class_docstr: Formal representation of a local level model.
  
  The local level model posits a `level` evolving via a Gaussian random walk:
  
  ```
  level[t] = level[t-1] + Normal(0., level_scale)
  ```
  
  The latent state is `[level]`. We observe a noisy realization of the current
  level: `f[t] = level[t] + Normal(0., observation_noise_scale)` at each
  timestep.
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code: def _make_state_space_model(
      self,
      num_timesteps,
      param_map,
      initial_state_prior=None,
      **linear_gaussian_ssm_kwargs,
  ):
      if initial_state_prior is None:
          initial_state_prior = self.initial_state_prior
      linear_gaussian_ssm_kwargs.update(param_map)
      return LocalLevelStateSpaceModel(
          num_timesteps,
          initial_state_prior=initial_state_prior,
          **linear_gaussian_ssm_kwargs,
      )
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import linear_gaussian_ssm
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.sts.internal import util
  from tensorflow_probability.python.sts.structural_time_series import Parameter
  from tensorflow_probability.python.sts.structural_time_series import StructuralTimeSeries
label_desc: Use image node_id 3 for calling the LocalLevel obj's underlying member method code with example usage: obj._make_state_space_model(num_timesteps, param_map, initial_state_prior) and returns: LocalLevelStateSpaceModel