWsvP+xp2ZyHNd5JdmSzKVRjLN0b9PmdRyS7hZEopT39aFrmQq\n7Goowzf/PQMAUHXfyJBrQtghO+gQoT6k8XFDzLe2tmu7/82dASfgDPTqucg0q+sNbhB8mcu3NmFR\ndaPn38e8KQvh+0/PxlG3jw+7GgThi7cXbBF2rRdmbsJPXyjdyFSlMmySqEDEGRJ8iDyC1oTE1dRN\nFgwsFPOHUmP2xobs5w8Wb0XFqErUaVF5goSEUkIkv31jsdDrTS3x5IeE2piXL6RBz6fU5xgSfAQQ\nprO+SOJxF87xMx4WelYin2NQTcvJZEATRi8vz84EfVhb2xJYGfS8iShSavMGoTbmOVPF5ZmKdSol\nSPARAKUxcEeY67sghdQo2RKX0sBLOSkIgiCcU0LTA1GCkOAjgGSaHMTdIHNQDaosKxFHVgJTwh1C\nEywG+G6p3RAEQfgjCqZuQUVrdYqKz0QmJPgIIMjM1Uu2NGLltubArm9FifcJ3wQ+qAl4QaU08DV2\n9OC+cauQTEVjg6KU3g1BRIntTZ3YXB9/DTINQUScIcFHAEEmrvzGE5/hkkenAZCgKQlpEyIsHynR\nC8woaXyc1JEBmFvVgJrGjsDrEyR3fbgCo6esx8crdoRdFUdEof0QRCny6Cdrcc4Dk8KuBkH4otTn\nGBJ8BMCjsZHsmDjvOIvq8LLGDav6BvF67K757dEzcdY/Pg2gRHnoeUf8aGZl+G/Fud8RMabUV1Ex\nJKpvdMHmXdgUAY1cFLvMpFW12N5kn/A8SlACUxeE3VZpXeSflIQRJ+x2IpooDtJWxOQ2CIIgCAu+\npSWUJcTz4+fmYr/B/THr1gvCropvSOMjAFmOakGXErbDnQzuG7cq+9nPLr7VL0UKCNubOtHZIy9D\nevzfvBj+Pm5l2FUgCIIIFNpkjTderQv0JPdRR4jgwxh7ljFWyxhbZnP8y4yxJsbYIu2/O0SUSwRD\nlMIyq4gIAej0ez/Bz14s3QzpItHfh4hWvawmuEAjcdGsEQRBEPaEPdaHXX7YiNL4PAfg4iLnTOOc\nn6j9d6egcpUgLo0orPsIstwov5spa+ostXBB+IKUgqgb4aZAEMpC/Sp+xOmdMnKeJEwIEXw451MB\nNIi4FhE+NE4Ux2pi6BWy4jRtqE9tSyeenrbBNjqgiPYso09QvyMIIkziOAaFFTWWUBeZPj5nMMYW\nM8bGMcaOtTqBMXYDY2weY2xeXV2dxKo5g/pP9CjktyR6kG/s6BZyHVUG6qjslP361YW4u3IlVu9o\nsTwu4nEar1Hf2oU/vblEqg8WQRC5NLX3hF2F2KHI1EMQgSJL8FkA4CDO+QkAHgfwrtVJnPOnOOen\ncM5PGTZsmKSq+SfM0MZxYElNU9hVcI2VSPCNJz4D4P89xfU9B0VzRxIAkEzJeXD3j1+N1+dV492F\nNVLKIwgil2lr63DCnR9jyppwNkjX2myyxIVobHk5Q8UNPFlT/GtzNmPWhvq87xV8JFKRIvhwzps5\n563a57EA+jDGhsoom3BOWAvuy//1WTgFB4Tfx5gu8iIo+EQuMpqtcaLQtYhBTR4k9xJRIox5Y/6m\nXZl/q8KxsK9r7QqlXMI9j0xYE3YV8pBl1THq7aX47lOzLMqXUryySBF8GGP7MU3sZoydqpWbL4Yq\nQFcyhU31ba5+I6sRy5LSY7WsLvBq/NxnoTfutz0Y82yW+gClMvRuCCIc9M0f6oLiqG5ox6LqRgDx\neq4bdrpbzxHxR1Q461cBzARwJGNsC2PsOsbYjYyxG7VTrgSwjDG2GMBjAL7LVXFkMPG7Nxbj3Acm\no6NbPfv9oJ9YEHl82ruTeH5GlTJ+K36RIRQW1fjESjL1T7HHoXrTS6V5Tv+g10uohIr53fQxUPW+\nHSXOvn9SbPK0EEQhykVchHN+dZHjTwB4QkRZQaPbDHcn09itb1nOMbvFO4299txduRKvzN6MA/fa\nDecfta/08gu+GzJVigWyn3drV8anSIQAuqutGyfdNQG3jTwawwb1839BgigB9K4XllBG5sbqsXxr\n9HyFiXCQGdVNeZo6etDSmVnU0Lgmhsb2TKSzdoMGLcq7dL96dWHgZegaH5maHauy4tIFeneHxTS8\nsUu3a9fzf61tTZkd1jfnb/F/MaIglGg7PoSt8VFRCyaSKI79Ix+bHnYVfPPoxLV48KPVYVcj9pDg\nY+CXrywIuwpKEDVTqrauJB6duBbJVDrvWFATY6FH5LdM3cdHxmtwuoCwigwjircXbEHFqErsahMT\nDjwIZPaJeC+pQuU5lHCiba+oqN1QMVJXnKAxKBwenrgGT0xaF3Y1Yg8JPgY2enSCC2/XSfD1JN2H\n6DnroQlr8PDENXh30VaxFy5AUI/qbx8sx/hlGY1CmgP3jVuVd04YU75VZBhRPD9zEwCgymVQETO7\n2rstc+uorGGk9Zs8KNG2N1TWbqhbM4JQl1LvN0J8fOKCcRGi4oJE3gQk7uaNu4Wcc7wwcxMaBO/s\n64vdDqtFbwhd3I9J1X8/q8r5e/q6nT5rUzr84Jk5OPPQvcOuhidUFs5KjDO0IDxbAfyec7487Aqp\nThhjbNimbipqwYjoQON9uJDGxwY3w1p4DpZiCfouVm5rwV/eX45N9e1Cr1uWyDyJlIWpWyGiOnkF\nYeZheUlZj0fgLDBjvZJR8m2Js29VBHGUaBsAGGM3MMbmMcbm1dWFk0SzlOkNZx3O3Cu73KNuHye1\nPIIwEpeovDok+GhwznMyv+uLy5nr6zFm6oawqpWDrIV6UNquHpeCiVMSWoVTAffNGQ61L82dSdQ0\ndgRbGUIJNte3x25SKFXcJNrmnD/FOT+Fc37KsGHDpNaTMFAiXa+zJ5i50464bb60diVRMaoSr8ze\nHHZVCAUgwUfjmekbsxGWjFw9ZhbuGbsSgNgx9o251TjmjvGWDvl2mHeZSmTML4qu8Umn859IoTWp\nWwHPTeStdxZQlC5ZyBA8rDYdZm2oxzkPTBISkc3Yt6lfh0OUEm2XOllTN8N3D09Yg1PuniCsjMb2\nbjz32UbL8eW5z6rwg2dmCytLNeI2Bu3Q8hM9Pc1+E/vFmVWe/bzdoo/3nHO8OGsTmjp6pJRLZCDB\nR+ONedU5f7taE3sYJf787lK0d6fQHZAWxAtBrx/r27oCuW7W1C3gG+hMOk9qq7oSQEUthcqRmlIW\nQvWaHa0AgEmraz1ft61LvUTJcSVOibZVIownlM3jYyj80U/WYmerOP/RP765BH/9YAUWbG7MO/bx\nih2Ytpb8L+MC5xy3v7cclz0hNyT2wupG3P7uMtzy9hKp5brts8bzrQIuRQ0SfDR6graTsinPj/ma\nyGXi5vp2TFy5Q/h1dTgHfvLcvACubDB1MyxOu5IptHT2CN256pJsbmCHLPFAtg+U1zVmMYFJRBuY\nWSCc99il27M7im75w5uLAUC43xuRD+f8as75cM55H875CM75M5zz0Zzz0drxJzjnx3LOT+Ccn845\nnxF2nUuVa56ejfcW1dgelxHcoFHbhe9OqjHuE94xt5OeVBpN7flalmY9j6Mk9MBM5oBP86rUDT45\nesr6sKvgGxJ8NPwMbn7GXlVChV5jUtsv2LwLL8ysAgBMXLHD88JOBmVaKzaaun3zXzNw3F8/Lvg7\nt8t6Nxof1VFZu+IWFTbla5u9aTN104ouw/gTnzdDEN6Yvm4nbn5tke1xmZsyehLuUiLuY9CvXlmI\nE+4svD4IEvOUZf77ytEzgy1fkXVnWJDgo5FM5wo+X314Kqobgt+F9bNmE9l027tzdzq+9e8ZuOO9\nTCTX61+YhytHq7v5WcbyTd1WbGsGIHZR3JOMz2Dh5Lm8JdlPKShhrCtiAmt8WhlBiGF9Xavl9zL6\nyk0vh5PYPExNU9zHoPHLt+f8TWHRCxO39kCCj4bZ1K2msQNn3z8p5zsFNpaloy+Qqxs8RimT0K8T\nBYIbhIU6NSlMnDQ/dmyoy2hVmjt68JPn5mJnazC+ZgQRBhWjKnHzawtDKz/osW78sm244J9TMH7Z\ntux3Mkzdwh4Zn59RFXIN4oTWUMJ+qYrgV+BaVtOEilGVgmojHxJ8NHr8mLqFpLUR1YfNu//GxXAU\nhL1yj8ENIrvoF1ht/d3LeBL3VK4IdbB8efZmfLqqFmMKRPZxg4jmE9EWSCjGe4u2hl2FwFixrQUA\nsGp7S96xOJvsWCXkJggR+O03HyyJ9nhDgo9G0BHB7HBqirV8a1NgdXh4whrbY1GYVnSNT9JlOGui\nFxmPacyeX+yXAAAgAElEQVS0jRJKcQC1CYKINPqmFY3vhB9ma0FrZDcjc3mqN2MV/GhFQoKPAPxI\nzxyZyB4PT1hTMJb7XR+uyHeI81xqLi+ZknoFEtUtgGvqlJXYJCjy/aig9YrqaxPR3lR4/gQRNYr1\nmu5k2nfC7FLumvdUrgy7CkKZtcE6Stq7IWlKs5YWhja2qV5ODqFMBfz9XDcfjyok+CjAzPX1ePST\ntbj7wxW256RD8nOMgqRvFc6aIAiCiCfFhJIjbhuHrz48VU5lAkKm3GUOpFDT6NGnV1HsIvOpJNxO\nWLEjsGuv2t4s9HpB1lUGJPho+Gn/vnx8OLJJTHdZxJXXSVsUEpSPjxFZmYwBYGdrF754z0TXnTSb\nwNSl4ON20DNq9iqXbFM61r5Ttjd1oKGtO9K+Jk6TAIvWrqg0aRJEUBSb38LcG1Nl7oo6S7bkJ2mN\nAiu3OVsr6M3EPGRbJcINiyC1/y/PyrXqcTvhh/90xEKCj0axRlcxqhJra61DavqCO2uDYfkg/Vxi\nKM9PVu5AXUsXnp3uzhekkOAT5GMrFGtfgXHUEVc8ORNfuGtC2NUA4F2Qd6p2D3Jy8zpnkexEEO7R\ntfxBDrNRCTVcylzy6LSwqxBNIrI+CQoSfFwwN4Ad/lz/IPvWaKXMCKrtGhdxMvuHHpygLOGuWSY8\nRnWLKkHsDKnw5FSogyp09qTw+CdrffspEIRfVNFsPjJxbfZzkOGsX52zGastIsjJRuZzV+Udyyas\nOSevXIcb4IGVX2KQ4CMAv43IyUI2rBw1fnfJ3XTmVFbwcVeGHtzA7TNyO9C4eRQPT1wTqUXrws3R\nNHVwA5OwSyyKf09ah39OWIPX5mwufjJBlChBhLO+5e2luOiRaPsHuadEJR+NKMwJYRK3PWUSfELG\n2KAKNS4rMy5RQ5VZ8MrR+Ehs8FnBx+X2ky4obW3qzDsWZp6HXW3WDpUqoOI4VopTr11Tb+vO5PDo\n7ImO8EzEk6I+PiGMJr2+GQGWUYoDUokR9oJ+/LLtAICedJram0RI8NFw0ubszvGjFeEW173siem4\n7d2lOd+lOQ+sk6rg2Af0Cj666ZpTdHvvqWvqAtWMqfGUiDhRzI+AJkOCsCCgjqHKXEjEHK2ZPTej\nCoD8TdJSn1ZI8NEJqSUYB1r90+ItTXjJFIXD0nE/yIoJLsPJhKJHrit3KfiUGc7PSwwWw3lMxJxf\nygMfLW4IwjkqCt9ZjY/FMT/9W6WMCDJzfKn4joMg/5lmXrgKU4LKPj5hWs4EAQk+LkgENDo4uWya\nc2mDk3EXWtYisb07iTHTMtHc3Gp8ylyerxP081R5qFCpbipMOl4Q0n5KZMFBECLpDW4gNpJnKeaC\nS6bSofkQlwJPTl6PT1aqlfem1Df/ysOugCqElscn5zqFo7qZD/up88z19Y7Ok9U97h+/GnUtXQDc\na3yMAml3Mo1/T16X/Vtk/VUZLKKyVuacY/6mXTj5oD0d7V5GbddxWY3YpHA6PECzVoJwS9G2GEJb\n1TfnXp1TjS27OrDVkHDTKuedU4yCTzJVGp3wzPs+Ra029xLi+cf4VQCAqvtGZr+zalkyNXylDml8\nQoZz5xofkdS3ORvo/JbrtDMbMyu7DW5gFJRenFWFxz9dV+BseZT6MPb+4q24cvRMvL2gxtH5QS/2\nVX8fNPERhHumrd2J9YZcXn6GkWS6N5jInBgkqHZCKQs9+pwTtimX6iJ23DbiSPBxQRDrEmOHc293\n6Z18p2rrmwujwbs1dTO+l+5kbhSswlqaYBeaMRsrXKNnTt/U0O7o/DTnePCj1ahtyY/OZ8eymiZP\ndXODm/q4xdgCRWoUa1s6Ud9augsaQhxF570Q5PVCdfLTjXS5x63VARENnL7VSatrUe1w3hKF3LxN\npd2+SfDRCLMhOMkQHbwAYigghEcRBSEhCnVUCd1qxGlzmlvVgCcmrcOf3lziuIxLH5/uul5u+9Kp\n93ziugw/MMZ870Cees8nOPnuiYJqRJQysk3dnGwAFBpT/PQdXePjdvONiBc//u9cnPfg5MCub27j\nDJKDG8RNheMSEnzCxmEeH6vBXEZHCaN/xKVPBvV+IrNbo73IutYu7HSgfUhqklJXsnRy1xR7lQ9P\nWINkhBLhEoQMgtL4pLi3XHLpNCcNa4SxajNJ2akxJM7rMVlieYYEHw0/bc53cAOHZYtsrPn3yyw+\nRX9noKChWwlHdZOBPm+8MnszTnGhfYiKXBcUxj7X1p3C+4u3hlgbgpCLkynHiZWEF7JJtF1qfB7/\ndB1Ovnsitlsk0SbUJazlTcSXVZGHBJ+QMXaAQn3BqqP48/FxRkpSD3X6HNxcRzQ0WLnDq8lJUM95\nxbZgorCJwk6T10MaHyJEorYRISKctVtLt09WZcIVb28WJ/hE7bkT/hD9updsaRR8xfhAgo+Gk0Zn\ntzDxa4/vtMGL7BhOB1VZa66gZAoSVsJD1dQQQVRr2tqdnn7nJGdWULvbBOGEYmNoKN28kKmbjxrp\n99rcmfR8DRHMXF+PdTtaQ60DERxBR5FLpzm+8cRntsfdzihxW0eR4OOCIJYfOVHdCrSuLbs68rqK\nqPpww//zjkWgxUegikKJyjLYayj0KO506rka3GK815tfW6R9x0quTROEjt+mH4e+c/WYWXh7obM0\nAET0SFtsKIuc94rNvTHoIr6gBKYC8OXj4+K33cmU94LysO9lRs2W7zw+Xn4kdOYSd604DRYyFgdO\nywg7hwJBEPFBdM47J+jzXBQ2CnU459nNllLBTriI0GtzRNFAjDG7X7eQxkcjrEhZHMC/J613dK4x\nSZv+W684Dm7goww3hDFhuC7S5Q+CuqWoaETSitq6qT7oR2nxRBCi8dv+wzG9i8igbKCjhwKnhLXp\nZtXGRZo0i55C4rY5SYJPyDS0doeSIdpxcIMQFq9+SvzvjCpR1Yg1MubpeA2Vclm9vaX3j+itqYgY\nUXz3WK2erlh1CCKPoJuobK2namNAMUjw0XCytkjYPC0/r9xPAxW1HjJfx1gjUe1ZVr9oaOsOpVwr\nVN4ElPFcnLTtBz5ahWU1mWhr+uml5Mxv94hmbqiXWxGCUATfQ5Mk03Mi+ujvW7ZGw8nc+NqczRJq\nUpqQ4KMR1iI1rHHWaNpXqA6ydg7iON+U+iRa7P5TaY5/OTTzJLzxycodYVeBkETFqMpQNPSqEYZZ\nzuLqRq1sgiiO1dxoXoP6CW4hOrhB0ciOEWv4JPj44LInpuMXryzwpeZ74tO12c9uLxNUHh/j/UhT\nmRrz+Ah8DiK7vzp927+U3iU0UIY1UVN/q4qft33d8/OE1YNQnyByPr2zYIvwaxbC77BBww5R6lAf\nKAwJPi4wm+As3tKEyiXbfF1z4spaX78PGquwi0T02dXeE3gZbsfeju7ghTHViJvTKBEuQWh8np+5\nSfg1C3HBQ5N9/d5XAuwS6o9RNSlu6/KeY8l8z/rbli0oWBUn8m3IbsVR6zUk+LggaHM4t4Oun+oU\nuhdjLcIIDSpy8glz56OUJlEr3LadJyatA6BePyOIqJAMIxiN4CKrGzp8/Z40zfHmKw9NCbsKvrGM\n6iYxj49bohbgpBiUxyeL91YXrVeewWkn853HRyvn01WFNVu5iVx9FekY1yZ1AZ8fN4qtwaI2WBKE\n6gSh8VGxn45dam9pMWFFeH5tCj6q2LG1qdP1b1R7LZY+PoJ0PrM31KMrSaY6hSDBR8OJIBC0Yliu\nj48zlY+oebTU8wWIROVocUaKLZgWb2mUVJNoE1aOMSJ6JAPw8VFt0VjX0oXJq+tsj496e6nna5Pg\nEk+KjaBKmoZ5rNRVT80qfmnJGiHVIFM3AdBg6R/jMxT5OIWazUU2GEI4FPMPe2rqBjkVMaFSf1Wp\nLkT0CcTUTWIbfWb6xqLnBBHAoRQhk9/wxl9LwUPh/S0Vtb5+IMFHIdy2LV/9pKDCJ1qNXFafrGvp\ncnV+UIOFwuNjDlFrRwQRdaKWcFonneaobmjHXR+uEHC1MAnu+f/61YWBXTsq+G3fqijP4zYzRk0u\nIsHHDba9Jpi3fsd7ywK5LiBu8ZxOc9zx3jKsr2v1dZ2gOk6h67opcvyy7djR7FbwcXV67Cj1+ycI\n2QSh8ZHRjZ+ZvhFn3z/J0blBLl5VHrJEm4tHcXzuDsh3RbZGw1xcVX07/vb+cnnlSytJTUjw0VBh\nI8C8Q/5CkTCiQTVeN2PA+rpWvDBzE3724vxQypfF/E0NYVchchSPBCOlGkrj5BGoMDYR0eCBj1YJ\nv6aMReHC6l2Oz43ruFHf2oV1tS1hV0NZ2rqSOPqO8b6usWp7C6435DYLyyrBqk+1KZzOoehcHjFR\nigQfDRVUoLIG9EXVjWho65ZSVtiPVdQzlfFu4mZHG7PbCY0Z6+vDrgIREcYu3S78mjK68e59Kc7S\nBQ9NwYUPTS14zpodLejsEbNAjtrwvKGuzfNvjfc6cWV4Uf90ovbs4wYJPi6wNXQLqRV7FSou/9dn\n+O0bi4XWRSRR2z2wI6h24VdIlyVgFXuPdkdLPYqZ+bm8tWBLKPUgCKD4OCZiONmtb5n/iwjA79jo\n5+eNWlLp1dvttT5ffXgqfqfw3B0kqSDmLZ7zDwBg9JT14suxKTcuRG1zjgQfDScx1INPrKgGXsYX\n3xNGQHdf6LpBr/9VFeCkCepq3n4k8dO/2ru9ZzonCBnjmJupNe77Ihc9UljrM6dKjNl11CwMZAXu\nuG+ceHNRM9F68sXXDD/+71w5FREECT4CCKsRiyqXc++TiahJyM8YrKqA4RZZ85DorM52kI+PAyRE\nNb3rw5WCr0iUEtRP1ULU+BC11+pn3rL7qcxn8POXe/2gZc3BtkTt5QuGBB8XBL7RFGJjDCqPjidC\nr0A+XqoU9thmh6yIt1HbUVSFD5fkZ6Uft8y774bbMOwEUapEYcRKxF3lZcGq7c14dc5mz78vanYt\n4cUH4X9HeIMEH40ojiVeqmy1GBVx76pOGAXDWbsY7TyZ/wV0vtkss0+ZuxeoisbH7owIdsXAufk1\nyuFBhIOqY7uKyHhW25s7hZh9RWlf6uJHpuHtBTVhV0MYsp+9b6sYBz//72fFkw+rAgk+AhAWOUzC\nsGk1YJrr/9MX5uWdkznPqn5ilqnc5nOUkRZEwGUx0gQfj8XE5f2LxN8roydK+EBC81EloInfoVHW\ngvb5GVVyCoo5CzbvQsWoSizf2iS13NBN3VzQlUxhrYMw649+slZCbcRAgo+Gk2FXxuD86SrnoRa9\ndB0/kVGmrd0ptjJ2l1JwUJAhlDq9b7/NUJqpW7HjIb1mldqXnHYVeBGE4jR39mD8snwTSifExYcy\nTuxsFWC+WkKv1W4M3NHcCQCYXmhtEwCyH735/t306VvfXoYrR88UXKNwIcHHBQmLBec+g/oJmxg4\nB96aH6w6105F7mQx7SV/gCo7eWGgavA0eRofj+GsxVcl8vh5YyW0viFs+O3ri3HjSwuwcaf3XCh2\nkGBERJW+ZZklcHmZ5KVwhLrMrA3RClXtBBJ8NJws0K3OCTP/gJcFop3g42QtbGnoxuyPuSEnuILL\nixX24ylwzF0xrnF9H57LcffLtCSVT4TGduVRSUtFRI/qhnYAzjevdjR3omJUJd5dWKOcxtBJ6gnv\nKHazNojxy43GvYrA7k6T2lzYx2pXO0BkP/vSedPOIMHHBVZdQ2SEFRmN049TpGVgBD+Vyb26sCvl\nXlWcNi4uyDJ1U3W0ValaTtuVL41PnBov4Ql9HHQ6Xa3ZkbHp/9/86qJt792F8XE6jwrBCn+lQzKV\nad2dSffWLH4Ie0jeUCde8xslSPBxgdWkwZjYRly51Lkdtpdi31u01fJ7JxNi2J3VC+HW2V3hTutq\nflXKmroVDSEawQYVQegpE3pXc7pRZ1xYF+unu9p7UDGq0nPdgGhGVQ0TIRqfUhoYbG62O5UGAPxr\n0nqZtQndx2fV9uLBCuIMCT4usNplETlee/GhcYsfG+9CmgKRi1iRg0KhRX7QA7+qE4sqUd3s2hMt\ngvLxleBX0XZIyEPv8166ljQNsQL4j+om52HREBltaNMvXEjw8UmCMWELi+Vbm12d72Xwm7qmzsOv\nMlgtmEUFL5i4slbIdcyIGl68DFRuf+HULM/8zF2Hs067O98rNLYXhx4RIQO9nbkdr1u75JoAOWHG\nerkRuOLKk1PkajnCRLVxVn59VHsC4UKCj4YjUy+LxhO13ekNPjQ+0lxDBAYFoJ2VfJQxdZNSCyIp\nS9IllKXX1M3Z+fq8lkyp13Yenrgm7CqEj4CFx1NTNwioCOEFZ8Gk1Jgh47iGIsFHw6uPCwMLrYHK\ntxONXgcIs8oio9OJRBVTt7BQtV5BISLLOxFtsqZuLhfMbq0Qoo7v6KRCalEcGfut7y2ioBVxodTm\nvGKQ4KPhN5xzKWD1jHRhSGROFpGCZEG/pICnKZkCsRuhVNYgWKwYuzqXUJeSAsk9hN7VItm3TOME\nLeL8rzs+W1fcXPDm1xb5K0QhothmVKmzItUQCgk+Gl4bWZgJOmWXXGghr0onNSNKS+XlKm6L7uqR\nY1YiSwMQdJvokhyCNLIo2jcJebgNZy17bnETnlnVuQaQVze/4ay///RsQTWJBk9Ni55Zn8LNPPKQ\n4OMGG42HygOxSKxcBVS/dVFrfBnv+PmZVZ5/66Z+skzdgm4dPSnVW19xZJiPynvfhKq4DWctW/JR\nxXKCuop3Vm1v9hU8KUi6k+r5qslERlRQRbqwI0jwiTCyI5ZZnaX/VKRZl37Nna1dvsJvA8DiLY22\nx6avDTY6kNvBxo8jsZuiRCp8OrpTWnb3/IsGHy7cWwGqOI3KIkWruZLHbRNQKUEmtd58VBEUjVz8\nyDRc++ycsKtBhESU+ikJPhpOFlGltmAyY71zHNwzOfO+T3Heg5OLnlfo3d03bpXtsX9Pdh7O08u7\nd/0bAbNZS2cPTrzzY8xcX297jkgtw92VK/Cb1xdh9saG/HKK/NauGk7NR0u7NzqHfHzii+NNqxgJ\nv3G6F68oKPcQghGbG9FqY9Lphnf8+hsJPj4JcwyWPvhZmvoFV1xc1dPdyTTSFqtRp+/TSi5IpTle\nn7sZi6ub0Njeg4cn2Id8FakB2NrYAQBo707mHSs2sPodUOOw/pFxC7RQJPQWEIemEOQt+B6TJC0S\nVdT4ENEiDmOBV0jw0XDSBqwaSt/y0nmElrsG+r8Cw7rFZaFmdRs9qTSOuG0c/j52pdCy3lqwBX96\naymenp5x4iw0AYtM66K72ViZxnjV+DjG4+9j0rwcQz4+hNs2oNLC2lx1as7hBlUi5CCymVv1mTGC\nAz5EqUWWzqq9CE4G03mbduV9N3i38tAGYtnFWpnMBHHvi6obLTUIsvjemFl4YWZVznei7jOpSQov\nztqUd8yx37HFeXUtXQCAxvaeor8XuRDWhVSrOgXu46OYCt6LP5qMyULBHJSEZPSxW7U+o+OmHzR2\ndAdWD4JQBS/zp5uIrU9P3+i+gJhAgo+G1wmhlHafLPP4aM9ty64OYeUs3tKE31jkEPhg8VZULtkm\nrBydpo4ebG/qzP49Y3097nhvOXp8rhgLPa+kpamb92VwmyYo6pnZC7VLEW22J5XGVf+ZiVkb6rVy\nxWl87EwBzajmu9LW5V5YJ1M3wg9OX202CI2CTYFzXtD81jg3dyfT6Aww7L+KzyeO/GvSOlSMqhQ2\nNn1vzCwh14k6J989wfJ7q6fsVEhyPMY4O00JYi34pNLchfOn93LC2kWTrVr83/zqvOcZ1ESxqLo3\nGlt1Qzs45/jVqwvxi1cWCC/rm//6DKff+0ne96/M3pz9LOo29bHGatDxY73Q2plZdJcnMl26UH1F\n+Pgsqm7E7I0N2ZDSq7e35J3j1cdn+rqdOOTWsfj9/xYX1KJ41Vy9MmczltU0efptIdq73ecVkrHQ\nosUcoY8ITpuCzLnlvnGr8N/Pqhyd2+1yM+q3b0hOwimpr8mydAtq0+TBj1cDEJdTbkaBYD6q8utX\nFwq/phOLDx1Z+fxUJLaCz5yNDTj01rE4+JaxYVclMP4zdQP+/M5S2+OiB62FmxvxxrxqnPb3iZi9\nIX+gqRhVic4eMUklazXTLQA4+/5JuO75edm/P16+XUgZQKbzb9AW17M21Odoef7y/vLsZy+P0mph\nbxxszNmz/cxlk1dn8idocg8459hQ14qKUZXYUNeac65XgWFZTRMqRlXiP1Pyo+HdM3YlKkZVukoq\nWqwab87fgvMenIyKUZWoGFWJcUtztX1OtEJ25V76+HRUjKrEky4i+xXjO/+ZiYpRlZhblR/hzg4Z\nCxgKZ02ovMZxk7/MbXd5e0GNy19EA1nhxoMaOnQLASvLh1Lh/cVbCx4P2sfH6fzpZbOkozuF6oZ2\nh7+UjxDBhzH2LGOsljG2zOY4Y4w9xhhbxxhbwhj7gohy7eCc4zv/mZn9+635W4r/xnNZHn8ogG1N\nnXjZoJXQ+Wj5dlz//DwcfMtY23s3L7icDqOvz63GjuYuXPXULIyZuiHv/j8SKJQY+XRVbfbzDS/O\nF3bdWQYB7rtPzcI/CoS/dkuHhQbAKIx6zZ5tNenVaNHV9GMcwPn/nAJo/xoj5HkViC99fDoA4N5x\nq/Dt0TMtz1lW02wox1Mxttz08gIceuvYbL4jEXPmP8aLe986v3tjcfZzTWMH7hu3SsiGQGdPCrva\n3Ps3kKlbfHH6ZvU24LQtyHSe71NWeBlirPIcF5sKXqCukktQgVF0k+xSFnyKEnBjDHJD7GcvzcfZ\n909ydG5XMoWdrV3FTxSIKI3PcwAuLnD8EgCHa//dAOBJQeVaUmd6iL/732J8b8ysHD8OM36G+bAH\nS7PK8mcvzsfElTsAAONNgsjh+wwEAPQzRaNzegsLNveaoP1j/Ko8rUZdSzAN+Ih9B+b8PctC4+QF\ns/Dx+rzq7OcRe+7m69pXPTULFaMqMXtDfXZ3xfyujLsiXtYa5t90ahqXhZtzE7c+MWld9rPIuWb/\nIf1z/jZqfIqZgHqphlFDJ2JSPmyfgcVPcsiHvzoLAPCNE/bPfjfysWkYPWU9jrp9PK57bm7eb9zc\nws2vLcRJd03IMcF0QtjjE6EOjndvJdq6FRN8jIyZ6j4SlVfNsBdklbStSZxPbSGCenS6YJ1K0eAk\nAysTUadCp5f5Y+qajAWKk6TsN720AKfcPdF9IT4QIvhwzqcCKLQVcxmAF3iGWQD2YIwNF1G2FW1d\nKZx56N544SenZr+bsb4ej35in9skymGpD701Y8733qIaPKTZzup8sWLPnL/1Nmx0EG3u9BZBbVD/\n/Ih2TR3ObUyd8tVj9s3TcgThowHAd0ADK656ahaem1EFIH8iMfosed1lPWHEkOzng/fe3fIc446K\nyIXAL84/zPZY0QGzyPED97IWPAf0LQPgXfAZfU0wCudDhmWefSLR+x6P2HdQ9vMnBq2lFz5antnM\nuPWdpe5MCn2VSsQBldtAeaLwuOe37nE09Xxh5ibUS9glF+G/bKVl1F95j8jcCjFDZKv9wMKsToaP\njxOfPN2aR6ZlgqzV/gEAqg1/b9G+C4SDh+6OV356Os45YhguO3F/7Du4HwCgX3mZ7W+8Cj4cHDua\n7TVJskinOW5+bREe+3Rdzvf9++Tes0jV9S6TI115guVd368t8tQ/nIfysvzrunHic4MuEJYlmGnh\n7u+53fnhCssoNvsM6pf97EXu4RwY0Lc8+3eLTWQxs2ZGFFeePAK/vuBwjLn2FMu6eeXNG8/AuJvP\nKXiO1+sfts9ArLn7Elzy+f2EDrYJxsAY0N6VxMG3VOK9RTU4/ZC9AWSEuFMP3ktYWT20U0rA/WJh\nc307nlEsjG1ZEcHHiBcndjcLPJGBilJpjioPIe6d0uJxw9INVs1L9/Vcb/IdteKfH6/GwbeMzUtG\nXqZrfBQ3dQsziXrQckCQz17v026eX5fEZ62UmoMxdgNjbB5jbF5dXZ2Qaz763ZMw+9YLMbBfuWXI\nXZ2+LtTtRmZtaMjxOakYVYlRby3Bqu3NOeetq23Nyw0jkpdm5+aF+fs3jwNg0XkEt/XalozQd/fl\nnxd7YQA/OrMCn9t7ABjLF3xE8vxPTsXtlx4DoFdA2M0kMHopfve++YK2efdx/z3cm9OZm/HNFx6e\n/TxhxY6cY+ccMQwAMGS3Pq7LseLEA/fI+btfeRl++5UjMKh/ed65xU3d7I+fUrEXBvbrveaauy/B\nvd86Lucc722CoW95AokEE74TnmAMczftAufAza8tQldPCowBXzxoL6zZkR/5zitpzvH3sStx+J/H\n4r1FNQUXwOTjEx22NXV4ygfllB8/Nxd3fbgCLZ2FN46ilIywGGEtrh/8eDW+/OBkTFptrent7Em5\nijxrhjGgvTsZaP/Wx9i35m/B1zUfT92P972F1oEjjOOcbulgzsunWzcEYWEhEqPpu5kZ63fi0Ylr\nC/7ez7uxciMwB/hRiWSaZ033dcHWiTDTp8z5uaKQJfjUADjQ8PcI7bscOOdPcc5P4ZyfMmzYMKEV\nSLDCC6V+fey1QW55bW41Ln5kGn7wTK//yKWPT8Md7y1HxahKzNko3kHzjveW5/xtnVCy1z/CjNOJ\n7pChuaZU09ZmIpPtN7i/VobDCzlAdwhPMLP2JRe/ZZ57xDBcd9bBAICtmh9Y/z7+u8atI4/O+67Q\nHOxVO3b6IXtj1V3WLnb6FUW8lkOG7p7dybno2H1x4dH75p9kKKjYe3Hz3vqWJ7Jl67/zu55hgNCN\nAMYyi6zFhlDszZ09SDCGvQf29b17OHRgP3ztuP0AZDS8T03dgJ5URtO7uoBQpfimaskzeXVt1ib+\njHs/xXkPThZehrmvtXYl8Z8p64WaS+l5rOZvanDlh1ZsA8Pv+P7DZ+dkN+iCxlhXXTv14//m+/YB\nwFG3j/cVeZZz4Jg7PsL/vR5cyG79fn73v8VYWtOEZCqdtVLYZuMzvXJb76av7r9l9ifR1yeqa3za\nuzbczxAAACAASURBVJL4zWsL8d6i3OVqS2cPvjdmNh6eaO8+IZqfPDcXN728oOimhXicvaOWziTO\nvn8SdrZ2udL46EoHN+bbfpEl+LwP4FotutvpAJo451JF14SFGZaRQ4da+0b4QRcKgFyfmvHLgol8\nZsRqCV1Vbx9esNZhUAKzzaZun82YeIdY3fwwwYKz037ux1+0/N6sIfFSvD7oX3nyCFx58ggM6lee\n519jvK7T52d1mtmk8funfc729571JKxXILzhnEPw9A97zdus6uTTxSe/fNPfXjU++nNOMLEaHyvB\nNZnicGHJU5B5t12I0w7OmM6Z1wvNHb07quZw72HlGSOc8aP/zsW1z87xtKBxG9VN56KHp+Lecatw\n00vWedG8jOXH/uUjbGvqwBVPzsStBdIsmAl67Ttv0y48MH41qna2oaGtG1t2taPJxlRa5DRT6BGK\n0NLowsS7iwqHRfbDOwtrcqKTtveksuXazckJxrCtqQM1jR3Z3fykyTQ3kdX4qD02NbR1491FW3Hz\na4tyInNe7TBhqq/8kKbfbtmVWb8FmcBXBI3tPdl1oRMtjq506JJ4X/n2KR5gjL0K4MsAhjLGtgD4\nC4A+AMA5Hw1gLICvAVgHoB3Aj0WU64ayIuZSIoIb3HXZsfjSYUPR2pXEt/49Iydi1DlHDMvu6g0P\nyN/iWycdgLc19bPVxFUmQDIxS/D6wGV3aT+qbH0nQH93Rw8fnN1NErWYO3r4YMvvTzhwD8ze4E8z\np0fOG9ivHH3LE0imecE26HeBPPfPF+KL92Sioxid6kWRYAwPfvsEvDBzE046cM/iPwh4TvO7eGBF\ntMBermcmmeZZsw5zUV5CXCcMO6V7794X9RbhrRdvyY3oR5Zu0cAqNUFQ6AFttgqODnbGvZ9mP+9s\n7cLQgf0KnJ2hWLAVEWP9/+Zvwf8MqR32HdwPs2+90Pd17Vi4eVfBsUXEgj8pITDAbe8uy0km3taV\nzGr2+iSs10y/MiTmPEAz5TavA3bvW4amjh7Ut3YJjawpGqM/3BVPzkDlr88GYE7bwKWEfy/TnrdV\nmgw/7GrrxsD+5bbRFd3OH3OrGlCWNV8rXld9nRQ5UzfO+dWc8+Gc8z6c8xGc82c456M1oQdaNLdf\ncM4P5ZwfxzmfV+yaosn4idgfF7E4GLHXABwybCCOH7EHLjh6n5xrcs5xZACLUQC45vTPoeq+kXjo\nqhOz3/UutnorYTNOueLJa07O+Vsf0OzMtLb7CPzwRc0ZnDGGdDqz6Lvw6H2KRgFyg9WV5tx6gZAE\ncSOPG47fXHg4fn/RkShLMCTT6Wwb/M4pI7K7YaIYNqgfZt96AT781VnYXfOPETkeJxjD8CG74U8X\nH5UTucyOoj4+Pjud151iZvg3qB3ef1yR8UdKpjkYrCP2ecldoD/3tDbZHqxpqo3P0tx2SfBRF+N7\nm7JajF+rGwr5vfrFqQlWkP6bduxo7sLWRvEhoTk45lU14Jv/noElW+wjjzqJdlWMnqSc5/amQWBs\n60qhTVt4O7HC0Oc4s+Bz4F4DAACT18hv824wmugt39pseY5R2Dcj4g01tfdgXW1rdr5o6xYX1IJz\njpPumoDD/zwO13jMK2jmlreXZoNPOTF16xV85Jm6CdH4RIEEy+wspdMcTR092HP3vsLLME4hZSbT\nup5UGn3Kg5lkrKLVWZXkJnqOHSeZnNvNKmxzR/fj1/DVYzI+JAmW6aCZMUjwMzRc7u7LP48D9xqA\nfQb3zxMYvOw6lpcl8JsLjwAA9Ekw9KQ4XpyZCULxpcOGYuLK2pzrOhW2Cpks7ju4P/Yd3B8H7jkA\nJ4wYgl+dfxgmr64Tsvh1u0Yq6uPjvSoA/C+YGGNCzcCMwk02T0U6bbu49LJLqGtt05wjlU5bbgLk\ntV2SfJTFmL19piE3mchd5JHHD8c0mwXm5oZ2VDe0Zxeivfgv26lGMyw/j2888Rnm3SZW6/POgpqs\n1YUdi6obcfm/PvNdVhihoK99ZjYu1OZlJzv05TY+PhV7747ZGxtiEUTDz+ZuITjPBMsy0y5Q42P0\nL5u+bqflOX56Z1uXE42PZuoWNY1PFNAFkUNuHYuT7pqACx+aYmvnKwJzJLKeFM+qEkUutv548ZH4\n7VeOsCzfjAhTN/NOf3Ynh/kPXW1Gv4cEY0hxri0GhBaRU+drTj8I5x4hNqhGthyt4s9+llGdJ1j+\n0xJ5b0MG9MF7vzwLBw/NNyPwug52Ijhzm8+W57qsh7lN+533dY3Pdc/NxR/fXOzvYsg1VdT7mtHH\nR0S/T2QFKo5kirt+J4RaLK621goYfbYK4aQP9StLFGwDTv0V3KKHci+G33xfXmloE58Hx0roMZtT\nixB6ABSNKBYEW5s60aqZunU5EGzX1WZCXps1PvraSOZiNwz8bDqt2GatYQoyuMGmerERJW95Z0nR\nc3Q3E5k+PiUj+CQYg7HvrattxWOfih04jAszcySyZCptaxPrlbIEw8+/fFjWrCmnLhbnOzFPckuP\ntpNjd2URi/lMYArklJNjRihhaffBYn+xOJZvzV3giNC+AXIzrLs1iyk26Ic16WX7Kcsk3P1kVS3e\nmLfFd1LcnP6vdfVkmlsKuV5JGCLbJdMc5ZopifFJm8sihY+62HWpB02JqP1Q7PVv2ZVv8iViXDHn\nmZmypg4VoyqzIW91wjB1A4ArvjBCSjlefPmcMCUkM7F2bRe/08X43dGdwlG3j8uazelmckE9G1UI\nomXf8rbzwCFusQpy5Ud4q24obk4ahqlbyQg+jAGdpgd7gIccKk4xRyLrSXHhpm7m0NI55WtvttCC\nSAQ92uBn58AtAt3UjXPx0eMKXcvY4Tt8DtDmZK9WuTpkOEj6oZCsZlX3Yk1hpc2OliwSjOVMvJc+\nPl3YbpouJGaCG1if4+VtG4MbpNIc5VabKaYCKaqbuljlvwICMJ8p0gTMC1ARI5FZ8Bk9eT0A5CW+\nlG3pdvWpmYiXh+8rThteCHMOm6CYv6lBiiAxXosa6UTjo9PU0YPOnjRuf3cZgN7nHHeNTxA0dQSn\n8RGtRbz2jIOyn5duacJ/pqxHR3cKN7wwD/M3ZYJH6RofmRH+SkbwKUswpLQHe+ahGRW8aOfynPJM\npm7JdDoblUPU4Pr4906yPWa1xyyyWV1w1D4AegdBlv2feBJaYAoOLt6czuX3Xrn3W8fh+rMOxo/O\nrAAAfPvkzG6jrM1OEX4ebgUzEff22ajzMfOW84Ve1xjcwDzYfrrKOtmgW3pN0tJCNwV0TWGKc/TY\n+PiYUTxVhjQYY88yxmoZY8tsjjPG2GOMsXWMsSWMsS8EXSc7B/gJK3ZgkoO26FSoLXbWdpucLH4w\nbyLoCzZzqoCieXzEVgu3X3q0Vq7gC9uwo7kr+3yNgsk7Pz8TN19wuN3PXLFxZxuueHIm/vbB8uIn\nC6IrmQbnPOuj1a9AZNwHP87ku9E3EPV3vlZgUuegOe6AIa5/I2LMv/rUA3P+/qG2hggCOz8fv/zh\nf4vx9Sem495xq/D+4hp8vGIH/j52FYDetB9+89y5oWQEH91PBACO3T9jc5vrjyDC/r73sx6JzO64\nCEbsaXZINZYvtiwjK+68CKN/kBvdTeSutpkEg5bhWq5pl0iO2HcQbrv0GPz1G8ei6r6RlgEUgrg1\nq2t6betu26+IdcUBe+yG4UMymtk8Ey6fJRif/++/mvGTEzX46gJK1sfH4tl5actGHx/O0WvqZngU\nZOpmy3MArDP9ZrgEwOHafzcAeDLoChUStH/8nHXiS7fYbXqcc8Sw7OZfEBHOzBofO81H8UTHYhtw\nosBGRFDa0QkrMhuExud8yDBxYZxrNQ2h7lPzxtxqjFsqLlWivmYysqu9G/+evB6H3joWtS2dBd+j\nWbuvC0tLapoiEXxl+JD++FxeABA5/OD0ipy/ZT8uPxsEuqBvDCVfuTTTF/SAF7rgIzJaXTFKRvDZ\nuLMNE1bsABBcCE/jdRMsdydLdmMN0vRsQN/8mO+6JkbUxPHAlcdnP+vJZznECz4qmZd5qYrM2rvu\nN4pOaL0uPr33Y5fDwCu6kKj7+Ii7buZauoBmVe/84tR8D7LhnE8FUCg512UAXtDSL8wCsAdjbLiM\nuq2882JcduL+OGjvAfjDRUfKKBJDduuDCf93LoCM07oRLy3mxnMPzUbiBJB1gtfRwyCbF1KFwiJX\njKrEmGkbPdSml4H9ynH35Z/P+16Gb9GXDstYl0xZk9lJN0bkMmq+/C7+dX8bPZH1H99agpteXoAG\nizxfXthjQK6W7sh9B2FTfTse+CjjizZzfb3jEN3NnT1ZoZjzcMzd9h1cPL+Ukb7liYLtZayNkOl3\nPfTGz87AMQahsywhNhLpFz6XidJ7wzmHAMjkLzTjpm3+6eKjcv5Opng275OOns9ygNZW+2ouIH98\ns3ggBFGUjOCTQ0CrReNlzeGsjcdFNdtCt2G50y94nD/GEK2mprFd6GO1ChTBOTcIWILKKXBM9nJx\nQN/8sOQq4WQBnxt0Qm2Mt6PbGYuqc66Pj7ieocs5uiOokyAZZOrmmAMAVBv+3qJ9FzhlCYZHv3sS\npvzhPDS29y5W61q6CkYf9TOm33juIdmFsjnqlpfr7tanDI9dfRK+csy+2HdwvzxTt/YufbGbe/Gg\nd/yX/e0iXHP6QXjl+tPw/i+/FGjuIgA4cK9e3+FrTsv4OExcmdl0NQuDoqrSoe2Wm1NbXPnkDF9J\nxIcO7AvGgJ9/+bCc779wUG4C66ddCKfH//XjnMAMIsMzO2Xczee4Or9feaJg2PU5G633U1Zt82fK\nd6qWy3DWLRdgxZ0XoU8ZE7qOe/PGM7Hunktw69eOxlmHDYWV94eb8vYb0g+zbrkAG+/9WrYf2Anf\n/fpkJjPj5p2s0PYlKfgUUnX7wmzqZrq+1wVQ/z7uX5MMRcYj3+1NmNrQZj05e71n46907RnXDohM\n0GhroidJlWKs+gF7BKdKF9HUCwUltHpeQa1nRO14GessXuOjm7qls+WYa+3FX03vT1c8ORMALIMb\nmK8bVtSsOMMYu4ExNo8xNq+uzn90LaMAe57mPwkAX7xnIk6482NfwgFHvnBx+6XH4Nj93fssFIKx\njMZhzLWn4KzDhqEnxdHY3p2Nlmin8ZHFmYcNxfEj9shqY9MWFRHRVYyanM8fMAT7De6Pr5+wP4DC\nUczOOmyo5zJ14aF/n0TOfW3Y2Ya1O1rtflaUIbv1wcZ7R+JLhw3F6zecjjsvOxaL7/hqjnAH+ItS\n6jdwkBecVne/wf0BZMZZq3Z7hBYgY25VAxZVN+Ydv0xQ6PL9hvTHgL7l2iawmA509uFDkUiwbK4l\nuzWPm9ISjGG/If17LY4KnLuH1k+MbWfjTu9t1Q0lKvgEdd1cUzdjA/XTVP/7o1Mtvy+0OBcdBMCK\nI/YdhJm3nI9j9x+MK74gdmPUeG+ZUOQc4MFmGpdP8Pdi+bg8NkbX4awF63zyE3N6vI723I1CuZMg\nAW7IBiFIZ3x8rIONuL8BcyTKY7L+ir3XMj+nxvYeqY6jEaYGgNGTeIT2XR6c86c456dwzk8ZNsx/\n7i9j8zvz0PwFsN+1TpthV3313RfjurMOtj23vrULP/HgX5Rr8ZBp+1f9ZxYufXx6znlh+3To/T4I\nAexn5x6CR67q3RAcPqQ/hg3qlzX36dRylbx4Xe6czjmwm0uNf1mCZX0TewWfsjzTMT+ChfERnXbI\n3rj2jAoMGdAHew7ITQDvJ1BUh0/fjiDb02s3nI6PfnMOEgnrcp74Xib+yfKtzcLyMxWCIdNu35y/\nBfM37fJ1Latxxgo3G2fGNULWOsfm54P6ZwQfY1AMWeu7khR8ghIKcrUULE9tlzV1c9lPyz0MKr1J\nE4Nl+JDdUPnrs7GPtjMiqsAcwSeRUe+mORf+5mQIiEEi00fJfR6fgCoiCHN/BSC8/Yr28fn8AUPw\n5o1n4LGrT8JbN52ZjVBZjC272oufRLwP4FotutvpAJo45+I8xAtg7sc3ffnQwMoym0MBuX312c82\negqZa7yFskQCyTTHai1qlzEDvShLCK+ITChs5pZLjs4JOlRelsDu/crQqvm06CaqvQFbeu/d7Xg5\n8rjh2bxeHQaNj9lnaltTBy5+ZKqjKIFO2dPk8+NnjOvo9rcpU9fqPhGt03l/YP9yHLnfIC2ybO9z\n/cox+yLBMpu/h+0jLkhFMZhm9v/7/y3GFU/O8HUtJ3lzupNpV6aI5nZgFBYvO3F/y9/s3rc3pL9T\nPzG/lIzg8/BVJ2Q/Zwc+0dFiDNt2ZYlcUzfO7fN5FL0uA1756Wl53xfqvJamRy4H+l+ed1jxk4qU\n6XU4NN6b0dQtuz6V4ORjLCOonE+qCwdGYhLboDe4gUm4FkmuxsdaqPIqdJ9SsRe+ccL+ONlkZ997\n3XzipSn1BmPsVQAzARzJGNvCGLuOMXYjY+xG7ZSxADYAWAdgDICfh1RV/Op8d2OvV6yaRZnHRNtG\nAaYsYb9TvLSmETPW94bNla0BKqTxEWF2ZTb7GtivD1q6kvh4+XbM2lAPwJv5uhV6v9br3a+8LG/D\n9a/vr8Cq7S3eogTavJo9TBqfpA/1md88R+feP9nX7wuhP1/GGMxpZo7cL6NtHz6kf/a7oH1UGBMn\nsFsFlTBf+S/vW0b+tyU3srF+zdzobacevBf22r2v+aeZOvWQ4COUUw/u3Rm1TLYooC0Zr9rU0YOm\njh5UjKo0OBd6FwOs1JKF1zLiFzoy105mU7e0nsAUkBLKzLwo3XP3PjZn+ijDbLqleDgAR8ENDPcQ\n1N2IWicZ37HoJqXvqrd1JYUn3bXE8EysyiLBB+CcX805H84578M5H8E5f4ZzPppzPlo7zjnnv+Cc\nH8o5P45zPi+suu7WR1ygE7f9pY+ATYAyxmydmv8+dhW+N2Z29m9dQ3LCCLE+R4VgDJYP5ndvLPZ9\nbd1s9sZzM1q7gf3K0NrVgxtenI9X52RiZ/Q3vV9u+L9TGOtdaD40YY123USe75IuWOjCVm2L85xN\nf7vsWMvv9xmUGxXNjyntVU/NwryqQsEWC+NJWHXYxPXTyliuT1hXsjeHmvFdBplcVK+PMVjFOwu3\n2J9chHMOL26iO9smaIMd5g3EjH9h5rNe70L5nmRF+CsZwcdKEhWNUaDSd3YA4OVZm3LOk7HADWud\nE8Sd6YEiOMRGyMpc29l5qmovnCIi2lqh9ZCM5iY+lHnvZ32TW1Tf1B2ca1u6Au2LTi9Nck+0YIzl\nhGAOevgxtvsyj/4axjbW7SIL+/GawPOzc72b951oEYa3ELqvhJkaAfmMGGOoum8kRl2SCe3br7ws\nTzDQF8t++6U5AfO/Jq3H+f+cDAC487Jj0bcskTVV0jUUW3Y5v8ezbRbHhwwbiJ+e3esntrTGOhGv\nU95dZOlKFzq61rK1K4np63bilrczIZerdrbhoL0zArtR8AkiH5aRRILl5Md6ZrrzaHpmznBgJu1W\noM318clF1+aYhX6dkz63hzBNaDFKSPBheZ+FB3XLsXHu/WPjzjZfOWj8JDrkHle8fcry48UXq4aV\n6Y6IBVeZbpqQFhOm+wJD1KS4rwdF+jC59/FRW1q0GhNEYVTlVzdkJkNzfwpqA8Rqc0C0KR8RPNec\nfhB++5UjCp7jt4tZtYo+Hk3djCzc7M7x+pBhu/uKDOZ2rEkwsflQCpaVYDC7LvS32PV2+y4ZgPW1\n+VGwdmnhzxOM5YxDnZoA1FdQBMszXUShszPL1emUZOKk43S41/2l1miR8V6dU43qhnZsbmjP5jYa\n2K/XR+W658UkHbaDAaht7vVpCtolxq0Gxty0OO9ddur+O72pI3r1nLv3LcM7P/8Sjh/hbgPDKyUj\n+OQ6MgdThnHx9OJ1p+HR756Yk/jLu6GbnN8YWfrXi5TRcujvK5Xmvm+sLMFw+Um9EegKaZByzLYC\nexb+Liwr7j3gfvGsSvtxgmjBZ5jJFMTq6o9/uk5YecUeNck9pYvbbuglmI4Zc4Sy/n0S+L8LrYU4\nvX5++qDbYTCZ5li9XU7o3PIEQyqdzvm73LRC9LJJxBgr6FtTlsgVfMy+Fn5xEwlTz0djxBi+W6Rp\np0iGDcwdxyv2HoCz758EABi/LJObyTjW72h2H2jBDYwxzDRYEzkNbuMUczPscmlGaFxPMdPm+1H7\nDQIAXHzsfnnzofQAJ1JLCxGrFyK8DMPng4fujstOzCywzUOT650dm/oWDGcdwC06eW5B7PLrC+5U\nTgJT7+U4+aWMfmguQnVBwe3iOagdVb9XtQxuEEBur0V3fAW3jTwab954huXxKav9539xOpaRj4+a\n9C1LBBrBzQsiQrsnTSZY3/rCCJx7pL1PAYM/4dxLrqqJK3dgQ13wwk9ZIldAGdS/V0NgvGUvQ4+u\nkfj1BYfnHUuwXG2EjgjBFnCXu8fsN3b5iftj9A9Ozv4ty8SpGDcazC3/9o1j88bXqvre6Ji7tETD\nRsHnZ+ceEmj9zI/8wD3FBV2ymkvMppTFyIvqZvh85H6DsOLOi7I5rcJEjdYmATk+Phbf6R+4D1M3\n2+/tLxiI4FPsuOX9+6+Ift10OhMZz88VMz6tPOfvUsA4AHld3DsKbsCtP4tAZOJa8/UE5y8FkIl8\ndP3Zh+CUisxup7m+MrV1JPeoSYpzXwt+UZsLxrbpNaqbET1U7l2an9KZh+5d9D7DEM7fXbQ18DIS\njOWEBNZN0fxinMvMGma93D7lvc9Uf8ciTBndYtZw7da3PEcos6p/kNi1NLt8RK/+9PS8Y09+P5PD\nZ9jAXq2aMTRzMKg9kJcZFQzav8YRakDgz8cZJST4WPj4CHD4tivDiF6OVyHAy3xgGbnOZZmqKCD0\n55pMB5DHx2lwA8HlSkPgAyu0MLEOn64met/IrXOwE4rV8/ETAtaMcSyjqG7RIc15zmLBDj+a9IK/\ntShahMZH99k467ChmHfbhbj0+P2Lzn9+/NC8Ph4REeyKUV6Wm9Pv1Ip8s69M9Cv3N6Fb0Fm1obIE\nszRrEzUUtHU5N4Mya4fM1RJlfucXYy2Nz+mMQ/fGuUfsk3OuLqzpiThloD9GK02eCPzOSObuxLVo\nvAXLDGGhoEZrk0Cu4COv3Kydo59r2EwYBU3dfJSnl2lukGGtnXqDG4hNBlkMGR1SdfM2I+7z+Kh9\nc1Z+fyJqfLjDhHZGu3+vOH0nJPioh74oKGSuGMpbE1BoZ09vQs2hmp9EsSboT/Pljb0GWucTEYm5\n751+qDG1ho8Ls14TP6tnZyf4iMJN/h2zMK0/kye+d5LQOjnF3n3A/oWY70GPTjagrzz/JL16Vj5T\nvq9t8Z1bjXLO5oXpguZnm7NZ56oU/6ihd5JBzvsI5jEXW1zIjOrm14/Jq9la3lpXwKPW65JMG5LA\nCtLWeREqRRG1tagqi2e/ApV+F+aEwyJYddfF9ppf099BmbpZtWkKbqAe+utXoV8ZW6LX2hjb3dCB\n/VDb0oUBfXqXGLb3KeA5qLzJYpY9mm1yvbi9A4beJOlW2rIEY8IiuFlxxiHWjvUH7T0Amwy+MEC+\nqdtkzb9RD3Ag8/Xd963jbI8x+3V7noZ+Ty2JqzmQR5DofUwvU7VWb92H82sZ9pBXQhqf3s/6Qw8y\nnLURXWoWnoekwLHsDrZHnwsvVQ2qLeudKcW5kGSQxcyCrH8T/BATZAnG+nv1C3C7eA4r55Lj6xk+\ni1p89u9Tlg3XmVtW/vVFCj7Gd2q5aRH2TEPkoe/W+1mbOhmWjKdcd9bBOceC2gR89kdfxCNXnYgh\nhqimhVxLGGO++veXj9yn+EkhsWJrc87fSQtNr9fphWc1Ptambp+s2tF7rrcibNlncH9U3TcSVfeN\nxGejzs9+f+dln88794ovHJBj4tfW5VxbJJrvnvo5T37Tda25Edv2HZzRZB6576Dsd0EvE/Q5eICi\nUfDyTN1gdPVQhxISfPJ9fIIsQyeroDAuPF32Dk8aH5/NzHKhFFLLzQln7fu+/NcnSoi8Xyc2+DJ3\noHw7dltshsgkJWCWdFpt0viohy74OIqWKahMJ81AhJC835D+OWkDMmUX8fHxUe6IPXfD5N9/2fXv\nZGgazDlqjBHvjM/afbTXwsJzgjHp+XEAa7+pPQb0xRs3noGTPpfJ0/L6zzKRLoMSvL2S0wRN7dEY\n2vkfVxxn8BWVdw96WYFpmXx2COMawfxUiikGZFIypm45GWUDaqe2L9ZvcANbc6wCtuE+75HBW4MM\nognrZkgpo6mbD5zel4zuqLCFRh6FFybiOtXoa74g7FpWZMNZG+ocRDhrM+YNDwEuPpZYvQkVzKmI\nXPT37zZoiB+cbF4E1VKK+/j4K1nVNt5q0G4M6leOmy/MDz3tlULmkgkGXH3qgXh1TrWw8pxQyGz4\n1Z+ejvbuVE5+IZUobEWTOfrcj7/oWMM4qF85WgRqt/QkoHZ5jw4ZtjuOHj4YlUu2ub62iO6T1w65\n/Toq53vJXbdkND7G99G7yDFqYfyXYdXfc8zqBPv4BNlWWEbycVWelSAmoo45OZi0f72+rp4Ud2jq\nFnxPVG23y46fnp0xj5GlNTh6+GAp5RjfvSgfHydl6YjQ+OhQVLfoIcTUzeX5tq1Awg5MtyELvDGx\nt74R5acLijCBDorGju7s55euPw3Dh4jJvcLQG1nMKsoXYwwj9hyQ/Vtf7wT9qvtYmPrq9O9TZin0\nyN7/c9JWzKeMPH44AGD/Pezfn3lT1U+kQisa2jJtqZ8m+Fi9yzC7Qa5LSe5mYu56J9zOWqKCT2Cl\nODrqRaXtuiZZgcvet2PIbu7CMIY1sZi1dSIFBpWED1kOukHm8ZGB36ekv/Owh2ERPj5OX4kir44w\nkOYcfcsTQvLmFKRA5AJrf7BgqmHMZbPf4P65ZcKfyVCY4/igIqGF77m815neKPAZ4R4Nfv72jWPx\nwJXH44s2Ub5OOWhPAMABBRbronEV5UyBcSlnbVhgcXjTuYdi2h/PwxEGn55ieEmsWwh9zpi/Xjzw\n5gAAIABJREFUqcH6BMFLCLfVz09gWtj3NCxKRvDJeSEBvYEiQWts/y56XU/hrIvf4542g3Dm2t6e\nkah+brxOp8G21uq+pAQeCKiIoO1bRWrcZNkyO13EeH0n+m0YIw31hp0P7n3I2tW0juqm0KxDAMjk\n/1hz9yV5AQeCJEwBYfd+vQtiq77rV+vqpYkL6ZNFyj3niGF45KoTcfJBe+ZoYHwXy4Dd+5Xj26cc\naFkFBuC0Q/bG2nsuwYmab40M+per6XhvxDge/u0bx1qeY26PiQTDgXu5e39BrRvszKQ5/M3T/vP4\nWFnnqGfPX5KCT1AaH+vFRa+6T2+QoncBrHDS9u+/8oSCx/MjUzvTaLmtRzGaDOE/B/SzH1TH3Xw2\n9nZgO+w0qlvQrylqa1En/UaEEBr0c9Ev389gkhG4qVugVzdt6luaugVcASJQ7LqV2/5m1w6KtR8R\nHLv/ELx2w+m48Ghr/wi/bdTNgu/ZH53irzADTjYVLj/pALx105kFxxnXQY8Mo0qhe5eRHNRY+sD+\n7l3HwwxHbgzCUVbAOf//23vveEuKMv//U/eem9OEOznnYWDywMzAEAYGmAEEiQISRPiiCCrqgoDK\nV1FX1FV/6+p+lXVZXXXFXdldUTGAuhhWlCCC5CHJ4ACTYJh0Y/3+ON3n9unTobq7Uvd53vOa1z2h\nTz1PV1d6qp56KoqFE8urQP7bUHVkgTtZEpR62moko9r7yzfn8VHd6ABThXifh6rZz7gH634/nLAy\nhO7xidoUG6GHy5TR0cvftpyN4PVfPjZiU2FnS6lyqFgUImdW5M0oUYmbFVGdtkVBAONxFPMaPvk1\nDMQUpxWffCJjlTXK3SR4qk5dWVkzeyxKIa59Wctokl8vniJvBURKwB0FXW3APnPnr9p+vbezBbdd\nvgbXblwQe60N7vPe195DSpOUx59cfVTg56omucNcG2WP2ZKmFnRsTNh7OsBUA0Eb5GUTGM7as9fG\nfZ10EiCNvq6valQ9SJquqUbqgjUz8NyOvTh+0QSsdg5Nk1XBbTrfRO05Ptnl2DJ4zvro3YGd1/Dx\nb8QsGpY8OsIwSfYn6KIyMZihjNoc3CCKrPdceR13bXoxqVgzeyyeeuWNsmwLn0vYnu+mKvfn7HJM\n9Ccm89s/nvKu+FRfp0mhEOpmxceLqtC1cQ+zsuKj5RyfeJIOZEWu9s8myZg9bGtuxN+evhjrndUe\nE5VG1SxZVFE4xYkikwUphh1z08qelJA4wRXOtE/ETWbXvhEXStWuboC+UNmB0RVN9zSEEkSL1NrZ\nY9HZUsKpSyfHXqujqNT0E0zGio+ZMq56QugDx89P9TtbqrwlalQRtuLTkHLFJwwd2xq8ZJWWVd1q\nV0FnnF3Ryp6SUJeGTxAyBrbh9aQ67eRhbJMXGJE6GzfW06CmdETuu2qQGH2l5zepVQolTtUzV0yV\nLzQFbgOma8VH9SDdvY9JPSORpTpUHQjnojGgigXVkLAIzssDsEWTu0Prls7xWVhVyGz4pPm5hBuX\nNWcSpkpYpLHE8Zo0rz7Y2A79+WMnAqjWLWhDPgCkCbboz+Igw0fmHJvf84XzDHt8JPRR/nvj4FJW\ndGVTV4aPu/H9sa27laQfdY5NVXADSXt8Yn5V80lNsIKYhJNeH8eMseIRUZLEv0+6MVfkehvqaHPE\neQgmEApuIEGO6rx30z/xkImVz3qcCIcF9XQjck6WiTnOnWhPAd/ZtBKY6RyfpNdLve30iYmsUoUb\nhNG/DQt+oGM/UfV34V/qLn3ufuEwVzcvclZ8MidRxb9dtho/fPe61PmdmAzhrJOoobsdsmtkpZif\nf+BoHDmvFxesmQFAvvtSKaQG1QQ30DC6clWJEpW0oxGaVPIJ9JbnO65aJywrzWoUY/EdyX+963As\n0nRAZlZkRuOpOs8ppatl0o5AVUSbrOaJexvdrU24esM8fPuy1Vad55QGHVG5iHzCnePTRcuF7kGI\n2xxlPexxUEfHGoCs7Aobj4h0A3lvv3TjLePefk1mCQqb4M5Svw6f24tDpvSEfp91TOv/ff9QSNzs\nEPx1ODqAgblpxroJbgAAo9qb8c1LV2Nv36CS9IMMn0pwA88zFh0Qzu7twLPb96bSRaRyyXZdikst\nyYGpjSlnieJuafn00b7rxfJAyxDeJ6RFwoqPzCecdOYuaaM5IkdMRtoAF95BwtUbyv7z297oS5WW\nuEx96XqblyvXz8E0iWeHEHYhUgXKKz4cLGKeU0awmCyDLgaWuT8aTNneZEVG3Y7K/rB8ic0u07ZQ\ngudpMqiMVNdLz42YuKVMrm4S5AeN20Zc3YJdCk1QV4aPakpxUzMVI6hcEmaP68Cz29IZNnEEFSx/\n52ZiZvg3H1yP/sFhHPu5eyKvyzr7JwP15/hE32MSQ1ElrpZJF6AGh7IbJkmY1duB50QmCqKSL0BY\nN+9M4zUnLjSoCWEDHK6rde13ge5vqhVCcDXL5OrG0q2QS3HNzai3S1jTs0dgojapi7dOoicqzffz\nYQPyxMGfzN9KGYN6RJ2zGKaWiWNT6srVzU+S/BYp1PGubs4eH+f99ISnACdBRN/YPT6+/ElTPP0i\npo5ux6Se6PODgOgVn0h5qX4VkI7hRuz5m09Gk3V7fJJlyvjullRy0ua9uCuPPJlJUdbIe1eUC2C8\nEdVkeaSVPT6WDMxUBTeYPKoNX7tI7GBSqSvhikeaD734WojcaFjIa92tgy3lTgRv3mSdew1r62Vk\nR7gRISHxGG44KXwyraO5ei2FQ2wlWHcZqcsVn8BMjnk2DYzFDihKjQGubgFF1E1HZZQsd/Yregk9\nOo2krguRrlAs+HUYaSKqxOlgIzLPWRKVkzhYH3P/JtOoTeAw2TT482zVjNG4/4VdwvXJRAlRVSyD\nnom6vVWEbmLLjcCjHhwexgMv7JKij0pk1JENiyZkTyQhMvT2Rr/yY8v5aVEEjXPs1zoaVWMJ1Y8z\niyEuYjhNGRU+YV8Vztq5UYrqlmNErP+wE6ldA8J98K4rispyMHV0/KpK0oqts9zGNfZRe2NUoGM5\n1i0n//HOtQDkdHhSOmV387FIWgLZNG98Z+T3YWLi7kV0hi6q3BfBZEgaNZIoNlt27Y+9xmRwDLfd\ny8MAP4gseov8Mm17WHuYpKHgDwJ3qeqcvCyk2uIT8lonqj0mwvj5B46u+awquAEL/043ZPgIImIk\nBB2COBLcYOQp6zjUKsjf2S92aJjjm5ceplSPwJkggQoWdaBk2LNgjEnttE01Ys3Os7NlHODee1QH\nn8SIjs1LwaTcdJJGnTO1rwFQ19h7Bw5k9xBeooL5BO/7SetmnL4NYCyrAZHutzZ5hYYN/kP7u6pw\n1dFpG+1LImRb0sUFouKAd9l4y++evkG89Np+fO+BLQY0GTkuxsXNj6BSXbW3TZlG4dSlq5tLkgxX\nsddeVH4m/+4AKVeun4P25hJ62ppw5LxxUuWK+LWKNCjG9/hY0CSb16DM/v4hAMnLAwewZGoPHt7y\nevXnMQklzXs3uSBX08D0DWSszvLU2Vpu1hdHhD0lioHITLmqKKZpCasLmfrYhL+V6cYkc1VdZvrh\n+0D0DDVtmbhLS/Y9PnL0CCKo/O7YozYyaTp4pbxF9YG6i0pdrviEPYDJPa04bdnkwO+yzEb5z/Ex\nPcs0c2wHrlw/N/Hv4tQW3lwuck3eW01BIgcuilavkk6xfP1/nwcA/PdDLyWW295cu88na/EP+/0R\nc3tx5Lze2N9HNcCm62ZSgu5kyqiym+vH33yIXmUIK+kbjA/zHOWSogvRSJ62dQ2qo7qFZUvVvllr\npsnSYWO7m2bMZ9R9S0LFyKK/vwzWqGNREa1LwyeMxkYWenZK2mcWtNznvhadeUlSnt+6ejqWThsl\n/gPFBEfQir+h2eM6Ir8Py7o8GUxxPq9S9vhkaG3OXDEVx3s2C7srP6oJP8cn+l5KDQyfEBjspy2T\nMjDhMknkFxkD2qhgFzYMmEf2EKZ3VTV5VpXqPEzteujvXyToIiqr6ruUvzNNUt2EzwRU9CDCtgeI\nr/yL6b9kqrgngRtR0jbq29UtQaSrplIDkHLg592LoNq38ZOnLwYAHBgI1zXtIC/L3vYkh1c996mT\nEh+WqQKepIBIpBJFTZ/IQD595mKUGhsw87ofCf+magWLI/gmYvJS9L79HQhz/tmI6g6+Oi9s7GoI\nUyQNb26qBmVxLVo7Z2yq38lw+5JVt8M0CYtw6hUbu8cnjUIZsbUtFiXrZJiOgA1eGUGGz+nLp+DN\ny6fg4lv/IE3mtLBjWHziR/a3B35NwQ10k2ZG+R/OW55SVm2a7gPXsQlZZ+EKHOMGyE8ajSYOb+WX\ntsdH42xZuA5mOw6//DSzekF1ytTMo8g1piIfpSXymeR73EEIIFJckxZpHeUmqJ5FBbTx4m+XDprU\nnVi+zFtMux/Viz83PnLKosrrqGA+lddB34fcpe4WTiR7TLe6Jy+eVPNZGkNcV3S6INWCVky/8JZl\nNUEHdFIdLdJbXkdem+hy69LwScOUUW2YE+N+FUX14oH6J52mLX7i4xur3l+9YR7OWjlVjj7e16ri\n4yOHg70M5ywlEiOh/GVV5+/PXYaPnLIoPrhBXCjzqDwTyDSTZ07kzbAi6gOtpTKksgVFIk3wc2OI\n6h1EVVvkeQjeM9Bkhfk21fREb2q342mOam+q+SxrVDfd+S1jvBCn8juOnh36nT+7Rp5tfEbonuSt\na8Mn6QDwm5euTienstTnFAXufq63ZsSJa/UdODmqvRl/d/bSTDLc8rxgYleidGLlhIX+dCrb2tnp\nXB90Eu+eIGGPT4YkZEcyPG3ZFFy6blbsdaGrRwL6iOhsJqqbWqoMW7KtCoeyZxpYMOXsKUmKsOFT\nM8BSy5tDAh6pxJsVIvcXNHCsCn5goNHTIdK7MiabPEyiyvTGj7vfq9bPxfWbDgr/fcBnnPNQVzeT\n1LXhk5SOlvRborwDdffVGkODc1UFMKhxdWdNvuUzGv/mhPkZBEXpUP77oZMPwsMfPSG9DPgCUug4\nwNSyAasqH+d/fOtKnLFiSqa0XQleRM8CibrCskcQS+Rsqk09DZGKuGeoprymSzXtRIkrTdjVTeMQ\nqqu1hNExrkIyPDj8/Yu37ZU9AaWin4lSUXY75PVCEZlIS4vNB+omUU3W805aDmsmKCzKTjJ8NOA2\njP4HP76rBc/ffLIBjfSydNqoms7jqmPn4Ysp902J0MAYultrl69FMVlH3Y7d5GnWH9y4MNXvwlb8\nvCya3I3Pn7MsVfpBHD63HML6iLm9gqtC0bOjKsmbYUXUB1I2+WdoNRNNskgcUMmoj5lCAFd5unk2\nqnu+uHRdsHtRdTjrgO/TqwVgJCy+DpLkodIJQk/iqsJZq1JfRr5EtgMx+RG4nx3h9+uWdxPjnLo2\nfJIUFMZYqga21uplNYVrQncLmhoZulvlBdkL3lSuv4BFSTx1qToXAtn3KprahO4WaenKbOCrVhwF\n0s06u6MS/7NdNWM0Nn9yE9bMHiu24mPC1U2xzKBw+QQRR1C59LcPN5+xOHVaYWQpozKqkrCuhiqT\nN5LbosnJgzcEkbRP/MnVR2aSJ+ail0mEUpKHs1ajR1a628TGlXHqp17R9Wz1qMjS7K7qpy4Nn+DG\nvvx0Fk8NPgNnUk9rJplBvpiuhfzbDx6Lx27aWPsjRaisoEGNq879DaYjoSWZJYqbITU5gA2sIwnT\nSGu4hYZEj/hNyXGKFzN8wq+xzd0wjuhIe5b2xERiwgatOlxwj104Xmp6IqXymAXj0NSYj/IrZdUI\n1W2Ptx0LPcA0YiDpXBD0UpiuDB4TIWpIIawunHfY9AxpOkh0MdTRl1RPeAULnDG2A9+9fE1mWXH9\nif/bmvcWVee6NHzCYAy4YPV0/Oe7Dq/6/Mr1c2o2/vuJnB1xLV7nwfsrRKmxIVNkGFvQWa7jwmTL\nWfZN/psJ3ekNZL+4sMN0dSBr0MwY0Jlwb5zoYC6wDCSSJMaqGaMVpEoQ8VgxVhBUIu2eCG89PmzW\nGNxw0kJ8/ZLD8PQnTwpWx4pMUYs3L8PDWadNXVPI5UQrgEl83YI/ftvhM8XTECDNJKquebOk/fNq\nCfvJU4X35nZGMs3/aFsyjDHMn1AdgUzkuS2cGLwcXTF23Pfe72LSdIMpZNlkZ7rQicrvSjA4jsoN\nWRtB/Y2eaDb+41tXJJITlD+u6I6WklWD7rRZ+6tr1+Oea46RqUoV3keVtq5EdSTfu+Lw0O+Soqo6\nesuRhf0MkSP8xUd0kJXFIHF/+u/vWIvLj5oTc221oEx7fGLqis6q5JWVOJRy0L5FpOvDUhEZcEiu\npRp2G7LnjpOHs/bld4imMs59CsQRpyrSXfz5i8EfBI19vdA5PprQ7QIy8lyZ7300X71wJa7duAAz\nx4aclBuBiO+2CtIcWAoAj3zsRDzx8Y34zQfXZ5IftqqmizEJDgsTyZdvXZYuhHoUInkTpJtISPKg\ntMd0NGPG2PAzsK7eMK86jVgpEUiu2kEH26WhqOFkiXwjUkQaGNAsMKpMVNwkBQRInYbEhkLG5KI/\nCX92BwVBSnQHBtsC2aLPXhV2tmB2Sd4UZEfTc+kJODPIZd74zszptzalG9bHGzZxrm7BwQ08F1R/\nV7VFIVq2bOrS8Ikj7BmkeTaBhUGwoZw8qg3vOmau9EGTqkKWNd3WpkZMHZ3cyAtCfnADM5ZUnIul\nKMlPbx95mDMcw/uGk6Ji+Mc0ehFMTOgemCHwTHzavvdzJHREBKEKHa0SY0yo8xM5PLiSXhZ9Mv1a\nPpkCNXjywjsukNHnm574UDXBfPic3sDPh4aTP4mo8/6y6p/GHk563qHMc3ziSBzswfmryyU9CXVt\n+AjvJZAiq/y3+lAxCQkTVVRCQasO7RgmP5PbhfymK0qfvz83PKS092euWs0p9hyJdB4rfO58YdmQ\nNlS1EJrqoioDWmSTK5FfVK9gRx2AyyB4MLBUjSLk2NZxGqpuabPBRldYGTqN7kgWjGHzJzfh245H\nRVpPFT9Z7+MzZy0Rus5EUJu4vjUsaFjlOBfj5s4IdWn4xEWs8n9/4sETM8lzB7T+/T61eskvGDpn\nBHTJqNl/43ntRnaZOjr6DIJbLlyJz529VLZqiSt31vw6aXH6sjm7V2w1Q3XDNX9CFyZ7oib2tIl1\nYEF5J1tDe5rqYKI7QCLviHYJ5x02LWX68QLKCz4ilk/aEmduJG6NDcB9kxcCI2j/s4s97NbUzUbu\n/5Enpqkh2XC21NhQs0qZab+ohHtpb5Z3pEkaosrI4XOSBUigA0xzgv+5tDY14PmbT8ayacEhroXS\nrCRaa4GoLAhRSauzvIPTjZL22bOWZBq8V8thOH/1dDx/88kY2xl9ns4JB0/EmSvDfIVrEe00MpzD\nl4o547K5Yp2yJHgPi4wIeUlWsNxO/NfXrk+8suQtz25n1VJqwMMfPSFROoD5YCAEkQS3uC6aJOes\nF6B2xZBB7Ay7LHsisrSbqfqzRPKiL87k6hbyeQqvrci0jcy2WzTQTUNCO6oG3T1J1q4rrowsCTnq\nJRaLAoi4kOETQJi7VNoVGW9Ui6jCZfug69IjZ0lP8+xV0/CPb10pPd16IU2J9Jayz5+zDPd/eENk\nukFumiJpi/4mKSJ7fDiA7gTnUOiajVIX1U29DMJiTM2ieUgyQ25TAJMovK466mWNvBbZr2KTXRE1\nrpGp58dPO7jms/+97ljce/1x0qtAGrfpqLJy5froaIVpCDw30ZKCUdnj43sP3+fB36qlrg0fHQOE\noMep0wdfRNI5q6biLIHVj6s3zI+Xx6Pfy0KHkZhGgvY2J0WkFe9Pm0sN6A1YHRPdqCyohiTihbjn\nYb3z6GydTNLytXx69GyYsoAiMc+XIESI6pMamODBwIKyshbLmhWfiAQPnan+OAApUd18+T84PJxC\nj+r3YRO1uudFoiaMk65Euf2SN8rg5FFtmBhxwHxvp0ik1dpcSbqCWTOw9z2QM1aIe5ikwfTezrhI\nwt5yYHqfnlmHQkOYyPKgGdna8wjkaRaUVlgD/Zmz5O91idMlW3pSkxOSIe7qlnCPj8AerDuuOgJb\ndu3Hu779YK28RNLEqV7xKWuW1BhKJE/SRtLGBhYY+jUtonr917uOkCaTIPyEtQ1ZBzuBE3MBA2iR\naqBrs32SNlZKdDSFI4Yql2LP5x2S93qYGGeqFHnfhzagb3BI6NqFE7vxm83bU0jJ58xRFq2ztCdh\n41kbPRDq0vCJQ3YjUeUqxMwXBFvDWReBJFkgeiL3kqmj0DcYPAOY5XBbUcKWqkV/l+g3KX4k51wP\nu5kyKjpYhze3LexniJQIHx6qVAcxAVkMhER7fBJcGzdXI7JaEzcYVFHfihDO2kXIRVqw4XfLWPks\nnKbA79LID0oj1TxfxESm6seReY+PCrd0eKK6haRPB5hqpjqKSvT3UaybGxxXHvBYvVXpRheEIqFj\n+dW0IelF1TMd9vl8n7F8SiJ53o5FqJOpOl+i5iPpjOzNyT4QsZGss8ZhEXWin0kdNDCEFOL2zalc\n8VFJ2MSQqK43nBx+dpmLtP7Hk9Dg0DDa4s5wk5jfl62Tv39XBJVl5uSQwD1BBIezttHduxpZYbhl\nUBPFzf99gt+qpi4Nn/CZ9rj9EsF8y4kFH8bIjLn+Eqk7nHWQFF13Lb3yaMiwtCImjSr7NMfdsoxj\nbYbdcOwCTzKtv7tI2joaR1d9m4xpgFZxiBAUzPLWzlSLVbyVM8T302TZF5NsVT21GADAW1fP0NJv\n+7NjYJjj7g8cXTlnJog4vcL2nGSZ5A2VFaCK6X0cB0/uzqSHbO3lbmWo/czWPiKsqpvsY+vS8NGJ\niA+1LrlFkqk6C9N2don8z33vo8qF/6tzD52OST2tOCNBSO4kHL9oQo3spO12mnKe5Dcig6cfXLWu\n6v35q6cHXhc6GSK5VHtVjjsX4eYzFqdK1/bokERy4p6pSN386oXpomcyJra/b7ZgaP2s479d+waq\n04uSJaH+qlxhrtpL6Xk9ODSMKaPacESEN0laObrRJtsj6OyVUzF/fFfl4zNWTMEnTz8kUXJZn7qp\nZpiB4YI10yuGXxLCdH7T0skCcoPTSzt+UEld7/HRWTArh5givkKNam/Ca77GPa+oyGMT9UfnYDKo\ns/aLnzamHb+7/jhlOkzoHomSMxKMIzmihmCWRjHqp4un9oB59tUZ64xS3N+5h03Hdf/5CIBwvSMP\nMLWooyHSIfMZih7E7W/rRF3d0qKySmpZJQbHl85fjgMDySOxhTFtTHvsNXH3pvtMudC0BRSR2S53\ntZTw2bOX4hdPvOLILx/bIEL1+XXJlYr6hfo9PiPSP/Fm8Ukzl6jHFL/HVCS9MAH6O+W6NHx0jwcq\nRo+7jyHmOd/1vqOFT6+3DZt8TotE1lnHLC6PleenZRAhcE3SKFCC6Zblm7GMWkoNoQEsCEI10QM2\nptxtSZU7WegeH4kyOAdOWRI/Ix6ZhpPO+gXjcMNJB2HehC45yvlk6ERoX5gCue59Lp82GowB7zgq\n3dEGiftJ/904CXzklEW4dN0sPL99r3OdGO8+dm5CDSqKGCE4krCYAalbZXJ1S0Cqtr+yaTsoPeZ7\nX/5bamCJT68PwzuQUz3bXTQDR0dHITyrlFIZGRsKv3Xpaly2bhbGBZz3IwtXlaj8SFu8LjkifuNu\njdthSllxhBlWT3x8Y6zrW3S6RD2SeW+GoahxusqrjAj8cXnU1Ji+r64dAzBho0dgV6Qn3ZFPdU/u\nmNrjO7qjGc996mSsFWhXVY6N0t5/0Pl6I2mqydWwbBAZK9QGM2BVado0Pqxrwye2AZBUGbzJlB++\n+oYn2gVGXwnUclK9gvz0Z1Frc0yEnVRC/B+E34fJge2CiV348CmLkp9RlODaNGVSNP0Pn3wQ/vXt\nhwEAFk5MNpMap9ZbVk3DpwT24sTt9WOMBc5Obzqk7J4UVsZ1nZhO2I3qs2ZkdhnSNY1Q7t3Hzcuc\nvLfuHT1/XM33Jy0WcyFMIkcEmwaSWVWxceIm3R5Vz0Sz765UP6/M4awVtCHePKidXDT31OvS8ImL\niCEz3jgLeF3kvceBrm6SK1T4RnR1HDSxvFFwzewxCqWUiTsB2WZqI0Jl+70Qsb7uDEfNH4cfvnsd\nLlo7I1p+QgU+fdYSnHdYcMCEpAQ99+MOcoJM5OT5E2pQEXWrKv0IAY3uAXSKULl/ckHM6klS0e5E\nhJcPHL8gWSKBeozsAZaFacNIaJUgByGjRQnTzf3cndgalljeg+qOqSyquX/n/eBQWcdGz/Kr6edY\nl4ZPGO6zaG5swJrZY/CPb11R9X2W4hpsEOhG7ejJRLhuncwd32ls71VDxprqffJKAk5kfPQzx5Y3\n87bGnV2RgUOm9AgfGutn9axagzfNhk8tK6BkJNUNcc/6E2+OjmQlUm8bGpjygYqq4Cbh8oJ/dcyC\n2hUdb78WlN0iEe/C9Uj908T9bZKooTLRNcjNIqZytqLvU5m0ONsXZOzl1G04pBXHOdA3OAQgvG+n\nA0w1Ex4pieG2y9digyekb/n6kR+8//j5CQSNpMvAQqvT585eioMnd6Nb4uDatMFl+rDJe645Bvd9\naEOq3wbPppRzL+rQ2kQyBK9bM2ssrlqffLNj1IyackM1QYv29+ctxz9dtCrSmNDrolmt+zccVzkv\nv73uWOH0gleZs9cNW1xaCbOEPWrXNStJSTDdZ4jwvXeuDVx98ZO0CiyZOqrmM119mOwBYLW3icd4\n094l21Z6xJjdKxaePQx/Prv7tvsVB7HJ0u5nOl/Lv1/N+etGO2wthU9q0gGmGkhbMLxF4j2CvsPB\nkS6qo7y5HHfQBPzoPUdWLQmmxapBj0FVZoztwLiu5Jvyw1R2s1WGe1OSbGloYPibE7O7VOhGtBh2\ntzZVnR1kijBXPZUrURVZAZllUS0mcoisbkB2d5J18L1q5hhcduRsOcp4MbRcmmYDuKw5fjgWAAAg\nAElEQVRnsnLGaCXtTBL9ZGa7LNfJNCt5UZMGLc7Av39IjeGT+bYVFAKOkRWfliZ7zA17NMkBaQsW\nB6+bQwW1zY5pXkmqLIUrHInWSRGp8IW3LMW/XHJo6t/LzC9TbpqTelrxdV8eHDmvF1dvSL8p2/Qq\nK6GPuGftGtNxE2FV0T8D0gwLC50GWZNyYpGmxO87bRpZ8KacJkx/5Pe+DAq719uvODyZYAFZ1d9F\n/C6zZLnIdLn057a74uN9zkun1a4wVn4vUCBUr862OsaK3/tJBDcv3RWfFn+kYoPdVF0bPknzPY3x\nUrPfi+l93qr3dnjRtSm/poHRlKEMwPWbDsKGg8bj2IXj1clRMfNSlUfqMizJszh9+VSsX5A8H/0y\n8rS3zJ8908a04xgnD9y7ePsRs3D1hvmRv6tN155NroR84upV2LOWEc45Kv3Tl0/JnHba1ihLu5/I\n9U/jkRBRmn338jW4+/1HpUs1JNlTlkxKlV4i2col+OTZ5O2CEX1cTx7vftElU3rSpRnwmYoJr0Mm\n9+DwOWOxYvrowO/jggWBA9ve6ENTI0NX68gWjuqDYmVomgwphg9jbCNj7EnG2GbG2HUB37+NMbaN\nMfaQ8/8yGXJlImLUZNnEGGSAmBqw5WVjnExB/3LJobj1basSJRdUIqaNacfXLj4UbbLCW6s2RqO+\nC/myLYVbV1BZln1rOsqRWzfl6x7k8uq7hrmyec1n4ekSRSbrIE51H/OFtyyTkk4WLS0b56aC8/g2\nZ/XssZg73hOlLubGw771tjs3nRYd/CILScqezEF7mglqWYPv6nFebaL3XHMMbn1bei8HEbLUB7/G\nPCS91bPG4LBZY/DuY8M9E9znv31PH8Z1tkRu4dA9Fi5lTYAx1gjgywCOB7AFwH2MsTs454/5Lv0u\n5/yqrPKUEpP33a3pgg5UDJ3KwKa42OqulXRlQUdnatvMlMt7M7hZ+bHzDmuJC0WqTG7Iaz9JOnNb\n6yChH9G5uriogHa2VQJuagnVDkpR6aAs5BBzXchaEUyL1POhJCSW5VmLRAydMbaj6rsogy+5R1LC\nH/gIXEXiPDBPvvuOtQDKqzmROoHjwMCQlj2ySchs+AA4DMBmzvmzAMAYuw3AaQD8ho99aBgh+OsC\nQ3hwA1Fuv2Itnnl1b+Lfqb7b4OVXNYNf3YM7HfJovJoMk3tZVs0IXvqPI6nftmgbETxYFVSKyC2x\nxYlV/an92lAZsXkfGmM+Q1BbVLdkcuL3+PjTD0qjfNHEntZEspNg9eG3OSfQUJeYSWErPiKy3O/6\nBofRUkDDZwqAFz3vtwBYHXDdmYyxowA8BeB9nPMXA67Rhs4Gn3NIGdXecdURODAwjJUzxmDlDMGD\nNCNCMgfx8w8cjT0HBqs+u+3yNdixpz+RrlXyJOe1qc5ayd6bwL0Z8gUl8VOXtuyvaLyg8kRs0UHO\n9xJuCA47NLlqxce5KEm+kXFTJ8Tt8QlbsZTUlmQ9R8xL2j2ah88Zi/99Zoc8RUJkM+ifhAo+R0Ye\noe2E8/klR8zCuK4WvPe2h9Kln0RmAKJlQMYZOCqpNpjj8dbPJPvlAqMFC/9anLR7C70cGBiqDWyA\nEX1NTILoCm7wAwAzOedLANwF4BtBFzHGLmeM3c8Yu3/btm2aVEvP589ZiiuOmRN5jbdgZ+2Elkwd\nhcMCDlIMlZ1C3JxxnTWRRtbMHouTBTdB2juPl5y4Sh+7sS8GHWPWtLM16fEbJfKEaJ+s0ID3ntyX\nQaKTqEOubsUha5F3XZmyDkKV++ALKHhCSGSpNJolChtddYCp3Mqlcm9T2DPz3oNbPhobGE5arCbQ\nQVTZSdo/vLZvIKs6gch4qjG2ZSTXnLgAnz1riQQtwvO7t7M5/se89m10xL7ou+Mc2LWvvxIdLkzH\nPJ7j8xKAaZ73U53PKnDOd3DOXWfArwFYGZQQ5/wWzvkqzvmqceNqT1C2gR7P4aJnrJiKD25cKPQ7\ntzx5C0rRJmtN+YGrGOeJ3IvKjaEm2XBQ8khrRV15kF2mo8pqZQ+gZ+QpOuDUvjeBsALvc3/PsbUH\nHNu5Nycdpcbq4YqIgZ94j4/hSYO0BpkIQbeWJpBNUmQWwbVzxspLLAiZ3gMCZck1QrtaS6nKdxLu\n//DxWL+gPK7+/wKCkgS2FZxHB0iK+o6Vy9zmV/dgVm9HxJX6keHqdh+AeYyxWSgbPOcCON97AWNs\nEud8q/P2VACPS5CbGR7yOoqfXH0kntuWbH+N/wwYU42rEbmKNvnoHuhp2eNjwUz98zefbFoFcSzI\nr0z46oYbPKXZ4xawbl4vulpLuGxd2GGNxRnYEulhYJg6pr3m88qKT0g5EY3GaMMBps2NwfO0so07\n5t/kownZIquzpTaPvnrhyqrBts0tSXdrCWesmCrk8WJdt6Bw8qE6KIn4nXe3iQ39s+zxAYB9/UM4\nMDBcE9TBNJkNH875IGPsKgA/BdAI4FbO+aOMsZsA3M85vwPAexhjpwIYBLATwNuyys1KoE+qwO8m\n9bRhUk+buJxA3/5swQ2SoKMTi0O+QWKmiVay90bTpvSkvsdSZGqSJDO7dI93vGXqo6cdjIWTunD0\n/JHV7t7OFjzy0RMTpWldx08YQ9oeHwt2qJcaq3/oHi/R1BieYPwBppbgRn5NkDkFWswTOKdMsXxp\n4ax54Osw0tbPuJWWtKTNho6A4z0YGHbsKTt6jekQcLPTiIwVH3DO7wRwp++zGz2vrwdwvQxZeYTz\n5BFbZGCiXbRh1UIWIg3X2I5m7NhbHfjhnFViB3OaDpmtwxVKhQQlh+L6IyAp6mqjdO9ubcLlR0Xv\nGQxPNyBIRoEGRvVOWHn0PvcgA4C5E/oxZSGuf1JZlERrmv8ckGVTR+HK9XNw4ZqZiWXGtX0tpQZc\n67ixj24fcW9XuSqjL3qcFjEKVuIEr5MqNQGhAUb0kPWxBunJeYyR5XwbJnu7Y/gE7S9y25zcHmCa\nV/TP7joRY0y5uhmY37I5bGkUoo1V0N195qyl2KRoo6jtqCzbeR7Iq9prkec8IeIRfr4MOGXJZLzj\n6BGXyH/7P6sr7VjoxutAt/6QUGcAfvzeI3HozHSh3KMQuc2XXttf9b6hgeGaExdKDcXs9ldPfmIT\nLl03CwDwjqPTTUakIdken2TUHJhcIzt9YxLpDpU61XTkc8RRy5zxnaZVAAePLhcR2zcYA4adz8d2\ntNR8F5CMNurW8NG96bNqP1HljSmXLc3yCjA4y6sBp4uQfZFK0f1Efn/DcVLTU7eqRGW1HmlqbMD1\nmw4CAEwf047D5/RKc1E7bWk51O7EbnXnvUTx2bOW4LiFwVHdokhz+/6fNAeE4nXxR0BNC0fy9jI2\nqhsLfq0bobFW7BkLAnLE1AlJXk448arbyNgMe12eQ+UlOKYiDbErPoIZZpurW90aPjoZiejmvtc3\nMNF+0KeJ5XpNN6l+7426+zCQXQDMu/MJpyHQ5U2QMejzz7oWYFKAsIt7rz8Od773SAAj5StrOXvP\ncXPx2E0nYnTIAGbBhK5E6SVpg3o7W3D2qmmRe3nSkrUt/Pw5SzP9XmZY359cfaTQYFkXwouVwi5s\ndjeWSVZVM8uKDDiQXqB/0oxzsdW8qLFLqYEpPSA3DVL2+OQVHYP0mkLoCRijetCje1VL16x/6GF9\nBiIPZREZHmlJD3kddOuw26T78stNTnm6RD4IKqfeQYZwOPSY4CeMMbQ3lwLT/Nn7jko0KeD/fdQK\n5e9vOA5tzsbpNP2Z7MGoX1OZQR+SNjn+/Fg4sRtzx3finqfKZyDWPntz+7iyksc1bBMbC7IQdsh2\nVMkQqZOTRrWiKSQi44gMvdSt4VPzuBTmfjm4gbr0k+hhglwOsPOoc8HRMePnH4SpLLs2tAlEPpBx\ngrq03/l+ND/hak9gkiGKeA0qHU1yYnczyXJVtXFBqeqaGJUlRpf3wJ9uPEFumpqP2ZApjXOeOt/f\nODAIALh47czgtD2vdU/S163hE4SKzPem6KZveryjtIyZvjlFhHWIWW+3aHuHdLrUyUxfV7vrf96y\n5JoIV06oJ/OegzSlwdIClGq/TppVooCfdLaUsKdvMLkCCWTJ2JtXPd5In85vPrg+odxaYUnk21Tk\nejxR/JLgL2tqA/3UZq4qedFR3cJlDw4PAyhHSEySpg7q2vDROeNatXmuMrujT64OwpdKVWBTU5mO\nmhDKKm/Jk7iKMhE+q6W+lOdhRVGVjpHhynOQL4Ra2poacerSyXjr6umR1+WhNTW5x+PX167Hnr5B\n/I/jRqYMo0EIRl5PHV17GK6MdAO/F0gjiVGYqh8tUDRS2ef4iBxgGvS7waHyp40N9oUSsE8jTegt\njNWekqYOdjTm6ia5Ndf16EwNBmSXzdA9UXLFaENl1DJyPysujLGNjLEnGWObGWPXBXz/NsbYNsbY\nQ87/y0zomYYwA5gxhi+etxyrZ49N9DuV2GSQi/bFozuaMW1Me00DIW3FNsWoIElUN2BE9by2cXFZ\nLeNZWFQ0tRN07/EutuE5NuTEsvYfPJxUhgrq1vDRharoG9koVvVWUW9sjyCTB6Q/F4WPJHy9SqF/\nttS0yNctCsZYI4AvA9gEYBGA8xhjiwIu/S7nfJnz/2talYxA5yONG4Ib67UcwSqiu7kkNj4y5ob/\n10lSO2JOb216CQL/qH6Obt40CoxzZAx+bTDoeELPiiPmlp/h4ik96eSFvJYFB48s41GPtmL4NNg3\nlqpvVzddcjzBDRhjel3stIez9svX6GqnMO2wu/DKnD8h+YFjNjTWKrFpdtcGag8RlDtwqvqOMt/L\nYQA2c86fBQDG2G0ATgPwmFGtYoh7hkVpP2yKehhVJ/3Z3d1mbggVd4ZQ0rZFVXvREDPwFZGbh2Ke\nxLPiR+9Zhwf/8ho2HjIRj3z0BHS1pttXFCxPYqTBmHDWIxfWfjTg7PEphUR0M9l21a3hUxNSU1HV\nqio0Ef6QymXrkBcWnlmBHiYqTeCGQufv/R/egN7Olprvk6DTEM9j2jWyFOSYKvXJBDHOFAAvet5v\nAbA64LozGWNHAXgKwPs45y8GXAPG2OUALgeA6dOj98/oIHMQhICC/61LV2Nmr7y9HllJ2o/86f+e\nEDvozsK91x+HUe1yDmbkHMo7AKXJR2SzrJDfSV37kqBktSQi0YMn9+DgyeVVHllGj4pVM470+erY\nPYErPqYn5cjVzYPuFQPdj74oEcS01ZkE2aVrF9OyaaNw0doZiVNMumkxKTrbMf+9yBCtMyLPiAx5\nQnQdhFtwfgBgJud8CYC7AHwj7ELO+S2c81Wc81XjxtlzaGRSovz6Z4/rkLrJvVpG8jJaiYoq+NOe\ntvgBZZYqKONQxppj/jI2pN7fe5PyTkpq22PsiBSxPeN00jWhlrUfU93PipBWXnCkODFXt6Dn59Zx\ncnWzDF2VqTpeuV73ryDUnk1SfW95HYLZ6iX031cekfg3pm5Fdjk3cR/SD8WVnL7IqdoEAOAlANM8\n76c6n1XgnO/wvP0agM9o0CsTKgexqg6KTht0R2Z5DrsHk12zbtG6+ri4FR9RNeKMQlfOmA45q3BJ\nqSnXOoQqjtbKgcgH5BpFQfXG/Sg2uIGBUWL9Gj6aKr3XWq4UEve9Bh10FilbjQUZqOoQ82oYipK3\nIqHsLIQiV458cB+AeYyxWSgbPOcCON97AWNsEud8q/P2VACP61UxnLhJhOwz1d4BlH5E6oe2KhQh\nR3U/INW488swZNU1aprxb21qxM1nLK4EDEiCyrxRdihtgj1FotQYITxuH2nlshqGKys+IXt8AtLR\nRf0aPhrhnBvpTWr2MRV0lK1tGTzgs97OZuzc25/Kj7nWvUrPjRSlGEg9wFReUkaocnUrygOWCOd8\nkDF2FYCfAmgEcCvn/FHG2E0A7uec3wHgPYyxUwEMAtgJ4G3GFHZQPSBIdSioodrinzgsAlUuaCoH\n355Hpru/lLL3RPCpn3uY+f12psiaz4Fur0g/aeee49PW3CgkSydk+Cimys+WlR+46YGJzr1MymbI\nAtKVPasu0tj+69tX456nXsVoicvr+oJByBekY9m6JjKaivxSdB/V4U6zkzbUaD3COb8TwJ2+z270\nvL4ewPW69bKVsLJFe8iA8V3ZAtmEIduNUKWsuLRe3LUPAPDY1t2xvxcZJ+ShOauegDJTT2xp9/uH\nytENWku1ho9p6jq4QdXyvtZoVGVsKaCyCLsd2QaJjhnH8Pht1UzsacVbDrV7lsmkm5XczlX9ffgH\ndbIkFq2uE3agot/SEtgjxW/cOiQlmElGo27jIRMlaOGXLRfTbc72Pf1C14noqbpMyhiThfZPKvdU\ne18raQt4tKub5zo/A47h09YcY2YYsA/rdsUn6GGqaijKkSrN+KKYXl0qEkUJy62CvI/rTQ8SssJD\nXhP1QeY9PoEr6OLXJsGfrqixURlkZRMvLCcIVbP4broij/ELb1mKeeO7kstI/ItsSD9oNmdttOq+\nPWKtP3WaScNZuy7+6xeMr/luwHF1axFY8aE9PgWD+V4zxjwbGVU7cKtNnshGUaKtmSKPdyF7hi46\nqhs1AEVBVVlPs4qqQhchLSwqztJUSbH35vTlU4XS877xfqyr3Qzb1B5EHtvyIIKMeF3FNrMLaoCi\nPCa4QUMDw6+vXY9xAa6fQ8Phe3zKaZt76vVt+OgKZ83tmtlX6TIUNGOgRI6BvSR5JrBBVtwiq8u/\nmqDQ8lLm1X9locsNlCgWcaVDRRWLa1ubQ05iV81I6Fx5d500Lbe+qqq3OtsD1RMjois+Inro6oqz\n5IktLXlUERrTUTZQRPfdcPDYMjltTPRZX61NAbIMZ1bdGj76QmOympdFGlB7CasgsrO6xlVCQYbW\nypAuwk25VrZkCSbbGJmdq8r7CC27tvRmMVQFTSho+0KEk7WeBRWZsBQ/8eZDcMzf/Q/aQ2ZyheSl\nKKNaDlyO0eucVdPwxMu78b4N8+UpA2dyVPLwXl8fFlxWGhOs+IjJyElj7EPnRHMcHzvtYCybPgpr\n54wVTj+r9q2l6HJgoruqW8PHJJUGTkM9NhXAwQSqs1N2+5WXQXUSil7GslIV9UexrCKWL0I9cXV4\n0qhWAMDZKyNcriJIe8yCgbnKGtqaG/GpM5bIkxXzPnl6YRM4+huDPEX/k9Vv6ez/ao/DiP9NZ0sJ\nF66ZEfp9jccOR+ZCWRJYIdZt1Na14aOrjHKPLJ3hrEPdaxTKrLk1GgnngramRuwfGEr9+6B+VXXH\np2KlT5nGmlZDiWISVtRN+Mm3lBrx6MdODHZhSYnIuNwd6BW1R9HlXqvtvDhHzIaDaje+h10bfkF2\nfUTIFtWt+r2poU/aWwg1mlOm+JULVuJHj2yNv9AAdWv46Drcszq4AQv9rgiE72PQqoY0dLdbKhvK\nwKhNntd3f+BoPLdtr3S5OiLhqTnHJ7/kaaaViEGwcCtpYyPS7GjRP3TIaTeSDIk32dgQk1jA1xet\nnYFNh0ySpwSAeRNiItAJ3nNexxFqJ5rVt/Vp833jIRMjw76b7KXq1vBxGRrmeOjFXQDULbdxzskH\nXzLqQ0X6DGNFcnQcxCqS3JRRbZgyqk2qXNmY6Pj85eDPHzsxU3rKDkYN+Cyn4wTCBJIP1lWJzHKt\nKxhPGN62XnaUxzbfipzISs9Npx2SXQmfvDj7Sygt60tlLap11hEwR9XqoOm+qa4NH845Pvmjx3Hr\nb5/D7HEdaJK8GQ+obogYK//XWoWr9hToiIRWLcNY6FMlcvUbJIRddKac6Q581BI6FSpDRFbCz11U\nuCk7xd7TSnAgCfKj7szkJnqZslszBJ9IStBg232uYlHb4p+qVhd9ienoaqOVHGAK9eMtE+66dWv4\nuIXx+w+9VH6hMO9//fT2EB3UFqnQDk2V2NAOVC35mwsyi1KXOk1ylKFZaToUl8hCHh91UJkXGRzn\nNaqXCCrqrH/FpyJLvqhIOXFtXHGeqp7tE8m0SPC7gGi5e/sG0dKkPnw9HWCqmR17+8svFGX8H//y\nWuW1uxm0KAdI2obsyqP7OalckQueiZKZYfrCQXPfX1l4dVX27NUdbFVLcUYUdY/yQCFVbyztnwpY\nnmuiumW8R/fnTY0MTb5oWupOPwtm2ClHMuSoLpI3nLQQw5zjhEXhe1LSonJQXx0lVG4m/XbzDuw+\nMIjJlrvAp8HMSWSWUFWZNLT1JyyaAG8zoKMdNz0Dn7fxo4uOs4IAPePVosyUhkedUShTUuLBUe9k\npFuMZ0sEo+Pp5iUojYpz8PxJmZyUlCl7YKg6LaPPUkS4wK2rvIdJPW348vkr0JbRPVBvOOtkn4vg\nLYO/fnobAGDVjDHpE4wUpiZZEerW8PGXDdXP4F8uOTQ+uolkwmP6q5OpfmXB3GBP+jk+BTFIwrB0\n3jgS7ZubFaSZx3wn0mHr4owKROtKmItXVVoRjbmJ7sWdrS9SjzCyx6c+qJks1Sw/a1vgVf+mHzyG\nr/7qWQDAoTNHZ0s4SJY3qIf01OOpe1c3l2HOlTZ46xeMxLIvamdVLw1c3tEZISdPxl1+NK0l6Jnm\nKe+JbEiN5CQtpQgZKVx0RO/xNx9cjzcODKZRSztV7rWS0wtD1/hDeI+PnAUha9HdDmdtCvoHh3Hr\nb5/zpKdef909VV0bPiYqUzmqG6+8Ljp5DEPpkl/Na4k7x0eeHI25pilIg83p1kETQmhAV/sAhPR7\nAsJE9Rnb2YKxnS1JVLIGpWOCmvPP5AmLilopa+Cft4kcHX2hCglvHBiovO7QGBlQJ/Xr6uar9FrH\na1plaT6fQdsAO/i1LHS5Qmo530lDf6HTiK89wFTmuQXSkqoiqNNW9ryLuqRcz4Q+Uhkh0e2IRhWH\njgiIpu5dttzzV0+vem/CaBh2Xd0krOjkJyBUwMq7oqxXcdYgB/D8jvJB5vPGd+LBG4+XkKp91K3h\n40fnykRlCVhxY6T7fAa3A936+n4c+Zlf4MWd+5TICZWvOj9lp6c73LhmZHdWqvPFxN4xWTKDD8KV\nkjRhENFnqOJRFzlwRtSd6bzr6kiSEtJztG8t2TNTH5efov227cVR977xMNKOg9z8fXV3HwDg8+cs\nQ4vCcuSOuU3YtHVt+KheNQjC8rorhdsf2IIXd+7Hd/7wF2tnD+sZrc+kHgp8AmTnve2DASIf6HZJ\nThNtNG+uTmmQdY/NpdqhnWbfj7py6bcBWRONA85SXVuzOvOgNoy73kJSt3t8aqxzRW3B589ZisYG\nvyuBGWtAj88prxTiJEvdNqLjMek7TE7fErwuVAzWVM1CBYez1hcinSDCqO0LlR8WIPBJwK9y3l7F\nIbM98Bs+wYfGqmWk/4+XFFfm8tKm6R7aBcrL+GCHhocBAKWG4q6L1K3h40dVY3/Giqm1spy/Ohpy\nFRUjDDfZynkLuWmuAtDg967L+DY1XtCxLwqQd3+Be64VV1JZqf/HA1tw/KIJ2NM3iM/97CmpaRP2\nonKgVY/lx1SPJWtMMOAMWpsbzUcSE17JM+jOKZPas/9UC/TJk5Ak5yNnQPkn7IsEGT4Ouhq8V9/o\nw6tv9GmRZarYNjgtAOcqDzA10EVpmnLMo0tHmMYy7yR/uVKL7HLrlpUHXtiFlZ+4W2rahD3ElZqi\nr4YAioIbBK6E68tMt/7e+P0/S+kr+wcdwyfA1U03Ml3d8uwyr9uNK600tywOOUt1TY16ypCJR2u+\ndhjE2+iZqFhF7avciYLh4co8ltT0TR8Ulnfy3ImoRueqmwxZBwaGQr8raeq4CHXomgRxy+LA0DBe\neaMPpQaG9hZ7NsgDcvPCFkNxx95+AMCvn96O/QND2Vd8hsqGT9Cg1V0p19b+u65uhR3p1OLN2+tu\nf9iY7CwMOmVI9YqPV186x0cXNYPn4o8Gddwh5yMNnfYczWH7qrsT8pL3DknpPhxdwU4yPoI23zkL\nf7rxBLQ1N+LxrbvR09aULXHCemQUU7cMHhgYwsKP/AQAsHBil9qITt7AQqjfPT53P/ZK5bWMQ1d3\n7CkbUv5Ba7Abb2ZxkSRx3xNqyy0vAG5/evsDW7BgYhfuf2GX87k6VKxYuq5uTQrdJU0/yvo1fOoE\njVt8KoW5sseH53c1RktoSv8+IhUyasXoQ5mbYzUq70920rKNtQndrfjVNevxT79+FpsWT0RPe9nY\nWTptlFxBhNXImMR49K+7K68bFFaqwI32AuJ0NGO6V8MHnT05I2S7yx//+WUAwJ2PbMVFa2dmSisr\n7gpTfDjrYvGB//iTFjm1+Za98HLwiqsb7fEpKCbCWVdRwHNJAOATP3ocAPCjR/6KMR0tagamBXZN\nND0bkgWVh4vqeACqilVl35uCtKePbcfH33yIgpQJW1DdP3FUu03+9fX9agVaQuD5Vxrl9w+pebD7\nB/wGldouM+qAZllGdF67Rd39eeo9Ps4P3QAZuvb4mKC4dxaDLYdN6URLeGaPjFd293n2+cgjz4ZB\nUSnCYYdKXf9q9r3RXisinrhqJfPQy339I4ZPW5Nd+3sAuW2MLe3VKYsnVb3PqtaUUW0AgA9uXBCa\nrm7vaiFXN6Wa6GNYezxrz0tJovf2DaKxgaFFU4AME8e71K3h44dzbk1jKJPgE93V3OdTr+zBTx59\nueqzJ195A/v7wzdg247qSmlii4/BbUW5Qfaev/BZz+K1OYR+ZDTp+/pH9piod3PhgS+jKGJNOWtl\n7XEXWSg5+zImdrfGXqt6j6d4OOtoPUyde5gUxoC+wdqVNpXyknwuAufA6/sH0N1aUj4ernqsmis3\nGT4OuurW6cunVF6rbnhs6Sh+s3m70vRVNIy1MfkVyJCeYpicYEk67HwlXo68+q+KtF1k5ZGbjPYZ\nQYIQ5AP/PrI34e1HzFImJ3CjvUBLoeXcO83TNU3+g0Yzpue2LzbszxgJZy3J1c38LcUSFGUzL0GE\n3PzdvX8Q3YoD45jOk7o1fPyVUVdzp3LTqG3M6u3QKk911iqPgkOD4khCDThZja7IT+kAACAASURB\nVKjC5+segu2PZkUQWZA1UO8bGMag44b5/M0n4+3r1Bk+aVExAx2Yexq7aNmRs9xYCUHjDN3dy+pZ\nYwAAS6b2xF4bpVueusXdBwZqPlM9btiyax8e++tuaf3J7gMDhY8IWreGzxsHBvD1/32+8l7XoLPk\nmYnRbwOpvccvnb+86v2/vv0wZbJy1BZGUjRjpyhh4aWHynZGU/4VnzqaByEyEFevMhUjBvQN5scd\nebbmCTWVtJQa8d7j5lXeZ20PKpHUfOl4jUYVXU6Q3hsPmYQ/fuR4HDpzjBwZlq+cMACv768NSe4/\nckAmHMC6T/8SJ33x1x49suVT2dVNn+FDB5hqxL/nXtuKj+Yl6MA474pknbJkMib3VPsWn7BoAq44\nZo5UOTobQJV2ic5Br9fA0uW2l0ejzl9fZD2iSoh3SekR9YGuJuJAQBQwG/nWpavx3XesNa2GVGaP\nk2fIueMaEVc3Hf3P6I7meD3Uq6GN3ftHVnzcSe6OZjXBk/3joP7B7AePcgAv7tyPyaPi94jJRHcZ\nqFvDx6WzpVwodY3RSjoNHwMtyk/ed1Tl9YGBIdxy0Sp8cONC/YpkxPbZpSSYXFVQIds1TmRXWZXZ\n5M645tEYJIrPAc0rPtUun+J1Yt28XozralGgkaOL4eqZtd9xV5QDXd1yOu2SJ6237emrvJ42ph0A\n0K5wxcfLdkd2b2e8sRkMA+fArn39SuuYi8nyWPeGzy0XrgRQbjB0jA+91rju8aiORt27RNrbqb7y\n6ERjnCMiABVRbHThqlh9dhg9cSIbcsJZA30Bm7JVkfYAUyUEZKBuVWTuXXJXfGpc3aRJUIPIINj2\ndp4xVll1AUYCHegyfF53VpvcQ6zTMDg8jKFhjnZFq1Qupp9lXR9g2lJq0N4i2BBtRXWhe/Ajx2No\nmAstc6elyING042CLHL5hHjVH2m4M7B+F9uCPGrCMFnbjLsff1WOIjnClnbWq4asPT5RQZSs6zst\neQ6ycUNbd7SoG2Z7n6V7DlcW1zo3jVad53gZKI51a/jcfsXhmDq6Dc9s21P+QFPmV634aGh5OQe+\n/tvn8My2vTht2WTl8gBgjEKDB4iY+VcgS+1p12bQ2c7kqU9rbmzAgO8kddmhWPPqbkKYxbaxKiEP\nmcOAKFe3GrnyxCrFOkNNgGMXjse9z+4AoC64gf8Ru0ZLlsOH3RUrXatULrrP0KxbV7eVM0ZjQndr\nxadWV9XyGj6qK7Qr6aM/eAzfvPcFpbKKRu05Pmb0kEXwQbZ65MgX4sqSK6y1uRH7BwYDz2LIilvt\n816OCL3E1VHZ5elvTpgvN0EB6r1OePf1/Dzjypu7ohzkWGJzPovolhdDbdm0Ubj1bYdizeyxAIB2\nTasn+/oG0dbUmDqAlretyWI85YG6XfFxqczEamoVGj2la29/behDlbz02n6t8oqG8lkJRUVQR4cR\nlDXyQ0KrZdfefnznDy/iO394EZ85a4nk1CmcNaGS9AXJWwaXTB0lQZd4/E2DTfVA9+yzV1zWPnp4\nOOTQUIvy10+cahbba4G4AbO+dP5ybH39AEqNetYX9g0MSVupURmC28WkIV63Kz4ulU3HmuRte2Mk\n6kef5hCi773tIQDFiFiWt8YwjOCVmPw/H5c83cugZwPOzx59RWratOJD5IEmDYM0m/of26rjxWtn\nZPr9iKtb+DW23bMoeelKXMOnvbmEOeM6lcravqe/8np//xDaWyQZPgVf8SHDx6lN+/qHtFSsgeFh\nz2v1TdATL+9WLkM3Ots/lSuBD/7lNfxm83Zl6YeS155PMf/3TYsqr6c45xjIahNGwlmPfEZGECFK\nWFGRvWdszWw5h03mAVvG0V49Dp/bmykttzQEh7OOEExIQ2UwAy8MwG8944e9fYNob5IjW8eKj4uJ\nfa9k+Giu/H2ecIeDQ2pXfBhj2Lm3v+bzfZpd7FSj5CRqv4ycWwtB2udpNcYL9/2VxSVHzMJznzoJ\nADAkuVBVVnxqDkjN5zMg9CBaPrJUZe/hpXltE/KMzL0VQ8PBwQ3y0M7sPjCAf/j505V7cMnLBNGA\nM57ratW3g+RVjwfR/oGhTAaLt4ToXvGhA0w1ozvDve5tA4oNHwB4adeIz/DJiyfh+k0LccyC8crl\nmkB1p60qdR0GsC7SHkwoQth9yLw9xhgaG1hN55s93fJfDYu8BGE93pV0m6qEmQhiIw1Y1jDCM8d2\nAAAaNI/ssrTBbrv+tz96HJ+76ymc8IV7Attf241y1wgxNbG8r38IHbJc3TRHddMNGT7aV3xGIkb1\nD6lvZPc6IQ4vWDMdX37rCrzj6DloLtX9Y7eKvX3lZ5TnVaWwGUW7u6pgSg2sJqx1VkZc3fL7jAlC\nBoEHmBpqKXRFu4xC5orPty5bjX++eBVaSuHp2NoEueGYn9m2txIKGshfv/jjP79sRO7evkG0yXJ1\n07DiY/Kp0ghYc4Or09XNy8oZo7XJ0oGuxluHmMHh6nKgukTmrSPRTamBVeqmrGfhpjOscEWMqD9k\ntoOTelrlJZYDbFlB8GrR2pRtSDauqwXHHTQh+Muaw5PtuH+gPCHkPerDO0GcN24+Q3ZEUDH2Z4zq\n5q0Oqg8w9dY9E4Z43Rs+Jvf4zOrtUCrr9f0DlddLNYUp1YGODkvnOT6DGvyfAmc2DcjMA40NDHc9\nJjuqW3D8SEvGXoTlxK0UyihGd77nSAmpEEkx0Z/ZhKubd1/SAc0Rb2Wy8ZCJWuR4vRJ6O1uwe/+A\ntP1Fqg+h96O7fNa94aObC1ZPBwDcfsVabXttNh0yEbMVh1WsB1RVTtV7vYz2eQoPSVVlWO0+MFhx\nEd36+gEpadIeHyIVGivvaM2DHReb3D/N7vDR8wxsXWn2RlLf0zeyT8ai4iFEY8oDRJPS7xk3DA0P\n47X9Axjb2ZI53aPnj8uchu3UveHjLaI6rM6zV03D8zefjJUz9IUN/dw5S7XJMoGtDXkcF64pn9ng\nbuTMWwMfhMpbCKufKl02Xt4tx/BpqOzxGfmsCM+bINJgywGmQX2HblW8GvRKGLiGy1HX4Mhog70r\nPtd+7+FaGRavWplmb98QOAe6JITSlhUgwWbq3vCRHb3JRtqb9YVX1EVgeGYVchQWj1Uzy/uu/Bvp\nVTfwSsJ/F7BT+ruzl+LDJy+Kv1CAkT0+5OpGyMeW/Soi2KKpLXro2M8SdK82FRkOoMG3UvKH53Zi\naJjjvud3mlEqR7irP1kCV7nGq67xomrPjSiKNyJOiI79FYRcdLTX/oGEisrpnpLuD26ggsCZzRwa\nWLo4bOYYnLVyqrT0gg4wJQiCqIfJ1yjcbqjR1yGd89XfYXJPK/7quBsHHcpKVCMjYm+HhlDW/iep\nO9AGGT4aQkoTxUB25XR9gZWXQYP9hYoGbcSIU5Nvmz+5SfoMupucTfsZiPwQVmyoOMnFRH66Mid2\na4qqZ2mZCdob81fPHstnXt2jU51c0tSY3fBpl+AuZzvFv8MYdMy2m6Snrcm0CkQITY2O4ePu8TGp\nTA4IM0VkTwSWJHQefoJiutHzJuIQLdp5ngu3rR7odht0J3JWz1a779dv1NlUZjgHWpqj2933HT9f\nkzb5xR1TpOF3ztlJ99eBayEZPgVe8fnhu9dhgq5ZJM3omjl3OyUVG0NLzvHaQzXn+KjtklTmHK1o\nBOP6r9fs8bFq+EHUI397+mJpYXBFsaWZsEEPVweVLYFqWy5L+rv2DeCb976A6zctDL2mq6WEyaPa\n0gupE5ozTNptdlbUHt7yuix1rIUMH49/bdEGIYdM6TGtgho0nrETJTcrJWcw7A9uoILgc3zq+/wI\nnVRWfCwYaBGEl/OdIxZ0YUsgBkvUGDF8bFHIELv2DYR+99P3HaVRE7P8/bnLKpOiScni6jZlVBte\nem0/vvCWZanTSIa6SeU4yPApuKtbPZG3fsN1qVK96pizbInFZDSYtLjP+sd/3op1c3vR0MBodYzI\nTF5D+duKifx0JSo/UNr31za+cs8zod+VNJ2NYwOnLZuS+rftGUJRu2PhmWM7Uqchin+sRgeYaqbI\nrm6E3VSCG5DxLUToOT456BNbnGg73/nDi/jlk6+OfJED3QlziK4C5KEOhGGb/a/9HB8Nvm5Bq/t5\nWmHSdShoHrls3azK644Moahdz5Ms+4TyQt0bPusXjjetAmExKlcXKsENhvQeYKpPjmUjGoO0eMKM\nXvqN+w1qQhDm8bcNpgbhNrRQIys+xR9wpoUMn3C8VSfL4aMDzllAMiLD2U7x7zCGnrYmrFEcTYWQ\nj+oOa3xX+QTtLbv2Vz6T3fS6frz+FR9dYwAVcmpOZJcvIpe0NNV2SDYMuoh8Q3ML6bHG0HCeYZ7H\n9qpVJ8MnHO+kQWtAPyNKxfCRcBZQEky0YXVv+AAWNYCEEDqe1tTR7QCAm3/8OPoH1biilXzhrFWh\nYzZV54St7b7qQYRF26GWh5BBjryWrMbEIMyN9Kj+QGm9ngWizJ/QGXtN2s3+Ovn+lUfgV9es1y7X\nW2zaM7i6tTlGU1sG4ykJbjl88C+78PyOvVpkuthfmjTg1inqPPKJinbcDe969+OvYv6Hf6xAwsiG\nzSLtMzswMIShYY45N9xZdficHIIraB4mLka103laRHpsG6wWGd3jgOHKio86wUFJ2zLeuXL93Nhr\nbNE1iqXTRmH62Hbtcr0Tm+3N6Y2WL5+/Aleun4MxHc0y1IrE+zy37+nHgQG9+5ylGD6MsY2MsScZ\nY5sZY9cFfN/CGPuu8/3vGWMzZciVhcoGh9CHzAHwwZO7q96rGHe4vrT/8+SrePqVN7RFFFIp58bv\nP4o5N9yJIac31z2TYytev2ldM2pE/olr0YpgD5m8BxsMSrc9rtdhyKlLJ0d+P2Nse6bzaYqOt9y0\nZHBTO3xuL645MfwspSKRuTQxxhoBfBnAJgCLAJzHGFvku+xSALs453MBfAHAp7PKlUmeopsQDoo7\nLMYYHv3YiZX3f3huJ944MChVhjuz8t8P/RXHf+FX+NOLr5VlS5VSJijQgI5Sf9/zuzRIyQcPfHgD\n3rR0MvYPDOGHD/+1GKNWwgrysOrpxYq9gJZk2UjTnN+Dq7PgH3+N9a043HPN+soB0EQt3pyhsawY\nMszowwBs5pw/yznvB3AbgNN815wG4BvO6+8BOI5Z9ISsUYQQQlfR6WgpobNlxGf2sa27pac/sbu1\n8v7JV/ZITd/Fnpomh7xGixvb2YIZY8quEFf92x8BUEdF1B82F3kTLUslqpvCfGlsYOAcGB7mSlb8\ns7ZjJy+eVHn9g3evww/fvS6rSnWD4i3ChUSG4TMFwIue91uczwKv4ZwPAngdwFgJsqVAkwn55eEt\nr+HmHz+hLP09fXJXefwcMbe38vqN/eEnV8tieJjjEz98XHq6JgIo5NH+6Wip+zOjCYnkdRLAbjQP\nCNzgBgpFNDqN55CnvNg07Pns2Usqr8d2NuOQKT0GtckXbjS2vGGy5bLKcZIxdjlj7H7G2P3btm3T\nJpf2+OSTXXv7ceqXfmtajUy87/h5ldfPble7H+acr/wOs2+4E/sHhgAEh1iWza1vW6VcRp6qr9dV\nnYashCixs/Q5qgM1GDTedO2rjNahjNIVHyeC6JClywPefY80HktGHg0f0665MqYfXwIwzfN+qvNZ\n0DVbGGMlAD0AdvgT4pzfAuAWAFi1apW2Gkr1LF/8Zec+bH611i1M5XP83NlLsXiq/FkoN2y2l97O\nFulyOIA/PL+z8v7MFVPR06Y20thTn9iEZgVnAry2bwAzr/sRTl4yKf5iy/CPO6jpIaKol77JxH3a\nkrWu3adyMOhGELXV8PF6DHhz4ZQctvG6yaPhYxoZhs99AOYxxmahbOCcC+B83zV3ALgYwO8AnAXg\nF9yiNXq30tnSEBLRuOfq/OTRlyufNTWyTId3hdHVWsIbBwZxxoopWty5Fk/pkb6Rk6F2UvWm0w6W\nKiMIFUYPADz1yhsAgB89vFVJ+ioZtqfZIwhzWFoNTFRPruEcH3cVZXCYW+sivGrGaNz/wq6Krk9/\nclPFRY8IZ8A5DuMzZy6JuZJwyWz4cM4HGWNXAfgpgEYAt3LOH2WM3QTgfs75HQD+GcA3GWObAexE\n2TiyBtrjk0/c2av/c+QsXHFM/FkAafjBVevw8EuvazF6/ve6YzGuS/5qD2MM37z3harPVO016W4t\nYbfk6HcubqjOPM9wDVs640rkkzyWJtNuLnHoHmtXXN0UynBXfLztj8z7lJHUv1xyKF7Ysa8y8ddE\nIayFGHT6Q/dAdCIeKaMfzvmdAO70fXaj5/UBAGfLkKUC8inNN1etn4ceRQdEzuztwMzeDiVp+5k8\nqk1Jujv39itJNwiVBqJ7KrW7R6kiU5lE+XhtNosWvQnLiSsqeaoDRDXTHHfnueM7lcv6rz++pEVO\nGrpamyioQQp0HICrApP9H5nUqB8/6qLS0UIHQiZBRXnXUYXcU6l1n/IskyFfY09tDxFFPZQPo+a/