Updates embedding vectors for tokens.


        Parameters
        ----------
        tokens : str or a list of strs
            A token or a list of tokens whose embedding vector are to be updated.
        new_vectors : mxnet.ndarray.NDArray
            An NDArray to be assigned to the embedding vectors of `tokens`. Its length must be equal
            to the number of `tokens` and its width must be equal to the dimension of embeddings of
            the glossary. If `tokens` is a singleton, it must be 1-D or 2-D. If `tokens` is a list
            of multiple strings, it must be 2-D.

def update_token_vectors(self, tokens, new_vectors):
        """Updates embedding vectors for tokens.


        Parameters
        ----------
        tokens : str or a list of strs
            A token or a list of tokens whose embedding vector are to be updated.
        new_vectors : mxnet.ndarray.NDArray
            An NDArray to be assigned to the embedding vectors of `tokens`. Its length must be equal
            to the number of `tokens` and its width must be equal to the dimension of embeddings of
            the glossary. If `tokens` is a singleton, it must be 1-D or 2-D. If `tokens` is a list
            of multiple strings, it must be 2-D.
        """

        assert self.idx_to_vec is not None, 'The property `idx_to_vec` has not been properly set.'

        if not isinstance(tokens, list) or len(tokens) == 1:
            assert isinstance(new_vectors, nd.NDArray) and len(new_vectors.shape) in [1, 2], \
                '`new_vectors` must be a 1-D or 2-D NDArray if `tokens` is a singleton.'
            if not isinstance(tokens, list):
                tokens = [tokens]
            if len(new_vectors.shape) == 1:
                new_vectors = new_vectors.expand_dims(0)

        else:
            assert isinstance(new_vectors, nd.NDArray) and len(new_vectors.shape) == 2, \
                '`new_vectors` must be a 2-D NDArray if `tokens` is a list of multiple strings.'
        assert new_vectors.shape == (len(tokens), self.vec_len), \
            'The length of new_vectors must be equal to the number of tokens and the width of' \
            'new_vectors must be equal to the dimension of embeddings of the glossary.'

        indices = []
        for token in tokens:
            if token in self.token_to_idx:
                indices.append(self.token_to_idx[token])
            else:
                raise ValueError('Token %s is unknown. To update the embedding vector for an '
                                 'unknown token, please specify it explicitly as the '
                                 '`unknown_token` %s in `tokens`. This is to avoid unintended '
                                 'updates.' % (token, self.idx_to_token[C.UNKNOWN_IDX]))

        self._idx_to_vec[nd.array(indices)] = new_vectors