code: def main(args):
      if args.gpu < 0:
          device = "cpu"
      else:
          device = "cuda:{}".format(args.gpu)
  
      with torch.no_grad():
          data = prepare_data(device, args)
      val_accs = []
      test_accs = []
      for i in range(args.num_runs):
          print(f"Run {i} start training")
          best_val, best_test = run(args, data, device)
          val_accs.append(best_val)
          test_accs.append(best_test)
  
      print(
          f"Average val accuracy: {np.mean(val_accs):.4f}, "
          f"std: {np.std(val_accs):.4f}"
      )
      print(
          f"Average test accuracy: {np.mean(test_accs):.4f}, "
          f"std: {np.std(test_accs):.4f}"
      )
  
imports: import argparse
  import time
  import dgl
  import dgl.function
  import numpy
  import torch
  import torch.nn
  from dataset import load_dataset
label_desc: Use image node_id 7 for calling a global function with example usage: main(args) without return types