code: def variance_increase_distance(cluster1, cluster2, data=None):
      """!
      @brief Calculates variance increase distance between two clusters.
      @details Clusters can be represented by list of coordinates (in this case data shouldn't be specified),
               or by list of indexes of points from the data (represented by list of points), in this case
               data should be specified.
  
      @param[in] cluster1 (list): The first cluster.
      @param[in] cluster2 (list): The second cluster.
      @param[in] data (list): If specified than elements of clusters will be used as indexes,
                 otherwise elements of cluster will be considered as points.
  
      @return (double) Average variance increase distance between two clusters.
  
      """
  
      # calculate local sum
      if data is None:
          member_cluster1 = [0.0] * len(cluster1[0])
          member_cluster2 = [0.0] * len(cluster2[0])
  
      else:
          member_cluster1 = [0.0] * len(data[0])
          member_cluster2 = [0.0] * len(data[0])
  
      for i in range(len(cluster1)):
          if data is None:
              member_cluster1 = list_math_addition(
                  member_cluster1, cluster1[i]
              )
          else:
              member_cluster1 = list_math_addition(
                  member_cluster1, data[cluster1[i]]
              )
  
      for j in range(len(cluster2)):
          if data is None:
              member_cluster2 = list_math_addition(
                  member_cluster2, cluster2[j]
              )
          else:
              member_cluster2 = list_math_addition(
                  member_cluster2, data[cluster2[j]]
              )
  
      member_cluster_general = list_math_addition(
          member_cluster1, member_cluster2
      )
      member_cluster_general = list_math_division_number(
          member_cluster_general, len(cluster1) + len(cluster2)
      )
  
      member_cluster1 = list_math_division_number(
          member_cluster1, len(cluster1)
      )
      member_cluster2 = list_math_division_number(
          member_cluster2, len(cluster2)
      )
  
      # calculate global sum
      distance_general = 0.0
      distance_cluster1 = 0.0
      distance_cluster2 = 0.0
  
      for i in range(len(cluster1)):
          if data is None:
              distance_cluster1 += euclidean_distance_square(
                  cluster1[i], member_cluster1
              )
              distance_general += euclidean_distance_square(
                  cluster1[i], member_cluster_general
              )
  
          else:
              distance_cluster1 += euclidean_distance_square(
                  data[cluster1[i]], member_cluster1
              )
              distance_general += euclidean_distance_square(
                  data[cluster1[i]], member_cluster_general
              )
  
      for j in range(len(cluster2)):
          if data is None:
              distance_cluster2 += euclidean_distance_square(
                  cluster2[j], member_cluster2
              )
              distance_general += euclidean_distance_square(
                  cluster2[j], member_cluster_general
              )
  
          else:
              distance_cluster2 += euclidean_distance_square(
                  data[cluster2[j]], member_cluster2
              )
              distance_general += euclidean_distance_square(
                  data[cluster2[j]], member_cluster_general
              )
  
      return distance_general - distance_cluster1 - distance_cluster2
  
imports: import time
  import numpy
  from numpy import array
  from PIL import Image
  import matplotlib.pyplot
  from sys import platform
  from pyclustering.utils.metric import distance_metric, type_metric
label_desc: Use image node_id 13 for calling a global function with example usage: variance_increase_distance(cluster1, cluster2, data) and returns: unknown