code: def compile_prog(args, program, loss_name=None, is_train=True):
      """
      Compile the given program, which would fuse computing ops or optimize memory footprint
      based building strategy in config.
  
      Args:
          args(Namespace): Arguments obtained from ArgumentParser.
          program(paddle.static.Program): The main program to be compiled.
          loss_name(str, optional): The name of loss variable. Default: None.
          is_train(bool, optional): Indicate the prupose of strategy is for
                                    training of not. Default is True.
      Returns:
          compiled_program(paddle.static.CompiledProgram): A compiled program.
      """
      build_strategy, exec_strategy = create_strategy(args, is_train)
  
      compiled_program = paddle.static.CompiledProgram(
          program, build_strategy=build_strategy
      )
      return compiled_program
  
imports: import time
  import logging
  from profile import Profiler
  import numpy
  from optimizer import build_optimizer
  from lr_scheduler import build_lr_scheduler
  from utils.misc import AverageMeter
  from utils.mode import Mode, RunScope
  from utils.utility import get_num_trainers
  import models
  import dllogger
  import paddle
  import paddle.nn.functional
  from paddle.distributed import fleet
  from paddle.distributed.fleet import DistributedStrategy
  from paddle.incubate import asp
  from paddle.distributed.fleet.meta_optimizers.common import CollectiveHelper
label_desc: Use image node_id 6 for calling a global function with example usage: compile_prog(args, program, loss_name, is_train) and returns: compiled_program