code: def _best_dev_gains(rpg):
      
      sg, delta = rpg.sg, rpg.delta
  
      best_dev_gains = ((1-delta)/delta *
                        (np.max(sg.payoff_arrays[i], 0) - sg.payoff_arrays[i])
                        for i in range(2))
  
      return tuple(best_dev_gains)
docs: Calculate the normalized payoff gains from deviating from the current
      action to the best response for each player.
  
      Parameters
      ----------
      rpg : RepeatedGame
          Two player repeated game.
  
      Returns
      -------
      best_dev_gains : tuple(ndarray(float, ndim=2))
          The normalized best deviation payoff gain arrays.
          best_dev_gains[i][ai, a-i] is normalized payoff gain
          player i can get if originally players are choosing
          ai and a-i, and player i deviates to the best response action.