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 __len__(self) -> int:
      images_file = self._FILES[self._split]["images"][0]
      with self.h5py.File(
          self._base_folder / images_file
      ) as images_data:
          return images_data["x"].shape[0]
  
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 2 for calling the PCAM obj's underlying member method code with example usage: obj.__len__() and returns: images_data