class_docstr: Inherits time series methods from TSM class.
  
  **** GENERALIZED AUTOREGRESSIVE SCORE (GAS) REGRESSION MODELS ****
  
  Parameters
  ----------
  
  formula : string
      patsy string describing the regression
  
  data : pd.DataFrame or np.array
      Field to specify the data that will be used
  
  family : GAS family object
      Which distribution to use, e.g. GASNormal()
class_docstr_tok: ["Inherits","time","series","methods","from","TSM","class",".","*","*","*","*","GENERALIZED","AUTOREGRESSIVE","SCORE","(","GAS",")","REGRESSION","MODELS","*","*","*","*","Parameters","--","--","--","--","--","formula",":","string","patsy","string","describing","the","regression","data",":","pd.DataFrame","or","np.array","Field","to","specify","the","data","that","will","be","used","family",":","GAS","family","object","Which","distribution","to","use",",","e.g",".","GASNormal","(",")"]
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])
          ]
      )
      coefficients = np.zeros(
          (self.X.shape[1], self.model_Y.shape[0] + 1)
      )
      coefficients[:, 0] = self.initial_values
      theta = np.zeros(self.model_Y.shape[0] + 1)
      (
          model_scale,
          model_shape,
          model_skewness,
      ) = self._get_scale_and_shape(parm)
  
      # Loop over time series
      theta, self.model_scores, coefficients = self.recursion(
          parm,
          theta,
          self.X,
          coefficients,
          self.model_scores,
          self.model_Y,
          self.model_Y.shape[0],
          model_scale,
          model_shape,
          model_skewness,
      )
  
      return (
          np.array(theta[:-1]),
          self.model_Y,
          self.model_scores,
          coefficients,
      )
  
imports: import sys
  import numpy
  import pandas
  import scipy.stats
  import scipy.special
  from patsy import dmatrices, dmatrix, demo_data
  from ..None import families
  from ..None import tsm
  from ..None import data_check
  from .gas_core_recursions import gas_reg_recursion
label_desc: Use image node_id 6 for calling the GASReg obj's underlying member method code with example usage: obj._cythonized_model(beta) and returns: np,  self,  self,  coefficients