assignments/assignment_set_2/intro_networkx_pyg.ipynb

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  "metadata": {
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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
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        "name": "ipython",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
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    "colab": {
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      "machine_shape": "hm",
      "include_colab_link": true
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/zahta/graph_ml/blob/main/assignments/assignment_set_2/intro_networkx_pyg.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Assignment Set 2 (part 2)"
      ],
      "metadata": {
        "id": "-4W_rlybXvQO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook is borrowed from the [CS224W: Machine Learning with Graphs](http://web.stanford.edu/class/cs224w/)"
      ],
      "metadata": {
        "id": "n2JCaHG2uxmy"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XuXWJLEm2UWS"
      },
      "source": [
        "In this Colab, we will introduce two packages, [NetworkX](https://networkx.org/documentation/stable/) and [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/).\n",
        "\n",
        "For the PyTorch Geometric section, you don't need to understand all the details already. Concepts and implementations of graph neural network will be covered in future lectures and Colabs."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gzsP50bF6Gb"
      },
      "source": [
        "\n",
        "# NetworkX Tutorial\n",
        "\n",
        "NetworkX is one of the most frequently used Python packages to create, manipulate, and mine graphs.\n",
        "\n",
        "Main parts of this tutorial are adapted from https://colab.research.google.com/github/jdwittenauer/ipython-notebooks/blob/master/notebooks/libraries/NetworkX.ipynb#scrollTo=zA1OO6huHeV6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZLU5b4g-Thi"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7p2pHdwnsu7-",
        "outputId": "7ce653dd-8cd8-4410-d37c-8fd8ef57bbd5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.9/dist-packages (1.10.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.9/dist-packages (3.0)\n",
            "Requirement already satisfied: numpy<1.27.0,>=1.19.5 in /usr/local/lib/python3.9/dist-packages (from scipy) (1.22.4)\n"
          ]
        }
      ],
      "source": [
        "# Upgrade packages\n",
        "!pip install --upgrade scipy networkx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "mARzvBi4-Thj"
      },
      "outputs": [],
      "source": [
        "# Import the NetworkX package\n",
        "import networkx as nx"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MCIeGAfLfAMK"
      },
      "source": [
        "## More precisely working with vertex and edge features, and graphs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qSI_n6P-e0PW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0ba4f13-7889-4bd8-a969-25d193077d04"
      },
      "source": [
        "# Create an undirected graph G\n",
        "G = nx.Graph()\n",
        "print(G.is_directed())\n",
        "\n",
        "# Create a directed graph H\n",
        "H = nx.DiGraph()\n",
        "print(H.is_directed())\n",
        "\n",
        "# Add graph level attribute\n",
        "G.graph[\"Name\"] = \"Bar\"\n",
        "print(G.graph)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n",
            "True\n",
            "{'Name': 'Bar'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x0pLs0-Ka9j8"
      },
      "source": [
        "### Node\n",
        "\n",
        "Nodes (with attributes) can be easily added to NetworkX graphs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQ8ApAL5H1GB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7eb20e4d-aef0-4916-bbc4-981a67f4916e"
      },
      "source": [
        "# Add one node with node level attributes\n",
        "G.add_node(0, feature=0, label=0)\n",
        "\n",
        "# Get attributes of the node 0\n",
        "node_0_attr = G.nodes[0]\n",
        "print(\"Node 0 has the attributes {}\".format(node_0_attr))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Node 0 has the attributes {'feature': 0, 'label': 0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "btOdMYqJaKia",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "504091b8-25be-423e-f239-4d68d6111002"
      },
      "source": [
        "# Add multiple nodes with attributes\n",
        "G.add_nodes_from([\n",
        "  (1, {\"feature\": 1, \"label\": 1}),\n",
        "  (2, {\"feature\": 2, \"label\": 2})\n",
        "])\n",
        "\n",
        "# Loop through all the nodes\n",
        "# Set data=True will return node attributes\n",
        "for node in G.nodes(data=True):\n",
        "  print(node)\n",
        "\n",
        "# Get number of nodes\n",
        "num_nodes = G.number_of_nodes()\n",
        "print(\"G has {} nodes\".format(num_nodes))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, {'feature': 0, 'label': 0})\n",
            "(1, {'feature': 1, 'label': 1})\n",
            "(2, {'feature': 2, 'label': 2})\n",
            "G has 3 nodes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0AdoaZPgbRis"
      },
      "source": [
        "### Edge\n",
        "\n",
        "Similar to nodes, edges (with attributes) can also be easily added to NetworkX graphs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0szH5F6EH079",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "745e2011-fdf4-4030-906d-259b8b0a263a"
      },
      "source": [
        "# Add one edge with edge weight 0.5\n",
        "G.add_edge(0, 1, weight=0.5)\n",
        "\n",
        "# Get attributes of the edge (0, 1)\n",
        "edge_0_1_attr = G.edges[(0, 1)]\n",
        "print(\"Edge (0, 1) has the attributes {}\".format(edge_0_1_attr))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Edge (0, 1) has the attributes {'weight': 0.5}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QRTmi4EUaf_I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "128d025f-63ec-4796-a5d1-ae1bd1665dd9"
      },
      "source": [
        "# Add multiple edges with edge weights\n",
        "G.add_edges_from([\n",
        "  (1, 2, {\"weight\": 0.3}),\n",
        "  (2, 0, {\"weight\": 0.1})\n",
        "])\n",
        "\n",
        "# Loop through all the edges\n",
        "# Here there is no data=True, so only the edge will be returned\n",
        "for edge in G.edges():\n",
        "  print(edge)\n",
        "\n",
        "# Get number of edges\n",
        "num_edges = G.number_of_edges()\n",
        "print(\"G has {} edges\".format(num_edges))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, 1)\n",
            "(0, 2)\n",
            "(1, 2)\n",
            "G has 3 edges\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9u1Utjn4bc7k"
      },
      "source": [
        "### Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lykPzFOEahuP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "db4a82e9-ab32-4b7d-8827-fa42157efde9"
      },
      "source": [
        "# Draw the graph\n",
        "nx.draw(G, with_labels = True)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Q6YTP2FDbOS"
      },
      "source": [
        "### Node Degree and Neighbor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GFA3B6Z_DE3q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08ff611c-9ff0-463c-9cf6-c6577affdddd"
      },
      "source": [
        "node_id = 1\n",
        "\n",
        "# Degree of node 1\n",
        "print(\"Node {} has degree {}\".format(node_id, G.degree[node_id]))\n",
        "\n",
        "# Get neighbor of node 1\n",
        "for neighbor in G.neighbors(node_id):\n",
        "  print(\"Node {} has neighbor {}\".format(node_id, neighbor))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Node 1 has degree 2\n",
            "Node 1 has neighbor 0\n",
            "Node 1 has neighbor 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MDcCjrW3JWzG"
      },
      "source": [
        "# PyTorch Geometric Tutorial\n",
        "\n",
        "PyTorch Geometric (PyG) is an extension library for PyTorch. It provides useful primitives to develop Graph Deep Learning models, including various graph neural network layers and a large number of benchmark datasets.\n",
        "\n",
        "Don't worry if you don't understand some concepts such as `GCNConv` -- we will cover all of them in the future lectures :)\n",
        "\n",
        "This tutorial is adapted from https://colab.research.google.com/drive/1h3-vJGRVloF5zStxL5I0rSy4ZUPNsjy8?usp=sharing#scrollTo=ci-LpZWhRJoI by [Matthias Fey](https://rusty1s.github.io/#/)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WU7NCZtQ6msa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b29f7df-4c15-4f38-d1bd-7d07abe365a1"
      },
      "source": [
        "import torch\n",
        "print(\"PyTorch has version {}\".format(torch.__version__))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PyTorch has version 1.13.1+cu116\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n7gMjMCT677n"
      },
      "source": [
        "## Setup\n",
        "\n",
        "The installation of PyG on Colab can be a little bit tricky. Execute the cell below -- in case of issues, more information can be found on the [PyG's installation page](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tRNEKu-R66Cw"
      },
      "source": [
        "# Install torch geometric\n",
        "#!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"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Install torch geometric \n",
        "try:\n",
        "    from torch_geometric.datasets import KarateClub\n",
        "except:\n",
        "    !pip install -q torch_scatter -f https://pytorch-geometric.com/whl/torch-1.13.1%2Bcu116.html\n",
        "    !pip install -q torch_sparse -f https://pytorch-geometric.com/whl/torch-1.13.1%2Bcu116.html\n",
