class_docstr: !
  @brief Represents output dynamic of PCNN (pulse-coupled neural network).
class_docstr_tok: ["!","@","brief","Represents","output","dynamic","of","PCNN","(","pulse-coupled","neural","network",")","."]
docstr: !
  @brief (list) Returns sampling times when dynamic is measured during simulation.
docstr_tok: ["!","@","brief","(","list",")","Returns","sampling","times","when","dynamic","is","measured","during","simulation","."]
code: def time(self):
      """!
      @brief (list) Returns sampling times when dynamic is measured during simulation.
  
      """
      if self.__ccore_pcnn_dynamic_pointer is not None:
          return wrapper.pcnn_dynamic_get_time(
              self.__ccore_pcnn_dynamic_pointer
          )
  
      return list(range(len(self)))
  
imports: import random
  import numpy
  import matplotlib.pyplot
  import matplotlib.animation
  from PIL import Image
  from pyclustering.nnet import *
  from pyclustering.core.wrapper import ccore_library
  import pyclustering.core.pcnn_wrapper
  from pyclustering.utils import draw_dynamics
label_desc: Use image node_id 2 for calling the pcnn_dynamic obj's underlying member method code with example usage: obj.time() and returns: list, wrapper