code: def normalized_table_calc(classes, table):
      
      map_dict = {k: 0 for k in classes}
      new_table = {k: map_dict.copy() for k in classes}
      for key in classes:
          div = sum(table[key].values())
          if div == 0:
              div = 1
          for item in classes:
              new_table[key][item] = numpy.around(table[key][item] / div, 5)
      return new_table
docs: Return normalized confusion matrix.
  
      :param classes: classes list
      :type classes:list
      :param table: table
      :type table:dict
      :return: normalized table as dict