class_docstr: PL Lightning Module for the FastFlow algorithm.
  
  Args:
      hparams (DictConfig | ListConfig): Model params
class_docstr_tok: ["PL","Lightning","Module","for","the","FastFlow","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,
          pre_trained=hparams.model.pre_trained,
          flow_steps=hparams.model.flow_steps,
          conv3x3_only=hparams.model.conv3x3_only,
          hidden_ratio=hparams.model.hidden_ratio,
      )
      self.hparams: DictConfig | ListConfig  # type: ignore
      self.save_hyperparameters(hparams)
  
file_path: anomalib/src/anomalib/models/fastflow/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.fastflow.loss import FastflowLoss
  from anomalib.models.fastflow.torch_model import FastflowModel
label_desc: Use image node_id 1 to create a new FastflowLightning object from inherited base classes: Fastflow with example: obj = FastflowLightning(hparams)