code: def test_basic_statistics_no_latent_variance(self):
      batch_shape = [4, 3]
      num_timesteps = 10
      num_features = 2
      drift_scale = 0.0
  
      design_matrix = self._build_placeholder(
          np.random.randn(
              *(batch_shape + [num_timesteps, num_features])
          )
      )
  
      initial_state_loc = self._build_placeholder(
          np.random.randn(*(batch_shape + [num_features]))
      )
      initial_state_scale = tf.zeros_like(initial_state_loc)
      initial_state_prior = mvn_diag.MultivariateNormalDiag(
          loc=initial_state_loc, scale_diag=initial_state_scale
      )
  
      ssm = DynamicLinearRegressionStateSpaceModel(
          num_timesteps=num_timesteps,
          design_matrix=design_matrix,
          drift_scale=drift_scale,
          initial_state_prior=initial_state_prior,
      )
  
      predicted_time_series = tf.linalg.matmul(
          design_matrix, initial_state_loc[..., tf.newaxis]
      )
  
      self.assertAllClose(
          self.evaluate(ssm.mean()), predicted_time_series, atol=1e-5
      )
      self.assertAllClose(
          *self.evaluate(
              (ssm.stddev(), tf.zeros_like(predicted_time_series))
          ),
          atol=1e-5,
      )
  
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.dynamic_regression import DynamicLinearRegression
  from tensorflow_probability.python.sts.components.dynamic_regression import DynamicLinearRegressionStateSpaceModel
label_desc: Use image node_id 1 for calling the _DynamicLinearRegressionStateSpaceModelTest obj's underlying member method code with example usage: obj.test_basic_statistics_no_latent_variance() without return types