\nBXMPGxZNwO1XrMUFa2Yok/H6/vIq/E0/fEyZDMIMOoJjyMa0rhRbFXSWRt4p0ZJ4IlS6EKisSm2O\n4fO9B7aoE6IYcnUjZJLXRUMbDjANbqvMZOjKGWOUpr+nb+SohJwWGSIE8hxIDo0Ykb8lQoLIwoIJ\nXcrSVlmT2jWE31aNN7jB8zv2GdSEKBJ5mryzVdUHXtiJ3z+3s5Bug68HnhFXxDutP0ZWfOh5ikIr\nPvBUfyo4ueLX165Hd6vakMxF5BtvP0xZ2iob37CVvTxV2xpXN0N6EPlmYGgYf/zLa2ikyDzSOPP/\n/c60CsqgRYECUwmHTohCKz6gqG55ZdqYdmVBDYrKZetmYUxHs7L0qSpFQ65uRBr8pebvfvYkzvnq\n7/DwlteM6CMTk4PyeqmN1590kNL08zT5VCQmdMuPAqsLk3WPVnxArm5EfXDGiil4/wnzlaTtrUK/\nvna9kjOVwtjfn59gB2T3EEkIC/38jHOA88u7DzjX5RsTbjq2h9WWyZiOZoxub8Lr+wcqky804Ztv\n7n7/URjb0YLP3/UUAKBT0REVKjBd9PKTUwoh30iiHvj8OcuUy2CsvBKnk4Hh/Bg+fsgOItJQaig7\nawwO5bME2bghe0xHs9bQ/7o5fG4vnti6G7sPlPf7dJGbeK6ZO768V/f6kxZi/oROHLNgnGGN0qNy\n33EQ5OoGWqYlCHnoq0wXrpmBfzhvOZZPG6VNpmy+/9BfTatA5JBG57DCoRwuIdrY3XLOC230AEAj\nY+AclQOmu9to3rsItDeXcOHambmdwB/d3oTDZqmNauiHDB/Qki9ByEJ123vU/JFZrXccPRtvWjo5\ntw0+QaSlyem0Bobyu9ppA+7K012PvWJYE/U0MODZ7Xtxz5PbAIACAxFGcRd9OfQvPpDhg5E9PjR8\nIohsqK5D/3TRysrr0e3qgjToYuWM0aZVqBsYYxsZY08yxjYzxq4L+L6FMfZd5/vfM8Zm6tcyGL9r\nmBvh0DV88mz7c0MOn9482z8wBABobmzAoTOLWSfdcc7dj7+CllKD1H2YNPlEJMJTXjjXP/amtU7k\nu9OoR5ZM7cGMsR2m1SA86Noo3FJqxFcuWAEA6MjRZs4g3nPcPLzn2Lmm1agLGGONAL4M4HgAWwDc\nxxi7g3PuPcr+UgC7OOdzGWPnAvg0gLfo13aEsL6pyXF1y+0eH997013w/v6y4fPLa47BlFFthrVR\nw08ffbnyum+QVgoJO+Ccazec8z1ykATNVuSLO65aZ1oFIgQdVWnjIZPUC1HMlFFtuPq4eWggP1td\nHAZgM+f8WQBgjN0G4DQAXsPnNAAfdV5/D8CXGGOMW7gT3w1uMJDHPT4WFvmd+8r7e8YUYBU5jL2O\ncUcQNmGiBSPDB7THhzDLF89bjpd27TetRiZMuavklUuOmElGj16mAHjR834LgNVh13DOBxljrwMY\nC2C7Fg0TUKqs+DiubsbXTPKJa9Lu2tuP1qYGtDXrC8NPEAQArn8yhAwf0Dk+hFlOXTrZtAqZ4ZXT\no6kuRWHf2gGRBsbY5QAuB4Dp06crl+cvNk2VPT75L1A21ImdewcKvdoDAB3NjZVVn3MPnWZYG6Le\n4Z6/uscNFNwA5v2LCaIo0BwCYSkvAfCO9qY6nwVewxgrAegBsCMoMc75LZzzVZzzVePG6T8/o9FZ\nLRzM8RlWXky0G16Rr+3rR0/BDR+vV+SFa2eYU4Soe7x1r7zHR698MnwwsseHBm0EkQ4LJm1zAbUx\nxrgPwDzG2CzGWDOAcwHc4bvmDgAXO6/PAvALG/f3ACPhrN3gBnkrV7blav/QMFqbij0c6hsc2eND\nXi6ETVBUNwNQI0AQ2XDHh1STorFtwFcvOHt2rgLwUwCNAG7lnD/KGLsJwP2c8zsA/DOAbzLGNgPY\nibJxZCX+cNZ5Ytsbfdg/MIShYV5ZuTLN4BBHyRJdVOGu+Kyb24u54zvNKkMQDhTcwBBk9xBENip7\nfKgyEZbCOb8TwJ2+z270vD4A4GzdeqWhuVQ2fA7kMCzxfz/0VwDAbzZvx9Hz9bsJenEHXTYZYar5\n+iWHVgxngjANNxDcgEo/KKobQRAEYS/+lUK3z9rXN6hfGUm4Z+eYWgX1TtIMDg9XAkYUFfd2yegh\nbMD1EuGgc3yMQK5uBCEHqkoEIY+wAYHrtrTXMXzyWO36q9z0zN7BX3bux4KJxXb/+sl7j8KDf9ll\nWg2CqBoncE57fIxA7jkEkQ3au0IQ+hh2KlweXd1c+gbKKz6Pbd1tNDjKgYEhbN/Th+2b+wxqoZ4F\nE7uwYGKXaTUIogoOaLd8aM0TQFMjGT4EkQX3AFOaQyAI9bgTDQM5Nnz6h4Yr7i6Pb91tRAfOgb4c\n5yFB5B5O5/gYodRQzoZhmrUmiFTQAaYEoQ/XYKi4i+Ww2vUPDvvc3fTiZlk/GT4EYYzyHh+9Msnw\nAdBUcs9EoAaQINLgzhnQig9BqKB6Vs6dpBvM8Wxd3+AwvvzLZwAAF67Rf6DmG32DuPW3z2HLrn3a\nZRMEUcbEHh8yfAA0NeT3TASCsAmye6LhdNQrkYCw+uTu8RnKs+EzMIwv/vxpAMCiyd3G9Pi+E167\no7nRmA4EUU942zUO/ROmFNwAQKmx+hRsgigSi6f0oKWkdo7D0gPuCaKQ+KtbHl1Mb/3tc5XX7QaN\nDnePz+fOWWpMB4KoVzjn2tsvMnw87D4wYFoFgpDOD969TrmMEVe3/A3ACCJv5HmiobnUgP7B4aoD\nQ1ubTBo+5ehyzYonhwiCqMXEig/VdAC3P7gFALB9T79hTQgin4wENyBEIAORSILfzsmxh1vFaGvz\nGDsmV3zc4AYtJXJ1Iwjd0B4fQ7z/+PmmVSCInEOWTxLyPGNP6CPMPvbvFcuTHV05fLV/sPKZyRWf\nAwOu4UPDIYLQRVUXqLkBI1c3AMfMH49zVk3FBQYiyxBEEaAVH4LQR55XfNzADK/tG3Et72o1NxS5\n+/FXAJCrG0HowvV4cCcAdY8byPAB0NDA8JmzaGMjQWSFXLjEoHwisjCc4xXDINXnjuvUr4gPcnUj\nCDPQHh+CIAiCIELJsd0TSKlR/1DkFx84uup9dxvNAxOEDgaGhrGnb9BYO0Y1nSCIzBRsHEYQVuCG\nefW7tvn3iNH6YXJm9XZUvZ/Y3WpIE4KoLx7e8joATzRYzS0YrfgQBJEZ2uNDEPJxQz4PDlcfrj3M\naTN+Vrzupq1NDeR+ShCaqezxIVc3giDyhhtlisYO0RTNRYlQS3OpXKEGfIdrD3NetRnfhKtYkWgz\nGFWOIOqVQWcp23umlw6otSQIIjMzxnSgt7MZ1286yLQqBFEYSg3lLnpwqHrFx3/2xcGTuzVqJYcn\nPr7RtAo4ev44ABRshCBMsH1PHwD94expjw9BEJlpa27E/R8+3rQaBFEoSo3BKz6cczQ0eF218rdi\n0drUiHcePQedLeZ0P2nxRNzz1Dbs3EuHlxOEbrbvKde71ia9azBk+BAEQRCEhTQ5LmxBe3waGMO9\n1x+Hl3cfMKGaFK7btNCo/B2OwTOuq8WoHgRRj3zx508D0O9qSoYPQRAEQViIa/gMDPkNHw4GYGJP\nKyb25Csa2cyx7Xh+xz7TagAAzlwxFc9t24sb37TItCoEUXf84olXAQDtzXoNH9rjQxAEQRAWUmoI\nC26Q330p37vicPz7O9aaVgMAMKG7FZ89eym6WptMq0IQdcNPrj6y6v3c8V1a5dOKD0EQhGbyOWQl\ndFNxdRvyhwPk0BwISRq9nS3o7STXMoKoV6aPaa96r/vwYFrxIQiCIAgLcYMb1OzxGS7v8SEIgsgb\nraVq17aOZjJ8CIIgCKLuaXZWfPoHA/b4kN1DEEQOafAtV+sObkCGD0EQBEFYyMiKjy+cNWjFhyCI\n/PO5s5fWGEKqIcOHIAiCICwk7ABTWvEhCKIInLxkknaZZPgQBEEQhIU0hR5gSis+BEHkHxOHL1NU\nN4IgCIKwkMhzfMjuIQgip9z1vqPw6ht9RmST4UMQBEEQFhK6x4dWfAiCyDHzJnRh3gS95/e4kKsb\nQRCEZmjMSojQ1EBR3QiCIGRCKz4EQRAEYSENDQzNjQ14ZtseXPC13+M3m7cDAOaM66AVH4IgiBSQ\n4UMQBEEQltLS1IAfPry16rNntu3FvPGdhjQiCILIL+TqRhAEQRCWEhb16OlX92jWhCAIIv+Q4UMQ\nBEEQltJSom6aIAhCFtSiEgRBEISlmDjngiAIoqiQ4UMQBEEQltLaFNxN//Ejx2vWhCAIIv+Q4UMQ\nBKEJznn8RQThoaUUvOIzuqNZsyYEQRD5hwwfgiAIgrCUsBUfgiAIIjnUohIEQRCEpQwM0SohQRCE\nLMjwIQiC0AwdPUmI8ofndppWgSAIojCQ4UMQBKEZmsMnCIIgCP2Q4UMQBEEQOeCdR88xrQJBBHL6\n8in42kWrTKtBELGUTCtAEARRb5CrG5EUxoDrNi3EEy/vxqSeNtPqEEQVoghkEAAACRpJREFUX3jL\nMtMqEIQQZPgQBEEQhKXM6u3Ac9v3oru1CQDw9UsOM6wRQRBEfiFXN4IgCE1cdew8HL9oAs5cOdW0\nKkRO+O93HYGVM0bjm5eSwUMQBJEVWvEhCILQxLiuFvwT+cETCehpb8LtVxxuWg2CIIhCQCs+BEEQ\nBEEQBEEUHjJ8CIIgCIIgCIIoPGT4EARBEARBEARReMjwIQiCIAiCIAii8JDhQxAEQRAEQRBE4SHD\nhyAIgiAIgiCIwkOGD0EQBEEQBEEQhYcMH4IgCIIgCIIgCg8ZPgRBEARBEARBFB4yfAiCIAiCIAiC\nKDyZDB/G2BjG2F2Msaedv6NDrhtijD3k/L8ji0yCIAiCIAiCIIikZF3xuQ7Azznn8wD83HkfxH7O\n+TLn/6kZZRIEQRAEQRAEQSQiq+FzGoBvOK+/AeDNGdMjCIIgCIIgCIKQTlbDZwLnfKvz+mUAE0Ku\na2WM3c8Yu5cxRsYRQRAEQRAEQRBaKcVdwBi7G8DEgK8+5H3DOeeMMR6SzAzO+UuMsdkAfsEYe4Rz\n/kyArMsBXA4A06dPj1WeIAiCIAiCIAhChFjDh3O+Iew7xtgrjLFJnPOtjLFJAF4NSeMl5++zjLH/\nAbAcQI3hwzm/BcAtALBq1aowI4ogCIIgCIIgCCIRWV3d7gBwsfP6YgDf91/AGBvNGGtxXvcCOALA\nYxnlEgRBEARBEARBCJPV8LkZwPGMsacBbHDegzG2ijH2NeeagwDczxj7E4BfAriZc06GD0EQBEEQ\nBEEQ2mCc2+lRxhjbBuCFjMn0AtguQR1VkH7ZsV1H0i87tutou35Adh1ncM7HyVKmSEjoq2wvP6Rf\ndmzX0Xb9APt1JP2yo6WfstbwkQFj7H7O+SrTeoRB+mXHdh1Jv+zYrqPt+gH50LFesf3ZkH7ZsV1H\n2/UD7NeR9MuOLh2zuroRBEEQBEEQBEFYDxk+BEEQBEEQBEEUnqIbPreYViAG0i87tutI+mXHdh1t\n1w/Ih471iu3PhvTLju062q4fYL+OpF92tOhY6D0+BEEQBEEQBEEQQPFXfAiCIAiCIAiCIIpp+DDG\nNjLGnmSMbWaMXWdIh2mMsV8yxh5jjD3KGHuv8/lHGWMvMcYecv6f5PnN9Y7OTzLGTtSk5/OMsUcc\nXe53PhvDGLuLMfa083e08zljjH3R0fFhxtgKxbot8OTTQ4yx3Yyxq03nIWPsVsbYq4yxP3s+S5xn\njLGLneufZoxdHCRLon6fZYw94ejwX4yxUc7nMxlj+z15+RXPb1Y6ZWOzcw9MoX6Jn6nKeh6i43c9\n+j3PGHvI+dxEHoa1L9aUQyIaleU3gQ7UT2XXjfopefpRP5VdR+qn4uCcF+o/gEYAzwCYDaAZwJ8A\nLDKgxyQAK5zXXQCeArAIwEcB/E3A9YscXVsAzHLuoVGDns8D6PV99hkA1zmvrwPwaef1SQB+DIAB\nWAPg95qf68sAZpjOQwBHAVgB4M9p8wzAGADPOn9HO69HK9TvBAAl5/WnPfrN9F7nS+cPjs7MuYdN\nCvVL9ExV1/MgHX3ffw7AjQbzMKx9saYc0v/I50f9VDI9nwf1U0l1oX5Kvn6Jnqnqeh6ko+976qcC\n/hdxxecwAJs5589yzvsB3AbgNN1KcM63cs4fdF6/AeBxAFMifnIagNs4532c8+cAbEb5XkxwGoBv\nOK+/AeDNns//lZe5F8AoxtgkTTodB+AZznnUQYFa8pBz/isAOwNkJ8mzEwHcxTnfyTnfBeAuABtV\n6cc5/xnnfNB5ey+AqVFpODp2c87v5eWW51899yRdvwjCnqnSeh6lozMbdg6A70SloTgPw9oXa8oh\nEQn1U9mhfioC6qfk6xcB9VPB+lnZTxXR8JkC4EXP+y2IbsiVwxibCWA5gN87H13lLOPd6i7xwZze\nHMDPGGMPMMYudz6bwDnf6rx+GcAEwzoCwLmorsA25SGQPM9M6vp2lGdVXGYxxv7IGLuHMXak89kU\nRyed+iV5pibz70gAr3DOn/Z8ZiwPfe1LnsphPWNdvlM/JQXqp+RB/VQ2qJ8KoYiGj1UwxjoB3A7g\nas75bgD/D8AcAMsAbEV5KdIk6zjnKwBsAnAlY+wo75fODIDR0H+MsWYApwL4D+cj2/KwChvyLAzG\n2IcADAL4tvPRVgDTOefLAbwfwL8xxroNqGb1M/VxHqoHN8byMKB9qWBzOSTsgvqp7FA/JQ/qp6RA\n/VQIRTR8XgIwzfN+qvOZdhhjTSg/7G9zzv8TADjnr3DOhzjnwwD+CSNL3Eb05py/5Px9FcB/Ofq8\n4roGOH9fNakjyp3dg5zzVxxdrcpDh6R5pl1XxtjbAJwC4K1OYwNnaX6H8/oBlP2R5zu6eN0MlOqX\n4pkaedaMsRKAMwB81/3MVB4GtS/IQTkkAFiU79RPSYP6KQlQP5Ud6qeiKaLhcx+AeYyxWc4MzLkA\n7tCthONf+c8AHuecf97zudfX+HQAbjSOOwCcyxhrYYzNAjAP5Q1nKnXsYIx1ua9R3lj4Z0cXN2rG\nxQC+79HxIifyxhoAr3uWK1VSNXNhUx56SJpnPwVwAmNstLNcfoLzmRIYYxsBXAvgVM75Ps/n4xhj\njc7r2Sjn2bOOjrsZY2ucsnyR555U6Jf0mZqq5xsAPME5r7gGmMjDsPYFlpdDogL1U+I6Uj8lD6vb\nB+qnpEH9VBRcUnQJm/6jHBniKZQt2g8Z0mEdyst3DwN4yPl/EoBvAnjE+fwOAJM8v/mQo/OTkBRV\nI0bH2ShHGfkTgEfdvAIwFsDPATwN4G4AY5zPGYAvOzo+AmCVBh07AOwA0OP5zGgeoty5bQUwgLKv\n6aVp8gxlH+bNzv9LFOu3GWUfWbcsfsW59kzn2T8E4EEAb/Kkswrlhv0ZAF8CygceK9Iv8TNVWc+D\ndHQ+/zqAd/quNZGHYe2LNeWQ/sc+Q+qnxHSkfiqdTtRPydeP+qlk+lnZTzEnQYIgCIIgCIIgiMJS\nRFc3giAIgiAIgiCIKsjwIQiCIAiCIAii8JDhQxAEQRAEQRBE4SHDhyAIgiAIgiCIwkOGD0EQBEEQ\nBEEQhYcMH4IgCIIgCIIgCg8ZPgRBEARBEARBFB4yfAiCIAiCIAiCKDz/P29JsW0veJcBAAAAAElF\nTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f65b79cb5f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ckpt_path = 'models/mdnmodel.ckpt-999'\n",
     "seq_len = 2000\n",
@@ -1245,7 +233,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.8.9"
   }
  },
  "nbformat": 4,
diff --git a/TestRNNModel.ipynb b/TestRNNModel.ipynb
index e0e1512..39269f0 100644
--- a/TestRNNModel.ipynb
+++ b/TestRNNModel.ipynb
@@ -3,9 +3,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%load_ext autoreload\n",
@@ -15,10 +13,18 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From /usr/local/lib/python3.8/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "non-resource variables are not supported in the long term\n"
+     ]
+    }
+   ],
    "source": [
     "import data_utils\n",
     "import model_utils\n",
@@ -28,9 +34,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -40,18 +44,17 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import tensorflow as tf\n",
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior() \n",
     "import numpy as np"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,10 +63,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# To get reasonable outputs, should use something bigger than 150 !\n",
@@ -72,1019 +73,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "980   0.00920263\n",
-      "981   0.00907012\n",
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-      "985   0.00956815\n",
-      "986   0.00918221\n",
-      "987   0.00909319\n",
-      "988   0.00888774\n",
-      "989   0.00870784\n",
-      "990   0.0101294\n",
-      "991   0.0109354\n",
-      "992   0.00936776\n",
-      "993   0.00919522\n",
-      "994   0.00966628\n",
-      "995   0.0101648\n",
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-      "997   0.00888422\n",
-      "998   0.00928454\n",
-      "999   0.00901097\n"
+     "ename": "AttributeError",
+     "evalue": "module 'tensorflow' has no attribute 'reset_default_graph'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-7-da090cfa5c5d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_session_and_model\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[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mtrain_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModelConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mtest_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModelConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m#train_config.num_layers = 1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Desktop/sensegen/model_utils.py\u001b[0m in \u001b[0;36mreset_session_and_model\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0mResets\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mTensorFlow\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0mgraph\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \"\"\"\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_default_graph\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[0m\u001b[1;32m     20\u001b[0m     \u001b[0msess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'reset_default_graph'"
      ]
     }
    ],
@@ -1166,7 +167,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.8.9"
   }
  },
  "nbformat": 4,
diff --git a/model.py b/model.py
index 288a8c7..79de4c1 100644
--- a/model.py
+++ b/model.py
@@ -10,7 +10,8 @@
 """
 
