class_docstr: Generates the house-structured motif from the `"GNNExplainer:
  Generating Explanations for Graph Neural Networks"
  <https://arxiv.org/abs/1903.03894>`__ paper, containing 5 nodes and 6
  undirected edges. Nodes are labeled according to their structural role:
  the top, middle and bottom of the house.
class_docstr_tok: ["Generates","the","house-structured","motif","from","the","`","``","GNNExplainer",":","Generating","Explanations","for","Graph","Neural","Networks","''","<","https",":","\/\/arxiv.org\/abs\/1903.03894",">","`","__","paper",",","containing","5","nodes","and","6","undirected","edges",".","Nodes","are","labeled","according","to","their","structural","role",":","the","top",",","middle","and","bottom","of","the","house","."]
code: def __init__(self) -> None:
      structure = Data(
          num_nodes=5,
          edge_index=torch.tensor(
              [
                  [0, 0, 0, 1, 1, 1, 2, 2, 3, 3, 4, 4],
                  [1, 3, 4, 4, 2, 0, 1, 3, 2, 0, 0, 1],
              ]
          ),
          y=torch.tensor([0, 0, 1, 1, 2]),
      )
      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 HouseMotif object from inherited base classes: CustomMotif with example: obj = HouseMotif()