def METHOD_NAME(
    model,
    booster,
    dataloader,
    criterion,
    optimizer,
    lr_scheduler,
    num_runs=10,
):
    for i, data in enumerate(dataloader):
        if i > num_runs:
            break
        inputs, labels = data[0].cuda(), data[1].cuda()
        outputs = model(inputs, labels=labels)
        loss = criterion(outputs)
        booster.backward(loss, optimizer)
        optimizer.step()
        lr_scheduler.step()
        optimizer.zero_grad()