class_docstr: `German Traffic Sign Recognition Benchmark (GTSRB) <https://benchmark.ini.rub.de/>`_ Dataset.
  
  Args:
      root (string): Root directory of the dataset.
      split (string, optional): The dataset split, supports ``"train"`` (default), or ``"test"``.
      transform (callable, optional): A function/transform that takes in a PIL image and returns a transformed
          version. E.g, ``transforms.RandomCrop``.
      target_transform (callable, optional): A function/transform that takes in the target and transforms it.
      download (bool, optional): If True, downloads the dataset from the internet and
          puts it in root directory. If dataset is already downloaded, it is not
          downloaded again.
code: def download(self) -> None:
      if self._check_exists():
          return
  
      base_url = "https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/"
  
      if self._split == "train":
          download_and_extract_archive(
              f"{base_url}GTSRB-Training_fixed.zip",
              download_root=str(self._base_folder),
              md5="513f3c79a4c5141765e10e952eaa2478",
          )
      else:
          download_and_extract_archive(
              f"{base_url}GTSRB_Final_Test_Images.zip",
              download_root=str(self._base_folder),
              md5="c7e4e6327067d32654124b0fe9e82185",
          )
          download_and_extract_archive(
              f"{base_url}GTSRB_Final_Test_GT.zip",
              download_root=str(self._base_folder),
              md5="fe31e9c9270bbcd7b84b7f21a9d9d9e5",
          )
  
file_path: vision/torchvision/datasets/gtsrb.py
imports: import csv
  import pathlib
  from typing import Any, Callable, Optional, Tuple
  import PIL
  from .folder import make_dataset
  from .utils import download_and_extract_archive, verify_str_arg
  from .vision import VisionDataset
label_desc: Use image node_id 5 for calling the GTSRB obj's underlying member method code with example usage: obj.download() without return types