GNN_Simulation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/katiavas/sql/blob/main/Untitled9.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Resa1r9YXjH"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K5711z9BAAOh"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1OM4DccLOQP_"
      },
      "outputs": [],
      "source": [
        "notebook_dir = \"/content/drive/MyDrive/Colab Notebooks/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OeoFnn_GOQQC",
        "outputId": "4081a3ae-b997-429a-bcdb-d3051215e464"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "import requests\n",
        "drive.mount('/content/drive')\n",
        "import os\n",
        "os.chdir(notebook_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OeAWef020ZLF",
        "outputId": "9e87bd15-599e-402e-be17-aa2c40015493"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/, https://download.pytorch.org/whl/cu117\n",
            "Collecting torch==1.13.1+cu117\n",
            "  Downloading https://download.pytorch.org/whl/cu117/torch-1.13.1%2Bcu117-cp310-cp310-linux_x86_64.whl (1801.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 GB\u001b[0m \u001b[31m1.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchvision==0.14.1+cu117\n",
            "  Downloading https://download.pytorch.org/whl/cu117/torchvision-0.14.1%2Bcu117-cp310-cp310-linux_x86_64.whl (24.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.3/24.3 MB\u001b[0m \u001b[31m72.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchaudio==0.13.1\n",
            "  Downloading https://download.pytorch.org/whl/cu117/torchaudio-0.13.1%2Bcu117-cp310-cp310-linux_x86_64.whl (4.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.2/4.2 MB\u001b[0m \u001b[31m112.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch==1.13.1+cu117) (4.5.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision==0.14.1+cu117) (1.22.4)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision==0.14.1+cu117) (2.27.1)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision==0.14.1+cu117) (8.4.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.14.1+cu117) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.14.1+cu117) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.14.1+cu117) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision==0.14.1+cu117) (3.4)\n",
            "Installing collected packages: torch, torchvision, torchaudio\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.0.1+cu118\n",
            "    Uninstalling torch-2.0.1+cu118:\n",
            "      Successfully uninstalled torch-2.0.1+cu118\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.15.2+cu118\n",
            "    Uninstalling torchvision-0.15.2+cu118:\n",
            "      Successfully uninstalled torchvision-0.15.2+cu118\n",
            "  Attempting uninstall: torchaudio\n",
            "    Found existing installation: torchaudio 2.0.2+cu118\n",
            "    Uninstalling torchaudio-2.0.2+cu118:\n",
            "      Successfully uninstalled torchaudio-2.0.2+cu118\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchdata 0.6.1 requires torch==2.0.1, but you have torch 1.13.1+cu117 which is incompatible.\n",
            "torchtext 0.15.2 requires torch==2.0.1, but you have torch 1.13.1+cu117 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed torch-1.13.1+cu117 torchaudio-0.13.1+cu117 torchvision-0.14.1+cu117\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting torch_geometric==2.0.1\n",
            "  Downloading torch_geometric-2.0.1.tar.gz (308 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torch_geometric==2.0.1) (1.22.4)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from torch_geometric==2.0.1) (4.65.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from torch_geometric==2.0.1) (1.10.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch_geometric==2.0.1) (3.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from torch_geometric==2.0.1) (1.2.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torch_geometric==2.0.1) (2.27.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from torch_geometric==2.0.1) (1.5.3)\n",
            "Collecting rdflib (from torch_geometric==2.0.1)\n",
            "  Downloading rdflib-6.3.2-py3-none-any.whl (528 kB)\n",
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            "\u001b[?25hRequirement already satisfied: googledrivedownloader in /usr/local/lib/python3.10/dist-packages (from torch_geometric==2.0.1) (0.4)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch_geometric==2.0.1) (3.1.2)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.10/dist-packages (from torch_geometric==2.0.1) (3.0.9)\n",
            "Collecting yacs (from torch_geometric==2.0.1)\n",
            "  Downloading yacs-0.1.8-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from torch_geometric==2.0.1) (6.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch_geometric==2.0.1) (2.1.2)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->torch_geometric==2.0.1) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->torch_geometric==2.0.1) (2022.7.1)\n",
            "Collecting isodate<0.7.0,>=0.6.0 (from rdflib->torch_geometric==2.0.1)\n",
            "  Downloading isodate-0.6.1-py2.py3-none-any.whl (41 kB)\n",
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            "\u001b[?25hRequirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric==2.0.1) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric==2.0.1) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric==2.0.1) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric==2.0.1) (3.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->torch_geometric==2.0.1) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->torch_geometric==2.0.1) (3.1.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from isodate<0.7.0,>=0.6.0->rdflib->torch_geometric==2.0.1) (1.16.0)\n",
            "Building wheels for collected packages: torch_geometric\n",
            "  Building wheel for torch_geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch_geometric: filename=torch_geometric-2.0.1-py3-none-any.whl size=513798 sha256=a5bc0484da532982f1f2f393e2dde47a1229ec2697dc6cc90d8865c90a198867\n",
            "  Stored in directory: /root/.cache/pip/wheels/55/82/9d/0d0cd9de414365d6f6b3c0467652108972e1602e4a91afcc9b\n",
            "Successfully built torch_geometric\n",
            "Installing collected packages: yacs, isodate, rdflib, torch_geometric\n",
            "Successfully installed isodate-0.6.1 rdflib-6.3.2 torch_geometric-2.0.1 yacs-0.1.8\n"
          ]
        }
      ],
      "source": [
        "!pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117\n",
        "!pip install torch_geometric==2.0.1\n",
        "\n",
        "\n",
        "# Optional dependencies:\n",
        "# !pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.0+cu117.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2M8PxBC1tJ7R",
        "outputId": "03e4b1dc-9985-4793-be76-6d5bbabbdf21"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://data.pyg.org/whl/torch-1.13.0+cu117.html\n",
            "Collecting pyg_lib\n",
            "  Downloading https://data.pyg.org/whl/torch-1.13.0%2Bcu117/pyg_lib-0.2.0%2Bpt113cu117-cp310-cp310-linux_x86_64.whl (2.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m27.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torch_scatter\n",
            "  Downloading https://data.pyg.org/whl/torch-1.13.0%2Bcu117/torch_scatter-2.1.1%2Bpt113cu117-cp310-cp310-linux_x86_64.whl (10.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.1/10.1 MB\u001b[0m \u001b[31m54.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torch_sparse\n",
            "  Downloading https://data.pyg.org/whl/torch-1.13.0%2Bcu117/torch_sparse-0.6.17%2Bpt113cu117-cp310-cp310-linux_x86_64.whl (4.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m4.7/4.7 MB\u001b[0m \u001b[31m63.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torch_cluster\n",
            "  Downloading https://data.pyg.org/whl/torch-1.13.0%2Bcu117/torch_cluster-1.6.1%2Bpt113cu117-cp310-cp310-linux_x86_64.whl (3.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m56.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torch_spline_conv\n",
            "  Downloading https://data.pyg.org/whl/torch-1.13.0%2Bcu117/torch_spline_conv-1.2.2%2Bpt113cu117-cp310-cp310-linux_x86_64.whl (872 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m872.1/872.1 kB\u001b[0m \u001b[31m40.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from torch_sparse) (1.10.1)\n",
            "Requirement already satisfied: numpy<1.27.0,>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scipy->torch_sparse) (1.22.4)\n",
            "Installing collected packages: torch_spline_conv, torch_scatter, pyg_lib, torch_sparse, torch_cluster\n",
            "Successfully installed pyg_lib-0.2.0+pt113cu117 torch_cluster-1.6.1+pt113cu117 torch_scatter-2.1.1+pt113cu117 torch_sparse-0.6.17+pt113cu117 torch_spline_conv-1.2.2+pt113cu117\n"
          ]
        }
      ],
      "source": [
        "!pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-1.13.0+cu117.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ea_A1pBrOQQE"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch import nn\n",
        "torch.backends.cudnn.deterministic = True\n",
        "torch.backends.cudnn.benchmark = False\n",
        "np.random.seed(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tYjZuXBdVfMs"
      },
      "outputs": [],
      "source": [
        "def load_array(filename, task):\n",
        "    datapoint = np.load(filename)\n",
        "    if task == 'task 1':\n",
        "        initial_state = datapoint['initial_state']\n",
        "        terminal_state = datapoint['terminal_state']\n",
        "        return initial_state, terminal_state\n",
        "    elif task == 'task 2' or task == 'task 3':\n",
        "        whole_trajectory = datapoint['trajectory']\n",
        "        # change shape: (num_bodies, attributes, time) ->  num_bodies, time, attributes\n",
        "        whole_trajectory = np.swapaxes(whole_trajectory, 1, 2)\n",
        "        initial_state = whole_trajectory[:, 0]\n",
        "        target = whole_trajectory[:, 1:, 1:]  # drop the first timepoint (second dim) and mass (last dim) for the prediction task\n",
        "        return initial_state, target\n",
        "    else:\n",
        "        raise NotImplementedError(\"'task' argument should be 'task 1', 'task 2' or 'task 3'!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ATBayvUXEKq",
        "outputId": "bac1d211-b4a5-4eab-8739-c74e41882555"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-52-60d65b43fd47>:31: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  initial_state_train = np.array(initial_state_train)\n",
            "<ipython-input-52-60d65b43fd47>:32: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  remaining_trajectory_train = np.array(remaining_trajectory_train)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2,)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "# directory_path = 'data/task 1/train/'  # Path to the directory containing .npz files\n",
        "\"\"\"\n",
        "This cell gives an example of loading a datapoint with numpy for task 1.\n",
        "\n",
        "The arrays returned by the function are structures as follows:\n",
        "initial_state: shape (n_bodies, [mass, x, y, v_x, v_y])\n",
        "terminal_state: shape (n_bodies, [x, y])\n",
        "\n",
        "\"\"\"\n",
        "directory_path = '/content/drive/MyDrive/data/task 1/train/'\n",
        "\n",
        "initial_state_train = []  # List to store the data\n",
        "remaining_trajectory_train = []  # List to store the data\n",
        "\n",
        "for file_name in os.listdir(directory_path):\n",
        "    if file_name.endswith('.npz'):\n",
        "        file_path = os.path.join(directory_path, file_name)\n",
        "\n",
        "        # Load the .npz file\n",
        "        loaded_data = load_array(file_path, task='task 1')\n",
        "\n",
        "        initial_state, remaining_trajectory = loaded_data\n",
        "\n",
        "\n",
        "        # Append the data to the list\n",
        "\n",
        "        initial_state_train.append(initial_state)\n",
        "        remaining_trajectory_train.append(remaining_trajectory)\n",
        "\n",
        "\n",
        "initial_state_train = np.array(initial_state_train)\n",
        "remaining_trajectory_train = np.array(remaining_trajectory_train)\n",
        "remaining_trajectory_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "tMWpTiJNlO3a",
        "outputId": "ed5e46d6-a7fc-43ec-e6cf-7bebeef8b949"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-53-8e11258f9d44>:21: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  initial_state_test = np.array(initial_state_test)\n",
            "<ipython-input-53-8e11258f9d44>:22: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  remaining_trajectory_test = np.array(remaining_trajectory_test)\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-53-8e11258f9d44>\u001b[0m in \u001b[0;36m<cell line: 23>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0minitial_state_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minitial_state_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0mremaining_trajectory_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mremaining_trajectory_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mtime_feature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mremaining_trajectory_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
          ]
        }
      ],
      "source": [
        "# directory_path = 'data/task 1/test/'  # Path to the directory containing .npz files\n",
        "# Get all the trajectories into one list\n",
        "directory_path = '/content/drive/MyDrive/data/task 1/test/'\n",
        "\n",
        "\n",
        "initial_state_test = []  # List to store the data\n",
        "remaining_trajectory_test = []  # List to store the data\n",
        "\n",
        "for file_name in os.listdir(directory_path):\n",
        "    if file_name.endswith('.npz'):\n",
        "        file_path = os.path.join(directory_path, file_name)\n",
        "\n",
        "        # Load the .npz file\n",
        "        loaded_data = load_array(file_path, task='task 1')\n",
        "\n",
        "        initial_state, remaining_trajectory = loaded_data\n",
        "\n",
        "        initial_state_test.append(initial_state)\n",
        "        remaining_trajectory_test.append(remaining_trajectory)\n",
        "\n",
        "\n",
        "initial_state_test = np.array(initial_state_test)\n",
        "remaining_trajectory_test = np.array(remaining_trajectory_test)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "This cell gives an example of loading a datapoint with numpy for task 2 / 3.\n",
        "\n",
        "The arrays returned by the function are structures as follows:\n",
        "initial_state: shape (n_bodies, [mass, x, y, v_x, v_y])\n",
        "remaining_trajectory: shape (n_bodies, time, [x, y, v_x, v_y])\n",
        "\n",
        "Note that for this task, you are asked to evaluate performance only with regard to the predictions of the positions (x and y).\n",
