class_docstr: A generic data loader where the samples are arranged in this way: ::
      root/class_x/xxx.ext
      root/class_x/xxy.ext
      root/class_x/[...]/xxz.ext
      root/class_y/123.ext
      root/class_y/nsdf3.ext
      root/class_y/[...]/asd932_.ext
  Args:
      root (string): Root directory path.
      loader (callable): A function to load a sample given its path.
      extensions (tuple[string]): A list of allowed extensions.
          both extensions and is_valid_file should not be passed.
      transform (callable, optional): A function/transform that takes in
          a sample and returns a transformed version.
          E.g, ``transforms.RandomCrop`` for images.
      target_transform (callable, optional): A function/transform that takes
          in the target and transforms it.
      is_valid_file (callable, optional): A function that takes path of a file
          and check if the file is a valid file (used to check of corrupt files)
          both extensions and is_valid_file should not be passed.
   Attributes:
      classes (list): List of the class names sorted alphabetically.
      class_to_idx (dict): Dict with items (class_name, class_index).
      samples (list): List of (sample path, class_index) tuples
      targets (list): The class_index value for each image in the dataset
code: def make_dataset(
      directory: str,
      class_to_idx: Dict[str, int],
      data_per_class_fraction: float,
      extensions: Optional[Tuple[str, ...]] = None,
      is_valid_file: Optional[Callable[[str], bool]] = None,
  ) -> List[Tuple[str, int]]:
      return make_dataset(
          directory,
          class_to_idx,
          data_per_class_fraction,
          extensions=extensions,
          is_valid_file=is_valid_file,
      )
  
imports: from torchvision.datasets import VisionDataset
  from PIL import Image
  import os
  import os.path
  from typing import Any, Callable, cast, Dict, List, Optional, Tuple
  import numpy
label_desc: Use image node_id 2 for calling the DatasetFolder obj's underlying member method code with example usage: obj.make_dataset(directory, class_to_idx, data_per_class_fraction, extensions, is_valid_file) and returns: make_dataset