docstr: This function handles the following functionalities:
  
  - Injecting a `DistributedDataSamplerWrapper` into the `DataLoader` if on a distributed environment
  - Wrapping the dataloader based on strategy-specific logic
code: def _prepare_dataloader(
      self, dataloader: object, shuffle: bool, mode: RunningStage
  ) -> object:
      """This function handles the following functionalities:
  
      - Injecting a `DistributedDataSamplerWrapper` into the `DataLoader` if on a distributed environment
      - Wrapping the dataloader based on strategy-specific logic
  
      """
      # don't do anything if it's not a dataloader
      if not isinstance(dataloader, DataLoader):
          return dataloader
  
      if _graphcore_available_and_importable():
          from lightning_graphcore import IPUAccelerator
  
          # IPUs use a custom `poptorch.DataLoader` which we might need to convert to
          is_ipu = isinstance(self.trainer.accelerator, IPUAccelerator)
      else:
          is_ipu = False
      if (
          self._requires_distributed_sampler(
              dataloader
          )  # sets the distributed sampler
          or mode
          == RunningStage.PREDICTING  # to track indices for the predictions
          or is_ipu
      ):
          sampler = self._resolve_sampler(
              dataloader, shuffle=shuffle, mode=mode
          )
          return _update_dataloader(dataloader, sampler, mode=mode)
  
      return dataloader
  
imports: import os
  from dataclasses import dataclass, field
  from typing import Any, Iterable, Optional, Tuple, Union
  import torch.multiprocessing
  from torch.utils.data import BatchSampler, DataLoader, RandomSampler, Sampler, SequentialSampler
  from torch.utils.data.distributed import DistributedSampler
  import lightning.pytorch
  from lightning.fabric.utilities.data import _auto_add_worker_init_fn, _replace_dunder_methods, _set_sampler_epoch, has_iterable_dataset, suggested_max_num_workers
  from lightning.fabric.utilities.distributed import DistributedSamplerWrapper
  from lightning.pytorch.overrides.distributed import UnrepeatedDistributedSamplerWrapper
  from lightning.pytorch.trainer import call
  from lightning.pytorch.trainer.states import RunningStage, TrainerFn
  from lightning.pytorch.utilities.combined_loader import CombinedLoader
  from lightning.pytorch.utilities.data import _is_dataloader_shuffled, _update_dataloader
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.imports import _graphcore_available_and_importable
  from lightning.pytorch.utilities.model_helpers import is_overridden
  from lightning.pytorch.utilities.rank_zero import WarningCache, rank_zero_warn
  from lightning.pytorch.utilities.types import EVAL_DATALOADERS, TRAIN_DATALOADERS
  from lightning.pytorch.utilities.warnings import PossibleUserWarning
label_desc: Use image node_id 8 for calling the _DataConnector obj's underlying member method code with example usage: obj._prepare_dataloader(dataloader, shuffle, mode) and returns: dataloader, dataloader, _update_dataloader