How to use the sense2vec.util.get_similarity function in sense2vec

if self.index is None:  # use the less efficient default way
            avg = vecs.mean(axis=0, keepdims=True)
            result_keys, _, scores = self.vectors.most_similar(
                avg, n=n_similar, batch_size=batch_size
            )
            result = list(zip(result_keys.flatten(), scores.flatten()))
            result = [(self.strings[key], score) for key, score in result if key]
            return [(key, score) for key, score in result if key not in keys]
        else:  # index is built, use annoy
            avg = vecs.mean(axis=0, keepdims=False)
            nns = self.index.get_nns_by_vector(avg, n_similar, include_distances=True)
            result = []
            for row, dist in zip(*nns):
                key = self.strings[self.vectors.find(row=row)[0]]
                if key not in keys:
                    score = 1.0 if dist == 0.0 else get_similarity(avg, self[key])
                    result.append((key, score))
            return result

keys_b: Union[Sequence[Union[str, int]], str, int],
    ) -> float:
        """Make a semantic similarity estimate of two keys or two sets of keys.
        The default estimate is cosine similarity using an average of vectors.

        keys_a (unicode / int / iterable): The string or integer key(s).
        keys_b (unicode / int / iterable): The other string or integer key(s).
        RETURNS (float): The similarity score.
        """
        if isinstance(keys_a, (str, int)):
            keys_a = [keys_a]
        if isinstance(keys_b, (str, int)):
            keys_b = [keys_b]
        average_a = numpy.vstack([self[key] for key in keys_a]).mean(axis=0)
        average_b = numpy.vstack([self[key] for key in keys_b]).mean(axis=0)
        return get_similarity(average_a, average_b)