Multiclass_with_convolution_deepnet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import gzip\n",
    "import pickle\n",
    "from urllib.request import urlretrieve\n",
    "import zipfile\n",
    "\n",
    "import nengo\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import nengo_dl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "urlretrieve(\"http://deeplearning.net/data/mnist/mnist.pkl.gz\",\n",
    "            \"mnist.pkl.gz\")\n",
    "with gzip.open(\"mnist.pkl.gz\") as f:\n",
    "    train_data, _, test_data = pickle.load(f, encoding=\"latin1\")\n",
    "train_data = list(train_data)\n",
    "test_data = list(test_data)\n",
    "for data in (train_data, test_data):\n",
    "    one_hot = np.zeros((data[0].shape[0], 10))\n",
    "    one_hot[np.arange(data[0].shape[0]), data[1]] = 1\n",
    "    data[1] = one_hot\n",
    "\n",
    "for i in range(3):\n",
    "    plt.figure()\n",
    "    plt.imshow(np.reshape(train_data[0][i], (28, 28)),\n",
    "               cmap=\"gray\")\n",
    "    plt.axis('off')\n",
    "    plt.title(str(np.argmax(train_data[1][i])));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/nengo_dl/tensor_node.py:325: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.conv2d instead.\n",
      "WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: 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",
      "WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/nengo_dl/tensor_node.py:325: average_pooling2d (from tensorflow.python.layers.pooling) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.average_pooling2d instead.\n",
      "WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/nengo_dl/tensor_node.py:325: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use keras.layers.dense instead.\n"
     ]
    }
   ],
   "source": [
    "with nengo.Network() as net:\n",
    "    # set some default parameters for the neurons that will make\n",
    "    # the training progress more smoothly\n",
    "    net.config[nengo.Ensemble].max_rates = nengo.dists.Choice([100])\n",
    "    net.config[nengo.Ensemble].intercepts = nengo.dists.Choice([0])\n",
    "    neuron_type = nengo.LIF(amplitude=0.01)\n",
    "\n",
    "    # we'll make all the nengo objects in the network\n",
    "    # non-trainable. we could train them if we wanted, but they don't\n",
    "    # add any representational power. note that this doesn't affect\n",
    "    # the internal components of tensornodes, which will always be\n",
    "    # trainable or non-trainable depending on the code written in\n",
    "    # the tensornode.\n",
    "    nengo_dl.configure_settings(trainable=False)\n",
    "\n",
    "    # the input node that will be used to feed in input images\n",
    "    inp = nengo.Node([0] * 28 * 28)\n",
    "\n",
    "    # add the first convolutional layer\n",
    "    x = nengo_dl.tensor_layer(\n",
    "        inp, tf.layers.conv2d, shape_in=(28, 28, 1), filters=32,\n",
    "        kernel_size=3)\n",
    "\n",
    "    # apply the neural nonlinearity\n",
    "    x = nengo_dl.tensor_layer(x, neuron_type)\n",
    "\n",
    "    # add another convolutional layer\n",
    "    x = nengo_dl.tensor_layer(\n",
    "        x, tf.layers.conv2d, shape_in=(26, 26, 32),\n",
    "        filters=64, kernel_size=3)\n",
    "    x = nengo_dl.tensor_layer(x, neuron_type)\n",
    "\n",
    "    # add a pooling layer\n",
    "    x = nengo_dl.tensor_layer(\n",
    "        x, tf.layers.average_pooling2d, shape_in=(24, 24, 64),\n",
    "        pool_size=2, strides=2)\n",
    "\n",
    "    # another convolutional layer\n",
    "    x = nengo_dl.tensor_layer(\n",
    "        x, tf.layers.conv2d, shape_in=(12, 12, 64),\n",
    "        filters=128, kernel_size=3)\n",
    "    x = nengo_dl.tensor_layer(x, neuron_type)\n",
    "\n",
    "    # another pooling layer\n",
    "    x = nengo_dl.tensor_layer(\n",
    "        x, tf.layers.average_pooling2d, shape_in=(10, 10, 128),\n",
    "        pool_size=2, strides=2)\n",
    "\n",
    "    # linear readout\n",
    "    x = nengo_dl.tensor_layer(x, tf.layers.dense, units=10)\n",
    "\n",
    "    # we'll create two different output probes, one with a filter\n",
    "    # (for when we're simulating the network over time and\n",
    "    # accumulating spikes), and one without (for when we're\n",
    "    # training the network using a rate-based approximation)\n",
    "    out_p = nengo.Probe(x)\n",
    "    out_p_filt = nengo.Probe(x, synapse=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Build finished in 0:00:00                                                      \n",
      "|#                Optimizing graph: creating signals                  | 0:00:00"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/nengo_dl/simulator.py:131: UserWarning: No GPU support detected. It is recommended that you install tensorflow-gpu (`pip install tensorflow-gpu`).\n",
      "  \"No GPU support detected. It is recommended that you \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization finished in 0:00:05                                               \n",
      "Construction finished in 0:00:07                                               ##################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################################| ETA: 0:00:00\n"
     ]
    }
   ],
   "source": [
    "minibatch_size = 200\n",
    "sim = nengo_dl.Simulator(net, minibatch_size=minibatch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 30, 784)\n"
     ]
    }
   ],
   "source": [
    "# add the single timestep to the training data\n",
    "train_data = {inp: train_data[0][:, None, :],\n",
    "              out_p: train_data[1][:, None, :]}\n",
    "\n",
    "# when testing our network with spiking neurons we will need to run it\n",
    "# over time, so we repeat the input/target data for a number of\n",
    "# timesteps. we're also going to reduce the number of test images, just\n",
    "# to speed up this example.\n",
    "n_steps = 30\n",
    "test_data = {\n",
    "    inp: np.tile(test_data[0][:minibatch_size*2, None, :],\n",
    "                 (1, n_steps, 1)),\n",
    "    out_p_filt: np.tile(test_data[1][:minibatch_size*2, None, :],\n",
    "                        (1, n_steps, 1))}\n",
    "print(test_data[inp].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(outputs, targets):\n",
    "    return tf.nn.softmax_cross_entropy_with_logits_v2(\n",
