code: def test_invalid_model_spec_raises_error(self):
      observed_time_series = tf.ones([2])
      design_matrix = tf.eye(2)
      with self.assertRaisesRegexp(
          ValueError, "Weights prior must be a normal distribution"
      ):
          gibbs_sampler.build_model_for_gibbs_fitting(
              observed_time_series,
              design_matrix=design_matrix,
              weights_prior=student_t.StudentT(
                  df=10, loc=0.0, scale=1.0
              ),
              level_variance_prior=inverse_gamma.InverseGamma(
                  0.01, 0.01
              ),
              observation_noise_variance_prior=inverse_gamma.InverseGamma(
                  0.01, 0.01
              ),
          )
  
      with self.assertRaisesRegexp(
          ValueError, "Level variance prior must be an inverse gamma"
      ):
          gibbs_sampler.build_model_for_gibbs_fitting(
              observed_time_series,
              design_matrix=design_matrix,
              weights_prior=normal.Normal(loc=0.0, scale=1.0),
              level_variance_prior=lognormal.LogNormal(0.0, 3.0),
              observation_noise_variance_prior=inverse_gamma.InverseGamma(
                  0.01, 0.01
              ),
          )
  
      with self.assertRaisesRegexp(
          ValueError, "noise variance prior must be an inverse gamma"
      ):
          gibbs_sampler.build_model_for_gibbs_fitting(
              observed_time_series,
              design_matrix=design_matrix,
              weights_prior=normal.Normal(loc=0.0, scale=1.0),
              level_variance_prior=inverse_gamma.InverseGamma(
                  0.01, 0.01
              ),
              observation_noise_variance_prior=lognormal.LogNormal(
                  0.0, 3.0
              ),
          )
  
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 5 for calling the GibbsSamplerTests obj's underlying member method code with example usage: obj.test_invalid_model_spec_raises_error() without return types