class_docstr: PL Lightning Module for the UFLOW algorithm.
  
  Args:
      hparams (DictConfig | ListConfig): Model params
class_docstr_tok: ["PL","Lightning","Module","for","the","UFLOW","algorithm",".","Args",":","hparams","(","DictConfig","|","ListConfig",")",":","Model","params"]
code: def configure_optimizers(self):
      def get_total_number_of_iterations():
          return 25000
  
      # Optimizer
      optimizer = torch.optim.Adam(
          [{"params": self.parameters(), "initial_lr": self.lr}],
          lr=self.lr,
          weight_decay=self.weight_decay,
      )
  
      # Scheduler for slowly reducing learning rate
      scheduler = torch.optim.lr_scheduler.LinearLR(
          optimizer,
          start_factor=1.0,
          end_factor=0.4,
          total_iters=get_total_number_of_iterations(),
      )
      return [optimizer], [scheduler]
  
file_path: anomalib/src/anomalib/models/uflow/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
  from anomalib.models.components import AnomalyModule
  from anomalib.models.uflow.loss import UFlowLoss
  from anomalib.models.uflow.torch_model import UflowModel
label_desc: Use image node_id 3 for calling the UflowLightning obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list, int