code: def overall_statistics(**kwargs):
      """
      Return Overall statistics.
  
      :param kwargs: inputs
      :type kwargs: dict
      :return: overall statistics as dict
      """
      result = {}
      POP = kwargs["POP"]
      population = list(POP.values())[0]
      TP = kwargs["TP"]
      P = kwargs["P"]
      TOP = kwargs["TOP"]
      table = kwargs["table"]
      classes = kwargs["classes"]
      result["Overall ACC"] = overall_accuracy_calc(TP, population)
      result["Overall RACCU"] = overall_random_accuracy_calc(
          kwargs["RACCU"]
      )
      result["Overall RACC"] = overall_random_accuracy_calc(
          kwargs["RACC"]
      )
      result["Kappa"] = reliability_calc(
          result["Overall RACC"], result["Overall ACC"]
      )
      PC_AC1 = PC_AC1_calc(P, TOP, POP)
      PC_S = PC_S_calc(classes)
      result["Gwet AC1"] = reliability_calc(
          PC_AC1, result["Overall ACC"]
      )
      result["Bennett S"] = reliability_calc(
          PC_S, result["Overall ACC"]
      )
      result["Kappa Standard Error"] = kappa_SE_calc(
          result["Overall ACC"], result["Overall RACC"], population
      )
      result["Kappa Unbiased"] = reliability_calc(
          result["Overall RACCU"], result["Overall ACC"]
      )
      result["Scott PI"] = result["Kappa Unbiased"]
      result["Kappa No Prevalence"] = kappa_no_prevalence_calc(
          result["Overall ACC"]
      )
      result["Kappa 95% CI"] = CI_calc(
          result["Kappa"], result["Kappa Standard Error"]
      )
      result["Standard Error"] = SE_calc(
          result["Overall ACC"], population
      )
      result["95% CI"] = CI_calc(
          result["Overall ACC"], result["Standard Error"]
      )
      result["Chi-Squared"] = chi_square_calc(
          classes, table, TOP, P, POP
      )
      result["Phi-Squared"] = phi_square_calc(
          result["Chi-Squared"], population
      )
      result["Cramer V"] = cramers_V_calc(
          result["Phi-Squared"], classes
      )
      result["Response Entropy"] = entropy_calc(TOP, POP)
      result["Reference Entropy"] = entropy_calc(P, POP)
      result["Cross Entropy"] = cross_entropy_calc(TOP, P, POP)
      result["Joint Entropy"] = joint_entropy_calc(classes, table, POP)
      result["Conditional Entropy"] = conditional_entropy_calc(
          classes, table, P, POP
      )
      result["Mutual Information"] = mutual_information_calc(
          result["Response Entropy"], result["Conditional Entropy"]
      )
      result["KL Divergence"] = kl_divergence_calc(P, TOP, POP)
      result["Lambda B"] = lambda_B_calc(
          classes, table, TOP, population
      )
      result["Lambda A"] = lambda_A_calc(classes, table, P, population)
      result["Chi-Squared DF"] = DF_calc(classes)
      result["Overall J"] = overall_jaccard_index_calc(
          list(kwargs["jaccard_list"].values())
      )
      result["Hamming Loss"] = hamming_calc(TP, population)
      result["Zero-one Loss"] = zero_one_loss_calc(TP, population)
      result["NIR"] = NIR_calc(P, population)
      result["P-Value"] = p_value_calc(TP, population, result["NIR"])
      result["Overall CEN"] = overall_CEN_calc(
          classes, TP, TOP, P, kwargs["CEN_dict"]
      )
      result["Overall MCEN"] = overall_CEN_calc(
          classes, TP, TOP, P, kwargs["MCEN_dict"], True
      )
      result["Overall MCC"] = overall_MCC_calc(classes, table, TOP, P)
      result["RR"] = RR_calc(classes, TOP)
      result["CBA"] = CBA_calc(classes, table, TOP, P)
      result["AUNU"] = macro_calc(kwargs["AUC_dict"])
      result["AUNP"] = AUNP_calc(classes, P, POP, kwargs["AUC_dict"])
      result["RCI"] = RCI_calc(
          result["Mutual Information"], result["Reference Entropy"]
      )
      result["Pearson C"] = pearson_C_calc(
          result["Chi-Squared"], population
      )
      result["TPR Micro"] = result["Overall ACC"]
      result["TPR Macro"] = macro_calc(kwargs["TPR"])
      result["CSI"] = macro_calc(kwargs["ICSI_dict"])
      result["ARI"] = ARI_calc(classes, table, TOP, P, population)
      result["TNR Micro"] = micro_calc(
          item1=kwargs["TN"], item2=kwargs["FP"]
      )
      result["TNR Macro"] = macro_calc(kwargs["TNR"])
      result["Bangdiwala B"] = B_calc(classes, TP, TOP, P)
      result["Krippendorff Alpha"] = alpha_calc(
          result["Overall RACCU"], result["Overall ACC"], population
      )
      result["SOA1(Landis & Koch)"] = kappa_analysis_koch(
          result["Kappa"]
      )
      result["SOA2(Fleiss)"] = kappa_analysis_fleiss(result["Kappa"])
      result["SOA3(Altman)"] = kappa_analysis_altman(result["Kappa"])
      result["SOA4(Cicchetti)"] = kappa_analysis_cicchetti(
          result["Kappa"]
      )
      result["SOA5(Cramer)"] = V_analysis(result["Cramer V"])
      result["SOA6(Matthews)"] = MCC_analysis(result["Overall MCC"])
      result["SOA7(Lambda A)"] = lambda_analysis(result["Lambda A"])
      result["SOA8(Lambda B)"] = lambda_analysis(result["Lambda B"])
      result["SOA9(Krippendorff Alpha)"] = alpha_analysis(
          result["Krippendorff Alpha"]
      )
      result["SOA10(Pearson C)"] = pearson_C_analysis(
          result["Pearson C"]
      )
      result["FPR Macro"] = complement(result["TNR Macro"])
      result["FNR Macro"] = complement(result["TPR Macro"])
      result["PPV Macro"] = macro_calc(kwargs["PPV"])
      result["NPV Macro"] = macro_calc(kwargs["NPV"])
      result["ACC Macro"] = macro_calc(kwargs["ACC"])
      result["F1 Macro"] = macro_calc(kwargs["F1"])
      result["FPR Micro"] = complement(result["TNR Micro"])
      result["FNR Micro"] = complement(result["TPR Micro"])
      result["PPV Micro"] = result["TPR Micro"]
      result["F1 Micro"] = result["TPR Micro"]
      result["NPV Micro"] = micro_calc(
          item1=kwargs["TN"], item2=kwargs["FN"]
      )
      return result
  
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 43 for calling a global function with example usage: overall_statistics() and returns: result