code: def _dataloader_init_kwargs_resolve_sampler(
      dataloader: DataLoader,
      sampler: Union[Sampler, Iterable],
      mode: Optional[RunningStage] = None,
  ) -> Dict[str, Any]:
      """This function is used to handle the sampler, batch_sampler arguments associated within a DataLoader for its re-
      instantiation.
  
      If the dataloader is being used for prediction, the sampler will be wrapped into an `_IndexBatchSamplerWrapper`, so
      Lightning can keep track of its indices.
  
      """
      is_predicting = mode == RunningStage.PREDICTING
      batch_sampler = getattr(dataloader, "batch_sampler")
      batch_sampler_cls = type(batch_sampler)
  
      if batch_sampler is not None and (
          batch_sampler_cls is not BatchSampler or is_predicting
      ):
          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
  
              if is_predicting:
                  success, args, kwargs = _replace_value_in_saved_args(
                      "drop_last",
                      False,
                      args,
                      kwargs,
                      default_kwargs,
                      arg_names,
                  )
                  if not success:
                      rank_zero_warn(
                          f"Trying to inject `drop_last=False` into batch sampler since you are predicting, however "
                          f"it seems the class `{batch_sampler_cls.__qualname__}` does not support it. "
                          "Your predictions might be incomplete. To mitigate this, expose `drop_last` in "
                          "the `__init__` method of your custom class."
                      )
  
              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=(
                          False
                          if is_predicting
                          else 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` and"
                      " instantiate your custom batch sampler inside the `*_dataloader` hook of your module,"
                      " or set `Trainer(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 or
              # how to adjust the `drop_last` value
              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 `Trainer(use_distributed_sampler=False)`. If you choose the latter, you will be"
                  " responsible for handling the distributed sampling within your batch sampler."
              )
  
          if is_predicting:
              batch_sampler = _IndexBatchSamplerWrapper(batch_sampler)
  
          # batch_sampler option is mutually exclusive with batch_size, shuffle, sampler, and drop_last
          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 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 6 for calling a global function with example usage: _dataloader_init_kwargs_resolve_sampler(dataloader, sampler, mode) and returns: dict, dict