code: def _get_dataloader_init_args_and_kwargs(
      dataloader: DataLoader,
      sampler: Union[Sampler, Iterable],
      mode: Optional[RunningStage] = None,
  ) -> Tuple[Tuple[Any], Dict[str, Any]]:
      if not isinstance(dataloader, DataLoader):
          raise ValueError(
              f"The dataloader {dataloader} needs to subclass `torch.utils.data.DataLoader`"
          )
  
      was_wrapped = hasattr(dataloader, "__pl_saved_args")
      if was_wrapped:
          dl_args = dataloader.__pl_saved_args
          dl_kwargs = dataloader.__pl_saved_kwargs
          arg_names = dataloader.__pl_saved_arg_names
          original_dataset = (
              dataloader.__dataset
          )  # we have this saved from _wrap_init
      else:
          # get the dataloader instance attributes
          attrs = {
              k: v
              for k, v in vars(dataloader).items()
              if not k.startswith("_")
          }
          # We cannot be 100% sure the class sets dataset argument. Let's set it to None to be safe
          # and hope we can get it from the instance attributes
          original_dataset = None
          # not part of `vars`
          attrs[
              "multiprocessing_context"
          ] = dataloader.multiprocessing_context
          arg_names = ()
  
      # get the dataloader instance `__init__` parameters
      params = dict(inspect.signature(dataloader.__init__).parameters)  # type: ignore[misc]
      has_variadic_kwargs = any(
          p.kind is p.VAR_KEYWORD for p in params.values()
      )
      if has_variadic_kwargs:
          # if the signature takes **kwargs, assume they will be passed down with `super().__init__(**kwargs)`
  
          if was_wrapped:
              # if the dataloader was wrapped in a hook, only take arguments with default values
              # and assume user passes their kwargs correctly
              params.update(
                  {
                      k: v
                      for k, v in inspect.signature(
                          DataLoader.__init__
                      ).parameters.items()
                      if v.default is not v.empty
                  }
              )
          else:
              params.update(
                  inspect.signature(DataLoader.__init__).parameters
              )
              params.pop("self", None)
  
      if not was_wrapped:
          # keep only the params whose default is different to the current attr value
          non_defaults = {
              name
              for name, p in params.items()
              if name in attrs and p.default is not attrs[name]
          }
  
          # add `dataset` as it might have been replaced with `*args`
          non_defaults.add("dataset")
          # kwargs to re-construct the dataloader
          dl_kwargs = {
              k: v for k, v in attrs.items() if k in non_defaults
          }
          dl_args = ()
  
      dataset = dl_kwargs.get("dataset", original_dataset)
      if isinstance(dataset, IterableDataset):
          dl_kwargs["batch_sampler"] = None
          dl_kwargs["sampler"] = None
      else:
          dl_kwargs.update(
              _dataloader_init_kwargs_resolve_sampler(
                  dataloader, sampler, mode
              )
          )
  
      required_args = {
          p.name
          for p in params.values()
          if p.kind in (p.POSITIONAL_ONLY, p.POSITIONAL_OR_KEYWORD)
          and p.default is p.empty
          and p.name not in dl_kwargs
          and p.name not in arg_names
      }
      # the dataloader has required args which we could not extract from the existing attributes
      if required_args:
          sorted_required_args = sorted(required_args)
          dataloader_cls_name = dataloader.__class__.__name__
          missing_args_message = ", ".join(
              f"`self.{arg_name}`" for arg_name in sorted_required_args
          )
          raise MisconfigurationException(
              f"Trying to inject custom `Sampler` into the `{dataloader_cls_name}` instance. "
              "This would fail as some of the `__init__` arguments are not available as instance attributes. "
              f"The missing attributes are {sorted_required_args}. If you instantiate your `{dataloader_cls_name}` "
              "inside a `*_dataloader` hook of your module, we will do this for you."
              f" Otherwise, define {missing_args_message} inside your `__init__`."
          )
  
      if not has_variadic_kwargs:
          # the dataloader signature does not allow keyword arguments that need to be passed
          missing_kwargs = (
              set(dl_kwargs) | set(arg_names)
          ) - params.keys()
          if missing_kwargs:
              sorted_missing_kwargs = sorted(missing_kwargs)
              dataloader_cls_name = dataloader.__class__.__name__
              raise MisconfigurationException(
                  f"Trying to inject parameters into the `{dataloader_cls_name}` instance. "
                  "This would fail as it doesn't expose all its attributes in the `__init__` signature. "
                  f"The missing arguments are {sorted_missing_kwargs}. HINT: If you wrote the `{dataloader_cls_name}` "
                  "class, add the `__init__` arguments or allow passing `**kwargs`"
              )
  
      return 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 5 for calling a global function with example usage: _get_dataloader_init_args_and_kwargs(dataloader, sampler, mode) and returns: dl_args,  dl_kwargs