code: def dist_optimizer(args, optimizer):
      """
      Create a distributed optimizer based on a given optimizer.
  
      Args:
          args(Namespace): Arguments obtained from ArgumentParser.
          optimizer(paddle.optimizer): A normal optimizer.
      Returns:
          optimizer(fleet.distributed_optimizer): A distributed optimizer.
      """
      build_strategy, exec_strategy = create_strategy(args)
  
      dist_strategy = DistributedStrategy()
      dist_strategy.execution_strategy = exec_strategy
      dist_strategy.build_strategy = build_strategy
  
      dist_strategy.fuse_all_reduce_ops = True
      all_reduce_size = 16
      dist_strategy.fuse_grad_size_in_MB = all_reduce_size
      dist_strategy.nccl_comm_num = 1
      dist_strategy.sync_nccl_allreduce = True
  
      if args.amp:
          dist_strategy.cudnn_batchnorm_spatial_persistent = True
          dist_strategy.amp = True
          dist_strategy.amp_configs = {
              "init_loss_scaling": args.scale_loss,
              "use_dynamic_loss_scaling": args.use_dynamic_loss_scaling,
              "use_pure_fp16": args.use_pure_fp16,
          }
  
      dist_strategy.asp = args.asp
  
      optimizer = fleet.distributed_optimizer(
          optimizer, strategy=dist_strategy
      )
  
      return optimizer
  
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 4 for calling a global function with example usage: dist_optimizer(args, optimizer) and returns: optimizer