        "    !pip install -q torch-geometric"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lbleyuURF5NU",
        "outputId": "0b96233d-c77c-4425-f5b4-0ff6e8f57325"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.4/9.4 MB\u001b[0m \u001b[31m25.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.5/4.5 MB\u001b[0m \u001b[31m62.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m565.0/565.0 KB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m280.2/280.2 KB\u001b[0m \u001b[31m899.2 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HLxnaKsN8GVf"
      },
      "source": [
        "## Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qywlcjyr8USw"
      },
      "source": [
        "# Helper function for visualization.\n",
        "%matplotlib inline\n",
        "import torch\n",
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Visualization function for NX graph or PyTorch tensor\n",
        "def visualize(h, color, epoch=None, loss=None):\n",
        "    plt.figure(figsize=(7,7))\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "\n",
        "    if torch.is_tensor(h):\n",
        "        h = h.detach().cpu().numpy()\n",
        "        plt.scatter(h[:, 0], h[:, 1], s=140, c=color, cmap=\"Set2\")\n",
        "        if epoch is not None and loss is not None:\n",
        "            plt.xlabel(f'Epoch: {epoch}, Loss: {loss.item():.4f}', fontsize=16)\n",
        "    else:\n",
        "        nx.draw_networkx(G, pos=nx.spring_layout(G, seed=42), with_labels=False,\n",
        "                         node_color=color, cmap=\"Set2\")\n",
        "    plt.show()"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bbny-iTO7NQN"
      },
      "source": [
        "## Introduction\n",
        "\n",
        "Recently, deep learning on graphs has emerged to one of the hottest research fields in the deep learning community.\n",
        "Here, **Graph Neural Networks (GNNs)** aim to generalize classical deep learning concepts to irregular structured data (in contrast to images or texts) and to enable neural networks to reason about objects and their relations.\n",
        "\n",
        "This tutorial will introduce you to some fundamental concepts regarding deep learning on graphs via Graph Neural Networks based on the **[PyTorch Geometric (PyG) library](https://github.com/rusty1s/pytorch_geometric)**.\n",
        "PyTorch Geometric is an extension library to the popular deep learning framework [PyTorch](https://pytorch.org/), and consists of various methods and utilities to ease the implementation of Graph Neural Networks.\n",
        "\n",
        "Following [Kipf et al. (2017)](https://arxiv.org/abs/1609.02907), let's dive into the world of GNNs by looking at a simple graph-structured example, the well-known [**Zachary's karate club network**](https://en.wikipedia.org/wiki/Zachary%27s_karate_club). This graph describes a social network of 34 members of a karate club and documents links between members who interacted outside the club. Here, we are interested in detecting communities that arise from the member's interaction."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g3uPffzbyqn9"
      },
      "source": [
        "## Dataset\n",
        "\n",
        "PyTorch Geometric provides an easy access to the dataset via the [`torch_geometric.datasets`](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets) subpackage:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YrpL9CtS7nx2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de3593bb-4124-49d8-b1ea-4a9d605daa8c"
      },
      "source": [
        "from torch_geometric.datasets import KarateClub\n",
        "\n",
        "dataset = KarateClub()\n",
        "print(f'Dataset: {dataset}:')\n",
        "print('======================')\n",
        "print(f'Number of graphs: {len(dataset)}')\n",
        "print(f'Number of features: {dataset.num_features}')\n",
        "print(f'Number of classes: {dataset.num_classes}')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset: KarateClub():\n",
            "======================\n",
            "Number of graphs: 1\n",
            "Number of features: 34\n",
            "Number of classes: 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lCeRGa2q7sdl"
      },
      "source": [
        "After initializing the [`KarateClub`](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.KarateClub) dataset, we first can inspect some of its properties.\n",
        "For example, we can see that this dataset holds exactly **one graph**, and that each node in this dataset is assigned a **34-dimensional feature vector** (which uniquely describes the members of the karate club).\n",
        "Furthermore, the graph holds exactly **4 classes**, which represent the community each node belongs to.\n",
        "\n",
        "Let's now look at the underlying graph in more detail:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTLapYhP7uCn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "818b92d4-e69b-4250-feaa-9130a4df8ad4"
      },
      "source": [
        "data = dataset[0]  # Get the first graph object.\n",
        "\n",
        "print(data)\n",
        "print('==============================================================')\n",
        "\n",
        "# Gather some statistics about the graph.\n",
        "print(f'Number of nodes: {data.num_nodes}')\n",
        "print(f'Number of edges: {data.num_edges}')\n",
        "print(f'Average node degree: {data.num_edges / data.num_nodes:.2f}')\n",
        "print(f'Number of training nodes: {data.train_mask.sum()}')\n",
        "print(f'Training node label rate: {int(data.train_mask.sum()) / data.num_nodes:.2f}')\n",
        "print(f'Contains isolated nodes: {data.contains_isolated_nodes()}')\n",
        "print(f'Contains self-loops: {data.contains_self_loops()}')\n",
        "print(f'Is undirected: {data.is_undirected()}')"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data(x=[34, 34], edge_index=[2, 156], y=[34], train_mask=[34])\n",
            "==============================================================\n",
            "Number of nodes: 34\n",
            "Number of edges: 156\n",
            "Average node degree: 4.59\n",
            "Number of training nodes: 4\n",
            "Training node label rate: 0.12\n",
            "Contains isolated nodes: False\n",
            "Contains self-loops: False\n",
            "Is undirected: True\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/torch_geometric/deprecation.py:12: UserWarning: 'contains_isolated_nodes' is deprecated, use 'has_isolated_nodes' instead\n",
            "  warnings.warn(out)\n",
            "/usr/local/lib/python3.9/dist-packages/torch_geometric/deprecation.py:12: UserWarning: 'contains_self_loops' is deprecated, use 'has_self_loops' instead\n",
            "  warnings.warn(out)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lIzbIoc-y8J4"
      },
      "source": [
        "## Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I5zhmKIH72Rf"
      },
      "source": [
        "Each graph in PyTorch Geometric is represented by a single [`Data`](https://pytorch-geometric.readthedocs.io/en/latest/modules/data.html#torch_geometric.data.Data) object, which holds all the information to describe its graph representation.\n",
        "We can print the data object anytime via `print(data)` to receive a short summary about its attributes and their shapes:\n",
        "```\n",
        "Data(edge_index=[2, 156], x=[34, 34], y=[34], train_mask=[34])\n",
        "```\n",
        "We can see that this `data` object holds 4 attributes:\n",
        "(1) The `edge_index` property holds the information about the **graph connectivity**, *i.e.*, a tuple of source and destination node indices for each edge.\n",
        "PyG further refers to (2) **node features** as `x` (each of the 34 nodes is assigned a 34-dim feature vector), and to (3) **node labels** as `y` (each node is assigned to exactly one class).\n",
        "(4) There also exists an additional attribute called `train_mask`, which describes for which nodes we already know their community assigments.\n",
        "In total, we are only aware of the ground-truth labels of 4 nodes (one for each community), and the task is to infer the community assignment for the remaining nodes.\n",
        "\n",
        "The `data` object also provides some **utility functions** to infer some basic properties of the underlying graph.\n",
        "For example, we can easily infer whether there exists isolated nodes in the graph (*i.e.* there exists no edge to any node), whether the graph contains self-loops (*i.e.*, $(v, v) \\in \\mathcal{E}$), or whether the graph is undirected (*i.e.*, for each edge $(v, w) \\in \\mathcal{E}$ there also exists the edge $(w, v) \\in \\mathcal{E}$)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFA6Xi4O79r0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "320e6989-2517-4ddb-9c55-578c4be36d83"
      },
      "source": [
        "from IPython.display import Javascript  # Restrict height of output cell.\n",
        "display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 300})'''))\n",
        "\n",
        "edge_index = data.edge_index\n",
        "print(edge_index.t())"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "google.colab.output.setIframeHeight(0, true, {maxHeight: 300})"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[ 0,  1],\n",
            "        [ 0,  2],\n",
            "        [ 0,  3],\n",
            "        [ 0,  4],\n",
            "        [ 0,  5],\n",
            "        [ 0,  6],\n",
            "        [ 0,  7],\n",
            "        [ 0,  8],\n",
            "        [ 0, 10],\n",
            "        [ 0, 11],\n",
            "        [ 0, 12],\n",
            "        [ 0, 13],\n",
            "        [ 0, 17],\n",
            "        [ 0, 19],\n",
            "        [ 0, 21],\n",
            "        [ 0, 31],\n",
