class_docstr: PL Lightning Module for Reverse Distillation Algorithm.
  
  Args:
      hparams(DictConfig | ListConfig): Model parameters
class_docstr_tok: ["PL","Lightning","Module","for","Reverse","Distillation","Algorithm",".","Args",":","hparams","(","DictConfig","|","ListConfig",")",":","Model","parameters"]
docstr: Configure model-specific non-mandatory callbacks.
  
  Note:
      This method is used for the existing CLI.
      When PL CLI is introduced, configure callback method will be
          deprecated, and callbacks will be configured from either
          config.yaml file or from CLI.
docstr_tok: ["Configure","model-specific","non-mandatory","callbacks",".","Note",":","This","method","is","used","for","the","existing","CLI",".","When","PL","CLI","is","introduced",",","configure","callback","method","will","be","deprecated",",","and","callbacks","will","be","configured","from","either","config.yaml","file","or","from","CLI","."]
code: def configure_callbacks(self) -> list[EarlyStopping]:
      """Configure model-specific non-mandatory callbacks.
  
      Note:
          This method is used for the existing CLI.
          When PL CLI is introduced, configure callback method will be
              deprecated, and callbacks will be configured from either
              config.yaml file or from CLI.
      """
      callbacks = []
      if "early_stopping" in self.hparams.model:
          early_stopping = EarlyStopping(
              monitor=self.hparams.model.early_stopping.metric,
              patience=self.hparams.model.early_stopping.patience,
              mode=self.hparams.model.early_stopping.mode,
          )
          callbacks.append(early_stopping)
      return callbacks
  
file_path: anomalib/src/anomalib/models/reverse_distillation/lightning_model.py
imports: from __future__ import annotations
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning.callbacks import EarlyStopping
  from pytorch_lightning.utilities.types import STEP_OUTPUT
  from torch import Tensor, optim
  from anomalib.models.components import AnomalyModule
  from .anomaly_map import AnomalyMapGenerationMode
  from .loss import ReverseDistillationLoss
  from .torch_model import ReverseDistillationModel
label_desc: Use image node_id 2 for calling the ReverseDistillationLightning obj's underlying member method code with example usage: obj.configure_callbacks() and returns: callbacks