How to use the textblob.decorators.requires_nltk_corpus function in textblob

    @requires_nltk_corpus
    def train(self):
        """Train the Naive Bayes classifier on the movie review corpus."""
        super(NaiveBayesAnalyzer, self).train()
        neg_ids = nltk.corpus.movie_reviews.fileids('neg')
        pos_ids = nltk.corpus.movie_reviews.fileids('pos')
        neg_feats = [(self.feature_extractor(
            nltk.corpus.movie_reviews.words(fileids=[f])), 'neg') for f in neg_ids]
        pos_feats = [(self.feature_extractor(
            nltk.corpus.movie_reviews.words(fileids=[f])), 'pos') for f in pos_ids]
        train_data = neg_feats + pos_feats
        self._classifier = nltk.classify.NaiveBayesClassifier.train(train_data)

    @requires_nltk_corpus
    def pluralize(self):
        '''Return the plural version of the word as a Word.'''
        raise NotImplementedError

    @requires_nltk_corpus
    def lemma(self):
        """Return the lemma of this word using Wordnet's morphy function.
        """
        return self.lemmatize(pos=self.pos_tag)

    @requires_nltk_corpus
    def tokenize(self, text):
        '''Return a list of sentences.'''
        return nltk.tokenize.sent_tokenize(text)

    @requires_nltk_corpus
    def singularize(self):
        """Return the singular version of the word as a Word."""
        raise NotImplementedError

    @requires_nltk_corpus
    def tag(self, text, tokenize=True):
        """Tag a string `text`."""
        if tokenize:
            text = list(word_tokenize(text))
        tagged = nltk.tag.pos_tag(text)
        return tagged

    @requires_nltk_corpus
    def train(self):
        train_data = nltk.corpus.brown.tagged_sents(categories='news')
        regexp_tagger = nltk.RegexpTagger([
            (r'^-?[0-9]+(.[0-9]+)?$', 'CD'),
            (r'(-|:|;)$', ':'),
            (r'\'*$', 'MD'),
            (r'(The|the|A|a|An|an)$', 'AT'),
            (r'.*able$', 'JJ'),
            (r'^[A-Z].*$', 'NNP'),
            (r'.*ness$', 'NN'),
            (r'.*ly$', 'RB'),
            (r'.*s$', 'NNS'),
            (r'.*ing$', 'VBG'),
            (r'.*ed$', 'VBD'),
            (r'.*', 'NN'),
            ])

    @requires_nltk_corpus
    def lemmatize(self, pos=None):
        """Return the lemma for a word using WordNet's morphy function.

        :param pos: Part of speech to filter upon. If `None`, defaults to
            ``_wordnet.NOUN``.

        .. versionadded:: 0.8.1 (``textblob``)

        """
        # if pos is None:
        #pos = _wordnet.NOUN
        #lemmatizer = nltk.stem.WordNetLemmatizer()
        # return lemmatizer.lemmatize(self.string, pos)
        raise NotImplementedError

    @requires_nltk_corpus
    def lemmatize(self, pos=None):
        """Return the lemma for a word using WordNet's morphy function.

        :param pos: Part of speech to filter upon. If `None`, defaults to
            ``_wordnet.NOUN``.

        .. versionadded:: 0.8.1
        """
        if pos is None:
            tag = _wordnet.NOUN
        elif pos in _wordnet._FILEMAP.keys():
            tag = pos
        else:
            tag = _penn_to_wordnet(pos)
        lemmatizer = nltk.stem.WordNetLemmatizer()
        return lemmatizer.lemmatize(self.string, tag)

    @requires_nltk_corpus
    def train(self):
        # train_data = nltk.corpus.brown.tagged_sents(categories=['news','science_fiction'])
        self.tagger = nltk.PerceptronTagger()
        self._trained = True
        return None