    "        logits=outputs, labels=targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = tf.train.RMSPropOptimizer(learning_rate=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|####################Calculating loss (100%)###################| ETA:  00:00:00WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (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 check for files with this prefix.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /anaconda3/lib/python3.7/site-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (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 check for files with this prefix.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculation finished in 0:04:00                                                \n",
      "error before training: 87.50%\n"
     ]
    }
   ],
   "source": [
    "def classification_error(outputs, targets):\n",
    "    return 100 * tf.reduce_mean(\n",
    "        tf.cast(tf.not_equal(tf.argmax(outputs[:, -1], axis=-1),\n",
    "                             tf.argmax(targets[:, -1], axis=-1)),\n",
    "                tf.float32))\n",
    "\n",
    "print(\"error before training: %.2f%%\" % sim.loss(\n",
    "    test_data, {out_p_filt: classification_error}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "do_training = False\n",
    "if do_training:\n",
    "    # run training\n",
    "    sim.train(train_data, opt, objective={out_p: objective}, n_epochs=10)\n",
    "\n",
    "    # save the parameters to file\n",
    "    sim.save_params(\"./mnist_params\")\n",
    "else:\n",
    "    # download pretrained weights\n",
    "    urlretrieve(\n",
    "        \"https://drive.google.com/uc?export=download&\"\n",
    "        \"id=1u9JyNuRxQDUcFgkRnI1qfJVFMdnGRsjI\",\n",
    "        \"mnist_params.zip\")\n",
    "    with zipfile.ZipFile(\"mnist_params.zip\") as f:\n",
    "        f.extractall()\n",
    "\n",
    "    # load parameters\n",
    "    sim.load_params(\"./mnist_params\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculation finished in 0:02:16                                                \n",
      "error after training: 0.75%\n"
     ]
    }
   ],
   "source": [
    "print(\"error after training: %.2f%%\" % sim.loss(\n",
    "    test_data, {out_p_filt: classification_error}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Simulation finished in 0:01:50                                                 \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bty9Dhw6lT58+VdKmafcXUX6zBvvtRkR2jcSjYRw9F51n5aBJvBYexe/BtVFbGtGvR2+Wb1zFmat7cD1oR4Pn6vJVm68YuX0kHx3+iC9bf1nj9k6vCqKnLgiPSZKkcZIknZQk6WRCQoKhwylBlmVGjx5NQEAAkydPrrJ2Fc6+WAZaULDtFi52/UhK20Cwr4bOp3ZzOCWD76PiAbAL8aBXw84oJJltO9YRF5lEsEMwrzd6nZ2RO1lzeU2VxVyTiKQuPHUkSdolSVKYjs8L5alHluVFsiyHyLIcYm9vX1nhPrZDhw6xYsUK9uzZQ3BwMMHBwWzdurVK2rbq2xu5EOwOmaLVOODU9Ab+YUeoG3WZz6/HEZqeBYDXC/XpbP8seVIOv/68kuzMXEbWG0kL1xbMPjFb7/MQTwOR1IWnjizL7WVZDtTx2WDo2PTpueeeQ5Zlzp07x9mzZzl79ixdu3atkraNuozHyCaf9DWbqVPnQ3ILbtDxrWfoc+EwJhmpjDoWRmZBAZJCosGYVrRSNyCdu/z87QqQ4bPnPsNKa8Xbf79NZn5mlcRcU4ikLgiC/pnaYvVsLXLj0jCLccDO9nnuJC9j+HsTGX/7MrEKFSPWbSEnMwOFkYrnXu5E48LaxOfEsPib5WgLtHze6nOi0qPEdgLlJJK6INxDkqTekiTFAM8AWyRJ+svQMT2pLAe8hEJVROrS7/Hzm4EkSVyPmsXk0WPoX5jOQXtPPlq0iKLCQlR2xrQb2pWG+V7E3Y3k6y/nI9+QGF9/PFuub2H+6fkUyUWGfqQngkjqgnAPWZbXy7LsJsuyVpZlR1mWOxk6pieVIrgXFl75pO07ijrPDG/vySQl/U1Cwla+bNcSP6mQ3/2asn/7FgBM/G1p37czvfKbYllgzNa/NqM4rKavS1+WhC3hrX1vkZWfZeCnqv5EUhcEoXJoTLHq2Aw5v4jUjX/i7jYcc/MgLl/5BKkwnQWNA8gxMuHz6ETSEotXD5k2csR3RDN6yU1olu9P/O07KA4reMXxFXZH7Wbk9pHcybxj4Aer3kRSFwSh0hh3Ho2RdR4pK5cCCgL8P6WgIIVrEV8QaG7CCDtzzvo1YvHvv/3vO342OIyrT32NJ/0Ln0WTZ0bCsURm+M8gMi2SIVuGcDGpcveweZKJl48MwNTUtETZnDlzSpSNHz9e5/dPnTpVoqx///46742MjCxndIKgR95tsApQcPtwHDlhYZgHBeHu/hJRUYtxcuzFtLqN2fT3aX62q03XE8fwb1K8jYHWwwKHVxqg+CmMvmnB/JF/kos7w5nffz7Tz09nxLYRzGo5i/ae7Q37fNWQ6KkLQg2Vk5ND06ZNadCgAfXq1eOjjz6q+iCUKiy6d0NSyqT8+gsA3l6TMDJy49LlaZgoCvi8Xm0S7JyZdfAYeTnZ/35V7WCCwysNMHe0ok9BQ5R5Egc2HGBhi4X4Wvvy5r43WXJ+iVgZ8wCR1AWhhtJqtezZs4fQ0FDOnj3L9u3bOXr0aJXHoWwyFAvPLFK3bqUgKQml0gR/v0/IyrrOzZsL6eZoTUutgj31mrFx3e/3f9dSi+OEYKwaetAtvyH5mXlsXrWJb577hi61ujDv9Dw+PPSh2Lb3HiKpC0INJUkSZmZmQPEeMPn5+YbZS8UtBNumVsh5+SQu+B4AW9vWODm+wM3I70lLO8uXDf2QlCrm56i4cyPivq9LKgWOA/0wfdaXdrn1SUtNY81Pq5nZbCavNHiFDREbGLtzLHdz7lb9s1VDYkxdEKrA7c8+Izdcv6+8awP8cXr//YfeU1hYSOPGjbl27RoTJkyokq13S5AktK0GYn3qv9z9bTXWw4ah9fbC1/djUlJPEnbhTZo13cQbHg7MViiZuXoN/3lpJFZOzvdV493Dm9spuTQJy+No6nk2zfmVoc264tvAi3fOf8DwbcP5qdNP2JtUvy0bqpJI6gbg7Oxcomzs2LElyoqKdL9s0bhx4xJl3bt313nvggULyhmdUJMolUrOnj1LSkoKvXv3JiwsjMDAwKoPJGQUdvXnkxplSfzcubh/vwC12oJ69b7m9OnBXLo8nQn+c9mWkMIfzbtwZ91mFvbpjt0Dib3ZYD/Wz83APjWZMG0kbvvO4l9ky+9u3/Bt2jLG/DWGJZ2XYGdsV/XPWE2IpC4IVeBRPerKZmVlRZs2bdi+fbthkrqFM6qmA7CN2ETCnj1kHjuOabOmWFk2xqvWa1y/MQ9bm5ZsbNaLKWfCWRvQhG77T7OiRUN8XV3+rUapUtB5fBBbFhaSnHWXPUaXeKlxb4wvpvFW0otExcfx3d0veG3wu9ga21b9c1YDYkxdEGqohIQEUlJSAMjOzmbXrl34+/sbLqAWk7DxSUVlbUL87NnI//wmWqvWq1hZNeXylY+Rc6NYEFKPz2y13LKyp8u5m2y9HnVfNWbWRvR7pynNAtuSV5jLymN7kHv7YDPEH2cTZ8Zc6cmVuXtJPB/9VK6MEUldEGqouLg42rZtS/369WnSpAkdOnQodZiuStjVQRHYDYfAu+RcuEDaluLtASRJSb26XyJJKsIuvEFRUR6j6gfwu6cVJtmZjL6ZxNLz989HKJUKOg5qQtOGz5KpuMOq77dzMTYTz7dakNRBQpOnIueXm9z55Txy4dOV2EVSF4Qaqn79+pw5c4Zz584RFhbG9OnTDR0StJiEhUsSRp4OxH/1NUU5OQAYGbkQ4D+L9PTzXL78EbJcRHM/XzYGeuCWEMOHcWksXrKI9KTE+6rr3LMd7m7uZFlGcHjzRU5uj6RBu+fIGmPLL/ZbKAhLJWrVKeSipyexi6QuCELVcW+K5PksDkGJFMTFkbxs+b+XHBw6UcvzFW7FrSH80vvIciFevn6s7dgSU4XEl7a1+f7t1zn02wrysos39lIoFPTp2welWkGB23WOb77O6R2RtPRsSZshvfndaRfKsGzOr9z31AzFiInSSlTaaTjLli2r4kgEoRppMQnTqIGYNWpD0k8/YTNsKIp/ts7w9n4LSVJz4+Y3yEX5BAR8gYe1FUtDAuh75hr7eo5Eueobzu/dSa8pH+Dk44u1tTXdunVj/fr1uPkmcWSdhFqjpFmbZniM8WDXst9pe7Ehu35aQ+uRvdEoNQb+AVQu0VMXBKFq1ekI9v7Y+sRRlJpKytp1/16SJAlv70l4e0/m9p0/uXBxMkVF+TS3MuMjHxfOWDqQN/VzVGo1az55nxtni/dBatCgAUFBQcSmh2MXILF/9RUuHrqFs5kzA8e/zCXvWwRcdWHVou9Jyk4y1JNXCZHUBUGoWgoFtJiEieIyxnW9SVr6M3L+/a/5e9WagI/Pu8THbyHswuvk56cx1s2eFxys+C6tAM+pM7FyduHP2Z9w4e/dAHTr1g1LS0tuFZ3F2d+MvSsvceX4bTQqDe3GDCDZN492kY05sWAzWdEphnjyKiGSuiAIVS+wH5i7YOufScGtONK2lzxgytNjLL51PiQhYQcHDzXnwoU3mGYfSW0TLZMik/Ce/BFuAYFs//5rjm/4A61WS9++fUlLS6PA+SbOtS3Z9fNFrp64g6SQCBr5PPGN8/BKdiZ5wXkSl14gNyrNAA9fuURSFwSh6qk08OxEzNRn0Lg7k/TTTzonMt3dR9KkyQZcXAaQlHyQq2GjeD3vXUzkdAZejObWsAn4tmjNgV+Xsm/Zj7i5udG2bVsuhl/EvZWMU21Ldv50gasnixN7o/7t2NnjEsvsN5J+I5GE70NJWHKeguQcA/wQKoeYKNWT119/vURZr169dN7btGlTvbffqlUrneUKRcl/t0NDQ3Xeu3//fr3GJFQPhYWFhISE4OrqyubNmw0dzv80Hol04EtsgxXEbQon8/BhzFq0KHGbhXkgFuaB1PF5j8TEfcTdXstHieNYrn6PWTehbas+DLK24/TmtRQVFdJmxDiuX7/Ojp1/MeqlMRz5FXb+dBFJkvBp7MArzSYwIWMCQ2KnstThW6Tj6cR/dwabwf4Y1bGu8h+DvomeuiDUcPPnzycgIMDQYZSkMYXmr2JhdAKVrRXJS5Y89HaFQouDQyca1F9Es6AveaVgFmOVqziUks5H3k2g30hO7vqLfct+pHfv3qhUKrZu20y3CfVx8rZgx5ILXDsVj1Kh5ItWX2BtYcvLGe+gHOWOwlxD4k9hpO178t9CFUldEGqwmJgYtmzZwpgxYwwdim5Nx6IwscS6oSmZh4+QfeFCmb7mYN+JJiFr6KQ8xAz5fbTkMMfOh+/GTOddrSPv7TmAfev23IyJ5cq1S3Sf2ODfxB5z+S6WWkvmt51PZn4mk8+/g/XLdTGub0/a9pskrQynKKegkh+88ojhF0GoAgfWXCExOkOvddq5m9FygO9D73njjTeYPXs26enpem1bb4wsodk4rHd9SZKJF8k//Yzrl3PL9FVz87o0CVmP9vwrfJT2IkmOH3BN05qd+dmsVxmzLluB5TOduHXyPAt8fek+sQGrZx7n6J8R9J3amDrWdfj0uU+ZvG8y885/w9RBU8lwMyd123XufHsGqx61Mfa3qeQfgP6Jnrog1FCbN2/GwcFB51bN1UqzV1CaGmHV2Ia07dvJi4kp81e1WnsaNfwFT8euuNyZTv+8uexv14jFsWd5YfsvWBUVsKVWXVofPs+hzCwadnDnzo00Yq8UL2ns4NmBIf5DWBm+kv0x+zFv6Yr9mPpIComkpRdI+CmM/PisynrySiF66oJQBR7Vo64Mhw4dYuPGjWzdupWcnBzS0tIYNmwYK1eurPJYHsrUFkJGYZP2X5IlZxK+nofL3DllPqVJqdRSt+5cTE19iLg+l+zsaNoNWIi0+C5+y+dwq/dLbDSzY/C56zxnaUoLBy2ntt3Eza94UnRyyGRO3TnFB4c+4I8ef+Do7YjjpEZkHIkjbXckd+adxuwZZyzaeaAwUVfmT0IvpKqcFJAk6cmegXiIwsLCEmWlHXJRUbpWtJSnrcjISJ3lAwcOLFF26tSpsgdWDciybIDz2iAkJEQ+efLkfWXh4eHVZoJy3759zJ07V+fql2oRZ/ptmFefhPimJO6MwPmzz7Dq07vc1cTH/8WFi2+hVlvh7/M1a6Z9jamzKzeNrIiv34Ttlk7UyVfQ/fd4+r3TGCcvSwBupN5g4OaBBNoF8mOHH1EqlAAUZuSRtjOSzOO3UVposR1eF42rmV4fvSwkSToly3JIWe4Vwy+CIBieuRM0ehE726OYNGrA7ZkzyY2IePT3HuDg0InGjVcDMmGXRtF8WFMSr4Tj5+SA/anDvGql5ayigHBfY05v/1/nxsvSi/ebvc+J2ydYfH7xv+VKMw3Wvevg8GowIJPwQyhZofF6eODKI5K6IDwF2rRpU73WqOvSYhKSQsalpyMKIyNi35z879a85WFhHkiTkHWYmHiRIv+Adws7Eg7vxsLcHONDe2hsYcKOYBMuhCeRFPu/yesXar9AN+9uLAxdyOk7p++rU+NujsPEhqhdzUhedZnU7Teq7Xa+IqkLwj0kSZojSdIlSZLOSZK0XpIkK0PH9NSw8oD6g1Bf+w2Xj98l98oV7nz++WNVpdU60jB4BWamvlgGHcPIOhH7ojzi79xhdFEG2QqZ3Y1NOXVPb12SJD5s/iEuZi68/ffbXLt77b46leYa7McEYdrUifR9MSQtu1Atlz6KpC4I99sJBMqyXB+4Arxn4HieLs+9CYW5mClOYjN6FCmrfyNt+/bHqkqttiQ4eCkmJrXw7hJDRsxObK0siTlykFfdHTjroWFnVBIp96xuMVWbMr/tfIooYvi24Zy4feK+OiWVAus+dbDq5UPO1RSSfr1U7XrsYvVLOW3dulVnua7Jy8qSlFRy69CMDN1roD09PUuUeXl56bz3+PHjJcqUSmU5o3uyybK8456/HgX6GSqWp5KdD9TrAyeW4DDhDFknTxL3wYdoPDwwqlu33NVpNDY0bLiC06cG49Mtkshjp4gp9KFHVhIbtGq2hJjSfkcknYb9b6K4jnUdfun6C6/seoXxO8fz2XOf0dmr8331mjV3BglS1l8jfV80Fs97VPjR9UX01AWhdKOAbaVdlCRpnCRJJyVJOpmQkFCFYdVwraZAXgbS6cW4fvkVChMTbg4cRPKKlY/1Cr+rCsDmAAAgAElEQVRWY0ejRivRauxxDzmGrVUqxw8eZI6/O8lmShanpZBxN/e+77iYubC8y3KC7IJ4e//bLLuwrETbpk2dMA62J21nJDkR1WcrX5HUhaeOJEm7JEkK0/F54Z57pgEFwC+l1SPL8iJZlkNkWQ4p7ZQr4TE4BEBADzj2Axo7M7w2/Ilpixbc+fRTYiZMpODu3XJXqdU60rT57yglE+r4biM19SpOibfpZWXBIT8jdh2OLvEdS60lizouoqNnR+aenMuru19l2YVlnEs4R35hPpIkYd27Dio7Y5JXXaIwPU8fT19hIqkLTx1ZltvLshyo47MBQJKkEUB3YKj8pO/u9KRqOQVyUuH4j6isrXFb+D2O779H5oED3OjVm8xjJYcKH8XIyIWGjZYjIRMUtIvDh7YyI8ANBRI/J96lqLDkux5apZY5refwSoNXuJF6g7kn5zJ061CeWfUMI7ePJDT1HLbDApBzC0leVT3G10VSF4R7SJLUGXgH6CnL8pP1frgOtWrVIigoiODgYEJCyvTuSvXgElx87N2RBZCXiSRJ2AwfTq3fVqMwNiZq1CgyDhwod7W2Dg1wMH4btSoba9uVZMReoYuRCadcVJw+q3v9uUJS8Grwq2zvu509/ffwVZuvGOA3gNiMWF7f8zoJpqlY9fIh93oqaTt1v9hXlURSF4T7fQeYAzslSTorSdIPhg6oovbu3cvZs2d58I3Xaq/V25CdDCd//rfIqG5dav3xB9o6dYh9czK51649pALdgtuMJv1SS0xMUrl0eSKT6zlQoJL44WrcI79rb2JPB88OTG0ylcUdF1NQVMCb+95EFWyFSYgj6XujubvhmkGHYsTql4do3bp1iTI/Pz+d9+p6Tb+i2wT88IPufLJjx44SZampqTrvff7550uUTZs2rcwxvPLKKzrLFy5cWOY6niSyLPsYOgbhH+5NwasVHP4GmowBtREASjNT3Bd+z40BA4h++RVqrfkNlU3Zd1OUJInnB8xkzdIJ1G56nrQrk2haMIXdVvnExWXg7Fy2bQA8LTz57LnPeH3v63x69FM+7jkdSaUg81gcWafiMW/lillLNxTaql1BJpK6IFSBvUsXER95Xa91Onh603bkuIfeI0kSHTt2RJIkxo8fz7hxD7+/2mk1FZZ1h4NfQ9v/vTKgdnbGfcECIl8cTszE1/BY+jMKjabM1Vo7u1K3dn/Cr2qo7XuQkc5deDXBn+9PRjGzR9mXTrb1aMvYoLH8eP5HguyD6N+rP2YtXEj76yZpu6LIOBqHZadamDZxKtdjV4QYfhGEGuzQoUOcPn2abdu2sWDBgifvyEKvllB/IOyfAzH3Dx8Z16+Py+ezyD59mtsfTi/3csdn+wwgM9KDu8nOWCZ8Su2cQtZKOeTmlu8t0QnBE2jh0oJZx2ZxPuE8ansTbIfVxf7VBqhsjbm79io5l5PLVWdFiJ66IFSBR/WoK4uLiwsADg4O9O7dm+PHj5d6nm211XUORB6GdePg5QPFx+D9w6JLF3KvXyfx2+/QeHtjN77sP2elSk3PwUNZuzGbhs130MdkF3OKOvHzyWhebqH7BT2d9SiUfN7ycwZtGcSb+95kZdeVOJk6ofWwwH5sEHfmn+bun9dwfLMxCk3lD8WInrog1FCZmZn/nniUmZnJjh07CAwMNHBUj8HIEnothOTrsOODEpftXn0Vi65dSZg/n6zTZ8pVde36DfHUuhAbHUD9wsXY5eTwc3JKuXv9VkZWfNXmKzLyMxi6ZSjhSeHAP9sK9K5D4d1c0nZHlavOxyV66hQv+9Jl9erVJcrs7Owq3J6u/czXrl1bomzGjBk6v5+VVfaVdrraKm1cVdcLNLNnz9Z5r5GRUYmy7777Tue9+fn5DwtRqCR37tyhd+/iPckLCgoYMmQInTt3fsS3qimvlvDsRDj8Lfh2Ad+O/16SJAmnT2aQHRrKrXfewWv9epRmpg+p7H4dho5kyScXsHc0potyHSvMhrD9agJdfB3KFWJd27os77KcCbsnMGL7COa2nksrt1ZovS0xCXEk40AMJg3s0bhU7n7soqcuCDWUt7c3oaGhhIaGcuHChXKteqqWnv8QHOrBhgmQmXjfJaWZGS6zvyA/NpY7sz4rV7U2Lq40atqSqFNePK/ehGlBNl9H3KboMd4787X25Zeuv1DLohav7XmNVZdWAWDV1QuFiZq7669V+gtKIqkLgvBkUGmhzyLISYFNk+CBpGvSuDG2Y8aQunYd6bt2lavqZ/oOJj9CTe5de7pLf3DOqIgu689y6lAM+bklTzV7GAcTB5Z2Xkor11Z8duwzvjj+BUVGElbdvcmPTifz2KPXw1eESOqCIDw5nALh+Q/g0ma4XHKvNfuJEzCqW5e4D6dTUI5N1kwsrWjasz9R243pxib6Z6/hvJXM6PQrLP71A3ZvnMqVCz+Tm1u2Ok3UJsxrO49hAcNYGb6S8TvHk+WrQFvHitTtNylMzX10JY9JJHVBEJ4szV8FO9/iSdOC+9/clDQaXObMpigri1sffFCuCc/G3V5Aq3Qk/VogPdTrmFr0HzI0auZ4duOsyU2i7/yHg4ee4eSpAURFLSE7O+ah9SkVSt5p+g4zW8wkNCGUQVsGcadVAXKhXKnDMCKpC4LwZFGqoeOnkBwBJxaXuKytXRuHKVPI/Hs/Kb+tKXO1aq0Rzw4Yxs29eVhJ88k+0YRR16/iaWrLl8r3WXH3Z65dGkBKfDJXr33G4SOtOXGyHzGxv5Kfn1Zqvb18erGiywpUChUvHh3F9cZ3ybmUXGn7xEhVuQmdJEmG38JMBx8f3W+Gh4eHl7kOXYdk7N27V+e9gwYNKlGWmJio487K8dprr+ks/+qrr0qUlXb4h64tEPz9/XXeG/EYBwg/LlmWpSpr7B4hISHyg3urhIeHExAQUMo3qo8nJc77yDKs7AOxp+D1s2By/zYBclERUS+NIvfyZWrv+AulhUWZqi0qLGT51NfIzczAt2tv9p85h5uPD2eCW7A2PgWlDAFRubRPSaRbm2tk5G4jM/MqCoUGO7v2uDj3w8amFZJU8j/D1NxU3j3wLgdjDjIn8x0Coz2x7u+LaWPHR8YlSdIpWZbLtCOb6KkLgvDkkaTi3npuOuwreY6ppFDg+M5UClNSSPqxZG++NAqlki4TJqM1NeP0L0uwyU4j+upVuty8wOFmAYx0t+N6LSO+buDKS1dbcPn2PIICfsfFZSDJyYc5GzqKK1dmIMslOz2WWksWtFvAhIYT+NB8HudMr5K49hKp13TvDvm4RFIXhBosJSWFfv364e/vT0BAAEeOHDF0SPrjWBcajywegkm4UuKyUd26WPTsQfLy5eTHlX3FiaO3DyPmfEe3SVMxy81EnXSbEydOcG7zemb6uHL2uUCmeziRaaXiA+NsZi/OIv7MEBrW24uH+2hiYlcQHv4uslxy1YxCUvByg5dZ3/tPDja/Qpwqgdilp9l+ZjNFOv4heBwiqQtCDTZp0iQ6d+7MpUuXCA0NffKGWR6l7bTibQN0vGkK4DBpEhQVkfDNt+WqVlIo8H+2FSO+XECv/gMwzsvizMVLfDP7cwrSUnm1thP7WtWjtpkRq1qYsToinl+mnyL6WG/c3SYSd3stYRfeoKhI94t3buZufNbhc4yH1EKFEov1WXz693/K/fi6iKQuCDVUWloa+/fvZ/To0QBoNBqsrKwMHJWemdoVn2l69S+I2FPistrVFesXXyT1zz/JuXy53NUrFErqtXqeyR/NpLa1BXczs/h2/jyOHzqAg1bNhiZ1aGJlyrpnzIju4MDlw7cJXdeSWh5vEx+/lfNhEygsLH35YgP/ENxHNsY934nBkR3KHZ8uYqIU/UyUnj59ukRZ//79dd4bFVU1e0CUxtPTU2f5b7/9VqKsSZMmOu8VE6X3e9REacqmCPJuZeq1TY2LKVY9apd6/ezZs4wbN466desSGhpK48aNmT9/Pqam979C/0ROlN6rIBcWNAWtRfGGXw8oTE3lWsdOGNevj8ePiyrU1Kl9u9m2YycFGiM8nRwY8tJoilRqxl+4yY6kNEYbmeO6Mgp7NzOaDAwn4uYMbKyfo0GDJSgUpe/Kkh2WiNrNHJWVVud1MVEqCAIFBQWcPn2aV155hTNnzmBqasrnn5ecVHziqbTwzES4fQ5uh5W4rLS0xG78eDIPHCDz8OEKNdW4TTsmvvkmdhQSGXeH7+d9jaIgn58CvRjgZM2SnHTyR3iRGJPBid8C8PH6hOS7B4mJWf7Qeo0D7UpN6OUlNvQShCrwsB51ZXFzc8PNzY1mzZoB0K9fv5qZ1AHq9YHt78L534vfOn2A9dAh3F25kjtz5+L1xx9IpSzVLQsre0denf4xq+d9yZXUTJYs+i9jxr/MPH8PonPyWJCRxooxdTm9+CLHV9ehdqfWXL/xNQ4OnTEycqnIU5aJ6KkLQg3l5OSEu7s7l/8ZS969ezd165b9VJ8niqkt1H4ezv8BOoYGFVot9m++Qe7FcNK2ldxeoLwUCiX9J07CIS+D+MQkVixbRkF+Pl/5eZBfJLNAmUmncYEkxmRyY28/ZFnm8pWPy72l72PFVuktCIJgMN9++y1Dhw6lfv36nD17lvfff9/QIVWeoAGQFgPRR3VetujWDY23N0lLlugluao1Wga/PhmzxFiiY2JYvWoVbmoFU72c2ZGURqiTik5jA7l91Yiiu4NJTNxNQsJfFW73UcTwy0OU9jalLv//K+6TQNfbbqD7ecvzM/j44491lr/44otlrkPQr+DgYB6cwK2x/LqA2gTOrQHPZ0tclhQKbF4aye0Pp5N17BimzZtXuElrZ1d6Dn+JdUsWcV2S+O233xg1cCAb41OYdjWG/U0DaNzZk1N/FRI04ACXr8zAxqYFKpV5hdsujeipC4JQM2jNwL8bXPyzxEZf/8+yZ0+UtrYk/fyz3pr1bdaCZs+2QBt3k2vXrrFm9Wrm+DiTXlDEB1djaNrDCxcfW67vHUheXiIREV/qrW1dRFIXBKHmCOoP2XchYrfOywqtFuuhQ8j8ez+5V6/qrdlWw17C094W08RYIiIiOPnnWl53t+PP+BR23k2n4+h6FGX7kBXbgZjYlaSmlu/YvfIQSV0QhJqj9vNgbFO8CqYU1oMHIxkZkbR0qd6aVarUdH/jXSyK8jFLjCUyMhKrv//C30TLlMvRRKmK6DCqHjHHu0OBDZcuTaOoSPdvExUlkrogCDWHUg31esOlrcWbfemgsrbGqk9v0jZuKtdBGo9iYWfP4BmzsVFJmNy+ya3oaNpfOoUky3Q/fZWL9koadfQn+shgMjIvc/PmQr21fS+R1AVBqFnqD4CC7OLEXgqbESOQCwpI/uUXvTZtYe/AwI+/wNHMFJPYCIpuRvDi5RPUNtYw8vwNjgaZYGXZhtTIZly/sYDoiJJvoleUWP0CvPzyyzrLdb0KXxP06NFDZ3nDhg1LlJX2M9BVXtrqlyeJJEkzgReAIiAeGCnL8i3DRiWUi1tTsPSA82ugwUCdt2g8PTFv3467q1ZjN24cChMTvTVvamXNgI8+Z90XHxMddZVMWWaUtTX7/Boy68Ztere3ZsSd90jLG07omSlcOfwNzXvWwdRSP2+Uip66INxvjizL9WVZDgY2A9MNHdDjunz5MsHBwf9+LCwsmDdvnqHDqnwKBQT1g4i9kFH68IrNS6MoSk0lZd16vYdgZGZGv2kz8faqhSYhlkvnQhmTncR7Xs6sT0jhP1Za3P1nYmwTSULyclZOP0ronmi9tC2SuiDcQ5ble88lMwWq5SZ0ZeHn58fZs2c5e/Ysp06dwsTEhN69exs6rKoR1B/kQrhQesI2adQQ4+BgkpctQy4sufd5RWmMjOk9dTp+ri4oM1LZumUzA7RF/FivFqHp2byeWAeN7Qs41N+EZ4MslCr9pGOR1AXhAZIkfSpJUjQwlCe4p36v3bt3U7t27VJ36KxxHOuCcwM4uqB4F8dS2IwcSX50NJmHDlVKGCqNhhemTMPXxgI5L5eVy5bRwcKIpUFeXM3K4YOcUaQrnLCrv4S6zz36WLsytamXWgThCSJJ0i7AScelabIsb5BleRowTZKk94CJwEel1DMOGAfg4eHx0Da3bdvG7du3KxT3g5ycnOjSpUuZ7l29ejWDBw/Wa/vVXrvpsLIvHF8Ez+o+l9f8+bYoLC1J3bQZs1atKiUMlUZD3ynT+HX2f7ienc3yxYsYO+E1Vtb35sVzN/hMNZu30l7FIXYFHu4vVbw9PcT8xCtt4vBJYm9vr7Nc1wZO+tj/I0HHUrD8fN2nvFQ3siy3L+OtvwJbKCWpy7K8CFgExfup6yc6/cvLy2Pjxo3MmjXL0KFULZ/2xZ/9cyB4aInDqQEkjQaLjh1J3bKFoqwsvU6Y3kul0TBk6gcs/vw/3EpMZv3K5fR5cQSrG3gz9Nx1PlN+xeKiRB7eNShjW3qoQxBqDEmS6siy/P+vGvYELumj3rL2qCvDtm3baNSoEY6O+vn1/onS8T+w8Fn4+wvo8oXOWyx6dCfl999J370Hyx7dKy0UlUbDqHfe57vPZ3Eu4gbatb/TrW9/fg/2YXBoBEszPSjTKRiPIMbUBeF+n0uSFCZJ0jmgIzDJ0AFV1KpVq56+oZf/5xAAjYYXH06deE3nLSYhIaicnUndvKnSw9FojXh58luYFuRy4vwFtm3cQLC5MRsa1eELPze9tCGSuiDcQ5blvrIsB/6zrLGHLMuxho6pIrKysti5cyd9+vQxdCiG03YaqIxgp+45b0mhwLJbVzIPHqIgObnSwzExt2Ds65Mwykrj2OkzbN28CV8TLaZKpV7qF0ldEGowExMTkpKSsLS0NHQohmPmAM+9AZe3wM2DOm+x6NEDCgtJ2769SkKycnBixNjxaFOTOHHqNBs3btTby44iqQuCUPM9MxEs3OCv93WejGTk54e2Th3SNm2uspCc6/jRd+AgNIlxnDlzhq1bS9/WoDzERGkNMW3aNJ3lEyZMqFC9N2/e1Fk+YsSIEmVRUVEVaksQKo3auHiJ4/pxEL6heNOvB1j06EHCV1+RFxODxk0/49uP4tu8Be3vxLF7+zYUSWJMXRAEoeyC+oOpPVzaovOyZbeuAKRtrrreOkCTnn1p3qwZ/vUb6KU+kdQFQXg6KBTF+61f2w1FJbcFULu6Yty4MambNlfJAdH/T5Ik2o4ch3fDJnqpTyR1QRCeHj7tITsZ4s7qvGzZozt5ERHkhodXcWD6I5K6IAhPj9rPAxJc26PzsnmnTqBSkVqFE6b6JiZKn0C6Zsn9/Pwqpa2LFy/qLD94UPfSMKF6+frrr1m8eDGSJBEUFMTPP/+MkZGRocMyHFM7cAmGa7ug9dslLqusrTFr2ZLUjRuxGTkC9RP4Fq7oqQtCDRUbG8s333zDyZMnCQsLo7CwkNWrVxs6LMPzaQ8xx4sPqNbBbuIE5Jwcoka+REFiYhUHV3EiqQtCDVZQUEB2djYFBQVkZWXh4uJi6JAMz6c9yEVw/W+dl43r1cN90X/Jv32bqJdGUXBXd/KvrsTwiyBUgStXZpKeod/JN3OzAHx9Pyz1uqurK1OmTMHDwwNjY2M6duxIx44d9RrDE8k1BLSWxUMw9XrpvMWkcWPcF35P9PiXiRo9Gs+lS1FaWFRxoI9H9NQFoYa6e/cuGzZs4MaNG9y6dYvMzExWrlxp6LAMT6kC79bFSxsfsnTRtHlz3L77ltyr14gaO5bCjMwqDPLxiZ66IFSBh/WoK8uuXbvw8vL6d6/9Pn36cPjwYYYNG1blsVQ7Pu0hfCMkXCreybEUZi1b4jbva2ImvUHkiy/iOncO2tq1qzDQ8hNJneLF/7ooFGX/RaY8+2UvWrSoRFl5xjp1xaWvzYAeVBMOEHlaeXh4cPToUbKysjA2Nmb37t2EhOhjx+4awKdd8Z/Xdj00qQOYt2uH23ffEvfe+9zo0xeHKVOwHjoEqRz5oSpVz6gEQaiwZs2a0a9fPxo1akRQUBBFRUWMGzfO0GFVD5ZuYB9QnNTLwLxNG7w3bsC0eXPufPop0WPGkn/nTiUH+XhEUheEGmzGjBlcunSJsLAwVqxYgVarNXRI1YdPO4g8DHllGytX2dvj9sNCnD7+mKwzZ7je8wUyjx6r5CDLTyR1QRCeTj7toTCv1D3WdZEkCetBA/Fevw6VnR2xb7xB/q1blRhk+YmkLgjC08njGVCbFK+CKSdNrVq4ffctcn4+MW++iZyXVwkBPh4xUQosXLhQZ/ns2bPLXMdmHdt1lmfysqITnfqYKP3hhx8qXIcgPDHURlDruTKPqz9I6+WF86efEvvGG9yZOxen99/Xc4CPR/TUBUF4evm0h+QISL7+WF+36NwJ6+Evcnf5iio7Cu9RRFIXBOHpVadD8Z9Xdz52FY5TpmDcoAFx708j9/oNPQX2+ERSFwTh6WXjDbZ14Mrj97IljQbXeV8jaTTETppEUVaWHgMsP5HUBaEGmz9/PoGBgdSrV4958+YZOpzqybdT8QqY3IzHrkLt7IzLnDnkRkQQO/kt5IICPQZYPiKpC0INFRYWxo8//sjx48cJDQ1l8+bNXL161dBhVT91OhYvbbyhe9fGsjJr+RxOH35Axr593J7xSZUeiXcvsfoFWLdunc7yt98uuYn+/++jUd0kJCToLA/XcSxXaW8VxsXF6TUmwbDCw8Np3rw5JiYmALRu3Zr169czdepUA0dWzXg8A1qL4iEY/24Vqsp68GDy426TtGgRKmcn7F99VU9Blp1I6oJQBT68GkNYRrZe6ww0M2ZmHbfSrwcGMm3aNJKSkjA2Nmbr1q1i7xddVBqo3Rau7CjetbGUvaDKyv7NNyi4c4fEb75F7eiEVd8+egq0bERSF4QaKiAggHfeeYcOHTpgZmZGgwYNUKnE/+V1qtMJLm6AuNDi4+4qQJIknGd+QkFCAnHTp6Oyt8OsVSs9Bfpo4n9hQdBBkqQpwBzAXpblCp9p9rAedWUaPXo0o0ePBuD999/Hzc0wcVR7dToAElzdUeGkDv+siPnmGyKHv0jMG2/ivX4dGk/PisdZBmKiVBAeIEmSO9ABiDJ0LBUVHx8PQFRUFOvWrWPw4MEGjqiaMnMA10Zw5S+9Vak0M8X9+++RVCpip06tshUxoqcOREZG6iwfNGhQibJevXQffzVp0iS9xlRen376qc7yBQsWVHEkNcLXwFRgg6EDqai+ffuSlJSEWq1mwYIFWFtbGzqk6qtOJ9g3CzISwEw/CyLUTk44f/wRsZPfIvG//8V+wgS91PswIqkLwj0kSeoJxMqyHFra4SlPkgMHDhg6hCeHbyfY9xlc2wnBQ/RWrUXXrqTv3Ufi9wsxa9UK46AgvdWtixh+EZ46kiTtkiQpTMfnBWAaML2M9YyTJOmkJEknS1tSKjxBnBuAmZNeh2D+n9OHH6BycODW21Mr/Y1TkdSFp44sy+1lWQ588ANcB7yAUEmSbgJuwGlJkpxKqWeRLMshsiyHVNf3F4RykKTiCdOIPVCYr9eqlRYWuMyaRV5kJHfmzNFr3Q8SSV0Q/iHL8nlZlh1kWa4ly3ItIAZoJMvy7QrUqbf4KkN1j6/K+XaG3DSIOqL3qk2bN8Nm5EhSVq0mfd8+vdf//0RSF4RKYmRkRFJSUrVNnLIsk5SUhJGRkaFDqT6824BSUylDMAD2b0xC6+tLzISJxH30Mfn/rE7SJ6kq/4OTJKl6/tetB507dy5RVtrr+D169ChRtnHjxhJlixYt0vl9XRN4Fy9e1HlvVNQTvyqvXGRZNsjsZkhIiHzy5Mn7yvLz84mJiSEnJ8cQIZWJkZERbm5uqNVqQ4dSfSzvBSmRMPEUKPTf7y1ITibxuwXcXbMGSa3GZvhwbMeMRmluXup3JEk6JctymV4HFqtfBKGSqNVqvLy8DB2GUF4Nh8Ha0XBiMTTT3TGrCJWNDU7TP8RmxHAS5n9D0n//S8rq1di/NRnrAQMqXL8YfhEEQbhXYF+o3Q52fQwplfebrsbTE9evvqTW2j8wCgzU26oYkdQFQRDuJUnQ45+95ze9UbzJVyUyrlcPjyWLsRk+XC/1iaQuCILwICsPaP8xROyG0NVV0qSkp/F7MVEq1CjVaaJUeMIVFcHPnSHxCkw4Xrw/jIGUZ6JU9NQFQRB0USig57eQlwnbnpyDRURSFwRBKI29H7SeChfWQ/hmQ0dTJiKpC4IgPEyLN8AxCDa8CncuGDqaRxJJXRAE4WGUahj8K6hNYGXfSl3mqA8iqQuCIDyKlQcMWwt5WbCiD2QlGzqiUomkLgiCUBaO9WDwquKe+q8DiidQqyGR1AVBEMqqVgvouxhiT8HvL+l9i159EEldEAShPOr2hK5z4epfsH+uoaMpQSR1QRCE8moyGgJ6wtHvIfuuoaO5j0jqgiAIj6P1O8UHahz9wdCR3EckdUEQhMfhFAj+3eHoQshOMXQ0/xJJXRAE4XG1ngq5qXDsv4aO5F8iqQuCIDwu5wbg1xWOLoCcNENHA4ikLgiCUDGtp0JOKhyvHr11kdQFQRAqwqUh+HaGIwsgN93Q0YikLgiCUGGtpxYvbTyu+7D4qiSSuiAIQkW5NgafDnD4O4P31kVSFwRB0Ic270F2ssHfMhVJXRAEQR/cGkPwMDjyHcSHGywMkdQFQRD0pcMnoDWHLW9BFZ7/fC+R1AVBEPTF1BbafwyRhyB0lUFCUFVlY4Y66V0QBKHKNBwOZ36BHR8UL3U0sanS5kVPXRDuIUnSx5IkxUqSdPafT1dDxyQ8YRQK6P5V8X4wu2dUffNV3qIgVH9fy7Ic/M9nq6GDEZ5ATkHQ7GU4tQyiT1Rp0yKpC4IgVIa274G5M2x5E4oKq6xZkdQFoaSJkiSdkyTpJ0mSrA0djPCE0ppDx5lw+zxcWF9lzYqkLjx1JEnaJUlSmI7PC8BCoDYQDMTB/7V3hzFy1MA7GokAAAYmSURBVHUYx7+/uwttFCOhLQlYpdemIO0VbTzQGIUUY1BetBRoQtSiCVEJiS9MRKn0lQlBQd6BMfjC+MIEQo1JqW8BCcRQD7h6NO1pazECxh6iUNpQ0P58MXNxPdu7dXd29nb6/SSTzs7O/udpkz75Z2Z2lvvnGedrETERERMzMzM1pddAWb8VVnwYnroPTp2q5ZCRfbqXUlrsImIVsCczxxbad3x8PCcmJnqeSQNoahf84la46acwdkNHQ0TEc5k53s6+ztSlFhFxYcvLrcCL/cqihli/FZZfAr++t5bZuqUu/bd7I2IqIn4HbAK+2e9AGnBDw3DVt2HmABzY3fvD9fwI0gDJzO2ZuSEzL8/MzZn5l35nUgOM3QDL1tYyW7fUJanXhobhqjvg6H44uKe3h+rp6JKkwtiNcP6aYrbewxtULHVJqsPwSDFb/+sUTPfui8qWuiTVZcM2OH81PHlPz86tW+qSVJfhEbj6O8W3TA8+1pNDWOqSVKcN22D5pfD43T15JoylLkl1GhqGTd+F16aLb5tWPXzlI0qS5nfZ5uLxvE/eA/96t9KhLXVJqtvQEGzaCX8/ApM/r3boSkeTJLXnkmth5RXFfevvvl3ZsJa6JPVDBFyzE958BZ7/WWXDWuqS1C+jV8OqT8NTP4R3jlcypKUuSf0yO1s/fhT2/qSSIS11SeqnD30CNn4Jzr2gkuFGKhlFktS5LQ9WNpQzdUlqEEtdkhrEUpekBrHUJalBLHVJahBLXZIaxFKXpAax1CWpQSJ7+KvW0tkiImaAPwHLgdf6HKdTg5wdmp3/4sxc0c4glrpUoYiYyMzxfufoxCBnB/PP8vSLJDWIpS5JDWKpS9V6qN8BujDI2cH8gOfUJalRnKlLUoNY6lIbIuJzETEdEYci4s7TvL8kIh4p3382Ila1vLej3D4dEdfWmbslQ0f5I2JZRDwREW9FxAN1527J12n+z0bEcxExVf55zQBlvzIiJstlX0RsbeuAmeni4jLPAgwDh4HVwDnAPmDdnH1uB35crt8MPFKuryv3XwKMluMMD1D+9wKfAm4DHhjAf/+NwEXl+hjwygBlfw8wUq5fCBydfT3f4kxdWtiVwKHM/GNmvgM8DGyZs88WYPYn4XcBn4mIKLc/nJknM/MIcKgcr04d58/M45n5NPB2fXH/Rzf5X8jMV8vt+4GlEbGkltSFbrKfyMx/ltuXAm1dALXUpYV9APhzy+uXy22n3af8j/gGsKzNz/ZaN/kXg6ry3wi8kJkne5TzdLrKHhEfj4j9wBRwW0vJn5GlLi0sTrNt7qzpTPu089le6yb/YtB1/ohYD/wA+HqFudrRVfbMfDYz1wNXADsiYulCB7TUpYW9DHyw5fVK4NUz7RMRI8D7gdfb/GyvdZN/Megqf0SsBH4J3JKZh3ue9gy5Sh3922fmAeA4xXWBeVnq0sJ+C6yNiNGIOIfiYtbuOfvsBr5crt8EPJ7FFa7dwM3lHQ6jwFpgb025Z3WTfzHoOH9EnAf8CtiRmc/Ulvg/usk+WpY8EXExcCnw0oJH7MfVbBeXQVuA64DfU9zJcFe57XvA5nJ9KfAoxYXQvcDqls/eVX5uGvj8AOZ/iWLm+BbFrHLdoOQHdlLMcCdblgsGJPt2iou7k8DzwPXtHM9vlEpSg3j6RZIaxFKXpAax1CWpQSx1SWoQS12SGsRSl1SJiDgvIm4v1y+KiF39znQ28pZGSZUoHxm7JzMX/Najemek3wEkNcb3gTURMQn8AbgsM8ci4ivA9RSPoR0D7qd4DO124CRwXWa+HhFrgAeBFcAJ4KuZebD+v8Zg8/SLpKrcCRzOzI8Cd8x5bwz4AsWjaO8GTmTmRuA3wC3lPg8B38jMjwHfAn5US+qGcaYuqQ5PZOYx4FhEvAE8Vm6fAi6PiHOBTwKPFo+hB4ofFtH/yVKXVIfWZ5ifanl9iqKHhoB/lLN8dcHTL5Kqcgx4XycfzMw3gSMRsQ0gCh+pMtzZwlKXVInM/BvwTES8CNzXwRBfBG6NiH0UTyec+7NvaoO3NEpSgzhTl6QGsdQlqUEsdUlqEEtdkhrEUpekBrHUJalBLHVJahBLXZIa5N8Q73H7jmXJhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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n2YSXiYb7knPZX1nbpRlJkpg1axZWVlZ88cUXNDU19W8/riBiQy9B6Af/PvZvUipTerXNQLtAHhn6yEXVzc7O5tSpUwwbNqxXY/ifFEp0/v6osg1kx5/EN2oIJlFRGLL2o7Tz4x+2j/Bc4VP8zU1PYd4HbIx6jJsTM1mSmMnz/jpucXfobMrU1JT58+fz8ccfs3nzZhYtWiS26u2BGKkLwgBXX1/PvHnzeP3117Gysur366u8huJhVk12/AkAbEcMxynvGIXoicj1Ich5DMcbVOQXrMdBqmJLtD/jbC35a1o+/0gvwHDBHLtOp2PChAmkpKT0/7eOK4QYqQtCP7jYEXVva2trY968eSxZsoS5c+caJQY8huJtvo59xUXUlJZgNmQIAD9WZ+GGH/8c8ygr0pOINSskPetdQgL/wSdhvvwtvYCVeWXkNLXyTrAXpsqOMWhMTAz79u0jOTkZnU5nnD5dxsRIXRAGKFmWuf322wkKCuLBBx80XiDusXiaVQOQl5SIaWgIkokJquJjlKnBsK+CR0a8yPEGFQWF62lpKUWlkHjO351n/NzZXl7Dv7OKOpszMzPDx8eHs2fPiiWOPRBJXRAGqEOHDrF27Vr27t1LZGQkkZGRbN++vf8DMbfH3tUFU41EblIikkaDWXQUw+qyebOtkfaSRsJKfLF1XQKynl0n70KWZSRJ4v88HLnZzZ7/5Jdxuu7nh4+Cg4OpqqqiuLi4//tzmRNJXRAGqNGjRyPLMomJicTHxxMfH8+0adOMEovkMQQP81rykhKRZRmzIUOwKMwhhQaKTCRqd+VwR8SjpOGLaVM8r+ybS2Vzx4NIT/i6YqtS8ZfU/M417IGBgUiS1P9LNK8AIqkLwgUkSVogSVKSJEkGSZJijR3PgOE1Ck9tMfWVFVQVFXbMq8syd9rX8VJzPfqqFlqOl3HXhO00a3wIlRO5e9sMDuQfwEat4hl/d+LrGjv3hzE3N8fb25ukpCQxBfMLIqkLQldngLnAAWMHMqAEzcTToh6AvKQETMLDkbRaxjXlk6CUybNUUrsvD6lNwbVD/4uJxoGF1lU8vPdunj3yLNPszRhva8nzmUUUtbQCHVMwlZWVlJaWGrNnlx2R1AXhArIsJ8uynGrsOAYcMztsgkZgoWkn90wiCo0G08hI9McOs2iIjucbajHUt1H/QwEajT1R4e9ho5R51NuZz1M38F7ie7wQoKNdlnnyXMdSxqCgjn1lxBRMVyKpC8IfJEnSckmSTkiSdEKc0vO/SWHz8TStIO/0SWSDAaupU2hNz2CZcytJGMixU1N3IB99QxvW1lH4+z2GdXs293r7syZpDVJbMQ96u7CtrIbvymuwsLDAy8tLJPVfEElduOpIkrRbkqQzPfzM+j3tyLL8gSzLsbIsxzo6OvZVuANHwDQ8LBpoamikPD8Xq6lTkTQaTPbsZFaEG8/W1iC36KneeA653YBOdwvOTjMYpD9DoCm8cOwFVugcCDA34bG0fKra2gkODqasrEwcfXcBkdSFq44sy5NkWQ7t4WezsWMb0Eys8AyLBCDvTDxKa2ssJl5D7datrBjlSXJ7O/H+FjQlVVC+Jgm5zUBg4HOYmfmw1KGdxKKDHC48yGsBHpS1trP0dBa+AQGAmIK5kEjqgjBANTc3M3ToUCIiIggJCeHpp582dkhYDV2ItbqJ3OP7AbCZMwd9dTWuyXFMCnLmifwyzGYNoiW9mvIPT6No1RAW+jZqDCx3VvDisRcIsVDxVrAnR2saeDS/CncPD5HULyCSuiBcQJKkOZIk5QMjgG2SJH1r7Jj+KK1Wy969e0lISCA+Pp6dO3dy5MgR4wblPxlPywbyz2ViMOgxHzkSpaMDNV9v4u4Jg6hpamN9cyP2S4JoLain9P1ETA1eBAY8g07VQLgyi9VnVjPLyZZ/+LmxrayGI/4RFJeUUFFRYdy+XSZEUheEC8iy/LUsyzpZlrWyLDvLsnydsWP6oyRJwsLCAujYA6atrc34uxpqzPDw96OlzUBZRhqSSoX19ddTf+AA4eYGJgc788buc2TYqnG4LRR9VQulKxNw1E7FzXUh11q1czDtXQrrC1nh4cQKnSPb9CoSdH5itH6e2NBLEPpB8XPP0ZLcu1vvaoMCcXn88d+so9friYmJIT09nXvuuaf/t97tgcfYOXB0FbkHN+HsH4TN7NlUfvQxtVu38sL8xUx94wD3rT/FN38ajePyMMo+OkP5x2cYtPwxKmtOsciQztvH/8lzE97jaT83ilrb2EIo63PSGD1aNv4Hl5GJpN5LfjqM4EIbN27ssa63t3cfR/PHTJ48uVtZcnJyj3Xz8vL6OhyhFyiVSuLj46murmbOnDmcOXOG0NBQo8ZkETkTO5OV5CacYAig9ffHJDSU6q834Xvrrby2KJIlHx7lH98k8eL8CBxuDabswzNUfZJBxE3vcvjUdAY17+FcZSr+dgG8GehJRkUVW1x8mX/mLNeEhRi1f8Ymkrog9IP/NaLuazY2NowfP56dO3caPamj0uDh4cjZjGr0TXUoTS2xnjObkmeepTk5mZFBQdw9fhDv7MtgjL8jMyPcsL8xkIpPz9L0lQqviY+hyPwHP6avxH/o65goFayKCWTU4bM8lVnM2KAAVKqrN7WJOXVBGKDKysqoru7Y8rapqYndu3cTGBho5Kg6eA6dQJtBSfGB9QBYTZsGajU1mzYB8MCkwUR52vD4xtPkVTZiGmyP7Rx/Ws5VY3ckhnpZC9V7Ovd98TQ35Q4bDelW9rx7IsFo/bociKQuCANUUVEREyZMIDw8nCFDhnDttdcyY8YMY4cFgMe4BQDk/bgDAJWtLZYTJlDzzVbktjbUSgVv3hAFwH0bTtGmN2A+xAWr67xpjq/EvnYqOlUjSYU/L056PCoEl5ZG3qxpo6qpuf87dZkQSV0QBqjw8HBOnTpFYmIiZ86c4amnnjJ2SJ1MrW1xtDcnN7cMTn0KgPWc2egrK6k/0LGXmoedGc/NDeNUbjUv7Oi4yWw5Xof5CFc84q6lzSCRkvVuZ5tqpYJnvZ2oV2t5+Fhi/3fqMiGSuiAIRuE14loKm21o3fIXKD6DxejRqJydKX3tNQxNTQDMjHBj6UhvPvohi40n85EkCespPqhU1igro7FsOUtLy8/r02cE+jGysYrtejXHyqqM1TWjunrvJvSy667rvpxZq9UaIZI/bubMmd3Kli1b1mPdG264oa/DEQY478hYTmzdRF6bC4O+uBVp+X7cnn+O3NvvoPhf/8Lt2WcBeGJ6ECnFtTy68TR+ThaE62ywGO6Cz4k55IyMIzHzXYYE/a2z3ReiAplyOpf7T6fzw4RYlFfZEkcxUhcEwSjcA0NQabVkO8yEykz45n7MR4zAfvlyar78ipqt24COaZV3bozG0ULLirVxlNW1YDHSDZNGDxpqXako/hKDob2z3cHubtzQVkOWpObd9Ktv6a1I6oIgGIVKrcYzJJzszCK45kk48xUc/xDHP92LaXQ0xU8/TWtODgD2FlrevzmGqsZW7v4sDr2ZGrNIR3yy5qKW6ykr+65L2w+PGoKuqpTX8sqpaG3v6fIDlkjqgiAYjXdENNUlRVQNWgT+k+Hbx5GKE3B/+SVQqSh48CEMrR0nHYW6W/PveeEcz67in1uTsBjtjk1JDM0tZqRlv9elXTs7O5ap2mgC/p2eb4SeGY9I6oIgGI13ZAwA2afjYc77YOkCa2ejbs3C7V/P0pyURNkrr3TWnxXpzv+N8eHTI7mkyXpUPpbY515La0MSdXVJXdqeFRNJcGE2n5ZUkdLQ1K/9MiZxo/R3+rUn1Yx1SntviouL61b24IMP9ljX3Ny8W1lDQ0OvxyRcOr1eT2xsLO7u7mzdutXY4XRh6+KGjbMr2fFxRF03A27bAWvnwNo5WC5Yhe1NN1G55hPMR4/GYswYAO6d4M+awzl8cSKPR8d707p2MqmDtpKT+yEhwa927v3i7u7O9KYqMvSePHWugP9GDLoq9oURI3VBGODeeOONzvM8L0fekdHkJiXS3tYG1jpY9i24hMF/b8LpWjc0gwZR9PTTGM4PGqzN1FwX4sKm+ELwsaLVQouqYDglJVs4m/wwen1jZ9vjY6KIzkrmQFU9uytqjdXFfiWSuiAMYPn5+Wzbto077rjD2KH8Ku+IGNpbWihIOT99YmYHt2wG3/EodjyA6/xQ2guLKHvzzc73LIjRUdPUxp6UUmzGeuOffDt1bWMoLt7M8RNzaWjIBCA0NJToigKc9a38Pb2QNoNshB72LzH9Igj94ODnaZTn1fdqmw4eFoxZOPg36zzwwAO8+OKL1NXV9eq1e5NHSBhKlYrshJN4nT/uDq0FLP4vbLoTszMrsb1uNpWfrMVq+nRMw8MZ5eeAm7UJX5zIZ/rNsaTuPIc6bgxBd9xEeupjHD8xm6DA53F2nk50eDhZKafYFjKMVQVlLPdwMm6H+5gYqQvCALV161acnJyIiYkxdii/SWNiintgCNnxv7ino9LA3A/BORRHz9OonJwoevJvyG1tKBUS82J0HDxXRnFjC1KMNRG1g9melMbQIZuxMB/MmaT7yM5+l9jYWHTlRURI7bySXTLglziKkbog9IP/NaLuC4cOHWLLli1s376d5uZmamtruemmm/j000/7PZb/xTsimgOfraKushxLO4efX1AoYNxfUX5+Cy63PkT+i+up+