def METHOD_NAME(the_surr, the_xs):
    # Set model to evaluate mode
    the_surr.eval()
    the_surr.m_likelihood.eval()
    with torch.no_grad(), gpytorch.settings.use_toeplitz(False), gpytorch.settings.debug(False):
        y_pred = the_surr(torch.tensor(the_xs, dtype=torch.float32))
    return y_pred.mean().data.numpy().astype(numpy.float64)