code: def test_basic_statistics_no_latent_variance_one_frequency(self):
      # fix the latent variables at the value 1 so the results are deterministic
      num_timesteps = 10
      period = 42
      frequency_multipliers = [3]
      drift_scale = 0.0
  
      initial_state_loc = self._build_placeholder(np.ones([2]))
      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 = SmoothSeasonalStateSpaceModel(
          num_timesteps=num_timesteps,
          period=period,
          frequency_multipliers=frequency_multipliers,
          drift_scale=drift_scale,
          initial_state_prior=initial_state_prior,
      )
  
      two_pi = 6.283185307179586
      sine_terms = tf.sin(
          two_pi * 3 * tf.range(0, num_timesteps, dtype=tf.float32) / 42
      )
      cosine_terms = tf.cos(
          two_pi * 3 * tf.range(0, num_timesteps, dtype=tf.float32) / 42
      )
      predicted_time_series_ = self.evaluate(
          (sine_terms + cosine_terms)[..., tf.newaxis]
      )
  
      self.assertAllClose(
          self.evaluate(ssm.mean()), predicted_time_series_
      )
      self.assertAllClose(
          *self.evaluate(
              (ssm.stddev(), tf.zeros_like(predicted_time_series_))
          )
      )
  
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.smooth_seasonal import SmoothSeasonal
  from tensorflow_probability.python.sts.components.smooth_seasonal import SmoothSeasonalStateSpaceModel
label_desc: Use image node_id 2 for calling the _SmoothSeasonalStateSpaceModelTest obj's underlying member method code with example usage: obj.test_basic_statistics_no_latent_variance_one_frequency() without return types