def METHOD_NAME(Y: np.ndarray, M: int, resamples: int, conf_level: float):
    """Compute CIs.
    Infers ``N`` from results of sub-sample ``Y`` and re-estimates omega (ω)
    for the above ``N``.
    """
    # Use half of available data each time
    T_data = Y.shape[0]
    n_size = math.ceil(T_data * 0.5)
    res_S1 = np.zeros(resamples)
    res_ST = np.zeros(resamples)
    for i in range(resamples):
        sample_idx = np.random.choice(T_data, replace=True, size=n_size)
        Y_rs = Y[sample_idx]
        N = len(Y_rs)
        omega = math.floor((N - 1) / (2 * M))
        S1, ST = compute_orders(Y_rs, N, M, omega)
        res_S1[i] = S1
        res_ST[i] = ST
    bnd = norm.ppf(0.5 + conf_level / 2.0)
    S1_conf = bnd * res_S1.std(ddof=1)
    ST_conf = bnd * res_ST.std(ddof=1)
    return S1_conf, ST_conf