Ex2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Ex2_mainLoss.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JwRTIXeJqab_"
      },
      "source": [
        "## Basic Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z4Zqx3ZBOKnO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0d83bd0-c89f-4c10-d5c0-55bb9713f6fb"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import torch\n",
        "import networkx as nx\n",
        "\n",
        "\n",
        "from platform import python_version\n",
        "print(\"Python Version\", python_version())\n",
        "print('PyTorch Version', torch.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python Version 3.7.12\n",
            "PyTorch Version 1.10.0+cu111\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "evkPHCXNqISe",
        "outputId": "225648ad-ba93-4dcc-b7f0-0adac4d97a21"
      },
      "source": [
        "# Device\n",
        "DEVICE = 'cpu'\n",
        "if torch.cuda.is_available():\n",
        "    DEVICE = \"cuda\"\n",
        "print(\"device =\", DEVICE)\n",
        "# Reproducibility\n",
        "SEED = 3407\n",
        "import random\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "torch.cuda.manual_seed_all(SEED)\n",
        "# Parallel processing\n",
        "NUM_CORES = 20\n",
        "torch.set_num_threads(NUM_CORES)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "device = cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjVT4mJ8qLpp",
        "outputId": "5149801c-ca0f-48c8-c9dd-d0942097cc28"
      },
      "source": [
        "if DEVICE==\"cpu\":\n",
        "    !pip install -q torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cpu.html\n",
        "else:\n",
        "    !pip install -q torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cu111.html\n",
        "import torch_geometric as pyg\n",
        "print('PyTorch Geometric Version', pyg.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 291 kB 34.8 MB/s \n",
            "\u001b[K     |████████████████████████████████| 639 kB 39.6 MB/s \n",
            "\u001b[K     |████████████████████████████████| 328 kB 44.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 138 kB 57.4 MB/s \n",
            "\u001b[K     |████████████████████████████████| 325 kB 30.1 MB/s \n",
            "\u001b[K     |████████████████████████████████| 407 kB 51.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 45 kB 3.5 MB/s \n",
            "\u001b[?25h  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "PyTorch Geometric Version 2.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ojccKMV1qdrh"
      },
      "source": [
        "## Prepare graph"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "th2rr-XwqW5z"
      },
      "source": [
        "def to_node_idx(x,y):\n",
        "    return (8*x) + y\n",
        "\n",
        "def to_coord(node_idx):\n",
        "    return node_idx//8, node_idx%8\n",
        "\n",
        "edge_index = [[],[]]\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        if i<7:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i+1,j))\n",
        "            edge_index[0].append(to_node_idx(i+1,j));    edge_index[1].append(to_node_idx(i,j))\n",
        "        if i>0:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i-1,j))\n",
        "            edge_index[0].append(to_node_idx(i-1,j));    edge_index[1].append(to_node_idx(i,j))\n",
        "        if j<7:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i,j+1))\n",
        "            edge_index[0].append(to_node_idx(i,j+1));    edge_index[1].append(to_node_idx(i,j))\n",
        "        if j>0:\n",
        "            edge_index[0].append(to_node_idx(i,j));    edge_index[1].append(to_node_idx(i,j-1))\n",
        "            edge_index[0].append(to_node_idx(i,j-1));    edge_index[1].append(to_node_idx(i,j))\n",
        "\n",
        "edge_index = torch.tensor(edge_index, dtype=torch.long).to(DEVICE)\n",
        "COINS = set([10, 30, 50])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n41CppvVqmv9",
        "outputId": "60617a12-9832-47c8-ebc9-29ba15ab2912"
      },
      "source": [
        "data = pyg.data.Data(edge_index = edge_index).to(DEVICE)\n",
        "NUM_NODES = data.num_nodes\n",
        "NUM_EDGES = data.num_edges\n",
        "print(\"No. of nodes =\", NUM_NODES)\n",
        "print(\"No. of edges =\", NUM_EDGES)\n",
        "print(\"edge_index.shape =\", data.edge_index.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of nodes = 64\n",
            "No. of edges = 448\n",
            "edge_index.shape = torch.Size([2, 448])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "RJVtbHoVqnr0",
        "outputId": "cf9b7278-6b17-4bca-82bd-f7897e278b94"
      },
      "source": [
        "graph = pyg.utils.to_networkx(data)\n",
        "pos = {}\n",
        "for idx in range(64):\n",
        "    x,y = to_coord(idx)\n",
        "    pos[idx] = np.array([x,y])\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8, 8))\n",
        "color = ['y' for i in range(64)]\n",
        "for coin in COINS:\n",
        "    color[coin] = 'r'\n",
        "nx.drawing.nx_pylab.draw_networkx(graph, node_color=color, pos=pos, node_size=750, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SOxrYvn07o0eP5uuvv27y7HWczm0t3jtrlnR33XyzHNmfempYIuD1luDzVTX+e+XKlZx33nmsXBneYGfOnDlcc801x7y31dXr6jO/BMZfm5oO8hr+f88ef6XIdIgN+Hw+3n33Xc466yw8nvDWza6//nr++te/UlV19HeqqvopoDu2thbefRfuvNN/uRs3Qnl5y54oXXcd896VlpYybdo07gxUeABKS0sZNWoU8+fPR9ePGi0ZGNMyc+bI5abJk6UrtoGKCulZu/ZaeO016R3wh6KYcTp3NP5706ZNXHzxxfzrX/8KuT0AixYtYty4caxvIpAc3PpXeJomB86VlbLfX3ml9Ag09a4FqxtApkOMJTE1mC7XDlQ19A2pxcWwYQOMGRPqnToez2G++uorBg8ezOWXX87PP//MgUDDmgCsWrWKuXPn0qtXr0bD6XRuDSukPzkZ3ngDPv4Y3nwTnE65VtYSTueWRkOZkZHBn//8Z77/PvzR/5IlS3jooYfo2rVro+F0OrcGfb/PJ9fKxoyBnJyjny9aJP+mTJFuQH9oWg0u1/ZGQ9m7d2/eeecdVq0KbxtNYWEhP/30E+PHj2fIkCF8/fXX1NUdDMrle++9cm3v5ZfhzDPl7CscVNWOy7WTlStXcsYZZ/C73/2OpUuXsse/RQrIunXr+Oc//8ngwYO56qqryM/Pr98a43/G3EBzbTrlFFi6FHbtkors/fflQLS5JYMG3O7djYYyOzubKVOmsHLlSlyBNHgAlixZwlNPPUVmZmaj4ZTuWP/umnfegQsukLMVf3z1lTSW9hbUjBAeamrWNRrKnJwcXnrpJZYtWxb4Rj+Ulpbyyy+/MGHCBHr27Mn8+fPRNA2Pp+UtLLfdJteT58+XxuTPf5Yzr5wcaUg//VS6z3fsgNdf91+OophwubazadMmLrjgAkaOHMnixYvZtq3lwWJz7Nixgy+//JLTTz+d8847j/Xr1+Ny7UBR/K/slZdLnbBsmXzn3n4bdu6EDz44ek2wugHA6z2Mrrf8nkeLmGb6KS9fyubNl6JpoUVffvCB9Iv7X2NpHq/XzC23qBQUHDsKNpvNqMH5144rz3tCBOSDD8YzZkzL0WJz58KRI0eDfo6nrAwuv1wqN0eAiUN5uYXx4098gazW8AIyjp8hKIrCggUmkpNbDhfXdWnkG4y9uZl+pOtw2WXSTZPq54CZRYvimTnz2N9QURQsYVgsXdfx+Y6VvXt3K2++qWOxBB8C/+KL0K2bfCYNXHmlDDIJFPQD4PXaePhhhV9+OTZq2mQyYQp2wboJPp/vmFkLwE03xfP739ehKOG1acEC+H//Tz67yy+Hjz6Cp57yv+Th9SqMHaucIIfFYgkrCK25mem778aRm9t8pHl+vnzH5syRRn/ePGlUHn746DVut2zLE0/AsGEty7B2rZ2pU080+OH0JSEEXu+x/dJuN7FokRakK/8o998vPQHjj1syzsuDhx6ChQubv0/TbMyebWLRomNdwOG+d5qmoWnHrvuPHetg6lQNs7n50VV1NVx8sQzyaQhOWrZMBmbNmXPstcHoBkUxc8YZ5VFPeeov009Mg37CzUj/9dfSJREqVquF119/ndmz/87y5cvRNA2LxcKMGTMYPHhwyOU9/PDDrF+/nri4OOLi4rj55pu55JJK3O55oQvnB72FJYj09C689NKfePXVV9m/fz91dXVYLBYWLAgvg8lFF12EruvYbDa6devGXXfdRVra02haUcD7hIDnn5cjymeead5YNlzndkNJif9OccUV1+Lx2Hn//fepq6vD7XZz8sknMyOYKJvjWL9+PY8//jherxdVVTn77LO5555rsVrvCikXqaYdjeYLFZvNxtNPP8nLL3/Dl19+iRACRVGYMmUK55xzTsjlvfbaa/znP//BZrNhMpm45ppruPXWdDyel8Ju02WXyT+Q7vS///1Yl/rxWK125sx5hZdffpmtW7c2/r4ffvgh8fHxIbfp+uuvp6KiApvNRnp6OpMmTaJPn/l4PM3vn16/XkYpX321/LfLJfvKvn0ywARg+XJITGx5QNPAaaedxZ//PII5c+ZQXV2N2+0mJyeHv/3tbyG3p7CwkLvuuguv14vZbObkk0/m7rvvQlWvC7ksRcFvXESgeAmLxcqDDz5EUtJmPvvsM3Rdx+v1csMNN3DVVaEHAy1YsIB33nkHk8mEqqpceuml3HnnMIR4yu82mcRE6QFoOobyN54KRjcIocf0JJOYGkyrNTPkDCubN8sfdPTocGr0ccEF1zFu3O/Zt28fjzzyCB9++CGjR49m5MiRIZc2d+5cioqKePLJJ7n55puxWCwUFr7Krl0f+92DqWnyT9fln8cjo2IbAmSysuSo7JVXZEdPaGEgFR/fnSlTpjBlyhSWLVvGtGnT2LFjBxdccEHI7QFwOBwMHDiQ559/njPPPBOAtWs/pKoqsLWYNUsqq5kzjw17X71aupt79JCdYe5c2Ym6+TndS1UdZGeP4OWX7+CFF15g3rx5TJ8+ne7du4fVpuTkZB555BFuuukmZsyYQU5ODprmZsWK2/3eU14O69bJ4CWrFdaskcEl06fL75tOhrxe+W+LJdB6uoczzriU3/xmMocPH+bJJ5/kjTfe4NRTTw2rTUuWLGHFihU88sgj/PGPf8ThcHDkyL/Ytu1vaFrz712gNnk8cnaWmyvD/mfOlLOZxET/MthsnZkwYQITJkxg3bp1TJs2jaVLl3LBBReEZTA7duxIRkYGL7zwAueffz6KopCXt5nDh5s3mOPGyW1ZDXzyiVyqubfJduvQ4hxUOnUaxpNPPsnjjz/O/Pnzuf/+++natWtYz2jfvn34fD4uvfRSnnrqKfrVh/EuX34Hmlbl976aGjlzHDpU6oUlS+Q67OTJ8vllZMio0iNH5AztjDP8y6AoCoMGncOHHz5EVVUVs2bN4rnnnmPo0KFhtWnv3r3YbDamTp3K1KlTSUlJoapqNRs2PBnwvrFjpQfjlFPkQPrTT+V7GKpuALm8oSjR387kj5i6ZIUQLF+eGNKG15kz5drKn/8cen12ey9Gjtx5zGeVlZUkJSWF5Uaqra3FarUe4yqsrFzFxo1j/XaKefPkfqSm3HwzZGdL/35FhXTBjhgBd9wh9yz5x0ROzkP06PH4CW1K9h9DH5Dm7t216wEKCl7A3+bxhj2kFsuxW2Luu09+9s47soPbbDKsfOJE6NnTT4tMiZx00nckJf1f42derxePxxOWIhZCUFVVdUKbfvqpO2733mbvqaiARx+Va3pCSAU1frxU0iADsw4dOvaejz6SEX/+2jRqVOUx71hNTQ1xcXGY/U3FA9AQkBXXZNOu213Af//b128ihkBtqqmR2xiKiuRa3/nny32Ngbx26enXMHDgR8fVUUFKSkrI7YHm++GBA39j166pQSWXON4le+SIfE7vvw+ZQeTAN5mS6NdvHunplzV+pus6NTU1JIUZ0t7c77F27ZlUVa3wc4d8Tg8+KLf9qKpct5wwAU4+Gf75T7muWV0to+xHjYJbb/W/ZKMoZkaNqjomcbnT6cRsNoflZvZ4PPh8PhxNKtT1OpYvTwy4fu7zyQnAd9/Jwdro0VK3rVwZmm4ASEwcyYgR0U8y0W5PK1m79rQ2yrKh0LnzjfTv/17Ll7YCTXOyYkVKUAEYrcVkSqJ//w/p2HFcVOs5cmQB27bdHLFk3oFQFEt9J49r+eJWkJd3Xf2euOiTlDSK4cOXR7UOIQQrVqQEnL1EClW107PnTDIz/xjVeuTs5ZyI58VtDlWN45RTdhAXlx3Venbv/gv79z9DNFLIHU9cXC6nnhpeYFko/PxzH1yunS1f2GpMZGdPpWfPZ6NeU7s9rSQj43ZUNfpnVqpqPBkZ0T9twWRykJLym6jXI9FJTY3+IatpaWPa6LQAhbS0sVE3lgAZGbdgMrXFe5dAZuYdUa9HURQ6d76etlhlEUKQnn55yxe2ksTE4ZhMAfzCEcRu7xt1YwnQufP1bbIGp6p2MjImRr0egIyMiWHtdggVVbXSufMNLV8YTRliWjvQqdPVbVKPxZJGcvJZbVJXTs60qCtjRbHSpcutbWJcTCYHXbr8AYhuR5frl9OiWkcDKSnnYjJF/5xURVFIT78i6vUAZGXd3QbKWCEtbQxWa6co1wOKopKdPTXqythkSiAn54Go1tFAfHw/4uMHtUFNog0N5gSikez/eByOviQkhB6cGUlibjBNJnt9R29503W4qGo8ubl/bbNDl1NSzsFmyyHQht7WoihmsrPbLqG8VFzRnL2o2O09SU4eFcU6jqIoCrm5M1DV0NdFg0VVHWRlTW2zE+Mdjr4kJ59JNGeZqhpHbu4jUSv/eLp0uSXqQR4mU2LU0/w1RebhjZ6+U5Q40tOvxmoNsEE1glgsHejc+UYUJXqDd1V10L17eMlLIipHrAUAyM19BKvVT8REK1EUM4mJI+jSxU8W86jUqTBw4D9R1ei8QKoaT48eTxMXl9PyxRHCbs8lN/fxqBkYVbUxYMAnbTaoAemWTUgYAoS+J61lFGy2LLp1CyM6rRX06zcvaqfTq6qdrl3/SGKinzx0UcBiSaFfv7lRMzCqamfAgI/bbFADkJZ2Hh06XISiRKdOszmB3r1fjkrZ/ujZ80XM5uh4bBTFSlraWDp0aCa5bhvTLgymqloZOHB+VDqFqjro3//DNlXEAPHxA+nW7S8Rb5Oi2EhIGEJm5uSIlhsM2dn3Eh8/IOIjfukBeIz4+H4tXxxBFEVhwICPMJmi8d7Z69/ptt0zZrNl0KfPG1F478xYrRn06BHdwwuaIz39ctLSxkTcNauqDrp0mUBKStss1TSlT5836g1MZPWSqtrp3/+jqCeRPx6zOaF+4BHpwZqC2ZxE375zWr60DWgXBhPkAn+kf3BVTeCkk74lLi4rYmWGQk7Og3TufEPElJei2LDbuzNkyBdRP82j+fpVhgz5mri4bhEzmlJp3Ux29p8iUl6oxMV1Y8iQryM6c24wlnL22vZ07nwdOTkPRfC9M2OxdGTYsB/adCbWlP79/0FCwrCI6QdVdZCa+ht69w4xXViEsFhSGDbsh4gaTVW107v3K6SlRT8QsDlSU0fTp8/fIqjDFUymJIYOXYbFEnB/XZvRbgwmQMeOFzFw4Gf1ATPhr8Moig2zuQPDhi07Zj9fW6MoCn36vEFm5qRWv0SqGk9i4jCGD/8paq6PYLBYUhg+/L8kJJzUaiOjqnYyM6fQu/erbe4BaEpy8qkMHfo9ZnNqK91kFkymBAYNWkiHDuEljogUubnT6d79yfr3LvzfVlUdxMX1YMSINdhsQWxojBImUxwnnfQdKSnnROC9c5CefgUDB36GokTDHR8cDkdfhg//L1Zr11bqBxVVtdOnz1tkZNwSMfnCoUuXm+nXb179YC3831ZV7VitXRgx4mfi4wdETsBWEvN9mM1RV3eAvLzrqa5eHeIp3gqqaqdDh4vo0+cNLJbwNlFHg4qKZWzZcg2aVhXS8T6KYkFRLHTv/hRZWXfFZGbZHELoFBbOZs+e6ei6Dwh+36mqOjCbkxkw4J+kpLRNkE8weL1lbN9+O2Vl/wn5CCZVjScp6VT69/8Amy0jShKGTk3NBrZsuZK6uqIQ+5IJVbWSlXUfubmPxjQdWVOEEBw69Hd27pyErnsRIkCG+ONQ1ThU1U6/fu/RseNFUZQyNDTNSX7+nzh06D103Y2/BCHNoarxOBx9GDDgExyO3tETMkRcrl1s2XINTufWsHR458430KvXi5hM0QvKCyhFe01c4A8h5CG1+/c/Q03NWkD1q8RUNR4hfKSmnktOzkOkpJzZtsIGiabVUlT0NgUFz6Nplei6x09qQLX+RRF06TKB7Oz7iIsLkC8qhrhceykomElx8bsoioqm1dJch1cUK6pqxWxOJTv7T2Rk3BqVtcNIUFGxjH37nqKi4gcUxeS3w8tRtE5Cwgi6dXuItLQLYjpT9oeuezl06AP273+GuroihPD5MTQKJlMCQnhJT7+GnJz7iY/v3+byBoPHc4jCwpc4cOB1QEfTnEBzB4KbMZnsqKqNzMx7yMychMXiJ1FpjKmuXsv+/U9TUvJvVNXiN2GDothRFLl3NFAAQY0AACAASURBVCfnATp1uqrdDKSbIoTOkSOfsn//s40nHvnL2qSq8r3r0OFCcnIejKlnEH6FBrMpTmc+FRVLqKxcQXX1L5SWFmI2m0lOziI5+TSSkk4nLe08bLausRY1KIQQVFb+SFXVSioqluJ0bqO4uICOHTvjcHQjOfkskpJGkpY2JmoRj5FG05yUlX1FVdVPVFb+QG1tAWVlh+jcORuHYwApKWeRnDyKpKTT2qVRaQ63u5Dy8m+orFxJVdVPVFQUousaaWmZJCb+H8nJo0hN/Q12e4BcXu0IedjvWioqllNRsYza2k0cOVJIUlIK8fFZpKScSVLSaaSljW3zoJFw0XUP5eXf1T+fZbhceyguLqRr1xzi4nqRkjKa5ORTSUkZHVP3ayh4PCWUl39FZeUqKit/pLq6EJfLSceOmSQkDCcl5UxSUkYTHz8w1qIGTW3tVioqvqeiYjk1NWspLS3EZrOTlJRFcvIZ9e/dmDbbCtMS/gwmQgi/fyNGjBDtkQ4dOohBgwbFWoyI4XQ6BSDmzJkTa1EixquvvioURREulyvWokSMfv36ifT09FiLETE0TRMmk0lMmjQp1qJEjF9++UUA4vDhw7EWJWKMHTtW2O32WIsRURwOhzjnnHNiLYZfgNWiGZvY/ubxLbBt2zbKysrIy8ujrKws1uJEhIbjg2bPnh1jSSLHK6+8ghCCN998M9aiRISSkhJ27NhBSUkJ+fn5sRYnInz22WdomsZHH310wrmWv1aeqD91/emnn46xJJHB7Xbz/fff43K5WL48uvmI24qffvoJp9PJjz/+GPaB4zGjOSsq2vEM87LLLhOAUFVV3H///bEWp9U4nU6RkpIiAGGz2cTKlStjLVKrWb58ubBarQIQqamp/xOzzPvuu0+oqioAcdVVV8VanFajaZro3r27AITVahX//Oc/Yy1Sq9m5c6ew2WwCEHa7/X9ilvnyyy8Ls9ksADFy5MhYixMRRo0aJQBhNpvFCy+8EGtxmgU/M8xflcHcunVrY4do6BSlpaWxFqtVzJw5UzgcjsY2nXHGGbEWqdWceuqpje1xOBxi9uzZsRapVRw5ckTY7fbGNtlsNrFz585Yi9Uq5s+fL+Lj4xvblJubKzRNi7VYreKqq65qHNRYrVZx7733xlqkVuFyuURqauox+u6HH36ItVitYtWqVcf0peTkZOF0OmMt1gn4M5i/KpfsN998g6bJSDhFUfB6vfz4448xlqp1LFq0CE/9icSqqrJhwwaqq6N/jFa0qKysZNOmTaiqfLU8Hg+LFi2KsVStY8WKFXi93sZgJU3T+Oabb2IsVetYtGgRdXUyUlZVVYqLi9mzJ/pHQUWTpUuXNj4jXdf56quvYixR69i4cSM1NTWNbfJ6vSxevDjGUrWOL774Aq9XbkFTFAWn08natWtjLFUINGdFRTudYTYwdOhQcemll8ZajIhiNpvFt99+G2sxIsbixYuF1WqNtRgR5cILLxTttU+ES3Jyspg1a1asxYgYhw4dElKt/e8wZcoUkZ2dHWsxIkr37t3F7bffHmsx/ML/wgzTwMDAwMAgVhgG08DAwMDAIAgMg2lgYGBgYBAEhsE0MDAwMDAIAsNgGhgYGBgYBIFhMA0MDAwMDILAMJgGBgYGBgZBYBhMAwMDAwODIDAMpoGBgYGBQRAYBtPAwMDAwCAIDINpYGBgYGAQBL8qg1lUVMTChQuprKzk4MGDLFy4kPLy8liL1SrWrl3LwoUL0XWdH3/8ka+//vpXfTahpml89dVXrFq1Cl3XWbhwIevXr4+1WK2irKyMhQsXUlxcTEVFBQsXLuTgwYOxFqtVbN26lYULF+L1etm0aROLFy9uPATg18rSpUv54osvAFi4cCGrVq2KsUStw+l08vnnn7Nr1y5cLhcLFy5k9+7dsRarVezdu5eFCxdSW1vL3r17+fzzz6mpqYm1WMHTXIJZ0U6TrzecDaeqqjCbzUJRFLFo0aJYi9UqzjzzzMazI+Pi4oTVahU1NTWxFitsqqqqhMViaTyGzWq1inPPPTfWYrWKBQsWCEVRGt89k8kkXn/99ViL1SpuuukmYbFYGo8rUxRF7N27N9ZitYrU1FQRFxfX2Jd69+4da5FaxerVqwUgLBaLUBRFWCwW8cADD8RarFbxl7/8pVF3N7x/q1atirVYJ8D/wnmYVVVVIiEhofEstZycHOHz+WItVqv49ttvG88ltFgsYvLkybEWqdXccccdjYfexsfHi++//z7WIrUKn88nMjMzG9+7pKSkX/WgRgghtmzZ0nguoaIoYty4cbEWqdU888wzjW2Kj48X77//fqxFajUnn3xy43sXFxcnioqKYi1Sqzh06NAx52EOGTJE6Loea7FO4H/CYAohxOOPPy4URREmk0l89NFHsRan1ei6Lk466aTG2VhxcXGsRWo1RUVFjbPm9vgOhcMHH3wgTCaTUBRFPP3007EWJyJcdNFFjQO1zZs3x1qcVlNTUyMSExMFIDIzM4XX6421SK3m+++/bxx8tufjsELhrrvuEoAwm83im2++ibU4zfI/YzCrqqqEqqrCbrf/6meXDXz77bcCEJdcckmsRYkY559/vgB+9bPLBnw+n7DZbEJV1V/97LKBLVu2CEAMGDAg1qJEjOnTpwvgf2J22UCnTp0E8KufXTbQcGZpWlpau5xdCvE/ch6mEDpmcyljx/bhhhtOx+s9KK3+rxhdr+PUUzMZMMDK3XdfjNdbEWuRWo3XW84991zCwIE2TjklA12vi7VIrUMITAcPMvHUU7m6f3/ijxyBX/175yM318Lw4cncffcleDwlsRap1fh8Ndxyy2/p1QsuuWQomuaMtUitpq6umDvuGMOZZ3YlJcWJEFqsRWoVQmgkJtZw9tlZTJz4Wzye4liLFBJKIINz8skni9WrV7ehOCfi8RyiqGgOJSULcDrzABWfT6AoCiaTQFFMxMcPolOna+nS5WbM5uSYyhsM1dXrKSp6jfLy76mr24+q2nC7PdhsVoSow2xOIynp/8jIuI0OHS5AUUyxFjkguu6jrGwxRUVzqK7+BZ+vHEWxUVfnIS7Oiq7XYbPlkJr6GzIz7yQhYUisRW6ZigqYNw8+/hg2bwZdx6soCCGwKoo0mAMGwGWXwcSJ0KlTrCVuEadzBwcOvE55+Ve4XLtQVRt1dV4sFgvgwWRKJCFhGBkZE0hPH4+q2mItckCEEJSXf0dR0ZtUVa3C6z2Mqsbhcrmx2+PQdRdWayYpKWfStesfSUo6DUVRYi12QDTNyeHDH1Fc/D41NRvQdTdCmNE0HxaLGSF8OBx9SEu7kMzMPxIXlxNrkVvE7S6kqOgNSkv/jdO5HUUx4/VqmEwmVFVDUWwkJAyhc+cb6Nz5ekym+FiLjKIoa4QQJ5/weXs1mG53Ifn5UygtlWHiQrgDXq+qDkCnU6fr6NnzBSyW1DaQMjTKy5eSn38XLtfu+llX4NGiyZSIotjIzf0LmZmTUZT25RAQQqOw8GX27XsSITxoWnULd5hQVRt2ey96936FlJSz2kTOkCgthalT4ZNPQFXB2cIsJS5O/veCC+DllyEzM/oyhkh19Vp27ryzXgH7AG/A602mBEAlK+seunV7GFW1tomcwSKE4NChD9i9+yE0rQpNa2lbgoKqOrBaO9Or14t07HhJm8gZCppWy5490ykqmoOiKC22SVGsgEpy8hn06fM6DkefthE0BJzOfHbunExl5bJ6l2ZgT5OqJgA6GRm30L37U5jNCW0jaDP8agymEIKDB+eSn39PvVHxhXS/otgwmRz06/c+HTuOi46QIeLz1ZCffzeHD3+ErrtCvl9V43E4+jJgwCc4HL2iIGHoOJ072LLlKlyufHS9NuT7VdVO58430avXzHYxogRg4UL4/e+lkQx1T6LZLI3nK6/AzTdDO5jJ6LqHvXtnUFg4O8z3zoHVmsHAgfNJTBwWBQlDp66uiLy866mu/iXM985BWtoY+vadg8XSIQoShk5FxQ/k5V2Dz1cRxnNSUVUbubmPkZ19X7vwRgmhU1g4mz17ptfr8ND2latqHCZTMgMGfERq6jnREbIFfhUGUwiNbdtu4ciRT8PqDE1RVQfZ2dPIzX00pm4Yj+cQ69aNoq6uEF0PPEsOjIrJ5GDw4P+QknJmxOQLh/LypWzaNK6+c4efZEFV7dhs2QwbtgKrNT1yAoaKEPCXv8CsWS3PKFsiPh6uvhrmzJEz1Bjh81WzYcPvqK3dhK63rk2q6qB//w9ITx8fIenCo6ZmA+vXn4PPV02oA+mmKIoViyWNYcN+xG7vETkBw+DAgTfYteu+sAY0TVFVBykpZzFo0L9i6krXdQ9btlxJefl3EdDhdnr0eI6srMkRki54/BnMduPjE0KwbdsfOHJkfqt/aABdd1JQ8Dx79z4eAenCw+stZe3aU3G59rbSWALoaFoNGzeOpbJyZUTkC4eKihVs2nRh/TNqXUYiXXfhcu1h7drT8HpjmLHpkUdg9uzWG0uA2lq57nnrrTELDNI0F+vXn0NNzfpWG0uQfWnr1hsoKVkYAenCo7Y2j3XrzsLnK6c1xhJACA8ez2HWrj0Vt7sgMgKGQVHRHHbtmtpqYwnyGVVULGPTpovr3e5tjxAamzdfTnn5txHS4S52736AwsLXIyBdZGg3BrOo6G8cOfJZRDp4A9JoPktp6ZcRKzNYhBBs2XIldXVFtLaDN0XXnWzadCFeb2nEygwWj+dwvbGMZPShl7q6AvLyro5NxPPixfDii9LQRQqnU66Bvv125MoMgZ07J+F0bmlxzSgUdN1FXt51uFy7IlZmsGiakw0bzgtijTwUdLzeMjZuPD8mBqaqajX5+XdHWN+5qKxcwd69MyJWZijs3fsEFRVLIq7Dd+/+E5WVP0WszNbQLgymy7WXXbumRWRUcjwNo2OfrzLiZQeiuPh9qqr+ixCRz8+paS62b7814uW2xLZtEyIwUz4RITxUVv7I4cMfRbzsgJSXw403RmZmeTxOJ9x7LxS07QymvPw7Dh/+JCrPSdfr2LLlGoRo21zHu3bdj89XhkwOE0k03O69FBQ8F+FyA6PrdeTlXRWRmeWJZTspLHyR6uq2zd9cU7OZgoJnIzyYlsjB2lVoWuTf6VBpFwZzx447orpXT9Nq2L374aiVfzwyyOeuqAwAAISoo6zsG8rLv49K+c1RVvYNFRVLozIAANnRd+y4o233zj30UHSMZQN1dfDHP0av/OMQQmfr1puiooglGk7nVg4d+jBK5Z9Ibe0WiovfiVqbdL2WffueoK6u7ZLpFxTMxuM5FLXydd3Ntm03R6385ti27fdRGaQ14PWWUlDwfNTKD5aYG0y3ez8VFctoaYtFaxCijuLid9G06Biw4zl06MOoj8J1vZb9+5+Nah1N2b//magNABoQQnD48CdRraORmhr44ANp1KKFzwfffQdFRdGrowllZV9F2G15IvK9ezqqdTSloOBFdD3ap6gIioreiHId9TUJjcLCmVGZiTWpBZcrn+rqtVGs4yg1NRtxOrcSeQ/AUeTMeXbM1mcbiLnBPHDgNUINHlmyREbun38+XH89bNzY8j2KonL48MfhCRkiBQXP+TUuCxbA7bfDeefBM880f/9778E558CaNYHrqahYhttd2EppW8bt3k9Vlf9AI48HnnsOrrlGbke89Vb4+Wf5XV4e/OlPcPHFcOmlMGOG3OrYHLpew/79fn6USPP3vwfc+nEDkAEkAX2ApquR3wH9AAdwDrAvUD1CwOttE7Swf/9zAQ3mk0/C5ZfDhRdKT/TixUe/+/572acuuEDurFmxwn89bvc+qqqiHz3v81XXu+kDD6YLC2V/evLJo59VVMDjj8O4cXDRRfDEE/7v13U3Bw680ibKuKzsyxZnYvfcI9tz/vny76abTrzm2WeljjhwoPkydL2OgoKZEZC4ZQoKZrXoIWxOZ4eiGwCE8FJauiiywodIzA1mScmCkNx8q1fDW2/BAw/IDj97NmRktHyfptVw+PD8VkgaHHV1xdTV+XmLgQ4dpLI6//zmvz9wAJYtk9e1hKqaqaj4LkxJg6es7GvA//4uTZOJbmbPhkWL4JZb4LHHoLgYqqul0vroIxk8arfLzu4Pt3tv2wQ0zZ8fMNDnIWAvUAV8DkwH1gAlwHjgcaAMOBm4OlA9dXXw2WcRETkQuu6jqurHgNdcd518DosXS+Mydy5s3w5HjsBTT8GkSfK722+XBsbfUbO67qGs7IsotOJYqqpWoiiWFq976SXo1+/Yzx55BNLS5Du3YIHc6RMIIXw4nVtaIW1wlJQsDMoLcPfd8MUX8u/994/9btOmYJwWWps8I4CyssUEGtT409mh6gZNq6ak5F+Rb0AIxNRg6roHt3tvSPfMmycNzoABcptberr8C4aammNdFC6Xi3nz5lFVVRWSDA0sX778hENqa2rWoKpxfu856ywYNQqSkpr//qWX4Lbb5D74ltC0mhO2mBQUFPCPf/yj5Zv98OGHH3LguGFrVdWPAd2xdruclXTpIp/JaafJDrF9O4wcCaNHy+2JcXEyk9zmzf7rV1U71dXHTq1//PFHVgSa8gSgoqKC9957D5fruDWwFg61Hgg07GZT6v92AZ/Vf3clEAfMADYA2wIVlp8v3bP1eL1ePv744xN+52BZt24dX3/99TFRxU5nXsD3DqB7d7A2SdqjKFLxHjkCCQnyWSmKfH5xcYGUsq9+GeUoJSUlvPPOO2FHOi9YsIAdO3Yc81lV1Wo0LfDa5ZIl8t0aPvzoZ7/8AocPwx13yHaZzdC7d+D6hdBPeO82bNjAl1+GF2Hv8Xh4++23T9AtlZWBBzUtoWkyodSUKcFc68TjOdz474YD3bdv3x5W3fn5+SxYsOCYA+693tIWAyr96exQdQMQ0NPVFsTUYDqd21rs5E3RNKmEKyvltP7KK6WBCXYZyuerwOerwuVy8eKLL5KRkcGtt97KunXrwpL/iSeeYNSoUZxxxhmsXCkfZHX1+rDXSpcuBYsFTj01+HuqqmS4dUFBARMmTKB3795MmDAhrPoB/vCHP9CrVy8mTpzYqNCrqv4bUhllZTI4tHv3E7/buBFyc/3fq+tOamqkMVuxYgWnnnoqZ511Fs/481+3wJo1a5gwYQJdu3Zl1qxZ0nCWlck1zBaYhHS79kO6Zy8AtgAnNbkmHuhZ/7lf4uJgxw68Xi9z584lOzub66+/nqVLl4bVpjlz5nD++eczePBgvvzyS4QQ1NRsIJg1pFmzYOxY6R7r0EG+a337Qk4O/Pij7GMrVsj3sEeAPf21tXIdpKSkhKlTp5KTk8Ott94a9uDzzjvvZNCgQVxxxRWNhlPOmP2n8authXffhTvvPPbzvDzIzpZLHpdcIg1nC+MjdL2Wqiq5jrBhwwbGjh3LiBEjmDZtWljt2bt3L7fffjtdu3ZlxowZVFVVIYTA5doZ1P1z5kjZJ08+Vvb582HIEOjZs+UyVDWufi+uzqeffkqvXr0YP348CxYsCKtNn3/+OVdccQU9evTgk08+Qdd1amo2oqp2v/eEorNb0g0AbveeNo/SbkpMM/1UVPzApk0Xo2nBbfkoKZE/eJ8+0oVkNsPDD8PQoXLdrCV03coLLwzm66/XI4RA13VMJhODBg0iJSUlZPk3bNhARYU8XURVVRISEnjuuVz69m15UXXuXDmyf/BB+W+nU84sX3hBztSuuQamTYMRIwKX43anMHFiAkVFRY0jP0VROOus8PK0Llt2dOagKApZWVnMmVOJzRacIvT5pOula1eZkrUpu3bJnRZPPCE7vT/y8k7iwQd3U1tb29imtLQ0Bg8eHHJ7ysvL2bJlC5qmoaoqiqJw2dCh/GPzZixBjLQ0YBWwFHgAuANIB5qa7zOAicDv/ZThdTi4f9AgXlu3Dk3TGt+73r1707lz55DbtGPHDg4elFGdqqoSFxfH9OkDOe20DUDLyxuaJo3K+vVw7bWyHy1eDK++KtejLRZ49FE50/RfhoU77+zBzp07G48+UhSF008/HXMw7pHjWLlyJV6vNI6KopCens5bb5lJTvbve3zlFejYUbZh3jy5nPHww7IPLV4s18fGjIEffpBbbT/8EJIDnM1w8GAvJk8uo6KiovG9czgc/N///V/I7XE6naxZswZd11EUBUVROOmkQcycuQlFCTywycuThsNsljPol1+WBtRikf3nzTflzPmcc+RSvP/0xQ6+/HIwL720AY/H09im7t27k5MTetL2goICdu/eDcj3zmKxMHnySYwbtxloPogpWJ0drG5QFDOjRlViMjlClj8U2n2mn2Cw1fvILrtMjo6Tk+XDaAgwCYZobo5vTdnz5sHvfieNZbhELwVgcOXquuwUFotcg2nKgQNycDB5cuAO0d4wAaOAQuBvQAJyXbMpVUBiG8vVGkwmGDxYDtgWLpTBZW+9JdeWvvlG/veFF6QnORCxPFovP1/KfcUVJ35ns8l+dOGFUkGfe65cY9+0qeVy28NxgQMGgMMh3edjx8KgQVLHvfqqDABKCCEneazbE4zO/jXphtCHgpGs3JxCKBGyiYnS993ULoRiI0wmwb///T1er4XXX3+dJ554gurqal566SXOPvvs4AuqZ8yYMXz77beMHDmS559/njPOOIO9e59k7948Qs3us3btUQUG0oXx2GNy9Hzttf7v69Ahh4KCDRQUFPDoo482rl+G6+6zWq2oqsoNN9zAjBkzyMrK4r//HYjTGdgLIAQ8/7wMFHnmmWPXYIuL5Wzzxhtl9F8gFMXCuHHXMWnS/Sxfvpxp06bxyy+/cNppp7FoUegRct999x1jxowhJSWFRx55hDvuuAO7yxVcpFgTfMg1zIHAe00+r23yuT8sJhOz3n2X53r35r333uPhhx+mpKSE6dOnc/3114faJCZNmsSbb75J//79eeGFFxgzZgyHDn3Azp13omnBB9Bpmlyn9PmkourbV37erx/07y8NUi8/uf7j4lLYvn07R44c4emnn+aNN97A7XazePFikgNN4/zQtWtXSkpKuPjii3nqqafo06cPGzeeT1lZ8zPM9evh0KGjwTwulxyw7dsnXZnHhRYEpSeGDz+XsrI3Wb9+PQ888ADfffcdPXr0CKsv7dixg/79+xMfH8+f/vQn7r33XpKSkvjhBxtCBD4t5ngaTpNbu1au8b355tHv7rxTGprf/vbE+0wmCw888Feefvp3fPbZZ9x///3s27eP2267jQcbXFsh8OKLLzJt2jSysrJ47rnnuPLKK6msXMrmzePR/MT8tKSzQ9ENDYSyjBdpYmowHY5+IW92HTtWRr2dcopUyp9+Gth11BSzOQWzOQmzGaZOncqkSZP4+OOPGTYsvJMYpk+fzqOPPsrpp5/e+Fli4lBMJgea1rwLU9Pkn67LP49HjvhnzuSYl+6OO2TU4siRgWVISpILntnZ2bzzzjs89thj/PDDD2G1B+Cdd95h9OjRZGVlNWnTKfVnkfpn1iyprGbOPDqqBDkImDpVjjAvvrjl+lXVTkLCUADOPPNMfvrpp7ADfgBGjBjB22+/zdVXX43dXr/WYrfLYXpZWbP3HAaWAOMAO/At8FH932nANOD/ARcCfwWGINc5/eJ2Q58+WMxmbr31Vm6++WY+/fTTsN3mEydO5JJLLuG8885r9CrI38y/VSgvh3XrZF+xWqUxXLIEpk+XQRf/+IectfXqBTt3ytnYJQFOwYqPl1OB9PR0XnzxRR566CE+//xzkvxFs7XAq6++yqBBg+jT5+gxVUlJoygr+47m1jHHjZMzxwY++UQq33vvlYElb7wBX34pvTYrVsj3MJBHX1XjSUqSnW3o0KF89dVXrF+/vtH1HSq5ubm8+eabXHnllccMIOz23gH7Uk2NdMkOHSr1wpIlcm1v8mTZ3qYTxssvlx4df+uZuu4mIWEoqqpyxRVXMH78eP7973/T7/iQ4iC5+OKL6d69O5dccglq/cECCQkntZhUwp/ODlU3AMTFdY/pMYcxP63k55/7BL0QDnI0/Morcj+41SqjrO6449joP3+kpo7hpJOim1e2rq6Yn37K9ZvHc948uc+yKTffLKNMmxLMGqbJlEDv3q/SpUt0s3oUFb1df9xa88FMxcVyFmyxyE7ewH33yRnMvHlHj41s4As/Ee+KYuX004uif/TSb34jtVEzHAGuQEa/6kA3YApynRKkAZ2M3H85EpgH5Aaqq39/qQWjiK77WL7c4Xf2UlEh1yV37ZJKt3NnGD9eGh6QCu3TT6VhTUmR++KuuspfbWZycx8hN/cvUWlLA2VlX7Fly1V+B59NabqGCdLIzJ4NBw/KgKY77wzs7jOZEhk2bDkJCSf5vygCbN9+GwcPzvH7fUWFdE/u3y8Nf04OTJgAJ5+wmtbyGqbZnMqoUc0PCiPJjz92wus94vd7fzr7o49C0w0AnTvfTP/+8yIhdkDa7fFeu3Y9QGHh7KilXGvAZEqgV6/ZZGTcEtV6AH76qSdu9+6o16MocYwcuZO4uKyWL24Fbvd+/vvfvlFNfdWA3d6HkSPDC3sPiTfekFEhkUy63hw2m6wn0M75CLFu3WgqK5e1fGErUdV4hg5dSlJSM1o8gvh81axc2TmKqf6OYjancvrph1HV6DrdSksXk5d3bdQzMoGJTp2uZsCA6Kcx3Lr1Dxw69AHRzNYGclDTr9/7pKdfGtV6oB0H/WRm3tkmYgih06lTgMXACJKdfT+qGv1DkVNSzo66sQSIi8shKen0li9sJaqaQE5O6GsrYXHDDW1z/JaiSN96G5CT8wAmU/TDj+T7EF1jCWA2J9b32egeiqyqcWRm3hV1YwmQlja2TdbgVNVGdvbUli+MANnZ97bJGZyKYqFDh3FRrycQMTeYcXE5pKScTTQ7haLY6NLlD1EPRW6gc+fro+5nV9V4cnIeiGodTcnJeSjqgwBFTdQVXwAAIABJREFUUejUqYWULJEiIUFGGtii2NHNZun67do1enU0IS1tTNQNpnzvHopqHU3Jzr4PVQ1ivaVVKHTtekeU66ivSTGRlTUVVY2mLlKw23uRmDi85UsjQELCEByO/gQbTR8OquogK+ueNhnUBJQjprXX06fPG1EddZlMCfTo8WTLF0YIszmBXr1eiZqBURQbaWnnkZp6TlTKb460tN+SkjIaRYmO8lLVePr0eaPNBjUAPP20jN+PFjYb/O1v0Sv/OBRFpX//DwJuJG8dJhyO/nTuHHpkb7jExw+kS5cJUWuTqsbTrdt0bLbQoqZbQ3b2PVitoe+/DRZVjaNfv/davjCC9Os3L6o63GLpQHZ2eEkkIkm7MJh2ey49ez4XFQOjqg769/87ZnPooe6toUuXm0hKOiWoXJihYjLZ6dvXf+BAtOjX792odApFsZKcfEabucwbSU2VJ5ZEw2g6HDJ0ODs78mUHIDX1XDp1ujoqz0lVbQwc+HGbRyn27PkcZnMakZ/BmIiL696mnhpo+B3nR2UQIGdi95GYODTiZQciIWEQ2dkPRGXmrKp2Bg6cj8kUu+0kjbLEWoAGunb9I+np4yP6g6uqg+zsaXToMDZiZQaLoigMHDgfmy2TSO7eUVUHgwcvjn4UaTNYrekMHrw4wp3Cgs2WxYABH0cx8UIALrxQ7kWIpNF0OGSIaTDpp6JA796v43AMQFEi525WVTsDBvwDuz2InGwRxmRycNJJX0fY3axisaQyZMhiFCW6a6TNkZg4gl69XoqwvrOTnHwmubkzIlZmKOTmTicl5ZyIDgRU1UGPHs83bvmJNe3GYCqKQr9+75KefkVEXqIGY5mb+2gEpAsPi6UDw4f/hN2eG4ERv4rJlMCQIV+QnBz9ABx/pKSMYvDgRfXegNa9Pqoah93eneHDV2GxpEZGwHB4/PHIGc34eLmbfu7c0LJqRBCTyc7QoUvr9+BFpi/16/c+HTsG2JgZZeLjBzBs2A/1yU5aNwBVFCtWayeGD/+ZuLjQU8RFiq5dJ9Kz5wsRMTCq6iAl5WwGD14Ys3U+RTExaNBnpKb+NiLeQmksnyEr686WL24jYr6t5HiEEBw8OLd+318doWbMURQbJpODfv3eo2PHi6IjZIj4fDXk59/D4cP/CCtEXlXjcTj6MmDAJzgcflKvtDFO5w62bLkKlys/rIOlVdVO58430qvXi5hM0Y8oDoqFC+WGWKdTZpQIBbNZbih75RW5sTZGxrIpuu5h794Z9QfvhvPeObBaMxg4cD6JieEl94g0dXVF5OVdT3X1L2G+dw7S0sbSt+9bMfHSNEdFxQ/k5V2Dz1cRxnNSUVUbubmPkZ19X0xmy8cjhE5h4Uvs2fNwvQ4PLVm6qsZhMiUzYMBHbRqn0ZR2uw/TH253ITt33kVZmUw0IETgPYByJC23jvTsOTO2MxY/lJd/T37+FFyu3fUvUuB9SyZTIopiIzf3L2RmTo5phovmkKfHv8y+fU8ihCeIvWUmVNWG3d6L3r1fISUlvEw3UaW0VKYf+eQTuXPc2XxS6UYadl1fcIHMku0/E3bMqK5ex86dk+pPrtAIdAIIyCA5UMjKupdu3R5ugyjV0BBCcOjQ++ze/Wc0rQpNa+nkGaXe+HemV68XYzpT9oem1bJnz3SKimRsQkuDAUWxoigqSUln0KfPazgcfdtCzJBwOvPZufPOxqPg/CVzaUDOSgUZGbfQvftTmM0hJM2NML86g9mAx3OIoqK3KCn5V31KKRM+nzwBwGQSKIqJ+PhBpKdfQ0bG79s8uCccqqvXceDAa1RULKWubj+qasPt9mCz2RDCjdmcRmLiyXTtejsdOlzQLkaNgdB1H2Vliykqeovq6tX4fOUoio26Og9xcVZ0vQ6brRupqeeSmTkp6tlUIkJFhUxD8vHHMoGnruNVFIQQWBuSe/bvL/N63XabzO7dznE6d3DgwGuUl3+Ny7ULVbVRV+fFYrEAHkymBBIShpORMaE+niD6e+tagxCCioolHDjwBlVVq/B6D6Oqcbhcbuz2OHTdhdWaSXLyKDIzJ5GUdFps1slDQNOcHD78EcXF71NTswEh6tB1E5qmYbGYEMKHw9GHtLQLyMycFFOXcrC43QUUFb1BaekinM7tKIoZr1fDZDKhqhqKYiMhYQidOl1Ply43tAuPkz+D2Xg0T3N/I0aMEO0JXdeE07lHXHhhP3Hbbb8VLleB0HU91mK1Ck1zi5qarWLgQJtYsuQd4fGUxVqkVuPxlImvvnpLDBoUJ2prtwlNc8dapNah60IUFIi7Ro8W1w0aJMSePUJoWqylahWa5hG1tTvE8OHJ4s03HxJ1dYdjLVKr8XqrxJ49S0Xv3oqorNwofL7aWIvUatzug+KRR24UZ57ZVTid+ULXfbEWqVXouk84nbvE6NHZ/5+98w6Pqkr/+OfeOz2VFAKEBCIgVURlVRTFAgjiTxQX29qwsesiAmJlLbiWBUVQFBVU0GXta9wVBdRFUewQkN4hDQghZZJMJtPu+f1xk5BAZjI1E3zm8zx5lCnnfs/cc9/3Pe9p4sEHrxF1dQeiLalFgLWiBZ94QjlMIYSoqqoSsiwLs9ks3O4Tu/E08NVXXwlAjB07NtpSwsall14qAPH1119HW0pYcLvdwmg0ClmWRU1NTbTlhIUtW7YIQPTr1y/aUsLGI488IgDx9ttvR1tK2OjYsaMAxIED7dO5BEpJSYkAREpKSrvt8HhzmO1rUMwPXnjhBYQQOJ1OPvzww2jLCRkhBPfWn7S8fPlySkpKoqwodA4ePMiXX34JwPTp06OsJjy8++67uN1uhBDMnz8/2nLCQsMRTzt37mTLli1RVhM6NpuNuXPnAvDQQw/hdgc2YbA98s0331Bef6rOzJkzo6wmPDxZv69yVVUV//vf/6KsJkBa8qKinfYwq6qqRHx8vAAEILKzs0/4XuZXX30l4uLiBCD0er2YNGlStCWFzF/+8heh0+kEIOLi4k74Xqbb7RaZmZmN7S4xMfGE72Vu2bJFmM1mAQhJksRll10WbUkhM2vWrMY6xcXF/S56mYMHD25sdyaT6YTvZZaUlDTeI0AMHDiwXfYy+T2kZF988UWh0+mELMtCp9MJSZLEsmXLoi0rJM477zxhMBgEIIxGozAYDCe0Ma6qqhJ6vV4YjUYBCIPBIC666KJoywqJ3NxcIUmSUBRFyLIsFEURCxYsiLaskLjpppuEXq9vvEeSJIn9+/dHW1ZIdOjQQZhMpkbn0qtXr2hLCom1a9c2BtKSJAm9Xi8eeOCBaMsKiUceeaRZfQDx448/RlvWcfwuHGZRUZHIzc0VOTk54qyzzhK5ubmivPzEniSzbt06kZubKxRFEY8//rhYsWKF8JzAE0rcbrdYvny5mDFjhtDpdCI3N1fk5eVFW1ZIlJWVidzcXHHGGWeIHj16iNzc3BM+0t+6davIzc0VFotF3HrrreLTTz8VDocj2rJCYtWqVWLx4sUCELm5ueL777+PtqSQqKmpEZ988okYM2aMSE1NFbm5uWLPnj3RlhUSe/fuFbm5uaJjx45i5MiR4pNPPhHV1dXRlnUcvwuH2cCgQYPEFVdcEW0ZYUWn04mvvvoq2jLCxmeffSYMBkO0ZYSVMWPGiPb6TARLUlKSmDt3brRlhI2GCSW/JyZPniyysrKiLSOs5OTkiIkTJ0Zbhle8OcwTbtJPjBgxYsSIEQ1iDjNGjBgxYsTwg5jDjBEjRowYMfwg5jBjxIgRI0YMP4g5zBgxYsSIEcMPYg4zRowYMWLE8IOYw4wRI0aMGDH8IOYwY8SIESNGDD+IOcwYMWLEiBHDD2IOM0aMGDFixPCDmMOMESNGjBgx/KGl/fJEO91Ldv78+Y073Dfsdv/pp59GW1ZIXHTRRY1HYSmKIhISEkRVVVW0ZQVNZWWliI+PF4qiCEDodDoxcuTIaMsKidzc3MYTFqg/PeKVV16JtqyQuPnmmxvbXcOB7Lt37462rJDIyMhorJNOpxP9+/ePtqSQ+Pnnn4XBYBCyLDfW6cEHH4y2rJB49NFHG++RJEnCYDCINWvWRFvWcfB72Et2+PDhyLImWQiBTqfjnHPOibKq0BgzZgwGgwEAj8fDwIEDSUhIiLKq4ElKSqJ///54PB4ADAYDl156aZRVhcbQoUPR6XTaaQWALMsMHz48yqpC47LLLsNoNAKgqioZGRnk5OREWVVoDBs2rNk9GjlyZJQVhcbAgQOJi4tDVVUA9Hr9Cf8sjR49Gr1eD2g23Gw2c/rpp0dZVQC05EVFO+1hCiHElVde2RgV33///dGWEzK1tbUiKSmp8TzMH374IdqSQua7775rPA+zQ4cOwm63R1tSyEybNq0x0r/66qujLSdkPB6PyMnJaTwP84MPPoi2pJDZtWtXY7szm83i8OHD0ZYUMi+88EJjj+yss86KtpywMHTo0MYe83PPPRdtOS3C7+V4r23btjUe5ltWVhZtOWFhzpw5AjjhU0hN6d27twDEvHnzoi0lLJSWlgpZloUkSWLXrl3RlhMWPvzwQwGIlJSUE/oM1qaMHTtWAGLq1KnRlhIW7HZ7YxDw7bffRltOWPjxxx8bA7Xa2tpoy2mR343DFEKI1NRUMWDAgGjLCBu1tbUCEIsWLYq2lLDx0ksvCUmSfhe9ywb69Okj0tPToy0jbHg8HqEoivjLX/4SbSlh49dffxXA76J32cCoUaOEyWSKtoywYrFYxAUXXBBtGV7x5jAlUZ/zb4nBgweLtWvXRiob7Dd2+x4qKv6H1bqG6upfKSsrQqfTkZTUlaSkISQmnkNKykiMxi7RluoXQgiqqn7Aav2eyspvqK3dzqFDhaSldcRi6UZS0jCSks6mQ4eRKIo52nL9wuOppbz8C6qqfsRq/RabrYDy8sNkZGRhsfQlOXkYSUlDSUwcgiRJ0ZbrFw5HMeXlX2C1/kBV1U9UVhahqh5SUjJJSPgDSUlD6dBhOGbzSdGW6hdCCGpq1lNZ+S2Vlaux2TZRWlpEYmIycXFZJCdr9yclZRQ6XWK05fqFqjqpqPhf/f1Zjd2+j0OHiujcORuzuQfJyReSlDSE5ORhSJISbbl+4XKVUV6+Aqv1R6zW76muLsJut5GW1pX4+NNITj6f5OQLiYvrF22pfmOzbaOy8msqK7+lpmY9ZWVFGI1mEhO7kpR0bn27uwSDIT3aUgGQJGmdEGLwca+3V4cphKCs7DMKCp6hpiYPkFHV2hY/K8vxgJvk5AvJzn6I5OTz2lSrv3g8Ng4ceJ2ioudwuytRVSdCOFv4pIyixAMqnTrdSlbWNEymbm0t1y/s9v0UFs7h0KHFSJKMx2MD1OM+J0kGZNmATteBrKz76Nz5NhTF0vaC/aCycjX5+c9QWbkaSVJQVVuLn5NlC6ASHz+Ybt0eIiVldLsMBlTVSUnJUgoKZuFwFCOEGyEcLXxSQlHiEcJFevq1ZGffT1xc3zbX6w9OZwmFhfM4cOAVQNS3O08Ln9SjKCZk2Uhm5hQyM+9Cr+/Qxmr9o7o6j4KCZygrW4Yk6fB4alr8nCSZkSQwm3uTnf0AHTtejSS1v/mbQqiUlv6bgoJ/UFu7DQBVtbf42QYbnpJyKdnZD5KY+Ic2VHo8J5TDdDiK2br1Bqqrf/VqrFpGQpbNpKb+Hyef/Cp6fXLENAZKZeVqtmy5Fo+nyqvjbwlJ0iNJenJynqZr17vbzYMhhEpR0Tz27XsEVXUBLr+/K8sWdLpk+vV7n+TkoZETGSAuVwU7dtxJefnnAd0jAFmOIzHxbPr2/SdGY+cIKQycmprf2Lz5jzidBwN8lhRk2UDXrtPo3v0xZFkfMY2BIISgpOSf7Nr1V1TV5cXxt4wsm5BlM336vEVa2v9FUGVgeDy17NkznUOH3kJV62gp4PSGLMdhsZxMv34fYLH0jJzIALHb97JlyzXU1m5HVVt2/C2j2fCMjBvo2fN5FCUuYhp9qjhRHGZ5+Uq2bPkjHk8d4A6qDEkyoijxnHrqFyQkRHfKshCCvXsfoLj4Ja/RlT/Ichzx8QMZOHA5Ol1SGBUGjstVycaNl2CzbQnQCDdHls1kZk7hpJOeinrPrKrqVzZuHIXHYwvICDdHj6IY6d8/l5SU6C87KSp6gb17H6o3wt6fc1/IsgWjMYtBg1ZFfcjD46ljy5Y/Uln5TYjtzkJ6+nh6934dWdaFUWHg1NbuYsOGi3C7y0KwDzKybKR370VkZPwprPqC4fDhD9i+fQKq6qDlXn/ryLIZnS6ZQYO+xmLpHV6BfuDNYbaP7ko9R44sY/PmcfWpiOCcJYAQDtzuMtavH0ZV1a/hExiwDsHOnX+muPjlkJwlgKraqK7OIy9vCG63NUwKA8flqiQv7yxqajaEZLRAS88UF7/Arl2T8BW4RRqr9Sc2bLgQt7s8BGcJ4MLjqWHz5sspK/s8bPqCYf/+p9i79+H6dhf8b6uqtdTV7WHdusE4HMXhExggHk8dv/12MZWVq8LQ7mopLf2QLVuuQojgDHo4qK3dSV7eWTidB0K0DyqqamfHjjs4ePCNsOkLhkOH3mb79lvqMzTB/7aqasfpPMS6dWdjs20Ln8AQaTcOs7o6j61brwk4FeYLVa3ht9+GU1dXFLYyA6Gg4B+UlCwNW52EcGC372XjxtEI4X/aJlwIobJx40jq6vZ7GXsNHFWt5dChtygsnBOW8gKlrq6AjRtHhmyEm6KqdrZsGU9NzcawlRkIJSXvUFDwdBjbnRuXq5T164fVZ37anm3brqemZn3IgWcDqlpLRcVX7No1JSzlBYrLVcn69efhdlcSSArWF6pqZ9euyZSXfxWW8gKlouIbdu78c9jukTY2bWXDhvNxucrDVGZotAuHqapOtmwZH1ZnebTsWrZtu77NezA1NZvJz/972OskhIOamo0UF78U1nL9obBwLjbb1rA5ywZU1cb+/Y9is20Pa7mtIYRg69br8Hgi0e7s9W3a/7HdcOBwHGTnzokRaHdunM4D7Ns3I6zl+kNp6b8pL18ZRkOsoQVrb1BZ+V1Yy/WHnTv/XJ8pCq9d0uzddbjdVWEttzXc7pr6Dk947xEI3O4qduy4I8zlBke7cJj79/8dp/NQRMoWwk11dR6HDr0dkfJbvqaobzyRicZV1cbevQ9RV1cQkfJbwm7fz/79j4a1J9YUVXWwdes1bRrYHDz4BjU1vxFK6sg7AoejiPz8pyNQtne2b5+AxxNKWtk7qmrnwIFXqK7Oi0j5LeFyVbJ9+20RCaZBq5P2rEbmN2uJ8vIvKCv7NMT0v3fc7po27znv2XNvxJy0EE7Ky1dQVvZZRMoPhKg7TI/HTlHR3Ig9EHC0B9NWxriy8mscjgLCHT02RQg3hYVzI1b+sRQWzolwb0nFbt+D1bomgtc4ihCC/fsfi1gAAFq0X1Q0p82McW3tDqzW1QQyYzlQVLWO/fufiFj5x3Lo0Bthz2gci8dTTWnpxxG9RlP27ftbRO2dEHWUlr6L01kasWs0xeUq49ChtxEicul6Va1l376/Rax8f4nuFDHg8OH3Af9nSE6ZAlu3glK/Bjk9Hd72o/PocpVhtX5HcvL5wQkNgMLC57yuocrNhRUrYN8+uOgiePBB7fX9++GZZ+DAAe3fJ58Md98N3bu3fA0hnBw69DonnfQMimIKex2a4vHUcujQYrwZYqcT5s2DvDyoqoIuXeCOO+Css8DlgiefhB07oKQE5s6FQYNavo6q1lJY+GybrKOtrFyFx9NyROyrPgCffQbvvAPl5XDKKXD//ZCW1vJ1hBCUln7UJrMXi4peQFW9T5Z76imtTnV1kJIC114LY8bAl1/C88831QwOB7z6KvQ+boKioLx8JU7nYQyGjhGpx1Edan2g1nqar6gIbr0Vhg2DGTNgwwaYNg3q95cH4J57YNSo47/r8dRQUDCLjIzrwqi+ZWy2bdhsm72+782+rV8P8+fD4cPaewMHwuTJ2vstI3Hw4CK6dXs47HU4loMH30SSJHz1R1atgrfe0vSnpMADD0Dfvv7bBtACwpqazcTHDwh/Jfwk6g7z4MHXAlynozX8MWMCu46q1nLw4BsRd5gejzaZwBupqXDjjfDrr5pRaiAtDWbOhIwMUFX45BP4+9/hDZ+T3mQqKr4iLe2ysOlvifLylT7Xf3o80LGj5mQ6doSff9bq8uabWn1POQX++Ed4/PHWriQoL1+Bx1MX8SDg4ME3vQY1vupz6BC8/rrmYLp2hZde0u7TCy+0fB1VreHAgVcj7jC19Yn/wtfs8uuvh/vuA4MBCgo049yzJ4wYof01sGIF/POfWtDWEpIkU1r6bzIz/xLeShxDdXUeHk+1X5994QXo06f5a6mp8OGH/l3Lbt9BXV0hJlNWgCoDo6TknVYzNS3Zt27dYPZszU44nVpbnDdPC4JaQlXtbegwF/kMatauhYUL4dFHtXtUVnb0Pf9tQ8MGHP8kPn5W6KKDJKopWW2rrk1tdTWqqn447lWr1Rp0qtZms+FyNW/8NTW/Icvet7M7/3wYOhQSj9l5LD4eOnWChuWIsgzFrczi93hsVFX9fNzrVmvwy05a+m5V1U/1O6m0jNkMt9yi6ZdlGDIEOnfWIke9XnsgTjlFe681ZNmEzda8TbhcLmy24FKn2jaEx/ckW2oLDfiqz48/ar2YnBytbjfeCBs3+r5XNTW/HdfGampqcLuDWzpVV1eHw9E8zetwFCGEb0Ock6M5ywYk6WhGoykrV8LIkUfb4rGoai2Vld8e93qo7e7Y36i6+le/ZoOvWgVxcRDKKVGSZKC6uvmac1VVqa72z2G3REu/R2XlNwSzZC4lpXkWQ1Fatw8ORxEeT3NHZrfbcTqDS3E7nU5qa5unklXVQV3dfp/fW7JEe0769dOep/R07S9Q2wAeKitXB6U9XETVYdbV5RPMlOpFi2DsWJg0SUu9BHI9VdUaS0FBAbfccgupqan88ssvAWsAuOmmm8jKyuKNN95odJzV1esarxEMl12mGav58+FPrXZKPFitRw3Xt99+y1lnnUW3bsFvo9e1a1fOPvts1qw5OpaoXcP/+1ReDoWFmoEOFCE8VFevAzRHuWjRIjIzM7n11lsDLwz44YcfSElJ4dZbb6WwsBDQ1vQ5HP4vNTq2Pk3tesP/79vn/ftCuBrXMJaWljJlyhRSU1P5z3/+E1BdGvjb3/5GRkYGc+bMwW7XDGJ19TokqfXdeObO1dKSN9+s9cDOPrv5+4cOaQFAa0dJVlcfDdTWr1/PiBEjSE1NDTqwGTx4MP379+fzzz9vdJxW65pWx/psNli8GP761+Pfq6yEcePguuvg5ZfB7iOz6/HUUFWl2QFVVfnggw846aSTgj5TMz8/n5SUFMaNG8eOHTua6G19qZE3+1ZSotmHUaPg/fe1lLovZNlSP6kNqqqqmDlzJmlpaSxYsCCoOr3++uukp6fzyCOPNAYDNTWbkGXv2SCPRws0rVbNno0fr2UDHEEO69tsm6O6ZjuqKVmnsxhJMgD+T0W+805tXE+n0yLLhx/WGlhmpj/f1vH55+8wb95SvvvuOzweD3q9nm+++YaypnkCP9m7dy8lJSVMmjSJ6dOnc8stt3DNNZUhDX4vW6Y92CtXaunZ1rDZ9jF//nxeeukl8vPzcTgc6PV6Pv88uIXztbW1/PzzzwwfPpzu3btz9913M3Cg/7Nx3W4tTXTJJZCdHfj1tXHMPGbPvoe33noLh8NBXV0d+/btC6pOGzZsQK/X8/bbb7N06VKGDRvG1KnXExdnwJ9I/9j6nHkmPPEEXH65lpJ9+22tJ+bbABj4/vv/MH/+lyxfvlw79UCS+OmnnzCbA99cf9u2bVitVmbMmMGjjz7Ktddeyx13pPsVqE2dqo19bd2qGWP9MT72iy+0iL9zK7v7ORyHWbx4MS+++CLbtm3D6XQiyzKff/45cXGBb2d25MgRKisrGTduHOnp6dx1110MG9b6gvU334RLLz1+LC87W7ML2dmao/nHP2DBArj3Xm8lqRw+vJ4335zB66+/TlVVFXV1dQghgmp3RUXaARGffPIJy5YtY/Dgwdxzz91kZPieSerLvmVkaPahqkobR2/t+RJCsHnz1yxYMJ+PP/4YVVVxuVxs2LAhqDqtX7+euro6Zs2axbPPPssVV1zBX/96GkJ4n4NSUaE9Q6tXw4svavWaMUNL+d9+e8ASUFU7QjiRJGPrH44AUd0ar6LiGzZvvgKPJ/hUzv33a1HyuHGtf9bl0nHbbTKFhc0Ni06nQ/YvJ3BMea7jop0HH4zjkktaj7LfeANKS49O+jkWVYUrr9TSGR187BVdUaFn3LjjU3GGprm3ADg2XSNJErm5CklJrTsXVdUG8Wtrtf/qjgnHxo/XHhZfA/sAy5bFMWdO899QkqTGk9oDQVXV41KfOTkGXntNRa/3XSdv9cnNhX//W3v9qqvg3Xfh6ae1iRgt4XIZmTFD4tdfmwdSiqKgKIGfoOF2u1HV5j3+m26K45ZbHEiS/+m+55/XxsauuuroazfcoPUERo/2/V2XS2LUKOk4HXq9PqhtDltKEy5ebKJ7d+/B5+7d2n1ZtEhz/EuWaGnKGS0sFd26FR56CHx16vPyzNx77/HBezDPkhDiuOEas1lh2TKPn+lHDW/2rbxcczgffnh0gtCxeDxG5s1TWLaseS892Hbn8XjweJovwRo1ysK993rQ6VqOGKurteDygQeOTrhavRqWLtXuWwP+2gZJ0nHuuRXodPEB6w+Edrk1nnZ0VWjda0nC5+ysphiNOnbtKmD58uX079+fuLg4jEYjX331FQ6HI+C/ESNGoCgKJpOJO+64g6KiIu64456Q6tOAENpsxiNHfH+uS5fu1NbW8vzzz5OcnIzFYsFoNAZVH4fDgU6nw2Kx0KFDB+bNm0dtbS0ZGa1PhBACnn1WiyhnzjzeWfqPxKRJ91FYWMgTYdYzAAAgAElEQVRtt92GyWRClmVGjx4dVH1WrFiB0WgkLi6OAQMGsHLlSrZu3YPR6Nv5+qrPlVdqD/zHH2tj0h6P7/SzyWTkf/9bw5o1axgyZAgWiwW9Xt/Ygw70b+LEiUiShMlkYvz48ezcuZNnnpmDogRm2D2e5mOYmzZpEzKGDWv9uyZTAg6HgzfeeINOnToRFxeHoiiUlpYGVadOnTphNBqJj49n5syZWK1WTj3V94kVGzZovcdrrtEcyvvvw7ffar20lmjNTowadSWlpaXce++9jfeof//+QdVn8+bNGAwG4uPj6d69Ox988AHV1Q5kObBgwpt983i0tukrA24wGFm8+B02btzI6NGjMZvNyLLMk08+GVSdZs+ejaIomM1mRowYwfr161m69AOMRu+9vYQErfffNIYKZdtoITzIcnR6lxBlh2k2nxzQzhA1NfDLL9osMY9Hmw6/caOWJvMPGYOhI6NGjWLTpk189NFHnHnmmWT6l889jnPOOYdbb72V3bt3s3DhQjIzM7FY+tYfzdUyHo+mX1W1v4a6rF0Lu3Zp/2+zaWMuCQlaD8AXFkt/zGYzU6dO5cCBAzz55JNceOGFQdUH4KKLLuLpp5/mwIED3HPPPZhMJiyW1o94mjsX8vO1ntaxz4/Tqf2BtszE6fRuvBQlHrO5N127duX1119n165dTJgwgSFDhgRVn65du3LWWWfx8ccfs3HjRkaO1M5NbW0yibf6OJ3aeKUQmrGeM0cz1gkJ3stSVTtmcy/OPfdcfvjhB7744guGDRtGTjCDvMBpp53G+PHj2bhxIx988AG9evXCbO7tcwyzokJL8dntWhv75Rft300nyqxcCeedBxY/Tl0zmU5Cp9M1jg2/+OKLDBkyJKgUM8CFF17Iww8/THFxMY8++iiJiYnEx5+GryVnl10G//qXNmv59de1nszZZ2uzSdev18ZjhdCWMixaBOee6/36kmQgPv400tLSeO6558jPz2fy5MkM8yd6aIHU1FT+8Ic/8Oabb7Jnzx7Gjx+PoigYDN5z3b7s27ffajObVVUbm335ZejV6/jJg00RwoPZ3JtTTjmFzz//nJ9//pkxY8bQ59jpxH7Su3dvRo0a1diGBw0ahNncGyF8ZzVGjdKyMhUVWo/zo4+0iXQQmG0A0Os7RvXknKifVvL99xm4XIf9+mxlpZbCLCjQZlVlZ2trrwYf13Fumfj4wQweHNnN2G22baxbd6bXpTJLlmjrkZpy883auMWbb2ppWqNRm359xx3Qo4f3a0mSkZNO+gdZWZHd1aOgYDb79j3idQH5oUPaxAq9vnl6aNo0bbnCtddqzqUp776rzUI9Flm2MHjwBiyWXmGswfH8+usgbLbfWnzPV32GDNGm/R84oM2mHT1aa4O+MlwGQ2fOOaeF6ahhxO228v336V5nylZWwmOPwZ49mkHKyNAc/WX1K5KcTu3fM2fCGWe0djWJzMxJ9Or1YljrcCwlJe+yc+dEv5eWNE3JfvCBlq6srtacytChWgrTWzCgKEkMGJBLhw7BB5v+sGnTWMrK/tvie77s28cfa/WprNTa3aBBWk+6pWeoAUkycv75tRE9ElAIwXffxfns+Ljd2iTG//1Pm6V9wQXw5z9r/x+IbQBISRnNwIGRP9ig3R7vtXnzOI4c+YRI7ooDWgTZteu99OgR2a3KhPCwZk2y1zV+4USW4xg0aBWJiX53sYPCav2BjRsvaZM6KUoiQ4dWRPzczz177qOo6IVWl2KEjkR6+tX07/9ehK8DP/2U0+oU/3CgKAn06bOE9HQ/Jg6EQF1dPr/80idiW0w2RZJMnHtuCTqdjy5bGCguXsCePfdFdKefBhISzuKMM36K+HXy8s6jqiryO3TJspmcnKcj3kGAdjqGCdC16z31J9dHFkmS6dLFy+BGWK+j0KnTbX5N8Q8Vg6EjCQmRP5k8MXEIOl3kT6mXJANdutzZJodkd+48EUkKfOJDoMiyha5dJ0f8OgCZmVPa5FkCidTUyG6WAWAydSM+vpVZIGFBJjV1TMSdJUDHjtcRrtNJfKEoCWRlTYv4dQCysqaiKD7GJMKGoFOnG9vgOt6JusNMSjofvd7LvmJhJCHhLMzm7hG/DkDXrndH3BjLchxZWdPb5OBlSZLIyrqvDYyxTGbmpAhfQ8Ni6Ul8fKu5x5AxGDJITAxu/DVQOnW6mUgbYy2omYgsBzcLO1Cysh7wOScgHMiymawsr+tNwope34G0tHFApIM1mbS0KyJ8DY3U1MuRpEivUJRJTb0MvT41wtdpTUWUkSSJXr3mR9QYy7KZnj3bbqNys7kH6elXI0mR295Nr+9Ap04TIlb+sXTufBs6XXLEypdlExkZf8JkCn7ThUDp2XOez12ZQkWWLfTq9VKbBDUAen0yWVn3R/xZysq6P2LlH0ta2v9hMvUgcqZKT2LiEBITz279o2EiJ+epiM70lOU4evSY1WZBjSzr6NHjOWQ58PW3/l/DRE7OMxEr328d0RYA2kORknJpRBajaimxKSQknBb2sn3Rq9d8dLrIpClk2Uy/fh/WL8tpGxTFQr9+70XMwShKEj17zotI2d5ITBxMly53RaROkmQkLW0sqamtLGgMM926/Q2jMYtADjTwF1m20KfPYgyGyGeEGpAkhf7934+Yg1EUE337vt1mQQ2A2dydHj1mR8TBSJKO+PhBdO4c+eGnpnTqNIGEhMFEYi8cWY4jJ+dJLJaeYS87YC3RFtBA794L608/CF+qQpKMWCy96d798bCV6S86XSL9+/877NG+LFvIyppOUlLbRcQNJCefR2Zm+MecZdnCgAEfR3wxckucdNJTmM0n1+84FR4kSYfB0IlevYLbgiwUZFnPgAEfhz2NKcsW0tOvIj39yrCW6w8WS2969JgbgXZnpk+ftzAaW9nWKAJ06fIXkpLODXOwJqEoifTr926bBgCgZQr79XsHvT6ZcAZrsmwiMfFMunYNz/r2UGk3DlOv78Bpp32PwdCJcEQpmrM8mUGDvm6z1MSxJCefR//+H4TtQZdlC50730737jPDUl4wnHTS03TqdEvYomNZttC//79JSjonLOUFfn0jgwZ9g9ncM0wZDj0GQ2dOP/37euPR9sTF9ePUU78M20QMWbaQkjKKPn0Wh6W8YMjMnEj37o+G8Vky06vXgqgEAKBNQhww4D8kJPwhTE5TRqdL5rTT1kT8xBVvGI1dOO2079HpUghHx0eWzcTHn84pp3zWJhMB/aF9qKjHZMpi8OB1JCaeGZJBlmULqaljOO20H9DpksKoMHBSU8cwcOBK9Pr0EMY0ZWTZQk7OU/TsOa/No8emaGPOL9G9++P1D3pwTUiWTej1GZx66pekprZwSGEbotcnc/rpP5GScklIBlmW40hKOpszzliL0RjcZhjhIjHxLE477XtMppwQ62QmM3MS/ft/2Cazin2Rnf0AJ5/8KrIcF/QkE0kyoChJ9O//IZ073xJegQGiKCZOPfVLMjL+FJLTlOU44uL6c8YZa4mLa32TkUhisZzM4MHriIsbEKINN9Ox4zUMGrSqTYeeWiPq6zBbQgjBwYOL2L1bmxatqv6dgCDL8ciynj59FpOWNjaSEgPG7a5m9+7JHD78HkKofp4iLyHLFiyWXvTr9z4Wi5cDCqOEzbadrVuvwW7fU7+urPW1tFrqUyYj40/07DkvKmlYX5SWfsyOHbchhNvvdacNhqFnz3l07nxbVAOaY1FVB/v2PUJR0XwAvw8GUJQE9Pp0+vV7n8REP3cGaSPq6orYtu0Gqqt/RVUdgKfV74AOWdbTocMIevd+o03HYf2houIbtm27Ho+nxu+NGjQnK+jW7VGys++PekDTFCE8FBbOZf/+RwHJ73WnipJQP0fjHTp0uDiyIn3Qbjcu8IXbXU1JyVIKC2fjcBxElk315zJqWzFJkgFZNqOqtVgsvcnOfpD09D9Gda/B1rDb91Fc/BIHDy5CCBVJkps9ILJsQZJ0qKqdlJQxZGdPJzHxnHZlhJsihMBqXUNh4bOUl69Als0I4W7ygEgoSnx9XRW6dLmTLl3+2mZLfILB46mjtPRDCgpmYbfvqm9j9iZBjg5F0XY3MRozycq6n4yMG9qd82+Kw3GIAwdeo7h4PqpqR5J09QGBtgxFlk1IkgFVrSUp6Xyys++nQ4cR7SYV1hLV1esoLHye0tJ/I8sGhFCbBdcN47hCeMjIuJGuXadEvQfmC1V1U1b2KQUFs6ipyUOWLahqHUI0bGyuoChxCOFEp+tA167T6Nz5VvT6lKjq9oXLVcGhQ4spLJyD212OJBnqbbgW5EiSEVk2oaq1xMefSnb2g6SmXh7V7e80XSegw2yK01lCdfU6amo28vHHb5Gc3IGLLx5HQsJpxMefjl4f+YX14UQIFbt9N9XV67DbdzJ//myuvfYmMjMHER9/BvHxp7Rrx98SHk8dNtsmqqvXUVS0ng8//BeTJt2H2dybhIQzMJt7tGsD3BIuVznV1XnU1Gzgiy8+pKamhiuvvJH4+FNJSDijfqLaiYMQgrq6fGpq1mGzbeX111/g/PMvpl+/oSQknEF8/CAUpS02PwgfquqitnYr1dXrqKrawfz5z3HvvTMwm3uSkDAYi6V3u+p9+YPbXU1NzXqqq/P4+ecV7NixhRtu+DNxcafUt7su7TaI9obDcYDq6nXYbJv4178W0qNHL84551ISEk4nPv60Ntk4wl+8OUyEEF7/zjjjDNHeUFVVGI1GkZGREW0pYSM/P18AYvr06dGWEjYmT54sAFFYWBhtKWEjLS1NmEwmoapqtKWEBavVKgBxySWXRFtK2HjvvfcEIPLy8qItJWz0799fyLIsHA5HtKWEBZfLJRRFESeffHK0pXgFWCta8IknVrgPfPbZZzidTkpLS9m0aVO05YSFRx99FIA333zzuLMbT0RcLhdv1e8w//jjj0dXTJhYv3495eXlOJ1OVq5cGW05YWHuXG0zj1WrVmG1Bn8mbXtBCNHY3u67777oigkTxcXFbN++HSEES5YsibacsLB06VJUVWXPnj3k5+dHW05gtORFRTvtYaqqKnr37i3QZpeI0aNHR1tSyOTn5wuTySQAYTKZxJtvvhltSSGzcOFCYTQaG+v0e+hljhgxorHd9e/f/4TvZVqtVhEfHy8AodfrxWOPPRZtSSGzYsUKYbFYBCDMZvPvopd5++23C1mWBSDS09NP+F6my+USnTp1EoCQJEncdNNN0ZbUInjpYZ5QDvPTTz8VOp2u0XBJkiQ2btwYbVkhcfPNNzc+EIDo1KmTcLlc0ZYVNE6nU6SnpzfWR1EUcdttt0VbVkisX7++2T1SFEUsX7482rJC4vHHH2/2LFksFmG1WqMtKyQGDBjQWB9AXHzxxdGWFBJFRUVCUZTG+uj1evHaa69FW1ZILF68WOj1+mbPUn5+frRlHYc3h3lCpWR1Oh0jRowgISGBjIwMLr30UlyuSB/PFFlycnK45JJLkCSJM888k/PPP/+ErpPT6eTCCy9k8ODBSJLEyJEj6dbaKdjtHLfbzahRo0hPTycxMZGRI0ei+DoA8wQgNTWVESNGoNPp6Nu3L5dccgl2u/+HubdHTj/9dC666CIARo8ezYABA6KsKDTq6uoYPXo03bp1w2QyMXz4cOLiIrdfa1tgsVgYPnw4ZrOZrKwsRo8eTV1d5I9vCxsteVHRTnuYDQwaNEhcccUV0ZYRVnQ6nfjqq6+iLSNsfPbZZ8JgMERbRlgZM2aMaK/PRLAkJSWJuXPnRltG2CgpKRGaWfv9MHnyZJGVlRVtGWElJydHTJw4MdoyvMLvoYcZI0aMGDFiRIuYw4wRI0aMGDH8IOYwY8SIESNGDD+IOcwYMWLEiBHDD2IOM0aMGDFixPCDmMOMESNGjBgx/CDmMGPEiBEjRgw/iDnMGDFixIgRww9iDjNGjBgxYsTwg5jDjBEjRowYMfwg5jBjxIgRI0YMPzihHOZXX33F+PHj2bdvH7/88gtXX301GzdujLaskJg1axbjx4/H4/HwxBNPMGHChBNrM+JjqK2tZcKECTzzzDO4XC7Gjx/Ps88+G21ZIbF+/Xquvvpq1q1bx549exg/fjxff/11tGWFxKJFixg/fjw2m40lS5Zwww03UFpaGm1ZIXHXXXdx++23AzB+/HgeeuihKCsKjX379nHttdfy+eefc+TIEcaPH89HH30UbVkhkZuby/jx4ykpKeHLL7/k2muvZffu3dGW5T8tbTAr2unm6x9//HGz424kSRLr16+PtqyQuP7665sdHZWeni6cTme0ZQWNw+EQqampjfWRZbndnnnnL2vXrj3ueK9PP/002rJCYsaMGc2O9zKZTKKioiLaskKiT58+zY73GjZsWLQlhURBQUGzdqfT6cTLL78cbVkhsXDhwmbtTpZlsXfv3mjLOg5+D+dhqqoqevbs2fhjjxgxItqSQmbv3r3NDpA+0c+7E0KIBQsWNKvT/v37oy0pZC644ILGdte7d+8T/gDpiooKERcX12iIH3744WhLCplly5Y1O0D6l19+ibakkGl6Xm5qaqqoq6uLtqSQaHperiRJ4vrrr4+2pBb5XThMIbRepizLQpbl38WJ6kJovUxAJCcnn/Anqguh9TITExMFcML3Lhv49ddfG9vdid67bGDGjBkCEAaD4YTvXQrRPKA+0XuXDeTn5zdm1U703mUDCxcuFJIkCUVRxJ49e6Itp0V+Nw5TVVVhMBhEenp6tKWEjb179wpATJ06NdpSwsZdd90lgN9F77KB1NRUYTQaT/jeZQMVFRUCEMOHD4+2lLCxdOlSAfwuepcN9O3bV8iyfML3LhtwOp1ClmXRs2fPaEvxijeHqTz++ONexzcXLlz4+J133hn6QGkYcLkqqar6gSNH/ktq6j7GjMkiI6MWIVzodCnIsiHaEgNCCIHDUUhFxSo8nm9xOn/gxhtPQ1EOAzp0uhQk6YSak4WqurHZtlBWtpxu3Q5iMm1l6NBUnM4jKEocipKIJEnRlhkQbncNVVW/cOTIf+na9QDnnhtPdrYTVbWj16cgy6ZoSwwYh+MgVuu31NSsRFHWc+ONg7BYygHQ61ORJCXKCgNDCJXa2h1UVHxBWtpObLbvuPTS7jidB5FlEzpdhxOu3Xk8dmpq8jhyZBknnXSEPn3s9Okj4fFUoyjJKIol2hIDxuUqw2pdQ0XFMhISdnDVVSeTmlqNEB70+lRkWR9tiY3MnDnz4OOPP77w2NclzZm2zODBg8XatWsjKswXHo+dw4ffp7BwNnb7bmTZjKo6EMIBSMiyEUkyoKq1xMefRnb2A6SmXt6ufvhjcTiKKS5+hQMHXkFV7UiSHo+nFiHcSJKMLFuQJFBVF2lp48jOvpeEhDOiLdsnVVW/Ulj4HEeO/AdZ1iOEQFVrEUIgSToUxYIQLmTZQmbmXXTp8heMxs7Rlu0VVXVx5MgnFBTMwmb7DVm2IIQTVXUAAkkyIstGVNWO2dyL7Oz7SU+/BkVpv87T5SrjwIE3KC6eh8tV3qhfVV1IkoQsm5EkBVWto0OHkWRn30dS0vnt2tHYbFsoLJzL4cPvADKSJOPx2AAVUOrbnQdJUujU6Va6dp2M2XxSlFV7RwiV8vKVFBTMpqpqTX27c6OqdrR2Z0CWTahqHUZjF7p2nU6nTjei0yVGW7pX3O5qSkqWUlj4HA5HUaN+IZxoNtyEJOlR1VoSE88hO/t+UlJGRT1okyRpnRBi8HGvt0eHKYSgpOSf7No1qd741vj1PUVJQJZN9OnzNqmpoyKsMjA8nlp2755OScni+u69w49vyciymfj4U+jb9x3M5pyI6wwEu30PW7deh822tf6hVlv9TkOPrFOn2+nRYzaKYo6wysAoK/uMbdtuRggnHk+1X99RlHhAplevl8nI+FO7cjKq6iI//+8UFj4LSPX3qTUkZDkOkymLfv3eIz5+YKRlBoTDcYjt2ydgta5GVV2Au9XvSJIeSVJITR3LySe/il6fHHmhAWC1/sDWrdfhdpfj8fhn72Q5DhDk5DxF166T21VGSgiV4uIF7N37AFq7s/n1PUVJQFES6dfvXZKTz4usSB+cMA7T6TzC1q3XUFX1s98/8rHIsoW0tLH07v16u0hdVFX9zObN43C7K/w0WMeiIMtGevSYQ2bmn8OuLxiKil5i794H6ntdnoC/L8tmdLoUBgzIJTHxD+EXGCAej43t22+lrGwZqlobVBmybCEpaSj9+r2DXp8aZoWBY7NtZ/PmK3A4CoOsk9YDyMq6n+7dH2sXgcDhwx+yY8dt9b0UV8DflyQjihJHv37vkZIyIgIKA0NVXezZM42DB98I0jZojtNi6cOAAbmYTFlhVhg4DkcxmzePw2bbEoINN5ORcQu9er0QlYzhCeEwHY6D5OWdjdN5MKiHoSmybMJi6c+gQV+j0yWESWHglJd/yebNVwRthJuipTTvpkePf4RBWXAIIdiz5776lHJ46nTKKZ/SocNFYVAXHG53FRs2DMNm244QoW0aIUkGjMZMTj/9RwyGjDApDJzq6jw2bLiwvpfs/Rn3By0AvYK+ff8Z1V5MYeE89u2bEaZ2Z6Z37zfIyLguDMqCQ1WdbNp0GVbrmqCd5VEU9PoOnHbaD1gsvcKiLxjs9r3k5Q3B7S5HiNZ7/r6QZTOJiWczcOByZNkYJoX+4c1htps+vNttZf36c3E6D4TsLAFUtQ6bbTO//TaiPm3T9litP4XNWQKoai3FxfPJz386LOUFQ37+38PmLEGr06ZN/0dV1a9hKS/w6zv57beLsdm2hewsAYRwUldXSF7eubjd/qV0w01t7c56Z1lFqM4StHt05Mgn7NwZvezGwYNLwuYsAVTVzo4dt1FW9nlYygsUIQSbN18VJmcJ4MHlKmP9+nNxOA6GobzAcTpLyMs7B5frSMjOErR7VFWl2VAhWh/uaQvajcPcufMvOBwHwvJDNyCEA5ttE/n5T4WtTH9xu2vC6iwbUNVa8vOfpKqq7ceWrdafKCj4R0TqtHnzFXg84S3XH/bvfxybbaufY8r+4sbhKGbXrrvDWKZ/qKqbzZvH+T3+6n+5tZSU/IsjRz4Na7n+YLfvYdeuv0ag3dnZuvU6nM7DYS3XHw4eXERl5ddhcpYNCFyuCrZuvQ5fmcNIIIRg27YbcbnK8Wcug7+oqp3Kyu84cOCVsJUZCu3CYZaVreDIkf+E2WhpqGothYWzqanZFPayfbFnzzQ8HmtEytYe9PH144dtg8dTV3/NcD7gR3G7K9i9e3pEyvZGdfV6iormhd0QAwhRR2nph5SXfxX2sn1RWDiburp9hKNneSyqWsv27TfjclWEvWxvCKGyZcs1qGpk9ldW1Tq2b78lImV7o66ukN27pwU9vucbN9XVazl0aHEEyvZOSck7WK0/AOHP5qmqjT177sdu3xf2sgMl6g5TCBGR6LEpqlrH7t1TIlb+sdjt+ykp+WfEHnIAp7OUQ4fejlj5x3Lo0OL66DEyqKqdQ4cWU1dXGLFrHMvu3VMjFgCA5mB27bqrzaJ9t7uK/PwnI/oseTy1FBY+F7Hyj6Ws7HNqa3cQzl5LU4RwUlm5uk2HBPbt+1tEg13NwUxvs6EoITzs3j0lQgGAhqo62Lfv4YiV7y9Rd5hW6/c4nSUBf6+oCEaOhKf8yrYKqqp+oK6uIODrBENx8Utec+65uTBxoqb9H03m7mzdCtOnw+WXwxVXwOOPQ1mZ92uoqo2CglltYoyFEBQUzPZqiJ1OmD0brr0WLr0Ubr8dfv756Pvr1sFNN8GoUTB1Khw65PVKFBe/HHb9LWG376O6+ucW32utPl9/DTffrL13yy2wZo336zgcxVRX/xJe8V7QAijvj/RTT8FVV8GYMXDjjfDZZ0ffq6uDuXNh7Fi47DK4556WyxDCQXHxgjYzxtoQQOvLLHzZg1mz4MILobi45e+qal2bBQFut5XS0g/wthRmyhStHqNHa3833aS9/uOPcPfd2r0ZNw6efRZqfcRFQrgpK/tv+CvQAmVly1rNDq5apT0zo0fDn/4EDYdM+W8bPBw58kmbZjdaIuoOs7j4xaAi4hdegD59/P+8ECoHDy4K+DqBol1nYf3C3ONJTdWM1ejRzV+vrtYehnffhffeA7NZe9B94XQeorp6XZiUe6eq6mfc7iNe3/d4oGNHmDcPli2D226DmTO1xm+1wmOPwa23wn//C717wxNPtFyOEA4OHHi1TYKAAwde8xrU+KpPaSk8/TTcdZfmcCZOhCefhAovz7Gq1lFU9GIEa3IULb3sPcq//nqtfX32meZY3ngDduzQ3pszR2uDb70F//mPVj/veCgr+8zXB8JCXV0hNTX+tW9v9mDTJjhwoLVvqxw58t82maR1+PB7gO9F+ffcA8uXa39v1yeRbDbNbnz0kXaPjhyBV1/1XobHU01h4fPhE+6DwsK5PsfM166FhQvhgQe0tjdvHnTuHJht0JApKVkadv2BEHWHWVn5LYGOt6xaBXFxcPrp/n9HCCfl5SsDExcEtbU7fRr888+HoUMh8ZjNOc46Cy64QKuXyQRXXgmbN7d2NQ9VVd+HKrlVtJl8LQcAoDn3W26BTp1AlmHIEO2B2LEDvv0WunfX6mYwaFHmnj1Q4KWzL4SLurq9kahGMyoqvvAa1PiqT2kpxMdr90uStPdMJl9GWaWycnWkqtGI212Nw+E7g5KTo92DBiRJ011QAD/8APfeC8nJoCia8fKGx1NDZeU34RHug6qqH5Ck1tfgebMHHg+8+CJMntz6tWTZSE1NXpBK/ae8fGVQqcvhw+HMM7W2lpCgZQlasw81NXkRn10qhKC62nc6e8kSzdn366c9T+np2l+gtkFVa9vEhvsiqg7T5arE7Q5sXMxmg8WL4a9/Dfx6NtuWZs7sm2++YdSoUezdG5yBfvrpp5kyZUqzg3drataFZdXVobQAACAASURBVIH3xo1aY/KFqtbVBxwaTqeT1157jXHjxgV93bFjx7Jo0SJcrqMpN6t1tVfn0hLl5VBYqBno/fuhR4+j75nN0KUL7PMyfi9Juma95sOHDzN58mRmz54daFUA2LVrF6NHj+bbb4/+Ttreo9v8LqNpfXr3huxs+P57zSCvWQN6PZzkY8c1l+tws95LXl4eY8eO5eefW04Jt8abb77JhAkTyM/Pb3ytpmY9stz6Jh1z52rpr5tv1rIdZ58N27ZBRoZm2MaO1SL+1T59vMBq/a7xXx6Ph/fee4+RI0fidPrfTpoyYcIEnn32WWqb5Bmrqn5qddcbX/bgww9h4MDm7c8bqlrXrN1ZrVYeffRRpk2b5ncdmlJeXs6IESNYtmxZM5vTmnMBWLRIuw+TJsGGDS1/xh/7AAp2+9HDmXfs2ME111zDsmXLWq9AC6xYsYKrr76abduOPjvaBDPveDxaoGm1aqnY8eO1bIDDEbhtAPzOOESKqDpMu30nshzY1mhvvqmNHaWnB349ITy4XKV88803DB48mMsuu4yvv/6awsLgJpqsXr2al156iW7dujU6TpttW8hT+vfs0VIxf/Zj2ZvNtrnRUWZmZjJt2jQ+/zz4tWWff/45U6ZMITMzs9Fx2mxb/f6+262l+y65RHMsdrsW/TclLk57vSU8nmpqa7c3Osru3buzYMECVvu24F4pKChg1apVjB49mjPPPJNvv/0Wp/MQ4F9Qc2x9FEUbY3ryyaP/nTZNe9i9Ictm7PZd5OXlcfHFF3PeeeexfPnyoE+aX7t2LW+//TZ9+vTh5ptvJj8/n9ra7X6tX546VUuLvfginHee5uxLSzUjFRenpfwmT9bG15v44+Ow2/c0OsqTTjqJ22+/nVWrVmH3dmNbYcWKFTzyyCN06dKl0XFWV+fRWvbJmz04fBg+/RQmTPDv+kI4qK7Oa3SUmZmZ/OMf/+CLL74Iqj5Hjhxh9erVXHfddfTt25dly5ahqioOh+/88J13wjvvaM7+ssvg4YePH3tduxZWrmy9bpKkUFu7jR07dnDVVVcxaNAgPvroIza3nrpqka1bt/Lxxx9z+umnM3bsWLZt20Zt7XafWYCKCu0ZWr1aa3Ovvw67dsE//xm4bQBtraeqhm/pYaBEdaefiopv6tff+bf8YvduzUAtWqQ96EuWaI1pxgz/rud267j9dh35+c1nrxoMBmQ58NjB6XSiqs1THg89lMjIkVWtfveNNzRD9eCDzV8vLtYG/u+4QzPIrVFRYeCqq1zHpYFNpuA2Aq+ra/7bSJJEbq6OpKTWjbGqaventlb7r04H8+drD8zUqUc/d+utWg9n2LCWy1m+PJHZs5v/hrIsYzAEfiKNqqrH9Xp69DDzyitu9HrfdWqpPuvWaeMss2dDr16wc6fW/mbNgp49Wy7H5TLyyCMKP//cfKxer9ejKIFvMu1yufB4mm9HOGFCIjfeWIsk+W9Mnn8eunXT6rlwIaxYoQUEoBnq00+HP/7RmwaZ0aOl43QYjcagMizHtjuAJUvMdOvm3Xr6sgePPgrnnqsFOqBN+lm6FDIzvWvYsCGOqVObp0slScJoDHyXGe00ouYTYeLi9Pz3vy4CMTX3369lARqSRlu3avfmkUfgjFbOZPB4jLz4ooH//rd5AK/T6dDpdP6LqMftduN2N29fY8YkMHWqE0VpedJPdbU2kfGBB7TMBmjOc+lSrfcfqG2QJB3nnluBThcfsP5A8LbTT+C/WhiRpMAuv2EDlJTANddo/7bbtYc9P1974FtDr9fxzDPP8uKL/2LdunV4PB4MBgP3338//fr1C1j/U089xaZNmzAajej1esaPH8+ll7pwu4MbmD50SBtHuvFG/5wlQFpaR554YiILFy7k8OHDOBwOdDodS5YsCUrD9ddfj6qqGI1GMjIymDhxIh06vISq+t49RAht5l5FhdY7aXgeu3fXouEG7HZt3CzHxz7yl102jpISwUcffYTb7cbhcHDqqafywAMPBFyfTZs2MWfOHJxOJ4qiMHjwYKZMuRaD4SGfPTJv9dm9W3vQG8b4+vSBvn01R+rNYRqNRv72t78xd+4KvvvuO4QQyLLMnXfeydChQwOu0xtvvMGXX37ZGOhdfvnl3HxzF+DVgDb+8Hi0e3HOOce/15rPMxhMzJnzNAsWLGD//v2Nv+8rr7yCxRL4/s0TJ07EarViNBpJTExkwoQJ9OixErf7N6/f8WUPDhzQxvhee+3o5//6Vy3NOXx4y+UNHjyESZP6sHTpUux2Ow6Hg8zMTJ57LvAZtAcOHODBBx/E6XSi1+vp168fkybdhSxPDKgcSdLaImg9sxkzNCfamrME0OsN3H33FITYwMqVKxFC4Ha7GT9+PGPHjg24TsuWLeOdd95BlmVkWWbEiBHcc89g9PrnvS6TSUjQev9N21PD/wdjG4RQo3oaVVR7mHb7Pn79dYDfs2Tr6ppPpX7/fc3JTJ2qTVZoDUnSc9551ciykd27d/Pwww/z73//m++//56zzz47YP1XXnklq1ev5oknnuCOO+7AaDRSVDSfvXvv97oG0+PR/t56S+thTp+uRfXl5VrP8vLLjxoAf0hMPJfTT1+DEIIvv/yS6dOnk5+fj9Ua3KYJcXFx9OrVi+eee46LL74YSZJYt+5sr0swGnj+ec2ZzJnTPD1ZWQk33AD33adNkFm8GH77DRYsaLkcWTbTo8dzZGbeRV1dHa+99hozZ85k+PDhfPDBBwHX57vvvuOCCy7g6quv5sknn6RHjx54PHbWrEn06Vy81WfDBm1m35w5moPctUu7h3/7G/zByx7ysmzmzDO3YzJlU1xczMyZM1m8eDHvvvsuf/TWhfPBtGnTWLhwIQ8++CD33HMPCQkJlJbmsn37LfXb4R1PRQWsX6/dA4NBc/CPPabpPussLaq/5BJtnGnrVq1H8OqrWhq6JYzGbgwZsh+AH3/8kfvuu48ff/wRq9VKfHzg0X+P+sGs2bNnc+WVVyLLMlu2XEdp6Xtev+PLHghx1NGAtpzm5Ze1MbOWO4wyWVn30qPHbNxuN//617946KGHyM7O5qeffgq4Pvv37ycnJ4dRo0Yxa9YsBg7UTnz57rtEr0M2NTXabz9okGYTVq3S2uHChVpP7N57NYd/kZ/bLitKIgMHLicp6RzKy8uZNWsWL7zwAs888wxTm3br/OSll15i+vTpTJo0iQcffJC0tDSqqn7lt9+Ge213oKXNf/kFnnlGCzxnzNDqOG5cYLYBtH2NzzuvJuIHAbTLzdeFEHz3XXzQC60DTcmaTD04++zm40alpaWkpqYGlZK1Wq2YTKZmKRur9Xs2brzUawNaskRzlk25+WYt6lqyRJsF15Tly30pUMjOfoCTTjq6+EwIQWlpKR07dgyoLg0cPnyY9PT0Zg1y9+77KCp6Hm+Lxw8dguuu09JiTTOM06bBiBGacX7hBa030Levlobu1MlLjZRETj31SxITz2x8ra6uDqfTSeKxU4v9QFVVysvLSUtLa/b6jz92x+FoeZCutfrk5mpjfRUVWqB2xRVw9dXeNShKAkOHWpv9phUVFcTHx6PXBx4t22xa2jCuyQBQXV0Bv/zSx+tGDJWVmoPcs0dzJBkZmsG67DLt/X374LnnYO9e7b3bbtPGOL2Rnn4N/fs3d2aHDx8Out0dOXKElJSUZs9hcfGC+gX4/o2L+rIHraVkFSWRPn0Wk55+dMKc2+2mqqqKlJSUQKrSSEu/R17euVRV/dDi5ysrtWejoECbTZqdraUoBw/WUv4rVzZ39g0TtbwhSTqGDrU2O7GpqqoKg8EQ1JBNS8+hx1PHmjUJPoNPt1sbmvnf/7Rg7YILtPkZDYGbv7YBICHhD5xxRuTXNbdLhwmwbt1ZbbawOyPjBvr2/WdEr+Hx2FizpkNYNpBvDUVJpG/ff5KWdnlEr1Na+m+2b58Q9v1JW0KS9AwdWhXxw5i3bLmW0tL3I3qNBhqyAJFECMGaNUltco+aZgEiSVXVL/W9l7ap05lnbsNk6hbR6+zdO4OCgtn4c4ZnqDTNAkSSn37qSV3dnohfBxS6dp1Kz57PRvxK7fa0ki5d7qw/gDeyKEo8nTr5OWUupOvEkZzsZcQ67Kh06BD5M/1SUkYhROBnXgaORIcOIyLuLAE6d761zdpdly53Rvw6kiTRseN1tMW0BCFU0tKCX7rkLwkJg/1aKhMOTKYeEXeWABkZ17fJGJwkmejc+baIXwegc+fbA17tEAyybKRTpxsifh2fGqJ6daBjx+va5OgWRUkiOfnCiF8HICvr/ogbY0nS06nTBBQl8g1VUeLIyLiJSBtjRYkjK+u+iF6jgQ4dhreJwwRIT/eRrw0jXbtOaQNjrAU1RqOPvFm4riTJdO06LeLGWFHiyc4OfEJZMMTF9cdi6dsm12qLQA00hxmJzf6PxWzuQXz8qRG/ji+i7jAVxUJm5qSIRpKyHEf37o+32YnxHTpcjMHQBX/X+gWDJOno2jW4RdXBkJ09PcLGWMJozG6z3rkkyXTr9hiyHNf6h4NEO/B7Spv0mAHi4vqSmDgk4NnngSDLJrp3fzRi5R9Lly63+7XbTyjIsoX09MAnXwVLTs7fI2rvJMlIevpVbXaAucGQRnr6dUhS5Nq5LMeRk/P3iJXvt45oCwDo3n0men0QOxH4hUJ8/MA2S0+AZoz79/8AWY5MA9ICgL9jNnePSPktYTb3oFu3RyLmYGTZRP/+H7RZUANaBB4X14/W9vYMFoOhE927PxKRsr3Rp89bETNcsmymc+fbSEz0Mh04Auj1KZx88qsRbHcW+vV7t82CGoDU1EtJSRmFJAW+rtgfFCWOXr1eikjZ3ujVa17E1kZKkoHk5AtJSwt8KUy4aRcOU1FM9Q4m/FGXopjp1+/dNjXEAPHxp/L/7J13eFRV+sc/985MpqQXWiDUIE26qIii2EXXH6srq6Cr7uraEQuCZddesCFrwa6Ioiwq6ooKiigWQDpCiEAoCYSEkD6T6ff8/rhJTCAzmXInN/jM53nmgdyZOfd75p5z3ve8p+Xk3KV5niQpAZutPzk54U8Lj5bu3e/CZuureUWXZRs9etxLYuIgTdNtDUmSGTjwg5iEtWXZxqBBC5Hl2DSKgbBYutG37/MxqEsGTKaO9O4d2RaF0dCx46WkpZ2muQMqyzY6dZpEenqI6zQ0pF+/1zAaU9A6CiXLNgYMeBeTKYR1dhpiNKYwYMD8mITPDYZE+vd/U/N0I6FdGEyAlJTj6d9/rqY/uCwnMmTIkjYZzG+Jnj3vp0OHv2jWeElSAmZzDkOHLkWS2v7RSZKBoUO/wWzuqpnRlGUbHTteSvfu+px1Z7X2ZvDgLzQ1MLJsZcCA90hODuN0AA3p0uWq+rE/rfJkwGTKYPjwFW0yZn44kiQxaNB/sdkGadZ7lmUbqamn0LfvHE3SCxeTKYNhw77DYNDOaMqyjd69nyQz87zWPxwDMjLOIjd3tqZtuMGQwrBh35GQEKsIZHi0G4MJ0LHjXxg48IP68EvkYTJJSsBoTGPYsGWkprawjUkbIUkS/fu/RefOf4+68ZLlRBITBzFy5C+YTJGtC9MCkymTkSPXYLMN0CBPVrKz/0m/fq+3eQSgKWlppzB06NcYDClROQKSZESWExk0aCEdOkzQUGH49O79cH0IPbrGS5ZtWCzdGTlyLRZLgF0M2gCDwcbw4StITR0TdXhWlm1kZl7A4MH/Q5b12+wsMXEQI0b8jMnUIUpHQEKWrfTt+x+6dYvgVAoNyc6+tj6EbiUa8yJJFkymDgwf/hNJSUO0Exgluq/DbAmncw/btl2G3f5r2EfhyHIi6eln0K/fGyQkZLX+hTaiomIp27ZNxu93hLwQW8WILJvo0ePfdO8+DUmKzXhbuCiKj6Kimezd+2j90V+hLzuRZSsGQzIDB84nPf2M2IkME4+njPz8q6mq+i6icpeUNIyBA+fralgOp6ZmDVu3XoLXWxbmBiEysmwmO/sGevV6tE3H+IIhhODAgdfYufN2hPCEtd5ZkszIspl+/d6gY8e2m+TTGj5fLTt3TuHgwf/Wtw2hzziV5UQslh4MGrSwfjy+feBw5JOXNxGnc1eYdUk1/h06XEzfvi/Uh63bnna7cUEg1B1rPqSw8In6o5hEwO3mZNmGEAopKSfSo8c9ZGTEfm1iJPh8NezfP4d9+55FUZz4/U5aXsAsYTAkIYSPTp0uJyfnLmy2ABuV6kxd3XYKC2dy8OD7SJKx/jimlsqUEYPBiizb6NbtNrp2vRGjMbmt5YZERcUS9u59rH5DDSmgg6OOqUnYbIPo0WMGWVkX6dpTDoTf7+LAgTcoKnoKn68cRXEHNDSyrJa7rKwL6d79bpKTh7Wx2tBwufZRVPQMJSWvA1KQcidjMCQiSQays2+iW7db201473Cqq1eyd+9jVFZ+jSQZAxoaSUpAkkxYLDnk5EynU6fLde0pB0IIP6Wl71FYOBOXaw9C+BGi5T1nZTkRIXykp59O9+73kJYW/h7LWnLUGcymOBx5VFZ+Q1XVCuz29dTUlCLLBpKTO5GcfAKpqSeTkXFum84ajQYh/FRWLqem5ieqqr6jrm47FRWlpKZmYLV2IzX1FFJSRpOZeUHMd+XXCp+vlvLyz6mu/pnq6h9xufZTXV1BRkZnbLZjSEs7ldTUU0hLO02X8ddIcDp3UVGxhOrqH6mt/YXa2hIURSElpRNJSSNISxtLevpZJCa2zbq6aBFCUFPzM1VVK6iq+p66uq1UVpZisyVjs3UhNXUMqaljyMw8H5MpU2+5IeH3u6io+LK+3K3A5dpLRcVBMjM7Y7H0ri93J5Gefraum3aHg9t9gIqKL6iu/omampXY7QfweFykpnYiKWkoaWmnkpZ2GsnJIezA3k6ord1AVdVyqqq+x27fRHV1KQkJZhITO5OSMrq+3J2H2RzkOJk2JJDBRAgR8DVy5EjRHunQoYMYOnSo3jI0w+l0CkmSxJtvvqm3FM2YM2eOkCRJuFwuvaVoxqBBg0Tnzp31lqEZiqIIo9EopkyZorcUzVi/fr0AxKFDh/SWohnnn3++sNlsesvQlKSkJHHWWWfpLSMgwFrRgk08Olz9JuzcuZNDhw6xZcsWqqqq9JajCa+++ipCCJ577jm9pWjG888/jxCCN954Q28pmlBRUcG2bdsoLS1l165desvRhM8++wyfz8f8+fOPOE/1aOXRR9WDCGbOnKmzEm3weDwsW7aMuro6Vq5cqbccTVi7di12u53vv/++xXNQ2zUtWVHRjnuYEydOFICQZVnce++9esuJGqfTKdLT0wUgEhISxC+//KK3pKhZuXKlSEhIEIDIzMz8Q/Qy77rrLiHLsgDEpEmT9JYTNYqiiNzcXAEIk8kkFi1apLekqNm1a5cwm80CEDab7Q/Ry3zppZeE0WgUgBgzZozecjThtNNOE4AwGAziueee01tOixCgh3lUGcwdO3YIi8UiUEf3RWJioqisrNRbVlTMnj1bJCYmNubp1FNP1VtS1Jx88snNntGLL76ot6SoKC8vFzabrTFPFotFFBQU6C0rKj755BORlJTUmKfc3FyhKIresqJi8uTJwmAwCECYzWYxbdo0vSVFhdvtFllZWY3PyGaziZ9//llvWVGxZs2aZnUpPT1dOJ1OvWUdQSCDeVSFZD///PNmXXiHw8H333+vo6Lo+fDDDxvPNwT1MN7a2tgfZxQrqqurWb3698OmHQ4HCxcu1FFR9Hz33XfUNTmp2OVy8cUXX+ioKHoWLlyI3W5v/HvXrl1Hfaj5iy++wO9Xlze53W4++eQTnRVFx/r16zl06FDj33V1dXz88cc6KoqeRYsWNatLlZWVrFmzRkdFYdKSFRXttIfZwLBhw8SECRP0lqEpRqNRfPPNN3rL0IzFixeLhIQEvWVoyvnnny/aa52IlNTUVDFr1iy9ZWhGaWmpUJu1Pw5TpkwROTk5esvQlF69eonrrrtObxkB4Y/Qw4wTJ06cOHH0Im4w48SJEydOnBCIG8w4ceLEiRMnBOIGM06cOHHixAmBuMGMEydOnDhxQiBuMOPEiRMnTpwQiBvMOHHixIkTJwTiBjNOnDhx4sQJgbjBjBMnTpw4cUIgbjDjxIkTJ06cEIgbzDhx4sSJEycEjiqDefDgQZYsWUJNTQ2lpaWN/z+a2bp1K0uWLEFRFNasWcP333+Poih6y4oYRVH4/vvvWbduHYqisGTJEvLy8vSWFRXV1dUsWbKEsrKyZv8/mtm5cydLlizB6/WSn5/PsmXL8Hq9esuKipUrV7J8+XIAlixZwvr163VWFB0ul4ulS5eyd+9enE4nS5YsoaioSG9ZUbFv3z6WLFlCXV0dRUVFLF26tNlm7O2eljaYFe108/VZs2YJo9EoZFkWRqNRSJIkPv30U71lRcXJJ5/ceIafxWIRJpNJ2O12vWVFTHV1tTAajY3HsJnNZnHaaafpLSsqPvroo8YyZzAYhMFgEC+88ILesqJi8uTJjWeWNpS/3bt36y0rKlJTU4XVam2sS7m5uXpLiorVq1c3npMrSZIwmUzizjvv1FtWVNxzzz3CZDI15gcQP/74o96yjoA/wnmYVVVVzc6O7Nq1q/D5fHrLioqvvvqqMU9Go1Fcf/31ekuKmmuuuabx0NvExETx9ddf6y0pKrxer+jcuXNjuUtKShI1NTV6y4qKzZs3NxoXSZLEueeeq7ekqHnkkUca85SYmCjeeOMNvSVFhaIoYujQoc3OYS0qKtJbVlQUFxc3O9N44MCB7fIc1j+EwRRCiPvvv19IkiQMBoOYN2+e3nKiRlEUMWjQoEZPsri4WG9JUbNv377G3svQoUPbZYUIlzfffFMYDAYhSZJ4+OGH9ZajCeedd54AhMlkEps2bdJbTtTU1tY2HorduXNn4fV69ZYUNd98802j8/mPf/xDbzmacMMNNzR2EL788ku95bTIH8ZgVlVVCUmShMViOep7lw189dVXAhAXXHCB3lI04+yzzxbAUd+7bMDr9Qqz2SxkWT7qe5cNbN68WQCif//+ekvRjBkzZgjgqO9dNqAoisjKyhLAUd+7bKC4uFgAIj09vd0604EM5lE16UcIgcXi5PTTj2HixJPw+yv1lhQ1iuJj7Nh+9Olj4tZbL8HvP4oGwAPg9zuYOnUiubkJjBmTixB+vSVFjaJUcfHFJ3Lmmf1JSHCo3uZRjBAKffumMWhQCrfccjE+39E9eQ7A73dx3XUX0r07TJx4Cori0VtS1Ph8VVx77XhGjepMhw7SH6DcCTIyBKNHd+Xqq8/G56vSW1JYSMEewHHHHSfWrl3bhnKOxOut4MCBNzl0aBEOx68I4cXnE0iSjCz7MRgSSUoaSseOl9Gp02QMhkRd9YaCw7GN4uKXqaz8BqdzJ5JkxO32YjYnIISbhIQupKSMpkuXf5CefiaS1L79GiEUKiqWcuDAG9TWrsLjKUGSzLjdHsxmE0L4sFr7kp5+Jl273oDN1k9vya3i89kpLX2XsrIF2O2b8PsdKIoBIQQGg0CWE0hMHEyHDhfTufPVmEzpektuFadzD8XFr1BR8SVO52+AjMfjw2QyAl5MpkySk0fRufNVZGZeiCwb9ZYcFCEE1dU/ceDAK1RX/4TbXYQkmXG53FgsZoTwYLH0JC3tNLKzryc5eYTekltFUdyUlX1ISck72O0b8PmqEMKE3+/DZDIAEjbbQLKy/kSXLtdiNnfRW3KreDylFBe/Tnn5pzgcWwGB16tgMMhIkh+DIYXk5OF06nQFHTpcgsFg0VsykiStE0Icd8T19mow3e4Sdu68nfLyRYCMogTveclyEqDQufPV9O79GEZjSpvoDIfq6p/ZufNWHI6tKIoX8AX9vMGQhMGQTM+eD9Cly7VIktQ2QkNECIXi4lfZu/dB/H47fr+9lW8YkWUTiYmDyc2dTWrqiW2iMxx8vmoKCu6mtPRt1HLnCPp5WbYBCllZF5Ob+wwJCZ3aQmZY2O1b2LnzFqqrVwEKQgTveRkMyUiSkZycu8jJubPdGU4hBGVlC9m1azoeT1l92xCs52VAls1YLD3IzZ1FRsY5bSU1ZPx+F3v3Psz+/c/X/10b9POybEEIQXr6mfTt+zxWa6+2kBkWLtdeduy4lYqKJQAI4Qr6eYMhCYDs7Bvo2fMBDAZbzDUG4qgxmEIISkvfY8eOG1EUF0KEtzZMkiwYjUkMGPAeGRlnx0hlePj9dRQU3EVJyZsoijPs78tyIklJgxk48AMslh4xUBg+Tudu8vL+isOR16pRaQlZttKlyz/p3fuJduFRApSXf8m2bZejKHUoSvDKfTiSZEKWrfTr9yodOkxsF86NovjYu/cxioqeqM9PeOE8WbZhsXRn4MCFJCUdGxuRYeLxlJKffxVVVStadaJbQpZtZGX9H8ccMwejMTUGCsOnunoVeXkT8XoPRdA+qM5A795P0LXrTe0iGiWEoLj4ZQoKptWXu/CGZGTZitGYwaBBC0hNHRMbka1wVBhMIRS2b7+R0tJ5EVWGpsiyjZ49/0337tM1UhcZHs8hNm4ci8u1JyJj+TsGDAYbQ4Ys1b1nVl39E5s3n1c/3hr5+KQsW7FYejN8+ApMpgztBEbAnj2PUFj4uCblrnPnq+jb9wVdjabf72Dz5vOorV0XZZ4kZNnKwIELyMq6QDN9keBw5LFhw1j8/tpWe8nBkGULRmMWI0b8hMXSXUOF4XPgwNv1nYNo2gbVqU5PP4tBg/6LLJs0Uhc+iuIjL+8yKiq+jMiRboosW8nN/Q/Z2ddopC50AhlM/d2ReoQQbN9+gybGEkBR6tiz5yEKC5/UQF1keL1VbNhwEk7nzqgrBPjx+2vZtOlMamr0G1euqVnNpk3n1IeMopvMoyhOnM4dbNgwBp+vWhuBEbBnz6OaGEtQy11Jydvs2HGTbhM0FMVdX07W7Wn80wAAIABJREFUaJAngaLUkZc3kfLyLzXRFwl1ddvry0lFVMYSQFFceDwHWL/+BNzuYo0Uhk9JyTuaGEsARXFQWbmUrVv/otskOyEU8vL+SkXFF1EbS1Dbh507p3DgwBsaqNOGdmMwDxx4g9LS9zRptBpQjeaDVFYu0yzNUBFCkJd3KS7X3rDDysFQFAebN5+D19v2M4S93nI2bz5Xk8rQgBCe+vDuZM3SDIeKiiUUFj6mebkrKZlLSck7mqUZDjt2TMFu39TqmFE4KIqTrVsvweXaq1maoeL3u9i06ax6p0orJ8SP13uIzZvP18XA2O2b2L79Bk2MZQOKUkdl5Tfs2fOoZmmGQ2HhTCoqlmhcl5zs2HELtbXrNEszGtqFwXS5iti5c6qmDXEDqnc8CZ8v+CC61hw8+D7V1T9G7Q23hN/vYPv26zVPtzV+++2amCx7EcJNVdVyDh78UPO0g+HzVZOXN1nTCt6AotSxc+fNbd6DqapaUR+l0a4hbkBRXOTlXdbmPefdu+/B6y1DO2OpIoQPp3MHRUWzNE23NRTFy9atl8ToGdVRVPQEdvsWzdMOhsOxjb17H45RG+6s/73cmqcdLu3CYG7ffgNCxO7H8Plq2L373zFL/3D8/rp671H7wgOqgSkv/5yqqh9ikn5LVFYup6Li65g4AKBWdNUga9+IBGLXrntDmNkbOX6/i+3bb4pZ+ocjhFI/aSlWv6Efu30zBw9+EKP0j8ThyKe4+OWY5UlRHOzZ8288ntKYpN8S+/b9B7d7P1o7AA0oiov8/CtjknYg8vOvDnuiXDh4PKUUFT0bs/RDRXeD6XLto7JyGUIEX2IRDUK4KCl5vc0a44MH3495mEdRnBQWPhHTezRFHeOLjQPwO37KyhbG+B71d/I7KCl5K6aOGvioqPgSt7skhvf4ncrKb/D5YhuqVxQHe/c+FtN7NGXfvln1S7Bihzqr89WY3uP3eykUFT0Vk6hGk7tQV5eP3b4phvf4HYdjKw7HZmLlAEBDz/lZ3TdB0X2BVXHxHML5oR99FNavB5cLMjLg0kvh/PNb/54QcPDgArp0uSpiraFSWPhkQOOyaBF89RXs3g2nnw4zZqjXvV545BH47TcoLYVZs2DYsGB3EVRWLsPtLsZsztY8D01xufZRXR24NxsoTwCLF8P8+VBRAYMHw113QVZWy+n4/XYKC5+gc+e/aZyDIyktnQ+0PIvV44HnnlPLWU0NZGfDtdfCCSfA11/Ds00cXSHA7YaXX4Z+LezHIEkSxcVz6NXrwdhkpAmFhU8G7TEHqjslJXDZZWBpsrrnssvgbwEeg8u1i9raDSQnD9c4B83x+x2Ulr5La+uV9+2Dv/8dTj0V7r0XNmyA55+HgwfBYIAhQ2DKFOjQoeXvC+Fi377Z9OhxD5Jk0D4jTaioWBrU8Zw6FfLyVN2gan7nHSgvV8vdb7+p/3//fejcOfB9FMVNUdEzDBgQ+3H0oqLgTk2gPAkB770H//sf2O1q/brjDkgMsPeMGln7gqysP8UgF6Ghu8EsK/swLC9/0iSYNg0SEqCwUH0YubktN1ZNURQ7ZWX/jbnB9HgOBp0YkZkJV1wBa9aoDW1TBg+Gv/wFHnggtHvJsonKymV07nxF5IJDoLJyKRC4IQmUp40b4fXX1YrerRu88AI8/DDMnh34Xk5nAV5vRcyXmZSV/Tdgw+X3Q8eOqtHs2BFWr4YHH4Q334SzzlJfDXz1FcybB8cc0/J9FMVFWdlHMTeYiuIL6tRA4LqTWr8c8fPPf2/Ugt/LQ0XFlzE3mNXVPyNJrTdRs2dD//6//92jBzz5pOqYeTzqc3vuOdVhCIQQbhyOPJKSBmugPDCHDn3S6jDArbce2QmQJBg1Sn2GN98cyp38lJd/EbHOcCgv/x+tOTUt5WnJEli6VHVukpPVDsN//gN3391yGn5/LYcOLdLVYOoaklUULy7X7rC+06uXWuEbkCQoDnFeRW1t8wNlPR4PH3zwAXZ7ZONYq1atOuKQ2tradciyOeB3xo6Fk0+GlMM2IjKZVGM5eDDIIT4Vv99OTc3KZtdKSkr4+OOPQ0ugBT788EMOHjzY7Fp19U9BveJAeVq5UvX6e/VS83fFFbB5M+zfH/j+smw5Ykbc2rVr+eWXX8LOC0BNTQ0LFizA42k+9lpbuyHgd6xWuOoq1YOXZRg9Grp0Ub37w1myBM4+Wy2HgXA6d6Aovzcofr+fTz75hJKSyEK1v/76KytWrGh2ra5uW9ByB9HVneb4qKr6rtmVqqoq5s+fH/GEoMWLF7Nnz55m12pr17Q6dvntt2qPZESTXe8yMppHMQyG4GUO1N5ObW3z5Vr5+fl8++23ocg/Aq/Xy7vvvovD0bzeVFf/GFF6GRkwYUJzx6A1/P5aPJ7fDzoXQrBkyRIKCgoi0rB7926+/PLLZs/Y662IeBhg5UoYP151Sq1WNaqxfLkaAQlEdfXPEd1LK3Q1mHV1+chy+Lu8zJoF554LV16p9m5ODHEdv89Xgc9Xi8fjYc6cOXTt2pXJkyezbl1kU5YfeOABRo0axRlnnNFoOO32DW26gXp1tWowS0pKuPnmm+nZsyeTJk2KOL1JkybRo0cPpkyZ0mg4a2pWR5xe0/az4f+7g/hIiuLEbt8IqIZy3LhxnHDCCTz00EMR3X/NmjVMmjSJrl278vLLL+PxePB6K1vdeqwpFRVQVKQanKaUlKgOwNmtbCglywk4nTvw+/3Mnz+fnj17cvHFF7NsWWTLnebMmcNpp53Gcccdx3fffQcQ8nhVsLpz6aVwySUwcyZUt7IstuF+VVVV3HfffXTr1o3LL7+cmprINnG/9tprOeaYY7jiiisaDWd19U9Bl2Q5HPDWW3BTC/OqSkvhggvUvC5YoOYtGIpibyzn+fn5/PnPf+bYY49l6tSpEeVn9+7d/O1vfyM7O5snnnii0XA6nTta/e5rr8H//Z/ak9y4MaLbA+rCf7t9I0IIFi9ezMCBAznvvPNYuDCyeQKLFi3i/PPPp1+/fnz22WcIIbDbN9dvDxmcQHk6vH3wetUQeyBcrgKEUCLSrwW67vRTVbWCX3+9EL8//EXrfr8aF9+4UfVMjCEEl4UwM3v2SL74Yg2KouD3+zEajQwfPpyMjPBDgGvXrqW8vBwAWZZJS0vjySd70qfP+la+CW+8AWVlzcf7GrjkEnUsJvgYporbnc5NN2Wwe/fuxiNoJEni7NZa8QAsXbq0MQ1JkujduzcvvlhGQkLrz+jwPK1bBw891Dwku3ixmrczzgiczvbtxzFjRgHV1dUoilo5srKyGDlyZNj5KS8vZ+PGjfh8PgwGA7Isc+GFx3PTTeuQpNZn9fl8MH26Oo55xx3N33vnHXVM8LnngqchRCILFgzn7bfX4PP58Pv9GAwGBg4cSHZ2+OPPeXl5FBUVAWq5S0xM5N//Hsxxx60FWp/FfHjd8XrVEG1urmooZ8+Gujp46qnAaShKAlOn9iMvLw8hBIqiIEkS48aNw2QKf6eZ5cuX4/F4GstddnY2L70EycmBW8/nn1d7kpddBm+/rfYi7723+WdqatQyN3QoDBwYXENpaT9uvbWKsrKyxnKXlJTEmDHhb8/mcDhYuXIlfr8fWZaRJInjjx/Bo4+uRZICt7l5edCzp9qeffutGqJ87TXo2lV93++HM89sfQxTxcayZSOZNWs9LpcLv1+dMNO3b1969+4ddp727NnD9u3b6w8gMGA2m7n11hGcffYGIHAEKlCeNm6EDz5Qy1lSEjz+OPz8s9pODBrUclqSZOTkk2swGKxh6w+HQDv96DyGGfmsKoNBDV9+/TV8+ilcfHEIdxOC2tqaxoLTcM1utyOHGgdtgtfb3Pt1uVy4gsUTYoCiKNjt9t8POK2nqiqyY3Ma0mj41263NzYe4TJypBravP9+tQG++GKw2QJPvmjA5XLiPmyA1+v1RpSnht8G1N+qoQyoTkHw7yoKPPaYGk6+9dYj31+6FCaHsN+Ces9afD5fs9+3rq4uojwd/tt4PB6cztY2IP+dlupOwxyAjAw1rxdfrD4zW4DOQ0OeDi8b1dXVGEPxXltIr+m/DocDny9wiHnnTtUhe+214OmmpMA558A118DChcHHaL1eD3V1zaNDfr8/omfkcrma5anh92qNpkb93HNVA7N6NVx0UdgSEAIcDjWi1rRtcLlcEeXJ6XQ2y5PX68XhsLdalwLlacIEdWLW1KmqIzBxomowW2sfQL8epq4G02hMI9rM+/2hj8MYDIIlS37E6ZSZPXs2M2fOxOl0MmfOHE499dSw733OOeewbNkyhgwZwtNPP824cePYu/dR9ux5gGi3jQuVrKzulJRspqCggPvuu49FixYB6vhqJCQkJCDLMhdffDEPP/wwvXv35pdfBlJXty2i9P78Z/UFaljz3XePDG02x8iECVdwyy13sWzZMu688062bNnCmDFjWLx4cdj3X7ZsGeeccw7JycnMmDGDKVOmYDa7+fnnbIIEVxBC9XwrK+GJJ46MYPz6qzpbMZRiYzIZePXV93jzzVxee+017r//fqqrq3nwwQeZHIrFPYwbb7yRV155hV69evH0009z4YUXcvDgPLZvvzmsJRit1Z1gfpLFksru3bs5cOAADz74IHPnzsXtdrNs2TJSU8Pf1Dw7O5tDhw5x1lln8cQTTzB48GA2bTqXysqWx3k3blTDrn/9q/q306nq3bsXXj1shYjfrz5Hh+PIcfamjBp1JjU1r7J69WqmTZvGypUr6dOnT0R1afv27QwYMACbzcZNN93E9OnTycjIYMUKc1g7f0kSQctpMIxGE/fe+zhPPXUW77//PjNmzODAgQPceOONzGgptNUKzz77LNOmTaNTp048/vjjTJ48mdra79my5WL8YTR3DXmSZbj6avUF6qTBrKzAs+gbkOXY9i6DoavBtNn6h7XYtbJSnTI+erQ6eWHdOtVbue++0L5vNKZiNKaSnAz33Xcft956K/PmzWPo0KER6Z82bRrTp09n3LhxjRttJyUNw2BIxO9veSzH71dfiqK+PB7V6zUY1P834PWqf5tMwSeUpKSog1B9+vTh/fffp6CgoHFcKxJefPFFzjjjjGYhm+TkUUENZqA8+f1qmKxnT9WTfOYZ1VNOTg58f4PBRmLiUCRJ4swzz2TDhg0sW7YsoggAwPDhw3n++ee54oorSEpKqr+ahMGQiM8XOHw5a5ba+D7zDJhb6OgsWQKnnBK4B9YURXFhtR6DLJu4+eabufbaa3nvvfcYO3ZsRHm66qqrOOecc/jTn/7U+Ls0/GaBCFZ38vLUkFi3blBbq4Y6hw1TrwUiMVGdTdqlSxdefvll7r//fj766COSgz3cIDz99NMMHjyYwYN/n6WamnpS/baWR87AvOACdQlTAwsWqGPKt90GK1aoZa5bNzUk++KL0LdvcGMpy4kkJx8PwAknnMCKFStYvXo1Bw4ciCg/PXr04LnnnmPSpElkZmY2Xrda+1BXl9/id+x29VkMG6bWn2+/VcfIG2bFejw0GiaPR301ncR1OIriqm+PDFx++eVcdtllLFy4sNlvHA7jx4+nc+fOTJw4sTGKkJg4NOjErGB5qqlRy1t2tlrXXnpJXcoUrKpbLD11PZFF99NKVq3KxeUKbdZWVZUa3isoUD2UTp3UBviCEA9RSE8/m6FDl0ShtnXc7gOsWtUr4FKZt9+GuXObX7vySjV0eemlqtfclGBjFbKcSN++L8R8qUxx8Wv1Wxe2PJkpUJ7+8hc1vFdcrM6CO+88db1csLCYJCUwevQ+EhJajctExcaN446Y6dlAw7pEk6m51ttvV5eUeDxquXvwQTXs3BpWaz9OOKHlRlIrFMXLDz8kBuy9BKs7y5apy3+qqlQHYORIuP56NTzbMkZ69ryPnj3vj1l+QD1uLS/v0oDOZ1OajmF+/LEafq2qUsvdsGHwz38GH/MzGJIZNuz7mC+Vyc//ByUlb7b4XlWVOv5fWKgaje7d1fpyXP1I2rhxR35n+fLA9zIa0zj55NjvOf3jj1n4fOUtvhcsT0VF6vM6eBDS0tTyOHFi8Ht16nRFm6wtbbfHe+3ceSf79z8fsy3XGpDlRPr0eYauXa+L6X0AVq3qhcu1J+b3kWULxx//W8yPKHI6d7NmzcCYbn3VgMXShxNP3Bnz++zb9yK7dk2P+e5FkmSmW7ep9OkT+12Z1q8/hZqayJYthIMsJzJs2DJSUk6I6X18vhp+/rlTm5Q7gyGNMWMOxvxorEOHPmPbtsvDmqUdGTIdOvyFQYMWxPg+sG3b3+o3AontMJTBkEz//m/RoUMIE1aipN0e79V2h54KOnVqmxMxcnKmIcsBtqvQkJSUk9rkPD+rtVdjuCqWyHIS3bvfFfP7APWbPbTN5IGuXdtmP9nu3e/CYIgsJBoOZnN2m5QHozGFDh0uIdimGVogSWa6dr2xTc6RzMgYjyQFiaNqhCxbycm5M+b3AejWbWqra4C1QJIMZGZeGPP7BEN3g2m19iIl5aSYSpEkM506XY7RGGRQRkM6dYrtzjugevndu4c/cB8p3bvPiLkTIEm0mVNjNKbQseNlMW68DKSlnYbFkhPDe/xOZub4kNbERYNa7u5us8Oxc3LuRJZja2AkSSY7+4aY3qMBWTbSrdttMX9OFksPUlJGxfQeDSQnj8Bq7UugrSa1QJatZGffouvh2NAODCZAv36vRrSBQagYDDZ69267jcqNxmRyc5+NmYGRpATS0k4lI+Os1j+sERkZ55KaehKSFJsCK8uJ5OY+j8EQ+555A336PBnTGXeybOaYY16OWfqHI0kGBgyYG8PGWMZq7dsme/02kJQ0hE6dLo9Z+yDLNnJypmOxdItJ+i2Rk3MHJlNm6x+MEFm2tsk4X1P69387pm240ZhOjx5t10EIRLswmFZrH3r1ejQmBkaWbfTvPxeTKV3ztIPRpcu19RMItDcwsmyhf/+3NE83GJIk0b//OzGpFJJkIiXleDp3btsjiUymzPqKrr2BkWUbffo8hdXaU/O0g5GRcQ5ZWROQJO1DZLJsZtCgBTHfoPxwcnNnYTCkxSBlGbO5Oz163Nv6RzXEYLAwaNDCmDhrsmyla9ebSU4Of5OPaEhOHhaznrMsWxk4cAEGQ2x75SFp0VtAA926TSEz83xNf3BZttGt2xRdNuuVJIlBgz7CbO4U0gbSoSLLNgYP/h8JCR01SzNUzObOHHvsp5o+I0kykpCQzcCB/22zMF9TOnSYQHb2jZqXu6ysP7dZmO9w+vV7FZutn6bhZlm20r//XGy2ALvMxxCDIZGhQ5doPD4rYTSmMWTIF8hy26+uS0k5gd69n9S43FlISTmRXr2C7DIfQ3r2fJDU1DGaOgKybKNXr0dISztZszSjod0YTEmSGTDgPTIzL9CkpynLNrp2vZlevdru7L7DSUjoyIgRq0lI6KqBxy8hy4kMHvwZaWmRrd/TgvT0cRx77KL6ih6dgZMkC2ZzDiNGrCQhoZXVyjGkT58nyc6+XpPGS5YTycqawIABc3VxAEA1MMOGfU9i4kBNGi9ZttGv3+t07HiJBuoiIylpCEOHfovBkEK0k4AkKQGTKZMRI1ZitQbdRSOmdOt2c31kTYtyZyMl5SQGD16s2zifLBsZPPh/pKaeolmeevZ8gJyc2zVQpw3txmCC+oMPHPg+vXs/Xv+Dh18xJCkBgyGV/v3n0qfPTN0arQbM5mxGjdpEhw4XR1yIZNmGzTaQkSN/IT09yCasbURGxtmMGLEaq7VfVHnq2HEixx23EbO5i8YKw0OSJHJzn6F//zcxGFIi7JkZ6pcuPcWAAe+2edjycEymNIYPX0mXLtdFbDRl2YrZnMPQocvo1CnyDf21IiXlOEaN2kxKyvERO9WynEh6+hmMGrVVl97y4eTkTOXYYz/BZMpCkiIZ7pDqZ8ROZ8iQJTHfY7U1ZNnM4MGL6dHj3vpyF377K8sWjMZMBg36iO7dp2kvMgp0X4cZCKdzN9u339C4uLy1MzNl2YoQCllZF9G37+yYL3yPhPLyr9i58xbc7gP1u2MEX9aghqAM9Ogxg27d7tAldBQMRfFRVPQkhYUzAaXVc/5Arm+Es+nb98U2nbQUKh5PKTt2TOHQoU+RJLnV46XUyIFEevo4jjlmDhZLj7YRGgbV1avYseMG6up+Q1E8tLZeTpaTAEHXrjfSs+dDGAyxm8wRCUIIDhx4ld2770NR3CGtaTQYkjAYUunT52k6dvyr7o704fh8NRQU3EVp6TuA3Or6YEkyIUkGkpJGcswxL5OUdGzbCA0DhyOP7duvp7Z2DUL4W90SUHW+FTp1upw+fZ7GaAx/i0WtaLcbF7SGy1XE/v0vUV7+GU7nDmTZjNfrR5Lk+l1Y/Fit/enY8a906XKNrqG9UBBCUFOzmv37n6e6egUeTwmybMXlcmM2mxHCg8FgIylpONnZ15OVNUH3qdStoSgeDh1aRHHxK/XHmzmRpATcbjcWiwVFqSMhoQtpaafStestJCePancN1uF4PGUcOPAaBw/+l7q6fCTJiN8PQigYjQaE8GC15pKVNYHs7BvadJZlpNjtv7J///NUVn6D212ELFtxuz2YTAlIkg9JMpKYOIQuXf5Bx45/bReTLIIhhJ/y8i8pLp5Dbe0afL5qZNmC0+nCarWgKE5MpkxSUkbTtevNpKWNa/flzueroaRkLqWl7+JwbAFAUWT8fh8JCSYUxYXF0pOMjPPo2vVmbLa+OituHaezgH37XqCi4gtcrt3IshmPp+H0IAEIbLZBdOp0OV26XKWroWwgkMFs3Em/pdfIkSNFe8Lv9wq7fZu46KIB4qabzhQOxw6hKH69ZUWF11srams3iuHDzeK7714TLtcBvSVFjct1QHz99UtixAiLqK3dKHw+u96SokJR/MLh2CGuv/40ccklxwq7fZvw+716y4oKn88pams3i9Gj08Sbb84QTmehUBRFb1lR4fEcErt3LxWDBkmiqmqN8Hqr9JYUFYqiiLq63eKRR64QZ57ZVdjtW4Tf79ZbVlT4/W5ht28V55zTXfzrXxNFXd2udlnugLWiBZt4VBlMIYSw2+1ClmVhs9mE3390G8sGli9fLgBx0UUX6S1FM/70pz8JQKxYsUJvKZrg8/mExWIRsiyLuro6veVoQn5+vgDEscceq7cUzXjwwQcFIObPn6+3FM3o3LmzAERJSYneUjShrKxMACIrK0tvKQEJZDDb1aSfUHj++ecRQuB2u/n444/1lqMJ06apA9uff/45ZWVlOquJnoMHD/LVV18Bv+ftaGfhwoV4vV6EELz00kt6y9GEe+65B4D8/Hzy82O7OXxb4HQ6eeaZZwCYPn16s3Nvj1Z+/PFHDh06BMAjjzyisxptePzxxwH1zN5oTlbShZasqGinPUy73S5SUlLUoDeIXr16HfW9zOXLl4vExEQBCJPJJG677Ta9JUXNLbfcIkwmkwCEzWY76nuZPp9PdO/evbHcpaWlHfW9zPz8fGGxWAQgJEkSf/7zn/WWFDXPPPOMsFqtAhCJiYl/iF7miSee2FjurFbrUd/LLCsra3xGgGhvNqYB/ggh2RdeeEEYDAYhy7IwGAxCkiTxxRdf6C0rKk499VSRkJAgAJGQkCDMZrNwOBx6y4qYmpoakZCQ0CxPZ555pt6youLTTz8VkiQ1K3svv/yy3rKi4sorr2x0akwmk5AkSRQWFuotKyoyMzOF2WwWgDCbzaJfv356S4qK9evXN3s+JpNJ3H333XrLior7779fGI3GxvwAYvXq1XrLOoJABvOoCslecMEFvPvuu+Tk5DB8+HDmz5/PqFFts8FwrHj88ceZO3cuBoOBO++8kwULFmBu6cTiowSbzcaCBQuYOnUqBoOBuXPnHvWhpNGjRzN//nyGDBlCjx49ePfddxk/frzesqLitttu45133sFms3HppZfy3//+l44d2373KC2ZN28es2fPBuDtt99mzpw5OiuKjtzcXD744APOOOMM0tPTeeedd7jyyrbdPlJrJk+ezLx588jKymLs2LF88MEH9O/fX29ZodOSFRXttIfZwLBhw8SECRP0lqEpRqNRfPPNN3rL0IzFixeLhIQEvWVoyvnnn99uQ0iRkpqaKmbNmqW3DM0oLS0VarP2x2HKlCkiJydHbxma0qtXL3HdddfpLSMg/BF6mHHixIkTJ45exA1mnDhx4sSJEwJxgxknTpw4ceKEQNxgxokTJ06cOCEQN5hx4sSJEydOCMQNZpw4ceLEiRMCcYMZJ06cOHHihEDcYMaJEydOnDghEDeYceLEiRMnTgjEDWacOHHixIkTAnGDGSdOnDhx4oRCS/vliXa6l+xLL70krFarkCSp8RDpxYsX6y0rKs4555zG427MZrPIzMwUNTU1esuKmKqqKpGRkdF4WonVahXjx4/XW1ZUfPrpp8JmswlZloUkScJqtYpXXnlFb1lRcc011zSWO5PJJFJSUkRBQYHesqIiJyen8cgyq9Uqhg0bprekqPjll19EUlJS4+keVqtV3HfffXrLioqHHnqosQ03GAwiMTFR/PTTT3rLOgL+CHvJjh07FkVREEKgKAqKonD88cfrLSsqxo0b1/h/t9tNbm4uycnJOiqKjtTUVHr16oXH42m8dvrpp+uoKHpOPPHExvLWUPbGjh2rt6yoOOOMM5AkCQCv10tqaio9evTQWVV0jBo1qrHc+f1+Tj75ZJ0VRcexxx6LwWDA5/OpZzGiPrejmXHjxjUaH7/fjyzLDB06VG9ZodOSFRXttIcphBAXXHCBAIQsy+L222/XW07UND0U22w2H/WHLQuhHordcC7hH+GwZSHUQ7ElSRLAH+Kw5aaHYickJPwhDltueij2H+GwZSHUQ7GNRmO7Pmw5XBoOxTYajeLxxx/XW06L8Ec4QFoIIbZs2dLYnS8rK9NbjibBtLr/AAAgAElEQVQ88cQTAhD9+/fXW4pm5ObmCkA8/fTTekvRhNLS0saQ7LZt2/SWownvv/9+o1Pj8/n0lqMJ559/vgDEzTffrLcUTairq2sc3li+fLnecjThhx9+aBwKsNvtestpkT+MwRRCiPT09D+UcbHb7QIQc+bM0VuKZsyaNUsAf4jeZQO5ubkiMzNTbxma4fP5hCzL4tprr9VbimasWrVKAH+I3mUDZ555pjCbzXrL0BSr1SpOOeUUvWUEJJDBlER9bLwljjvuOLF27doYBYNDx+UqoqrqW6qrf6am5hcOHtyNyWQiI6MnqaljSEk5gfT0M0lI6KC31NAQAtavh59+ghUrIC+PfTt30qlbN0y9esHYsXDCCXD66ZCQoLfakFAUN5WV31Jb+wtVVd9jt++mtHQ/OTm52GyDSEsbS2rqGJKShjeOnbV3PJ6DVFYuo6ZmFdXVP1Nevge/30eHDj1JSTmB1NSTSEs7HYulm95SQ8bh2EpV1Q9UV/+A3b6J/fsLSE/PIjW1J6mpYxvrksFg01tqSAjhp6rqe2pqVlNdvYK6ugL27i2gZ8++2GzHkJZ2KikpJ5KSMhpJOkqmbFRXw9dfw+rV8OOP2Pfsoc5up2OvXjByJIwZA6edBrm5eisNGadzF1VVy6mu/ona2nWUlOzCak0kPb0nqakn15e7szCZ0vSWCoAkSeuEEMcdcb29GkwhBJWV31BY+AQ1NT8jSUb8fnsLn5QwGJIQwktGxni6d59OSko7nQjkcsHcufDkk1BaCn6/eu1wjEaw2UCS4Lrr4NZbITu77fWGgMu1j/37Z1Nc/Aog4ffXAb4jPifLFsCA2dyFnJy76NTpCgwGS1vLDYnq6lUUFs6kouIrZNlUX+6OrCcGQxKK4iUt7RS6d59Benr7nJChKD7KyhZQWDgTp7MAECiKs4VPGjAYEhHCR+fOf6Nbtzuw2dpno+z1lrN//0vs2/cfhHCjKC6E8B7xOUkyI8smDIYUcnJup0uXazEaU3RQHAJbtqhtw8KFYDKBwwGKcuTnEhPV60OGwPTpMGGC2la0M4QQlJf/j8LCmdjt6wEDiuJo4ZNyfbnzkpV1Ed2730VSkr4TgY4qg+nxlJKffxVVVT8E+IEDISPLFjp2nEhu7n8wGtvRbNNVq+CSS6CyUq0IoWI2qwb0mWfgn/9sNxVDCEFx8RwKCqYhhA8hPK1/qR5ZTsRkymLQoP+2K+fG56th+/abOHTo43qDErhuHI4sJ5KWdhr9+7/VriIdDkceW7dOxOXaE2ZdMiHLRnr0uJecnOnIsjFmGsPl4MGF/PbbNSiKByFacDgDIMs2DAYbAwa8R0bG2TFUGCYuF9x7L8yZAx6P6kiHSlISDBoEH3wAPXvGTGK4uFyF5OVNwm7fhKK01NEJhAFZTqBLl2vp3fsJDAZrzDQG46gxmJWVy9my5f9QFHdYjXBTJMmC0ZjC0KHfkJQ0WGOFYSIEPPAAPPUUOFvy6kMkMRGOOw4+/1ytJDri89Xw66/nU1u7IcxGuDmybKV79xn06PEv3cO0tbUb2bz5LHw+e1iNcFMkKQFZtjB48GekpZ2qscLw2b//ZQoKbkdRXIRj/Jsiyzas1j4MHfoNCQkdtRUYJoriJi9vEhUVX6EodRGnI8s2OnW6gmOOeUn/MO3u3erQy8GDUBdhngwG1bGeOxf+8hdt9UXAoUOfkpc3GSHcCHFktCkUZNmKydSBYcO+xWrto7HC1glkMNtVUL+iYgm//noBfn9txMYSQAgXXu9BNmwYQ23tRg0Vhi1EDac+/XR0xhLUXumqVer4hT0cj01bfL4a1q8/iZqaNVEZSwBFcVJYOJOCgmkaqYuM2tp1bNw4Fq/3UMTGEkAID35/DZs3n0dFxTcaKgyfoqJnKCi4I+ye8uEoSh0ORz7r1h2Hx1OqncCwdXjYvPlcKiq+jMpYqmnVUVo6j7y8SxGihZBnW7FrF4waBYWFkRtLUHukdXXwt7/Bu+9qpy8CSksXkJd3GYriiNhYgto2uN37WLduFHV1OzRUGB3txmDa7ZvZsuWiqCtDU/z+WjZtGofbfUCzNMPi2WfhjTeiqwxNcbvht9/g/PNVY9zGCKGwefN4nM6dCOHWJE1FqaO4eA779j2vSXrh4nbvZ+PGM/D7azVLU1GcbNkyAYdjq2ZphkNZ2Ufs3v1vDeuSF4+nhA0bTkVRIndko2HbtiupqVkdYOw1fBSljvLyxfo5azU1cPLJ6hBNS+OUkeB0qsM233+vTXphUlX1I7/99nfNnhEo+HxVbNhwMl5vlUZpRke7MJiK4mXr1ks0/KF/x+dzsG3bFQQLPceE/Hz417+0M5YNuN2wbh28+qq26YbA/v0vYrdv1MxYNqAodezaNZ26up2aptsaQgjy8iZF3VNuCUWpY+vWiShK5F52JHg8B8nP/7umjieAEF7c7iL27HlA03RD4dChzygv/0zz9qHBWauuXqlpuiFx883aGssGnE7461/bPArl99eRlzdR83IHAp+vmh07btA43choFwazsPBx3O59RBM6CoyXmppVHDz4fgzSDoAQcOmlqnGLBQ4H3HEH7N8fm/RbwOUqZNeuGTExLtAwPnVpmzo2paXzqK1dF1XoKDACl2svRUVPxSDtwPz22z/qxyy1R1Hq2LfvOez2zTFJvyV8vhry86+KQUOsoijOeme9DXvOy5fDRx+1PENeC6qr4fbbY5N2AAoKpuPzxaYXKISbQ4c+o6JiaUzSDwfdDabf76Ko6KmYVQgARXGwe/d9bdcY//ADFBRo7z02xeuF556LXfqHUVT0bIvT9rVDoa4un5qatvH2hRDs3n1fzBwAUMtdYeETbdYY19XtpLLym6jG/1tDUdzs2fNQzNI/nAMH3kJRYuR41uP3V3Po0CcxvUcz7rlH+8hTU1wumDcPystjd48meL2VlJS8HpMIYQOKUsfu3ffGLP1Q0d1glpUtDPmzHo+6TOnSS2H8eLjmGnVtb2jfPUhNzc8RqgyTp54KuHTkBeA4wAxcddh7y4D+gA0YB+wNdg+PB155JXa92Cb4/U5KSt4IaDAXLVKXi559NjzxRPP3li+HK69Un9dVV8GPPwa+j6LUUVT0tHbCg1BV9T0+X2WL77VWzlwumDUL/u//4IIL1HldgVEoK/tYU+2B2L//PwgReEnCo4/CxRerQ+BXXAGLFx/5mblzYdw4NerfMgoVFYvxeA5pojkYQoiQnOl9+9Sy9+ij6t8rV8Itt6jP5qKL1OoYzD75/XYKC2dqqDwI27fDxuATEU8DLEBS/atfk/fmAz2ARGACUBEoEUlS50+0ASUlbxHMlEydqj6f885TX3/72+/vffwxXHaZWiavuw5+/TXwfRyOrTgc27QTHgG6G8zi4pcDbEhwJH4/dOyodqw+/xz+8Q948EEoKWn9u4pSx4EDbVCAnE5YujTgpJxs4D7g74ddPwRcBDyMWgmOA/7a2r0kCb6J/WzMysqlBCsqmZlqA3zeec2vl5XBY4/BjTeqjfN118Ejj6hDNy0jKC//POY9CoCSkjfw+1t2alorZ888A7W1qnH59FM1f4Hw++0UF8+JQQ6aI4SgpGRe0CjApEnw/vvqs3j0UbU9/e2339/fv1+dL5KZ2drdZA4d+kgT3cGw2zfg91e3+rnZs6F//9//djjU8vjhh+ozOnQIXn45eBp1dXm43W0wxDF/fkjrLF8A7PWvhke0FbgOmAeUojrWAYue06k61G1AcfGrrTo1t94KX36pvt55R72Wlwevvaauuvv8c9U5/de/Av88iuKltPQdbcWHia4GUwiB3b4p5M9brWovpXNnkGUYPRq6dGle6YPcjerqI7s3Tqcz4lCt2+3Gf/jT3bQJLIF3sLkI1TM8vE36GBgEXILqXT4AbALygwlwOOCXX4647IxiCUtL362uXhXUqRk7Vp3wl3LYBiplZeqS0RNOUG376NHqT1NcHPj+smzF4djS7Jrf78cdYU9aCBEgTz8TaMw8WDkrLISff1aHkNPS1CVw/fq1mEwjLZVxl8uFEmHI3uPx4PV6D7tW3OqSmF69mu+0KEnNn8Xs2eokS2MrexQoSh1VVSuOuF4XRZixrq7uiHpYW7um1WUf336rLlEeMeL3a2eeCccfr5a15GS197JlS+A0QF1DW1vbfM15oLITKi3+HsuXq8MpEfAe8CdgLGrP82HUdiPg/O7CwiPGST0eDz5fZGP2Pp+v2bF9oC73cbl2RZReSYm610K/fmpZPPtsdfi1KuBQqI+qqu8iupdW6Gow3e4iIPJxvooKKCpSG4JQUHc7UR/4gQMHuOGGG0hNTSXSzRkmT55Mz549mT9//u+Gc/36iCrEVqDpZlCJQJ/66wHx+5tNIf/ll1849dRT6dq1a9j3b6Bz586MGzeu2W9SXb2CSJ5Tv37Qvbu6Za7fr4ZjTSbo3Tvwd4TwUVurxgP9fj/vvvsuPXr04Oqrrw77/gArV64kNTWVm266iZL6LqLf78LtLgw5jablbNs26NQJ3n5bDcn+/e+tz+IXwo3brVqmyspK7r77bjIyMvjkk8jGze699166dOnCSy+91OhI1NauQ5Ja33d41iw491w1TJ6ZCSeeqF7/7jv12TT83Ro1Nb/HqPPy8rjwwgtJS0vDEc4uVk0YNmwYI0eO5Ntvv200nNXVPwbtuTgc8NZbcNNNwdPevLn1TXD8fjs1NarzKYTgf//7H/379+ess84KJxuNFBYWkpKSwuTJk9m9e/fvb2wKrYNwN5AFjAG+q792eBvRB0gAtgdKxGZTMw84HA5mzpxJhw4deOmll0LOR1Nee+01srKyeOyxx7DXz8J1OLbUb3vZ2nfV+nLzzb9HpE84QW0X8vLUf7/8Ut0eNyMjcDoOx5a2X/HQBF33u3K7i+orefhenM+nhpXOOUdtlEPDyIoV/2P27Hf58ssv8fv9GI1GVq1aFVEPprCwkH379vGPf/yDqVOncsMNNzC1pIT0CLxSO3D4hmqpBPEe6/Ht2cN7c+cya9Ys8vPzcbvdmEwmfgw2WBgEh8PBd999x5gxYxg4cCB33HEHPXuGblyaYjCoXuMjj6jjgiYT3H+/2oMLhBo638ScOf/i5ZdfxuFw4HQ62bNnT0R5Wrt2LQaDgVdffZXXX3+d8ePHM3XqJCQpIaTZsYeXsxUr1M1Zxo5VQ35bt8Ldd6sNcqDzlyUpgfXrl/DSS9/y4YcfoigKkiSxceNGOnYMf/ecnTt3Ul5ezh133MGMGTO45ppruPzy5JAmZd12G0yZojZSGzeqz6SuDl5/Xd1fI1Q8nhI+/vhjnn32WdasWYPP50OSJH744QeSItiJqry8nB07djB+/HhycnK47bbbGDEi+HjVm2+qYbwOQXYiXLsWliyB1m2EwqFDm/joo6f4z3/+Q3l5OU6nE6/XG1G5KywsxGAw8MEHH/Dhhx8yduxYbr/1Vs6trqa1Pa1mAgNRjeEHqL3KjahtROphnw3WRghg748/MvOtt5g3b15jpCYvLy+iPG3duhWHw8GDDz7II488wuWXX8611w5CiOA5+uc/1fphNKoRgXvuUQ1odrZaj6ZMUUewkpJg5szgu3/6/XUI4UGSzGHr1wJdt8arrPyOLVsmhDRO0RRFURvhujr139ZCSA14vUauvhr272/eUMqyHNHWbEeEY4GXrVauC8Fg3gfsA96u//tWwAs0rdeDUUOzFwdJp9hopGsLIRaDwdCqhpZoKU+LFhlJS2vduLzxhhqGnTFD/XvdOnjoIXUCTd++6nyHe+9VK0WwgxY+/dTCc88dGV6MJE9CiCNCnz16mHjtNYHJFDxPLZWzhQvVJbBffaU6BKA2ACNGBN6VzOs1c/fdgnXrmoezJElClsMP8iiKcoSXffnlVq6+2osshx5ue/ZZ1ciXlqphzSuvVK9feilMm6YejBEIr1finHM4QoeWdemNNxLo3bvlGb87d6rP5LXXVKP/9tvqGOy9TSZS5uWpz+Zf/wqelwbWrjUzbdqRjrNWdckqy9QqCuGmdi5wPvANao/zribvJaP2QFvKntts5jpg7mGdgUjLXUt16eyzLUybJjAaQ+9w3HWXGskwm9Ux9ccfh65dVefmiSfU+pWV1fJ3JcnImDGVGI2x3R60XW6NF0pX/nCEUGe9VVaqEzFCNZYAZrORgoIiFi1aRG5uLklJSZjNZr799lt8Pl/Yr7PPPhtZlrFYLFx++eXs3r2b6267Lew8gTp+2TRY4wAK6q8HI7tXL2pra3n00UdJTk7GarViNpsjyo/P58NoNGK1WklJSeHxxx/HbrfTuXNORHnauVM9UKFfP3UssH9/GDAg2AxMlSlTplNQUMCkSZOwWCzIssz48eMjys/SpUsxm80kJSXRt29fPv30U/Lzd2E2m4JqCFTOWgont2YfLBYz3377I8uWLWP48OEkJiaSkJDAvHnzIsrT9ddfjyRJWK1WLrjgArZs2cLMmbMwGsM7Cs7vV8cw169XZytedJH6KitT8/x+kKXLFksyTqeTF198kczMTBITEzEYDFRUVESUp86dO5OQkIDNZmP69OmUl5czfPgJAe+/caNq6P/6V1XzggVq7/+f/1Tf37FDNZ533RWasQQYP/4iDhw4wI033ojVasVoNDJo0KCI8pOXl0dCQgJJSUlkZ2fzzjvvUONyYYjAmZBQe4uHtxG7ADdwTIDvmc1m3v7gA9asWcNpp52G1WpFlmUee+yxiPL01FNPIcsyNpuNU045hVWrVjF//oeYzeG145Kk1q+dO9X5ATk5avtw/PFqOHZrkHEoIfzIsj69S9DZYNpsfcNeuzNrFuzdq86+NIf9u0kkJHRiwoQJbN++nXnz5jFkyBC6dOkSbkIAjBw5kkmTJrFt2zbmzZtHz4YR7CAhKR/gAvz1L1f9tT8DW4CP6q89BAxBXWYSlAEDSEpK4p577qG4uJj77ruP0aNHR5QfgJNOOol///vfFBcXM2PGDBITE7HZgs9q8fvVkKuiqK+GAxf69VOHUHbWb+CzY4c6bTzYGKbBkIzNdgy9e/fmvffeY+vWrVx66aWMDLXVO4zs7GyGDh3Ke++9x2+//caFF16I2dyl1ckkgcrZ0KHqDNr33lPz+OuvsGGDWtkDoShObLZjOP3001m3bh2fffYZxx9/PN1DH0v4f/bOOzyqYv3j3zNneza9kIQkkFACBOliAVRsVxRE8YqAaLjiRaxYULFguTZEBbHwUxEVsOsFvICKYgkgikDooSYhvSeb7Gb7OfP74yQhgWw/Z0/Cs5/nyaNs9sx8J2dm3pl33pnpQFZWFiZOnIhdu3Zhw4YNyMrKglbbDwzjehDQ0CC4wywWQffffwv/HjFCiPr9+GPBLfvhh8La5sMPC7dGuUKj6Q21Wo177rkHZWVlWLx4MYYPHw6Nm4A3d1x88cV46KGHUFJSgkWLFiEmJgZhYUMAFw7MiROFd9Cq+frrhVnL4sWCy/zxx4WtJRdf7F3+DKNCWNhQJCYm4t1330VBQQHmzJnjd1uKiYnBeeedh+XLl6OoqAi33XYbFEqlEEnmBgOAzTjdL3wGYCuEWeatADYA2AZhQP0MhCBCl3cy8TzQvz9GjRqF3377DVu3bsUVV1yBvn7eo9mnTx+MHz8ev/76K7Zu3YoLLrgAWm0/t0sbJpNQ11r7hJ9/FvqE0aOF/uGvv4RBG6XCDLO01H1MilIZB0LcD3alRPbbSv74Ix4Oh3d7uiorhT07SuVpdxggNG5v1ub1+hEYNcrD9CZQDh8WWq6Lo6meA/D8GZ892/L5FgD3Qdh/eQEEd21vd3mp1UKPLvGpHkVFi3Dq1DMu18g++UQI329PdrYQabpunbDW19AgRJXecAMwdarrvAgJw6hRudDpXI2bxWHXrqFobu78xBpP9aywUFjvKygQAoBmzwbGjXOdl0qViIsvlvY8Y4fDgB07Ely+I4NBWD/Ozxc6px49hJnZxIlnf9cbl2xy8r3o3/8dkdR3TlXV5zh+fK5X5/y2d8m++qqwbtl+oNMaqOUKlo3E4MFrER19ecC63XL99cCGDS5/XQPgWgjR8SyEAfMLAFq7t88BLABQB+BKAB8DcBkjo1YLkVF+upS9gVIe27aFuTxdymAQlmiKi4VZZFqaECg3apRQDz/+WHhXRqOwFn3rrULcgyuio/+BoUN/lKg0p+my13sdPHgD6uq+kzQPAGAYJVJSHkKfPhJvUOY4IDLStzsv/UWvF/Z8BjCj9AaDYRsOHrzW6/2ygcCyeowd2yj5tUsnTz7ccuC71Ge9MoiLuwmDB3t/QIe//PlnL5+if/2FZcORmbkSCQk3S5qPxVKIXbuyJD1BphWGUWPMmCooFGeG1YjMO+8I018pT/ppZdQoYNcuybPJzR0TlENhCNEiPf1FpKZKf+xfl1zDBICUlAfAstLf78gwLJKT50ieD1hWmFopg+A2iInxfh9AAERGjgXLStyRQHCLJSbeGZQ7CpOT7wqKa4cQHVJSHpA8H0BoS4TogpJXXNz1kueh1aYjLGyw5PkADGJirpHeWAKC60LKIzNbCQ8P2nmyKSkPgmVdOoZFhKJHj9uCkI9rZDeYUVHjg9IZ6/Ujg3cR6QMPSOoGASDssXr0Uc8RJyLAMAxSUx+RvDNmGIKUlPslzaMVnS6zZY1MWpTKeERGjpU8HwBITLwDgexr9gaGUSEpaU7QAi/S0h6XfEBNiA5paUG65is2VnDL+hGl6hMMI/jcg0Bc3A1gGIn7OxDExEyASuVmD1EQkN1gMgyDfv3ekrQzJkSLvn2XSpb+WfTvLyzW+R6V5D2RkcJiQJBISvq3pKNIhtEgPv5maLVuIoJEpm/fpSDEzabQACFEh379lvm1zcIflMpopKQ8JHFb0iAt7THPXxSJ2NjJUKvT4Cr4J1AYRoHw8JGIiPAyOkgMXnpJ2r4hLEz6PNpBiBIZGYtASJiEeaiRkfGKZOl7rUNuAQAQHz8F0dFXeHVSia8QokNy8t2IiDhf9LTd8n//J1RcKdBqhTh6XXDcbwCgUOgxaNAXkhkYhUKPfv2kDSI5k8jIi5CUdKckZWIYNWJiJgTFddme3r2fg0rlX9S3JwjRITPzA6hUvh+24H+eCmRlfePXFjRvYBgNBg36PGiDGgDCJuQXX5Smf2BZICvL/QHHEpCUNAd6/VAwjPhn4RAShl69nvEYrR8MuoTBBIDMzI+gVMZCTEkMo4JWm4H09JdES9NroqKEXe7ujrXxB51OOF/KXVimRERHj0dy8lzRZzCEaJGV9S0UigjPXxaZjIxXodGkizxYY6FUxiEzM/iXfBOiwuDB60Qf7ROiQ2zsJMTHuwlxloiwsEHIyHhFkno3YMCHUKv9P0rSbx58EDj/fLfnTvtFeLgwmJba5XsGDMNg0KAvwbIRENMbwDAa6PXDg+cy90CXMZgqVRyGD98BpTJelFEKw6ih1WZg2LAcsKw0o1OPXH458Omn4hnNsDBg5kwhbl4m+vR5AwkJ00TrvAjRYtCgrxAVdako6fkKy2oxfPhWaDS9RDKaCqhUCRgxYgeUSjeHYkqIXn8ehgz5UTSjSYgOUVGXYeDANcGdibUjJWUeUlMfE7Xe9emzBAkJHu8EkgZChGtjhg0Tp39gGGGZJifH88G5EqHRpGL48G1QKKIghmkhRIOwsMEYOvTHIKyRekeXMZgAoNX2xqhRexAWNjSgxk5IGKKjL8eIETtl67TamDJFuLsmOtr/NQWGERrVk08K9xTJ1GkJUhhkZn6ItLQFLa5M/7QQooFCEYvzzvsecXGTxBXpI0plLEaM+BtRUZcFXO/Cw0dg5Mg90Gj8O5RALKKixmL48K1QqXoG5HImRIukpDtx3nn/k3XDOACkpz+Lvn3fbDGa/nWgDKMEy4ZjwIA16NlzrrgCfUWnE24vmTIlsOUVnU44e/Lvv4WjtWQkLGwQRo7cBZ0uM6DBjeDRmIzhw7eCZaVbG/UV2fdhdgalPEpL30Zh4ZMA4PGutVYICQPDsMjM/ADx8VNlGw13isEAzJ0L/O9/wm0m3l6xo9cLu32/+goYHIwQe+8xmQ7g8OGpsNvLfNijqQQhLOLipqB//+XBCeX3Ekopqqu/wPHjc0EpD573bi9ta8eQkbEIPXveG5RtMd7CcRYUFDyG8vIPAfCgtPOzWc+EZcPBshHIyvoakZFBDIjxAoulEHl509HcfKilb/Dm9goWhKgRGTkWAwasglrt/sSdoLN5s+A9slpdHnpyFq3u3EcfFQ7MDcZWNi/heSeKi19BcfHLABiv99ISogchKgwcuBqxsddJK9INXfbgAnc4HA2orPwYJSVvwOlsAMMoWzrm1tB5BVhWOGVCo0lFWtoCJCRMB8sGLxjGZ44eFS4eXL1acMtQ2vGQA41GuLTQYgHGjxcOw7z8cllnle6glEdDwy8oKVkMgyEHhGjB8zZQevowZkL0LedHUiQmzkJKygOSn+QTCBzXjKqqz1BcvBg2WykIUbdcNt16mDYBy+pBqQMKRTRSUx9FYuIsKJVRcsp2i9VajLKyd1Fe/h4o5cAwpKUtCe2fYVQgRAOetyA8fBTS0h5HTMx1IETWC41cQilFU9NfKCl5DXV1m0CIBpQ6OnTMhOjAMCx43o6EhKlISXkI4eHDZVTtAZtNONR30SKhn9BqhQMOWq8LZBhhAM1xwu/mzRMOz+3RQ17dbrDbq1FevgJlZcvAcc1gGLZDvTvdhwvHR6alLUB8/D9lPS8W6KYGsxVKKWy2EhiNe2Ay7cWmTV8iMjIK48Zdj/DwEdDrR3S9EaMnnE7hcsU9e4C8PKx6911MuvlmxIwYIZxJNmyYdFG2EuF0mmAy7YPJtAdlZXuwYcNa/Otf9yAsLAt6/UjodAO6bAfsCputAiZTLozGXOTkfIfmZhMmTLgFev1whIePhFqd0rU8GeNaiaoAACAASURBVB6glIfZfBwmUy5MpgP4/PMVuPDCccjMHIvw8JHQ64d3acPfGRxnQXPzARiNe9DYeAQffbQcc+c+CJ1uAMLDRyIsbDAIET8CX1Jqa4VT8ffswaEtW5B/7Bgmz5oluFxHjhQOZO5W9Y7Cai1s6cP3Y9261ejVKwPnn391S70bCZXKxRUlMuDKYIJS6vJn5MiRtKvB8zzVaDQ0MTFRbimiUVpaSgHQBQsWyC1FNB566CEKgJaXl8stRTTi4+OpVqulPM/LLUUUjEYjBUCvvfZauaWIxrfffksB0P3798stRTSGDBlCCSHU4XDILUUUnE4nZVmWDhgwQG4pLgGwm3ZiE7vOYouXbN68GTabDdXV1cjLy5Nbjig899xzAIQbzZ3erm12YZxOJz7++GMAwH/+8x+Z1YjDwYMHUVdXB5vNhl9++UVuOaKwbNkyAMCWLVtgNHo+4LyrQynFs88+CwB4/PHHZVYjDhUVFTh8+DAopVi9erXcckThiy++AM/zOHHiBEpLS+WW4xudWVHaRWeYPM/TQYMGUQgOcDpp0iS5JQVMaWkp1Wq1FADVaDR09erVcksKmI8++ohqNJq2Mp0Ls8wJEya01buhQ4d2+1mm0Wik4eHhFABVKpX0hRdekFtSwGzZsqWtLWm12nNilnn33XdTQggFQBMTE7v9LNPpdNKePXtSAJRhGDp79my5JXUKXMwwu5XB/OGHH6hCoWjruAgh9PDhw3LLCog777yTsizbVqaePXtSp9Mptyy/cTgcNDExsa08LMvSuXPnyi0rIA4cONDWaQGgCoWC/vzzz3LLCogXX3yRKpXKtjKFhYXRpqYmuWUFxNChQ9vKA4Bec801cksKiPLy8g79nVKppCtXrpRbVkCsWbOmQ71jWZaWlJTILessXBnMbuWSdTgcGDduHPR6PeLi4jB+/HiYg3FNjoTEx8fjkksuAcMwGDZsGEaNGgWbzeb5wS6K1WrF6NGjMWTIEDAMg0suuQRxcV1nMd8fzGYzLrvsMsTGxiI8PBzjxo2Dw9H5vZPdBZ1Oh7Fjx4JlWfTp0weXXHIJmoNxJZ2EDBgwAGPGjAEAjB8/Hr169ZJZUWCYTCaMHz8eKSkpUKvVbe+ruzN27FhoNBokJyfj8ssvh8nbbTRdgc6sKO2iM8xWhg0bRm+44Qa5ZYiKQqGgW7ZskVuGaGzatImqVCq5ZYjKddddR7tqm/CXyMhIunTpUrlliEZVVRUVurVzhwceeICmpqbKLUNU0tPT6V133SW3DJfgXJhhhggRIkSIEHIRMpghQoQIESKEF4QMZogQIUKECOEFIYMZIkSIECFCeEHIYIYIESJEiBBeEDKYIUKECBEihBeEDGaIECFChAjhBSGDGSJEiBAhQnhByGCGCBEiRIgQXhAymCFChAgRIoQXhAxmiBAhQoQI4QXdymDm5OQgOzsbRUVF2LNnD2bNmoVDhw7JLSsg3nzzTWRnZ4PjOLz66qu45557YLVa5ZblNxaLBffccw+WLFkCp9OJ7OxsvPXWW3LLCogDBw5g1qxZ2LdvHwoLC5GdnY1t27bJLSsgVq9ejezsbJjNZnz++eeYM2cO6urq5JYVEI888gjuv/9+AEB2djaef/55mRUFRlFREe644w789NNPqKurQ3Z2NtavXy+3rIDYsGEDsrOzUVNTg99++w133HEHCgsL5ZblPZ0dMEu76OHr33zzTYersBiGobt375ZbVkDccsstlGGYtjLFxsZSu90utyy/sVqtNDo6usMVbLfeeqvcsgLir7/+6vCOWJal69evl1tWQDz++OMd2pJarab19fVyywqIfv36dbjea8yYMXJLCojCwsKzrpVbtmyZ3LIC4v/+7/86XFnGMAw9efKk3LLOAufCfZgcx9HevXu3/bEvu+wyuSUFzIkTJzpctvzuu+/KLSlgli1bRtVqdVuZ8vPz5ZYUMGPGjGmrd3379u32F0jX1dVRnU7X1hE/+uijcksKmPXr19OwsDAKgOp0Orpjxw65JQXMrbfe2jZYi46OphaLRW5JAWGz2WhcXFybsbz55pvlltQp54TBpJTSr7/+mhJCKMuydNeuXXLLEYWpU6dSADQiIoJarVa55QSMxWKher2eAqAzZsyQW44o/Pnnn5RlWUoIoevWrZNbjig89thjbRcT19bWyi0nYHiep+np6efE7LKVgoICyrIsZRiGvvnmm3LLEYXly5dThmEoy7L0xIkTcsvplHPGYHIcR1UqFY2Li5NbimicOHGCAqD333+/3FJE46677qIAzonZZSvR0dFUrVZ3+9llK3V1dRQAHT9+vNxSROOTTz6hAM6J2WUrmZmZlGGYbj+7bMVms1FCCM3IyJBbiktcGUyFhMujosJxZphM+2AyHcDChf0QExOD8vIPER4+HGFhg0GIWm6JPmO3V8No3AON5jhmz9bhttvUqK7+Cnr9SGi1fcAwjNwSfYJSHhZLPozGPZg1SweeD4NKtQF1dZkIDx8JlSpebok+w/M2mEwHYTLtw3/+MwhNTU0oL38fev1Q6PVDwbI6uSX6jMPRAJMpF2ZzHu6/Pw4TJvRCZeWnCA8fAZ0uEwzDyi3RJyilsFqLYDLlYsyYcsycSZCS8hdqa2ta6l1yt2tLPO+A2ZwHo3EvnnkmEwcOWFBXtxJhYYOh1w+HQhEht0SfcTqNMJn2orn5EBYs6IXMzAGoqPgYev1whIVlgRCl3BI9wgjGtHNGjRpFd+/eHUQ5HeF5G2pq1qKkZDGamw+BEB0odYDn7QAYEKIGwxDwvBURERchLe0xxMRc06UbvN1ejfLyFSgvfwcORwMIUYPnbeB5BwhhQYgGlHIAgISEW5CS8hD0+vNkVu0ek2k/SkqWoqbmawAEDEPAcVZQyoEQZUsZrVAqY9Gz5wNISroTKlWc3LJdQimHurrvUVz8KozGv9veiVDvaEu9U4DnzQgLG4K0tMcQF3cjCFHJLd0lTmcjKitXobR0KWy2MhCiBaV2cJwDDEPAssKAk+cdiI2diNTU+YiIuKBLGxqz+STKyt5CZeUnoNQJhlGC563geScIUbS8NzsI0SEpaQ569rwXGk2K3LJdQimFwfA7SkpeQ0PDlhb9FDxvBaUULKsEw6jA8xZoNBlIS3sUCQnTu/SgjePMqK7+CiUlr8FiOdlS7zrvw6OixiM19VFER18he71jGGYPpXTUWZ93VYNZXf0Njh+fA0o5cJzRq2dYNhwsG46BAz9FdPR4iRX6BsdZUVj4FMrLlwNgwPMWj88wjAIMo0R4+AUYOHBNl2vsVmsxjhy5DUbjbvC8DQDn8RlCtAAoeva8D+npL3Y5z0BDwy84cuQ2cJzJp3rHMApkZq5AfPxNEiv0DUo5FBcvRlHRCxDqndmLpwgI0UKr7YtBg75EWNgAqWX6hN1ei2PH7kRDw2ZQyoFSh8dnGEYNgEGPHtPQt+9bUCjCpRfqA01Nu5GXNx0ORyU4rhlCfJl7WFYPSin69n0DSUlzZDcy7aGUoqJiJfLzHwKlAM+bvHqOZfVQKhMwaNAXiIgYLbFK13Qbg+lwNODIkZkwGH73snGfDSFaJCRMR79+74JlNSIr9B2jcS8OHboBDketX2USDKca/fq9g6SkWeIL9IOKipU4cWIeKLWBUqfPzxOig0qVgMGD10OvHyqBQt/gOAuOH78bNTXfBFDvwhAdfTkGDFgNpTJKZIW+Y7Hk49ChG2GxFIDnm/1IgYAQNXr3fhapqY91iQ65tnYDjhy5DTxvAaV2n59nGA0UinBkZX2DqKhLJVDoG5RyKCh4AmVl73g1iO4MQsKg1w9FVtY3UKuTRVboOzZbJfLypsJozPWz3gl9eHLy3cjIeBWEBH/lsFsYTLu9Grm5F8NmK/GrMbSHEC30+qEYOvQXWV0WBkMODhy4zu+K0x5CdEhNfQzp6c+KoMx/CgqeRmnpUr8NS3sICcOQIT8iKmqsCMr8w+k0Yf/+y9HcfMjvTqsVhlFBo+mF4cN3yOp2NpkOYt++S+B0NgHgA0qLkDAkJExFZuZKWY1mWdl7yM9/RKR6p8PAgWsQHz9FBGX+wfMOHDo0BQbDryKUSQGlMhYjRvwJrTZdFH3+YLUWIzf3Itjt1QB8H0i3hxAdoqIuweDB3wV9ucOVwewyJ/04nUbs3TsWVmtxwMYSAHjeAqNxHw4cuAY8H9iL85empt2iGUsA4HkzSkoWo6TkDVHS84eiokWiGUsA4PlmHDhwDYzGvaKk53v+Dhw48A+YTAcCNpYAQKkdVusp7Ns3rsW1FnwsloIWY2lAoMYSEN5RdfVXOHlyXuDi/KSq6nPk5z8sYr0z48iRmaiv/1mU9HyFUoq8vBkiGUsAcMLhqMHevWNajFXwsdtrkZt7Mez2KgRqLAHhHRkMOTh8eCrcTeyCSZcxmCdO3A+rtQSA5/UIb6HUCqNxD0pKXhUtTW/hODMOHZosmrFshefNKCxcCKNxn6jpekNT024UFf1HtE6rFZ5vxqFDk8FxwT8SsKjoZZhM+0CpTbQ0KXXAaj2FkycfEi1N7/PmcOjQlJaZpXjwvBkVFStRV/ejqOl6g8VyCseOzRFlQNMenrfg8OGb4XAE/0jAyspVqK//QeS2xMNur8WRI7fJYmCOHs2Gw1EDb2IZvIXnLWho2IKKihWipRkIXcJg1tdvQU3NN6BU/A6T580oKnoJzc15oqftjvz8R+F0NkiSNs9bcfjwzS2RZsGB523Iy7tZ9E6rFYejFgUFj0uStitMpoMoKXlV9AEAILyjqqrP0NDwu+hpu6OkZCkslpMQY2Z5Jq2zMqezUfS0XSHMxKaB56UZTPG8BUePzpYkbVfYbOU4efJ+0QfTAg40Nv6BqqrPJUjbNdXV38BgyBHFO3gmPN+MkycfhtVaLHraviK7waSU4sSJuyXptFrheWtQR/tWazEqKz+SzLgAFHZ7BaqqPpUo/bOprFwFu71GsvR53oKKig9gs5VJlseZnDz5oGQdMSAYmBMn7pYs/TNxOk04depZiTpiAY5rDuqSQH39j2huPgwxZy3todSOhoafYTTmSpJ+ZxQWLpTUmyIYmHlBW4qilMOJE/dKWu943oqCgqckS99bZD+4oKlpJ2y2Cq++u24d8OOPQGEhcPnlwIIF3uZCYTBshdVaGpStGWVl74JS70f4RUXAsmXA8eNAZCQwdy4wbpz7Z3i+GcXFi5GUdEeAaj1DKUVx8WKXDcLVe/n5Z2DJkvbpADYb8N57QGZm5/mUlb2HjIwXJChFR6zWIjQ17UBn4ft2O/Dmm0BuLtDUBCQnA//+N3DBBUBlJTB9OqBpF3w9fTpw++2u8ilGU9NuREScFT8gOsIAynVQzksvCWWyWoGYGGDaNOC664BTp4BXXgHKy4Xv9e8P3H8/0Lv32WlQakVZ2Tvo1euZoEQvCvXO/ZaE0lLgjjuASy8FnnoKqKsT6t2xY8L/f/EFkJjo+nmet6GkZAkGDZJ+AOp0GlFd/QU8rfGdWSZKgc8+AzZsAEwmoS4+8ggQFtb585TaUV+/CXFxk8UvxBnU1//oduD54INAXh7AtmyPj48HVq8GPv1UKFMrPA84HEJ/Ehl5Ziocamu/hcPxtqwR6LLPMEtL3/R6JhYbC9x2GzBhgj85UVRUfOjPg77lQnmUl7/vtWuC44CnnwYuvBD47juhEbz8MlBS4vlZm600KMEyRuNu2O2VLn/v6r1cdRXwww+nfx58UDA+/ft3ng6lNpSXvxuU9Zfy8g9cDmo4DkhIEIzmxo3A7NnA888LxrKVjRtPl8uVsQSEkXFZWXCuNystXeJ2lD9jhmA8Nm0SjOfKlYJRiYsTyve//wHr1wMXXwy84GbMQqkT9fU/SFCCjthsZTAa//L4vWXLgAHttooyDHD++UKZvINDbe1/4XR6t1cwEKqrv4Q33e6ZZdq8GfjpJ+Dtt4FvvxUGnu5uzeM4Y9A8ASUlSzzuWZ4373R7Wb1a+GzmzI79w/TpwLBhnRnLVgiqq4Praj5bgcw0NubA2/WWSy4Bxo4FIvw4FYpSG+rrpQ9YsFhO+rQvsbgYqK0Fbr5ZGIGNGAEMHizMzjzDobFxu99avaWxcZvbMnn7XjZvBq6+WujQXMHzNlitp/wT6gP19ZtdDmq0WmDWLGFWQghw0UVAUpJgXHyHh8HwWyBSvcLpNHn8u6WnA6p20fkMI8wq9XqhrK3vhRCgzI1nnONMQSlTY+MOMIz77QS//irMskaMOP1ZTAxwww0dDY4nGEYNk0n6wWd9/WaPrsvOyvTnn8C11woDOa1WMC6//SZ4C1xhNO7yydPlD5RSNDXtFCEdYUBw9dWuv8Pz5qD04e6Q1WA6nY1BjVBrbj7UYfby119/4cYbb8SpU6f8Su/111/HE088gYaG08E9RuMeMExgf1ZKBfemJ3jeCoNha9u/nU4nVq9ejRkzZvid9y233IJPP/0UHHd6zUhYzA8sirSyEjhwwH2DAACGYWE0nt77W19fj8cffxxLly71K9/8/HxMmTIFO3eebtSUUpjN3geB1dcLM/70dtvbpk0TBjmvvgo0eoiBsdsrO8xeDh06hGnTpsHfPc5r1qzBPffcg4qK00sZJtPellOU3LN0KXDNNUB2tuAZuPDC07+bOFF4P2+/Ddx6q7tUaId6RynF+vXrMXnyZNjt/gV93H333XjnnXdgs52uZ01Nf7mduTQ3Ax9/DNx7r19ZdoDnrR3qXXNzM1555RU88cQTfqVnMBgwadIkbNmypUOfYzT+7fY5d2Vq73ihVHBflpa6S42FxZLf9q/CwkLMmjULP/zgn3dgy5YtuP3225GffzpNYZDm2SivWAFMngzcdx+wr5MA/wMHgIYGwQXtjvbvSA5kNZhm8zGvGrlYUOqEw1GLv/76C+PGjcMVV1yB77//HkVFRX6l9/PPP+P1119HSkpKm+Fsbj7s9ZFqAJCaCkRHA19+CTidwK5dwP797keO7WluPthmKHv16oW7774ba9eu9as8ALB27VrMnTsXvXr1ajOczc2H/E6vlZ9+As47T5ipuYPjTDCbj7QZytTUVCxZsgRbtmzxK99Tp05h06ZNuPzyy3HJJZdg586dbt3LZ+J0Cu7Lf/wDSEsT3EXvvSe8r/ffB8xm4MUX3adBiA4Wy3EcOnQIEydOxOjRo7F27Voc82/Kij///BPvv/8+MjIyMHfuXFRUVMBsPuqVZ+OhhwSX7FtvCevkynbnXW/cKPw88ADQt6/7dARPimAo+/fvj9tuuw0bN26ExeJfoNv69evx6KOPIjk5uc1wmky5cHdE3EcfCbOueBHO9KfUBqNxT5uhTE5OxnPPPYcNGzb4lV51dTU2b96MG264AcOGDcOWLVvA87zHoDZXZRo9Gvj+e2HgaTIJ9Q8QXLOuYBgWZvMRFBYWYubMmRg0aBDWrFmD/fv3+1WmAwcO4PPPP8fgwYMxbdo05Ofnw2w+6tELMGcO8PnnwDffCIOyJ58824OxebNgLLUezIHdXinbvnpA5pN+Ghp+x6FDN4DjfAtTX7kSqKnxJehHwOlUYM4cFQoLO0bkajQasKzvB7ZbrdYOMzEAeOqpKFx5pcGndPLzhQ7s1ClhfS8qSujIHnvM87MGgxo33eQAz3cc5YW5igbwQHNzR3cRIQTr1ikQEeF55uDuvcycKcxavFl/3rw5CosWdfwbsiwLjcb3Yw45joP1jNFHv35hWL7cDoXC/Z5fnheMYatRVHQS41JfD9x0k2CEdC4OlHI61Xj2WRV27Og4kFKr1VB0lqgHbDYbnM6Oncadd0ZjxgwjGMb7zmTJEqBXL0F/e3geuPFG4JNPhMFcZzgcBNddx8Lh6Pg31Ol0fp0GZDabO8zEGIbBqlU6pKZ27r48eVJ4JytWCG3lk0+ETvipdoGUHAdceaXnoJ9WDhwIx7x5Hd8RwzDQuXqxbhC8GB37mfBwNdavt4G4mKa4KxPPA6tWCYaF44CpU4Hly4GvvhLctJ3BcRq8+64a69Z17F+VSiVUKt9PznE4HGd5EK6/PgoPPGAFy3of9fvYY4JnY0rLIUtWq1AHX3wRGD7c/bMMo8CYMQ1QKPS+yvcJVyf9yBolyzDBzV6pVODJJ5/B229/jiNHjsDpdEKtVuO+++5DZmdhmx5YunQp8vLyoFKpQAjBpEmTcOWVBMBXPqXTp4+wyN/Kffd5dl22EhMTh0cemYFVq1ahsbERNpsNCoUCb775pk8aWrnrrrvA8zzUajWioqIwa9YsREauAqXez8rO5OBBIVrRk7ullauvvg7Hj1vw/fffg+d52O12DBo0CA888IDPeefl5WH58uWw2+1QKBQYNGgQ5s2bBqXyBbeHdlMKvPaa4CZatKhzY9ke3o1XSqVSY968h8HzP2LPnj3gOA4KhQIzZ87Ehe19ol7y2Wef4ffff4dCoQAhBFdeeSWmTu0NQj7yaf2c405HxraHUqETq611bTCVSg0WLlyADz/8EJWVlbDb7WBZFq+88opfBuaRRx5BU1MT1Go1tFotpk+fjl69toPnD3b6/X37gKoq4JZbhH9bLMI7KCoCPvjA5+wBAMOGjcLtt6di7dq1sNvtsNvtSEpKwvPeRw+1UVlZiRdeeAF2ux1KpRLp6em4665/g5DH4GrW7KlM//qX8AMInqi4OOHHFUqlEnfcMQeNjXuwbds2UErBcRwmTZqECX5ETv7000/473//C4ZhQAjBmDFj8O9/Xwil8l2ftmcxTEf38rZtQHi4EPDjCUqprNeAyTrDtFjysWvXEK/3YHKc8LNqlTCTmT9fCJTxdnLIMAqMHWsEy2pw+PBhLFiwAJs2bcL27dtx8cUX+6x/8uTJ+P333/H000/j3nvvhU6nQ2npMuTnP+7Tml9+vuCa5XkhUnb9eqGM3gwCIyIuxogRf4BSig0bNmD+/PmoqKiA0ei9W7g9Op0OaWlpeP3113HdddeBYRjs2TMaRuMul894ei+vvy5s1XjySc/5E6JFnz6voWfPe9Hc3Iy3334bL7/8Mq6++mp8++23Ppdn69atuOyyyzBp0iQsWrQIAwcOBMeZsX17pFvjsmSJMOJ/442ObqK8PCFIJiUFMBqFSFqDQVgbdFem0aOPQKPphVOnTmHhwoX4/PPP8cUXX2Dq1Kk+l+mhhx7Ce++9h4cffhjz589HdHQ0amr+i6NH7wDHdX7CT0MDsHevEMCkUgF79gDPPitEaKvVgqs5I0MwlCtXAlu3Cm40V3VQrU7DRRcJSxm///475s+fj9zcXDQ1NUGv9330n5GRAYfDgUWLFmHatGlgWRaHD9/ScmXc2Vitwsy/la++EtyVDz0keGjsdqFOXnutUC8TEz21J4KUlIfRt+9rsNvt+Oijj7Bw4UKkp6fj77/drzt2RmFhITIyMnDppZfitddew/nnnw8A2LYtHBzXeTSuuzIRItS35GTBgD7/vDBDmzTJtQaWjcCQId8jMnIMqqqq8NJLL+G9997DK6+8gkceecTnMr399tt4+OGHMWfOHCxcuBCJiYloatqJ/fuvdlnvTCahzQwbJvQHv/4qtK0PPhD6PAB49FFg4EBhG40nCNFi3Lhmyc807pKHr1NKsW1bmNfbSj75RKj87cnOFiIavUGjycCFF+Z3+KysrAxJSUkgrvwkbqirq4NWq+0wojYYtuPgwetcVqDOeO89waXndAJDhghrSD17evMkQVra48jIeLntE2EvYxlSUvzbb1paWoqePXt2qJAnT85HaekSuBoZu3svdrvQsJ9/Hhg50nP+QiPfjMjI0zOv5uZm2Gw2xMTE+FwejuNQVVWF5OSOtzj8+WcabLbO9+607rVUKjsOxh5+WOi4PvxQMJI6nVCmuXOFyExXEBKGceOMHf6mVVVViI6O9ss11tTUBEopItvF31ssp7BrV5bLwafBIBjI/HxhdN+jh/BeJk4Efv9dWDurqRGM54ABwr7TPn1ca4iL+ycGD/6mw2clJSVIbe0FfaS8vBw9evTosDRSWvoO8vMfA6We+4czXbLjO7nd7zc3gb0sG4HMzJVISPhn22d2ux0NDQ3o0aOHt8XoQGd/j9zci9DU5HmrDNCxTCUlwn+rq4UBwZQpglvWHcIEwQCWPb08U19fD7Va7deSjdlshsViQWxsbNtnHGfB9u0RLgefBoOwRFNcLLSdtDTBMI5qMUU1NUIA3erV3vV5ev0ojBrlevAuFl3SYALAnj3nBy3yKSFhBgYN+szzFwPA6TThjz9ivLqjL1BYNhwDBqxGfPwNkuZTXf0Njh2b7VMwk78IjbwJLCttMNihQzejttb3Gas/RERchBEjdkiaB6UU27dHuJy9iAkhWmRkLEZKyn2S5tPY+BcOHLg6KPWOEC3OPz8PWm1vSfPJz38CJSWvQaqTi9qjVqfiooukP07ur78yYLV6EdYfMAQpKfPQt+8Sz18NkC57W0li4mwQ4l+Aii+wrB6JibMkz0eh0CMyMjhXVVHKISbmKsnziYm5xq87L/0hKmq85MYSAJKS/gWWlf4SYUL0SEq6U/J8GIZBfPzNQYoLoIiPv1HyXCIizg/aBeMaTS/JjSUA9OgxLShlIkQTlP4OABIT/wWGkf7eYUI06NFjpuT5uNUga+5Ayx9A2s21gDAbi46+QvJ8ACA19TGwrLRRXIASiYm3d3C3SIVCEY6EhBmQOkaMZfVITfUiNFgEYmL+EaQtTRQJCdOCkA+QkvIwGEb6gIjIyMugVnu1ZhAQDMOiZ88HJX9PhOiRmhqcg//1+qHQaj3s2RGJ5OTgnGOcnDwnKPloNL0RHj7C8xclRHaDqVDokZw8V9JGQUgYevVaGPCBAt4SE3M1lEr/1j28hRAlUlPnS5pHe9LSHpM4Oo2BSpUctEENw7Do1espSb0bhOjQs+e9QbvAXK8fjPDwUQB83yLlLYRo0bv3M5KlArzEaAAAIABJREFUfybJyXeBYaQrDwAQokZCwi2S5tGe9PQXJK13DKNCbOz1UKs9bHoWCZWqBxIS/gmGkW7mTEgYevf2PVpZdB1yCwCA9PSXoFD4HtDhHSx0uoFITr5LovTPhmEIsrK+kmwQIAwAnoZW6yYqQ2R0uv5ITV0AQqTp/AnRICvra8mj39rTs+e90Gr7QapmoFTGBb2RDxy4BoRI4x4jRIsePW5DZORFkqTfGSpVHPr1e1cyA0OIDoMGfRaUZYBW4uKuR1TUeI8b/v2FZXXo33+5JGm7om/ftyQbGDKMEpGRYxEff5PnL0tMlzCYLKtFVtbXkhgYQrTIyvoyaLPLVsLDRyIlZZ7oBoZhlNBq+yItLTiuy/b06vUkNJp0iO2aJUSH1NRHodcPFTVdTzAMK2G90yEr62uwrPRrO+3RaHqhb98lEgxsCBSKmKAEXJxJq5EWe52MEC3i429CTMw/RE3XGwYM+EiS5RRCdBgwYBWUyljPXxYRpTIaAwd+KsmAWijTJ0EdTLvUIreAViIjL0Zm5gpROy9CdBgyZGNQZ2LtSU9/CbGxE0WrRAyjhEqVjKFDt0jupuoMQhQYNuzXFlePOEaTEB3i4m5A797PiZKer+h0/XDeef8TtaETokVm5keIiLhAtDR9ITl5DpKT7xGxTAQKRTSGD98alDXzM2EYBllZ66DT9RfN7UeIFhERFyAzU/objDpDpYrHsGG/iRp4RogO6en/QVzc9aKl6QuxsdciI+NVUftwltW39DleHNUUBLqMwQSAHj1uxYABq1oauv/SGEYJlg3HkCE/IirKy+NlJIBhCAYN+hwJCdMD7rwICYNW2x8jR+6CSuXmeA+JUakSMHLkLuh0fUUokw49eszEwIFrZB09RkdfjvPO29QSqBXIOi0LQnQYOPAz9OgRvDWxzujTZzFSUx8NuPMiRAuVKhkjR+6CVpshkjrfUSj0GD58O8LDRwXsniUkDNHRV2LIkB9BiDRuUW/Q64di+PDtUCrjAhwIMG1bfVJTfT+QQExSUu5D377LWuqd/22aYdRQKGIwbNhW2QN92iP7PszOMJtPIC/vFpjNx32+xZuQMERGjsGAAau6zKgEAGprN+Do0WxwnAWU+nLbOgtCVEhNfawlSEW+Y6Haw/N2nDr1n5Y7GG3wJdKZYTRgWR0GDlyD2NhrpRPpIzZbBY4cuQ1NTX/5Ve90uoHIyvpSNo9GZxgM25GXdwucToPXJ2oJMC1h/LPQt+/rQQtc8gSlPEpL30Zh4ZPgeTs8XcTcHoZRgRAV+vVbjh49ZnYJFx8AOBwGHD8+F3V1G3x8R0K9U6mSkJX1DcLDvThbLkiYTAdw+PBU2GylfrWlmJgJyMx8H0qlVLEt7umyBxe4glIeVVWfobh4EazWU6DU6fL+QoZRg2EI9PqhSEt7ErGxE7tMY2iPw9GA0tI3UVb2Nih1guOa4crQEKIH4ERc3E3o1etJhIUNCqpWbzGZDqG4+GXU1KwDwyjA8642zhOwbBgYRoWePe9HSso8WW9OdwWlFHV1/0NR0ctobj4ISnmXxxwyjAoMo4BWm4G0tAVISJge9LVyb+A4M8rKlqO0dAk4zgSOM8PVxnlCdKCUR0zMVejV62lERIwOrlgvsVgKUVz8Kqqq1oBhWDeHGzBtW7ySkv6N1NT5QYse9ZWGht9RVPQimpr+AMC4OQFNCULUUKnikJr6GJKSZss6U3YFzztQUfERSkoWw26vahlYdz7AaV2fjoi4EL17Px20aHlXdDuD2R6jcS/q639CY2MOTKb9aG6uAyEEOl08wsNHITJyHGJjr4VO119uqV7B8040NGyGwbAVjY1bYbGcRFNTPcLCIqDRJCEiYgyiosYiNnZylzQqneFwNKC29js0Nm5HU9MOWK0VMJubEB4eA622HyIjL0FU1CWIjr4ahMh65r/XNDcfRX39jzAYcmAy7YHZXAtKeeh0cdDrhyIq6lJER1/dpUb27qCUh8HwOwyGHBgMOTCbj8BkqodarYNWm4CIiIsQGTkWcXGToVJJuy1KLDiuGbW1/0Nj43Y0Nm6HzVaCpiYDIiJioNH0RlTUuJb+4bqgHYIQKFZrMerqNsBg2Aaj8W9YLNVwOOzQ62Oh02UhKuoyREdfjoiIi7rkxOBMWi+Zbmj4BY2Nv6O5+RBMpnooFMqWPnx0yzuaGJTDI7zBlcEEpdTlz8iRI2lXJDExkXZVbf5gs9kowzB09erVcksRjRUrVlBCCLXZbHJLEY0hQ4bQ5ORkuWWIBs/zVKlU0ocfflhuKaKxf/9+CoA2NDTILUU0Jk+eTPV6vdwyRCUiIoJec801cstwCYDdtBOb2PX8Rx4oKChAVVUV9u3bh6Ym7w8478qsXLkSlFIsdXflRTfjrbfeAs/z+OSTT+SWIgoGgwGHDh1CRUWF3xeOdzW+//57OBwOrFmzpsNdlN2Zl18WLiJ47bXXZFYiDg6HAz/99BNMJhN27ZL+0PFgsHfvXjQ1NeG3336Dzd0N2F2Rzqwo7cIzzBkzZlAAlBBCn3vuObnlBIzNZqNxcXEUAFWpVDQ3N1duSQGza9cuqlKpKAAaHx9/Tswyn3rqKUoIoQDo7bffLrecgOF5nmZmZlIAVKlU0g0bNsgtKWCKioqoWq2mAGhYWNg5Mcv84IMPqEKhoADopZdeKrccUbjqqqsoAKpQKOi7774rt5xOgYsZZrcymPn5+VSj0VAI90xRvV5PGxsb5ZYVEMuXL6dhYWFtZbryyivllhQwl112WVt5wsLC6Pvvvy+3pIBoaGjo8I40Gg09deqU3LICYuPGjR3KlJmZSXmel1tWQGRnZ7cZF41GQ5988km5JQWE3W6n8fHxbe9Ip9PRv//+W25ZAZGbm0u1Wm1bmWJjY6nVapVb1lm4MpjdyiW7fv16WK2nt2SYTCb8+uuvMioKnM8++wzmlltjGYZBTk6O35c/dwUaGxuxffv2tn+bzWZ89pm0V6pJzZYtW9DcfDo03mq14rvvvpNRUeCcWe+OHz+O/Px8D091bdavXw+OE6J/bTYbvvrqK5kVBcbu3btRU1PTFthjNpu7fZm+/vprWCxC9C/DMKirq8POnTtlVuUDnVlR2kVnmDzPU6vVSocMGUKvv/76Ljky8RWn00mtVitVKBT0+++/p3a7XW5JAWO32+n69eupUqmkVquVOp1OuSUFjNVqpRMmTKAjRoygVqu128/GOI6jVquVRkZG0sWLF58TbnOHw0GLi4spAGq1WqnD4ZBbUsBYrVZ677330pSUFGq1WinHcXJLCojWete7d286e/bsLtuHw8UMs3vE97fAMAzUajUIISCEQK3uHmHi7mBZtu2WeZVKBaWyaxxMEAhKpRJKpbLtfZ0LtNa7c6VM7duPUqmEStX19vH5ikKhaCvTufCOAKEcLMuec/WOYZgO76u70K1csiFChAgRIoRchAxmiBAhQoQI4QUhgxkiRIgQIUJ4QchghggRIkSIEF4QMpghQoQIESKEF4QMZogQIUKECOEFIYMZIkSIECFCeEHIYIYIESJEiBBeEDKYIUKECBEihBeEDGaIECFChAjhBd3KYNbX1yMnJwcmkwm1tbXIycnpcCh2d+TEiRPIyckBpRT79+/Hzp07u/XdhJRS7Ny5EwcPHgTP88jJycHJkyfllhUQJpMJOTk5qKurg9FoRE5ODurr6+WWFRDFxcXIycmB0+nEyZMn8ccff7QdXN5d2bt3L3bs2AEAyMnJweHDh2VWFBh2ux1bt25FaWkprFYrcnJyUFFRIbesgKiqqkJOTg4sFgvKy8uxdevW7nUnZmcHzNIuevj6G2+8QRUKBSWEUIVCQRmGoevWrZNbVkBcfPHFbXf4aTQaqlAoqNFolFuW3xgMBsqybNs1bGq1mo4bN05uWQHxzTfftNU5QghlWZYuW7ZMblkBMX36dKpUKtveEQCan58vt6yAiIiIaLs6SqPR0PT0dLklBcSff/7Zdk8uwzBUqVTSRx55RG5ZAbFgwYK2vrv1ztytW7fKLesscC7ch3nmvYSJiYnd/kaCjRs3Ur1e33ah6uzZs+WWFDDZ2dmUZdm2+zB/+OEHuSUFxJn3Eur1emowGOSWFRDt7yVkGOacuIf1ueeeaxuonQv3sPI8T7Oyss6pe1hLS0s73GncVe9hPScMJqWUPvnkk5RhGMqyLP3444/llhMwPH/65nuVSkVLSkrklhQwRUVFbaPHrKysLtkgfOWDDz6gLMtShmHos88+K7ccUbjyyispAKpUKmlubq7ccgKmsbGR6nQ6CoDGx8efE1eW/fjjj22egNtvv11uOaJw5513tk0QNmzYILecTjlnDGZDQwNlGIaq1epuP7tsZePGjRQAveaaa+SWIhpXXHEFBdDtZ5et2O12qlKpKCGk288uW8nNzaUAaP/+/eWWIhrz58+nALr97LIVnudpTEwMBdDtZ5etlJaWUgA0MjKyyw6mXRnMbhX0AwBhYcCYMX1x441jAFjklhMwlFJceeUIpKYq8eCDM8HzdrklBQzP2/HggzORlqbC+PHDunUQUysMY8XkyRfikksyIUxiujeUUmRlpaBfv3Dce+9UcFz3b0s878Q990xFUhIwY8Y/QGn3DmICAJ4341//ug5DhvRAcrJebjmikJCgxfDhybj99mvA82a55fgE464zGzVqFN29e3cQ5ZyN09mEyso1qK1dC5NpHzjOCI5jwDAMCOGhUMQiPHwEEhKmIT7+ZrCsRla93mCx5KO8fAUaGn6G2XwEAIXN5oRKpQTghEbTCxERY5CUdAciI8eBYRi5JbuFUgqDIQeVlR+hsXEHbLZiAArY7Q6o1QoADHS6QYiOvgrJyXOg1abLLdkjHGdBTc3XqK7+CkZjLpzOevA8AaUULEvBshHQ64chPv4m9OgxEwpFuNySPWKzlaOiYiXq6jbBbD4MnnfA4eChVLIAnFCpeiIi4kIkJt6OmJh/gGFYuSV7pKlpNyoqVqCxcSsslnwwjAJWqx0ajQqU8tDp+iMqajySk+cgLCxLbrke4XkH6ur+h6qqT9HUtAsORxUoVYDjOCgUDAjRQq8fgtjYSUhM/BdUqji5JXvE4ahHZeUnqK39DibTfvC8BU4nbbkY2wmlMgHh4eejR49bERd3AwhRyi0ZDMPsoZSOOuvzrmow7fZaFBQ8jurqLwAQ8Lz77SMsK4y+kpLmIj39ObBsWBBU+kZT027k5z8Mo3EXKOVAqcPNtxmwbBgUilikp7+AHj1mdjnDSSlFZeUqnDr1DJzOBnBcM4S1/M5hGBUYhiA8fDT69n0T4eHDgyfWS5xOE06dehYVFR8AADjO5Pb7hIQB4NGjx63IyFgEpTI2CCp9w2w+jpMnH0RDw69gGAY8b3X7fZbVgxAd0tKeRErKfV3ScNbWbkBBwWOwWotbysO7+TYLQlTQ6QagT58liI6+LEgqvYfn7SguXozS0jdAKQeOM7r9PiFaABSxsRPRp88SaDSpwRHqAzZbGU6enI+6uvUQ+nD3s0mhD1cgJWUeevV6AoSog6KzM7qVwayp+S+OHp0NnreAUt9clIRowbKRyMr6ElFRl0qk0Dd43obCwqdRVvYueN531xchYYiIGI2BA9dAre4pgULfsVpLcOTIrTAacz0OZjqDEC169pyH9PTnQYhKAoW+09DwG/LypoPjGj0alTNhGBVYVocBAz5BXNxkiRT6BqUcSkqW4NSpZ8HzNrg3KmdDSBi02r7IyvoaOl1/aUT6iMNRh2PH/o36+s1+ufMI0SIhYTr69l0GhaJruDiNxlwcPnwz7PZKP8qkACFq9O27FElJd3aJQXXrQPrkyfvBcVYATp+eJ0QHlSoBgwZ9g4iIs2xWUOgWBpNSipMnH0JFxYqAfduEaJGRsQgpKQ+IpM4/HI4G7Ns3HhbLiYDKxDBKEKLDsGG/IDx8pIgKfaep6W/s338VeN4MSn1rDO0hRAedLhPDhv0GhSJSRIW+U1LyBgoLF/o1oGkPITokJ9+NPn1ek7Xz4jgrDh6ciKamv/wa0JyGgBAtBg9ei5iYq0XT5w9m8wns3TsWTqfB54F0ewjRQKVKxPDhf0CtThZRoe9UVX2BY8fuFKG/C0Ns7CQMHLgGhChEUuc7lHI4evRfqKlZG2C9E/rw/v3fR2LibSKp8x5XBrPLBP0IxvIBUYwlAPC8BQUFT6C09C0R1PmH09mEvXvHwGw+EnCZKHWA4xqxb99lMBr3iaTQd4zGXOzbdwU4rikgYwkIAQ3NzXktnaB716eUCMbymYCNJSCUqbz8/5CfP18EZf5qsOPAgWvQ1LQj4E4L4MHzzTh06EY0NPwiij5/sFgKkJt7IRyOmoCMJQDwvBU2Wyn27LkAdnu1SAp9p6rqKxw7Nluk/q4ZdXX/Q17edFDqmydBLCjlceTIbaip+a8I9U7ow48fvwuVlZ+KoE4cuozBrKpag4qKj0WNmuJ5MwoKFsBgyBEtTV/Iy7sVFktBwA28PRxnwoEDV8HpbBQtTW9xOBqwf//V4HnxjBulNpjNJ3D0aLZoafpCQ8OvLTNLcetdefl7qKr6QrQ0fSE//xEYjX+LMgBohefNOHhwMqzWUtHS9D5vO/bvvwpOpwHu1sh9gVIn7PZKHDw4URYDYzIdwrFjd4j+jurrv0dx8WLR0vSFkpKlqK39TuS2ZMHx43NgMu0XLc1A6BIG02Yrx4kT94oyKjkTnre0rEsF98zZ6upvYTD8BkrFPyeR40w4fvw+0dP1xPHjcz0GwfgDpTbU1/+I2trvRE/bHU6nCXl500XttFrheTOOH78LdnuV6Gm7o7FxByoqVkpSJkptOHJkRtC3CRUWLoTdXglf12A940Rzcx7Kyt4ROV338LwTeXlTJat3RUX/QXPzUdHTdofZfAKnTok78GyF5604fPhm8Ly7IMng0CUM5vHj97QsDkuD02nAqVPPSZb+mXCcpWVdQhojzfNW1NauRWPjDknS7wyDYRvq6jZKMgAAhIZ+9Oi/WoJTgkNh4UI4nU2Spc/zVpw4EbyBDaUUR47cKklHLKTvhNGYi5qabyVJvzPM5hMoK3tbsv16PN+MgoInYLfXSJJ+Z5SXL4fVWgyxZstnwvO2oHtspG27FDZbGUpL35Qofe+R3WDabOWor/8RvkZS+QLPW1Be/r6kRrk9NTVfA5B20zTPW1Bc/KqkebSnuPgVyTriVnjeEbTOmOPMqKhYAUqlqxOUOlBbuyFo62QGw69wOGolzYPnm1FU9JKkebSntHQpeF66vkGAoqJihcR5tOREeRQXL5JsMC3Ao7n5IEymQxLmcZrm5iMwmXIhvgfgNDxvRknJ67Ktz7Yiu8EsL3/Pp2jCpiZg4UJgwgRg2jRgyxZvn6SoqfnGL42+Ulz8qk+uy8pKYMECYNIkYMoUYNkywPNNSxT19Zths1UGpNUbbLZyGAy/wdWIeN064K67gKuvBhYt6vg7qxVYuhSYPBmYOBGYN891PjxvQnHxItdfEJHq6q8AdF7v7HZg8WKhfl17LXDnncDOnWd/b9UqYPx4YM8edzkxKC9/XwzJHikuXuy23r30EnDTTcB11wG33QZs2iR8npcHzJ8PXH89cMMNwHPPAXV1rvOxWE7AZDogrvhO4DgzKitXAfDsiistFerfS+1s+ZYtwjucMAF4+mmh7+gMnregtPTNoJwM1NDwi8c9lkDn5Vm7Fpg+XXh/d90FHDzo+nmet6O0dIkIij1TWrrMrbv0wQeFskyYIPzcfvvp3xkMwAsvCH3DpEnAiy+6zofnLS2TK/mQ3WBWV3/j0563ZcsAhUKoPE89Bbz5JlBY6Pk5jjO1dJLSYrfXwGLJ9+mZN98EoqKA//4X+PBDYP9+YP16z88xjBINDV6PGPymvn4zANeh6rGxQgc8YcLZv3vjDcBoFIzLd98B99zjPi+z+RgcjobABHtBdfWXLoOXOA5ISBDey8aNwOzZwPPPCwObVsrKgJwcoezuoNSK6uqvRVTuKh8OBsPvbr8zYwbwxReCoXzpJWDlSuDYMeH9TJwo/O7LLwGtFnjVjfOCUgfq6jaJW4BOaGzcAYbxbovEsmXAgAGn/11YCCxZAjzxhNBXqNXC+3QFz1vQ3HwkQMWeqa1d59Vg+szy5OUBK1YIg5mNG4WB3MKF7gbWHGprN4gh2SO1tevhyUM4bx7www/Cz+rVpz9/5hkgJkaod+vWAbfc4joNjjOipua/4oj2E1kNJs87YLV6b1wsFmDrVuCOO4RGfd55wMUXAz//7N3zJlPHqYDT6cT69ev9voR6z549Z11SazTuaTmFw3sqKoDLLgNUKqHyjB4NnDrl+TmeN6Gp6c8On9XV1WHTJv87s40bN551OXJj4w63kbGXXAKMHQtERHT8vLgY2LEDeOQRYUDAskBmpvv8CdG1uHdOc/DgQezdu9encrRiMpnw3Xffwel0nvF5rosnhLo1axaQmAgQAlx0EZCUJBiXVpYtA+bMEQZvnrBYTnSYvfA8jx9//BE1Nf6tmx05cgR///13h8/M5qMeD4BITxfqWCsMA5SXAxdcINS/sDBAowFuvBE45MabR6njrMhzk8mEtWvX+h0QtGXLFpSVlXX4zGjc7dUywK+/CtpHjGifnvDehg4V3ucddwDbtgFmF0uhlJ7dPxQUFGD79u0+lwUQ+pZvv/0WVmvHyUBjo+f0OitPZSXQu7fQfhhGmLE1NgozNFdwXBMcjtOuAkopcnJycMqbzqUTiouL8dtvv3V4xw6HAU6nf5ep79oFVFcDc+cCer3Qlvr1c/9MU1Pw4jY6Q1aDaTYfAyHen/1aWip0uqntToHq08c74wIIp4Q4nUY4nU58/PHHSE1NxZQpU+Dv4QxPPfUUhgwZgokTJ+JQSw9jMu31OSL3ppuERmK1AjU1gvtv9Gjvnm01mHV1dXj00UeRkpKCf/7znz7l354pU6YgJSUFjz/+eJvhbGr6y6+0jhwBevQAPvlEcMnecYcwK3MHz1tgNArG8eDBg7j22msxdOhQLFy40C8NO3fuxI033oi0tDSsWrUKTqezpZF7vy2nvh4oKREMDgD8/jugVAIXXujd84QoYTafAM/zWLt2Lfr3749rr70WP/30k+8FAvDOO+/gwgsvxNixY/Hnn8L7N5m825u7dClwzTVAdrYwO+6sDAcOCJ2zO1rzM5lMeOmll5CcnIybbroJTa78nh7Izs5Geno65syZ02Y4Gxu3ezg+EmhuBj7+GLj33o6fnzoF9O17+t89ewodcklJ5+kIg0/B715QUIAZM2agf//+uMeTS8QFBQUFmDp1KpKTk7Fs2bI2w2k2H/erPBdcIMwm8/KE//7wg1C+mBjXaRGihdG4F5RS/PLLLxg+fDjGjx+PL7/80q8yffvtt7jiiiswZMgQ/PTTT6CUorl5PwjReXx2xQqhD7jvPmBfS1XNyxP68kWLhN/NnXv6d66wWPJlXceU70gIoGVk4r3NtlgA3RnvJizM9ajxTHieYObMG7B+/R9wOp0tBxor8PTTTyMuzvdDjHNzc8HzPDZt2oQffvgB8fHxWLSo1/+3d+fBUVX5HsC/99zebqc7nX1PJxCaxCRkkU0UZGpGJhEYa0S0xpfh8RBwQcxDEhaBcRsGLQiooD5F0PBcphCdcXxVqKPm1UMUcFiEkZIlMJDJBiEJIWun0/e+P06CAdKd252+SXzv96lKFXS675K+5/zO8rvnIjnZtySF7Gw+zDJjBiDLQF4e77Gp0dBwHhkZGTh58iQURYEsy2CM4e677/b5fAB0BxQXSkpKUFJSgvT0dGzaVAm9H+sh19XxobHbbwc++AA4fpwPkSUnA0lJfX9GUTrxxRcf4oknNuDSpUuQZV44Dh486Nc5Xbx4EaIooqamBvPnz8dDDz2E2bMnY/58EYLQ/9xYVxcfvszLA+x2fq1t2waUlKg/BrdbwapVi7F9+350dHTA7XZDFEW88MIL+OAD35Ocjh49CkVR8PXXX2Py5MkICQnBU0+NQVZW//f7Pv44UFjIK6vvvsMN3+uZM3zIzNtcEgA4nY2YMGECjhzhFbLb7QZjDAUFBdD7cbHU19fD5XJh27Zt2L59O5KTk7F5cxeC+lkS+s03+fBkZOS1r7e344bPWiz8dU+OHfsf/OIXiaiurr563Z07d86v6665uRmMMTQ2NqKoqAjLli3DlCmTsGZNJ7ylbHg6H7OZl6PCQt4btlj4sLm3bbndXXjlledRUnIPWltb4e4ev3377bdxoK9J+X6Ul5cDAL7//ntMnz4dQUFBWLp0PKZO9V7fPfggL/M6He8YrFrFA2hdHXDwIJ8/X7GCjx6uWQO8+y5g87jwlwJZdkIUfRvFC5QhDZi+plVL0o3Bsa3txiDqcW+Kgqqqf6Kz88eKRZZl1NbWoqXF9/sLew+38Cd2XMaVK74t8SbL/GKZORN4+WVeoNevB15/nbe4+uN2d6G2thayLF8dKlEUxe9hl55tyLIMQRBQU1ODrq4uvwKm0cgLyZw5fGQgJwfIzeVDMZ4CJgA0NTWiqenaHmBbW5tf59Tc3Hy18pNlGZ2dnaiqqoSiKF4rG/5+YN06HlR6kpVKS4Fp0/hwrVqyrKCmpgbt7e3XfEd1dXVXKzFfXN+La25uRn39JagtT6LIpzM+/5zPK99zD3+9qoonny1eDGRled8GL0tVcLvd15xTRUUFRNH3xdp7/g4926qrq4PTKXkNmOXlPOHqjT4SXCWJ99Z6a23lr3vS2tqChoaG64YcXX5dd06n85qyJMsy/umpe9vN2/ns3g18+invfcbH80CzahWwdSvgqa2vKAouXqxFW1vbNefU2Njo1zn1/tvIsozW1lZcuHCh37KUnv7jv/PzedA8cIDXDzExvKMAAD//OfDOOzyZyXuH4f9pD1MUbfDl5BMS+HBEZSX/N8Avsv7TfsDDAAAPGUlEQVSGj37cn4Kysm/R2gqUlJTgxRdfhNPpxLZt2zB1qu8Ltefl5aGsrAwOhwMbN25Efn4+zp9fi3PnnoHa20qam4ELF3h2osHAf+68kydkqAmY4eF21Nf/HSdOnMDKlSvx6ac8i8zfOT+DwQDGGGbMmIF169YhNTUV3357E9rafL8ReuTIG1/rPyFah1mz/g1LlqzA7t27UVxcjPLyckyZMsWvudkvv/wS+fn5kCQJS5cuRVFRESTJhX374uFtuk1RgA0bgMZGPmTUM1d5+DBvGf+le42FpiaeEHT//fynL3q9iDff3InS0pF49dVXsXbtWrS0tGDdunUoKCjw+ZwWLVqErVu3Ii4uDuvXr8e9996LixffxunTj/l0c7fbzecwAT5HVlTEGze/VLFkrNFoQ1VVFSoqKvDkk09i586dcDqd+Oqrr2Dz3D3wKDY2Fg0NDZg8eTLWr1+PsWPH4ujRPDQ2eh62/u47XnZ6EkXa23kj5/x5YPx43lvuUV0NuFzXTudcb+LEaWht3YY9e/Zg2bJlOHLkCFJSUvwqS6dOnUJ6ejokScKCBQuwevVqREVFYc8eg8clJb2dT0YGn5PtOf4JE/hw7PHjgKeqS6fT4+mnN2DTpjuwY8cOrF69GvX19SgsLMTKlSt9PqdNmzZh+fLlCA0Nxdq1azFv3jy0tOzB8eOzVWT1/0gQePkaORLYt+/G33mjKIpP03iBNqQB02xO8+nePkkCpkzhraziYh4sv/kG2LJF3edFMRh6fQhCQoC1a9eiuLgYb731FsaMGePX8S9ZsgRLlixBfn7+1VtjLJZsiGIQ3G51czk2G08o+fhjXlDa24HPPus72PQlOJhPdqalpeGjjz7CiRMnUFZW5tf5ALxQTJs2Dam9snOs1nFeA6bbzX9kmf90dvJeTHY2zzZ9912goIAPAx45wlPiPRFFMyyWbAiCgBkzZmD69OnYvXs3dGqya/qQnZ2NDRs2YN68eddU5KJoRleX5yHMF17gFdXGjbwl3GPjxmszEx9+mGf+Tpzo+RhkuQNm82gwZkBxcTEWLVqEHTt24LbbbvPrnAoKCjB16lTMnj37am+u52/mSWMj/9tPmsQbZYcO8Zb+mjW8AVBUxJN97rpL3TFYLJkAALvdjtLSUjz77LPYtWsXrFb/ngv63HPPYcyYMRg79scHC9hst6KxsQyeMjBnzuS9kh47d/LA//jj/HwXL+bzsQ4HrzOmTPE8GsWYGcHB/Eu8/fbbceDAAezZswe1tf7dtmW32/H8889jzpw5iI6Ovvq6yZSC9vaTfX7G2/ns38/L0d138/ri0CHeceiZV++LojhhsWRDr9djwYIFmDt3Lt577z3k5vr3WL28vDyEhoaioKAAhu7sMYsly2sd3tLCy31ODq8Tysr4d7J4Ma/7XnuN95ynTQP27uXXorfq2GRKHtLHzQ3500r2709BR8dZ1e+/coUPWR46xLMyFy4E7rhD3WdDQu5ATo7KlFo/OZ1VOHBglE+3ypSX8+HYM2d4VmZuLp+r8DahD/AnFDgcmxEb+8AAj9q76urXUV6+1ONqK6Wl/LaR3ubO5Zmm//gHn+87e5YnAM2fzysuTwTBiEmTKmAwRAXs+Pty5MjP0NTUdwZSbS3vLer1vJD3WLqUF+zefvMbYNkyYKyXB8hI0mhMnNh3JRkostyJr76yeEySuXwZeOopfo0pCv8uZs3ilfSOHfw7NF3XcP/kE097E5GUtAYjRjwdwDO4UX397u5lLdU1PktL+bDy6tX8/198wYc3r1zhGacrVtyYyd1DFIORk/PfsFpv7vsNAXLixAOorX1L1Xt7n4+i8KD/2Wd8VCoykjdCvY0GiKINU6Z4SaMNkL17wz1myl6+zIf5Kyp43Wa38+S/cd3PATl2jN/uU1PDf/foo96nA6KiCpCerv1i7MP28V6nTy9BdfWr/WbDDRRjQUhJWY/4eP+y3tRSFAX79yfD6azQdD8Af0zR+PE/QJKSNd1Pe/sZ/O1vmT4/I9IfJlMybrlFxY21A1RZuRlnzz6h2ZJrPQTBgISEQqSkbNB0PwBw+PCtN9xmpAXGgpCd/VfYbLdquh+X6zL27YsdlOtOFINx222XwJgfk/U+qKv7M06cmKtq8YKBERARMQuZmdqvnHX8+L+grm4ntJ5bFEUrUlO3ISrqPk33Awzjx3slJDw2SF1sGdHR2j9XTRAEJCYWqUq1HiirdaLmwRIAJCkFFou2LW+AV8SJics13w8AREf/KwYneYAhPn5w1pNNTFwOUfRvSNQXBkM0goMnab4fvT4EERGzoHU1JQgGxMU9rHmwBIDw8F+pXoxhIBgzIzFxcB4xl5i41Od7z/3DEBHx60HYj7cjGGKSlAKrdYKmhyIIBkRF3Q+dTvvKBABiYrRf+JixINjtKzTfTw+7/Qkw1k+OfwBER/9W830AvDKOjLwPgqBlJclgs90Kk8lLSnAAhYfP1Dwhoue6G6yHYycmFoMxY/9vHABBEBEf/2j/bwwAxnSIjy/UPMAYjfFX52S1Fhw8DiZTsqb7YMyEuLhH+l2cQ2tDHjABIDX1DU0LBWMSRo4cvIXKdTobUlLWaxhg9LDZbkVYWL5G279RePgMWK3joVWeGGNBGDVq06A1agAgJWWDphUXYyakpm7VbPs37k+H1NQ3NRzdEGAyJSMmRts5896s1tzuho02DQHGzEhIWAKTya7J9vtity+HTheq2fYZk5CWtmPQGjUAkJZWqmlZEsUQJCWt1mz7ag2LgGk2j0Zy8jOaBBjGzEhLewsGg+8LEwxEXNwjCArKhBYBRhRNSEv7z0EtEIIgID39HY0aNjpYLDmIjV2owbY9MxiiuhtrgQ8wjAVhxIg/QJJSAr5tbyIiZiI8fAYEIfDfE2MmZGTsAmODm1zvcGzRqCElwGCIQ3Ly0xps2zNRNCM9fadG152E2NiFsNlULkMVIMHB4xAf/6hm55Se/kfodJaAb9vnYxnqA+iRmLgUoaF3BLSVwpgZsbEPIjLSv1VvBkIQGDIz/wyDIRJA4OZoGZOQkfEnGI0+3DkfIEZjPDIzPwxwS1KEwRCNzMwPB7UB0CMq6j7ExDwQ0ILOmBlhYflISCgM2DZ9kZq6DZI0EoIQuOErxswYPfp1BAXdFLBtqqXTWZGV9UmAG9QCdDobsrJ2D8kwX0jI5O5OQuCuO0EwwWLJRUrK+oBt0xcjRvwBVuu4gE4LMGZGUtLvEBr6s4BtcyCGTcAUBBEZGbsQGvrLgBQMxoIQGzsfo0YNziNu+mI0xuLmm/fDYIgJQOUlgDEzMjI+RFiYyvtoNBAWlof09PcDUtAFwQijMa77bxTd/wc04nBsRkzM3ABddzxYpqf/EYIwNMVLpwtGbu5eSJIjII0bxsxwOLYgJkb7pDlPrNaxyM7+vDupaaB/Vx10ulDk5u6F2dzPat8astuLkZT0u4CUJcbMsFrHITv7r5rP+Xo+BgOysj6F1ToxYOdkt69EUtITATi6wBg2ARPgi1RnZn6I5OSnugu6P4enhyhaMHr0f2DUqJeGpNfSm8lkx/jxx7oTMvy7iBgzQ5JG4eabv0F4eB/P0BpkEREzkZu7FybTyAGdU0TEXRg37ihMpoQAH6FvBEGAw/EKHI6XIYoW+DeMzsCYhBEjft89bKl9xqU3en0Yxo49gOjoOX4HTcYkGAyxyMr6RPN7fdWw2SZh7NjDsFhy/G7cMBYEm20yxo//O4KCMgJ8hL5LSlqJ9PSd0OlC/R5GZ0xCfHwhcnK+hChqn5jnjShKyM7+HAkJPZmzvte/gmCATheCm256F8nJ/j10QStDfh+mJ21tp3Dy5EI0N38LRXH3e58mYyYoioKwsDsxevSrMBpjB+lI1bt06S84fboQLldD9xPXva/9yStvICGhCElJq4e8Er6eLHfi3Lnfo6qKP2iw/+f8CRDFIOh04XA4tiAi4lfaH6SPnM4qnDq1CA0Nn0EQhH7vARQEAwSBITj4Fowe/QbM5lFe3z8ULl/eg1OnHkZHx/nu8/F+Ow0PRjJiY+dj5Mjnh7wSvp6iyKis3Izz55+BorhV3dMoitaryX8xMXOHvCF9PZerEeXlS1BX9z4ApuL+YB0Y08NsTkdq6lbNF1zwR0vLUZw8uRCtrd93L9nofZF2HmAVRETcA4fjJej1/TxsVkPDduGC/rS3n0Vl5RbU1/8XnM6K61otMmS5A5KUgsjI+xAX98iQzO35QlEUNDXtQWXlS2hq+hpdXY3XnJOidAJgsFiyERf3MCIj74UoDt3aiWq43e2oq3sf1dWvo6XlGAC51xC0Alluh04XBpttMhIS/h022+RhV2Fdz+msRnX1a6ir24X29jPd8zI9Ix78ujMa7YiIuAvx8Y9BkrysUTZMXLlyEFVVL6GxsQwuV911150LgAKz+SbExs5HdPScQc1Y9ocsu1Bf/zGqql5Bc/NhKIrzml6aLLdDFC0IDp6I+PjHEBaWN2TD5Gq5XA2oqdmOCxfeRlvbKQiCvtd96gpkuQ0GQ9zVOfLh0EvuT2vrD6is3IyGhk/Q2VnVPSrVU/7dkGUXJMmB6OjfIi5uwZAGyh4/2YDZmyw70dr6Q/dSWQx6fSgkKXXQs/YCyeVqRHv7KchyBwRBD6MxAUZj4rAPKJ4oigKnswJOZxUUxQXGJEiSA3q9dmn0WpNlF9raTqKr6zIAGTqdDWZz2pDNFQVCV1cz2tpOQpZbIQg66PXR3YlCwzugeON0VqOj4xwUxQVBMEKSUrqT7n6aFMWNtrbT6Oqqh6K4IYoWmM1pEEXtF0XRitvdjra2H+B2t0AQROh04TCbHUO6Pmxf/k8ETEIIIURrw3ZpPEIIIeSngAImIYQQogIFTEIIIUQFCpiEEEKIChQwCSGEEBUoYBJCCCEqUMAkhBBCVKCASQghhKhAAZMQQghRgQImIYQQogIFTEIIIUQFCpiEEEKIChQwCSGEEBUoYBJCCCEqUMAkhBBCVKCASQghhKhAAZMQQghRgQImIYQQogIFTEIIIUQFCpiEEEKIChQwCSGEEBUoYBJCCCEqCIqieP6lINQBOD94h0MIIYQMuSRFUSKvf9FrwCSEEEIIR0OyhBBCiAoUMAkhhBAVKGASQgghKlDAJIQQQlSggEkIIYSo8L8L3jYUbpU1hAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YM9i6NEEqno8"
      },
      "source": [
        "def plot_policy(policy):\n",
        "    u = [[0.75 for i in range(8)] for j in range(8)]\n",
        "    v = [[0.75 for i in range(8)] for j in range(8)]\n",
        "    for state in range(64):\n",
        "        x,y = to_coord(state)\n",
        "        action = np.argmax(policy[state])\n",
        "        if action==0:\n",
        "            u[y][x] = 0\n",
        "            v[y][x] = 0.75\n",
        "        elif action==1:\n",
        "            u[y][x] = 0.75\n",
        "            v[y][x] = 0\n",
        "        elif action==2:\n",
        "            u[y][x] = 0\n",
        "            v[y][x] = -0.75\n",
        "        else:\n",
        "            u[y][x] = -0.75\n",
        "            v[y][x] = 0\n",
        "\n",
        "    graph = pyg.utils.to_networkx(data)\n",
        "    pos = {}\n",
        "    for idx in range(64):\n",
        "        x,y = to_coord(idx)\n",
        "        pos[idx] = np.array([x,y])\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(8, 8))\n",
        "    color = ['y' for i in range(64)]\n",
        "    for coin in COINS:\n",
        "        color[coin] = 'r'\n",
        "    nx.drawing.nx_pylab.draw_networkx(graph, node_color=color, pos=pos, node_size=750, ax=ax, edgelist=[])\n",
        "    \n",
        "    X = np.arange(0,8,1)\n",
        "    Y = np.arange(0,8,1)\n",
        "\n",
        "    ax.quiver(X, Y, u, v, scale_units='xy', scale=1)\n",
        "    \n",
        "    ax.xaxis.set_ticks([])\n",
        "    ax.yaxis.set_ticks([])\n",
        "    ax.set_aspect('equal')\n",
        "    plt.grid()\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49yosqtWNi7B"
      },
      "source": [
        "## 3. Define random walker and Initialize policy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JWCtmMGjBfr"
      },
      "source": [
        "POLICY = {}\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        u = to_node_idx(i,j)\n",
        "        POLICY[u] = np.array([0.0, 0.0, 0.0, 0.0])\n",
        "        if j<7: # up\n",
        "            POLICY[u][0] = 1.0\n",
        "        if i<7: # right\n",
        "            POLICY[u][1] = 1.0\n",
        "        if j>0: # down\n",
        "            POLICY[u][2] = 1.0\n",
        "        if i>0: # left\n",
        "            POLICY[u][3] = 1.0\n",
        "        POLICY[u] /= sum(POLICY[u])\n",
        "\n",
        "Q = {}\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        u = to_node_idx(i,j)\n",
        "        Q[u] = {}\n",
        "        if j<7: # up\n",
        "            Q[u][0] = torch.rand(1)\n",
        "        if i<7: # right\n",
        "            Q[u][1] = torch.rand(1)\n",
        "        if j>0: # down\n",
        "            Q[u][2] = torch.rand(1)\n",
        "        if i>0: # left\n",
        "            Q[u][3] = torch.rand(1)\n",
        "\n",
        "RET = {}\n",
        "for i in range(8):\n",
        "    for j in range(8):\n",
        "        u = to_node_idx(i,j)\n",
        "        RET[u] = [[], [], [], []]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R3eBev53qnmX"
      },
      "source": [
        "def sample_episode(walk_len, start_node=None):\n",
        "    if start_node==None:\n",
        "        start_node = np.random.randint(0,NUM_NODES)\n",
        "    states = []\n",
        "    actions = []\n",
        "    rewards = []\n",
        "    curr_state = start_node\n",
        "    while len(states) < walk_len:\n",
        "        states.append(curr_state)\n",
        "        if curr_state in COINS:\n",
        "            rewards.append(1.0)\n",
        "        else:\n",
        "            rewards.append(0.0)\n",
        "        \n",
        "        actions.append(np.random.choice(a=[0,1,2,3], size=None, replace=True, p=POLICY[curr_state]))\n",
        "        if actions[-1]==0:      # up\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0], to_coord(curr_state)[1]+1)\n",
        "        elif actions[-1]==1:    # right\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]+1, to_coord(curr_state)[1])\n",
        "        elif actions[-1]==2:    # down\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0], to_coord(curr_state)[1]-1)\n",
        "        else:                   # left\n",
        "            curr_state = to_node_idx(to_coord(curr_state)[0]-1, to_coord(curr_state)[1])\n",
        "    return states, actions, rewards"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_MlK2uxrJpM"
