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_cluster_rays(ax, color, sample, cluster, center):
      dimension = len(sample[0])
  
      for index_point in cluster:
          point = sample[index_point]
          if dimension == 1:
              ax.plot(
                  [point[0], center[0]],
                  [0.0, 0.0],
                  "-",
                  color=color,
                  linewidth=0.5,
              )
          elif dimension == 2:
              ax.plot(
                  [point[0], center[0]],
                  [point[1], center[1]],
                  "-",
                  color=color,
                  linewidth=0.5,
              )
          elif dimension == 3:
              ax.plot(
                  [point[0], center[0]],
                  [point[1], center[1]],
                  [point[2], center[2]],
                  "-",
                  color=color,
                  linewidth=0.5,
              )
  
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 4 for calling the kmeans_visualizer obj's underlying member method code with example usage: obj.__draw_cluster_rays(ax, color, sample, cluster, center) without return types