CS224W_Colab_4.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Copy of CS224W - Colab 4.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XuXWJLEm2UWS"
      },
      "source": [
        "# **CS224W - Colab 4**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gzsP50bF6Gb"
      },
      "source": [
        "In this Colab, we will shift our focus from homogenous graphs to heterogeneous graphs. Heterogeneous graphs extend the traditional homogenous graphs that we have seen before by specifically incorperating different node and edge types. This additional information allows us to extend the graph neural nework models that we have worked with before. Namely, we can apply heterogenous message passing, where different message types now exist between different node, edge type relationships. \n",
        "\n",
        "At first, we will learn how to transform NetworkX graphs into DeepSNAP representations. Additionally, we will dive deeper into how DeepSNAP stores and represents heterogeneous graphs as PyTorch Tensors.\n",
        "\n",
        "Then, we will build our own heterogenous graph neural netowrk models using PyTorch Geonetric and DeepSNAP on node property prediction task. To evaluate these models, we will use our model on the heterogeneous ACM dataset.\n",
        "\n",
        "**Note**: Make sure to **sequentially run all the cells in each section**, so that the intermediate variables / packages will carry over to the next cell\n",
        "\n",
        "Have fun on Colab 4 :)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MSaetj53YnT6"
      },
      "source": [
        "# Device\n",
        "You might need to use GPU for this Colab.\n",
        "\n",
        "Please click `Runtime` and then `Change runtime type`. Then set the `hardware accelerator` to **GPU**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "67gOQITlCNQi"
      },
      "source": [
        "# Installation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_m9l6OYCQZP"
      },
      "source": [
        "!pip install -q torch-scatter -f https://pytorch-geometric.com/whl/torch-1.7.0+cu101.html\n",
        "!pip install -q torch-sparse -f https://pytorch-geometric.com/whl/torch-1.7.0+cu101.html\n",
        "!pip install -q torch-geometric\n",
        "!pip install -q git+https://github.com/snap-stanford/deepsnap.git\n",
        "!pip install -U -q PyDrive"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRfgbfTjCRD_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1b26fe8f-45e1-44de-dfcf-d0622e3f7eae"
      },
      "source": [
        "import torch_geometric\n",
        "torch_geometric.__version__"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.0.4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZoXlf4MtYrbz"
      },
      "source": [
        "# 1 DeepSNAP Heterogeneous Graph\n",
        "\n",
        "First, we will explore how to transform NetworkX graphs into the format supported by DeepSNAP. \n",
        "\n",
        "In order to extend the traditional DeepSNAP representation of graphs to include heterogenous graphs, we include the following graph property features:\n",
        "* `node_feature`: The feature of each node (`torch.tensor`)\n",
        "* `edge_feature`: The feautre of each edge (`torch.tensor`)\n",
        "* `node_label`: The label of each node (`int`)\n",
        "* `node_type`: The node type of each node (`string`)\n",
        "* `edge_type`: The edge type of each edge (`string`)\n",
        "\n",
        "The key new features we add are `node_type` and `edge_type`, which enables us to perform heterogenous message passing.\n",
        "\n",
        "In this first question we will work with the familiar [karate club graph](https://networkx.github.io/documentation/stable/auto_examples/graph/plot_karate_club.html) seen in assignment one. To start, since each node in the graph belongs to one of two clubs (club \"Mr. Hi\" or club \"Officer\"), we will treat the club as the `node_type`. The code below demonstrates how to differentiate the nodes in the NetworkX graph.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8LQ_z5gcBVA1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "outputId": "3f17ff30-16d5-4810-d180-af33e6312723"
      },
      "source": [
        "from pylab import *\n",
        "import networkx as nx\n",
        "from networkx.algorithms.community import greedy_modularity_communities\n",
        "import matplotlib.pyplot as plt\n",
        "import copy\n",
        "\n",
        "G = nx.karate_club_graph()\n",
        "community_map = {}\n",
        "for node in G.nodes(data=True):\n",
        "  if node[1][\"club\"] == \"Mr. Hi\":\n",
        "    community_map[node[0]] = 0\n",
        "  else:\n",
        "    community_map[node[0]] = 1\n",
        "node_color = []\n",
        "color_map = {0: 0, 1: 1}\n",
        "node_color = [color_map[community_map[node]] for node in G.nodes()]\n",
        "pos = nx.spring_layout(G)\n",
        "plt.figure(figsize=(7, 7))\n",
        "nx.draw(G, pos=pos, cmap=plt.get_cmap('coolwarm'), node_color=node_color)\n",
        "show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1JpFb9fTw1lg"
      },
      "source": [
        "### Question 1.1: Assigning Node Type and Node Features (Not Specifically Graded)\n",
        "\n",
        "Using the `community_map` dictionary and graph `G` from above, add node attributes `node_type` and `node_label` to the graph G. Namely, for `node_type` assign nodes in the \"Mr. Hi\" club to a node type `n0` and nodes in club \"Officer\" a node type `n1`. \n",
        "\n",
        "Then for `node_label`, assign nodes in \"Mr. Hi\" club to a `node_label` `0` and nodes in club \"Officer\" a `node_label` of `1`.\n",
        "\n",
        "Lastly, assign every node a feature vector [1, 1, 1, 1, 1]. \n",
        "\n",
        "**Hint**: Look at the NetworkX function `nx.classes.function.set_node_attributes`.\n",
        "\n",
        "**Note**: This question is not specifically graded but is important for later questions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zev_hMJHJXK1"
      },
      "source": [
        "import torch\n",
        "\n",
        "def assign_node_types(G, community_map):\n",
        "  # TODO: Implement this function that takes in a NetworkX graph\n",
        "  # G and community map assignment (mapping node ids --> 0/1 labels)\n",
        "  # and adds 'node_type' as a node_attribute in G.\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~2 line of code)\n",
        "  ## Note\n",
        "  ## 1. Look up NetworkX `nx.classes.function.set_node_attributes`\n",
        "\n",
        "  new_map = {node: f'n{class_}' for node, class_ in community_map.items()}\n",
        "  nx.classes.function.set_node_attributes(G, new_map, 'node_type')\n",
        "\n",
        "  #########################################\n",
        "\n",
        "def assign_node_labels(G, community_map):\n",
        "  # TODO: Implement this function that takes in a NetworkX graph\n",
        "  # G and community map assignment (mapping node ids --> 0/1 labels)\n",
        "  # and adds 'node_label' as a node_attribute in G.\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~2 line of code)\n",
        "  ## Note\n",
        "  ## 1. Look up NetworkX `nx.classes.function.set_node_attributes`\n",
        "\n",
        "  nx.classes.function.set_node_attributes(G, community_map, 'node_label')\n",
        "\n",
        "  #########################################\n",
        "\n",
        "def assign_node_features(G):\n",
        "  # TODO: Implement this function that takes in a NetworkX graph\n",
        "  # G and adds 'node_feature' as a node_attribute in G. Each node\n",
        "  # in the graph has the same feature vector [1., 1., 1., 1., 1.]\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~2 line of code)\n",
        "  ## Note\n",
        "  ## 1. Look up NetworkX `nx.classes.function.set_node_attributes`\n",
        "\n",
        "  new_map = {node: [1., 1., 1., 1., 1.] for node in community_map}\n",
        "  nx.classes.function.set_node_attributes(G, new_map, 'node_feature')\n",
        "\n",
        "  #########################################\n",
        "\n",
        "assign_node_types(G, community_map)\n",
        "assign_node_labels(G, community_map)\n",
        "assign_node_features(G)\n"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mafN0P3EOhSb"
      },
      "source": [
        "### Question 1.2: Assigning Edge Types (Not Specifically Graded)\n",
        "\n",
        "Next, we will assign three different types of `edge_type` to the edges: \n",
        "* Edges within club \"Mr. Hi\": `e0`\n",
        "* Edges within club \"Officer\": `e1`\n",
        "* Edges between clubs: `e2`\n",
        "\n",
        "**Hint**: Use the `community_map` from before and `nx.classes.function.set_edge_attributes`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsbYWEVwSV5n"
      },
      "source": [
        "def assign_edge_types(G, community_map):\n",
        "  # TODO: Implement this function that takes in a NetworkX graph\n",
        "  # G and community map assignment (mapping node ids --> 0/1 labels)\n",
        "  # and adds 'edge_type' as a edge_attribute in G.\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~5 line of code)\n",
        "  ## Note\n",
        "  ## 1. Create an edge assignment dict following rules above\n",
        "\n",
        "  def edge_value(n0, n1):\n",
        "    if community_map[n0] == community_map[n1] == 0:\n",
        "        return 'e0'\n",
        "    elif community_map[n0] == community_map[n1] == 1:\n",
        "        return 'e1'\n",
        "    else:\n",
        "        return 'e2'\n",
        "  edge_map = {(n0, n1): edge_value(n0, n1) for n0, n1 in G.edges()}\n",
        "  nx.classes.function.set_edge_attributes(G, edge_map, 'edge_type')\n",
        "\n",
        "  #########################################\n",
        "\n",
        "assign_edge_types(G, community_map)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBsTdTPVTQ52"
      },
      "source": [
        "## Heterogeneous Graph Visualization\n",
        "\n",
        "Now we can visualize the Heterogeneous Graph we have generated."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2sdufbODHtp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "90f5d3ad-8eed-4753-bef5-3f1b9287418a"
      },
      "source": [
        "edge_color = {}\n",
        "for edge in G.edges():\n",
        "  n1, n2 = edge\n",
        "  edge_color[edge] = community_map[n1] if community_map[n1] == community_map[n2] else 2\n",
        "  if community_map[n1] == community_map[n2] and community_map[n1] == 0:\n",
        "    edge_color[edge] = 'blue'\n",
        "  elif community_map[n1] == community_map[n2] and community_map[n1] == 1:\n",
        "    edge_color[edge] = 'red'\n",
        "  else:\n",
        "    edge_color[edge] = 'green'\n",
        "\n",
        "G_orig = copy.deepcopy(G)\n",
        "nx.classes.function.set_edge_attributes(G, edge_color, name='color')\n",
        "colors = nx.get_edge_attributes(G,'color').values()\n",
        "labels = nx.get_node_attributes(G, 'node_type')\n",
        "plt.figure(figsize=(8, 8))\n",
        "nx.draw(G, pos=pos, cmap=plt.get_cmap('coolwarm'), node_color=node_color, edge_color=colors, labels=labels, font_color='white')\n",
        "show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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pFO4eLpMAT9UxlzOZLEhL1yL+Uh6uJGtwO8fo7iFJxpPqnMTOOMlktH8dF4mzB4EgCIj74AvEPjoE11ZugEWnhzlfA4veAMFghFmjhUWnR/b+4zgxbBxiOw2B8Xauu4fNJCATBE+pwGDl3Y1MPf7cdA1/bkqDySxAIZdBEACj0YKG0RXw/NO10b5NFSgU5WfeR6sFQkOBpCSgcmX3jqVPH+CNN4C+fcU7Zk4OUKcOEBdHKwkZK48EQcDpVz5C+m+bYC7Q2vQcma8v1OE10Ong7/AJriDxCJkrccaJSc5kFvD5nAt49pVDWLXuKvI1Zuh0FmgKzCjQmmE0CTgTl4ups+Ix4MUDOBtffu7S1GragmXLFnePRPyMEwAEBwNPPw0sXSrucRnzJElzf7IraAIAwWCA7mo6jg4cK+HImDtw4MQkZTRa8O7Hp/DPnhswGAUYjaUnOAu0ZmTnGPHWxFM4fLz8rHPv398zuoiLXeNU6NVXgUWLALNZ/GMz5m4WoxEXp84rM2iS+fig9apv0S1hB2KMF1C588P0XL0BOcfO4vaR064aLnMBDpyYpD79Jh7nLuRBr7fY/By93oIJM84h4Uq+hCNznZgYYOtWwOjmUi4pMk4A0KYNtSfYulX8YzPmbtfXb4dgtn79ytp/HCdfGg9d+o1inzfr9Lgy+0ephsfcgAMnJpn4hDzEHr4FvaH0i06b5hWxfEFbbF/bCXOmt0BoiB8AQKez4JvvE1w1VEnVrAlERQGxse4dh1QZJ4CLxFn5dXnWYpjzNQCAbgk7EPXOSDx6/C/0yjyKVsu/gdzPF4LRiKQ5PyE79tj9QZbFguvrt8OYneOG0TMpcODEJLNq3VUYjKUHTcFBSkyf0ASLf01E36GxiL+Uh6njG9/5enxCPtLSba8p8GT9+rl/dZ1UGScAePZZCgxTUqQ5PmPukneu+A1cjcF9cDhmNHZG90BQswYIe/Epq8eQq/yQH39FqiEyF+PAiUkiN9+IPQdvwWIB1ixuh6EDw7BsThtsWdURU8Y3gq+PDF3ahyAxRYOdsZkwGAUsXZGEepEBCA9TAwAsgoC1G9Lc/J2IwxPqnKTMOAUEAM8/D/zwgzTHZ8wdBIsFFp2+2OeS5v0CffoNGLNzcH3jTgS1aGTDgQQYc8rPopcHHQdOTBKnz+XAR1nUVqBbpxCMm3wGg0cfQt06AejTozoiw/1xKVFz5zE6vQVpGTpEhlNaxGQSsPdQpsvHLoVWrYC8PODiRfec32KhLuZqtXTnGDMGWLLE/bVcjIlFJpdDpizexFKXcfPOx+YCLZSBNtyNyGRQqFViD4+5CQdOTBK5eSZYLEUr6NZuSMOtLAPy8k2IPZyF6KhAqFUKaApMxZ6XrzHBX110odJoysdSLbncvdN1Wi2gUtE4pNKkCVCvHvDXX9KdgzFX861SyeljWAxGqGqGijAa5gk4cGKSkN3zysrKNtz5WK83Q61SQKszI8C/+N1cgL8CBdqiYKk87YHmzsBJyvqmu736KrBggfTnYcxVar88BHKVr9XHyX19IPfzve9jAPCPqo2A6DpSDZG5GAdOTBIVg3wgl5cd9SSmFKBuncA7/6/yk6NWDTUSU4qm7wIDy892ij17AkePArdvu/7cUtY33W3QIOD0afdNSTImtohXngVg/Q6uy7kt6JN/Buqw6mi3eSl9HFELisAA1H3/ZekHylym/LwrMY/SsmlFmM1l7+az50AmXhsRhS4dquLAkVsY8WwELidqkJJKK+l8fWTo1bWaK4brEv7+wKOPUr+jIUNce25XZZz8/IARI6gh5qxZ0p+PMampalRD1Z4dcHPrXuyM7lHsawnT5t35+N6vFVIGV0CNQb0lHSNzLc44MUmoVQo83i0UZW0OfjvXiIkzzuGVYZHYvLIjGjcIwqSZ54s9ZkDvmhKP1LXctbrOVRknAHjlFeCnn6gYnbHyoMXiGfCrVsXuIkG5WoW26xZCofKTaGTMHXiTXyaZ5KsFGPn2sTIbYJZGJgMebl0JX01uLsHI3OfqVaBlS+D6dUDpwnzvrl3ApEnA7t2uOd/jjwMvvAAMG+aa8zEmtYLkNBzo9jz0V69BsGHqThGgRuuV36Jany4uGB1zJc44MclE1PbHgD41oPKz/2Xmr1bg3VejJRiVe9WuDYSHAwcOuPa8rsw4AdxJnJU//hG10PmJ1qhdpyoU/iooAu7v7SH384VcBlRtVhcddq/ioKmc4sCJSer1kXXRuX1Vm4MnmQyQQQE/QwuEVpWw6ZAb9evn+uk6V9U4FerfH0hOpkJxxsqFY8fgs+JXNNv3J3peO4DGX01AxcoBUFcJhiqsOgKbRCPyzRfRdcEEtJPfQFDzBu4eMZMIB05MUnK5DB+/2xCPtAqHxSIvNYCSywE/PzkiwwPw45zWMOoqYORIatxY3vTv7/q2BK7OOCmVwOjRwPffu+6cjEnGaARGjaIVD6GhUAb4I3zUM+gY6Yvuv3+DHom70eXk32j42XtQjxhGf3D//uvuUTOJcI0Tk5zBADRvDnw63QzfwOtY/vtVXMvQQS6Xw2wW4KMEuncKwZABYahftwIAuu706QM0bgx891356udksdDGv7GxQN26rjnnd98BZ864tsdSair93lNSgMBA649nzGNNnw7s2wds2lT8YlSlChAXB1S7Z/Xvjz8CK1cC27a5dpzMJThwYpL76itg+/bi1xyj0YINf5uwcKEcW7coICshMsrLo95HnTsDX35ZvoKnUaMoqHjrLdecb9YsID2dfheuNGAA0LcvrbRjzCvFxVEfkWPHgIiIos9nZQF16gA5OfdfnAwGICqK2ui3bu3S4TLp8VQdk1R6OjBjBjB7dvFri4+PHNVDfZGfpywxaAKAChWAzZvppm3aNBcN2EVcXefk6hqnQoVF4nx7xryS2Ux3OVOmFA+aACAhAYiOLvmOztcXePdd4IsvXDNO5lIcODFJ/fe/wMiRQIMS6iQDA4H8/LKfX7kyBU7Ll7s+WyKlxx4DDh0Ccl20Ybqra5wK9epFndKPHHH9uRlz2vz5gEIBjB17/9cKA6fSvPwy1Tldvizd+JhbcODEJHPgAE3RffxxyV8PDKTpOGtCQ4EdO+gaVl6KjQMDgY4dqYu4K7gr4ySXA2PGcGsC5oWSkoCpU4HFi0tufGktcKpQgV783EK/3OHAiUnCbAbeeIMy1RUqlPyYChWsZ5wKhYUB//wDfPop8Ouv4o3TnVy5us5dGSeAtmD54w8gO9s952fMboJAGaP33y85XQ4Aly6VHTgBwJtvAqtXU8dbVm5w4MQksXQp7Vv2/POlP8aWqbq71a1LGZr336c3Ym/Xrx8VzJvN0p/LXRkngBYc9ekD/Pyze87PmN2WLaPi73HjSn+MtYwTQC/+Z58F5swRdXjMvThwYqLLzqbpublzy14Jp1JRexSTyfZjN25MwcbYscCWLc6P1Z0iIoAaNajWSWruzDgB9PviInHmFdLTgQ8+AJYsKX1fJEGwLXACgPfeoxoDVxU0Mslx4MREN2kSLUO3tgpXJrM/6wQArVoB69YBw4e7bu81qbhqdZ07M04AreaWyYA9e9w3BsasEgTgtdeof0bLlqU/7tYt+rdKFevHjIqi1SCLFokzRuZ2HDgxUZ05A6xaRbVItqhQwbYC8Xu1b0/nGTwYOHzY/ud7ClfVObk74yST8f51zAv8/jsQHw9MnFj248pqRVCS8eOpJ4te7/wYmdtx4MREIwhUED5pElC1qm3PcSTjVKh7d2rQ27+/9+6J9vDDVDealCTteQoK3JtxAoBhw6gvF9fJMo906xZdwJYsoTqCstg6TVeoVSugSRPqq8K8HgdOTDRr1lB905gxtj/H0YxToZgYYN48oHdv4MIFx4/jLgoFddaWOuuk0bg34wQAlSoBTz1FwS5jHufdd4FnngE6dLD+WHsDJ4Dqpr78snxuwPmA4cCJiUKjoRrIuXNLr6csiTMZp0KDBwOffUZlBImJzh3LHVxR5+QJGSeAisS//57fO5iH2bKFCvCmT7ft8Y4ETt260Z3iX3/ZPz7mUThwYqKYMYMaOnbubN/znM04FXrpJbqh69kTuHbN+eO5Uq9ewP794vwcSuMJGScAeOihom7wjHmEvDxKk3//ve27UTsSOMlktJXC55/z8lIvx4ETc9rly8CCBcDMmfY/V4yMU6HXX6fFMD17AjdvinNMVwgKAh55hBp8SkEQ3F8cXqiwSHzBAnePhLH/+/BDoEcPuoOxRWErgnr17D/XgAHUH4qXl3o1DpyY0959l6bpwsLsf65YGadCH3wADBpUtEeat5BydZ3BQDtG+PhIc3x7DR0K7N0LXL3q7pGwB97evdRN156NMG/coD+mypXtP59CQR18efNfr8aBE3PKli3AuXMUPDlCzIxToalTga5dqVu12MeWSr9+wMaN0tT+eEp9U6HAQOC552gLMMbcRqsFRo+m1SWVKtn+PEem6e42bBhw8qT3LgVmHDgxxxkMwFtvUXsSPz/HjiF2xgmg6aCvvwaaNgWeeIKuj54uKopaOBw5Iv6xPaW+6W5jxlDgZDS6eyTsgTV1KtC8OS31tIezgZNKRRfOL790/BjMrThwYg779lua5u/Xz/FjSJFxAih4WrgQqF6dVt0ZDOKfQ2xSra7ztIwTADRrBkRGuqZrOmP3OX6c+jXNnWv/c50NnAAq9Nu8WfoGbkwSHDgxh1y7RtP0s2c7d5zAQOlWkykUwE8/UXuEF16wb088d5CqzskTM04AdxJnbmI0AqNG0WqW6tXtf74YgVNwMPDyy/bVVjGPwYETc8gHH1B5gLPXjwoVpK1D8vEBVq+mQvHRoz27f9AjjwCpqUBKirjH9cSMEwA8/TRw4gRw6ZK7R8IeKDNnAqGhtNmlIy5dcv7CB9B03fLl3rUEmAEA7GhVyBiJjQV27qQtnZwl1VTd3fz8gD//pO7ib75J2Xlbt5hyJaWSCto3bqRGkWLx1IyTSkX9txYutKBn35s4dyEHObkmqFUK1KqhQq+uoahezcrWF4zZIz6eCiCPHXPsIiAI4gVONWrQ3cO8ecCUKc4fj7kMB07MLmYzbef05Ze294orixTF4SUJCKBpsJ49i3rQeWLw1L8/sGyZuIGTp2acMm7oEFgtFVt3ZuBUMqDVme98zUcpw7JVyWjeJBjDng5HmxZ2rHpirCRmM03RTZ4MREQ4doyMDECtpqk2Mbz3HnUOfv99cS6ozCV4qo7ZZfFiehMeOlSc47ki41QoOJjaJ2zaZPvOCq72+OPAvn2UJRKLJ2acTpy5jeH/OYqd+69BrjAXC5oAwGgSYDAKOHryNj6YdhbzllyGwN2WmTO++47ull57zfFjiFHfdLf69YEuXbg3h5fhwInZLCsL+OQTcae6XJVxKlSlCnXo/vln5wvbpRAcDLRtC2zfLt4xPS3jdPp8Dt6bcgYFWjNMJuvBkE5vwbrN1zD7ey6GYg5KSqLpsCVLqBuso8QOnAAqGP36a+7N4UU4cGI2++QT6srdsqV4x3RlxqlQ9eoUmMyeDfzwg2vPbYt+/cRdXedJGaecXCPen3IGen3pVfptmlfE8gVtsX1tJ8yZ3gKhIX7Q6S3YuD0D/+y+4cLRsnJBEGgvpnHjgAYNnDuWFIFT27Z0zJUrxT0ukwwHTswmp04Bv/0GTJsm7nFdnXEqFB5OwdPkycCKFa4/f1kK2xKItQLQkzJOG/5JLzPLFBykxPQJTbD410T0HRqL+Et5mDq+MQDKPC1ZnshTdsw+P/0EZGZSPZGzpAicAMo6ffmlZy/7ZXdw4MSsEgQqCJ8yhaa6xBQQQBknd7wX1qsHbN1K28WsX+/685emXj2asjt+XJzjeUrGyWIRsHpdKvQGC9YsboehA8OwbE4bbFnVEVPGN4Kvjwxd2ocgMUWDnbGZMBgFLF2RhHqRAQgPUwMAMrMMOH/RDZE2804ZGcD48TRFJ8ZmjVIFTo89Bvj6UgEm83gcODGrVq+mrNArr4h/bKWS2gW4a1uUpk1p+f/LLwPbtrlnDCXp31+8rtoFBZ4ROB07fbvYFF23TiEYN/kMBo8+hLp1AtCnR3VEhvvjUmJRZbxOb0Fahg6R4ZQy0xss+MdKwVQAACAASURBVG19qsvHzrzU669TA7dWrZw/lsUCXL5MdzZik8kowOPNf70CB06sTPn5tFJ27lzqxC0Fd9Q53a1NG+rz9PzztFm6JxCzzkmj8YypupTUgmLTdGs3pOFWlgF5+SbEHs5CdFQg1CoFNAXFW7zna0zwV9OLTxCAy8kiLjlk5dfvv9MO5J98Is7xrl2j2oIKFcQ53r2efprOERsrzfGZaDhwYmX67DNaLdupk3TncFed0906dqTazEGDgKNH3TuWwvEkJgJpac4fy1MyTgVaM0zmooxTVnbRBoJ6vRlqlQJanRkB/sUj9AB/BQq0Re0KtNrirQsYu09WFtUXLFlCnVbFINU0XSGlkuqwOOvk8ThwYqW6dAlYtEj6v2N3Z5wK9exJ19l+/YAzZ9w7FqWSOp1v3Oj8sTwl46RWKaBUlN3HIjGlAHXrFDUCVPnJUauGGokpRVkmtUqi1CcrP959l+6COnYU75hSB04AtdI/fJgyZcxjceDESvXOOzRNV6uWtOfxhIxTof79qU1B797AxYvuH4sYdU6eknGqVUMFpbLsS86eA5mIighAlw5V4esjw4hnI3A5UYOUVCqCk8mAiDAP+GaY59q6Fdi1C5gxQ9zjuiJwUqspUzZzprTnYU7hwImVaNMm4MIF4O23pT+Xp2ScCj37LLVdeOwxIDnZfePo3RvYvZsCH2d4Ssbp4VaV4aMsO+N0O9eIiTPO4ZVhkdi8siMaNwjCpJnn73xd5SfHM09KHMkz75WXB4wZA3z/vfhbmLgicAKos/lffwFXr0p/LuYQ3quO3Uevp427v/2WVrxJLTDQczJOhUaOpGCuRw8qGK9Rw/VjqFQJaN0a+Pdfmj50lKdknBQKGZ7uXwu/rE3B4NGHin1t6cqiCPXoqdt4fuyREo8RHOSD5o1F2ieMlT8TJgBdu9LeRWJzVeBUqRIwYgR1E//mG+nPx+zGGSd2n9mzgYYNgb59XXO+ChU8K+NU6M03KYDq2ZP657lDv37OT9d5SsYJAJ7sXRMKB7e8UPnJ8dKzEZB54u7MzP327aOVdF9/Lf6xLRbgyhVpWhGU5J13qHFnVpZrzsfswoETKyYtjabXXXmj42lTdXebMAF48km6gc3Jcf35C7uIO9Mg1FMyTgBQuZIvPpvQGL4+9l16VH5ydO0Qgpie1SUaGfNqOh31a5o7F6hcWfzjp6bScV11BxIWBgwYAMyfDwDIj7+MzH8P4PqmXcjadxTGbDdcjNgdPFXHihk/nkoEXHVjBXhWcXhJpk+nwK5vX2qS6crsTf36FPScOEHTdo7wpIwTADRtWBkFNxvDr/J5ABaYrewyYTHL8UjravjwrfqcbWIlmzqVutkOGiTN8V01TXcX83/exLXu/XF5xV7orl2HzMfnzh2UxWBE6BM9EPXOSFR8qJlLx8U448TusncvsGcP8OGHrj2vJ2ecAFrJNXs27Q/65JN0c+vKcxdmnRzlSRknQaDpz8iwKvh5Xhs81jUUvr5yKBXFL0UyADKZHA2jK6Bt40aI3c5BEyvFiRPA4sXAvHnSncPFgVPmvwew/bFROKcLgOZSMswFOphy8mDKzYcpNx8WnR7pa7fgYI9hONBjGIy5HnwBLYc4cHrAmMwCbucYcSvbAL2h6FbfbC5aBSv2YhRrPD3jBAByOfDDD0DVqsAzzwBGo+vO7Uydk9kMGAzi9QB01rRp1Nhz6VIgorY/Jr7TEH/93B41K0Ui52YImtSvhEcfqYLuHcNw/WJr/PBVa3z1eVXotDJJ3xeZlzIaKRL/8kuguoTTuC4MnDLWb8eRAa/ClJsPs7mMOXqLBeYCLW4fOonYRwbBmOPhF9FyhKfqHgCCIODUuRys+OMqDh3LhlwByGQymIwWRNT2xwtPhyPhXAiCg+UYMsT14/P0jFMhhQL45ReaDRg2DFi+XLptaO726KPUjDQ93f7VfYXZJk9I1vz2GzUYPXSI2tUUCgxQIj0pDPHHgCO7KMgTBGDpfCA+HmjUCPj5Z6B9e6BXL1q4wBgAYNYsoFo14MUXpT1PQoK02yf83+2jZ3Bi2DhYtLantS16A7Qp13A4ZjQ67FkJmYOLL5jt+CdczsVdzMUzow/j/SlncOBoFswWAUajAIPBAotAnZpnfpeAFZv24+nn09zyBusNGadCPj4UAGRm0qbHFiv1OWKds1cvx7qIe0p905EjtN/q+vX3JwYEgWZbKlYsyozJZMX364uOBqZMofdHU/Gt7NiDKj4e+Oor6tkk9YUrIcElhZ9x739eZtAk8/FB61XfolvCDsQYL6By54cBUPCUd+4ibv6zT/IxMg6cyrWDx7LwxoRTSL+hg1ZnKXVllk5nhlxhxvptVzB/6WXXDhLek3EqpFIB69bRdfudd5xb8WYrR+ucPKG+KTUVGDiQpjpbtrz/68nJFBzWqVP88/dudDx2LBAUBHz+uaTDZd7AYqFVdJMm3f/CEZvZTPPLdetKepqCK1dx+6j1vZ6y9h/HyZfGQ5d+o9jnzfkFuDJrsVTDY3fhwKmcir+Uh4kzzkGntz0lotNb8Oema1j5p/Qda81mAQeO3sIbH57ErMX7kKfcg56D92Lw6ENY8cdV5Oa5sIjIAYGBlAHatw+YOFH68/XpQ40w7S1Md3fGSaOhgvo33qDV1SU5cQKoXRsIDy/++e7d6WuFrWzkcqqN+vZb4PhxacfNPNx339G/r78u/blSUoCQEMnvQBLn/wLBTBtYd0vYgah3RuLR43+hV+ZRtFr+DeR+vhCMRiTN+QnZsccglLAcNfvgSRQkpUo6TsaBU7n12ex4q0FTm+YVsXxBW2xf2wlzprdAaIgfdHoLfvglCdk5hjKf64wN29Ix4MUD+OTLOJw4mwOd3gyZTIBOZ0H6dR2WrEjCgJcOYsqsOORrPHdepmJF2hZr3Trxt8W6V5UqQIsWwM6d9j3PnRkniwUYPpxWiY8fX/rjTpyg7+/ewEmtpibQW7cWfa52bepvOHy4a1c3Mg+SnAxMnkwr6VxRz+OiwvCbW/dCMBZd72oM7oPDMaOxM7oHgpo1QNiLT1k9hkypQNa+o1IOk4EDp3Lp4uU8XMso+10lOEiJ6ROaYPGvieg7NBbxl/IwdXxjAFQusGFruujjEgQB3/5wCd8uuoTsHCO0WnOJj9PrLTAYLNi1/yZGvnUMmbf0oo9FLFWrAv/8Q0XPc+dKey5HVte5M+P0ySfA9evAokVll6CcOAH4+lJQdK97p+sA4IUXqDXExx+LO17mBQSBigvffdd1qwRcFDiZ7lkVlzTvF+jTb8CYnYPrG3ciqEUjq8cQjCYYs3OlGiL7Pw6cyqHV61NhMFK2ac3idhg6MAzL5rTBllUdMWV8I/j6yNClfQgSUzTYGZsJg1HA0hVJqBcZgPAwNfQGC377K63spbAO+HFlMjZsTbd5+tBoFHA9U4//fHgSmgLPzTzVrAns2EELfJYule48jnQRd1fGaflyYMUK4M8/re93eOIEFXzfm3ECgJgYYMuW4gXhMhmwcCHw66/Ue4w9QH7+GbhxA3j/fded00WBk0xZfImuLuPmnY/NBVooA234Q5bLIffhxfJS48CpHNp78Fax1V7dOoVg3OQzGDz6EOrWCUCfHtURGe6PS4maO4/R6S1Iy9AhMpzSEwaDBZeTxKvYTkzRYPnvV8sMmkqaOjSbKXj64ddE0cYihYgIyjx9/DGwerU052jUCFAqgdOnbX+OOzJOBw5Q0fxff1FpSFlu3qSFAbdulZxxqlWLfrb79xf/fEgIBU8vveQ9KzKZkzIyKGBasoRWE7iKiwIn36rObxUjUyrgW62KCKNhZeHAqZwRBAFaXfEpsLUb0nAry4C8fBNiD2chOioQapXivixOvsYEfzXd9chlQE6eeFme1etSYTKVHjSVNXVoNArY+E8G9PqSp/Y8Rf36lB156y3nN+YtiSNdxF2dcUpOpj5Xy5ZRbZM1J07QSrurV0vOOAElT9cBVHTeuTPw3ntODZl5i//8Bxg1yvG9hxzlosCp9qjBUASorT5O7usDuZ/vfR8DAMxmhPSSvt/Ug44DpwdAVnZRobdeb4ZapYBWZ0aAf/HUcIC/AgX/rzsSAAgWcabqCgpM+Gf3DZgtjk0dFtqx92YZZ/EMzZpR0DRqFLB9u/jHt7fOyZUZp7w8Cuzef5/29bPFiRP0M8vJAUJDS35MaYETQFvhbNkCbN7s2JiZl/j9d+DMGWo/4EomE62qi4qS/FRhMZ0h6K0vyulybgv65J+BOqw62m1eSh9H1AKUCtR67gkoAz2gcVs5x5Oh5YxMJoOfnxw6Xdl1RIkpBejdveidSuUnR60aaiSm0PSdxQIEVRAnHX7gWBYUiqLq4MKpQ4PBggVftkSfHtVRp3bpU4cpqVpodRas35qOvj0l3FZBJG3b0nX+qadoxV3HjuIdu0sX6h91/XrpgcbdXJVxMpuB554DHnkEePtt25934gTQpg3ViZW2QOqhh2gq7/Ll+1vpBAcDP/5IndzPnKEN7Fk5k51N/Sx++831ewclJ1PHVinPq9UCc+dCOWsWakY2QlpSJnZG9yj2kIRpRfsN3fu1QnK1H+q8IXEHdQaAM07l0sMtK1ttpLvnQCaiIgLQpUNV+PrIMOLZCFxO1CAlVQsAyM8DRo8MwIwZwKlTzjV5zL5tLDZN58jUIVA8c+bpHn2UipcHDgSOHRPvuL6+QM+ewKZNtj3eVRmnDz+kWqV58+xr4nziBFCpUunTdAAFVDExpXdO794dePpp17T0YW7w7rt0F+KCLU/uI+U0nclE9Vr169M+RHv2oNG+9fALrWp3mwWFvxpR74xEhcbSdzdnHDiVS0OfCoOPsuxf7e1cIybOOIdXhkVi88qOaNwgCJNmngcA+PjI8MIzNTHxIznS0+maVbs2rQJet87+YlyT2VKsWN2RqUM6jgtadIvo8cdpKX5MDHDunHjHtafOyRUZpx9/pNVza9dSYGer/HyqbZLJyg6cgLKn6wDqo3XihHSF+cxNtm2j5mVSN0orjRSBkyDQ3kPNm9MqwTVrKEXdsCF8K1dE+53L4Ve9KmQ2FsAr/NUIGz4Q9Se/Je44Wal4qq6cSU4Gpk8JgibfFz5+OgwefajY15euTL7z8dFTt/H82CP3HUMGYPATtVCtKtC7N3VqvniRshzz59O0yCOPUB1L3750w1RWliEwQAmlj6zMwMfa1CEABPq7YEddkQ0YQMFLr17A7t3ibHfVpw/w5puAXm99qb9GI+2OFHv2AB98QP9WsXMxz6lTQJMmwLVrJa+ou9tjj9E+dXl5tLfhvfz96T2of38qGLd3M2TmgfLz6W5t4cKSf+muIHbgtHcv8N//0gt51iz6Y77n4ulfJwydj63HqZcnIHP7fgACLLr7s+2KCgGQ+yhRf8rbqPPqc+KNkVnFGadyIi8PmDCBFpw0bCjD15/Wh5+v/b9elZ8cg58IQ7WqRe/IMhk1HHznHVpyf+0aTYvExQE9elAw8OabVKSr1d5/zOaNgiFYad1kbepQLpOhReNKdn8/nuC556jRcc+eVGfqrGrVKODYvdv6Y6XMOF25AjzzDPVscqQXYeGKupQU6xmnChWADh3o9Veahx8GxoyhLcxcsX8gk9iECVTU17u3+8Yg1ua+Z89SVD9sGPDqq/Ti79u31DtO36qV0fbPheh2cTui3nsZftVDIPv/Y+X+KlRs1xItf/wSPdP2c9DkBhw4eTmzmXYeaNAASEujHj+TJwMdH66ED96wL3hS+cnRuX1VvPpiZJmPq1CBMimLFtFUy59/0h3+9OlUsNy/P7BgAWW/ACA8zB9RdcoutClr6hAALIIMX0ythRdeoNVqFtu34PMIL79MRdM9e1I7GmfZurpOqhqnnBwawyefUDbIESdOAK1a0WvIWsYJsD5dB9C+genp9DfBvFhsLM39fvONe8fhbMYpJYWajfXoQf9duEDBk8K27LmqRjU0mPQmel7dh75dw9H3r9nok3MKHfetRvUne0Ku5Ekjd5AJAt+beasdO6huMjiY9u966KH7H3PoeBYmfRkHi0UoVi90Nz9fOQRBwNCBtTH6hTp37mwckZ1NZQkbN1IGKiSEbqzq1L+JddviobWy2q80LZsGY/K4llixgnoE3bpFUzcvvij5puWimjaNFgft2mX/1NbdzpwBnniCsj5l/bpiYoCxYynoEIvJRMerV4+KwR3VujVN/Y4aRT8Ta32frlwB2renwKis2tmzZ2mPuyNHgMiy7wGYJ9LpKKKeNo2q/t3FaKTdvPPy7CveA+gCNWMGFQC+9ho1GwsOdm487dsDX31FqVfmVpxx8kIXLlBW55VX6I5/9+6SgyYAaNe6Mv7+tT3++2YDNKgbCB+ljFaqWRTwUcpRuaIPRj1XB38ua4+Xh0U6FTQBtEJqyBCqN8nIoOtGQACwbFFVZGWqQRVU9vHzleP1EVGoWpWmBI8fp67Uubl0LenShYKpfPEanUtm4kQKJHv3pvE7qmlTyrpZKzqXIuM0bhyde/Zsx49hMNBUb7NmZTe/vFtUFO0NeNTKHqZNm1IZyYsvUkaWeZlp06hN/qBB7h1HYiK1rrcnaCoooICpQQP6+OxZ+n6cDZoAumC4q9aLFcOBkxe5dYsCh44dKVg4f56uLdZiHaVSju6dQrBkdhv8sewRfD+rFYLkLTHy6bZY91N7PDeoNoKDxN/CQC6nupPJk4EjR2RY9UMLBKh9YU/w5Ocrx8R3G6BR/aBin2/Rgt64U1NpCuyPP2i6Z8QIKlT21DyqTAZ8/jn9XGJiKLBx9Di2rK4Tu8Zp4UJg61bKEDkzSxAXR0XrRiO9ToKCrD4FgG3TdQDV4wHOBXfMDU6eBH74gVKRTt7EOc2eaTqTiWoXoqPpezhwAPjuO3FXKeTl2f6HwiTFgZMXMBjoDaBhQ/r7jIujzK+1FVUlqRTsi8jwAFSrEgiTQQW53HUXp+h6PvhtcRsE+QcAQtkvPbVKDrVKgRkfNUG3jtVKfZyvL/VK+usv+rk0aUJTU/Xq0Y2eGMXYYpPJgLlzaYpx4EBaHecIW+qcxMw4/fsvBcF//w1UrOjcsQrrm2wpDL+brYGTQkFZyM8/F7cVBJOQyQSMHAl88YVnLIu0JXASBGol0KQJ9cJYv57+laL3E2ecPAYHTh6ssN1H06Z0l79rF93EWNs41RaVKwNZWc4fx17XM3xweEdrfPxOM7R/qDJ8fWQI8FdArVLAR6kAIIdeq0LBrSh0avYIfGSVbc4eVa9OAeXZs8CqVVQL06oVFS+vWFHyij93kcupgDk4mKY2jUb7j9G1K32vmZmlP0asjNPFi8DQofRzdWSRkcUiIO5iLmIP38LuA5nYsz8bTZuZbJ6mK9S+PS06SE21/tioKFqwMHy4Yz9f5mKzZtFc7EsvuXskxFrgtGsX9WX59FO6E9q+vfSaCWcJQum9OJjLcXG4hzp5kgq/r1+nekCxV+R++ikFEtOni3vcsggCLSx54omibTluZRtwJSkfeRoz/HzlqFbVD2E1ArBvnwwbN1LvqPx8ancSE0Or0uzJVut0FHz++CNw+DAweDBN57Vr5/6ZAICyiQMHUgbn559tXmxzx1NP0QrH4cNL/nq1alRIbsv2LKXJzqaf1/jxtNTfHrdzjPj7n3SsXp8Kvd5y52eu0QBKHwG1Q0OgMIZh2ZJAm4/5/PPUq2nMGOuPFQR63bRtC0yZYt/YmQtduEA1CEePStt4zB69etGF6t6NF0+dolb58fF0AR0yxO5O33bTaCio9KS7vwcYB04eJj2dCog3bqRpkdGjnaslKc1339Eb6oIF4h+7NMuW0SqsQ4fsCxASEiiA2rQJ2L+f3gRjYuh61rCh7QFQairwyy8URCkUdGM7bBjtk+ZOWi19P3XrUpmEPQHd0qW0evG330r+emAgvaYcvVE1Gilobd6cVm7aY+P2dHy14BJkAPSG0ldTyuVyPNquMia93wi+PtbfgFaupAyirZsdX7tGmce//6bXDvMwFgsVbQ4eTEWcniIykpYIF2adkpKAjz+mZmITJ9LqHHtX2zkqI4MKO69fd835WJl4qs5DaLWUBWralJapX7hAfdKkatPh6qm6zEzqML1okf1Zleho4K23aLoyI4MKfxMSaEuTqChqxrlxI01LlSUsjG4UL1ygabJLl+jnHRNDux44WmvkLLWasmJnz1KW0Z5bmZgYurYbStjGTxCcm6oTBHof8/MDZs6077kr/riKrxdegsFgKTNoAgCLxYKDx7Lwnw9PwmC03q7i8cdpJam133ehmjWp+/3w4XzD7pEWLKDlj5602aBeTxF3nTrAzZuUeWrThu5uEhKA//zHdUETwPVNHoYDJzezWKjzcoMGlAE+cgT48ktxVq+WpUoV1wZO48bRFEvr1s4dJyCAVpMtXEi1Lhs2ABER9MZevTploebNo5XEpZHJaFbghx9oKfyzz9K1u1Yt2oT9+HHXr8qrUIEyart2AZMm2f680FB67ezde//XdDq6ttsbqBaaN4+Ou3KlfcfYvf8mlixPgl5ve88uvcGCy4kaTJ0VZ/WxlStTBmnnTtvH9OyzdMM+YYLtz2EukJxML/glSxx/oUrhyhW60/r8c2qNYDbTMubJk90TwPCKOo/CgZMb7d9Pxa6zZ1PwtGYNZVBcwZUZpx07KCCYOlXc48pklDEaP56On5JC9UvHjtHPtVEjCth27Cg5IwNQIDZsGK0YO3qUyggGDaI32W++AW7cEHfMZalUibJHa9bQwiJb9e9f8rSVMyvqtmwBPvuMjmvP9VoQBMxedMlqlqlN84pYvqAttq/thDnTWyA0xA96gwUHjmbhSrL1Hg32bHRcaP58mtK0J+BiEhIESqu/8w79sXoKoxGYM4e2YoiLo9qCuXOdKxR0FmecPAoHTm6QlET1hEOGUMb30CHg0UddOwZXBU46HV0b582jehspVaxIZRI//khZ9l9+oczdRx9RkfRTT9EUXVpayc+vU4dufi9fpqmdkydpA+MBA2gqzRUrs0JCaHHOokX0Rm+LwrYE92bJHJ2mO3+eprXWrLG/8/bx07eRrzGV+ZjgICWmT2iCxb8mou/QWMRfysPU8Y0BAEaTBavXWV8yV9iWwJ7MYJUqlGUcMcK55qNMJL/8QgV448e7eyTEYqFWAo0b053UwIFUTOcJWxNwxsmjcODkQrm5VGPTpg21/YiPp2yH1AsySuKqwGn6dMre9O8v/bnuJpfTyuBPPgEOHqSyhIEDKfvUrBltLvvRR7Qllsl0/3O7dQN++omyWP3700rpsDCqQTpzRtqx16pFwdMXX9AYrGnRgjJq8fHFP+9Ixikzs+j77dTJvucCVNtUuK3OmsXtMHRgGJbNaYMtqzpiyvhG8PWRoUv7ECSmaLAzNhMGo4ClK5JQLzIA4WFqWCzA9j03UFBQdvDVoAHVXp0+bd/4+vYtWizF3Oj6dQjj3kPWG+NxfuLXODH8PZx48T3E/fdLZB84AZevWdqxg7rSzpxJdQDdu9N8vqfIzeXAyYNw4OQCZjNlEBo0oOLm06fpDV2KzVdtFRREGQkpsyjnz9M1aM4c6c5hq5AQClJXrqTpt/nzKVvx+uuUgX/uOeDXX6kO9G5BQbSX2t69wL59VMjdty8FZfPnSxd8Fi7o+fBDyvyURSYruTGkvRkng4GmKZ95pvT2BtacjiueyunWKQTjJp/B4NGHULdOAPr0qI7IcH9cSiyajtPpLUjL0CEynP4glEoZEhLL3j+ntO/ZFl99RVO7tq7KY+Iya3VIihmKnZaaODxuJhK/+RHXVm7AtRUbcOXrpTjUZwR2NeyF5MWrYdaXMsculuPHKZJ+9VXKfB0+TD1TEhIca1gmFe7h5FE4cJLYP/9QIevy5bTy68cfKaPgbjIZTW3dvi3N8S0W6rMzebL7l/vfS6mkm8nPPqPpuFOnKMP0++90rXzkEarHOnaMvo9C0dGUQUtKoufu20c1ac88A2zefH/mylkNG9Jx//Mfeu2UpaQ6J3syToXlJpUrO9fbS68vvjnc2g1puJVlQF6+CbGHsxAdFQi1SgHNPRmlfI2J9lD8v7x86z9MRwOnChUokzdmzP2BMpOW/sYt7GvSC3Gn06DN1cKsKSg+3yoIMGu0KLiSgvPjZmD/o0NgyJLgInX5MnV07dePUtHnz9MfcmH6357tVlyBM04ehQMnicTF0d/k2LHUeG/XLudXlIlNyum6JUsom/Xqq9IcX0xhYcDLLwN//knZqOnTgZwcWgVYsybVxKxZQ58DaPFPr16UvUpMpKz+5Mm0uu+//6V2B2Jp0YK2kxkxgsouStOtGwWBd/8+7ck4ff013Xz/8otzU8f3buGTlV2UMdDrzVCrFNDqzAjwL76CKsBfgQJtUdDlY0M/p86d6e/MkQL+Rx+lLOPYsZ67r2F5Y7ydi9j2g6C5mgGLDT9zS4EWeecSsL/TEJjyHdzU8V7Xr9OdSLt2VC9x8SK9CHzu2qtTp6PHRUSIc04xcMbJo3DgJLLMTFrS3rkzvZmdO0c3NJ7QpfpeUgVOGRlUP+RIzyZ38/OjTP1XX1HNUGws1aQtXUoBVteu1C7i7Fl6w61UiYLDQ4doas1spsd06EDff2Gw5Yx27ShwGzKE9g4tiVpNr7fNm4s+Z2vGacMGCpw2bHC+gD8wwHrjscSUAtStU3QilZ8ctWqokZhCb44Wi4BKwdY3nfb1pU7yd3/P9vj0Uwq8Vqxw7PnMPseGvAldagaEEjb5lvn4oPWqb9EtYQdijBdQufPDAADBYIQ25RpODn/PuZPn5dHKj8aNKUiKi6MmliW94C9fpqBJqiZ6juCMk0fhwEkkBgO9+TRqRG+ocXG0FN6RjXhdRarA6d13KUPSvLn4x3a1unXpBnXzZroJff99aj3Trx+twhs7lgIOjYZuYGfOpN5Q2+hIQwAAIABJREFUEyZQw86ICOCFF6j21GJ7W6P7dOlCW7I8+SRtkFuSe6eubMk4nT5N+6r+8QdQu7bj4wPoZxBWLRQQyr5L2HMgE1ERAejSoSp8fWQY8WwELidqkJJK3SkD/JWoF2lbBGfLRselUakow/bOO7btfccclx9/Gdn7jkIoI9WUtf84Tr40Hrr04ilEi96Am//sQ0FyKcthy2IwUCuB6GhKDx87Rn1Gytrw09Om6QDOOHkYDpycJAg0xdOkCb057t5NS++rVnX3yKyTInDasoVWsdnTxNFb+PtTp+758+kavHkzBVbffEPNN3v3pkL4pCR6Q//9d+pO/vDDtPlwZCQtCrhyxbHz9+lDxfZ9+1Jgfq+YGArWCgv+rWWcrl+nfQPnzqWsliMEgX7fL79MGbmUhFpQKMsOnG7nGjFxxjm8MiwSm1d2ROMGQZg08zwAyj4NHVj7vim/0vTpQ3WEpfXpsqZ1a8oQjxzJU3ZSSvx6CQSDAd0SdiDqnZF49Phf6JV5FK2WfwO5ny8EoxFJc35CduwxCOb77zAEC5C8YLntJ7RYKJXYsCFdlLZtozsPW/bB88TAiTNOHoUDJyecOEH1LR9/TG+mGzdSJthbiB04FRQAr71G++A5us2Ht5DJ6Hf93ntUe5SWRsHDqVNUP1O/PmUyTpygIuQTJ6gXVG4uFZ936UJ79+WXvXjsPk89RVOFjz1GMwp3q1mTitX37aP/LyvjpNPRFPKLL1JXbXvduEEZ1qZNabViVBRNS2/aoEKLxkGQy4HBow/h6Kmiwt6lK5Mx7WvqmXD01G08P/YIejy9D29MOIWMG7TfjV4PdHq4us3jCA2lLO+ePfZ/D4U+/JAWSbhy38YHicVgQNov6+5M0dUY3AeHY0ZjZ3QPBDVrgLAXn7J6DMFgQMoPqyGYzVYeKNDdQ5s21Ixt6VK6MNuT/vbEwIkzTh6FAycHXLtGU1F9+tCbzsmTVCzsbcQOnKZMocxF797iHdNbBAXRUv4lSyiIWr2aGi5OmkTNN598klY6jxtH00JvvUUZqbAwei3t2WN7xmPYMCrP6Nnz/immzt3zMPuHOAx55RD+3rsfp5MP4vX/nsS/+27CZKI7eUEoyhDZkxk0m2lbmEGDKDA8dYqCjYsXKfgoXD054e0GCPC3vz7Ez1eOWpXqo1NHZYlbyJTG0dV1hZRKSkZ88gllCJm49Ju2FltymjTvF+jTb8CYnYPrG3ciqIVtXcMtegOMt8voXHrkCBUovvkm3c0ePEgFh/byxMCJM04ehQMnOxQUANOmUQPF0FB6wxgzxrNqCO0hZuB0+jS1Wpg9W5zjeTO5nFpQTJxI2+pcvkyF3bt30+cfeoiCqPHjqci8SROqlYqOpoLllBTr53j1Vaq96tmTptz2HszEc2MP4+Slk8jMvYG0dB0MJiP0Rj1OncvB53MuoN8L+7F4eRKmT7cgPp4yXrasoLt8mYr9IyIoOH78carz+uknWgRx78KH6tVUmPtZC1QIVNq8Qs/PV46xL0Vi1c+hmDOHVoZ/9JFtU3CFrRicmWpr2JDea4cPpwCRiUSng2nc+1RQVvipjKIeEOYCLZSBtqWnZUoFTLklpGgvXqQtAwYMoBYD585RatbRFTmeGDhxxsmjcOBkA4uFmiM2bEhvdEeP0t6P3n4DIFbgZDYDr7xCy/jduZ2Tp6palZa+L19OQc7339PCnrfeohmEI0eo6Hz+fMpmtmpFU3ErVgBabenHHTeOMp79nk7GpJlxSEnVwmC0lPh+UaA1I19jxq9rrmLdjlNYtdpU5nRqQQG95rt1o33/CgqoVOTQIfpdW9uEul5kIH78tg1aN6sIXx8ZfEqqexIAtUqO6tX8MPWDxni6fxgACoQK+2t16HB/R/R7NW9OAZazbSDeeIPe32fOdO447C6ffgpldD1RtkcQTGYoK9xVtJeeTnccHTtSsVpCAqVSnbmTLSgAbt1yfqWE2Djj5FHKdeCk1ZmRcUOH1Gta5OQaHWrjv28f1aTMmUN9e1avtn//Lk8lVuC0cCEFAqNGOX+s8k6hoEBk2jTqm3T2LGVw/v6bslJHj9J7QZculNGpVYuymgcPlpxRadjyKtSVUmCwsqluIZPZgsDgPHz1/Zk7U3eFBKHo/GFhFOi9/jpNB37zDdUz2aN6NRVmf9oCKxY+jMFPhKFSsA8sZjmUChl8fRTw962CWZObY83iduj4cJVizw0NpSzS6NFUM7ZgQekZJWe6iN9NLqes6Vdf2b+VCyvByZPAokXwXTAPgmD99Sn39YHcz/e+jwvJlAr4VAqmHh8TJ9ILMjCQIusPPxSnsPLSJbrAe1ofFc44eRQvnWQqncUi4OjJbCz/4ypOnc2BUimDTAaYTAIqVfTF0KfC0Kd7dav9ZhITgQ8+oDesGTMoA+yOPeWkJEbgdO0aNX/cvbv8/XxcoWZNWtE1ciRlTWJjqY7ozz+pq3X37vQ+8cILFJy+9BLVONWsCSRd1eCHX5JgKeNNqU3zinh3bDRCQ/xw/kIeps+Ox/Wbely4lI+Vf17FsMERuHWLsktLl9L1eeRIyvaIddNdvZoKr42IwuOdo/DYY7SqcMoUKntp0aT058lkNCXZrRs1I/37bxpjSVnNfv0oU/Sek+1+IiKo+H7YMJpO9eR2Ih7NZKI7qc8/hyKyDmo+E4O05evLfEqXc1vgX4eyju02LwUA/FuvO7TJaZD5KFF7+EDI5syhC3JMDK24CA8Xd9yeOE0nCBw4eRiZ4PLdFKVz7kIuPvrsHDQFpjsbjd5L5SeHRQBeGFQbI4ZGQHbPvEZODm2nsXgxbQQ6blz5XSGWkEBL2xMSHD/G00/TqqZp08QbFyOFLQ82baLANCqKmj5euEBZmNoNLiDucgZKWL0NAAgOUmL1onb4Yu4FxB6+hdEvRKJF42CMeZ8aQfmrfVAR7bFtmwz9+lHA1LWrdAHwwYM0PXnoEL2nPvIIzazYwmikYGvJEprqfOKJ4l/XaimgSk6mpqTOEAQql2nShK4FzAFffEE7VW/bBshkSD58ECe7jYDSxszoveQ+SnSurEFA6+b0S7E3/WmrL76gO5ZZs6Q5viM0Guo7VVDg7pGw/ys3OYKDx7Lw5kenkJllKDVoAmhDUYPBghV/XMWnX8ffmb4zmWjKqUED+rs5c4aKRctr0AQ4n3HasIEyEx99JN6YWJHISGrv8PfftPz/888p2KhcGTh4yIzT8TdgtgBrFrfD0IFhWDanDbas6ogp4xvB10eGLu1DkJiiwc7YTBiMApauSEK9yACEh6kBABqNBdGNs5CYSBmn7t2lzRreuFHUd/DqVfuSBT4+VDi/Zg0FX2PG0PtJIbWapje3bnV+nDIZdX1furT0Tu2sDBcvUvpv0SKYBDPmH56PtrufQH5UEKCw/wUmA1BJLUPAql9p/yGpgibA8zb3Bbi+yQOVi8DpwqU8TJxxDnq97XczOr0Fuw9k4rtlidi2DWjZkuqXNm+mC6anbUwrhYoVKcPmSEfr/Hxa1bVwYbEFM0wiajW1v5g7lzJRX8/NhO9dJSDdOoVg3OQzGDz6EOrWCUCfHtURGe6PS4lF0YVOb0Fahg6R4VRgK5Obocc1pzM0trp5syhwSklxbCqwUycK1vV6KqI/fLjoa2LUORUKDaVi/eHDiwdozAqLhQrTPv4Y+xRpeGjRQ1gbtxb/vvgvnh7/FvwsJsjsiM5lAPyqVkSbhH30y5eaJ07V8TSdxykXgdMX8y5CZyVoatO8IpYvaIvtazthzvQWCA3xg05vwcrf0/DG2zp8+ik1MmzVykWD9gAKBf09OrKf2ief0LROjx6iD4tZIZMBPn4GAEWv+bUb0nAry4C8fBNiD2chOioQapUCmgJTsefma0zwVxcVvt7I1Llq2HcCJ0FwPHAC6OZ72TJaxdm/P00Tm0xU9rJ5c7GWQU4ZNIgyfB98IM7xHggLF8KgL8DwWocx9Peh+LDTh/h3+L9ouuko/MaPQ8c/5kIVURNylfXiMbmPEv7166Dj8Q3wqVzRBYOHZwZOnHHyOF4fOF1J1iD5atlzv8FBSkyf0ASLf01E36GxiL+Uh6njqcW3TC7gtbfTMGCAZ27EKzVHpuuOHaMVV199Jc2YmHUGg6VYv6Gs7KKGR3q9GWqVAlqdGQH+xVcHBfgrUKAteuKN3GysObcGe5L3ID4zHtnabIdWn9qiMHDKyqIspbM30YMH08rEPXuon5ReT9N/Yk6vzZ1LHd//+Ue8Y5ZXxsTL0E54H107XUKtiuGIez0OQ5oOgWzWLFpBsmsX1P1649Gj61Fvwlj4Vq0EReD9ewIpfJXwrVYZ0VPeRqdDf0BVo5prvoH8fGohHxbmmvPZijNOHsfrV9X9tj4Vxv8vq16zuB3+2JiGx7uFono1FQ4dz8L0b+KL1XoAwNIVSdi4vAPCw9RISdVi4/Z0jB0RCV8fr48j7VYYONWta9vjTSbq4/PFF96xH195YTTSSumzZ6m/3+FTClgsMsjkpQc5iSkF6N29aAmayk+OWjXUSEwpmnsyKzRYdW4Vrudfxw3NDVzXXIfOpEO1gGoIDQhFaGAo/Xv3x///t1pANfyPvfMOa+psw/h9skjYQ8DBciFDxVG31rr31moddVut1s9R96q21lW3ddXVOmu1rlpXrVucOFGcKKIyZM+Qcb4/ngYCJGRD1PO7Li6RJCcvAXLu8zz3ez9utm7gMfr93cTHU3isKdWmgpQrR76mVauoOlS/PnnvmjQxz/Gdnal1P3QoRRQ4F1Ph433j9LN/IOzcFZEty2Lb9KPwd/On0uLkyTTy5OLFXEEidLRH5WmjUGnyCMTt+xsxP66CNOIxYGMDm26dUaZvZ7i3amxQS88sPH1KOzCsbXswV3GyOt574XTh6rt8Hh2V1yMnR4l1i2ugXYvS8PPW7vWIis4CwwAPHqWiRtWP713R1ZXy3vRlzRoKPxw40HJr+phRKGi7fnh4nkgKD6cOgpcX+WKDg4FWzRxw5ByDnBztwul86Dt8PbgCmjYshdDrCRjcxxfPIjMQFU2pmizLwJ1fDVMD9qNatbyKa5YsK1dExabH5v77POk5QqNDc78WlxGHVGkqStmW0imyPO09ERfnAXd3HqKizLuLnMejHbAtWtD8vXPnqL3m5qb7sfrQqhW1BMeOpdEsHHm8SnmFiScnwuvQGcxRuKPJrnAwIlHeFdbDh1QSLPjDyMoCs2YNPBcvhmfXrsCE/sDevcCvJbibzRrbdABXcbJC3nvhpN52APK8HgByvR4CPoPkVFm++xX0eqSmmckY8Z5hSKsuKop2Nl2+/HG2Nc2JUkmvp0ocqf599Ihm2wUHk0hq354u2gMCyCCugmUdEXpPhDcx2j1KyakyzFwQjvEjK2P2hAA8eJyGOUse5N4uFDBwEpdD1655W/C7dAEaN5bA19kXvs6+Or+PHEUO4jPiC4msN2lvcCvmVr5KVty9v3Hl+DRIYpuCn1kVfff/rrWS5WnvCRFfpPP51alWjapC7u70+v32m/lmSC5eTBtI/vyTpnl87EjlUiwNXYplocswpdIgfHuKB+bvfZSXkZVFwXfZ2RRJYKfWjlMo6AczZw7NHjp/nvJMTp3Sb76OJbFW4cRVnKyO9144FaSg16OUqwipaTKdXo+PFX2FE8vSLrqxY2nAK4d+sCwN/VUXR/fv04W4szOd4IODKQrgm2+AoCAKQ9YFwzDo190bq7c8Q69hV/PdtmX3y9zPb9xJRr9R1zUeQ8hzwPffSbB2Na3p4EEKkHzxgozWXbuS8LArbEPJRcQXoZxjOZRzLKdzzb6rWfzx1U6sW8eC55uCZpXb54qth+8e5qtkxWfEw05kp1cly8POA7ZCyg2xtaUEdltb2tzVrRvFOKiLTmOws6PzfbduNOHjYxotlHrvEWL/+hfZb+IAlkWUIBHLBMfgUDsI14ZfQ4WR0yiZtXZt2mnSuTP1UPfuRe7WT5alKIHp06n69PvvFKGvQiSyDuHUsGHJrkETXMXJ6njvhZOthI8UWdHVIl1ej8xM4PgxAUQM7ar7mNKC9RVOBw7Q+8off1h+Te8jLEtz6FStNfU2m1ic12KrX59O6EFBpvtlWjfzxNY9LyGV5hg84FYk4sGnVHkEBVGw5IgRVLGZNYsylg4fBtaupZZs06Ykojp1omqYscTHMwjyc0d2ItCpkQf6Vtd+da9klUjKSipUyYrNiMWV6Cv0f7WvifiiXCEld+mMN2c6oe/Ph/HPqp7wr+aB71c/R+N69vC084S9yL5Q8K0+NGgADB5Mr9XBgx921VUplyNm/wk8W7IR6Y9fQJkjy51+rGRYTBCLYeeVDWHoZijCboG/bRv9AbRtS7EBK1fmeYUuXqTeaWoqle7aty/84lmLcLJGDwJXcbI63nvh1KReKfx9OqbILCJdXg+BAEiIc8TIkfS3ExJCb5Kqj3K6L6bfW1xdKW25KFJSqNK0a9fHJSq18e6dZoEE5LXYatSgMSHBwZYz0UvEfKxZUAPDJoQhI1NugHjiYcJXldCxtRPujqZ5dJs2UW5R/fpk3B7939eTkym5/NAhStGvWpXaeV27GtbVyMggcWlnp585nMfw4GbrBjdbNwS5BxV5X5ZlkSpNzRVSL6olYPj6SgBPjtrfLMHNEwEY/vkgODRfj+w68wFGmdsO1FXJchG75BNZ330H1K1LcwQHDdL/+3+fkKel43qXr5ASFg5FRuEp0zyWAbJykPHkBcKfRCKygjfqh0dA1KcXBV/NmkXC6P59qjDduUOZEf36aZ8BJxRah3Cy1ladKVcsHGbnvRdOn3fxwqlzcZAWEeVflNdDIGDQo2MZjBlKV0dpaTStPjSUsmJGjqSyv7qQqlkT+cIH32dcXWnkU1HMmEEXkp9+WjxrshZSUgq32MLDycKhqiBVrUp5P8HB1L4p7iqEV1kJfllaC1+MuA2BUAG5Qnv7mWF4EAmBnOQqOHfKAx1bA9Wrk81k507y7rRtS60t1fu0szPQty99SKXAmTNUbWnalG5Tiag6dYrejKSKImAYmN0czjAMnMROcBI7wd/NH018ge2NgPo5M9GtG4DOwItJwJdfTgP/9DSs35QJoWtMoUpWxLsInHt5LvdrcRlxyJJnwd3WPZ+gqj06BGPGj0BKmZOoWtkh9zZDdhhaK4psKUKbD0D6w6dQSnULGQUYpL+KwcVGn6PJgjEQThhHP+A5c2g33dSp1LLTlZJb0hWn1FSKI7DG5OO0NK7iZGV8ELPqhoy7icfP0o16rEjIw851dVDGU/MfNsvShUhoaN7H06dUUVAXU9b496YPR47Q7C9tictXr9KJMTycRNaHSHo68OBBYZGUnEwtNXWRFBxMFUhratOsWAHs26/EjO/isW1PFF69zoadHSBXsODzGPB4DLKzGbx5XhZnT5aFrdgGDRoA48fThYGK1FSqqGzfTv+OHKm9QKBUAjdukIg6dAhISiJrS5cu5NcqWJm8fp2Od/UqVZ3S06nIYClWraJCx+bNeV9TKGgSyLJldHufPrqPky3PJnN7AZF1dEs1PL1eHoETxyIuMwaxGbFIlabCTeJWuHqloaLlbucOAc/6rltvD5qMt38ehzJLqvF2RihEze0/wal2Vdj6eSG0xQAknr8GnoAPlwY1Ub92WcpvGDmSdjU4Oen3xBER9EYTEWHG70Y7mS+i8WLtDrzdewyylFRAoYAgRwrPwZ/Db+wgOATqmc9SHHTvTlcuPXuW9Eo4/uODEE6PnqZh9NTbOtPDCyK24aFbh3IYPbiCQY9LS6NRDyohdeUKeffUhVRIyPtRlbp0CZg0iXbKFUQmo40vU6bQ3+37TlYWmbILttliY2nXmkoYqUSSj4/1RboU5MkT+n27coVGbK1fD5w5n46BQ9KRnimH2IYPj1I2cHN0QXAwg5UrgVGjgGfPyIqyZQuNclHn/n1q06WmUvtOH7/skyckoA4epMe3bk3nwfbtqTL1998kVjZupONFR1vm9VDx7BmZuN+8KfwzDAujrlHt2hSvYYzXTKGgrKg+faiNDQAyhQzxmfGFRFbuv2qfJ2YlwsnGSS+R5WHnARuB5Xvk2THx+LdSc7BFVJoYoRC+o/oi5eZ91Nq9Arf6T0TieZp7wwOLxj3qwWHZIsOvJJ8/B1q2pH8tSPrjSNz7ejaSr94Bq1SCzcm/2xoCPnhCARyCKqPqmu/g/Ek1i65HL1q1ojdpc20R5TCZD0I4ATTkd4YB8+rENjw0bVAKMycEGGUUVYdlaa6lelXq+XNq6amLqdKlTXoai/DwIe0U0nSht3gxcPo0cPy4dVVYdCGV0s9D3X90/z6drCtXzl89Cg6mzDttlRVrRqGgllmvXjT4FqCfZc+eJAwKUqcO7fCLjiYxcfkyiZtTp0joq8OywO7d9H7dqhUFnuq7kyw2liqYhw4BZ88C9erR735GBjBhAhUiNAl1cxMURF6kOnUK35aZSev46y+6T9Omhh9fJVovXiThbQgKpQIJWQl6iaz4jHjYCm31Elme9p65OwwNgWVZHBg7EsymMxDJGTR7chov1+5EuQFdIfEpi/gTF3BnyJR87bvmkedwe+CkXOEEPg8+g3uh2rp5Bj8/oqPJYGdBRZ0UegvXOgyFPD0T+hgC+bZi1Ny1Ap4dmllsTXpRrx6VldV3IXKUKB+McAKA8EepmPFjODIyFcjK1uz1ENvwwLJAvx7eGPyFr8miSRupqYWrUk5OhatSlmxXFEVMXDb2/fUa/5yLR2ycHA4OgL2dAE3qu6FXJy/IpBLUqUOtFX1TxYsbVZp2wRbbixeAn1/hFlulSiX3eluC5ctpt+PZsySEZDLyET1+rNlLevAg0L8/sGcPDcQFaJfkhAn0O6pp0kRqKjBvHomLWbOAr7+mzRT6kpEBnDxJc+XCw0lA2dnRRgP10E1LMHkyxRDMnav9Pn//TbscBwyg79PQzQ9r15IX8vJlw14XQ2BZFknZSXqJrNj0WAh4Ar1FloPIAeHx4RhzdDS+Hh8Du/9ygps9OY2c+ETc6DEaymwpGp7bjcg1vyFq457cdRUSTgB4EjFax14FX2Lg5O+4OPpDjYszx0tWiLQHT3Gp0edQpBs2sZknEaPe35vh2vgTi6xLL4KC6A81OLjk1sCRjw9KOAGAUsni5p0k7PzzFW7fS4FAwIBhGMjlSri6iPBFN2+0a+EJO9vi9RcolYWrUpGRQK1a+cWUpfNhoqIz8dPaJ7gfkQKWBWTy/D9+gYABj2Egl9qjca3K+PF7PUKFLIx6mra6SFKlaauLo6pVKWfqQ9/99/gxtbxULTqAKh9jx1IrShNyOf1+lS9P/iQVixZRdenCBe1xMQ8eUI5XQgK1twwdaTJlCvlbo6Jo1mFiYsHQTfMLj/Pnycd182bR94uPB4YPp92lO3fSeUpfWBZo04Zej1mzTFuvOWBZFmk5aXqJrJi0GEgVUrBgUcnGG0vm2ID/3/Vmsyen8XjOSrzedRgAELBgEgSO9rg/ek7uc2kSTnw7W3wadhi2FQycqZOcTFc7ycmmvgQaOVutPTIePddaadLm3QIAoZsLWkZfBM9SylgXXl6kzM25o4LDJKzPnWgiPB6DOjVdUaemK7KyFUhJlUEuZ+FgL4Cjg8BiFSbd66JyfkAAZcEAtGtLVZXasIG+7uKSX0hVr26+KsndByn49rt7yMpWaK1Uy+UsABYsk4prD2/h+q1g1KlZPK7wgmnaKpGkKU170iR6LW0N70q89ygU9Lsye3aeaAJoZltRNgiBgMTPsmX0e1e3Ln198mTyBPXuTflNms4PQUHUtt27l0KhmzenVq6+7ef4eGqTvn1LUTljxgD37uXFHLx8qX/opr40bEjVx9evi44UcXenyt3mzdSymz2b1qfPWwXDkE+sVi1af61apq/bFBiGgaONIxxtHFHZTfPWeiWrxG93fsO009PQtlJbjP5kNHJi4pH4/SRAkef5yY6Jz/1ckZkFcVndW+IZPg+yVCM26lgwjiD5+l1kR73R2Z5LvByGyNW/odbuFfm+rszJQdzRsyjdpaVF1qcTbled1WHl1lfTkIj5KO0hhldZCZwchSUmmrTh5ET+kdmzgWPH6Gr+6FGauXXnDkWiuLjQm/nUqXSSMbaS/fxlBibOuYvMLO2iSR2GoZl+0+aH4+HjVOOeVAssS1aGEyeApUuBIUPoJO7kRJWHNWvo+2zWjMzOsbFUnfvrL9oqP2AAnaA+RtEEULagSgSpc/IkVT+K4quvSHgtWpT3NYYhE7hCQenl2n4/GIbE1cOHJJiqViXrhVyPaUWqOAJVhhPD0EXBrFlUEQoLIy/S2rVAmTIUtrl5s2mdG4GA4hWOHtV9X4ahlt3ly8COHWSYf/tWv+fx8iIx+uWXNGXEmgl7G4bGWxpj3Y11ONznMLZ22YpPyn2COpUag6c0vfnAKpUQ2BkR027BOILnK7ZCkU27BJs9OY0K44egSdhhtH53AzV3LgfPRgRWJsOLVb8i6dJNsIr8PllFWgae/fSLRdamE5alLaj6jBPgKDY+aOH0vsHj0dimIUOAX36hikt0NGXIicV0UvH3J89R//50sgsL033iYlkWM34M17nrsHZ1Z+xcVwf/7GuMVfND4OlukyuelEa8qbIsEBNDlYpVqyhxuVEjEoOffEIVi6go8oSuWEGJ1dHRZEZfupSqKnXrcu8Z6jx6BPz4I1U51HeLJSSQoNG1A65sWdq8dOJE/g1MQiHZKC5dote+KBwc6Gd34QKJ2Zo1qS1WFOrCSVPHwceHhOCpU3SfL76gNfr7k5hesoRas4bSsaP2qA1NVK5MLc/69en7OnBAv8f160cVUGto12kiITMBo/4ahfY722NozaEIHRqKOuXyXPMCR3vwbPTbBswTCXPvq/45ALAyOWzKuBu+QIGAlHtRScZGwCqViDlwKt+9RkgeAAAgAElEQVRxy/Rqh2sdhuFM5RZwrFYFXgN1Dx9MvfUA0ng9h3qak4wMevMvqTYhh0a4n4aV4+xMVQRVJUGppBOkyif1888kOGrXzt/ic1d777ofkYr4RGmRlSYnRwHmTw/GotWPcOlaAob1L495k4Pw1aRbyMhU4NqtJNSvrb1ll5BQuMUWHk7iSeU/Ko407Q8ZVYtuzpzChv1//qGAUn28XWPHkjdq+XJg9eq8rzs6UnWmYUPyQfXoUfRxVLNZ9+0jId+kCQkcTTvR4+Op3frqlW6rhq7QTZUvSlfoJkB/N199RVEU+s6rEwopx6pNG6pu/vUXCfuixoUxDLBuHW346NzZcA+YpVAoFdgUtgmzz87G50Gf4+Hoh3CRuBS6H8Mw8B7cEy/X7wYrk2k4Uh5Nw4/D1o92EtQ7tgUA8G+l5sh69RYeHZtBYG9En5VhqOokk5nVoChPTQcKNBperNkO6VsqZcYePQPHkECdx+GJhMiJS4CNezGH2aWmcnPqrBBOOL1n8Hh52+iHDaOvJSXR7rfQUGpzDRhAwqlBAzoJ3ouMzo1p+GNTPfx59DXaNPNEaQ8xroYlYv7yCDRt4I7IqAycufQOALBl1wsc3dkQPl4SREVnYdf+V6hf27VQmrbqc/U07eBgymwrqTTtD5UVK+jcMnp04dv0adOpaN6cqni//ko7ztSDTb29yefUujX5gurXL/pYDENxCO3a0c656tWBadNInKl78+LjSbhkZdGMV32xsaF2W9u2VHG9fp1a1oMHk4+4c2cSUs2aaT7furpS5ejMGfLGGUKDBpSqP348HWP79qJ3hLu7U2t54EBqtZf0+S70VSjGHBsDW6EtTvY/iZDSIUXev/yYLxH1y+9gZcCZyi3y3fbk+zW5nxe8TQXfVoIK44cav2BVu86MwkmZIwPD40H9mtEY7xYYRq8kdbPD+ZusEk44fQC4uOSdXACqTKiqUpcvK/E4PiGfeGnW2B0Tv7uHnBwl1i2ugXYtSsPP2xZPI/O26mZLlXgdk43yPnaIis5C2N0U+PjKkZggyJem3b69daZpf2g8egQsWECm7oJVFpalttbkyfodi8cj8bV2LVVJZszIf3vNmrTFvls3at1V0CMf1t6e1jdoEImmLVuoGvrZZ1Q1kkppM4TK32QMPB5F2tSrR+3Kx49JRP3wAwVRFgzdVKFq1xkqnAASP5s2UcuuWzeqXs2cqX3DRufOVB379lva8FESxKbHYurpqTj57CQWt1yMvtX66uXvtK3gDddGtZFw4XrhYEgdMHw+bCv6wLludWOXbbLPKUuWhccJjxHxLgIR7yLwKOERnsQ8xGxpNngFy04GwioUEDqXgIDhBvxaJZzH6QOEzydhM3w4sGy5HDai/G8a+468RkJiDtLS5bh0LRGVK9hDIuYjIzO/WSo9Qw5bCSVDCoUM9u6T5eZTbdlCu6HatCFzLCeaLIeqRffdd5pFzMOHZIHw99f/mIMGkfF61SrNhuYOHUggdOhAFU19qVKFPGrff0+Vly++AO7epdZsdLTu4b6G4O9PuysvXiRh2aYNxSr4+NCmizVrqDWoEk6mBK9060bVp6tXqQ1XlN9qxQoSsseOGf98xiBXyrHq6ipUXVcVbhI3PBz9EP2q9zNoU0zNXcth4+EGRmBAIizDQODkgDqHNpi2AUcP4cSyLGLTY3HuxTlsuLEB44+PR7ud7VB+ZXm4LHJB3z/7Yu+DvchR5KBdpXZY23Uj7Cr66vX0RXm3eCIhxD5ljP/ejCUtreRLlxyF4CpOHzg5MiUKzh1NTMp7c5JKFSjlKkJqmgx2tvnfLO1s+cjMomAXoZCBh4fS6keQfIgsX07di6+/1nz7iRMkGgw5Z7m5UTv12jXKLhqqocMyejTFFHTvTs+h7wghhqHHtGlDlaFWrWgH5PPnloui8fSk72Ho0LzQzYMHyQ/m60sbk/bvJ9+Wsef2MmVIDK1ZQy3wBQvo+Qoez9ER2LqVWuZ37xbPjMdzL85hzLEx8LTzxLlB5xDkbkAYlRoiV2c0urgXoS36I/t1LJTZmmfWqWBEQohcnVH/9HZIvE0UFmrCSaaQ4VnSs3zVI9XnPIaHwFKBCCgVgIBSAWhRoQUCSgXAz9lP4/y/V5O/Qvj4+VBkZBb59Nq8W9LYePiOHlAyOU5cxckq+eACMDnyk5kpR7u+l6FQ0I/5j031sGj1I9y4Q0FzQ77wRbkyEtwJT0Hb5p74esptAJSw/tfOhhgy7iaiorNgI+Jhz8a6cHf7wJMlrYyICKpwXLtGhm1NtGtHfjddZu6ChIbSeBZHR/KqaRLFCgV5mFSeKGNEx9atVBkCqF3222+GH8NY5HKqSI0fT5lOKnN51660w9PYc2F4OBnifX1pB6y7ho1k48ZRlMbu3SZ9C0XyOvU1Jp2ahItRF7GszTL0COxhltgVeVo6nq/YihdrdkApk0GRlj9xm29nC4bPg8/w3qj47TCIShmnDpOyknJFUccO4zHr29o4I4zGi+QX8HL0yhVH6h+lbA3bWaLIzMKpsg2gyMgyao08GxGaPTkNcRk9vFDmZvt2umrZsaP4n5tDK1zF6QNHIuGjlKsIsfFFXzmeD32HrwdXQNOGpRB6PQGD+/jiWWQGoqLpzUYi5sPV+T2YWvwBoWrRzZ2rXTRlZ5MwMObkXL8+tdAyM2n0iGoMizp8Pr1nf/YZjSSZM6fwfXQhEpH/KDaWdu19/jnlHmka8WJuBAJa++LFtPb166kSNWGCaaGbwcG0M3H2bNpJt3lz4WHJCxZQ3tjvv1P+lTnJUeRgxZUVWHxpMUZ+MhK/dPoFdiIzpIb+h8DBHv6zvkGlaaMQ9/dZvNn9F6Sx78DKZBCFXkSZb4ag9Mzx4OsRYaBQKhCVEpVbMVKvIGXIMnIFUQehEN0qdsSY+q1QybWS2QYb820lqDRjNJ7+8DMUmYaJJ56tBN5fdisZ0QRwFScrhRNOHzgMw6BfD2+s3fq8yByn5FQZZi4Ix/iRlTF7QgAePE7DnCUPAAA2Ih56dS4HPp8zMhUny5bRTrSRI7Xf58IFmvmmbobWF4YBRo2ik/5PP2kWTgC12Y4cIaFVoQK1oAxBleEUG0um8+vXKZpi0iSqBOnbAjSFTz+lKlGZMiR2Zs+mvKjDh8nEPnAgCawuXSh8U9Osv4LY2FCQaLt29PhOnUigqYJZJRKqrnXsSFXDsmUBaY4S5y7H42lkOpJSZLCzFcCrjAQtPnWHi5N+L8TJZycx9thYVHStiCvDrqCSayXdDzISnkCA0p1bonRntdRse3uAyQAKiKaMnIx85uyIBPr3ScITuNm6kUByC0B1z+r4PPhzBJQKQFmHsnkVsrk10NrnM8DD/DPZKn47DBmPI/F27996iye+rRgujWsjeMVMs69HbziPk1XCteo+AjIz5eg0IBTSHOPC5URCBvu31tf7jZ3DdB4+pJPt9evaq00AiQ97e+MqQQC9L/v4ULXlwAHKRtLGgwckLvbupX/1ZcaMPBFx5AgZyJ8+pVbW06eUJdWqlXHrN4QePUgYffll4duSksi/dPAg+aOqVqX7du1KoZi6SE4mT1hYGHnG1EevzJkDXL2ejQ7dX+PISYojV3kHAbowYVkWDT5xQ7+e3gjy11xheJn8EhNOTsCtt7ewsu1KdKrSyaDv3xywLAt5oD/S3BywZ8WwfNWjuIw4VHatXKi15u/mD3uRHim2deuSgUw1C8gCa380ezkiV2wDq1CAlWlPDuaJhCjbpyOqbfih5GbUAZR+bGdXeOsrR4nCCaePhM07X2D3gVc608MLIrbhoVPrMvjfCMtd1XLkRy4n/82gQVQRKorq1YGNG3XnLRXF11+TcdvJidpKRXH6NAVTnj1LAZj6MHw4BbSOH09Bqeqjco4cAf73PxIay5ZZdo7ptm3UKvzjj6LvJ5UC//5LUQeHDlHch76hm7t30/czYQKJWj4fuBaWhPGzwsHnK6Es4u2WYUhEDerji349vHMrMdnybCy5tAQrrq7AuHrjMKnRJIgFYiNeAf2RyqX5zNnqH7/tyUHjlyymbx+YTyD5OvmCzzNgN15BGjemmUqNG5vvG9FA5vNXiPx5O15t+h1MZhbgYA8WVJ1XyuQoW78qKkSHwyH8VslvF/7mG1LuY8eW7Do48sEJp48ElmXx3ZKHuHQtQW/xZGPDQ61qzlg4syrXpitGFi2iqsepU0WfpN++Ja9NXJxpExnu3qUMsJwc3RUugATIvHnk8dGnpaUSHZMmAe/eFb49K4taXKtXk+CYONGsGYi5xMZStSsuTv/2oFJJr8nBgySi9AndjIqi1p1CAUyblYyfNtzLDaDVB7END/16+mBwH18ceXQE406MQ43SNbC09VL4OfvpfRx9SMhMyC+M/muvvUp5BV9n39z2mkocVSlVBa4bt9MPKT2dxoGYi+bNKQOjeXPzHbMIFL/vRdLydciZMAmsUgmRqxNcGtSk5PPatamf27VrsaxFKwMH0i/aoEEluw6OfHDC6SNCqWSxevMzHD7+FnKFEgqF5vvxeIBQwEPLph6YNNofAk40FRsPHtBokevXAT+/ou/766+UT6SrgqIPjRrRln4vL8p20sXs2STuzpzRPcqkUSMyua9dS60sbTx/Tu27iAhagyrQ1ZzUr08J5y00h1/rRBW6efAgeaa0hW4qlcCPC3Nw9OI18Pla/tBA8yEnjKoMT3cbPHiUhvkrIhAbL4VIxEDY4A+8FJ/Cqnar0Lpia+MWDDJnv0h+oVEg5Shycrf2V3GrkiuQKrpWhIivRV2eOUPGrfPnSWCYi7Zt6RfAEj94TcycSW928+YVvu3wYfolDwvTPdfHknTvTnOqDN0yy2FROOH0ERIVnYk/Dkfj2L+x4PMZsCzAAuAxgFzOouWnHujd1QsVfM23S4dDN3I55QMNGVK0IVxF3750MTp8uOnPvX07JWTfu0e+I13ZQyxLJvHsbPI8FXVu8fenjsM//5Do0MXRo9SZCAmhDCtf/fIL9eKHH6hduHy56ceKjSXhevAgcO4cJZp36UIf3t7UHt++7xXkcs3VJidHAX7fWC/ffMiQICd8NekW3V46HQfWt9IuYAqQJk3Ll3ek8h89TXwKTztPjVv7Pe08DY8viIkhVf/zz5oDwIylc2fK1ejc2XzHLIp27egPrUuXwrexLPVlp00rWdHSsiUwZUrxmAA59IYTTh8xWdkK3AlPQWqaDCwLODoIEBLkBFtbbrNlSbBwIYmLU6d0WyuUSqoQ3bhhHmGRnU0n+6ZNyW80fbrux0ilVHGpW5eG+2rDxYXGwbx5k3+osK71LFkCrFxJRYhvvzVPV+j2bcquevLEvPaV9HSqwB06RGLKz4+Fg/dlyBVyrfMh2zYvjXYtPDFqcl522tGdDTFYLTtt84pa8PPOu4BhWRav015r9B4lZSfB382/UPXI380ftkJbbUs3HJYlo1rfvrQl01z06EFR8z17mu+Y2mBZ+gMKC9Oei3H0KDB1Kg0eLKmqU7169EdgiomRw+xwZ8iPGImYj/q1i3naN4dGwsOBpUtJCOlzQr91i9K/zVWNEYvJRvHuXZ7XSJdQsbGhnXgNGgAVK2qukslkJCrevTNs3IpYDMyaRVWt8eNpl9uqVcbNm1MnJIRE2ePH5HcyF/b21FXp3p0qh79sS8Duw3nXpMbMh5TJlVj063mUbXIjt7X26N0jONg45PMedfLvhIBSAfB28gav4JgAS8AwZIS7ft28xzVxVp1BREeTGCpXTvt92renNt6+fRQ+VhJwOU5WCSecODhKGLmcRMsPP+gvhE6epJEm5mTECPIj1aqlfQxLQVxdKTyzcWNae8EQyHfv6D6vXhUddaANPz8SZ8ePU7tvwwaaBafLwK4NhsmbXWdO4aSOQADYO2WCYRTUA0fefEgAufMhBXwGyan5h+mqz4dUKoGHz1Lg0iALbSq2wdi6Y1GlVBU4i40I7TI3NWoAf/5JizRXNUYkIqVdHNy4Qf6soq5SGIbSZydMoGoY34Qdg8bC5ThZJdzkMQ6OEmbJEjIWjxih/2NU8+nMSeXKdD6sVYsCMZV6bgSrWJHmwA0cSF0NdVThl69emRY10LYtcP8+tQU/+YTOZ1nGTdDIFU6WJCNTDoXa61dwPqREzEdWtqLI+ZAA4CWpiB9b/IgvQ75EPa961iGaAPpFsbEhQ5y5KM6K082b+hnb27ShP869ey2/Jk1wFSerhBNOHBwlyP37lF+0aZP+npu0NHrfb9rU/OsZOZKMzhIJVZL0pWFD8gp37EhdEBUq4RQVZVirThM2NpQDGBZGEQpVqxongJo3p9cvOdm09RSFrYSvsxATGZWJin55wZBiGx7KlZEgMiqvfScRl0CVQx8CA6mfevu2+Y5pjcJJVXWaOxdatyFbCqWSJlbb6xEeylGscMKJg6OEkMtpm/78+YZ5lc6epcqLIbPV9KVzZxqG27s3VZ0MoVcvYMwYEk9pafS1+HjyYsXH07gTc+DrSxWudesoTqhTJ4oy0BdbWxrBcuKEedajiXJlJBDbFC16zoe+QwVfOzRtWAoiIVNoPiSPB/h6m9HUbU4CAkjk3LplvmMWl3BiWRJOn3yi3/1btiT1b8lpzZrIyKArmJJoEXIUCSecODhKiMWLaceZoXECJ07QbjZLIBDQeiIj6cNQ/+/kySTqevcmYRgfT+/9ZcqYFtKpidatqfLUqBE955w5+rfvOnYE/jyUgQN/v8Gvv7/Ezv1ROHY6BimppntslEpAmlZK51pU8yFHDCiPY7sbIaiKY+58SAAQCXno2bEI83JJUr48ndhv3jTfMYtLOL16Raq0bFn97q9edZJrH9Nidjh/k9XCxRFwcJQA9+9TBtPNm4Z7f/z9yXJRo4Zl1hYdTaNcJk+mgoKuMSwFkclImFSoAJQqRcd78gS4eNEy6wXoXDhxIgm9FSuocqap9SmTKXH+yjts3fUKz19mQiJhIZOxYHgkVBQKFo3rlcIX3bwQqGVmnDbi44GtW8nA7uAAlK7wFKnSN+DxjHuLLVdWhN83NDDqscVClSoUiqUpDt4YZs0i8TRrlnmOp40DB6g3fvSo/o9hWfqDHTyYzHzFQUQEZUw9elQ8z8ehN1zFiYOjmJHJaBfdjz8aLpoiI8kvWr26RZYGgGJtmjalltbp0/SchiAUUpr5pUuUS8Wylp1BB5B/au9e4JdfKC+wQ4fCvuWklBwMHX8TC1c9wovodPD4SkhzWChZsq9kZSuRI2Nx5lI8xky/g5W/PIVSWbToYVkK0O7bl8z1ERG0+08qBXLSykFsY1xYFE8gx03Hn7Dr3i5Y7bVttWr0jcbEmOd4QmHxVJz09Tepo6o6zZtXfDv/uIqT1cIJJw6OYmbxYvL9DBtm+GNPnKAQYUvn8Y0aRdWToUONS9l2dKQL+jt3SMCYagzXl5YtqX332WeUGThrFpCZCaSkyjB0XBiiojORlV30dkGWBaRSJY6ceIsfVz7SKFySkiiXMDiYDPX169P3++YNea8WLwZOn5Rg5oQqsBEZ9sMS2/DQobkXdv1vChZdWoQOuzrgZfJLg45RLAQFUUnRXD6n4mrVGSOcALqa8POjmP3igNtRZ7VwwomDoxi5d49aSb/8YlxytSXymzTRsiVd8DZpAuzYASQmGn4Mb2/afHXtWvFuSBKJqM14+za1CIOCWAz93z0kJedAbsA6sqVKnL0Uj98P0TZBlgWuXqVuTfnyNOR4/Xoy6z98CHTrRpmJ9++TYZ1hgOaNPTDlG3/YiHh6/bzFNjy0aeaJb7/2R12vurgx/AYa+zRG7Y21serqKiiUxbyzqygCAujFNtfOuuLIcTLUGF6QuXOB778vHoGXmspVnKwUTjhxcBQTqhbdggXGta5kMuDff4tnbBWPB3z1FYUmd+lCAsEYsrLIk7V5s2E738yBlxewZw8w5/tUvI3LgEyuveVVu7ozdq6rg3/2Ncaq+SHwdLcBQOJp6+4orFunRK1aNBEkIICSx7dtIyEVFERRCRERNGNPKMx/7NafeWLtohpoXM8NIiEDG1F+BcXj0WOk9pGYOq4SJo32B/+/wdpCvhDTm0zHpSGXsP/hfjTa0gj34+6b9XUymsBAKue9TxUnQ43hBWncmHqyv/5q3nVpIi2NqzhZKZxw4uAoJhYtol3Nxs5FvXaNKh2enuZdlzYGDaIBtsOG0RiW7GzDjxEfT9WmkSPJd5SUZPZl6uTRy1fg8bW355wcBZg/PRibdkSi/ReXEPE0DfMmB+XenprK4sjxBCxcSG3HyZPJ1xQYCFy4QF6uFSuKHoxcpZIDFsyoiv1b6uPLz33hU9oNYoEj6tVyQfcOZbF5+Sdwab8Lr+yOa358qSo4M/AMhtYciua/NsfsM7ORLTfiB2JOqlQB4uLeL+GkatOZMqhw7lyK+bf0WrmKk9XCCScOjmLg7l3yxBjbogMskxZeFO7utDvu2jWgZk0aw2IICgUJpTdvgEmTaBxL9+7Fl3EIkCH8algiWBb4Y1M9fNHNC9tW1cbxPY0wd3IgREIGTRu4IzIqA2cuvUOOjMWWXS9QqbwdfLwkAAC+QIGyFV+hTRvSCE2bUrfml1+Aw4cNG93i4izCwN6+qBtcFUHeNbF0bnWMG1EZFXztMLXxVCy6tAhKVrPI4zE8DK89HLdH3sb9uPuouaEmLkZZcKuiLmxtgdKlgdev84K7TKE4hZMpNGhAZcYtW8yzJm1wFSerhRNOHBwWRtWiW7jQNJO0JfObtDFqFLXpJk6kIcT6jmEByBfl6EgCysWFRsu4uFAFq7g2ir2IyoRImPc2pxq222vYVVT0s0O7FqVR3kf7sF0VkS8zMHAgeZe+/JLSy1u0MH5dqamAk1P+rzXzawZHG0ccjDhY5GPLOpTFn73/xPzm89F7X298ffRrpGSnGL8YUwgKop5owVk7xlBcwslYf5M6c+dScq1UavqxtMFVnKwWTjhxcFiYhQupvTZkiPHHSEwkA3KjRuZblz40bEjnM4AmbBgyhiU+nsZ8eXtTlY3PJ6N5RATt6i4OMjLzBxaqhu2mpctzh+1KxPxC91MftguQmCpXjiJ1hg0zPcw5JaVwMYFhGExrPA0LLy7UK4Kge2B3hH8dDoVSgarrquJQxCHTFmUMgYHUozSHQdzScQQsmzfc11Tq1gVCQigPylJwFSerhRNOHBwW5O5d8geZ0qIDKA+pSRMyIRcnDJNXdZo0ybAxLPHx1M1RN8Lb2gJHjpCxujh2ddsUGHti7LBdoZDBjz+arwCgqeIEAF0CuiBVmoozL87odRxnsTM2dNqAHd12YNKpSej1Ry/EpJspV0kfAgLIbG0On5OlK06vXpHiNdYYXpC5cymMzdhp07rgKk5WCyecODgshKpFt2gRdTNMobhiCDTRvz8Jt0aNDBvDEh9P58KC7UlPT8o8mjiRtvJbEs9SNpAriq7e6DNs19VZZNZ1aao4AeRjmtJoChZeXGjQ8Zr6NcWdkXfg7+qP6uuqY3PY5uIJzgwMpBO8OSpOlo4jMIcxXJ3atantt3GjeY5XEK7iZLVwwomDw0IsWEAz2gYNMu04LFv8xnB1HB2Bzz8HfvsNGDdO/6pTfDz9qyl6ISiIZqb27k0tSEvh42WLMh7iIu+ja9iujYiHbu3NVKX4D20VJwDoV70fHr57iJtvDJsDJxFKML/FfJwacArrb65Hi99a4GniU90PNIWAAJqp8/Ch6aLH0hUnc/mb1PnuO7oyysw073EBruJkxXDCiYPDAty+DaxZQxejpl7gRkRQN8Tf3zxrM4aRI+l7GTxY/zEs8fE0E1VbZlWLFnTO6dCBdrVbin49vSG20W5K0jVsl2WBTq3LmHVNRYVCi/giTGwwEQsvGVZ1UhFSOgRXhl5BJ/9OqL+pPhZdXASZwkKVHHd3mt5crpzpCtjSwslc/iZ1atak2Hhjg86Kgqs4WS2ccOLgMDM5OVRlWryYziemoqo2mavDYAw1a5I15Px5MkfrM4YlPp7sH0XtJBw0iFqBnTtb0CoS74GMDKDXsKu4cSc59+tbdr/E98siAAA37iSj36jraNHzIr6ZfgcxcbRbSihk0KS+G5ydhBqPbSwpKdorTgAwrNYwnH1xFo8THht1fD6Pj/ENxuP68Ov498W/qLuprsEVLL0JDCR1bKrPyZLCSZUYbm7hBFDVackSICND510Ngqs4WS2ccOLgMDM//kieJnMNUS+JGAJNqEziY8fqN4YlPp7e+3WlpM+dC1SqBAwYYFjcgS4SEmj47uzZPHwzuJrBM+NYRg5XFwEmjTZ/qU/XGDJ7kT1G1xmNJZeWmPQ85V3K43i/45hQfwLa72qPSScnIVNm5rZSQAB9M9YsnMxtDFenenXaubF2rXmPy1WcrBZOOHFwmJHbt+n9c8MG81SIsrOBixdNywwyF59/TmGYUql+Y1ji4ykAU5cxnmFoJEt8PDBlinnWeuQInc88Pel83vdzJyyYEUziSQ/PtFDIQOKUjVc1ZoEVmL8Ups0crs43db/B/of78Tr1tUnPxTAMBoQMwL1R9/Am/Q2qrauGf57/Y9Ix8xEYSBUdUw3ilhRO5jaGF2TOHDL/paeb75hcxclq4YQTB4eZyMmhKtOSJeZp0QEkmqpVo+DIkkYiofDHjRtpR5yuMSxv3gD29vQ4XdjYAAcOUBK3KXaR5GRq/40bR+bz5cspAiEhAdi+1RUR12uhdClXCIUMRBoqUBIxD7YSPnp0LIdD61ujum95dN/bHVK5+YIOlUrq6ug6J7rZumFgyEAsv6JHX1QPPOw8sLP7TqxutxpDDw/F4EODkZCZYPqBAwPphb9927RkU0vmOFnCGK5OcDDQvDkZG82BUkmGc3t73fflKHY44cTBYSbmzyc/z5dfmu+YJ09aR5tOxVdf0aSJypWBGjWKHsMSF2dYDIOrK/2VgjwAACAASURBVAVszp0LHDtm+NqOHyeRaWdHQdaffkrn4eXLqZvEssCdMDvs21YN+zbXR7/u3qhSyR7lyojh6yVBzWrOmDTaH3/tbIgxQyrCzk6I9R3Xw0HkgP4H+kOhVOhehB6kpZGY0ydEc0KDCdhyawsSs3T0RQ2gfeX2uD/qPhxFjqi6rir23N9jWnRBQADw7Bmd5F+8MP44lqw4WcIYXpDZs4Fly6hSZCrp6fr/knAUPywHB4fJhIWxrLs7y75+bd7jVq/Ospcvm/eYptK8Ocvu2sWyp0+zbGAgyyoUhe+jULAsn8+ynTsbfvxLl+i1vH1bv/unpLDssGEs6+vLsv/8Q19TKln2wAGWrVSJZdu1Y9nwcMPXoSJLlsU2/7U5O+LwCFapVBp/oP+IimLZcuX0v/+gg4PY7899b/LzaiL0VSgb/HMw23FXRzYqOcq4gygULCuRsGybNiy7f7/xi3n9mmXLlDH+8dpQKlm2VCmWjY42/7EL0q8fy/7wg+nHiY62zGvBYRa4ihMHh4modtH99JN5vadv3wJRUUCdOuY7pjlQmcSbNdM+hiU5mTovfn6GH79hQ+Dnn2nAcHR00fc9fZq8TACltLdoQR2jFi2AGTOoc/L335QbZSxigRgHex9EWEwYZv470/gD/Yc+/iZ1JjecjNXXVpvf1A2gvld9hH0Vhnrl6qHWxlr4+drPWocMa0WVlVGunGk+J0tVnCxpDC/I7NnAihX0QzYFXbsHOEoUTjhxcJjIDz/QzrEBA8x73FOnSAAIBOY9rql06QI8eQI8eAB8+63mQMz4eBJVunbUaaNXL2DMGBJPaWmFb09PB0aPJsG6fj2NtMnIAIYOBdq2JSP7nTvmCw11sHHA333/xv6H+7EsdJlJxyoq/FITge6BaOTdCJvDNpv0vNoQ8UWY+elMXBh8AXvC96DxlsYIjws37CCBgdQjNWVnnaWEk8rfVBx5Hv7+QPv2wMqVph0nLY0zhlsxnHDi4DCBsDA6cZtrF5061hJDUBChkATKhg0kcDSNYYmPp0JEURlOupg8mWap9u5NQZoqzp+n+aoZGcC9e0DTpuQvq1YNcHOjQbwjR5pfcLrbuePkgJNYcWUFfr39q9HHMaaYMKXRFPwU+pPlgiwBBJQKwLlB5zCg+gB89utn+O7sd/qb4gMDKTncGitOxeFvUmfWLGDVKiq7GgtXcbJqOOHEwWEkqhbd0qXm7wIolVRxKqkxK7oYPpyM4Tk5msewxMfT92BsxQkgIfrzz4BCAXzzDQmlceOAL74gw/fWrdSGCwigQsfVqxQ6akg1x1B8nHxwov8JTPlnCg4/OmzUMXSFX2qinlc9VHSpiD339xj1nPrCY3gYVWcUbn11C7dibqHmhpq4/Oqy7gcGBAAxMVQpeffOuCdX7aoz94w9SwVfaqNSJUp01SclVhtcxcmq4YQTB4cW4t5J8fc/Mdhz8BV+PxiN4//GIik574r4++/Jw9O/v/mf+/Zt2mXm62v+Y5sDHx8a+rt7NyWJFxzDEh9PeU+mCCeAzqV//EG7C/38aKfe3bs06aNBA9rEtH07sG8fULGiac+lL4HugTjyxREMPTwU516cM/jxxhYTpjaeioWXFhruQTICL0cvHOx9EPOazUPPvT0x5u8xSJUWsVssMJBmA4WEGN+u4/OpTKkwz+5FAJZNDC+KmTNJ9etKidUGV3GyajjhxMGhBsuyuH47CeNn3UWfEVexfMMTrP81Eut/fY6l6x6jx5ArmD7/PvYdSMGGDaxFWnSA9bbp1FGZxB0cSDytWJF329u3JJw8PU17juxs8pClpVEF69NPyfvUqxc9/7Vr9LXipk65OtjdYzd6/dELt94aJhSMqTgBQKsKrSAWiPHX478Mf7ARMAyDnkE9Ef51OLLl2ai6tqr2565cmZRzSIhp7TpzZzkVpzFcnQoVgG7dSNkbA1dxsmo44cTB8R/Z2QpM/O4eps+/j+u3k5AjY5GVrYRczkImp89zZCwuXE3A8k130eHzCLi7W+bq/+RJ623TqWjThoIlr1+nMSzbt+ddYL94QeLAlBia69eBWrWA58+BK1eArl2Br78mD/KjRxQ2yivBd7CWFVpiXYd1aL+rvUEz5YwtJjAMg6mNpmLBxQWm5S4ZiIvEBZs6b8K2rtsw7vg49NnXB7HpsfnvJBZTaFeZMtZlEC9OY3hBZswA1q0zrnXJVZysGk44cXAAyJEp8c30O7h9PwVZ2UWLIZYFGJ4Sr2LeYcr396FQmPcklp5OftamTc16WLPD41Eg5rp1dEGvPobl1StqpxmDVEqdjo4d6d82bYDGjcl7vHUrcPQoEBur+zjFQY+gHpj32Ty02dFG79EohsYRqNM9sDveZb7DhagLxh3ABJqXb457o+7Bz9kP1dZVw7bb2/ILuMBAioA31SAuM6MBvriN4er4+VFpdOlSwx/LVZysGk44cXAAmL8iAs9fZiAnR/8KUrZUiTvhKVi79blZ13L2LGU3vQ/TFoYMoVEpSUl5Y1ikUvIJGzN25tYt+t7v3qUMpsWLgd9+Aw4don8HDiQx1aEDPac1MLz2cIysPRKtd7TWK+Hb0DgCdfg8PiY3nIwFFxcYdwATkQglWNhyIU70P4HV11aj1fZWeJb4jG4MCCAH/4sXNC7EGCxRcSop4QQA06fTjKL4eMMex1WcrBpOOHF89MTEZeNCaAKkOkRT7erO2LmuDv7Z1xir5ofA090G2VIlDhx7g7R0eZGPNYQTJ6y/TafCwwNo145ETdWqNIZlxw5q2RlibJfJaNRKmzZ5ZvspU0gknT+fPwR09Gh6zu7dLTehw1AmN5qM9pXao/3O9kjPKXrQqykVJwD4MuRL3I29i9sxJg7VNYGaZWri6rCraFupLeptqoefLv8ERRV/CvgKCKCcCGMwp3AqKWO4Oj4+QJ8+NMDSELiKk1XDCSeOj54//3qt0zPi5CjA/OnB2LQjEu2/uISIp2mYN5niqHkMcOx0jNnW8z4Yw9UZORLYuCkba7Y8g437NWzeFwqv4FDEZN7A3kPROkXl/ftA/frAhQu0i3vxYmrNPXgA9Oyp2Z6yZAkNPh42zPy7142BYRgsbrUYQe5B6LG3B3IU2k/+plScAMBGYIPx9cdj0aVFxh/EDAh4Anzb8FtcHXYVx58ex9AnPyHj7k2gZs0ifU45icl4tnQTLn/aB2ertcO5Gh1xpfVARO88BIXAjMLp1SsK8ypuY3hBpk0DNm82rL/MVZysGk44cXzUyOVKHDrxFjI5nX3/2FQPX3TzwrZVtXF8TyPMnRwIkZBB0wbuiIzKwJlL75AjY7Fl1wtUKm8HHy8JsqVK7D7wyizriYykikRIiFkOZ3GeRKZj34k7cPa5hj8Ov8a7pCzwBDkQiXOQlpmBjdsj0WVgKL5f9hCJSflPiHI5sGABjW7x96ekb4GABNPkyeQ31gafT5WtiAhg3jwLf5N6wjAMNnbaCIlAgi8PfKl1KLA5zolf1f4Kp56dymuTlSAVXSvi1IBTaN1hLNiHD3FI/BLysBuF7pfxLAphfcfhtO+neDx3NZJCbyEj4jnSw58g4cwV3B8zF6deAg8WboQsRUNcvKGo/E0lYQxXx8sL6NePrgj0has4WTWccOL4qIl7Jy1k7m7W2B0Tv7uHXsOuoqKfHdq1KI3yPrZ4GpmRe59sqRKvY7JR3scOAJCQlAOp1PT8mZMngVatSna3mL5cuZmIUZNuIexuMhgeq9Ekny1VIidHidPn4zBo7A1EvSbvy8OHlAO1Zw+JiIQE4N9/yVzu4aHf89vaAkeOANu20Y4+a0DAE2BPzz2ISY/BmL/HaKxkGhtHoI6DjQNGfTIKSy4b2AKyEAzDoO+noyFxLoWntlkIP7kD/0b+m3t74uUwXKzTFW/3n4AyWwplVnahYyjSM6BggRe/H8eFOl2R9eqtaYsq6TadOlOn0s6Gt3p+T1zFyap5D96eOTgsR0amAnx+/ivSfUdeIyExB2npcly6lojKFewhEfORkZm/5ZSeIYethPbb83k8PHoiN9oTq+J9iCEAgHsPUzBjQTiypfqZ6eUKIClFhq+n3Mb8H6Vo2JAEhFRKhvITJ2hkiqF4etIuu4kTyVRvDYgFYhz+4jCuvbmG2WdmF7rdXOfEsfXGYm/4XrxNM1FgmBF+UDAmNp6M4Dhg6P6BGHpoKKKvXsW19kMhT8ugMC4dsDI5sqPe4vKnfZCTYMIOAGsSTmXL0s6GRXq2V7mKk1XDCSeOjxqRkFfII6PeUpJKFZCI+cjKVsDONn8okZ0tH5lZVGXKkbHo0J4PV1dAIqHqfEgI0Lw57UgeOZI22CxdShWSI0eAy5cpj+jdOwpLlsup6tKqlaW/a9OQyZSYPO8+pEWIJk1GepYFkpJl+PNEBPh8CrK8d49moprSTQkKogTz3r2pkmUNONo44li/Y/g9/HesuLIi323mqDgBNDuvX7V+WHnVxIGy5iQgAIiKgqCcN+613A8JT4xz7fpDnqH5ioIRClFrz0o0e3IaHWSP4PppXQAAq1BAGpeA24OnGLcOazCGF2TKFNpF8VqP2Aqu4mTVWNncdQ6O4sXVRQSZTPdVcGRUJto2z4vBFtvwUK6MBJFR1L6zEQEvX/DBMLQTOzGR2k8FP6KjyctT8PaUFBJcOTmUh+TmVvjD1bXw1+zsit/Ccf7KO8iLyK5SGekXrX6ES9cSMKx/ecybHISvJt0CwwC2Tqm4cDkLgf4Ss62pRQu6mO/QgcIy9W33WRIPOw+cGnAKTbY2gZvEDQNCBkAmoyqbra15nmNiw4movbE2pjWeBiexBYf06UtgIKnXGjVgH/4Esxy744byAgDtw4ITL4chcvVvqLU7v8Bkc2RI+DcUWdExkHiVNmwd1mIMV6d0acrvWLiQyqxFwVWcrBpOOHF81DjYC1CzmjOu3Sq6JXA+9B2+HlwBTRuWQuj1BAzu44tnkRmIis4Cn8+gZVMP8HikYOzs6MPbW/91KBRkg0hOBoYOLSy47t3L+1xddMnlRQsrTcLL1ZUmWxjLjn2vkJWlwB+b6uHPo6/RppknSnuIcTUsEfOXR+Qz0gPAll0vcHRnQ/h4Sf57vVj8c/4NAv3NO1xu0CBKGe/cGThzhoRoSePr7Ivj/Y+j+a/N4Sx2RsNSneDoaD6x6+fshw6VO2Dt9bWY1mSaeQ5qCgEBwMGDQMuWwO3beHb1FZAhRbMnp/Fy7U6UG9AVEp+yiD9xAXeGTIFSmoMXq34FALAKzRcwLzfsQsD3Ewxbh7UYwwsyeTKJy8mTtb9BKJV09fU+BLl9pHDCieOjp28Pb9x7mIqsbO3m7uRUGWYuCMf4kZUxe0IAHjxOw5wlDwAAAj6D3l28TFoDn0/b8efPp635+pKdXVhkqYRVbCztUCt4W1ISVTx0VbMK3u7oCMTGZ+NldF7bRWWkz8lRYt3iGmjXojT8vLUb6aOisyCXszj6z1t8M8z8U3nnziXxNGAAsHevdZjsg9yDcKjPIXTc3RFrmvwJJ6cmZj3+5EaT0fK3lhhXfxwkwhJWi6qK06RJyP5xMVJu5Y0bKdOrHa51GAZlthQNz+2G18DuiNq4p8jDKaU5iNr4u+HCydradCo8PChDY8ECYO1azfdJT6c/UGv45eXQCCecOD56ald3hpOjANlSBXoNu5rvti27X+Z+fuNOMvqNup7vdh4P8POxRUU/064OExNJ5DRubNjjxGJK6DYkpVupJAuFplZiYiL5rjTdlp0NlPWRolwVBrz/7F4qIz2AXCO9gM8gOTX/2Ax1Iz1Apnylks2t0pkLhqHInNatyVJiaO6gpajnVQ+7uu9C77094OZ9EkANsx27qkdV1C1XF9tub8OoOqPMdlyj8PKiE3/58si69xA8sReUUvr9eLFmO6Rv4wAAsUfPwDEkUK9DypJSwCqVYAwREjdv0mBDa2TSJKBKFfoF1ZQSy/mbrB5OOHF89DAMg8Wzq+Grb8N0zqnL/zjAzlaA+dOCTV7D6dNAkyY06svS8HiAszN9VDSg6JOTA5y/rMSinwHVbvKCRvpSriKkpsmKNNID9NrJZErY2JgwBVgLNjY0BqZBA/r+Ro40+1MYRauKrfC/yj/jx8Yd8DTxHCq5VjLbsac2nor+f/bH8NrDIeCV4Ns6w5AoSE6Ggs/Pt4suOyZv7IgiMwvisvoZ0RgeD0ppDviSIoK91FEZwz/5xKClFxulStGQx/nzaRxLQVJTOX+TlcPVAjk4AFTwtcPy70NgZ8vXyxbB5wGODkL8vLAGSnvo+YZeBCdPWn9auEgE+HgLdHYQIqMy81XgChrp845nubcfV1fg77+pdXfsmMWexmBq2fSC/9vZaL29Nd6kvTHbcRt6N4S3kzf2hu812zGN5r92nbCKP5n3TIQFC57YgCuKqCjrM4YXZOJEYP9+SrwtSFoaV3GycjjhxMHxH1UDHLFlRW18Wr8UREIGNhpO7GIbHkQiHlo29cCvq2qjgq+dyc/Lsu/PfDrvcraQy4uecXI+9B0q+NqhaUN6HdWN9CrK+9iBsbBxt2JFOjcNHEg7Ga2B1FSgWs5XGFZrGNrsaIOkLPNNKp7aaCoWXlyoc3yQxQkMBCIiYFu/NpQ5Mp1354mE4NmICn2uwq6Cj2G/K9bqb1LHzY1aiT/8UPg2ruJk9XDCiYNDjXJlJJg/PRj7t9bHwN4+qFzeDqXdbVDGQ4wqlewxYkB5HP6tAWZNCEQpN/P01SIi6N8qVcxyOIsiEfPRqqkH+EW8c6iM9CMGlMex3Y0QVMUx10gPALYSPvr1MGDLoQk0bAj8/DPQsSNFQZQ0KvvKtMbT0KpCK3TY1QEZORm6H6gHbSu1BcMwOPa0hEtsAQFUcapXB2U87cEU9csCoGn4cbRLvweJV2nUO7aFPvcl0x7f3hYVvh1m2PO/D8IJACZMAA4dAp4VGJvDVZysHoYt8csTDo6Pm5UradDtL7+U9Er04/nLDAyfEAZpjv5+MHVsJXz8tbMhRMLiu25btIhCMi9cKNmL+YULaVfjokWAklVi8KHBiMuIw6E+hyDii3QfQAe77+3GuhvrcH7weTOs1kgePqRMiKNHkdKsLULTHaDILDxiRR/4thK0ehsKvq0BuwXbtgVGjwY6dTLqOYuVuXOByEgoN25EzKF/kHLrAXIuX4UgPha2o4ajbO8OsHF3LelVchSAqzhxcJQw70ubTkV5Hzs42TlDqTD87UMk5GFgb99iFU0AxebUrUvp4nK57vtbCvUNUzyGh02dNkHIE2LgwYFQssYJUXV6BffC67TXuBR1yeRjGU3FihRA6e0Np9R4ONUILNR+0we+rQR+Y780TDSxbF6G03tAdu9+iNj3L0551sPdETPxfMkviL50Fy8exyJi+hL8W6EpbvQag6SrVtJr5gDACScOjhJFKgUuXqTk6/eB9HQSH68eBsHXWwKRUH/vCZ/Hg4B1R9/upmVeGQPDUMtOoQC++QaFxuwUFykp+bswQr4Qv/f8HW/S3mDssbEm+5MEPAEmNZyEhZcWmrhSExCJAD8/akFVr45PJg+EuJwnGJH+qat8WzHcmtVHlbnjDHvuqChKd7VmY/h/JF68gXONeiNSbgd5eiYU6flbtsosKZTZOYg99A+utv4ST35cV/L+NQ4AnHDi4ChRLl4EgoMBF5eSXolunj0jz5CdHXDhAh+bV9RE1QBHSMRFv40olYCSJ0XNhjKEXaiCFy9KJs1ZKAT++AO4dIlmBpYEqamF59RJhBIc7nMYl15dwtxzc01+jkE1BuHGmxu4F3vP5GMZjSoIs2ZNCB9HoFHoPjgEVwbfTvesGb6Qj9Ld26D2vjWGZTcB742/KfFyGK51GAp5ajqUWhLTc2FZKDKz8WzRBjz+zormEn7EcMKJg6MEOXHC+mMIAOD4cRJNI0cCW7ZQ8KathI+V80OwYGZV1K3pApGQga2ED5GQBxsRD7YSPmxEPPh4ekLoDBy0GYIBw9KxYoXu57MUjo7A0aPkK9u/v/ifv2DFSYWT2AnH+x3Hzns7sfqqjjlmOhALxPhfvf9h8eXFJh3HJAICaNdDjRrA7dsQuTqj0aW9qP7LfDjWDAJPYgOeWPTfSBQWPFsxeDYieFTxRp0u9RCyZRF4AiPyqN4D4ZTzLhHXOw3X6vvSNvhYkZmF5yu2IvbI6eJcLocGuABMDo4S5ORJ7ZMXrAGWzZtJum8fhXSqwzAMPglxwSchLoiNz8ad8BSkpcvB5zNwchSibk0XsEoBKlUKQINmzZFYdgqOjFyLOXMoa6kk8PYGDh8mwVqunGEjbkxFU8VJhae9Z+5QYFeJK/pV72f084z6ZBQqrqqIyKRIlHcpb/RxjCYwkNR2+/bAqlUAAJ5QiLK92qNsr/ZIC3+Cd2euQJaYDGbBAth8Px0evdpD/PsuyjYyNqri5k0yhlsxUZv/gFJWdEyDtsHHysxsPPpuJTw7vSe9/Q8UTjhxcJQQMTHAy5dkWrZG0tKAwYPJ53vtGk3TKApPdzFaf6Y5DHTSJODs0WX4t2E11Pm8J9avb47p0y2waD2pWRPYtg3o1o1adxUqFM/zaqs4qfBz9sOxfsfQ4rcWcJG4oH3l9kY9j5PYCcNrDcfS0KVY036Nkas1gcBAYMUK6kM/eULzesR5vxsOwZXhEFyZ/rNlKdCpKVDGg/xROTlaDqqD98AYzioUiFy5Dcos4wcfZz59idS7EXCsHlDcy+f4D65Vx8FRQpw6BTRvTiHH1saTJ1SJcXICzp3TLZp0MWoUcPOSMyYFrkd4xWFYuS4dUql51mosHToAM2fSv0nmy6EskqIqTiqqelTFwd4HMfDgQJN2x/2v/v+w694uxGXEGX0Mo6lShYYeikRA5cpAeLj2+7q50TBEgO6voxqjlffAGP7u9GUosvJ+8VWDj89UbgHHalXgNbC7zmMopTJErv7Nksvk0AEnnDg4SghrjSE4ehRo1Ih2n23alK9QYDS2tsDUqcCpnzugReUmEHechh07TD+uqYweDbRrB3TvbnyhwxD0nd/awLsBdnTbge57u+Nu7F2jnqu0fWn0Du6NVVdXGfV4k3B0pB0PUVFU3rt1S/t9CwonY38Q74G/Ke3BMyjVvj/V4GNZUoreg49ZhQKpdx5acpkcOuCEEwdHCaBUWt98OqWSJkCMGEFDckeONN5qookRI4Dbt4F+bsuR4fMn5v12Tn0GbImxZAmd44cNs2xMActSq05XxUlFm0ptsKrtKrTb2Q7PEp/pfoAGJjWahPU31iNVmmrU402igEFcK66uQGIifW6qcLLWwb7/IU9LB6s2hqbg4GOBve5dhwAgT003+9o49IcTThwcJcCdO3Sy9vMr6ZUQaWlAjx5Ubbp+nSpO5kYsBmbMAJb+4IrN3dYhpu5QHPgr0/xPZCB8PrBjB53j582z3PNIpSREbQyY1NO7am/MbDITrXe0xtu0twY/ZwWXCmhdsTU23Nhg8GNNRi2SoFgqTlbubwIAgb0tGKHpvXm+nQGhoBxmhxNOHBwlgDXFEDx6BNSrB3h4AGfPWtYiMmQI8Pgx4PauMz4pUw//OzzDck9mALa2wJEjZBjfvt0yz2FItUmdUXVGYXCNwUYPBZ7SaAqWX1mObLlxY0+MRiWcQkKAu3ehtbxojooTy74XrTrb8t7giXUr5yIHHzMM7KsU024GDo1wwomDowQ4edI6/E1HjlDEwLhxwIYNhlVDjEEkAmbNoo/9Q1fhrdsebDpRguNB1PD0pIrbxIkkIM2Nvv4mTcxoMgPNyzdHp92dkCkzrEoXUjrk/+ydd3jTZReG7yRNJwXasimjjDIse+8NMmXKliEgqKCoCA6GoAIyREBB9vzYG2TvPQXK3nvTltHdJN8fx9KWrjRN2gLvfV1ctMlvvAnaPD3neZ9DyWwlmX/KRoowPiJbdW5ukCkTXLkS93HWqDi9AcZwgCyNaqAxo/+d4OBjZyfyftbZ1ktVJIASTgpFCvPypbTDatZMvTUYjTJftE8fGdDeq1fK3fujj+DOHTh33IOPPP7kq93dCQ4PTrkFJEDRojIMuG1bKZZYk8SiCBJCo9EwvsF4vNy8aLOsDeGGpO08+67qd/x24DcMRoNlC7CEyIoTJOxzii6c9HrLhNMbUG0C0Nrbk7tXOzT29uwsWIenOw6+eu7yiMmc7DIAgJ0F67BBXyjGn+CbdwGwz+KOW+XSqbJ+haCEk0KRwuzaJR7WdOlS5/7Pnkl+0datIuAqVUrZ+9vZwdChUnX6o3dLQm+W5IvVQ1J2EQlQpw6MHi0xBY+suJPfnCiChNBqtMxqNgsNGrqt6ZakocBVc1cli0sWVpxPwbj0bNlEBD15krDPyRqtujfAGB5J3k87obWz7KNX5+xEge/7mFW1UtgOJZwUihQmNdt0Fy6InylnTtixA7JnT511tG8v2UkHDkAvz8ksPDOfQ3cOpc5i4qBrV+jUCZo1g2ArFcOSU3GKRK/Ts7TNUm4+u8mXm740e+irRqNhUJVBjNo3KuUGxWo0UnWK3FkXn3CyRo7TG2AMj8QpV3ZKzvkNrS5p4kfr7Ei25vXI1aWVjVamMBclnBSKFCa1jOGrV4ufacAAGfNib5/4ObZCp4Nhw6TqNLBvZjSbJvLRym4pb2BOgJ9+ggIFoHPn+H3NSSG5FadInPXOrGu/jt03d/Pznp/NPq+xd2PCDGFsvbY1+Yswl+g768xp1VlScXpDjOGvMBrJtmEZxQtmRGvmDjudixPZW71P8ZkjVbUpDaCEk0KRgty4AQEB8gt4SmE0wpAhEmi5YQN8/HHK3TshWreWz8gTJ6BVkTbYB7zHsF3DUntZr9BoYOZMePwYBg5M/vWSYw5/nYyOGdncaTNzT83lr6PmDTvUarQMqjqIkftGWmcR5hBpEPf0hIgIuB9HpEJyW3VviDEcEJH31Vdw9iw5y0E5lwAAIABJREFUVy+ksqMfWWuWQ+tgH3u3nVaD1tkR12KFKD7tV0rMHGXZ4GOF1VH/CgpFCrJlC9SrB9oU+pUlIEAqJgEB4mfKli1l7msOWq1UdYYMgXnzNNRt/ieznYrTqkgryuUsl9rLA2SX4apV4gPLn19CQS3F0jiC+MiWLhtbOm+h+uzquDm60b5Y+0TPafteW37c8SOH7hyiomcKTDcuUkRm9mg0UQbx1/vDGTOKqjQYLBNOb5C/iaFD5f3YsQM++YQMn3Sl7MiRhD56yq1Zywg49C/hfs/Q3bmFS86s5J48kvQl1Ey6tIaqOCkUKUhKtunOnZMBwnnzwvbtaUs0RfLBB2IWv3QJyhTKygeOv9NtTTdCI1J5kF003N3hn39E5G3caPl1rFlxiiSfWz42dtzIl5u/ZNOVTYker9fp+abyN4zeP9q6C4mP6Dvr4jOI63TyxgQEWCac3hR/05gxsGyZ/BDYu1feiyGyKcIhiwcFB/Wm3Oq/qbxnMRW+6oBP0SxKNKVRlHBSKFKIiAj5RbNePdvfa+VKqFEDvv8eJk1KXT9TQmg0ktY9dCj07w97p7Qnv1sBRuwZkdpLi0H+/LBiBXTpIqnvlmDtilMkxbIWY1XbVXRe1ZkDtw8kenz3Ut05ePsg5x+nwLwzLy948ACCgsyLJLC04pTWhdPUqTBlimxldXSUIYnTpoFTPAngBQvKpG1FmkQJJ4UihThyBHLntu1ONoMBfvxRRMjGjbI7LK3TsKEUHJ48ASdHDa0dpzDt+DRO3D+R2kuLQeXK8Oef0KSJ5FAlFVtUnCKpnKsy85rPo8WSFvg+9E3wWGe9M33L902ZqpOdnajOS5cSjiSIFE5JzXF6E4zhCxbAL7+IaPL0hO++g/ffh1q14j/H21sJpzSMEk4KRQph6xgCf39o2hT27RM/05ti+9BoYMQIaYX17w+zJmZnXP1xdF3dlTCDhXPLbESbNvD55yKeXrxI2rnWiCNIiIYFGzKhwQQaLmzIdf/rCR77ablPWXtxLbee3bLdgiKJNIh7e8O9e3G/cZEG8aRWnG7dknPSqjF81SrZxrp5swjI/ftle+tvvyV8Xu7csishKPVnOSpio4STQpFCbN5sO+F05gyUKyefTVu3yty5N4k6daLyEq9dg8JhncidITe/7v01tZcWi2+/Fe9Y27bSfjUXa8URJET7Yu0ZVHUQ9RfU5+HLh/Ee5+bkxselPmb8wfG2XRBE+Zzs7MDHJ+5eZ/RWXVJynNKyv2nLFvjkE9nKWrSoTHnu0QMmTpQxNAmh00mb8+rVlFmrIkko4aRQpAD+/nD2LFSpYv1rL1smVf+hQ2HCBOl2vGlEVp1+/VViE8aN0/B3k7/56+hfnHwQjy8mldBopGVnMMhazc2TtHXFKZLPy39Op2KdaLCgAQEhAfEe179Sf+admseToCe2XZA5BvHorbrwcPPf1LTaptu7Fzp2lIpT6f/Go/z6q1TfWrY07xrK55RmUcJJoUgBtm+HqlXFF2otDAaxS0R2Ajq/4XM/q1eHfPkkAmDrVgj3y8nouqPptqZbkmez2Rq9XgTr/v0wbpx556RExSmSITWGUC13NZotahbvHMAcrjloXbQ1kw5Psu1iIlt1EL9BPLJVp9FEiSdzSIvC6dgxaNVKhh5G/qZ05oykzk6eLK/RHJRwSrMo4aRQpADWjiHw85NZakeOiJ+p9Fsy83PECBg7Vkztv/8OXUt2JatL1pTbPp8E0qeXLswff8iOu8SwpTn8dTQaDX80/APP9J60Xd42XuE5oPIA/jr2Fy/DXtpuMYUKiQAwGBKvOIH5Pqe0aAw/e1YMcNOnQ9268pjBAD17yn/cOXOafy0lnNIsSjgpFDbGZLKuMfz0afEzvfeeCLLMma1z3bRAxYpig3F3h/nzwd9fw/Sm0/nj8B+J7hZLDXLlgrVrJRjzUAKj9kymlBVOIEnhc5vPJcIYwcdrP45zKHBBj4LUyluL6cen224hLi6QNStcvw7Fikn16XVhZEl6eFozhl+5Ir8djR8vAWWRTJki/q5evZJ2PSWc0ixKOCkUNubiRRl7UtgKWXZLloiResQIaRG9jRMYhg+XrkajRhJ/kytDLn6t/Svd1nQjwpgEN3YKUaoUzJkDLVqIsT2SR09C+XveNdr3PkLTTgcoW+cAXfodZe6Sm/gHpMxuQb1Oz/IPl3PN/xpfbf4qzgG/g6oOYvyh8bbdwRjZrnN2lkTW869lSEWvOJkbSZCWjOG3b0tA29Ch0KFD1OO3bslQxunTkz4uQAmnNIsSTgqFjYmsNiVnNmdEhOzmGjRIrhf9Z/PbRpkyUlHLkUPCO0NDoUfpHrg5uTFm/5jUXl6cNG4s+VmNG8PpM4F8NeQ07XodZvHqO9y+G0zA83D0DuHcvB3E3KU3adn9EN/9coYHj2w/1DhyKPCO6zvi3KVYOntpimYuyoLTC2y3iOgG8bh8Tpa06tJKm+7hQ2nL9e0bs6pkMkGfPvDll5b91uTpKWnqL23YRlVYhBJOCoWNSW4MwdOnEhJ54oT4mUqVst7a0irDh8O8edK2W7hQPDszms5g3MFxnHt8LrWXFyeffQZVagbw6aB/OXrSn7BwE+HhsSs8YWHy+P4jT+nW7zgXryQxEMoC3Jzc2NxpM7NOzmLqsamxnh9UZRC/7f8Ng9FgmwVEN4jH5XN6vVVnjjk8LQgnf39pz7VvL8N7o7NkiVScvv3WsmtrtbJb4sqV5K9TYVWUcFIobEhoqOxMrlPHsvNPnpTqS6lSsGkTZMpk3fWlVYoXl112efOKWdxohDwZ8/Bz7Z/TbMvu8rWX3HjiCxqDWbvpjUZ4ERhBvx9Ocede3DvfrEl21+xs6bSFEXtGsPTs0hjP1cxbkwyOGVhzcY1tbv56JEFyK05pwRj+4oX8RlO3rrToovP0qaS5zpiRvHlHql2XJlHCSaGwIfv3S/adu3vSz120SGwTv/4qQcNvo58pIYYNgzVr5HVHDtftVaYXLnoXfj/4e6qu7XWMRhPfDvclJCS2ATs6ZYpnZOGUcmxbXpWJv5Qga2YHgoIN/PDr2RRZZ373/PzT4R8+/+dztlzd8upxjUbDd1W/Y9S+UXH6oJJNpHAymaJaddHv4+oKISEimMwRTjdvpq4xPDgYmjUThT92bOw+/NdfS0JqhQrJu48STmkSJZwUChtiSQxBRAR88414ZrZtg3btbLO2tE7RovLeFSokn00gO8VmNJvB6P2jufDkQuouMBrHTvrzMijhNleG9Hb88v17zFhwnUbt93PhyguGf1sUkwnuPghOkZYdQIlsJVjZdiUdV3bk0J2orYDNCjXjRdgLdlzfYf2bZsokradHj+RrV1fZZReJRpO0sSupWW0KC5PZOzlyyI6510XT1q2wcyf8/HPy76WEU5pECSeFwoYkNYbgyRM53tdX/EwlSthubW8CQ4fKZ9Dly7KJCiCfWz6G1RxG9zXdbefJSSILV94mOFjWsmxGBdq38GTOxDJsWlyFn74tgr1eQ41Kmbl+K5Cd+58QFm5i1v9uUMDLhdyeToSFG1my2oLJwRZSNXdV5nwwh+aLm3P2kVS7tBotA6sMZNT+Uda/oUZjvkE8LQsng0GSZu3sZCulThfz+aAgyaaYOhXSpUv+/ZRwSpMo4aRQ2IiHD+HGDfOr9SdOyGDe8uXhn38sa++9bRQsKB2RIkWiqk4gQ2r1Oj0TD09MvcX9x4uXEZw++yzGY7WqZubrYb606XGY/HldaFgnG165nblyPfDVMSGhRu4+CMErtwtGI+zc/xiDwQZtsnho7N2YcfXH8f7C97kRcAOADsU6cPHJRY7dO2b9GyY2eiWtV5yMRgmy9PODxYvjnm00dKiEkTVsaJ17KuGUJlHCSaGwEVu2yAw5c7xJCxZIpWnMGBg5MvYvsu8ygwfL5+TmzSJEQaojM5vN5Je9v3D5aep+sPgHhKHXx/xRunzdXZ76hfHiZQT7j/hRMF86nBx1BAbFNLW/DIzA2em/f2wNsZ63NR2Ld2RA5QHUny9Dge119nxd6WvbJLUnNnol+ry6hIRTahjDTSYxe1+8CKtXxz076fhx2Qo6YYL17psjh8QRPHuW+LGKFEMJJ4XCRpjTpgsPl5/HP/0kLak2bVJmbW8SXl7w4Yfg7S1jWCIp4F6AH6v/SPe13eNMxU4pQsONsWwufv5RH/yhoQacHHUEhxhwcY6piF2cdQT91+LTajSEhaX86+hXoR/tfdrTcGFDnoU8o0fpHuy+sZuLTy5a90aJVZzMbdWlhjF88GDZHrthgyShv054OPToIWVRa0b5azRQoICqOqUxlHBSKKyEyWQiKCiCoGADBoOJLVsSNoY/eiTPX7woM+d8fFJurW8aP/wg79PcuVFxPwB9y/fFZDIx+cjkVFtbOmc7s1ps128FkT9vlO/F0UFLzuxOXL8l7buICBPpXFJn6+SwmsOo5FmJZoubodVo+azcZ4w5YOWw0egVp7x5pZLy+HHU89FbdQnlOKV0tWn0aFi5UkqeGTPGfcz48ZAlC3TqZP37q3ZdmkMJJ4UiGRiNJg4d96Pf96eo0XwPjTocoGH7/dRquZfcRU/j/8I/zu3dx45JPlOVKrBuHbi5pcLi3yBy5YKPPpIZqX//HfW4Tqtj1gezGL57OFf9rqbK2jJncsDOLvEfpXsOPiFfHhdqVM6EvV5Dt3Z5uHo9kFt3JMMpk4c9Dg6p8yNZo9EwqdEkcrjmoN2KdvQu25uV51dy9/ld690kTx7Z/fDypVRSXm/XmVtxSknh9NdfMG2a7JSLr5J0+bL02KdOTd54gPhQwinNoYSTQmEhB44+pXnXQwwedY4TvgEYjRBhMGEwmDAaTTik82fgiLO06n6YE74Br86bN0+8o7//LjuWlZ/JPL77Du7eFQtJaGjU494e3gyqOoge63qkSsvOTqehZeMc6PUJf2gGPA/nx5Fn6dXZi42LqlC0UHqGjpEUdJ3eQJsPsqGxxQevmUQOBQ6NCGXgtoF0KdGF3w9ZMS9Lp5N+a3wJ4uaaw48fl10UtmbePDEcbtsmij0uTCYZs/LDD9JTtgVKOKU5lHBSKCxgzaZ7DB51Dj//MIJD4t8SHxxi4NGTUAYM82Xzzkf06ydiadcuaNky5db7NpA9u9hIHB1lDEt0+lfsT0hESJzjRFKCFo1yECl52vQ4zLFTUUJ51qKbjBgvYuHYqQA69jlKndb76Pv9KR48EgVoMBoZcLEeU45Ose2w3USw19mz4sMVXHx6kcDwQGaemIlfsF/iJ5pLQgZxcypOJlPKDPddsQIGDhSjYkKCaNYsqaD162e7tXh7K+GUxlDCSaFIInsPPWHi9KuEJsHIGxpmZMS4i1y85s+RI/DeezZc4FvMwIEyHmzkSNkdHolOq2NWs1kM2Tnk1db6lCSzhwN1a2SxqNWm0xvo1DwfKzv+j7WX1uI9yZsZJ2YQbjBjXpsNcLF3YUOHDRy4fYD87vn588if1rt4QgZxc4TTzZvg4CAq2lZs2gSffiqZIEWKxH/cgwdSBp0xw7ZlY1VxSnMo4aRQJIGwcCM//34hUdEU12gNNEYc3M6TPn3KZfW8bWTOLMN0/f2jxrBEUiRzEQZUHkCPtT1sMzYkEb79zJvCBVxxsDf/x6q9vYbAjKe4m3sOZXOUZWPHjfyv1f9YfGYxRf4swrxT81Il5NPdyZ3NnTbz4OUDxhwYQ2BYYOInmUN04VSkiAihwP+ubU6rztb+pj17xEy3enXi07T79ZMSqK1TarNkEbO8nxUrf4pkoYSTQpEEdh94kugA1/hGawAEhxo58q9/Cqz07WXAAPE4jRgR+7mvK3/N89DnTD8xPcXXZWen5fcRxalY1h1HB22CPmGtVnbVvV8rK+v+6MDGK//QZ0MfDEYDlXNVZttH25jRbAbTT0znvb/eY/GZxSnu38qZPic7u+wk3BhO3419rXPR6K06vV7Ek6+vfG9OjpMthdPRo9C6tQyJrFQp4WPXrJE24+DBtllLdDQaVXVKYyjhpFAkgYUrbr3K3bFktEZwsIH/rbidyq/izcbdHb78Uj5vj70WcG2ntWPWB7P4YccP3Hp2K8XXZq/X8vOgokz4uQTVK8nuOWcnHQ4OWgyaEJydtNjba6lXPQt/jS7Jt58XIrtrVnZ22ckVvyu0W9GO0AjxPdXMW5M9XfcwqeEkJhyaQImpJVh5fmWKVtMKehRketPpzD01l02XNyX/gt7ecO1aVNxAdJ+TucLJFsbwM2egaVOYORPq1En42GfP4PPPYfp0cHKy/lriQgmnNIUSTgqFmfg/C+Pm7aAYjyV1tAbAybMBhIenXmDj28A338gv4kOGyPcPHoVw6mwAh477gV9uPi3+DT3X9UyVlp1Go8GncHp++e49VsyuyMC+3vT9OD9PCsylzydZWTO3EoO/LoJ3ftdX57g6uLKhwwYAGv2vES9CX7y6Vr389Tj48UFG1RnFz3t+psy0Mqy/tD7FXlun4p0okbUEHy7/kCN3jyTvYo6OskPt2jX5PrrPyclJ/lG12rhznGxlDL98WZJqJ0wQ8ZQY330n22Jr1LDuOhJCCac0hRJOCoWZPHsebpXRGnY6LS9epuxojbeNDBmg/1dGTpx5TOdPj9Ohz1G+HX6Gob+d4+uhp9nzdyWe7W7M6HX/SxXxFIlbBnvqVMtC84Y5sMv/L97FwnFNF3fIpYOdA4tbLcbb3Ztac2vxKPDRq+c0Gg2NvRtzvNdxBlcfzHfbv6PSzEpsubolRV7f6LqjyeiYkWaLmnH+8fnkXex1g/jrO+vCw+OuONnCGH7rFtSrJ9H97dolfvy+fdKm++03663BHJRwSlMo4aRQmIkxjiKRJaM1NBowpuKH+dvAhSsvOHHlEF7vXeT67ZeEhRkJDDK8+hMebkL/sARrZ2aiW//DPH+ROjvUouPu5I5/SML+Np1Wx1+N/6JxwcZUnVU11g5BjUZDiyItONX7FP0r9ueLTV9QfU51dl7facOVQ918dcnikoV2Pu1osKABNwNuWn6x6MKpeHFpk0X894uEu7t8HZdwsra/6cEDqFtX+r49eiR+fGioDPmdNCn+BHFboYRTmkIJJ4XCTNK72hERkfzRGuERRlzTxTFZXWEWp84G8Pmgkzx7EY7OLv4dZyYTaI2OXLkeSNd+x/EPSL18JAA3RzezMpE0Gg0/1fqJfhX6UW12NXwf+sY6RqvR0tanLWf6nKFX6V70XNeT2nNrs//WflssHY1Gw6Cqgzh89zD9K/an/oL6PA58nPiJcRHdIO7qKq27i//NxfPwENEUn3Cylr/Jz0/mHXXuLMLJHH79VdaeGgFskcJJ/cKVJlDCSaEwEw83e9wyJi54EhutkS+PS5K2rCuiuH0viAE/nSEkNAkeMaMdT/xC+OLHU6nqLXN3csc/2PwdlZ+X/5wx9cZQd37deAWRTqujc4nOnP/sPB2LdaTjyo68v+D95HuR4qBF4Rb4BftROntp2hRtQ8OFDXke+jzpF4pecYLYBvHQ0LiFk7X8TS9eiEepQQP48UfzzjlzRsavTE6lmYgeHlKqfvIkde6viIH66a1QmIlGo6Fjq1w4JhJymNBoDWcnHR1b5U6J5b6VTJ9/g5DQhHON4srQMho13H0QzM79FlZJrIC5FafotPNpx/wW82m+pDnrL62P9zi9Ts/HpT/mUt9LNC/cnFZLW9FsUTNOPjgZ7zlJRafV8W3lbxm1fxQjao2gbI6yNF/cnJCIkKRdKLLiFFk9iW4Qd3ePWziZTNZp1QUFiQG8dGnxKZkz4sZgkFbezz/HP3rF1qhIgjSFEk4KRRJoUCvrq5/3lozW0GigRuVMKb7ut4Fnz8PZd/hpnF6zSBLK0AoNNbFwRcpHFETi7uRu0fiS+vnrs779enqs7cG8U/MSPNZeZ0/vsr253PcydfPVpdHCRrRe2pqzj85auuwYdCreCd+Hvpx6eIo/G/1JZpfMtF/RnghjEjY7uLvLDrp79+T71ytOwcGxhZM1jOFhYZLTlCsX/Pmn+QN5//pLQjl79rT83tZACac0gxJOCkUScHG2o2fnvIlWneLCwUHLFz0LYK9X/9tZwvqtD9D899ZZkqEFcPPuSy5ff5kq6zfHHB4fFTwrsLPLTgbvHMy4A+MSPd7RzpF+Ffpxpd8VKnpWpPa82nRY0YGLTy5adP9IHOwc6F+xP6P2jUKn1TG/xXyCwoP4ZN0nSdvdF9foFZNJhFNQUGzhlFx/U0QEdOwo4mv2bIk8MIdbt2TH3fTp5p9jK5RwSjOon+AKRRJp1zwXTepnT5J4cnTQ0rFlLhrVzWbDlb3d7Dv8hNBo3iZLMrTCIiLYfTR1qk5uTklv1UWnSOYi7Ou2j5n/zmTg1oFmCRVnvTPfVP6GK32v4JPFh6qzq9JldReu+l21eB29yvRi+/XtXPG78moo8NnHZxm0bZD5F4luEM+WTSo6d+5INSooKHaOU3L8TUajtNqePYPFi8Eu7jiIWJhM0KcP9O8PhQpZdm9rooRTmkEJJ4XCAr7omZ/uHfJir9ckONjV0VGLg72Wfj3keIXlvJ59ZUmGltakZ+nJlAuPjI6lrbro5MqQi73d9rL75m4+Xvux2S0yVwdXvq/2PVf6XsEroxcVZlSg59qeFsUKuDq40qdsH8YeGAtAOvt0bOiwgfWX1/PbfjPzjeIyiP/7r1ScAgPjrjhZIpxMJvjiC7h6FVatkoqTuSxeDLdvy4yftIASTmkGJZwUCgvQaDR0aJmL1XMr0aNjXjzc7XF00OLirMPFWUZsZM3swKdd87F2fiWavZ8jtZf8xqPTxfSkWJKhBRoeBz9g6dmltl5uLJLTqouOh7MH2z/azr0X92i1tBXB4cFmn5vBMQPDag7jUt9LZHbJTOlppflsw2fcfX43SWvoW74vS88u5f6L+6/WtKXTFqYcm8KMEzMSv8DrwikyCNPdPbZwSo4x/Icf4OBBWL8eXFzMP+/pU/jqK5gxQ6phaQEVSZBmUMJJoUgG6V31tG+Ri1WzKzJnYll+H1GcCSOKM29yWZbPrEDLxjlxcTazNaBIEA+3xD/AEsvQMmlDeRxxg44rO9JoYSOG7BzCsrPLuPDkQtIMzhZgya66+HCxd2Ft+7Wks09HgwUNCAgJSPykaLg7ufNrnV+58NkFnPROFJtSjP6b+vPw5UOzzs/skplOxTsx4dCEV4/lTJ+TzZ02M3jnYFaeX5nwBaK36iBmxenFi5jCyVJj+MiRkvK9aZNEzSeFr7+Gtm2hfPmknWdL3NzkfXjwILVX8s6jhJNCYQW0Wg2eOZwo6p2eIt7pyZnNCY25u3YUZtG4XrZXLbf4SCxDy0HnyPFhC+hSogv+wf6YTCYW+i6k8f8ak2FUBspMK0PX1V0Zd2AcW65u4f6L+1Zr6yU1xykx7HX2zG8xn1LZSlFjTo1X1Z+kkNklM2Prj+Xsp2cxmAwU+bMIA7cO5ElQ4nlBX1f6mhn/zogh2rw9vNnQYQO91/dmx/Ud8Z+cKxc8fy6+I4gyiHt4yOPRhZMlxvDJk2Vg79atkCmJu1i3boVduyR+IK2h2nVpAiWcFArFG0H1ipkS3UGeUIaWRgNlS7qR1cOZyY0m4x/iT4lsJVjdbjVX+13l4TcP+avRX1TNXZVbz24xat8oik0pRuYxmak1txb9NvZj+vHpHLpziJdhSd+Z5+bkRkBIAEaT9UI4tRotE96fQNv32lJ1dlWu+F2x6DrZXbMzseFETvc5zfPQ5xSaXIjBOwYnKPTyZMxDE+8mTDk6JcbjpbOXZmmbpbRd3pZj947FfbJGI1WnyHZd/vzSHoPYFaekGsPnzJGMpm3bIEcSW+SBgfDJJzBlCqRLl/jxKY0STmkCjSk1J2AqFApFEpg65xpL194hLDzpP7YcHbSMHVaMkj4yZ+zA7QO0WtoK3z6+ZHKOuyphMpl48PIBvo988X3oK38/8uX84/NkS5eN4lmLUyxLMYplLUaxLMUo6FEQO238rdn0I9Nzq/8tMjpaf9bZ9OPTGbprKBs6bKBU9lLJutaNgBuM2D2CNRfX0K9CP76s+CXpHdLHOu7so7PUmVeH619cx0nvFOO5NRfW0HtDb3Z22UnhTIVj36RTJ5kV17WrfF+1KowYAU2aQJ48cE4EL/XrQ79+8nhiLF8ux+7cadlOuG++kVbYggVJPzclGDFCxN2oUam9kncaJZwUCsUbQ1BQBB9/dYJ7D0IwGMz/0eXooKVOtcwM6lcoRgv1q81f8eDlA/7X6n9JWofBaOCK35UYgur0w9Pce3GPQpkKiZiKJqhyuOZAo9GQd0JednbZiZebV5LuZy4rz6+k9/reLG2zlJp5ayb7epefXmb4nuFsvrKZryp9Rd/yfXGxj2my/mDxBzTI34BPy30a6/w5J+cwdNdQ9nXbR64MuWI++csv0pYbPVq+//xzqTyNGwd6PVy/LkboTJlk5EliHqd//oFu3WDLFihRIukv9tgxEWe+vpA5c9LPTwmWLIGlS2HFitReyTuNEk4KheKN4olfKH2+PckTv1DCzag8OTpoqVDanZ8GFsXutZ15QeFBlJhagjH1xtC8cPNkry0wLJCzj8/GqE75PvTFYDLgk8WHc4/P0at0L5p4N8Eniw+uDq7Jvufr7Ly+k7bL2zK1yVRaFrHOQNrzj88zbPcwdt/YzbdVvqVP2T6vKkwHbx+kw8oOXO57Oc5q2/iD45l+Yjp7u+2NWdlbsQLmzRMDN4gnafduOHEC/P3h7l24cQOqVJGvE2LXLvjwQ1i7FipWTPoLDA+HcuWk4tSpU9LPTylOnJAK3enTqb2SdxolnBQKxRvHy8AIRk68yMGjT0EDYWGxf4w5OepAA+1beNKtXZ54zfp7b+6l7fK2nPn0DO5O7jZZ78OXD/F95EufDX3I75afp8FPOff4HFn4z3WyAAAgAElEQVRcssSqTnl7eKPXJT5MOiH+vf8vjf/XmJ9q/kTPMtYbFXL64WmG7hrKkbtH+K7qd/Qs3RMHOwdqzqlJrzK96FCsQ5znfb/9e7Zd28b2j7ZHicVz56B5c7h0Sb6PFAUZM8pzT55I623ePBFE8XHkiFSKliyBWrUse2GjRon42rjR/FEsqcGLFxIY+uJF6ieZv8Mo4aRQKN5Y/PzDWLPpHqs33sf/WRgmk+Q95crhRMdWualVNTMO9ol/wHyx8Qv8QvyY32K+TdfbZlkb2hRtw4fvfYjBaOCq/9VY1anbz2/j7eEd5Z/6T1TldM2ZpJ2aV/yuUH9+fXqU7sF3Vb+z6i7P4/eOM2TXEHwf+vJj9R/J4ZqD77d/z6nep+K8j8lk4pP1n3DN/xobOmzAwc5BDODp08vOOgcHGe7r5gZ16kjl6flz+O47cHSEoUPjXsjp0+KBmjkTGje27MVcvgyVKkmrLm9ey66RkmTLJmv19EztlbyzKOGkUCjeCkwmEwYjsdpx5hAYFkjxqcWZ0GACTQs1tcHqhF7relE6e2l6l+0d7zFB4UGce3wuhqA6/fA0YYawWNUpnyw+ZHCMP6Po/ov7vL/wfWrlrcX4BuPRaqxbpTh4+yBDdw3lit8VIowRTG44mWaFm8V5rMFooO3ytpgwsbT1UnRanRi4V66E996Tg0qUAC8v2LxZhv0mZAy/dEkqTL//Lm06SzCZoHZtaNZMRqu8CVSrBsOHW15dUyQbJZwUCoUC2HVjF51WdsK3jy9uTm42ucegbYNI75Ce76t9n+RzHwU+ihJT//197vE5PJw9YlWnCnkUetXuCwgJoNmiZuTKkIvZH8zGXmf9JOw9N/fQe31vrgdcZ0bTGbTzaSfC6DVCI0Jp/L/GeGX0YlrTaWhatBBPUevWAJg++oiwew+I2L0X7dWr2Jcshu5sHMbwmzehenWpRHXvbvnCZ86Ev/+WdHFdwhlhaYbu3cXH1atXaq/knUUJJ4VCofiPzzZ8RlBEELM/mG2T6/+2/zceBz5mTP0xVrme0WTkmv+1WO2+m89uUtC9YAzf1F9H/8JOa8eKD1fE2hlnDcIN4eSZkAcPJw+MGBlWYxitiraKVeV6EfqCOvPqUMerDiO3A87OhPbqw83pi7kxZhqG4FA0RiM4O2EMDiFry/fJ1787GSuUkDbg/fsimvr2lWqUpdy/LxWurVst24WXWowcCX5+MMY6/w0pko4STgqFQvEfL8NeUmxKMf5q9BcNCza0+vUjAzRnfjDT6teOTnB4sLT7Xsufehr0FAc7B1oXaU35nOUpllXafdbKlfr72N+su7SOz8p9xpBdQwg3hPNTzZ9oVqhZDO/Tk6AnVJtdjXF3iuO54g537z0HjQZjSGjsi2q16JwccMyVnXKzR+HSrRO0aydz6JJDmzbg7S2xCG8Sy5dLztTq1am9kncWJZwUCoUiGtuvbafrmq6c6XMmQf+QJaw4t4KFvgtZ2TaRWW424nHgY/pu7MuuG7uo5VWL6/7XOfNIdhNGVqci232FMxVOclsvJCIErz+82NJpCz5ZfFh7cS1Ddg3BXmfP8JrDeb/A+68E1M1HV9lSrj6e93UYTWb40rQa7DBRsUMtMsyakrzdb6tXw8CBcOqUmM/fJE6dgg4d4OzZ1F7JO4sSTgqFQvEan6z7BKPJyPRm06163R3XdzB893B2dd1l1esmlTH7xzD56GQ2d9qMt4c31/2vx6pO3Qi4QX63/K8EVaSPKneG3Anu0Bu9bzS+j3xZ0FLSt40mIyvOrWDorqFkdMzIiFojqJW3Fifa9OXRlj2YQsLivVZc6N0yUO3YapxyJ3GcSiTPnoGPj1RtatSw7BqpSWCghIIGBqpIglRCCSeFQqF4jeehzyk2pRjTm06nfv76VrvuyQcn+WjVR5zuk/oBhnNOzmHQtkGsbb+W8jnLx3o+JCKE84/PxxJUL8Ne4pPFJ9YOv0hD/fPQ5+T7Ix9Hex6NkZBuMBpYfGYxP+3+iQr3PGj350sIjls0afR6Ss0fS4YyPjjn9eRgnc747TkiT2q1ZG/9PqUX/m7ZC//0UzAYxBT+ppIzJxw4IKNpFCmOEk4KhUIRB5uvbKbX+l749vGNc06bJdx6dosqs6pwu/9tq1wvuay7uI7ua7uzsOVCswXi06CnscTUmUdnyOCQ4ZWIOv/kPC56F+Y2nyuZTdGIMEawtlYz9AeuoCXuypVGrydPnw48O36G0osm8G+nr6OEE6B1dKDOzT3YuyfRm7Vvn/ijzpyRoM03lZo14ccfZdafIsVRwkmhUCjiocfaHthp7ZjaZKpVrvci9AXZxmUj8PtAq1zPGuy7tY9WS1sx8f2JtPVpa9E1jCYjNwNuvhJUR+8dZd2lddjr7Mnnli+md8qYg6vlumEMDaPW5e3c/GshOTs3xyl3Dh5v3sup7gMxhkZVompf383JLgNiCidnR7yH9iP/Vx+bv8iQEChVSszgLa0ziibV6NkTSpeGPn1SeyXvJPGP8VYoFIp3nHH1x+EzxYcd13dQ26t2sq+Xzj4dYYYwQiNCY1ViUouquauytfNWGi1sxJOgJ3xW/rMkX0Or0eLl5oWXmxfNCkkA5qcbPsXV3pX2xdq/qk5NOTYFl3/O086kxwnx52Rv05AjjXtgDAml8u5FeHZpya1pixO8nzEohHuL1ydNOP36KxQp8uaLJoCCBSXxXJEqKOGkUCgU8ZDBMQN/N/mbHmt7cLrPadLZp0vW9TQaDe5O7viH+JMtXTYrrTL5FM9anL3d9lJ/QX0eBT5iWM1hyR7RMqDyAMpNL8cP1X+gZLaSrx6/9mg2F1aPxUQEADcmzyf0/iMAHm7YSfoSRcy6frj/M/MX4+sLU6bIjrS3gYIFYe/e1F7FO4uy5CsUCkUCNCrYiOp5qjNo2yCrXM/N0Q2/YD+rXMuaeLl5sb/7ftZfXs9n/3yGwWhI9vUaFGjA38fiMmFHibKQB49ffW0ICsYunbN5N4iIAKMx8eMMBmlt/fIL5LBwJ15aQ1WcUhUlnBQKhSIRfm/wO6surGL3jd3Jvpa7kzv+wf5WWJX1yeKShZ1ddnLhyQXar2hPaEQcgZRJYGCVgfx+6HdCIkJePWbvkRGtvT65S0X/4B6kSwclS4rhe9gwWLxYqkrBwVEH/vmnDBHu0SPZ90wz5M8PN26IeFSkOEo4KRQKRSK4ObkxtfFUuq/tTmBY8ozdbk5ps+IUSXqH9PzT8R+MJiON/9eYF6EvLL5W8azFKZW9FPNOzXv1WKa6lTGFJ/6Br7XXo3Wwj/U1gFanJccnneDBA5k317SpiIjly6FjR3B3l2HBNWtK0GWdOrKj7tEjGez7hhN47zE3XbJz+bvRXBk1lVvTlxB850FqL+udQe2qUygUCjPptLITmZwzMeH9CRZfo/OqztT1qkuXkl2suDLrYzAa+Oyfzzh27xgbO24ks0tmi66z79Y+uq7uysXPL6ILDYN58zgyYDyPg6HW5e2c/uRHnu44CEDBwZ/jUiAPJ7sMoNbl7Tjn9YxxrR0FahN88y5aOx11soZin84JunWTQcFZs0YdGBEB169D+/aQObO06C5cgPPn5fnCheVPkSJRX3t5gV3atf2aDAYebdzN1THTeXbiLISFSuK6RoPW0R4MRtyrlyf/Nz3wqFUx2R41Rfwo4aRQKBRm8jToKcWmFGNpm6VUzV3Vomt8sfELvNy8+LLil1ZenfUxmUwM3TWUJWeXsLnTZvJmzGvRdZpMKM/oS3l4b8UeqFCBp3WbcnTInxgCgxM/+XV0WrK3bkjp+ePEID17toxQqVZNRFTjxmBvD4sWyUDc48dBr498QfDkiQioCxdi/rl/H/LliymmCheGQoXA1dWi120tIl685OgHvXl24iyGwKAEj9W5OJO5fhVKLRiP1j5pI3MU5qGEk0KhUCSBVedXMXDbQE72Pomz3kwjczSG7RqG0WRkeK3hNlidbZh0eBK/HfiNjR034pPFx/wTL16E8eMJW7yQdSUcafn3XjRFimAymTje5nMeb9mHMTgk8etEI86RKy9fSptu9mwRRS1bwrJlsHEjlI+dih4nwcFw6VJsQXXpEri5xRZUhQtLJcvGlR1DUDD7q7Yl8NL1GPlWCaF1csStYknK/zMTbRquor2pKOGkUCgUSaTd8nZ4pvdkbP2xST534uGJXHp6icmNJttgZbZjke8ivtz8JSs/XEmV3FXiP9BkkkrQuHFw6BD06YOpTx+Kr6zL2HpjaVCgAQCG0DCONOxGwPEzGIPME092Dnoq7l1KhlJF4z/o6lVo0ULM0wUKSBWqQwfw8EjCq42G0Qi3bsUWVOfPi9h6XUwVLiz3tVK151jrz3i8ZS/G4KQZ9XXOjnh2aYXPxCFWWYciCiWcFAqFIok8DnxM8anFWfnhSirlqpSkcxecXsDGKxtZ2HKhjVZnOzZf2UznVZ2Z/cFsGns3jvlkRASsXAljx0JAAHz1FXz0EThLVW7h6YXM+HcGO7vsfHWKMTycM31/4u6CNRAagjHO/UomdI6OOObMQrmn53BZsgBqJxBGumUL9Oolu+uOHpUq1IYNMp6kWzdo0MB6XiY/P6mqRRdTFy6I0MqTJ25R5eZm9uUDr95iT8kmGEPiFk0JzvRDRtPUvbUXvVuGZL9URRRKOCkUCoUFLDu7jME7B/PvJ//ipHcy+7wNlzYw+ehkNnbcaMPV2Y7Ddw7zweIP+K3eb3xU4iN48QJmzYIJE8DTE775Rna5aWOKoAhjBAUnFWRRq0VU9KwY47nQh0+4VaIq14MdMISEojEZMWl1mIxGsuX2wKtEXjIuX4hmxw7ZNXfkCOTOHXtxgYHg4yNhl++/H/V4QAAsWSIi6tYt6NwZunaV9pstCA2VytfrgurCBXBxiS2mihSBXLlivWdnv/qFm1MXYQoPj/M2ic70c3ak0LAvyNe/u21e5zuKEk4KhUJhIW2WtSFfxnyMrjfa7HMO3j5I/839OdTjkA1XZlvOPz5Plz/rMvlqIcpvPC0VoK+/hgoVEjzvzyN/su36Nla1XRX7ydatMel0hGfOSvjmbeiKFEKf3wudTivC7PZtER1jxsDSpdIOdHSMeY2vv5bIgfnzE1j8eZgzR47JnVuqUO3aQYYUqMqYTHDvXmwxdeEC+PuDt/crMWUoUJCtfUZjCA6xeKYfgEP2LNS5uUftsrMiKsdJoVAoLOTPRn8y59Qcjtw9kvjB/+Hu5J6mc5wSxdeXIgNGc/CPQC7cOcno31tjWrIkUdEE0L1Udw7ePsi5x+diP1m4MJqzZ7EvXxYXF3scNUZ0tWtJy61qVRFLIBUtLy/47LOYmUzHjsHChfD77wkvokgRGD1aKk9DhsC2bdJW69gRtm6VpHFbodFAzpySK/X55zB5stz/zh3Z1Td9OjRqBGFhhMz/nwwm/o/ImX47C9YhfbFCeHYxb+Ze2KOnSTbgKxJGCSeFQqGwkCwuWfjj/T/otqab2SnbaT0AM05MJhEVDRrIn0KF0F29RuMNl1kVepIea3sQYUw81NJJ70S/Cv34bf9vsZ8sUkSqSj4+Eh8QGiqC6dAhqQpNmybHaTRSgTp0SIQGQHi4JIOPGweZMpn3muzsRKQsWyZttYoVJSzTywsGD4YrV8x8c6xE+vRQtqy0EX/5hYhfR6NxjZqNGDnTL9z/WZJm+mns9YQ/szzEVBEbJZwUCoUiGbR9ry3eHt4M321evICboxsBIQEYTWbMWUttwiSwkpIlxezdvr0ES373Hbi74+HswbaPtnH3xV1aLW1FcHjiuUyflvuUdZfWcevZrZhPFCggfqkiRWRHWmioGKnz5ZNwy9u3ZVgvyKiVVavgxx9FQI0bB9mzy+45S/DwgL594cQJWLdO4g0qV4bq1cUX9fKlZddNBjpnR0zGqIqapTP9TAYDOmfzPXiKxFHCSaFQKJKBRqNhSuMpTD8xneP3jid6vF6nx0nvlKxRJjYnIEDaWfnyiRfot9/g9GkxVDs4xDg0nX061rZfi4vehQYLGhAQEpDgpTM6ZuTjUh8z/uD4mE9EenDs7eVP2H/+nWrV4MAB6N49qsIE4geaMQOaNxff05Qp1slUKlFC2n137kD//iLQcuWSqtfu3SkzsuXmTRy2b8EUlHDYpTlodFrsXF2ssChFJEo4KRQKRTLJli4b4xuMp9uaboQZEg8pTLM+pxs3RCzkywdnz8L69VEtugREib3OngUtF1AiawlqzKnB/Rf3E7xN/4r9mXdqHk+CnkQ9ePUqODnJGqILp+rVYc8e+Phj8TBFH+DbpIkYxN3cxDtkTeztJQ9q7Voxb/v4iK+qQAEYMUI8Utbi6VMJ8OzdGwoWhLJl0e/djUeRvImKwYRm+mns7PDs1ByNVn3UWxP1bioUCoUV6FisI3ky5uGXPb8keqy7kzv+If4psCozOXpUdpaVKSP+otOno1p0ZqLVaJnYcCJtirah6uyqXPW7Gu+x2V2z06ZoGyYdnhT14Jkz0m47fz52xWnfPqn6lC8vAiOSWbNkFl3BguJPshVZs8qOPV9fWLxYhguXKiXZUAsXQlIrQ4GBsHkzfPstlC4tvqpZs6SKtnw5PHwIixeT/4+f0Lkk3GarcXYTDV/64uSZjQobZ8nXeUREavR2ePVL2zMR30RUHIFCoVBYiXsv7lFyakm2dN5CyWzxi47ac2vzfbXvqZuvbgqu7jWMRgmGHDtWqjxffilVnfTpk33pv4/9zU+7f2JDhw2Uyl4qzmOu+F2h0sxKXP/iOuns00GzZrKmmjXFt3T2bNRQXm9vWLECLl+WvKg9e2QXWokSsivN0xPKlYOffxYfVkoQEgJr1ogH6sgRaNNG2nkVKsSuEoWHizjdvl3+HDsmwqtOHRFf5cvHmTRuMpnYVageQTfuJLlFqNFoyFC+OFX2LU3Oq1TEgao4KRQKhZXI4ZqD3+r9RtfVXQk3xB1aCP9VnIJTqeIUHCw71IoWhZ9+gj59pE3Wv79VRBPAJ2U/YWLDiTRY0IBdN3bFeUwB9wLU9qrN9OP/+ZbOnBHxc/68tN+ihz5WqyZiqWlTEU8XLoiZu1cvKF4c3N0ltbxfP6mWpQSOjtC2LWzaJPfMm1eS0osWhVGjYMcOEXlNm8pOv08/Fe/Yt99KxWrvXhg2THYOxjOeRaPRUG7dNLON4NFORG+noYy9Pzx7luyXqoiJEk4KhUJhRbqU6EIO1xyM3Dcy3mNSxeP0+LEIJS8v2Tn2999RLTobDIJtXbQ1i1sv5sNlH7LqfByBl8CgKoMYd3Acof5PRExUqyaiyMEhpnCK9Dnp9WJQHzhQ2mY//hh1TIkSIlRatpQwyZTE01N29A0cKF8PHw716sH48eKJOnMGTp6U3X+NGsmuQDNJVygfFbfOwy5jetAl/pGt0dvhkNWDSsfX4fheYYlZSOlohbccJZwUCoXCimg0GqY1ncakI5Pwfegb5zFujimY5XTpklSVvL1lp9jOnSKcatSwzi60BKjtVZuNHTfy6T+fMuPEjFjPl8peCp8sPmxZN0ESs318ojxOrwunvXulXdW2rZjW//wzdnJ4x45iGO/USdp+tuTJE8mAijR0lysnRvoPPxSh9Pw5/PKLBHiWKhUVd2CBOyZDGR+qHVtNznZN0Do6xBkvoHNxQufsSK6urah2fC3pihSASZPgiy+gShWpgCmsghJOCoVCYWU803syss5Iuq6Ju2Vnc3O4ySSG6ubNpRWUObNUcqZPt918tngok6MMe7ru4Ze9vzBy70het9UOqjqIw5tmYPTxkXVqNLL+iGiBmnnzyhy3q1elUpY9uwiXuBgzRvKghpuXq2U2kYbuAQPE0J0vn4xuiTR0P3ggxvGePeU5FxcJs9yxQyp7Hh5SDYuMO3j8ONFbRsc5T05KzhlD3dv7KDTiSzJWLImLtxcuhfPhVrUsRcd9T917Byn213AcsnhEndi7NyxaJN6vqVOt+568oyhzuEKhUNgAk8lEgwUNqJm3Jt9X+z7Gc9OOT+PI3SPMaBa7CpMsIiIkd2jsWPDzk9DKLl3AOYkeGRtw78U9GixoQF2vuoxrMA6tRn5vN5lMLKmXg/dK1qPY2HmYKlcmPGdujFu3ob93G62To8xZ69BBZsstWCBxAAsWiNE6Lh48kArQlClSgbKE6Ibubdvg+PGonXR16sRr6E4Qo1GyoGbPlpiDWrXEUN6wobQhbcmVK+K3qltXhJsN2rPvCko4KRQKhY249ewWZaaVYVeXXbyX5T1AhMKKwwtYe3QJf7eYgb1HRnROjolcKRFevpTt7BMmQI4csnW+WTPQ6azwKqyHf7A/TRc1JW/GvMz+YDZ6nYiFx5WK80feEFrqmvBg2T9oAIwGTDo9jp5ZyfdNTzxf3Mfu5+FS5WnUSOIJDhwQD1FcHDwIH3wA+/dLKy0xTCbZybdtm4ilPXvEDxa5861atSR5kxLl+XNp9c2eLaKmY0cRUT4+1rvH6wQEiKfNYJDZf25utrvXW4wSTgqFQmFD/j72NzP/ncmeD7fyYNEGro2dQfDdB4TpjDjpHDCGReBRozz5vulBptqVkhZWeP+++FimTZPqxddfixk4DRMUHkTb5W0xGA0sa7MM041HHC9eD3+tFr1BC8bYH0k6FydMIaHkMwXgHXJPKlDffCNVk1Gj4r/Z1KnihTp4MG7Rc/NmVEVpxw6pzEVWlGrVgixZrPjKE+DSJRGE8+ZBtmwioNq3l92C1iYiQtqN//wjXjdvb+vf4y1HCSeFQqGwIUajke/bV6DKhkD0dg4YAuMOS9Slc0afwZXSiyfiVjGR4MkzZ2SH1po1Uqn48kvIn98Gq7cN4YZweq7ryfMTZ+k1KRzDyyAgcaO6DiOZG9Wi9KqpaC5fFoP7rVvxt8xMJsmmCgoSn8/Tp2KOj8xTevYMateOEkteXtZ9oUnFYBARN3u2xBw0aCAiql4961cPZ8yAH36Qlme9eta99luOEk4KhUJhI0wmE76f/MidJesxBYWYdY7WyZEyyyaRpUH11y8mH/Zjx8pOrb59xfhri6pEChB47TZbS7+PPjAi8YOjobPX49m9DT6Thopw6tcPWrWK5yaB8p716iXVqefPpeUW2X7z8RHTeVrE31/E3uzZcO+eZER162bdCtHu3bJL8ccfZZyMjXdZvi0o4aRQKBQ24uLQCVyfMAdDUHDiB0dD5+xEpR0LyFDGR0aPLFkiFabwcGnHdewYa9jum8bR5p/waOOeeGMDNHo9peaPJUMZH5zzenKwTmf89hwBRFxWPbAM11NHZQjxpk1yUnyG7rJlYe5c2fVWv35KvUTrceaMtPIWLJDKYrduEntgjcDSa9fENF69OkycaHuT+luAEk4KhUJhA4Jv32dXkfoYQ+Me+puQMABI7+NNtQ7V4Y8/JOPo66/h/fffiqpAyP1H7PSuizEkNN5jNHo9efp04NnxM5ReNIF/O30d9f7odHh2bk6JiYMlmqBfP/j334QN3du2STzAkSNiLH8TCQ8XkTh7tniymjYVEVWzZvIqZ8+fi6cqOFgM6x4eiZ/zDpNGa5QKhULxZnNzysJYmUWv43fgBCe7fkvI/Uexnnt55iLPdx8UA++2bbJl/S0QTQA3py1+9XWty9vJ17871U6spf6TY5Ra+DtaB3tM4eHcmDgX//3HMRleq0oZDNybv5LwPF7Swly/XkIvr1yJSuhu2DCmIbxuXRkr06qVzJl7E9HrRSytXCmjZ8qUkdeULx8MHQrXr1t23fTpJR6hTBnZXBA5I1ARJ0o4KRQKhZUxhoVx8+9FmMLCLRMGgEmn43q2QtJqesu4t3h9jGpT9jYNOdK4BzsL1iF9sUJ4dmmZ6DW0ej1+v46XuIGHD0UQZc6c8EkDBkgWVL9+yX0JqU/mzLIp4ORJye7y95dsqVq1ZHdeYGDSrqfTSXjoDz+Idyyy/amIhUrAUigUCivz7PjZGN9HCgNjSCiVdy/Cs0tLbkWrusSFyWDk4dp4Ah5tickku7siIuTv6F9b+vdrj0U8jJmafWPyfEL/q7o93LCT9CUSTzc36XSE2TmIwTtPHtle36xZwidpNNLmqlBBUtR79rT4bUozaDQirkuVEuGzbp28xi++kKTybt1k5Iq51cquXSX3qnVrGDRIROZbUum0Fko4KRQKhZUJ8wuI8b0lwgAgIuC5fPBZSbCY9bfRKH4ZOzupQiT37zgeM4XG9DaFPIgSUoagYBxzmJGfZAJTpLG8Vy8RQokJJwBXV6nQVKsm40/Klzfr3+KNwMFBBE/r1pLxNX++iEODQQRR587m+buqVJHsq2bNxJj+559JT0l/i1HCSaFQKKzNa94mi4QBgFYju52SKVSS9LdWa/MKg513XcKv307WNTQ6LfbuGeWbNm1kvMydO+DpmfjJhQpJaGjr1nDsWMoFXaYk2bPDt99Ke/LwYdmVV6KEjKLp1k3mGL4+JDk6efNKG7RTJ8l5WrECMmWK81BjRAT+B/8l9MFjjKFh6DOmJ0PZYjhmS6R1+oaihJNCoVBYGX3kB3oysXNNJx9ybxnZW9Tj+uT5mMJiD0COjtZe/0rEae31aB3sX+1SNIaE4l6trBzo4iK7wmbNgiFDzFtE8+Yimj78UMz3b+vsNo1GDN8VK8qMulWrYOZMyW368EP576tcubjFcmR17ocfpL25di28996rp0MfPuHm9MXcmDQfU3g4JgCTCY1WizE0jEx1K5Pv6x64Vy0rae9vCcocrlAoFFYmQxnz5o1FioHXvwbQ6HRkaVrbJutLbfJ82smsD9IaZzfR8KUvTp7ZqLBxlnydJydoNGTNnx17j2iz1nr2lDRsg8H8hfz0k1RdBg604FW8gTg5ybDkrVslviFnTvnex0eCVR88iH2OVgsjR8KwYWI837ABgNtzV7KjQG2ujp5GuF8AES8CMbwIxPAyiDXhYu0AACAASURBVIjnLzGGhvHon90cbdqLg7U6Ev78Zcq+VhuicpwUCoXCBpwf9BvXJ82j5tlNnP7kR57uOAhAwcGf41IgDye7DKDW5e04543ZWtpRoDbBN++idXakyp4lpC9RODWWb3MO1enM0z2HMWfUyuvoHOyp6J2OjCcOxnyiXDkYPlyiCMzFz08CMn/9VQbgvmuYTLB3rxjKI71f3bpBkyaxfU2HDkGrVlwrW4eLO09hDDYzDd/BHqc8OalycDn69FYclJxKKOGkUCgUNiDo5l12v/d+vAGYieFarBDVT6y18qrSDi/W/MP+Nv0xJPETSKuBrNkzUPrJWZk9F92nM3267K5btSppFz15Unw8O3ZAsWJJO/dt4uVLWL5cRNS5c1KN6tYNSkbNTnw4Zykneg2OaxZzgmgd7MlQrjiVdix449t2qlWnUCgUNsA5T07y9uuCztkpyedqnR0pPvVnG6wq7eAa9IyylbyS9P7onJ3IWLUsJSv9N9D4v7bRK9q1g127ZEdZUihZUvw/LVpAQEDix7+tpEsnu+9275bqUvr0srOuVCmYOBHT48ecGzU9QdGk0espvfgPal3eTuPwi7hXl12LxtAwnv97Dv/9x1PmtdgQJZwUCoXCRhT+5WuytaifJHGgdXak9MLfyVi+uA1XlgY4c4ZMtSpScft8HLJlRpfOOd5DtfZ2aDGRo1UDKmyZi3bR/6B0aamGHDoUdaCrq+ywmz076evp1AkaN5a/45mf906RPz+MGCFp5GPGwOHDBHgVIfTGnURPjS8R3xAUzNVxM2y14hRDCSeFQqGwERqNhhKzR5Pvmx5oHR0SFFC6dC7YZ/WgwoaZZG3ydprCY+DrC8WKkbFsMerc3EPpxX/gUaYoGkDn6oKdyYDO3g47nYZ8H9SkVouyFM9uh9bOTnaAffONfLg3ayZ5RZFEmsQtET9jx8rcthEjrPYy33h0OhlXs3Ah1+q1wvDf+2pRIr7JxJOt+2PEc7yJKOGkUCgUNkSj0eA9+HPq3tlPoZ+/wtEzG1oHe3TpnNG5OKHR2+FevTylF/1O3Vv7cK9aNrWXnDKcOSO7uQCNVkuWBtWp2KEW9TpWoerB5VTSPqB6/zbUG/4xhVwjcBz/G0ydCrf/y3+qWhWuXpUdYsOGSWaRwSBG7wwZxK+UVPR6WLpUvFLr11vvtb4l+B0+DdHadBaNynGw59mJs4kel5Z5S4MrFAqFIm2hz+CKV9+PyPt5Z8Ke+BPu/wytvR57j4yS1/Qu8eKFbH0vUCDm4/v3o2/cGH2hfKA3gV4DzT+QbfBTp0KfPjB4sIQ5enjIqJXwcDhyRFp0TZvCokVSdZo2TSolSSVbNli2DD74AA4ciL3GdxhDYHCM7y0alWM0SSL+G4yqOCkUCkUKotFocMjsTjpvL5zzer57oglkx1bhwtIGisRkkqTqKlXke71ednl5e0vA5cmTkre0ebNkEIFsnd+zR0TU5s2QL19U2OOWLfDoUex7m0OlSpLx1KJF0oflvsVo9DFrLa8n4tsl4FN7dQ2NJkZe2ZuIEk4KhUKhSFn+8zfF4PZtqR7lyyff6/VRoqVJExle6+oKQ4eKv8lkknE0e/ZEHT95Mnz5peQ4VagAc+davsbevaXt16NHrBE67yr2mdwSPyhRTDhkjXt0y5uCEk4KhUKhSFmi+ZteEVltisz4sbeHoCD5umnTKM9Rjx5w7x5s3CgVp717YxrBP/lEfEpHj8puMEt3yGk08NdfcOkSTJhg2TXeMnJ1b4PWKYH5dv+RUCK+1sEet0qlbLbGlEAJJ4VCoVCkLHFVnKK36SCmcKpaFS5flnwmOzsRRAMGQNaskDEjnD8f81o1asgcuufPRXSFhlq2TicnWLkSRo+WfKh3nNzdW5slROMblaN1dCBvvy5oordo30BUcrhCoVAoUpasWeH4cfCMNm6mVCmYMkX8SSAeqIIFpUUHEm5Zrx58/LG0zurUkccOHoTy5cU4/jqjR0v7Lk8eEUBZsli23q1b4aOPpIrl6Zn48W8xJ9p/wYM12zCFRyT5XK2TA7Wv7MQhi4cNVpZyqIqTQqFQKFKOR48gLEwGzEby4oVUlEpFa+E4OEBwtF1cTZpEtes0GslcGjZM5tNF+pxep2dPuXaFCiKuTp60bM316ol3qlUry6tXbwk+k4Zin9lDhv8mAa2TI8X+Gv7GiyZQwkmhUCgUKUmkvyn6vLLDh0U0OThEPeboCCHRhsg2bCjZTJGPlS4tcQPnz4vPKa7mibu7CK5cuaT6VK8erFhh2bq//Vau06+fZee/JdhncqfyroU4ZMsca5ddfGidHCn881d4dmpu49WlDEo4KRQKhSLlMMffBOIvii6cPDygePGYXqOff4b//U92412/Hvf9evaUQMsPP4RNm6B/f6lUJdU0rtHIKJc9eySZ/B3G2SsX1Y+vJlvzemgd7OM2jGu16FyccC6Qh9KL/8CrX5eUX6iNUMJJoVAoFClHQjvqovN6xQlitusAcueGXr0k5ym+dl316hARIWGWZcpIWOaWLSKkkprR5OoKq1bB99/Ldf7f3p2HN1Wm/QP/Zm+6pFDWslOQRQUsAmUruzhQUWBcxnFBEZzR0bn0VccVZ3FGfd8RUYZxGR0U+SFuI6PIpkJBZN9pQaEgRXaKLaVtkjZN8vvjNjRJk+YkOae08P1cFxfJaXLO0/EPvvM893M/lzBz8zT0ff8VjD78Dbo9+yASu3SAMTUFhiQrzK2ao/XEa5C1Yh5G7F2BVuNHXOjhqorF4UREVH8GDpRdcdnZ8t7tliW1gweB5n79fSZPlpC1f3/NtT17gPHjgcLCmqW+0lJZQhs1Cvjvf0M/86WX5F7vvivvKyulT9OOHcBnn0nxeDQWLZKapy1bYi84p0aLM05ERFQ/PB4JP/4zTvn5QHp6YGgCZKmuqirw2uWXS7fx/Pyaa6mpwIMPyixSuHmAKVMkVJ09K+8tFmDuXLk+cCDw7bfR/R6TJgG33y67+qqj311GjRuDExER1Y/DhyXoNPXrQB1qmQ6Q5bfg4KTT1V6uA4Bnn5VZpPffD/3cFi2Aa68FFiwIvNfDD0vd0uTJ0dct/eUv0q38ySej+x41egxORERUP5TWNwFAYmLt4ATUHL/iz2KR+qUnngg/A3TvvXLwb/Cs1C9+Ibvy/v532TGndAbJYJCg9p//SKdyumQwOBERUf0ItaNu/Xpg8ODan01ODh1ihg+X5b6iosDrN98sS4G+OqZgI0fKocFbttT+WffuwMaNwL590vaguFjRr4NmzSQ4/e53gcuHdFFjcCIiovoRPON0/Lg0qOzevfZnwwUni0X6Ny1bFnh92DCpi3r2WQlIwfR6OefurbdCj61pU2DJEml5kJVV+xiXcDIzgZdfluU+Xw0VXdQYnIiIqH4EzzitWyezTf7NMH2SksIvm4VarsvMlK7kQ4bILrpQ7r4b+OQTCWuhGI3AzJnSbmD4cGDp0si/EwDccYfUUN15Z+yHClOjweBERETaq6oCDhwAevasuRauvgkAbLbwwWn8eDk/zr8GymSSHXJjxwL/+IfMZgVr3VqW7BYurHusd98tu/CmTZPaJyVde2bOlCW+v/0t8mepUWNwIiIi7e3fLw0rrdaaa+HqmwBpNhlu9qZVKzkEOLjp5bBh8pxp02TJLhRfkXgkgwfLUTALF0rbguBmnMHMZuDjj4E331Q+U0WNEoMTERFpL7i+yW6XIu9+/UJ/PjlZZnrCzTpNmFC7LUF2toSpJ5+Upbzdu2t/75prpLB8x47IY27fXno8VVbK0l2oWSx/6enAhx/KjNXBg5HvT40SgxMREWkvuL5p82YpxPafgfJntUpBd2Vl6J/76pz8l9EGDJCAZjQCzzwjB/MGMxjqLhIPlpgIfPCBBLWsrNC78vwNGSKzXZMmRX+kCzUKDE5ERKS9/PzaheHh6psACU46Xfglst695XDf778P/E7fvsCGDcBvfgP88AOwYkXt7959t4QhpcFGp5MgNnu21FeFa7Tpc//9Uqx+773K6qOoUWFwIiIi7eXlBS7V1VXfBMghvzpd+BmncF3Es7OloaXZDPzv/wKPPSbn4flr105CW7SNKydNAlauBJ5+WnbehavB0umAN94A9u6VsEUXFQYnIiLSVnk5cPIk0LWrvPd4ZFYo0owTUHdRdqjgNGxYTdH4xIlyxMu8ebW/q7RIPFjv3rLMuG6d3P/cufDj//RT4PnngTVron8ONVgMTkREpK09e2QXnMEg77/7DkhLk91x4SQkyDJXXcFp5Ehg587ATt+DBwNbt8pMlU4nPZ1mzKi9LDduHHDkiMyERatFC2mHkJ4uzwtXCN65MzB/PnDrrcCxY9E/hxokBiciItJWtPVNgLIZJ6sVGDECWL685prNJiFt61Z5n5Uls1AzZwZ+12gEpk5VXiQezGyW5bj77pPwtGpV6M+NHStn4N14Y/hlR2pUGJyIiEhb0dY3ATLj5PFEDhvh6pz8ezw9/zzw6qvAiROBn7vnHin0djgi/w6h6HRyTt3ChTKr9NproYvBH39cZqceeii251CDwuBERETaimXGybdUFynU5OTIjJPLVXPNv84JkCWzqVOBP/4x8LsdOwL9+8tBvfEYNUrC4D//KTNQ/h3NAQlY774LrF4NzJ0b37PogmNwIiIibfnPOJ06BZw5A1x+ed3f0eslcIQ7V86nTRsgI0OCi8/QofLefzfdU0/JMSr5+YHfnz49tiLxYF26SMH7sWOyPHfmTODPbTZg0SLgiSdqlhGpUWJwIiIi7Zw+LTMwbdvK+/XrgUGDJBhFYjBEDk5A7eW6Fi2k5cCuXTXXmjaVNgLBTTEnTAAKCgL7QcXKZpNwNmiQNOMM7lzeo4ccyfLLX0r3cmqUGJyIiEg7vqNWdDp5r6S+ySea4LR4ceC14DonQJbR9u+XHXE+JhNw112xF4kHMxiAF14AnnsOGD1agpS/SZOA224DfvWr8MfJUIPG4ERERNqJpb7Jx2iUHlCR9O0r/ZQKCmquBdc5ATVNMR99NHAZb9o0aRug5q63226Tw34feAD4618Di8afe04C1lNPqfc8qjcMTkREpB3/+ianU5avBgxQ9l2TSVlw0uulSHzJkpprvg7iwbvcJk+WA4Tnz6+51qWLNLZctEjZuJTq31+aZS5eLDNMdrtcNxhkJ97HH8sfalQYnIiISDv+M05btwI9ewJJScq+azIpP08ueLmufXsJSMG1Szqd9HR65pmaIANIkbhay3X+2rSR3XRms4S5I0fkerNmspvv/vulQSg1GgxORESkDY9HQoFvxima+iYguuA0ZgywZQtQWlpzLdRyHQAMHCjLhS+/XHNt4kSZHTtwQPn4lLJagffeA265RRpybtgg1/v2lc7mkyYFjpsaNAYnIiLSxo8/yk6zpk3lfTT1TYDM0vjPCtUlKUnaEKxYUXMtXHACpIB71ixpjwAAFgtw553A228rH180dDrZ0ffWW8ANN0hfJwCYMkXaF9x5Z/hDg6lBYXAiIiJt+Nc3eb0y46RVcAKktYB/WwJfcArVzTsjQ3bT/elPNdemT5dAE9zAUk05OXLo79/+BjzyiOyse/ll6fv0/PO1Pu71eOAqKYXj6ElUl5XDG+p3oXrF4ERERNrwr2/av19mhXz9nJSwWKI7DiUnB1i2rGbHXNeu0lH88OHQn3/6aakz2rtX3nfvLn+CWxuorWdPYNMm6TN13XUSDj/+GHj9dRk/gPLvDyLv/mexvEkmvmo3BKuvuBZftszC120Ho+DFN1BZVBzhIaQVBiciItKG/4xTtPVNgBy7Ek1w6tBBirE3bpT3Ol3dy3VpacCTT8pZcj5aFYmHevayZUC3blL3VFYGfPghnHfejfVZk7C2/yQceec/8Dic8Fa54LE74a2uRlVRMQ48/xpWdR6O3b95Gh4tZ8coJAYnIiLShv+MU7T1TYAEJ6czuu+EW64L5/77ZcZp1Sp5/8tfyu6/wsLonhsLkwmYPVv6SmVnw37wMNZWt0bJ9r3wOCvhDdMg0+OohKeyCsc++AIbRt8JtyPK/40oLgxORESkPpdLGlL27Cnv6ys4BR+/4uvnFI7FArz4ooQXj0d2wN12G/Dvf0f33HhMn46qufOw/t4/oapC+e/rsTtxbtdebLv5QXhZWF5vGJyIiEh9+/fL0pnVCvz0E3D8eGAHcSWs1ui7effvL+fj+WaMrrxS3p88Gf47N94oAWrBAnk/fTowd269HolSsGo7qgzm0IXsAHQmE/p+8CpGFqxEjmsf0oZJE1GPoxLFa7fi9LI19TbWSx2DExERqc+/vmnDBukWbjBEd4/ExOh3uBkMwPjxNbNOBoO0Kahr1snXFPPpp6Wm6sorgY4d5ciUeuB2OHFk7ifwuuoOasXrt2PnXX+A88TpwO9X2PHDTI3aKFAtDE5ERKS+eOubgNiCE1B7uW7YsLqDEyCF6wMGAK+8Iu/vvbd+isQBnPhkOfDzGcgjC1Yi4+GpyN7+Ocae2YrMBbOgt5jhdblQOHseStZtg9dde1nu7JY82H84Ui/jvdQxOBERkfr8Z5zqOzhdc43s4isrk/fZ2XUXiPu8+KLMPJ0+Ddx0k4z76NHonx+lYws+g7u8pl9V+k3jsDlnGnIvGw1br+5oN2WyovucWrpaoxGSPwYnIiJSn2/GqaoK2L5dttxHKylJisyjZbPJsSpffy3v+/YFDh4ESkrq/l7XrsDttwN//rM8+1e/klonjVWeCezJVDhnPipPnIarpBSnluTC1qdnxHt4nJWo+inC70eqYHAiIiJ1lZcDJ04AXboAO3ZIILHZor9PcnLsBdr+y3VmswS3desif2/GDOCjj+Rw4HvvlSNYfA01teIJLAh3niw6/9ptd8CYnKjsPiGW8Eh9DE5ERKSuvXuBHj0AozH2ZTog/uC0ZEnN+W9K6pwAoFkzaYj5+OPAVVcBrVoBX34Z2xgUMjdvGvc99BYzzM2aqDAaioTBiYiI1KVGfRMQX3DKyJAQtHWrvFda5wQADzwA7N4NrF4ts07/+ldsY1Ao/aZxMCRFnlXSm03QW8y1XgMAdDq0uDZbqyGSHwYnIiJSl6++yeuNLzilpMS3TOa/XJeVJWGooiLy9xISgBdekKaYN98sAerEidjHEUHbWycA3sjLbMP3LMe48jxY27VG1rK58rqjnP2XcmU3JPfootkYqYbOy6OWiYhITWPGAI88IgfmZmfLzjSdLvr7LF8u4SfWWadvvwUefFDqrAAJcM89B4waFfm7Xq8UmP/+98CaNUDnznKunUby7puBI+9+GvaYlboYkhLRZ+6LSJ98rQYjo2CccSIiInX5Zpx8s02xhCZAZpziOUpk4EDgxx9rWgpEs1yn0wEvvQQ89RRwxx3S00nDY00um/EAjKnJUX9PbzEjpXd3tLp+tAajolAYnIiISD1FRXK+XNu28S3TARKcvN7Yl+uMRmDcuJrlukgH/gbLzgauvlp+D5ut5iBgDSS0aYWBX70HY2oKoFf2T7M+wYLELh0x4Iu3oTcaNRsbBWJwIiIi9fhmm3S6+IOT1Sr3ifa8On8TJtQEpyFDgM2bo2uq+eKLMvN0662aF4nbenVH9uZFSLqskxSLh5mp05lM0CdY0OKaIRiy/iOYbNHPVFHsGJyIiEg9vh11Z8/KQbt9+sR+L6tV/nY6Y7/HtdfKLJPdDqSmAt26Adu2Kf9+t27Ar38tDTS//FK6igNwV1bBVVoGr8rLd4kZ7TE8bykGLP03Wl0/GnqLGYbkRBhtyTAkWWFITkTH396KYTu/QL9PX4dRwW48Uhfn9oiISD35+dL/aONGoF8/wGSK/V4JCfJ3PMGpSRNZblu5UmaffHVOgwYpv8ezzwI9eqAiaygKb/wNjuQfhrvCAZ3RAK+rGokZ7ZHx6DS0vXWCKkFGp9MhbXBfpA3ui+qyclSePIPqCgdMqcmwtGkFg38bAqp3nHEiIiL1+Gac4l2mA9SZcQICl+uirXMC4HR5sCHtCnyTW4DDG/bCXVYBeDzwVrkArxf2gz/iu0dfwNdtBmHfH19RdRbKmJKMpMs6IfWqnkjs3J6hqQFgcCIiInV4vcCePYE76uKRkCD3jDc4+fo5eb0y47RuneKC84oDh7H26okoPlIEjxcI17/HXeGA2+7EoVfexbabH4RX62Na6IJhcCIiInUcPiy7z1JSgC1bolsOC8VgkL/Ly+O7T7ducmjvjh1Ay5ZAerrMjEVQWVSMDSNvQ9WZYqBaWRBy2x0o+moddt83I74xU4PF4EREROrIz5dlul27gI4dpb4oXno9UFoa/338l+sU9nPa98xMVP1UIjNVIehMJvT94FWMLFiJHNc+pA0bAADw2B048eFSlGzcGf+4qcFhcCIiInXk5am3TOdjMADnzsV/H//jVxTUOVWXlePYwi/gddXdybt4/XbsvOsPcJ44HXDd7azED7PmxjVkapgYnIiISB2+GSe1g1O8S3UAMHQoUFAgZ84NGwasXRt2JgkAjr6/GDq99FEaWbASGQ9PRfb2zzH2zFZkLpgFvcUMr8uFwtnzULJuG7zuoIJwjwenl+TKMh9dVBiciIhIHb4Zp/Xr1QtORiNQVhb/fUwm6em0dCnQoYMUnu/fH/bjx+Z9CneF4/z79JvGYXPONOReNhq2Xt3RbsrkiI/UmYw4vTy6HXzU8DE4ERFR/FwumdFJTpbO3BkZ6txXrRknIKrlusqgmaLCOfNReeI0XCWlOLUkF7Y+PSM+zlPlQtWZs3ENmRoeBiciIorf/v0yk7NtW3wH+wYzmdQLTuPGyXlzTmfkOidP4DKe82TR+dduuwPGZAWNLr1eeD1sS3CxYXAiIqL4aVHfBEhwqqhQ517NmgG9ewOrV9fUOYV7bBNb3I/Tm02q3IcaFgYnIiKKnxb1TQBgNss5c2rxLdd16wY4HNJ7KoTWk66BPsES8XZ6swn6n7t5+78GAK/bg+aj4uxlRQ0OgxMREcUvPx/o0kWW7DIz1buv2azejBMg/ZwWL5bX2dlhZ506TLulzl13PsP3LMe48jxY27VG1rK58rpjWwBAkwF9kNipnWpDp4aBh/wSEVH88vKAG26Q0GSJPFOjmNksM0Nq6dlTCs7z82uW626/vdbHLK2ao8XYoTj1RS5yLxsd8LOC5+acfx38Mx9DUiK6PDZNvXFTg8EZJyIiik9FhfRHOnRI3WU6QNoGqBmcdDpZrlu8OGIH8R4vPAZDkoIi8CB6ixmpfS9Hi7HZ8YyUGigGJyIiis+ePUCPHsCmTeoHJ4sl/kN+g/mOX+ndWwLf6dMhP5bcPQP9P38ThiSr4lvrEyxI6toR/T97Ezo9/4m9GPG/KhERxSc/H7jiCmDjxvgP9g2WkKB+cBo2TMJecbEEvTp21zXL7o9Bue/D0ro5DClJYT+nMxqht1rQfNQgDFn/MYwpyeqOmRoMBiciIopPXh7QvDmQni5/q8lqVT84WSzAmDHSRTxCWwIASM28HKMLv0Hm/JloOuRq6C1mGG3JMKamwJiSBENiAtpPvRHZmxeh/2dvwpCofIaKGh8WhxMRUXzy82V7v9rLdIAEp8pK9e/rW657+GHggQciflxnMKBVzki0yhkJ58kiVB4/DbfDCVNqChIz2jMsXUIYnIiIKD55eUBKihRdqy0xUY5wUdu4ccBDDwHvvCMtFEpLgdRURV9NaN0CCa1bqD8mahS4VEdERLErKpKltO3bgcGD1b+/VsGpVSspaN+4ERgwQDqeEynA4ERERLHLzwe6d5fz5Lp3V//+SUlygLAWfMt1CuqciHwYnIiIKHb5+bLENXiwegf7+tMyOPn6OQ0dWveBv0R+GJyIiCh2eXlAdbU2heEAkJysXXDq3Vvu3bQpsHOnuo026aLF4ERERLHLz5cmklrUNwESnNxube7t6yK+apWEqE2btHkOXVQYnIiIKDZerwSnw4eBfv20eUZKisxoaeW666TOKcLxK0Q+DE5ERBSbH3+UZpJ9+ki/JS3YbIDHo829AWDUKFmmy8xkcCJFGJyIiCg2eXlSH6RVfRMgM05aLdUBcqTLiBGyK3DTJu3qqeiiweBERESxyc+XZTSt6psA2bGn5YwTIMt1ublAly7Sj4qoDgxOREQUm927gVOntJ1xstmklkpLOTnA8uXye3C5jiJgcCIioths2wakpUkXbq3YbPK3lst1bdoAGRlAy5YMThQRgxMREUXP5QIOHZKu21ryFZ1rcdCvv+uuA06elKNXtF4apEaNwYmIiKJXUCChpr6Ck9Op7XMmTJA6p+bNpXaLKAzjhR4AERE1fGV7D6Bwzns4u2U3qs+VQ19ViURnKjoZktHc44FOr9H/Dzf+/M9UebksC2olMxM4d66mzql3b+2eRY2azuvVuuqOiIgaq6Iv12LfjFko++4APK5qoNq/1sgLQ3ISjCnJ6PLYNHT63R3aBCi9Hti1C+jVS/17+5s+HaiokJ2CH32k7bOo0eJSHRERhXRw5tvYeuMDKN2+Bx5HZVBoAgAd3OV2VJ44jX3PzMKWib+F26lBLZJeD5SWqn/fYBMmAIWFwNq12u/ko0aLwYmIiGopfH0BCv4yBx6Hstoit92Bn1ZvxPZbfg+v2sXVer0so2lt9Ghp6mkwAAcOaP88apQYnIiIKEDZdwfx3eP/B7fdEfLnOpMJfT94FSMLViLHtQ9pwwYAADyOSvy0ehMOv7lQ3QEZDEBZmbr3DCUpSYrdO3ViWwIKi8GJiIgCHHrlHXgiHD1SvH47dt71BzhPnA647rY7cPD/3oKq5bP1FZwAaUtQXc3gRGExOBER0XnV5RU4tnAxUO3GyIKVyHh4KrK3f46xZ7Yic8Es6C1meF0uFM6eh5J12+B1116Wc5WUovibzeoNqj6DU04OsG+f1DkRhcDgRERE553871fQGWr+RFzt9AAACVRJREFUaUi/aRw250xD7mWjYevVHe2mTI54D7fdgcOvv6/eoEwm2e1WHzp0kD8lJcCRI/XzTGpUGJyIiOg859GTcNtrCsIL58xH5YnTcJWU4tSSXNj69Ix8E68X9kMqhg6TSfo41ZcJE+T4Fc46UQgMTkREdF51hT3gyBHnyaLzr912B4zJiYru41a4G08Rkwmw29W7XyTXXSe7+BicKAQGJyIiOs/UxAadKf5DJYypKSqM5mf1uVQHAAMGyNl4K1fW3zOp0WBwIiKi81Kvuhx6iyXi5/RmE/QWc63XAKAzm9B0UKZ6g7JY6nfGSa+X5bojR4Ciosifp0sKgxMREZ3XbORAGFMiL8cN37Mc48rzYG3XGlnL5srrjm0BADq9Hp3uu029QVksgCN0TynNXH+99HX69tv6fS41eDyrjoiIAvwway72/fFVxV3Dg6UN649BK/+fegMaOBBITQVWrFDvnpGUlQHNm8Mz9R64/vQXqe9KTZGlTK0ONKZGgcGJiIgCuEpKkdtjLFzFZ6P+rt6agKyl/0ba0H7qDWjECDk7bs0a9e4ZQemOvTh07U04UeICEhKgM+jhcVXDZEtGp99PQYdpt8DSIq3exkMNB2MzEREFMDVNxcAv58GgcAedjz4xAVfMfErd0AQACQmAU8VdenWwHz6Gtf0mYsOIW3H8rBse6OBxVsJd4YC3yoWqMyU48MIbWJUxHLvvmxGxwzpdfBiciIioFlufHhi8ZiFMaU1gSLTW+Vmd2QS9NQG9/vlndJh+i/qDsVpll5vGyvYU4Nt+E1GWvw9uuzPssTEehxMeZxWOvf85No29C+7KKs3HRg0HgxMREYVk690DI/d/je5//R8ktGsNQ3Ki7J7T6aAzm2BMSYIhOREdf/trDN/1BdrdPlGbgSQmah6cnMdPYeOYO+AqLQt5jEwoHrsTZ7flY8evH1L3bD5q0FjjREREEXm9XhR/sxnn8vaj+lwZDFYrrB3S0TJnJAwJkdsXxGXaNCA3Fzh4ULNH7LrnCRx7fzG81dUhf64zmZA5/yWkXn0lEju1w4bRd5w/j8+QlIh+n76G5qMGaTY+ajji73JGREQXPZ1Oh2bDs9BseFa9Prd8/yHYz9jhPueGYcU3SO7WGYmd26v6DNe5chz/aGnY0ORTvH47Dv3jPfRd+ErAdXeFHQdfepvB6RLB4ERERA2Kp6oKJxd9hYN/fwvl+3+A3u0GqszAbf8DT2UVUjMvR5fHpqPl+BHQGQxxP+/o/EXnDzYeWbASh19bgLZ3TIS1QxsUrViLXVMfh6eyCoWz5wFAyKW84m82w3H0JKztWsc9HmrYWONEREQNxrnd32Nlp+HY/dsZOLfrO3gclaiuqkY1dKguLYPHWYmSDTuw487HkNttDOw/xH+Y8PGFX8BdUdNgM/2mcdicMw25l42GrVd3tJsyOeI9dEYDilbwbLtLAYMTERE1CGe37Mb6YbeiqqgY7vK6z6Zzl1fAcfQk1g6YhPJ9P8T13KqfSgLeF86Zj8oTp+EqKcWpJbmw9ekZ8R6eShdcxSURP0eNH4MTERFdcI4jJ7Bp3FS4K6I4k87jQfW5cmwcfQdcJaWqjcV5suZ8OrfdAaOCflZecJ/VpYLBiYiILriDf38rYLksmM5kQt8PXsXIgpXIce1D2rAB8gOvF65zZSh8c2HMzzanNYn5uz56sxmmpvHfhxo+BiciIrqg3HYHjr73qaJdbTvv+gOcJ04HXPc4KlE4ex68bndMz0+/eXzEJp8AoDebpI9V0GsZhBvNrxkS0/OpcWFwIiKiC+r4h0sAnQ6A7GrLeHgqsrd/jrFntiJzwSzoLWZ4XS4Uzp6HknXbQu5qczsrcTrG4uz2UybD64nc9HL4nuUYV54Ha7vWyFo2V153bAsAaDqoLxJ/fk0XN7YjICKiC6roy2/hLq+pbfLtavM4KzF4zUK0mzIZP/7rgzrv4S6rwE9rNqHV+BFRP9/UxIbWk8fi+IdLkHvZ6ICfFTw35/zr4J/5GJIS0eWx6VE/lxonzjgREdEFVXWmOOB9LLvaAKDq1E8xj6HHXx+ByZYS9ff0CRakDbkazUcPjvnZ1LgwOBER0QUVUCuE2Ha1AYA+wRz5Q2FY26cj68t5MNqSAb2yfxr11gSk9OqOqz/+B3QKv0ONH/9LExHRBWVt3+Z8jVOsdEYjrO3T47pH6lU9MXTTp0jq2hGGJGvYMektZugtZrSeeA0G5S5QVFhOFw8GJyIiuqDa3n4DDIkJET9X1642ndGA9JvGxz2WpK4dMTx/GbKWvYNWN4yB3mKGIdEKQ0oS9NYEGFNT0PnhuzHi+6+Q+d5LMFhin+WixonF4UREdEE1HdwX5pbN4Dh0tM7PDd+zHImd2gEAspbNBQCs6joKjsPHYOvTE8ndOqsyHp1Oh6aDMtHv4zlw2x2oLCqG2+6EKTUZ5pbNoDfyn85Lmc7r9bLdKRERXVCH3/oA3z36Itz28E0wwzEkWXHVvJfQ+oYxGoyMKBCX6oiI6IJrf/eNSO3fq1aheCR6awJajhuOVteHbhVApDbOOBERUYNQXV6BTb+YinN538Njd0b8vCHRirThA9DvkznQm1lrRPWDM05ERNQgGJOTMGjVfHSYdosUZIdpQ2BIToLRloyMR+5B//++wdBE9YozTkRE1OBUV9hx/MMlOPTKO3AcOQGPswp6qwVJGR2Q8cg9aD35Wu5oowuCwYmIiIhIIS7VERERESnE4ERERESkEIMTERERkUIMTkREREQKMTgRERERKcTgRERERKQQgxMRERGRQgxORERERAoxOBEREREpxOBEREREpBCDExEREZFCDE5ERERECjE4ERERESnE4ERERESkEIMTERERkUIMTkREREQKMTgRERERKcTgRERERKQQgxMRERGRQgxORERERAoxOBEREREpxOBEREREpBCDExEREZFCDE5ERERECjE4ERERESnE4ERERESkEIMTERERkUIMTkREREQKMTgRERERKcTgRERERKQQgxMRERGRQgxORERERAoxOBEREREpxOBEREREpBCDExEREZFCDE5ERERECjE4ERERESnE4ERERESkEIMTERERkUIMTkREREQKMTgRERERKcTgRERERKQQgxMRERGRQgxORERERAoxOBEREREpxOBEREREpBCDExEREZFC/x+XOWPEKu55sAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRANkF1jRxLV"
      },
      "source": [
        "which include edges within each clubs (2 types) and edges across two clubs (1 type). Different types of nodes and edges are visualized in different colors. The NetworkX object `G` in following code can be transformed into `deepsnap.hetero_graph.HeteroGraph` directly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4DW8L0hxbxw4"
      },
      "source": [
        "## Transforming to DeepSNAP representation\n",
        "\n",
        "The NetworkX object `G` in following code can be transformed into `deepsnap.hetero_graph.HeteroGraph` directly!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZfHZ_eoVVGd"
      },
      "source": [
        "from deepsnap.hetero_graph import HeteroGraph\n",
        "\n",
        "hete = HeteroGraph(G_orig)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izq4t_O9WxDH"
      },
      "source": [
        "## Question 1.3: How many nodes are of each type (10 Points)\n",
        "\n",
        "Submit your answers on Gradescope.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRNSP6nnW78C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b37667a5-48b8-49d2-b0a1-1b7406ea4402"
      },
      "source": [
        "def get_nodes_per_type(hete):\n",
        "  # TODO: Implement this function that takes a DeepSNAP dataset object\n",
        "  # and return the number of nodes per `node_type`.\n",
        "\n",
        "  num_nodes_n0 = 0\n",
        "  num_nodes_n1 = 0\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~2 line of code)\n",
        "  ## Note\n",
        "  ## 1. Colab autocomplete functionality might be useful.\n",
        "\n",
        "  num_nodes_n0 = hete.num_nodes('n0')\n",
        "  num_nodes_n1 = hete.num_nodes('n1')\n",
        "\n",
        "  #########################################\n",
        "\n",
        "  return num_nodes_n0, num_nodes_n1\n",
        "\n",
        "num_nodes_n0, num_nodes_n1 = get_nodes_per_type(hete)\n",
        "print(\"Node type n0 has {} nodes\".format(num_nodes_n0))\n",
        "print(\"Node type n1 has {} nodes\".format(num_nodes_n1))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Node type n0 has 17 nodes\n",
            "Node type n1 has 17 nodes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vEsHJp2ZYaE2"
      },
      "source": [
        "## Question 1.4: Message Types: How many edges are of each message type (10 Points)\n",
        "\n",
        "Submit your answers on Gradescope.\n",
        "\n",
        "When working with heterogenous graphs, as we have discussed before, we now work with heterogenous message types (i.e. different message types for `node_type` and `edge_type` combinations). For example, an edge of type `e0` connecting two nodes in club \"Mr. HI\" would have a message type of (`n0`, `e0`, `n0`). In this problem we will analyze how many edges in our graph are of each message type.\n",
        "\n",
        "**Hint**: If you want to learn more about what the different message types are try the call `hete.message_types`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qobKuqbAYvJ7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd317b93-ccd6-4e3c-f348-e221b0b68059"
      },
      "source": [
        "def get_num_message_edges(hete):\n",
        "  # TODO: Implement this function that takes a DeepSNAP dataset object\n",
        "  # and return the number of edges for each message type. \n",
        "  # You should return a list of tuples as \n",
        "  # (message_type, num_edge)\n",
        "\n",
        "  message_type_edges = []\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~2 line of code)\n",
        "  ## Note\n",
        "  ## 1. Colab autocomplete functionality might be useful.\n",
        "\n",
        "  counts = hete.num_edges()\n",
        "  message_type_edges = [(transition[1], count) for transition, count in counts.items()]\n",
        "\n",
        "  #########################################\n",
        "\n",
        "  return message_type_edges\n",
        "\n",
        "message_type_edges = get_num_message_edges(hete)\n",
        "for (message_type, num_edges) in message_type_edges:\n",
        "  print(\"Message type {} has {} edges\".format(message_type, num_edges))\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Message type e0 has 35 edges\n",
            "Message type e2 has 11 edges\n",
            "Message type e1 has 32 edges\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bjMVik1JbJ76"
      },
      "source": [
        "## Question 1.5: Dataset Splitting: How many nodes in each dataset split? (10 Points)\n",
        "\n",
        "Submit your answers on Gradescope.\n",
        "\n",
        "DeepSNAP has built in Dataset creation and splitting methods for heterogeneous graphs. Here we will create train, validation, and test datasets for a node prediction task and inspect the resulting subgraphs. Specifically, write a function that computes the number of nodes in each dataset split.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ct10Oh4gcqgD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98253b6f-dfdf-4b4f-94bc-07b368079f0c"
      },
      "source": [
        "from deepsnap.dataset import GraphDataset\n",
        "\n",
        "def compute_dataset_split_counts(datasets):\n",
        "  # TODO: Implement this function that takes a dict of datasets\n",
        "  # and returns a dict mapping dataset names to the number of labeled\n",
        "  # nodes used for supervision in that respective dataset.  \n",
        "  \n",
        "  data_set_splits = {}\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~3 line of code)\n",
        "  ## Note\n",
        "  ## 1. DeepSNAP `node_label_index` will be helpful.\n",
        "  ## 2. Remember to count both node_types\n",
        "\n",
        "  for name, dataset_ in datasets.items():\n",
        "    data_set_splits[name] = sum(list(map(len, dataset_[0].node_label_index.values())))\n",
        "\n",
        "  #########################################\n",
        "\n",
        "  return data_set_splits\n",
        "\n",
        "\n",
        "dataset = GraphDataset([hete], task='node')\n",
        "# Splitting the dataset\n",
        "dataset_train, dataset_val, dataset_test = dataset.split(transductive=True, split_ratio=[0.4, 0.3, 0.3])\n",
        "datasets = {'train': dataset_train, 'val': dataset_val, 'test': dataset_test}\n",
        "\n",
        "data_set_splits = compute_dataset_split_counts(datasets)\n",
        "for dataset_name, num_nodes in data_set_splits.items():\n",
        "  print(\"{} dataset has {} nodes\".format(dataset_name, num_nodes))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train dataset has 12 nodes\n",
            "val dataset has 10 nodes\n",
            "test dataset has 12 nodes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFY2PaDbVKe4"
      },
      "source": [
        "## DeepSNAP Dataset Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iiyEw-agbgV8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "294e7987-8fc0-4dc6-d8cf-a1b4a1f97b53"
      },
      "source": [
        "from deepsnap.dataset import GraphDataset\n",
        "\n",
        "dataset = GraphDataset([hete], task='node')\n",
        "# Splitting the dataset\n",
        "dataset_train, dataset_val, dataset_test = dataset.split(transductive=True, split_ratio=[0.4, 0.3, 0.3])\n",
        "titles = ['Train', 'Validation', 'Test']\n",
        "\n",
        "for i, dataset in enumerate([dataset_train, dataset_val, dataset_test]):\n",
        "  n0 = hete._convert_to_graph_index(dataset[0].node_label_index['n0'], 'n0').tolist()\n",
        "  n1 = hete._convert_to_graph_index(dataset[0].node_label_index['n1'], 'n1').tolist()\n",
        "\n",
        "  plt.figure(figsize=(7, 7))\n",
        "  plt.title(titles[i])\n",
        "  nx.draw(G_orig, pos=pos, node_color=\"grey\", edge_color=colors, labels=labels, font_color='white')\n",
        "  nx.draw_networkx_nodes(G_orig.subgraph(n0), pos=pos, node_color=\"blue\")\n",
        "  nx.draw_networkx_nodes(G_orig.subgraph(n1), pos=pos, node_color=\"red\")\n",
        "  show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5LsVSRuI3hU"
      },
      "source": [
        "# 2 Heterogeneous Graph Node Property Prediction\n",
        "\n",
        "In this part we will use PyTorch Geometric and DeepSNAP to implement a GNN model for heterogeneous graph node property prediction (node classification). This part of Colab requires you having good understandings on the heterogeneous graph and how to implement the GNN layers by using PyG.\n",
        "\n",
        "At first let's take look at the general structure of a heterogeneous layer by an example.\n",
        "\n",
        "Let's assume we have a graph $G$, which contains two node types $a$ and $b$, and three message types $m_1=(a, r_1, a)$, $m_2=(a, r_2, b)$ and $m_3=(a, r_3, b)$.\n",
        "\n",
        "Thus, for $G$ a heterogeneous layer will contains three Heterogeneous GNN layers (`HeteroGNNConv` in this Colab) where each `HeteroGNNConv` layer will perform the message passing and aggregation with respect to only one message type. The overview of the heterogeneous layer is shown below:\n",
        "\n",
        "<br/>\n",
        "<center>\n",
        "<img src=\"https://web.stanford.edu/class/cs224w/images/colab4/hetero_conv.png\"/>\n",
        "</center>\n",
        "<br/>\n",
        "\n",
