How to use the pyopenms.MRMFeatureFinderScoring function in pyopenms

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] 

    # // 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";

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] 

    # // 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";

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] 

    # // store transformation, using a linear model as default
    trafo_out = pyopenms.TransformationDescription()
    trafo_out.setDataPoints(pairs_corrected);
    model_params = pyopenms.Param()

def main(options):
    precursor_tolerance = options.precursor_tolerance
    product_tolerance = options.product_tolerance
    out = options.outfile
    chromat_in = options.infile
    traml_in = options.traml_in

    # precursor_tolerance = 0.05
    # product_tolerance = 0.05
    # out = "/tmp/out.mzML"
    # chromat_in = "../source/TEST/TOPP/MRMMapping_input.chrom.mzML"
    # traml_in = "../source/TEST/TOPP/MRMMapping_input.TraML"

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

    output = algorithm(chromatogram_map, targeted, precursor_tolerance, product_tolerance)

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

def main(options):
    precursor_tolerance = options.precursor_tolerance
    product_tolerance = options.product_tolerance
    out = options.outfile
    chromat_in = options.infile
    traml_in = options.traml_in

    # precursor_tolerance = 0.05
    # product_tolerance = 0.05
    # out = "/tmp/out.mzML"
    # chromat_in = "../source/TEST/TOPP/MRMMapping_input.chrom.mzML"
    # traml_in = "../source/TEST/TOPP/MRMMapping_input.TraML"

    ff = pyopenms.MRMFeatureFinderScoring()
    chromatogram_map = pyopenms.MSExperiment()
    fh = pyopenms.FileHandler()
    fh.loadExperiment(chromat_in, chromatogram_map)
    targeted = pyopenms.TargetedExperiment();
    tramlfile = pyopenms.TraMLFile();
    tramlfile.load(traml_in, targeted);
     
    output = algorithm(chromatogram_map, targeted, precursor_tolerance, product_tolerance)

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

# the RT space (e.g. for 100 second RT space, set it to 100)
    rt_normalization_factor = 100.0

    pp_params = pp.getDefaults();
    pp_params.setValue("PeakPickerMRM:remove_overlapping_peaks", options.remove_overlapping_peaks, '')
    pp_params.setValue("PeakPickerMRM:method", options.method, '')
    if (metabolomics):
        # Need to change those for metabolomics and very short peaks!
        pp_params.setValue("PeakPickerMRM:signal_to_noise", 0.01, '')
        pp_params.setValue("PeakPickerMRM:peak_width", 0.1, '')
        pp_params.setValue("PeakPickerMRM:gauss_width", 0.1, '')
        pp_params.setValue("resample_boundary", 0.05, '')
        pp_params.setValue("compute_peak_quality", "true", '')
    pp.setParameters(pp_params)

    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: