notebooks/workflow_molecule_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "af3ba92b",
   "metadata": {},
   "source": [
    "## Molecule Workflow with ESOL example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d925ab75",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras as ks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a7450a2",
   "metadata": {},
   "source": [
    "just for downloading the some data in place of your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da90a3d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "from kgcnn.data.datasets.ESOLDataset import ESOLDataset\n",
    "ESOLDataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b04b7b9c",
   "metadata": {},
   "source": [
    "move the data from \"~/.kgcnn.datasets.esol\" to your local \"esol\" folder. The ESOL data is just some example data for this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9d19299",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kgcnn.data.moleculenet import MoleculeNetDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c73e274",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = MoleculeNetDataset(\n",
    "    data_directory=\"esol/\",\n",
    "    dataset_name=\"esol\",\n",
    "    file_name=\"delaney-processed.csv\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c2b7431f",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.prepare_data(\n",
    "    overwrite=True,\n",
    "    smiles_column_name=\"smiles\",\n",
    "    add_hydrogen=True, \n",
    "    sanitize=True,\n",
    "    make_conformers=True, \n",
    "    optimize_conformer=True,\n",
    "    external_program=None, \n",
    "    num_workers=None\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a90fbe11",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "from kgcnn.molecule.encoder import OneHotEncoder\n",
    "data.read_in_memory(\n",
    "    nodes = [\n",
    "        'Symbol', 'TotalDegree', 'FormalCharge', 'NumRadicalElectrons', 'Hybridization',\n",
    "        'IsAromatic', 'IsInRing', 'TotalNumHs', 'CIPCode', \"ChiralityPossible\", \"ChiralTag\"\n",
    "    ],\n",
    "    encoder_nodes = {\n",
    "        'Symbol': OneHotEncoder(\n",
    "            ['B', 'C', 'N', 'O', 'F', 'Si', 'P', 'S', 'Cl', 'As', 'Se', 'Br', 'Te', 'I', 'At'],\n",
    "            dtype=\"str\"\n",
    "        ),\n",
    "        'Hybridization': OneHotEncoder([2, 3, 4, 5, 6]),\n",
    "        'TotalDegree': OneHotEncoder([0, 1, 2, 3, 4, 5], add_unknown=False),\n",
    "        'TotalNumHs': OneHotEncoder([0, 1, 2, 3, 4], add_unknown=False),\n",
    "        'CIPCode': OneHotEncoder(['R', 'S'], add_unknown=False, dtype='str'),\n",
    "        \"ChiralityPossible\": OneHotEncoder([\"1\"], add_unknown=False, dtype='str'),\n",
    "    },\n",
    "    edges = ['BondType', 'IsAromatic', 'IsConjugated', 'IsInRing', 'Stereo'],\n",
    "    encoder_edges = {\n",
    "        'BondType': OneHotEncoder([1, 2, 3, 12], add_unknown=False),\n",
    "        'Stereo': OneHotEncoder([0, 1, 2, 3], add_unknown=False)\n",
    "    },\n",
    "    graph=['ExactMolWt', 'NumAtoms'],\n",
    "    encoder_graph = {},\n",
    "    add_hydrogen=False,\n",
    "    make_directed=False,\n",
    "    has_conformers=True,\n",
    "    sanitize=True,\n",
    "    compute_partial_charges=None,\n",
    "    label_column_name=\"measured log solubility in mols per litre\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "28e51efa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kgcnn.graph.preprocessor import SetRange, SetEdgeIndicesReverse\n",
    "data.map_list(SetRange(max_distance=5.0, in_place=True));\n",
    "data.map_list(SetEdgeIndicesReverse(in_place=True));\n",
    "data.map_list(method=\"count_nodes_and_edges\");\n",
    "data.map_list(**{\"method\": \"count_nodes_and_edges\", \"total_edges\": \"total_reverse\"});"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1fd08df6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['node_symbol', 'node_number', 'edge_indices', 'edge_number', 'graph_size', 'node_coordinates', 'graph_labels', 'node_attributes', 'edge_attributes', 'graph_attributes', 'range_indices', 'range_attributes', 'edge_indices_reverse', 'total_nodes', 'total_edges', 'total_reverse'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0].keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f64b7ab9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([934])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.clean(\"edge_indices\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a84f128c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "labels = np.array(data.obtain_property(\"graph_labels\"))\n",
    "if len(labels.shape) <= 1:\n",
    "    labels = np.expand_dims(labels, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c0310227",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "kf = KFold(n_splits=5, random_state=42, shuffle=True)\n",
    "train_test_indices = [\n",
    "    [train_index, test_index] for train_index, test_index in kf.split(X=np.zeros((len(data), 1)), y=labels)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "91fbbb67",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_config= {\n",
    "    \"class_name\": \"make_model\",\n",
    "    \"module_name\": \"kgcnn.literature.DMPNN\",\n",
    "    \"config\": {\n",
    "        \"name\": \"DMPNN\",\n",
    "        \"inputs\": [\n",
    "            {\"shape\": (None, 41), \"name\": \"node_attributes\", \"dtype\": \"float32\"},\n",
    "            {\"shape\": (None, 11), \"name\": \"edge_attributes\", \"dtype\": \"float32\"},\n",
    "            {\"shape\": (None, 2), \"name\": \"edge_indices\", \"dtype\": \"int64\"},\n",
    "            {\"shape\": (None, 1), \"name\": \"edge_indices_reverse\", \"dtype\": \"int64\"},\n",
    "            {\"shape\": (), \"name\": \"total_nodes\", \"dtype\": \"int64\"},\n",
    "            {\"shape\": (), \"name\": \"total_edges\", \"dtype\": \"int64\"},\n",
    "            {\"shape\": (), \"name\": \"total_reverse\", \"dtype\": \"int64\"}\n",
    "        ],\n",
    "        \"cast_disjoint_kwargs\": {},\n",
    "        \"input_node_embedding\": {\"input_dim\": 95, \"output_dim\": 64},\n",
    "        \"input_edge_embedding\": {\"input_dim\": 5, \"output_dim\": 64},\n",
    "        \"input_graph_embedding\": {\"input_dim\": 100, \"output_dim\": 64},\n",
    "        \"pooling_args\": {\"pooling_method\": \"scatter_sum\"},\n",
    "        \"edge_initialize\": {\"units\": 128, \"use_bias\": True, \"activation\": \"relu\"},\n",
    "        \"edge_dense\": {\"units\": 128, \"use_bias\": True, \"activation\": \"linear\"},\n",
    "        \"edge_activation\": {\"activation\": \"relu\"},\n",
    "        \"node_dense\": {\"units\": 128, \"use_bias\": True, \"activation\": \"relu\"},\n",
    "        \"verbose\": 10, \"depth\": 5,\n",
    "        \"dropout\": {\"rate\": 0.1},\n",
    "        \"output_embedding\": \"graph\",\n",
    "        \"output_mlp\": {\n",
    "            \"use_bias\": [True, True, False], \"units\": [64, 32, 1],\n",
    "            \"activation\": [\"relu\", \"relu\", \"linear\"]\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e6a9f2ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running training on fold: 0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"DMPNN\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"DMPNN\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ node_attributes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_nodes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_edges (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>), (<span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_attributes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedIndicesToDisjoin…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │                 │ edge_indices[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
       "│                               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]   │                 │ total_nodes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],         │\n",
       "│                               │                           │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_attributes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gather_nodes_outgoing         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GatherNodesOutgoing</span>)         │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),      │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ edge_attributes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedAttributesToDisj…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]           │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ gather_nodes_outgoing[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices_reverse          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)                  │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_reverse (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,784</span> │ concatenate[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>), (<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ edge_indices[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedIndicesToDisjoin…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │                 │ edge_indices_reverse[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]   │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],         │\n",
