code: def get_parallel_exe(program, loss):
      """get_parallel_exe"""
      exec_strategy = F.ExecutionStrategy()
      exec_strategy.num_threads = 1  # 2 for fp32 4 for fp16
      exec_strategy.use_experimental_executor = True
      exec_strategy.num_iteration_per_drop_scope = 10  # important shit
  
      build_strategy = F.BuildStrategy()
      build_strategy.enable_inplace = True
      build_strategy.memory_optimize = True
      build_strategy.remove_unnecessary_lock = True
  
      # return compiled_prog
      train_exe = F.ParallelExecutor(
          use_cuda=True,
          loss_name=loss.name,
          build_strategy=build_strategy,
          exec_strategy=exec_strategy,
          main_program=program,
      )
      return train_exe
  
imports: import argparse
  import time
  import os
  import glob
  import numpy
  import paddle.fluid
  import paddle.fluid.layers
  from pgl.utils.logger import log
  from pgl.graph import Graph
  from pgl.sample import graph_alias_sample_table
  from pgl import data_loader
  import mp_reader
  from reader import GESReader
  from model import GESModel
label_desc: Use image node_id 5 for calling a global function with example usage: get_parallel_exe(program, loss) and returns: train_exe