class_docstr: The risk commodity detection dataset (RCDD) from the
  `"Datasets and Interfaces for Benchmarking Heterogeneous Graph
  Neural Networks" <http://shichuan.org/doc/156.pdf>`_ paper.
  RCDD is an industrial-scale heterogeneous graph dataset based on a
  real risk detection scenario from Alibaba's e-commerce platform.
  It consists of 13,806,619 nodes and 157,814,864 edges across 7 node types
  and 7 edge types, respectively.
  
  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.HeteroData` 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.HeteroData` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
code: def download(self) -> None:
      path = download_url(self.url, self.raw_dir)
      extract_zip(path, self.raw_dir)
      os.unlink(path)
  
file_path: pytorch_geometric/torch_geometric/datasets/rcdd.py
imports: import os
  from typing import Callable, List, Optional
  import numpy
  import torch
  from torch_geometric.data import HeteroData, InMemoryDataset, download_url, extract_zip
  from torch_geometric.utils import index_to_mask
label_desc: Use image node_id 4 for calling the RCDD obj's underlying member method code with example usage: obj.download() without return types