code: def main():
      parser = argparse.ArgumentParser(description="OGBN-Arxiv (MLP)")
      parser.add_argument("--device", type=int, default=0)
      parser.add_argument("--log_steps", type=int, default=1)
      parser.add_argument("--use_node_embedding", 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")
      split_idx = dataset.get_idx_split()
      data = dataset[0]
  
      x = data.x
      if args.use_node_embedding:
          embedding = torch.load("embedding.pt", map_location="cpu")
          x = torch.cat([x, embedding], dim=-1)
      x = x.to(device)
  
      y_true = data.y.to(device)
      train_idx = split_idx["train"].to(device)
  
      model = MLP(
          x.size(-1),
          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, x, y_true, train_idx, optimizer)
              result = test(model, x, y_true, 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: ogb/examples/nodeproppred/arxiv/mlp.py
imports: import argparse
  import torch
  import torch.nn.functional
  from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 3 for calling a global function with example usage: main() without return types