code: def preprocess(is_train=False):
      FLAGS = ArgsParser().parse_args()
      profiler_options = FLAGS.profiler_options
      config = load_config(FLAGS.config)
      merge_config(FLAGS.opt)
      profile_dic = {"profiler_options": FLAGS.profiler_options}
      merge_config(profile_dic)
  
      if is_train:
          # save_config
          save_model_dir = config["save_model_dir"]
          os.makedirs(save_model_dir, exist_ok=True)
          with open(
              os.path.join(save_model_dir, "config.yml"), "w"
          ) as f:
              yaml.dump(
                  dict(config),
                  f,
                  default_flow_style=False,
                  sort_keys=False,
              )
          log_file = "{}/train.log".format(save_model_dir)
      else:
          log_file = None
      logger = get_logger(log_file=log_file)
  
      # check if set use_gpu=True in paddlepaddle cpu version
      use_gpu = config["use_gpu"]
  
      print_dict(config, logger)
  
      return config, logger
  
imports: import numpy
  import os
  import sys
  import platform
  import yaml
  import time
  import shutil
  import paddle
  import paddle.distributed
  from tqdm import tqdm
  from argparse import ArgumentParser, RawDescriptionHelpFormatter
  from utils import get_logger, print_dict
label_desc: Use image node_id 3 for calling a global function with example usage: preprocess(is_train) and returns: config,  logger