        "If you use the velocity of the remaining trajectory for training,\n",
        "this use should be purely auxiliary for the goal of predicting the positions [x,y] over time.\n",
        "While testing performance of your model on the test set, you do not have access to v_x and v_y of the remaining trajectory.\n",
        "\n",
        "\"\"\"\n",
        "directory_path = '/content/drive/MyDrive/data/task 2_3/train/'\n",
        "\n",
        "initial_state_train = []  # List to store the data\n",
        "remaining_trajectory_train = []  # List to store the data\n",
        "\n",
        "for file_name in os.listdir(directory_path):\n",
        "    if file_name.endswith('.npz'):\n",
        "        file_path = os.path.join(directory_path, file_name)\n",
        "\n",
        "        # Load the .npz file\n",
        "        loaded_data = load_array(file_path, task='task 2')\n",
        "\n",
        "        initial_state, remaining_trajectory = loaded_data\n",
        "\n",
        "\n",
        "        # Append the data to the list\n",
        "\n",
        "        initial_state_train.append(initial_state)\n",
        "        remaining_trajectory_train.append(remaining_trajectory)\n",
        "\n",
        "\n",
        "initial_state_train = np.array(initial_state_train)\n",
        "remaining_trajectory_train = np.array(remaining_trajectory_train)\n",
        "remaining_trajectory_train[0].shape\n",
        "for trajectory in remaining_trajectory_train:\n",
        "    time_feature = trajectory[:, :, 0]\n",
        "    time_feature_shape = time_feature.shape\n",
        "    time_feature_shape\n",
        "    # Do something with the time feature\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8mScl5UxzaU",
        "outputId": "ad37bf4c-2a42-4b21-90a6-3fdeddc90e43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-67-3260515f1737>:35: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  initial_state_train = np.array(initial_state_train)\n",
            "<ipython-input-67-3260515f1737>:36: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  remaining_trajectory_train = np.array(remaining_trajectory_train)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for trajectory in remaining_trajectory_train:\n",
        "    time_feature = trajectory[1, :,1]\n",
        "    print(time_feature)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4YSP6PxGzex5",
        "outputId": "b38ba916-3e3b-4880-aaf9-0a655e6c0761"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "[4.78798396 4.76411175 4.74017636 4.71619482 4.6921843  4.66816222\n",
            " 4.64414637 4.62015502 4.59620699 4.57232174 4.54851945 4.52482107\n",
            " 4.50124848 4.47782453 4.45457322 4.43151977 4.4086908  4.3861145\n",
            " 4.36382079 4.34184155 4.32021086 4.29896527 4.27814413 4.25778996\n",
            " 4.23794888 4.21867112 4.20001163 4.18203076 4.16479512 4.14837839\n",
            " 4.13286257 4.11833965 4.10491352 4.0927023  4.08184121 4.07248604\n",
            " 4.06481816 4.05905079 4.05543709 4.05428078 4.05595053 4.06089967\n",
            " 4.06969382 4.08304884 4.10188334 4.12738871 4.16111067 4.20499801\n",
            " 4.261232  ]\n",
            "[5.50556556 5.40871149 5.31124817 5.21319159 5.1145575  5.01536144\n",
            " 4.91561913 4.81534679 4.71456181 4.61328218 4.51152578 4.4093094\n",
            " 4.30665054 4.20356797 4.10008103 3.996209   3.89197108 3.7873875\n",
            " 3.68247916 3.57726689 3.47177184 3.36601592 3.26002217 3.15381513\n",
            " 3.04742032 2.94086424 2.83417298 2.72737053 2.62048118 2.51352888\n",
            " 2.4065385  2.29953495 2.1925436  2.08559041 1.97870282 1.87190884\n",
            " 1.76523627 1.65871374 1.55237062 1.44623681 1.34034361 1.23472351\n",
            " 1.12941023 1.02443845 0.91984304 0.81565944 0.71192455 0.60867632\n",
            " 0.50595376]\n",
            "[5.37406395 5.38025794 5.38590908 5.391018   5.39558534 5.39961171\n",
            " 5.4030977  5.40604385 5.40845066 5.41031865 5.41164835 5.41244029\n",
            " 5.41269498 5.41241289 5.41159445 5.41024006 5.40835011 5.40592498\n",
            " 5.40296504 5.39947072 5.39544254 5.390881   5.38578684 5.3801613\n",
            " 5.37400586 5.36732224 5.36011223 5.35237776 5.34412078 5.33534319\n",
            " 5.32604668 5.31623282 5.30590325 5.29505969 5.28370393 5.27183786\n",
            " 5.2594634  5.24658255 5.23319735 5.21930984 5.20492196 5.19003556\n",
            " 5.17465252 5.15877478 5.14240435 5.12554328 5.1081937  5.09035791\n",
            " 5.07203831]\n",
            "[5.57491728 5.67951843 5.78466976 5.89066196 5.99797761 6.10751421\n",
            " 6.22136709 6.33894653 6.40786477 6.47481996 6.54113195 6.60662228\n",
            " 6.6711263  6.73451737 6.79669729 6.85758823 6.91712722 6.97526126\n",
            " 7.03194446 7.08713788 7.14080886 7.19293012 7.24347699 7.29242757\n",
            " 7.33976345 7.38547017 7.4295393  7.47197201 7.51277737 7.55197371\n",
            " 7.58959112 7.6256744  7.66028704 7.69351666 7.72548323 7.75635177\n",
            " 7.78635281 7.81582377 7.84529058 7.87565082 7.90868544 7.94913745\n",
            " 8.03500496 8.2152682  8.33065001 8.42149231 8.49666135 8.55997897\n",
            " 8.61355209]\n",
            "[ 6.68977876e-01  5.74859347e-01  4.80303352e-01  3.85255690e-01\n",
            "  2.89659469e-01  1.93455125e-01  9.65811098e-02 -1.02546639e-03\n",
            " -9.94285045e-02 -1.98692472e-01 -2.98881930e-01 -4.00060976e-01\n",
            " -5.02291829e-01 -6.05630749e-01 -7.10123748e-01 -8.15806000e-01\n",
            " -9.22702036e-01 -1.03082513e+00 -1.14017633e+00 -1.25074340e+00\n",
            " -1.36250010e+00 -1.47540559e+00 -1.58940435e+00 -1.70442643e+00\n",
            " -1.82038816e+00 -1.93719327e+00 -2.05473431e+00 -2.17289438e+00\n",
            " -2.29154905e+00 -2.41056833e+00 -2.52981867e+00 -2.64916489e+00\n",
            " -2.76847191e+00 -2.88760630e+00 -3.00643764e+00 -3.12483955e+00\n",
            " -3.24269055e+00 -3.35987469e+00 -3.47628194e+00 -3.59180841e+00\n",
            " -3.70635653e+00 -3.81983506e+00 -3.93215905e+00 -4.04324992e+00\n",
            " -4.15303537e+00 -4.26144914e+00 -4.36843031e+00 -4.47392226e+00\n",
            " -4.57787185e+00]\n",
            "[6.39500594 6.56590013 6.66962166 6.73954053 6.78281888 6.79808817\n",
            " 6.77120147 6.62523571 6.23811734 5.96777174 5.75997893 5.57943444\n",
            " 5.41510847 5.2627551  5.1206478  4.9882712  4.8659596  4.75502674\n",
            " 4.65781969 4.57614395 4.51139531 4.46780315 4.44897652 4.1293225\n",
            " 3.79097359 3.41753928 3.13216901 2.91152333 2.80854629 2.87254854\n",
            " 3.122383   3.2078748  3.18855571 3.15309103 3.11862291 3.08672899\n",
            " 3.05369684 3.01896377 2.98847909 2.9794156  3.03119387 3.21163415\n",
            " 3.48825871 3.69508902 3.81274914 3.87210046 3.8934578  3.88827158\n",
            " 3.86338668]\n",
            "[4.82559173 4.83537684 4.84445222 4.85282265 4.86049317 4.8674691\n",
            " 4.87375599 4.87935962 4.88428599 4.88854131 4.89213197 4.89506453\n",
            " 4.89734573 4.89898248 4.89998182 4.90035094 4.90009716 4.89922794\n",
            " 4.89775085 4.89567359 4.89300397 4.88974993 4.88591949 4.88152081\n",
            " 4.87656214 4.87105185 4.8649984  4.85841036 4.85129643 4.8436654\n",
            " 4.83552618 4.82688777 4.8177593  4.80815003 4.79806931 4.78752661\n",
            " 4.77653155 4.76509386 4.75322337 4.74093008 4.7282241  4.71511569\n",
            " 4.70161522 4.68773323 4.6734804  4.65886754 4.64390562 4.62860576\n",
            " 4.61297925]\n",
            "[5.42190317 5.43044127 5.43780611 5.44398209 5.44895341 5.45270399\n",
            " 5.45521755 5.4564776  5.45646744 5.45517021 5.45256882 5.44864575\n",
            " 5.44338118 5.4367508  5.42873057 5.41929725 5.40842752 5.39609751\n",
            " 5.38228249 5.36695674 5.35009329 5.33166387 5.31163871 5.28998647\n",
            " 5.26667419 5.24166717 5.21492858 5.1864182  5.15609238 5.12390433\n",
            " 5.089804   5.05373777 5.01564805 4.97547282 4.9331451  4.88859237\n",
            " 4.84173584 4.79248965 4.74075989 4.68644349 4.62942687 4.56958445\n",
            " 4.50677689 4.44084952 4.37163193 4.29894487 4.22259588 4.1423312\n",
            " 4.05782483]\n",
            "[7.36518913 7.32668383 7.28527008 7.24093777 7.19367788 7.14348472\n",
            " 7.09035505 7.03427965 6.97524513 6.9132362  6.84823624 6.78022742\n",
            " 6.70919066 6.63510545 6.55794966 6.47769926 6.39432791 6.30780653\n",
            " 6.21810282 6.12518061 6.0289992  5.92951268 5.82666913 5.72041025\n",
            " 5.6106716  5.49738716 5.38048667 5.25987764 5.13544583 5.00705491\n",
            " 4.87454397 4.7377236  4.59637081 4.45022236 4.29896618 4.1422303\n",
            " 3.97956817 3.8104392  3.6341824  3.44998013 3.2568077  3.05336263\n",
            " 2.83796591 2.60843069 2.36191723 2.09490828 1.80391912 1.48918634\n",
            " 1.16530043]\n",
            "[6.641412   6.67370127 6.70549948 6.7368022  6.76760499 6.7979034\n",
            " 6.827693   6.85696934 6.88572796 6.9139644  6.94167422 6.96885293\n",
            " 6.99549607 7.02159916 7.04715772 7.07216727 7.09662332 7.12052138\n",
            " 7.14385695 7.16662554 7.18882265 7.21044379 7.23148445 7.25194016\n",
            " 7.27180642 7.29107875 7.3097527  7.32782379 7.3452876  7.36213971\n",
            " 7.37837572 7.39399128 7.40898204 7.42334374 7.43707213 7.45016302\n",
            " 7.46261229 7.4744159  7.48556987 7.49607033 7.5059135  7.51509572\n",
            " 7.52361344 7.53146325 7.53864189 7.54514625 7.55097339 7.55612053\n",
            " 7.56058511]\n",
            "[4.42838979 4.45882372 4.4874609  4.51428488 4.5392828  4.56243767\n",
            " 4.58373268 4.6031554  4.62068969 4.63631843 4.65002949 4.66180792\n",
            " 4.67163649 4.67950306 4.68539382 4.68929155 4.69118398 4.69105856\n",
            " 4.68889841 4.68469097 4.67842459 4.67008291 4.65965309 4.64712424\n",
            " 4.63248082 4.61570978 4.5968005  4.5757384  4.55251028 4.52710557\n",
            " 4.49951072 4.46971257 4.43770031 4.40346123 4.36698245 4.32825272\n",
            " 4.28725997 4.24399174 4.19843636 4.15058214 4.10041739 4.04793024\n",
            " 3.99310923 3.93594396 3.87642327 3.8145368  3.75027699 3.68363552\n",
            " 3.61460502]\n",
            "[5.57850592 5.51395943 5.44878765 5.38301295 5.31665728 5.24974135\n",
            " 5.18228635 5.11431444 5.04584818 4.97691032 4.90752364 4.83771089\n",
            " 4.76749473 4.69689757 4.62594102 4.55464577 4.48303288 4.41112381\n",
            " 4.33894037 4.26650476 4.19383973 4.12096882 4.0479168  3.97470845\n",
            " 3.9013681  3.82792084 3.75439277 3.6808111  3.60720414 3.5336013\n",
            " 3.46003307 3.38653099 3.31312762 3.23985654 3.16675229 3.09385044\n",
            " 3.02118757 2.94880133 2.87673051 2.80501514 2.73369665 2.66281803\n",
            " 2.59242408 2.5225617  2.45328028 2.38463221 2.31667401 2.24946666\n",
            " 2.18307476]\n",
            "[2.62265429 2.72628665 2.83095794 2.93678539 3.04389905 3.15244372\n",
            " 3.2625812  3.37449308 3.48838426 3.6044873  3.72306794 3.84443227\n",
            " 3.96893605 4.0969971  4.22911198 4.36587909 4.50803099 4.65648083\n",
            " 4.81238977 4.97726447 5.1530858  5.34239414 5.54766293 5.76376516\n",
            " 5.91424046 5.88221192 5.79157187 5.70051598 5.61810339 5.54587596\n",
            " 5.4842183  5.43354601 5.39462366 5.36871655 5.35771808 5.36423806\n",
            " 5.39147175 5.44238179 5.51771269 5.61400219 5.72468438 5.84370699\n",
            " 5.96757621 6.0953176  6.22813514 6.36990345 6.53002084 6.73731987\n",
            " 7.09736586]\n",
            "[7.43924012 7.44983568 7.45899848 7.46672718 7.47302099 7.4778799\n",
            " 7.48130457 7.48329597 7.48385553 7.48298521 7.48068721 7.47696358\n",
            " 7.47181649 7.46524804 7.45725956 7.44785116 7.43702126 7.42476628\n",
            " 7.41108046 7.39595473 7.37937689 7.36133753 7.34182909 7.32084448\n",
            " 7.29837644 7.27441708 7.2489575  7.22198726 7.193494   7.16346281\n",
            " 7.13187567 7.09871092 7.06394303 7.02754193 6.98947207 6.94969172\n",
            " 6.90815191 6.8647952  6.81955395 6.7723482  6.72308281 6.67164375\n",
            " 6.6178931  6.56166215 6.50274181 6.44086887 6.37570667 6.30681626\n",
            " 6.23361026]\n",
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            " 3.9885188  3.92678494 3.85548432 3.77218321 3.67338196 3.55395812\n",
            " 3.40652232]\n",
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            " 4.99471256]\n",
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            " 4.32177571 4.16505136 3.88221276 3.26346204 2.82641527 2.49097593\n",
            " 2.19504393]\n",
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            " 4.94190579 5.05690124 5.16354131 5.26222226 5.35323839 5.43669206\n",
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            " 5.88873926 5.92451655 5.96987573 6.02697876 6.10091406 6.21327518\n",
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            " 6.73240042]\n",
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            "  1.11498085  1.17336099  1.23203079  1.29110851  1.35073126  1.41105628\n",
            "  1.47225605  1.53448987  1.5978589   1.66246378  1.72848225  1.79617287\n",
            "  1.86585766  1.9378545   2.01215677  2.08669065  2.14506562  2.11746036\n",
            "  2.0221861 ]\n",
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            " -6.00735543]\n",
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            " 5.69259325 5.58996859 5.48587838 5.38033362 5.2733465  5.16493044\n",
            " 5.05509994 4.94387026 4.83125572 4.71727202 4.60194014 4.48528821\n",
            " 4.36735391 4.24818599 4.1278444  4.00640088 3.8839404  3.7605632\n",
            " 3.63638705 3.51154955 3.38620996 3.26054993 3.13477174 3.00909382\n",
            " 2.88374681 2.75897005 2.63500577 2.51209091 2.39044892 2.27028357\n",
            " 2.15177696 2.03509317 1.9203881  1.80782515 1.69759558 1.58994212\n",
            " 1.48518423 1.38374368 1.28616838 1.19315222 1.10554672 1.0243595\n",
            " 0.95073305]\n",
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            " -2.8289847 ]\n",
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            "  0.73787523  1.00244761  1.2670783   1.53244515  1.79928062  2.0684506\n",
            "  2.34106801]\n",
            "[1.9968666  2.07679758 2.15641953 2.23572915 2.31472319 2.39339838\n",
            " 2.47175148 2.54977926 2.6274785  2.704846   2.78187856 2.85857304\n",
            " 2.93492627 3.01093513 3.08659652 3.16190735 3.23686457 3.31146514\n",
            " 3.38570606 3.45958435 3.53309707 3.60624128 3.6790141  3.75141266\n",
            " 3.82343411 3.89507566 3.96633451 4.03720791 4.10769315 4.17778765\n",
            " 4.24748915 4.31679575 4.38570586 4.45421828 4.52233183 4.59004525\n",
            " 4.65735721 4.72426635 4.7907713  4.85687066 4.92256308 4.98784724\n",
            " 5.05272186 5.11718573 5.18123771 5.24487668 5.30810145 5.37091073\n",
            " 5.43330337]\n",
