class_docstr: `The Food-101 Data Set <https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/>`_.
  
  The Food-101 is a challenging data set of 101 food categories with 101,000 images.
  For each class, 250 manually reviewed test images are provided as well as 750 training images.
  On purpose, the training images were not cleaned, and thus still contain some amount of noise.
  This comes mostly in the form of intense colors and sometimes wrong labels. All images were
  rescaled to have a maximum side length of 512 pixels.
  
  
  Args:
      root (string): Root directory of the dataset.
      split (string, optional): The dataset split, supports ``"train"`` (default) and ``"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. Default is False.
code: def __getitem__(self, idx: int) -> Tuple[Any, Any]:
      image_file, label = self._image_files[idx], self._labels[idx]
      image = PIL.Image.open(image_file).convert("RGB")
  
      if self.transform:
          image = self.transform(image)
  
      if self.target_transform:
          label = self.target_transform(label)
  
      return image, label
  
file_path: vision/torchvision/datasets/food101.py
imports: import json
  from pathlib import Path
  from typing import Any, Callable, Optional, Tuple
  import PIL.Image
  from .utils import download_and_extract_archive, verify_str_arg
  from .vision import VisionDataset
label_desc: Use image node_id 3 for calling the Food101 obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: image,  label