How to use the fasttext.model.WordVectorModel function in fasttext

:rtype: np.array
        """
        if node is None:
            return self.zero
        if node.token_id == 0:
            return self.root
        if hasattr(node, self.emb_field):
            return getattr(node, self.emb_field)

        f = getattr(node, self.key_field)
        emb = None

        if isinstance(self.vsm, KeyedVectors):
            vocab = self.vsm.vocab.score()
            emb = self.zero if vocab is None else self.vsm.syn0[vocab.index]
        elif isinstance(self.vsm, WordVectorModel):
            emb = np.array(self.vsm[f]).astype('float32')

        setattr(node, self.emb_field, emb)
        return emb

:param emb_field: where the emb_matrix with respect to the key is saved in NLPNode.
        :type emb_field: str
        """
        self.vsm = vsm
        self.key_field = key_field
        self.emb_field = emb_field

        if isinstance(vsm, KeyedVectors):
            vector_size = vsm.syn0.shape[1]
            # root
            np.random.seed(9)
            self.root = np.random.uniform(-.25, .25, (vector_size,)).astype('float32')
            np.random.seed()
            # zero
            self.zero = np.zeros((vector_size,)).astype('float32')
        elif isinstance(vsm, WordVectorModel):
            self.root = np.array(vsm[structure.ROOT_TAG]).astype('float32')
            self.zero = np.array(vsm['']).astype('float32')