code: def sample(self, nsims=1000):
               
          if self.latent_variables.estimation_method not in ['BBVI', 'M-H']:
              raise Exception("No latent variables estimated!")
          else:
              lv_draws = self.draw_latent_variables(nsims=nsims)
              mus = [self.smoothed_state(self.data, lv_draws[:,i])[0][0][:-1] for i in range(nsims)]
              data_draws = np.array([np.random.normal(mus[i], np.sqrt(self.latent_variables.z_list[0].prior.transform(lv_draws[0,i])), mus[i].shape[0]) for i in range(nsims)])
              return data_draws
docs: Samples from the posterior predictive distribution
  
          Parameters
          ----------
          nsims : int (default : 1000)
              How many draws from the posterior predictive distribution
  
          Returns
          ----------
          - np.ndarray of draws from the data