code: def main():
      parser = argparse.ArgumentParser(description="OGBN-Arxiv (GNN)")
      parser.add_argument("--device", type=int, default=0)
      parser.add_argument("--log_steps", type=int, default=1)
      parser.add_argument("--use_sage", action="store_true")
      parser.add_argument("--num_layers", type=int, default=3)
      parser.add_argument("--hidden_channels", type=int, default=256)
      parser.add_argument("--dropout", type=float, default=0.5)
      parser.add_argument("--lr", type=float, default=0.01)
      parser.add_argument("--epochs", type=int, default=500)
      parser.add_argument("--runs", type=int, default=10)
      args = parser.parse_args()
      print(args)
  
      device = (
          f"cuda:{args.device}" if torch.cuda.is_available() else "cpu"
      )
      device = torch.device(device)
  
      dataset = PygNodePropPredDataset(
          name="ogbn-arxiv", transform=T.ToSparseTensor()
      )
  
      data = dataset[0]
      data.adj_t = data.adj_t.to_symmetric()
      data = data.to(device)
  
      split_idx = dataset.get_idx_split()
      train_idx = split_idx["train"].to(device)
  
      if args.use_sage:
          model = SAGE(
              data.num_features,
              args.hidden_channels,
              dataset.num_classes,
              args.num_layers,
              args.dropout,
          ).to(device)
      else:
          model = GCN(
              data.num_features,
              args.hidden_channels,
              dataset.num_classes,
              args.num_layers,
              args.dropout,
          ).to(device)
  
      evaluator = Evaluator(name="ogbn-arxiv")
      logger = Logger(args.runs, args)
  
      for run in range(args.runs):
          model.reset_parameters()
          optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
          for epoch in range(1, 1 + args.epochs):
              loss = train(model, data, train_idx, optimizer)
              result = test(model, data, split_idx, evaluator)
              logger.add_result(run, result)
  
              if epoch % args.log_steps == 0:
                  train_acc, valid_acc, test_acc = result
                  print(
                      f"Run: {run + 1:02d}, "
                      f"Epoch: {epoch:02d}, "
                      f"Loss: {loss:.4f}, "
                      f"Train: {100 * train_acc:.2f}%, "
                      f"Valid: {100 * valid_acc:.2f}% "
                      f"Test: {100 * test_acc:.2f}%"
                  )
  
          logger.print_statistics(run)
      logger.print_statistics()
  
file_path: graph4nlp/graph4nlp/pytorch/test/graph_embedding/ogbn_test.py
imports: import argparse
  import torch
  import torch.nn.functional
  import torch_geometric.transforms
  from ogb.nodeproppred import Evaluator, PygNodePropPredDataset
  from torch_geometric.nn import GCNConv, SAGEConv
  from .logger import Logger
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types