      },
      "source": [
        "## Build models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H6FJwSCOqnj-"
      },
      "source": [
        "# Set Hyperparameters\n",
        "EMBED_DIM = 512\n",
        "EPS = 1e-15\n",
        "INIT_LR1 = 0.1\n",
        "\n",
        "WALK_LEN = 128\n",
        "INIT_LR2 = 1e-2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SgWVxsR1qnhh"
      },
      "source": [
        "class Node2Vec(torch.nn.Module):\n",
        "    def __init__(self, embedding_dim=EMBED_DIM, num_nodes=NUM_NODES, sparse=False):\n",
        "        super(Node2Vec, self).__init__()\n",
        "        self.embedding_dim = embedding_dim\n",
        "        self.embedding = torch.nn.Embedding(num_nodes, embedding_dim, sparse=sparse)\n",
        "        self.reset_parameters()\n",
        "\n",
        "\n",
        "    def reset_parameters(self):\n",
        "        self.embedding.reset_parameters()\n",
        "\n",
        "\n",
        "    def forward(self, batch=None):\n",
        "        emb = self.embedding.weight\n",
        "        return emb if batch is None else emb[batch]\n",
        "\n",
        "\n",
        "    def loss(self, pos_rw, neg_rw):\n",
        "        start, rest = pos_rw[:, 0], pos_rw[:, 1:].contiguous()\n",
        "        h_start = self.embedding(start).view(pos_rw.size(0), 1, self.embedding_dim)\n",
        "        h_rest = self.embedding(rest.view(-1)).view(pos_rw.size(0), -1, self.embedding_dim)\n",
        "        out = (h_start * h_rest).sum(dim=-1).view(-1)\n",
        "        pos_loss = -torch.log(torch.sigmoid(out) + EPS).mean()\n",
        "\n",
        "        start, rest = neg_rw[:, 0], neg_rw[:, 1:].contiguous()\n",
        "        h_start = self.embedding(start).view(neg_rw.size(0), 1, self.embedding_dim)\n",
        "        h_rest = self.embedding(rest.view(-1)).view(neg_rw.size(0), -1, self.embedding_dim)\n",
        "        out = (h_start * h_rest).sum(dim=-1).view(-1)\n",
        "        neg_loss = -torch.log(1 - torch.sigmoid(out) + EPS).mean()\n",
        "    \n",
        "        return pos_loss + neg_loss\n",
        "\n",
        "model1 = Node2Vec().to(DEVICE)\n",
        "optimizer1 = torch.optim.Adam(model1.parameters(), INIT_LR1)\n",
        "platLR1 = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer1, mode='min', factor=0.5, patience=1, threshold=0.0001, \n",
        "                                                    threshold_mode='abs', cooldown=0, min_lr=0, eps=1e-10, verbose=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DvylOvFqneX"
      },
      "source": [
        "def get_contexts(walks, size):\n",
        "    batch = torch.tensor([], dtype=torch.int, requires_grad=False).to(DEVICE)\n",
        "    for sequence in walks:\n",
        "        for i in range(0,len(sequence)-size):\n",
        "            batch = torch.cat((batch, sequence[i:i+size].reshape(1,size)), axis=0)\n",
        "    return batch"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_ZEVaearYQI"
      },
      "source": [
        "class InferNet(torch.nn.Module):\n",
        "    def __init__ (self, input_len=EMBED_DIM):\n",
        "        super(InferNet, self).__init__()\n",
        "        self.input_size = input_len\n",
        "        self.lin1 = torch.nn.Linear(self.input_size,512)\n",
        "        self.lin4 = torch.nn.Linear(512,1)\n",
        "\n",
        "    def forward(self, X):\n",
        "        X = torch.tanh(self.lin1(X))\n",
        "        X = self.lin4(X)\n",
        "        return X\n",
        "\n",
        "model2 = InferNet().to(DEVICE)\n",
        "optimizer2 = torch.optim.Adam(model2.parameters(), INIT_LR2)\n",
        "platLR2 = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer2, mode='min', factor=0.5, patience=5, threshold=1e-5, \n",
        "                                                    threshold_mode='abs', cooldown=0, min_lr=0, eps=1e-10, verbose=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tKwUWa8rYNv"
      },
      "source": [
        "mse = torch.nn.MSELoss(reduction='sum')\n",
        "def getLoss2(imm_rewards, rewards):\n",
        "    main_loss = mse(torch.sum(imm_rewards), torch.sum(rewards)) / WALK_LEN\n",
        "    return main_loss"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7Q1o1QIrYA-"
      },
      "source": [
        "def printRewards(rewards):\n",
        "    matrix = np.array([[float(rewards[to_node_idx(i,j)][0]) for i in range(8)] for j in range(8)])\n",
        "    cm = sns.light_palette(\"blue\", as_cmap=True)\n",
        "    x=pd.DataFrame(matrix).iloc[::-1]\n",
        "    x=x.style.background_gradient(cmap=cm, axis=None)\n",
        "    display(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bqK-2Z1Ur6sT"
      },
      "source": [
        "## Train models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Zppsa_AgrYI4",
        "outputId": "f2be744c-13fe-451e-8ac7-784c1a1c85ba"
      },
      "source": [
        "NUM_ITER = 300\n",
        "CONT_SIZE = 3\n",
        "NUM_NEG = 1\n",
        "losses1 = []\n",
        "losses2 = []\n",
        "# GAMMA = 0.5\n",
        "\n",
        "model2.eval()\n",
        "all_states = model1.forward(torch.tensor([i for i in range(NUM_NODES)])).reshape(NUM_NODES,EMBED_DIM)\n",
        "imm_rewards = model2.forward(all_states).reshape(NUM_NODES,1)\n",
        "printRewards(imm_rewards)\n",
        "\n",
        "for iter in range(1,NUM_ITER+1):\n",
        "    states, actions, rewards = [], [], []\n",
        "    for start in range(NUM_NODES):\n",
        "        s, a, r = sample_episode(WALK_LEN, start)\n",
        "        states.append(s)\n",
        "        actions.append(a)\n",
        "        rewards.append(r)\n",
        "    neg_samples = torch.randint(low=0, high=NUM_NODES, size=(NUM_NODES,(WALK_LEN*NUM_NEG)-1))\n",
        "    neg_samples = neg_samples.reshape(NUM_NODES, (WALK_LEN*NUM_NEG)-1)\n",
        "\n",
        "    states = torch.tensor(states).to(DEVICE)\n",
        "    actions = torch.tensor(actions).to(DEVICE)\n",
        "    rewards = torch.tensor(rewards).to(DEVICE)\n",
        "    neg_samples = torch.cat((states[:,0].reshape(NUM_NODES,1), neg_samples.to(DEVICE)), axis=1)\n",
        "\n",
        "    model1.train()\n",
        "    optimizer1.zero_grad()\n",
        "    pos = get_contexts(states, CONT_SIZE)\n",
        "    neg = get_contexts(neg_samples, CONT_SIZE)\n",
        "    loss1 = model1.loss(pos.to(DEVICE), neg.to(DEVICE))\n",
        "    loss1.backward()\n",
        "    optimizer1.step()\n",
        "    loss1 = float(loss1)\n",
        "    platLR1.step(loss1)\n",
        "    \n",
        "    model2.train()\n",
        "    loss2 = 0.0\n",
        "    for i in range(NUM_NODES):\n",
        "        optimizer2.zero_grad()\n",
        "        ep_states = model1.forward(states[i,:].reshape(WALK_LEN)).reshape(WALK_LEN,EMBED_DIM)\n",
        "        imm_rewards = model2.forward(ep_states).reshape(WALK_LEN,1)\n",
        "        L = getLoss2(imm_rewards, rewards[i].reshape(WALK_LEN,1))\n",
        "        L.backward()\n",
        "        optimizer2.step()\n",
        "        loss2 += float(L)\n",
        "    loss2 /= float(NUM_NODES)\n",
        "    platLR2.step(loss2)\n",
        "\n",
        "    # if len(losses1)>0:\n",
        "    #     loss1 = (GAMMA*loss1) + ((1-GAMMA)*losses1[-1])\n",
        "    # if len(losses2)>0:\n",
        "    #     loss2 = (GAMMA*loss2) + ((1-GAMMA)*losses2[-1])\n",
        "\n",
        "    print(\"iter : \"+str(iter)+\" |  loss1 : \"+str(np.around(loss1,6))+\" |  loss2 : \"+str(np.around(loss2,6)))\n",
        "    losses1.append(loss1)\n",
        "    losses2.append(loss2)\n",
        "\n",
        "    if iter in [10, 50, 150, 300]:\n",
        "        model2.eval()\n",
        "        all_states = model1.forward(torch.tensor([i for i in range(NUM_NODES)])).reshape(NUM_NODES,EMBED_DIM)\n",
        "        imm_rewards = model2.forward(all_states).reshape(NUM_NODES,1)\n",
        "        printRewards(imm_rewards)"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "        }</style><table id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row0_col0\" class=\"data row0 col0\" >-0.096312</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row0_col1\" class=\"data row0 col1\" >-0.052164</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row0_col2\" class=\"data row0 col2\" >-0.184436</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row0_col3\" class=\"data row0 col3\" >-0.042942</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row0_col4\" class=\"data row0 col4\" >0.181065</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row0_col5\" class=\"data row0 col5\" >-0.219515</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row0_col6\" class=\"data row0 col6\" >0.109105</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row0_col7\" class=\"data row0 col7\" >0.447374</td>\n",
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              "                        <th id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row1_col0\" class=\"data row1 col0\" >-0.286214</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row1_col1\" class=\"data row1 col1\" >-0.258522</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.076433</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.412653</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.138243</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row1_col5\" class=\"data row1 col5\" >-0.810946</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row1_col6\" class=\"data row1 col6\" >0.302585</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.075944</td>\n",
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              "                        <th id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row2_col0\" class=\"data row2 col0\" >-0.269227</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.007240</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row2_col2\" class=\"data row2 col2\" >-0.231572</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.403893</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row2_col4\" class=\"data row2 col4\" >-0.066074</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row2_col5\" class=\"data row2 col5\" >-0.009555</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row2_col6\" class=\"data row2 col6\" >-0.278564</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row2_col7\" class=\"data row2 col7\" >0.350786</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.367310</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.407358</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row3_col2\" class=\"data row3 col2\" >-0.123323</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row3_col3\" class=\"data row3 col3\" >-0.238423</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row3_col4\" class=\"data row3 col4\" >-0.136397</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.134321</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row3_col6\" class=\"data row3 col6\" >0.319811</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row3_col7\" class=\"data row3 col7\" >-0.014502</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row4_col0\" class=\"data row4 col0\" >0.110826</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.155772</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row4_col2\" class=\"data row4 col2\" >-0.073064</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.769978</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.033892</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.320755</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.226202</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row4_col7\" class=\"data row4 col7\" >-0.199564</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.114600</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row5_col1\" class=\"data row5 col1\" >-0.193299</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row5_col2\" class=\"data row5 col2\" >-0.078518</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row5_col3\" class=\"data row5 col3\" >-0.069977</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row5_col4\" class=\"data row5 col4\" >-0.132767</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.170152</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row5_col6\" class=\"data row5 col6\" >-0.052116</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row5_col7\" class=\"data row5 col7\" >-0.596430</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row6_col0\" class=\"data row6 col0\" >-0.014354</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row6_col1\" class=\"data row6 col1\" >-0.128355</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row6_col2\" class=\"data row6 col2\" >-0.262778</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.079164</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row6_col4\" class=\"data row6 col4\" >-0.305579</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row6_col5\" class=\"data row6 col5\" >-0.148568</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.325136</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row6_col7\" class=\"data row6 col7\" >-0.569716</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.130310</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row7_col1\" class=\"data row7 col1\" >-0.110663</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.048013</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row7_col3\" class=\"data row7 col3\" >-0.363037</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row7_col4\" class=\"data row7 col4\" >-0.104800</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row7_col5\" class=\"data row7 col5\" >-0.144950</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.050885</td>\n",
              "                        <td id=\"T_2eb89202_45e2_11ec_a4d7_0242ac1c0002row7_col7\" class=\"data row7 col7\" >-0.215876</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3d192b6a10>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 1 |  loss1 : 18.202168 |  loss2 : 51.455153\n",
            "iter : 2 |  loss1 : 13.48706 |  loss2 : 3.560434\n",
            "iter : 3 |  loss1 : 12.355205 |  loss2 : 2.405142\n",
            "iter : 4 |  loss1 : 11.641489 |  loss2 : 2.885881\n",
            "iter : 5 |  loss1 : 11.203625 |  loss2 : 90.784861\n",
            "iter : 6 |  loss1 : 11.188174 |  loss2 : 49.526362\n",
            "iter : 7 |  loss1 : 10.942301 |  loss2 : 30.159016\n",
            "iter : 8 |  loss1 : 10.546155 |  loss2 : 12.52868\n",
            "iter : 9 |  loss1 : 10.246436 |  loss2 : 9.103377\n",
            "iter : 10 |  loss1 : 9.943521 |  loss2 : 2.811324\n"
          ]
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        {
          "output_type": "display_data",
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              "                <tr>\n",
              "                        <th id=\"T_423521d8_45e2_11ec_a4d7_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
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              "                        <th id=\"T_423521d8_45e2_11ec_a4d7_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
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              "                        <th id=\"T_423521d8_45e2_11ec_a4d7_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
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            "iter : 11 |  loss1 : 10.056853 |  loss2 : 0.877638\n",
            "iter : 12 |  loss1 : 9.796163 |  loss2 : 0.93323\n",
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            "iter : 50 |  loss1 : 8.730254 |  loss2 : 0.018085\n"
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              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row1_col3,#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col6{\n",
              "            background-color:  #0000ff;\n",
              "            color:  #f1f1f1;\n",
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              "            background-color:  #a5a5f7;\n",
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              "            background-color:  #bebef5;\n",
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              "            background-color:  #c1c1f5;\n",
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              "            background-color:  #b1b1f6;\n",
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              "            background-color:  #b3b3f6;\n",
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              "            background-color:  #7b7bf9;\n",
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              "            background-color:  #dedef4;\n",
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              "            background-color:  #a1a1f7;\n",
              "            color:  #000000;\n",
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              "            background-color:  #dfdff4;\n",
              "            color:  #000000;\n",
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              "            background-color:  #8989f8;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row3_col1,#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row7_col6{\n",
              "            background-color:  #a2a2f7;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row3_col2{\n",
              "            background-color:  #7171f9;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row3_col3{\n",
              "            background-color:  #cecef4;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row3_col4,#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row4_col3{\n",
              "            background-color:  #9d9df7;\n",
              "            color:  #000000;\n",
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              "            background-color:  #a6a6f7;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row3_col6{\n",
              "            background-color:  #c3c3f5;\n",
              "            color:  #000000;\n",
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              "            background-color:  #8888f8;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row4_col0{\n",
              "            background-color:  #ccccf5;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row4_col1{\n",
              "            background-color:  #7070f9;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row4_col2{\n",
              "            background-color:  #9494f7;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row4_col4{\n",
              "            background-color:  #8a8af8;\n",
              "            color:  #000000;\n",
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              "            background-color:  #c9c9f5;\n",
              "            color:  #000000;\n",
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              "            background-color:  #4a4afb;\n",
              "            color:  #f1f1f1;\n",
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              "            background-color:  #f0f0f3;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col0{\n",
              "            background-color:  #2e2efd;\n",
              "            color:  #f1f1f1;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col1{\n",
              "            background-color:  #0a0afe;\n",
              "            color:  #f1f1f1;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col2{\n",
              "            background-color:  #6868fa;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col3{\n",
              "            background-color:  #a3a3f7;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col4,#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row7_col7{\n",
              "            background-color:  #a8a8f6;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col5{\n",
              "            background-color:  #7a7af9;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col7{\n",
              "            background-color:  #3838fc;\n",
              "            color:  #f1f1f1;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col0{\n",
              "            background-color:  #e4e4f3;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col1{\n",
              "            background-color:  #6b6bfa;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col2{\n",
              "            background-color:  #e6e6f3;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col3{\n",
              "            background-color:  #b7b7f6;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col4{\n",
              "            background-color:  #b0b0f6;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col5{\n",
              "            background-color:  #c5c5f5;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col6{\n",
              "            background-color:  #7676f9;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col7{\n",
              "            background-color:  #dadaf4;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row7_col0{\n",
              "            background-color:  #cdcdf5;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row7_col2{\n",
              "            background-color:  #adadf6;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row7_col4{\n",
              "            background-color:  #9b9bf7;\n",
              "            color:  #000000;\n",
              "        }#T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row7_col5{\n",
              "            background-color:  #cecef5;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row0_col0\" class=\"data row0 col0\" >-0.167199</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.064851</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row0_col2\" class=\"data row0 col2\" >-0.269096</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.209824</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row0_col4\" class=\"data row0 col4\" >-0.139174</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.076785</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row0_col6\" class=\"data row0 col6\" >-0.194169</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row0_col7\" class=\"data row0 col7\" >-0.059719</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row1_col0\" class=\"data row1 col0\" >-0.050086</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row1_col1\" class=\"data row1 col1\" >-0.070198</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.153886</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.706085</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.019060</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row1_col5\" class=\"data row1 col5\" >-0.083493</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row1_col6\" class=\"data row1 col6\" >-0.099616</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.078622</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row2_col0\" class=\"data row2 col0\" >-0.033857</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.064667</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row2_col2\" class=\"data row2 col2\" >-0.040117</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.196031</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row2_col4\" class=\"data row2 col4\" >-0.218526</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.032248</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.076598</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row2_col7\" class=\"data row2 col7\" >-0.226839</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.134424</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row3_col1\" class=\"data row3 col1\" >0.030933</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.231949</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row3_col3\" class=\"data row3 col3\" >-0.154648</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.049452</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.015388</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row3_col6\" class=\"data row3 col6\" >-0.107858</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row3_col7\" class=\"data row3 col7\" >0.137781</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row4_col0\" class=\"data row4 col0\" >-0.142317</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.236433</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.089125</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.049749</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.129870</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row4_col5\" class=\"data row4 col5\" >-0.132181</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.398469</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row4_col7\" class=\"data row4 col7\" >-0.298146</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.510005</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.660083</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.271472</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.024127</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.006954</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.199045</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.703075</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.472450</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col0\" class=\"data row6 col0\" >-0.246556</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.257775</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col2\" class=\"data row6 col2\" >-0.252659</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col3\" class=\"data row6 col3\" >-0.058521</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col4\" class=\"data row6 col4\" >-0.029086</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col5\" class=\"data row6 col5\" >-0.117127</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.212617</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row6_col7\" class=\"data row6 col7\" >-0.200240</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row7_col0\" class=\"data row7 col0\" >-0.149005</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.018907</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row7_col2\" class=\"data row7 col2\" >-0.013218</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row7_col3\" class=\"data row7 col3\" >-0.131306</td>\n",
              "                        <td id=\"T_8fc49ab4_45e2_11ec_a4d7_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.059632</td>\n",
