How to use the textblob.nltk.corpus.reader.wordnet.Synset function in textblob

def _synset_from_pos_and_line(self, pos, data_file_line):
        # Construct a new (empty) synset.
        synset = Synset(self)

        # parse the entry for this synset
        try:

            # parse out the definitions and examples from the gloss
            columns_str, gloss = data_file_line.split('|')
            gloss = gloss.strip()
            definitions = []
            for gloss_part in gloss.split(';'):
                gloss_part = gloss_part.strip()
                if gloss_part.startswith('"'):
                    synset.examples.append(gloss_part.strip('"'))
                else:
                    definitions.append(gloss_part)
            synset.definition = '; '.join(definitions)

"""
        Get the path(s) from this synset to the root, counting the distance
        of each node from the initial node on the way. A set of
        (synset, distance) tuples is returned.

        :type distance: int
        :param distance: the distance (number of edges) from this hypernym to
            the original hypernym ``Synset`` on which this method was called.
        :return: A set of ``(Synset, int)`` tuples where each ``Synset`` is
           a hypernym of the first ``Synset``.
        """
        distances = set([(self, distance)])
        for hypernym in self.hypernyms() + self.instance_hypernyms():
            distances |= hypernym.hypernym_distances(distance+1, simulate_root=False)
        if simulate_root:
            fake_synset = Synset(None)
            fake_synset.name = '*ROOT*'
            fake_synset_distance = max(distances, key=itemgetter(1))[1]
            distances.add((fake_synset, fake_synset_distance+1))
        return distances

def path_similarity(self, synset1, synset2, verbose=False, simulate_root=True):
        return synset1.path_similarity(synset2, verbose, simulate_root)
    path_similarity.__doc__ = Synset.path_similarity.__doc__

    def lch_similarity(self, synset1, synset2, verbose=False, simulate_root=True):
        return synset1.lch_similarity(synset2, verbose, simulate_root)
    lch_similarity.__doc__ = Synset.lch_similarity.__doc__

    def wup_similarity(self, synset1, synset2, verbose=False, simulate_root=True):
        return synset1.wup_similarity(synset2, verbose, simulate_root)
    wup_similarity.__doc__ = Synset.wup_similarity.__doc__

    def res_similarity(self, synset1, synset2, ic, verbose=False):
        return synset1.res_similarity(synset2, ic, verbose)
    res_similarity.__doc__ = Synset.res_similarity.__doc__

    def jcn_similarity(self, synset1, synset2, ic, verbose=False):
        return synset1.jcn_similarity(synset2, ic, verbose)
    jcn_similarity.__doc__ = Synset.jcn_similarity.__doc__

    def lin_similarity(self, synset1, synset2, ic, verbose=False):
        return synset1.lin_similarity(synset2, ic, verbose)
    lin_similarity.__doc__ = Synset.lin_similarity.__doc__

    #////////////////////////////////////////////////////////////
    # Morphy
    #////////////////////////////////////////////////////////////
    # Morphy, adapted from Oliver Steele's pywordnet
    def morphy(self, form, pos=None):
        """
        Find a possible base form for the given form, with the given

share a single root which disallows this metric from working for
            synsets that are not connected. This flag (False by default)
            creates a fake root that connects all the taxonomies. Set it
            to True to enable this behavior. For the noun taxonomy,
            there is usually a default root except for WordNet version 1.6.
            If you are using wordnet 1.6, a fake root will need to be added
            for nouns as well.
        :type use_min_depth: bool
        :param use_min_depth: This setting mimics older (v2) behavior of NLTK wordnet
            If True, will use the min_depth function to calculate the lowest common
            hypernyms. This is known to give strange results for some synset pairs
            (eg: 'chef.n.01', 'fireman.n.01') but is retained for backwards compatibility
        :return: The synsets that are the lowest common hypernyms of both synsets
        """

        fake_synset = Synset(None)
        fake_synset.name = '*ROOT*'
        fake_synset.hypernyms = lambda: []
        fake_synset.instance_hypernyms = lambda: []

        if simulate_root:
            self_hypernyms = chain(self._iter_hypernym_lists(), [[fake_synset]])
            other_hypernyms = chain(other._iter_hypernym_lists(), [[fake_synset]])
        else:
            self_hypernyms = self._iter_hypernym_lists()
            other_hypernyms = other._iter_hypernym_lists()

        synsets = set(s for synsets in self_hypernyms for s in synsets)
        others = set(s for synsets in other_hypernyms for s in synsets)
        synsets.intersection_update(others)

        try:

def wup_similarity(self, synset1, synset2, verbose=False, simulate_root=True):
        return synset1.wup_similarity(synset2, verbose, simulate_root)
    wup_similarity.__doc__ = Synset.wup_similarity.__doc__

    def res_similarity(self, synset1, synset2, ic, verbose=False):
        return synset1.res_similarity(synset2, ic, verbose)
    res_similarity.__doc__ = Synset.res_similarity.__doc__

    def jcn_similarity(self, synset1, synset2, ic, verbose=False):
        return synset1.jcn_similarity(synset2, ic, verbose)
    jcn_similarity.__doc__ = Synset.jcn_similarity.__doc__

    def lin_similarity(self, synset1, synset2, ic, verbose=False):
        return synset1.lin_similarity(synset2, ic, verbose)
    lin_similarity.__doc__ = Synset.lin_similarity.__doc__

