class_docstr: Base class for structural time series models.
  
  A StructuralTimeSeries object represents a declarative specification of a
  structural time series model, including priors on model parameters.
  It implements a joint probability model
    `p(params, y) = p(params) p(y | params)`,
  where `params` denotes a list of real-valued parameters specified by the child
  class, and `p(y | params)` is a linear Gaussian state space model with
  structure determined by the child class.
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docstr: Python `int` dimensionality of the latent space in this model.
docstr_tok: ["Python","`","int","`","dimensionality","of","the","latent","space","in","this","model","."]
code: def latent_size(self):
      """Python `int` dimensionality of the latent space in this model."""
      return self._latent_size
  
imports: import collections
  import types
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import joint_distribution_auto_batched
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.internal import distribution_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.python.util import deprecation
label_desc: Use image node_id 5 for calling the StructuralTimeSeries obj's underlying member method code with example usage: obj.latent_size() and returns: self