class_docstr: `Stanford Cars <https://ai.stanford.edu/~jkrause/cars/car_dataset.html>`_ Dataset
  
  The Cars dataset contains 16,185 images of 196 classes of cars. The data is
  split into 8,144 training images and 8,041 testing images, where each class
  has been split roughly in a 50-50 split
  
  .. note::
  
      This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.
  
  Args:
      root (string): Root directory of 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 _check_exists(self) -> bool:
      if not (self._base_folder / "devkit").is_dir():
          return False
  
      return (
          self._annotations_mat_path.exists()
          and self._images_base_path.is_dir()
      )
  
file_path: vision/torchvision/datasets/stanford_cars.py
imports: import pathlib
  from typing import Any, Callable, Optional, Tuple
  from PIL import Image
  from .utils import download_and_extract_archive, download_url, verify_str_arg
  from .vision import VisionDataset
label_desc: Use image node_id 5 for calling the StanfordCars obj's underlying member method code with example usage: obj._check_exists() and returns: unknown, False