How to use the quantlib.math.optimization.LevenbergMarquardt function in QuantLib

if ival is None:
        ival = {'v0': 0.1, 'kappa': 1.0, 'theta': 0.1,
        'sigma': 0.5, 'rho': -.5}

    process = HestonProcess(
        risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
         ival['theta'], ival['sigma'], ival['rho'])

    model = HestonModel(process)
    engine = AnalyticHestonEngine(model, 64)

    for option in options:
        option.set_pricing_engine(engine)

    om = LevenbergMarquardt(1e-8, 1e-8, 1e-8)
    model.calibrate(
        options, om, EndCriteria(400, 40, 1.0e-8, 1.0e-8, 1.0e-8)
    )

    print('model calibration results:')
    print('v0: %f kappa: %f theta: %f sigma: %f rho: %f' %
          (model.v0, model.kappa, model.theta, model.sigma,
           model.rho))

    calib_error = (1.0 / len(options)) * sum(
        [pow(o.calibration_error() * 100.0, 2) for o in options])

    print('SSE: %f' % calib_error)

    return merge_df(df_option, options, 'Heston')

ival = {'v0': v0, 'kappa': 3.7, 'theta': v0,
        'sigma': 1.0, 'rho': -.6, 'lambda': .1,
        'nu': -.5, 'delta': 0.3}

    process = BatesProcess(
        risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
         ival['theta'], ival['sigma'], ival['rho'],
         ival['lambda'], ival['nu'], ival['delta'])

    model = BatesDetJumpModel(process)
    engine = BatesDetJumpEngine(model, 64)

    for option in options:
        option.set_pricing_engine(engine)

    om = LevenbergMarquardt()
    model.calibrate(
        options, om, EndCriteria(400, 40, 1.0e-8, 1.0e-8, 1.0e-8)
    )

    print('BatesDetJumpModel calibration:')
    print('v0: %f kappa: %f theta: %f sigma: %f\nrho: %f lambda: %f nu: %f \
    delta: %f\nkappaLambda: %f thetaLambda: %f' %
          (model.v0, model.kappa, model.theta, model.sigma,
           model.rho, model.Lambda, model.nu, model.delta,
           model.kappaLambda, model.thetaLambda))

    calib_error = (1.0 / len(options)) * sum(
        [pow(o.calibration_error(), 2) for o in options])

    print('SSE: %f' % calib_error)

ival = {'v0': v0, 'kappa': 3.7, 'theta': v0,
        'sigma': 1.0, 'rho': -.6, 'lambda': .1,
        'nu': -.5, 'delta': 0.3}

    process = BatesProcess(
        risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
         ival['theta'], ival['sigma'], ival['rho'],
         ival['lambda'], ival['nu'], ival['delta'])

    model = BatesModel(process)
    engine = BatesEngine(model, 64)

    for option in options:
        option.set_pricing_engine(engine)

    om = LevenbergMarquardt()
    model.calibrate(
        options, om, EndCriteria(400, 40, 1.0e-8, 1.0e-8, 1.0e-8)
    )

    print('model calibration results:')
    print('v0: %f kappa: %f theta: %f sigma: %f\nrho: %f lambda: \
    %f nu: %f delta: %f' %
          (model.v0, model.kappa, model.theta, model.sigma,
           model.rho, model.Lambda, model.nu, model.delta))

    calib_error = (1.0 / len(options)) * sum(
        [pow(o.calibration_error(), 2) for o in options])

    print('SSE: %f' % calib_error)

    return merge_df(df_option, options, 'Bates')

ival = {'v0': v0, 'kappa': 3.7, 'theta': v0,
        'sigma': .1, 'rho': -.6, 'lambda': .1,
        'nu': -.5, 'delta': 0.3}

    process = HestonProcess(
        risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
         ival['theta'], ival['sigma'], ival['rho'])

    model = BatesDoubleExpDetJumpModel(process, 1.0)
    engine = BatesDoubleExpDetJumpEngine(model, 64)

    for option in options:
        option.set_pricing_engine(engine)

    om = LevenbergMarquardt()
    model.calibrate(
        options, om, EndCriteria(400, 40, 1.0e-8, 1.0e-8, 1.0e-8)
    )

