MDnn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Deep-dentistry model for decay analysis</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setting up resources and libraries required\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Activation\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.optimizers import Adam\n",
    "from keras.metrics import categorical_crossentropy\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.core import Flatten, Dense\n",
    "from keras.layers.convolutional import *\n",
    "import numpy as np\n",
    "import itertools\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Local and relative paths to the files are used..\n",
    "train_path = 'Data/train'\n",
    "valid_path = 'Data/validate'\n",
    "test_path = 'Data/test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1081 images belonging to 2 classes.\n",
      "Found 250 images belonging to 2 classes.\n",
      "Found 596 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train_bconvert = ImageDataGenerator().flow_from_directory(train_path,target_size=(64,64),classes=['DECAYED','DECAYFREE'],batch_size=20)\n",
    "valid_bconvert = ImageDataGenerator().flow_from_directory(valid_path,target_size=(64,64),classes=['DECAYED','DECAYFREE'],batch_size=10)\n",
    "test_bconvert = ImageDataGenerator().flow_from_directory(test_path,target_size=(64,64),classes=['DECAYED','DECAYFREE'],batch_size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function plots images with labels\n",
    "def plots(ims, figsize=(24,12), rows=1, interp=False, titles=None):\n",
    "    if type(ims[0]) is np.ndarray:\n",
    "        ims = np.array(ims).astype(np.uint8)\n",
    "        if (ims.shape[-1] != 3):\n",
    "            ims = ims.transpose((0,2,3,1))\n",
    "    f = plt.figure(figsize=figsize)\n",
    "    cols = len(ims)//rows if len(ims) % 2 == 0 else len(ims)//rows + 1\n",
    "    for i in range(len(ims)):\n",
    "        sp = f.add_subplot(rows, cols, i+1)\n",
    "        sp.axis('Off')\n",
    "        if titles is not None:\n",
    "            sp.set_title(titles[i], fontsize=16)\n",
    "        plt.imshow(ims[i], interpolation=None if interp else 'none')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "imgs, labels = next(train_bconvert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u7Nq1S9Zqtchms8hms9f9bpVQqXr5GzTXN0Oj0UCSJPYjut0uOp0O1Go1NBoNZFlGs9nk5+r1erTbbXQ6HT4fms0mtFotv16324VOp+Pzld4L2Uuj0eDs2bP8Hi5evIiJiYlb7n/R+9zsVyr9STrDtVotWq0WP3YtkA20Wi2vy0qlwvsSIZ/P879nZ2cxOjq67TP2K1/5CjQaDZrNJvL5PHQ6HVQqFdLpNNRqNZ97rVYLbrcbWq2Wv99oNMqxQqfTgdvtxrFjxxAKhSDLMhqNBvx+P+bn5/kzDA4OolgsolAowGKxwGq1IpVK4dChQ5iZmYFGo4Hf74fVasXFixdhMplgNptRKBTg8Xj4XKd1FI1G8cQTT/R8xq3uG2q1uq+5cav2QSXsdjssFgt0Oh0kSeK922g0AgCCwSBSqRR8Ph8uX76MxcXFbc+Nl156iddzKpVCPp9HIBDA8vIygsEgVCoV1tbWYDAY4HQ6USgU0O12MTg4iNXVVahUKphMJphMJlSrVV77dE632210u13Y7Xa0220YjUZYLBa0Wi3Y7XY4HA7eU0wmE+8x5K+q1WpYLBY0Gg3Issz7ELC+7xgMBkSj0Z7P2O/cUO5HSmxe461Wi/dD+v1rxafNZhM6ne6acaharebPYLFY+HW63S7q9TpCoRBGRkYgyzKcTie0Wi3MZjPUajUqlQocDgdarRY6nQ4OHz6MWCyGQqGAcDgMs9mMP/iDP+CxfvCDH+DZZ5/d9ty41nlxrXiboIynrxfvq9VqPisA9Ox7ZBetVsuxarfb5TNisz1lWYZer+fndTodtNttGAwG9u1WV1d57GPHjuHBBx+85WfK9XDPPfdgZGQEdrsdTqcTer0eFosFRqMRrVYLOp0OnU4HJpMJly5dwvLyMtxuN06ePIkjR47g+PHjyOfzOHz4MJ544glUq1X24zudDn73d3+Xx7r63Wx7bsRiMczPzyOVSkGv16NSqSAYDOLcuXPQaDTYu3cvUqkU1Go1fD4fisUi5x7q9TosFgu0Wi1qtRoWFhYQCoVw8eJFdLtd9j3MZjM0Gg0SiQRkWUYmk4EsyxgbG+OcGABUKhUUi0V4PB60223s27cP586dg8PhgF6vR61Wg8lkgtvtRrlchizLmJiYwOjoaM9n3Mq+IRiwNwGUfN0uKDBzu90YGBiAwWBgh3tgYAC1Wo0nmFarxenTpzEzMwOtVotisci/T8mxcrmMVqsFk8mEVquFeDyORCKBVCqFZDKJRqPBv99ut1GtVlGv1zE6OsrOWyqVgtvthsFgQCKRwMGDB2G321Eul5FKpaDT6aDRaFAul5HP59FoNHhh2u12xOPxvu2g3Izp38rNVflzcrKVoIBo859Op8MbuCRJHFiWSiUUCgXU63VOvEiSxM+jTZfGudZ49L6u9d7vVHi9XkSjUTidTnaE6OBSJlUlSYLVaoXJZEIoFILX60W5XOaDjZ5nMpl6AjD6PrcDWZZRKBQ4sWexWBCJRAAAfr8flUoFOp0ObrcbuVwORqORHfBKpYJUKoVWq4Vut4tCoYDl5WU0Gg0sLi5Cr9dDpVLBarVClmXcc889qFarcDgc8Pv9SKVSePHFFxEOhzl5AqzPA6vVykkuesxut0OSJGQyGbRaLU5w1ut1RKNRqNVqLC4uYt++fdu2x5kzZzg5XKvVYDabYTAY8LnPfQ6yLCMSiXCC7MKFCzCZTOh0OhgaGkKj0cDw8DCWl5cRjUYRj8exvLyMubk5DA4OIhAIwGQyoV6v48SJE1haWoJGo8F3v/tdTsKbzWbk83m4XC40m0088sgj8Hq9GBgYgFar5YNfkiTYbDa0Wi3YbDa43W4UCoWewPpm4PTp05iamkK320U8HkelUuGill6vRzwex9zcHGw2G+r1Oq5cucJzJhqNotFowGg0olarwe/3o9FowO12Q6PRQKvVwuFwsCPocrkQjUZht9uRz+fR7XYRiUSQzWY5oZXNZnmtpFIpxONxxGIx2O12NJtNxONxPPTQQxgYGEA6ne5JGnxY/PSnP0U2m0Umk2F7KJNnpVKJk7LZbJYD5GQyiWQyiUAggGq1isuXL2N5eRnvvPMO4vE4pqenkUgkcPnyZZjNZoyOjqLVamFpaQmXL1/mPeDcuXOIx+PodDowGAyczGw0GlheXsalS5fgdrvh8XhQKBQQCATQbDaRTqdx/vx5nDt37kN9fpVKxQ55u92G1WrFwMAAPB4Pf4eUbKR1f/HiRSwsLKBWqyEej2NlZQUDAwP8/dOeT05+vV5HOp2GyWSC3W6H1WqFy+XC1NQUSqUSVCoVWq0WJ+LsdjsnV2mf0el0cDqdWFlZQblchtFoRL1eh1ar5fNJkiS0Wi3827/9G3++kZGRvuxBezgVXScmJjhpRklwl8sFl8sFj8eD4eFhDA8PIxAIcCKBzrYbJegooLxWEHo74rnnnkO1WsW5c+dQrVZhsVhQLpeRyWSgVquh1+vh8Xh4z3M6nXC5XJxsymQy0Ol0cDgcXOyWZZmD7Gq1Cq1Wi1KphHA4DIPBgFqthnK5jGw2i3w+j2w2i1qthm63i0ajwX5LqVRCu91GJpOB3++H0WiEw+FANBqFy+XCxMQE7HY7fD4frFYrJzcMBkNfNrBarQgGgwiFQpxo7hftdpt9QPpDj7daLTQaDdRqNQ5s6U+n0+kp8FarVQDgJEG5XOYigdKulMjVarUcJFPArdFo8PDDD/N7m5iY2NZnuhmgdaBcD0r/djMJgOxGyVcC2YeSCcDG/tVsNjmB+eyzz+LXf/3X8cgjjwBYTyw9/fTT/DqbA8V+UavVIMsycrkc1Go1DAYDtFotbDYbTCYTBgcHodPp4Pf7uQhrNBoRjUZRLpcxNDTE5JJcLgeXywUATCZZWVlBIBCATqdDs9lELBaDJEnQ6/XodrvIZrNotVq4fPkyUqkUjEYjcrkcE1FMJhMqlQpsNhtCoRBSqRSazSa8Xi8sFsuHjtf6we2wB5JPStBoNHA4HJzobDabcDqd0Gg07EtvFxqNBgsLC6hUKnwGpNNpTExMcLJvenqak+gejwdarRb5fJ5/v9FoIJfLcUJMkiSYzWYumIdCIU4elUol5PN53m8ovtXpdGi1WkwycrvdCAQCHLMC4HOQCsObC0/9otvtcjKX/q08F4l4AKAnBlLOkc1JRvLXyE9WvgbtfxSv1ut1tNttNJtNGAwG+P1+TiI5nU7YbDb4/X4ulITDYbz22msolUqwWq2Ix+OwWq0c7/j9fiSTSR7v2Wef3ZZdruULbE5Gb46tlTE0EVuUZwY9j753ZaIWAJ8LdFY0m010Oh0mALXbbZ5rANhHL5VK7DOqVCrYbDa0222OOe+9915+Xw8++OC27PFRYHx8HG63G06nE+FwGA6HA9PT05iYmIDH48HIyAimpqZgNpthNpsxNTUFv98Pg8GAkZERqNVqJqKMjo4iEAiwf7O6uopgMIg33niDx7sR8eyDYDQasbq6CkmS4PV6+TxJpVKYmJjAnj17kEwm4XQ6ucBIORvywSqVCmKxGGq1GsbHxzn5SoU/yl21220kk0l4PB5Eo1GMjY1heXkZrVYL+XweCwsL6Ha7MBqNSCaT0Ol0+OlPfwpgw8+lOZLNZhGLxWCxWFAsFrf12T9SBuztDsqS6/V6rpro9Xpm0W02sl6vx8jICCqVCgKBAFKp1LbGJUbMysoK6vU6isUiV6yKxSLC4TAWFxfR6XSwsLCAK1euYHh4mB12ZcVGp9OhUCgAABYWFtixpUoRAHboDQYD1Go1TCYTAGBqagrtdht2ux3dbhdOpxPNZhNmsxmdTgfnzp1Dt9vFnj17cP/990Or1aJarXJVgCoDFND1g2uxUOmQooOMDjMKGjudDvR6PQftZ86cwfHjx7G0tIRjx46hXC6j0Wig2WxyAKt83et9F2q1mgMqv98Pl8uFvXv3Ynp6Go8++ii8Xi+Pr9x4KHBSfgZ6v3cKJElCKBSCx+OBWq1GrVbDwMAAH1jdbhfDw8Oo1+ucoDaZTDAajdi3bx+OHTvGSQZyJiipQHNwc2DRD37yk59Ap9Px2FRlN5vNKJfL8Hq9MJlMWFtbg8lk4uq30+nE2toaVwHb7TZcLhe0Wi1kWUa1WuXElM/n489OTki9XofJZEI6nUY6nYbP50OhUIDL5WKnYN++fXj77bcRDoeh0WiwuLgIv9+PTCaDdruN5eVlfi9nz56F3W5nB6Ff5PN5PPfcc8jn84jFYhgfH0cymYTD4UC1WkU8HodWq8Xy8jL2798PSZLgdDohSRLOnDkDtVqNQCCAEydOYGRkBJlMBlNTU7h48SIqlQpMJhMWFhY4ael0OjnZHQqFoFarkcvlmM1Je49arcbly5extraGSCTCDq8kSWi322xDk8nE+wetU5vNtu158R//8R/4/ve/j2g0iuXlZUQiESwvLzODqlAowGq1olKp8J6WTqcxPDzMDItMJoM9e/bgxIkTsFqtqNfr0Gg0zOZpt9sol8uwWq1c/KKkgMFgQKPRQKVSwZ49e5BOp6HX6/HQQw9Br9fznj49PY2LFy/izJkzOHLkCBeEms0mJiYmUKlU8MYbb+Dw4cPb3jdKpRKee+45xGIxAOsBc7VaxdjYGNbW1vj/gUAArVYLXq8XV65cgdFoRCwWg8vlQjqdxvz8PAwGAzNlhoaGcPnyZZw7dw5WqxUqlQoXL17kuVSr1bj7oVKpYGRkBPF4HAaDAfl8HqVSCVNTU1wxPnv2LD7+8Y8z49NkMjGzzWazYWVlBd/4xjfw+OOPY3p6um872O12nmv1eh1nz56Fy+VCJBLhRDsFNNQtYjQaEQ6Hkc1mEYlEEIlEcPHiRbhcLnQ6HU4kGY3GnoTs8ePHMTk5iUqlgl27dqHZbMJoNHISmLpSiNVHc6vdbqNWq6FYLEKr1XLRpl6vY2lpCWq1Gk6nEyqVCvV6vScg+tjHPtaXPZRMxEajAafTyQktAD1MQwpgr1cwMxgMHKxSwnVzElaJW8mK/SC8+OKLvE9ptVqsrKzA4XAwS2ppaYm/a1oDZDMKbmk/1+v1cLlcXJQGwEzmeDzOZ8m+fft4nwHAZxLNl5GREZw8eZLZf8QaJ9YWsF4A8nq9KJVKCIVCfNbRWdjPfur1etmPczqdiMfjvDY2f78qlQqhUAjLy8v8fCWz81rQaDRQqVT8msQ0arfbHDRTspsCZvL7KBFDTC1iMlEgTa9PvjDZXKfTbfnz7zQ2M92AX+6i2uyjb7YlzTkq2jSbTWYLA8Dw8DCeeeYZtovP54PT6cQLL7yw7dhEia9+9asoFArMMB0cHGTCApE9iLHn8/ng8/lQLpd5/200GlhZWUEqlUK1WmVWY7lcRrfbxYEDB2CxWJihKEkSz3uVSsV+zuzsLNrtNoaGhuByuaDX61EoFHiNFotFHDp0iDs6iHmZzWa5UPz8888jEAj0JOl3Ctf67oFr75s7AZojREJRq9XceUE+m9Vq5WTLdvDaa6+hWCyiVCrB4XAgm83C6XTyOVGv1xGPx2Gz2bC6ugqTyQSDwYBMJgO73Y5EIoFgMIjV1VU4HA5OpBOZqFgscmF8aGgIGo0Gly9fZt+C9j9iydZqNeTzefh8PiYHeb1e6HQ6VKtVNJvNniSx0WjkTr7Tp0/DarVieHi4bzsoWa1UWLpeQfJGLFl6HsVQVMilx59++mns27cPAwMDmJ2dxTe/+U0uQmm1WnQ6HTgcDo7n1Wo13G43Wq0WF35zuRyeeeYZzMzMYGlpCV6vF7lcDoODg+zrUMfMh4FyntNnJiaz0r/YXIAiELufmM0qlarnHKTn0ZliMBi4k4f2TMqBUOKe9kzKH9C55HK5ONaTZRm1Wo0T3M1mEysrKx/aHh8FvF4v9u7dy3Gb0+lEo9GAwWBAJBLhPZc681599VVEo1EkEgns3r0bdrsdJpMJDocDqVQKL7/8Mh5++GGOI+v1+nULCv3gRz/6ETKZDI4fP47x8XE0m03upKOiAvmtVFxotVqIRqOYm5vj/b5YLEKtVqPZbOKtt96CLMsIhUJM8qHipCRJ8Pl8TLii7shms4lcLod8Pg9JkpDNZjE+Pg5gveOJWP1utxt6vR4OhwOVSgV6vZ73p+8KAAAgAElEQVRfe2ZmBh6PhzvBtwJpq23cNwO3S2u1EoODg7DZbDCbzWg2m1zxoKqLJEm8CJvNJqrVKhYWFuDxeGA2m7Fv3z54PB4+fH784x9vmZLearWwsrKCTqcDr9eLer2OfD6ParWKSqXCGyK1dc/NzUGn06FUKuFrX/sa6vU6s96o7XNlZYWdXarwqdVqlEolPmxrtRonjahK9KlPfQqhUAif+MQnYDab0Wg0uHVZr9cjlUrh7/7u7/DOO++g1Wrh61//OqrVKrc+VKtVZDIZDA4OolQqoVgs4qtf/eqWKen1er1nbiidzzfeeAPf/e538ZOf/ATxeJwrmNdybK7X/vFBP+/neZudJmL3DQ4OYnp6Gs888ww+97nP/VLb2J0gQbB//354vV524onlScl6SrRRoC5JEkwmEzO83nnnHd5EnU4ntzgQC4IYDK+//npf7QqNRgM//elP0e12cfHiRbjdbg42KYmp0+m4EEAMIpPJxIWBRqOBcDjMVTSqihG7rVarQaVSoVQqMZuWAouFhQXMzc31VIt3796Nd999F51OhxmfFEifOXMG09PTePnllzE+Ps4OQ7PZRCQSwYULF9BoNKDVavHAAw/ga1/72pbssbKyIi8tLSGRSECSJLz55ps4cOAA4vE4HA4HtFotQqEQYrEYs7zJKTOZTPD7/Xj33XdhtVq5sDM9PY1z584hk8lw0E4JsYGBAVQqFbTbbSQSCT7IqZ0pFArBYDAgnU6jVCoBWA9+o9Eos528Xi/sdntPyxclPEwmE7MciGGmUqm2PDe+/e1vyxcvXuRWaafTyfO13W6zrIvFYkGlUuEAcm1tDQcPHsTCwgLPy/HxcRSLRVy5cgXZbBbFYpElKtxuNzOzDAYDqtUqnE4nM7E0Gg1sNhsikQg7ehaLhRMJZrMZy8vLkCQJw8PDmJ2dhd1u70lQA0Aul8OnP/1pZmZMT09j165dW7LHz372MzmRSCCZTDKbH1gvDOn1eqytrfWwP8PhMOr1OtxuN0ssEHN1dXUV7XabWeSRSIQlN7RaLbxeL2ZmZjA+Po5MJsPznoLwYDCI8+fP85k6ODiITCbDjs3JkyeZaW+z2WC1Wrml8L333uP2VJLKIbb27/zO72x5bvzN3/yNTIw6g8GAeDyOpaUlPPjgg7Db7ex8pVIpXhuvvvoqjhw5grm5OZYlGBsbY+a40+lkxjgljmdnZ5HNZtFsNnHPPfdgdnYW+/fvZ5YzsS8MBgO63S7PVZrzKpWKW2wlSUI8HufAhOxHSelUKoWBgQHYbDZ89rOf7UuS4ROf+ITc6XSQy+UwNzeHQ4cOcWKEWFLdbpeLUcRY7Ha7WFxcRDAY5MSzzWbjIoxKpcLp06d/abydTi5c6/X7kSD48z//c7zxxhvQaDR47LHH0Gg0YLVa8cILL2BqagrxeBxDQ0OcGFpeXka5XMbevXvx3nvvYWRkhNl3p0+fxhe+8AVOvsbjcTz11FMAwH4SMTm73S4qlQrOnj2Lt99+G/v378fQ0BAsFgu3gnY6Hbz11lsIBAIsI+N0OuF2u3HhwgW0Wi3U63WUy2V84xvfwB/+4R/igQceQC6Xw8WLF/H444/DbDZveW589rOflSnhWSgUkE6nf+k7HR0dZfkpSoqsrKywnI7Sf/rYxz7G7X8WiwUrKyvweDzcAl2r1ZBIJFiKgcYmtrpS9kLJdCJ2ktVqZZYZBebK39fr9exDWq1WvP7669uWp7gZ2OyrUqFJmVhQJqKvFdAqi/4HDx5EKBTiOKVcLuOBBx6AVqvl85n8oEKhgKmpKahUKoyPj+PQoUN97Ruf+cxnZEqQ1mo1TE1NIZ1Ow+VycYdDrVbj76ZarcLv93PATIkQSn45HA6e92q1GvF4nCUXKJay2WzQarU4f/489uzZw2dXpVLB4OAg0uk0+57lchkqlQoejweLi4sc01GnHhFkqAhotVoRCASQy+Vgt9tht9vx2GOPbckeer1evp4UGX1HlCgjbKUYdaPfvx5GR0eZaUznCxFiaP6cOHECPp8PRqORZXWoc4n8XpPJxEU5g8GA733ve1ueG0ePHpWbzSaGh4c5Bna5XFheXmYfgST0SGbIbrejVCphaGiIE5VU/KGiaKFQgN/vZ1YikTxIzoy62dRqNbxeLydo6ZwkMgUV+X0+H3eSuFwuGI1G3o8pGdVqtTiJT4XSUqmEe++9t+9940YFF2Uhi7CV73x0dBSHDh3Cvffei0uXLiEajbK8wOzsLLRaLb7+9a9zp2wwGITVauVuO7JxNptFOByG1Wrl7gyDwcC+/+DgIMeu1LJ+4MABBAKBbcuDXc8ewI07RpW/Q+cEJeZovzQYDFycU0oy0PdJbE6SRiGyxNX3yKQVtVrNjH2StKPkHPlsNpsNnU4HkUgE77zzzi09U26EP/uzP8PQ0BCvF4rliXxC5KRSqQRJkvCTn/wEkUgEk5OTkCQJKysr+Na3voWHHnqICxRutxvAejdJvV5HLBaDXq/H0NAQHnjggb7mxt/+7d/KbrcbyWQS5XKZSQvKYh5Ji1QqFRiNRng8HlitViSTSRQKBZ6za2trvPbX1taYoev3+7G6usqyBuRPm81m3neomJnL5bhTV5mMJ9lE8omJxEREQ4qFKpUK3G43XC4XJ2QnJyeFBMEHwefzcXKJgnfKlGs0GmYA1et1OJ1OGI1GZsYqNwJqc98qarUaa9IBwNraGjOhbDYbbDYbcrkcQqEQcrkcM+eq1SoGBgaQy+X4PeZyORSLRdbQIa1CmnA0oShApqQAtf7SIe7xeNgGZrOZE23A+gZ6//33c3tdOBwGsM4coqCbaOHUNtEvlLIBX/va1zA9PQ2z2YzHHnsM3/zmNzE3N8dOxLXauq71/8243s+38jxlewmw0TpBCe4LFy7g+eefx+/93u/B7/fj85//PH74wx9yQvxWg1qonU4nfD4fS0wooVKpYLFYoFarOTFFlVxi+5HDQtVIksiIx+MYGxvjFhhioVDQSYEXMeq2ikajge9///uIx+NcIW+327DZbCgUCj1MRK/XC7/fD71ez200xOiyWCxYXV1lXT9K0HY6Hdbeo9cAwJ/D6/Wi0WhwgpGSy/Pz81wpNhgMWF1dxdmzZ7mVa2lpCfv370c6nUa1WkWtVoPBYMDy8jLsdjszDt97770t2+LFF19ErVbD66+/jkwmg127diGVSmHv3r1cBaSWO7LLxMQEqtUqVldX2fmamJhAOp2G0WiEXq9HPp/H+Pg4B3XU5rGysoJkMsnthkNDQ9zWYjQaUSqVOPiempriAJuSecSSo39TEooOfmKXkkZdIpHoa24cP34cBoOB30O73UY+n0cqlUIul0MikWAtvWAwyKx2qpCbzWZmRtPz1Go1du3ahUgkAlmWec34/X6YzWY+3N1uNywWC2suTk5OIhgM4siRI1xlJt3U+fl5nDx5kpPNS0tLCAaD0Ol0GBwcxMDAAHbt2oWRkRGcOHECs7OzWFtbw1tvvbVlW8zMzHAgRixXYL3Kr9PpMDQ0xC3M5EiQBAGxjkirmdiAQ0NDCIfDHPjSmlepVBgZGWEt5Uqlwufg4uIiTpw4wbpzlUoFMzMziMfjrDdFyX2SBykUCtDpdJibm2N9JXrtpaUlmEwmzM3N9TU3KFlM0iLEwKb9gLpbaB9PJpPcvkoMaYfDgbm5OWY9UytrLpeDVqtFPB6HxWLhqr3T6cS+ffvYySedZAoEVCpVD/OVGNCUKC6Xy7Db7SwnRIUm2ueMRiMymQwymQyee+65vuwBoOes1mq1sFqtHMBQUg/YYKhotVouRJGcC+mCazQalka699572XcgfFBRsx9cr2i6XXz1q1/FhQsXWBrjpZdewvPPP4+XX34Z99xzDwCwLvNbb72FF198EfF4HD//+c/xD//wD3jppZcQDodx9uxZTjT6/X6oVCr4fD6USiU+O4lZQe+XAn+dTodkMglZlpHP52G323ta9w8ePIhisQifz4exsTE0Gg3EYjHMzs5ibm6OZZPK5TIMBgNeeeUVAEAkEulbAooY/tSpotFo4PF4+OeBQABOpxPdbhcWi4WlPPx+PwKBAHekAMDAwAD0ej3sdjs8Hg9cLhf27NnD3UQOhwMDAwPYt28fdu/eDWCdQUN+gbJ1lH5GRALyO6rVKs9ZWZa5RZV+v9PpoFKpwO/3w2Qy4dFHH+17jtwMXM9X3Zy4U8YOys4vJTqdDmw2Gx5//HGMjo7CYDBgYGCA/0QiEWi1Wvh8Pg64A4FAz/dUr9fx7rvv9vUZdDod7rnnHpw+fRqdTgdXrlxBp9PB0tIS7HY7kskkXC4XywdQwYz0qxOJBBf0yQ8ireSZmRmYTCYUi0UMDw9zIrVareLSpUuYmJhAKBRCNpuFLMuoVCq4fPkyAoEACoUCVCoVa6aT5qvFYoEsy3C73ajX6xgbG8Pq6ipLalGhlToONrfo94PNTGXylQlK9uNmFqTyMdobtrqnhcNhjI2NsS65zWaDxWKB3++Hz+eD3+9HNBrFU089xcVYWqNK2Q8iFtGa73dP9fl8CIfDTE6iPaLb7SKfz2NychKzs7PcZTk2Nga3282SPQ6Hg78z6i6hdVsoFHjtG41GLnLSGUZ6lMSQpKJmLpdjXXciISn12jOZDFKpFMujUXGLul9brRbK5TIWFxf7lsei378WMUgZSysLMVuxuUql4jPk9ddfx4ULF3D69Gm8//77qFQqGBoawuTkJP7yL/+SizlarRbRaBSFQgF79uwBANYRj8ViSKfTMJvNnFwzGAwIBAJcOKYkdbfbxZUrV/D+++/3ZQt63/Q5N0MpJ3AtmT+KrYGNM4GKPcrfpzs/SAKH/FAlc1XJAifCBHW7Uqs9EXVIBggAJ6iJhUldrmtrazhy5Ejf9vioEA6HMT4+zvJZgUAAVqsVkUgEdrsdBoMBbrcbbrebCTJerxdarZbl9SKRCCedSc+efGPaW9PpNJaWlvCjH/2or/eXTqexsLDAfhMRB4lwQXdrNJtN1vGWZRn1ep2/P/q/VqvlYrTBYOCzcWVlBQaDge+qIFIS5QGIOEXycKR/T/EZSQSurKzw+UV+PflKVASmPYnWM82fD8Kd0xu9BUiShEgk0sP0pHZ8AD0BEbDuqNpsNnaGCTqdjv8vyzK3EBJLljQqKIlJLQ39bNb0ZTabTdae0Wq1cLvdkCSJDw9yMuhwoQQqOczUrkDtjRRYkmNAAZXy0gxqt6CWlC996UuwWq1oNpu4dOkStw9R6yyJM1OSlpLRlUoFLpcLpVIJHo+HL/myWq3c/toP3njjDfzTP/0TXnzxxZ7vDdiallI/Ad92gsPNrAT6TmiOUCWXEt8vvPACfvSjH8Hr9eLIkSP413/9177Gu1kYGhqCSqViujxVfihYJOdnZWWFW9FzuRy63S5sNhuMRiOz6IhhYbFYuFKsUqk4uTo0NITTp08jHo9jdHSUW42oskjrph98+9vfRrFY5IDWYDCgUCiw00CJDGp1qlarrH2Uy+VgtVpZi5MSgMQ+oQN2fn6ePxddBDEwMIClpSWcPHmS9ZUoEDYajUin07BarXyhDl2+c+rUKdx3330YHh7G3NwcV9eJEUk6tj6fj7Vktgo6FOjyLqronzhxgqtyq6ur2L9/P95++22uzlGiOZ1OY/fu3chms5yYMhqNGBsb65EloYDQ5XJxizTpplKyWq/Xs6C7Xq/HlStXWL8rmUxifHyc90Tas/L5fM9FKSSibrPZ+IKafvDUU0/hrbfeQjgcxuDgIOs7U1HE6/VyVXV1dRVDQ0NYWlrC9PQ0qtUq9u7dy9qYpB1GwXE0GsXo6ChWVlZQKBT4dUgeRqPRcJdArVZDpVJhB/DNN9/kCx3uv/9+vPTSS3jiiSfgcDiQz+dhMpmwtLTEYvOyLHMBjRgiJBmwVZAWUT6fh16v50LC8vJyT9Go2WxidXUVkUgERqMRXq+XL+eiggP9jPRjSUPM6/XiwoULSCaT2LNnD4LBILNoTpw4wawcq9Xaw1wplUrIZrNYW1vD0NAQrFYrS5UQG+fEiRMccDWbTdjtdtRqNU72b6dbhxyhV155BRqNBnv27OEAnVp+iU2n1+tx7tw5LoCGw2E+g8khJK12kgSoVCoYHx/HpUuXEAwGOeFPyValX1Eul3tYbJTcpACYtOpyuRwzitvtNgYHB2G1WjE7O8v2odfqB8SAsVgs0Ov1rF1KQSfNfWAjOKLkeiQS4WQrBTF0ntClVRMTE8hkMpidneUxbxYD9nqdLtt9/V/84hdotVrMnLhy5QoikQhqtRqOHTvGSVWyBznd4XAYer0eBw4cwLe+9S1mZlD7Kv2+x+NBuVxmCRRKGlYqFU7009k6Pz+PBx54AMlkkhmDVOAnNh8F7Uo5n0qlwjIGmUyG9xRiZfQDWZZhNpuhUqm4ldXhcLBeZiAQ4L2CpDXoMjK1Wo3x8XGcP38ewWAQo6OjcLlcLLVhMpmQSCRgMpkQCAQgSRIX0sfGxmC32/Huu+8yU5LmHrUd0jkBrAfiFODodDp8/OMfxzvvvAMAzGChQsITTzzByfB+18rNwrX8TFo/wEbCQcmA3cwI27VrFy5cuACj0YiHH34YFosFbrcbpVKJdfFIt9rj8XDBu1QqwWaz4cEHH4QkSYjFYswu6ge7du3CmTNn+OJFj8fDMhx0yWUymeQAngrOVGyk8352dhb3338/fv7zn2NychK/+MUvcPjwYbz88ssYGBiAWq3m84Y6hZxOJ958803ulCE/O5lMsuYsXabicDggSRJWV1fh8/m486Jer2Nqagr1ep0LnC6Xi329ftYKMeHIl1Umka6FrXTVKZOv12JFbt7nSMu2XC4jevVSMyLu0Nyi9ez1enHu3DkUCgXYbDYmXGg0Gi540FlCjM9+cOLECQwODvLZQskOSkxcuXKFk+q0tsmPJ2ZbpVJBp7N+dwj5w3TOUocSdQuZzWYmXpD0Uz6fh9vtRjab5aJuKpXCzMwMRkZGmOlHMkDK9Uf+AZGtgHUSwOjoKFZXV2E2m/uyx+a1dT35ASoa3QjK59DaSCaTfKfLfffdhxdeeAFf/OIX+bLgcDjcI/VFNiEmMcX0NpsN2WwWQ0ND8Pv9KBaLGB0d5dwA+eG0xqnA/mHuq7ieFAfQKztI62tzgpZyJEqSGbWoUwGBiDgA+PygQgOdq0rfj+JhkrcgWQ7at6jLmGxBvqlKpUKlUtm2LXYaRIKjted0OrGwsMASL9SNQAzSXbt2we12c2w8Pz/PF3oq/UKSNyLpMpLuUubVtgIiZtFdCXQ5M3U3k+9PuYdsNst3h/h8Ppw9exZerxdra2swm818ya189RJs6kQkeRKSJFTKoxQKhZ57SKgjli5Kpr1Qo9HA5XLh/fffh8/n43tPyL+neURnOHVbbgV3TQKWnGJqQaBEDFVKqSreaDQ40KXLFEg3hqqEFEQrgxKluDsFVwDgdrvR6XT4MN8qqFWagttkMsmt/5QQoOCgVCqhWq3ypSmUPKMNhpKVdCMqbfjVavWXkq0UQNGEUalUmJycxOTkJN59910MDw8jk8nw5tbtdjE0NIRXX30VmUwGX/nKV/DlL3+Zg3Vq/yGGVK1Ww9raWt8HeTAY5EndLzYfcltJrvYjW3Aj0GZPAQJt7sp2iEKhgH//93/f1ut/WITDYQSDQb50hyo5KpUKe/fuxeLiIrMDvV4v5ufnkc/n4fV6kU6n+dDyer09bBv6nMR4pEvd9Ho96vU6XC4Xz2NiJQ4NDSGRSPSt50hsPmUgZjKZWGOVigGJRAIOh4MdS9IVrdfr7LjRxSpUydTr9ahWq2i32/B6vZyETKfTrFtFQYXZbMbi4iKA9bVmNBp5IwfWb8icnp5m9t99993HQRCJhOv1egSDQQwODmJhYQHLy8usA7gVkP3IuSQpA9InpGBTlmUcPHgQiUQCMzMziEQi3C586tQp3HvvvayN/P7777N8yJkzZzA4OMjrIZVKYWxsDPPz8+zg0GVjlFygZAW1ccdiMb79mCqndPnM2toa61xSUSCbzbLT108XAbCuz3P48GEsLy/zeBaLhS9AogsgqFhECXiDwYC5uTmoVCocPnyY2aw//vGPcenSJdjtdtaQpfY5o9HI3RCxWAxTU1O4dOkSbDYbMxeffvppnDhxAoFAgNuRz5w5A4PBgLfeeosvVXQ6nSiXy9waubS0xCwzmoPKxOlW0Gw2mV2kbN3zer0cILtcLiwuLnKCVa/Xc1KHtE89Hg+y2Swz9/P5PJxOJ65cuQKNRoOJiQm+ROX48eMIBoOoVCrweDyYn5+HJEl8KZ1arcbZs2f5UoBms4nBwUGYzWZ4PB6k02m+7bRer3OBj+ZwMpmE1WrlVsN+QMksYuVarVbWilO2OdN3WqvVsLq6iu9973vweDzYu3cvrFYrHn30UQ4oZ2dnuS2JEqHUulmtVvH+++/jN3/zN7nbhBx22rs2a4fTJTSnTp1Ct9tFJpNBOp3G0NAQZFnm74ESXbTX0kUD/YAkh6gNixJuxCIix1HJFOl01i/so4QgBc6UUKTCLTGxdDodTCYTt69vJ0m6mTVzvXP9wyR36b2SRrDyRltKLpKPRvsKsZUrlQqvTbvdjmPHjmH37t1cWItGoxgcHMTa2hpGRkbYPyIGFwBcvHgRfr8fu3btYs3Der3Ot8NT4rHVauE73/kOX2xI528mk0G1WsXQ0BBmZmZ4L6YLWfsFtVxS4E1FZLIVFWDo53Q/gdvtZlt2u124XC5m1lA3DUkUKROyVNzI5/NcBHjttdd4nnW7XTz55JMcmNHFS8eOHcO+ffsQDAaxZ88eNJtN+P1+/PjHP+YLVnQ6HbxeL783SszeKtxonlIwD6xfInrgwAFm08zMzMDn8/XIPhAriVhMxF6nrhtiKdH+Sd0N3W4XgUCAL+/tB5cvX8bQ0BACgQAymQxra3q9Xrz66qvMKqWLT+gywkQiwUnifD7PxWPqInj33Xdx8uRJOJ1O7N69G/F4HK1WC+FwGI1GAxcuXGDJgG63y+uD5no6neZbyZVt58TcI+1gYJ0dS23G0WgUDocDKysrcLlcmJubY9b7B4GSrtey4WZWM+FaLedb6dy7FnuQ9NapsySVSvHlVrSeSIqKLsF9+OGHUa/X8corr8BoNDJ5gggzlJRvtVqw2+1bsgMhGAxSazrHmtTB0el04PF4uIBNydL9+/ezbFG32+Uki9lsxvz8PJN56vU6S4FR3EIkF4qbi8UiYrEYJicnOalE5IZAIIDl5WWEw2HIssy+MSUna7UaAoEA75darZaTrpRQ6jeOvd53qMSN9gPleansAlCr1firv/orJldMTU3BZDLB5XJBpVqXACJJh7//+7/Hl7/8ZdbpXFxcxOTkJEZHR1nege4zCIfD6Ha7zIIkG1JsROxSYHuXjd9IaoOgJHy02+1f8pEI1KFICVV6Per2Vb4ekYyUMlyUdKXzhfZIkkqhs0PZQQGA34uycEiJttsZnU4Hfr+fCy+hUIgl+Ww2G8fH58+fx+DgIJNn6PORb0eXR5P2qtVqRaFQQC6Xw8jICNbW1nruJtgKKBZfW1vD2NgYgI34JRgMsqQm3T9CF8ES89XpdHI8TUl1ujB8dHQUKtW6ZjLJ1ZDEGiVaKVenLBATGcjpdEKtVnMcW6vVcObMGe5aDwaDKJfL8Hg8XAAk/5D8Z6U+7o1wVyRgXS4XHnzwQW45pOrf2NgYCoUCEokEb+KlUgnRaBRLS0vsZFPylLR7KOtP4vHU+kRMJ0poUtBFi78fIWLKotdqNXZOSXfQbrdzCyYx50qlEjQaDQv//vZv/zb++Z//uUfvFQAHCyRKD6zT6Ek4ny5PUm50VGFwu918kUQqlUIgEOALeogxRgxcckKIFaxSqXhzJ1ZYPyC9xe3geslUZYX5RsnZfsfdrF2j/KwUtBKrlNp/P8zFU9sFtShRApMuUfL7/eww+f1+vkyDLuWJxWI4dOgQ0ul0j/QAVdS0Wi071vT5E4kExsbG+HWp4khJfErEXc95vRFSqRQsFgvrv5BQPCXySLuUROZDoRCzu+hAofdA3xkdrtSCQno0JLyeTCZ7Kud0CBEThA4vSqp0u10MDg4ikUjA6/UiEAjgzTffZLYSaScqmWU2mw27d+/uK5Fy4MABZhhQ4oduPG2320in02g2m8jn86xxRjpOKpWKW0BjsRgqlQpKpRI/32q1wuv1wmaz4cqVK7Db7XA6nbh06RJfyEctKJQMI9YwBYGU5KdDkgIv6j6gqinZnlgNpIXa7+3d1EpIySRgfW+llna6BI20ahcXF5FIJPDII49wUv7SpUsol8u4cOEChoeHMTIyAo/Hw5/F5/PhypUr0Ol0yGazqFQqCIVCCAQC3B4+MDCAT37yk5ibm8Pp06fxyCOPIJ1OIxaLYWhoCGazGUtLS1z4OHXqFPbt24dEIsEFjXg8jkajwS2ZZJ+tgtjolIjI5XJwOp1ot9sYGBiAwWBAMpnkLguSzyFNONrHVar1S3Z+9rOf8XeazWY50CKNP2oxpAQcOSKNRgNzc3MYGRnB+fPnEQ6HWTrH4/FgenoauVyO58AvfvEL1leidjElK5qSKadOneprbthsNmg0GsRiMW6tItYqsL5XFwoFvoyKGI/EVH/nnXdYwoOKncTcpiJWJpPBysoKLBYLXn75ZfzRH/0RnwHUClcsFplJQ+ckFXjJoRwYGMDMzAxCoRAXT6nNKZ/Psw9A7NoTJ070nVRSSsqMjY1xYEGXfFHxl4qK1NXgdrsRiUSYuUnBOnV7kE6aUldsZGSE2f/94kbPIZ/lWmd8P8jn8yiXyxzAEuOaksu07ogVQnsItVsDG2wdl8uFkydPYu/evWwPYsuSXBN1T1Biu91u47/8l//C616SJOzdu5eZK0rGycMPPwyj0QiDwYBTp05xUspkMlEZSg0AACAASURBVPHFixcuXMDevXtZOmOzHMQHgXwWStyo1Wp4PB4sLS1hamqKf4duTqfkKhX1ScoiGo1ytwVpNRL7zuPxwGazsfQHnV0kDzQxMYG5uTlO3FAXFu3TtVoNv/ZrvwaTyYSpqSlmjlutVm4fJbmZqakpaLVaBAIBlMtllmO5naD0Oz/zmc/AaDTyedBoNPDUU0/B6/Vyx8TMzAzrpsdiMTgcDqyurmJkZIST3LRfjoyMoFarYXl5GbFYjH076uToB0rJM5r77777LsxmM+677z4kEgn2szqd9ZvpKYFFTGjS9KzVanjooYfw3nvvMet1ZGQEiUSCyTAkPUPyHdFolFlWlNglFj91A62srPRo+5GGuHL/pG4lm82G06dPIxqNotls8mUrW4Fyz92cbL0eE7YfMshmbN7fRkZG+N4D0vUkJj9p3RPLlM5qOuMfeughvP32278Ur1K8bLfb+VzeKkizlfYtSnRSKzedfZTIoNjS4XDw3kGydpSETafTcLvdrF9MiWLqOLXZbKwD2e12sXfvXiwsLPA8IEITrQN63cXFRQQCAXQ6HU5SazTr2v1kp2AwiFKphEajwRcy9YvtFAw3zwEqvmm1Wuzfvx9/8id/gmKxiE9+8pM4efIkXyTq8XiQyWQQjUbx2muv4fHHH8fCwgLq9Tqmp6dRKpUwODjI/u/x48fhcrmQzWZx//33w2w2s4QZ5Rey2WxPd22z2US9Xr8pF/ldqxhBvhV9buU62vw42UXpgykTq8rXpvmtTKqS/0D6rjQ3yCekQgYxGwFwtwkRG+iSwQ9zf5IkSQgEAlxooH2l0+n0LcN2LYRCIT4/SbLB4XDwJcLA+tolP4VyO+RXpFIp2O12Lk6QXIOSYe/xeJBIJFjmoR+srq7i0KFDmJ+fRzwe50666NVLlInYRJfcUzHB7/dzXEbx0ezsLF8gT2xoip/o8nqS5nO5XLh8+TKi0SiTCC0WC59d09PTSCaTmJ+f5/VPDFqKm0qlEsv9EIGF/GLqyqDC4Afh1gtTfkg88MADeOihhzhxSBp21Eo1MTGBcDjMwQbpN1LgT5skOeBUzSeWKzmbpO2g1JShy6dIe6QfBixt+OQY5PN5HD58GJ/61Ke4LYOy6blcrkfjymazYXx8nDcS+jkltihJSxoolNSgDYMSzfQ5aHOl5+j1egwPD0Ov1yOXy3H7tcVi4YR0IpHgSghVPtVqNesB9bsgPyyu1a5zrZYH5XepbP3Z/Ef5upsfu1bbkfLnkiQxc4YSjh81A8Pj8WB0dJTbF0OhEB566CGMjIwgFAqx3ufExASiV0XaPR4PtFotTp48yYlHSgbQelKr1chms7+ke0KsamUbJhUoqCWQHLV+5wat1UQiAbfbzdUzCjiolZ6q6aSlbDKZYLFYWKaAPhMdosQGILYasHHg0yUNPp+PWxQWFxdx5MgRNBoNhEIhZtNpNBqWMtBqtRgdHcXZs2f5cKUL8DweDwYGBlj/hliaQ0NDW7YFJUOVmmzJZBLFYpFv2/Z4PJidnYXVasXi4iJ0Oh3Onz+PbreLdDrNt8dSqzkliEmXlBgvrVaLWQLhcBitVgsDAwOsDUuXBSkPQzqoAfDe2Gw2+bZbYrpSApkqqx6PB8PDwxzgbRWUXB4bG+PbTykQcblciEaj2L9/P2w2G+LxOAKBAO6//350Oh2kUinEYjEO0J999lmEQiG+zT6Xy7GzT8lWSiTs3bsXHo8H3W4XyWQSDz74IK5cuYKTJ09i//79uHjxIuLxOB599FEUCgWMj4/jj//4j1l71+12Y35+npP9lNBQ6pmZzWZkMpkt24KKhqSxtnfvXtjtdtY7Jr1Es9kMh8MBlUrFZ02pVOrRRKZECV2u5/P5sLy8jOXlZWbfkOabknFgNpvR6XRYW9fpdGJ4eBiPPvoohoeHYTQamelUr9c5sU0FTroYwuv1YnFxEQ6HAyaTCW+//fYNWz2vBWLAUpsUXRxHTma73UYymWT91VarxVp1tEc98cQTnCS12WwYGRnpSYwnEgmcPXsWr732Gt8sDYDPSaCXyUJtVdT2RvIGsVgM9913HzO3SD6J2GZUTDKbzQiFQohGoz0anVsBJZBbrRYOHjzI7AEAXGijixbIlyB/qtFosPY2saqpuETrnxJqTqfzmglAs9nM7e1KfbfNuFbLJp3jFFTd6IzfCqgAVq/X+fIzctoNBgOz8knWhloU6fPShT5UkFxYWOhpa6V9joJD+pv8AI1Ggy9/+cv44he/yDpnyWSSb52muU42o6RzsViE1WpFvV5nqQqSraBkrlqt7lsDVq/X9zAqKYECrCfRXC5Xz23A5P/ShR6k50ivQYU+umgvEonA5/PBarXC4XCwlvj4+DgmJiYwNTWFJ598kn1TSrSSZAExtAYGBljLk9qSHQ4HnnzySQDg90e62vl8nqUTbkeQb0mXhA4ODmJsbAwDAwPYs2cPDh48iEgkArfbjd/4jd/ghIHX60U8Huf1azKZMDQ0hPvuuw+jo6NcjLVarSgWi7wHPvLII337X9QdRC3i9XodBw4cwL59+zgZfPLkSSa/UHcLsZ+peN9oNJBIJHDlyhVO9FHwS4Vwp9PJen8ejweHDh2CJEncVtrtrmsQk9anUkZGKS3jcrnQarVw9uxZZueTpjmxtWZnZ/u+2XwrRIoPSsz0UyzaXFyiIgNdqEMsVzp7qZOBdFWVesoGgwFHjhzhs5riR7qYqd/kK7DeKkz7f7FYZAJGtVrluUrkBdITpYQ4JcUo+apSqdgPIQY+7S90pwMV/ijZYTAYmCxAt5RT50mtVkO5XOZznrSC6QxUan9S0Y0ky9rtNorFYl9xm9J3VSbOlfHjxMQEHnvsMRw8eJC/3+u15Pt8Pnz+85/H008/jUKhAK/Xy3IdxBSMRqN48MEHkUwmUSqV+KJcKmZTvNPpdLCwsAC/3w+HwwGLxYL3338fiUQC4+PjvMZISovkEM1mM7MH6ZKureJajO/Nn5eKkWQfKqoRKKZQJteo9ZvIZ1T0pOdREYbmPu135NPQ+NQ5SJeS79mzh2Na5UV6xIxVMi09Hk/fbfdKTE5OcoGI5Dn0ej1cLhdCodC2X5dQLBaZBEWF9Var9f9T9+axjZ7X1fghRVKUuIv7JpJaqWWkGc3Is2jGntjxJK7HsR0nad0sbRC0bps0RdoAaZAALYqiTdGkBYq0aNGiLZrWblEkMZLCWZzYbhyPPZ590YxmtIsSRYqLSJEUJVEkf3/I586rseuI0w/5vt8DDOLMIlHv+zz3uffcc84VogUbaolEAi0tLcL8ppqPA7Q3NzdlUBZxIlou8RzmcrmGrTpMJhOWlpbEfoD5y+3bt+VcKgeul0olWK1WAVTJ4qaVHv1daV8SDAbFLjQQCECj0aC7uxtTU1NytpnD8A7Z2NiQWUokghSLRRnsRWIIWfFra2uikKXqlsr7vaqQ/n8NwL7//e9HNBoV4MhqtcJkMon3EAeJWK1W3H///Xjf+96HQ4cOoaenRwoLYOfQs7vBAKH0D2Fw0Ov1GB8fF7YVAVx6MTbK7KO8U6vV4qGHHpKLZ9++fQK4UnpEH0adTodcLicJOi8YHhYWKAy6ysBVrVZ3gWX88/HxcRnMQiCblHyCr0yinE4nOjs7xfBer9ejUCjIFEIAMmyokdVoV/6dgFH+vvLPlUH0nRKfd/q9d5MDvdPiBcJ3wkuBgZ9Jxs9zEfSlLxdZEi6XS5oQZrMZbW1tePDBB4UdxUBKP0+j0YhAICDACsEzBi4mpAQpyTLgn1G2YbFYEI/Hsbm52bBEkv6hTqdTij96TG5vb8skXloBcAowizEOSSJroFbbmULNYSgsaBnkWQgzAJfLZTElN5vNGBgYkK+l0Whk2BWZCQaDAR0dHbBarbDb7QL8zM/PC4ts3759kmw2YkFAlgLZjfTHYpGxvr6OQCCAQCCA8FsT5slC5ruiTJXSaCbIgUBAwFiDwYAjR44IoFIqlYRBeuPGDQGaOGCK7Fhe6JFIBKVSCTqdDuFwGF6vV4AIWqPQA7G9vV3AskYXf7ZsNiuDksj0JcOFLI2+vj4B9CYmJmQYU3d3txSCN27ckCnjw8PDiEajeOCBB6DVauHz+bC2toZAIIDz58/j2rVr0Gq12LdvH+LxOGKxGNRqNVKplNwl09PT2L9/P/R6PV5++WXcunULMzMz0jVOpVIi8bfZbNjY2IDJZILX65Umzl4XQQlgB/SjHJWMMnpDcr+1tbWJnI8xjMlmsVhEb2+vsKKXl5eluCUwS/nN+vq6sJLYBOH56urqgsPhkOFm9BCs1WqiDqhWq2LJQMAxlUqho6MDHo9HWGGNFohsMBaLRVSrVXzoQx+C1+vd5dnOHIB7hDHLYDCgXq+LrA+AsOqVvlcc+FEoFPCpT31KhvUZjUaUy2VUKhUBl2dmZmS/kk1ANkUoFMLly5eh1WrR3t6OEydOIBQKYXBwEGq1WpoB+Xwely9fliKy0efBGM6kkY3bUqkEtVot4AT9uGw2m1jY0KuazTQ2WJTWBPT8Vto0kYHDYUG9vb0YGRnBfffdh3A4/LbPqbyTlYWbcjV6R9+9yIgGIHGb77Ner4u6gg0RskVoVcHGJhkrzJEY47jeSRoJ3GE4mc1m/MEf/AFisRiam5sxNjYmwyWYTyhlwffff780AzQaDTKZDEKhEFKpFDY2NgTkbJTxWa3uzA9oa2uTn5vPU6fTieyZ3sW03uBwG0p7TSaT5IU8X1tbW0I0YBHPe5fsM0pLh4eH8cADD+yycrLZbLvAPPoW0qfWYDCgu7tb/NWZa9FKgvfMz2NxLsBeFvd5NBoVoIp5TSQSwcMPP4xgMLirQZtMJiUeUN0Qj8eFjQ1AimWyi7PZLPL5vAya3KvcnouM6FqtJjEtm81ifHwcExMTcDqd6OnpkRxDSYAhm5fALFVyDocD/f39wq7m/mEjGNhpgr/++uuSp/Hv8efU6XTo6OhAsViERqPBpUuXpCmRy+WwsbEhgyM1Gg2cTidu3rwJtVot6h7G3kbWz6pV3u3976VZVKvVJI++++8SGOJUbn4e3qckOTA3ZmONTWY2Xtn85vdic6fROkypXCKQTjCYDE7m/bSpI8OQoBkVnwAkpsTjcWl4MZbdzWRUDpJcWFiQO9Hj8cg+IzEin8/LHAqCfGQI8/OTYc3aiV6pe13vf//7YbVa3/aOH3nkEfzt3/4tvv71r+OZZ57BU089hc9//vM/U7nxxBNPIBQKoaenR5QYY2NjMJlMMoeB8dfj8WByclLAo7//+7+XGMB3S+BsZWVFhk9yYOLa2prI0+k9nsvlsL6+DpfLhUQi0TDI9rOITMDb73e+Y+AO0Yl1AXEO7l9lTU0sQ9nw5F4B7pwRkgMOHz6MRx99FOFwGJVKBaOjo2hra8PY2JionBnr+PmoklapVHA4HPc0aByA5BZK0hNxK87Z+Z+a03tdjBMtLS2iEKE6hPGSlkgLCwvI5XJiOZhMJlGv1zExMYGOjg60tbXJGaXqRmkxRLytkZXJZLC1tYW2tjYsLy+jqakJRqMRkUhEiFMHDhzA2tqaxLJkMolsNivxoFKpwO12SzOnqWnHi54WI8lkUmYdkGjD2RMkXZLFSgUsLQo9Ho8QM4iVAXcsV0jSJG7CQc7ZbFbe517W/9MALC8hpa+jcnF6ITcBfSCZeNFUPBwOw2g0IpPJSALJApV+r7QUoCyBg3YACLOKjAi73Q6r1YpCoSDMM61W2xCwROCDHQflqtVq2L9/P7a2tkR2yANDYDMUCklRpASDAEgyS0nI1taWBC4CY/w79Xod//Ef/yHT3zh8iTIxTkgkWFMsFtHX1ydDwAhEb2xsiMyY37eR9W7eSXf/N/DuE0Tv7rA1Kk9UXhbK7/Fun+fun0PpY8Pn/X9ykVni9/vlnPh8PnR3d8PhcGB5eVmYJuFwGKFQSKwCpqamkEgkxLeRhTh9kYAdiRn/jO+TAA+9G2miT1sKFuU04gfuvAsm0JQSNrKSySTy+bzsOVp1cCgDpQL0yOMeJ6DDM0q2Nj8ju2XVahXFYlFYJUwQmew3Nzfj8uXLMBgMuHnzJnw+nzAQkskkRkZGdk0tZ6eURWJPT4/YchB4HB8fFzCoEbbS5cuXxf82HA6jq6sLuVwOb775Jubn52UYUiqVEtNwTnFUet/Qr5dsKcqps9ksMpmMMB7ZnSyVSujp6RFpU6VSEWmIkvFN4Coej8Pn8wHYYZpks1nE43GRMxHEYqIDYFfCtNdFQ3kyPcmSMhqNSCQS6Onpgc1mQ2dnJ2q1nWmuvFS7u7tFvud2u8W/lPF+fn4eX/3qV/H6668LsPCBD3wAAHD06FF0dHTIsLOzZ88ilUoJc2xubk6GOU5OTuKNN96A3+9HIpEQqTOfPyd4Evihr9nMzExDz4LTrpVsGIvFIsAFrQb4fdik8Hq9wnwjEElgenh4GDabDcFgEP39/WJZwvuWTQuv1wuVSgWfzyfMiSNHjsDv96Ner8PpdMo9lcvlkM/ncfHiRfmcCwsLwpKirJSJMAdMNspy5P68fPmydOw5/ZiJ/fHjxwUML5fL0kigTy9jGwAsLCwIg3V1dVXYZ2Tgk41PIJpFKKVfvJ/pn7W1tYVbt27hpz/9qfivW61WLCws4MaNGwL6Xb9+XYZANTc3IxwOC1uskaVkmZAZQ8CKrGZOwyXLkHGQ+QXvAzLPTSYTNBqNAK8cyMbvt2/fPmFCejwedHd3IxKJyDADj8eD4eHhdyx092otoGyy7nVx+EI+nxfwVTmkkc1u5kUclqYc9MT3Sz81siaUYBjzAOZgXIybBLBPnDiBBx54QIozxiAA0ojnz8ozabfbMTQ0BKPRiPHxcaTTaUxNTWFjYwM9PT17fhYAZK9ns1lEo1Fsb28Lo4uAREtLi9ylvb29ohzJ5/NinWKz2UTdRWsbYAfEy2azmJ6eFvUKBwwSUGppacHo6Kg0Zjj9mM+IqgYOSVEW1SQZsNHGpjdz1HstlhtZ7e3tiEajcLvdwm76WaBFZ2cn/H6/eLqSqf+pT30K6+vr0Ov1sFqt6OzsRLVaxYkTJxAMBnH8+HFEo1F0dnaio6MDPT09UsTq9Xrxp2NxS1YfFXON/lwajUZY+JyHQQUQY188HhfGkcvlQrlcxszMDGKxmLD1Dxw4IHGM1jblclks2cjOm5ycFIk1peS5XE6YeE1NO0M2FxYWRHmwb98+2O12+X46nQ5erxfValVAZL1eL3dWW1sbmpqaGlKZ8Ey+k0fl3X/nndY73WFkQJ86dQpjY2N45JFH0N/fj/379+PkyZM4fPiw1CHMlUiIIAOc8YXsYcYykhX4Ps6ePYv3ve994pVKOxyekUZl5gSOlLUyZyPQdoBMVZfLhdXVVXR2du7yblQ+N6/XC6PRKM0IZU1CCwOCU/wZNzY2JJ+ZmZlBMpnE8vIyVCqVNHPZbGYtQ1sPgvHValWG+LDJbDQaMTc3t+dn4XA48Oijj+56r08//TS8Xq80h5LJJCwWC1577bX/cT8AwDPPPCPDzQqFAmKx2C62LhnLJpNJ7M3sdruo//x+v+AK4XBYFDdut1vICUeOHBFiDJng3BNU1WUyGcFN/jeMTy6S28ja5nN6p6ar8r/558RqeMcqgUr+vtLXlfcCCSM9PT0YHBxEOBxGqVTCwYMHodfr4fP5xN6iv78fAHDixAmMjY0J6MrPE41GZe/dy+JgzUgkIg0INnSpMPJ6vQ3nu8pFkhD3i9lsRqVSEeLV9va2gK7j4+MoFApIp9NCuKF/+vPPP4/V1VUBitXqHW/V1dVVYQ47nc6G90Y4HBbrNd7/zBE4KIs5MImFra2t6O3tlTkJjCfADh6m0+lw7tw5+Xwcos3zzGYdlYXc88zFyd7n3CjWHVTesk5hPa3X6yXno2UFz+Je393/Ux6wOp0Ox44dExN3sig4bfX27duScAUCAVgsFgEKOOFMKWekHIugBYPQ2toaVlZW4PP50NXVhe3tbVitVgEl2Bkki4VUdfotUYaj1+vFA4MTbPe6OLTFarUiGAzuSs6Jzo+NjcnvMQlVqVQ4f/48VldXxc+JBZXS84TPgAGLQY7PlEycer0u9gPsVgMQ3yhlZ4lsqsHBQbzyyivo7+9HZ2enFG4MVPSI+z+xlCDo3b+vXLyYjUajFLzs7LW0tIjMqFarIRKJ7GKWkX3FApL/9sKFCwJc0aNkc3NTLm+yicvlMlKplAQTpW8NJQAs1u91RSIRdHZ2Qq/XC9uF/j5bW1tYW1tDrVaDz+fDiRMnYDQahXHp9XoxMTGB6elpPPLIIwiFQrIHXnnlFTzyyCNyMSoD6Te/+U188IMflOKPbFaCE9xv7LZxCqfRaERHR4cUZzRyb2lpgd/vb7gYolfO4uKiDBIqFovCymTxC+z4xdJQHNgBqmu1moBR6XQafX19SKVSIqfn11hcXJRu8q1bt8QLslKp7PJbpa9jqVRCX18f5ubm4HA45OelHy3PECX4lUoFly5dwn333Sc+sAaDAZ2dnXt+FvV6HePj4+jq6sLKyor4cJIJXKlU0NHRIWw1An68WNRqNeLxuPjk+nw+8Xcm6Dg4OIhsNov5+Xmsr6/LcD0Op3rllVdw6tQpABCvNVptcBol5Y+tra2IRqPSWSdQRwCCLJ67k6m9Ltq00H83GAxifn4egUAAc3NzmJycRKVSkSSSQAKlx/TGK5VKwvg+e/YsnE6nMCA4mVmr1eLKlSvo6elBKpVCIpHAysoKDh48KPLLQqGAyclJGRTDydTFYlEGKtJLL5/Po729Xd4VB0Q6HA7xQGzED4pNAAL7qVRKvrbVapUuMdkv1eqOST+ffaVSkamiZIlQ4tzUtDMNl16gwE5jJBwOY3p6WkD5Wq2GY8eOSTJClrfL5UI+n0exWEQ8HheGH4sKMh8YvznQa2pqShomjQ6BINDKBEvpY8xYXqvtTNwmE4/n/MknnxRfWvqT0/eJE77J7Mvn8/joRz+6S7LIAoHgXSqVEsCbjAECrCsrK6jVarh48aIMtgIgzVeybkulklgKkd3QyKIdA7DjwcUGGoEaMurL5TJUKhV6e3uxtrYmkjAW+g6HQ6xmmAsRtCcroFgs4vjx47tYxky4eac7HA5oNBphV8zNzWFtbU3e871YC+x1sQAhU49yWcrL+H0JPvJzM27z31ECrGzSA7uHcRLg4LobvKlWq4hGowDu+PWz2U9yARuOAETCS3CtUqng05/+NE6dOgW/399wEwuAAM5ms1nyGhaYbLCsrKzgyJEj0ijp7e2F2+2WRhoH5rW3tyMQCEixplarsbCwIKyraDSK5uZmUYNw0A6lgWq1WuYR1Ot1GVTHOywSieySiXI/fuxjHxMGHPN2st+8Xu897BLIQCiePyXDlos+6x6PBz6fT/LgdDqNdDoNrVaLVCol+7qpqQlPP/00XC6XNCIqlQoGBwfhcDjkbtZqtVheXkatVsPJkyeFhToxMQGNRiOsQSp8+BnL5bLkAmRHnzt3DtlsFkNDQw3nX/SXpZWBRqPB3NwcVCoV2tvbZZDi4OCg1BRU9hWLRclHmCP29fUJ8EWwrlwuo6OjA9PT0/Is2MRSqVSwWq3C4mWuRuCSw/IICrhcLhSLRfh8PmH2HzhwADdu3EBXV5cQSajmuhcghbnK3Z6VwP/sBXv3ovqMvsr0x29ubpYJ3dVqVRQENpsN3/zmN3cNxGOeS2YrwVeqUiKRiDA9qVJ8/vnnceDAAVy5ckVIQ3xOjTLn5+bmZLiWMsYpFVJkHVIBw+dH5SR9uIvFImZmZsQ7WqvViscp7QkYU1wuF1ZWVqBSqcROKJ1OIxKJIB6PSwNncnJS5hoQwGbewUG7zFWCwaCAN0r/9b2uBx98EC+++CKeeOIJfOc738Hp06dRLpdFdfujH/0I9XodZ86cwY0bNwDsttBhzf7hD39YiDSs6bRarfiOKhVLrIXGx8eFzcm7vV6vIxwOo6OjQzw9Dx06JDUK845QKISFhQWxouKgVp61+fn5d6zB73W9WzP9bs9XPhcCXLSqUIK2nF0wOjqK0dFRmfdTqVTw7LPPSrN5c3MThw4dEqtJ5tef/vSnMTExIQ2lcrmMJ554QlRb4XAY//qv/4qmpia43W65/xtliwM7ALDZbBbVJZm8JLCw0Ubi29zcnJCZGlnnzp2TZgvzlO3tbUxPTyOXy2FxcVFmw7Ahplarce7cOXi9Xsm7Ozo6cPPmTdTrdRw+fFiaeMp6amFhoWH1IgFhqiWY07ApVi6X4fP5RLVtNpthNBpx+fJlacAy/47H42hvb0exWITL5ZK9zyZyoVDAyMgILly4IIorr9eLtbU1WK1WrK6uQqfTwW63Y2NjA7Ozs/B6vUKcO378OM6ePSsWpsxTGNdWVlaE2EIf970OM/y5ArB3Bxv5EBoNBgcH4fP55EJWeoS1trYiEolIcqeUhd28eVPAPqVXGyn1fCgsOtVqtUhF2cmlfIwyR6XfGQPB3NycBA12WkqlkiRblEPsdTEJoLxZuQiUArsvcx6UWCyG559/XqRiLII4NINsHzLKGDyZkHIioJKdeOvWLZnkSyr38vKy+H6RwbS6uopvf/vbOHPmDA4ePCjT4TntfnV1FVqt9p6KAC4WFe/EblUu+lKRSUJD5Xw+L11xv9+PpqYmWK1WXL9+HQAk+AGQS5mdM2Wh4/F4kEgkhBHCgtrpdEqhTek5GQzlchlTU1O7gFheZP+b1dPTA7fbjcnJSQmCLIbo43P8+HHMzc1JF6ler+O73/0uTp48KZ5Hzz77LJ588knxRGOnU6/XCwuPi9+HpvocBEI2Fin6er1e5LFarRY2m03YmOwq8uwx6Da6crmcgGUAJKGibyQZbJyY29TUBLPZvMv3lQXt4uIimpqa5GJLJpMyUZTgULVa3eVPw2bJlStXMDAw/6x1zAAAIABJREFUIIOZLBYLEokEOjo68OabbyISiYglgdfrRTKZhMFgkAbB0NCQXHzBYFAkDHtdNpsNBw8exPnz53H8+HF873vfw8TEBEZGRrC8vAyXyyXe0hxGMjs7i2q1ivPnzyMSiUjXkB6U2WxWhk5ZrVZhSvt8PrnQOH2ZCgOTyYSZmRkBX3lm6X1Jpm+xWBQfWjZpSqWSAAuMRZR5NFoQcbgg5c8svPl1OJCGrL7l5WU540z6t7a2EA6HxXy9r69PBjdRbkOmiMViQT6fR2dnJ5LJpEj+bt26hRMnToh03mAwiL8tgbNqdcfknUxbjUaD6elp8eljoWkymXDz5k1hdex1cZhZNptFLpeDw+FAc3OzdJe9Xi/i8bgMniBLnAby9ExiEkRfdDZm+vv7ZQDQ/Pw89u3bJwDe1tbOZGt6tHG4JWNpKpWSIWSlUgk2mw16vR4Wi0WGtqhUKuk0z87OYmlpCR6PR2w2Jicn9/wsAAjw6vF4EI/HJVbp9Xq5OxmPSqWS+B5vbW0hGo1ifn5eQHl2+hlHeddyz4+MjGBiYkIaxGxYzs3NyV2rVqsFXGPuQPYk7RKoJKAXmVK2TuYpJ7feiwVBc3MzLBaLyDHJfGYhy6RTOdClpaVFQGMWQiym2azks+Wdx/1AayQmwryX+XwYr7PZrIBddwPtewVi76UgAnZAAsr0qOhgDsOmCvMiDphUFi60geIz5vvn/yeAC9wBZpWL/5YDa9gEZVFSqVQwOTmJnp4ekdRycBpZpE8//fSuvKtRxQ33M+XtyiEkZIaz+UqAzeFwYH19HRcvXkQ0GhWf2O3tbfGjJrOOBQmL6mKxiPb2duRyOWGxkfTAvJuevIylBoMBt27dQvitIboEC+j92draisuXL4vcGYAwZ+9Wme1lkeXNf89zwLjPRb9SgtBUFFAOS1Yn9zUH7blcLgEdI5GIvFs+P7KK19fXsbCwIHc7B1ZRfUbF2sbGBpaXl9HW1ia5EM/59vbODIeLFy8Kw2uvizkum6u8zwjYk+2rHOoZi8XQ0dEBi8UiUteNjQ2ZVk9WJO/HWq2GhYUFAd3p9acEJVl36XQ6dHV1CWDX1taGoaEhmebNxiE9CkkiIeOSjFuCwPF4vOG9oXw2wB3AiHXXzwJhjxw5IgPKaPuhtIfxeDxwu924evUqDAYDDh48iEqlgs9+9rMolUp4+eWX0d3dLV6IBDRJRKKPcyaTgd1ulwYzG64k7tDXniQUqhf2uqjOVKr9CMJrNBphGxNAJxDLHJKxjvVSMBhEsViUpvDy8rLUGZxxQMUFbUzYFFtdXUUikRAPTdpzBINBAJB9REIMG5sARK3FmQ7Xr1+XAa30l/5ZK5FICHGMjXDaZBBcI9NbpVLhpz/9qXxPAHJOh4eHBZiixz4BVz4XxlmCjw899BBeeukl8fO1Wq0YGRkRj/qBgQHBN5jD0EsW2ME0RkZG8PLLL2N7exuLi4syP2dychIDAwMNgdF3L9byrCGtVitmZmZE5RAKhVAoFEQtp8wjlHeRWq3GRz7ykV3EmWKxiBdffFFqNa1WK0zsX/mVX8E3vvENea/lclmGAXIwLS1faL9IMofD4ZCvz/qEnuQA3obbvNsKBALS5FYCnGSyM7dgsysYDEoD614A2AsXLqCjowPXrl2Dx+MRcF6n0+H73/8+HA4HVCqVNPtp28I7j3k4836HwyGkIw683N7eFv/8RvGetbU1uN1uLC0tIfzWkEUA8vVJ4FGpVMhms9Io4TNhncMmTbFY3PW+XC4XZmZmhMzJHE85m8FisQj5A9ghuQE7ZyGVSom10c2bN2E0GuH1ejE3NyexMhQKCV5ot9tx8+ZNqZH26jn/c7cgUIKwwI7/0YMPPoiBgQE5jLwQKIczGAyw2WwIh8Po7+8XT0p29/jylUUMi3kCZ7lcTv7/lStXANyRoXEzAXf8e+iNpvRCS6fTIgng16I0jwF3r4syTr504J2najJwcJER8PLLL0sRVK1W5eKjtIOXIenQLDjJZuHn57P74he/KF+PnVcC2PSEsdvtOHPmDM6cOQO1Wo0//dM/RVNTE86cOYPz58/LhUBp6r0uFpbvBr56vV6RWTGgk71BKTStJuhzdOLECQB39qDBYBCzeg6cIouHX5NAgcFgkJ+P0jKCMpQ6UApjNBp3fVZ2Xe91fehDH4Lf70c8HhfGyPDwMFwuF9xuN37nd34HTz75JMxms3gssYMZCASQSqXEQ1Wv1+M///M/8dxzz+HFF19EJpORAK+cJM/FgpFyTTLnNjc3YTKZpLChXNVisYi/0/r6uhSQDNJMnhpZ7PomEgmRwNXrdXR2dsJoNO5K/DlZlwxfJi+U7dDShKbdBAe0Wi0mJyfFwoBWC2SZazQa3L59W7rQtOqgrMNoNGJwcBA3b97E9PS0+KayqL169SqOHj2KcDiMWq0Gp9MphWIjzE9e1vF4XNiRHo8HFy5cQF9fn7wfg8EgzFVKhaPRqEiIt7e3ZVpsqVRCLpcTdrLdbhf7BMYXjUaDXC4nYNXExATK5bJ0Hvn3OQyDcXZrawuDg4Po7++H3+8XFosyZivPd6PgPOX0jMlk001NTWFmZkbkMdvb2zh//rz8/CwcaVXDBg67m4yXBBbq9br4KB87dgyrq6uSqJD9fPXqVZjNZvT29soZIYuyr68PXq9X9k4sFkMoFJJkvFwuY3p6Gk1NTZibmxOpeSNACi037Ha7NBfvNpR3uVxyJgiQ6PV6eDwemM1maTJxYAWbmpRRkzFjNpvhdDrR3NyMrq4u+P1+8WkEdppjtLwpFArSBCBIzOeSTqeF3dbZ2QmNRoPx8XHxbSKLeGNjo+EBbWQTbm1tiaSXgCEBGzZbstksVlZWUK/XceHCBRw+fFieBRM/ABLPSqUSXn/9deRyOXz4wx/GjRs34PF4JH8hSNfV1SWezLwbyS6m3J0gAYsjqhloj8T4V6vVJOG9+9zsZbGhxkFYbMoy5jEnYNON4KsytyB4XSwWBZikuoC/T8COck+CJ3z3TPrJ/KN1C/3slIuWBq2trT/zZ27keTA3Unqa8i5n3ObX43kplUoin6e0nI1vZY7GvJCLADvwdmBUSTTg32Uzn4oalUolzB4yxTlUR6fTwefzve3ebtSTjc+XDDo2pdVqtfiFNzc3i98xQTY+RwJsWq1WgAcCPQaDQd79/v374fP55F6xWCy7FG8c+AVA9jtVJ6VSCfv3798FhOfzeaTTaTnnOp0OlUoFv/ALvyBFNQkEjSw2Mfn5mQO2tbXJADout9stShDKEre3t8XPm/7eLGr379+Pw4cP49ChQ2hubsb9998Pj8cDq9Uq55L3h1qtlljMs0AVQjabRSwWw8zMDOLxuLwnTmCm0q9SqcBoNEo8efPNNxt6Fpubm8hkMsjlchgcHMTk5KRY1BDoZcOVOXkkEsHExATS6TQ6OzuhUqkkl2L8ItBMBQLvR8b60dHRXapGkmuy2Syy2azkOAaDAVevXpW4wjPGe3xgYOBt8zHo8am0ktnLuvucMTdkfk9ywjstnskDBw6IjyB/Pkrky+UyFhYWRHVz+vRpHDp0CDqdTtRl9XodR48eFck8m0RGo1FyL9Y4vFs4dJBS9YsXL4p6jkA1rcgaWawllU0mgodkoJEdlkwmUavd8RHm3+f9w/wxHo/LGahUKvD7/XKuGKO8Xq/E7rW1NVEwUC3AnC8cDov9GOM9G/C0NGCuQjUOfdhnZmYa8j0Nv2V1SFsRg8EgnzMej6OtrU2sMKh6JbEJ2Lln2LSg92YgEBAP7tbWVmkosfELQIaPkYDU2dkplmyf//zn0d3dLXct8zHGKHoD2+12/PjHP8bQ0BCq1SrGxsawubmJiYkJhMNhXLp0qeEm1uDgIAAI0/zIkSM4cuQIurq64HK5cPToUUQiEfj9fmlSHTp0CCdPnhTLMOAOLtLd3Y0Pf/jDsNls8Hg8MmxuZGREBrGHQiHxFierm7GGz4mNgVKpJDNtenp6UKlUEIvFxNqBsZgNElrgMfY30siy2+2i3qR3OWuRQ4cO4fDhw2K3xaZiKBTaM5Py7rW+vo54PI7JyUn84Ac/wPXr15FIJHDp0iVR67COpmKApC0qbPv6+sQGoL29XUhx9GglAKuc/7PXRUKU2+1GJpPB0tLSLsKlzWZDPp9HqVQS9vLs7Czy+bzUA4wlJJgQc3I6nZKvUeFGax6C+zwH9KZnrkGCjsvlQq1Ww4ULF8S68Ny5c0in0zKvhBZxXq9XGsRNTU2irN3L+r/iActLTK1Wo6OjA+3t7fD7/WI4zwfCYtzpdIo0h0lptbozxToSiYg0gUkfpY9kKdDPVa1WY35+Xj7H6uqqJJh8+QzclKNz4A0LV15UvNxYZJBNstf1T//0T5ibm8PVq1d3yVm4CL7yv/m8OLiFMgkOfGFiTvaf0rNVCRYzseeFqUwePvaxj6FaraK3txfBYBBerxebm5u4fv06vvOd7+DZZ5/FX/zFX8j729jYwF/+5V9Klz6Xy8kBvtduGYNuV1cXgsEggsEgXC7X2wIRWQZqtXpXF5bPqVqtSoedHkWUE9LLTvkMCDIRWCCYr9FoZFBNvV6XrheTa0p0aeTM34tEIhgaGkJ/fz+OHj0qXmj3snw+n0xedLvdsNlsOHLkCI4dO4Zjx45JF5TPzmAwiE/hxsYGbt26hY2NDTidThw7dkx8UCORCLq6unDr1i2xsLg7kM7Nze3yRWLCwqDL4pWsLBblBGhbWlqks0fAo1GmEoFdk8mE3t5enDt3TkzCyVT1+XwCFPF9EJBnMeNyuUSmR1uDqakpac4wWSR7hz6lPT09aGtrg9frFZP+er0Om80mSfVLL70kjFNO7SSb0Gg0or29XSR4y8vLKBQKsNvtAgTtdbndbpEQKX2hNjY2BKyiZcry8jJWVlaEkcF/S+9cFj7pdFoAVjIRu7q6YLfbMT8/L8UbwbKOjg6YzWa0t7fj3LlzsFqt4j3FvchuKuMtgZ22tjZpmrABRObWvYBK9Xodc3NzAmiTMbG4uAiDwYB4PC5sO7IDyGxLp9OoVCryczPex2IxAc47OzuhVqvhdrtx69YthEIhYT6TWU+Jq9lsxtxb09D9fr8MxMnn87hy5QpWVlbQ1dUlnd1YLCYMVyZABFZoldNI44bFCf0pE4mEAA+8N0ulkjQp+ItebXwH9Dli0trU1CTejrSxYOHIzw5AwBQC2vl8HplMBiqVCpFIRIY5EZCkpzRZYhMTE1haWoLL5UI4HBaAxW63o729veFhKRxQpFKp0N/fv6soZQJXrVaxtLSEX//1X8cTTzwh05TZlOJgIbK+OCWYQ9f+/d//HU899ZQUMhqNRhobfL7MC7i/eSdtbm4in8/Lvc5mMSWo9XodxWIR2WxWgAkmzpRZNbIIQtBKiIUwYz4Ld37O5eVlGajJwoHxnOxqnvlarSaDb5qadjwnOzs7BVDd2tqSpil/fp4h2imwMdLU1ITR0VHs378fAwMDOHToEA4cOIDDhw/j5MmTch//b9b29vYucIK+xHwutKPgPqbagnFBCdIy1+Iiu0zZZHqnuxWA5GIAxNaICT6HowE7eREHvHBgICWkuVwO4+Pj8vXupdnLxhM9PWk3BADpdFruRP4ca2trWFtbQyqVkoYDAJFosolOy5WWlha0tbXJnAAWcmxmK+1RWltbxQqEnp8E68gYByDe2ZyU/vDDD2NsbAyPP/645HF2ux2BQEDUT3tdLDDJSHQ6nZIPtra2CusTgDyzbDYLn8+HI0eOwO12S35K1Z5Wq8WJEyfwsY99DGNjYzKskyozxhnm8Yyd9Emt1+tyjgiOE1xIJpO7mEhUFZCIQJ9eMuoaWXq9Xr4nm4omkwmzs7OSD3PyM0G87e1tWCwWDA0NSfPt+PHjMrW6paVFimAliGi324VVdPXqVSQSCWHNZzIZbG5uCtGFMn0qbPiLQy5ZB+l0OrFMYP6ztbUl/qKN5KOspf6n9W5fi/Gaijmqopqbm7G4uIilpSVUKhU4HA6o1Wo0Nzdjbm4OhUIB+Xwet2/fRmtrqzDGlpaWhFTDZiobdbRuIPmIOamSUMScmsA+mwWNLDY81Go10um05P60Q3C5XAJasC5hrU6yDHCH6UjJPfN3/nudTidNFwKJtB5jU5N/j033lZUVOXd8HtXqztDbu+tT5f1L9no+n2/Ih5/2Zy0tLTh9+jTMZjPcbrfUm7RS2t7eloFGbObxHH/hC1+QPJ3NLdY2JGvQPoPEE6o+6fPLYUJutxvf+MY3cPbsWYmjmUwGTqdTbBObmpqwtraGXC6HYDCI7e1tLC0tYXl5WSyhbt++Dbfb3fC9otPpsH//fqmL7HY7PB4POjo6EAqF0NXVJR75jOG0WxgcHMTRo0dx8uRJuev7+/ulnqJHL++AwcFBwWC4vzhA9eMf/zgA4Nd+7deQyWTQ09ODU6dOQafTSd1CDGj//v1CylpbW4PJZJJaluo0fr9GGLBmsxkWi0WUhB0dHRgcHEQoFJI6IRKJiBLJaDSKIu5e1vj4OJaXl5HJZGCxWHDp0iWMj49j7q3ZFGyKulwu2We0cCEpkbGJgCwb5VQvsCl/t7/9XhaJgMCdGUskQtH/mRaANptN7p21tTVotVpRpRgMBhk07XK5UK1W5dx2dnYKAYB5PhmzVPJxDg5Z6pyj4/F44HK5ROVdLBYxNzcnOTKb9ZVKRXIgNgaJg+1l/V8bwkVAy+fzwe12o6WlRSa2U0Jar9cRCATgcDjg8XikMCgUCnJhaTQa8Zmj3xJ9vDixli8unU5jdnZWPgOH0rCTBtyZWksAhlKF27dvy9+hLxop2kqp2l7X+Pg4nnnmGZRKJXz1q1/d1QUD3vkip0cMixWCiARXecEwcCpBbCWTh8xQ/rzs4ur1evzmb/4mPv/5z2NiYgI3btzAm2++ibW1NRiNRnzpS196Wwd4aWkJyWQSuVwOc3NzYoDeaLBm4mA2m/HII4/A5XIhGo3i2LFj6O/vx8DAAKLR6K7no0xAKc9kh5BJBZMddrKsVqtQ6Mk8Y7LIn40sIyb+3I8s/FiMMOGjHKJarWJ5eRljY2NwuVwwGo3yNTiE4F4WvbYCgQBCoRAOHDgAt9stFxMPO4MhfWdTqRR0Op2wPVk49fX1ob+/Hzdv3sSVK1ekw8SLBthhpnd3d2Nqagqrq6syOZyedwRdlM0Bdgh5TvR6vchyeMHabLaGrDr4c5nNZuh0OkxNTeHQoUPys6VSKZEwplIpmUKuHIrCZFzpZUgwnr8/OzuL+fl5uFwu+XwcbsbnR8YzzxfZoj6fD6dPn8Ybb7wBnU6HwcFBKc7ILjh48KA0czo6OqQpRObOXpfBYMDcW4MB1tfXxaB///79uH79Orxer4Bj3P8E1zlkhQAOi7b29nYZPsail1YMbGaQgZFIJJDNZvH8888LGKOMMcqJmysrK5iYmACAXX+HPy+BB8qtybJsZNELMZvNCmBisVgQDAZlGIjdbsfw8LBI3h0Oh1jT0AeWYMOxY8fE89Rms0mDjMNFOGhkbm5OEmrGZa1Wi97eXiQSCXk2wB1mRC6XE0ZHKBRCU9OdSb6cfJpKpbC1tSX+f434fBJY55AnNkToeUvWCc86QT7GbwAyDIndYxbwnCTOQpuNS7KDDQaD+CKtrKxgcXERGxsbMiGetj4E81KplHiPt7a2IpVKyVmbm5sTH1gAAsQ2MuQS2JlKzIYgC7Z0Oi2+igQK/viP/1gGvHC4GBt1nMRMsICSXlperK2tSbHChFUJMFEOR3aPkrm3vb2Nxx57TIA05hCMsQSyGCN4PvjeGgVgyeggsEuwmc+Be4PNXfraE2DjFGDemUyEmXuYzWZUq1UkEgnodDvD59h4o2KErH0Cy2TEGgwGOJ1O1Ot1kecyvpKtbjQaYbPZcODAAVES3OtSxmcy4MiKsdlsAo6wIUKvNiVrmV+D74H5A3MUxjdlbqfMn/g+mYNms1kBsMj2437hM2PzSq1WSyFNb2l+z3fypfxZi+BMLpcTQsPU1BQA4Pr16wI6EyBm0UT1F2M45YEEkZSsWt47ZEOz8UhmEoulQ4cO4YEHHsDg4CBGR0extrYmoH0ymYTJZJIY+vDDD2N7e8fPV9k4oE81C7dGzgrtQui1yc/P/U2lEItjs9mM1tZWHD9+HF1dXcLGGx0dxfve9z6srq6ivb0dR48eFR98MmwNBoPsf61WK8xgsrxYYN64cUOIDbQvoX0Q73jeG7RW2draEtUQffsJhDWyOBTS6XSiUCjA4XAgFAphYGBA3jVZha2trZh7y8uZgHG9XpfcnOeZoB2b+8wNNzc3hclLlRH3MoetcqgSgZi2tjaR7NJ6iGeaZ3h7e1uYSSyQyVxqhDhDxSPzIi4lWYZnUrl4TgCI1zZnbGxsbIhMn6QQZZNpaWlJcgyCyHxuzHnYSOJnIuOcfshUKjQ1NaG9vV3u2s7OTmSzWfFybhScJ0OcHs6MXVRR0NOduYfJZJJ6WWmXwqbV0tKS3C9U7TidTqRSKRQKBXmOjCFU8LBpbbFYMD8/L41veinzfKjVO5Pb2fyjfQEbRYxRer0e0WgU7e3te34WXq9XGjUWiwVXrlzBwsICtrd3/GmvXr2Kubk5VKtVLCws4KMf/agMHQwGg1Cr1fjBD34Ap9MpgDiBWO4b/q+STKTT6XDt2jWcO3dOat99+/YJ6OzxeDA+Po6zZ89Cr9dLPOW/J8Dv8XiQTqfFco+WAbSCUGIne1kcigtAGlaMlSaTSRpWrJ11Op2QS2jD4fP58PDDD4tahwxUg8EAq9WKkydPSm7gdrsln2YTlaQZ5hOnT58WdmNXVxeGhobg8Xjg8Xhgs9nQ3d2NgYEBmM1mAf+Yl9ACb2RkBH19fQ3NZ+Dgdjb2iEuEQiFRPPn9fgwPDwt5h6rae1mzs7MCZhIroqUcnxdtA9mgKZfLWFpakrxwZmYG3d3d0himxYey1iYBrtG4QQvB1tZWacgxv8zn80gkEoLlpVIpGRrIXIwEu1QqJV74jJPMJxiDmTMTAF9dXZUzxFyezRt6KKdSKbHt6+3tFdVPW1ub5D1tbW1YXV2F3W6XIaKsHd9pqOw7rZ8rAEsK+tGjRzEyMiIDGlpaWsTPRqfTIRwOIxwOo7u7G16vVzYHQVGycpLJpAyqWFxclAfJASsEIykvq9VqiMVicllyk7P7D0AMlhmkAIjnE4sPdl8I9vEiaaQgYAL6la98BXq9Ht/85jdRrVYxOzsrHb67F5PIL3/5y5L0MsFlcs9LmgeXFxYDKi8+ZQed/3vlyhX58z/6oz/Cc889h6mpKdy4cQPf+ta3JGFSLrVajVdeeQX5fB7JZBKJREJYI40sdhVoGq3szFLW73K50NvbCwDCImMXl742/Dl5eVDKyQSB8lMlqEeJjJKJwWdCljOnPdOigIkugyVlhh0dHbuekVarhdVqbUhKcPLkyV3//9VXX4Xf70cgEEBbWxt6e3vR3t4u02kZyMh6UzJwaAWQy+WQyWRQrValC93V1QWdTodMJiMMYbPZLJ0+dlQ9Ho98Hxr8k/VMRgj9DG02mwDSDHBMovg8GpVHptNpxGIxSZz5c6yurgqjS6vVyuRnFlBM+hg7KP1gR69SqcBisSCXy+HWrVvQ6XTCQqCEmAULp0S2traKHy19y65duyZxoVarCVON4IxysBmZZNVqFVNTU0in0w2BbPTWdbvdIp0iK8rn8+HChQvCmqHvF995W1sb9u/fLyCTcmJ1U9OO8TnB66amHZN6jUYj/lhWq1XYw4FAQLxumMgSWGJy4XA48MwzzwgTRckIAyCFrbIQa3RYXSaTkYEulKcsLy/LeycgeOXKFWkaMMErFAowm83CgkkkElhfXxc/562tLQQCgV2sPjZcvF6vSL1yuZz4/abTaQSDQWQyGQGpaAPhdrsRi8UQCARk/xaLRVy9ehXAnWnIHFhFJuleFwFDNomYODJGpVIpkZ0xIaYEnSBbIpEQT+hqtYr5+Xmsrq4ik8mIfM7n88HhcEjioWSWEoxsbW3F5OQk2tvbMTU1hWKxiEwmI6xkFm4LCwuYnJyU+OJwOLBv3z5JiJSgT6OAG33jCIh++tOfxmc+8xn8xm/8Bj7wgQ+gWCxKvkDlyPPPPy+A8VNPPbXLJ5iyyk9+8pMoFAr4t3/7N2Hu0A6JvygHZZHMn5cMB9oqUbVD8FqlUon8n4BgpVIRSSq/lkq1e7DTXhbjARkMZDySAaI8o7RlYOxSNjn5+5RlM8cgOK+0/lGpVKL+YJJqNpvh8/kQiUTg8/mkcVMoFHDkyBG5b3jf0ffPbrfDYDDA7/eLZcXd+7+RvUGZOIs2ng1K38hCYU5BkIM5Aff82tqa5G+8+5VsfuZbyntPaT1Av0+TySRnQGmfw19kKuVyOVFvEUBQNieU6qm9Lua3BIC2trYExKTUj3mPcigYmXo6nU4AYjZq6J3HoSOVSkWYRM3NzQIoEEDa2NjA2toa/H4/PB6PnMuhoSEMDAwgm83i0KFDQn6IRqNS6ChzZt79BDGTyST27du352fR3NwsOSFrDQLwBA6ZH9GuaHR0VM40m7RkSbM5UalUZMgKc1taWrndbvGgVqlUwrhXnsnXXntNCtF9+/bJ36UNA5nB2WxW3tnW1pbYBRUKBdlTjSwyIx0OB8bHx6HRaIQZ2NbWhkQiAQAywI93B+8Wfl+CK8y/mZ/R+5v7kPUVPU35ddgs4UBc5jBkcRUKBdy4cQNGoxGZTAZra2u47777BAQm05Bxl8qDRkAOZa2nBFnvBl7vznHJnALu2HqpVCr4/X5RV3HYXSAQkPvEYDCI6o1NGubhLS0tOH/+PDY3N0XFxZyHdzXrAYIX9Ap2Op1wuVyiOCgWi5IHNLJYB7Fxzv2qVt+ZrbG0tIT19fVdDVWC9SQ5ARA/Y6pj5ueRSCQhAAAgAElEQVTn5WtwkBB/Np1OJ0OqmAtz4O/Kygrm5+eRSCRw+/btXaqA9fX1XeA1m45KJWQikUA8Hsfm5mZD1iVUNTkcDpw/f14a4fl8HmfOnIFKpRLlDPe+2WxGZ2cn8vk8HnvsMTz22GNQqVS7ZjnwziZeQoY4c9LNzU10dXXBarUKkEhcYnNzE7du3cLq6iruv/9+zM3NYWZmRpobtMtzOp1IJpNobm5GKBSSHIVEpWPHjjVMImKM5xk1GAzw+XwCflNmTz/rY8eOYWRkBE899RQef/xxfOITn8BTTz2F4eFhnDp1Siz0lEAspev8mgTtq9WqqGSBHULd6Ogotre3hRlKa7ZgMAi3240DBw7I+aYSO5vNSn1ES0xaPTRyx3KewsrKiigG3W63EEGUi9gH89d7XalUCq+88op4J09NTQmxY2JiQvYV6yc2NeLxOAqFglhqud1u8cQlK505KO/DVCrV8OfjHmX9xZyHTRO1Wi0MYRIKScAk3sO8k0QYkgJUqjvzSvj1eZ+SSMX6tbm5Gaurq/LzeL1e+Z7333+/KFGojmU9wPyGn4Xvi/nhXtbPFYClDJG+PUeOHBG2BBFlmt6zQCRzhGASJSZcNHhnF5USvqamJun0Uk7OzcxEjVI6BgN6BpHpwBdCkAbYORyUczJ5IODTyGISlkwmodFoEIvF8Cd/8id44YUX8PWvfx3PPvusXBS82LVaLR555BEpbFgcKOVi7H4T0CToyA1xtz8Zi/Ompibkcjm8+OKLeOmll3bJq/L5PL71rW8BuOPHqfxVKBTw3//933KJU/LcyKrVavjQhz4Eq9WKUCgkQU7pVUiPKYLyHLrDhJdBmUkapzwTGOMlzoBKFhA7MAwGBKFNJpMMF2GCzwuLACNZDGRaHjt2TFiZBGx4aex1fe973xPgIJ/P4+LFi6hUKkKTZwKi1Wp3+QBVq1UEAgFJ9mi6DkDsCPisCeJxiJJer4fdbhemMIeJPfjgg1hdXZWOIIFrgkOcFM9iikGd+4QTblkskE3W6KJf69bWlgzH0uv1CAaDsNlsMpGeoC/9LOkNzPhACTGTvZaWFpFO5XI5SV7y+bxIWLg3CFKSqWi1WiVp+clPfiLDQgh2U3Kv0+mQTqcFzKI8n13RRph9HGJhNBoxMzOD4eFhNDc34+bNm1heXpaJ0gRcCQYpLzoOdSB7hNYLBAWUjLharYYrV65IHLJYLIhGo6hWq1hcXES9XhdGBmMm5b39/f3CrGLSrZQ4KyWTBOD+7u/+rqF9wQFilKyQMUYpSjqdht/vRzabRUdHh4D2HBxHwIxFNwF2p9OJvr4+kUwSOAiHwyLjJIuRRQ1BZzIsKpWdibostpxOJ7q6urC+vo5UKgWHw4FgMIhoNAqXy4WHHnoIg4ODcLlcGBoaEll7I6u1tRWBQAB6vR6ZTAbBYFBY/2yW1Os7fsgAhPnP4pbMuvX1daysrCAYDCKbzQrDLpPJCGDHBiUl+QS+Nzc3kUwmEQgEpPliMplw+/ZtxGIxAVOXl5dhsVhw8OBBbGxsYHJyUsDfcnlnKja70jabrWEAdmVlRRocm5ubiMViWFlZwcrKCsrlMn7xF38Rv/qrv4r5+fldHp+0cCBzWOlV+PDDDyOVSuHP//zPdzHtCXbzXg8EArtAKDaCCfRRjcCmLiW2AMRDW9kgBiCFFwGNRtniAES+TgYnFQA8C2xUMrHl56jX67Db7aKYASC5D/cVwbmNjQ0sLCxIg44FABuibBKQ8cBhCKOjo0in03A6nYhGoyKDI6uSDVebzYaHHnqo4QGGyrWxsSFAF/NOq9W6670sLCzI8EiqPwjarq6uIpVKCSASjUZ3DSED7gDCvPPeyT+SseEnP/mJyM4Zu5SArkajEXk/8y6tVotsNovW1ta3DRJqNP+iFzLzHKWlES1lGO/IdC2VSgIIbm5uir1LJpOR4bIWiwWrq6tyxzL+KAuY69evY35+XtjWZNgwJ2Gh3NPTI01Y7slYLCb7JJlMShOIoBvv9EbUaTx/bOL19vaiv79f8kjGS94JtIEol8uIx+MolUqYmZlBNpsVYPTq1avCOOMZ4PMgu5Es/GKxKHuQrHjeU5cvX5azT7Yyh8DdvHlTWFn8HvRT5fejbLORRfuLs2fPCjmmp6dHwB/WGzyrwWAQpVIJqVRK7DuCwaAMTGLMYN3mdrvlndGahc+3XC7LZ+ZwlubmnTkcLpcLra2tuH37tjScrVYrXC6XgLhbW1vytSk5JosY2PGVvJcBbQB21Tk864zJ0WgUo6Oj+NznPofPfOYz+L3f+z381m/9FiwWCyYmJkQizT1EC6+enh6pF2gfkM1mhQnJJgxt53g/VSoV2QdsJPE5kQxD2xrWQa2trUgmk2KRQrl8I0tp48Y6ibGSjUS/3y/PmyQG3kGMM2wmsTnI/JzPmYAXG4NswgAQ1UClUsHc3JzEstXVVbkrmf+zKU5FI9m2tDIgKYDgeCMScJ1Oh8XFRWmmffnLX8Z9990nNl+M2fV6HW63WwBTv9+PcDiMzc1NsZmgpSAVIozB3GNssJNc8bWvfQ3ZbFast1KpFAKBAI4dO4b9+/dDr9fjhRdegFarxcDAADY2NpDNZnHt2jVhpbJJn0wmd9UNXq8XMzMzDc/uePTRR9+WQ4yPj6OtrQ2PPPIIPvrRjyKVSsFoNGJkZARtbW3o7OwUm7Lp6WlkMhmEw2Hcd999u3yDSSxiTc7he3q9XrxT19bWMDQ0hIMHD4p9DuMOSWpk0fNZut1u2cOlUkmGRRKjstvt6O7uxrlz5xoii7DueOONN2Cz2URhlE6nEY/HZUgtsaVMJiPWKfe6Lly4AJ/Ph0qlImdtcXFRzk9bWxsqlQrsdjvC4bA0SVdXV4U8QEX60tKS5GFkk1osFmkUNKpqJWlLaZnC+pHex/SCnp6eFg9XZQNYydKm6kSpgiBQHwgExHOXuQhzXmAn9wkGg1JX12o1Ue7lcjmpBer1utTwjHfE/vi8qLLeK8ns5wrAsiNy+PBhPP300/j4xz+O3/7t3wYAeYFMgtva2sTTgR3n9fV1YajV6zueaBcvXpThFbwMmQDSR4psNlK/ObzHYrGI9x4LaoPBgEAgILIFejyw08YHzQKGQGij7C2CuwaDAV/72tewsbEhoJFGo8Hc3Bz+7M/+DMDuybqvvfaadMJ48fEXsONhqxymAtxhzjKRUkpnWIAxceCghZdeegnXr1/HP//zP+PZZ5+Vf/9u6/r165IQPv/883t+FnweLS0t4qFjt9sFpFF2LCnXuFvyw43PKYtkPRJIJgi/trYmlhUEppUMPJ/PJ4Aqix36A7HDTwbj1taWAHo8oCxwc7ncriKoEZYjF0HmSqWC3t5ezM7Ooru7G+l0GlNTU7h9+zb0ej0WFhaka0UpHgDxLtbr9YjH4wKWkTE7OzsrDCu+fxY79CYymUy4du3argEgyv3DhgQDI1kflMBT4kXQ6+79t5el1+tlyiFBIhY6ZL4zmTebzTIIhoxg4I7vFjtZTNJ4Afh8PmGtkQlMP1ECktwvvHA2NjbE43RsbAwqlQo3btxAa2srnE4nIpGIxBGNRoOOjg44HA5JmFdXV2GxWGTIxl4WpTX1el08be+77z6o1WpcunQJAwMDALCLCT43NycG/sri/dVXX0WhUJD4WSwWRVZCILVarcokyEKhAIPBgNdeew1GoxGPPfaYAP3Kc8bJ9T/60Y/kXLEwAN4+1Zzx67nnnkM0Gm1ob9B/WGk1ogQOLBYLqtWd6aWcvFkqlQRMYLLLZ8MJsgTK2VnlYBNgRxJIwIpx2WKxwOfzCeisVqtlwAe98WZnZxGLxXDx4kUpKgmy0NebcQrYaVjSEmQvi1JTTv9kR5mSVnqoNjc3y51FZQgHFqysrGB6elq+5vLysjS9GJcJWPOdzszMCIjApCcYDMp50+l0mJ+fRzAYRKFQQEdHh6hdkskkLl26JABdR0cHmpqaMDk5KXYVWq1Wpq43stbW1lAsFuH3+4XNB9xpVvEs8ZmTocozQv/kjo4OHD58eFf3PZlMCljLRS9yysoJHhEMIiOF74SxhIA04yiTSMYJ3nsEhcjqaPRO4T7nv1Wqg5RKGSULipYWZNoxrnNQknIYFYea0t6Ce4RqAwL5BJMI4OVyOfFjzmQy4vPpcDhgtVrhdrvlHbGYjsViUgxxNQI6EgAmw45AOsFHfkZK9JT5Jd+R0stwfX0dzz333NuKVGWzUQleE+gkCMtcl4UjlSlUY5BFqcw5VlZWYDab0dbW9jbmWqNKAoKvS0tL4kOqjNFUUnBwERuRzI0qlYrYFZGYoAQZKQNlUUt5P+XC9NTn3UqQnnuSckqDwSCDVMiQJGDNfJ0DGROJhJyXRoAU2mZwMBg9ozs6OnDgwAFRQLBGYa5N2550Og2PxyP++FT9BINBjI6OYu4tX30AchcRMCWgS+Y98w7Ka8mmq1Qq0mClGmFgYGDXADP+W97RBFQaLZaBnXvW5/NJbcJGC9mPDodD9sSVK1fErzIQCMDv92NhYQEAhEDAZi5whwlFgJHNuu7ubmHHkSlG6y3aB/Ecra6uyiBJnsdjx46hXC4jmUxKvkZGJQG4d2Kg/ax1t/UA70GTyYTTp0/jsccew8jICDo7O3Hq1CkcPXoUQ0NDGBsbw9/8zd/gPe95Dy5fvixKGO43AvlKP1s2xzlMjiwrjUYj9k5sIPJ5kNVPUIJnmUoxxm/GeiWg0GjDl3cY5eNkiRHwIUmDQ40IGAN3fOmVAw9VKhWmpqbE0omDPXkHUmFmMBjEj16lUmF5eRltbW2Sp1HJRCCehAI+RzYGeQ+xwUpPUDYUG1nMHWhPkUgkJF4zL0qlUkin00JYYnN5YGAAR48excsvv4zFxUV5901NTVKjcbYN8yDuc9qs8C6meogWP6xrCDwy11OSCWZmZlAqlRAMBqVhbrfbJR4TFG1kbW5u4oknnsAnP/lJBINBnDhxAp/4xCcwODgoapjf//3fx0MPPYQLFy7IED4+R87+oQqGxIBisYgHH3wQDz/8MA4fPiwWGFqtVqzHDh06hNHRUQH1Tp06BbVajVgsJvuSjUPmZ83NzXjzzTexvr6Oy5cvw2azybmhD3Zvb680nBuJG8SrgB1igHJPKu2r5t7yGQUg9fa9Lo1Ggxs3bsDtdgsBhOzO1tZWlEolAWMZA0KhEJxOp9QQRqMRs7OzMl8kFouJDR2HucXjcVy7dq2hz0aP+3K5vGs+Dt9NMpkU4tTQ0JA0lUjwIG5DjIjKKuZLZHDzTiIBUwn4MvfUaDRi0cjYyFrAZDKJyrqvr0+awqwP+T2oBCyXy6JW29M7auip/S/XAw88gM7OTmEJUXb02GOPwWAw4OzZs4jH44hEItLBI5hVq9Vw48YNST6Xl5cRi8WwsLAgkhT6hDCpVRZZLDKU0m1OKFQmuwx6lGQph4isr6+LvyI71ZTEuVyuhg4kO4L8Gv/wD/8gHnODg4M4ePAgAoEAvvCFL8Bms+Gv/uqvsL6+Ll5q7BazuOehZcJPsJmWDUwo6efKw80uKpnDSo/GeDwOrVYLk8kkXctSqSSyXWB3osd3BEAkRXtdH//4x9Hb2wuj0YhkMgmr1Sp7QCl3Z8fv9ddfx4MPPohkMimABf+XF6wSZNBoNJL0EpxjoUdGIgF7FruXLl0CAAnE7JDQgyqXy4kHCuUq58+fh0ajET9jvo9Gngf9iP7xH/8R6+vr+NznPidTcwHgi1/8IrRaLUKhEEKhEOr1uvhbvf766wB2ZNYEEYeHh2EymTA/Pw+fzycFNUEPsk4IFkajUUla+e+ZLAE7gd1ms4k3LN8/9xc7VQAE8GLBtbKyApfL1dDeSKVScDqdIsmhvL9YLMLr9Yo3lFqtFmZupVIRtky9XpcppGS/ktl4/fp1kZNx6BzPA9mzBDspS6TXZiwWk1gxPj4uQGMqlcLIyIgkX2Robm9vY3x8XJo+0WgUBoOhIXa0w+HA4uIiKpWKgP1k4ur1erz66qt49NFHpRFC0JhyRHbfl5eX8d73vlesEPx+PwqFAnp6enD16lW4XC6RD8XjcRw4cEDA2UAggEqlgjNnzmBsbAwLCwvSFVWpVEgkEmhtbYXVakUsFhMLAwCSbBAE5n4eGRmBSqXaBXTuZQ0ODmJ2dhbNzc2IRqPIZrNy5pubm2WK7MzMDFwuF7a3t5HNZqXAYXEwOzsrLJ6pqSm8973vlY6v3++XxJ3AxrVr16To4t9ratqZzp3NZuH1euF2u1Eul+F0OsVnSa/Xy/CBWCyG9fV19PT0SNNiY2NDbArIuN7rYqOiUqng1Vdfhc/nQ7lcFta+UopDNrjyniwWi+jr6xPJGtkALK5KpZIw2hwOhzx33gEE6KlY4YR4MsGZbF28eBFOpxMbGxuw2WwwGAy4cOECuru7cfv2bYTDYfT19YncsLm5WZKeRtba2pqw4q5cuYJf/uVflnfOhIkyZzZEuQgkvuc97wGwc68ePnxY7lmyYilDByDDQxknAAhYRNXChQsXpEloMBjwwgsvyF2l9J9lTOLn4z2oLBiVn3cvi37N9DG12WyYnZ2VIrpUKsHtdiOVSu0anghgV35AoEDZ2CYbWKPR4Pz58/D5fDL0R61Wy3tgA4yDC2gzsb6+jmQyiVAoJMUqgde1tTVhdbNAYzPh9u3b8Hq9+OlPf9rQszCbzQKsUo7Gu5xAAWM/n7OSrcX4xX10+vRpvPjii7h69SpKpZJMuAcg7xfYKTwSiYTEyL/+679GKBTC8PCwWMpQbpxOp5HJZDA/P4++vj7k83m0t7cLYJ/P58UH1mQyYXx8HENDQ/ekMKHtBpsAd68f//jHOH78uDQhFhcXJT6k02lplLM5TSYjJwrT5w6AAOlkSCqH9lGJRdsvAu7MZ6lYS6fT8tkIQs7OzkoM5p3a1dUlDLe9LjKjOE25UCggm83C5XIhl8shEAiIP3cgEMBrr72GsbEx9Pb2YmFhQZpfq6ur0Ov14h8bi8XwX//1X3j88ccFLCNgyjkDbKBT8UVLD95zzNt/9KMfIR6PY3h4GC0tLUgkEqK6YF7LwVn0R2eO04h3IQB558y9o9Go3CNKSTybjlQCERCv1+vo7u6WBoTNZhOmPX8eFs6cG3D48GEsLCygu7tbCAGbm5uSS5AYQcXH7OwsWltbpRbk3asswNnoDL/lyZ5MJhvKvbiq1Z2BYkajUawtarWa3HFkifn9flHtmUwm2YP79u1DqVTCV77yFQSDQdkjBBMpvye7j3GXcnh+ZpVKJcM/r1+/LkxAgpdsXirZlFSJUf6dyWQwPDyMyclJaZA2ujcqlQpWVlbEeov3k8PhwPr6Onw+H5aWlkQNR+CJfuoARIVIYEep4CKRgjGfTTclmaO7uxvxeBzVahXXrl0TAPi9732vxBHGqHq9LqQENkYIvjQ1NUkeQ/XkXtfExAQMBgMqlQq+//3v44Mf/CDUajVmZ2cxMjIiVmb0WqVKt1wuIxQKyd1P3IONJDZllIqV1tZWsdaanZ1FOBxGZ2enEHVu3boltkoOh0OaRLQ1MpvNGB8fx9jYGCYmJlCpVDA7Owuz2YyOjg5MTk7uIihRGdfIam5uFkA7lUphYGBAajXWJATxnnjiCckXuDfIsK/Vaujr6xPv7Hq9jsXFRflMDocD2WwW+/btk4HMVHwmEgkMDw9j//79SKfTYi9JPCOVSom6Z3JyEhaLBdlsVizW2traUCgU8Eu/9EvSJF5dXcXk5GRD1iUE0O12O7773e/i1KlTohhgHmq322Uux8rKCqamphqee8DFmGwymfDKK69gaGhI1LqBQEAarRqNBvF4XOzl8vm8sKHJhGU9MT09jcHBQWn4sIFRLpfR1dXV0Ofj3cB9HIlEpKGwsbGBQqGA5uadYbQLCwtieURFi1KBTaUqMTWq2Vhzx+Nx8SRnPjE/Py/xY2VlBZ2dnUIcIBmLDU+eg4WFBRnwx/sjkUjggQceEPUfFZR7VRL8XAFYMnpyuRyWl5cxODgIg8EgrDxSyDltmtMAGaitVqscgubmZmGf0W+JMlKlNxUv9lptZ8ATwS36/DG5YIdNCZpRvsCuIQChrrOQIY2bhcJeV3Nzs4BASgCUk8Nv3rwpHUw+IxYfvFRrtZp04Mnm2t7ehsvl2tVdA+6AkizG+bX4s25sbEjiSkaC2WzexRDixUiJFkEgsiMo1yd7pZFlNBplAIHL5RKZBZMJ/nc+nxcQlDT6bDYrE/R4sSmHhzApUjLjgN3+RfTiUjKCp6enRX6o1WoliaH30Pb2tsgJtra2YDKZcO7cOXR2dspzIEuaz30vS6VS4Yc//KF4QTIQHzhwAADwpS99CZ/97Gfxxhtv4CMf+QjK5bJIHFgAMQCw8J2fn8fw8LB42/EZ8HKOx+MyHICXUCQSwYULF8TXi51oAk+5XE6SFibRZLHUajUBOjgIgPuxUeCASRO7+5SukxVPuwAWcsouPmMD9zQ9DMkW5s/JIKs82yxcCBqQaWO320VaTYlba2srxsfH4fF4ZLpisVgUBiNlHATGZ2Zm0NPTI95Ve130PGWTgpLLvr4+RKNR3L59G5cvX8aVK1fwgQ98QJhCZNQEg0GZ+Nnc3Iwf/OAHOHDggIARZCv7/X4sLi7uGhTBZk44HMatW7fQ29srpu5NTU04c+aMxDOTyYSjR4+K5cndrHtg5/yNjo6iWq3i0Ucfxfz8fMMMDKWh+vz8vMQCFqIsnldWVsS3t1AoSKHCAVAE5zUaDfr7+7G2tgaHw4GtrS1EIhFMT0+LuTstL8h0pe8r2db0l+UQKhaNJpMJsVhMLnBKZYCdRhxZ/gTt6Jm310UZMJkMLFwZF9ggUbL56CVH8IRep6FQCNPT0yID5VAUSkDp85xIJGSYibIBw/dO0HZychJ9fX3CENvc3JR9y6RHq9Xi8OHD+Pa3v43HH38ctVpNlBCM642sbDaLM2fOCHjwZ3/2ZxIH1Go1fvd3f1eeB+NeLBaTLjvvEXbllUoSJQBLpicA6abze/DPKIc+ceIEzp07J/fDm2++KU0lpXcnk3IWQJRr8X2wY9/IYo7AGE5GDFkw/EUVBActWCwWYTLRR5H7Rumfy2KcrEOC/ADkMyt9gykRJdOAEjhKlGOxmORz9PGtVHamJtOzjMV+b28vIpHInp8F5abb29sYHh6GwWCQoo7vuFAowGq1ih0A70wy6Jhw00v4zTffFP9aygldLhdeeOEF6PV6GWjB8/Ld735X1Ff/8i//gj/8wz8UwJrFHZknjPdUNpDx4/P5kE6n0d7ejsXFRVy6dEl8LU/e5SX/bot7W8mGVi4O1ePwDo1Gg8XFRRmGROsE3iP79u2TJhzPBnMkxk0CThw8l81mEQ6H4ff7BTBUyoxZkNOqgyxuAjtknJlMJmF8kinciLSaxTuZ6IwFvOeVJASyBnO5nEwV5zmmpJcN53q9joWFBbz66qs4ffo0mpp2PDo9Ho/UL7wD2MwgKWBrawtdXV2YmJiQ/WOz2TA+Pg6tVovZ2VkMDw+L3ZHVakU8HodOp0MwGEQsFkNbWxvW1tbEK3+vi7GCwAjPdT6fFwCdzMZUKiX3IIdb9vX1yb4i695oNMr+2d7elmdgNpsRDocxOzsLm82G27dvi+WV2+2WfcFGOQBhp1EhRUkxGwos0lnb8fzSFqmR3NxqteL+++8XX+qWlhZcvXoVhw8fRiwWw6FDh3D16lUh6hB4ZU1EP3jWmn6/XwAhnqtqtSp/l4P5CBhxgGUsFoPFYkEgEMDKygra29sRiUSQy+VkqBvPGr1WmfdzAngqlZJcnySke1nKBhZzWQKnly9fhsfjEaIEnxvvLhJryDQn2YkMPAKHvDNIjKA1RzQaxfT0tNT5jC1NTU148sknd6lT+fVZQ3NQIAk+PO8ktTTauJmdncX6+jp6e3vlOZN4Q69k+qHSS5J5p7LJNDk5KXjE3XtTrVbD7XYjm83ihz/8odgoEjC+desWwuGw5L3cf9vb2+jo6BD14MLCAqrVKl555RVUKhUMDAxgdXUVTqdTagw2LYxGo8xHaGRdvHgRPp8PExMTgkcwp1OyoR0OB/L5vCjPAIgl0Pr6OkKhEK5cuSI+qnxX29vbguXwTAeDQaRSKRlsSlLCwP/H25cHx32X5z+rXWl17r3a1epYrU7r8iHHTmzHSchhchSSkBZIQ8pwdCjHH/QITIG2w3RCBgiUKW0K0wmlwwwQwmTaHA6E2JDE8RHbkmXJ1r3aW3tqL0mrXe2ufn+oz5uvAi3adn75zDA4PqTV9/v5vJ/3fd7ned6hIdx222147LHHxB5KrVZjaWlJfIrJyGc+SI9uet7v27dPCCsGg6GiZkVraytSqRRaW1vFEoDWTMxl6urqBAT1+XwVk9iUS6vV4s4774Tf75f4CbytGGZz2WQySUOnXC6Lb65Op8OFCxekGcg5TcQ0CKCyuV6pHRbVB0prRCo+2FBmI4T7MpVKCcmRQ7rYdOSdQoCczH7ainK/cBgZ7WpCoRDsdruQ0gjyklVNYt3c3BwGBwexsrKCUCiEm266SRi5sVgMZrNZWL3Eonaz3lUAtqenB6XS9mCPaDQqB0mj2R7+0dnZKWa2DBrV1dUCrFAOyuLV6XQKCEcgicwEfl0GXIK3fNnsbPGF+nw+MdFngcJgwaKDX4+fA3jbB00Jdu5mMcCSgs6flZcxJQX0LbLb7VLM8TJh4kMWndJknZuMoBK7BtzkZrNZutAqlUqYimR6ceMrBwXQ84LgKC87yjcYtPjZKllkr66trUk3iMOzOCWUFwr/bjAYhMPhEHCY/p0s+vi56M/KwRUmk0mM+JV+uSw8KHNj8c1EmD83gWoCJSwQKc9kwUG5Pvfjbtfly5dht9uxd+9eYdkYjUZ4/mvyula77XEbDAZx8uRJHDt2TOTGJpNJJCj0XzOIDRcAACAASURBVFpaWsLg4KAkXgQ0Oc28VCpJF5iAQqGwPZGUAXF5eRltbW0C0LMAJRjKZLS6ulomxXOaNH392LGqdPGyAiAMPNL9yQAl44PgFgdt8fOzm8b4EY/HcezYMUxPT4s3Tm1tLbq6ulBbW4tAILDDv665uVnYo2R51NTU4ODBg3j11Veh0WjQ19eHZDKJkZER8T3lGWGSarFYkM1mZUIsAfHdLiaFjElMQMbHxyX+aLVa3H///RITyIAg68hgMMjevuGGG6ThlUgk8Oabbwojs7GxEdFoFDabTbx+2Qjq7+/HmTNnMDg4KAnF4cOHcf36dayvr8Plcu2Q2ABvs1/532ST6fV6PP7448Km/NznPrfr56FkLWg0GpkmzPfPrvO+ffsERGZ8ZRFLv7rq6mpMT0+js7MTNpsN8XhckuPBwUH4fD4sLCxIIUvJcGdnp7CDOjo6hLHBIV7RaFRASDLHmeQBEGYbG1xMZpxOZ0WMYIKgxWJRFCb0l6P1islkEq9u/l3GboIA0WhUkmUOuWSirNfrUV1dLQO9enp6pPFFVjGLXjKxlAwfJv7z8/MYHh7GxMQErFargHNra2twOp3I5XIiUeJnrjR20CMR2C70lpaW0NPTg7a2NtTV1eGJJ55AsVgUe41kMomTJ0/Kc1Cye4G39ysbFTzbAMRihc+V6hKlPyKVBrxzeS/wziCznywQpccim01saLEpWMniO2BuVCxue38z0WUsByD7l8MtAEiDXCkj5a8ZF/gzMW+j2oRTeevr6+XeoaLmypUr+NjHPibAo9IzlmAvGx319fUIh8Oora1FNBrFww8/jLW1Nbz11lu4du1aRc+DseH+++9HLBbDE088IWAbi3ayPGjHwnyAIBDztnw+j0996lN44IEHUFNTgw9/+MOYn5/HzMwMXn/9dXzkIx9BJBIRRt6FCxcEDJ+dncX169ehVquxsrIiE3wJTrHpHY/HZRDG6uqqsMnL5TK+//3vI5lMwuVy4Y477qjYgoBxmtZKvwvcJ2OLZ5FMT/o379mzB+vr62LJxNkCBFxoZ2Gz2XYwbjc2NkQJwEGKlB8CkKKKsZEs5Xg8LjZi2WwWs7OzIrNk3GPsrYStBACzs7PCwKaShDkgwUKNRiPDRy5evChAKwkiVOTxXDDHPH36tMhw9Xo94vE4GhsbRUbNpgMbcPw6KpUKd955J375y19Kc9FiseDy5ct4//vfLwQQWp2QHEE5byaTgcvlqniwy/z8PHQ6Hfbs2bNjMA1ZzZTP05+fQ3wmJyfR+V/elsx1qNpTDkDin5dKJcRiMWQyGWlys2Ykc5JECLVajZaWFpTLZZEV0z6Ktg9kS5F4QqCXfoJUZVXS2Dt06JDUD8yjT5w4IRLVS5cuieegknWczWbl7JPlumfPHrS3t+Ott96SxiBjMwFS5om0RGOsZX3MWQNVVVWiIqGVFAfibm1tyZ5V3scOh0MsGDY3N6XWqGTRt5jNPNZKbMqxscz8Y2BgQGoTNvYJfDHmTU9PSwxWqVRwu93w+/0YGBgAgB1kKFpO8N/39vaKmrW2thbd3d1yfticyeVyoowCIHNESO5hjUuG3m4Xa0rW87z3q6urYbVa4fP5hM1cX1+Prq4uYcWygUmgaWhoSBrYbJ6T4Tc3N4ezZ8+iuroaRqNR7NnC4bAoCDhnh9YhPDvr6+sYGhrC2NgYbrjhBsmbI5EI+vr6EIlEsLGxgY6ODoljVDRWCtCzce1yuRAIBGA2m+VrUFJObGdtbQ1ms1nqdzZTotEonn/+eXR0dAjARqyoVCoJBkLA2O/3S6xnXpVIJNDa2ir5utJ7mPldubzt+8k8i2pXAsO0dUwmk1heXhYV8W4X7aioCqSaeWNjQ2bRUIlFFfL/ZrAVV3t7+w6VFxUXNptNan4ygBOJhNTI8XgcLpcLKysrQsDb2tpCZ2cnFhcXUVdXh0AgIBgcFaWV2lOQiU8lsfJ+JDZRW1sreBSbrazxEomE5EPBYFAUp8Dbjc5UKiV7o7a2VqwkeEaVQ2OJXaXTaWFlswakVRVZ+qOjo9JsyefzcDqdUKvVUr+wDtiN0vddBWDpZ3blyhWYzWb89Kc/hdVqFV85ejREIhExBx8ZGZGEnoOy6uvrsby8LEGW3g8cEsFNrGSukNnY0tIigAEvHmVhwkSRPlgajUbkLAAE0CRIZTabZbNW0gWw2Wz47Gc/i6985SuCmNPwmgGckg6yoMxmswQXes0xmeCURHZb2e2l7w07bPTMYrAn84EFtkq1bfyuvBTpr8Vkl5uYAZ7FJyVXDIyVrIMHDwq7me+OrF12fcksI6NhYmICR48eRSKRkKKAQY2DNej9tLW1hddff106okpQiPJAMh3z+TxisdiO4ppsDrK8yAJl0VxfXy8FAANfW1ubmIxXsuhtp/QKVHqbJBIJfPSjH8Xjjz8utg8jIyPQ6/XSUaeUgMXHysqKMK2YQPNzJRIJkU8xEXC5XJidnRUJXzKZREtLi8iiGTD53Fi8E6CmVYjSY4sG9GT97XZFIhGRALJjTkYAmxGDg4PSBVM2AnhOKYlTypwuXboErVaLa9euydfh92FHkqxRdu9ramqwtLQkIBNBaPrFUl6m0+mwsrICvV4vDD8WKrFYTMB+yo53uwhQLS8vSzFTKBRgMpnkAhsfHxc/v1tuuUXO68rKitg20O+TDR23243jx48LYLKwsCAm62QQU6kQCoXw6KOP4j3veY8AMSqVCvl8Xtg1ysJTKTNj8QQA3/ve93DzzTdjcnIShUJB2GCVLJPJBJ/PJ9+Hvs39/f1Ip9NiEXDhwgX09PRIV5zgFRPzhoYGRKNRHDx4EAsLC/B6veL9+ctf/hL5fB7Nzc0iMWUzIxQKQaPRYHZ2FseOHZOOOX2VaQ9SLBbR0dEhhUe5XMbU1JQMd1E2t8iEoQx7t4ssm0wmg9deew0DAwMCNpKpT+CYzTibzYZUKiU+vslkElevXsUf/MEfSPJDsIHFGxm/6XRamANkFyv3dSaTEV9Dsuk7OzsRDoelk97a2orp6WkcP34ci4uLWFtbk0FzVD8oVR+VrDfffFN+7fP5xDOZk7KpgGloaEBVVZUMwMjn81LY0TuNzATeq2fOnBF7Ap47MuIIAFD6CLx9z1BZQrYNB5ewocP3CEASYLLvmeQSIKu0WCZ4qpwozbPAM0qpFhkiBPLeyWCnH51WqxUWeqFQwPT0NOrr68Url/YvBJMJ2jDJZoNhc3MT8/PzkoPwGZBVxeY3JWPpdBr33nuvDKUZGRnBzTffvOtnQXuYz3/+82hubobH48FTTz2FV155Bc899xy2trZ2DP9SxiXGfmV+wGbGiy++iGKxiJ/97GfSeKupqcHjjz8urOn+/n7x9aScPxwO47bbbkM6ncaJEyfQ1dUFm82Gz3zmM+K5dv36dZH033TTTQgEAvD5fOK1/eKLLwIATp48ic7OTtx99927fh583yxG8/m8MM0aGxt3DIji2WVDgZK+crmMe+65RxhUZIbQL493PwsangECTqurqzsGmzEuseGrLIYIjtB+i0O7CP7QR5eN2kpjx8bGBqanp3H48GFRVbBxBUBAJOUAp9OnT+ODH/wgQqGQsApzuRw8Hg/6+vrEd66npwc//OEPceDAARw+fBhOpxNerxctLS0itST4lMvlkEqlpFlosVhw6NAhTE5OSm7T39+PQCAgqqTNzU243W4ZhhUMBsVCifuxkkXFVzQaleFI9GKkDyfzgO7ubly5ckXsIkhoYX7C5hMteXinUDGk1WpFoj00NCTe3QQwCKyygbO5uYl0Oo2+vj6p9WhFBbw9LJf7SJk3AdihbtzNcrlcoujiIDA2LzY2NrB//35hzgeDQTQ2NkosoXUIlWYf+MAH8B//8R+ikCGIRE/bYDAoZI9wOCwgNwE8MkTJSN67dy+CwaAMvyOzVKfT7QBfCWIR7Gf9ylhaySKZgLGQDTnmPg0NDfD7/ejs7ITb7caNN94o75l3eiKREMZysVhES0sL5ubmJF6QhJPJZNDV1SX7j7GZdk20qOjr68NTTz2FVCol3tpKyyvuEWIAxeL2QCT+HtVTJKtUsjQajdwbBLai0SgMBgMGBgak2UCwkeeeA0o3N7cHiWWzWdxyyy0yrJWA36lTp5BOp7G0tIQHHngA4XBY6iKCVozfDocD8/PzQhwhLjA3Nwer1Qqv1yvgPHEMYhsXL17E/v37EQwG0dDQIPV0Jau6uho+nw8HDhxAKBSS5hUJNc3NzaLGUtpnMb8IBAKYnJxELpfDwYMHRammUql22AmwQU1bIRLkzGYzvF6vgOokYlGdpNFsD9WKx+PS6CRDneqmUqmE4eFhTE9Po1AowO/34/jx4/jFL35RkaqCoB5BOYK8rAei0agQlnin/l8Y6SMjI3LGNRoNFhYWpD4hoQ/YzoNmZ2extbWFkZERma1is9lQKpUwNzcnHqwqlQorKys4fvw4JicnJbYnk8mKBxnSliWRSCCRSAgQTVIi4+D4+DjuuusuvPXWWwgGg+js7BTf+NXVVcmlp6amZHAXm8NUAnE4F1UrtF3g4Gb+PuMWiVcEwkmAI+4UiUSQTCZlQHFbWxsSiYR4pBM/3M16VwHYcrksE80IKOXzeUxOTgrdOhQKCVDK4oeSZZpDs3s5NTWF4eFh6TZz8BKLWXbflV05gkQsmHjZUbLEl0ffyXg8jnA4DJ1OJ51YgoT8fBz6VMn6y7/8S5TLZTzxxBN44oknBPCktEw56Z1dRCZ8nLK2ubkpgAzZCwxmlHQAkMTEZDLJz8r3kc1mBeAlUE0AgMV0c3OzPC8mXMlkcgfTgfYDLNQqpc+r1Wp0dHTg/PnzaGpqksuSrEV6ZZF5wgudYBop9ZQjE/ziz0TpgkajkcBPvzWyFSmBNBqNiEQiEmDL5bIUhry02alRdrm///3v4z3veY90dpQsmkrkonv27JGiT8lQ4nMnK5lTbv1+Pw4ePCjdTP6cNOK3Wq2w2+1IpVJwu91oamqCx+PB6OioMNUog2DiVltbK4khgZ319XXpULLrRXYQAVEm4wTkCSIojcYrPStWq1UuJrIF4/G4SOlXVlbQ29sr1gHBYFAKZp7l5eVl2ZOUNjJGtLe3y4XMeMBOKEFTJjnsItJyYWxsDFNTU+LFNT4+jhtuuAEWiwXNzc0iCSKAxwBPr7dMJiN7ezerrq5OBrxFIhFhubDL7/f7UVdXJwb2SosTyk1YrLJ7zqFhlI8Fg0FotVr09vZKo4vSS5PJhEcffVSGmJG5R6kK37Oym1lTU4OZmRm89tprcDgcWF1dhcvlgsvlgtPpFPZXqVSq2INsbW1tB6OV/puhUAgOh0MYhZSw0tObbAA2Cba2trC4uIh4PA6j0SjWC2S29vT0SHGSTCbFl4venMvLy6LAIIDE4kun08lkWf6bdDotjGVKnFkkc2hDS0uLNDl2s7xerwym4XRlerLRO6uqqkoaREajUSYqk0kTj8eh0+kwNjYmA7F4Zs1mM86dOyeyZzJNyMYOh8OwWq1YWVmRwSEcVkUGOEG5trY2uZdcLhc8Hg+Gh4cxOTmJxsZG8W1nklpfXw9PhRNh+TNz0cbAZDKJfyD3AAtJt9sNp9OJr3zlKwJeb2xs4MCBA3C5XCIr/+IXv4iXX35ZZH20W6GVA+8dMoHYYGWcJBNbrVZLHFWCnkqfVe4ZshN5f1fa2GOTmmeejCs2Zfk+CRYTmGWspOSfzV82O9mUo4+YVqsViR6bwKlUCmazWXIlspuVLA/eHwSvCbQpcxk2EAiMtbe3AwAGBgbQ0dGx62fR0NCAP//zP0dLS4uABRMTE2hoaMDf//3fY2NjA7FYDP/yL/8iZ/DIkSN444035K5gPkC5KVk1XPy18r4rFAoysIIgLoExvv+TJ0/CYDDg7rvvxkc+8hHceOONuHjxIoC3h6R++tOfxsDAgHiUTk1NyfdYXFzcMUhvN6uurm6Hlyt9dgn2syHEvci7kN7RbW1t0Gg0+PnPf45jx47h5ZdfxtbWFt7znvcIqYG5N4kFZMTS4uK5555DuVzGgQMHpAHPZ8wzRYCBjSGqOgqFAoxGI5599lkcOXIEXq9X8tb19fWKilkqp/x+P2w2mzQf+TOQVUsLCq5cLidAA8F2m82GSCQiDS82+OmN6PV6EQ6H4XA4YLfb5Uzx7zPHtdvtstddLhdqa2tx9uxZ6HQ6hEIhpNNpYajSf5OsM1qMME+vpOELQAb1OZ1OrK6uwmw2Y2lpSRpU72Qzk6UUj8exuroKp9MpIBRBaN6RbMxQjtrX14eamhrxeywUCrBarWLDwHqgWCxKQ5m1WLlcFh9h1oDlchlDQ0Miy2fNZjAYkMlkKn4WVLlxHoher4fZbJa4xVpgY2MD+/btw8LCggAA8/Pz6OnpkTOm1Wrx0ksv4ZFHHtmRJ7B+o1omkUgIcYhKDeY1VqsVgUAA2WwWc3Nz6OrqEq9C1qvMd8mo5v7m2aJSKBqNVkyOUKowGBd4JnjHjY2NwWQyidyb8ZwqLOaOVLNRlnz16lV5X1RGuFwuAVUI+tfV1e1oGrW1taG/v1/IQVTaUp3Dz03Px6qqKgGmCQhms1kkEomK6lgSyJ5//nmxC+I8AHo0u1wuZLNZrK2toVzeHvrK70Mm3pUrV9DR0QGdTieDBLn3enp6sLa2Joopi8UiE9tJPIpGo8KAJJuRtfGZM2fEFoQ2BVarFTMzMzAajTAYDAgEArj77rvh8/lEScAmQiVLCcCfOnUKDz74oCjelNZ+mUxGGOMajQZ2ux1utxter1dIcny+vCt5trnnaNVBlTBreABYXl6WGGU0GjExMSHs6Gw2K7MMyJi1WCxi49Db24tkMinNwnK5jKtXr8LhcIjVym4W70piFR6PR+wvyNp3u91CsiOG8b9ZVVVVWF5eln0NbNtx+f1+FItFOBwO1NbWIhKJyMDknp4e2TdUd7FJEo1GZdBnPp/H1NQUrFarWO4wj61k0T6UwDkbYyT/kagQCoV23BU6nQ5vvvkmtra2MDAwIPMK3G63kCNow8YcnHGU9wwXFT1s6JEMpaxfzWYzisWi+LXz73CWRFtbm7xHKnXoSbyb9a4CsB6PRxIvsiQpJ2IwpnH69PQ0ent7pQhgMGKnhxcdE3oOMAAgBxmAXLaNjY1YXFwUBiE782SCqdVq8fyj/x+TIIJZDPRk5FIm4nA4BBjZ7aKZf6FQwPe+9z186lOfEkbL0aNHEQ6HMTs7K/YBNTU1wjQi+KG8tI1Go4AHBIn5PNm5YpHIpJrSC2666upq8XVl4USgKZ/PiydRqVSCTqeD2WxGLBbbIQ8ie6jSYpkmyPv27YPH40EoFILZbEY6nUY8Hpfkm4VcPp+H2+3GM888gwceeEBkbtw/ZDQCEGCOe0JpUq+0C2C3PBaLiT2DRqPB9PQ0AEgBycSNJvFMeNndUhasDKaVSAIp1wIgXoosjGkcz+EdvBR/85vf4Pjx478FgjJx2draQiQSQVdXl1wsfr8fTqcTBoNBfE94NsnCsFqtwnLjEB2CZZTPcT9RksBfGwwGKczJuuB+q2TRQoRADuMAE55jx44JuMTBJMDb7KnNzU352fnZyQ5hR5Am5cvLy3A6ndBoNHJGo9GogGKUhVM2wz07PT0tNgzNzc1IJpPiAUZvp/X1dTQ3N6OzsxOZTEaSqUqYbGTl8mLVarXCOKqrq0MkEoFWq8XLL7+MPXv2QKfTSWeX74sy0Hg8LuBpc3OzxMBAICDeZm1tbQKWcPgI3wm/P5n/3HuMTQRxvv/976O9vV32Dq07yMhk8kz2ZyWLBSsHMyqHOzHZZ8wjy4HFB+MFAdXm5mZUVVWhublZpIBer1cGHwKQydiUt/T29grLwev1Yu/evcIiYDHKDjttSyih5mRgu92OxcVFOBwO+Hw+SfQqZTi6XC5kMhmR9fNzs2FDuRtBHLIquJ8IyvN8xGIx9Pf3S4Ks0+mwd+9eaVyScbW2toZcLofOzk6x1KA0mSwpxuNgMChAUzqdxuzsLBoaGtDV1YWVlRUpPslEZTFH0OL/sjweDzweD6xWK2pqauB0OqHVapHNZmVK92233Sa+xmTm9PT04OWXX8bHP/5xuFwukVE+9NBDGBkZQXNzM06cOIFkMon77rsPhUIB8Xhc7hwy4dgso3LnmWeekfyGRXm5XJaGCUFS7iWyBWm5w7trt4t7iglzNBoVWTeZd8xtlpeXRe3BRJXNRu4hyoeZFxFYaWtrE3UIGVycJMviPxQK7fg6tJWh5QUbEfRNI5i9tbUlVgQEefbs2SNT4ne71Go1PvShD4nP8eDgIHp6eqTRRHbQ17/+dRSLRSwtLcHj8eBTn/oU1tbW8N3vfhe9vb1SMMRiMWn+8w4iy48gCP2ByajW6/XCSqNXN+NqLBbDuXPnoNVq8cYbb0jBODU1BYvFIkwmFpt79uyRHJXFSyWLjWWyzeiBVi6XxfOfe5MgvjLu83zS65Dvi5PeyeSmxYPSV5xgGT0d6f9JcGd1dVUscMjiopUOwQGqcMiUU1oArK2tVTSToLu7W3K+S5cu4fDhwwLgEChnQ1yZy1RVVeFXv/oVjh49is3NzR3PQ6PRSA6Qy+WwuLiInp4ezM/Pw+VyyeCc/v5+KTSpBNzc3B4imE6n0dvbKyDTyMgILly4ALPZjCNHjmB8fByJRALDw8MyRHFjYwOpVApOp1MahJXGUbJ76JG3tLQk+WUikZCGGUkP3Cfr6+vifUgQkCxHKg/J2stkMhgYGBDVAEFUgve5XA5NTU1obm7G9evXkUql0NnZKYxsgr9kduXzeRmIRPZZY2OjyNZ5F5OBttvV19eHjY0NHDlyRIgVGo1G9hebyG1tbZIPU15NcLSurg7Ly8tYXl5GoVDAv//7v8tQHtop1dbWwmKxyGApWsgRYDIYDPJzKgkk9KbnXUobF2A7V6LMmvkjVX+8tysdkJvL5cQXHtjOBxkbeOfF43E8/fTTwsKkkoyfizkp62kCsdevX5f4SJtBpaWVWq0WEI+sSRJO7rnnHrzwwgty/tmYZy1PNjab7MxJ2MznqgSQpvXH+Pg4VCoVvvCFL+Dxxx+XIa/19fWioCJ5ivEkEomgvr4e8Xhc1FsGgwFzc3OwWCziVxuLxWToEH2DFxYWZCjg6dOn8f73v19Y2BqNRoZS1dTUYGhoSAYdra+vyzPnz8y7dX5+XpjlxAUqJUfw85LoxWY02ZQbGxvY3NxEV1cXgsEg7HY7YrEYNjc3YbfbMTMzI41B3gUkkPFuYtObVgMELlnPVFdXo729HaFQCACkYUQmP/N8PhPa/6jVavT19cm/M5vNOHXqlOSt73vf+/CTn/xk18+Cqta1tTVh71LCzjqE55BY1zsbuv/dM+bfIf41MjICjUaD9vZ2jI+Po6WlBQAwPj6Oo0ePii8wPUxvvPFGhMNhUbkwntXV1UkeRqILvVXHxsbgcrkEmKx0b1B9zUYH43KpVBKFLhtLyWQS586dw+bmJq5evYrW1lZpOvh8PhkcR9ykublZcinmISSXxWIxqFQq9Pb2yp5U3kUmk0msXnhGlUphkhWUvuzMa2gXVCqVdt24edctCBis+f+UAFJecfXqVTFMj8ViMj1zdXUVHo9HfGIIBGQyGTHaJeOrVCpJB5DeQvl8XpiBRK3JLimVSrDZbJLIkR6+tLQktgf8/GSNkfXCAv6dgfv3LavVKhtuenoaKpUKR48eFa/MlpYWvPe970Uul8PFixcxOzsroCEDGotHu90Ov98vno28ZMi8IGuHgCJZSDy8VVVVQv/WarUy3Z3vJ5FICPOUfoEMrOl0Wuj6nBjH7n4li0lyIpHAgQMHcP78efFcBSBm8ZTb8MKnxHdtbU08jwhSsUuq9OriRU3ZIMFl/qxk9wAQwNbr9eLQoUOwWCxIpVIwGAxYXl6GzWaT53nlyhU0NzdL55PJOX9dicyJXRoCobxgyBL4wQ9+IF0aADIIhOAp/72SnUpQJRgMSseNxTeZBnzX9fX1AtawCO/o6EA4HBa7EAC/BWqTFcQOEWVBvCCUw3kqWfRy5NlWBsHe3l7cdNNNcnETYGByRaYp5QJkmq+srMDpdKK2thYejweDg4Pwer3o7OwUZjO7q5T4k01YLm9PJF1fX8eFCxdgt9uxf/9+5PN5XLx4ET6fT9jDuVwOg4ODiEajKBQKcLvdiMfjsNlsuHbtmngT7XaRLUSZDBNU7i+al5PpzX1Iti2BdQJw2WwWra2t8Pl82LNnD8bGxrB//36Zxrm2tibFOZM1JlK8bLj4+9y7dXV1qKqqgt1uh0qlkphM1tLGxga+8Y1vCOOUgGwli8VVdXU1Ojo6hCHDmM+BXASHaXUTCoUEACEodPToUZw+fVr2bG1tLcxmszAfLRaLWE3o9Xo4HA6Rs6+trcHlcknXnk0wjUYj/nRtbW0Ih8Po6enB5uYm/H4/rl69in379mFxcVEY436/H729vXKx73YR8FSr1di7dy8sFgvy+bxI4ZjwZTIZNDY2irSYSajSmiUYDGJ4eFjkSLR+YdHKopfslHK5jHA4jGg0ira2NmEVs0FK1YLFYpFigIlXV1cXCoWCWKlQVqr0xdNqtRUxDgDsiOXKxbvgv/PXpVdcNpuVWFBfX49vfOMbSCQSO/YXWV0vv/wyHnroISmAmCco/Q4bGhrg8XikkCSYxvPF3INAKJugBFopxS4Wi+J9X8li4aLVarGwsCASbd69BI0Ya1OplMRuFqj0DmMRRTuOZDIpyglOiqVEljmKUspOlQ0LUzIt2JSm5xxZewB2eM5TGUIAnHftbtcHP/hBSfDJ/GEzU6VSob+/H1tbW7h69Sqam5tx/vx5AMDQ0BDOnj2LL37xi3jyySfR1dUl+SffM4FB3okE36g2YWNZSUCg1RMbSNx3fMeM2x/5yEfkXWYyX4ZyawAAIABJREFUGQFN+XfJVK/UA5Z7PJvNSsOAvpEEUyjppfc6SRO0KSCDzO12y/txu91wOBx47bXXRBGQy+XgcDjQ3d0Nl8uFUCiE+vp6aQafPn0aDz/8sAAsVGbQXmttbQ1VVVVYWVlBd3c30um0xBkqVmiBQAukSgDYd+4jAvt8t5FIROSwLHqBnYxnvnOCnfPz83A6nUIeAYCXXnoJ/f39UKvV6O7uFnl9Op1Gf38/uru7ceHCBbk3vV4vPB4PbrnlFjQ2Nsod8Ytf/EJ+zvX1dbzxxhsYHR0VqT2VFgTEKmU5ckp2MplEOBwWQIR7gL69ZKFFo1FcunQJDz74oOQqHBhKAJiDMBnLOLSSTYxIJCITzVkEr66uIhwOo1QqweFwIJ1OI5FI4NixY9BqtbBYLPB4PLjhhhtETq7RaNDa2ipFtN1ul7NFJmmlcZQs0lQqhd7eXmFoM8fisGneeadOnRKW/cbGBl555RU0NjbCYrFgY2N7YOnk5CQmJydxxx13oKqqColEQrz66U/KfJ2DpxhnyBhU5jxerxdarRbt7e0SI6jKY7OPwzW5LxwOR8WyZzIH8/k8Wltb5c5kXqBSqTA5OYnq6mr09/fL92ZjkZ7mZDYyD7n55pthtVrx7LPPAoBY3ilZ07wjlTYjbDIEAgFRdRHU5h5ifGW9B0DuX95x3N+V5F/Mx2+77TacOXMGxWIRzzzzDP7kT/4Eer0emUxGmiK8K+gbSbUQm9htbW149tln8bGPfQynTp1Cb2+v3BkEV+krvLGxAZ/Ph2KxCL1ej+eee07OBEEhMsU7Oztx6dKlHeeSHpqBQAAWi0WYmH19ffK+VldXhbW428Vm8crKioClZBpWVVUJo59gGQkSBLCUTcpMJoPPfOYz+Ld/+zexceO/Jy5AP0+Cbswjl5eXBfT/5Cc/iU9/+tOiiiBRiliB3W6HTqeTwUusfwGIFaLVasWFCxfgdDp3/Sw2NjaEuMOZGwTgWScrST38GVibANhx13C9E6B1Op0y6Ovy5ctoaWmRs8KzHolE0NraKjVkU1MTfD4fXC4Xzpw5g1tvvVUGRjc2NsLtdsv78nq9YlnBuTwjIyM4depURXuDSm/m2hqNRoZckpVOYg5JbrQNoy0ZlZm0aIxGo2LnxnuFsZ7NN9ZqHChLTJDYCms85VwhxmCSPldWVkR9wVoPgMyQqq2t3TUD9rff6P/HFQwGJUhxGnokEsHq6qowF7kRm5qacPnyZQGeCCoSlWZRv7GxIYGI3pw0rWaxAECSRAYUBlqyjfjSyO5kMGARwE1AOjqTCbJ7KgUcCYJubW3hrrvuwsmTJ/HEE0/g0UcfxfDwsBh0b2xsoKurC5ubm0gkElhfX4fP58Pm5ia+853v4OTJk/jBD36Ajo4OGRhE1uGRI0dw4sQJHDt2DD6fDz6fD0tLS/Ks6J0aDodFwnL9+nWRRbjdbkSjUZHBpNNpKSppRp3JZBAOhxEKhUS2x39TyZqZmRF5D4MPL0sygZqamlBfXy+gJ6evM8mltJIsNQYvHkLg7cFYZHTyv7lXotHojo4oPwsBgM3NTbGBIDNErVbjF7/4hTCnOLmbMnyVSlWRlIWXI4soMo1yuRx+9rOfSSBiwU6GWjQalQIEeHvCaHNzs4AjIyMj8rUTiYQUNXV1dQiHwxLMOMFTpVKhtrYWS0tLYifAnxmAdJIZCMlwVErLeLbIxlPKlnazeM4JOLNLl0qlcMstt4hlADv7vHxZ2HBgAr0UaQFw+fJlXLt2De3t7eJdSPY3O7V89slkEisrKyJ1pDSXrKlr164hGo0KCE+QxuPxCEhqs9lk76ysrAiIUQkjOBgMyvRhxiAa+7MwstvtSCQS4n/FC4QFPxPWra0t+Rpra2tobW2Fy+VCMpmU92YwGGCz2cT/x2w248knn5TimE0SpZSMzZt8Pi8JRjQa3RF3Gxoa8Dd/8zcCUGazWTm/layVlRUZVkHwkH7cjNd+vx9msxnBYFDYt2wuUILj8XhQKBTQ3d0tDTi1Wo2pqSmRZodCIZhMJtnbKpUK3d3dSCQSwjijlY1erxfmIuP2/Py8XOypVArXr1/HjTfeCADo7OyUxGNra0vAoUAgsOtnEQqFcP78efzZn/2ZyHqUQ52UXlerq6syVZTdaLIDMpkMuru7hQ3h9XrFg5CScybFarVauvq0daCvlF6vFwaFXq+X4Wh9fX3SKGptbd0xRAHYZjkUCgUB9ShVr2TKPYCKGaJcqVQKc3NzAhbwTisWi5Ikcq+fP38eZ8+eRUNDA55//nn853/+Jz7zmc/g3nvvxV//9V+jpaVF7CwsFot4v335y1+We0w5/ITxa3V1FclkUuIsi1o2kmn9U8liY4wDjQhmko1OO5/6+noB2yiZ457mncfmGxl2BoMBVVVV4p1LRgFZBlSU8D3znlKr1aLAsFqtEqfYNGGsp38bz/jMzAw+8IEPyBBOZaGym3X48GHxDjebzVKo8vwReBwaGoLFYsEnPvEJfPSjH8WhQ4fw2GOPIZ1O40Mf+hDS6bTsMyou2ATi5yeBQFkQ0MMsnU5LLkWGOADZb6lUCltbW4hGo7hy5QqWlpYwMTGBsbExXLt2DZcvX4bP5xM/5uXlZUxMTOCNN96oaG+wIU3VApl2BFPJlqIFBIfwUY3FBltjYyP6+/vlvZORSvskj8cjeQb9BwlSd3d3y2choK0cyMYGLC2Nqqqq5GsRmNXpdHjhhRfErqirqwvDw8MV5V9U0HAR5GDDhfuZbH2yLrl/xsfHhVHFe5L2YfRS7O7ulmZTT08Ptra24PV6EQgEoNPpcOnSJWQyGRw/flykyvQ7PHnypPgYdnR04N5778Xs7Cz27NmDwcFBDAwMIBKJiO9de3u7NMf/N/kXvz+b3PTq3drawvLyMgKBACKRCCYnJ8VD2m63yzsh4EaAgcU3B5vw3/CsV1dXw2AwiG8+76+1tTWxzOHZuvHGGyVHW11dlSGqtbW1QiihagzYnr3BeoJWMJQq72bl83m4XC7U1NRgdHQUDQ0NaGtrg8PhgNFoRE9Pj6g9NjY2UFNTIwPXqBSkrJqzFxi/1tfXEQwGpVETj8cFdOSUbsYnkoV8Pp/YG7HpbbVaRaJeKpWkQcH3z5il9H632+1yripZytjHOl2lUkksoaQfgNznVD1RWcEclk1i1hSHDh3C/fffj1wuh4ceekhmvXCxfuc9xt9TqVRYWlpCQ0ODeAGTjEF2G0EuKik4bJufLxwOY2ZmpiIrF8bEcDiMm2++GV1dXdJY7+vrk5+ZFgtra2sIh8OixiKImMvlcPr0aaRSKXzrW9+CRqPB2NgYbDabWO3l83ksLS0hkUigrq5OQP+BgQH80R/9EcbHxzE+Po6pqSmkUil0dHTI8HO73Y62tjaYzWZhXl69ehXr6+sIBALo7e2VWn5iYgIrKyuIRqMVqyr8fr/cI8Db3vYkL5DcQRY5G4dmsxmzs7PI5XLwer1QqVQIBoNCZOLwRTKlmRcwdy0Wi7h06ZLEbQ7BY37CuUO86/r7+8WfFgCam5uxtLQkcVul2h4E19LSgrW1NVEZxuPxXT8L3hXMvWitxedDX2YSN0geJAbwzkYf/8c/q6mpgdVqxfDwsFh91dbWYn5+XjCzra0tXLhwAaurq5idnUUmk0EgEJAh0+VyGTfddBMmJiZgMBhgsVhgMBjQ0tIiuA9VwMq7+IUXXqjYVpDECpL6lNZatAMpFotyd9FOYX5+XuYRhUIhWCwWuWMZz1mXkCVOtjGJSQ0NDZJr0sKAsZUEMTaquGerq6vR1dUlIK/SwqypqQmdnZ1IJpNYWFhAPB7fdRx9VxmwTOBYlBN45ZAgSo9oCq7RaERSwcsGgATJ/v5+XLt2Tbxh6UVGEIDdNHZC6dVDP6BSqQS32w2bzSYbgAlvIpGAXq8XmQ0LSAIRBJU2NzdhsViE2bXbZTAY4PF40N/fv4NJZjKZfufwBLfbjfn5eXzpS1/Cvn37AEAuEgD4x3/8x98yhSb7EwC+9rWvYX19HU8++SQmJiZw8eJFqNVqMY4nS5BAkd/vx+nTp0W68c6hNfX19eju7obFYpHiYmpqSnzCfle35n9aU1NT2LNnD+rq6kTCQdN9h8MBj8ezQ86t0Whw4MAB/OY3v8Gzzz6LRx99FI2NjXLgFhYWMDAwIF1pdrFGR0cFFHU4HFheXt5x4HlhU9bDS5MFSrFYhN/vBwDxFqbnqNLbiRdFU1MT2tvbdyQOv29xkjaL1tdffx2NjY04ffq0DJpgwmM2myUpnp2dxcjICPr7+3ckZYFAQPxS2DH3+/0yEVc5KZYdZKV8i4kwfaRYEPOs8TORgRsMBqUwZlLNLjmTrkoWhx2wY8if/4//+I8BYAczkx1XgiRMtNnwACDFajabxa233ipD6th15f8zOBeLRblgyKikb0x3d7cw4AKBAB555BFhghgMBpHJ0i+ttrYWdrsdV65cgVarxczMTEVACvctE0l2ZMk+J3unuroaL7zwAs6ePYsvfelLMoyO0nG/34+amhq0t7djbm4OLpcLJ0+exJEjR1BfXy+MpYaGBtnHt9xyC+rr66XoASBxlr9+5plnkMlk8Prrr2N6elpA7vr6eumkfu1rX0NHRwecTqdYl5C9VKk9BdmD/DyULNfX1wt4yG4k2c+tra0ol8s4c+YM6uvrxSOODGGyZa5duyZeT2QA09+Ucj8yZHlxU0JJpiL3FpmBXq8XHR0duHbtGnp7e4U5RCYhCzKycCthbnV0dOC+++5DNBoVIBSAsIasVqsMwMrnt32d0+m0SNHX1tawf/9+/OQnP8Hy8jK8Xi9MJhOcTieqqqqk+CU4X1VVBY/Hg5WVFYyOjgrrJhKJYGRkRN4H4waTervdLgM/Ojo6JDkdHR0V5qlKpcL8/Lz4LlksFpEF//9aSkVIKpXCuXPndvw5z5ZyeAzZ9mfPnhUpPrBd8IXDYfzVX/2VFAosIN58800ZDEEgks1f3iMEmGihQQYJFRUAKj4r9H9kDCBAyrhIBistmex2uwzGWF9fl5yMrIX6+vodftnr6+toa2uTApwS8+bmZlGuAG9b9JBhSTk3QRb6I8fjcbS1tUnspt9oIpFAe3s7JicnZahHIpGQvGg3i76SVBEpQRAyIvgeAQiQWiptD6Z4+OGHEQ6H8a//+q9oaWmRyeSMh2SikQhQLG5PL6cfoXJwzebmpjAE2Txjkc2md21t7Q5bgEwmI77uBCej0ah4tP9vrFwIhlCaSBIC4xiBegCSnxWLRZEFVldXY2lpCZFIBF6vFxaLBe3t7ZIPLS0tiS3Sm2++iQcffFAGXlJFNjMzA71ej+effx5HjhwRlhpZL62trZiampLBtSRG8HO3trYin8/j/PnzePTRRwFAnt1u18rKChwOh8hPaRNAoJhTvaenp+FwOGQCOfMPABgbG8PevXsBAG1tbchkMojFYvLz9vT04LXXXhNPO5fLhba2Nly7dg3JZBInTpyQWmR+fl6GPRGQPHXqFPR6PY4fPy4Dvl588UXYbDY0NjbCarUKuUU5AI3FZSXLZrMJi5QxStnwWF9fh9/vx7lz5+D1esWehXcOLWkKhYIwLK1Wq1iira6u4tZbbxUyDRsUfPckuoTDYTmvVCMSuOPwNnptkiDBHJDzKghWFAoFLCwsSHNpt+uGG26A0WiUmMUmHW26gO17hDn1mTNnUF1djXg8LjGH+zkWi0Gr1cJoNEpjMhaLIZPJYHBwEDqdDrW1tZient5BREomk+KN6nA4pJHOXJeARn9/vwyfBCB3Smtrq5AJ+LU4NK5SX3GC3QQQWTcxn+Mck42NDWlskNBEixbWGvT/VLJO77jjDjidTgHMqqqqEI/HUVNTI00zvmfKpPP5PE6cOIGnnnpK7MmYn7F5xMaRWq0WhjfvaM7JIO6w28V5DJ2dnSgUCvD5fFCpVPj5z3+Ozc1N/Omf/imWl5exvr4Oi8WCmZkZ2Gy2HbYgFosFS0tL6OzshFarxfnz5zExMYGtrS2x9uHwocHBQfkeBNwXFxfh9Xpx++23C4jHOtJoNKK5uRmpVEo+h9VqxfLyMtra2mTPkjzU2NiIsbExyWErtS7h4EIuzrvZ2NjA0tIS9uzZI0Ae3yPtEunfzbO8tLSEvr4+/MVf/AW+/vWvY2pqSkgVbNBT3cP8kVY9JAfQ2uXVV1+VoWi0Yezv74fNZpOzyLkMxWIRgUBgB2u6qakJhw8frgicZ340NjaG1dVVFItFiafV1dUwGo3CymR8YxOaz4TngrYAAAScvPnmm6XJyfPGOUL0Kg2FQtizZw9effVVvPe97wWwHatCoRA+8IEP4IUXXoDJZBL7h/7+ftjtdrS0tMBoNGJ2dlasTth8JbO6UuViLpcTkgkbMLSN4Z+zhp+dnRXVeS6Xw8TEhGBPzJep6iVLmTUYm4Z8dowFJHARAGddRyU18y4ytpPJpOTh9NLOZDK4cOECbrvtNmlusem82zv2XQVgudl4GWs0GkleOWWcFxsp+wTUCKTq9frfopmTvt7U1CST2QjSkcGn9BokcEmQhKwMXtLc3KlUCg6HQ+QNDOCUBKXTaZHyk2a/20Vfy1AoJJ0X5XN65/rCF76Ab33rWxgdHZXA8M7FjaX8M+WvtVotBgYGZGBDLpfDiRMnoNFsT4Wj3+f73/9+zM/Pi/9fIBCQr2swGNDR0SHPgF12MnS4KnkW/JmZRF++fFlkyWQHEQQjBbyhoUEKYCZYDQ0NItXhhUyJMe0pCoUCstkshoeHJelQdk9Y5K2trYmHKX9NuQLB+oaGBsRiMZkC3dTUJMMDaAvAZKsSewoWXxsbG/jpT38qniVHjx7FxYsXJaHlBceOIIuzxsZGBAIBAc1pCk3/rubmZmF1UlpPRgqLR4I/ZJ4SlGDALZVKIvmh/JPdZyZVBO7J1mSR+b8xc2eAJxvmgQceEMCXDRFgu5htamoSuTRtHPbs2SMNHU6QJaMZgNiBMEao1Wo4nU6EQiF5f0xCGIOy2azEL8ohFxcXMTw8jGAwiI6ODinCeUnU1dVhdnYWdrsdb731Fm6//faK2OLcG5QrEiQFIP5eTFyqqqpwzz33yDRrsiZyuRz6+vqwtLQEq9WKtrY2rKyswGg04sKFCzh27Bj2798Pj8eDra0t/OEf/qGARGSyKOViGo0Gn/3sZ9HV1YXOzk7YbDaR8HK6bV1dnQxW7OnpkW4wGe0cclYpA6OrqwvXrl0TRub+/ft3WJAopUJssPBnHRkZQS6XEw/d/v5+uN1uaajs3bsXXq8XGo1G/KU44VKl2p7kbLFYRPJnMBgkoY9EIiKbo0ceQTsWboFAAI2NjRgeHobH4xH2MT0GKVnb7RoeHkY4HBbQ1mazCWjn8XiQSqXEuoPWJvyZ6P1NVlU8HsfRo0eRTCbhcDhkMnkymZQJtoyZvb29UvjpdDpcuHBBPByZWPJcEsiw2WzCrna73UilUrjvvvukOcbYStP7bDaLzs7OivZGpXcQ//5/9+8YF3in0gqI8XF1dVUYpYwV6XRaJgzX1tbC7XaLyoDMfqotyKBjkU5VUENDgySK9L2vpKHHxeYoGSccTFkul5HL5YTpyCE4HKzJJJeJLAtIAnUcwkZAmN5hZKFzaCWtKoDtphoBFQLsGxsbaG9vlz3GxnSpVBJGKuNQNpvFd77zHXz1q19FY2MjTCZTRYznubk59Pf34/r169LoZb7Iz06vSDL1yAjt6OiAWq2Gw+GAWq3GwsICvvnNb8rnr6urw9/+7d/KwCKCtwcPHhR7BbPZLGoS5oL0wyTwzZ/HarWKDFtZTGazWRw4cED8UHnuKbetdNF6oqmpCclkElqtVqTnymnilJBSGcAY4vf7ZagKcyDa7cTjcZHh0y4gEAjIXXHp0iXU1tZi7969MsiKfnP8+VkMGY1GtLS0yPRk3i96vV5iPZmOlF2WSiXxwvt9i8AEAVgAWFpakoG/ZO/wrqJqjg0aDhFJJBISBzUajdxJzKn0ej36+/vh9/sFROB9TH88k8mE+++/Hz/60Y9QVVWFgYEBASbz+Tx+/OMf49ixYxgaGsKRI0cEkODn4j7gvaNsJu92GQwGKS5XV1clBzObzVheXpbnsbW1hWAwiIMHDwobM5vNysAqxj16PFMVNTQ0JFJjtVotw88o9WcuQ+UVi+zBwUEB78maJdhIhplyhggZbaVSCX6/H263G3V1dRgeHt71s2A8oHyV+WUymUQgEEAgEIDRaEQ2m5WmZiAQENshxhHmrawbyHzT6/XydfhMBgcHsbCwIDGApB+v1wuHw4FgMCj1CO9Pso1Zp1FtwWYF8+VSqSSNH7LkKlm0WuHXo/RX2Vz67Gc/iyeffBKPPPKIgLVUepTLZWmuMS7wbPFeJSOftR4BeQBSk1NJsba2JsPEOOAOgLwr3h/82dlw48DLUml7GOXKyoo0s3a7CoXtAbvNzc24cuUKBgcHUVW1bXU4NTWFX/7yl2hvbxdrKKoQS6WSzHmZnp5GV1cXwuEw0uk0mpub4XA4EI1G8dxzz+HgwYO477774PP5ZOYB6/CRkRFUVVVhcnISPp9PYg9rvqGhIRlsdv36dQwPD0vdX19fj4mJCQwMDMh5YiOnoaEBs7OzFedTZFkzV6FKb2JiAvl8Ht3d3aKE4pCrYDAIm82GK1euiJ857xMCbFTsut1u2O12qWcJchJoNZvNoqbiHUzrj9dee03IDgMDAzI3gl70bMYq8R6/3w+TyYSenh48++yzaGtr2/Wz4HMg+522d2TnszHO2E5LCsZR1qUApFkBbAOo+/btE/U4bUv4rCi/V3qm63Q6rK2tSSxXq9X49a9/jZ6eHvG+dzqdWF5eFkyguroaVqtVVFn8zMxrKyVV0Wd+dXVVbI0MBoN4+VI1wWYSFfNUGzC+U6VXKBSkua3X67G8vIxyuQy9Xg+dTie1D/PbqqoqWCwWRKNRrK6uolwuI5lMCvkqnU4LwYDPbXNzUwYQV1dXi+UNm1jEHCvBN95VAJaUbUrcCoUCAoGADJbiZtzc3BQ/DwZoJbuAQF+pVILT6ZQJxuz+cigQL0AWNjSk5oFmt4esHoJ9wWBQWH78WpTZUXpE7wx2G+hnsdtVLpcFSGan439a7e3t+PSnP73j33MTKpfyvwm48NelUgkPPvggnE4nXn31Vdxzzz3iC/fVr34VGxsbuOuuu1AsFvHoo4/ic5/7HM6fPy9A3+DgIAwGA4xGI0wmE+LxuIB7NTU1IiUEfrdfyf+0OMglFouJNAbAju41QWAyIbRaLW6//XZcvXoVa2trAijymSq9mcgAIJBWVVUljDBgu3upZHgyWbVardjc3JRkiZKAbDaLTCaDlpYWzM7Oim+fEpikjyH3yG7XT37yE+Tz28OdWPD7/X60traiq6sL8/Pz4v+m9CTk+aHcTumFwiBF7xez2Qy/3y+dPXZs6I3KgEVGoNfrFRYtfSbpp0tAOp/PC9CazWbF51mZjJFlU8mKRCIiT6Qkm53BTCaDYDCIlpYWKWY1Go0A5vRiJIuzrq5OTNSB7S4tQSYAAmTzTNP7hckgGbDc//F4HNevXxdQUclco4RS6edz8OBBXL16VWTXlDTvdmUyGRmoxK89Pz+P3t5eKYw4XCsajeK+++4Tzx8AAjLwIuawIMq7aEIObBvH+3w+6RoSPCCwr9Vq8a1vfQulUgk333yzgCyU+S8tLe2Qpfl8Pmi120OnDAYDTCaTeOLRKqTSlU6nYbfbUVVVtcNDSKvVYnJyUgY/sEhWsgfOnTuHYrGIoaEh8dcLhUJiFcH3Tsk07ywyoGpra3H9+nV0dnbukOZwInU8Hhe/abV6eyAAm38EGDo6OnDx4kV0d3fL4CMCDvl8viKZE8F1Sm0oQyoUCujo6EAwGJTPQ9YFh3lQtkMGmU6ng8/nw8jICGKxmNgPkMlH9YrVasX8/LyoSjKZDG655RZJRDgw5o033hAbGTYJWLypVCrccccdEjvL5bKoDMiGpFyvkvXYY4/JHa9kgPNeUMY+gn1sYjIBJLuIgBLBUTah2FzhZ+edyySVhSUZN6Ojo9LJZ6HMPIe/z69PKRqbwoylLDIquVMASFyqr68X1jjBIH7WQqEg1gNms1nOKPMqxnzeS2SqkT1OSxMCy2QNMnZotVq5SymRpRqpsbFRQC8W4rQU0mg0Yq9EdYLBYMA3v/lNfOlLX5LPvNvFhg8bJGRWkTnKr8ecgkwYDvMrFApob2+H2+3Gxz72MdhsNvGHfuWVV3DixAlsbW3h7/7u76BSqfDJT34SwWAQbrcbly9flsa9cvH3uB95r7MByHuWoLRGo5GCjiwyfo2GhoYd+eLvW2QdMm4wLydIzsLQarUKm4y+uel0WuYq1NXVwWg0wu/3i+8vczODwSAs7HQ6jZdeegmf/OQnYbPZdhSb9fX1MJlM8Pl8MhiTvoiJRELk/AQ6ydalDLe+vl5ALKpj6Be4m1VVtXO4FhdtdMxmMywWCxYWFqSgo7djY2OjeMTTG7WlpUUGhrJZXSgUxDf+0KFDyGQyiEajMBgMePPNN7GysoL9+/ejs7MTfr8fFotFJt67XC6YTCZR5k1MTADYbsBRRUJGJq2n2tvbdzA0K1kcnqdkhRNcZTNgbm5O/r7L5ZIcgjkJyRGsqzioieBTqVQSaybOx2DMLRaLUjQzDwO2QfalpSUBi3gemJuSDcmGNQFksim1Wi2am5srAg8oeSawq9Fo4Pf7pYnOxlRTU5P48pK9zEHNfJZszG5tbWF0dBRnzpyBwWCQYZpUVpJhRnspqiKYp1CxSasGfm/gbTuzWCwmg3VIamJDh3+P9kiVLMYmAhhKyyuSJpQMahKdisXiDvsCNmjZPCdrm17x4XBYcolUKiWft1wuC2DKPDsejyOfz+Po0aPw/Nfgb6PRKAPjaBFD3+0E4GP8AAAgAElEQVRSqYRoNCoqSDbZaXmx20Vwl1Y0ZJGur6+jo6MDAHDmzBk88sgjWF5eFk9NTlDnO/N6vWL/RaYnAGlenz59GhaLRQglzCU7OzsxOzv7W6q7Bx98EK+++qpYwZAxz0Yqmxl+vx/RaBRutxvHjx/H3Nyc7J3BwcGKFTeM8/T8D4fDGB8fx9DQEKLRKAKBADo7O0UlxeY09zxrJO4Po9GItrY2PP300ztIIfX19ejr68Pc3BzU6u2p9yQE0POazey6ujqsrKzg4YcfxszMDPbu3Yvm5mbE43HZA8wzGhoaEI1G4XA4oNPpZOD62bNnMTAwsKNBt5uVSCSEkc/GB319Cf4WCgWxOyR5hXcH2f5k+mq1WoyMjEjuSeIVyRHBYBBDQ0NYWVmRHJZs59nZWfT19WF+fh4tLS2CJWxubmJgYABnz54VAlEoFJI8rrW1FR6PR/JxYmuV1vTMH5lbcUAWG3RKn2SdTiekO5I+WIeyzmZzMxqNCjGCz4tsfxIQeA7YPD906JAw+ale5XsCIHaBxChZK4TDYSwvL0sTQakO2S1B4l0FYDnZj+wG+olxMtnU1BR6e3tht9thtVrFO89kMoktARmOBAUok+KfbW5uwmazCUuCDAz6jRAAYleBG4jFBaVkLCgZFHmIyWRUDoKqqakROvZuFzdHoVDA/Pw8BgcHAfxu9itf7MDAgBSJBAi5lBcSl1I+wZ+vVCph37590Gq1GB4eRiqVwg9/+MMdFG1+ve9+97t47bXX8A//8A944403BFTQ6/VSMHHD0S+koaHhvx1u8j+tXC6HpaUl8UGsrq6WDgyBUxrm2+128ZllYJqdncWJEyd2MLkikYh47m1ubmJkZAQ1NTXw+/1SZL8ThNfpdDLIy+l0YnNzE1arVcB47h12cZeXlwX07OjoEPYEO/WU2gwMDOz6WcRiMWHqsatNdoTZbMbMzAx0Oh1yuZzYBjDxZuBRSiGB7f3LIJVKpaDT6dDb2ytSZSZkHCzGfUTAqLq6GslkUthxSq9Upa8yp1DW1taiq6sLXq9XvFgoE6h0QAgHX7S1tUnnL5VKIRKJyPu4fPmydJxZlNKOQMk6JJPxwIEDuHLlCsrlMrq6ugSwpachmTwEZPguOcBHCSLRn3JsbAxWqxV1dXWy52prt6fdk50KbJ/Fzs5OLC0tybPb7VpYWMDw8DAymYzIDAm863Q6Kcr6+vrw05/+FBcvXsQPf/hDfPe73xUbB8p06FFMmQ+9iS5duoRDhw6hp6dHZG0AdrC4a2tr8YUvfAFdXV3C1GACnUqlsLa2hitXroj3GBNeJgHsdBPw411QqZe22+0WjzVg2yOXYCIv+JWVFQHUyOQslUoyzMLtdkOv1yORSCCfzyMWiwnrtaamZof1DQEYyoc4uA2AsFxpuK/X6xGPx+FyuVBXVwefzydsC/pv0VuLjAvK68icrCSx8fl8wjZjd79UKgnYTpYLmcD0A+UADFormM1maS7ROkCn08n9yO/B4XQWi0Um7RIo0+v1MozJZDKhvb0di4uLmJqawvve9z6R4jOWGAwGGQBIr9WGhgbYbDbxTHunN+PvW+vr6wIys8Ah2JjNZuXOZELMJi2AHQUiGUvcQ2xMkU2mbIZwn5NNw0SMoC9BNN49bC5SLUFlEAdEkoFDuyM2IWOxWMVejgTFlT50bM6xmGEBwjNJsJY/JwCRDSYSCbHAyefzwhqkFJMyfLL5LBYL5ufnpbDiQEc2upRyO9rIsJHJOER/OEoBAeDHP/4xenp6cO+99+76WZjNZsk7q6urEYlE0NjYKIPhCHJQHUCJYzAYFHlwVVUVbr31Vnz729+WPKRcLuOuu+6Cx+NBbW0t7rrrLnz961/HgQMHcPToUZTLZTz//PMIh8N4+umnxQdua2sLhw8fxrlz53bkarQ1Ye5BMI/NCBYTyoFp/5fFgobFu5L8wJhAFiALfzKUCY6Pjo6KfI/naGlpCRaLBfF4XMCl9vZ2YS3ddNNNuHjxIhoaGjA1NYX29nYcOHAAkUhEQBKqCticD4VCMJvN4k/NBlggEIBarZYBjWazWRhHu1m0VnhnYclYzWYDiRlkcnGIHIv+QqEgzQL66BHk5zBC+qRSNrm8vIyhoSFh1LjdbvGknJqawp133ine3nzOrIU41I8MzYsXL0Kv10t8JnuwUnsKMoAI/rMxwP8B2MG4Zpzl75EdTwkv3xPjEBVFBAGUPsmsaZhbkm1F65TR0VHMzc1JMU4mIT3aeYZKpZI0JmkdRPIAc7rdrLGxMfT19Ql5g+pB3vs6nU7sufR6PcrlspxdSowBSM5Odj29k61Wq9jwRaNRAdr27dsnXrU8m8zHyMTb3NyUxpFyECIJR6yH+WzIQgUgQ3wrzc3pLU97DALd9PVdX19Hc3Mz7rzzTnnf3EcEFgm6qNVqWCwWWCwWiQu8Azc3t4eWsla/fv26sPK5N6hK46BHyvnr6+slv/L7/QLacZ7CysoKvF4vfD4fDh8+LO8nk8nIsO/dLIK8vM8pgaZqJZlM4sMf/jDOnTuHY8eOyRwC+qzHYjF0dHSIpV+5XBaWN8+vxWJBIBDATTfdJH+HjdxAIACPxwOn04m6ujqYTCZcv35dmh4cmD07O4v6+nrxcHY4HFhcXJR69uDBg6ipqZHGhtvthsfjEZLKbpfdbkehUEBfX5/Yy8zOzmJ0dBSNjY144YUX8PGPfxyxWEwIRIxXd999N7785S9LHPH5fNDr9WhpaUEmk5GhmO973/vEfo6DfDc2NsQjlDgP1WiMKdlsVj4H7XVIBqmpqUE0GpVhkalUSu67YDCI9773veLJvduVTqexuLgIs9kMvV4vX4/kG+aXOp0OkUhEbCD5Pbjv2bzTaDQYGBiQhrzdbofP58PGxgbi8biQKVZWVuQ+Z/PS6XTiV7/6Fbq6umCz2QRrcTqdUKvVmJ6exvHjx/HKK6+go6ND8jCyjEm4o9pAo9HsaMDtZrHGYZ6ZTqdFWUhMgnGO+5aqMlovsSnH+EFWOeMgB47zvqP6hOQL1oEkDdTW1goBib6wxPk0Gg0WFhYwOjoKg8EAr9eLYrGIkZERXL58GR0dHdL4JhFtN+tdHcIFvG1Uzy4d2Vc1NTUyrIJFOh8c2Vc8pCxOaKZLw3ai2Cw8CVayy0j/BsqlOXACgEhC4vG4yKQ57IqeWvQLInOHG4Sfpb+/f9fPgZRp+sryM/yuRXbiW2+99Vt/xn+jBM3e+edE+5Vf/6677pIuLJH+Uqkkci0m/nfccYcUZ5zkTcDVbDbL9FoCo5UMO1AuJXuUrAYCX+wU0QePLI/m5mZ53yz0KVklQEzWhcPhQFtbm0gIyF5i8UOgCICAkQQj6FNC6WC5XIbRaJQgwA4cg4Ry8JfD4ZC9u9vFRJ3er0wuZmdnBfDk/+j/RtCYYBGLaUorNjc3pXvD80PvSUr42MXh1yKbg/uKz5nTIwGILEh51ghaki0+ODgIrVYrfl8M3LtdpP77/X6ZsssOKX31QqEQJiYmxGyb0m1akvBn4/Pk0AjaG5BxyM4bvW/JHFHKL8mQ7uzshMFgEIBVp9PhrrvukiYCE0OyMSj1DYfD8Hg8SCQSaGpqwtDQ0K6fRSQSkT1PBhoTKU53LJfL8Pl8aG1txUsvvQSVSgWXyyXeublcTn6udxbupVIJQ0ND8hzHx8dFnsVnWFtbi89//vPo7e0V0KGmpgaxWEyaZuPj4zLUjF49jP20fEkmkxgbGxOpF9lFlayJiQn80z/9E06dOoWRkRFhkDKhYvedCTmL00KhIJ6ldrtdJINkyxIYKRQKsNlsiEajIs1m4aVWq+FyuaDX61EsFqHT6aT7bjAY5FyRhUUpOUHLhYUF6T4rbRN497FpuNvFwYItLS3SHGDcIXuBSSj3M33xCFjncjn4/X6sra1h7969kliQsWQwGMQTF4CcmfHxcWGxMDbQz0+lUmF0dFSajXNzc/B4PALoUrLK788Od319PWKxmAwKqXSwo/Ju5OegpA2AyLrImieoQh9vAJIQssihAkNpl0FGDu8nxn++PwIWLA4IBrNJxL/DhjClVfTB4jAXMgPoaV6pzJyJu9ILkV+DagE2ltra2qRxrdFoJH4pB8XpdDrJn3jfEFghqMBinP76/Fnz+bwAasz5+M4IEpEpx7hLUGrfvn1SGJBNtn///ooaWT6fb4dPV2dnpwA9m5ubwmYNhUK/pbrgMLKxsTE88cQTAuQSDNvY2IDT6URXVxf279+P5557Dp/4xCcwODiI1tZWPPTQQ7j11lvx2GOPiXKgsbERTz31FC5evIgHHngA9957L44dO4Zvf/vb+OhHP4pvfOMb+NCHPoS2tjb88z//M55++mk88MADuOeee3Y02qmUOnHiREV7g3cagR2eTcYvNrIps6X9Fz3kU6kU9uzZI8CUEjQnk8zzX37RCwsLUKvVuHTpEq5duwaz2YzOzk6YTCbJw2pra3H16lW5ixsbG8UXkI0KlUoFo9EoA+nYYGxubkYsFhMvP+YelTwLfr13Lko9t7a2MDAwgPX1dRniVlNTg0gkgmAwiIWFBSEj8H6j1UWpVEIymZRmqNfrRTKZRH9/vxSJi4uLApTQn/D2228XWXFfXx+CwSD8fr/465KRyuE39913n6igCA5ns9mKPWDZgCeLiOeaX4fxjgANgVI2oLkIfNE+jOzZpqYmsdBgYU8wj9+bRT8AiZNs1tFj1+FwoLm5Ge3t7eLnRwsEkhXcbrcQN+LxON544w2Mj4/v+llsbW3B4/GIfUE0GhVPa5J06DHK3IrzMigJZkxtaGhAR0eHePJnMhnMz8+L3yut+Bj7qPhiHCTrlMBBX18fnE4nuru7RXnBHMRoNAqQyVqGz5q2YuVyueImJ2dNkLBAuyHm0aVSSeL/W2+9JXcPGybcHyRzsIYgkHT58mVEo1HEYjG43W4EAgEsLCygVCphcXFRbMRYT7OJceHCBUxOTuJ73/sezp07B4/HI16e2WwWCwsLmJycxNmzZ/GrX/1KrKbOnj2Ls2fP4s0338SVK1cqsiAgAev69etwOBwYGRmReFgqbVsWcigWzwxjDW2gcrmcDEnjHa1SqRAKhcSqQafT4de//jXS6bT4uEciEWxsbODIkSPQ6XQwmUwyuOrSpUtoaWnB4uIiTCaTEOAikYjYB9GybmhoSOawcLC20WjE4OBgxT6fpVIJJpMJMzMzcubJYqQnLht+BChff/11GI1GzM/PS03GBmN3dzdeffVVqXcOHDggeRnzUZVKha6uLmEu8z7RarW46aabsLq6iuXlZczMzOCOO+6ATqeD3+8X1e3W1pbMBWLDm/uZDRSebzaAd7NYW5CpzTuM9TYBTrIsST5js5/YA+/IQ4cOSY5qMpmQSqXEDoukqXw+L2x/2uUZjUZcvnwZw8PDYjfAWMAatqWlBcFgUAZ6sZFJAJR3Cus5fr9KFmtj4ie5XA5NTU3SBCIuyHuc+RiVBzz3nNPCvIDWihwYBkB8x6ncocI1FouJbQObZsx1GY/W19cRiUTE5513HpXNVDyQ+EOm9W7JEe8qAEvQgIeOTCy/34+ZmRkAEMozixnKxyORCNRqtUitjEajeEVxkAkTLprkqtVqkcCQ3UWUnS+eSTw7DNzklPxQdkzgRAnGKYEQWhfsdpFtw+7kiy+++D8mRmq1Wp4RWToEAX7fYnGnBGF/9KMfwWKxiKyHoCu/Hv+7WCzixIkTcsFT5kLGVDablaBBr6jq6mrcdtttu34WAITlQUo+pTSUIJBxxK4f3wl97MjWI0hPWYdarRbpMRkH9AKJRqPS3eH7JFsym83i8uXLyGazsNvtYojPQMEuc2NjI7LZ7I4Jp5xmSR8ZJUNvN4v+sWwKABDG3fr6OkZHR6Wj19DQIDR+Agi0xyiXyyKNVrKmrVYruru7ZXBGTU2NsJ4ItvOidDqd0Ol0AkIGg0HxCSI7hvI+JnpGoxEGgwHt7e3ic8xzxX1VySJrnkMc1tfXsbT0/7h78+C47/pu/LWrY7XSarX3oWN1H74d4/iI40AyQChNCUkIw0APylFgpu1QwgyTUjLQTCnDEBpSQktKgTKU24QjNG7s4NhxfEm2bF2rY1crrfY+tKd2dexqf3+or7e/Stun2udpmef5fWYYEsc69nt8Pu/363r7RBlgNptx9913o6mpCX6/XzJ8mQsKQEBsgvt8ZgnGUalMVow2BKoG+K4CkGabwAstHrlcDqFQCBqNBnq9Xiy2PEQ50Ob9738/bDYbVldXEQ6Hq7J9sYjgEIdKpYLl5WWZ8EqVIzMKmeP4xS9+USx4HIDETFK+U5y6SUB1dXUVhw8fxsc+9jF88YtfxHPPPYd8Po+PfvSjkhtFUIokVC6Xw+zsLK5evSp7OBtDXluj0Sh7Un19PS5evIjx8XGxyVSzKpUKDAYDgsEgPv3pT+P8+fO4ePEiFv4tv5aqax6qVPvyXfB6vbh48aIotgkwcyhWKpWS7Gt+HsaS8N6S1CNQTZttoVBAJpMRyw6bUqpmuru7YTAYMDs7C5/PB71eD5vNBovFIu9zNfEUtM1ls1kkk0nJESX4SmUfQS9+b+Zn0z1AwovFj9vtlmKR/89mzuPxCBm0uroqli3+bCregC0VAACZ9FwsFkWloFT9NTc3C8gFQACPanK0AYi6M5VKiWKRlm6qLgHIPysn0PIdKpVKothhjcDCU6le53vAJpngE/cbFmSsNejyICnFIps1jjLnnsrVYrEoyqI3vOENMBqNVV0PArDct3jm8pmtq6uTa8DrprS/cl9kLiNBcQLEzDJlPcGzm9cukUigr69PPltNTY0oDBg1xbOP57DT6RR1HBXnJpNJcsHYlA4ODlZFciobawLjJPQ4LIdAMetUKrTorPja174m1jnWlHzWqf4CIPm1VI52d3fjrrvuQqFQwKOPPoqVlRUsLCygpaUFXV1d+OEPf4hTp07h29/+NhwOBz74wQ/isccew7e//W186lOfgk6nw/79+/EHf/AHGBwcxLvf/W489NBD+PjHP46vfOUr0Ov1+OQnP1nVs8H3lYP2NBqNgCvcszo7O9HV1YWVlRVRJpbLZRE/JJNJDAwMIJvNoqWlBalUCpFIRKJIAGBhYQH3338/9Ho9Ojs7MTk5iUAgICR4IpHAnj17UFtbKyp41pbLy8uiNGejE4vF0NvbK80ahRJ79uyB1WqV864asoJANMEhEmrMxSPBz7MiEomgvr4e7e3tMgCSipoDBw5IrU+ymwqtwcFBhMNhcayQMKQ6koqjXC4nQDIJMhIVe/fuxfz8vET8nDlzBh0dHUgmk0gkEjhx4gSamprg8/kwMzMDk8lU9RnL/Z0uJ54hJNuZ9R0KhUSIQLJFOXhO6d7jfk6HIoESlUq1TcDA6A0KBACIkpcNL+tdkmKMhZqcnJR4ikQiIcC0cs4D4zF2uuiMYXweQRvuCa2treIOIyhgNBoRDAZlLykWi3JN2HdQ8XvnnXeKo45qLl53ZvlzYM3m5ia6u7uhVqslO54CELPZLI4xKt/sdrsM+OF+yzNKOTCrmkUlH4UdBJiVg7lYa2m1WiwtLeHWrVu4dOkSfD6fOEAIoM7NzeGVV17BhQsXMDc3h0QigVQqhW984xtYWFgQcpjDRFOpFKampgSkmZubQzAYRLlcRigUQk1NDa5evYrnn38eL7/8Mm7cuIEbN27gu9/9LoaHhxGNRiXyh/s0n1Glg2UnS+kuIeBNB97m5tZgY7qqRkZGUKlUBOtgL8c5AiaTCQsLC6LgdLlcMJvNiEQiMlTJ4/GI62hwcBD5fB75fB7RaBRra2viQhoYGJCoLBIRJDB4ljK7XKvVYmxsDDU1NXC5XEKqB4PBqmpRYIuYnpubA3B7+JFOp8Nzzz2HRCKBfD4vuaLM8mRMCnsirVYrDjGfz4eHH34YdrsdavXWANgXXnhByEC6degWJZHKjE5eV5vNhve9730S40enLOPdxsfHEQgEhExxOp1wOBzo6uoSAjoSiVR1plC0R9cHrweAbXOOWEeyZ6fAkHtkJBLZphIlWLu4uCi1K99tpYuDTiISUrFYDBqNBolEAn6/X6I+5ubm4PP54PF4ZK+IRCJYXV3F+Pi41HYE/evq6hCLxaqOpyDwzPeMPQkdYgR1iWnwvWBPSyyEgDPjJjj3hf0rcT4CxIz2Ys9eLBYRjUalxiX5zPpFo9HA5XJtI3LZA1IU0dLSgl//+tfSDxND2cn6jQKwPCTZCLIpUbLvZIaNRqPYQ1hY89Dh19TW1krWJBsXNnEsqrmxk4WjrZh2Ud5IFvi0kVAeTXUGFbQqlUoKKbVaLYMAGGa800UpNydREq1//feoVG4Pz3nzm9+87b+RKeXiNVF+Lb8f/57y38vlMmw2m6hWXg+OsakIhUI4efIkGhoa0N3dLZm9vGcEqwwGA5xOp7DQ1SyNRgO32y3sB60tZF8ACPvBl61cLiOfz0tYuM/nE3BF2ciur6/D4XDIteR95JAN/p3a2lpkMhkB6pxOJ3K5HLq7u6UI5ObHPODNzU3J3OGGrszLLJVKwkLtdBG0KRaLGBsbEzC2VCphenpa7jGVhGS++H4QNOb1WV5eloE3dXV18Hg8WFhYQCQSkWaKRa0yw48NKJtkvV6PtrY2uQf87ErQmwpRKsCoUvf7/VJIVNMoA5D3ra2tTZSUly9fxsGDB1FXV4f9+/djcHAQv/3bvy0xE9zMCYDw/vL54BCYcrmM9vZ2Uccyk6lUKol9mSpZHngcJsSBdIuLixgdHcWjjz4KvV6PQCAgId4c0JBKpeTaMqoilUrhXe96V1UWuPr6ekxNTSGTyaC1tRUmkwldXV2igmDkAQ9z3qcbN26IQpyRDiy4eH3IyNNOSXLr4YcfRiwWw/Xr1/HMM8+gp6dH3ALMoEokEpiensaPf/xjfO1rXxN1NoEZ4HZGFkP+qV6jAuzWrVtVT7qnXTubzeKhhx6Cw+EQGxNtymzQaJNWknHt7e0S1k8wSbkHtre3w2AwSI6nEsi1WCxYX18X8Dufz4vKgs8eACGustmsKH6OHj2KsbExAFuZX1ar9d8d3ATMd7qU9lAWVAS/AAioyXtDEIFAXKlUkkKE+93q6iq6uroEDKDqkWASAWN+P6fTKcz6xsYG2traxB535MgR7N+/H+3t7QgGg9BoNHj88cdRU1MjoBtV9mTCmcVEJVE1i3mtjY2NomxVDoRiZjSJOja3bLqUYCvVxYw3oWIF2DpTyd6TyGCdwKgHNgdUAJGQUKqc9Hq97J0knHK5HIxGI4aGhqQJ+PznP49HHnmkKss9cNvGRrv/2tqaXAuqWznkiNmWTU1NYpdWgiOsn6icYlPHM5JnFMkXnuV+v18GqPT19cnXU23APZI5gHzvqGK32WxiE6ODQKk+3enS6/WikiuVtgY/8LOurKxgdnYWa2tr4jJQqqcJPDEnj+8dnzWlm4bvF4EeklWFQgEf+chH0NjYiAceeGAbqEdwv6urCw8//DAOHTokrpL7778f9913Hw4ePIgHHngAX/ziF1GpVDA9PY2//uu/xlve8hZ87nOfw8GDB6t6Ntjo0DpPwlCj0Ygqk888lVl85hsbG6XeKxaL6O/vlwaQKmtmDnJPoX2fPUBnZ6fksjc2NsLr9eLw4cNiJwyFQtK4Uhm5b98+abg5zJFkNFWNdP9Uo1ZibU7CoaurS5TB/f39cs7ynWVzT2Ke+0QkEhGygzUUHWcc4GswGGC1WrG6ugqDwYCjR48imUzC4XAgGAzi4sWLyOVyeMtb3oJoNIp0Oo2+vj74/X4Eg0GUSiUcOHAAXq8XLS0taG9vxyuvvIKenh54PB6cPXsWAwMD4orY3NzE1atXq3o21tbWkEwmBURkvcD/bWxsCIjJQUBUtJLYA24rkJRDW1jXEsjlnzM3VqfToa2tTTLjbTabEEW//OUvce3aNSwsLGBkZATRaBSjo6NYXFzE9PQ0isUi5ubmMDw8LD0EzwTGV9Gqv9NFyzLrLd43/s7cU6kyVKvVQsqTDOUZUiwWYbFYhIwwm80IhUKisOX7xtis5uZm5PN5ASbYr5TLZbhcrm1RUMyPNZlMopqmiIN7OEFS/n3WjNWsjY0NmM1mqeV4vjFyYnV1VQAdAm2xWAzLy8uYnp7G2bNn8cILL+DMmTP4l3/5F4yOjko/k0wmkU6nsbi4iNXVVXz1q1/FzMwM5ufnBdzkvJjp6Wl4vV74fD4kEgn4fD5cvnxZ+oVisQi3240LFy7gxo0bst+RXGLNRFUh649qYl0YM7CwsCD1X6lUQnd3Nzo6OuQ554Ba9lPcE+kKczqdouRvbGwUFwaz+GmF57VeWFjA6OgoNJqtYZa9vb1obGyUmKUbN27Is0XiwOfzobW1VcQcCwsLUKvVMgBbGb2zsLCAxsZGLC4uVvVs1NbWioWf5whj9KiCHR0dxaFDh4QkKBQKeOqpp2C32/HEE0+IAImxeS+++CKsVit2796NyclJ2Se4v3KQE7M86cBTq7eGVR09ehTvf//7EY1G8bOf/UwiUILBIMLhsORl7969W941gpKMWYxEIujr66tKEMB4FJIkxKQ4d4Yza3jd6DJgDcjanIOoSLQsLi4im82ira1NMBnWJwaDAYFAQJxuJJ0AiOrU6/UikUhsG3Kq1+tlCKLNZkNXVxcuXrwIi8UisR0NDVvDWTnwrdqVyWTEzctngcQe+yfWpnQljY2NCV62vLyMfD4Pi8Ui71J7e7sA5mq1WgaiE6egoMFgMKCtrU2EQHQO86ziGUcHJ++TVqvFyMiI9Azt7e3IZDIiNAuHw0IMjY+P7+g6/EYzYKnqAbYKc0rLy+Uy+vr6pLhllgoPIDZfLS0tMhwlmUxicXFRciY5rIPs08LCgryYtLcSBVdaAmmr4gbAZpEbJhtOBngnEgkYjUZ5OJjV4TKFvvAAACAASURBVPf7qwKWOB2XqiidTocrV67g+PHj25SqBKu/8pWv4D3veY9cR67/DNgj86GUur8e3OVD+pWvfAV//Md/LH/Oe8SmlQAah2XQsssCjDJsNlIOh6PqLCGqebhBrK6uor29XbLE+FmovmLmKbNumBHDTYuqHhYrLBoJqNNyyoxhNka0izEvqLu7WzY/Tt8dGRmB2WyG2WxGMBjclpfD4oPMpFarlWd6p6tcLqO3t1dYckYdEJigcoDgMX8GJ1eHQiHJjqIs32g0im3K4XBIg08lIpXgJpNJJk3W1dXJVHXK89fXtyYs8vdbW9s+GZxMXyqVEsCUqkdubNUWeRzeUC6Xpcn96Ec/ilKpBJvNhkgkIs2PxWJBS0uL2EgaGhok34iNEK8xv56KRTb8lUpl25ReKuRsNpvsE1SQsgjmz9LpdGhtbZXhf2azWRjNZDIpyvpUKoX+/n7MzMxUBc6Hw2GMjo7iyJEj8hzTKuz3+9Hb2yuTq3nNGTzPgSnMB2XRx4EeZFyZs0d2fu/evUilUvjpT38q77VGo8H8/DxKpZI0ht///vcFZCJRwetIpQQZRj7btJuyAK5GKQ5sKapmZmaECKKtKplMore3F/l8Xoa8EIQFthopnU6HaDSKrq4uKQCofqX1lTEEjGUwGAxiXeEzTgCaqnwOU+Fkz3A4vK2ZWl9fx9WrV8X2zOKLCmISRCT2drqoFqCSm0NyNBqN2CFpC02lUrDb7aJCpn28trYWY2NjeOyxxxCLxWC1WmW/Z6YZFVpKpS2bFZ4pVHszL1Gn02HXrl34xS9+AafTKQAMQTeTyYRwOCxg99raGhwOhygbampqqhpIBgCTk5Pbzjylk4JgBJ89qjUJeLMYVkYjUCnD84jgMNl6At0A5L3ge6AE7wi08XciUcqzvru7G/X19Xjzm98sNvjBwUHs27cPwWBQiKNq91E6emhb5n3nGUG1Fd+Lzs5Osd2xeed1I+AUi8XEIq5SbU05djgcoqpX2pHZYHGoGglL5eAFnm/c5znogWoMADL1m0AHG7VqmmU6Vux2u9xT2u45eTqdTqO7uxvxeFyypBcXF+F0OnHq1Cm0trZi165daG5uhtfrlSxlKs9Ye5BIIIDIYYRUYnz/+9+Hx+MR1RvJcT5/AOSc5Z8pa8RvfetbmJ+fx61bt8SCWe2ANn7uRCIhymMCgzU1NTJVm+8mhxER3PT7/RJPZDabYTQapS4gOQwAJ06cwI0bN1BfX48777wTxWIRr732mryDPCc5EGNpaQmdnZ0C+K6urqJYLKK9vV3yo0mo8bldWVmB0+lEKBTCsWPH4Pf7EY1GceLEiR1dC51OJ84oDkDR6/WiGJyfn8f+/fulXuRzrtFsTTVvb2+XYSBDQ0NIpVKiyOJzSyDZbrfD7Xajv78f2WxW9jgOJCFpRvEKsCWeOHDgAPx+vwwCPXbsmKiNOzs7ceXKFQwODiIajcLn8+HAgQM4d+6c2NSrWbwOFDUQzNzc3BSgjaQKyTvmBRPgoaKNSvBsNiv1JPss/j/V9QQKCcIwxoT1EolADi2bm5tDR0eH1BUkCdvb2+XnJBIJeecjkQiCwSB8Pt+OrwVzyhl1xrqc6n8SFDyzlH0uBSCsBylgoaKThB1JDQ4F4pCySqUiMxi6urrkXSOwlkql0NXVJeIj1hEul0uydZU9EklPDs+kUrWaRRcm4zTYv5XLZek7OFjZ7Xajp6dHPjdrE54pVDtT9JHL5ST6iM/Ws88+ize+8Y0CMvK6UyABAFevXpUBUsDt2ENGq/Gco4qfjj/WygDkvKqmp2d91dfXJ+BNKpUS9wQty0tLS0gmk5idnZW9ktnQPFuDwSAcDoeotvv6+tDQ0IDFxUVR/FO1PDExIQTR0tKSgOsDAwNIp9MwmUwC2nF+SKVSwdTUlPR27AlpZ6fbk0RTOBxGT09PVc8G3cEUPjQ3N6O1tVUAWBKX6XQaq6urOHPmDI4fPw6HwyEkCdWa3Ef6+voQCoVw8OBByf6uqamBzWaDz+dDOp2WvUOlUokbIB6Pi0uG8Wi7d+9GTU0NdDodZmZm0NHRIaD8K6+8gsHBQczPz0vsIMVe09PTqKmpqWoIF52U3Pe0Wq3knhqNxm3OcADSmzGuhG44Zqf7/X7BLFjTUTjIc5IuPPaugUBAVLtLS0tob2+XGQusuxi/SVCyubkZmUwGvb29CAaDUtNTvU0S4H/XuagUEVKIyc8FbDnirVarDIvmjAGNRoNdu3ZhfX1d6i72UBRR0kFBdX48HhfxA7Eg9mxOpxORSERESpVKRfK0SeB0dXXB6/VKvAEj8xhpc+3aNZlbsdPr8RsFYLkBku1Uq9Xo7u6G3++XgHtOgKai8tatW9DpdDCbzZJTQrWKyWTCxMSEKDUpYyfTzqaG9lACtLzhVKfRYmaxWDA/Pw+LxQK/34+6ujqxU6yvr0tmGQdSNDU1bZOvV5OfwxwKgtJ8qC9cuIC6ujocO3ZMrtm5c+ewd+9eCR3n4kCO2tpa/OhHP4LZbMab3vSmbc0wALH5KL/2/e9/P97whjcIiPLUU0/hscceQ01NDZ599ll87GMfw5//+Z/DbDYLy80sJpvNhl27doniR6VSSTYlcDvfo5pF6yFBQRYdnM5OeX2lUpHBMa2trVhb25ooThUgwSM2U319fRJTwHBtNkbMNOSBrFarhe0g4Muijdm3kUgElUoFHR0dWF9fx9LSkjQl3Bh4iOn1erz1rW8Vi91Ol8/nQ29vr/x8AkK00fL3pfq0UCgI086v4ZABgrZkXsnkBYNBmEwmrKysCINP5onqtGw2KwAnwRwOWlIqOJQ2ewDCqFNlRXaSyrdqN2turIxVKBaLuHnzJo4ePSrPGllWAqicolupVGR6Mg+1+vp6eDweyakzGo1oamoSiyxtPtyv2CzFYjFh46isJNCwvLwsuV4jIyOwWq2IxWLIZrPo7e2VqApmdk1PT+Mb3/iGANw7Xbdu3YLP50NbWxui0aj8XKocmB+l0WjQ3NyMxx57DH/5l38pf+fmzZuoq6sTEJZfA0BAfeA28VMulzE+Po7a2lo88sgj+PjHP479+/fDZDJheHhYojYAyCRoZcYY7wWbR4JWVESxUeJ1VoJYO1k//OEPoVar8Vd/9Vfwer1wOBwywCoWi8mwmn379m0b8EFgjwppg8EAvV4Pl8uF9fV1WK1WKf44nIC5xmNjY+jq6sLy8jIcDoc81wQCCDozn5fvDa8TCzAWGiz6WYARkKutra1qH+U5xa9RkpcEjNn4ECBioUprKZWbtDL39vaiv79fmjVarAnqu91uLCwswGq1itWVezHV48w9bW1tRSaTweXLlyWLme8zrxcbUzaCzc3NQoRUo2IDgMuXL1cFyv2vFp9LNpxsHGkj5TmojBqgmpG1DoFm4DaJSnVuc3MzisUiHnroIQQCAXziE59AOBwWmz/tZwRv+DOqWcp8bjaeLNQ7OzuxsLAgZCbrJ1p96S5SxpZQUcuGDbiduUalH218HNbU0NAgmWu0Q3MIBs967rcEeNvb2zE5OSkuI/49uj/Y5FXzrpCgJZFO4IPq45qaGnnu2ejn83lRb/Jc5WAfjUYjvysHm/As5nOg1+vlWeBzWalUhNQIBAISl/EfOaIIKJEsYh2sVqtlcnw4HBYArJrFPZu/n0ajkcEatPVtbGwNaKMziPuhEnC32WzinKGKjSCJ1+uF2+2GVrs14HRlZQUWi0UidLq6unDixAlMTEzAarUik8ng0KFD28ghkgB0n3HADi38BJ5VKhWWl5dx/vx5nDx5sqqYHzrOmE1KqzvjfHp6eqQHYINKVSSHORJI8fv9Aog0NDTAbrdvI9TZWEciEQFJOEiKsQqLi4swGo3o6emRPD9GDKytrcHpdAo4ShUdVdydnZ0oFAqYnZ3Fgw8+iHPnzgmgudNFEQQBlZWVFWnySeBzf2QGOhWfAOQ+x2IxORsJiG1ubuL06dNwuVyShU8gu7m5GY2NjchkMjh//rz0BXSbkGRcW1vDpUuXBMRYWVnBwMCAvKO8f3q9HlqtFq+++iqi0SgikQj+6Z/+SYZY7fRarK2toaurS2oWDunhWU8BBx017PMI5HCQm06nQ6lUQltbmwz8uX79OtbX1yVPWRnLQhcFazT+fKq6qOZSqVSitrdarSiVSjLYq7GxEX6/Hz09PdJLk/xSZoLvdCmnjVN0QsU/B4ARALt16xYefPBBiVCor6+H2WwW9T/nLsTjcbnHc3NzoiYjKHPu3DmcP38ed9xxh+x73JvdbrcMRgYgBBD3URJK3EsoCuCzqOyXeHbvdFUqFRkYlEgksLm5iQMHDsggJCrFeY1zuRzMZjPS6bTk8NJdQlCNkYmM8CAR3NDQIHMshoaGJDapvb0dKpVK4hibm5vhdDplmF9dXR0cDgeSyaT0biaTCQaDATdv3hQge3NzE+3t7RgfH5f4AfYJO13K/ml1dRV33323kKyMl2FPr9PpcPz4cXE6fOYzn8FnP/tZtLa2oq6uDr/zO78jZxJxHOWQKeXQ1oWFBZndAkBmwJTLZbS0tEClUsHj8WBoaAjf//738cADD0g2LJXD3KdUKhVmZ2fR2toq6sgDBw7g5ZdfrqpXCYfDaGtrAwCJ5mpsbITVapUoOfZKAJBIJEQQxGgLOgAIstNZlcvlhAyiwIDXlrUeY7+4P7a0tEif1NPTI9+ztbVVesfNzU0sLi6K4wCAEDbJZFIcuOzlqllK8h+APOPpdBo6nU7I6kqlAqfTCb/fj3Q6LT0Gf6fNzU05a0jW0cXE+ogkqXLvBLZIol27dmF8fBwPPvggAoEA1Gq17JU8n9fW1gTYdTqdcLvdOHHiBCwWi/SvyggvRmTuZP1GAVg2qATCCADQlpxOp5FMJuUB4UAIFvpUWRLRJ1jKRo/T8Cjzpuw9EAggEomgv79fDgiHwyGSeubWrK6uir3NYrHAaDQil8uJypFAJjd3MkbcEKvJi6HdD7htHWURt7a2hjNnzgjIW1NTg6mpKbzxjW8EcDsDlvZdvpxNTU2Ym5uD0WiE3++XwTOzs7PCztXU1ECr1eLee++FyWSC1+sVsPsb3/iGNOH/+I//iL6+PlEoUdHDAq+xsRFmsxler1dUmBy2tLS0VPXERABwOp2yMfK+cGBMKBQSBTSVSGS19+zZg0uXLqGhoQErKyu44447MDg4iCtXrogln2pFsrRWq1Wm5FksFmSzWZmUycOMw1/YHPNlHBgYgEqlks85MTEhmbd8eVkQezweUXPsdDErSaPRSHGmHJYTiUSk4SGTSNu8MkuGTRgzFVdWVjA4OCiZQ8DtBo8Mklp9e5IfG26XyyXXjCAdAfJyeWsadkdHh1i+qQJm48lidGpqShj4ahYbblol2tra8NOf/hQPPPDANlUwlUNs/pTgOllVxlQYjUYJyGfjSRs4wWOCsnzeenp6BARhWP76+jqefvppPPnkk9jc3MS5c+cAbL2btK5mMhkhfti0vetd78LFixdhtVqrUouT8GG2Gg8ZXmsqgZnhSIXYk08+ibNnz+Jtb3sbJicnUSgUkEgk5LrpdDpEIhE0NTUhnU4jlUqhvb0dhUJBAvDz+Tw+97nP4XOf+5yQDbTcErxXRpxQda60rdLWrbS0EgxUAlg7XWq1Go8//rg0ESze6UhgpunPf/5z+P1+fPKTn5R84HQ6jYGBAZRKJezfvx8LCwtobm6WOIJYLAan0ynAAqdVu1wuhMNhUc/a7XYpUpVODTbjdrsd8Xhcsmq//vWv4wMf+ACmp6cxOTmJO++8c1ukSrlcFkXMwsLCjq+F2+2W+BdlbirPKaU9iXmGBAJpJbxw4YJMmGa0AAESZhESOK+vr8fBgwexsrICvV6P1dVVUQzSEtzQ0CA51P39/aLAyOfzePzxx4XcoV1ZCRQzwoSxFdVYRXfy3ADY0V5EoFNpT+RzS8BYqWJV5oeSGCmXy/J1dXV1aGtrE4WCMnpi7969okhnQ8+mjLYzZb5gNYsKPCrE+D8lgUhVH+1YSucJVbfr67cHMVHxSrUeC1Cq3+PxOGw2G6LRqCjzY7EYbDabgAu8B1QbKIt8ZiS/4Q1vQCAQgN/vh9lsFvDbZDLhmWeeETDwqaee2tG1oMqRyhYCena7HU1NTUilUlhaWkIoFBJF6+rqKnp7e2G323H9+nW8973vFcVXqVQSEIDW83g8LvfZYDAIaUklWrlcxv79++V6cg+cm5uTLH3aCpktuLi4KOAc1ZF8pjg9mU1ENYuKEd5jDsGora2VRo9At1KBSOUNn+9IJCLOEp1OB6fTiUQigUKhgKNHj4qilUDthQsXsGfPHszOzkouul6vF+FFR0cHpqamhDCj3R/Yqhc5ddzn88Fut0ssApVGQ0NDCAQCMpRlJ4vkAJWM9fX1YtFlHdLb2yvkFT87+w+DwYCWlhYZ3kLxyO7du4W8jcViaGlpwfr6Ovbt2yeWeZJlVD8ZDAaMj4/j3nvvxbFjxzA5OSn2dqvVKsNibt68ib1794qSkWQZY25CoRBWVlYEKKxmsbkFIHsbQS5GOM3NzQlwRdccAInroUOLZBwAqc3K5TJu3bqFp59+GtFoFG9961u3DVWmJZf7IIn2QqGAiYkJZDIZmYo+MjICi8WCN73pTTAYDAJE0IXBuu3ChQsSd8QheztZXq9X3BJGo1HU7Wz+qQqPxWLirJufn5daeteuXQJgU6RBN0EqlcIb3/hGzMzMSDwd+9iWlhao1WoEAgEYDAb5b1Txr6+vy55KwIDDQePxOBwOh/zMPXv2YOHfptozf5LnWTU9LAARtACQa814n0KhgJmZGZw/f17EPz//+c+3fT2HwfI6uN1u3Lx58798Xzc3N3H9+vV/9+dKgRbfS9amJL5YZzCOiSAg91k6Ili/7nQRFKVriVEpdOS1t7fLmarVaqWnpRNHqf5raWnB0tIS9uzZg3Q6jVgsJvgEe9Wenh6xbbMPmp+fl1rMbDZjYWFB7PRWq1WU+qxRlcAwnS4cxjQyMoLNzU309/fD4/GIUGeni5Fvzc3NSCaTGBsbw/3334/6+nqcPHlSVL9XrlwRJf3evXvx6quv4p577sFXv/pV/Nmf/Rm+8IUvwGKxwO12C35z8+ZNOBwOHDhwAMFgUOqD2tqtAaKLi4uIRCIYGhqSQXeZTEbiKwje6nQ6UUtzaB+jZQh8trW1ySAntVqNK1euSM9YzYrFYqhUKrJnG41GhMNhUcAqY/MYAVRbWwuz2SxuCbqz6coJhUJIpVLo6elBPB4XYpmCKYqgmpqatgltbDYbisWiEETEwMxmM8LhMMLhMAAglUqJCIL1HJ1OjGFRqs93uhiXyOvIIYvs5YlR0NkUDoexubmJ+fl5lMtlfOxjH5N4AQ4LValUsFgssh/xrFJmzZJootsol8vh4MGDePbZZ3H33XejUCjA4/Ggrq5OFNKMDGLd09zcjJGRERw9ehRNTU3o6OjAxMTENofWTpXzv1EAlvkaVBgw+4Q2Rp1OJ0rDmZkZQZQ5JY7oeCKREPsWVT/MgmW2i1qtlgfUZDKJDZVDnqhkpBXb6XQKg8rMKWZA9vX1wWg0iiRemUsWj8exsrIiBdlOF5thAowNDQ2Ix+NSaDgcDmkKqNJV2s6A2yq1559/HmazWQAq5TT0qakpAWnT6bRkgzJ3j1mVzDckkEzJP1n7CxcuyM/kdZqbmxOGplQq4eDBg5idnZUNvJrFe11TU4NYLCa/Jws/KtIIahF8JXDIyAiCaAxz5gvOZpjAv9LGyOvLplw5VS8UCkkeCm2EZrNZNgfaAthcKgtTPiOxWKwqtoybiM1mE7X42toaOjs7hZHT6XQCXHHQFUFmyuNJJvDQ4vfhYZfL5URVS1uISqWSwgCATAikyosbGQ90NigEdtjQU/WXz+dFzUOmqtpJq/X19TIUyWQywe12y/VkoccGiApo2s1ZVPHdtFgsyGQyKBaLuHTpEt7znvfI78uCi+oWHly5XA4ul0sIHZ/Pt03BRHCCFhVaTYxGo6ihybyzUKLKpdpIBhZN3OP4virzjpV5paurqxgZGcF73vMePPPMM9i7d68oTPj5gC1rIBsUZqMFAgG0traiVCohHA5vU7rx81DBR3KCRS7V69wryBTy0CQzSzCbDRWflWoWgbnW1lZpWFjMstDW6/WwWCxiOV9dXYXVaoXX68Xv//7vY25uDk1NTTJEKhaLwWKxSJQHyS6CTB0dHUJ8kZhiAUvLP60qVOazodbpdPjlL3+JvXv34ubNmzh8+LAMmaHikHEKZM13spjTyfuk1+thNBol/oF5aDxrGhoaRMmm0+nEKvjII4/gxz/+sQCATU1N8j6zaVxbW5MClhYdklv8GSqVShRLtPnQ8cL8u0wmI+8S1dhUENG+z/eq2qD//xW4ulMSSNmUAZD92OFwiPqLewAzatnk8czm+01Sju+TRqMRa57b7d5mm3rttddw+PBhOQPpSOB+rdVqMTc3hwMHDuz4emxsbIgKk+A7h0bSzsdrzKiAXC4njRdJ7tbWVlFakpjjM0Dix2QyCTA4Pz8vn58KMEYM8BoCELUhaxMqZ6j+5ffn3sy9sK6uDjabDXv37t3xtdBqtbDZbGK/m5+fF8JNp9NBr9eL/ZC/l9PpRDabxbFjx8QCS4cHXRDKJpXnM+vMjY0NAU84oG9oaAjj4+OS38ffa+HfhggODAwgEomImonvBMkAxi3FYjE5twnCVbP4jPP84J9xFgKdR3wOuSdSuUVlENXErEW47/BMoPPiwoULeNOb3oTu7m55htbW1hCNRgWQbGpqQjAYhNFolLqTz77SCk/XSyQSgdFoRD6fRy6Xg81mg8fjkQZzpyuTyYiogwpdKo75mXj/SGgvLy9vUy+x3iZAfs899wgJy/2Ywzxu3LghxLFOp0NnZ6ec52q1WuKNOFxmenpagGgOwd29ezcCgQDK5bIoZb1er5y5/HrGSFSzlH0V3XUqlUpmIITDYbGnkuwj0A5AlGPKc1AZAbO8vIxAIIBbt27Jz6PaiCQq87qpxCaJypzK1/++P/nJTzAwMICOjg7YbDYAkJp9dna26qx5rqamJrhcLtTWbg23am1tlbggZYwMY1NIQPFZWF5eRlNTEzY3N8X2zb1Tufcp55lw3kRTU9M2YoXOGboaUqmUuCl8Pp+QoMwUJfhO8oe1Et0njJSrZpFwYT3KGigYDGJ6eloGR/9na2lpCUtLS9XfiNctug3Y4/BcppoUwLaanXsyv473TpmRyTi1nS7mty4vL2N5eVl6fNYz7BGcTifMZrMog5lTzftPgqmjo0MysNkHM56C8RnBYBButxvd3d3Q6XQSDUNb9r59+xCNRpFIJGSPVavVQhIqHUbAVn8fDoeFyNLpdPB6vRgcHKx636itrUUgEMDhw4cxMjKCffv2yYBG1kh0uxA0drvdOHr0KC5cuCCDkE6cOAGPxyNih5mZGRw8eFDIwkwmA7fbLeQj96C3v/3tOHv2LFwul+RRBwIBEVW99tprOHr0qAynYh1QV1cHp9MJAOjq6kI6nRYL+ssvvyzPTTVD1/lsMjKEoDnJfZVKJVGC3OtIGvBsiEQiApA2NDRgamoKd955J9RqtURu8rxZXV2FTqeTORB0Smg0GmQyGdmfM5kM8vm81EKsqygC27dvnwy8ZpQaBWHKSNBqF8HWQqEgbjheV4pzCHqyliSxcuedd8r9IoFOdx3PGRLydKLxzKmrq5N4NDqbiIWwtqJrMB6PixCUkVFKsmZpaQn79++Hz+cTXCWTycDhcOw4Lu03CsACEDsOcBs8YZOm0Wjg8/mE2SfzSvsh/5xMHSdaE0iiFaimpgZms1kKpuXlZdlweWAqmUuj0QiPxyNqW2Y0Et1PJBJiz6Nikg0MFW1U8Ox0sdli0QhAilqqfWmBaGlpESut0pIGAFeuXJGJ65wYS0agWCyKbXN5eVkUZp/61Kfw2GOPic2HB0Rtba1MYuUDZ7Vasby8LIOoeCAR4KGqjVYcHvDVStKplONQAp/PB5PJJOq8aDQq4HQymdw2JXt9fWsiMu3Ei4uL2LVrFyYmJtDS0iJAKRteKnjr6urks9I+xsONEyetVis6OjqkASdrUyqV0NvbC7VaDZ/PJ1EBRqMRgUBAmkyqW6ph2blZkynnz97Y2MD8/LzYzxmZ0N3dLfeQRAQL1La2NmkMWMwmEgnE43GxGjC03Gq1ora2VnJQCKjwfSE7SIk9m5rXW21ramrgdDoFEGVm8/LysuTUVrOotjMYDFLo6nQ6/N7v/R5+8pOfCEPM5pb7ARtHMpU6nU4U1iMjI6iv3xow9/osGgByCDFmJJFISFOTTqdhsVjQ3NyMnp4esfawUePUZiomaSXj9VpYWIBGo8HAwADm5+ersvYwNF45yIHNPUEv3h+y/wcPHoTD4cDzzz8v14OMI4EOAkN8pzY2NmRoGwEaTprlnzHbkupW5URc/nfeP4JTtHcqwXy+TyTkql0s6BirQTcFVavc63U6HZ588kkcP34cjz76KG7cuIGTJ0+KVfDixYtiiy4Wi+ju7kYul0NnZ6fEe9BKubCwIM+A0WhER0eHgNm0R3OvJiFkMBgkFmHv3r3weDwCFgQCARluxmaNOXU7XcFgEJ2dnaJ4oZKPTYhKpYLdbhfQk8QF32s2eHQ9kEDgP9fXbw2hmZiYkHwk3m9GwvA+KovLUqmErq4uTE5O4o477sDc3Bx27dol9mFgq5mkZczhcCAej0ueMJv2ags9Ap4nT55EpVLB+973Pvz617+GzWaD2+3Gz372sx19DwDyTAOQc4jOFRbMJCGUKhrWNyz0XC4XXC4XdDqdFItGoxEPPPAA/vZv/xaFQkEaZRaPJMAMBoOQ01REVrN4nwiGA5ChQSSuuJewAOa7TXsmpzLTotbQ0IBgMCjEH9WyjPxhYuc70QAAIABJREFUM0g1jM1mQywWQyaTEWCAoAPJHABih/X7/UIk9vb2IplMIhAIwGazCfhqtVpRLpdFtbGT1dvbK5PaTSYTRkdHUVdXJ5nezJ7lJG2z2SwZp+VyGR6PB2q1ehtRzX2C8SK1tbVCdLBwJ1gUi8Uk/+/ixYvQaDTYv3+/kJpsPicnJ2WYRHNzs2SVz83Nwel0CphA5waJ5mrPWLomOFCH9TfPmO7ubnF/DA0NwWQyIZ/PS9SP1WoVEviee+4RgQNrGNaztIIz95Cupbq6Opw9exbvfOc7AUBszUtLS+jp6cH09DSsViusVqsoownEMf6BUWAcuEHgr62trSrXDRXArCczmQy6urokK5RKP9rwSYJSDMLMT5Id2WwW4+PjyGQyOHDgAFQqFfr7+6UvGRgYEDCT+yDfm/r6rYx2EnVarRaHDx/G6Ogoenp64PV65awtlUqwWCyi0iUppIzCqVYZDUDe6UgkImcb9wLGTBBoi0ajsFgsKBQK8Pl8ohLlu5XNZqUWByC1WzQalZ938+ZNmRbOmo8kPGu5ZDKJYrGI2dlZjIyM/Ie/9+zsrMRo/Hetjo4OiRSgzVQZJ0ECjfsq81U5iV6r1cJgMAhoS4UaRR8ET5XZqhx6w/6LM0usVqv0uFSnVSpb2Yy08ptMJolp4aAdnveVSkXixAhOVOuq4Gcul8vIZDIS6zU+Pl51DEq1Sxnlooy14CLxyb/DukSp9KV4hCpgAtIEcavdN7g3ECCiipvvgDL3k4OAORiWc2zoguD33LVrl9wr7tOslfv7+6FSqYQc5PyQ8fFxGRJLARqVwJOTkzAajejq6pJsUiovOWSOmeDz8/Oyj1dDYvF6UxDwyCOPCMlZV1cn5y97crVaLT0c++nGxkbcvHkTv/Vbv4WxsTHYbDacP39e6gq73S49B/c1Ao2xWAznzp3DsWPH5Lrn83l0dHSIc4PKcWArPs3v96O/v1/eEw4OBSC1BZWZyijLnSxGpbCmokOKoiJltA77NjqTSQZQwEGQlMTr+vq6nDV0BUWjUYkUyGazUKlU8nyUy2U5J1pbW0VZr9FoEIlEJPqAeEJTU5OAxqzr2PPznarWVcEzi0QiMZhYLCZCIvaKXCR6jhw5gkQiIeQuxRqsBRg1QCKLzlA6WRlRyvfVarViz549cDgc8Pv9IjbJ5XKiVO/v75c9nc4xtVqNVCq1LZeceBPJsf9qVUeT/x8uNlF84JjBysnzZP1pW1Fm+tBCn8/nxSLKvKza2lophJeXl2UD5Y1YW1tDMBhEd3e3bI5U1i4vLyMajUpGT7FYxNraGkwmE1paWsROQ0VdNpuVzB82Ftysq2GIYrGYZLzE43H4fD4BQ9j0MWODYeV+vx+PP/44/u7v/g7f/e538c///M9wu92oVLYCg6kwpAKPOSPZbBYrKysCaJJpX1/fmupOgI7KSALjfLBeffVVUVDyJeBADU7w48uZSCQwNDRUdQNgNpvFolQqbU3w44FDOwo3J5vNJsA72QkqYRmITBVZPB4XEJOgJidoKjNS+M+cNAhADiYOGqMKrqmpCRqNBtPT05iYmJDnhn+XL77RaMTi4qIoJHa6CPLTxkdZPgcoLS0tiUXUbDaLDZBfRzCdm00kEoHP50MqlcLs7KxMbm1vb5cDkGpuMre08PPz0iLDw595RFSX8Ot4jdjE8fkjy65UvO10MfeMjD6biebmZjz00EOSHc0CgSpWFmO07xKAOn/+POLxOEqlrQmhLHo4KIeNEQO89+/fj2KxiHA4LIcG80HJni4tLcnXsXm0Wq2iZtFqtTAajdBqtTIUYd++fTLdd6eLNmEqPNkI9PT0bMsEK5VK8Pl88lzHYjH8xV/8Bb7+9a+LxZJkgnII1srKCnQ6naiKAoEAstksrl69Co/Hg1OnTkkToAS3GWrOjEIC94yk2NjYkHeQShYWzTy86HSoZmk0GnzhC1+QQRRU41O9BUBs8MlkEgcPHsTFixfxjne8A4FAAK+++ip+9rOf4fTp0zLcpa6uDrt27ZL9mBlyBCOoAHI4HFIA8rrz2SwUChJlA2w1sdFoFA6HQ5R9BA7IsvJadHV1wWAwYHBwsKqGmZ8R2AL5f/CDH0geJIdoKRvdUqkkNl8OEmJBurGxgUgkIjZb7ps1NTUSN0KlG0EsAuvMPlapVFhYWMDS0hLcbjeSySTe8pa3AADe+973yiRUZXaaXq+XgprgDs+XavPpPvCBD+Dxxx+HTqfDV7/6VRw8eBB/8id/gve+97347Gc/i8nJSVFnArdjCbi4z9EaxrOPVjVmavFd4YAt7n18LnjtWLwCEBbdYrGIVfXDH/4wAMh+f+3aNdk3gsEgZmZmMD09Lfnl1bpMGhsbRc3BGomgLABRy/E9pSKU9ZIyAoKT4hnrw3qoXC6jqalJ7ilJOdrUCFpzwBOJAlrnqDgHIMQWlSNjY2Ny9rKwZh5pIpGoamCdz+dDd3c3LBYLcrkcDh06JEMaCLDncjn09PRI3nulUsG73/1ueUaB21mjygnoPH95bQioUc1EdWRPTw9GR0dx3333SeYun3nWDcxX4x5dLpel7lU6QdbX12U/YYRINUur1aKurk6aHgKLLpcL/f39CAQC6O/v3xYHxLgwn88nrhaC02yMGOvC4YXM+2WkgcvlgtPpxMzMjACnw8PDuP/++6HX62EwGKBWq7Fv3z5YLBakUil0dHRIs2U0GrcN4SR5RMULc46rsVbrdDrMz8/LOc44LapwNzY2EI1GZVASz3wSAowOo6qLefC7d+8WsEqpHk6n01KLK62OhUIB6+vruHHjBoLBIFZXV6UprK+vh9frFdApEAggHA6L+tBms8kcgEwmI++d2+2uOgOW5xpBg3w+LwQ1I0W4FhcXtw1iCgaDQvYTQKTdfWVlBYFAAHNzc3j11Vfle4RCIVy9ehUvvPACzpw5g+vXr8Pn88lZEggEMDU1he9973v/Kfj6P7V8Pp+AYSRwaN9W2tnpDmH0QXNzM2pqahCPx0WpzPqN2c7M/OXXEeBQqVQy0JHzKxihwuFsq6uraG1tRaFQQEtLC2KxmBAEzc3NMJlMkrHOAWEUsygHUVYThwUAly5dQigUkgny58+fx2uvvfY/Dr4C/97Fwn9XCqyUNnEl+EoH7vr6uoAxJEmB24O7qtlHCWzNzs7KGUVHJwDZG2pqarC0tIS2tjYcPHgQDQ0N8vMJ5Hd3dwuxRZGM3+9HTc3WIC1GXBGoj8VikrXudrvR0tKCsbExicRhL8TYJCVOQCEVldvEN+jgOnHihOTCVrOUE+x/9atfyb1gL9vS0iJE/tLSkohRtFotOjo6kM1msbi4KBjRD3/4Qxw9ehQ9PT0y/2V+fh733HMPzGYzCoWCDFVua2vD/v37EY1GMTc3JxEDsVgMHo8HxWIRRqMRzc3Ngp2Q2KDIyOVyCZ7U1dUlRBTjzV4vhvtfLcYYsQZYWVmRM4m1I2smOha4X1A8yHksBB45XJBAcjKZRCaTQTgclmsfDAa3iQhIhBPcDYfD8Pv94hrlWcEhkCTfKeTjoEDWJZw3Uu2+wf2mUChInUcsiXUOY1iYqU43AMVVdJcRA+SsIKWyXOmQX15elqg3DhDnAG6XywVgq5+02WzbAOBwOCzxa+l0WkDu+vp6nDlzBi+99BLi8bi4Wkhu7WT9RgFYAmVkwym512g0WF5eFladDx2l1ErZL4fmZLNZGWZDFQV/RqVSQTgcFqWI2WyWYonM7crKimQbcnI4lUecGE1lBxt4hsvT3s0JzwzoZSOzk0WwiBYeZvEwV41seCQSkcLnhRdegM1mkwPUbrfLxhAOh7c9vMlkUpgNHtTJZFJYToKzVLISPFUqeJin8/LLL8sUdTbhm5ub8pISeEwkEvibv/kbUW9Us6guUalUuHHjxjZQg0OdCHJTvdrX1yebWLlchkqlQjAYRLFYREtLyzabHhumgYEBqNVqCSKn/YFWWk5bpp23oaEBDodDDnO+tLRLr62tyQbIpoJ5vBsbtydiVtMsU+XCA8tsNosag1ZxZb4RN29a7fk+MHCcwA4ni1KpTPl+OByGxWKB0+kU4INRFgQNGWDOgo45NrlcDuVyGYODg2LD5uCy9fV1IUqobrLZbFW9JwAE0E4mkxgdHRW2jFaFd73rXULmUL3FKcS0Y7Modrvdot5SqVQ4deoULl++jKGhIdjtdiFzMpkM4vE4kskkCoUC+vv7t4F8ZNEIcBHcYMMF3B6SYLFYhPih6r1YLCKfz1eVTQdsZQxTTUnVbj6fx/z8vCgSmTPF55kqy/r6eoyMjCCdTsPlcsmzwf/GbCzmo0ajUeTzeVFtnj59Wg5b5f5AYoRWF06yVxaw3MOpxACw7Wv/dxUYBCD+4R/+QYAiKjbJGBNAs9vtEnej1WoxPz8v9kA6M37wgx/I/udwOOBwOORrjUajKB+dTuc25RKwVWDTqcGJr8wE5ECCfD4PAPB4PLh586aQMwRQOPSwsbERKysrsNvtO74WLDK4R9XX12NychKRSERy6NjwJZNJyXFeX1/H9PQ0KpUKvF4vJiYmRJ1AdQzzbP1+v6h6aH9nphjt2nv27MGuXbvEFsc9u1AoSN473xdGVdBuSgKOTSWtaoyqqGYNDg5CpVLhne98pwy6IGlYLBalBpiamgKwvYEjy/+nf/qnePLJJ/HMM8/gQx/6ED7xiU/gU5/6FD796U/jjjvukAKOqi7uf/xd77rrLhw8eBDf+c53cOrUKTzxxBP40Ic+hN/93d/Fww8/LCQPv5bgK7/n2NgYxsfHxSLF2A4WxtUsrVYrhSOnKFMdV19fL6Ab98BQKCSNKAt0RibRMpZOp2E2m0WdSQCX6g6eRVRlUslKxasyn49gNeMbmGNJsI9kB4t/NqparVZA050uTtTd2NhAV1cX1Go1BgYGJEe6vr5enCh8Jul4KJfLOH78OF555RWJk6GyVa1Wi8qJw5C4Fzc3NyMajSKVSsFgMKBYLOKDH/wgyuUyHA6HOCiy2SxMJpMop/isBgIB2d+pUvryl7+Mr3/961hfX5fYIqUiY6eLICVFBrwHoVAIkUgE9fX1mJ2dhcvlkkw03qPGxkbJGHW5XBgYGEBtbS0WFxclZ9/pdIp7wOPxiO21VCohnU7jwIED0Gq18Hg8KJfLiEajSKfTWFpakugN1n98zqjk6+/vl0F1VqsVxeLWVG/mHTI2YaeLmcZUmbERdTgcACCDDBkbQoCStTOzsFm7E+QZHh7GzMwMVlZWJLYmm83KkFC+8wT3pqenRWnb3t6O06dPi+L+yJEjsNlsIjIwmUw4cOAApqamZP+JRCLyPtJa2dbWVnWznMvlkM1mZf9kjAibTqXKdHZ2FoFAQHoWYEtsMjk5KTU9wePZ2VncunULly5d+k9/djQaxejoKM6dO4dLly7h0qVLePHFF3H58uWqPsN/tKqN6QC2FLA801gn08nDuC8O/aEalkBRT0+PCJC4j9NhQACJfQIJWvbLBPiZXcj9mmQM91AAQviRgPb7/QiHw1IfNzU1wWKxiJOKcRtKMm6ny+Px4Nq1axgZGcHk5GRVcVr/U4vvIkUKBLF5prMXZD1KVVtdXZ2As+zFqzlTOBHd5XLJfSf2wNqLZCX7bxIsjY2N4iokOM79nIQpHTB2u13qIwJU7JEmJibkmWLmLLGPdDoNr9crAOjGxgYcDoe4PNra2pBIJNDf34+ZmRmpMRYXF2UuRjWLsY7sU4gVkLBkL0sHL52mer1eIm9KpRKmpqbwxBNP4L777pP7t7S0hMXFRWSzWWQyGczNzUm0mtVqlZqB/ZlarZb4pt7eXlitVmxsbEgW7Pr6Ou69917k83mpOTh3JxgMYnR0FMPDwxIdQqfjThdjhNRqNZaXl6W35znPGSUE6CuVrYFc7FWVAzDpjqNbmfESnBPDepJ7CIWBfMZ57ioFL4VCAZOTkyiXy1hcXJR6gnEUr68V2aswhqlawpe/T7lcFvI5nU4Lcc13hQ5A1rr33XefCJ24X2UyGYlvI/FCBSoxAsa5sH/n5yD5XyqVtg1to4AiGo1KHB9jQekU5fN77do1ZLNZAXXX19d37Gr9jUYQ8ALwl6R1gw8YM/XMZjM8Ho9MVmYRTws1m2gGkxMUYuOgtCpyYigPx66uLgF29+7di5GRkW1KBSqUmMkH3FbuUi6dSCRQqVSE1eSE653mPgAQJRgVmF6vF62trRKqzM2Paja9Xi9B2HxwaGEkU5rJZJBIJOQhCYVCcDgcwrCXSiXJ1iVYSxbC6XTi8uXLkjvK7Etu2Pw92KDRAk/whv+u0+lgs9mquhYApKkslUrYvXu3TK0nE8fClPeRRTUbFFr/jx07Jps+GUK1eiswf2FhQb4nAAFMyUBRAcsMEDa8PNzYiPEasADKZDJoa2uTF7hUKsHhcAjgTyXLTheZayoz/X6/TLckI8MNgEBvKBSSTD3GRtCKbjQaodPppGkyGAwC2jKegUO/+LNpC6FysVTaGuqlVKs5nc5tCjmSFgBEubK5uTVczmKxSMFRLehIwmR4eFjATQ7+KZVK2NzcxDve8Q585jOfQWdnp6hyL168iKGhIfj9frhcLgwPD2NqagqDg4MIBAJCznz2s5/Fd77zHZw5cwZjY2Ow2+2S+0pFLIvkWCwmKreNjQ1MT0/D6XTK4b6ysoIrV67g2LFjAjTxvWDUw+bmJvbt2yeh8dUo+3p7e7GwsCDEAw8dqpMJmJFh5mFCxffnP/95jI6OolKpyHTxvXv3QqVSidWNEyeBrQOMygbmcFKBQ6CXhSyLb9qvuGcrrfiMGSFQyj2QyqBqbU585svlMr75zW/iwx/+MDQajezR/Nws/r7zne9gbW0NH/nIR2CxWMRSXFtbi0gkgp6eHrz88stYX1/HH/7hHyIYDIq6iqABlU0sHJiHzKKfJFUymUR3dzfS6bQ0Y5lMBj09PQgGg9KQKnOWqSTnvlxNJhuvuTKHlQzwzMyM7Ou0LU9MTGB1dVXs3Dxvf/CDH6C+vh4+n0+aCWYxrqysCGHIa2G1WrF3715RZ3FPWlxcFLIyFAqhs7MTwWAQpVIJs7OzWFxcxNve9jYBnZjTm81mEQgEZBhTKBRCKBTCoUOHqno2jEYjvF4vbt68ib6+vm15iFQVcn/3eDzYt2+fgGUbGxt46aWXpLHlhGGq3IxGI+666y6sr6/D6/Xiy1/+8jaC4eMf/zje/va3S8QDyQFGP9BCTZUd1aEkOQlgcI9jA76xsSEqhWrJChbtWq1WCmuCjFTSAJCiuLW1FZOTk1KgMqfQ6XTKXgFAlDLMweI5oszeImnLIQd0ETQ3N29TJ/K6kMSjJZAKPqrbqZ68du0aNBoNOjs7qwLZmCfLibmcFk2lMifP0zVUX18vkRjMR+OzWS6XJX+aTQqzSElUUy3MWpTqeUZJpFIpUbBpNBoBVVi3cU+pq6sTgQCwZeFmFhvvMa18zODcyVpZWZEJyASBlpaWxIGTSCRgs9mQzWZhsVgk252xBQShmBk4NDQEt9st71gymUQ4HBawh2rburqt4WGtra3w+/3Y2NjA8ePH8a//+q9CcjIHuqOjA5OTkwLw86xgZivrL4PBgEgkIoQk8/t3uqxWK/x+v0QjaDQaLPxbxl5fX58MnuUzOjg4KMB1bW0tBgYGcOvWLTmH2Z90dHRAo9HI52U0VaFQwO7du7GwsCDgrtlshsvlkp6A6rpTp07h7W9/O1paWrBv3z4MDw+js7NT6tfOzk74/X4hxlOplGSgAhCVdjWrVCqJWpfOIjasXq/33/39paUlcWPx5xHE5UyJxcVFnD9/Xu7Zf7VWV1dlaPJ/16qWwAJuxwtxr+eeyveW80n4uRkNlkgkpClnpALdVASllKpX1hoqlQqBQADHjx9HPp+XeDSC6mazWdTasVhMeoZcLifnJ4UyyoxM9j7A1v1iBmy1k+7/b1t0pQCQ2olnOu+XMmaAfTiJLbqTCGZVs5QZk6xp0uk0CoUCTCYT9Ho9lpaW4HQ6JSs9lUqJoIwkH4dRMhM8Go0iEonAbreLO5eDhEnoUVVJ59f6+joikQhOnDiB4eFhqd1dLhdyuZwM2OZATJKGwNbzoJztQEC0WscNFYPlchlve9vbUCwWMTo6iqNHjyKVSsFsNmN2dlYGvN57773QarV46aWX0NzcjHvvvRdnz57FxsaGxCmYzWYsLy/j0KFDEiHocrmkD6HQjAQIHYoej0fOeIKMjY2NmJqaEkHg6OioDCPme/Tiiy/CbDbLcxUOhyXOoRpBAPseKv85f4NDu61WqwCSrHMASF3MoZDhcFjU8wQ+e3t74fP5BHRkrCIAATG5t3BgJiNPOGR+9+7dEgvU09Mj4ioS63Q0s/Zi3ZzJZKQfqmZtbGwNe1VGeRIQpouWmfHsVwBIT0chDN0UBLfpVK9UKpJdznefvSjBf9bVVOByHgDBfz7vFL4pyW+6lIaHhwFsOQE4PFin0+14lslvVAFLW7ler5esBipG7HY7IpEIYrEYZmZmoNfrBZ2msozotcFggNlsFpUjs8RY5EajUQHS7Ha7ZBxubm4KSBkKheB2u4UVo02dFj3lEC4yU2x6uru7AWwVJpxM6/P5qrI5sRDlwwZAin8q5rgBcgMh60oLMlUjPp8P169fF8adSlCj0SgZQbTff+lLX5LGaHV1VYK4qbplMcEm8Etf+pLI1gls04JEVWZjYyNsNhu+9a1vAYAU49UsZiEdOXJE7DZkiZmFRUsz1XZGoxF+v1+YCsZYmM1mGR6SSqW2AcUENmhrZJYJAAG277jjDimE+PdDoZBY8anu6e7ulpeULyfl8GSeCoWC5LxUsxhrkU6nZbOlHZwsGXP8aINltAKtzhxAwGZeqRBhs0hFFAewsUgBIHmEjGUol8syeddkMm2zWpL8UBICVL5wqN3MzExVmZZcFosFV69eleeZLBfvqcVigd1ux3PPPYd3vvOd2NzcFFve2bNnsbKyghdeeAG3bt2SsPuXX35ZwHm9Xo90Oi2D+qanp+W+kU1+5ZVXJDeIKniqsGtra9Hb2yt2WZVKhStXruDq1asIBoM4ffo03G635PWwGN7Y2EA8HsfFixd3fC2GhobELgNAlMC0sJMc4KCdzc1N+RwmkwmZTAbf/e53US6XEYvFYLfbMTw8jOnpafh8PgEIqEz6xS9+gRdeeEHuK/PulIPACLQTSGUOMFl87mU8NKmgozKbZEA1jDIXLU4M1q+t3Qr+X1paQjwex+TkJBKJBKLRKK5duyZ7NA9rDpFgo01QtKOjA88995wUyyx6Ozs7xb7DeACPx4O1tTUBWqjU4lRcZYadVqvFww8/LCRBfX09WltbYTabBfThvt7c3Cyqq52sa9eu4fr16zh16hSefvppCaVnfhO/L5W8bDjm5+eRSCTg8/nw9NNPy34QCoUwPz+PhYUFsSUTqI9Go3L/uQ/FYjGUSiWxALW2tgoxBkAKvpMnT2JtbQ39/f0yXIhRO/F4XAbY8V1xOBwYGBiQXLSdrmKxKIAeGxqe8yREaOdi1pVarca3vvUtTExMwGg0wmq1wmAwwOFwwGazwel0ilqB5IbVasWb3/xmNDU14e6778a1a9dw6NChbeQvzzcWjATMbDabqMA5GII2ag5JU6oLSGrw/almce8k8MnzkM4G3k+qG+iAYOFKC+XKyso2dSiJE+aGEaghgcWsw7a2NlF1kjghqcg9kdEVAGSwQrFYlP1jbW1NmhOSV+l0Gn6/v6rYo5WVFeTzeQHYXS4XbDYbTpw4IQ6bt771rdDpdFheXkY2m8UjjzwCYKv+oVq7XN4aoMpGj40JFb7KLDASUHRa/OQnP5EhqyTQmKc2Pz8vjiCHw4GGhgYYDAYkk0nY7XbZTx588EG0t7cjGAyKqp0Ku2oW31vWwPPz82htbZUIFuZ+9/f3Q61WY2hoSCJMhoeHpakkAF8sFtHf3y8ZoNlsFl1dXfLOsS7J5XKw2+0SXcHmiQ0dB0Om02nJbadCnMQB47E4YJfkFgAZvFoNsbe+vr4tw55N8sbGhggS1Gq1uBVI4hNMJ+nMaAKdToeuri5UKhUBcoHbGZaMDaBajQr1cnkr15ixaFqtFhaLBdevX5d3+NixY4jH43KGJpNJifJizh+bSgJ91Yojrl+/jpmZGQQCATkTxsbG/kPwFQAmJibg8/kwOzsrlvtwOIxoNIr5+Xn86Ec/wosvvrhj8PX/ppVIJCRHm30IyTPWFOwtlOIORjMxjoqgIAm32tqtWSVUqRIwoDWc+wsdVPy+3HN4rjDzfc+ePRgaGoJWq0VnZyc6OjrkLOYZsLm5KS4bg8Egmf//ry8lmUgylfdHpVIJiMR6iPsHz1leA55PO13M/B0YGJBMdQ7Q47sYi8Wwuroq+MW+fftgNBqlZmfEjtJhxlrQarXCZrMhHo9LjEF9fT36+vpkP21raxNijFmyvb29aGlpgV6vFwEJzz2HwyHuGxLnvH48TwAIoVPNam1tlYirs2fPynDCYrEoSt3m5mbs27cPZrMZp0+fFvcF+86TJ08KOGaxWHDr1i0hpTnI9PTp03JuEcspFApQqVQwm80SA8behVZ8kmgUNW1ubmJpaQlmsxnpdBp9fX0oFotCKNFxQYdJNcIZCu0YEabT6aQvUGJLdHwxFqShoUGUoYzYoaJ7c3NTBrgRD9Hr9SK6oMuS5w2JmXw+j2g0iqmpKal7CdqXy2WEQiFxAvOfWXOtra1J3EmlUhGgsVoFrPJzARDBDOsq9pjKfNne3l4Zvsl6sK6uTupw1jBK1wqxAAAihiIADUDqTbqHCS7X1NRI3A6JLl5nDuhaW1vD+Pi4/H6zs7MYHh7GjRs3MDExsbPnoqqr9t+wqAogO8ucBYJrAITdZjFHkJZ2WyqvNjY2kE6nRULf1dUloAIH8tTW1op0mNMDuqi1AAAgAElEQVS2qW4jU0LbNoFhbrpsGtLptGR8qdVqsYgyd5bZZ9XYRePxuABparVaJPEEgThYiQDk9evXcc899wi4RUCEjYfJZJK8ElqgNjc3JRMlFApJQUmrH62BVDQ6HA7ZyLg5kkHgS5zNZuV72u12YeyVIdiBQKDqg5z3ZmxsTBpCghFUqiwuLsomRIWly+WSRmBiYgK5XA7hcBhOpxNarRbT09PCBvMzejweZLNZ2O12WCwWGI1GpNNpLC4uolAoiLqF8nq/34/Dhw8LI82AcCpd+WK3t7cLoFBXVycqH1opd7pqamrEykYFBxVhHHzBYowDIqieYM4TFVcdHR3SMHKgDoOzyXCVy2VhYHktqUhmviyZnXg8LnYEWuhpAaXyubu7W+wjHNrEhiAajVYNRl+7dg0Oh0OYZTZejBbghklr+KOPPoo77rgDLpdL2D8SHU6nE88///y2HCQAMoTt2WefxVNPPYVkMgmv1yuxKD6fD319fVIgLC8vS9NpMBiwubmJ9vZ2IWuYE5lIJORgYCGgUqkwOjqKw4cPI5FIVLVv0ILJ5yAWi4lNjcoJFpYNDQ1CvNTX12N0dBS9vb34oz/6IzzxxBN4/PHHZS/2+Xwwm80YHx8XVdLMzIxMgmUhy0MSuM0M0g7CHB4C/nyOeUjqdDpRbrOR5nvPPanag5z3kEXM3//930uxwPgSgmxKlpah+1Qu19fXo6OjA9FoFC6XC6lUCp2dnfje976H973vffJ7UrHH+IH6+nrcunVLYhdo9yJJ0NLSsm1AXl1dHQYGBlCpVNDX1ye2XLvdjubmZrHS0tlRzWAhr9e7TaU8PDws94sgxJEjR5DL5bZlZVPR9M1vflOs9HSJcFCOyWQSAI9qXQDSVK6srMgwNE5IZUYgp/jyuTx06BDOnDmDoaEhLC4uwu1248SJE2hubpYBdyyeCMxQpVLts0HHAhX4Xq9X7J6vBzHX1tbw7W9/G319fdLYKgsyNmQbGxvyPPP9uOuuu/DRj35Uzg/al8noswFkHUHCIhwOC4lQU1ODgYEBGUbByeo8Q9iMkcGv9nrQ1aLM8Gajptfr5ZxghJHFYpHfjYAPlV0Ejvx+v9higds5maVSCYFAQDI+SUSw2NVoNIjH47BYLCiVSqIMIWCu1Wrh9XrR19cn5wfzD6nsotqsVCqhv7+/qsnmlUpF8uEOHDiAcrmMXbt2YXR0VAYY0k3U1NSEwcFB7Nq1CwaDAX6/HxaLRf7O4OCgEGAEtwiAUqXB5tBgMIgF7le/+pU8Y62trbBarYhGozIMlA3QysqKZL1zzzWbzYhGo9KklEoljI+Py95EkcBOF+tpDo3kRHXWTYzTuXbtGtra2iTyYXNzEydOnJC4LoKX3IcmJydx5MgRAdyz2awM7GLetdvtluy1bDaLsbExxONx7N+/X+p65rw2NzfLMMiWlhZYrVa0tbWhUtkaPlQoFNDR0YHZ2VnkcjmpJZnjutNrYbVaBegIBoPo6+vblmVKxZVKpUIqlUJ7e7s4nficE9SgwotzGHhGrK+vw+VySR3R1dUl4AFzCOfm5gTI12q16OnpQT6fx/e+9z3cc889sFgs6O7ulqFXHF7F/bK7uxvhcFga9vb2dvj9/qqeDS4SCztZ6XS6aqfT/wtLGauUy+XEXcasReWEc/aHFNFotVqJ3qG6kedQpVJBV1cXZmZmZM6BkqyenJzE4OCg9B1Ub7IXLRaLCAQCMrCQtQCjEYxGI1KplGRmE+RqbGxEIBAQVfv/HxbBa+C2+pX/U/43ZS1L4BG4nU/Jv7PTxeGI3Ps5VIvOppaWFgwMDCASiaCzs1PiEDncjmcNa+ra2lpMT08L8ZtIJGA2m0WY1tPTI0RAU1MTenp6BEy+fPky9u/fj6amJiwtLcmezjqdfRgH5fX09GB8fBxOpxNerxc2m01U2tz/qwVg2RtR7ckIP5LLjOvxeDwiRltaWpIh28Q/XnrpJQSD/1977x7b5nlfjx+SoiSS4p0Ur5Kom2U5kmOnaW510qxFu65bgm5r12bt2mEdigD7o1j/GNZ16ApsxYCh2AXptqLb2nXYBqxOk7ZJmyxpnDmJ4yZ1HFuxdbMs8SJSpHgXRYm6kPz9wd/5+JW+Tiq6c4Z1zwGC+CLR1Mv3fZ7Pcz7nnE9SXJmHDx/G8vIyBgcH8eSTTyIcDku8GK3sbCYwIpBNs62tLZmNkUgk0N/fL9dzZGRE3DtbW1t4/fXXpWFYKBT2KF7ZHDko2IQh0cpnNxaLwWq1olAoSFMcaNVqFDSx2cLnniIPNu85TD0ej4uDm0Qu1x7u12yUOxwOcS3zzxijSfKdQhrua3xGms0mFhYW5IzD5l87oHOY5/BarSa5xnRGUukLQAhvbQMegAjSGJlGwpk1K3kizs/hM8B4Bp5DdTodUqkU3G63uBIBSEOUg9noLtrd3cX3vvc9AJB1g87p9fX1A4tF3lYClp15m80muSW0l01PT8uhj4TO4uKiKO5of+OCypBxrcIlk8lIHqfP58PCwoLEFfAGyefz8sDZbDakUikZvEL15+DgIJaXl6HT6bC4uChdSH7I7LaTrKLCth0FhtFoxNjYmARmk6To7e2Vh5M3Bm1e7ILxgLO5uYlYLCbkILO3OJHU6XSKSrejo0OKXtqquWB3d3djaWkJoVBIgowrlQoeeeQRsZry2tJ2Va/XEQ6HZTBRKpVCs9kUuxNVUQcFB2iQpKAMnK+1uLgoBQyVckajUT7P7e3WtEcqe91utyxgWqUEALzrXe8SAo/ZVlQvejweUTxR7k8ynNJyPvBcMEk8cahMKBSScGmg1e1qh3QsFApwOp3o7e2VZ4S2GR7mwuGwDH1hMDQ7qXyOqGLiAksVdW9vryjruMH29/dLPiJVJ1TnpdNpCe6m6pnFAwdidHR0IJlMQqfTIZvNCmFFtRMXd5I77UDbDSSpww2Dh6yuri6x0IRCISQSCckwnZubE1WNw+EQmzPtkeyosXj93Oc+J2p5h8OBX/mVXxHyG2gdLvL5PEwmE9xuN77yla9gYmJCJkhSZUu7Q61Wk7XPZDLhtddeg06nw9NPP42BgYG21ErcGBYWFnDHHXdI4U+ilfnLJFb4HObzeZRKJczNzYna/Stf+Yq8J3bvOdyMg8sACBnGHC2tQonkCbvqJJZIVLHYYkeRajZmSDabTRlSwwNHO7Db7WL9pKpbe5Bgnitze/h5s3u+tbUFj8cj1jQ2nSKRCIrFIkZGRvBf//VfyGQymJqawsMPP4wHHngAJpMJX/3qVyW7SqtgY/MqGAzK+sQBO9FoFEeOHMHOzg4WFhZgMpkkF5nNllQqhZ6eHhmod1DwniUJcOXKFYm5YOzP97//fbzvfe+T3KxCoSANR65ltHnz1zMzM9jZaU1MZW6W0WjE4uIifv3Xfx1TU1N7IlB4j6TTafT19ckAgWg0Cr1ej5MnT+LEiRMSkZLL5fDUU0/BbrcLuUaXRrValeZQrVbDww8/fODrcfLkSfzqr/4q7HY7/uM//kMKOKBVWN17771CXvHnHRsbk2aGltyncoM2a1rESc6ura2JuoGqbu6djALhM8Kc+UQiga2tLWkSAK34GQ7a4jrJWCBGKPCQ1W7eHpURVImwoUxEIhGUy2U5/KVSKXg8HhmWxUY1G5HMMqPqfnt7W1QutC5yijeJw/HxcVy5ckUKY+bvsRHC2oYRCGyiAK0DLe2G5XIZt99+O6anp3H48GFR5R4UVLFwQNI999yDfD6PoaEh9Pb2YmdnR4ZTNBoN/O3f/i0mJydxxx134OWXX4bf75e9hM4tNsB7enrENk4V4z333CNKaJIxVKPpdDoZ1NLT0yOZdVQImkwmnD17VgZtapUi5XIZyWQSf/VXf4UvfelLKJVKeOmll/DpT3+6rXuDjQrtes6cu1wuh5WVFbjdbsnK58GPTimq2al+XVhYwOHDh3HixAk8+eST+OVf/mWcOnUK999/P5599lm8973vxdWrV/HSSy/JYYiq8kKhgHA4LHEX3NfZ/GJNxmeUeaq0fNNybbPZRPHZDrlEe7DP55O5EC6XS+J4tANZuX4mk0mJ5ujp6ZGYD5vNhsHBQayuropQ4dKlS1I7d3d3i9K70Wjg9ddfh9lslinmJIObzSZmZ2dl7Tl+/Diy2awMEfT5fBgaGgIAOZCTYBkbG8PZs2cRiUQkT1jhxsD7gE6SbDYrTTXugYxnYs3PRixjX+ii468phtje3pbzHfc9s9mMQCAgg2h4fxmNRmkCGAwG+TM+v2zsdHR0wGq1SjwBXSVms1nWZD7LWmXY/2Zoa0iqlFn3AZCmKfcL7oEUL1BAALQXU3Hp0iUYDAZ4vV7hIBhv09XVJU6RoaEhZLNZ6HQ6vPrqqxgcHJRzGyeuLy4uYnt7WzJaObMhk8lItAddmKVSSWKJtra2ZKjq1tYWnE6nkI4c9plMJuH3+8XFYrFYsLi4CLPZjIWFBdxyyy1CUtEFoxWmHRTMsB4cHBQO55577oHFYsH58+fxwAMPYG1tDe94xztw+fJl+P1+IRoTiQSOHDmCYrGIu+66C/Pz8/jUpz6FH/7wh8hms2g0GlhaWsKJEyfQ2dmJSqWCQCAgZ0VGUeZyOZhMJsTjcRliVSwWpQ7jsLxMJiN2eGYos25mXe/1emXt5r5/UHR0tAYhh0IhuR+Z40wlKx2G5GfYZGbTkSpqOlHtdjtyuZzM8iAByexhzodhtADPYDabTc6nxWIRdrtdaheeo7T1JecckNhkU5Mxctr4uYMim83Kz05HLt3udB0xaojnCQo1yYOQd+CfU2hTrVaFuKbI02g0igCRAojt7W0ZMMroFZ6DGRnB4bLr6+soFotYXFxEsVjE7OzsHqU9AHltnpMPgrc1goA5FalUClarVSyCJAFoB3Y6nVhaWhKCtVKpYHZ2VjIUk8mkHGI40IBdEHabqVZi5h3ZaR7EOzo6EA6HceTIEVm4zGazWINHR0cxMTEhmxynAOt0OolRCAQCMlhieXm5rSm8LCapdABa2StUyfDAVygUsLCwgNHRUQSDQbEmAK3Nob+/X8jBRCIhZCsAuZFoZbDb7QgEArjrrrswODgoii2qTRjPQDsob/Z6vS7KDp/PJ8QkFaNWqxWPPvooOjo6cP/990sHuB2QeGZOCg+rzPegZYaqJd78HIZGGzat77Q9UrnL/F7m0LHAYaeNBQ0VOgCEGGf+DYcZ7ezsCEnFDCd2wvl5UlVXr9clx+mgYMNgdnZWcoipNF5bW0O93po0vLS0JPm37BbxXiCJUSwWxQLGn6tUKiGbzcqhlRmHTqcTgUBALArRaFTICJIJwWBwz4R7qtUzmQwMBoPkXJKoZ0g4h0Xw9doBF3oWRnxe+Zw3Gg3Jhdnebg3+4mZDazqbOlR40+YB7FUpUe3JTqXJZMK//du/obOzE/F4XFRiTqcTtVoNf/EXfyHKIIvFgnQ6LUU5s3aZecrBLIFAAM1mUz6DdvJzWGi98MILyOVyst5RHUULJy3mP/zhD3H69Gmsrq5iZWUF8/Pz+Pa3vy0dbRbwVqtV7i2S9iQC+CxSycvMJO3nwSxXdoy1UQRra2tS1GjV3fz39Hp9W80rLfYX0do4Bm7gXA95+Ojs7MTXv/51bG9vCynPeyiXy4kS1+l07smIHR0dxbe//W184xvfwOc//3lEo1G5x7gHkJSLRqNCDLCjy/zNM2fOwO/3o9Fo4LOf/awo0KkUYNwLX/egYOwBrz0PYrwGdHi8/PLLmJmZkYMImyvMpGTcAAAhHxmnkM1mZfpzpVKRgZlUenq9XimC6TwBWkMI2OziusUIHO7Hdrtd1jCuMSSlScC2AxK+XNd5L3N91kZksAgmwcocLR6k2QjlesOahYUj11bej1R4cGAZ10Zatqli0KpzqtWquASo2GC+Fu9nkpN8btsBm2JUGPM5YRZZuVyWZgAPMGyYs7FCFwHVuJFIRA4NLHxDoZDcN1xn6AqIx+Ny2KSzhV8LQEh35lgCkFqEzh+qyrgO8eDYDslGezj3sWeffVZUoLu7uwiFQrj33ntFjXr//fdjdHR0TyOaxJvdbpdoEkZdUSxQrVZx7Ngx+Hw+eZ5tNhu++MUvylA+Rkvx3mONa7FYYLfbkUgkhKhhDA9zUUnqce9j7dZuljYjOVjDkjCo1WqiaD169CiAa5mgWvsg4zZ4nzcareEtCwsLyGQy2NjYkMFaFotFFKC7u7u47bbbUCqVsLCwIAdKNoYZ50SS0uFwyGCaarUqZPn29raoqrnOJ5NJ2dNJVB4Em5ubcnZgI5sqKA5UZPPdZDIhn89LxilrV1q/zWYzZmZmUCqVZOo2D2gcsJJIJLCzsyN55cwM39nZEfIDgOylzFymgh1oxb9QPRQIBGCxWOT1Xn31VRGYxGKxtqNLFK6hu7sb8/Pz0jTZ3d0VWzAJTg6vLBaL0uihiowDf1j3sP7WNvi5B9NOvLS0BAByz7BZzIgc7m9U5dPdoN2n2NAgAUMrMp8Vui9YE/9vhlY4oCVjed9TbMPf8zrxcwLQtkgEaDVu6FzhGYAkHbM2GSVBBaXNZkMwGMSVK1fkDMazDL/f6XRKLOKtt96K7e1tEUisrKwgkUigXq/jyJEj0Ol0MkzbYrHgjTfekBqKg7Y5WLlSqUhcAQkqKvI3NzflXna5XLK+t4OBgQFMTEyI6GNhYQEzMzN48skn0d/fj5WVFVHJ0wkcCoUQCARw9913i/qXIrypqSn80i/9EhKJhAwjZz0aj8fRbLaGyFarVaTTaVFp0kG0tbUlwj3meDLrm+Qf60GqVR0OB3K5nFy7D3/4w7DZbKJcPSjYcAFa5xDyX2azWRxxdMdQaERxBs+5bCqy7kyn0+JQYROmXq/L883PnWIxCl9ot+f+RQcwz3n8/MltMfqGUUQ8w1PAos1jPyhIDGvnILDe0EZVkMsBWnwMxU8AhJdjjAtr4s3NTXGekvdjvcp6l883xUjkTxgXSRdJvV5HOp1GPB5HKpXCK6+8goWFBan5+b5ZC/HPD9qseFsVsMx4MpvNkmU1NTWFSqUi9lwWUxy2Q9KWG8zAwICQBHxoOYyLBziHw4FqtYqjR4+KdJy2aUqygVanmBP5OASBUnCtWoHkK8OYdTod8vk8uru7odPppCvazqAlFkQsoEqlEo4dOyZFU29vLxYWFhAKhSSjhOQQD9dUlpBcpDqB9ifmgNAKEIvF0N/fL5l+JEw4NZaHzFKptEdNODExIXZLHix4Lake5cGTQeLt5jlSHcec36WlJbGlkdjkNNBarSZdHBa7zEplB6NQKIjNhwdEfh0PCCRHaH/ktDtaUGgVoOWTnZSdnR0pXvj58zDIA0gsFoPL5ZJDaztZjsxK46GYlnM+B1w8GQ1AaxyV2EBrcXK5XHLophWJVmk+Q9qsFCpLI5EIksmkZNBqZfck9rSh1Lwu2oBvHm5pcchkMgAgh/J2wHuVxCJz/9iNAyBKdFqWGaFA6zyfWx5ymTFDNRn/HRI/VE1znfna176Gjo4O3HrrrTCbzXjmmWf2EAzd3d1IJBIyqIeHawCilNFu6M8//zz6+/vhdDoPHNgNQMiZM2fO4IEHHkB3dzey2axkXTPLKJPJoFwuY2ZmBpVKBc8995yQeVQL0+aoJf8ASPQFBwJw46JScD/R8WbNFl4bPhMkZrlJaofqkaRp93Co1+vF7sq1UWvTBq4NZmBMBT/rxx57DL/2a7+GlZUV+XoWJ6lUSoLqS6WSNFZuv/12LC4u4vjx45iZmZFOrcFgkOFtzWYTfr8fmUwGfX19aDQaWF1dFYWM1WrF0aNHZSgN9xYqLnt6euTg1NfXd+BrwYaV1javPYTw3t7a2hJlLZXkPLSQLOb3cZ3kgaHZbCIWiyGTyWB0dBRLS0sSobG0tCR7AZtbpVJJlF6dnZ3S6NIqhekeYF5YOp2Wv2ceKtV+7YBEPw8fJBW1RCQPyPV6HR/+8Ifl1yyWtc4J7fVlQcemIPdHHmDZuNMSfUajEU899RT6+vrEkcOaiJ3+zs5OTExMiMWcpCv3NSoPtAr1g4Lr5f7njKRgvV7H8vIy3G63qLfYTCPR1NvbKwQUiX06JoDWM8/8+bW1NYm5YTb6+vo6BgcHZf9hUc99mGs1SVaqa0nIscjmwYDPKAc5HBTMYXS5XJienkY4HEaxWITJZBKVI+30q6urSCaTUpQHg0Ekk0m5x7kfARCilEqMrq4ueDweFItF+fy4rrJO4rC9SCQi+bnb29sy0dlqtSKRSGB8fFyeAR7omMvJJgivWbuT7hlpQ8UUM66np6clpol1Ee2AdPzkcjmx3lLVcvz4cRQKBQQCAVitVly8eBHj4+OIxWJyQOEeyenBJPuZe+twOCTmgdbDZDIJk8kkA0CZT8961eFwSMOeQo12yZTp6WmMj49LPcH9kUSEdg/jv3frrbeiWCzilltukQMhlVlsxjKDk4fVXC4nKl3W8HRyZbNZqW04qKdSqSAcDkudWalU4PV64XQ64XA4xP7OLFgOU2XDKZfLwWg0ytlKoX1ks1kMDQ1JHiQAEfVwRgQdL8lkUpp3tVpN9gjtXAmqDOnaorKasSNA657m/UGXJAkD1pXcaygW4fmLDkgOnGHT0WQyiUWZdRprxv/tILnN2l77/HPfo1BBq/YnAUuiirXQQcEGnt1ux9zcnJB4bJjyHuAaz/U0Ho9LjArFDGz+33nnnXjmmWdkFgHXtGAwiNnZWfT19UmjfW5uTjKG6eolqdlsNlEqlUTdmEgkRG3KWsvr9cLtdiOVSokblA4+xlS1g5mZGZTLZbzvfe/DCy+8gEOHDmF8fFyERSaTCdPT0/D7/eJ+isfjMJvNeP311+F0OmGxWFAsFjE5OQmDwYAvf/nLcLlc4o62WCw4fPgwLl++DLvdLmIBo9GIN954Qz7L9fV19Pf3w+FwiPqSLthqtYpYLCaNbgAiJGFTi0Kr7373u3A6nbLOHhTc+ylqIUHPGs7hcMjnwzzWZrMptSbvI0aWNRoNHD58GBcuXJCIBXIE3J9YhwOtOs5kMskgbQobmRvNMzlFAYyBYoOIzjpt3Bv33BshYJvNpnBJrN3IDfJMyFqKEWZUdJOHYPzKxsaGnPl5r3KtM5vNMiCcfA7FDrVaTXi4paUlRKNRDA4OolAoyOBtulrm5uYQj8f3iB+5ZvBsTK6Hdd9B8La2QlmgUQm2tLSExcVFlMtlzM/Piw2Kaond3V2Ew2EpxEgSMHeOA28qlQrm5+cxPz+PnZ0dFItF1Go1zM/PY3u7NQ1wa2sLFy9elOzVra3W1Of+/n709PSgv78fADAyMgIAMrmYlhIe0NmJIlnJAy0AscwfBCxemR/KDjvf2/LyMqrVqijOyuWy5DQyL5GdcKpmmdnG1yT7r41SqFarMoFda/+lBJ2E2aVLl/DRj34Ut912mxDdvKHZbWC3hbm+9913nxxo21XAsqPLiIE77rgDdrsdlUoFExMTYsdoNBoIBAIoFotiDSNBOzo6KtfH6/WiXC7D4/GI+pMyfT74XPD4eZhMJkQiEbkGvAeZ38gihgcibnIkaHhN+BnwQEQi8KAgCb26uorNzU2xuzKXltYWh8MhtqbBwUE5NG9tbcFut6NarWJkZATDw8MyEIdEGLtq7IQyW7daraJUKiEQCCASichrktDkAWF0dFSy4Ej46/V6GQKwvr6O5eVlvPHGG0in09IcCAQCbWeDkTzjZ8IFmpm2bDKQ9OBCzI4acG3gBcljLvz8mfh3JHDZEOBnyGf03LlzePrpp6XQ1qrfnnrqKVy9ehWNRkMUQtomTj6fRyqVwsmTJ2V6JJs7B4XX68W9994rKgcSEblcDhcuXEA0GhVF+Le//W1MTU0hHo/LZ8QoCxJh2kysYrGItbU1UWhwICE3Ne17vd79TAKUv6a9e3NzU4bpcA3lhgpcUw1SndgONjY2pHDitFatYpokorZLyfupVCrhxRdfRCaTEeUd83Q7OjokxgJokZfJZBKvvvqqXG92S/n6PJRxiGJ/f78EwmuV31QYA5A1v1qtCvHa1dWFkZERbG9vI5lMHvhasDBhE45FNwApmtgsYW4Tnxs2dkhAknQkucX/aC9ngZvP57G0tIRqtSqDvKgcpTrb6XTKMJl//Md/lM9Nm68FtEjx+fl5rKysyPtMJBJIp9MS+9MOTCYT/umf/kkOUixoNzc3kUwmcfbs2T151x6PR1SyJG74bJOc1zYaWRSTeCXJrI3ooNrG7/fj1KlTeP/7378nw49rlFYBMDIygnQ6LXl9tFlx0Cg/H9rWD4pIJAKLxQKPxyOkq9vtlp9Br9cjEonA4XAIMU6Vg06nQ19fn+TxckApyXMeaHk/c20cHh7G4OAgQqEQent7EQqFxDlD5Q2JVO7xPT096Orqklw32sWoMCJxz+FDWvv8QcHhH7OzszKpmoQ6lWwc+JpKpbC6uipiAOaYakmz+fn5PTErPExQAMC6RK/X4+mnn5aD1sbGhqikMpkMkskkent74XQ6EYvFkEwmEY/HUa/XUSgU9qi2VlZW5PnhNSGR2+5hmQQhm2TZbBaXLl2SWosNbjqp8vm8qKXY0GPDiDnGdKl1dXVhamoKCwsLyGazuOWWWyTGigM6qfI1GAwiBmC+ajAYRF9fn0QqMX89l8uJa8Hr9SKdTos7q1KpSC3KZslBQZeUxWKR+obEeTgcFpeQdn3lc5rJZCS3l3naAwMD6OrqQjQahdlsRiQS2ZMp5/F49liVDQYDhoeHJfN+Y2ND4jAymQwymQzOnTuHsbExGWysbTDTqs7BJMy85nOSSCTaujcUroF7fS6X25MBzhqdg5a0dSMbetomCRXUrDnT6TSGhoak2cn5EVSmGY1GJJNJZLNZZDIZUYlR/cYGNBsY3FtI3miV1OVyWYiZwcFBsV3z/z8PoJKVtS3rfz12TxYAACAASURBVADSkGANrHXDaZ0lnHtxUFDYlcvlREUXiUSwsbGBfD6PQ4cO4fDhwzAYDBgcHEQkEoHZbJYmCRtRbK5UKhVcvXoVgUAAMzMzCIfDIrTixHa9vjVIi7FQ8XhcZrjMzc3J4EYOWNTr9SLm8vl8Qh4VCgUZMEWFKMngWq0Gu93edkYwRQRnzpwRdysJ6o6ODvzZn/2ZiBUofGLcUr1eRzabxWuvvYbp6WkkEgm88MILGBoaklqEDbtkMgm3242ZmRkRDq2treHQoUPieg4Gg/KaFHZR2DYzMyPPDGvner0uP7NOp8OhQ4fk/ORwOITAPCiMRqOQpNwXt7a2UCqVEI1GJW+82WyKyp1nMYoBNjY2sLCwIGvKwsKC1O7cjyjM42uRONe6e3nWZUwiz0vExsYG7Ha71HqsOUjWUoTFszfFTO2A52Y2gnj24hmT/zGSTcvbUJSgPeOzDmX0DgVbdITznMi4l52dHdkXm83W0FPm6zJaa2lpCd/4xjfw9NNPIx6P77lGXKcpCOO6QY7soPXX20rAcsIjrfUk8Higq1QqyGQyqFQqcpimfZqWing8vscOx9yngYEBBINByZtkAZbJZBAIBORhpTWbxC2zW7e2tlAsFpHJZDA2NiZ2DNrI2CnkosKuFknhcDjclkTf4XCgq6tLvmd0dFSIpaGhIbjdbrGGXr16VQ4y3EQ4CKJev5abyJtLq/oDINOfXS6XFO1U4bCzzk6PwWBAoVCQxcvpdCIajcJut8v7yeVyYv3ioj05OYl4PI5Dhw7JtWkHXIyazdagtfPnz2N4eHhPfsnKyoqEKHMYgjYcnB1F2j2oeqXs3OFwCEmq1+vh8/ng9Xol4HtxcRGpVApAqzCxWCyw2WyyYfBe4IP48ssvS4QDbQ42m03yWjgNlQf8g6JQKEhxxcWVIdV86A8dOiTZrsvLy5ienkY2m5WuFzs1PKTSPksygNmZBsO1wO1sNitFCQ+SzH6kIpT3ZrFYFKUPu0hc4Dc2NvYM63I4HFKE6nQ6yTE9KEhgc7HmYZuEEQDpoDKWg+sJiRcu3lplAJV4XH/Y0aKq0el07smOYmODlgYetElodnZ24tlnn8UPfvADABClSiKRQK1WQzabxfe//33Z0M6cOYO1tbW2iJR6vS5EUTKZRLVaxfz8PBKJhNiPXnjhBSG6tIQRiR8qhqjm0Xa3tc+H9j/+PPy/1j6txf7MLP5+/9dSwUElNe2o7RIHPFSQIOe/w0NLs9kU6x6bKVz/DQYDZmZmcOrUKWQyGcTjceRyOUSjUVy8eBErKyvI5XKYmZlBPB7H448/LvncuVxOutRerxe1Wk2aMNzcef/odDqZis1sMJ1OhwcffHBPNjHQyvbl0IXTp0/jxz/+8YGvBd8bC1it2mP/58SiTnsteK9z/eZBhcUpySbe+11dXXjyySfxox/9CI8//jhMJhMKhQLS6bSsAalUCi+99BJeeOEFPProo/L5PPfcc1hZWcHq6qocBrWFYqFQEOIpGo0iFoshGo22dW+QSGFWJJ93HjZPnTola7nBYMDi4qIoVnkdtJ1tADK8hI1XrYVKG91Adfn29jbOnDmDJ554QuzUvLbavZuHc0YAPfPMM3tywniPs3Du6OhoSznP7+f+yuYbVaCBQAButxudnZ2i+Ovp6UE4HBZlF4k+ErVUXnLgqTbyKZvNClFx5coVrK6uitKThSobaly3a7UaYrGYDOHg3kFysKenRwZtNRoNUbxoSbKDYm1tDcViEadOncLhw4dF0UkXCNWna2trkkcXDAZRLBYxNjaGer018DEQCEjTjuQw3SnAtYNGo9EQ66ler4fD4cAnPvEJDA0N4ZZbbkFHR2vYqsvlQj6fx9bWFgYGBkQ1zoM51eqsXxmZ0t/fL9ZDn88n9+tBwX2MzTagVQOtra0hm80CgAyyKJVKcLlcSKfTkqnHptbq6irm5uYwOTkJm80mtXtvby+6urrwnve8BysrK3IOYPZeX18fXC6XHKy1zb/FxUWsrq7CaDTC5XLB4/EgkUjI3tfd3Y033nhD6lXafFlf8985KAYHB8X6SLW21WqF2+0WxVSxWIROp5PDZ7lc3jPVeWBgQOoVKqiZoZ1Op5HL5TAwMCADEXlwpgV5cXFRbME8WGezWej1eskLTKfTcvBjjQxA1NY8gNKO2tfXt+c9KrSP7e1tlEolDA8PI5vNSuONdl0O5qRbggp9NtwASPOBsR8kS9lYoCONazQJupGREfh8PrhcLlFjT09Py1A1RqQxi501EbMwSa6SSNrc3MT58+eh0+nEafjzEEGgBd0U2v8D1+phnnXotKH7h3/fzp7C+SGzs7PiNOTrUIFOsrxcLuPixYtYX1/H/Pw8JicnRcBitVolCojcQygUQjqdxvr6uvzfbrcLAVmr1ZDP5zEwMACXyyUNZTYBOLCXdSqbhXRa8N9jIxho1ZOZTEYIzVAo1Na19/l84tB73/veh76+PnR1dSGTycBiseCuu+7C6OiokI7Dw8MoFApwuVw4ceIEjh49CqfTiVtvvRXlchkjIyPIZDKSvc5zHfNsA4GAWNIpBvB6vUJSJhIJseK//vrr4nJjncezMO+DhYUFpFIpUSPncjlYLBapE9pZR9PptETYkLwkX8Eaj/UZa2tyTXw/rDU5INPj8cDlcslzzs+MCnkKFSk86OjoQDAYFMd2d3c3/H6/uK/4vlj/r6ysyHlZG9XF5gCFRVSYtgNtVApdLKxNyeFpnebkVhjBw7Mcm4p0QrJhTxEIhSU8y1AIxUjEeDyOaDQqdX+hUMDFixdx/vz5PVEDVFZrRTz8tdZ5TJHGQdfRt3Un7ujoQCwWQ6PRCu2Px+OYmJgQmw8tGVwMeUAwm82ipAEgHRna0phPSYkxJ/55PB6Uy2WRe2s7kVSQ1uutrDqSvt3d3SgWi4jH42K38vl8yGQyMqWamT2bm5vSmV9cXJSi+yAIhULIZrPw+/2Ix+NygMnn8+jp6ZFMWdqM+IBR3djX1ycPMxfTYDAoUQNUcdLClU6nYTab0dHRIYOySOixa9rd3Y1yuYy+vj5ks1m4XC489dRTyGazMtmZw554DQ0GA1588UW8+93vFmsnbQTtgKQeieBIJIIrV65gaGgIr776qkx0JBGp1+tlQBUJDl6Lw4cPi1K6VCohGAzKxre9vS2231QqJeHgJPHZdSIZzMO2x+ORiALagrkwOJ1OsdLxAEX1JK9jO5ZAEr6c/Mt8VXbcC4WCRD9wgTWbzUJk+Hw+2Sz4exLELPRIGtBGyYFBXPR5yHK73ZJvxsM4FS/agGwWD3yfzJHlgYKHZZfL1bYCo7e3VzqG2mD0QqEgRQ5tysy8odKAB1VaZhlBwWeG9hSbzSYbEItT5h7zAE3lJi1kBMkEFlokEnK5HDweDzo6OvC9731PNkFtc+Lll1/GxMTEga8Fc//q9ToeeeQR+P1+saQAe8lRqlWvp1rl12ixn2jVvub+r9n/fW/2tfxzrf1f+/rar2l3EwcgAzG48dGarc3OJCHZ2dkp5JWW7L1y5Qrm5uYkizAWi103O0yv1+MnP/kJuru7Zcoqh/j09vbKGk1XANAi4tgUYoed9/8HPvABeY60+YanTp0S9RUPbQcB7TgsJrX2JWJ3d1cKHBLIJMCZM8VDB1VotDYCrc+RsSW8piQ0n3vuOQCtIYdAa78/ffr0nmuovabnz5+Xw6TRaMT8/LyoBPhMX7x48cA//37QYlkul+Uzr9Vqso6YTCZpwBoMBvzoRz/CZz7zGQDX7IT8Pq0lUatmnp+fx9GjRyUOSJstp80f49CG5557Tg4mJOP42iTK2UD7u7/7O3ziE58AAMmiZcO0u7u7bQK2p6cHuVwOzWYTgUBAbFZ6fWuoKQ8DWiu4TqdDOBxGLBaTicpAq4BmHUK1Hck7LYnNBiVtX93d3XC5XJI5zUMmG2vMJM7lcqIGpeqCGZ9UQPC+pmW+HZdJT08P/v3f/x3BYBAejwexWAx+v1/UFNzTu7u7cdddd6FWq+HUqVN48MEHMT8/Ly6UUqmE/v7+PbEbbDLS8k3igyq5kZERfPOb38RTTz0Fo9GIYrGIw4cPw2g0YnV1VXKDeRBfXV2VYp8WQ71ej97eXgwMDMDpdOKRRx7B5uYmDh8+3JZtVns9uA7w86VbgarM/v5+uQ+XlpYkT497bjqdRjgchtPpxNmzZ6U5u7KyIsMgFxYWMDw8LH/GOqZWq8lka9byHR0dGBgYQCqV2mMNzefz8rrBYFAyAWkjJalSq9UkP3F4ePjA14JZtceOHZMIAa7n+Xxe6l4S74yYYZ03NjYmWbaMY6KSmurHUqkkQ2E4rI4xDIxz4rNWLpfx7ne/Gx0dHVhaWpK5DqxbSJw0m01RN7J50dnZKapgusa0+4FCe6Ci0OPxCFnAuo/OqWazKQ4pfp5sOPP3fIYZl8bzKrNg6Txgw5jKWirzmSnL/YaWblqZucfx7EzXi1ZswDMKyX+r1SpxQj9PYJN1v7tqf83KdZMiC20000HACB2bzQav1ytOHlq3tUNGKWwh6T03NyeDl9l4ajabsNvtUhsmk0kZxMg5H1w/5ubmMDQ0hJ2dHTkHbm5uYnV1FUePHkU6ncbw8DAcDgdmZmYktgwAxsfHJWKrXq8LT8DaThuj0Q66urrg9Xrh8XjwrW99C41GQ2rsYDCI06dP49ChQ6jVarjllluwvLwMv9+PcrmMS5cuSSwgHVCc8TMwMIClpSV0d3ejr68PS0tLEp1D0UNfXx82NjawuLgo8RoTExN4/fXXkUwm91jJeSbWnmPdbjfC4TBKpRIikQguX74s0Zg8N7STAUtRCGNzfD4fisWiNI8ZZbOzsyNnWp736/U6hoaGRJCgjYTjXsCZR/zZLBYL8vm8uEYsFovEo1Etz32Nez+FFj6fD2traxgfHxey12g0yhqjfY7adToTGxsbKBaLEofDxjXvOcaHkKQFWsK0kZEROfczI5Z1PmtCLSHM+ouuXO6tJNHn5ub2kKXc1/efm1h/ad2a/BpeH64jjPM6CN5WApbFSC6Xw/T0tBTlBoNBco5Y+JE4BFoEh8/nk6wJstgMkOaNmMvlZPHUqle0Ifqc6ut2u2WYgMFgQCAQQK1Wk0FYtDfztSKRCGKxmAykYLclm83KZteO6pOHuUuXLmF9fR3Hjh0T8q+npweBQACxWAw7O62BTz09PZLDFYvFEA6HRbXBATArKyuyWLrdbmxttSZz8iGtVCo4duyYLD6cFEdJNqMWqAz+13/9V/T19cFsNiMcDot9mze/9qDADNz19XVRhbQDqiA4oKFYLKK/vx/pdBonTpzAysoKAOzp+DCPjgsIh0jQHjwyMiKHQv49s91sNpsUK/39/RgaGkImk5HDHACxgW5ttSalT01NAWgVxJFIBNPT02KFpJWVdnYOjqFyOhgMHvhakDzv7+/H8vKyZErRNkrVNYeAafMVeaDj88Owa3ZwqNblIsdcWdo4eNjb2dmRwo3XqlqtyufKDTuXy4kyhB0oZt55vV5RVJP42NraQiQSaeveGB8fx8WLF4UgtlgsYiXR2hF47ZkFzU5vo9EQJSSt6JVKZQ+pSpKG1nseOvn6WrKQhA0LNr42FcvNZhOnTp3asx5QOdZoNP4fovHSpUsHvhbJZBKPP/64vKZ2EqeWfL0eIar9OkL7XrTf/9Ne83pkrvZ79xPC7EDuV8/eCOmqBQsC2u9JtGvVm8C1wQy8H7TdS72+lak9MzMjSjOt3ZwkHNAalMj73Ov1yjPJnChajNj8YPwAC2q/349cLoezZ8/i3nvvFecASRqz2SzPodvtbsv2xeea15VNCCqfqeKncp0/vzaKgteGRKt2QBIJQ15rQpuLqtfrcfbsWfmztyLF9Ho9SqWSDJp8s6+50XuE7+n8+fOYnJyUZhEbuz09PZK1TpUE9zZeN2Y5AhClMIt91h1PPPEElpeX8elPf1qagGzSplIpyQjnekSSXHs45p7MCbjNZivDmyQ3C1ESl263u21FijYaiBZxp9Mpan8e+NnM0+l0MmSzr69PhoRx6jLt4yQMyuWyuARoJ2TDg0Ux8w4ZIZNIJCSKoNFoyMGQE+CpJqPygw1TNjV4IKIb5KCIRCLo6emB2+2WBi7VhYyw2djYwKVLl3D8+HG88cYb+L3f+z384Ac/2EOsjY2NYXFxUQZCNZtNsTcygqarqwtmsxk9PT34zne+g+HhYSwsLODYsWM4ffq0KFlJ2LOOYr6nNl+X94nJ1BpmywPMhz70IYkl4H7fDthQIKm/uroqB1o2BPL5vDRuqahjZJjD4YDf78fw8DDy+TxWVlZgs9nkfZRKJbmHZmdnJXNXmydHZwcbH/l8XjLzarUakskktre3MTg4KO6tQqGATCaDWCwmhy02PBgzo1WrHAQWi0Uy+5kn5/P5xN7r9XrlEMj6k/c1B3KxmeHz+bCwsCC1JPNhGWsFtPZ0utnoKKNykYRdJpPBzk5rMrPb7RbxSC6Xw/z8vKgkqSTnntRsNjEyMoLp6WlZP9pxYynsBZ1/XKeoqNMqwbhGc6+nm5P7J+vM1dVVsWRzT6V9nE05KrVIoLL2ZGwc91fO/OCf8WxGQk0774CiJmZcrq+vS3xJOw3fnxdoa1USVBxC2G50Sa1W2zPbwGQySRYshTlra2uiXmRONBttXHOpnO/s7ITX65Wm69DQkDgSKPAoFArw+/2Yn59HOp3G/fffjytXrqCzs1MyXTmErVwuy/nU5/OJmn5jYwObm5vw+XzSiOPez1q6Xecis23j8bgMk8pkMtJwuPvuu/H444+Lc5fNBja4eX5MpVLY3t7Gj3/8Yxw/fhwXL16ETqdDIBCQCL58Po9MJiMxhfV6HefOnUOz2YTb7YbFYsHJkyfh9XqlQUFhD4k+nrN5Tl1fX8dtt90mZC9rUTrrKGQ7CJjrzQZnPp+XyEWeRekwZlY+a5xjx44hFouJst1kMsnMCA7z4n3KM7BWgMhGOvcc7qdUjdKVxP2KKmw2xil4ZDQD/7tR8hWAWPfpvtTGdnH/53kFaMV7jI2NSR1BdyvP51oHCH8+AKL4pvBuZ2dHhtWvrq7uUQ+TVCWxqiXNCe25hoILNifIGZBvOwje1ggC3uhaS2Oz2QoN51Q7MvOUQQeDQYyMjCAYDO7pPHCwBvNYAaC/vx+HDh2C1+uVw2coFILZbMadd96Ju+++G52dnVIkkZArl8ti4aWNgOQkYwmq1SoCgQAOHTokhzN2KbjQMTT/IPD7/fD5fBgdHRXVD21NtEAfOnQIY2Nj6O/vF9l1R0eHTDmlXP78+fOw2+24cuWKTJ0nUW2326U4y+fz2NjYQCAQQHd39x6bZSQSkTyNc+fOyVAUdhjy+Tx2d3cRi8X2EE2VSgWDg4OSgUZVZbvdssOHDwuhTTWLXt+ycEej0T1ZIVobNj8LRlFwQWF+IB8qEpTj4+PY2tqSidyUsVMlTGWPTqfDnXfeCavVilAohI2NDXg8HlE0ut1umUjIpgEfWJfLJWHpBoNB7IkHBQm9VCqFnp4eeDweGbhFooSB8VxEebjnBsYcSx66GZ2gneLNLjitkSTrGX/BTDLmrTF/zWAwIJvNykGYRcTly5exvLwsuWwrKyuwWCzwer1ig6Gith38yZ/8CcbHx+Wa7+7uynvTRjzwwKedGsl7iKQI7/eenh74/X54vV75Wb1er5DlPKTQNsz3z9w/3nck2BlHoC10tbmQWhuzlhhsFydPnpRfX48Q3U9w/jTw6/arX7Wvtf/r3+q19r/um0US7Cei21GvacFNmAcUFigsIvnZc+8Brg1g4OfHAoAkpZZw1f6a73t9fV0IVj5rzOXS6XQyIZXNH6rYGQdCe4zdbherDdcNTgF1uVyiLD0oePAnEcHv5f1Aoo/FGslWrTJ7e3t7jzoWwB4ilvuBFvx3uP5cj+zQEkLaKBDg/1VeX69BcKP3R1dXF86ePSsRJFSjcA24cOGC2LuMRiP+8A//UNRKXLtJhAKQddfhcGBhYUEatKFQSO53Tq89d+6cfA+HR9JaSjeH0WgUtahWzc2DwQ9/+EN532wKjI+Po7e3F/fdd1/b14LkYU9PD7xeL/L5vBA+rBFYnNOmRmUGG24c8rKxsSHvv7OzU/ZtToPnMKvd3VaWP/dDZpuXSiWEw2HJyqQihIU98+ZZE3G4FBUGjUZD7Ly0qB0UR48exUMPPQSv14uFhQWYTCbY7XZcvXpVDgUmkwlTU1NYXl6WLNSPfOQjcLlckrtH1w6fi93dXZTLZbHBs87d2dnB0tIShoeH8eyzz+LkyZOIx+O47777EIlEJO6HxO+xY8cQDAYlK5z57xxgyxropZdeQiaTQTQaxdDQkLi52o2A4mFvaWlpjzuEyhStuplKoOHhYSEIeH+++OKLonBi7jvzCXO5nKisyuUyksmk1O933nknNjc3sby8LDWO1WoVlXatVkNvb69EHyQSCaysrMjQSR4uqQyjRRIAnE4n/uEf/uHA1yKTyUgd2Wy2BlpxCAkP3iTJ7XY7ent7RcVKF5jFYsHm5iauXLmCzc1NuFwuGbrLpkqhUMDKyor8XIODg9JEYGwUcxq57gDA8vKyKMapLudZiNZ2m82Gy5cvo1KpYG5uToi9rq4u/P3f/31b94bCNZDcttvtSKfTiEajSKfT0gQmIcAcYw4yYiQL/57nBSqfef7lICTGd2ndfTz7sWnEaBYKP+jy5B7DZgnz7bnO0kXGn4ckhLbx8H8R2pqUKr926nSPxyPCMDYNGd2zvb2NsbExIWhJ8C4uLsp+43a7UavVxGVrt9tRKBSENLNYLIjH47LPstZk7Ws2mzEwMADgWvTN+Pg4vF4vpqenMTc3h0QiIXE+zPsuFotoNBrwer1y9mTtzPvVYrHg93//99u6nqOjo9DpWrn+PA+Mjo6KEndoaAjHjx/H2bNn8eqrr4rzgA4/xlHynh0fH5fhhFriMJfLyZk8l8thbW0NCwsL0izP5XKYnZ3F6OioNChCoZCIssi30PbPn/n222/H1NSUkNNutxtWqxVGoxHBYBCPPfbYga8FI3Sq1arUx5yHREUzlfN0ZVPUxLkKWmdbsVhEKBRCMpmEXq/H0NCQNB+dTqeoaPX6VlwUhQfMdaUim/U69xODwQCHwyHNYwoKeEa6Hrl4I7V5qVRCsVgUAWY6nRYimusjZwuwbmYDIZvNyqAyRhzlcjlpYNBBxzq0VCohn88jFovhJz/5CWZmZpBOp0VMsh/7Y8G0ZxeeEbnOAhDXLM9x/P9BoGu3UPtZoNPpmgDwl3/5l2IBNxgMEidA9RrJRk4NpcVpd3dX8sYA7GH+OaW0XC6LGhO4ppAiCcdNkp0mEnlUgebzeekEMMdOp9PhHe94B6amprC1tSWTS0OhkBzo/uiP/ggA0Gw2D7piN7m4X758WZRIPNxTWcUpt16vVyIJOCSHKtB6vY4zZ85IwDcPyKVSCRaLBbFYDN/73vfQ1dWFBx98EGfOnMG73vUubGxsyGGTKgceFqanp6XbajabcejQIVitVqyurkpAvMPhwNLSkmTNUO3DLtoXv/jFA+9evDcWFxcxOzuLxcVFrK2tYXR0FAsLC7IAkGzk9dJ2Smi/4BRbEixGo1E6f7y/eL8A1zZbZqtOTU2hUCjA5/OJnZ8dMk5kXFhYEAscJyvSluT1ehEKhVCpVPDud79bOmUHvTd4Lb7whS9geXlZupebm5vwer1STLGo5uYLQAag8HDP3DYudpxGTYUWACnImF3Cv6fdhfY6dvhpfyOZw83V4XAI8Vsul+H3+yXQmzmcDzzwQFvX4v+HPCsPPvggyuWyqHFYOOp0Ookl4ILNQkK7GDLMe3d3V6JKePhk0cLhUVxbdDqdHOq0OaMESRda/FgYaC3dWiUmiWEtAdnuvXE9BSp/TWj/7Hq/1n6/Ftf7+/3f/9P+zeu9/n4V7X5yja9Zr9cPfG+Mj483c7mcFC7avCM2gbTkGf8tEktUe7Mhoc055fdxnaEV0Gq14uMf/zjq9TrGxsbQaLSyyVlgM+ydahOr1Yp0Oi0xJH/+53+Ozs5OfPazn90TzN/T0yPZf+xAm0wm/PVf//WBroff72+SNNDa2/kMsDDgM6tVe2qHdO0/dLAAY5F6vcB9PitsePE1tOrxnwbt17HBxz/X/DsHvjdCoVCTDZDbb78dw8PDqNfr4iAgKcqA/bm5Oeh0Onzyk59EoVDAwMAA7rzzTtlr2dCyWCxYWloSsooDAD71qU/BbDbj0UcflUxPAGLdAq6R/zabDel0GqVSCd3d3XI45n6cTqcxPT2NtbU19Pf347bbboPJZEJ3dzc+9rGP3dD14Nrxta99TbI9aU9jk5W2VUb7mEwmuFwuaSzxMFmr1eR7QqGQ2GJ5bdmQok2WOckk0Ehe8vDf2dkpDWM+vzs7OxgbG0M8HofL5ZIp71QUd3R0wOv1wu/340Mf+lC710P2lG9+85uYnJzEK6+8gvvuu08yNJ955hl88IMfxDPPPAO/34/e3l4ZesR7k3nznGXAZh0b9IwMaDabuHTpEmZnZ7G9vY2HHnoIS0tLoqSj4ieTyeyxgLIhyn/DbrdLnvHU1BSOHDkCo9GICxcu4Pjx4zAajTLt2Ww2H/je+O3f/u0mlV/RaBS7u7sS42K1WrG+vo5sNovh4eE9amU2i3Q6ndQWLpdLSFzWKgMDA9jY2EAul8PExITEb/F1mZE+MDAgNbbX60U2mxXnF3N3X3vtNfT19YlyvVQqSRaiw+EQNYper8cTTzwh54WD3hvvf//7m9lsFu94xztQqVSQzWbR19cnyiAOQwGuEdfNZlOssczgdblcQgjQPjs8PIzz58+LQnF8fFyaGLRKsjm0sbGBSqWCzs5O3HbbbXLIpCvLarXC6XQilUqJ+pwkMMk05hcXi0WcXLXJmAAAFfBJREFUPHlSmhs3sm78PKPd+uvrX/+6DFvb2tqC3+9HNpvF6OioHM65bpJkWVtbQ6lUkgYxs1ndbjdcLpfMReHXU+msrUf5LDCHmOp67d6+ubkpZyJtLaq11zJLHGjFjwSDwRs5w6p7Q4PPfe5zTQAiUuIgLjZcnE6nDG9mHcXGlM1mQy6Xk/10eHgYyWQSfr9fSEkKw1ZXV6XxQtVoo9HApUuXMDo6inw+D4fDIeIXABJLQOKKvMH6+joikYic/xnX2Gg0ZL995JFH2r4WAPCxj32sSe5ic3NT9k232y3qcDYI7rnnHpw7dw6NRkMakiS7qtWqkIUcrM1B0cztNJvNoijf3d2VSBxyPozHWV5elsHlq6urMvB0fX1dmmQcSspniHVNd3c3dDodLly40Pb1OHLkSDMajcJoNErMktFolEHKFAEcOXIEXV1dEj/D/YbrCfdToOXQHRgYgNFoxNWrV4Vg5brA1+S153mdzXa73S6NdLp/WPuw6c7YNq29n4Mnb/QMC7TWDboBGMnyZmKTg54zKSY56HnjINgvJqErhZFYrNfJHfDPurq6sLy8/FOvx9uqgCU+97nP4Qtf+AKazabk3nCAA7sR6+vrMowhFArJ1Fna4Dm9jJsXrSC80ahsAyDdBXafjEajkEXZbBYrKyswGo1iq+ZQrkqlAoPBgMnJSVHrAq0PYWBgALu7u/jSl74kG1c70A7s6uvrkzxNbpqZTAbr6+solUrycDF6gQMBQqEQnn/+eSwuLmJpaUmmY5KotNlsuHr1KnQ6Hfr7+zE2NobHH38c999/P0qlkhz69Ho9PB6PDEfjQZuDEJityM5CtVqVfBQqVzKZjBQDVHndCIaGhvDBD35QPldOM9zc3MTx48fh9/uF8GUmMFUSXV1dsFgsGB8fh9VqxeDgoGT+Hj9+XHKxqNxpNBqibshmsygUCrh06ZJkrvCwPjg4KDZJdiU5KbKnpwcul0uGhgSDQUQiEaysrOChhx5qy6awH1/+8pfxrW99SxSV7NDRWsYCj5mxXCQ5SITFFge7+Hw+OWxTUUHrM8kaDuoym82i6uBr9/T0YGBgAAMDA+jv74fJZJLnttFoYHl5GVtbW1JgsrO7traG3/md3xHytV187Wtfk1/ffffdALAnQ5E5izyIaIlTHuypEmDnmE0X3tsM69bpdBJzQdKEVmkuuiSuecimcny/zZ0dVl5P4JqN+2dR9l1PIaj99f7X3K9Mvd7r7VfC8vWuF0nwZu/npxG7b2Yl1yoP2rWb78+1Y/eUymVuigBEKcuil0UKG08ApFm3/+djZ5z3zZUrV7C2toZkMilTM7l+knwlQVStVjE8PCzKewDSOODPoNPp5EBHkks7If0gYHYYfy6dTodqtSqkNIkIPvs8jJN8Ba6R1VpQYUjFBMF7n6Stlujm3/P6vRnerKhig+1niag4ceKEqEo4vIlOCKB1GCHxxX2iXC6ju7tbmmp6/bUs5UcffRRbW1v47ne/K1FFXDM8Hg9+4zd+A5/85CfFnbO8vCwNXdpL2TBMJpPS8eeazmKvq6sLLpcLu7u7+MVf/EXcfvvtUgy3k2X5Znj44YfxB3/wBzCbzejr6xPbHpU5W1tb8uyQ1KECmu9taGgIoVBI6hIW07Rd6vWtKKiJiQkZruDz+eQe4X3K+5sNPK1KrK+vD/l8HtlsVhoY9Xpdcu86Ozvx8MMPC/naDjhsEwAeeughpFIp3HLLLWLZu3jxIsbGxvD8889jaWkJi4uLWFxcRCKREGUNB0ttbm5KPWmz2TA/Py9RQZubmygWiwBazxmVo8ViEYODg1hfX0dvby/C4TAAiFKFNZ7L5YLZbIbFYoHJZEI0GsX29jai0SjGx8fF0r+7uyuqT97L7YDT1umW4VrV0dGB+fl5aWYzuiiRSMh7NxgMsFqtqFQqKBQKomo9dOiQKGEZDxYIBKTmiMfjSKVSYqWcnJzE2tqaOJ1IPCSTSZRKJTQaDZw6dQoGgwGxWEzeY71el6xW1vj1el2UaO2Ch1wqYcfGxsR5RWKgr69P8g1JtszMzMBobA0KY11IMp1T7l9//XW5jx0Oh+TR8T6hI48ChHA4LA4tvV4vJL1e34ofYgQCSQQqLYGWRbm7u1uesZ/FMqqwF5/5zGfwpS99CYFAAJFIRJRSdIh1dXVJk4i5rcy85lrLhlSj0ZDYGZ5rrVbrHqGEzWaTOJJMJoN6vS7nH9bc3G/ZgCVpY7PZRFFHIUm1WpUhxF/96ldv6AyrsBd0iDKKZXFxUVT01WoVKysreM973iMOEKPRiFAohPHxcXF68LNh7cGB2My87uzsxNDQkAyo6uhoTabv7e3F7bffLpyINvOeDsdSqQSHwwGdToerV6+iWCyKy6JUKgkhr9PphPtoN3ZAi9XVVXFLkrchAXz27FmJy6rX69jY2BCeJ5PJiCOE0Q2pVArRaBROp1MiInK5HJaXl9HV1SXPDgcaa983c8s50KlQKKBUKokzjeQZiWtGhpTLZQAQQcHa2toN1198zwaDAevr6wiHw7KX0J3JOQHaPZAkOy303Ie3t7cRDAZRrVaFJ7NYLNjd3RV1K6MXGIkEtGrL/v5+4U/IEXDAFRvfPCcxzo0NVZKv/x3gGVnr1rueG+5659rrnS33OxW1/2m/53rfu/9rCZ7x+do80/N6kHwFrp2jNjY2DuwA/x8hYIlwOCzhvlQX8UEKBoOyWZBAovqAtj8qBGjRo904FApJ14IWKoPBIHmmVEVSiu90OkUFc9ttt+Gd73wn8vm85HgxL4J2fma3/CzkmraLQls1pzHyA7x69SoqlQqGh4flQB6JRLC+vo7Z2VmUy2XccccdOH36tHQxOX2bg6pYhG1sbGBmZgZ9fX2YmpqSvJparYZMJoN0Oi15MSSpJicnJYtNa+H2+Xxi2U2lUsjlcrBarRgYGJDuQCwW+5nujcnJSeneTU9Pw+PxiJWKUx/ZKTYajbIwceEpl8tiqa/X60gkEnsOuZOTk1IM9ff3w+12Y2BgQGwAOp0OoVBoT2ak1u6Qy+UkK+/q1auixo5Go7BYLBgeHm7LPvxW4GdPxRIVV8wE4zUfGBgQiyYHUmhJNRL4tVpNyACqlWjfD4VC8lyl02lRZ9EaUyqVJI+Xyrjt7W04nU6J4djc3JQ4Edqk2o2k0GJ8fFx+/fnPf14aNuyoUj1jt9slImJrawvZbFYsLDx8NBqNPbmY7ApS/UiVwn7ruZZYINio0KpbWYjzGaKSj2py4r+rQ8fX2r9Jaf/srTYc7d/v//9+Qlb7/dqu5JuRZPs7pFqS92e1lhNUbZMA0lqU2bjQZqGS7GJWLAsMKlwBiNqKkSr7Lfd6vR7PPfccVlZWkMlkkEwm5fDFAqhSqUhu4erqKs6dO4dSqSRFFXAtviAYDMrgOL1eL9EuN5LHxiJM2ywk2DzgfUmwi3s90B2gtU5poVXMal9TG39wPbzZ5/9WXe527xWXy4WRkRGZNk4Lt81mQz6fh81mk4MPn2N2/LUNL6vVCo/HA7vdjueffx533XWXkKpaqx4PWlSVcu1kc8fhcGB1dRXpdFoartxXDAaDqE5JtjocDgwODgqZV6/X8c53vrOta/BW4GAkvh+32y22c6vVKgNhyuUy6vXWoFSSR/l8HsViUSyF/f39CIfDuP322+HxeHDixAkMDAwgk8nI5G4+Z8yeq1arQv5yf6PSWqfTIRqNSiYycO1e5r7T7hAyLaanp+XXbDry2dvZ2cGFCxcwPz+P9fV1uN1u9Pf347777pP1Y3V1Ve6pd73rXaLWef7556HX68WFYTAYMDIyIteXttBarYaVlRXceuutACDNUO5h/Mw5vJIN4/7+fhgMBoyNjWF5eRnnz5+XmpB5qW9mrXsrRKNRUZRq3UQkcra2tiRnjfc91TupVErqCqPRiHA4jFAohFQqhaWlJVQqFQSDwT35itoBmly3mfXHjG2u3S6XS1w9JAs6OjoQj8dFxc9afXV1VbLZvv71r9/QvcHPzmg04o033pAzBgffMsvQZDIhHo/L+m02m6VR5fV6kUgkRMEeiUSQTCbldamC4lBZiiSKxaIQJ9yz6GLb3t5GpVJBKBSSwZs8PzF/2GQyCYGgnej9L//yLzd0LRTeGn19faKUY7Qb17mNjQ0UCgWk02nk83n4fL49Ob3a+CQO9KHIg4TV2tranjMhrdYkyfi8MnbLbDbD4/HIuYfqNm1G4srKimQ3txObp/DWSKfTIsrhMGvWynSWXL58WYQdtVoNCwsLeOyxx2RuAPNfk8mkqDJffPFFWR+Alpo1FothZWUFuVxOBC/5fB7BYFAamHTUcj1hjBDnFbD+0b5nqmAZTfenf/qnN3w9KNRiw9lqteIXfuEXMD4+jve+970itOvt7ZXGXi6XE/J0ZWUFlUoFfX19wgGxAdtsNoVsnZubw/nz56VeYJwkm2+BQAClUkniIgGIIp0kmtvtRr1eF0Hezs6OzIvhGcDn8+E73/nODV0LRghxDsTVq1cld5nNOJ2uldPK4aYkX/V6PUZGRiTfvV5vDU4nSU4BEBs3Pp9PhnUzdrCvr0/Oy2tra+JI4hmHrhq6w9fX1/c4rUulksxk2n+G++/EW4l23uxcehDF6/XOuNc7u77Vz7WfiGVNznMka2TWRwfB/ygBy66byWSS6AAqEGjT5aGWA5J4cZhPxqKPRQ2LIOayMjuWykcucOxoc8pcIBAQZQEVp729vbjjjjtEWaQdhGW1WvH5z39eVCC8OQ+K3/zN39zz+7GxMRmqRDKU+WwulwvBYBAmkwmJREJUO9FoFK+99ppYwKhCBa7dSIxY4OCVkZERUUzy60hIcNOfn5/HwsKCDGZiRgkXzHq9LplnHITFa0LClkHhN4qJiQmEw2EcOnQIR48ehd/vlwWE74e5JbQU7OzsoFqtIp1Ow+l0SufG7/fLoW1+fh5utxtXrlwRq5vdbsfo6CgsFgtGRkbgcDjg8/kwMjICv98v+YFOpxMnTpwQMp4NAqpAjUYjIpEIjEYj7rvvPkxOTgKADDS5UfzN3/yNWASoZtPpdEJCs6CbnZ0VRS+D99kBJXnKLmtvb690jdgd5QRiLijsxBYKBbFCMY6B3Tu9Xo9yuSw5WOVyWRQAtCT+1m/9lij/SHC3g/vvv3/P7wOBgBxy2YzZ3NzE+vq6vL7ZbIbdbpcONDezbDYrhzuTySQqb25GWqs2/1+v7x1kQBUriTISvvvt19zcmbOjtbTzPd3IJvZmpOd+q8b+bqJW0ardxLSRAtfb8N7s9wd5j9cjcd/s/d0oeO15P/PZIBnJz4qqSqB1H1Jhwmw//jnB4kMbScBuu16vxwsvvIAnnnhCnoGNjQ1cuXJFyIzLly8LkUOXwD//8z/L66fTaVy8eFGKSubABoNBUfi1MzCFPyPtjXzvtMDRvcDDmZZ4pSqW14TgvawdWsb7mI1MFiZsVvHZIaGpva5USe7/zLX7kRbXU2YfFGazGcPDwxgdHcXk5CRKpZIcQLq7u1EqlWTwpDZCgU4bEu2vvvoqXnnlFQCtwmt2dlYaQMynpN3eYDDg7/7u75DJZLC2toZisSjNOS2ZabPZ5L/d3V1Zu3hdeZDmQAaqZf478fDDDwspTeUrm1I8vPGwSIX37u6urLkulwuVSgXLy8sAWkruqakppNNpnD59WmoCKl+p+uG12N7eFleKx+PBwMAAfD4fbDab7OvMO+W/T4VKOBzG7/7u76JSqeA///M/8eyzz7bVEP/oRz+65/cnTpxAJpMBADz22GP4wAc+gGq1KoO6xsfHcebMGXn/jGfgYBUqaw4fPozOzk6JN6Hafnt7G6FQCOFwGLFYDGfPnsXp06dFdMChO52dnchms3jyySfx4osvIhqN4tKlS1hcXESz2ZqKnclkcPHiRRkQx2YAn0e6WdoBHVVUjjJii1b7nZ0dmY6cSCTEYUa3DTPoAQjxGo/HYbfb4XK5EI/HUavVsLa2JjMCWL90d3dLQ5ukJhVLq6uryGQyeOWVVyQT3+FwiM2Xh24qljgA6Wdpfg8NDcFqtSIajWJiYgKNRkOI1Y2NDYm9WFxchNfrlQFrFHnU63VcuXJF1uNMJoOZmRlp/rA5Q5W9zWaTPDuqXr1eL7a2trCwsACr1Sr3drlcRiaTQbPZFLeZwWAQ0pjrOoea8VlTuDn4yEc+IlPtud9ms1nMz88jHo+Ly0Kn04lbh7Ug7wG6UTo7OxEMBuUsyygJNsTY+GJzhPMcAMg5IJfLSf3PBiLvQ+2AL5vNhmAwiD/+4z+Wv79w4cL/6QzYnxWsB6hGZ6QAnYU2mw0ej0cGNrPZ++lPfxq9vb245557ZK4I80bX1tYwMTEhA4vcbreoVelE5CA1RirybE5Sc319XbJBaeEfHR1FT0+PDHOkY5FirFQqJU2BG8XHP/5xzM7OYmNjAz6fD263Gz/4wQ9w7tw5nD59Go1GQ+KLZmZmxI3IPc3v98Nut8vskGKxiJmZGVitVmmgFgoFETWkUinJAZ+enpY8+bm5OWlIsZnZ0dEhM266urok6oYDpQ0GgwxSYwwMHWs3AhK+nP9AsZjf7xfnB/PjGSOjnbmgjRFh45kNFvJBJItTqZQIiLxer4jR2PAkJ1StVmXfIK9TKpVkJgAdfsViUeKi3i78NCL2eiKin9Uxp/139v8b2temChbY64Lln7GOPgje1gxYBQUFBQUFBQUFBQUFBQUFBQUFBYX/S/gfVcAqKCgoKCgoKCgoKCgoKCgoKCgoKPw8QxGwCgoKCgoKCgoKCgoKCgoKCgoKCgo3CYqAVVBQUFBQUFBQUFBQUFBQUFBQUFC4SVAErIKCgoKCgoKCgoKCgoKCgoKCgoLCTYIiYBUUFBQUFBQUFBQUFBQUFBQUFBQUbhIUAaugoKCgoKCgoKCgoKCgoKCgoKCgcJOgCFgFBQUFBQUFBQUFBQUFBQUFBQUFhZsERcAqKCgoKCgoKCgoKCgoKCgoKCgoKNwkKAJWQUFBQUFBQUFBQUFBQUFBQUFBQeEmQRGwCgoKCgoKCgoKCgoKCgoKCgoKCgo3CYqAVVBQUFBQUFBQUFBQUFBQUFBQUFC4SVAErIKCgoKCgoKCgoKCgoKCgoKCgoLCTYIiYBUUFBQUFBQUFBQUFBQUFBQUFBQUbhIUAaugoKCgoKCgoKCgoKCgoKCgoKCgcJOgCFgFBQUFBQUFBQUFBQUFBQUFBQUFhZsERcAqKCgoKCgoKCgoKCgoKCgoKCgoKNwkKAJWQUFBQUFBQUFBQUFBQUFBQUFBQeEmQRGwCgoKCgoKCgoKCgoKCgoKCgoKCgo3CYqAVVBQUFBQUFBQUFBQUFBQUFBQUFC4SVAErIKCgoKCgoKCgoKCgoKCgoKCgoLCTYIiYBUUFBQUFBQUFBQUFBQUFBQUFBQUbhIUAaugoKCgoKCgoKCgoKCgoKCgoKCgcJOgCFgFBQUFBQUFBQUFBQUFBQUFBQUFhZuE/w9QITpbQ/re3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1728x864 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plots(imgs, titles=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Network Architecture</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:4: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:8: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "  \n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:14: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "  \n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:22: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:28: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:35: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:42: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:49: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:59: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:65: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:71: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:79: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:87: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:95: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:107: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:112: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:119: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:127: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:135: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:143: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:149: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n",
      "C:\\Users\\DesignerBe\\AppData\\Local\\conda\\conda\\envs\\MLDL_env\\lib\\site-packages\\ipykernel_launcher.py:154: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization(axis=-1, epsilon=0.001, momentum=0.99, weights=None)`\n"
     ]
    }
   ],
   "source": [
    "input1 = keras.layers.Input(shape=(64, 64, 3))\n",
    "C1 = Conv2D(32, (3, 3), strides=(2, 2), activation='linear', input_shape=(64,64,3),\n",
    "                kernel_regularizer=regularizers.l2(9.999999747378752e-05), padding='valid')(input1)\n",
    "BN1 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C1)\n",
    "A1 = Activation('relu')(BN1)\n",
    "C2=Conv2D(64, (3, 3), strides=(1, 1), activation='linear', input_shape=(64,64,3),\n",
    "                kernel_regularizer=regularizers.l2(9.999999747378752e-05), padding='same')(A1)\n",
    "BN2 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C2)\n",
    "A2 = Activation('relu')(BN2)\n",
    "C_sep_1 = SeparableConv2D(64, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(A2)\n",
    "BN3 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_1)\n",
    "add_1 = keras.layers.add([A2,BN3])\n",
    "A3 = Activation('relu')(add_1)\n",
    "\n",
    "#Bottleneck 1\n",
    "\n",
    "C3 = Conv2D(128, (7, 7), strides=(1, 1), activation='linear', input_shape=(64,64,3),\n",
    "                kernel_regularizer=regularizers.l2(9.999999747378752e-05), padding='same')(A3)\n",
    "BN4 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C3)\n",
    "A4 = Activation('relu')(BN4)\n",
    "C_sep_2 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(A4)\n",
    "BN5 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_2)\n",
    "add_2= keras.layers.add([A4,BN5])\n",
    "A5 = Activation('relu')(add_2)\n",
    "C_sep_3 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(A5)\n",
    "BN6 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_3)\n",
    "add_3= keras.layers.add([A5,BN6])\n",
    "A6 = Activation('relu')(add_3)\n",
    "C_sep_4 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(A6)\n",
    "BN7 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_4)\n",
    "add_4= keras.layers.add([A6,BN7])\n",
    "A7 = Activation('relu')(add_4)\n",
    "C_sep_5 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(A7)\n",
    "BN8 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_5)\n",
    "add_5= keras.layers.add([A4,BN8])\n",
    "A8 = Activation('relu')(add_5)\n",
    "\n",
    "#Bottleneck 2\n",
    "MP1 = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='valid',data_format='channels_last')(A8)\n",
    "#To tackle overfitting as acc=0.998 and val_acc = 0.66  in previous version\n",
    "DO1 = keras.layers.Dropout(rate=0.3, noise_shape=None, seed=None) (MP1)\n",
    "C_sep_6 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(DO1)\n",
    "BN9 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_6)\n",
    "add_6= keras.layers.add([BN9,DO1])\n",
    "A9 = Activation('relu')(add_6)\n",
    "\n",
    "C4 = Conv2D(128, (7, 7), strides=(1, 1), activation='linear', input_shape=(64,64,3),\n",
    "                kernel_regularizer=regularizers.l2(9.999999747378752e-05), padding='same')(A9)\n",
    "BN10 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C4)\n",
    "A10 = Activation('relu')(BN10)\n",
    "C_sep_7 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(A10)\n",
    "BN11 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_7)\n",
    "add_7= keras.layers.add([A10,BN11])\n",
    "A11 = Activation('relu')(add_7)\n",
    "\n",
    "C_sep_8 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(A11)\n",
    "BN12 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_8)\n",
    "add_8= keras.layers.add([A11,BN12])\n",
    "A12 = Activation('relu')(add_8)\n",
    "\n",
    "C_sep_9 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(A12)\n",
    "BN13 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_9)\n",
    "add_9= keras.layers.add([A12,BN13])\n",
    "A13 = Activation('relu')(add_9)\n",
    "\n",
    "C_sep_10 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(A13)\n",
    "BN14 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_10)\n",
    "add_10= keras.layers.add([A10,BN14])\n",
    "\n",
    "A14 = Activation('relu')(add_10)\n",
    "\n",
    "#Bottleneck3\n",
    "MP2 = MaxPooling2D(pool_size=(2,2),strides=(2,2),padding='valid',data_format='channels_last')(A14)\n",
    "#To tackle overfitting as acc=0.95 and val_acc = 0.69  in previous version\n",
    "DO2 = keras.layers.Dropout(rate=0.3, noise_shape=None, seed=None) (MP2)\n",
    "\n",
    "C_sep_11 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(DO2)\n",
    "BN15 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_11)\n",
    "add_11= keras.layers.add([BN15,DO2])\n",
    "A15 = Activation('relu')(add_11)\n",
    "C5 = Conv2D(128, (7, 7), strides=(1, 1), activation='linear', input_shape=(64,64,3),\n",
    "                kernel_regularizer=regularizers.l2(9.999999747378752e-05), padding='same')(A15)\n",
    "BN16 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C5)\n",
    "A16 = Activation('relu')(BN16) #Big Node 2\n",
    "\n",
    "C_sep_12 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(A16)\n",
    "BN17 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_12)\n",
    "add_12= keras.layers.add([A16,BN17])  #add_7\n",
    "A17 = Activation('relu')(add_12) \n",
    "\n",
    "C_sep_13 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(A17)\n",
    "BN18 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_13)\n",
    "add_13= keras.layers.add([A17,BN18])  #add_8\n",
    "A18 = Activation('relu')(add_13) \n",
    "\n",
    "C_sep_14 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(A18)\n",
    "BN19 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_14)\n",
    "add_14= keras.layers.add([A18,BN19])  #add_9\n",
    "A19 = Activation('relu')(add_14) \n",
    "\n",
    "C_sep_15 = SeparableConv2D(128, (3, 3),dilation_rate=(1, 1), depth_multiplier=1, strides=(1, 1),padding='same',\n",
    "            activation='linear',depthwise_initializer='Zeros', kernel_regularizer=regularizers.l2(9.999999747378752e-05),\n",
    "                         use_bias=True, pointwise_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                          depthwise_regularizer=regularizers.l2(9.999999747378752e-05), pointwise_regularizer=regularizers.l2(9.999999747378752e-05))(A19)\n",
    "BN20 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C_sep_15)\n",
    "add_15= keras.layers.add([A16,BN20])  #add_9\n",
    "A20 = Activation('relu')(add_15)\n",
    "\n",
    "C6 = Conv2D(128, (7, 7), strides=(1, 1), activation='linear', input_shape=(64,64,3),\n",
    "                kernel_regularizer=regularizers.l2(9.999999747378752e-05), padding='same')(A20)\n",
    "BN21 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(C6)\n",
    "A21 = Activation('relu')(BN21)\n",
    "F1 = Flatten() (A21)\n",
    "D1 = keras.layers.Dense(units=128, activation='linear', use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                        kernel_regularizer=regularizers.l2(9.999999747378752e-05))(F1)\n",
