train_derm1_singlevar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from jax.config import config\n",
    "config.update(\"jax_enable_x64\", True)\n",
    "\n",
    "import jax\n",
    "import jax.numpy as jnp\n",
    "import numpy as np\n",
    "from jax import grad, vmap, jit, jacfwd, jacrev, value_and_grad\n",
    "from functools import partial\n",
    "from jax.flatten_util import ravel_pytree\n",
    "import jax.random as random\n",
    "from jax.lax import cond, scan\n",
    "import jax.example_libraries.optimizers as optimizers\n",
    "key = random.PRNGKey(2022)\n",
    "\n",
    "from utils_node import init_params_damage, init_params_damage_simple\n",
    "from utils_node import RK_forward_pass_nobias as NODE_nobias, RK_forward_pass as NODE\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.patches import Patch\n",
    "import pandas as pd\n",
    "import pickle\n",
    "\n",
    "\n",
    "from diffrax import diffeqsolve, ODETerm, SaveAt#, Heun as mysolver\n",
    "from diffrax import Dopri5, Dopri8, Heun, Tsit5, Kvaerno3, Kvaerno4, Kvaerno5\n",
    "from diffrax import PIDController\n",
    "\n",
    "mysolver = Heun"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zGGlvPXIUjBBCCCEqyZtX9e57CAaDCAaDAH7b/BsaGlBTUwMg+usTRbHDy/GAln4SqbmWghFCCCEkDNJwse7WLxIfH8/ueJH6Rfr374+kpKSorsfv90MQBBgMhnYv5ystLYXP50NsbCwGDhwY1bVpgYIRQgghupP6IUpKSgB0n2CkO5ZoOusXGTJkiC5HjtWiYIQQQojupGCku5RpOgtGRo8e3S3WI9eT+0UACkYIIYR0A263Gz6fD+Xl5QD03VQFQYDP5wPQPTMj7aFghBBCCFEhGAwiEAigpKSEXZCXkZGh23q8Xi9EUTxunodewQjHcQgEAgA6DkYOHjwIQP+MUrgoGCGEEKIrqURTWVkJoOXpXs8L8trrz2hubkZFRQWA6A88kz6fuLg41kzbFmVGCCGEEBWk+1+kEo3eT/ftlUSkkzR5eXlRv/elsyO9QMvnJwVKFIwQQgghComieFzzqp4bqnyeR0/pF5Hmi2RkZCA1NTVq69ISBSOEEEJ0Ix8udvToUQD6BiOBQAA8z8NgMMBisbCv6xWMdNRMK9fTSzQABSOEEEJ0JJVoLBZLt7h1VsrSWCyWVn0regUjHTXTyvX05lWAghFCCCE6kjZ/t9sNp9MJo9Go6wV5HfVn6BWMdHU5HkCZEUIIISRsoige17xaWFiIuLg43dcjD0ZcLhebDKvXSZqOmlcBCkYIIYSQsPn9ftaf0R0mrwYCAXAcB4PB0O5JmqysLKSnp0dtPYIgsExNYmJih9/Tky/Ik1AwQgghRBfyEsThw4cB6LuhyvtFjMbftke9SjQej4f1i8TGxrb7PeXl5fB4PIiJiUFhYWFU16clCkYIIYToQiqJJCYmsibM7hCMtM1C6BWMyD+frvpFBg8e3GGDa09AwQghhJCok88XSUhI0L3voaN+EUD/5tXe3i8CUDBCCCFEB16vF4IgwGg0doueEXn/iny+CKDPbb08z8Pr9QLouF8EoGCEEEIICZs8C3HkyBGIogibzYbMzExd1iPvX5H3i3g8Hhw7dgxAdDMj0npiY2M7Lb9QMEIIIYSESQpGkpKSWm2oel2Q11G/yK+//gpRFNGvX7+o3iQs7xfpTG8YeAaEEYxs3rwZF110EXJzc2EwGLBu3bpOv/+bb76BwWA47kd1dXW4ayaEEKJQY2MjRFHUexkAWkoQ8iOrek9ebdu/Ited+0XcbjfKysoA9MHMiNvtxrhx4/D8888ret2vv/6Kqqoq9kOvVBwhhPQ1gUAAWVlZyMrKYre76klegoiNjdX9JI3P5wPP8zAajR32i0QzGAkGg/D7/QA6D0ak+Sf9+vWL6vyTSDArfcGMGTMwY8YMxb9QZmYmUlJSFL+OEEKIOnv37gXHcXC73cjJydF7OceVIPTue5BnIdqWifQIRqT1xMfHw2zueJveuXMnAGDixIlRWVckRa1nZPz48cjJycG5556LrVu3RuuXJYSQPm/37t0AgKFDh7ZqztSL0+kE0BKMiKKoezDS0ZFeQJ9gJNR+kd4UjCjOjCiVk5ODlStX4qSTToLf78eqVaswdepU/PDDDx1+gH6/n6WoAMDhcER6mYQQ0mvt2bMHQPT7Htrj9/sRDAZhMBiQkJCA6upqOBwOGI1GDBkyJOrrEQShw/4Mv9+PI0eOAIjeZ9dZ/0pbP/30EwAKRkIyfPjwVtHuqaeeiqKiIjz99NNYs2ZNu69ZtmwZHnzwwUgvjRBCej1RFFlPxgknnKDzan576rdarTCZTCwrMnDgQMTHx0d9PdLIdZPJdNyvf+jQIQiCgJSUFGRnZ0dlPYFAoFWw1pFgMIhffvkFQO8IRnTJ102aNIlFm+2566670NzczH5I3cKEEEKU8Xq97N/bcePG6bya7tsv0t7IdXmJJlpHjju6H6et/fv3IxAIwGazYdCgQVFZWyRFPDPSnt27d3faRBUXF6fbFdKEENKb2O12Nt00mhNE2yMvibQNRvQ61ttZf0ZP6RfRazaLlhQHIy6Xq1VW49ixY9i9ezfS0tLQv39/3HXXXaioqMBrr70GAFi+fDkKCwsxevRo+Hw+rFq1Chs3bsT69eu1+10QQghp1759+8BxHBISEtC/f39d1+LxeCAIQquSiJ7HejmO63Tk+r59+wBQv0g0KA5GduzYgbPOOov978WLFwMA5s2bh9WrV6OqqgqlpaXs5wOBAG655RZUVFTAarVi7Nix+Oqrr1q9ByGEEO0JgoC9e/cCAEaOHKn7E7R86qq0Fin7oEdmRNr44+Li2h25Hu3MiHzeidVq7fR7e9NJGiCMYGTq1KmdTvFbvXp1q/99++234/bbb1e8MEIIIep4PB6WyR4zZozOq/ntSG9SUhKAlpOSJSUlAPRpru2sJBIIBHD48GEA0QtG5PfjdBY48jzPjmufeOKJ0VhaxOl/4JwQQkhEOJ1OFBUVAdC/XyQQCBw3VVQqg+Tm5iItLS2q6xFFsdNg5MiRI+A4DklJScjPz4/KmpTcR+P1epGYmIihQ4dGY2kRR8EIIYT0QqIotgpG9J4xIj/SK00Vleaf6JG16eoIbbRP0giCwO7r6apfRCrRjB8/vlsMsdNC7/hdEEIIacXv98PtdnebkzTyqasSqZ9FzxKN1Wptd0OPdr+I1+s9rrm3I72tXwSgYIQQQnolp9OJsrIycByHxMREXU/SyI/0Sv0iwG+Zke7WLwJEPxjp7H6ctqRgpLf0iwAUjBBCSK8kL9HofZJGOtJrNpvZU78oirqVaeRHaLtLMBJqv4ggCNi1axcAyowQQgjpxjiOa3WSpjuVaKSgqKamBg0NDTAajVHvZ2lv3okcx3FsGFs01iYIApt30lW/yJEjR+B0OhEfH6/boLhIoGCEEEJ6GWnz7y79IvL5IhIpKzJkyBBYLBZd1tPeCHgAOHr0KAKBAKxWa1TKW263G6IoIiYmBrGxsZ1+r1SiGTduHGsE7g0oGCGEkF5GCkaOHj0KQN+TNMFgkB3plZcg9DxJ09WUU6lEM3LkyKicVlHSLyJNXu1N/SIABSOEENKrSPMzgsFgt5gxImUhLBYLTCYT+7peJ2l4nmdHaHtavwjQO0/SABSMEEJIryL1Q1RUVCAYDCIhIQEFBQW6raejLIReJ2mkQKSzkkg0g5FgMAifzweg62BEFEUKRgghhHR/UommoqICQMuGqtdgrI4ufuN5nk1fjXaZJpQsRDSDEWk98fHxXfaAFBcXw263IzY2Vvc+IK1RMEIIIb2ItLlJd77ouWl1NOX02LFj8Hq9iI+Px5AhQ6K6pq6CEZ7nceDAAQDRDUZCKdFI/SInnHBCl42uPQ0FI4QQ0kvIU/7dYQy8lBWxWCytsjNSiWbUqFGt+kgijeO44+7HaaukpAQ+nw9xcXEoLCyM6Hrk9+PITxp1pLeWaAAKRgghpNeQSjQWi4WVGvTMjHTVL6JXiaazkohUPhoxYkTEAyWfzwee52E0GkM63kzBCCGEkG5PvtlKQ7v0CkY6uxVXr5M0XR3pBRDVIE76fBISErrs65E3r/a2Y70ABSOEENIrCILANre6ujrdT9L4/X7wPA+DwXDcU78eJ2k6C47kfvnlFwDRKW+1d3lgR8rLy1FXVweTyaTLXT6RRsEIIYT0AvIR59IYeD1P0sizEPI1+Hw+HD58GEB0g5GOmmnbitZQsVDmnchJWZHRo0d3eatvT0TBCCGE9ALyRshoD+3qbD1tN/6DBw+C53mkpqYiJycnauvpqJlWzuFwsPJWpIMRaT0xMTGIi4vr8vt7c78IQMEIIYT0CvKUv97Nqx3NFwFal2iieZNwKCUaacMfMGAAMjIyorKeUE7RAL13DLyEghFCCOnh2t7/Ip0I0SsY8Xq9EASh3VMi3blfZMeOHQCAk046KeJrUjJfBKDMCCGEkG5Ofv8LAFZq0KtM09mtuNJJmmge65WCI5PJ1OkR2mgFI4FAAIFAAEDnJ3skVVVVqKqqgsFgwLhx4yK6Nr1QMEIIIT2cvERz5MgRdpKmf//+uqyns6d+PTIj8v6VzkpDUikk0sGI9OdltVpDmmWya9cuAC2zT0IJXnoiCkYIIaQHazvFUyrR6HWShud5eL1eAMc/9Tc1NaG8vBxAdDMjoZREmpqa2CmkSJdCqF/keBSMEEJIDyY/0iufvKpXicbj8UAUxXZvxZUCpYKCAthstqisJ9QjtFJPxqBBg5CWlhax9cjnwVC/yG8oGCGEkB6sbX+G3s2rnfWL6FGikU71xMbGdnq5XLT6RaRgzWw2hzwvhIIRQggh3VrbKZ7yMo0eumu/SFdZiGgFI/I/r1CONtfX16O0tBQAMH78+EguTVcUjBBCSA8VCATYLb1JSUngOE7XO2nkR4zba7TU4yRNdwtGlPaLSFmRoUOHRq20pQcKRgghpIeSn8owm804fPiwridppJJIe7fiiqIY9cxIqEdoGxoaUFxcDCCypZBAINBqHkwo+kKJBqBghBBCeiyHwwEASE5OBgD8/PPPAFo2ez1O0nSWhaioqIDdbofJZMKIESOiup6ujtBKp1UinX0IdT1yFIwQQgjptnieZ5kIKeW/e/duAPr0Foii2OkttFKJZtiwYSHdxaKFUG/F1aNfJFRSoETBCCGEkG5H2tji4uLY5q5nMOJ2u8HzPEwmU7slkWiXaARBOC5Y60g0ghEl65E0NTXh6NGjACgYIYQQ0g1JwYh8Y5PKNHoEI1LJKCkpqd1TItEORqT5K6EcoY1GMKJkPRIpuBw4cGBEZ590BxSMEEKIAjzPY8OGDaivr4coirqsQRAEFoxI/SLV1dWorq6G0WiM6tFZoKVE07Z/pS2pTBOttcmDtc6O0NbW1qKsrAwGgwETJkyI+HpCPdIL9J0SDQCYu/4WQgghkn379uGcc85BcnIyGhsbQ25E1JL8KVu6+E3KigwbNgxWqzWq6/F6veA4Dkajsd1+CI7j2GTYaB3rDbU/Q9rwhw8fHnL5JBxKj/QCfad5FaDMCCGEKLJ9+3YAQGFhoS6BCND+lFMppa/Hra7yEk17p3iOHDkCv9+PhIQEFBYWRnw9fr8fgUAABoOhWzSvhnOkF/gtGOnNd9JIKBghhBAFpA1Cz6vc2ztCq1fzqiiKaG5uBtB1iWb06NFROXKs5FbcaAQjUrCWkJAQcgDrdDpx6NAhAIho+ai7oGCEEEJCxPM8a8SM9DHQjnAcx6audodgxOfzIRgMdpqFkD6zaJVolJREohGMtNds3JWdO3dCFEXk5eUhKysrUkvrNigYIYSQELndbjZuXa9gRNpo5VNO5euKdjAiPfUnJiZ2+NQfzeZV+RHarkoiVVVVqKyshNFojNjn1t48mFBs2bIFAHDqqadGZF3dDQUjhBASomPHjqGurg4GgwFjx47VZQ3tlWj27t0LURSRlZWF7OzsqK6n7ame9kTzWK/L5YIoioiJielyuJrUvDpy5MhOx8WrXQ/Qeh5MKKRg5IwzzojIurobCkYIISQEoiiylP6QIUMitnl1tYb2ghG95osEg8FWF/W1x+Vy4ciRIwCiU6YJ9UgvEN1+ESVZEY7jsG3bNgAUjBBCCJHx+Xys3KBXQ6Hf7wfHcTAYDK2O7+rVLyIFRhaL5biL8SQ//fQTRFFEQUFBxHsf5CPpu0O/iDx4VBKM/Pzzz3C5XEhOTo76zBi9UDBCCCEhsNvtOHjwIAD95j7IL1qTn0rR61hvKLM8fvjhBwDA5MmTI74e+byTrjJX8kxXpIIRj8fDRuQrmf0ilWhOO+003Y6PRxsFI4QQ0gXp+KoUjOgxbh1ov1+E53n88ssvAKK7rlCf+qVgZNKkSRFfkzw46uoIcUVFBWpqamAymSIWxHU1Ir8jUjDyu9/9LiLr6o4oGCGEkC64XC44HA6UlJQA0CcY6ehURlFREdxuNywWC4YNGxa19UhTYE0mE5sC255oZka6GkkvJ2VFRo8e3en6w6W0ZCR/XV9rXgUoGCGEkC7Z7XYcOnQIoigiJydHl7kPDocDoigiLi6u1UVrUonmhBNOiGpKP5S7VioqKlBRUQGTyRTxKaJ+v59NOQ1l85dO0kSqRBMIBEKeAiv366+/oq6uDnFxcbodH9cDBSOEENIJQRDgcDh0m+Mhkaac2my2Vl/Xu3k1lBLNmDFjIn76SAqOQp1yGul+kXCmrgK/lWgmT56s6ChwT0fBCCGEdEKaWyEFI3qcpOE4jm3+3SEYkR/p7S7Nq0pKNNFoXg2nRAP0vfkiEgpGCCGkE9KmcvjwYQD6ZEakjTY+Pv64p2U9ZoyEcqQX+O1SwUgHIxzHwePxAAht8y8tLUV9fT1iYmIiMrxO6XrkKBghhBDSinRihOM4dpJGj8xIRyWa2tpaVFZWwmAwRHUeRShHenmeZ9mHSAcj0nri4+MRGxvb5fdL6zrhhBMiUgpRuh5JeXk5iouLYTQaMWXKFM3X1Z1RMEIIIR3w+/0IBoMoKSmBz+dDYmIiBg0aFNU1cBzHTtG0DUakrMiQIUMUNUmqIQgCy4x0VhLZv38/XC4XEhMTMWLEiIiuSUmJBvgtGIlUU63aEs348eND/r30FhSMEEJIB6RN5dixYwBahop1Nb9Ca/ISTdunbD36RdxuNwRBgNlsbnWqpy2pX+Tkk0+O6CkfnudDCo7kInmSRh6shRuM9KX5IhIKRgghpANt+0X0KNF0dhGdHsFIqHe/RKt51el0QhRFxMbGhlRyiXTzqjxYUzq/pK/2iwAUjBBCSLt4nmdNiAcOHAAQ/ebVrp76ox2MKBnkFa1gRMoc2Wy2kKacHjt2DE1NTYiNjY3IxX1KLuqTa2xsZHcfnX766Zqvq7ujYIQQQtohbXKxsbFs3Hq0MyPSseKYmJjjnvq9Xm/Ux9NLPTRdDfJyuVzYt28fgMgGIzzPd5o5ao+UFRk3bpyi5tJQhDt1FQC2bt0KABg+fDgyMzM1XVdPQMEIIYS0w263A2hJuzc0NMBsNmPUqFFRXYN8o237lL13714IgoCMjAzk5OREdT0JCQmd9s789NNPEAQBBQUFEV2bvETTWf+KXCSbV30+X0jBWnv6cokGoGCEEEKOEwwG2QkWqXl15MiRIW94WpA/ZXdVolFSDlCju5Vo5EeeQ/0MpNknkegXUXJRX1ubN28GQMEIIYSQ/0/KilitVlZuiHaJpqvr56VjvZG6cbYtJYO8ohGMyPtp2h557ojX68V3330HIDKbfrglGrfbzU74UDBCCCEEoiiyYCQlJQW7du0CEP3m1a6un49286qSQV5SMDJp0qSIrke6ODDUwWXfffcdAoEAcnNzMXToUE3XEwwG4fV6ASgPRn744QdwHIe8vDwMHDhQ03X1FBSMEEKIjM/ng9/vh8FggM1m0+X4rCiKrYKRtgRBiPoYeClA66pRNFo39Uolmvb6aTry9ddfAwDOOusszUtbUrBmsVgQExOj6LXy+SLRKrl1NxSMEEKIjLTpJiUlwel0sp6RaAYjXq+XNUK2F4wcPXoULpcLcXFxGD58eMTXEwgEWA9NSkpKp98bjZt6wynRAK2DEa0pnQIr19ebVwEKRgghhBFFkT1xp6SksKzIgAEDkJqaGrV1yJ/622uElNZ1wgkndHpRndbrsVqtIZdoItkvEk6Jxu12s+ZVrYMRnudZsKY0GAkGgxHtY+kpFAcjmzdvxkUXXYTc3FwYDAasW7euy9d88803mDhxIuLi4jBkyBCsXr06jKUSQkhkSZfimUwmJCYmsqbCSJYb2pKXaDp66pfWFY3mVXkPTSgBWTRu6g1nsNjWrVsRDAbRv39/FBYWaroeaR5MqFNg5Xbt2gWPx4PU1NSoHx3vThQHI263G+PGjcPzzz8f0vcfO3YMM2fOxFlnnYXdu3dj0aJFuPbaa/HFF18oXiwhhESStOnabDYYjcaIjg3viFSiMRqNHc6qkI6BnnbaaVFZj9RD09VTfzRu6pVuUgaUNYpGsl9EixLN6aefHvV7j7oTxfm9GTNmYMaMGSF//8qVK1FYWIgnn3wSQMtZ/W+//RZPP/00pk+frvSXJ4SQiOB5nm0qUl+EHsGIVBJJSkpqd3PyeDz48ccfAUTnQjV542pXF95JN/UmJSVF7KZer9cLnudhNBrbPfLckUj1iwiCoHgKrBz1i7SIeBj23Xff4Zxzzmn1tenTp7MaWXv8fj8cDkerH4QQEkkOh4Ol2i0WC+x2O44cOQIAmDhxYlTWIO9Z6ahE88MPPyAYDCIvLw+DBg2K6HoEQWjVQ9OVaNzUKx8sFmqGw+l0ssBS62BEzcV4giDg22+/BUDBSMSDkerqamRlZbX6WlZWFhwOBzuT3dayZctgs9nYj4KCgkgvkxDSx8lnixgMBuzcuRMAUFhYiPT09KiswePxgOO4kEo00TgG6nK5wPM8zGZzSOPNozVfBFBWotmyZQt4nsegQYPQv3//iK1H6Z/HgQMH0NDQAIvFErWAt7vqlgWqu+66C83NzexHWVmZ3ksihPRi8vHvepZo5CWRjvoH5MFIpMl7IULZaCN9kiYYDMLn8wEIv19ES/JmYzUlmilTpmh+aV9PE/EzYdnZ2aipqWn1tZqaGiQnJ3eY0lJyXIsQQtRq7+hqtE/SyEsiHZ1aCQQCrMQd6WCkq7tx2orGTb1S46rFYlF0pDlSwYjX62WZrHBmqlC/yG8inhmZMmUKNmzY0OprX375JaZMmRLpX5oQQkIiL9FIop0ZaW5uhiAIiImJ6bAxc8eOHfB6vejXrx9GjhwZ0fVId+OEutFG46Zeeb9IqJqamthIf62DEfmU3HBOwlAw8hvFn57L5cLu3bvZ0J1jx45h9+7dKC0tBdBSYpk7dy77/htuuAFHjx7F7bffjoMHD2LFihV455138I9//EOb3wEhhKjg8/ng8/nY+HcAaGxsxNGjRwFEr3lVPsujo5JINPtFlPZCRLpEE+6R3s2bN0MQBAwbNgy5ubmarqezkf1dKSkpQVlZGcxmM0455RTN1tVTKQ5GduzYgQkTJrAbLBcvXowJEyZgyZIlAICqqioWmAAtzV+ffvopvvzyS4wbNw5PPvkkVq1aRcd6CSHdgnz8u3QCRGpeHTx4cFQmr4Y6bj1a/SLh9EJEOhiRTq2YTCZFp1YiWaIJBAIwGo2q+kUmTpwYsbH5PYninpGpU6dCFMUOf7696apTp05laTJCCOku2t7QK4l2iUZaQ0JCQoeNjBzHsWOgkQ5G/H4/AoEADAZDyCWRSAcj4Z5aiVQwIg9iqUSjXrc8TUMIIdHQ0VFaKRiJVvNqKOPWf/75ZzidTthsNowdOzai65E2/oSEhJDmhchv6o1EWUsQhFbTcUNVX1+PX375BUDLQ7FW2t5hFA4KRlqjYIQQ0md1dCGddJImGpkReRais94DqURz+umnR2ygmERpiUbK2JxwwgkRKTk4nU5F804kmzZtAgCMGjXquHlXaiidv9JWfX09Dhw4AKDlz5NQMEII6aM6mnZaX1+P4uJiANFpXpWaMq1Wa6dBRrT6RYLBIBtIGWpj5ueffw4Ax03b1krbgXShinSJxmazhdVI/M033wBoCZKiNVCvu6NghBDSJ7ndbvA8z27olUhZkaFDhyoqCYQrlOOqgiBELRiR1mOxWBATE9Pl94uiyIKR888/X/P1BINBtialzcSRCEbau8NIqU8//RRAZD6vnoqCEUJInyQv0cifbqNZohEEgZ2i6SwLsX//fjQ2NsJqtUY8W6O0RPPLL7+guroaCQkJESk5SFkIi8WiaBhmTU0N9u/fDwA488wzNVuP/A6j+Ph4xa/neZ4FIxdeeKFm6+rpKBghhPQ58qOrbbMf0WxedbvdEEURZrO5041WyopEemw4z/MhBUdy//vf/wAA06ZN03xytvy0k9KsiFQKGTt2LPr166fZmsItGUl+/PFH1NXVwWazUb+IDAUjhJA+R5ouajKZjmu4jGZmRD7Eq7ONTQpGtHzC72g90lN/qIFFJEs0Xq8Xfr+/1UC6UEklmmnTpmm2nvbuMFLq448/BtDyeYVSBusrKBghhPQ58smZ8iCgtrYWpaWlMBgMbLBjJEnBSGf9IqIoslMhke4XUXoxnsPhwNatWwFEJhhpampi61F6gigS/SLt3WGk1CeffAIAuOiiizRbV29AwQghpE+RXwDXthQhZUWGDRsW1lRNJQKBAPx+P4DOg5EjR46guroasbGxmDRpUsTW09nn0pENGzaA4zgMGzYMgwYN0nQ9oVwc2JHKykocOnQIRqNRswBOFEUWHIWbFSkpKcEvv/wCo9FIzattUDBCCOlTOpsuGs0SjbTxh3qkd9KkSYrGoCslH7fe0UV9bUWyRONwONjFgUpnl0hZkQkTJoQdOLTl8/nCLhlJpMbV0047jY70tkHBCCGkT+lsumi0xsCLoojGxkYAXU8UjVa/SEelq46IosiaV2fMmKH5euRZCKWNopEo0agpGUmkfhE6RXM8CkYIIX1KZ6WIaJ2kkTdmdvXkHo35IvISTajlqQMHDqCsrAzx8fGaB0ryiwPDuahQ62BEXjIKN9PicrmwceNGANQv0h4KRgghfUYgEIDH4wFw/KZbXV2NioqKqDSvyrMinT1ll5aWori4GCaTCVOmTInYelwuF4LB4HF39HRGKtFMnTpV8/JRKBcHdqS0tBRHjx6FyWTS7N4XtePfAeCrr75CIBDAoEGDMGLECE3W1ZtQMEII6TPq6uoAtDSMtj1WKfWLjBgxIuwNJxQ8z7On7LS0tE6/V8qKTJw4MeSm0nDU19cDaMlChHoDrVSi0bpfpKOblEMlZUVOOukkzT4zNSUjifwUTbjv0ZtRMEII6ROCwSDb5DIyMo77+Wj1i9jtdoiiiLi4uC4zCtEo0Xi9XlYSCbWp0u12s7VpHYx4PB4EAgEYjcawGkW1LtFwHMdKWOGWaARBYMEI9Yu0j4IRQkifUF9fD1EUYbVa2z2dsW3bNgDAySefHNF1SE/ZaWlpXT4hS1NEIxmMSFkRm80Wcknk66+/RiAQQGFhIYYNG6bpeqTPx2azhZylkYiiiA0bNgDQLhiRslgWiyWs8e9AS9atpqYGSUlJEZ8V01NRMEII6fU4jmN9GpmZmcf9fCAQwJYtWwBof8OrnN/vh8/nA9D1KZri4mIcPnwYJpMpYidpAoEA22yVjEyXH+nVsuQgCIKqS+h2796N8vJyWK1WzfpF1M4WAX47RTN9+vSIjvPvySgYIYT0es3NzRBFEfHx8e1mRb7//nt4vV5kZmZi9OjREVuHtNEmJibCbDZ3+r3r168HAJxyyikRuz24oaEBQEujqJIm1EjNF5Fmi8TGxoY860Tuo48+AgCcd955mjTVer1e+Hw+VbNFAJq6GgoKRgghvZ78WGZ7T/LSkctp06ZFtLlQyY24UjBy3nnnRWQtgiCwp34lWZHDhw+jqKgIMTExmt77Avz252Sz2cL6c5CCkYsvvliT9chni3QVPHakvLwcu3btgsFgiMg8lt6CghFCSK8mP87b0dOt1Geg9ebadh1erxdA18EIx3FsTZEKRuQTTpWcHpKyImeccYamp444jmN39YSThSgrK8POnTthMBgwc+ZM1esRBCHsG4PlpKmrU6ZMabdxmrSgYIQQ0qtJ2Qir1druLalutxvff/89AODss8+Oyjq6esr+8ccfYbfbkZKSErGGWvnxWSVZiEiWaKRSWjiNolJfxqmnntpuX5BSzc3NYY+jb29ddIqmcxSMEEJ6NWnT7ehp+9tvvwXHcRgwYAAKCwsjtg4pGAnlqV8q0Zxzzjlhjx7vTDAYZFkIJY2ZPp+PHZ3VuuQgL9GEI1IlmtTU1LBLdx6Ph2W4qF+kcxSMEEJ6rVBOr0ibxdlnnx2xfpFgMMhKRaEM4op0v4gUoFmtVsTFxYX8us2bN8Pr9SIvL0/TRt9gMMhmnYQTjDgcDtb3o0Uw4vP52J+XmhLNhg0b4PP5MGDAgIg2RvcGFIwQQnot6ThvQkJCh6URefNqpEhZEYvF0uXRTrvdjh9++AFAZIIRNRNO5VNXtQzcpKyI1WoN6+jr+vXrEQwGMXToUAwfPlz1eqSsSFJSUrulvVDR1NXQUTBCCOmVAoEAC0Y6Oi3S2NiInTt3AojcfBFRFNkR2lA2/6+//ho8z2P48OEYMGCA5uvx+Xzskj6lWQipX6S7lWg+/PBDAC1ZEbWbvlaNq6Io0tRVBSgYIYT0SrW1tRBFEQkJCR2e+ti0aRNEUcTIkSORm5sbkXU4nU4EAgGYTKaQgpEvvvgCQORLNMnJyYr6UYqLi3Hw4EGYTCZNG339fj87ZRROMMJxHDuxcskll6hej9PpZJfiqbnbZteuXaisrERCQgKmTp2qel29HQUjhJBex+fzsU03Kyurw6flaJRo5JfQdbX5i6IY0WBE/tSvtEQjZUWmTJmiahppW1L2KpRBcO3ZunUrmpqakJ6ersnNxlo0rgK/naI577zzFPXl9FUUjBBCeh0pAEhKSup0kqe8eTUSPB4PPB4PDAZDSJfQFRUVobi4GDExMRF5mna5XOypX+mMEKnkoGWJhud5tvmHeklfW9IpmpkzZ4Y9mEwin3WiNuCiqavKUDBCCOlV5PebdDZZtKqqCgcOHIDBYIjY3S/yS+hCaYSUTtGcdtppmg4Uk8iPOSt56nc6nfjqq68AAJdeeqmm65HGv4fz+xVFsVW/iFryS/HUZDMqKyuxY8cOGAwGXHDBBarX1RdQMEII6VXkw6o6y4pIJZqJEyciLS1N83UEAoGQgiK5SJZoOI6D0+kEoLwx84svvoDf78fQoUMxcuRITdYjb+xNT08PqyRy4MABFBUVITY2VpPPLNwSVlufffYZAGDSpEnIyspSuaq+gYIRQkivIj8J0dkGF+l+Efmx4lAmigaDQbamSAQj8ssClU44XbduHYCWrIhWR1Slxl6j0Rj25i+VaM4++2xVzaaA+kZaOZq6qhwFI4SQXiMQCLDhWV1tcNLGH4l+EfkldKH2Qnz//fdwuVzo168fJkyYoPmawn3qDwaDrP9ByxKNlBVJS0sLe8qsllNXpc8n3EZaidfrZSUt6hcJHQUjhJBeQz5ZtLPhWUePHkVxcTHMZjNOP/10zdfR3NwMnucRExMT8hO71C9y7rnnwmjU9p9m+VO/0mBk06ZNaG5uRlZWFiZPnqzJenw+Hwsawy2R1dTUsDuF1GYg1AyCa2vDhg3weDwoKCjA2LFjVb1XX0LBCCGkV5BvKF31REhZkVNOOUXVJWgdrUP+1B9qWSOS/SLyiaJKn/qlEs3FF1+s2T050ueTnJwc1sRVoOW0iiiKOPHEE5Gfn69qPV6vF8FgEEajscsblbvy1ltvAdC2pNUXUDBCCOkVvF4vAoEADAZDlxtKJI/0er1e+Hw+GAyGkBtFGxoasGPHDgAtmREtyUtGSp/6RVFs1S+iBZ7n2amVcI/zApEp0SQnJ6vKSrndbvZ5/fnPf1a9rr6EghFCSK8Q6mRRURQj2rxaV1cHoKUJMtQsxIYNGyCKIkaPHo28vDxN19PY2MhKRkqf+n/66SdUVFQgMTFRs89KOs4bFxfX6Wmnzng8Hnz55ZcA1E9dFQRB9Th6yUcffQS3243Bgwdj0qRJqt6rr6FghBDS44miyDaUrp7+f/75Z9TW1sJqtWrWAyHxer3s+GxGRkbIr5P6RaZPn67pegRBYLNOMjIyFJcNpKf8GTNmKD6B0x5RFNkpIyUlrLY2bNgAr9eL/v37q+7LkI9/VzvbZe3atQCAOXPmUIlGIQpGCCE9npINRbp59pxzztF8TLc8KxLqe0dyBHxTUxM4joPZbA6rMVPrEo3b7Ybf71d1nBdoXaJRu+lL/Stqx7/X19ezkfmzZ89Wtaa+iIIRQkiPp2SyqDSQSuubZ30+HxtypiQrsn//fpSXlyMuLg5nnHGGZusRRbFVVkRpL8Thw4exb98+mM1mzaaISlmRlJSUsJthBUFgczzU9ovIx/WrHXz33nvvgeM4TJgwQbPBcH0JBSOEkB4tGAyy0khXT9tNTU3Ytm0bAO2DkdraWgAtPStKShrvvfcegJZm2nB7KNpjt9sRDAZhNpsVT1wFwMasn3XWWZpcjBcMBlmwpmbj3759O2pqapCcnKx6jL+UFQl1XH9npBINNa6Gh4IRQkiP1tDQAFEUYbVauwwCvvzySwiCgFGjRmHAgAGarcHv97ONNjMzU9Fr3333XQDAn/70J83W03bUejgnRLQu0UhZkVD+nDojlWhmzJgR9rFgoCU40uJUDwCUlJRgy5YtMBgMmDVrlqr36qsoGCGE9Fg8z7NNrl+/fiGXaLS+vExaQ2JioqKNdv/+/di3bx9iYmJUnwqRC+d4sVxNTQ3LIGlxdFYURXa8WG05RKsjvVKwZrVaYbFYVL2XNFvkzDPPVD3zpK+iYIQQ0mM1NDSwY6JdTToVBIE1GGpZohEEgfWsKH3ClrIi5513nialEIkUHCk5Xiz38ccfQxRFnHzyyZpsrk6nExzHwWQyqRoqdvDgQezbtw8mk0nVn6F89kqolxh2hko06lEwQgjpkQRBYE+3oRxb3bVrF2pqapCYmKjpCHj56HelR0PfeecdAMDll1+u2Xo4jmPlh3CzEFqXaKSNPzU1VdVQsf/85z8AWoLJcDI+Ervdrnhcf0f27t2LX375BTExMfjDH/6g6r36MgpGCCE9kjwICGVYlVSiOffcc1X1GrQV7tyM/fv3Y//+/ZqXaOx2O7udN5zyg9PpZBe9aRGMyBuM1QQQPM9jzZo1AIB58+aF/T7yU0ahlPa6ImVFLrjgAlW/v76OghFCSI8Tzv0v0nwRLUs0Ho8HXq83rN6MSJRotBgq9sUXX8Dv92Po0KGaHFGVsiJWq1XVXJeNGzeioqICqampqm7DdTqdCAQCqmedAC2ft3zQGQkfBSOEkB7H4/EoatBsaGhgN7xqGYxIG39ycrLi3oxIlGjcbjfbaMMdbS4v0ajNGmjZuCqVaK644oqwgxpRFNlguvT0dNUX/3333XcoKSlBYmKiqgCJUDBCCOmB5MOzQgkC1q9fD1EUccIJJ2h22oHn+bDnZuzbty8iJRopWxTuULFgMIhPPvkEgPo7XwDA5XIhGAyqblx1Op14//33AQBz584N+33kmSy1wREAvPHGGwCAyy67TPWJnL6OghFCSI8inw8R6oYSiSO9DocDgiAgNjZW8bCySJRofD4f680Id27Gpk2b0NzcjMzMTJxyyimq1yS/LVhN4+p7770Hr9eLYcOGqbpPSOoVSUlJUT3kLBgMsuwWlWjUo2CEENKjSBtKqPMhInWkV35CRGk5IxKDzqTPJTk5OewyhjR19eKLL1ZdwpAPglPb2CmVaObNmxd26UgerGlxnPerr75CfX09MjMzcfbZZ6t+v76OghFCSI/BcRwr0YR6/8uOHTtQX1+P5ORknHrqqZqsw+/3w+PxAOh6BH1b8hKNFgPFACAQCLBZJ+FutIFAgD3p//73v1e9Jqk3Q+kguLaOHTuGTZs2wWAw4K9//WvY76NFsCYnlWhmzZoV1iwX0hoFI4SQHkMa/R4fHx/yTA/5kV61qXmJlBVJSkpS/J6RKNFIvSIJCQlh32/zySefoLa2Fjk5OapvD/b7/Sw4Ujoevy3pOO+0adNQUFAQ1nvIS3taZEXcbjdr9KUSjTYoGCGE9Ag8z7NNNzMzM+R0vXSkV6t+EVEU2UYbTvlB6xINx3GaTBN95ZVXAABXXnml6id96dLApKQkVZf/iaKI1157DYC6xtX6+np2f5EWlxF+/PHHcLvdGDRokKoeFvIbCkYIIT2CktHvkrq6Ovz4448AgPPPP1+TdTQ1NYHjOJjNZsXTOyNRomlsbIQgCIqyRW2VlZWxvpqrr75a1Xp8Ph/LQqjNimzbtg1FRUVISEjAZZddFtZ7BINBxaW9rkglmjlz5qg+/kxaUDBCCOn2lI5+l3z++ecQRRHjx49Hbm6uJuuQeiHCmd4pZUWmT5+uSYlGni1SM0109erVEAQBU6dOxZAhQ1StScqKJCcnqz7uKjWu/vGPfww70JKyIhaLJez3kGtoaGCBG5VotEPBCCGk22tsbFQ0+l0iPcFeeOGFmq0jGAzCbDaHNadC60FnTU1N4HkesbGxYQ85EwSBlWiuvfZaVevxer3sBI3arIjX68Xbb78NIPzx7/KsSFZWliZZjPfeew8cx2H8+PGaTKglLSgYIYR0a4IgsJMQSrIiJSUlWL9+PYCWPgi1eJ5nWZHMzEzFczP27duHAwcOaFaiCfdzaWvDhg0oKSlBSkpK2KUQiZSlSU5OVnWCBmg5ZuxwODBgwACceeaZYb1HXV0d6xVJSEhQtR4J3dAbGRSMEEK6tebmZtajoaS08e9//xuiKGLatGkYPHiw6nU0NDSwLEQ4jatvvfUWAO1KNFLvSkxMjKr3W7VqFQDgL3/5i6qyivy2YC1OrEglmr/+9a9hDUwLBoOssVdJw3NnSktLsXnzZhgMBlxxxRWq34/8hoIRQki31fYukVA3JZ7n8e9//xsAcN1116leB8/zLAsRzsbGcRxeffVVANo8UavtXZHU19ezI6rXXHONqjU1Njay3gy1J1YqKytZVivc2SKRyIpIf4a/+93vNLtWgLSgYIQQ0m3Z7XYEAgGYTCZFPRpffPEFysvLkZaWhksvvVT1Ourr69lJnnB6Mz7//HNUVFQgPT1dk4Fi8myRmummr7/+OgKBAE488USMHz8+7PeRNxiHO4pebu3atRAEAVOmTMGwYcMUvz4SWRGfz4cVK1YAAG644QbV70dao2CEENItybMi/fr1UzSeXCo9zJ07V3XvAsdxbKMNtwny5ZdfZuvRYvqn1JSZlpYW9p0voiiyz0lt42pzczN4nofZbA67kVa+Lvn493DI54polRV58803UVtbi/z8fPzhD3/Q5D3Jb8L6W/z8889j4MCBiI+Px+TJk7F9+/YOv3f16tUwGAytfqj9x4EQ0vs1NzeHlRWprq7Gxx9/DED9Jgu0pPsFQYDFYlE8VwQAKioq2E24WpSMvF6vJjfP/vDDD9i3bx8sFgtmz54d9vuIotgqK6I2C7Fr1y7s3bsXcXFxmDVrluLXy0/QaJUVEUURTz/9NADg5ptv1mySL/mN4mDk7bffxuLFi3H//fdj586dGDduHKZPn87OlrcnOTkZVVVV7EdJSYmqRRNCere2vSJKsiL/+c9/wHEcpkyZgtGjR6taRyAQUL2xvfrqqxAEAaeffromR0Gl9SQnJ6ualCplRf70pz+pymZ4PB74fD4YDAbVF+IBvzWuXnLJJWE15kpXBlgsFs2yIhs3bsSePXuQkJCgSUBJjqc4GHnqqadw3XXX4aqrrsKoUaOwcuVKWK1W1izWHoPBgOzsbPYjKytL1aIJIb2by+WC3++H0WhU1IOgZekBaN0EGc7ALPkMD60aaaUTK2o2fqfTyU73qPmcRFFETU0NgJYLA9WOkQ8EAuzobDjj3+UlNa2yIkDLvgcAV111lSYBFzmeomAkEAjgp59+wjnnnPPbGxiNOOecc/Ddd991+DqXy4UBAwagoKAAl1xyCfbt29fpryNdPS3/QQjpO6STK6mpqYqyIps2bcKRI0eQlJSk+u4Xv9/PmiDD7RX56quvUFxcjJSUFE0GnTU3N7NGWjVP/e+88w7cbjeGDx+O0047TdV6PB4PDAaDJqPW33vvPdTX1yMrKwvTp09X/Hqtp60CwMGDB/HZZ5/BYDBg4cKFmrwnOZ6iYKS+vh48zx+X2cjKykJ1dXW7rxk+fDj+/e9/48MPP8Trr78OQRBw6qmnory8vMNfZ9myZbDZbOxHuDc1EkJ6Hp/PB7fbDUD5yQypUXT27NmqNyOp9JyYmBj2xv/SSy8BUD/DA2jJQkglmtTUVFVP/VL26Jprrgn7fQRBYP/uZ2RkIDY2Nuz1SO/3yCOPAAAWLFigOMvCcVyrO2i0yoo888wzAICLL75Y9ah80rGIn6aZMmUK5s6di/Hjx+PMM8/E+++/j4yMDLz44osdvuauu+5Cc3Mz+1FWVhbpZRJCugkpK5KcnKxog2tsbMR///tfAOpLIvLL3sItK9fU1ODDDz/UZD1AS4ZZi96MvXv34vvvv4fZbFZ1E25dXR0buqbFkLMPPvgA+/fvh81mw/z58xW/XrpIMT4+PqxG447eU+ph+cc//qHJe5L2KQo9peN1Uo1QUlNTg+zs7JDeIyYmBhMmTMCRI0c6/J64uDhNjr8RQnoWr9cLu90OQPkUz9dffx1+vx/jxo3DiSeeqGodWlz2JjXSTp48GWPHjlW1HkEQUFVVBaDlOK+S0lVbUg/LxRdfHHagFQgEWNCYnZ0d9vFiiSiKePjhhwG0ZEWUNtTyPB+RrMiLL74Ir9eLCRMm4He/+50m70nap+hvUGxsLE488URs2LCBfU0QBGzYsAFTpkwJ6T14nseePXuQk5OjbKWEkF5NFEVUVFQAaAkClEzxFEWRlWiuu+46VZuR1LMGhH/ZW9v1qNXQ0MCOOau5gM7j8WDNmjUA1DWu1tTUQBRFJCQkIDk5Oez3kXz22WfYvXs3EhMTw+rLkF8YqMV6gJaA67nnngMALF68WLMAh7RPcevz4sWLMW/ePJx00kmYNGkSli9fDrfbjauuugpASwd0Xl4eli1bBgB46KGHcMopp2DIkCGw2+14/PHHUVJSokmnOyGk96ivr4fP54PJZFL8sLJp0ybs3bsXVqtV9bh16TRGUlJS2DORvvnmGxw5cgSJiYlhzcqQCwaD7Jhzdna2qqzIiy++iIaGBhQWFuK8884L6z28Xi8rYWVnZ6vepEVRxNKlSwEAN954o+I+IfmcEzWj8dt6++23UVVVhZycHNXN0KRrioORWbNmoa6uDkuWLEF1dTXGjx+Pzz//nKX7SktLW6XsmpqacN1116G6uhqpqak48cQTsW3bNowaNUq73wUhpEcLBAKsNJKdna14qNSzzz4LoOVhSM2lcRzHsRM0asaaS1mRP//5z6obaaurq9nQNTW/N6/Xi3/+858AgLvvvjvsoEYq09tsNtVNuUDLrcE//PADLBYLFi9erPj1zc3NCAaDii9S7Ix8yNlNN92kujmXdM0giqKo9yK64nA4YLPZ0NzcrFkKjhDSfZSWlsLhcCAhIQEDBw5U9HRbXFyMwYMHQxAE7Nu3T9WDTm1tLWpraxEfH4/BgweH9ZTd0NCA3NxcBAIB7NixQ1X/itvtxrFjxwAAgwcPVrX5P/PMM1i0aBEGDBiAQ4cOhbXBejweHD16FAAwdOhQTXr7pk6dik2bNmHhwoVYvny5oteKooiioiL4fD5kZmaqKmHJbdq0CVOnToXFYkFZWZkm9+30VaHu33Q3DSFEVy6Xi/Vo5OTkKA4Ann/+eQiCgHPPPVdVICK/7E1Nun/NmjUIBAKYMGGCqkBEFEV2dDY1NVVVIOLz+fDYY48BaMmKhBOIyAecpaamahKIbNmyBZs2bUJsbCxuvfVWxa+XThgpHY7XFWnI2bx58ygQiRIKRgghuhFFsdUpEaU9Gm63m83MWLBggaq1NDY2gud5xMTEhD0ened5rFy5EgDwt7/9TdV6HA4HvF4vjEaj6if+l19+GVVVVejfvz+uvPLKsN7D7XbD7XZrNuAMADtBc9VVVyE/P1/x68MdjteZI0eOsLuNFi1apMl7kq5RMEII0Y3dboff7w/7lMiaNWtgt9sxePBgXHDBBWGvg+M41rOiZoz4u+++i19//RWpqamYM2dO2OuRDxTr16+fqovZfD4f/u///g+ANlmRtLQ0TXootm/fjvXr18NkMuGOO+5Q/HopOALU9fe09cwzz0AURcycORPDhw/X7H1J5ygYIYToou0pCKUTN0VRZI2rN998s6pZF7W1tWxgVrhNkDzP46GHHgLQcupQTX9bY2Mja8pUO1DslVdeQWVlJQoKCtipR6Wam5tZlkbrrMhf//pXFBYWKnqtIAiorKwE0JIV0arBtKmpid2zRkPOoouCEUKILtxut6qJol999RUOHDiAxMTEsEsPQEvmQBqYpeao6rvvvosDBw4gNTUVN998c9jrkZ8syszMVBVk+f1+NmbhrrvuCmvTbpulUXsZHgDs3r0bH3/8MQwGA+666y7Fr6+rq2MZNS0vXn355Zfh8XgwduxYTJs2TbP3JV2jYIQQogt5vT+cDU7Kilx55ZVh93jIn7CTkpLCPobbNiuiZj2lpaXsKK/aG2JfeeUVVFRUID8/H1dffXVY76H12HcAePTRRwG0jIoYNmyYotf6fD72dyc3N1eT4Ahomefyr3/9C0BLVoSGnEUXBSOEkKjz+XxwuVwAwqv3HzlyBJ9++ikAhJ2FEAQBJSUl8Hg8MBqNIV9p0R6tsiJVVVVs8FtBQYHqSbJSVuTOO+8M6/SLfOx7Tk6O6rHvAHDgwAG89957AIB77rlH0WulKb2iKCIpKUnTUQ9vv/02ysvLkZWVhdmzZ2v2viQ0FIwQQqJKEAR2a3dSUlJYm+Rzzz0HURQxY8YMxU/WQMumVlZWBrfbDaPRiIEDB4Z9VJXneTz44IMA1GVFmpqa2MC1/Px81X0Qr776KsrLy5Gbm4trrrkmrPeorq5mY9+1unzu0UcfhSiK+P3vf48xY8Yoem1DQwPrXcnNzdUse+F2u1m5aMGCBXQ3mg4oGCGERI0oiigvLw977DsAOJ1O1mQY7nHexsZGOJ1OGAwGDBgwQNE9OG298847OHjwIFJTU8NeD8/z7IhzZmam6o0/EAi0yoqEM9Ze7fyX9hw5cgRr164FoDwrEgwGVU3p7cyyZctQXl6OgQMHUuOqTigYIYRETUNDAxwOBwwGA/r37x/W0/+qVavgdDoxfPjwsO5XkZ/iycrKQkJCguL3kMh7RW655ZawywYNDQ0QBAFxcXGanFZZvXo1SktLkZOTE9ZFffJemnDmv3TknnvugSAImDFjhuKBcFVVVRAEAVarVXUvjVxRUREef/xxAMDTTz+tyYh7ohwFI4SQqJDP8sjJyQkrCPD5fHjiiScAtJREwulhcDqdCAQCMBqNqjc1eVYk3F4RnudZcJSRkaE6AxEIBFiD6B133BFWIFFXV4dAIACz2azZaZVt27bhnXfegcFgYHNPQuV0OlmWRsvyDNDSrBoIBHDeeefhkksu0ex9iTIUjBBCoqK+vp7N8gg3CFi9ejUqKyuRn5+PefPmhb0OoOWJX83UTq2yIk1NTeB5HrGxsWH3m8i99NJLKCkpQXZ2dlhTYP1+f6umVS0mm4qiiFtuuQUAcPXVV2Ps2LEhv1YQBFbCSk9P1yxLAwCfffYZPv74Y5jNZjzzzDN0gkZHFIwQQiIuGAy2Ko2E849+MBhkT9S33357WE2GHo8HHo8HBoNB9dROLbIigiCwjV+LrEhzczMeeOABAMCSJUsUlxzkp1USExM1O63yzjvv4Pvvv0dCQgKWLl2q6LX19fUsS6PVRXhAS9C1cOFCAC1j30eMGKHZexPlKBghhERcTU0NRFGE1WoNe5bHG2+8gZKSEmRmZuLaa68N6z2kjd9ms6lqgNQyKyLN8Ah38qvco48+ioaGBowcOTKsXhG73c6CNa3KIT6fj417v+OOOxQ1LQcCAdTV1QFoaVrV6v4ZoKU/5MiRI8jOzsZ9992n2fuS8FAwQgiJKIfDAbvdDiD8Cac8z7M+iFtvvTWsJkP56RC1w7tefPFFTXpFpI1WzS3BkuLiYixfvhwA8PjjjyseBiYIArt/JjMzU7MR688++yxKSkqQl5fHSjWhqqqqYkeLtShhSSoqKtg4+n/+85+azish4aFghBASMYFAABUVFQBaNtxwj9C+++67OHz4MNLS0nDDDTcofr2Wp0MqKipw5513AgAeeuihsDey2tpacByH2NhYTU6H3H333QgEApg2bVpYlwY2NDSwLI1WF8/V1dXhkUceAdCStVHy5+9wOOB0OgFod7RYctttt8HtduO0007DX/7yF83el4SPghFCSERI/Qc8z8NisYRd7xcEgW1oixYtCmsGh7zvQO3pkAULFsDpdGLy5Mn4+9//HtZ7+Hw+1kOjxWTT7du3480334TBYMCTTz6peOPmOI5labKysjSZtAoADzzwABwOByZOnKho05c3rfbr10/TptXNmzezz+pf//oXNa12ExSMEEIiorm5GW63GwaDAfn5+WFvcB999BH27t2L5OTksEoifr+fbbRqT4d8+OGHeP/992E2m/HSSy+F9V6iKLIsTXJysuoBZ6IoYvHixQCAefPmYfz48Yrfo66ujp100qoccuDAAbz44osAgCeffFLRn39dXR27tVirW4KBlqDrpptuAgBcf/31mDBhgmbvTdShYIQQojn5Ta8ZGRlhj9cWRZHV9m+66SbFTZ6CIKCsrEyT0yFOp5NtZLfeequi46lyzc3NrElUzX04kvfffx9bt26FxWJhn5USgUBAk1uL27rtttvA8zwuueQSTJ06NeTXReJosWTlypXYs2cP0tLSwvqsSORQMEII0ZxWN72uX78eP/30E6xWKxYtWqT49fKL5/Ly8lRttPfeey/Ky8sxaNAgLFmyJKz38Hq9LCuSkZGhukk0EAiwkyq33nor8vLyFL1eFMVWTaLhnnRq66uvvsKnn34Ks9mMf/7zn2GtR8ujxUDL30np1MzDDz+sWV8M0QYFI4QQTTU3N7Mn2+zs7LDLMzzPs83jhhtuUJyut9vt7OK5goICVUd5t2/fzq6XX7lyZVineQKBAEpKSiAIAhISEjQpP6xYsQJFRUXIzs7G7bffrvj1NTU1rElUiywN0PLnJp2aufHGGxVdZNjQ0ACXywWDwaB50+o999wDu92O8ePHhzUMjkSWsrNfhBDSifr6elaeSU5OVvVku2LFCvz4449ITk7Grbfequi1fr+/VQZCzRN/MBjE3/72N4iiiD//+c8499xzFb8Hz/MoLS0Fx3GIi4tD//79VW+0jY2NbNbJ0qVLFf8eGxsbWdCYl5en2Z0s//nPf/DLL78gJSVFUQbJ6XSyvzvZ2dma3py7Y8cOrFq1CkDLjc9aln6INigYIYRowm63s80kLS1N1ZNtaWkp7r77bgDAY489pmhQliAIKC0tZRkItVM7ly9fjp9//hlpaWl46qmnFL9eflOx2WzGgAEDNNkMH3nkETQ1NWHMmDG46qqrFL3W6XS2Cta0unjO5XKx23jvu+++kEshfr8f5eXlAICUlBSkpaVpsh6gZeruX//6V4iiiL/85S847bTTNHtvoh0KRgghqnEc1+ooZrgj34GWzfvGG2+Ey+XC6aefrjilXlVVBb/fD7PZjPz8fFUZiGPHjuH+++8HADzxxBNhBTb19fVwOp2qbipuq6ioiJWNnnjiCUXBTSAQQFlZGYCWjV/LEetLly5FdXU1Bg8ejPnz54f0GilrJB0B1/oivFtuuQUHDx5ETk4Onn76ac3el2iLekYIIapVVVWB53nEx8erCkSAlntMPv30U8TGxuKll15S1HPS3NzM+kTy8/NV9YmIooibbroJXq8XU6dOxZVXXqn4PVwuF5tqmpOTE/bQt7brmj9/PoLBIKZPn47p06crem1FRQUEQYDVatV049+2bRu7Ufmpp54KucxSWVnJgsf+/ftrNuMEANatW4eVK1cCAF577TXVk3dJ5FAwQghRxeFwoLm5GQBUn1hpbGzEggULALRMFB05cmTIr+V5nmVn1PaJAC0b2WeffYaYmBisXLlS8e8rGAy2ykBoVQp55ZVX8MUXXyA+Pp6Nfw9VY2Mjm/2Sl5en2cbvdrsxd+5cCIKAuXPn4uKLLw7pdfK/O/3791cVPLZVWVnJ7jC67bbbcM4552j23kR7FIwQQsIWDAZbjXtX2wR52223oba2FiNHjmQj10NVU1PDxqurPanidrvZja633XYbhg8frvg95NkirTIQJSUlbMDZI488ouim2UAgwLI0WjeI3n777SgqKkJ+fj6eeeaZkF7D8zzrW1FzVUB7pKCooaEBEydOpJkiPQAFI4SQsEiNmdKGq7b3YOPGjfj3v/8Ng8GAVatWKdosvV4vG9yVm5ur+ol/6dKlKCsrw4ABA1hDphLyS/nUTJ+VEwQBV199NZxOJ0477TQWLIVC+rOSyjNaNoh++eWXWLFiBQDg1VdfDXkwnTx41LJvBWiZ+LphwwZYrVasXbtWs0v/SORQMEIICUttbS3cbjeMRiMKCgpUbbgejwfXX389AODvf/87Tj311JBfy/M8y87YbDbV5Zn9+/fjySefBAD861//UvzELr9XJT09XbN7VV588UVs3LgRFosFr776qqKm1YaGBng8HhiNRtVNvXJ2u52d5Jk/f37IpRC3261p8Cj3008/sQDymWeeCSurRaKPghFCiGJ2u73VfS9qUv6iKOK6667DkSNHkJeXh2XLloX8Wp7nUVJSwqasKjkC3NFa5s+fD47jcNFFF+Giiy5S/B4NDQ3w+/0wmUyaPfEfPXoUt912GwDg//7v/zB06NCQX+v1eluVZ7TMEixYsAAVFRUYMmQIHnvssZBeIwgCCx5TU1M1m/oKtAQ5c+bMQTAYxGWXXYZrrrlGs/cmkUXBCCFEEY/HwzaT9PR01Y2ZTz31FNauXQuTyYTXX3895EFp0jwR6Yl/4MCBMJvVTStYu3YtvvnmG1gslpB7H+QCgQAL0rKzszWZJyKVZ9xuN84880x2P04oeJ5nd/MkJydr1kQLAB988AHWrFkDo9GI1157DQkJCSG9rrq6mt2grNXUV8miRYtw6NAh5OXl4eWXX6YbeXsQCkYIISGTZkKIooikpCTVm8n69evZGPPly5eHfKGadPOtVCYaOHCg6uZZu93Oxpjfc889KCwsVPR66VI+QRBgsVgUX+rXkeeffx6bNm1CQkIC/v3vfysqaVRVVSEQCCAmJkb1SSe52tpaVla7/fbbMWXKlJBe19zczMozeXl5mk5C/e9//4tVq1bBYDBgzZo1mvbFkMijoWeEkJDV1tayC/DU9h4UFRXhiiuuYE/+oQ7JAoCmpibY7XYALUdCtTiJsWTJEtTU1GDYsGGKx88DLQ2ZXq9X076Mw4cPs4vwHn/8cQwaNCik14miiLq6OvYZ5efna7bxi6KIG264AXV1dTjhhBPwwAMPhPQ6v9/f6uRVUlKSJusBgLKyMlx33XUAgDvuuANnnXWWZu9NooOCEUJISDweDxoaGgCof6p1uVy45JJL0NTUhMmTJ2PFihUhb94+n481iGZlZWnSc7Bz5048//zzAFoyEUp7YOx2O/ts8vPzNTk2y/M8rrrqKni9Xpx99tksE9GVQCCA8vJyeDweAEBmZmbIJZRQvP766/jggw8QExOD1157LaTfqzxrZLVakZWVpdl6eJ7H3Llz0dTUhJNOOgkPPvigZu9NooeCEUJIp3w+X6tMhNoTK4IgYN68edi3bx+ys7Px/vvvh7x5BwIBViZKTEzUZKKm0+nEtddeC0EQMGvWLEXDsXieR0NDA+sT6devn2bX3j/11FPYunUrEhMT8corr4RUnmlubmYTVo1GI7KzszXtEykrK8PNN98MALj//vsxfvz4Ll8jiiKqqqpYk7GWp3kA4MEHH8Q333yDhIQEOsbbg1EwQgjpkMvlQklJCURRBADExMSoPrHy8MMP4/3330dsbCzef/995ObmhvQ6r9eLkpISzcpEQEugdckll2DXrl1IS0tjR3pD0dDQgNraWvA8DwBITEzU7In/jTfeYOWZJ598EgMGDOjyNVJGRBRFWK1W5Ofna7oxO51O/PGPf0RzczMmT57M1hfKmqQsTV5enqZrevHFF7F06VIALRktJaeMSPdCwQghpF1+v59lIRISEpCeno7ExERVMyHefPNNdvHcihUrQm58dLvdKCkpgSAIiI+Px4ABA1SfnAkGg7j88svx9ddfIzExEZ9//jny8vJCeq3dbmelImlol81m0+SJ/91338XcuXMhiiL+/ve/s16IzkgNvVIgUlhYqGn2wePx4KKLLsL27duRnp6ONWvWdPn5OxwOVFRUgOd5GI1G5ObmapY1AlrG9d94440AWm4InjdvnmbvTaKPghFCyHGk+R3SyZABAwaoHky1bds2NiDrlltuCXkGhHTHiyAISEhIQP/+/VU3Y0p9Bp988gni4+PxySef4OSTTw7ptYFAoNUYc7UXA8p99NFHmDNnDmvqfe6550J6b4fDAZfLxe6c0TIQ8fv9+MMf/oBNmzYhOTkZX3zxRZcZiPr6elRXVwMALBaLZn00ki1btrDm5+uuu476RHoBCkYIIcepqalhR0K1CESKiopwySWXwO/349JLLw15QJY0xpzjOMTFxWmyFuk0yFtvvQWz2Yz//ve/OPPMMxWtRwrStAxEvvjiC1x++eXgOA5//vOfQ76xWH5BYL9+/TTd9DmOw5w5c/D555/DYrHg008/xYknntjpa9xuNwtE0tPTkZWVpemE1b179+Liiy+G3+/HxRdfrKj5mXRfFIwQQlrx+XytZkGoLYc0NTXhwgsvRH19PU488US8/vrrIWc25CPntbheXhRF3HLLLVi1ahWMRiPWrl2LCy64IOTX19fXR2Ss+tdff41LL70UgUAAf/zjH7F69eqQPyMtLwiUk7IzUn/Phx9+iNNPP73T13Ach/LycgAtjc7Z2dmaBgqlpaU4//zzYbfbceqpp7KAkvR89KdICGlFeqpNSkpSfWxW2lwPHjyI/Px8fPTRRyEfM21sbGSnVHJzczV54n/ooYfw9NNPAwBefvllXH755SG/trm5mY1VVzsCX+7bb7/FhRdeCJ/Ph4svvhhr164NaYPlOA5VVVVobm4GoO0dL6Io4qabbsKaNWtgMpnwzjvv4Nxzz+3yNRUVFQgGg4iNjdXspmJJY2Mjzj//fFRUVGDUqFH4+OOPVQ+6I90HBSOEEAAtm0lDQwPrPVA7XVVqwNy4cSMSExPx6aefhnxyprGxkfVlpKenazLNdNmyZWxA1/Lly3H11VeH/FqHw4GysjIALfepaDVddfv27bjgggvg8Xgwffp0vPPOO4iJienydU6nExUVFeA4DoB281aAlj+3O++8Ey+88AKbZnrJJZd0+bqGhgY4nU4YDAYUFBRoOl3V4/HgwgsvxIEDB5Cfn4/PP/+cJqz2MhSMEEIQDAZRXl4Ot9sNoCUAUPvkv2TJEja+/O2338bYsWNDel1TU1OrQESL+0uWLVuGu+++G0DL0eKFCxeG/Fqn08kCEZvNptkTv5QRcTqdOOuss0Ket9LU1MQmmcbGxiI/P1+TCbSSRx55BP/85z8BtBydnT17dpevaW5uZhm17OxsTTMWHMdh1qxZ+O6775CSkoLPP/8cBQUFmr0/6R4oGCGkj5PP75AyImqfOp999lk8/PDDAIAXXngh5L4MaWgXAKSlpWnScyAPRJYuXcqulw+Fy+Vix5uTk5M16xP57LPP8Mc//hFerxdnnHEGPvroo5ACCpfL1erG25ycHE1LM8uWLcN9990HoGXoWijHiuVZLJvNpmnGQmo2lp96Gj16tGbvT7oRsQdobm4WAYjNzc16L4WQXqW5uVncu3evuGfPHvHQoUOiz+dT/Z5vvPGGCEAEID788MMhv87hcLC1lJeXi4IgqF7Lo48+ytaydOlSRa91uVxsPcXFxZqsRxRF8fXXXxfNZrMIQJw5c6bodrtDep3X6xX37dsn7tmzRywtLdVsPaIoirW1teIFF1zAPqsHH3ww5Nft2bNH0z8zidfrFa+//noRgGg0GsV169Zp9t4kekLdvykYIaSPcrvdbLM9duyYyHGc6vf83//+xzbaBQsWhLQ5CYIg1tfXs7VotdHKAxElQZEotnw20sZ/7Ngxked51esRRVF89tln2Zr+8pe/iIFAIKTXBQIB8eDBg+KePXvEoqIizdYjiqL41VdfidnZ2SIAMS4uTlyxYkWXn78gCGJVVRULRKqqqjQNRH799Vdx/Pjx7LN66aWXNHtvEl0UjBBCOuT3+8X9+/eLe/bsEUtKSjTZSLZt2yZarVYRgDhnzpyQNsxgMCiWlJSwTU2rtagJRBwOBwtEjh49qsnGLwiCuGTJEramhQsXhvy+Pp+PBSK//vqrGAwGVa9HFFsCnDvvvFM0GAwiAHHUqFHiL7/80uXrBEEQy8vL2Z9ZbW2tJuuRvP7662JiYqIIQOzXr5/42Wefafr+JLpC3b+pZ4SQPkQURTidTlRXV4PnecTHx6ue2CmKIjZs2IA//elP8Hg8OP/88/Hqq6922ssgiiIaGxvZ3S7yXhU1a6mvr8fdd9+Nl19+GUBLs6qSHhG73c7mZEjTXtX2ZAiCgAULFrBbgR966CHce++9If0+vV4viouLwfM8YmJiMHDgQE3mahw9ehSzZ8/G9u3bAQDXX389nnrqqS77VgRBQHl5ORwOB4CW48Ra9Yi43W7cfPPNePXVVwEAU6dOxRtvvBHyCSzSs1EwQkgf4fP5UFFRAa/XCwAwm82qRqsLgoBPPvkEjz76KH744QcAwJQpU/Dee+91ehmay+VCVVUV/H4/ACAuLg75+fmqTmBwHIcXX3wR9913H5qamgC0nAqRGlc7EgwG0dTUhEAggEAgwC50s9lsyMvLUx2INDc3s2mvBoMBzz33HLtPpTOCIMDhcKCyspLdx6NVIPLmm2/i+uuvh9PpREpKClatWoU//OEPXb5OGmgmHf3Oz8+HzWZTvR4A2LNnD2bNmoUDBw7AaDRiyZIluPfeezU9Hky6NwpGCOnlxP8/P6SmpgaiKMJoNCI9PR39+vUL6x97juPwzjvvYNmyZdi7dy8AID4+Htdeey2WLl3a4VCzYDCIqqoq9lRtMpmQlZWF1NRUVdmQTZs2YcGCBfjll18AAOPGjcO//vUvnHHGGZ2+LhgM4ujRowgGg62+Lh0nVrMmt9uNf/3rX/jnP/+JpqYmmM1mrFmzBldccUWXr2tqaoLD4YAgCACg2X08DocDCxcuxOrVqwEAp59+Ot544w3079+/09dxHIf6+no0NjZCEAQYDAb0798fSUlJqtYDtPzdfPnll7Fw4UL4fD7k5ORg7dq1mDp1qur3Jj0LBSOE9FKiKKK5uRm1tbUIBAIAWqaq5ubmhjRYqz0//vgj5s2bhwMHDrD3mz9/PhYtWoSsrKwOX+d0OlFeXg6e5wG0HNvNzMxU9aRfXl6O2267DW+99RZ7z4cffhh/+9vfuty4g8Egjh07hmAwiJiYGKSlpSEmJgbx8fGIj48Pe00+nw8rV67EsmXLUFtbCwAYOXIknnvuOUybNq3D10n3y9jtdvY1s9mM1NRUZGRkqM7QfPPNN7jyyitRUlICo9GI++67D/fee2+nnz/P86ivr0dDQwMLjCwWC3JycjSZa9Lc3Izrr78eb7/9NgBgxowZ+M9//qPpSHvSc1AwQkgvFAgEUF5ezsoOarMQHMfh0UcfxUMPPQSe55Geno5//OMfmD9/frvTSEVRhN/vh9/vh9vtZnfdSD0qakoygiDghRdewJ133gmXywWj0Yjrr78eS5cuRXp6epevDwQCKCkpYRcBFhYWdlpWCkUgEMCrr76KpUuXsjkggwcPxgMPPIDZs2d3Ghy53W6Ul5ezDI004dVqtaqeaeL1enHPPfewEfiFhYX4z3/+02nWiOd5NDY2or6+ngWP8fHxyMzMRFJSkuo1ORwOPPvss3j66afR2NgIs9mMRx99FLfccoumF+qRnoWCEUJ6GbvdznoNjEYjMjIykJaWFnaa//Dhw/jrX//K+kL+9Kc/4YUXXuiwcZHneZSUlLBASKLFDa4HDx7Eddddh2+//RZAS4/K888/jwkTJnT52mAwiLq6OjQ1NUEURZjNZgwcOFBVIMJxHF5//XU89NBDOHbsGACgoKAAS5Yswbx58zrNQImiiLq6OpZBiYmJQX5+fsh393Rlx44dmDt3Lsti/e1vf8MTTzzRYXlFFEXY7XbW3Ay09PNkZmYiOTlZdRBit9tZECJlgIYPH47Vq1fjlFNOUfXepOejYISQXqLt5ma1WpGfnx/2ZisIAl5++WUsXrwYHo8HNpsNK1aswOzZszvcmARBYIGIwWBAfHw8YmNjkZKSoqrHIBgM4vHHH8eDDz6IQCCAxMRELFu2DDfeeGOXwY0oiqivr0dtbS1EUQTQ0oeh5vI9nufx9ttv48EHH8ShQ4cAtIxBv+eee3Ddddd1+b48z6O8vBxOpxNASzYkOztbk4bNYDCIRx99FEuXLgXP88jOzsYrr7zS6RRcnudRWVnJLt2LjY1FZmYmbDabJkHI8uXLsXz5cvb+I0aMwH333YdZs2ZRkyoBQMEIIT0ez/NwOByw2+2t7pYJtwmT4zi8+eab+L//+z/s378fAHDWWWdh9erVHTY7iqIIr9eLmpoaeDweGI1GFBYWanJHyU8//YRrrrkGP//8MwDg/PPPx8qVKzFgwIAuX+v1elFRUQGfzwegJUDLzMwM+1I5QRDwwQcf4P7778e+ffsAtHzWd9xxB+bPn99hL4XH42GBB9BSqvD7/TAYDMjNzUVqampY62nrwIEDmDt3Lnbs2AGgJYu1YsWKTstXHo8HZWVlrEyUmZmJjIwM1UFIY2Mjli9fjmeeeYY1LY8aNQr33XcfLr/8cgpCSCsUjBDSA0nzQux2O5xOJ3viB1qutw+ld6Itn8+H1atX47HHHkNxcTEAIDk5GQ888AAWLlzYbgZCFEXU1taiqamJ3SBrNBoxcOBAVYFIXV0d3n33XaxduxZbt24F0LLpL1++HH/+85+73CgFQUBtbS3q6+sBtPTMZGdnIyUlJaxNVhRFfPLJJ1iyZAl2794NAEhJScGtt96KBQsWdJr1kV9sJycdrVbbDFpcXIwPPvgA77//PrZu3QpRFJGSkoIXXnih3dM7oijC5/PB6XTC4XCwQC0mJgYFBQWq1+NwOPDkk0/i6aefZgHYmDFjcN999+GPf/wj9YWQdlEwQkgPIgUhtbW1bBMBwEohNptNcemhtrYWr7zyCp599ll282pGRgb+8Y9/4MYbb+xwlkQgEEBZWRmbW2I0GpGYmIiMjIywAhGn04kPP/wQa9euxfr161nfgsFgwOzZs/H0008jMzPzuNdJm6sUDPE83+oEUXJyMnJzc8M6uSOKIr766ivce++9bEBYUlISFi1ahMWLF7fbvCtXX1/PPtPExERWMjOZTOwETzgOHDiA999/H++//z527tzZ6ucuvPBCrFy5Enl5ea2+zvM8mpqa0NjYyD4bSUpKCnJyclRlK/x+P1544QU88sgjLAgcO3YslixZgt///vcUhJBOUTBCSDcmbbQ+nw9erxfNzc1skzYajUhLS4PNZkN8fLyiJ35RFLFlyxa88MIL+O9//8tS9AUFBbjttttwzTXXsCdkjuPQ1NTEfl2e5+H3++Hz+ViTbG5uLpKTkxVvOBUVFfjss8/w6aefYv369SywAYCTTjoJc+bMwaxZs46bwikdW3Y4HHC73Wxtcmazma0rHN9++y3uuecebN68GUDLsdYFCxbgtttu6zDzJA1PCwaDcLvdrFFT7ewSURSxc+dOFoAcPHiQ/ZzRaMTvfvc7XHbZZbj00ktRUFDQ6rVerxeNjY2w2+0sg2YwGJCYmIjk5GQkJSWpOmLN8zxef/11LFmyBKWlpQBaGlMffvhhXHbZZRSEkJBQMEJINyUIAsrKylr1GgAtT9Wpqano16+f4k2krq4Ob7/9NlauXMl6HgBg0qRJuPHGGzF79uxWDa+BQADFxcXHPUlLLBYLCgoKQm6S5XkeP/74Iz755BN8+umnrOQhGTp0KObMmYM5c+Zg2LBhx71eFEU4HA7U1tayCa5Ay4YsX0NCQgIyMzPDetLfsWMH7r33XnzxxRcAWrJOf//733HnnXciOzu73dcIgoDq6mp2hFkuKysL/fr1UxyI8DyPbdu2sQBE2uiBlpLKueeei8suuwwXX3zxcbM5BEFAc3MzGhsbWwV4cXFxSEtLQ0pKiuqeDVEU8fHHH+Puu+9mf5fy8vLwwAMP4Morr9RkWizpO+hvCyHdkCiK7LSFwWCA1WpFXFwckpKSkJiYqGhjs9vtWLduHd566y189dVXLItgtVoxZ84c/P3vf8fEiRMBtGyALpcLoiiyQVzSvShShsFoNCIuLg6xsbGwWCydrsXn82HHjh3YsmULvv32W2zdupWdqABantAnTZqECy+8EDNnzsT48ePbfb9gMAi73c5GtwO/lTqSkpK6XEdn3G43KisrUVJSgueffx7r1q0D0JJZufrqq3HvvfeybIM0P4XjOMTExCAmJgY+nw/l5eVsXXFxceznkpKSFGVmAoEANm7ciA8++ADr1q1jJ6OAlj+vCy64AL///e8xc+bM48pnPM/D6XSyH9KgMoPBgOTkZKSlpWkyuwRoyRrdeeedrJ8nJSUFd911F26++WZNmpZJ32MQ5Z1vIXr++efx+OOPo7q6mo1enjRpUoff/+677+K+++5DcXExhg4discee6zTY2ZtORwO2Gw2NDc3h51ybY802thgMEAURXAcB1EUERMTw/4Py3EcjEYjSzUKggBBEGAymcBxHEwmE/vHuiMcx6G0tBQFBQUwm83gOA5msxk8z0MURZhMpuNSmYIgsJ+T7oRIT08P6XikIAhdrglo2aSam5tZPV1aV319PURRREZGBptQGUrDoCAIMJvNaGpqgt1ux8CBAzX5h6+zX1M6rpmTk9Ph9zU3N8Nut6N///6q1yP9mRkMBpSXl8NqtXY6MZLjOJSVlSE2NhY5OTkwGAxwu91ss5D+/gEtG67P52O/hvTz+fn5SExMbPdJVpqdUVpaimAwCIfDgcbGRtYbsHv3bvzvf/8Dz/MYO3Ys8vLyMGTIEEybNg1jx45tlU3w+XzsNI5cfHw8BgwY0OXfp0AggIMHD2LPnj346aefsGXLFvz888/HjVu32WyYPn06Zs6ciRkzZrDPT/pcpH+SRFFEMBhEIBA4LguiZJy9KIooKyvDjh07sHPnTpSUlKCyshKVlZXwer2orKxstUaDwYArr7wSCxcubNWj4vf74fF42CbfltlsZn9WSu3fvx8vvPAC1qxZ0ypYS01NxUUXXYTLLrsM5513HuLj4xEMBuH1euH1ehEMBsFxHPuc5KTJsqmpqaqyFMFgEHv37sWPP/6IHTt2YPv27exkk8ViwcKFC3H77bdrdiKI9C6h7t+Kg5G3334bc+fOxcqVKzF58mQsX74c7777Ln799dd2m8u2bduG3/3ud1i2bBkuvPBCrF27Fo899hh27tyJMWPGaPqbUaq4uBgejwexsbHw+/0QRRGiKCIQCLCGuPT0dASDQRQVFbFTAtL3Ay0pXIPBAJPJhPj4eHi9XrjdbvZk+euvv2L37t0oKiqCwWDAddddx9LvdXV1yMvLw/Dhw5GZmcn+cXE6nawRccuWLXjvvfcwZswYnHHGGbBarUhKSkIgEEB9fT2qqqrQ1NSElJQUCIKAtLQ0jBo1CklJSeA4Dj6fj71nWVkZamtr4XK52K8rzTY4duwY3nzzTezZswennXYaZsyYgYEDB8JkMrGnQWmjq6urQ3l5Oerr65GWloZx48Zh0KBBsFqtOHz4MDZs2IAdO3bAZDIhMTERAwcOxPjx4zFixAhkZ2cjLi6ODVjat28fKioqYDab4XQ6kZ6ejuTkZPA8D5PJBJPJhIaGBtTV1cHhcKCiogJGoxGnnHIKpk6dCovFgr1792L79u0s4MjLy4PD4cAvv/yC6upqeL1ejBo1CpmZmWwGxqRJkzBy5EgWaMXHx8Pv97M/e5fLhYaGBlRXV6OqqgqCICAmJgZGoxFNTU1seBbHceB5HmlpaSgsLERBQQHy8vKQnp6O+Ph4lJWVYePGjdiyZQu8Xi9GjhyJyZMnIzs7G/X19SguLkZJSQm8Xi/7rC0WC2u6nDBhAoYMGYLU1FS4XC4cPHgQO3bsYL0IcXFxyMjIQL9+/Vjj6IEDB7Bt2zYcOnQIBQUFSEhIQFNTE6qrq9mmGxcXh/79+7OTN/Hx8UhPT8eYMWMwePBgZGdng+M4HDlyBLt378Z3332HuLg49r0Gg6FVX0RtbS3q6upgt9tZL4lECh5sNhsSEhJgs9mQlZWFk08+GSeffDKGDBkCl8vFgqmKigqUl5ejsrISNTU1qKioQF1dHcxmMxvXLq3B4/HAbrezz2/MmDEYNGgQ4uPj4Xa7UVpaiu+//75VicNsNmPcuHEYOXIkzGYzSktLceTIkeOagoGWbERqairS0tLYxi6NaLdYLIiLi4PVakVaWhpyc3ORkJCAlJQUJCQkwGKxwO12o66urtUP6bOqra3FoUOHUFNTw46+Ai0NspMnT8a5556LadOmIS8vD0lJSezkVHv9MRIpe5acnKw4WyQdzy4uLsaPP/7Igo/du3e3CgaBlqzUtddeiyVLltCtuqRTEQtGJk+ejJNPPhnPPfccgJan04KCAtx888248847j/v+WbNmwe1245NPPmFfO+WUUzB+/HisXLlS09+MEjzPs8mE7ZGefqVmvWAw2GqIU1uCIMDlcsHhcLDUupTVMBqNiI+PR3JyMmw2GwKBAA4cOIC9e/eivLwc06dPZ2ly+foAsA2qs39UOI6Dw+FQfeEY0NJTIJ/WKTUothUIBGAwGMI+DSCpr6/Hpk2bkJaWhiFDhqBfv34szSuKIlwuF3vKl0Zmjxs3DqmpqbDZbF02x0kpePmP0tJS1NfXw2QyscZQ+X+2/Yc3HGazGVlZWcjNzQXHcWxDDSMRqSmj0QiTyXRctkISExODAQMGIDMzE83Nzeyzb7tJh8JsNsNsNnf4WqPRiOzsbBQUFKChoQGVlZXHTW3Vg5R96GzT10NSUhILiKTgMzMzk/2wWq0IBAJwu93weDxwu92t/nvb/2zvv3fEZrPhpJNOwsknn4yTTjoJp556aqfZSEIkEQlGAoEArFYr3nvvPVx66aXs6/PmzYPdbseHH3543Gv69++PxYsXY9GiRexr999/P9atW8dSfW1Jd1rIfzMFBQWaBiOCILCBTnI8z0MQBBw+fBjr1q1rNw0sHR00Go2wWq2wWq2qm8GkiYzNzc0YPHjwcSOha2pqWHbGYrHAarXCZrMhLS2tVQq2srIShw4dQm1tLXJyclBQUIDc3FyYTCZ4PB626Uo/XC4XLBYL8vPzkZmZyYKZmpoaHD58GGVlZUhISEB6ejr7IQ9WOI5DSUkJjhw5wjI0CQkJrJbu9Xrh9/thNpthNBpZ9ik5OZnd/GmxWBATE9OqxNDQ0AC73d7phmAwGNhTYEpKCpsj0dTUxAIPecpbqZiYGPZUHBcXB47j0NzcDFEUUVhYiLFjxyIzMxPl5eUoLi5GVVUVamtr0djY2OG64+Pj0a9fP8TGxiIQCLBGywEDBiA7OxsejwdNTU3sR3V1NWpqaljmrismkwnp6enIy8vDgAEDkJubi8zMTJSVleHbb79FSUkJRowYgQEDBiAlJQU+nw8NDQ2oqKjAsWPHOg06YmJiYLPZkJSUhLi4OBgMBnacdsiQIZg8eTIGDRrE7laR/jw5joPb7UZxcTEOHTqE4uJiHDt27LjGXInFYkFeXh4GDRqEtLQ0VqJqbGyEw+FgPS0ZGRlITk5mGUCv1wu73Y66ujpWbpLWKP08x3Gw2WzIzMxEMBhETU0NbDYbTjzxREybNg2nnHIKkpOT2RFq6c+hsbGRXW8vfd3j8RwXyMr/PyaVOKW/Q1JWRf7fU1NT2Wkl6dex2+1obGxk/1+w2+0dloYiISEhARMnTmwVfAwePJhOxZCwhBqMKCokShcntb2dMysrq9VRM7nq6up2v186e9+eZcuW4cEHH1SyNMWMRiPrk5CTSgOJiYl48803I7qGjkhXoatVUlKCkpKSkL63syyRw+GAw+Fgd290pr6+ns0Y6Izdbm+VOu9MXFwc+4db2qwdDgc7WeFwOFBeXs6us29L+vOUUunDhg1jw52ksoDf78fQoUMxfvx4dkuqlAErLS1FXV0d/H4/vF4vbDYbgsEgKioqcPToUWRkZODKK69EUlISS71v3boVwWAQo0aNwpAhQzBkyBDk5OQgLS0NoijC7XazzdHj8cDj8cBkMrHAKiYmBm63Gy6XCzU1NSguLmYnNdLS0pCVlYX09HQ4nU5WwkhOTkZxcTHcbjeSk5ORk5MDjuOwbt06bN++HXFxcRg/fjzS09PZxj1ixAj84Q9/QHZ2No4ePYqdO3eioqICLpcLfr8fVVVVqKqqQk5ODiZOnIiJEyciJSUF1dXVqK6uxrFjx/Dzzz/jf//7H/73v/9h9OjRGD58OIqKihAIBFipID09HSNGjMB5552HjIwMVFVVoaamhpVjEhISWEkvJycHycnJiI2NhcvlwuHDh1FUVISTTz4Z559/PguEGxoa8OWXXyIlJQVDhw6FzWZDSkoKYmNjIQgCm4Mi/RrV1dWwWq1sZH18fDwSExORlJQEm80GQRCwd+9e2Gw2VvqKi4tDbW0tfv75Zxw9ehRxcXE45ZRTMHbsWKSkpIDjOMTGxrLXCILAAlOpjBcfHw+Px4N9+/Zh7969+OGHHzB16lRMnz4dU6ZMAcdx7OGg7b9HAFg2tr2Sj/xHMBhEQkICrFZrq/8M52sUeJBo65anae666y4sXryY/W8pM6K1oUOHIhgMsh9SSUWaXXD55ZezTAnHcWyGQNtGsbZllJiYGMTGxiI2NpadOpD+u9R/IX8vqUEvJiYGgwcPZjMMpCbH+Ph4xMTEhNysx/M8e9+unqhiYmLY6QwpaxHqP0RSeUjKmCQlJSEhIQHx8fGwWCzs5EdcXBx7anQ4HKirq0MgEADP82wj9vl8SElJQb9+/diFapmZmUhNTUVCQgI7vSBv9pTuGykuLmZPw42Njax/w2KxYOLEiSxTIy89ycsUJpMJZrMZbrcbDQ0NyMjIYI3JbrcbI0aMYJ+RIAhoampiAcLevXvh9/vR0NDAbrM955xzcMUVVyA9PR05OTmsdCf1TEh9RtKfl9PpZL0qCQkJrRoBpTkjUvalqKgIXq8X5513HjIzM2G329lmKG38UvOryWSCIAiYP38+jEYjqqqqkJWV1aq0xvM8Dh8+jH379qG8vByTJ0/G6aefzsa+i6KImpoadruq/O+51PQdFxfH5lVIGRPp56VMUnJysursYTAYxL59+1BcXIyamhrk5+fjH//4R7szP6T/306YMOG40qW8cbyzsibP8zAYDDjxxBNx1VVXhbzOtiPz/X4/9u/fD5vNhsGDB+Piiy/GiBEj2n2t1IMkMRgMrcrCI0eODHkdhPQk3bJM01akGlgJIYQQEjmh7t+KcnGxsbE48cQTsWHDBvY1QRCwYcMGTJkypd3XTJkypdX3A8CXX37Z4fcTQgghpG9RXKZZvHgx5s2bh5NOOgmTJk3C8uXL4Xa7WRpz7ty5yMvLw7JlywAACxcuxJlnnoknn3wSM2fOxFtvvYUdO3bgpZde0vZ3QgghhJAeSXEwMmvWLNTV1WHJkiWorq7G+PHj8fnnn7Mm1dLS0lY9B6eeeirWrl2Le++9F3fffTeGDh2KdevWhTxjhBBCCCG9W1gTWKONekYIIYSQniciPSOEEEIIIVqjYIQQQgghuqJghBBCCCG6omCEEEIIIbqiYIQQQgghuqJghBBCCCG6omCEEEIIIbqiYIQQQgghuqJghBBCCCG6UjwOXg/SkFiHw6HzSgghhBASKmnf7mrYe48IRpxOJwCgoKBA55UQQgghRCmn0wmbzdbhz/eIu2kEQUBlZSWSkpJgMBg0e1+Hw4GCggKUlZXRnTcRRJ9z9NBnHR30OUcHfc7REcnPWRRFOJ1O5ObmtrpEt60ekRkxGo3Iz8+P2PsnJyfTX/QooM85euizjg76nKODPufoiNTn3FlGREINrIQQQgjRFQUjhBBCCNFVnw5G4uLicP/99yMuLk7vpfRq9DlHD33W0UGfc3TQ5xwd3eFz7hENrIQQQgjpvfp0ZoQQQggh+qNghBBCCCG6omCEEEIIIbqiYIQQQgghuurVwcjmzZtx0UUXITc3FwaDAevWrevyNd988w0mTpyIuLg4DBkyBKtXr474Ons6pZ9zVVUV5syZg2HDhsFoNGLRokVRWWdPp/Rzfv/993HuueciIyMDycnJmDJlCr744ovoLLYHU/o5f/vttzjttNOQnp4Oi8WCESNG4Omnn47OYnu4cP6NlmzduhVmsxnjx4+P2Pp6C6Wf8zfffAODwXDcj+rq6oitsVcHI263G+PGjcPzzz8f0vcfO3YMM2fOxFlnnYXdu3dj0aJFuPbaa+kf8C4o/Zz9fj8yMjJw7733Yty4cRFeXe+h9HPevHkzzj33XHz22Wf46aefcNZZZ+Giiy7Crl27IrzSnk3p55yQkICbbroJmzdvxoEDB3Dvvffi3nvvxUsvvRThlfZ8Sj9rid1ux9y5c3H22WdHaGW9S7if86+//oqqqir2IzMzM0Ir7ENHew0GAz744ANceumlHX7PHXfcgU8//RR79+5lX7viiitgt9vx+eefR2GVPV8on7Pc1KlTMX78eCxfvjyi6+ptlH7OktGjR2PWrFlYsmRJZBbWy4T7OV922WVISEjAmjVrIrOwXkjJZ33FFVdg6NChMJlMWLduHXbv3h3x9fUWoXzO33zzDc466yw0NTUhJSUlKuvq1ZkRpb777jucc845rb42ffp0fPfddzqtiBDtCIIAp9OJtLQ0vZfSq+3atQvbtm3DmWeeqfdSeqVXX30VR48exf3336/3Unq98ePHIycnB+eeey62bt0a0V+rR1yUFy3V1dXIyspq9bWsrCw4HA54vV5YLBadVkaIek888QRcLhf+9Kc/6b2UXik/Px91dXXgOA4PPPAArr32Wr2X1OscPnwYd955J7Zs2QKzmbavSMnJycHKlStx0kknwe/3Y9WqVZg6dSp++OEHTJw4MSK/Jv1pEtIHrF27Fg8++CA+/PDDiNZ9+7ItW7bA5XLh+++/x5133okhQ4Zg9uzZei+r1+B5HnPmzMGDDz6IYcOG6b2cXm348OEYPnw4+9+nnnoqioqK8PTTT0es9EjBiEx2djZqampafa2mpgbJycmUFSE91ltvvYVrr70W77777nFlSKKdwsJCAMAJJ5yAmpoaPPDAAxSMaMjpdGLHjh3YtWsXbrrpJgAtpUdRFGE2m7F+/XpMmzZN51X2XpMmTcK3334bsfenYERmypQp+Oyzz1p97csvv8SUKVN0WhEh6rz55pu4+uqr8dZbb2HmzJl6L6fPEAQBfr9f72X0KsnJydizZ0+rr61YsQIbN27Ee++9x4JBEhm7d+9GTk5OxN6/VwcjLpcLR44cYf/72LFj2L17N9LS0tC/f3/cddddqKiowGuvvQYAuOGGG/Dcc8/h9ttvx9VXX42NGzfinXfewaeffqrXb6FHUPo5A2Dd7y6XC3V1ddi9ezdiY2MxatSoaC+/x1D6Oa9duxbz5s3DM888g8mTJ7MZARaLBTabTZffQ0+g9HN+/vnn0b9/f4wYMQJAy5HqJ554AgsWLNBl/T2Jks/aaDRizJgxrV6fmZmJ+Pj4475OWlP6d3r58uUoLCzE6NGj4fP5sGrVKmzcuBHr16+P3CLFXuzrr78WARz3Y968eaIoiuK8efPEM88887jXjB8/XoyNjRUHDRokvvrqq1Ffd08Tzufc3vcPGDAg6mvvSZR+zmeeeWan30/ap/RzfvbZZ8XRo0eLVqtVTE5OFidMmCCuWLFC5Hlen99ADxLOvx1y999/vzhu3LiorLUnU/o5P/bYY+LgwYPF+Ph4MS0tTZw6daq4cePGiK6xz8wZIYQQQkj3RHNGCCGEEKIrCkYIIYQQoisKRgghhBCiKwpGCCGEEKIrCkYIIYQQoisKRgghhBCiKwpGCCGEEKIrCkYIIYQQoisKRgghhBCiKwpGCCGEEKIrCkYIIYQQoisKRgghhBCiq/8HA0FHVEVeH/gAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "specimen = 'PA_Belly_Dermis1'\n",
    "\n",
    "metadata = pd.read_csv('data/Dermis/DamageData.csv')\n",
    "width = float(metadata[metadata.Specimen == specimen]['Width'])\n",
    "thickness = float(metadata[metadata.Specimen == specimen]['Thickness'])\n",
    "A = width*thickness # Cross sectional area\n",
    "\n",
    "\n",
    "data = pd.read_csv('data/Dermis/DamageData/' + specimen + 'Data.csv')\n",
    "def extract_numbers_and_cycle(value):\n",
    "    parts = value.split('-')\n",
    "    number = int(parts[0])\n",
    "    cycle = parts[1] if len(parts) > 1 else None\n",
    "    return pd.Series({'#': number, 'Cycle': cycle})\n",
    "\n",
    "#Extract the unloaded length so we can find the stretches\n",
    "data2 = data[data.Cycle != '1-Preload']\n",
    "L0 = np.min(data2['Size_mm'])\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "Currently the data has a column similar to 2-Recover, where the \"2\" indicates\n",
    "the cycle number and \"Recover\" the stage of loading within that cycle. \n",
    "Change this so that the column \"Cycle\" only contains the cycle number, say, \"2\"\n",
    "and add a new column called \"Type\" that contains the stage of loading.\n",
    "\"\"\"\n",
    "data[['#', 'Cycle']] = data['Cycle'].apply(extract_numbers_and_cycle)\n",
    "data = data[['SetName', '#', 'Cycle', 'Time_S', 'Size_mm', 'Displacement_mm', 'Force_N']]\n",
    "data = data.rename(columns={'Cycle': 'Type', '#': 'Cycle'})\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "The specimen is loaded to each lmb_max 5 times. In theory all the curves after \n",
    "the first should overlap, since the maximum amount of deformation hasn't \n",
    "increased. But the stress curves still keep decreasing. Our current formulation\n",
    "of damage cannot account for this, so only keep the last cycle.\n",
    "\"\"\"\n",
    "data = data[data.Cycle==5]\n",
    "setnames = np.unique(data['SetName'])\n",
    "data_l = data[data.Type == 'Stretch']\n",
    "l = np.min([len(data_l[data_l['SetName']==setname]) for setname in setnames])\n",
    "# Collect the relevant variables in numpy arrays\n",
    "time_l_gt = []\n",
    "disp_l_gt = []\n",
    "sgmx_l_gt = []\n",
    "for setname in setnames:\n",
    "    time_l_gt.append(data_l[data_l['SetName']==setname]['Time_S'][:l])\n",
    "    disp_l_gt.append(data_l[data_l['SetName']==setname]['Displacement_mm'][:l])\n",
    "    sgmx_l_gt.append(data_l[data_l['SetName']==setname]['Force_N'][:l]/A)\n",
    "time_l_gt = np.array(time_l_gt)\n",
    "disp_l_gt = np.array(disp_l_gt)\n",
    "sgmx_l_gt = np.array(sgmx_l_gt)\n",
    "lmbx_l_gt = 1.0+disp_l_gt/L0\n",
    "\n",
    "\n",
    "\n",
    "data_u = data[data.Type == 'Recover']\n",
    "l = np.min([len(data_u[data_u['SetName']==setname]) for setname in setnames]) #get the minimum length of data_u so we have a common length\n",
    "# Collect the relevant variables in numpy arrays\n",
    "time_u_gt = []\n",
    "disp_u_gt = []\n",
    "sgmx_u_gt = []\n",
    "for setname in setnames:\n",
    "    time_u_gt.append(data_u[data_u['SetName']==setname]['Time_S'][:l])\n",
    "    disp_u_gt.append(np.flip(data_u[data_u['SetName']==setname]['Displacement_mm'][:l]))\n",
    "    sgmx_u_gt.append(np.flip(data_u[data_u['SetName']==setname]['Force_N'][:l]/A))\n",
    "time_u_gt = np.array(time_u_gt)\n",
    "disp_u_gt = np.array(disp_u_gt)\n",
    "sgmx_u_gt = np.array(sgmx_u_gt)\n",
    "lmbx_u_gt = 1.0+disp_u_gt/L0\n",
    "\n",
    "\n",
    "# # Get rid of the jumps in time\n",
    "# t0 = 0.0\n",
    "# for i in range(len(time_u_gt)):\n",
    "#     time_u_gt[i] = time_u_gt[i] - time_u_gt[i][0] + t0\n",
    "#     t0 = time_u_gt[i][-1]\n",
    "\n",
    "\n",
    "n_cycles = len(time_u_gt)\n",
    "for i in range(n_cycles):\n",
    "    plt.plot(lmbx_l_gt[i], sgmx_l_gt[i], 'lightgray')\n",
    "    plt.plot(lmbx_u_gt[i], sgmx_u_gt[i], 'k-')\n",
    "\n",
    "n_damage = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Psi functions\n",
    "def Psi1_fn(params, I1):\n",
    "    params, bias = params\n",
    "    return NODE_nobias(I1-3, params) + jnp.exp(bias)\n",
    "def Psi2_fn(params, I2):\n",
    "    params, bias = params\n",
    "    return NODE_nobias(I2-3, params) + jnp.exp(bias)\n",
    "def Psiv_fn(params, Iv):\n",
    "    params, theta = params\n",
    "    Psiv = NODE_nobias(Iv-1, params)\n",
    "    return jnp.maximum(Psiv, 0.0)\n",
    "def Psiw_fn(params, Iw):\n",
    "    params, theta = params\n",
    "    Psiw = NODE_nobias(Iw-1, params)\n",
    "    return jnp.maximum(Psiw, 0.0)\n",
    "Psi_fns = (Psi1_fn, Psi2_fn, Psiv_fn, Psiw_fn)\n",
    "\n",
    "def f(d, params):\n",
    "    return 1.0-d\n",
    "f_vmap = vmap(f, in_axes=(0, None))\n",
    "def G(tau, params):\n",
    "    tau = tau/500.0 #divide by 500 for normalization\n",
    "    params, a = params\n",
    "    # tau = jnp.log(tau)/10.0 # normalization\n",
    "    # return NODE_nobias(tau, params)\n",
    "    return jnp.tanh(jnp.exp(a)*NODE_nobias(tau, params))\n",
    "G_vmap = vmap(G, in_axes=(0, None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "# These functions are needed for predictions under realistic loading cases\n",
    "def eval_Cauchy_singlevar(lmbx, lmby, d, params):\n",
    "    params_Psi1, params_G = params\n",
    "\n",
    "    lmbz = 1.0/(lmbx*lmby)\n",
    "    F = jnp.array([[lmbx, 0, 0],\n",
    "                   [0, lmby, 0],\n",
    "                   [0, 0, lmbz]])\n",
    "    C = F.T @ F\n",
    "    Cinv = jnp.linalg.inv(C)\n",
    "\n",
    "    I1 = C[0,0] + C[1,1] + C[2,2]\n",
    "\n",
    "    f1 = jnp.clip(1.0-d, a_min=0.0, a_max=1.0)\n",
    "    Psi1 = jnp.exp(Psi1_fn(params_Psi1, I1))\n",
    "\n",
    "    p = -C[2,2]*(2*Psi1)\n",
    "    S = p*Cinv + 2*Psi1*jnp.eye(3) # do (1-d)* the usual S\n",
    "    S = f1*S\n",
    "\n",
    "    sgm = F @ (S @ F.T)\n",
    "    return sgm\n",
    "eval_Cauchy_vmap = vmap(eval_Cauchy_singlevar, in_axes=(0,0,0,None), out_axes = 0)\n",
    "eval_Cauchy_vmap2 = vmap(eval_Cauchy_singlevar, in_axes=(0,0,None,None), out_axes = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "@partial(jit, static_argnums=(3,))\n",
    "def ddot_singlevar(params_G, y, taudot, ddot_fn):\n",
    "    \"\"\"\n",
    "    Difference between this function and, say, ddot_vahid: \n",
    "    1. This function checks for damage criterion.\n",
    "    2. This function returns both ddot and rdot\n",
    "\n",
    "    Differences from the ddot_fn in the other notebooks:\n",
    "    1. It works with only 1 damage function (Psi_I1)\n",
    "    2. It assumes f=1-d\n",
    "    \"\"\"\n",
    "    d, r, tau = y\n",
    "\n",
    "    #check if g<0\n",
    "    g = G(tau, params_G) - G(r, params_G)\n",
    "    def true_fun(): #What to return for rdot & ddot if g<0\n",
    "        return 0.0, 0.0, taudot\n",
    "    def false_fun():\n",
    "        ddot = ddot_fn(params_G, tau, taudot)\n",
    "        rdot = taudot\n",
    "        return ddot, rdot, taudot\n",
    "    ydot = cond(g<0, true_fun, false_fun)\n",
    "    return jnp.stack(ydot)\n",
    "\n",
    "def Psi1_uniaxial(t, params_Psi, lmb_dot):\n",
    "    lmbx = 1.0 + lmb_dot*t\n",
    "    I1 = lmbx**2 + 2.0/lmbx # = lmbx**2 + lmby**2 + lmbz**2\n",
    "    return Psi1_fn(params_Psi, I1)\n",
    "\n",
    "def I1_fn(t, lmb_dot):\n",
    "    lmbx = 1.0 + lmb_dot*t\n",
    "    return lmbx**2 + 2.0/lmbx # = lmbx**2 + lmby**2 + lmbz**2\n",
    "\n",
    "@partial(jit, static_argnums=(3,))\n",
    "def integrate_d_uniax(params, lmb_max, y0, ddot_fn, saveat):\n",
    "    params_Psi, params_G = params\n",
    "    t_peak = 1.0\n",
    "    lmb_dot = (lmb_max-1.0)/t_peak\n",
    "    def yprime(t, y, args=None):\n",
    "        I1, I1dot = value_and_grad(I1_fn)(t, lmb_dot)\n",
    "        taudot = jnp.exp(Psi1_fn(params_Psi, I1))*I1dot\n",
    "        return ddot_singlevar(params_G, y, taudot, ddot_fn)\n",
    "    \n",
    "    solution = diffeqsolve(terms=ODETerm(yprime), \n",
    "                           solver=Dopri5(), \n",
    "                           t0=0.0, \n",
    "                           t1=1.0, \n",
    "                           dt0=1.e-4, \n",
    "                           y0=y0, \n",
    "                           saveat=SaveAt(ts=saveat), \n",
    "                           max_steps=1048576)\n",
    "    return solution.ys\n",
    "\n",
    "# @partial(jit, static_argnums=(2,3,))\n",
    "def repeat_uniax(params, lmb_max_list, ddot_fn, saveat):\n",
    "    y0 = jnp.array([0.0,0.0,0.0]) # y0 = (d0, r0, tau0)\n",
    "\n",
    "    # t and lmb are dummy vars, but it is convenient to return them from here\n",
    "    t_list   = []\n",
    "    lmb_list = []\n",
    "    d_list   = []\n",
    "    t0 = 0.0\n",
    "    for i in range(len(lmb_max_list)):\n",
    "        lmb_max = lmb_max_list[i]\n",
    "\n",
    "        y = integrate_d_uniax(params, lmb_max, y0, ddot_fn, saveat)\n",
    "        d_load   = y[:,0] #y consists of [r, d, tau], but we don't need to return r\n",
    "        t0+= 1.0\n",
    "        #Unloading curves\n",
    "        t_unload   = t0 + saveat\n",
    "        t0+= 1.0\n",
    "        y0 = y[-1]\n",
    "        y0 = y0.at[2].set(jnp.float64(0.0))\n",
    "\n",
    "        t_list.append(t_unload)\n",
    "        d_list.append(d_load)\n",
    "\n",
    "    return jnp.array(t_list), jnp.array(lmb_list), jnp.clip(jnp.array(d_list), a_min=0.0, a_max=1.0)\n",
    "\n",
    "@jit\n",
    "def ddot_vahid(params_G, tau, taudot):\n",
    "    ddot = taudot*grad(G)(tau, params_G)\n",
    "    return ddot\n",
    "ddot_vahid_vmap = vmap(ddot_vahid, in_axes=(None, 0, 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = init_params_damage_simple(key, G_layers=[1,5,5,5,1])\n",
    "lmbx_max_list = [np.max(x) for x in lmbx_u_gt]\n",
    "saveat = jnp.linspace(0,1.0)\n",
    "t, lmbx, d_pr = repeat_uniax(params, lmbx_max_list, ddot_vahid,saveat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "@partial(jit, static_argnums=(0,2,3,))\n",
    "def step(loss, i, get_params, opt_update, opt_state):\n",
    "    params = get_params(opt_state)\n",
    "    g = grad(loss)(params)\n",
    "    return opt_update(i, g, opt_state)\n",
    "\n",
    "def train(loss, get_params, opt_update, opt_state, key, nIter = 10000, print_freq=1000, metric_fns=None):\n",
    "    train_loss = []\n",
    "    metrics = []\n",
    "    for it in range(nIter):\n",
    "        key, subkey = random.split(key)\n",
    "        opt_state = step(loss, it, get_params, opt_update, opt_state)         \n",
    "        if (it+1)% print_freq == 0:\n",
    "            params = get_params(opt_state)\n",
    "            train_loss_value = loss(params)\n",
    "            train_loss.append(train_loss_value)\n",
    "            to_print = \"it %i, train loss = %e\" % (it+1, train_loss_value)\n",
    "            print(to_print)\n",
    "    return get_params(opt_state), train_loss, metrics\n",
    "\n",
    "\n",
    "lmbx_max_list = [np.max(x) for x in lmbx_u_gt]\n",
    "def loss(params):\n",
    "    saveat = jnp.linspace(0,1,2)\n",
    "    t, lmbx, d_pr = repeat_uniax(params, lmbx_max_list, ddot_vahid, saveat)\n",
    "    loss = []\n",
    "    for i in range(5):\n",
    "        di = d_pr[i]\n",
    "        lmbx = lmbx_u_gt[i]\n",
    "        lmby = 1.0/jnp.sqrt(lmbx)\n",
    "        sgmx_pr = eval_Cauchy_vmap2(lmbx, lmby, di[-1], params)[:,0,0]\n",
    "        loss.append(jnp.mean((sgmx_u_gt[i]-sgmx_pr)**2))\n",
    "\n",
    "    return jnp.mean(jnp.array(loss))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strategy 1: Train directly against stress data (doesn't work well)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 1000, train loss = 2.647062e-01\n",
      "it 2000, train loss = 2.289590e-01\n",
      "it 3000, train loss = 1.824340e-01\n",
      "it 4000, train loss = 1.669759e-01\n",
      "it 5000, train loss = 1.608701e-01\n",
      "it 6000, train loss = 1.576964e-01\n",
      "it 7000, train loss = 1.558783e-01\n",
      "it 8000, train loss = 1.546284e-01\n",
      "it 9000, train loss = 1.542735e-01\n",
      "it 10000, train loss = 1.535645e-01\n",
      "it 11000, train loss = 1.525832e-01\n",
      "it 12000, train loss = 1.482910e-01\n",
      "it 13000, train loss = 1.293395e-01\n",
      "it 14000, train loss = 1.267223e-01\n",
      "it 15000, train loss = 1.261115e-01\n",
      "it 16000, train loss = 1.256101e-01\n",
      "it 17000, train loss = 1.251266e-01\n",
      "it 18000, train loss = 1.247665e-01\n",
      "it 19000, train loss = 1.245024e-01\n",
      "it 20000, train loss = 1.242981e-01\n"
     ]
    }
   ],
   "source": [
    "params = init_params_damage_simple(key, Psi_layers=[1,3,3,3,1], G_layers=[1,5,5,5,1])\n",
    "opt_init, opt_update, get_params = optimizers.adam(5.e-4) #Original: 1.e-4\n",
    "opt_state = opt_init(params)\n",
    "\n",
    "params, train_loss, _ = train(loss, get_params, opt_update, opt_state, key, nIter = 20000, print_freq=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2a31b0d00>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'time [s]'), Text(0, 0.5, '$f(d)$')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OI4ovmc87aDfGoRKgRJRCoUhAZD9Ufb34gllg6ZuqH0qiwiUizoMPPsjFF19MTk4OpaWl3HrrrRwPIlP/N7/5DYsXLyY9PZ0VK1bw4osvxmC1CoUiIamrg6uvhoULw3v+zp3w6qvjLRpx4sQJcTt/fhRP4raJL4CxNnGrKlEKhUIxPbLqVF8PCxaMfyxpsZ4Rt1PttvlizpWIihSvv/46n/3sZ3n77bd5+eWXcTqdvOtd78IaOO35PLZv384HP/hBPv7xj7N3715uvfVWbr31Vg4dOhTDleuPLVu28Mgjj8R7GQqF/jAaRW9Tbm54z8/IELdjY5FbU5g0ey8/ixZF8STSymfO9m8aTpRaG2VUT5RCoUg4ZCUqcKcr6StR8kIx0YwoibyIqFlREeMvf/nLuO+feOIJSktL2b17N1ddddWEz/ne977HjTfeyFe+8hUAvvWtb/Hyyy/zgx/8gMceeyzqa1YoFLOMdG8qnc0W12XY7aJHGWD58iieyDkobk3ZMHpW3FeVKIVCoZgeKaIWLPDb+QIjz5OSYOx8WcrOF22GvA3ghYWFkx7z1ltvcd1114177IYbbuCtt96a9Dl2u53h4eFxXwqFYhYwMAD79vnVRzjopBI1OOh3FIbrTAwKe7+49dhBc4MxDTIqonjCiVEiSqFQJByBlShp50v6SpQUUVNZFuTPRs+B2xH1Jc02PB4PX/jCF7j88stZPsU2a2dnJ2VlZeMeKysro3OKfoUHH3yQvLw831d19dTpipqmhbb4JGI2/+6KJKS3F1pbZ9bPJEVUnCtRJ0+KJRiNUFsbpZNoHnAMiPvyNrsWDLGXNEpEKRSKhMJmE9cbEAJKiqhz5+K+CRc9xs2Iqp38uPQyMGUAGoy2xmBhs4vPfvazHDp0iF/96lcRf+3NmzczNDTk+2ptnfi/n9lsBmB0dDTia0gU5O8u/y0UioRGVp1DnQ8ViLTzxfkiePiwuK2oiGJSu3PIW30yw1iHeCwr9lY+UD1RCoUiwWhuFpoiOxtKS8VjubniOnT6NCxbFt/1RQVbtzeJaJIZURKDQYis4aMiXCInmvFIs4t7772XF154ga1bt1JVVTXlseXl5XR1dY17rKuri/Ly8kmfk5aWRloQnzpMJhP5+fl0d3cDkJmZicFgCOI3SHw0TWN0dJTu7m7y8/MxmUzxXpJCMXPkjKiZiChZiXI6xeDelPh8vJeOkKgm80krX2ohdG8V9+MQKgFKRCkUigRDpvAtWCA0A4i+qN27hZUgKUWUTNubakaURIoo1RcVETRN43Of+xy/+93v2LJlC3V101+s161bxyuvvMIXvvAF32Mvv/wy69ati8iapBiTQmq2kZ+fP6UgVSgSBjnfCcJP5gMhmmprIXWa60MUGRvzu0Sim8znFVFpRWA5Le7HIVQClIhSKBQJhhRRgTtdCxb4RVRSYmkRt8EME1QJfRHls5/9LE899RR/+MMfyMnJ8fU15eXlkeHd/d20aRNz5szhwQcfBODzn/88V199Nd/5zne4+eab+dWvfsWuXbv4yU9+EpE1GQwGKioqKC0txamD4ZqxxGw2qwqUInmQVr60tJn731asmPl6ZsDAAHR43XXSZh8VZCUqrdC/wahElEKhUExP4KBdSdKHSwTTDyVRA3cjyo9+9CMA1q9fP+7xxx9/nI985CMAnD17FqPR32J82WWX8dRTT/HP//zP/NM//RP19fX8/ve/nzKMIhxMJpMSFApFIhOJfiid0N/vz8aImohyWsDjECES5ryASpSy8ykUCsW0TDQjKuljzn3JfLXTH+sbuNsSnbXMMoJJgtuyZcsFj/3d3/0df/d3fxeFFSkUiqRBBkHMxMoncbv90XiyRyqG9Pf7K1FR64mSVr7UAnBZwd4rvg/GpREFlIhSKBQJReCMKIm8n7QiKphBuxJViVIoFIrEYPFicQHzeGb+WidPwokTUFMDDQ0zf70QcLuhvR1GRsT386LlrrP3idvUQrB4vf1pRZAan0qeijhXKBQJg8sFLS3i/vk9UQBnz4qJ6UlHMDOiJFJo2TrBlayZ7wqFQpEkpKREJhAijgN3Bwf9VaiyMsjJidKJAkMlRrz+/ez6yY+PMroSUVu3buWWW26hsrISg8HA73//+6Cfu23bNlJSUli1alXU1qdQKOLL2bNCSKWnw5w5/sdLS8WbtqaJmPOkQvOA9Yy4n1Uz/fGphZDivYLJ5ykUCoUiuYnjwN2YWPncdnB55+OlFvhFVI4SUQBYrVZWrlzJo48+GtLzBgcH2bRpE9dee22UVqZQKPRAYD9UQB8/BkMSh0vYusBjF420U82IkhgMAQl9ytKnUCgUuqSzE7Zvj9zOXxwH7sYkmc8xKG7NOWBMCRBR0YwCnBpd9URt3LiRjRs3hvy8T3/609x5552YTKaQqlcKhSKxmChUQrJgAezd649ATxpkvHlGlZjQHgzZdTB4QPVFKRQKhV4ZHIS+PjE5PhIEDtx1uyFGyZ2aFqtQiQFxm1ogbke8HwhUJSp8Hn/8cU6fPs3Xv/71oI632+0MDw+P+1IoEpKWFuFjq631TzxPciYKlZAkbUJfKMl8El9CnxJRCoVCoUvk589IJPOB6K1K8dZGYmjps1qFbuvqEt9HTUQ5B8Vtar64VXa+mdHU1MTXvvY1fvGLX5CSElxR7cEHHyQvL8/3VV0dhD1GodAj585BTw+cOROzHad4M10lCpLQzhfKjCiJjHuVVSyFQqFQ6ItIiyjwi6gYDuHu92Y9SBEVfTtfPjiHhdUdlIgKB7fbzZ133sk3v/lNFi5cGPTzNm/ezNDQkO+rtbU1iqtUKKJI4P+7SaccJkZa9eoneM9M2phzX7x5CHMwVE+UQqFQ6Ben09+7FMkou9pacYFMS4vca07DwIBIxe3pEd9HpRLlsoLH6R2ym+O38qWVxC3eHHTWExUKIyMj7Nq1i71793LvvfcC4PF40DSNlJQUXnrpJa655poLnpeWlkZaDP/nUiiixrFj/vuHDkFjY/zWEgM8Hr+ImqoSdeYMOByRSYzVBb5KVBDJfBI1cFehUCj0ixyolJEB5iB7XYNhoh3GKDMw4K9C5eVBUVEUTuLrh8oXQsrXDxW/UAlIYBGVm5vLwYMHxz32wx/+kFdffZXf/va31NXFZ3qxQhEzAkXUeX8LyUhbm9jtSkmBuXMv/Hl5OWRlCX92czMsWhT7NUaFUGZESeSx9j5wjoidO4VCoVDog2hY+eKA0yn0YGCohMEQhRMFWvlAF/1QoDMRZbFYOBngxWlubmbfvn0UFhYyd+5cNm/eTFtbG08++SRGo5Hly5ePe35paSnp6ekXPK5QJCWBIurIkfitI0bIKlRtrd/2HYiMOd+/X1j6kkJEhTojSmLOEcMI7X3CDlgQ2+n1CoVCoZgCTROWu0hPpXU6hRUjJSUmlr7BQXHb1yduo5fM5z2RjkIlQGc9Ubt27aKxsZFGry3pvvvuo7Gxkfvvvx+Ajo4Ozp49G88lKhT6wO2GEyf83wcKqiRlqmQ+SdL1RY11gscBBlNwM6ICUQl9CoVCoU/q6uBd74LFiyP7ukeOwKuvisn0MWDA67KTIioqoRKaB5zeBGKdiShdVaLWr1+PpmmT/vyJJ56Y8vnf+MY3+MY3vhHZRSkUeuTsWRFhajaLnafmZvG9HLaXhAQjoqQdPGlyNqSVL7NKDBcMhaw66N+tEvoUCoVCr0Ta+xbjdD4pojo7xW1UKlHOYSGkjGZIyRKPWfTRE6WrSpRCoQiSo0fF7cKFUFAgUhcCK1NJyFTx5pKkq0SFk8wnUQl9CoVCMbuQIRUuV0xOJ0XUuXPiNiqVKJ+Vzztk1zkMtm5xX9n5FApFyEj73pIl4gv8wipJCcXOl3SVqFAG7Uqknc+iRJRCoVDohq4uePllkaobaWQlKgYiymIRBS+Pxz9xJSqVqMBkPvBb+dJLwRzfYA4lohSKRCRQRElPdRKLKE2bOt5cIu18LS2itzbhCWfQrkRVohQKhUJ/DA8L+300LHeyEhUDO5+sQtntQrOlpUFlZRRO5PBO800tFLfD+uiHAiWiFIrERIqoRYtmRSWqu1vsehkMMG/e5MdVVEBmptgZa2mJ2fKiRzgzoiTSAmhpFipUoVAoFPFHxptHOpkPRCWqqQk++EG4667Iv34AUkQNeTMf5s8HY6RVhdsGrlFx8Zd2PtkPlR3ffihQIkqhSExmmZ1PWvmqq6dObZUx55Aklj4ZChFOT5QUXi6LfydPoVAoFPFFDtqNxoyolBRxwWxuhn37Iv/6AchEvqjGm0srX0qOP1xJJ8l8oESUQpF49PdDT4+4H1iJOnFCRJ8nIVJEBTOMXYooaf9LWDxuGPXOiAqnJyolA9LLxX1Z0VIoFApF/PB4hK0ColOJysryl4amsm3MELvd/2t0dYnbqIRK2L0bgGmF/seUiFIoFGEjq1DV1eINs6ZGlGfs9iTxsF1IMP1QkqSJObd1gMcJhhTImBPea2QHWPoUCoVCEV8sFmGvNpshIyPyr5+ZKTZaIfIzqAKQ1afcXFH0gmhVos7rhwIlohQKxQyQtj35BmkyiYpU4M+SjGCS+SRJE3MurXzhzIiSqIQ+hUKh0A/SyheNKhSIhIczXgfD8uXROQfQ2ytui4uDGz8SFh5XwJBdr4hyDILde/I4z4gCJaIUisQjsB9KkuR9UaG8SSdNT5Qv3jyMfiiJSuhTKBQK/WAyQWGhmO8YDQYHob1d3F+6NDrnwF+JKiiA06fF/Yjb+RyDomqXkiG+AEa8HwbSy8AcJSEaAmFubyoUirgRmMwnSfKY83DsfGfOiJRXmfiacMwk3lziS+hrmeFiFAqFQjFjysvFV7RobfX3RFVXR+UUNpu/H8rhgLExoQ1rwgiRnRKdW/lAVaIUisRjqkqU/FkSMTDgt3gHI6IqKoTVPNDVkJBEQkTJQApViVIoFIrk58ABcZuTI/qjooCsQuXlwdmz4n5NTRQ2LJWIUigUEcVm89fOA5tGZdn+6NGkmwkkq1BlZZCdPf3xRqNfbCV0X5Qv3nwG23uyEmVtSbr/LxQKhSKh8Hiim6Craf6N1IqKqA3clSKqqCg0l0hIaJo/3nzCZL7490OBElEKRWJx8qR4I87NHW8JWLhQqIfBQX/eaJIQSry5JCnCJSLRE5U1FwxGMbDQllz/XygUCkVCMTgIL74I27dH5/UtFmHnA6iqEnaMKBCTUAnXiAiWMKaIGVES2ROlKlEKhSJkAq18BoP/8bQ0/0yIJOuLCmenK+Fjzj1uGPX6JGZSiTKaIaNK3FcJfQqFQhE/ZDKfyRSd1+/r84dKVFZGpRJlt4PVKu4XFvqvsaFscgaFrEKlFoz/rGNRdj6FQhEuE4VKSJI0oS+UeHNJwleiIjEjSqIS+hQKhSL+DA+L22jFm/f3Q0eHuF9ZGZVK1OCguM3JET1QU30kmRFSRJnzxz9m93oJs5WdT6FQhIoUSIGhEpIkTegLxy6Q8CLKNyOqOvwZURI1K0qhUCjijxRRubnRef3zK1FREFEDXm2Tny/au2QlKuJzfQMrUZJh78kyKsAcRIN0DFAiSqFIJOS2z0TvWEleiQrFLiCPPX06arbw6BKJfiiJqkQpFApF/JF2vmiIqNFR0awkhdq6dVE5j6xEFRSIZD6bDVJTobY2gifxOMHpzVAPFFEW74cBnVShQIkoRQIz5hzjG1u+wWO7HsOjeeK9nOijaXD8uLg/S0SUxQKdneJ+KJWoOXNEm5jL5Y9gTSh88eYRGLyhZkUpFApFfLHZRI+SwRBczGyoBFr5ysth7VqR/BBBNM1fiSoo8H8cqa+PcJuXY1DcpmSCKdX/uM7izUGJKEUC8+eTf+abr3+Tz/zpM9hd9ngvJ/qcOyc6OlNSJlYUUli1t/uH7SU4Ms091AHvgTHnCRkuIa13WaoSpVAoFAmPrBBlZ4sLVKQJtPJFPOVBYLWKjUmTSfRERa8falDcpp530VciSqGIHBaHxXd/2D4cx5XECFlhWrBg4ql2+fn+2PMkGbo7k/hU2Rcl0/0SCp+dr3bmryV7oqxnReqfQqFQKGJLaipUV48fTRJJAitRCxaIGD2bLaKnkFWovDxRUJvKGDMjfP1Q+eMfVyJKoYgcR3qO+O7v69wXv4XEiqn6oSRJZumTAiiUZD5JQsec++x8tTN/rYxKEXWuuWCsbeavp1AoFIrQyM+HVauioDgAh0N432UlqqwMXnoJ9u2L6GkCrXwQg2S+CypRckaU6olSKGbMga4Dvvt7O/fGcSUxYhaKqHBCJSQJm9DncYuqEUSmJ8pogkzv66iEPoVCoUgu+ryx313egeo5ObBtG7RFdtPsfBEVlUqUaxQ8DjEk3pznf9zeD45+cV+JKIVi5gSKqFlViZoo3lwif6bsfD4RlXCVqLF2UTUymmc+I0qi+qIUCoUiPmiaSObzRCkAq98rLmQlamAA/uM/4Fe/itgp3G5/uGB+vmjxku7BiFaifPOhcoWQkkgrX0YlpGRF8IQzQ4koRULSP9ZP24h/lyVQUCUtwdTOk7QSNRM7X3OzuAAkDNLKl1ktqkiRQM2KUigUivhgscCWLfDyy9F5/b4+oXBkoJQUVRFUN0NDQgump0NGhr8KVVYmeqQiRgKFSoASUYoE5WDXQQDm5Iid+uN9x7G5IttEqSuC3faRIurUKdFYmsDY7dDaKu6HU4mqqhK9vA6H/3USgkgm80l8laiWyL2mQqFQKKYnMJkv0jidQuEEDtmVsbYRFFFSl0XVygd+y96kIko/Vj5QIkqRoMjK00UVF1GcWYxH83C4+3CcVxVF5DtWebmopU9GRYXYFvJ4EtDHNp7mZrHzlZMDpaWhP99kgnnzxP2E6ouKZDKfxDcrSlWiFAqFIqZIEZWTE/nXlo1Ksi+qvj4qiQ/y5QsLxW1UQiU8LnB6q2lpReN/5guVUJUohWLGSBHVUNZAQ1kDAPu79sdzSdFF2vOm2/YxGPzHJHhfVGA/lMEQ3mtIS19CiqhIJPNJpCBTlSiFQqGILbKZKDc38q8t1Y0UUzU1/gn1EVI4muavRBV5tU1UKlGOfnGylEwwpY//mbLzKRSR40C3X0StLFsJwP7OJBZRwSTzSZKkL2om/VCShAyX8ImoCNr55GuNngO3I3Kvq1AoFIqpkb1K0RRRMplPniOCzUojI2LIbkqK/+WjUomye3+X86tQmqZbEZUS7wUoFKHi0Twc6j4ECBE15hwDkrwSFUwyn0QKrQQXUXJGVDj9UJKEjDm3tIjbSMSbS9JLwZQB7jEYPas7X7lCoVAkJU6nf+htpEWU2w2Dg+K+bPw1ecOIomTlMxjEaeXGZEQrUVJEpRZd+LhzUNzPnsEHgiigRJQi4Tg9cJpR5yjpKenUF9b7AiX2d+1H0zQM4Xq/9IyqRIVFwlWiPC4Y9V4MsyNYiTIYhD1w+Kjoi1IiSqFQKKKPrEJlZopSTiTp7/dH5sldx7//e7j99oie5vx+qLNnRfBTWppwD0YEj9svlM6vRFm8HwYy5girn45Qdj5FwiFte0tLlmIymlhSvIQUYwqDtkHODZ+L8+qigNPpVxShiKjjx6M3lyIGRKISJXuiTp1KkJjzsTbvjKhUyKiI7GurhL6w2Lp1K7fccguVlZUYDAZ+//vfT3n8li1bMBgMF3x1yj4FhUIxe8jMFNft2trIv3Zvr7hNSfFXpFauhCuuEF8R4vx+KLmnu2CBv/A1YxwD3n6ojAuFkk6tfKBElCIBkaESshcqLSWNxcVCXCSlpe/0aSGksrJEbvd01NWJbO+xMThzJvrriwIul0jng5lVoqqrwWwWMefnEkFfB1r5DBF+e1YJfWFhtVpZuXIljz76aEjPO378OB0dHb6v0nAiJhUKRcKxp2MP9d+v5/Znbhciqr5+ZruBkyFFlEz/q6oSQ5wiiNUqqk5Goz8YODqhEpNY+QCGT4hbHYooZedTJBwHu8WMKCmi5P1D3YfY37mfdy98d7yWFh3kts/CheKdbDpSUsSxhw4JS19dBG1hMeLsWSGk0tJgzpzwXyclRfz6J06IYl7ErAfRwhcqEYWF+ipRSkSFwsaNG9m4cWPIzystLSV/qnEECoUiKdnZtpOTAydJTUmN3knkfCjwi6n6evjKV8Ttpk3C5jdDpJWvoMD/8SOmoRIAQ+IzH3nLInjCyKAqUYqEQ1aiVpSt8D0mBZVM7UsqQgmVkCR4X5R0L9bVBacbpyKhYs6jkcwnkZHpqhIVE1atWkVFRQXXX38927Ztm/Z4u93O8PDwuC+FQpF4yI3e+oIFIm58dDTyJ+nrE/a37GxoaRGPlZfDQw/BvfdGrP/q/H4oiEIlSvMIOx9MLKIG9onbgpUX/izOKBGlSChG7COcGhDNMnI+VOD9pIw5D3ZGVCAJLqJkP1R9BKr3CZXQJwVOVCtRLZF/bYWPiooKHnvsMZ599lmeffZZqqurWb9+PXv27JnyeQ8++CB5eXm+r+rq6hitWKFQRJITfcJ+1phdDzt3wuuvC8ETSWT1qbjYf3HLyhK38+dHTETJ0xQFaJuIV6IcA0JImdIgJeu8nw2C1duWoEMRpex8ioRCRptXZFdQnFnse3xlufjjaupvYtQ5SqZZXwkuMyKUZD6JFFEJOnA3cNDuTJFCLCES+qTAiWQyn0S+pq0LXKO6SzlKFhYtWsSigE8Xl112GadOneLhhx/m//7v/yZ93ubNm7nvvvt83w8PDyshpVAkGA63g+ZBsRl2UXY9WBDR5pFODQ4UUd6Lm8WZxi6uZkHNAoLonp4Wq1WksxuN/krU0FDEZ/lObeUb9LqLMqshtSBCJ4wcqhKlSChkmTywCgVQllVGSWYJHs3D4e7D8VhadNC08Ox8gbOiIr0DFgOiUYlKKBElrXeRJLUAzHnjz6OICWvXruXkNKXQtLQ0cnNzx30pFIrEom+0j7bhNgCWpXmlTKT/lu12MQEXRInIe3Fr7s5ihBz251wekdME9kPJFD5p5Ssvj9gs38nnQwEMeN1F+fqrQoESUYoEw