code: def update_lr(self, loss):
      lr_sche = self.optimizer._learning_rate
      if isinstance(lr_sche, paddle.optimizer.lr.LRScheduler):
          if isinstance(lr_sche, paddle.optimizer.lr.ReduceOnPlateau):
              lr_sche.step(loss)
          else:
              lr_sche.step()
  
imports: import abc
  import paddle
  from paddlepanseg.core import infer
label_desc: Use image node_id 1 for calling the PanSegRunner obj's underlying member method code with example usage: obj.update_lr(loss) without return types