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            " -4.26295099 -4.27305431 -4.27753186 -4.27655516 -4.27020581 -4.25836356\n",
            " -4.2408339 ]\n",
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            " 1.14436835 1.14418463 1.11905027 1.05701079 0.93627219 0.73348366\n",
            " 0.5379472  0.44730488 0.41756119 0.41558842 0.426465   0.44232414\n",
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            " 0.28306322 0.33236601 0.42694346 0.60298423 0.86965294 1.17324631\n",
            " 1.49733137 1.86076912 2.16493996 2.3881072  2.57890971 2.75096894\n",
            " 2.90879327 3.05397296 3.18680249 3.30682431 3.41302496 3.50389267\n",
            " 3.57742254 3.63114259 3.66229665 3.66842888 3.64870176 3.60600116\n",
            " 3.54822908]\n",
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            " -2.66557349 -2.69546348 -2.74619385 -2.84060978 -3.00191315 -3.04505444\n",
            " -3.0586108 ]\n",
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            " -5.72869743 -5.76854977 -5.802655   -5.83081099 -5.85271638 -5.86794999\n",
            " -5.87593557]\n",
            "[ 4.47960761  4.41484146  4.34944212  4.28341661  4.21677131  4.14951155\n",
            "  4.08164139  4.01316312  3.94407686  3.8743799   3.80406606  3.73312469\n",
            "  3.66153954  3.5892871   3.51633442  3.44263597  3.36812897  3.29272623\n",
            "  3.21630458  3.13868498  3.05959542  2.97859465  2.89488949  2.80678227\n",
            "  2.70900529  2.53526204  2.32365953  2.14785157  1.98763279  1.83719333\n",
            "  1.69379905  1.55587831  1.4224193   1.2927208   1.16627076  1.04268054\n",
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            " -0.39030815]\n",
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            " -13.36379864 -13.50100533 -13.63636399 -13.76994153]\n",
            "[5.27094942 5.13523122 5.05796229 5.03099338 5.06164633 5.19829386\n",
            " 5.45157748 5.50099308 5.48505074 5.42213375 5.31292096 5.14139192\n",
            " 4.81979437 4.64951266 4.54890363 4.48424558 4.44977211 4.44895396\n",
            " 4.50792279 4.55908837 4.5172284  4.45561064 4.38108866 4.29510792\n",
            " 4.19601695 4.11014959 4.0815416  4.04200566 3.99318497 3.93446369\n",
            " 3.86212873 3.75481818 3.54195371 3.40503276 3.30411593 3.23211227\n",
            " 3.19127172 3.19794968 3.37165208 3.45248258 3.45616003 3.41236822\n",
            " 3.324889   3.1839435  2.92303256 2.56293622 2.38578788 2.27471762\n",
            " 2.21512314]\n",
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            " -3.87813404]\n",
            "[2.89620498 3.03561889 3.18999618 3.36331324 3.56200877 3.79645465\n",
            " 4.08291135 4.43290777 4.80909445 5.07890874 5.34686506 5.42518192\n",
            " 5.37618553 5.19932426 5.07319593 5.03226863 5.09618112 5.12750825\n",
            " 5.14988638 5.16575066 5.17329013 5.16578475 5.09279065 5.01795292\n",
            " 5.16694468 5.28252011 5.38876288 5.3332778  5.18723104 5.10052206\n",
            " 5.08252727 5.11258727 4.96267349 4.5942813  4.32188861 4.05656869\n",
            " 3.93644253 3.88083023 3.8698764  3.90166087 3.98577372 4.00075695\n",
            " 3.9809328  3.97775777 3.9917168  4.02352496 4.07500257 4.14832691\n",
            " 4.24527929]\n",
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            " 1.63283936 1.60745333 1.5831979  1.56007275 1.53807863 1.51721736\n",
            " 1.4974918  1.4789058  1.46146423 1.44517295 1.4300388  1.4160696\n",
            " 1.40327417 1.39166232 1.38124484 1.37203357 1.36404134 1.35728204\n",
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            " 1.34588526 1.34962247 1.35476874 1.36134993 1.36939327 1.37892743\n",
            " 1.38998253 1.40259012 1.41678323 1.43259627 1.45006493 1.46922598\n",
            " 1.49011691 1.5127753  1.53723789 1.56353897 1.5917078  1.62176433\n",
            " 1.65371205]\n",
            "[ 1.75227191  1.66845784  1.58349573  1.49735536  1.41000262  1.32139905\n",
            "  1.23150129  1.14026051  1.04762163  0.95352257  0.85789323  0.76065445\n",
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            " 4.54753219 4.20787148 3.99463619 3.87104333 3.81801275 3.83478186\n",
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            " 4.95609626 5.00274231 4.99030844 4.94667311 4.88593691 4.81504422\n",
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            " 1.78419165 1.77868425 1.77270688 1.76626261 1.75935407 1.75198342\n",
            " 1.74415229]\n",
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            "  1.735164    1.64232855  1.54974742  1.45744452  1.36544319  1.27376613\n",
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            " 5.20147131 5.20552352 5.2090527  5.21206113 5.21455118 5.21652536\n",
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            " 5.15997897 5.15228384 5.1441405  5.13555181 5.12652008 5.11704765\n",
            " 5.10713717 5.09679146 5.08601344 5.07480607 5.06317239 5.05111543\n",
            " 5.03863825]\n",
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            " 3.81578131 3.91675107 4.08044559 4.31421765 4.40852782 4.46735578\n",
            " 4.50601585]\n",
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            " 3.76668059 3.71043754 3.65696504 3.60501178 3.55354356 3.50166272\n",
            " 3.44852392 3.39330784 3.33513087 3.27295222 3.20547513 3.13103868\n",
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            " 2.30881411 2.5205302  2.836357   3.27786384 3.6416843  3.93312347\n",
            " 4.1824403  4.40358761 4.60489335 4.79162223 4.96727588 5.13433984\n",
            " 5.29460291]\n",
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            " 5.87981921 5.88990575 5.89617652 5.89838524 5.8963459  5.89013502\n",
            " 5.88074543 5.87223924 5.87727705 5.91584226 5.98641879 6.07067698\n",
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            " 7.01638691 7.07144009 7.12426859 7.17492398 7.22345301 7.26989797\n",
            " 7.31429705 7.35668474 7.39709201 7.43554703 7.47207557 7.50670128\n",
            " 7.5394459 ]\n",
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            " -6.25157631]\n",
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            " 3.95631051 3.94211578 3.92638938 3.9091209  3.89030365 3.86993602\n",
            " 3.84802346 3.8245814  3.79963951 3.77324856 3.74549158 3.71650401\n",
            " 3.68651305 3.655927   3.6255873  3.59785026 3.59347266 3.5950069\n",
            " 3.57934285 3.55546575 3.52572551 3.49120145 3.45250636 3.41003112\n",
            " 3.36404444 3.31474123 3.2622691  3.20674425 3.14826206 3.08690462\n",
            " 3.02274686 2.95586217 2.8863284  2.81423509 2.73968005 2.66271755\n",
            " 2.58337767 2.50169301 2.41770031 2.33144244 2.24297224 2.15235875\n",
            " 2.05969701]\n",
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            " 3.74769127 3.75226438 3.75525181 3.75660453 3.75626933 3.75418879\n",
            " 3.75030155 3.74454151 3.73683726 3.72711204 3.71528405 3.70126713\n",
            " 3.68497181 3.66630694 3.64518192 3.62151181 3.59522871 3.56627692\n",
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            " 3.28588223 3.23448231 3.18022243 3.12310517 3.06313805 3.00033424\n",
            " 2.9347134  2.86630249 2.7951366  2.72125969 2.64472505 2.56559563\n",
            " 2.48394402]\n",
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            " -8.57772611 -8.73916791 -8.68480239 -8.59891595 -8.70766277 -8.84478912\n",
            " -8.84133994]\n",
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            " 2.43396831 2.56963556 2.7090301  2.85254244 3.00063419 3.15385874\n",
            " 3.31289048 3.47856741 3.65195539 3.83444879 4.02793459 4.23506662\n",
            " 4.45969656 4.70702052 4.97350215 5.11808684 5.06765788 4.99009273\n",
            " 4.91615534 4.84915    4.7889562  4.73504953 4.68775645 4.64791863\n",
            " 4.61585367 4.59142475 4.5742245  4.56370722 4.55926742 4.56027939\n",
            " 4.56611063 4.57611683 4.58961901 4.60585854 4.62391414 4.64254027\n",
            " 4.65981853 4.67228802 4.67225882 4.63603831 4.44711749 4.09874684\n",
            " 3.85744019]\n",
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            " -8.24971519]\n",
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            " -3.03669572]\n",
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            " 3.70298003 3.86594236 4.02961344 4.1938208  4.35835996 4.52300162\n",
            " 4.68750367 4.85162834 5.01515677 5.17782543 5.33931674 5.49934451\n",
            " 5.65765378 5.81400399 5.96814969 6.1198195  6.26869255 6.4143718\n",
            " 6.55635312 6.69399015 6.82645633 6.95270931 7.07146867 7.18122607\n",
            " 7.28031378 7.3670542  7.43998833 7.49813383 7.54118067 7.56953921\n",
            " 7.58422286 7.58662722 7.5782967  7.56074371 7.5353404  7.50327214\n",
            " 7.4655315 ]\n",
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            " 8.01867639 8.03996334 8.05940931 8.07729509 8.09380941 8.10908169\n",
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            " 8.18625143 8.19346199 8.19998378 8.20599452 8.21166636 8.21716314\n",
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            " 8.26288663 8.27202661 8.28219481 8.29351909 8.30613125 8.32016847\n",
            " 8.33577468]\n",
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            " 5.55895466 5.5144857  5.46941544 5.42376162 5.37754178 5.3307736\n",
            " 5.28347596 5.23566846 5.18737105 5.13860403 5.08938799 5.03974377\n",
            " 4.98969242 4.93925529 4.88845398 4.83731035 4.78584655 4.73408511\n",
            " 4.6820489  4.62976118 4.57724568 4.52452667 4.47162894 4.41857791\n",
            " 4.36539971 4.31212127 4.25877041 4.20537597 4.151968   4.09857793\n",
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            " 3.72834386 3.67644302 3.62493082 3.57386169 3.52329353 3.47328795\n",
            " 3.42391051]\n",
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            " 3.55282189 3.50075801 3.44700388 3.39156014 3.33442637 3.27560097\n",
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            " 1.82002731 1.72345882 1.62445359 1.52289453 1.41864754 1.31155849\n",
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            " 0.46274926]\n",
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            " 3.75156088 3.76553353 3.81940757 3.90547869 3.92072763 3.91762484\n",
            " 3.90449105 3.88452122 3.85939072 3.83012554 3.79741636 3.76175788\n",
            " 3.72351826 3.68297529 3.64033336 3.59572592 3.54920203 3.50068807\n",
            " 3.44989943 3.3961305  3.33767392 3.26959945 3.16494306 3.11307542\n",
            " 3.10042678 3.10885318 3.13777148 3.18994326 3.27073944 3.38898007\n",
            " 3.55872309]\n",
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            " -4.44879451]\n",
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            " 2.20190585 2.19823283 2.19742106 2.19961909 2.20499519 2.21374128\n",
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            " 3.38428287 3.31365691 3.24178035 3.16972765 3.09802706 3.02696545\n",
            " 2.95670968 2.88736182 2.81898682 2.75162714 2.68531094 2.62005675\n",
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            "  3.06319458  3.15809055  3.24774111  3.33221734  3.41154667  3.48571655\n",
            "  3.55467843]\n",
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            " 4.48400519]\n",
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            " -1.87368208 -1.85345957 -1.74059504 -1.59301771 -1.54287562 -1.56084424\n",
            " -1.50293138]\n",
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            "  9.38435749  9.53623829  9.68628897  9.8346391   9.98140349 10.12668369\n",
            " 10.27057098 10.41314749 10.55448796 10.69466001 10.83372462 10.9717384\n",
            " 11.1087533  11.24481639 11.37997154 11.51425977 11.64771845 11.78038238\n",
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            " 12.68927213 12.81659375 12.9433504  13.06956072 13.19524227 13.3204119\n",
            " 13.44508556]\n",
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            " 8.31306608 8.34291568 8.32520801 8.27121609 8.19626048 8.11798302\n",
            " 8.05411327]\n",
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            " 8.68775333 8.7288706  8.76838961 8.80628919 8.84254861 8.87714776\n",
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            " 9.17408892 9.18713087 9.19998464 9.21345284 9.22875454 9.24775897\n",
            " 9.2733397  9.30983704 9.36338547 9.44123697 9.54870929 9.68471407\n",
            " 9.84124797]\n",
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            "  2.32875138e+00  2.57035472e+00  2.65005858e+00  2.60138358e+00\n",
            "  2.29732223e+00]\n",
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            " 5.53898603 5.53917883 5.540579   5.5432244  5.54718437 5.55256618\n",
            " 5.55952378 5.5682701  5.57909463 5.59238939 5.60868908 5.62873649\n",
            " 5.65359677]\n",
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            " 3.56518149]\n",
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            " 3.10149325 3.33477993 3.58810556 3.86355542 4.16378229 4.49272883\n",
            " 4.85775529]\n",
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            " -2.40982797e-04]\n",
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            " 5.89313227 5.86350502 5.83641665 5.8115803  5.78875626 5.76774192\n",
            " 5.7483645 ]\n",
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            " -1.2944272 ]\n",
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            "  5.7252037   5.85166556  5.97785143  6.10374241  6.22931997  6.35456606\n",
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            "  8.64905931  8.76414678  8.87853719  8.99221128  9.10515019  9.21733558\n",
            "  9.32874977  9.43937591  9.54919768  9.65819974  9.76636896  9.8736949\n",
            "  9.98016927 10.08578516 10.19053614 10.29441424 10.39740908 10.49950769\n",
            " 10.60069483]\n",
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            " 2.64770601 2.59951042 2.55141374 2.50342402 2.45554924 2.40779733\n",
            " 2.36017614]\n",
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            " 4.95730014 4.93379162 4.90901096 4.88295026 4.85560392 4.82696931\n",
            " 4.79704709 4.7658439  4.73337712 4.69968119 4.6648175  4.62889061\n",
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            " 4.52376807 4.59211126 4.67260138 4.76703969 4.87934326 5.01710121\n",
            " 5.19396547 5.42248166 5.6671856  5.87260061 6.03625296 6.16605905\n",
            " 6.25576403 6.29873539 6.31167885 6.30711968 6.29000232 6.26267295\n",
            " 6.22655208]\n",
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            " 7.77838807 7.77440141 7.76685837 7.75548393 7.73994713 7.71984307\n",
            " 7.69466653 7.66377168 7.62630752 7.58110802 7.52648989 7.45983781\n",
