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 __init__(
      self,
      root: str,
      transform: Optional[Callable] = None,
      pre_transform: Optional[Callable] = None,
      force_reload: bool = False,
  ) -> None:
      super().__init__(
          root, transform, pre_transform, force_reload=force_reload
      )
      self.load(self.processed_paths[0], data_cls=HeteroData)
  
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 1 to create a new AMiner object from inherited base classes: InMemoryDataset with example: obj = AMiner(root, transform, pre_transform, force_reload)