class_docstr: !
  @brief Visualizer of EM algorithm's results.
  @details Provides services for visualization of particular features of the algorithm, for example,
            in case of two-dimensional dataset it shows covariance ellipses.
docstr: !
  @brief Closes figure object that was used or allocated by the visualizer.
  
  @param[in] figure (figure): Figure object that was used or allocated by the visualizer.
docstr_tok: ["!","@","brief","Closes","figure","object","that","was","used","or","allocated","by","the","visualizer",".","@","param","[","in","]","figure","(","figure",")",":","Figure","object","that","was","used","or","allocated","by","the","visualizer","."]
code: def close(figure):
      """!
      @brief Closes figure object that was used or allocated by the visualizer.
  
      @param[in] figure (figure): Figure object that was used or allocated by the visualizer.
  
      """
      plt.close(figure)
  
imports: import numpy
  import random
  from pyclustering.cluster import cluster_visualizer
  from pyclustering.cluster.center_initializer import kmeans_plusplus_initializer
  from pyclustering.cluster.kmeans import kmeans
  from pyclustering.utils import pi, calculate_ellipse_description, euclidean_distance_square
  from enum import IntEnum
  import matplotlib.pyplot
  import matplotlib.animation
  from matplotlib import patches
label_desc: Use image node_id 2 for calling the ema_visualizer obj's underlying member method code with example usage: obj.close(figure) without return types