code: def create_multi_optimizer(config, parameter_list=None):
      """ """
      # create learning_rate instance
      lr_config = config["LEARNING_RATE"]
      lr_config["params"].update(
          {
              "epochs": config["epoch"],
              "step_each_epoch": config["total_images"]
              // config["TRAIN"]["batch_size"],
          }
      )
      lr = LearningRateBuilder(**lr_config)()
  
      # create optimizer instance
      opt_config = deepcopy.copy(config["OPTIMIZER"])
      opt = OptimizerBuilder(**opt_config)
      return opt(lr, parameter_list), lr
  
imports: import sys
  import math
  from paddle.optimizer.lr import LinearWarmup
  from paddle.optimizer.lr import PiecewiseDecay
  from paddle.optimizer.lr import CosineAnnealingDecay
  from paddle.optimizer.lr import ExponentialDecay
  import paddle
  import paddle.regularizer
  from copy import deepcopy
label_desc: Use image node_id 2 for calling a global function with example usage: create_multi_optimizer(config, parameter_list) and returns: opt,  lr