def METHOD_NAME(data):
    data = utils.process_data(data)
    device = utils.get_bench_device()
    g = data[0]
    g.ndata["features"] = g.ndata["feat"]
    g.ndata["labels"] = g.ndata["label"]
    g = g.remove_self_loop().add_self_loop()
    in_feats = g.ndata["features"].shape[1]
    n_classes = data.num_classes
    # Create csr/coo/csc formats before launching training processes with multi-gpu.
    # This avoids creating certain formats in each sub-process, which saves momory and CPU.
    g.create_formats_()
    num_hidden = 16
    num_heads = 8
    num_layers = 2
    fan_out = "10,25"
    batch_size = 1024
    lr = 0.003
    dropout = 0.5
    num_workers = 4
    iter_start = 3
    iter_count = 10
    train_nid = th.nonzero(g.ndata["train_mask"], as_tuple=True)[0]
    # Create PyTorch DataLoader for constructing blocks
    sampler = dgl.dataloading.MultiLayerNeighborSampler(
        [int(fanout) for fanout in fan_out.split(",")]
    )
    dataloader = dgl.dataloading.DataLoader(
        g,
        train_nid,
        sampler,
        batch_size=batch_size,
        shuffle=True,
        drop_last=False,
        num_workers=num_workers,
    )
    # Define model and optimizer
    model = GAT(
        in_feats, num_heads, num_hidden, n_classes, num_layers, F.relu, dropout
    )
    model = model.to(device)
    loss_fcn = nn.CrossEntropyLoss()
    loss_fcn = loss_fcn.to(device)
    optimizer = optim.Adam(model.parameters(), lr=lr)
    # Enable dataloader cpu affinitization for cpu devices (no effect on gpu)
    with dataloader.enable_cpu_affinity():
        # Loop over the dataloader to sample the computation dependency graph as a list of
        # blocks.
        # Training loop
        avg = 0
        iter_tput = []
        for step, (input_nodes, seeds, blocks) in enumerate(dataloader):
            # Load the input features as well as output labels
            blocks = [block.int().to(device) for block in blocks]
            batch_inputs = blocks[0].srcdata["features"]
            batch_labels = blocks[-1].dstdata["labels"]
            # Compute loss and prediction
            batch_pred = model(blocks, batch_inputs)
            loss = loss_fcn(batch_pred, batch_labels)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            # start timer at before iter_start
            if step == iter_start - 1:
                t0 = time.time()
            elif (
                step == iter_count + iter_start - 1
            ):  # time iter_count iterations
                break
    t1 = time.time()
    return (t1 - t0) / iter_count