code: def test_random_riemannian_normal_belongs(
      self, n_samples, random_mean, precision_type, atol, max_iter=100
  ):
      mean = self.space.random_point() if random_mean else None
      precision = self._get_random_precision(precision_type)
  
      sample = self.space.random_riemannian_normal(
          mean=mean,
          precision=precision,
          n_samples=n_samples,
          max_iter=max_iter,
      )
  
      expected = gs.ones(n_samples, dtype=bool)
      self.test_belongs(sample, expected, atol)
  
imports: import random
  import pytest
  import scipy
  import geomstats.backend
  from geomstats.learning.frechet_mean import FrechetMean
  from geomstats.test.random import EmbeddedSpaceRandomDataGenerator, HypersphereIntrinsicRandomDataGenerator
  from geomstats.test.test_case import TestCase
  from geomstats.test.vectorization import generate_vectorization_data
  from geomstats.test_cases.geometry.base import LevelSetTestCase
  from geomstats.test_cases.geometry.manifold import ManifoldTestCase
  from geomstats.vectorization import get_batch_shape
label_desc: Use image node_id 9 for calling the HypersphereExtrinsicTestCase obj's underlying member method code with example usage: obj.test_random_riemannian_normal_belongs(n_samples, random_mean, precision_type, atol, max_iter) without return types