class_docstr: MNIST superpixels dataset from the `"Geometric Deep Learning on
  Graphs and Manifolds Using Mixture Model CNNs"
  <https://arxiv.org/abs/1611.08402>`_ paper, containing 70,000 graphs with
  75 nodes each.
  Every graph is labeled by one of 10 classes.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      train (bool, optional): If :obj:`True`, loads the training dataset,
          otherwise the test dataset. (default: :obj:`True`)
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.Data` 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.Data` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      pre_filter (callable, optional): A function that takes in an
          :obj:`torch_geometric.data.Data` object and returns a boolean
          value, indicating whether the data object should be included in the
          final dataset. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
  
  **STATS:**
  
  .. list-table::
      :widths: 10 10 10 10 10
      :header-rows: 1
  
      * - #graphs
        - #nodes
        - #edges
        - #features
        - #classes
      * - 70,000
        - 75
        - ~1,393.0
        - 1
        - 10
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code: def processed_file_names(self) -> List[str]:
      return ["train_data.pt", "test_data.pt"]
  
file_path: pytorch_geometric/torch_geometric/datasets/mnist_superpixels.py
imports: import os
  from typing import Callable, List, Optional
  import torch
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_zip
label_desc: Use image node_id 3 for calling the MNISTSuperpixels obj's underlying member method code with example usage: obj.processed_file_names() and returns: list