code: def _reset_dataloader_for_stage(self, running_state: RunningStage):
      dataloader_name = f"{_STAGES_PREFIX[running_state]}_dataloader"
      # If the dataloader exists, we reset it.
      dataloader = (
          getattr(self.trainer.datamodule, dataloader_name)
          if is_overridden(dataloader_name, self.trainer.datamodule)
          else None
      )
  
      if dataloader:
          setattr(
              self.trainer._data_connector,
              f"_{dataloader_name}_source",
              _DataLoaderSource(
                  self.trainer.datamodule, dataloader_name
              ),
          )
          setattr(self.trainer, dataloader_name, None)
          # TODO: Resolve this within PyTorch Lightning.
          with contextlib.suppress(Exception):
              getattr(self.trainer, f"reset_{dataloader_name}")(
                  self.trainer.lightning_module
              )
  
imports: import contextlib
  from copy import deepcopy
  from typing import Any, Dict, Optional
  from pytorch_lightning import LightningModule
  from pytorch_lightning.loops import Loop
  from pytorch_lightning.loops.fit_loop import FitLoop
  from pytorch_lightning.trainer.connectors.data_connector import _DataLoaderSource
  from pytorch_lightning.trainer.progress import Progress
  from pytorch_lightning.trainer.states import TrainerFn, TrainerStatus
  from pytorch_lightning.utilities.model_helpers import is_overridden
  from torch import Tensor
  import flash
  from flash.core.data.utils import _STAGES_PREFIX
  from flash.core.utilities.imports import requires
  from flash.core.utilities.stability import beta
  from flash.core.utilities.stages import RunningStage
  from flash.image.classification.integrations.baal.data import ActiveLearningDataModule
  from flash.image.classification.integrations.baal.dropout import InferenceMCDropoutTask
label_desc: Use image node_id 17 for calling the ActiveLearningLoop obj's underlying member method code with example usage: obj._reset_dataloader_for_stage(running_state) without return types