class_docstr: !
  @brief Model of oscillatory network that is based on the Kuramoto model of synchronization.
  
  @details CCORE option can be used to use the pyclustering core - C/C++ shared library for processing that significantly increases performance.
docstr: !
  @brief Finds neighbors of the oscillator with specified index.
  
  @param[in] index (uint): index of oscillator for which neighbors should be found in the network.
  
  @return (list) Indexes of neighbors of the specified oscillator.
docstr_tok: ["!","@","brief","Finds","neighbors","of","the","oscillator","with","specified","index",".","@","param","[","in","]","index","(","uint",")",":","index","of","oscillator","for","which","neighbors","should","be","found","in","the","network",".","@","return","(","list",")","Indexes","of","neighbors","of","the","specified","oscillator","."]
code: def get_neighbors(self, index):
      """!
      @brief Finds neighbors of the oscillator with specified index.
  
      @param[in] index (uint): index of oscillator for which neighbors should be found in the network.
  
      @return (list) Indexes of neighbors of the specified oscillator.
  
      """
  
      if (self._ccore_network_pointer is not None) and (
          self._osc_conn is None
      ):
          self._osc_conn = wrapper.sync_connectivity_matrix(
              self._ccore_network_pointer
          )
  
      return super().get_neighbors(index)
  
imports: import math
  import numpy
  import random
  import matplotlib.pyplot
  import matplotlib.animation
  import pyclustering.core.sync_wrapper
  from pyclustering.core.wrapper import ccore_library
  from scipy.integrate import odeint
  from pyclustering.nnet import network, conn_represent, conn_type, initial_type, solve_type
  from pyclustering.utils import pi, draw_dynamics, draw_dynamics_set, set_ax_param
label_desc: Use image node_id 11 for calling the sync_network obj's underlying member method code with example usage: obj.get_neighbors(index) and returns: super