class_docstr: CLEAR Image Dataset (base class for CLEARImage)
docstr: Return self._paths_and_targets with root appended or not
docstr_tok: ["Return","self._paths_and_targets","with","root","appended","or","not"]
code: def get_paths_and_targets(
      self, root_appended=True
  ) -> Sequence[Sequence[Tuple[Union[str, Path], int]]]:
      """Return self._paths_and_targets with root appended or not"""
      if not root_appended:
          return self._paths_and_targets
      else:
          paths_and_targets: List[List[Tuple[Path, int]]] = []
          for path_and_target_list in self._paths_and_targets:
              paths_and_targets.append([])
              for img_path, target in path_and_target_list:
                  paths_and_targets[-1].append(
                      (self.root / img_path, target)
                  )
          return paths_and_targets
  
imports: from pathlib import Path
  from typing import Optional, Sequence, Tuple, Union, List
  import json
  import os
  import torch
  from torchvision.datasets.folder import default_loader
  from avalanche.benchmarks.datasets import DownloadableDataset, default_dataset_location
  from avalanche.benchmarks.utils import default_flist_reader
  from avalanche.benchmarks.datasets.clear import clear_data
label_desc: Use image node_id 5 for calling the _CLEARImage obj's underlying member method code with example usage: obj.get_paths_and_targets(root_appended) and returns: self, paths_and_targets