code: def on_advance_end(self) -> None:
      if (
          self.trainer.datamodule.has_unlabelled_data
          and self.should_reset_weights
      ):
          # reload the weights to retrain from scratch with the new labelled data.
          self._lightning_module.load_state_dict(self._model_state_dict)
      self.progress.increment_completed()
      return super().on_advance_end()
  
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 8 for calling the ActiveLearningLoop obj's underlying member method code with example usage: obj.on_advance_end() and returns: super