 import numpy as np
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior() 
 
 import model_utils
 
@@ -166,7 +167,8 @@ def _build_model(self):
             self.optimizer = tf.train.AdamOptimizer(self.learning_rate)
             #self.loss = tf.reduce_mean(tf.squared_difference(self.preds, self.y_holder))
             print(self.y_holder)
-            mixture_p = tf.contrib.distributions.Normal(self.mu, self.sigma).prob(tf.reshape(self.y_holder,(-1,1)))
+            
+            mixture_p = tf.distributions.Normal(self.mu, self.sigma).prob(tf.reshape(self.y_holder,(-1,1)))
             mixture_p = tf.multiply(self.pi, mixture_p)
             output_p = tf.reduce_sum(mixture_p, reduction_indices=1, keep_dims=True)
             log_output_p = tf.log(output_p)
diff --git a/model_utils.py b/model_utils.py
index 01f28d1..185eca9 100644
--- a/model_utils.py
+++ b/model_utils.py
@@ -9,7 +9,8 @@
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
 """
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
+tf.disable_v2_behavior() 
 
 
 def reset_session_and_model():

From 0dcc36aa2711423ab89b059ae74865205a818b8f Mon Sep 17 00:00:00 2001
From: James Timothy Meech <james.t.meech@gmail.com>
Date: Mon, 19 Apr 2021 17:33:43 +0100
Subject: [PATCH 2/7] Clearing output

---
 SenseGenModel.ipynb | 58 ++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 57 insertions(+), 1 deletion(-)

diff --git a/SenseGenModel.ipynb b/SenseGenModel.ipynb
index b231227..6c8a310 100644
--- a/SenseGenModel.ipynb
+++ b/SenseGenModel.ipynb
@@ -144,7 +144,63 @@
       "4   0.1319322\n",
       "5   0.12631807\n",
       "6   0.124129064\n",
-      "7   0.12099192\n"
+      "7   0.12099192\n",
+      "8   0.11737048\n",
+      "9   0.1044701\n",
+      "10   0.07797714\n",
+      "11   -0.06915333\n",
+      "12   -0.18561403\n",
+      "13   -0.23944949\n",
+      "14   -0.30283973\n",
+      "15   -0.32037136\n",
+      "16   -0.3280427\n",
+      "17   -0.36503723\n",
+      "18   -0.28163645\n",
+      "19   -0.23247373\n",
+      "20   -0.27357972\n",
+      "21   -0.30006894\n",
+      "22   -0.3751812\n",
+      "23   -0.33349535\n",
+      "24   -0.4730928\n",
+      "25   -0.38327885\n",
+      "26   -0.4159511\n",
+      "27   -0.37187916\n",
+      "28   -0.44080728\n",
+      "29   -0.3905839\n",
+      "30   -0.46327212\n",
+      "31   -0.51284206\n",
+      "32   -0.43359837\n",
+      "33   -0.47039986\n",
+      "34   -0.46653005\n",
+      "35   -0.47923234\n",
+      "36   -0.34236607\n",
+      "37   -0.491854\n",
+      "38   -0.5133849\n",
+      "39   -0.54073936\n",
+      "40   -0.5220748\n",
+      "41   -0.5718595\n",
+      "42   -0.5100446\n",
+      "43   -0.541494\n",
+      "44   -0.5273764\n",
+      "45   -0.4299085\n",
+      "46   -0.49229184\n",
+      "47   -0.502245\n",
+      "48   -0.554855\n",
+      "49   -0.52185005\n",
+      "50   -0.46928453\n",
+      "51   -0.61792344\n",
+      "52   -0.6143366\n",
+      "53   -0.6856599\n",
+      "54   -0.61845237\n",
+      "55   -0.63214624\n",
+      "56   -0.531037\n",
+      "57   -0.4642566\n",
+      "58   -0.6153024\n",
+      "59   -0.68375504\n",
+      "60   -0.7403096\n",
+      "61   -0.6888944\n",
+      "62   -0.6331447\n",
+      "63   -0.62586933\n"
      ]
     }
    ],

From 78917b97bab578f86e70d92472758d41bea687aa Mon Sep 17 00:00:00 2001
From: James Timothy Meech <james.t.meech@gmail.com>
Date: Mon, 19 Apr 2021 17:35:10 +0100
Subject: [PATCH 3/7] Clearing output

---
 SenseGenModel.ipynb | 133 ++++----------------------------------------
 1 file changed, 11 insertions(+), 122 deletions(-)

diff --git a/SenseGenModel.ipynb b/SenseGenModel.ipynb
index 6c8a310..a15dff8 100644
--- a/SenseGenModel.ipynb
+++ b/SenseGenModel.ipynb
@@ -2,20 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\nAuthor: Moustafa Alzantot (malzantot@ucla.edu)\\nAll rights reserved Networked and Embedded Systems Lab (NESL), UCLA.\\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\\n'"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Author: Moustafa Alzantot (malzantot@ucla.edu)\n",
@@ -28,18 +17,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n"
@@ -47,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -95,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,96 +94,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
-      "Tensor(\"mdn_model/add_1:0\", shape=(40, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model/strided_slice:0\", shape=(40, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/Exp:0\", shape=(40, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/y:0\", shape=(4, 10, 1), dtype=float32)\n",
-      "WARNING:tensorflow:From /Users/james/Desktop/sensegen/model.py:171: Normal.__init__ (from tensorflow.python.ops.distributions.normal) is deprecated and will be removed after 2019-01-01.\n",
-      "Instructions for updating:\n",
-      "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n",
-      "WARNING:tensorflow:From /usr/local/lib/python3.8/site-packages/tensorflow/python/ops/distributions/normal.py:153: Distribution.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\n",
-      "Instructions for updating:\n",
-      "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n",
-      "WARNING:tensorflow:From /usr/local/lib/python3.8/site-packages/tensorflow/python/util/dispatch.py:201: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "keep_dims is deprecated, use keepdims instead\n",
-      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
-      "Tensor(\"mdn_model_1/add_1:0\", shape=(1, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model_1/strided_slice:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model_1/Exp:0\", shape=(1, 24), dtype=float32)\n",
-      "0   0.40117803\n",
-      "1   0.23122519\n",
-      "2   0.18913844\n",
-      "3   0.14960684\n",
-      "4   0.1319322\n",
-      "5   0.12631807\n",
-      "6   0.124129064\n",
-      "7   0.12099192\n",
-      "8   0.11737048\n",
-      "9   0.1044701\n",
-      "10   0.07797714\n",
-      "11   -0.06915333\n",
-      "12   -0.18561403\n",
-      "13   -0.23944949\n",
-      "14   -0.30283973\n",
-      "15   -0.32037136\n",
-      "16   -0.3280427\n",
-      "17   -0.36503723\n",
-      "18   -0.28163645\n",
-      "19   -0.23247373\n",
-      "20   -0.27357972\n",
-      "21   -0.30006894\n",
-      "22   -0.3751812\n",
-      "23   -0.33349535\n",
-      "24   -0.4730928\n",
-      "25   -0.38327885\n",
-      "26   -0.4159511\n",
-      "27   -0.37187916\n",
-      "28   -0.44080728\n",
-      "29   -0.3905839\n",
-      "30   -0.46327212\n",
-      "31   -0.51284206\n",
-      "32   -0.43359837\n",
-      "33   -0.47039986\n",
-      "34   -0.46653005\n",
-      "35   -0.47923234\n",
-      "36   -0.34236607\n",
-      "37   -0.491854\n",
-      "38   -0.5133849\n",
-      "39   -0.54073936\n",
-      "40   -0.5220748\n",
-      "41   -0.5718595\n",
-      "42   -0.5100446\n",
-      "43   -0.541494\n",
-      "44   -0.5273764\n",
-      "45   -0.4299085\n",
-      "46   -0.49229184\n",
-      "47   -0.502245\n",
-      "48   -0.554855\n",
-      "49   -0.52185005\n",
-      "50   -0.46928453\n",
-      "51   -0.61792344\n",
-      "52   -0.6143366\n",
-      "53   -0.6856599\n",
-      "54   -0.61845237\n",
-      "55   -0.63214624\n",
-      "56   -0.531037\n",
-      "57   -0.4642566\n",
-      "58   -0.6153024\n",
-      "59   -0.68375504\n",
-      "60   -0.7403096\n",
-      "61   -0.6888944\n",
-      "62   -0.6331447\n",
-      "63   -0.62586933\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_utils.reset_session_and_model()\n",
     "with tf.Session() as sess:\n",
@@ -266,9 +157,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }

From 975c90a829db3961c53250c58f2ce89c8f4570e7 Mon Sep 17 00:00:00 2001
From: James Timothy Meech <james.t.meech@gmail.com>
Date: Mon, 19 Apr 2021 17:37:39 +0100
Subject: [PATCH 4/7] Clearing output

---
 TestRNNModel.ipynb | 70 ++++++++--------------------------------------
 1 file changed, 12 insertions(+), 58 deletions(-)

diff --git a/TestRNNModel.ipynb b/TestRNNModel.ipynb
index 39269f0..f67a645 100644
--- a/TestRNNModel.ipynb
+++ b/TestRNNModel.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,19 +12,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /usr/local/lib/python3.8/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "non-resource variables are not supported in the long term\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import data_utils\n",
     "import model_utils\n",
@@ -33,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -54,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -63,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -73,22 +63,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "module 'tensorflow' has no attribute 'reset_default_graph'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-da090cfa5c5d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_session_and_model\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[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mtrain_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModelConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mtest_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModelConfig\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m#train_config.num_layers = 1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Desktop/sensegen/model_utils.py\u001b[0m in \u001b[0;36mreset_session_and_model\u001b[0;34m()\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0mResets\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mTensorFlow\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0mgraph\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \"\"\"\n\u001b[0;32m---> 19\u001b[0;31m     \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_default_graph\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[0m\u001b[1;32m     20\u001b[0m     \u001b[0msess\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_default_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: module 'tensorflow' has no attribute 'reset_default_graph'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_utils.reset_session_and_model()\n",
     "with tf.Session() as sess:\n",
@@ -113,30 +90,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f5a1e52fef0>]"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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z+mLa2Hhf2Q26GSJgvlBJm01pz2IWPeP1uEhPYJR7dij6Ucx6xkTlO+SYTrhm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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f5905ad1278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "plt.plot(sample_preds)"
    ]
@@ -144,9 +100,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }

From f75bc3c68dd610bc8f07a2f49a36698b12115abf Mon Sep 17 00:00:00 2001
From: James Timothy Meech <james.t.meech@gmail.com>
Date: Tue, 20 Apr 2021 00:15:57 +0100
Subject: [PATCH 5/7] working now

---
 SenseGenModel.ipynb | 1135 ++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 1120 insertions(+), 15 deletions(-)

diff --git a/SenseGenModel.ipynb b/SenseGenModel.ipynb
index a15dff8..c18f3f6 100644
--- a/SenseGenModel.ipynb
+++ b/SenseGenModel.ipynb
@@ -2,9 +2,20 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 107,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nAuthor: Moustafa Alzantot (malzantot@ucla.edu)\\nAll rights reserved Networked and Embedded Systems Lab (NESL), UCLA.\\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\\n'"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\"\"\n",
     "Author: Moustafa Alzantot (malzantot@ucla.edu)\n",
@@ -17,9 +28,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 108,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n"
@@ -27,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -48,9 +68,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 111,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Disabling eager execution\n",
+      "INFO:tensorflow:Disabling v2 tensorshape\n",
+      "INFO:tensorflow:Disabling resource variables\n",
+      "INFO:tensorflow:Disabling tensor equality\n",
+      "INFO:tensorflow:Disabling control flow v2\n"
+     ]
+    }
+   ],
    "source": [
     "import tensorflow.compat.v1 as tf\n",
     "tf.disable_v2_behavior() \n",
@@ -59,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -92,9 +124,1039 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 114,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
+      "Tensor(\"mdn_model/add_1:0\", shape=(40, 72), dtype=float32)\n",
+      "Tensor(\"mdn_model/strided_slice_2:0\", shape=(40, 24), dtype=float32)\n",
+      "Tensor(\"mdn_model/strided_slice:0\", shape=(40, 24), dtype=float32)\n",
+      "Tensor(\"mdn_model/Exp:0\", shape=(40, 24), dtype=float32)\n",
+      "Tensor(\"mdn_model/y:0\", shape=(4, 10, 1), dtype=float32)\n",
+      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
+      "Tensor(\"mdn_model_1/add_1:0\", shape=(1, 72), dtype=float32)\n",
+      "Tensor(\"mdn_model_1/strided_slice_2:0\", shape=(1, 24), dtype=float32)\n",
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+      "988   -1.487782\n",
+      "989   -1.5004134\n",
+      "990   -1.5639615\n",
+      "991   -1.5386683\n",
+      "992   -1.5314955\n",
+      "993   -1.4756889\n",
+      "994   -1.5384191\n",
+      "995   -1.5632467\n",
+      "996   -1.5629617\n",
+      "997   -1.530855\n",
+      "998   -1.5546094\n",
+      "999   -1.5758102\n"
+     ]
+    }
+   ],
    "source": [
     "model_utils.reset_session_and_model()\n",
     "with tf.Session() as sess:\n",
@@ -128,9 +1190,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 115,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
+      "Tensor(\"mdn_model/add_1:0\", shape=(1, 72), dtype=float32)\n",
+      "Tensor(\"mdn_model/strided_slice_2:0\", shape=(1, 24), dtype=float32)\n",
+      "Tensor(\"mdn_model/strided_slice:0\", shape=(1, 24), dtype=float32)\n",
+      "Tensor(\"mdn_model/Exp:0\", shape=(1, 24), dtype=float32)\n",
+      "Tensor(\"mdn_model/y:0\", shape=(1, 1, 1), dtype=float32)\n",
+      "INFO:tensorflow:Restoring parameters from models/mdnmodel.ckpt-999\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Fake data')"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "ckpt_path = 'models/mdnmodel.ckpt-999'\n",
     "seq_len = 2000\n",
@@ -154,6 +1252,13 @@
     "axes[1].set_title('Fake data')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -178,7 +1283,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.9"
+   "version": "3.9.4"
   }
  },
  "nbformat": 4,

From eca40273e6dd7b540641773159b0b9c7ab3b903c Mon Sep 17 00:00:00 2001
From: James Timothy Meech <james.t.meech@gmail.com>
Date: Wed, 21 Apr 2021 14:41:08 +0100
Subject: [PATCH 6/7] Now working

---
 SenseGenModel.ipynb            | 1128 ++------------------------------
 SenseGenModel_ECGDataset.ipynb |   70 +-
 TestRNNModel.ipynb             |  702 +++++++++++++++++++-
 model.py                       |    4 +-
 4 files changed, 815 insertions(+), 1089 deletions(-)

