class_docstr: The GED datasets from the `"Graph Edit Distance Computation via Graph
  Neural Networks" <https://arxiv.org/abs/1808.05689>`_ paper.
  
  GEDs can be accessed via the global attributes :obj:`ged` and
  :obj:`norm_ged` for all train/train graph pairs and all train/test graph
  pairs:
  
  .. code-block:: python
  
      dataset = GEDDataset(root, name="LINUX")
      data1, data2 = dataset[0], dataset[1]
      ged = dataset.ged[data1.i, data2.i]  # GED between `data1` and `data2`.
  
  Note that GEDs are not available if both graphs are from the test set.
  For evaluation, it is recommended to pair up each graph from the test set
  with each graph in the training set.
  
  .. note::
  
      :obj:`ALKANE` is missing GEDs for train/test graph pairs since they are
      not provided in the `official datasets
      <https://github.com/yunshengb/SimGNN>`_.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The name of the dataset (one of :obj:`"AIDS700nef"`,
          :obj:`"LINUX"`, :obj:`"ALKANE"`, :obj:`"IMDBMulti"`).
      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: 20 10 10 10 10 10
      :header-rows: 1
  
      * - Name
        - #graphs
        - #nodes
        - #edges
        - #features
        - #classes
      * - AIDS700nef
        - 700
        - ~8.9
        - ~17.6
        - 29
        - 0
      * - LINUX
        - 1,000
        - ~7.6
        - ~13.9
        - 0
        - 0
      * - ALKANE
        - 150
        - ~8.9
        - ~15.8
        - 0
        - 0
      * - IMDBMulti
        - 1,500
        - ~13.0
        - ~131.9
        - 0
        - 0
code: def processed_file_names(self) -> List[str]:
      # Returns, e.g., ['LINUX_training.pt', 'LINUX_test.pt']
      return [f"{self.name}_{s}.pt" for s in ["training", "test"]]
  
file_path: pytorch_geometric/torch_geometric/datasets/ged_dataset.py
imports: import glob
  import os
  import os.path
  import pickle
  from typing import Callable, List, Optional
  import torch
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_tar, extract_zip
  from torch_geometric.utils import one_hot, to_undirected
label_desc: Use image node_id 3 for calling the GEDDataset obj's underlying member method code with example usage: obj.processed_file_names() and returns: unknown