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

best_correct_centroids = None
        min_inertia = numpy.inf
        n_successful = 0
        n_attempts = 0
        while n_successful < self.n_init and n_attempts < max_attempts:
            try:
                if self.verbose and self.n_init > 1:
                    print("Init %d" % (n_successful + 1))
                n_attempts += 1
                self._fit_one_init(X_, rs)
                if self.inertia_ < min_inertia:
                    best_correct_centroids = self.cluster_centers_.copy()
                    min_inertia = self.inertia_
                    self.n_iter_ = self._iter
                n_successful += 1
            except EmptyClusterError:
                if self.verbose:
                    print("Resumed because of empty cluster")
        self._norms_centroids = numpy.linalg.norm(self.cluster_centers_,
                                                  axis=(1, 2))
        self._post_fit(X_, best_correct_centroids, min_inertia)
        return self

def __init__(self, message=""):
        super(EmptyClusterError, self).__init__()
        self.message = message

def _compute_dist(self, K, dist):
        """Compute a n_samples x n_clusters distance matrix using the kernel
        trick."""
        sw = self.sample_weight_

        for j in range(self.n_clusters):
            mask = (self.labels_ == j)

            if numpy.sum(mask) == 0:
                raise EmptyClusterError("try smaller n_cluster or better "
                                        "kernel parameters")

            # NB: we use a normalized kernel so k(x,x) = 1 for all x
            # (including the centroid)
            dist[:, j] = 2 - 2 * numpy.sum(sw[mask] * K[:, mask],
                                           axis=1) / sw[mask].sum()