OhjHO5cwfwYHW/tTWfjyQLunBhF3tEf4WgtBVFNREev42zyX8nIfJXoqFi8vbxoTz5JUtBQ5sWn80mYD56mV9YT3xdLJPXfacKECT2WjxgxolvZiy++2Nfh9CpbW9tuZcHBwT3WNTMz61YmVr9cXp5//nmef/55APbv38/LL798WSZ06FjaeOCzVWQnnCRswi8OawmcCU7BWDZswXLyZMrffRfL6ybj5ePDMB87vjiRx93jB2ES5cA1J4fy6sGXeWvGuwQG/Iva2kSSzj5ITMzL5GzcyYujTfh7dStT487xcag3w2wsjNPhPiSmXwRBMDoHDy8sbO3Ijj/Z/UWFAsY9AhXncJkfgWRiQvFTTyPLMgtjPciuaOR4dhX243zRyGrcUy3ZnrUdlcqc0JDXaW0tA+kzzM3N4PRJtscMxlqlZH58BhuKBt5h1SKpC8JVYPz48ZfdGvULSZKEd2QMOadPYdDru1cIuh6cglElrsTpoQdpPH6cup07mRrmgoVWxRcn8lA7maENsGF2zUReP/oada11WFmF4+vzAGVl24mKauHcuXM4tDazPcafETbmPJCSx9/TCzDIA2dVjEjqgiBcFrwjYmhpaKAoPa37i+fn1ilPwyZAQhsQQOkrr2KCgelhrmw7XURDSzuWY3RYtpkRXjqId+LfAcDLazk2NkNRqj7HxKSOI0eOYKNW8Vn4IG5zd+C9vDL+kpo3YBK7SOqCIFwWvMIikSQFyQf39VwhaBY4BiH98DJODz9IW34+VevWsXCIjsZWPdtOF6EdZIPaxZyl9XNYn7yelMoUJElJSPArKBQqIqNOEhd3jNraWtQKief83XnAy5nPiip5NC2/83i8K5m4Ufobejqgd/369T3WzcjI6Fb23HPP9XpMfWnWrFnGDkG4iplYWBB+7VQSvtuGLiiEwFHjulb4abT+5W1Y2JZhPmoU5SvfI2L2bHwdzfniRB4LYz2wGONO2xcNjGmN5dUTr/LB5A8wMXEjMOBZziTdh4fnMX744QemTZuGJEk84uNCuyzzdm4pSqkj0V/J2wmIkbogCJeNCbfegXtgMN++9yYlWd0HSgTPBsdA+P5FnB5+EENtLRUf/IfFQzw5nl1FUmENZhGOKCzV3NG4gMNFh0muSAbA2Xk6HrqluLsnU1DwKTU1NUDHfP4Tvq7c5eHIqoJynkovuKJH7CKpC4Jw2VCq1Mz882OYWFqy+eVnaayt6VpBoYCxf4GyFEwMaVjPnk3V2rXMdVdgplGy6lA2kkqBxQg3HIrMCdT7surMqs63+/s/jrX1OHx8j3Ho0MrOckmSeGqQG8t1jvwnv5xnM4v6q8u9TiR1QRAuK+Y2tsx++Emaamr45rXn0bf/4gnQkDlg7wcHX8Hxvj+BUknzyneYF61jS3wh5fUtmA9zRVIxtA0TAAAgAElEQVQreKDpNr7N+Zb8uo491SVJSVTk28gGHSr1KgoLD3c2K0kS//Bz4yZXe97JLSW5/srcrlckdUEYwLy9vQkLCyMyMpLY2Fhjh3PRnH39mLziT+SfPcP+Tz7s+qJCCaP/DMWJqOtOY3frrdRu3cot9o206g2sO5qL0lyNxRh3vPLsGVIfwidnP+l8u1JpRmTUh7S3aUk6ezfNzYWdr0mSxOODXDFVKHgnt7S/uturRFIXhAFu3759xMfHc+LECWOH8rsEjZlAzIw5xH+7ldTDB7u+GLYQrHRw8GXs77gdpZ0dmo9WMj7AkbVHcmhtN2B1jScqJ1MeKl3KjpTtVDZXdr7d0cEPSboPg6GJuJO30dr682t2ahVL3OzYVFpFfnNrf3W314jVL7/hySef7FbW0+EQAFOmTOlWVl/fu7vy9RY7O7sey8eNG9etzGAw9HU4gvCrxt64lPyzp9m35j94R8Sg/Wl7CpUGRt0PO/6Csjwe+9uXUfrSy9yxeAU3pbaw7XQhc6J02M4bTNvKBBYXX8eGlA3cHXl3Z9ujRi1kzZqTBAXv4UTcPCLCP8Lc3BeAFR5OrCoo5/28Up7x1xmj63+YSOqCcAFJkl4CZgKtQAZwmyzL1Zfa7r7VH1Cak3mpzXTh5OXLhKXLf7OOJElMnjwZSZJYsWIFy5f/dv3LjUKpZNLtd/PZkw/x4+efdu1v9M1w4CU4+ArWc1dR9uZbDPpxB4McJ7DqUDazI93RellhOdKNmT+O4+kT79MYshQzdccHg5WVFf7+15OYoCAm5ggn4uYRFvYudrYj8DDRMMfJlk8LK/mztwt26isnVYrpF0HoahcQKstyOJAGPGbkeC7JoUOHOHnyJDt27OCdd97hwIEDxg7pd3PxG0zEpKmc2rm16zJHtSmMuAcy96FqzMRq2jRqvtnK7VFOJObXcDK3CgCr67wxWCq4I3c2m1K/7tL26NGjaWpypbJyBVqtM/HxSyks/BKAezydaDIYWF1Q3m997Q1XzsePIPQDWZa/u+DXI8D83mj3f42o+4qbmxsATk5OzJkzh2PHjjF27FijxHIpRt9wC+eO/cieD99l8TMvISnOj0eH3A4/vAYHXsH2xsep+fprrsk9jpWJCx//kE2Mlx0KrRLnBSEoP5ZJ2HOQtuA21Ao1ABYWFgwdOpRDhw4xfPg7FBf/k+SUR2huzifI9wEm2lnxYX4Zd3o4Yaa8MsbAV0aUgmAcy4Adxg7ij2poaOg88aihoYHvvvuux6ekrwQmFhaMu2kZRempnN57weeu1hKG3Qmp2zB1kjAJD6fh889ZPMSDnUnFFFZ3LEs0GWxLbYCBqUUj+f7kri5tjxw5Eo1Gw8GDcUREfIiLy2yyst+irj6Fe72cqGzTX1G7OYqROjB/fs+DsWnTpnUrS09P77HulbSy4IknnuixvKebovv37++xbnX1JU8zG40kSbsBlx5eekKW5c3n6zwBtAOf/UY7y4HlAJ6enn0Q6aUpKSlhzpw5ALS3t3PjjTf2eEP/ShE0ZgKn933HwXWr8Rs6AjMr644Xhq2Aw2/DwVexXbyYosceY7GmlP/IMqt/zObxaR2Hbg9eMJzM5w/QsrcEOUbu3ArA3Nyc4cOHc+DAAUpKxjDY/ynKynaRk/Mew4NfI8bKjJV5Zdzi5oBKcflvHyBG6sJVR5blSbIsh/bw81NCvxWYASyRf+N5cVmWP5BlOVaW5VhHR8f+Cv+i+fr6kpCQQEJCAklJSb/6YX6lkCSJSbffTWtzEwc+/fkpUczsIPY2SNqI1YgQlNbWqL75musj3PjkcDaltc0AqCy0VIcbiKj04/iZQ13aHjFiBFqtlv3796NWW+PufiMlJdtoasrlXk8n8ppb+absyhjIiKQuCBeQJGkK8AhwvSzLjcaOR+jKXudJ7Iw5JH2/m9LsC1YTDbsTkFAkrMZ6/jzq9uzh/kgb2vUyb+39+dt1xLQxNCtaKd19rku7pqamjBw5ktTUVAoKCvD0WIZCoSIn9wOuc7DG30zLq9nFtBsu/z1hRFIXhK7eBiyBXZIkxUuS9J6xAxK6GjJrPhpTU45t+uLnQmtdx/YBcWuwnTMDDAYsvtvKDUM9WH8sl9yKjs9nraUZJQGNhJX4cDY9oUu7w4cPx9TUlL1796LVOuHqMo+ioo20tZbyuK8r5xpbWHsFzK2LpC4IF5Bl2U+WZQ9ZliPP/9xp7JiErkzMLYiYPJ20I4eoKir4+YUR90BrHZqyvZiPHUPVF5/zp7HeqJQSr+5K7awWNn00Bkkm69v4Lu1qtVpGjRpFRkYGubm5eHktR5bbyc39iCkO1oywMeelrCJq2n6xF81lRiR1QRCuODHTZqFQKTm+5aufC92jwXMkHHkPuxsWoS8rx/ToD9w2yofNCYUkF9UCYOVgS55PFcH5HuQUdN3ed+jQoZibm7N3715MTT1xdp5BQeF62ttr+KefO1Vtel7PKenPrv5uYvULsGDBgh7LzX56JPkC7777bl+H06u8vb27lS1ZsqTHuvoezoZ89tlne6zb1tZ2SXEJwqUwt7EldPy1nN77HSMW3IilnUPHCyPvhQ03Ym5fhVqno2rdeu58/yM+O5LDy9+m8tHSIQAEzhhO05tpnN1+BK//G9TZrkajYdSoUXz33Xfk5+fj7XUnJSVbyMtfS5jPn1jkYsdH+eXc6u6At6nWGF3/n8RIXRCEK9KQ6+ciywbitm76uXDwFLDzRTq2EptFC2k8fhyT4jzuHD+IPSmlnMju2LjLyc2NLF0ZflnOlFUUd2k3OjoarVbL4cOHsbAIwMFhInl5q2lvb+BRX1dUColnMgq5XImkLggDWHV1NfPnzycwMJCgoCAOHz78v990hbB2ciFw5FgSd++kqa5jagWFEobfDfnHsRnmA2o11f/9L7eN9MHJUsuLO1M7TzXymRqJqUHLqW1dt04wMTEhJiaGs2fPUlVVhbfXnbS3V1NYuAEXrZp7PZ3YVlbDkerLc8M+kdQFYQC7//77mTJlCikpKSQkJBAUFGTskHrV0FnzaWtp5tTOrT8XRt4IJjaoUj7B6tprqf56E1pDG3+a6M+x7Er2p5YB4DXIn3SnQjxTramtqerS7rBhw5AkiaNHj2JtHY2t7Uiyst+iubmIOz2ccNOqeSq9AMNleOydSOqCMEDV1tZy4MABbr/9dqBjvtjGxsbIUfUuB09vfGOGcmrnN7Q2nz+pSGPe8TBS8jfYTB+HobaW2h07uWGIB172Zrz4bSqG8+vNXaYEYarXEr9uX5d2ra2tCQkJ4eTJkzQ3NxMY8CyyrCc5+VFMFRKP+7qSWNfEqstws6+r7kaptbV1t7Lhw4df9PtXrlz5vytdRnraatXBwaHHusnJyd3K9u3b10NN4feq/iaD1sKGXm1T42aOzcxBv/p6ZmYmjo6O3HbbbSQkJBATE8Mbb7zxq2cCXKmGzV7I+r89zOk93xIzfXZH4dDl8ONbmLUdQePrS/WGDdjMmc1DkwO4b/0ptiQUMjvKncDgcL4Z/AlRaT5kHD3NoGFhne2OGDGC06dPExcXx6hRo/Dze4zU1L9RUPAZc92XsLGkiifPFWCpUrLQpeczCoxBjNQFYYBqb2/n5MmT3HXXXZw6dQpzc3NeeOEFY4fV69wGB+IRHEbc9s3IP+1fZOUGgyYipW7H9oZFNCUk0JyczIwwV0LcrHj5u1Ra2jtWe41eOINM0wLatxbRXtvyc7tubnh7e3P06FH0ej3ubouxsxvDufTnaW7K5qNQH0bbWnB/ci4bS6p6Cs0orrqRuiAYw2+NqPuKTqdDp9MxbNgwoGPjuoGY1AHCJk1h+5svUZByFl3w+Z0o/a+Fc99ifX0EpVotVf/9L65//zuPTAnklo+Pse5oLreN8sHWwo7aKRp0m1SkffojQXeN79zsa+TIkaxbt46kpCTCw8MJCnqBo0encvbsX4iO3sCaMF9uSszk3rM5KCWY5WRrxL9CBzFSF4QBysXFBQ8PD1JTO56m3LNnD8HBwUaOqm/4xQxDrTUh+Yf9FxROBEBZehSradOo3fIN+voGxvg7MHKQPW/vTae+pePp0KlDZrHD+yhWuSoqj+b+3ISfHw4ODhw+fBhZljHRuhAw+B/U1J4iN/c/mCkVrA3zYai1OXefzWFrqfE3/RJJXRAGsLfeeoslS5YQHh5OfHw8jz/+uLFD6hNqExP8hgwn7cgP6NvPPxhn59vxk74b2xsWYWhspHbrViRJ4pEpgVQ0tPKfAx2bgikVSsbNnUGCWRq1W7Nor+zY2VGhUDBixAiKiorIzs4GwNl5Jk5O08jMeoPy8n2YKRV8Gu5LtKU5d57NNnpiv+qmX7Ta7k+Bubu791h3/fr1fR1Onxs06OK/9p85c6YPIxGMITIy8ora6/9SBI4eR/IP+8mKP4lfbMeUE4MmQvxnmCwMQBsURNWGDdgsWkiEhw3Twlz48GAmN4/wwsFCS6hjGDtHbsN/rweF6xLwuHsokkIiPDycPXv2sHfvXpYuXYpSqSQw4J/U1iaSkHgHFhaBeHos49Owqdx8poDlSdm8YfBkgZFunoqRuiAIA4JXWBSmllakdJmCmQRtjUh5R7BdtIiWlBSaEzp2Z3x4cgDN7QbevmBr3ttHr2C1biuK/Fbq9ucBoFarmTp1Knl5eWzfvh1ZllGrbRk+7DuCAp9HlvWcTf4riceu4QWb3Yy0MeO+5Fw+MdJyR5HUBUEYEJQqFYNHjCEj7hitTee3wvceDUoNpO/GasYMFNbWlL31NgC+jhYsjPXgs6M5ZJZ1PB1qa2JLyPhhfG8ZR83uHFrzOo4DDAsLY/To0cTFxXH8+PGO6ym1uLktZNjQHURGrsHSIoii7Jd5QvMhE+0s+WtaPu/nlfb730EkdUEQBoyg0eNpb20h/fiRjgKtBXiOgPQ9KC3Mcbz7LhoOHaL+4EEA/jzJH1O1kkc3nu58IGmO/xzWeG6lTtNI5YYUDC0dSx+vueYaBg8ezI4dO8jM/PmADkmSsLcbTWTkKnx9HqCy5CuetvqOmY42PJ1eyEtZRf365KlI6oIgDBhugwOxcnT+xSqYSVB6FmoKsF28GLWnJ6Uvvojc3o6TlQlPTg/mWFYl6451rHoxU5sxPWQmzzq/T1tlM9VbOrbnVSgUzJs3