How to use the pyopenms.TransformationDescription function in pyopenms

# set up featurefinder and run
    featurefinder = pyopenms.MRMFeatureFinderScoring()
    # set the correct rt use values
    scoring_params = pyopenms.MRMFeatureFinderScoring().getDefaults();
    scoring_params.setValue("Scores:use_rt_score",'false', '')
    featurefinder.setParameters(scoring_params);
    featurefinder.pickExperiment(chromatograms, output, targeted, trafo, empty_swath)

    # get the pairs
    pairs=[]
    simple_find_best_feature(output, pairs, targeted)
    pairs_corrected = pyopenms.MRMRTNormalizer().rm_outliers( pairs, 0.95, 0.6) 
    pairs_corrected = [ list(p) for p in pairs_corrected] 

    # // store transformation, using a linear model as default
    trafo_out = pyopenms.TransformationDescription()
    trafo_out.setDataPoints(pairs_corrected);
    model_params = pyopenms.Param()
    model_params.setValue("symmetric_regression", 'false', '');
    model_type = "linear";
    trafo_out.fitModel(model_type, model_params);
    return trafo_out

# set up featurefinder and run
    featurefinder = pyopenms.MRMFeatureFinderScoring()
    # set the correct rt use values
    scoring_params = pyopenms.MRMFeatureFinderScoring().getDefaults();
    scoring_params.setValue("Scores:use_rt_score",'false', '')
    featurefinder.setParameters(scoring_params);
    featurefinder.pickExperiment(chromatograms, output, targeted, trafo, empty_swath)

    # get the pairs
    pairs=[]
    simple_find_best_feature(output, pairs, targeted)
    pairs_corrected = pyopenms.MRMRTNormalizer().rm_outliers( pairs, 0.95, 0.6) 
    pairs_corrected = [ list(p) for p in pairs_corrected] 

    # // store transformation, using a linear model as default
    trafo_out = pyopenms.TransformationDescription()
    trafo_out.setDataPoints(pairs_corrected);
    model_params = pyopenms.Param()
    model_params.setValue("symmetric_regression", 'false', '');
    model_type = "linear";
    trafo_out.fitModel(model_type, model_params);
    return trafo_out

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()
            f_fxml_tmp = pms.FeatureXMLFile()
            f_fxml_tmp.setOptions(f_fmxl.getOptions())
            f_fxml_tmp.load(in_file, map_)
            if in_file == file_:
                trafo.fitModel("identity")
            else:
                algorithm.align(map_, trafo)
            if out_files:
                pms.MapAlignmentTransformer.transformRetentionTimes(map_, trafo)
                addDataProcessing(map_, params, pms.ProcessingAction.ALIGNMENT)
                f_fxml_tmp.store(out_files[i], map_)
        else:
            map_ = pms.MSExperiment()
            pms.MzMLFile().load(in_file, map_)

scorer = pyopenms.MRMFeatureFinderScoring()
    scoring_params = scorer.getDefaults();
    # Only report the top 5 features
    scoring_params.setValue("stop_report_after_feature", 5, '')
    scoring_params.setValue("rt_normalization_factor", rt_normalization_factor, '')
    scorer.setParameters(scoring_params);

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

    trafoxml = pyopenms.TransformationXMLFile()
    trafo = pyopenms.TransformationDescription()
    if trafo_in is not None:
        model_params = pyopenms.Param()
        model_params.setValue("symmetric_regression", "false", "", [])
        model_type = "linear"
        trafoxml.load(trafo_in, trafo, True)
        trafo.fitModel(model_type, model_params);


    light_targeted = pyopenms.LightTargetedExperiment();
    pyopenms.OpenSwathDataAccessHelper().convertTargetedExp(targeted, light_targeted)
    output = algorithm(chromatograms, light_targeted, pp, scorer, trafo)

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

def main(options):

    # load TraML file
    targeted = pyopenms.TargetedExperiment();
    pyopenms.TraMLFile().load(options.traml_in, targeted);

    # Create empty files as input and finally as output
    empty_swath = pyopenms.MSExperiment()
    trafo = pyopenms.TransformationDescription()
    output = pyopenms.MSExperiment();

    # load input
    for infile in options.infiles:
        exp = pyopenms.MSExperiment()
        pyopenms.FileHandler().loadExperiment(infile, exp)

        transition_exp_used = pyopenms.TargetedExperiment();

        do_continue = True
        if options.is_swath:
            do_continue = pyopenms.OpenSwathHelper().checkSwathMapAndSelectTransitions(exp, targeted, transition_exp_used, options.min_upper_edge_dist)
        else:
            transition_exp_used = targeted

        if do_continue:

def algorithm(chromatograms, targeted):
    # Create empty files as input and finally as output
    empty_swath = pyopenms.MSExperiment()
    trafo = pyopenms.TransformationDescription()
    output = pyopenms.FeatureMap();

    # set up featurefinder and run
    featurefinder = pyopenms.MRMFeatureFinderScoring()
    # set the correct rt use values
    scoring_params = pyopenms.MRMFeatureFinderScoring().getDefaults();
    scoring_params.setValue("Scores:use_rt_score",'false', '')
    featurefinder.setParameters(scoring_params);
    featurefinder.pickExperiment(chromatograms, output, targeted, trafo, empty_swath)

    # get the pairs
    pairs=[]
    simple_find_best_feature(output, pairs, targeted)
    pairs_corrected = pyopenms.MRMRTNormalizer().rm_outliers( pairs, 0.95, 0.6) 
    pairs_corrected = [ list(p) for p in pairs_corrected]

def algorithm(chromatograms, targeted):
    # Create empty files as input and finally as output
    empty_swath = pyopenms.MSExperiment()
    trafo = pyopenms.TransformationDescription()
    output = pyopenms.FeatureMap();

    # set up featurefinder and run
    featurefinder = pyopenms.MRMFeatureFinderScoring()
    # set the correct rt use values
    scoring_params = pyopenms.MRMFeatureFinderScoring().getDefaults();
    scoring_params.setValue("Scores:use_rt_score",'false', '')
    featurefinder.setParameters(scoring_params);
    featurefinder.pickExperiment(chromatograms, output, targeted, trafo, empty_swath)

    # get the pairs
    pairs=[]
    simple_find_best_feature(output, pairs, targeted)
    pairs_corrected = pyopenms.MRMRTNormalizer().rm_outliers( pairs, 0.95, 0.6) 
    pairs_corrected = [ list(p) for p in pairs_corrected]