class_docstr: Custom data loader for multiple datasets.
class_docstr_tok: ["Custom","data","loader","for","multiple","datasets","."]
code: def _create_samplers(
      datasets: List[AvalancheDataset],
      batch_sizes: List[int],
      distributed_sampling: bool,
      loader_kwargs: Dict[str, Any],
  ):
      samplers = []
  
      for dataset, dataset_mb_size in zip(datasets, batch_sizes):
          sampler = _make_sampler(
              dataset,
              distributed_sampling,
              loader_kwargs,
              dataset_mb_size,
          )
  
          samplers.append(sampler)
  
      return samplers
  
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 8 for calling the MultiDatasetDataLoader obj's underlying member method code with example usage: obj._create_samplers(datasets, batch_sizes, distributed_sampling, loader_kwargs) and returns: samplers