code: def test_get_seasonal_latents_shape_no_chains_constrained_and_unconstrained(
      self,
  ):
      model = sum_lib.Sum(
          [
              local_level.LocalLevel(),
              seasonal.Seasonal(
                  name="s1",
                  num_seasons=6,
                  constrain_mean_effect_to_zero=True,
              ),
              seasonal.Seasonal(
                  name="s2",
                  num_seasons=10,
                  constrain_mean_effect_to_zero=False,
              ),
          ]
      )
      model.supports_gibbs_sampling = (
          True  # Work-around to simplify testing.
      )
      self.assertAllEqual(
          [
              3,
              4,
              120,
              # 5 from the constrained, 10 from the unconstrained.
              15,
          ],
          gibbs_sampler.get_seasonal_latents_shape(
              timeseries=tf.ones(shape=[3, 4, 120]), model=model
          ),
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import square
  from tensorflow_probability.python.distributions import inverse_gamma
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions.linear_gaussian_ssm import linear_gaussian_update
  from tensorflow_probability.python.experimental.distributions import mvn_precision_factor_linop
  from tensorflow_probability.python.experimental.sts_gibbs import gibbs_sampler
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.stats import sample_stats
  from tensorflow_probability.python.sts.components import local_level
  from tensorflow_probability.python.sts.components import local_linear_trend
  from tensorflow_probability.python.sts.components import regression
  from tensorflow_probability.python.sts.components import seasonal
  from tensorflow_probability.python.sts.components import semilocal_linear_trend
  from tensorflow_probability.python.sts.components import sum
  from tensorflow_probability.python.sts.forecast import forecast
  from tensorflow_probability.python.sts.internal import missing_values_util
  from tensorflow_probability.python.sts.internal import util
label_desc: Use image node_id 16 for calling the GibbsSamplerTests obj's underlying member method code with example usage: obj.test_get_seasonal_latents_shape_no_chains_constrained_and_unconstrained() without return types