def METHOD_NAME(self):
    seed = 1
    n = 20 * ht.MPI_WORLD.size
    data = create_spherical_dataset(
        num_samples_cluster=n, radius=1.0, offset=4.0, dtype=ht.float32, random_state=seed
    )
    kmedoid = ht.cluster.KMedoids(n_clusters=4, init="kmedoids++")
    kmedoid.fit(data)
    self.assertIsInstance(kmedoid.cluster_centers_, ht.DNDarray)
    self.assertEqual(kmedoid.cluster_centers_.shape, (4, 3))
    for i in range(kmedoid.cluster_centers_.shape[0]):
        self.assertTrue(
            ht.any(ht.sum(ht.abs(kmedoid.cluster_centers_[i, :] - data), axis=1) == 0)
        )
    # More Samples
    n = 100 * ht.MPI_WORLD.size
    data = create_spherical_dataset(
        num_samples_cluster=n, radius=1.0, offset=4.0, dtype=ht.float32, random_state=seed
    )
    kmedoid = ht.cluster.KMedoids(n_clusters=4, init="kmedoids++")
    kmedoid.fit(data)
    self.assertIsInstance(kmedoid.cluster_centers_, ht.DNDarray)
    self.assertEqual(kmedoid.cluster_centers_.shape, (4, 3))
    # check whether result is actually a datapoint
    for i in range(kmedoid.cluster_centers_.shape[0]):
        self.assertTrue(
            ht.any(ht.sum(ht.abs(kmedoid.cluster_centers_[i, :] - data), axis=1) == 0)
        )
    # different datatype
    n = 20 * ht.MPI_WORLD.size
    data = create_spherical_dataset(
        num_samples_cluster=n, radius=1.0, offset=4.0, dtype=ht.float64, random_state=seed
    )
    kmedoid = ht.cluster.KMedoids(n_clusters=4, init="kmedoids++")
    kmedoid.fit(data)
    self.assertIsInstance(kmedoid.cluster_centers_, ht.DNDarray)
    self.assertEqual(kmedoid.cluster_centers_.shape, (4, 3))
    for i in range(kmedoid.cluster_centers_.shape[0]):
        self.assertTrue(
            ht.any(
                ht.sum(ht.abs(kmedoid.cluster_centers_[i, :] - data.astype(ht.float32)), axis=1)
                == 0
            )
        )
    # on Ints (different radius, offset and datatype
    data = create_spherical_dataset(
        num_samples_cluster=n, radius=10.0, offset=40.0, dtype=ht.int32, random_state=seed
    )
    kmedoid = ht.cluster.KMedoids(n_clusters=4, init="kmedoids++")
    kmedoid.fit(data)
    self.assertIsInstance(kmedoid.cluster_centers_, ht.DNDarray)
    self.assertEqual(kmedoid.cluster_centers_.shape, (4, 3))
    for i in range(kmedoid.cluster_centers_.shape[0]):
        self.assertTrue(
            ht.any(ht.sum(ht.abs(kmedoid.cluster_centers_[i, :] - data), axis=1) == 0)
        )