DwcHBz7//HMqKrofmOHtfS8uzrPIzXqZvzsms9DFlleyS1gQn0FOU0u3+n1BJHVBEAYMSZIIGj2OnNPxNFSffyDIb1LHfzP2IGk0OD30EC3n0qneuBGABbE6RvnZ88KOFAqrO7YaWBy4mGSLLJIG59EYV0JjYsd+MVqtlsWLFyNJEuvXr6e5ubnb9QMDn8faOpq05Id5xq2aVwI8SKhrZMLxVFYXlPf5qF36jXN1e/9ikmT03W9MTU27lR08/1Xsl9RqdbeyCRMm9Fi3srLy0gK7RE5OTj2WFxUVXXQb9913X7eyd9555w/HZAyyLBvluPfY2Fj5l6tMkpOTr4gNtK6UOC9WeV4Oax6+hwlLVxA9dSbIMrwaDB5DYOEnyLJMzpKbaM3Nxe/bnSjMzcmrbGTyawcY5mvHqqVDkCSJxw8+zv6cfXxV/TaG8lacH4hGZdOxei4rK4u1a9cSERHBrFmzusXQ2lrO8RPzMBhaGBK7kXIceCglj++r6hhtY8GLAR74ml38fuySJMXJshx7MXXFSF0QBqjU1FQiIyM7f6ysrHj99deNHVafc/DwwtHLh5RD+zsKJKnjQaSM/aBvR5IknB/5K/rycio++hgADzsz/nJdAPtTy9gc37FX+s3BN1Onr+dATDIYZCrWnkVf3wqAj48Pw4cP59SpUxQUFHSLQaNxICL8P+j1TSQkrsBNI7MhwpeXAnScqmtk5NFkrjmWwguZRcTVNPTq6F0kdUEYoAICAoiPjyc+Pp64uDjMzMyYM2eOscPqF4GjxlF0LpXq4vPfVP0mQUsNFHR8mzKNiMBq2lQqPv6YtpKO4+luHelNlKcN//gmifL6FoLsgxjqMpQPC9ZgvdiPtpJGyt5PpL2qY8pl7NixmJubs3PnTnqa8bCwGExoyGvU158lO3slkiRxs5sDPwwL5KlBblirlbyVW8L0k+cIP5TEO7m9s1JGJHVBuArs2bOHQYMG4eXlZexQ+kXgqHEAJP80WvcdD5IS0nd31nF88EHQ6yl7400AlAqJF+eF09Ci5+9bkgC4JfgWihuKOWgSh+MdoejrWilbmUBbSQMmJiZMnDiRvLy8X31wz8HhGlycZ5Gd8x719WkAuGo13O3pxNdR/pwZFcrKYC/G2FpgoeyddHzVPVEqCMawY8cOiouL/3fF38HFxYWpU6deVN0NGzawePHiXr3+5czKwRFdUCgphw4wfO4NSKY2oBvSkdSveRIAjU6H7c03U7lqFXY334RJUBD+zpbce40fr+5KY05UCRMCx+Bt5c2apDVMmT4FxxURlH98mtL3EnFYGkJkZCTHjx9n165dBAQEoNFousXi7/8E5RXfk5L6BDHR/0WSfk7etmoVc5xtmePcexuBXXVJvampqVtZRkZGDzVh3rx53cq2bdvWY91XX3310gLrQWhoaI/lvr6+3cp6OmAa6PFr4a8x/LRtqTCgtLa2smXLFp5//nljh9KvAkeNZfeH71KWk4WTt2/HvPq+56ChHMw7zhRwuHMFNV99RelLL+P58UcA3DluEFsTC3ly0xl2PTiOm4Nv5pkjz3Cq9BTRrtE43RVJ2UenKf/wNHY3BDJlyhRWrVrFoUOHelxIodHYM9j/cc4m/5WCgvXodD0f/N5brrqkLgjGcLEj6r6wY8cOoqOjcXZ2NloMxuA/bBR7V71Pyo8HLkjq/4L0PRCxCACllRX2d91J6Qv/pv7gD1iMGY1GpeCFeeHMW/kjL+1M4dHpM3nz1JusTlpNtHM0KjsTnO6MoHxNEhVrz2IzTkdoSCiHDh0iKiqqx9OlXFzmUly8mfSMF3FwnIiJ1qXP+i3m1AVhgFu/fv1VNfXyEzMra7zCIkn98UDHN1bXKLBwhtSu37Ztb7wRtU5H6UsvIes7nh6N9rTl1hHefHIkh7MFzSwKWMT+vP1k1pzf1dFSg9OKCMyHulD3fT7RZToAvvvuux5jkSSJgIBnkOV20lL/3nedRiR1QRjQGhsb2bVrF3PnzjV2KEYROGoctWWlFKalgEIBAdPg3G5o+3kaVqHR4PTgn2lJS6Nm0+bO8oevC8DVyoTHNiaywP8GNEoNq8+s7nxdUiuwneuP7YLBaAr1RBi8OXv2LAcOHOhxKtPMzAtfn/soK99FXv5aamtP09CQTnNzIW1tVRgMvfPEqUjqgjCAmZmZUVFR0ePZvFcDvyHDUak1pBz6vqMgaAa0NUDm/i71LKdOxSQ8nLI33sBw/r6bhVbFM7NDSSup5/Oj1cz2m803md9Q0lDS5b3mMc443RNJlIkfvnon9u7dy+rVq6mu7r6vuofHMiwtQkhL+zvHT8zmyNHrOPTjGA4cjCXt3LO90meR1AVBGLA0pmb4Rg8h7cgPGPR68B4LWmtI2dqlniRJOP/1L7SXllK5Zk1n+cQgZ2aEu/L23nQmuCxAlmU+Tf60+3VczXH7UywTNVFMsomluLiYlStXkpiY2KWeQqEmOno9UZGfEB72PiHBrxEU+DyD/f+Gk+OUXumzuFEKPP300z2W/3RO4YWmT5/eY92+OFCjvLzn/Zh7WtHi4OBwyddbvXr1JbchCJebwFHjSDt6iNwzCXhHRMPgyZC6A/TtoPw5BZrFxmIxcSIV//kQmwULUNnbA/D0zBAOnivn/b01TPabzOepn3NH2B1Ya7t++1GYqrCa4In31nb8bljCtrjdbNy4kczMTGbNmtWZT1Qqc+zsRvVZf8VIXRCEAc0nKhaNqRkphw50FATOgMYKyDvSra7TQw9haG6m/II9jxwttSwf68uBtDImui6isb2Rz1M/7/FaFsNcUFhqUBytZunSpYwYMYL4+Hjy8vL6pG89EUldEC4gSdIzkiQlSpIUL0nSd5IkuRk7JuHSqDQa/IeO4NyxH2lvbe3YMkCpheSt3epqfX2wmT+fqs+/oP2CrXVvHOqJVqVgX6KaUW6j+DT5U5rbm7u9X1IrsRqvozWrlrasOsaPH49arSY+Pr5P+3ghkdQFoauXZFkOl2U5EtgKPGXsgIRLFzhyLK1NjWTFn+g4OGPQNR3z6j1MZdrdtATa26nZvKWzzNZcw9xoHRtPFTB/0M1UNleyJWNLt/cCmA91RWmloXZXDhqNhqCgIJKSkmhra+uz/l1IJHVBuIAsy7UX/GoOGH27aOHSeYZFYmpl/fMUTNAMqMmDooRudbX+/phGRFD91Vdd7l8tG+VNa7uB5CxHQu1DWZ20Gr1B3+39klqB5QQPWnNqaUmvJjIykpaWFlJTU/usfxcSN0qBlJSUHssXLlzYrSwyMrLHun5+fr0aE8CXX3550XXXXHDH/kJLllz8I8k9baFwNZIk6V/ALUAN0PMG+leI1157jQ8//BBJkggLC2PVqlWYmJgYO6x+p1AqGTx8NEn7dtHa1Ihm8BSQFB2jdbfu/6at58+j+G9P0RQfj1lUFAD+zpaMHezI2iO5/GPxbfz14ENszdzKLL/u+6mbD3Ghbn8+tbty8L4zHCsrK+Lj4391649e7WufX0EQLjOSJO2WJOlMDz+zAGRZfkKWZQ/gM+De32hnuSRJJyRJOlFWVtZf4V+0goIC3nzzTU6cOMGZM2fQ6/Vs2LDB2GEZTeCosbS3tZJ29MeOvV88R/Y4rw5gNXUakpkZ1b8YWC0b5U1pXQuN1QGEO4bzz8P/5Hjx8W7vl1QKLK/xoDW3jtZz1URERJCRkUFdXV2f9O1CIqkLVx1ZlifJshzaw8/mX1RdB3Tf1e3ndj6QZTlWluVYR0fHvg36D2pvb6epqYn29nYaGxtxc7t67/u6Dw7Czt2Dkzu2dEyrBM2AsmSo6L6hn9LCHKupU6jdsRN9fUNn+Vh/RwY5mrPqUC5vTXgLd0t37tt7H8kVyd3aMI9xRmmjpWZ3LuHh4ciy3G3del8Q0y+CcAFJkvxlWT53/tfrgZ7n5n6ntLRnqKvv/g//UlhaBDF48N9+9XV3d3cefvhhPD09MTU1ZfLkyUyePLlXY7iSSAoFMdNnseuDt8lLSsQzcDrsfBSSv4HRD3SrbzNvPjVfbaRu5w5s5s8HQKGQWDbahye+PkN6MXxw7QfcvONm7tx9J59M/QQvq5/3q5dUCizHe1C9KR3HRi3u7u4kJCQwcuTIHp+B6S1ipC4IXb1wfiomEZgM3G/sgP6oqqoqNm/eTFZWFoWFhTQ0NPDpp92fhryaBI+5BlMra05s/RpsPME1otvTpT8xjYpE4+tL9ZdfdSmfG6XD2lTNxz9k4WLuwvvXvo9BNrBi1wpKG7ueXmQW5YikUdJwrJjIyEhKS0t/17nBf4QYqQvCBWRZ/tXplkvxWyPqvrJ79258fHz4aWpo7ty5/Pjjj9x00039HsvlQqXREDl5Ooe/XEdFfh72gTNh37NQkw/Wui51JUnCZv58Sl98kZb0dLTnF0OYapTcOMyT97/PIK+yEV87X1ZOWsnt397Oil0reHPCm3hYeQCg0Kowi3Sk8VQpwZPC2ancSUJCQp9Og4mk/jv92kME/flwQU8yMzMvuY2e7sz/2jFdwuXP09OTI0eO0NjYiKmpKXv27CE29qIOpB/QIq+bzvHNXxK37WsmL1wI3/8bDr4KM7ofdGM963pKX32V6i+/wvnRRzrLbx3hzX8OZPLarjReXRRJqEMob1zzBvftvY/rN1/PDQE3sDx8ObYmtpgPcaHhWDFyah0BAQGcPn2ayZMno1Qq+6R/YvpFEAaoYcOGMX/+fKKjowkLC8NgMLB8+XJjh2V0ZlbWBI+9hrMH99EgWUH0LXByDVRmdaursrfHcsIEajZvRm5t7Sx3sTbhrvGD2HiqgN1nO3ZtHO46nG1ztjHbbzbr/r+9O4+OqsgXOP6tztZZyUJYkgBhEwgJARLZMQ6LIETBAURcUGQZQWd5qIPCqKOMT51xF30KOOqgDAiCsm+CgArEsCYsQkSWhCWQhUBCIEu9P7rBhO6EkNzO0vw+59Shc2/dur/OCb9Tp27dqgNzGbRoELOTZlPYyAW3xt7kbjtFdHQ0eXl5HDp0yOZeRpGkLoQTe/HFFzlw4ADJycnMmTMHDw+Pmg6pVug8eAhFBQXsWrMCbnsaTK6WHrsd/iOGU5SVxfkN35U6/sc+rWnbyJepi5M4l2d5WzTYK5gXur/AorsXEdsolnd2vMPwpcNxjQmk4GQuTT0b4u3tTWJi4g1tNXkjJKkLIW46QaFNaNH5VnavWU6BORC6jIc98+GM7Vuf3j174tqoEdnzS8/xd3c18fqIaDJyL/Pisr2lzrX0b8l7fd5jRp8ZHD9/nKWe61FuJvITz9CjRw9SUlLYsGGDQ76bJHUhxE0p9q7fc/F8Dvs2roee/wNu3pY9TK+hXFwIGDWK3B+3kL+/9LTUyNB6TLq9JYt2/DYMU1Jckzh6h/bm40Of4BbpT96uM3SL6UKnTp3YtGkTiYmJhn8vSepOQilV5ZKcnGxThHBWYe0iadiiFduXf432DIDuk2DfN3DCdtJDwKj7MHl5kfHxv23O2RuGKWli9ESyL2WzKXgn+nIRF/ecJT4+nlatWrF8+XLD14SRpC6EuCkppYgZPJSsk2kc2bMTuj8OngGw3nZbORc/P/xHjiRn5Uoup6aVOldqGGbpXptro4Kj6B3am3dOf4SpgZnchFO4uLgwYsQIGjduzIIFC0hNTTXse0lSF0LctG7p1hNPv3okfbsazPWg518gZS0cs91AI/Dh0WAykWlnh7DI0Ho8bp0N8+1+22GYidETyb6czd4mRylIvcDlExfw8PDg/vvvx9fXlyK8BNAAABPQSURBVLlz55JRYv32qpCkLoS4abm4utE+ri+/bN/GhaxM6DIBfBrCty/ZrLXu1qgR9eLjyV64kMKsLJu2nujTmjYNffnb18mczy89DHOlt/7GpZngqsjdanmr1MfH5+rLYJs2bTLkO0lSF8KJvfPOO0RGRtK+fXvefvvtmg6nVorqM4DioiL2frcO3L0g7q9w9AfL+Po1gh4dg87PJ2vuXJtz7q4mXh0WxamcfF5bZbtk0MToiaQVneRYeBa5CafIWXcUrTVBQUGMGTOG+Ph4Q76PJHUhnFRycjKzZs0iISGB3bt3s2zZMoe+9FJXBYaE0iQiiqT1q9HFxRAzBhpGweppcDmvVF2P1q3xuf12sj7/gmI7+w90ahrAmB7N+XzrMRJ+zSx17kpvfar367h3CiRn3TGyF6egizTBwcG4ubkZ8n0kqTsJrXWVi3Au+/fvp1u3bnh5eeHq6kpcXByLFy+u6bBqpah+AzmXfpqjybvB5AKD/gU5qfC97dIBQePHUZSVRfaiRXbbemrALYQFePLMV3vILyi9M9LE6IlkXM5kSbst+P6uCbkJp8j4fB/Fl213UKosWftFiGrw3KFUki8Yu7NUpI8n01uHlX0+MpJp06aRkZGBp6cnK1askLVfytD61u6Yff1IWreK8A6doFl3iLoXfngXOt4PgS2u1vXs3BnPjh3J/ORTAkaORLmWTqNe7q787z1RjP53Au+tP8TTA9pePRcVHEVcWByzkmYx8O6B+Pu1JHvJL5ydnUTQw+1x8a56b1166kI4qXbt2jFlyhT69+/PwIEDiY