       "│                               │                           │                 │ total_reverse[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directed │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dense[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],               │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], dense[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                  │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], dense[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], dense[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], dense[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>], dense[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>] │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ aggregate_local_edges_5       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">AggregateLocalEdges</span>)         │                           │                 │ dropout_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">169</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ aggregate_local_edges_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │          <span style=\"color: #00af00; text-decoration-color: #00af00\">21,760</span> │ concatenate_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">PoolingNodes</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ dense_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ mlp (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MLP</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">10,368</span> │ pooling_nodes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_disjoint_to_batched_gra… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ mlp[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                  │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastDisjointToBatchedGraphS…</span> │                           │                 │                            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ node_attributes (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_nodes (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_edges (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m), (\u001b[38;5;34m2\u001b[0m, \u001b[38;5;45mNone\u001b[0m),   │               \u001b[38;5;34m0\u001b[0m │ node_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),   │                 │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
       "│                               │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]   │                 │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],         │\n",
       "│                               │                           │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_attributes (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gather_nodes_outgoing         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mGatherNodesOutgoing\u001b[0m)         │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),      │               \u001b[38;5;34m0\u001b[0m │ edge_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]           │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate (\u001b[38;5;33mConcatenate\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ gather_nodes_outgoing[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices_reverse          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)                  │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_reverse (\u001b[38;5;33mInputLayer\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │           \u001b[38;5;34m6,784\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m), (\u001b[38;5;34m1\u001b[0m, \u001b[38;5;45mNone\u001b[0m),    │               \u001b[38;5;34m0\u001b[0m │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
       "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),   │                 │ edge_indices_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
       "│                               │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]   │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],         │\n",
       "│                               │                           │                 │ total_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directed │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],               │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m16,512\u001b[0m │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add (\u001b[38;5;33mAdd\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation (\u001b[38;5;33mActivation\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                  │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_1 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_1[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m], dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_1 (\u001b[38;5;33mActivation\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_2 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_1[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m], dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_2 (\u001b[38;5;33mActivation\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_3 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_1[\u001b[38;5;34m3\u001b[0m][\u001b[38;5;34m0\u001b[0m], dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_3 (\u001b[38;5;33mActivation\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_4 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_1[\u001b[38;5;34m4\u001b[0m][\u001b[38;5;34m0\u001b[0m], dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m] │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_4 (\u001b[38;5;33mActivation\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ aggregate_local_edges_5       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mAggregateLocalEdges\u001b[0m)         │                           │                 │ dropout_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_1 (\u001b[38;5;33mConcatenate\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m169\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ aggregate_local_edges_5[\u001b[38;5;34m0\u001b[0m… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m21,760\u001b[0m │ concatenate_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes (\u001b[38;5;33mPoolingNodes\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ dense_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ mlp (\u001b[38;5;33mMLP\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │          \u001b[38;5;34m10,368\u001b[0m │ pooling_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_disjoint_to_batched_gra… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ mlp[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                  │\n",
       "│ (\u001b[38;5;33mCastDisjointToBatchedGraphS…\u001b[0m │                           │                 │                            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">55,424</span> (216.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m55,424\u001b[0m (216.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">55,424</span> (216.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m55,424\u001b[0m (216.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 21ms/step \n",
      "Print Time for training:  0:02:24.984375\n",
      "Running training on fold: 1\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"DMPNN\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"DMPNN\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ node_attributes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_nodes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_edges (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>), (<span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_attributes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedIndicesToDisjoin…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │                 │ edge_indices[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
       "│                               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]   │                 │ total_nodes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],         │\n",
       "│                               │                           │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_attributes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gather_nodes_outgoing_6       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GatherNodesOutgoing</span>)         │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),      │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ edge_attributes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedAttributesToDisj…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]           │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ gather_nodes_outgoing_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices_reverse          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)                  │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_reverse (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,784</span> │ concatenate_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>), (<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ edge_indices[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedIndicesToDisjoin…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │                 │ edge_indices_reverse[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]   │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],         │\n",