    print('BatesDoubleExpDetJumpModel calibration:')
    print('v0: %f kappa: %f theta: %f sigma: %f\nrho: %f lambda: %f \
    nuUp: %f nuDown: %f\np: %f\nkappaLambda: %f thetaLambda: %f' %
          (model.v0, model.kappa, model.theta, model.sigma,
           model.rho, model.Lambda, model.nuUp, model.nuDown,
           model.p, model.kappaLambda, model.thetaLambda))

    calib_error = (1.0 / len(options)) * sum(
        [pow(o.calibration_error(), 2) for o in options])

    print('SSE: %f' % calib_error)

# initial values for parameters
    if ival is None:
        ival = {'v0': 0.1, 'kappa': 1.0, 'theta': 0.1,
                'sigma': 0.5, 'rho': -.5}

    process = HestonProcess(
        risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
        ival['theta'], ival['sigma'], ival['rho'])

    model = HestonModel(process)
    engine = AnalyticHestonEngine(model, 64)

    for option in options:
        option.set_pricing_engine(engine)

    om = LevenbergMarquardt(1e-8, 1e-8, 1e-8)
    model.calibrate(
        options, om, EndCriteria(400, 40, 1.0e-8, 1.0e-8, 1.0e-8)
    )

    print('model calibration results:')
    print('v0: %f kappa: %f theta: %f sigma: %f rho: %f' %
          (model.v0, model.kappa, model.theta, model.sigma,
           model.rho))

    calib_error = (1.0 / len(options)) * sum(
        [pow(o.calibration_error() * 100.0, 2) for o in options])

    print('SSE: %f' % calib_error)

    # merge the fitted volatility and the input data set
    return merge_df(df_option, options, 'Heston')

if ival is None:
        ival = {'v0': v0, 'kappa': 3.7, 'theta': v0,
        'sigma': 1.0, 'rho': -.6, 'lambda': .1,
        'nu': -.5, 'delta': 0.3}

    process = HestonProcess(
        risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
         ival['theta'], ival['sigma'], ival['rho'])

    model = BatesDoubleExpModel(process)
    engine = BatesDoubleExpEngine(model, 64)

    for option in options:
        option.set_pricing_engine(engine)

    om = LevenbergMarquardt()
    model.calibrate(
        options, om, EndCriteria(400, 40, 1.0e-8, 1.0e-8, 1.0e-8)
    )

    print('BatesDoubleExpModel calibration:')
    print('v0: %f kappa: %f theta: %f sigma: %f\nrho: %f lambda: %f \
    nuUp: %f nuDown: %f\np: %f' %
          (model.v0, model.kappa, model.theta, model.sigma,
           model.rho, model.Lambda, model.nuUp, model.nuDown,
           model.p))

    calib_error = (1.0 / len(options)) * sum(
        [pow(o.calibration_error(), 2) for o in options])

    print('SSE: %f' % calib_error)

ival = {'v0': v0, 'kappa': 3.7, 'theta': v0,
        'sigma': 1.0, 'rho': -.6, 'lambda': .1,
        'nu': -.5, 'delta': 0.3}

    process = BatesProcess(
        risk_free_ts, dividend_ts, spot, ival['v0'], ival['kappa'],
         ival['theta'], ival['sigma'], ival['rho'],
         ival['lambda'], ival['nu'], ival['delta'])

    model = BatesModel(process)
    engine = BatesEngine(model, 64)

    for option in options:
        option.set_pricing_engine(engine)

    om = LevenbergMarquardt()
    model.calibrate(
        options, om, EndCriteria(400, 40, 1.0e-8, 1.0e-8, 1.0e-8)
    )

    print('model calibration results:')
    print('v0: %f kappa: %f theta: %f sigma: %f\nrho: %f lambda: \
    %f nu: %f delta: %f' %
          (model.v0, model.kappa, model.theta, model.sigma,
           model.rho, model.Lambda, model.nu, model.delta))

    calib_error = (1.0 / len(options)) * sum(
        [pow(o.calibration_error(), 2) for o in options])

    print('SSE: %f' % calib_error)

    return merge_df(df_option, options, 'Bates')