class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE SCORE LOCAL LINEAR TREND MODELS ****
  
  Parameters
  ----------
  data : pd.DataFrame or np.array
      Field to specify the univariate time series data that will be used.
  
  integ : int (default : 0)
      Specifies how many time to difference the time series.
  
  target : str (pd.DataFrame) or int (np.array)
      Specifies which column name or array index to use. By default, first
      column/array will be selected as the dependent variable.
  
  dist : str
      Which distribution to use
docstr: Creates the structure of the model
  
  Parameters
  ----------
  beta : np.array
      Contains untransformed starting values for latent variables
  
  Returns
  ----------
  theta : np.array
      Contains the predicted values for the time series
  
  Y : np.array
      Contains the length-adjusted time series (accounting for lags)
  
  scores : np.array
      Contains the scores for the time series
docstr_tok: ["Creates","the","structure","of","the","model","Parameters","--","--","--","--","--","beta",":","np.array","Contains","untransformed","starting","values","for","latent","variables","Returns","--","--","--","--","--","theta",":","np.array","Contains","the","predicted","values","for","the","time","series","Y",":","np.array","Contains","the","length-adjusted","time","series","(","accounting","for","lags",")","scores",":","np.array","Contains","the","scores","for","the","time","series"]
code: def _cythonized_model(self, beta):
      """Creates the structure of the model
  
      Parameters
      ----------
      beta : np.array
          Contains untransformed starting values for latent variables
  
      Returns
      ----------
      theta : np.array
          Contains the predicted values for the time series
  
      Y : np.array
          Contains the length-adjusted time series (accounting for lags)
  
      scores : np.array
          Contains the scores for the time series
      """
  
      parm = np.array(
          [
              self.latent_variables.z_list[k].prior.transform(beta[k])
              for k in range(beta.shape[0])
          ]
      )
      theta = np.zeros(self.model_Y.shape[0])
      theta_t = np.zeros(self.model_Y.shape[0])
      (
          model_scale,
          model_shape,
          model_skewness,
      ) = self._get_scale_and_shape(parm)
  
      # Loop over time series
      theta, self.model_scores = self.recursion(
          parm,
          theta,
          theta_t,
          self.model_scores,
          self.model_Y,
          self.model_Y.shape[0],
          model_scale,
          model_shape,
          model_skewness,
          self.max_lag,
      )
  
      return (
          np.array(theta),
          theta_t,
          self.model_Y,
          np.array(self.model_scores),
      )
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .gas_core_recursions import gas_llt_recursion
label_desc: Use image node_id 6 for calling the GASLLT obj's underlying member method code with example usage: obj._cythonized_model(beta) and returns: np,  theta_t,  self,  np