       "│                               │                           │                 │ total_reverse[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dense_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ dense_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ dense_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ dense_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ dense_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ dense_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ aggregate_local_edges_11      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">AggregateLocalEdges</span>)         │                           │                 │ dropout_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">169</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ aggregate_local_edges_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │          <span style=\"color: #00af00; text-decoration-color: #00af00\">21,760</span> │ concatenate_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_1               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">PoolingNodes</span>)                │                           │                 │ dense_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ mlp_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MLP</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">10,368</span> │ pooling_nodes_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_disjoint_to_batched_gra… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ mlp_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastDisjointToBatchedGraphS…</span> │                           │                 │                            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ node_attributes (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_nodes (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_edges (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m), (\u001b[38;5;34m2\u001b[0m, \u001b[38;5;45mNone\u001b[0m),   │               \u001b[38;5;34m0\u001b[0m │ node_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),   │                 │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
       "│                               │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]   │                 │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],         │\n",
       "│                               │                           │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_attributes (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gather_nodes_outgoing_6       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mGatherNodesOutgoing\u001b[0m)         │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),      │               \u001b[38;5;34m0\u001b[0m │ edge_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]           │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_2 (\u001b[38;5;33mConcatenate\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ gather_nodes_outgoing_6[\u001b[38;5;34m0\u001b[0m… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices_reverse          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)                  │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_reverse (\u001b[38;5;33mInputLayer\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │           \u001b[38;5;34m6,784\u001b[0m │ concatenate_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m), (\u001b[38;5;34m1\u001b[0m, \u001b[38;5;45mNone\u001b[0m),    │               \u001b[38;5;34m0\u001b[0m │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
       "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),   │                 │ edge_indices_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
       "│                               │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]   │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],         │\n",
       "│                               │                           │                 │ total_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m16,512\u001b[0m │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_5 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_5 (\u001b[38;5;33mActivation\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_6 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_4[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_6 (\u001b[38;5;33mActivation\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_7 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_4[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_7 (\u001b[38;5;33mActivation\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_8 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_4[\u001b[38;5;34m3\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_8 (\u001b[38;5;33mActivation\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_9 (\u001b[38;5;33mAdd\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_4[\u001b[38;5;34m4\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ dense_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_9 (\u001b[38;5;33mActivation\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ aggregate_local_edges_11      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mAggregateLocalEdges\u001b[0m)         │                           │                 │ dropout_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],           │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_3 (\u001b[38;5;33mConcatenate\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m169\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ aggregate_local_edges_11[\u001b[38;5;34m…\u001b[0m │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m21,760\u001b[0m │ concatenate_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_1               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mPoolingNodes\u001b[0m)                │                           │                 │ dense_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ mlp_1 (\u001b[38;5;33mMLP\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │          \u001b[38;5;34m10,368\u001b[0m │ pooling_nodes_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_disjoint_to_batched_gra… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ mlp_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "│ (\u001b[38;5;33mCastDisjointToBatchedGraphS…\u001b[0m │                           │                 │                            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">55,424</span> (216.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m55,424\u001b[0m (216.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">55,424</span> (216.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m55,424\u001b[0m (216.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 21ms/step\n",
      "Print Time for training:  0:02:24.656250\n",
      "Running training on fold: 2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"DMPNN\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"DMPNN\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ node_attributes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_nodes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_edges (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>), (<span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_attributes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedIndicesToDisjoin…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │                 │ edge_indices[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
       "│                               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]   │                 │ total_nodes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],         │\n",
       "│                               │                           │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_attributes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gather_nodes_outgoing_12      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GatherNodesOutgoing</span>)         │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),      │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ edge_attributes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedAttributesToDisj…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]           │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ gather_nodes_outgoing_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices_reverse          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)                  │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_reverse (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,784</span> │ concatenate_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>), (<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ edge_indices[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedIndicesToDisjoin…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │                 │ edge_indices_reverse[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]   │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],         │\n",
