code: def on_advance_start(self, *args: Any, **kwargs: Any) -> None:
      if self.trainer.datamodule.has_labelled_data:
          self._reset_dataloader_for_stage(RunningStage.TRAINING)
          self._reset_dataloader_for_stage(RunningStage.VALIDATING)
          if self.trainer.datamodule.has_test:
              self._reset_dataloader_for_stage(RunningStage.TESTING)
      if self.trainer.datamodule.has_unlabelled_data:
          self._reset_dataloader_for_stage(RunningStage.PREDICTING)
      self.progress.increment_ready()
  
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 6 for calling the ActiveLearningLoop obj's underlying member method code with example usage: obj.on_advance_start() without return types