How to use the tslearn.metrics.SquaredEuclidean function in tslearn

...          [1., 2., 2.1, 3.2],
    ...          gamma=0.01)  # doctest: +NORMALIZE_WHITESPACE +ELLIPSIS
    0.089...

    See Also
    --------
    cdist_soft_dtw : Cross similarity matrix between time series datasets

    References
    ----------
    .. [1] M. Cuturi, M. Blondel "Soft-DTW: a Differentiable Loss Function for
       Time-Series," ICML 2017.
    """
    if gamma == 0.:
        return dtw(ts1, ts2)
    return SoftDTW(SquaredEuclidean(ts1[:ts_size(ts1)], ts2[:ts_size(ts2)]),
                   gamma=gamma).compute()

def _func(self, Z):
            # Compute objective value and grad at Z.

            Z = Z.reshape(self.barycenter_.shape)

            G = numpy.zeros_like(Z)

            obj = 0

            for i in range(len(self._X_fit)):
                D = SquaredEuclidean(Z, to_time_series(self._X_fit[i],
                                                       remove_nans=True))
                sdtw = SoftDTW(D, gamma=self.gamma)
                value = sdtw.compute()
                E = sdtw.grad()
                G_tmp = D.jacobian_product(E)
                G += self.weights[i] * G_tmp
                obj += self.weights[i] * value

            return obj, G.ravel()

def _softdtw_func(Z, X, weights, barycenter, gamma):
    # Compute objective value and grad at Z.

    Z = Z.reshape(barycenter.shape)
    G = numpy.zeros_like(Z)
    obj = 0

    for i in range(len(X)):
        D = SquaredEuclidean(Z, X[i])
        sdtw = SoftDTW(D, gamma=gamma)
        value = sdtw.compute()
        E = sdtw.grad()
        G_tmp = D.jacobian_product(E)
        G += weights[i] * G_tmp
        obj += weights[i] * value

    return obj, G.ravel()