class_docstr: PL Lightning Module for the CFA model.
  
  Args:
      hparams (DictConfig | ListConfig): Model params
class_docstr_tok: ["PL","Lightning","Module","for","the","CFA","model",".","Args",":","hparams","(","DictConfig","|","ListConfig",")",":","Model","params"]
docstr: Configures optimizers for the CFA Model.
  
  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","the","CFA","Model",".","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) -> torch.optim.Optimizer:
      """Configures optimizers for the CFA Model.
  
      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 torch.optim.AdamW(
          params=self.model.parameters(),
          lr=self.hparams.model.lr,
          weight_decay=self.hparams.model.weight_decay,
          amsgrad=self.hparams.model.amsgrad,
      )
  
imports: from __future__ import annotations
  import logging
  import torch
  from omegaconf import DictConfig, ListConfig
  from pytorch_lightning import Callback
  from pytorch_lightning.callbacks import EarlyStopping
  from pytorch_lightning.utilities.types import STEP_OUTPUT
  from torch import Tensor
  from torch.optim.optimizer import Optimizer
  from anomalib.models.cfa.loss import CfaLoss
  from anomalib.models.cfa.torch_model import CfaModel
  from anomalib.models.components import AnomalyModule
label_desc: Use image node_id 3 for calling the CfaLightning obj's underlying member method code with example usage: obj.configure_optimizers() and returns: torch