            "        [ 1,  0],\n",
            "        [ 1,  2],\n",
            "        [ 1,  3],\n",
            "        [ 1,  7],\n",
            "        [ 1, 13],\n",
            "        [ 1, 17],\n",
            "        [ 1, 19],\n",
            "        [ 1, 21],\n",
            "        [ 1, 30],\n",
            "        [ 2,  0],\n",
            "        [ 2,  1],\n",
            "        [ 2,  3],\n",
            "        [ 2,  7],\n",
            "        [ 2,  8],\n",
            "        [ 2,  9],\n",
            "        [ 2, 13],\n",
            "        [ 2, 27],\n",
            "        [ 2, 28],\n",
            "        [ 2, 32],\n",
            "        [ 3,  0],\n",
            "        [ 3,  1],\n",
            "        [ 3,  2],\n",
            "        [ 3,  7],\n",
            "        [ 3, 12],\n",
            "        [ 3, 13],\n",
            "        [ 4,  0],\n",
            "        [ 4,  6],\n",
            "        [ 4, 10],\n",
            "        [ 5,  0],\n",
            "        [ 5,  6],\n",
            "        [ 5, 10],\n",
            "        [ 5, 16],\n",
            "        [ 6,  0],\n",
            "        [ 6,  4],\n",
            "        [ 6,  5],\n",
            "        [ 6, 16],\n",
            "        [ 7,  0],\n",
            "        [ 7,  1],\n",
            "        [ 7,  2],\n",
            "        [ 7,  3],\n",
            "        [ 8,  0],\n",
            "        [ 8,  2],\n",
            "        [ 8, 30],\n",
            "        [ 8, 32],\n",
            "        [ 8, 33],\n",
            "        [ 9,  2],\n",
            "        [ 9, 33],\n",
            "        [10,  0],\n",
            "        [10,  4],\n",
            "        [10,  5],\n",
            "        [11,  0],\n",
            "        [12,  0],\n",
            "        [12,  3],\n",
            "        [13,  0],\n",
            "        [13,  1],\n",
            "        [13,  2],\n",
            "        [13,  3],\n",
            "        [13, 33],\n",
            "        [14, 32],\n",
            "        [14, 33],\n",
            "        [15, 32],\n",
            "        [15, 33],\n",
            "        [16,  5],\n",
            "        [16,  6],\n",
            "        [17,  0],\n",
            "        [17,  1],\n",
            "        [18, 32],\n",
            "        [18, 33],\n",
            "        [19,  0],\n",
            "        [19,  1],\n",
            "        [19, 33],\n",
            "        [20, 32],\n",
            "        [20, 33],\n",
            "        [21,  0],\n",
            "        [21,  1],\n",
            "        [22, 32],\n",
            "        [22, 33],\n",
            "        [23, 25],\n",
            "        [23, 27],\n",
            "        [23, 29],\n",
            "        [23, 32],\n",
            "        [23, 33],\n",
            "        [24, 25],\n",
            "        [24, 27],\n",
            "        [24, 31],\n",
            "        [25, 23],\n",
            "        [25, 24],\n",
            "        [25, 31],\n",
            "        [26, 29],\n",
            "        [26, 33],\n",
            "        [27,  2],\n",
            "        [27, 23],\n",
            "        [27, 24],\n",
            "        [27, 33],\n",
            "        [28,  2],\n",
            "        [28, 31],\n",
            "        [28, 33],\n",
            "        [29, 23],\n",
            "        [29, 26],\n",
            "        [29, 32],\n",
            "        [29, 33],\n",
            "        [30,  1],\n",
            "        [30,  8],\n",
            "        [30, 32],\n",
            "        [30, 33],\n",
            "        [31,  0],\n",
            "        [31, 24],\n",
            "        [31, 25],\n",
            "        [31, 28],\n",
            "        [31, 32],\n",
            "        [31, 33],\n",
            "        [32,  2],\n",
            "        [32,  8],\n",
            "        [32, 14],\n",
            "        [32, 15],\n",
            "        [32, 18],\n",
            "        [32, 20],\n",
            "        [32, 22],\n",
            "        [32, 23],\n",
            "        [32, 29],\n",
            "        [32, 30],\n",
            "        [32, 31],\n",
            "        [32, 33],\n",
            "        [33,  8],\n",
            "        [33,  9],\n",
            "        [33, 13],\n",
            "        [33, 14],\n",
            "        [33, 15],\n",
            "        [33, 18],\n",
            "        [33, 19],\n",
            "        [33, 20],\n",
            "        [33, 22],\n",
            "        [33, 23],\n",
            "        [33, 26],\n",
            "        [33, 27],\n",
            "        [33, 28],\n",
            "        [33, 29],\n",
            "        [33, 30],\n",
            "        [33, 31],\n",
            "        [33, 32]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nLLsT0ROzffp"
      },
      "source": [
        "## Edge Index"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wQJyi9OB8dh_"
      },
      "source": [
        "By printing `edge_index`, we can further understand how PyG represents graph connectivity internally.\n",
        "We can see that for each edge, `edge_index` holds a tuple of two node indices, where the first value describes the node index of the source node and the second value describes the node index of the destination node of an edge.\n",
        "\n",
        "This representation is known as the **COO format (coordinate format)** commonly used for representing sparse matrices.\n",
        "Instead of holding the adjacency information in a dense representation $\\mathbf{A} \\in \\{ 0, 1 \\}^{|\\mathcal{V}| \\times |\\mathcal{V}|}$, PyG represents graphs sparsely, which refers to only holding the coordinates/values for which entries in $\\mathbf{A}$ are non-zero.\n",
        "\n",
        "We can further visualize the graph by converting it to the `networkx` library format, which implements, in addition to graph manipulation functionalities, powerful tools for visualization:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3KfJHtlV8h3W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "0874cce7-0c7d-4222-d1f8-eadf8683e8af"
      },
      "source": [
        "from torch_geometric.utils import to_networkx\n",
        "\n",
        "G = to_networkx(data, to_undirected=True)\n",
        "visualize(G, color=data.y)"
      ],
      "execution_count": 17,
      "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": "NUdHZY2u8vn3"
      },
      "source": [
        "## Implementing Graph Neural Networks\n",
        "\n",
        "After learning about PyG's data handling, it's time to implement our first Graph Neural Network!\n",
        "\n",
        "For this, we will use one of the most simple GNN operators, the **GCN layer** ([Kipf et al. (2017)](https://arxiv.org/abs/1609.02907)).\n",
        "\n",
        "PyG implements this layer via [`GCNConv`](https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#torch_geometric.nn.conv.GCNConv), which can be executed by passing in the node feature representation `x` and the COO graph connectivity representation `edge_index`.\n",
        "\n",
        "With this, we are ready to create our first Graph Neural Network by defining our network architecture in a `torch.nn.Module` class:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6tQGQF8r8zIr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b742e8e1-fdc1-4174-9302-66507bf8e89d"
      },
      "source": [
        "import torch\n",
        "from torch.nn import Linear\n",
        "from torch_geometric.nn import GCNConv\n",
        "\n",
        "\n",
        "class GCN(torch.nn.Module):\n",
        "    def __init__(self):\n",
        "        super(GCN, self).__init__()\n",
        "        torch.manual_seed(12345)\n",
        "        self.conv1 = GCNConv(dataset.num_features, 4)\n",
        "        self.conv2 = GCNConv(4, 4)\n",
        "        self.conv3 = GCNConv(4, 2)\n",
        "        self.classifier = Linear(2, dataset.num_classes)\n",
        "\n",
        "    def forward(self, x, edge_index):\n",
        "        h = self.conv1(x, edge_index)\n",
        "        h = h.tanh()\n",
        "        h = self.conv2(h, edge_index)\n",
        "        h = h.tanh()\n",
        "        h = self.conv3(h, edge_index)\n",
        "        h = h.tanh()  # Final GNN embedding space.\n",
        "        \n",
        "        # Apply a final (linear) classifier.\n",
        "        out = self.classifier(h)\n",
        "\n",
        "        return out, h\n",
        "\n",
        "model = GCN()\n",
        "print(model)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GCN(\n",
            "  (conv1): GCNConv(34, 4)\n",
            "  (conv2): GCNConv(4, 4)\n",
            "  (conv3): GCNConv(4, 2)\n",
            "  (classifier): Linear(in_features=2, out_features=4, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5zgbaD5P8_M_"
      },
      "source": [
        "Here, we first initialize all of our building blocks in `__init__` and define the computation flow of our network in `forward`.\n",
        "We first define and stack **three graph convolution layers**, which corresponds to aggregating 3-hop neighborhood information around each node (all nodes up to 3 \"hops\" away).\n",
        "In addition, the `GCNConv` layers reduce the node feature dimensionality to $2$, *i.e.*, $34 \\rightarrow 4 \\rightarrow 4 \\rightarrow 2$. Each `GCNConv` layer is enhanced by a [tanh](https://pytorch.org/docs/stable/generated/torch.nn.Tanh.html?highlight=tanh#torch.nn.Tanh) non-linearity.\n",
        "\n",
        "After that, we apply a single linear transformation ([`torch.nn.Linear`](https://pytorch.org/docs/stable/generated/torch.nn.Linear.html?highlight=linear#torch.nn.Linear)) that acts as a classifier to map our nodes to 1 out of the 4 classes/communities.\n",
        "\n",
