class_docstr: !
  @brief Class represents clustering algorithm HSyncNet. HSyncNet is bio-inspired algorithm that is based on oscillatory network that uses modified Kuramoto model.
  
  Example:
  @code
      from pyclustering.cluster.hsyncnet import hsyncnet
      from pyclustering.nnet.sync import sync_visualizer
      from pyclustering.utils import read_sample, draw_clusters
      from pyclustering.samples.definitions import SIMPLE_SAMPLES
  
      # Read list of points for cluster analysis.
      sample = read_sample(SIMPLE_SAMPLES.SAMPLE_SIMPLE2)
  
      # Create network for allocation of three clusters.
      network = hsyncnet(sample, 3)
  
      # Run cluster analysis and output dynamic of the network.
      analyser = network.process(0.995, collect_dynamic=True)
  
      # Get allocated clusters.
      clusters = analyser.allocate_clusters(eps=0.1)
  
      # Show output dynamic of the network.
      sync_visualizer.show_output_dynamic(analyser)
  
      # Show allocated clusters.
      draw_clusters(sample, clusters)
  @endcode
docstr: !
  @brief Destructor of oscillatory network HSyncNet.
code: def __del__(self):
      """!
      @brief Destructor of oscillatory network HSyncNet.
  
      """
  
      if self.__ccore_network_pointer is not None:
          wrapper.hsyncnet_destroy_network(self.__ccore_network_pointer)
          self.__ccore_network_pointer = None
  
file_path: pyclustering/pyclustering/cluster/hsyncnet.py
imports: import pyclustering.core.hsyncnet_wrapper
  from pyclustering.core.wrapper import ccore_library
  from pyclustering.nnet import initial_type, solve_type
  from pyclustering.cluster.syncnet import syncnet, syncnet_analyser
  from pyclustering.utils import average_neighbor_distance
label_desc: Use image node_id 2 for calling the hsyncnet obj's underlying member method code with example usage: obj.__del__() without return types