code: def _update_dataloader(
      dataloader: DataLoader,
      sampler: Union[Sampler, Iterable],
      mode: Optional[RunningStage] = None,
  ) -> DataLoader:
      dl_args, dl_kwargs = _get_dataloader_init_args_and_kwargs(
          dataloader, sampler, mode
      )
      return _reinstantiate_wrapped_cls(
          dataloader, *dl_args, **dl_kwargs
      )
  
imports: import inspect
  from dataclasses import fields
  from typing import Any, Dict, Generator, Iterable, Mapping, Optional, Sized, Tuple, Union
  import torch
  from lightning_utilities.core.apply_func import is_dataclass_instance
  from torch import Tensor
  from torch.utils.data import BatchSampler, DataLoader, IterableDataset, RandomSampler, Sampler, SequentialSampler
  from typing_extensions import TypeGuard
  import lightning.pytorch
  from lightning.fabric.utilities.data import _reinstantiate_wrapped_cls, _replace_value_in_saved_args, has_iterable_dataset, sized_len
  from lightning.pytorch.overrides.distributed import _IndexBatchSamplerWrapper
  from lightning.pytorch.trainer.states import RunningStage
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.rank_zero import WarningCache, rank_zero_warn
label_desc: Use image node_id 4 for calling a global function with example usage: _update_dataloader(dataloader, sampler, mode) and returns: _reinstantiate_wrapped_cls