def METHOD_NAME(args):
    # load and preprocess dataset
    if args.dataset == "cora":
        data = CoraGraphDataset()
    elif args.dataset == "citeseer":
        data = CiteseerGraphDataset()
    elif args.dataset == "pubmed":
        data = PubmedGraphDataset()
    else:
        raise ValueError("Unknown dataset: {}".format(args.dataset))
    g = data[0]
    if args.gpu < 0:
        cuda = False
    else:
        cuda = True
        g = g.int().to(args.gpu)
    features = g.ndata["feat"]
    labels = g.ndata["label"]
    train_mask = g.ndata["train_mask"]
    val_mask = g.ndata["val_mask"]
    test_mask = g.ndata["test_mask"]
    in_feats = features.shape[1]
    n_classes = data.num_labels
    n_edges = data.graph.number_of_edges()
    print(
        """----Data statistics------'
      #Edges %d
      #Classes %d
      #Train samples %d
      #Val samples %d
      #Test samples %d"""
        % (
            n_edges,
            n_classes,
            train_mask.int().sum().item(),
            val_mask.int().sum().item(),
            test_mask.int().sum().item(),
        )
    )
    # add self loop
    if args.self_loop:
        g = dgl.remove_self_loop(g)
        g = dgl.add_self_loop(g)
    n_edges = g.number_of_edges()
    # normalization
    degs = g.in_degrees().float()
    norm = torch.pow(degs, -0.5)
    norm[torch.isinf(norm)] = 0
    if cuda:
        norm = norm.cuda()
    g.ndata["norm"] = norm.unsqueeze(1)
    # create GCN model
    model = GCN(g, in_feats, args.n_hidden, n_classes, args.n_layers, F.relu, args.dropout)
    if cuda:
        model.cuda()
    loss_fcn = torch.nn.CrossEntropyLoss()
    # use optimizer
    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
    # initialize graph
    dur = []
    for epoch in range(args.n_epochs):
        model.train()
        if epoch >= 3:
            t0 = time.time()
        # forward
        logits = model(features)
        loss = loss_fcn(logits[train_mask], labels[train_mask])
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if epoch >= 3:
            dur.append(time.time() - t0)
        acc = evaluate(model, features, labels, val_mask)
        print(
            "Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
            "ETputs(KTEPS) {:.2f}".format(
                epoch, np.mean(dur), loss.item(), acc, n_edges / np.mean(dur) / 1000
            )
        )
    print()
    acc = evaluate(model, features, labels, test_mask)
    print("Test accuracy {:.2%}".format(acc))