code: def _dataloader_init_kwargs_resolve_sampler(
      dataloader: DataLoader,
      sampler: Union[Sampler, Iterable],
  ) -> Dict[str, Any]:
      """This function is used to handle the sampler, batch_sampler arguments associated within a DataLoader for its re-
      instantiation."""
      batch_sampler = getattr(dataloader, "batch_sampler")
  
      if (
          batch_sampler is not None
          and type(batch_sampler) is not BatchSampler
      ):
          batch_sampler_cls = type(batch_sampler)
          if hasattr(batch_sampler, "__pl_saved_args"):
              # This is a PyTorch `BatchSampler` subclass for which we captured the init args
              args = batch_sampler.__pl_saved_args
              kwargs = batch_sampler.__pl_saved_kwargs
              default_kwargs = batch_sampler.__pl_saved_default_kwargs
              arg_names = batch_sampler.__pl_saved_arg_names
  
              success, args, kwargs = _replace_value_in_saved_args(
                  "sampler",
                  sampler,
                  args,
                  kwargs,
                  default_kwargs,
                  arg_names,
              )
              if not success:
                  raise TypeError(
                      "Trying to inject a modified sampler into the batch sampler; however, it seems the class "
                      f"`{batch_sampler_cls.__qualname__}` does not have an argument called `sampler.` To mitigate "
                      "this, expose an argument `sampler` in the `__init__` method of your custom class."
                  )
  
              batch_sampler = _reinstantiate_wrapped_cls(
                  batch_sampler, *args, **kwargs
              )
          elif hasattr(batch_sampler, "batch_size") and hasattr(
              batch_sampler, "drop_last"
          ):
              # This is a sampler for which we could not capture the init args, but it kinda looks like a batch sampler
              # even if it does not inherit from PyTorch's interface.
              try:
                  batch_sampler = batch_sampler_cls(
                      sampler,
                      batch_size=batch_sampler.batch_size,
                      drop_last=batch_sampler.drop_last,
                  )
              except TypeError as ex:
                  import re
  
                  match = re.match(
                      r".*__init__\(\) (got multiple values)|(missing \d required)",
                      str(ex),
                  )
                  if not match:
                      # an unexpected `TypeError`, continue failure
                      raise
  
                  # There could either be too few or too many arguments. Customizing the message based on this doesn't
                  # make much sense since our MisconfigurationException is going to be raised from the original one.
                  raise TypeError(
                      " Lightning can't inject a (distributed) sampler into your batch sampler, because it doesn't"
                      " subclass PyTorch's `BatchSampler`. To mitigate this, either follow the API of `BatchSampler`"
                      " or set`.setup_dataloaders(..., use_distributed_sampler=False)`. If you choose the latter, you"
                      " will be responsible for handling the distributed sampling within your batch sampler."
                  ) from ex
          else:
              # The sampler is not a PyTorch `BatchSampler`, we don't know how to inject a custom sampler
              raise TypeError(
                  " Lightning can't inject a (distributed) sampler into your batch sampler, because it doesn't"
                  " subclass PyTorch's `BatchSampler`. To mitigate this, either follow the API of `BatchSampler`"
                  " or set`.setup_dataloaders(..., use_distributed_sampler=False)`. If you choose the latter, you"
                  " will be responsible for handling the distributed sampling within your batch sampler."
              )
  
          return {
              "sampler": None,
              "shuffle": False,
              "batch_sampler": batch_sampler,
              "batch_size": 1,
              "drop_last": False,
          }
  
      return {
          "sampler": sampler,
          "shuffle": False,
          "batch_sampler": None,
      }
  
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 6 for calling a global function with example usage: _dataloader_init_kwargs_resolve_sampler(dataloader, sampler) and returns: dict, dict