code: def distributed_loader_specifics(
      distributed_launch, rank, dataset, loader_kwargs
  ):
      """Prepare loader_kwargs for DDP when necessary.
  
      Parameters
      ----------
      distributed_launch (bool)
          DDP flag
      rank (int)
          node rank in DDP
      dataset: Dataset
          The dataset to make a DataLoader for.
      **loader_kwargs : dict
          Keyword args to DataLoader, see PyTorch DataLoader for
          options.
  
      Returns
      -------
      loader_kwargs
          augmented keyword args to DataLoader
      """
      sampler = loader_kwargs.get("sampler", None)
      shuffle = loader_kwargs.get("shuffle", False)
      # Possibly make a DistributedSampler or a wrapper for some other sampler
      if distributed_launch and not isinstance(
          dataset, IterableDataset
      ):
          drop_last = loader_kwargs.get("drop_last", False)
          # num_replicas arg is equal to world_size
          # and retrieved automatically within
          # DistributedSampler obj.
          if sampler is not None:
              sampler = DistributedSamplerWrapper(
                  sampler,
                  rank=rank,
                  drop_last=drop_last,
                  shuffle=shuffle,
              )
  
              # with DistributedSamplerWrapper, one must disable shuffling for dataloader
              loader_kwargs["shuffle"] = False
              loader_kwargs["sampler"] = sampler
          elif loader_kwargs.get("batch_sampler") is None:
              # no sampler and batch-sampler
              sampler = DistributedSampler(
                  dataset,
                  rank=rank,
                  drop_last=drop_last,
              )
  
              # with DistributedSamplerWrapper, one must disable shuffling for dataloader
              loader_kwargs["shuffle"] = False
              loader_kwargs["sampler"] = sampler
          else:  # batch_sampler was specified
              sampler = DistributedSamplerWrapper(
                  loader_kwargs.get("batch_sampler", None),
                  rank=rank,
              )
              loader_kwargs["batch_sampler"] = sampler
      elif distributed_launch and isinstance(dataset, IterableDataset):
          logger.warning(
              "Cannot automatically solve distributed sampling "
              "for IterableDataset."
          )
      return loader_kwargs
  
imports: from torch.utils.data import DataLoader
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import _BaseDataLoaderIter
  import logging
  import warnings
  import functools
  from torch.utils.data import DistributedSampler
  from speechbrain.dataio.batch import PaddedBatch, BatchsizeGuesser
  from speechbrain.dataio.dataset import DynamicItemDataset
  from speechbrain.dataio.sampler import ReproducibleRandomSampler, DistributedSamplerWrapper
  from speechbrain.utils.checkpoints import register_checkpoint_hooks, mark_as_saver, mark_as_loader
label_desc: Use image node_id 1 for calling a global function with example usage: distributed_loader_specifics(distributed_launch, rank, dataset, loader_kwargs) and returns: loader_kwargs