notebooks/workflow_qm_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "85f131d4",
   "metadata": {},
   "source": [
    "## QM Workflow with QM7 example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "01403a2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras as ks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d3134c5",
   "metadata": {},
   "source": [
    "just for downloading the some data in place of your data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "491bda82",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "from kgcnn.data.datasets.QM7Dataset import QM7Dataset\n",
    "QM7Dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c4aa717",
   "metadata": {},
   "source": [
    "move the data from \"~/.kgcnn.datasets.qm7\" to your local \"qm7\" folder. The QM7 data is just some example data for this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fcad446d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kgcnn.data.qm import QMDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "245e1140",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = QMDataset(\n",
    "    data_directory=\"qm7/\",\n",
    "    dataset_name=\"qm7\",\n",
    "    file_name=\"qm7.csv\",\n",
    "    file_name_xyz=\"qm7.xyz\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d55c2455",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.prepare_data(\n",
    "    overwrite=True,\n",
    "    file_column_name=None,\n",
    "    make_sdf=True\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "45787e49",
   "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",
    "    \n",
    "    add_hydrogen=True,\n",
    "    make_directed=False,\n",
    "    sanitize=False,\n",
    "    compute_partial_charges=None,\n",
    "    label_column_name=\"u0_atom\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f7926ab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from kgcnn.graph.preprocessor import SetRange\n",
    "data.map_list(SetRange(max_distance=5.0, in_place=True));\n",
    "data.map_list(method=\"count_nodes_and_edges\", total_edges=\"total_ranges\", count_edges=\"range_indices\", \n",
    "    count_nodes=\"node_number\", total_nodes=\"total_nodes\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8dad449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'node_symbol': array(['C', 'H', 'H', 'H', 'H'], dtype='<U1'), 'node_number': array([6, 1, 1, 1, 1]), 'node_coordinates': array([[ 0.9983, -0.0025, -0.0044],\n",
      "       [ 2.0902, -0.0024,  0.0041],\n",
      "       [ 0.6338,  1.0269,  0.0041],\n",
      "       [ 0.627 , -0.5277,  0.8781],\n",
      "       [ 0.6414, -0.5075, -0.9054]]), 'edge_indices': array([[0, 1],\n",
      "       [0, 2],\n",
      "       [0, 3],\n",
      "       [0, 4],\n",
      "       [1, 0],\n",
      "       [2, 0],\n",
      "       [3, 0],\n",
      "       [4, 0]], dtype=int64), 'edge_number': array([1, 1, 1, 1, 1, 1, 1, 1]), 'graph_labels': array(-417.96), 'node_attributes': array([[0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
      "        0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
      "        1., 0., 0., 0., 0., 0., 0., 0., 2.],\n",
      "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
      "        0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
      "        1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
      "        0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
      "        1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
      "        0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
      "        1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
      "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1.,\n",
      "        0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
      "        1., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32), 'edge_attributes': array([[1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
      "       [1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
      "       [1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
      "       [1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
      "       [1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
      "       [1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
      "       [1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
      "       [1., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0.]], dtype=float32), 'graph_attributes': array([16.0313,  5.    ], dtype=float32), 'range_indices': array([[0, 1],\n",
      "       [0, 2],\n",
      "       [0, 3],\n",
      "       [0, 4],\n",
      "       [1, 0],\n",
      "       [1, 2],\n",
      "       [1, 3],\n",
      "       [1, 4],\n",
      "       [2, 0],\n",
      "       [2, 1],\n",
      "       [2, 3],\n",
      "       [2, 4],\n",
      "       [3, 0],\n",
      "       [3, 1],\n",
      "       [3, 2],\n",
      "       [3, 4],\n",
      "       [4, 0],\n",
      "       [4, 1],\n",
      "       [4, 2],\n",
      "       [4, 3]]), 'range_attributes': array([[1.09193309],\n",
      "       [1.09206083],\n",
      "       [1.09201876],\n",
      "       [1.09279623],\n",
      "       [1.09193309],\n",
      "       [1.78341231],\n",
      "       [1.78347143],\n",
      "       [1.78363048],\n",
      "       [1.09206083],\n",
      "       [1.78341231],\n",
      "       [1.78345266],\n",
      "       [1.7837128 ],\n",
      "       [1.09201876],\n",
      "       [1.78347143],\n",
      "       [1.78345266],\n",
      "       [1.78367252],\n",
      "       [1.09279623],\n",
      "       [1.78363048],\n",
      "       [1.7837128 ],\n",
      "       [1.78367252]]), 'total_nodes': array(5), 'total_ranges': array(20)}\n"
     ]
    }
   ],
   "source": [
    "print(data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d1141978",
   "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": 10,
   "id": "858a4cb6",
   "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": 11,
   "id": "064ea7df",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_config= {\n",
    "    \"class_name\": \"make_model\",\n",
    "    \"module_name\": \"kgcnn.literature.Schnet\",\n",
    "    \"config\": {\n",
    "        \"name\": \"Schnet\",\n",
    "        \"inputs\": [\n",
    "            {\"shape\": [None], \"name\": \"node_number\", \"dtype\": \"int32\"},\n",
    "            {\"shape\": [None, 3], \"name\": \"node_coordinates\", \"dtype\": \"float32\"},\n",
    "            {\"shape\": [None, 2], \"name\": \"range_indices\", \"dtype\": \"int64\"},\n",
    "            {\"shape\": (), \"name\": \"total_nodes\", \"dtype\": \"int64\"},\n",
    "            {\"shape\": (), \"name\": \"total_ranges\", \"dtype\": \"int64\"}\n",
    "        ],\n",
    "        \"cast_disjoint_kwargs\": {\"padded_disjoint\": False},\n",
    "        \"input_node_embedding\": {\"input_dim\": 95, \"output_dim\": 64},\n",
    "        \"last_mlp\": {\"use_bias\": [True, True, True], \"units\": [128, 64, 1],\n",
    "                     \"activation\": [\n",
    "                         {\"class_name\": \"function\", \"config\": \"kgcnn>shifted_softplus\"}, \n",
    "                         {\"class_name\": \"function\", \"config\": \"kgcnn>shifted_softplus\"}, \n",
    "                         'linear'\n",
    "                     ]},\n",
    "        \"interaction_args\": {\n",
    "            \"units\": 128, \"use_bias\": True, \n",
    "            \"activation\": {\"class_name\": \"function\", \"config\": \"kgcnn>shifted_softplus\"}, \n",
    "            \"cfconv_pool\": \"scatter_sum\"\n",
    "        },\n",
    "        \"node_pooling_args\": {\"pooling_method\": \"scatter_sum\"},\n",
    "        \"depth\": 4,\n",
    "        \"gauss_args\": {\"bins\": 20, \"distance\": 4, \"offset\": 0.0, \"sigma\": 0.4}, \"verbose\": 10,\n",
    "        \"output_embedding\": \"graph\",\n",
    "        \"use_output_mlp\": False,\n",
    "        \"output_mlp\": None,\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a24751c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running training on fold: 0\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step\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: \"Schnet\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"Schnet\"\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_coordinates (<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\">3</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",
       "│ node_number (<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\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ range_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_ranges (<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_attributes_to_d… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_coordinates[<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_nodes[<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: #00d7ff; text-decoration-color: #00d7ff\">None</span>),       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_number[<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>),   │                 │ range_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_ranges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_position (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">NodePosition</span>)  │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)]    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_attributes_t… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,080</span> │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_distance_euclidean       │ (<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> │ node_position[<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\">NodeDistanceEuclidean</span>)       │                           │                 │ node_position[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">1</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\">8,320</span> │ embedding[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gauss_basis_layer             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_distance_euclidean[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GaussBasisLayer</span>)             │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction           │ (<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\">68,608</span> │ dense[<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\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer[<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",
       "│ sch_net_interaction_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\">68,608</span> │ sch_net_interaction[<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\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer[<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",
       "│ sch_net_interaction_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\">68,608</span> │ sch_net_interaction_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer[<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",
       "│ sch_net_interaction_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\">68,608</span> │ sch_net_interaction_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer[<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\">24,833</span> │ sch_net_interaction_3[<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",
       "│                               │                           │                 │ cast_batched_attributes_t… │\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\">1</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_attributes_t… │\n",
       "│                               │                           │                 │ mlp[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],                 │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\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> │ pooling_nodes[<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_coordinates (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\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",
       "│ node_number (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ range_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_ranges (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),       │               \u001b[38;5;34m0\u001b[0m │ node_coordinates[\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_nodes[\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;45mNone\u001b[0m),       │               \u001b[38;5;34m0\u001b[0m │ node_number[\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),   │                 │ range_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_ranges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_position (\u001b[38;5;33mNodePosition\u001b[0m)  │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)]    │               \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_t… │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │           \u001b[38;5;34m6,080\u001b[0m │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_distance_euclidean       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ node_position[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],       │\n",
       "│ (\u001b[38;5;33mNodeDistanceEuclidean\u001b[0m)       │                           │                 │ node_position[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\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;34m8,320\u001b[0m │ embedding[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gauss_basis_layer             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ node_distance_euclidean[\u001b[38;5;34m0\u001b[0m… │\n",
       "│ (\u001b[38;5;33mGaussBasisLayer\u001b[0m)             │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ dense[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],               │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],   │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_1         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],   │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_2         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],   │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_3         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer[\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;34m24,833\u001b[0m │ sch_net_interaction_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes (\u001b[38;5;33mPoolingNodes\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_t… │\n",
       "│                               │                           │                 │ mlp[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],                 │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\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 │ pooling_nodes[\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\">313,665</span> (1.20 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\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\">313,665</span> (1.20 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\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": [
      " Compiled with jit: False\n",
      "Print Time for training:  0:40:00.375000\n",
      "Running training on fold: 1\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\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: \"Schnet\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"Schnet\"\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_coordinates (<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\">3</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",