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              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.847771</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.108828</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row1_col5\" class=\"data row1 col5\" >-0.048212</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row1_col6\" class=\"data row1 col6\" >-0.024392</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.065962</td>\n",
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              "            <tr>\n",
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              "            <tr>\n",
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              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row3_col6\" class=\"data row3 col6\" >-0.124780</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row3_col7\" class=\"data row3 col7\" >0.183684</td>\n",
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              "            <tr>\n",
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              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.839635</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.124151</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row5_col3\" class=\"data row5 col3\" >-0.054095</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row5_col4\" class=\"data row5 col4\" >-0.025836</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.121851</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.778294</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.296601</td>\n",
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              "            <tr>\n",
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              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.129135</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row6_col2\" class=\"data row6 col2\" >-0.097331</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.061699</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row6_col4\" class=\"data row6 col4\" >-0.020138</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row6_col5\" class=\"data row6 col5\" >-0.059910</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.144554</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row6_col7\" class=\"data row6 col7\" >-0.194239</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.082297</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row7_col1\" class=\"data row7 col1\" >-0.063084</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.058144</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row7_col3\" class=\"data row7 col3\" >-0.074257</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.075685</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row7_col5\" class=\"data row7 col5\" >-0.033125</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row7_col6\" class=\"data row7 col6\" >-0.035647</td>\n",
              "                        <td id=\"T_57cc90c0_45e3_11ec_a4d7_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.080849</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3d172dcc10>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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              "            background-color:  #a6a6f7;\n",
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              "                <tr>\n",
              "                        <th id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row0_col0\" class=\"data row0 col0\" >-0.059596</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.112657</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row0_col2\" class=\"data row0 col2\" >-0.167608</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.170489</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row0_col4\" class=\"data row0 col4\" >-0.130200</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row0_col5\" class=\"data row0 col5\" >0.081157</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row0_col6\" class=\"data row0 col6\" >-0.025497</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row0_col7\" class=\"data row0 col7\" >-0.019101</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.035744</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row1_col1\" class=\"data row1 col1\" >-0.078987</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row1_col2\" class=\"data row1 col2\" >0.137326</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row1_col3\" class=\"data row1 col3\" >0.847848</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row1_col4\" class=\"data row1 col4\" >0.108112</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row1_col5\" class=\"data row1 col5\" >-0.046135</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row1_col6\" class=\"data row1 col6\" >-0.021829</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row1_col7\" class=\"data row1 col7\" >0.066119</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row2_col0\" class=\"data row2 col0\" >-0.044241</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row2_col1\" class=\"data row2 col1\" >0.060555</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row2_col2\" class=\"data row2 col2\" >-0.112130</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.136232</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row2_col4\" class=\"data row2 col4\" >-0.098108</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row2_col5\" class=\"data row2 col5\" >0.020097</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row2_col6\" class=\"data row2 col6\" >0.059909</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row2_col7\" class=\"data row2 col7\" >-0.106770</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.109853</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row3_col1\" class=\"data row3 col1\" >-0.082835</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.099415</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row3_col3\" class=\"data row3 col3\" >-0.112185</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row3_col4\" class=\"data row3 col4\" >0.061215</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row3_col5\" class=\"data row3 col5\" >0.029379</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row3_col6\" class=\"data row3 col6\" >-0.121749</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row3_col7\" class=\"data row3 col7\" >0.183283</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row4_col0\" class=\"data row4 col0\" >-0.181648</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row4_col1\" class=\"data row4 col1\" >0.145239</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row4_col2\" class=\"data row4 col2\" >-0.108307</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.065239</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.007590</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row4_col5\" class=\"data row4 col5\" >-0.103270</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.198377</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row4_col7\" class=\"data row4 col7\" >-0.272418</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.174625</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row5_col1\" class=\"data row5 col1\" >0.841056</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.122944</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row5_col3\" class=\"data row5 col3\" >-0.053756</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row5_col4\" class=\"data row5 col4\" >-0.024622</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.121756</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row5_col6\" class=\"data row5 col6\" >0.780405</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.294152</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row6_col0\" class=\"data row6 col0\" >-0.142704</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.129312</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row6_col2\" class=\"data row6 col2\" >-0.097509</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.061364</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row6_col4\" class=\"data row6 col4\" >-0.019438</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row6_col5\" class=\"data row6 col5\" >-0.059211</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.143629</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row6_col7\" class=\"data row6 col7\" >-0.193418</td>\n",
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              "                        <th id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row7_col0\" class=\"data row7 col0\" >0.085062</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row7_col1\" class=\"data row7 col1\" >-0.061930</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.058566</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row7_col3\" class=\"data row7 col3\" >-0.076025</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.074263</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row7_col5\" class=\"data row7 col5\" >-0.031800</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row7_col6\" class=\"data row7 col6\" >-0.036823</td>\n",
              "                        <td id=\"T_84b650fc_45e4_11ec_a4d7_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.080324</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3d172a1510>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "NpSZTqB_rYGL",
        "outputId": "ed712c45-7ad8-41ee-a180-3a01bd1193e8"
      },
      "source": [
        "printRewards(rewards[:,0].reshape(NUM_NODES,1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "            <tr>\n",
              "                        <th id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
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              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row4_col3\" class=\"data row4 col3\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row4_col5\" class=\"data row4 col5\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row4_col6\" class=\"data row4 col6\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row4_col7\" class=\"data row4 col7\" >0.000000</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row5_col0\" class=\"data row5 col0\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row5_col2\" class=\"data row5 col2\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row5_col3\" class=\"data row5 col3\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row5_col4\" class=\"data row5 col4\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row5_col6\" class=\"data row5 col6\" >1.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row5_col7\" class=\"data row5 col7\" >0.000000</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
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              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row6_col1\" class=\"data row6 col1\" >0.000000</td>\n",
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              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row6_col3\" class=\"data row6 col3\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row6_col4\" class=\"data row6 col4\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row6_col5\" class=\"data row6 col5\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row6_col6\" class=\"data row6 col6\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row6_col7\" class=\"data row6 col7\" >0.000000</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
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              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row7_col1\" class=\"data row7 col1\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row7_col2\" class=\"data row7 col2\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row7_col3\" class=\"data row7 col3\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row7_col4\" class=\"data row7 col4\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row7_col5\" class=\"data row7 col5\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row7_col6\" class=\"data row7 col6\" >0.000000</td>\n",
              "                        <td id=\"T_84bde588_45e4_11ec_a4d7_0242ac1c0002row7_col7\" class=\"data row7 col7\" >0.000000</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3d194abb90>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "LjyGbLSKsa1K",
        "outputId": "7bca75cf-35b0-4e64-87c2-3e4ddd33f5f0"
      },
      "source": [
        "x = [i for i in range(1,NUM_ITER+1)]\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(x, losses1, 'r', label='Node2Vec Loss')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('loss')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.title(\"Node2Vec Loss vs Iterations\")\n",
        "fig.savefig('Node2VecLoss_iter.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "V1gwNscPs3jJ",
        "outputId": "f6afdfba-d3c0-4426-ca7c-f0b9bcc9bdde"
      },
      "source": [
        "x = [i for i in range(1,NUM_ITER+1)]\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(x, list(losses2), 'g', label='InferNet Loss')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('loss')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.title(\"InferNet Loss vs Iterations\")\n",
        "fig.savefig('InferNetLoss_iter.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3UheoJv6s7E0"
      },
      "source": [
        "## Improve policy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LyLo7KEIrYDo"
      },
      "source": [
        "def improvePolicy(policy, Q, state, eps=0.1):\n",
        "    q = torch.tensor([0.0, 0.0, 0.0, 0.0])\n",
        "    for a in [0, 1, 2, 3]:\n",
        "        if a in Q[state].keys():\n",
        "            q[a] = Q[state][a]\n",
        "\n",
        "    policy[state] = np.array([0.0, 0.0, 0.0, 0.0])\n",
        "    idx = 0\n",
        "    _, indices = torch.sort(q, descending=True)\n",
        "    policy[state][indices[idx]] = (1.0-eps)\n",
        "\n",
        "    while ((state+1)%8 == 0 and indices[idx]==0) or (state>=56 and state<=63 and indices[idx]==1) or (state%8 == 0 and indices[idx]==2) or (state>=0 and state<=7 and indices[idx]==3):\n",
        "        policy[state][indices[idx]] = 0.0\n",
        "        idx += 1\n",
        "        policy[state][indices[idx]] = (1.0-eps)\n",
        "\n",
        "    num_actions = 4.0\n",
        "    if (state+1)%8 == 0:\n",
        "        num_actions -= 1.0\n",
        "    if state>=56 and state<=63:\n",
        "        num_actions -= 1.0\n",
        "    if state%8 == 0:\n",
        "        num_actions -= 1.0\n",
        "    if state>=0 and state<=7:\n",
        "        num_actions -= 1.0\n",
        "\n",
        "    if (state+1)%8 != 0:\n",
        "        policy[state][0] += (eps/num_actions)\n",
        "    if not (state>=56 and state<=63):\n",
        "        policy[state][1] += (eps/num_actions)\n",
        "    if state%8 != 0:\n",
        "        policy[state][2] += (eps/num_actions)\n",
        "    if not (state>=0 and state<=7):\n",
        "        policy[state][3] += (eps/num_actions)\n",
        "    \n",
        "    return policy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6P5N73szrYLY"
      },
      "source": [
        "def walk_freq():\n",
        "    freq = {}\n",
        "    for i in range(1000):\n",
        "        for start in range(NUM_NODES):\n",
        "            states, a, r = sample_episode(8, start)\n",
        "            for s in states:\n",
        "                if s not in freq.keys():\n",
        "                    freq[s] = 0\n",
        "                freq[s] += 1\n",
        "    matrix = np.array([[int(freq[to_node_idx(i,j)]) for i in range(8)] for j in range(8)])\n",
        "    cm = sns.light_palette(\"green\", as_cmap=True)\n",
        "    x=pd.DataFrame(matrix).iloc[::-1]\n",
        "    x=x.style.background_gradient(cmap=cm, axis=None)\n",
        "    display(x)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "ImEZzhAGs3eM",
        "outputId": "b0e9d29b-16f5-4cd1-8371-63f5fc8c5b5a"
      },
      "source": [
        "plot_policy(POLICY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RRSMeU2ms2x_"
      },
      "source": [
        "model1.eval()\n",
        "model2.eval()\n",
        "all_states = model1.forward(torch.tensor([i for i in range(NUM_NODES)])).reshape(NUM_NODES,EMBED_DIM)\n",
        "imm_rewards = model2.forward(all_states).reshape(NUM_NODES,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Asl5Pz8WrX-S",
        "outputId": "cafb0a38-47dc-45db-9acc-ee888761f651"
      },
      "source": [
        "cum_reward = []\n",
        "epsilon = 0.98\n",
        "model2.eval()\n",
        "NUM_ITER = 300\n",
        "WALK_LEN = 5\n",
        "walk_freq()\n",
        "\n",
        "for iter in range(1,NUM_ITER+1):\n",
        "    states, actions, rewards = [], [], []\n",
        "    for start in range(NUM_NODES):\n",
        "        s, a, r = sample_episode(WALK_LEN, start)\n",
        "        states.append(s)\n",
        "        actions.append(a)\n",
        "        rewards.append(r)\n",
        "    \n",
        "    for i in range(NUM_NODES):\n",
        "        G = 0.0\n",
        "        for j in reversed(range(WALK_LEN-1)):\n",
        "            G += float(imm_rewards[states[i][j+1]])\n",
        "            s = states[i][j]\n",
        "            a = actions[i][j]\n",
        "            vis_prev = False\n",
        "            for k in range(j):\n",
        "                if states[i][k]==s and actions[i][k]==a:\n",
        "                    vis_prev = True\n",
        "                    break\n",
        "            if vis_prev:\n",
        "                continue\n",
        "            \n",
        "            RET[s][a].append(G)\n",
        "            Q[s][a] = np.mean(RET[s][a])\n",
        "            POLICY = improvePolicy(POLICY, Q, s, epsilon)\n",
        "\n",
        "    epsilon *= 0.98\n",
        "    rewards = torch.tensor(rewards).to(DEVICE)\n",
        "    \n",
        "    cm = float(torch.sum(rewards))/float(NUM_NODES)\n",
        "    # if len(cum_reward)>0:\n",
        "        # cm = (GAMMA*cm) + ((1-GAMMA)*cum_reward[-1])\n",
        "    cum_reward.append(cm)\n",
        "\n",
        "    print(\"iter : \"+str(iter)+\" |  cum_reward : \"+str(cum_reward[-1]))\n",
        "    if iter in [5, 10, 20, 30, 50, 75, 150, 300]:\n",
        "        walk_freq()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                        <th id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row0_col0\" class=\"data row0 col0\" >5437</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row0_col1\" class=\"data row0 col1\" >7528</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row0_col2\" class=\"data row0 col2\" >7260</td>\n",
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              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row0_col5\" class=\"data row0 col5\" >7304</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row0_col6\" class=\"data row0 col6\" >7712</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row0_col7\" class=\"data row0 col7\" >5492</td>\n",
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              "                        <th id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row1_col0\" class=\"data row1 col0\" >7668</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row1_col1\" class=\"data row1 col1\" >9299</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row1_col2\" class=\"data row1 col2\" >8913</td>\n",
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              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row1_col4\" class=\"data row1 col4\" >8857</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row1_col5\" class=\"data row1 col5\" >8865</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row1_col6\" class=\"data row1 col6\" >9333</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row1_col7\" class=\"data row1 col7\" >7622</td>\n",
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              "                        <th id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row2_col0\" class=\"data row2 col0\" >7392</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row2_col1\" class=\"data row2 col1\" >9053</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row2_col2\" class=\"data row2 col2\" >8402</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row2_col3\" class=\"data row2 col3\" >8205</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row2_col4\" class=\"data row2 col4\" >8349</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row2_col5\" class=\"data row2 col5\" >8422</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row2_col6\" class=\"data row2 col6\" >8770</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row2_col7\" class=\"data row2 col7\" >7226</td>\n",
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              "                        <th id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row3_col0\" class=\"data row3 col0\" >7270</td>\n",
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              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row3_col2\" class=\"data row3 col2\" >8312</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row3_col3\" class=\"data row3 col3\" >8230</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row3_col4\" class=\"data row3 col4\" >8297</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row3_col5\" class=\"data row3 col5\" >8450</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row3_col6\" class=\"data row3 col6\" >8845</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row3_col7\" class=\"data row3 col7\" >7275</td>\n",
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              "                        <th id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row4_col0\" class=\"data row4 col0\" >7041</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row4_col1\" class=\"data row4 col1\" >8793</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row4_col2\" class=\"data row4 col2\" >8323</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row4_col3\" class=\"data row4 col3\" >8313</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row4_col4\" class=\"data row4 col4\" >8347</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row4_col5\" class=\"data row4 col5\" >8443</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row4_col6\" class=\"data row4 col6\" >8912</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row4_col7\" class=\"data row4 col7\" >7200</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
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              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row5_col1\" class=\"data row5 col1\" >8992</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row5_col2\" class=\"data row5 col2\" >8448</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row5_col3\" class=\"data row5 col3\" >8420</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row5_col4\" class=\"data row5 col4\" >8421</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row5_col5\" class=\"data row5 col5\" >8500</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row5_col6\" class=\"data row5 col6\" >9120</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row5_col7\" class=\"data row5 col7\" >7180</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
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              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row6_col1\" class=\"data row6 col1\" >9360</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row6_col2\" class=\"data row6 col2\" >8934</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row6_col3\" class=\"data row6 col3\" >8714</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row6_col4\" class=\"data row6 col4\" >8770</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row6_col5\" class=\"data row6 col5\" >9004</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row6_col6\" class=\"data row6 col6\" >9600</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row6_col7\" class=\"data row6 col7\" >7525</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row7_col0\" class=\"data row7 col0\" >5393</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row7_col1\" class=\"data row7 col1\" >7521</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row7_col2\" class=\"data row7 col2\" >7391</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row7_col3\" class=\"data row7 col3\" >7220</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row7_col4\" class=\"data row7 col4\" >7105</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row7_col5\" class=\"data row7 col5\" >7258</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row7_col6\" class=\"data row7 col6\" >7661</td>\n",
              "                        <td id=\"T_926f7b38_45e4_11ec_a4d7_0242ac1c0002row7_col7\" class=\"data row7 col7\" >5428</td>\n",
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              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3d339c5c90>"
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          "metadata": {}
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        {
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          "text": [
            "iter : 1 |  cum_reward : 0.265625\n",
            "iter : 2 |  cum_reward : 0.28125\n",
            "iter : 3 |  cum_reward : 0.265625\n",
            "iter : 4 |  cum_reward : 0.203125\n",
            "iter : 5 |  cum_reward : 0.375\n"
          ]
        },
        {
          "output_type": "display_data",
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              "            background-color:  #add5ad;\n",
              "            color:  #000000;\n",
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              "            background-color:  #a2cfa2;\n",
              "            color:  #000000;\n",
              "        }#T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row7_col6{\n",
              "            background-color:  #8ec58e;\n",
              "            color:  #000000;\n",
              "        }#T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row7_col7{\n",
              "            background-color:  #dfeddf;\n",
              "            color:  #000000;\n",
