code: def recognize(self, input_pattern):
      index_neuron = self.__network.simulate(input_pattern)
  
      decoded_capture_objects = []
  
      for index_capture_object in self.__network.capture_objects[
          index_neuron
      ]:
          # print("\t%s" % decode_map[index_capture_object]);
          decoded_capture_objects.append(
              self.__decode_map[index_capture_object]
          )
  
      frequent_index = max(
          set(decoded_capture_objects),
          key=decoded_capture_objects.count,
      )
      print(decoded_capture_objects)
      return frequent_index
  
imports: from pyclustering.nnet.som import som, type_conn
  from pyclustering.samples.definitions import IMAGE_DIGIT_SAMPLES
  from pyclustering.utils import read_image, rgb2gray
  from tkinter import *
  from tkinter import messagebox
  import math
  import pickle
  import os
  import random
label_desc: Use image node_id 3 for calling the recognizer obj's underlying member method code with example usage: obj.recognize(input_pattern) and returns: frequent_index