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: Construct a specification for a structural time series model.
  
  Args:
    parameters: list of Parameter namedtuples, each specifying the name
      and prior distribution of a model parameter along with a
      bijective transformation from an unconstrained space to the support
      of that parameter. The order of this list determines the canonical
      parameter ordering used by fitting and inference algorithms.
    latent_size: Python `int` specifying the dimensionality of the latent
      state space for this model.
    init_parameters: Python `dict` of parameters used to instantiate this
      model component.
    name: Python `str` name for this model component.
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code: def __init__(
      self,
      parameters,
      latent_size,
      init_parameters=None,
      name="StructuralTimeSeries",
  ):
      """Construct a specification for a structural time series model.
  
      Args:
        parameters: list of Parameter namedtuples, each specifying the name
          and prior distribution of a model parameter along with a
          bijective transformation from an unconstrained space to the support
          of that parameter. The order of this list determines the canonical
          parameter ordering used by fitting and inference algorithms.
        latent_size: Python `int` specifying the dimensionality of the latent
          state space for this model.
        init_parameters: Python `dict` of parameters used to instantiate this
          model component.
        name: Python `str` name for this model component.
      """
  
      self._init_parameters = init_parameters
      self._parameters = parameters
      self._latent_size = latent_size
      self._name = name
  
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 1 to create a new StructuralTimeSeries object from inherited base classes: object with example: obj = StructuralTimeSeries(parameters, latent_size, init_parameters, name)