How to use the lmfit.model.save_modelresult function in lmfit

def test_save_load_modelresult(dill):
    """Save/load ModelResult with/without dill."""
    if dill:
        pytest.importorskip("dill")
    else:
        lmfit.jsonutils.HAS_DILL = False

    # create model, perform fit, save ModelResult and perform some tests
    model, params = create_model_params(x, y)
    result = model.fit(y, params, x=x)
    save_modelresult(result, SAVE_MODELRESULT)

    file_exists = wait_for_file(SAVE_MODELRESULT, timeout=10)
    assert file_exists

    text = ''
    with open(SAVE_MODELRESULT, 'r') as fh:
        text = fh.read()
    assert_between(len(text), 8000, 25000)

    # load the saved ModelResult from file and compare results
    result_saved = load_modelresult(SAVE_MODELRESULT)
    check_fit_results(result_saved)

    clear_savefile(SAVE_MODEL)

# 
import numpy as np

from lmfit.model import save_modelresult
from lmfit.models import GaussianModel

data = np.loadtxt('model1d_gauss.dat')
x = data[:, 0]
y = data[:, 1]

gmodel = GaussianModel()
result = gmodel.fit(y, x=x, amplitude=5, center=5, sigma=1)

save_modelresult(result, 'gauss_modelresult.sav')

print(result.fit_report())
# 

#
import numpy as np
from lmfit.models import GaussianModel
from lmfit.model import save_modelresult

import matplotlib.pyplot as plt

data = np.loadtxt('model1d_gauss.dat')
x = data[:, 0]
y = data[:, 1]


gmodel = GaussianModel()
result = gmodel.fit(y, x=x, amplitude=5, center=5, sigma=1)

save_modelresult(result, 'gauss_modelresult.sav')

print(result.fit_report())

plt.plot(x, y,         'bo')
plt.plot(x, result.init_fit, 'k--')
plt.plot(x, result.best_fit, 'r-')
plt.show()
#

def onSaveFitResult(self, event=None):
        dgroup = self.get_datagroup()
        deffile = dgroup.filename.replace('.', '_') + '.fitresult'
        wcards = 'Fit Results(*.fitresult)|*.fitresult|All files (*.*)|*.*'

        outfile = FileSave(self, 'Save Fit Result',
                           default_file=deffile,
                           wildcard=wcards)

        if outfile is not None:
            try:
                save_modelresult(dgroup.fit_history[-1], outfile)
            except IOError:
                print('could not write %s' % outfile)

def autosave_modelresult(self, result, fname=None):
        """autosave model result to user larch folder"""
        xyfitdir = os.path.join(site_config.usr_larchdir, 'xyfit')
        if not os.path.exists(xyfitdir):
            try:
                os.makedirs(xyfitdir)
            except OSError:
                print("Warning: cannot create XYFit user folder")
                return
        if not HAS_MODELSAVE:
            print("Warning: cannot save model results: upgrade lmfit")
            return
        if fname is None:
            fname = 'autosave.fitresult'
        fname = os.path.join(xyfitdir, fname)
        save_modelresult(result, fname)

def autosave_modelresult(self, result, fname=None):
        """autosave model result to user larch folder"""
        confdir = os.path.join(site_config.usr_larchdir, 'xas_viewer')
        if not os.path.exists(confdir):
            try:
                os.makedirs(confdir)
            except OSError:
                print("Warning: cannot create XAS_Viewer user folder")
                return
        if not HAS_MODELSAVE:
            print("Warning: cannot save model results: upgrade lmfit")
            return
        if fname is None:
            fname = 'autosave.fitmodel'
        save_modelresult(result, os.path.join(confdir, fname))

def onSaveFitResult(self, event=None):
        deffile = self.datagroup.filename.replace('.', '_') + 'peak.modl'
        sfile = FileSave(self, 'Save Fit Model', default_file=deffile,
                           wildcard=ModelWcards)
        if sfile is not None:
            result = self.get_fitresult()
            save_modelresult(result, sfile)

def autosave_modelresult(self, result, fname=None):
        """autosave model result to user larch folder"""
        xyfitdir = os.path.join(site_config.usr_larchdir, 'xyfit')
        if not os.path.exists(xyfitdir):
            try:
                os.makedirs(xyfitdir)
            except OSError:
                print("Warning: cannot create XYFit user folder")
                return
        if not HAS_MODELSAVE:
            print("Warning: cannot save model results: upgrade lmfit")
            return
        if fname is None:
            fname = 'autosave.fitresult'
        fname = os.path.join(xyfitdir, fname)
        save_modelresult(result, fname)

'guess': guess,
           'param_group': param_group,
           'confidence_intervals': confidence_intervals,
           'confidence_report': confidence_report,
           'f_test': f_test, 'chi2_map': chi2_map,
           'is_param': isParameter,
           'isparam': isParameter,
           'minimize': minimize,
           'ufloat': ufloat,
           'fit_report': fit_report,
           'Parameters': _Parameters,
           'Parameter': Parameter,
           'lm_minimize': minimize,
           'lm_save_model': save_model,
           'lm_load_model': load_model,
           'lm_save_modelresult': save_modelresult,
           'lm_load_modelresult': load_modelresult,
           }

for name in ('BreitWignerModel', 'ComplexConstantModel',
             'ConstantModel', 'DampedHarmonicOscillatorModel',
             'DampedOscillatorModel', 'DonaichModel',
             'ExponentialGaussianModel', 'ExponentialModel',
             'ExpressionModel', 'GaussianModel', 'Interpreter',
             'LinearModel', 'LognormalModel', 'LorentzianModel',
             'MoffatModel', 'ParabolicModel', 'Pearson7Model',
             'PolynomialModel', 'PowerLawModel',
             'PseudoVoigtModel', 'QuadraticModel',
             'RectangleModel', 'SkewedGaussianModel',
             'StepModel', 'StudentsTModel', 'VoigtModel'):
    val = getattr(lmfit.models, name, None)
    if val is not None: