class_docstr: Stanford Cars dataset.
  homepage="https://ai.stanford.edu/~jkrause/cars/car_dataset.html",
  dependencies=scipy
class_docstr_tok: ["Stanford","Cars","dataset",".","homepage=","''","https",":","\/\/ai.stanford.edu\/~jkrause\/cars\/car_dataset.html","''",",","dependencies=scipy"]
code: def __init__(
      self,
      root: Union[str, pathlib.Path],
      *,
      split: str = "train",
      skip_integrity_check: bool = False,
  ) -> None:
      self._split = self._verify_str_arg(
          split, "split", {"train", "test"}
      )
      self._categories = _info()["categories"]
      super().__init__(
          root,
          skip_integrity_check=skip_integrity_check,
          dependencies=("scipy",),
      )
  
imports: import pathlib
  from typing import Any, BinaryIO, Dict, Iterator, List, Tuple, Union
  from torchdata.datapipes.iter import Filter, IterDataPipe, Mapper, Zipper
  from torchvision.prototype.datasets.utils import Dataset, EncodedImage, HttpResource, OnlineResource
  from torchvision.prototype.datasets.utils._internal import hint_sharding, hint_shuffling, path_comparator, read_categories_file, read_mat
  from torchvision.prototype.tv_tensors import Label
  from torchvision.tv_tensors import BoundingBoxes
  from .._api import register_dataset, register_info
label_desc: Use image node_id 1 to create a new StanfordCars object from inherited base classes: Dataset with example: obj = StanfordCars(root)