def METHOD_NAME(args):
    # load and preprocess dataset
    data = load_data(args)
    g = data[0]
    if args.gpu < 0:
        cuda = False
    else:
        cuda = True
        g = g.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_classes
    n_edges = g.num_edges()
    print(
        """----Data statistics------'
      #Edges %d
      #Classes %d
      #Train samples %d
      #Val samples %d
      #Test samples %d"""
        % (
            n_edges,
            n_classes,
            train_mask.sum().item(),
            val_mask.sum().item(),
            test_mask.sum().item(),
        )
    )
    # graph preprocess and calculate normalization factor
    g = g.remove_self_loop().add_self_loop()
    n_edges = g.num_edges()
    us, vs = g.edges(order="eid")
    udeg, vdeg = 1 / torch.sqrt(g.in_degrees(us).float()), 1 / torch.sqrt(
        g.in_degrees(vs).float()
    )
    pseudo = torch.cat([udeg.unsqueeze(1), vdeg.unsqueeze(1)], dim=1)
    # create GraphSAGE model
    model = MoNet(
        g,
        in_feats,
        args.n_hidden,
        n_classes,
        args.n_layers,
        args.pseudo_dim,
        args.n_kernels,
        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
    mean = 0
    for epoch in range(args.n_epochs):
        model.train()
        if epoch >= 3:
            t0 = time.time()
        # forward
        logits = model(features, pseudo)
        loss = loss_fcn(logits[train_mask], labels[train_mask])
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        if epoch >= 3:
            mean = (mean * (epoch - 3) + (time.time() - t0)) / (epoch - 2)
            acc = evaluate(model, features, pseudo, labels, val_mask)
            print(
                "Epoch {:05d} | Time(s) {:.4f} | Loss {:.4f} | Accuracy {:.4f} | "
                "ETputs(KTEPS) {:.2f}".format(
                    epoch,
                    mean,
                    loss.item(),
                    acc,
                    n_edges / mean / 1000,
                )
            )
    print()
    acc = evaluate(model, features, pseudo, labels, test_mask)
    print("Test Accuracy {:.4f}".format(acc))