synth_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "# from arc_prize.synth_data.move_many_random import generate_dataset\n",
    "# from arc_prize.synth_data.flip import generate_dataset\n",
    "from arc_prize.synth_data.ecaa0ec1 import ARCecaa0ec1Dataset\n",
    "from arc_prize.synth_data.eval.eval_00576224 import ARCEval00576224Dataset\n",
    "from arc_prize.synth_data.eval.eval_009d5c81 import ARCEval009d5c81Dataset\n",
    "from arc_prize.synth_data.eval.eval_00dbd492 import ARCEval00dbd492Dataset\n",
    "from arc_prize.synth_data.move_diagonal import MoveDiagonalDataset\n",
    "from arc_prize.synth_data.move_diagonal_and_scale import MoveDiagonalScaleDataset\n",
    "from arc_prize.synth_data.rotate import RotateDataset\n",
    "from arc_prize.synth_data.rotate_and_scale import RotateScaleDataset\n",
    "from arc_prize.synth_data.scale import ScaleDataset\n",
    "\n",
    "\n",
    "\n",
    "dataset = ARCEval00dbd492Dataset(grid_dim=12, max_train_pairs=4)\n",
    "training_tasks = dataset.generate_full_tasks(num_tasks=10)\n",
    "eval_tasks = dataset.generate_full_tasks(num_tasks=2)\n",
    "\n",
    "\n",
    "file_path_root = \"data/00dbd492\"\n",
    "os.makedirs(file_path_root, exist_ok=True)\n",
    "\n",
    "with open(f\"{file_path_root}/training_challenges.json\", \"w\") as f:\n",
    "   json.dump(training_tasks.to_dict()[\"challenges\"], f)\n",
    "with open(f\"{file_path_root}/training_solutions.json\", \"w\") as f:\n",
    "   json.dump(training_tasks.to_dict()[\"solutions\"], f)\n",
    "with open(f\"{file_path_root}/evaluation_challenges.json\", \"w\") as f:\n",
    "   json.dump(eval_tasks.to_dict()[\"challenges\"], f)\n",
    "with open(f\"{file_path_root}/evaluation_solutions.json\", \"w\") as f:\n",
    "   json.dump(eval_tasks.to_dict()[\"solutions\"], f)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x900 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5118L769R1siLnaewB7n+mk3Kz8Hk+mXc87nW5NFh3o4HAAAAQO4MoQAAAADInSEUAAAAALkzhAIAAAAgd4ZQAAAAAOTOEAoAAACA3JUGg4zPy9xGq9WKer0e09PTUalUhq4pl8vR7/cz71H0PIU9yJ3hYvJOpxONRiOazWbUarXMeyw3ReivFPYgd4Yp5/pr4f01yhp5sfMU9iDXX8MU4TmYXL+Mez7Xmjw6rDyfxb1OJ0oZM6uJajW67XbmtUXPU9iD3BkuJu91u5nXjYOU+yuFPcidYcq5/lp4f42yRl7sPIU9yPXXbFJ+DibXL+Oez7Umjw7zdjwAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5M4QCAAAAIHeGUAAAAADkzhAKAAAAgNyVBoPBYK5FrVYr6vV6TE9PR6VSGbqmXC5Hv9/PvEfR8xT2IHeGi8k7nU40Go1oNptRq9Uy77HcFKG/UtiD3BmmnOuvhffXKGvkxc5T2INcfw1ThOdgcv0y7vlca/LosPJ8Fvc6nShlzKwmqtXottuZ1xY9T2EPcme4mLzX7WZeNw5S7q8U9iB3hinn+mvh/TXKGnmx8xT2INdfs0n5OZhcv4x7PteaPDrM2/EAAAAAyJ0hFAAAAAC5M4QCAAAAIHeGUAAAAADkzhAKAAAAgNwZQgEAAACQu9JgkPF5mdtotVpRr9djeno6KpXK0DXlcjn6/X7mPYqep7AHuTNcTN7pdKLRaESz2YxarZZ5j+WmCP2Vwh7kzjDlXH8tvL9GWSMvdp7CHuT6a5giPAeT65dxz+dak0eHleezuNfpRCljZjVRrUa33c68tuh5CnuQO8PF5L1uN/O6cZByf6WwB7kzTDnXXwvvr1HWyIudp7AHuf6aTcrPweT6Zdzzudbk0WHejgcAAABA7gyhAAAAAMidIRQAAAAAuTOEAgAAACB3hlAAAAAA5M4QCgAAAIDclQaDjM/L3Ear1Yp6vR7T09NRqVSGrimXy9Hv9zPvUfQ8hT3IneFi8k6nE41GI5rNZtRqtcx7LDdF6K8U9iB3hinn+mvh/TXKGnmx8xT2INdfwxThOZhcv4x7PteaPDqsPJ/FvU4nShkzq4lqNbrtdua1Rc9T2IPcGS4m73W7mdeNg5T7K4U9yJ1hyrn+Wnh/jbJGXuw8hT3I9ddsUn4OJtcv457PtSaPDvN2PAAAAAByZwgFAAAAQO4MoQAAAADInSEUAAAAALkzhAIAAAAgd/P6dDwW55//+Z9j/fr1mfnU1JR8jvyoI4/MzIH86K/Z81HWbNq0adbrAQBguSsNBhmfl7mNVqsV9Xo9pqeno1KpDF1TLpej3+9n3qPo+c64R6fTic2bN2fmk5OT8jnyrD9/EUtzhkXOO51ONBqNaDabUavVMu+x3BShv1LYg/5aXD7KmrVr1yZ9xinn+mvh/TXKGnmx8xT2INdfwxThOZhcv4x7PteaPDpsXq+E6nU6UcqYWU1Uq9FttzOvLXq+M+6xecuWpP+X/CLks70SainOsMh5r9vNvG4cpNxfKexBfy3NK6FSPuOUc/218P4aZY282HkKe5Drr9mk/BxMrl/GPZ9rTR4d5mdCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5m9en47FrPSb2jA2l1cs6B5anFPolzxwAAJhbaTDI+LzMbbRarajX6zE9PR2VSmXomnK5HP1+P/MeRc93xj06nU5s3rw5M5+cnJw1P27y6Ghv/n5mXp08rPD5jZtvycwnJycz//xFLM0ZFjnvdDrRaDSi2WxGrVbLvMdyU4T+SmEP+mtxeUTEisnVs/4erF27NukzTjnXXwvvr1HWyIudp7AHuf4apgjPweT6Zdzzudbk0WHzeiVUr9OJUsbMaqJajW67nXlt0fOdcY/NW7bE+vXrM/OpqalZ809MbYyb37wxM3/sRecVPp/r9+eoI4/MzJfiDIuc97rdzOvGQcr9lcIe9Nfi8oiI3S965ay/B5s2bUr6jFPO9dfC+2uUNfJi5ynsQa6/ZpPyczC5fhn3fK41eXSYnwkFAAAAQO4MoQAAAADInSEUAAAAALkzhAIAAAAgd4ZQAAAAAORuXp+OR9oOeNoJ8Zh/vTYzb7Vauzy/OTMFxtl34j/igsFtmflUAjkAALA4pcEg4/Myt9FqtaJer8f09HRUKpWha8rlcvT7/cx7FD3fGffodDqxefPmzHxycnLW/LjJo6O9+fuZ+T5PWRPlcvZcsd/v7/L87q/clJlXJw+LGzffkplPTk5m/vmLWJozLHLe6XSi0WhEs9mMWq2WeY/lpgj9lcIednV/rZhcvaj7L0W+dfNtmXnE3N/D2rVrkz7jlHP9tfD+GmWNvNh5CnuQ669hivAcTK5fxj2fa00eHTavV0L1Op0oZcysJqrV6LbbmdcWPd8Z99i8ZUusX78+M5+ampo1/8TUxrj5zRsz82d89WOz/sFotVq7PJ9t/4+96Lw5f3+OOvLIzHwpzrDIea/bzbxuHKTcXynsYVf31+4XvXJR91+K/P43/2VmHjH397Bp06akzzjlXH8tvL9GWSMvdp7CHuT6azYpPweT65dxz+dak0eH+ZlQAAAAAOTOEAoAAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyF1pMMj4vMxttFqtqNfrMT09HZVKZeiacrkc/X4/8x5Fz3fGPTqdTmzevDkzn5ycnDU/bvLoaG/+fma+z1PWRLlczsz7/f4uz+/+yk2ZeXXysLhx8y2Z+eTkZOafv4ilOcMi551OJxqNRjSbzajVapn3WG6K0F8p7GFX99eKydWLuv9S5Fs335aZR8z9PaxduzbpM045118L769R1siLnaewB7n+GqYIz8Hk+mXc87nW5NFh2RODIXqdTpQyZlYT1Wp02+3Ma4ue74x7bN6yJdavX5+ZT01NzZp/Ympj3PzmjZn5M776sVn/YLRarV2ez7b/x1503py/P0cdeWRmvhRnWOS81+1mXjcOUu6vFPawq/tr94teuaj7L0V+/5v/MjOPmPt72LRpU9JnnHKuvxbeX6OskRc7T2EPcv01m5Sfg8n1y7jnc63Jo8O8HQ8AAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyN28Ph2P5e0n1/9TfH6OT6+r/e7Tl25DACN6TOwZG0qrZ80BAIBdqzQYZHxe5jZarVbU6/WYnp6OSqUydE25XI5+v595j6LnO+MenU4nNm/enJlPTk7Omh83eXS0N38/M9/nKWuiXM6eK/b7/Vnzzl13z3r/6uRhUdlvnwXfv9/vx91fuWnW+9+4+ZbMfHJyMvPPX8TSnGGR806nE41GI5rNZtRqtcx7LDdF6K8U9rCr+2vF5Opc7z9Kv8z1+Fs335aZR8z9PaxduzbpM045118L769R1siLnaewB7n+GqYIz8Hk+mXc87nW5NFh83olVK/TiVLGzGqiWo1uu515bdHznXGPzVu2xPr16zPzqampWfNPTG2Mm2d5pdIzvvqxWf9gtFqtWfOfXP9Ps97/sRedFwc++vAF37/Vas15/7l+f4468sjMfCnOsMh5r9vNvG4cpNxfKexhV/fX7he9Mtf7j9Ivc+X3v/kvM/OIub+HTZs2JX3GKef6a+H9NcoaebHzFPYg11+zSfk5mFy/jHs+15o8OszPhAIAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJA7QygAAAAAcjevT8djeTvgaSfEY/712sy81Wot4W4ARved+I+4YHBbZj4V/7F0mwEAAIYqDQYZn5e5jVarFfV6Paanp6NSqQxdUy6Xo9/vZ96j6PnOuEen04nNmzdn5pOTk7Pmx00eHe3N38/M93nKmiiXs+eK/X5/l+d3f+WmzLw6eVjcuPmWzHxycjLzz1/E0pxhkfNOpxONRiOazWbUarXMeyw3ReivFPawq/trxeTqRd1/KfKtm2/LzCPm/h7Wrl2b9BmnnOuvhffXKGvkxc5T2INcfw1ThOdgcv0y7vlca/LosHm9EqrX6UQpY2Y1Ua1Gt93OvLbo+c64x+YtW2L9+vWZ+dTU1Kz5J6Y2xs1v3piZP+OrH5v1D0ar1drl+Wz7f+xF5835+3PUkUdm5ktxhkXOe91u5nXjIOX+SmEPu7q/dr/olYu6/1Lk97/5LzPziLm/h02bNiV9xinn+mvh/TXKGnmx8xT2INdfs0n5OZhcv4x7PteaPDrMz4QCAAAAIHeGUAAAAADkzhAKAAAAgNwZQgEAAACQO0MoAAAAAHJnCAUAAABA7kqDQcbnZW6j1WpFvV6P6enpqFQqQ9eUy+Xo9/uZ9yh6vjPu0el0YvPmzZn55OTkrPlxk0dHe/P3M/N9nrImyuVyZt7v93d5fvdXbsrMq5OHxY2bb8nMJycnM//8RSzNGRY573Q60Wg0otlsRq1Wy7zHclOE/kphD7u6v1ZMrl7U/Zci37r5tsw8Yu7vYe3atUmfccq5/lp4f42yRl7sPIU9yPXXMEV4DibXL+Oez7Umjw7LnhgM0et0opQxs5qoVqPbbmdeW/R8Z9xj85YtsX79+sx8ampq1vwTUxvj5jdvzMyf8dWPzfoHo9Vq7fJ8tv0/9qLz5vz9OerIIzPzpTjDIue9bjfzunGQcn+lsIdd3V+7X/TKRd1/KfL73/yXmXnE3N/Dpk2bkj7jlHP9tfD+GmWNvNh5CnuQ66/ZpPwcTK5fxj2fa00eHebteAAAAADkzhAKAAAAgNwZQgEAAACQO0MoAAAAAHJnCAUAAABA7gyhAAAAAMhdaTDI+LzMbbRarajX6zE9PR2VSmXomnK5HP1+P/MeRc93xj06nU5s3rw5M5+cnJw1P27y6Ghv/n5mXp08rPD5jZtvycwnJycz//xFLM0ZFjnvdDrRaDSi2WxGrVbLvMdyU4T+SmEP+mtxeUTEisnVs/4erF27NukzTjnXXwvvr1HWyIudp7AHuf4apgjPweT6Zdzzudbk0WHl+SzudTpRyphZTVSr0W23M68ter4z7rF5y5ZYv359Zj41NTVr/ompjXHzmzdm5o+96LzC53P9/hx15JGZ+VKcYZHzXrebed04SLm/UtiD/lpcHhGx+0WvnPX3YNOmTUmfccq5/lp4f42yRl7sPIU9yPXXbFJ+DibXL+Oez7Umjw7zdjwAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5M4QCAAAAIHeGUAAAAADkrjQYZHxe5jZarVbU6/WYnp6OSqUydE25XI5+v595j6LnO+MenU4nNm/enJlPTk7K58iz/vxFLM0ZFjnvdDrRaDSi2WxGrVbLvMdyU4T+SmEP+mtx+Shr1q5dm/QZp5zrr4X31yhr5MXOU9iDXH8NU4TnYHL9Mu75XGvy6LDyfBb3Op0oZcysJqrV6LbbmdcWPd8Z99i8ZUusX78+M5+ampLPkR915JGZ+VKcYZHzXrebed04SLm/UtiD/lpcPsqaTZs2JX3GKef6a+H9NcoaebHzFPYg11+zSfk5mFy/jHs+15o8Oszb8QAAAADInSEUAAAAALkzhAIAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJC70mCQ8XmZ22i1WlGv12N6ejoqlcrQNeVyOfr9fuY9ip7vjHt0Op3YvHlzZj45OSmfI8/68xexNGdY5LzT6USj0Yhmsxm1Wi3zHstNEforhT3or8Xlo6xZu3Zt0meccq6/Ft5fo6yRFztPYQ9y/TVMEZ6DyfXLuOdzrcmjw8rzWdzrdKKUMbOaqFaj225nXlv0fGfcY/OWLbF+/frMfGpqSj5HftSRR2bmS3GGRc573W7mdeMg5f5KYQ/6a3H5KGs2bdqU9BmnnOuvhffXKGvkxc5T2INcf80m5edgcv0y7vlca/LoMG/HAwAAACB3hlAAAAAA5M4QCgAAAIDcGUIBAAAAkDtDKAAAAAByZwgFAAAAQO5Kg0HG52Vuo9VqRb1ej+np6ahUKkPXlMvl6Pf7mfcoep7CHuTOcDF5p9OJRqMRzWYzarVa5j2WmyL0Vwp7kDvDlHP9tfD+GmWNvNh5CnuQ669hivAcTK5fxj2fa00eHVaez+JepxOljJnVRLUa3XY789qi5ynsQe4MF5P3ut3M68ZByv2Vwh7kzjDlXH8tvL9GWSMvdp7CHuT6azYpPweT65dxz+dak0eHeTseAAAAALkzhAIAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJA7QygAAAAAclcaDDI+L3MbrVYr6vV6TE9PR6VSGbqmXC5Hv9/PvEfR8xT2IHeGi8k7nU40Go1oNptRq9Uy77HcFKG/UtiD3BmmnOuvhffXKGvkxc5T2INcfw1ThOdgcv0y7vlca/LosPJ8Fvc6nShlzKwmqtXottuZ1xY9T2EPcme4mLzX7WZeNw5S7q8U9iB3hinn+mvh/TXKGnmx8xT2INdfs0n5OZhcv4x7PteaPDrM2/EAAAAAyJ0hFAAAAAC5M4QCAAAAIHeGUAAAAADkzhAKAAAAgNwZQgEAAACQO0MoAAAAAHJXGgwGg7kWtVqtqNfrMT09HZVKZeiacrkc/X4/8x5Fz1PYg9wZLibvdDrRaDSi2WxGrVbLvMdyU4T+SmEPcmeYcq6/Ft5fo6yRFztPYQ9y/TVMEZ6DyfXLuOdzrcmjw8rzWdzrdKKUMbOaqFaj225nXlv0PIU9yJ3hYvJet5t53ThIub9S2IPcGaac66+F99coa+TFzlPYg1x/zSbl52By/TLu+Vxr8ugwb8cDAAAAIHeGUAAAAADkzhAKAAAAgNwZQgEAAACQO0MoAAAAAHJnCAUAAABA7kqDQcbnZW6j1WpFvV6P6enpqFQqQ9eUy+Xo9/uZ9yh6nsIe5M5wMXmn04lGoxHNZjNqtVrmPZabIvRXCnuQO8OUc/218P4aZY282HkKe5Drr2GK8BxMrl/GPZ9rTR4dVp7P4l6nE6WMmdVEtRrddjvz2qLnKexB7gwXk/e63czrxkHK/ZXCHuTOMOVcfy28v0ZZIy92nsIe5PprNik/B5Prl3HP51qTR4d5Ox4AAAAAuTOEAgAAACB3hlAAAAAA5M4QCgAAAIDcGUIBAAAAkDtDKAAAAAByVxoMMj4vcxutVivq9XpMT09HpVIZuqZcLke/38+8R9HzFPYgd4aLyTudTjQajWg2m1Gr1TLvsdwUob9S2IPcGaac66+F99coa+TFzlPYg1x/DVOE52By/TLu+Vxr8uiw8nwW9zqdKGXMrCaq1ei225nXFj1PYQ9yZ7iYvNftZl43DlLurxT2IHeGKef6a+H9NcoaebHzFPYg11+zSfk5mFy/jHs+15o8Oszb8QAAAADInSEUAAAAALkzhAIAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJC70mCQ8XmZ22i1WlGv12N6ejoqlcrQNeVyOfr9fuY9ip6nsAe5M1xM3ul0otFoRLPZjFqtlnmP5aYI/ZXCHuTOMOVcfy28v0ZZIy92nsIe5PprmCI8B5Prl3HP51qTR4eV57O41+lEKWNmNVGtRrfdzry26HkKe5A7w8XkvW4387pxkHJ/pbAHuTNMOddfC++vUdbIi52nsAe5/ppNys/B5Ppl3PO51uTRYd6OBwAAAEDuDKEAAAAAyJ0hFAAAAAC5M4QCAAAAIHeGUAAAAADkzhAKAAAAgNyVBoOMz8vcRqvVinq9HtPT01GpVIauKZfL0e/3M+9R9DyFPcid4WLyTqcTjUYjms1m1Gq1zHssN0XorxT2IHeGKef6a+H9NcoaebHzFPYg11/DFOE5mFy/jHs+15o8Oqw8n8W9TidKGTOriWo1uu125rVFz1PYg9wZLibvdbuZ142DlPsrhT3InWHKuf5aeH+NskZe7DyFPcj112xSfg4m1y/jns+1Jo8O83Y8AAAAAHJnCAUAAABA7gyhAAAAAMidIRQAAAAAuTOEAgAAACB3hlAAAAAA5K40GGR8XuY2Wq1W1Ov1mJ6ejkqlMnRNuVyOfr+feY+i5ynsQe4MF5N3Op1oNBrRbDajVqtl3mO5KUJ/pbAHuTNMOddfC++vUdbIi52nsAe5/hqmCM/B5Ppl3PO51uTRYeX5LO51OlHKmFlNVKvRbbczry16nsIe5M5wMXmv2828bhyk3F8p7EHuDFPO9dfC+2uUNfJi5ynsQa6/ZpPyczC5fhn3fK41eXSYt+MBAAAAkDtDKAAAAAByZwgFAAAAQO4MoQAAAADInSEUAAAAALkzhAIAAAAgd6XBIOPzMrfRarWiXq/H9PR0VCqVoWvK5XL0+/3MexQ9T2EPcme4mLzT6USj0Yhmsxm1Wi3zHstNEforhT3InWHKuf5aeH+NskZe7DyFPcj11zBFeA4m1y/jns+1Jo8OK89nca/TiVLGzGqiWo1uu515bdHzFPYgd4aLyXvdbuZ14yDl/kphD3JnmHKuvxbeX6OskRc7T2EPcv01m5Sfg8n1y7jnc63Jo8O8HQ8AAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5Kw0GGZ+XuY1WqxX1ej2mp6ejUqkMXVMul6Pf72feo+h5CnuQO8PF5J1OJxqNRjSbzajVapn3WG6K0F8p7EHuDFPO9dfC+2uUNfJi5ynsQa6/hinCczC5fhn3fK41eXRYeT6Le51OlDJmVhPVanTb7cxri56nsAe5M1xM3ut2M68bByn3Vwp7kDvDlHP9tfD+GmWNvNh5CnuQ66/ZpPwcTK5fxj2fa00eHebteAAAAADkzhAKAAAAgNwZQgEAAACQO0MoAAAAAHJnCAUAAABA7kb6dLzB//00g26vl72mVJr1J6cXPU9hD3JnuJj8wb+/g4xPJ1muitBfKexB7gxTzvXXwvtrlDXyYucp7EGuv4YpwnMwuX4Z93yuNXl0WGkwwt3+/d//PQ4++OCd9qDArnP77bfHIx7xiF29jSWjv2D50F9AUY1bf0XoMFhOdmaHjTSE2rp1a9xxxx1RrVajVCrtlAcGltZgMIh2ux0HHnhgrFgxPu/E1V9QfPpLf0FRjWt/RegwWA7y6LCRhlAAAAAAsBjjNY4HAAAAYJcwhAIAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5M4QCAAAAIHeGUAAAAADkzhAKAAAAgNwZQgEAAACQO0MoAAAAAHJnCAUAAABA7gyhAAAAAMidIRQAAAAAuTOEAgAAACB3hlAAAAAA5M4QCgAAAIDcGUIBAAAAkDtDKAAAAAByZwgFAAAAQO4MoQAAAADInSEUAAAAALkzhAIAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5M4QCAAAAIHeGUAAAAADkzhAKAAAAgNwZQgEAAACQO0MoAAAAAHJnCAUAAABA7gyhAAAAAMidIRQAAAAAuSuPsmjr1q1xxx13RLVajVKplPeegBwMBoNot9tx4IEHxooV4zN/1l9QfPpLf0FRjWt/RegwWA7y6LCRhlB33HFHHHzwwTvlAYFd6/bbb49HPOIRu3obS0Z/wfKhv4CiGrf+itBhsJzszA4baQhVrVYjIuLqq66KiVWrhq5ZValEr9PJvEfR8xT2IHeGi8m7vV6c8rznzfx9HhdF6K8U9iB3hinn+mvh/TXKGnmx8xT2INdfwxThOZhcv4x7PteaPDpspCHUgy+fnFi1KiYmJoaumahUojQYZN6j6HkKe5A7w53xezBuL4cuQn+lsAe5M0w9j9Bfw/izKU9hD3L9NUwRnoPJ9cu456Ou2ZkdNl5vTAYAAABglzCEAgAAACB3hlAAAAAA5M4QCgAAAIDcGUIBAAAAkLvSYDDHj0GPiFarFfV6Paanp6NSqQxdUy6Xo9/vZ96j6HkKe5A7w8XknU4nGo1GNJvNqNVqmfdYborQXynsQe4MU87118L7a5Q18mLnKexBrr+GKcJzMLl+Gfd8rjV5dFh5Pot7nU7mR/dNVKvRbbczry16nsIe5M5wMXmv2828bhyk3F8p7EHuDFPO9dfC+2uUNfJi5ynsQa6/ZpPyczC5fhn3fK41eXSYt+MBAAAAkDtDKAAAAAByZwgFAAAAQO4MoQAAAADInSEUAAAAALkzhAIAAAAgd6XBIOPzMrfRarWiXq/H9PR0VCqVoWvK5XL0+/3MexQ9T2EPcme4mLzT6USj0Yhmsxm1Wi3zHstNEforhT3InWHKuf5aeH+NskZe7DyFPcj11zBFeA4m1y/jns+1Jo8OK89nca/TiVLGzGqiWo1uu515bdHzFPYgd4aLyXvdbuZ14yDl/kphD3JnmHKuvxbeX6OskRc7T2EPcv01m5Sfg8n1y7jnc63Jo8O8HQ8AAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5Kw0GGZ+XuY1WqxX1ej2mp6ejUqkMXVMul6Pf72feo+h5CnuQO8PF5J1OJxqNRjSbzajVapn3WG6K0F8p7EHuDFPO9dfC+2uUNfJi5ynsQa6/hinCczC5fhn3fK41eXRYeT6Le51OlDJmVhPVanTb7cxri56nsAe5M1xM3ut2M68bByn3Vwp7kDvDlHP9tfD+GmWNvNh5CnuQ66/ZpPwcTK