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"]
          )
          torch.cuda.manual_seed(config["seed"])
          torch.cuda.manual_seed_all(config["seed"])
          torch.backends.cudnn.deterministic = True
          cudnn.benchmark = False
      else:
          config["device"] = torch.device("cpu")
  
      print("\n" + config["out_dir"])
  
      runner = ModelHandler(config)
      t0 = time.time()
  
      test_acc = runner.test()
  
      runtime = time.time() - t0
      print("Total runtime: {:.2f}s".format(runtime))
      runner.logger.write("Total runtime: {:.2f}s\n".format(runtime))
      runner.logger.close()
  
      return test_acc
  
imports: import argparse
  import json
  import os
  import time
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  import yaml
  from torch.utils.data import DataLoader
  from graph4nlp.pytorch.datasets.trec import TrecDataset
  from graph4nlp.pytorch.modules.evaluation.accuracy import Accuracy
  from graph4nlp.pytorch.modules.utils import constants
  from graph4nlp.pytorch.modules.utils.generic_utils import to_cuda
  from graph4nlp.pytorch.modules.utils.logger import Logger
  from .run_text_classifier import TextClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: main(config) and returns: test_acc