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_center(ax, center, color, marker, alpha):
      dimension = len(center)
  
      if dimension == 1:
          ax.plot(
              center[0],
              0.0,
              color=color,
              alpha=alpha,
              marker=marker,
              markersize=kmeans_visualizer.__default_2d_marker_size,
          )
      elif dimension == 2:
          ax.plot(
              center[0],
              center[1],
              color=color,
              alpha=alpha,
              marker=marker,
              markersize=kmeans_visualizer.__default_2d_marker_size,
          )
      elif dimension == 3:
          ax.scatter(
              center[0],
              center[1],
              center[2],
              c=color,
              alpha=alpha,
              marker=marker,
              s=kmeans_visualizer.__default_3d_marker_size,
          )
  
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 5 for calling the kmeans_visualizer obj's underlying member method code with example usage: obj.__draw_center(ax, center, color, marker, alpha) without return types