code: def master_param_to_train_param(
      master_params_grads, params_grads, main_prog
  ):
      for idx, m_p_g in enumerate(master_params_grads):
          train_p, _ = params_grads[idx]
          if train_p.name.find("layer_norm") > -1:
              continue
          with main_prog._optimized_guard([m_p_g[0], m_p_g[1]]):
              append_cast_op(m_p_g[0], train_p, main_prog)
  
file_path: PGL/ogb_examples/linkproppred/ogbl-ppa/utils/fp16.py
imports: from __future__ import print_function
  import paddle
  import paddle.fluid
label_desc: Use image node_id 5 for calling a global function with example usage: master_param_to_train_param(master_params_grads, params_grads, main_prog) without return types