        "In this Colab, all the $l^{th}$ Heterogeneous GNN layers will be managed by a ($l^{th}$) Heterogeneous GNN Wrapper layer (the `HeteroGNNWrapperConv`). The $l^{th}$ Heterogeneous GNN Wrapper layer will take in the input node embeddings from $(l-1)^{th}$ layer and aggregate (across message types) the Heterogeneous GNN layers' results. For example, the wrapper layer will aggregate node type $b$'s node embeddings from Heterogeneous GNN layers for $m_2$ and $m_3$. The \"simplified\" heterogeneous layer structure is shown below:\n",
        "\n",
        "<br/>\n",
        "<center>\n",
        "<img src=\"http://web.stanford.edu/class/cs224w/images/colab4/hetero_conv_1.png\"/>\n",
        "</center>\n",
        "<br/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOTCyuRcJikS"
      },
      "source": [
        "<font color='red'>We recommend you implement the heterogeneous GNN model in following steps:</font>\n",
        "\n",
        "1. Implement the `HeteroGNNConv` first.\n",
        "2. Implement the `mean` aggregation in `HeteroGNNWrapperConv`.\n",
        "3. Implement the `generate_convs`.\n",
        "4. Implement the `HeteroGNN` model and the `train` function.\n",
        "5. Train the model with `mean` aggregation across the message types and make sure your model has reasonable performance.\n",
        "6. Implement the `attn` aggregation in `HeteroGNNWrapperConv`.\n",
        "7. Train the model with `attn` aggregation across the message types and make sure your model has reasonable performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pkFjcktiJJLm"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAm9_OcJJJ-W"
      },
      "source": [
        "import copy\n",
        "import torch\n",
        "import deepsnap\n",
        "import numpy as np\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch_geometric.nn as pyg_nn\n",
        "\n",
        "from sklearn.metrics import f1_score\n",
        "from deepsnap.hetero_gnn import forward_op\n",
        "from deepsnap.hetero_graph import HeteroGraph\n",
        "from torch_sparse import SparseTensor, matmul"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n2prITo3JSbo"
      },
      "source": [
        "## Dataset\n",
        "\n",
        "You need to login to your Google account and enter the verification code below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QvQwxJX4JTJX"
      },
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "# Authenticate and create the PyDrive client\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Igoy4F_xJbVn"
      },
      "source": [
        "id='1ivlxd6lJMcZ9taS44TMGG72x2V1GeVvk'\n",
        "downloaded = drive.CreateFile({'id': id})\n",
        "downloaded.GetContentFile('acm.pkl')"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBlboS5kJmJL"
      },
      "source": [
        "## Heterogeneous GNN Layer\n",
        "\n",
        "Now let's start working on our own implementation of a heterogeneous layer (the `HeteroGNNConv`)! Similar to what we did in Colab 3, we will implement the layer using PyTorch Geometric. In general, our heterogeneous GNN layer draws ideas from the **GraphSAGE** ([Hamilton et al. (2017)](https://arxiv.org/abs/1706.02216)).\n",
        "\n",
        "At first, let's implement the GNN layer for each message type :\n",
        "\n",
        "\\begin{equation}\n",
        "m =(s, r, d)\n",
        "\\end{equation}\n",
        "\n",
        "Each message type is a tuple containing three elements where $s$ refers to the source node type, $r$ refers to the edge (relation) type and $d$ refers to the destination node type. The update rule is very similar to that of GraphSAGE but we need to include the node types and the edge type. The update rule is described as below:\n",
        "\n",
        "\\begin{equation}\n",
        "h_v^{(l)[m]} = W^{(l)[m]} \\cdot \\text{CONCAT} \\Big( W_d^{(l)[m]} \\cdot h_v^{(l-1)}, W_s^{(l)[m]} \\cdot AGG(\\{h_u^{(l-1)}, \\forall u \\in N_{m}(v) \\})\\Big)\n",
        "\\end{equation}\n",
        "\n",
        "where $[m]$ indicates that the weight matrices or embeddings with respect to message type $m$, $W_s^{(l)[m]}$ computes the messages from neighboring nodes, $W_d^{(l)[m]}$ compute messages from the node itself, and $W^{(l)[m]}$ aggregates messages from both node types. In the equation above, $v$ has the node type $d$, and $u$ has the node type $s$.\n",
        "\n",
        "For simplicity, we use mean aggregations for $AGG$ where:\n",
        "\n",
        "\\begin{equation}\n",
        "AGG(\\{h_u^{(l-1)}, \\forall u \\in N_{m}(v) \\}) = \\frac{1}{|N_{m}(v)|} \\sum_{u\\in N_{m}(v)} h_u^{(l-1)}\n",
        "\\end{equation}"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Z1b0Mf8Jova"
      },
      "source": [
        "class HeteroGNNConv(pyg_nn.MessagePassing):\n",
        "    def __init__(self, in_channels_src, in_channels_dst, out_channels):\n",
        "        super(HeteroGNNConv, self).__init__(aggr=\"mean\")\n",
        "\n",
        "        self.in_channels_src = in_channels_src\n",
        "        self.in_channels_dst = in_channels_dst\n",
        "        self.out_channels = out_channels\n",
        "\n",
        "        # To simplify implementation, please initialize both self.lin_dst\n",
        "        # and self.lin_src out_features to out_channels\n",
        "        self.lin_dst = None\n",
        "        self.lin_src = None\n",
        "\n",
        "        self.lin_update = None\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~3 lines of code)\n",
        "\n",
        "        self.lin_dst = nn.Linear(in_features=in_channels_dst, out_features=out_channels)\n",
        "        self.lin_src = nn.Linear(in_features=in_channels_src, out_features=out_channels)\n",
        "\n",
        "        # input is twice the output channel because it will be the concatenation of the outputs\n",
        "        self.lin_update = nn.Linear(in_features=2 * out_channels, out_features=out_channels)\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "    def forward(\n",
        "        self,\n",
        "        node_feature_src,\n",
        "        node_feature_dst,\n",
        "        edge_index,\n",
        "        size=None,\n",
        "        res_n_id=None,\n",
        "    ):\n",
        "        ############# Your code here #############\n",
        "        ## (~1 line of code)\n",
        "\n",
        "        return self.propagate(\n",
        "            edge_index,\n",
        "            size=size,\n",
        "            node_feature_src=node_feature_src,\n",
        "            node_feature_dst=node_feature_dst,\n",
        "            res_n_id=res_n_id\n",
        "        )\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "    def message_and_aggregate(self, edge_index, node_feature_src):\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~1 line of code)\n",
        "        ## Note:\n",
        "        ## 1. Different from what we implemented in Colab 3, we use message_and_aggregate\n",
        "        ## to replace the message and aggregate. The benefit is that we can avoid\n",
        "        ## materializing x_i and x_j, and make the implementation more efficient.\n",
        "        ## 2. To implement efficiently, following PyG documentation is helpful:\n",
        "        ## https://pytorch-geometric.readthedocs.io/en/latest/notes/sparse_tensor.html\n",
        "        ## 3. Here edge_index is torch_sparse SparseTensor.\n",
        "\n",
        "        # self.aggr == 'mean'\n",
        "        out = matmul(edge_index, node_feature_src, reduce=self.aggr)\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "        return out\n",
        "\n",
        "    def update(self, aggr_out, node_feature_dst, res_n_id):\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~4 lines of code)\n",
        "\n",
        "        out = torch.cat((self.lin_dst(node_feature_dst), self.lin_src(aggr_out)), dim=-1)\n",
        "        aggr_out = self.lin_update(out)\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "        return aggr_out"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKq8ScTiJthn"
      },
      "source": [
        "## Heterogeneous GNN Wrapper Layer\n",
        "\n",
        "After implementing the GNN layer for each message type, we need to somehow aggregate the the node embedding results (with respect to each message types) together. Here we will implement two types of message type level aggregation.\n",
        "\n",
        "The first one is simply the mean aggregation:\n",
        "\n",
        "\\begin{equation}\n",
        "h_v^{(l)} = \\frac{1}{M}\\sum_{m=1}^{M}h_v^{(l)[m]}\n",
        "\\end{equation}\n",
        "\n",
        "Here node $v$ has the node type $d$ and $M$ is the total number of message types that the destination node type is $d$.\n",
        "\n",
        "The other one is the semantic level attention introduced in **HAN** ([Wang et al. (2019)](https://arxiv.org/abs/1903.07293)). Instead of directly averaging on the message type aggregation results, we use attention to learn which message type result can be more important, then aggregate from all the message types. Following are the equations for semantic level attention:\n",
        "\n",
        "\\begin{equation}\n",
        "e_{m} = \\frac{1}{|V_{d}|} \\sum_{v \\in V_{d}} q_{attn}^T \\cdot tanh \\Big( W_{attn}^{(l)} \\cdot h_v^{(l)[m]} + b \\Big)\n",
        "\\end{equation}\n",
        "\n",
        "where $m$ refers to message type and $d$ refers to the destination node type. Then we can compute the attention and update the $h_v^{(l)}$:\n",
        "\n",
        "\\begin{equation}\n",
        "\\alpha_{m} = \\frac{\\exp(e_{m})}{\\sum_{m=1}^M \\exp(e_{m})}\n",
        "\\end{equation}\n",
        "\n",
        "\\begin{equation}\n",
        "h_v^{(l)} = \\sum_{m=1}^{M} \\alpha_{m} \\cdot h_v^{(l)[m]}\n",
        "\\end{equation}"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_bun02xJwFm"
      },
      "source": [
        "class HeteroGNNWrapperConv(deepsnap.hetero_gnn.HeteroConv):\n",
        "    def __init__(self, convs, args, aggr=\"mean\"):\n",
        "        super(HeteroGNNWrapperConv, self).__init__(convs, None)\n",
        "        self.aggr = aggr\n",
        "\n",
        "        # Map the index and message type\n",
        "        self.mapping = {}\n",
        "\n",
        "        # A numpy array that stores the final attention probability\n",
        "        self.alpha = None\n",
        "\n",
        "        self.attn_proj = None\n",
        "\n",
        "        if self.aggr == \"attn\":\n",
        "            ############# Your code here #############\n",
        "            ## (~1 line of code)\n",
        "            ## Note:\n",
        "            ## 1. Initialize self.attn_proj here.\n",
        "            ## 2. You should use nn.Sequential for self.attn_proj\n",
        "            ## 3. nn.Linear and nn.Tanh are useful.\n",
        "            ## 4. You can create a vector parameter by using:\n",
        "            ## nn.Linear(some_size, 1, bias=False)\n",
        "            ## 5. The first linear layer should have out_features as args['attn_size']\n",
        "            ## 6. You can assume we only have one \"head\" for the attention.\n",
        "            ## 7. We recommend you to implement the mean aggregation first. After \n",
        "            ## the mean aggregation works well in the training, then you can \n",
        "            ## implement this part.\n",
        "\n",
        "            self.attn_proj = nn.Sequential(\n",
        "                nn.Linear(in_features=args['hidden_size'], out_features=args['attn_size']),\n",
        "                nn.Tanh(),\n",
        "                nn.Linear(in_features=args['attn_size'], out_features=1, bias=False)\n",
        "            )\n",
        "\n",
        "            ##########################################\n",
        "    \n",
        "    def reset_parameters(self):\n",
        "        super(HeteroConvWrapper, self).reset_parameters()\n",
        "        if self.aggr == \"attn\":\n",
        "            for layer in self.attn_proj.children():\n",
        "                layer.reset_parameters()\n",
        "    \n",
        "    def forward(self, node_features, edge_indices):\n",
        "        message_type_emb = {}\n",
        "        for message_key, message_type in edge_indices.items():\n",
        "            src_type, edge_type, dst_type = message_key\n",
        "            node_feature_src = node_features[src_type]\n",
        "            node_feature_dst = node_features[dst_type]\n",
        "            edge_index = edge_indices[message_key]\n",
        "            message_type_emb[message_key] = (\n",
        "                self.convs[message_key](\n",
        "                    node_feature_src,\n",
        "                    node_feature_dst,\n",
        "                    edge_index,\n",
        "                )\n",
        "            )\n",
        "        node_emb = {dst: [] for _, _, dst in message_type_emb.keys()}\n",
        "        mapping = {}        \n",
        "        for (src, edge_type, dst), item in message_type_emb.items():\n",
        "            mapping[len(node_emb[dst])] = (src, edge_type, dst)\n",
        "            node_emb[dst].append(item)\n",
        "        self.mapping = mapping\n",
        "        for node_type, embs in node_emb.items():\n",
        "            if len(embs) == 1:\n",
        "                node_emb[node_type] = embs[0]\n",
        "            else:\n",
        "                node_emb[node_type] = self.aggregate(embs)\n",
        "        return node_emb\n",
        "    \n",
        "    def aggregate(self, xs):\n",
        "        # TODO: Implement this function that aggregates all message type results.\n",
        "        # Here, xs is a list of tensors (embeddings) with respect to message \n",
        "        # type aggregation results.\n",
        "\n",
        "        if self.aggr == \"mean\":\n",
        "\n",
        "            ############# Your code here #############\n",
        "            ## (~2 lines of code)\n",
        "\n",
        "            # xs == [Tensor[number_of_nodes, num_features], Tensor[number_of_nodes, num_features]]\n",
        "            # output.shape = Tensor[number_of_nodes, num_features]\n",
        "            return torch.stack(xs).mean(dim=0)\n",
        "\n",
        "            ##########################################\n",
        "\n",
        "        elif self.aggr == \"attn\":\n",
        "\n",
        "            ############# Your code here #############\n",
        "            ## (~10 lines of code)\n",
        "            ## Note:\n",
        "            ## 1. Store the value of attention alpha (as a numpy array) to self.alpha,\n",
        "            ## which has the shape (len(xs), ) self.alpha will be not be used \n",
        "            ## to backpropagate etc. in the model. We will use it to see how much \n",
        "            ## attention the layer pays on different message types.\n",
        "            ## 2. torch.softmax and torch.cat are useful.\n",
        "            ## 3. You might need to reshape the tensors by using the \n",
        "            ## `view()` function https://pytorch.org/docs/stable/tensor_view.html\n",
        "\n",
        "            # xs == [Tensor[num_nodes, num_features], Tensor[num_nodes, num_features]]\n",
        "            xs = torch.stack(xs, dim=0)\n",
        "\n",
        "            # e_m.shape == [2, num_nodes]\n",
        "            e_m = self.attn_proj(xs).mean(dim=-1)\n",
        "\n",
        "            # self.alpha.shape == [2, num_nodes]\n",
        "            self.alpha = F.softmax(e_m, dim=0).detach()\n",
        "\n",
        "            # hidden_state.shape == [num_nodes, num_featires]\n",
        "            hidden_state = (self.alpha.unsqueeze(-1) * xs).sum(dim=0)\n",
        "\n",
        "            # convert alpha to a numpy array\n",
        "            self.alpha = self.alpha.cpu().numpy()\n",
        "\n",
        "            return hidden_state\n",
        "\n",
        "            ##########################################"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tn_pnCOKJw-d"
      },
      "source": [
        "## Initialize Heterogeneous GNN Layers\n",
        "\n",
        "Now let's initialize the Heterogeneous GNN Layers. Different from homogeneous graph case, heterogeneous case can be a little bit complex.\n",
        "\n",
        "In general, we need to create a dictionary of `HeteroGNNConv` layers where the keys are message types.\n",
        "\n",
        "* To get all message types, `deepsnap.hetero_graph.HeteroGraph.message_types` is useful.\n",
        "* If we are initializing the first conv layers, we need to get the feature dimension of each node type. Using `deepsnap.hetero_graph.HeteroGraph.num_node_features(node_type)` will return the node feature dimension of `node_type`. In this function, we will set each `HeteroGNNConv` `out_channels` to be `hidden_size`.\n",
        "* If we are not initializing the first conv layers, all node types will have the same embedding dimension `hidden_size` and we still set `HeteroGNNConv` `out_channels` to be `hidden_size` for simplicity.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hSBImHClJzf4"
      },
      "source": [
        "def generate_convs(hetero_graph, conv, hidden_size, first_layer=False):\n",
        "    # TODO: Implement this function that returns a dictionary of `HeteroGNNConv` \n",
        "    # layers where the keys are message types. `hetero_graph` is deepsnap `HeteroGraph`\n",
        "    # object and the `conv` is the `HeteroGNNConv`.\n",
        "\n",
        "    convs = {}\n",
        "\n",
        "    ############# Your code here #############\n",
        "    ## (~9 lines of code)\n",
        "\n",
        "    for msg_type in hetero_graph.message_types:\n",
        "\n",
        "        if first_layer:\n",
        "            src_node, _, dst_node = msg_type\n",
        "            in_channels_src = hetero_graph.num_node_features(src_node)\n",
        "            in_channels_dst = hetero_graph.num_node_features(dst_node)\n",
        "        else:\n",
        "            in_channels_src = hidden_size\n",
        "            in_channels_dst = hidden_size\n",
        "\n",
        "        convs[msg_type] = conv(\n",
        "            in_channels_src=in_channels_src,\n",
        "            in_channels_dst=in_channels_dst,\n",
        "            out_channels=hidden_size\n",
        "        )\n",
        "\n",
        "    ##########################################\n",
        "    \n",
        "    return convs"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U39dX8EpJ3FG"
      },
      "source": [
        "## HeteroGNN\n",
        "\n",
        "Now we will make a simple HeteroGNN model which contains only two `HeteroGNNWrapperConv` layers.\n",
        "\n",
        "For the forward function in `HeteroGNN`, the model is going to be run as following:\n",
        "\n",
        "$\\text{self.convs1} \\rightarrow \\text{self.bns1} \\rightarrow \\text{self.relus1} \\rightarrow \\text{self.convs2} \\rightarrow \\text{self.bns2} \\rightarrow \\text{self.relus2} \\rightarrow \\text{self.post_mps}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rplknA8aJ6J5"
      },
      "source": [
        "class HeteroGNN(torch.nn.Module):\n",
        "    def __init__(self, hetero_graph, args, aggr=\"mean\"):\n",
        "        super(HeteroGNN, self).__init__()\n",
        "\n",
        "        self.aggr = aggr\n",
        "        self.hidden_size = args['hidden_size']\n",
        "\n",
        "        self.convs1 = None\n",
        "        self.convs2 = None\n",
        "\n",
        "        self.bns1 = nn.ModuleDict()\n",
        "        self.bns2 = nn.ModuleDict()\n",
        "        self.relus1 = nn.ModuleDict()\n",
        "        self.relus2 = nn.ModuleDict()\n",
        "        self.post_mps = nn.ModuleDict()\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~10 lines of code)\n",
        "        ## Note:\n",
        "        ## 1. For self.convs1 and self.convs2, call generate_convs at first and then\n",
        "        ## pass the returned dictionary of `HeteroGNNConv` to `HeteroGNNWrapperConv`.\n",
        "        ## 2. For self.bns, self.relus and self.post_mps, the keys are node_types.\n",
        "        ## `deepsnap.hetero_graph.HeteroGraph.node_types` will be helpful.\n",
        "        ## 3. Initialize all batchnorms to torch.nn.BatchNorm1d(hidden_size, eps=1.0).\n",
        "        ## 4. Initialize all relus to nn.LeakyReLU().\n",
        "        ## 5. For self.post_mps, each value in the ModuleDict is a linear layer \n",
        "        ## where the `out_features` is the number of classes for that node type.\n",
        "        ## `deepsnap.hetero_graph.HeteroGraph.num_node_labels(node_type)` will be\n",
        "        ## useful.\n",
        "\n",
        "        convs1 = generate_convs(\n",
        "            hetero_graph=hetero_graph,\n",
        "            conv=HeteroGNNConv,\n",
        "            hidden_size=self.hidden_size,\n",
        "            first_layer=True\n",
        "        )\n",
        "        self.convs1 = HeteroGNNWrapperConv(\n",
        "            convs=convs1,\n",
        "            args=args,\n",
        "            aggr=self.aggr\n",
