class_docstr: CLEAR Image Dataset (base class for CLEARImage)
code: def _load_metadata_old(self) -> bool:
      if not super(_CLEARImage, self)._load_metadata_old():
          print("CLEAR has not yet been downloaded")
          return False
  
      train_folder_path = self.root / "training_folder"
  
      if self.split == "all":
          filelist_folder_path = train_folder_path / "filelists"
      else:
          filelist_folder_path = (
              train_folder_path
              / "testset_ratio_0.3"
              / f"split_{self.seed}"
          )
  
      filelist_name = f"{self.split}.txt"
  
      self._paths_and_targets = []
      for bucket_index in self.bucket_indices:
          f_path = (
              filelist_folder_path / str(bucket_index) / filelist_name
          )
          try:
              self._paths_and_targets.append(
                  default_flist_reader(f_path)
              )
          except Exception as e:
              print(f"Error reading {f_path}")
              return False
  
      self.paths = []
      self.targets = []
  
      for path_and_target_list in self._paths_and_targets:
          for img_path, target in path_and_target_list:
              self.paths.append(img_path)
              self.targets.append(target)
  
      return True
  
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 3 for calling the _CLEARImage obj's underlying member method code with example usage: obj._load_metadata_old() and returns: True, False, False