9cPVbpi3OMGg8FXjUqqhL6uLrH1YzSGFlO3aJHY+ervh56e6K0vSkSjEnX6tM4T3z0uGPVGCEZDRAW+rkwBVMSEffv2UVER4ch6hUKhO471HmPMNUaKMYW55IsHIy2i5DU9Lw8GBkQ6n8GAo2cQgMVrciJymomsfDHvhxr0fp7ToZUPlJ1PkWBIgSQFUyAry1byt9N/S66+KFmFqqsLLWknI0PMpWhuFtWoBIpX9nj8laiZxJtL5s4V9nCbDdraROy5LhlrA83tnRFVHp1zZNeJi5JK6Asai8UyrorU3NzMvn37KCwsZO7cuWzevJm2tjaefPJJAB555BHq6upYtmwZNpuNn/70p7z66qu89NJL8foVFApFjJCfP2ryakgZHRMP5kRG1PiQIqqkxN/3XFeH5YwY6JS9LDI9tYGOQYkUUZHrhxr0X/fME/w7yUpUwaoInTCyqEqUImHQNI2DXRfGm0t84RLJVIkKJ1RCkqDhEu3tQvCYTEIAzRQZcw46t/QFhkpEekaURM2KCpldu3bR2NhIY2MjAPfddx+NjY3cf//9AHR0dHD27Fnf8Q6Hgy996UusWLGCq6++mv379/O3v/2Na6+9Ni7rVygUscHhdnCsT2x8LitYJGY1QuRFlFQ3ASJKW7gIa48VgOyGeVitMxsTOTLinw9VENCKFPFQian6oTwuGBJ98MrOFwShToZ/8803ufzyyykqKiIjI4PFixfz8MMPx2axipjTOtzKkH2IFGMKi4ov/Av2xZx3HUBLwD6gCQknVEKSoCIqMN7cbI7MayZEuEQ0Z0RJsmvHn0sxLevXr0fTtAu+nnjiCQCeeOIJtmzZ4jv+q1/9KidPnmRsbIy+vj5ee+01NmzYEJ/FKxSKmNFj7eHcsLBkN2Z5bRTp6ZG7kIFQN4FpD0eOAGBbvAr3E7/A+O8PQlERr70mRkW63eGdJjDaPHDMSMTtfI4pRNRIE7htIrEvJwrDiiOArux8cjL8xz72MW677bZpj8/KyuLee++loaGBrKws3nzzTT71qU+RlZXFPffcE4MVK2KJ7IdaVLSIVNOFQ+yWlCzBbDQzZB/i7NBZavL1Plk1CMIJlZAkqIiSVr5IDniXtkD52rpE9ilFY0aURFWiFAqFIip0W7t9IqquajnMuSjyjbjSyldUJNSNV0RZ5jVAYSFZNYVkZUFqqqgkDQ2Nn/EULBNZ+dxu/0ZkRCpRmhbcfKi8FdFzZ8wQXYmoUCfDB1osAGpra3nuued44403Zo2I0jQNQ6SjM3WK9BpP1A8FkGpKZUnJEg50HeBA14HkElHhvGNJEeV9k00UZCUqEv1QkoRI6PPFm9dG7xy+WVFKRCkUCkWk0DSNbmu3L5lvYcVymDMn8icKtPKB7/ruXrCIrDS/c7CgQESRDwyELqI0zV+JCgyVOHNGCLO0NKiJxMcr55Cw7BnNkDKB5VHnoRKgMzvfTNm7dy/bt2/n6quvnvSYZJoKv2XLFtLT06mqqsJut8d7OVFHxptP1A8lkT9Lir4oqxVkr8VM7Hxtbf441AQgGiJK2vl0PXDXZ+eLZiWqVtw6BsAxFL3zKBQKxSxiyD7EoG2Q3jEhciZqOZgxHs94EdXf7xvaVL7/r1zz139ktVk4dmQf08BA6KcZGQGHQ/Ql5+f7H5d7uvX1/sjzGeGLNi+ceI7WgBJRMaGqqoq0tDTWrFnDZz/7WT7xiU9MemwyTYU/cOAADoeD9vZ2TBH5P1rfSBG1vHT5pMcklYiS5uPi4vE19WApKICyMnE/gYbuRjLeXBJo59NtzHkseqLM2ZBWPP58CoVCoZgRPdYe2kfaASjNKqWwaxi6uyN7wRkcFJ661FRRcpJW/epq+O1v4T//U8zyYGYiKmb9UFOFSoC/EqXTUAlIEhH1xhtvsGvXLh577DEeeeQRnn766UmP3bx5M0NDQ76v1tbWGK40sshEKE3T2L8/CUTDFNhcNo73ir/gqSpRvoS+ZIg5n0mohEQ+N0FElKb5RVQkBu1KamrEztnYmEj/0x0eJ4x634uyo1iJAtUXpVAoFBGmZ9QfKrEsfyEcPAjvvBN+ssNEBHrsDAa/VX/p0guyx/PzxSE2mz8kMJzTBBLRZD5NA4eIZJ9QRNl6YKwDMED+igt/rhOSQkTV1dWxYsUKPvnJT/LFL36Rb3zjG5Mem0xT4Xfu3Om7v23btjiuJPoc7TmKW3NTmFFIZU7lpMfJfqmT/SexOqyxWl50mEmohCTBwiV6esBiEW/+tbWRe12z2f96urT0jZ4TQweNaZBeFt1z+RL6lIhSKBSKmeLyuBgYG/CJqCJ3Kv/0yj/xm1N/jGwyX79XdEh14xVRzqo6/mK5nG3GK/HUCQuHyeSf8RtqNWoyERXRSpRzSGweGs1gzrvw57IKlT1/4vlROiEpRFQgHo9nVvQHud1udu/e7fv+7bffjuNqoo9M5ltRumLKII3SrFLKssrQ0Hz2v4QlEpWoBBNRUuBUV4c2WzgYdN0XFWjli3YKkapEKRQKRcToG+3Do3notIj+JOOIlYPdhzg0NoNBTeejaReKKO913ZJejBMzo+XzMKb7k4sXL4ZLL4XS0uBPM1k/FER40K7d29uVVpSw/VCgs3S+UCfDP/roo8ydO5fF3g+ZW7du5aGHHuL//b//F5f1x5KjR49itVpJT0/HZrOxY8eOeC8pqkgRJe16U7GyfCUvnXqJA10HuLTq0mgvLXrMZNCuJEFFVCT7oSS6TujzxZvXRv9cvoS+luifS6FQKJKcnlERO95uEV5x15Ao/RSURmBavGR4GFwuUdmSEXwy3tyZBkB27fje6VDEk0RWoQoKxvdDDQ35MiwiLKIm6fce0H8/FOhMRO3atWvcUML77rsPgLvvvpsnnnjigsnwHo+HzZs309zcTEpKCvPnz+c//uM/+NSnPhXztccaaeVbvXo127Zt49SpU/T29lIcTgBBAnCgW4ioqfqhJCvLhIhK6L4otxtOnBD3I2HnO3lSbC+lXjhfS09Eox9KouuBu7548yj3Q4GqRCkUCkUE6bH24Pa4OTskPp9KEVVfc1HkThKY9mAwCFHl7em3DIm+q+z5M7eCy9Oc/1FSVqEqKvw2wbDRPGCX/VCTfGYd3CduVSUqeORk+MmQE+Iln/vc5/jc5z4X5VXpE1l5WrduHb29vRw/fpwdO3Zw0003xXll0cFn5yubvsHQFy6RyAl9LS1C9KSnw9wZ7GbNmSN2rUZGhEJZujRiS4wG0Ri0K9H1wN1YJPNJAnuiNG1iK4VCoVAopsXmsmFxWOgZ7cHhdpCeko5jqJ8UYMm8tZE70fmNStLuX1GBpV2M6sleXHXB07q7RSp6dbW/gBXKaSTSzBKRKpRjADQ3mNIm7ndy22HIe0Kdi6ik64maLchK1Nq1a7nkkksAeOedd+K5pKjRZemi29qNAQPLSpZNe7ysVh3sPjilKNc1cttnpgMZDIaESuiLxowoSWBPlO7+t4jFjCiJFGouqz9iVqFQKBQh02MVVr6BMVF9qs6sIMXpxmxMoWbO9J9XgiKwH0pOzpXJfEuWMHL/f8H//oycOzZe8NQTJ8TGoTWInC2LRQzTNRov7IeSAdAN03dUTM90Vr7ho6C5wJwPmRG0REYBJaISEJvN5os0v/jii1m7Vux2JKuIklWoBYULyErNmvb4xcWLSTWlMmwfpmWwJcqrixKRCJWQyNdIgL6oaPZEyZjz0VHo6Ij8688IX09UDCpRpnTI8CZcqoQ+hUKhCJveUSEI+sbEhlR2Wi5vzIW+xTWY0iKUjmSx+NMe8rxJdl4R5V68DOuYEUpKyKnOv+CpKV6/mcs1/WnkHN/z50MB7N0rbletCn35FxBsP1RBg+6dEkpEJSD79u3D5XJRUlJCTU2NrxK1Y8eOxK28TIFM2QsmVALAbDKztETY1qQASzgiESohSZBwicFB/2ZbNCpRqalCSIHOEvo8ThgT0bgx6YkCf4CFCpdQKBSKsDk/VCIzPRtjQQGF9RGcbTRR2oOMN1+0nNJS0aeUlnbhU6WIcjqDP835Vj5Ng337xP3GxtCWfgEel7DzQRChEqtmeLLoo0RUAiKtfBdffDEGg4GGhgZSU1MZGBjglC4bPmZGYLx5sEhLX8L2RUViRpQkQUSUFDbl5ZA1fcExLHQZLjHaKhptTenRnxElyVbhEgqFQjETRuwj2F12TEYTLQMtAFwx9wqefN+TPHXbU5E7kSwRBaY9eEVUuqWXS/7z/Vzd8vMJnxpsJUrTJj4NwJkzYpPTbI5AW7WjX5wsJVN8TcRgYsSbgxJRCYkMlZAVqNTUVBq92wPJGHUuhVCwlajAYxNeREWii1OKqGPHwOOZ+etFiWj2Q0nka+uqEhUYbx4r64KsRCkRpVAoFGEhrXyFGYUc6xPX7A/lXcm1zCMjCPtc0JxfIhodFeFTAG1t8Nxz4N1cPx8563c6ETXVfChp5Vu2LAIBv9NZ+TRNiShFdJFC6eKLL/Y9lqx9UU63kyM9YsdlZXnwf1CyEpWQdr6+Pv+WUCRE1Lx54p10dBTOnZv560WJaPZDSXQpomKZzCfxzYpSIkqhUCjCQVr5jAajT1AtGDSScfyU6GOKBBOpm+PHhdgoLsZ90vsePknig8ylmk5EBSaon98PFTErH4BN/JtNKqLG2kXgkcEEeREK5ogiSkQlGIODg5zwzg8KFFGBfVHJxIm+EzjcDrJTs6nNrw36ebISdar/FBZHhN7MYoWsQlVXR8bXZjb71YOOLX3SiTr7KlHei2AskvkkqidKoVAowkbTNPpGhfKQyXxzsiv5zDOb+Obr30SLlCd9InUjk/mWLmXLrmxe4nqG562a8OnBVqIms/JBBEMl3HZwijj2yfuh9onb3EXC4q5zlIhKMHbv3g1AbW3tuMG6UkTt3bsXh8MRl7VFAxkqsaJ0BUZD8P+7lmSVUJFdgYbGwa6D0VpedJBCJxL9UJIE6IuKtZ1PNxks8axEWVpEP5ZCoVAogmbANoDL4yLVlErrsBh6W0wmfdZeWi3tGDIyInMiqW4C0x5kMl/tfEb7x7CTRnrjxJ8XKivh8sunNrVo2uRDdsEvomZciZJWPnOumBE1EdLKl69/Kx8oEZVwBM6HCmT+/PkUFRVht9t98efJgLTjhdIPJZH2v4Sz9EUyVEKSACIqmoN2JXV1YjPPYoGuruidJySkiIpVMh9AZjUYjOCxw1hn7M6rUCgUSUC3tRsQG7bHe8Vcx3ynSHEoLa2LXH/rRJF5XhFlSROPpZYVklo08STdjAxRxMqcJMMBYHhYpPelpPgT1CW9vf4ugJUz1TV2r5UvvWTyYwYSpx8KlIhKOGTPU6CVD8BgMPgeSyZL34xEVKIm9EUyVEKicxFltfpnN0WzEpWWBnO9s/t0Y+nzVaJqY3dOoxkyvNPtVV+UQqFQhESXRezClWWVcbjnMAAZdjcAFRULI3OSydIevCJqxC5SHnIWls/oNIHFrvO1n+yHmj9fxKjPCF+oxBQiajBx4s1BiaiEQ1aipH0vkGTsi5qVImoW2vlOnxa3BQXiK5roqi/K7YDRNnE/liIKZkXM+fe+9714L0GhUCQZNpeNYbvo7SnJKvG1HRitowBUzYnQtbtbVLvG9UPZ7b6L14gxD1LM5CypnvQl7HZxfZVhfhPR4y0QTWTli1iohNMCrjHhgEgtnPgY1yiMeOePqEqUItJ0dHTQ1taG0Wj0RZoHkmwJfQNjAz6vcSgzoiRSeB3oOoAnUfo+bDb/u10kBu1KZFWrt9e/7aQjYtEPJdGViBo7B2jeGVGlsT23L6GvJbbnjSEHDx7kU5/6FG632CE+cuQIH/zgB+O8KoVCkchIK19BRgFWh5Vzw8Lv5hoeBGBeVYQG7XZ6rdblAZWmEyfEqJK8PEY+/RX4zW/I+fzHJn0Jmw0OH558NqLH4x9yXzJBgShioRKyCpVaCEbTxMcMHhI9uumlkDGz6lqsUCIqgZAVpqVLl5KdnX3Bz6Wd7/jx4wwODsZyaVFB7u7MzZtLXnreNEdfyKLiRaSaUrE4LLQMtkR4dVGiqcn3BklZBAevZmVBjTe4QIfVqFjEm0t0NXDXl8xXG7sZUZKs5K9E/fSnP2Xx4sXceOON3H777WzatIn3v//98V6WQqGIIufOneOhhx7izTffjMrrB1r5DnUfAqAoo4hXS61sq4b5Cy90CoWMwwEDIvVv3GcBef1eupQRiwFMJnLKJm94mm7Ybn8/uN2Qng45E7RVRawSZZ8m2hwSLlQCIiSilGUiNkgRdX6ohKSkpIR58+YBfttfIjMTKx9AijGFZSVizsD+zgSx9AWGSkT6Q3Xg0F2dEYt4c4muKlHx6IeSzIJZUTt37uSNN95gYGCAvXv38uyzz3L77bfHe1kKhSKK/PWvf+UrX/kKH/3oRyP+2pqm+WZClWaV+jZ7K7IrWFy+nLl1K8nOnsSuFgrd3SI2LzdXpENIAuLNy8pEH9NE4kcSKKImSqSdyso3Our/uDCjSpSmidlPkFShEhAhEaUsE7FhOhEV+LNksPT5RFRpeCIK/Al9CdMXFY1QCYmO+6KkoJFVomgSWImKe8y5rALFMplP4qtEtcT+3DHii1/8Ip/+9KfZtWsXv/rVr7j11lvZtm1bvJelUCiiyF6vB21JJPuKvQzaBnF5XJhNZnLTcn0jVBrKGvjFbb9g36f3ReZE0sp3viNFiihNY/mHVnLZS98gNXXyl5EiCkTF6Xxk21XpBG7ygweFMaa0FCoqgl/6BTgGwOMEYyqYp3AVDe4Tt7OtEqUsE9FH0zR27doFwJo1ayY9ToZLqEqUQAqwhIk5j0a8uUT2WOlYRMXCzlfnTZ8dGfHvwsWNeMyIkkjhNnoWPNNMYkxQ3nzzTd71rncBwu78wgsv8NWvfjXOq1IoFNHk0CFhsZuod3ym9I2JikpRRhEGg8FXiXp/7qWs7EvxR5LPBI/Hf3EqP683SIooux0OHIAzZ6Z8KZPJb2o539Jnt4t4c5g6VGLVqhkaY2xepZZeMvkLaR4Y8H5Om22VKGWZiD4nT55kcHCQtLQ0GhomFxWBlSgt7tvs4ePRPD6v8Yqy8Js0E64SFY1kPolOK1F2O5w9K+7HQkSlp0O1N8wo7pY+n4iKQyUqo0LsDGpuGGuL/fljwPlW8zlz5vDKK6/EaTUKhSLajI6Octob9zqVaydcpJWvOLMYTdN8n1OWeApFDJ5MaZgJfX1C8aSljR/c5HSKYAlgtNuCCxNM8XlQMllflMyYys0VpzqfyA3ZlSJqivCkoaPgGoGULMiNwuefKBEREaUsE9FHVpYaGxsxm82THtfY2EhKSgpdXV20trbGankR5/TAaaxOK2mmNBYWhT9zQcacnx447Ysk1S0eDxwXQ/uiauc7c0YMZtIJzc3CVpeVFdksjanQTbiEtNLFoyfKYPRXwJI0XGIiq3k0+iQUCoU+aGtro61NbAqtmnGk3Hg8mof+MSGSijOLOTd8jiH7ECnGFB7445f55B8/yXFHx8xPJKtQpaXjKzenTgkhlZXF3iNp/JmNdM5ZPe3LTSai5GkmSuWDCIVKuO3gGBL3p5oP1btd3BZeDMaUyY/TGRERUcoyEX0mG7J7PhkZGb5KVSL3RUn73fLS5aTM4A+qKLOIOTlzAHzeZd1y7pzo5DSbwRsQElGKi/01eynWdIAMlZg/P3YBdboIl3DbYaxd3I9HTxT4xVuSiihlNVcoZhc7duzA4/GQl5dHZWVlRF970DaI2+Mm1ZRKTlqOz8pXmVnOSH8nnZYuissjcO2eTN1IVbN0KcNtIwBkrZm+atPYCJdfDueHOk8lotxu4RaEGYZKSCtfah6YJih3SXrf8i7mshmcLPZEJeJcWSYiz1RDds9HHpMMImomVj5Jwlj6ZD/UggVCSEUDWY2SvmodEMsZURJdVKKsZxEzojKnjn2NJkk+K0pZzRWK2YPL5WL37t0ArFixAkOEd+UCrXzg35idayrEAGRm5lFUVDWzk0zVqOTtix+dU48LE8a8XLJqp6jueCkqEvN6A0MmhobEDCmTSfz8fE6cgLEx4RCZ0bVZWvnSppmDKEVU8boZnCz2zFhEud1uDh8+zK9//Wv+5V/+hfe9730ApKenz3hxCoHT6fSlzUxXiYLkSOiTOzzhDNk9H2np0324RDST+SQ6jDmPZTKfRBeVKClcsmtjPyNKkmSzoiwWy7jvldVcoZg9dHZ20tws3suiESrRYxWlm6JMoTrk55Qit4jHKy6rnflJpmpU8grE4VQhrrIXlGMM81O8TOUrKWHC15D9UA0NQmiFhaaBzVvumqofyt4Pw97PJEWXhnmy+BCST+r06dMcPHiQQ4cO+b6amppwOp2kpqayZMkSVqyI0KRmhY9Dhw5hs9nIy8tjQRBbArIStXv3blwuFykpieMvlfgqUREQUTLdT/eVqGiGSkh0GC4Ry2Q+ifwzkjHncdEwVm+qUmYckvkk0s6XJJWovLw8nnnmGZ9lL3DYprSa33HHHUpIKRRJSHt7O2e8aXWR/ixqd9l9/VBlWaJ5V86fzBpzMwyUV4Tfv+1jMo+dx+MXURWLoMpJ7rLqoF6yr08UtwoKID9fPNYl5gVPGG0OEQqVcA56o83NkFow+XG9b4vbnHpIj5MrI0yC/nT94Q9/mKeffhqDwUBmZiZWq5Wbb76Z+++/nxUrVlBfX48pbLmqmIrA+VDGILYdFi1aRG5uLsPDwxw6dCjizZXRxuKwcKpfNMpIK95MkJWog10H8WgejIaouFhnjqwOySjyaKBDERXLQbsS2X81PCw2/iZrrI0qvkpUnPqhAs+dJAN3NU3jxz/+Md/97ncxGAxcfPHF3Hnnnb4KvrKaKxTJidPppKenJ2oiqssqVEdeeh4Z5gycbifH+0RvsTYqgppqqpbP/ESTiaiTJ8VcjvR0hv/+Hrg2hbylwSUwnzsnEnAXLxYiyuGAgQHxs8nCnALjzcMmmGhzCLDyJVY/FIRg5/vtb3/L97//fSwWC+3t7dx777289NJL7Ny5k5qaGiWgoogUUcFY+QCMRqPv2ES09B3qPoSGRnl2OaVZ0/hog6C+qJ40UxpWp5XTA6cjsMIoEQsRJV+7qUmk/MQZl0uk80FsK1Hp6VDlta7HzdInLXTxmBElkZWo0TYRdJEE7N27l4suuogrrriCQ4cOceWVV/LlL3/Z93NlNVcoko++vj4GBwfp90aML18eAUETQMeISN2ryBZTZ5v6m3B6nKSnpPOnnE5emg/LGt81s5NYLKJRyWgUTUyBePuhWLWK4VFR/8jJDc5CcX46n7Ty5eaKa+H5aFqEKlG25O6HghBE1Be/+EU2bdpEeno62dnZfO9732Pbtm289tprLFu2jL/85S/RXOesRoZKhDLzIJGH7spmzUhY+QBSjCksLxVvqLL8rjsGB/0TyqMpoqqrITNTvJuejr+gbG31j8OommE/bqjEPVxCD5Wo9FIRbIEGo4k7EiGQp556iv/+7//m3//933n55ZfZsWMHzz77LA8//HC8l6ZQKKJET08PLS0tAMybN4/s86PoZoDL4/KFSlTkCBElWw6qc6u5dfGtrKhezYqqi2Z2IqluiooubETyWvm46CLqajzMnTt+hNRUTCaiJqtCtbUJC6DJBGFrUbcDHIPifvoUVg+PG/q8m/3JLKIefPBBcnJyxj22evVqduzYwec//3k+8IEPcOedd9IjS5GKiGC1Wjl8+DAQfCUK/IJLVrESCdmsKXuZIoG09Om2L0pGjldWiu2haGE0+kWaDix9sgo0b97Eza3RJO7hElJExbMnymAQwRaQFOEShYWFVFeP7xNoaGjgBz/4AT/60Y/itCqFQhFtAq18DUEMoA2FLksXHs1Ddmo22alCnMnN3mWly3j81sfZdc8uUk2pMzvRVJnjUkSlp1O3MpeV/3EnqUGeLlBEaZpfRE3XD7VkycSVqqCwe38Xcy6YpniRoUPgskBKDuQtC/Nk8WPGH1sMBgOf//znOXLkCHa7ncXR3EWfhezZswePx8OcOXNCmnkgK1GHDx++ILFK70ihE1ERVa7zhL5YWPkkOuqLike8uSQwXCLm6GFGlMQXLpH4ImrVqlU8/vjjFzy+YMECzp49G4cVKRSKaDM6OorVavVVoqLVD1WeXe577FDPIQBuNTdg3LnL7yQJF49HlH9g4lCJPXv831utF07OnYJAEdXfL5z8ZrMImpiIiAzZtXmTK6ZK5YMAK98lYEy8tqCI7f3OmTOHZ599lieffDJSL6kg9H4oSXl5OdXV1Xg8Ht/chERA07SI2/kgARL6YhFvLlm6VNzOchEl7XxxqURZvR/o4zkjSpJEMef/+q//yve//33uuusu3nrrLaxWK93d3Xz729+mri7OYlWhUEQF6YA6d+4cEFkRpWka3VZRuinL9vvf5OeUolENZ/s5MVRpJvT1iQm36ekXulGamkSoREYG/WctDJODZ3nwm8yBIkpqvbKyybMeZCUq7FCJYKPNAXq2i9sEtPJBFIbt3nzzzZF+yVlNOP1QkkS09LWNtDFgG8BkMLG0ZGnEXlfa+VoGWxiyDUXsdSPGLK9ExTJUQnJ+zHlMkVWf7Lr4zYiSZCePiLr00kt5++23aW1t5corryQ3N5eKigp++9vf8p3vfCfey1MoFFGgq6sLj8fjq0RF0s43aBvE6XZiNpkpSBelm8GxQZoHxfvlD17+Nz7w2w/QabLN7ESBg5vOJyBU4vBeB69zNZ3VwW+sSxHldPpFVEXF5MfPOFTCOQQeBxhTpo42h4QOlYAoiChFZJEiKtRKFCTm0F0Z/LC4eDFpKWnTHB08BRkFVOeKXgldWvpiMSNKIoXasWNxUA/jkfHmsRy0K5k3T9wODQmLQ0yR/VDSShdPfDHnLXFdRqRYuXIlW7Zsob29nRdeeIHnn3+eM2fOcNNNN8V7aQqFIsLIaPPOzk7GxsZIT08Pap5msEgrX0lmCQbvhtdb58QH/xJjDmanh8zULMoqZ3gRk/1QEzUqed1EnsbVDJ8R2eT5a4OfSZWfD2vXQm0tjI6KwIjJxnoMDoJXi4ZfifKl8pXAVCNlbD1g8e6kFifWkF2JElE6pre3l9PeBLU1a9aE/PxEFFFS4ESyH0qi274op9OvJmJRiVqwQGxNWSwihidOeDz+XzselajMTH8iYMz7oiwt4lYPIioruWZFSUpLS9m4cSM333wzxcWJNcBRoVAER2dnJx6Ph25vJWfp0qURHbkzkZVvR7tw99RoIh6vumophpSgx65eyNiYsOsZDFNWoixFNXhcbszpKWQuCT6QKC1N2Pek47Ck5MLwP4n8uFhXN3nP1LT45kNN1w/lHbKbu2T6ipVOUSJKx8gq1MKFC8mXY6ZDYM2aNRiNRs6dO0d7e3uEVxcdZDJfJPuhJA2lOu2LOn1amJWzsmKT8202+71scbT0tbeLkRgpKVATp4C6uIVL+OLNa2N84gmQa7B1g8sa16UoFApFKLR5NwJlX1QkrXw2l81n/y/J9Isb2Q+VbxeVqfk1M4w2l1Wo/HxxfQ7E7fb564bGRBxfXn1pWHG20spXXj75MW+8IW6vuCLklxd4nOAcFPenFVGJ3Q8FSkTpmpn0QwFkZ2f7Bs69/fbbEVtXNJFVIlk1iiTyNXUnoqSQWbQodv0xOog5l/1QNTV+z3askSJKVsRihp7sfKkFYPYOHJEVMoVCodA5DoeD3l4xv0mmb0YyVEJWofLT833tBWPOMU70nQDAMCQE1uL6GVrR5Cb3RIObTpwQrpHMTIZqV8LFa8lbH1qzkscjXkZuFk42Hwr8IurKK0M6hR9bt2gTmC7aHBK+HwqUiNI1MhAiHCufJJGG7tpddo71ioCFaFSiZLjEwa6DuD3uiL9+2MQyVEKig3CJePZDSeI2K8oSECyhB5KsLyqSbN26lVtuuYXKykoMBgO///3vp33Oli1buOiii0hLS2PBggU88cQTUV+nQjHb6OjoQNM0cnNzOeq9lkWyEtUx0gGMjzbvtHRyZkjMo+pzDuMxwIr6cBUHYLeDVwgy0Rgbma68ahVDq66Gf/kX8v7x0yGf5vXX4cwZUeyabL6U3e6384UvooKMNvc4oc/7ubTksjBPFn+UiNIpmqb5RJQUQuGQSAl9R3uP4tbc5KfnU5UbeVvbgsIFZKRkMOYa49RArEsPUyBFVCxCJSQ6EFHxTOaTxEVEucbA5vVVxHPQbiBJFHMeaaxWKytXruTRRx8N6vjm5mZuvvlmNmzYwL59+/jCF77AJz7xCf76179GeaUKxexCtink5+fT5C2zREpEuTwuekeFuAkUUYe7DzNsH8aAgT2V0HLpYvLK54Z/ovZ2UbnJzxeW/vPx9kNpq9fgLXyRlxfaKYxGGB4W94uKJj9u1y4hpEpKwpy2omkB/VBTlLsABg+AexTM+ZCbuPNl42SiUUzHmTNn6OnpISUlhVVhR6SMr0R5PB6MYfhoY4XPyle20peCE0lMRhPLS5ezs30n+zv3s7Ao+HSbqCKFTCwrUYEJfXEinjOiJHERUaPeGVEp2ZA2xRUtliTRwN1Is3HjRjZu3Bj08Y899hh1dXW+SPUlS5bw5ptv8vDDD3PDDTdEa5kKxazCbrfT5x1O29fXh6ZpVFZWUjpRul0YdFm68GgeslOzyUnLAcCjeXi7TbRG1BfW89HGj5Kdmj0zG7608s2ZM/HPZSVq0SLWVLYzlFU5odaaCo9HpPKB0GqTEdgPFdav5BgQFSajefqgiJ6AIbtTJfjpnMRdeZKzy7v70NDQQHr6NL7SKVi6dClZWVmMjIxwLI4fmINBiqhoWPkk0tKnm74oTYtPJUqKqK6uOOR7C/RUierrg4GBGJ00MJkv3jOiJMrOFzHeeustrrvuunGP3XDDDbz11ltTPs9utzM8PDzuS6FQTEx7ezuappGfn+/7bDOTDefz6bQIt0BgFcpoMGJ1iPCda2s28LUrvsa9a+8N/yRjY/7r70RWvoBQCcO5Vkob51D/wF0hXzb6+8WlxmyGjIzJj4tIPxQIK990i0yCfihQIkq3zGQ+VCAmk4nVq1cD+rf0RTPeXCJfWzcx552dos5uNMa2JJOT408CjIO41jS/iIpnT1RWln/oYMyqUb5kPp30Q0FSDdyNN52dnZSd17ldVlbG8PAwYzJjeAIefPBB8vLyfF/V1dXRXqpCkbBIK9+cOXPY6xUajWFPhx2PR/P4QiUqcsZPpZWVqPd1F8Jrr/l9cuEgq1BFRTDRZvnx42C1iguVHN60MHQHTU+PiDTPyxNBwBPhdsO2beJ+1PuhIEBEJW4/FCgRpVuk4Ak3mS+QRJkXFc1kPonuEvqkgKmrE8McYsnSpeI2Dn1R3d0icMhgEL96PIm5pc+XzKeTfihQPVE6YPPmzQwNDfm+Wltb470khUKX2Gw2+r0VnIqKCvbt2wdErhLVZenC5XGRYc4gL83fgDTmHGN3+27MLhgb6KGr89TUpZ1pT+QVHRUVE/9cWvkaG2l5q4MOynGtDr1HvqdH7NNOJaIOHRKD57Ozwxyy6xoDp1dQpk0josY6vdZxg7DzJTBKROkQt9vts/PNtBIFiREu0WXposvahQEDy0qWRe08shJ1dugsA2Ox8m9NgRQwsbTySeIYLiGT+aqrY68dz0dWwmI2K8onovRUiaoVt85BcAzGcSGJT3l5OV3yw5GXrq4ucnNzyZjiA1daWhq5ubnjvhQKxYXIKlRhYSFms5mDB8XcpkhVotpGxOypOTlzfP3ZPdYe/nfv/+L0OKnxZPM/e3/Kv+773oVznYLF6fRb+SbLHJehEkuXceRMJrtYg215aGnNdrsQRyYT5OZOLqKklW/dujBHjti9Vr7UAjBNEv8nkVWovGUiCj2B0ZWICjVK9rnnnuP666+npKSE3Nxc1q1blxQJSMePH8disZCZmclSWS2YATJc4sCBA1PaSeKJHLI7v3A+Wakhdk2GQH56PjV5ogKgC0tfPPqhJDoQUfEMlZDEfOCurydKR5WolCxI8w6TVH1RM2LdunW88sor4x57+eWXWbcusb3/CoVekAN2KysrOXbsGHa7nZycHOoiYGtwup10WcQmyJxcf9hD+0g7u9qFqKnziOpUbe0MRFtvr/C1Z2dDZubEx3grUSNpxbgxkVJZRlZ1YcinAZG2d/nlInlvImbcDzXmTZydLpUPkqYfCnQmokKNkt26dSvXX389L774Irt372bDhg3ccsstPn9sohI4H8pkMs349aqrqykvL8flcrFnz54Zv140iEU/lERXlj4posLKE50hcRRRegiVkMhKVOzsfDqbESVRfVETYrFY2Ldvn88y1NzczL59+3zDPTdv3symTZt8x3/605/m9OnTfPWrX+XYsWP88Ic/5JlnnuGLX/xiPJavUCQVo6OjDA4OYjAYqKys9P1drly5MiLpw52WTjyah5y0HHLTRJVE0zQ6LZ0c7RXXyswRGwCLFs6gn0dWqydLEwwIlRgcFr9X/vKqkEMlenrE7fz5ouA1UeuVps1QRHmcYPeqtYzyqY8F6N0ubhN4PpREVxHnoUbJPvLII+O+//a3v80f/vAH/vjHP0asrBsPZKhEJPqhAAwGA2vXruX5559nx44dXH755RF53UgiK1ENpdEXUQ2lDTx//Hn2d+pIRMWjEiUT+lpaRErQTLzdIaLHSlRMRJRrzN98q6dKFIi0wL4dqhJ1Hrt27WLDhg2+7++77z4A7r77bp544gk6Ojp8ggqgrq6OP/3pT3zxi1/ke9/7HlVVVfz0pz9V8eYKRQTo7BQVj8LCQtLS0iIeKnFu+BwgrHySvrE+7C47x3uPY/SAe0DY8FYuWR/eSTRNNAbD5Fa+Y8dELnlWFgMnRZR7wcWhXTA9num1GsDp09DRIZyJYY0lHesEzQPmHPE1FW4H9ImKXjJUonQlomaKx+NhZGSEwsLJy512ux273e77Xo8xsrISFYl+KMkll1zC888/r9twCV+8eVn04s0lshJ1oDvOdj6LBWTzeCxnRElKSqCwUPiyjx8Ps5s0PPRUiZJr6O2FwcGp52jMGKuYdE9KDqSGZsuIOqoSNSHr169H07RJf/7EE09M+JxEd0QoFHqko6MDEIESQERDJVweF31jQrBU5vgjx9uG2+iwdDBkH6LCngKai5y8EqoqwnSQDA2JZqWUFHENngjZw97YyOCNH4SiY+RfF1o/VE8POByi7zg1Fc6dE87B808pq1Br1oS5lzrmTRnMmCCm/XwG9oHHLq5/OTqZ1TkDdGXnmykPPfQQFouFO+64Y9Jj9B4ja7PZ2L9fVEgiVYkCf1+UHkWUy+PicPdhIDZ2PnmOQ92HcHvcUT/fpJw4IW6lmIk1BkPcLH16GLQrycmBcq8DIerVKF+8ea1+ZkRJ5MBdJaIUCoUOOT+VT9M032ZFJERU36gY2puVmuXrzXZ73LSPtHO0R1wjs7MLOZsH9Q0bfKETISOrUMXFIjZvIl57DQDX5VczsvIK+MQnyL80tM1Wb+sYlZXilHv3QkDR3MfMrXxez2AwIqrHe7LiS/V3DQyDpBFRTz31FN/85jd55plnppxYrfcY2f379+N0OikuLqamJnJ2n4svvhiDwUBLSwvd8g9YJzT1NWF328kyZzGvYF7Uzze/YD6Z5kxsLhtN/bFKE5iAeCbzSWQFLIYianBQDLcFmBf9/9xBEbNwCV8yX22UTxQGMi3QqkSUQqHQHzL1sqCggPT0dFpbWxkYGCAlJYVly2ae6ts7Kvp6ijOL/ee0irhz+VmhO8XOgXJYc82Hwz/RdFY+TYO//Q2AoUvehaaJCtFE/UyT4XKJMZQgRkLK9nr3BPvGMxJRYx1iveZcMGdPf3yHN/yt/Lqpj0sQkkJE/epXv+ITn/gEzzzzzAWT4s9H7zGygf1QYe9yTEBubi6LvR+Y5Tn0guyHWl66HKMh+v9LmowmVpQK22Bc+6LiGSohiUMlSvZDlZWJKpAeiFlflB5nREmknc/aIi6KCoVCoSOkla/cax2QVailS5eSFoFZGT2joqISKKIyUjKoyKngRL9wjvz3xv/m/973f1xTd014J3E4YMA7XmWyDf+jR0WTUno6RZljXGN+g4sWWUM6TWenEExZWcKiPpmI6uwUm4cGg0jvC5lQrHyuUejeKu5X3BjGyfRHwouop59+mo9+9KM8/fTT3HzzzfFezoyJRj+URK/zoqSQiYWVTyLPFdeY83iGSkjiIKL0ZOWTxCyhzxdvrrNkPvAKOwO4rP6kJYVCodABLpeLPq+FQYoo2Q8ViVAJu8vOiH0EGC+iCjIKmF8wn5P9J8m2w43Fl/LhFR8KfxSLrELl5k5eWvJWobjySvjWt8jaeBWFrz0b0mmklW+ONx9jMhH15pvidvlyKCgI6RQiJEJeKzKDEFHdr4t+qMxqyI1DH3gU0JWICjVK9qmnnmLTpk185zvf4ZJLLqGzs5POzk6GhobisfyIEAsRpbe+KF8yXwxF1MoyHYRL6KkSdeLE5FP4IoyeQiUkMa9EyeG2esKU5t9NVH1RCoVCR/T19eHxeMjMzCQ7W9jGItkPJa18eel5pJ43LPbtc28DsN5TTcmeY/5rdzhMZ+UDv4hav943KyqU2Dy73R9tXlUlbicTUTOy8tkCrHwpQYjK9r+I24obk6IfCnQmonbt2kVjY6NvV+G+++6jsbGR+++/H+CCKNmf/OQnuFwuPvvZz1JRUeH7+vznPx+X9c+UoaEhjh8/DkQ2VEIiwyV27NgxZdpUrPEl85VGP5lPIgVb3Ox8Lpc/WCKelaiaGmG2djqhOTYfnKWdT1Z/9EDseqK8/8Z67IkCv7hTfVEKhUJHyH6owJ73SCbzSStfSaZ/Gm1TXxNWh5VtrdsASO8f4bdHfou7MNSSjZfAaPPJrHwuF2zZAoCtoo6dtuWczmkI6YLZ3i5OlZ8v7HwQJRE16i13Zc6Z+jhJh1dEVSaHlQ90FnEeapTsFu//aMnCrl0iO7+2tpaSycZKz4AVK1aQlpbGwMAATU1NLFwY/3jJIdsQZ4ZE7HM87Hytw60MjA1QkBHmm2K4tLQIb3R6uhAy8cJoFJWwffuEpS8GykbPlaieHhgeFk6LiOMaBZv3AqpXEZVVBz3b1KwohUKhK2QglhRRAwMDnDkjPjvMVES5PW46RkS/VWmWeP3e0V6O9R7j1MAptrduJ9sONssgLze/wu3FYX4+GxgQG5Zm8+TeuZ07YWQECgvp63TSSTljixYyL4RBwudb+WBiETU8DN4w6NBFlNsGdm9CVEYQIsrSDCMnwGCCsmtDPJl+0VUlarYT6SG755OamspFF10E6MfSJ618VblVMRUyeel51ObXArC/Kw7VKGkHWLhw8ojTWBHjvig99kTl5vo3BqNm6ZMzosy5kBpj0R4salaUQqHQGSMjI4yNjWE0GikuFv1KsgpVW1tL/gyH+3VaOnF5XGSaMynMEONGzgyK9+uyrDLeaXuHcos4dsnSq/yKJFQCJ99OZmeTVr5rrmFgh7BGFDYGv9FqtQqtZjCMF1HZ2XDRRbAiwPCzfbsYyFtXN/7YoJCBEmmFkBLEcCmZyle8DlLzQjyZflEiSkfIfqhoiajA19ZLQp+08skepVgizxkXS58eQiUkMRRRVqsIHQJ9VaIgBpa+wGQ+vfrBZYVMVaIUCoVOkG0cxcXFmLwCJpKhEq3DYtRNVW4VBoMBm8tGh0VcqIYdw4w6R6nwiqiLL7olvJN4PP4hTd5BwRMiRdR119G/TxxfuC74vmlZhSouFkN2JampQigFmpwiYuULpgoFfitfkqTySZSI0hHRDJWQBPZF6YF49ENJfCIqnpWoeIZKSGIook6fFreFhfGZLzwVUU/ok5UoPSbzSVQlSqFQ6Ain0+mz7c0LGCwYqVCJMecYPVbRD1WdVw1Ay2ALmqZRmFHIgc4DpDkhzybizi9uDDMFuq1NWPgzMvzT3c/HYoG33gLAteZShlvEYOHCDcFvMk9k5ZuMsEWUywqOQbEZGEy0udsBna+K+0nUDwU664mazbS3t9PW1obRaPRZ7qKBrETt3bsXu90ekdkKM0GKqFj2Q0lWlutARC3WQcynXMOxY6IbNYpVEj32Q0miXomy6DxUAsZXojQPxGBum0KhiC52u52hoaFxyXaDg4Ps3LkTp9OJx+PBYDCQkpKC2WwmNTWVqqoqamtr47tw4MyZM7jdbnJycsb1ir/9tkjMm+nnpbYRoTqKMovINGfi9rhpGWwBYF7BPL73zvco9Y5oql9wMalZYTbMyuCm2trJr7FvvCF6pmpqGKhYivbQd8hoO0l67SSi6zyGhoQOMxovLHZ5PCLTwu0WAmt0FORe+lVXhfi7yCpUWgmcl2Q4Ib1vgWtEHF8w88qhnlAiSidIe93SpUt9b3LRYN68eRQVFdHX18eBAweiWvWaDo/m8fVESUETS6RwO9x9GJfHRYoxhn8OerLz1deLd93hYeG1qwxiZylM9NgPJYl6zLme480lmdWi8dfjgLHO4GZ/KBQKXTE2NkZPTw+9vb309/czNjYGwOLFi6n3ltxNJhM2m833HE3TcDgcOBwOrFYrZQER3DabjbfffpvS0lIqKiooCHmgUPi0tLQAMD9g562jo4OmpiYMBgOXhzUh1k+XRfQpVeaI97pzw+dwup1kmjMpzSplS8sWevLAkgp/f8Vt4Z2kv18oHKMR5s6d/LhXXhG3113HgMUMCxdSuCH4ADBZhSovh5TzPs54PCKzQv78tddEFHpNTRh5UmNhpvJVvCvpNuaUiNIJ0Q6VkBgMBtauXcuf//xn3nnnnbiKqJbBFiwOC6mmVOoLY593Pa9gHtmp2VgcFk70nWBpydLYnLi3F7xDA3WR852WJkpDTU3C0hcDEaXHSlT07Xwt4jYrjmmM02FMEULK2iJizpWIUigSBqvVys6dOxkZGRn3uMFgICsri5SAT9ZZWVlcddVVmM1mjN5wI6fTidPpxG63k5OT4zu2v7+fkZERRkZGOHXqFFlZWdTU1DB37lzMZnPUfp/BwUHGxsYwmUxUBlyX3vD60BoaGmYk6OwuO/1jwjJXni2qPR7Ng9lkZl7BPI70HKFntAeT0YQ9N41rLr4jvBN57YjMmSOakyYjoB/K6RSaKxTbu+w3nugSHpiF4fHAiy+K+xs3hmg+cQyA0yI229KDq5Alaz8UqJ4o3RCLfiiJ7IuKd0KftPItLVmK2RS9N+LJMBqMvl6smIZLyN6jmhr/EId4E6O+KD3OiJLISlRXlyjKRRyfiNJxTxSoviiFIkGw2Wz09/f7vs/IyMBms2EwGCgsLGThwoWsW7eOG2+8kQ0bNlBX53/vMRqN5OXlkZmZSXp6Ounp6eTk5FBYWEhFRcU4R0xJSQmrV69mzpw5mEwmrFYrR44c4eWXX+bw4cPY7fao/H6dnZ2AiDU3BaiArVu3AnD11VfP6PW7rSI2PS89j/SUdADqCuq4bt51zM2by6vNoo/nunnXMfC1ASpypgiEmAy3G7y/x5TjTLq7/Xnj69ax7DsfY2Pv/1Fd6Z78OQEMDQmLnsk08Qgqg8EfBOxywZ//LO7fdFOQv4dk9Jy4zSgXm27TMdYJA/vE/Yp3hXgy/aMqUTrA4/H4KlGXhDCVOlz0Ei4Rz2Q+SUNZA2+de4sDXQf44IoPxuakeuqHkixZAs8/H3URpedKVF6eSC7q6RHrjGhrosvqnxGlZzsf+PuilIhSKHSHpmn09vbS0tJCV1cXGRkZXHutmLtjNBpZu3YtOTk5Ea0Qmc1mKisrqaysxOVy0d7eTnNzM8PDwzQ3N1NbWxuV/ur2dhGjXXFeg8/rr78OwFUhN/OMp9MixE1ZVtm4x6W1/9WWV1nVAXfULSHV7oLMIPp/zqerS6iWjIzJZ0MBvOoNXli5UrhCHn8c48svw6YPB3UaWYUqLZ08gd1oFFWoY8dEi1ZqKlxzTQi/i+YJGLBbHdxzOl4StwUXQfokA4YTGCWidMDJkycZHBwkLS2N5cuXR/18stp14sQJBgYGYupvDiSeyXySuCT06VVEQVRFlN3uT3jVY08UiHX19IiKWURFlG9GVJ5+Z0RJZKXMqkSUQqEX3G43ra2tnD59GqvV6ns8PT0dh8NBqtcmVhjl2NOUlBTmzp3L3Llz6enpYWRkhKwAR4XFYolIX/fIyAhWqxWj0TiuP6uvr49Dhw4BcGVY2dwCj+ahZ1Sk8pVnl9M72oumaZRkifAKl8fFtqZXuXQYrvRUi9ClcPAKwWnj8qSV79pr0V55FQMIhROk106eZqr0dJNJ6Lm/eN11V10VohnG1gUeJ5jSIa04uOdIK1+SpfJJlJ1PB+zatQsQ8w6i6S+WFBUV+Zo04zkvKp7JfJK4JPTpKVRCIgVdFEVUc7O4DmVnT2w30APSZhjxhD5Li7jVcz+URNr51KwohUIXtLW18be//Y2DBw9itVpJSUlh3rx5bNiwgcsvv9wnoGJNSUnJuNjxoaEhtmzZwp49e3A6nTN6bRlrXlJSMq6X68033wRESEbpDC4k3dZu3B436Snp5KblcrDrIG+fe9s3ZHdvx16y+ywYgBe63wzPeu90+gfsTiWiNG1cP9SuP3bwOlfRe/HGoE4zMiJmMBqNUFY2+XGyQvWStzgUtpUvsyo4cedxQ6f3ZEnYDwVKROkCKWRiGfIgAyziZemzOqyc7BferniKKFkFax9pp3e0NzYn1dOMKIkUUZ2dMDgYlVPIfqj58/U7azZqCX2+ZD6d90OBsvMpFDojIyMDh8NBZmYmy5cv5/rrr2fZsmVRTfINh4GBAUCIvi1bttAnA5RCxGazTTgbCiLXDyVjzKtyq2gdbvWFXMmUvr+d/htV3t7YecvDrHh1dgr/XHY25E4RjX76tAifMJvRVjTQd6SLYXJJWX9FUKeRVaiSkgtT+QIxmcBmg+3bxfcbg9NoArdDVKJAiKhgGNgD9j5IyYHiS0M4WeKgRJQOiFUyXyDxDpc43HMYDY3SrFLKsqfYOokyOWk5zC8QVTlZGYsqNpt/XoSeKlF5ef5IHynyIoye480lURdRep4RJZFCb7QVPK74rkWhmGW4XC5OnDhBU0A5vLCwkEsvvZRrrrmGurq6cZUZPVFbW8sVV1xBVlYWNpuNt956i6amJrQQrXCnTp3C4/FQWFhIcfF425gUUTPph7I4LPRYezAYDFTlVnG89zgA9UX1vpCrP+17hgIbpJlSueGqj4Z3olCtfOvWMfTGAZyaCXNlKXnLphcrmuaPNp8uWHfxYjFHyuEQ46pC2scdaxcnS80Dc870xwO0e6185deBMfbhYbFAiag443Q62b17NxCfStQ777wT8htcJNCDlU8i1xCThL6mJvFGlJ+vP09blPuiAitReiVqA3d9g3YToBKVUQHGVNDcfvuGQqGIKh6Ph+bmZl555RWOHz9OU1PTuNS7kpISDHot4QeQn5/P1VdfTXV1NZqmcezYMXbv3o3LFdyGjNvt9lWhFi4cPyNpeHiYPXv2ADPrh2oeEO/HZVlldFg6sLlsZJozqc2vBcDhdjB4QnxGWbL0atKz80M/icMhGmxhehElm5Suu46+l8TnwcJL6oNybPT2Ciuf2Tx1PxSI+VCyg+Omm0J0hIx5BWGwVShI+n4oUCIq7hw6dAibzUZeXp5vCF4saGxsJCUlhZ6eHs7Kbv8Y4hNRpfEXUTENl5ACZfFi/XnaotwXlUiVqM5OsWMXMRJhRpTEYPSvU4VLKBRRp7Ozky1btnDo0CEcDgdZWVmsWrUqbr1OM8VkMrFq1SpWrlyJ0Wiko6OD1tbWoJ7b2dmJ2+0mKyuLkpKScT/bvn07Ho+Huro6qquDTIc7D5fHxblhsTlUll1GU5/YMVtSsgSjdxDsX46/yJxBDwB/f/M/hnUeOjrEhmle3tT9VHY7vPyyuH/TTfSeEaEhxdcE99nIO4uYqqrJU/kkmuaPNg/NymcTtjyA9CBj3h0D0Pe2uF9xQwgnSyz0WROeRUgr3+rVq30D72JBeno6K1euZPfu3ezYsYOaqeYXRAE9VaJiGi6hx1AJiapEUVAARUViFvKpUyJtNiJIMZIIPVEgKmYjTSIQI35uW4UiqbFarRw4cIDeXtGPm5aWxqJFi6iuro7p54FoMXfuXHJycmhraxs3o2oq2rzetMoJvGmR6IdqG27D5XGRnZpNl6ULj+ahOLPY1wsF8MMdP+BMHixw5bBsRSgZ4IEn8nrspqtCbd0qSknl5WirGun/ymr46ABF104voG02f25FMB/hdu8Wois1FTZsmP54H2Pe/PS0QkjJCO45na+ISPTcxYmxeRgmif9XmuDIYIdY9kNJ4tUXpWkaB7sPAvoQUXINR3qO4HTPLFFoWvQYKiFZulTcRkFEuVz+VjA9V6IgCn1RTot/Fy8ReqIgIKFPVaIUimhhMBjo7+/HaDRSX1/PNddcQ01NTVIIKElBQcG40S1ut9snGs/H6XTS47XAzZlAfESiH+rskHDe1OTXMDdvLtmp2SwvHT9a5q3OnRwvgcJ33RqeY8RmEztxMH2j0p/+JG5vuonBYSMuF5hLCsitmD4N8MwZUV0qKoKcINqUnnpK3F5ySYhhg9LKlxHCsGFp5UvSVD5J8vylJihSRMViyO75BPZFxZL2kXb6x/oxGUwsLVka03NPRG1+LTmpOTjcDo73HY/uyfQ4I0oiK1HNzeIiEEFaW0Xaa2rq9Btz8SbiMefSypdaIJpyEwGV0KdQRIX+/n7f/czMTBobG9mwYQOLFy/WbWBEpNA0jb179/L222/7Kk6BdHR04PF4yM3NJec8VTA6Our7vBSuiBqyDTFoG8RoMFKVW0VZdhnra9eTk+Y/16hzFJtLXP/uu+y+sM7jC5QoLBRDdqdCiqibb8Zk8DBnjtBd02k3TfPPXaytDW5ZMpUvpHYytw3s3v9nM6YRhBKPG9q8v5cSUYpoMTo6yuHDh4H4VqJ2794945kOoSCrUAuLFpKWEvkp56FiNBhZUSaizqMaLiFHhYM+7XxlZSLwwuOBEyci+tKyqjN//vS+7XgT8XCJROqHkqhKlEIRUUZGRti+fTvbtm0bJ6QqKyvJzMyM48piS0pKCpqmsWfPngv6saey8r3zzjs4nU4qKysviD0PljNDIrCiOLOYVJOwy50f1rF327PkDzmoypnj65cOGdn/Nd2O4YkT4uJoNsP115N71Sou+up1NGSdmvYU3d1irzM1VQRGTIfFAt5MDq4ILjldIKtQaYViyG4w9G4HW6cYLl8Wim8w8VAiKo7s2bMHj8dDRUXFhG8a0WbhwoXk5eUxNjbmmwAeC6RQ0YOVTyLfLKMac97aCmNj4g0zzItAVDEY/OIuwjHnidAPJZEi6tT017Hg8A3aTZB+KPCvVVWiFIoZ4fF4OHbsGFu3bqWvrw+TycTo6Gi8lxUXDAYDK1eupNZbOtm/fz+nvG+0drvdN1dqOitfOCmFTreTc8Pn6Bvto3mg2WfrC2R4qJvjb/6etW3w7rIrw0tD7OmB4WGxWzidiJJVqKuugoEBOHgQtmwRA5+mQerP6moxZHc6Xn1VuEHKy8VzgmZUWvlC+Ix69jfituq9YErMcJRgUSIqjsSzHwrAaDT6YtVjOXT3QLd+QiUkMUnok8Kkvn7qiXjxJErhEomQzCeRdr6I9USNit3PhKxEjXWA2z71sQqFYkL6+vrYsmULTU1NeDweysrK2LBhA1VVIcREJxkGg4EVK1awwHsxOHLkCEeOHKG9vR1N08jPz5+wMvc37yyl9evXh3XelsEWrA4r3dZuslKzGHVeKGQf/fVXeOHY8/RnwKXLwrShyd23mhqxYToVAVY+65+3MkwOrFkz9WBexgdKzJ0b3LJkKt9FFwmzSVA4LSJlz2AIvh9K80Drs+J+9e1BnihxUSIqjshkvljOhzqfeIRL6CmZT+KbFRULEaXHUAlJlGLOE0lEyTW2tUFENoxlJSpRkvkA0orBlAloYD0T79UoFAnHsWPH2L59O1arlfT0dNasWcPatWvJmK5HZpawZMkSlng37U6dOsWbb74JTGzlGxkZ4e23RVz29ddfH/K53B43Tf1NnOw/SWlWKYUZhSwqGn8dPtt3mm1v/hKnx8WpAthQF4YNbXhYVKIMBpgujXBkRCTzAdx8M6dfOMLrXM2Rhr+f9jTnzomeqMJCyM6eflmB0earV4PbPf1zAL8VPb0sBCvfW8ICaM6FincFeaLERYmoOBLvSlTguWMlohxuB8d6hZjQk4haUbYCAwY6LZ30WHuicxIpTPTYDyWJciUqEex8hYUi6hwiVI1KxJ4og8Ev+pSlT6EImSxv/NncuXNZv349FdNNQp2FLFiwgMbGRtxuNyZvs+xEImrLli24XC7mzZsXVj9U61Arx3qP4fK4KM8uZ3Xl6gusej/+zdcwuNxYUiG/ZiFz84Is8QQiq1CVlTBdn9vLLwt/3YIFUF9Pz9viYlN4/eppTyOtfMFWoY4dE0l+qanQ0BCkiPK4YdTb2xVKqqy08s15D5ji3/MebZSIihO9vb2cPn0a0Ecl6ujRo4yMjET9fPKNLC8tj+rc8IblRYPs1GzmF4pP+FGrRuk5VEIi13b8eAjbVVPj8YD3f3ViOE96RkS0L0qGMyRSTxT41ytFoEKhmBSXy8Xw8LDv++rqaq666ipWrlyJeTpb1yymqqqKmpoacnJyKC0tnbBS97J3GG04VSiArWe3MjA2QGVOJWur1pKeMr6q0tR5hH3bhAXtRBHcuuR9oZ/E6fSn8gUj9AKtfPtPYu0dw5CSQvGNa6Z8Wl+fGCuVkgLB6vIXXxS3V14p3IJBjQUdawOPC1KyhDMhGAKtfHP/LrjnJDhKRMWJXbt2ASLcIT8/P27rKCsrY+7cuWia5ltTNJFWvhVlK8Jr2owivr6oaCX0JYKdr7YW0tLEFHU5Cn2GdHSIPA2TKfids3gTsZhz50jAjKgEqkSBSuhTKIKkt7eXLVu2+BLkJHl5CTLSII44nU46OsQw13nz5jE0NMQbb7wxLnxjJiKqqa+Jpr4mjAYjG2o3UJhReMExP/71VzC6PVjTTbTnwK2Lbw39F2lrEzuGubki5XYqPB6/srn5ZnpeEE6gwoYqUnKmtnvK4L/KyuBbq6Veu+UWcYkPIrdivIMi2M9qve/A6DlIyZkVVj5QIipuSCtfPKtQklj2RUmBEnZ0aBTxJfR1RyGhb3DQ3wmqxxlREpPJL/IiZOmTlrja2un7bPVCxAbuyn6iRJoRJVF2PoViStxuN4cOHeKtt95ibGwMo9HI2NhYvJeVUJw9exa3201ubi4lJSUcPHiQwcFBtm7dSk9PD+fOnePYsWMYjUauueaakF/f6rBSnlNOY0Uj8wovrBAd6DrA/517kZFUOFLopjynnLVzwmixCIzLm469e6GzU0y8veoqunPmw6pGSq+busXB7RabksGeBsRpXn9d3H/Pe4J7Do4BcAyBwQiZITiGfFa+W4LvoUpwlIiKE3oSUbIvKhYJfXpM5pPINUUl5lxWoSorgxstHk8i3BeVSP1QksiJqBZxG4qnXC/INSs7n0JxAUNDQ2zdupXmZrHJUFNTw9VXX03uNMlqCj+apnHmjNhoqvMGMaxZs4b8/HycTidvv/02Tz75pO/xAtmsGiQuj4t2Sztzc+dyVc3EA3pfOPEC3dlwrnEeXTnw3kXvxWgI8aPx8DAMDYms8WCSF2Vp6Prrcaek0Vu/Dh54gJJ/+uSUT+voAJdLaK/CCwtqE/LMM6LwdemlYmm9vcISOCUyDClzTvAR5ZoGrb8V92eJlQ+UiIoLmqb5kvniGSohiWUl6mCXGLS7onRF1M8VKlJEHek5gtMd4eHDiRAqIYmwiJJ9RYnSDwURtPMlYjKfRFWiFIoL0DSNpqYm3njjDSwWC+np6VxyySU0NDSQotfRFTqlr68Pq9VKSkqKbzZUeno6l19+OTXexp2//vWvAFwR5IRYl8vF7178HT978WecGz6H2+MmKzVrQhsfwD9d+U+8+dE3GXFagDCtfNJjV1Ym0humI6AfqrdXVJjS02E696c8TSgJ+U8/LW4/+EERCPjWW7B/qo4Ft80/YDeUPt6+HSKIIiUbKm4I/nkJjvqLjwNnz56lu7ublJQUGhsb470cLrroIkwmE+3t7Zw7dy5qMyx6R3vpsIha9Ioy/Ymo2vxactNyGbYPc6z3WGTXePy4uE0EESXthhEauJvIlahz50Q/V9ipxLKfKDPB+qHAX4my94DLKhqMFYpZjsFgYHh4GE3TqKiooKGhgdRgPjgrLkBWoaqqqnzpfCBmWDY0NFBYWMh+7yf+4uJiLBYL2ZNketvtdlpbW3nhry+w+YebceFi8zc3c/GKiydO2tM0ePttKC0lxWyk29pNTmoOG2pDjDZ3u/3qJpim3+5u8G6ic9NNlLzzAuvqVuMomjolYmxMVJEgeBHV3Cx+RaMR7rhDuPXlkifFekYERKQVhmZB91n53g0psyfGX4moOCCrUA0NDaSnx983mpWVxfLly9m/fz87duyImoiSNrl5BfPITg1iuEGMMRgMrChdwbbWbezv2h9ZEZUIoRKSwEqUpgXfVDoJiTQjSlJUJHYFh4ZEsuCyZWG+kLTCJWIlKjUfzPngHBQVtfxw/xEUisTH4/FgNArzTkNDA+Xl5b7qiSJ07HY7nZ2dAL6q0/n09vYyNDRERkYGl1566TgBdeCA+Dzh8XgYGRlhaGiIzp5Ovv6Tr2Nz2WhY1sCKReIaPifnwv9Ov3/p+1zan0X54CB/1F4F4Kb6m0hLCTGW+9w5kcyXmRlcYsOf/yyuq42NkJuL8QN/R7HN5v2MMPnng3PnxG1R0fTp6ZJf/UrcbtgA5eWiEgVTiCjN4+/jDaUKNUutfKDsfHFBT/1QklhY+vQ4ZPd8opbQl0giauFCsXUVGIYRJpqWmJUogyFClj7fBSkBK1EA2bXiViX0KWYpLpeLPXv2sGfPHt9jZrNZCagZ0tzcjMfjoaCgYNI+MpnKt2HDBq6++mrf45qm0draypkzZ2htbWVwcBCL1cI3f/pNhm3DVFdU86P//BHpqekUZRaRYR5fGdnf/DY//cV9/L8/f46z5Rk82/wCEKaVT87vqKsLbsPx+efF7c03i4Q+m01cHBcunPQpHo+oKkFoCbeBVj7wV6I8nkmeMNYObrsIhcgoD/5E/bvEtc6UCRU3Bv+8JECJqDighyG75xOLobs+EVWqYxFV7hVRkZwV5XT6G4MSwc6Xnu6ftj7DvqieHrH7ZTAENzpDT8jK2YxElOwnSsRKFPh3I1VflGIWMjAwwOuvv05bWxudnZ0MDQ3Fe0lJgdvtpsU7QmP+FLtrgdHmsgoIQkQtX76cRYsWsWTJEpYsXcIjzzzCud5z5OXk8acf/wlHigOAqtzxzhqH28F//ejDGNweFiy4BOucUo71HsNsNLNxwcbQfpGeHrBYRNZ4MOpmYABeEIKN97+f0z9/g8MsZeTdH5xSgJ09K6aOZGSIbKpgOHwYDh4Uibi33SYem9bOZ/EKwqxakcwXLOOsfEGWyZIEZeeLMR6Ph927dwP6rETt3r173PTwSCJFlBQqesRXiYqkiDp1SkTqZGdDouxeLlki1n30qPAChInUjlVVQpslEjNO6HOOgKNf3E/YSpQauKuYfWiaxqlTpzh27BiappGZmUljY6Oa+xQhWltbcTqdZGZmUl4+ccXDZrPxxhtvABfOhzIajT4LoN1u55oPXcM7h97BbDbz6+/+muqaalrOtGA0GKnIHt9r9P1nvsTQuVPkpOfyxU89zs9OPAPANXXXkJce4n9fWYWqrg5uaNMzz4DDAQ0NsGgRZ149hYV5FFx/G5Nl9no8490cxiC1jaxCbdwIMtQw8GOd2z3+e2y9/ljzUK5XmgZnZ6eVD1QlKuYcP34ci8VCZmYmS3RUlViyZAnZ2dlYLBaORiiVLRCXx8XhnsOAPpP5JMtLl2PAQLe1my7LzKxsPuS/5+LFM+4vihkRCpeQb/6JlMwnkWsOW0RJ4ZFaCOYEjT1WlSjFLMNut/POO+9w9OhRNE2jsrKSq666isJgM6UVU6JpGqe94mPevHkYJrkmvvnmm9hsNiorK1m6dOmkr5eSksKcrDmkmlL51UO/4oarbuBkv3jTrsipwGzyDyfcfew1tv7phwBs+sCDlJTV8fvjvwfCsPJZLCIkAvzOjen4v/8Tt3fdheX3f8NiM2EsLqL0XasmfUpbmwiVSEsL3sqnaRda+eBCETUOi/dClzU3+FhzgIE9wu5tyoTKm4J/XpKgRFSMkXa51atX6yoO1WQysWbNGiA6lr6T/SexuWxkmjOZX6jf5pis1Czqi8Sn54jNi0qkfihJhGLOE7EfSjLjSlSiW/lA9UQpZh07duygp6cHk8nEypUrWb16NeZEmRKeAHR1dWG1WjGbzcydQhX84Q9/AEQVajKhBeKzy9M/e5p3fvUOt914GyP2ETpGRApwfaF/987isPDl396DW/OwcsX13Piuz9Ax0sHb594G4D2Lgp1E60XaLMrLxeCmYI7ftk2Uku68k/ZfbQWg+NqVpJgn/v0Ce4rnzTuvcjQFO3eKIllmJtxyi/9xgwGWLoXly897LccQ2HrEAdkhXqylla/yplln5QMlomKOHvuhJHJNMj0wkgTOhwp5kF2MibilT4ooHVUep0WJKF8lqrVV9P6GjG/QboJa+UBVohSzjmXLlpGXl8dVV1015Yd8RXjIKlRNTc2kbQMOh4OnvaWUDwaWUry0trdy1+a76LWIzG+TycSqhlUANPWLJtaKnApy0vwmuYffepgt7pOcXFLKFz/zczAYeP64CHm4ZM4lVOYE2WwEokFJxuUFe3H7xS/E7XXXQVkZbVuFCJtzx+WTPqWvz99yVVsb/PJkFeo977lQ382fLwpn4/7pZRUqozI0ITTLrXygeqJijhQoeuqHkkQzXEJWdfRs5ZOsLFvJb478JvIiSlrkEgEpotraxDT2SdKTpiORRVRxsfi1h4fFJmLIMedy0G4oUbF6Q1ainEPgGBSx5wpFEjE2NsbIyAilpaUAFBYWcuWVV05Z/VCEx9DQEH19fRgMBuqmsMD9+c9/pq+vj4qKCq699tpxP3t799u85x/eQ89gD26jm1/+6y99/62sDivtI2JQbGAVCo+Hr13xNXpGe7hzxZ0U5Ys+qV8f/jUQhpWvuVk0KxUUQDA2T02DJ58U9zdtYshiwvLDJzHu3U35u9dM+jRv9gZVVcG1XIGw6f1a/FpMoD8vxGWFUe9w3ZwQ55AM7AHLKTBlzEorH6hKVEyx2+2+2QZ6FlEHDx5kdHQ0oq8tBYme480lco0RsfNpWmLa+fLzhU0BZtQXJR0PiTQjShIYcy5/j5DwVaJqI7SiOJCSBWne2SezvBr16KOPUltbS3p6OpdcconPVTARTzzxBAaDYdyXHmYCKsbT1dXF66+/zq5duxiRQ3RACagocdK7qzZnzpwp/x6e9AqOD33oQ+PaHn7+25+z/u719Az2MKdsDl++88vj/ls19TehaRpl2WX+kIjOTtiyBbN1jO9v/D6XVl0KwJnBM7zW8hoAH1wejNrw4vH41U2wF7bt24W/LisLbr2V9nYgO5uyO64mJXXij+E2m1g6wCRjtCZk61bo6BCX8BtuuPDnw8OiwuV0eh8Y8e50ppeF3rt78ifids57wKy/2Z+xQImoGLJv3z4cDgfFxcVT7sLEi6qqKioqKnC73eNmYkSCREjmk0gRdbTnKA63Y2Yv1tEh3rWMxsRLV5ihpW9wULxZQ2KKKJhhzHkiD9oNxJfQN3tF1K9//Wvuu+8+vv71r7Nnzx5WrlzJDTfcQLdsLJ+A3NxcOjo6fF9nzpyJ4YoVU+HxeDhy5Ag7duzA6XSSnZ0dlURahR+r1UpHh+hVmirWvK+vjz/+8Y8AbNq0CRD2vn+4/x/4yD9/BLvTzuplq9n17C4uWnaR73mjzlHODQuL3cIiMXPplV2/4bHHPol7ZFj4sgP4vwMi5GFD7QZq8kNQKR0dQoFkZEBZWXDPkYESt98OWVmYzSKtdqqw3rNnxR5sYWFoRhBp5Xv/+0UYxfns2SM03dAQ4LbBqNeWGGoVyjEEzV6L4sJ/CO25SYQSUTFEWvnWrl2ry50ug8Hgq0ZNtcsaKsP2Yc4MiQ8Qy0uXR+x1o8XcvLnkpeXh9Dg52jPDpEJZxZk3b+J3ND0zQxElrXxlZSLdPRGZUbhEMvREgeqLAr773e/yyU9+ko9+9KMsXbqUxx57jMzMTH72s59N+hyDwUB5ebnvqyzYD1yKqGKz2Xjrrbc45S0v19XVccUVV5CZOfua4mPJqVOn0DSN0tLSSYfrgtiwcDqdNDY2smLFCppONdH4nkZ+9OsfgQZ33HAH2361jfKS8dHoTX2iClWSVUJ+ej6HTr3Fo4/ezZ9OvMAzva+LRAUvmqbx5H5R7bp75d2h/SJSjFVXB5e2a7P5/XWbNsGrr7LggxdzXdOPmCTdHU0DuecSSi+UwwG/9bYoTWblGzcrytICmgfSCsVXKDT/HNyjkLcMSq4M7blJhBJRMUTP/VASOS8qkn1RMlSiMqeSwgz9x8QaDAZfNWrGfVGJGCohiZCISrQCXCBhiyjnMDgGxP1EtvOBvy9qloooh8PB7t27ue6663yPGY1GrrvuOt56661Jn2exWKipqaG6upr3vve9HD58OBbLVUxBT08Pr7/+Ov39/ZjNZtasWcPy5cvHDXJVRB6n08k5bxDDgmlsCdLKJ6tQeOB002kyMzL5wT//gF9//9ekpY7fkBxzjo2rQu09sZVvfvsGnPYxamtX8f67vj1O8Lx17i2a+pvIMmfx/qXvD/4XGRsTA3ZBiKhg+NOfhC2jqgrWr4ff/AZ27cKwZ/ekGqy9XWiv1FSoqJj4mIl46SUxz7e8XJxqInwiyukKcEuE2LSsadAkouKp/4fEGd0SBXT1zrF161ZuueUWKisrMRgM/P73v5/y+I6ODu68804WLlyI0WjkC1/4QkzWGS6JIKKiUYmSQkSm3iUCvoS+TiWiwu2Jkn1EiRgqIZECMGQ7nxQcaUWJ7xWXlahZOnC3t7cXt9t9QSWprKyMTtm0cB6LFi3iZz/7GX/4wx/4xS9+gcfj4bLLLvN9kJwIu93O8PDwuC9FZOnr68PhcJCXl8eVV15JRSifUBVh09raitvtJicnh6KiokmPO378OO+88w7GNCPvv0OIm/r6ep5+5Gl2P7ubz9712Qmfd7L/JB7NQ1FmEcdO7+SBf9+IbWyEyjmLeeBrfyU1NWPc8T/f93MA3r/0/WSnhvD+LKtQxcUiPzwYZKDEhz+Me9hK9//9FQ0mLRV5POMNLKHo+5/+VNzeccfkcejy9QxjZ8HjFNen9BCr5F2vwfBxSMmGurtCe26SoSsRZbVaWblyJY8++mhQx9vtdkpKSvjnf/5nVq7U9wf0oaEhjnn/MvQsoi6++GIMBgMtLS1T+v1DQVaiEkpElUco5lxWcRIpVEIi0wRPnRI+gRCR1ZtE7YcC/9rPnhWptkHjs/IleD8UqJ6oMFi3bh2bNm1i1apVXH311Tz33HOUlJTw4x//eNLnPPjgg+Tl5fm+qoPd6VYEzaJFi1i2bBlXXHEFWcHM9lHMGE3TaPEGMUzXC/6/P/tfKAStRuNPu/7ke/zWW25l8fyJ021tLhtnh84CcG74HP/4g/fiso1SV7OSh76+jYLc0nHHjznHfKl8IVn5NG28lS8YenrgxRfF/bvuouMHz/KOdRnbq/8errlmwqe0tMDoqOiZmjcv+OUdPw7Pi8R2Pv3pyY8zmQDNg3HUu8uZPT/0SlKT9zN63SYw50x9bJKjKxG1ceNG/vVf/5X3ve99QR1fW1vL9773PTZt2kReXl6UVzczdu3ahaZp1NbW+qJU9Uhubi5LvBWISFn6DnR7483L9B9vLpGC70DXATRNC/+FErkSVVkpOlrd7rCSFRI53lxSWir6uTRNpNoGjS/evDYKq4ox8newtIh/iFlGcXExJpOJrq6ucY93dXVRPllTw3mYzWYaGxt96WQTsXnzZoaGhnxfrec1witCp6+vj507d+LxeABh1Z43b56y78WQzs5OrFYrKSkpzJkkScHj8fDjX/6Y7zz3HSgGDY2//O0vuD3uaV//9MBpPJoHs9HMPX+8h7dL7NQsv5z/+Jet5GRf2D7w/PHnGbIPMTdvLutr1wf/i7S2CnVjNgfvsfv1r8HlgtWrYckSzv7PXwEo+cjNEwoXpxNOnBD3Fy0KfrguwHe/K96eb7ll6o8bJhOkax1oLhuY0iCzKviTgAiiOCcGIVP/mdCem4TMuneSeFkmEsHKJ4lkX5SmaRzqPgQkRry5ZFnpMowGIz2jPXRZu6Z/wkSMjPgH8iViJcpg8FejwuiLSuR4c0lgzHlIfVE+r3lthFcUB7JqAINoIrb3xHs1MSc1NZXVq1fzyiuv+B7zeDy88sorrFu3LqjXcLvdHDx4cEr7WFpaGrm5ueO+FOFz6tQp3nrrLTo7O30hEorY4na7fb2A8+bNGxdXLnn2xWdZesNSPv3Ap/EYPeCGRzY/wu++9ztMxqlVhMvj4szgGVL7h7io8iKeev9T3LHqTr77j6+SkTnx38/P9wsr310Nd2E0BPkR2OXyb4guXBicutE0ePxxcf+uu7D+aQt9Zy0Y0tOp/ux7JnzKqVNCSOXkBF/sAujuhp+LX4svf3nqY00myPScxuNBOCWC/TeQnPwf0NxQehXk6z8oLNrMOhEVL8tEIoko2Rcl1zwTzgydYdg+jNloZlFR4giJTHOmb1hf2POi5JZSSUlwA/n0SJjhElarSIKFxBZREGbMeTLMiJKY0sQke5i14RL33Xcf//M//8PPf/5zjh49ymc+8xmsVisf/ehHAdEEv3nzZt/xDzzwAC+99BKnT59mz549fPjDH+bMmTN84hOfiNevMGtwOp3s3LmTI0eOoGkaVVVVzAvFF6WIGCdOnGBsbIzMzMwJAyXu/tzd3P7F2zl+9rgQNP3w0fUf5fMf+XxQCcZHOg7Su+1lyg+3UNJt5ab6m/jlbb8k1ZQ64fEdIx389ZSoBm1auSn4X+TkSeHnzsoKPi7vtddEnnhGBtx5J2cfeQ6AkndfQkbZhQLP6fS7HRYvDs1h94MfiOWtXQtXThOUV1k8QF3VIDk5xtCTYz1O/2yo+on702Ybs05ExcsykYgiaseOHT4bRLhIAbK0ZClmk3nGa4slvr6ocMMlpPBIRCufJEwRdfq0uM3PF0PdE5mwEvqSSURBQF9US1yXES8+8IEP8NBDD3H//fezatUq9u3bx1/+8hdf2MTZs2d9M3AABgYG+OQnP8mSJUu46aabGB4eZvv27SwNiFlWRJ6hoSG2bt1KZ2cnRqORhoYGGhsb1QyoODA6Ospp74Vg2bJlmEwm3G43w6N+988Nl91ACincds1tGM8YoRf+373/L6jX39f0Bt/412t54bXHMJtSCUZz/PLgL/FoHtZVrfPNkpoWm81/QVuyJPikh//4D3H7sY/hKSqh9fqPwdpLmPu59054+OnTouCVmxv8+CkQDkMZI/CVr0wvvkoymqmsgJyyOTCJ2JyU1t+BrRPSy6Hq1tCem6RcWFtNctLS0kiL8byezs5OWltbMRgMrF69OqbnDocVK1aQnp7O4OAgTU1NLJqBFU0KkESy8kkaSht45vAz4YdLyPL/4okbYhMCKaKOHAnpackQKiEJK6HP1xOV4DOiJFm10PPmrK1EAdx7773ce++9E/5sy5Yt475/+OGHefjhh2OwKoWko6ODPXv24PF4yMzMZM2aNbrvlU5mjh8/jsfjobi4mPSMdL71/W/xP7/9H2559y08+lXxqf8Dd3yAyy67jF89/Suesz3HJZdcwqpVq6Z8Xbfbxf89/y1+/9y3cbtdpGYX4Fp7MUwTWqFpms/KF1KgRFOT6AsuLAy+F2rPHpE3bjLBl75EWxvYlzaSflEjZVdceLjL5a9C1deHVoV6/HHo7xchFNPGCbhtMNYu7ocTeiRjzRd8MnQBlqTMukpUPJBVqCVLlpCTo/8kE7PZzEUXiUngM406P9gtkvkSUUTNOKEvkUMlJHLtJ06I7NUgSYZ4c0nIlSjHIDgHxX1ViVIoYkJubi4mk4mysjKuuuoqJaDiyPDwMOfOneP0udM88tQjzLlqDvc/ej+tXa288NILWOwWAEwmE3Or5/KTnwiL2Kc+9akpX/dQ8w4+9c8refY3D+B2uyipWcL/+8qzNCzbMO2atrdu51D3IdJMadyx7I7gfpHRURHNCqFthv7nf4rbD3wA6uro7RXf1tVNXMhqbhZ2vuzs0OZCud0iUALgvvuCaNWytOB0alichVidIf59DB6G7tfBYIIF94T23CRGV5Uoi8UyLr2oubmZffv2UVhYyNy5c9m8eTNtbW2+YWwA+/bt8z23p6eHffv2kZqaqivbhBQi0iaXCKxdu5bt27fzzjvvcNdd4c8BkHa+FaWJk8wnkQl9R3uOYnPZSE9JD+0FpIhKxFAJSV2dmPg3NiZGqE+z2ydJhkG7Evk7nDkjkt5Tp9uAk0IjrTjxZ0RJ5K7lLK5EKfSH0+nEbBY28aysLK688koyMzOD6qdRRI+HH3uYJ//0JKc7T/seqyqv4uO3fZzPf+TzZKf53xdffvllmpubycvL4wMf+MCEr6dpGl/f8nV++PK3WXfGTWpqOouvvI1Lr/h7LqoLLtzl37f9OyACJQoygvSYy83DkhKYYr7VOE6dEgN1Ab76VXjmGRp37aLmg58lp+ZCZ4LT6d90DLUK9bvfCRtgYSF85CPTHOy2gbWZ/n7Y3zqPwjmwZk3w5/JVoareG3qiXxKjKxG1a9cuNmzw7yjcd999ANx999088cQTdHR0cFbuCnhpbGz03d+9ezdPPfUUNTU1vrkEeiARRVQkEvrGnGM09QsPVCJWoqpyqyjMKKR/rJ+jPUdprGic/kkSl8vv/0pkO19KikgjOnRI9EWFKKKSoRJVViZ2CC0WccGa9j+nz8qXBDOiJDJlUM2KUuiEc+fOcfDgQdasWUNJSQmAmv0UJzRNw6N5MBlNOBwOXn7lZU53nsZgNHBZw2V87q7PcfvG2yfsTZOz0zZt2kTmRANsR0Yw5OTQP9ZPX5qbinXvYuOld5GSm8f8wvlBbW4e7DrICydewICBr17+1eB+qcFBf7puKNfw73xHCK8bb4SGBvjYx2DPHgqLiqDxHy84/ORJfxVqkgT4CdE0+K//Evc/+1mReTElQ4fB4wJzAXZDeSjGEnCOQLO3eFH/DyE8MfnRlYhav379lDN5nnjiiQsem9EMnxigaZrPzpdIIkqudf/+/djt9rD6yA73HMajeSjOLKY8O7h5KnrCYDDQUNbAlpYtHOg6EJqIam4WZYuMDJhg9ymhWLLEL6JuuimopwTurCU6BoOw9O3bJ36vaa+nyRRvLpGC0HoGNE/osbgKRYRwu90cOnTIt6Ha2trqE1GK2HLi9Al+8uuf8Ju//IbvPPAdbr/6dlpbW3nvde+lvKmcBzc/SH3d5BeB9vZ2nvdOiA208mmaxmt7nqW6zUK9sRjWr+fb136b6+ddz6LiRTT1NZFiTGFBYXBNt/+5Xdjrbl96O/VFQVyUXC7YvVsolTlzREJSMHR1+WPN//EfsW3dgXHPQVLT0uDjH7/g8PMzK0KpQr35JuzYAWlpMEmrZsCJemG0HQwG3LkrxO30I7j8tPwCXBbIXQRlEw8Jnq3oSkQlI6dOnWJgYIC0tDRWrEgcS1tdXR3FxcX09vayf//+sATgwS7RD7WybGXC2isaSoWICrkvKnCmRKIPdpSqQf5O02C3+23kyVCJAr+ICqovSlrekqkSlVklvPAeB4x1QGYIW6YKRYQYHh5m9+7dWCyip2bRokXUJ8NOTQLR3tXO4799nN/85TfsP+G/Lj79wtO8Z917OH36NIsXL+YDH/gAc+fOnfK1/vd//xe3280VV1zBsmXL0DSN7ftf4Klnv8HZU3tYWdbAv173bRgcJLeqioayBt/cyWWlyyaNMg+kZbCFpw8+DcA/Xn5hJWhCDhwQ/VAZGRDK57bvf18oo0sugauv5vDGf6aL61j53sXMKS6+4PDjx0XRqrAQgpzb7eOhh8Tt3XeLofCTonlgSHwWI6sWo030QgUtotx2OOIteS34TGhKbxagRFSUkVa+VatWkTptM4V+MBgMrF27lhdffJF33nknLBGVyP1QEmlDDFtEJbKVTxJizHlLi7gwZGWFFtWqZ0IKl0jGSpQxBTKrxe9maVYiShFTNE2jpaWFI0eO4PF4SE9Pp7GxkeIJPpgqokNXVxe3/cNtvH3k7XGjT1YuXMmd776Tj97+UXbv3o3NZiM7O5s503jT3G43//M//wPAJ+75OL977Ue88Ofv0X3uOAApphTy6lfgXH8V5qwcRp2jHO4Rg3sXFy9mbt7UAk3yne3fwa25uX7e9ayuDCIdubcX2tqEWLjoIjAHOZplZAR+6O0b+sd/ZGT7Qdr/ehAwkfPZC2dSDQ+DnLATagv/sWPw/PNiid6ul8mxNIPTIub95S7C5BQPBy2iTjwqbNwZFbBAzbk7HyWiokwiWvkkUkSFm9B3oFuIqETsh5LIhL4DXQfQNC34iloyzIiSyN8hyEpUYLx5smxahSWikiWZT5JdJ343awswQU6vQhEl+vr6OHRIVCDKysoSblMyEeno7uCdw+/w3vXvxWAwUFxczLHDx/BoHupr6rntutv4+N993GfXa25upre3F5PJxJo1a6adzfWHP/yB1tZWsi7J4l+6/pklL7WR6oaUFDOrL76Vu2//FtUV/lCmlsEWNE2jOLM4OEse0G3t5qd7fwrA1674WnC/uPSi19SIElGw/OQnoo9q0SJ4z3s4sfYzgEbFuxrIvWrVBYcfOiTcgpWVoc9S/MY3xO173ztNbpXbDiMnxP3cJWA0+xL8ghJR9n44/K/ifsO3IEX1HJ6PElFRRgqQRBiyez4zCZfQNC2hZ0RJlpUsw2gw0jvaS6elk4qcIPNHk6kStWiRUEN9fdDTI5KKpiCZ4s0lUkQFNSvKJ6ISvBfufLLqgNdUQp8i5hQXFzN37lzy8vKora2N93KSlubWZp56/in+8Mof2H10N3n5eRx96ShlOWWYTCZ+9t2fMbdmLo3LxvcHezweX7Ly0qVLpxzl4na7cHZ38NwDDwCw4foNvGB9gYo5hdy99EPc8e6vUlw4Pv3N7XFzdkh4xOcVzAv69/n+O9/H5rKxds5aNtROH4POyAh0d4vr3bzgz4PN5s8a/8pXGHnmz7Tv6QBzKgu/c2Fse3u7uJwajaFXofbuhV//Wizxm9+c5uDh4yJMIjXPl6gXkog6/G/gGIC85VD3kdAWOktQIiqKOJ1O9uzZAyRuJQqgqamJ/v5+CkPYlemwdNA31ofRYGRpiX7i5kMlw5zBwqKFHOs9xv6u/cGJKE0TZmdIjkpURgbU1oqwjKNHpxVRyTRoVyJ/l5YWkaQ0qcPDMQjOYXE/GStRoBL6FFFndHSUo0ePsnz5cl+o0cqVK+O8quTkaNNRfv7cz3nh9Rc4fPowBGR1FWUV0dXdRVmO8GW/96b3TvgaZ86cwWazkZGRMWEflKZpHDy1nZdff5xdu/7AhqxLGdq/n0qzme/d8z3e1/s+7lxx56RJe20jbTjdTjLNmZRmTdUA5GfYPsyjO8VQ369d/rXgXCRyB7C8PIi4uwB+9COhjKqr4cMf5vDrdnhPJxXlGrnLx/97uN3+2fX19eLyGgr/3/8nbj/4QRH+NynOYRj1NifnLffZQtLTxfVs2qwwy2k48d/ifuNDYJxuCNXsRImoKHL48GFsNht5eXkJ2fxaWFhIfX09TU1N7Ny5kxtuuCHo58pQiYVFC8kwh/guoTNWlq0UIqpzPzcuuHH6J/T2ihHiMtYtGVi82C+irrpqykOTUURVVIiL3diYEFKT/jnLKk1aCaRMENebyEhRqCpRiijh8Xg4ffo0J06cwO12YzAYfIPfFZHB7Xbj8rhIM4tP0T/88Q/5wR9/4Pv5knlLuPmqm9l02yZWLJq+n9npdNLkLdHX19djDAhSOn5uPy+99lN27voDAz2tvsf/1vM6LcCtH/wg86rnMa968qqPpmmcHhARdrX5tUFb6h/b9RiDtkEWFS3ivYsnFn/jsNlELxSEZqMYGYFvf1vc//rX6RpMo8eehvGeT7Bk/YWHnz4triMZGaG7Nd54A/78ZzF5ZNoq1NARsaGbUQFp/g3wtLQg93b3/RN4nFB+PVQG/9lvtpHgsWH6Rlr51qxZM+6NJZGQ1ahQ+6JkqEQiW/kkcuhu0OES0so3dy5MNPciEQkhXCKZZkRJjMYg+6J8oRJJlMwnUZUoRRTp7Oxky5YtHD16FLfbTVFREQsXLoz3spKCEcsITz73JLf9w22UXFrC/77wv76fffC2D7J2yVoeuPcBTr50kiN/PsJ/bf6voAQUwKFDh7Db7WRlZVFdXQ0uF5qmcfH/XMzaR1fx0p9/wEBPKyZTCkuXb+DWW7/Fb38/wiHg3q9N36d0dugsI/YRzCZz0GESXZYu/u2NfwNEL5QxmJEMx46JRKSiotCalL73PbFxWl8PH/gAVouG0SjcgOcXswIH6y5Z4rfWBYOmwT/9k7j/8Y9Ps0k51gW2HjGKIi8MJ1Dv23D214ABGv8r9OfPIlQlKorIUIlE7IeSrF27ll/+8pch90X5QiVKk0BElYcpopLByieRv4v0IUyCyyUqNZBclSgQv8/Bg6IvauPGSQ5K1n4o8Ee2j54TPnujunwoZs7g4CBHjhyhr68PgLS0NJYuXUpVVdU0z1RMxemzp3nmT8/wp9f/xI5DO3A4Hb6fvfzGy/zD+8TQ1Msuu4x3fh963zMI4Xvy9AlOdb6NSevlGrcbSkowrFpFbX4t+zP3U7v8Etavfj/XX34XudlFfOYzn0Fzw7vf/W6WTHONdLqdHOsV19NFRYswm4JLytv8ymaG7cOsrljNXQ13Tf+EwKi8UK7b/f3+ibcPPABf+hLzTpyg7D8fJa3+QvFy6pQQUjk5IlAiFP7yFzEbKj0d/uVfpjhQ88Cw9zqdPW9CR8ToqH8dF+zvaxrs/bK4P+8jUKBstFOhroJRRFZvErEfShJYiQolnc4Xb16WuPHmElmJOtZ7jDHn2PT2xGQKlZAEmdDX2irenNPSINk+A0kL35SVKEuLuE2mGVGSjHIwpoHHDqOtyVltU8Sczs5O+vr6MBqNzJs3j/r6elJS1EeTmXDy5Enqr6uHgL6X0sJSrrnkGm57123ccu0tYb+2pmk0nd3Htt2/4/U3fkN/zwk0b+T5nQWXM8f7GeGh6x/ip7f8lLz0PN9ze3p6eOKJJwD48pe/PO25jvUew+F2kJOWQ01+cBtT75x7h8f3iYG3P7jpB5iC6eU5LKLTQ47K+8//FAKsoUEEMH3oQ+DxkGXvv+DTtcMhHPHgz2oKFo/HX4W6914x/3dSrGdEpLkxFXIm9p2/8YZYz9VXQ27ueT8893vo2QamDJHIp5gS9U4VJaxWK4e9f5iJLKJWrVqF2Wymp6eHlpYW6uqm/+DkcDs42iNsX1KAJDKVOZUUZRTRN9bHkZ4j08+akJa3ZBRRra1gsUB29oSHSYExb17izxg+n9DsfLVRXk0cMBhFhW3khOiLUiJKEQZjY2O4XC5fgtv8+fNxOBzU19eTEWqX/Synf7Cf5/7yHH987Y8Ysg08+1/PYjKamD9/PlUFVeSW5/Kuy9/F7TfezmWrLwt/6L3bDSYTTx98ms2vbKZm/xly7f4f5+eXs2T5elwXr4YF4vo4kej54Q9/iM1mY82aNVw1TW9tt7WblsEWAJaXLg/KkufRPNz753sBuHvl3Vxaden0v9u5c8KOZzSGVoXq6BDDdQHtgW9x4J5HqfVkk3fHjXDFhSMgTp0STo28PNFjGwq//a0Y9p6TA/841bxgj1Mk8gHkLZ7ULWA2CxHlcp33A7cD9n5V3F/yZTUPMAiUiIoSe/fuxe12U1FRMe3QOT2Tnp7OypUr2bVrF++8805QIup473GcHie5ablBe5j1jMFgYGX5Sl5tfpX9XfunF1HJaOcrLBRj0bu7xe+3Zs2EhyVjqIQkJBGVjJUoEMJp5IT/91QogsThcNDU1ERLSwt5eXlc4f2gaTabaZgyZkwh0TSN3Qd38+xfn+WlbS+x7/g+3+DbrKws2ofaqS6oxmAwcGL7ibBFqc0xyo4Df2H3wb+yMX8Ni1Mr4cYbyU7N5szQGXJyzdRSS3lZAzdevYn1l90yrUAbHh7mBz8QARZf/vKXpzze4Xawr3MfAHUFdRRnBjdU+Wd7f8au9l3kpObw79f9+/RPGByE/V6bfn19aD3M//ZvIiFi3TqaXz7J2V1ddJiv5rp/+48LPlj39Ph7oaac6zQBLpffvvflL8OU86UHDwohZc6BzMk/e8lC7wUi6uSPwXIS0kthyVdCW+gsRYmoKCGtfHLWUiJzySWXsGvXLnbs2MHf//3fT3u8z8pXuiL8nS+d0VDawKvNr/p+t0mR8W0Q+rul3lm8WIioo0enFVHJFCohkXa+5mZx8bnAcaRp/uS6ZOyJAr84VAl9iiBxu92cPn2akydP4vJ+ajMajTidTsyTzgpQTMQNd9zAywdeHvdYVVkVGy7ewK3X30p5Trnv8VAElNPtZPfxLeze9yJHj27lXMsB3G7x36p46QCLV94FAwNsqNvAr9/za1LbUzF5TEIMr7siqOv8N77xDXp7e1m4cCHvf//7pzx2f+d+7C47OWk5LCkObjNyYGyAza9sBuCb679JeXb51E+w22HnTuGVKyubInJ1AlpaxHBdYPiaWzn6b68ARpb8x0dIWVA77lCrFXbvFpeH6mpxqlB44gk4cUKIpy9+cYoDrWdgtE34BPMbpvQLyj87pzPgQXsfHPJG/q14QAgxxbQoERUlkiFUQnLJJZfw6KOPBh0ukUzJfJKgwyWamsS7ZUGBqNwkE0uWwNatU/ZFyd22BEz0n5bKStHUa7PBmTMTCEXHALhGxP1kmxElkTZFVYlSBEFnZyeHDx9mdHQUgLy8PJYsWULJNLPmZjOaprH38F6ee+k5XnnrFX72yM9YMkcIiTXL1vDakde4aPFF3HjFjbx/4/tpWBzGddbjgaEhyMqi2drGih+toKTbyvJu/yFZOYXUL1pH9cW3wMXXQ3o6mR4PJYMlWDwWCgsLWbt2bVDJw4cOHeL7Xuvb97///Sl73s4MnqHT0onRYOSiiouC62kCvr7l6/SO/v/s3Xl8VNX9//HXzCSZ7Alk39j3LWCACEipiqJSLFqtW6t1a/ULrUptXepaq2jdqyjVWrefe6toFVFEAVFkD/u+hS07ZF9n7u+PmwlEAiSQzM1k3s/HI4/JTO7M/dwE5sz7nnPPKWBA3ACmjpx6/I0NwxwfV1VlDk0fNqxlFyk9+CDU1lKbeQbLnvoWN3bif3kmXW+7uNFmLhcsXWqGlU6dTrCuUxMqKg5PZX733eZwvibVlsChdeb3kf0aTWnelCZ7opb/wQxSUQOh5/UtK9SPKUS1kY4WogBWrlzZrLOHnpn5OsL1UB6eY1mTu+b4E2x4rodq6ZWjvqAZ05x35J4ou908rvXrzeM86hg9wSI4AQI66LUdnp4oTXMuJ5Cbm9vQDgYHBzNgwACSk5M7zOiE1lRcWszHcz/ms/mfsWD5AnILcxt+9r9v/kf/X5nvvX+65U/c+cc7iYz48WwAx1dVU8HKjV+zav08Nm/+jj62WKZm3Azp6XRN60qQIwgjNpgBiekM7n8mI9LPp0/X0476W+3YsYOysjKcTicjR45sVk+iYRhMnToVl8vFRRdddNz1JitqK1ifb15L3i+2H5HO5h3nurx1vLDsBQD+cd4/TjyL386d5qgKu90cVdGSHtFVq+CNNzCAVb/4GxX5HxEaZuO0V39/1KZbtpiXEAcHm7tp6XXCDz5oXrKVlgY333yMjdwuKFxuzsoXnADhJ258PSGqoSdqz4ew+22wOeD0VzXzagvoN9UGioqK2F5/Sn74MYY9+ZLevXvTqVMnDh48yNq1a0+4+GFHmpnPo39cfxw2B0WVRewv3U9K5DGuc+uI10N5DKifsvUYIcrtPtwT1RGviQKzh239erPD8ajPAg3XQ3XzclVepAV3pZni4+Pp3LkzMTEx9O7dG0dLFsXxIx/O+pDL7r6MOtfhboEARwBD+w7l3DHncuHoCxse79SCmePm7ZjHdxu/JOf7L9ifvb5heB5AWWgsxul/wFZXh91mJ+umLFIjU487eUNFRQVbtmwBYMCAAc0eivnuu++yYMECQkJCePrpp4+77fq89bjcLmJCY+jR6dgL8B7JMAymfTENl+Hi4v4Xc3aPs4//hJKSw23YoEHH6d5pgssFv/0tuN1snHAbuf3GYX/8dIYPKycwtPHvo6TkcHs4ZIgZpFpi1Sp48knz++efP87zS7dCXTk4gqFz83rUPH+6ujrM9aSW3mQ+MOAOiPH9E//epBDVBjzXQ3nCh6+z2WyMGDGCL7/8kqVLlx43RBVWFLK/dD9gXhPVUQQHBNMvth/r89ezOne1f4YozzFt22ZO7RMU1OjHBw6YoyMcDnOd4Y7ouJNLNExv3kGvh4LDM/JV7gdXldlwiwDV1dVs2bKFgQMHYrfbsdlsjB59CjPCdTD5hfnM+nIWsxfOZvjI4fzl2r8AMHTIUOrK6khITWDc8HFM/OlELhx/IdGR0c1/7YP72LptCaND+5rD07p25Y9f/pH1+1dzXv35jtCwaLr1Gs7AvmeQMXgCdB/Z0DVyogmgDMNomCwrJiam2Wt4lZaWNkxlfvfdd9O167HfGwsqCsgpy8Fms7XoeurZW2czd8dcghxBPH5OMxaGXbfOPOOXmAjHqadJzz8Py5djhEdQcevdUAvpI51EpTobbWYY5nwVhmHOxNfS66Dq6uCGG8zM9stfwoUXHmvDciirT2rRg8DevGAbF2e20zExwPKpUJ0PUYNg0H0tK1QUotpCRxrK5zFy5Ei+/PJLlixZwk033XTM7Ty9UD069SDC2bEuTBySMMQMUTmruaD3BU1v1BHXiPJISTHP2pWWmqfYfhQUPcGiW7eWjY7wJZ5rvbZubeKHDdObd9CZ+QCcsRAQZjbe5dkQ2cfqiqQdyM/PZ9WqVVRXV+NwOBhQ32vtzwHK5XKxYMkCPv7qY75Z8g3rd6xvmEnvQPkB/nz1nwl0BNKjRw82zttI3159m/X7MgyDHXvXsmL9XDZsXsTO7SsoKthDgM3Bu5e8izM+Cbp25ZIBlzA4YTATRg9mZL/x9O467KT/Hjt27KCoqIiAgACGDh3a7Of99a9/Zf/+/fTs2fO460K53C7W5ZnX9HSL7tbszw61rlqmfTkNgNtOv+3EvVc5OVBYaIbHQYOadxAe2dnwFzP42srLyFj8PAVT7icu/ujf6c6d5sR/AQEt3w3AM8/AypXmdVT1l5I17dDa+mF8cRDS/HnTExLqg93u9yH7fXMY36jXweE84XOlMYWoNtBRQxQc7mU7liNn5uto0hPSeWfdO8eeXMLths31azR0xBBls5nHtWyZORziGCGqow7lgxP0RPnDcD6bzbwuqnidOaRPIcqvGYbBtm3b2FR/8igiIoK0tDSLq7JeTU0NKRkpFNQUNHq8S1IXxg0fx+RzJjeaMKFf7+O0F2632cUfGsoT3z/BE98/QfrqXIJcjTeLjksjNzaYLvXLkNzzk3ta5VgKCgrYWD/8beDAgYQ2cxrwr7/+mqeeegowJ5MIPsZ4NLfhZvn+5ZRWlxLkCKJvTPNntX1h2QtsKdxCfFg8d4+9+/gbu92wYYP5fc+e0JLp3w0DpkwhrzyUOMqxGQa2yoomA1RZ2eHRgv37t3wY3/btcF99h9CTTx6nF6vygDkUz2aH6JP4vFWZC8v/z/x+4F+g8/Ev05CmKUS1MsMwGoKGLy+y+2OeY9m4cSMlJSVEHrXMtakjzsznccIZ+vbsMac4DwyEZqyn5ZOODFE/4hn/3REnlfA47jTnDdObd9C/vUdYNzNEaXIJv1ZXV8eqVavIyckBoEuXLgwaNMivrn2qqKxg9vzZfPbNZ+w/tJ8Pn/+QsKAwgoKC6J7UnfLcck4ffDrnjjmXi869iL49TxwQDpbksXLDPNZuWsC2bUv5y5ApJHXqAuecg91mJ7c8l5LwAIZG96dXr5EM7j+O4YPOpXNkC8eMNUN1dTUrVqzAMAxSU1Pp0sxx2gcOHODKK6/E7XZz3XXXccEFxxi5AazNXUteeR4Ou4ORKSNPPClEvcKKQh5Y8AAAfzvzbyeehGLHDnO+caez5Wf63n2XzZ9uYQuZdCGb9CevgWnTjtrMM+mf220OmevWrWW7MQz43e/MjxFnnQW/+c0xNnRVw8H65VYiepmjA1rA7TJw//B/BFQXQnS6GaLkpChEtbLs7Gxyc3MJCAhg2LBhVpfTahISEujatSu7d+9m2bJlnH120xdvrs1bC3TcniiALYVbqKitIDTwR2fkPEP5evduYhGhDuI4M/T5Q09USorZBldXm6M7enhGjxjGET1RHfiaKDg8XFGTS/ityspKli5dSklJCXa7ncGDBzf7A7Yv8yx2O+urWXy9+GtWbFxBTW2N+UMbbNm3hWHdzXb/v//+L/Hx8TiDjj9EKqcshznb5rB1yefs27iEwrzdjX6+MX4dSZEpUFPDLwf+kpEpIxmePJzggLa/HnHDhg3U1NQQGRnZ7AWR6+rquPLKK8nNzWXw4ME899xzx9x2f+l+souzsdlsZCRl0Cmk+deQPzD/AQ5VHWJIwhCuG3bd8Tc+dKjx9cotaJ/rtu0i6+p/cIA+YLMT8uQjcNvEo7YzDLNZPHjQPI+afhKTE7/+OsybZ/Ze/fOfx5kj4uBqcNdAYCREtHw9kdL17xF14EPcBGAf9Ro4gk74HGlaB/2kZ53ly5cDMHjw4JNeLby9yszMZPfu3SxdurTJEHXkuGZPr01HkhieSFxoHPkV+azPW8+IlB8N1zyyD7+jakaI6sg9UZ5pzjdsMI+3IUTVFEFdmfm9v4QorRXlt1wuF5WVlTidTkaMGNEhJlBqjt9M+Q1vzHuj0WOdIzsz5rQxTBgzgW4x3RoeT0s9elhjZVUZWZsXkFIXQhd3BJx2Gov3LObaj69lYC50P1T/mjGpdO0xjP59xtB36M8h2VwyIzUoldTI5k3qcKoKCgrYu3cvAOnp6c3uYXzwwQeZP38+4eHhfPDBB8cc/ldZW9kwcqV3594khDe/J21D/gZeXP4iAM9MeOb4a0nV1poXGBmGudhfC4ablpYYrBh+K6V1sdhtMPiNP9LlVz85arvqanM2vfx88/6gQS0bLQiQm3u4c+vBB49zMrJ8N1TlmsP4Op9m3rZEZQ4Rm6cAsCvsXnp0Gtqy50sjClGtrCNeD+UxcuRI3n///WNeF7WtaBuVdZWEBITQs1PH+yRts9kYkjCEeTvnsSZ3zdEhynOmq2/zx3T7HM+1Xps2mWMW6md3MoyOP725R69eZojauhXOPbf+QX9YI8ojTD1R/i48PJyRI0cSEhLS4U4WVlVXMXfRXGbPn838pfN58q9PckGmORztjIwzeHv+2wzrN4yzR53NhWdfyOnDTj/mhA178raxct1cNm79nh3bl5O7fwtut5srB11Bl8FXwKFDjE4bzZi0MZyZnsGYmGFkDBxPXGfvBKWmeIbwFRYWAuYwzejo6GY994svvuDhhx8G4KWXXqLvMdrCqroqftj7A7WuWqKDo+kd0/zelNLqUn714a9wGS4m95vMmd3PbHpDwzCHC2zebKackJBmr3ZrGOaQ7Y1vrsBd7MZJNcOfuZrOTQSo2lr49ltzCJ7DYQaoZk5e2Gh/U6aYvVjDhjU5UtBUeQAOmetoEdUfAls4eZerBr67AnttEcX2YWxz3kXzJpKXY1GIamWegNERQ5Rn0d0lS5Y0+XNPL9TA+IHNXmXc16QnpDeEqKP4Q09Uz57mUIiKCvMasPopYgsLzXUxoONeDubR5OQSDdObd/CDhyN6ohSi/Mm+ffsIDg4mJiYGgM6dO1tcUeswDIOsDVl8/NXHzFs8j+UbllNVXdXw8zmL5jSEqKuuuIorr7iSsNAmrkFxu6G4GMLC2FOZy6hXRhG0ex8D8xtvFhbRmYrYKBg4EEJDSQgJYdF1i9ryEJvN7XazfPlyioqKsNlsJCQkNMy0eCI7d+7kyiuvxDAMbrrpJq644oomt6uqq+K77O+oqK0gOCCYjOSM465PdaRaVy2XfnApq3JWERcaxzMTnjn2xuvXm0kIICwMMjKaN23sF19QFxrFlhXRuB95lHhySb9yEMF/+FWTm2/ebAao0FDIzDRnmG+pe+6B//7XDGH/+lcTow0NN5RsgtL6M5XB8S1vawwDlt0MefMxAiJYGfT/qHV10Gl0vUghqhW5XK6G4XwdaVIJj9NOOw2Hw8GBAwfYu3fvUWtFeCZcGBLf8SaV8PBMmNHk5BIdeXpzj8BA85qvjRvNr/oQ5QkUaWktH8bgazyTSzQKUZ5AEd7N2+V4n2f2weoCqC2DwJP41CA+ZefOnaxbt46AgAB+8pOfEBbWsgvZ2xuX29Vwou+rr77i3JvPhSPO+0WFRzEqfRTjR4/nF+f9ouHxI4em7cvbTtbGb1i/6Vt27FzBwKA0fj/i/2DoUJJTkimpLiEw1E5Cck969MhgQJ8zOG3A2aQlNW8qcyts2LCBoqIiAgMDOeOMMwhvZiIoLy9n8uTJFBUVMWLEiGMuqmsYBisPrKSitoKwoDBOTz396GuLj8EwDG783418sf0LQgND+ezKz+gafYyh04WFhwPUwIHmDA/24we1qk27cP71L9jeeZvAbt0YeDAcd00JXSf0h1dfbPI5paWwa5f5/ZAhJxegXngBHnnE/P6ll6DJZTgProIKc/1NInpCZL9mLarbyMYnYMe/wWbHdfp7lK0eAG5zLSo/mgum1SlEtaLNmzdTWlpKaGhos8/e+JLQ0FAGDx5MVlYWS5YsOSpEeSaV6Igz83l4jm1N7hoMwzjcGB48CHl55vcdeTgfmD1tnhB13nmAf1wP5eHpiWq0VpQ/9UQFRUFQJ6g5aIbHk5leV3zG1q1bG6YwT0tLa/YU1+1JVXUVXyz8gtnzZ7Ng+QKGDR/GOw+/A8CYMWMIJZT+/fvz08yfcuHZFzImY8xR1wAZhsFzS59j5aZvqPnhO0qLG3cxGeFF5gLkdXU47A6+vfZbenXuRViQbwTO0tJSdtUngmHDhjU7QBmGwfXXX8+aNWuIj4/nww8/POZ05tsPbqewopAAewCZKZnNDlAA98+/n9dXv47D5uCDSz84eji9h8tlrnQL5qrvPY4/YK3iu1Vs/9s7ZH+xkdOM5STZbFBYSFrpLhg5Ev7zn6MWlvdYt87s4ElMNGfja6mPPoKpU83v//pXuK6p+THK95gBynMNVAvWg2qwZxZk3WF+f9ozONLOh/pfUV2dQtSpUIhqRZ7roU477TQCOujsbCNHjmwIUb/4xS8a/awjT2/uMSBuAA6bg4NVB9lbspe0qPqLVD29UKmp5oK0HZlnuKLnmPGvEOXpidqx44izeA0L7XazqCovC+tuhqiyXQpRHdimTZvYWn+2oE+fPse8xqW9MQyD5WuW8/G8j/nmh29YsXEF1TXVDT+vsddQXlNOWFAYoaGhHNxwkKD6D8qGYbAvdxsrN86j+MAuft3lZ5CQgK13b15e+TKb969jQrF5jWx8Yk+6dz+Nfn1GM7T/mZB2uO3ztcmVNm3ahGEYJCYmknDMxYmO9sQTT/Dee+8REBDAf/7zn6NOrnoUVBSwqcBsMwbFD2pRuHxpxUs8tPAhAGb+bOaxF7sHWLvWnMo8OBiOczK7YO4qdt3zL3KW7sbAPBmaP2IiSYXfmG/uffvCZ5812b3kdpvNX0GB2cE1cGCzD6XBd9/BlVeaIey3vzWH9B2lrsJcTgIgsu/JBaiilfD9VYABvf8P+kzFZjOHDNbVmV9OrbF70jrmJ32LdMT1oX4sMzOTl1566ajJJUqrS9lxcAfQsUOUM8BJv9h+rM9fz+rc1UeHqI48lM+jiRn6PJNK9G75bKs+JzW18TTn3btzxPTmftATBWZYPLhS10V1YOvXr2fHDvM9fcCAAfRs52dIioqLiI6Mbri+5vzLz6fQXdjw86iIKEYNGcU5Y85h8jmTGz7EV9RWsHL/cjYunc3W7UvJ3rWa8tIiAIIcgVz+i5EEBgRA797clHETxYOKGRs+kGF9xxEeGu3142wLu3fvJicnB5vNRv8WXNP75ZdfcueddwLw7LPPMnbs2Ca3O1R1iGX7lmEYBimRKYfbzWb4cOOH3PzZzQDc95P7uOG0G4698aZN5rW6NhsMHXr0NVDV1Wzb4yQ7G8p/sMHSbLAHEDc+nd7TJhHz0K1mgEpOhi++gNjYo3ZRWQnLl5uzpoPZ5Le0c3bjRpg0yVxDedIkmDGjidF5hmEO43PXgbMzhJ/E/7+KfbBgErgqIPFcyHi2YUeeNazUC3VqFKJaUUeeVMLDM7nE8uXLcblcDUMePEP5ksKTiAmNsaw+b0hPTGd9/nrW5K7hZ31+Zj7oTyHqyBn66vlTT5TDYY4Q2bjRPO7u3fxojSiPhhn6dllahrSN7OzshgA1ePBgurV01VAvKC0rZfb82cxZOIeFKxZSVF7E5q82Ex8ej81m47yfnMeW/C2cdfpZTDprEqcPOx273c7OvevoTqD5YTstjas+vIpZG2cxYRsEus3XttvsxCWZvUzlA/sQnWIOCZsycoqFR9z63G43q1evbpjKvFu3bs0exrd+/XouvfTShgV1b7755ia3K68pZ8neJdS564gNjWVo4tBm1/fl9i+5/D+X4zbcXD/seh746QPH3njnzsNjrIcMMcfXGQYVKzYS+uUsePtt+MlPyP/1C5SXQ0BGOil/mEz3G8cT0SPOTDPffQfR0WaA6nr0e3ltLSxebHZ0edaCSmph59CBA3D++eYVAJmZ8O67TU0kUR+gqovAHgCdhrX8Gqi6clhwIVTuh6gBcMb75mvV68jzX3mTQlQrqa6uZnX9OFxP0OiI+vXrR3h4OGVlZaxfv75hAT7PUD5fG8JwMtIT0nl77duNJ5fwh5n5PDwhKj/fvIA3JsYvFto9Uq9eh0PUOWMLzQYL/CdEaYa+Di01NZWcnBwSExPb1SK6K9es5K1P32LB0gWs3rKaOlddo5+v2byG8RnjAfh///x/FJcWsGrjN2Rt+Q///fwO9uxaQ1VlKa9M+hfx8d0gLY3MlEyW7F1Cz4yBpKecxqA+ZzC0308JDe7Yw7Lr6upYtmwZBQUF2Gw2+vXr1+zexpycHC644AJKSkoYO3YsL7zwQpOTZVTXVfPD3h+ocdUQHRzNiJQRzZ6J77vs77jovYuodddy6YBL+efP/nnsCTn27TMvUAJqImMp/GQZ+V++RP53W6goquQc5hJMNRw6RM/7nic11U5Sko2AC66HsjKYOBHmzzeH7n36qTlP+Y8YBqxYYQaokBAYM6blkygtXw6XXAK7d5ujNj79tIleLE+AqthnBqdOwyCghV1ddRXw3RXmaAFnLIz71LyWVVqdQlQrWb16NbW1tcTExLTLs3atxeFwMGLECL755huWLl3aEKLW5po9UYPjO/71EekJZlBsNM25P6wR5REWZl6wm50NGzdSPPgMCgrMH/lDTxT8aJpzT5AISQKHnwwu98zQp7WiOowjJ8qx2+3tYlj6hq0bSEhIICbSHN3wxltv8OysZxt+HtcpjtFDR3PumHO5cPwkUiPNadffW/cef134V2KXbSCyuvFrOhwB7LaXEJ+YCIbB7aNv544xd7TbGfPagmEYrFq1ioKCAgICAhg+fDhxzZwZoaKigkmTJpGdnU3v3r356KOPcDZxUU1ZTRnL9i1rmIlvZMpIAuzN+8iZlZPFxLcnUlFbwXm9zuP/Xfz/ml42pbYWli7l4N4ysnMjKQrvQtnUJ8y2qZ49MIDiMZMJ/vW5cPHFxEcfEeJKS+GCC2DRIvNa5i++gFGjmqxpwwbzvKHDYc430ZIAZRjm1OVTp0JNjdlONjla8McBqvNwCEls/o7AfE/+9mI4mAX2IPjJrMMnvY5QW2t+BQY2b+Z3aZpCVCvxTCoxcuTIDv9mPHLkSL755huWLFnCDTeY45PX5NX3RCX4QU9UfW/blsItVNRWEOp2mOOowT96osA8zuxs2LSJ7aFnAJCQ0PHn1PDwXPu1dSv+dz0UHNETtcvSMqR1GIbB2rVrcTgcDDyZq+RbSV5BHp989QlfLPqC71Z9x4GCAzz3yHNM/YU5hdlFF1zEqt2rOHPUmYzJyKDWnc+mrYvZsPlfjHOVkxo3AM4/H5vNxob8DQwOhi6hSaR1G0KfniMZ3G8cg3qNxhl0+BNwgM3/PgZt2bKFnJwc7HY7p59+Op06dWrW81wuF7/61a9Yvnw5MTExzJ49u2HdsCPtKd7Durx11LnrCA4IJjMlE2dA804wrctbx7lvnktxdTFndDmD//7yvwTZA6nedYDS5ZspzdpOybpsuuz5jk4bvoO6OipfmEW2KwViu8HQYUQ6a4gZO4C484cTc94IAsKbmC2wpMScXXbxYoiKMlPNMUYRbdx4uIkfOhQiI5t1KIB5DdWUKfDqq+b9n/8cXnvNHDXYiOE+PJW5zQ6dM1oeoPZ/Ad9fYU7644wzh/DFjWly0zVrYP9+c1KME0xgKMfhf+8ebcQfJpXw8AxX9ARHwzAaemUGJ3T8nqjE8ETiw+LJK89jfd56RhSHmdO0RUS0fIC0r+rf32x0Nm5kW32D4i+9UPCjnqiG6c39ZCgfHD7W2mKoOQRB0VZWI6fAE6B2796NzWYjJSWF6KM+4bWd7OxsHv7nwyxcvpDN2Zsx3EbDz+x2O7t27wJgf+l+VgStYMjYcLase5EVS/Iavc6G8EEMTBgE5eWc1f0sPrn8EzJTRhIf3vyZ5jq6kpISVq9ezaH6WREGDx7crABlGAazZ8/mvvvuY+XKlQQFBTFr1ix6/Wj8dllNGWty11BYYU7oERMaQ0ZSRrMCVGVZKS9+fC+zv57BzwrqWPuT0/lbv9ms+CGU8r/PoPbTOY22D2MXnaiC8HA6h1TSa3QPOsXY6HzudQQFH2fyCYCiIrMHaskS6NQJvvwShg8/ajOXywxQRy45lZx8wkNpsGMH/OIXkJVlzuL3yCPwpz81sWSV4TZn0as8UB+ghkNIC/7dGm7Y8CisvgcwIGYkjP0vhDY9UyIc7n2qqzvmJtIMClGtxBMoOvKkEh6eoLh27VrKy8spqC0wFxa0B9Iv1g8mVsCcgfCrHV+xOnc1I3aZQ0jo27flF3/6qiNm6NtWfyLSH2bm8/B8dti+Hdxlu7HD4SFu/iAgDILjoSrPHD7SeZjVFclJODJAAQwdOrRNA5TL5WLxysVUuis5J/McAMrKynjprZeg/nN2WlIamemD6J4azaTeZzC28yCoqOBA6QH++OUf6VEEA4rBBsQkdKdbt6H07XU6IwafD2mDwGYjlggm9Z3UZsfhawzDYOfOnWzcuBG3243D4aBv377Nut7tyy+/5N577204URwWFsarr77KGWec0ej1txRuYVvRNtyGG4fdQZ+YPvTs1BOb222uoZibS023PuQVO6mqgur/fUnlp/OoOnCQ4gOFuA5VcyGbmIb5qX7XrU+ytqB+aEOneLDZCUuOIjw2mMiUCGJ6ZUD/RBgxguBhw+jfkEyOc81VTY25uu1DD5lBqnNn+OorGNb4/au83DxBtn//4ZAxZEiTc00c08cfw29+Y87iFxdnTiBx1lk/2sgwoHIflGyCusqTC1C1JbD4Gtg7y7zf67eQ8Y8TDi33TGZRW9v8XcnRFKJaQXFxccNihP4QolJSUkhOTmb//v2sXLmSQ/GHAOgf158gR9OL0nU06QnpZojKWQ2b6nuf/GUoHzQOUfUjDvypJ6pLF/NMXk0NVBXuIhSaHHfeoYV1M0NUuUKUr1q3bl1DgBo2bNgx1/g5FZu3b+bjrz5m3uJ5LFm7hOKyYsaOHctZI87CYXcQ1zWOiT8bR0JEHQGUUpi7nar8z9mYD2nFBYzNTIODBxmSOISL+1/M6Z3TGdVpCOn9xhER1rxhaP6ssLCQNWvWUFZWBkBiYiJDhgxp8jqmI5WVlTF16lRef/11AEJCQpg6ZQrTbp5KnN1udq8UFlKWfhpfbtlJXskhohdsJe77LTjLbOw7+AE7isrpUbyKXpgzD1UtXs+q/Pq1m1Yegq83NOzPho1iRzClKYmE9x1MWnQ4dWnmJbjhGecR+vQ5OLJWQEWF+WBCgjmuLjX1xCcvDQM++ADuuqvx0Pt33zXT0RH27DGXmnK5zPuhoeamze2Bqqoye5uef968P2qUueuUlB9vmA8lG6Gm2LzvCIZOQyG4Bav25i2CpTdAyWbz+qfhM6DXCXrh6qknqnUoRLWClStXYhgGXbp0IT4+3upyvGLkyJHMmjWLpUuXUjWiCvCPSSU8PGthrc5dDZvq3wT9YVIJD0+I2r2b7VtcgMNvZuaDw9Ocb94M7pL6sR7+NJwPzGvACpdqcgkftW7dOnbt2gWYPVCtGaAMw+CaW65h3sp57M/f3/C4zQbxoYH0KnNQc7CAmrBgEp5IICHYYPjuw88PCAgkMaUfkf2HQkYGxMQQ6Ajkv7/8b6vV2NEZhsGWLVvYsmULAEFBQfTr14+uXbti1LmoOVBIbf4haguKqS0oJrSykLCKfDh4kOUjxnHpbx9n1659XMRoroyMJdwRjOuZrSx7Yip92UwfzOnEv33nBVYdzMBujyR6ZycqFu6h4og6qnCaf/iYGIKri4mLM9fZ25rpYlFuNltD8tjfqYqzfvIL7rv8DSJCzWDsAHqZBwL7S2DVRvMCo7Awc2q85q4Qu2gR3H67OXQPIDER/vpXuPbaRnOLu93mdUJ79pj3Y2PNJr1z5+b/zjdtgssvh/qJmrn9dnj4YQg68txyXTkcWmeegAJz2vHwXhDeA5qaPKMpB7Ng9V9g/2zzfmgqnPFfiG3+5STqiWodClGtwJ+G8nlkZmYya9YslixZgq2reRaoIy+y+2NHztBnbKoy1zv3p56o2FiIiYHCQrbVhyh/6okCc0jf5s0Gzrpd5tgif5pYAjS5hA87dOhQowCVltb8xU9/rKKygi8WfkHWlizuuvkuggOCsdlsrFq9ikNl+0lJgq4JkcSE2qG2hIExfZg+fhoUFhMSk0CfmD4ERboZmtSHPr0yGdT3JwzqeTqBgX4y0+XJMAwoK8MVHEZ5pZ26OqjNWk/t2k3UFpVSXVRK3s69RJRsY3jtfiJqa6l69UOytnZhwwaoe/kN+OjDRi95ZDD6XWBndtWOpnPngVwwfDjOL7+g9ohoVBMQSlWnTtRERxBEDakpDoYk96NTZydBvd0446MISorBmRxDcGosJMdCQABBQHptJXfPu5tnCp+BMdCjUw9en/wBZ3Q5g6OUl5s9XkXm4seEhMDppzcvQG3aBHfeaY6rAzN8/elP8Mc/mlOZH6GmBpYtM3djs5nhqVev5o/ONwxzsoipU82Osrg4eOMNc96KRipzDi+ga7OZvfkRfaC5I3hKt8Ga+2D3O+Z9mwN63gBDHmpZDxbqiWotClGtYEn9GY6OvD7Uj3mui1q6dCkh48yZjvxhZj6P/nH9CbQHUlxVjLFpoxmi/GGh3SP170/FohXszzcbAH/qiQLzGrDYiAICbRWADcLaz3o6XuEJUeqJ8jnR0dEMGzYMl8vV4gDlcrlYtHwRn33zGfOXzidrSxa1tbU47TauuGAy/XoO46oPr6Jw7E7O2uF5VglG/VTj+6sLMOLjsdV/kF190+pmz9zWUbira6krLKa2sIS6ohKCKw/irDwEhw5RceHl7CkIMYPRfz+h9utvqSupoLasmtrSKnpVrKFr2XpwuynOyua77Pq/36tLjwpGiWwmqT4YGSWHqKysf48KCwPAERxEYEQwgRFOiu3VzM3NIbu0lIO1JfzkJwk8/fRjJFaVEnjjYILioigJd1MQVsVOCthps9Enpg9nRHfjbM/fb/hguLTpESl17jrmbJvDn+f+mY0F5rqKv8v4HU+c+wThQU0s8LtrlzmvuMtldpv06mV2/ztO0FuTkwMPPGDOKe5ymdtff7352BETP+XkmLOrulxQXW0GqcBAs+OzmbO9YxiwahX8/e/w3nvmY2efDW+++aM5pmrLzJNNnvdKZ2dz6F5AWPN2VLEf1v0Vtr8CRn3q6XoFDPkrRJxcw+vpiVKIOjUKUa3An2bm8xg+fDg2m43d+3ZjLzQv5PSHmfk8ghxB9I/rT8HWNdhLy8w3an9LEf37s33RIcCc4Kglwx46gl69oHucZ42oZP9ZI8rD0/OmBXd9Rl1dHQH1n55SjrpI48QeeuIhHn/7ccoqSomKhOgIGNQdYgLsjC0/jYAN26HnMIoqizgQUA6BgSQn9aZ712H07X066X3H0T11UKNlQHwuQLlcUFJCXXg0FZU2c72d5aup27SNukNl1B4qp7a4giTXXjpVHYDiYopmvs/qnZFmMHr2BVyffd7oJQexju7sAqAqazxbPMEoqxAWbGq0bTVVgBuAwPJDBAWlUVNTTnmEi8g+nbGFBkBIEI4IJ4H9T4d+MRAdTWifVM4IMIeWBY67kIB/XYQRYGfWrFk8+OCDrF1rrvUYHR3N/fffzx/+8AfsdjsQQ507laycLA6UHqivwkaPTj3oG3viIezr89bzWtZrvLnmTXLLcwFzhtt/X/hvzu99/uENPQsX1dUdXpQJzFEPQ4eeeGGmsjJ44gnzq7x+8fOf/xymTz9qlMi+fWb4MQ5PBEloqLn+U3OW6Vi3zryc6r33aFho3uEw56u444762ffctVCxB8qzobb08JMjekJkP3MSiROpq4CNT5oz77nqewKTL4D0h80QdgrCwsyJMsKbyK/SfApRpygnJ4e9e/dis9k47bTTrC7HayIjI+nfvz8bDm7AbbiJCYkhKdxPpveul56Qzr7F9Qvu9ujxo4HPfqB/f7Zjjuv2t/wI5jF3i9tl3vG366HgiJ6oXeanEX+ZmdJHbdq0if379zN69GiCg5tYN+cI+3L28clXnzD3+7ncdvNtjB08ljnb5jC37kuG9CglKhDsjf7cbqZeMZXO8eaH/7/+9K88dOZDDIkfTFA7Cknu6lrqiszen9rCEuoOlhJedwhnxUEoLqb01//Hnn31w+PeeIe6RT9QW1pFXXk1tWXVDKhcQUqF2bNTuKWYpZvq13d4biHM/bLRvkJYR6f6YERxMWVl9dsGHf7dO4KDCAwLwtapF8QnQ1QUIcEGXbvWL4J6eTqBGYEERIcT2DmCwM4RhCZEQEI0hS4XBysrCTe+o6ioiLBLEnBfeilxcXGkpqaSmJiI44heGwfgmYajuLiaf7/wb/7xj380DOuMjIxk2rRp3HrrrbiCXGws2IiBQXVdNQerDlJZW4ndZictKo2k8CTiwprurjEMgw35G5i1aRYfbvqQlQdWNvwsLjSOXw/5NXePvZuY0BgzMO3caXYL1U+73sBuN8NP9+7Hf2+pq4N//xvuv998HTDXe3r8cRg7tmGzkhIoLjbX192xw3zLSk2FtDTz5aOjj9/JtXmzGZree8/MeB4hIfCzn5mjBDMzMa95KtlpBih3fTePzQ7OWHP4XnNm3jMM2P0uZN1hvg5A7GgY+ijEjz3+c5spIuKoOTXkJChEnSLP9VD9+/cnwl9WGq2XmZnJhlXmu8mQhCEdfpHhH0tPSCei4E3zjr8N5QPo359t7AP8a2Y+jyNDlBHWHf/61w+EpgE28wxpVV7LpuUVr9q8eTNbt5of/vPz848awldSWsLnCz5nzsI5fL96PofKdhEdDp22QFZoOGOfGMunWz7l2+pFjAsHew2EhEaS2mUQPXpkMKD3GEIGnw+hZlAYkdLK1wcbhjmpQEkJlJZS3aU3JSXm5+farxZQt3k7dcVmD1BdSQVd3TuJrsqBkhJyPvyeNevMYOR65Cn4blGjlx7GKlLr38cqJlzN9u31YWdNASzZ0WjbGg5fhR9UVYzTGUlAAAQOSCbwUHcCIkIIjAwhIDKUyORxkBQEUVFEpEYxKqU+GGVcTMALkwnoHIk96OiPYCFAw2fb/qcBR5+c3bhxI9s8XSD1kpOT6devH2FhTQ8RMwyDpUuX8vrrr/Pmm282zNbXuXNn/u///o9p06bRqVMn9pfuZ+Uec7KsIwUHBDM8eTidQo6eEbGitoLFexbzxfYv+GjTR2wrOlxbgD2ASX0m8Zuhv+H8XucT6Ag8HJ62bDHH0Xk4HGaiiYoyP+Efr5vEMGD2bPjznw+nmp49zZ6nSy5pCF5ud+PFcj26dDF3cbyPLTt3wvvvm71OWVmHHw8KgvPPh8sug0mTIDzMDdUFULjbvO7JIzDCPNkUkgz2wGPv6EgFP8CK26DwB/N+WFcY+nfocqlOVLVD7SpELVy4kMcff5wVK1Zw4MABPvroIyZPnnzc58yfP59p06axfv160tLSuOeee/jNb37jlXrh8FA+f7oeymPkyJG8ut9chtufJpXwGJIwhKCC+jv+NKmER79+bKs/09qrh5vjrs/RAXXtCj3idwFQ6u5KCxax7xgcTkiaAI4QcFdbXU2bmzFjBo8//jg5OTmkp6fz3HPPHXcI9wcffMC9997Lrl276N27N4899hgXXHCB1+o1DAObzcbWrVsbZmgbOHBgowBVWVvJq3Ne5e//mErnEINOwdAnBqhf+y3jtNM4I7EvhtvNpD6TCLQHcsb4gWR0G03X5P5Nnjjz7Pfw/fqwk32AutxC6g6WUnuwjLpDZcQYBQRXFEF1NYVT7iN7t2GO6HrmeWqXr6a2rApXeTV15dVkuJeSSC7YbOTvqmPV6vr3m5mL4fvvGtUQwyqi64ORraKc6ur6E5yhoQA4nIEEhgUREObEHjsU4gZCZCThwXX06GGGHcd1pxM0IYHATuEEdIogsHM4IXEREBsJUVF0cjo51zOC/axfAL845t8iEIj13ImMOuaHYbf78EKsxcXFVFZWNvysqqqKsrIyqqqqOHDAHFaXlJRE586dSU5ObuhddLuhsrKCwsICioqKKCws5IcffuCNN95gy7Yt2JxgOKDPaX248cYbuejii6ikkqxDWbgOuqisNfeZEJ5ApDOSQHsg4UHhxITGEGA3PzIWVBSw6sAqFu1eyNe7v+GHPUupc9fiOZPkdDg5p+c5TO7zc34eN5bYoCjzH0L2XjMIH7kAU1iYeUYqPh6a6iEtKYHly80UtHev+bVvn3l/yxbcNiiM7o1r2p/gmmsorQ6icImZuUNCzCnHS+tH08XGmruLjj7cA/VjZWXmpBBvvHF4Qj8wryEaPx4uu8zNRROLiQotA1cV1JRBSa45fM8jON6cba85kz0YBpRtN8PTvv9B9vv1OwyDgXdD39sg4ARDGU+SZwRlSIjy2clqVyGqvLyc9PR0rrvuOi6++OITbr9z504mTpzITTfdxFtvvcW8efO44YYbSEpKYsKECV6o+HCI8qeZ+TwyMzNhsfn9wLiB1hZjgfTEdGz1w7are3aj/Qxa8ZIuXdgWlQHF0DO5EmjmRbIdREAAXHL+LqiBsAQ/m5nP48zPT7xNB/Dee+8xbdo0Zs6cSWZmJs888wwTJkxg8+bNTS5r8f3333PFFVcwffp0fvazn/H2228zefJkVq5cyaBBg9q83uy83cz79n84dsaxdVkJ2a5c6pL34n7jD0TsOkCd28mWmJFUdEqiOHoTYyNT6VtcxMEqGzucTupcqQSXxbC1ppQHXn6Sg088RUSnK+gcOpb/hPTmfwG5dBmwkZEVc6ncU8z6rXGUFAbSk2wu6DQPR6idwrpENvTI4JC9D4Y9kJhVWSQ4DmDE2DhkRFN+KIzeJVtJte8jOLCSxbNmUVg8kKqAEFwVEFdQjMNwURXvpKp7OJvdvagqDyS4toaVD13Nts3JBFZUkly7naSMfZQVB7Hs0HCW1oyhOvR3DEzLYVh6DYO2f08COezesI+yrjuIviaSkKpowpwBxEVVU2IPYllhAEv29GDHRf+l0h6MI8BGWVksJa5E4qOCGBVXwLkhXzG4eCEVG9dyqCaQ7PBEClLSKOp7PnWpgwgyKrBTjs0BDhsEuCoIqssnrHwNUcYmwpxFBDpqCaIaozKEGkcq+2wD2V7Zn415XVm7NZaNuxLpFFrB2T2+ZnSXRfSI30bnyIMEOyugOhIqY6moScCwxeKOiGLrmhgKS7txID+BgkIbh/JLqKpyERpYRs+QDSSEr4eQbFLjc3nj2oN0i7ERF2pQ6Q6l0B3E3orvWDpjH3sqe3GothO22hpKy0IoKw/FaT9At5TvSYvfQVznPUQE78Fu7MddU0aIy2BwoIsznVXcYQtie3AvtuT0Zld5CpWOfqR2/wkZkW76535JwI6nMSpLqK0uwxVcjdudj2HUUu0OoZIISg90Jj8rntyDnamtgYSIEuJsxcQdyiFux0Zcmw7grKyFOKAbkAyMAOMsMDrbsIcYRFFItf0Zaha/SQTRBNpTqLKlUGeLxGmEEFwTSERMFL1SonGGhpmTM+zNg+o8c9KGqhzKDxayb9chNmyNxl2QwLnJ8Qw7O4G4LglkjInjp+MMokIrwF0D1Qb8+LyRwwkhSea1ooE/6kFz10JdmTnUr67cHKJXsMQMToU/mL1YDWzQ41pI/5v5em3E7YY5c8zvzzvv8Gx90jI248d9tu2EzWY7YU/UHXfcwWeffca6desaHrv88ss5dOgQczz/Ok6gpKSEqKgoiouLiYxs2blkwzCIjY2lqKiI5cuXk5GR0aLn+7ra2loi/xZJlb2Kxdcu5vQup1tdktdVJcQSnFeI8d132EaPtrocr+vaFbKzzaU4xoyxuhoLfNrfXG3+rLmQON7qanzOqbz/elNmZiYjRozg+foVNN1uN2lpafz+97/nzjvvPGr7yy67jPLycj799NOGx04//XSGDh3KzJkzm7XPU/ndvPX4P1j37TaKi+ooK67B5S4mtLQ7QQHBBIW4cTschEWWEBTkxkUARQEh1NQEY2AQXOnEqA4Cw4ERZKMmIIBaRyAuzE9ZlTUhlFZG0DnsIA5qiQvIJ4pDBNTUEhxURShVgI1aAikjlJK6KAKpxUkNoQFl2DAIoA4HLsIpIwCzR8JFAOV1odQSRAmRVDhCKbFFUUYYRn0XRxA1RFNMFIcIrysj0l7CoYDOFAVHUxsUiDOwmuCAapyB1bgMB1W1wVTXBlNdFUh0bQlGqUG1zUlk5xKCQ6sICakkNLSS4NBqIkJKqXUFUljWmeKKKA6VR1NWHUZpZQRut534yHy6Ru2ma9RuIiNLiYoqJiaiiFBnBcGBVTjs7hb9jX6sui4QOwaBAdZMl+Y2bFTWBBPmrDzxxs1QWRNMSFBVq7xWu2IPwgiIwgjqjBEYgxEYBdjAqMPmKsfmqsTuLq8PTWUYdRXYjBMsxmQPguih1EYNpy75ctxR6eZreiafsDmwOQIJ9XRI1ZVTUXnkxBhHbmvH5ghqtG1llRmafrztl3Pt1LmDOPfc+nk76iqoqjKOuDbMRm2d3dyPzQZ2Jw2jResqqK42GnpOAWrrHA3b2hxOoqM821bimQzl8DEfcQGa/YgeyJZs66oCw3XsbZs7+2ETmvv+2656olpq8eLFjB/f+IPLhAkTuPXWW4/5nOrqaqqrD59CKCkpOen9b9u2jaKiIpxOJ4MH+8/MdB55lXlU2atw2BykJ/nP9OYNDh0iOK8QANtA/+uJq6o6vDBh797W1mIJw314ytpwP7wozE/U1NSwYsUK7rrrrobH7HY748ePZ/HixU0+Z/HixUybNq3RYxMmTGDWrFnH3E9rtk0De/WgcEkW6QOyCQgoI7CiloKaCK7/2asn/Zpyaj5ePolal4HNXsewrhvoEZ/d6OfOgMMftA+WR1NYHElucQzRYeUM7LLlmK9bWhmOA5f55XAREOQ65rbPf3kzG/cNoFunnYwfOI9hvVc3/MxuMxoFqLKqMMorQqmqDMFwQLfE7KZeEoCPlv2c1MA9pMbsIzq2mJCQqmMGqAUbz2Bvfhph9nJSOu1lxMCVTW4HUFTWCbdhIyigllpXIDHhRcfc9ttNY8gviSMwoJaUTvs4rXvWMbetqA6hqLwz5VVh1LoCGJS24ZjbHiqPIiDQRnhwudmj5K7BVpOPrSb/mM850rFGye0pTGX5/vO46Nf9zOumFv2CwKKlBO584ahtZ2edx2db/o8Zz9c/sOhSQo8xhHr+xp/w5rLbeeVf9Q8s/hUhtU2/l6RuH84TX9xH1/peqPSqG4g08prcdv3eAdz9weP85W7z39egqilEGHua3HZXfld+98o/+WJ2/fVuK6dB2bYmt8UZC2PeqQ9PwOq7oXhd09s6QuGnnx2e7XDdX6FoedPbAlzZ9n1EPh2icnJySEhofDFzQkICJSUlVFZWEtLElJjTp0/nwQcfbJX9b926FafTSUZGBkH+NjMbsP3gdoIcQfTu3JuQwLYZs9uu7dxpLvoXF2deCOtn9u41hwB4fgV+pyofMMwFD0NPfrFSad8KCgpwuVxNtjWbNm1q8jnHaptycnKa3B5at20afNYgSsqLcO2eRVhkPO68Aly7ilvltdsbt2EDbNhtx+4JKiqLJiyknCB77Qmv/fhy7XhGDj5AELk4jYM4bMcOJX9+ZzojRm4jOP4gXVzfkR6de8xt31h0DWlnvExF/EIIqKTHcWqY8trzDB74LT0H7Cco/BgfKOtNff050mz/IajvBgYPquGi2H3H3HbNnnR+ErGOkYM24oopO+7r3vnudDKN7ZzbeTlhifsg8djbfrflDAZF1ZEWsRt36XYImXvMbeeunUCS28GQ2nk495XCcc4/zpg7hZqw4fTvV0u0YwcXhN9xzG235vRmS/lERp7upCJ0O5B1zG3f++Ey1ueNpqbOiVFbwXNX33zMbRduGste13n837QkqC6CZTcec9tvN53B9rKz+M11wWZvz+qje6k9lu0YwaL9F3JR++2Al2bw6eF8ffr04dprr210hnD27NlMnDiRioqKJkNUU2f70tLSTno4SU1NDXl5eaSmprb4uR1BrauWvPI8UiJbvuZIh1BXB7m5cBJrrnQELpd5+MnJVldiEcMNVbltOna9I/OF4Xz79+8nJSWF77//nlGjRjU8/uc//5kFCxY0LLZ+pKCgIF5//XWuuOKKhsdeeOEFHnzwQXJzm/6g3dptEwBuV6PFcCorqnDX1K+hgw3qXOZ72I+H6lBLlcuB68j8YHMcfp7DSViwYf77N2qoqnYfY1vAEUxocP2F665KqmvcjRf4PNa27iqqq13H3TYk2IY9wAnuampqXNTWHmNbu5OQEBt2mwF15dTU2Y6zbTDBweZEcUZdFbU1ddTWug9v86PXDQ62mcOfXDXU1tY2mmzux9s6nTYCAqCutgLDZRxn2yCcTru5IKqrhtraGmpqjkx/9sMzAdiDCAyyERgANqOOupoaqo983SO3tQUSFOwgMABw1+KqraGqGtyGG7vNftS2gU4HQYGAuw5XbTVV1cd63QACnQEN27rrqqmsasa2rjrcruNvGxAUgDMIcLswXFVUNBpteOQwtiO2NdwYdZXH3dYRGECwEwy3G5u7kvKKY2yLA0dQIMFOzP9LrorjbmsPDCQkuDnb2rEHBpnbwgmG6LXNcD5sdrAHERbq2baCyiqj6W09/++P2Laq2jj+e0TDthrO1y4lJiYe1SDl5uYSGRnZZIACcDqdOJ2tNwVAUFCQ3wYogEBHoP8GKDBnF/DTAAXmBw2/DVBgNjAKUB1abGwsDoejybYmMbHp0/PHapuOtT20ftsENP5AAYREhAPNW13z+CtJ/WjbZg9ECMcZQjMn4QnHGdzMbR3mh/JmjQdxRBHkbN62tqAwgoKa+br2EAIDQwgMPfGmAU5ztsDmbNuS14UAAgKCCWjW6zpwBAQT1py/nd2BI8DZ7G3tAU7CmvMPyO7AHtj8bW0BQYQ17x8ENkdgs7a12R1AIGHNHUzkiGybbYMiCW2jbUOavW1Ei7YNbva2LVjRtyXb2q2fzMqn5yQeNWoU8+bNa/TY3LlzG50tFBEROVlBQUFkZGQ0amvcbjfz5s07ZlujtklEpONrVyGqrKyMrKwssupXNdu5cydZWVlkZ5sXNN51111cffXVDdvfdNNN7Nixgz//+c9s2rSJF154gffff5/bbrvNivJFRKQDmjZtGi+//DKvv/46Gzdu5Oabb6a8vJxrr70WgKuvvrrRsPJbbrmFOXPm8OSTT7Jp0yYeeOABli9fztSpU606BBERaWXtajjf8uXLOfPMMxvue2Y3uuaaa3jttdc4cOBAQ6AC6N69O5999hm33XYbzz77LKmpqfzrX//y2hpRIiLS8V122WXk5+dz3333kZOTw9ChQ5kzZ07D5BHZ2dnYj5jrd/To0bz99tvcc8893H333fTu3ZtZs2Z5ZY0oERHxjnY7sYS3+MKFzSIiHZHef49NvxsREWs09/23XQ3nExERERERae8UokRERERERFpAIUpERERERKQFFKJERERERERaQCFKRERERESkBdrVFOdW8ExOWFJSYnElIiL+xfO+6+eTxDZJbZOIiDWa2zb5fYgqLS0FIC0tzeJKRET8U2lpKVFRUVaX0a6obRIRsdaJ2ia/XyfK7Xazf/9+IiIisNlsLX5+SUkJaWlp7Nmzxy/X8tDx6/h1/Dr+kz1+wzAoLS0lOTm50WK1orbpVOn4dfw6fh1/W7dNft8TZbfbSU1NPeXXiYyM9Mt/qB46fh2/jl/HfzLUA9U0tU2tQ8ev49fx6/hPRnPaJp36ExERERERaQGFKBERERERkRZQiDpFTqeT+++/H6fTaXUpltDx6/h1/Dp+fz3+9szf/zY6fh2/jl/H39bH7/cTS4iIiIiIiLSEeqJERERERERaQCFKRERERESkBRSiREREREREWkAhSkREREREpAUUok7BjBkz6NatG8HBwWRmZrJ06VKrS/KK6dOnM2LECCIiIoiPj2fy5Mls3rzZ6rIs8+ijj2Kz2bj11lutLsWr9u3bx69+9StiYmIICQlh8ODBLF++3OqyvMLlcnHvvffSvXt3QkJC6NmzJw899BAddZ6ehQsXMmnSJJKTk7HZbMyaNavRzw3D4L777iMpKYmQkBDGjx/P1q1brSnWD5zo79GU+fPnc9ppp+F0OunVqxevvfZam9fZVlp6/AcOHODKK6+kT58+2O12n3+vbunxf/jhh5xzzjnExcURGRnJqFGj+OKLL7xTbBto6fEvWrSIMWPGNLRV/fr14+mnn/ZOsW3gZP7/e3z33XcEBAQwdOjQNquvrbX0+OfPn4/NZjvqKycn55RrUYg6Se+99x7Tpk3j/vvvZ+XKlaSnpzNhwgTy8vKsLq3NLViwgClTpvDDDz8wd+5camtrOffccykvL7e6NK9btmwZ//znPxkyZIjVpXjVwYMHGTNmDIGBgXz++eds2LCBJ598kk6dOlldmlc89thjvPjiizz//PNs3LiRxx57jL///e8899xzVpfWJsrLy0lPT2fGjBlN/vzvf/87//jHP5g5cyZLliwhLCyMCRMmUFVV5eVK/cOJ/h4/tnPnTiZOnMiZZ55JVlYWt956KzfccIPPfpBu6fFXV1cTFxfHPffcQ3p6ehtX1/ZaevwLFy7knHPOYfbs2axYsYIzzzyTSZMmsWrVqjautG209PjDwsKYOnUqCxcuZOPGjdxzzz3cc889vPTSS21cadto6fF7HDp0iKuvvpqzzz67jSrzjpM9/s2bN3PgwIGGr/j4+FMvxpCTMnLkSGPKlCkN910ul5GcnGxMnz7dwqqskZeXZwDGggULrC7Fq0pLS43evXsbc+fONcaNG2fccsstVpfkNXfccYdxxhlnWF2GZSZOnGhcd911jR67+OKLjauuusqiirwHMD766KOG+26320hMTDQef/zxhscOHTpkOJ1O45133rGgQv/y479HU/785z8bAwcObPTYZZddZkyYMKENK/OO5hz/kTrae3VLj99jwIABxoMPPtj6BXnZyR7/RRddZPzqV79q/YK8rCXHf9lllxn33HOPcf/99xvp6eltWpe3NOf4v/nmGwMwDh482Or7V0/USaipqWHFihWMHz++4TG73c748eNZvHixhZVZo7i4GIDOnTtbXIl3TZkyhYkTJzb6d+AvPvnkE4YPH86ll15KfHw8w4YN4+WXX7a6LK8ZPXo08+bNY8uWLQCsXr2aRYsWcf7551tcmfft3LmTnJycRv8PoqKiyMzM9Mv3w/Zo8eLFR71PTZgwQX8fP+V2uyktLfW7Nttj1apVfP/994wbN87qUrzm1VdfZceOHdx///1Wl2KZoUOHkpSUxDnnnMN3333XKq8Z0Cqv4mcKCgpwuVwkJCQ0ejwhIYFNmzZZVJU13G43t956K2PGjGHQoEFWl+M17777LitXrmTZsmVWl2KJHTt28OKLLzJt2jTuvvtuli1bxh/+8AeCgoK45pprrC6vzd15552UlJTQr18/HA4HLpeLhx9+mKuuusrq0rzOM668qffD1hhzLqcuJyenyb9PSUkJlZWVhISEWFSZWOGJJ56grKyMX/7yl1aX4lWpqank5+dTV1fHAw88wA033GB1SV6xdetW7rzzTr799lsCAvzvY39SUhIzZ85k+PDhVFdX869//Yuf/vSnLFmyhNNOO+2UXtv/fpvSqqZMmcK6detYtGiR1aV4zZ49e7jllluYO3cuwcHBVpdjCbfbzfDhw3nkkUcAGDZsGOvWrWPmzJl+EaLef/993nrrLd5++20GDhzYcJ1JcnKyXxy/iPimt99+mwcffJCPP/64da4J8SHffvstZWVl/PDDD9x555306tWLK664wuqy2pTL5eLKK6/kwQcfpE+fPlaXY4m+ffvSt2/fhvujR49m+/btPP3007z55pun9NoKUSchNjYWh8NBbm5uo8dzc3NJTEy0qCrvmzp1Kp9++ikLFy4kNTXV6nK8ZsWKFeTl5TU6g+FyuVi4cCHPP/881dXVOBwOCytse0lJSQwYMKDRY/379+e///2vRRV515/+9CfuvPNOLr/8cgAGDx7M7t27mT59ut+FKM97Xm5uLklJSQ2P5+bm+vQMUB1JYmJik+1VZGSkeqH8yLvvvssNN9zABx984JfD0Lt37w6Y79e5ubk88MADHT5ElZaWsnz5clatWsXUqVMB8ySoYRgEBATw5ZdfctZZZ1lcpfeNHDmyVU7+65qokxAUFERGRgbz5s1reMztdjNv3jxGjRplYWXeYRgGU6dO5aOPPuLrr79ueGPyF2effTZr164lKyur4Wv48OFcddVVZGVldfgABTBmzJijprXfsmULXbt2tagi76qoqMBub/z26XA4cLvdFlVkne7du5OYmNjo/bCkpIQlS5b4xfuhLxg1alSjvw/A3Llz9ffxI++88w7XXnst77zzDhMnTrS6HMu53W6qq6utLqPNRUZGHvV55aabbqJv375kZWWRmZlpdYmWyMrKanTS72SpJ+okTZs2jWuuuYbhw4czcuRInnnmGcrLy7n22mutLq3NTZkyhbfffpuPP/6YiIiIhuseoqKi/OKsZkRExFHXf4WFhRETE+M314XddtttjB49mkceeYRf/vKXLF26lJdeeslnp4xtqUmTJvHwww/TpUsXBg4cyKpVq3jqqae47rrrrC6tTZSVlbFt27aG+zt37iQrK4vOnTvTpUsXbr31Vv72t7/Ru3dvunfvzr333ktycjKTJ0+2rugO7ER/j7vuuot9+/bxxhtvAHDTTTfx/PPP8+c//5nrrruOr7/+mvfff5/PPvvMqkM4JS09fjA/NHmem5+fT1ZWFkFBQUf1qPuClh7/22+/zTXXXMOzzz5LZmZmQ5sdEhJCVFSUJcdwKlp6/DNmzKBLly7069cPMKd8f+KJJ/jDH/5gSf2nqiXHb7fbj/pcEh8fT3BwsM9+Xmnp3/+ZZ56he/fuDBw4kKqqKv71r3/x9ddf8+WXX556Ma0+358fee6554wuXboYQUFBxsiRI40ffvjB6pK8Amjy69VXX7W6NMt0tGlzm+N///ufMWjQIMPpdBr9+vUzXnrpJatL8pqSkhLjlltuMbp06WIEBwcbPXr0MP7yl78Y1dXVVpfWJjxTxP7465prrjEMw5zm/N577zUSEhIMp9NpnH322cbmzZutLboDO9Hf45prrjHGjRt31HOGDh1qBAUFGT169PDp9+uTOf6mtu/atavXa28NLT3+cePGHXd7X9PS4//HP/5hDBw40AgNDTUiIyONYcOGGS+88ILhcrmsOYBTdDL//o/k61Oct/T4H3vsMaNnz55GcHCw0blzZ+OnP/2p8fXXX7dKLTbDMIxTj2IiIiIiIiL+QddEiYiIiIiItIBClIiIiIiISAsoRImIiIiIiLSAQpSIiIiIiEgLKESJiIiIiIi0gEKUiIiIiIhICyhEiYiIiIiItIBClIgXzZ8/H5vNxqFDh7y+b5vNhs1mIzo6ulnbe2q12WxMnjy5TWsTERER8SUKUSJt5Kc//Sm33npro8dGjx7NgQMHiIqKsqSmV199lS1btjRrW0+tv/zlL9u4KhERERHfohAl4kVBQUEkJiZis9ks2X90dDTx8fHN2tZTa0hISBtXJSIivurOO+/E6XRy5ZVXWl2KiFcpRIm0gd/85jcsWLCAZ599tmFI3K5du44azvfaa68RHR3Np59+St++fQkNDeWSSy6hoqKC119/nW7dutGpUyf+8Ic/4HK5Gl6/urqa22+/nZSUFMLCwsjMzGT+/PktrnP16tWceeaZREREEBkZSUZGBsuXL2+l34KIiHR0d911F08++STvvPMO27Zts7ocEa9RiBJpA88++yyjRo3ixhtv5MCBAxw4cIC0tLQmt62oqOAf//gH7777LnPmzGH+/PlcdNFFzJ49m9mzZ/Pmm2/yz3/+k//85z8Nz5k6dSqLFy/m3XffZc2aNVx66aWcd955bN26tUV1XnXVVaSmprJs2TJWrFjBnXfeSWBg4Ckdu4iI+I+oqCiuv/567HY7a9eutbocEa8JsLoAkY4oKiqKoKAgQkNDSUxMPO62tbW1vPjii/Ts2ROASy65hDfffJPc3FzCw8MZMGAAZ555Jt988w2XXXYZ2dnZvPrqq2RnZ5OcnAzA7bffzpw5c3j11Vd55JFHml1ndnY2f/rTn+jXrx8AvXv3PskjFhERf1VXV0doaCjr1q3joosusrocEa9QiBKxWGhoaEOAAkhISKBbt26Eh4c3eiwvLw+AtWvX4nK56NOnT6PXqa6uJiYmpkX7njZtGjfccANvvvkm48eP59JLL21Ui4iIyIncc889lJWVsW7dOqtLEfEahSgRi/14+JzNZmvyMbfbDUBZWRkOh4MVK1bgcDgabXdk8GqOBx54gCuvvJLPPvuMzz//nPvvv593331XZxJFRKRZVqxYwcyZM5k4caJClPgVhSiRNhIUFNRoMojWMmzYMFwuF3l5eYwdO/aUX69Pnz706dOH2267jSuuuIJXX31VIUpERE7I7Xbzu9/9jqlTp5KZmcmvfvUramtrdW2t+AVNLCHSRrp168aSJUvYtWsXBQUFDT1Jp6pPnz5cddVVXH311Xz44Yfs3LmTpUuXMn36dD777LNmv05lZSVTp05l/vz57N69m++++45ly5bRv3//VqlTREQ6tueee46CggL++te/MnjwYGpra9m0aVPDz8eMGcOSJUsAuP7663n66aetKlWk1aknSqSN3H777VxzzTUMGDCAyspKdu7c2Wqv/eqrr/K3v/2NP/7xj+zbt4/Y2FhOP/10fvaznzX7NRwOB4WFhVx99dXk5uYSGxvLxRdfzIMPPthqdYqISMe0b98+7r33Xt555x3CwsLo3bs3TqeTdevWMXjwYADuvfdeHn30UcaOHYvdbue2226zuGqR1mMzDMOwuggRaXs2m42PPvqIyZMnt+h5v/nNbzh06BCzZs1qk7pERMT3/OIXvyAgIID33nuv4bFhw4ZxwQUX8PDDDzc8lpGRQXR0NHPmzNEwP+lQNJxPxI9cccUVpKamNmvbb7/9lvDwcN566602rkpERHzJp59+ytdff82zzz7b6PHBgwc3mlxi2bJlFBUVERUVpQAlHY56okT8hGcleYfDQffu3U+4fWVlJfv27QPMWf9OtN6ViIiIx759+7jggguYNWsWv/jFL3jjjTcYNGiQ1WWJtBqFKBERERFpNZWVlZx99tk8+uij/OQnP+GDDz7gP//5T6OhfyK+TiFKRERERESkBXRNlIiIiIiISAsoRImIiIiIiLSAQpSIiIiIiEgLKESJiIiIiIi0gEKUiIiIiIhICyhEiYiIiIiItIBClIiIiIiISAsoRImIiIiIiLSAQpSIiIiIiEgLKESJiIiIiIi0QIDVBVjN7Xazf/9+IiIisNlsVpcjIuI3DMOgtLSU5ORk7Had0zuS2iYREWs0t23y+xC1f/9+0tLSrC5DRMRv7dmzh9TUVKvLaFfUNomIWOtEbZPfh6iIiAjA/EVFRkZaXI2IiP8oKSkhLS2t4X1YDlPbJCJijea2TX4fojzDJCIjI9VQiYhYQMPVjqa2SUTEWidqmzQIXUREREREpAUUokRERERERFpAIUpERERERKQFFKJERERERERaQCFKRERERESkBdpViFq4cCGTJk0iOTkZm83GrFmzTvic+fPnc9ppp+F0OunVqxevvfZam9cpIiL+Qe2SiIg0pV2FqPLyctLT05kxY0aztt+5cycTJ07kzDPPJCsri1tvvZUbbriBL774oo0rFRERf6B2SUREmtKu1ok6//zzOf/885u9/cyZM+nevTtPPvkkAP3792fRokU8/fTTTJgwoa3KFJF2wDCgosLiAlxmAaGh4PWljo7YPw4rCjiC1ftvQ2qXRESkKe0qRLXU4sWLGT9+fKPHJkyYwK233nrM51RXV1NdXd1wv6SkpK3KE5E2sHMnPPsslJbCv/9tXR2hzgrK/x1uXQHtyS/LICDM6irahZNpl6B126ZPP/2UTz/9lMjISC666CJGjRp10q8lIiJNa1fD+VoqJyeHhISERo8lJCRQUlJCZWVlk8+ZPn06UVFRDV9paWneKFVEWskjj5ghysoAJXIsJ9MuQeu2TcuWLeOf//wnjz/+OGPGjOH9998/6dcSEZGm+XRP1Mm46667mDZtWsP9kpISBSkRH7Jrl3nbsydcfrn5/Z/+BAHefjczQil3lQEazocj1Lp9dxCt2TadddZZOBwOsrKy+Oijj7jlllv4+c9/jtPpbK1yRUT8nk+HqMTERHJzcxs9lpubS2RkJCEhIU0+x+l0qiER8WH79pm3//wnnH22lZXYAIuHsAVqOGF7czLtErRu2zRu3DjGjRtHbW0tXbt25cCBA3z55ZdMmjSpVV5fRER8fDjfqFGjmDdvXqPH5s6dq/HfIh1YYaF5GxtrbR0iTWlP7VJgYCAXX3wxgGYHFBFpZe0qRJWVlZGVlUVWVhZgThWblZVFdnY2YA53uPrqqxu2v+mmm9ixYwd//vOf2bRpEy+88ALvv/8+t912mxXli4gXFBebt9HRlpYhfsLX26Vx48YB5oQXIiLSetpViFq+fDnDhg1j2LBhAEybNo1hw4Zx3333AXDgwIGGhguge/fufPbZZ8ydO5f09HSefPJJ/vWvf2kaWZEOqrra/AKIirK2FvEPvt4uDR8+HIB169ZRW1trSQ0iIh2RzTAMw+oirFRSUkJUVBTFxcVERkZaXY6IHEdeHngmPqurA4fD2nrk1Oj999ha63fjdruJioqirKyMDRs20L9//1asUkSk42nu+2+76okSETkez1C+iAgFKJHmsNvt9OnTB4CtW7daXI2ISMehECUiPuPgQfO2Uydr6xDxJb169QIUokREWpNClIj4jKIi81YhSqT5unfvDsDu3bstrkREpONQiBIRn6GeKJGW69q1K6AQJSLSmhSiRMRnHDhg3sbHW1uHiC9JTU0FYJ9npWoRETllClEi4jO2bTNve/a0tg4RX+IJUXv37rW4EhGRjiPA6gJE5CRVV8Ojj0LnznD11RDg3f/OlZXw0r/r9xkUBDZbm+/zxRfN296923xXIh1GcnIyAHl5edTV1RHg5fcKEZGOSO+kIr5qzx544AHz+z/8weu7LyeGWynw+n4BBg60ZLciPik2Nha73Y7b7SY/P5+kpCSrSxIR8XkKUSK+qrDQ0t07qeYK3jbvXPpLr/WE9esHI0Z4ZVciHYLD4SA+Pp6cnBwOHDigECUi0goUokR8VWWledu7N6xa5fXdR4AnQkGoA9p+NJ+InKSEhARycnLIz8+3uhQRkQ5BIUrEV3kWTYqNhbAwa2sRkXYtvn5Ky7y8PIsrERHpGDQ7n4iv8gzni4mxtg4Raffi4uIAyM3NtbgSEZGOQSFKxFcV1E/qEBtrbR0i0u55QlShxddSioh0FApRIr7K82FIIUpETiC2/n2ioMCaGTVFRDoahSgRX+X5MKThfCJyAp6eKE0sISLSOhSiRHyVJ0TVfzgSETmWmPqTLRrOJyLSOhSiRHyVrokSkWZSiBIRaV0KUSK+yjMsRyFKRE7AE6J0TZSISOtQiBLxVZ4QpeF8InICnhBVVFSEYRgWVyMi4vsUokR8UXU1lJaa3ytEicgJdO7cGQCXy0VJSYnF1YiI+D6FKBFf5OmFCgiA6GhLSxGR9i8kJISQkBDA7I0SEZFToxAl4ovy8szbuDiw2aytRUR8giaXEBFpPQpRIr7IE6Li462tQ0R8hmdIn3qiREROnUKUiC9SiBKRFvKEqIMHD1pciYiI71OIEvFFubnmbUKCtXWIiM9QT5SISOtRiBLxRQpRItJCnhCla6JERE6dQpSIL8rJMW8VokSkmdQTJSLSehSiRHyRJ0QlJVlbh4j4DIUoEZHWoxAl4os8ISox0do6RMRnaDifiEjrUYgS8UUHDpi3ClEi0kzqiRIRaT0KUSK+pqoKPB+CkpOtrUVEfIZnsV2FKBGRU6cQJeJrPL1QTid06mRtLSLiMzwhSsP5REROnUKUiK/Zt8+8TU4Gm83aWkTEZxzZE2UYhsXViIj4NoUoEV+zf795m5JibR0i4lM8IcrlclFcXGxxNSIivk0hSsTX7N1r3qamWluHiPgUp9NJeHg4AAUFBRZXIyLi2xSiRHzNnj3mbVqatXWIiM+JjY0FID8/3+JKRER8m0KUiK/xhCj1RIlIC3lClHqiREROjUKUiK/JzjZvu3a1tg4R8Tnx8fEA5OXlWVyJiIhvU4gS8TW7d5u3XbpYW4eI+Jy4uDhAw/lERE6VQpSILykvB88Z5G7dLC1FRHxPQkICALm5uRZXIiLi29pdiJoxYwbdunUjODiYzMxMli5detztn3nmGfr27UtISAhpaWncdtttVFVVealaES/btcu8jYrSQrsiXtRR2qbExEQAcnJyLK5ERMS3tasQ9d577zFt2jTuv/9+Vq5cSXp6OhMmTDjm2O23336bO++8k/vvv5+NGzfyyiuv8N5773H33Xd7uXIRL9m+3bzt0cPaOkT8SEdqmzwh6sCBAxZXIiLi29pViHrqqae48cYbufbaaxkwYAAzZ84kNDSUf//7301u//333zNmzBiuvPJKunXrxrnnnssVV1xxwjOEIj7LE6J69rS2DhE/0pHappT6Rbr37dtncSUiIr6t3YSompoaVqxYwfjx4xses9vtjB8/nsWLFzf5nNGjR7NixYqGhmnHjh3Mnj2bCy644Jj7qa6upqSkpNGXiM/YutW87dPH2jpE/ERHa5uODFGGYbTJPkRE/EGA1QV4FBQU4HK5Gi569UhISGDTpk1NPufKK6+koKCAM844A8MwqKur46abbjrukInp06fz4IMPtmrtIl7j+b+gECXiFR2tbUqtX1+usrKSgoKChtn6RESkZdpNiDoZ8+fP55FHHuGFF14gMzOTbdu2ccstt/DQQw9x7733Nvmcu+66i2nTpjXcLykpIS0tzVslSytyu918uOlD0hPSSY5I9n4B1dXY164DIDggGJvN1rb7Mwz45hvz+/7923ZfInLS2nPb5HQ6SU5OZv/+/ezcuVMhSkTkJLWbEBUbG4vD4Thq2tXc3NyGC2F/7N577+XXv/41N9xwAwCDBw+mvLyc3/72t/zlL3/Bbj96tKLT6cTpdLb+AYjXbTu4jUs/uNSy/Xc7CDuftWDHTicMGmTBjkX8T0dsm3r27Mn+/fvZunUrI0eO9Mo+RUQ6mnYTooKCgsjIyGDevHlMnjwZMHsa5s2bx9SpU5t8TkVFxVGNkcPhANBYbz+wYv8KS/fvssHuKPP7tKg07LRxTxSA3Q433gihoW2/LxHpkG1Tv379+Pbbb9m4caPVpYiI+Kx2E6IApk2bxjXXXMPw4cMZOXIkzzzzDOXl5Vx77bUAXH311aSkpDB9+nQAJk2axFNPPcWwYcMahkzce++9TJo0qaHBko5r1YFVAFwz5BpmTJxhTRGPmTe2wFBo6+F8ImKJjtY2DRkyBIBVq1ZZXImIiO9qVyHqsssuIz8/n/vuu4+cnByGDh3KnDlzGi7ozc7ObnR275577sFms3HPPfewb98+4uLimDRpEg8//LBVhyBe9N3e7wD4afefEhYUZm0xItJhdbS2KTMzEzCnYne5XO0i2ImI+Bqb0R7GFliopKSEqKgoiouLiYyMtLocaaaymjI6P9aZWnct2/+wnR6dtPisiK/R+++xteXvpq6ujoSEBIqKipgzZw4TJkxo1dcXEfFlzX3/bTfrRIm0xDc7v6HWXUu36G50j+5udTkiIj4jICCAq666CoDbb7+dnJwciysSEfE9ClHik/635X8AXNDrgrafWlxEpIP5y1/+QkJCAuvWrWPw4MF88sknVpckIuJTFKLE59S4avhw44cAXNT/IourERHxPQkJCSxYsIAhQ4ZQUFDAz3/+cx599FGryxIR8RkKUeJzPtn8CYWVhSSGJ/LTbj+1uhwREZ/Ut29fli5dyi233AKYC/4+9dRTFlclIuIbFKLEpxiGwdM/PA3A9cOuJ8DeriaYFBHxKU6nk2eeeYZHHnkEMK+Rmjt3rsVViYi0fwpR4lO+2vEV3+/5HqfDyZQRU6wuR0SkQ7jzzju54YYbMAyD3/zmNxQXF1tdkohIu6YQJT7DMAzu/eZeAG4afhNJEUkWVyQi0jHYbDaeffZZevXqxf79+3nggQesLklEpF3TWCgf5na7ef755zn77LPp1q2b1/dfUl3C4n2LAXNISFvPkpeVk8WSfUsICQjhrjPuatN9iYj4AWVuawAAMO9JREFUm9DQUGbMmMGECROYMWMGt9xyiyVti4iIL1CI8mHLli1ruCDYEvHA/3l/tzcPv5mE8ATv71hEpIM799xzGT9+PF999RWPPvooM2fOtLokEZF2SSHKh82ePdvaAuqA/ea3w4YNw25v+9GhQxKG8Ncz/9rm+xER8Vf33nsvX331Fa+//jp/+9vfiI2NtbokEZF2RyHKhy1atAiAp59+mhtvvNHSWkJDQ7XorYhIBzB27FhOO+00Vq5cyWuvvcbtt99udUkiIu2OQpSPcrvdLFu2DICzzjqLsLAwiysSEZGOwGazcdNNN/Hb3/6Wf//73/zxj3/USTIRkR/R7Hw+aseOHZSWluJ0OhkwYIDV5YiISAdy2WWXERwczMaNG1m1apXV5YiItDsKUT5q/fr1AAwYMICAAHUoiohI64mMjORnP/sZAO+//77F1YiItD8KUT5q06ZNAPTv39/iSkREpCO65JJLAPjwww8trkREpP1RiPJR27ZtA6B3794WVyIiIh3R+eefT2BgIFu3bmXLli1WlyMi0q4oRPmoHTt2ANCjRw+LKxERkY4oMjKSsWPHAvD5559bXI2ISPuiEOWjsrOzAbSavIiItJkJEyYAMHfuXIsrERFpXxSifJDb7WbPnj0ApKWlWVyNiIh0VOPHjwdg4cKF1NXVWVyNiEj7oRDlgwoKCqiurgYgJSXF4mpERKSjSk9PJzo6mtLSUrKysqwuR0Sk3VCI8kH79u0DID4+nqCgIIurERGRjsrhcDBmzBgAFi1aZHE1IiLth0KUD9q/fz+gXigREWl7nhD1/fffW1yJiEj7oRDlgw4cOABAUlKSxZWIiEhHN2rUKAB++OEHiysREWk/FKJ8UE5ODqAQJSIibS8jIwObzcaePXsa2h8REX+nEOWDPI1YYmKixZWIiEhHFxERQb9+/QBYsWKFxdWIiLQPClE+KDc3FzAnlhAREWlrGRkZAKxatcriSkRE2geFKB/kCVEJCQkWVyIiIv5g6NChAJrmXESknkKUD8rPzwfUEyUiIt6Rnp4OwOrVqy2uRESkfVCI8kF5eXmAQpSIiHjHkCFDANi+fTvl5eUWVyMiYj2FKB9TV1dHUVERAHFxcRZXIyIi/iA+Pp64uDgMw2Djxo1WlyMiYjmFKB/jCVA2m42YmBiLqxEREX8xaNAgANavX29xJSIi1lOI8jGe66E6d+6Mw+GwuBoREfEX/fv3B1BPlIgIClE+p6CgAIDY2FiLKxEREX/iCVGbNm2yuBIREespRPkYT4jSUD4REfGmvn37AgpRIiKgEOVzPCFKk0qIiIg3eULUjh07qKurs7gaERFrKUT5mMLCQkDD+URExLtSU1MJDg6mtraW3bt3W12OiIilFKJ8jIbziYiIFex2Oz179gTM9aJERPyZQpSP8fREKUSJiIi39erVC4CtW7daXImIiLUUonyMeqJERMQqPXr0AMzrokRE/JlClI9RT5SIiFhFIUpExNTuQtSMGTPo1q0bwcHBZGZmsnTp0uNuf+jQIaZMmUJSUhJOp5M+ffowe/ZsL1XrfQpRIiLep7bJ5AlRO3futLgSERFrBVhdwJHee+89pk2bxsyZM8nMzOSZZ55hwoQJbN68mfj4+KO2r6mp4ZxzziE+Pp7//Oc/pKSksHv3bqKjo71fvJcoRImIeJfapsO6d+8OKESJiNgMwzCsLsIjMzOTESNG8PzzzwPgdrtJS0vj97//PXfeeedR28+cOZPHH3+cTZs2ERgYeFL7LCkpISoqiuLiYiIjI0+p/rbmcrkIDAzEMAxycnJISEiwuiQRkZPmK++/apsOKy8vJzw8HICDBw92iGAoInKk5r7/tpvhfDU1NaxYsYLx48c3PGa32xk/fjyLFy9u8jmffPIJo0aNYsqUKSQkJDBo0CAeeeQRXC7XMfdTXV1NSUlJoy9fcfDgQTyZt3PnzhZXIyLS8altaiwsLKxhncJdu3ZZW4yIiIXaTYgqKCjA5XId1buSkJBATk5Ok8/ZsWMH//nPf3C5XMyePZt7772XJ598kr/97W/H3M/06dOJiopq+EpLS2vV42hLRUVFAERGRp702U0REWk+tU1H69q1KwB79uyxuBIREeu0mxB1MtxuN/Hx8bz00ktkZGRw2WWX8Ze//IWZM2ce8zl33XUXxcXFDV++1AjoeigRkfavo7dNnhC1e/duiysREbFOu5lYIjY2FofDQW5ubqPHc3NzSUxMbPI5SUlJBAYG4nA4Gh7r378/OTk51NTUEBQUdNRznE4nTqezdYv3Ek+I0lA+ERHvUNt0tNTUVEA9USLi39pNT1RQUBAZGRnMmzev4TG32828efMYNWpUk88ZM2YM27Ztw+12Nzy2ZcsWkpKSmmykfJ1nOJ96okREvENt09E8Qw0VokTEn7WbEAUwbdo0Xn75ZV5//XU2btzIzTffTHl5Oddeey0AV199NXfddVfD9jfffDNFRUXccsstbNmyhc8++4xHHnmEKVOmWHUIbUrD+UREvE9tU2OeELV3716LKxERsU67Gc4HcNlll5Gfn899991HTk4OQ4cOZc6cOQ0X9GZnZ2O3H859aWlpfPHFF9x2220MGTKElJQUbrnlFu644w6rDqFNaTifiIj3qW1qLCUlBVCIEhH/1q7WibJCe12Loyk333wzM2fO5L777uPBBx+0uhwRkVPiS++/3taefze7du2ie/fuOJ1OKisrsdlsVpckItJqfG6dKDkxDecTERGrJScnA+baVp5rdUVE/I1ClA/RxBIiImK1oKAg4uLiANi3b5/F1YiIWEMhyoeoJ0pERNoDT2/U/v37La5ERMQaClE+RCFKRETag6SkJEAhSkT8l0KUD1GIEhGR9sATog4cOGBxJSIi1lCI8hGVlZVUVFQAClEiImIthSgR8XcKUT7C0wvlcDja3XS3IiLiXxSiRMTfKUT5CE+Iio2N1ZocIiJiqcTERABycnIsrkRExBoBVhcgzVNQUABoKJ+ISHPU1taSk5NDRUUFcXFxdO7c2eqSOhSFKBHxd+qJ8hGaVEJE5PhKS0t58cUXGTduHJGRkXTr1o3+/fsTFxdH165dufHGG1m2bJnVZXYIClEi4u8UonyEpyfKs8ChiIgc9tRTT9GtWzdeffVVxo8fz6xZs8jKymLLli0sXryY+++/n7q6Os4991zOO+88tm7danXJPi0hIQGAiooKysvLLa5GRMT7NJzPR2g4n4jIsS1btoyFCxcycODAJn8+cuRIrrvuOmbOnMmrr77Kt99+S+/evb1cZccRHh5OSEgIlZWV5Obm0qNHD6tLEhHxKoUoH6HhfCIix/bOO+80azun08lNN93UxtV0fDabjYSEBHbt2qUQJSJ+SSHKR+Tn5wMazici0pTRo0czdOjQhq8hQ4YQHBxsdVkdWnx8PLt27SIvL8/qUkREvE4hykd4hvPFxsZaXImISPtzwQUXsGbNGp588km2b9+OzWajd+/ejYLV0KFDiY+Pt7rUDsNzXZRClIj4o5MKUaNGjWLYsGE64+dF6okSETm2e+65p+H7pUuXMnnyZAYNGoTNZuO1115j06ZNDUPQ9u/fb2GlHYenPcrNzbW4EhER7zupEDVx4kSd8fMy9USJiDTPzTffzIwZM7jooosaHps9eza//e1vueaaayysrGPxtPGek3wiIv7kpEKUzvh5l2EY6okSEWmmjRs3MnTo0EaPXXDBBbzwwgs8/fTT1hTVASlEiYg/O+V1ojxn/N5//33eeustNmzYwKeffkpSUhLXXntta9To98rKyqiurgYUokRETmTEiBG8/vrrRz0+ePBgli5dakFFHZOnPdI1USLij045RB3vjN/3339/qi8vHD7LFxISQlhYmMXViIi0b0899RRPP/001157LWvWrMHtdlNVVcWTTz6pIdGtyBOi1BMlIv7olEOUzvi1PU8DpWvMREROLCMjgyVLlrBnzx6GDh1KSEgIERERvPLKK0yfPt3q8joMTyBViBIRf3TKU5w/9dRTnHXWWezevZvbbruNQYMGUVNTozN+rcgzVEJD+UREmqdfv3589dVXZGdnk5WVhd1uJyMjg6SkJKtL6zA8bVJBQQGGYWCz2SyuSETEe045RHnO+E2dOpWhQ4cSGBiI2+0mICCAV155pTVq9HsKUSIiJ6dLly506dLF6jI6JM+J0traWkpKSoiKirK4IhER72mVxXZ1xq9teUKUZ2FDERFpLDs7u0Vhad++faSkpLRhRR1faGgooaGhVFRUUFBQoBAlIn7llK+JOlKXLl248MIL+dnPfqYA1Yo8IUrXRImING3EiBH87ne/Y9myZcfcpri4mJdffplBgwbx3//+14vVdVyaXEJE/FWr9ERJ2/KsBq+eKBGRpk2cOJHw8HDOOeccgoODycjIIDk5meDgYA4ePMiGDRtYv349p512Gn//+9+54IILrC65Q4iNjWX37t0UFhZaXYqIiFcpRPkAhSgRkeP7f//v/7Fnzx4eeugh4uLiSEpKoqCggMrKSmJjY7nqqquYMGECgwYNsrrUDsVzXVRBQYHFlYiIeJdClA9QiBIROb7k5GSysrKYMGEClZWVPPLIIxoC7QUKUSLir1r1mihpGwpRIiLH98c//pFJkyYxduxYbDYbb731FsuWLaOystLq0jq0mJgYAA3nExG/oxDVztXW1jac4UtMTLS4GhGR9un3v/89y5cv57zzzsMwDGbMmMGoUaOIjIykf//+XH755Tz66KN8/vnnVpfaoXhClHqiRMTfaDhfO+eZmc/hcDQ0ViIicrQhQ4YwZMgQXnvtNRYvXkxYWBhr1qwhKyuLrKwsPv74Yx5++GFKS0utLrXD8AznU0+UiPgbhah27sCBA4A5lM9uV8ehiMiJbN26teH7zMxMMjMzG+4bhmFFSR2WeqJExF/pU3k75wlRWndLROTU2Ww2q0voUHRNlIj4K4Wodk4hSkRE2iuFKBHxVwpR7ZwnRCUnJ1tciYiISGNHXhOloZIi4k8Uotq5/fv3AwpRIiLS/nTu3BkwZ5ItKyuzuBoREe9RiGrnPCFKw/lERKS9CQ0Nxel0AhrSJyL+RSGqndu3bx8AKSkpFlciIiLSmM1ma7guqqioyOJqRES8RyGqnVOIEhGR9kyTS4iIP1KIaseqq6sbFttNTU21uBoREZGjea6LUogSEX/S7kLUjBkz6NatG8HBwWRmZrJ06dJmPe/dd9/FZrMxefLkti3QizzXQzmdzoYzfSIi4n1qm45NPVEi4o/aVYh67733mDZtGvfffz8rV64kPT2dCRMmNPTGHMuuXbu4/fbbGTt2rJcq9Y49e/YAZi+UFogUEbGG2qbjU4gSEX/UrkLUU089xY033si1117LgAEDmDlzJqGhofz73/8+5nNcLhdXXXUVDz74ID169PBitW3PE6K6dOlicSUiIv5LbdPxaWIJEfFH7SZE1dTUsGLFCsaPH9/wmN1uZ/z48SxevPiYz/vrX/9KfHw8119/fbP2U11dTUlJSaOv9io7OxuAtLQ0iysREfFPaptOTNdEiYg/ajchqqCgAJfLRUJCQqPHExISyMnJafI5ixYt4pVXXuHll19u9n6mT59OVFRUw1d7DiieENW1a1eLKxER8U9qm05MPVEi4o/aTYhqqdLSUn7961/z8ssvExsb2+zn3XXXXRQXFzd8eYbMtUe7d+8GNJxPRMRX+EPb9GO6JkpE/FGA1QV4xMbG4nA4yM3NbfR4bm4uiYmJR22/fft2du3axaRJkxoec7vdAAQEBLB582Z69ux51POcTmfD6urtnSdEdevWzdpCRET8lNqmE9NwPhHxR+2mJyooKIiMjAzmzZvX8Jjb7WbevHmMGjXqqO379evH2rVrycrKavi68MILOfPMM8nKyvKpoRBNMQyDXbt2ARrOJyJiFbVNJ6aeKBHxR+2mJwpg2rRpXHPNNQwfPpyRI0fyzDPPUF5ezrXXXgvA1VdfTUpKCtOnTyc4OJhBgwY1en50dDTAUY/7ory8PCoqKrDZbBrOJyJiIbVNx+cZtnjw4EFcLhcOh8PiikRE2l67ClGXXXYZ+fn53HfffeTk5DB06FDmzJnTcEFvdnY2dnu76TxrUzt27AAgJSXFZ4d4iIh0BGqbjs8znA/MINWSa8FERHyVzTAMw+oirFRSUkJUVBTFxcVERkZaXU6Dt956i1/96leMGzeO+fPnW12OiEira6/vv+2Br/1uoqOjKS4uZtOmTfTt29fqckRETlpz33/999RZO7d9+3aAJi9AFhERaU90XZSI+BuFqHZq69atAPTu3dviSkRERI7PM4SvoKDA4kpERLxDIaqd8oSoXr16WVyJiIjI8aknSkT8jUJUO2QYBlu2bAGgT58+FlcjIiJyfOqJEhF/oxDVDhUUFHDw4EFAPVEiItL+eUJUfn6+xZWIiHiHQlQ7tGnTJgC6dOlCaGioxdWIiIgcn3qiRMTfKES1Qxs3bgRgwIABFlciIiJyYgpRIuJvFKLaofXr1wMKUSIi4hvi4uIADecTEf+hENUOeULUwIEDLa5ERETkxBSiRMTfKES1Q2vXrgVg8ODBFlciIiJyYvHx8QDk5eVZXImIiHcoRLUzOTk55OXlYbfb1RMlIiI+wROiSktLqaqqsrgaEZG2pxDVzmRlZQHQu3dvzcwnIiI+ISoqisDAQEBD+kTEPyhEtTOrVq0CYNiwYRZXIiIi0jw2m62hNyo3N9fiakRE2p5CVDuzfPlyADIyMiyuREREpPkSEhIAc1i6iEhHpxDVzixduhSA4cOHW1yJiIhI8yUmJgIKUSLiHxSi2pF9+/axd+9e7Ha7eqJERMSnJCUlAXDgwAGLKxERaXsKUe3I4sWLAXNq84iICIurERERaT5PiNq/f7/FlYiItD2FqHbk22+/BWDMmDEWVyIiItIyKSkpgDmqQkSko1OIakcWLlwIwE9+8hOLKxEREWmZ1NRUQCFKRPyDQlQ7UVhYyOrVqwEYN26cxdWIiIi0TFpaGgDZ2dkWVyIi0vYUotqJr776CsMwGDRoUMMMRyIiIr6ia9euABQUFFBRUWFxNSIibUshqp34/PPPAZgwYYLFlYiIiLRcVFQUUVFRAOzcudPiakRE2pZCVDvgcrn47LPPAJg4caLF1YiIiLSczWajZ8+eAGzfvt3iakRE2pZCVDuwaNEiCgoK6NSpE2eccYbV5YiIiJyUPn36ALB582aLKxERaVsKUe3Ae++9B8DPf/5zAgMDLa5GRETk5PTv3x+A9evXW1yJiEjbUoiyWHV1dUOIuvLKKy2uRkRE5OQNGTIEgKysLGsLERFpYwpRFvvoo48oKioiJSWFs846y+pyRERETtrw4cMBWLduHWVlZRZXIyLSdgKsLsDfPffccwBcf/31OBwOi6sRERE5eampqXTt2pXdu3fzxRdfcN5553m9hk2Fm1iTtwaAoKAgbDZbm+7PbrMzrus4kiKS2nQ/ItK+KERZaNGiRXz//fcEBQVx0003WV2OiIjIKRs/fjyvvPIKl1xyiTUFjAK8vFpIXGgca29eS0J4gnd3LCKWUYiyiGEY3HPPPQD85je/ISlJZ7BERMT3XXjhhbzyyivWFVAM1M+wfuZZZ7b5KI/l+5eTX5HPo4se5enznm7TfYlI+6EQZZEPPviABQsW4HQ6G8KUiIiIr5s0aVK7uR4qNDS0zYfzfbn9Syb8vwn8c8U/+fOYP2tYn4ifUIiyQE5ODr///e8BuOOOO0hLS7O4IhERkdZhs9kICwuzugyvOafHOYxKHcXivYt55NtHeO6C56wuSUS8QLPzeVlVVRWXXnopeXl5DB48mLvvvtvqkkREROQk2Ww2/nbW3wB4eeXL5JXnWVyRiHiDQpQXlZSUcOGFF7Jo0SIiIyN5//33cTqdVpclIiIip+DMbmcyInkE1a5qXllp4fVgIuI1ClFesnjxYjIyMpg7dy6hoaF88skn9OvXz+qyRERE5BTZbDZuGm7OsvvGmjcwDMPiikSkrSlEecFbb73F2LFj2bZtG2lpaSxYsIBx48ZZXZaIiIi0kksGXILT4WRTwSbW56+3uhwRaWMKUW1sz5493HDDDbhcLi6//HJWr17dsKK7iIiIdAyRzkjG9xgPwP82/8/iakSkrSlEtbGZM2dSVVXF2LFjeeutt+jUqZPVJYmIiEgbuKD3BQB8ueNLiysRkbamENXGPvnkEwBuvvlm7Hb9ukVERDoqT0/U4j2LqaqrsrgaEWlL+lTfhg4dOsS6desAGD9+vMXViIiISFvq3bk3CWEJVLuqWbF/hdXliEgbaneL7c6YMYPHH3+cnJwc0tPTee655xg5cmST27788su88cYbDUElIyODRx555Jjbe9uqVasA6NatG3FxcRZXIyIiJ6sjtU3Sdmw2G6enns7Hmz9mwe4FDE0c6v0a1q3D8c0CAIIcQdhstrbfaWgo/PKXEBXV9vsSaSfaVYh67733mDZtGjNnziQzM5NnnnmGCRMmsHnzZuLj44/afv78+VxxxRWMHj2a4OBgHnvsMc4991zWr19PSkqKBUfQ2MaNGwEYPHiwxZWIiMjJ6mhtk7StYYnD+Hjzx/zl67/wl6//4vX937gcXvrU67uFVavghRcs2LGINWxGO1rMIDMzkxEjRvD8888D4Ha7SUtL4/e//z133nnnCZ/vcrno1KkTzz//PFdffXWz9llSUkJUVBTFxcVERkaeUv0/duutt/Lss89y++238/jjj7fqa4uI+Lq2fP9tTR2tbZK29dX2rzjn/51j2f7P2gHXrzS//+XASwmwt/H58u+/h927oUcP2L69bfcl4gXNff9tNz1RNTU1rFixgrvuuqvhMbvdzvjx41m8eHGzXqOiooLa2lo6d+58zG2qq6uprq5uuF9SUnLyRZ/Azp07AejRo0eb7UNERNpOR2ybpG2d3eNsyu4qs7oMAByBodDWw/ny8iAhAXbuhMpKCAlp2/2JtBPtJkQVFBTgcrlISEho9HhCQgKbNm1q1mvccccdJCcnH3cSh+nTp/Pggw+eUq3NtXv3bgC6du3qlf2JiEjr6ohtk7Qtm81GWFCY1WV4T1wcdO4MRUWwZQukp1tdkYhXdJjZ+R599FHeffddPvroI4KDg4+53V133UVxcXHD1549e9qspr179wKQlpbWZvsQEZH2qz22TSKtymaDPn3M77dssbYWES9qNz1RsbGxOBwOcnNzGz2em5tLYmLicZ/7xBNP8Oijj/LVV18xZMiQ427rdDpxOp2nXO+JVFVVUVhYCKALiUVEfFRHa5tE2kSvXvDDD7omSvxKu+mJCgoKIiMjg3nz5jU85na7mTdvHqNGjTrm8/7+97/z0EMPMWfOHIYPH+6NUpslJycHgODgYDp16mRxNSIicjI6Wtsk0iZ69jRvFaLEj7SbniiAadOmcc011zB8+HBGjhzJM888Q3l5Oddeey0AV199NSkpKUyfPh2Axx57jPvuu4+3336bbt26NQSX8PBwwsPDLTsOOByiEhMTvbNGg4iItImO1DaJtAnPBFo7dlhbh4gXtasQddlll5Gfn899991HTk4OQ4cOZc6cOQ0X9GZnZ2O3H+48e/HFF6mpqeGSSy5p9Dr3338/DzzwgDdLP4qn0fzxxcgiIuJbOlLbJNImunc3b+tnJRbxB+1qnSgrtNVaHC+99BK/+93vmDRpEp988kmrva6ISEehtZCOTb8b8Sl79kCXLhAYaE5z7nBYXZHISWvu+2+7uSaqo8nPzwcgLi7O4kpERERE2lBSkhmcamuhfiSOSEenENVG8vLyAIUoERER6eACAsAzE7Gm5xc/oRDVRgoKCgCFKBEREfEDqanmrUKU+AmFqDbiWSMqNjbW4kpERERE2lhamnm7d6+1dYh4iUJUG/H0RMXExFhciYiIiEgb8wzn27fP2jpEvEQhqo2oJ0pERET8hkKU+BmFqDbiCVHqiRIREZEOLznZvN2/39o6RLxEIaoN1NbWUlpaCkDnzp0trkZERESkjXlC1IED1tYh4iUKUW2gqKgIAJvNRnR0tLXFiIiIiLS1pCTzVj1R4icUotqAJ0RFR0fj0KrdIiIi0tF5QlR5OZSVWVuLiBcoRLUBT4jSUD4RERHxC+HhEBZmfq8hfeIHFKLagCdEaVIJERER8RuJieZtbq61dYh4gUJUG/CEqE6dOllciYiIiIiXJCSYtwpR4gcUotqAeqJERETE7yhEiR9RiGoD6okSERERv+MJUXl51tYh4gUKUW1AIUpERET8Tny8easQJX5AIaoNFBYWAhrOJyIiIn7EE6I0nE/8gEJUG9A1USIiIuJ34uLM2/x8a+sQ8QKFqDbg6YnSOlEiIiLiNxSixI8oRLUBDecTERERv+MJUbomSvyAQlQbUIgSERERv+MJUQcPgstlbS0ibUwhqpXV1NRQVlYGKESJiIiIH/FcxmAYUH99uEhHpRDVyjyTStjtdqKjo60tRkRERMRbAgPBs7yLrouSDk4hqpXl179pxMTEYLfr1ysiIiJ+JDbWvK2/tEGko9Kn/FZWUFAAQKznTURERETEX3guZVCIkg5OIaqVHdkTJSIiIuJXPJ9/6k8qi3RUClGtzBOi4j2rdouIiIj4C89IHIUo6eAUolpZXv3aCApRIiIi4nc8M/Rpdj7p4AKsLqCj8fRExXnWShARERHxF54QlZMD5eVe331hIezYVd9HEBwMNptX9jtoEISEeGVX0k4oRLWynJwcABITEy2uRERERMTLIiLM2zffNL+87Auu4Cre9vp+R46EJUu8vluxkEJUKztw4AAASUlJFlciIiIi4mUWX84QSgVd2G3eSUsDW9teuVJUBGVlsHSp2fEWFtamu5N2RCGqlXlClHqiRERExO9cfjlceKFlu59c/wVAqA28MJovNtYcRrh1Kwwd2vb7k/ZBIaoVud1u9u/fD0BKSorF1YiIiIh4mc3md90xvXqZIWr7doUof6LZ+VpRQUEBtbW12Gw2DecTERER8QPdupm32dmWliFephDVirLr//ckJiYSGBhocTUiIiIi0tY86wsfPGhtHeJdClGtaNeuXQB085ySEBEREZEOrVMn81Yhyr8oRLWi7du3A9C9e3eLKxERERERb1CI8k8KUa1o69atAPTu3dviSkRERETEGxSi/JNCVCtav349AP3797e4EhERERHxhshI87a42No6xLsUolqJy+Vi7dq1AAwePNjiakRERETEG6KizFuFKP/S7kLUjBkz6NatG8HBwWRmZrJ06dLjbv/BBx/Qr18/goODGTx4MLNnz/ZSpY2tWbOG8vJyIiIi6Nu3ryU1iIhI2/DVtklE2p5ClH9qVyHqvffeY9q0adx///2sXLmS9PR0JkyYQF5eXpPbf//991xxxRVcf/31rFq1ismTJzN58mTWrVvn5crh888/B2Ds2LE4HA6v719ERNqGL7dNItL2oqPNW10T5V9shmEYVhfhkZmZyYgRI3j++ecBcLvdpKWl8fvf/54777zzqO0vu+wyysvL+fTTTxseO/300xk6dCgzZ85s1j5LSkqIioqiuLiYSM+g1mZyuVxUVlZy8OBBxowZw549e3jllVe47rrrWvQ6IiL+6FTef73J19omEfGu0tLD10Xt3w92u/kVGmpBMYYBrgrA3L/NZt3+cVhRwBFOcv/Nff8NOJXaWlNNTQ0rVqzgrrvuanjMbrczfvx4Fi9e3ORzFi9ezLRp0xo9NmHCBGbNmnXM/VRXV1NdXd1wv6Sk5KRrXrx4MWPHjm2436VLF6644oqTfj0REWlffLFtEhHvioiA8HAoK4PkZGtrCXVWUP7vcGuLaC9+WQYBYW328u1mOF9BQQEul4uEhIRGjyckJJCTk9Pkc3Jyclq0PcD06dOJiopq+EpLSzvpmu32w7++4cOH89lnnxESEnLSryciIu2LL7ZNIuJ9v/iF1RWIt7WbnihvueuuuxqdISwpKTnpxiozM5Py8nICAwMJDAxsrRJFRMTPtGbbJCLe99pr8OKL5mi2igpzOJ/TaUEhRijlrjJAw/lwtO14ynYTomJjY3E4HOTm5jZ6PDc3l8TExCafk5iY2KLtAZxOJ85W+lftcDgItWTAq4iIeIMvtk0iYg3PYCRrPxragLYbwtYsgf4xnLDdDOcLCgoiIyODefPmNTzmdruZN28eo0aNavI5o0aNarQ9wNy5c4+5vYiISEuobRIRkaa0m54ogGnTpnHNNdcwfPhwRo4cyTPPPEN5eTnXXnstAFdffTUpKSlMnz4dgFtuuYVx48bx5JNPMnHiRN59912WL1/OSy+9ZOVhiIhIB6K2SUREfqxdhajLLruM/Px87rvvPnJychg6dChz5sxpuEA3Ozu70WQOo0eP5u233+aee+7h7rvvpnfv3syaNYtBgwZZdQgiItLBqG0SEZEfa1frRFlBa3GIiFhD77/Hpt+NiIg1mvv+226uiRIREREREfEFClEiIiIiIiItoBAlIiIiIiLSAgpRIiIiIiIiLaAQJSIiIiIi0gLtaopzK3gmJywpKbG4EhER/+J53/XzSWKbpLZJRMQazW2b/D5ElZaWApCWlmZxJSIi/qm0tJSoqCiry2hX1DaJiFjrRG2T368T5Xa72b9/PxEREdhsthY/v6SkhLS0NPbs2eOXa3no+HX8On4d/8kev2EYlJaWkpyc3GixWlHbdKp0/Dp+Hb+Ov63bJr/vibLb7aSmpp7y60RGRvrlP1QPHb+OX8ev4z8Z6oFqmtqm1qHj1/Hr+HX8J6M5bZNO/YmIiIiIiLSAQpSIiIiIiEgLKESdIqfTyf3334/T6bS6FEvo+HX8On4dv78ef3vm738bHb+OX8ev42/r4/f7iSVERERERERaQj1RIiIiIiIiLaAQJSIiIiIi0gIKUSIiIiIiIi2gECUiIiIiItICClGnYMaMGXTr1o3g4GAyMzNZunSp1SV5xfTp0xkxYgQRERHEx8czefJkNm/ebHVZlnn00Uex2WzceuutVpfiVfv27eNXv/oVMTExhISEMHjwYJYvX251WV7hcrm499576d69OyEhIfTs2ZOHHnqIjjpPz8KFC5k0aRLJycnYbDZmzZrV6OeGYXDfffeRlJRESEgI48ePZ+vWrdYUK2qb1DYB/tk2qV3yn3YJrG+bFKJO0nvvvce0adO4//77WblyJenp6UyYMIG8vDyrS2tzCxYsYMqUKfzwww/MnTuX2tpazj33XMrLy60uzeuWLVvGP//5T4YMGWJ1KV518OBBxowZQ2BgIJ9//jkbNmzgySefpFOnTlaX5hWPPfYYL774Is8//zwbN27kscce4+9//zvPPfec1aW1ifLyctLT05kxY0aTP//73//OP/7xD2bOnMmSJUsICwtjwoQJVFVVeblSUduktgn8s21Su+Rf7RK0g7bJkJMycuRIY8qUKQ33XS6XkZycbEyfPt3CqqyRl5dnAMaCBQusLsWrSktLjd69extz5841xo0bZ9xyyy1Wl+Q1d9xxh3HGGWdYXYZlJk6caFx33XWNHrv44ouNq666yqKKvAcwPvroo4b7brfbSExMNB5//PGGxw4dOmQ4nU7jnXfesaBC/6a26TC1Tf7VNqld8t92yTCsaZvUE3USampqWLFiBePHj294zG63M378eBYvXmxhZdYoLi4GoHPnzhZX4l1Tpkxh4sSJjf4d+ItPPvmE4cOHc+mllxIfH8+wYcN4+eWXrS7La0aPHs28efPYsmULAKtXr2bRokWcf/75FlfmfTt37iQnJ6fR/4OoqCgyMzP98v3QSmqbGlPb5F9tk9oltUtH8kbbFNAqr+JnCgoKcLlcJCQkNHo8ISGBTZs2WVSVNdxuN7feeitjxoxh0KBBVpfjNe+++y4rV65k2bJlVpdiiR07dvDiiy8ybdo07r77bpYtW8Yf/vAHgoKCuOaaa6wur83deeedlJSU0K9fPxwOBy6Xi4cffpirrrrK6tK8LicnB6DJ90PPz8Q71DYdprbJ/9omtUtql47kjbZJIUpOyZQpU1i3bh2LFi2yuhSv2bNnD7fccgtz584lODjY6nIs4Xa7GT58OI888ggAw4YNY926dcycOdMvGqv333+ft956i7fffpuBAweSlZXFrbfeSnJysl8cv0h7p7bJ/9omtUtql7xNw/lOQmxsLA6Hg9zc3EaP5+bmkpiYaFFV3jd16lQ+/fRTvvnmG/5/e3cTElXfh3H8mntszLecEtMsJqdwUrOFKUVKkNSiFoGVaSaZRbuk1BSCkF6o3EXaQnAzhRSusiirTaVEkJpkCEEiaQMRSYGYKRLjeRah5FN0O90zc6z5fsCFh79nLgc8Fz/nP2dWrFhhdpyg6enp0fDwsNavX6+wsDCFhYWpo6NDDQ0NCgsLk9frNTtiwC1btkzp6emzjqWlpcnj8ZiUKLhqamp08uRJ7du3T+vWrdOBAwdUWVmpuro6s6MF3fQ1L9Svh/MB3fQN3RSa3UQv0UvfC0Y3MUT9BpvNpqysLD18+HDm2NTUlB4+fKhNmzaZmCw4DMNQeXm5Wltb9ejRIzmdTrMjBdXWrVvV19en3t7ema/s7GyVlJSot7dXVqvV7IgBl5ub+8Otg/v7+7Vy5UqTEgXX+Pi4/vln9uXTarVqamrKpETmcTqdSkxMnHU9HB0dVWdnZ0hcD+cTuoluCuVuopfope8Fo5vYzvebqqqqdPDgQWVnZ2vDhg26fPmyvnz5okOHDpkdLeCOHj2qGzdu6Pbt24qJiZnZWxobG6uIiAiT0wVeTEzMD3vso6KiFBcXFzJ77ysrK5WTk6OLFy+qsLBQXV1dampqUlNTk9nRgmLnzp26cOGCHA6H1q5dqxcvXujSpUs6fPiw2dECYmxsTAMDAzPfDw4Oqre3V0uWLJHD4VBFRYXOnz+vlJQUOZ1O1dbWKikpSfn5+eaFDlF0E930vVDqJnoptHpJmgfd5Jd7/IWoK1euGA6Hw7DZbMaGDRuMZ8+emR0pKCT99MvtdpsdzTShdBvZaXfu3DEyMjKM8PBwIzU11WhqajI7UtCMjo4ax48fNxwOh7Fw4UJj1apVxqlTp4zJyUmzowXE48ePf/o3f/DgQcMwvt1Ktra21khISDDCw8ONrVu3Gq9fvzY3dAijm+imaaHWTfRS6PSSYZjfTRbD+Is/yhgAAAAA/Iz3RAEAAACADxiiAAAAAMAHDFEAAAAA4AOGKAAAAADwAUMUAAAAAPiAIQoAAAAAfMAQBQAAAAA+YIgCgqi9vV0Wi0UjIyNBf2yLxSKLxSK73T6n9dNZLRaL/z7dGwAw79BNgO8YooAA2bJliyoqKmYdy8nJ0fv37xUbG2tKJrfbrf7+/jmtnc5aWFgY4FQAgGChmwD/YIgCgshmsykxMVEWi8WUx7fb7Vq6dOmc1k5njYiICHAqAICZ6CbAdwxRQACUlZWpo6ND9fX1M9sOhoaGftgycfXqVdntdt29e1dr1qxRZGSkCgoKND4+rmvXrik5OVmLFy/WsWPH5PV6Z84/OTmp6upqLV++XFFRUdq4caPa29t9zvny5Uvl5eUpJiZGixYtUlZWlp4/f+6nZwEAMJ/QTYD/hJkdAPgb1dfXq7+/XxkZGTp37pwkKT4+XkNDQz+sHR8fV0NDg1paWvT582ft3r1bu3btkt1u17179/TmzRvt2bNHubm5KioqkiSVl5fr1atXamlpUVJSklpbW7V9+3b19fUpJSVlzjlLSkqUmZmpxsZGWa1W9fb2asGCBX55DgAA8wvdBPgPQxQQALGxsbLZbIqMjFRiYuIv1379+lWNjY1avXq1JKmgoEDNzc368OGDoqOjlZ6erry8PD1+/FhFRUXyeDxyu93yeDxKSkqSJFVXV+vBgwdyu926ePHinHN6PB7V1NQoNTVVknwqOQDAn4VuAvyHIQowWWRk5ExJSVJCQoKSk5MVHR0969jw8LAkqa+vT16vVy6Xa9Z5JicnFRcX59NjV1VV6ciRI2pubta2bdu0d+/eWVkAAKGJbgJ+jSEKMNn/b1GwWCw/PTY1NSVJGhsbk9VqVU9Pj6xW66x135fbXJw5c0b79+9XW1ub7t+/r9OnT6ulpUW7du36jd8EAPC3oJuAX2OIAgLEZrPNesOtv2RmZsrr9Wp4eFibN2/+z+dzuVxyuVyqrKxUcXGx3G43RQUAfym6CfAP7s4HBEhycrI6Ozs1NDSkjx8/zvy37r9yuVwqKSlRaWmpbt68qcHBQXV1damurk5tbW1zPs/ExITKy8vV3t6ut2/f6unTp+ru7lZaWppfcgIA5h+6CfAPhiggQKqrq2W1WpWenq74+Hh5PB6/ndvtdqu0tFQnTpzQmjVrlJ+fr+7ubjkcjjmfw2q16tOnTyotLZXL5VJhYaF27Nihs2fP+i0nAGB+oZsA/7AYhmGYHQJA4FksFrW2tio/P9+nnysrK9PIyIhu3boVkFwAgNBFN+FPxStRQAgpLi7WihUr5rT2yZMnio6O1vXr1wOcCgAQyugm/Il4JQoIEQMDA5K+bZVwOp3/un5iYkLv3r2T9O3OSv/2mSIAAPiKbsKfiiEKAAAAAHzAdj4AAAAA8AFDFAAAAAD4gCEKAAAAAHzAEAUAAAAAPmCIAgAAAAAfMEQBAAAAgA8YogAAAADABwxRAAAAAOADhigAAAAA8MH/AHAQHQ5rDaMOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f_params = params[1]\n",
    "saveat = np.linspace(0,1.0,lmbx_l_gt.shape[1])\n",
    "t, _, d = repeat_uniax(params, lmbx_max_list, ddot_vahid, saveat)\n",
    "d = np.array(d)\n",
    "d = np.clip(d, a_min=0.0, a_max=1.0)\n",
    "fig, ax = plt.subplots(2,2,figsize=(10,8))\n",
    "fig, ax2 = plt.subplots(figsize=(20,4))\n",
    "\n",
    "cols = ['black', 'green', 'red', 'blue', 'orange']\n",
    "lnstyles = ['solid', 'dashed', 'dotted', 'dashdot']\n",
    "\n",
    "t0 = 0.0\n",
    "for i in range(n_cycles):\n",
    "    # Loading\n",
    "    col = cols[i]\n",
    "    di = d[i]\n",
    "    lmbx_l = lmbx_l_gt[i]\n",
    "    lmby_l = 1.0/np.sqrt(lmbx_l)\n",
    "    time_l = t0 + np.linspace(0,1,lmbx_l.shape[0])\n",
    "    sgmx_l = sgmx_l_gt[i]\n",
    "    t0 += 1.0\n",
    "\n",
    "    sgmx_l_pr = eval_Cauchy_vmap(lmbx_l, lmby_l, di, params)[:,0,0]\n",
    "\n",
    "    ax[0,0].plot(time_l, lmbx_l, col)\n",
    "    ax[0,1].plot(lmbx_l, sgmx_l_pr, col, linestyle='dashed', alpha=0.3)\n",
    "    ax[0,1].plot(lmbx_l, sgmx_l, col, linestyle='solid', alpha=0.3)\n",
    "    ax[1,0].plot(time_l, di, col)\n",
    "    ax[1,1].plot(time_l, f_vmap(di, f_params), col)\n",
    "\n",
    "    # ax[0,2].plot(time_l, sgmx_l, col, alpha=0.3)\n",
    "    ax2.plot(time_l, sgmx_l_pr, col, alpha=0.3)\n",
    "    ax2.plot(time_l, di, col, linestyle='dashed', alpha=0.3)\n",
    "\n",
    "    # Unloading\n",
    "    lmbx_u = np.flip(lmbx_u_gt[i])\n",
    "    lmby_u = 1.0/np.sqrt(lmbx_u)\n",
    "    time_u = t0 + np.linspace(0,1,lmbx_u.shape[0])\n",
    "    t0 += 1\n",
    "    sgmx_u = np.flip(sgmx_u_gt[i])\n",
    "    sgmx_u_pr = eval_Cauchy_vmap2(lmbx_u, lmby_u, di[-1], params)[:,0,0]\n",
    "\n",
    "    ax[0,0].plot(time_u, lmbx_u, col)\n",
    "    ax[0,1].plot(lmbx_u, sgmx_u_pr, col, linestyle='dashed')\n",
    "    ax[0,1].plot(lmbx_u, sgmx_u, col, linestyle='solid')\n",
    "    ax[1,0].plot(time_u, di[-1]*np.ones_like(time_u), col)\n",
    "    ax[1,1].plot(time_u, f_vmap(di[-1]*np.ones_like(time_u), f_params), col)\n",
    "\n",
    "    ax2.plot(time_u, sgmx_u_pr, col)\n",
    "    ax2.plot(time_u, di[-1]*np.ones_like(time_u), col, linestyle='dashed')\n",
    "\n",
    "    sgmx_l = sgmx_l_gt[i]\n",
    "\n",
    "ax[0,0].set(xlabel='time [s]', ylabel = '$\\lambda_x$')\n",
    "\n",
    "legend_elements = [Line2D([0], [0], color='k', linestyle= '-', label='gt'),\n",
    "                   Line2D([0], [0], color='k', linestyle='--', label='pr')]\n",
    "ax[0,1].legend(handles=legend_elements)\n",
    "ax[0,1].set(xlabel='$\\lambda_x$', ylabel='$\\sigma_{xx}$')\n",
    "\n",
    "legend_elements = [Line2D([0], [0], color='k', linestyle= '-', label='stress'),\n",
    "                   Line2D([0], [0], color='k', linestyle='--', label='damage')]\n",
    "ax2.legend(handles=legend_elements)\n",
    "ax2.set(xlabel='time [s]', ylabel='$\\sigma_{xx}$')\n",
    "\n",
    "ax[1,0].set(xlabel='time [s]', ylabel='$d$')\n",
    "ax[1,1].set(xlabel='time [s]', ylabel='$f(d)$')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Strategy 2: Train just Psi_params + 5 damage parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 10000, train loss = 1.121702e-02\n",
      "it 20000, train loss = 5.204323e-04\n",
      "it 30000, train loss = 2.920508e-04\n",
      "it 40000, train loss = 2.719141e-04\n",
      "it 50000, train loss = 2.660218e-04\n",
      "it 60000, train loss = 2.623921e-04\n",
      "it 70000, train loss = 2.598360e-04\n",
      "it 80000, train loss = 2.582557e-04\n",
      "it 90000, train loss = 2.587077e-04\n",
      "it 100000, train loss = 2.573336e-04\n"
     ]
    }
   ],
   "source": [
    "def loss_Psi(params):\n",
    "    d, params_Psi = params\n",
    "    loss = []\n",
    "    for i in range(len(lmbx_u_gt)):\n",
    "        lmbx = lmbx_u_gt[i]\n",
    "        lmby = 1.0/jnp.sqrt(lmbx)\n",
    "        sgmx = sgmx_u_gt[i]\n",
    "        di = jax.nn.sigmoid(d[i])\n",
    "        sgmx_pr = eval_Cauchy_vmap2(lmbx, lmby, di, (params_Psi, None))[:,0,0]\n",
    "        loss.append(jnp.mean((sgmx-sgmx_pr)**2))\n",
    "    return jnp.mean(jnp.array(loss))\n",
    "\n",
    "\n",
    "params = init_params_damage_simple(key, Psi_layers=[1,3,3,3,1], G_layers=[1,5,5,5,1])\n",
    "params_Psi = params[0]\n",
    "d = np.linspace(0,0.8,5)\n",
    "params = (d, params_Psi)\n",
    "opt_init, opt_update, get_params = optimizers.adam(5.e-4) #Original: 1.e-4\n",
    "opt_state = opt_init(params)\n",
    "\n",
    "params, train_loss, _ = train(loss_Psi, get_params, opt_update, opt_state, key, nIter = 100000, print_freq=10000)\n",
    "# min so far: 2.524991e-04: 5.e-4, 200k, Psi_layers=[1,3,3,3,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "d =  [0.98018274 0.99460978 0.99850241 0.9995633  0.99985402]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_gt, params_Psi = params\n",
    "d_gt = jax.nn.sigmoid(d_gt)\n",
    "print('d = ', d_gt)\n",
    "fig, ax = plt.subplots()\n",
    "cols = ['black', 'green', 'red', 'blue', 'orange']\n",
    "\n",