diff --git a/SenseGenModel.ipynb b/SenseGenModel.ipynb
index c18f3f6..c630afa 100644
--- a/SenseGenModel.ipynb
+++ b/SenseGenModel.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -11,7 +11,7 @@
        "'\\nAuthor: Moustafa Alzantot (malzantot@ucla.edu)\\nAll rights reserved Networked and Embedded Systems Lab (NESL), UCLA.\\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\\n'"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -28,18 +28,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n"
@@ -47,9 +38,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Enabling eager execution\n",
+      "INFO:tensorflow:Enabling v2 tensorshape\n",
+      "INFO:tensorflow:Enabling resource variables\n",
+      "INFO:tensorflow:Enabling tensor equality\n",
+      "INFO:tensorflow:Enabling control flow v2\n",
+      "INFO:tensorflow:Disabling eager execution\n",
+      "INFO:tensorflow:Disabling v2 tensorshape\n",
+      "WARNING:tensorflow:From /usr/local/lib/python3.9/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "non-resource variables are not supported in the long term\n",
+      "INFO:tensorflow:Disabling resource variables\n",
+      "INFO:tensorflow:Disabling tensor equality\n",
+      "INFO:tensorflow:Disabling control flow v2\n",
+      "INFO:tensorflow:Disabling eager execution\n",
+      "INFO:tensorflow:Disabling v2 tensorshape\n",
+      "INFO:tensorflow:Disabling resource variables\n",
+      "INFO:tensorflow:Disabling tensor equality\n",
+      "INFO:tensorflow:Disabling control flow v2\n"
+     ]
+    }
+   ],
    "source": [
     "import data_utils\n",
     "import model_utils\n",
@@ -58,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -91,9 +107,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[1.012817  1.018851  1.023127  ... 0.7548917 0.9279268 0.7980909]\n",
+      " [1.022833  1.02238   1.021882  ... 0.8043137 0.9129872 0.8192417]\n",
+      " [1.022028  1.020781  1.019178  ... 0.831714  0.9246597 0.8658821]\n",
+      " ...\n",
+      " [1.018445  1.014788  1.021041  ... 0.6956257 0.6753473 0.8980947]\n",
+      " [1.019372  1.016499  1.022935  ... 0.7479103 0.6603377 0.8283723]\n",
+      " [1.021171  1.017849  1.022019  ... 0.776768  0.719353  0.8002428]]\n"
+     ]
+    }
+   ],
    "source": [
     "data = data_utils.load_training_data()"
    ]
@@ -107,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,1039 +154,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
-      "Tensor(\"mdn_model/add_1:0\", shape=(40, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model/strided_slice_2:0\", shape=(40, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/strided_slice:0\", shape=(40, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/Exp:0\", shape=(40, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/y:0\", shape=(4, 10, 1), dtype=float32)\n",
-      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
-      "Tensor(\"mdn_model_1/add_1:0\", shape=(1, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model_1/strided_slice_2:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model_1/strided_slice:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model_1/Exp:0\", shape=(1, 24), dtype=float32)\n",
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-      "984   -1.5121973\n",
-      "985   -1.4623721\n",
-      "986   -1.5438272\n",
-      "987   -1.4973439\n",
-      "988   -1.487782\n",
-      "989   -1.5004134\n",
-      "990   -1.5639615\n",
-      "991   -1.5386683\n",
-      "992   -1.5314955\n",
-      "993   -1.4756889\n",
-      "994   -1.5384191\n",
-      "995   -1.5632467\n",
-      "996   -1.5629617\n",
-      "997   -1.530855\n",
-      "998   -1.5546094\n",
-      "999   -1.5758102\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_utils.reset_session_and_model()\n",
     "with tf.Session() as sess:\n",
@@ -1190,14 +190,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
+      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.9/site-packages/tensorflow/python/keras/layers/legacy_rnn/rnn_cell_impl.py:909: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
+      "  warnings.warn(\"`tf.nn.rnn_cell.LSTMCell` is deprecated and will be \"\n",
+      "/usr/local/lib/python3.9/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1700: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n",
+      "  warnings.warn('`layer.add_variable` is deprecated and '\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Tensor(\"mdn_model/add_1:0\", shape=(1, 72), dtype=float32)\n",
       "Tensor(\"mdn_model/strided_slice_2:0\", shape=(1, 24), dtype=float32)\n",
       "Tensor(\"mdn_model/strided_slice:0\", shape=(1, 24), dtype=float32)\n",
@@ -1212,13 +228,13 @@
        "Text(0.5, 1.0, 'Fake data')"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1008x576 with 2 Axes>"
       ]
diff --git a/SenseGenModel_ECGDataset.ipynb b/SenseGenModel_ECGDataset.ipynb
index dfcab68..173f4f7 100644
--- a/SenseGenModel_ECGDataset.ipynb
+++ b/SenseGenModel_ECGDataset.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -11,7 +11,7 @@
        "'\\nAuthor: Moustafa Alzantot (malzantot@ucla.edu)\\nAll rights reserved Networked and Embedded Systems Lab (NESL), UCLA.\\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\\n'"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -28,9 +28,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n"
@@ -38,18 +47,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/malzantot/anaconda3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
-      "  from ._conv import register_converters as _register_converters\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import data_utils\n",
     "import model_utils\n",
@@ -58,7 +58,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,19 +68,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Disabling eager execution\n",
+      "INFO:tensorflow:Disabling v2 tensorshape\n",
+      "INFO:tensorflow:Disabling resource variables\n",
+      "INFO:tensorflow:Disabling tensor equality\n",
+      "INFO:tensorflow:Disabling control flow v2\n"
+     ]
+    }
+   ],
    "source": [
-    "import tensorflow as tf\n",
+    "import tensorflow.compat.v1 as tf\n",
+    "tf.disable_v2_behavior() \n",
     "import numpy as np"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'dataset/ecg_data/1 NSR'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-9-be93d435cb86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_training_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ecg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/Desktop/sensegen/data_utils.py\u001b[0m in \u001b[0;36mload_training_data\u001b[0;34m(dataset)\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'ecg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m         data_files = [f for f in os.listdir(ECG_data_dir)\n\u001b[0m\u001b[1;32m     30\u001b[0m                         if f.endswith('.mat')]\n\u001b[1;32m     31\u001b[0m         \u001b[0mdata_list\u001b[0m \u001b[0;34m=\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;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'dataset/ecg_data/1 NSR'"
+     ]
+    }
+   ],
    "source": [
     "data = data_utils.load_training_data('ecg')"
    ]
@@ -725,7 +751,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.9.4"
   }
  },
  "nbformat": 4,
diff --git a/TestRNNModel.ipynb b/TestRNNModel.ipynb
index f67a645..2c28840 100644
--- a/TestRNNModel.ipynb
+++ b/TestRNNModel.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,9 +12,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Enabling eager execution\n",
+      "INFO:tensorflow:Enabling v2 tensorshape\n",
+      "INFO:tensorflow:Enabling resource variables\n",
+      "INFO:tensorflow:Enabling tensor equality\n",
+      "INFO:tensorflow:Enabling control flow v2\n",
+      "INFO:tensorflow:Disabling eager execution\n",
+      "INFO:tensorflow:Disabling v2 tensorshape\n",
+      "WARNING:tensorflow:From /usr/local/lib/python3.9/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "non-resource variables are not supported in the long term\n",
+      "INFO:tensorflow:Disabling resource variables\n",
+      "INFO:tensorflow:Disabling tensor equality\n",
+      "INFO:tensorflow:Disabling control flow v2\n",
+      "INFO:tensorflow:Disabling eager execution\n",
+      "INFO:tensorflow:Disabling v2 tensorshape\n",
+      "INFO:tensorflow:Disabling resource variables\n",
+      "INFO:tensorflow:Disabling tensor equality\n",
+      "INFO:tensorflow:Disabling control flow v2\n"
+     ]
+    }
+   ],
    "source": [
     "import data_utils\n",
     "import model_utils\n",
@@ -23,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,9 +58,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "INFO:tensorflow:Disabling eager execution\n",
+      "INFO:tensorflow:Disabling v2 tensorshape\n",
+      "INFO:tensorflow:Disabling resource variables\n",
+      "INFO:tensorflow:Disabling tensor equality\n",
+      "INFO:tensorflow:Disabling control flow v2\n"
+     ]
+    }
+   ],
    "source": [
     "import tensorflow.compat.v1 as tf\n",
     "tf.disable_v2_behavior() \n",
@@ -44,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,7 +102,652 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
+      "WARNING:tensorflow:From /Users/james/Desktop/sensegen/model.py:52: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
+      "WARNING:tensorflow:From /usr/local/lib/python3.9/site-packages/tensorflow/python/keras/layers/legacy_rnn/rnn_cell_impl.py:987: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.9/site-packages/tensorflow/python/keras/layers/legacy_rnn/rnn_cell_impl.py:909: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
+      "  warnings.warn(\"`tf.nn.rnn_cell.LSTMCell` is deprecated and will be \"\n",
+      "/usr/local/lib/python3.9/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1700: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n",
+      "  warnings.warn('`layer.add_variable` is deprecated and '\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
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+      "407   0.010597008\n",
+      "408   0.010352265\n",
+      "409   0.010509173\n",
+      "410   0.0105036395\n",
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+      "419   0.010599396\n",
+      "420   0.01106106\n",
+      "421   0.010478071\n",
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+      "423   0.010799255\n",
+      "424   0.010526717\n",
+      "425   0.010784077\n",
+      "426   0.01046394\n",
+      "427   0.010170026\n",
+      "428   0.00967541\n",
+      "429   0.00969765\n",
+      "430   0.01110092\n",
+      "431   0.0118172495\n",
+      "432   0.010806804\n",
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+      "434   0.010168827\n",
+      "435   0.011455486\n",
+      "436   0.010383791\n",
+      "437   0.011053469\n",
+      "438   0.010549251\n",
+      "439   0.0104897795\n",
+      "440   0.010227136\n",
+      "441   0.010704505\n",
+      "442   0.009745198\n",
+      "443   0.009446754\n",
+      "444   0.009932296\n",
+      "445   0.010484893\n",
+      "446   0.010961694\n",
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+      "448   0.009611452\n",
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+      "450   0.011752001\n",
+      "451   0.010761133\n",
+      "452   0.009796828\n",
+      "453   0.010256304\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "454   0.010037214\n",
+      "455   0.010916758\n",
+      "456   0.010337343\n",
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+      "468   0.009725121\n",
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+      "470   0.010122486\n",
+      "471   0.0098930495\n",
+      "472   0.01074515\n",
+      "473   0.010915479\n",
+      "474   0.010536116\n",
+      "475   0.01074855\n",
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+      "485   0.010152603\n",
+      "486   0.009876495\n",
+      "487   0.010498512\n",
+      "488   0.010146117\n",
+      "489   0.011658595\n",
+      "490   0.010447956\n",
+      "491   0.011065344\n",
+      "492   0.010312888\n",
+      "493   0.010148713\n",
+      "494   0.010357324\n",
+      "495   0.009862019\n",
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+      "505   0.010476113\n",
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+      "523   0.010624662\n",
+      "524   0.010775345\n",
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+      "528   0.010144826\n",
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+      "530   0.010272845\n",
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+      "533   0.00995081\n",
+      "534   0.010005272\n",
+      "535   0.010290168\n",
+      "536   0.01098619\n",
+      "537   0.009916031\n",
+      "538   0.010124595\n",
+      "539   0.009871271\n",
+      "540   0.010032132\n",
+      "541   0.009660124\n",
+      "542   0.01079159\n",
+      "543   0.01045295\n",
+      "544   0.01005266\n",
+      "545   0.010399198\n",
+      "546   0.009220087\n",
+      "547   0.011556671\n",
+      "548   0.011044651\n",
+      "549   0.010805067\n",
+      "550   0.010190822\n",
+      "551   0.010426733\n",
+      "552   0.011011118\n",
+      "553   0.012163961\n",
+      "554   0.011302302\n",
+      "555   0.010109657\n",
+      "556   0.009936721\n",
+      "557   0.009808655\n",
+      "558   0.010132302\n",
+      "559   0.010315281\n",
+      "560   0.011321313\n",
+      "561   0.010176626\n",
+      "562   0.009217857\n",
+      "563   0.010162047\n",
+      "564   0.010774081\n",
+      "565   0.01035694\n",
+      "566   0.010384877\n",
+      "567   0.009544257\n",
+      "568   0.010793575\n",
+      "569   0.0103996\n",
+      "570   0.010108681\n",
+      "571   0.010837473\n",
+      "572   0.01001897\n",
+      "573   0.009025779\n",
+      "574   0.00985795\n",
+      "575   0.010714112\n",
+      "576   0.009709858\n",
+      "577   0.009722699\n",
+      "578   0.010266235\n",
+      "579   0.009680011\n",
+      "580   0.010841243\n",
+      "581   0.010513518\n",
+      "582   0.010374136\n",
+      "583   0.009637525\n",
+      "584   0.009725406\n",
+      "585   0.009368896\n",
+      "586   0.010587522\n",
+      "587   0.011053329\n",
+      "588   0.011217826\n",
+      "589   0.01086165\n",
+      "590   0.010253424\n",
+      "591   0.010843137\n",
+      "592   0.010402182\n",
+      "593   0.011237535\n",
+      "594   0.0102094505\n",
+      "595   0.011088045\n",
+      "596   0.009966487\n",
+      "597   0.010214208\n",
+      "598   0.011916663\n",
+      "599   0.009912753\n",
+      "WARNING:tensorflow:From /usr/local/lib/python3.9/site-packages/tensorflow/python/training/saver.py:969: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Use standard file APIs to delete files with this prefix.\n",
+      "600   0.01067204\n",
+      "601   0.011182453\n",
+      "602   0.010615521\n",
+      "603   0.011008811\n",
+      "604   0.00972122\n"
+     ]
+    }
+   ],
    "source": [
     "model_utils.reset_session_and_model()\n",
     "with tf.Session() as sess:\n",
@@ -121,7 +803,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.9"
+   "version": "3.9.4"
   }
  },
  "nbformat": 4,
diff --git a/model.py b/model.py
index 79de4c1..d7e3536 100644
--- a/model.py
+++ b/model.py
@@ -156,6 +156,7 @@ def _build_model(self):
         mu_ = gmm_params[:, : self.num_mixtures]
         sigma_= gmm_params[:,  self.num_mixtures: 2* self.num_mixtures]
         pi_ = gmm_params[:,  2*self.num_mixtures:]
+        print(pi_)
         self.mu = mu_
         self.sigma = tf.exp(sigma_ / 2.0)
         self.pi = tf.nn.softmax(pi_)
@@ -168,7 +169,7 @@ def _build_model(self):
             #self.loss = tf.reduce_mean(tf.squared_difference(self.preds, self.y_holder))
             print(self.y_holder)
             
-            mixture_p = tf.distributions.Normal(self.mu, self.sigma).prob(tf.reshape(self.y_holder,(-1,1)))
+            mixture_p = tf.compat.v1.distributions.Normal(self.mu, self.sigma).prob(tf.reshape(self.y_holder,(-1,1)))
             mixture_p = tf.multiply(self.pi, mixture_p)
             output_p = tf.reduce_sum(mixture_p, reduction_indices=1, keep_dims=True)
             log_output_p = tf.log(output_p)
@@ -214,6 +215,7 @@ def predict(self,sess, seq_len=1000):
                     self.init_state: cur_state
                 }
             )
+
             # chose one
             select_mixture = np.random.choice(self.num_mixtures, p=pi_[0])
             #new_pred_  = np.random.normal(loc=mu_[0

From 4651d70b672cc42be6ad1e8c58901980e7d288f0 Mon Sep 17 00:00:00 2001
From: James Timothy Meech <james.t.meech@gmail.com>
Date: Sat, 26 Jun 2021 11:57:53 +0100
Subject: [PATCH 7/7] Pushing

---
 SenseGenModel.ipynb            | 138 +------
 SenseGenModel_ECGDataset.ipynb | 584 +--------------------------
 TestRNNModel.ipynb             | 700 +--------------------------------
 download_ecg_dataset.sh        |   0
 4 files changed, 36 insertions(+), 1386 deletions(-)
 mode change 100644 => 100755 download_ecg_dataset.sh

diff --git a/SenseGenModel.ipynb b/SenseGenModel.ipynb
index c630afa..390bf4f 100644
--- a/SenseGenModel.ipynb
+++ b/SenseGenModel.ipynb
@@ -2,20 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\nAuthor: Moustafa Alzantot (malzantot@ucla.edu)\\nAll rights reserved Networked and Embedded Systems Lab (NESL), UCLA.\\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\\n'"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Author: Moustafa Alzantot (malzantot@ucla.edu)\n",
@@ -28,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,34 +27,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Enabling eager execution\n",
-      "INFO:tensorflow:Enabling v2 tensorshape\n",
-      "INFO:tensorflow:Enabling resource variables\n",
-      "INFO:tensorflow:Enabling tensor equality\n",
-      "INFO:tensorflow:Enabling control flow v2\n",
-      "INFO:tensorflow:Disabling eager execution\n",
-      "INFO:tensorflow:Disabling v2 tensorshape\n",
-      "WARNING:tensorflow:From /usr/local/lib/python3.9/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "non-resource variables are not supported in the long term\n",
-      "INFO:tensorflow:Disabling resource variables\n",
-      "INFO:tensorflow:Disabling tensor equality\n",
-      "INFO:tensorflow:Disabling control flow v2\n",
-      "INFO:tensorflow:Disabling eager execution\n",
-      "INFO:tensorflow:Disabling v2 tensorshape\n",
-      "INFO:tensorflow:Disabling resource variables\n",
-      "INFO:tensorflow:Disabling tensor equality\n",
-      "INFO:tensorflow:Disabling control flow v2\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import data_utils\n",
     "import model_utils\n",
@@ -74,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,21 +48,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Disabling eager execution\n",
-      "INFO:tensorflow:Disabling v2 tensorshape\n",
-      "INFO:tensorflow:Disabling resource variables\n",
-      "INFO:tensorflow:Disabling tensor equality\n",
-      "INFO:tensorflow:Disabling control flow v2\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import tensorflow.compat.v1 as tf\n",
     "tf.disable_v2_behavior() \n",
@@ -107,23 +59,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[1.012817  1.018851  1.023127  ... 0.7548917 0.9279268 0.7980909]\n",
-      " [1.022833  1.02238   1.021882  ... 0.8043137 0.9129872 0.8192417]\n",
-      " [1.022028  1.020781  1.019178  ... 0.831714  0.9246597 0.8658821]\n",
-      " ...\n",
-      " [1.018445  1.014788  1.021041  ... 0.6956257 0.6753473 0.8980947]\n",
-      " [1.019372  1.016499  1.022935  ... 0.7479103 0.6603377 0.8283723]\n",
-      " [1.021171  1.017849  1.022019  ... 0.776768  0.719353  0.8002428]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = data_utils.load_training_data()"
    ]
@@ -190,61 +128,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.9/site-packages/tensorflow/python/keras/layers/legacy_rnn/rnn_cell_impl.py:909: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
-      "  warnings.warn(\"`tf.nn.rnn_cell.LSTMCell` is deprecated and will be \"\n",
-      "/usr/local/lib/python3.9/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1700: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n",
-      "  warnings.warn('`layer.add_variable` is deprecated and '\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tensor(\"mdn_model/add_1:0\", shape=(1, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model/strided_slice_2:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/strided_slice:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/Exp:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/y:0\", shape=(1, 1, 1), dtype=float32)\n",
-      "INFO:tensorflow:Restoring parameters from models/mdnmodel.ckpt-999\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 1.0, 'Fake data')"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ckpt_path = 'models/mdnmodel.ckpt-999'\n",
     "seq_len = 2000\n",
diff --git a/SenseGenModel_ECGDataset.ipynb b/SenseGenModel_ECGDataset.ipynb
index 173f4f7..1852532 100644
--- a/SenseGenModel_ECGDataset.ipynb
+++ b/SenseGenModel_ECGDataset.ipynb
@@ -2,20 +2,9 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'\\nAuthor: Moustafa Alzantot (malzantot@ucla.edu)\\nAll rights reserved Networked and Embedded Systems Lab (NESL), UCLA.\\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\\n'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\"\"\"\n",
     "Author: Moustafa Alzantot (malzantot@ucla.edu)\n",
@@ -28,18 +17,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "%load_ext autoreload\n",
     "%autoreload 2\n"
@@ -47,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,21 +48,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Disabling eager execution\n",
-      "INFO:tensorflow:Disabling v2 tensorshape\n",
-      "INFO:tensorflow:Disabling resource variables\n",
-      "INFO:tensorflow:Disabling tensor equality\n",
-      "INFO:tensorflow:Disabling control flow v2\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import tensorflow.compat.v1 as tf\n",
     "tf.disable_v2_behavior() \n",
@@ -91,22 +59,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] No such file or directory: 'dataset/ecg_data/1 NSR'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-9-be93d435cb86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_training_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'ecg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/Desktop/sensegen/data_utils.py\u001b[0m in \u001b[0;36mload_training_data\u001b[0;34m(dataset)\u001b[0m\n\u001b[1;32m     27\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'ecg'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m         data_files = [f for f in os.listdir(ECG_data_dir)\n\u001b[0m\u001b[1;32m     30\u001b[0m                         if f.endswith('.mat')]\n\u001b[1;32m     31\u001b[0m         \u001b[0mdata_list\u001b[0m \u001b[0;34m=\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;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'dataset/ecg_data/1 NSR'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "data = data_utils.load_training_data('ecg')"
    ]
@@ -120,7 +75,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,484 +94,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /home/malzantot/Nesl/sensegen/model.py:137: LSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.\n",
-      "WARNING:tensorflow:From /home/malzantot/Nesl/sensegen/model.py:137: MultiRNNCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "This class is equivalent as tf.keras.layers.StackedRNNCells, and will be replaced by that in Tensorflow 2.0.\n",
-      "WARNING:tensorflow:From /home/malzantot/Nesl/sensegen/model.py:142: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
-      "WARNING:tensorflow:From /home/malzantot/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/tensor_array_ops.py:162: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Colocations handled automatically by placer.\n",
-      "Tensor(\"mdn_model/add_1:0\", shape=(1280, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model/strided_slice:0\", shape=(1280, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/Exp:0\", shape=(1280, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/y:0\", shape=(128, 10, 1), dtype=float32)\n",
-      "\n",
-      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-      "For more information, please see:\n",
-      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-      "  * https://github.com/tensorflow/addons\n",
-      "If you depend on functionality not listed there, please file an issue.\n",
-      "\n",
-      "WARNING:tensorflow:From /home/malzantot/Nesl/sensegen/model.py:169: Normal.__init__ (from tensorflow.python.ops.distributions.normal) is deprecated and will be removed after 2019-01-01.\n",
-      "Instructions for updating:\n",
-      "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n",
-      "WARNING:tensorflow:From /home/malzantot/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/distributions/normal.py:160: Distribution.__init__ (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.\n",
-      "Instructions for updating:\n",
-      "The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use `tfp.distributions` instead of `tf.distributions`.\n",
-      "WARNING:tensorflow:From /home/malzantot/Nesl/sensegen/model.py:171: calling reduce_sum_v1 (from tensorflow.python.ops.math_ops) with keep_dims is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "keep_dims is deprecated, use keepdims instead\n",
-      "WARNING:tensorflow:From /home/malzantot/anaconda3/lib/python3.6/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.cast instead.\n",
-      "Tensor(\"mdn_model_1/add_1:0\", shape=(1, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model_1/strided_slice:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model_1/Exp:0\", shape=(1, 24), dtype=float32)\n",
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-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
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-      "311   -1.5307882\n",
-      "312   -1.5252777\n",
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-      "378   -1.5297729\n",
-      "379   -1.5215596\n",
-      "380   -1.5360425\n",
-      "381   -1.5321892\n",
-      "382   -1.532932\n",
-      "383   -1.5289149\n",
-      "384   -1.5318658\n",
-      "385   -1.5302466\n",
-      "386   -1.5342908\n",
-      "387   -1.5274019\n",
-      "388   -1.5325809\n",
-      "389   -1.5345662\n",
-      "390   -1.5339288\n",
-      "391   -1.5312002\n",
-      "392   -1.5296836\n",
-      "393   -1.5252416\n",
-      "394   -1.5309366\n",
-      "395   -1.5332506\n",
-      "396   -1.5334619\n",
-      "397   -1.5359544\n",
-      "398   -1.5284281\n",
-      "399   -1.5376202\n",
-      "400   -1.5310094\n",
-      "401   -1.5351522\n",
-      "402   -1.5362556\n",
-      "403   -1.5293115\n",
-      "404   -1.5338084\n",
-      "405   -1.5322225\n",
-      "406   -1.5319463\n",
-      "407   -1.5354942\n",
-      "408   -1.5331061\n",
-      "409   -1.5368834\n",
-      "410   -1.5373237\n",
-      "411   -1.5329795\n",
-      "412   -1.5356042\n",
-      "413   -1.5374467\n",
-      "414   -1.5344105\n",
-      "415   -1.5393586\n",
-      "416   -1.5312672\n",
-      "417   -1.5361015\n",
-      "418   -1.5382856\n",
-      "419   -1.5382527\n",
-      "420   -1.5330033\n",
-      "421   -1.5371863\n",
-      "422   -1.5346358\n",
-      "423   -1.531743\n",
-      "424   -1.53284\n",
-      "425   -1.5348405\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_utils.reset_session_and_model()\n",
     "with tf.Session() as sess:\n",
@@ -651,41 +129,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Tensor(\"mdn_model/add_1:0\", shape=(1, 72), dtype=float32)\n",
-      "Tensor(\"mdn_model/strided_slice:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/Exp:0\", shape=(1, 24), dtype=float32)\n",
-      "Tensor(\"mdn_model/y:0\", shape=(1, 1, 1), dtype=float32)\n",
-      "INFO:tensorflow:Restoring parameters from ./models/ecg_mdnmodel.ckpt-999\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5,1,'Fake data')"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x576 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ckpt_path = './models/ecg_mdnmodel.ckpt-999'\n",
     "seq_len = 1200\n",
@@ -711,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/TestRNNModel.ipynb b/TestRNNModel.ipynb
index 2c28840..0441702 100644
--- a/TestRNNModel.ipynb
+++ b/TestRNNModel.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,34 +12,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Enabling eager execution\n",
-      "INFO:tensorflow:Enabling v2 tensorshape\n",
-      "INFO:tensorflow:Enabling resource variables\n",
-      "INFO:tensorflow:Enabling tensor equality\n",
-      "INFO:tensorflow:Enabling control flow v2\n",
-      "INFO:tensorflow:Disabling eager execution\n",
-      "INFO:tensorflow:Disabling v2 tensorshape\n",
-      "WARNING:tensorflow:From /usr/local/lib/python3.9/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "non-resource variables are not supported in the long term\n",
-      "INFO:tensorflow:Disabling resource variables\n",
-      "INFO:tensorflow:Disabling tensor equality\n",
-      "INFO:tensorflow:Disabling control flow v2\n",
-      "INFO:tensorflow:Disabling eager execution\n",
-      "INFO:tensorflow:Disabling v2 tensorshape\n",
-      "INFO:tensorflow:Disabling resource variables\n",
-      "INFO:tensorflow:Disabling tensor equality\n",
-      "INFO:tensorflow:Disabling control flow v2\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import data_utils\n",
     "import model_utils\n",
@@ -48,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,21 +33,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "INFO:tensorflow:Disabling eager execution\n",
-      "INFO:tensorflow:Disabling v2 tensorshape\n",
-      "INFO:tensorflow:Disabling resource variables\n",
-      "INFO:tensorflow:Disabling tensor equality\n",
-      "INFO:tensorflow:Disabling control flow v2\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import tensorflow.compat.v1 as tf\n",
     "tf.disable_v2_behavior() \n",
@@ -81,7 +44,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,652 +65,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
-      "WARNING:tensorflow:From /Users/james/Desktop/sensegen/model.py:52: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
-      "WARNING:tensorflow:From /usr/local/lib/python3.9/site-packages/tensorflow/python/keras/layers/legacy_rnn/rnn_cell_impl.py:987: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Call initializer instance with the dtype argument instead of passing it to the constructor\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/usr/local/lib/python3.9/site-packages/tensorflow/python/keras/layers/legacy_rnn/rnn_cell_impl.py:909: UserWarning: `tf.nn.rnn_cell.LSTMCell` is deprecated and will be removed in a future version. This class is equivalent as `tf.keras.layers.LSTMCell`, and will be replaced by that in Tensorflow 2.0.\n",
-      "  warnings.warn(\"`tf.nn.rnn_cell.LSTMCell` is deprecated and will be \"\n",
-      "/usr/local/lib/python3.9/site-packages/tensorflow/python/keras/engine/base_layer_v1.py:1700: UserWarning: `layer.add_variable` is deprecated and will be removed in a future version. Please use `layer.add_weight` method instead.\n",
-      "  warnings.warn('`layer.add_variable` is deprecated and '\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:`tf.nn.rnn_cell.MultiRNNCell` is deprecated. This class is equivalent as `tf.keras.layers.StackedRNNCells`, and will be replaced by that in Tensorflow 2.0.\n",
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-    {
-     "name": "stdout",
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-     "text": [
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-      "599   0.009912753\n",
-      "WARNING:tensorflow:From /usr/local/lib/python3.9/site-packages/tensorflow/python/training/saver.py:969: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use standard file APIs to delete files with this prefix.\n",
-      "600   0.01067204\n",
-      "601   0.011182453\n",
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-      "603   0.011008811\n",
-      "604   0.00972122\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_utils.reset_session_and_model()\n",
     "with tf.Session() as sess:\n",
diff --git a/download_ecg_dataset.sh b/download_ecg_dataset.sh
old mode 100644
new mode 100755