       "│                               │                           │                 │ total_reverse[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dense_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ dense_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ dense_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ dense_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ dense_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ dense_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ aggregate_local_edges_17      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">AggregateLocalEdges</span>)         │                           │                 │ dropout_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">169</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ aggregate_local_edges_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │          <span style=\"color: #00af00; text-decoration-color: #00af00\">21,760</span> │ concatenate_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_2               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">PoolingNodes</span>)                │                           │                 │ dense_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ mlp_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MLP</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">10,368</span> │ pooling_nodes_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_disjoint_to_batched_gra… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ mlp_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastDisjointToBatchedGraphS…</span> │                           │                 │                            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ node_attributes (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_nodes (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_edges (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m), (\u001b[38;5;34m2\u001b[0m, \u001b[38;5;45mNone\u001b[0m),   │               \u001b[38;5;34m0\u001b[0m │ node_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),   │                 │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
       "│                               │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]   │                 │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],         │\n",
       "│                               │                           │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_attributes (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gather_nodes_outgoing_12      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mGatherNodesOutgoing\u001b[0m)         │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),      │               \u001b[38;5;34m0\u001b[0m │ edge_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]           │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_4 (\u001b[38;5;33mConcatenate\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ gather_nodes_outgoing_12[\u001b[38;5;34m…\u001b[0m │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices_reverse          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)                  │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_reverse (\u001b[38;5;33mInputLayer\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │           \u001b[38;5;34m6,784\u001b[0m │ concatenate_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m), (\u001b[38;5;34m1\u001b[0m, \u001b[38;5;45mNone\u001b[0m),    │               \u001b[38;5;34m0\u001b[0m │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
       "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),   │                 │ edge_indices_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
       "│                               │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]   │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],         │\n",
       "│                               │                           │                 │ total_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dense_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m16,512\u001b[0m │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_10 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ dense_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_10 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_10 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_11 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_7[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ dense_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_11 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_11 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_12 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_7[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ dense_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_12 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_12 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_13 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_7[\u001b[38;5;34m3\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ dense_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_13 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_13 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_14 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_7[\u001b[38;5;34m4\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ dense_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_14 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_14 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ aggregate_local_edges_17      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mAggregateLocalEdges\u001b[0m)         │                           │                 │ dropout_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_5 (\u001b[38;5;33mConcatenate\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m169\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ aggregate_local_edges_17[\u001b[38;5;34m…\u001b[0m │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m21,760\u001b[0m │ concatenate_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_2               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mPoolingNodes\u001b[0m)                │                           │                 │ dense_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ mlp_2 (\u001b[38;5;33mMLP\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │          \u001b[38;5;34m10,368\u001b[0m │ pooling_nodes_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_disjoint_to_batched_gra… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ mlp_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "│ (\u001b[38;5;33mCastDisjointToBatchedGraphS…\u001b[0m │                           │                 │                            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">55,424</span> (216.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m55,424\u001b[0m (216.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">55,424</span> (216.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m55,424\u001b[0m (216.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
      "Print Time for training:  0:02:25.218750\n",
      "Running training on fold: 3\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"DMPNN\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"DMPNN\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ node_attributes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_nodes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_edges (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>), (<span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_attributes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedIndicesToDisjoin…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │                 │ edge_indices[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
       "│                               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]   │                 │ total_nodes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],         │\n",
       "│                               │                           │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_attributes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gather_nodes_outgoing_18      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GatherNodesOutgoing</span>)         │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),      │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ edge_attributes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedAttributesToDisj…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]           │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ gather_nodes_outgoing_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices_reverse          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)                  │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_reverse (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,784</span> │ concatenate_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>), (<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ edge_indices[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedIndicesToDisjoin…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │                 │ edge_indices_reverse[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]   │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],         │\n",
