class_docstr: PL Lightning Module for the STFPM algorithm.
  
  Args:
      hparams (DictConfig | ListConfig): Model params
class_docstr_tok: ["PL","Lightning","Module","for","the","STFPM","algorithm",".","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,
          layers=hparams.model.layers,
      )
      self.hparams: DictConfig | ListConfig  # type: ignore
      self.save_hyperparameters(hparams)
  
file_path: anomalib/src/anomalib/models/stfpm/lightning_model.py
imports: from __future__ import annotations
  import torch
  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 anomalib.models.stfpm.loss import STFPMLoss
  from anomalib.models.stfpm.torch_model import STFPMModel
label_desc: Use image node_id 1 to create a new StfpmLightning object from inherited base classes: Stfpm with example: obj = StfpmLightning(hparams)