code: def main(args):
      env_info = get_sys_env()
      place = (
          "gpu"
          if env_info["Paddle compiled with cuda"]
          and env_info["GPUs used"]
          else "cpu"
      )
  
      paddle.set_device(place)
      if not args.cfg:
          raise RuntimeError("No configuration file specified.")
  
      cfg = Config(args.cfg)
  
      val_dataset = cfg.val_dataset
      if val_dataset is None:
          raise RuntimeError(
              "The verification dataset is not specified in the configuration file."
          )
      elif len(val_dataset) == 0:
          raise ValueError(
              "The length of val_dataset is 0. Please check if your dataset is valid"
          )
  
      msg = "\n---------------Config Information---------------\n"
      msg += str(cfg)
      msg += "------------------------------------------------"
      logger.info(msg)
  
      model = cfg.model
      if args.model_path:
          utils.load_entire_model(model, args.model_path)
          logger.info("Loaded trained params of model successfully")
  
      config_check(cfg, val_dataset=val_dataset)
  
      evaluate(model, val_dataset, args)
  
file_path: PaddleSeg/contrib/MedicalSeg/nnunet/single_fold_eval.py
imports: import argparse
  import os
  import shutil
  import paddle
  import numpy
  import pickle
  import sys
  from medicalseg.cvlibs import Config
  from medicalseg.utils import get_sys_env, logger, config_check, utils
  from nnunet.utils import DynamicPredictor, save_segmentation_nifti_from_softmax, aggregate_scores, determine_postprocessing, resample_and_save, predict_next_stage
label_desc: Use image node_id 4 for calling a global function with example usage: main(args) without return types