code: def _dummy_model(
      self,
      num_timesteps=5,
      batch_shape=None,
      initial_state_prior_batch_shape=None,
      latent_size=2,
      observation_size=1,
      dtype=None,
  ):
      batch_shape = batch_shape if batch_shape is not None else []
      initial_state_prior_batch_shape = (
          initial_state_prior_batch_shape
          if initial_state_prior_batch_shape is not None
          else batch_shape
      )
      dtype = dtype if dtype is not None else self.dtype
  
      return linear_gaussian_ssm.LinearGaussianStateSpaceModel(
          num_timesteps=num_timesteps,
          transition_matrix=self._build_placeholder(
              np.eye(latent_size), dtype=dtype
          ),
          transition_noise=mvn_diag.MultivariateNormalDiag(
              scale_diag=np.ones(batch_shape + [latent_size]).astype(
                  dtype
              )
          ),
          observation_matrix=self._build_placeholder(
              np.random.standard_normal(
                  batch_shape + [observation_size, latent_size]
              ),
              dtype=dtype,
          ),
          observation_noise=mvn_diag.MultivariateNormalDiag(
              loc=self._build_placeholder(
                  np.ones(batch_shape + [observation_size]), dtype=dtype
              ),
              scale_diag=self._build_placeholder(
                  np.ones(batch_shape + [observation_size]), dtype=dtype
              ),
          ),
          initial_state_prior=mvn_diag.MultivariateNormalDiag(
              scale_diag=self._build_placeholder(
                  np.ones(
                      initial_state_prior_batch_shape + [latent_size]
                  ),
                  dtype=dtype,
              )
          ),
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import linear_gaussian_ssm
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.sts.components.local_linear_trend import LocalLinearTrendStateSpaceModel
  from tensorflow_probability.python.sts.components.sum import AdditiveStateSpaceModel
label_desc: Use image node_id 14 for calling the _AdditiveStateSpaceModelTest obj's underlying member method code with example usage: obj._dummy_model(num_timesteps, batch_shape, initial_state_prior_batch_shape, latent_size, observation_size, dtype) and returns: linear_gaussian_ssm