code: def nucleus_sample(
      self,
      text,
      max_len=32,
      end_word=None,
      repitition_penalty=1.0,
      temperature=1.0,
      top_k=0,
      top_p=1.0,
  ):
      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_logits = output[0, -1, :]
          for id in set(ids):
              next_token_logits[id] /= repitition_penalty
          next_token_logits = next_token_logits / temperature
          filtered_logits = self.top_k_top_p_filtering(
              next_token_logits, top_k=top_k, top_p=top_p
          )
          next_token = paddle.multinomial(
              paddle.nn.functional.softmax(filtered_logits, axis=-1),
              num_samples=1,
          ).numpy()
          ids += [int(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_logits = output[0, -1, :]
              for id in set(ids):
                  next_token_logits[id] /= repitition_penalty
              next_token_logits = next_token_logits / temperature
              filtered_logits = self.top_k_top_p_filtering(
                  next_token_logits, top_k=top_k, top_p=top_p
              )
              next_token = paddle.multinomial(
                  paddle.nn.functional.softmax(
                      filtered_logits, axis=-1
                  ),
                  num_samples=1,
              ).numpy()
              ids += [int(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 4 for calling the GPT2_Base_CN obj's underlying member method code with example usage: obj.nucleus_sample(text, max_len, end_word, repitition_penalty, temperature, top_k, top_p) and returns: self