original_docstring: 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.
      
docstring: Instantiate this model as a Distribution over specified `num_timesteps`.
docstring_tokens: Instantiate this model as a Distribution over specified ` num_timesteps ` .
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)
code_tokens: def make_state_space_model ( self , num_timesteps , param_vals , initial_state_prior = None , initial_step = 0 , ** linear_gaussian_ssm_kwargs ) : 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 )
short_docstring: Instantiate this model as a Distribution over specified `num_timesteps`.
short_docstring_tokens: Instantiate this model as a Distribution over specified ` num_timesteps ` .
comment: """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.
      """