class_docstr: Generates the grid-structured motif from the
  `"GNNExplainer: Generating Explanations for Graph Neural Networks"
  <https://arxiv.org/abs/1903.03894>`__ paper.
class_docstr_tok: ["Generates","the","grid-structured","motif","from","the","`","``","GNNExplainer",":","Generating","Explanations","for","Graph","Neural","Networks","''","<","https",":","\/\/arxiv.org\/abs\/1903.03894",">","`","__","paper","."]
code: def __init__(self) -> None:
      edge_indices = [
          [0, 1],
          [0, 3],
          [1, 4],
          [3, 4],
          [1, 2],
          [2, 5],
          [4, 5],
          [3, 6],
          [6, 7],
          [4, 7],
          [5, 8],
          [7, 8],
          [1, 0],
          [3, 0],
          [4, 1],
          [4, 3],
          [2, 1],
          [5, 2],
          [5, 4],
          [6, 3],
          [7, 6],
          [7, 4],
          [8, 5],
          [8, 7],
      ]
      structure = Data(
          num_nodes=9,
          edge_index=torch.tensor(edge_indices).t().contiguous(),
          y=torch.tensor([0, 1, 0, 1, 2, 1, 0, 1, 0]),
      )
      super().__init__(structure)
  
imports: import torch
  from torch_geometric.data import Data
  from torch_geometric.datasets.motif_generator import CustomMotif
label_desc: Use image node_id 1 to create a new GridMotif object from inherited base classes: CustomMotif with example: obj = GridMotif()