        "We return both the output of the final classifier as well as the final node embeddings produced by our GNN.\n",
        "We proceed to initialize our final model via `GCN()`, and printing our model produces a summary of all its used sub-modules."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48uhs_0j9AMX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "outputId": "e9c1d65d-73a0-4cc9-c05d-a291d686e496"
      },
      "source": [
        "model = GCN()\n",
        "\n",
        "_, h = model(data.x, data.edge_index)\n",
        "print(f'Embedding shape: {list(h.shape)}')\n",
        "\n",
        "visualize(h, color=data.y)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Embedding shape: [34, 2]\n"
          ]
        },
        {
          "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": "nDtJ9Zjw9I_Y"
      },
      "source": [
        "Remarkably, even before training the weights of our model, the model produces an embedding of nodes that closely resembles the community-structure of the graph.\n",
        "Nodes of the same color (community) are already closely clustered together in the embedding space, although the weights of our model are initialized **completely at random** and we have not yet performed any training so far!\n",
        "This leads to the conclusion that GNNs introduce a strong inductive bias, leading to similar embeddings for nodes that are close to each other in the input graph.\n",
        "\n",
        "### Training on the Karate Club Network\n",
        "\n",
        "But can we do better? Let's look at an example on how to train our network parameters based on the knowledge of the community assignments of 4 nodes in the graph (one for each community):\n",
        "\n",
        "Since everything in our model is differentiable and parameterized, we can add some labels, train the model and observe how the embeddings react.\n",
        "Here, we make use of a semi-supervised or transductive learning procedure: We simply train against one node per class, but are allowed to make use of the complete input graph data.\n",
        "\n",
        "Training our model is very similar to any other PyTorch model.\n",
        "In addition to defining our network architecture, we define a loss critertion (here, [`CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html)) and initialize a stochastic gradient optimizer (here, [`Adam`](https://pytorch.org/docs/stable/optim.html?highlight=adam#torch.optim.Adam)).\n",
        "After that, we perform multiple rounds of optimization, where each round consists of a forward and backward pass to compute the gradients of our model parameters w.r.t. to the loss derived from the forward pass.\n",
        "If you are not new to PyTorch, this scheme should appear familar to you. \n",
        "Otherwise, the PyTorch docs provide [a good introduction on how to train a neural network in PyTorch](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#define-a-loss-function-and-optimizer).\n",
        "\n",
        "Note that our semi-supervised learning scenario is achieved by the following line:\n",
        "```\n",
        "loss = criterion(out[data.train_mask], data.y[data.train_mask])\n",
        "```\n",
        "While we compute node embeddings for all of our nodes, we **only make use of the training nodes for computing the loss**.\n",
        "Here, this is implemented by filtering the output of the classifier `out` and ground-truth labels `data.y` to only contain the nodes in the `train_mask`.\n",
        "\n",
        "Let us now start training and see how our node embeddings evolve over time (best experienced by explicitely running the code):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FI3DETGi9ND6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "ad3c192b-a65c-4ecd-c50a-090e702240fc"
      },
      "source": [
        "import time\n",
        "from IPython.display import Javascript  # Restrict height of output cell.\n",
        "display(Javascript('''google.colab.output.setIframeHeight(0, true, {maxHeight: 430})'''))\n",
        "\n",
        "model = GCN()\n",
        "criterion = torch.nn.CrossEntropyLoss()  # Define loss criterion.\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)  # Define optimizer.\n",
        "\n",
        "def train(data):\n",
        "    optimizer.zero_grad()  # Clear gradients.\n",
        "    out, h = model(data.x, data.edge_index)  # Perform a single forward pass.\n",
        "    loss = criterion(out[data.train_mask], data.y[data.train_mask])  # Compute the loss solely based on the training nodes.\n",
        "    loss.backward()  # Derive gradients.\n",
        "    optimizer.step()  # Update parameters based on gradients.\n",
        "    return loss, h\n",
        "\n",
        "for epoch in range(401):\n",
        "    loss, h = train(data)\n",
        "    # Visualize the node embeddings every 10 epochs\n",
        "    if epoch % 10 == 0:\n",
        "        visualize(h, color=data.y, epoch=epoch, loss=loss)\n",
        "        time.sleep(0.3)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "google.colab.output.setIframeHeight(0, true, {maxHeight: 430})"
            ]
          },
          "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": {}
        },
        {
          "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": {}
        },
        {
          "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": {}
        },
        {
          "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": {}
        },
        {
          "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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vCBUAgDeECgDAG0IFAOANoQIA8IZQAQB4Q6gAALwhVAAA3hAqAABvCBUAgDeECgDAG0IFAOANoQIA8IZQAQB4Q6gAALwhVAAA3hAqAABvCBUAgDeECgDAG0IFAOANoQIA8IZQAQB4Q6gAALwhVAAA3hAqAABvCBUAgDeECgDAG0IFAOANoQIA8IZQAQB4Q6gAALwhVAAA3hAqAABvCBUAgDeECgDAG0IFAOANoQIA8IZQAQB4E610AQBQqq6elHYfOKGeZFp18YhmTRujuji7s2rAbwHAiHHyVFJrN+zR9j3HFAlMmYyTZX/OnzVOly+fodGj4pUu84xGqAAYEY4e79a/3fuiepJpOUnptAtnZH++tPOodu0/ofdeu0hNY+oqV+gZjjEVAFUvnc7oR2teUnc2UApxTuruSelH921RJtNfK5QboQKg6m3bfUzJ3vSg7ZzCYNmx91jZa0JhhAqAqrdh00H1pjJFte1NZbRh06EyV4T+ECoAql77iZ7S2h/vLlMlGAyhAgDwhlABUPWaGks7TXjcmESZKsFgCBUAVW/Z0imKRYvbXcWigZYtnVLmitAfQgVA1Zs/e1zRoZKIRzR3ZlN5C0K/CBUAVS8aCXTTyoVKxCMyK9zGJNUlIrpp5UIFQT+NUHaECoARYUJTvW5+2xLNndmkSMQUiwaKZn9GIqb5s8fpA29bqnFcTV9R3KYFwIgxdnRCb79ynjq7e7Vrf3hDyUQ8opZpY1Vfx+6sGvBbADDijKqLafFZEypdBgogVACcOZyTup+XevdKFkjx+VLirEpXVVMIFQC1zznp+N3S0W9K6XaFw/omKSXF50jNH5caLq1wkbWBgXoAtc056dAXpda/lVL7JdcluU7JnZJcj9SzWdr/San9+5WutCYQKgBq27E7pZO/ktwA9wNz3VLbP0mn1g9fXTWKUAFQu1xKOvIvAwfKK227pSNfL39NNY5QAVC7Tj0mud7i2/e8JCX3lK+eMwChAqB2JbeF4ybFspiUfLl89ZwBCBUAtctlpH6/gLg/xX0ZGAojVADUrvhsyUq4bYtLSbFZ5avnDECoAKhdDW9Uv3egLCQ2i4shXyNCBUDtCuJS0/uLO1qxOmnCx8pfU40jVADUtgkflUZdMHCwWJ3U9D6p8arhq6tGESoAaptFpGn/Vxp/mxSMkaxBUlyyhGSjpOhkadKnpYmfqHSlNYF7fwGofRaRJnxEGn+rdOpxqXePpEBKzJfql5U27oIBESoAzhwWk0ZfXukqahrdXwAAbwgVAIA3hAoAwBtCBQDgDaECAPCGUAEAeEOoAAC8IVQAAN4QKgAAbwgVAIA3hAoAwBtCBQDgDaECAPCGUAEAeEOoAAC8IVQAAN4QKgAAbwgVAIA3hAoAwBtCBQDgDaECAPCGUAEAeEOoAAC8IVQAAN4QKgAAbwgVAIA3hAoAwBtCBQDgDaECAPAmWukCAAAh55y2Hj+sBw9s0eGukwos0LwxE3XVtAWaWN9Y6fKKQqgAQBVo7Tqpf3z+ISV727Us/qxW1W/SKOtWV3ed1r24VO111+gDC69WPFLdu+3qrg4AzgBt3R360rNrtDz2hG6a8KAyTqoLUtm5xzU5ckRmD+vhFx/XlUs+r0hQvSMX1VsZAJwhvrNlvS6Krde7Gh9U3FI5gRJKBCnFLaU3RO/V9l1fqVCVxSFUAKCC2ro71NaxUzc0PqiEpQZsmwh6NSf5Q7UdfnmYqisdoQIAFfR0625dWvdM0e2dpK0vfUNHj3eXr6jXgFABgAo6nuzSxXXPK27potrHLa2l49brvsd2lrewISJUAKCC6iIx1QelHXUkIt06dOSUjp2ovqMVQgUAKmjxuClKuVhJy6QzcaXTTmse3anjJ3vKVNnQECoAUEHzxkzU5tRCpVxxu+N0JtCO9nMlSfsOd+g7dz+vTdvaylliSQgVAKggM9P4KbcrU+TuOOMienr/Na88T6Wdfr1+l7bvPlamCktDqABAhc2feJHa41eoJzPw9ei96Zi2HFmuI13TT5ueSjvd8/B2dff0lrPMohAqAFAFJrf8rQ73rlAyE1PG2WnzMs6UTMe17ej5un/HLQWXT6ed7rjrdzreUdkxFkIFAKqBRdU87//oF9s+pa1HlimVCQfvU5moth89Xz9+4ZP65baPKOP6P5pJZ5y+e/cmZTKZ4ar6Vbj3FwBUifr6uN50xVt116/mac2OXqXSaZX62b83ldFvXzis5WdPKU+Rg+BIBQCqyJjRCX3ohrP1hmUzNNRd9JPPHfBbVAkIFQCoMpEg0HmLJmlsY2JIy3cn03pp51Gl08PfDUaoAEAVMjNdcPYURaND203f9+hOrf7Z8zp5Kum5soERKgBQpc6e16ypzQ1DWrY3ldGJU0n98N4X1d0z8N2PfSJUAKBKBYHp+qvnq3FUabdx6eOcdKqzV48+s89zZf0jVACgikUjgd533eIhL+8kbXypVXf+5yZ1dZf/4khCBQCqXEN9XCvOm1bSMmMTrTp38kNaNnWNlk58RCdOHNa3f/KcOsscLFynAgAjwMWvm6ZoJNDDT+8dsF3zqD26as4PNGX0TjlnMksr46J601nf1472c3Tvbz6sd117WdnqNOdcvzOXL1/uNmzYULaVAwBKk8lk9OP7tmjPoY5XzZveuEXXL/6qYkGPzAos60zdqdE6NXG1Jk6aO+QazOxp59zyQvPo/gKAESQIAl150WxFI6enRiJySu9c9I+KRwoHiiQF5lQfPamG1tuUThf3TZMl11eWVwUAlE3zuHqNH1t/2rSzJz2iwAa/2NFMqo8e046n/1tZLo4kVABgBFp12RzFci6MfP3UBxSLFHeho5k0d8w6PfLIv3uvi1ABgBGoeVy9/mDVIkUiJsmpIX6spOWDwGnB6Lu0a98Jr3URKgAwQk2aMErvfcti9TOEMqjmUXu17onHvdZEqADACDZpwigtnjtBx7ubS1427aKqDw6oo9Pf/cEIFQAY4a5ZMUe7ut+pdCZS0nJ9Rzjb9x7zVguhAgAjXBCYzrvgQ1IQ1wCXHr5KJEjpaNdUJZP+Ti8mVACgFgSjFJn1rZIWOdjRopPJCaqL+7u5CqECALWibola7UalM4MP3fem43p8z9tlJs2ZMdZbCYQKANSQSfM/o+3tFyiZ7v92+b3puNbtvkF7TizWzCmNGj0q7m39hAoA1BIL1Lz4q1q369062dOkZLpOyXRCPamEetMxHT41U/+55Y/07MGrFY0Guvri2V5Xz12KAaDGjG+q1+su+hOtvvcqTazforGJVjlF1Hpqho50TZeZlIhH9O5VC9U0ps7rugkVAKhBzePq9cfvfb227Z6jx5/drxOnknKSmpsSuvCcKZo3e5yiEf+dVYQKANSoIDAtaBmvBS3jh2+dw7YmAEDNI1QAAN4QKgAAbwgVAIA3hAoAwBtCBQDgDaECAPCGUAEAeEOoAAC8IVQAAN6YG+BrwsysVdKu4SsHADACzHbOTSw0Y8BQAQCgFHR/AQC8IVQAAN4QKgAAbwiVKmZmt5qZ6+dxrMK1rTazvZ5f821m9gMz22JmGTN7qJ92N5vZo2bWamY9ZrbTzL5lZrMKtF1qZveZWYeZHTGzfzWzIX25hJm1ZLf9Hw5l+Uoys3ea2TNm1m1mu8zss2YWKWH5D5rZU2bWaWbHzOwRMzsnr82V2eldZnbUzL5nZpPz2qwe4G96c067LwzQrvu1bxGUC1/SNTLcJCl/B56qRCFl9k5J50laL2mg7zidIOnXkv5O0jFJCyV9TtI1ZrbEOXdSksxsmqSHJG2W9C5JTZK+LOkeM7vMOZcpw3uoOma2UtKPJX1L0n+XdL6kL0lqlPQ/ilj+S5L+VOH2/gtJoyRdmP3Z1+YNku6TtEbSjQp/R38t6ddmtsw515Nt+r8kfSNvFS2Sfijp5znT/kXSr/LaNWSn/VyoXs45HlX6kHSrJCdpXqVrKVDbakl7Pb9mkPPvRyQ9VMKyK7Pb6sacaf+gMHSacqZdnm13wxDqa8ku+4eV3v4l1v2MpLV50/5SUlLSlEGWvURSRtI7B2n3gKRtkqI505Znt9d/GWTZz2XbLR2k3Qey7d5a6W3Ko/8H3V81IKeb7HIz+1lOV8/XzKw+r+1UM/uumbVlu45+Z2Y3F3jNOdnui4PZdjvM7KsF2p1vZuuy3SJbzexjQ30f7rUdORzJ/sw9gnu7pF84547lrONhSbslveM1rGtA2e65jdmuprbsdpya1+Z92e6oDjM7YWbPmdlHc+ZfYGb3Z3+PXdnt//Uh1DJT4dHfnXmzvicpJuktg7zEH0l62Tn3s0HaXSzpfufcK9vfObdB4e/l+kGW/aCkp51zmwZpd4ukQwqPhlClCJWRIWJm0bxHod/dnQo/Ld6g8FP6RyT9c99MM2uQtFbhjuTTCrubnpP0PTO7PafdHElPKvxU/5eSVkn6oqTmvPWNkfSD7HrfIekpSf9sZlfmNsoG3uqhvfX+mVnEzBJmdq6kr0h6QdkdTjZM50h6vsCimyQt8V1Pdr23K9xhv6jw9/A/FR5FrTWz0dk2lyncZmsV/g7eJen/KeyeU7bdGklphUerb5H0V8rrri5yuy7N/jxtOzjnXpbUqcG3w2WSNprZX5jZPjNLmdnzZnZTXru0wiOffD2Szu7vxc3sUknzJH1noCKy4XilpO/nBheqUKUPlXj0/9Dvu78KPe4p0O4bect/RuF/9gXZ53+SbXdFXrsHJB2WFMk+/66kDknTBqhtdfa1rsyZllD4yfSbeW1Tkr5V4nsftPtLUlvO9nhK0tScedOy0z9WYLk7JW0fwu+jRQN0f0mKKPwk/WDe9Muyy30i+/zPJB0dYD193UbnDlLPoNtV0vuyr7WowLy9RSzfLemEpJezr/VmST/KvuY7cto9KemJvGVnK+w66xng9e9QGEbNg9TxqWK2CY/KPzhSGRmul3RB3uNPC7S7K+/5vyk8Gr0w+/xySfuccw/ltbtT0kT9/lPrNQpDa/8gdXU65x7se+LCwdgtkk47C8s5F3XO3TbIaw3FmyStkHSbwk/595tZUxnWU6yFkiZJ+n7uROfcIwpvd/TG7KSnJI0zszvN7LoCNW9VOBZ0R7YrbWahlZVxu+YKFA7o3+ic+4Fz7n5Jf6DwaO/TOe2+KulCM/trM5tkZosUHrFlso9XMbM6Se9W+LfWNkgdH5T0jHPud6/t7aDcCJWR4Xnn3Ia8x7YC7Q7183x69ud4SQcKLHcwZ74UnrlTzOnC7QWm9WjgM7e8cc5tdM497pz7tsIupiWS+sZ0jin8ZDuuwKLjJR0tQ0l926+/bTxekpxzaxWe0TdT0k8ltZrZA9luPDnnjivs6tkv6euSdme7nG4cQk19v6NC22GcBt8ORxQeVf22b4ILx75+rXCspm/a9xWe7fVJhX93L0jaJ+leFd4eUjjm1aTBu74ulLRosHaoDoRKbZncz/N92Z9HJU0psNyUnPlS2K00vUC7quWc26Gw/nnZ552Sdur3Ywq5lijc6fnWt/3628av7MCdc//hnHujwh379ZKmSvpV31iZc+5Z59yNCoPoEknbJd1lZv2OT/Sjb/D7tO1gZi0KTwkebDsMNHh+2o0DnXOfUzjudq7Crsj3SpqvsCuzkFsU/q3dO0gNt0jqVTh+hypHqNSWd+c9f4/Crocnss/XSpqRHRzN9T6FYyp9O5j7JF2Xf8ZSNTOzpQqPsLbnTP65pLea2dicdpcp7Osvx7UOLyn8lP6evNpWZNf5UP4CzrkO59w9CscWpip8D7nzU8659QpPuw0kLS6lIOfcbkkbJb0/b9bNCnfUvxzkJX4qabyZLc95P4HCsZWnCqzvlHPuOefcITNbpfAII/+6FFl4UeRKST9wzvX2t3Iziyvcnr90zrUOUiuqABc/jgznmVn+mVeStMGdfibMtWb2ZYWhcKGkz0v6rnNua3b+akn/VdJPzOwzCru43q9wB/FR51w62+7zkq6V9Fj2wrdtCo9cVjnnXnX68WDMLCXpO4P1/5vZbIXjRVK4c82Y2buyz59yzu3KtntE4c5us8KB5HMVdrvsVXgWVZ8vK9x5/tzM/reksQov4Hsiu3zfelsUDkR/0Tn3hSLe0jIrfEeDnys8W+4OM7tT4VjVdEl/o3Cc5NvZ9f2VwqPIBxV2cc2Q9AlJzzrnWs3sOkm3S/pZtq6G7PyTkh7Pqbuo7apw7OMeM7tD4UWG50v6rKSvOuf6uj5lZn+ZrX9u37ZWeMHkH0v6sZl9VuGRxe0Kx4+uyVn2fIVnqfV1k10m6c8l/Z1z7rECNb1f4YkNg3VpXafwaI2ur5Gi0mcK8Oj/oYHP/nLKnjGT0+5ySXcrPHPrqKSvSarPe82pCgdQ2xSOf/xO0s0F1j1X4Q6oTeGOe7ukr+TMX60CFz8q/DT+UN40J2n1a3y/t+a0+3uFp0KfyL7XFxQGyKQCr3mOpPslnVI4vrBa0oS8NkvVz5liee1aivx93Kzw6KBH4ZjE93T6mWlvVXjK8IFsmz0Kd97TsvMXSvp3hYHSLalVYRfRRUPZrtm2N+TUtFtheETy2nwh+5otBf5m7sz+TXUrDLZrCmzDRxSOZXUpDJcPDVDPRknPFVH33dltGK/0/0cexT34PpUaYGa3SvpXSfNd4QF8DCB7bcnfKPzioc5K1wOMZIypAOGpvv9AoACvHWMqOOM55/IHsQEMEd1fAABv6P4CAHhDqAAAvCFUAADeECoAAG8IFQCAN/8fagrxDehD0A4AAAAASUVORK5CYII=\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": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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ioDIRKMAgTp7pLOropFcsZjrV1l3CioDKRKAAg3BOKv74pHeZoS4BjH4ECjCI+roqJeLF/1cJQ6f6uuoSVgRUJgIFGMSCS6YMqf2USdVqmEygYPwhUIBBJOKBViyZrnjMBm0bjwW6bkXTCFQFVB4CBSjC6lWz1Th94oChEo8FWrl0uhbNG9oRDTBWEChAEWJBoHfdvkirLpupZCJQMhHITAqC6AhmQk1Ct1w7V2uumVvuUoGy4V5eQJFiQaAbr56j61Y2ad+hM2prTykw07QptbqksU5mg3eJAWMZgQIMUTwWaHFzQ7nLACoOXV4AAC8IFACAFwQKAMALAgUA4AWBAgDwgkABAHhBoAAAvCBQAABeECgAAC8IFACAFwQKAMALAgUA4AWBAgDwgkABAHhBoAAAvCBQAABeECgAAC8IFACAFwQKAMALAgUA4AWBAgDwgkABAHhBoAAAvCBQAABeECgAAC8IFACAFwQKAMALAgUA4EW83AUAKK9D7a165vg+nU51qjoW1/KG2Vo5dY5iAZ83MTQECjBOHets0z/ufEotXeeUDrNyufkvnDqkb+8xvWf+Kq2etaCsNWJ0IVCAcej1jjb95ZZHlMqmzwdJr1Q2I0n63t7N6sikdMecZSNfIEYljmmBccY5p69sW6/uAmGSryfM6icHX9bhjtYRqw2jG4ECjDO7206oPZ0qqm02DPXo4Z0lrghjBYECjDMbju5RKswU1TaU06aWgwpdWOKqMBYQKMA4c7K7Y8jLdGXSJagEYw2BAowziSGeDhzKKR7ESlQNxhICBRhnltbPUsKKD4gpyVolCRQUgUABxpk1jQvlBjy/6w3JIKY75lwmMytxVRgLCBRgnJmUrNGNsxYMetRhkmrjSV0389KRKQyjHoECjEPvW3C1ljfMVjIofG1zwgJNStbof664XdWxxAhXh9GKK+WBcSiwQB9bulovnjqshw9t1+GOVsWDQKFzSsbienvTUq1pXKQJiWS5S8UoQqAA45SZadW0uVo1ba7aerrUnk4pEcQ0rXqCAqPzAkNHoADQ5GSNJidryl0GRjk+hgAAvCBQAABeECgAAC8IFACAFwQKAMALAgUA4AWBAgDwgkABAHhBoAAAvCBQAABeECgAAC8IFACAFwQKAMALAgUA4AWBAgDwgkABAHhBoAAAvCBQAABeECgAAC/4TnkAo5JzTifPdKmrO6NEPND0hlrFY3xGLicCBcCoks2G2rKrRZu3HVOqJyuzaL5z0vLF0/SWKxo1oSZR3iLHKQIFwKiRzmT1w0d2q+V0lzLZ8KLHX9rZoh17T+uedyzVlMnVZahwfOP4EMCo8X837NeJ050Fw0SSwtCpK5XR9x/eqXQ6O8LVgUABMCq0tnXr4OttymbdoG3T6VA79p0agaqQj0ABMCq8sOO4wnDwMJGkdCbUpm3HS1wR+iJQAIwKr59oV5F5IklqO5cqOoDgB4ECYFQI3dDCwRSdWoyRQ6AAGBXq64Z21lYyEVOM61JGFFsbwKhw1WUzlIgXt8uKBabli6eVuCL0RaAAGBXmzqpTbXVxl86ZmVYunVHiitAXgQJgVDAzvevti1WVjJ2/Or6QeMy0dnWzJk2sGrniIIlAATCKTJlcrf901zI1TZ+oWMwU5O3BEvFAkyYmdfctC7Xk0obyFTmOcesVAKNKfV2V3veOpTpztlu7D7aqoyutZCKm5tmT1DR9omygwxeUFIECYFSqn1Sta5c3lrsM5KHLCwDgBYECAPCCQAEAeEGgAAC8IFAAAF4QKAAALzhtGACGwzkptUvKtEhBUqpaKsUml7uqsiJQAGAoXFY6829S679K2TOSYrl75fdIE26Rpn1SSjaXt8YyIVAAoFguLR35lNT1vOS68+bnfravkzo2SLO/ItVeXY4Ky4oxFAAo1om/ujhMLhBKrks68jtS+uiIllYJCBQAKEb2jHT2oQHCJI9LS63fKXlJlYZAAYBitD2k4neZaantR1GwjCMECgAUo3tLcUcn54VS5kTJyqlEBAoAFMNlh7iADWOZ0Y1AAYBiJOdraCfGZqT49FJVU5EIFAAoxuR3SVbsLjOIrkkJakpaUqUhUACgGMm5UvVVkhKDt7Wk1HBvqSuqOAQKABSr6S9z3VgDhIpVSdP/m1R92YiVVSkIFAAoVqxemvc9aeKt0VGIVeceCCSrkeKzpFmfl+rfV84qy4ZbrwDAUMQmRUcq2TPSuXXRzSEtKdWskGqukczKXWHZECgAMByxeqn+PV6fsq09pZd2nNDug61KZ7KqSsS0dP5UrVgyXRNrk17XVQoECgCUmXNOG54/rJd2npCclA2ju012p7La/MoxbX7lmG64qklvuaKxzJUOjEABgDJ7cvNhbd3VomzWXfRYb7g8/eLrSmeyun7FbFmFdqsRKABQRqfOdGnrrhPKFAiTXomgWwsbXlB4slVPPR5XXcMqzV90q+omVo1gpYMjUACgjF7Ycfz8UUhf8SClm+Z9X8umPyPnAsVjPXIuUNb9RN37vqjt4ce1bOU9I1xx/wgUACijPQdb5QrkSSLo1j1XfEFTqk8oHsu7a7FlFVNWyVhKC8Mv6acbt2juvN/VyrnlH1/hOhQAKKN0Oiw4//YF39SUmuMXhkkfySCjO6at07pD9+sbLz+tsFAyjSACBQDKKB6/eDdcEz+rhQ0vKh5kBl0+YRmtnfiMNp0+qK9u/rk6usr3HSwECgCU0YK59RddC7ls+tNyrrgzuQKTLqs6qNpYp7Z1HtFXH9qkhx7fo87ukQ8WAgUAyujqZTMVCy4Mj2m1rysxQFdXXxkXU0PsrJw5naxr1f7Dbbr/p9tH/GiFQAGAMpreUKvL5k9VPPZGqIRu6LtmJ5NMap/QrtBJ7Z1p/eMDW/ToMwd0um0o3zQ5fAQKAJTZbdfNuyBUjrVfqp5s8deYxCxUS3ayJCm0Nwb5TRm99to2/fu6dXrimVfkSjxoz2nDAFBmQWC6/YZmXXXZTL2w47j27H+rbm7+XlHLZp1pc/cSpVwUQEEYaGLytFY1PqrlMzYoFmQlhQos1MmXmnTcPqbLV7yzJFfb20CJdc0117jNmzd7XykAoH/pdFa7Nv2xFk95TMlYz4BtUy6uz5/6iI5lp0pOauqSPjv3b5WIpRTYhfv33t39iY55mrD4W5o4cfKw6jOz551z1/SdT5cXAFSYRCKmRVf/qU50LVdPtvBdhkMXhck/n7k7ChNJ5qTfbPqWquLdF4WJFN1Z30yaMeGgsvt+TYeOtHitm0ABgApUVVWlOVf9k47bb6u1p17dYUKdYVKdYZXSLqZtqfn64ukPaGvPQkmShaYV9rpmJ48P+txm0qSq0zq998916kynt5oZQwGASmWB5i79qE6feb9+8PID6kwcUFqBXsvO1NlwYtTGSeZMDefq9NH5Pyh4ZFLwqU26fPrTemDjdr3/rot6r4aFQAGACtdQX6sPv+0jOtl1Tj/bu11dLQcVWKggDFTXMVH1Z+s1t+rIkL8t0sypIdio9s4rvXyBF4ECAKPEtJo6feSKt+qOlsv1w0d2K5MJ1Xs8EqvJSBraacGBhZpUdVL7j7Rp+aLpb7o+xlAAYJRpnD5RH7r7ci1bMPX8VfbtPfUKrPCNJvvjJIUuplRP1ktdBAoAjEL1dVVae+Ol+sT7Vmr+3MlqT8/Qme4ZQ3qOTJjUiY5LVJWIeamJQAGAUawqGdM7b1molUtn6Lkjdw3pti2ZMKmDbct16ZzhXY/SF4ECAKOcmenmt8zVmps+qkNnLy/4hV19pbMJPXPobjXNnORlQF4iUABgzKibWKVLrr5Ph9rfIufUb7D0ZBPacvxmvdJyq+64vtnb+jnLCwDGEAtiuuTqf9Jre9er+uyXNK32iJwzOZmcM53qmq3nDt+lQ+1X6wN3LVX9pGpv6yZQAGAMumTBzQrDm/Tyjl167cBL6smkdTY1VS4+V9evaNQvNzcoHvPbSUWgAMAYFQSmFZcv1YrLl47M+kZkLQCAMY9AAQB4QaAAALwgUAAAXhAoAAAvCBQAgBcECgDACwIFAOAFgQIA8IJAAQB4YW6A+xybWYukgyNXDgBgFJjnnLvoO4MHDBQAAIpFlxcAwAsCBQDgBYECAPCCQKlgZnavmbl+pjNlru0+Mzvs+Tl/xcy+Y2a7zSw0s/WDtP+wmW0ys04zO2NmG81seZ82c83sh2bWZmZnzexHZnbJm6jRmdnnhrt8uZjZjWb2tJl1mdkxM/uymdUMYflfMrMNZtae246bzezWvMdvM7P7zWxvbh17zewfzGzGIM/72dw23djP47PN7F9yNafMbL+ZfaH4V46RxBdsjQ7vkdR3550pRyEl9quSVkp6VtKA30tqZp+X9ClJfyXpM5JqJV2b+9nbplbS45JSkj4iyUn6nKQnzOxK51yH7xdQiczsSknrJD0s6S5Jl0r6oqTZkt5XxPK/Lekruel/KfogulJ521rSJyRNVLR990laJOnPJK3Nbev2As87X9IfSzrRz3qbJT0lab+k35V0XFKzpIWD1Ywycc4xVegk6V5FO8GF5a6lQG33STrs+TmDvH9vlLS+n3bXSwol/eogz/d7krL520/RzjQj6dPDrNFJ+ly5t/8Qa35Q0h5Jibx5H869llWDLNssqUvSpwZpN73AvDW5dfxmP8s8LOnrktZL2ljg8f+Q9Iv8upkqe6LLawzI6xpbY2Y/znVLnDKzr/bt1jCzRjP7lpmdzHUhbDWzDxZ4zkvN7Nt5XQ37zOz/FGh3lZn9PNfttMfMPjHc1+GcC4ts+klJ+51zPx6k3d2SnnXOvZq3jv2KPvW+c1hFFsHM7jSzZ3JdP22592RJnzZrc11Qbbn3a5eZ/Une44vN7EEzO2Fm3Wb2mpk9YGZD6lUws4SkOyX9wDmXznvoB5J6NPh2+E1F4f21gRo551oKzN6U+zm7QF0fkLRK0h/0U/cCSWsl/V2fulHBCJTRIWZm8T5ToffufkmvSnqXpL+R9DFJ/9D7oJlNkPSkpHdI+kNFXUwvS/q2mX08r92lij4ZrpH0J4p2SH8maVqf9U2S9J3cet+paAfyD2Z2S36jXNjdN7yXXtCNkraY2WfM7IiZZczsFTN7T592l0t6pcDy2yQt81jPeWZ2p6SfSWpX1J30SUlXSNpoZrNzbeZL+omirpz3KQq+L0uakPdUP1O0I/6koh3rZxV13Z1/383swGDjTJIWKOo+vGA7OOe6Je3V4NvhRkk7Jd2TGxfJmNmrZvZfBllOkm7K/dyRP9PMpij6+/yMc+50P8uuzv3sMrN1uQ81rbkPQ1OLWDfKodyHSEz9T3qjy6vQ9O8F2n2tz/J/pKjLZ3Hu9/+aa3dzn3aPKurHjuV+/5aiHWLTALXdl3uuW/LmVUk6Jekf+7TNSPrGEF/7QF1e3ZLOKtohf0DS7ZIeyNXzzrx2PZL+osDyn5OUGeZ7MmCXl6TNirqX4nnzLpWUlvTl3O/vzj3PpH6eY1ru8bsHqeVVSY8N0uaG3HPd2c82Hmz5nblt3aLoA8qtij6kOEm/N8Bydbllt+dvi9xj/yzp53rjwur16tPlpShAXW7dX82t9+O5v6/NyuseZaqciUH50eHXdPGg/JkC7X7Q5/fvKdp5Xitpt6IjjiPOufV92t0v6V8VfVp9WdIdigLr9UHq6nTOPdH7i3MuZWa7JV1wFpVzzvffWaBoh3Wzc+4FSTKzxyRtVXTk9ZDn9RUldwS4StLnnXPnT5pwzu03s6f0xif2lxQFzPfM7F8kbXDO5Q9Mn1I0sP0XZjZTUbDu6bs+59xIDE73but7nXM/ys17PDdg/gdm9rculwC9ct1y31V0hLU6f1uY2dsUjd+s6rtcgfVK0WvvPRp63MzaFP1dr5X0/97cS4NvdHmNDq845zb3mV4t0O54P7/39mE3SDpaYLljeY9L0lRdHGCFtBaYl9IgZ2h5cErS6d4wkc6Pvzym6OyjXq2SphRYvkGFa3+zpkgy9b+NGyQp996tVfT/79uSjpnZs2Z2U+5xp+ioa7OkL0janRvD+uQwaup9nf1th/66nHqdyv1c12f+I5JmSmrMn5nriv2mpLcrOmlia5/lvi7pG5IOm1m9mdUrOts0lvu9qoj1StJVg9SNMiBQxpaZ/fx+JPfztKRZBZablfe4JJ1UgYHUCrJtgMfyP/VuUzSO0tcyRV0xvrXm1t/fNj6/83bOPeGcu1NSvaKdb0bSz8xsWu7xfc65D0uarmjn+bikvzezdwyxpr2KQv6C7WBm1ZLma/DtMNC2lqIB+3xfUzQudI9z7rEC7S9TdIpxa960WtJ1uX/3huZQ14sKQKCMLe/t8/s9iv7jPZf7/UlJc8xsdZ92H1A0htK7c3lE0l1m1qjK9KCkBjO7pndG7pPx7XrjzCIpGvi+LjcI3tuuWdEO7Ce+i3LRdS3PS3qPmcXy1jlP0VjG+gLLpJxzjyu6nmaCovGW/Medc+4lSZ/OzbpiiDX1KDr99r19zhB7t6Ixr8G2w4O5n2v7zL9T0WnjvUe3MrO/lvRbkn7D9X8G3i0Fpi2KThq4RdIPc+2eVXRUV2i90oXvMypFuQdxmPqf9MZg+7sVfYLrO8X7tHtN0QVrtysakO+R9K95zzdB0VjKcUX/8e9U1OXiJH08r12zokHY/YoGYm+R9EFJ9+e1uU8FrkNRtNNc32deUYPykublXuu7FZ0ZtC3v93l57aoVhd9BSR9SdNbag4pOQLitz+t9VdG40DsVnU21RdH4xMQ+63aS7iuiRqdop/fuAtOU3DbNKurf/xVJ789t8xblTnJQ9An9O7ltepOkX1fUvXVEUo2kKyU9kWv3dkU71e8qGne5Oq+WQQflc+1WKjqR4UeSbpP0UUVHSw/0affh3Ht1U948U3R0dCpXzx2S/im3He7Na/f7uXnf0MV/pwsGqW+9Cl+H8pHcc34tt97/rOgo5gnlBvSZKmsqewFMA7w5A5/l5SRN69NujaIB6fbcDuOrkmr6PGejohA5qagrZKukDxZY94LcTuxkbme0V7mzlHKP36fiA6XYnfVAr/feAq/j/tzr7Jb0jKQ7CjznJZL+TdHZQuck/VhSc582E3LruOiMsALPN9D7cU2uzZ25erokteXekyV5z3F9bt6h3HtwVNFZaktyj89QNA6xW1Jn7jU+KWltn1oO9N3WA9S9JldTt6IPFP9bUm0/2//mPvMn5f6Wjiv6kLJV0gcKvO/9bZcB33v1Eyi5xz6k6Oildzv9nfp8GGCqnInvQxkDzOxeRWdpLXKFB+sxADO7Q9JPFX2S9np/MmA8YQwFiLqdvkmYAG8O16Fg3HPO/VG5awDGArq8AABe0OUFAPCCQAEAeEGgAAC8IFAAAF4QKAAAL/4/RKOugHUc000AAAAASUVORK5CYII=\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": {}
