class_docstr: `PCAM Dataset   <https://github.com/basveeling/pcam>`_.
  
  The PatchCamelyon dataset is a binary classification dataset with 327,680
  color images (96px x 96px), extracted from histopathologic scans of lymph node
  sections. Each image is annotated with a binary label indicating presence of
  metastatic tissue.
  
  This dataset requires the ``h5py`` package which you can install with ``pip install h5py``.
  
  Args:
       root (string): Root directory of the dataset.
       split (string, optional): The dataset split, supports ``"train"`` (default), ``"test"`` or ``"val"``.
       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 into ``root/pcam``. If
           dataset is already downloaded, it is not downloaded again.
code: def _download(self) -> None:
      if self._check_exists():
          return
  
      for file_name, file_id, md5 in self._FILES[self._split].values():
          archive_name = file_name + ".gz"
          download_file_from_google_drive(
              file_id,
              str(self._base_folder),
              filename=archive_name,
              md5=md5,
          )
          _decompress(str(self._base_folder / archive_name))
  
file_path: vision/torchvision/datasets/pcam.py
imports: import pathlib
  from typing import Any, Callable, Optional, Tuple
  from PIL import Image
  from .utils import _decompress, download_file_from_google_drive, verify_str_arg
  from .vision import VisionDataset
label_desc: Use image node_id 5 for calling the PCAM obj's underlying member method code with example usage: obj._download() without return types