How to use the pyopenms.FeatureXMLFile function in pyopenms

pp_params = pp.getDefaults();
    pp_params.setValue("PeakPickerMRM:remove_overlapping_peaks", options.remove_overlapping_peaks, '')
    pp_params.setValue("PeakPickerMRM:method", options.method, '')
    pp.setParameters(pp_params);

    chromatograms = pyopenms.MSExperiment()
    fh = pyopenms.FileHandler()
    fh.loadExperiment(chromat_in, chromatograms)
    targeted = pyopenms.TargetedExperiment();
    tramlfile = pyopenms.TraMLFile();
    tramlfile.load(traml_in, targeted);

    output = algorithm(chromatograms, targeted, pp)

    pyopenms.FeatureXMLFile().store(out, output);

mse.updateRanges()
                sizes.append((mse.getSize(), in_f))
                plog.setProgress(i)
        plog.endProgress()
        __, file_ = max(sizes)

    f_fmxl = pms.FeatureXMLFile()
    if not out_files:
        options = f_fmxl.getOptions()
        options.setLoadConvexHull(False)
        options.setLoadSubordinates(False)
        f_fmxl.setOptions(options)

    if align_features:
        map_ref = pms.FeatureMap()
        f_fxml_tmp = pms.FeatureXMLFile()
        options = f_fmxl.getOptions()
        options.setLoadConvexHull(False)
        options.setLoadSubordinates(False)
        f_fxml_tmp.setOptions(options)
        f_fxml_tmp.load(file_, map_ref)
        algorithm.setReference(map_ref)
    else:
        map_ref = pms.MSExperiment()
        pms.MzMLFile().load(file_, map_ref)
        algorithm.setReference(map_ref)

    plog.startProgress(0, len(in_files), "Align input maps")
    for i, in_file in enumerate(in_files):
        trafo = pms.TransformationDescription()
        if align_features:
            map_ = pms.FeatureMap()

if in_types == set((pms.Type.CONSENSUSXML,)):
        link_features = False
    elif in_types == set((pms.Type.FEATUREXML,)):
        link_features = True
    else:
        raise Exception("different kinds of input files")

    algorithm_parameters = params.copy("algorithm:", True)
    algorithm = pms.FeatureGroupingAlgorithmQT()
    algorithm.setParameters(algorithm_parameters)

    out_map = pms.ConsensusMap()
    fds = out_map.getColumnHeaders()
    if link_features:
        f = pms.FeatureXMLFile()
        maps = []
        for i, in_file in enumerate(in_files):
            map_ = pms.FeatureMap()
            f.load(in_file, map_)

            # set filedescriptions
            fd = fds.get(i, pms.ColumnHeader())
            fd.filename = in_file
            fd.size = map_.size()
            fd.unique_id = map_.getUniqueId()
            fds[i] = fd
            maps.append(map_)
        out_map.setColumnHeaders(fds)
        algorithm.group(maps, out_map)
    else:
        f = pms.ConsensusXMLFile()

pms.IdXMLFile().load(id_file, protein_ids, peptide_ids)

    mapper = pms.IDMapper()
    mapper.setParameters(params)

    if in_type == pms.Type.CONSENSUSXML:
        file_ = pms.ConsensusXMLFile()
        map_ = pms.ConsensusMap()
        file_.load(in_file, map_)
        mapper.annotate(map_, peptide_ids, protein_ids, use_subelements)
        addDataProcessing(map_, params, pms.ProcessingAction.IDENTIFICATION_MAPPING)
        file_.store(out_file, map_)

    elif in_type == pms.Type.FEATUREXML:
        file_ = pms.FeatureXMLFile()
        map_ = pms.FeatureMap()
        file_.load(in_file, map_)
        mapper.annotate(map_, peptide_ids, protein_ids, use_centroid_rt,
                use_centroid_mz)
        addDataProcessing(map_, params, pms.ProcessingAction.IDENTIFICATION_MAPPING)
        file_.store(out_file, map_)

