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)
      ret = runner.predict(["How far is it from Denver to Aspen ?"])
      print(f"output: {ret}")
  
imports: import argparse
  import os
  from typing import List
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  import yaml
  from nltk.tokenize import word_tokenize
  from graph4nlp.pytorch.data.dataset import Text2LabelDataItem, Text2LabelDataset
  from graph4nlp.pytorch.inference_wrapper.classifier_inference_wrapper import ClassifierInferenceWrapper
  from graph4nlp.pytorch.modules.utils import constants
  from .run_text_classifier import TextClassifier
label_desc: Use image node_id 1 for calling a global function with example usage: main(config) without return types