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()
  
      val_acc = runner.train()
      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 val_acc, test_acc
  
imports: import argparse
  import os
  import time
  import numpy
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import yaml
  from torch.optim.lr_scheduler import ReduceLROnPlateau
  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.graph_construction import NodeEmbeddingBasedGraphConstruction, NodeEmbeddingBasedRefinedGraphConstruction
  from graph4nlp.pytorch.modules.graph_embedding_initialization.embedding_construction import WordEmbedding
  from graph4nlp.pytorch.modules.graph_embedding_initialization.graph_embedding_initialization import GraphEmbeddingInitialization
  from graph4nlp.pytorch.modules.graph_embedding_learning import GAT, GGNN, GraphSAGE
  from graph4nlp.pytorch.modules.loss.general_loss import GeneralLoss
  from graph4nlp.pytorch.modules.prediction.classification.graph_classification import FeedForwardNN
  from graph4nlp.pytorch.modules.utils import constants
  from graph4nlp.pytorch.modules.utils.generic_utils import EarlyStopping, grid, to_cuda
  from graph4nlp.pytorch.modules.utils.logger import Logger
label_desc: Use image node_id 1 for calling a global function with example usage: main(config) and returns: val_acc,  test_acc