How to use the tslearn.clustering.TimeSeriesKMeans function in tslearn

def _kmeans_init_shapelets(X, n_shapelets, shp_len, n_draw=10000):
    n_ts, sz, d = X.shape
    indices_ts = numpy.random.choice(n_ts, size=n_draw, replace=True)
    indices_time = numpy.random.choice(sz - shp_len + 1, size=n_draw,
                                       replace=True)
    subseries = numpy.zeros((n_draw, shp_len, d))
    for i in range(n_draw):
        subseries[i] = X[indices_ts[i],
                         indices_time[i]:indices_time[i] + shp_len]
    return TimeSeriesKMeans(n_clusters=n_shapelets,
                            metric="euclidean",
                            verbose=False).fit(subseries).cluster_centers_

random_state=seed)
y_pred = dba_km.fit_predict(X_train)

for yi in range(3):
    plt.subplot(3, 3, 4 + yi)
    for xx in X_train[y_pred == yi]:
        plt.plot(xx.ravel(), "k-", alpha=.2)
    plt.plot(dba_km.cluster_centers_[yi].ravel(), "r-")
    plt.xlim(0, sz)
    plt.ylim(-4, 4)
    if yi == 1:
        plt.title("DBA $k$-means")

# Soft-DTW-k-means
print("Soft-DTW k-means")
sdtw_km = TimeSeriesKMeans(n_clusters=3,
                           metric="softdtw",
                           metric_params={"gamma_sdtw": .01},
                           verbose=True,
                           random_state=seed)
y_pred = sdtw_km.fit_predict(X_train)

for yi in range(3):
    plt.subplot(3, 3, 7 + yi)
    for xx in X_train[y_pred == yi]:
        plt.plot(xx.ravel(), "k-", alpha=.2)
    plt.plot(sdtw_km.cluster_centers_[yi].ravel(), "r-")
    plt.xlim(0, sz)
    plt.ylim(-4, 4)
    if yi == 1:
        plt.title("Soft-DTW $k$-means")

y_pred = km.fit_predict(X_train)

plt.figure()
for yi in range(3):
    plt.subplot(3, 3, yi + 1)
    for xx in X_train[y_pred == yi]:
        plt.plot(xx.ravel(), "k-", alpha=.2)
    plt.plot(km.cluster_centers_[yi].ravel(), "r-")
    plt.xlim(0, sz)
    plt.ylim(-4, 4)
    if yi == 1:
        plt.title("Euclidean $k$-means")

# DBA-k-means
print("DBA k-means")
dba_km = TimeSeriesKMeans(n_clusters=3,
                          n_init=2,
                          metric="dtw",
                          verbose=True,
                          max_iter_barycenter=10,
                          random_state=seed)
y_pred = dba_km.fit_predict(X_train)

for yi in range(3):
    plt.subplot(3, 3, 4 + yi)
    for xx in X_train[y_pred == yi]:
        plt.plot(xx.ravel(), "k-", alpha=.2)
    plt.plot(dba_km.cluster_centers_[yi].ravel(), "r-")
    plt.xlim(0, sz)
    plt.ylim(-4, 4)
    if yi == 1:
        plt.title("DBA $k$-means")

TimeSeriesResampler

seed = 0
numpy.random.seed(seed)
X_train, y_train, X_test, y_test = CachedDatasets().load_dataset("Trace")
X_train = X_train[y_train < 4]  # Keep first 3 classes
numpy.random.shuffle(X_train)
# Keep only 50 time series
X_train = TimeSeriesScalerMeanVariance().fit_transform(X_train[:50])
# Make time series shorter
X_train = TimeSeriesResampler(sz=40).fit_transform(X_train)
sz = X_train.shape[1]

# Euclidean k-means
print("Euclidean k-means")
km = TimeSeriesKMeans(n_clusters=3, verbose=True, random_state=seed)
y_pred = km.fit_predict(X_train)

plt.figure()
for yi in range(3):
    plt.subplot(3, 3, yi + 1)
    for xx in X_train[y_pred == yi]:
        plt.plot(xx.ravel(), "k-", alpha=.2)
    plt.plot(km.cluster_centers_[yi].ravel(), "r-")
    plt.xlim(0, sz)
    plt.ylim(-4, 4)
    if yi == 1:
        plt.title("Euclidean $k$-means")

# DBA-k-means
print("DBA k-means")
dba_km = TimeSeriesKMeans(n_clusters=3,