class_docstr: The heterogeneous AMiner dataset from the `"metapath2vec: Scalable
  Representation Learning for Heterogeneous Networks"
  <https://ericdongyx.github.io/papers/
  KDD17-dong-chawla-swami-metapath2vec.pdf>`_ paper, consisting of nodes from
  type :obj:`"paper"`, :obj:`"author"` and :obj:`"venue"`.
  Venue categories and author research interests are available as ground
  truth labels for a subset of nodes.
  
  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:
      fs.rm(self.raw_dir)
      path = download_url(self.url, self.root)
      extract_zip(path, self.root)
      os.rename(osp.join(self.root, "net_aminer"), self.raw_dir)
      os.unlink(path)
      path = download_url(self.y_url, self.raw_dir)
      extract_zip(path, self.raw_dir)
      os.unlink(path)
  
file_path: pytorch_geometric/torch_geometric/datasets/aminer.py
imports: import os
  import os.path
  from typing import Callable, List, Optional
  import torch
  from torch_geometric.data import HeteroData, InMemoryDataset, download_url, extract_zip
  from torch_geometric.io import fs
  from torch_geometric.utils import coalesce
label_desc: Use image node_id 4 for calling the AMiner obj's underlying member method code with example usage: obj.download() without return types