text: import torch
  from torch_geometric.data import InMemoryDataset, download_url
  from torch_geometric.read import read_npz
  
  
  class CoraFull(InMemoryDataset):
      r"""The full Cora citation network dataset from the
      `"Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via
      Ranking" <https://arxiv.org/abs/1707.03815>`_ paper.
      Nodes represent documents and edges represent citation links.
  
      Args:
          root (string): Root directory where the dataset should be saved.
          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`)
      """
  
      url = 'https://github.com/abojchevski/graph2gauss/raw/master/data/cora.npz'
  
      def __init__(self, root, transform=None, pre_transform=None):
          super(CoraFull, self).__init__(root, transform, pre_transform)
          self.data, self.slices = torch.load(self.processed_paths[0])
  
      @property
      def raw_file_names(self):
          return 'cora.npz'
  
      @property
      def processed_file_names(self):
          return 'data.pt'
  
      def download(self):
          download_url(self.url, self.raw_dir)
  
      def process(self):
          data = read_npz(self.raw_paths[0])
          data = data if self.pre_transform is None else self.pre_transform(data)
          data, slices = self.collate([data])
          torch.save((data, slices), self.processed_paths[0])
  
      def __repr__(self):
          return '{}()'.format(self.__class__.__name__)
  