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
        ctx = mx.cpu(0)
    else:
        cuda = True
        ctx = mx.gpu(args.gpu)
        g = g.to(ctx)
    features = g.ndata["feat"]
    labels = mx.nd.array(g.ndata["label"], dtype="float32", ctx=ctx)
    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.sum().asscalar(),
            val_mask.sum().asscalar(),
            test_mask.sum().asscalar(),
        )
    )
    # add self loop
    if args.self_loop:
        g = dgl.remove_self_loop(g)
        g = dgl.add_self_loop(g)
    # normalization
    in_degs = g.in_degrees().astype("float32")
    out_degs = g.out_degrees().astype("float32")
    in_norm = mx.nd.power(in_degs, -0.5)
    out_norm = mx.nd.power(out_degs, -0.5)
    if cuda:
        in_norm = in_norm.as_in_context(ctx)
        out_norm = out_norm.as_in_context(ctx)
    g.ndata["in_norm"] = mx.nd.expand_dims(in_norm, 1)
    g.ndata["out_norm"] = mx.nd.expand_dims(out_norm, 1)
    model = GCN(
        g,
        args.n_hidden,
        n_classes,
        args.n_layers,
        "relu",
        args.dropout,
    )
    model.initialize(ctx=ctx)
    n_train_samples = train_mask.sum().asscalar()
    loss_fcn = gluon.loss.SoftmaxCELoss()
    # use optimizer
    print(model.collect_params())
    trainer = gluon.Trainer(
        model.collect_params(),
        "adam",
        {"learning_rate": args.lr, "wd": args.weight_decay},
    )
    # initialize graph
    dur = []
    for epoch in range(args.n_epochs):
        if epoch >= 3:
            t0 = time.time()
        # forward
        with mx.autograd.record():
            pred = model(features)
            loss = loss_fcn(pred, labels, mx.nd.expand_dims(train_mask, 1))
            loss = loss.sum() / n_train_samples
        loss.backward()
        trainer.step(batch_size=1)
        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.asscalar(),
                    acc,
                    n_edges / np.mean(dur) / 1000,
                )
            )
    # test set accuracy
    acc = evaluate(model, features, labels, test_mask)
    print("Test accuracy {:.2%}".format(acc))