code: def predict(self, word, n=1):
      result = self.model.most_similar(word, topn=self.top_k + 1)
      result = [w for w, s in result if w.lower() != word.lower()]
      return result[: self.top_k]
  
file_path: nlpaug/nlpaug/model/word_embs/word_embeddings.py
imports: import nlpaug.util.math.normalization
label_desc: Use image node_id 7 for calling the WordEmbeddings obj's underlying member method code with example usage: obj.predict(word, n) and returns: result