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 _prepare_sample(
      self,
      data: Tuple[
          Tuple[str, BinaryIO], Tuple[int, int, int, int, int, str]
      ],
  ) -> Dict[str, Any]:
      image, target = data
      path, buffer = image
      image = EncodedImage.from_file(buffer)
  
      return dict(
          path=path,
          image=image,
          label=Label(target[4] - 1, categories=self._categories),
          bounding_boxes=BoundingBoxes(
              target[:4], format="xyxy", spatial_size=image.spatial_size
          ),
      )
  
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 3 for calling the StanfordCars obj's underlying member method code with example usage: obj._prepare_sample(data) and returns: dict