text: import os
  import os.path as osp
  import shutil
  from tqdm import tqdm
  
  import torch
  from torch_geometric.data import InMemoryDataset, download_url, extract_zip
  from torch_geometric.io import read_tu_data
  
  
  class TUDataset(InMemoryDataset):
      r"""A variety of graph kernel benchmark datasets, *.e.g.* "IMDB-BINARY",
      "REDDIT-BINARY" or "PROTEINS", collected from the `TU Dortmund University
      <https://chrsmrrs.github.io/datasets>`_.
      In addition, this dataset wrapper provides `cleaned dataset versions
      <https://github.com/nd7141/graph_datasets>`_ as motivated by the
      `"Understanding Isomorphism Bias in Graph Data Sets"
      <https://arxiv.org/abs/1910.12091>`_ paper, containing only non-isomorphic
      graphs.
  
      .. 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.
          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`)
      """
  
      url = 'https://www.chrsmrrs.com/graphkerneldatasets'
      cleaned_url = ('https://raw.githubusercontent.com/nd7141/'
                     'graph_datasets/master/datasets')
  
      def __init__(self, root, name, transform=None, pre_transform=None,
                   pre_filter=None, use_node_attr=False, use_edge_attr=False,
                   cleaned=False):
          self.name = name
          self.cleaned = cleaned
          super(TUDataset, self).__init__(root, transform, pre_transform,
                                          pre_filter)
          self.data, self.slices = torch.load(self.processed_paths[0])
          if self.data.x is not None and not use_node_attr:
              num_node_attributes = self.num_node_attributes
              self.data.x = self.data.x[:, num_node_attributes:]
          if self.data.edge_attr is not None and not use_edge_attr:
              num_edge_attributes = self.num_edge_attributes
              self.data.edge_attr = self.data.edge_attr[:, num_edge_attributes:]
  
      @property
      def raw_dir(self):
          name = 'raw{}'.format('_cleaned' if self.cleaned else '')
          return osp.join(self.root, self.name, name)
  
      @property
      def processed_dir(self):
          name = 'processed{}'.format('_cleaned' if self.cleaned else '')
          return osp.join(self.root, self.name, name)
  
      @property
      def num_node_labels(self):
          if self.data.x is None:
              return 0
          for i in range(self.data.x.size(1)):
              x = self.data.x[:, i:]
              if ((x == 0) | (x == 1)).all() and (x.sum(dim=1) == 1).all():
                  return self.data.x.size(1) - i
          return 0
  
      @property
      def num_node_attributes(self):
          if self.data.x is None:
              return 0
          return self.data.x.size(1) - self.num_node_labels
  
      @property
      def num_edge_labels(self):
          if self.data.edge_attr is None:
              return 0
          for i in range(self.data.edge_attr.size(1)):
              if self.data.edge_attr[:, i:].sum() == self.data.edge_attr.size(0):
                  return self.data.edge_attr.size(1) - i
          return 0
  
      @property
      def num_edge_attributes(self):
          if self.data.edge_attr is None:
              return 0
          return self.data.edge_attr.size(1) - self.num_edge_labels
  
      @property
      def raw_file_names(self):
          names = ['A', 'graph_indicator']
          return ['{}_{}.txt'.format(self.name, name) for name in names]
  
      @property
      def processed_file_names(self):
          return 'data.pt'
  
      def download(self):
          url = self.cleaned_url if self.cleaned else self.url
          folder = osp.join(self.root, self.name)
          path = download_url('{}/{}.zip'.format(url, self.name), folder)
          extract_zip(path, folder)
          os.unlink(path)
          shutil.rmtree(self.raw_dir)
          os.rename(osp.join(folder, self.name), self.raw_dir)
  
      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]
              new_data_list = []
              for data in tqdm(data_list):
                  new_data_list.append(self.pre_transform(data))
              data_list = new_data_list
              self.data, self.slices = self.collate(data_list)
  
          torch.save((self.data, self.slices), self.processed_paths[0])
  
      def __repr__(self):
          return '{}({})'.format(self.name, len(self))
  