code: def main(config):
      # configure
      np.random.seed(config["seed"])
      torch.manual_seed(config["seed"])
  
      if not config["no_cuda"] and torch.cuda.is_available():
          print("[ Using CUDA ]")
          config["device"] = torch.device(
              "cuda" if config["gpu"] < 0 else "cuda:%d" % config["gpu"]
          )
          cudnn.benchmark = True
          torch.cuda.manual_seed(config["seed"])
      else:
          config["device"] = torch.device("cpu")
  
      runner = ModelHandler(config)
      test_scores = runner.translate()
  
      return test_scores
  
imports: import argparse
  import os
  import numpy
  import torch
  import torch.backends.cudnn
  from nltk.tokenize import word_tokenize
  from graph4nlp.pytorch.inference_wrapper.generator_inference_wrapper import GeneratorInferenceWrapper
  from graph4nlp.pytorch.modules.config import get_basic_args
  from graph4nlp.pytorch.modules.utils import constants
  from graph4nlp.pytorch.modules.utils.config_utils import get_yaml_config, update_values
  from graph4nlp.pytorch.modules.utils.generic_utils import EarlyStopping, to_cuda
  from main import SumModel
label_desc: Use image node_id 4 for calling a global function with example usage: main(config) and returns: test_scores