How to use the ms2pip.cython_modules.ms2pip_pyx.get_mzs function in ms2pip

if type(modpeptide) == str:
            if modpeptide == "Unknown modification":
                continue

        pepid_buf.append(pepid)
        peplen = len(peptide) - 2
        peplen_buf.append(peplen)

        ch = charges[pepid]
        charge_buf.append(ch)

        model_id = MODELS[model]["id"]
        peaks_version = MODELS[model]["peaks_version"]

        # get ion mzs
        mzs = ms2pip_pyx.get_mzs(modpeptide, peaks_version)
        mz_buf.append([np.array(m, dtype=np.float32) for m in mzs])

        # Predict the b- and y-ion intensities from the peptide
        # For C-term ion types (y, y++, z), flip the order of predictions,
        # because get_predictions follows order from vector file
        # enumerate works for variable number (and all) ion types
        predictions = ms2pip_pyx.get_predictions(
            peptide, modpeptide, ch, model_id, peaks_version, colen
        )  # SD: added colen
        prediction_buf.append([np.array(p, dtype=np.float32) for p in predictions])

        pcount += 1
        if (pcount % 500) == 0:
            sys.stdout.write("(%i)%i " % (worker_num, pcount))
            sys.stdout.flush()

float(numby) / (2 * (len(peptide) - 3)),
                            float(numall) / (18 * (len(peptide) - 3)),
                        )
                    )
                else:
                    # Predict the b- and y-ion intensities from the peptide
                    pepid_buf.append(title)
                    peplen_buf.append(len(peptide) - 2)
                    charge_buf.append(charge)

                    # get/append ion mzs, targets and predictions
                    targets = ms2pip_pyx.get_targets(
                        modpeptide, msms, peaks, float(fragerror), peaks_version
                    )
                    target_buf.append([np.array(t, dtype=np.float32) for t in targets])
                    mzs = ms2pip_pyx.get_mzs(modpeptide, peaks_version)
                    mz_buf.append([np.array(m, dtype=np.float32) for m in mzs])
                    predictions = ms2pip_pyx.get_predictions(
                        peptide, modpeptide, charge, model_id, peaks_version, colen
                    )  # SD: added colen
                    prediction_buf.append(
                        [np.array(p, dtype=np.float32) for p in predictions]
                    )

                pcount += 1
                if (pcount % 500) == 0:
                    sys.stdout.write("(%i)%i " % (worker_num, pcount))
                    sys.stdout.flush()

    f.close()
    if tableau:
        ft.close()