            " 7.37660704 7.26719882 7.09930198 6.88758026 6.84716221 6.83388869\n",
            " 6.832421   6.83798801 6.84834871 6.86223355 6.87883908 6.89761781\n",
            " 6.9181764  6.94022079 6.96352419 6.98790733 7.01322556 7.03936021\n",
            " 7.06621254 7.09369938 7.12174995 7.1503035  7.17930748 7.2087161\n",
            " 7.23848927 7.2685917  7.2989922  7.32966309 7.36057974 7.3917202\n",
            " 7.42306483]\n",
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            " 3.67741143 3.79130457 3.77749367 3.73687771 3.68363856 3.6224574\n",
            " 3.55562315]\n",
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            " 6.4223567 ]\n",
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            " 4.55895554]\n",
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            " 5.33008334 5.29893965 5.26025633 5.21257341 5.15395899 5.08202862\n",
            " 4.99475444]\n",
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            " 3.30972923]\n",
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            " -4.65531502]\n",
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            " -11.15381712 -11.19129899 -11.22802676 -11.26400244]\n",
            "[5.73486379 5.75108595 5.76698058 5.78278126 5.79880836 5.81551022\n",
            " 5.83353246 5.85384392 5.87799056 5.90869304 5.95165828 6.02505539\n",
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            " 6.02479061 6.00867565 5.98357293 5.95092131 5.91156662 5.86607292\n",
            " 5.81483724 5.75808712 5.69596523 5.62858401 5.5560582  5.47856534\n",
            " 5.39633008 5.30962494 5.21877242 5.12413896 5.02612131 4.92512735\n",
            " 4.8215534 ]\n",
            "[5.02970905 4.95553801 4.90640549 4.87901389 4.871589   4.88338458\n",
            " 4.9145088  4.96595673 5.03986935 5.14020271 5.27445555 5.45928307\n",
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            " 3.73258568 3.62091659 3.53128612 3.46249908 3.41375185 3.38474255\n",
            " 3.37536605 3.38606964 3.41718541 3.46820917 3.5379576  3.62556623\n",
            " 3.73153044]\n",
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            " -0.86780274 -0.67962282 -0.77073089 -0.56306247 -0.33410515 -0.20903471\n",
            "  0.0968417 ]\n",
            "[ 1.89455368  2.09459153  2.29782858  2.50467598  2.71565935  2.93140822\n",
            "  3.15268886  3.38045914  3.61595771  3.86085745  4.11755255  4.38976398\n",
            "  4.68407036  5.01522896  5.45162296  5.96503407  6.31974503  6.64792259\n",
            "  6.97202754  7.30483516  7.65513398  8.01699445  8.34891182  8.61554216\n",
            "  8.82731627  9.00302703  9.15428489  9.2874483   9.40606512  9.51216591\n",
            "  9.60691268  9.69094975  9.76462874  9.82819553  9.88197545  9.92653326\n",
            "  9.96272365  9.99157262 10.01406273 10.03097106 10.04282492 10.04993787\n",
            " 10.05247245 10.05050294 10.04407342 10.03325385 10.01819635  9.99918916\n",
            "  9.97670005]\n",
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            "  1.27451644  1.49250621  1.60554954  1.64300753  1.86494189  1.72447972\n",
            "  1.60344842]\n",
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            " -6.06784064]\n",
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            " -3.78351742 -3.74402426 -3.73866806 -3.82454621 -3.87833625 -3.87844811\n",
            " -3.69093012]\n",
            "[0.76830826 0.86735079 0.96589588 1.06394305 1.1614959  1.25856205\n",
            " 1.35515315 1.45128469 1.54697594 1.64224979 1.73713258 1.83165396\n",
            " 1.92584675 2.01974677 2.11339277 2.20682625 2.30009141 2.39323506\n",
            " 2.48630654 2.57935762 2.67244251 2.76561769 2.85894189 2.95247589\n",
            " 3.04628235 3.14042549 3.23497064 3.32998359 3.42552965 3.52167229\n",
            " 3.61847123 3.71597975 3.81424098 3.91328276 4.01311066 4.11369871\n",
            " 4.21497723 4.31681745 4.41901304 4.52125946 4.62313395 4.72408111\n",
            " 4.82341194 4.92032512 5.01395721 5.10345998 5.1880913  5.26729609\n",
            " 5.34075482]\n",
            "[ 7.01865382  7.08001772  7.13893069  7.19542443  7.24953095  7.30128284\n",
            "  7.35071356  7.39785778  7.4427518   7.48543405  7.52594561  7.56433095\n",
            "  7.60063883  7.63492333  7.6672451   7.69767271  7.72628554  7.75317731\n",
            "  7.77846047  7.80227113  7.82477455  7.84617256  7.86671444  7.88671253\n",
            "  7.90656452  7.92678564  7.94805629  7.97129465  7.99777061  8.02929118\n",
            "  8.06851277  8.11949058  8.18866841  8.28662812  8.43060665  8.64417196\n",
            "  8.93853069  9.27901383  9.61238314  9.91457134 10.18358406 10.42368055\n",
            " 10.63959673 10.835287   11.01385371 11.17770828 11.32874034 11.46845088\n",
            " 11.59804898]\n",
            "[2.25273804 2.44473451 2.63446689 2.82183065 3.00672782 3.18906966\n",
            " 3.36877888 3.54579165 3.7200594  3.89155069 4.06025363 4.22617516\n",
            " 4.38931486 4.54966934 4.70724891 4.86207658 5.01418541 5.16361574\n",
            " 5.31041281 5.4546247  5.59630089 5.735491   5.87224404 6.00660779\n",
            " 6.13862848 6.26835056 6.39581665 6.52106744 6.64414183 6.76507693\n",
            " 6.88390817 7.00066939 7.11539292 7.22810963 7.33884893 7.44763883\n",
            " 7.5545058  7.65947479 7.76256902 7.86380989 7.96321677 8.06080689\n",
            " 8.15659509 8.25059372 8.3428125  8.4332584  8.52193561 8.60884559\n",
            " 8.69398747]\n",
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            " -8.55216578 -8.63344089 -8.71401853 -8.79390552 -8.87310848 -8.95163388\n",
            " -9.02948802]\n",
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            " -6.70210202 -6.63039241 -6.55032246 -6.46125417 -6.36259555 -6.25390577\n",
            " -6.13496469]\n",
            "[3.31077507 3.38487073 3.45799916 3.53012406 3.60120873 3.6712157\n",
            " 3.74010642 3.80784102 3.87437813 3.93967483 4.0036866  4.06636738\n",
            " 4.12766975 4.18754515 4.24594421 4.30281711 4.35811406 4.41178575\n",
            " 4.46378385 4.51406144 4.56257338 4.60927656 4.65412982 4.6970935\n",
            " 4.73812982 4.77720355 4.81428154 4.84933795 4.8823533  4.91330407\n",
            " 4.94216452 4.96890438 4.99349005 5.01588765 5.03606399 5.05398704\n",
            " 5.06962609 5.08295189 5.0939366  5.10255379 5.10877826 5.11258588\n",
            " 5.11395343 5.11285831 5.10927827 5.10319117 5.09457458 5.08340552\n",
            " 5.06966007]\n",
            "[ 2.966328    3.09021039  3.19469349  3.30558373  3.50233427  3.57568078\n",
            "  3.59616083  3.60481209  3.62673054  3.78965951  3.83752226  3.79601742\n",
            "  3.6999069   3.54382411  3.29802805  3.19717993  3.28134392  3.34901291\n",
            "  3.39294756  3.41419632  3.41404222  3.39335887  3.35256234  3.29161144\n",
            "  3.20998551  3.10667333  2.98035307  2.82736947  2.63590849  2.34893748\n",
            "  1.93991796  1.66671476  1.41307359  1.15608188  0.88047613  0.5652173\n",
            "  0.15346653 -0.03826282 -0.19358935 -0.34152382 -0.48413926 -0.62089216\n",
            " -0.75306448 -0.88948337 -1.01700259 -0.93302903 -0.77900863 -0.59853248\n",
            " -0.4120473 ]\n",
            "[5.20208619 5.16699901 5.13092522 5.09391277 5.05601694 5.01730171\n",
            " 4.97784131 4.93772205 4.89704434 4.85592511 4.81450067 4.7729313\n",
            " 4.73141    4.69016468 4.64945074 4.60955155 4.57077986 4.5334767\n",
            " 4.49800736 4.46475505 4.4341146  4.40649443 4.38237864 4.36254391\n",
            " 4.34790096 4.33934534 4.3378085  4.34433777 4.3602621  4.38761003\n",
            " 4.43044411 4.50225179 4.7024268  4.86644676 5.00252746 5.12896933\n",
            " 5.25132239 5.37200756 5.4922077  5.61248136 5.73299742 5.85369763\n",
            " 5.97448959 6.09527681 6.21593795 6.33632457 6.45626559 6.57557276\n",
            " 6.6940457 ]\n",
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            " -3.83358099 -3.88578873 -3.93744065 -3.98727325 -4.03131252 -4.0514834\n",
            " -3.95098938]\n",
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            " -0.56063783 -0.435091   -0.3074332  -0.17767212 -0.04581378  0.08813789\n",
            "  0.22418187  0.36232167  0.50256723  0.6449331   0.78943937  0.93611356\n",
            "  1.0849892   1.23611053  1.38953575  1.5453404   1.70362061  1.86449148\n",
            "  2.02809204  2.19459415  2.36420891  2.53719538  2.71387536  2.89465707\n",
            "  3.08006391  3.27077691  3.46771045  3.67214813  3.88600787  4.11242397\n",
            "  4.35726857  4.63469422  5.01417637  5.21939858  5.3556472   5.47780832\n",
            "  5.59308419  5.70429881  5.81295773  5.9199981   6.02604796  6.13153273\n",
            "  6.2367248 ]\n",
            "[3.75009436 3.86111348 3.97561286 4.09381311 4.2159725  4.34239612\n",
            " 4.47344827 4.60956987 4.7513032  4.89932864 5.05452108 5.21804119\n",
            " 5.39149261 5.57721601 5.77890598 6.00314669 6.26452915 6.61996396\n",
            " 6.95736471 7.11753976 7.24581294 7.35861376 7.46162415 7.55762367\n",
            " 7.64822339 7.73445864 7.81704291 7.89649346 7.97319998 8.04746497\n",
            " 8.11952904 8.18958737 8.25780092 8.32430429 8.38921134 8.45261939\n",
            " 8.51461231 8.57526294 8.63463488 8.692784   8.74975952 8.80560505\n",
            " 8.86035937 8.91405704 8.96672889 9.01840249 9.06910244 9.11885072\n",
            " 9.16766688]\n",
            "[-4.81727345 -4.81497444 -4.81200173 -4.80835247 -4.80402416 -4.79901471\n",
            " -4.7933224  -4.78694588 -4.77988411 -4.77213636 -4.7637022  -4.75458149\n",
            " -4.74477439 -4.73428128 -4.72310287 -4.71124006 -4.69869405 -4.68546623\n",
            " -4.67155827 -4.65697204 -4.64170963 -4.62577334 -4.60916568 -4.59188938\n",
            " -4.57394734 -4.55534265 -4.53607861 -4.51615867 -4.49558647 -4.47436582\n",
            " -4.45250067 -4.42999517 -4.40685361 -4.38308053 -4.3586807  -4.33365904\n",
            " -4.3080206  -4.28177054 -4.25491415 -4.22745678 -4.19940391 -4.17076108\n",
            " -4.14153392 -4.11172814 -4.08134951 -4.05040386 -4.01889709 -3.98683516\n",
            " -3.95422408]\n",
            "[6.24438928 6.31032303 6.30592631 6.23551565 6.06975573 5.65167006\n",
            " 5.43748593 5.27886823 5.1147138  4.81994444 4.59416613 4.47045747\n",
            " 4.39326741 4.34924512 4.33306809 4.34270873 4.37834228 4.44265253\n",
            " 4.54342597 4.71228246 4.87477642 4.95396423 5.02349946 5.09245836\n",
            " 5.16493882 5.24417499 5.33408028 5.44140963 5.58225217 5.9222999\n",
            " 6.28145044 6.51252705 6.67734998 6.77922888 6.71846629 6.69869185\n",
            " 6.75169896 6.85776758 7.04684654 7.36200494 7.49728303 7.55416419\n",
            " 7.54422101 7.42133696 7.37373827 7.40854264 7.4679226  7.54715365\n",
            " 7.5932402 ]\n",
            "[3.90452309 4.0130235  4.06878114 4.08446718 4.08534333 4.08657251\n",
            " 4.10088378 4.13912312 4.20561046 4.28083936 4.28537266 4.08066419\n",
            " 3.70602618 3.3523022  3.25343278 3.2189145  3.16101272 3.06612996\n",
            " 2.8976149  2.58266687 2.47403434 2.43772786 2.42891423 2.42598609\n",
            " 2.29284765 2.05202417 1.9283233  1.87803603 1.89804514 2.01179315\n",
            " 1.92891602 1.78250706 1.66478599 1.59043817 1.60475075 1.77759082\n",
            " 1.75992187 1.67274314 1.55063968 1.41102598 1.4237623  1.52434039\n",
            " 1.53384023 1.47517823 1.34128721 1.09260371 1.06293324 1.08717095\n",
            " 1.09923435]\n",
            "[6.60838661 6.5928904  6.51736413 6.39529875 6.21329805 6.04653584\n",
            " 6.03573069 5.99505722 5.90344451 5.73218801 5.6461917  5.68954659\n",
            " 5.75018883 5.80608951 5.84978973 5.87707968 5.88446015 5.86784723\n",
            " 5.82082741 5.72959798 5.54203761 5.27107803 5.12684647 4.98352219\n",
            " 4.73523532 4.34724937 4.03135768 3.79773662 3.64653724 3.54806415\n",
            " 3.4510605  3.50241728 3.72237587 4.00952399 4.24515974 4.47595066\n",
            " 4.75299409 4.93844152 5.07961565 5.18734905 5.26609278 5.31755341\n",
            " 5.34149159 5.33540371 5.29255206 5.19205704 4.94112461 4.8135722\n",
            " 4.73773316]\n",
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            " -5.95104339 -5.89412936 -5.84218482 -5.79530286 -5.75382351 -5.7183927\n",
            " -5.69009618 -5.67074826 -5.6635763  -5.67519181 -5.72356985 -5.88654424\n",
            " -6.06560277 -6.1384778  -6.15324507 -6.03765427 -5.80684252 -5.55683879\n",
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            " -2.96808071 -3.18183921 -3.42593525 -3.65155992 -3.91499308 -4.18231171\n",
            " -4.17440364]\n",
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            "  0.26409156  0.31814682  0.3554064   0.38408494  0.40801806  0.42923126\n",
            "  0.44888786]\n",
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            " -3.2004979  -2.95057555 -2.7058608  -2.40135667 -2.15547014 -1.99843097\n",
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            "  0.14088276  0.26409134  0.37512366  0.47385325  0.56023461  0.63428045\n",
            "  0.6960736   0.7458167   0.78395338  0.81141283  0.8300669   0.84352072\n",
            "  0.85826411]\n",
            "[2.19043175 2.24808976 2.30623385 2.36484247 2.42389313 2.48336236\n",
            " 2.54322571 2.60345766 2.66403167 2.72492012 2.78609431 2.84752447\n",
            " 2.90917976 2.97102831 3.03303721 3.09517259 3.15739963 3.21968267\n",
            " 3.28198525 3.34427021 3.40649979 3.46863576 3.53063949 3.59247218\n",
            " 3.6540949  3.71546882 3.77655533 3.83731625 3.89771391 3.95771142\n",
            " 4.01727277 4.07636302 4.13494844 4.19299665 4.25047682 4.30735968\n",
            " 4.36361776 4.4192254  4.47415886 4.52839641 4.58191834 4.63470706\n",
            " 4.68674709 4.7380251  4.78852989 4.83825247 4.887186   4.93532587\n",
            " 4.9826697 ]\n",
            "[3.78633681 3.56260832 3.4584108  3.60254109 3.68715455 3.28082956\n",
            " 3.01090686 3.14948716 2.98331888 2.8444476  2.95229871 2.74112322\n",
            " 2.64072286 2.84789387 2.75979127 2.76528093 3.07383971 3.04024009\n",
            " 3.02601574 3.29350695 3.18361794 3.12574274 3.1735187  3.31893721\n",
            " 3.59006941 4.06326072 4.20033584 4.16431988 4.0200107  3.85766917\n",
            " 3.77775856 3.76046101 3.82317284 3.76748857 3.73381186 3.70304702\n",
            " 3.66838585 3.62364959 3.55350596 3.54653846 3.63894286 3.76718081\n",
            " 3.80587856 3.962919   4.10136142 4.18024473 4.19310983 4.12319746\n",
            " 3.8290083 ]\n",
            "[3.17286302 3.27473059 3.37718025 3.48028347 3.58411971 3.68877656\n",
            " 3.79435163 3.90095557 4.00871452 4.11777284 4.22829642 4.34047679\n",
            " 4.45453633 4.57073554 4.68938209 4.8108422  4.93555516 5.06405561\n",
            " 5.19700577 5.33524264 5.47984944 5.63226951 5.7945015  5.96945655\n",
