class_docstr: An extended TUDataset from `Pytorch Geometric 
  <https://pytorch-geometric.readthedocs.io/en/latest/index.html>`_, including
  a variety of graph kernel benchmark datasets, *e.g.* "IMDB-BINARY", 
  "REDDIT-BINARY" or "PROTEINS".
  
  .. note::
      Some datasets may not come with any node labels.
      You can then either make use of the argument :obj:`use_node_attr`
      to load additional continuous node attributes (if present) or provide
      synthetic node features using transforms such as
      like :class:`torch_geometric.transforms.Constant` or
      :class:`torch_geometric.transforms.OneHotDegree`.
  
  Args:
      root (string): Root directory where the dataset should be saved.
      name (string): The `name
          <https://chrsmrrs.github.io/datasets/docs/datasets/>`_ of the
          dataset.
      task (string): The evaluation task. Either 'semisupervised' or
          'unsupervised'.
      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`)
      use_node_attr (bool, optional): If :obj:`True`, the dataset will
          contain additional continuous node attributes (if present).
          (default: :obj:`False`)
      use_edge_attr (bool, optional): If :obj:`True`, the dataset will
          contain additional continuous edge attributes (if present).
          (default: :obj:`False`)
      cleaned (bool, optional): If :obj:`True`, the dataset will
          contain only non-isomorphic graphs. (default: :obj:`False`)
      processed_filename (string, optional): The name of the processed data file.
          (default: obj: `data.pt`)
code: def process(self):
      self.data, self.slices = read_tu_data(self.raw_dir, self.name)
  
      if self.pre_filter is not None:
          data_list = [self.get(idx) for idx in range(len(self))]
          data_list = [
              data for data in data_list if self.pre_filter(data)
          ]
          self.data, self.slices = self.collate(data_list)
  
      if self.pre_transform is not None:
          data_list = [self.get(idx) for idx in range(len(self))]
          data_list = [self.pre_transform(data) for data in data_list]
          self.data, self.slices = self.collate(data_list)
  
      torch.save((self.data, self.slices), self.processed_paths[0])
  
imports: import os.path
  from itertools import repeat
  import os, shutil, torch
  import numpy
  from torch_geometric.data import InMemoryDataset, download_url, extract_zip
  from torch_geometric.io import read_tu_data
label_desc: Use image node_id 11 for calling the TUDatasetExt obj's underlying member method code with example usage: obj.process() without return types