class_docstr: The user-item heterogeneous rating datasets :obj:`"Douban"`,
  :obj:`"Flixster"` and :obj:`"Yahoo-Music"` from the `"Inductive Matrix
  Completion Based on Graph Neural Networks"
  <https://arxiv.org/abs/1904.12058>`_ paper.
  
  Nodes represent users and items.
  Edges and features between users and items represent a (training) rating of
  the item given by the user.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The name of the dataset (:obj:`"Douban"`,
          :obj:`"Flixster"`, :obj:`"Yahoo-Music"`).
      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,
      name: str,
      transform: Optional[Callable] = None,
      pre_transform: Optional[Callable] = None,
      force_reload: bool = False,
  ) -> None:
      self.name = name.lower().replace("-", "_")
      assert self.name in ["flixster", "douban", "yahoo_music"]
  
      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/igmc_dataset.py
imports: import os.path
  from typing import Callable, Optional
  import torch
  from torch import Tensor
  from torch_geometric.data import HeteroData, InMemoryDataset, download_url
label_desc: Use image node_id 1 to create a new IGMCDataset object from inherited base classes: InMemoryDataset with example: obj = IGMCDataset(root, name, transform, pre_transform, force_reload)