class_docstr: !
  @brief Visualizer of K-Means algorithm's results.
  @details K-Means visualizer provides visualization services that are specific for K-Means algorithm.
code: def __draw_centers(
      figure, offset, visualizer, centers, initial_centers
  ):
      ax = figure.get_axes()[offset]
  
      for index_center in range(len(centers)):
          color = visualizer.get_cluster_color(index_center, 0)
          kmeans_visualizer.__draw_center(
              ax, centers[index_center], color, "*", 1.0
          )
  
          if initial_centers is not None:
              kmeans_visualizer.__draw_center(
                  ax, initial_centers[index_center], color, "*", 0.4
              )
  
imports: import copy
  import numpy
  import matplotlib.pyplot
  import matplotlib.animation
  import pyclustering.core.kmeans_wrapper
  from pyclustering.core.wrapper import ccore_library
  from pyclustering.core.metric_wrapper import metric_wrapper
  from pyclustering.cluster.encoder import type_encoding
  from pyclustering.cluster import cluster_visualizer
  from pyclustering.utils.metric import distance_metric, type_metric
label_desc: Use image node_id 6 for calling the kmeans_visualizer obj's underlying member method code with example usage: obj.__draw_centers(figure, offset, visualizer, centers, initial_centers) without return types