How to use the nyoka.skl.skl_to_pmml function in nyoka

pml_pp = pp.get_preprocess_val(ppln_sans_predictor, col_names, model)
            trfm_dict_kwargs['TransformationDictionary'] = pml_pp['trfm_dict']
            derived_col_names = pml_pp['derived_col_names']
            col_names = pml_pp['preprocessed_col_names']
            categoric_values = pml_pp['categorical_feat_values']
            mining_imp_val = pml_pp['mining_imp_values']
        PMML_kwargs = get_PMML_kwargs(model,
                                      derived_col_names,
                                      col_names,
                                      target_name,
                                      mining_imp_val,
                                      categoric_values,
                                      model_name)
        pmml = pml.PMML(
            version=PMML_SCHEMA.VERSION.value,
            Header=sklToPmml.get_header(description),
            DataDictionary=sklToPmml.get_data_dictionary(model, col_names, target_name, categoric_values),
            **trfm_dict_kwargs,
            **PMML_kwargs
        )
        pmml.export(outfile=open(pmml_f_name, "w"), level=0)

pml_pp = pp.get_preprocess_val(ppln_sans_predictor, col_names, model)
            trfm_dict_kwargs['TransformationDictionary'] = pml_pp['trfm_dict']
            derived_col_names = pml_pp['derived_col_names']
            col_names = pml_pp['preprocessed_col_names']
            categoric_values = pml_pp['categorical_feat_values']
            mining_imp_val = pml_pp['mining_imp_values']
        PMML_kwargs = get_PMML_kwargs(model,
                                      derived_col_names,
                                      col_names,
                                      target_name,
                                      mining_imp_val,
                                      categoric_values,
                                      model_name)
        pmml = pml.PMML(
            version=PMML_SCHEMA.VERSION.value,
            Header=sklToPmml.get_header(description),
            DataDictionary=sklToPmml.get_data_dictionary(model, col_names, target_name, categoric_values),
            **trfm_dict_kwargs,
            **PMML_kwargs
        )
        pmml.export(outfile=open(pmml_f_name, "w"), level=0)

Name of the Target column.
    mining_imp_val : tuple
        Contains the mining_attributes,mining_strategy, mining_impute_value.
    categoric_values : tuple
        Contains Categorical attribute names and its values
    model_name : string
        Name of the model

    Returns
    -------
    mining_models :
        Returns Nyoka's MiningModel object
    """
    model_kwargs = sklToPmml.get_model_kwargs(model, col_names, target_name, mining_imp_val, categoric_values)
    if 'XGBRegressor' in str(model.__class__):
        model_kwargs['Targets'] = sklToPmml.get_targets(model, target_name)
    mining_models = list()
    mining_models.append(pml.MiningModel(
        modelName=model_name if model_name else "XGBoostModel",
        Segmentation=get_outer_segmentation(model, derived_col_names, col_names, target_name, mining_imp_val,categoric_values,model_name),
        **model_kwargs
    ))
    return mining_models