code: def is_closed(self):
          
          old_training_data = self.training_data
          self.training_data = {x: [] for x in self.sm_vector}
          for t in self.smi_vector:
              src_state = t[:-1]
              symbol = t[-1:]
              found = False
              for dst_state in self.sm_vector:
                  if self.observation_table[dst_state] == self.observation_table[t]:
                      self._add_training_data(src_state, dst_state, symbol)
                      found = True
                      break
              if not found:
                  return False, t
  
          assert self.training_data != old_training_data, \
              "No update happened from previous round. The algo will loop infinetely"
          return True, None
docs: _check if the observation table is closed.
          Args:
              None
          Returns:
              tuple (bool, str): True if the observation table is closed and false otherwise.
                                 If the table is not closed the escaping string is returned.