code: def preprocess(is_train=False):
      FLAGS = ArgsParser().parse_args()
      profiler_options = FLAGS.profiler_options
      config = load_config(FLAGS.config)
      config = merge_config(config, FLAGS.opt)
      profile_dic = {"profiler_options": FLAGS.profiler_options}
      config = merge_config(config, profile_dic)
  
      if is_train:
          # save_config
          save_model_dir = config["Global"]["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["Global"].get("use_gpu", False)
      use_xpu = config["Global"].get("use_xpu", False)
      use_npu = config["Global"].get("use_npu", False)
      use_mlu = config["Global"].get("use_mlu", False)
  
      alg = config["Architecture"]["algorithm"]
      assert alg in [
          "EAST",
          "DB",
          "SAST",
          "Rosetta",
          "CRNN",
          "STARNet",
          "RARE",
          "SRN",
          "CLS",
          "PGNet",
          "Distillation",
          "NRTR",
          "TableAttn",
          "SAR",
          "PSE",
          "SEED",
          "SDMGR",
          "LayoutXLM",
          "LayoutLM",
          "LayoutLMv2",
          "PREN",
          "FCE",
          "SVTR",
          "SVTR_LCNet",
          "ViTSTR",
          "ABINet",
          "DB++",
          "TableMaster",
          "SPIN",
          "VisionLAN",
          "Gestalt",
          "SLANet",
          "RobustScanner",
          "CT",
          "RFL",
          "DRRG",
          "CAN",
          "Telescope",
          "SATRN",
          "SVTR_HGNet",
      ]
  
      if use_xpu:
          device = "xpu:{0}".format(os.getenv("FLAGS_selected_xpus", 0))
      elif use_npu:
          device = "npu:{0}".format(os.getenv("FLAGS_selected_npus", 0))
      elif use_mlu:
          device = "mlu:{0}".format(os.getenv("FLAGS_selected_mlus", 0))
      else:
          device = (
              "gpu:{}".format(dist.ParallelEnv().dev_id)
              if use_gpu
              else "cpu"
          )
      check_device(use_gpu, use_xpu, use_npu, use_mlu)
  
      device = paddle.set_device(device)
  
      config["Global"]["distributed"] = dist.get_world_size() != 1
  
      loggers = []
  
      if (
          "use_visualdl" in config["Global"]
          and config["Global"]["use_visualdl"]
      ):
          save_model_dir = config["Global"]["save_model_dir"]
          vdl_writer_path = save_model_dir
          log_writer = VDLLogger(vdl_writer_path)
          loggers.append(log_writer)
      if (
          "use_wandb" in config["Global"]
          and config["Global"]["use_wandb"]
      ) or "wandb" in config:
          save_dir = config["Global"]["save_model_dir"]
          wandb_writer_path = "{}/wandb".format(save_dir)
          if "wandb" in config:
              wandb_params = config["wandb"]
          else:
              wandb_params = dict()
          wandb_params.update({"save_dir": save_dir})
          log_writer = WandbLogger(**wandb_params, config=config)
          loggers.append(log_writer)
      else:
          log_writer = None
      print_dict(config, logger)
  
      if loggers:
          log_writer = Loggers(loggers)
      else:
          log_writer = None
  
      logger.info(
          "train with paddle {} and device {}".format(
              paddle.__version__, device
          )
      )
      return config, device, logger, log_writer
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import sys
  import platform
  import yaml
  import time
  import datetime
  import paddle
  import paddle.distributed
  from tqdm import tqdm
  import cv2
  import numpy
  from argparse import ArgumentParser, RawDescriptionHelpFormatter
  from ppocr.utils.stats import TrainingStats
  from ppocr.utils.save_load import save_model
  from ppocr.utils.utility import print_dict, AverageMeter
  from ppocr.utils.logging import get_logger
  from ppocr.utils.loggers import VDLLogger, WandbLogger, Loggers
  from ppocr.utils import profiler
  from ppocr.data import build_dataloader
label_desc: Use image node_id 9 for calling a global function with example usage: preprocess(is_train) and returns: config,  device,  logger,  log_writer