How to use the pyopenms.Param function in pyopenms

# 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 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

def getModelDefaults(default_model):
    params = pms.Param()
    params.setValue("type", default_model, "Type of model")
    model_types = [ "linear", "interpolated"]
    if default_model not in model_types:
        model_types.insert(0, default_model)
    params.setValidStrings("type", model_types)

    model_params = pms.Param()

    pms.TransformationModelLinear.getDefaultParameters(model_params)
    params.insert("linear:", model_params)
    params.setSectionDescription("linear", "Parameters for 'linear' model")

    pms.TransformationModelInterpolated.getDefaultParameters(model_params)
    entry = model_params.getEntry("interpolation_type")
    interpolation_types = entry.valid_strings
    model_params.setValidStrings("interpolation_type", interpolation_types)

def getModelDefaults(default_model):
    params = pms.Param()
    params.setValue("type", default_model, "Type of model")
    model_types = [ "linear", "interpolated"]
    if default_model not in model_types:
        model_types.insert(0, default_model)
    params.setValidStrings("type", model_types)

    model_params = pms.Param()

    pms.TransformationModelLinear.getDefaultParameters(model_params)
    params.insert("linear:", model_params)
    params.setSectionDescription("linear", "Parameters for 'linear' model")

    pms.TransformationModelInterpolated.getDefaultParameters(model_params)
    entry = model_params.getEntry("interpolation_type")
    interpolation_types = entry.valid_strings
    model_params.setValidStrings("interpolation_type", interpolation_types)

    params.insert("interpolated:", model_params)
    params.setSectionDescription("interpolated", "Parameters for 'interpolated' model")
    return params

# 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);