Creates the structure of the model

        Parameters
        ----------
        beta : np.array
            Contains untransformed starting values for latent variables

        mini_batch : int
            Size of each mini batch of data

        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

def _uncythonized_mb_model(self, beta, mini_batch):
        """ Creates the structure of the model

        Parameters
        ----------
        beta : np.array
            Contains untransformed starting values for latent variables

        mini_batch : int
            Size of each mini batch of data

        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
        """

        rand_int =  np.random.randint(low=0, high=self.data.shape[0]-mini_batch-self.max_lag+1)
        sample = np.arange(start=rand_int, stop=rand_int+mini_batch)

        data = self.data[sample]
        X = self.X[sample, :]
        Y = data[self.max_lag:]

        parm = np.array([self.latent_variables.z_list[k].prior.transform(beta[k]) for k in range(beta.shape[0])])
        model_scale, model_shape, model_skewness = self._get_scale_and_shape(parm)
        theta = np.matmul(X[self.integ+self.max_lag:],parm[self.sc+self.ar:(self.sc+self.ar+len(self.X_names))])

        # Loop over time series
        theta, self.model_scores = gasx_recursion(parm, theta, self.model_scores, Y, self.ar, self.sc, Y.shape[0], self.family.score_function, 
            self.link, model_scale, model_shape, model_skewness, self.max_lag)

        return np.array(theta), Y, self.model_scores