class_docstr: `Describable Textures Dataset (DTD) <https://www.robots.ox.ac.uk/~vgg/data/dtd/>`_.
  
  Args:
      root (string): Root directory of the dataset.
      split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``.
      partition (int, optional): The dataset partition. Should be ``1 <= partition <= 10``. Defaults to ``1``.
  
          .. note::
  
              The partition only changes which split each image belongs to. Thus, regardless of the selected
              partition, combining all splits will result in all images.
  
      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/dtd.py
imports: import os
  import pathlib
  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 DTD obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: image,  label