            " 6.16168095 6.37902655 6.63716426 6.95881464 7.02718011 6.93076492\n",
            " 6.82568446 6.72292514 6.62364701 6.52764829 6.43451078 6.34383096\n",
            " 6.25527017 6.16856091 6.08350277 5.99995687 5.91784092 5.83712309\n",
            " 5.75781824 5.67998721 5.60373358 5.52919848 5.45655242 5.38598155\n",
            " 5.31766511]\n",
            "[ 2.18040808  2.08914966  1.99766093  1.90594877  1.81402011  1.72188197\n",
            "  1.62954147  1.53700575  1.44428205  1.35137765  1.25829992  1.16505626\n",
            "  1.07165412  0.97810101  0.88440449  0.79057215  0.6966116   0.60253051\n",
            "  0.50833655  0.41403742  0.31964085  0.22515456  0.13058627  0.03594373\n",
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            " -0.6279867  -0.72294693 -0.8179128  -0.91287689 -1.00783185 -1.10277039\n",
            " -1.19768529 -1.29256944 -1.38741578 -1.48221735 -1.57696731 -1.67165888\n",
            " -1.76628542 -1.8608404  -1.9553174  -2.04971015 -2.14401249 -2.23821843\n",
            " -2.33232214]\n",
            "[-3.16926265 -3.08921028 -3.0006543  -2.90247835 -2.79311016 -2.67023178\n",
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            "  0.03810316  0.04548807  0.0469965   0.04272259  0.03275619  0.01718559\n",
            " -0.00390249]\n",
            "[2.14216855 2.31312158 2.48143128 2.64712988 2.81023745 2.9707693\n",
            " 3.12873485 3.28413433 3.43695944 3.58719736 3.7348285  3.87983235\n",
            " 4.02218852 4.16187304 4.29886933 4.43316775 4.56474618 4.69356486\n",
            " 4.81958138 4.94275869 5.06306619 5.18047581 5.29496129 5.40649774\n",
            " 5.51506049 5.62062336 5.72315903 5.82263761 5.91902527 6.01228151\n",
            " 6.10235816 6.18919993 6.27273579 6.35287139 6.42948634 6.50242663\n",
            " 6.57149272 6.63647681 6.6971842  6.75342816 6.80503042 6.8518008\n",
            " 6.89356319 6.93014216 6.96135177 6.9870096  7.00690213 7.0208335\n",
            " 7.02864091]\n",
            "[2.26125085 2.36921358 2.47963099 2.59333866 2.71168954 2.83719039\n",
            " 2.97582443 3.16131457 3.34579895 3.47641368 3.59122912 3.69630245\n",
            " 3.79411248 3.88597926 3.97270871 4.05483677 4.13274134 4.20669951\n",
            " 4.27691986 4.34356549 4.40676759 4.46663427 4.52325667 4.57671372\n",
            " 4.62707474 4.67440146 4.71874992 4.76017191 4.79871611 4.83442879\n",
            " 4.86735404 4.89753453 4.92501193 4.94982705 4.9720198  4.99162899\n",
            " 5.00869217 5.02324547 5.0353234  5.04495887 5.05218307 5.05702556\n",
            " 5.0595142  5.05967491 5.05753025 5.05309911 5.04640078 5.03745459\n",
            " 5.0262794 ]\n",
            "[4.69067104 4.78885269 4.8871012  4.98539294 5.08370438 5.18201215\n",
            " 5.28029291 5.37852344 5.47668054 5.57474105 5.67268182 5.77047964\n",
            " 5.86811129 5.96555344 6.06278264 6.15977527 6.25650753 6.35295535\n",
            " 6.44909435 6.54489973 6.6403462  6.73540788 6.83005816 6.9242695\n",
            " 7.01801323 7.11125919 7.20397537 7.29612741 7.38767843 7.47859148\n",
            " 7.56883525 7.65838253 7.74720529 7.8352733  7.92255377 8.00901173\n",
            " 8.09461057 8.17931239 8.26307825 8.34586839 8.42764239 8.50835938\n",
            " 8.58797814 8.66645728 8.74375536 8.81983102 8.89464313 8.96815093\n",
            " 9.04031405]\n",
            "[-1.13457265 -1.47215456 -1.6528145  -1.75216878 -1.80808316 -1.82912236\n",
            " -1.81988843 -1.78609302 -1.73893864 -1.71452317 -1.69332653 -1.78190417\n",
            " -1.86508249 -1.82046251 -1.65315597 -1.37041194 -0.82883092 -0.21718495\n",
            "  0.23368173  0.63557119  1.0145042   1.38071324  1.7400398   2.09627263\n",
            "  2.45149674  2.80509971  3.15345626  3.49228989  3.81960123  4.13580575\n",
            "  4.44234752  4.74076065  5.03235994  5.31819881  5.59911536  5.87577973\n",
            "  6.1487207   6.41836098  6.68504832  6.94906962  7.21066548  7.47003965\n",
            "  7.72736296  7.98278391  8.23643585  8.48844169  8.73891442  8.98795323\n",
            "  9.23564443]\n",
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            " -4.62909352]\n",
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            " -4.03672646 -3.95310525 -3.84411278 -3.69394658 -3.4507384  -3.06960273\n",
            " -2.80067761]\n",
            "[5.86626484 5.77665776 5.67378279 5.55070803 5.38287817 5.14078073\n",
            " 5.00264768 4.89248032 4.79668644 4.71037402 4.63105918 4.55726145\n",
            " 4.48801214 4.42263891 4.36065705 4.30170914 4.24552923 4.19192036\n",
            " 4.14074012 4.09189116 4.04531504 4.0009885  3.95892182 3.91915835\n",
            " 3.88177575 3.84688893 3.81465538 3.78528344 3.75904507 3.73629558\n",
            " 3.71750495 3.7033096  3.69460353 3.69271278 3.69977312 3.7197177\n",
            " 3.76191222 3.88036804 4.02694072 4.08671106 4.11976918 4.13670987\n",
            " 4.14198067 4.13799343 4.12624013 4.10772046 4.08313972 4.05301267\n",
            " 4.01772265]\n",
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            " -5.38251292 -5.36046434 -5.34214691 -5.32727679 -5.31565271 -5.30714124\n",
            " -5.30166814 -5.29921418 -5.29981473 -5.30356305 -5.31061792 -5.32121702\n",
            " -5.335699   -5.35453977 -5.3784141  -5.40830716 -5.4457344  -5.49323973\n",
            " -5.5558069  -5.64708231 -5.8151103  -5.88427911 -5.91851409 -5.9372031\n",
            " -5.94563539 -5.94623164 -5.94034564 -5.92882032 -5.91221437 -5.89091078\n",
            " -5.86517476 -5.83518682 -5.80106236 -5.76286345 -5.72060526 -5.67425871\n",
            " -5.62374986]\n",
            "[3.82970479 3.75937453 3.68859841 3.61738711 3.54575148 3.47370253\n",
            " 3.40125146 3.32840958 3.25518837 3.1815994  3.10765434 3.03336479\n",
            " 2.95874216 2.88379783 2.80854332 2.7329901  2.65714938 2.58103219\n",
            " 2.5046496  2.42801254 2.3511317  2.27401769 2.19668129 2.11913344\n",
            " 2.04138509 1.96344716 1.88533049 1.80704587 1.72860394 1.65001529\n",
            " 1.57129037 1.49243956 1.41347316 1.33440136 1.25523432 1.17598211\n",
            " 1.0966548  1.0172624  0.93781489 0.85832207 0.77879344 0.69923836\n",
            " 0.61966616 0.54008607 0.46050721 0.38093851 0.30138871 0.22186633\n",
            " 0.14237969]\n",
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            "  0.40173074  0.46152577  0.49749024  0.49972671  0.55722523  0.63210598\n",
            "  0.71778335  0.81396434  0.92676343  1.01174237  1.08679003  1.16135298\n",
            "  1.23670803  1.31496425  1.42268045  1.52934546  1.62388863  1.70862259\n",
            "  1.78118885]\n",
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            " 3.4258242  3.53736476 3.67566281 3.85829089 4.15642839 4.5081191\n",
            " 4.62842073]\n",
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            " 5.0287641  5.11151918 5.194528   5.27778886 5.36129995 5.4450593\n",
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            "  1.42450213  1.48036433  1.54990877  1.63252224  1.72805237  1.83682614\n",
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            "  3.95258473  4.39031675  4.77743637  5.14295795  5.49578383  5.83980416\n",
            "  6.17722993]\n",
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            " 4.74899182 4.74637468 4.73807283 4.72355583 4.70216583 4.6730686\n",
            " 4.63517657 4.58702125 4.52652672 4.45056957 4.35401384 4.22721987\n",
            " 4.04971905 3.98007204 4.15027814 4.32863995 4.50093016 4.66659777\n",
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            " 5.68631421 5.81660678 5.94377282 6.06796488 6.18931491 6.30793782\n",
            " 6.42393434 6.53739324 6.64839311 6.7570038  6.86328763 6.96730034\n",
            " 7.06909187 7.16870709 7.26618632 7.36156583 7.45487825 7.54615292\n",
            " 7.63541616]\n",
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            "  1.70857034  1.77452383  1.84052182  1.90655506  1.97261425  2.03869003\n",
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            "  2.50088076  2.56674201  2.63253163  2.6982393   2.76385453  2.82936692\n",
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            " 5.88419504 5.71695832 5.65444182 5.64476359 5.66421356 5.69643848\n",
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            " 6.17359621 6.20164155 6.22806311 6.25273412 6.27555502 6.2964526\n",
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            " 6.38715629 6.3921393  6.39517083 6.3962557  6.39536187 6.39242855\n",
            " 6.38739084]\n",
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            " 1.03927488 1.0569391  1.07465877 1.09242851 1.1102433  1.12809846\n",
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            " -5.24068025]\n",
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            " 5.29674789 5.45949664 5.54752006 5.36563756 5.1873344  5.02055807\n",
            " 4.85423307 4.67764804 4.64405403 4.86959616 5.15047493 5.61346116\n",
            " 5.95555792 6.23376513 6.48129456 6.70604781 6.91159158 7.09987336\n",
            " 7.2719998  7.428545   7.56968794 7.69526879 7.8047964  7.89742108\n",
            " 7.97188509 8.02649052 8.05926101 8.06908993 8.06103146 8.05986417\n",
            " 8.09980673 8.17715933 8.27046027 8.3664662  8.45874316 8.54398192\n",
            " 8.6202362  8.68615748 8.74064226 8.782653   8.81111574 8.82485283\n",
            " 8.82252788]\n",
            "[1.36780202 1.43024933 1.49245845 1.55441157 1.61608993 1.6774717\n",
            " 1.73853446 1.79925627 1.85961578 1.91959213 1.97916492 2.03831418\n",
            " 2.0970202  2.15526357 2.21302499 2.27028519 2.3270247  2.38322312\n",
            " 2.43885753 2.49390451 2.54834154 2.60214614 2.6552955  2.70776629\n",
            " 2.75953457 2.81057566 2.86086411 2.91037363 2.95907701 3.00694615\n",
            " 3.05395203 3.10006479 3.14525435 3.18949391 3.23275977 3.27502799\n",
            " 3.31627374 3.35647106 3.39559278 3.43361034 3.47049384 3.50621196\n",
            " 3.54073199 3.57401993 3.60604052 3.63675742 3.66613328 3.69413\n",
            " 3.72070882]\n",
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            "  1.38755498  1.53951943  1.69633107  1.85848369  2.0265077   2.20097446\n",
            "  2.38250895  2.57179301  2.76957661  2.97667827  3.19391628  3.42190102\n",
            "  3.66100379  3.91131584  4.17261616  4.44444771  4.7262405   5.01726187\n",
            "  5.3162805   5.62125866  5.92958642  6.23880792  6.54688974  6.85194173\n",
            "  7.15261283  7.44816732  7.73829459  8.02291679  8.30206787  8.57583297\n",
            "  8.84432351  9.10767094  9.36603783  9.61965375  9.86876168 10.11358392\n",
            " 10.35432666 10.59118505 10.82434662 11.0539931  11.28030122 11.50344263\n",
            " 11.72358339]\n",
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            " -6.64416449]\n",
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            " 4.19738496 4.13628605 3.825897   3.55321291 3.38398065 3.26792935\n",
            " 3.08653896 2.90456422 2.75397351 2.76578716 2.72399429 2.62964137\n",
            " 2.33701047 2.06894928 1.9657992  1.99014975 2.27996349 2.36637117\n",
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            " 2.84241376 2.79206455 2.74286388 2.69481474 2.64792682 2.60221671\n",
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            " 4.30525015 4.2344583  4.16245567 4.08924398 4.01482309 3.93919283\n",
            " 3.86235475 3.78431206 3.70506911 3.62463106 3.54300338 3.46019124\n",
            " 3.37619913]\n",
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            " 5.21355592 5.27526021 5.33629135 5.3965894  5.45609141 5.51473106\n",
            " 5.57243828 5.6291388  5.68475366 5.73919874 5.79238408 5.8442133\n",
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            " 5.39499987]\n",
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            " 3.42493798 3.22092119 3.28081817 3.43330304 3.61326337 3.808088\n",
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            " 4.87350197 4.83740516 4.81106685 4.79264222 4.78083495 4.77469663\n",
            " 4.77351087 4.77672261 4.78389298 4.79466931 4.80876465 4.82594334\n",
            " 4.84601068 4.86880529 4.89419352 4.92206523 4.95233058 4.98491768\n",
            " 5.01977078]\n",
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            "  4.06462051  3.82262782  3.58627249  3.35181783  3.11588912  2.87553001\n",
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            "  1.93758056  1.94076709  1.96903787  2.07798127  2.31021214  2.54429606\n",
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            " -1.09982326]\n",
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            " 4.85047851 4.85272717 4.8251355  4.82258092 4.84653016 4.8976396\n",
            " 4.98207225 5.12106209 5.43030303 5.67134779 5.89764173 5.82162025\n",
            " 5.69108646 5.50509004 5.31423897 5.0222987  4.80403516 4.70629415\n",
            " 4.66295408 4.65914249 4.69038213 4.75360059 4.84804193 4.9764337\n",
            " 5.14691799 5.3804348  5.76239524 6.24237568 6.59880058 6.77569441\n",
            " 6.87363541 6.90790917 6.87613133 6.74590807 6.37738216 6.21432986\n",
            " 6.1264509 ]\n",
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            " 6.26546705 6.26770162 6.2703195  6.27325152 6.27644914 6.2798819\n",
            " 6.2835358  6.28741275 6.29153062 6.29592417 6.30064688 6.3057738\n",
            " 6.3114059  6.31767663 6.32476169 6.33289407 6.34238819 6.35368043\n",
            " 6.36740233 6.38452599 6.40669558 6.43716614 6.48484419 6.6279363\n",
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            " 7.07887744]\n",
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            " -4.2553564  -4.20952502 -4.16021037 -4.10732339 -4.05074871 -3.99034004\n",
            " -3.92591385]\n",
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            " 4.90402015 4.82192026 4.75349527 4.68745654 4.62097199 4.55370177\n",
            " 4.487551   4.42294842 4.3595557  4.29778638 4.2890854  4.34508728\n",
            " 4.37967695 4.40413626 4.42170784 4.43357639 4.44005282 4.44094237\n",
            " 4.43565408 4.42317444 4.40190133 4.36921398 4.32030511 4.24393868\n",
            " 4.08417194 3.70554358 3.4911461  3.33867944 3.22546591 3.14265585\n",
            " 3.08597954 3.05339583 3.0442998  3.05931707 3.10049454 3.17205997\n",
            " 3.2826722 ]\n",
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            " 2.14734569 2.14914347 2.16026445 2.17898756 2.20407702 2.23461437\n",
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            " 2.55683462 2.61457571 2.67465516 2.73694861 2.80135023 2.86777129\n",
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            " 3.38447554]\n",
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            " -2.3070166 ]\n",
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            " 3.4080801  3.43249432 3.44760592 3.45442891 3.45374785 3.44618428\n",
            " 3.43223985 3.41232536 3.38678105 3.35589106 3.31989408 3.27899121\n",
