How to use the pyopenms.MRMRTNormalizer function in pyopenms

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";
    trafo_out.fitModel(model_type, model_params);
    return trafo_out

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";
    trafo_out.fitModel(model_type, model_params);
    return trafo_out