class_docstr: !
  @brief Observer of K-Means algorithm that is used to collect information about clustering process on each iteration of the algorithm.
  
  @see kmeans
docstr: !
  @brief Returns amount of steps that were observer during clustering process in K-Means algorithm.
docstr_tok: ["!","@","brief","Returns","amount","of","steps","that","were","observer","during","clustering","process","in","K-Means","algorithm","."]
code: def __len__(self):
      """!
      @brief Returns amount of steps that were observer during clustering process in K-Means algorithm.
  
      """
      return len(self.__evolution_clusters)
  
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 2 for calling the kmeans_observer obj's underlying member method code with example usage: obj.__len__() and returns: len