    #////////////////////////////////////////////////////////////
    # Morphy
    #////////////////////////////////////////////////////////////
    # Morphy, adapted from Oliver Steele's pywordnet
    def morphy(self, form, pos=None):
        """
        Find a possible base form for the given form, with the given
        part of speech, by checking WordNet's list of exceptional
        forms, and by recursively stripping affixes for this part of
        speech until a form in WordNet is found.

        >>> from nltk.corpus import wordnet as wn
        >>> print(wn.morphy('dogs'))
        dog
        >>> print(wn.morphy('churches'))

lch_similarity.__doc__ = Synset.lch_similarity.__doc__


def wup_similarity(synset1, synset2, verbose=False, simulate_root=True):
    return synset1.wup_similarity(synset2, verbose, simulate_root)
wup_similarity.__doc__ = Synset.wup_similarity.__doc__


def res_similarity(synset1, synset2, ic, verbose=False):
    return synset1.res_similarity(synset2, verbose)
res_similarity.__doc__ = Synset.res_similarity.__doc__


def jcn_similarity(synset1, synset2, ic, verbose=False):
    return synset1.jcn_similarity(synset2, verbose)
jcn_similarity.__doc__ = Synset.jcn_similarity.__doc__


def lin_similarity(synset1, synset2, ic, verbose=False):
    return synset1.lin_similarity(synset2, verbose)
lin_similarity.__doc__ = Synset.lin_similarity.__doc__


def _lcs_ic(synset1, synset2, ic, verbose=False):
    """
    Get the information content of the least common subsumer that has
    the highest information content value.  If two nodes have no
    explicit common subsumer, assume that they share an artificial
    root node that is the hypernym of all explicit roots.

    :type synset1: Synset
    :param synset1: First input synset.

return ic


######################################################################
# Similarity metrics
######################################################################

# TODO: Add in the option to manually add a new root node; this will be
# useful for verb similarity as there exist multiple verb taxonomies.

# More information about the metrics is available at
# http://marimba.d.umn.edu/similarity/measures.html

def path_similarity(synset1, synset2, verbose=False, simulate_root=True):
    return synset1.path_similarity(synset2, verbose, simulate_root)
path_similarity.__doc__ = Synset.path_similarity.__doc__


def lch_similarity(synset1, synset2, verbose=False, simulate_root=True):
    return synset1.lch_similarity(synset2, verbose, simulate_root)
lch_similarity.__doc__ = Synset.lch_similarity.__doc__


def wup_similarity(synset1, synset2, verbose=False, simulate_root=True):
    return synset1.wup_similarity(synset2, verbose, simulate_root)
wup_similarity.__doc__ = Synset.wup_similarity.__doc__


def res_similarity(synset1, synset2, ic, verbose=False):
    return synset1.res_similarity(synset2, verbose)
res_similarity.__doc__ = Synset.res_similarity.__doc__

# More information about the metrics is available at
# http://marimba.d.umn.edu/similarity/measures.html

def path_similarity(synset1, synset2, verbose=False, simulate_root=True):
    return synset1.path_similarity(synset2, verbose, simulate_root)
path_similarity.__doc__ = Synset.path_similarity.__doc__


def lch_similarity(synset1, synset2, verbose=False, simulate_root=True):
    return synset1.lch_similarity(synset2, verbose, simulate_root)
lch_similarity.__doc__ = Synset.lch_similarity.__doc__


def wup_similarity(synset1, synset2, verbose=False, simulate_root=True):
    return synset1.wup_similarity(synset2, verbose, simulate_root)
wup_similarity.__doc__ = Synset.wup_similarity.__doc__


def res_similarity(synset1, synset2, ic, verbose=False):
    return synset1.res_similarity(synset2, verbose)
res_similarity.__doc__ = Synset.res_similarity.__doc__


def jcn_similarity(synset1, synset2, ic, verbose=False):
    return synset1.jcn_similarity(synset2, verbose)
jcn_similarity.__doc__ = Synset.jcn_similarity.__doc__


def lin_similarity(synset1, synset2, ic, verbose=False):
    return synset1.lin_similarity(synset2, verbose)
lin_similarity.__doc__ = Synset.lin_similarity.__doc__