code: def _get_predicate_guards(self, state, state_training_data):
          
          # choose the sink transition.
  
          # First option: Just the maximum transition
          # sink = max(state_training_data, key=lambda x: len(x[1]))[0]
  
          # Second option: Heuristics based on RE filters properties
          max_size_trans = max(state_training_data, key=lambda x: len(x[1]))
          max_size_trans_l = [x for x in state_training_data if
                              len(x[1]) == len(max_size_trans[1])]
  
          target_states = [t[0] for t in max_size_trans_l]
          if len(max_size_trans_l) == 1:
              sink = max_size_trans[0]
          elif '' in target_states:
              sink = ''
          elif state in target_states:
              sink = state
          else:
              sink = random.choice(target_states)
          # End of sink selection
  
          transitions = []
          known_symbols = []
          for (t, data) in state_training_data:
              if t == sink:
                  continue
              pred = SetPredicate(data)
              transitions.append((t, pred))
              known_symbols += data
          transitions.append(
              (sink, SetPredicate(set(self.alphabet) - set(known_symbols))))
          return transitions
docs: Args:
              state (DFA state): The dfa state
              state_training_data (list): The training data set
          Returns:
              list: A list of transitions