       "│                               │                           │                 │ total_reverse[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dense_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ dense_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ dense_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ dense_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ dense_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ dense_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ aggregate_local_edges_23      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">AggregateLocalEdges</span>)         │                           │                 │ dropout_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">169</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ aggregate_local_edges_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │          <span style=\"color: #00af00; text-decoration-color: #00af00\">21,760</span> │ concatenate_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_3               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">PoolingNodes</span>)                │                           │                 │ dense_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ mlp_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MLP</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">10,368</span> │ pooling_nodes_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_disjoint_to_batched_gra… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ mlp_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastDisjointToBatchedGraphS…</span> │                           │                 │                            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ node_attributes (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_nodes (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_edges (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m), (\u001b[38;5;34m2\u001b[0m, \u001b[38;5;45mNone\u001b[0m),   │               \u001b[38;5;34m0\u001b[0m │ node_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),   │                 │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
       "│                               │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]   │                 │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],         │\n",
       "│                               │                           │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_attributes (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gather_nodes_outgoing_18      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mGatherNodesOutgoing\u001b[0m)         │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),      │               \u001b[38;5;34m0\u001b[0m │ edge_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]           │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_6 (\u001b[38;5;33mConcatenate\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ gather_nodes_outgoing_18[\u001b[38;5;34m…\u001b[0m │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices_reverse          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)                  │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_reverse (\u001b[38;5;33mInputLayer\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │           \u001b[38;5;34m6,784\u001b[0m │ concatenate_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m), (\u001b[38;5;34m1\u001b[0m, \u001b[38;5;45mNone\u001b[0m),    │               \u001b[38;5;34m0\u001b[0m │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
       "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),   │                 │ edge_indices_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
       "│                               │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]   │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],         │\n",
       "│                               │                           │                 │ total_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dense_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],             │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m16,512\u001b[0m │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_15 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ dense_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_15 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_15 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_16 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_10[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ dense_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_16 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_16 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_17 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_10[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ dense_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_17 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_17 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_18 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_10[\u001b[38;5;34m3\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ dense_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_18 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_18 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_19 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_10[\u001b[38;5;34m4\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ dense_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]              │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_19 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_19 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ aggregate_local_edges_23      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mAggregateLocalEdges\u001b[0m)         │                           │                 │ dropout_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_7 (\u001b[38;5;33mConcatenate\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m169\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ aggregate_local_edges_23[\u001b[38;5;34m…\u001b[0m │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m21,760\u001b[0m │ concatenate_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_3               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mPoolingNodes\u001b[0m)                │                           │                 │ dense_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ mlp_3 (\u001b[38;5;33mMLP\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │          \u001b[38;5;34m10,368\u001b[0m │ pooling_nodes_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_disjoint_to_batched_gra… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ mlp_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "│ (\u001b[38;5;33mCastDisjointToBatchedGraphS…\u001b[0m │                           │                 │                            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">55,424</span> (216.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m55,424\u001b[0m (216.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">55,424</span> (216.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m55,424\u001b[0m (216.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "Print Time for training:  0:02:24.843750\n",
      "Running training on fold: 4\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"DMPNN\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"DMPNN\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                  </span>┃<span style=\"font-weight: bold\"> Output Shape              </span>┃<span style=\"font-weight: bold\">         Param # </span>┃<span style=\"font-weight: bold\"> Connected to               </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ node_attributes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_nodes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_edges (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>), (<span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_attributes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedIndicesToDisjoin…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │                 │ edge_indices[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
       "│                               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]   │                 │ total_nodes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],         │\n",
