How to use the text2vec.embeddings.word_embedding.WordEmbedding function in text2vec

def setUpClass(cls):
        from text2vec.embeddings.word_embedding import WordEmbedding
        cls.embedding = WordEmbedding(sequence_length=SEQUENCE_LENGTH)

def load_model(self):
        if not self.model:
            if self.embedding_type == EmbType.BERT:
                from text2vec.embeddings.bert_embedding import BERTEmbedding
                self.model = BERTEmbedding(model_folder=self.bert_model_folder,
                                           layer_nums=self.bert_layer_nums,
                                           trainable=self.trainable,
                                           sequence_length=self.sequence_length,
                                           processor=self.processor)
            elif self.embedding_type == EmbType.W2V:
                from text2vec.embeddings.word_embedding import WordEmbedding
                self.model = WordEmbedding(w2v_path=self.w2v_path,
                                           w2v_kwargs=self.w2v_kwargs,
                                           sequence_length=self.sequence_length,
                                           processor=self.processor,
                                           trainable=self.trainable)
            else:
                raise ValueError('set error embedding type.')

def info(self):
        info = super(WordEmbedding, self).info()
        info['config'] = {
            'w2v_path': self.w2v_path,
            'w2v_kwargs': self.w2v_kwargs,
            'sequence_length': self.sequence_length
        }
        return info