6OxtVV+nH2uLq70/62PqQkbiU32/oQtP9L4OIGq6aWqquUImjcWApSU8lZscJue7fdEsywzmF8tPEwe0+cK3VuatepmJSJad9Pw7NrQ4IeaMflExfIXmq7WUelvoshrQghylVej9qRxo4dy9ixYwGYOnUqYWE1E0ddENV3ANuXf83ejd/SZchw8GsMcVNg7XNwcA3c8tsGIz59+mCOiOD0q6/h3asXroGBNu09F9+OjQfTeearJL5+vCcuJsvGGCE+IUztOpWp30/l072fMjZqLMHjO+AaZMzesdJTF8KJpadbNm04duwYixYtYtSoUTUcUe0VFNqEsHaRvz0wBej6GAS1hlVToPDS1brKZKLxK69QfP48p174u91nUv5e7jwXH0FS2jkW7Sj9clF8i3j6N+vPjF0z+DnzZzya+eHi427I95Ck7iTMZrPdYs/FixftFuF8hg0bRkREBHfddRfvv/8+AQEBNR1Srdah7wCyT53k+L4kywFXd7jzNcg8bFlzvQRzm1uo/6c/cn7tWnKW2d896a4OIXQIq8ebaw+WemiqlOL5bs8T4BHAM5uf4VLRJbvXV4YkdSGc2ObNm9m3bx+7d++mb9++NR1Orde6a0/M3j7sWbfqt4Ot+loemm58FTb+s9RLSUGPPopnx46cmv4PCk7bvklqMimeubMtJ8/l8+mPR0qd8zf781LPl0jJTuG9He8Z9h0kqQshhJWruzsRcX05lLDltwemAEM/gOhRllUcV/4VrMMzysWFkFdfQRcUcPJvz9kdhunRsj63twnmgw0pZOddLnWuV2gvRrYZyX/2/YeEkwmGfAdJ6kIIUUKHfgMpLiokecPa3w66uMGQD6D7E5AwExaNg0JLgnYPD6fBU0+Su3kz2V8usNvmlIFtOX+pkPc3pNicmxwzmaZ+TVl/fL0h8UtSF8KBavtLXbU9vpoQFNqEppEd2PPtKoqLS7wUZDLBgJeh34uQ/BX8dyRcugBAwKhReHXvxunXXqMgLc2mzXaN/RjWOYzPfjxKalbpt1S93LyYc+ccptw6xea6ypCkLoSDmM1mMjIyam3i1FqTkZFR5gP1m1n0HYPJOZPOrzu3257s9Re4ewYc/g5WPwtYZsOEvPwyFBdz+vXX7bY5uf8tKAVvrjlocy7AHIBSypDYZZ66kxgzZozd49nZ2TbHpk+f7uhwBBAWFkZqaipnzpyp6VDKZDabZe66HS1juuIdEMjuNctpGdPFtkLnhyB9H2z7ELpOhIYRuIWEEDRuHGdnzCDvwQfxiokpdUmIvyeP9Axn5qbDjOvdgogQP4fELkldCAdxc3OjefPmNR2GqAQXV1c69B3Alq/mkX36FP4NG9lWuu1p2PUFrH0eHlwIWFZxzF6wgNOvvEr4l/NRptKDIZPiWjEv4TjPfZPMnLFd8HI3PgXL8IsQQtgR1XcASin2rFtpv4JXIPR+yrKpxi+WTaRNXl40eHIy+cnJnFuyxOaSel5uTB8ayc5jWTw4e5vd7e+qSpK6EELY4RtYn1ax3UjasJbCy5ftV+oyAfybwprnwPpQ1S8+HnOHDpx58y2K8/JsLrk7OoQPHuhMcloOI2duIf18vqFxS1IXQogyRPcfRP75HA5u+8F+BTcz9H0BTifBnvmA5aFpw2efoTA9nYzZH9u9bGBkYz5+JJajGXnc++EWmxkxVaGq88m8Uqp2TgNwAkuXLrV7/M03bdeD3rBhg6PDqTFaa2OmENyg2NhYnZiYWBO3Fg6ki4v5ZPJEzL6+3D/d/qwWiothdl+4cBqeSLTsngSkTX6S899+S8uVK3ALCbF76fajmYz55Ce8PVyZM7YrrRr42K2nlNquta7QusnSUxdCiDIok4no/oM4efAA6UcO269kMsEd/4CcNNj6wdXDDZ6cDED6m2+V2X5Ms0DmTehOQVExH200ZuldSepCCFGO9nF9cXX3YNdq+4t2ARDeE9oMhu/fgvOWNWDcQkMJHPMIOcuWcTEpqcxLI0L8WDypJ9OHRhoSryR1IYQoh9nHh/Zxfdi7cT05Z9LLrtj/JSgqgKV/vrroV9C48bgEBpL+z3+V+xJak0AvzG4uhsQrSV0IO5RSTymltFKqfk3HImpel6H3ohRsXTSv7Er1W0G/v8PBlbDjMwBcfLyp//gk8n76iQsbN1ZLrJLUhbiGUqoJ0B84VtOxiNrBr34wHfrdSfJ368g+dbLsil0fg+Zxli3wMixj5AH33ot7s2aceeMNdJFxG0yXyYhd6G9gt3otRYoji0F/pwuBaOAIUL8i18TExGjh3M5nZui3H7hHr5jxRvkVs1O1fqWJ1jP7aF1YoLXW+tyq1Xpfm7Y6a+HCSt0bSNQV/PuVnroQJSil7gbStNa7K1B3glIqUSmVWJvXdxHG8AkIJHrAYPZv/o6MtONlV6wXCvFvQVoibH4DAN87+uMZHc2Zd96l2MG7jElSFzcdpdQ6pVSynTIEmAY8X5F2tNYztdaxWuvY4OBgxwYtaoUuQ4bj6u7OloX/Lb9i5DCIGgEbX4PU7SilaPD0UxSmp5P52X8cGqMkdXHT0Vr301pHXluAw0BzYLdS6ggQBuxQStlZzUncjLz86tFpYDw/b9nM2WNHyq886HXwbQyLJ0BRAV6xsfj06UPGrFkUZmY6LEZJ6kJYaa2TtNYNtNbhWutwIBXorLU+VcOhiVok9q7f42428+PCueVX9PS3bFqdkQL7LW98N3hyMsUXL3Lmrbccts6+JHUhhLgBnr5+dB40lEPbfuT0r9d5C7TNnZYFv36aDYBHy5YEjh5N9oKFnHr+eXSBrNIoRLWx9tjP1nQcovaJGTwEs7cPm+d+Wn6P2+QCsWPh6A9wei8ADab8laCJj5G9YCHHH5tI0YULhsYmSV0IIW6Q2duH7sNHcXTPTn7deZ2F3DqPBlfz1d66UooGf/4zjf8xndytWzl6/wMUnDJuhE+SuhBCVEL0HYMIaBzCxjkfU1RYWHZFr0DLbJjd8yH/3NXD/sOH0+SjjyhIS+PIvSPJ37/fkLgkqQshRCW4uLpx24NjyTyRWvbuSFfcOg4KcmFX6amQPr160mzuF2AykTVvviFxSVIXQohKahnThaaRHfhxwVzyyxsbD+0MobGWIZhrxuDNbdoQ/uV8Gk2bakhMktSFEKKSlFLEPTSO/NwLbF10nReSuoyHjENw+DubU24NGqDc3Q2JSZK6EEJUQYPwFkTe3p+dq5aTdTKt7IoRQ8ErCBJmOTQeSepCCFFFve57CBdXVzZ98UnZldzMlpkwB1dCdjlrx1SRJHUhhKgib/8AugwdQcpPWzmyZ2fZFWMftfyb+G+HxSJJXQghDBATP5SAxiGsnTmDgvx8+5X8m0KbQbD9UyhwzGqN1ZrUtdZKihRHlur8exaiJDd3D+6Y8Cdyzpzm+3nlrMTY9TG4mAl7vnRIHNJTF0IIg4RFRBJ9x2B2rFrKiYNlvEwU3gsaRsHW/7OZ3mgESepCCGGg3qMexjewPqs/fJdCewt2KQXdJsKZ/XanN1aVJHUhhDCQh5cX/cc/TmbacbYtLuMt0chh4B1s6a0bTJK6EEIYrHmnWCJ6/46ErxeQfuSwbQU3s2XpgEOr4WyKofeWpC6EEA5w+8PjMfv4suajdykuLrKtEPsouLjDNmN765LUhRDCATx9/bh99DhOH07hwA+bbCv4NLDsY7prLlzMMuy+ktSFEMJB2va4jeDwFvz45ecUFdp5aNptIhTkwQ7jNqOWpC6EEA6iTCZ63zeac+mnSVq/1rZCoygI7w3bZkJROWuy3wBJ6kII4UDhHWMIbRvB1kXzKLhk503TbpMgJxX2LzHkfpLUhRDCgZRS9Br1MLlZmexctcy2wi0DIKA5HE8w5H6S1IUQwsHC2raneadYfvpmIfm512ymYXKBCd/Bna8aci9J6kIIUQ16jnyI/NwLJC5dbHvS09+w+0hSF0KIatCweUvadO/NjhXfkJtt3BTGa0lSF0KIatLj3gcpLLjMtsWOWaERJKkLIUS1CQwJJarPHexas5wzx4445B6S1IUQohr1um80Ht4+rJv9Abq42PD2JakLIUQ18vT1I+6BMZz4eR/JG9cZ3r4kdSGEqGbt4/oS2jaCTZ9/Ql7OOUPblqQuhBDVTJlM9Bs7icsX89g891ND25akLoQQNaB+03BiBg8lecNa0g7sM6xdSepClKCU+rtSKk0ptctaBtV0TMJ5dR82Ct/6wayb/T5FhbKglxCO8pbWuqO1rKjpYITzcjOb6fPIHzh7/Cg7VsqCXkIIUee1urUb7eP64V3PmKUCXA1pRQjn8oRSajSQCDyptbb7TrdSagIwAaBp06bVGJ5wNgMn/cWwtpTW2rDGhKgLlFLrgEZ2Tk0DtgJnAQ1MBxprrR+9XpuxsbE6MTHR0DiFuEIptV1rHVuRutJTFzcdrXW/itRTSs0C7CyALUTtJWPqQpSglGpc4sd7gOSaikWIypCeuhCl/VMp1RHL8MsR4A81G44QN0aSuhAlaK0fqukYhKgKGX4RQggnIkldCCGciCR1IYRwIjJPXQgDKKXOAEeB+ljmuddFdTl2cO74m2mtgyvSiCR1IQyklEqs6EsitU1djh0k/itk+EUIIZyIJHUhhHAiktSFMNbMmg6gCupy7CDxAzKmLoQQTkV66kII4UQkqQtRAUqpgUqpn5VSKUqpZ+yc91BKzbee36aUCi9x7lnr8Z+VUgOqM+4SMVQqfqVUkFJqg1LqglJqRnXHXSK+ysbfXym1XSmVZP23Tx2KvUuJbRV3K6XuqdANtdZSpEgppwAuwC9AC8Ad2A1EXFNnEvCh9fN9wHzr5whrfQ+gubUdlzoUvzfQC3gMmFEHf/+dgBDr50ggrQ7F7gW4Wj83BtKv/FxekZ66ENfXBUjRWh/WWl8G5gFDrqkzBPjM+nkh0FcppazH52mtL2mtfwVSrO1Vp0rHr7XO1Vp/D+RXX7g2qhL/Tq31CevxvYBZKeVRLVFbVCX2PK31ld2ozVhWDr0uSepCXF8ocLzEz6nWY3brWP8jngOCKnito1Ul/trAqPiHATu11pccFKc9VYpdKdVVKbUXSAIeK5HkyyRJXYjrU3aOXdtrKqtORa51tKrEXxtUOX6lVHvgNap/ffwqxa613qa1bg/cCjyrlDJf74aS1IW4vlSgSYmfw4ATZdVRSrkC9YDMCl7raFWJvzaoUvxKqTBgMTBaa/2Lw6MtIy6rSv3utdb7gVwszwXKJUldiOv7CWitlGqulHLH8jBryTV1lgAPWz8PB9ZryxOuJcB91hkOzYHWQEI1xX1FVeKvDSodv1LKH1gOPKu1/qHaIv5NVWJvbk3yKKWaAW2w7MZVvpp4mi1FSl0rwCDgIJaZDNOsx14C7rZ+NgMLsDwITQBalLh2mvW6n4E762D8R7D0HC9g6VVG1JX4gb9h6eHuKlEa1JHYH8LycHcXsAMYWpH7yRulQgjhRGT4RQghnIgkdSGEcCKS1IUQwolIUhdCCCciSV0IIZyIJHUhhCGUUv5KqUnWzyFKqYU1HdPNSKY0CiEMYV0ydpnW+rpvPQrHca3pAIQQTuNVoKVSahdwCGintY5USj0CDMWyDG0k8AaWZWgfAi4Bg7TWmUqplsD7QDCQB4zXWh+o/q9Rt8nwixDCKM8Av2itOwJPX3MuErgfy1K0LwN5WutOwBZgtLXOTOCPWusY4Cngg2qJ2slIT10IUR02aK3PA+eVUueApdbjSUAHpZQP0ANYYFmGHrBsLCJukCR1IUR1KLmGeXGJn4ux5CETkG3t5YsqkOEXIYRRzgO+lblQa50D/KqUGgGgLKKNDO5mIUldCGEIrXUG8INSKhn4VyWaeAAYq5TajWV1wmu3fRMVIFMahRDCiUhPXQghnIgkdSGEcCKS1IUQwolIUhdCCCciSV0IIZyIJHUhhHAiktSFEMKJSFIXQggn8v/zasY03fJOawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim.run_steps(n_steps, data={inp: test_data[inp][:minibatch_size]})\n",
    "\n",
    "for i in range(5):\n",
    "    plt.figure()\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.imshow(np.reshape(test_data[inp][i, 0], (28, 28)),\n",
    "               cmap=\"gray\")\n",
    "    plt.axis('off')\n",
    "\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(sim.trange(), sim.data[out_p_filt][i])\n",
    "    plt.legend([str(i) for i in range(10)], loc=\"upper left\")\n",
    "    plt.xlabel(\"time\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}