class_docstr: CLEAR Feature Dataset (base class for CLEARFeature)
code: def _load_metadata_new(self) -> bool:
      if not super(_CLEARFeature, self)._load_metadata_new():
          print("CLEAR has not yet been downloaded")
          return False
  
      self.tensors_and_targets = []
      splits = (
          ["test", "train"] if self.split == "all" else [self.split]
      )
      for split in splits:
          folder_path = self.root / self.split
          feature_folder_path = (
              folder_path / "features" / self.feature_type
          )
          metadata = _load_json(feature_folder_path / "features.json")
          tensors = []
          targets = []
          for bucket, data in metadata.items():
              for class_idx, class_name in enumerate(self.class_names):
                  feature_path = data[class_name]
                  try:
                      features = torch.load(folder_path / feature_path)
                  except Exception as e:
                      print(f"Error loading {feature_path}")
                      return False
                  for _id, tensor in features.items():
                      tensors.append(tensor)
                      targets.append(class_idx)
              if self.split == "all" and split == "train":
                  _tensors, _targets = self.tensors_and_targets[
                      int(bucket)
                  ]
                  _tensors += tensors
                  _targets += targets
                  self.tensors_and_targets[int(bucket)] = (
                      _tensors,
                      _targets,
                  )
              else:
                  self.tensors_and_targets.append((tensors, targets))
  
      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 4 for calling the _CLEARFeature obj's underlying member method code with example usage: obj._load_metadata_new() and returns: True, False, False