class_docstr: The MovieLens 100K heterogeneous rating dataset, assembled by GroupLens
  Research from the `MovieLens web site <https://movielens.org>`__,
  consisting of movies (1,682 nodes) and users (943 nodes) with 100K
  ratings between them.
  User ratings for movies are available as ground truth labels.
  Features of users and movies are encoded according to the `"Inductive
  Matrix Completion Based on Graph Neural Networks"
  <https://arxiv.org/abs/1904.12058>`__ paper.
  
  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`)
  
  **STATS:**
  
  .. list-table::
      :widths: 20 10 10 10
      :header-rows: 1
  
      * - Node/Edge Type
        - #nodes/#edges
        - #features
        - #tasks
      * - Movie
        - 1,682
        - 18
        -
      * - User
        - 943
        - 24
        -
      * - User-Movie
        - 80,000
        - 1
        - 1
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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/movie_lens_100k.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
label_desc: Use image node_id 1 to create a new MovieLens100K object from inherited base classes: InMemoryDataset with example: obj = MovieLens100K(root, transform, pre_transform, force_reload)