class_docstr: `Oxford-IIIT Pet Dataset   <https://www.robots.ox.ac.uk/~vgg/data/pets/>`_.
  
  Args:
      root (string): Root directory of the dataset.
      split (string, optional): The dataset split, supports ``"trainval"`` (default) or ``"test"``.
      target_types (string, sequence of strings, optional): Types of target to use. Can be ``category`` (default) or
          ``segmentation``. Can also be a list to output a tuple with all specified target types. The types represent:
  
              - ``category`` (int): Label for one of the 37 pet categories.
              - ``segmentation`` (PIL image): Segmentation trimap of the image.
  
          If empty, ``None`` will be returned as target.
  
      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/oxford-iiit-pet``. If dataset is already downloaded, it is not downloaded again.
code: def __len__(self) -> int:
      return len(self._images)
  
file_path: vision/torchvision/datasets/oxford_iiit_pet.py
imports: import os
  import os.path
  import pathlib
  from typing import Any, Callable, Optional, Sequence, Tuple, Union
  from PIL import Image
  from .utils import download_and_extract_archive, verify_str_arg
  from .vision import VisionDataset
label_desc: Use image node_id 2 for calling the OxfordIIITPet obj's underlying member method code with example usage: obj.__len__() and returns: len