    "for i, di in enumerate(d_gt):\n",
    "    lmbx = lmbx_u_gt[i]\n",
    "    lmby = 1.0/jnp.sqrt(lmbx)\n",
    "    sgmx = sgmx_u_gt[i]\n",
    "    col = cols[i]\n",
    "    sgmx_pr = eval_Cauchy_vmap2(lmbx, lmby, di, (params_Psi, None))[:,0,0]\n",
    "    ax.plot(lmbx, sgmx_pr, col)\n",
    "    ax.plot(lmbx, sgmx, col, linestyle=':')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train params_G with these d values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it 10000, train loss = 5.849455e-07\n",
      "it 20000, train loss = 1.251721e-08\n",
      "it 30000, train loss = 2.456660e-09\n",
      "it 40000, train loss = 2.409282e-09\n",
      "it 50000, train loss = 2.364515e-09\n",
      "it 60000, train loss = 2.323474e-09\n",
      "it 70000, train loss = 2.285825e-09\n",
      "it 80000, train loss = 2.250896e-09\n",
      "it 90000, train loss = 2.218466e-09\n",
      "it 100000, train loss = 2.188217e-09\n",
      "it 110000, train loss = 2.159853e-09\n",
      "it 120000, train loss = 2.133224e-09\n",
      "it 130000, train loss = 2.108108e-09\n",
      "it 140000, train loss = 2.084366e-09\n",
      "it 150000, train loss = 2.061962e-09\n",
      "it 160000, train loss = 2.040361e-09\n",
      "it 170000, train loss = 2.019890e-09\n",
      "it 180000, train loss = 2.000336e-09\n",
      "it 190000, train loss = 1.981535e-09\n",
      "it 200000, train loss = 1.963487e-09\n",
      "it 210000, train loss = 1.946060e-09\n",
      "it 220000, train loss = 1.943087e-09\n",
      "it 230000, train loss = 1.912808e-09\n",
      "it 240000, train loss = 1.896864e-09\n",
      "it 250000, train loss = 1.881260e-09\n",
      "it 260000, train loss = 1.865979e-09\n",
      "it 270000, train loss = 1.850937e-09\n",
      "it 280000, train loss = 1.836087e-09\n",
      "it 290000, train loss = 1.821490e-09\n",
      "it 300000, train loss = 1.806764e-09\n",
      "it 310000, train loss = 1.792143e-09\n",
      "it 320000, train loss = 1.777507e-09\n",
      "it 330000, train loss = 1.762771e-09\n",
      "it 340000, train loss = 1.747887e-09\n",
      "it 350000, train loss = 1.732820e-09\n",
      "it 360000, train loss = 1.717534e-09\n",
      "it 370000, train loss = 1.701944e-09\n",
      "it 380000, train loss = 1.686020e-09\n",
      "it 390000, train loss = 1.672350e-09\n",
      "it 400000, train loss = 1.653047e-09\n",
      "it 410000, train loss = 1.635952e-09\n",
      "it 420000, train loss = 1.618401e-09\n",
      "it 430000, train loss = 1.600466e-09\n",
      "it 440000, train loss = 1.582213e-09\n",
      "it 450000, train loss = 1.563676e-09\n",
      "it 460000, train loss = 1.568064e-09\n",
      "it 470000, train loss = 1.526236e-09\n",
      "it 480000, train loss = 1.507580e-09\n",
      "it 490000, train loss = 1.489153e-09\n",
      "it 500000, train loss = 1.471143e-09\n",
      "it 510000, train loss = 1.453466e-09\n",
      "it 520000, train loss = 1.436349e-09\n",
      "it 530000, train loss = 1.419771e-09\n",
      "it 540000, train loss = 1.403747e-09\n",
      "it 550000, train loss = 1.388220e-09\n",
      "it 560000, train loss = 1.373169e-09\n",
      "it 570000, train loss = 1.358579e-09\n",
      "it 580000, train loss = 1.344385e-09\n",
      "it 590000, train loss = 1.330564e-09\n",
      "it 600000, train loss = 1.317081e-09\n",
      "it 610000, train loss = 1.303911e-09\n",
      "it 620000, train loss = 1.291029e-09\n",
      "it 630000, train loss = 1.278427e-09\n",
      "it 640000, train loss = 1.266074e-09\n",
      "it 650000, train loss = 1.253960e-09\n",
      "it 660000, train loss = 1.242088e-09\n",
      "it 670000, train loss = 1.230447e-09\n",
      "it 680000, train loss = 1.219033e-09\n",
      "it 690000, train loss = 1.207841e-09\n",
      "it 700000, train loss = 1.196857e-09\n",
      "it 710000, train loss = 1.186081e-09\n",
      "it 720000, train loss = 1.175502e-09\n",
      "it 730000, train loss = 1.165120e-09\n",
      "it 740000, train loss = 1.154929e-09\n",
      "it 750000, train loss = 1.144917e-09\n",
      "it 760000, train loss = 1.135110e-09\n",
      "it 770000, train loss = 1.125477e-09\n",
      "it 780000, train loss = 1.116020e-09\n",
      "it 790000, train loss = 1.113467e-09\n",
      "it 800000, train loss = 1.097614e-09\n",
      "it 810000, train loss = 1.088638e-09\n",
      "it 820000, train loss = 1.079835e-09\n",
      "it 830000, train loss = 1.071165e-09\n",
      "it 840000, train loss = 1.062658e-09\n",
      "it 850000, train loss = 1.054284e-09\n",
      "it 860000, train loss = 1.046058e-09\n",
      "it 870000, train loss = 1.037952e-09\n",
      "it 880000, train loss = 1.029972e-09\n",
      "it 890000, train loss = 1.022141e-09\n",
      "it 900000, train loss = 1.014422e-09\n",
      "it 910000, train loss = 1.006827e-09\n",
      "it 920000, train loss = 9.993427e-10\n",
      "it 930000, train loss = 9.919715e-10\n",
      "it 940000, train loss = 9.847107e-10\n",
      "it 950000, train loss = 9.775494e-10\n",
      "it 960000, train loss = 9.704837e-10\n",
      "it 970000, train loss = 9.635099e-10\n",
      "it 980000, train loss = 9.566434e-10\n",
      "it 990000, train loss = 9.498633e-10\n",
      "it 1000000, train loss = 9.432966e-10\n",
      "it 1010000, train loss = 9.365750e-10\n",
      "it 1020000, train loss = 9.300586e-10\n",
      "it 1030000, train loss = 9.236018e-10\n",
      "it 1040000, train loss = 9.172336e-10\n",
      "it 1050000, train loss = 9.109349e-10\n",
      "it 1060000, train loss = 9.047023e-10\n",
      "it 1070000, train loss = 8.985509e-10\n",
      "it 1080000, train loss = 8.924574e-10\n",
      "it 1090000, train loss = 8.864456e-10\n",
      "it 1100000, train loss = 8.804820e-10\n",
      "it 1110000, train loss = 8.745842e-10\n",
      "it 1120000, train loss = 8.687560e-10\n",
      "it 1130000, train loss = 8.633392e-10\n",
      "it 1140000, train loss = 8.572659e-10\n",
      "it 1150000, train loss = 8.515938e-10\n",
      "it 1160000, train loss = 8.459920e-10\n",
      "it 1170000, train loss = 8.404518e-10\n",
      "it 1180000, train loss = 8.349541e-10\n",
      "it 1190000, train loss = 8.295042e-10\n",
      "it 1200000, train loss = 8.241110e-10\n",
      "it 1210000, train loss = 8.187645e-10\n",
      "it 1220000, train loss = 8.134785e-10\n",
      "it 1230000, train loss = 8.082427e-10\n",
      "it 1240000, train loss = 8.030537e-10\n",
      "it 1250000, train loss = 7.979179e-10\n",
      "it 1260000, train loss = 7.933114e-10\n",
      "it 1270000, train loss = 7.877790e-10\n",
      "it 1280000, train loss = 7.827694e-10\n",
      "it 1290000, train loss = 7.778136e-10\n",
      "it 1300000, train loss = 7.729060e-10\n",
      "it 1310000, train loss = 7.680389e-10\n",
      "it 1320000, train loss = 7.632201e-10\n",
      "it 1330000, train loss = 7.584497e-10\n",
      "it 1340000, train loss = 7.537355e-10\n",
      "it 1350000, train loss = 7.490532e-10\n",
      "it 1360000, train loss = 7.444110e-10\n",
      "it 1370000, train loss = 7.398142e-10\n",
      "it 1380000, train loss = 7.352693e-10\n",
      "it 1390000, train loss = 7.307779e-10\n",
      "it 1400000, train loss = 7.263296e-10\n",
      "it 1410000, train loss = 7.219231e-10\n",
      "it 1420000, train loss = 7.175547e-10\n",
      "it 1430000, train loss = 7.132346e-10\n",
      "it 1440000, train loss = 7.089607e-10\n",
      "it 1450000, train loss = 7.047317e-10\n",
      "it 1460000, train loss = 7.005364e-10\n",
      "it 1470000, train loss = 6.964040e-10\n",
      "it 1480000, train loss = 6.923250e-10\n",
      "it 1490000, train loss = 6.882718e-10\n",
      "it 1500000, train loss = 6.842662e-10\n",
      "it 1510000, train loss = 6.803036e-10\n",
      "it 1520000, train loss = 6.763807e-10\n",
      "it 1530000, train loss = 6.725049e-10\n",
      "it 1540000, train loss = 6.686771e-10\n",
      "it 1550000, train loss = 6.648919e-10\n",
      "it 1560000, train loss = 6.611452e-10\n",
      "it 1570000, train loss = 6.574351e-10\n",
      "it 1580000, train loss = 6.537772e-10\n",
      "it 1590000, train loss = 6.501607e-10\n",
      "it 1600000, train loss = 6.465829e-10\n",
      "it 1610000, train loss = 6.430390e-10\n",
      "it 1620000, train loss = 6.395389e-10\n",
      "it 1630000, train loss = 6.360824e-10\n",
      "it 1640000, train loss = 6.326622e-10\n",
      "it 1650000, train loss = 6.292806e-10\n",
      "it 1660000, train loss = 6.259423e-10\n",
      "it 1670000, train loss = 6.226395e-10\n",
      "it 1680000, train loss = 6.193850e-10\n",
      "it 1690000, train loss = 6.161633e-10\n",
      "it 1700000, train loss = 6.129780e-10\n",
      "it 1710000, train loss = 6.098277e-10\n",
      "it 1720000, train loss = 7.105566e-10\n",
      "it 1730000, train loss = 6.036391e-10\n",
      "it 1740000, train loss = 6.179976e-10\n",
      "it 1750000, train loss = 5.975861e-10\n",
      "it 1760000, train loss = 5.946059e-10\n",
      "it 1770000, train loss = 5.916738e-10\n",
      "it 1780000, train loss = 5.887684e-10\n",
      "it 1790000, train loss = 5.860603e-10\n",
      "it 1800000, train loss = 5.830593e-10\n",
      "it 1810000, train loss = 5.802553e-10\n",
      "it 1820000, train loss = 5.774726e-10\n",
      "it 1830000, train loss = 5.747287e-10\n",
      "it 1840000, train loss = 5.720178e-10\n",
      "it 1850000, train loss = 5.700535e-10\n",
      "it 1860000, train loss = 5.666840e-10\n",
      "it 1870000, train loss = 5.640633e-10\n",
      "it 1880000, train loss = 5.614749e-10\n",
      "it 1890000, train loss = 5.589150e-10\n",
      "it 1900000, train loss = 5.599245e-10\n",
      "it 1910000, train loss = 5.538698e-10\n",
      "it 1920000, train loss = 5.513904e-10\n",
      "it 1930000, train loss = 5.489465e-10\n",
      "it 1940000, train loss = 5.465143e-10\n",
      "it 1950000, train loss = 5.441128e-10\n",
      "it 1960000, train loss = 5.417372e-10\n",
      "it 1970000, train loss = 5.393907e-10\n",
      "it 1980000, train loss = 5.370664e-10\n",
      "it 1990000, train loss = 5.596877e-10\n",
      "it 2000000, train loss = 5.334455e-10\n",
      "it 2010000, train loss = 5.302357e-10\n",
      "it 2020000, train loss = 5.280078e-10\n",
      "it 2030000, train loss = 5.258035e-10\n",
      "it 2040000, train loss = 5.236601e-10\n",
      "it 2050000, train loss = 5.214653e-10\n",
      "it 2060000, train loss = 5.193525e-10\n",
      "it 2070000, train loss = 5.172183e-10\n",
      "it 2080000, train loss = 5.151230e-10\n",
      "it 2090000, train loss = 5.840701e-10\n",
      "it 2100000, train loss = 5.110045e-10\n",
      "it 2110000, train loss = 5.089790e-10\n",
      "it 2120000, train loss = 5.070047e-10\n",
      "it 2130000, train loss = 5.049765e-10\n",
      "it 2140000, train loss = 5.030072e-10\n",
      "it 2150000, train loss = 5.010553e-10\n",
      "it 2160000, train loss = 4.991268e-10\n",
      "it 2170000, train loss = 4.972121e-10\n",
      "it 2180000, train loss = 4.953124e-10\n",
      "it 2190000, train loss = 4.934381e-10\n",
      "it 2200000, train loss = 4.915760e-10\n",
      "it 2210000, train loss = 4.897354e-10\n",
      "it 2220000, train loss = 4.879157e-10\n",
      "it 2230000, train loss = 4.861113e-10\n",
      "it 2240000, train loss = 4.843535e-10\n",
      "it 2250000, train loss = 4.825530e-10\n",
      "it 2260000, train loss = 4.807959e-10\n",
      "it 2270000, train loss = 4.790536e-10\n",
      "it 2280000, train loss = 4.791963e-10\n",
      "it 2290000, train loss = 4.756299e-10\n",
      "it 2300000, train loss = 4.739284e-10\n",
      "it 2310000, train loss = 4.722502e-10\n",
      "it 2320000, train loss = 4.705929e-10\n",
      "it 2330000, train loss = 4.689409e-10\n",
      "it 2340000, train loss = 4.673593e-10\n",
      "it 2350000, train loss = 4.656898e-10\n",
      "it 2360000, train loss = 4.890026e-10\n",
      "it 2370000, train loss = 4.624953e-10\n",
      "it 2380000, train loss = 4.609172e-10\n",
      "it 2390000, train loss = 4.593581e-10\n",
      "it 2400000, train loss = 4.578093e-10\n",
      "it 2410000, train loss = 4.562751e-10\n",
      "it 2420000, train loss = 4.547496e-10\n",
      "it 2430000, train loss = 4.534247e-10\n",
      "it 2440000, train loss = 4.517524e-10\n",
      "it 2450000, train loss = 4.502597e-10\n",
      "it 2460000, train loss = 4.492837e-10\n",
      "it 2470000, train loss = 4.473227e-10\n",
      "it 2480000, train loss = 4.471289e-10\n",
      "it 2490000, train loss = 4.444352e-10\n",
      "it 2500000, train loss = 4.430092e-10\n",
      "it 2510000, train loss = 4.415984e-10\n",
      "it 2520000, train loss = 4.401998e-10\n",
      "it 2530000, train loss = 4.388034e-10\n",
      "it 2540000, train loss = 4.374230e-10\n",
      "it 2550000, train loss = 4.360583e-10\n",
      "it 2560000, train loss = 4.346982e-10\n",
      "it 2570000, train loss = 4.333525e-10\n",
      "it 2580000, train loss = 4.320179e-10\n",
      "it 2590000, train loss = 4.306923e-10\n",
      "it 2600000, train loss = 4.293764e-10\n",
      "it 2610000, train loss = 4.280716e-10\n",
      "it 2620000, train loss = 4.714690e-10\n",
      "it 2630000, train loss = 4.254915e-10\n",
      "it 2640000, train loss = 4.242153e-10\n",
      "it 2650000, train loss = 4.229522e-10\n",
      "it 2660000, train loss = 4.216930e-10\n",
      "it 2670000, train loss = 4.204459e-10\n",
      "it 2680000, train loss = 4.192106e-10\n",
      "it 2690000, train loss = 4.179808e-10\n",
      "it 2700000, train loss = 4.167633e-10\n",
      "it 2710000, train loss = 4.155540e-10\n",
      "it 2720000, train loss = 4.143529e-10\n",
      "it 2730000, train loss = 4.131615e-10\n",
      "it 2740000, train loss = 4.119790e-10\n",
      "it 2750000, train loss = 4.108038e-10\n",
      "it 2760000, train loss = 5.622727e-10\n",
      "it 2770000, train loss = 4.084830e-10\n",
      "it 2780000, train loss = 4.073348e-10\n",
      "it 2790000, train loss = 4.061970e-10\n",
      "it 2800000, train loss = 4.050636e-10\n",
      "it 2810000, train loss = 4.039376e-10\n",
      "it 2820000, train loss = 4.028214e-10\n",
      "it 2830000, train loss = 4.017130e-10\n",
      "it 2840000, train loss = 4.054849e-10\n",
      "it 2850000, train loss = 3.995202e-10\n",
      "it 2860000, train loss = 3.984474e-10\n",
      "it 2870000, train loss = 3.973555e-10\n",
      "it 2880000, train loss = 3.962840e-10\n",
      "it 2890000, train loss = 4.144433e-10\n",
      "it 2900000, train loss = 3.941647e-10\n",
      "it 2910000, train loss = 4.246005e-10\n",
      "it 2920000, train loss = 3.920775e-10\n",
      "it 2930000, train loss = 3.910631e-10\n",
      "it 2940000, train loss = 4.229500e-10\n",
      "it 2950000, train loss = 3.890072e-10\n",
      "it 2960000, train loss = 3.879793e-10\n",
      "it 2970000, train loss = 3.869731e-10\n",
      "it 2980000, train loss = 3.859744e-10\n",
      "it 2990000, train loss = 3.849829e-10\n",
      "it 3000000, train loss = 3.839964e-10\n",
      "it 3010000, train loss = 3.830178e-10\n",
      "it 3020000, train loss = 3.820450e-10\n",
      "it 3030000, train loss = 3.810785e-10\n",
      "it 3040000, train loss = 3.801160e-10\n",
      "it 3050000, train loss = 3.791590e-10\n",
      "it 3060000, train loss = 3.782091e-10\n",
      "it 3070000, train loss = 3.772764e-10\n",
      "it 3080000, train loss = 3.763286e-10\n",
      "it 3090000, train loss = 3.753985e-10\n",
      "it 3100000, train loss = 3.744744e-10\n",
      "it 3110000, train loss = 3.735544e-10\n",
      "it 3120000, train loss = 3.726405e-10\n",
      "it 3130000, train loss = 3.717300e-10\n",
      "it 3140000, train loss = 3.708281e-10\n",
      "it 3150000, train loss = 3.699306e-10\n",
      "it 3160000, train loss = 3.694309e-10\n",
      "it 3170000, train loss = 3.681543e-10\n",
      "it 3180000, train loss = 3.672756e-10\n",
      "it 3190000, train loss = 3.664112e-10\n",
      "it 3200000, train loss = 3.655315e-10\n",
      "it 3210000, train loss = 3.646678e-10\n",
      "it 3220000, train loss = 3.638094e-10\n",
      "it 3230000, train loss = 3.629541e-10\n",
      "it 3240000, train loss = 3.621048e-10\n",
      "it 3250000, train loss = 3.661007e-10\n",
      "it 3260000, train loss = 3.604215e-10\n",
      "it 3270000, train loss = 3.598878e-10\n",
      "it 3280000, train loss = 3.587583e-10\n",
      "it 3290000, train loss = 3.579337e-10\n",
      "it 3300000, train loss = 3.571135e-10\n",
      "it 3310000, train loss = 3.562993e-10\n",
      "it 3320000, train loss = 3.554896e-10\n",
      "it 3330000, train loss = 3.546913e-10\n",
      "it 3340000, train loss = 3.538822e-10\n",
      "it 3350000, train loss = 3.530860e-10\n",
      "it 3360000, train loss = 3.522949e-10\n",
      "it 3370000, train loss = 3.515062e-10\n",
      "it 3380000, train loss = 3.507243e-10\n",
      "it 3390000, train loss = 3.531342e-10\n",
      "it 3400000, train loss = 3.491724e-10\n",
      "it 3410000, train loss = 3.484044e-10\n",
      "it 3420000, train loss = 3.476377e-10\n",
      "it 3430000, train loss = 3.827682e-10\n",
      "it 3440000, train loss = 3.461203e-10\n",
      "it 3450000, train loss = 3.453681e-10\n",
      "it 3460000, train loss = 3.489494e-10\n",
      "it 3470000, train loss = 3.438764e-10\n",
      "it 3480000, train loss = 3.431371e-10\n",
      "it 3490000, train loss = 3.424010e-10\n",
      "it 3500000, train loss = 3.416685e-10\n",
      "it 3510000, train loss = 3.409421e-10\n",
      "it 3520000, train loss = 3.402180e-10\n",
      "it 3530000, train loss = 3.394972e-10\n",
      "it 3540000, train loss = 3.387815e-10\n",
      "it 3550000, train loss = 3.398855e-10\n",
      "it 3560000, train loss = 3.373628e-10\n",
      "it 3570000, train loss = 3.366591e-10\n",
      "it 3580000, train loss = 3.359583e-10\n",
      "it 3590000, train loss = 3.352605e-10\n",
      "it 3600000, train loss = 3.345676e-10\n",
      "it 3610000, train loss = 3.338776e-10\n",
      "it 3620000, train loss = 3.331933e-10\n",
      "it 3630000, train loss = 3.325118e-10\n",
      "it 3640000, train loss = 3.318433e-10\n",
      "it 3650000, train loss = 3.311564e-10\n",
      "it 3660000, train loss = 3.304840e-10\n",
      "it 3670000, train loss = 3.298138e-10\n",
      "it 3680000, train loss = 3.317319e-10\n",
      "it 3690000, train loss = 3.285204e-10\n",
      "it 3700000, train loss = 3.278271e-10\n",
      "it 3710000, train loss = 3.271712e-10\n",
      "it 3720000, train loss = 3.265191e-10\n",
      "it 3730000, train loss = 3.258725e-10\n",
      "it 3740000, train loss = 3.252239e-10\n",
      "it 3750000, train loss = 3.245807e-10\n",
      "it 3760000, train loss = 3.239406e-10\n",
      "it 3770000, train loss = 3.233037e-10\n",
      "it 3780000, train loss = 3.227360e-10\n",
      "it 3790000, train loss = 4.508889e-10\n",
      "it 3800000, train loss = 3.214132e-10\n",
      "it 3810000, train loss = 3.207898e-10\n",
      "it 3820000, train loss = 3.201696e-10\n",
      "it 3830000, train loss = 3.195534e-10\n",
      "it 3840000, train loss = 3.189386e-10\n",
      "it 3850000, train loss = 3.183273e-10\n",
      "it 3860000, train loss = 3.177198e-10\n",
      "it 3870000, train loss = 3.171158e-10\n",
      "it 3880000, train loss = 3.165122e-10\n",
      "it 3890000, train loss = 3.159127e-10\n",
      "it 3900000, train loss = 3.153165e-10\n",
      "it 3910000, train loss = 3.147228e-10\n",
      "it 3920000, train loss = 3.141302e-10\n",
      "it 3930000, train loss = 3.135428e-10\n",
      "it 3940000, train loss = 3.130359e-10\n",
      "it 3950000, train loss = 3.123758e-10\n",
      "it 3960000, train loss = 3.117963e-10\n",
      "it 3970000, train loss = 3.171195e-10\n",
      "it 3980000, train loss = 3.106454e-10\n",
      "it 3990000, train loss = 3.100895e-10\n",
      "it 4000000, train loss = 3.095039e-10\n",
      "it 4010000, train loss = 3.089355e-10\n",
      "it 4020000, train loss = 3.083710e-10\n",
      "it 4030000, train loss = 3.098043e-10\n",
      "it 4040000, train loss = 3.072492e-10\n",
      "it 4050000, train loss = 3.612276e-10\n",
      "it 4060000, train loss = 3.061375e-10\n",
      "it 4070000, train loss = 3.055881e-10\n",
      "it 4080000, train loss = 3.050367e-10\n",
      "it 4090000, train loss = 3.044903e-10\n",
      "it 4100000, train loss = 3.039462e-10\n",
      "it 4110000, train loss = 3.034044e-10\n",
      "it 4120000, train loss = 3.028668e-10\n",
      "it 4130000, train loss = 3.023299e-10\n",
      "it 4140000, train loss = 3.017951e-10\n",
      "it 4150000, train loss = 3.013556e-10\n",
      "it 4160000, train loss = 3.007323e-10\n",
      "it 4170000, train loss = 3.002053e-10\n",
      "it 4180000, train loss = 2.996797e-10\n",
      "it 4190000, train loss = 2.991566e-10\n",
      "it 4200000, train loss = 2.986360e-10\n",
      "it 4210000, train loss = 2.981205e-10\n",
      "it 4220000, train loss = 3.032179e-10\n",
      "it 4230000, train loss = 2.970885e-10\n",
      "it 4240000, train loss = 2.965761e-10\n",
      "it 4250000, train loss = 2.960676e-10\n",
      "it 4260000, train loss = 2.955603e-10\n",
      "it 4270000, train loss = 2.950546e-10\n",
      "it 4280000, train loss = 2.945514e-10\n",
      "it 4290000, train loss = 2.940501e-10\n",
      "it 4300000, train loss = 2.935502e-10\n",
      "it 4310000, train loss = 2.930529e-10\n",
      "it 4320000, train loss = 2.925585e-10\n",
      "it 4330000, train loss = 2.920664e-10\n",
      "it 4340000, train loss = 2.915768e-10\n",
      "it 4350000, train loss = 2.911456e-10\n",
      "it 4360000, train loss = 3.025487e-10\n",
      "it 4370000, train loss = 2.901167e-10\n",
      "it 4380000, train loss = 2.896338e-10\n",
      "it 4390000, train loss = 2.891536e-10\n",
      "it 4400000, train loss = 2.886751e-10\n",
      "it 4410000, train loss = 2.881981e-10\n",
      "it 4420000, train loss = 2.877232e-10\n",
      "it 4430000, train loss = 2.872646e-10\n",
      "it 4440000, train loss = 2.867805e-10\n",
      "it 4450000, train loss = 2.871411e-10\n",
      "it 4460000, train loss = 2.860101e-10\n",
      "it 4470000, train loss = 2.854378e-10\n",
      "it 4480000, train loss = 2.849165e-10\n",
      "it 4490000, train loss = 2.844539e-10\n",
      "it 4500000, train loss = 2.839941e-10\n",
      "it 4510000, train loss = 2.835362e-10\n",
      "it 4520000, train loss = 2.830809e-10\n",
      "it 4530000, train loss = 2.826267e-10\n",
      "it 4540000, train loss = 2.821752e-10\n",
      "it 4550000, train loss = 2.818377e-10\n",
      "it 4560000, train loss = 2.812758e-10\n",
      "it 4570000, train loss = 2.808281e-10\n",
      "it 4580000, train loss = 2.803829e-10\n",
      "it 4590000, train loss = 2.834200e-10\n",
      "it 4600000, train loss = 2.794967e-10\n",
      "it 4610000, train loss = 2.791115e-10\n",
      "it 4620000, train loss = 2.786180e-10\n",
      "it 4630000, train loss = 4.650252e-10\n",
      "it 4640000, train loss = 2.777462e-10\n",
      "it 4650000, train loss = 3.785955e-10\n",
      "it 4660000, train loss = 2.768815e-10\n",
      "it 4670000, train loss = 2.764512e-10\n",
      "it 4680000, train loss = 2.760230e-10\n",
      "it 4690000, train loss = 2.755959e-10\n",
      "it 4700000, train loss = 2.751705e-10\n",
      "it 4710000, train loss = 2.747466e-10\n",
      "it 4720000, train loss = 2.743241e-10\n",
      "it 4730000, train loss = 2.739045e-10\n",
      "it 4740000, train loss = 2.734854e-10\n",
      "it 4750000, train loss = 2.730669e-10\n",
      "it 4760000, train loss = 2.726507e-10\n",
      "it 4770000, train loss = 2.722359e-10\n",
      "it 4780000, train loss = 2.719516e-10\n",
      "it 4790000, train loss = 2.714116e-10\n",
      "it 4800000, train loss = 2.710062e-10\n",
      "it 4810000, train loss = 2.705932e-10\n",
      "it 4820000, train loss = 2.701853e-10\n",
      "it 4830000, train loss = 2.697797e-10\n",
      "it 4840000, train loss = 2.693753e-10\n",
      "it 4850000, train loss = 2.689719e-10\n",
      "it 4860000, train loss = 2.685724e-10\n",
      "it 4870000, train loss = 2.681821e-10\n",
      "it 4880000, train loss = 2.677752e-10\n",
      "it 4890000, train loss = 2.673788e-10\n",
      "it 4900000, train loss = 2.669839e-10\n",
      "it 4910000, train loss = 2.665900e-10\n",
      "it 4920000, train loss = 2.693848e-10\n",
      "it 4930000, train loss = 2.658093e-10\n",
      "it 4940000, train loss = 2.654152e-10\n",
      "it 4950000, train loss = 2.650268e-10\n",
      "it 4960000, train loss = 2.646397e-10\n",
      "it 4970000, train loss = 2.642539e-10\n",
      "it 4980000, train loss = 2.638697e-10\n",
      "it 4990000, train loss = 2.634865e-10\n",
      "it 5000000, train loss = 2.631045e-10\n",
      "it 5010000, train loss = 2.627237e-10\n",
      "it 5020000, train loss = 2.623441e-10\n",
      "it 5030000, train loss = 2.619661e-10\n",
      "it 5040000, train loss = 2.615888e-10\n",
      "it 5050000, train loss = 2.612142e-10\n",
      "it 5060000, train loss = 2.608398e-10\n",
      "it 5070000, train loss = 2.604668e-10\n",
      "it 5080000, train loss = 2.600945e-10\n",
      "it 5090000, train loss = 2.597237e-10\n",
      "it 5100000, train loss = 2.593542e-10\n",
      "it 5110000, train loss = 2.589854e-10\n",
      "it 5120000, train loss = 2.586192e-10\n",
      "it 5130000, train loss = 2.582536e-10\n",
      "it 5140000, train loss = 2.578889e-10\n",
      "it 5150000, train loss = 2.575253e-10\n",
      "it 5160000, train loss = 2.571634e-10\n",
      "it 5170000, train loss = 2.568030e-10\n",
      "it 5180000, train loss = 2.564428e-10\n",
      "it 5190000, train loss = 2.560845e-10\n",
      "it 5200000, train loss = 2.557269e-10\n",
      "it 5210000, train loss = 2.553970e-10\n",
      "it 5220000, train loss = 2.550146e-10\n",
      "it 5230000, train loss = 2.546601e-10\n",
      "it 5240000, train loss = 2.543063e-10\n",
      "it 5250000, train loss = 2.539563e-10\n",
      "it 5260000, train loss = 2.536034e-10\n",
      "it 5270000, train loss = 2.540181e-10\n",
      "it 5280000, train loss = 2.529042e-10\n",
      "it 5290000, train loss = 2.525560e-10\n",
      "it 5300000, train loss = 2.522101e-10\n",
      "it 5310000, train loss = 2.518642e-10\n",
      "it 5320000, train loss = 2.515197e-10\n",
      "it 5330000, train loss = 2.511764e-10\n",
      "it 5340000, train loss = 2.508347e-10\n",
      "it 5350000, train loss = 2.504927e-10\n",
      "it 5360000, train loss = 2.501530e-10\n",
      "it 5370000, train loss = 2.529909e-10\n",
      "it 5380000, train loss = 2.494761e-10\n",
      "it 5390000, train loss = 2.683204e-10\n",
      "it 5400000, train loss = 3.254517e-10\n",
      "it 5410000, train loss = 2.484673e-10\n",
      "it 5420000, train loss = 2.481328e-10\n",
      "it 5430000, train loss = 2.477993e-10\n",
      "it 5440000, train loss = 2.480696e-10\n",
      "it 5450000, train loss = 2.471366e-10\n",
      "it 5460000, train loss = 2.468071e-10\n",
      "it 5470000, train loss = 2.464779e-10\n",
      "it 5480000, train loss = 2.461497e-10\n",
      "it 5490000, train loss = 2.478298e-10\n",
      "it 5500000, train loss = 2.454959e-10\n",
      "it 5510000, train loss = 2.451702e-10\n",
      "it 5520000, train loss = 2.448457e-10\n",
      "it 5530000, train loss = 2.451547e-10\n",
      "it 5540000, train loss = 2.506514e-10\n",
      "it 5550000, train loss = 2.438777e-10\n",
      "it 5560000, train loss = 2.436225e-10\n",
      "it 5570000, train loss = 2.432366e-10\n",
      "it 5580000, train loss = 2.429174e-10\n",
      "it 5590000, train loss = 2.425996e-10\n",
      "it 5600000, train loss = 2.422828e-10\n",
      "it 5610000, train loss = 2.419662e-10\n",
      "it 5620000, train loss = 2.416510e-10\n",
      "it 5630000, train loss = 2.496425e-10\n",
      "it 5640000, train loss = 2.410223e-10\n",
      "it 5650000, train loss = 2.407101e-10\n",
      "it 5660000, train loss = 2.403975e-10\n",
      "it 5670000, train loss = 3.554410e-10\n",
      "it 5680000, train loss = 2.398183e-10\n",
      "it 5690000, train loss = 2.394650e-10\n",
      "it 5700000, train loss = 2.420467e-10\n",
      "it 5710000, train loss = 2.388474e-10\n",
      "it 5720000, train loss = 2.385400e-10\n",
      "it 5730000, train loss = 2.382357e-10\n",
      "it 5740000, train loss = 2.379281e-10\n",
      "it 5750000, train loss = 2.376241e-10\n",
      "it 5760000, train loss = 2.380556e-10\n",
      "it 5770000, train loss = 3.172871e-10\n",
      "it 5780000, train loss = 2.367146e-10\n",
      "it 5790000, train loss = 2.364128e-10\n",
      "it 5800000, train loss = 2.361117e-10\n",
      "it 5810000, train loss = 2.358115e-10\n",
      "it 5820000, train loss = 2.355471e-10\n",
      "it 5830000, train loss = 2.352134e-10\n",
      "it 5840000, train loss = 2.349158e-10\n",
      "it 5850000, train loss = 2.379208e-10\n",
      "it 5860000, train loss = 2.343232e-10\n",
      "it 5870000, train loss = 2.340256e-10\n",
      "it 5880000, train loss = 2.337307e-10\n",
      "it 5890000, train loss = 2.334361e-10\n",
      "it 5900000, train loss = 2.331429e-10\n",
      "it 5910000, train loss = 2.329885e-10\n",
      "it 5920000, train loss = 2.325586e-10\n",
      "it 5930000, train loss = 2.322669e-10\n",
      "it 5940000, train loss = 2.319807e-10\n",
      "it 5950000, train loss = 2.316860e-10\n",
      "it 5960000, train loss = 2.313965e-10\n",
      "it 5970000, train loss = 2.311082e-10\n",
      "it 5980000, train loss = 2.308206e-10\n",
      "it 5990000, train loss = 2.305329e-10\n",
      "it 6000000, train loss = 2.302466e-10\n",
      "it 6010000, train loss = 2.299609e-10\n",
      "it 6020000, train loss = 2.296806e-10\n",
      "it 6030000, train loss = 2.293902e-10\n",
      "it 6040000, train loss = 2.291067e-10\n",
      "it 6050000, train loss = 3.910082e-10\n",
      "it 6060000, train loss = 2.285405e-10\n",
      "it 6070000, train loss = 2.282582e-10\n",
      "it 6080000, train loss = 2.279767e-10\n",
      "it 6090000, train loss = 2.276957e-10\n",
      "it 6100000, train loss = 3.676987e-10\n",
      "it 6110000, train loss = 2.271349e-10\n",
      "it 6120000, train loss = 2.550754e-10\n",
      "it 6130000, train loss = 2.265775e-10\n",
      "it 6140000, train loss = 2.262996e-10\n",
      "it 6150000, train loss = 2.260222e-10\n",
      "it 6160000, train loss = 2.257745e-10\n",
      "it 6170000, train loss = 2.260321e-10\n",
      "it 6180000, train loss = 2.251946e-10\n",
      "it 6190000, train loss = 2.249294e-10\n",
      "it 6200000, train loss = 2.246434e-10\n",
      "it 6210000, train loss = 2.243694e-10\n",
      "it 6220000, train loss = 2.240955e-10\n",
      "it 6230000, train loss = 2.238227e-10\n",
      "it 6240000, train loss = 2.235504e-10\n",
      "it 6250000, train loss = 2.232787e-10\n",
      "it 6260000, train loss = 2.230075e-10\n",
      "it 6270000, train loss = 2.227368e-10\n",
      "it 6280000, train loss = 2.224665e-10\n",
      "it 6290000, train loss = 2.221967e-10\n",
      "it 6300000, train loss = 3.087659e-10\n",
      "it 6310000, train loss = 2.216628e-10\n",
      "it 6320000, train loss = 2.213905e-10\n",
      "it 6330000, train loss = 2.211224e-10\n",
      "it 6340000, train loss = 2.208548e-10\n",
      "it 6350000, train loss = 2.205888e-10\n",
      "it 6360000, train loss = 2.203225e-10\n",
      "it 6370000, train loss = 2.264320e-10\n",
      "it 6380000, train loss = 2.197914e-10\n",
      "it 6390000, train loss = 2.195267e-10\n",
      "it 6400000, train loss = 2.192620e-10\n",
      "it 6410000, train loss = 2.189974e-10\n",