       "│                               │                           │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_attributes (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gather_nodes_outgoing_24      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GatherNodesOutgoing</span>)         │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">11</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),      │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ edge_attributes[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],     │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedAttributesToDisj…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]           │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">52</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ gather_nodes_outgoing_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices_reverse          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)                  │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_reverse (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)                    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,784</span> │ concatenate_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>), (<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ edge_indices[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],        │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastBatchedIndicesToDisjoin…</span> │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),   │                 │ edge_indices_reverse[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│                               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>)]   │                 │ total_edges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],         │\n",
       "│                               │                           │                 │ total_reverse[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dense_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │          <span style=\"color: #00af00; text-decoration-color: #00af00\">16,512</span> │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ dense_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ dense_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_21[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_21[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_21[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ dense_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_22[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_22[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_22[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ dense_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">DMPNNPPoolingEdgesDirected</span>)  │                           │                 │ dropout_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Add</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ dense_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ dense_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ add_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ aggregate_local_edges_29      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">AggregateLocalEdges</span>)         │                           │                 │ dropout_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">169</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ aggregate_local_edges_29[<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │          <span style=\"color: #00af00; text-decoration-color: #00af00\">21,760</span> │ concatenate_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_4               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_indices_to_d… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">PoolingNodes</span>)                │                           │                 │ dense_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],            │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ mlp_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MLP</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">10,368</span> │ pooling_nodes_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_disjoint_to_batched_gra… │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ mlp_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]                │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">CastDisjointToBatchedGraphS…</span> │                           │                 │                            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                 \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to              \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│ node_attributes (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_nodes (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_edges (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m), (\u001b[38;5;34m2\u001b[0m, \u001b[38;5;45mNone\u001b[0m),   │               \u001b[38;5;34m0\u001b[0m │ node_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),   │                 │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
       "│                               │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]   │                 │ total_nodes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],         │\n",
       "│                               │                           │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_attributes (\u001b[38;5;33mInputLayer\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gather_nodes_outgoing_24      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mGatherNodesOutgoing\u001b[0m)         │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m11\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),      │               \u001b[38;5;34m0\u001b[0m │ edge_attributes[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mCastBatchedAttributesToDisj…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]           │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_8 (\u001b[38;5;33mConcatenate\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m52\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ gather_nodes_outgoing_24[\u001b[38;5;34m…\u001b[0m │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ edge_indices_reverse          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "│ (\u001b[38;5;33mInputLayer\u001b[0m)                  │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ total_reverse (\u001b[38;5;33mInputLayer\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_12 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │           \u001b[38;5;34m6,784\u001b[0m │ concatenate_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_indices_to_disj… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m), (\u001b[38;5;34m1\u001b[0m, \u001b[38;5;45mNone\u001b[0m),    │               \u001b[38;5;34m0\u001b[0m │ edge_indices[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],        │\n",
       "│ (\u001b[38;5;33mCastBatchedIndicesToDisjoin…\u001b[0m │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),   │                 │ edge_indices_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
       "│                               │ (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m), (\u001b[38;5;45mNone\u001b[0m)]   │                 │ total_edges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],         │\n",
       "│                               │                           │                 │ total_reverse[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dense_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_13 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m16,512\u001b[0m │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "│                               │                           │                 │ dmpnnp_pooling_edges_dire… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_20 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ dense_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_20 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_20 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_21 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_13[\u001b[38;5;34m1\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ dense_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_21 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_21 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_22 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_13[\u001b[38;5;34m2\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ dense_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_22 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_22 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_23 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_13[\u001b[38;5;34m3\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ dense_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_23 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_23 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dmpnnp_pooling_edges_directe… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mDMPNNPPoolingEdgesDirected\u001b[0m)  │                           │                 │ dropout_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ add_24 (\u001b[38;5;33mAdd\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ dense_13[\u001b[38;5;34m4\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ dense_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]             │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ activation_24 (\u001b[38;5;33mActivation\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ add_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]               │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dropout_24 (\u001b[38;5;33mDropout\u001b[0m)          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ activation_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ aggregate_local_edges_29      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mAggregateLocalEdges\u001b[0m)         │                           │                 │ dropout_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],          │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ concatenate_9 (\u001b[38;5;33mConcatenate\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m169\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ aggregate_local_edges_29[\u001b[38;5;34m…\u001b[0m │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_14 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m21,760\u001b[0m │ concatenate_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_4               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │ cast_batched_indices_to_d… │\n",
