code: def show_clusters(sample, clusters, centers, initial_centers = None, **kwargs):
          
  
          visualizer = cluster_visualizer()
          visualizer.append_clusters(clusters, sample)
          
          offset = kwargs.get('offset', 0)
          figure = kwargs.get('figure', None)
          display = kwargs.get('display', True)
  
          if figure is None:
              figure = visualizer.show(display = False)
          else:
              visualizer.show(figure = figure, display = False)
          
          kmeans_visualizer.__draw_centers(figure, offset, visualizer, centers, initial_centers)
          kmeans_visualizer.__draw_rays(figure, offset, visualizer, sample, clusters, centers)
          
          if display is True:
              plt.show()
  
          return figure
docs: !
          @brief Display K-Means clustering results.
          
          @param[in] sample (list): Dataset that was used for clustering.
          @param[in] clusters (array_like): Clusters that were allocated by the algorithm.
          @param[in] centers (array_like): Centers that were allocated by the algorithm.
          @param[in] initial_centers (array_like): Initial centers that were used by the algorithm, if 'None' then initial centers are not displyed.
          @param[in] **kwargs: Arbitrary keyword arguments (available arguments: 'figure', 'display', 'offset').
          
          <b>Keyword Args:</b><br>
              - figure (figure): If 'None' then new is figure is created, otherwise specified figure is used for visualization.
              - display (bool): If 'True' then figure will be shown by the method, otherwise it should be shown manually using matplotlib function 'plt.show()'.
              - offset (uint): Specify axes index on the figure where results should be drawn (only if argument 'figure' is specified).
          
          @return (figure) Figure where clusters were drawn.