            " 3.23335189 3.18311844 3.12840946 3.06932243 3.0059356  2.9383094\n",
            " 2.86648729]\n",
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            " 4.9185631  4.89096329 4.81750843 4.69044434 4.50014165 4.25215261\n",
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            " 2.34804636 2.20734886 2.02921057 1.79129262 1.43652597 1.13873736\n",
            " 1.1069852 ]\n",
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            " 2.81991428 2.84499401 2.86676815 2.8852244  2.90034377 2.91209241\n",
            " 2.92042277 2.92528174 2.92660989 2.92433989 2.9183954  2.90868998\n",
            " 2.89512667]\n",
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            " 3.42868985 3.24885341 3.06408288 2.87532564 2.68440762 2.49347159\n",
            " 2.30417978 2.11843801 1.93883864 1.76768622 1.60622807 1.45504652\n",
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            "  0.7339412   0.79431923  0.86795905  0.96091361  1.08369993  1.25803625\n",
            "  1.52548373  1.58031959  1.43860306  1.26905371  1.09166893  0.91086389\n",
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            " -1.35513666]\n",
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            "  0.32633077  0.4470331   0.57658883  0.71632287  0.8677856   1.03270606\n",
            "  1.21280214  1.40928949  1.62190127  1.84751413  2.07940198  2.30878194\n",
            "  2.52833546  2.73463417  2.9278857   3.11041722  3.28542636  3.45635389\n",
            "  3.62666864  3.79978046  3.97871334  4.16469662  4.35290276  4.52624986\n",
            "  4.6621435 ]\n",
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            " -6.67959477]\n",
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            "  0.13379992  0.60391836  1.08572146  1.53044927  1.92754202  2.28532088\n",
            "  2.61221908  2.91412402  3.19563329  3.46028079  3.71079844  3.94943078\n",
            "  4.17807192  4.39827971  4.61126805  4.81794754  5.01899825  5.21493741\n",
            "  5.40616899  5.59301707  5.77574787  5.95458438  6.12971639  6.3013075\n",
            "  6.46950015  6.63441935  6.7961755   6.95486653  7.11057964  7.26339265\n",
            "  7.41337509  7.56058911  7.70509029  7.84692818  7.98614681  8.12278492\n",
            "  8.2568763 ]\n",
            "[6.16297833 6.24552094 6.32726196 6.4083527  6.48916983 6.57197458\n",
            " 6.65148852 6.72813412 6.80304523 6.87646875 6.9485092  7.01922006\n",
            " 7.08863037 7.15675497 7.22359924 7.28916225 7.35343817 7.41641706\n",
            " 7.47808535 7.53842608 7.59741841 7.65503728 7.71125216 7.76602545\n",
            " 7.8193107  7.87104907 7.92116287 7.9695427  8.01601923 8.06029155\n",
            " 8.1016894  8.13762482 8.13851127 8.14514371 8.15874286 8.17557463\n",
            " 8.19426809 8.21411964 8.23470923 8.25576499 8.27710274 8.29859423\n",
            " 8.32015009 8.34171108 8.36324392 8.38473828 8.40620521 8.4276779\n",
            " 8.44921392]\n",
            "[4.1446361  4.15849421 3.97321351 4.01011544 4.2288466  4.56086195\n",
            " 4.93538719 5.21948677 5.41170225 5.540547   5.57263376 5.47025515\n",
            " 5.28705977 5.35426194 5.56400166 5.78236526 5.95789656 6.04909944\n",
            " 5.99325645 5.68565485 5.2629172  5.40248564 5.42076696 5.35005525\n",
            " 5.23233598 5.07177925 4.8127644  4.45386884 4.16573355 3.93787162\n",
            " 3.75684925 3.61407912 3.50257834 3.41819907 3.35817987 3.32010477\n",
            " 3.30148544 3.29964062 3.31181007 3.33550768 3.36870212 3.40982423\n",
            " 3.4576899  3.51139926 3.57025301 3.63368704 3.70124288 3.77259217\n",
            " 3.8475466 ]\n",
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            " -8.6929606  -8.70577472 -8.71788656 -8.72928938 -8.73997622 -8.74994004\n",
            " -8.75917355]\n",
            "[5.11900542 5.11457635 5.10832094 5.09998037 5.08913792 5.07503845\n",
            " 5.05595405 5.02396953 4.77131925 4.41935665 4.1729552  4.00757679\n",
            " 3.89998073 3.83542027 3.80519365 3.80390967 3.82793584 3.87434239\n",
            " 3.93954354 4.01550985 4.07191885 3.97810313 3.71114779 3.47544614\n",
            " 3.29483558 3.1601196  3.06417621 3.00269127 2.9733411  2.97529898\n",
            " 3.00902708 3.07618608 3.17902287 3.31398086 3.40602542 3.26687251\n",
            " 3.05652996 2.86549422 2.70341388 2.57048513 2.46627847 2.39106419\n",
            " 2.34618541 2.33443799 2.36078261 2.43374836 2.56732649 2.76144463\n",
            " 2.82117633]\n",
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            " -3.90039638]\n",
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            "  0.75788748  0.87830248  0.99648022  1.11190385  1.22451588  1.33450597\n",
            "  1.44217202  1.54784403  1.65184717  1.75448595  1.85603886  1.95675778\n",
            "  2.05686928  2.1565765   2.25606092  2.35548377  2.45498297  2.55464799\n",
            "  2.65451798  2.75459345  2.85483538  2.95516039  3.05543331  3.15545643\n",
            "  3.2549538   3.35354734  3.45071762  3.5457339   3.63751522  3.72431135\n",
            "  3.80278254  3.86382657  3.8689945   3.93686331  4.02781707  4.12830731\n",
            "  4.23450211]\n",
            "[5.68490364 5.64693518 5.60854673 5.56976337 5.53061482 5.49113769\n",
            " 5.45136938 5.4113469  5.37110647 5.3306831  5.29011042 5.24942077\n",
            " 5.20864611 5.16782301 5.12699423 5.08620438 5.04549972 5.00492803\n",
            " 4.96453841 4.92438117 4.88450793 4.8449724  4.80583444 4.76716049\n",
            " 4.72902183 4.69149586 4.65466669 4.61862538 4.58346989 4.54930495\n",
            " 4.51624285 4.48440784 4.45393723 4.4249842  4.39772318 4.37235339\n",
            " 4.34910147 4.32822332 4.31000512 4.29476532 4.2828726  4.27477433\n",
            " 4.27101148 4.27224674 4.27929441 4.29314744 4.31499215 4.3461949\n",
            " 4.38825865]\n",
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            "  2.16945926  2.25360269  2.26521912  2.2376957   2.18136898  2.10023688\n",
            "  1.99553778  1.86651125  1.70997509  1.51800765  1.26789645  1.14886012\n",
            "  1.18303139]\n",
            "[6.94520965 6.95130693 6.9540037  6.95327762 6.94910115 6.9414418\n",
            " 6.93026194 6.91551832 6.89716168 6.87513614 6.84937851 6.81981747\n",
            " 6.7863726  6.74895317 6.70745671 6.66176721 6.61175292 6.55726361\n",
            " 6.49812705 6.43414455 6.365085   6.2906771  6.21059854 6.12446105\n",
            " 6.03178858 5.93198466 5.82428096 5.70765128 5.58065669 5.44113503\n",
            " 5.28547709 5.10646307 4.8831557  4.8348198  4.86238452 4.87539292\n",
            " 4.87600531 4.86525965 4.84334156 4.80973617 4.76310425 4.70075437\n",
            " 4.61698749 4.49594429 4.24372528 4.08429417 3.98846224 3.90808176\n",
            " 3.83355136]\n",
            "[4.05777992 4.04604565 4.02674583 3.99851344 3.95874469 3.93721407\n",
            " 4.07893945 4.19397245 4.29161957 4.37571372 4.44782629 4.50851608\n",
            " 4.55768347 4.59461838 4.61783898 4.62461974 4.60975844 4.56169334\n",
            " 4.44355655 4.20930418 4.15034091 4.15045431 4.17133806 4.20254977\n",
            " 4.23954825 4.27987732 4.32202793 4.36499274 4.40805972 4.45070441\n",
            " 4.49252854 4.53322243 4.57254064 4.61028536 4.64629412 4.68043046\n",
            " 4.71257759 4.74263283 4.77050273 4.79609847 4.81933114 4.84010649\n",
            " 4.85831828 4.87383921 4.88650714 4.89610247 4.90230754 4.90462581\n",
            " 4.90219843]\n",
            "[6.32778007 6.29041881 6.2462616  6.19472811 6.1350816  6.06636653\n",
            " 5.9873079  5.89613886 5.79028248 5.66570004 5.51535436 5.324803\n",
            " 5.05888101 4.7815096  4.65256751 4.56540371 4.48778903 4.41011675\n",
            " 4.32784196 4.23786376 4.13723755 4.02225905 3.88700045 3.71898904\n",
            " 3.47205699 3.32954776 3.32483387 3.31146164 3.2479919  3.12639108\n",
            " 2.976583   2.81834622 2.65886562 2.50048288 2.34387933 2.1891494\n",
            " 2.0361844  1.88485118 1.73511186 1.58696462 1.44041028 1.29544975\n",
            " 1.15208547 1.01032349 0.87017542 0.73165992 0.59480382 0.45964276\n",
            " 0.32622159]\n",
            "[4.96486304 4.99780609 5.03716257 5.10796659 5.26220052 5.37265825\n",
            " 5.47070883 5.56541857 5.66390313 5.77737154 6.01952744 6.1563836\n",
            " 6.25541752 6.33520795 6.40176333 6.45791254 6.50514199 6.54426289\n",
            " 6.57587215 6.60054265 6.61879677 6.63103078 6.63752312 6.6384335\n",
            " 6.63381298 6.62362973 6.6077892  6.5861458  6.55849888 6.52459783\n",
            " 6.48414599 6.43680449 6.38219234 6.31993039 6.24976031 6.17179846\n",
            " 6.08713951 5.99925187 5.91681592 5.85677888 5.83954904 5.8724538\n",
            " 5.94527628 6.04343069 6.15615681 6.27688434 6.40175601 6.5284807\n",
            " 6.65566336]\n",
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            " -0.27185078 -0.31945842 -0.42004838 -0.71613864 -0.88922681 -0.96203108\n",
            " -0.99419587]\n",
            "[3.47712323 3.42360175 3.37102332 3.31945366 3.26896421 3.219633\n",
            " 3.17154557 3.12479637 3.07949168 3.03575146 2.99371173 2.95352814\n",
            " 2.91537976 2.87947541 2.84606629 2.81546101 2.78804776 2.7643295\n",
            " 2.74498322 2.73096715 2.72373567 2.72573806 2.74188769 2.78665628\n",
            " 2.90523334 2.93006523 2.92665639 2.91073826 2.88699021 2.85762899\n",
            " 2.82393435 2.78673547 2.74661276 2.70399601 2.65921757 2.61254356\n",
            " 2.56419338 2.51435245 2.46318086 2.41081956 2.35739469 2.30302088\n",
            " 2.24780368 2.19184144 2.13522674 2.07804768 2.02038937 1.96233551\n",
            " 1.9039693 ]\n",
            "[2.52807471 2.62003602 2.71661692 2.81810891 2.92486435 3.03731509\n",
            " 3.15599982 3.2816062  3.41503899 3.55753796 3.71090056 3.8779587\n",
            " 4.06381404 4.28035506 4.59631891 4.98991629 5.26431394 5.50762117\n",
            " 5.73053757 5.93429997 6.11940657 6.28782674 6.44129098 6.58016246\n",
            " 6.70427374 6.81305689 6.9055296  6.98035288 7.03602451 7.0711899\n",
            " 7.08512539 7.0783785  7.0517445  7.006358   6.94400479 6.86700569\n",
            " 6.77791304 6.67918953 6.57297658 6.46098288 6.34447105 6.22430383\n",
            " 6.10101815 5.97490914 5.846119   5.71473405 5.58089805 5.44494738\n",
            " 5.30755786]\n",
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            " -6.21527369 -6.22542627 -6.23315062 -6.23825079 -6.24050225 -6.2396468\n",
            " -6.23538593 -6.22737289 -6.21520306 -6.19840242 -6.17641356 -6.14857931\n",
            " -6.1141232  -6.07212803 -6.02151373 -5.96101887 -5.88919358 -5.80441921\n",
            " -5.70497948]\n",
            "[-4.07347168 -4.04936588 -4.14877773 -4.23788248 -4.2127245  -4.11128981\n",
            " -3.85666494 -3.71765067 -3.66742103 -3.70957499 -3.87547083 -3.8821608\n",
            " -3.8053738  -3.59006056 -3.36926455 -3.28914856 -3.2957524  -3.47564839\n",
            " -3.53066019 -3.48390807 -3.33799058 -3.05025681 -2.93676838 -2.91489422\n",
            " -3.03179216 -3.18095276 -3.17263736 -3.07605811 -2.80158368 -2.64630708\n",
            " -2.60316349 -2.66140698 -2.88029725 -2.9289432  -2.88801321 -2.72512462\n",
            " -2.48848318 -2.43471945 -2.48073149 -2.72603046 -2.89866627 -2.94972714\n",
            " -2.90677869 -2.65066965 -2.5930805  -2.68227193 -2.94771984 -3.44820195\n",
            " -3.69096409]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PW0UfPn6tHgJ",
        "outputId": "a845ed6a-1c3d-4af3-c878-c8f1d044cbfd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-12-ae4060bd0b28>:35: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  edge_attr_tensor = torch.tensor(relative_locations, dtype=torch.float)  # Use relative_locations as edge attribute\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[8, 5], edge_index=[2, 8], edge_attr=[8], y=[8, 2]),\n",
              " Data(x=[5, 5], edge_index=[2, 5], edge_attr=[5], y=[5, 2]),\n",
              " Data(x=[9, 5], edge_index=[2, 9], edge_attr=[9], y=[9, 2]),\n",
              " Data(x=[4, 5], edge_index=[2, 4], edge_attr=[4], y=[4, 2]),\n",
              " Data(x=[7, 5], edge_index=[2, 7], edge_attr=[7], y=[7, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2]),\n",
              " Data(x=[6, 5], edge_index=[2, 6], edge_attr=[6], y=[6, 2])]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# add relative locations as a feature because the gravitational dynamics depend only in relative location\n",
        "'''import torch\n",
        "from torch_geometric.data import Data\n",
        "\n",
        "def calculate_distance(x1, y1, x2, y2):\n",
        "    return torch.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)\n",
        "\n",
        "def get_data_object(x, y):\n",
        "    data_list = []\n",
        "    for i in range(len(x)):\n",
        "        x_tensor = torch.tensor(x[i])\n",
        "        y_tensor = torch.tensor(y[i])\n",
        "\n",
        "        num_nodes = x_tensor.shape[0]\n",
        "        positions = x_tensor[:, 1:3]  # Extract positions (x, y) from initial state tensor\n",
        "\n",
        "        relative_locations = torch.zeros(num_nodes, dtype=torch.float)  # Initialize tensor for relative locations\n",
        "\n",
        "        for j in range(num_nodes):\n",
        "            distances = []\n",
        "            for k in range(num_nodes):\n",
        "                if j != k:\n",
        "                    x1, y1 = positions[j]\n",
        "                    x2, y2 = positions[k]\n",
        "                    distance = calculate_distance(x1, y1, x2, y2)\n",
        "                    distances.append(distance)\n",
        "            distances = torch.stack(distances)\n",
        "            relative_locations[j] = torch.mean(distances)\n",
        "\n",
        "\n",
        "        edge_index_tensor = torch.tensor([\n",
        "            [j for j in range(num_nodes)],\n",
        "            [j for j in range(num_nodes)]\n",
        "        ], dtype=torch.long)\n",
        "\n",
        "        edge_attr_tensor = torch.tensor(relative_locations, dtype=torch.float)  # Use relative_locations as edge attribute\n",
        "\n",
        "        data = Data(x=x_tensor, edge_index=edge_index_tensor, edge_attr=edge_attr_tensor, y=y_tensor)\n",
        "        data_list.append(data)\n",
        "    return data_list\n",
        "\n",
        "data_list = get_data_object(initial_state_train, remaining_trajectory_train)\n",
        "\n",
        "data_list = get_data_object(initial_state_train, remaining_trajectory_train)\n",
        "\n",
        "data_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IsMMG4lijZcB",
        "outputId": "fca4a1bc-f523-4393-f023-3bca2834557e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[8, 6], edge_index=[2, 8], y=[8, 2]),\n",
              " Data(x=[5, 6], edge_index=[2, 5], y=[5, 2]),\n",
              " Data(x=[9, 6], edge_index=[2, 9], y=[9, 2]),\n",
              " Data(x=[4, 6], edge_index=[2, 4], y=[4, 2]),\n",