    "BN22 = BatchNormalization(axis=-1, epsilon=0.001, mode=0, momentum=0.99, weights=None)(D1)\n",
    "A22 = Activation('relu')(BN22)\n",
    "DO3 = keras.layers.Dropout(rate=0.5, noise_shape=None, seed=None)(A22)\n",
    "out = keras.layers.Dense(units=2, activation='softmax', use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', \n",
    "                        kernel_regularizer=None)(DO3)\n",
    "model = keras.models.Model(inputs=[input1], outputs=out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(Adam(lr=.0001), loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_2 (InputLayer)            (None, 64, 64, 3)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 31, 31, 32)   896         input_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 31, 31, 32)   128         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 31, 31, 32)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 31, 31, 64)   18496       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 31, 31, 64)   256         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 31, 31, 64)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_12 (SeparableC (None, 31, 31, 64)   4736        activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 31, 31, 64)   256         separable_conv2d_12[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 31, 31, 64)   0           activation_17[0][0]              \n",
      "                                                                 batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 31, 31, 64)   0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 31, 31, 128)  401536      activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 31, 31, 128)  512         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 31, 31, 128)  0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_13 (SeparableC (None, 31, 31, 128)  17664       activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 31, 31, 128)  512         separable_conv2d_13[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 31, 31, 128)  0           activation_19[0][0]              \n",
      "                                                                 batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 31, 31, 128)  0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_14 (SeparableC (None, 31, 31, 128)  17664       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 31, 31, 128)  512         separable_conv2d_14[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 31, 31, 128)  0           activation_20[0][0]              \n",
      "                                                                 batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 31, 31, 128)  0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_15 (SeparableC (None, 31, 31, 128)  17664       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 31, 31, 128)  512         separable_conv2d_15[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 31, 31, 128)  0           activation_21[0][0]              \n",
      "                                                                 batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 31, 31, 128)  0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_16 (SeparableC (None, 31, 31, 128)  17664       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 31, 31, 128)  512         separable_conv2d_16[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_16 (Add)                    (None, 31, 31, 128)  0           activation_19[0][0]              \n",
      "                                                                 batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 31, 31, 128)  0           add_16[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 15, 15, 128)  0           activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 15, 15, 128)  0           max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_17 (SeparableC (None, 15, 15, 128)  17664       dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 15, 15, 128)  512         separable_conv2d_17[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_17 (Add)                    (None, 15, 15, 128)  0           batch_normalization_24[0][0]     \n",
      "                                                                 dropout_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 15, 15, 128)  0           add_17[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 15, 15, 128)  802944      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 15, 15, 128)  512         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 15, 15, 128)  0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_18 (SeparableC (None, 15, 15, 128)  17664       activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 15, 15, 128)  512         separable_conv2d_18[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_18 (Add)                    (None, 15, 15, 128)  0           activation_25[0][0]              \n",
      "                                                                 batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 15, 15, 128)  0           add_18[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_19 (SeparableC (None, 15, 15, 128)  17664       activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 15, 15, 128)  512         separable_conv2d_19[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_19 (Add)                    (None, 15, 15, 128)  0           activation_26[0][0]              \n",
      "                                                                 batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 15, 15, 128)  0           add_19[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_20 (SeparableC (None, 15, 15, 128)  17664       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 15, 15, 128)  512         separable_conv2d_20[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_20 (Add)                    (None, 15, 15, 128)  0           activation_27[0][0]              \n",
      "                                                                 batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 15, 15, 128)  0           add_20[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_21 (SeparableC (None, 15, 15, 128)  17664       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 15, 15, 128)  512         separable_conv2d_21[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_21 (Add)                    (None, 15, 15, 128)  0           activation_25[0][0]              \n",
      "                                                                 batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 15, 15, 128)  0           add_21[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 7, 7, 128)    0           activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dropout_4 (Dropout)             (None, 7, 7, 128)    0           max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_22 (SeparableC (None, 7, 7, 128)    17664       dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 7, 7, 128)    512         separable_conv2d_22[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_22 (Add)                    (None, 7, 7, 128)    0           batch_normalization_30[0][0]     \n",
      "                                                                 dropout_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 7, 7, 128)    0           add_22[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 7, 7, 128)    802944      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 7, 7, 128)    512         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 7, 7, 128)    0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_23 (SeparableC (None, 7, 7, 128)    17664       activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 7, 7, 128)    512         separable_conv2d_23[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_23 (Add)                    (None, 7, 7, 128)    0           activation_31[0][0]              \n",
      "                                                                 batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 7, 7, 128)    0           add_23[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_24 (SeparableC (None, 7, 7, 128)    17664       activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 7, 7, 128)    512         separable_conv2d_24[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_24 (Add)                    (None, 7, 7, 128)    0           activation_32[0][0]              \n",
      "                                                                 batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 7, 7, 128)    0           add_24[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_25 (SeparableC (None, 7, 7, 128)    17664       activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 7, 7, 128)    512         separable_conv2d_25[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_25 (Add)                    (None, 7, 7, 128)    0           activation_33[0][0]              \n",
      "                                                                 batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 7, 7, 128)    0           add_25[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "separable_conv2d_26 (SeparableC (None, 7, 7, 128)    17664       activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 7, 7, 128)    512         separable_conv2d_26[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "add_26 (Add)                    (None, 7, 7, 128)    0           activation_31[0][0]              \n",
      "                                                                 batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 7, 7, 128)    0           add_26[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 7, 7, 128)    802944      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 7, 7, 128)    512         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 7, 7, 128)    0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 6272)         0           activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 128)          802944      flatten_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 128)          512         dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 128)          0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_5 (Dropout)             (None, 128)          0           activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 2)            258         dropout_5[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 3,895,362\n",
      "Trainable params: 3,890,178\n",
      "Non-trainable params: 5,184\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'batch_input_shape': (None, 64, 64, 3), 'dtype': 'float32', 'sparse': False, 'name': 'input_1'}\n",
      "[]\n",
      "{'name': 'conv2d_1', 'trainable': True, 'batch_input_shape': (None, 64, 64, 3), 'dtype': 'float32', 'filters': 32, 'kernel_size': (3, 3), 'strides': (2, 2), 'padding': 'valid', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': {'class_name': 'L1L2', 'config': {'l1': 0.0, 'l2': 9.999999747378752e-05}}, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
      "[array([[[[ 7.42815733e-02, -2.07459405e-02,  1.32754996e-01,\n",
      "          -7.37847388e-02,  5.68770915e-02, -1.06634945e-02,\n",
      "           1.27182469e-01,  8.06067139e-02,  2.51204520e-02,\n",
      "          -8.84796679e-03, -5.93518019e-02, -7.61036798e-02,\n",
      "           1.02868840e-01,  9.81663465e-02,  1.12676665e-01,\n",
      "          -9.41796601e-03,  8.70216787e-02,  7.32742846e-02,\n",
      "           4.83665615e-02, -5.83636984e-02, -1.08711012e-01,\n",
      "           5.25566489e-02,  9.54654813e-02, -1.20830685e-02,\n",
      "           1.21317312e-01, -8.32082406e-02,  1.62622333e-03,\n",
      "          -5.87824807e-02,  9.13071781e-02,  3.14770490e-02,\n",
      "           8.78281444e-02,  3.46189439e-02],\n",
      "         [-6.89400733e-03,  7.81360865e-02,  2.45780051e-02,\n",
      "          -4.19843197e-03, -1.14191122e-01, -6.49915338e-02,\n",
      "          -1.44720078e-04, -5.81184253e-02,  1.07577607e-01,\n",
      "           1.34640500e-01, -1.35202110e-01,  8.12567174e-02,\n",
      "          -3.91429663e-02, -2.81524658e-03, -1.13106802e-01,\n",
      "           1.26016304e-01, -5.31966761e-02, -8.24485272e-02,\n",
      "           9.42990929e-02, -3.64034325e-02, -6.22034371e-02,\n",
      "           7.79018700e-02,  1.27886996e-01, -4.37521860e-02,\n",
      "          -9.47319269e-02,  1.07721895e-01,  7.82086104e-02,\n",
      "          -1.11459129e-01,  9.75883305e-02, -9.76832658e-02,\n",
      "           5.65076619e-02, -3.84361669e-02],\n",
      "         [ 2.25570947e-02,  1.04042634e-01, -1.23624392e-01,\n",
      "          -1.69772133e-02, -1.03258803e-01,  3.52841169e-02,\n",
      "           1.30755052e-01, -9.48772356e-02,  2.01621801e-02,\n",
      "          -1.18430622e-01, -8.77243727e-02,  4.10618782e-02,\n",
      "          -4.38228622e-02, -1.23076737e-02, -5.44423535e-02,\n",
      "           4.29709256e-02,  1.10005721e-01, -1.14872120e-01,\n",
      "          -6.09133989e-02, -3.04917768e-02,  8.41380358e-02,\n",
      "          -1.25356942e-01, -8.34015608e-02, -1.26235589e-01,\n",
      "          -5.37633970e-02,  2.00489163e-02, -6.41229749e-02,\n",
      "           5.40749431e-02, -4.99785468e-02,  1.15503654e-01,\n",
      "          -1.16959803e-01, -9.62566063e-02]],\n",
      "\n",
      "        [[ 1.79555714e-02,  1.11549675e-01, -8.80872756e-02,\n",
      "          -9.45985615e-02, -1.13375641e-01,  3.83814871e-02,\n",
      "          -9.41948593e-03,  1.01603657e-01,  2.93897539e-02,\n",
      "           4.83514965e-02,  2.23332047e-02, -8.14145952e-02,\n",
      "          -4.56472412e-02,  1.14023551e-01,  4.26822156e-02,\n",
      "           5.49579710e-02, -1.30833462e-01, -2.86422223e-02,\n",
      "          -4.78144586e-02, -4.94577587e-02, -6.64401725e-02,\n",
      "           1.37629285e-01,  3.21330428e-02,  8.48740786e-02,\n",
      "           1.02641314e-01,  3.49997133e-02, -9.98650566e-02,\n",
      "          -6.01219684e-02, -3.00212353e-02, -4.23331559e-03,\n",
      "           4.90028858e-02,  5.71321696e-02],\n",
      "         [ 4.24456596e-03,  1.25207379e-01, -1.54117271e-02,\n",
      "           1.35778233e-01, -4.99245450e-02,  7.01294392e-02,\n",
      "           3.34171802e-02, -7.16924816e-02, -1.65110156e-02,\n",
      "           8.55313838e-02,  6.85531199e-02, -9.03372467e-02,\n",
      "          -5.22940978e-02, -1.11483678e-01, -1.16745099e-01,\n",
      "           2.05174088e-02, -6.77419528e-02, -5.42807579e-02,\n",
      "           9.06884372e-02, -3.06653157e-02,  4.60879654e-02,\n",
      "          -6.88681901e-02, -1.05932713e-01, -1.06487989e-01,\n",
      "           1.09985247e-01, -4.81117219e-02,  1.16597846e-01,\n",
      "           1.29720584e-01, -9.67890397e-02,  7.48893172e-02,\n",
      "          -8.70070755e-02, -3.15462127e-02],\n",
      "         [ 2.24474519e-02, -2.89499834e-02,  1.42600238e-02,\n",
      "          -1.24971949e-01,  1.25027612e-01, -7.96835348e-02,\n",
      "          -6.25996739e-02,  1.24473438e-01,  1.08370647e-01,\n",
      "          -6.48430660e-02, -6.84896186e-02,  1.28512979e-02,\n",
      "          -5.67918643e-02,  1.21159300e-01,  5.58032095e-02,\n",
      "          -2.15981081e-02, -2.07020417e-02, -7.47973546e-02,\n",
      "           2.71423608e-02,  7.86513984e-02,  8.74865055e-03,\n",
      "           1.15550295e-01, -1.20175004e-01, -8.20051059e-02,\n",
      "           3.67854685e-02,  7.97412544e-02,  1.08031258e-01,\n",
      "           5.85338473e-03, -2.67043263e-02,  4.83522564e-02,\n",
      "          -1.33474275e-01, -9.51272473e-02]],\n",
      "\n",
      "        [[-1.35594398e-01, -1.29046798e-01,  1.32560745e-01,\n",
      "           1.07703105e-01, -2.30014026e-02,  6.13190830e-02,\n",
      "           7.19515085e-02, -7.08029643e-02, -3.63579392e-03,\n",
      "           4.27077115e-02, -1.04325674e-01,  2.34349668e-02,\n",
      "          -9.23795849e-02, -1.11817427e-01,  1.15525112e-01,\n",
      "           6.24133646e-02, -2.32997909e-02, -1.72054395e-02,\n",
      "           4.48488444e-02, -1.16755813e-02,  1.13697693e-01,\n",
      "          -6.57301843e-02, -8.04536343e-02,  3.26952189e-02,\n",
      "          -1.35548398e-01, -6.88715428e-02,  4.10516411e-02,\n",
      "           9.09307897e-02, -8.25780481e-02,  2.63644159e-02,\n",
      "          -8.44681337e-02,  1.36584237e-01],\n",
      "         [-1.91555843e-02, -1.02944165e-01, -7.26355612e-03,\n",
      "          -5.56618795e-02, -1.49677098e-02, -2.44589970e-02,\n",
      "           1.02181658e-01,  1.36918575e-02, -9.34369862e-02,\n",
      "          -4.49617654e-02, -1.65933445e-02, -1.24589056e-02,\n",
      "          -1.17411658e-01,  9.56083536e-02,  2.86067873e-02,\n",
      "          -1.11425795e-01, -6.73507452e-02,  1.13479778e-01,\n",
      "          -3.76504958e-02, -1.02999412e-01,  5.02714664e-02,\n",
      "           4.96053398e-02,  5.91812581e-02, -3.84029970e-02,\n",
      "          -3.56692299e-02, -1.17702141e-01, -1.14546463e-01,\n",
      "          -1.15609720e-01,  9.90605652e-02, -1.11863926e-01,\n",
      "          -1.08532302e-01, -1.09344959e-01],\n",
      "         [ 1.29228815e-01,  1.14470974e-01,  1.41242594e-02,\n",
      "           1.34308055e-01, -1.06565841e-01,  5.74174225e-02,\n",
      "          -2.17214376e-02, -5.09101152e-02,  4.45829928e-02,\n",
      "           1.23851821e-01,  4.82364595e-02,  7.94909447e-02,\n",
      "           8.25887024e-02,  3.38872969e-02,  5.08045256e-02,\n",
      "           9.17281657e-02, -1.53282136e-02, -7.06022754e-02,\n",
      "           1.04293883e-01, -2.65667513e-02,  3.79220694e-02,\n",
      "          -1.28263846e-01,  1.28306746e-02,  5.49264252e-03,\n",
      "          -1.06836088e-01,  7.74087906e-02, -9.88005549e-02,\n",
      "          -1.49497464e-02,  1.17222890e-01,  5.68532348e-02,\n",
      "          -1.81928799e-02, -1.35828033e-01]]],\n",
      "\n",
      "\n",
      "       [[[-6.22342378e-02,  1.13528207e-01, -3.00582871e-02,\n",
      "          -4.65017483e-02, -1.37994587e-02,  2.68166363e-02,\n",
      "           1.27893701e-01,  3.93790603e-02, -1.25156775e-01,\n",
      "           8.07127655e-02,  1.28810093e-01,  3.08684707e-02,\n",
      "          -5.91858253e-02,  1.06210366e-01, -4.08919603e-02,\n",
      "           6.35775477e-02,  1.26632735e-01, -1.27786338e-01,\n",
      "           2.08986849e-02,  5.62277138e-02,  1.35812268e-01,\n",
      "           1.31627068e-01,  1.61467493e-03, -7.01685920e-02,\n",
      "          -1.46577135e-02, -1.36984080e-01, -7.70035982e-02,\n",
      "          -9.65952873e-04, -4.97357696e-02,  2.63904035e-03,\n",
      "           4.22518849e-02, -1.30197436e-01],\n",
      "         [ 7.49018341e-02, -5.67395762e-02,  1.04958996e-01,\n",
      "          -5.86736649e-02, -3.29850763e-02, -9.07418132e-02,\n",
      "          -4.92270291e-02, -6.65902793e-02, -8.44003186e-02,\n",
      "          -3.13506573e-02, -2.48330235e-02,  9.89681631e-02,\n",
      "          -6.53357804e-02,  5.69284260e-02, -2.54875645e-02,\n",
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      "           6.33667260e-02,  1.25877246e-01],\n",
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      "          -8.60249624e-02,  1.00937337e-02]],\n",
      "\n",
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      "\n",
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      "           5.46569973e-02,  1.20150134e-01],\n",
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      "          -3.43286842e-02,  9.06324387e-02]]],\n",
      "\n",
      "\n",
      "       [[[-4.32705879e-03, -1.13993987e-01,  8.64617378e-02,\n",
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      "\n",
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      "\n",
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      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "      dtype=float32)]\n",
      "{'name': 'batch_normalization_1', 'trainable': True, 'axis': -1, 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
      "[array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
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      "      dtype=float32), array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
      "      dtype=float32)]\n",
      "{'name': 'activation_1', 'trainable': True, 'activation': 'relu'}\n",
      "[]\n",
      "{'name': 'conv2d_2', 'trainable': True, 'batch_input_shape': (None, 64, 64, 3), 'dtype': 'float32', 'filters': 64, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'linear', 'use_bias': True, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': {'class_name': 'L1L2', 'config': {'l1': 0.0, 'l2': 9.999999747378752e-05}}, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
      "[array([[[[-0.01065604,  0.07415166, -0.05952656, ...,  0.0241098 ,\n",
      "           0.00997382,  0.00102812],\n",
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      "          -0.05751876,  0.01494062],\n",
      "         ...,\n",
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      "           0.06055554, -0.058227  ]],\n",
      "\n",
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      "[]\n"
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      "        [[ 2.26631798e-02,  2.33901963e-02,  3.08717228e-03, ...,\n",
      "          -5.18656150e-03, -5.44922054e-03, -1.39632933e-02],\n",
      "         [-2.51363982e-02,  1.34562477e-02, -7.97106512e-03, ...,\n",
      "          -1.70935094e-02, -9.51339863e-03,  4.81503643e-03],\n",
      "         [ 1.81634128e-02,  7.66802952e-03,  1.38204135e-02, ...,\n",
      "          -2.00369209e-03, -1.20867770e-02,  6.34543225e-03],\n",
      "         ...,\n",
      "         [-2.37320047e-02,  1.35125741e-02, -1.69515591e-02, ...,\n",
      "          -1.45577984e-02,  1.47360265e-02, -5.19677810e-03],\n",
      "         [-6.58778101e-03, -2.25000046e-02, -2.17491109e-02, ...,\n",
      "          -2.16566343e-02, -3.98520194e-03, -5.62665798e-03],\n",
      "         [ 7.88012892e-03,  1.00004598e-02,  1.82822384e-02, ...,\n",
      "          -1.35884434e-03,  3.89804691e-03,  1.26909688e-02]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 9.28445160e-03, -2.17853505e-02,  1.22751966e-02, ...,\n",
      "           1.85377188e-02,  1.33155212e-02, -1.29448865e-02],\n",
      "         [-1.43548315e-02,  1.34651475e-02,  1.48638859e-02, ...,\n",
      "          -1.28938286e-02,  4.05155867e-03, -1.70459449e-02],\n",
      "         [ 1.53102651e-02,  5.85548580e-03,  5.90794720e-03, ...,\n",
      "           2.01149620e-02, -3.93849611e-03,  1.91520229e-02],\n",
      "         ...,\n",
      "         [-1.79115739e-02, -7.09170662e-03, -2.01452188e-02, ...,\n",
      "          -1.02171609e-02, -2.51170360e-02, -1.13364905e-02],\n",
      "         [-7.04683177e-03, -1.44847855e-03,  3.50449234e-03, ...,\n",
      "          -1.34565979e-02,  1.93694234e-03,  1.72351822e-02],\n",
      "         [ 1.59041770e-03, -1.46337533e-02,  7.03797117e-03, ...,\n",
      "          -4.45313379e-03, -2.05651037e-02,  1.96214765e-02]],\n",
      "\n",
      "        [[-1.51263531e-02, -1.95210949e-02,  1.29468180e-02, ...,\n",
      "           2.20128484e-02,  1.22989565e-02, -1.49490898e-02],\n",
      "         [ 4.76266630e-03,  2.05059722e-02,  8.86362791e-03, ...,\n",
      "           1.87196843e-02, -1.93400327e-02, -1.83422547e-02],\n",
      "         [-4.78560664e-03, -6.86175935e-03, -4.90036048e-03, ...,\n",
      "          -1.57641675e-02,  1.63066275e-02,  3.82047333e-03],\n",
      "         ...,\n",
      "         [ 1.32624470e-02,  1.21286064e-02, -6.82967342e-03, ...,\n",
      "           1.30017363e-02, -1.75251048e-02, -6.89317659e-03],\n",
      "         [ 1.36016607e-02, -1.33847976e-02, -7.25712068e-03, ...,\n",
      "           2.02453882e-02,  6.28867000e-03, -1.18268393e-02],\n",
      "         [ 1.16569400e-02,  8.71748477e-03, -7.21125305e-03, ...,\n",
      "           7.21265003e-03,  1.42814890e-02, -4.19265404e-03]],\n",
      "\n",
      "        [[-1.21304533e-02, -5.86507656e-03,  2.39132531e-02, ...,\n",
      "          -1.57357249e-02, -1.04701621e-02, -6.64128922e-03],\n",
      "         [-2.16484070e-04,  2.42874660e-02,  5.58439642e-05, ...,\n",
      "          -1.09632500e-02,  3.57875600e-03,  6.23515621e-03],\n",
      "         [-2.21315455e-02, -2.47610807e-02, -1.66576914e-03, ...,\n",
      "          -1.81411244e-02,  8.16633180e-03,  2.19646357e-02],\n",
      "         ...,\n",
      "         [ 8.54087994e-03,  1.79956704e-02, -1.88670009e-02, ...,\n",
      "          -2.23648399e-02,  1.99920982e-02, -1.92145258e-02],\n",
      "         [ 1.09182969e-02, -2.49518324e-02,  1.68320313e-02, ...,\n",
      "           2.26626508e-02, -2.21319124e-03,  4.74031642e-03],\n",
      "         [ 1.85449794e-02, -4.82140109e-03,  1.94291435e-02, ...,\n",
      "          -1.53900785e-02,  1.80280805e-02,  2.33746916e-02]]],\n",
      "\n",
      "\n",
      "       [[[-2.47191638e-02, -1.04173645e-02, -2.29522772e-03, ...,\n",
      "           1.26679502e-02,  1.84715651e-02, -4.54659760e-03],\n",
      "         [-6.75361790e-03, -2.40652915e-02,  1.96090713e-03, ...,\n",
      "           1.89179033e-02, -9.67794005e-03,  2.98092142e-04],\n",
      "         [-2.08675787e-02, -6.15186989e-04,  1.91556290e-02, ...,\n",
      "          -2.30738278e-02,  1.59250014e-02,  4.03206795e-04],\n",
      "         ...,\n",
      "         [ 1.97850130e-02, -6.97338954e-03, -2.17621215e-02, ...,\n",
      "           1.89254396e-02,  1.40172169e-02, -2.03111432e-02],\n",
      "         [-1.87158808e-02,  5.37529401e-03, -1.68876462e-02, ...,\n",
      "          -1.20313717e-02, -1.67040117e-02, -9.82164871e-03],\n",
      "         [-1.12265348e-03, -1.92792863e-02,  1.70793198e-02, ...,\n",
      "           1.45323016e-03,  2.16880143e-02, -1.62456706e-02]],\n",
      "\n",
      "        [[ 2.45523043e-02,  1.01199746e-02,  1.33536346e-02, ...,\n",
      "           1.20049827e-02,  7.80567527e-03,  3.18950042e-04],\n",
      "         [ 5.75915538e-03,  1.04825944e-04,  7.38834962e-04, ...,\n",
      "           1.90858319e-02,  1.12326480e-02, -1.81508772e-02],\n",
      "         [ 2.50954367e-02,  1.95153654e-02,  4.07520309e-03, ...,\n",
      "          -1.97747778e-02,  1.10840201e-02,  1.47280656e-02],\n",
      "         ...,\n",
      "         [ 1.44530758e-02,  1.96555816e-02,  2.36394517e-02, ...,\n",
      "           1.43025331e-02, -2.01939698e-02, -9.31612216e-03],\n",
      "         [ 1.98521279e-03, -1.91011094e-02, -9.93981678e-03, ...,\n",
      "           1.81367695e-02, -9.90896486e-03, -1.47207500e-02],\n",
      "         [-2.49714293e-02,  1.27137005e-02,  9.15236399e-03, ...,\n",
      "           1.63810998e-02, -1.77147966e-02,  1.87443048e-02]],\n",
      "\n",
      "        [[-5.10334596e-03, -1.43437348e-02,  1.90099627e-02, ...,\n",
      "           9.47670266e-03,  5.80237433e-03,  1.96623765e-02],\n",
      "         [-2.44817492e-02, -7.91909173e-03,  1.51249878e-02, ...,\n",
      "           1.25462078e-02,  1.60184391e-02,  2.47255862e-02],\n",
      "         [ 9.98817012e-03, -1.67155117e-02,  1.58477314e-02, ...,\n",
      "           8.70943069e-03, -2.39736866e-02,  1.96120739e-02],\n",
      "         ...,\n",
      "         [-7.20515475e-03, -4.24360298e-03,  7.97357410e-03, ...,\n",
      "          -2.15157308e-02, -1.26200793e-02, -1.10994149e-02],\n",
      "         [ 8.57306644e-03,  1.47347897e-02, -3.04611586e-03, ...,\n",
      "          -1.63886882e-02,  7.66846910e-03, -8.44590366e-03],\n",
      "         [ 2.97924504e-04, -6.32091984e-03, -2.01271307e-02, ...,\n",
      "          -9.90931410e-03,  1.60554387e-02, -2.09486391e-02]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 2.05140747e-03,  1.58935636e-02,  1.36811510e-02, ...,\n",
      "           1.73204467e-02,  7.53624365e-03, -9.83414799e-03],\n",
      "         [-1.33273397e-02,  4.31213528e-03, -9.23932344e-03, ...,\n",
      "          -1.38901928e-02,  9.54154506e-03,  1.96996592e-02],\n",
      "         [ 1.06677450e-02, -2.46970654e-02, -4.06389497e-03, ...,\n",
      "           7.28307292e-03,  8.45501944e-03, -1.96318403e-02],\n",
      "         ...,\n",
      "         [-7.71990046e-03,  8.17637891e-03, -1.19751319e-03, ...,\n",
      "           4.98147495e-03, -1.88146420e-02,  2.41765119e-02],\n",
      "         [ 3.53490375e-03, -8.64239223e-03,  2.08215304e-02, ...,\n",
      "          -8.33534077e-03, -5.19894063e-03,  8.88608024e-03],\n",