              "        }</style><table id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >0</th>        <th class=\"col_heading level0 col1\" >1</th>        <th class=\"col_heading level0 col2\" >2</th>        <th class=\"col_heading level0 col3\" >3</th>        <th class=\"col_heading level0 col4\" >4</th>        <th class=\"col_heading level0 col5\" >5</th>        <th class=\"col_heading level0 col6\" >6</th>        <th class=\"col_heading level0 col7\" >7</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row0_col0\" class=\"data row0 col0\" >4854</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row0_col1\" class=\"data row0 col1\" >7882</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row0_col2\" class=\"data row0 col2\" >7211</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row0_col3\" class=\"data row0 col3\" >8586</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row0_col4\" class=\"data row0 col4\" >7655</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row0_col5\" class=\"data row0 col5\" >7056</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row0_col6\" class=\"data row0 col6\" >6650</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row0_col7\" class=\"data row0 col7\" >4800</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row1_col0\" class=\"data row1 col0\" >6574</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row1_col1\" class=\"data row1 col1\" >8989</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row1_col2\" class=\"data row1 col2\" >9850</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row1_col3\" class=\"data row1 col3\" >12335</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row1_col4\" class=\"data row1 col4\" >9928</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row1_col5\" class=\"data row1 col5\" >7973</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row1_col6\" class=\"data row1 col6\" >8487</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row1_col7\" class=\"data row1 col7\" >7439</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row2_col0\" class=\"data row2 col0\" >6788</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row2_col1\" class=\"data row2 col1\" >8641</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row2_col2\" class=\"data row2 col2\" >8868</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row2_col3\" class=\"data row2 col3\" >9796</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row2_col4\" class=\"data row2 col4\" >8317</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row2_col5\" class=\"data row2 col5\" >7242</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row2_col6\" class=\"data row2 col6\" >7587</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row2_col7\" class=\"data row2 col7\" >6270</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row3_col0\" class=\"data row3 col0\" >6332</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row3_col1\" class=\"data row3 col1\" >8412</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row3_col2\" class=\"data row3 col2\" >7392</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row3_col3\" class=\"data row3 col3\" >8325</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row3_col4\" class=\"data row3 col4\" >7430</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row3_col5\" class=\"data row3 col5\" >7334</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row3_col6\" class=\"data row3 col6\" >8573</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row3_col7\" class=\"data row3 col7\" >6901</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row4_col0\" class=\"data row4 col0\" >7096</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row4_col1\" class=\"data row4 col1\" >9620</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row4_col2\" class=\"data row4 col2\" >7473</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row4_col3\" class=\"data row4 col3\" >7245</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row4_col4\" class=\"data row4 col4\" >8336</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row4_col5\" class=\"data row4 col5\" >9054</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row4_col6\" class=\"data row4 col6\" >10438</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row4_col7\" class=\"data row4 col7\" >7732</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row5_col0\" class=\"data row5 col0\" >9582</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row5_col1\" class=\"data row5 col1\" >12035</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row5_col2\" class=\"data row5 col2\" >8348</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row5_col3\" class=\"data row5 col3\" >6992</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row5_col4\" class=\"data row5 col4\" >7335</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row5_col5\" class=\"data row5 col5\" >9070</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row5_col6\" class=\"data row5 col6\" >12398</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row5_col7\" class=\"data row5 col7\" >8831</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row6_col0\" class=\"data row6 col0\" >8147</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row6_col1\" class=\"data row6 col1\" >9656</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row6_col2\" class=\"data row6 col2\" >7622</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row6_col3\" class=\"data row6 col3\" >7626</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row6_col4\" class=\"data row6 col4\" >8065</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row6_col5\" class=\"data row6 col5\" >8339</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row6_col6\" class=\"data row6 col6\" >10489</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row6_col7\" class=\"data row6 col7\" >7852</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row7_col0\" class=\"data row7 col0\" >5824</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row7_col1\" class=\"data row7 col1\" >7372</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row7_col2\" class=\"data row7 col2\" >5938</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row7_col3\" class=\"data row7 col3\" >6017</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row7_col4\" class=\"data row7 col4\" >6801</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row7_col5\" class=\"data row7 col5\" >7168</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row7_col6\" class=\"data row7 col6\" >7811</td>\n",
              "                        <td id=\"T_9f1c19fe_45e4_11ec_a4d7_0242ac1c0002row7_col7\" class=\"data row7 col7\" >5211</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3d1934c210>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 6 |  cum_reward : 0.40625\n",
            "iter : 7 |  cum_reward : 0.3125\n",
            "iter : 8 |  cum_reward : 0.328125\n",
            "iter : 9 |  cum_reward : 0.4375\n",
            "iter : 10 |  cum_reward : 0.40625\n"
          ]
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              "            background-color:  #b3d8b3;\n",
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              "            background-color:  #cbe4cb;\n",
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              "                <tr>\n",
              "                        <th id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row0_col0\" class=\"data row0 col0\" >3893</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row0_col1\" class=\"data row0 col1\" >6451</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row0_col2\" class=\"data row0 col2\" >7212</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row0_col3\" class=\"data row0 col3\" >9445</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row0_col4\" class=\"data row0 col4\" >6797</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row0_col5\" class=\"data row0 col5\" >6655</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row0_col6\" class=\"data row0 col6\" >5537</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row0_col7\" class=\"data row0 col7\" >3793</td>\n",
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              "                        <th id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row1_col0\" class=\"data row1 col0\" >5507</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row1_col1\" class=\"data row1 col1\" >8753</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row1_col2\" class=\"data row1 col2\" >14039</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row1_col3\" class=\"data row1 col3\" >16227</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row1_col4\" class=\"data row1 col4\" >11292</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row1_col5\" class=\"data row1 col5\" >9674</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row1_col6\" class=\"data row1 col6\" >7781</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row1_col7\" class=\"data row1 col7\" >5769</td>\n",
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              "                        <th id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row2_col0\" class=\"data row2 col0\" >5921</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row2_col1\" class=\"data row2 col1\" >7953</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row2_col2\" class=\"data row2 col2\" >9011</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row2_col3\" class=\"data row2 col3\" >9365</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row2_col4\" class=\"data row2 col4\" >8071</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row2_col5\" class=\"data row2 col5\" >6507</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row2_col6\" class=\"data row2 col6\" >6296</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row2_col7\" class=\"data row2 col7\" >5133</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row3_col0\" class=\"data row3 col0\" >5187</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row3_col1\" class=\"data row3 col1\" >7509</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row3_col2\" class=\"data row3 col2\" >6694</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row3_col3\" class=\"data row3 col3\" >7411</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row3_col4\" class=\"data row3 col4\" >7478</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row3_col5\" class=\"data row3 col5\" >6608</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row3_col6\" class=\"data row3 col6\" >8029</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row3_col7\" class=\"data row3 col7\" >6479</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row4_col0\" class=\"data row4 col0\" >5744</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row4_col1\" class=\"data row4 col1\" >9167</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row4_col2\" class=\"data row4 col2\" >8156</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row4_col3\" class=\"data row4 col3\" >6300</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row4_col4\" class=\"data row4 col4\" >7519</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row4_col5\" class=\"data row4 col5\" >8911</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row4_col6\" class=\"data row4 col6\" >12516</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row4_col7\" class=\"data row4 col7\" >8095</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row5_col0\" class=\"data row5 col0\" >7717</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row5_col1\" class=\"data row5 col1\" >15049</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row5_col2\" class=\"data row5 col2\" >11695</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row5_col3\" class=\"data row5 col3\" >6684</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row5_col4\" class=\"data row5 col4\" >7207</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row5_col5\" class=\"data row5 col5\" >11806</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row5_col6\" class=\"data row5 col6\" >16262</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row5_col7\" class=\"data row5 col7\" >9990</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row6_col0\" class=\"data row6 col0\" >8009</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row6_col1\" class=\"data row6 col1\" >12255</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row6_col2\" class=\"data row6 col2\" >9090</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row6_col3\" class=\"data row6 col3\" >6706</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row6_col4\" class=\"data row6 col4\" >8764</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row6_col5\" class=\"data row6 col5\" >7604</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row6_col6\" class=\"data row6 col6\" >9645</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row6_col7\" class=\"data row6 col7\" >7431</td>\n",
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              "                        <th id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row7_col0\" class=\"data row7 col0\" >5638</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row7_col1\" class=\"data row7 col1\" >6673</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row7_col2\" class=\"data row7 col2\" >6354</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row7_col3\" class=\"data row7 col3\" >5319</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row7_col4\" class=\"data row7 col4\" >6737</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row7_col5\" class=\"data row7 col5\" >5486</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row7_col6\" class=\"data row7 col6\" >6612</td>\n",
              "                        <td id=\"T_abffcf80_45e4_11ec_a4d7_0242ac1c0002row7_col7\" class=\"data row7 col7\" >4382</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
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            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3d339a9390>"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 11 |  cum_reward : 0.359375\n",
            "iter : 12 |  cum_reward : 0.328125\n",
            "iter : 13 |  cum_reward : 0.46875\n",
            "iter : 14 |  cum_reward : 0.484375\n",
            "iter : 15 |  cum_reward : 0.515625\n",
            "iter : 16 |  cum_reward : 0.59375\n",
            "iter : 17 |  cum_reward : 0.5625\n",
            "iter : 18 |  cum_reward : 0.609375\n",
            "iter : 19 |  cum_reward : 0.671875\n",
            "iter : 20 |  cum_reward : 0.578125\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "            background-color:  #bdddbd;\n",
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              "            background-color:  #e0eee0;\n",
              "            color:  #000000;\n",
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              "                <tr>\n",
              "                        <th id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row0_col0\" class=\"data row0 col0\" >2890</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row0_col1\" class=\"data row0 col1\" >5262</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row0_col2\" class=\"data row0 col2\" >7390</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row0_col3\" class=\"data row0 col3\" >11495</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row0_col4\" class=\"data row0 col4\" >7450</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row0_col5\" class=\"data row0 col5\" >6136</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row0_col6\" class=\"data row0 col6\" >4090</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row0_col7\" class=\"data row0 col7\" >2786</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row1_col0\" class=\"data row1 col0\" >3626</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row1_col1\" class=\"data row1 col1\" >6398</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row1_col2\" class=\"data row1 col2\" >11172</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row1_col3\" class=\"data row1 col3\" >23974</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row1_col4\" class=\"data row1 col4\" >21140</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row1_col5\" class=\"data row1 col5\" >11013</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row1_col6\" class=\"data row1 col6\" >6660</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row1_col7\" class=\"data row1 col7\" >4597</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row2_col0\" class=\"data row2 col0\" >3464</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row2_col1\" class=\"data row2 col1\" >4715</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row2_col2\" class=\"data row2 col2\" >5512</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row2_col3\" class=\"data row2 col3\" >9315</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row2_col4\" class=\"data row2 col4\" >9361</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row2_col5\" class=\"data row2 col5\" >6960</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row2_col6\" class=\"data row2 col6\" >4822</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row2_col7\" class=\"data row2 col7\" >3795</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row3_col0\" class=\"data row3 col0\" >5211</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row3_col1\" class=\"data row3 col1\" >7992</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row3_col2\" class=\"data row3 col2\" >5177</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row3_col3\" class=\"data row3 col3\" >6334</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row3_col4\" class=\"data row3 col4\" >7053</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row3_col5\" class=\"data row3 col5\" >5252</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row3_col6\" class=\"data row3 col6\" >5915</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row3_col7\" class=\"data row3 col7\" >5268</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row4_col0\" class=\"data row4 col0\" >6462</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row4_col1\" class=\"data row4 col1\" >10288</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row4_col2\" class=\"data row4 col2\" >7147</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row4_col3\" class=\"data row4 col3\" >4986</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row4_col4\" class=\"data row4 col4\" >6980</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row4_col5\" class=\"data row4 col5\" >8563</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row4_col6\" class=\"data row4 col6\" >16161</td>\n",
              "                        <td id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002row4_col7\" class=\"data row4 col7\" >7588</td>\n",
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              "                        <th id=\"T_b9516c2a_45e4_11ec_a4d7_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
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              "<pandas.io.formats.style.Styler at 0x7f3d339c50d0>"
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          "text": [
            "iter : 21 |  cum_reward : 0.65625\n",
            "iter : 22 |  cum_reward : 0.703125\n",
            "iter : 23 |  cum_reward : 0.671875\n",
            "iter : 24 |  cum_reward : 0.65625\n",
            "iter : 25 |  cum_reward : 0.6875\n",
            "iter : 26 |  cum_reward : 0.8125\n",
            "iter : 27 |  cum_reward : 0.515625\n",
            "iter : 28 |  cum_reward : 0.640625\n",
            "iter : 29 |  cum_reward : 0.640625\n",
            "iter : 30 |  cum_reward : 0.828125\n"
          ]
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        {
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              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row4_col3\" class=\"data row4 col3\" >3432</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row4_col4\" class=\"data row4 col4\" >3371</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row4_col5\" class=\"data row4 col5\" >8947</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row4_col6\" class=\"data row4 col6\" >21252</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row4_col7\" class=\"data row4 col7\" >8012</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
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              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row5_col2\" class=\"data row5 col2\" >8709</td>\n",
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              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row5_col4\" class=\"data row5 col4\" >4185</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row5_col5\" class=\"data row5 col5\" >14746</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row5_col6\" class=\"data row5 col6\" >30784</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row5_col7\" class=\"data row5 col7\" >12398</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
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              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row6_col1\" class=\"data row6 col1\" >11150</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row6_col2\" class=\"data row6 col2\" >6240</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row6_col3\" class=\"data row6 col3\" >3417</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row6_col4\" class=\"data row6 col4\" >3248</td>\n",
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              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row6_col6\" class=\"data row6 col6\" >9421</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row6_col7\" class=\"data row6 col7\" >6024</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row7_col0\" class=\"data row7 col0\" >2836</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row7_col1\" class=\"data row7 col1\" >3717</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row7_col2\" class=\"data row7 col2\" >4076</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row7_col3\" class=\"data row7 col3\" >2652</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row7_col4\" class=\"data row7 col4\" >2614</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row7_col5\" class=\"data row7 col5\" >4280</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row7_col6\" class=\"data row7 col6\" >5376</td>\n",
              "                        <td id=\"T_c5eaeaa6_45e4_11ec_a4d7_0242ac1c0002row7_col7\" class=\"data row7 col7\" >2859</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
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            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3d338b8550>"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 31 |  cum_reward : 0.796875\n",
            "iter : 32 |  cum_reward : 0.765625\n",
            "iter : 33 |  cum_reward : 0.90625\n",
            "iter : 34 |  cum_reward : 0.90625\n",
            "iter : 35 |  cum_reward : 1.03125\n",
            "iter : 36 |  cum_reward : 0.75\n",
            "iter : 37 |  cum_reward : 0.890625\n",
            "iter : 38 |  cum_reward : 0.84375\n",
            "iter : 39 |  cum_reward : 0.84375\n",
            "iter : 40 |  cum_reward : 0.921875\n",
            "iter : 41 |  cum_reward : 0.765625\n",
            "iter : 42 |  cum_reward : 0.90625\n",
            "iter : 43 |  cum_reward : 0.921875\n",
            "iter : 44 |  cum_reward : 0.921875\n",
            "iter : 45 |  cum_reward : 1.125\n",
            "iter : 46 |  cum_reward : 1.0\n",
            "iter : 47 |  cum_reward : 0.828125\n",
            "iter : 48 |  cum_reward : 0.953125\n",
            "iter : 49 |  cum_reward : 1.0625\n",
            "iter : 50 |  cum_reward : 1.015625\n"
          ]
        },
        {
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              "                        <th id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1606</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row0_col1\" class=\"data row0 col1\" >3219</td>\n",
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              "                        <th id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
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              "                        <th id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
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              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row2_col1\" class=\"data row2 col1\" >2333</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row2_col2\" class=\"data row2 col2\" >6210</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row2_col3\" class=\"data row2 col3\" >33082</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row2_col4\" class=\"data row2 col4\" >6591</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row2_col5\" class=\"data row2 col5\" >2918</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row2_col6\" class=\"data row2 col6\" >2297</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row2_col7\" class=\"data row2 col7\" >1939</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row3_col0\" class=\"data row3 col0\" >3367</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row3_col1\" class=\"data row3 col1\" >2622</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row3_col2\" class=\"data row3 col2\" >2522</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row3_col3\" class=\"data row3 col3\" >5540</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row3_col4\" class=\"data row3 col4\" >2356</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row3_col5\" class=\"data row3 col5\" >2129</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row3_col6\" class=\"data row3 col6\" >2421</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row3_col7\" class=\"data row3 col7\" >3508</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row4_col0\" class=\"data row4 col0\" >7739</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row4_col1\" class=\"data row4 col1\" >9499</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row4_col2\" class=\"data row4 col2\" >4185</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row4_col3\" class=\"data row4 col3\" >2325</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row4_col4\" class=\"data row4 col4\" >2167</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row4_col5\" class=\"data row4 col5\" >5745</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row4_col6\" class=\"data row4 col6\" >7140</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row4_col7\" class=\"data row4 col7\" >7747</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row5_col0\" class=\"data row5 col0\" >35394</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row5_col1\" class=\"data row5 col1\" >40365</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row5_col2\" class=\"data row5 col2\" >7356</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row5_col3\" class=\"data row5 col3\" >3598</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row5_col4\" class=\"data row5 col4\" >2873</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row5_col5\" class=\"data row5 col5\" >14866</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row5_col6\" class=\"data row5 col6\" >42387</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row5_col7\" class=\"data row5 col7\" >36266</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row6_col0\" class=\"data row6 col0\" >6979</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row6_col1\" class=\"data row6 col1\" >9904</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row6_col2\" class=\"data row6 col2\" >4829</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row6_col3\" class=\"data row6 col3\" >2141</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row6_col4\" class=\"data row6 col4\" >2268</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row6_col5\" class=\"data row6 col5\" >6792</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row6_col6\" class=\"data row6 col6\" >6990</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row6_col7\" class=\"data row6 col7\" >6682</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row7_col0\" class=\"data row7 col0\" >2031</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row7_col1\" class=\"data row7 col1\" >2523</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row7_col2\" class=\"data row7 col2\" >3008</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row7_col3\" class=\"data row7 col3\" >1730</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row7_col4\" class=\"data row7 col4\" >1815</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row7_col5\" class=\"data row7 col5\" >3196</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row7_col6\" class=\"data row7 col6\" >2256</td>\n",
