code: def kl_divergence_calc(P, TOP, POP):
      """
      Calculate Kullback-Liebler (KL) divergence.
  
      :param P: number of actual positives per class
      :type P: dict
      :param TOP: number of positives in predict vector per class
      :type TOP: dict
      :param POP: population or total number of samples per class
      :type POP: dict
      :return: KL divergence as float
      """
      try:
          result = 0
          for i in TOP.keys():
              reference_likelihood = P[i] / POP[i]
              response_likelihood = TOP[i] / POP[i]
              result += reference_likelihood * math.log(
                  (reference_likelihood / response_likelihood), 2
              )
          return result
      except Exception:
          return "None"
  
imports: from __future__ import division
  import math
  import operator
  from functools import reduce
  from .pycm_interpret import *
  from .pycm_ci import kappa_SE_calc, CI_calc, SE_calc
  from .pycm_util import complement
label_desc: Use image node_id 28 for calling a global function with example usage: kl_divergence_calc(P, TOP, POP) and returns: result, str