        "        )\n",
        "\n",
        "        convs2 = generate_convs(\n",
        "            hetero_graph=hetero_graph,\n",
        "            conv=HeteroGNNConv,\n",
        "            hidden_size=self.hidden_size,\n",
        "            first_layer=False\n",
        "        )\n",
        "        self.convs2 = HeteroGNNWrapperConv(\n",
        "            convs=convs2,\n",
        "            args=args,\n",
        "            aggr=self.aggr\n",
        "        )\n",
        "\n",
        "        for node_type in hetero_graph.node_types:\n",
        "            self.bns1[node_type] = nn.BatchNorm1d(num_features=self.hidden_size, eps=1.0)\n",
        "            self.bns2[node_type] = nn.BatchNorm1d(num_features=self.hidden_size, eps=1.0)\n",
        "            self.relus1[node_type] = nn.LeakyReLU()\n",
        "            self.relus2[node_type] = nn.LeakyReLU()\n",
        "            self.post_mps[node_type] = nn.Linear(\n",
        "                in_features=self.hidden_size,\n",
        "                out_features=hetero_graph.num_node_labels(node_type)\n",
        "            )\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "    def forward(self, node_feature, edge_index):\n",
        "        # TODO: Implement the forward function. Notice that `node_feature` is \n",
        "        # a dictionary of tensors where keys are node types and values are \n",
        "        # corresponding feature tensors. The `edge_index` is a dictionary of \n",
        "        # tensors where keys are message types and values are corresponding\n",
        "        # edge index tensors (with respect to each message type).\n",
        "\n",
        "        x = node_feature\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~7 lines of code)\n",
        "        ## Note:\n",
        "        ## 1. `deepsnap.hetero_gnn.forward_op` can be helpful.\n",
        "\n",
        "        x = self.convs1(node_features=x, edge_indices=edge_index)\n",
        "        x = forward_op(x, self.bns1)\n",
        "        x = forward_op(x, self.relus1)\n",
        "\n",
        "        x = self.convs2(node_features=x, edge_indices=edge_index)\n",
        "        x = forward_op(x, self.bns2)\n",
        "        x = forward_op(x, self.relus2)\n",
        "\n",
        "        x = forward_op(x, self.post_mps)\n",
        "\n",
        "        ##########################################\n",
        "        \n",
        "        return x\n",
        "\n",
        "    def loss(self, preds, y, indices):\n",
        "        \n",
        "        loss = 0\n",
        "        loss_func = F.cross_entropy\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~3 lines of code)\n",
        "        ## Note:\n",
        "        ## 1. For each node type in preds, accumulate computed loss to `loss`\n",
        "        ## 2. Loss need to be computed with respect to the given index\n",
        "\n",
        "        for node_type in preds:\n",
        "            node_type_index = indices[node_type]\n",
        "            node_type_pred = preds[node_type][node_type_index]\n",
        "            node_type_y = y[node_type][node_type_index]\n",
        "\n",
        "            loss += loss_func(node_type_pred, node_type_y)\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "        return loss"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o9e7q_hUJ8zB"
      },
      "source": [
        "## Training and Testing\n",
        "\n",
        "Here we provide you with the functions to train and test. You only need to implement one line of code here.\n",
        "\n",
        "**Please do not modify other parts in `train` and `test` for grading purposes.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CI5Hl_5TJ_YL"
      },
      "source": [
        "def train(model, optimizer, hetero_graph, train_idx):\n",
        "    model.train()\n",
        "    optimizer.zero_grad()\n",
        "    preds = model(hetero_graph.node_feature, hetero_graph.edge_index)\n",
        "\n",
        "    loss = None\n",
        "\n",
        "    ############# Your code here #############\n",
        "    ## Note:\n",
        "    ## 1. `deepsnap.hetero_graph.HeteroGraph.node_label` is useful\n",
        "    ## 2. Compute the loss here\n",
        "\n",
        "    loss = model.loss(preds, hetero_graph.node_label, train_idx)\n",
        "\n",
        "    ##########################################\n",
        "\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    return loss.item()\n",
        "\n",
        "def test(model, graph, indices, best_model=None, best_val=0):\n",
        "    model.eval()\n",
        "    accs = []\n",
        "    for index in indices:\n",
        "        preds = model(graph.node_feature, graph.edge_index)\n",
        "        num_node_types = 0\n",
        "        micro = 0\n",
        "        macro = 0\n",
        "        for node_type in preds:\n",
        "            idx = index[node_type]\n",
        "            pred = preds[node_type][idx]\n",
        "            pred = pred.max(1)[1]\n",
        "            label_np = graph.node_label[node_type][idx].cpu().numpy()\n",
        "            pred_np = pred.cpu().numpy()\n",
        "            micro = f1_score(label_np, pred_np, average='micro')\n",
        "            macro = f1_score(label_np, pred_np, average='macro')\n",
        "            num_node_types += 1\n",
        "        # Averaging f1 score might not make sense, but in our example we only\n",
        "        # have one node type\n",
        "        micro /= num_node_types\n",
        "        macro /= num_node_types\n",
        "        accs.append((micro, macro))\n",
        "    if accs[1][0] > best_val:\n",
        "        best_val = accs[1][0]\n",
        "        best_model = copy.deepcopy(model)\n",
        "    return accs, best_model, best_val"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpNz9B5AKBUU"
      },
      "source": [
        "# Please do not change the following parameters\n",
        "args = {\n",
        "    'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'),\n",
        "    'hidden_size': 64,\n",
        "    'epochs': 100,\n",
        "    'weight_decay': 1e-5,\n",
        "    'lr': 0.003,\n",
        "    'attn_size': 32,\n",
        "}"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRHbWD4hKED8"
      },
      "source": [
        "## Dataset and Preprocessing\n",
        "\n",
        "In the next, we will load the data and create a tensor backend (without a NetworkX graph) `deepsnap.hetero_graph.HeteroGraph` object.\n",
        "\n",
        "We will use the `ACM(3025)` dataset in our node property prediction task, which is proposed in **HAN** ([Wang et al. (2019)](https://arxiv.org/abs/1903.07293)) and our dataset is extracted from [DGL](https://www.dgl.ai/)'s [ACM.mat](https://data.dgl.ai/dataset/ACM.mat).\n",
        "\n",
        "The original ACM dataset has three node types and two edge (relation) types. For simplicity, we simplify the heterogeneous graph to one node type and two edge types (shown below). This means that in our heterogeneous graph, we have one node type (paper) and two message types (paper, author, paper) and (paper, subject, paper).\n",
        "\n",
        "<br/>\n",
        "<center>\n",
        "<img src=\"http://web.stanford.edu/class/cs224w/images/colab4/cs224w-acm.png\"/>\n",
        "</center>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJy03_IsKGh6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9c513e8-4414-4907-ad88-69a96aa8d86a"
      },
      "source": [
        "print(\"Device: {}\".format(args['device']))\n",
        "\n",
        "# Load the data\n",
        "data = torch.load(\"acm.pkl\")\n",
        "\n",
        "# Message types\n",
        "message_type_1 = (\"paper\", \"author\", \"paper\")\n",
        "message_type_2 = (\"paper\", \"subject\", \"paper\")\n",
        "\n",
        "# Dictionary of edge indices\n",
        "edge_index = {}\n",
        "edge_index[message_type_1] = data['pap']\n",
        "edge_index[message_type_2] = data['psp']\n",
        "\n",
        "# Dictionary of node features\n",
        "node_feature = {}\n",
        "node_feature[\"paper\"] = data['feature']\n",
        "\n",
        "# Dictionary of node labels\n",
        "node_label = {}\n",
        "node_label[\"paper\"] = data['label']\n",
        "\n",
        "# Load the train, validation and test indices\n",
        "train_idx = {\"paper\": data['train_idx'].to(args['device'])}\n",
        "val_idx = {\"paper\": data['val_idx'].to(args['device'])}\n",
        "test_idx = {\"paper\": data['test_idx'].to(args['device'])}\n",
        "\n",
        "# Construct a deepsnap tensor backend HeteroGraph\n",
        "hetero_graph = HeteroGraph(\n",
        "    node_feature=node_feature,\n",
        "    node_label=node_label,\n",
        "    edge_index=edge_index,\n",
        "    directed=True\n",
        ")\n",
        "\n",
        "print(f\"ACM heterogeneous graph: {hetero_graph.num_nodes()} nodes, {hetero_graph.num_edges()} edges\")\n",
        "\n",
        "# Node feature and node label to device\n",
        "for key in hetero_graph.node_feature:\n",
        "    hetero_graph.node_feature[key] = hetero_graph.node_feature[key].to(args['device'])\n",
        "for key in hetero_graph.node_label:\n",
        "    hetero_graph.node_label[key] = hetero_graph.node_label[key].to(args['device'])\n",
        "\n",
        "# Edge_index to sparse tensor and to device\n",
        "for key in hetero_graph.edge_index:\n",
        "    edge_index = hetero_graph.edge_index[key]\n",
        "    adj = SparseTensor(row=edge_index[0], col=edge_index[1], sparse_sizes=(hetero_graph.num_nodes('paper'), hetero_graph.num_nodes('paper')))\n",
        "    hetero_graph.edge_index[key] = adj.t().to(args['device'])\n",
        "print(hetero_graph.edge_index[message_type_1])\n",
        "print(hetero_graph.edge_index[message_type_2])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Device: cuda\n",
            "ACM heterogeneous graph: {'paper': 3025} nodes, {('paper', 'author', 'paper'): 26256, ('paper', 'subject', 'paper'): 2207736} edges\n",
            "SparseTensor(row=tensor([   0,    0,    0,  ..., 3024, 3024, 3024], device='cuda:0'),\n",
            "             col=tensor([   8,   20,   51,  ..., 2948, 2983, 2991], device='cuda:0'),\n",
            "             size=(3025, 3025), nnz=26256, density=0.29%)\n",
            "SparseTensor(row=tensor([   0,    0,    0,  ..., 3024, 3024, 3024], device='cuda:0'),\n",
            "             col=tensor([  75,  434,  534,  ..., 3020, 3021, 3022], device='cuda:0'),\n",
            "             size=(3025, 3025), nnz=2207736, density=24.13%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hrmU5-QQKJv6"
      },
      "source": [
        "## Start Training!\n",
        "\n",
        "Now lets start training!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0HplV9hKMkc"
      },
      "source": [
        "## Training the Mean Aggregation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgwfyzLbKOUw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9d122b38-8a44-4f25-9c4f-5a352e515994"
      },
      "source": [
        "best_model = None\n",
        "best_val = 0\n",
        "\n",
        "model = HeteroGNN(hetero_graph, args, aggr=\"mean\").to(args['device'])\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'], weight_decay=args['weight_decay'])\n",
        "\n",
        "for epoch in range(args['epochs']):\n",
        "    loss = train(model, optimizer, hetero_graph, train_idx)\n",
        "    accs, best_model, best_val = test(model, hetero_graph, [train_idx, val_idx, test_idx], best_model, best_val)\n",
        "    print(\n",
        "        f\"Epoch {epoch + 1}: loss {round(loss, 5)}, \"\n",
        "        f\"train micro {round(accs[0][0] * 100, 2)}%, train macro {round(accs[0][1] * 100, 2)}%, \"\n",
        "        f\"valid micro {round(accs[1][0] * 100, 2)}%, valid macro {round(accs[1][1] * 100, 2)}%, \"\n",
        "        f\"test micro {round(accs[2][0] * 100, 2)}%, test macro {round(accs[2][1] * 100, 2)}%\"\n",
        "    )\n",
        "best_accs, _, _ = test(best_model, hetero_graph, [train_idx, val_idx, test_idx])\n",
        "print(\n",
        "    f\"Best model: \"\n",
        "    f\"train micro {round(best_accs[0][0] * 100, 2)}%, train macro {round(best_accs[0][1] * 100, 2)}%, \"\n",
        "    f\"valid micro {round(best_accs[1][0] * 100, 2)}%, valid macro {round(best_accs[1][1] * 100, 2)}%, \"\n",
        "    f\"test micro {round(best_accs[2][0] * 100, 2)}%, test macro {round(best_accs[2][1] * 100, 2)}%\"\n",
        ")"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1: loss 1.09893, train micro 48.0%, train macro 42.17%, valid micro 34.33%, valid macro 18.76%, test micro 34.82%, test macro 20.67%\n",
            "Epoch 2: loss 1.08895, train micro 90.67%, train macro 90.32%, valid micro 86.0%, valid macro 85.39%, test micro 69.41%, test macro 64.47%\n",
            "Epoch 3: loss 1.0566, train micro 71.0%, train macro 66.22%, valid micro 66.67%, valid macro 58.48%, test micro 61.22%, test macro 50.26%\n",
            "Epoch 4: loss 0.99167, train micro 65.33%, train macro 59.65%, valid micro 62.0%, valid macro 50.93%, test micro 57.27%, test macro 45.96%\n",
            "Epoch 5: loss 0.88277, train micro 66.0%, train macro 61.04%, valid micro 62.0%, valid macro 51.37%, test micro 56.99%, test macro 45.71%\n",
            "Epoch 6: loss 0.73447, train micro 67.33%, train macro 62.81%, valid micro 63.33%, valid macro 52.93%, test micro 57.93%, test macro 46.73%\n",
            "Epoch 7: loss 0.57835, train micro 71.67%, train macro 68.09%, valid micro 66.0%, valid macro 57.19%, test micro 59.76%, test macro 48.66%\n",
            "Epoch 8: loss 0.44712, train micro 75.33%, train macro 72.65%, valid micro 68.67%, valid macro 61.58%, test micro 61.27%, test macro 50.28%\n",
            "Epoch 9: loss 0.34623, train micro 79.0%, train macro 76.95%, valid micro 72.0%, valid macro 66.82%, test micro 63.11%, test macro 52.8%\n",
            "Epoch 10: loss 0.26861, train micro 84.83%, train macro 83.72%, valid micro 75.0%, valid macro 70.83%, test micro 64.99%, test macro 55.7%\n",
            "Epoch 11: loss 0.20854, train micro 89.67%, train macro 89.2%, valid micro 80.0%, valid macro 77.89%, test micro 66.64%, test macro 58.17%\n",
            "Epoch 12: loss 0.16197, train micro 93.0%, train macro 92.81%, valid micro 84.0%, valid macro 82.95%, test micro 69.04%, test macro 61.91%\n",
            "Epoch 13: loss 0.126, train micro 96.17%, train macro 96.13%, valid micro 88.33%, valid macro 87.86%, test micro 71.06%, test macro 65.44%\n",
            "Epoch 14: loss 0.09874, train micro 97.33%, train macro 97.32%, valid micro 90.33%, valid macro 90.03%, test micro 73.84%, test macro 69.78%\n",
            "Epoch 15: loss 0.07867, train micro 98.5%, train macro 98.5%, valid micro 91.33%, valid macro 91.1%, test micro 76.28%, test macro 73.35%\n",
            "Epoch 16: loss 0.06362, train micro 99.0%, train macro 99.0%, valid micro 92.33%, valid macro 92.16%, test micro 78.49%, test macro 76.42%\n",
            "Epoch 17: loss 0.05199, train micro 100.0%, train macro 100.0%, valid micro 93.67%, valid macro 93.57%, test micro 80.42%, test macro 78.96%\n",
            "Epoch 18: loss 0.04262, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.62%, test micro 81.79%, test macro 80.65%\n",
            "Epoch 19: loss 0.03493, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.39%, test macro 82.57%\n",
            "Epoch 20: loss 0.02871, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.52%, test macro 83.87%\n",
            "Epoch 21: loss 0.02374, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 84.66%, test macro 84.09%\n",
            "Epoch 22: loss 0.01975, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 85.04%, test macro 84.53%\n",
            "Epoch 23: loss 0.0165, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 85.32%, test macro 84.87%\n",
            "Epoch 24: loss 0.01364, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 85.69%, test macro 85.32%\n",
            "Epoch 25: loss 0.01119, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 85.93%, test macro 85.58%\n",
            "Epoch 26: loss 0.00917, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 86.16%, test macro 85.85%\n",
            "Epoch 27: loss 0.00755, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 85.98%, test macro 85.65%\n",
            "Epoch 28: loss 0.00628, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 85.98%, test macro 85.66%\n",
            "Epoch 29: loss 0.00529, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 85.98%, test macro 85.67%\n",
            "Epoch 30: loss 0.00451, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 85.79%, test macro 85.48%\n",
            "Epoch 31: loss 0.00388, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 85.74%, test macro 85.42%\n",
            "Epoch 32: loss 0.00337, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 85.69%, test macro 85.38%\n",
            "Epoch 33: loss 0.00296, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 85.6%, test macro 85.29%\n",
            "Epoch 34: loss 0.00261, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 85.51%, test macro 85.19%\n",
            "Epoch 35: loss 0.00233, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 85.36%, test macro 85.04%\n",
            "Epoch 36: loss 0.00208, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 85.18%, test macro 84.84%\n",
            "Epoch 37: loss 0.00188, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 85.13%, test macro 84.8%\n",
            "Epoch 38: loss 0.0017, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.99%, test macro 84.66%\n",
            "Epoch 39: loss 0.00155, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.85%, test macro 84.52%\n",
            "Epoch 40: loss 0.00142, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.89%, test macro 84.57%\n",
            "Epoch 41: loss 0.00131, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.8%, test macro 84.48%\n",
            "Epoch 42: loss 0.00121, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.75%, test macro 84.45%\n",
            "Epoch 43: loss 0.00113, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.75%, test macro 84.44%\n",
            "Epoch 44: loss 0.00105, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.8%, test macro 84.5%\n",
            "Epoch 45: loss 0.00098, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 84.71%, test macro 84.41%\n",
            "Epoch 46: loss 0.00092, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.66%, test macro 84.38%\n",
            "Epoch 47: loss 0.00087, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.66%, test macro 84.39%\n",
            "Epoch 48: loss 0.00082, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.61%, test macro 84.34%\n",
            "Epoch 49: loss 0.00078, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.66%, test macro 84.39%\n",
            "Epoch 50: loss 0.00074, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 84.61%, test macro 84.37%\n",
            "Epoch 51: loss 0.00071, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 84.61%, test macro 84.38%\n",
            "Epoch 52: loss 0.00068, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 84.56%, test macro 84.34%\n",
            "Epoch 53: loss 0.00065, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 84.56%, test macro 84.35%\n",
            "Epoch 54: loss 0.00063, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 84.38%, test macro 84.17%\n",
            "Epoch 55: loss 0.0006, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 84.28%, test macro 84.09%\n",
            "Epoch 56: loss 0.00058, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 84.14%, test macro 83.95%\n",
            "Epoch 57: loss 0.00056, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 84.09%, test macro 83.91%\n",
            "Epoch 58: loss 0.00054, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 84.19%, test macro 84.02%\n",
            "Epoch 59: loss 0.00053, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 84.19%, test macro 84.02%\n",
            "Epoch 60: loss 0.00051, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 84.28%, test macro 84.13%\n",
            "Epoch 61: loss 0.0005, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 84.33%, test macro 84.18%\n",
            "Epoch 62: loss 0.00049, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.33%, test micro 84.28%, test macro 84.13%\n",
            "Epoch 63: loss 0.00047, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.33%, test micro 84.28%, test macro 84.14%\n",
            "Epoch 64: loss 0.00046, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 84.28%, test macro 84.15%\n",
            "Epoch 65: loss 0.00045, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.67%, test micro 84.19%, test macro 84.06%\n",
            "Epoch 66: loss 0.00044, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 84.09%, test macro 83.98%\n",
