text: """Unit tests for Riemannian KMedoids."""
  
  import geomstats.backend as gs
  import geomstats.tests
  from geomstats.geometry import hypersphere
  from geomstats.learning.kmedoids import RiemannianKMedoids
  
  
  @geomstats.tests.np_autograd_and_torch_only
  class TestRiemannianKMedoids(geomstats.tests.TestCase):
      _multiprocess_can_split_ = True
  
      def test_hypersphere_kmedoids_fit(self):
          gs.random.seed(55)
  
          manifold = hypersphere.Hypersphere(2)
          metric = hypersphere.HypersphereMetric(2)
  
          data = manifold.random_von_mises_fisher(kappa=100, n_samples=200)
  
          kmedoids = RiemannianKMedoids(metric=metric, n_clusters=1)
          center = kmedoids.fit(data)
  
          self.assertTrue(manifold.belongs(center))
  
      def test_hypersphere_kmedoids_predict(self):
          gs.random.seed(1234)
          dim = 2
  
          manifold = hypersphere.Hypersphere(dim)
          metric = hypersphere.HypersphereMetric(dim)
  
          data = manifold.random_von_mises_fisher(kappa=100, n_samples=200)
  
          kmedoids = RiemannianKMedoids(metric, n_clusters=5)
          centroids = kmedoids.fit(data, max_iter=100)
          result = kmedoids.predict(data)
  
          expected = gs.array(
              [int(metric.closest_neighbor_index(x_i, centroids)) for x_i in data]
          )
          self.assertAllClose(expected, result)
  