class_docstr: The MalNet Tiny dataset from the
  `"A Large-Scale Database for Graph Representation Learning"
  <https://openreview.net/pdf?id=1xDTDk3XPW>`_ paper.
  :class:`MalNetTiny` contains 5,000 malicious and benign software function
  call graphs across 5 different types. Each graph contains at most 5k nodes.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      split (str, optional): If :obj:`"train"`, loads the training dataset.
          If :obj:`"val"`, loads the validation dataset.
          If :obj:`"trainval"`, loads the training and validation dataset.
          If :obj:`"test"`, loads the test dataset.
          If :obj:`None`, loads the entire dataset.
          (default: :obj:`None`)
      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`)
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code: def download(self) -> None:
      path = download_url(self.data_url, self.raw_dir)
      extract_tar(path, self.raw_dir)
      os.unlink(path)
  
      path = download_url(self.split_url, self.raw_dir)
      extract_zip(path, self.raw_dir)
      os.unlink(path)
  
file_path: pytorch_geometric/torch_geometric/datasets/malnet_tiny.py
imports: import os
  import os.path
  from typing import Callable, Dict, List, Optional
  import torch
  from torch_geometric.data import Data, InMemoryDataset, download_url, extract_tar, extract_zip
label_desc: Use image node_id 4 for calling the MalNetTiny obj's underlying member method code with example usage: obj.download() without return types