      "         [-4.47563827e-04,  2.50585899e-02,  1.84723251e-02, ...,\n",
      "          -1.03576845e-02,  1.93601660e-02,  2.15869658e-02]],\n",
      "\n",
      "        [[-1.77081432e-02, -2.22930163e-02, -1.07548758e-02, ...,\n",
      "           1.33096091e-02, -1.95997544e-02,  6.29932806e-03],\n",
      "         [ 1.93543509e-02,  2.26931982e-02, -1.55042056e-02, ...,\n",
      "           2.42787525e-02,  1.78243183e-02,  2.08290443e-02],\n",
      "         [-2.88362056e-03,  1.18735991e-02, -4.27954830e-03, ...,\n",
      "          -6.09062240e-03,  2.37720646e-02,  1.44329891e-02],\n",
      "         ...,\n",
      "         [-6.12995774e-03,  8.25748220e-03, -1.57809723e-02, ...,\n",
      "           6.39285892e-03, -1.88149251e-02, -8.88979435e-03],\n",
      "         [ 6.47335127e-03, -3.23609449e-03, -5.53447753e-05, ...,\n",
      "           5.63988090e-03,  1.32374689e-02, -3.28649767e-03],\n",
      "         [-2.38166004e-02, -2.35203616e-02, -8.09150934e-03, ...,\n",
      "           1.83009878e-02,  4.50510159e-03,  1.37308426e-03]],\n",
      "\n",
      "        [[-3.40289995e-03, -1.72009431e-02, -1.59485228e-02, ...,\n",
      "           6.46249205e-03, -1.83410384e-03, -1.63773205e-02],\n",
      "         [ 1.76689811e-02, -1.76364388e-02, -1.66770257e-03, ...,\n",
      "           1.66164488e-02, -1.23343989e-03,  8.24693590e-04],\n",
      "         [ 1.65756978e-02, -2.21758056e-02,  2.05651857e-03, ...,\n",
      "          -4.01719101e-03, -2.51986980e-02, -1.70705020e-02],\n",
      "         ...,\n",
      "         [ 1.54379457e-02, -2.07211003e-02,  3.19545344e-03, ...,\n",
      "           4.71967645e-03, -6.23628497e-04, -2.32968219e-02],\n",
      "         [ 6.66377693e-03, -2.68588029e-03,  8.34958628e-04, ...,\n",
      "           6.49936870e-03, -2.50328332e-04, -2.44442690e-02],\n",
      "         [ 1.48963742e-03, -7.29094073e-03,  3.20466608e-03, ...,\n",
      "           1.35009773e-02,  1.67872943e-02, -1.84868965e-02]]],\n",
      "\n",
      "\n",
      "       ...,\n",
      "\n",
      "\n",
      "       [[[-3.02390940e-03,  1.68576241e-02, -1.26675125e-02, ...,\n",
      "          -2.34664436e-02, -2.08404362e-02, -1.35978442e-02],\n",
      "         [ 2.30075605e-02,  2.50511244e-02,  1.80623755e-02, ...,\n",
      "          -2.43023429e-02,  1.98964030e-04, -2.39216909e-02],\n",
      "         [ 1.26834661e-02,  3.14078294e-03,  1.31735466e-02, ...,\n",
      "          -8.62499140e-03,  1.75600946e-02, -1.12347426e-02],\n",
      "         ...,\n",
      "         [ 9.22391191e-03, -1.60511509e-02,  2.28622444e-02, ...,\n",
      "          -1.15595320e-02, -2.08530575e-03, -1.57758482e-02],\n",
      "         [ 2.18411423e-02, -2.04510894e-02,  4.86266240e-03, ...,\n",
      "          -1.65437702e-02,  7.13660568e-03, -2.43310574e-02],\n",
      "         [ 2.08326578e-02, -1.18949851e-02, -2.49805879e-02, ...,\n",
      "           1.08453520e-02, -7.53286481e-03,  2.65475176e-03]],\n",
      "\n",
      "        [[ 1.25676319e-02,  1.36631541e-02,  8.47984478e-03, ...,\n",
      "           1.59475505e-02, -2.25127153e-02,  1.78812630e-02],\n",
      "         [-5.87613694e-03, -2.36961376e-02,  1.26167238e-02, ...,\n",
      "           1.73828788e-02,  1.04945786e-02,  2.42121257e-02],\n",
      "         [ 2.39129178e-03,  1.72352605e-02, -5.28683886e-04, ...,\n",
      "          -2.38967817e-02,  1.02546550e-02,  2.45814286e-02],\n",
      "         ...,\n",
      "         [-7.35923648e-03,  1.74826682e-02, -5.47888502e-03, ...,\n",
      "           1.13763288e-03, -9.68920067e-04, -8.72910582e-03],\n",
      "         [ 1.27339363e-02, -1.94248334e-02, -4.79191542e-04, ...,\n",
      "           1.97007693e-03, -1.38105638e-03, -8.61478038e-03],\n",
      "         [-3.59964184e-03, -1.12103336e-02,  9.87482443e-03, ...,\n",
      "           8.79534706e-03, -1.34621970e-02, -2.76176259e-04]],\n",
      "\n",
      "        [[-1.49568506e-02, -1.59248710e-04, -1.50796250e-02, ...,\n",
      "           2.10439414e-02,  1.51642971e-02,  1.19208582e-02],\n",
      "         [-4.81129810e-03,  1.03230923e-02, -2.50289179e-02, ...,\n",
      "          -8.12528655e-03,  1.09828860e-02,  1.21479332e-02],\n",
      "         [ 1.77804194e-02,  5.34343719e-03, -7.48007745e-03, ...,\n",
      "           1.10969171e-02,  5.77533990e-03, -8.62793624e-03],\n",
      "         ...,\n",
      "         [ 1.52905099e-03, -9.89126321e-03,  1.49328448e-02, ...,\n",
      "          -1.22945365e-02,  1.11138299e-02,  1.73825100e-02],\n",
      "         [ 1.89124346e-02,  7.78356567e-04,  6.86088204e-03, ...,\n",
      "          -1.27448635e-02,  1.39578916e-02, -7.98941776e-03],\n",
      "         [ 1.03111193e-02, -1.28658731e-02, -2.24138387e-02, ...,\n",
      "           1.28266029e-03, -1.16199823e-02,  2.04489455e-02]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 2.35767923e-02,  9.38598439e-03, -1.96051542e-02, ...,\n",
      "           2.03689560e-02, -1.78333782e-02, -1.60632767e-02],\n",
      "         [-6.72744960e-03,  2.20471993e-03, -8.51124898e-03, ...,\n",
      "           2.14092508e-02,  1.27284378e-02, -1.11626051e-02],\n",
      "         [ 1.00115798e-02, -6.45116530e-03, -2.67255493e-03, ...,\n",
      "          -7.45837204e-03,  6.75721094e-03,  4.75917011e-04],\n",
      "         ...,\n",
      "         [-1.32530583e-02,  6.52671605e-03, -3.26706097e-03, ...,\n",
      "           5.77838719e-03, -2.00894214e-02,  9.91915911e-03],\n",
      "         [ 8.97119194e-03, -3.25188786e-03, -2.48791352e-02, ...,\n",
      "           1.33061111e-02, -1.21630030e-02,  2.05107592e-03],\n",
      "         [-1.57338399e-02, -1.04388529e-02, -1.16580231e-02, ...,\n",
      "          -1.76564045e-02, -2.14919299e-02, -6.91393763e-03]],\n",
      "\n",
      "        [[-1.95051525e-02, -2.40821764e-03,  9.42464173e-03, ...,\n",
      "          -1.92375984e-02,  1.72564611e-02,  2.43988074e-03],\n",
      "         [-7.45112263e-03, -9.58560221e-03, -1.77733228e-03, ...,\n",
      "           9.80435684e-03, -2.29740553e-02,  3.86778638e-03],\n",
      "         [-7.55668990e-03, -7.52069615e-03, -1.93814747e-03, ...,\n",
      "          -4.62715887e-03,  1.74407773e-02,  5.11317141e-03],\n",
      "         ...,\n",
      "         [-1.84560809e-02, -1.46675967e-02, -1.40379593e-02, ...,\n",
      "          -1.09639606e-02,  1.07934587e-02,  9.10502672e-03],\n",
      "         [-6.16258010e-03,  2.49872953e-02, -2.22120024e-02, ...,\n",
      "          -5.43006696e-03, -1.04307365e-02, -5.59212826e-03],\n",
      "         [ 6.59781322e-03, -7.32554123e-04, -2.38497574e-02, ...,\n",
      "           8.34133103e-03,  1.99697725e-02,  1.91751905e-02]],\n",
      "\n",
      "        [[ 1.24877989e-02, -5.55014424e-03, -1.89160723e-02, ...,\n",
      "           3.09998542e-03, -1.12336464e-02, -1.36785312e-02],\n",
      "         [-4.39168327e-03, -2.14246884e-02,  1.85625069e-02, ...,\n",
      "          -1.85361300e-02, -1.58247929e-02, -4.20603901e-03],\n",
      "         [-1.61507800e-02,  5.01014292e-05, -7.77016208e-03, ...,\n",
      "           2.51801535e-02, -2.12515127e-02,  7.18628988e-03],\n",
      "         ...,\n",
      "         [-6.38740882e-03, -3.87779996e-03,  2.28368491e-02, ...,\n",
      "           5.32449596e-03, -2.13089474e-02, -5.86855784e-03],\n",
      "         [ 2.47366950e-02,  5.34180552e-03, -2.19597854e-02, ...,\n",
      "          -1.65967196e-02, -1.36685539e-02,  1.63740814e-02],\n",
      "         [ 2.31260620e-02, -2.20534652e-02, -3.43906879e-03, ...,\n",
      "           1.19554959e-02,  1.04263313e-02,  2.97514722e-04]]],\n",
      "\n",
      "\n",
      "       [[[-1.92152783e-02,  1.55057535e-02,  2.47814730e-02, ...,\n",
      "          -2.47855447e-02, -1.13700340e-02,  1.39655918e-03],\n",
      "         [ 2.34203525e-02, -2.11302377e-02,  2.45098919e-02, ...,\n",
      "           2.51379050e-02,  6.55986369e-03,  7.31275976e-03],\n",
      "         [ 2.17810385e-02,  4.70746495e-03, -4.55278158e-03, ...,\n",
      "           3.91716510e-03, -2.11882330e-02, -1.53382979e-02],\n",
      "         ...,\n",
      "         [-1.70590207e-02, -2.34783534e-02,  5.09254634e-04, ...,\n",
      "          -1.89256258e-02,  9.17495415e-03, -2.26772949e-03],\n",
      "         [-2.60083005e-04,  1.19962208e-02, -1.22902682e-02, ...,\n",
      "           2.41014957e-02,  5.51891886e-03,  1.66082084e-02],\n",
      "         [-7.07291067e-04,  2.30980329e-02,  7.10625201e-04, ...,\n",
      "           1.79382041e-02,  2.10609473e-02, -1.05351526e-02]],\n",
      "\n",
      "        [[ 1.40204243e-02, -1.04146069e-02,  1.53811052e-02, ...,\n",
      "           1.71636604e-02, -2.22471543e-02,  1.77471936e-02],\n",
      "         [ 6.51430711e-03, -2.50300858e-02, -2.45716069e-02, ...,\n",
      "           1.89538002e-02, -3.82961892e-03, -1.31735513e-02],\n",
      "         [ 1.28942691e-02,  1.90142989e-02, -2.23940834e-02, ...,\n",
      "          -1.62824765e-02, -8.28981586e-03, -1.96007527e-02],\n",
      "         ...,\n",
      "         [-8.60054046e-03, -1.41803315e-02,  3.25476564e-03, ...,\n",
      "           1.00663230e-02, -1.06943231e-02, -1.01812817e-02],\n",
      "         [ 8.55270773e-03,  1.79469660e-02,  1.61285140e-02, ...,\n",
      "          -1.46574154e-03,  1.44561157e-02,  1.90151483e-03],\n",
      "         [-8.81934166e-03, -1.34584280e-02,  3.74606065e-03, ...,\n",
      "          -2.13416480e-02,  9.72585008e-03, -1.78270154e-02]],\n",
      "\n",
      "        [[-1.79354530e-02, -6.63655065e-03,  1.77233927e-02, ...,\n",
      "           2.48949081e-02, -2.47008950e-02,  1.66865513e-02],\n",
      "         [ 3.61781940e-03,  1.44801885e-02, -1.63477100e-02, ...,\n",
      "          -5.31272963e-04,  5.46997413e-03,  1.67422369e-03],\n",
      "         [ 1.25207268e-02, -1.11813359e-02,  1.69974267e-02, ...,\n",
      "          -2.37239785e-02,  9.56276432e-04, -1.30607300e-02],\n",
      "         ...,\n",
      "         [ 2.21734084e-02, -1.96974743e-02, -1.71685182e-02, ...,\n",
      "           1.03889592e-03, -3.48914415e-03, -1.26131726e-02],\n",
      "         [ 1.02819875e-02,  1.22447237e-02, -2.16938928e-02, ...,\n",
      "          -7.40253367e-03, -1.52248926e-02, -2.18193866e-02],\n",
      "         [-2.00767051e-02, -1.22499876e-02, -2.02770177e-02, ...,\n",
      "           1.70624964e-02, -1.16926199e-02,  1.26443841e-02]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-1.06470883e-02,  1.86501630e-03,  1.06831454e-03, ...,\n",
      "           1.49504617e-02,  2.02464424e-02, -2.87330151e-03],\n",
      "         [-1.81772187e-03, -3.38185765e-03,  1.69794410e-02, ...,\n",
      "          -5.94408438e-03, -1.44920554e-02,  6.42424077e-03],\n",
      "         [ 2.48736925e-02,  6.99602813e-03, -1.79639738e-02, ...,\n",
      "           5.40921092e-03,  1.50497891e-02,  2.29056813e-02],\n",
      "         ...,\n",
      "         [-1.78115480e-02, -4.14544344e-03,  9.23283771e-03, ...,\n",
      "          -1.41071947e-02,  5.08405454e-03, -2.00038031e-03],\n",
      "         [ 2.01161131e-02, -1.91876777e-02, -3.57342698e-03, ...,\n",
      "           7.42172822e-03,  2.40480937e-02,  5.59202582e-03],\n",
      "         [-4.08752263e-03, -2.50435304e-02, -1.93785075e-02, ...,\n",
      "           5.02168201e-03,  9.31499526e-04,  2.29865909e-02]],\n",
      "\n",
      "        [[ 1.20092258e-02,  2.34048329e-02,  2.14248337e-02, ...,\n",
      "           2.24440955e-02, -1.64954159e-02,  2.88203172e-03],\n",
      "         [ 3.38963047e-03, -1.14637623e-02,  2.49620676e-02, ...,\n",
      "          -1.00729037e-02, -1.28054246e-03,  8.32152367e-03],\n",
      "         [-1.49968723e-02,  2.03128010e-02,  2.48241946e-02, ...,\n",
      "          -1.92901008e-02, -1.15482425e-02,  2.23926194e-02],\n",
      "         ...,\n",
      "         [ 6.69277459e-03, -1.94839872e-02, -1.93301402e-02, ...,\n",
      "           3.24857049e-03, -6.56088628e-03,  1.62224062e-02],\n",
      "         [-2.28406545e-02,  2.38907672e-02, -3.64928506e-03, ...,\n",
      "           1.02563500e-02, -1.00766849e-02, -3.98414209e-03],\n",
      "         [-2.27296501e-02, -1.72947124e-02,  9.44370031e-03, ...,\n",
      "          -1.40330102e-02,  1.39079243e-03, -3.15978564e-03]],\n",
      "\n",
      "        [[ 2.73836590e-03, -5.80157340e-03, -2.15245876e-02, ...,\n",
      "          -7.50012882e-03, -1.65436603e-02, -1.54496562e-02],\n",
      "         [ 2.21122429e-02, -1.91429425e-02,  1.74906850e-02, ...,\n",
      "           1.14158951e-02, -4.11349721e-03,  7.60931522e-04],\n",
      "         [ 1.79440565e-02,  9.72207636e-05,  2.50552222e-02, ...,\n",
      "          -1.21931192e-02, -2.28045695e-02,  8.08437541e-03],\n",
      "         ...,\n",
      "         [ 1.40561797e-02, -7.83117861e-03,  1.19430274e-02, ...,\n",
      "          -1.04776286e-02,  1.62410662e-02, -1.27898287e-02],\n",
      "         [-1.76422745e-02, -3.41774896e-04,  2.03612559e-02, ...,\n",
      "           2.22485997e-02,  1.26606785e-03,  5.65077923e-03],\n",
      "         [-5.88916801e-03, -1.97205711e-02,  1.90907232e-02, ...,\n",
      "          -6.98305666e-05, -3.49539518e-03, -6.89342991e-03]]],\n",
      "\n",
      "\n",
      "       [[[-5.14702871e-04,  2.10965760e-02, -1.10373870e-02, ...,\n",
      "          -9.39175673e-03, -3.67863104e-03, -2.04228871e-02],\n",
      "         [ 9.89699364e-03, -8.94964300e-03,  1.00009888e-02, ...,\n",
      "          -9.38955881e-03,  1.04490817e-02, -1.22399023e-02],\n",
      "         [ 2.40608901e-02,  1.39669068e-02,  5.38746826e-03, ...,\n",
      "          -2.04507224e-02,  6.20029494e-03, -1.34302499e-02],\n",
      "         ...,\n",
      "         [ 4.09251079e-04, -6.79484196e-03,  2.41296738e-03, ...,\n",
      "           1.27239339e-02,  2.28413418e-02,  3.04381549e-03],\n",
      "         [-1.69523470e-02, -2.49649156e-02, -1.36113130e-02, ...,\n",
      "           5.66619262e-03, -7.27305375e-03, -1.12670660e-03],\n",
      "         [-5.69500215e-03, -1.19366683e-02, -7.55717605e-03, ...,\n",
      "          -9.80820972e-03, -1.05782868e-02, -1.62869692e-02]],\n",
      "\n",
      "        [[ 1.61635876e-02, -1.89788286e-02, -3.17429006e-03, ...,\n",
      "          -2.68145464e-03,  1.44636407e-02,  2.32397541e-02],\n",
      "         [-1.91066712e-02,  2.77478620e-03, -1.98041536e-02, ...,\n",
      "           1.31301656e-02, -1.79275349e-02, -3.56041454e-03],\n",
      "         [-1.15191126e-02, -1.35289580e-02, -1.34112481e-02, ...,\n",
      "           2.63231061e-03,  2.10146531e-02,  3.58458981e-03],\n",
      "         ...,\n",
      "         [-7.50534795e-03, -9.24259983e-03, -2.31714100e-02, ...,\n",
      "          -2.26999745e-02, -1.49199422e-02, -8.86463188e-03],\n",
      "         [-9.95310489e-03, -2.00942680e-02,  6.24210387e-03, ...,\n",
      "           2.40271129e-02, -1.67055726e-02,  5.12964465e-03],\n",
      "         [ 2.20570602e-02,  8.77352804e-03, -1.80949662e-02, ...,\n",
      "           2.51320340e-02, -8.15911777e-03, -1.14727756e-02]],\n",
      "\n",
      "        [[-7.69704394e-03, -4.83794697e-03, -2.29164157e-02, ...,\n",
      "           1.13396160e-02,  1.68006197e-02, -7.96433911e-03],\n",
      "         [ 1.10578574e-02, -1.41872251e-02, -2.15412248e-02, ...,\n",
      "          -4.13380563e-03, -8.34892318e-03,  1.12168901e-02],\n",
      "         [ 1.67788453e-02,  2.42565833e-02,  1.40548907e-02, ...,\n",
      "          -2.34124605e-02, -2.43004635e-02, -2.21298635e-03],\n",
      "         ...,\n",
      "         [ 2.46327631e-02,  1.60567947e-02, -1.23004979e-02, ...,\n",
      "           2.06282884e-02,  2.20003165e-02,  1.64364278e-02],\n",
      "         [-9.10189562e-03,  2.06247307e-02, -4.01581265e-03, ...,\n",
      "           5.31299599e-03, -1.95479188e-02,  8.69645923e-03],\n",
      "         [-1.47007005e-02, -4.65058535e-03, -7.85724446e-03, ...,\n",
      "          -2.29780581e-02, -1.29095977e-02,  6.78611919e-04]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 1.30349360e-02,  2.48395689e-02,  1.35210752e-02, ...,\n",
      "           5.34449704e-03,  1.94378123e-02,  1.97655521e-03],\n",
      "         [ 2.35169344e-02,  1.43235996e-02, -2.63619423e-03, ...,\n",
      "           3.54144722e-05,  7.84599036e-03,  1.36411451e-02],\n",
      "         [ 2.41087079e-02,  1.45963803e-02,  1.85290910e-02, ...,\n",
      "          -1.57086253e-02, -1.77818164e-02, -5.11748157e-03],\n",
      "         ...,\n",
      "         [-2.14472730e-02,  1.16622820e-03, -2.17509717e-02, ...,\n",
      "           7.62736425e-03, -1.73092298e-02, -1.56855471e-02],\n",
      "         [ 8.42184201e-03, -9.75231081e-03, -1.51751954e-02, ...,\n",
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      "           2.08839215e-02,  2.06767134e-02, -3.80705856e-03]],\n",
      "\n",
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      "         ...,\n",
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      "\n",
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      "{'name': 'activation_4', 'trainable': True, 'activation': 'relu'}\n",
      "[]\n",
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      "{'name': 'add_2', 'trainable': True}\n",
      "[]\n",
      "{'name': 'activation_5', 'trainable': True, 'activation': 'relu'}\n",
      "[]\n"
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     "name": "stdout",
     "output_type": "stream",
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      "{'name': 'add_3', 'trainable': True}\n",
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      "{'name': 'add_4', 'trainable': True}\n",
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      "{'name': 'add_5', 'trainable': True}\n",
      "[]\n",
      "{'name': 'activation_8', 'trainable': True, 'activation': 'relu'}\n",
      "[]\n",
      "{'name': 'max_pooling2d_1', 'trainable': True, 'pool_size': (2, 2), 'padding': 'valid', 'strides': (2, 2), 'data_format': 'channels_last'}\n",
      "[]\n",
      "{'name': 'dropout_1', 'trainable': True, 'rate': 0.3, 'noise_shape': None, 'seed': None}\n",
      "[]\n"
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    {
     "name": "stdout",
     "output_type": "stream",
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      "{'name': 'add_6', 'trainable': True}\n",
      "[]\n",
      "{'name': 'activation_9', 'trainable': True, 'activation': 'relu'}\n",
      "[]\n",
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      "           1.18748229e-02,  2.35762820e-03,  1.97299328e-02],\n",
      "         ...,\n",
      "         [-8.78451858e-03,  6.68564998e-03, -1.30625395e-02, ...,\n",
      "           7.49297254e-03, -4.18111868e-03,  1.47264395e-02],\n",
      "         [ 1.90888178e-02,  5.94221056e-05,  1.98670644e-02, ...,\n",
      "          -2.04776675e-02,  3.78878228e-03, -1.94757413e-02],\n",
      "         [-2.44956836e-03,  1.41639095e-02, -2.63755955e-03, ...,\n",
      "           1.67075749e-02,  2.06418131e-02, -1.31759048e-03]],\n",
      "\n",
      "        [[ 9.21746716e-04,  2.00728644e-02, -9.84501187e-03, ...,\n",
      "          -7.18096830e-03, -3.54155898e-04,  8.30601528e-03],\n",
      "         [-1.94729939e-02,  1.28593538e-02, -5.90344518e-03, ...,\n",
      "           1.45658087e-02,  1.01255979e-02,  2.19830498e-03],\n",
      "         [-1.40032498e-02,  8.43900256e-03,  1.27776917e-02, ...,\n",
      "           9.38700326e-03,  9.93808918e-03, -1.72939803e-02],\n",
      "         ...,\n",
      "         [ 1.22106504e-02,  1.46032311e-03,  1.77576076e-02, ...,\n",
      "           5.85748628e-03, -1.41231790e-02,  1.63230672e-03],\n",
      "         [-1.92203540e-02,  2.60248221e-03, -1.09002860e-02, ...,\n",
      "          -8.97049252e-03,  3.70934792e-03, -3.40870582e-03],\n",
      "         [-1.45677831e-02,  1.58218667e-03, -1.18733663e-02, ...,\n",
      "           4.83296998e-03,  8.08491372e-03, -8.73569120e-03]],\n",
      "\n",
      "        [[-1.60208941e-02,  1.79126170e-02,  1.96333770e-02, ...,\n",
      "          -1.07610952e-02, -1.21871540e-02,  6.67291135e-03],\n",
      "         [ 1.36119779e-02,  3.84875759e-03, -1.07897269e-02, ...,\n",
      "           1.06968861e-02, -3.36354971e-03,  6.95877150e-03],\n",
      "         [-8.22765473e-03,  9.78482887e-04, -3.05737928e-03, ...,\n",
      "           2.10244115e-02,  1.55959278e-05, -1.21026719e-02],\n",
      "         ...,\n",
      "         [ 2.13496201e-03, -1.17596732e-02, -1.75958592e-02, ...,\n",
      "          -4.88219969e-03,  1.01750661e-02, -8.79556220e-03],\n",
      "         [-2.15539970e-02, -1.84642877e-02, -1.07062245e-02, ...,\n",
      "           6.24986924e-03, -1.04490491e-02,  5.86718507e-03],\n",
      "         [-1.89808179e-02,  2.60222703e-03, -2.13831551e-02, ...,\n",
      "          -1.25895115e-02, -6.23357296e-03, -1.30308727e-02]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 6.03195652e-03,  5.82236238e-03,  4.34338860e-03, ...,\n",
      "          -2.06061229e-02, -2.08237935e-02,  1.96031854e-03],\n",
      "         [ 2.47390755e-03,  6.16754405e-03, -6.32487610e-03, ...,\n",
      "           1.94730591e-02, -2.80313566e-03,  2.11319532e-02],\n",
      "         [-2.07914710e-02, -2.22946145e-03,  4.27243114e-03, ...,\n",
      "          -1.88184083e-02,  4.70850989e-03,  4.40608710e-05],\n",
      "         ...,\n",
      "         [ 2.17877124e-02,  2.99817696e-03, -2.02804618e-02, ...,\n",
      "          -1.01045389e-02, -1.66201815e-02,  8.05899501e-04],\n",
      "         [ 8.44951905e-03,  1.55702792e-03, -6.74124528e-03, ...,\n",
      "          -2.15210579e-03, -4.73867543e-03, -2.16626693e-02],\n",
      "         [-3.23598087e-03, -6.23397529e-04,  7.26845488e-03, ...,\n",
      "          -1.89507324e-02,  5.76746091e-03, -1.95888914e-02]],\n",
      "\n",
      "        [[-4.40101139e-03, -2.12225541e-02, -6.26607984e-03, ...,\n",
      "          -1.33688906e-02, -7.55462702e-03, -1.45319402e-02],\n",
      "         [ 8.53588432e-03, -6.54777326e-03, -9.44775343e-03, ...,\n",
      "          -1.81262586e-02, -1.81211159e-03,  1.34468619e-02],\n",
      "         [ 1.65912881e-03, -1.81779489e-02, -1.00860372e-04, ...,\n",
      "          -6.03925437e-05, -6.94272667e-03, -3.07366438e-03],\n",
      "         ...,\n",
      "         [ 1.00702588e-02,  1.84900928e-02, -1.88547373e-02, ...,\n",
      "           1.08098444e-02, -9.82653257e-03,  8.05702619e-03],\n",
      "         [ 1.18779149e-02,  6.48569129e-03,  1.07900295e-02, ...,\n",
      "           1.51500646e-02,  1.95103083e-02,  1.50307994e-02],\n",
      "         [-2.03551985e-02, -1.42933223e-02,  2.06679106e-04, ...,\n",
      "          -1.53913433e-02,  1.51874330e-02, -1.88241154e-03]],\n",
      "\n",
      "        [[ 1.45888887e-03,  3.99156660e-03,  1.75686982e-02, ...,\n",
      "          -4.91382927e-03,  1.31677482e-02,  6.40491582e-03],\n",
      "         [-2.04298571e-02,  7.93300383e-03, -9.41540394e-03, ...,\n",
      "          -6.68270420e-03, -9.77468118e-03, -1.45369247e-02],\n",
      "         [-7.54274335e-03, -1.13504380e-02, -1.21671986e-02, ...,\n",
      "          -5.73286787e-03, -1.22252293e-02, -1.35337943e-02],\n",
      "         ...,\n",
      "         [-1.12723690e-02,  7.37034157e-03,  1.50708128e-02, ...,\n",
      "          -1.93572920e-02,  1.84332412e-02, -1.66051947e-02],\n",
      "         [-2.10870542e-02,  1.87634137e-02,  2.11695340e-02, ...,\n",
      "          -2.09886543e-02,  3.65966000e-03,  6.70021400e-03],\n",
      "         [ 1.50193330e-02, -1.05955247e-02,  2.04329807e-02, ...,\n",
      "          -1.49619179e-02, -3.39204632e-03, -2.93288380e-03]]],\n",
      "\n",
      "\n",
      "       [[[-2.05707643e-02, -8.71574506e-04, -7.07821548e-03, ...,\n",
      "          -1.84788313e-02,  7.66092911e-04, -2.13288888e-02],\n",
      "         [ 9.15108435e-03,  5.22781909e-03, -1.48418210e-02, ...,\n",
      "          -6.07473962e-03,  1.29669160e-03,  1.58677902e-02],\n",
      "         [-9.84537695e-03,  9.54968296e-03, -5.73484972e-03, ...,\n",
      "           1.31397229e-02,  3.96700576e-04, -1.79195795e-02],\n",
      "         ...,\n",
      "         [-1.56106893e-02,  1.38464961e-02,  1.45037454e-02, ...,\n",
      "           1.33477505e-02, -2.68533826e-03,  7.24184513e-03],\n",
      "         [-7.27551989e-03, -1.16029102e-02, -6.29560836e-03, ...,\n",
      "          -1.91780664e-02, -6.42845873e-03,  1.01294573e-02],\n",
      "         [-1.40217319e-03,  9.37072188e-03,  2.26882286e-03, ...,\n",
      "          -4.27730195e-03, -1.52417812e-02,  1.45912822e-02]],\n",
      "\n",
      "        [[ 4.32219729e-03, -1.95403825e-02,  6.99532405e-03, ...,\n",
      "           5.12774102e-03,  1.12279225e-02,  8.89709592e-03],\n",
      "         [-2.01942064e-02,  8.63568857e-04,  4.25915606e-03, ...,\n",
      "          -1.90509092e-02, -1.80185474e-02,  1.92875471e-02],\n",
      "         [ 1.15486141e-02,  9.73738916e-03, -1.65308230e-02, ...,\n",
      "          -1.30681759e-02,  3.33618373e-05, -1.02304285e-02],\n",
      "         ...,\n",
      "         [ 6.41057454e-03, -1.86628755e-02, -1.64821222e-02, ...,\n",
      "          -1.18741542e-02,  1.67629588e-02,  1.17389504e-02],\n",
      "         [-8.39523226e-03, -7.22874794e-03, -1.93791725e-02, ...,\n",
      "          -3.75222415e-04,  6.29138015e-03,  8.12309794e-03],\n",
      "         [-8.32846388e-04,  8.50985944e-03, -2.15522293e-02, ...,\n",
      "           3.69014218e-03, -1.50869042e-03,  1.66767184e-02]],\n",
      "\n",
      "        [[ 1.71117578e-02, -1.24950018e-02, -1.49489706e-02, ...,\n",
      "          -8.31198040e-03,  2.85417214e-03,  5.01980074e-03],\n",
      "         [-1.78514421e-03, -4.38293256e-03, -2.46354192e-03, ...,\n",
      "           1.05869975e-02, -9.89526510e-04, -1.02338018e-02],\n",
      "         [ 1.79644283e-02, -3.13189812e-03, -8.81027151e-03, ...,\n",
      "           1.47082824e-02, -1.79921724e-02, -8.93706270e-03],\n",
      "         ...,\n",
      "         [-1.04990415e-03,  1.40877198e-02,  2.16859672e-02, ...,\n",
      "           1.95592549e-02,  6.99769519e-03, -3.05406377e-03],\n",
      "         [ 5.32872975e-04,  2.16448288e-02, -1.56564135e-02, ...,\n",
      "           1.36692505e-02, -9.43941623e-03,  1.49833504e-02],\n",
      "         [-8.58865678e-04, -2.05439106e-02,  3.32844630e-03, ...,\n",
      "          -5.39610721e-03,  5.15289605e-04, -1.03700208e-02]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[-4.90493327e-03, -9.98836942e-03, -1.18768606e-02, ...,\n",
      "          -1.57226883e-02,  3.41039523e-03,  1.15256254e-02],\n",
      "         [-3.12447548e-04, -8.28892272e-03, -1.16907610e-02, ...,\n",
      "          -1.31765036e-02,  1.52791087e-02,  4.44376841e-03],\n",
      "         [ 4.29341942e-03,  8.31281580e-03,  2.02959031e-03, ...,\n",
      "           8.99191760e-03, -1.32587384e-02, -1.72241144e-02],\n",
      "         ...,\n",
      "         [ 8.06264207e-03, -1.22006489e-02, -1.59767289e-02, ...,\n",
      "           5.89089468e-03,  4.23085317e-03,  1.66150462e-02],\n",
      "         [-6.74651656e-03, -1.96791068e-03, -1.29570384e-02, ...,\n",
      "          -2.14612652e-02,  8.05773027e-03, -3.51643749e-03],\n",
      "         [ 1.68905128e-02, -4.99296933e-04,  1.18267518e-02, ...,\n",
      "           4.62776050e-03,  9.33714211e-03, -1.79304518e-02]],\n",
      "\n",
      "        [[ 1.69573743e-02,  9.53934155e-03, -3.41506675e-03, ...,\n",
      "          -1.19610317e-03,  1.03493501e-02, -1.64528005e-02],\n",
      "         [-1.00242747e-02,  5.47805987e-03, -2.17630602e-02, ...,\n",
      "           1.22503657e-02, -4.21090238e-03, -8.31903610e-03],\n",
      "         [-2.12955382e-02, -1.70707777e-02, -4.82082739e-03, ...,\n",
      "          -1.09584071e-03, -3.18762846e-03, -1.55213308e-02],\n",
      "         ...,\n",
      "         [-5.60719892e-03, -2.02752091e-03,  8.15146789e-03, ...,\n",
      "          -1.14961574e-02,  1.15812775e-02,  6.70466200e-03],\n",
      "         [-2.04747263e-02, -1.58397388e-02,  2.15405542e-02, ...,\n",
      "          -1.60931051e-03,  3.18626687e-03,  7.82320276e-04],\n",
      "         [-1.61555801e-02, -2.01802850e-02,  4.65678982e-03, ...,\n",
      "           8.09157640e-03, -2.13075113e-02,  8.74553062e-03]],\n",
      "\n",
      "        [[-1.90919098e-02,  4.06615809e-03,  4.39983793e-03, ...,\n",
      "          -2.05214843e-02,  2.01536138e-02, -1.74989514e-02],\n",
      "         [ 4.97130677e-03,  1.70441438e-02,  3.76617350e-03, ...,\n",
