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 Get method to return allocated clusters at specific iteration of clustering process.
  
  @param[in] iteration (uint): Clustering process iteration at which clusters are required.
  
  @return (array_like) Clusters at specific iteration.
docstr_tok: ["!","@","brief","Get","method","to","return","allocated","clusters","at","specific","iteration","of","clustering","process",".","@","param","[","in","]","iteration","(","uint",")",":","Clustering","process","iteration","at","which","clusters","are","required",".","@","return","(","array_like",")","Clusters","at","specific","iteration","."]
code: def get_clusters(self, iteration):
      """!
      @brief Get method to return allocated clusters at specific iteration of clustering process.
  
      @param[in] iteration (uint): Clustering process iteration at which clusters are required.
  
      @return (array_like) Clusters at specific iteration.
  
      """
      return self.__evolution_clusters[iteration]
  
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 7 for calling the kmeans_observer obj's underlying member method code with example usage: obj.get_clusters(iteration) and returns: self