class_docstr: A variety of graph datasets collected from `SNAP at Stanford University
  <https://snap.stanford.edu/data>`_.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      name (str): The name 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`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
code: def process(self) -> None:
      raw_dir = self.raw_dir
      filenames = fs.ls(self.raw_dir)
      if len(filenames) == 1 and fs.isdir(filenames[0]):
          raw_dir = filenames[0]
  
      raw_files = fs.ls(raw_dir)
  
      data_list: Union[List[Data], List[EgoData]]
      if self.name[:4] == "ego-":
          data_list = read_ego(raw_files, self.name[4:])
      elif self.name[:4] == "soc-":
          data_list = read_soc(raw_files, self.name[:4])
      elif self.name[:5] == "wiki-":
          data_list = read_wiki(raw_files, self.name[5:])
      else:
          raise NotImplementedError
  
      if len(data_list) > 1 and self.pre_filter is not None:
          data_list = [
              data for data in data_list if self.pre_filter(data)
          ]
  
      if self.pre_transform is not None:
          data_list = [self.pre_transform(data) for data in data_list]
  
      self.save(data_list, self.processed_paths[0])
  
imports: import os
  import os.path
  from typing import Any, Callable, Dict, List, Optional, Union
  import fsspec
  import numpy
  import torch
  from torch_geometric.data import Data, InMemoryDataset
  from torch_geometric.io import fs
  from torch_geometric.utils import coalesce
label_desc: Use image node_id 7 for calling the SNAPDataset obj's underlying member method code with example usage: obj.process() without return types