How to use the tslearn.utils.ts_size function in tslearn

self_similarity = True
    else:
        dataset2 = to_time_series_dataset(dataset2, dtype=numpy.float64)
    dists = numpy.empty((dataset1.shape[0], dataset2.shape[0]))
    equal_size_ds1 = check_equal_size(dataset1)
    equal_size_ds2 = check_equal_size(dataset2)
    for i, ts1 in enumerate(dataset1):
        if equal_size_ds1:
            ts1_short = ts1
        else:
            ts1_short = ts1[:ts_size(ts1)]
        for j, ts2 in enumerate(dataset2):
            if equal_size_ds2:
                ts2_short = ts2
            else:
                ts2_short = ts2[:ts_size(ts2)]
            if self_similarity and j < i:
                dists[i, j] = dists[j, i]
            else:
                dists[i, j] = soft_dtw(ts1_short, ts2_short, gamma=gamma)

    return dists

def _check_full_length(centroids):
    """Check that provided centroids are full-length (ie. not padded with
    nans).

    If some centroids are found to be padded with nans, the last value is
    repeated until the end.
    """
    centroids_ = numpy.empty(centroids.shape)
    n, max_sz = centroids.shape[:2]
    for i in range(n):
        sz = ts_size(centroids[i])
        centroids_[i, :sz] = centroids[i, :sz]
        if sz < max_sz:
            centroids_[i, sz:] = centroids[i, sz-1]
    return centroids_

2.0...

    See Also
    --------
    gak : Compute Global Alignment kernel
    cdist_gak : Compute cross-similarity matrix using Global Alignment kernel

    References
    ----------
    .. [1] M. Cuturi, "Fast global alignment kernels," ICML 2011.
    """
    random_state = check_random_state(random_state)
    dataset = to_time_series_dataset(dataset)
    n_ts, sz, d = dataset.shape
    if not check_equal_size(dataset):
        sz = numpy.min([ts_size(ts) for ts in dataset])
    if n_ts * sz < n_samples:
        replace = True
    else:
        replace = False
    sample_indices = random_state.choice(n_ts * sz,
                                         size=n_samples,
                                         replace=replace)
    dists = pdist(dataset[:, :sz, :].reshape((-1, d))[sample_indices],
                  metric="euclidean")
    return numpy.median(dists) * numpy.sqrt(sz)

"""
    dataset1 = to_time_series_dataset(dataset1, dtype=numpy.float64)
    self_similarity = False
    if dataset2 is None:
        dataset2 = dataset1
        self_similarity = True
    else:
        dataset2 = to_time_series_dataset(dataset2, dtype=numpy.float64)
    dists = numpy.empty((dataset1.shape[0], dataset2.shape[0]))
    equal_size_ds1 = check_equal_size(dataset1)
    equal_size_ds2 = check_equal_size(dataset2)
    for i, ts1 in enumerate(dataset1):
        if equal_size_ds1:
            ts1_short = ts1
        else:
            ts1_short = ts1[:ts_size(ts1)]
        for j, ts2 in enumerate(dataset2):
            if equal_size_ds2:
                ts2_short = ts2
            else:
                ts2_short = ts2[:ts_size(ts2)]
            if self_similarity and j < i:
                dists[i, j] = dists[j, i]
            else:
                dists[i, j] = soft_dtw(ts1_short, ts2_short, gamma=gamma)

    return dists