How to use the nyoka.keras.keras_model_to_pmml.KerasToPmml function in nyoka

def generate_submodel(self, submodel):
        """
        Generates multiple PMML object for the regression and classification submodel of RetinaNet for each connected pyramid layers

        Parameters
        ----------
        submodel :
            The Regression or the Classification submodel

        Returns
        -------
        List of Nyoka's NetworkLayer object for all the submodels
        """
        net_layers_group=list()
        for idx, name in enumerate(self._pyramid_layers):
            nyoka_pmml_reg_mod = kerasAPI.KerasToPmml(submodel)
            del nyoka_pmml_reg_mod.DeepNetwork[0].NetworkLayer[0]
            nyoka_pmml_reg_mod.DeepNetwork[0].NetworkLayer[0].connectionLayerId = name
            for idx_, lay in enumerate(nyoka_pmml_reg_mod.DeepNetwork[0].NetworkLayer):                
                lay.layerId = lay.layerId+"_"+name
                if idx_ != 0:
                    lay.connectionLayerId = lay.connectionLayerId+"_"+name
            net_layers_group.extend(nyoka_pmml_reg_mod.DeepNetwork[0].NetworkLayer)
        return net_layers_group

def __init__(self, keras_model, model_name=None, description=None,copyright=None,\
        dataSet=None, predictedClasses=None, script_args=None):
        if not dataSet:
            dataSet = 'input'
        data_dict = KerasDataDictionary(dataSet, predictedClasses, script_args)
        trans_dict = None
        if script_args:
            self.validate_script_args(script_args)
            trans_dict = KerasTransformationDictionary(dataSet,script_args)
        super(KerasToPmml, self).__init__(
            version="4.4", Header=KerasHeader(description=description, copyright=copyright),
            DataDictionary=data_dict, TransformationDictionary= trans_dict, DeepNetwork=[
                KerasNetwork(keras_model=keras_model, 
                model_name=model_name, 
                dataSet=dataSet, 
                predictedClasses=predictedClasses,
                script_args=script_args)])

Returns
        -------
        Nyoka's PMML object

        """
        from keras.models import Sequential
        mod = Sequential()
        for l in model.layers[1:]:
            if l.__class__.__name__ == "Model":
                break
            mod.add(l)
        if trained_classes == None:
            warnings.warn(f"trained_classes are not provided. Maximum 80 classes will be considered.")
            trained_classes = ["Category_"+str(i+1).zfill(2) for i in range(80)]

        group1_pmml = kerasAPI.KerasToPmml(mod,model_name=self.model_name,dataSet=input_format, description=self.description,
         predictedClasses=trained_classes, script_args=self.script_args)
        return group1_pmml