def METHOD_NAME(
    model: Any,
    data: tuple,
) -> None:
    # build model and input
    meta_args, concrete_args, sequence = data
    if concrete_args is None:
        concrete_args = {}
    # init inputs
    inputs = [meta_args[i] if i in meta_args else concrete_args[i] for i in sequence]
    inputs = [i.cuda() if isinstance(i, torch.Tensor) else i for i in inputs]
    model.cuda().eval()
    # bench
    para_mem = float(parameter_size(model)) / 1024**2 * 6
    act_mem = _benchmark_memory(model, inputs)
    speed = _benchmark_speed(model, inputs)
    print("gpt origin, time: %.4fs, act mem: %.2fMB, para mem: %.2fMB, all mem: %.2fMB" %
          (speed, act_mem, para_mem, act_mem + para_mem))
    return act_mem