      "           1.89559367e-02,  2.10523997e-02, -8.71612225e-03],\n",
      "         [-2.65158713e-04,  1.69116631e-03, -9.52623971e-03, ...,\n",
      "           1.42992493e-02, -1.41507257e-02,  3.09405848e-03],\n",
      "         ...,\n",
      "         [-5.37231378e-03, -9.27999336e-03, -6.31768070e-03, ...,\n",
      "           4.01667878e-03,  1.33270342e-02, -1.08002070e-02],\n",
      "         [-4.11288813e-03, -2.74841674e-03,  1.62064265e-02, ...,\n",
      "          -4.07764502e-03, -8.90964642e-03,  2.04664785e-02],\n",
      "         [ 3.28726880e-03,  8.52971524e-03,  6.58074766e-03, ...,\n",
      "          -1.52438199e-02, -7.47550465e-03,  1.86029803e-02]]],\n",
      "\n",
      "\n",
      "       [[[-1.47895953e-02, -8.29239096e-03,  2.51903757e-03, ...,\n",
      "          -3.75591218e-03,  4.96133603e-03,  7.73357227e-04],\n",
      "         [-6.06941618e-03, -1.66627206e-03, -1.23259332e-02, ...,\n",
      "           9.94295813e-03, -1.40198050e-02,  1.33890603e-02],\n",
      "         [ 8.40499438e-03,  1.46514643e-02,  2.12422330e-02, ...,\n",
      "          -2.24914402e-03,  2.18666475e-02, -1.47676840e-02],\n",
      "         ...,\n",
      "         [-8.81655514e-03,  1.05794836e-02, -1.04768360e-02, ...,\n",
      "           1.79908220e-02, -8.62667058e-03,  8.22040625e-03],\n",
      "         [-4.56219167e-03, -2.02055480e-02,  1.07383709e-02, ...,\n",
      "           6.01807609e-03, -9.89502762e-03, -1.29259452e-02],\n",
      "         [ 1.97440963e-02, -1.41286068e-02,  1.13719981e-02, ...,\n",
      "           4.00721468e-03,  1.78342070e-02, -1.29718725e-02]],\n",
      "\n",
      "        [[ 1.90945435e-02,  1.97593104e-02,  1.80384144e-03, ...,\n",
      "           4.16838005e-03, -7.26939272e-03,  2.07445007e-02],\n",
      "         [-1.03223724e-02,  1.11605395e-02,  1.68783683e-02, ...,\n",
      "           1.71785671e-02, -1.85727049e-02,  1.65044870e-02],\n",
      "         [-1.47899240e-02, -3.20659950e-04,  1.18647143e-03, ...,\n",
      "           5.66957705e-03, -1.51256789e-02, -1.35076595e-02],\n",
      "         ...,\n",
      "         [-4.27300483e-03, -1.23545025e-02, -9.30560101e-03, ...,\n",
      "          -6.52809441e-03, -1.32620754e-02, -1.34096146e-02],\n",
      "         [ 1.60452034e-02, -7.69601855e-03, -1.46730337e-02, ...,\n",
      "           1.26171801e-02, -9.91487317e-03, -1.66490227e-02],\n",
      "         [ 1.97075661e-02, -1.40164625e-02, -2.14587990e-02, ...,\n",
      "          -2.08077952e-03, -1.04621612e-03, -8.09772965e-03]],\n",
      "\n",
      "        [[ 1.20160934e-02,  1.67922322e-02, -2.12328061e-02, ...,\n",
      "           4.21610102e-03,  1.54985767e-02,  1.79303251e-03],\n",
      "         [ 2.09805164e-02, -9.54263844e-03, -1.72824878e-02, ...,\n",
      "           1.95865389e-02,  4.96230088e-03, -1.47913154e-02],\n",
      "         [ 1.91593375e-02,  9.35291499e-03,  1.71555001e-02, ...,\n",
      "           1.81448031e-02, -1.65104195e-02,  1.98132489e-02],\n",
      "         ...,\n",
      "         [ 1.49805192e-02, -1.47212204e-02,  1.90155264e-02, ...,\n",
      "           1.00987982e-02, -1.86481886e-03, -5.13986498e-03],\n",
      "         [ 8.05690140e-03, -2.15433333e-02, -8.25292896e-03, ...,\n",
      "           8.02640803e-03,  4.53712046e-03,  2.05730516e-02],\n",
      "         [ 1.62400063e-02,  7.38474354e-03,  1.20631922e-02, ...,\n",
      "           8.70317966e-03, -1.71280354e-02,  1.00741852e-02]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 1.42476521e-03, -1.63073167e-02,  7.14372285e-03, ...,\n",
      "           1.06741097e-02,  1.54973175e-02, -2.11593043e-02],\n",
      "         [ 3.14720161e-03,  1.81407761e-02,  7.01608136e-03, ...,\n",
      "           1.39918048e-02, -1.57443527e-02, -3.92261334e-03],\n",
      "         [-1.36989057e-02,  2.06856597e-02, -1.72952116e-02, ...,\n",
      "           3.63663957e-03, -1.54140983e-02, -3.42132524e-04],\n",
      "         ...,\n",
      "         [-1.45690087e-02,  5.01620211e-03,  2.18131337e-02, ...,\n",
      "           9.78221186e-03, -1.82835702e-02,  9.79811884e-03],\n",
      "         [-8.67156312e-04, -1.67582203e-02,  1.13155507e-03, ...,\n",
      "           1.58021282e-02, -5.46934269e-03, -1.02720121e-02],\n",
      "         [-2.03028321e-07,  1.01869572e-02, -3.28164361e-03, ...,\n",
      "          -2.10517645e-02, -1.74307991e-02, -9.26496554e-03]],\n",
      "\n",
      "        [[ 1.63797233e-02, -8.17225222e-03,  7.82624073e-03, ...,\n",
      "          -9.50920396e-03, -1.25632370e-02,  4.93447296e-03],\n",
      "         [ 1.18683148e-02, -1.67641751e-02, -1.14005161e-02, ...,\n",
      "           1.75741315e-03,  1.91443618e-02,  3.78174707e-04],\n",
      "         [-1.90957785e-02, -1.39676100e-02, -2.98227742e-03, ...,\n",
      "           4.16203402e-03, -8.27226881e-03,  7.52253830e-03],\n",
      "         ...,\n",
      "         [-6.99047372e-03,  1.41019188e-03, -1.45974420e-02, ...,\n",
      "           1.57978144e-02, -1.49300694e-02,  1.34090502e-02],\n",
      "         [-1.94085017e-02, -1.71173885e-02, -1.51977250e-02, ...,\n",
      "          -9.52883624e-03,  1.69027597e-04, -1.81788560e-02],\n",
      "         [-1.65873524e-02,  7.08901323e-03, -1.31741781e-02, ...,\n",
      "           1.39322113e-02,  1.90000292e-02,  6.62610307e-03]],\n",
      "\n",
      "        [[-1.63204595e-03,  1.43796001e-02, -1.39703993e-02, ...,\n",
      "           5.77201694e-03,  2.11585853e-02, -9.90397017e-03],\n",
      "         [-7.64889177e-03,  2.07129028e-02,  4.36176918e-03, ...,\n",
      "          -1.55404573e-02,  1.07225310e-02, -1.42233875e-02],\n",
      "         [ 3.32950056e-03, -8.62955861e-03, -6.68956060e-03, ...,\n",
      "          -2.05332357e-02, -3.62071395e-03, -1.84170716e-02],\n",
      "         ...,\n",
      "         [ 1.47832911e-02, -1.91547014e-02,  1.32099371e-02, ...,\n",
      "          -7.34992325e-03, -2.09342744e-02, -1.97200682e-02],\n",
      "         [ 2.05453094e-02,  1.28740016e-02, -1.40331741e-02, ...,\n",
      "          -8.26471858e-03,  3.22416611e-03, -1.80118214e-02],\n",
      "         [-1.39000742e-02,  8.50637071e-03,  2.35828571e-03, ...,\n",
      "          -1.33018401e-02,  1.68969724e-02,  3.18865664e-03]]],\n",
      "\n",
      "\n",
      "       ...,\n",
      "\n",
      "\n",
      "       [[[-7.43839424e-03,  1.44609530e-02, -1.72355752e-02, ...,\n",
      "          -7.60731194e-03,  1.67222712e-02,  1.29746143e-02],\n",
      "         [-3.30927409e-03, -9.55339614e-03,  4.35074605e-03, ...,\n",
      "           2.16227230e-02,  2.05311161e-02,  1.14141200e-02],\n",
      "         [ 7.86354952e-03, -1.28895976e-03,  2.01818552e-02, ...,\n",
      "          -5.09656966e-03,  2.88470276e-03, -6.60894252e-03],\n",
      "         ...,\n",
      "         [ 1.35901961e-02,  1.08714048e-02,  7.36411661e-03, ...,\n",
      "           1.73992161e-02, -1.09572625e-02,  1.64950807e-02],\n",
      "         [ 6.70013949e-04,  8.96435417e-03,  1.87027827e-03, ...,\n",
      "          -2.58861855e-03,  1.97004620e-02, -1.70389116e-02],\n",
      "         [ 6.35934249e-03, -6.45600632e-03,  1.30669214e-03, ...,\n",
      "          -1.85126252e-02, -2.04797368e-02,  5.87660074e-03]],\n",
      "\n",
      "        [[ 1.51114184e-02, -7.10207596e-03,  2.10464392e-02, ...,\n",
      "           1.07367393e-02, -1.66458301e-02,  1.69428606e-02],\n",
      "         [-5.33509441e-03,  1.24158841e-02, -6.12146594e-03, ...,\n",
      "           2.00930741e-02,  2.00551581e-02,  1.13467369e-02],\n",
      "         [-3.38722765e-03, -6.18629530e-03, -5.26349992e-04, ...,\n",
      "           5.71593642e-04,  1.98166948e-02, -1.32380268e-02],\n",
      "         ...,\n",
      "         [ 2.24700198e-04,  5.48460521e-03,  2.17168052e-02, ...,\n",
      "           1.42458919e-02, -2.24630907e-03, -1.65827572e-02],\n",
      "         [ 1.10419076e-02, -5.42942621e-03, -1.90089345e-02, ...,\n",
      "           1.59822572e-02,  8.35630298e-03,  1.04572233e-02],\n",
      "         [ 4.48416919e-03,  8.67489167e-03, -7.11522158e-03, ...,\n",
      "          -1.03270551e-02, -1.63864288e-02,  1.73871759e-02]],\n",
      "\n",
      "        [[ 1.72987301e-02,  6.39213249e-04, -2.02012621e-03, ...,\n",
      "          -2.14266311e-02, -1.94408838e-02,  1.19402613e-02],\n",
      "         [-3.07258032e-03,  1.18271876e-02, -2.04917360e-02, ...,\n",
      "          -1.99836530e-02, -6.18298911e-03,  1.04523394e-02],\n",
      "         [-1.03909820e-02,  9.26061161e-03, -6.97981101e-03, ...,\n",
      "           1.16126053e-03, -2.24798173e-03,  2.06914227e-02],\n",
      "         ...,\n",
      "         [ 1.24089178e-02, -1.21484902e-02, -7.84806255e-03, ...,\n",
      "          -1.95682216e-02, -1.16420332e-02, -1.00866593e-02],\n",
      "         [-8.61087814e-04, -1.83253214e-02, -1.58596560e-02, ...,\n",
      "          -1.02403015e-03, -1.76666640e-02, -9.55220312e-05],\n",
      "         [ 1.40597057e-02, -1.52973495e-02,  1.41469706e-02, ...,\n",
      "          -1.92062594e-02, -2.84918211e-03, -4.01269086e-03]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 1.20154936e-02,  5.18988818e-05,  2.10428610e-04, ...,\n",
      "          -5.45619614e-03,  1.55157577e-02,  2.06724014e-02],\n",
      "         [ 2.18615215e-02, -7.92227779e-03,  1.31538529e-02, ...,\n",
      "          -4.81320359e-03,  1.43488478e-02,  1.93309877e-02],\n",
      "         [-1.86412353e-02, -1.37769794e-02,  1.60937384e-03, ...,\n",
      "           1.53526310e-02, -1.91056281e-02,  5.70088997e-03],\n",
      "         ...,\n",
      "         [-1.97563656e-02, -2.17581429e-02,  8.66582431e-03, ...,\n",
      "           6.68522157e-03, -2.04253774e-02,  1.89491007e-02],\n",
      "         [-4.89933416e-03, -6.51334319e-03,  3.93319875e-03, ...,\n",
      "           1.59533676e-02,  1.56209301e-02, -4.09037806e-03],\n",
      "         [-1.27914781e-02, -6.83444086e-03,  3.40062380e-03, ...,\n",
      "          -2.85050273e-03, -4.71191481e-04,  1.85294505e-02]],\n",
      "\n",
      "        [[ 1.27599854e-02,  1.22879986e-02,  4.39250655e-03, ...,\n",
      "           5.59350476e-03, -9.24142264e-03,  1.57346074e-02],\n",
      "         [-2.62876227e-03,  1.39039010e-04, -2.38895603e-03, ...,\n",
      "          -7.83225801e-03,  8.27820227e-03,  1.81494635e-02],\n",
      "         [-7.52851367e-03, -9.09794122e-04,  5.57634048e-03, ...,\n",
      "           1.03894267e-02, -6.26944005e-04,  1.19270887e-02],\n",
      "         ...,\n",
      "         [-6.92185387e-03,  8.03725980e-03,  1.33635011e-02, ...,\n",
      "          -1.54321454e-03,  7.15807825e-03,  1.12165827e-02],\n",
      "         [ 1.28268171e-02,  1.91295426e-02,  9.06278007e-03, ...,\n",
      "          -1.41761880e-02, -2.02819686e-02,  1.40960868e-02],\n",
      "         [-4.45326976e-03,  6.61367737e-03,  9.50471498e-03, ...,\n",
      "          -8.71203840e-04, -1.62020400e-02,  1.81699153e-02]],\n",
      "\n",
      "        [[ 3.19303945e-04,  1.63800996e-02,  1.48182269e-02, ...,\n",
      "          -2.18637958e-02,  1.70209948e-02, -7.61551969e-03],\n",
      "         [-6.74626138e-03, -1.13736000e-02, -4.62463312e-03, ...,\n",
      "          -1.56683102e-03, -1.82967447e-03,  1.61091797e-03],\n",
      "         [ 1.71811152e-02,  1.23784319e-03,  2.73675658e-03, ...,\n",
      "           1.54478494e-02,  1.02495756e-02,  4.78835776e-04],\n",
      "         ...,\n",
      "         [-9.65730660e-03,  2.88074464e-03, -1.43229607e-02, ...,\n",
      "          -1.46472547e-02,  2.15035491e-03, -1.51611120e-03],\n",
      "         [-2.12171618e-02, -6.95902854e-04,  1.00623611e-02, ...,\n",
      "           1.96036603e-02, -6.50083367e-03, -1.17036887e-03],\n",
      "         [ 1.83331389e-02, -1.38289090e-02,  6.20139018e-03, ...,\n",
      "           2.09419057e-03,  3.92979383e-03, -7.58801959e-03]]],\n",
      "\n",
      "\n",
      "       [[[ 4.91069071e-03,  1.83432158e-02,  1.18427444e-02, ...,\n",
      "          -8.14709906e-03,  2.11996678e-02,  1.49805974e-02],\n",
      "         [-2.93116830e-03, -3.69208865e-03,  1.67247113e-02, ...,\n",
      "           2.06406470e-02,  1.44262295e-02,  1.20763462e-02],\n",
      "         [ 9.29310173e-03, -1.94734521e-03,  1.76696721e-02, ...,\n",
      "          -8.14688485e-03, -3.34051251e-03, -4.48783487e-03],\n",
      "         ...,\n",
      "         [ 1.08280461e-02, -4.73808497e-03,  2.00518724e-02, ...,\n",
      "           3.11469659e-03,  2.50150822e-03, -3.35840322e-03],\n",
      "         [ 1.33737866e-02, -4.94941697e-03,  1.84085462e-02, ...,\n",
      "          -1.29585452e-02, -3.39222327e-03, -2.02844255e-02],\n",
      "         [-1.71909854e-03,  7.60886632e-03, -1.61211863e-02, ...,\n",
      "           1.33104529e-02, -2.89846770e-03, -1.47230029e-02]],\n",
      "\n",
      "        [[ 1.37796812e-03,  1.14531014e-02, -1.83748156e-02, ...,\n",
      "          -1.36828711e-02, -2.18733959e-03,  1.69845615e-02],\n",
      "         [-1.22277159e-02,  1.61619764e-02,  3.13300267e-03, ...,\n",
      "           1.06616933e-02, -3.05296853e-03, -1.59342159e-02],\n",
      "         [-8.75743479e-03, -1.42650083e-02, -1.87867675e-02, ...,\n",
      "           1.53977666e-02,  2.14209948e-02, -2.08154190e-02],\n",
      "         ...,\n",
      "         [-6.77389745e-03, -1.53147820e-02, -9.34755523e-03, ...,\n",
      "          -1.73582882e-02, -7.71510322e-03, -4.65770625e-03],\n",
      "         [ 1.88592207e-02,  1.44152958e-02, -9.38771572e-03, ...,\n",
      "          -2.63278931e-03,  1.55624058e-02, -8.13128892e-03],\n",
      "         [ 1.49541330e-02, -2.42519565e-03,  1.60144549e-02, ...,\n",
      "           1.31617505e-02, -3.71224061e-03, -1.72060616e-02]],\n",
      "\n",
      "        [[-2.04159766e-02, -1.36566376e-02,  4.15492617e-03, ...,\n",
      "          -1.84167325e-02, -1.36951506e-02,  2.00850908e-02],\n",
      "         [ 2.02131402e-02,  1.37686413e-02,  2.09204983e-02, ...,\n",
      "          -1.31047592e-02, -1.78502183e-02, -7.52278324e-03],\n",
      "         [ 5.84697910e-03,  1.33954007e-02,  1.64912473e-02, ...,\n",
      "          -1.59561690e-02,  8.05523247e-03, -8.18521529e-03],\n",
      "         ...,\n",
      "         [ 4.83144075e-04, -2.05224473e-02,  1.68403257e-02, ...,\n",
      "           2.17962842e-02, -8.10733996e-03,  1.06269028e-02],\n",
      "         [-1.18693309e-02,  4.47562337e-03,  1.12955999e-02, ...,\n",
      "           7.93218613e-03, -1.72915664e-02, -3.64389643e-03],\n",
      "         [ 2.11831182e-03, -1.61933843e-02, -1.60507467e-02, ...,\n",
      "          -1.80505421e-02,  1.84173454e-02, -1.12823546e-02]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 4.64477576e-03, -1.11812856e-02, -1.26519902e-02, ...,\n",
      "           1.74323078e-02,  1.88975055e-02, -2.87793577e-04],\n",
      "         [ 2.83986516e-03, -1.59460269e-02, -6.81596063e-03, ...,\n",
      "           7.09730014e-03, -6.53057080e-03, -2.00326312e-02],\n",
      "         [-5.63882850e-03, -2.26389617e-04,  5.37485257e-03, ...,\n",
      "           1.24843139e-02, -1.57186259e-02,  2.12034862e-02],\n",
      "         ...,\n",
      "         [-5.92702441e-03, -2.04105750e-02, -6.31694030e-03, ...,\n",
      "           1.80011299e-02, -1.45377275e-02,  5.64109161e-03],\n",
      "         [ 2.05943789e-02, -1.00015560e-02,  3.19622457e-04, ...,\n",
      "          -1.65633559e-02, -2.06249766e-02,  5.47770411e-04],\n",
      "         [ 1.89866964e-02, -1.30299665e-03, -5.82027622e-03, ...,\n",
      "          -1.00513790e-02,  2.99442746e-03,  4.53765318e-03]],\n",
      "\n",
      "        [[ 1.75439660e-02,  3.97298299e-03,  1.48702618e-02, ...,\n",
      "           1.12549383e-02, -1.96628943e-02,  4.92513180e-04],\n",
      "         [ 1.12414118e-02,  1.61999296e-02, -1.07072620e-02, ...,\n",
      "           1.71002001e-03, -7.08131306e-03,  1.28010530e-02],\n",
      "         [-2.00933050e-02,  4.22493927e-03, -1.47143211e-02, ...,\n",
      "          -7.82144908e-03,  1.14265028e-02,  1.68545637e-02],\n",
      "         ...,\n",
      "         [ 5.17722592e-03,  1.71291456e-03,  2.16468070e-02, ...,\n",
      "           1.99672636e-02, -2.80166417e-03,  1.49469543e-02],\n",
      "         [ 3.01119126e-03,  1.90324467e-02, -1.14869326e-05, ...,\n",
      "          -8.48784950e-03, -1.75883081e-02, -1.33652361e-02],\n",
      "         [-8.43099225e-03, -1.90788899e-02,  1.11335758e-02, ...,\n",
      "          -1.86249875e-02,  7.14598596e-03,  4.12320346e-03]],\n",
      "\n",
      "        [[-9.91821289e-05, -2.14895010e-02, -1.03348345e-02, ...,\n",
      "           6.70924410e-03,  6.68992661e-03, -1.42864445e-02],\n",
      "         [ 1.65441614e-02,  2.09037494e-02,  6.14701584e-03, ...,\n",
      "           7.85900280e-03,  1.89397465e-02, -1.87557377e-02],\n",
      "         [-1.98630448e-02,  1.52922440e-02, -1.50381615e-02, ...,\n",
      "           7.83064216e-03, -1.03601133e-02,  1.77741889e-02],\n",
      "         ...,\n",
      "         [ 3.21963429e-03, -5.58528677e-03,  4.26468812e-03, ...,\n",
      "           1.20391976e-02,  8.41173157e-03, -7.00115785e-03],\n",
      "         [-8.75315908e-03, -1.66698899e-02, -1.97293498e-02, ...,\n",
      "          -1.01166358e-02,  9.05768573e-03,  1.57927815e-02],\n",
      "         [-1.01625845e-02,  4.04491462e-03,  1.40532535e-02, ...,\n",
      "          -2.65472569e-03,  7.08783604e-03, -9.01468843e-03]]],\n",
      "\n",
      "\n",
      "       [[[-1.45696914e-02, -9.95786022e-03,  5.37753105e-04, ...,\n",
      "          -1.81150641e-02, -1.57339089e-02, -4.45347838e-03],\n",
      "         [ 6.64835237e-03, -1.78014636e-02,  4.55525517e-03, ...,\n",
      "          -1.15130935e-02,  1.04204211e-02, -8.33596662e-03],\n",
      "         [-7.44083896e-04, -4.92757373e-03,  1.34162363e-02, ...,\n",
      "           8.58947635e-03, -1.83527488e-02,  1.55817065e-02],\n",
      "         ...,\n",
      "         [ 5.93539886e-03, -9.86506045e-03,  1.82533097e-02, ...,\n",
      "          -1.79995093e-02, -8.19577929e-03,  1.91194508e-02],\n",
      "         [-1.96148641e-03, -2.75860541e-03, -1.87601335e-02, ...,\n",
      "           1.29075851e-02, -1.80929452e-02, -4.48668748e-03],\n",
      "         [ 1.17039960e-02, -1.19393226e-02,  2.78182887e-03, ...,\n",
      "          -7.12291803e-03,  1.11902114e-02, -6.01041131e-03]],\n",
      "\n",
      "        [[ 3.00842337e-03,  5.74100204e-03,  6.62022084e-03, ...,\n",
      "          -2.13311780e-02,  1.08814631e-02,  2.70719081e-03],\n",
      "         [-4.59737144e-03, -1.14071285e-02,  1.09246001e-04, ...,\n",
      "          -8.24688561e-03, -2.09606122e-02, -1.94035545e-02],\n",
      "         [-1.93190463e-02,  8.04439746e-03, -1.93621572e-02, ...,\n",
      "          -6.56332728e-03, -1.51390638e-02,  9.66381468e-03],\n",
      "         ...,\n",
      "         [-4.30519320e-03, -1.16783455e-02, -2.08242480e-02, ...,\n",
      "           1.32028926e-02, -1.53856352e-03,  6.19648956e-03],\n",
      "         [ 6.64773211e-03,  7.87941739e-04, -1.73941329e-02, ...,\n",
      "          -1.12212896e-02, -2.82467529e-03, -1.38073266e-02],\n",
      "         [-4.52999771e-03,  9.99547727e-03, -1.80666335e-02, ...,\n",
      "          -3.86844203e-03, -2.05744058e-03,  8.52522068e-03]],\n",
      "\n",
      "        [[-9.84759256e-03,  5.62289357e-03,  1.39273647e-02, ...,\n",
      "           1.75509062e-02,  1.31634045e-02,  1.43234711e-02],\n",
      "         [ 1.60974730e-02,  2.09976546e-03,  6.14041463e-03, ...,\n",
      "          -1.00112548e-02, -1.28770238e-02,  4.02749330e-03],\n",
      "         [ 6.09171204e-03, -1.59081351e-02, -1.82037391e-02, ...,\n",
      "           1.11056399e-02,  6.12045452e-03, -4.39276174e-03],\n",
      "         ...,\n",
      "         [ 1.21083204e-02, -8.29270389e-03,  4.54115681e-03, ...,\n",
      "          -1.64598506e-02,  2.89356709e-03, -1.61527582e-02],\n",
      "         [-1.17707280e-02, -1.19571760e-02,  1.86819602e-02, ...,\n",
      "           2.22844444e-03, -1.91809237e-02, -5.99997304e-03],\n",
      "         [-2.76916288e-03, -8.78464337e-03, -1.17201908e-02, ...,\n",
      "          -1.63280703e-02,  3.19487602e-03,  2.44520791e-03]],\n",
      "\n",
      "        ...,\n",
      "\n",
      "        [[ 1.58759374e-02,  1.31195411e-03, -8.60681385e-03, ...,\n",
      "          -1.29975639e-02, -4.94059920e-03,  1.72397494e-03],\n",
      "         [ 1.73605625e-02,  7.82385841e-03, -3.99067625e-03, ...,\n",
      "          -1.69916190e-02,  6.27968833e-03, -4.79821302e-03],\n",
      "         [ 2.05142405e-02,  1.45161245e-02,  1.26772206e-02, ...,\n",
      "          -1.51406750e-02,  1.83844473e-02,  1.66617613e-02],\n",
      "         ...,\n",
      "         [ 9.35070030e-03, -5.09142317e-03,  9.62012820e-03, ...,\n",
      "          -7.26860017e-03,  1.34538282e-02,  1.70126539e-02],\n",
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     "name": "stdout",
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      "{'name': 'activation_10', 'trainable': True, 'activation': 'relu'}\n",
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      "{'name': 'add_8', 'trainable': True}\n",
      "[]\n",
      "{'name': 'activation_12', 'trainable': True, 'activation': 'relu'}\n",
      "[]\n",
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      "{'name': 'add_9', 'trainable': True}\n",
      "[]\n",
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      "{'name': 'add_10', 'trainable': True}\n",
      "[]\n",
      "{'name': 'activation_14', 'trainable': True, 'activation': 'relu'}\n",
      "[]\n",
      "{'name': 'max_pooling2d_2', 'trainable': True, 'pool_size': (2, 2), 'padding': 'valid', 'strides': (2, 2), 'data_format': 'channels_last'}\n",
      "[]\n",
      "{'name': 'dropout_2', 'trainable': True, 'rate': 0.3, 'noise_shape': None, 'seed': None}\n",
      "[]\n",
      "{'name': 'separable_conv2d_11', 'trainable': True, 'filters': 128, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'same', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'linear', 'use_bias': True, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'bias_regularizer': None, 'activity_regularizer': None, 'bias_constraint': None, 'depth_multiplier': 1, 'depthwise_initializer': {'class_name': 'Zeros', 'config': {}}, 'pointwise_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}}, 'depthwise_regularizer': {'class_name': 'L1L2', 'config': {'l1': 0.0, 'l2': 9.999999747378752e-05}}, 'pointwise_regularizer': {'class_name': 'L1L2', 'config': {'l1': 0.0, 'l2': 9.999999747378752e-05}}, 'depthwise_constraint': None, 'pointwise_constraint': None}\n",
      "[array([[[[0.],\n",
      "         [0.],\n",
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      "\n",
      "\n",
      "       [[[0.],\n",
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      "         [0.],\n",
      "         ...,\n",
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      "         [0.],\n",
      "         ...,\n",
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      "\n",
      "        [[0.],\n",
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      "         [0.],\n",
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      "         [0.],\n",
      "         [0.],\n",
      "         [0.]]]], dtype=float32), array([[[[-0.11477443, -0.13363706, -0.1235035 , ..., -0.04398064,\n",
      "          -0.08718083,  0.02464226],\n",
      "         [ 0.12130402, -0.06046933, -0.07213492, ..., -0.06380239,\n",
      "          -0.06247465,  0.06457292],\n",
      "         [ 0.06214915,  0.12167664, -0.12202881, ...,  0.14238547,\n",
      "          -0.02219006, -0.04427154],\n",
      "         ...,\n",
      "         [ 0.07079931, -0.07171093, -0.01680449, ..., -0.06238176,\n",
      "           0.06102924, -0.03797682],\n",
      "         [-0.02496642,  0.15066291,  0.05344172, ...,  0.032447  ,\n",
      "          -0.05994055, -0.0861201 ],\n",
      "         [-0.14124879, -0.08745079, -0.06493229, ...,  0.04340729,\n",
      "          -0.00765629,  0.10626508]]]], dtype=float32), array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)]\n",
      "{'name': 'batch_normalization_15', 'trainable': True, 'axis': -1, 'momentum': 0.99, 'epsilon': 0.001, 'center': True, 'scale': True, 'beta_initializer': {'class_name': 'Zeros', 'config': {}}, 'gamma_initializer': {'class_name': 'Ones', 'config': {}}, 'moving_mean_initializer': {'class_name': 'Zeros', 'config': {}}, 'moving_variance_initializer': {'class_name': 'Ones', 'config': {}}, 'beta_regularizer': None, 'gamma_regularizer': None, 'beta_constraint': None, 'gamma_constraint': None}\n",
      "[array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1.], dtype=float32), array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32), array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "       0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32), array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
      "       1., 1., 1., 1., 1., 1., 1., 1., 1.], dtype=float32)]\n",
      "{'name': 'add_11', 'trainable': True}\n",
      "[]\n",
      "{'name': 'activation_15', 'trainable': True, 'activation': 'relu'}\n",
      "[]\n",
      "{'name': 'flatten_1', 'trainable': True, 'data_format': 'channels_last'}\n",
      "[]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'name': 'dense_1', 'trainable': True, 'units': 2, 'activation': 'softmax', 'use_bias': True, 'kernel_initializer': {'class_name': 'VarianceScaling', 'config': {'scale': 1.0, 'mode': 'fan_avg', 'distribution': 'uniform', 'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}\n",
      "[array([[-0.02689167, -0.01078583],\n",
      "       [-0.01037381,  0.02613532],\n",
      "       [-0.00688134,  0.02128256],\n",
      "       ...,\n",
      "       [ 0.01852953,  0.01511767],\n",
      "       [ 0.01886596, -0.01959814],\n",
      "       [ 0.02098321,  0.00955082]], dtype=float32), array([0., 0.], dtype=float32)]\n"
     ]
    }
   ],
   "source": [
    "for layer in model.layers:\n",
    "    g=layer.get_config()\n",
    "    h=layer.get_weights()\n",
    "    print (g)\n",
    "    print (h)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      " - 154s - loss: 0.7171 - acc: 0.8518 - val_loss: 1.6777 - val_acc: 0.6920\n",
      "Epoch 2/5\n",
      " - 93s - loss: 0.5907 - acc: 0.8827 - val_loss: 0.8671 - val_acc: 0.7720\n",
      "Epoch 3/5\n",
      " - 68s - loss: 0.5116 - acc: 0.9027 - val_loss: 0.6938 - val_acc: 0.8400\n",
      "Epoch 4/5\n",
      " - 75s - loss: 0.4692 - acc: 0.9200 - val_loss: 0.6306 - val_acc: 0.8720\n",
      "Epoch 5/5\n",
      " - 87s - loss: 0.4773 - acc: 0.8957 - val_loss: 0.6624 - val_acc: 0.8240\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x16615179898>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit_generator(train_bconvert, steps_per_epoch=55, \n",
    "                    validation_data=valid_bconvert, validation_steps=25, epochs=5, verbose=2) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Results</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 596 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "test_bconf = ImageDataGenerator().flow_from_directory(test_path,target_size=(64,64),classes=['DECAYED','DECAYFREE'],batch_size=100)\n",
    "test_imgs, test_labels = next(test_bconf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 0., 1., 0., 1., 1.,\n",
       "       0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1., 1., 0., 1., 1., 1., 1.,\n",
       "       1., 0., 1., 0., 0., 1., 0., 0., 1., 1., 0., 0., 0., 1., 0., 0., 1.,\n",
       "       0., 1., 0., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1., 0.,\n",
       "       0., 0., 1., 0., 1., 0., 1., 0., 1., 0., 0., 0., 1., 0., 1., 0., 1.,\n",
       "       0., 0., 0., 1., 0., 1., 1., 0., 0., 0., 0., 1., 1., 1., 1.],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels = test_labels[:,0]\n",
    "test_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict_generator(test_bconf, steps=1, verbose=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions=np.round(predictions,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(test_labels, predictions[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[40 14]\n",
      " [ 2 44]]\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"
    }
   ],
   "source": [
    "cm_plot_labels = ['DECAYED','DECAYFREE']\n",
    "plot_confusion_matrix(cm, cm_plot_labels, title='Confusion Matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TF-GPU",
   "language": "python",
   "name": "mldl_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}