class_docstr: The Amazon Computers and Amazon Photo networks from the
  `"Pitfalls of Graph Neural Network Evaluation"
  <https://arxiv.org/abs/1811.05868>`_ paper.
  Nodes represent goods and edges represent that two goods are frequently
  bought together.
  Given product reviews as bag-of-words node features, the task is to
  map goods to their respective product category.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The name of the dataset (:obj:`"Computers"`,
          :obj:`"Photo"`).
      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`)
      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
  
      * - Name
        - #nodes
        - #edges
        - #features
        - #classes
      * - Computers
        - 13,752
        - 491,722
        - 767
        - 10
      * - Photo
        - 7,650
        - 238,162
        - 745
        - 8
code: def download(self) -> None:
      download_url(self.url + self.raw_file_names, self.raw_dir)
  
file_path: pytorch_geometric/torch_geometric/datasets/amazon.py
imports: import os.path
  from typing import Callable, Optional
  from torch_geometric.data import InMemoryDataset, download_url
  from torch_geometric.io import read_npz
label_desc: Use image node_id 6 for calling the Amazon obj's underlying member method code with example usage: obj.download() without return types