How to use the nlu.messages.parsed_input_message.ParsedInputMessage function in nlu

import nltk
from nltk.corpus import wordnet

from nlu.messages.parsed_input_message import ParsedInputMessage
import utils

class YesNoMessage(ParsedInputMessage):
    """
    >>> YesNoMessage.confidence("Hmmm... No thanks.")
    1.0
    >>> ynm = YesNoMessage("Hmmm... No thanks.")
    >>> ynm.frame['decision']
    {'decision': False, 'word': 'No', 'id': 2}
    >>> ynm.getDecision()
    False
    >>> YesNoMessage.confidence("Ok")
    1.0
    >>> ynm = YesNoMessage("Ok")
    >>> ynm.frame['decision']
    {'decision': True, 'word': 'Ok', 'id': 0}
    >>> ynm.getDecision()
    True
    >>> YesNoMessage.confidence("Sounds good")

from nlu.messages.parsed_input_message import ParsedInputMessage

class OutOfDomainMessage(ParsedInputMessage):
    def _parse(self, raw_input_message, generators):
        pass

import nltk
from nltk.corpus import wordnet

from nlu.messages.parsed_input_message import ParsedInputMessage
from nlu.messages.msgutils import extract_close_keywords
from nlu.messages.msgutils import get_keyword_confidence
import utils

class SystemMessage(ParsedInputMessage):
    frame_keys = ['action']
    exit_keywords = ['adieu.n.01', 'bye.n.01', 'farewell.n.02', 'exit.v.01']
    restart_keywords = ['restart.v.01', 'reload.v.02']
    keywords = exit_keywords + restart_keywords
                
    def _parse(self, raw_input_string):
        """
        Fills out message meta and frame attributes
        """
       
        tokenizer = nltk.WordPunctTokenizer()
        tokenized_string = tokenizer.tokenize(raw_input_string)
        tagger = utils.combined_taggers
        tagged_string = tagger.tag(tokenized_string)

        wordActionMap = {'exit':SystemMessage.exit_keywords, 'restart':SystemMessage.restart_keywords}

from nlu.messages.parsed_input_message import ParsedInputMessage

class PrefferenceMessage(ParsedInputMessage):
    def _parse(self, raw_input_message):
        pass

from nlu.messages.parsed_input_message import ParsedInputMessage
from nlu.stanford_utils import extract_subject_nodes
from nlu.stanford_utils import get_node_string
from nlu.stanford_utils import get_parse_tree
from nlu.messages.msgutils import get_keyword_confidence
from nlu.messages.msgutils import extract_close_keywords


import nltk
from nltk.corpus import wordnet

class PreferenceMessage(ParsedInputMessage):
    """
    >>> from nlu.generators import *
    >>> cache_size = 16
    >>> generators = Generators()
    >>> generators.add(Generate_Tokenized_String, cache_size)
    >>> generators.add(Generate_Stanford_Parse_Tree, cache_size)
    
    >>> pm = PreferenceMessage('I like Japanese food.', generators)
    >>> print pm.frame
    {'word': 'like', 'temporal': 'permanent', 'prefer': True, 'subject': [u'Japanese', u'food']}
    >>> pm = PreferenceMessage('I like carrots.', generators)
    >>> print pm.frame
    {'word': 'like', 'temporal': 'permanent', 'prefer': True, 'subject': [u'carrots']}
    >>> pm = PreferenceMessage('I want carrots.', generators)
    >>> print pm.frame
    {'word': 'want', 'temporal': 'temporary', 'prefer': True, 'subject': [u'carrots']}

3 chinese
    5 mexican
    """
    
    stemmed_string = utils.stem_words(tokenized_string)
    cuisines = set.difference(wordlists.cuisines, wordlists.meal_types)
    cuisines = cuisines.union(wordlists.list_of_adjectivals)
    stemmed_cuisines = utils.stem_words(cuisines)
    results = extract_words_from_list(stemmed_cuisines, stemmed_string, True)
    if enum:
        return [(i, tokenized_string[i]) for i, w in results]
    else:
        return [tokenized_string[i] for i, w in results]


class SearchMessage(ParsedInputMessage):
    """
    >>> # Test Confidence
    >>> SearchMessage.confidence('I like apples and carrots.')
    1.0
    >>> SearchMessage.confidence('I am looking for a breakfast dish.')
    1.0
    >>> SearchMessage.confidence('What can I make with bricks?')
    1.0
    
    >>> # Test _parse
    >>> sm = SearchMessage('I like apples or carrots.')
    >>> sm.frame['ingredient']
    [{'descriptor': [], 'relationship': 'or', 'id': 2, 'preference': 0, 'name': 'apples'}, {'descriptor': [], 'relationship': 'or', 'id': 4, 'preference': 0, 'name': 'carrots'}]
    >>> sm.frame['dish']
    []
    >>> for ingredient in sm.frame['ingredient']: print ingredient['name']