code: def main():
      parser = argparse.ArgumentParser(
          description="OGBN-Products (SIGN)"
      )
      parser.add_argument("--device", type=int, default=0)
      parser.add_argument("--log_steps", type=int, default=1)
      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=200)
      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-products")
      split_idx = dataset.get_idx_split()
      data = SIGN(args.num_layers)(
          dataset[0]
      )  # This might take a while.
  
      xs = [data.x] + [
          data[f"x{i}"] for i in range(1, args.num_layers + 1)
      ]
      xs_train = [x[split_idx["train"]].to(device) for x in xs]
      xs_valid = [x[split_idx["valid"]].to(device) for x in xs]
      xs_test = [x[split_idx["test"]].to(device) for x in xs]
  
      y_train_true = data.y[split_idx["train"]].to(device)
      y_valid_true = data.y[split_idx["valid"]].to(device)
      y_test_true = data.y[split_idx["test"]].to(device)
  
      model = MLP(
          data.x.size(-1),
          args.hidden_channels,
          dataset.num_classes,
          args.num_layers,
          args.dropout,
      ).to(device)
  
      evaluator = Evaluator(name="ogbn-products")
      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, xs_train, y_train_true, optimizer)
  
              train_acc = test(model, xs_train, y_train_true, evaluator)
              valid_acc = test(model, xs_valid, y_valid_true, evaluator)
              test_acc = test(model, xs_test, y_test_true, evaluator)
              result = (train_acc, valid_acc, test_acc)
              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/products/sign.py
imports: import argparse
  import torch
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_geometric.transforms import SIGN
  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