              " Data(x=[7, 6], edge_index=[2, 7], y=[7, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2]),\n",
              " Data(x=[6, 6], edge_index=[2, 6], y=[6, 2])]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "import torch\n",
        "from torch_geometric.data import Data\n",
        "\n",
        "def calculate_distance(x1, y1, x2, y2):\n",
        "    return torch.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)\n",
        "\n",
        "def get_data_object(x, y):\n",
        "    data_list = []\n",
        "    for i in range(len(x)):\n",
        "        x_tensor = torch.tensor(x[i])\n",
        "        y_tensor = torch.tensor(y[i])\n",
        "\n",
        "        num_nodes = x_tensor.shape[0]\n",
        "        positions = x_tensor[:, 1:3]  # Extract positions (x, y) from initial state tensor\n",
        "\n",
        "        relative_locations = torch.zeros(num_nodes, dtype=torch.float)  # Initialize tensor for relative locations\n",
        "\n",
        "        for j in range(num_nodes):\n",
        "            distances = []\n",
        "            for k in range(num_nodes):\n",
        "                if j != k:\n",
        "                    x1, y1 = positions[j]\n",
        "                    x2, y2 = positions[k]\n",
        "                    distance = calculate_distance(x1, y1, x2, y2)\n",
        "                    distances.append(distance)\n",
        "            distances = torch.stack(distances)\n",
        "            relative_locations[j] = torch.mean(distances)\n",
        "\n",
        "        x_tensor = torch.cat([x_tensor, relative_locations.view(-1, 1)], dim=1)  # Add relative location feature\n",
        "\n",
        "        edge_index_tensor = torch.tensor([\n",
        "            [j for j in range(num_nodes)],\n",
        "            [j for j in range(num_nodes)]\n",
        "        ], dtype=torch.long)\n",
        "\n",
        "        data = Data(x=x_tensor, edge_index=edge_index_tensor, y=y_tensor)\n",
        "        data_list.append(data)\n",
        "    return data_list\n",
        "\n",
        "data_list = get_data_object(initial_state_train, remaining_trajectory_train)\n",
        "data_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x88KMSVdbboD"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from torch_geometric.loader import DataLoader\n",
        "\n",
        "\n",
        "train_data, val_data = train_test_split(data_list, test_size=0.2, random_state=42)\n",
        "train_loader = DataLoader(train_data, batch_size=30)\n",
        "val_loader = DataLoader(val_data, batch_size=30)\n",
        "##train_loader = DataLoader(data_list, batch_size=30)\n",
        "#train_loader"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = get_data_object(initial_state_test, remaining_trajectory_test)\n",
        "\n",
        "test_loader = DataLoader(test_data, batch_size=30)\n",
        "test_loader"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uew2RPApijQr",
        "outputId": "fcb8930d-dcfb-415b-b350-88db8fc15e43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch_geometric/deprecation.py:13: UserWarning: 'data.DataLoader' is deprecated, use 'loader.DataLoader' instead\n",
            "  warnings.warn(out)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch_geometric.loader.dataloader.DataLoader at 0x7fadab54f580>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Baselines\n",
        "import torch\n",
        "from torch_geometric.data import DataLoader\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "\n",
        "# Define your linear baseline model\n",
        "def LinearBaseline(x):\n",
        "      positions_x = x[:, 1] + x[:, 3] * 5\n",
        "      positions_y = x[:, 2] + x[:, 4] * 5.0\n",
        "      stacked_positions = torch.stack((positions_x, positions_y), dim=1)\n",
        "\n",
        "      return stacked_positions\n",
        "\n",
        "# Define your linear baseline model\n",
        "def ConstantBaseline(x):\n",
        "      positions_x = x[:, 1]\n",
        "      positions_y = x[:, 2]\n",
        "      stacked_positions = torch.stack((positions_x, positions_y), dim=1)\n",
        "\n",
        "      return stacked_positions\n",
        "# Create a data loader from the data_list\n",
        "#data_loader = DataLoader(data_list, batch_size=1, shuffle=True)\n",
        "\n",
        "# Create an optimizer\n",
        "def evaluate_linear_baseline(data_loader):\n",
        "    total_loss = 0\n",
        "\n",
        "    for data in data_loader:\n",
        "        x = data.x  # Input features\n",
        "        y = data.y  # Target labels\n",
        "        # print(y, 'Y TRUE')\n",
        "        out = LinearBaseline(x)\n",
        "        loss = mean_squared_error(out, y)\n",
        "        total_loss += loss\n",
        "        # print(loss)\n",
        "    avg_loss = total_loss / len(data_loader)\n",
        "    print(avg_loss)\n",
        "\n",
        "def evaluate_constant_baseline(data_loader):\n",
        "    total_loss = 0\n",
        "\n",
        "    for data in data_loader:\n",
        "        x = data.x  # Input features\n",
        "        y = data.y  # Target labels\n",
        "        # print(y, 'Y TRUE')\n",
        "        out = ConstantBaseline(x)\n",
        "        loss = mean_squared_error(out, y)\n",
        "        total_loss += loss\n",
        "        # print(loss)\n",
        "    avg_loss = total_loss / len(data_loader)\n",
        "    print(avg_loss,'Constant Baseline on Train Data')\n",
        "\n",
        "\n",
        "\n",
        "baseline_constant_train = evaluate_constant_baseline(train_loader)\n",
        "baseline_constant_val = evaluate_constant_baseline(val_loader)\n",
        "baseline_constant_test = evaluate_constant_baseline(test_loader)\n",
        "\n",
        "\n",
        "baseline_on_train_data = evaluate_linear_baseline(train_loader)\n",
        "baseline_on_validation_data = evaluate_linear_baseline(val_loader)\n",
        "baseline_on_test_data = evaluate_linear_baseline(test_loader)\n",
        "\n",
        "\n",
        "# Evaluation\n",
        "# avg_loss = evaluate(model, data_loader)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PnOjUcQYCEsB",
        "outputId": "b151e6bb-55e9-4e04-dbdf-71c139c9cf4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14.076583133353578 Constant Baseline on Train Data\n",
            "13.281135515064955 Constant Baseline on Train Data\n",
            "11.544181071746573 Constant Baseline on Train Data\n",
            "22.366187333384815\n",
            "21.168016585892353\n",
            "22.457220100054865\n",
            "22.457220100054865\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define the model names and corresponding losses\n",
        "model_names = ['NN Model', 'Linear Baseline', 'Constant Baseline']\n",
        "train_losses = [5.6339, 22.366187333384815, 14.076583133353578]\n",
        "val_losses = [5.3033, 21.168016585892353, 13.281135515064955]\n",
        "test_losses = [4.7995, 22.457220100054865, 11.544181071746573]\n",
        "\n",
        "# Set the bar width\n",
        "bar_width = 0.25\n",
        "\n",
        "# Set the positions of the bars on the x-axis\n",
        "r1 = range(len(model_names))\n",
        "r2 = [x + bar_width for x in r1]\n",
        "r3 = [x + bar_width for x in r2]\n",
        "\n",
        "# Create the bar chart\n",
        "plt.bar(r1, train_losses, color='b', width=bar_width, edgecolor='black', label='Train Loss')\n",
        "plt.bar(r2, val_losses, color='g', width=bar_width, edgecolor='black', label='Validation Loss')\n",
        "plt.bar(r3, test_losses, color='r', width=bar_width, edgecolor='black', label='Test Loss')\n",
        "\n",
        "# Add x-axis labels and tick labels\n",
        "plt.xlabel('Models')\n",
        "plt.ylabel('Loss')\n",
        "plt.xticks([r + bar_width for r in range(len(model_names))], model_names)\n",
        "\n",
        "# Add a legend\n",
        "plt.legend()\n",
        "\n",
        "# Display the bar chart\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "BumWO3epoLoF",
        "outputId": "0eaee2c2-00fd-4127-ef96-752533b20b27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oxj2dF5s6C2t",
        "outputId": "b4769f74-13f1-4ac9-9ad4-f93fc8b1277b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100, Training Loss: 10.2523\n",
            "Epoch 1/100, Validation Loss: 6.9555\n",
            "Epoch 2/100, Training Loss: 7.4685\n",
            "Epoch 2/100, Validation Loss: 6.4371\n",
            "Epoch 3/100, Training Loss: 7.2034\n",
            "Epoch 3/100, Validation Loss: 6.2925\n",
            "Epoch 4/100, Training Loss: 7.0094\n",
            "Epoch 4/100, Validation Loss: 6.2373\n",
            "Epoch 5/100, Training Loss: 6.8867\n",
            "Epoch 5/100, Validation Loss: 6.2434\n",
            "Epoch 6/100, Training Loss: 6.7692\n",
            "Epoch 6/100, Validation Loss: 6.1666\n",
            "Epoch 7/100, Training Loss: 6.6733\n",
            "Epoch 7/100, Validation Loss: 6.0104\n",
            "Epoch 8/100, Training Loss: 6.5114\n",
            "Epoch 8/100, Validation Loss: 5.8515\n",
            "Epoch 9/100, Training Loss: 6.3872\n",
            "Epoch 9/100, Validation Loss: 5.8940\n",
            "Epoch 10/100, Training Loss: 6.3137\n",
            "Epoch 10/100, Validation Loss: 5.8604\n",
            "Epoch 11/100, Training Loss: 6.2753\n",
            "Epoch 11/100, Validation Loss: 5.9269\n",
            "Epoch 12/100, Training Loss: 6.2289\n",
            "Epoch 12/100, Validation Loss: 5.8967\n",
            "Epoch 13/100, Training Loss: 6.1814\n",
            "Epoch 13/100, Validation Loss: 5.8846\n",
            "Epoch 14/100, Training Loss: 6.1438\n",
            "Epoch 14/100, Validation Loss: 5.8803\n",
            "Epoch 15/100, Training Loss: 6.1013\n",
            "Epoch 15/100, Validation Loss: 5.8681\n",
            "Epoch 16/100, Training Loss: 6.0708\n",
            "Epoch 16/100, Validation Loss: 5.8981\n",
            "Epoch 17/100, Training Loss: 6.0407\n",
            "Epoch 17/100, Validation Loss: 5.8519\n",
            "Epoch 18/100, Training Loss: 6.0092\n",
            "Epoch 18/100, Validation Loss: 5.8498\n",
            "Epoch 19/100, Training Loss: 5.9738\n",
            "Epoch 19/100, Validation Loss: 5.7741\n",
            "Epoch 20/100, Training Loss: 5.9486\n",
            "Epoch 20/100, Validation Loss: 5.7810\n",
            "Epoch 21/100, Training Loss: 5.9295\n",
            "Epoch 21/100, Validation Loss: 5.7312\n",
            "Epoch 22/100, Training Loss: 5.9113\n",
            "Epoch 22/100, Validation Loss: 5.7243\n",
            "Epoch 23/100, Training Loss: 5.9016\n",
            "Epoch 23/100, Validation Loss: 5.7050\n",
            "Epoch 24/100, Training Loss: 5.8836\n",
            "Epoch 24/100, Validation Loss: 5.6451\n",
            "Epoch 25/100, Training Loss: 5.8740\n",
            "Epoch 25/100, Validation Loss: 5.6579\n",
            "Epoch 26/100, Training Loss: 5.8619\n",
            "Epoch 26/100, Validation Loss: 5.6244\n",
            "Epoch 27/100, Training Loss: 5.8481\n",
            "Epoch 27/100, Validation Loss: 5.5949\n",
            "Epoch 28/100, Training Loss: 5.8393\n",
            "Epoch 28/100, Validation Loss: 5.5795\n",
            "Epoch 29/100, Training Loss: 5.8311\n",
            "Epoch 29/100, Validation Loss: 5.5884\n",
            "Epoch 30/100, Training Loss: 5.8281\n",
            "Epoch 30/100, Validation Loss: 5.5500\n",
            "Epoch 31/100, Training Loss: 5.8163\n",
            "Epoch 31/100, Validation Loss: 5.5544\n",
            "Epoch 32/100, Training Loss: 5.8071\n",
            "Epoch 32/100, Validation Loss: 5.5313\n",
            "Epoch 33/100, Training Loss: 5.8020\n",
            "Epoch 33/100, Validation Loss: 5.5403\n",
            "Epoch 34/100, Training Loss: 5.7985\n",
            "Epoch 34/100, Validation Loss: 5.5170\n",
            "Epoch 35/100, Training Loss: 5.7949\n",
            "Epoch 35/100, Validation Loss: 5.5349\n",
            "Epoch 36/100, Training Loss: 5.7881\n",
            "Epoch 36/100, Validation Loss: 5.4985\n",
            "Epoch 37/100, Training Loss: 5.7857\n",
            "Epoch 37/100, Validation Loss: 5.5335\n",
            "Epoch 38/100, Training Loss: 5.7798\n",
            "Epoch 38/100, Validation Loss: 5.4817\n",
            "Epoch 39/100, Training Loss: 5.7733\n",
            "Epoch 39/100, Validation Loss: 5.5465\n",
            "Epoch 40/100, Training Loss: 5.7719\n",
            "Epoch 40/100, Validation Loss: 5.4393\n",
            "Epoch 41/100, Training Loss: 5.7694\n",
            "Epoch 41/100, Validation Loss: 5.5638\n",
            "Epoch 42/100, Training Loss: 5.7705\n",
            "Epoch 42/100, Validation Loss: 5.4150\n",
            "Epoch 43/100, Training Loss: 5.7683\n",
            "Epoch 43/100, Validation Loss: 5.5999\n",
            "Epoch 44/100, Training Loss: 5.7797\n",
            "Epoch 44/100, Validation Loss: 5.3778\n",
            "Epoch 45/100, Training Loss: 5.8000\n",
            "Epoch 45/100, Validation Loss: 5.7242\n",
            "Epoch 46/100, Training Loss: 5.8341\n",
            "Epoch 46/100, Validation Loss: 5.3435\n",
            "Epoch 47/100, Training Loss: 5.9561\n",
            "Epoch 47/100, Validation Loss: 5.7969\n",
            "Epoch 48/100, Training Loss: 5.8680\n",
            "Epoch 48/100, Validation Loss: 5.3640\n",
            "Epoch 49/100, Training Loss: 5.8900\n",
            "Epoch 49/100, Validation Loss: 5.7144\n",
            "Epoch 50/100, Training Loss: 5.8640\n",
            "Epoch 50/100, Validation Loss: 5.3635\n",
            "Epoch 51/100, Training Loss: 5.9453\n",
            "Epoch 51/100, Validation Loss: 5.6346\n",
            "Epoch 52/100, Training Loss: 5.8403\n",
            "Epoch 52/100, Validation Loss: 5.3974\n",
            "Epoch 53/100, Training Loss: 5.7856\n",
            "Epoch 53/100, Validation Loss: 5.5558\n",
            "Epoch 54/100, Training Loss: 5.7683\n",
            "Epoch 54/100, Validation Loss: 5.3707\n",
            "Epoch 55/100, Training Loss: 5.7304\n",
            "Epoch 55/100, Validation Loss: 5.5303\n",
            "Epoch 56/100, Training Loss: 5.7504\n",
            "Epoch 56/100, Validation Loss: 5.3474\n",
            "Epoch 57/100, Training Loss: 5.7412\n",
            "Epoch 57/100, Validation Loss: 5.5480\n",
            "Epoch 58/100, Training Loss: 5.7563\n",
            "Epoch 58/100, Validation Loss: 5.3319\n",
            "Epoch 59/100, Training Loss: 5.7631\n",
            "Epoch 59/100, Validation Loss: 5.5742\n",
            "Epoch 60/100, Training Loss: 5.7650\n",
            "Epoch 60/100, Validation Loss: 5.3279\n",
            "Epoch 61/100, Training Loss: 5.7998\n",
            "Epoch 61/100, Validation Loss: 5.6102\n",
            "Epoch 62/100, Training Loss: 5.7724\n",
            "Epoch 62/100, Validation Loss: 5.3270\n",
            "Epoch 63/100, Training Loss: 5.7985\n",
            "Epoch 63/100, Validation Loss: 5.5979\n",
            "Epoch 64/100, Training Loss: 5.7645\n",
            "Epoch 64/100, Validation Loss: 5.3418\n",
            "Epoch 65/100, Training Loss: 5.7808\n",
            "Epoch 65/100, Validation Loss: 5.5782\n",
            "Epoch 66/100, Training Loss: 5.7533\n",