       "│ node_number (<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\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ range_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_ranges (<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_attributes_to_d… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_coordinates[<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_nodes[<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: #00d7ff; text-decoration-color: #00d7ff\">None</span>),       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_number[<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>),   │                 │ range_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_ranges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_position_1               │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)]    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_attributes_t… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">NodePosition</span>)                │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,080</span> │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_distance_euclidean_1     │ (<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> │ node_position_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\">NodeDistanceEuclidean</span>)       │                           │                 │ node_position_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_21 (<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\">8,320</span> │ embedding_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gauss_basis_layer_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_distance_euclidean_1… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GaussBasisLayer</span>)             │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_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\">68,608</span> │ dense_21[<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\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_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\">68,608</span> │ sch_net_interaction_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_6         │ (<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\">68,608</span> │ sch_net_interaction_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_7         │ (<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\">68,608</span> │ sch_net_interaction_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\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\">24,833</span> │ sch_net_interaction_7[<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",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_1               │ (<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> │ cast_batched_attributes_t… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">PoolingNodes</span>)                │                           │                 │ mlp_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],               │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\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> │ pooling_nodes_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_coordinates (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\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",
       "│ node_number (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ range_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_ranges (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),       │               \u001b[38;5;34m0\u001b[0m │ node_coordinates[\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_nodes[\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;45mNone\u001b[0m),       │               \u001b[38;5;34m0\u001b[0m │ node_number[\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),   │                 │ range_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_ranges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_position_1               │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)]    │               \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_t… │\n",
       "│ (\u001b[38;5;33mNodePosition\u001b[0m)                │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │           \u001b[38;5;34m6,080\u001b[0m │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_distance_euclidean_1     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ node_position_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mNodeDistanceEuclidean\u001b[0m)       │                           │                 │ node_position_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_21 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │           \u001b[38;5;34m8,320\u001b[0m │ embedding_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gauss_basis_layer_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ node_distance_euclidean_1… │\n",
       "│ (\u001b[38;5;33mGaussBasisLayer\u001b[0m)             │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_4         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ dense_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_5         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_6         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_7         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_1[\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;34m24,833\u001b[0m │ sch_net_interaction_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_1               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_t… │\n",
       "│ (\u001b[38;5;33mPoolingNodes\u001b[0m)                │                           │                 │ mlp_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],               │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\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 │ pooling_nodes_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\">313,665</span> (1.20 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\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\">313,665</span> (1.20 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\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": [
      " Compiled with jit: False\n",
      "Print Time for training:  0:39:06.453125\n",
      "Running training on fold: 2\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\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: \"Schnet\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"Schnet\"\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_coordinates (<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\">3</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",
       "│ node_number (<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\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ range_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_ranges (<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_attributes_to_d… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_coordinates[<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_nodes[<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: #00d7ff; text-decoration-color: #00d7ff\">None</span>),       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_number[<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>),   │                 │ range_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_ranges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_position_2               │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)]    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_attributes_t… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">NodePosition</span>)                │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,080</span> │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_distance_euclidean_2     │ (<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> │ node_position_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\">NodeDistanceEuclidean</span>)       │                           │                 │ node_position_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_42 (<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\">8,320</span> │ embedding_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gauss_basis_layer_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_distance_euclidean_2… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GaussBasisLayer</span>)             │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_8         │ (<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\">68,608</span> │ dense_42[<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\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "│ sch_net_interaction_9         │ (<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\">68,608</span> │ sch_net_interaction_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "│ sch_net_interaction_10        │ (<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\">68,608</span> │ sch_net_interaction_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">…</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "│ sch_net_interaction_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\">68,608</span> │ sch_net_interaction_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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_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\">24,833</span> │ sch_net_interaction_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_2               │ (<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> │ cast_batched_attributes_t… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">PoolingNodes</span>)                │                           │                 │ mlp_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],               │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\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> │ pooling_nodes_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_coordinates (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\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",
       "│ node_number (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ range_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_ranges (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),       │               \u001b[38;5;34m0\u001b[0m │ node_coordinates[\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_nodes[\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;45mNone\u001b[0m),       │               \u001b[38;5;34m0\u001b[0m │ node_number[\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),   │                 │ range_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_ranges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_position_2               │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)]    │               \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_t… │\n",
       "│ (\u001b[38;5;33mNodePosition\u001b[0m)                │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding_2 (\u001b[38;5;33mEmbedding\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │           \u001b[38;5;34m6,080\u001b[0m │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_distance_euclidean_2     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ node_position_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mNodeDistanceEuclidean\u001b[0m)       │                           │                 │ node_position_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_42 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │           \u001b[38;5;34m8,320\u001b[0m │ embedding_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gauss_basis_layer_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ node_distance_euclidean_2… │\n",
       "│ (\u001b[38;5;33mGaussBasisLayer\u001b[0m)             │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_8         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ dense_42[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_9         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_10        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m…\u001b[0m │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_11        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_10[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_2[\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;34m24,833\u001b[0m │ sch_net_interaction_11[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_2               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_t… │\n",
       "│ (\u001b[38;5;33mPoolingNodes\u001b[0m)                │                           │                 │ mlp_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],               │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\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 │ pooling_nodes_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\">313,665</span> (1.20 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\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\">313,665</span> (1.20 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\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": [
      " Compiled with jit: False\n",
      "Print Time for training:  0:39:04.390625\n",
      "Running training on fold: 3\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 26ms/step\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: \"Schnet\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"Schnet\"\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_coordinates (<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\">3</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",
       "│ node_number (<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\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ range_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_ranges (<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_attributes_to_d… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_coordinates[<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_nodes[<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: #00d7ff; text-decoration-color: #00d7ff\">None</span>),       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_number[<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>),   │                 │ range_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_ranges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_position_3               │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)]    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_attributes_t… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">NodePosition</span>)                │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,080</span> │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_distance_euclidean_3     │ (<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> │ node_position_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\">NodeDistanceEuclidean</span>)       │                           │                 │ node_position_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_63 (<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\">8,320</span> │ embedding_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gauss_basis_layer_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_distance_euclidean_3… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GaussBasisLayer</span>)             │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_12        │ (<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\">68,608</span> │ dense_63[<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\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_13        │ (<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\">68,608</span> │ sch_net_interaction_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_14        │ (<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\">68,608</span> │ sch_net_interaction_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_15        │ (<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\">68,608</span> │ sch_net_interaction_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\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\">24,833</span> │ sch_net_interaction_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_3               │ (<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> │ cast_batched_attributes_t… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">PoolingNodes</span>)                │                           │                 │ mlp_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],               │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\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> │ pooling_nodes_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_coordinates (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\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",
       "│ node_number (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ range_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_ranges (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),       │               \u001b[38;5;34m0\u001b[0m │ node_coordinates[\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_nodes[\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;45mNone\u001b[0m),       │               \u001b[38;5;34m0\u001b[0m │ node_number[\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),   │                 │ range_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_ranges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_position_3               │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)]    │               \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_t… │\n",
       "│ (\u001b[38;5;33mNodePosition\u001b[0m)                │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding_3 (\u001b[38;5;33mEmbedding\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │           \u001b[38;5;34m6,080\u001b[0m │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_distance_euclidean_3     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ node_position_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mNodeDistanceEuclidean\u001b[0m)       │                           │                 │ node_position_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_63 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │           \u001b[38;5;34m8,320\u001b[0m │ embedding_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gauss_basis_layer_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ node_distance_euclidean_3… │\n",
