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"]
code: def __init__(self, hparams: DictConfig | ListConfig) -> None:
      super().__init__(
          input_size=hparams.model.input_size,
          backbone=hparams.model.backbone,
          gamma_c=hparams.model.gamma_c,
          gamma_d=hparams.model.gamma_d,
      )
      self.hparams: DictConfig | ListConfig  # type: ignore
      self.save_hyperparameters(hparams)
  
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 1 to create a new CfaLightning object from inherited base classes: Cfa with example: obj = CfaLightning(hparams)