5fxj2fa00eHebteAAAAADkzhAKAAAAgNwZQgEAAACQO0MoAAAAAHJnCAUAAABA7gyhAAAAAMhdaTDI+LzMbbRarajX6zE9PR2VSmXomnK5HP1+P/MeRc9T2IPcGS4m73Q60Wg0otlsRq1Wy7zHclOE/kphD3JnmHKuvxbeX6OskRc7T2EPcv01TBGeg8n1y7jnc63Jo8PK81nc63SilDGzmqhWo9tuZ15b9DyFPcid4WLyXrebed04SLm/UtiD3BmmnOuvhffXKGvkxc5T2INcf80m5edgcv0y7vlca/LoMG/HAwAAACB3hlAAAAAA5M4QCgAAAIDcGUIBAAAAkDtDKAAAAAByZwgFAAAAQO5Kg0HG52Vuo9VqRb1ej+np6ahUKkPXlMvl6Pf7mfcoep7CHuTOcDF5p9OJRqMRzWYzarVa5j2WmyL0Vwp7kDvDlHP9tfD+GmWNvNh5CnuQ669hivAcTK5fxj2fa00eHVaez+JepxOljJnVRLUa3XY789qi5ynsQe4MF5P3ut3M68ZByv2Vwh7kzjDlXH8tvL9GWSMvdp7CHuT6azYpPweT65dxz+dak0eHeTseAAAAALkzhAIAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJA7QygAAAAAclcaDDI+L3MbrVYr6vV6TE9PR6VSGbqmXC5Hv9/PvEfR8xT2IHeGi8k7nU40Go1oNptRq9Uy77HcFKG/UtiD3BmmnOuvhffXKGvkxc5T2INcfw1ThOdgcv0y7vlca/LosPJ8Fvc6nShlzKwmqtXottuZ1xY9T2EPcme4mLzX7WZeNw5S7q8U9iB3hinn+mvh/TXKGnmx8xT2INdfs0n5OZhcv4x7PteaPDrM2/EAAAAAyJ0hFAAAAAC5M4QCAAAAIHeGUAAAAADkzhAKAAAAgNwZQgEAAACQu9JgkPF5mdtotVpRr9djeno6KpXK0DXlcjn6/X7mPYqep7AHuTNcTN7pdKLRaESz2YxarZZ5j+WmCP2Vwh7kzjDlXH8tvL9GWSMvdp7CHuT6a5giPAeT65dxz+dak0eHleezuNfpRCljZjVRrUa33c68tuh5CnuQO8PF5L1uN/O6cZByf6WwB7kzTDnXXwvvr1HWyIudp7AHuf6aTcrPweT6Zdzzudbk0WHejgcAAABA7gyhAAAAAMidIRQAAAAAuTOEAgAAACB3hlAAAAAA5M4QCgAAAIDclQaDjM/L3Ear1Yp6vR7T09NRqVSGrimXy9Hv9zPvUfQ8hT3IneFi8k6nE41GI5rNZtRqtcx7LDdF6K8U9iB3hinn+mvh/TXKGnmx8xT2INdfwxThOZhcv4x7PteaPDqsPJ/FvU4nShkzq4lqNbrtdua1Rc9T2IPcGS4m73W7mdeNg5T7K4U9yJ1hyrn+Wnh/jbJGXuw8hT3I9ddsUn4OJtcv457PtSaPDvN2PAAAAAByZwgFAAAAQO4MoQAAAADInSEUAAAAALkzhAIAAAAgd4ZQAAAAAOSuNBhkfF7mNlqtVtTr9Zieno5KpTJ0Tblcjn6/n3mPoucp7EHuDBeTdzqdaDQa0Ww2o1arZd5juSlCf6WwB7kzTDnXXwvvr1HWyIudp7AHuf4apgjPweT6Zdzzudbk0WHl+SzudTpRyphZTVSr0W23M68tep7CHuTOcDF5r9vNvG4cpNxfKexB7gxTzvXXwvtrlDXyYucp7EGuv2aT8nMwuX4Z93yuNXl0mLfjAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5M4QCAAAAIHeGUAAAAADkrjQYDAZzLWq1WlGv12N6ejoqlcrQNeVyOfr9fuY9ip6nsAe5M1xM3ul0otFoRLPZjFqtlnmP5aYI/ZXCHuTOMOVcfy28v0ZZIy92nsIe5PprmCI8B5Prl3HP51qTR4eV57O41+lEKWNmNVGtRrfdzry26HkKe5A7w8XkvW4387pxkHJ/pbAHuTNMOddfC++vUdbIi52nsAe5/ppNys/B5Ppl3PO51uTRYd6OBwAAAEDuDKEAAAAAyJ0hFAAAAAC5M4QCAAAAIHeGUAAAAADkzhAKAAAAgNyVBoOMz8vcRqvVinq9HtPT01GpVIauKZfL0e/3M+9R9DyFPcid4WLyTqcTjUYjms1m1Gq1zHssN0XorxT2IHeGKef6a+H9NcoaebHzFPYg11/DFOE5mFy/jHs+15o8Oqw8n8W9TidKGTOriWo1uu125rVFz1PYg9wZLibvdbuZ142DlPsrhT3InWHKuf5aeH+NskZe7DyFPcj112xSfg4m1y/jns+1Jo8O83Y8AAAAAHJnCAUAAABA7gyhAAAAAMidIRQAAAAAuTOEAgAAACB3hlAAAAAA5K40GGR8XuY2Wq1W1Ov1mJ6ejkqlMnRNuVyOfr+feY+i5ynsQe4MF5N3Op1oNBrRbDajVqtl3mO5KUJ/pbAHuTNMOddfC++vUdbIi52nsAe5/hqmCM/B5Ppl3PO51uTRYeX5LO51OlHKmFlNVKvRbbczry16nsIe5M5wMXmv2828bhyk3F8p7EHuDFPO9dfC+2uUNfJi5ynsQa6/ZpPyczC5fhn3fK41eXSYt+MBAAAAkDtDKAAAAAByZwgFAAAAQO4MoQAAAADInSEUAAAAALkzhAIAAAAgd6XBIOPzMrfRarWiXq/H9PR0VCqVoWvK5XL0+/3MexQ9T2EPcme4mLzT6USj0Yhmsxm1Wi3zHstNEforhT3InWHKuf5aeH+NskZe7DyFPcj11zBFeA4m1y/jns+1Jo8OK89nca/TiVLGzGqiWo1uu515bdHzFPYgd4aLyXvdbuZ14yDl/kphD3JnmHKuvxbeX6OskRc7T2EPcv01m5Sfg8n1y7jnc63Jo8O8HQ8AAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5Kw0GGZ+XuY1WqxX1ej2mp6ejUqkMXVMul6Pf72feo+h5CnuQO8PF5J1OJxqNRjSbzajVapn3WG6K0F8p7EHuDFPO9dfC+2uUNfJi5ynsQa6/hinCczC5fhn3fK41eXRYeT6Le51OlDJmVhPVanTb7cxri56nsAe5M1xM3ut2M68bByn3Vwp7kDvDlHP9tfD+GmWNvNh5CnuQ66/ZpPwcTK5fxj2fa00eHebteAAAAADkzhAKAAAAgNwZQgEAAACQO0MoAAAAAHJnCAUAAABA7gyhAAAAAMhdaTDI+LzMbbRarajX6zE9PR2VSmXomnK5HP1+P/MeRc9T2IPcGS4m73Q60Wg0otlsRq1Wy7zHclOE/kphD3JnmHKuvxbeX6OskRc7T2EPcv01TBGeg8n1y7jnc63Jo8PK81nc63SilDGzmqhWo9tuZ15b9DyFPcid4WLyXrebed04SLm/UtiD3BmmnOuvhffXKGvkxc5T2INcf80m5edgcv0y7vlca/LoMG/HAwAAACB3hlAAAAAA5M4QCgAAAIDcGUIBAAAAkDtDKAAAAAByZwgFAAAAQO5Kg0HG52Vuo9VqRb1ej+np6ahUKkPXlMvl6Pf7mfcoep7CHuTOcDF5p9OJRqMRzWYzarVa5j2WmyL0Vwp7kDvDlHP9tfD+GmWNvNh5CnuQ669hivAcTK5fxj2fa00eHVaez+JepxOljJnVRLUa3XY789qi5ynsQe4MF5P3ut3M68ZByv2Vwh7kzjDlXH8tvL9GWSMvdp7CHuT6azYpPweT65dxz+dak0eHeTseAAAAALkzhAIAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJA7QygAAAAAclcaDDI+L3MbrVYr6vV6TE9PR6VSGbqmXC5Hv9/PvEfR8xT2IHeGi8k7nU40Go1oNptRq9Uy77HcFKG/UtiD3BmmnOuvhffXKGvkxc5T2INcfw1ThOdgcv0y7vlca/LosPJ8Fvc6nShlzKwmqtXottuZ1xY9T2EPcme4mLzX7WZeNw5S7q8U9iB3hinn+mvh/TXKGnmx8xT2INdfs0n5OZhcv4x7PteaPDrM2/EAAAAAyJ0hFAAAAAC5M4QCAAAAIHeGUAAAAADkzhAKAAAAgNwZQgEAAACQu9JgkPF5mdtotVpRr9djeno6KpXK0DXlcjn6/X7mPYqep7AHuTNcTN7pdKLRaESz2YxarZZ5j+WmCP2Vwh7kzjDlXH8tvL9GWSMvdp7CHuT6a5giPAeT65dxz+dak0eHleezuNfpRCljZjVRrUa33c68tuh5CnuQO8PF5L1uN/O6cZByf6WwB7kzTDnXXwvvr1HWyIudp7AHuf6aTcrPweT6Zdzzudbk0WHejgcAAABA7gyhAAAAAMidIRQAAAAAuTOEAgAAACB3hlAAAAAA5M4QCgAAAIDclQaDjM/L3Ear1Yp6vR7T09NRqVSGrimXy9Hv9zPvUfQ8hT3IneFi8k6nE41GI5rNZtRqtcx7LDdF6K8U9iB3hinn+mvh/TXKGnmx8xT2INdfwxThOZhcv4x7PteaPDqsPJ/FvU4nShkzq4lqNbrtdua1Rc9T2IPcGS4m73W7mdeNg5T7K4U9yJ1hyrn+Wnh/jbJGXuw8hT3I9ddsUn4OJtcv457PtSaPDvN2PAAAAAByZwgFAAAAQO4MoQAAAADInSEUAAAAALkzhAIAAAAgd4ZQAAAAAOSuNBhkfF7mNlqtVtTr9Zieno5KpTJ0Tblcjn6/n3mPoucp7EHuDBeTdzqdaDQa0Ww2o1arZd5juSlCf6WwB7kzTDnXXwvvr1HWyIudp7AHuf4apgjPweT6Zdzzudbk0WHl+SzudTpRyphZTVSr0W23M68tep7CHuTOcDF5r9vNvG4cpNxfKexB7gxTzvXXwvtrlDXyYucp7EGuv2aT8nMwuX4Z93yuNXl0mLfjAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5M4QCAAAAIHelwSDj8zK30Wq1ol6vx/T0dFQqlaFryuVy9Pv9zHsUPU9hD3JnuJi80+lEo9GIZrMZtVot8x7LTRH6K4U9yJ1hyrn+Wnh/jbJGXuw8hT3I9dcwRXgOJtcv457PtSaPDivPZ3Gv04lSxsxqolqNbrudeW3R8xT2IHeGi8l73W7mdeMg5f5KYQ9yZ5hyrr8W3l+jrJEXO09hD3L9NZuUn4PJ9cu453OtyaPDvB0PAAAAgNwZQgEAAACQO0MoAAAAAHJnCAUAAABA7gyhAAAAAMidIRQAAAAAuSsNBhmfl7mNVqsV9Xo9pqeno1KpDF1TLpej3+9n3qPoeQp7kDvDxeSdTicajUY0m82o1WqZ91huitBfKexB7gxTzvXXwvtrlDXyYucp7EGuv4YpwnMwuX4Z93yuNXl0WHk+i3udTpQyZlYT1Wp02+3Ma4uep7AHuTNcTN7rdjOvGwcp91cKe5A7w5Rz/bXw/hpljbzYeQp7kOuv2aT8HEyuX8Y9n2tNHh3m7XgAAAAA5M4QCgAAAIDcGUIBAAAAkDtDKAAAAAByZwgFAAAAQO4MoQAAAADIXWkwyPi8zG20Wq2o1+sxPT0dlUpl6JpyuRz9fj/zHkXPU9iD3BkuJu90OtFoNKLZbEatVsu8x3JThP5KYQ9yZ5hyrr8W3l+jrJEXO09hD3L9NUwRnoPJ9cu453OtyaPDyvNZ3Ot0opQxs5qoVqPbbmdeW/Q8hT3IneFi8l63m3ndOEi5v1LYg9wZppzrr4X31yhr5MXOU9iDXH/NJuXnYHL9Mu75XGvy6DBvxwMAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyF1pMBgM5lrUarWiXq/H9PR0VCqVoWvK5XL0+/3MexQ9T2EPcme4mLzT6USj0Yhmsxm1Wi3zHstNEforhT3InWHKuf5aeH+NskZe7DyFPcj11zBFeA4m1y/jns+1Jo8OK89nca/TiVLGzGqiWo1uu515bdHzFPYgd4aLyXvdbuZ14yDl/kphD3JnmHKuvxbeX6OskRc7T2EPcv01m5Sfg8n1y7jnc63Jo8O8HQ8AAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5Kw0GGZ+XuY1WqxX1ej2mp6ejUqkMXVMul6Pf72feo+h5CnuQO8PF5J1OJxqNRjSbzajVapn3WG6K0F8p7EHuDFPO9dfC+2uUNfJi5ynsQa6/hinCczC5fhn3fK41eXRYeT6Le51OlDJmVhPVanTb7cxri56nsAe5M1xM3ut2M68bByn3Vwp7kDvDlHP9tfD+GmWNvNh5CnuQ66/ZpPwcTK5fxj2fa00eHebteAAAAADkzhAKAAAAgNwZQgEAAACQO0MoAAAAAHJnCAUAAABA7gyhAAAAAMhdaTDI+LzMbbRarajX6zE9PR2VSmXomnK5HP1+P/MeRc9T2IPcGS4m73Q60Wg0otlsRq1Wy7zHclOE/kphD3JnmHKuvxbeX6OskRc7T2EPcv01TBGeg8n1y7jnc63Jo8PK81nc63SilDGzmqhWo9tuZ15b9DyFPcid4WLyXrebed04SLm/UtiD3BmmnOuvhffXKGvkxc5T2INcf80m5edgcv0y7vlca/LoMG/HAwAAACB3hlAAAAAA5M4QCgAAAIDcGUIBAAAAkDtDKAAAAAByZwgFAAAAQO5Kg0HG52Vuo9VqRb1ej+np6ahUKkPXlMvl6Pf7mfcoep7CHuTOcDF5p9OJRqMRzWYzarVa5j2WmyL0Vwp7kDvDlHP9tfD+GmWNvNh5CnuQ669hivAcTK5fxj2fa00eHVaez+JepxOljJnVRLUa3XY789qi5ynsQe4MF5P3ut3M68ZByv2Vwh7kzjDlXH8tvL9GWSMvdp7CHuT6azYpPweT65dxz+dak0eHeTseAAAAALkzhAIAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJA7QygAAAAAclcaDDI+L3MbrVYr6vV6TE9PR6VSGbqmXC5Hv9/PvEfR8xT2IHeGi8k7nU40Go1oNptRq9Uy77HcFKG/UtiD3BmmnOuvhffXKGvkxc5T2INcfw1ThOdgcv0y7vlca/LosPJ8Fvc6nShlzKwmqtXottuZ1xY9T2EPcme4mLzX7WZeNw5S7q8U9iB3hinn+mvh/TXKGnmx8xT2INdfs0n5OZhcv4x7PteaPDrM2/EAAAAAyJ0hFAAAAAC5M4QCAAAAIHeGUAAAAADkzhAKAAAAgNwZQgEAAACQu9JgkPF5mdtotVpRr9djeno6KpXK0DXlcjn6/X7mPYqep7AHuTNcTN7pdKLRaESz2YxarZZ5j+WmCP2Vwh7kzjDlXH8tvL9GWSMvdp7CHuT6a5giPAeT65dxz+dak0eHleezuNfpRCljZjVRrUa33c68tuh5CnuQO8PF5L1uN/O6cZByf6WwB7kzTDnXXwvvr1HWyIudp7AHuf6aTcrPweT6Zdzzudbk0WHejgcAAABA7gyhAAAAAMidIRQAAAAAuTOEAgAAACB3hlAAAAAA5G6kT8cb/N9PM+j2etlrSqVZf3J60fMU9iB3hovJH/z7O8j4dJLlqgj9lcIe5M4w5Vx/Lby/RlkjL3aewh7k+muYIjwHk+uXcc/nWpNHh5UGI9zt3//93+Pggw/eaQ8K7Dq33357POIRj9jV21gy+guWD/0FFNW49VeEDoPlZGd22EhDqK1bt8Ydd9wR1Wo1SqXSTnlgYGkNBoNot9tx4IEHxooV4/NOXP0Fxae/9BcU1bj2V4QOg+Ugjw4baQgFAAAAAIsxXuN4AAAAAHYJQygAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5M4QCAAAAIHeGUAAAAADkzhAKAAAAgNwZQgEAAACQO0MoAAAAAHJnCAUAAABA7gyhAAAAAMidIRQAAAAAuTOEAgAAACB3hlAAAAAA5M4QCgAAAIDcGUIBAAAAkDtDKAAAAAByZwgFAAAAQO4MoQAAAADInSEUAMvO6aefHqtXr97V2wAAALZhCLWESqXSSP9df/31i36sXq8XF1544cj3uv7666NUKsXVV1+96MfeWS666KL45Cc/uau3Acm5/PLLZ+2Qr33ta7t6iyP5t3/7t7jwwgvjtttu26X7GAwG8ZGPfCR+4zd+I/baa69YtWpVPPaxj43/+T//Z3S73QXfd6m/v0suuSQuv/zyJXksAABYiPKu3sA4+chHPrLdrz/84Q/HF77whR2+Pjk5uejH6vV6sWHDhoiIeNrTnrbo++0KF110UZxyyilx8skn7+qtQJL+5//8n3HooYfu8PVHPepRu2A38/dv//ZvsWHDhnja0562y1619MADD8SLXvSi+PjHPx5PfepT48ILL4xVq1bFP/zDP8SGDRviqquuik2bNsV+++0373sv9fd3ySWXxN577x2nn3567o8FAAALYQi1hP7gD/5gu19/7Wtfiy984Qs7fB1gFM985jPjuOOO29XbKLQ//dM/jY9//OPxute9Lt7xjnfMfP0Vr3hFPP/5z4+TTz45Tj/99PjMZz6zC3cJAADLg7fjJWbr1q2xcePGOProo2OPPfaI/fbbL175ylfGL37xi+3W3XjjjbFu3brYe++9Y88994xDDz00XvrSl0ZExG233Rb77LNPRERs2LBh5i06F1544bz2cuGFF0apVIpbb701Tj/99Nhrr72iXq/HGWecEb1eb7u1pVIpzj333Pjrv/7reMxjHhN77LFHrFmzJr785S9vty7r57Q8+Fjb3q/b7cYVV1wxs3//6z7MzwUXXBArVqyI6667bruvv+IVr4jdd989/uVf/iUi/t/bca+88sp485vfHPvvv39MTEzE7/7u78btt9++w33/6Z/+KX77t3876vV6rFq1KhqNRvzjP/7jDut+/OMfx8te9rI48MADY+XKlXHooYfG2WefHffff39cfvnl8bznPS8iIn7zN39z6NuRP/OZz8RTn/rUmJiYiGq1Gs961rPilltu2eFxPvnJT8YxxxwTe+yxRxxzzDFxzTXXjPT78x//8R/xjne8I4444oiYmpraIT/ppJPitNNOi89+9rPbvcUxq09Xr14901NzfX+rV6+OZz/72fH5z38+jj322Nhjjz3iqKOOik984hPb3fNXu/FBD74l88G3+q1evTpuueWWmJ6ennmsor4KFgCA5csroRLzyle+Mi6//PI444wz4tWvfnX84Ac/iHe/+93xzW9+M/7xH/8xHvKQh8RPf/rTeMYznhH77LNPvOlNb4q99torbrvttpl/vOyzzz5x6aWXxtlnnx3Pec5z4rnPfW5ERDzucY9b0J6e//znx6GHHhpTU1PxjW98I97//vfHvvvuG29/+9u3Wzc9PR1XXnllvPrVr46VK1fGJZdcEr/9278dX//61+OYY46Z12N+5CMfiTPPPDOOP/74eMUrXhEREYcffviC9g/LVbPZjHvuuWe7r5VKpXj4wx8eERF/9Ed/FJ/61KfiZS97Wdx8881RrVbjc5/7XLzvfe+Lt73tbfH4xz9+u2v/+I//OEqlUrzxjW+Mn/70p7Fx48Y48cQT41vf+lbsueeeERHxxS9+MZ75zGfGmjVrZoZcH/rQh+K3fuu34h/+4R/i+OOPj4iIO+64I44//vi499574xWveEUceeSR8eMf/ziuvvrq6PV68Ru/8Rvx6le/Ov7X//pf8eY3v3nmbcgP/t+PfOQjcdppp8W6devi7W9/e/R6vbj00kvjKU95Snzzm9+cGWZ//vOfj9/7vd+Lo446KqampuJnP/tZnHHGGfGIRzxizt+/r3zlK/GLX/wiXvOa10S5PPz/HZ566qnxoQ99KP7+7/8+nvjEJ454MjHn9xcR8b3vfS9e8IIXxFlnnRWnnXZafOhDH4rnPe958dnPfjae/vSnj/xYEREbN26MV73qVVGpVOIP//APIyIW9BZCAADI1YBd5pxzzhlsewT/8A//MIiIwV//9V9vt+6zn/3sdl+/5pprBhEx+Od//ufMe999992DiBhccMEFI+3lS1/60iAiBlddddXM1y644IJBRAxe+tKXbrf2Oc95zuDhD3/4dl+LiEFEDG688caZr/3whz8c7LHHHoPnPOc5M1877bTTBocccsgOj//gY21rYmJicNppp420fxgnH/rQh2b+zv3qfytXrtxu7c033zzYfffdB2eeeebgF7/4xeCggw4aHHfccYP/+q//mlnz4N//gw46aNBqtWa+/vGPf3wQEYN3vetdg8FgMNi6devg0Y9+9GDdunWDrVu3zqzr9XqDQw89dPD0pz995munnnrqYMWKFUN76sFrr7rqqkFEDL70pS9tl7fb7cFee+01ePnLX77d1++8885BvV7f7uvHHnvs4IADDhjce++9M1/7/Oc/P4iIoV2zrY0bNw4iYnDNNddkrvn5z38+iIjBc5/73JmvZXXrIYccsl1nZX1/D66NiMH//t//e+ZrzWZzcMABBwx+7dd+beZrw7pxMPh/fwZ+8IMfzHzt6KOPHjQajczvBQAAdjVvx0vIVVddFfV6PZ7+9KfHPffcM/PfmjVrolKpxJe+9KWIiNhrr70iIuLv//7v47/+679y39dZZ5213a+f+tSnxs9+9rNotVrbfX3t2rWxZs2amV8/8pGPjP/+3/97fO5zn4sHHngg933CuHnPe94TX/jCF7b771d/dtExxxwTGzZsiPe///2xbt26uOeee+KKK64Y+sqfU089NarV6syvTznllDjggAPi05/+dEREfOtb34rvfe978aIXvSh+9rOfzXRUt9uN//bf/lt8+ctfjq1bt8bWrVvjk5/8ZJx00klDf2bVsLeXbesLX/hC3HvvvfHCF75wuy7cbbfd4oQTTpjpwp/85CfxrW99K0477bSo1+sz1z/96U+Po446as7fv3a7HRGx3ff8qx7MfrXvdoYDDzwwnvOc58z8ularxamnnhrf/OY3484779zpjwcAALuat+Ml5Hvf+140m83Yd999h+Y//elPIyKi0WjE7/3e78WGDRvine98ZzztaU+Lk08+OV70ohfFypUrd/q+HvnIR27364c+9KEREfGLX/wiarXazNcf/ehH73DtEUccEb1eL+6+++7Yf//9d/reYJwdf/zxI/1g8te//vXxN3/zN/H1r389LrrooswBza/+HS6VSvGoRz1q5ucOfe9734uIiNNOOy3zsZrNZtx///3RarXm/TbcBz34OL/1W781NH+wd374wx8O3XdExGMe85j4xje+MevjPDhgenAYNcwog6qFetSjHrXDQO6II46IiF/+bD+dCQDAcmMIlZCtW7fGvvvuG3/91389NH/wh42XSqW4+uqr42tf+1p86lOfis997nPx0pe+NP78z/88vva1r0WlUtmp+9ptt92Gfn0wGMz7XlmvgPBKKcjP97///ZnBzs0337zg+2zdujUiIt7xjnfEscceO3RNpVKJn//85wt+jG0f5yMf+cjQQUzWz2+arwd/PtO3v/3tOPnkk4eu+fa3vx0RMdIrq/LoMZ0JAMByYgiVkMMPPzw2bdoUT37yk2d+CPBsnvjEJ8YTn/jE+OM//uP46Ec/Gi9+8Yvjb/7mb+LMM8+c8+0ueXjwH7nb+u53vxurVq2aGaA99KEPjXvvvXeHdQ++omFbu+J7gOVm69atcfrpp0etVovzzjsvLrroojjllFNmPrBgW7/6d3gwGMStt94686EGD344QK1WixNPPDHzMffZZ5+o1Wrxr//6r7PuLevv+IOPs++++876OIcccsjQfUdEfOc735n1sSMinvKUp8Ree+0VH/3oR+MP//APhw7cP/zhD0dExLOf/eyZrw3rsfvvvz9+8pOfbPe1uTrs1ltvjcFgsN267373uxERMz94/cFXnt57770zb8WO0JkAABSTnwmVkOc///nxwAMPxNve9rYdsn6/P/OPnl/84hc7vArpwVcl3HfffRERsWrVqoiIoQOfvNxwww3bvf3l9ttvj7/927+NZzzjGTP/uDv88MOj2WzOvLog4pc/12XYR6pPTEws6f5hObr44ovjq1/9arz3ve+Nt73tbfGkJz0pzj777B0+VS/ilwOXbd+advXVV8dPfvKTeOYznxkREWvWrInDDz88/uzP/iw6nc4O1999990REbFixYo4+eST41Of+lTceOONO6x7sL8mJiYiYseeWrduXdRqtbjooouG/ty7Bx/ngAMOiGOPPTauuOKKaDabM/kXvvCF+Ld/+7dZf18iftmTr3vd6+I73/nOzCfKbevaa6+Nyy+/PNatW7fdJ+Mdfvjh8eUvf3m7te9973t3eHVS1vf3oDvuuGO77mu1WvHhD384jj322JlXgD04kNv28brdblxxxRU73E9nAgCQOq+ESkij0YhXvvKVMTU1Fd/61rfiGc94RjzkIQ+J733ve3HVVVfFu971rjjllFPiiiuuiEsuuSSe85znxOGHHx7tdjve9773Ra1Wi9/5nd+JiIg999wzjjrqqLjyyivjiCOOiIc97GFxzDHHLPhntIzimGOOiXXr1sWrX/3qWLlyZVxyySUREbFhw4aZNb//+78fb3zjG+M5z3lOvPrVr5752PUjjjhih5/fsmbNmti0aVNcfPHFceCBB8ahhx4aJ5xwQm77h6L5zGc+E1u2bNnh60960pPisMMOi82bN8db3vKWOP300+Okk06KiIjLL788jj322Pgf/+N/xMc//vHtrnvYwx4WT3nKU+KMM86Iu+66KzZu3BiPetSj4uUvf3lE/HK49P73vz+e+cxnxtFHHx1nnHFGHHTQQfHjH/84vvSlL0WtVotPfepTERFx0UUXxec///loNBrxile8IiYnJ+MnP/lJXHXVVfGVr3wl9tprrzj22GNjt912i7e//e3RbDZj5cqV8Vu/9Vux7777xqWXXhoveclL4glPeEL8/u//fuyzzz7xox/9KK699tp48pOfHO9+97sjImJqaiqe9axnxVOe8pR46UtfGj//+c/jL/7iL+Loo48eOij7VW9605vim9/8Zrz97W+PG264IX7v934v9txzz/jKV74Sf/VXfxWTk5M7DHzOPPPMOOuss+L3fu/34ulPf3r8y7/8S3zuc5+Lvffee7t1s31/Eb/8+U8ve9nL4p//+Z9jv/32iw9+8INx1113xYc+9KGZezzjGc+IRz7ykfGyl70sXv/618duu+0WH/zgB2d+P7a1Zs2auPTSS+P/+//+v3jUox4V++67b+bP1QIAgF1il34235g755xzhn709nvf+97BmjVrBnvuueegWq0OHvvYxw7e8IY3DO64447BYDAYfOMb3xi88IUvHDzykY8crFy5crDvvvsOnv3sZw9uvPHG7e7z1a9+dbBmzZrB7rvvnvmR4g968CPar7rqqpmvPfjR4Hffffd2a4d9NHhEDM4555zBX/3VXw0e/ehHD1auXDn4tV/7taEfTf75z39+cMwxxwx23333wWMe85jBX/3VXw39GPItW7YMfuM3fmOw5557DiJiu48+h3H24N/BrP8+9KEPDfr9/uDXf/3XB494xCMG995773bXv+td7xpExODKK68cDAb/7+//xz72scH69esH++6772DPPfccPOtZzxr88Ic/3OHxv/nNbw6e+9znDh7+8IcPVq5cOTjkkEMGz3/+8wfXXXfddut++MMfDk499dTBPvvsM1i5cuXgsMMOG5xzzjmD++67b2bN+973vsFhhx022G233QYRsV1nfOlLXxqsW7duUK/XB3vsscfg8MMPH5x++uk7dN3//t//ezA5OTlYuXLl4Kijjhp84hOfGJx22mmDQw45ZKTfzwceeGDwoQ99aPDkJz95UKvVBnvsscfg6KOPHmzYsGHQ6XSGrn/jG9842HvvvQerVq0arFu3bnDrrbcODjnkkB16Kuv7O+SQQwbPetazBp/73OcGj3vc4wYrV64cHHnkkdt18INuuummwQknnDDYfffdB4985CMHF1988dAevvPOOwfPetazBtVqdRARg0ajMdL3z2imp6cHz372swcHHHDAICIG11xzzZzXfOlLXxr82q/92mD33XcfHH744YMPfehDue8T4FfpLyAlpcFgAT9dGn5FqVSKc845Z+bVCUBxXH/99fGbv/mbcdVVV8Upp5yyq7czFlavXh3HHHNM/P3f//2u3goj+sxnPhP/+I//GGvWrInnPve5cc0112T+QPuIiB/84AdxzDHHxFlnnRVnnnlmXHfddXHeeefFtddeG+vWrVu6jQNjT38BKfF2PACAOTzzmc+c+floo7jsssvi0EMPjT//8z+PiF9+GuNXvvKVeOc73+kfccCS0l9ASgyhAAB2shtuuGGHT3dct25dnHfeeZnX3HfffTMfMBLxy0+3/PnPfx4Pf/jDffohFNRgMIh2ux0HHnhgrFhRjM+EWkh/RegwWI7y6DBDKACAnezOO++M/fbbb7uv7bffftFqteI//uM/Ys8999zhmqmpqe0+zANYPm6//fZ4xCMesau3MZKF9FeEDoPlbGd2mCEUO4UfLQbF9bSnPc3f4SV222237eotkKD169fH+eefP/PrZrMZj3zkI+P222+PWq22C3cGLFSr1YqDDz44qtXqrt5K7nQYLD95dJghFADATrb//vvHXXfdtd3X7rrrrqjVapmvIli5cmWsXLlyh6/XajX/gIOCK9Lb0RbSXxE6DJazndlhxXhjMgBAgaxduzauu+667b72hS98IdauXbuLdgQwGv0F5GmkV0Jt3bo17rjjjqhWq4Wa4gP/TxF/MObOoL+g+FLor06nE7feeuvMr3/wgx/Et771rXjYwx4Wj3zkI2P9+vXx4x//OD784Q9HRMRZZ50V7373u+MNb3hDvPSlL40vfvGL8fGPfzyuvfbaXbJ/YHzpLyAlIw2h7rjjjjj44IPz3guwBIr0gzF3Bv0Fy8eu7K8bb7wxfvM3f3Pm1w/+3JPTTjstLr/88vjJT34SP/rRj2byQw89NK699tp47WtfG+9617viEY94RLz//e/38ebAktNfQEpKgxF+Gm2z2Yy99torrr7qqphYtWromlWVSvQ6ncx7FD1PYQ9yZ7iYvNvrxSnPe17ce++9Ua/XM++x3BShv1LYg9wZppyPa3+1Wq2o1+vRbDb9PBUoqHH+ezzO3zssF3n8PR7plVAPvoVlYtWqmJiYGLpmolKJ0izzrKLnKexB7gx3xu/BuL0lrQj9lcIe5M4w9Txi/PoLAGC5GZ8fDAMAAADALmMIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5M4QCAAAAIHcjfTreg1ZVKjFRqQy/UbkcE9Vq9gMVPE9hD3JnuJh8MOafKpVyf6WwB7kzTDkf9/4CAFgu5jWE6nU6mR+fPFGtRrfdzry26HkKe5A7w8XkvW4387pxkHJ/pbAHuTNMOR/3/gIAWC68HQ8AAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5K89n8apKJSYqleE3KpdjolrNfqCC5ynsQe4MF5MPSqXM68ZByv2Vwh7kzjDlfNz7CwBguZjXEKrX6URpMBiaTVSr0W23M68tep7CHuTOcDF5r9vNvG4cpNxfKexB7gxTzse9vwAAlgtvxwMAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuyvNZvKpSiYlKZfiNyuWYqFazH6jgeQp7kDvDxeSDUinzunGQcn+lsAe5M0w5H/f+AgBYLuY1hOp1OlEaDIZmE9VqdNvtzGuLnqewB7kzXEze63YzrxsHKfdXCnuQO8OU83HvLwCA5cLb8QAAAADInSEUAAAAALkzhAIAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJC78nwWr6pUYqJSGX6jcjkmqtXsByp4nsIe5M5wMfmgVMq8bhyk3F8p7EHuDFPOx72/AACWi3kNoXqdTpQGg6HZRLUa3XY789qi5ynsQe4MF5P3ut3M68ZByv2Vwh7kzjDlfNz7CwBgufB2PAAAAAByZwgFAAAAQO4MoQAAAADInSEUAAAAALkzhAIAAAAgd4ZQAAAAAOSuPJ/FqyqVmKhUht+oXI6JajX7gQqep7AHuTNcTD4olTKvGwcp91cKe5A7w5Tzce8vAIDlYl5DqF6nE6XBYGg2Ua1Gt93OvLboeQp7kDvDxeS9bjfzunGQcn+lsAe5M0w5T6m/3vOe98Q73vGOuPPOO+Pxj398/MVf/EUcf/zxmes3btwYl156afzoRz+KvffeO0455ZSYmpqKPfbYYwl3DaC/gDR4Ox4AwAiuvPLKOP/88+OCCy6Ib3zjG/H4xz8+1q1bFz/96U+Hrv/oRz8ab3rTm+KCCy6IzZs3xwc+8IG48sor481vfvMS7xwYd/oLSIUhFADACC6++OJ4+ctfHmeccUYcddRRcdlll8WqVavigx/84ND1X/3qV+PJT35yvOhFL4rVq1fHM57xjHjhC18YX//615d458C4019AKgyhAADmcP/998dNN90UJ5544szXVqxYESeeeGLccMMNQ6950pOeFDfddNPMP9q+//3vx6c//en4nd/5naHr77vvvmi1Wtv9B7BYS9FfEToMGM28fiYUAMA4uueee+KBBx6I/fbbb7uv77fffrFly5ah17zoRS+Ke+65J57ylKfEYDCIfr8fZ511VubbWaampmLDhg07fe/AeFuK/orQYcBovBIKACAH119/fVx00UVxySWXxDe+8Y34xCc+Eddee2287W1vG7p+/fr10Ww2Z/67/fbbl3jHAL803/6K0GHAaLwSCgBgDnvvvXfstttucdddd2339bvuuiv233//ode85S1viZe85CVx5plnRkTEYx/72Oh2u/GKV7wi/vAP/zBWrNj+fwtcuXJlrFy5Mp9vABhbS9FfEToMGM28hlCrKpWYqFSG36hcjolqNfuBCp6nsAe5M1xMPiiVMq8bByn3Vwp7kDvDlPMU+mv33XePNWvWxHXXXRcnn3xyRERs3bo1rrvuujj33HOHXtPr9Xb4h9puu+0WERGDwSDX/QI8SH8BKZnXEKrX6UQpo3QmqtXottuZ1xY9T2EPcme4mLzX7WZeNw5S7q8U9iB3hinnqfTX+eefH6eddlocd9xxcfzxx8fGjRuj2+3GGWecERERp556ahx00EExNTUVEREnnXRSXHzxxfFrv/ZrccIJJ8Stt94ab3nLW+Kkk06a+cccwFLQX0AqvB0PAGAEL3jBC+Luu++Ot771rXHnnXfGscceG5/97Gdnftjvj370o+1eOfBHf/RHUSqV4o/+6I/ixz/+ceyzzz5x0kknxR//8R/vqm8BGFP6C0iFIRQAwIjOPffczLevXH/99dv9ulwuxwUXXBAXXHDBEuwMYHb6C0iBT8cDAAAAIHeGUAAAAADkzhAKAAAAgNwZQgEAAACQu3n9YPJVlUpMVCrDb1Qux0S1mv1ABc9T2IPcGS4mH5RKmdeNg5T7K4U9yJ1hyvm49xcAwHIxryFUr9OJ0mAwNJuoVqPbbmdeW/Q8hT3IneFi8l63m3ndOEi5v1LYg9wZppyPe38BACwX3o4HAAAAQO4MoQAAAADInSEUAAAAALkzhAIAAAAgd4ZQAAAAAOTOEAoAAACA3JXns3hVpRITlcrwG5XLMVGtZj9QwfMU9iB3hovJB6VS5nXjIOX+SmEPcmeYcj7u/QUAsFzMawjV63SiNBgMzSaq1ei225nXFj1PYQ9yZ7iYvNftZl43DlLurxT2IHeGKefj3l8AAMuFt+MBAAAAkDtDKAAAAAByZwgFAAAAQO4MoQAAAADInSEUAAAAALkzhAIAAAAgd+X5LF5VqcREpTL8RuVyTFSr2Q9U8DyFPcid4WLyQamUed04SLm/UtiD3BmmnI97fwEALBfzGkL1Op0oDQZDs4lqNbrtdua1Rc9T2IPcGS4m73W7mdeNg5T7K4U9yJ1hyvm49xcAwHLh7XgAAAAA5M4QCgAAAIDcGUIBAAAAkDtDKAAAAAByZwgFAAAAQO4MoQAAAADIXXk+i1dVKjFRqQy/UbkcE9Vq9gMVPE9hD3JnuJh8UCplXjcOUu6vFPYgd4Yp5+PeXwAAy8W8hlC9TidKg8HQbKJajW67nXlt0fMU9iB3hovJe91u5nXjIOX+SmEPcmeYcj7u/QUAsFx4Ox4AAAAAuTOEAgAAACB3hlAAAAAA5M4QCgAAAIDcGUIBAAAAkDtDKAAAAAByV57P4lWVSkxUKsNvVC7HRLWa/UAFz5fiMTqdTmzesiUzn5ycXPZ5peBnmHI+KJUyrxsHKfdXCnvQX4vLIyLWrl2b9BkVOR/3/gIAWC7mNYTqdTpRGgyGZhPVanTb7cxri54vxWNs3rIl1q9fn5lPTU0t+/yoI4/MzItwhinnvW4387pxkHJ/pbAH/bW4PCJi06ZNSZ9RkfNx7y8AgOXC2/EAAAAAyJ0hFADAiN7znvfE6tWrY4899ogTTjghvv71r8+6/t57741zzjknDjjggFi5cmUcccQR8elPf3qJdgvw/+gvIAXzejseAMC4uvLKK+P888+Pyy67LE444YTYuHFjrFu3Lr7zne/Evvvuu8P6+++/P57+9KfHvvvuG1dffXUcdNBB8cMf/jD22muvpd88MNb0F5AKQygAgBFcfPHF8fKXvzzOOOOMiIi47LLL4tprr40PfvCD8aY3vWmH9R/84Afj5z//eXz1q1+NhzzkIRERsXr16qXcMkBE6C8gHd6OBwAwh/vvvz9uuummOPHEE2e+tmLFijjxxBPjhhtuGHrN3/3d38XatWvjnHPOif322y+OOeaYuOiii+KBBx5Yqm0D6C8gKV4JBQAwh3vuuSceeOCB2G+//bb7+n777RdbtmwZes33v//9+OIXvxgvfvGL49Of/nTceuut8T/+x/+I//qv/4oLLrhgh/X33Xdf3HfffTO/brVaO/ebAMbSUvRXhA4DRmMIlZDHxJ6xobQ66RxgmBT6SX+Rmq1bt8a+++4b733ve2O33XaLNWvWxI9//ON4xzveMfQfcVNTU7Fhw4ZdsFOA7c23vyJ0GDCa0mAwGMy1qNVqRb1ej+np6ahUKkPXlMvl6Pf7mfcoer4UjzHo9KK9+fuZeXXysF2e37j5lsx8cnIyNm/evKg8689XRDHOMOW80+lEo9GIZrMZtVot8x7LTRH6K4U96K/F9VdExNq1a5M+oyLnKfTX/fffH6tWrYqrr746Tj755Jmvn3baaXHvvffG3/7t3+5wTaPRiIc85CGxadOmma995jOfid/5nd+J++67L3bfffft1g97FcHBBx88dr0Ny8mDz0OWe39F6DBYjvLosHm9EqrX6UQpY2Y1Ua1Gt93OvLbo+VI8xgNbfhg3v3ljZv7Yi87b5fn69esz86mpqUXnRx15ZGZehDNMOe91u5nXjYOU+yuFPeivxfVXRMSmTZuSPqMi5yn01+677x5r1qyJ6667buYfcVu3bo3rrrsuzj333KHXPPnJT46PfvSjsXXr1lix4pc/hvO73/1uHHDAAUP/Abdy5cpYuXJlbt8DMJ6Wor8idBgwGj+YHABgBOeff368733viyuuuCI2b94cZ599dnS73ZlPmzr11FO3G1aeffbZ8fOf/zxe85rXxHe/+9249tpr46KLLopzzjlnV30LwJjSX0Aq/EwoAIARvOAFL4i777473vrWt8add94Zxx57bHz2s5+d+WG/P/rRj2ZeMRARcfDBB8fnPve5eO1rXxuPe9zj4qCDDorXvOY18cY3vnFXfQvAmNJfQCoMoQAARnTuuedmvn3l+uuv3+Fra9euja997Ws57wpgbvoLSIG34wEAAACQO0MoAAAAAHLn7XgFcvcTI677/PBP94qI2L+8uPyxNy5qewCZvhP/ERcMbsvMpxaZf2LhWwMAAJZIaTDI+MzybbRarajX6zE9PR2VSmXomnK5HP1+P/MeRc+X4jEGnV60N38/M1/5pMPi37dm549Ysbj8sOZhsz5+dfKwuHHzLZn55ORkbN68eVF51p+viGKcYcp5p9OJRqMRzWYzarVa5j2WmyL0Vwp7yLu/VkyuXnQ/zJYfN3n0Lu2viF/+7IyUz6jI+bj317h937CcjPPf43H+3mG5yOPv8bxeCdXrdKKUMbOaqFaj225nXlv0fCke44EtP4yb37wxM9//i+fFX/ffmZm/uPzaReXnbX7trI//2IvO2+6jW3/V1NTUovOjjjwyMy/CGaac97rdzOvGQcr9lcIe8u6v3S96Za798Ympjbu0vyIiNm3alPQZFTkf9/4CAFgu/EwoAAAAAHJnCAUAAABA7gyhAAAAAMidIRQAAAAAuTOEAgAAACB38/p0PNK2z9ci/tubS9kLvrh0ewGYj8fEnrGhtHpXbwMAAMhRaTDI+MzybbRarajX6zE9PR2VSmXomnK5HP1+P/MeRc+X4jEGnV60N38/M1/5pMPi37dm54c1D8v1+urkYXHj5lsy88nJydi8efOi8qw/XxHFOMOU806nE41GI5rNZtRqtcx7LDdF6K8U9pB3f62YXD3r3//jJo/O9fq8+ysiYu3atUmfUZHzce+vcfu+YTkZ57/H4/y9w3KRx9/jeb0SqtfpRCljZjVRrUa33c68tuj5UjzGA1t+GDe/eWNmvv8Xz4u/7r8zMz9v82tzvf6xF50X69evz8ynpqYWnR915JGZeRHOMOW81+1mXjcOUu6vFPaQd3/tftErZ/37/4mpjblen3d/RURs2rQp6TMqcj7u/QUAsFz4mVAAAAAA5M4QCgAAAIDcGUIBAAAAkDtDKAAAAAByZwgFAAAAQO7m9el4pO3uJ0Zc9/nhn/4VEfHiJdwLwHx8J/4jLhjclplPLd1WAACAnJQGg4zPLN9Gq9WKer0e09PTUalUhq4pl8vR7/cz71H0fCkeY9DpRXvz9zPzlU86LP59a3b+iBWLyw9rHjbr41cnD4sbN9+SmU9OTsbmzZsXlWf9+YooxhmmnHc6nWg0GtFsNqNWq2XeY7kpQn+lsIe8+2vF5OpF98Ns+XGTR+/S/oqIWLt2bdJnVOR83Ptr3L5vWE7G+e/xOH/vsFzk8fd4Xq+E6nU6UcqYWU1Uq9FttzOvLXq+FI/xwJYfxs1v3piZ7//F8+Kv++/MzF9cfu2i8vM2v3bWx3/sRefF+vXrM/OpqalF50cdeWRmXoQzTDnvdbuZ142DlPsrhT3k3V+7X/TKXPvjE1Mbd2l/RURs2rQp6TMqcj7u/QUAsFz4mVAAAAAA5M4QCgAAAIDcGUIBAAAAkDtDKAAAAAByZwgFAAAAQO4MoQAAAADIXWkwyPjM8m20Wq2o1+sxPT0dlUpl6JpyuRz9fj/zHkXPl+IxBp1etDd/PzOvTh62y/MbN9+SmU9OTsbmzZsXlWf9+YooxhmmnHc6nWg0GtFsNqNWq2XeY7kpQn+lsAf9tbj+iohYu3Zt0mdU5Hzc+2vcvm9YTsb57/E4f++wXOTx97g8n8W9TidKGTOriWo1uu125rVFz5fiMR7Y8sO4+c0bM/PHXnTeLs/Xr1+fmU9NTS06P+rIIzPzIpxhynmv2828bhyk3F8p7EF/La6/IiI2bdqU9BkVOR/3/gIAWC68HQ8AAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5Kw0GGZ9Zvo1WqxX1ej2mp6ejUqkMXVMul6Pf72feo+j5UjxGp9OJzZs3Z+aTk5PLPs/68xVRjDNMOe90OtFoNKLZbEatVsu8x3JThP5KYQ/6a3F5RMTatWuTPqMi5+PeX+P2fcNyMs5/j8f5e4flIo+/x+X5LO51OlHKmFlNVKvRbbczry16vhSPsXnLlli/fn1mPjU1tezzo448MjMvwhmmnPe63czrxkHK/ZXCHvTX4vKIiE2bNiV9RkXOx72/AACWC2/HAwAAACB3hlAAAAAA5M4QCgAAAIDcGUIBAIzoPe95T6xevTr22GOPOOGEE+LrX//6SNf9zd/8TZRKpTj55JPz3SBABv0FpMAQCgBgBFdeeWWcf/75ccEFF8Q3vvGNePzjHx/r1q2Ln/70p7Ned9ttt8XrXve6eOpTn7pEOwXYnv4CUmEIBQAwgosvvjhe/vKXxxlnnBFHHXVUXHbZZbFq1ar44Ac/mHnNAw88EC9+8Ytjw4YNcdhhhy3hbgH+H/0FpKI0GGR8Zvk2Wq1W1Ov1mJ6ejkqlMnRNuVyOfr+feY+i50vxGJ1OJzZv3pyZT05OLvs8689XRDHOMOW80+lEo9GIZrMZtVot8x7LTRH6K4U96K/F5RERa9euTfqMipyn0F/3339/rFq1Kq6++urt3pJy2mmnxb333ht/+7d/O/S6Cy64IL797W/HNddcE6effnrce++98clPfnLo2vvuuy/uu+++mV+3Wq04+OCDx663YTl58HnIcu+vCB0Gy1EeHVaez+JepxOljJnVRLUa3XY789qi50vxGJu3bIn169dn5lNTU8s+P+rIIzPzIpxhynmv2828bhyk3F8p7EF/LS6PiNi0aVPSZ1TkPIX+uueee+KBBx6I/fbbb7uv77fffrFly5ah13zlK1+JD3zgA/Gtb31rpMeYmpqKDRs2LHarANtZiv6K0GHAaLwdDwBgJ2u32/GSl7wk3ve+98Xee+890jXr16+PZrM589/tt9+e8y4BdrSQ/orQYcBo5vVKKACAcbT33nvHbrvtFnfdddd2X7/rrrti//3332H9//k//yduu+22OOmkk2a+tnXr1oj45VsPv/Od78Thhx++3TUrV66MlStX5rB7YJwtRX9F6DBgNF4JBQAwh9133z3WrFkT11133czXtm7dGtddd12sXbt2h/VHHnlk3HzzzfGtb31r5r/f/d3fjd/8zd+Mb33rW3HwwQcv5faBMaa/gJR4JRQAwAjOP//8OO200+K4446L448/PjZu3BjdbjfOOOOMiIg49dRT46CDDoqpqanYY4894phjjtnu+r322isiYoevA+RNfwGpMIQCABjBC17wgrj77rvjrW99a9x5551x7LHHxmc/+9mZH/b7ox/9KFas8CJzID36C0iFIRQAwIjOPffcOPfcc4dm119//azXXn755Tt/QwAj0l9ACkqDQcZnlm+j1WpFvV6P6enpqFQqQ9eUy+Xo9/uZ9yh6nsIe5M5wMXmn04lGoxHNZjNqtVrmPZabIvRXCnuQO8OU83Hvr3H7vmE5Gee/x+P8vcNykcff43m9EqrX6UQpY2Y1Ua1Gt93OvLboeQp7kDvDxeS9bjfzunGQcn+lsAe5M0w5H/f+AgBYLrzxFwAAAIDcGUIBAAAAkDtDKAAAAAByZwgFAAAAQO4MoQAAAADInSEUAAAAALkrz2fxqkolJiqV4Tcql2OiWs1+oILnKexB7gwXkw9KpczrxkHK/ZXCHuTOMOV83PsLAGC5mNcQqtfpRGkwGJpNVKvRbbczry16nsIe5M5wMXmv2828bhyk3F8p7EHuDFPOx72/AACWC2/HAwAAACB3hlAAAAAA5M4QCgAAAIDcGUIBAAAAkDtDKAAAAAByZwgFAAAAQO7K81m8qlKJiUpl+I3K5ZioVrMfqOB5CnuQO8PF5INSKfO6cZByf6WwB7kzTDkf9/4CAFgu5jWE6nU6URoMhmYT1Wp02+3Ma4uep7AHuTNcTN7rdjOvGwcp91cKe5A7w5Tzce8vAIDlwtvxAAAAAMidIRQAAAAAuTOEAgAAACB3hlAAAAAA5M4QCgAAAIDcGUIBAAAAkLvyfBavqlRiolIZfqNyOSaq1ewHKniewh7kznAx+aBUyrxuHKTcXynsQe4MU87Hvb8AAJaLeQ2hep1OlAaDodlEtRrddjvz2qLnKexB7gwXk/e63czrxkHK/ZXCHuTOMOV83PsLAGC58HY8AAAAAHJnCAUAAABA7gyhAAAAAMidIRQAAAAAuTOEAgAAACB3hlAAAAAA5K48n8WrKpWYqFSG36hcjolqNfuBCp6nsAe5M1xMPiiVMq8bByn3Vwp7kDvDlPNx7y8AgOViXkOoXqcTpcFgaDZRrUa33c68tuh5CnuQO8PF5L1uN/O6cZByf6WwB7kzTDkf9/4CAFguvB0PAAAAgNwZQgEAAACQO0MoAAAAAHJnCAUAAABA7gyhAAAAAMidIRQAwIje8573xOrVq2OPPfaIE044Ib7+9a9nrn3f+94XT33qU+OhD31oPPShD40TTzxx1vUAedJfQArK81m8qlKJiUpl+I3K5ZioVrMfqOB5CnuQO8PF5INSKfO6cZByf6WwB7kzTDlPpb+uvPLKOP/88+Oyyy6LE044ITZu3Bjr1q2L73znO7HvvvvusP7666+PF77whfGkJz0p9thjj3j7298ez3jGM+KWW26Jgw46aBd8B8C40l9AKkqDwWAw16JWqxX1ej0+c+21MTExMXTNRLUa3XY78x5Fz1PYg9wZLibvdrvxzGc9K5rNZtRqtcx7LDdF6K8U9iB3hinnqfTXCSecEL/+678e7373uyMiYuvWrXHwwQfHq171qnjTm9405/UPPPBAPPShD413v/vdceqpp865/sH+2tXfN7Bwqfw9Xur+ikjnewcWLo+/x96OBwAwh/vvvz9uuummOPHEE2e+tmLFijjxxBPjhhtuGOkevV4v/uu//ise9rCHDc3vu+++aLVa2/0HsFhL0V8ROgwYjSEUAMAc7rnnnnjggQdiv/322+7r++23X9x5550j3eONb3xjHHjggdv9Q3BbU1NTUa/XZ/47+OCDF71vgKXorwgdBozGEAoAIGd/8id/En/zN38T11xzTeyxxx5D16xfvz6azebMf7fffvsS7xJgR6P0V4QOA0Yzrx9MDgAwjvbee+/Ybbfd4q677tru63fddVfsv//+s177Z3/2Z/Enf/InsWnTpnjc4x6XuW7lypWxcuXKnbJfgActRX9F6DBgNF4JBQAwh9133z3WrFkT11133czXtm7dGtddd12sXbs287o//dM/jbe97W3x2c9+No477ril2CrAdvQXkBKvhAIAGMH5558fp512Whx33HFx/PHHx8aNG6Pb7cYZZ5wRERGnnnpqHHTQQTE1NRUREW9/+9vjrW99a3z0ox+N1atXz/zslUqlEpVKZZd9H8D40V9AKuY1hFpVqcRERumUy+WYqFazH6jgeQp7kDvDxeSDUinzunGQcn+lsAe5M0w5T6W/XvCCF8Tdd98db33rW+POO++MY489Nj772c/O/LDfH/3oR7Fixf97kfmll14a999/f5xyyinb3eeCCy6ICy+8cCm3Dow5/QWkYl5DqF6nE6XBYGg2Ua1Gt93OvLboeQp7kDvDxeS9bjfzunGQcn+lsAe5M0w5T6m/zj333Dj33HOHZtdff/12v77tttvy3xDAiPQXkAI/EwoAAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyJ0hFAAAAAC5K89n8apKJSYqleE3KpdjolrNfqCC5ynsQe4MF5MPSqXM68ZByv2Vwh7kzjDlfNz7CwBguZjXEKrX6URpMBiaTVSr0W23M68tep7CHuTOcDF5r9vNvG4cpNxfKexB7gxTzse9vwAAlgtvxwMAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJA7QygAAAAAcmcIBQAAAEDuDKEAAAAAyF15PotXVSoxUakMv1G5HBPVavYDFTxPYQ9yZ7iYfFAqZV43DlLurxT2IHeGKefj3l8AAMvFvIZQvU4nSoPB0GyiWo1uu515bdHzFPYgd4aLyXvdbuZ14yDl/kphD3JnmHI+7v0FALBceDseAAAAALkzhAIAAAAgd4ZQAAAAAOTOEAoAAACA3BlCAQAAAJA7QygAAAAAcleez+JVlUpMVCrDb1Qux0S1mv1ABc9T2IPcGS4mH5RKmdeNg5T7K4U9yJ1hyvm49xcAwHIxryFUr9OJ0mAwNJuoVqPbbmdeW/Q8hT3IneFi8l63m3ndOEi5v1LYg9wZppyPe38BACwX3o4HAAAAQO4MoQAAAADInSEUAAAAALkzhAIAAAAgd4ZQAAAAAOTOEAoAAACA3JXns3hVpRITlcrwG5XLMVGtZj9QwfMU9iB3hovJB6VS5nXjIOX+SmEPcmeYcj7u/QUAsFzMawjV63SiNBgMzSaq1ei225nXFj1PYQ9yZ7iYvNftZl43DlLurxT2IHeGKefj3l8AAMuFt+MBAAAAkDtDKAAAAAByZwgFAAAAQO4MoQAAAADInSEUAAAAALkb6dPxBv/3E6W6vV72mlJp1k+vKXqewh7kznAx+YN/fwcZnxC3XBWhv1LYg9wZppyPa38BACw3Iw2h2v/3I5NPed7zct0MkL92ux31en1Xb2PJ6C9YPlLor/e85z3xjne8I+688854/OMfH3/xF38Rxx9/fOb6q666Kt7ylrfEbbfdFo9+9KPj7W9/e/zO7/zOEu4Y4Jf0F5CCkYZQBx54YNx+++1RrVajVCrlvScgB4PBINrtdhx44IG7eitLSn9B8aXSX1deeWWcf/75cdlll8UJJ5wQGzdujHXr1sV3vvOd2HfffXdY/9WvfjVe+MIXxtTUVDz72c+Oj370o3HyySfHN77xjTjmmGN2wXcAjCv9BaSiNPDadgCAOZ1wwgnx67/+6/Hud787IiK2bt0aBx98cLzqVa+KN73pTTusf8ELXhDdbjf+/u//fuZrT3ziE+PYY4+Nyy67bM7Ha7VaUa/Xo9lsRq1W23nfCLBkUvl7vNT9FZHO9w4sXB5/j0d6JRQAwDi7//7746abbor169fPfG3FihVx4oknxg033DD0mhtuuCHOP//87b62bt26+OQnPzl0/X333Rf33XffzK+bzWZE/PIJIFBMD/793ZX/u/9S9FeEDoPlKI8OM4QCAJjDPffcEw888EDst99+2319v/32iy1btgy95s477xy6/s477xy6fmpqKjZs2LDD1w8++OAF7hpIxc9+9rNd9jPtlqK/InQYLGc7s8MMoQAAErB+/frtXnlw7733xiGHHBI/+tGPdvkPZF+oVqsVBx98cNx+++2FfDtO0fcfUfzvoej7bzab8chHPjIe9rCH7eqt5G65dVjR/+wVff8Rxf8eir7/iHw6zBAKAGAOe++9d+y2225x1113bff1u+66K/bff/+h1+y///7zWr9y5cpYuXLlDl+v1+uFffL6oFqtVujvoej7jyj+91D0/a9YsWKXPfZS9FfE8u2wov/ZK/r+I4r/PRR9/xE7t8N2XRsCABTE7rvvHmvWrInrrrtu5mtbt26N6667LtauXTv0mrVr1263PiLiC1/4QuZ6gDzoLyAlXgkFADCC888/P0477bQ47rjj4vjjj4+NGzdGt9uNM844IyIiTj311DjooIP+//buPrTG/4/j+Hs252xqmLTZNLQxZCSTtaFFK0Vu/rKiNYXRprQV1oYjt0tIacjt/IETQrI190uYhK1kQ2xIOYsiy93u3t8/9nO+38OGcznXdXZ+no/aHy6fq/N6b7teuT7OjWzZskVERJYvXy5paWmyfft2mTFjhjidTrl7967s27fPn2MA+AvRXwC6CzahAAAAfkNGRoa8efNG1q5dKy6XS8aOHSsVFRXuN+99+fKlx9PVU1NT5dixY7J69WopLCyUYcOGydmzZyUxMfG3Hs9ut4vD4ej05S2BItBnCPT8IoE/A/l9w+r+Euk+sxtFfv8L9BkCPb+IOTMEqT8/LxQAAAAAAAB/Bd4TCgAAAAAAAKZjEwoAAAAAAACmYxMKAAAAAAAApmMTCgAAAAAAAKZjEwoAAMBPSkpKZMiQIRIaGirJycly586dn64/efKkjBgxQkJDQ2X06NFSXl5uUdKueTPD/v37ZfLkyRIRESERERGSnp7+y5nN5u3P4Bun0ylBQUEyZ84ccwP+grf5379/L7m5uRIdHS12u10SEhL8/nvk7Qw7d+6U4cOHS1hYmMTGxkpeXp58+fLForSerl+/LjNnzpSYmBgJCgqSs2fP/vKcyspKGTdunNjtdhk6dKiUlpaantMM9Bf95QuB3mH0V6n3D6wAAACwnNPpVJvNpocOHdKHDx/q4sWLtW/fvtrY2Njp+ps3b2pwcLBu3bpVa2trdfXq1dqzZ0998OCBxcn/5e0M8+bN05KSEq2urta6ujpdsGCB9unTR1+9emVx8g7e5v+moaFBBw4cqJMnT9bZs2dbE7YT3ub/+vWrjh8/XqdPn643btzQhoYGrays1JqaGouT/8vbGY4ePap2u12PHj2qDQ0NeuHCBY2Ojta8vDyLk3coLy/XoqIiPX36tIqInjlz5qfr6+vrtVevXpqfn6+1tbW6a9cuDQ4O1oqKCmsC+wj9RX/5QqB3GP1lrL/YhAIAAPCDCRMmaG5urvvPbW1tGhMTo1u2bOl0/dy5c3XGjBkex5KTk3XJkiWm5vwZb2f4Xmtrq4aHh+uRI0fMivhTRvK3trZqamqqHjhwQLOysvx6E+dt/j179mhcXJw2NzdbFfGXvJ0hNzdXp06d6nEsPz9fJ06caGrO3/E7N3ErV67UUaNGeRzLyMjQadOmmZjM9+gv+ssXAr3D6C9j/cXL8QAAACzW3Nws9+7dk/T0dPexHj16SHp6ulRVVXV6TlVVlcd6EZFp06Z1ud5sRmb43qdPn6SlpUX69etnVswuGc2/fv16iYyMlIULF1oRs0tG8p87d05SUlIkNzdXoqKiJDExUTZv3ixtbW1WxfZgZIbU1FS5d++e+yUv9fX1Ul5eLtOnT7ck85/qbtexEfRXB/rrzwR6h9FfHYxcxyG+DAUAAIBfe/v2rbS1tUlUVJTH8aioKHn06FGn57hcrk7Xu1wu03L+jJEZvrdq1SqJiYn54R+1VjCS/8aNG3Lw4EGpqamxIOHPGclfX18vV69elfnz50t5ebk8ffpUcnJypKWlRRwOhxWxPRiZYd68efL27VuZNGmSqKq0trbK0qVLpbCw0IrIf6yr6/jDhw/y+fNnCQsL81Oy30d/daC//kygdxj91cFIf/FMKAAAAFiuuLhYnE6nnDlzRkJDQ/0d55eampokMzNT9u/fL/379/d3HEPa29slMjJS9u3bJ0lJSZKRkSFFRUWyd+9ef0f7bZWVlbJ582bZvXu33L9/X06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      "text/plain": [
       "<Figure size 1200x900 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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X/PtrlDXycucp7EGuv3Yk5edgcv0y7vlsa4roMG/HAwAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAUDhDKAAAAAAKZwgFAAAAQOEqWZbzeZlb6XQ60Ww2Y2pqKmq12tA11Wo1pqenc+9R9jyFPcid4ULyXq8XrVYr2u12NBqN3HssNmXorxT2IHeGKef6a/79NcoaebnzFPYg11/DlOE5mFy/jHs+25oiOqw6l8WDXi8qOTOriXo9+t1u7rVlz1PYg9wZLiQf9Pu5142DlPsrhT3InWHKuf6af3+NskZe7jyFPcj1146k/BxMrl/GPZ9tTREd5u14AAAAABTOEAoAAACAwhlCAQAAAFA4QygAAAAACmcIBQAAAEDhDKEAAAAAKJwhFAAAAACFq2RZls22qNPpRLPZjKmpqajVakPXVKvVmJ6ezr1H2fMU9iB3hgvJe71etFqtaLfb0Wg0cu+x2JShv1LYg9wZppzrr/n31yhr5OXOU9iDXH8NU4bnYHL9Mu75bGuK6LDqXBYPer2o5MysJur16He7udeWPU9hD3JnuJB80O/nXjcOUu6vFPYgd4Yp5/pr/v01yhp5ufMU9iDXXzuS8nMwuX4Z93y2NUV0mLfjAQAAAFA4QygAAAAACmcIBQAAAEDhDKEAAAAAKJwhFAAAAACFM4QCAAAAoHCVLMv5vMytdDqdaDabMTU1FbVabeiaarUa09PTufcoe57CHuTOcCF5r9eLVqsV7XY7Go1G7j0WmzL0Vwp7kDvDlHP9Nf/+GmWNvNx5CnuQ669hyvAcTK5fxj2fbU0RHVady+JBrxeVnJnVRL0e/W4399qy5ynsQe4MF5IP+v3c68ZByv2Vwh7kzjDlXH/Nv79GWSMvd57CHuT6a0dSfg4m1y/jns+2pogO83Y8AAAAAApnCAUAAABA4QyhAAAAACicIRQAAAAAhTOEAgAAAKBwc/p0PHbsX//1X2P9+vW5+eTkpHyB+ZFHHJGbA/OnvxaWj7Lmhhtu2OH1AACw2FWyLOfzMrfS6XSi2WzG1NRU1Gq1oWuq1WpMT0/n3qPs+Shrer1ebNiwITdfvXq1fIF53t+/iJ1zhos57/V60Wq1ot1uR6PRyL3HYlOG/kphD/prYfkoa4499thk+yH1XH/Nv79GWSMvd57CHuT6a5gyPAeT65dxz2dbU0SHzemVUINeLyo5M6uJej363W7utWXPR1mzYePGpP+X+sWQ7+iVUDvjDBdzPuj3c68bByn3Vwp70F+75pVQqfZD6rn+mn9/jbJGXu48hT3I9deOpPwcTK5fxj2fbU0RHeZ3QgEAAABQOEMoAAAAAApnCAUAAABA4QyhAAAAACicIRQAAAAAhZvTp+OxuD2ttia+ufqvcvNubdVuz38UO/54SWA87e7+GmXNXTu8GgAAFr9KluV8XuZWOp1ONJvNmJqailqtNnRNtVqN6en8AUHZ81HW9Hq92LBhQ26+evXqpPPj1hwd1U2D3Hx65bLdnm/eO/+v6844w8Wc93q9aLVa0W63o9Fo5N5jsSlDf6WwB/21sHyUNZsfX0+2H1LP9df8+2uUNfJy5ynsQa6/hinDczC5fhn3fLY1RXTYnF4JNej1opIzs5qo16Pf7eZeW/Z8lDUbNm6M9evX5+aTk5NJ5zdfcl3UL70tN+++YtVuz+87OP8HaGec4WLOB/1+7nXjIOX+SmEP+mth+Shr+pccmWw/pJ7rr/n31yhr5OXOU9iDXH/tSMrPweT6Zdzz2dYU0WF+JxQAAAAAhTOEAgAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAULg5fToe7Lvkotxsc+U1se+SixeUm4sCRVmz4VW52WRvMtZv2MGn882SR0TcHNfNe28AADAOKlmW83mZW+l0OtFsNmNqaipqtdrQNdVqNaanp3PvUfZ8lDW9Xi82bNiQm69evTrp/Lg1R0d10yA3n165LJZ9/xu5+U9XHhp7bfregvIlt1V2+Pib987/67ozznAx571eL1qtVrTb7Wg0Grn3WGzK0F8p7GEc+uvz3/xSYY8/yh42P76ebD+knuuv+ffXKGvk5c5T2INcfw1Thudgcv0y7vlsa4rosDm9EmrQ60UlZ2Y1Ua9Hv9vNvbbs+ShrNmzcGOvX7+B/SZ+cTDq/+ZLron7pbbl59xWr4qDLL87N7zjrNXHQZQvLl16a/0qo7itWxX0H5/8A7YwzXMz5oN/PvW4cpNxfKexhHPqryMcfZQ/9S45Mth9Sz/XX/PtrlDXycucp7EGuv3Yk5edgcv0y7vlsa4roMO99AgAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAUDhDKAAAAAAKN6dPx4PZ3Lvl3NzsgWzVLtwJwNx8c/Vf5Wbdmv4CAICFqmRZzudlbqXT6USz2Yypqamo1WpD11Sr1Ziens69R9nzUdb0er3YsGFDbr569eqk8+PWHB3VTYPcfHrlslj2/W/k5j9deWgsua1S6PWb987/67ozznAx571eL1qtVrTb7Wg0Grn3WGzK0F8p7GEc+uvz3/zSDu+//PY95n39KHvY/Ph6sv2Qeq6/5t9fo6yRlztPYQ9y/TVMGZ6DyfXLuOezrSmiw+b0SqhBrxeVnJnVRL0e/W4399qy56Os2bBxY6xfvz43n5ycTDq/+ZLron7pbbl59xWr4qDLL87N7zjrNbH00vx3eO6M6+87OP8HaGec4WLOB/1+7nXjIOX+SmEP49Bfs93/MR+cmPf1o+yhf8mRyfZD6rn+mn9/jbJGXu48hT3I9deOpPwcTK5fxj2fbU0RHeZ3QgEAAABQOEMoAAAAAApnCAUAAABA4QyhAAAAACicIRQAAAAAhZvTp+PBbPZdclFutrnyml23EYA5WrPhVbnZZG9yF+4EAAAWp0qW5Xxe5lY6nU40m82YmpqKWq02dE21Wo3p6ence5Q9H2VNr9eLDRs25OarV69OOj9uzdFR3TTIzadXLotl3/9Gbv7TlYfGXpu+t6B8yW2VHT7+5r3z/7rujDNczHmv14tWqxXtdjsajUbuPRabMvRXCnsYh/76/De/VNjjj7KHzY+vJ9sPqef6a/79NcoaebnzFPYg11/DlOE5mFy/jHs+25oiOmxOr4Qa9HpRyZlZTdTr0e92c68tez7Kmg0bN8b69etz88nJyaTzmy+5LuqX3pabd1+xKg66/OLc/I6zXhMHXbawfOml+e8Q7b5iVdx3cP4P0M44w8WcD/r93OvGQcr9lcIexqG/inz8UfbQv+TIZPsh9Vx/zb+/RlkjL3eewh7k+mtHUn4OJtcv457PtqaIDvM7oQAAAAAonCEUAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAws3p0/HYsafV1sQ3V/9Vbt6trdqt+XWx8N9sf++Wc3OzB7JVC86XRv4nSwHFGYf+KnJ/ERE7/twRAACgkmU5n5e5lU6nE81mM6ampqJWqw1dU61WY3p6OvceZc9HWbP0vypR3TTIzadXLtut+d0rHowNGzbk5setOTrp/U+vXBab987/67ozznAx571eL1qtVrTb7Wg0Grn3WGzK0F8p7EF/LSwfZc3mx9eT7YfUc/01//4aZY283HkKe5Drr2HK8BxMrl/GPZ9tTREdNqdXQg16vajkzKwm6vXod/P/d+Cy5yPd4z+rUb80/5U83Ves2q35TSf3Y/369bn5zZdcl/T+u69YFfcdnP8DtFPOcBHng/7CX0lSZin3Vwp70F8Ly0dZ07/kyGT7IfVcf82/v0ZZIy93nsIe5PprR1J+DibXL+Oez7amiA7zO6EAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUzhAKAAAAgMLN6dPxWJg9K/8R+y65ODffXHnNgvJuXDL/zUXETb1vxvoN+Z8+Ndmb3O35kXFEbg4Up+h+uDmuS3p/s+WjrLkhbtjh9QAAsNhVsizn8zK30ul0otlsxtTUVNRqtaFrqtVqTE9P596j7Pkoa5b+VyWqmwa5+ZZVWey16Xu5+U9XHrqgfHDI43f4+HeveDA2bNiQm69evTr5PO/vX8TOOcPFnPd6vWi1WtFut6PRaOTeY7EpQ3+lsIfZ+qvo/jhuzdGl7q9R1hx77LHJ9kPquf6af3+NskZe7jyFPcj11zBleA4m1y/jns+2pogOm9MroQa9XlRyZlYT9Xr0u93ca8uej3SP/6xG/dLbcvPNZ2+Jgy67ODe/46zXLCi/7cxLdvj4N53cj/Xrd/C/9E9OJp8feUT+K6F2yhku4nzQ7+deNw5S7q8U9jBbfxXdHzdfcl2p+2uUNTfccEOy/ZB6rr/m31+jrJGXO09hD3L9tSMpPweT65dxz2dbU0SH+Z1QAAAAABTOEAoAAACAwhlCAQAAAFA4QygAAAAACmcIBQAAAEDhDKEAAAAAKFwly3I+L3MrnU4nms1mTE1NRa1WG7qmWq3G9PR07j3Kno+yZul/VaK6aZCbb1mVxV6bvpebDw55fKHX373iwdiwYUNuvnr16uTzvL9/ETvnDBdz3uv1otVqRbvdjkajkXuPxaYM/ZXCHmbrr9n647g1R+/W63d3P42y5thjj022H1LP9df8+2uUNfJy5ynsQa6/hinDczC5fhn3fLY1RXRYdS6LB71eVHJmVhP1evS73dxry56PdI//rEb90tty881nb4mDLrs4N7/tzEsKvf6mk/uxfv363HxycjL5/MgjjsjNd8oZLuJ80O/nXjcOUu6vFPYwW3/N1h83X3Ldbr1+d/fTKGtuuOGGZPsh9Vx/zb+/RlkjL3eewh7k+mtHUn4OJtcv457PtqaIDvN2PAAAAAAKZwgFAAAAQOEMoQAAAAAonCEUAAAAAIUzhAIAAACgcIZQAAAAABSukmU5n5e5lU6nE81mM6ampqJWqw1dU61WY3p6OvceZc9HWbP0vypR3TTIzbesymKvTd/LzX+68tAF5YNDHr/Dx797xYOxYcOG3Hz16tXJ53l//yJ2zhku5rzX60Wr1Yp2ux2NRiP3HotNGforhT3M1l9F98dxa44udX+NsubYY49Nth9Sz/XX/PtrlDXycucp7EGuv4Ypw3MwuX4Z93y2NUV0WHUuiwe9XlRyZlYT9Xr0u93ca8uej3SP/6xG/dLbcvPNZ2+Jgy67ODe/46zXLCi/7cxLdvj4N53cj/Xr1+fmk5OTyedHHnFEbr5TznAR54N+P/e6cZByf6Wwh9n6q+j+uPmS60rdX6OsueGGG5Lth9Rz/TX//hpljbzceQp7kOuvHUn5OZhcv4x7PtuaIjrM2/EAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUzhAKAAAAgMIZQgEAAABQuEqW5Xxe5lY6nU40m82YmpqKWq02dE21Wo3p6ence5Q9H2XN0v+qRHXTIDefXrlst+Z3r3gwNmzYkJuvXr06+Tzv71/EzjnDxZz3er1otVrRbrej0Wjk3mOxKUN/pbAH/bWwfJQ1xx57bLL9kHquv+bfX6OskZc7T2EPcv01TBmeg8n1y7jns60posOqc1k86PWikjOzmqjXo9/t5l5b9nyke/xnNeqX3pabd1+xarfmN53cj/Xr1+fmk5OTyedHHnFEbr5TznAR54N+P/e6cZByf6WwB/21sHyUNTfccEOy/ZB6rr/m31+jrJGXO09hD3L9tSMpPweT65dxz2dbU0SHeTseAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAwhlCAQAAAFA4QygAAAAAClfJspzPy9xKp9OJZrMZU1NTUavVhq6pVqsxPT2de4+y56Os6fV6sWHDhtx89erV8gXmeX//InbOGS7mvNfrRavVina7HY1GI/cei00Z+iuFPeivheWjrDn22GOT7YfUc/01//4aZY283HkKe5Drr2HK8BxMrl/GPZ9tTREdVp3L4kGvF5WcmdVEvR79bjf32rLno6zZsHFjrF+/PjefnJyULzA/8ogjcvOdcYaLOR/0+7nXjYOU+yuFPeivheWjrLnhhhuS7YfUc/01//4aZY283HkKe5Drrx1J+TmYXL+Mez7bmiI6zNvxAAAAACicIRQAAAAAhTOEAgAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAULhKluV8XuZWOp1ONJvNmJqailqtNnRNtVqN6enp3HuUPU9hD3JnuJC81+tFq9WKdrsdjUYj9x6LTRn6K4U9yJ1hyrn+mn9/jbJGXu48hT3I9dcwZXgOJtcv457PtqaIDqvOZfGg14tKzsxqol6Pfrebe23Z8xT2IHeGC8kH/X7udeMg5f5KYQ9yZ5hyrr/m31+jrJGXO09hD3L9tSMpPweT65dxz2dbU0SHeTseAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAwhlCAQAAAFA4QygAAAAAClfJspzPy9xKp9OJZrMZU1NTUavVhq6pVqsxPT2de4+y5ynsQe4MF5L3er1otVrRbrej0Wjk3mOxKUN/pbAHuTNMOddf8++vUdbIy52nsAe5/hqmDM/B5Ppl3PPZ1hTRYdW5LB70elHJmVlN1OvR73Zzry17nsIe5M5wIfmg38+9bhyk3F8p7EHuDFPO9df8+2uUNfJy5ynsQa6/diTl52By/TLu+Wxriugwb8cDAAAAoHCGUAAAAAAUzhAKAAAAgMIZQgEAAABQOEMoAAAAAApnCAUAAABA4QyhAAAAAChcJcuybLZFnU4nms1mTE1NRa1WG7qmWq3G9PR07j3KnqewB7kzXEje6/Wi1WpFu92ORqORe4/Fpgz9lcIe5M4w5Vx/zb+/RlkjL3eewh7k+muYMjwHk+uXcc9nW1NEh1XnsnjQ60UlZ2Y1Ua9Hv9vNvbbseQp7kDvDheSDfj/3unGQcn+lsAe5M0w511/z769R1sjLnaewB7n+2pGUn4PJ9cu457OtKaLDvB0PAAAAgMIZQgEAAABQOEMoAAAAAApnCAUAAABA4QyhAAAAACicIRQAAAAAhatkWc7nZW6l0+lEs9mMqampqNVqQ9dUq9WYnp7OvUfZ8xT2IHeGC8l7vV60Wq1ot9vRaDRy77HYlKG/UtiD3BmmnOuv+ffXKGvk5c5T2INcfw1Thudgcv0y7vlsa4rosOpcFg96vajkzKwm6vXod7u515Y9T2EPcme4kHzQ7+deNw5S7q8U9iB3hinn+mv+/TXKGnm58xT2INdfO5LyczC5fhn3fLY1RXSYt+MBAAAAUDhDKAAAAAAKZwgFAAAAQOEMoQAAAAAonCEUAAAAAIUzhAIAAACgcJUsy/m8zK10Op1oNpsxNTUVtVpt6JpqtRrT09O59yh7nsIe5M5wIXmv14tWqxXtdjsajUbuPRabMvRXCnuQO8OUc/01//4aZY283HkKe5Drr2HK8BxMrl/GPZ9tTREdVp3L4kGvF5WcmdVEvR79bjf32rLnKexB7gwXkg/6/dzrxkHK/ZXCHuTOMOVcf82/v0ZZIy93nsIe5PprR1J+DibXL+Oez7amiA7zdjwAAAAACmcIBQAAAEDhDKEAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUrpJlOZ+XuZVOpxPNZjOmpqaiVqsNXVOtVmN6ejr3HmXPU9iD3BkuJO/1etFqtaLdbkej0ci9x2JThv5KYQ9yZ5hyrr/m31+jrJGXO09hD3L9NUwZnoPJ9cu457OtKaLDqnNZPOj1opIzs5qo16Pf7eZeW/Y8hT3IneFC8kG/n3vdOEi5v1LYg9wZppzrr/n31yhr5OXOU9iDXH/tSMrPweT6Zdzz2dYU0WHejgcAAABA4QyhAAAAACicIRQAAAAAhTOEAgAAAKBwhlAAAAAAFM4QCgAAAIDCVbIs5/Myt9LpdKLZbMbU1FTUarWha6rVakxPT+feo+x5CnuQO8OF5L1eL1qtVrTb7Wg0Grn3WGzK0F8p7EHuDFPO9df8+2uUNfJy5ynsQa6/hinDczC5fhn3fLY1RXRYdS6LB71eVHJmVhP1evS73dxry56nsAe5M1xIPuj3c68bByn3Vwp7kDvDlHP9Nf/+GmWNvNx5CnuQ668dSfk5mFy/jHs+25oiOszb8QAAAAAonCEUAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAwhlCAQAAAFC4SpblfF7mVjqdTjSbzZiamoparTZ0TbVajenp6dx7lD1PYQ9yZ7iQvNfrRavVina7HY1GI/cei00Z+iuFPcidYcq5/pp/f42yRl7uPIU9yPXXMGV4DibXL+Oez7amiA6rzmXxoNeLSs7MaqJej363m3tt2fMU9iB3hgvJB/1+7nXjIOX+SmEPcmeYcq6/5t9fo6yRlztPYQ9y/bUjKT8Hk+uXcc9nW1NEh3k7HgAAAACFM4QCAAAAoHCGUAAAAAAUzhAKAAAAgMIZQgEAAABQOEMoAAAAAApXybKcz8vcSqfTiWazGVNTU1Gr1YauqVarMT09nXuPsucp7EHuDBeS93q9aLVa0W63o9Fo5N5jsSlDf6WwB7kzTDnXX/Pvr1HWyMudp7AHuf4apgzPweT6Zdzz2dYU0WHVuSwe9HpRyZlZTdTr0e92c68te57CHuTOcCH5oN/PvW4cpNxfKexB7gxTzvXX/PtrlDXycucp7EGuv3Yk5edgcv0y7vlsa4roMG/HAwAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAUDhDKAAAAAAKZwgFAAAAQOEqWZbzeZlb6XQ60Ww2Y2pqKmq12tA11Wo1pqenc+9R9jyFPcid4ULyXq8XrVYr2u12NBqN3HssNmXorxT2IHeGKef6a/79NcoaebnzFPYg11/DlOE5mFy/jHs+25oiOqw6l8WDXi8qOTOriXo9+t1u7rVlz1PYg9wZLiQf9Pu5142DlPsrhT3InWHKuf6af3+NskZe7jyFPcj1146k/BxMrl/GPZ9tTREd5u14AAAAABTOEAoAAACAwhlCAQAAAFA4QygAAAAACmcIBQAAAEDhRvp0vOz/fZpBfzDIX1Op7PA3p5c9T2EPcme4kPyhn98s59NJFqsy9FcKe5A7w5Rz/TX//hpljbzceQp7kOuvYcrwHEyuX8Y9n21NER1WyUa423/+53/GihUrdtqDArvP7bffHgcffPDu3sYuo79g8dBfQFmNW39F6DBYTHZmh400hNqyZUvceeedUa/Xo1Kp7JQHBnatLMui2+3GgQceGEuWjM87cfUXlJ/+0l9QVuPaXxE6DBaDIjpspCEUAAAAACzEeI3jAQAAANgtDKEAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUzhAKAAAAgMIZQgEAAABQOEMoAAAAAApnCAUAAABA4QyhAAAAACicIRQAAAAAhTOEAgAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAUDhDKAAAAAAKZwgFAAAAQOEMoQAAAAAonCEUAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAwhlCAQAAAFA4QygAAAAACmcIBQAAAEDhDKEAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUzhAKAAAAgMIZQgEAAABQOEMoAAAAAApnCAUAAABA4QyhAAAAACicIRQAAAAAhTOEAgAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAUDhDKAAAAAAKZwgFAAAAQOGqoyzasmVL3HnnnVGv16NSqRS9J6AAWZZFt9uNAw88MJYsGZ/5s/6C8tNf+gvKalz7K0KHwWJQRIeNNIS68847Y8WKFTvlAYHd6/bbb4+DDz54d29jl9FfsHjoL6Csxq2/InQYLCY7s8NGGkLV6/WIiLjm6qtjYtmyoWuW1Wox6PVy71H2PIU9yJ3hQvL+YBAnPe95Mz/P46IM/ZXCHuTOMOVcf82/v0ZZIy93nsIe5PprmDI8B5Prl3HPZ1tTRIeNNIR66OWTE8uWxcTExNA1E7VaVLIs9x5lz1PYg9wZ7ox/B+P2cugy9FcKe5A7w9TzCP01jL+b8hT2INdfw5ThOZhcv4x7Puqandlh4/XGZAAAAAB2C0MoAAAAAApnCAUAAABA4QyhAAAAACicIRQAAAAAhatk2Sy/Bj0iOp1ONJvNmJqailqtNnRNtVqN6enp3HuUPU9hD3JnuJC81+tFq9WKdrsdjUYj9x6LTRn6K4U9yJ1hyrn+mn9/jbJGXu48hT3I9dcwZXgOJtcv457PtqaIDqvOZfGg18v96L6Jej363W7utWXPU9iD3BkuJB/0+7nXjYOU+yuFPcidYcq5/pp/f42yRl7uPIU9yPXXjqT8HEyuX8Y9n21NER3m7XgAAAAAFM4QCgAAAIDCGUIBAAAAUDhDKAAAAAAKZwgFAAAAQOEMoQAAAAAoXCXLcj4vcyudTieazWZMTU1FrVYbuqZarcb09HTuPcqep7AHuTNcSN7r9aLVakW73Y5Go5F7j8WmDP2Vwh7kzjDlXH/Nv79GWSMvd57CHuT6a5gyPAeT65dxz2dbU0SHVeeyeNDrRSVnZjVRr0e/2829tux5CnuQO8OF5IN+P/e6cZByf6WwB7kzTDnXX/Pvr1HWyMudp7AHuf7akZSfg8n1y7jns60posO8HQ8AAACAwhlCAQAAAFA4QygAAAAACmcIBQAAAEDhDKEAAAAAKJwhFAAAAACFq2RZzudlbqXT6USz2Yypqamo1WpD11Sr1Ziens69R9nzFPYgd4YLyXu9XrRarWi329FoNHLvsdiUob9S2IPcGaac66/599coa+TlzlPYg1x/DVOG52By/TLu+Wxriuiw6lwWD3q9qOTMrCbq9eh3u7nXlj1PYQ9yZ7iQfNDv5143DlLurxT2IHeGKef6a/79NcoaebnzFPYg1187kvJzMLl+Gfd8tjVFdJi34wEAAABQOEMoAAAAAApnCAUAAABA4QyhAAAAACicIRQAAAAAhTOEAgAAAKBwlSzL+bzMrXQ6nWg2mzE1NRW1Wm3ommq1GtPT07n3KHuewh7kznAhea/Xi1arFe12OxqNRu49Fpsy9FcKe5A7w5Rz/TX//hpljbzceQp7kOuvYcrwHEyuX8Y9n21NER1WncviQa8XlZyZ1US9Hv1uN/fasucp7EHuDBeSD/r93OvGQcr9lcIe5M4w5Vx/zb+/RlkjL3eewh7k+mtHUn4OJtcv457PtqaIDvN2PAAAAAAKZwgFAAAAQOEMoQAAAAAonCEUAAAAAIUzhAIAAACgcIZQAAAAABSukmU5n5e5lU6nE81mM6ampqJWqw1dU61WY3p6OvceZc9T2IPcGS4k7/V60Wq1ot1uR6PRyL3HYlOG/kphD3JnmHKuv+bfX6OskZc7T2EPcv01TBmeg8n1y7jns60posOqc1k86PWikjOzmqjXo9/t5l5b9jyFPcid4ULyQb+fe904SLm/UtiD3BmmnOuv+ffXKGvk5c5T2INcf+1Iys/B5Ppl3PPZ1hTRYd6OBwAAAEDhDKEAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUzhAKAAAAgMJVsizn8zK30ul0otlsxtTUVNRqtaFrqtVqTE9P596j7HkKe5A7w4XkvV4vWq1WtNvtaDQaufdYbMrQXynsQe4MU8711/z7a5Q18nLnKexBrr+GKcNzMLl+Gfd8tjVFdFh1LosHvV5UcmZWE/V69Lvd3GvLnqewB7kzXEg+6PdzrxsHKfdXCnuQO8OUc/01//4aZY283HkKe5Drrx1J+TmYXL+Mez7bmiI6zNvxAAAAACicIRQAAAAAhTOEAgAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAULhKluV8XuZWOp1ONJvNmJqailqtNnRNtVqN6enp3HuUPU9hD3JnuJC81+tFq9WKdrsdjUYj9x6LTRn6K4U9yJ1hyrn+mn9/jbJGXu48hT3I9dcwZXgOJtcv457PtqaIDqvOZfGg14tKzsxqol6Pfrebe23Z8xT2IHeGC8kH/X7udeMg5f5KYQ9yZ5hyrr/m31+jrJGXO09hD3L9tSMpPweT65dxz2dbU0SHeTseAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAwhlCAQAAAFA4QygAAAAAClfJspzPy9xKp9OJZrMZU1NTUavVhq6pVqsxPT2de4+y5ynsQe4MF5L3er1otVrRbrej0Wjk3mOxKUN/pbAHuTNMOddf8++vUdbIy52nsAe5/hqmDM/B5Ppl3PPZ1hTRYdW5LB70elHJmVlN1OvR73Zzry17nsIe5M5wIfmg38+9bhyk3F8p7EHuDFPO9df8+2uUNfJy5ynsQa6/diTl52By/TLu+Wxriugwb8cDAAAAoHCGUAAAAAAUzhAKAAAAgMIZQgEAAABQOEMoAAAAAApnCAUAAABA4SpZlvN5mVvpdDrRbDZjamoqarXa0DXVajWmp6dz71H2PIU9yJ3hQvJerxetViva7XY0Go3ceyw2ZeivFPYgd4Yp5/pr/v01yhp5ufMU9iDXX8OU4TmYXL+Mez7bmiI6rDqXxYNeLyo5M6uJej363W7utWXPU9iD3BkuJB/0+7nXjYOU+yuFPcidYcq5/pp/f42yRl7uPIU9yPXXjqT8HEyuX8Y9n21NER3m7XgAAAAAFM4QCgAAAIDCGUIBAAAAUDhDKAAAAAAKZwgFAAAAQOEMoQAAAAAonCEUAAAAAIWrZFmWzbao0+lEs9mMqampqNVqQ9dUq9WYnp7OvUfZ8xT2IHeGC8l7vV60Wq1ot9vRaDRy77HYlKG/UtiD3BmmnOuv+ffXKGvk5c5T2INcfw1Thudgcv0y7vlsa4rosOpcFg96vajkzKwm6vXod7u515Y9T2EPcme4kHzQ7+deNw5S7q8U9iB3hinn+mv+/TXKGnm58xT2INdfO5LyczC5fhn3fLY1RXSYt+MBAAAAUDhDKAAAAAAKZwgFAAAAQOEMoQAAAAAonCEUAAAAAIUzhAIAAACgcJUsy/m8zK10Op1oNpsxNTUVtVpt6JpqtRrT09O59yh7nsIe5M5wIXmv14tWqxXtdjsajUbuPRabMvRXCnuQO8OUc/01//4aZY283HkKe5Drr2HK8BxMrl/GPZ9tTREdVp3L4kGvF5WcmdVEvR79bjf32rLnKexB7gwXkg/6/dzrxkHK/ZXCHuTOMOVcf82/v0ZZIy93nsIe5PprR1J+DibXL+Oez7amiA7zdjwAAAAACmcIBQAAAEDhDKEAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUrpJlOZ+XuZVOpxPNZjOmpqaiVqsNXVOtVmN6ejr3HmXPU9iD3BkuJO/1etFqtaLdbkej0ci9x2JThv5KYQ9yZ5hyrr/m31+jrJGXO09hD3L9NUwZnoPJ9cu457OtKaLDqnNZPOj1opIzs5qo16Pf7eZeW/Y8hT3IneFC8kG/n3vdOEi5v1LYg9wZppzrr/n31yhr5OXOU9iDXH/tSMrPweT6Zdzz2dYU0WHejgcAAABA4QyhAAAAACicIRQAAAAAhTOEAgAAAKBwhlAAAAAAFM4QCgAAAIDCVbIs5/Myt9LpdKLZbMbU1FTUarWha6rVakxPT+feo+x5CnuQO8OF5L1eL1qtVrTb7Wg0Grn3WGzK0F8p7EHuDFPO9df8+2uUNfJy5ynsQa6/hinDczC5fhn3fLY1RXRYdS6LB71eVHJmVhP1evS73dxry56nsAe5M1xIPuj3c68bByn3Vwp7kDvDlHP9Nf/+GmWNvNx5CnuQ668dSfk5mFy/jHs+25oiOszb8QAAAAAonCEUAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAwhlCAQAAAFC4SpblfF7mVjqdTjSbzZiamoparTZ0TbVajenp6dx7lD1PYQ9yZ7iQvNfrRavVina7HY1GI/cei00Z+iuFPcidYcq5/pp/f42yRl7uPIU9yPXXMGV4DibXL+Oez7amiA6rzmXxoNeLSs7MaqJej363m3tt2fMU9iB3hgvJB/1+7nXjIOX+SmEPcmeYcq6/5t9fo6yRlztPYQ9y/bUjKT8Hk+uXcc9nW1NEh3k7HgAAAACFM4QCAAAAoHCGUAAAAAAUzhAKAAAAgMIZQgEAAABQOEMoAAAAAApXybKcz8vcSqfTiWazGVNTU1Gr1YauqVarMT09nXuPsucp7EHuDBeS93q9aLVa0W63o9Fo5N5jsSlDf6WwB7kzTDnXX/Pvr1HWyMudp7AHuf4apgzPweT6Zdzz2dYU0WHVuSwe9HpRyZlZTdTr0e92c68te57CHuTOcCH5oN/PvW4cpNxfKexB7gxTzvXX/PtrlDXycucp7EGuv3Yk5edgcv0y7vlsa4roMG/HAwAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAUDhDKAAAAAAKZwgFAAAAQOEqWZbzeZlb6XQ60Ww2Y2pqKmq12tA11Wo1pqenc+9R9jyFPcid4ULyXq8XrVYr2u12NBqN3HssNmXorxT2IHeGKef6a/79NcoaebnzFPYg11/DlOE5mFy/jHs+25oiOqw6l8WDXi8qOTOriXo9+t1u7rVlz1PYg9wZLiQf9Pu5142DlPsrhT3InWHKuf6af3+NskZe7jyFPcj1146k/BxMrl/GPZ9tTREd5u14AAAAABTOEAoAAACAwhlCAQAAAFA4QygAAAAACmcIBQAAAEDhDKEAAAAAKFwly3I+L3MrnU4nms1mTE1NRa1WG7qmWq3G9PR07j3KnqewB7kzXEje6/Wi1WpFu92ORqORe4/Fpgz9lcIe5M4w5Vx/zb+/RlkjL3eewh7k+muYMjwHk+uXcc9nW1NEh1XnsnjQ60UlZ2Y1Ua9Hv9vNvbbseQp7kDvDheSDfj/3unGQcn+lsAe5M0w511/z769R1sjLnaewB7n+2pGUn4PJ9cu457OtKaLDvB0PAAAAgMIZQgEAAABQOEMoAAAAAApnCAUAAABA4QyhAAAAACicIRQAAAAAhatkWc7nZW6l0+lEs9mMqampqNVqQ9dUq9WYnp7OvUfZ8xT2IHeGC8l7vV60Wq1ot9vRaDRy77HYlKG/UtiD3BmmnOuv+ffXKGvk5c5T2INcfw1Thudgcv0y7vlsa4rosOpcFg96vajkzKwm6vXod7u515Y9T2EPcme4kHzQ7+deNw5S7q8U9iB3hinn+mv+/TXKGnm58xT2INdfO5LyczC5fhn3fLY1RXSYt+MBAAAAUDhDKAAAAAAKZwgFAAAAQOEMoQAAAAAonCEUAAAAAIUzhAIAAACgcJUsy/m8zK10Op1oNpsxNTUVtVpt6JpqtRrT09O59yh7nsIe5M5wIXmv14tWqxXtdjsajUbuPRabMvRXCnuQO8OUc/01//4aZY283HkKe5Drr2HK8BxMrl/GPZ9tTREdVp3L4kGvF5WcmdVEvR79bjf32rLnKexB7gwXkg/6/dzrxkHK/ZXCHuTOMOVcf82/v0ZZIy93nsIe5PprR1J+DibXL+Oez7amiA7zdjwAAAAACmcIBQAAAEDhDKEAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUrpJlOZ+XuZVOpxPNZjOmpqaiVqsNXVOtVmN6ejr3HmXPU9iD3BkuJO/1etFqtaLdbkej0ci9x2JThv5KYQ9yZ5hyrr/m31+jrJGXO09hD3L9NUwZnoPJ9cu457OtKaLDqnNZPOj1opIzs5qo16Pf7eZeW/Y8hT3IneFC8kG/n3vdOEi5v1LYg9wZppzrr/n31yhr5OXOU9iDXH/tSMrPweT6Zdzz2dYU0WHejgcAAABA4QyhAAAAACicIRQAAAAAhTOEAgAAAKBwhlAAAAAAFM4QCgAAAIDCVbIs5/Myt9LpdKLZbMbU1FTUarWha6rVakxPT+feo+x5CnuQO8OF5L1eL1qtVrTb7Wg0Grn3WGzK0F8p7EHuDFPO9df8+2uUNfJy5ynsQa6/hinDczC5fhn3fLY1RXRYdS6LB71eVHJmVhP1evS73dxry56nsAe5M1xIPuj3c68bByn3Vwp7kDvDlHP9Nf/+GmWNvNx5CnuQ668dSfk5mFy/jHs+25oiOszb8QAAAAAonCEUAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAwhlCAQAAAFC4SpblfF7mVjqdTjSbzZiamoparTZ0TbVajenp6dx7lD1PYQ9yZ7iQvNfrRavVina7HY1GI/cei00Z+iuFPcidYcq5/pp/f42yRl7uPIU9yPXXMGV4DibXL+Oez7amiA6rzmXxoNeLSs7MaqJej363m3tt2fMU9iB3hgvJB/1+7nXjIOX+SmEPcmeYcq6/5t9fo6yRlztPYQ9y/bUjKT8Hk+uXcc9nW1NEh3k7HgAAAACFM4QCAAAAoHCGUAAAAAAUzhAKAAAAgMIZQgEAAABQOEMoAAAAAApXybKcz8vcSqfTiWazGVNTU1Gr1YauqVarMT09nXuPsucp7EHuDBeS93q9aLVa0W63o9Fo5N5jsSlDf6WwB7kzTDnXX/Pvr1HWyMudp7AHuf4apgzPweT6Zdzz2dYU0WHVuSwe9HpRyZlZTdTr0e92c68te57CHuTOcCH5oN/PvW4cpNxfKexB7gxTzvXX/PtrlDXycucp7EGuv3Yk5edgcv0y7vlsa4roMG/HAwAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAUDhDKAAAAAAKZwgFAAAAQOEMoQAAAAAoXCXLsmy2RZ1OJ5rNZkxNTUWtVhu6plqtxvT0dO49yp6nsAe5M1xI3uv1otVqRbvdjkajkXuPxaYM/ZXCHuTOMOVcf82/v0ZZIy93nsIe5PprmDI8B5Prl3HPZ1tTRIdV57J40OtFJWdmNVGvR7/bzb227HkKe5A7w4Xkg34/97pxkHJ/pbAHuTNMOddf8++vUdbIy52nsAe5/tqRlJ+DyfXLuOezrSmiw7wdDwAAAIDCGUIBAAAAUDhDKAAAAAAKZwgFAAAAQOEMoQAAAAAonCEUAAAAAIWrZFnO52VupdPpRLPZjKmpqajVakPXVKvVmJ6ezr1H2fMU9iB3hgvJe71etFqtaLfb0Wg0cu+x2JShv1LYg9wZppzrr/n31yhr5OXOU9iDXH8NU4bnYHL9Mu75bGuK6LDqXBYPer2o5MysJur16He7udeWPU9hD3JnuJB80O/nXjcOUu6vFPYgd4Yp5/pr/v01yhp5ufMU9iDXXzuS8nMwuX4Z93y2NUV0mLfjAQAAAFA4QygAAAAACmcIBQAAAEDhDKEAAAAAKJwhFAAAAACFM4QCAAAAoHCVLMv5vMytdDqdaDabMTU1FbVabeiaarUa09PTufcoe57CHuTOcCF5r9eLVqsV7XY7Go1G7j0WmzL0Vwp7kDvDlHP9Nf/+GmWNvNx5CnuQ669hyvAcTK5fxj2fbU0RHVady+JBrxeVnJnVRL0e/W4399qy5ynsQe4MF5IP+v3c68ZByv2Vwh7kzjDlXH/Nv79GWSMvd57CHuT6a0dSfg4m1y/jns+2pogO83Y8AAAAAApnCAUAAABA4QyhAAAAACicIRQAAAAAhTOEAgAAAKBwhlAAAAAAFK6SZTmfl7mVTqcTzWYzpqamolarDV1TrVZjeno69x5lz1PYg9wZLiTv9XrRarWi3W5Ho9HIvcdiU4b+SmEPcmeYcq6/5t9fo6yRlztPYQ9y/TVMGZ6DyfXLuOezrSmiw6pzWTzo9aKSM7OaqNej3+3mXlv2PIU9yJ3hQvJBv5973ThIub9S2IPcGaac66/599coa+TlzlPYg1x/7UjKz8Hk+mXc89nWFNFh3o4HAAAAQOEMoQAAAAAonCEUAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAwlWyLOfzMrfS6XSi2WzG1NRU1Gq1oWuq1WpMT0/n3qPseQp7kDvDheS9Xi9arVa02+1oNBq591hsytBfKexB7gxTzvXX/PtrlDXycucp7EGuv4Ypw3MwuX4Z93y2NUV0WHUuiwe9XlRyZlYT9Xr0u93ca8uep7AHuTNcSD7o93OvGwcp91cKe5A7w5Rz/TX//hpljbzceQp7kOuvHUn5OZhcv4x7PtuaIjrM2/EAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUzhAKAAAAgMIZQgEAAABQuEqW5Xxe5lY6nU40m82YmpqKWq02dE21Wo3p6ence5Q9T2EPcme4kLzX60Wr1Yp2ux2NRiP3HotNGforhT3InWHKuf6af3+NskZe7jyFPcj11zBleA4m1y/jns+2pogOq85l8aDXi0rOzGqiXo9+t5t7bdnzFPYgd4YLyQf9fu514yDl/kphD3JnmHKuv+bfX6OskZc7T2EPcv21Iyk/B5Prl3HPZ1tTRId5Ox4AAAAAhTOEAgAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAULiRPh0v+3+fZtAfDPLXVCo7/M3pZc9T2IPcGS4kf+jnN8v5dJLFqgz9lcIe5M4w5Vx/zb+/RlkjL3eewh7k+muYMjwHk+uXcc9nW1NEh1WyEe72n//5n7FixYqd9qDA7nP77bfHwQcfvLu3scvoL1g89BdQVuPWXxE6DBaTndlhIw2htmzZEnfeeWfU6/WoVCo75YGBXSvLsuh2u3HggQfGkiXj805c/QXlp7/0F5TVuPZXhA6DxaCIDhtpCAUAAAAACzFe43gAAAAAdgtDKAAAAAAKZwgFAAAAQOEMoQAAAAAonCEUAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAwhlCAQAAAFA4QygAAAAACmcIBQAAAEDhDKEAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUzhAKAAAAgMIZQgEAAABQOEMoAAAAAApnCAUAAABA4QyhAAAAACicIRQAi86pp54aK1eu3N3bAAAAtmIItQtVKpWR/rnpppsW/FiDwSDOP//8ke910003RaVSiWuuuWbBj72zXHjhhfHRj350d28DknPFFVfssEO++MUv7u4tjuTf//3f4/zzz49Nmzbt1n1kWRYf+tCH4ld/9Vdjn332iWXLlsXjH//4+OM//uPo9/vzvu+u/v4uvfTSuOKKK3bJYwEAwHxUd/cGxsmHPvShbf78wQ9+MD796U9v9/XVq1cv+LEGg0FccMEFERHxtKc9bcH32x0uvPDCOOmkk+LEE0/c3VuBJP3xH/9xrFq1aruvP+Yxj9kNu5m7f//3f48LLrggnva0p+22Vy09+OCD8aIXvSg+8pGPxFOf+tQ4//zzY9myZfG5z30uLrjggrj66qvjhhtuiOXLl8/53rv6+7v00ktj3333jVNPPbXwxwIAgPkwhNqFfu/3fm+bP3/xi1+MT3/609t9HWAUz3zmM+PJT37y7t5Gqf3Zn/1ZfOQjH4nXvva18fa3v33m6y9/+cvj+c9/fpx44olx6qmnxsc//vHduEsAAFgcvB0vMVu2bImLL744Hve4x8Vee+0Vy5cvj9///d+Pn/zkJ9us+/KXvxzr1q2LfffdN/bee+9YtWpVvPSlL42IiE2bNsV+++0XEREXXHDBzFt0zj///Dnt5fzzz49KpRK33nprnHrqqbHPPvtEs9mM0047LQaDwTZrK5VKnHPOOfE3f/M38Qu/8Aux1157xdq1a+Ozn/3sNuvyfk/LQ4+19f36/X5ceeWVM/v3v+7D3Jx33nmxZMmSuPHGG7f5+stf/vLYc88949/+7d8i4n/ejnvVVVfFH/7hH8ajHvWomJiYiN/6rd+K22+/fbv7/t//+3/jN37jN6LZbMayZcui1WrFv/zLv2y37o477ojTTz89DjzwwFi6dGmsWrUqzjrrrHjggQfiiiuuiOc973kREfFrv/ZrQ9+O/PGPfzye+tSnxsTERNTr9XjWs54V3/rWt7Z7nI9+9KOxZs2a2GuvvWLNmjVx7bXXjvTv57/+67/i7W9/exx++OExOTm5XX7CCSfEKaecEp/4xCe2eYtjXp+uXLlypqdm+/5WrlwZz372s+NTn/pUHHXUUbHXXnvFkUceGX//93+/zT1/vhsf8tBbMh96q9/KlSvjW9/6VkxNTc08VllfBQsAwOLllVCJ+f3f//244oor4rTTTotXvepVcdttt8U73/nO+OpXvxr/8i//Eg972MPiRz/6UTzjGc+I/fbbL974xjfGPvvsE5s2bZr5j5f99tsvLrvssjjrrLPiOc95Tjz3uc+NiIgnPOEJ89rT85///Fi1alVMTk7GV77ylXjve98b+++/f7ztbW/bZt3U1FRcddVV8apXvSqWLl0al156afzGb/xGfOlLX4o1a9bM6TE/9KEPxRlnnBFHH310vPzlL4+IiMMOO2xe+4fFqt1ux7333rvN1yqVSjzykY+MiIg/+qM/iuuuuy5OP/30+MY3vhH1ej0++clPxnve855461vfGk984hO3ufZP/uRPolKpxBve8Ib40Y9+FBdffHEcf/zx8bWvfS323nvviIj453/+53jmM58Za9eunRlyfeADH4hf//Vfj8997nNx9NFHR0TEnXfeGUcffXTcf//98fKXvzyOOOKIuOOOO+Kaa66JwWAQv/qrvxqvetWr4q/+6q/iD//wD2fehvzQ//3Qhz4Up5xySqxbty7e9ra3xWAwiMsuuyyOO+64+OpXvzozzP7Upz4Vv/M7vxNHHnlkTE5Oxo9//OM47bTT4uCDD57139/nP//5+MlPfhKvfvWro1od/v8OTz755PjABz4Q//RP/xS//Mu/POLJxKzfX0TEd7/73XjBC14QZ555ZpxyyinxgQ98IJ73vOfFJz7xiXj6058+8mNFRFx88cXxyle+Mmq1WrzpTW+KiJjXWwgBAKBQGbvN2WefnW19BJ/73OeyiMj+5m/+Zpt1n/jEJ7b5+rXXXptFRPav//qvufe+5557sojIzjvvvJH28pnPfCaLiOzqq6+e+dp5552XRUT20pe+dJu1z3nOc7JHPvKR23wtIrKIyL785S/PfO373/9+ttdee2XPec5zZr52yimnZIcccsh2j//QY21tYmIiO+WUU0baP4yTD3zgAzM/cz//z9KlS7dZ+41vfCPbc889szPOOCP7yU9+kh100EHZk5/85Oy///u/Z9Y89PN/0EEHZZ1OZ+brH/nIR7KIyN7xjndkWZZlW7ZsyR772Mdm69aty7Zs2TKzbjAYZKtWrcqe/vSnz3zt5JNPzpYsWTK0px669uqrr84iIvvMZz6zTd7tdrN99tkne9nLXrbN1++6666s2Wxu8/WjjjoqO+CAA7L7779/5muf+tSnsogY2jVbu/jii7OIyK699trcNffdd18WEdlzn/vcma/ldeshhxyyTWflfX8PrY2I7P/8n/8z87V2u50dcMAB2S/+4i/OfG1YN2bZ//wduO2222a+9rjHPS5rtVq53wsAAOxu3o6XkKuvvjqazWY8/elPj3vvvXfmn7Vr10atVovPfOYzERGxzz77RETEP/3TP8V///d/F76vM888c5s/P/WpT40f//jH0el0tvn6scceG2vXrp3586Mf/ej47d/+7fjkJz8ZDz74YOH7hHHzrne9Kz796U9v88/P/+6iNWvWxAUXXBDvfe97Y926dXHvvffGlVdeOfSVPyeffHLU6/WZP5900klxwAEHxMc+9rGIiPja174W3/3ud+NFL3pR/PjHP57pqH6/H//rf/2v+OxnPxtbtmyJLVu2xEc/+tE44YQThv7OqmFvL9vapz/96bj//vvjhS984TZduMcee8Qxxxwz04U//OEP42tf+1qccsop0Ww2Z65/+tOfHkceeeSs//663W5ExDbf8897KPv5vtsZDjzwwHjOc54z8+dGoxEnn3xyfPWrX4277rprpz8eAADsbt6Ol5Dvfve70W63Y//99x+a/+hHP4qIiFarFb/zO78TF1xwQfzlX/5lPO1pT4sTTzwxXvSiF8XSpUt3+r4e/ehHb/Pnhz/84RER8ZOf/CQajcbM1x/72Mdud+3hhx8eg8Eg7rnnnnjUox610/cG4+zoo48e6ReTv+51r4u/+7u/iy996Utx4YUX5g5ofv5nuFKpxGMe85iZ3zv03e9+NyIiTjnllNzHarfb8cADD0Sn05nz23Af8tDj/Pqv//rQ/KHe+f73vz903xERv/ALvxBf+cpXdvg4Dw2YHhpGDTPKoGq+HvOYx2w3kDv88MMj4me/209nAgCw2BhCJWTLli2x//77x9/8zd8MzR/6ZeOVSiWuueaa+OIXvxjXXXddfPKTn4yXvvSl8Rd/8RfxxS9+MWq12k7d1x577DH061mWzfleea+A8EopKM73vve9mcHON77xjXnfZ8uWLRER8fa3vz2OOuqooWtqtVrcd999836MrR/nQx/60NBBTN7vb5qrh34/09e//vU48cQTh675+te/HhEx0iuriugxnQkAwGJiCJWQww47LG644YZ4ylOeMvNLgHfkl3/5l+OXf/mX40/+5E/iwx/+cLz4xS+Ov/u7v4szzjhj1re7FOGh/8jd2ne+851YtmzZzADt4Q9/eNx///3brXvoFQ1b2x3fAyw2W7ZsiVNPPTUajUa85jWviQsvvDBOOumkmQ8s2NrP/wxnWRa33nrrzIcaPPThAI1GI44//vjcx9xvv/2i0WjEN7/5zR3uLe9n/KHH2X///Xf4OIcccsjQfUdEfPvb397hY0dEHHfccbHPPvvEhz/84XjTm940dOD+wQ9+MCIinv3sZ898bViPPfDAA/HDH/5wm6/N1mG33nprZFm2zbrvfOc7EREzv3j9oVee3n///TNvxY7QmQAAlJPfCZWQ5z//+fHggw/GW9/61u2y6enpmf/o+clPfrLdq5AeelXC5s2bIyJi2bJlERFDBz5Fufnmm7d5+8vtt98e//AP/xDPeMYzZv7j7rDDDot2uz3z6oKIn/1el2EfqT4xMbFL9w+L0UUXXRRf+MIX4t3vfne89a1vjV/5lV+Js846a7tP1Yv42cBl67emXXPNNfHDH/4wnvnMZ0ZExNq1a+Owww6LP//zP49er7fd9ffcc09ERCxZsiROPPHEuO666+LLX/7yduse6q+JiYmI2L6n1q1bF41GIy688MKhv/fuocc54IAD4qijjoorr7wy2u32TP7pT386/v3f/32H/14iftaTr33ta+Pb3/72zCfKbe3666+PK664ItatW7fNJ+Mddthh8dnPfnabte9+97u3e3VS3vf3kDvvvHOb7ut0OvHBD34wjjrqqJlXgD00kNv68fr9flx55ZXb3U9nAgCQOq+ESkir1Yrf//3fj8nJyfja174Wz3jGM+JhD3tYfPe7342rr7463vGOd8RJJ50UV155ZVx66aXxnOc8Jw477LDodrvxnve8JxqNRvzmb/5mRETsvffeceSRR8ZVV10Vhx9+eDziEY+INWvWzPt3tIxizZo1sW7dunjVq14VS5cujUsvvTQiIi644IKZNb/7u78bb3jDG+I5z3lOvOpVr5r52PXDDz98u9/fsnbt2rjhhhvioosuigMPPDBWrVoVxxxzTGH7h7L5+Mc/Hhs3btzu67/yK78Shx56aGzYsCHe/OY3x6mnnhonnHBCRERcccUVcdRRR8UrXvGK+MhHPrLNdY94xCPiuOOOi9NOOy3uvvvuuPjii+Mxj3lMvOxlL4uInw2X3vve98Yzn/nMeNzjHhennXZaHHTQQXHHHXfEZz7zmWg0GnHddddFRMSFF14Yn/rUp6LVasXLX/7yWL16dfzwhz+Mq6++Oj7/+c/HPvvsE0cddVTsscce8ba3vS3a7XYsXbo0fv3Xfz3233//uOyyy+IlL3lJPOlJT4rf/d3fjf322y9+8IMfxPXXXx9PecpT4p3vfGdERExOTsaznvWsOO644+KlL31p3HfffXHJJZfE4x73uKGDsp/3xje+Mb761a/G2972trj55pvjd37nd2LvvfeOz3/+8/HXf/3XsXr16u0GPmeccUaceeaZ8Tu/8zvx9Kc/Pf7t3/4tPvnJT8a+++67zbodfX8RP/v9T6effnr867/+ayxfvjze//73x9133x0f+MAHZu7xjGc8Ix796EfH6aefHq973etijz32iPe///0z/z62tnbt2rjsssvif//v/x2PecxjYv/998/9vVoAALBb7NbP5htzZ5999tCP3n73u9+drV27Ntt7772zer2ePf7xj89e//rXZ3feeWeWZVn2la98JXvhC1+YPfrRj86WLl2a7b///tmzn/3s7Mtf/vI29/nCF76QrV27Nttzzz1zP1L8IQ99RPvVV18987WHPhr8nnvu2WbtsI8Gj4js7LPPzv76r/86e+xjH5stXbo0+8Vf/MWhH03+qU99KluzZk225557Zr/wC7+Q/fVf//XQjyHfuHFj9qu/+qvZ3nvvnUXENh99DuPsoZ/BvH8+8IEPZNPT09kv/dIvZQcffHB2//33b3P9O97xjiwisquuuirLsv/5+f/bv/3bbP369dn++++f7b333tmznvWs7Pvf//52j//Vr341e+5zn5s98pGPzJYuXZodcsgh2fOf//zsxhtv3Gbd97///ezkk0/O9ttvv2zp0qXZoYcemp199tnZ5s2bZ9a85z3vyQ499NBsjz32yCJim874zGc+k61bty5rNpvZXnvtlR122GHZqaeeul3X/Z//83+y1atXZ0uXLs2OPPLI7O///u+zU045JTvkkENG+vf54IMPZh/4wAeypzzlKVmj0cj22muv7HGPe1x2wQUXZL1eb+j6N7zhDdm+++6bLVu2LFu3bl126623Zocccsh2PZX3/R1yyCHZs571rOyTn/xk9oQnPCFbunRpdsQRR2zTwQ+55ZZbsmOOOSbbc889s0c/+tHZRRddNLSH77rrruxZz3pWVq/Xs4jIWq3WSN8/o5mamsqe/exnZwcccEAWEdm111476zWf+cxnsl/8xV/M9txzz+ywww7LPvCBDxS+T4Cfp7+AlFSybB6/XRp+TqVSibPPPnvm1QlAedx0003xa7/2a3H11VfHSSedtLu3MxZWrlwZa9asiX/6p3/a3VthRB//+MfjX/7lX2Lt2rXx3Oc+N6699trcX2gfEXHbbbfFmjVr4swzz4wzzjgjbrzxxnjNa14T119/faxbt27XbRwYe/oLSIm34wEAzOKZz3zmzO9HG8Xll18eq1atir/4i7+IiJ99GuPnP//5+Mu//Ev/EQfsUvoLSIkhFADATnbzzTdv9+mO69ati9e85jW512zevHnmA0Yifvbplvfdd1888pGP9OmHUFJZlkW3240DDzwwliwpx2dCzae/InQYLEZFdJghFADATnbXXXfF8uXLt/na8uXLo9PpxH/913/F3nvvvd01k5OT23yYB7B43H777XHwwQfv7m2MZD79FaHDYDHbmR1mCMVO4VeLQXk97WlP8zO8i23atGl3b4EErV+/Ps4999yZP7fb7Xj0ox8dt99+ezQajd24M2C+Op1OrFixIur1+u7eSuF0GCw+RXSYIRQAwE72qEc9Ku6+++5tvnb33XdHo9HIfRXB0qVLY+nSpdt9vdFo+A84KLkyvR1tPv0VocNgMduZHVaONyYDAJTIscceGzfeeOM2X/v0pz8dxx577G7aEcBo9BdQpJFeCbVly5a48847o16vl2qKD/yPMv5izJ1Bf0H5pdBfvV4vbr311pk/33bbbfG1r30tHvGIR8SjH/3oWL9+fdxxxx3xwQ9+MCIizjzzzHjnO98Zr3/96+OlL31p/PM//3N85CMfieuvv3637B8YX/oLSMlIQ6g777wzVqxYUfRegF2gTL8Yc2fQX7B47M7++vKXvxy/9mu/NvPnh37vySmnnBJXXHFF/PCHP4wf/OAHM/mqVavi+uuvjz/4gz+Id7zjHXHwwQfHe9/7Xh9vDuxy+gtISSUb4bfRttvt2GeffeKaq6+OiWXLhq5ZVqvFoNfLvUfZ8xT2IHeGC8n7g0Gc9Lznxf333x/NZjP3HotNGforhT3InWHK+bj2V6fTiWazGe122+9TgZIa55/jcf7eYbEo4ud4pFdCPfQWlolly2JiYmLomolaLSo7mGeVPU9hD3JnuDP+HYzbW9LK0F8p7EHuDFPPI8avvwAAFpvx+cUwAAAAAOw2hlAAAAAAFM4QCgAAAIDCGUIBAAAAUDhDKAAAAAAKN9Kn4z1kWa0WE7Xa8BtVqzFRr+c/UMnzFPYgd4YLybMx/1SplPsrhT3InWHK+bj3FwDAYjGnIdSg18v9+OSJej363W7utWXPU9iD3BkuJB/0+7nXjYOU+yuFPcidYcr5uPcXAMBi4e14AAAAABTOEAoAAACAwhlCAQAAAFA4QygAAAAACmcIBQAAAEDhDKEAAAAAKFx1LouX1WoxUasNv1G1GhP1ev4DlTxPYQ9yZ7iQPKtUcq8bByn3Vwp7kDvDlPNx7y8AgMViTkOoQa8XlSwbmk3U69HvdnOvLXuewh7kznAh+aDfz71uHKTcXynsQe4MU87Hvb8AABYLb8cDAAAAoHCGUAAAAAAUzhAKAAAAgMIZQgEAAABQOEMoAAAAAApnCAUAAABA4apzWbysVouJWm34jarVmKjX8x+o5HkKe5A7w4XkWaWSe904SLm/UtiD3BmmnI97fwEALBZzGkINer2oZNnQbKJej363m3tt2fMU9iB3hgvJB/1+7nXjIOX+SmEPcmeYcj7u/QUAsFh4Ox4AAAAAhTOEAgAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAUDhDKAAAAAAKV53L4mW1WkzUasNvVK3GRL2e/0Alz1PYg9wZLiTPKpXc68ZByv2Vwh7kzjDlfNz7CwBgsZjTEGrQ60Uly4ZmE/V69Lvd3GvLnqewB7kzXEg+6PdzrxsHKfdXCnuQO8OU83HvLwCAxcLb8QAAAAAonCEUAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAwhlCAQAAAFC46lwWL6vVYqJWG36jajUm6vX8Byp5nsIe5M5wIXlWqeReNw5S7q8U9iB3hinn495fAACLxZyGUINeLypZNjSbqNej3+3mXlv2PIU9yJ3hQvJBv5973ThIub9S2IPcGaacp9Rf73rXu+Ltb3973HXXXfHEJz4xLrnkkjj66KNz11988cVx2WWXxQ9+8IPYd99946STTorJycnYa6+9duGuAfQXkAZvxwMAGMFVV10V5557bpx33nnxla98JZ74xCfGunXr4kc/+tHQ9R/+8IfjjW98Y5x33nmxYcOGeN/73hdXXXVV/OEf/uEu3jkw7vQXkApDKACAEVx00UXxspe9LE477bQ48sgj4/LLL49ly5bF+9///qHrv/CFL8RTnvKUeNGLXhQrV66MZzzjGfHCF74wvvSlL+3inQPjTn8BqTCEAgCYxQMPPBC33HJLHH/88TNfW7JkSRx//PFx8803D73mV37lV+KWW26Z+Y+2733ve/Gxj30sfvM3f3Po+s2bN0en09nmH4CF2hX9FaHDgNHM6XdCAQCMo3vvvTcefPDBWL58+TZfX758eWzcuHHoNS960Yvi3nvvjeOOOy6yLIvp6ek488wzc9/OMjk5GRdccMFO3zsw3nZFf0XoMGA0XgkFAFCAm266KS688MK49NJL4ytf+Ur8/d//fVx//fXx1re+dej69evXR7vdnvnn9ttv38U7BviZufZXhA4DRuOVUAAAs9h3331jjz32iLvvvnubr999993xqEc9aug1b37zm+MlL3lJnHHGGRER8fjHPz76/X68/OUvjze96U2xZMm2/1vg0qVLY+nSpcV8A8DY2hX9FaHDgNHMaQi1rFaLiVpt+I2q1Zio1/MfqOR5CnuQO8OF5FmlknvdOEi5v1LYg9wZppyn0F977rlnrF27Nm688cY48cQTIyJiy5YtceONN8Y555wz9JrBYLDdf6jtscceERGRZVmh+wV4iP4CUjKnIdSg14tKTulM1OvR73Zzry17nsIe5M5wIfmg38+9bhyk3F8p7EHuDFPOU+mvc889N0455ZR48pOfHEcffXRcfPHF0e/347TTTouIiJNPPjkOOuigmJycjIiIE044IS666KL4xV/8xTjmmGPi1ltvjTe/+c1xwgknzPzHHMCuoL+AVHg7HgDACF7wghfEPffcE295y1virrvuiqOOOio+8YlPzPyy3x/84AfbvHLgj/7oj6JSqcQf/dEfxR133BH77bdfnHDCCfEnf/Inu+tbAMaU/gJSYQgFADCic845J/ftKzfddNM2f65Wq3HeeefFeeedtwt2BrBj+gtIgU/HAwAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAULg5/WLyZbVaTNRqw29UrcZEvZ7/QCXPU9iD3BkuJM8qldzrxkHK/ZXCHuTOMOV83PsLAGCxmNMQatDrRSXLhmYT9Xr0u93ca8uep7AHuTNcSD7o93OvGwcp91cKe5A7w5Tzce8vAIDFwtvxAAAAACicIRQAAAAAhTOEAgAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAULjqXBYvq9ViolYbfqNqNSbq9fwHKnmewh7kznAheVap5F43DlLurxT2IHeGKefj3l8AAIvFnIZQg14vKlk2NJuo16Pf7eZeW/Y8hT3IneFC8kG/n3vdOEi5v1LYg9wZppyPe38BACwW3o4HAAAAQOEMoQAAAAAonCEUAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAwlXnsnhZrRYTtdrwG1WrMVGv5z9QyfMU9iB3hgvJs0ol97pxkHJ/pbAHuTNMOR/3/gIAWCzmNIQa9HpRybKh2US9Hv1uN/fasucp7EHuDBeSD/r93OvGQcr9lcIe5M4w5Xzc+wsAYLHwdjwAAAAACmcIBQAAAEDhDKEAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUrjqXxctqtZio1YbfqFqNiXo9/4FKnqewB7kzXEieVSq5142DlPsrhT3InWHK+bj3FwDAYjGnIdSg14tKlg3NJur16He7udeWPU9hD3JnuJB80O/nXjcOUu6vFPYgd4Yp5+PeXwAAi4W34wEAAABQOEMoAAAAAApnCAUAAABA4QyhAAAAACicIRQAAAAAhTOEAgAAAKBw1bksXlarxUStNvxG1WpM1Ov5D1TyfFc8Rq/Xiw0bN+bmq1evXvR5reRnmHKeVSq5142DlPsrhT3or4XlERHHHnts0mdU5nzc+wsAYLGY0xBq0OtFJcuGZhP1evS73dxry57visfYsHFjrF+/PjefnJxc9PmRRxyRm5fhDFPOB/1+7nXjIOX+SmEP+mtheUTEDTfckPQZlTkf9/4CAFgsvB0PAAAAgMIZQgEAjOhd73pXrFy5Mvbaa6845phj4ktf+tIO199///1x9tlnxwEHHBBLly6Nww8/PD72sY/tot0C/A/9BaRgTm/HAwAYV1dddVWce+65cfnll8cxxxwTF198caxbty6+/e1vx/7777/d+gceeCCe/vSnx/777x/XXHNNHHTQQfH9738/9tlnn12/eWCs6S8gFYZQAAAjuOiii+JlL3tZnHbaaRERcfnll8f1118f73//++ONb3zjduvf//73x3333Rdf+MIX4mEPe1hERKxcuXJXbhkgIvQXkA5vxwMAmMUDDzwQt9xySxx//PEzX1uyZEkcf/zxcfPNNw+95h//8R/j2GOPjbPPPjuWL18ea9asiQsvvDAefPDBXbVtAP0FJMUroQAAZnHvvffGgw8+GMuXL9/m68uXL4+NGzcOveZ73/te/PM//3O8+MUvjo997GNx6623xite8Yr47//+7zjvvPO2W7958+bYvHnzzJ87nc7O/SaAsbQr+itChwGj8UooAIACbNmyJfbff/9497vfHWvXro0XvOAF8aY3vSkuv/zyoesnJyej2WzO/LNixYpdvGOAn5lrf0XoMGA0lSzLstkWdTqdaDabMTU1FbVabeiaarUa09PTufcoe74rHqPX68WGDRty89WrVy/6PO/vV0Q5zjDlvNfrRavVina7HY1GI/cei00Z+iuFPeivheUREccee2zSZ1TmPIX+euCBB2LZsmVxzTXXxIknnjjz9VNOOSXuv//++Id/+Iftrmm1WvGwhz0sbrjhhpmvffzjH4/f/M3fjM2bN8eee+65zfphryJYsWLF2PU2LCYPPQ9Z7P0VocNgMSqiw+b0drxBrxeVnJnVRL0e/W4399qy57viMTZs3Bjr16/PzScnJxd9fuQRR+TmZTjDlPNBv5973ThIub9S2IP+WlgeEXHDDTckfUZlzlPorz333DPWrl0bN95448x/xG3ZsiVuvPHGOOecc4Ze85SnPCU+/OEPx5YtW2LJkp+9+Pw73/lOHHDAAUP/A27p0qWxdOnSwr4HYDztiv6K0GHAaLwdDwBgBOeee2685z3viSuvvDI2bNgQZ511VvT7/ZlPmzr55JO3GVaeddZZcd9998WrX/3q+M53vhPXX399XHjhhXH22Wfvrm8BGFP6C0iFX0wOADCCF7zgBXHPPffEW97ylrjrrrviqKOOik984hMzv+z3Bz/4wcwrBiIiVqxYEZ/85CfjD/7gD+IJT3hCHHTQQfHqV7863vCGN+yubwEYU/oLSIUhFADAiM4555zct6/cdNNN233t2GOPjS9+8YsF7wpgdvoLSIG34wEAAABQOEMoAAAAAArn7Xhj5Gm1NfHN1X+Vm3drq3Z7/qPY8cfYA+Mp9f6KiLhrhykAAFDJspzPLN9Kp9OJZrMZU1NTUavVhq6pVqsxPZ0/QCh7viseo9frxYYNG3Lz1atXLyg/bs3RUd00yM2nVy7b7fnmvfP/OpbhDFPOe71etFqtaLfb0Wg0cu+x2JShv1LYg/5aWB4Rsfnx9aTPqMz5uPfXuH3fsJiM88/xOH/vsFgU8XM8p1dCDXq9qOTMrCbq9eh3u7nXlj3fFY+xYePGbT4a9edNTk4uKL/5kuuifultuXn3Fat2e37fwfn/gVKGM0w5H/T7udeNg5T7K4U96K+F5RER/UuOTPqMypyPe38BACwWficUAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAwhlCAQAAAFC4OX06HovbnpX/iH2XXJybb668ZsG5uSdQhJt634z1G3bw6Xu9yQXnJ8TEgvYIAADjrpJlOZ9ZvpVOpxPNZjOmpqaiVqsNXVOtVmN6ejr3HmXPd8Vj9Hq92LBhQ26+evXqBeXHrTk6qpsGufmWVVnstel7uflPVx664HzJbZXcfHrlsti8d/5fxzKcYcp5r9eLVqsV7XY7Go1G7j0WmzL0Vwp7KHt/3b3iwUIff/Xq1bH89j1y84iIzY+vJ31GZc7Hvb/G7fuGxWScf47H+XuHxaKIn+M5vRJq0OtFJWdmNVGvR7/bzb227PmueIwNGzfG+vU7+F/iJycXlN98yXVRv/S23Hzz2VvioMsuzs3vOOs1C86XXpr/SqjuK1bFfQfn/wdKGc4w5XzQ7+deNw5S7q8U9lD2/rrp5H6hjz85ORmP+eCOXwnVv+TIpM+ozPm49xcAwGLhvVEAAAAAFM4QCgAAAIDCGUIBAAAAUDhDKAAAAAAKZwgFAAAAQOHm9Ol4cO+Wc3OzB7JVu3AnAHPzzdV/lZt1a/oLAACKVsmynM8s30qn04lmsxlTU1NRq9WGrqlWqzE9PZ17j7Lnu+Ixer1ebNiwITdfvXr1gvLj1hwd1U2D3HzLqiz22vS93PynKw+NJbdVcvPplcti2fe/saDrN++d/9exDGeYct7r9aLVakW73Y5Go5F7j8WmDP2Vwh7K3l93r3hw1vsvv32P3Hx65bL4/De/NO/rIyI2P76e9BmVOR/3/hq37xsWk3H+OR7n7x0WiyJ+juf0SqhBrxeVnJnVRL0e/W4399qy57viMTZs3Bjr16/PzScnJxeU33zJdVG/9LbcfPPZW+Kgyy7Oze846zWx9NL8d3B2X7EqDrp8Ydffd3D+f6CU4QxTzgf9fu514yDl/kphD2Xvr5tO7s96/8d8cCI3775i1YKuj4joX3Jk0mdU5nzc+wsAYLHwO6EAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUzhAKAAAAgMIZQgEAAABQuEqW5Xxm+VY6nU40m82YmpqKWq02dE21Wo3p6ence5Q93xWP0ev1YsOGDbn56tWrF5Qft+boqG4a5OZbVmWx16bv5eY/XXnogvMlt1Vy8+mVy2Lz3vl/HctwhinnvV4vWq1WtNvtaDQaufdYbMrQXynsoez9dfeKBwt9/NWrV8fy2/fIzSMiNj++nvQZlTkf9/4at+8bFpNx/jke5+8dFosifo6rc1k86PWikjOzmqjXo9/t5l5b9nxXPMaGjRtj/fr1ufnk5OSC8psvuS7ql96Wm28+e0scdNnFufkdZ71mwfnSS/NffNd9xaq47+D8/0ApwxmmnA/6/dzrxkHK/ZXCHsreXzed3C/08ScnJ+MxH5zIzSMi+pccmfQZlTkf9/4CAFgsvB0PAAAAgMIZQgEAAABQOEMoAAAAAApnCAUAAABA4QyhAAAAACicIRQAAAAAhatkWc5nlm+l0+lEs9mMqampqNVqQ9dUq9WYnp7OvUfZ813xGL1eLzZs2JCbr169ekH5cWuOjuqmQW4+vXLZbs83753/17EMZ5hy3uv1otVqRbvdjkajkXuPxaYM/ZXCHvTXwvKIiM2Pryd9RmXOx72/xu37hsVknH+Ox/l7h8WiiJ/j6lwWD3q9qOTMrCbq9eh3u7nXlj3fFY+xYePGWL9+fW4+OTm5oPzmS66L+qW35ebdV6za7fl9B+f/B0oZzjDlfNDv5143DlLurxT2oL8WlkdE9C85MukzKnM+7v0FALBYeDseAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAYETvete7YuXKlbHXXnvFMcccE1/60pdGuu7v/u7volKpxIknnljsBgFy6C8gBYZQAAAjuOqqq+Lcc8+N8847L77yla/EE5/4xFi3bl386Ec/2uF1mzZtite+9rXx1Kc+dRftFGBb+gtIhSEUAMAILrroonjZy14Wp512Whx55JFx+eWXx7Jly+L9739/7jUPPvhgvPjFL44LLrggDj300F24W4D/ob+AVFTnsnhZrRYTtdrwG1WrMVGv5z9QyfNd8RirV6+OycnJwvK7VzwYN52c/zHXqxPIa7Vyn2HKeVap5F43DlLurxT2oL8WlkdEHJv4GZU5T6G/Hnjggbjlllti/fr1M19bsmRJHH/88XHzzTfnXvfHf/zHsf/++8fpp58en/vc53b4GJs3b47NmzfP/LnT6Sx848DY2xX9FaHDgNHMaQg16PWikmVDs4l6Pfrdbu61Zc93xWNs2Lhxm//n8PMmJycXfX7kEUfk5mU4w5TzQX/H/wG92KXcXynsQX