code: def _model(self, beta):
               
  
  
          Y = np.array(self.data[self.max_lag:])
  
          # Transform latent variables
          z = np.array([self.latent_variables.z_list[k].prior.transform(beta[k]) for k in range(beta.shape[0])])
  
          return neural_network_tanh(Y, self.X, z, self.units, self.layers, self.ar+len(self.X_names)), Y
docs: Creates the structure of the model (model matrices etc)
  
          Parameters
          ----------
          beta : np.ndarray
              Contains untransformed starting values for the latent variables
  
          Returns
          ----------
          mu : np.ndarray
              Contains the predicted values (location) for the time series
  
          Y : np.ndarray
              Contains the length-adjusted time series (accounting for lags)