code: def _update_inernal(self, varp_list):
          
          # The probability for the binary variable for the same latent dimension of any of the models is on.
          self._b_prob_all = 1.-param_to_array(varp_list[0].gamma_group)
          [np.multiply(self._b_prob_all, 1.-vp.gamma_group, self._b_prob_all) for vp in varp_list[1:]]
docs: Make an update of the internal status by gathering the variational posteriors for all the individual models.