class_docstr: PL Lightning Module for Reverse Distillation Algorithm.
  
  Args:
      input_size (tuple[int, int]): Size of model input
      backbone (str): Backbone of CNN network
      layers (list[str]): Layers to extract features from the backbone CNN
      pre_trained (bool, optional): Boolean to check whether to use a pre_trained backbone.
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docstr: Configures optimizers for decoder and bottleneck.
  
  Note:
      This method is used for the existing CLI.
      When PL CLI is introduced, configure optimizers method will be
          deprecated, and optimizers will be configured from either
          config.yaml file or from CLI.
  
  Returns:
      Optimizer: Adam optimizer for each decoder
docstr_tok: ["Configures","optimizers","for","decoder","and","bottleneck",".","Note",":","This","method","is","used","for","the","existing","CLI",".","When","PL","CLI","is","introduced",",","configure","optimizers","method","will","be","deprecated",",","and","optimizers","will","be","configured","from","either","config.yaml","file","or","from","CLI",".","Returns",":","Optimizer",":","Adam","optimizer","for","each","decoder"]
code: def configure_optimizers(self) -> optim.Adam:
      """Configures optimizers for decoder and bottleneck.
  
      Note:
          This method is used for the existing CLI.
          When PL CLI is introduced, configure optimizers method will be
              deprecated, and optimizers will be configured from either
              config.yaml file or from CLI.
  
      Returns:
          Optimizer: Adam optimizer for each decoder
      """
      return optim.Adam(
          params=list(self.model.decoder.parameters())
          + list(self.model.bottleneck.parameters()),
          lr=self.learning_rate,
          betas=(self.beta1, self.beta2),
      )
  
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 ReverseDistillation obj's underlying member method code with example usage: obj.configure_optimizers() and returns: optim