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

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)
            else:
                if 'window_size' not in self.metric_params.keys():
                    window_size = 0.1
                else:
                    window_size = self.metric_params['window_size']
                region = sakoe_chiba_band(n_timestamps,
                                          window_size=window_size)
            self._clf = SklearnKNN(
                n_neighbors=self.n_neighbors, weights=self.weights,
                algorithm='brute', metric=_dtw_region,
                metric_params={'region': region},
                n_jobs=self.n_jobs, **self.kwargs
            )

        elif self.metric == 'dtw_itakura':
            n_timestamps = X.shape[1]
            if self.metric_params is None:
                region = itakura_parallelogram(n_timestamps)
            else:
                if 'max_slope' not in self.metric_params.keys():
                    max_slope = 2.
                else:

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)
            else:
                if 'window_size' not in self.metric_params.keys():
                    window_size = 0.1
                else:
                    window_size = self.metric_params['window_size']
                region = sakoe_chiba_band(n_timestamps,
                                          window_size=window_size)
            self._clf = SklearnKNN(
                n_neighbors=self.n_neighbors, weights=self.weights,
                algorithm='brute', metric=_dtw_region,
                metric_params={'region': region},
                n_jobs=self.n_jobs, **self.kwargs
            )

        elif self.metric == 'dtw_itakura':
            n_timestamps = X.shape[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',
    options={'window_size': window_size}, return_path=True
)
band = sakoe_chiba_band(n_timestamps_1, n_timestamps_2,
                        window_size=window_size)
matrix_sakoechiba = np.zeros((n_timestamps_1 + 1, n_timestamps_2 + 1))
for i in range(n_timestamps_1):
    matrix_sakoechiba[i, np.arange(*band[:, i])] = 0.5
matrix_sakoechiba[tuple(path_sakoechiba)] = 1.

plt.subplot(2, 2, 2)
plt.pcolor(timestamps_1, timestamps_2, matrix_sakoechiba.T,
           edgecolors='k', cmap='Greys')
plt.xlabel('x', fontsize=12)
plt.ylabel('y', fontsize=12)
plt.title("{0}\nDTW(x, y) = {1:.2f}".format('sakoechiba', dtw_sakoechiba),
          fontsize=14)

# Dynamic Time Warping: itakura
slope = 1.2

def plot_sakoe_chiba(n_timestamps_1, n_timestamps_2, window_size=0.5, ax=None):
    """Plot the Sakoe-Chiba band."""
    region = sakoe_chiba_band(n_timestamps_1, n_timestamps_2, window_size)
    scale, horizontal_shift, vertical_shift = \
        _check_sakoe_chiba_params(n_timestamps_1, n_timestamps_2, window_size)
    mask = np.zeros((n_timestamps_2, n_timestamps_1))
    for i, (j, k) in enumerate(region.T):
        mask[j:k, i] = 1.

    plt.imshow(mask, origin='lower', cmap='Wistia', vmin=0, vmax=1)

    sz = max(n_timestamps_1, n_timestamps_2)
    x = np.arange(-1, sz + 1)
    lower_bound = scale * (x - horizontal_shift) - vertical_shift
    upper_bound = scale * (x + horizontal_shift) + vertical_shift
    plt.plot(x, lower_bound, 'b', lw=2)
    plt.plot(x, upper_bound, 'g', lw=2)
    diag = (n_timestamps_2 - 1) / (n_timestamps_1 - 1) * np.arange(-1, sz + 1)
    plt.plot(x, diag, 'black', lw=1)