code: def gelman_rubin(mcmc):
      
  
      if mcmc.n_chains < 2:
          raise ValueError(
              'Gelman-Rubin diagnostic requires multiple chains '
              'of the same length.')
  
      # get Vhat and within-chain variance
      Vhat, W = _vhat_w(mcmc)
  
      # compute and return Gelman-Rubin statistic
      Rhat = np.sqrt(Vhat / W)
      return pd.DataFrame({'gelman_rubin': Rhat}, index=mcmc.levels)
docs: Args:
          mcmc (MCMCResults): Pre-sliced MCMC samples to compute diagnostics for.