class_docstr: !
  @brief Container of output dynamic of the chaotic neural network where states of each neuron during simulation are stored.
  
  @see cnn_network
class_docstr_tok: ["!","@","brief","Container","of","output","dynamic","of","the","chaotic","neural","network","where","states","of","each","neuron","during","simulation","are","stored",".","@","see","cnn_network"]
docstr: !
  @brief (uint) Returns amount of simulation steps that are stored.
docstr_tok: ["!","@","brief","(","uint",")","Returns","amount","of","simulation","steps","that","are","stored","."]
code: def __len__(self):
      """!
      @brief (uint) Returns amount of simulation steps that are stored.
  
      """
      return len(self.output)
  
imports: import math
  import numpy
  import random
  import matplotlib.pyplot
  from matplotlib import rcParams
  from matplotlib.font_manager import FontProperties
  from mpl_toolkits.mplot3d import Axes3D
  from enum import IntEnum
  from scipy.spatial import Delaunay
  from pyclustering.utils import euclidean_distance_square, average_neighbor_distance, heaviside, draw_dynamics
label_desc: Use image node_id 2 for calling the cnn_dynamic obj's underlying member method code with example usage: obj.__len__() and returns: len