class_docstr: The synthetic BA-Multi-Shapes graph classification dataset for
  evaluating explainabilty algorithms, as described in the
  `"Global Explainability of GNNs via Logic Combination of Learned Concepts"
  <https://arxiv.org/abs/2210.07147>`_ paper.
  
  Given three atomic motifs, namely House (H), Wheel (W), and Grid (G),
  :class:`~torch_geometric.datasets.BAMultiShapesDataset` contains 1,000
  graphs where each graph is obtained by attaching the motifs to a random
  Barabasi-Albert (BA) as follows:
  
  * class 0: :math:`\emptyset \lor H \lor W \lor G \lor \{ H, W, G \}`
  
  * class 1: :math:`(H \land W) \lor (H \land G) \lor (W \land G)`
  
  This dataset is pre-computed from the official implementation.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` object and returns a transformed
          version. The data object will be transformed before every access.
          (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      pre_filter (callable, optional): A function that takes in an
          :obj:`torch_geometric.data.Data` object and returns a boolean
          value, indicating whether the data object should be included in the
          final dataset. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
  
  **STATS:**
  
  .. list-table::
      :widths: 10 10 10 10 10
      :header-rows: 1
  
      * - #graphs
        - #nodes
        - #edges
        - #features
        - #classes
      * - 1000
        - 40
        - ~87.0
        - 10
        - 2
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code: def download(self) -> None:
      download_url(self.url, self.raw_dir)
  
file_path: pytorch_geometric/torch_geometric/datasets/ba_multi_shapes.py
imports: import pickle
  from typing import Callable, List, Optional
  import numpy
  import torch
  from torch_geometric.data import Data, InMemoryDataset, download_url
label_desc: Use image node_id 4 for calling the BAMultiShapesDataset obj's underlying member method code with example usage: obj.download() without return types