8tLI+IuOGGG5I+ozLnKfTXvffeGw8++GAsX758m68vX748Nm7cOPSaz3/+8/G+970vvva1r430GJOTk3HBBRcsdKsA29gV/RWhw4DReDseAMBO1u124yUveUm85z3viX333Xeka9avXx/tdnvmn9tvv73gXQJsbz79FaHDgNHM6ZVQAADjaN9994099tgj7r777m2+fvfdd8ejHvWo7db/x3/8R2zatClOOOGEma9t2bIlIn721sNvf/vbcdhhh21zzdKlS2Pp0qUF7B4YZ7uivyJ0GDAar4QCAJjFnnvuGWvXro0bb7xx5mtbtmyJG2+8MY499tjt1h9xxBHxjW98I772ta/N/PNbv/Vb8Wu/9mvxta99LVasWLErtw+MMf0FpMQroQAARnDuuefGKaecEk9+8pPj6KOPjosvvjj6/X6cdtppERFx8sknx0EHHRSTk5Ox1157xZo1a7a5fp999omI2O7rAEXTX0AqDKEAAEbwghe8IO655554y1veEnfddVccddRR8YlPfGLml/3+4Ac/iCVLvMgcSI/+AlJhCAUAMKJzzjknzjnnnKHZTTfdtMNrr7jiip2/IYAR6S8gBZUsy/nM8q10Op1oNpsxNTUVtVpt6JpqtRrT09O59yh7nsIe5M5wIXmv14tWqxXtdjsajUbuPRabMvRXCnuQO8OU83Hvr3H7vmExGeef43H+3mGxKOLneE6vhBr0elHJmVlN1OvR73Zzry17nsIe5M5wIfmg38+9bhyk3F8p7EHuDFPOx72/AAAWC2/8BQAAAKBwhlAAAAAAFM4QCgAAAIDCGUIBAAAAUDhDKAAAAAAKZwgFAAAAQOGqc1m8rFaLiVpt+I2q1Zio1/MfqOR5CnuQO8OF5FmlknvdOEi5v1LYg9wZppyPe38BACwWcxpCDXq9qGTZ0GyiXo9+t5t7bdnzFPYgd4YLyQf9fu514yDl/kphD3JnmHI+7v0FALBYeDseAAAAAIUzhAIAAACgcIZQAAAAABTOEAoAAACAwhlCAQAAAFA4QygAAAAACledy+JltVpM1GrDb1StxkS9nv9AJc9T2IPcGS4kzyqV3OvGQcr9lcIe5M4w5Xzc+wsAYLGY0xBq0OtFJcuGZhP1evS73dxry56nsAe5M1xIPuj3c68bByn3Vwp7kDvDlPNx7y8AgMXC2/EAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUzhAKAAAAgMIZQgEAAABQuOpcFi+r1WKiVht+o2o1Jur1/AcqeZ7CHuTOcCF5VqnkXjcOUu6vFPYgd4Yp5+PeXwAAi8WchlCDXi8qWTY0m6jXo9/t5l5b9jyFPcid4ULyQb+fe904SLm/UtiD3BmmnI97fwEALBbejgcAAABA4QyhAAAAACicIRQAAAAAhTOEAgAAAKBwhlAAAAAAFM4QCgAAAIDCVeeyeFmtFhO12vAbVasxUa/nP1DJ8xT2IHeGC8mzSiX3unGQcn+lsAe5M0w5H/f+AgBYLOY0hBr0elHJsqHZRL0e/W4399qy5ynsQe4MF5IP+v3c68ZByv2Vwh7kzjDlfNz7CwBgsfB2PAAAAAAKZwgFAAAAQOEMoQAAAAAonCEUAAAAAIUzhAIAAACgcIZQAAAjete73hUrV66MvfbaK4455pj40pe+lLv2Pe95Tzz1qU+Nhz/84fHwhz88jj/++B2uByiS/gJSUJ3L4mW1WkzUasNvVK3GRL2e/0Alz1PYg9wZLiTPKpXc68ZByv2Vwh7kzjDlPJX+uuqqq+Lcc8+Nyy+/PI455pi4+OKLY926dfHtb3879t9//+3W33TTTfHCF74wfuVXfiX22muveNvb3hbPeMYz4lvf+lYcdNBBu+E7AMaV/gJSUcmyLJttUafTiWazGR+//vqYmJgYumaiXo9+t5t7j7LnKexB7gwXkvf7/Xjms54V7XY7Go1G7j0WmzL0Vwp7kDvDlPNU+uuYY46JX/qlX4p3vvOdERGxZcuWWLFiRbzyla+MN77xjbNe/+CDD8bDH/7weOc73xknn3zyrOsf6q/d/X0D85fKz/Gu7q+IdL53YP6K+Dn2djwAgFk88MADccstt8Txxx8/87UlS5bE8ccfHzfffPNI9xgMBvHf//3f8YhHPGJovnnz5uh0Otv8A7BQu6K/InQYMBpDKACAWdx7773x4IMPxvLly7f5+vLly+Ouu+4a6R5veMMb4sADD9zmPwS3Njk5Gc1mc+afFStWLHjfALuivyJ0GDAaQygAgIL96Z/+afzd3/1dXHvttbHXXnsNXbN+/fpot9sz/9x+++27eJcA2xulvyJ0GDCaOf1icgCAcbTvvvvGHnvsEXffffc2X7/77rvjUY961A6v/fM///P40z/907jhhhviCU94Qu66pUuXxtKlS3fKfgEesiv6K0KHAaPxSigAgFnsueeesXbt2rjxxhtnvrZly5a48cYb49hjj8297s/+7M/irW99a3ziE5+IJz/5ybtiqwDb0F9ASrwSCgBgBOeee26ccsop8eQnPzmOPvrouPjii6Pf78dpp50WEREnn3xyHHTQQTE5ORkREW9729viLW95S3z4wx+OlStXzvzulVqtFrVabbd9H8D40V9AKuY0hFpWq8VETulUq9WYqNfzH6jkeQp7kDvDheRZpZJ73ThIub9S2IPcGaacp9JfL3jBC+Kee+6Jt7zlLXHXXXfFUUcdFZ/4xCdmftnvD37wg1iy5H9eZH7ZZZfFAw88ECeddNI29znvvPPi/PPP35VbB8ac/gJSMach1KDXi0qWDc0m6vXod7u515Y9T2EPcme4kHzQ7+deNw5S7q8U9iB3hinnKfXXOeecE+ecc87Q7Kabbtrmz5s2bSp+QwAj0l9ACvxOKAAAAAAKZwgFAAAAQOEMoQAAAAAonCEUAAAAAIUzhAIAAACgcIZQAAAAABSuOpfFy2q1mKjVht+oWo2Jej3/gUqep7AHuTNcSJ5VKrnXjYOU+yuFPcidYcr5uPcXAMBiMach1KDXi0qWDc0m6vXod7u515Y9T2EPcme4kHzQ7+deNw5S7q8U9iB3hinn495fAACLhbfjAQAAAFA4QygAAAAACmcIBQAAAEDhDKEAAAAAKJwhFAAAAACFM4QCAAAAoHCGUAAAAAAUrjqXxctqtZio1YbfqFqNiXo9/4FKnqewB7kzXEieVSq5142DlPsrhT3InWHK+bj3FwDAYjGnIdSg14tKlg3NJur16He7udeWPU9hD3JnuJB80O/nXjcOUu6vFPYgd4Yp5+PeXwAAi4W34wEAAABQOEMoAAAAAApnCAUAAABA4QyhAAAAACicIRQAAAAAhTOEAgAAAKBw1bksXlarxUStNvxG1WpM1Ov5D1TyPIU9yJ3hQvKsUsm9bhyk3F8p7EHuDFPOx72/AAAWizkNoQa9XlSybGg2Ua9Hv9vNvbbseQp7kDvDheSDfj/3unGQcn+lsAe5M0w5H/f+AgBYLLwdDwAAAIDCGUIBAAAAUDhDKAAAAAAKZwgFAAAAQOEMoQAAAAAonCEUAAAAAIWrzmXxslotJmq14TeqVmOiXs9/oJLnKexB7gwXkmeVSu514yDl/kphD3JnmHI+7v0FALBYzGkINej1opJlQ7OJej363W7utWXPU9iD3BkuJB/0+7nXjYOU+yuFPcidYcr5uPcXAMBi4e14AAAAABTOEAoAAACAwhlCAQAAAFA4QygAAAAACmcIBQAAAEDhRvp0vOz/faJUfzDIX1Op7PDTa8qep7AHuTNcSP7Qz2+W8wlxi1UZ+iuFPcidYcr5uPYXAMBiM9IQqvv/PjL5pOc9r9DNAMXrdrvRbDZ39zZ2Gf0Fi0cK/fWud70r3v72t8ddd90VT3ziE+OSSy6Jo48+Onf91VdfHW9+85tj06ZN8djHPjbe9ra3xW/+5m/uwh0D/Iz+AlIw0hDqwAMPjNtvvz3q9XpUKpWi9wQUIMuy6Ha7ceCBB+7urexS+gvKL5X+uuqqq+Lcc8+Nyy+/PI455pi4+OKLY926dfHtb3879t9//+3Wf+ELX4gXvvCFMTk5Gc9+9rPjwx/+cJx44onxla98JdasWbMbvgNgXOkvIBWVzGvbAQBmdcwxx8Qv/dIvxTvf+c6IiNiyZUusWLEiXvnKV8Yb3/jG7da/4AUviH6/H//0T/8087Vf/uVfjqOOOiouv/zyWR+v0+lEs9mMdrsdjUZj530jwC6Tys/xru6viHS+d2D+ivg5HumVUAAA4+yBBx6IW265JdavXz/ztSVLlsTxxx8fN99889Brbr755jj33HO3+dq6deviox/96ND1mzdvjs2bN8/8ud1uR8TPngAC5fTQz+/u/N/9d0V/RegwWIyK6DBDKACAWdx7773x4IMPxvLly7f5+vLly2Pjxo1Dr7nrrruGrr/rrruGrp+cnIwLLrhgu6+vWLFinrsGUvHjH/94t/1Ou13RXxE6DBazndlhhlAAAAlYv379Nq88uP/+++OQQw6JH/zgB7v9F7LPV6fTiRUrVsTtt99eyrfjlH3/EeX/Hsq+/3a7HY9+9KPjEY94xO7eSuEWW4eV/e9e2fcfUf7voez7jyimwwyhAABmse+++8Yee+wRd9999zZfv/vuu+NRj3rU0Gse9ahHzWn90qVLY+nSpdt9vdlslvbJ60MajUapv4ey7z+i/N9D2fe/ZMmS3fbYu6K/IhZvh5X9717Z9x9R/u+h7PuP2LkdtvvaEACgJPbcc89Yu3Zt3HjjjTNf27JlS9x4441x7LHHDr3m2GOP3WZ9RMSnP/3p3PUARdBfQEq8EgoAYATnnntunHLKKfHkJz85jv7/27vb0CrrP47jn7l5zgyWN8jmJqvYyhbeICobm8lIBoJi9WgDZc7QVFwQCpk460TWNkRFkGVslevB7FChIjlmaY6YGpFtIG0ZtpkEnZFQNLTa3bcH+3vq6Kaey3NdZ+ff+wV74OXv4ny+264PXj/PTX6+9u/fr+vXr+u5556TJK1Zs0YzZ85UTU2NJOnFF19UcXGx9u7dqxUrVigYDOrrr79WfX19PMcA8B9EfwEYL9iEAgAAuAdlZWX65Zdf9OqrryoUCmn+/PlqaWkJv3nv1atXI56uXlRUpMOHD2vnzp3asWOHHnvsMR07dkxz5sy5p8fz+/0KBAKjvrwlUST6DImeX0r8GcgfG173lzR+ZneK/PGX6DMken7JnRmSLJ6fFwoAAAAAAID/BN4TCgAAAAAAAK5jEwoAAAAAAACuYxMKAAAAAAAArmMTCgAAAAAAAK5jEwoAACBO6urq9Mgjjyg1NVUFBQX66quv7rj+o48+Ul5enlJTUzV37lw1Nzd7lHRs0czQ0NCgJUuWaOrUqZo6dapKSkruOrPbov0Z3BQMBpWUlKRnn33W3YB3EW3+3377TZWVlcrMzJTf79esWbPi/nsU7Qz79+/X448/rkmTJik7O1tbtmzRn3/+6VHaSF988YVWrlyprKwsJSUl6dixY3c9p7W1VQsWLJDf79ejjz6qxsZG13O6gf6iv2Ih0TuM/mqM/oENAAAAngsGg+bz+ey9996zb7/91p5//nmbMmWK9fb2jrr+7NmzlpycbLt377bOzk7buXOnTZw40S5evOhx8n9EO8OqVausrq7O2tvbraury9auXWuTJ0+2n376yePkI6LNf1NPT4/NnDnTlixZYs8884w3YUcRbf6//vrLFi1aZMuXL7e2tjbr6emx1tZW6+jo8Dj5P6Kdoampyfx+vzU1NVlPT4+dPHnSMjMzbcuWLR4nH9Hc3GxVVVV25MgRk2RHjx694/ru7m574IEHbOvWrdbZ2WkHDhyw5ORka2lp8SZwjNBf9FcsJHqH0V/O+otNKAAAgDjIz8+3ysrK8J+HhoYsKyvLampqRl1fWlpqK1asiDhWUFBgGzdudDXnnUQ7w60GBwctLS3N3n//fbci3pGT/IODg1ZUVGTvvPOOVVRUxPUmLtr8Bw8etJycHOvv7/cq4l1FO0NlZaUtXbo04tjWrVtt8eLFrua8F/dyE7dt2zabPXt2xLGysjJbtmyZi8lij/6iv2Ih0TuM/nLWX7wcDwAAwGP9/f26cOGCSkpKwscmTJigkpISnT9/ftRzzp8/H7FekpYtWzbmerc5meFWN27c0MDAgKZNm+ZWzDE5zf/6668rPT1d69at8yLmmJzkP378uAoLC1VZWamMjAzNmTNH1dXVGhoa8ip2BCczFBUV6cKFC+GXvHR3d6u5uVnLly/3JPP9Gm/XsRP01wj66/4keofRXyOcXMcpsQwFAACAu7t27ZqGhoaUkZERcTwjI0PffffdqOeEQqFR14dCIddy3omTGW718ssvKysr67Z/1HrBSf62tja9++676ujo8CDhnTnJ393drc8//1yrV69Wc3OzLl++rM2bN2tgYECBQMCL2BGczLBq1Spdu3ZNTz75pMxMg4OD2rRpk3bs2OFF5Ps21nX8+++/648//tCkSZPilOze0V8j6K/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      "text/plain": [
       "<Figure size 1200x900 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 9\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m batch \u001b[38;5;129;01min\u001b[39;00m train_loader:\n\u001b[1;32m      8\u001b[0m     grids, masks, target_grid \u001b[38;5;241m=\u001b[39m batch\n\u001b[0;32m----> 9\u001b[0m     \u001b[43mvisualize_tensors\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgrids\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msqueeze\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_grid\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msqueeze\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/work/arc-prize/arc_prize/vis.py:253\u001b[0m, in \u001b[0;36mvisualize_tensors\u001b[0;34m(grids, output_grid, prediction)\u001b[0m\n\u001b[1;32m    250\u001b[0m     axes[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m]\u001b[38;5;241m.\u001b[39mset_title(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPrediction\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    252\u001b[0m plt\u001b[38;5;241m.\u001b[39mtight_layout()\n\u001b[0;32m--> 253\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/matplotlib/pyplot.py:612\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    568\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    569\u001b[0m \u001b[38;5;124;03mDisplay all open figures.\u001b[39;00m\n\u001b[1;32m    570\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    609\u001b[0m \u001b[38;5;124;03mexplicitly there.\u001b[39;00m\n\u001b[1;32m    610\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    611\u001b[0m _warn_if_gui_out_of_main_thread()\n\u001b[0;32m--> 612\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_get_backend_mod\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshow\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/matplotlib_inline/backend_inline.py:90\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(close, block)\u001b[0m\n\u001b[1;32m     88\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     89\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m figure_manager \u001b[38;5;129;01min\u001b[39;00m Gcf\u001b[38;5;241m.\u001b[39mget_all_fig_managers():\n\u001b[0;32m---> 90\u001b[0m         \u001b[43mdisplay\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_fetch_figure_metadata\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfigure_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     95\u001b[0m     show\u001b[38;5;241m.\u001b[39m_to_draw \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/IPython/core/display_functions.py:298\u001b[0m, in \u001b[0;36mdisplay\u001b[0;34m(include, exclude, metadata, transient, display_id, raw, clear, *objs, **kwargs)\u001b[0m\n\u001b[1;32m    296\u001b[0m     publish_display_data(data\u001b[38;5;241m=\u001b[39mobj, metadata\u001b[38;5;241m=\u001b[39mmetadata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    297\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 298\u001b[0m     format_dict, md_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minclude\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mexclude\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    299\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m format_dict:\n\u001b[1;32m    300\u001b[0m         \u001b[38;5;66;03m# nothing to display (e.g. _ipython_display_ took over)\u001b[39;00m\n\u001b[1;32m    301\u001b[0m         \u001b[38;5;28;01mcontinue\u001b[39;00m\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/IPython/core/formatters.py:182\u001b[0m, in \u001b[0;36mDisplayFormatter.format\u001b[0;34m(self, obj, include, exclude)\u001b[0m\n\u001b[1;32m    180\u001b[0m md \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    181\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 182\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[43mformatter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    183\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m    184\u001b[0m     \u001b[38;5;66;03m# FIXME: log the exception\u001b[39;00m\n\u001b[1;32m    185\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/decorator.py:232\u001b[0m, in \u001b[0;36mdecorate.<locals>.fun\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kwsyntax:\n\u001b[1;32m    231\u001b[0m     args, kw \u001b[38;5;241m=\u001b[39m fix(args, kw, sig)\n\u001b[0;32m--> 232\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mcaller\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mextras\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/IPython/core/formatters.py:226\u001b[0m, in \u001b[0;36mcatch_format_error\u001b[0;34m(method, self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    224\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"show traceback on failed format call\"\"\"\u001b[39;00m\n\u001b[1;32m    225\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 226\u001b[0m     r \u001b[38;5;241m=\u001b[39m \u001b[43mmethod\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mNotImplementedError\u001b[39;00m:\n\u001b[1;32m    228\u001b[0m     \u001b[38;5;66;03m# don't warn on NotImplementedErrors\u001b[39;00m\n\u001b[1;32m    229\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_return(\u001b[38;5;28;01mNone\u001b[39;00m, args[\u001b[38;5;241m0\u001b[39m])\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/IPython/core/formatters.py:343\u001b[0m, in \u001b[0;36mBaseFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    341\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[1;32m    342\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 343\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mprinter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    344\u001b[0m \u001b[38;5;66;03m# Finally look for special method names\u001b[39;00m\n\u001b[1;32m    345\u001b[0m method \u001b[38;5;241m=\u001b[39m get_real_method(obj, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_method)\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/IPython/core/pylabtools.py:170\u001b[0m, in \u001b[0;36mprint_figure\u001b[0;34m(fig, fmt, bbox_inches, base64, **kwargs)\u001b[0m\n\u001b[1;32m    167\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackend_bases\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FigureCanvasBase\n\u001b[1;32m    168\u001b[0m     FigureCanvasBase(fig)\n\u001b[0;32m--> 170\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbytes_io\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    171\u001b[0m data \u001b[38;5;241m=\u001b[39m bytes_io\u001b[38;5;241m.