code: def average_inter_cluster_distance(cluster1, cluster2, data=None):
      """!
      @brief Calculates average inter-cluster 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 where each element can represent index from the data or object itself.
      @param[in] cluster2 (list): The second cluster where each element can represent index from the data or object itself.
      @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 inter-cluster distance between two clusters.
  
      """
  
      distance = 0.0
  
      if data is None:
          for i in range(len(cluster1)):
              for j in range(len(cluster2)):
                  distance += euclidean_distance_square(
                      cluster1[i], cluster2[j]
                  )
      else:
          for i in range(len(cluster1)):
              for j in range(len(cluster2)):
                  distance += euclidean_distance_square(
                      data[cluster1[i]], data[cluster2[j]]
                  )
  
      distance /= float(len(cluster1) * len(cluster2))
      return distance**0.5
  
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 11 for calling a global function with example usage: average_inter_cluster_distance(cluster1, cluster2, data) and returns: unknown