code: def greedy_search(self, text, max_len=32, end_word=None):
      with paddle.no_grad():
          # # 终止标志
          if end_word is not None:
              stop_id = self.tokenizer.encode(end_word)
              length = len(stop_id)
  
          # 初始预测
          ids = self.tokenizer.encode(text)
          input_id = paddle.to_tensor(
              np.array(ids).reshape(1, -1).astype("int64")
          )
          output, cached_kvs = self.model(input_id, use_cache=True)
          next_token = int(np.argmax(output[0, -1].numpy()))
          ids.append(next_token)
  
          # 使用缓存进行继续预测
          for i in range(max_len - 1):
              input_id = paddle.to_tensor(
                  np.array([next_token]).reshape(1, -1).astype("int64")
              )
              output, cached_kvs = self.model(
                  input_id, use_cache=True, cache=cached_kvs
              )
              next_token = int(np.argmax(output[0, -1].numpy()))
              ids.append(next_token)
  
              # 根据终止标志停止预测
              if (end_word is not None) and (ids[-length:] == stop_id):
                  break
  
          return self.tokenizer.decode(ids)
  
file_path: PaddleHub/modules/text/text_generation/GPT2_Base_CN/module.py
imports: import paddle
  import numpy
  import paddle.nn
  from paddlehub.module.module import moduleinfo, serving
  from paddlenlp.transformers import GPT2ForPretraining, GPT2ChineseTokenizer
label_desc: Use image node_id 2 for calling the GPT2_Base_CN obj's underlying member method code with example usage: obj.greedy_search(text, max_len, end_word) and returns: self