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 __repr__(self) -> str:
      return f"{self.__class__.__name__}(name={self.name})"
  
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 9 for calling the IGMCDataset obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str