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.
class_docstr_tok: ["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","."]
docstr: Instantiate this model as a Distribution over specified `num_timesteps`.
  
  Args:
    num_timesteps: Python `int` number of timesteps to model.
    param_vals: a list of `Tensor` parameter values in order corresponding to
      `self.parameters`, or a dict mapping from parameter names to values.
    initial_state_prior: an optional `Distribution` instance overriding the
      default prior on the model's initial state. This is used in forecasting
      ("today's prior is yesterday's posterior").
    initial_step: optional `int` specifying the initial timestep to model.
      This is relevant when the model contains time-varying components,
      e.g., holidays or seasonality.
    **linear_gaussian_ssm_kwargs: Optional additional keyword arguments to
      to the base `tfd.LinearGaussianStateSpaceModel` constructor.
  
  Returns:
    dist: a `LinearGaussianStateSpaceModel` Distribution object.
docstr_tok: ["Instantiate","this","model","as","a","Distribution","over","specified","`","num_timesteps","`",".","Args",":","num_timesteps",":","Python","`","int","`","number","of","timesteps","to","model",".","param_vals",":","a","list","of","`","Tensor","`","parameter","values","in","order","corresponding","to","`","self.parameters","`",",","or","a","dict","mapping","from","parameter","names","to","values",".","initial_state_prior",":","an","optional","`","Distribution","`","instance","overriding","the","default","prior","on","the","model","'s","initial","state",".","This","is","used","in","forecasting","(","``","today","'s","prior","is","yesterday","'s","posterior","''",")",".","initial_step",":","optional","`","int","`","specifying","the","initial","timestep","to","model",".","This","is","relevant","when","the","model","contains","time-varying","components",",","e.g.",",","holidays","or","seasonality",".","*","*","linear_gaussian_ssm_kwargs",":","Optional","additional","keyword","arguments","to","to","the","base","`","tfd.LinearGaussianStateSpaceModel","`","constructor",".","Returns",":","dist",":","a","`","LinearGaussianStateSpaceModel","`","Distribution","object","."]
code: def make_state_space_model(
      self,
      num_timesteps,
      param_vals,
      initial_state_prior=None,
      initial_step=0,
      **linear_gaussian_ssm_kwargs,
  ):
      """Instantiate this model as a Distribution over specified `num_timesteps`.
  
      Args:
        num_timesteps: Python `int` number of timesteps to model.
        param_vals: a list of `Tensor` parameter values in order corresponding to
          `self.parameters`, or a dict mapping from parameter names to values.
        initial_state_prior: an optional `Distribution` instance overriding the
          default prior on the model's initial state. This is used in forecasting
          ("today's prior is yesterday's posterior").
        initial_step: optional `int` specifying the initial timestep to model.
          This is relevant when the model contains time-varying components,
          e.g., holidays or seasonality.
        **linear_gaussian_ssm_kwargs: Optional additional keyword arguments to
          to the base `tfd.LinearGaussianStateSpaceModel` constructor.
  
      Returns:
        dist: a `LinearGaussianStateSpaceModel` Distribution object.
      """
      return self._make_state_space_model(
          num_timesteps=num_timesteps,
          param_map=self._canonicalize_param_vals_as_map(param_vals),
          initial_state_prior=initial_state_prior,
          initial_step=initial_step,
          **linear_gaussian_ssm_kwargs,
      )
  
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 12 for calling the StructuralTimeSeries obj's underlying member method code with example usage: obj.make_state_space_model(num_timesteps, param_vals, initial_state_prior, initial_step) and returns: self