code: def __CI_class_handler__(cm, param, CV, binom_method="normal-approx"):
      """
      Handle CI calculation for class parameters.
  
      :param cm: confusion matrix
      :type cm: pycm.ConfusionMatrix object
      :param param: input parameter
      :type param: str
      :param CV: critical value
      :type CV: float
      :param binom_method: binomial confidence interval method
      :type binom_method: str
      :return: result as dictionary
      """
      result = {}
      item1 = cm.class_stat[param]
      if param == "TPR" or param == "FNR":
          item2 = cm.class_stat["P"]
      elif param == "TNR" or param == "FPR":
          item2 = cm.class_stat["N"]
      elif param == "PPV":
          item2 = cm.class_stat["TOP"]
      elif param == "NPV":
          item2 = cm.class_stat["TON"]
      elif param == "ACC" or param == "PRE":
          item2 = cm.class_stat["POP"]
      for i in cm.classes:
          temp = []
          if param == "PLR":
              SE = LR_SE_calc(cm.TP[i], cm.P[i], cm.FP[i], cm.N[i])
              CI = LR_CI_calc(cm.PLR[i], SE, CV)
          elif param == "NLR":
              SE = LR_SE_calc(cm.FN[i], cm.P[i], cm.TN[i], cm.N[i])
              CI = LR_CI_calc(cm.NLR[i], SE, CV)
          elif param == "AUC":
              SE = AUC_SE_calc(cm.AUC[i], cm.P[i], cm.N[i])
              CI = CI_calc(item1[i], SE, CV)
          else:
              SE = SE_calc(item1[i], item2[i])
              if binom_method == "wilson":
                  CI = CI_calc_wilson(item1[i], item2[i], CV)
              elif binom_method == "agresti-coull":
                  CI = CI_calc_agresti(item1[i], item2[i], CV)
              else:
                  CI = CI_calc(item1[i], SE, CV)
          temp.append(SE)
          temp.append(CI)
          result[i] = temp
      return result
  
file_path: pycm/pycm/pycm_ci.py
imports: from __future__ import division
  import math
label_desc: Use image node_id 9 for calling a global function with example usage: __CI_class_handler__(cm, param, CV, binom_method) and returns: result