        },
        {
          "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": {}
        },
        {
          "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": {}
        },
        {
          "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": {}
        },
        {
          "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": {}
        },
        {
          "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": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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YBwAwVxQtUBrLqxUp4OytVJDW/FhFsZoDACiyogXKyxpXFVR+Tc0C1cTKitQaAECxFS1Q6uKV2jC/SX4eRykxz9ftyzYXqykAgIugqFcUvmP9yzQ/Vj5tqMQ8Xzcv2aCNtU3FbAoAoMiKGiiV0bjeu+N2balbrIjxLrgiPu5HVBGJ6a6VV+r1zduL2QwAwEVQlN+Un6gyGtdvb7pRfYkR7W4/rs7RQUU9X6trFmhb/ZK8usQAAHNf0QNl3LxYuW5duvFiVQcAuMg4PAAAOEGgAACcIFAAAE4QKAAAJwgUAIATBAoAwImLdtrwixEEVkdO9eroqV4lkmlVVsS0aXW9mhoqS900AEDGnA+UZ4906eHdJxUEVslUeHt7Y6QDLZ2qqYrpjptWaUEtdykGgFKb011eTz3Xrgd2HddYIn0+TKTwJ4NT6UDdfaP68r3PqaN7uIStBABIczhQuvtG9YM9p5RK22nLJVOBvvVQi6ydvlw+UulAw6NJpdL80BcAFGrOdnk9ebBV6SC/kBhLpHX8bL9WLplXcD3WWh0/2689B1p1um1AnjEKrFVjfaWu2dKk1cvmy/NMwcsFgJ83FyVQrLU6PtilZ3taNZJKqjpWpisblqmhrGrK8gePdCvfg45kKtC+59oLDpR0EOg/Hz2qk+f6z3eppTOVtnYO6fuPH1NjfYXuvHmtolG/oGUDwM+bogfKod42ffHwbvWMDSsZpGUlRYyn7xzfp1U1DXrbupdOCpZUOlA6KKzbqX8wUXDb7nv8uE6c7Z+yiyuZCnSuY0jfeeSwfunWdTKGI5ViGhlN6umWTh080qXRRErRiK+1y+dr87p6HR1tV9fYsHxjtKKqTmvnLczr8xgZTel024ASybTK4xEtW1StaGTyzoG1VqdaB3SuY1Dd/WNKpwPVzytT3bxyrV4+XxF/5t7hdDqQ7Ij80b1SMCB5lVL5VZJfPav1calLpwP1DyUUBFaVFVGVxfLf3ASBVSodKBrxXvz3zlpJVuLO5kVX1EDZ13Van33uR0oG6Qump2y4AW/p69AHn/ye3nPFq9RYUXP+dd/z8j46OT+PX9gfXVfviA6f7JlxjCYdWJ3rGNLZ9kEtafz53DBcDPsOtevRJ07JSOc/k0AJfa/1tP59qFe+5ylQIGOMIp6nykhMr2++Qi9Z2JxzeX0DY/rhk6d15FSvfM/I2vDswMBKm1fX67odS1QeD//8Dx7p1A/3ntHIaFLZvayekXzf0/b1C3T9jiXys4JlaCSp/Yfa9VzLUV3Z+HVtWrBLaXnyPSPP82SUkqpvk+p/V4o2ul5tc9LAUEI/e7ZN+5/vCNe7wu/R8kU1umZrk5ZO8T1KB4EOn+zVE0+fU3v3iIyRjDFau6JWV29uVGN9fpcJ9A2Maf+zx2QHvqvtC76nmniXJCnw6+TX/qo07w1SpM7V282pdbhPD545pL2dJzWWTirq+dpSu1i3Lt2o5ur6gpdnrdXZjkHtOdCmM+0DCgKr8nhEyxfXqHcgoc7uYQXWqrI8qh0bF2rjqnrF/JQ08D2p65+lVJukQJInRRZL9f9NqrlVMlGn79tMN5h99dVX2z179sxqwX2JEb3vif9QIitMJjVA4c8Ff/Ca18qbsCfyr986oJ7+0bzq8j2jKzc16uVXLc27fQ/sOq4DLZ15B9eqpfP0+pvX5r185G/foXY99sTpC44UAxPo1KLTGouNyXq5P6SY5+sXlm3Wq5dvuWB6R/ewvvL9Q0qm0jk/X88zqiyP6tfu2Kinn+/Q7qdbZzwRI+IbNTZU6q5b150PlZPn+vWdhw+rPNKtX9n0QZVHB+R7uf7efcmrkpbfI8VWTL8yLnHnOgb1jQeeVzptc46BRnxPV29p0nVXLL5g+shYSl+/75B6B8YuOKNTCncEfM9ox8ZG3XDlkimPWKy12rXvnI4eeUK/tOGj8r2EYv6FPRepICbfj8gs+Tup4toX92anaMO3ju/Tw2cPKR0ECvTCOjCSop6vbXVL9I7118n38jtiSiTT+vZDLWrrGp60bnKJRjwtqDiuX978cfkakpTr+2Mkb5607P9K8TX5vbmJcxuz11p7dfb0oh0DPna2RUEeW2sraTA5pkO9bRdMv3Zrk6KR/Jt3xYaFBbXv+Jn+go6CTrcNFLR85Gd0LKVHnzg1aYPeXt8xbZhIUiJI63unntGzPa3npyWTaX3t/ueVSOYOEynsThkcTuhL9z6bV5hI4VFTW+eQfrD3tKRwjO3bDx1WMpXSGzb8rcqj/VOEiSSlpaBfOvWbUlB41+ylon9wTN944HklksGUJ9Sk0oH2HGjV/kMd56el04G+dt8hdfWN5txghpcJWP3suXbtfrp10uvjfvr0OR16/oDu2vhhlUUGJ4WJJEW8hIwdls78d2n0mVm8y+n9vxP79cjZQ0oG6QvCRAq3dYkgrX3dZ/S5Qz/O68zUdBDo6/c/r3MdQ3mFiSRVR0/rDRs+Il+Dyh0mmdYEvdLJt0mJU3ktNx9FC5RHzz1/vmtrJmNBSg+fPXTBtHXNdSovi2im7tOI72nDqjpVV8YKal+6wFOD8z3jDIU50NKp7I84bdLqr+qfNkzGJYK0vnvywPnnzx7tyuuztVYaGEwUdIp4Km319POdSibTemT3SaXSgVbMe1ZVsR753kzLsVIwJA0+lHd9l5rdT5/La6OXSgd6/MnTCjLfqZYTPeobGDv/fMr5UoF+sv+sxhKpSa8NjST1033n9JLF31TMH51xuyE7KrV/eMa2FqJ3bFj3nX52xl6ZZJDW/u4zOj7YNeMyDx3rVmfvSEHbn1tW3aOoN5ZfYTskdXwk72XPpCiBEthAQ6nC9sTaRvoveB6NeHrT7RtUVRGbcnwkGvG0fFG1bnlZ4d0I5WWFDR/FY5zlVQwHj3RNGscaqCrsaPDYQKd6xsKLW/cebMt7T242uwjGSD97rl3tmYtpr1p8fwFf3mGp5+5Z1Dr3JVPpgs7MTGdupyRJuw+05v2ZGRk9c3jyhnjfc+2KRYa1tn6vPJNnI8aelxLH8yubhx+cO5x32WSQ1gOnn52x3O6nW5XKc91I0rx4u5qqjs8cqBMN/VRKdcxcLg9FOkIxk/Y6Z5Lrt+WrK2P69ddu0nXbF6uiLKKI7ykWDQc8F9aV67brm/W6V67Juy9yom3rFiiSZ5ea7xltWdNQcB2YWa69zbFoIq+jk3FRz1fX6KAkqW+guF1KyVSg020D5/++GypOF/blTZwsRrNKrrtvVIV8DZOpQGfaB5RKB+rqHcl7vlQ60OGTPZOmHzreraaKFgW2kB1FKw39pIDy09vbeTLvXhkr6UD3uWnLDI0k1TeQ585KxqrafTIF7yoZaejxAufJrShneXnGaEF5tdpH8tvT9GS0qjr3Bjsei+iarYt09ZYm9Q0mlEoFKi+LqLL8xZ2dsGl1vX6490x+hY20fX1hYzTIT3h9T/KCabM5SfSFgdrid00GgZ1wNlih9V2eXafptFWhn1wyFSiZCuQZc/76r3zny5ZIBopWjKmg9WvTks0/zGYymk7OXGiC7LNfsyUSaXmeKai7Kx4ZkWemX+5kaSntZoy4aGMor1q6SXEvv7zyPU83L9kwbRljjOZXx9VQW/6iw0QKg+r2G5oVmeF044jvaec1ywoeo0F+1jXXTurSjCfKZIL8N07JIK3G8vBU1Koif07RiKe6eWXyM3dP6B0p8FTg6OKZy1yCKiuiBV075nlG86riikf9gi8RqMjx/Y/HfI0kc18oPSUTlfzC764xlYpIYX97cX/67WM85s84rpRtLFWuwBbaPe9LfoHrbgpFC5RrF6xQ3I/MuM8SNZ7W1izQ4kp3H2y+1jXX6Y4bVyse9SedURaNeIpGPN380uUcnRTR9vULJ+1UVg/l/8dtJG2uXaSqaJkk6apNjXmfHTib6+U8z+gl2xafP4Nx77nbNJaK59nYCqn2rYVXegmYVxVXXU1Z3uWNpI2r6uV5RquW5f/dj0Y8bVu7YNL0Davq1Da8ToUdJQVS5c4Cyk/vZY0rFfXy25h7MrqyYdm0ZSrKo5pfneffVsbRnu2yBR/jW6nyhgLnya1ogRLzI3r3tltUGYnLn+KbG/V8Laqcp9/a9PJiNWNGq5fP17vetF23Xd+s5iU1amyo0IrFNXrlS5brXW+6QpsZOymqyvKortm66ILxLM96quutzesoJeL5umP51vPPN62uv+B6pqkYI1WVR/O6Av58Xb6na7c2qbI8qjXLa+UZ6WjPNiXSFQpsHl9iE5Wqb8+7vkvNtdsW5RXmnpGWNVWfP+q/enNT3p9DxPe0cunkANq2boHSNqJ9bTuVSs/cM2LlSRXXO73A8frG1XmX9T1Pt8zQKyNp0ndjJn1jC9U62FzYUV/FtVLEzU5zUe9F0FhRo/df9Wrd1LRWcS+iMj+icj+quB9RTbRMr1m+VX+6/TaV+W6v1iyU73ta31ynN9yyTm++Y5N+6dZ12rymoaDrYDB7L9u+SFduXKiIbzR+H8763npVDVdOGypRz9db175EK6pf2CjEYxHdeevaaT87Y6SyeERv+oUNevWNK/PamEUintY31+rqzU2SpFdcu0zlZVHJ+Pr6wT9WIl2udJB7OVYmPDpZ+mnJK5+xrkvVuhW1WjPDbWqMkcrLorr9hpXnpy1eWKUtaxtm/BwivqfX7Fyd82atFWVR3bBjifaee436Ew1KBVMfKQTWk/HnSY3vyeNd5a8yGtcbV12p2AxHKTHP102L1mpx5fwZl7l+Za0W1Jaf72LNx4NHf13JIM/uN1MpLfwfeS97xsUV60r5bIl0SicHe5QIUqqMxLWsqjavPUn8/OjpH9WTB9t06Hi3kslAnm/kLR3TyVibRtKJ8wPvaZvWquoFel3zNq2umdz9IYW31nlk90mdaR+Up/AO0p4X/rt62XztvGaZqirCL11b15B+/NRZnTjTJytdsHfne0bVlTG9dPsibVxVf8FV2oPDCX3rwRb1Doyp3OvQzpVfUvP8AwqsL8+kZBVRxLMyFVeGX9pYc7FW3ZxhrdWPfnZGTx5skzHm/AC6ZyTjGS1qqNQdN62eNA46Pt/eg20X3H5HCru5fN/oNTvXaFnT9Lc/2vNMq/bsO6TXrv97Lag4Kc+kzl8jFFgpUJn8WKPM0k8XbTzrsXMt+trRJyVdOPAeyZzJevOSDbqzeXve9yhLJNP6zsOH1do5pFQq+3LJyaIRTwsrjumuzZ/I40r5z0rxwu8AMtWV8hctUIDZstbqxGC3ukaHFPE8LausVV1Zfvd1GhhK6PjZPiWTgcriEa1cOu/8PbyyDY0k1dE9rIGhhJKpQNWVUc2rLtOC2vJpb/fR2jmkJw+2qatvVOV+nzY2PaPmRqOqqlqp8nop2jTr936pSiTTevZol063DigdWNXWlGnL2gbVzjDOMn6D0COnepVMBaoqj2rrugUF/YzE4HBC+w91qO3sXm2q+54WVp5QLGoUrVij2IK3hjfsLPLO7GByTD9qPaJdbUc1nE4q7vm6smG5di5ep9p44b8wO/53tueZNp1uy9zLqyyiFYtq1NM3qvaeYQWBVFUx8V5eSWng+5l7ebXqhXt5LZHq3yVV3yp5szuJhUABADhx0e/lBQD4+UKgAACcIFAAAE4QKAAAJwgUAIATBAoAwAkCBQDgBIECAHCCQAEAOEGgAACcmPbWK8aYDkknLl5zAACXgBXW2kl3Zp02UAAAyBddXgAAJwgUAIATBAoAwAkCZQ4zxrzdGGOnePSWuG13G2NOO17mh40x+40xvcaYEWPMc8aY9xtjpvxFImPMKmPMcGadrMnx+g3GmB9nltdqjPm4MWZWv8NrjNmZqeeW2cxfSsaYd2bW55gx5pAx5l15zOMbY95tjHnYGNNmjBkwxjxpjPkNY4yXVfadxph7jTFnjDFDxpgDxpg/McZM+wtOxpjvZ9bpB7OmN0/ztz9/VisBRZf7p+sw1/yypOyNd6oUDSmyGkn/KumQpDFJ10l6r6SrJL1uinn+UVKfpEkhYYzZJukBSfdJ+kVJKyV9VNISSW9y3PY5yxjzTkmfkfQhSQ9KulnSPxpjjLX209PMWi7pfZLukfRJSYOSXi3ps5I2SPqTCWXfr3Bdf05Sl6QbJP0vSdcq/PvN1a5flbR9huZ/SNJ/ZE0bmGEelIq1lsccfUh6u8IfhF5T6rbkaNvdkk5fhHo+lFkHDTle+zVJbZL+INd6kvQtSS2SohOm/Xqm7JWzaMvOzLy3lHr9F9DmiKR2Sf+WNf1zkjonrpsc8/qS6nJM/5ykUUnlE6YtyFHu/Zn1tSrHa7WSWiX9aqbMB7Neb85M/81Sr0Me+T/o8roMTOgau9EY821jzKAxpssY86ns7h1jzCJjzD3GmM5M98d+Y8xbcixzpTHm85luojFjzFFjzCdzlNthjPlhptupJZ+ulAJ1Zf694IjMGFMr6eOS3i2pN0e7opJul/RVa21ywktflZTQ1Ec8L4oJ/WGmWylhjDlnjPkHY0xNVrnfN8Y8m+mK6zHG7DHG3Dnh9Vdluur6Mp/nIWPM+2fRpJdJWiDpC1nTPy+pXuGRRE7W2rS1tjvHS09IiktqmFC2Y4pyUnhEmO0jkg5Ya780ddNxqSFQLg2+MSaS9cj12X1B0mFJb5D0CUnvlHS+S8MYUynpMUm/IOnPJb1e0tOSPm+M+a8Tyq2UtFvSjQr3Mm+X9D81YQOSUSPp3zP1vk7hBuTTxphXTCyUCbu7832zmfdXlRmr+CNJn7PW9mYV+9+SnrPWfn6KxayWVCbpwMSJ1tpRSUckbcq3PQX6a4VB94Ck12Ta+XZJ3x3/zIwxb5b0MUlfUtiF9GZJX5dUl3l9lcJunmMKu+Zem1lm5XglE8YYPjBDezZn/j2QNf2ZzL+zWQ83KQzxc3mUCyQ9P3GiMeYGhUeKv5NHXR8yxqQywfofxpits2gvLpZSHyLxmPqhF7q8cj3+M0e5f8qa/72S0pLWZZ7/bqbczqxyDyrsFvEzz+9R2F++eJq23Z1Z1ismTIsrPKL456yyKUn/kud73pL1Pv9tvF0Tyrxc4RjLpqz3v2ZCmesy027PUcfjkh6axeexU9N0eSkMhDFJd2dNf0tmvtdmnv+DpCenqeeuTPmaacqsyKzX98/Q5j/PLKssa3okM/0vClwHr1IYEu+dodw2SSOSPps1PaYwzD44YVquLq9Fkv5J4c7RyxXuHB1TOH6y0eX3jIe7B0col4Y7JV2T9fiDHOW+mvX8ywqPQq/NPL9R0hlr7aNZ5b6gsFtkfG/1NoWBdXaGdg1bax8Zf2KtHVO4N7p8YiFrbcRa+xszLGvcYYXvb6fCjeGdCgNOkpQ5a+gzkj5hrT2Y5zIvlpcq3GBmdy99WeHG/6bM8yckXWGM+XtjzC1m8llsT0lKSvqyMeYuY8zC7IqstScy6/WvnL6DaRhjNik8qnpEYZfVVOUWSfqOwiPBP8p6+U8VDvb/9XR1WWvPWWvfZa39prX2h9bazyr8+7UKd5QwB3GW16XhgLX2cB7l2qZ4Pt6HXafc3RStE16Xwr71fE4J7skxbUxhV9Os2LBLak/m6WPGmHOS/tUY8/fW2p8oDNJaSf9nwumj4xvkamNMtbV2YELbanNUU6cXunxcGl9/F6xja23KGNM14fV7FK6j35D025KSxph7Jf2Rtfa4tfawMeZVkv5M4VhH3BizW9KfWWsfK7BNE9fDxHaNtyXXGMkkmW64BxQeJdxprc15lqExpj5Tzkh6VeazGH9tucIw+E2F7yk+YdZ45vMcsNamcy3bWnvKGPO4wh0OzEEcoVxeGqd4fibzb7ekphzzNU14XQrP/sk1kFoK4+Eyfo3JJoXtPaNwY9kj6VOZ156U9MPM/48oDLfxMQRJkjGmTNIqScU4uhlffxesY2NMRGFId0uSDX3GWnutwnGptyk8ivzK+DzW2kestbdLmi/pFoVHON81xmSPY81kPDg3Z00fPxqdcT0YY5ZKekhSv8KQ6J+iXI3CU7TrFXYLnskqskphkH5BL3x244H37sz/8xkj4QaEcxSBcnl5Y9bzX1HY3/3TzPPHJC01xlyfVe7XFI6hjG9c7pf0i5mui1Ib7yY6kvn3w5JekfUY7355i8K9X1lrE5K+L+mNmQ36uLsUjvVkX9vgwk8UnkH2K1nT36SwN+DR7BmstT3W2q8o7K7ckuP1MWvtwwoH9ysVXktTiF0KdxDenDX9LQoD7kfTzWyMWaBwjE2SbrXWdk5RrkLSdzPtu22KI+qnNPmzGz+B4wuZ/095JJ45wrlB4QkjmIPo8ro0XDHFnumerK6HVxtjPqowEK6V9JeS7rHWtmRev1vS70v6pjHmvQq7td4s6VZJvzWhq+EvFZ599GNjzN8o/JIvUTjAPekU45kYY1IKr4OYchwlcxHi30r6mqSjCjf6N2ba+z1r7S5JstY+J+m5rHmbM//9adaG7AMKN/JfNcZ8SuG1DR+V9HVr7d4J8+9UOC7wX6y1d+fxll6e42rtlLX228aYj0l6jzFmSNK9kjZK+qDCEwG+m6nvnxUOLu9SGOTrJL1V4eemzKnXN2bmP6XwKOY9ks4qc7aWMWaFwpD9q+nGUay1SWPMXyi8kPGMwnB4paR3SPq9TPCOr4d/kfQ2a20k87xc4RFHc6b80szRyriDE45WviHpeoWfV6Ux5qUTyh2x1nbY8Ey9R7PbaIyRpBMTx/Yy69HLrKMOSesz6yDQDOMvKKFSnxXAY+qHpj/L6/zFfhPK3ahwMHRQ4d7npzTh4rNM2UUK++U7FXYJ7Zf0lhx1r1Y4ANup8CK2I5I+PuH1u5XjwkaFG4xHs6ZZZZ35lGO+RoWnIB9TeHZQl8LB69+RFM9zPU26ADSzTnZl3kObpL+TVJFV5g5NcUZYVrmd03wWg5kyRtIfKrzaP6Fw3OJTmnDGlsIurkcVhslY5j1/YryMwmtHvqMwTMYyy/iapPUTltGcqfcDef4t/ZbCEybGFF7s+ds5ytwdbhIm1THVY2fWZzzV4+0ztC3XWV7vyHz+PQpPUGjN/H2sz+f98ijNg99DuQwYY96u8JYla21+g/eYIHMU9lpJWy1fCGDWGEMBwnGavyFMgBeHMRT83LPWZp+kAGAW6PICADhBlxcAwAkCBQDgBIECAHCCQAEAOEGgAACc+P/xiuta0ojH7QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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lniirfE2sol9rDwA4T7wHyrWtS8adKX9m5ab1LQt8NwEAUAXeA+XGtqUlT1p0kt48Z6XvJgAAqsB7oExN1KghUVtSWZN0eLC8L+YCAFyYvAfK4YEencymSiobyumB9u2+mwAAqALvgfLM0eh2K6U6PNCjntSg72YAAM4z74FyIjVw1u1WxhMPYurLDPtuBgDgPPMeKDWx8oYNO+dK+lIuAMCFzXugrJ7eVtbckpiZZtbyzY0AcLHzHihrm+cpJiupbNwC3TJnBbewB4BJwPuePBYEeveSdSV1YzlJN7Ut890EAEAVVOTU4Ma2Zbq2dfGE5UzS13ZvUljizSQBABeuigSKc04vnjg0YbmsC7W3r0tbjh2sRDMAAOdRRQJlZ0+nhrKZksqmwqx+cpDJjQBwsatIoGzq2lfWbewPD/aqn7koAHBRq0ig9KVLu/XKiJgFGsimK9EUAMB5UpFAKfc7UXIuVG2ZEyIBABeWigTKupYFZU1unF4zpeQ7FAMALkwVCZQrmucqZqVNbkwGMd0+b5WsxPIAgAtTRQIlZoHuXnbNhN/cGDNTS+3UkuasAAAubBX7Qvf1Mxcqlcvqm3s3yzmnbNHkxZogrpl10/S7l71ZSb5XHgAuehXdk9/QtlSrZrTp8cO79VTnXg1m0grMtGhas26ft1prmmYrMO7jBQCTQcVPDZpq6nXX4rW6a/HaSlcFAKgiTg8AAF4QKAAALwgUAIAXBAoAwAsCBQDgBYECAPCCQAEAeEGgAAC8IFAAAF4QKAAALwgUAIAXBAoAwAsCBQDgBYECAPCCQAEAeEGgAAC8IFAAAF5ckF/mnsnmtHPfCe3e361UJqf6uoQuXdqixfMaFQRW7eYBAEZxwQXK8zuP6onN7TKTMtnw1PMHDvcpHgv0Czcv0YLZDVVsIQBgNBdUl9ezW4/oic3tyubCM8JEisJlKJXV9x7ZrQOH+6rUQgDAWC6YQOnuHdbG5w8rmwvHLZfNOf3gsb0TlgMAnF8XTKBs2dGpMHQllXXOafeB7gq3CABQjopfQ+lND2nT0f3qGu5XMohpWcNMXdY8VzE7M8t2vHJcJeaJMtlQL+zq0qolzSW3o6t7UFu2deqVQ73KZkPVJGNatbRZa1e2alp9spxfCQAwiooFykAmpa++/Iy2dR+RScq4qIvqiY49ilmguxZeoZvmLJcUnXGkM+V1YQ0MZUoqF4ZOD/7HPr18oFu50MnlQyuTDbVlW6e2bO/UDevmav2lbWXVDwA4U0UCpT+T0p/8/H71poeVc2cGRSqXlSR9Z98WnUgP6s5FV8jMFJhKPkORpHhs4uHDzjnd/+Qr2nuwR9nc2SvP5St8+ueHFQSmK1fNKr0BAIAzVCRQ7t31tHrTQ8q5sRMiHeb0yKGdumT6LF0yvU1tM+t1+OhASeuPBabFc6dPWO5I14D2tveOGiaFsrlQTz7XrtVLmlVbc8GNpL7oHRsa0A92btWuE13KZHOqSdVoTqZF16yYq8uWt6iuNjHqcrkw1L72XnX3pSRJzdNrtWhO6XORwtBp36FeHezoU09fSplsTg1Ta9TUWKflC6drSr7eRDyQ2RjrTO2Whl6QXFqKt0r1N0pBbfkbYRIbGs6qfzAtM1PjtBol4uNfmg1Dp3Qmp0QiUCzwdBk3TEkWk4zPbzV53/rHhwe0q6dz3DAZkQ5zuv/gNl0yvU1Xr5mtHz/xylnDhUdl0rpVrRMW27ytQ9lS1ifJZNq6+5jWr6Hry5d0Lqu/3/60Xuo+JDnJJZ2UlPprB3RC3dp/+LCefmmO7rx5uRbNbTy1nHNOm146os3booEa2VwokxSLBYrFTG+4bLauXD1r7BCQtG3PMT2+uV2ZTO7UmWikX5L05HPtkiSTlEzGdMXKmbqi8Hra4M+ko38pZQ7mGxVKlpAUSo13SS2feN0HS3vHSW166YgOdpxULN9jEDrp0qXNWn9pmxqn1Zwq65zTwY6TenZrh1490iczk3NObS31unpNm5bMm37qQCGdyWnHK8e1ZXun+gczkkktjXVav6ZNS+efLqfMIan7n6Xe70puWJKTEvOkGR+QGt4mBXXndXsc7O/WQ+079MKJdqVzOSVjMV3RNE+3zVul+VNnnNM6nXNq7zypzVs7deRYv8LQqa42obaWKeo6MaSBoYzMpJkzpujqNW1aOKfh9OciTEkn75eO/52U7ZQUSgqk+Byp+aNSw235v2l/zI2z41+/fr3bvHlzWSv84YGXdP/Bbcq60nbkcQv0p9fcqfp4jb754x3q6h4ad7RXPBZo9dJm3XrdwgnX/YVvPKfcBGcnhVqb6nTPOy4tuTzGlglz+vPnH9TBkz1ywejvgYWmRCahpZ2L9N7bLtGc1qlyzumHj+/Vvva+MYeGx+OBVi6coQ03LBo1VDa9dETPvHCkrKHlscAUBKZ3vmmpFtY/JR39tORSoxe2GimxUFrw1fO+07pQbHzhsJ59qWPUbRxYFP53vmWZ5rc1KAyjrudX2ntHPWBMxAPNap6iu96yXF3dQ/ruw7sVOndW2UQ8UF1tXO+9faUacg9JR/9YcjlJ2TNXaHVSMFWaf6+UnO/z1x5V6EL90+5ntalrv7JhqFCn/94DmeJBoGtaF+vuZesVWOlnZMPprL770G4d6xkq6UA7EQ80rT6p92xYoanBHqn9N6WwX9Jonz+TgkZp/j9INctKbtOppc2ec86tL37e+7DhzqGTJYeJJMWDmHrSQwoC07tvW6GW6bVjnjIn4oGWLZiuN79hwYTrdc6VFSaSlEozt8WX+1/dpkMDvWOGiSS5wCmTyKij4age2rhfkvTc9s5xw0SSstlQuw50a+vuY2e9dqSrv+wwkaLraZlsqKc2Pi7XOU6YSNFrmf1SxyfLqmOy2Lr72JhhIkVnKZlsqO89skcneof10Mb92nuwZ8ydYiYb6sixAX3nwZf1rw/uUiqTG7VsJhvq5EBaTz/1Tbmjf5x/j7Jnr9ANSbnj0sEPSLnKTy/45p7N2tS1X+kwd0aYSFIop3SY06aj+/TNPaUfnGdzob7zwC51nhgsrddG0fbp6UvpgUcekTv4QSk8qdHDRNHzYY/06vul9MGS2zUR74ESLyOBI+7UEOLamrh+5W2rdOt1C9Uyoy5/pGMykxbMnqZ33LJUb33j4pL60M2spAv3hWqSsTLbjtHkwlA/PbxLOU38QXCBU09Dr7r7h9VxbGDcHVWhbDbUMy8eUfEZ9rNbS1t+LOvafiTnShhB6NLSwBNS9ug513UxCkOnJ7e0l/Ye5UI9+dxB7dx3YsLrmLmcU+exgQnLOed047z7ZOMFftRSKdcnnfjGhO18LY4M9mrj0X1Kh7lxy6XDnDYe3aeOwd6S1rt19zF19w2XPDdvROicrpv9FSkcKm0BNyB1/VlZdYzH+zWUZY0z9dyxV5UKRzlyGEXonFpq60/9HIsFWrWkWauWNCudP1KpScYUj5WffYvmNmrvqz1jZnSheCzQqqVNZdeBs+3s7SjpGtoIc1JfzUk9v/NoWWEwnMrqSNeA5rROlSRlMjntay/tAzuaeJDSiuYtCqzENjhJvd+Tmj98znVebPYf7i35uqRz0ivtvWMfJBeXL6HMgsYdSsZK3FkqI/V+W2r5iPdrBSMeObRLubC07ZELQz1yaJfuXn7NuOWcc9H13zJ7WCSpseaoZk3dr3EuL55t4GdStkuKzyy7vmLez1CunrlQrsS/oJhM181aomRs9FxLJmKqr0ucU5hI0vo1bYpNMOJkhJPTpctazqkenKknNXTWmcN4nEmZWE79g+mylpOknpOnj1QHhrOv6W7UU5PdCsNy/tbSUurlc67vYnTk6EDJXTBSFCrl7xbHtqhxqxLBRGcnhQ3ISam9HltwpheOt5/VzTWWUE7PH2+fsFzfQFqDJc6zK7Zkxouysre4SQNPnVN9xbwHSjIW14Z5q5QMJu4+isdi2jBvle8mnDK7pV4rFkyfMJDisUA3r5+v2iRDDn1IBLFxR2CNJpApmSi/y7GwmlhgKjOPitcWDfsqd5nXkWrfQy8ZHyrv6NuC/Aiwypioq6tYpoTy6XTunA+MauJDCqy8Nkk5KXfynOorVpF7eb19wWVaP3OhksHoO+hApppYXL996S1qqZ1aiSZIiq6jbLhhsS5Z0qR4/lpMoVjMFIuZ3njVXK29ZOJhyCjNkoYWhWUMzJCk+lSdVi5uKiuInJNapp8eZVVflzg1fPVc9KdnlHd0ZzVSbeUOiC5EDVOTZV+b9Kk/PUO5cs4iXVaKnduQ3VLUxcrrSqstoXwyGSv72smIVLZOoSv3wCwmxfzshysSKGamX1v+Bn1gxbWaXz9DiSCmulhCtbGEEkFM181arE+ue6uWN1Z+Jx4Epg3XL9I971ity5a3qL4uoZpkTA1Tk7pmTZt+492Xax0z5L1qqZ2qJQ2ldx/WpJNa2NikFQtnqK6MiaWN05Ka2TTl1M9BYFp7Sati53h0lw2T2nnsmjJ2WE5quPOc6rpYrVzUVFaHSn1d/Jy7rEez69jV5e0w4zOlxMSjQs/VNa0LSx6IFLdAb2hdNGG5hvqk6uvO7ZrPK92Xy5V91uyiCbseVKyPx8x01cwFumrmAh0b7ld3alBxCzR7SqNq45W5QDaepsY63XrdIt163Xmv+nXp3YvX6S9ffHjCU3wLTXN6Z2nDhmhOyQ3r5uqhjQcm7FqJx0w3rpt31vNrL2nV8zuOKldmV8SILR1v1erWZyVN0E9vNdLUDVL89TWQY0pdQkvmTdfegz0THkXH44GuWztXj23yNyy1e3i2jg/NU2v9fgU2QbRZbTTJsczu13LcMmeFHjm0q+TyN+fvXzgeM9P6NW16fHN7yQMgRvSmWtXZv0hzpu0p/deeck10FwgPzsvt61tqp2p5Y6sWN7RUJUxw/i2a1qwPX3KjkkFMo37uXRQmC3vn6oNvuUozGqJZ56uWNuvK1a2KjzOYIh4LdO0Vc7R0wfSzXquvS+g9t69QMhEr+zgtMCkTzFdu1mejndFYrEaqWSXNen3OQ7nt+oWaNiUx7plgPB5o2fzpumx5i+64cVFJ1zGvXtOmZGL8cmbSE4c+KgvqNe71K6uRatdIje8ad32vVVNNvd6+8LIJrxkng5jevvAyNdXUj1tuxKXLWtTcWFv22XZgpo1HPlj6hFurl1r/e1l1jLs63zPlgUKdQ316uH2nNnbuUxiOjP9zass26a0LL9X6xXNHvQC5+0C3Nr5wWD19KY3c7ikMnZoa63T9ujlaMm/6uPX29af07NYObdtzTGHoxr3xqJkUCwLNbKrTnW9eFt1bbOgl6djfSsMv6PSJvEX3ippxt9T06xUbinoxGE5ldf+T+/RqR5+c06mzlUQ8kHPSlatbdcO6uaeuie15tVsPPLVPzp351d4j5W++ep6uWNmqrhOD+teHXlY2e/a3tibigRqn1ujdG1aoPjgoHfq4lOuV3GBhqehCfP1NUttnpKBGleac00PtO/TvB16UyZRxp8+OExaTk9O7Fl2uDfNWl7XeVDqn7z2yW0dLnNx4bjPl/16qmfis6aylx5gpT6DgvMiEOfWmo/kDjck6JUoYBShJx7qH1HMyGqXT1Finpsby7p+VyeZ0vGdYqUxWqVROtTVxpTM57TvUq64TUXtaptdp3arWM67HnF7BIWl4q+QyUX983VXcgLDAyYG0tu7pUndvSkFgmjOzXquWNCsxyoi9bC7UngPd2rb3uIaGs0omY7pk0YyzyudyofYc7NFz2zrV259SYKaZM6J7ec1vm3Z64IZz0T3Xer4lZdqjgK+7Upr+K1Ly7O7QSutLD+upjj362dH9Gs5lVBtL6A2ti3Rj2zI1JM/tvm/OOR3u6o/u5dXVr1zoNCV/L6/OE4MaGDx9L6/1a9q06Kx7eT0gHf+ylO3Q6Xt5zZWaPyJNu00Kzu27oAgUAIAX5+1eXgCA1ycCBQDgBYECAPCCQAEAeEGgAAC8IFAAAF4QKAAALwgUAIAXBAoAwAsCBQDgxbi3XjGzLkkHzl9zAAAXgYXOubO+hH7cQAEAoFR0eQEAvCBQAABeECgAAC8IlAuYmX3AzNwYj54qt+0+M2v3vM4/NbMXzazHzIbMbKeZfcrMphSVu2+MbfL5UdZ5o5k9nV9fh5n9tZmV+P2oZ63rlnw9t57jr1g1Zvah/PZMmdkuM/tICcvEzOz3zOynZtZpZifNbIuZfdDMztp3mNmlZvagmfWb2XEz+0czayoq89g4f9MPFJWdYWb/YGbHzGzAzB42s8te+9ZApfDVcxeHX5JUvPPOVqMhFdYg6R8l7ZKUknS9pP8p6SpJxV8O3iXpnUXPHSn8wcwul/SQpJ9IerukxZL+QtJcSe/z3PYLlpl9SNKXJX1O0sOS3iLp/5qZOee+NM6idZL+l6SvSfqCpH5Jb5P095IukfT7BXXMkfSYpJ2S3iNpuqJt/UMzu9E5N/Idth9T9D4Xuk7SX0v6fsH6TNIPJC2S9NuSuiX9D0mPmtla55zXgxl44pzjcYE+JH1A0RdCL6t2W0Zp232S2s9DPZ/Lb4OWcuuW9F1JuyUlCp77tfz6rjyHttySX/bWam//Mtocl3RU0leLnr9X0rHCbTPKsjFJTaM8f6+kYUl1Bc/9jaQeSdMLnrspv71+cYI2fkXRAURTwXPvyi/7poLnGiWdkPS31d6uPEZ/0OU1CRR0jd1kZt8r6HL4YnH3jpnNNrOv5bsRUvkupntGWediM/t6vpsoZWavmNkXRim3zsyeNLNBM9tdSldKmY7n/y3rjMzMEpLukPRt51ym4KVvS0rr7DMeLyzyu/lupbSZHTGz/2NmDUXlfsfMduS74rrNbLOZ3VXw+u35rrre/Pu5y8w+dQ5Nuk7STEnfKHr+65KaJd041oLOuZxz7sQoLz0rqUZSS8Fz75T0I+dcT8HyT0h6VeNs63x35i9J+kFRXe+UdNg592jB+noVnbVU5L3Da0egXBxiZhYveoz23n1D0h5Jv6joiPFDkk51aZhZvaTHJb1V0h9IulPSS5K+bmYfLii3WNImRUeYn1K0Y/4jnbkDkaKui3/O1/suRTuaL5nZmwoL5cPuvlJ/2fzvNzV/reK/SLq3cEeV15oPxayZvWxm/83MYgWvL5VUK2lr4ULOuWFJeyWtLrU9ZfoTRd03D0l6h6Q/V3Sm+aOR98zM7pb0V5K+qagL6W5J/yqpKf/6EkXdP/sUdc29M7/O+pFKzGxRfrv+4QTtuTT/79ai57fl/z2X7XCzorORI/m21CnqTiyuY6Se8eq4S9I0SV8tev7Scda3wMymltdknA9cQ7k47BzluR8pui5Q6MfOud/L//9BM3OSPm1mn3XOvSzp1yUtV9SN8Fi+3P1mNkvSZ8zsK865nKLwqJN0hXPucMH6iz/00yR9bOQo0syekHS7pF+R9GhBuVz+MSEzW6Mo5EZ8TdKHi4o9L+k5RTuXWkU7pc/lf7ffyJcZuRjcPUo1Jwpe9yZ/Afq/Kupe+q380z+x6I4TX1f0fn1f0VnDi865Txcs/uOC/18pKSnpo865vvxzPy2qzinapqHGN9Z2OFH0eknM7HZJ75X0SefcyFnjDEk2Sh0j9awcZ5W/pqhL7v6i55sk7R9jfSN19pfWapwvBMrF4S6dfVG+Z5Ry3y76+VuSPiPpGkkvKzrjOFQQJiO+oehi+GpFO/MNkn5YFCajGSzqkkiZ2cuSFhQWcs6V83e2R9LVio7Gr1d0ITau6Ch+ZH2fL1rmx2bWL+k/m9mfOed2l1GfT9cqCoLi7qVvKdq+NysKlGclfczM/rekf5f0tHNusKD885Iykr5lZvdKesI5d7Rwhc65AzrPn18zW63orOpRSX/mYX1zJN0q6QsF4YSLGF1eF4etzrnNRY89o5TrHOPnufl/m1Q0Eiqvo+B1KepbL2UUzWhHpClFZw3nxDk3nP/9HnfOfU7SJyT9qpldO8Gi38z/u76obTNGKduk00e6Po1svzO2cX5nebzg9a9J+qikNygagXbCzP7NzBbly+9RdKYXKDqz6TCzZ8zs5nNo01jbYaQtJW2HfDfcQ4q64e4qCoAeRWdM5W7rexT9jsVnvlLU7rHWN/I6LjAEyuQya4yfD+X/PSGpbZTl2gpel6LRP3NHKVcNm/P/Liux/MjN6fYqCrdLC180s1pJSyRt99K6M41svzO2sZnFFYX0CUlykS87565RdF3q/YrOIv9lZBnn3KPOuTsUDb+9VdGghB+ZWfF1rImMXCu5tOj5kesaE24HM5sn6RFJfZJuL+iGG2nroKLuqeI6RuoZq473S3rBOffCGO0ea32vOufo7roAESiTy3uLfv5lRX3sP8v//LikeWZ2Q1G5X1XUjz3ywX9Q0tvNbHalGlqGkaPyvROUu1tRmDwrSc65tKQHJL03v0Mf8R5FI5S+f9YaXrtnFI0g++Wi59+nqHvqseIFnHPdzrl/UdRduWaU11POuZ8qurhfr+jidzk2KjpAuLvo+XsUBdx/jLewmc1UNHdFkm5zzh0bo+j3Jf2CmTUWLHujpIUaZVub2XpF4TDa2cnI+uYWnpXlR8q9Y7T14QJR7XHLPMZ+6PQ8lPco6p8vfsSLyr2qaDLZbYomBKYl/WPB+uoVXUvpVHTx+g5FXSpO0ocLyi1SNHFwn6KRYm9StAP6RkGZ+zTKXBBFO83Hip7LSvrKBL/r5YqC7EOKJt69TdKfShpSNNhgpNxCSU8omiC3QdEO5l5FwfmlonWuVTRf4t/y6/ygop3od4rK3ZLfBh+YoI0j5f4o/54UPu7Ml/lsvszn8+37HUknJT0pKciX+TtFo7zeo+i61m/kt/d3869/RNHouXsUBeq7FZ2pHVJ+7kd+O2QlfaqEv6OP5LfPZ/K/w6fzP3+8qNxXJGULfq6TtCW/DX9VZ//9NRSUnasouB7P/129T9FXXzwz8nsX1fW3iq4TtY7R5kDS05IOKgro2/N/Wyckza/2Z5PHGH9r1W4Aj3HenNNBMdajpajcTYou8vbnP3hfVMHks3zZ2YpC5JiiLqEXJd0zSt1LFV2XOJbfoeyV9NcFr9+n0gPFSbpvgt91Vn4nuk9RiBxXdLbxcUk1BeWaJH0vv7MaljSY3+n91hg7rpsUHaUPKwrSz0uaUlTmF/JtvGOCNt4yznvRny9jkn5X0Wz/tKLrKV8s2vm+P7+djubfg32Khnk35F+/Lv8+Hsy/fkTSdyStLFjHony9f1ji39JvKjqYSCma7PmxUcrcp6hHrriOsR63FC1/maLrLAOKrnHcJ6l5lHoSigL0BxO0uUnRwcKJ/Pv8iKKRh1X/bPIY/cH3oUwCZvYBRaOIlrvRL9ZjHGb2WUVzPS5zfCCAc8Y1FCDqVvosYQK8NsxDweuec654kAKAc0CXFwDAC7q8AABeECgAAC8IFACAFwQKAMALAgUA4MX/B/ZgycVnXdCcAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ],
