code: def _get_dataloader_init_args_and_kwargs(
      dataloader: DataLoader,
      sampler: Union[Sampler, Iterable],
  ) -> 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
              )
          )
  
      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 TypeError(
                  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 functools
  import inspect
  import os
  from collections import OrderedDict
  from contextlib import contextmanager
  from functools import partial
  from typing import Any, Callable, Dict, Generator, Iterable, Optional, Sized, Tuple, Type, Union
  from lightning_utilities.core.inheritance import get_all_subclasses
  from torch.utils.data import BatchSampler, DataLoader, IterableDataset, Sampler
  from typing_extensions import TypeGuard
  from lightning.fabric.utilities.enums import LightningEnum
  from lightning.fabric.utilities.exceptions import MisconfigurationException
  from lightning.fabric.utilities.rank_zero import rank_zero_warn
  from lightning.fabric.utilities.seed import pl_worker_init_function
label_desc: Use image node_id 5 for calling a global function with example usage: _get_dataloader_init_args_and_kwargs(dataloader, sampler) and returns: dl_args,  dl_kwargs