code: def __CI_overall_handler__(
      cm, param, CV, binom_method="normal-approx"
  ):
      """
      Handle CI calculation for overall 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 list [SE, (CI_DOWN, DI_UP)]
      """
      result = []
      population = list(cm.POP.values())[0]
      if param == "Kappa":
          SE = kappa_SE_calc(
              cm.overall_stat["Overall ACC"],
              cm.overall_stat["Overall RACC"],
              population,
          )
      else:
          SE = SE_calc(cm.overall_stat[param], population)
      if binom_method == "wilson":
          CI = CI_calc_wilson(cm.overall_stat[param], population, CV)
      elif binom_method == "agresti-coull":
          CI = CI_calc_agresti(cm.overall_stat[param], population, CV)
      else:
          CI = CI_calc(cm.overall_stat[param], SE, CV)
      result.append(SE)
      result.append(CI)
      return result
  
file_path: pycm/pycm/pycm_ci.py
imports: from __future__ import division
  import math
label_desc: Use image node_id 10 for calling a global function with example usage: __CI_overall_handler__(cm, param, CV, binom_method) and returns: result