class_docstr: PL Lightning Module for the CFLOW algorithm.
  
  Args:
      hparams (DictConfig | ListConfig): Model params
class_docstr_tok: ["PL","Lightning","Module","for","the","CFLOW","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,
          pre_trained=hparams.model.pre_trained,
          fiber_batch_size=hparams.model.fiber_batch_size,
          decoder=hparams.model.decoder,
          condition_vector=hparams.model.condition_vector,
          coupling_blocks=hparams.model.coupling_blocks,
          clamp_alpha=hparams.model.clamp_alpha,
          permute_soft=hparams.model.permute_soft,
      )
      self.hparams: DictConfig | ListConfig  # type: ignore
      self.save_hyperparameters(hparams)
  
imports: from __future__ import annotations
  import einops
  import torch
  import torch.nn.functional
  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 torch.optim import Optimizer
  from anomalib.models.cflow.torch_model import CflowModel
  from anomalib.models.cflow.utils import get_logp, positional_encoding_2d
  from anomalib.models.components import AnomalyModule
label_desc: Use image node_id 1 to create a new CflowLightning object from inherited base classes: Cflow with example: obj = CflowLightning(hparams)