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

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

                # TODO: Check if 30 is good default CE!
                # RG: removed `if ce == 0` in get_vector, split up into two functions
                colen = 30
                if "ce" in data.columns:
                    try:
                        colen = int(float(ces[title]))
                    except:
                        sys.stderr.write("Could not parse collision energy!\n")
                        continue

                if vector_file:
                    # get targets
                    targets = ms2pip_pyx.get_targets(
                        modpeptide, msms, peaks, float(fragerror), peaks_version
                    )
                    psmids.extend([title] * (len(targets[0])))
                    if "ce" in data.columns:
                        dvectors.append(
                            np.array(
                                ms2pip_pyx.get_vector_ce(
                                    peptide, modpeptide, charge, colen
                                ),
                                dtype=np.uint16,
                            )
                        )  # SD: added collision energy
                    else:
                        dvectors.append(
                            np.array(
                                ms2pip_pyx.get_vector(peptide, modpeptide, charge),

numby,
                            numall,
                            explainedby,
                            explainedall,
                            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()