class_docstr: CLEAR Feature Dataset (base class for CLEARFeature)
code: def _load_metadata_old(self) -> bool:
      if not super(_CLEARFeature, self)._load_metadata_old():
          print("CLEAR has not yet been downloaded")
          return False
  
      train_folder_path = self.root / "training_folder"
  
      feature_folder_path = (
          train_folder_path / "features" / self.feature_type
      )
  
      if self.split in ["train", "test"]:
          split_folder_path = (
              train_folder_path
              / "testset_ratio_0.3"
              / f"split_{self.seed}"
          )
  
          split_name = f"{self.split}_indices.json"
      else:
          split_name = None
  
      self.tensors_and_targets = []
      for bucket_index in self.bucket_indices:
          f_path = feature_folder_path / str(bucket_index) / "all.pth"
          try:
              tensors, targets = torch.load(f_path)
              if split_name:
                  indices_json = (
                      split_folder_path / str(bucket_index) / split_name
                  )
                  chosen_indices = _load_json(indices_json)
                  tensors = [tensors[i] for i in chosen_indices]
                  targets = [targets[i] for i in chosen_indices]
              self.tensors_and_targets.append((tensors, targets))
          except Exception as e:
              print(f"Error loading {f_path}")
              return False
  
      self.tensors = []
      self.targets = []
      for tensors, targets in self.tensors_and_targets:
          for tensor, target in zip(tensors, targets):
              self.tensors.append(tensor)
              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 _CLEARFeature obj's underlying member method code with example usage: obj._load_metadata_old() and returns: True, False, False