    elif in_type == pms.Type.MZQ:
        file_ = pms.MzQuantMLFile()
        msq = pms.MSQuantifications()
        file_.load(in_file, msq)
        maps = msq.getConsensusMaps()
        for map_ in maps:
            mapper.annotate(map_, peptide_ids, protein_ids, use_subelements)
            addDataProcessing(map_, params, pms.ProcessingAction.IDENTIFICATION_MAPPING)
        msq.setConsensusMaps(maps)

def parse_featureXML_GT(feature_file):
    featuremap = pyopenms.FeatureMap()
    featurexml = pyopenms.FeatureXMLFile()
    featurexml.load(feature_file, featuremap)
    
    hulls = pd.DataFrame(columns=['rt_min', 'rt_max', 'mz_min', 'mz_max', 'detected', 'pic_id'])   
    for i in range(featuremap.size()):
        feature = featuremap[i]
        chs = feature.getConvexHulls()
        for j in range(len(chs)):
            pts = chs[j].getHullPoints()
            hulls.loc[len(hulls)] = [pts.min(0)[0], pts.max(0)[0], pts.min(0)[1], pts.max(0)[1], False, -1]
    return hulls

algorithm = pms.MapAlignmentAlgorithmPoseClustering()
    alignment_params = params.copy("algorithm:", True)
    algorithm.setParameters(alignment_params)
    algorithm.setLogType(pms.LogType.CMD)

    plog = pms.ProgressLogger()
    plog.setLogType(pms.LogType.CMD)

    if reference_file:
        file_ = reference_file
    elif reference_index > 0:
        file_ = in_files[reference_index-1]
    else:
        sizes = []
        if align_features:
            fh = pms.FeatureXMLFile()
            plog.startProgress(0, len(in_files), "Determine Reference map")
            for i, in_f in enumerate(in_files):
                sizes.append((fh.loadSize(in_f), in_f))
                plog.setProgress(i)
        else:
            fh = pms.MzMLFile()
            mse = pms.MSExperiment()
            plog.startProgress(0, len(in_files), "Determine Reference map")
            for i, in_f in enumerate(in_files):
                fh.load(in_f, mse)
                mse.updateRanges()
                sizes.append((mse.getSize(), in_f))
                plog.setProgress(i)
        plog.endProgress()
        __, file_ = max(sizes)

pms.IdXMLFile().load(id_file, protein_ids, peptide_ids)

    mapper = pms.IDMapper()
    mapper.setParameters(params)

    if in_type == pms.Type.CONSENSUSXML:
        file_ = pms.ConsensusXMLFile()
        map_ = pms.ConsensusMap()
        file_.load(in_file, map_)
        mapper.annotate(map_, peptide_ids, protein_ids, use_subelements)
        addDataProcessing(map_, params, pms.ProcessingAction.IDENTIFICATION_MAPPING)
        file_.store(out_file, map_)

    elif in_type == pms.Type.FEATUREXML:
        file_ = pms.FeatureXMLFile()
        map_ = pms.FeatureMap()
        file_.load(in_file, map_)
        mapper.annotate(map_, peptide_ids, protein_ids, use_centroid_rt,
                use_centroid_mz)
        addDataProcessing(map_, params, pms.ProcessingAction.IDENTIFICATION_MAPPING)
        file_.store(out_file, map_)

    elif in_type == pms.Type.MZQ:
        file_ = pms.MzQuantMLFile()
        msq = pms.MSQuantifications()
        file_.load(in_file, msq)
        maps = msq.getConsensusMaps()
        for map_ in maps:
            mapper.annotate(map_, peptide_ids, protein_ids, use_subelements)
            addDataProcessing(map_, params, pms.ProcessingAction.IDENTIFICATION_MAPPING)
        msq.setConsensusMaps(maps)