class_docstr: PL Lightning Module for the GANomaly Algorithm.
  
  Args:
      hparams (DictConfig | ListConfig): Model params
class_docstr_tok: ["PL","Lightning","Module","for","the","GANomaly","Algorithm",".","Args",":","hparams","(","DictConfig","|","ListConfig",")",":","Model","params"]
code: def __init__(self, hparams: DictConfig | ListConfig) -> None:
      super().__init__(
          batch_size=hparams.dataset.train_batch_size,
          input_size=hparams.model.input_size,
          n_features=hparams.model.n_features,
          latent_vec_size=hparams.model.latent_vec_size,
          extra_layers=hparams.model.extra_layers,
          add_final_conv_layer=hparams.model.add_final_conv,
          wadv=hparams.model.wadv,
          wcon=hparams.model.wcon,
          wenc=hparams.model.wenc,
          lr=hparams.model.lr,
          beta1=hparams.model.beta1,
          beta2=hparams.model.beta2,
      )
      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.callbacks import Callback, EarlyStopping
  from pytorch_lightning.utilities.types import EPOCH_OUTPUT, STEP_OUTPUT
  from torch import Tensor, optim
  from anomalib.models.components import AnomalyModule
  from anomalib.models.ganomaly.loss import DiscriminatorLoss, GeneratorLoss
  from .torch_model import GanomalyModel
label_desc: Use image node_id 1 to create a new GanomalyLightning object from inherited base classes: Ganomaly with example: obj = GanomalyLightning(hparams)