       "│ (\u001b[38;5;33mPoolingNodes\u001b[0m)                │                           │                 │ dense_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ mlp_4 (\u001b[38;5;33mMLP\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │          \u001b[38;5;34m10,368\u001b[0m │ pooling_nodes_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_disjoint_to_batched_gra… │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ mlp_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]                │\n",
       "│ (\u001b[38;5;33mCastDisjointToBatchedGraphS…\u001b[0m │                           │                 │                            │\n",
       "└───────────────────────────────┴───────────────────────────┴─────────────────┴────────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">55,424</span> (216.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m55,424\u001b[0m (216.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">55,424</span> (216.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m55,424\u001b[0m (216.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
      "Print Time for training:  0:02:24.640625\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 246/300\n",
      "29/29 - 1s - loss: 0.0700 - scaled_mean_absolute_error: 0.1470 - scaled_root_mean_squared_error: 0.2065 - lr: 2.8225e-04 - 571ms/epoch - 20ms/step\n",
      "Epoch 247/300\n",
      "29/29 - 1s - loss: 0.0680 - scaled_mean_absolute_error: 0.1427 - scaled_root_mean_squared_error: 0.2090 - lr: 2.7730e-04 - 649ms/epoch - 22ms/step\n",
      "Epoch 248/300\n",
      "29/29 - 1s - loss: 0.0695 - scaled_mean_absolute_error: 0.1458 - scaled_root_mean_squared_error: 0.2157 - lr: 2.7235e-04 - 686ms/epoch - 24ms/step\n",
      "Epoch 249/300\n",
      "29/29 - 1s - loss: 0.0663 - scaled_mean_absolute_error: 0.1392 - scaled_root_mean_squared_error: 0.2063 - lr: 2.6740e-04 - 572ms/epoch - 20ms/step\n",
      "Epoch 250/300\n",
      "29/29 - 1s - loss: 0.0677 - scaled_mean_absolute_error: 0.1423 - scaled_root_mean_squared_error: 0.2087 - val_loss: 0.2113 - val_scaled_mean_absolute_error: 0.4437 - val_scaled_root_mean_squared_error: 0.6105 - lr: 2.6245e-04 - 684ms/epoch - 24ms/step\n",
      "Epoch 251/300\n",
      "29/29 - 1s - loss: 0.0657 - scaled_mean_absolute_error: 0.1380 - scaled_root_mean_squared_error: 0.2057 - lr: 2.5750e-04 - 596ms/epoch - 21ms/step\n",
      "Epoch 252/300\n",
      "29/29 - 1s - loss: 0.0661 - scaled_mean_absolute_error: 0.1389 - scaled_root_mean_squared_error: 0.1972 - lr: 2.5255e-04 - 539ms/epoch - 19ms/step\n",
      "Epoch 253/300\n",
      "29/29 - 1s - loss: 0.0618 - scaled_mean_absolute_error: 0.1298 - scaled_root_mean_squared_error: 0.1954 - lr: 2.4760e-04 - 536ms/epoch - 18ms/step\n",
      "Epoch 254/300\n",
      "29/29 - 1s - loss: 0.0654 - scaled_mean_absolute_error: 0.1374 - scaled_root_mean_squared_error: 0.1997 - lr: 2.4265e-04 - 542ms/epoch - 19ms/step\n",
      "Epoch 255/300\n",
      "29/29 - 1s - loss: 0.0646 - scaled_mean_absolute_error: 0.1356 - scaled_root_mean_squared_error: 0.1981 - lr: 2.3770e-04 - 586ms/epoch - 20ms/step\n",
      "Epoch 256/300\n",
      "29/29 - 1s - loss: 0.0652 - scaled_mean_absolute_error: 0.1370 - scaled_root_mean_squared_error: 0.2003 - lr: 2.3275e-04 - 538ms/epoch - 19ms/step\n",
      "Epoch 257/300\n",
      "29/29 - 1s - loss: 0.0621 - scaled_mean_absolute_error: 0.1304 - scaled_root_mean_squared_error: 0.2019 - lr: 2.2780e-04 - 536ms/epoch - 18ms/step\n",
      "Epoch 258/300\n",
      "29/29 - 1s - loss: 0.0645 - scaled_mean_absolute_error: 0.1355 - scaled_root_mean_squared_error: 0.2016 - lr: 2.2285e-04 - 536ms/epoch - 18ms/step\n",
      "Epoch 259/300\n",
      "29/29 - 1s - loss: 0.0592 - scaled_mean_absolute_error: 0.1242 - scaled_root_mean_squared_error: 0.1875 - lr: 2.1790e-04 - 538ms/epoch - 19ms/step\n",
      "Epoch 260/300\n",
      "29/29 - 1s - loss: 0.0655 - scaled_mean_absolute_error: 0.1375 - scaled_root_mean_squared_error: 0.2035 - val_loss: 0.2102 - val_scaled_mean_absolute_error: 0.4415 - val_scaled_root_mean_squared_error: 0.6011 - lr: 2.1295e-04 - 640ms/epoch - 22ms/step\n",
      "Epoch 261/300\n",
      "29/29 - 1s - loss: 0.0681 - scaled_mean_absolute_error: 0.1430 - scaled_root_mean_squared_error: 0.2079 - lr: 2.0800e-04 - 544ms/epoch - 19ms/step\n",
      "Epoch 262/300\n",
      "29/29 - 1s - loss: 0.0629 - scaled_mean_absolute_error: 0.1320 - scaled_root_mean_squared_error: 0.1965 - lr: 2.0305e-04 - 538ms/epoch - 19ms/step\n",
      "Epoch 263/300\n",
      "29/29 - 1s - loss: 0.0647 - scaled_mean_absolute_error: 0.1359 - scaled_root_mean_squared_error: 0.1973 - lr: 1.9810e-04 - 539ms/epoch - 19ms/step\n",
      "Epoch 264/300\n",
      "29/29 - 1s - loss: 0.0660 - scaled_mean_absolute_error: 0.1385 - scaled_root_mean_squared_error: 0.2038 - lr: 1.9315e-04 - 570ms/epoch - 20ms/step\n",
      "Epoch 265/300\n",
      "29/29 - 1s - loss: 0.0620 - scaled_mean_absolute_error: 0.1302 - scaled_root_mean_squared_error: 0.1994 - lr: 1.8820e-04 - 538ms/epoch - 19ms/step\n",
      "Epoch 266/300\n",
      "29/29 - 1s - loss: 0.0602 - scaled_mean_absolute_error: 0.1265 - scaled_root_mean_squared_error: 0.1876 - lr: 1.8325e-04 - 536ms/epoch - 18ms/step\n",
      "Epoch 267/300\n",
      "29/29 - 1s - loss: 0.0591 - scaled_mean_absolute_error: 0.1241 - scaled_root_mean_squared_error: 0.1813 - lr: 1.7830e-04 - 541ms/epoch - 19ms/step\n",
      "Epoch 268/300\n",
      "29/29 - 1s - loss: 0.0594 - scaled_mean_absolute_error: 0.1247 - scaled_root_mean_squared_error: 0.1867 - lr: 1.7335e-04 - 534ms/epoch - 18ms/step\n",
      "Epoch 269/300\n",
      "29/29 - 1s - loss: 0.0591 - scaled_mean_absolute_error: 0.1241 - scaled_root_mean_squared_error: 0.1870 - lr: 1.6840e-04 - 539ms/epoch - 19ms/step\n",
      "Epoch 270/300\n",
      "29/29 - 1s - loss: 0.0617 - scaled_mean_absolute_error: 0.1295 - scaled_root_mean_squared_error: 0.1963 - val_loss: 0.2143 - val_scaled_mean_absolute_error: 0.4499 - val_scaled_root_mean_squared_error: 0.6136 - lr: 1.6345e-04 - 639ms/epoch - 22ms/step\n",
      "Epoch 271/300\n",
      "29/29 - 1s - loss: 0.0612 - scaled_mean_absolute_error: 0.1286 - scaled_root_mean_squared_error: 0.1896 - lr: 1.5850e-04 - 532ms/epoch - 18ms/step\n",
      "Epoch 272/300\n",
      "29/29 - 1s - loss: 0.0598 - scaled_mean_absolute_error: 0.1256 - scaled_root_mean_squared_error: 0.1851 - lr: 1.5355e-04 - 549ms/epoch - 19ms/step\n",
      "Epoch 273/300\n",
      "29/29 - 1s - loss: 0.0607 - scaled_mean_absolute_error: 0.1274 - scaled_root_mean_squared_error: 0.1877 - lr: 1.4860e-04 - 557ms/epoch - 19ms/step\n",
      "Epoch 274/300\n",
      "29/29 - 1s - loss: 0.0585 - scaled_mean_absolute_error: 0.1228 - scaled_root_mean_squared_error: 0.1858 - lr: 1.4365e-04 - 533ms/epoch - 18ms/step\n",
      "Epoch 275/300\n",
      "29/29 - 1s - loss: 0.0580 - scaled_mean_absolute_error: 0.1218 - scaled_root_mean_squared_error: 0.1805 - lr: 1.3870e-04 - 541ms/epoch - 19ms/step\n",
      "Epoch 276/300\n",
      "29/29 - 1s - loss: 0.0560 - scaled_mean_absolute_error: 0.1175 - scaled_root_mean_squared_error: 0.1780 - lr: 1.3375e-04 - 585ms/epoch - 20ms/step\n",
      "Epoch 277/300\n",
      "29/29 - 1s - loss: 0.0593 - scaled_mean_absolute_error: 0.1246 - scaled_root_mean_squared_error: 0.1847 - lr: 1.2880e-04 - 540ms/epoch - 19ms/step\n",
      "Epoch 278/300\n",
      "29/29 - 1s - loss: 0.0585 - scaled_mean_absolute_error: 0.1227 - scaled_root_mean_squared_error: 0.1832 - lr: 1.2385e-04 - 536ms/epoch - 18ms/step\n",
      "Epoch 279/300\n",
      "29/29 - 1s - loss: 0.0577 - scaled_mean_absolute_error: 0.1211 - scaled_root_mean_squared_error: 0.1838 - lr: 1.1890e-04 - 537ms/epoch - 19ms/step\n",
      "Epoch 280/300\n",
      "29/29 - 1s - loss: 0.0570 - scaled_mean_absolute_error: 0.1198 - scaled_root_mean_squared_error: 0.1774 - val_loss: 0.2129 - val_scaled_mean_absolute_error: 0.4471 - val_scaled_root_mean_squared_error: 0.6161 - lr: 1.1395e-04 - 690ms/epoch - 24ms/step\n",
      "Epoch 281/300\n",
      "29/29 - 1s - loss: 0.0586 - scaled_mean_absolute_error: 0.1231 - scaled_root_mean_squared_error: 0.1826 - lr: 1.0900e-04 - 570ms/epoch - 20ms/step\n",