            "Epoch 66/100, Validation Loss: 5.3453\n",
            "Epoch 67/100, Training Loss: 5.7602\n",
            "Epoch 67/100, Validation Loss: 5.5572\n",
            "Epoch 68/100, Training Loss: 5.7457\n",
            "Epoch 68/100, Validation Loss: 5.3379\n",
            "Epoch 69/100, Training Loss: 5.7869\n",
            "Epoch 69/100, Validation Loss: 5.5827\n",
            "Epoch 70/100, Training Loss: 5.7688\n",
            "Epoch 70/100, Validation Loss: 5.3496\n",
            "Epoch 71/100, Training Loss: 5.7885\n",
            "Epoch 71/100, Validation Loss: 5.5778\n",
            "Epoch 72/100, Training Loss: 5.7404\n",
            "Epoch 72/100, Validation Loss: 5.3371\n",
            "Epoch 73/100, Training Loss: 5.7602\n",
            "Epoch 73/100, Validation Loss: 5.5923\n",
            "Epoch 74/100, Training Loss: 5.7263\n",
            "Epoch 74/100, Validation Loss: 5.3280\n",
            "Epoch 75/100, Training Loss: 5.7541\n",
            "Epoch 75/100, Validation Loss: 5.5781\n",
            "Epoch 76/100, Training Loss: 5.7202\n",
            "Epoch 76/100, Validation Loss: 5.3195\n",
            "Epoch 77/100, Training Loss: 5.7431\n",
            "Epoch 77/100, Validation Loss: 5.5786\n",
            "Epoch 78/100, Training Loss: 5.7092\n",
            "Epoch 78/100, Validation Loss: 5.3184\n",
            "Epoch 79/100, Training Loss: 5.7295\n",
            "Epoch 79/100, Validation Loss: 5.5833\n",
            "Epoch 80/100, Training Loss: 5.7024\n",
            "Epoch 80/100, Validation Loss: 5.3087\n",
            "Epoch 81/100, Training Loss: 5.7282\n",
            "Epoch 81/100, Validation Loss: 5.5863\n",
            "Epoch 82/100, Training Loss: 5.6999\n",
            "Epoch 82/100, Validation Loss: 5.3082\n",
            "Epoch 83/100, Training Loss: 5.7239\n",
            "Epoch 83/100, Validation Loss: 5.5798\n",
            "Epoch 84/100, Training Loss: 5.6917\n",
            "Epoch 84/100, Validation Loss: 5.2956\n",
            "Epoch 85/100, Training Loss: 5.7233\n",
            "Epoch 85/100, Validation Loss: 5.5793\n",
            "Epoch 86/100, Training Loss: 5.6873\n",
            "Epoch 86/100, Validation Loss: 5.2939\n",
            "Epoch 87/100, Training Loss: 5.7208\n",
            "Epoch 87/100, Validation Loss: 5.5693\n",
            "Epoch 88/100, Training Loss: 5.6806\n",
            "Epoch 88/100, Validation Loss: 5.2930\n",
            "Epoch 89/100, Training Loss: 5.7042\n",
            "Epoch 89/100, Validation Loss: 5.5602\n",
            "Epoch 90/100, Training Loss: 5.6726\n",
            "Epoch 90/100, Validation Loss: 5.2828\n",
            "Epoch 91/100, Training Loss: 5.6986\n",
            "Epoch 91/100, Validation Loss: 5.5486\n",
            "Epoch 92/100, Training Loss: 5.6701\n",
            "Epoch 92/100, Validation Loss: 5.2795\n",
            "Epoch 93/100, Training Loss: 5.7007\n",
            "Epoch 93/100, Validation Loss: 5.5460\n",
            "Epoch 94/100, Training Loss: 5.6676\n",
            "Epoch 94/100, Validation Loss: 5.2805\n",
            "Epoch 95/100, Training Loss: 5.6869\n",
            "Epoch 95/100, Validation Loss: 5.5278\n",
            "Epoch 96/100, Training Loss: 5.6531\n",
            "Epoch 96/100, Validation Loss: 5.2915\n",
            "Epoch 97/100, Training Loss: 5.6765\n",
            "Epoch 97/100, Validation Loss: 5.5221\n",
            "Epoch 98/100, Training Loss: 5.6515\n",
            "Epoch 98/100, Validation Loss: 5.2879\n",
            "Epoch 99/100, Training Loss: 5.6743\n",
            "Epoch 99/100, Validation Loss: 5.5062\n",
            "Epoch 100/100, Training Loss: 5.6339\n",
            "Epoch 100/100, Validation Loss: 5.3033\n",
            "Average loss, over epochs 5.940717119472909\n"
          ]
        }
      ],
      "source": [
        "# prediction of terminal positions for 49 timestamps based on initial data using a graph neural network\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from torch_geometric.nn import MessagePassing\n",
        "from torch_geometric.utils import add_self_loops, degree\n",
        "from torch_geometric.nn import SAGEConv\n",
        "from torch_geometric.nn import GATConv\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "device = 'cuda:0'\n",
        "class GNNPredictor(MessagePassing):\n",
        "    def __init__(self, in_channels, hidden_channels):\n",
        "        super(GNNPredictor, self).__init__(aggr='mean')  # Use \"mean\" aggregation for message passing\n",
        "        self.lin1 = nn.Linear(in_channels, hidden_channels)\n",
        "        self.lin2 = nn.Linear(hidden_channels, hidden_channels)\n",
        "        self.lin3 = nn.Linear(hidden_channels, 2)  # Output 2-dimensional terminal positions\n",
        "\n",
        "    def forward(self, x, edge_index):\n",
        "      x = self.lin1(x)\n",
        "      x = F.relu(x)\n",
        "      x = self.lin2(x)\n",
        "      x = self.propagate(edge_index, size=(x.size(0), x.size(0)), x=x)\n",
        "      x = self.lin3(x)\n",
        "      return x\n",
        "\n",
        "\n",
        "    def message(self, x_j, edge_index, size):\n",
        "        row, col = edge_index\n",
        "        deg = degree(col, size[0], dtype=x_j.dtype)\n",
        "        deg_inv_sqrt = deg.pow(-0.5)\n",
        "        norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]\n",
        "        return x_j * norm.view(-1, 1)\n",
        "\n",
        "\n",
        "num_features = 6\n",
        "hidden_channels=64\n",
        "#model_gnn = GraphSAGE(num_features, hidden_channels) # initialize our GNN with a hidden size of 16#\n",
        "#model_gnn = model_gnn.double()\n",
        "\n",
        "\n",
        "# same loss and optimizer as before\n",
        "# optimizer = torch.optim.Adam(model_gnn.parameters(), lr=0.01, weight_decay=5e-4)\n",
        "# Create an instance of the GNNPredictor model\n",
        "model = GNNPredictor(in_channels=6, hidden_channels=64)\n",
        "model = model.double()\n",
        "\n",
        "# Define the loss function\n",
        "loss_fn = nn.MSELoss()\n",
        "\n",
        "# Define the optimizer\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=0.01)\n",
        "total_epoch_loss = 0\n",
        "num_epochs=100\n",
        "# Training loop\n",
        "model.train()  # Set the model in training mode\n",
        "for epoch in range(num_epochs):\n",
        "    total_loss = 0\n",
        "    for batch in train_loader:\n",
        "        batch = batch # Move the batch to the device (e.g., GPU)\n",
        "\n",
        "        optimizer.zero_grad()  # Clear gradients\n",
        "\n",
        "        # Forward pass\n",
        "        x = batch.x\n",
        "        # x = x.float()\n",
        "        # print(x)\n",
        "        edge_index = batch.edge_index\n",
        "        # print(edge_index)\n",
        "        y_true = batch.y\n",
        "        y_pred = model(x, edge_index)\n",
        "\n",
        "        # Compute loss\n",
        "        loss = loss_fn(y_pred, y_true)\n",
        "\n",
        "        # Backward pass and optimization\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        total_loss += loss.item()\n",
        "\n",
        "\n",
        "\n",
        "    avg_loss = total_loss / len(train_loader)\n",
        "    total_epoch_loss += avg_loss / num_epochs\n",
        "    print(f\"Epoch {epoch+1}/{num_epochs}, Training Loss: {avg_loss:.4f}\")\n",
        "    # Validation phase\n",
        "    model.eval()  # Set the model in evaluation mode\n",
        "    with torch.no_grad():\n",
        "        total_val_loss = 0\n",
        "        for batch in val_loader:\n",
        "            batch = batch  # Move the batch to the device (e.g., GPU)\n",
        "\n",
        "            x = batch.x\n",
        "            edge_index = batch.edge_index\n",
        "            y_true = batch.y\n",
        "            y_pred = model(x, edge_index)\n",
        "\n",
        "            val_loss = loss_fn(y_pred, y_true)\n",
        "            total_val_loss += val_loss.item()\n",
        "\n",
        "        avg_val_loss = total_val_loss / len(val_loader)\n",
        "        print(f\"Epoch {epoch+1}/{num_epochs}, Validation Loss: {avg_val_loss:.4f}\")\n",
        "print('Average loss, over epochs', total_epoch_loss)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "naJYZLth_HSi",
        "outputId": "a053d319-87d1-4f23-9395-712c98daab47"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Difference: 1.3772622218933668\n",
            "Standard Deviation Difference: 0.9385655901626421\n",
            "Minimum Difference: 0.034908957514182326\n",
            "Maximum Difference: 3.628986275556466\n",
            "95th Percentile Difference: 3.1991105393500043\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Difference: 1.3028639050768334\n",
            "Standard Deviation Difference: 1.5976837802771473\n",
            "Minimum Difference: 0.1942724926103354\n",
            "Maximum Difference: 4.883755872302281\n",
            "95th Percentile Difference: 4.460857558191358\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Difference: 1.9706470665500018\n",
            "Standard Deviation Difference: 1.067119785809331\n",
            "Minimum Difference: 0.5158341871117815\n",
            "Maximum Difference: 3.923094354346663\n",
            "95th Percentile Difference: 3.8260716701028916\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Difference: 0.908806055542449\n",
            "Standard Deviation Difference: 0.6967663404135024\n",
            "Minimum Difference: 0.08427747150119602\n",
            "Maximum Difference: 2.626881543561682\n",
            "95th Percentile Difference: 2.086366599308022\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Difference: 1.8357220455144279\n",
            "Standard Deviation Difference: 1.481634679119702\n",
            "Minimum Difference: 0.22289473549376648\n",
            "Maximum Difference: 6.057461590385688\n",
            "95th Percentile Difference: 4.166101767341433\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Select samples for evaluation (e.g., randomly select 5 samples)\n",
        "evaluation_samples = data_list[:5]\n",
        "\n",
        "# Set the model to evaluation mode\n",
        "model.eval()\n",
        "\n",
        "# Set the threshold for failure prediction\n",
        "threshold = 11.54  # Adjust this value according to your needs\n",
        "\n",
        "# Evaluate the selected samples\n",
        "for sample in evaluation_samples:\n",
        "    x = sample.x\n",
        "    edge_index = sample.edge_index\n",
        "    y_true = sample.y\n",
        "\n",
        "    with torch.no_grad():\n",
        "        y_pred = model(x, edge_index)\n",
        "\n",
        "\n",
        "    # Convert tensors to numpy arrays for visualization\n",
        "    x_values = x.squeeze().numpy()\n",
        "    y_true_values = y_true.squeeze().numpy()\n",
        "    y_pred_values = y_pred.squeeze().numpy()\n",
        "\n",
        "    # Compute the differences between y_pred and y_true\n",
        "    differences = np.abs(y_pred_values - y_true_values)\n",
        "\n",
        "    # Calculate the summary statistics\n",
        "    mean_difference = np.mean(differences)\n",
        "    std_difference = np.std(differences)\n",
        "    min_difference = np.min(differences)\n",
        "    max_difference = np.max(differences)\n",
        "    percentile_95 = np.percentile(differences, 95)\n",
        "\n",
        "    # Print the summary statistics\n",
        "    print(\"Mean Difference:\", mean_difference)\n",
        "    print(\"Standard Deviation Difference:\", std_difference)\n",
        "    print(\"Minimum Difference:\", min_difference)\n",
        "    print(\"Maximum Difference:\", max_difference)\n",
        "    print(\"95th Percentile Difference:\", percentile_95)\n",
        "    threshold = np.percentile(differences, 95)\n",
        "\n",
        "\n",
        "    # Compute the Euclidean distance between predicted and ground truth positions\n",
        "    distances = np.linalg.norm(y_pred_values - y_true_values, axis=1)\n",
        "\n",
        "\n",
        "    # Find the indices where the distances are above the threshold (failure cases)\n",
        "    failure_indices = np.where(distances > threshold)[0]\n",
        "\n",
        "    # Plot the failure cases\n",
        "    plt.figure()\n",
        "    plt.scatter(x_values[:, 0], x_values[:, 1], color='b', label='Initial Positions')\n",
        "    plt.scatter(y_true_values[:, 0], y_true_values[:, 1], color='g', label='Ground Truth')\n",
        "    plt.scatter(y_pred_values[:, 0], y_pred_values[:, 1], color='r', label='Predictions')\n",
        "\n",
        "    # Add labels for each point\n",
        "    for i, (x, y) in enumerate(zip(x_values[:, 0], x_values[:, 1])):\n",
        "        plt.annotate(f'{i}', (x, y), textcoords=\"offset points\", xytext=(0,5), ha='center', color='b')\n",
        "    for i, (x, y) in enumerate(zip(y_true_values[:, 0], y_true_values[:, 1])):\n",
        "        plt.annotate(f'{i}', (x, y), textcoords=\"offset points\", xytext=(0,5), ha='center', color='g')\n",
        "    for i, (x, y) in enumerate(zip(y_pred_values[:, 0], y_pred_values[:, 1])):\n",
        "        plt.annotate(f'{i}', (x, y), textcoords=\"offset points\", xytext=(0,5), ha='center', color='r')\n",
        "\n",
        "    # Highlight failure cases with a larger marker size\n",
        "    plt.scatter(y_pred_values[failure_indices, 0], y_pred_values[failure_indices, 1], color='r',\n",
        "                label='Failure Predictions', s=120)\n",
        "\n",
        "    plt.legend()\n",
        "    plt.title('Sample Evaluation')\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zw1XapVCmKfs",
        "outputId": "bf77f3ca-e9e3-41f3-d835-57a266099987"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<torch_geometric.loader.dataloader.DataLoader at 0x7f789377dcf0>"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_data = get_data_object(initial_state_test, remaining_trajectory_test)\n",
        "\n",
        "test_loader = DataLoader(test_data, batch_size=30)\n",
        "test_loader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KgBUhC9Bl7y7",
        "outputId": "1ed8f9fa-b371-4c78-9c70-c1cb9d5a57f8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Loss on Test Data: 4.7995\n"
          ]
        }
      ],
      "source": [
        "model.eval()  # Set the model in evaluation mode\n",
        "total_loss = 0\n",
        "\n",
        "with torch.no_grad():\n",
        "    for data in test_loader:\n",
        "        x = data.x\n",
        "        edge_index = data.edge_index\n",
        "        y_true = data.y\n",
        "        y_pred = model(x, edge_index)\n",
        "\n",
        "        loss = loss_fn(y_pred, y_true)\n",
        "        total_loss += loss.item()\n",
        "\n",
        "avg_loss = total_loss / len(test_loader)\n",
        "print(f\"Average Loss on Test Data: {avg_loss:.4f}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XLEdSdVVl-Eu"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP+IjSnc9VCt7zN5s9biMgz",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}