              "                        <td id=\"T_d2d534ba_45e4_11ec_a4d7_0242ac1c0002row7_col7\" class=\"data row7 col7\" >1964</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
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              "<pandas.io.formats.style.Styler at 0x7f3d339bcd10>"
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 51 |  cum_reward : 1.0\n",
            "iter : 52 |  cum_reward : 1.09375\n",
            "iter : 53 |  cum_reward : 1.03125\n",
            "iter : 54 |  cum_reward : 1.125\n",
            "iter : 55 |  cum_reward : 1.046875\n",
            "iter : 56 |  cum_reward : 1.078125\n",
            "iter : 57 |  cum_reward : 1.15625\n",
            "iter : 58 |  cum_reward : 1.1875\n",
            "iter : 59 |  cum_reward : 1.09375\n",
            "iter : 60 |  cum_reward : 1.15625\n",
            "iter : 61 |  cum_reward : 1.21875\n",
            "iter : 62 |  cum_reward : 0.96875\n",
            "iter : 63 |  cum_reward : 1.15625\n",
            "iter : 64 |  cum_reward : 1.140625\n",
            "iter : 65 |  cum_reward : 1.203125\n",
            "iter : 66 |  cum_reward : 1.296875\n",
            "iter : 67 |  cum_reward : 1.171875\n",
            "iter : 68 |  cum_reward : 1.203125\n",
            "iter : 69 |  cum_reward : 1.171875\n",
            "iter : 70 |  cum_reward : 1.09375\n",
            "iter : 71 |  cum_reward : 1.203125\n",
            "iter : 72 |  cum_reward : 1.1875\n",
            "iter : 73 |  cum_reward : 1.1875\n",
            "iter : 74 |  cum_reward : 1.15625\n",
            "iter : 75 |  cum_reward : 1.203125\n"
          ]
        },
        {
          "output_type": "display_data",
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              "                        <th id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1303</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row0_col1\" class=\"data row0 col1\" >2591</td>\n",
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              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row0_col6\" class=\"data row0 col6\" >1608</td>\n",
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              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row1_col1\" class=\"data row1 col1\" >2908</td>\n",
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              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row1_col6\" class=\"data row1 col6\" >5048</td>\n",
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              "                        <th id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1377</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row2_col1\" class=\"data row2 col1\" >1627</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row2_col2\" class=\"data row2 col2\" >4802</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row2_col3\" class=\"data row2 col3\" >44752</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row2_col4\" class=\"data row2 col4\" >5154</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row2_col5\" class=\"data row2 col5\" >1765</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row2_col6\" class=\"data row2 col6\" >1587</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row2_col7\" class=\"data row2 col7\" >1455</td>\n",
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              "            <tr>\n",
              "                        <th id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row3_col0\" class=\"data row3 col0\" >2681</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row3_col1\" class=\"data row3 col1\" >1765</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row3_col2\" class=\"data row3 col2\" >1701</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row3_col3\" class=\"data row3 col3\" >4475</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row3_col4\" class=\"data row3 col4\" >1650</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row3_col5\" class=\"data row3 col5\" >1518</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row3_col6\" class=\"data row3 col6\" >1587</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row3_col7\" class=\"data row3 col7\" >2644</td>\n",
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              "                        <th id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row4_col0\" class=\"data row4 col0\" >6284</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row4_col1\" class=\"data row4 col1\" >7640</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row4_col2\" class=\"data row4 col2\" >3160</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row4_col3\" class=\"data row4 col3\" >1607</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1527</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row4_col5\" class=\"data row4 col5\" >4434</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row4_col6\" class=\"data row4 col6\" >5270</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row4_col7\" class=\"data row4 col7\" >6148</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row5_col0\" class=\"data row5 col0\" >43842</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row5_col1\" class=\"data row5 col1\" >48450</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row5_col2\" class=\"data row5 col2\" >5947</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row5_col3\" class=\"data row5 col3\" >2748</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row5_col4\" class=\"data row5 col4\" >1985</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row5_col5\" class=\"data row5 col5\" >13018</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row5_col6\" class=\"data row5 col6\" >50958</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row5_col7\" class=\"data row5 col7\" >45103</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row6_col0\" class=\"data row6 col0\" >5325</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row6_col1\" class=\"data row6 col1\" >8348</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row6_col2\" class=\"data row6 col2\" >4001</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row6_col3\" class=\"data row6 col3\" >1545</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row6_col4\" class=\"data row6 col4\" >1598</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row6_col5\" class=\"data row6 col5\" >5431</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row6_col6\" class=\"data row6 col6\" >5279</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row6_col7\" class=\"data row6 col7\" >5188</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row7_col0\" class=\"data row7 col0\" >1462</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row7_col1\" class=\"data row7 col1\" >1796</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row7_col2\" class=\"data row7 col2\" >2499</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row7_col3\" class=\"data row7 col3\" >1372</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row7_col4\" class=\"data row7 col4\" >1359</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row7_col5\" class=\"data row7 col5\" >2574</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row7_col6\" class=\"data row7 col6\" >1585</td>\n",
              "                        <td id=\"T_dff42444_45e4_11ec_a4d7_0242ac1c0002row7_col7\" class=\"data row7 col7\" >1452</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
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              "<pandas.io.formats.style.Styler at 0x7f3d3396e890>"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 76 |  cum_reward : 1.234375\n",
            "iter : 77 |  cum_reward : 1.15625\n",
            "iter : 78 |  cum_reward : 1.265625\n",
            "iter : 79 |  cum_reward : 1.09375\n",
            "iter : 80 |  cum_reward : 1.234375\n",
            "iter : 81 |  cum_reward : 1.28125\n",
            "iter : 82 |  cum_reward : 1.28125\n",
            "iter : 83 |  cum_reward : 1.328125\n",
            "iter : 84 |  cum_reward : 1.3125\n",
            "iter : 85 |  cum_reward : 1.375\n",
            "iter : 86 |  cum_reward : 1.359375\n",
            "iter : 87 |  cum_reward : 1.296875\n",
            "iter : 88 |  cum_reward : 1.203125\n",
            "iter : 89 |  cum_reward : 1.375\n",
            "iter : 90 |  cum_reward : 1.28125\n",
            "iter : 91 |  cum_reward : 1.328125\n",
            "iter : 92 |  cum_reward : 1.296875\n",
            "iter : 93 |  cum_reward : 1.34375\n",
            "iter : 94 |  cum_reward : 1.40625\n",
            "iter : 95 |  cum_reward : 1.328125\n",
            "iter : 96 |  cum_reward : 1.3125\n",
            "iter : 97 |  cum_reward : 1.4375\n",
            "iter : 98 |  cum_reward : 1.390625\n",
            "iter : 99 |  cum_reward : 1.375\n",
            "iter : 100 |  cum_reward : 1.390625\n",
            "iter : 101 |  cum_reward : 1.375\n",
            "iter : 102 |  cum_reward : 1.3125\n",
            "iter : 103 |  cum_reward : 1.453125\n",
            "iter : 104 |  cum_reward : 1.25\n",
            "iter : 105 |  cum_reward : 1.28125\n",
            "iter : 106 |  cum_reward : 1.34375\n",
            "iter : 107 |  cum_reward : 1.4375\n",
            "iter : 108 |  cum_reward : 1.4375\n",
            "iter : 109 |  cum_reward : 1.4375\n",
            "iter : 110 |  cum_reward : 1.375\n",
            "iter : 111 |  cum_reward : 1.453125\n",
            "iter : 112 |  cum_reward : 1.390625\n",
            "iter : 113 |  cum_reward : 1.3125\n",
            "iter : 114 |  cum_reward : 1.4375\n",
            "iter : 115 |  cum_reward : 1.421875\n",
            "iter : 116 |  cum_reward : 1.453125\n",
            "iter : 117 |  cum_reward : 1.40625\n",
            "iter : 118 |  cum_reward : 1.453125\n",
            "iter : 119 |  cum_reward : 1.453125\n",
            "iter : 120 |  cum_reward : 1.421875\n",
            "iter : 121 |  cum_reward : 1.46875\n",
            "iter : 122 |  cum_reward : 1.34375\n",
            "iter : 123 |  cum_reward : 1.40625\n",
            "iter : 124 |  cum_reward : 1.46875\n",
            "iter : 125 |  cum_reward : 1.453125\n",
            "iter : 126 |  cum_reward : 1.515625\n",
            "iter : 127 |  cum_reward : 1.484375\n",
            "iter : 128 |  cum_reward : 1.46875\n",
            "iter : 129 |  cum_reward : 1.46875\n",
            "iter : 130 |  cum_reward : 1.53125\n",
            "iter : 131 |  cum_reward : 1.40625\n",
            "iter : 132 |  cum_reward : 1.453125\n",
            "iter : 133 |  cum_reward : 1.4375\n",
            "iter : 134 |  cum_reward : 1.484375\n",
            "iter : 135 |  cum_reward : 1.4375\n",
            "iter : 136 |  cum_reward : 1.53125\n",
            "iter : 137 |  cum_reward : 1.515625\n",
            "iter : 138 |  cum_reward : 1.515625\n",
            "iter : 139 |  cum_reward : 1.453125\n",
            "iter : 140 |  cum_reward : 1.53125\n",
            "iter : 141 |  cum_reward : 1.515625\n",
            "iter : 142 |  cum_reward : 1.5\n",
            "iter : 143 |  cum_reward : 1.546875\n",
            "iter : 144 |  cum_reward : 1.515625\n",
            "iter : 145 |  cum_reward : 1.53125\n",
            "iter : 146 |  cum_reward : 1.5\n",
            "iter : 147 |  cum_reward : 1.453125\n",
            "iter : 148 |  cum_reward : 1.515625\n",
            "iter : 149 |  cum_reward : 1.484375\n",
            "iter : 150 |  cum_reward : 1.515625\n"
          ]
        },
        {
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              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1049</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row0_col1\" class=\"data row0 col1\" >2105</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row0_col2\" class=\"data row0 col2\" >3248</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row0_col3\" class=\"data row0 col3\" >8111</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row0_col4\" class=\"data row0 col4\" >3332</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row0_col5\" class=\"data row0 col5\" >2189</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row0_col6\" class=\"data row0 col6\" >1115</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row0_col7\" class=\"data row0 col7\" >1054</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1066</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row1_col1\" class=\"data row1 col1\" >2142</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row1_col2\" class=\"data row1 col2\" >6249</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row1_col3\" class=\"data row1 col3\" >71486</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row1_col4\" class=\"data row1 col4\" >10280</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row1_col5\" class=\"data row1 col5\" >6280</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row1_col6\" class=\"data row1 col6\" >4202</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row1_col7\" class=\"data row1 col7\" >2107</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1059</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row2_col1\" class=\"data row2 col1\" >1094</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row2_col2\" class=\"data row2 col2\" >2679</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row2_col3\" class=\"data row2 col3\" >60373</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row2_col4\" class=\"data row2 col4\" >2735</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row2_col5\" class=\"data row2 col5\" >1135</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row2_col6\" class=\"data row2 col6\" >1106</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row2_col7\" class=\"data row2 col7\" >1081</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row3_col0\" class=\"data row3 col0\" >2080</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row3_col1\" class=\"data row3 col1\" >1115</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row3_col2\" class=\"data row3 col2\" >1086</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row3_col3\" class=\"data row3 col3\" >2637</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row3_col4\" class=\"data row3 col4\" >1082</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row3_col5\" class=\"data row3 col5\" >1077</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row3_col6\" class=\"data row3 col6\" >1083</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row3_col7\" class=\"data row3 col7\" >2109</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row4_col0\" class=\"data row4 col0\" >3831</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row4_col1\" class=\"data row4 col1\" >4813</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row4_col2\" class=\"data row4 col2\" >2164</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row4_col3\" class=\"data row4 col3\" >1085</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1078</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row4_col5\" class=\"data row4 col5\" >3233</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row4_col6\" class=\"data row4 col6\" >2719</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row4_col7\" class=\"data row4 col7\" >3851</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row5_col0\" class=\"data row5 col0\" >54272</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row5_col1\" class=\"data row5 col1\" >57640</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row5_col2\" class=\"data row5 col2\" >3673</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row5_col3\" class=\"data row5 col3\" >2114</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row5_col4\" class=\"data row5 col4\" >1170</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row5_col5\" class=\"data row5 col5\" >9859</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row5_col6\" class=\"data row5 col6\" >60462</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row5_col7\" class=\"data row5 col7\" >56146</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row6_col0\" class=\"data row6 col0\" >2824</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row6_col1\" class=\"data row6 col1\" >5794</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row6_col2\" class=\"data row6 col2\" >3151</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row6_col3\" class=\"data row6 col3\" >1072</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row6_col4\" class=\"data row6 col4\" >1103</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row6_col5\" class=\"data row6 col5\" >4217</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row6_col6\" class=\"data row6 col6\" >2778</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row6_col7\" class=\"data row6 col7\" >2798</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row7_col0\" class=\"data row7 col0\" >1063</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row7_col1\" class=\"data row7 col1\" >1116</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row7_col2\" class=\"data row7 col2\" >2068</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row7_col3\" class=\"data row7 col3\" >1054</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row7_col4\" class=\"data row7 col4\" >1057</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row7_col5\" class=\"data row7 col5\" >2085</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row7_col6\" class=\"data row7 col6\" >1102</td>\n",
              "                        <td id=\"T_ee88e3b4_45e4_11ec_a4d7_0242ac1c0002row7_col7\" class=\"data row7 col7\" >1062</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3d339bb750>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iter : 151 |  cum_reward : 1.578125\n",
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              "                <tr>\n",
              "                        <th id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >7</th>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1004</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row0_col1\" class=\"data row0 col1\" >2007</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row0_col2\" class=\"data row0 col2\" >3012</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row0_col3\" class=\"data row0 col3\" >7039</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row0_col4\" class=\"data row0 col4\" >3011</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row0_col5\" class=\"data row0 col5\" >2004</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row0_col6\" class=\"data row0 col6\" >1002</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row0_col7\" class=\"data row0 col7\" >1001</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >6</th>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row1_col0\" class=\"data row1 col0\" >1001</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row1_col1\" class=\"data row1 col1\" >2004</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row1_col2\" class=\"data row1 col2\" >5050</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row1_col3\" class=\"data row1 col3\" >74850</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row1_col4\" class=\"data row1 col4\" >9081</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row1_col5\" class=\"data row1 col5\" >6019</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row1_col6\" class=\"data row1 col6\" >4014</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row1_col7\" class=\"data row1 col7\" >2004</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >5</th>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row2_col0\" class=\"data row2 col0\" >1002</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row2_col1\" class=\"data row2 col1\" >1004</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row2_col2\" class=\"data row2 col2\" >2028</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row2_col3\" class=\"data row2 col3\" >64790</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row2_col4\" class=\"data row2 col4\" >2035</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row2_col5\" class=\"data row2 col5\" >1006</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row2_col6\" class=\"data row2 col6\" >1005</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row2_col7\" class=\"data row2 col7\" >1004</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row3_col0\" class=\"data row3 col0\" >2007</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row3_col1\" class=\"data row3 col1\" >1006</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row3_col2\" class=\"data row3 col2\" >1002</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row3_col3\" class=\"data row3 col3\" >2031</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row3_col4\" class=\"data row3 col4\" >1001</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row3_col5\" class=\"data row3 col5\" >1002</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row3_col6\" class=\"data row3 col6\" >1004</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row3_col7\" class=\"data row3 col7\" >2001</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >3</th>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row4_col0\" class=\"data row4 col0\" >3044</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row4_col1\" class=\"data row4 col1\" >4031</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row4_col2\" class=\"data row4 col2\" >2007</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row4_col3\" class=\"data row4 col3\" >1003</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1003</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row4_col5\" class=\"data row4 col5\" >3010</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row4_col6\" class=\"data row4 col6\" >2038</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row4_col7\" class=\"data row4 col7\" >3039</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002level0_row5\" class=\"row_heading level0 row5\" >2</th>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row5_col0\" class=\"data row5 col0\" >56885</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row5_col1\" class=\"data row5 col1\" >59880</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row5_col2\" class=\"data row5 col2\" >3029</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row5_col3\" class=\"data row5 col3\" >2004</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row5_col4\" class=\"data row5 col4\" >1007</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row5_col5\" class=\"data row5 col5\" >9043</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row5_col6\" class=\"data row5 col6\" >62901</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row5_col7\" class=\"data row5 col7\" >58859</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002level0_row6\" class=\"row_heading level0 row6\" >1</th>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row6_col0\" class=\"data row6 col0\" >2040</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row6_col1\" class=\"data row6 col1\" >5031</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row6_col2\" class=\"data row6 col2\" >3004</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row6_col3\" class=\"data row6 col3\" >1004</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row6_col4\" class=\"data row6 col4\" >1002</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row6_col5\" class=\"data row6 col5\" >4000</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row6_col6\" class=\"data row6 col6\" >2051</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row6_col7\" class=\"data row6 col7\" >2039</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002level0_row7\" class=\"row_heading level0 row7\" >0</th>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row7_col0\" class=\"data row7 col0\" >1000</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row7_col1\" class=\"data row7 col1\" >1005</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row7_col2\" class=\"data row7 col2\" >2004</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row7_col3\" class=\"data row7 col3\" >1002</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row7_col4\" class=\"data row7 col4\" >1000</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row7_col5\" class=\"data row7 col5\" >2000</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row7_col6\" class=\"data row7 col6\" >1002</td>\n",
              "                        <td id=\"T_00597a0e_45e5_11ec_a4d7_0242ac1c0002row7_col7\" class=\"data row7 col7\" >1002</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f3d33974a50>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "o2FN_u_3tnAp",
        "outputId": "5bdaa85c-53ce-4839-d856-510d33e0eb20"
      },
      "source": [
        "x = [i for i in range(1,len(cum_reward)+1)]\n",
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(x, cum_reward, 'r', label='Average Cumulative Reward')\n",
        "plt.xlabel('iteration')\n",
        "plt.ylabel('reward')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.title(\"Average Cumulative Reward per episode vs Iterations\")\n",
        "fig.savefig('CumReward_iter.png')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "U5Xk_sditm95",
        "outputId": "e38e7bd7-c7da-43b7-d115-35e2c670048f"
      },
      "source": [
        "plot_policy(POLICY)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZjsfBUNtm7S"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}