How to use the tslearn.cycc.y_shifted_sbd_vec function in tslearn

def _shape_extraction(self, X, k):
        sz = X.shape[1]
        Xp = y_shifted_sbd_vec(self.cluster_centers_[k], X[self.labels_ == k],
                               norm_ref=-1,
                               norms_dataset=self._norms[self.labels_ == k])
        S = numpy.dot(Xp[:, :, 0].T, Xp[:, :, 0])
        Q = numpy.eye(sz) - numpy.ones((sz, sz)) / sz
        M = numpy.dot(Q.T, numpy.dot(S, Q))
        _, vec = numpy.linalg.eigh(M)
        mu_k = vec[:, -1].reshape((sz, 1))

        # The way the optimization problem is (ill-)formulated, both mu_k and
        # -mu_k are candidates for barycenters
        # In the following, we check which one is best candidate
        dist_plus_mu = numpy.sum(numpy.linalg.norm(Xp - mu_k, axis=(1, 2)))
        dist_minus_mu = numpy.sum(numpy.linalg.norm(Xp + mu_k, axis=(1, 2)))
        if dist_minus_mu < dist_plus_mu:
            mu_k *= -1