code: def build(
      args, main_prog, startup_prog, step_each_epoch, is_train=True
  ):
      """
      Build a executable paddle.static.Program via following four steps:
          1. Create feeds.
          2. Create a model.
          3. Create fetchs.
          4. Create an optimizer if is_train==True.
  
      Args:
          args(Namespace): Arguments obtained from ArgumentParser.
          main_prog(paddle.static.Program):The main program.
          startup_prog(paddle.static.Program):The startup program.
          step_each_epoch(int): The number of steps in each epoch.
          is_train(bool, optional): Whether the main programe created is for training. Default: True.
      Returns:
          fetchs(dict): A dict of outputs from Program execution (included loss and measures).
          lr_scheduler(paddle.optimizer.lr.LRScheduler): A learning rate scheduler.
          feeds(dict): A dict to map variables'name to their values.
          optimizer(Optimizer): An optimizer with distributed/AMP/ASP strategy.
      """
      with paddle.static.program_guard(main_prog, startup_prog):
          with paddle.utils.unique_name.guard():
              mode = Mode.TRAIN if is_train else Mode.EVAL
              feeds = create_feeds(args.image_shape)
  
              model_name = args.model_arch_name
              class_num = args.num_of_class
              input_image_channel = args.image_channel
              data_format = args.data_layout
              use_pure_fp16 = args.use_pure_fp16
              bn_weight_decay = args.bn_weight_decay
              model = models.__dict__[model_name](
                  class_num=class_num,
                  input_image_channel=input_image_channel,
                  data_format=data_format,
                  use_pure_fp16=use_pure_fp16,
                  bn_weight_decay=bn_weight_decay,
              )
              out = model(feeds["data"])
  
              fetchs = create_fetchs(
                  out, feeds, class_num, args.label_smoothing, mode=mode
              )
  
              if args.asp:
                  sparsity.set_excluded_layers(
                      main_program=main_prog,
                      param_names=[model.fc.weight.name],
                  )
  
              lr_scheduler = None
              optimizer = None
              if is_train:
                  lr_scheduler = build_lr_scheduler(
                      args, step_each_epoch
                  )
                  optimizer = build_optimizer(args, lr_scheduler)
  
                  optimizer = dist_optimizer(args, optimizer)
                  optimizer.minimize(fetchs["loss"][0], startup_prog)
  
      # This is a workaround to "Communicator of ring id 0 has not been initialized.".
      # Since Paddle's design, the initialization would be done inside train program,
      # eval_only need to manually call initialization.
      if (
          args.run_scope == RunScope.EVAL_ONLY
          and paddle.distributed.get_world_size() > 1
      ):
          collective_helper = CollectiveHelper(
              role_maker=fleet.PaddleCloudRoleMaker(is_collective=True)
          )
          collective_helper.update_startup_program(startup_prog)
  
      return fetchs, lr_scheduler, feeds, 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 5 for calling a global function with example usage: build(args, main_prog, startup_prog, step_each_epoch, is_train) and returns: fetchs,  lr_scheduler,  feeds,  optimizer