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 (list[string]): A list of allowed extensions.
      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.
  
   Attributes:
      classes (list): List of the class names.
      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
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code: def __repr__(self):
      fmt_str = "Dataset " + self.__class__.__name__ + "\n"
      fmt_str += "    Number of datapoints: {}\n".format(self.__len__())
      fmt_str += "    Root Location: {}\n".format(self.root)
      tmp = "    Transforms (if any): "
      fmt_str += "{0}{1}\n".format(
          tmp,
          self.transform.__repr__().replace(
              "\n", "\n" + " " * len(tmp)
          ),
      )
      tmp = "    Target Transforms (if any): "
      fmt_str += "{0}{1}".format(
          tmp,
          self.target_transform.__repr__().replace(
              "\n", "\n" + " " * len(tmp)
          ),
      )
      return fmt_str
  
imports: import torch.utils.data
  from torch.utils.data import Dataset
  from torchvision import transforms
  from PIL import Image
  import os
  import os.path
  import sys
label_desc: Use image node_id 5 for calling the DatasetFolder obj's underlying member method code with example usage: obj.__repr__() and returns: fmt_str