code: def predict(self, word, pos=None):
      results = []
      for synonym in self.model.synsets(word, pos=pos, lang=self.lang):
          for lemma in synonym.lemmas(lang=self.lang):
              if self.is_synonym:
                  results.append(lemma.name())
              else:
                  for antonym in lemma.antonyms():
                      results.append(antonym.name())
      return results
  
imports: from nlpaug.model.word_dict import WordDictionary
label_desc: Use image node_id 3 for calling the WordNet obj's underlying member method code with example usage: obj.predict(word, pos) and returns: results