class_docstr: This class extends the basic Pytorch Dataset class to handle filelists as
  main data source.
docstr:         This reader reads a filelist and return a list of paths.
  
          :param root: root path where the data to load are stored. May be None.
          :param flist: path of the flislist to read. The flist format should be:
              impath label
  impath label
   ...(same to caffe's filelist).
          :param transform: eventual transformation to add to the input data (x).
          :param target_transform: eventual transformation to add to the targets
              (y).
          :param flist_reader: loader function to use (for the filelists) given
              path.
          :param loader: loader function to use (for the real data) given path.
          
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code: def __init__(
      self,
      root,
      flist,
      transform=None,
      target_transform=None,
      flist_reader=default_flist_reader,
      loader=default_image_loader,
  ):
      """
      This reader reads a filelist and return a list of paths.
  
      :param root: root path where the data to load are stored. May be None.
      :param flist: path of the flislist to read. The flist format should be:
          impath label\nimpath label\n ...(same to caffe's filelist).
      :param transform: eventual transformation to add to the input data (x).
      :param target_transform: eventual transformation to add to the targets
          (y).
      :param flist_reader: loader function to use (for the filelists) given
          path.
      :param loader: loader function to use (for the real data) given path.
      """
  
      flist = str(flist)  # Manages Path objects
      files_and_labels = flist_reader(flist)
      super().__init__(
          root,
          files_and_labels,
          transform=transform,
          target_transform=target_transform,
          loader=loader,
      )
  
imports: from pathlib import Path
  from typing import Callable, Generic, List, Tuple, Sequence, Optional, TypeVar, Union
  import torch.utils.data
  from PIL import Image
  import os
  import os.path
  from torch import Tensor
  from torchvision.transforms.functional import crop
  from .transform_groups import XTransform, YTransform
label_desc: Use image node_id 1 to create a new FilelistDataset object from inherited base classes: PathsDataset with example: obj = FilelistDataset(root, flist, transform, target_transform, flist_reader, loader)