class_docstr: Replacement of PyTorch DataLoader that also supports
  the additional loading mechanisms implemented in
  :class:`MultiDatasetDataLoader`.
class_docstr_tok: ["Replacement","of","PyTorch","DataLoader","that","also","supports","the","additional","loading","mechanisms","implemented","in",":","class",":","`","MultiDatasetDataLoader","`","."]
code: def __init__(
      self, datasets: AvalancheDataset, batch_size: int = 1, **kwargs
  ):
      super().__init__([datasets], [batch_size], **kwargs)
  
imports: from typing import Any, Dict, Iterator, List, Optional, Sequence, Sized, Union
  import numpy
  from torch.utils.data import DistributedSampler, Dataset
  from torch.utils.data.dataloader import DataLoader
  from avalanche.benchmarks.utils.data import AvalancheDataset
  from avalanche.benchmarks.utils.data_attribute import DataAttribute
  from avalanche.benchmarks.utils.ffcv_support.ffcv_components import HybridFfcvLoader, has_ffcv_support
  from avalanche.distributed.distributed_helper import DistributedHelper
  from torch.utils.data.sampler import Sampler, BatchSampler
  from torch.utils.data import ConcatDataset
label_desc: Use image node_id 1 to create a new SingleDatasetDataLoader object from inherited base classes: MultiDatasetDataLoader with example: obj = SingleDatasetDataLoader(datasets, batch_size)