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"]
code: def __init__(self, hparams: DictConfig | ListConfig) -> None:
      super().__init__(
          input_size=hparams.model.input_size,
          backbone=hparams.model.backbone,
          layers=hparams.model.layers,
          pre_trained=hparams.model.pre_trained,
          anomaly_map_mode=hparams.model.anomaly_map_mode,
          lr=hparams.model.lr,
          beta1=hparams.model.beta1,
          beta2=hparams.model.beta2,
      )
      self.hparams: DictConfig | ListConfig  # type: ignore
      self.save_hyperparameters(hparams)
  
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 1 to create a new ReverseDistillationLightning object from inherited base classes: ReverseDistillation with example: obj = ReverseDistillationLightning(hparams)