def embed_category(self, category, model=None):
        '''

        :param model: gensim word2vec.Word2Vec model
        :param term_acceptance_re : SRE_Pattern, Regular expression to identify
            valid terms, default re.compile('[a-z]{3,}')
        :return: EmbeddingsResolver
        '''
        self._verify_category(category)
        if self.term_acceptance_re is not None:
            acceptable_terms = set([t for t in self.corpus_.get_terms() if self.term_acceptance_re.match(t)])
        else:
            acceptable_terms = set(self.corpus_.get_terms())
        trained_model = CategorySpecificWord2VecFromParsedCorpus(self.corpus_, category, model).train()
        self.category_word2vec_model_[category] = trained_model
        word2dwe = {word: trained_model.wv[word] for word in trained_model.wv.key_to_index.keys()}
        self.category_embeddings_[category] = word2dwe
        return self


        :param model: gensim word2vec.Word2Vec model
        :param term_acceptance_re : SRE_Pattern, Regular expression to identify
            valid terms, default re.compile('[a-z]{3,}')
        :return: EmbeddingsResolver
        
scattertext/representations/CategoryEmbeddings.py