How to use the pyts.metrics.dtw function in pyts

y : array-like, shape = (n_samples,)
            Class labels for each data sample.

        Returns
        -------
        self : object

        """
        X, y = check_X_y(X, y)
        self._le = LabelEncoder().fit(y)
        self.classes_ = self._le.classes_

        if self.metric == 'dtw':
            self._clf = SklearnKNN(
                n_neighbors=self.n_neighbors, weights=self.weights,
                algorithm='brute', metric=dtw,
                metric_params=self.metric_params,
                n_jobs=self.n_jobs, **self.kwargs
            )

        elif self.metric == 'dtw_classic':
            self._clf = SklearnKNN(
                n_neighbors=self.n_neighbors, weights=self.weights,
                algorithm='brute', metric=_dtw_classic,
                metric_params=self.metric_params,
                n_jobs=self.n_jobs, **self.kwargs
            )

        elif self.metric == 'dtw_sakoechiba':
            n_timestamps = X.shape[1]
            if self.metric_params is None:
                region = sakoe_chiba_band(n_timestamps)

# Parameters
X, _, _, _ = load_gunpoint(return_X_y=True)
x, y = X[0], X[1]

# To compare time series of different lengths, we remove some observations
mask = np.ones(x.size)
mask[::5] = 0
y = y[mask.astype(bool)]
n_timestamps_1, n_timestamps_2 = x.size, y.size

plt.figure(figsize=(10, 8))
timestamps_1 = np.arange(n_timestamps_1 + 1)
timestamps_2 = np.arange(n_timestamps_2 + 1)

# Dynamic Time Warping: classic
dtw_classic, path_classic = dtw(x, y, dist='square',
                                method='classic', return_path=True)
matrix_classic = np.zeros((n_timestamps_2 + 1, n_timestamps_1 + 1))
matrix_classic[tuple(path_classic)[::-1]] = 1.

plt.subplot(2, 2, 1)
plt.pcolor(timestamps_1, timestamps_2, matrix_classic,
           edgecolors='k', cmap='Greys')
plt.xlabel('x', fontsize=12)
plt.ylabel('y', fontsize=12)
plt.title("{0}\nDTW(x, y) = {1:.2f}".format('classic', dtw_classic),
          fontsize=14)

# Dynamic Time Warping: sakoechiba
window_size = 0.1
dtw_sakoechiba, path_sakoechiba = dtw(
    x, y, dist='square', method='sakoechiba',

max_slope=slope)
matrix_itakura = np.zeros((n_timestamps_1 + 1, n_timestamps_2 + 1))
for i in range(n_timestamps_1):
    matrix_itakura[i, np.arange(*parallelogram[:, i])] = 0.5
matrix_itakura[tuple(path_itakura)] = 1.
plt.subplot(2, 2, 3)
plt.pcolor(timestamps_1, timestamps_2, matrix_itakura.T,
           edgecolors='k', cmap='Greys')
plt.xlabel('x', fontsize=12)
plt.ylabel('y', fontsize=12)
plt.title("{0}\nDTW(x, y) = {1:.2f}".format('itakura', dtw_itakura),
          fontsize=14)

# Dynamic Time Warping: multiscale
resolution, radius = 5, 2
dtw_multiscale, path_multiscale = dtw(
    x, y, dist='square', method='multiscale',
    options={'resolution': resolution, 'radius': radius}, return_path=True
)

x_padded = x.reshape(-1, resolution).mean(axis=1)
y_padded = y.reshape(-1, resolution).mean(axis=1)

cost_mat_res = cost_matrix(x_padded, y_padded, dist='square', region=None)
acc_cost_mat_res = accumulated_cost_matrix(cost_mat_res)
path_res = _return_path(acc_cost_mat_res)

multiscale_region = _blurred_path_region(
    n_timestamps_1, n_timestamps_2, resolution, x_padded.size, y_padded.size,
    path_res,
    radius=radius
)