      "it 6420000, train loss = 2.187350e-10\n",
      "it 6430000, train loss = 2.184711e-10\n",
      "it 6440000, train loss = 2.182083e-10\n",
      "it 6450000, train loss = 2.179454e-10\n",
      "it 6460000, train loss = 2.176839e-10\n",
      "it 6470000, train loss = 2.174222e-10\n",
      "it 6480000, train loss = 2.171617e-10\n",
      "it 6490000, train loss = 2.169012e-10\n",
      "it 6500000, train loss = 2.166410e-10\n",
      "it 6510000, train loss = 2.163807e-10\n",
      "it 6520000, train loss = 2.161217e-10\n",
      "it 6530000, train loss = 2.158630e-10\n",
      "it 6540000, train loss = 2.156041e-10\n",
      "it 6550000, train loss = 2.153485e-10\n",
      "it 6560000, train loss = 2.150871e-10\n",
      "it 6570000, train loss = 2.148293e-10\n",
      "it 6580000, train loss = 2.145785e-10\n",
      "it 6590000, train loss = 2.143282e-10\n",
      "it 6600000, train loss = 2.140582e-10\n",
      "it 6610000, train loss = 2.138016e-10\n",
      "it 6620000, train loss = 2.135456e-10\n",
      "it 6630000, train loss = 2.132897e-10\n",
      "it 6640000, train loss = 2.131229e-10\n",
      "it 6650000, train loss = 2.127792e-10\n",
      "it 6660000, train loss = 2.125242e-10\n",
      "it 6670000, train loss = 2.122699e-10\n",
      "it 6680000, train loss = 2.120153e-10\n",
      "it 6690000, train loss = 2.117614e-10\n",
      "it 6700000, train loss = 2.115077e-10\n",
      "it 6710000, train loss = 2.112539e-10\n",
      "it 6720000, train loss = 2.110010e-10\n",
      "it 6730000, train loss = 2.107477e-10\n",
      "it 6740000, train loss = 2.104948e-10\n",
      "it 6750000, train loss = 2.102419e-10\n",
      "it 6760000, train loss = 2.422470e-10\n",
      "it 6770000, train loss = 2.097387e-10\n",
      "it 6780000, train loss = 2.094871e-10\n",
      "it 6790000, train loss = 2.092358e-10\n",
      "it 6800000, train loss = 2.089847e-10\n",
      "it 6810000, train loss = 2.087338e-10\n",
      "it 6820000, train loss = 2.084833e-10\n",
      "it 6830000, train loss = 2.082325e-10\n",
      "it 6840000, train loss = 2.079827e-10\n",
      "it 6850000, train loss = 2.077337e-10\n",
      "it 6860000, train loss = 2.075921e-10\n",
      "it 6870000, train loss = 2.072322e-10\n",
      "it 6880000, train loss = 2.069826e-10\n",
      "it 6890000, train loss = 2.067331e-10\n",
      "it 6900000, train loss = 2.064837e-10\n",
      "it 6910000, train loss = 2.062889e-10\n",
      "it 6920000, train loss = 2.059849e-10\n",
      "it 6930000, train loss = 2.064076e-10\n",
      "it 6940000, train loss = 2.054870e-10\n",
      "it 6950000, train loss = 2.052379e-10\n",
      "it 6960000, train loss = 2.049975e-10\n",
      "it 6970000, train loss = 2.047401e-10\n",
      "it 6980000, train loss = 2.044909e-10\n",
      "it 6990000, train loss = 2.042431e-10\n",
      "it 7000000, train loss = 2.039945e-10\n",
      "it 7010000, train loss = 2.037469e-10\n",
      "it 7020000, train loss = 2.170191e-10\n",
      "it 7030000, train loss = 2.032499e-10\n",
      "it 7040000, train loss = 2.030015e-10\n",
      "it 7050000, train loss = 2.027530e-10\n",
      "it 7060000, train loss = 2.025046e-10\n",
      "it 7070000, train loss = 2.022561e-10\n",
      "it 7080000, train loss = 2.020084e-10\n",
      "it 7090000, train loss = 2.018103e-10\n",
      "it 7100000, train loss = 2.016007e-10\n",
      "it 7110000, train loss = 2.012626e-10\n",
      "it 7120000, train loss = 2.010143e-10\n",
      "it 7130000, train loss = 2.007652e-10\n",
      "it 7140000, train loss = 2.005161e-10\n",
      "it 7150000, train loss = 2.002701e-10\n",
      "it 7160000, train loss = 2.000174e-10\n",
      "it 7170000, train loss = 1.997684e-10\n",
      "it 7180000, train loss = 1.995255e-10\n",
      "it 7190000, train loss = 1.992691e-10\n",
      "it 7200000, train loss = 1.990185e-10\n",
      "it 7210000, train loss = 1.987687e-10\n",
      "it 7220000, train loss = 1.985189e-10\n",
      "it 7230000, train loss = 1.982687e-10\n",
      "it 7240000, train loss = 1.982488e-10\n",
      "it 7250000, train loss = 1.977677e-10\n",
      "it 7260000, train loss = 1.975173e-10\n",
      "it 7270000, train loss = 1.972661e-10\n",
      "it 7280000, train loss = 1.970144e-10\n",
      "it 7290000, train loss = 1.967622e-10\n",
      "it 7300000, train loss = 1.965102e-10\n",
      "it 7310000, train loss = 1.962577e-10\n",
      "it 7320000, train loss = 2.662477e-10\n",
      "it 7330000, train loss = 1.958043e-10\n",
      "it 7340000, train loss = 1.955084e-10\n",
      "it 7350000, train loss = 1.952441e-10\n",
      "it 7360000, train loss = 1.949896e-10\n",
      "it 7370000, train loss = 1.947342e-10\n",
      "it 7380000, train loss = 1.944786e-10\n",
      "it 7390000, train loss = 1.942225e-10\n",
      "it 7400000, train loss = 1.945903e-10\n",
      "it 7410000, train loss = 1.937087e-10\n",
      "it 7420000, train loss = 1.934507e-10\n",
      "it 7430000, train loss = 1.931925e-10\n",
      "it 7440000, train loss = 1.929333e-10\n",
      "it 7450000, train loss = 1.926744e-10\n",
      "it 7460000, train loss = 1.924142e-10\n",
      "it 7470000, train loss = 1.921894e-10\n",
      "it 7480000, train loss = 1.918919e-10\n",
      "it 7490000, train loss = 1.951941e-10\n",
      "it 7500000, train loss = 1.918206e-10\n",
      "it 7510000, train loss = 1.911024e-10\n",
      "it 7520000, train loss = 3.061011e-10\n",
      "it 7530000, train loss = 1.905715e-10\n",
      "it 7540000, train loss = 1.903053e-10\n",
      "it 7550000, train loss = 1.900632e-10\n",
      "it 7560000, train loss = 1.901689e-10\n",
      "it 7570000, train loss = 1.895003e-10\n",
      "it 7580000, train loss = 1.892299e-10\n",
      "it 7590000, train loss = 2.390021e-10\n",
      "it 7600000, train loss = 2.985179e-10\n",
      "it 7610000, train loss = 1.884121e-10\n",
      "it 7620000, train loss = 1.881373e-10\n",
      "it 7630000, train loss = 1.878621e-10\n",
      "it 7640000, train loss = 1.875853e-10\n",
      "it 7650000, train loss = 1.873071e-10\n",
      "it 7660000, train loss = 1.870275e-10\n",
      "it 7670000, train loss = 1.867515e-10\n",
      "it 7680000, train loss = 1.864650e-10\n",
      "it 7690000, train loss = 1.861816e-10\n",
      "it 7700000, train loss = 1.861373e-10\n",
      "it 7710000, train loss = 1.856113e-10\n",
      "it 7720000, train loss = 1.853236e-10\n",
      "it 7730000, train loss = 1.852883e-10\n",
      "it 7740000, train loss = 1.847432e-10\n",
      "it 7750000, train loss = 1.844503e-10\n",
      "it 7760000, train loss = 1.842099e-10\n",
      "it 7770000, train loss = 1.838594e-10\n",
      "it 7780000, train loss = 1.836478e-10\n",
      "it 7790000, train loss = 1.832597e-10\n",
      "it 7800000, train loss = 1.829555e-10\n",
      "it 7810000, train loss = 1.826500e-10\n",
      "it 7820000, train loss = 1.823476e-10\n",
      "it 7830000, train loss = 1.820448e-10\n",
      "it 7840000, train loss = 1.817198e-10\n",
      "it 7850000, train loss = 1.814045e-10\n",
      "it 7860000, train loss = 1.810867e-10\n",
      "it 7870000, train loss = 4.881838e-10\n",
      "it 7880000, train loss = 1.804436e-10\n",
      "it 7890000, train loss = 1.801175e-10\n",
      "it 7900000, train loss = 1.804251e-10\n",
      "it 7910000, train loss = 1.794543e-10\n",
      "it 7920000, train loss = 1.791181e-10\n",
      "it 7930000, train loss = 1.787795e-10\n",
      "it 7940000, train loss = 1.784394e-10\n",
      "it 7950000, train loss = 1.780863e-10\n",
      "it 7960000, train loss = 1.777338e-10\n",
      "it 7970000, train loss = 1.773775e-10\n",
      "it 7980000, train loss = 1.770161e-10\n",
      "it 7990000, train loss = 1.766490e-10\n",
      "it 8000000, train loss = 1.762764e-10\n",
      "it 8010000, train loss = 1.813273e-10\n",
      "it 8020000, train loss = 1.779399e-10\n",
      "it 8030000, train loss = 1.751303e-10\n",
      "it 8040000, train loss = 1.747429e-10\n",
      "it 8050000, train loss = 1.743563e-10\n",
      "it 8060000, train loss = 1.739623e-10\n",
      "it 8070000, train loss = 1.784440e-10\n",
      "it 8080000, train loss = 1.733703e-10\n",
      "it 8090000, train loss = 1.727689e-10\n",
      "it 8100000, train loss = 1.723660e-10\n",
      "it 8110000, train loss = 1.719575e-10\n",
      "it 8120000, train loss = 1.715477e-10\n",
      "it 8130000, train loss = 1.711318e-10\n",
      "it 8140000, train loss = 1.707139e-10\n",
      "it 8150000, train loss = 1.702919e-10\n",
      "it 8160000, train loss = 1.698666e-10\n",
      "it 8170000, train loss = 1.694379e-10\n",
      "it 8180000, train loss = 1.760140e-10\n",
      "it 8190000, train loss = 1.685709e-10\n",
      "it 8200000, train loss = 1.681291e-10\n",
      "it 8210000, train loss = 1.676845e-10\n",
      "it 8220000, train loss = 1.672378e-10\n",
      "it 8230000, train loss = 1.667865e-10\n",
      "it 8240000, train loss = 1.663304e-10\n",
      "it 8250000, train loss = 1.658726e-10\n",
      "it 8260000, train loss = 1.654084e-10\n",
      "it 8270000, train loss = 1.681464e-10\n",
      "it 8280000, train loss = 1.644706e-10\n",
      "it 8290000, train loss = 1.639949e-10\n",
      "it 8300000, train loss = 1.635169e-10\n",
      "it 8310000, train loss = 1.630329e-10\n",
      "it 8320000, train loss = 1.626290e-10\n",
      "it 8330000, train loss = 1.620554e-10\n",
      "it 8340000, train loss = 1.615572e-10\n",
      "it 8350000, train loss = 1.610576e-10\n",
      "it 8360000, train loss = 1.605543e-10\n",
      "it 8370000, train loss = 1.600458e-10\n",
      "it 8380000, train loss = 1.597611e-10\n",
      "it 8390000, train loss = 1.590161e-10\n",
      "it 8400000, train loss = 1.585272e-10\n",
      "it 8410000, train loss = 1.579692e-10\n",
      "it 8420000, train loss = 1.574394e-10\n",
      "it 8430000, train loss = 1.569055e-10\n",
      "it 8440000, train loss = 1.563691e-10\n",
      "it 8450000, train loss = 1.558260e-10\n",
      "it 8460000, train loss = 1.552803e-10\n",
      "it 8470000, train loss = 1.547295e-10\n",
      "it 8480000, train loss = 1.541753e-10\n",
      "it 8490000, train loss = 1.536171e-10\n",
      "it 8500000, train loss = 1.530550e-10\n",
      "it 8510000, train loss = 1.524868e-10\n",
      "it 8520000, train loss = 1.519169e-10\n",
      "it 8530000, train loss = 1.513403e-10\n",
      "it 8540000, train loss = 1.507595e-10\n",
      "it 8550000, train loss = 1.501763e-10\n",
      "it 8560000, train loss = 1.499135e-10\n",
      "it 8570000, train loss = 1.489969e-10\n",
      "it 8580000, train loss = 1.484019e-10\n",
      "it 8590000, train loss = 1.478035e-10\n",
      "it 8600000, train loss = 1.612118e-10\n",
      "it 8610000, train loss = 1.483647e-10\n",
      "it 8620000, train loss = 1.459876e-10\n",
      "it 8630000, train loss = 1.453658e-10\n",
      "it 8640000, train loss = 1.447455e-10\n",
      "it 8650000, train loss = 1.442132e-10\n",
      "it 8660000, train loss = 1.435303e-10\n",
      "it 8670000, train loss = 1.428658e-10\n",
      "it 8680000, train loss = 1.423753e-10\n",
      "it 8690000, train loss = 1.415965e-10\n",
      "it 8700000, train loss = 1.409535e-10\n",
      "it 8710000, train loss = 1.403100e-10\n",
      "it 8720000, train loss = 1.399507e-10\n",
      "it 8730000, train loss = 1.390123e-10\n",
      "it 8740000, train loss = 1.383597e-10\n",
      "it 8750000, train loss = 1.377046e-10\n",
      "it 8760000, train loss = 1.370463e-10\n",
      "it 8770000, train loss = 1.363833e-10\n",
      "it 8780000, train loss = 1.364586e-10\n",
      "it 8790000, train loss = 1.350519e-10\n",
      "it 8800000, train loss = 1.343813e-10\n",
      "it 8810000, train loss = 1.337069e-10\n",
      "it 8820000, train loss = 1.330313e-10\n",
      "it 8830000, train loss = 1.323543e-10\n",
      "it 8840000, train loss = 1.319410e-10\n",
      "it 8850000, train loss = 1.725380e-10\n",
      "it 8860000, train loss = 1.303074e-10\n",
      "it 8870000, train loss = 1.664504e-10\n",
      "it 8880000, train loss = 1.289311e-10\n",
      "it 8890000, train loss = 1.282391e-10\n",
      "it 8900000, train loss = 1.275458e-10\n",
      "it 8910000, train loss = 1.268503e-10\n",
      "it 8920000, train loss = 1.261539e-10\n",
      "it 8930000, train loss = 1.254595e-10\n",
      "it 8940000, train loss = 1.291343e-10\n",
      "it 8950000, train loss = 1.240534e-10\n",
      "it 8960000, train loss = 1.233516e-10\n",
      "it 8970000, train loss = 1.226486e-10\n",
      "it 8980000, train loss = 1.443696e-10\n",
      "it 8990000, train loss = 1.217488e-10\n",
      "it 9000000, train loss = 1.205308e-10\n",
      "it 9010000, train loss = 1.198228e-10\n",
      "it 9020000, train loss = 1.191270e-10\n",
      "it 9030000, train loss = 1.184065e-10\n",
      "it 9040000, train loss = 1.176989e-10\n",
      "it 9050000, train loss = 1.169894e-10\n",
      "it 9060000, train loss = 1.162804e-10\n",
      "it 9070000, train loss = 1.155712e-10\n",
      "it 9080000, train loss = 1.148554e-10\n",
      "it 9090000, train loss = 1.141441e-10\n",
      "it 9100000, train loss = 1.134340e-10\n",
      "it 9110000, train loss = 1.127228e-10\n",
      "it 9120000, train loss = 1.189131e-10\n",
      "it 9130000, train loss = 1.113017e-10\n",
      "it 9140000, train loss = 1.105926e-10\n",
      "it 9150000, train loss = 1.098925e-10\n",
      "it 9160000, train loss = 1.091732e-10\n",
      "it 9170000, train loss = 1.084667e-10\n",
      "it 9180000, train loss = 1.077578e-10\n",
      "it 9190000, train loss = 1.070509e-10\n",
      "it 9200000, train loss = 1.063465e-10\n",
      "it 9210000, train loss = 1.056409e-10\n",
      "it 9220000, train loss = 1.049375e-10\n",
      "it 9230000, train loss = 1.042354e-10\n",
      "it 9240000, train loss = 1.035333e-10\n",
      "it 9250000, train loss = 1.028322e-10\n",
      "it 9260000, train loss = 1.021324e-10\n",
      "it 9270000, train loss = 1.014337e-10\n",
      "it 9280000, train loss = 1.007366e-10\n",
      "it 9290000, train loss = 1.006053e-10\n",
      "it 9300000, train loss = 9.934738e-11\n",
      "it 9310000, train loss = 9.865475e-11\n",
      "it 9320000, train loss = 9.796520e-11\n",
      "it 9330000, train loss = 9.727678e-11\n",
      "it 9340000, train loss = 9.671089e-11\n",
      "it 9350000, train loss = 2.695093e-10\n",
      "it 9360000, train loss = 9.522177e-11\n",
      "it 9370000, train loss = 2.346566e-10\n",
      "it 9380000, train loss = 9.385971e-11\n",
      "it 9390000, train loss = 9.318448e-11\n",
      "it 9400000, train loss = 9.250817e-11\n",
      "it 9410000, train loss = 9.183550e-11\n",
      "it 9420000, train loss = 9.340415e-11\n",
      "it 9430000, train loss = 9.049435e-11\n",
      "it 9440000, train loss = 8.983028e-11\n",
      "it 9450000, train loss = 8.916866e-11\n",
      "it 9460000, train loss = 8.850770e-11\n",
      "it 9470000, train loss = 8.784993e-11\n",
      "it 9480000, train loss = 8.724145e-11\n",
      "it 9490000, train loss = 8.654056e-11\n",
      "it 9500000, train loss = 8.588953e-11\n",
      "it 9510000, train loss = 8.524102e-11\n",
      "it 9520000, train loss = 8.461396e-11\n",
      "it 9530000, train loss = 8.395215e-11\n",
      "it 9540000, train loss = 8.366485e-11\n",
      "it 9550000, train loss = 8.267477e-11\n",
      "it 9560000, train loss = 8.203931e-11\n",
      "it 9570000, train loss = 8.140852e-11\n",
      "it 9580000, train loss = 8.078006e-11\n",
      "it 9590000, train loss = 8.015493e-11\n",
      "it 9600000, train loss = 8.000790e-11\n",
      "it 9610000, train loss = 7.891107e-11\n",
      "it 9620000, train loss = 8.132686e-11\n",
      "it 9630000, train loss = 7.768148e-11\n",
      "it 9640000, train loss = 7.707710e-11\n",
      "it 9650000, train loss = 7.646157e-11\n",
      "it 9660000, train loss = 7.585793e-11\n",
      "it 9670000, train loss = 7.525651e-11\n",
      "it 9680000, train loss = 1.354540e-10\n",
      "it 9690000, train loss = 7.406770e-11\n",
      "it 9700000, train loss = 7.347485e-11\n",
      "it 9710000, train loss = 7.288795e-11\n",
      "it 9720000, train loss = 7.230266e-11\n",
      "it 9730000, train loss = 7.172155e-11\n",
      "it 9740000, train loss = 7.114405e-11\n",
      "it 9750000, train loss = 7.056922e-11\n",
      "it 9760000, train loss = 6.999649e-11\n",
      "it 9770000, train loss = 6.942766e-11\n",
      "it 9780000, train loss = 6.896154e-11\n",
      "it 9790000, train loss = 6.829935e-11\n",
      "it 9800000, train loss = 6.773910e-11\n",
      "it 9810000, train loss = 1.210239e-10\n",
      "it 9820000, train loss = 6.662910e-11\n",
      "it 9830000, train loss = 6.607986e-11\n",
      "it 9840000, train loss = 6.553438e-11\n",
      "it 9850000, train loss = 6.499285e-11\n",
      "it 9860000, train loss = 6.445262e-11\n",
      "it 9870000, train loss = 6.391624e-11\n",
      "it 9880000, train loss = 6.338483e-11\n",
      "it 9890000, train loss = 6.285576e-11\n",
      "it 9900000, train loss = 6.232834e-11\n",
      "it 9910000, train loss = 6.180577e-11\n",
      "it 9920000, train loss = 6.128617e-11\n",
      "it 9930000, train loss = 6.077225e-11\n",
      "it 9940000, train loss = 6.027381e-11\n",
      "it 9950000, train loss = 5.974858e-11\n",
      "it 9960000, train loss = 6.748415e-11\n",
      "it 9970000, train loss = 5.873960e-11\n",
      "it 9980000, train loss = 5.824058e-11\n",
      "it 9990000, train loss = 5.776624e-11\n",
      "it 10000000, train loss = 5.725187e-11\n",
      "it 10010000, train loss = 5.676184e-11\n",
      "it 10020000, train loss = 5.627625e-11\n",
      "it 10030000, train loss = 9.983263e-11\n",
      "it 10040000, train loss = 5.531283e-11\n",
      "it 10050000, train loss = 2.298038e-10\n",
      "it 10060000, train loss = 5.436387e-11\n",
      "it 10070000, train loss = 5.389393e-11\n",
      "it 10080000, train loss = 5.342731e-11\n",
      "it 10090000, train loss = 5.296662e-11\n",
      "it 10100000, train loss = 5.250280e-11\n",
      "it 10110000, train loss = 9.664254e-11\n",
      "it 10120000, train loss = 3.133351e-10\n",
      "it 10130000, train loss = 5.114184e-11\n",
      "it 10140000, train loss = 5.069477e-11\n",
      "it 10150000, train loss = 5.053839e-11\n",
      "it 10160000, train loss = 4.980944e-11\n",
      "it 10170000, train loss = 4.937106e-11\n",
      "it 10180000, train loss = 4.893628e-11\n",
      "it 10190000, train loss = 4.850337e-11\n",
      "it 10200000, train loss = 4.809322e-11\n",
      "it 10210000, train loss = 4.765175e-11\n",
      "it 10220000, train loss = 4.722978e-11\n",
      "it 10230000, train loss = 4.681078e-11\n",
      "it 10240000, train loss = 4.639573e-11\n",
      "it 10250000, train loss = 4.598325e-11\n",
      "it 10260000, train loss = 4.557282e-11\n",
      "it 10270000, train loss = 4.517864e-11\n",
      "it 10280000, train loss = 4.476067e-11\n",
      "it 10290000, train loss = 4.435959e-11\n",
      "it 10300000, train loss = 4.396165e-11\n",
      "it 10310000, train loss = 4.356660e-11\n",
      "it 10320000, train loss = 4.326063e-11\n",
      "it 10330000, train loss = 4.278518e-11\n",
      "it 10340000, train loss = 4.239977e-11\n",
      "it 10350000, train loss = 4.201605e-11\n",
      "it 10360000, train loss = 4.163605e-11\n",
      "it 10370000, train loss = 4.125885e-11\n",
      "it 10380000, train loss = 4.088367e-11\n",
      "it 10390000, train loss = 4.051285e-11\n",
      "it 10400000, train loss = 4.014332e-11\n",
      "it 10410000, train loss = 3.977660e-11\n",
      "it 10420000, train loss = 3.941295e-11\n",
      "it 10430000, train loss = 3.905261e-11\n",
      "it 10440000, train loss = 3.869520e-11\n",
      "it 10450000, train loss = 3.834062e-11\n",
      "it 10460000, train loss = 3.798775e-11\n",
      "it 10470000, train loss = 3.763833e-11\n",
      "it 10480000, train loss = 3.729252e-11\n",
      "it 10490000, train loss = 3.694956e-11\n",
      "it 10500000, train loss = 3.660882e-11\n",
      "it 10510000, train loss = 3.627064e-11\n",
      "it 10520000, train loss = 3.632295e-11\n",
      "it 10530000, train loss = 3.560185e-11\n",
      "it 10540000, train loss = 3.527398e-11\n",
      "it 10550000, train loss = 3.494344e-11\n",
      "it 10560000, train loss = 3.461771e-11\n",
      "it 10570000, train loss = 6.071154e-10\n",
      "it 10580000, train loss = 3.397403e-11\n",
      "it 10590000, train loss = 3.377203e-11\n",
      "it 10600000, train loss = 3.334093e-11\n",
      "it 10610000, train loss = 4.389740e-11\n",
      "it 10620000, train loss = 3.542660e-11\n",
      "it 10630000, train loss = 3.545334e-11\n",
      "it 10640000, train loss = 3.210652e-11\n",
      "it 10650000, train loss = 3.180500e-11\n",
      "it 10660000, train loss = 3.150611e-11\n",
      "it 10670000, train loss = 3.120996e-11\n",
      "it 10680000, train loss = 3.091695e-11\n",
      "it 10690000, train loss = 3.062581e-11\n",
      "it 10700000, train loss = 3.033814e-11\n",
      "it 10710000, train loss = 3.393291e-11\n",
      "it 10720000, train loss = 2.977393e-11\n",
      "it 10730000, train loss = 2.949030e-11\n",
      "it 10740000, train loss = 2.921275e-11\n",
      "it 10750000, train loss = 2.893912e-11\n",
      "it 10760000, train loss = 4.527080e-11\n",
      "it 10770000, train loss = 2.839782e-11\n",
      "it 10780000, train loss = 2.813071e-11\n",
      "it 10790000, train loss = 2.786820e-11\n",
      "it 10800000, train loss = 2.760580e-11\n",
      "it 10810000, train loss = 2.734640e-11\n",
      "it 10820000, train loss = 2.708938e-11\n",
      "it 10830000, train loss = 9.453193e-11\n",
      "it 10840000, train loss = 2.666656e-11\n",
      "it 10850000, train loss = 2.633292e-11\n",
      "it 10860000, train loss = 2.612159e-11\n",
      "it 10870000, train loss = 2.584019e-11\n",
      "it 10880000, train loss = 2.559741e-11\n",
      "it 10890000, train loss = 2.252754e-10\n",
      "it 10900000, train loss = 2.511925e-11\n",
      "it 10910000, train loss = 2.488384e-11\n",
      "it 10920000, train loss = 2.465041e-11\n",
      "it 10930000, train loss = 2.442022e-11\n",
      "it 10940000, train loss = 2.419148e-11\n",
      "it 10950000, train loss = 2.396516e-11\n",
      "it 10960000, train loss = 2.374039e-11\n",
      "it 10970000, train loss = 2.351802e-11\n",
      "it 10980000, train loss = 2.329814e-11\n",
      "it 10990000, train loss = 2.313342e-11\n",
      "it 11000000, train loss = 2.286318e-11\n",
      "it 11010000, train loss = 2.264947e-11\n",
      "it 11020000, train loss = 2.243745e-11\n",
      "it 11030000, train loss = 2.679430e-11\n",
      "it 11040000, train loss = 2.201937e-11\n",
      "it 11050000, train loss = 2.476476e-11\n",
      "it 11060000, train loss = 2.160849e-11\n",
      "it 11070000, train loss = 2.142139e-11\n",
      "it 11080000, train loss = 2.120604e-11\n",
      "it 11090000, train loss = 2.100770e-11\n",
      "it 11100000, train loss = 2.081206e-11\n",
      "it 11110000, train loss = 2.061685e-11\n",
      "it 11120000, train loss = 2.042418e-11\n",
      "it 11130000, train loss = 2.024629e-11\n",
      "it 11140000, train loss = 2.004518e-11\n",
      "it 11150000, train loss = 1.985830e-11\n",
      "it 11160000, train loss = 1.967272e-11\n",
      "it 11170000, train loss = 1.948894e-11\n",
      "it 11180000, train loss = 1.930699e-11\n",
      "it 11190000, train loss = 1.912830e-11\n",
      "it 11200000, train loss = 1.894885e-11\n",
      "it 11210000, train loss = 1.877234e-11\n",
      "it 11220000, train loss = 1.859673e-11\n",
      "it 11230000, train loss = 1.842354e-11\n",
      "it 11240000, train loss = 1.825248e-11\n",
      "it 11250000, train loss = 1.808273e-11\n",
      "it 11260000, train loss = 1.791451e-11\n",
      "it 11270000, train loss = 1.775354e-11\n",
      "it 11280000, train loss = 1.758283e-11\n",
      "it 11290000, train loss = 1.741917e-11\n",
      "it 11300000, train loss = 1.725714e-11\n",
      "it 11310000, train loss = 2.597045e-10\n",
      "it 11320000, train loss = 1.693823e-11\n",
      "it 11330000, train loss = 1.678127e-11\n",
      "it 11340000, train loss = 1.662562e-11\n",
      "it 11350000, train loss = 1.647151e-11\n",
      "it 11360000, train loss = 1.631838e-11\n",
      "it 11370000, train loss = 1.620079e-11\n",
      "it 11380000, train loss = 5.830786e-11\n",
      "it 11390000, train loss = 1.586870e-11\n",
      "it 11400000, train loss = 1.572172e-11\n",
      "it 11410000, train loss = 1.807261e-11\n",
      "it 11420000, train loss = 1.543224e-11\n",
      "it 11430000, train loss = 1.528996e-11\n",
      "it 11440000, train loss = 1.514860e-11\n",
      "it 11450000, train loss = 1.500903e-11\n",
      "it 11460000, train loss = 1.487068e-11\n",
      "it 11470000, train loss = 1.473366e-11\n",
      "it 11480000, train loss = 1.713193e-10\n",
      "it 11490000, train loss = 1.446287e-11\n",
      "it 11500000, train loss = 1.432951e-11\n",
      "it 11510000, train loss = 1.449304e-11\n",
      "it 11520000, train loss = 1.407997e-11\n",
      "it 11530000, train loss = 1.393723e-11\n",
      "it 11540000, train loss = 1.380924e-11\n",
      "it 11550000, train loss = 1.368225e-11\n",
      "it 11560000, train loss = 1.355656e-11\n",
      "it 11570000, train loss = 1.343213e-11\n",
      "it 11580000, train loss = 1.710238e-11\n",
      "it 11590000, train loss = 1.310960e-10\n",
      "it 11600000, train loss = 1.306578e-11\n",
      "it 11610000, train loss = 1.294584e-11\n",
      "it 11620000, train loss = 1.282721e-11\n",
      "it 11630000, train loss = 1.270995e-11\n",
      "it 11640000, train loss = 1.259335e-11\n",
      "it 11650000, train loss = 1.071024e-10\n",
      "it 11660000, train loss = 1.236508e-11\n",
      "it 11670000, train loss = 1.225132e-11\n",
      "it 11680000, train loss = 1.213961e-11\n",
      "it 11690000, train loss = 1.202897e-11\n",
      "it 11700000, train loss = 1.191937e-11\n",
      "it 11710000, train loss = 1.181084e-11\n",
      "it 11720000, train loss = 1.170334e-11\n",
      "it 11730000, train loss = 1.159663e-11\n",
      "it 11740000, train loss = 1.149096e-11\n",
      "it 11750000, train loss = 1.138623e-11\n",
      "it 11760000, train loss = 1.128284e-11\n",
      "it 11770000, train loss = 1.118086e-11\n",
      "it 11780000, train loss = 1.107918e-11\n",
      "it 11790000, train loss = 1.097869e-11\n",
      "it 11800000, train loss = 1.087937e-11\n",
      "it 11810000, train loss = 1.163299e-11\n",
      "it 11820000, train loss = 1.068302e-11\n",
      "it 11830000, train loss = 1.058630e-11\n",
      "it 11840000, train loss = 1.049071e-11\n",
      "it 11850000, train loss = 1.039574e-11\n",
      "it 11860000, train loss = 1.030233e-11\n",
      "it 11870000, train loss = 1.021766e-11\n",
      "it 11880000, train loss = 1.011706e-11\n",
      "it 11890000, train loss = 1.002969e-11\n",
      "it 11900000, train loss = 9.935956e-12\n",
      "it 11910000, train loss = 9.860599e-12\n",
      "it 11920000, train loss = 9.757637e-12\n",
      "it 11930000, train loss = 9.670120e-12\n",
      "it 11940000, train loss = 9.583544e-12\n",
      "it 11950000, train loss = 9.497448e-12\n",
      "it 11960000, train loss = 9.412542e-12\n",
      "it 11970000, train loss = 9.328230e-12\n",
      "it 11980000, train loss = 9.244566e-12\n",
      "it 11990000, train loss = 9.162008e-12\n",
      "it 12000000, train loss = 9.080209e-12\n",
      "it 12010000, train loss = 1.785363e-11\n",
      "it 12020000, train loss = 2.201385e-10\n",
      "it 12030000, train loss = 8.909886e-12\n",
      "it 12040000, train loss = 8.760963e-12\n",
      "it 12050000, train loss = 8.682851e-12\n",
      "it 12060000, train loss = 8.605622e-12\n",
      "it 12070000, train loss = 8.529070e-12\n",
      "it 12080000, train loss = 8.453423e-12\n",
      "it 12090000, train loss = 8.378283e-12\n",
      "it 12100000, train loss = 9.147425e-12\n",
      "it 12110000, train loss = 5.197828e-10\n",
      "it 12120000, train loss = 8.157609e-12\n",
      "it 12130000, train loss = 8.085507e-12\n",
      "it 12140000, train loss = 8.014372e-12\n",
      "it 12150000, train loss = 7.943514e-12\n",
      "it 12160000, train loss = 7.873687e-12\n",
      "it 12170000, train loss = 8.586363e-12\n",
      "it 12180000, train loss = 7.735404e-12\n",
      "it 12190000, train loss = 7.667283e-12\n",
      "it 12200000, train loss = 1.374875e-11\n",
      "it 12210000, train loss = 7.532982e-12\n",
      "it 12220000, train loss = 7.467068e-12\n",
      "it 12230000, train loss = 7.401634e-12\n",
      "it 12240000, train loss = 7.336888e-12\n",
      "it 12250000, train loss = 7.272441e-12\n",
      "it 12260000, train loss = 8.518525e-12\n",
      "it 12270000, train loss = 9.268989e-12\n",
      "it 12280000, train loss = 7.423995e-12\n",
      "it 12290000, train loss = 7.024630e-12\n",
      "it 12300000, train loss = 6.960733e-12\n",
      "it 12310000, train loss = 6.900071e-12\n",
      "it 12320000, train loss = 6.840182e-12\n",
      "it 12330000, train loss = 6.780852e-12\n",
      "it 12340000, train loss = 6.722208e-12\n",
      "it 12350000, train loss = 6.664179e-12\n",
      "it 12360000, train loss = 6.606523e-12\n",
      "it 12370000, train loss = 1.824056e-11\n",
      "it 12380000, train loss = 6.493269e-12\n",
      "it 12390000, train loss = 6.437362e-12\n",
      "it 12400000, train loss = 6.689744e-12\n",
      "it 12410000, train loss = 6.327137e-12\n",
      "it 12420000, train loss = 1.753621e-11\n",
      "it 12430000, train loss = 6.219249e-12\n",
      "it 12440000, train loss = 6.165997e-12\n",
      "it 12450000, train loss = 6.113283e-12\n",
      "it 12460000, train loss = 6.061103e-12\n",
      "it 12470000, train loss = 6.009424e-12\n",
      "it 12480000, train loss = 5.958262e-12\n",
      "it 12490000, train loss = 5.907852e-12\n",
      "it 12500000, train loss = 5.860070e-12\n",
      "it 12510000, train loss = 5.807709e-12\n",
      "it 12520000, train loss = 5.758515e-12\n",
      "it 12530000, train loss = 5.709645e-12\n",
      "it 12540000, train loss = 5.662324e-12\n",
      "it 12550000, train loss = 5.613637e-12\n",
      "it 12560000, train loss = 6.922071e-12\n",
      "it 12570000, train loss = 5.519491e-12\n",
      "it 12580000, train loss = 5.477525e-12\n",
      "it 12590000, train loss = 5.427053e-12\n",
      "it 12600000, train loss = 5.394792e-12\n",
      "it 12610000, train loss = 5.336534e-12\n",
      "it 12620000, train loss = 5.291887e-12\n",
      "it 12630000, train loss = 5.247596e-12\n",
      "it 12640000, train loss = 5.203942e-12\n",
      "it 12650000, train loss = 5.160580e-12\n",
      "it 12660000, train loss = 6.583524e-10\n",
      "it 12670000, train loss = 5.075115e-12\n",
      "it 12680000, train loss = 2.640772e-11\n",
      "it 12690000, train loss = 5.762293e-12\n",
      "it 12700000, train loss = 4.950081e-12\n",
      "it 12710000, train loss = 5.008262e-12\n",
      "it 12720000, train loss = 8.708325e-12\n",
      "it 12730000, train loss = 4.828747e-12\n",
      "it 12740000, train loss = 4.759236e-11\n",
      "it 12750000, train loss = 4.749722e-12\n",
      "it 12760000, train loss = 4.710766e-12\n",
      "it 12770000, train loss = 4.672255e-12\n",
      "it 12780000, train loss = 4.633997e-12\n",
      "it 12790000, train loss = 7.971610e-11\n",
      "it 12800000, train loss = 4.938846e-12\n",
      "it 12810000, train loss = 4.521829e-12\n",
      "it 12820000, train loss = 4.485103e-12\n",
      "it 12830000, train loss = 4.448568e-12\n",
      "it 12840000, train loss = 4.412566e-12\n",
