code: def datasets_from_filelists(
      root,
      train_filelists,
      test_filelists,
      complete_test_set_only=False,
      train_transform=None,
      train_target_transform=None,
      test_transform=None,
      test_target_transform=None,
  ):
      """
      This reader reads a list of Caffe-style filelists and returns the proper
      Dataset objects.
  
      A Caffe-style list is just a text file where, for each line, two elements
      are described: the path to the pattern (relative to the root parameter)
      and its class label. Those two elements are separated by a single white
      space.
  
      This method reads each file list and returns a separate
      dataset for each of them.
  
      Beware that the parameters must be **list of paths to Caffe-style
      filelists**. If you need to create a dataset given a list of
      **pattern paths**, use `datasets_from_paths` instead.
  
      :param root: root path where the data to load are stored. May be None.
      :param train_filelists: list of paths to train filelists. The flist format
          should be: impath label\\nimpath label\\n ...(same to Caffe's filelist).
      :param test_filelists: list of paths to test filelists. It can be also a
          single path when the datasets is the same for each batch.
      :param complete_test_set_only: if True, test_filelists must contain
          the path to a single filelist that will serve as the complete test set.
          Alternatively, test_filelists can be the path (str) to the complete test
          set filelist. If False, train_filelists and test_filelists must contain
          the same amount of filelists paths. Defaults to False.
      :param train_transform: The transformation to apply to training patterns.
          Defaults to None.
      :param train_target_transform: The transformation to apply to training
          patterns targets. Defaults to None.
      :param test_transform: The transformation to apply to test patterns.
          Defaults to None.
      :param test_target_transform: The transformation to apply to test
          patterns targets. Defaults to None.
  
      :return: list of tuples (train dataset, test dataset) for each train
          filelist in the list.
      """
  
      if complete_test_set_only:
          if not (
              isinstance(test_filelists, str)
              or isinstance(test_filelists, Path)
          ):
              if len(test_filelists) > 1:
                  raise ValueError(
                      "When complete_test_set_only is True, test_filelists must "
                      "be a str, Path or a list with a single element describing "
                      "the path to the complete test set."
                  )
              else:
                  test_filelists = test_filelists[0]
          else:
              test_filelists = [test_filelists]
      else:
          if len(test_filelists) != len(train_filelists):
              raise ValueError(
                  "When complete_test_set_only is False, test_filelists and "
                  "train_filelists must contain the same number of elements."
              )
  
      transform_groups = dict(
          train=(train_transform, train_target_transform),
          eval=(test_transform, test_target_transform),
      )
  
      # import here to prevent circular import issue
      from .utils import as_taskaware_classification_dataset
  
      train_inc_datasets = [
          as_taskaware_classification_dataset(
              FilelistDataset(root, tr_flist),
              transform_groups=transform_groups,
              initial_transform_group="train",
          )
          for tr_flist in train_filelists
      ]
      test_inc_datasets = [
          as_taskaware_classification_dataset(
              FilelistDataset(root, te_flist),
              transform_groups=transform_groups,
              initial_transform_group="eval",
          )
          for te_flist in test_filelists
      ]
  
      return train_inc_datasets, test_inc_datasets
  
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 3 for calling a global function with example usage: datasets_from_filelists(root, train_filelists, test_filelists, complete_test_set_only, train_transform, train_target_transform, test_transform, test_target_transform) and returns: train_inc_datasets,  test_inc_datasets