def METHOD_NAME(args, device, g, dataset, model):
    # create sampler & dataloader
    train_idx = dataset.train_idx.to(device)
    val_idx = dataset.val_idx.to(device)
    sampler = NeighborSampler(
        [10, 10, 10],  # fanout for [layer-0, layer-1, layer-2]
        prefetch_node_feats=["feat"],
        prefetch_labels=["label"],
    )
    use_uva = args.mode == "mixed"
    train_dataloader = DataLoader(
        g,
        train_idx,
        sampler,
        device=device,
        batch_size=1024,
        shuffle=True,
        drop_last=False,
        num_workers=0,
        use_uva=use_uva,
    )
    val_dataloader = DataLoader(
        g,
        val_idx,
        sampler,
        device=device,
        batch_size=1024,
        shuffle=True,
        drop_last=False,
        num_workers=0,
        use_uva=use_uva,
    )
    opt = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=5e-4)
    for epoch in range(10):
        model.METHOD_NAME()
        total_loss = 0
        for it, (input_nodes, output_nodes, blocks) in enumerate(
            train_dataloader
        ):
            x = blocks[0].srcdata["feat"]
            y = blocks[-1].dstdata["label"]
            y_hat = model(blocks, x)
            loss = F.cross_entropy(y_hat, y)
            opt.zero_grad()
            loss.backward()
            opt.step()
            total_loss += loss.item()
        acc = evaluate(model, g, val_dataloader)
        print(
            "Epoch {:05d} | Loss {:.4f} | Accuracy {:.4f} ".format(
                epoch, total_loss / (it + 1), acc.item()
            )
        )