      "Epoch 282/300\n",
      "29/29 - 1s - loss: 0.0574 - scaled_mean_absolute_error: 0.1205 - scaled_root_mean_squared_error: 0.1801 - lr: 1.0405e-04 - 571ms/epoch - 20ms/step\n",
      "Epoch 283/300\n",
      "29/29 - 1s - loss: 0.0559 - scaled_mean_absolute_error: 0.1175 - scaled_root_mean_squared_error: 0.1768 - lr: 9.9100e-05 - 570ms/epoch - 20ms/step\n",
      "Epoch 284/300\n",
      "29/29 - 1s - loss: 0.0593 - scaled_mean_absolute_error: 0.1245 - scaled_root_mean_squared_error: 0.1824 - lr: 9.4150e-05 - 533ms/epoch - 18ms/step\n",
      "Epoch 285/300\n",
      "29/29 - 1s - loss: 0.0559 - scaled_mean_absolute_error: 0.1173 - scaled_root_mean_squared_error: 0.1811 - lr: 8.9200e-05 - 542ms/epoch - 19ms/step\n",
      "Epoch 286/300\n",
      "29/29 - 1s - loss: 0.0577 - scaled_mean_absolute_error: 0.1212 - scaled_root_mean_squared_error: 0.1822 - lr: 8.4250e-05 - 536ms/epoch - 18ms/step\n",
      "Epoch 287/300\n",
      "29/29 - 1s - loss: 0.0560 - scaled_mean_absolute_error: 0.1175 - scaled_root_mean_squared_error: 0.1759 - lr: 7.9300e-05 - 535ms/epoch - 18ms/step\n",
      "Epoch 288/300\n",
      "29/29 - 1s - loss: 0.0546 - scaled_mean_absolute_error: 0.1146 - scaled_root_mean_squared_error: 0.1740 - lr: 7.4350e-05 - 548ms/epoch - 19ms/step\n",
      "Epoch 289/300\n",
      "29/29 - 1s - loss: 0.0560 - scaled_mean_absolute_error: 0.1176 - scaled_root_mean_squared_error: 0.1755 - lr: 6.9400e-05 - 598ms/epoch - 21ms/step\n",
      "Epoch 290/300\n",
      "29/29 - 1s - loss: 0.0556 - scaled_mean_absolute_error: 0.1168 - scaled_root_mean_squared_error: 0.1759 - val_loss: 0.2128 - val_scaled_mean_absolute_error: 0.4468 - val_scaled_root_mean_squared_error: 0.6120 - lr: 6.4450e-05 - 736ms/epoch - 25ms/step\n",
      "Epoch 291/300\n",
      "29/29 - 1s - loss: 0.0552 - scaled_mean_absolute_error: 0.1159 - scaled_root_mean_squared_error: 0.1708 - lr: 5.9500e-05 - 563ms/epoch - 19ms/step\n",
      "Epoch 292/300\n",
      "29/29 - 1s - loss: 0.0531 - scaled_mean_absolute_error: 0.1116 - scaled_root_mean_squared_error: 0.1680 - lr: 5.4550e-05 - 574ms/epoch - 20ms/step\n",
      "Epoch 293/300\n",
      "29/29 - 1s - loss: 0.0538 - scaled_mean_absolute_error: 0.1130 - scaled_root_mean_squared_error: 0.1728 - lr: 4.9600e-05 - 583ms/epoch - 20ms/step\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 294/300\n",
      "29/29 - 1s - loss: 0.0552 - scaled_mean_absolute_error: 0.1159 - scaled_root_mean_squared_error: 0.1771 - lr: 4.4650e-05 - 553ms/epoch - 19ms/step\n",
      "Epoch 295/300\n",
      "29/29 - 1s - loss: 0.0534 - scaled_mean_absolute_error: 0.1121 - scaled_root_mean_squared_error: 0.1707 - lr: 3.9700e-05 - 583ms/epoch - 20ms/step\n",
      "Epoch 296/300\n",
      "29/29 - 1s - loss: 0.0547 - scaled_mean_absolute_error: 0.1149 - scaled_root_mean_squared_error: 0.1717 - lr: 3.4750e-05 - 551ms/epoch - 19ms/step\n",
      "Epoch 297/300\n",
      "29/29 - 1s - loss: 0.0517 - scaled_mean_absolute_error: 0.1085 - scaled_root_mean_squared_error: 0.1651 - lr: 2.9800e-05 - 541ms/epoch - 19ms/step\n",
      "Epoch 298/300\n",
      "29/29 - 1s - loss: 0.0542 - scaled_mean_absolute_error: 0.1137 - scaled_root_mean_squared_error: 0.1735 - lr: 2.4850e-05 - 591ms/epoch - 20ms/step\n",
      "Epoch 299/300\n",
      "29/29 - 1s - loss: 0.0507 - scaled_mean_absolute_error: 0.1064 - scaled_root_mean_squared_error: 0.1671 - lr: 1.9900e-05 - 538ms/epoch - 19ms/step\n",
      "Epoch 300/300\n",
      "29/29 - 1s - loss: 0.0534 - scaled_mean_absolute_error: 0.1121 - scaled_root_mean_squared_error: 0.1741 - val_loss: 0.2115 - val_scaled_mean_absolute_error: 0.4441 - val_scaled_root_mean_squared_error: 0.6109 - lr: 1.4950e-05 - 645ms/epoch - 22ms/step\n",
      "Print Time for training:  0:04:32.187500\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from kgcnn.models.utils import get_model_class\n",
    "from keras.optimizers import Adam\n",
    "from kgcnn.training.scheduler import LinearLearningRateScheduler\n",
    "from kgcnn.literature.DMPNN import make_model\n",
    "from kgcnn.data.transform.scaler.molecule import QMGraphLabelScaler\n",
    "from kgcnn.data.transform.scaler.standard import StandardLabelScaler\n",
    "from kgcnn.metrics.metrics import ScaledMeanAbsoluteError, ScaledRootMeanSquaredError\n",
    "from datetime import timedelta\n",
    "\n",
    "history_list, test_indices_list = [], []\n",
    "model, hist, x_test, y_test, scaler, atoms_test = None, None, None, None, None, None\n",
    "splits_done = 0\n",
    "for i, (train_index, test_index) in enumerate(train_test_indices):\n",
    "    print(\"Running training on fold: %s\" % i)\n",
    "\n",
    "    # Make the model for current split using model kwargs from hyperparameter.\n",
    "    # They are always updated on top of the models default kwargs.\n",
    "    model = make_model(**model_config[\"config\"])\n",
    "\n",
    "    # First select training and test graphs from indices, then convert them into tensorflow tensor\n",
    "    # representation. Which property of the dataset and whether the tensor will be ragged is retrieved from the\n",
    "    # kwargs of the keras `Input` layers ('name' and 'ragged').\n",
    "    dataset_train, dataset_test = data[train_index], data[test_index]\n",
    "    x_train, y_train = dataset_train.tensor(model_config[\"config\"][\"inputs\"]), labels[train_index]\n",
    "    x_test, y_test = dataset_test.tensor(model_config[\"config\"][\"inputs\"]), labels[test_index]\n",
    "    \n",
    "    atoms_test = dataset_test.get(\"node_number\")\n",
    "    atoms_train = dataset_train.get(\"node_number\")\n",
    "    \n",
    "    scaler = StandardLabelScaler(with_std=True,with_mean=True, copy=True)\n",
    "    scaler.fit(y_train, atomic_number=atoms_train)\n",
    "    y_train = scaler.transform(y_train, atomic_number=atoms_train)\n",
    "    y_test = scaler.transform(y_test, atomic_number=atoms_test)\n",
    "\n",
    "    # If scaler was used we add rescaled standard metrics to compile.\n",
    "    scaler_scale = scaler.get_scaling()\n",
    "    mae_metric = ScaledMeanAbsoluteError(scaler_scale.shape, name=\"scaled_mean_absolute_error\")\n",
    "    rms_metric = ScaledRootMeanSquaredError(scaler_scale.shape, name=\"scaled_root_mean_squared_error\")\n",
    "    if scaler.scale_ is not None:\n",
    "        mae_metric.set_scale(scaler_scale)\n",
    "        rms_metric.set_scale(scaler_scale)\n",
    "    metrics = [mae_metric, rms_metric]\n",
    "\n",
    "    # Compile model with optimizer and loss\n",
    "    model.compile(loss=\"mean_absolute_error\", metrics=metrics, optimizer=Adam(learning_rate=5e-04))\n",
    "    print(model.summary())\n",
    "\n",
    "    # Build model with reasonable data.\n",
    "    model.predict(x_test, batch_size=2, steps=2)\n",
    "    model._compile_metrics.build(y_test, y_test)\n",
    "    model._compile_loss.build(y_test, y_test)\n",
    "\n",
    "    # Start and time training\n",
    "    start = time.process_time()\n",
    "    hist = model.fit(x_train, y_train,\n",
    "                     validation_data=(x_test, y_test),\n",
    "                     batch_size=32, \n",
    "                     epochs=300, \n",
    "                     validation_freq=10, \n",
    "                     # Change to verbose = 2 to see progress\n",
    "                     verbose=0,\n",
    "                     callbacks= [\n",
    "                         LinearLearningRateScheduler(\n",
    "                             learning_rate_start=0.001, learning_rate_stop=1e-05, epo_min=100, epo=300)\n",
    "                     ])\n",
    "    stop = time.process_time()\n",
    "    print(\"Print Time for training: \", str(timedelta(seconds=stop - start)))\n",
    "\n",
    "    # Get loss from history\n",
    "    history_list.append(hist)\n",
    "    test_indices_list.append([train_index, test_index])\n",
    "    splits_done = splits_done + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e49f35e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from kgcnn.utils.plots import plot_train_test_loss, plot_predict_true\n",
    "\n",
    "plot_train_test_loss(history_list, loss_name=None, val_loss_name=None,\n",
    "                     model_name=\"DMPNN\", data_unit=\"mol/L\", dataset_name=\"esol\",\n",
    "                     filepath=\"\", file_name=f\"loss.png\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e508ad31",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m8/8\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot prediction for the last split.\n",
    "predicted_y = model.predict(x_test)\n",
    "true_y = y_test\n",
    "\n",
    "# Predictions must be rescaled to original values.\n",
    "predicted_y = scaler.inverse_transform(predicted_y)\n",
    "true_y = scaler.inverse_transform(true_y)\n",
    "\n",
    "# Plotting the prediction vs. true test targets for last split. Note for classification this is also done but\n",
    "# can be ignored.\n",
    "plot_predict_true(predicted_y, true_y,\n",
    "                  filepath=\"\", data_unit=\"mol/L\",\n",
    "                  model_name=\"DMPNN\", dataset_name=\"ESOL\",\n",
    "                  file_name=f\"predict.png\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6d0f67e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}