class_docstr: Stream-51 Pytorch Dataset
code: def _load_metadata(self) -> bool:
      if self.train:
          data_list = json.load(
              open(str(self.root / "Stream-51_meta_train.json"))
          )
      else:
          data_list = json.load(
              open(str(self.root / "Stream-51_meta_test.json"))
          )
  
      self.samples = data_list
      self.targets = [s[0] for s in data_list]
  
      self.bbox_crop = True
      self.ratio = 1.1
  
      return True
  
imports: import os
  import shutil
  import json
  import random
  from pathlib import Path
  from typing import Any, List, Optional, Sequence, Tuple, TypeVar, Union
  from torchvision.datasets.folder import default_loader
  from zipfile import ZipFile
  from torchvision.transforms import ToTensor
  from avalanche.benchmarks.datasets import DownloadableDataset, default_dataset_location
  from avalanche.benchmarks.datasets.stream51 import stream51_data
label_desc: Use image node_id 3 for calling the Stream51 obj's underlying member method code with example usage: obj._load_metadata() and returns: True