      "it 12850000, train loss = 4.377017e-12\n",
      "it 12860000, train loss = 4.341753e-12\n",
      "it 12870000, train loss = 4.306784e-12\n",
      "it 12880000, train loss = 4.272104e-12\n",
      "it 12890000, train loss = 4.237862e-12\n",
      "it 12900000, train loss = 4.203793e-12\n",
      "it 12910000, train loss = 4.170253e-12\n",
      "it 12920000, train loss = 4.137071e-12\n",
      "it 12930000, train loss = 4.104146e-12\n",
      "it 12940000, train loss = 4.071428e-12\n",
      "it 12950000, train loss = 4.039119e-12\n",
      "it 12960000, train loss = 4.007086e-12\n",
      "it 12970000, train loss = 4.229576e-12\n",
      "it 12980000, train loss = 3.943896e-12\n",
      "it 12990000, train loss = 3.912728e-12\n",
      "it 13000000, train loss = 3.881961e-12\n",
      "it 13010000, train loss = 3.858117e-12\n",
      "it 13020000, train loss = 3.821265e-12\n",
      "it 13030000, train loss = 8.600832e-11\n",
      "it 13040000, train loss = 3.761759e-12\n",
      "it 13050000, train loss = 3.732498e-12\n",
      "it 13060000, train loss = 3.703552e-12\n",
      "it 13070000, train loss = 3.677615e-12\n",
      "it 13080000, train loss = 3.646220e-12\n",
      "it 13090000, train loss = 3.618069e-12\n",
      "it 13100000, train loss = 5.223344e-10\n",
      "it 13110000, train loss = 3.603674e-12\n",
      "it 13120000, train loss = 3.621289e-12\n",
      "it 13130000, train loss = 3.508099e-12\n",
      "it 13140000, train loss = 7.808990e-11\n",
      "it 13150000, train loss = 3.454803e-12\n",
      "it 13160000, train loss = 3.510800e-12\n",
      "it 13170000, train loss = 3.402471e-12\n",
      "it 13180000, train loss = 1.969252e-10\n",
      "it 13190000, train loss = 3.351153e-12\n",
      "it 13200000, train loss = 3.325795e-12\n",
      "it 13210000, train loss = 3.301476e-12\n",
      "it 13220000, train loss = 3.312570e-12\n",
      "it 13230000, train loss = 3.251358e-12\n",
      "it 13240000, train loss = 3.226996e-12\n",
      "it 13250000, train loss = 3.202899e-12\n",
      "it 13260000, train loss = 3.179047e-12\n",
      "it 13270000, train loss = 3.155410e-12\n",
      "it 13280000, train loss = 3.132023e-12\n",
      "it 13290000, train loss = 3.108758e-12\n",
      "it 13300000, train loss = 3.085925e-12\n",
      "it 13310000, train loss = 3.063013e-12\n",
      "it 13320000, train loss = 3.076396e-12\n",
      "it 13330000, train loss = 3.018293e-12\n",
      "it 13340000, train loss = 3.325001e-12\n",
      "it 13350000, train loss = 2.974330e-12\n",
      "it 13360000, train loss = 2.952650e-12\n",
      "it 13370000, train loss = 2.931276e-12\n",
      "it 13380000, train loss = 2.910015e-12\n",
      "it 13390000, train loss = 2.888988e-12\n",
      "it 13400000, train loss = 2.868187e-12\n",
      "it 13410000, train loss = 2.847486e-12\n",
      "it 13420000, train loss = 2.827047e-12\n",
      "it 13430000, train loss = 2.806768e-12\n",
      "it 13440000, train loss = 7.254251e-11\n",
      "it 13450000, train loss = 2.766806e-12\n",
      "it 13460000, train loss = 2.750775e-12\n",
      "it 13470000, train loss = 4.277574e-10\n",
      "it 13480000, train loss = 2.708509e-12\n",
      "it 13490000, train loss = 2.689431e-12\n",
      "it 13500000, train loss = 2.670520e-12\n",
      "it 13510000, train loss = 2.651823e-12\n",
      "it 13520000, train loss = 3.036395e-12\n",
      "it 13530000, train loss = 2.614919e-12\n",
      "it 13540000, train loss = 2.596717e-12\n",
      "it 13550000, train loss = 1.040115e-10\n",
      "it 13560000, train loss = 2.560839e-12\n",
      "it 13570000, train loss = 2.566989e-12\n",
      "it 13580000, train loss = 2.525739e-12\n",
      "it 13590000, train loss = 2.508474e-12\n",
      "it 13600000, train loss = 2.491286e-12\n",
      "it 13610000, train loss = 2.474340e-12\n",
      "it 13620000, train loss = 1.830767e-10\n",
      "it 13630000, train loss = 2.440840e-12\n",
      "it 13640000, train loss = 2.424335e-12\n",
      "it 13650000, train loss = 2.500489e-11\n",
      "it 13660000, train loss = 2.391965e-12\n",
      "it 13670000, train loss = 2.492793e-12\n",
      "it 13680000, train loss = 2.360159e-12\n",
      "it 13690000, train loss = 2.344625e-12\n",
      "it 13700000, train loss = 2.328907e-12\n",
      "it 13710000, train loss = 2.721933e-12\n",
      "it 13720000, train loss = 2.298289e-12\n",
      "it 13730000, train loss = 8.002774e-12\n",
      "it 13740000, train loss = 2.698655e-12\n",
      "it 13750000, train loss = 2.253331e-12\n",
      "it 13760000, train loss = 2.238791e-12\n",
      "it 13770000, train loss = 2.224189e-12\n",
      "it 13780000, train loss = 2.209781e-12\n",
      "it 13790000, train loss = 2.195575e-12\n",
      "it 13800000, train loss = 2.181429e-12\n",
      "it 13810000, train loss = 2.167821e-12\n",
      "it 13820000, train loss = 2.153863e-12\n",
      "it 13830000, train loss = 2.164943e-12\n",
      "it 13840000, train loss = 5.317039e-11\n",
      "it 13850000, train loss = 2.121524e-12\n",
      "it 13860000, train loss = 4.600048e-12\n",
      "it 13870000, train loss = 2.086311e-12\n",
      "it 13880000, train loss = 2.073300e-12\n",
      "it 13890000, train loss = 2.060384e-12\n",
      "it 13900000, train loss = 2.065451e-12\n",
      "it 13910000, train loss = 1.970265e-10\n",
      "it 13920000, train loss = 2.022302e-12\n",
      "it 13930000, train loss = 2.009869e-12\n",
      "it 13940000, train loss = 1.997571e-12\n",
      "it 13950000, train loss = 1.989923e-12\n",
      "it 13960000, train loss = 1.973298e-12\n",
      "it 13970000, train loss = 1.961381e-12\n",
      "it 13980000, train loss = 1.949530e-12\n",
      "it 13990000, train loss = 1.955201e-12\n",
      "it 14000000, train loss = 1.926187e-12\n",
      "it 14010000, train loss = 1.914678e-12\n",
      "it 14020000, train loss = 1.903280e-12\n",
      "it 14030000, train loss = 1.892001e-12\n",
      "it 14040000, train loss = 1.880839e-12\n",
      "it 14050000, train loss = 1.869806e-12\n",
      "it 14060000, train loss = 2.092866e-11\n",
      "it 14070000, train loss = 6.007558e-12\n",
      "it 14080000, train loss = 1.926221e-12\n",
      "it 14090000, train loss = 1.826529e-12\n",
      "it 14100000, train loss = 1.815973e-12\n",
      "it 14110000, train loss = 2.801269e-12\n",
      "it 14120000, train loss = 2.541042e-12\n",
      "it 14130000, train loss = 1.784985e-12\n",
      "it 14140000, train loss = 1.507744e-10\n",
      "it 14150000, train loss = 1.764950e-12\n",
      "it 14160000, train loss = 1.754833e-12\n",
      "it 14170000, train loss = 1.744957e-12\n",
      "it 14180000, train loss = 1.735238e-12\n",
      "it 14190000, train loss = 6.585672e-12\n",
      "it 14200000, train loss = 1.716017e-12\n",
      "it 14210000, train loss = 1.706526e-12\n",
      "it 14220000, train loss = 1.697123e-12\n",
      "it 14230000, train loss = 1.687786e-12\n",
      "it 14240000, train loss = 1.678589e-12\n",
      "it 14250000, train loss = 1.669413e-12\n",
      "it 14260000, train loss = 1.660394e-12\n",
      "it 14270000, train loss = 1.651740e-12\n",
      "it 14280000, train loss = 1.642579e-12\n",
      "it 14290000, train loss = 5.557175e-10\n",
      "it 14300000, train loss = 2.970322e-12\n",
      "it 14310000, train loss = 1.616526e-12\n",
      "it 14320000, train loss = 1.608016e-12\n",
      "it 14330000, train loss = 1.599535e-12\n",
      "it 14340000, train loss = 1.591188e-12\n",
      "it 14350000, train loss = 1.582917e-12\n",
      "it 14360000, train loss = 1.574801e-12\n",
      "it 14370000, train loss = 1.566552e-12\n",
      "it 14380000, train loss = 2.121231e-12\n",
      "it 14390000, train loss = 1.644374e-11\n",
      "it 14400000, train loss = 1.542931e-12\n",
      "it 14410000, train loss = 1.534835e-12\n",
      "it 14420000, train loss = 1.527087e-12\n",
      "it 14430000, train loss = 1.519444e-12\n",
      "it 14440000, train loss = 1.511814e-12\n",
      "it 14450000, train loss = 1.504318e-12\n",
      "it 14460000, train loss = 1.496843e-12\n",
      "it 14470000, train loss = 1.489516e-12\n",
      "it 14480000, train loss = 1.483661e-12\n",
      "it 14490000, train loss = 1.474848e-12\n",
      "it 14500000, train loss = 7.875418e-12\n",
      "it 14510000, train loss = 1.460583e-12\n",
      "it 14520000, train loss = 1.453520e-12\n",
      "it 14530000, train loss = 1.446540e-12\n",
      "it 14540000, train loss = 1.439651e-12\n",
      "it 14550000, train loss = 1.432805e-12\n",
      "it 14560000, train loss = 1.426046e-12\n",
      "it 14570000, train loss = 1.419358e-12\n",
      "it 14580000, train loss = 1.412740e-12\n",
      "it 14590000, train loss = 3.145184e-11\n",
      "it 14600000, train loss = 1.453519e-11\n",
      "it 14610000, train loss = 1.393157e-12\n",
      "it 14620000, train loss = 1.386748e-12\n",
      "it 14630000, train loss = 1.380430e-12\n",
      "it 14640000, train loss = 1.374121e-12\n",
      "it 14650000, train loss = 1.367910e-12\n",
      "it 14660000, train loss = 1.361738e-12\n",
      "it 14670000, train loss = 1.355648e-12\n",
      "it 14680000, train loss = 1.349625e-12\n",
      "it 14690000, train loss = 3.383773e-10\n",
      "it 14700000, train loss = 1.337709e-12\n",
      "it 14710000, train loss = 1.331834e-12\n",
      "it 14720000, train loss = 6.348567e-12\n",
      "it 14730000, train loss = 1.320256e-12\n",
      "it 14740000, train loss = 1.907318e-12\n",
      "it 14750000, train loss = 5.815395e-11\n",
      "it 14760000, train loss = 1.303337e-12\n",
      "it 14770000, train loss = 1.323883e-12\n",
      "it 14780000, train loss = 1.292274e-12\n",
      "it 14790000, train loss = 1.800844e-10\n",
      "it 14800000, train loss = 1.281436e-12\n",
      "it 14810000, train loss = 3.329947e-12\n",
      "it 14820000, train loss = 1.270813e-12\n",
      "it 14830000, train loss = 1.265577e-12\n",
      "it 14840000, train loss = 1.260381e-12\n",
      "it 14850000, train loss = 1.255269e-12\n",
      "it 14860000, train loss = 2.507209e-12\n",
      "it 14870000, train loss = 1.245100e-12\n",
      "it 14880000, train loss = 1.243536e-12\n",
      "it 14890000, train loss = 1.238700e-12\n",
      "it 14900000, train loss = 1.230256e-12\n",
      "it 14910000, train loss = 1.225401e-12\n",
      "it 14920000, train loss = 1.220602e-12\n",
      "it 14930000, train loss = 1.215836e-12\n",
      "it 14940000, train loss = 1.211113e-12\n",
      "it 14950000, train loss = 1.225419e-12\n",
      "it 14960000, train loss = 1.658043e-11\n",
      "it 14970000, train loss = 1.201879e-12\n",
      "it 14980000, train loss = 1.192671e-12\n",
      "it 14990000, train loss = 1.188219e-12\n",
      "it 15000000, train loss = 1.183727e-12\n"
     ]
    }
   ],
   "source": [
    "# Task: integrate ddot_vahid_vmap from 0 to whatever Psi0 (tau) reaches in the first curve, and compare the value of d at the peak\n",
    "saveat = [3.0]\n",
    "for lmbx in lmbx_max_list:\n",
    "    # get peak tau for each curve. To do this, we need to integrate Psi1_fn (i.e., dPsi/dI1)\n",
    "    I1 = lmbx**2 + 2.0/lmbx\n",
    "    saveat.append(I1)\n",
    "saveat = jnp.array(saveat)\n",
    "def yprime(I1, y, args=None): #The independent variable here is I1, not t.\n",
    "    return jnp.exp(Psi1_fn(params_Psi, I1))\n",
    "\n",
    "solution = diffeqsolve(terms=ODETerm(yprime), \n",
    "                       solver=Kvaerno5(), \n",
    "                       t0=saveat[0], \n",
    "                       t1=saveat[-1], \n",
    "                       dt0=1.e-5, \n",
    "                       y0=jnp.array([0.0]), \n",
    "                       saveat=SaveAt(ts=saveat),\n",
    "                       max_steps=1048576,\n",
    "                       stepsize_controller=PIDController(rtol=1e-9, atol=1e-9))\n",
    "tau_gt = solution.ys.flatten()\n",
    "\n",
    "\n",
    "def loss_d(params_G):\n",
    "    d_pr = []\n",
    "    for taui_gt in tau_gt[1:]:\n",
    "        d_pr.append(G(taui_gt, params_G))\n",
    "    d_pr = jnp.array(d_pr)\n",
    "    return jnp.mean((d_gt-d_pr)**2)\n",
    "\n",
    "\n",
    "params = init_params_damage_simple(key, Psi_layers=[1,3,3,3,1], G_layers=[1,5,5,5,1])\n",
    "params_G = params[1]\n",
    "opt_init, opt_update, get_params = optimizers.adam(1.e-4)\n",
    "opt_state = opt_init(params_G)\n",
    "\n",
    "params_G, train_loss, _ = train(loss_d, get_params, opt_update, opt_state, key, nIter = 15000000, print_freq=10000)\n",
    "#last run: 4.401112e-10 with 5.e-5, [1,5,5,5,1], 3m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2ba1e3e80>]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d_pr = []\n",
    "for taui_gt in tau_gt:\n",
    "    d_pr.append(G(taui_gt, params_G))\n",
    "d_pr = jnp.array(d_pr)\n",
    "\n",
    "plt.plot(tau_gt, d_pr)\n",
    "\n",
    "temp = np.hstack([np.zeros(1), d_gt])\n",
    "plt.plot(tau_gt[1:], d_gt, '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([  0.        ,   1.46071203,   7.00826479,  30.18195361,\n",
       "       122.41755082, 444.0904984 ], dtype=float64, weak_type=True)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tau_gt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Array([0.98018274, 0.99460978, 0.99850241, 0.9995633 , 0.99985402],      dtype=float64)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_gt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "d_pr = []\n",
    "for taui_gt in tau_gt[1:]:\n",
    "    d_pr.append(G(taui_gt, params_G) - G(tau_gt[0], params_G))\n",
    "d_pr = jnp.array(d_pr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gt:  [0.98018274 0.99460978 0.99850241 0.9995633  0.99985402]\n",
      "pr:  [0.98018272 0.99460991 0.99850226 0.99956226 0.99985622]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2bb019640>]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('gt: ', d_gt)\n",
    "print('pr: ', d_pr.flatten())\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(tau_gt[1:], d_gt, '.-')\n",
    "ax.plot(tau_gt[1:], d_pr, '.--')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def integrate_d(params_G):\n",
    "    def yprime(tau, d, args=None): # Here the independent variable is tau, the dependent variable is d\n",
    "        # yprime = dd/dtau = dG/dtau (see ipad notes)\n",
    "        return grad(G)(tau, params_G)\n",
    "    \n",
    "    solution = diffeqsolve(terms=ODETerm(yprime), \n",
    "                           solver=mysolver(), \n",
    "                           t0=tau_gt[0], \n",
    "                           t1=tau_gt[-1], \n",
    "                           dt0=0.001, \n",
    "                           y0=jnp.array([0.0]), \n",
    "                           saveat=SaveAt(ts=tau_gt),\n",
    "                           max_steps=1048576)\n",
    "    d_pr = solution.ys[1:]\n",
    "    return d_pr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gt:  [0.98018274 0.99460978 0.99850241 0.9995633  0.99985402]\n",
      "pr:  [0.98018271 0.99460991 0.99850226 0.99956226 0.99985622]\n"
     ]
    }
   ],
   "source": [
    "d_pr = integrate_d(params_G)\n",
    "print('gt: ', d_gt)\n",
    "print('pr: ', d_pr.flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'time [s]'), Text(0, 0.5, '$f(d)$')]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = (params_Psi, params_G)\n",
    "f_params = None\n",
    "saveat = np.linspace(0,1.0,lmbx_l_gt.shape[1])\n",
    "t, _, d = repeat_uniax(params, lmbx_max_list, ddot_vahid, saveat)\n",
    "d = np.array(d)\n",
    "d = np.clip(d, a_min=0.0, a_max=1.0)\n",
    "fig, ax = plt.subplots(2,2,figsize=(15,12))\n",
    "fig, ax2 = plt.subplots(figsize=(20,4))\n",
    "\n",
    "cols = ['black', 'green', 'red', 'blue', 'orange']\n",
    "lnstyles = ['solid', 'dashed', 'dotted', 'dashdot']\n",
    "\n",
    "t0 = 0.0\n",
    "for i in range(n_cycles):\n",
    "    # Loading\n",
    "    col = cols[i]\n",
    "    di = d[i]\n",
    "    lmbx_l = lmbx_l_gt[i]\n",
    "    lmby_l = 1.0/np.sqrt(lmbx_l)\n",
    "    time_l = t0 + np.linspace(0,1,lmbx_l.shape[0])\n",
    "    sgmx_l = sgmx_l_gt[i]\n",
    "    t0 += 1.0\n",
    "\n",
    "    sgmx_l_pr = eval_Cauchy_vmap(lmbx_l, lmby_l, di, params)[:,0,0]\n",
    "\n",
    "    ax[0,0].plot(time_l, lmbx_l, col)\n",
    "    ax[0,1].plot(lmbx_l, sgmx_l_pr, col, linestyle='dashed', alpha=0.3)\n",
    "    ax[0,1].plot(lmbx_l, sgmx_l, col, linestyle='solid', alpha=0.3)\n",
    "    ax[1,0].plot(time_l, di, col)\n",
    "    ax[1,1].plot(time_l, f_vmap(di, f_params), col)\n",
    "\n",
    "    # ax[0,2].plot(time_l, sgmx_l, col, alpha=0.3)\n",
    "    ax2.plot(time_l, sgmx_l_pr, col, alpha=0.3)\n",
    "    ax2.plot(time_l, di, col, linestyle='dashed', alpha=0.3)\n",
    "\n",
    "    # Unloading\n",
    "    lmbx_u = np.flip(lmbx_u_gt[i])\n",
    "    lmby_u = 1.0/np.sqrt(lmbx_u)\n",
    "    time_u = t0 + np.linspace(0,1,lmbx_u.shape[0])\n",
    "    t0 += 1\n",
    "    sgmx_u = np.flip(sgmx_u_gt[i])\n",
    "    sgmx_u_pr = eval_Cauchy_vmap2(lmbx_u, lmby_u, di[-1], params)[:,0,0]\n",
    "\n",
    "    ax[0,0].plot(time_u, lmbx_u, col)\n",
    "    ax[0,1].plot(lmbx_u, sgmx_u_pr, col, linestyle='dashed')\n",
    "    ax[0,1].plot(lmbx_u, sgmx_u, col, linestyle='solid')\n",
    "    ax[1,0].plot(time_u, di[-1]*np.ones_like(time_u), col)\n",
    "    ax[1,1].plot(time_u, f_vmap(di[-1]*np.ones_like(time_u), f_params), col)\n",
    "\n",
    "    ax2.plot(time_u, sgmx_u_pr, col)\n",
    "    ax2.plot(time_u, di[-1]*np.ones_like(time_u), col, linestyle='dashed')\n",
    "\n",
    "    sgmx_l = sgmx_l_gt[i]\n",
    "\n",
    "ax[0,0].set(xlabel='time [s]', ylabel = '$\\lambda_x$')\n",
    "\n",
    "legend_elements = [Line2D([0], [0], color='k', linestyle= '-', label='gt'),\n",
    "                   Line2D([0], [0], color='k', linestyle='--', label='pr')]\n",
    "ax[0,1].legend(handles=legend_elements)\n",
    "ax[0,1].set(xlabel='$\\lambda_x$', ylabel='$\\sigma_{xx}$', ylim=[0,3])\n",
    "\n",
    "legend_elements = [Line2D([0], [0], color='k', linestyle= '-', label='stress'),\n",
    "                   Line2D([0], [0], color='k', linestyle='--', label='damage')]\n",
    "ax2.legend(handles=legend_elements)\n",
    "ax2.set(xlabel='time [s]', ylabel='$\\sigma_{xx}$')\n",
    "\n",
    "ax[1,0].set(xlabel='time [s]', ylabel='$d$')\n",
    "ax[1,1].set(xlabel='time [s]', ylabel='$f(d)$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x320 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "plt.rcParams[\"mathtext.fontset\"] = \"dejavuserif\"\n",
    "\n",
    "params = (params_Psi, params_G)\n",
    "f_params = None\n",
    "saveat = np.linspace(0,1.0,lmbx_l_gt.shape[1])\n",
    "t, _, d = repeat_uniax(params, lmbx_max_list, ddot_vahid, saveat)\n",
    "d = np.array(d)\n",
    "d = np.clip(d, a_min=0.0, a_max=1.0)\n",
    "\n",
    "resize = 0.8\n",
    "n_ax = 3\n",
    "fig, ax = plt.subplots(1,3,figsize=(5*n_ax*resize,4*resize))\n",
    "\n",
    "t0 = 0.0\n",
    "for i in range(n_cycles):\n",
    "    # Loading\n",
    "    di = d[i]\n",
    "    lmbx_l = lmbx_l_gt[i]\n",
    "    lmby_l = 1.0/np.sqrt(lmbx_l)\n",
    "    time_l = t0 + np.linspace(0,1,lmbx_l.shape[0])\n",
    "    sgmx_l = sgmx_l_gt[i]\n",
    "    t0 += 1.0\n",
    "\n",
    "    sgmx_l_pr = eval_Cauchy_vmap(lmbx_l, lmby_l, di, params)[:,0,0]\n",
    "\n",
    "    ax[0].plot(time_l, lmbx_l, color=plt.cm.Dark2(i))\n",
    "    ax[1].plot(lmbx_l, sgmx_l_pr, color=plt.cm.Set2(i), alpha=0.3)\n",
    "    ax[1].plot(lmbx_l, sgmx_l, color=plt.cm.Set2(i), marker='.', alpha=0.3, lw=0, markersize=3)\n",
    "    # ax[2].plot(time_l, di, color=plt.cm.Dark2(i))\n",
    "\n",
    "    # Unloading\n",
    "    lmbx_u = np.flip(lmbx_u_gt[i])\n",
    "    lmby_u = 1.0/np.sqrt(lmbx_u)\n",
    "    time_u = t0 + np.linspace(0,1,lmbx_u.shape[0])\n",
    "    t0 += 1\n",
    "    sgmx_u = np.flip(sgmx_u_gt[i])\n",
    "    sgmx_u_pr = eval_Cauchy_vmap2(lmbx_u, lmby_u, di[-1], params)[:,0,0]\n",
    "\n",
    "    ax[0].plot(time_u, lmbx_u, color=plt.cm.Dark2(i))\n",
    "    ax[1].plot(lmbx_u, sgmx_u_pr, color=plt.cm.Dark2(i))\n",
    "    ax[1].plot(lmbx_u, sgmx_u, color=plt.cm.Dark2(i), marker='.', lw=0, markersize=3)\n",
    "    # ax[2].plot(time_u, di[-1]*np.ones_like(time_u), color=plt.cm.Dark2(i))\n",
    "\n",
    "ax[0].set(xlabel='time [s]', ylabel = '$\\lambda_x$')\n",
    "\n",
    "legend_elements = [Line2D([0], [0], color='k', marker='.', linestyle=None, label='Data', lw=0),\n",
    "                   Line2D([0], [0], color='k', linestyle='-', label='Model')]\n",
    "\n",
    "ax[1].legend(handles=legend_elements, frameon=False)\n",
    "ax[1].set(xlabel='$\\lambda_x$', ylabel='$\\sigma_{xx}$')\n",
    "# ax[2].set(xlabel='time [s]', ylabel='$d$')\n",
    "\n",
    "[a.spines['top'].set_visible(False)  for a in ax]\n",
    "[a.spines['right'].set_visible(False) for a in ax]\n",
    "labels='abc'\n",
    "for label, axi in zip(labels, ax):\n",
    "    axi.text(-0.05, 1.04, '('+label+')', transform=axi.transAxes)\n",
    "\n",
    "\n",
    "ax[0].plot(0, np.max(lmbx_l)*1.1, alpha=0.0)\n",
    "ax[0].legend(['Cycle 1', 'Cycle 2', 'Cycle 3', 'Cycle 4', 'Cycle 5'], ncol=2, frameon=False, handlelength=1, loc='upper left')\n",
    "\n",
    "x = np.linspace(0,500,1000)\n",
    "y = vmap(G, in_axes=(0,None))(x, params_G)\n",
    "y = np.arctanh(y)\n",
    "ax[2].plot(x,y)\n",
    "ax[2].set(xlabel='$\\\\tau$ [kPa]', ylabel='$tanh^{-1}(G(\\\\tau))$ [-]')\n",
    "\n",
    "fig.savefig('figs/fig_derm1.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils_node import forward_pass_nobias, init_layers_nobias\n",
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "plt.rcParams[\"mathtext.fontset\"] = \"dejavuserif\"\n",
    "\n",
    "def format_traj_fig(ax):\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.margins(x=0)\n",
    "    ax.set(xticks=[0,0.5,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale = 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 120x90 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot trajectories for the first figure\n",
    "p, bias = params_Psi\n",
    "y = []\n",
    "for I1 in [3.15,3.2,3.3,3.5,3.84,5,6,7,8]:\n",
    "    n = 100\n",
    "    dt = 1.0/n\n",
    "    t = np.linspace(0,1,n)\n",
    "    yi = [np.array([I1-3]) + jnp.exp(bias)]\n",
    "    for _ in t[1:]:\n",
    "        yi.append(yi[-1] + dt*(forward_pass_nobias(yi[-1], p)) + jnp.exp(bias))\n",
    "    y.append(np.array(yi))\n",
    "y = np.array(y)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4*scale,3*scale))\n",
    "for yi in y:\n",
    "    ax.plot(t, yi, 'k-', linewidth=0.5)\n",
    "\n",
    "format_traj_fig(ax)\n",
    "ax.set(xlabel='$\\omega$', ylabel='$\\Psi_0$')\n",
    "fig.savefig('figs/traj_Psi0.pdf', bbox_inches='tight', transparent=True, pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4MBkaGtKGDRvo+fPnjMcrLi6WpQ1BQUEkFAoZ7UpLS2nz5s3E5XLp0KFDjNUGiURCycnJZGdnR66urpSRkaHUOb+O7OxsWcmLqQP5tlB5uLWiooK8vb3p3XffJS0tLdLW1iZtbW3q1KkTrVq1qjV8VBtlxVpTU0P79u0jLpdLW7ZsobKyMjmb3Nxc+uGHH8jc3JyioqIYSzTFxcW0fv16MjQ0pICAgNfOVHJxcSE7OztKSUlRmI/evHmTPD09ycbGRuEcAekPw8jIiNavX692fVQsFtOpU6fI2tqaFi5cqDAVeZs0e26AWCymzMxMio6Opvj4eIVT0doDbxJrfn4+rVy5kkxNTSkkJESuQyTNIefMmUMeHh4Kp8BdvXqVXFxcyN7eXqH4RCIRhYaGkqmpKa1YsYIeP37M2Ja0MzRz5kzy8vJiLHcR/ZV6SaPy634YysLn88nPz4+4XC5t27ZNNtutPdBuJrK0JkxilUgklJSURHPnziUXFxe6cuWK3Puqq6tp7969shyS6QcpFArp6NGjZGJiQitXrmScj0tE9Pz5c9kkkH379iksupeXl8vywt27dzPOWZVOdnF2diYHBwe6ePGi2uueHj58KLtiREREMK7dUoXWWIfV4cRaXV0tu9Rv3LiRcf7ow4cPadmyZWRmZkahoaGMpaeioiLasGEDGRoa0sGDBxk7GxKJhK5cuUIuLi5ka2v72kV3N27coPnz55O1tbXCUSuBQECHDx8mY2NjWrNmjdrzR6WXehsbG5o/f36LTJrm8/m0d+9ehXV5dehQYl28eLFCATY2NtLJkyfJ2tqaPD09Gb84aefF0dGRHBwcFJanBAIBBQYGkomJCa1atUrhpV4oFNKxY8fIzMyMli1bRg8ePGC0e/z4Ma1atYqMjY0pMDBQ7Us9j8ej3377jTgcDu3YsaNFLvU5OTnk5eVFZmZmFBYW1irTBzuUWFNTU+VeKysrox07dhCHw6GdO3cyri2qqqqSpQMbNmxQWFfMy8ujlStXkrGxMR0+fJixFktEVFhYSOvWrSNDQ0M6cOAAY0rQ2Ngoi3pubm505coVtS+t169fl0XvuLg4tUtZDQ0NFB0dTZaWlrRo0aLXTupuCTqUWKU5q3TkxcXFhebMmaPwsnvr1i369ttvycLCgsLDwxmjRUNDA8XGxpK1tTW5u7vT1atXFU6EPn36NNna2pKLi4vCSSylpaWynHXHjh2MFQlVqK2tpYCAADIxMSFvb2+F0VsVioqKaOPGjcTlcmn37t1vbaVAh7qZcGFhIU6ePIm4uDhMnDgRCxcuxCeffNLEtq6uDlFRUQgPD8fQoUOxePFifPXVV3JtFhUV4fDhw0hNTcXMmTPh5uaG9957T86upKQEgYGBSEpKgpGRETw9PeXu009EuHjxIv744w+IRCJ4enrCxMSkyb2qVCU7OxsHDx7Ew4cPYW9vD3t7e3Tt2rXZ7UkkEpw7dw6BgYHQ0dHBwoULYWBg8HYnYb+Vn0QbI42sZmZmFBUVxdjTvXv3Lnl5eZGpqSkFBAQwXsIbGxspPj6ebG1tycnJiS5cuMAYHcViMZ09e5YcHBzIzs6OEhMTGSN3WVkZ/fbbb7LOnqJlJ8oiEAgoODiYzM3N6bvvvmuRy3JxcTH9/PPPssk4bVmi7FCR9X9v015TU4OIiAhER0djyJAhWLBgAeM98wsKChAQEIDLly/D1NQUbm5ujHs9FRUVITAwEBcvXgSHw4GHhwf69+/fxEYikeDChQsICgpCfX09XF1dYWZmJncvf1W4ffs2/vjjD+Tl5cHW1hb29vbo3r17s9trbGxEYmIigoODoaOjAw8PDxgaGqoV6VuCDifWHj164MqVKwgMDERJSQns7Oxga2srd4kUCoWIjY1FeHg4evXqBU9PT0ybNk3usldfX4/4+HiEhoaia9eucHd3B4fDkftiCwsLceTIEaSmpmLGjBlwd3dnvL25slRWViIsLAxxcXEYNmwY5s+fj5EjRza7PQDIy8tDYGAgrl27BlNTU7i4uMilLG1JhxLr5s2bkZqaiokTJ8LDw0NusR0RITMzE4GBgXj06BGsra3h4OCAXr16ybV5+/ZtBAUFITs7GxYWFnB2dpZbbFdXV4eYmBhERkaiZ8+ecHd3x/Tp05sdocRiMf78808EBwejpqYGjo6OmDVrllrr4aqrq3H8+HFER0ejf//+8PDwwJQpU9TKRZ88eYLg4GCsX7++2W0w0a5Xt7Y0X331FXx8fKCjo9Pk+efPn+PYsWM4f/48Ro0ahSVLljB2qsrLyxEaGopTp07hyy+/hIeHB0aPHt3Ehohw+fJlBAcHo6ioCNbW1jhy5Ajj6lJlefDgAY4cOYJr165hxowZ2LZtGwYOHNjs9iQSCVJSUnD06FHweDzY2toiIiJCrR0Ca2trceLECRw/fhy9evVqlY2GO1RkfTVnra2tRVxcHKKiotCtWzc4OzvD2NhYLncUCoWIj49HREQEdHR04OTkBDMzM3Tu3LmJ3aNHj3Ds2DGkpaVh8uTJcHV1xeeff95sn3k8HiIiIhAfH4+PP/4Ybm5umDx5sloR7/79+zh69CjS09NhYGAAFxcXxi2DlEUsFiMlJQUhISEoKyuDjY0NbG1tW237pg4l1pcvXyIjIwOhoaEoLy+HtbU15s6dKxf1JBIJ0tLScOzYMRQXF8PS0hL29vZyl3kej4eoqCjEx8ejf//+mDdvHr755ptmX+br6uqQkJCAyMhIAIC9vT0sLCzkdk5RhZKSEoSHhyMxMRGDBw+Gi4uL2qK/c+cOQkJCkJmZiRkzZsDZ2VmuBNgadCixGhgYwNjYGE5OTnIRhYhw584dhIaGIjMzE9988w2cnZ0xZMiQJnYCgQDx8fGIioqClpYW7OzsYGlp2WxBicViJCcnIzQ0FKWlpbCwsICdnR1jzVZZqqurERMTg5iYGHTt2hUODg4wNTWVuxqoQmFhIcLDw3HhwgUMHz4c8+bNw7hx495qnbVDibWyslIuij5+/BhhYWFISUnByJEj4eTkJPclNDQ0ICkpCeHh4eDxeLCysoKtrS3j3UuUgYhw7do1hIWFITs7GwYGBnB0dMSnn37a7HMUiUQ4c+YMIiMjIRAIYG1tDRsbG7UuyS9evEBUVBQSEhLQt29fODg4gMPhqFVmU4cOJVZpzvr8+XNERkYiMTERAwYMgKOjI6ZPn96k4yWRSHDp0iWEhYUhPz8fRkZGsLe3x4ABA5rtR3Z2NkJDQ3H16lVMmDABTk5OapWbpFE5LCwMz58/h5mZGezs7ORut6QKfD4fsbGxiI2NRadOnTB37lzMnDlTrdGvlqJDidXPzw/nz5+Hvr4+7OzsYGJi0qTsQ0S4efMmwsLCcPv2bUydOhUODg5ym0uoQkFBAcLDw5GcnIzPP/8cjo6OapWGiAhXr15FWFgY7t+/Dw6HA3t7e7WislAoxOnTpxEVFYW6ujpYW1tj9uzZalUwysrKWrxG26HE6u/vD0dHR8btyUNCQpCWlobRo0fDyckJo0ePbragXrx4gcjISJw6dQr9+vWDg4MDZsyYodblMzs7GyEhIUhPT8ekSZPg5OTEWF5TllejcklJCczNzTF37ly1BFZdXY3Y2FicOHECenp6CA8Pb3ZbTHQosb5aupKWhhISEjBgwAA4OTmp1ZMXCoU4efIkIiMjoaOjAzs7O8ycOVOtnnxpaSlCQ0Nx9uxZDB06FM7Ozmr35O/evYvg4GDcunULBgYGcHJyUisqNzY2IikpCSEhIaiqqsLs2bNhY2OjVlRWRIcSK4/Hw+XLlxESEoKGhgY4ODioVRqSdpSCgoJQUFAAKysrxhKXKkiHb8PCwqCrqyur6zLdDVBZXrx4IRvMGDZsGFxdXTF27Fi1RJ+Tk4OgoCDcuHEDhoaGcHZ2ViufVwaNEWtMTAy2bduGLl26QFtbG/v27VP6Mvhq6crCwgLz5s1TqxPy7NkzhISE4Ny5cxg3bhzc3d0Zt5ZUFiLCjRs3EBwcjAcPHsDCwgKOjo5qla8aGhqQmJiIkJAQiMViODs7w9zcXK3yVUVFBSIiIhAXF4dPP/0UHh4eGD9+/FsrX2mEWK9duwZDQ0NkZmbis88+Q3BwMNatW4ecnBy5e5ky8brSlbIUFhYiISEBZ8+ehb6+PpydnWFkZNTsPFQikeDGjRuIi4vD1atXMWrUKLi5ualVHRAIBEhJSUFcXBwKCwthbGwMZ2dntX6YZWVlOH36NBISEgAAc+fOxaxZs9RKb5qLRojVxsYGenp6CAsLA/DXF/3hhx/Cx8cHXl5eb3y/oimCTBAReDwecnJycPv2bWRkZKCkpASDBg2Cubk5jI2N0a1bN5X8l0gkePr0Ke7du4dbt24hMzMTdXV1GD16NCwtLTFp0iSVc2WRSIS8vDxkZWXhxo0buHfvHjp16oTp06fD0tJSbjBDGaqqqvDgwQPcuXMHGRkZKCwsRJ8+fWBqagpzc3O8++67KrfZkmiEWHv27Il//etfWLFihey5mTNnQldXF3FxcW98v1SsKSkpAP76UiorK/Hy5Uu8ePECpaWlKC8vR2NjIwCgT58+GDZsGEaOHInx48ejb9++ICLU19dDJBKhrq4OdXV1qK2tRU1NDaqrq8Hn81FZWQkej4fy8nKUlpbi5cuXAAAtLS0MHDgQw4cPx6hRozBmzBi88847kEgkEIlEEAqFjG1WVlaioqJC1mZJSQlqamoA/HX37yFDhmDEiBEYPXo0hg8fDh0dHYjF4iZtCgQC1NbWNvGxoqICL168QFlZGUpLS9HQ0AAA0NfXxxdffIERI0Zg3LhxGDRoUJvcjl0R7V6sPB4Pffr0wdGjRzFv3jzZ8/Pnz8f169dx584dufcw7SkwcOBAaGlpySLYq1+C9P+v+2L+10ZLS6vJn7a2tuxf6Z/0NSlEBC0tLdm/AKCtrQ0dHR3o6OjI/q+rqyv769y5Mzp16oTOnTtDT0+vSUfrf786LS0t6OjoQE9PD126dEHXrl3RrVs3vPPOO+jRowd69OiBXr16oXfv3ujTpw/ef/999O3bV6089m3S7qcINmdfAUV7CpiYmKjVq25vMP24xGIxBALBa/dceFucPHmyRdtr92KV5oevRkrpY0W549q1a7F8+XLZY2lkjYiIaLXpayytT7sX63vvvYeePXuitLS0yfMlJSUYPHgw43v09PTkhlGB9rHFUEejR48eLZb3tnuxAgCHw0FmZqbssbQu6ePjo9T7eTweAKg1u56leZSVlTEurmwOGiHWNWvWwMjICHl5eRg6dChCQkKgo6MDNzc3pd4vHVF68uRJqwwDthbS9OXp06cal75IfW/JzptGiHXixIkICgqCg4MDunbtCm1tbZw9e1apAQEAsgpAz549Ne5LB/4qKWmi38DrKyyqohFiBYDZs2dj9uzZbe0GSxvStnctYGFRgQ4hVj09PWzcuPGt7Dfbkmiq30Dr+N7uR7BYWKR0iMjK8veAFSuLxsCKlUVj+NuINSYmBhMmTMC0adMwffp03Lt3r0XtWwtV/Ni0aRNGjx4NAwMD2Z+Njc1b9LYp9fX1WLNmDXR1dVFQUPBG+0uXLmHy5MmYPn06Jk+ejLS0NNUO2ML3e20T0tPTqUePHrK9oo4cOUIfffQR8fn8FrFvLVT1Y+PGjZScnPwWPVRMfn4+TZ48mVxdXRl3M/9fCgoKSF9fX7avQ0pKCunr66u02fHfQqyzZ88mBwcH2WOxWEx9+/ZVuDWnqvathap+tCex3r17l3Jzcyk5OVkpsXp7e9PkyZObPDdhwgRavny50sf8W6QBFy5cwPjx42WPtbW1MW7cOCQlJbWIfWvRXvxoDiNGjMDQoUOVtv/fcwWACRMmqHSuGi9WHo8HPp8vtyiuX79+yM/PV9u+tWiuHwEBATAwMMDUqVPh5uaGR48etbarLcLjx4/V/sw1XqyqriRozsqD1qA5fgwaNAhjxoxBUlIS0tLS8Omnn2LcuHEoLi5udX/VRSAQqP2Za7xYVV1J0JyVB61Bc/zw9PSEt7c3dHV1oa2tjQ0bNqBLly7Yt29fq/urLt26dVP7M9d4saq6kqA5Kw9ag5bwQ0dHB5988olGpAKDBw9W+zPXeLECilcSGBoatoh9a6GqH8uWLZN77tmzZxg0aFCr+dhScLncJucKABkZGap95irXLNoh6enppK+vT7m5uUREdPTo0Sb1yqlTp9K6deuUtm+vfn/yyScUFxcne+zv709dunShnJyct+r3qygqXTk6OtK8efNkj6V11kuXLhERUWpqqsp1Vo2ZfP063rSSQCAQNMmX1F150FZ+b926Fbt378auXbtQX18PPT09JCUlqXWfreZSX18PY2NjVFZWAgAcHBwwcOBAREVFAfjrroqv3mXm448/RkJCAlasWIHOnTtDJBIhISFBpQ042CmCLBrD3yJnZekYsGJl0RhYsbJoDKxYWTQGVqwsGgMrVhaNgRUri8bAipVFY2DFyqIxsGJl0RhYsbJoDKxY2yFbtmwBl8uV7R4jJT4+Hl9//TWKiorayLO2hZ3I0s7Yvn07EhMTkZSUxLhZh4uLC7p164aDBw+2gXdtCxtZ2xFCoRC//vorvLy8ZELdt28fLl68KLPp168fcnNz28rFNoUVazvi6tWrqKiowNSpU2XPbdq0qclykMLCwjbf6a+tYMXajsjIyED37t3Rv39/AH9tHlFeXt5kP9eMjAx8/vnnbeVim8KKtR3B5/PRvXt32ePLly8DgGw2fVZWFvLz82FhYdEm/rU1rFjbEUZGRigrK8PDhw8hEomwc+dOfPTRR7h//z7q6+vx448/YsGCBU3ShI4EWw1oZ+zcuRMHDhzAwIEDsWLFCvTu3Rvff/89dHR0YG1tDR8fH+jo6LS1m20CK1YWjYFNA1g0BlasLBoDK1YWjYEVK4vGwIqVRWNgxcqiMbBiZdEYWLGyaAysWFk0BlasLBoDK1YWjeH/Advk1QTCF+ZGAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 120x90 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p, a = params_G\n",
    "y = []\n",
    "# for I1 in [3.15,3.2,3.3,3.5,3.84,5,6,7,8]:\n",
    "for I1 in [0,0.0001,0.001, 0.01, 0.1, 1,2,3,4,5,6]:\n",
    "    n = 100\n",
    "    dt = 1.0/n\n",
    "    t = np.linspace(0,1,n)\n",
    "    yi = [np.array([I1])]\n",
    "    for _ in t[1:]:\n",
    "        yi.append(yi[-1] + dt*(forward_pass_nobias(yi[-1], p)))\n",
    "    y.append(np.array(yi))\n",
    "y = np.array(y)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4*scale,3*scale))\n",
    "for yi in y:\n",
    "    ax.plot(t, yi, 'k-', linewidth=0.5)\n",
    "\n",
    "format_traj_fig(ax)\n",
    "ax.set(xlabel='$\\omega$', ylabel='$G$')\n",
    "fig.savefig('figs/traj_G.pdf', bbox_inches='tight', transparent=True, pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 120x90 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# key, subkey = random.split(key)\n",
    "params_f = init_layers_nobias([1,3,3,3,1], key)\n",
    "y = []\n",
    "# for I1 in [3.15,3.2,3.3,3.5,3.84,5,6,7,8]:\n",
    "for I1 in [0,0.1, 1,2,3,4,5,6]:\n",
    "    n = 100\n",
    "    dt = 1.0/n\n",
    "    t = np.linspace(0,1,n)\n",
    "    yi = [np.array([I1])]\n",
    "    for _ in t[1:]:\n",
    "        yi.append(yi[-1] + dt*(forward_pass_nobias(yi[-1], params_f)))\n",
    "    y.append(np.array(yi))\n",
    "y = np.array(y)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4*scale,3*scale))\n",
    "for yi in y:\n",
    "    ax.plot(t, yi, 'k-', linewidth=0.5)\n",
    "\n",
    "format_traj_fig(ax)\n",
    "ax.set(xlabel='$\\omega$', ylabel='$f$')\n",
    "fig.savefig('figs/traj_f.pdf', bbox_inches='tight', transparent=True, pad_inches=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}