How to use the nlu.messages.preference_message.PreferenceMessage function in nlu

"""
        Fills out message meta and frame attributes.
        """
        tokenized_string = g.generate_tokenized_string(raw_input_string)
        parseTree = g.generate_stanford_parse_tree(raw_input_string)
        
        subjects = extract_subject_nodes(parseTree)
        if subjects:
            self.frame['subject'] = [get_node_string(subject)
                                     for subject in subjects]
        words_temporary_pos = extract_close_keywords(
                                       PreferenceMessage.keywords_temporary_pos,
                                       tokenized_string,
                                       2)
        words_temporary_neg = extract_close_keywords(
                                       PreferenceMessage.keywords_temporary_neg,
                                       tokenized_string,
                                       2)
        words_permanent_pos = extract_close_keywords(
                                       PreferenceMessage.keywords_permanent_pos,
                                       tokenized_string,
                                       2)
        words_permanent_neg = extract_close_keywords(
                                       PreferenceMessage.keywords_permanent_neg,
                                       tokenized_string,
                                       2)
        words_temporary = words_temporary_pos + words_temporary_neg
        words_permanent = words_permanent_pos + words_permanent_neg
        if words_temporary and words_permanent:
            # Confused
            # self.frame['temporal'] = None
            # self.frame['word'] = None

self.frame['subject'] = [get_node_string(subject)
                                     for subject in subjects]
        words_temporary_pos = extract_close_keywords(
                                       PreferenceMessage.keywords_temporary_pos,
                                       tokenized_string,
                                       2)
        words_temporary_neg = extract_close_keywords(
                                       PreferenceMessage.keywords_temporary_neg,
                                       tokenized_string,
                                       2)
        words_permanent_pos = extract_close_keywords(
                                       PreferenceMessage.keywords_permanent_pos,
                                       tokenized_string,
                                       2)
        words_permanent_neg = extract_close_keywords(
                                       PreferenceMessage.keywords_permanent_neg,
                                       tokenized_string,
                                       2)
        words_temporary = words_temporary_pos + words_temporary_neg
        words_permanent = words_permanent_pos + words_permanent_neg
        if words_temporary and words_permanent:
            # Confused
            # self.frame['temporal'] = None
            # self.frame['word'] = None
            # This check is skipped due to an error in not using the POS
            # when looking up synsets.
            # TODO: Fix (example: fish)
            pass
        if words_temporary:
            self.frame['temporal'] = 'temporary'
            self.frame['word'] = words_temporary[0]
        else: # words_permanent

def _parse(self, raw_input_string, g):
        """
        Fills out message meta and frame attributes.
        """
        tokenized_string = g.generate_tokenized_string(raw_input_string)
        parseTree = g.generate_stanford_parse_tree(raw_input_string)
        
        subjects = extract_subject_nodes(parseTree)
        if subjects:
            self.frame['subject'] = [get_node_string(subject)
                                     for subject in subjects]
        words_temporary_pos = extract_close_keywords(
                                       PreferenceMessage.keywords_temporary_pos,
                                       tokenized_string,
                                       2)
        words_temporary_neg = extract_close_keywords(
                                       PreferenceMessage.keywords_temporary_neg,
                                       tokenized_string,
                                       2)
        words_permanent_pos = extract_close_keywords(
                                       PreferenceMessage.keywords_permanent_pos,
                                       tokenized_string,
                                       2)
        words_permanent_neg = extract_close_keywords(
                                       PreferenceMessage.keywords_permanent_neg,
                                       tokenized_string,
                                       2)
        words_temporary = words_temporary_pos + words_temporary_neg
        words_permanent = words_permanent_pos + words_permanent_neg

parseTree = g.generate_stanford_parse_tree(raw_input_string)
        
        subjects = extract_subject_nodes(parseTree)
        if subjects:
            self.frame['subject'] = [get_node_string(subject)
                                     for subject in subjects]
        words_temporary_pos = extract_close_keywords(
                                       PreferenceMessage.keywords_temporary_pos,
                                       tokenized_string,
                                       2)
        words_temporary_neg = extract_close_keywords(
                                       PreferenceMessage.keywords_temporary_neg,
                                       tokenized_string,
                                       2)
        words_permanent_pos = extract_close_keywords(
                                       PreferenceMessage.keywords_permanent_pos,
                                       tokenized_string,
                                       2)
        words_permanent_neg = extract_close_keywords(
                                       PreferenceMessage.keywords_permanent_neg,
                                       tokenized_string,
                                       2)
        words_temporary = words_temporary_pos + words_temporary_neg
        words_permanent = words_permanent_pos + words_permanent_neg
        if words_temporary and words_permanent:
            # Confused
            # self.frame['temporal'] = None
            # self.frame['word'] = None
            # This check is skipped due to an error in not using the POS
            # when looking up synsets.
            # TODO: Fix (example: fish)
            pass

def confidence(raw_input_string, generators):
        return get_keyword_confidence(raw_input_string,
                                      PreferenceMessage.keywords,
                                      3)