\u001b[39mgetvalue()\n\u001b[1;32m    172\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fmt \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msvg\u001b[39m\u001b[38;5;124m'\u001b[39m:\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/matplotlib/backend_bases.py:2178\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2176\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bbox_inches:\n\u001b[1;32m   2177\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtight\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 2178\u001b[0m         bbox_inches \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_tightbbox\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2179\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbbox_extra_artists\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbbox_extra_artists\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2180\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28misinstance\u001b[39m(layout_engine, ConstrainedLayoutEngine) \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m   2181\u001b[0m                 pad_inches \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlayout\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m   2182\u001b[0m             h_pad \u001b[38;5;241m=\u001b[39m layout_engine\u001b[38;5;241m.\u001b[39mget()[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh_pad\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/matplotlib/_api/deprecation.py:457\u001b[0m, in \u001b[0;36mmake_keyword_only.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    451\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m name_idx:\n\u001b[1;32m    452\u001b[0m     warn_deprecated(\n\u001b[1;32m    453\u001b[0m         since, message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    454\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[38;5;124m; the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    455\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter will become keyword-only \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    456\u001b[0m         name\u001b[38;5;241m=\u001b[39mname, obj_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m()\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/matplotlib/figure.py:1787\u001b[0m, in \u001b[0;36mFigureBase.get_tightbbox\u001b[0;34m(self, renderer, bbox_extra_artists)\u001b[0m\n\u001b[1;32m   1783\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax\u001b[38;5;241m.\u001b[39mget_visible():\n\u001b[1;32m   1784\u001b[0m     \u001b[38;5;66;03m# some Axes don't take the bbox_extra_artists kwarg so we\u001b[39;00m\n\u001b[1;32m   1785\u001b[0m     \u001b[38;5;66;03m# need this conditional....\u001b[39;00m\n\u001b[1;32m   1786\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1787\u001b[0m         bbox \u001b[38;5;241m=\u001b[39m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_tightbbox\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1788\u001b[0m \u001b[43m            \u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbbox_extra_artists\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbbox_extra_artists\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1789\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   1790\u001b[0m         bbox \u001b[38;5;241m=\u001b[39m ax\u001b[38;5;241m.\u001b[39mget_tightbbox(renderer)\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/matplotlib/_api/deprecation.py:457\u001b[0m, in \u001b[0;36mmake_keyword_only.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    451\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m>\u001b[39m name_idx:\n\u001b[1;32m    452\u001b[0m     warn_deprecated(\n\u001b[1;32m    453\u001b[0m         since, message\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPassing the \u001b[39m\u001b[38;5;132;01m%(name)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%(obj_type)s\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    454\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpositionally is deprecated since Matplotlib \u001b[39m\u001b[38;5;132;01m%(since)s\u001b[39;00m\u001b[38;5;124m; the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    455\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter will become keyword-only \u001b[39m\u001b[38;5;132;01m%(removal)s\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    456\u001b[0m         name\u001b[38;5;241m=\u001b[39mname, obj_type\u001b[38;5;241m=\u001b[39m\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m()\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 457\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/matplotlib/axes/_base.py:4505\u001b[0m, in \u001b[0;36m_AxesBase.get_tightbbox\u001b[0;34m(self, renderer, call_axes_locator, bbox_extra_artists, for_layout_only)\u001b[0m\n\u001b[1;32m   4502\u001b[0m     bbox_artists \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_default_bbox_extra_artists()\n\u001b[1;32m   4504\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m a \u001b[38;5;129;01min\u001b[39;00m bbox_artists:\n\u001b[0;32m-> 4505\u001b[0m     bbox \u001b[38;5;241m=\u001b[39m \u001b[43ma\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_tightbbox\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4506\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m (bbox \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   4507\u001b[0m             \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;241m0\u001b[39m \u001b[38;5;241m<\u001b[39m bbox\u001b[38;5;241m.\u001b[39mwidth \u001b[38;5;241m<\u001b[39m np\u001b[38;5;241m.\u001b[39minf\n\u001b[1;32m   4508\u001b[0m             \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;241m0\u001b[39m \u001b[38;5;241m<\u001b[39m bbox\u001b[38;5;241m.\u001b[39mheight \u001b[38;5;241m<\u001b[39m np\u001b[38;5;241m.\u001b[39minf):\n\u001b[1;32m   4509\u001b[0m         bb\u001b[38;5;241m.\u001b[39mappend(bbox)\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/matplotlib/artist.py:365\u001b[0m, in \u001b[0;36mArtist.get_tightbbox\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    349\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_tightbbox\u001b[39m(\u001b[38;5;28mself\u001b[39m, renderer\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    350\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    351\u001b[0m \u001b[38;5;124;03m    Like `.Artist.get_window_extent`, but includes any clipping.\u001b[39;00m\n\u001b[1;32m    352\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    363\u001b[0m \u001b[38;5;124;03m        Returns None if clipping results in no intersection.\u001b[39;00m\n\u001b[1;32m    364\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 365\u001b[0m     bbox \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_window_extent\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrenderer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    366\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_clip_on():\n\u001b[1;32m    367\u001b[0m         clip_box \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_clip_box()\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/matplotlib/spines.py:158\u001b[0m, in \u001b[0;36mSpine.get_window_extent\u001b[0;34m(self, renderer)\u001b[0m\n\u001b[1;32m    156\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m bb\n\u001b[1;32m    157\u001b[0m bboxes \u001b[38;5;241m=\u001b[39m [bb]\n\u001b[0;32m--> 158\u001b[0m drawn_ticks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_ticks\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    160\u001b[0m major_tick \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m({\u001b[38;5;241m*\u001b[39mdrawn_ticks} \u001b[38;5;241m&\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis\u001b[38;5;241m.\u001b[39mmajorTicks}), \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    161\u001b[0m minor_tick \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m({\u001b[38;5;241m*\u001b[39mdrawn_ticks} \u001b[38;5;241m&\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis\u001b[38;5;241m.\u001b[39mminorTicks}), \u001b[38;5;28;01mNone\u001b[39;00m)\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/matplotlib/axis.py:1310\u001b[0m, in \u001b[0;36mAxis._update_ticks\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1308\u001b[0m minor_ticks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_minor_ticks(\u001b[38;5;28mlen\u001b[39m(minor_locs))\n\u001b[1;32m   1309\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m tick, loc, label \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(minor_ticks, minor_locs, minor_labels):\n\u001b[0;32m-> 1310\u001b[0m     \u001b[43mtick\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_position\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1311\u001b[0m     tick\u001b[38;5;241m.\u001b[39mlabel1\u001b[38;5;241m.\u001b[39mset_text(label)\n\u001b[1;32m   1312\u001b[0m     tick\u001b[38;5;241m.\u001b[39mlabel2\u001b[38;5;241m.\u001b[39mset_text(label)\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/matplotlib/axis.py:434\u001b[0m, in \u001b[0;36mXTick.update_position\u001b[0;34m(self, loc)\u001b[0m\n\u001b[1;32m    432\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mupdate_position\u001b[39m(\u001b[38;5;28mself\u001b[39m, loc):\n\u001b[1;32m    433\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Set the location of tick in data coords with scalar *loc*.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 434\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtick1line\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_xdata\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    435\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtick2line\u001b[38;5;241m.\u001b[39mset_xdata((loc,))\n\u001b[1;32m    436\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgridline\u001b[38;5;241m.\u001b[39mset_xdata((loc,))\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/matplotlib/lines.py:1292\u001b[0m, in \u001b[0;36mLine2D.set_xdata\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m   1290\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_xorig \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mcopy(x)\n\u001b[1;32m   1291\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalidx \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m-> 1292\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstale\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/work/arc-prize/.venv/lib/python3.12/site-packages/matplotlib/artist.py:316\u001b[0m, in \u001b[0;36mArtist.stale\u001b[0;34m(self, val)\u001b[0m\n\u001b[1;32m    310\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    311\u001b[0m \u001b[38;5;124;03m    Whether the artist is 'stale' and needs to be re-drawn for the output\u001b[39;00m\n\u001b[1;32m    312\u001b[0m \u001b[38;5;124;03m    to match the internal state of the artist.\u001b[39;00m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    314\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stale\n\u001b[0;32m--> 316\u001b[0m \u001b[38;5;129m@stale\u001b[39m\u001b[38;5;241m.\u001b[39msetter\n\u001b[1;32m    317\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstale\u001b[39m(\u001b[38;5;28mself\u001b[39m, val):\n\u001b[1;32m    318\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stale \u001b[38;5;241m=\u001b[39m val\n\u001b[1;32m    320\u001b[0m     \u001b[38;5;66;03m# if the artist is animated it does not take normal part in the\u001b[39;00m\n\u001b[1;32m    321\u001b[0m     \u001b[38;5;66;03m# draw stack and is not expected to be drawn as part of the normal\u001b[39;00m\n\u001b[1;32m    322\u001b[0m     \u001b[38;5;66;03m# draw loop (when not saving) so do not propagate this change\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from arc_prize.data import ARCDatasetParams, make_data_loaders\n",
    "from arc_prize.vis import visualize_tensors\n",
    "\n",
    "\n",
    "train_loader, val_loader = make_data_loaders([file_path_root], batch_size=1, params=ARCDatasetParams(max_grid_size=30, max_train_grids=4))\n",
    "\n",
    "for batch in train_loader:\n",
    "    grids, masks, target_grid = batch\n",
    "    visualize_tensors(grids.squeeze(0), target_grid.squeeze(0), None)\n",
    "\n"
   ]
  }
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