code: def advance(self, *args: Any, **kwargs: Any) -> None:
      self.progress.increment_started()
  
      if self.trainer.datamodule.has_labelled_data:
          self.fit_loop.run()
  
      if self.trainer.datamodule.has_test:
          self._reset_testing()
          metrics = self.trainer.test_loop.run()
          if metrics:
              self.trainer.logger.log_metrics(
                  metrics[0], step=self.trainer.global_step
              )
  
      if self.trainer.datamodule.has_unlabelled_data:
          self._reset_predicting()
          probabilities = self.trainer.predict_loop.run()
          self.trainer.datamodule.label(probabilities=probabilities)
      else:
          raise StopIteration
  
      self._reset_fitting()
      self.progress.increment_processed()
  
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 7 for calling the ActiveLearningLoop obj's underlying member method code with example usage: obj.advance() without return types