class_docstr: !
  @brief Common cluster visualizer for 1-, 2- and 3-dimensional data.
  @details `cluster_visualizer_multidim` visualizer can be used to display clusters in N-dimensional data.
  
  The general difference between `cluster_visualizer` and `cluster_visualizer_multidim` is a way of representation
  of 3-dimensional data. In case of `cluster_visualizer` 3-dimensional data is displayed in 3-dimensional plot with
  `x0`, `x1` and `x2` axis. In case of `cluster_visualizer_multidim` it displayed on three 2-dimensional plots:
  `(x0, x1)`, `(x0, x2)` and `(x1, x2)`.
  
  There is an example where sample `Atom` is used for clustering and displayed by `cluster_visualizer`:
  @code
      from pyclustering.utils import read_sample
      from pyclustering.samples.definitions import FCPS_SAMPLES
      from pyclustering.cluster import cluster_visualizer
      from pyclustering.cluster.dbscan import dbscan
  
      sample = read_sample(FCPS_SAMPLES.SAMPLE_ATOM)
      clusters = dbscan(sample, 15, 3).process().get_clusters()
  
      visualizer = cluster_visualizer()
      visualizer.append_clusters(clusters, sample)
      visualizer.show()
  @endcode
  
  Visualized clustering results of `Atom` data:
  @image html dbscan_atom_visualizer.png "Fig. 1. DBSCAN clustering results (3-dimensional data 'Atom')."
  
  @see cluster_visualizer_multidim
docstr: !
  @brief Appends list of cluster to canvas for drawing.
  
  @param[in] clusters (array_like): List of clusters where each cluster may consist of indexes of objects from the data or object itself.
  @param[in] data (array_like): If defines that each element of cluster is considered as a index of object from the data.
  @param[in] canvas (uint): Number of canvas that should be used for displaying clusters.
  @param[in] marker (string): Marker that is used for displaying objects from clusters on the canvas.
  @param[in] markersize (uint): Size of marker.
docstr_tok: ["!","@","brief","Appends","list","of","cluster","to","canvas","for","drawing",".","@","param","[","in","]","clusters","(","array_like",")",":","List","of","clusters","where","each","cluster","may","consist","of","indexes","of","objects","from","the","data","or","object","itself",".","@","param","[","in","]","data","(","array_like",")",":","If","defines","that","each","element","of","cluster","is","considered","as","a","index","of","object","from","the","data",".","@","param","[","in","]","canvas","(","uint",")",":","Number","of","canvas","that","should","be","used","for","displaying","clusters",".","@","param","[","in","]","marker","(","string",")",":","Marker","that","is","used","for","displaying","objects","from","clusters","on","the","canvas",".","@","param","[","in","]","markersize","(","uint",")",":","Size","of","marker","."]
code: def append_clusters(
      self, clusters, data=None, canvas=0, marker=".", markersize=None
  ):
      """!
      @brief Appends list of cluster to canvas for drawing.
  
      @param[in] clusters (array_like): List of clusters where each cluster may consist of indexes of objects from the data or object itself.
      @param[in] data (array_like): If defines that each element of cluster is considered as a index of object from the data.
      @param[in] canvas (uint): Number of canvas that should be used for displaying clusters.
      @param[in] marker (string): Marker that is used for displaying objects from clusters on the canvas.
      @param[in] markersize (uint): Size of marker.
  
      """
  
      for cluster in clusters:
          self.append_cluster(cluster, data, canvas, marker, markersize)
  
imports: import itertools
  import math
  import matplotlib.pyplot
  import matplotlib.gridspec
  from mpl_toolkits.mplot3d import Axes3D
  from pyclustering.utils.color import color
label_desc: Use image node_id 4 for calling the cluster_visualizer obj's underlying member method code with example usage: obj.append_clusters(clusters, data, canvas, marker, markersize) without return types