class_docstr: Custom data loader for multiple datasets.
class_docstr_tok: ["Custom","data","loader","for","multiple","datasets","."]
code: def _extract_ffcv_args(self, loader_args):
      loader_args = dict(loader_args)
      ffcv_args: Dict[str, Any] = loader_args.pop("ffcv_args", dict())
      ffcv_args.setdefault("device", None)
      ffcv_args.setdefault("print_ffcv_summary", False)
  
      for arg_name, arg_value in loader_args.items():
          if arg_name in ffcv_args:
              # Already specified in ffcv_args -> discard
              continue
  
          if arg_name in HybridFfcvLoader.VALID_FFCV_PARAMS:
              ffcv_args[arg_name] = arg_value
      return loader_args, ffcv_args
  
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 6 for calling the MultiDatasetDataLoader obj's underlying member method code with example usage: obj._extract_ffcv_args(loader_args) and returns: loader_args,  ffcv_args