class_docstr: Stream-51 Pytorch Dataset
docstr: Creates an instance of the Stream-51 dataset.
  
  :param root: The directory where the dataset can be found or downloaded.
      Defaults to None, which means that the default location for
      'stream51' will be used.
  :param train: If True, the training set will be returned. If False,
      the test set will be returned.
  :param transform: The transformations to apply to the X values.
  :param target_transform: The transformations to apply to the Y values.
  :param loader: The image loader to use.
  :param download: If True, the dataset will be downloaded if needed.
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code: def __init__(
      self,
      root: Optional[Union[str, Path]] = None,
      *,
      train=True,
      transform=None,
      target_transform=None,
      loader=default_loader,
      download=True,
  ):
      """
      Creates an instance of the Stream-51 dataset.
  
      :param root: The directory where the dataset can be found or downloaded.
          Defaults to None, which means that the default location for
          'stream51' will be used.
      :param train: If True, the training set will be returned. If False,
          the test set will be returned.
      :param transform: The transformations to apply to the X values.
      :param target_transform: The transformations to apply to the Y values.
      :param loader: The image loader to use.
      :param download: If True, the dataset will be downloaded if needed.
      """
  
      if root is None:
          root = default_dataset_location("stream51")
  
      self.train = train  # training set or test set
      self.transform = transform
      self.target_transform = target_transform
      self.loader = loader
      self.transform = transform
      self.target_transform = target_transform
      self.bbox_crop = True
      self.ratio = 1.1
      self.samples: Sequence[Tuple[int, Any, str]] = []
  
      super(Stream51, self).__init__(
          root, download=download, verbose=True
      )
  
      self._load_dataset()
  
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 1 to create a new Stream51 object from inherited base classes: DownloadableDataset with example: obj = Stream51(root)