code: def _append_optimize_op(self, block, param_and_grad):
      if self.set_param_lr_func is None:
          return super(AdamWDL, self)._append_optimize_op(
              block, param_and_grad
          )
  
      self._append_decoupled_weight_decay(block, param_and_grad)
      prev_lr = param_and_grad[0].optimize_attr["learning_rate"]
      self.set_param_lr_func(param_and_grad[0])
      # excute Adam op
      res = super(AdamW, self)._append_optimize_op(
          block, param_and_grad
      )
      param_and_grad[0].optimize_attr["learning_rate"] = prev_lr
      return res
  
file_path: PaddleDetection/ppdet/optimizer/adamw.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle.optimizer import AdamW
  from functools import partial
  import re
label_desc: Use image node_id 2 for calling a global function with example usage: _append_optimize_op(block, param_and_grad) and returns: res, super