            "Epoch 67: loss 0.00043, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.91%, test macro 83.8%\n",
            "Epoch 68: loss 0.00042, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.81%, test macro 83.71%\n",
            "Epoch 69: loss 0.00042, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.76%, test macro 83.67%\n",
            "Epoch 70: loss 0.00041, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.76%, test macro 83.67%\n",
            "Epoch 71: loss 0.0004, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.72%, test macro 83.63%\n",
            "Epoch 72: loss 0.00039, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.58%, test macro 83.49%\n",
            "Epoch 73: loss 0.00039, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.53%, test macro 83.44%\n",
            "Epoch 74: loss 0.00038, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.58%, test macro 83.49%\n",
            "Epoch 75: loss 0.00038, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.62%, test macro 83.55%\n",
            "Epoch 76: loss 0.00037, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.62%, test macro 83.55%\n",
            "Epoch 77: loss 0.00036, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.62%, test macro 83.55%\n",
            "Epoch 78: loss 0.00036, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.48%, test macro 83.42%\n",
            "Epoch 79: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.44%, test macro 83.37%\n",
            "Epoch 80: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.34%, test micro 83.44%, test macro 83.38%\n",
            "Epoch 81: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.34%, test macro 83.29%\n",
            "Epoch 82: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.39%, test macro 83.35%\n",
            "Epoch 83: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.34%, test macro 83.3%\n",
            "Epoch 84: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.34%, test macro 83.3%\n",
            "Epoch 85: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.34%, test macro 83.3%\n",
            "Epoch 86: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.25%, test macro 83.21%\n",
            "Epoch 87: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.29%, test macro 83.26%\n",
            "Epoch 88: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.29%, test macro 83.26%\n",
            "Epoch 89: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.29%, test macro 83.26%\n",
            "Epoch 90: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.29%, test macro 83.26%\n",
            "Epoch 91: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.34%, test macro 83.31%\n",
            "Epoch 92: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.2%, test macro 83.18%\n",
            "Epoch 93: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.2%, test macro 83.18%\n",
            "Epoch 94: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.2%, test macro 83.18%\n",
            "Epoch 95: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.2%, test macro 83.18%\n",
            "Epoch 96: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.15%, test macro 83.14%\n",
            "Epoch 97: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.15%, test macro 83.14%\n",
            "Epoch 98: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.15%, test macro 83.14%\n",
            "Epoch 99: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.15%, test macro 83.14%\n",
            "Epoch 100: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.68%, test micro 83.15%, test macro 83.14%\n",
            "Best model: train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 85.32%, test macro 84.87%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtkKBI_nKS1T"
      },
      "source": [
        "## Question 2.1: What is the maximum **micro** F1 score you could get for the best_model on test set when using the mean aggregation? (10 points)\n",
        "\n",
        "Submit your answers on Gradescope."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIvw51jMKTvn"
      },
      "source": [
        "## Question 2.2: What is the maximum **macro** F1 score you could get for the best_model on test set when using the mean aggregation? (10 points)\n",
        "\n",
        "Submit your answers on Gradescope."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBiYvwcuKd0z"
      },
      "source": [
        "## Training the Attention Aggregation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6na5zyQKfvi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7cbfb1d-940a-4c1d-e937-bc11dfa607a3"
      },
      "source": [
        "best_model = None\n",
        "best_val = 0\n",
        "\n",
        "output_size = hetero_graph.num_node_labels('paper')\n",
        "model = HeteroGNN(hetero_graph, args, aggr=\"attn\").to(args['device'])\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'], weight_decay=args['weight_decay'])\n",
        "\n",
        "for epoch in range(args['epochs']):\n",
        "    loss = train(model, optimizer, hetero_graph, train_idx)\n",
        "    accs, best_model, best_val = test(model, hetero_graph, [train_idx, val_idx, test_idx], best_model, best_val)\n",
        "    print(\n",
        "        f\"Epoch {epoch + 1}: loss {round(loss, 5)}, \"\n",
        "        f\"train micro {round(accs[0][0] * 100, 2)}%, train macro {round(accs[0][1] * 100, 2)}%, \"\n",
        "        f\"valid micro {round(accs[1][0] * 100, 2)}%, valid macro {round(accs[1][1] * 100, 2)}%, \"\n",
        "        f\"test micro {round(accs[2][0] * 100, 2)}%, test macro {round(accs[2][1] * 100, 2)}%\"\n",
        "    )\n",
        "best_accs, _, _ = test(best_model, hetero_graph, [train_idx, val_idx, test_idx])\n",
        "print(\n",
        "    f\"Best model: \"\n",
        "    f\"train micro {round(best_accs[0][0] * 100, 2)}%, train macro {round(best_accs[0][1] * 100, 2)}%, \"\n",
        "    f\"valid micro {round(best_accs[1][0] * 100, 2)}%, valid macro {round(best_accs[1][1] * 100, 2)}%, \"\n",
        "    f\"test micro {round(best_accs[2][0] * 100, 2)}%, test macro {round(best_accs[2][1] * 100, 2)}%\"\n",
        ")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1: loss 1.10071, train micro 33.33%, train macro 16.67%, valid micro 33.33%, valid macro 16.67%, test micro 32.89%, test macro 16.5%\n",
            "Epoch 2: loss 1.09023, train micro 33.33%, train macro 16.67%, valid micro 33.33%, valid macro 16.67%, test micro 32.89%, test macro 16.5%\n",
            "Epoch 3: loss 1.05864, train micro 92.33%, train macro 92.4%, valid micro 89.0%, valid macro 89.22%, test micro 72.33%, test macro 73.3%\n",
            "Epoch 4: loss 0.99462, train micro 78.0%, train macro 75.34%, valid micro 74.67%, valid macro 71.14%, test micro 67.29%, test macro 60.07%\n",
            "Epoch 5: loss 0.88479, train micro 70.17%, train macro 62.73%, valid micro 68.0%, valid macro 59.22%, test micro 65.55%, test macro 55.08%\n",
            "Epoch 6: loss 0.72857, train micro 69.67%, train macro 61.76%, valid micro 67.33%, valid macro 57.86%, test micro 65.51%, test macro 54.56%\n",
            "Epoch 7: loss 0.55454, train micro 69.5%, train macro 61.15%, valid micro 67.33%, valid macro 57.69%, test micro 65.55%, test macro 54.39%\n",
            "Epoch 8: loss 0.40605, train micro 69.67%, train macro 61.47%, valid micro 68.0%, valid macro 58.62%, test micro 65.74%, test macro 54.69%\n",
            "Epoch 9: loss 0.29739, train micro 72.5%, train macro 66.42%, valid micro 68.0%, valid macro 58.45%, test micro 65.93%, test macro 54.81%\n",
            "Epoch 10: loss 0.21891, train micro 77.17%, train macro 73.49%, valid micro 70.67%, valid macro 63.12%, test micro 66.4%, test macro 55.78%\n",
            "Epoch 11: loss 0.16127, train micro 81.67%, train macro 79.5%, valid micro 76.0%, valid macro 71.62%, test micro 66.82%, test macro 56.78%\n",
            "Epoch 12: loss 0.11846, train micro 88.17%, train macro 87.37%, valid micro 80.67%, valid macro 78.2%, test micro 68.33%, test macro 59.5%\n",
            "Epoch 13: loss 0.08657, train micro 92.5%, train macro 92.26%, valid micro 83.33%, valid macro 81.74%, test micro 69.98%, test macro 62.8%\n",
            "Epoch 14: loss 0.06316, train micro 94.0%, train macro 93.88%, valid micro 84.0%, valid macro 82.8%, test micro 71.25%, test macro 65.3%\n",
            "Epoch 15: loss 0.04672, train micro 96.0%, train macro 95.97%, valid micro 86.0%, valid macro 85.22%, test micro 72.19%, test macro 67.13%\n",
            "Epoch 16: loss 0.03544, train micro 97.5%, train macro 97.49%, valid micro 88.33%, valid macro 87.89%, test micro 73.36%, test macro 69.16%\n",
            "Epoch 17: loss 0.02759, train micro 98.5%, train macro 98.5%, valid micro 89.33%, valid macro 89.03%, test micro 75.11%, test macro 71.81%\n",
            "Epoch 18: loss 0.02201, train micro 98.83%, train macro 98.83%, valid micro 92.33%, valid macro 92.22%, test micro 76.89%, test macro 74.31%\n",
            "Epoch 19: loss 0.01807, train micro 99.33%, train macro 99.33%, valid micro 94.67%, valid macro 94.65%, test micro 78.21%, test macro 76.08%\n",
            "Epoch 20: loss 0.01492, train micro 99.67%, train macro 99.67%, valid micro 95.0%, valid macro 94.98%, test micro 79.44%, test macro 77.68%\n",
            "Epoch 21: loss 0.01236, train micro 99.83%, train macro 99.83%, valid micro 95.67%, valid macro 95.66%, test micro 80.71%, test macro 79.27%\n",
            "Epoch 22: loss 0.01028, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 95.99%, test micro 81.84%, test macro 80.6%\n",
            "Epoch 23: loss 0.00858, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.33%, test micro 82.59%, test macro 81.54%\n",
            "Epoch 24: loss 0.00697, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 95.99%, test micro 83.11%, test macro 82.17%\n",
            "Epoch 25: loss 0.0056, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.65%, test micro 83.76%, test macro 82.93%\n",
            "Epoch 26: loss 0.00456, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.38%, test macro 83.65%\n",
            "Epoch 27: loss 0.00375, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.71%, test macro 84.1%\n",
            "Epoch 28: loss 0.00311, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.89%, test macro 84.35%\n",
            "Epoch 29: loss 0.00261, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.89%, test macro 84.39%\n",
            "Epoch 30: loss 0.00223, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.99%, test macro 84.52%\n",
            "Epoch 31: loss 0.00193, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 97.99%, test micro 85.04%, test macro 84.59%\n",
            "Epoch 32: loss 0.00169, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 97.99%, test micro 85.18%, test macro 84.77%\n",
            "Epoch 33: loss 0.0015, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 97.99%, test micro 85.18%, test macro 84.77%\n",
            "Epoch 34: loss 0.00134, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 85.27%, test macro 84.88%\n",
            "Epoch 35: loss 0.00121, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 85.27%, test macro 84.88%\n",
            "Epoch 36: loss 0.00111, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 85.32%, test macro 84.94%\n",
            "Epoch 37: loss 0.00102, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 85.32%, test macro 84.94%\n",
            "Epoch 38: loss 0.00094, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 85.18%, test macro 84.8%\n",
            "Epoch 39: loss 0.00088, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 85.04%, test macro 84.66%\n",
            "Epoch 40: loss 0.00082, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 84.94%, test macro 84.56%\n",
            "Epoch 41: loss 0.00077, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.99%, test macro 84.61%\n",
            "Epoch 42: loss 0.00072, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.89%, test macro 84.53%\n",
            "Epoch 43: loss 0.00069, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.71%, test macro 84.34%\n",
            "Epoch 44: loss 0.00065, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.61%, test macro 84.25%\n",
            "Epoch 45: loss 0.00062, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.52%, test macro 84.16%\n",
            "Epoch 46: loss 0.00059, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.65%, test micro 84.61%, test macro 84.27%\n",
            "Epoch 47: loss 0.00057, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.65%, test micro 84.52%, test macro 84.19%\n",
            "Epoch 48: loss 0.00055, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.65%, test micro 84.42%, test macro 84.11%\n",
            "Epoch 49: loss 0.00052, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.65%, test micro 84.33%, test macro 84.02%\n",
            "Epoch 50: loss 0.00051, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.33%, test macro 84.05%\n",
            "Epoch 51: loss 0.00049, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.38%, test macro 84.11%\n",
            "Epoch 52: loss 0.00047, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.42%, test macro 84.17%\n",
            "Epoch 53: loss 0.00046, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.38%, test macro 84.13%\n",
            "Epoch 54: loss 0.00045, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.24%, test macro 83.99%\n",
            "Epoch 55: loss 0.00043, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.24%, test macro 83.99%\n",
            "Epoch 56: loss 0.00042, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.24%, test macro 83.99%\n",
            "Epoch 57: loss 0.00041, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.24%, test macro 83.99%\n",
            "Epoch 58: loss 0.0004, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.19%, test macro 83.95%\n",
            "Epoch 59: loss 0.00039, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.14%, test macro 83.9%\n",
            "Epoch 60: loss 0.00039, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.14%, test macro 83.91%\n",
            "Epoch 61: loss 0.00038, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.09%, test macro 83.86%\n",
            "Epoch 62: loss 0.00037, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.0%, test macro 83.78%\n",
            "Epoch 63: loss 0.00036, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.0%, test macro 83.78%\n",
            "Epoch 64: loss 0.00036, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.95%, test macro 83.73%\n",
            "Epoch 65: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.91%, test macro 83.69%\n",
            "Epoch 66: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.81%, test macro 83.59%\n",
            "Epoch 67: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.81%, test macro 83.59%\n",
            "Epoch 68: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.81%, test macro 83.6%\n",
            "Epoch 69: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.76%, test macro 83.56%\n",
            "Epoch 70: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.72%, test macro 83.51%\n",
            "Epoch 71: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.72%, test macro 83.51%\n",
            "Epoch 72: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.72%, test macro 83.51%\n",
            "Epoch 73: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.62%, test macro 83.43%\n",
            "Epoch 74: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.58%, test macro 83.39%\n",
            "Epoch 75: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.62%, test macro 83.44%\n",
            "Epoch 76: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.62%, test macro 83.44%\n",
            "Epoch 77: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.58%, test macro 83.4%\n",
            "Epoch 78: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.58%, test macro 83.4%\n",
            "Epoch 79: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.62%, test macro 83.45%\n",
            "Epoch 80: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.62%, test macro 83.46%\n",
            "Epoch 81: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.62%, test macro 83.46%\n",
            "Epoch 82: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.58%, test macro 83.41%\n",
            "Epoch 83: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.58%, test macro 83.42%\n",
            "Epoch 84: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.62%, test macro 83.48%\n",
            "Epoch 85: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.62%, test macro 83.48%\n",
            "Epoch 86: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.48%, test macro 83.33%\n",
            "Epoch 87: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.44%, test macro 83.29%\n",
            "Epoch 88: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.44%, test macro 83.29%\n",
            "Epoch 89: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.39%, test macro 83.24%\n",
            "Epoch 90: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.39%, test macro 83.25%\n",
            "Epoch 91: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.44%, test macro 83.29%\n",
            "Epoch 92: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.44%, test macro 83.29%\n",
            "Epoch 93: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.44%, test macro 83.3%\n",
            "Epoch 94: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.44%, test macro 83.3%\n",
            "Epoch 95: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.48%, test macro 83.35%\n",
            "Epoch 96: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.44%, test macro 83.31%\n",
            "Epoch 97: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.44%, test macro 83.31%\n",
            "Epoch 98: loss 0.00024, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.44%, test macro 83.31%\n",
            "Epoch 99: loss 0.00024, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.44%, test macro 83.31%\n",
            "Epoch 100: loss 0.00024, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.44%, test macro 83.31%\n",
            "Best model: train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 97.99%, test micro 85.04%, test macro 84.59%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mtAhFLQQKgbl"
      },
      "source": [
        "## Question 2.3: What is the maximum **micro** F1 score you could get for the best_model on test set when using the attention aggregation? (4 points)\n",
        "\n",
        "Submit your answers on Gradescope."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_cnsMGbsqJG_"
      },
      "source": [
        "## Question 2.4: What is the maximum **macro** F1 score you could get for the best_model on test set when using the attention aggregation? (4 points)\n",
        "\n",
        "Submit your answers on Gradescope."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQgx5y4UqMHH"
      },
      "source": [
        "## Attention for each Message Type\n",
        "\n",
        "Through message type level attention we can learn that which message type is more important to which layer.\n",
        "\n",
        "Here we will print out and show that each layer pay how much attention on each message type."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hvK58gijqN_C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67f34a2e-f764-4fd0-c4d0-d3814ac1d516"
      },
      "source": [
        "if model.convs1.alpha is not None and model.convs2.alpha is not None:\n",
        "    for idx, message_type in model.convs1.mapping.items():\n",
        "        print(f\"Layer 1 has attention {model.convs1.alpha[idx]} on message type {message_type}\")\n",
        "    for idx, message_type in model.convs2.mapping.items():\n",
        "        print(f\"Layer 2 has attention {model.convs2.alpha[idx]} on message type {message_type}\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Layer 1 has attention [0.42440572 0.42171216 0.4547883  ... 0.44317695 0.4175288  0.41557276] on message type ('paper', 'author', 'paper')\n",
            "Layer 1 has attention [0.5755943  0.5782879  0.5452117  ... 0.556823   0.5824712  0.58442724] on message type ('paper', 'subject', 'paper')\n",
            "Layer 2 has attention [0.45600995 0.42799044 0.4334866  ... 0.5782609  0.31248942 0.5250837 ] on message type ('paper', 'author', 'paper')\n",
            "Layer 2 has attention [0.54399    0.57200956 0.5665134  ... 0.42173904 0.68751055 0.47491634] on message type ('paper', 'subject', 'paper')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7JXsMTBgeOI"
      },
      "source": [
        "# Submission\n",
        "\n",
        "In order to get credit, you must go submit your answers on Gradescope.\n",
        "\n",
        "Also, you need to submit the `ipynb` file of Colab 4, by clicking `File` and `Download .ipynb`. Please make sure that your output of each cell is available in your `ipynb` file."
      ]
    }
  ]
}