code: def test_stats(self):
      # Build a model with expected initial scale 0.
      level_scale = self._build_placeholder(1.0)
      initial_state_prior = mvn_diag.MultivariateNormalDiag(
          loc=self._build_placeholder([0.0]),
          scale_diag=self._build_placeholder([1.0]),
      )
  
      ssm = LocalLevelStateSpaceModel(
          num_timesteps=10,
          level_scale=level_scale,
          initial_state_prior=initial_state_prior,
      )
  
      # In expectation, the process is constant.
      mean = self.evaluate(ssm.mean())
      self.assertAllClose(mean, np.zeros(10)[:, np.newaxis])
  
      # variance of level[T] is T * level_scale
      expected_variance = np.arange(1, 11)[:, np.newaxis]
      variance = self.evaluate(ssm.variance())
      self.assertAllClose(variance, expected_variance)
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.sts.components.local_level import LocalLevelStateSpaceModel
label_desc: Use image node_id 2 for calling the _LocalLevelStateSpaceModelTest obj's underlying member method code with example usage: obj.test_stats() without return types