class_docstr: `STL10 <https://cs.stanford.edu/~acoates/stl10/>`_ Dataset.
  
  Args:
      root (string): Root directory of dataset where directory
          ``stl10_binary`` exists.
      split (string): One of {'train', 'test', 'unlabeled', 'train+unlabeled'}.
          Accordingly, dataset is selected.
      folds (int, optional): One of {0-9} or None.
          For training, loads one of the 10 pre-defined folds of 1k samples for the
          standard evaluation procedure. If no value is passed, loads the 5k samples.
      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.
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code: def __load_folds(self, folds: Optional[int]) -> None:
      # loads one of the folds if specified
      if folds is None:
          return
      path_to_folds = os.path.join(
          self.root, self.base_folder, self.folds_list_file
      )
      with open(path_to_folds) as f:
          str_idx = f.read().splitlines()[folds]
          list_idx = np.fromstring(str_idx, dtype=np.int64, sep=" ")
          self.data = self.data[list_idx, :, :, :]
          if self.labels is not None:
              self.labels = self.labels[list_idx]
  
file_path: vision/torchvision/datasets/stl10.py
imports: import os.path
  from typing import Any, Callable, cast, Optional, Tuple
  import numpy
  from PIL import Image
  from .utils import check_integrity, download_and_extract_archive, verify_str_arg
  from .vision import VisionDataset
label_desc: Use image node_id 9 for calling the STL10 obj's underlying member method code with example usage: obj.__load_folds(folds) without return types