       "│ (\u001b[38;5;33mGaussBasisLayer\u001b[0m)             │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_12        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ dense_63[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_13        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_12[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_14        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_13[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_15        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_14[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_3[\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;34m24,833\u001b[0m │ sch_net_interaction_15[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_3               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_t… │\n",
       "│ (\u001b[38;5;33mPoolingNodes\u001b[0m)                │                           │                 │ mlp_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],               │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\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 │ pooling_nodes_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\">313,665</span> (1.20 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\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\">313,665</span> (1.20 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\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": [
      " Compiled with jit: False\n",
      "Print Time for training:  0:39:06.609375\n",
      "Running training on fold: 4\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 16ms/step\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: \"Schnet\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"Schnet\"\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_coordinates (<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\">3</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",
       "│ node_number (<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\">0</span> │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ range_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_ranges (<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_attributes_to_d… │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>),       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_coordinates[<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_nodes[<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: #00d7ff; text-decoration-color: #00d7ff\">None</span>),       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_number[<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>),   │                 │ range_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_ranges[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_position_4               │ [(<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>), (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)]    │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ cast_batched_attributes_t… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">NodePosition</span>)                │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,080</span> │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_distance_euclidean_4     │ (<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> │ node_position_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\">NodeDistanceEuclidean</span>)       │                           │                 │ node_position_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_84 (<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\">8,320</span> │ embedding_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gauss_basis_layer_4           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ node_distance_euclidean_4… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GaussBasisLayer</span>)             │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_16        │ (<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\">68,608</span> │ dense_84[<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\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "│ sch_net_interaction_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\">68,608</span> │ sch_net_interaction_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "│ sch_net_interaction_18        │ (<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\">68,608</span> │ sch_net_interaction_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "│ sch_net_interaction_19        │ (<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\">68,608</span> │ sch_net_interaction_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SchNetInteraction</span>)           │                           │                 │ gauss_basis_layer_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",
       "│ 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\">24,833</span> │ sch_net_interaction_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_4               │ (<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> │ cast_batched_attributes_t… │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">PoolingNodes</span>)                │                           │                 │ mlp_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>],               │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\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> │ pooling_nodes_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_coordinates (\u001b[38;5;33mInputLayer\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\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",
       "│ node_number (\u001b[38;5;33mInputLayer\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m)              │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ range_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_ranges (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m)                    │               \u001b[38;5;34m0\u001b[0m │ -                          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ cast_batched_attributes_to_d… │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m),       │               \u001b[38;5;34m0\u001b[0m │ node_coordinates[\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_nodes[\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;45mNone\u001b[0m),       │               \u001b[38;5;34m0\u001b[0m │ node_number[\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),   │                 │ range_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_ranges[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_position_4               │ [(\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m), (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)]    │               \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_t… │\n",
       "│ (\u001b[38;5;33mNodePosition\u001b[0m)                │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ embedding_4 (\u001b[38;5;33mEmbedding\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                │           \u001b[38;5;34m6,080\u001b[0m │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ node_distance_euclidean_4     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ node_position_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],     │\n",
       "│ (\u001b[38;5;33mNodeDistanceEuclidean\u001b[0m)       │                           │                 │ node_position_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m1\u001b[0m]      │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ dense_84 (\u001b[38;5;33mDense\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │           \u001b[38;5;34m8,320\u001b[0m │ embedding_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ gauss_basis_layer_4           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │ node_distance_euclidean_4… │\n",
       "│ (\u001b[38;5;33mGaussBasisLayer\u001b[0m)             │                           │                 │                            │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_16        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ dense_84[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],            │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_17        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_16[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_18        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_17[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m], │\n",
       "│                               │                           │                 │ cast_batched_indices_to_d… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ sch_net_interaction_19        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)               │          \u001b[38;5;34m68,608\u001b[0m │ sch_net_interaction_18[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│ (\u001b[38;5;33mSchNetInteraction\u001b[0m)           │                           │                 │ gauss_basis_layer_4[\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;34m24,833\u001b[0m │ sch_net_interaction_19[\u001b[38;5;34m0\u001b[0m]… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\n",
       "├───────────────────────────────┼───────────────────────────┼─────────────────┼────────────────────────────┤\n",
       "│ pooling_nodes_4               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │ cast_batched_attributes_t… │\n",
       "│ (\u001b[38;5;33mPoolingNodes\u001b[0m)                │                           │                 │ mlp_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m],               │\n",
       "│                               │                           │                 │ cast_batched_attributes_t… │\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 │ pooling_nodes_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\">313,665</span> (1.20 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\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\">313,665</span> (1.20 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m313,665\u001b[0m (1.20 MB)\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": [
      " Compiled with jit: False\n",
      "Print Time for training:  0:56:44.171875\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.Schnet 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 = QMGraphLabelScaler([StandardLabelScaler(with_std=True, with_mean=True, copy=True)])\n",
    "    scaler.fit(y=y_train, atomic_number=atoms_train)\n",
    "    y_train = scaler.transform(y=y_train, atomic_number=atoms_train)\n",
    "    y_test = scaler.transform(y=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",
    "\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",
    "    model.summary()\n",
    "    print(\" Compiled with jit: %s\" % model._jit_compile)  # noqa\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=800, \n",
    "                     validation_freq=10, \n",
    "                     # Change to verbose = 2 to see progress\n",
    "                     verbose=0,\n",
    "                     callbacks= [\n",
    "                         LinearLearningRateScheduler(\n",
    "                             learning_rate_start=0.0005, learning_rate_stop=1e-05, epo_min=100, epo=800)\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": 13,
   "id": "c1d034b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjsAAAHHCAYAAABZbpmkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hT1f/A8XfaprtpSgcdQFsBoUwBAcsQEGSUPSxLZMkQEcGNKHv8xIVMGSLIFpCNLBFkyVJAxhdRRgtt2XRAV5rz+6P2SuiGhgJ+Xs+Th+bcc89KSD4599x7dUophRBCCCHEE8qmsBsghBBCCGFNEuwIIYQQ4okmwY4QQgghnmgS7AghhBDiiSbBjhBCCCGeaBLsCCGEEOKJJsGOEEIIIZ5oEuwIIYQQ4okmwY4QQgghnmgS7AjxHzZv3jx0Oh3nz5/P9747duxAp9OxY8eOAm/Xf9GmTZt45plncHR0RKfTcevWrcJuUp4FBQXRokWLwm6GENmSYEcIKwsKCkKn02X5KF26dK77jx8/ntWrV1u/oaLQXL9+nfDwcJycnJg2bRoLFizAxcXFqnX+8ccfdOjQgcDAQBwdHQkICODFF19kypQpVq23oG3cuJGRI0cWdjPEI04n98YSwrpWr15NQkKCRdqFCxf46KOPGDBgANOmTctxf1dXVzp06MC8efMKvG1paWmkpqbi4OCATqfL175ms5mUlBTs7e2xsZHfTQ9i06ZNNGvWjK1bt9KoUSOr17d3714aNGhAiRIl6N69O76+vkRGRvLrr7/y999/89dff+WrvKCgICpUqMD69eut1OLsDRw4kGnTpiFfZSIndoXdACGedG3atMmUNnbsWAC6du1aoHXdvn07XzMCtra22Nra3lddNjY2ODo63te+jzqlFElJSTg5OT2U+q5cuQKA0WgssDJzei+MGzcOd3d3Dh48mKnOjLYI8SSRn2PiP2v37t1Ur14dR0dHSpYsycyZMxk5cmSmGQ6dTsfAgQNZvnw55cqVw8nJidDQUP744w8AZs6cSalSpXB0dKR+/fp5Wv+yePFigoODqVWrVo75dDodt2/fZv78+dqhrx49egBobT158iRdunTBw8ODOnXqAHDs2DF69OjBU089haOjI76+vvTq1Yvr169blJ/Vmp2M9Re7d++mRo0aODo68tRTT/Hdd99Z7JvVmp369etToUIFTp48SYMGDXB2diYgIICJEydm6tuFCxdo1aoVLi4u+Pj4MGTIEDZv3pzndUCXLl2id+/e+Pv74+DgQHBwMK+99hopKSkW43OvnPq8efNmnn32WZycnJg5cyYVKlSgQYMGmcowm80EBATQoUMHi7RJkyZRvnx5HB0dKVq0KP369ePmzZs59qN+/fp0794dgOrVq1u8xgDLly+nWrVqODk54eXlxcsvv8ylS5csyujRoweurq78/fffhIWF4ebmlmMg/ffff1O+fPksgysfH59MaQsXLqRGjRo4Ozvj4eHB888/z5YtWzLly+09kzH2e/bs4a233sLb2xsXFxfatm3L1atXM5X3448/UrduXVxcXHBzc6N58+acOHHCot8ZM6N3Hx4W4l4ysyP+k/744w8aN26Mt7c3I0eOxGQyMWLECIoWLZpl/l27drF27Vpef/11ACZMmECLFi147733mD59OgMGDODmzZtMnDiRXr16sX379mzr/v333zl16hTDhg3LtZ0LFizg1VdfpUaNGvTt2xeAkiVLWuR56aWXKF26NOPHj9em8rdu3crZs2fp2bMnvr6+nDhxglmzZnHixAl+/fXXXL8Q/vrrLzp06EDv3r3p3r07c+fOpUePHlSrVo3y5cvnuO/Nmzdp2rQp7dq1Izw8nBUrVvD+++9TsWJFmjVrBqTPOrzwwgtER0fz5ptv4uvry+LFi/n5559zHROAqKgoatSowa1bt+jbty9ly5bl0qVLrFixgjt37mBvb5+ncu52+vRpOnfuTL9+/ejTpw9lypShY8eOjBw5kpiYGHx9fbW8u3fvJioqik6dOmlp/fr1Y968efTs2ZNBgwZx7tw5pk6dyu+//86ePXvQ6/VZ1jts2DDKlCnDrFmzGD16NMHBwdprnFFe9erVmTBhApcvX+arr75iz549/P777xbBislkokmTJtSpU4fPPvsMZ2fnbPsaGBjIvn37OH78OBUqVMhxXEaNGsXIkSOpVasWo0ePxt7env3797N9+3YaN26s5cvPe+aNN97Aw8ODESNGcP78eSZNmsTAgQNZtmyZlmfBggV0796dJk2a8Mknn3Dnzh1mzJhBnTp1+P333wkKCqJfv35ERUWxdetWFixYkGM/xH+cEuI/qE2bNsrR0VFduHBBSzt58qSytbVV9/63AJSDg4M6d+6cljZz5kwFKF9fXxUXF6elDx06VAEWee/19ttvK0CdPHkyT211cXFR3bt3z5Q+YsQIBajOnTtn2nbnzp1MaUuWLFGA+uWXX7S0b7/9NlN7AwMDM+W7cuWKcnBwUG+//baW9vPPPytA/fzzz1pavXr1FKC+++47LS05OVn5+vqq9u3ba2mff/65AtTq1au1tMTERFW2bNlMZWbllVdeUTY2NurgwYOZtpnNZqXUv+Nzr5z6vGnTJou8p0+fVoCaMmWKRfqAAQOUq6urNs67du1SgFq0aJFFvk2bNmWZnl2b7u5PSkqK8vHxURUqVFCJiYla+vr16xWghg8frqV1795dAeqDDz7IsZ4MW7ZsUba2tsrW1laFhoaq9957T23evFmlpKRY5Dtz5oyysbFRbdu2VWlpaRbbMsZZqby/ZzL62ahRI4v9hwwZomxtbdWtW7eUUkrFx8cro9Go+vTpY1FnTEyMcnd3t0h//fXXs3ydhbibHMYS/zlpaWls3ryZNm3aUKJECS09JCSEJk2aZLlPw4YNCQoK0p7XrFkTgPbt2+Pm5pYp/ezZs1mWYzabWbp0KVWqVCEkJORBuwJA//79M6XdvdYkKSmJa9eu8dxzzwHw22+/5VpmuXLlqFu3rvbc29ubMmXKZNuvu7m6uvLyyy9rz+3t7alRo4bFvps2bSIgIIBWrVppaY6OjvTp0yfX8s1mM6tXr6Zly5Y8++yzmbbf72GM4ODgTK//008/zTPPPGMx45CWlsaKFSto2bKlNs7Lly/H3d2dF198kWvXrmmPatWq4erqmucZq7sdOnSIK1euMGDAAIu1Uc2bN6ds2bJs2LAh0z6vvfZansp+8cUX2bdvH61ateLo0aNMnDiRJk2aEBAQwNq1a7V8q1evxmw2M3z48EyL0O8d5/y8Z/r27Wuxf926dUlLS+PChQtA+szkrVu36Ny5s8V42traUrNmzfsaT/HfJsGO+M+5evUqiYmJWZ72XaZMmSz3uTsoAnB3dwegePHiWaZnt05j586dXLp0qUAXJgcHB2dKu3HjBm+++SZFixbFyckJb29vLV9sbGyuZd7bXwAPD49c158AFCtWLNMX4b37XrhwgZIlS2bKV6pUqVzLv3r1KnFxcbkefsmvrMYRoGPHjuzZs0dbJ7Njxw6uXLlCx44dtTxnzpwhNjYWHx8fvL29LR4JCQn3teg344s/q/dk2bJlte0Z7OzsKFasWJ7Lr169Oj/88AM3b97kwIEDDB06lPj4eDp06MDJkyeB9LU9NjY2lCtXLtfy8vOeuTevh4cH8O//mzNnzgDwwgsvZBrPLVu2yCJqkW+yZkeIPMjujKXs0lU2p8EuWrQIGxsbOnfuXGBty+qMofDwcPbu3cu7777LM888g6urK2azmaZNm2I2m3MtM7/9Kqh9C1J2MzxpaWlZpmd35lXHjh0ZOnQoy5cvZ/DgwXz//fe4u7vTtGlTLY/ZbMbHx4dFixZlWYa3t3c+W59/Dg4O93UJAHt7e6pXr0716tV5+umn6dmzJ8uXL2fEiBH5Kic/r3tueTPeowsWLLBYK5XBzk6+ukT+yDtG/Od4e3vj5OSk/Xq82+nTp61Wb3JyMitXrqR+/fr4+/vneb/8Hpa5efMmP/30E6NGjWL48OFaelb9LSyBgYGcPHkSpZRF//JyfRdvb28MBgPHjx/PMV/GbMGtW7csFvLeOyOSm+DgYGrUqMGyZcsYOHAgP/zwA23atMHBwUHLU7JkSbZt20bt2rUL7HT1wMBAIP09+cILL1hsO336tLa9IGUcFoyOjgbS+2U2mzl58iTPPPNMgdeXnYwF2j4+Prled0jOvhJ5IYexxH+Ora0tTZo0YfXq1URERGjpp06dYvPmzVard+PGjdy6dSvfh7BcXFzydeuAjF/N9/6injRpUr7qtaYmTZpw6dIli/UhSUlJzJ49O9d9bWxsaNOmDevWrePQoUOZtmf0O+ML85dfftG2ZZzGn18dO3bk119/Ze7cuVy7ds3iEBakz6SlpaUxZsyYTPuaTKb7uvXDs88+i4+PD19//TXJycla+o8//sipU6do3rx5vsvM8PPPP2c547Jx40bg30Nnbdq0wcbGhtGjR2eaEbTmTF2TJk0wGAyMHz+e1NTUTNvvPk0941pCj9PtNcTDJzM74j9p1KhRbNq0ibp16zJgwABMJhNTpkyhfPnyHDt2zCp1Llq0CAcHB9q3b5+v/apVq8a2bdv44osv8Pf3Jzg4WFsInRWDwcDzzz/PxIkTSU1NJSAggC1btnDu3LkH7UKB6devH1OnTqVz5868+eab+Pn5sWjRIm0hbm6/1sePH8+WLVuoV68effv2JSQkhOjoaJYvX87u3bsxGo00btyYEiVK0Lt3b959911sbW2ZO3cu3t7eFkFuXoSHh/POO+/wzjvvUKRIkUyzDfXq1aNfv35MmDCBI0eO0LhxY/R6PWfOnGH58uV89dVXFtfkyQu9Xs8nn3xCz549qVevHp07d9ZOPQ8KCmLIkCH5Ku9ub7zxBnfu3KFt27aULVuWlJQU9u7dy7JlywgKCqJnz55A+hqqYcOGMWbMGOrWrUu7du1wcHDg4MGD+Pv7M2HChPtuQ04MBgMzZsygW7duVK1alU6dOmmv24YNG6hduzZTp04F0v9/AAwaNIgmTZpga2trcUkAIUCCHfEfValSJTZv3sxbb73F8OHDKVasGKNGjSI6OtoqwU5cXBwbNmygefPm2iLmvPriiy/o27cvH330EYmJiXTv3j3HYAfSL1r4xhtvaJfRb9y4MT/++GO+Dp9Zk6urK9u3b+eNN97gq6++wtXVlVdeeYVatWrRvn37XK/MHBAQwP79+/n4449ZtGgRcXFxBAQE0KxZM+36Mnq9nlWrVjFgwAA+/vhjfH19GTx4MB4eHtqXeV4VK1aMWrVqsWfPHl599dUsr5nz9ddfU61aNWbOnMmHH36InZ0dQUFBvPzyy9SuXTtf9WXo0aMHzs7O/N///R/vv/++dgG+Tz755IGutvzZZ5+xfPlyNm7cyKxZs0hJSaFEiRIMGDCAjz76yKLsjGv/TJkyhWHDhuHs7EylSpXo1q3bfdefF126dMHf35//+7//49NPPyU5OZmAgADq1q1r8fq1a9eON954g6VLl7Jw4UKUUhLsiEzk3lhC3GXkyJGMGjVK7rNTSCZNmsSQIUO4ePEiAQEBhd0cIcQTQtbsCCEKRWJiosXzpKQkZs6cSenSpSXQEUIUKDmMJYQoFO3ataNEiRI888wzxMbGsnDhQv73v/9le/q2EELcLwl2hBCFokmTJsyZM4dFixaRlpZGuXLlWLp0aaYznYQQ4kHJmh0hhBBCPNEKdc3OL7/8QsuWLfH390en07F69eps8/bv3x+dTpfpWiE3btyga9euGAwGjEYjvXv3JiEhwboNF0IIIcRjo1CDndu3b1O5cmWmTZuWY75Vq1bx66+/ZnnabNeuXTlx4gRbt25l/fr1/PLLL/Tt29daTRZCCCHEY6ZQ1+w0a9aMZs2a5Zjn0qVLvPHGG2zevDnTFUNPnTrFpk2bOHjwoHaZ8ylTphAWFsZnn32W52uKmM1moqKicHNzk0uPCyGEEI8JpRTx8fH4+/vneG+4R3qBstlsplu3brz77ruUL18+0/Z9+/ZhNBq1QAegUaNG2NjYsH//ftq2bZuneqKiojLdvVoIIYQQj4fIyEiKFSuW7fZHOtj55JNPsLOzY9CgQVluj4mJwcfHxyLNzs6OIkWKEBMTk225ycnJFveayVijHRkZicFgKICWCyGEEMLa4uLiKF68OG5ubjnme2SDncOHD/PVV1/x22+/FfihpQkTJjBq1KhM6QaDQYIdIYQQ4jGTW5zwyF5BedeuXVy5coUSJUpgZ2eHnZ0dFy5c4O233yYoKAgAX19frly5YrGfyWTixo0b+Pr6Zlv20KFDiY2N1R6RkZHW7IoQQgghCtEjO7PTrVu3THcWbtKkCd26ddNuAhcaGsqtW7c4fPiwdufb7du3Yzabc7xRooODAw4ODtZrvBBCCCEeGYUa7CQkJPDXX39pz8+dO8eRI0coUqQIJUqUwNPT0yK/Xq/H19eXMmXKABASEkLTpk3p06cPX3/9NampqQwcOJBOnTo9Mnd3FvmXlpZGampqYTdDCCHEI0Kv12Nra3vf+xdqsHPo0CEaNGigPX/rrbcA6N69O/PmzctTGYsWLWLgwIE0bNgQGxsb2rdvz+TJk63RXPEQJCQkcPHiRbnruBBCCI1Op6NYsWK4urre3/5yu4j01dzu7u7ExsbKAuVClJaWxpkzZ3B2dsbb21uueSSEEAKlFFevXuXOnTuULl3aYoYnr9/fj+yaHfHfk5qailIKb29vnJycCrs5QgghHhHe3t6cP3+e1NTU+zqc9ciejSX+u2RGRwghxN0e9HtBgh0hhBBCPNEk2BEiD4KCgti9e3dhN0MIIcR9kGBHCCGEEE80CXaEEEII8USTYEc8spRSJKWmWf2Rn6svJCUl8frrr+Pr60uJEiUYPXo0ZrMZgF9//ZUqVapgMBgICAjgyy+/zDFdCCHEwyGnnotHVrLJTNhXu6xez8Y36+Koz9upjGPGjOHEiROcOnWK+Ph4GjVqRIkSJejRoweDBw/mnXfeoWvXrty8eZPz588DZJsuhBDi4ZBgx8pS0lLQ2+jldOonxNKlS5kzZw4eHh54eHjw9ttvs2TJEnr06IFer+evv/7ixo0bFClSBA8PD4Bs04UQQjwcEuxY2ZlbZ/B09MTXJfu7sIusOdjZsPHNug+lnryKioqiRIkS2vPAwECioqIAmDNnDh9//DGlSpWiXLlyfPrpp4SGhmabLoQQ4uGQYOchuJN6p7Cb8FjS6XR5Prz0sPj7+xMREUHJkiUBiIiI0G46W6ZMGb7//ntMJhNff/01nTt35vz589mmCyGEeDhkgbK1/efvPPZk6dixI2PGjOHmzZtERkbyxRdf0KlTJyD9prTXr1/Hzs4ONzc37ZLm2aULIYR4OCTYESIfPv74Y8qUKUPZsmUJDQ2lU6dOdO/eHYCNGzdSpkwZ3NzcmDx5Mt99912O6UIIIR4Oues51r3r+YlrJ3C0c6SksWSBlvskSkpK4ty5cwQHB+Po6FjYzRFCCPGIyO77Ia/f3zKzY2VKjmMJIYQQhUqCHSGEEEI80STYeQjkSKEQQghReCTYsTIJdIQQQojCJcGOEEIIIZ5oEuw8BLJIWQghhCg8EuxYUcztGGJux5CcllzYTRFCCCH+syTYsaIRe0fw6aFPiYyPLOymCCGEEP9ZEuxYkY70O52blbmQWyIeJfPmzaNRo0b3tW/9+vVZuHBhAbfo0VDQfTt//jx2dnL7PyGEBDtWpdPpZL2OEI+BBwlAhRCPPgl2rMhGlz68cvq5EOJuJpMpT2nZUUphNsuMsRB5JcHOQ5Cm0gq7CY8npSA10fqPPASjZrOZQYMG4eXlhdFopHr16ly7do1z587RvHlzPD098fPzY/LkyQDs37+f6tWrYzAYCAwMZMqUKdmW/ccff/D888/j4eFBtWrVOHTokLbt4MGDVKpUCYPBQP/+/fP0BVe/fn2GDx9OtWrVcHV1pXfv3kRHR/PCCy9gMBho3749ycn/LppfsWIF5cuXp0iRIrRq1YorV65o29q1a4ePjw9FihThpZde4saNG8C/h4hmz56Nn58fvr6+zJ8/P9e2zZ07l6effho3NzcqVarEjh07LLafPn2aKlWq4OHhQffu3UlMTATgzz//pE6dOhgMBooWLcq7776r7TN9+nSeeuopvL29efnll4mNjc2y7qCgIHbv3q0979GjB2PHjuXs2bP079+fHTt24OrqSvny5QG4ceMGXbp0wcfHh6eeeipP/ctpn/r16/Pxxx/z7LPP4uLiwtatWylVqhQjRozAy8uLESNGcPPmTTp37oyXlxclS5Zk5syZFu0dOHAgL7zwAs7Ozvz999+5tkcIkU4OaFuRDTKz80BMSfB1HevX03836J1yzLJlyxb27t3L2bNncXFx4ejRozg6OvL8888THh7OypUrSUlJ4cyZMwDo9XpmzpzJM888w2+//UbDhg2pU6cOVapUsSg3ISGBpk2bMnnyZNq0acO6deto164df/75JzY2NrRr144PP/yQV199la+//po5c+bQt2/fXLu0cuVKNm3ahF6v55lnnuHYsWPMmzePEiVKUKtWLRYvXkzPnj05cOAAgwcP5scff6Rs2bIMGzaMAQMGsGLFCiA92FmwYAEmk4mOHTsyevRoJk2aBEBaWhrHjx/nwoUL7Nixg3bt2tGuXTvc3NyybZevry8//fQT/v7+zJ07l06dOnHhwgUcHBwA+O6779i6dSs+Pj60bduW8ePHM2bMGIYPH07z5s3ZtWsXd+7c4cSJEwBs3bqVMWPG8NNPPxEYGMgrr7zCm2++ybx583IdowxPPfUUX3/9NQsXLmTbtm1aerdu3QgJCSEyMpJz587xwgsvUKVKFSpVqpRtWbnts2TJEjZt2kSxYsXYt28f58+fx9bWlujoaEwmE6+++ioAERER/PXXXzRs2JCyZctSr149AJYuXcrmzZupXLmyfK4IkQ8ys2NFOp2usJsgCoheryc+Pp7//e9/2NjYULVqVY4ePUp8fDzDhw/H0dERg8FAtWrVAKhatSpVq1bFxsaGZ599lrCwMPbs2ZOp3PXr11O+fHnat2+Pra0tbdq0wcfHh19//ZV9+/ZhZ2fHa6+9hl6vZ+DAgfj5+eWpvb1796Z48eL4+vpSr149QkNDKV++PG5uboSFhXH06FEgfaZlwIABVKxYEb1ez8cff8yaNWu0Qyovv/wyLi4uuLu7M2TIEIuZEYDhw4djb29P48aN8zTbEBYWRvHixbG1taVPnz7odDotQATo2bMnTz/9NEajkWHDhrFs2TJt/C9cuEBMTAwuLi7UqFEDSP/y79u3L+XKlcPFxYXx48ezbNmyBw4EYmJi2LFjBxMmTMDBwYGyZcvSpUsXfvjhhwfap3fv3pQqVQpHR0d0Oh0ODg58+OGH6PV67O3tWb58OePHj8fZ2ZlKlSrx6quvsnjxYm3/9u3bU61aNezs7NDr9Q/URyH+S2Rmx4q0NTuySPn+2Dmmz7o8jHpy0bBhQ/r370/fvn2JiYnh5Zdfpnr16gQGBmJjk/k3w4kTJxg8eDBHjhwhJSWFpKQkypYtmylfREQEO3fuxGg0ammpqalERUVhY2NDsWLFtHSdTmfxPCc+Pj7a305OTpmeX79+Xat/wYIFTJw4UdtuZ2dHTEwMvr6+vPPOO6xatYqbN2+ilMLLy0vLZ2tri6enp/bc2dmZhISEHNu1evVqRo8ezdmzZwGIj4/X2gJQvHhxi7+jo6MBmDhxIsOGDeOZZ57B39+f0aNH07JlS6KioqhVq5a2T2BgIElJSdrhtvsVERFBUlIS3t7eWlpaWhpdu3Z9oH3uff18fX21M8auXbtGamoqJUqUsOjP8ePHs91fCJE3Euw8BHLq+X3S6XI9vPQwDRkyhCFDhhAZGUlYWBhFihThwoULKKUyzeINHDiQunXrsnbtWpycnOjcuXOWsw0BAQE0adKEtWvXZtq2c+dOLl68aJF27/MHFRAQwJgxY3jrrbcybZs/fz47duxg7969BAQEsHnzZvr163ffdSUnJ9O5c2d++OEHGjdujK2tLX5+fhbjEhkZafF3xkyWn58fc+fORSnF2rVrCQ8P5+bNm/j7+xMREaHtExERgaOjI0WKFCE+Pt6ifhcXF20NEMDly5cpVaoUkHkWNiAgAFdXV27evJnnGdq87HNv+t3Pvby80Ov1REREEBwcrPXH398/2/2FEHkjh7GsSGZ2nhyHDh3i4MGDmEwm3Nzc0Ov1FC9eHDc3N8aMGUNSUhJxcXEcPnwYSJ+xMBqNODo6smvXLjZs2JBluS1atOD3339n9erVmEwmEhMT2bRpE7GxsYSGhpKamsqsWbNITU1l2rRp2kxHQenZsydTp07VDmvduHGDNWvWaH1wdHTEw8ODa9eu8dlnnz1QXcnJyaSkpGizTF999RVXr161yDNv3jzOnDlDbGws48ePJzw8HEhfRB0VFYVOp8NoNKLT6dDpdHTs2JHZs2dz6tQpbt++zbBhwwgPD88yKKhcuTLff/89aWlpbNu2zWJxtI+PDxcvXtQO3wUEBBAaGspHH33EnTt3MJlM/Pbbb5w8eTLb/t3PPneztbWlQ4cO2v7Hjx/nm2++oVOnTnnaXwiRPQl2rCjjooIS7Dz+YmNj6dWrF0ajkTJlylC7dm26dOnC+vXr2bt3L35+fpQpU4Z9+/YB8MknnzBt2jQMBgOTJk2iVatWWZbr7u7Ohg0bmDJlCj4+PgQFBTFr1iwA7O3tWblyJVOmTMHT05Njx45ZHLIpCLVq1eKzzz7jlVdewWAwULVqVW1t0SuvvIKHhwdFixalbt26NG3a9IHqMhgMfPrppzRp0gRfX1+uX7+uzaxkePnllwkPDycwMJCAgAA+/PBDAA4cOKCdXfbaa6+xZMkSHBwcaNy4MUOHDiUsLIzAwED0er22gPpeo0aN4vfff8doNPLNN9/QunVrbdsLL7xAUFAQ3t7e2mLiRYsWcfHiRZ566il8fHwYPHiwxcxQVu5nn7tNnToVk8lE8eLFadWqFSNHjqRBgwZ53l8IkTWdkiX9xMXF4e7uTmxsLAaDocDKfXfnuxy5eoQ+FfsQXia8wMp9UiUlJXHu3DmCg4NxdMx9HY0QQoj/huy+H/L6/S0zOw+BxJNCCCFE4ZFgx4oy1g3IAmVR0Fq2bImrq2umx6ZNmwq7aVSuXDnLtp06daqwm1YgnvT+CfEkkrOxrChjgbIQBW3dunWF3YRsZSx2flI96f0T4kkk38ZWJHc9F0IIIQqfBDtWpAU7SLAjhBBCFBYJdqxIp9OBklPPhRBCiMIkwY4V2ehsQCdnYwkhhBCFqVCDnV9++YWWLVvi7++PTqdj9erV2rbU1FTef/99KlasiIuLC/7+/rzyyitERUVZlHHjxg26du2KwWDAaDTSu3fvXO/P87DIRQVFVubNm0ejRo3ua9/69euzcOHCAm6ReJQEBQVluuGqEOLBFGqwc/v2bSpXrsy0adMybbtz5w6//fYbH3/8Mb/99hs//PADp0+fznQl2q5du3LixAm2bt3K+vXr+eWXX+jbt+/D6kKOMk49l5kd8biSL14hxJOgUE89b9asGc2aNctym7u7O1u3brVImzp1KjVq1CAiIoISJUpw6tQpNm3axMGDB3n22WcBmDJlCmFhYXz22WcWN9ArDDKzIwqbyWTS7qotHr7CHv+s6s9Pm5RSKKWwsZEVD+Lx9li9g2NjY7UbAQLs27cPo9GoBToAjRo1wsbGhv3792dbTnJyMnFxcRYPa9BuBCozO489s9nMoEGD8PLywmg0Ur16da5du8a5c+do3rw5np6e+Pn5MXnyZAD2799P9erVMRgMBAYGMmXKlGzL/uOPP3j++efx8PCgWrVqHDp0SNt28OBBKlWqhMFgoH///pjNuZ/ZV79+fT7++GOeffZZXFxcSE1N5YcffiAkJAQPDw9atGjBpUuXtPy7du2iSpUqGI1G6tWrp10c79VXXyUiIoLGjRvj6urKokWLsq1z5MiRdOnShfbt2+Pq6krt2rWJiYmhf//+uLu7U7VqVc6ePZunPo8fP57AwEAMBgOhoaEcO3ZM2xYUFMTnn39OSEgIRqORgQMH5joe69evp0yZMri5uREUFMTSpUuB9Jnlrl27YjQaqVq1KsOGDdMOL+7YsSPTfbt0Op121/m5c+fy9NNP4+bmRqVKlSxuKprV+O/cuZNq1aphNBqpX78+f//9t5b/xx9/pFSpUhQpUoRRo0bl2h9IP3zfpUsXfHx8eOqpp5g/f3629W/dupVSpUoxYsQIvLy8GDFiBDdv3qRz5854eXlRsmRJZs6cqe3fo0cPBg4cyAsvvICzs7NFW4V4bKlHBKBWrVqV7fbExERVtWpV1aVLFy1t3Lhx6umnn86U19vbW02fPj3bskaMGKGATI/Y2NgH6sO9Ru8drRotb6RmH5tdoOU+qRITE9XJkydVYmKiUkops9msElMTrf4wm825tu3HH39U1apVU7GxscpkMqnDhw+r+Ph4FRISokaMGKESExNVbGysOnTokFJKqcOHD6vDhw+rtLQ0dfDgQWUwGNRvv/2mlFLq22+/VQ0bNlRKKRUfH6/8/f3VihUrlMlkUqtWrVLFixdXiYmJKjk5WRUrVkxNnz5dpaSkqMmTJytbW1u1YMGCHNtar149VbJkSXXmzBltTA0Gg9q1a5dKSkpSb7zxhqpfv75SSqlr164po9GoVq5cqVJSUtTEiRNVqVKlVGpqqlJKqcDAQLVr165cx2fEiBHK2dlZ/fLLLyo5OVm9+OKLKigoSC1btkylpqaqXr16qVdeeSXXPiul1MqVK9WVK1dUSkqK+vjjj1XlypW1egIDA1WdOnXU1atXVWRkpPL29lbbt2/PsW1FixZVu3fvVkopFR0drU6cOKGUUurdd99VDRs2VLGxserUqVOqWLFi2uvy888/q5IlS1qUA6jIyEillFIbNmxQERERymQyqVmzZqmiRYuqpKSkLMc/IiJCeXl5qV9++UWZTCY1efJk9eyzzyqllLpy5YpydXVV69atU8nJyerdd99Vtra2uY55WFiYevvtt1VSUpI6deqU8vPzU0ePHs2y/u3btytbW1s1atQolZKSou7cuaO6dOmiOnXqpG7fvq2OHj2qvLy81I4dO5RSSnXv3l15enqqQ4cOqdTUVJWSkpJjW4R4GO79fsgQGxubp+/vx2J+OzU1lfDwcJRSzJgx44HLGzp0KG+99Zb2PC4ujuLFiz9wufeSNTsPJjktmZfWvWT1epa3XI6jXc43HtXr9cTHx/O///2P6tWra3cHj4+PZ/jw4djY2ODo6Ei1atUAqFq1qrbvs88+S1hYGHv27KFKlSoW5a5fv57y5cvTvn17ANq0acPYsWP59ddf0el02NnZ8dprrwEwcOBAJk6cmKc+9e7dW5uZWLFiBW3atKFOnTpA+syJh4cH0dHRbNu2jUqVKtGuXTsA3n77bSZNmsTBgwcJDQ3NU10ZGjZsSN26dQFo27YtM2bMIDw8/Qa4HTp0YOjQobn2uX79+lpbAD788EPGjh1LQkICrq6uAAwePBgvLy8gfRbj6NGjOd4ZXK/Xc+rUKSpXroyvry++vr4ALF++nG+//RaDwYDBYKB79+78+uuveeprWFiY9nefPn0YPnw4Z86coUKFCoDl+C9atIi2bdtqY/PGG28wevRozp8/r834tGjRAkifIcvuru0ZYmJi2LFjB6tXr0av11O2bFm6dOnCDz/8oN2x/e76dTodDg4OfPjhh9jZ2WFjY8Py5cs5ffo0zs7OVKpUiVdffZXFixdTr149ANq3b6+9l4V4Ejzyh7EyAp0LFy6wdetWi7ua+vr6cuXKFYv8JpOJGzduaB9oWXFwcNA+4DIe1iBrdp4cDRs2pH///vTt2xc/Pz/eeecdLl68SGBgYJbrGU6cOMGLL76It7c37u7u/PDDD1y/fj1TvoiICHbu3InRaNQep06dIioqiujoaIoVK6bl1el0Fs9zcne+qKgoSpQooT13dXXF09OTqKioTNtsbGwoXrx4prMe88LHx0f728nJKdPzjLMkc+ozwOzZsylfvjzu7u74+vqilLIYu6JFi2p/Ozs753r25YoVK1i1ahXFihWjadOm2mG66Ohoix85+fnBs3r1aqpWraq1/8qVKxZtvHv8IyIiWLBggUV/b9++zaVLlzK1wdnZGU9PzxzrjoiIICkpCW9vb628mTNnEhMTk2X9kP5ZmbFO59q1a6Smplq87oGBgRaveV7fZ0I8Lh7pmZ2MQOfMmTP8/PPPmT4EQkNDuXXrFocPH9Z+hWzfvh2z2UzNmjULo8kWtDU7EuzcFwdbB5a3XP5Q6smLIUOGMGTIECIjIwkLC6NIkSJcuHABpZQ2i5dh4MCB1K1bl7Vr1+Lk5ETnzp2znOELCAigSZMmrF27NtO2nTt3amtEMtz7PDt3t8ff35+//vpLe3779m2uX7+Ov78//v7+bNy4UdumlCIyMlJb3H9vvwpCTn0+f/48gwcPZufOnVStWpXk5GRcXFweaHa0Zs2abNiwgeTkZIYPH85rr73Gjh078PPzIzIykpIlSwIQGRmp7ePi4kJiYqL2/PLly9rfycnJdO7cmR9++IHGjRtja2uLn5+fRRvvHreAgAD69Omjree6219//WVx89bExMQsg+K7BQQE4Orqys2bN7N9fe5Nv/u5l5cXer2eiIgIgoODgfQA6u4TOqzxugtRmAp1ZichIYEjR45w5MgRAM6dO8eRI0eIiIggNTWVDh06cOjQIRYtWkRaWhoxMTHExMSQkpICQEhICE2bNqVPnz4cOHCAPXv2MHDgQDp16lToZ2KBHMZ6UDqdDkc7R6s/8vLBfujQIQ4ePIjJZMLNzQ29Xk/x4sVxc3NjzJgxJCUlERcXx+HDhwGIj4/HaDTi6OjIrl272LBhQ5bltmjRgt9//53Vq1djMplITExk06ZNxMbGEhoaSmpqKrNmzSI1NZVp06YRHR2d73Hs0KEDq1evZu/evaSkpPDRRx9Rq1Yt/Pz8aNasGUePHmXNmjWYTCa+/PJLnJyctEX/Pj4+nD9/Pt915iSnPickJGBjY4O3tzcmk4kRI0Y8UF0pKSksXryYuLg49Ho9rq6u2NraAunjMn78eOLi4jh9+jTfffedtt/TTz/NzZs32blzJ8nJyYwZM0bblpycTEpKijZz9dVXX3H16tVs29ClSxeWL1/Orl27MJvNxMfHs2LFCiD9cNjhw4fZuHEjKSkpjBo1KtdF6AEBAYSGhvLRRx9x584dTCYTv/32GydPnszTmNja2tKhQwdt/+PHj/PNN9/QqVOnPO0vxOOoUIOdQ4cOUaVKFW0dw1tvvUWVKlUYPnw4ly5dYu3atVy8eJFnnnkGPz8/7bF3716tjEWLFlG2bFkaNmxIWFgYderUYdasWYXVJQtyGOvJERsbS69evTAajZQpU4batWvTpUsX1q9fz969e/Hz86NMmTLs27cPgE8++YRp06ZhMBiYNGlSputDZXB3d2fDhg1MmTIFHx8fgoKCtPevvb09K1euZMqUKXh6enLs2DFq1aqV77aHhIQwZ84cevbsSdGiRTl9+rR2YUIvLy9Wr17NiBEj8PT0ZNWqVdpaEID333+fDz74AKPRyOLFi+9n6PLV5woVKtCvXz8qVapEUFAQwcHB2NvbP1B98+fPJzAwEA8PD7Zu3crUqVMBtD4XL16czp07061bN4s2fvXVV4SHhxMcHEz16tW1bQaDgU8//ZQmTZrg6+vL9evXM525dbfg4GCWLl3Ku+++S5EiRShbtixr1qwBwNvbmyVLlvDGG29QtGhRnJyc8nQIadGiRVy8eJGnnnoKHx8fBg8ebDETlZupU6diMpkoXrw4rVq1YuTIkTmuexLicadTMu1AXFwc7u7uxMbGFuj6nYkHJ7L1wlbalW7Ha5VfK7Byn1RJSUmcO3eO4OBgHB1zXjAsREGbN28eCxcuZNu2bYXdFCHEPbL7fsjr9/cjv0D5cWbzz/Caldz1XAghhCgsEuxYkazZEdbSsmVLXF1dMz3uXuxa0CZOnJhlncOGDbNanXk1cODALNv2qBzSvh+VK1fOsk8ZZ5MJIfJODmNhvcNYXxz6gh/P/0irkq14o8obBVbuk0oOYwkhhMiKHMZ6hMnpm0IIIUThk2DHijKusyNrdoQQQojCI8GOFcmp50IIIUThk2DnIZBlUUIIIUThkWDHijIOY8nEjhBCCFF4JNixIm3NDrJm53EXFBTE7t27H1p9ixYtomXLlg+tPiGEeJJJsGNF2podOYwl8qlr166sW7eusJuhGTlyJK+++mphN0MIIe6LBDvW9M+Z57JAWdzLZDIVdhMspKWlFXYThBDCaiTYsaKM20VIsPPkSEtLY8SIEQQGBlK0aFHefvttLXD5+++/ef755zEajfj7+/Phhx9q+82bN48GDRrQr18/3N3d+fbbbwkKCuLzzz8nJCQEo9HIwIEDLfI3atQIgPPnz2NnZ8fs2bPx8/PD19eX+fPna3kvX75MkyZNMBgMNGjQgNdffz3XWZiRI0fSuXNn2rdvj6urK9u3b2f9+vVUrFgRNzc3SpcuzfLlywHYsWMH48ePZ/78+bi6utKsWTMAIiIiaN68OZ6enoSEhFj16s1CCPEgJNixIrldxINRSmFOSrL6Iz+vzxdffMGuXbs4dOgQp0+f5rfffuPrr7/Wto8ZM4Zr166xc+dOFi5cyOrVq7Vtu3btIjQ0lJs3b/Lyyy8DsHr1anbt2sXx48f5/vvv+fnnn7OsNy0tjePHj3PhwgW+++47Xn/9deLj4wEYMGAAxYoV48qVK0yYMIFFixblqS+rVq2iX79+xMXFUbduXdzc3FixYgWxsbF89dVX9OzZk5iYGOrXr8+HH35I9+7dSUhI4Mcff8RsNtOyZUuaNGnC5cuXmTt3Lt26dePy5ct5HkshhHhY7Aq7AU8yuajgg1HJyZxr09bq9QSvXoUuj7en+Oabb5g7dy7e3t4AvP3223z22WcMHDiQkiVLUrJkSQBKly5N165d2b17N23atAGgZMmS9OjRAwAnJycABg8ejJeXFwD169fn6NGjNGjQIMu6hw8fjr29PY0bN8bZ2Zm///6bChUqsGbNGs6ePYujoyPPPfdcnhc216tXj8aNGwPg6OhIvXr1tG1hYWFUrFiRQ4cO0aJFi0z7HjhwgMTERAYNGgRAaGgo9erV48cff9T6KIQQjwoJdqxMh04OYz1BIiIiaNasmcWsXUBAAACXLl1i4MCB7Nu3j8TERFJSUujUqZO2b7FixTKVV7RoUe1vZ2dnEhISsqzX1tYWT0/PTHmvXbtGWlqa1oaMeq5evZprX+5tz+7du3n//fc5deoUZrOZ27dvc/369Sz3jYiI4Ny5cxiNRi3NZDJRrVq1XOsVQoiHTYIdK5Lr7DwYnYMDwatXPZR68iogIIBly5ZRtWrVTNs++ugjPDw8+PPPPzEYDAwdOpTo6Oh/67HCvdK8vLywtbUlKiqK4sWLA3Dx4kUc8tCne9vTrVs3hg4dSo8ePbC3tyc0NFQ7xHdv3oCAAEJCQjh27FgB9UQIIaxH1uxYUcap53Kdnfuj0+mwcXS0+iM/QUivXr346KOPiI6ORinF+fPn2blzJwDx8fG4ubnh6urK8ePHWbhwobWGRmNnZ0erVq0YPXo0ycnJHDhw4L5PWY+Pj8fT0xO9Xs/KlSs5fPiwts3Hx4cLFy5owU/NmjUxm83MmDGDlJQUUlJS2LVrFxEREQXSLyGEKEgS7FhRxsyOLFB+crz77ruEhoZSu3Zt3N3dadmyJZGRkUD6mpqff/4Zg8HAoEGDaN++/UNp0/Tp07lw4QJeXl68//77hIeH52lm515Tpkxh0KBBeHh4sHnzZos1PB06dCAhIQEPDw9atGiBnZ0dGzZsYPPmzQQEBODv78+4ceMwmyWwF0I8enRKvomJi4vD3d2d2NhYDAZDgZU7/8R8Fp1aRJ2AOgwPHV5g5T6pkpKSOHfuHMHBwTjmccGwyKxz585UrFjR4tR3IYR4nGX3/ZDX72+Z2bEibWZHFu0IKzpx4oS2qHjbtm2sWbOGVq1aFXazhBDikSHBjhXJ7SLEw3Dr1i2aN2+Oq6srr732GtOnT6dChQpMnDgRV1fXTI9hw4YVdpOFEOKhkrOxrChj4atcZ0dYU+3atTl79mym9Pfee4/33nuvEFokhBCPFpnZsaKMmR0hhBBCFB4JdqxIzsYSQgghCp8EO1akrdmRBcpCCCFEoZFgx4pkzY4QQghR+CTYsSJr3B5ACCGEEPkjwY4V2SDX2RFCCCEKmwQ7VnT3nbHFf09QUBC7d+/OMU+PHj0YO3bsQ2qREEL8N0mwY0WyQFkIIYQofBLsWJHM7AghhBCFT4IdK5J7Yz0YpRSmlDSrP3ILRseOHUvPnj0t0ho0aMDChQt544038Pf3x2g00rhxYyIiIh6oz9OnT+epp57C29ubl19+mdjYWACuXr1Ks2bNMBqNeHl50blz5xzThRBC/EtuF2FFcm+sB5OWambZuINWr6fjsOrY2dtmv71jR2rWrElqaip6vZ6YmBgOHDjA2rVrsbOzY+zYsdjb2/P6668zaNAgVq9efV/t2Lp1K2PGjOGnn34iMDCQV155hTfffJN58+bx+eefExwczLp160hLS+Pw4cMA2aYLIYT4l8zsWJF2GEtmdh5rpUuXJigoiC1btgCwYsUKmjRpgpubG506dcLd3R0nJyfef//9XBck52Tp0qX07duXcuXK4eLiwvjx41m2bBlKKfR6PdHR0URGRuLg4ECtWrUAsk0XQgjxL5nZsSJZoPxgbPU2dBxW/aHUk5tOnTqxbNkymjdvzrJlyxg4cCAA48aN49tvv+XKlSvodDri4uLuux1RUVEWwUpgYCBJSUncuHGDd999l48//ph69erh7OzMu+++S+/evbNNF0II8S+Z2bEi7UagEuvcF51Oh529rdUfebn4Y3h4OGvXruXs2bMcOXKEFi1asHPnTqZPn87GjRuJjY3lwIEDD9Rff39/izU/ERERODo6UqRIEQwGA1999RURERHMmzePN954g7Nnz2abLoQQ4l8S7FhRxgJlM3K7iMddUFAQISEh9OnTh7CwMFxcXIiPj0ev1+Pl5cXt27cf+Ho5HTt2ZPbs2Zw6dYrbt28zbNgwwsPD0el0bNiwgbNnz6KUwt3dHZ1Oh62tbbbpQggh/iXBjhVpMwYys/NE6NixI9u3byc8PByApk2bUrt2bQIDA6lYseIDr5dp3LgxQ4cOJSwsjMDAQPR6PZMmTQLgzz//pEGDBri5udG8eXMmTZpEYGBgtulCCCH+pVNyqhBxcXG4u7sTGxuLwWAosHK3nN/CZ4c+o2yRskx+YXKBlfukSkpK4ty5cwQHB+Po6FjYzRFCCPGIyO77Ia/f34U6s/PLL7/QsmVL/P390el0mU7ZVUoxfPhw/Pz8cHJyolGjRpw5c8Yiz40bN+jatSsGgwGj0Ujv3r1JSEh4iL3IntwIVAghhCh8hRrs3L59m8qVKzNt2rQst0+cOJHJkyfz9ddfs3//flxcXGjSpAlJSUlanq5du3LixAm2bt3K+vXr+eWXX+jbt+/D6kKOMm4EalayZue/rGXLlri6umZ6bNq0qbCbJoQQ/wmFeup5s2bNaNasWZbblFJMmjSJjz76iNatWwPw3XffUbRoUVavXk2nTp04deoUmzZt4uDBgzz77LMATJkyhbCwMD777DP8/f0fWl+yIjM7AmDdunWF3QQhhPhPe2QXKJ87d46YmBgaNWqkpbm7u1OzZk327dsHwL59+zAajVqgA9CoUSNsbGzYv39/tmUnJycTFxdn8bCGjFPPZWZHCCGEKDyPbLATExMDQNGiRS3SixYtqm2LiYnBx8fHYrudnR1FihTR8mRlwoQJuLu7a4/ixYsXcOvTycyOEEIIUfge2WDHmoYOHUpsbKz2iIyMtEo92o1A5YQ3IYQQotA8ssGOr68vAJcvX7ZIv3z5srbN19eXK1euWGw3mUzcuHFDy5MVBwcHDAaDxcMa5HYRQgghROF7ZIOd4OBgfH19+emnn7S0uLg49u/fT2hoKAChoaHcunXL4k7P27dvx2w2U7NmzYfe5ntpNwKVmR0hhBCi0BTq2VgJCQn89ddf2vNz585x5MgRihQpQokSJRg8eDBjx46ldOnSBAcH8/HHH+Pv70+bNm0ACAkJoWnTpvTp04evv/6a1NRUBg4cSKdOnQr9TCyQmR0hhBDiUVCoMzuHDh2iSpUqVKlSBYC33nqLKlWqMHz4cADee+893njjDfr27Uv16tVJSEhg06ZNFldPXLRoEWXLlqVhw4aEhYVRp04dZs2aVSj9uZc2syPBzn9aUFAQu3fvfuA84r9Dp9Nx8eLFAitP3l/iv65Qg5369eujlMr0mDdvHpD+H3706NHExMSQlJTEtm3bePrppy3KKFKkCIsXLyY+Pp7Y2Fjmzp2Lq6trIfQmMxudDTp0chjrITCbFZdO3+TPgzFcOn0Ts1nGPC+uXr1K8+bNcXFxoUyZMhaHjfOTd8aMGVStWhW9Xs/IkSO19OTkZHr16kWJEiUwGAw899xz2qUj7rVv3z5sbGwe+Iaq+Wl3fvPf77YMEydOpHjx4ri5uVGlShXi4+MLrqNCiGwV6mGsJ512nR3MKKXkVHQr+fv3K+xadobbt5K1NBejA3U7lqZkFZ8c9hSvv/46vr6+XL16lW3bthEeHs6ZM2coUqRIvvL6+fkxcuRIFi9ebLGPyWTSZhWKFSvG999/T8uWLTl//rzFjxKz2cyQIUOoXr16ntveo0cP6tevT48ePQqsj7nlv99tANOmTWPTpk3s2bOH4sWL88cff2Bvb5/n/goh7t8ju0D5SZAR7MhRLOv5+/crbJp53CLQAbh9K5lNM4/z9+9Xstkzf8aOHUvPnj0t0ho0aMDChQsZP348gYGBGAwGQkNDOXbs2APVdeLECerWrYvRaKRatWrs2bMHSA8IBg0ahJeXF0ajkerVq3Pt2rVs03OTkJDA6tWrGTVqFM7OzrRq1YqKFSuyZs2afOdt06YNrVq1wmg0Wuzn4uLC8OHDKVGiBDY2NnTq1Al7e3tOnz5tkW/WrFnUrFmTkJCQ+xy1B+9jbvnvdxtAWloa48aNY/bs2ZQoUQKdTkelSpVwcHDItQ+rVq2iZMmS2n0Bz507R/PmzfH09MTPz4/Jk9NvMlzQ70MhniQS7FjR3Wt2ZN1OwTObFbuWnckxz+7vzxTIIa2OHTuyZs0aUlNTgfQLWh44cIDWrVtTtmxZDh06xPXr13nxxRd55ZVX7ruelJQUWrZsyUsvvcTVq1d57733aNmyJTdv3mTLli3s3buXs2fPcv36dWbOnImjo2O26QADBgxgwIABWdZ15swZXF1dKVasmJZWsWJFTpw48UB5c3LmzBlu3LhBqVKltLTr168zadIkRo0ala+y8lpfftqdU/773QZw8eJF7ty5w4oVKyhatChlypRh9uzZubZ/6dKlvPfee2zbto3SpUtjMplo3rw51atX59KlS5w+fZratWsDFOj7UIgnjQQ7VpRxUUGzMsu6HSuIPnMr04zOvRJuJhN95tYD11W6dGmCgoLYsmULACtWrKBJkya4ubnRrl07vL290ev1fPjhhxw7doyEhIT7qmf//v3aTI1er6djx46UKVOGTZs2odfriY+P53//+x82NjZUrVoVV1fXbNMBpk+fzvTp07OsKyEhIdM1pgwGQ5Ztz0/e7CQmJvLyyy8zdOhQ3N3dtfRhw4YxePDgTLNCWWnRogVGoxGj0cjixYsZMGCA9vz//u//HrjdOeW/320Aly5dIjY2lj///JPz58+zfPlyPvzwQ3bt2pVtX7/77juGDx/Otm3bCA4OBtLfH/Hx8QwfPhxHR0cMBgPVqlUDKND3oRBPGgl2rEhOPbeu23E5Bzr5zZebTp06sWzZMgCWLVtGx44dAZg9ezbly5fH3d0dX19flFJcv379vuqIiorKdPuSwMBAoqKiaNiwIf3796dv3774+fnxzjvvkJqamm16blxdXTPdFy4uLi7LBf75yZuV1NRUXnrpJUqVKqWdbQnw+++/c/DgQfr06ZOnctavX8+tW7e4desWXbp0Yfr06drzDz744IHbnVP++90G4OTkBMDw4cNxcnKiUqVKdOrUiY0bN2bb10mTJtGrVy8CAwO1tIsXLxIYGIiNTeaP7oJ8HwrxpJFgx4osZnYk4ClwLobc1zvkJ19uwsPDWbt2LWfPnuXIkSO0aNGC8+fPM3jwYObPn8/NmzeJjo5Gp7v/M/D8/f0z3b4kIiJCu27UkCFDOHLkCAcPHmTz5s0sWrQox/SclC5dmoSEBC5duqSlHT9+nPLlyz9Q3nuZzWa6deuGTqdj/vz5Fgv1d+7cyenTpwkICMDX15dly5bxySefZFofdb/y2+6c8t/vNoCnn34ae3t7i77ndsLCypUrmT59uhZgAxQvXpwLFy5ken8V9PtQiCeNBDtWZKuzBeQKytbiV9qIizHnQMbVwwG/0sYCqS8oKIiQkBD69OlDWFgYLi4uJCQkYGNjg7e3NyaTiREjRjxQHRlX/p46dSomk4nly5dz6tQpmjZtyqFDhzh48CAmkwk3Nzf0ej22trbZpufG1dWV1q1bM2LECBITE1m/fj3Hjh2jdevW+c5rMplISkoiLS3N4m+Afv36ER0dzfLly7GzszwBtG/fvvz1118cOXKEI0eO0KpVK15//XW+/PLLXNs/b968XM/Eyk8fc8t/v9sgfaF2hw4dGDduHMnJyZw6dYply5YRFhaWbduDg4PZvHkzQ4YMYcOGDQDUqFEDNzc3xowZQ1JSEnFxcRw+fLjA34dCPGkk2LEiG50N6GRmx1psbHTU7Vg6xzx1wktjY1Nwp/x37NiR7du3Ex4eDkCFChXo168flSpVIigoiODg4Ac6ndje3p61a9eyZMkSPD09mTBhAmvXrsXDw4PY2Fh69eqF0WikTJky1K5dmy5dumSbDtC/f3/69++fbX3Tp08nKioKT09P3nrrLZYtW6adKt2sWTPGjx+fp7xjx47FycmJOXPmMG7cOJycnFiwYAEXLlxgzpw5HDhwAC8vL+2QT8ZaFWdnZ3x9fbWHk5MTrq6u2a7fadasmVbGvY+725rXPua3n/e7DdJPPb927RpeXl6EhYUxZswY6tatm+1rA+lXiV+3bh29e/dm586d2NnZsX79evbu3Yufnx9lypRh3759Bf4+FOJJo1My7UBcXBzu7u7ExsYW6E1BT10/xZAdQ3B3cGdh2EL0NvoCK/tJlJSUxLlz5wgODra4SnZusrrOjquHA3XC5To7QgjxJMju+yGv399yUUErylizo5SSa+1YUckqPgRX9k4/OysuGRdD+qGrgpzREUII8fiSYMeK7l6gLKzLxkZHQBmPwm5GtqKiojLd6gTAy8uL8+fPP/wGCSHEf4gEO1YkZ2OJDP7+/nLNEyGEKCSyQNmKtMNYcgVlIYQQotBIsGNFchhLCCGEKHwS7FjR3QuU5aQ3IYQQonBIsGNFGRcVNCMzO0IIIURhkWDHimx0NuhIv2S7rNkRQgghCocEO1Ykdz0XQgghCp8EO1YkC5SFEEKIwifBjhXJqef/bUFBQezevfuh1Xf+/PlMN9rMq5EjR/Lqq68WcIseDdbo28N+bYUQD0aCHSuywQbSUlDmNMxmmd0R4r/mQQJQIUTBkWDHimxsbNABKDMmZSrs5ggh/gPS0tIsnptM+fvsyW9+IR4HEuxYkc1dwyvrdvJPKUVqSrLVH7ktHh87diw9e/a0SGvQoAELFy7kjTfewN/fH6PRSOPGjYmIiMhXH+fOnUtgYCBubm6UKVOGHTt2AHD79m0GDBiAv78/Hh4edOvWDYCbN2/StGlTvLy88Pb2pm/fviQnJ2dZ9o0bN+jSpQs+Pj489dRTzJ8/X9t29epVmjVrhsFgoEGDBly5ciXXto4cOZIuXbrQvn17XF1dqV27NjExMfTv3x93d3eqVq3K2bNntfx//PEHzz//PB4eHlSrVo1Dhw5p28aPH09gYCAGg4HQ0FCOHTumbQsKCuLzzz8nJCQEo9HIwIEDc23b/v37qV69OgaDgcDAQKZMmWKx/fbt27Rp0wY3Nzeef/557X5kiYmJdO7cmSJFilCkSBHq1q2r7bNr1y6qVKmC0WikXr16nDp1Ksu6e/TowdixY7Xn8+bNo1GjRgA0btyYtLQ0XF1dcXV1JSIigrS0NEaMGEFgYCBFixbl7bffzjXAyGmfkSNH0rlzZ+112b59OzqdjqlTpxIcHEyDBg0wm82MGDGC4sWL4+fnx6BBg7T3zbx582jQoAH9+vXD3d2db7/9NtfxFuJxI/OrVpSxZgcgTaXlkFNkxZSawoL3Blm9nm4TJ6O3d8h2e8eOHalZsyapqano9XpiYmI4cOAAa9euxc7OjrFjx2Jvb8/rr7/OoEGDWL16dZ7qvX37NoMHD+bw4cOULl2aCxcuaIc7Bw8eTHR0NMeOHcPd3Z39+/cDYDabef3112ncuDE3btygefPmzJgxg8GDB2fuV7duhISEEBkZyblz53jhhReoUqUKlSpV4vXXX8fb25srV67w22+/0bRpU8LDw3Nt85o1a9i0aRNLliyhRYsWhIaG8sknnzB16lT69evHqFGjmD9/PgkJCTRt2pTJkyfTpk0b1q1bR7t27fjzzz9xdHSkbNmyHDp0CKPRyJgxY3jllVc4cuSIVs/q1avZtWsXSUlJVK1alfbt29OgQYNs26XX65k5cybPPPMMv/32Gw0bNqROnTpUqVIFgJUrV/LDDz/w/fff89FHH/HKK6/wyy+/MH/+fG7fvs2lS5fQ6/Xs27cPgOvXr9OqVSu++eYbWrZsyaRJk2jVqhWnTp3K12GpLVu2UKpUKYv7on366afs2rWLQ4cOodfradu2LV9//XWOQd0XX3yR4z6rVq1i7dq1LF++nJSUFAC2bt3K0aNH0ev1fPPNN6xYsYJ9+/bh5OREq1atmDBhAiNHjgTSA7vu3bszY8aMbINnIR5nMrNjRf8GO0rW7DzGSpcuTVBQEFu2bAFgxYoVNGnSBDc3Nzp16oS7uztOTk68//77+V60qtPpOHHiBMnJyQQGBhIcHIzZbGbBggV8+eWXeHl5odfrqVOnDgCenp60bNkSBwcH/Pz86NevX5Z1xsTEsGPHDiZMmICDgwNly5alS5cu/PDDD6SlpbFq1SpGjx6No6MjtWrVolWrVnlqb8OGDalbty729va0bdsWNzc3wsPDsbOzo0OHDhw9ehSA9evXU758edq3b4+trS1t2rTBx8eHX3/9FYB27drh7e2NXq/nww8/5NixYxYBweDBg/Hy8qJYsWLUr19fKzc7VatWpWrVqtjY2PDss88SFhbGnj17tO21atWiRYsW2NvbM3LkSH799VeioqLQ6/Vcv36ds2fPYmdnp83sbNy4kUqVKtGuXTv0ej1vv/02d+7c4eDBg3kap5x88803jB07Fm9vb4xGI2+//TYrVqx4oH3q1atH48aNsbGxwdHREYAPPvgAg8GAk5MTS5cu5Z133qFYsWJ4enoyfPhwlixZou1fsmRJevTogY2NDU5OTg/cRyEeNTKzY0UZV1AGmdm5H3Z6e7pNnPxQ6slNp06dWLZsGc2bN2fZsmXaL+px48bx7bffcuXKFXQ6HXFxcXmu18XFhSVLlvDZZ5/Ro0cPXnzxRb766itsbW1JTk4mODg40z7x8fG8/vrrbN++nbi4ONLS0qhRo0amfBERESQlJeHt7a2lpaWl0bVrV65evYrJZKJ48eLatuLFi3P16tVc2+zj46P97eTklOl5RsASERHBzp07MRqN2vbU1FSioqIAmD17NpMmTeLixYvodOkX3rx+/Tqurq4AFC1aVNvP2dk51zvGnzhxgsGDB3PkyBFSUlJISkqibNmyFv27uzxPT0+io6Pp1q0bFy5coG3btiQnJ9O/f3+GDh1KVFQUJUqU0PaxsbGhePHiWvsfREREBM2aNUOn0wHph2sDAgIeaJ9ixYpl2ufutHv7ExgYaNGXrPYX4kkiMztWdPdhLJNZFv3ll06nQ2/vYPVHxhdITsLDw1m7di1nz57lyJEjtGjRgp07dzJ9+nQ2btxIbGwsBw4cyHcfw8LC2L59OxcvXsTBwYEPP/wQb29vHBwctHUld/viiy+4evUqR44cIS4uji+++CLLNUcBAQG4urpy8+ZNbt26xa1bt4iPj+frr7/G29sbOzs7IiMjtfx3/10QAgICaNKkiVb3rVu3uH37Nl26dOH8+fMMHjyY+fPnc/PmTaKjo7WA534NHDiQ0NBQIiIiiI2NpV27dhbl3d2/xMRErl+/jp+fH/b29owePZo///yTzZs389VXX7Fjxw78/f0t1l8ppYiMjMTf3z9T3S4uLiQmJmrPL1++rP2d1XsrICCAn3/+WRuX2NhYTp48mWP/ctsnq3ruTru3PxERERZ9ycv/ASEeZxLsWJGs2XlyBAUFERISQp8+fQgLC8PFxYX4+Hj0ej1eXl7cvn3bYpFqXly+fJn169eTmJiIg4MDzs7O2NraYmNjwyuvvMJbb73F9evXSU1N1Q7JxMfH4+zsjLu7OxcuXGD69OlZlh0QEEBoaCgfffQRd+7cwWQy8dtvv3Hy5EntsNLIkSNJSkri119/Zd26dQ88Rndr0aIFv//+O6tXr8ZkMpGYmMimTZuIjY0lISEBGxsbvL29MZlMjBgx4oHri4+Px2g04ujoyK5du9iwYYPF9r1797Jx40ZSUlIYNWoUNWrUwN/fn59//pkTJ05gNpsxGAzY2dlha2tLs2bNOHr0KGvWrMFkMvHll1/i5OTEs88+m6nuypUrs2HDBuLi4jh79izffPONts3Lywuz2czFixe1tF69evHRRx8RHR2NUorz58+zc+fOHPt3P/vcrWPHjnz++edcunSJGzduMGbMGDp16pTn/YV43EmwY0W6xR2xTbiMLs0kFxV8AnTs2JHt27drC3mbNm1K7dq1CQwMpGLFitSqVStf5ZnNZiZOnEjRokXx8fHh0qVLWsD0xRdf4O/vT/ny5SlatCizZs0C4M033yQ6OhoPDw/at29P27Ztsy1/0aJFXLx4kaeeegofHx8GDx6szUBMnTqVmJgYvL29GTp0KF27dr2fIcmWu7s7GzZsYMqUKfj4+BAUFKT1oUKFCvTr149KlSoRFBREcHAw9va5H0rMySeffMK0adMwGAzaYuK7tW/fnlmzZlGkSBF2797NggULAIiOjqZNmzYYDAaqV69Ov379qFu3Ll5eXqxevZoRI0bg6enJqlWrWL16NXq9PlPd3bp1o2TJkhQrVozOnTvTuXNnbZuLiwsffPABzzzzDEajkYiICN59911CQ0OpXbs27u7utGzZMteZtfvZ5269e/embdu21KhRg3LlylG5cmWGDh2a5/2FeNzplNy0ibi4ONzd3YmNjcVgMBRcwQva0TrlT5Kc3JncdC5lipQpuLKfQElJSZw7d47g4GBtkaUQQgiR3fdDXr+/ZWbHmnT/XmlHDmMJIYQQhUOCHWu6O9gxS7DzX9WyZUvtonJ3PzZt2lTYTctk4sSJWbZ12LBhhd00Bg4cmGXbMg6PPe6e9P4JUZjkMBZWPIy1uBMdEo8T72RgYqMZVPapXHBlP4HkMJYQQoisyGGsR5nOhowr7chhLCGEEKJwSLBjTTodGVevkGBHCCGEKBwS7FiTToetApTcCFQIIYQoLBLsWJMsUBZCCCEKnQQ71nRXsCMzO0IIIUThkGDHqnT/LFBWsmZHCCGEKCSPdLCTlpbGxx9/THBwME5OTpQsWZIxY8ZY3OBPKcXw4cPx8/PDycmJRo0acebMmUJs9V10NrJAWRAUFMTu3bsf+frq16/PwoULrdCiR4NOp7O4R9WDetivqxB5Ie/zrD3Swc4nn3zCjBkzmDp1KqdOneKTTz5h4sSJTJkyRcszceJEJk+ezNdff83+/ftxcXGhSZMmJCUlFWLL/6GzSV+gjBzGEuK/7En5wsiQnJxMr169KFGiBAaDgeeee459+/Zlm//eCyXa2Njw+eefA3DkyBFq166NwWDgqaeeYs6cOXnaD+Dq1as0b94cFxcXypQpw08//VSg/cxv+TNmzKBq1aro9XpGjhyZZZ59+/ZhY2NjcePgnOrJ71iLrD3Swc7evXtp3bo1zZs3JygoiA4dOtC4cWMOHDgApM/qTJo0iY8++ojWrVtTqVIlvvvuO6Kioli9enXhNh4sTj2XYMe6zOY0Ik8c49SenUSeOIZZFoSL/yiTyZSntPyWce/2jADu1q1bDB48mJYtW5KQkJBl/oSEBO3x559/YmNjQ7t27YD0G6k2adKEW7dusWLFCoYMGcKpU6dy3Q/g9ddfx9fXl6tXr/Lpp58SHh7OjRs3cu1fjx49mDdvXq758lu+n58fI0eOpH379lluN5vNDBkyhOrVq+e5nvyOtcjaIx3s1KpVi59++ok///wTgKNHj7J7926aNWsGwLlz54iJiaFRo0baPu7u7tSsWTPHyDc5OZm4uDiLh1VoFxWUNTvWdGb/Xma/3pvvR3/Ixsmf8v3oD5n9em/O7N9bYHWMHTuWnj17WqQ1aNCAhQsXMn78eAIDAzEYDISGhnLs2LF8lW02mxk0aBBeXl4YjUaqV6/OtWvXgPT3ePPmzfH09MTPz4/JkycD5LnOiIgIbf+QkBCLW1QcPHiQSpUqYTAY6N+/P2Zz3gJynU7HtGnTCAoKwmg0MnPmTPbu3Uu5cuXw8PBgzJgxeao/pz7odDpmzJhBcHAwXl5eTJgwIdd25WVMVq1aRYkSJfD19eXTTz8Fch7/EydOULduXYxGI9WqVWPPnj3Zjsndhw7uPiT46quvEhERQePGjXF1dWXRokU5jkt2ctpHp9MxdepUgoODadCgQbZpOfUnq/zZcXFxYfjw4ZQoUQIbGxs6deqEvb09p0+fzrUfixcvJjQ0lODgYADOnz9P586dsbGxoWrVqoSEhPC///0v1/0SEhJYvXo1o0aNwtnZmVatWlGxYkXWrFmTaxvy4n7Kb9OmDa1atcJoNGa5fdasWdSsWZOQkJA81/MgY71q1SpKliypLe140M+Tx9kjHex88MEHdOrUibJly6LX66lSpQqDBw+ma9euAMTExABQtGhRi/2KFi2qbcvKhAkTcHd31x7Fixe3Tgcy1uzIdXas5sz+vaz9YjwJN65ZpCfcuMbaL8YXWMDTsWNH1qxZQ2pqKpD+3jtw4ACtW7embNmyHDp0iOvXr/Piiy/yyiuv5KvsLVu2sHfvXs6ePcv169eZOXMmjo6OmEwmmjdvTvXq1bl06RKnT5+mdu3aAHmq02w207JlS5o0acLly5eZO3cu3bp14/Lly6SkpNCuXTtee+01rl+/Tvny5dm7N+9jtWvXLk6ePMmqVasYPHgwn3/+Obt372bfvn2MHz+es2fP5lh/Xvqwfft2/vjjD3bs2MGoUaP4+++/c2xTXsZk/fr1HD9+nB07dvDFF1/w008/ZTv+KSkptGzZkpdeeomrV6/y3nvv0bJlS27evJnncQKYM2cOJUqUYMuWLSQkJNC5c+ccxyUruY0lwNatWzl69ChbtmzJMi0v/bm3jAEDBjBgwIBc+3jmzBlu3LhBqVKlcs27YMECi9fmjTfeYOHChZhMJg4cOEBERATPPfdcrvudOXMGV1dXihUrpqVVrFiREydO5NqGvCjo8q9fv86kSZMYNWrUA9WT17FeunQp7733Htu2baN06dIP/Hny2FOPsCVLlqhixYqpJUuWqGPHjqnvvvtOFSlSRM2bN08ppdSePXsUoKKioiz2e+mll1R4eHi25SYlJanY2FjtERkZqQAVGxtbsB1YPUC9Mau8arygplpzZk3Blv0ESkxMVCdPnlSJiYl5yp+WZlJf9++uPgtvnu1j5mvdVVqaqUDaV6VKFbV+/XqllFJTpkxRbdu2zbIPOp1OxcfHa2mBgYFq165d2Za7bds29fTTT6v9+/crs9mspe/evVsVK1ZMpaWl5diue+vMqG/fvn2qdOnSFnnbt2+vvv32W7Vjxw4VFBSkpZvNZlWsWDG1YMGCHOtSSilAHT58WHvu4+OjVq5cqT2vUaOGWrVqVY7159YHQB06dEjbXr16dbVq1apc25ZdeRll/vzzz9rzYcOGqT59+mQ7/r/88osKDAy0KPe5555TixcvVkpZvq6AioyM1PLVq1fPYizvzpufccmQ2z6A2rt3r8X2e9Ny609WZeTFnTt3VI0aNdTIkSNzzXv06FHl6Oiobt68adGuoKAgZWtrq2xtbbXP97zsd29/PvzwQ9WvX78s627evLlyd3dX7u7uSq/XKycnJ+35hAkTMuXPb/l369evnxoxYkSmtBkzZiillOrevbsaM2ZMvuvJy1gDaty4cap06dLq/PnzWvqDfp4Utuy+H2JjY/P0/f1Iz+y8++672uxOxYoV6datG0OGDNGmtH19fQEy/SK6fPmyti0rDg4OGAwGi4d16LBRoENmdqzh0qkTmWZ07hV//RqXThXML71OnTqxbNkyAJYtW0bHjh0BmD17NuXLl8fd3R1fX1+UUly/fj3P5TZs2JD+/fvTt29f/Pz8eOedd0hNTeXixYsEBgZiY5P5v2le6oyIiODcuXMYjUbtsWnTJqKjo4mOjrb4JanT6Sye58bHx0f728nJKdPzhISEHOvPSx/unrF1dnbOdY1CXsbk7lnc4sWLEx0dne34R0VFZZr1DQwMJCoqKs/jlJXcxuV+98nq9bs7LS/9yc97ACA1NZWXXnqJUqVKMXz48FzzL1iwgJYtW2qHeW7cuEHz5s359NNPSU5O5rfffmPo0KH89ttvOe4H6YuX712CEBcXh6ura5Z1r1+/nlu3bnHr1i26dOnC9OnTtecffPBBpvz5LT8nv//+OwcPHqRPnz73XU9+xnrSpEn06tWLwMBALe1BP08ed3aF3YCc3LlzJ9MLY2trq60tCA4OxtfXl59++olnnnkGSH+T7N+/n9dee+1hNzezu6+gLGt2ClzCrbwdTshrvtyEh4drh2iOHDlCixYtOH/+PIMHD2bnzp1UrVqV5ORkXFxcLC6PkBdDhgxhyJAhREZGEhYWRoUKFXj66ae5cOECSil0Op2WN691BgQEEBISkuXx9507d2Y6PbUgT1fNrf6CGrf8lhcZGUnJkiW1v/38/ICsx79kyZJERkZa7B8REUHr1q0z1e/s7ExiYqL2/N4fYHe/fjmNS3byss/ddWSV5u/vn2t/siojO2azmW7duqHT6Zg/f36u+5rNZhYvXszXX3+tpf3999+4uLjQoUMHACpVqkStWrW01zG7/QBKly5NQkICly5dIiAgAIDjx48X2CGYgix/586dnD59WisnNjYWOzs7/v77b6ZMmZJrPfkd65UrV9K1a1eCg4O1H2XFixd/oM+Tx90jPbPTsmVLxo0bx4YNGzh//jyrVq3iiy++oG3btkD6f8zBgwczduxY1q5dyx9//MErr7yCv78/bdq0KdzGg8Vdz2Vmp+C5Gj0KNF9ugoKCCAkJoU+fPoSFheHi4kJCQgI2NjZ4e3tjMpkYMWJEvss9dOgQBw8exGQy4ebmhl6vx9bWlho1auDm5saYMWNISkoiLi6Ow4cP57nOmjVrYjabmTFjBikpKaSkpLBr1y4iIiIIDQ0lNTWVWbNmkZqayrRp03KcWbgfOdVfEON2t7yW98knnxAXF8fp06eZO3cu4eHh2Y5/zZo1AZg6dSomk4nly5dz6tQpmjZtmqncypUrs3TpUtLS0vjuu+/466+/LLb7+Phw/vz5XMclO/ezT1Zl5LU/edGvXz+io6NZvnw5dna5/27+6aefSE1N1U4wAXj66ae5c+cOa9asQSnFyZMn2bVrFxUrVsxxP0ifEWndujUjRowgMTGR9evXc+zYsSyD0XvNmzePHj165Jjnfso3mUwkJSWRlpZm8Xffvn3566+/OHLkCEeOHKFVq1a8/vrrfPnll3mqJ79jHRwczObNmxkyZAgbNmwAeODPk8fdIx3sTJkyhQ4dOjBgwABCQkJ455136Nevn8XZHu+99x5vvPEGffv2pXr16iQkJLBp0yYcHR0LseX/0E49l7OxrCEgpDyuRbxyzOPm6UVASPkCq7Njx45s376d8PBwACpUqEC/fv2oVKkSQUFBBAcHY29vn68yY2Nj6dWrF0ajkTJlylC7dm26dOmCnZ0d69evZ+/evfj5+VGmTBn27duX5zrt7OzYsGEDmzdvJiAgAH9/f8aNG4fZbMbe3p6VK1cyZcoUPD09OXbsGLVq1SqQMcpL/QUxbnfLa3kZszbPP/88gwYNolGjRtmOv729PWvXrmXJkiV4enoyYcIE1q5di4dH5uD5yy+/ZNGiRRQpUoTDhw9nGsv333+fDz74AKPRyPfff5/tuNzPWOZVfvqToX///vTv3z9T+oULF5gzZw4HDhzAy8tLuw7Orl27tDzNmjVj/Pjx2vMFCxbQqVMniy9rd3d3vv/+e0aMGIHBYKBZs2a89dZbFmfYZrVfhunTpxMVFYWnpydvvfUWy5Yto0iRIln2pVmzZpmu25PxuLud+Sn/3j6OHTsWJycn5syZw7hx43BycmLBggU4Ozvj6+urPZycnHB1ddUOy+VUT17GOishISGsW7eO3r17s3Pnzgf+PHnc6dSTNld1H+Li4nB3dyc2NrZg1+9seJuhlzbzu7ML3aoNomtI14Ir+wmUlJTEuXPnCA4OznOwmnE2VnZavfUhpWsW7Je4EEKIhyu774e8fn8/0jM7jz1Zs2N1pWvWotVbH2aa4XHz9JJARwghBPCIL1B+/Om0YEcm0KyndM1alKxeM/3srFs3cTV6EBBSHhsb29x3fkiioqJ4+umnM6V7eXlpazkeFY9yWx/ltj2oJ7lvQhQ2CXasSe6N9dDY2NhSvHylwm5Gtvz9/R+by7s/ym19lNv2oJ7kvglR2OQwljXJ2VhCCCFEoZNgx5p0OlmzI4QQQhQyCXasSadLn9lRSmZ2hBBCiEIiwY416WywkzU7QgghRKGSYMea5NRzIYQQotBJsGNVun8XKOfjSqdCCCGEKDgS7FiTzkY7t19mdoQQQojCIcGONelssPnnYoIS7Pz3BAUFsXv37odW3/nz5/N0k8CsjBw5kldffbWAWyQeJT169GDs2LGF3QwhCoUEO9akXWdHkWaWYEeIDPLFK4R4mCTYsSbdv2t20pBgRzxZ0tLkPV2YCnv8763fZDLla//85hfiQUiwY006W23NjixQfnyNHTuWnj17WqQ1aNCAhQsX8sYbb+Dv74/RaKRx48ZERETkq+y5c+cSGBiIm5sbZcqUYceOHQDcvn2bAQMG4O/vj4eHB926dQPg5s2bNG3aFC8vL7y9venbty/JyclZln3jxg26dOmCj48PTz31FPPnz9e2Xb16lWbNmmEwGGjQoAFXrlzJta0jR46kc+fOtG/fHldXV7Zv386JEyeoW7cuRqORatWqsWfPHi1/ZGQkYWFheHh4UK5cOdasWQPA/PnzWbRoEWPGjMHV1ZX+/ftnW+eOHTsoVaoUI0eOpEiRIgQHB7Nv3z5mzpyJv78/AQEBbNy4MU99Xr9+PRUrVsTNzY3SpUuzfPlybVuPHj0YNGgQDRs2xM3NjcaNG3Pjxo0cx+PPP/+kTp06GAwGihYtyrvvvqttGz9+PEWLFiUoKIjJkydbHF7U6XRcvHhRe16/fn0WLlwIwP79+6levToGg4HAwECmTJmS4/hHRETQvHlzPD09CQkJYdOmTVr+v//+m9q1a+Pm5kb79u1JTEzMsT+QHsCMGDGCwMBAihYtyttvv60FJVnVr9PpmDp1KsHBwTRo0ACz2cyIESMoXrw4fn5+DBo0SHt/zps3jwYNGtCvXz/c3d359ttvc22PEAVG3YeIiAgVGRmpPd+/f79688031cyZM++nuEIXGxurABUbG1uwBe+cqBZMK6eafvesGrlnZMGW/QRKTExUJ0+eVImJiUoppcxmszKnmKz/MJtzbNeff/6pPDw8VEpKilJKqejoaOXs7Kzi4uLUkiVL1K1bt9SdO3dUz549VevWrbX9AgMD1a5du7ItNyEhQbm5uak///xTKaXU+fPn1dmzZ5VSSr366quqefPm6urVqyolJUUr59q1a2rt2rUqKSlJRUVFqSpVqqgvv/xSKaXUuXPnlK2trVZ+WFiYevvtt1VSUpI6deqU8vPzU0ePHlVKKfXSSy+pbt26qcTERLVnzx7l5uamevfuneM4jBgxQjk4OKjNmzertLQ0FRsbq4KDg9VXX32lUlJS1NKlS5WHh4e6ceOGUkqp2rVrq3feeUclJSWpn3/+Wbm6uqozZ84opZTq3r27GjNmTI71KaXUzz//rGxtbdVnn32mUlNT1ciRI1Xx4sXVkCFDVHJysvr2229ViRIl8tTnHTt2qP/9738qLS1NbdiwQbm4uKjo6GitPUWLFlVHjx5ViYmJ6oUXXlDDhw/PsW0dO3ZU48ePV2azWSUkJKj9+/crpZTasGGD8vf3V6dPn1Y3b95UDRs2tHhdAIvPz3r16qkFCxYopZQ6fPiwOnz4sEpLS1MHDx5UBoNB/fbbb1mO/+3bt1WlSpXUV199pVJTU9XevXuVl5eXiomJUUopVb16dTV06FCVnJysVq1apezs7HId84kTJ6oGDRqoK1euqJs3b6r69eurKVOmZFl/YmKiAlSrVq1UbGysunPnjpo1a5YqV66cioyMVNeuXVO1atVSI0aMUEop9e233ypbW1v17bffqrS0NHXnzp2cX3wh7nLv90OGvH5/39dqxi5dutC3b1+6detGTEwML774IuXLl2fRokXExMQwfPjwgozHHl8Z19lRskD5vpjMXP7qd6tXU/TNKqDP/g7ppUuXJigoiC1bttC8eXNWrFhBkyZNcHNzo1OnTlq+999/n9q1a+erbp1Ox4kTJyhRogSBgYFA+izgggUL+OOPP/Dy8gKgTp06AHh6etKyZUsA/Pz86NevH1u3bmXw4MEW5cbExLBjxw5Wr16NXq+nbNmydOnShR9++IHy5cuzatUqzpw5g6OjI7Vq1aJVq1Z5am+9evVo3LgxAEePHsVsNjNo0CAAOnbsyKRJk9i0aRN16tTh0KFDbN26FQcHB+rXr0+LFi1Yvnw5Q4cOzdcYubq68tZbb6HT6QgPD2fkyJF89NFH2Nvb07FjR3r27MmtW7dISkrKts+VKlWiXr16WplhYWFUrFiRQ4cO0aJFCwBeeuklKlVKv5ls+/bt2bJlS47t0uv1XLhwgZiYGPz8/KhRowYAy5cvp0+fPtodzD/44ANtxi43VatW1f5+9tlnCQsLY8+ePVSpUgWwHP9jx46RmJiojX9oaCj16tXjxx9/pEGDBhw7doxffvkFe3t72rRpQ82aNXOt/5tvvmHu3Ll4e3sD8Pbbb/PZZ58xcODATPU7Ojpq/TMYDAAsXbqUd955h2LFigEwfPhwBg0axMiRIwEoWbIkPXr0AMDJySlPYyJEQbivw1jHjx/X/mN///33VKhQgb1797Jo0SLmzZtXkO17vMmp50+MTp06sWzZMgCWLVtGx44dARg3bhylSpXCYDBQo0YNrl+/nucyXVxcWLJkCZMnT6Zo0aK89NJLREVFcfXqVZKTkwkODs60T3x8PK+88grFihXDYDDw1ltvZVlnREQESUlJeHt7YzQaMRqNzJw5k5iYGK5evYrJZKJ48eJa/rv/zknGlxhAVFRUpv0CAwOJiooiKioKb29viy+0jG355eXlhU6nA9K/IG1tbSlSpIj2HCAhISHHPgPs3r2b2rVrU6RIEYxGI4cOHbIYu6JFi2p/Ozs753oH8okTJ5KSksIzzzxDlSpVWLduHQDR0dH3NbYAJ06c4MUXX8Tb2xt3d3d++OEHizbePf4RERGcO3dO66vRaGTTpk1ER0cTHR2Nt7e3FpDktR0RERE0a9ZMK69r164Whzjvrj+rtKioKEqUKKE9v/c1z2p/IR6G+5rZSU1NxcHBAYBt27ZpvwrLli1LdHR0wbXucXf3Xc9lzU7+2dmkz7o8hHpyEx4ezvjx4zl79ixHjhyhRYsW7Ny5k+nTp/Pzzz9TunRp/vzzT8qWLZuvqsPCwggLCyMhIYH+/fvz4YcfMnfuXBwcHDh//jylSpWyyP/FF19w9epVjhw5gpeXFzNnzmTJkiWZyg0ICMDV1ZWbN29qgUKGtLQ07OzsiIyMJCgoCEhfX3P3F2N27i7L39+fyMhIi+0RERG0bt0af39/rl69SlJSklZuREQEFStWzFROQcmpzwDdunVj6NCh9OjRA3t7e0JDQ1H/XBrifvj5+TF37lyUUqxdu5bw8HBu3ryJn5+fxbjcO0bOzs4W62cuX76s/T1w4EDq1q3L2rVrcXJyonPnzhZtvLtfAQEBhISEcOzYsUxtu3DhAteuXbMY/8jISMqXL59jnwICAli2bJnFDNPdshrXe98Td69bi4iIwN/fP8f9hXgY7mtmp3z58nz99dfs2rWLrVu30rRpUyA9qvf09CzQBj7edNj88zllUnLmQX7pdDp0elvrP/LwARwUFERISAh9+vQhLCwMFxcX4uPj0ev1eHl5cfv27XyfSn358mXWr19PYmIiDg4OODs7Y2tri42NDa+88oo2a5Oamqot/I2Pj8fZ2Rl3d3cuXLjA9OnTsyw7ICCA0NBQPvroI+7cuYPJZOK3337j5MmT2Nra0qZNG0aOHElSUhK//vqrNiuRHxmHRaZOnYrJZGL58uWcOnWKpk2bUrx4capWrcqIESNISUnhl19+Yd26dXTo0AEAHx8fzp8/n+86c5JTnyF97Dw9PdHr9axcuZLDhw8/UH0rVqwgKioKnU6H0WhMf7/qdHTo0IE5c+Zw5swZYmNjmThxosV+lStXZunSpaSlpfHdd9/x119/advi4+MxGo04Ojqya9cuNmzYkG39NWvWxGw2M2PGDFJSUkhJSWHXrl1EREQQGBhIhQoVGDt2LKmpqaxdu5YDBw7k2qdevXrx0UcfER0djVKK8+fPs3PnzjyPSceOHfn888+5dOkSN27cYMyYMRaHeoUoLPcV7HzyySfMnDmT+vXr07lzZypXrgzA2rVrtcNbgn9mdnSA3PX8SdCxY0e2b99OeHg4AE2bNqV27doEBgZSsWJFatWqla/yzGYzEydOpGjRovj4+HDp0iUtYPriiy/w9/enfPnyFC1alFmzZgHw5ptvEh0djYeHB+3bt6dt27bZlr9o0SIuXrzIU089hY+PD4MHD9ZmFKZOnUpMTAze3t4MHTqUrl275ns87O3tWbt2LUuWLMHT05MJEyawdu1aPDw8gPT1G0ePHsXHx4d+/foxf/58SpcuDaR/qe7fvx+j0ciAAQPyXff99HnKlCkMGjQIDw8PNm/ebLGG534cOHCAatWq4erqymuvvcaSJUtwcHCgefPm9OvXj9q1a1OpUiVtTVCGL7/8kkWLFlGkSBEOHz5s8b755JNPmDZtGgaDgUmTJuW4lsrOzo4NGzawefNmAgIC8Pf3Z9y4cdos8uLFi/npp58oUqQI8+bNy/G9kuHdd98lNDSU2rVr4+7uTsuWLTPNTOWkd+/etG3blho1alCuXDkqV66c7zVaQliDTt3nPG5aWhpxcXHaBxukX8HV2dkZHx+fAmvgwxAXF4e7uzuxsbHaQrsCsW86G3+bzhQ3R8oWr8uXDb4suLKfQElJSZw7d47g4OA8HVIR4nGQcThSrisjxP3L7vshr9/f9zWzk5iYSHJyshboXLhwgUmTJnH69OnHLtCxKp1OG2CZ2RFCCCEKx30FO61bt+a7774D4NatW9SsWZPPP/+cNm3aMGPGjAJt4GNNZ4PtP/NmcjbWf1fLli1xdXXN9Lj7AnCPiokTJ2bZ1mHDhlmtzn379mVZ5/PPP2+1OvNq2bJlWbYt4yKPj6OBAwdm2aeMQ6VCPInu6zCWl5cXO3fupHz58syZM4cpU6bw+++/s3LlSoYPH86pU6es0VarsdphrAOz+fnAV0w0OBLsX4PpjbJeTCrSyWEsIYQQWSmUw1h37tzBzc0NgC1bttCuXTtsbGx47rnnuHDhwv0U+cTKuBGoHMYSQgghCsd9BTulSpVi9erVREZGsnnzZu2KmleuXCnYmZHH3V3X2UlTaQ90TQ8hhBBC3J/7CnaGDx/OO++8Q1BQEDVq1CA0NBRIn+XJuKy5IP12EQp0pC9QVkiwI4QQQjxs93UF5Q4dOlCnTh2io6O1a+wANGzYME/XcvjPuOt2EWZlTp/ZkQuICiGEEA/VfQU7AL6+vvj6+nLx4kUg/Z4nckHBe+h0FoexhBBCCPHw3ddhLLPZzOjRo3F3dycwMJDAwECMRiNjxoyRe0Dd7a67nsthLCGEEKJw3NfMzrBhw/jmm2/4v//7P2rXrg2k31E4414748aNK9BGPrZ0Ntj9E99IsCOEEEIUjvua2Zk/fz5z5szhtddeo1KlSlSqVIkBAwYwe/Zs5s2bV8BNfJz9exhLW7Mj/nOCgoLYvXv3I19f/fr1WbhwoRVaJB4l8jqLgqLT6bSlLAXFWp+X9xXs3Lhxg7Jly2ZKL1u2LDdu3HjgRj0xMg5j8c+p5zKzYzXKrEj6+xZ3jlwh6e9bKLOM9ePgYQeC4vFUv359HB0dtas9N2vWLNu8J06c4Pnnn8dgMFCuXDl27Nhhsb1v3774+flhMBioWLEi69aty1TGvn37sLGx0W7MC3D16lWaN2+Oi4sLZcqU4aeffiqw/gEkJyfTq1cvSpQogcFg4LnnnmPfvn3Z5s+tnxMnTqR48eK4ublRpUoV4uPj87Q9P2P9OLmvYKdy5cpMnTo1U/rUqVOpVKnSAzfqifHPYSzt1HOZ2bGKxOPXiPnkANdm/8GNpae5NvsPYj45QOLxa4XdtCee3NyycBX2+GdVf37blNf8c+bMISEhgYSEBH788ccs86SmptK6dWs6dOjAzZs3mTx5Mh06dOD69etanrfeeovz588TFxfH3Llzefnlly22m81mhgwZQvXq1S3Kfv311/H19eXq1at8+umnhIeH5+nHfY8ePfJ0xMNkMmnB/61btxg8eDAtW7YkISEh3/2cNm0amzZtYs+ePcTFxTF//nzs7e21/XPbnpexftzcV7AzceJE5s6dS7ly5ejduze9e/emXLlyzJs3j88++6yg2/j40t1zGEtmdgpc4vFrXF94irTYFIv0tNgUri88VWABz9ixY+nZs6dFWoMGDVi4cCHjx48nMDAQg8FAaGgox44dy1fZZrOZQYMG4eXlhdFopHr16ly7lt7uc+fO0bx5czw9PfHz82Py5MkAea4zIiJC2z8kJMTiflwHDx6kUqVKGAwG+vfvn+eTC3Q6HVOnTiU4OJgGDRpw4sQJ6tati9FopFq1auzZs8cif3bbX331VSIiImjcuDGurq4sWrQoxzqnTZtGUFAQRqORmTNnsnfvXsqVK4eHhwdjxozJU59zGzudTseMGTMIDg7Gy8uLCRMm5DgWOb12947v888/rx0+unf6/95DS7m18e7xz63P9/M651ReVvVnlZbT+yKr/AXh9OnT3Lx5k0GDBmFra0ujRo2oUqUKq1at0vKULVsWBwcHrR0pKSlcunRJ2z5r1ixq1qxJSEiIlpaQkMDq1asZNWoUzs7OtGrViooVK7JmzZoCa7uLiwvDhw+nRIkS2NjY0KlTJ+zt7Tl9+nS++pmWlsa4ceOYPXs2JUqUQKfTUalSJa3PuW2/X6tWraJkyZKcOXMGePDProJ2X8FOvXr1+PPPP2nbti23bt3i1q1btGvXjhMnTrBgwYKCbuPjSwt2FGlmuYJyQVNmxa11f+eY59a6swVySKtjx46sWbOG1NRUAGJiYjhw4ACtW7embNmyHDp0iOvXr/Piiy/yyiuv5KvsLVu2sHfvXs6ePcv169eZOXMmjo6OmEwmmjdvTvXq1bl06RKnT5/WTgjIS51ms5mWLVvSpEkTLl++zNy5c+nWrRuXL18mJSWFdu3a8dprr3H9+nXKly/P3r1789zmrVu3cvToUdavX0/Lli156aWXuHr1Ku+99x4tW7bk5s2bAKSkpGS7fc6cOZQoUYItW7aQkJBA165dc6xz165dnDx5klWrVjF48GA+//xzdu/ezb59+xg/fjxnz57Nsc8Zchu77du388cff7Bjxw5GjRrF339n/x7L7rV70PHNrY0Z479ly5YCf53zMoZ3159VWk6ve1b5BwwYwIABA3Js15AhQ/D29ubFF1/M8Qvy3s9ZpRQnTpywSBswYABOTk5Ur16dF154gYoVKwJw/fp1Jk2axKhRoyzynzlzBldXV4oVK6alVaxYMVO5BenMmTPcuHGDUqVKZbk9u35evHiRO3fusGLFCooWLUqZMmWYPXu2li+37ZD3sc6wdOlS3nvvPbZt20bp0qUf+LPLKlQBOnLkiLKxsSnIIh+K2NhYBajY2NiCLfj4DypqUnkVNq+KClsZpm6n3C7Y8p8wiYmJ6uTJkyoxMTFv+f+6qSLf/yXXR+JfNwukfVWqVFHr169XSik1ZcoU1bZt2yz7oNPpVHx8vJYWGBiodu3alW2527ZtU08//bTav3+/MpvNWvru3btVsWLFVFpaWo7turfOjPr27dunSpcubZG3ffv26ttvv1U7duxQQUFBWrrZbFbFihVTCxYsyLEupZQC1N69e5VSSv3yyy8qMDDQYvtzzz2nFi9enKftuY3N3XUePnxYe+7j46NWrlypPa9Ro4ZatWpVjn3Oyr1jB6hDhw5p26tXr65WrVqVbbuye+1yG19ARUZGatvr1auX7dhn1caM8VdKFfjrnNsY3lt/Vmm5ve5ZlZGT/fv3q/j4eHXnzh31ySefKH9/fxUXF5cpX0pKigoODlaff/65SklJUT/++KOys7NTffr0yZTXZDKpbdu2qUmTJmlp/fr1UzNmzFBKKdW9e3c1ZsyYbPvz4Ycfqn79+mXZ3ubNmyt3d3fl7u6u9Hq9cnJy0p5PmDAh1/7euXNH1ahRQ40cOTLL7Tn1c8+ePQpQvXr1Unfu3FFHjx5VXl5e6pdfflFKqVy353WslUp/HceNG6dKly6tzp8/r6Xf72eXUtl/JmT3/ZDX7+/7mtkReaSzQf9P8G0ym+RmoAXMHJ+Se6Z85MtNp06dWLZsGQDLli2jY8eOAMyePZvy5cvj7u6Or68vSimLNQC5adiwIf3799cWTr7zzjukpqZy8eJFAgMDsbHJ/N80L3VGRERw7tw5jEaj9ti0aRPR0dFER0db/ErV6XQWz3OTkTcqKorixYtbbAsMDCQqKipP2/PDx8dH+9vJySnT84SEhBz7nCG3sStatKj2t7Ozc5ZrJjJk99o96Pjm1sa7yyro1zkvY5hVGXen5eV1z8941KhRA1dXV5ycnHjvvfdwc3Pj119/zZRPr9ezevVq1qxZg6+vL19++SUdO3bMsi5bW1saNmzItm3b2LhxI7///jsHDx6kT58+mfK6uroSFxdnkRYXF4erq2uW7V2/fr121KNLly5Mnz5de/7BBx/k2NfU1FReeuklSpUqxfDhw7PMk1M/nZycgPTbOjk5OVGpUiU6derExo0bAXLdntexzjBp0iR69epFYGCglvagn13WcN9XUBZ5oUP/z19mZZarKBcwGzf73DPlI19uwsPDtcMlR44coUWLFpw/f57Bgwezc+dOqlatSnJyMi4uLvk+ZDlkyBCGDBlCZGQkYWFhVKhQgaeffpoLFy6glEKn+/c+I3mtMyAggJCQkCynoXfu3JnplNH8nEKa0R5/f38iIyMttkVERNC6des8bb+7XwUhpz5D3scuP7J67YKDg3McX2dnZxITE7Xndx8iyksb7x63gn6dcxvDe+vPKi231z27MvLKxsYm29esUqVK7Ny5U3teq1YtXn755WzLMplM/PXXX/z555+cPn2agIAAAGJjY7Gzs+Pvv/9mypQpJCQkcOnSJW378ePHC/wQjNlsplu3buh0OubPn5/jGGXXz6effhp7e3uLfe/+O7ft98pprAFWrlxJ165dCQ4O1n4AFi9e/IE+u6zhkZ/ZuXTpEi+//DKenp44OTlRsWJFDh06pG1XSjF8+HD8/PxwcnKiUaNG2gKpQnfXzI5CkZyWXLjtecI4BLtj655zIGPr7oBDsHuB1BcUFERISAh9+vQhLCwMFxcXEhISsLGxwdvbG5PJxIgRI/Jd7qFDhzh48CAmkwk3Nzf0ej22trbUqFEDNzc3xowZQ1JSEnFxcRw+fDjPddasWROz2cyMGTNISUkhJSWFXbt2ERERQWhoKKmpqcyaNYvU1FSmTZtm8cs9r2rWrAmkn4lpMplYvnw5p06domnTpnna7uPjw/nz5/Ndb07tya7PQIG8XnfL7rXLbXwrV67M0qVLSUtL47vvvuOvv/7StuW3jQX9Ouc2hnmR2+ueH7du3WLr1q0kJyeTkpLCl19+yY0bN7Q67nXs2DGSkpK4c+cOn376KWazWas3NjaWxYsXk5CQoLXr559/5vnnn6dv37789ddfHDlyhCNHjtCqVStef/11vvzyS1xdXWndujUjRowgMTGR9evXc+zYMYvgLTvz5s2jR48eeeprv379iI6OZvny5djZ5TwXkV0/XVxc6NChA+PGjSM5OZlTp06xbNkywsLCAHLcnt+xBggODmbz5s0MGTKEDRs2ADzwZ5c15CvYadeuXY6PIUOGFGjjbt68Se3atdHr9fz444+cPHmSzz//HA8PDy3PxIkTmTx5Ml9//TX79+/HxcWFJk2akJSUVKBtuS86G21mByA1LbXQmvIk0tnoMLYsmWMeY8un0NkU3OxBx44d2b59O+Hh4QBUqFCBfv36UalSJYKCgggODrY4hTMvYmNj6dWrF0ajkTJlylC7dm26dOmCnZ0d69evZ+/evfj5+VGmTBn27duX5zrt7OzYsGEDmzdvJiAgAH9/f8aNG4fZbMbe3p6VK1cyZcoUPD09OXbsGLVq1cr3eNjb27N27VqWLFmCp6cnEyZMYO3atdr/0dy2v//++3zwwQcYjUYWL16c7/rz02comNfrbtm9drmN75dffsmiRYsoUqQIhw8fttiW3zYW9Ouc2xjmRW6v+7369+9P//79s9yWmprK0KFD8fLywtfXl3Xr1rFx40bc3f/9EdOsWTPGjx8PwLfffqvdu/HXX39l9erVWj6dTsfs2bMpVqwYnp6e/N///R+LFy/mmWeewdnZWdvP19cXJycnXF1dMRqNAEyfPp2oqCg8PT156623WLZsGUWKFMmyzc2aNdOuU3PvI6Od97pw4QJz5szhwIEDeHl5afl37dqVqY+59XPatGlcu3YNLy8vwsLCGDNmDHXr1s11e17GOishISGsW7eO3r17s3Pnzgf+7LIGncrH/NG9p95m59tvv73vBt3tgw8+YM+ePdqLfS+lFP7+/rz99tu88847QPqHT9GiRZk3bx6dOnXKUz1xcXG4u7sTGxuLwWAokLYDcPpH1MZ3aVnEniTnIsxqPIun3J8quPKfMElJSZw7d47g4GAcHR3zvF/i8WvcWve3xenntu4OGFs+hVMFL2s0VYh8q1+/Pq+++mqOh1SEEFnL7vshr9/f+VqzU1BBTF6tXbuWJk2a8NJLL7Fz504CAgIYMGCAtoDs3LlzxMTE0KhRI20fd3d3atasyb59+7INdpKTk0lO/veQ0r0LzwqMzgYd/w5ySlrBLJQVlpwqeOFYzpPkc7GY41OwcbPHIdi9QGd0hBBCPL4e6TU7Z8+eZcaMGZQuXZrNmzfz2muvMWjQIObPnw+kX+sELM+eyHiesS0rEyZMwN3dXXvce9ZAgdGlD6896V+6EuxYj85Gh2NJI87P+OBY0vjIBTpRUVFZTmkHBQUVdtMyKYy2Psrj8yi37X49iX0SIieP9NlYZrOZZ599VjtOWaVKFY4fP87XX39N9+7d77vcoUOH8tZbb2nP4+LirBPw/LMKXS/Bzn+ev79/jqcwP0oKo62P8vgUVNvuvXdRYXqUx1sIa3ikZ3b8/PwoV66cRVpISIh2VoCvry9gedpmxvOMbVlxcHDAYDBYPKzin5kdPaBDJ2djCSGEEIXgkQ52ateunem+IH/++ad28aLg4GB8fX0t7j4bFxfH/v37CQ0NfahtzVJGsKPSZ3aSTRLsCCGEEA/bI30Ya8iQIdSqVYvx48cTHh7OgQMHmDVrFrNmzQLSTyMcPHgwY8eOpXTp0gQHB/Pxxx/j7+9PmzZtCrfxQPr9zv+9sGCKWQ5jCSGEEA/bIx3sVK9enVWrVjF06FBGjx5NcHAwkyZNsrhh4Hvvvcft27fp27cvt27dok6dOmzatClfpy5bjXYYS9bs5MfDuJqmEEKIx8eDfi880sEOQIsWLWjRokW223U6HaNHj2b06NEPsVV5pLMBHdpVlGXNTs70ej06nY6rV6/i7e1d4LcSEEII8fhRSnH16lV0Oh16vT73HbLwyAc7jzUbWwD0KECRapYrKOfE1taWYsWKcfHixQK9hYAQQojHW8ZNbG1tbe9rfwl2rMkmPQK1l5mdPHN1daV06dKkpkpgKIQQIl3GfefulwQ71mSbftK5/p9jjSazqXDb85iwtbV9oDe1EEIIcbdH+tTzx55t+syOXqXfQM+kJNgRQgghHjYJdqzJNv1urjKzI4QQQhQeCXas6d6ZHQl2hBBCiIdOgh1rstGDToeDOX1mJzVNFt0KIYQQD5sEO9b0z2Ese3MaOuRsLCGEEKIwSLBjTbbpJ7s5/HPquVxnRwghhHj4JNixJlt7QIc9gFJyuwghhBCiEEiwY03/XFQwfWZHyY1AhRBCiEIgwY41/XO7CAcUKCVnYwkhhBCFQIIda9LpwM4eewU6uTeWEEIIUSgk2LE2G722QFnW7AghhBAPnwQ71marxx4dOpU+s2P+5wKDQgghhHg4JNixNlv7f089T5NgRwghhHjYJNixNhs9DqDN7Kh/7pMlhBBCiIdDgh1r+2eBMqRfVNCMzOwIIYQQD5MEO9Zmoyf90oLpp57LYSwhhBDi4ZJgx9psLc/GSjbJ/bGEEEKIh0mCHWuztdfW7CgUCakJhd0iIYQQ4j9Fgh1r+2dmxwYdANfuXMOcbCI15jbKLIuVhRBCCGuTYMfabPXYAsXsXFAozsWdgzSFOSkNZZL1O0IIIYS1SbBjbXZOgKK4nSsAF+IuAKBMaahUCXaEEEIIa5Ngx9r0jgCUsHFCh47Ldy6np5uUBDtCCCHEQyDBjrXpXQAdHv8M9R3THQBUmhlzstwFXQghhLA2CXasTe8IOnBTChudDXdS72hXUVaJaYXcOCGEEOLJJ8GOtdk5AeCaZkaHjiRTEmkqPchRqbJIWQghhLA2CXasTZ8e7LikpYEOEk2JpP1zFWVlMsu6HSGEEMLKJNixNr0zoMPVbEqf2UlL4tzNvzGnpKHSFCpVDmUJIYQQ1iTBjrVlzOyY0oMd/1hPHJfFkrA3CgBzigQ7QgghhDVJsGNt/5x67pKWig4dsbYJkGomLSEFbEAlyhlZQgghhDVJsGNteuf0f9JScLS157pDLKm69IXJKsWMOTn9cJYQQgghrEOCHWvTO4FOB6ZkfJ2LonSKKLsrpKk00m6noEwKZZJDWf/P3p9HyZbd9Z3oZ+8zxhw5553Hqro1qEpVpapSaUYIBFhgkMDYeHjG2G37ybRBPdh49fN6dL9nsZb7gdu2wDZNY9w2lhC2EUIgEBoKoRpUqnm483xzzoyMOc60935/7Mi4mXe+qlk6n7UiYzjn7LNPRGSc7/mNOTk5OTk5rxW52HmtcQuAAJXwA3u+D4FgMVhFG82g1c0zsnJycnJycl5jcrHzWjMMUCaLuXPsEAAXvCWMgXR9AIBJcrGTk5OTk5PzWpGLndcar2jdWCphKhwDYM5bBgy6nSCkQEd5kHJOTk5OTs5rRS52XmuG2VgYTUV6hE7Ior+GxmDaGbhW7Gy0kMjJycnJycl5dcnFzmvNsKgggEj7TBQmWPObKKEgM+hYQZp3QM/JycnJyXmteEuJnV/6pV9CCMHP/dzPjV6LooiPf/zjTExMUC6X+djHPsbS0tIbN8lLkQ44nn2c9JkqTqGFphMMMBjSboTJVC52cnJycnJyXiPeMmLnySef5N/+23/L3XffveX1n//5n+fzn/88n/3sZ3nkkUeYn5/nox/96Bs0y6uwEaQcd7lt7DZA8Lw8jDEG1YrBkIudnJycnJyc14i3hNjpdrv81b/6V/n1X/91xsbGRq+3Wi1+4zd+g1/+5V/mgx/8IPfffz+/+Zu/yaOPPsrjjz/+Bs74Eoadz0l73DFxBwBLfoOBjshaMYi8bUROTk5OTs5rxVtC7Hz84x/nL/yFv8CHPvShLa8/9dRTpGm65fVDhw6xe/duHnvssdd7mlfHt1WUSXrcM3UPQsBS0EAbhW4l4EpMP8/IysnJycnJeS1w3+gJXI9Pf/rTPP300zz55JOXLVtcXMT3fer1+pbXZ2ZmWFxcvOqYcRwTx/HoebvdftXme0U2LDtJF9/x+cG9P8TT/cfRRlux4whMaltICPctoT9zcnJycnLeMrypz6znz5/nH/7Df8h/+k//iTAMX7VxP/nJT1Kr1Ua3Xbt2vWpjXxFvw7LTxxEO9UKdNb9FxsUeWXkl5ZycnJycnNeGN7XYeeqpp1heXua+++7DdV1c1+WRRx7hX/7Lf4nruszMzJAkCc1mc8t2S0tLzM7OXnXcX/iFX6DVao1u58+ff20PZMONlfZxpEM9qJNJzYq/jjGGuNXD6Dz9PCcnJycn57XgTe3G+t7v/V5eeOGFLa/99E//NIcOHeIf/aN/xK5du/A8jy9/+ct87GMfA+Do0aOcO3eOhx9++KrjBkFAEASv6dy34F0MUAaYLEyCMCx4KxxKDrC6vMiO6n50kuHgv37zysnJycnJ+S7gTS12KpUKd91115bXSqUSExMTo9d/5md+hk984hOMj49TrVb52Z/9WR5++GHe+c53vhFTvjIbbqw0AmBbaRtCSBb9BirWmFZm43aiPCMrJycnJyfn1eZNLXZuhF/5lV9BSsnHPvYx4jjmwx/+ML/6q7/6Rk9rKxtiR9mgaN+4VESRpWAV1c4QHYGW2raN0AYhxRs42ZycnJycnO8s3nJi52tf+9qW52EY8qlPfYpPfepTb8yEbgQvBAQk1rJjUsWEU2fJXyMzGUHXJRUKN3MwqUYEDgA6ytD9DNVN8KaLyPAt93Hl5OTk5OS84eRnz9cDr2DbY6kBAL7jMeVPcNabIzOKMIUsStDStdadOEO1E3Q3xShtrT2eg78t/7hycnJycnJulvzs+XowitmxbqyxcJzt/gxKKhp+i5IqkrUiqBfJVvroWCGEQBYcpOehE4Vqxeiajyx6b+CB5OTk5OTkvPV4U6eef8fgDt1YmXVjCSGYdscxAk67cyQ6QbVjZNFqT6fq41R9hGfdWdJ3MEqTtWKMMW/UUeTk5OTk5LwlycXO68GGZWcodgAm3QkwhuWgQUd1Uc0Y6TvI0EWIywOUnaKLaiWYQd5WIicnJycn52bIxc7rwUadHRWDtoUDpzzb0HTZbwCQNuMrbrqB8ByMMmTrUW7dycnJycnJuQlysfN6sCF2shiMraVTcAv85YkfYdFfs4vaERc659Hm6lWUnaKL6iTovGloTk5OTk7ODZOLndcDrwBCWLGjLxYOfLD6dirlGgYDiabVanK2fY6z7bNXHEZ4EqNBXcO6Y1KNUbnlJycnJycnZ4Nc7LweeEVADN1YF60yDpLxoE7Da5OZjHi9j0YTbYrtuRSn6Nq09F665XWjNFkjIj7bIlsfvFZHkpOTk5OT85YjFzuvB25oLTtptCVI2ZUOk/4ES0GDzGS4Xch0RmYyrmabEa79yFQzts1DjUF1EpLzHZKFLjpWNpBZ5U1Fc3JycnJyIBc7rw9eAWvZSSDujF52hcOe4q5RJeX19VW6aY9UZWT66nE5cmjdUc2YdL5Lcr6NHmQ4FR+n4qMH2WWWn5ycnJycnO9WcrHzerCReg7QXhw9lI7DrcX9TFanAGg1Gvy/jv4iX298g0a0htGG3uEVBi+tbYnRsdYdQ7rcJ2vGyKKHU/ERUoxuqpXkWVs5OTk5OTnkFZRfH7wQxvfB8mGWn/kSj574U9Io4h0//FGCzHDfzH2oM+vMxOMYY/jDlT/m+4L3o59fRTQzXOnhjod420qjIWXZx2Qax7+8orIsuKheihspRCH/iHNycnJyvrvJz4SvF3vfC8uHcZsnWTvvYzBI18XXGlMrE8kOoQ6oZRUebN1B+VSCQWAAjSY+094idoQUCN+54q6EJzG9FNVJkLnYycnJycn5Lid3Y71eVHcAUHd7CEeSJQlJv48ymRUtRQcM/OzZn+RDqw+SqoTBtGH17hRjDMm5NkZpEn1jsTgycFDtGJPlgco5OTk5Od/d5GLn9aIwDghk0qFWrwDQXW+gdIYrJcVqBSkklaxA34n5D9u/wJE75lmd6KJCWz+neXaZc+2z1wxe3kCEjs3Mukagsk6uvTwnJycnJ+c7gdzH8XpRmQEpIelSr5dYWYDO6jLlsXGqhRqDGY1YSWlNx/zbyqdZEQ0eP/ssdW+M/3X253DOQHqqwaCqyXSGK6/90QkhEI5ANeNR8PJmdKJI57voSOFOhrjjhcvWuRF034qlvBt7Tk5OTs6bldyy83pRHAfhQNxhbMxadtorywjASE1p3ySN73do3WP4iT0/jsHgSJe2avN46XlSncJCTBrHKNS19zVEFlx0L0Vf0jzUpIp0sYfqpghXkC71SZd6N+XyMqkmWe4Tn+uQLg8wOs/8ysnJycl5c5KLndeLwpgtLKgS6tUAjKG9uox0XbI0JnB8wrBALaixr7QHpRUGg0DwvDgCZYc4jZk/cxalb0zsCEeOig5uYDJNuthHtWKcqo8MXZySh1qLSOa66OjaLjJjDKodE59vo5b7CEeg++nIwpOTk5OTk/NmIxc7rxdeEZwAgDG/Dxh66+tgIM0iEIId5R3sq++j5JeRQo6agh7uH+V3nC/QU10GZ5v00h4A/WywJX4nXezR+frcFkuODB10J0Enaih0emStGKcWjNxWwpXIqo/qpiQXOqhOYqszX3LTcUa60CO50MEkGlnzkQUXA1sEVU5OTk5OzpuJXOy8Xghha+0AlZUnCHwPg6HfbqJ0hjaakihScku4wuXj+/4eBacw2vy52nEADvZ2srS+xMnmKc53znOmfQawsTOdP7tAcrbN4KW1i7v1HXSi0J1kVITQqXiXxecIKXBrPiYzJHMd4lPNy27J2Q7ZemSLGJY9hLBjyNBBt1N0nHdjz8nJycl585GLndeT+/8mIBDtC5QrIRhNr7mOMhlGqFE/q+nSFPfW7+Ef7vm47YiOYc1vcSFcRiLpn11jkPVJVUI/G5ColO5jC5jEbp+caY9iaIQQCFeSrUdkjYEVOs7VP3an7CGDK9fvwRW4tWDUn2sD6TvoVKG6uSsrJycnJ+fNRy52Xk92PQQCRNymXPRAa9qrK2it0UJjlBUornAZC8eoehUbIzNs+/Bs7RgC8OY03axLqjNSlTI4uko83wFHIHyJjjLSpd5ot7LgomOFU/avKXQ2EJ6DDN3Lb1cpYgggfWmbk76KDUjzGkE5OTk5Oa8Gudh5PSmO20BloF4CjKK9ugyAFho2CYXQDQndkP/pwM/zN3b8FD82+yMcqZ3BAEFLcHjxML899xkGrS7RM2tkOkPdVaA3q1E6IzndHo1lXVSXW2ReTUTovioNSHWiUK2Y5EKH+Fz7ugHTOTk5OTk51yMXO6834wcBw77WnyJ0RntlCSEEmUnY3LdTADPFaW4p7+dWsY87C4dYdzqcKJ4HYPXMHIc7RzHfaqIzxWBCM9ht6G/TaGNIznVeV8uIkMO6Pq34phuQmkxbgTPXITnTJr7QQbUTdD/FxDeWeZaTk5OTk3M1crHzerPtbjCGmp8i1YA0ikiiAWkWX7bqmD9GLbGZWRVT4n1j7+K56jEM8Pb2rXxo7UFqvSKpk7F6Z0Qn7RLVUlTRCoju2Qaa16/+zUYDUjO4cWuMMYZ0qWcFTisBV+BUfZyqD1KicrGTk5OTk/MKycXO683e9wDgSKgWAAz9VpM0SzCYUdyOUQbVTnBrBUTVoWAKvGfsXVR2T6CEZiYe53tW3wHAswfO4JZCelmPvhqQ7JAYDM0Ti6xHjdft0IQrMZkhu4k0dN1LUa0Ep+TZuj++M8ryEo7A9NObthTl5OTk5ORsJhc7rzeTt6J2W8EzFqZgDL31BlqnGKMxyt50J8GtBxS2VylUKwig4pX5oR0/RDRp3VMCeLZ6nH+n/hNPdZ6l5JXp6T6D7YZMZ8illEGvx1x3/nWz8MiCg27buj7Xw2iDWrcWrSvFEwlP2gyzPFA5JycnJ+cVkIud1xuviNhxPwLDuNcFoNNYRekMhcIkCtVJcMYCvNkSfhCwf+oglVKVkBBXOpT2jTPmjVEsV/j92UcA+NzC5/nUmX/Hvzj9KZ5RLxFXlBUT5/p0kjbxFdxkrwWjuj43EKiseymqEyNLV+7zJTyJSRU6ycVOTk5OTs63Ty52Xm/cAGdsJ2AYd9tgNJ2VFVtNR2pMonEnCnizpZG1o1QqM16bZFe4Eykd3B0llt8Ws/ag5q/v+anR0OcHNnj5a2t/xtJUG2U08nxCrGLmu3Mk+rWvg7NR10c142v2yzLakDVs5eirpcNvuLPMDViJcnJycnJyrkYudl5vhECM70M4PmN+H6EzuquLqCxDORp3uog3XRoJAK0V7dUVRMHBUZJaUCP0Cnh7KoTVIrPhDH9/79/Zsov5ZIFfzv4d69k6NFKS9oBm3KSTtOlng9f8EGVoA5Wz9eiq8Ta6m6C66fW7pQuRp5/n5OTk5LwicrHzRuCGUN1O6GgCF4zRDFpNdJjhz5YQztCioTWN+QusnDtDnPYRAqaCSfZV97KjvJ2yV6bklqiLGjvC7aPhBYKuO+Bk6QLNtMX6qUVSndKMWpxqnuRs5xzdtIvJ9GvSrVw4AllwyJZ6ZI3LBY9RmqwRIRxGx3rVsTyJ7md5V/WcnJycnG+bKwdL5Ly2VGZgbC+icZp6UbPYEfSbqySDi1YXYwzNpUXW5+cxWhGnfXy/hkkVMnCZCCeYCCdoxk0K7hp/efZjJCLjX53+tdEYz1WPcbC3CzmXEtwdEKuYVtzCSxzWn+oQz1mtKwsusughi8P7govwJcJ3EJ60N9+x/bBuoAIzgAxcNJAt9cAY3InCyC2lOimqm9r08usgPImJFCZViCD/uubk5OTk3Dz52eONIKwht9+DOfU1xs0Si2aWbmOVLE1RWYrjenRWV1ibO09YLqOVYtBtU6mOYXoZBBeHqgd1pJA0ogZjfh2hsXlXEl6qnOJHFt/PVDpG0orohT0qpx3K52OEgkw4uNJF9zN0//quIhE4FO6cILx17IaqMVvBI8iW+gC4EwVQhqwxsAJKXtuqA4Aj0JmNZdp83Dk5OTk5OTdKLnbeIOTkQbSQjDtNZFqlu7ZmxU6aMmi3WTl/Fj8I8cMCWin6rSbZWIZzBXdO6IT4jk/Sj/lLMx/l00v/hUPF2zjaP8aRyhnubh9k8cmTjMUVQjOOcTSDqsLc7bNjZhd+7KD7Gaqf2qrFgwyTanSiManCJBqTKEys6D+9THS4QeFtkwQH6td1Q8nAGVp4+mBAOBLdT3FqN6ZchBAg8iDlnJycnJxvn1zsvFFMHkRIwXgYI1REp7GGylI6jTXaK8s4jktQKgEgHQetFJmKcGSIUXqLO8l3fKpeleW1ed6+/V72e3uJiXip+zLPVo9xd/sge/qzACx4q8S3N9m2bzdIxenkLAfqBwgnK9ecrtGG+FSLwfMr6H5G75uLDF5eo3j3FP6+6shFdSVk4KAFZMt9ROBsKRx4IwhHoPspTBRueJucnJycnJwNcrHzRlHdhfBL1IIOUkAa9Yl7PbqNNYwxFKtbxYcbBPR6LUKviEk0orDVjTTujNEptQjHy8RJTKVdZjqY4jBnWAwa1NISX5n8Fo+PvYBKNT/a/mHuKB7CGE0rbOFKl37Wp+JXuZIMEVIQHqwT7KsSHW8yeHEV3U3pPjpPsNSj9M5t1xY8vrXwmChDVq4fq7Nl355Ex+oykZeTk5OTk3Mj5GeONwq/gP7hT+E4kqqfgkoYtFuoNKVUq1++ehiSRH2UZzDp5S6dkiqwa/s+9k3sxysFBI7P39/9t/krO36c6od28/+59Tf4xsRzKGkL9P3e4uepBTXKfpm1qMGp9mkWugv00u41py0cSeHQOGM/epDi26dAQHyyRXTk+m0ppO/gVIObsuoACNfBpMO4nZycnJycnJskFztvIO6eB0mm7qEexAidkbRWKNXHrriu4/lkaUpGzKWla0yqEa5kamqGolfEKwZIz2HamWJPsAsk/JWdP8m4t3XsL699DQREKqIZNWknbWIVE2XRdecuXEnhrkmK980A0H96mWTh2kLpZtAqo7EwR5YmtkeW0teM2zGptfzk5OTk5ORcSi523kgcFzF1K2NBDDqhff44DJqjxVprTj3zLY4/+RgA0pFEcR/hSMymflF6kNq08IL1So5VxqnUalQp40qXglPkzsrt/N1dP8NPzf4EoWODg/9k+U/5xWOfZL4/z/HucY52j9OMW5xtn6UVt5jvLnCuc/6ahxAeGiPYXwMD3a/Podo33gT0WvRaLdsgNR62ubhGB3SjDMlCj6z5+rTEyMnJycl5a/GmFjuf/OQneeCBB6hUKkxPT/OjP/qjHD16dMs6URTx8Y9/nImJCcrlMh/72MdYWlp6g2Z88zizdzAWpgiV0r9wGHfum9BZotdq8o3P/N+88JU/5uU/+wqr587ghQWiqI1xzcjKYbQBA07tontoT3UP22d34+JS82t40qMdtZAIDpYP8slD/yvbQhuwnOiEw4Oj/OfF3+W3F36Hxf4iraTNXHeeXtZjcJ2Ky0IISg/N4k4WMImm88j5G2oCei2yNKHbWCONY7LEiqdrdUBX3QTVjtHtJC8+mJOTk5NzGW9qsfPII4/w8Y9/nMcff5wvfelLpGnK93//99Pr9Ubr/PzP/zyf//zn+exnP8sjjzzC/Pw8H/3oR9/AWd8c8rbvpzQxDRg6/YzS0/8HK1/8Fb72W79OY/4CaFv/5sKRl/CCkDRJyJwMnVrLjh5kw4KAtu1CMuiTpSkydKkUKxwo7ydwAgI3pBpUcaVLolM+sf+/56GxBwB4ZO3P7VyEZD5ewJUug2xApjMynV23X7pwJJX370QWXFQroffo/FXbRNwI/eY6aXeAT0AaWbF1tQ7oJtNkawOEtG0lTN5aIicnJyfnEt7UYueLX/wif/Nv/k3uvPNO7rnnHv79v//3nDt3jqeeegqAVqvFb/zGb/DLv/zLfPCDH+T+++/nN3/zN3n00Ud5/PHH3+DZ3xheZYLo3f8Ez/PQRvCnZ2Z44qU2WdxnfHqS+x6+D7Ri4dgRjNYYo0lVBMZadUyqcMYChCNIo4il0ydpLy8hAwe35ONmkqpfpR7WKXklHCHppz16aZf76/deNp+malMLamQmRQ3FTqavLyBkwaXy/p0gBcmFLoPnVr6t9yPuD+i+uELwvMB9TpGs9zHGXLUDumon6H6KLPsYZVCDXOzk5OTk5GzlTS12LqXVagEwPj4OwFNPPUWapnzoQx8arXPo0CF2797NY4899obM8WYRUlKc2kmpVgUMS/0AKQx37S/z7g8+xM6dExTKJdIkZunUcTw/YBB1EK5Ad60Fxyn5to/WwhzNxQV6rXW0UsiyD5lhqjjJrfVb2F3ZjSMcamEdZTQHi/sZ98e3zOf51gssRIv0sj79oXXndPs0zbhp+2ld41jcyQLld24DYPDiGt3HF7bEFl2PdG1A+4unkacyhBYIBWYhsUHKV+iAblJF1hjYuj1S2BT13JWVk5OTk3MJbxmxo7Xm537u53j3u9/NXXfdBcDi4iK+71Ov17esOzMzw+Li4lXHiuOYdru95fZGEtQmqGzbAwiqfsoP7F3gvuA5ZNpBqIQd+/YAcOHwS3hhSJJGKKnQibKp3J6k01jl6T/8HI/97m8zf/QwUa+LLLjgXgxmlkISuiGucAmdgNXBGj8y8YN87+QH+J8O/jyOcDjdP8MvHf/f+czi71LySgghWO4tMdeZ5+TaCVYHq9c+lv01CvdMARCfaNL8wmmy1WvH/ZhU03tqidYfnkI3EyteZj0QwFJGFg+Dni/pgJ61YkykRoHZMnS+bVeWjhVZM3rF8UY5OTk5OW8+3jJi5+Mf/zgvvvgin/70p1/xWJ/85Cep1Wqj265du16FGX77eEHInvf8MO+5q8wP3t5jsgLuYIWJJ/8ZtSO/xZ4ZW4Rv6dRx9NppVByTyRSn6OFUfeJ+n/mjh7nw8osgBPPHDhN12rZaceiiN2Ux7a3uZW9tL7squ5gqTnFL9SDvHXsXBUL+yvYfxwxtN3PRAk+2niJ0Amq6SHP+AqvnzrK0Poc2msxkNKL1Kx5P8W2TVD+0G1l00Z2E1h+fof/C6haLi1GadKlH//kVmn9wiujwGjpT6AmB8+4a8vYSOAIRQbpie2tt7oCuY4VqxIjwYjVm4UjryuqnN/S+G21Q3YRkoUtypk1yrkPWuH7afU5OTk7OW4u3hNj5B//gH/AHf/AHfPWrX2Xnzp2j12dnZ0mShGazuWX9paUlZmdnrzreL/zCL9BqtUa38+evnV79WuOFIWG5TOG9f4/eA/8Dnbv+FjYox+D25pnpPUN1fBydRiwcP4ojNIOshzsRgidozF/g+JOPj4RKY/4C7dUVtFI4VX+L68eVLhLBeDjO7souyl5lFEx8b/1e/uf9P29XNIbPXvgv/Jsjv0Z/YYVBp0MaDWiuLNKNuyz3VljpL5NeJZ7Hmy1R+wv78XdXwMDguRXaXzpL/7kVWl86S+Mzx2h/6RyD51fRvRQCSXIruPfWEIFEuAIx7SMEJKet+1J4ElLbr2vDCiPDrUXApS/RnfSariyTabJGRHK2TXKujVqPwRPIkotuJ1vE4c1gMo1qxTflusvJycnJee15U4sdYwz/4B/8A/7bf/tvfOUrX2Hfvn1blt9///14nseXv/zl0WtHjx7l3LlzPPzww1cdNwgCqtXqltsbieO6BMUSmVeFyizprvdifTj2hB0uPsFBcRQZt1h4/lE83yNJelBxaK0ss3r+LEsnjw/H8lBZxtKpE8S9LjJ0EK68YsG9wAmohzVSnSERuDiUeh7f770HnSpMqjgRn+V/7f0qv6++xlPuMdZay5xfPEknaY9ieq6GDBzK791B+eFtCFeSrQwYvLBqm4Jqgwxd/D1Vig9Mo+7zEJMejnNRvMjtPiBQ8xE606MO6KqdoJsxsnh5txMRXNuVZYwhXRmQzHesWCr5OFUf6Tu2S3uiUJ2bq9djlCFrxsTn2iTnO6jOq1NrKCcnJyfn1eFN3Rvr4x//OL/927/N5z73OSqVyigOp1arUSgUqNVq/MzP/Ayf+MQnGB8fp1qt8rM/+7M8/PDDvPOd73yDZ39zFCoVOmurML4TtAYhADm8h4PeCZ5Tu1hbicmiLso1tM8dobneZu7wi2itmNy5By8MmT92mJWzp+l3WhS21xCBg4kVoni5tg2cgKpXhgSKLYeFVpt7/APcNnaAJ/rP8nj7aQxwwpzjZHKOlmgxsTKJUgHFUuUysaPNxfggsHV4ggN13Okig6Ery5sp4s0UkRUfIQS95jrRXI/ipW0yxlxEQWIGmujsOsUDEyBsur1ONW7Ju+x4NruyNtLxt8yvm6LWI5ySh/Ccy5bLwEE1Y9xaaC1J18Bog+6mZI0I1UsQrgRXoFqJrXskb64tBlj3nkk1Iri5Zqk5OTk5OVfnTW3Z+bVf+zVarRYf+MAH2LZt2+j2mc98ZrTOr/zKr/CRj3yEj33sY7zvfe9jdnaW//pf/+sbOOtvDy8IQYDRGqSkc/ffBQxo61IpeynTxRgDLBx+DpIBvfNHGMwf48KRlwA48I4HmTlwC8YYVs+fo9tYQ+sMp+KP6vJcStEU2Cd2M5aVUKaPKmVk4x7lSpXv2/YBa2Aa6i4j4SV9gs6gzVdXvsYTjW+R6pR+1qcRNdBGc6Z1ltOtM5ftx6n4lN+1ncp7dhDeMjbqkaWyjG5jDS8IRgJpAyEEYluAwRCfHLqyHIFqxzibhM7g5TXWf/c46TAQWvoSdYWsrI2aPAiuKHQAxDDIWfWubZ3Rg4x0rkNyvo2OUpyKj1PykAUXPUhvOEjaaIOOMrJWTLLQJT7dIjnbQefWoZycnJxXjTe1ZedGCtOFYcinPvUpPvWpT70OM3rt8MIQd9j/ygsCor3fRz9xmXrxX4JRCAH7qx2W+wFzR15i575d9Dst5k+dQyvFxI5dlMYmGN9RwvV9kkGP1QvnmNq9j7BYRghB1rrontlwkmmdEusumegR1EuM61nOtM+QJYrADbizeoiX2kcubiTgaecoTwyegfhZPjDzXhzhoo1iLWpgjMFg6GcDim7huscddTvEg/4Vm58CyO0B6mSfbHFgCygWXGv5GFpd0uU+/WeWbVzQs8t4H9pjXVl968oSm6w7WSNCdVOc6tW7rgshEJ6DWo9xKrZ+0aWYVJEu9lC9FKfibenELhw5DHy+smVpM6qdkK0NbEf3TNn0ed/BaE22FiGLnrUW5eTk5OS8IvJf0jcJrh/ghyFRt0N7bYVup0dWmkW6LlJK2mP3sWsCpID24gXcFz/N+MJXOHfsBBjDrQ+9G6MV9ZlZpvfut9ads2cYdNrIgos3U8SfLeFvK+PvKOPtrGDGJevZMp20QWlijKBUwnd8JsNJyn6ZQTbgL0x9L98/9QGm/Am2hTMYCY8mTyEQoA3HG8doJU16aZ+znTN8eu6zrMSrXOhcuO4xa5XRXV/D9QOEuPJXUZQcTEVgjCY+3UI4chSUrBNF9xvzQwOYIpnv2mrKV8jK0v0U1YiQBfe67iVZcFG9FH0F647RhnS5b4VOzd8idEbb+w6qfe1AZZNp0pU+OsqQoYNbD3GqATJ0kSUP1Uu2iNOcnJycnG+fN7Vl57sJIQSFag2tNKV6nbBcIQjvwXnxk+hkwGD8bVQ6h9leHnChU2DuzHlSLaE/xvie26hOToJ0KNZq7L7r7cwfO8LahXP0muuMbduOO7HVypJEAxpzcyQqojI5NYoPGQ/HqfpVBtkAjaYbdzkQ7ObA1G4upAt8LvpjpJQIbW1Dx5pHML5kob/A51f+CAGcaZ3m7439NeJgB0GxeNVjHnQ6xP0BpVrt2m/OrAsnFfHJFuHt46O59r65iO6lyJJL6ihkQzF4uUHlvTtGrix3vAAGstUBRhuc4Mruqy2fhRQIR5A1Y2TZ3yKOssaAbD22Fp2rxNSIwLFB1APrQrwSdnm6pafZ5v3LwEU1Ipyyj7yBOefk5OTkXJ3csvMmYmx2Oztuv4PxHbso1uo4QRF+7N8iHvq7ODvuoTP2dvbXbV+wE+sVjjWqYBSHDkyStNeo1Go4gwZ7bt2PEJJec53W0iJRt7tlPypLWT1/jkG3Q2lsfMvJ1pUuoRsyFo7hC5+yX8aXPr7js8ObZYc/a781AoSQfK3zGP/n6f+LLyz+EUJpdJoxF83RXl9ldekCKrty7IpWiu76Go7nXdWqs4GcDTDCkDUj1LAOTny6RXKmDQK8+8ZIt2mM1sRnWqhuYoOyI4WJMlQ7JmsnyCsENF91n0UX3U3Rm6xDqpOQrUbIgnNFi84GQgqE5Kod4E2mydYjWzzxKoJJFmx9pGz92nV/dJRhrhKPdaOYVGNUXnU6JyfnO5fcsvMmQkjJZae+ne9AuD5VMcaiKTFW+APcuRfpZ/Zqf6qYcMvSp7kwtYtCFELDoyIcJnbuonHhLCtnT7Ht1kOU6mOADYBuzF2g21ilMj55zYwf37HZUrGKcYSDFJKf2v5RW6snEvzSuX9lrR4GK35wcFwHjOFL4nG2rc8QhEUmZndctp+o2yHqXzlWR2vN84/+MVGvwwPf+1Gcgkc6Bk7bWFdW4ND7ps3MK7xtkiTIoCRRVY3saaLDDUoPzGK0IWvH6E6KDJwrxt9c9bMYihnVipElD5No0uU+YJDB9UWTCIdiKVFIf6tlZrNV51rIootqxqhh8POlqHZCutRDBg7e9vK3Fd9jlCaZ74AGpx7glP3rZqHl5OTkvNXIf9Xe7EgHtt9LYWYX4cQ2OlPvZNueXTCURfdMrSPUgG2n/yPhc/8evvA/4LbPsWumAHGHtbMn6LeaZGkKKqN54TTNpQVK9TGkc7l7pNtY48KRl1g8eYxCS1MeuOhehJdJCm6B0A1t8cJA8N7gQTDw/srDNkVeAMOg8ueSw7TdiPPzx1lcOcdatMZib5F+1meQDlhbWcT1/MsysABefvKrnDv+AsvzZ1g8fwIpHbIJG7Aen27T/cY8JtW4UwX8Q3UG3TZeGCL2BGitiE40bSyMLy8KjmFLiWS+y/p/OU77K+dIFrrXDIKXBQfVSdG9lGy5bwOkb9A6JH0HnSpbMHETN2LV2TwG2qAa0dbq08bQW2mRzHcxSpMNRc+3Y51RncQeY6JI5jrEZ1ukq/1vu7BiTk5OzpuR3LLzFkFKh8rkNEtrM2x/z8c4N/ebjI1XmNoGNDsEvbOIc+cBAV/6p9w+ELyYbae1OEd37jjRjl2YxikaJ48R7rob17sYSxL1uswdeZkLh1+kubRw2b4TZRtx1idn2fm+h0hKGUIIPrD9Pdy9eBvT/hQeLn/a/Tra6FGz0CPpKe4UB3jyzKPs2nErwnfopT0GnTa9tSUObL8dgMxYV5crXM4ceYZTLz812vfcqZfZse8Q1AX4EhMrspUBwpOU372dJIlI45hSrYae9chOxuhEER1bp3DnJKqT4JStQMlWB3T/7AImMzZ1fL6HUw8ID40T7Kte5poSnoPpZzaLqz3MzrqJ2jfClZfV3LlRq84GsuTafdd8nGqA0YbBSofzp08xVhljrDZhBc96BI7Emy7ecH2fjUrSwpM4JQ9jXEykSBf7qEaMrPl4Uzc+Xk5OTs6blVzsvIUo1ur4tXH8yhjv++mfpRgIohP/lUrrFNIdWhyMAqMp+5rxMGYt7dM+/Cidiod/+k8Iiztxxe3o1GH+xDHOv/wCK2dPjywcUkrqM9vQWpMlMVmSoPptUIZeY43Dv/cFSnfuoXzXftzSGIVigUG/z0Pmbu4LbmferPCf499HofhM6/eZcidYzlaRxyTvGLuf2WCGoKXZL3aQkqHSmLVoDYCgnfHcY38CwO4Dd3H+5EssXzhFGkdI34cpYMHOs/TgLE7ZZ7CwipQCISSOgGSngzmhiI6uU7hjArduRYVqJ7S/eh6TGbzZIk49ID7RRDVjeo8v0H92mfC2cQp3jG8RPbLgXgxIvo4bTA0yOosrlHaM4/kBsuCg++koBd6Ki8ENWXU2EI4ER5CtRYjQJVuLUCsDlG9IvGy0jlPyyVYHCEfgThZuaHzVTtD9i8JLCIEouBfjhVbtXN2x8IbmmpOTk/NmJRc7byFcz6M6Oc3KudNUh3Ew67t+GCesUfP7cOEp6C6B0QjpsLvSo7ES0jn+BEFwknL7MDUB82MHefrr32R5aW009ti2Hey8/S523Ho7Qam0Zb+RijBRyktf/VPmjr5M94XTpHMNxj74PnRNYsouvlNAJX32yb18b+s9/FH7q0gka2odRzgorfhW62mkkOgs412Fd3CLup253jwvxke5NdnBuUe+RqZTZvbeyl3v/n7W1xboNhssnD3Gtj23kk1nBOsOwe4q/t4qWZYS9zq2IOMQb0cJfbaDjjLiUy3CW8fQg4z2V85hYoU7HlJ5/06E51C4e4r4eJPoaAPdzxg8t0I636Xyvp0jt5f0HcS4vKZ4MJlmcLhB/4UVVJwg3w3erdsuq7mj2gk6yq5q1dGDjP6zy5hUU37X9lEMjt02hoUeqhMjyi66t9VlJTyJLLpkK32EK68rUEyqrPAK3CsemwysCy1bHSCLLjLIfypycnLeuuS/YG8xyvUxWksLpNEA1w8wfhnv7T8O3ZNw4Hth4Xl45j8ivAK7az2eXTHM90sUmi8jXclK1+Xrv/s79DIfNyhw8MAsO+57L+UdB6+6z9AJoRTyjo/8GNsO3sbzX/4iSbPLid/7IrW330rtwB52ztzK8eZxhONwm3sLXx08RqQiHByEBkc4mMxgpEYIyYvpMb7R+ia/tf5Z3BQ+8MwkY0kBv15n530PcKF7gdKO7XTW1zh74gWm992K9hTlj+zFD+2JPOn3SeIY1/OYO3OE3Qffhuv79GfAmdMMDq/h763S/so5dDdFVjwq37NrVD1Z+g6FOycIbx8nOtWk8/gceqlH649OU/nALtxxu5+rCR1jDMm5Dv2nl9G9FKMyjNZEzzSo7J9BuHJUc8ep+Ve16hhjiI836T+zPMqsGpRXKd43bfcvha3900lsNWwy25bjkhAd6TtoZUiX+tbac43iiVkrRkcKp3b1dWTBJWvFZGsR3mzpptxZG1WhTaRwJ0Kc8tX3k5OTk/Nak4udtxheGFKemGB9YR4vUwSlEuG2g7DuQFCGPe+Gve+Fs99g/Mn/i7KX0k195nslBqnDk0sTaNOiVg55/+4FJuM/p5nuQ/UnoFAf9eK6GjsO3cHErt089yd/yOKp4yx/6wWWn3yBc96XUIFDUC7hlor8d1Mf5thMk1JY4Vvrz9JJutxtbsEkiq/LZ2mpNr+1/lnQhjtfCEi6PeYLEc8cWqCQ3sm0nKC8fTvqhWdYWTjD3PpZpv1JsjQZiZ2o20FKwVNf+xyN5XkGnRa3v+P9OLtC9EKMbie0vnAa3UsRoUP1g7tHFpstSEFcjYluywhOOuheSvtPzlB6eDvBnis3ic0aEb1vLZEt9wEQBYds1iBOK0wvo/fcMuX7Z23NnU5KthZd0aqTNWN6TyyQrdhWF07FR3USK9T2VUcWGhlctK4YZdBGjbrcbzmUYUHEdLEHxiCr/mXiSicKtR7bDLXrfN5OyUM1I5yyh1O9dpyRMQYzyMhaCboVozONkJAMUtzxAu54+JpWhDbKoPspsuTlcUY5OTlbyMXOW5Dy2ATtlRXifpfpfQeRQQFm77y4wvQhcH3kM/+RXZU+LzfLPLq0kySxdV/2VPu8a8d5/CwFISic+WOco5/BuAHt9/0SSAnCgaRrm5IWbdo66QAQhKUyD/7oT3D+pec5+vif02+30FqjeglRP0KIdbqnLzC5bZzqe/bw47MfwREOAoFsGV5YOUmDFlJpbn8poNZ0UI7h6bs6DHzBb7Z+BxeXu4NDHKiXGTQazJ0+jLf7TmrxLMVKlSxNiHpd1lcXaCzPgYZTLz3FvjvfgV8qEE0NkMsaeinClVS/Z9dVC/z1m006q6sEkxUir0t5PsA0FN2vz6FaMYU7J1DNmGx1QLYaka0ORp3NhSMI75zA7HTpLl6gUCijnu0SvbxGuL+OOxYihG0YutmqY5Rm8MIqg5cboA3ClRTePoV/sMb6l07Cakbvm4tUv3/PFS1B2lxJ6lickofqpSRzXdykgDtR2HLyV80YHV/bqrOBcCVISbo6sNWnr9BTzBiD7qU2Tb6bYJTNZHOHgeE6UaTLPfQgw5sqXLeNxpVIdcqp5in21/fjycu3N6kmXe7ZQpJTBbzJqxezzMnJ+e4jFztvQcJSmVK9Tr8JxeoVqg8Xx6E4jvjYr1P/8hdQ3zjKQAhkqHhg7Ax3jLeG9XEc0Ipg5VmbMi7AP/45kvE7rdhBg0ohi6AwBo1TgISZ2xFCsPvOu9l91z1orYl7XQadNlGnQ7fZ4Njj3yBZWKP/9ZfZ84MfJpWaVKcwBhPtcbrtFm97sUClLdHS8PJdMVHV1hnSWpOQ8kz0EotjDgdXfRqnH+fYdJOHl1PeUX8fqjeg3W1y/NnHUFmGdF1UlnL8mcd427s+xFJ9lW3LEwTSpfL+nZdVkN4gHvRprSzi+j6eH6DLiuQWQ7lRIzneYvD8KoMXVi9zGQH4e6sU751GljzWLpzFkQ7OTEg61sO0zUisyIKH6l/syaVjRedr50fWHH9nmeIDszglj367SbQtJmhIspUB8fEm4a1jW/ZrwFp2rpE275Q8KzKW+phU26wqT6LjDNWMbWHEGwySliUX1UpI1yL82a3xXDpRNmi6GYGxtYEutd5I30G4tgxAEmU4kwXcenhTdY+6SZdUp3STLmPh1vdDR5nNIOsmyMAhW7HuwutZonJycr57yMXOW5Tq1Iztp1W4RrPNiYNsP3QnpRfmMcZw//d+kMlwQCPu4UXLFBYfw+ucw54+DSCpvPSbGCdEhRMIk5EWt9Pb94NIJ6Bw+gsMdr4fbQ5Zq09zDiYPIh2XQuBQSDPYdgiEYHz7Lp74vd8hXmpw9ouP8OBH/xKZY3Adl+9Ze5DnHlmkGnkUgwr+g7dwtnCcCRlwIjmNARwkCFiZyjh4wsdtpTzafJxHs2f4+VqIaXRZO3WSVmMZ5Uv2fOBdnP/KNzh95GlqB/eThrB8S5vb9h/Amyld8e3JspTW0iJaZRQrdQD8sECvuY7aW6M0sc0WL9QG4UvcSWslcScLuJPhyK2URAOibm/0WZiDPubZbItYcYfuK9VN6Xz1HKqVIDxJ6Z3b8HdXEEKgtabbaJCJDH9PEc5o+s8s4++qbHG/GTTK6Cu6sTYjfVtIMWtEmETjzRbJmjY1fyNL7UYQQgwLHEaosodT9m3gdSchW7H1h5yyd9VO8mDjjpyqPxQmPUyi8GZuLg5Imctr/6iOrTGkE41Tta09VC+1cUuec2W3ZU5Ozncd+S/BW5RCuUKhXLnOSmOEE9t551/4IcLp/XjFEqZxBrKINNtBOnknE4//IuiUrLwTIQxOZw6hE9zeHCBwBiuEq89hpIvQCf76Udbr+yie+H2UExLXtoEOoHXeurmSSfAKTG7fxrt/4q/y2H/5z6wvzvPYZ3+bhz/2VxisNYi+8iKzSY2gUOKO93+Y+uQMdyfvoJN0+D+zz9DMWtzi7+OIOokuCJpjmrGGZHbV50wx4pMn/jlh5vH2ox5FLTmxI2J6h4c3O0Y0v8rRp/6c2QfuxXEMqZNwJTmojaa9vETU7VKq10evCyEIyyV6zXUKu/cw9rGDmFgjr9ELK+p20ErhDtP/vYqP2qWR580WsZKtR3S+ct4WJyy6VD64e4voiLodol6PYq1G4vcproeYVkrvW0tU3rtjtJ7BYIyGZsrgQgOn6uNOFi6r1AyMApVVN8Vc6IAyyOLFf/tsdUByvmMzrkoeouiiPYMiwyhFsVa3Ysd3UJEiW41ACNR6RNZKEK7Aqd94/SEZWstPthbZrLEbTJMHa/EbvQfakK1HI+uYuykY2yl5o+rS/o7yNUVYTk7Odwe52PlORgj8fQ8RpEdQWYYHML7XLota6CyjeeivUzr5OZp7fxRT30fp2O9QXH7C1mfeOAkJgTAZCIlM2ox/9R8ijAKtSHZ/ACMk7tpRNAJtDO5gkay4jfr2e3n3T/51Hvvd36a9ssyf/+ffIu73yNKU8W072Pfge9nt78YvFFBaYTLNz5b+Jo+oJ7hn8g6clsvh7nHuvP2dtB97ianFDqd3giMFE/OGIIJBoDi3O+HXzv57vveW+yjOx6ycf45Hd5zlp6Z/nEG7ieM6aAk93WeyOA2xobu2SqezRhoaTNaj7JZRKCQSx/WJ+wO6jQbj23dcM+1aq4xeq4UXXBQtjusTTfYI2gVMO6P3rSXCg3U6f3YBk2qcWkDlg7u2tIDQRtNdb+C4Lq7nE/V7yNsLqCdSkrNtkgM1/O1ljDFkSwPGnhE4jRZ92b+435o/tDoV8LaXR+MLKXCrPqqXggFnKIqy9Yj2l8+hkwyjrYCylZqN9XDWBeYhRXl2EgBZtinwJlHoVFlrzjV6hF31a+lupMkPEJ5zQ1Ymow0mUZhuRtYboPuZ7XcWSGR4+ecjKx6qGZMu9/G2lW/YgqRjW/XaqfqvejC1TpStZZS348jJed3Jxc53ONL1KNXqNOYuEJYraKVIBgOSKEFKgbvjg/QKuzF+FSq7iPf8GI3xB/Gbx9D1A1R7xyhd+AoIiZEeIhsMhY69yi69/B+RSQtv/djmnZKM3UZn+g6qk1MjwdNbb4AQTO89wDs+8qNIx0esZBBpJvxxxrIq4XQZoT0c1+GvVX6C052z1CiRPH2K6chQ6cUMiob950JAcWZvgnYADH8qnuLuesjEqotzao0/rz/NZG+CaNDnse63KKUhh4JbcEo+fubgyYCldBFf+cybBXx8HCEpukXGynX6rSbFWu2aFrSo1yONI0qbYqeEECAF8o4i6om2FSvn2mDAnSlSef/Oy6wwUbdL3OtSqNjsL88PGIg+lVvrxEeb9L65iLl3muhwg2SlR6AEOODNllDdBN1NUa0E1UqIT7YQjqD83h34Oy/OfbO4Ur2UzlfPYxKFCjXKUchMImKByATCCMSapvcnc4iHDMVbJ60rquRjtMYtXlug6ESRXugiAgdv2+Xuqo00+Wypb61DV0hNN8YGdqt2gm73cJoZKuyR+GI4l63xQTpRo7GFsG6zbD22gmrq2hYkYwy6nZCu9NH9DKce4M0Uryl0TaZt01lHIotXLzqpE2WDt5sxMvz2+5gBo9IEuWDKybk5crHzXUBQKoOA9toKQkiCQpGJHbsoVKt4YQHSu0C6GClhxcN0p+h530dr7jTLwQxh7S4KqokqTFO+8Kc2oFkOg5uXvoWNTHUYNscCo/AbLyObp9ETt1EeG+fdH/1xnv/D36UyVuf2d9yD7M3DxAFMHcxqip+4MBEi6i6z6SyucAmcgF7ao+yVyQ7ewYVjL/Hj3YdZ73aQepW4Ctm+CJE1EEJgjOH0gZSJVZepJcljK9/gT+NvIBBgDFVR5ufG/hbPZ0eouFUmBjWei1+GJOHs8efZlUzwwL0fxozN2vcNh87aKkGheMU+Ygbot1s40rmsc7vr+SRuTOm2caIjDTDg76nYYoGXWEO00fQaDaR0kNLuxwtDBu0W8pZp5PkuupvS/focAB3d5fmxE9z1jgeZ3bHbjjHIyNYG1i0130M1IjqPXKD88DaC/fWt+0sUna+eR/czKDvE+zOKE+OjPmUmM9BV6CN9VCuh99gCan5A+Z3bbUbWNVrqqXZCdKxBfLI1OjHLoktwsE5woL5FcG3UDkqXbCHEzRYaHdnjUa0EhtFJytdQ9XCDy4VWutSj87ULAFQ+sAtvpjgUIa6tLO1KWwX7StlkqSJdjVDrA3AkTj1AdRIb2D1TvEyIbYiwbG1gxc6w8rRTC3DK3sUSAakia8WodZv9JgOHrJ2AN7Dzu9G2Htq2N1GdBN1JwIAIbTySDF2E71wUP8pY65w2GKUve1+/GzDagDK5IMzZgjDXSun4LqHdblOr1Wi1WlSrV66r8lYmS1OWT5/ELxYoVuuEpfIVT9525RiSHhTHSaIB7bnTtA//OUq4FMplTFBDLx/HJF22nfhNhMkYVG8hHMzj6MiOYTQYgw7qJBN34nXOIKMmIu2BkCAE7UN/nfT2n4D+KnqlDV4BsXPnZVffq4NVyl6ZxqkzPPn5/4JfKNAdtNGZ4vbv+z523fE2vrDwJ3x+6Y8RCBzh8NGTd7By6hRrk4qX7ortQBK0MHjSJTEpEsnYmmTnOZfxtU0nWU+QvGMHhYlxfqjyQQqJy/SuvZiShytdYhUzEU7QTFsstuYpr2r8sIjreWRpQjzoUaqOkaUpaRwxtXMPyXNNnKpPeOfEFa0Lg26HlXNnKFaqI7ED0G83KdbHKKsqna9dQHiS8JYx/k73f6QhWvydPX+Lj+7+i5eNZ7Sh99g88ek2AMX7pincMTFa1vnqedKFHiJ0Ufd4xKpHsXJ5Vp/RBn16gDrRx3FcnIJH6YFZvI2MLMnI1ZmtDoiOrpPOdUfbOxUfnSjMRlNRAf6OMsEtY3jbSyOBqtoJTtnH3162Y61HViBsuMpcSTvpcKZ9hr3VPVT9rf+j8Zk23UfnYdgsVTiC8vt3jsbTg8yKDd9Bllxk2R+KBJshNrLmlLzRCXJD0AghcGeKo7ikUfbZegTCuvYwYKIMkw7FRdlHBNKm+EdW5IjQWptMqlG9FG9bCe8qGYKj9z9VqF6GasW2aKUxyNABITCJxihbWFJ6ElxpMyo1VugMBY/wHdzpIk7t8npLbxQb9Zj0ILNXDBvTGs5PuPKaMXLXQifKNu2NFd5U8dseJ+etw42ev7+7JP93Ka7nsf3WQze4cmBv2MykyX2HKGertHoJfVNGYpClOt7MPqJt/wtO+zxpOEur00GuHGa8/Qx+vGrje+Im4fyjjH7RhBgKIagc+x06pUnKL/wGKhije/Bj6GwS2TqHLk6D40DSZ9INwQ2Z2X8Qzw9IBgNQhtL4OHfc8y5cx+WHt/8Avnb4ryt/xPvq72T7O/ajLjRw1tqc7SS0axoEhLEkiDQzXZ9tFxxKXcnGL21jXOGlUOk4+N88z/N3nKCxs8kPy+/hD178Cs/IIzwwdj/vnno3Vb/GP37hf+Fc5xz/pPx32Ve4hd56k2988T+j4piHP/yTTG7bzXprCdFfYfu79jDIIk63zrCntptUpbTTDtOFKYwxdNfXcIaWscNPPULU7vC2d38/Xlgg6nao7p2i/hcPIAIH7Rq6jw9AQTNZJ1EJvrPV8iCkoPSu7YjQJTrcsBWeo4zivdP0nliwQscVFN41xVp7Hj+8ck0aIQXOgSJ6DNThCBFldL8xf92vkLejTHhozIoibatMRyeaZEt9kgtdkgtd/J1lSu/ablPEK74NKF7sWXE0yBAFB7d00YJjjMYYbStHb2JweI3+U8v2+7q7gskM6XyXztcuUHnvjlFwuAgdTKJRrcR2nfesANED21/sUjEghLBibZCRznfRiUKG1kqk++nW7DMBonixppBqxRhtkIFz+bieRIab0uOvUPvJKE3WjFGNCB1n9uRfcrdaBIduUGMMZAaT2e84rkBIiZDW1Wfn30EnRbyJwk2l+7/amFSj+qkVb/3UWhBhKHLMlp8Jd6p4WX2o66G6CemyFa7CFSQXOjgToT3u17CYZc5bg9yyw3e+ZecVk/QwTkiaJkjHxRmsIYpj1kqzdhKSPmkSMVg+R6evidcXGF/5OsXOMaRO7Rhmw9Vl7OON58a6vVRxBuP4uN0LDLa/B1XeiRysEE/di977PjCGZ774ec4dPYo2mvt/+MfYfdtdgO3aPuh3ebnxMn61QsErMPdn36R94iwUPJQ0iH7KIBsggIGKAIFyDEvbFfM7MnqhQmq486WQ8TUHBBy7JWZxh7Zz1gYjoOyU+Jldf4NPzf8GaZLgCMn2uM5Dz9UwUYIQklJtnHf84I+z3lrGr1W599aHOds+SzNucsvYLSx0F4hUzO3jh4h7PVbPnSUoFXnhsT/l/LEXQMCB2x7g9oc/QL/dZGLH7lHGWDNt8f/45t8m0Qk/PP1D/LV9f+UyS0caRySDAWGlSnLEtqEArHumGYOAyvt3EgUDmstLlOtb69ZciX5rHX/Fw10curku+dUQniTYXyO8bXxUTyiJIrrrtv+aEGC6GjMXo89G1mJSs33KnFqAURrVThG+dT1dejXejJucap1mf20f9aCOMYb+08tEhxsAhLeNUbx/xlq1Hp0nOdcBAeV3bSfYt9VqZYzBpBqTaLu/K2SxbVk/VTa4W0iEK644v5tFdROEK/F3Vra4mVQ3sZajTmz7loU3Xg/pauhEofspbj3EnS5edrwbAlNFmRWBnrQ3V15XJBhtMKnCRAo1yGzTW0fAcFvhSJACHWfodmKDtB0xysq7+nwzG2g/VbyuQDPa2KKfy31r/Sp7Iyuc7mc4Ff/bLmb5RmO0/V97I0Xqm53cspPz6uGXEIDvDM3u3uzFZdOHQGs8leAVKpT9EtHyBTrxA6ydPwpAmXU8NSALJkAKKqd+D691CrSyZ0Hp4AyWh6V+JIX5b9ixjaZ4/ivE5/4Et7/CLf4dnDeGqe072LVnD7QX6OsQIwTbtk2zmC6SRRmFQoHp++6ie/oCRAqBwRU+jufgFENKRYf+jMfsodu5L6jwucU/pjc4zz3hXUT3Rqy8NMfUguTWYwF+GnNmf2ZdFlrQUz3+jzO/Ojx7G8KeYfezEatpTK+kCWKBu9bgiSf/fyS7KnxEfA/RiuS3z3+a+6v3kpDyUvNl7qreQaISOo1VlFa8+MRXOH/sBYSUaKE5dexbbN97G2GtSr/TpFivI4ButuEmEnSyLs24RdkrI4UkSxL6rXW6zXXSOGF8+3Yqd04iQofe4wtW6AClB2ZxZgv0zizgBzfW0Tys1ujrdWq3z1Cdnh2K1OFCY2zG3qar8DSJWV+YI+71cNxhDAsGZgw6yAhPOahWTOuLZyi/azv+rgru2OWxOMbY+Asda8TAoGRKNogZvLBKcnboprt3mvCOcdI4orW8RPX+aYQriU+16H5jHqMM4cH6aEwhBMJ3RtaR6yE8B6cqX9WTjlP2yVo2Xsm62wxpY+ge0+BUg1et5cWo3tKwxpI3U7IFJgcZupugexk6HboaN4wtrkA4Q9HjOzbg3rXCZcN9qWOF7iYjl5qAoTsNe3EwvI4e2m2spesK7UuuOF8hyFb7oA3edPGqwshkmnRlQNaIkL7EKXhbxxkVs1S4UwVrabtGBqFRGt3LUJ3YuiWLHk7gjN6HV2ohMkpjlK2Yfq3PdyP4PWtEI3ekCN3hMQmE5yD8azcovu5ctPmuaquSW3bILTuvOsNWBoPVeVqrq/SW55BkFEIP6QZ4zeNUv/W/W5cWEE3eQ7j2Aqo4hdOzVghbU8XatYV0hu58w5p/AHfqFiqrj6PShHj6HYTbbiVMV1nZ/QGWoxrV7hwXPEUWhYQDQbFao1ir4xYCznTP2TgHYX9sGoMG9WCMqN1jvFsh0xkNWrx84lE6L50EDCtTiuZul/Hpnbw4ODo6zHJXcvdzIV4q6JY1z98bMbnscOvRgMw1PPFQnzF/jK4XE5kYEPjSI9Upt5dv4/85+7c4dvY55o+8AGfXMBKO3aPYu14lPN+nVpnivu/7MSIVIacqTNSmeWb9Wf7V6X9DqjMerN/PX5z5CLP+NOXMp9dskMYxQaFo422MYnKXbZyaXOjQe2qZ8ECNwl2T9FtNVs6fo1yvXxZcfTWyNCaJIsa377xy5e6N9bKU9bk5Bt0OpWGdns1oo+mvrFOaK0DTnmQLb5ukcPckJla2LcfKgHRlgFobYJRBGUWqUjzHwxFDkSKFDcDeV0OrjLW5C3QbDcrj44xv38ngqRXi400AggN1glvquBPhFU8QqpcSn2gSn24hQ5fivdN4M69tywmjbbySOxZY4dC39ZeuZ2n6tvdnDLqTIBxrudSpsu6ujZP5pvfFKG0teMOTM8awcabYWMsgkJ54VUTAFeerNLqT4tQCvNkiwnOGbjuNjm0smOqk6G5ia0RdIyB5FLcVOsiCZ2O3fAcRWCFnIoXqJtbFFimEBBxpA76NFQUjwSPFxffDwMYbIxwBnrVm2Zuw1wSpGloSbXkNFIhAIsuenUvojkS0SRVZO7HxXoPUihpHjD6LDYQjbZD6aAxni4jbeJ9MOrwpG7BuUo3J7G0konxnJAyFazNJN8d9mWEcGNqglQZlQGuMGh67tK5TXKwL1RFX7rEowakEr7qV6kbP37nYIRc7ryVGa3qtJq3lRfrtFq7n44ch/soLBKf+kGz8NtKZ+/GWnyUVIfWX/09ENkCVtuEWSojGSfrVW3H6q/jJqv1BFhKtNUKA4/lIo+0/18EPEfc6iLNfJx6/nf5D/yOUZsDxob0AvRUGE/vRQhA4AZnJWO2vMluapd+8QKEZEZdrzJs1xiuznHzuSc58/TFiFRE6IU4Y0JwWfLNyhlu9fex+PkHFCecrLZ67p48JXYwx3Pe4T7krWdipOHYgQkgHx3VQRlurghBorSAz7D/hsvOCTXlfubfMCxOLOLHmw9/ahow1c7s1c3s048VJ/vatP8Ojq4/x+8t/iDIaR0jeFd7H98p3MuNP4gQB/V6Lo998BGEkt7/jfYzP7mBs+46tJzJjWL1wlqQ/QOuM+TNH7A+6kMP5WSFYHZtmYnbXlm0HvS4YQ2VikvLY+GWB7irLWF+cp9+01ih5FSEVD+xVe3m9THLcWmhE6GCiy6skA6wla2RGMVmYxPU8ZMGldP8M3rYSBmgvL9JaXqJQrdFvtxmbmaUyOUX/mRWil9dG4zi1gOBAjWBfDRE6pPM94uPrJHPdy1xzwb4qxftmrliF2WSadKFHth5tPTGoYWCwZ7OgROhaAbPp/tJxVCex1Z5LF91jOhkKkRsQESMLyo0WZ4yVjY3xXpll4PXAKFup26n4yKKL7mVWNKTKXgo5wqb934CFYlSrKbUneoSw1hFPogdWiMjACqBLxzPKYDKFSTcUjhipvo230H4Hhp+H2fQ6wuZlOAKGQmBDhAhp5yArw+rfQ7ElfYkoXNlduhGnpYfiSQAicJAlH+lLK5yjbBTLtdnCZi1zF62wNnNNW0EjNspmMBR5Fw9z419DiOGf4f3QwH0xOP5qPfuMDTwP9lZf9ezAXOzcBLnYee3RStFdb9BZXSYeDFBxZOvzFKt4tWmy1jKD9SUqbp+Jc7+Pe99fRYY1WHmZpLKXbiTon3+B4uKjFDvHcYTGFWb4Y7BxySltdo826CxDOq41twdVZNwireyl8+7/N97Kc8SVfYhCDQx4C49TOPcV0vJuVHGawdTbcYaNVRuL85x94RkWjh1l0O8gEKQqxdESgaA0NcWxhyD1NO+qPcBz3Zc4efYl7nnKp6cHfOuBPmnF40BhP4f7xyg5RTzj0kya7Dvlsuu8jW85cnvE0s6LAbjTSy53vhBiBDx9/4BOWeH6PsrYwodaK6QRGK35YPgu7irextLhl2kePUGJIlIKquOz3PueH2J67z4q9QlacZOqX6Xf67B+/jxpGvPYFz9NmkSbPqmtP6618SkO3PUg2/behtQSMkOWxcTZgEKtRm1yZtQmQ2tNc3GeznqDUrW2JbPsUowx9Frr1KamCbsh3ScW7RUjG8URi7hTBdypAm23x8888/eITMy/vudXOFDdv2WsfqdN48I5/EIBx/HJsoQ0jpjYuZtCuUK61CM+0SQ517FiZHiYMnRHAcoA3kyR4GCddLk/sggJT1K4e4rwtjEbbD3XJTnXIZ3r2qvjm8QZC/B3Vwl2V3BqW912qpeSnOuQnGuPKkPbGCYPWfJGYklHmc1mipS1WAysm9XZWK9kLRdO2cOdKW1J938rYrTNjDNm6P7ZiCl6hULNqGG8kTLWsvEGpKobZQWYTmwG6+bMvRseY0PEJcp6lDdEsiOu6y670ljDzkGvqhA22jYLDvbVcrHzRpKLndcPozVJFJEM+gw6HQadFmkU4XgetakZqlPTuI1j4BWgvhvmnobiBEwcIFlfoLt4hnTuRcanxvDUAFrnYOUInP8m9gzmYLQiyzJrYR39o9tMMI0cmZ3TYGJoLdr0I2cMWXUP/Vs+Rjp7H4R1ZG8B/9jvc17v4dy5ZS5cWER015gpRNz7N/9nBkHAfLxOyStR9Ir00h6nvvQ1lk+coFuH8fffy6Fb7+d06yyTukZ6eo3DTz+CXO6SkHL0UMTCToXB8ED1HppZhxO909z5QsDUsku3rHnmvgHakzjCCp2SCTkUHOTp9GVKLcOhwwHFnj2OxqRioukjMk26vcI7H/gIL5cusJquUvGqPLf6LD+Wvp/eUy8z6LUp1yYoT05jjCJ0CnSTDgUCGvPnUZkNMA8KJcb3H+Sue95L6BTQUUaU9XBCj8rEFKVajdbqCu2VFYrVKqsL53j5ya+hVUZtfIb65CzViWlqEzMEw+yvNInJkoSpPXtxlItuJzjjITLYKpLO9y/w8Wd+nlQn/LM7fpF7J94+WpYmMavnz6GyjJWzJ9Fpxp6D9xDrAW7oM7Frz6iNh04Uydk28ckW2epFMRHsrxPeUt8iPrK1Ab1vLpKtWSEoy55NL88u/lzKomsLJm6cWDZdNZtUWyESWTFiBsOr7U04tcD2RfMlybnOSOC82nizRYJ9Nbuvq7TOsCe5oUvilcSBqOFx97PRvUmUdZUUXGRh49591dt4qK4tqJnOd3HqAcH+Ou70jbcjyXltMMOLBNWIRkkMr2asUC52boJc7LxxqCwlGQyQjktQHMZImE024HQATgByKEiyBNbPAhrGD4DjwmAdXvxvcOQPYNdD4JfJTnwNM/eU3YdXBpUiVYyUAiklYNBG2NIsRiPYsBJdNE8bt0Ra24/fOAzDJpTaK9O842+RPvGbTAYdHOmQlXeyft/HkcVZKNRBCOKTj/LFzz9CmmXMPng399xWIa7McvLpc1z45pNoZd1G8t5dNPY63FO/i27cYyIYIzYJ//78p6mrInd/Q9Lttzm2p8eF/Zo7xH5eUif4i9UPc6fZz+df+DRjZ22hudQzHD0UsTqjmVhxeNvzIRg4uT9mfq/GOAKBQPZT7n2mSLEv6Rc1z94Xk3nwkeCDnNVzvJgc5f2Fd7KXbcydfZngbIck6iORlAsVtt35NqKZEveHbyfAJSaiUK0x6LQxjuHkM09w/vgLV/3Mi+Uat9/7frbfcoheu0m5PsbY9p1bbEqby6+82HqZf/LiPyXVKf/4lv+B98++z34WRrM+P093fY2Tz3+TcyefBynYtusW7rn3w8RZn+q2aeoz2y474WWtGN1NbXG/qwW/GkN8skX/meVRrSBZ8vD3VPB3V68a/3M1VlsrMB/jL2jSxf6oLtBm3KkC/p4q/q6Kda/0M3QvRfdT+zjKrEtsQzAMCwtijF2nm6L62bDydLxFQAlX4O2s4G8v2yKF3QTdSe39sJWIPUiBcGyAMq7AKXs41QCn5uNUA2TVR3gS1bJVodWwcKJqJZcJumshXIEIhscSDgNwQwenFuBOFWwcznXeX5NpkvMd4hNN0qX+ZctlxSc8UMPfV3vLW7jeikQnm3S/PofupqPXnJpP/YcPULhr8lXZRy52boJc7HwHkEaw+LwNenZDEJL28gIrFy7g+gHliQmqp38f98xXERMH7DqV7WROEX32UUR7jijcjlQDvLTJxehD62MXcFEHCWlvWg0d2gaka/e9UUnaKL7Vu4vnlwoUxYDvmT7BY61DrLcGSJ1SmN3FPT/yU9SmZ1mL1qiHddLuIvUTv48++COs+hUW+gvosw2O/emX0dpQ3bMfT0HS7RC1GqhMYQREJub0TIfjt8ZMVWeZcSdxlaB5/DQ7jth/7xfvHLA2a3AywT1P+1Q6DnFgeObeAUk4nDIGKaXtLi4EBmPr/2jDzILDznMupZ69GtfCsLRN8YP3/CT1cIKTyWkWlk4TvrhCKfWQ0mHqjtsId0wR9gy9lTVaK0v0Wg0GWYQvPG6/8z0cuP0BUh0xsXfvqC1HP+sz351nb3UvrnR5dO1xfunI/06iEv7+vv+Ov7jrIwC011ZZnTvH0W99naW5E6PAUK0VtfEZ7n/wIwglGNu/i/L49dPrr4aOM9L5Hk7Vxxm/OYGzmePNE0gEB+oHRu00kvMdW6l5Z5lgd+VVT49W3YT4dJvkVAvVSV7Vsa+KtOn5suDaeBpfYhI9crmZQXZDLkAZutaVOVnAGQtsnEs0dN9FGSZSpIu9UaVuAG9bCX93hWw1IjnbvrgfAd5MyWZk1QPc8XCUor6ZDcuUiRU4w/R5X17RHWQ2FXHEfWUWse9EopNN2n905qrLJ/7a7a+K4MnFzk2Qi53vELTaZBUSGJ0xWDiBX5vCrUza5ae/Dq5nXWNGQ2cZqttQ/SaRKaCiLqyfxp9/HGfxGWRvCYA0GCf2JvD687hpx8boSWeUUQYM6wZpbISfJlPweyd300tdNi6bfcdw79QaB7Z5mOpOdHESr3mCrLYfkQ3wF78FwPq7/jdMaRJ/4Um+/PQKK+fm0MZaIFwVoXVM5pUojU0wef/dtLc7nOie4gP1d6J6EQQurdYazcMnWDh6hEikPH93zNvO1qk3BJGnWH6gyvniOkJK1tQ6UspRzJBgGEi9+fgMTKw67DnrU2vamklGCFZmrAtuZtH64qMiHLkzplVXYOBDk+/j9OAc+4t7WO4v0332BLfN1SjJAtv3HMI5uAPpedy+7z6OxMd4vv0iwsCeyl4enniIZ1rP8Wsn/x2JSvipXT/JX9v9k0T9PmdOvMDRR79Gq7GI9Fzu/8iPEpRKPPm53yUe9AkLZd7+zh+gVpxmbNdOvEpoBZGQvBanJQMYpdBKYYy2wfPyotXoaOMoCMFtY7e+Bnu/ztyMIVuLSE61bFHFkmeDfsseTtnHqXg2bVxtBFkPM3hSje7YnmuqHaPaiRVNxrrxnFqAU7c3txbYQNsbSIk2qbYuvqFo0Rsipp+iGpFNub7BM5Msezbbbl8Np3xRLJpUE58bui2XL7f6CM+2BhGuHLndTHLlAHlgU6YSo4D0i5MQOCXPZkeVPfu44KKToUDbcGlGCgHIqm/fu437imeLLnaGfe461uqmo+yipW1zHM6GW3CjbUjRs5llw/gdHStbEmAjA0xfzKbbKBshnGEmXuBcrOk0jIXaCLK2n8Ew8Fpre6/0xWB8scl969jgZiFtFtrafzqM6V3d0ufUAmb/0QOv2KWVi52bIBc730UsvQw6g21323/6uANh9aJIymJoXQC/DI2T1oLTmsNsu4d07TxJZx194iskSUqrehduUGTm5G8hsoikfpDC4hN2GwAMZ1plHrkwBcD+Wpf7Z9YIHXWJ5UhetAoZYx+LYco9ml7m8ecXJhFuyFgQU/c6lL0etTDDTN5FuudDGMehc+7rTDaOYu76K2RTO1nq9OibKQ7/2Tdozc+TZSmukQSOYdf3fYjZWw6hdI9mZlsfvNg6TNkrs8sdR0ifL63/OU+2nh0eiaHslOiqHhVZ5rbBLOnLF2wBxg0EnN+VcepAPHKZaS6KJ2D0Q7p93uOWIz7SQKeieeGOAVnJGdYvsnsUCMbdMVq6jTaKTGXscXfyk7Uf5sXmCyw//hSTvQL14jh7fuD93HLwHtbiBpXY59nPf4722gqO67L77gc4sP1utNJ4foAINqoMS/ywgF8Icf0QN/CvmD2mh5/HhkRSWYZKU7I02XSfkKUZRmu0Grq8HAcvDPHDIo7v8XL7MAW3xKGJ2y4Ovtl1hBjq9I2suDenpWAjjfm1SpEH657KGhHZiu33ptqJPSmHw5NzaN13Tj24obgc1U5I5rvW3bYekTXjK7oRAWuZCp2R2LvqejlXxWR6i+vqakz+nbcRHqi/on3lYucmyMXOdxHDbu3IG8i80GrolhqS9G0ae9xGNefo9SJajQ5Rc4VARASVOjJp4a+8QLTre3BWXiI88Xuc6dbJJu9kF8dxBKQ7Hqay9Che4wgCNdQ8No4IwIQ1GDTRWturKbh48hMSgyHRGUIIfDGM1zBWkLiORDru0LumMQb6mcMXT03Tin2kMHxw9xLbygN0fT9kMa2JO2jVD9CYOMSe5WepzT1Ge8/30yhvpxWUyDCUmmfwBw16s29HSYeCG/K7C3/I4sIZKid6OAPF2L23c8u+u/lm82l6WZ9m1mY9a7Iz3M5ivEwoA3qqT6RjBIJaU3LX8yFeIkh8zfHbEowU+KnASwRObPAzK360AC3BSIORgukllzASJL7hpXsTWpWMsluil/VBwH53F+85NsPC6eNkJiOuuzSDiLtrdzNemGZXbQ+q4uBKnzF/DEe69ETEs+lhtgXbuL1+G30nouKUOdc+RyhDxv0axAqVpagsszVHBEgpEdJBCoHQoAcax3UwEpSjUFnGnw+e5P9u/1d+LPx+fmjie1kZrFByS5TcEmSwIQeFkCMNLB2BKHg21d5xcVzX1jPBZjeqLENnmb3XCikl0nVxXAfHcRGOi+v7hOXKa2LJeiuzUeNIDUsHiKJr69UUbN2ZrTWHNtLOhzVmNqdvO0MLaDyMk+qmNsaqm6IHma2nM4yx2ohJwljxpVrx6H5z01xZ8W2c1DDd3mhsenhmRvWPTKJGLsENq9HF/HAu1s4JhjWQpF0w0vJC2LT0WI1qFt1QdqFgZMlhwyKzUXpBXZQSZli9+nqM/+XbKL59+vr7vQa52LkJcrGT8+2SJQnttRXaF06TZoZCfRyjFclggO418HWPYjG0HealxitVkcW6teC052xwdW8NZm6HXgPG9wECjv4ROukShzOohZeQ809jVIpyS6BT+tLDK4wTZm285knby2lT8DVg44gwaJXRjhyeXa6xt9ZnZ3lo0t+wJAEYQ1o/gNc5ay1fYpi1Jp2LwcIqw0gHkAijyMrbOHP33ybKBoTtsyTFGaa9Mq3qLtrRKnRWSYxkpVhkojBNNajSixp86cxv86LpMyBjr5lhxzcH+F2F0Rpf2FT8zGR0dG/TOy3wpEums2EPdMGgYAs59ot6ZDnSZvhYgDCCW48HbD+32WK0eUT71xUOkZORuYbU1SS+YVAwDIqGXlHRL2pSH35AvIu7xAFanVWWmheYTqp0+ut4SiBSSNOYNItxHRdcSaFUY7w6S3lign8XfZaOb2Nm/t74X+P5zkvc49zGtJxAuA6u7+F5AY7v43oejuuBEphIY3yDcYZupY2Ja0O/16LTXKPbWkWnirBcISxVCAslgkIJzy/gOJLq5DTlicktLrXvZGzZF33VGk9vNowxmFjdUHuOq46hN8YQtlXHt2EVNMq2UBmxqdHv5kzD683Dlmjo0Pr86evuM7fsvM7kYifnlZIM+rSWl+g01nAch0K1TqleJyiVcZ1hQPOGy2yDqHUx2Nkrbq06mvQhi8AvQX8N3Zwn6ayRRjGpLBD1u6SJIosHFDzNVHTEtuC4929YkRKUbHxSfw2OffFi/SGtMMJBaDW0Fm3scBR9vTUOaWhxst41NXSxSIweXrVJd5i6b92ARm9YqjYGNqyVD1CLFtHVvbhC4a4fY3n2IZKgjDP7AHr+WV56aYnlnk/ZUQS6g+PCqpdyrlxib/VtTDSOESddVqv7Wc06dOnywRmHb05NMq8i9jk7OBWfo+SWOBvNMdADmrqDAUp9SaknKQ4cwoEgjASFviSIBVJvOv7hfC+tNQRghEGYy1/feEUiRy473/FJdHKFisMbzwUbpdcKMsRzPLTRlN1hJ/ihC8/zA3yvgO8W8IMCfqmIlA7t9RXajWVbFmDT1fyWxwKkdJncvpttu25l121voz4zi+td3nh0MyrLUFlKlqZkSULc7SFdl0KljB8WcP1rb79lrDghbvfJBrawZnG6juNfOwA7iSJUluKF4ahkwM2QRAM6a2tkSUxlcopCufKmdQd+J2O0Ye0/vHxNV1Yes/MGkIudnFcDYwzJoI/jejd1Urhh0gGodBRjpJQii2NckeE4DiwfhtpOW6NISGicAp3CynFIOphwjDScJMkEUbfFwAQkUYJeP08hWqTQP4MoTkBxHB2OE879GcHyM6MKrLq2G78/j5i6HVXehjz1FSuetB5JhA1320hICWELiiEwSm046hDCwWw+Qw+F1Cgq0tixtBDg+OgsJlUJnuORTtxJ2DoBOiNzK6yV9xCKFLP9bRTOfQ0n7jJ/+0c5XruN8blnmOg1OFPZT7/9HE9Uxrm/fjdrTspKtIJnXJ5ff5G13grFzGOnKlHOqhRjn8rAo9tcI+50EcNpRgVDv6jpFa3lZxBqlKvJXDCuIHU0RoKfCAo9QaknhzcrtIwAI0A7Bi1BSytsnAxcJXAzYQtWDoPDBYJQ+mRakZqUggyRODhIil6J6sQUpZkpCF3azVWCHqysz5MNIhxjXWsSSRCU2HPwbm57+3upTVq3gcAWgkyjiGgwoLU6T3N1gXZzhXZrlW57jSjqUirXGRvfwdT2PUzvOUB1ehq/WEQO2xNsFKLTStFvNWnMXaC5skBrfYlOp0ES96iOz7Bj+yF23XYn5e3TNstvE0kU0W816bWaZEmKH/oEpTJhqYxfLNoWIRux/+JyC4NWGatzFzhz+BnWly5QrIwxPr2D2b0HqExM44c31gMu59Ujz8Z6E5KLnZzvCFRqXVebr2Q3/r2THgTli8+jFiaokgwGJPOHiTtNBqkhxUfhgckQcQuBoFwuUqsVCCe2M0zjgOI4nPlzmH8Os3oUJUOYvBV337vh9Ndg5Sj0lgFpaxT1VlCVnWRa4iw+bavhYqylaKPGkeOjqzuRgzWEim2wuLTutExnxFrhGYEUzkiASSGQrotAI6Rjz4fDjDizEeOkMszwPhvGQGmnADpBuWUaUw8iTML48uOIbIAAMreMKUwxcDwG/UWelgFnZ/bzrsn7uaAGDLpnOR0UeK8KOeP6RK6PSHqcHZxmyfNYiebRQuC7JdazNo6RBJkilR6pyNgozX9pcX2BAG1wM/BTiZcIvFTYxzE4StArKrpVQ1S22S9mKIrM5lpRylDsSbbNO+xeKhEox76HIiGbKlKQIWO6jBtrZGJIkgiFIjMKR9h2Ca5wL7qCDAwDw6jWJqlUJ8myhCSJSIe3JInQOsMMj01sjusw1k0ihcP07H723HEPU/v201tv0FxZpL22TNTvkGUpzjA+SUoH1/EJ3CLFSpVCtU5QKBGGJYKwRFAs0W6scO74s5w/+RLrq/PDuLaL33/PC5ic3cOO/bez57Z7KNXGLmYOOW/eIPDvFC589TnMC30CWRi9NtBd5DurHPjou1+VfeRi5ybIxU5Ojk0tTZOYNIqIB32yOKE8Pk6hUr36SUFlkPZsnSM3sEUVVQZx27rhkj50F20BSK8AGEx7EU78CUI4tkq2UVDbbYVYaRziLkRNOPIFWHgOZt9Gq76D071lbmkvUjz9qHXL7XoYt7+E6C7C+EFYfsnGG40a+lwsQ7CR/WYTvWxzx5GbSUor4BgKsKHbTmtjrVZGg5BobACxFRfDYGIh7HKGDjCjaZZ2UukvIIZZXC+5gj8LHf5yN8WUdrNYqDMZt1FujVUvJEi6jCdNnq5M80QocQ0ckTFt06PilG2qvFEoNaDq1llVPVpZmw0LmBVOchjKM7SXGTMSU46SzKx4bJ9zqbYujQkRo3AmLaFf0nQrmm7Z0CtrsqLk1ngblYYhXVyn1HdwhBVOrnBtY1uTodGj54VaDadaJKzX2D1zkI7o05lbpHd+kaTXsxY+ZYWqGX4mUopNWYyM0qPtFM1oPWD0XdwQdlorm8kmJfXJGSZ37qG73mB17ixZmtpAfW1rSPlhgXJ1nHJ1gmptikp9Ai8ooFRKplN7r1J0liKkg+f7uH6A4/p4ru1EnyUJaRIN72NUklhXX7lKoVKhUKkQFqsEhaItmhr3SaKIOOqTRLbIY6lap1Qdo1ipXde1uOXfTSn67SZZmlKsVPHC8E0bm3Tuxef5s//0fwGCcX+GkldnJTrP4uA0BsOPfOKfcMtD73rF+8nFzk2Qi52cnNeQpGdjjzajMlAJ+MWhBWcYn7ERRKu1fdxdBsdnXcWcahxhX2UX437ViiuT2XpJ7QVrtYpa0Jq3br4sgaUXoDwNE7dAZ8G2FZl7Cg5+yI7bX7Un2HOP22DxsGbnWt8FU7dDUEX3m5gzf4aJ2lYkBTVk2h4GQQ9ddRsxShuxTkJaF53R1r2zIZaGViW5YX0YiiEzFChYG5dNs3eLIBzcrG+bxo46LkInmCDzCvRdzcCRTJS3M2d6zCUtnioUORQnZFmMkB5S+HzWT+noGGk05bah2g3JHEPsQuJC5hoyz5D6DHWhQWLQ4mKgqxhm+wWRYLzhEMYOqWe3S1yN8iDxDHFgbLNLhuNIiUCijRUk92cHMfMtxGKHcuzjBAHFYhW3EFIpjNH3MyIdUdVFKk6BSEXoNCHqd2n213FTKOkAmVrB6jguEzt3Mb33AJN7LhamtF+xjPWFOVbPnWXt/Fk6jdXhkmEA++hdH9aV0ZsE1qWIi9uNMBtO2Y3nl0R7idGfKww1/P4AQViiUK7hByGO6+H4Ho7n4foeIBh02ww6bfq9NnGvY13HBhzpEBbLFMo1iuUapXIdKR20VmidobVGKxvQ7/oBXlDACwL8sIjn+0jXG7maN5c6kI6DlA5CSqQcPnYkcqMkguPYx8P/1ZGEMJskqRD86a//KlG3Y8dE4DsFGtECGTaOpzIxyd/+179xzR56N0Iudm6CXOzk5Ly5GWQDTjVPcaB+gNC9RvzFRuzPhjjYbJHS2rrWHN/WUUq61toUtYbbCSu8gqp93fGg37DrDdaheR5m77KWp84irJ+27UkWngMV2zGjNsx9C1aPwdhemDgIz/+OHXv6Dkj70Dxn57D7Ieit2n1GzeH8VmCTFQOz8eyigLCleOTFwHCwAsvoUR04iUEhcIxBC4ESEmE0DRR14SK0ZkEayni0i5MEaUQzaTLnQNUYikKyICGu7eRIoYiXZRSSPsiQo2bAGdWh67jMunX6qkPHJBghcd0CWjpkOkEPg++F0RghEMKxsVpDEWg/EjUSGQKBHJZWMJt0xYZ7zgwtblJIJJJQu9TDOrfUbqGRrHN+MMedlUOUnSLtrMu24iyxTlhL1qgFdd5eexv9tQbpeotqzydqtWisL+Fk4DsBUkgcx0U6Ln0d4WsHR0gbtK2ykXBwPM+62RzXlgVwXVSakkYDkkFEMhiQRtHoOF3Pw/UCXN/HL9hEhLjXYdDroLKL6dmjb6rZdC82vgMX17HxShvfh2FX800xcmwZx2x6LEbjbcofuDJXEmobwvcqm2xGZRlRr3v5sIaR2AH4S//0n7HrzrtvYMSrk4udmyAXOzk5OdflUvF0KSq1AinuWAHjl2wM1aBplwVlu32/YbPvjLa3bGCFUpZA+wIghwUth7FRC89ZC1bctgHonSUrilrn7XIMzD9nXytN2fWSnt1HoQbrZzZZnzaOYfMZ9OIxXTypDnPChK2GqzFsOEsUejjMZjeeHVMPW6doo/mCb5jRcLdy+DNP8YTvcqJU5ZZUk6Q9Hswk/aDMSc/lmBnQxjChoIkiFlaAhEJQEx4XTITIIgwC5RXQjmdnK6R9z7UGnWEcfySM5LBmkUEPrUzOKPAdbAad0rai8WQwwYHqfhb7S5zrniPTGTP+FPur+xjoiIpf4YPbPkA1qLIcrVByi3TSLqlOybTiYGUf24NZFtpzVE0RnaYkg8i6wYKQsFTCLxRxh24xozVZmhB1u3Qbq3Qaq0S9LjpJUaktWqkT21bD80P8YhEvLOAVCvhhgMGQJAPiqE8a28bK8aA/jJWSF2s/Da0mWRqTxQlZbJvvZkkyKn65xTJjhq5brUePtbLf09EyY0auwRHi4vsKgnjQo99sbvlWXXQkX9zuh/77/4nb3/3+a//fXYdc7NwEudjJycl5y6CGV8Yb/dk2+rHpzAotjO391l+FsG7FTjoYWpA6MLHPCqmFZ+xrQ3caQsLcM9b1V56GC09COGatYW4AhTG7/voZa9XKYjv+YI3h2Q5cG5eFdDFRy1qZNiqEC4dsKJrERqCQ0ehhfJURYhj3BIuOYEazkUUPQIzhcdcwYWw1awdYdiRHXYe6ztidab4Z+iTGoIQgdn16RhGohFQ4rPg+Cya1+5EuRkjEsKaUdnwrTHU6FIQSLSRyo+cdgBviSBdlhnFhw5O+FA5GQD2o0027KK3YVpql7JbJTMZkcYpUp1T8Cp70KHpFjDFU/Apj4RiOcDAY9lT3UHJLlP0yY+EY3aRLySvhCdfW0dEKrawQsUUkh64mZyhsnA1r3xUYxalZkbJhdTKY4TKwsWrXkAMjI5EZjbfBSOgM7+eOvMzn/vn/dt2vcm7ZeZ3JxU5OTk7OVVCpFUsbcVdJdygAhgJLZxez/IbxSaQDOPsNGwe14z5YfNGWQlh6Ecozdtvx/VZctOftrbNgRVV5FhaehYkD1uKlUhvsfv5xW8eJ4cl1uMxk0VCgDKcw/COlgzZ6FFBuhKAzPKGbkXUHBkLwdd+Kp0llOO8MY2mMIRESCcw58EjgEiCYyRSZEIRGkyI44wiMlBjpIoyymX/CGZ34zRYBIkBKGwMlxCgw3k7GNtC1AdvuMDDeZh8GToAQgtnSLAJBxa8wU5wBYKIwgTaawAkAmC3NUgtqCASudKn5NUI3pB7W8eSr22j2amit+PWP/wzdUZzU5eQxO98mn/rUp/jn//yfs7i4yD333MO/+lf/igcffPCGts3FTk5OTs7rgErtCf7Sas5a27gn6VlLVRZfjL3asK5oZS1XKrFjOC4gYO2E3SasW9eeSux+tLqYnYe2Yqu7DOefgOo28CvghVZrLL8MGCvEOosondoYImOsAEv7ZIMmoBFDV9jmjLcFYWhLQVEbjruCwNiAGwGsSnCBFMiEoC+gKQQDAQuOIAEcYN4RJJcG1YzCtWy8jUEgjMFIZ1jNnGEW2yb3qnTYKFZqvZdDi49w8BwPT3oETkjoFQjdAgWvTNErUnALBE5A6IYU3AK1oMYgGxA6IePhOKEbUvWruNLFEQ6udAndkKJXxJMernBxpTuy8hx/4lF+/5f/2VW/Cq93Npb7ivf0JuAzn/kMn/jEJ/g3/+bf8NBDD/Ev/sW/4MMf/jBHjx5levqV9d14JVxY7xO4DlOV4A2bQ05OTs6bBucqlgUpYVMtFrxLgtCFY0/i7sTl2xYfuPh4Yv/155D0rYAx+mJdqg3r1fC5k8V2rkP3G0Linv2GzQKszNpljm/v23PsXDtpg81dn7291a0usXjDEmbs/aBpyzVojYnWMVkMjo/pNNAYEqAvYCAMTQFFAw0p6AhwjMEDFiWkQ8F0QQrqxm6nhWBFCtpDL2FPWnGVAQpBhiEDBhviaKSRLrqhzIZLcqNsw5Z7ecmyjYw9+1gKSWEolohc2g9lkDZBa7atFDgwVwFRwSt+AOkfvP5n9SryHWHZeeihh3jggQf41//6XwO2KuiuXbv42Z/9Wf7xP/7H193+tbDsaK35R//5WxxeaHPf7jG21ULKoYubGpKz/YtN5Ib9RqQUo+8R2GuCzaGQQoqRW31LR+RRXQ0xrBGysa4YWkzFaJvRWMOrB7Hp+aVxlxuvjS4qNv1vXKxzceVtR2Ns+p9gY7vN3zaz6cKFi3VPhi7h4bEMj1duzGMjC2G4jd4IjTSMeiINsxI25ndZ+sDwwmlzxsumAx/Nfcvzy34cLtnuisd+hX1vDDGKDzVbjnfr9hefXD0uVmza1lzhWMTWY9m03AxrmVy3rtolP4g3VIftCsdzxUE3fcdumo3DveyDvPGxX+2icq90OHGNZ5ab/bne9H8vrj6/Gz0LXOv4Xo0xrrndlfZ52YNrb3jFXb+6X4FNw15r4KH4Uepi0LhKwNjnsr+KSCNQfZA+Iu0j4raNcSpN4kRN5KCB0Cmiv4bxiqAz24w2bmHSPujU1npSCeiYQdIlRZEYWzwyNppYGGIBCYJEQIy9RUNxVQB6AgYCIgF9QAnQCBSQAMlVv1Nbfyxvb9UZP/sxpLcTISTlsYC//v99lz33vQK+ayw7SZLw1FNP8Qu/8Auj16SUfOhDH+Kxxx674jZxHBPH8eh5u91+1efV7fT5sV/9x/xQqra8HhWmubDvJ171/eXk5OTkfCcSDe+Lm56HwPbh8z2v2p42Lph9YMOGVhwtg+uL7Wurztirj2KYuusxC8eb7Lht7Nud7k3xlhc7q6urKKWYmZnZ8vrMzAxHjhy54jaf/OQn+cVf/MXXdF7lwGWmEtDvR8SZQZlhpVATMd58EYODERs3Obq3XP6F2TAtmqH5x+Y0XKz4ykb5/dG68pJ7ccll8BW+lDdyabblskxc56u/9VLyoi3JsPnVi/Mavr5pHmaT6dRstAAAhNlYf7MZaGNO1pxkbuASUlzhmM0lx3jx4SXjXfP93Pi8Npm2Lllv9O6Za49hl8grLL9sMnYrAxcvqjaZdTYVptt4vzfWv2y0a1iSrv3cXOXxlba5aEb7dszLW78rV3jvXsHYV97bKx3ptTAh3KBJY/Pzqx7K9Y7vEhPJqzHGK2bTRK60a3HVJ685F63WV1723YYwwCVezF47vuK6rwVvebHz7fALv/ALfOITnxg9b7fb7Nq161XdhwgC9v3ef8OkKSZJXtWx3ypseEhv1FO6Yc0daZfN5+ZrMHJtXbqv65xrN9w4m8e4kfleaenGD9tmR8pWE/vFn77Rshv43d/qoTJXNY2bS85jl+//0plfwS13pZ1fS8uMnm4dd8u+b8jtd/koN8KVXD6bp2y2yOtND14N3fIW5nrlgl7rMW4mcOLS/Vxt2+vN5c0brGGu8GgrN/M2vxkO0xhYOtPla//hxOXLLskGK1Vfv3jWt7zYmZycxHEclpaWtry+tLTE7OzsFbcJgoAgeG3fZCEEIgwh77abk5OTk/NdRH2H4YkvLNFrXt1yUx4L2HZL/XWb05uzg9hN4Ps+999/P1/+8pdHr2mt+fKXv8zDDz/8Bs4sJycnJyfnuw8pBe/9yVuuuc57/tItrzg4+WZ4y4sdgE984hP8+q//Or/1W7/F4cOH+ft//+/T6/X46Z/+6Td6ajk5OTk5Od91HLh3mh/4u3dRqm/1opTHAn7g797FgXtf37Iwb3k3FsBP/uRPsrKywj/9p/+UxcVF3v72t/PFL37xsqDlnJycnJz/f3v3HhRV/f4B/L1cdl1ALnJH5aISKApjorRepikYFR1Lo1Jnc1atHBQNy0zUFJ2GdKbGsqahsRT/0GTUUSMVDVEpHQVEQFBEzeuoiEYIeEFln98fjmda0bKvwO6e3/s1szNwPh+W580HD497ztlD1DF69vdDWIwvrp6ux62GZri6Pzx01ZGv6DyiivfZeV58B2UiIiL786x/v1VxGIuIiIjoadjsEBERkaqx2SEiIiJVY7NDREREqsZmh4iIiFSNzQ4RERGpGpsdIiIiUjU2O0RERKRqbHaIiIhI1VRxu4jn9ehNpBsaGqxcCRERET2rR3+3/+1mEGx2ADQ2NgIAunfvbuVKiIiI6L9qbGyEh4fHU8d5bywAZrMZV65cQefOnaHRtN0NyhoaGtC9e3dcunRJtffcUntG5rN/as/IfPZP7RnbM5+IoLGxEUFBQXBwePqZOXxlB4CDgwO6devWbs/v7u6uyl/gv1N7Ruazf2rPyHz2T+0Z2yvfP72i8whPUCYiIiJVY7NDREREqsZmpx3pdDqkp6dDp9NZu5R2o/aMzGf/1J6R+eyf2jPaQj6eoExERESqxld2iIiISNXY7BAREZGqsdkhIiIiVWOzQ0RERKrGZqcdfffddwgNDUWnTp0QFxeHoqIia5f0TH777TeMGTMGQUFB0Gg02LZtm8W4iGDx4sUIDAyEXq9HQkICTp8+bTGnrq4ORqMR7u7u8PT0xLvvvoumpqYOTPF0y5Ytw8CBA9G5c2f4+flh7NixqK6utphz9+5dpKSkwNvbG25ubkhKSsK1a9cs5ly8eBGjR4+Gi4sL/Pz8MHfuXDx48KAjozxRZmYmoqOjlTfwMhgMyM3NVcbtOduTLF++HBqNBrNnz1a22XvGJUuWQKPRWDwiIyOVcXvPBwCXL1/GO++8A29vb+j1evTr1w9HjhxRxu19PxMaGtpqDTUaDVJSUgDY/xq2tLRg0aJFCAsLg16vR8+ePfHZZ59Z3KPKptZQqF1kZ2eLVquVNWvWyPHjx+X9998XT09PuXbtmrVL+1c7d+6UhQsXypYtWwSAbN261WJ8+fLl4uHhIdu2bZPy8nJ57bXXJCwsTO7cuaPMGTlypMTExMjhw4fl999/l169esnEiRM7OMmTjRgxQrKysqSyslLKyspk1KhREhwcLE1NTcqc5ORk6d69u+Tn58uRI0fkpZdeksGDByvjDx48kL59+0pCQoKUlpbKzp07xcfHR+bPn2+NSBZycnJkx44dcurUKamurpYFCxaIs7OzVFZWioh9Z3tcUVGRhIaGSnR0tKSmpirb7T1jenq6REVFydWrV5XH9evXlXF7z1dXVychISEyefJkKSwslLNnz8ru3bvlzJkzyhx738/U1tZarF9eXp4AkH379omI/a9hRkaGeHt7y/bt2+XcuXOyadMmcXNzk5UrVypzbGkN2ey0k0GDBklKSoryeUtLiwQFBcmyZcusWNV/93izYzabJSAgQL744gtlW319veh0OtmwYYOIiJw4cUIASHFxsTInNzdXNBqNXL58ucNqf1a1tbUCQAoKCkTkYR5nZ2fZtGmTMqeqqkoAyKFDh0TkYUPo4OAgNTU1ypzMzExxd3eX5ubmjg3wDLy8vOTHH39UVbbGxkYJDw+XvLw8efnll5VmRw0Z09PTJSYm5oljasg3b948GTp06FPH1bifSU1NlZ49e4rZbFbFGo4ePVqmTp1qse2NN94Qo9EoIra3hjyM1Q7u3buHkpISJCQkKNscHByQkJCAQ4cOWbGy53fu3DnU1NRYZPPw8EBcXJyS7dChQ/D09ERsbKwyJyEhAQ4ODigsLOzwmv/NzZs3AQBdunQBAJSUlOD+/fsWGSMjIxEcHGyRsV+/fvD391fmjBgxAg0NDTh+/HgHVv/PWlpakJ2djVu3bsFgMKgqW0pKCkaPHm2RBVDP+p0+fRpBQUHo0aMHjEYjLl68CEAd+XJychAbG4u33noLfn5+6N+/P3744QdlXG37mXv37mHdunWYOnUqNBqNKtZw8ODByM/Px6lTpwAA5eXlOHDgABITEwHY3hryRqDt4MaNG2hpabH4JQUAf39/nDx50kpVtY2amhoAeGK2R2M1NTXw8/OzGHdyckKXLl2UObbCbDZj9uzZGDJkCPr27QvgYf1arRaenp4Wcx/P+KSfwaMxa6uoqIDBYMDdu3fh5uaGrVu3ok+fPigrK7P7bACQnZ2No0ePori4uNWYGtYvLi4Oa9euRUREBK5evYqlS5di2LBhqKysVEW+s2fPIjMzEx999BEWLFiA4uJifPDBB9BqtTCZTKrbz2zbtg319fWYPHkyAHX8jqalpaGhoQGRkZFwdHRES0sLMjIyYDQaAdje3wo2O/T/WkpKCiorK3HgwAFrl9KmIiIiUFZWhps3b2Lz5s0wmUwoKCiwdllt4tKlS0hNTUVeXh46depk7XLaxaP/HQNAdHQ04uLiEBISgo0bN0Kv11uxsrZhNpsRGxuLzz//HADQv39/VFZW4vvvv4fJZLJydW1v9erVSExMRFBQkLVLaTMbN27E+vXr8dNPPyEqKgplZWWYPXs2goKCbHINeRirHfj4+MDR0bHVmfXXrl1DQECAlapqG4/q/6dsAQEBqK2ttRh/8OAB6urqbCr/zJkzsX37duzbtw/dunVTtgcEBODevXuor6+3nN2KWQAAB+1JREFUmP94xif9DB6NWZtWq0WvXr0wYMAALFu2DDExMVi5cqUqspWUlKC2thYvvvginJyc4OTkhIKCAnzzzTdwcnKCv7+/3Wd8nKenJ1544QWcOXNGFWsYGBiIPn36WGzr3bu3cqhOTfuZCxcuYM+ePXjvvfeUbWpYw7lz5yItLQ0TJkxAv379MGnSJHz44YdYtmwZANtbQzY77UCr1WLAgAHIz89XtpnNZuTn58NgMFixsucXFhaGgIAAi2wNDQ0oLCxUshkMBtTX16OkpESZs3fvXpjNZsTFxXV4zY8TEcycORNbt27F3r17ERYWZjE+YMAAODs7W2Ssrq7GxYsXLTJWVFRY/EPNy8uDu7t7q524LTCbzWhublZFtvj4eFRUVKCsrEx5xMbGwmg0Kh/be8bHNTU14Y8//kBgYKAq1nDIkCGt3u7h1KlTCAkJAaCO/cwjWVlZ8PPzw+jRo5VtaljD27dvw8HBsoVwdHSE2WwGYINr2KanO5MiOztbdDqdrF27Vk6cOCHTpk0TT09PizPrbVVjY6OUlpZKaWmpAJAVK1ZIaWmpXLhwQUQeXk7o6ekpP//8sxw7dkxef/31J15O2L9/fyksLJQDBw5IeHi4zVwSOn36dPHw8JD9+/dbXBp6+/ZtZU5ycrIEBwfL3r175ciRI2IwGMRgMCjjjy4LHT58uJSVlcmuXbvE19fXJi4LTUtLk4KCAjl37pwcO3ZM0tLSRKPRyK+//ioi9p3taf5+NZaI/WecM2eO7N+/X86dOycHDx6UhIQE8fHxkdraWhGx/3xFRUXi5OQkGRkZcvr0aVm/fr24uLjIunXrlDn2vp8ReXgVbnBwsMybN6/VmL2voclkkq5duyqXnm/ZskV8fHzkk08+UebY0hqy2WlH3377rQQHB4tWq5VBgwbJ4cOHrV3SM9m3b58AaPUwmUwi8vCSwkWLFom/v7/odDqJj4+X6upqi+f4888/ZeLEieLm5ibu7u4yZcoUaWxstEKa1p6UDYBkZWUpc+7cuSMzZswQLy8vcXFxkXHjxsnVq1ctnuf8+fOSmJgoer1efHx8ZM6cOXL//v0OTtPa1KlTJSQkRLRarfj6+kp8fLzS6IjYd7anebzZsfeM48ePl8DAQNFqtdK1a1cZP368xXvQ2Hs+EZFffvlF+vbtKzqdTiIjI2XVqlUW4/a+nxER2b17twBoVbeI/a9hQ0ODpKamSnBwsHTq1El69OghCxcutLgs3pbWUCPyt7c7JCIiIlIZnrNDREREqsZmh4iIiFSNzQ4RERGpGpsdIiIiUjU2O0RERKRqbHaIiIhI1djsEBERkaqx2SEiAqDRaLBt2zZrl0FE7YDNDhFZ3eTJk6HRaFo9Ro4cae3SiEgFnKxdABERAIwcORJZWVkW23Q6nZWqISI14Ss7RGQTdDodAgICLB5eXl4AHh5iyszMRGJiIvR6PXr06IHNmzdbfH1FRQVeffVV6PV6eHt7Y9q0aWhqarKYs2bNGkRFRUGn0yEwMBAzZ860GL9x4wbGjRsHFxcXhIeHIycnRxn766+/YDQa4evrC71ej/Dw8FbNGRHZJjY7RGQXFi1ahKSkJJSXl8NoNGLChAmoqqoCANy6dQsjRoyAl5cXiouLsWnTJuzZs8eimcnMzERKSgqmTZuGiooK5OTkoFevXhbfY+nSpXj77bdx7NgxjBo1CkajEXV1dcr3P3HiBHJzc1FVVYXMzEz4+Ph03A+AiP53bX5rUSKi/8hkMomjo6O4urpaPDIyMkTk4Z3qk5OTLb4mLi5Opk+fLiIiq1atEi8vL2lqalLGd+zYIQ4ODlJTUyMiIkFBQbJw4cKn1gBAPv30U+XzpqYmASC5ubkiIjJmzBiZMmVK2wQmog7Fc3aIyCa88soryMzMtNjWpUsX5WODwWAxZjAYUFZWBgCoqqpCTEwMXF1dlfEhQ4bAbDajuroaGo0GV65cQXx8/D/WEB0drXzs6uoKd3d31NbWAgCmT5+OpKQkHD16FMOHD8fYsWMxePDg/ykrEXUsNjtEZBNcXV1bHVZqK3q9/pnmOTs7W3yu0WhgNpsBAImJibhw4QJ27tyJvLw8xMfHIyUlBV9++WWb10tEbYvn7BCRXTh8+HCrz3v37g0A6N27N8rLy3Hr1i1l/ODBg3BwcEBERAQ6d+6M0NBQ5OfnP1cNvr6+MJlMWLduHb7++musWrXquZ6PiDoGX9khIpvQ3NyMmpoai21OTk7KScCbNm1CbGwshg4divXr16OoqAirV68GABiNRqSnp8NkMmHJkiW4fv06Zs2ahUmTJsHf3x8AsGTJEiQnJ8PPzw+JiYlobGzEwYMHMWvWrGeqb/HixRgwYACioqLQ3NyM7du3K80WEdk2NjtEZBN27dqFwMBAi20RERE4efIkgIdXSmVnZ2PGjBkIDAzEhg0b0KdPHwCAi4sLdu/ejdTUVAwcOBAuLi5ISkrCihUrlOcymUy4e/cuvvrqK3z88cfw8fHBm2+++cz1abVazJ8/H+fPn4der8ewYcOQnZ3dBsmJqL1pRESsXQQR0T/RaDTYunUrxo4da+1SiMgO8ZwdIiIiUjU2O0RERKRqPGeHiGwej7YT0fPgKztERESkamx2iIiISNXY7BAREZGqsdkhIiIiVWOzQ0RERKrGZoeIiIhUjc0OERERqRqbHSIiIlI1NjtERESkav8HjSRTv4Y4tMMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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=\"Schnet\", data_unit=\"kcal\", dataset_name=\"qm7\",\n",
    "                     filepath=\"\", file_name=f\"loss.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44d1e15a",
   "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
}