            "image/png": 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KyPlE/R0L1ijh5X76y+b6ha6bAAAoEuehcvnc+VpQWSt/mrmVmPH0nmWbVOIX9KR+AMAbyHmo+MbTLS2XTDu3EliriliJ6+oBAEXkPFSGM2ndu/cZTTddH8jqy69u0SCrvwDgouE8VJ45+ZrstJHyuqc69rtuAgCgSJyHyuPH9yo1zSVaRqWCrB5rb3PdBABAkTgPlb70SF7lBxj+AoCLRkEm6vMq7/E9YQBwsXD+ib5iTqPyuVDLsqp6100AABSJ81C5bf4qxT0/p7IJL6bbF6xx3QQAQJE4D5XFlXO1rLpBsWmGwWLG0/zKWq2obnDdBABAkTgPFWOMPrzmei2qrFN8kmAxkmoS5frImhtk8ryqMQBg9irILHnCj+meS65WfJJLsFhJfalh3df2rLKWb34EgItFQUIllc3oc688qpFMevIyQVY7eo7r6/u2FaIJAIAiKEiobO08pMF0SsE0Z9angqyePnFAp1PDhWgGAOANVpBQ+dGR3L+nXpIeP85Z9QBwMXAeKukgq86R/pzLZ2yg3b0drpsBACgC56GSCYK8v6c+neO1wgAAs5vzUCn1Y3mHytxEhetmAACKoCDnqVzZsFhejhdrSXgx3dCywnUzAABFUJCJ+ltbV+V8ocjyWIlW1TQXohkAgDdYQUKlpaJGP7PocpVMcw2whB/TR9bdkPdwGQBgdpr4lHcHbp2/SmWxuL594AUF1p5ZYmxkFPc81SUq9KHV16q1oqZQTQAAvMEKFiqSdE3zMl3VuEQvdh/RK6eOaySbVk1Jua5uWqLFVXMLWTUAoAgKGipS+CVcmxsWaXPDokJXBQAoMr52EQDgDKECAHCGUAEAOEOoAACcIVQAAM4QKgAAZwgVAIAzhAoAwBlCBQDgDKECAHCGUAEAOEOoAACcIVQAAM4QKgAAZwgVAIAzhAoAwBlCBQDgDKECAHCGUAEAOEOoAACcIVQAAM4QKgAAZwgVAIAzsWI3YLzu3mG9uOekTnYPSZIa68q0flWT6mvLitwyAMB0Zk2ojCQz+sGP96uja0DZwMra8P4T3YPatb9bzfUVettNy1WWmDVNBgCMMyuGv5KprP7lh7vV3jmgTPb1QJEka6VM1qq9c1Bf/+FuJVPZ4jUUADClWREqT714TP2DKWUDO2mZbGDVN5jSky8cfQNbBgDIR8FDJRsEOjrYo/19neoc7j/n8XQmq537uqYMlFFBYLVrf7fSGXorADAbFWyCoj81okeO7dHj7W2ysjIyythAcxMVumPBGl3VuFie8XToeJ+MyX2/xkgHj/VpxaLanLfJZgN19Q4rnQ5UXhZTbXWpTD6VAgByUpBQOTHcp8+8/LCGM2llbHDWYx3Dffr6vuf03MlD+vW112toJKMgh17KqCCwGh7J5FR2aDitbTs7tH1vpyTJyChrrSpKY9q8rlmXrmiQ5xEuAOCK8+GvkUxan335EQ2kk+cEyqhUkFVb30nd17ZVMd/Lq9dgjFEsNn2ze/pG9NUf7NQLu08qlQ6USgdKprPKZAKdHkjp8eeO6jsPvapMduI2AgDy5zxUnj75mkayaU3X90gHWW3rPKTKWk+BzaOnYq3mN1VOWSaVzuqbD+7R8BS9oEw2UHvXoB584rWc68b5s3m81wAuPM6Hvx4+ulupIPeJ9OdPH1JrQ6UOd5w7iT+RloZKVVcmpiyz+0C3UunpeyDZrNX+o73q7U+qpmrqfSJ3gbXa1dOun7S3qSc1JBMYVY1UKnukREHSk2eM5jVW6Ip1zVrcMuesIchsEOjAkdPq7h2WlVRbldDyRbWK+fkf/3T3DuvVg6fU25fUwHBaibivujkJzWuoVEtjpeIxX/Gper3pdmnwKSkYlPxqqeI6KVY/g1fk4jc8ktbAUFqeZzSnKjHl+2WtVSqdlReNOpz3/Ka1kh2RTEwy8fPbF86b01BJB1mdSg7lXD5jA+09fVL3XHGtvvHAnmmHomK+pxs2L5h2v9t2dCiTyW1Yy1rppd0ndOOVC3Mqj6kd7O/W3+16XCOZjJLB63NfJuiR5kk1fXPUcKpBx04M6GT3Ac2tKdM7b1uhkrivbTs7tPWVDgWBVTp6/+IxTw8/fUjrVzXqmg2tOc2B9Zwe0X8+eUCdp4Y0/k9q/xFJOnHm9zlVCV2xrlmrl9YpHvPDO5MHpJN/Jo28JMmTbCb6sPq0VH611PgxKd5yXq/TxeJwe5+e3d6u4ycH5PvmzDlm61bUa9OaprMOAHv7k3px9wntaOuKTnC2Ki+Na9PaJq1ZVqejJwa1bUdHeEBhpaqKuDauadLqpXNVEvfPrTx1SOq5X+r7d8mmJQVSfIFUd49UdafkvfFX4Xitv0sPH92jHaeOKx1klfBj2li/QLe2rlJLRc2M9tk3kNRLe05qz2unlExnFfM81VQnNDiU0kgqkO8ZtTRWaPPaZrU0Vr4e0jYt9f+X1P13UvqopECSkWIt0twPS9W3FySEzVTDEZs3b7bbtm3LeWfDmbT+1zPfUTaPIY6FlXX6+IY7dbi9T//2X/sUBPac5cW+Z+R5Ru+4ebkWzquecn/ZINBf3/dCzvVLUtPccr3vrWvy2gbnOjxwSp99+ZGzwmQ8ExhVDVSpuatJRuH7Wl9Tqrk1ZWo71DvpgUXMN2ptqtLdt6yYMli6e4f19Qd259RTPbPvmKfyREzvuWuVqvw26cgHJTssTTiI60lepbTwPqlkUc51XIyeeP6oXtxzcsIDOM+Er+vP3rpSLY2V2nOgWw9tOaQgCDR+RNr3jYKsle8bZbJnPxiPhT3bu29doZbGMcPevd+WOv8yDHyN+3szZWHPcsFXpHiro2c7tWwQ6CuvbtH2U8eUDrJn/eV4MvI9Tzc0r9C7lm7IuWdmrdXWV9r1zPZ2yWra0y7iMU8NdeW6+5blSuiYdOQDUrZbE/8dG8mrlhZ8WUosz/l5ntnamOettZsneszpnEqpH5Nn8ttlfWmFJGnhvGrdc/c6bV7XrESJL89InjFKlPjavK5Z99y9btpAkaQgUF5LlKXp3yxMz1qrL+x6YspAkSTrWfVX9muoLOzRBoFVV8+w9h7smbKnmslaHTsxoGe3H5+yDf/6SFtegSJJmUyg/qGUvvPgy7JHPyzZIU38H1GSAinol45+SLJv3vOlXtx9YtJAkaTASql0oO8+vFc79nXpoS0HlcmeGyhSOAxtpXMCRZLSmXCBzXcf3qvOU9EoSN+DUaCM6JxAkcIDgkyndPgeKds38yeZh3/a+7RePnVMqXGBIkmBrNJBVj/paNP3Dr6c8z6f29GhZ1/pUDZ77oH2RNKZQCe6BvXDR5+WPfx+Kdulyf+OrRSclg7/gpQ6knObcuF0+MsYo6sbl+jJjv0Kpp2qlxJeTDfNW3nm98ryEl2zoVVvWd9y1vBHPmOuMd8o5ntnts8F8ynnb+/pkxpMJ3Mqa43VqZpTqhgODygCKymH3m0mG+iFXSd15WXz5HvnHrwcbu/TSDK35ebntMlKCyufUJBNyZ/2z81K2X5p8Amp8sYZ1XchywaBtrx4PKch5kw20GPPHp4wMPKRzgR65JlD+vm7VkonPx0FylQCKeiVer8hzf3QedU9nYP93Xq5+6jS08wlp4KsHj22Rze1rFRtonzKskPDaT398nFl83zdsoHV2pr7w7/PXD427ZDU+edS69/mVc9UnK/+uqV11YT/4cczkqpLSrViTuO5jxmjkrivkrif9ySeMUbrVtQrhyZICkNr/apz24D8bDlxYNpeyhlGGiodVmDyX85tZfXa0dMTPvbinpN5HUyMt6HpIflmug+r0YYMST33zbiuC9n+w705r9i0Vkqdx3syVuepIQ10PhrNn+RSeUrq+ZeC9ygfPrpb6SD35/jY8b3Tlhk9ty5fCX9Qy2pfyG+0ZvDZsGfniPNQaS6v1nuWblTcm2BiLWIklfpx/cbaGwtyZvvG1U3yctxvWWlMC5qrnLfhzaY3jwUakmSsUdbP/z97Nmt1un/iHtFk9+eqKtGd3wapw+dV34Xq6In+8wrvmbJWGj71eDQ8metGSSntdnhnvB097bI5jMxI4eKkF7qm/7vZ89qpvHspkrSoZlcunf5zDT45g40mVpBrf103b4XuWXm1quOlSnivj7B5Mop7vhZVztXHN9yppvLp50hmYk5VQrdevUixKcYxjKREia933rqSS7Y4kPDzXEViwkn7mZhsov78r46Q5/Zv0r+b8x3KmqlsYKVgIL+NjCcFOfY+Z2i6Ya/xkjmUT6Zn1rtK+MMyeY8AZMPhMkcKdu2vTQ0LtaF+gXb1tGtXT7uGs2nVJsp1ZcMiNZfPKVS1Z6xZVq/SREyPPH1IyVT2zJGVZyTjGTXWleuua5eoprq04G15M7h8bqv29HbkPATmZ3z5weS92cl4nlF97cTj0fObqtTdO6w8RiLO0jPSpMaKPI5qS/JfNXMxqKlKyPNMXpdXcsH3jbJeg8Jj4RzfZJuW/JoCtkpK+DENZVI5ly/L4QAsEfc1qByH+cZIZstkrScpn1DyJX/qE8rzUdBvvPKM0bq6Fq2rK86a/qXza/TBd83RkY5+HTx+WqlUVpXlJbpkSZ1qCROnrmhYpG/sz235uQmM6nprZaKegdHka1TGK03EJr2iwoZVjdFY9Mw+7LYdv0O3LbtfcS+HI1tTLtX+wozqudCtXjpXT780+Sq8QqpseofU9R85TNRH4guleHNB27SpfqGeynFxUtzzdXXT0mnLrVpap63b2/PuFR7qXSNjZvD3X3Ft/ttMYlZ8n0ohGWO0cF61rt+0QLdevVhXXd5CoBRAiR/T3YvXq2SKuTRJUiDFsjHNGQh7qzHf0/zmqimHKkfFfE/XbGiZdLiyprpUS+fX5LSvibx2+goZr0LTD4P5UqxJKr9yRvVc6KoqSrRgXpVyGW2Mxzw11JY5GSmcV1+hytpLwxMccxmqNGXhiZAFdmvrJTktTpLCZe/XNi+bttxlKxtmdGiUzFZof8+m/OZVyq+UYu4WK130oYI3zs2tl+jOBWsV93x5E/ynN4FRPBPXwuMLFDe+fN9o7fK5eudtK3X95vlThkHM97RpTZPWLJv6Mil3XbdEzfUV8vOcX4n5npbMb5C/6MvhSWGaLBxjUmyuNP+L4Xj9m9Sd1y5ReVl8ymCJ+Z4uWVKnd9y8XKUlsfMKlpK4p9uuXhz+Mu/Pw57iVExCKtskVd0180pz1Fw+Rze3XDLtAVWJ5+vdyzaqMj79KQzlpXFds74178sT+Z7Rzt73S36Oi49MudT4u3nVMe0uXZ5RD0jSkYEePXx0t7Z1HZZkFVirGr9CTQNzVdlfpZKYr6Xza7R+VaOqKkpe366jT1teOq6OrsEoFIyCIFDdnDJdvb5FyxbU5FR/EFjtaOvU1lfaNTCUmXL5q+8byUqXrqzXjVcsDCf70yek7s9L/T8KrydlbTgpbwOp+u1S/YcLPk5/IRgaSeuBnxzQsZMDZ53xPXo9tc3rmnTVZWHPsm8gqe892qa+gZQymdcHikbD3/fDOZqJzqiPxzy987aVaqgbEyTJfdKxj4QnN561Gqwk7MRU3iE1f/INuxaYtVb/fmi7fnR0tySddYX2uOfLWqt3L9ukG+atyGu/z73Sri3RUKPbM+oleXOkBf8oJfJrkzT1GfWECgomsFbJbFoxz59yifl4fQNJnTo9ImulmurEjIcrrbXq7UtqOJnR0EhaMd/TqdMjOtTep6HhtHzf06KWal22skEVZRN8+GQHpOHnwxVHXrVUvrko15Oa7U4PJLWzrUs9/SOKeZ5am6p0yZLa16+lFrHWqqNrUC/uOame0yPyPKOWhkqtX92o6ooSHeno17adHeo8NSxrreZUJbRpTZOWLayZeHjJBtLQs1LvN6X08TBAyq+Uat5T8HmUyfQmh/ST9n16vuuwRrJplcdKdHXTEl3TtEwVOfRQJtI/mNLLr57U7gOnlEpnFfONaqoS6h/M99pfWUmeFJsXXvur6nbJK5mq6kkRKgAAZ96wa38BAN7cCBUAgDOECgDAGUIFAOAMoQIAcIZQAQA4Q6gAAJwhVAAAzhAqAABnCBUAgDNTXqbFGNMp6dAb1xwAwAVgkbW2YaIHpgwVAADywfAXAMAZQgUA4AyhAgBwhlCZxYwxv2SMsZPceovctnuNMUcd7/PPjDHbjTG9xphhY8weY8wnjTn3u2ONMWXGmE8ZY9qMMUljzAljzH8YY0rGlbvWGLMl2l+HMeZzxpgZfdOWMebG6LW/dabPsViMMR+MXs+kMeZVY8yv5rjdtdF7vcMYkzHGHJyi7E3GmCej1/qUMeY+Y0zTuDK3GGPuN8bsj8rtN8Z8wRhzzpekG2MWGmO+aow5HJXda4z5Y2NMRd4vAN4wsWI3ADn5OUnjP8AzxWhIgVVL+idJr0pKSnqLpI9L2iTpHaOFjDFxSf8paYmkT0vaJalB0m0a8+XyxpjLJD0s6UeS3hqV/4ykVknvKfizmSWMMR+U9PcKX6tHJN0i6e+MMcZa+4VpNr9F0nWStin8XtoJv/zcGHOdpIcUvtbvlDRX0h9LetQYs8lam4yK/qqkyuixA5JWSPpDSXcYYy6z1g5E+6uI2hqX9AlJhyVdEZVdoTfR+3fBsdZym6U3Sb+k8D/y8mK3ZYK23Svp6BtQz6ej16B+zH2/K6lP0oJptv2epDZJ8TH3/UK0v40zaMuN0ba3Fvv1z6PNMUknJX113P1fkdQ19rWZZHtvzL/vl3RwknKPSNonKTbmvs3R6/VrY+5rmGDb66Nyvzzmvtuj+24fV/bPFB5QlRf7teU28Y3hr4vAmGGy640x3zfGDBhjuo0xnx8/1GOMmWeM+ZoxpisaCtlujHn/BPtcEg1fdETlDhhj/nqCchuMMU8YY4aioaichlXy0B39HNsz+zVJ37bWHplso6g3c6ekb1lr02Me+paklMb0fFwyod+KhphSxph2Y8zfGmOqx5X7qDFmdzSs02OM2WaMuXvM43dEw3ano/fzVWPMJ2fQpKsV9uLuH3f/fQp7E9dOtbG1NsixnqskPWytPfM+WWu3KXz/7h5zX+cE2z4X/Wwdc9/oMGbfuLK9CoftTY7twhuMULkw+MaY2LjbRO/d/QqPFn9W0l9J+qCkM8Mb0ZDC45LukvT7kn5G0iuS7jPGfGhMuSWStio8gvykwg/nP5RUP66+akn/EtX7DoUfDl8wxtw0tlAUePfm+mSj51cZzV38tqSvWGt7o8cWSlog6YAx5kvGmD5jzIgx5lFjzPoxu1kmqVTSjrH7ttaOSNovaU2u7cnTn0j6nMJht7dJ+guFPc4fjr5nxpj3SfpLSV+X9FOS3ifpO5LqoseXSvqBpNcUDvO8PdrnmbkEY8zi6HX91DTtWRv93DHu/p3RT1evQ1ZhWI+XlLRumm1viH7uHnPfIwp7mX9ujFkT/T3cLOmjkr5orR083wajQIrdVeI2+U2vD39NdPuPCcp9cdz2H1f4n31l9PtHonI3jiv3iMIhEj/6/WuSBiS1TNG2e6N93TTmvoTCI9N/GFc2I+nLOT7ndeOe51dH2xU9flV0f5+kRxV+KN8tabvCo9iFUbm3ROXunKCOJyU9OoP340ZNMfylMBSSku4dd//7o+3eHv3+t5JemKKed0Xlq6cosyh6XT85TZt/P9pX6bj7Y9H9n8jj+U81/LVV0rMTtDGQlJxin1WS9iicF4uNe6xR0hPj/h6+pDFDctxm342eyoXhboWTlGNvvzlBuW+N+/0bCnujV0a/Xy/pmLX2sXHl7lc4RDJ61Hq7wtA6Pk27hqy1Px79xYaTsXslLRxbyFobs9Z+YJp9jdqn8PndqPAD8W6FITdq9G92SNLbrLUPWGu/J+mnJZVJ+vUc6ymEqxQO24wfavqGwgAYPSJ/TtJ6Y8zfGGNuNeeubntJUlrSN4wx75poZZS19lD0uv6R02cwc38t6cpodVajMWaVwiG2ILqdwxgTU9hba5X0Xjtm6MwYUyrpmwqD5X8ofO3+j8Ke2+cL+URwflj9dWHYYa3dl0O5E5P8PjpWXSepfYLtOsY8LoVj7bksF+6Z4L6kwmGnGbHh8NS26NfHjTHtkv7JGPM31tpn9Pocy1PW2qEx2x0xxuyRtGFc22onqKZOrw//uDT6+p31GltrM8aY7jGPf03ha/QBhfNDaWPMA5J+21p70Fq7zxhzh6SPKfxgThhjtkr6mLX28TzbNPZ1GNuu0bacynN/E7LW/nMUJP9bYQ/ZKgyFBzTB8Fc0FPhVSbdK+mlr7fZxRT6g8MBiubV2f3TfT4wxpyX9gzHmi9bal120HW7RU7m4NE3y+7Ho5ylJzRNs1zzmcSlcFdQ6QbliGA2Y5dHPA5KGpyg/elS8X2HArR37YHQEvFThcItro6/fWa9xdEQ+d/RxG/p7a+2VCuepflFhb/Kbo9tYa39srb1TUo3CD96MwnmZ8fNa0xkNz7Xj7h/tlTp7Hay1n1D4fC6TNM9a+/MKl/8+OUHxLyrsdbzXWvvoBI9fKqlnTKCM2hr9XO2m1XCNULm4vHvc7+9V+CH7bPT745LmG2OuGVfuvyucUxn9gHlI0luNMfMK1dA8jA4Z7ZckG67k+qGka8eeBBdN4K9StJLIWpuS9KCkd0cf6qPepXDu5wcFaOszCier3zvu/vcoHBV4bPwG1toea+03FQ5dnnNEb61NWmv/S+GEf4XCc23y8bTCg4T3jbv//QpD7qk89zcla+2gtfYVa+0JY8ydCt+TL44tY4z5S0n/U9I91trvT7KrDkm1xpjl4+7/b9HPY8KsxPDXhWH9JEeo28aOQ0v6KWPMZxSGwpWS/kDS16y1bdHj9ypcPfOvxpiPKxziep/CkwZ/xVqbjcr9gcIJ8C3GmD9VOM/RqnDS+5zlx9MxxmQUnicx6bxKdKLiZyV9W2FvJKFwDuijkv7TWvv0mOJ/oPCI9YfRB1RpdF+vwknwUZ9S+EH/LWPM5yUtVnjy43estc+PqftGST9W+CF3bw5P6TpjTM24+zLW2u9H7fk9Y8ygwqGf1QpP9HtSYRjKGPMPkvoVfuCflLRS4bzBQ9Hjvxo99wckHVF49P97ko4rWsVljFmkMGj/aKp5FWtt2hjzCYUnOx5TuCjjZkm/LOk3ovAdfR2+LOkXrbWxMfc16PVgXyip3Bjzruj3XdbaXVG5DQpXFb4QPXatwjmQv7DWbhmzv48pWtEnqc0Yc9WY5naO6ZncG5V7wBjzJwpPftys8ETI5+U4DOFQsVcKcJv8pqlXf505IXBMuesl/ZvClVunFE5olo3b5zyF4/RdCoeHtkt6/wR1L1M4idolaXQZ7ufGPH6vJjj5UeHR+GPj7rMatyJqgu2aFC5Pfk3h8Fa3wl7Hr0tKTFD+SoVBMCTptKTva4KTRKPX5OnoOZyQ9P807sQ5hZP8E64UG1fuxinei4GojJH0WwqvCpBSOI/xeY1ZyaVwuOsxhYGSjJ7zX42WUXhuyb8pDJRktI9vS7pkzD4WR/V+Kse/pV9RuIgiqXCp7q9NUObe8CMh5+f8qTHl1ioMzt7o/XtBYUhP9Pcx2f7uHVd2jcIe3JFon3sVHnjUFvv/JrfJb3yfykXAGPNLCi9vssLmNqGPMaLe2NslXWr5DwGcF+ZUgHB4508JFOD8MaeCNz1r7fiFCwBmiOEvAIAzDH8BAJwhVAAAzhAqAABnCBUAgDOECgDAmf8PCsGdGIJvtAgAAAAASUVORK5CYII=\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": {}
        },
        {
          "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": "F2B3X6tf9YpS"
      },
      "source": [
        "As one can see, our 3-layer GCN model manages to linearly separating the communities and classifying most of the nodes correctly.\n",
        "\n",
        "Furthermore, we did this all with a few lines of code, thanks to the PyTorch Geometric library which helped us out with data handling and GNN implementations.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9bELRjibIRO"
      },
      "source": [
        "## Documentation\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OmqyWVNObNcK"
      },
      "source": [
        "You can explore more PyG functions through its [documentation](https://pytorch-geometric.readthedocs.io/en/latest/)."
      ]
    }
  ]
}