code: def vectorize(self, sentence_list):
          '''
          Args:
              sentence_list:   The list of tokenized sentences.
                               [[`token`, `token`, `token`, ...],
                               [`token`, `token`, `token`, ...],
                               [`token`, `token`, `token`, ...]]
          
          Returns:
              `np.ndarray` of tokens.
              [vector of token, vector of token, vector of token]
          '''
          test_observed_arr = self.__setup_dataset(sentence_list, self.__token_master_list, self.__seq_len)
  
          inferenced_arr = self.__rbm.inference(
              test_observed_arr,
              training_count=1, 
              r_batch_size=-1
          )
  
          return inferenced_arf vectorize(self, sentence_list):
          '''
          Args:
              sentence_list:   The list of tokenized sentences.
                               [[`token`, `token`, `token`, ...],
                               [`token`, `token`, `token`, ...],
                               [`token`, `token`, `token`, ...]]
          
          Returns:
              `np.ndarray` of tokens.
              [vector of token, vector of token, vector of token]
          '''
          test_observed_arr = self.__setup_dataset(sentence_list, self.__token_master_list, self.__seq_len)
  
          inferenced_arr = self.__rbm.inference(
              test_observed_arr,
              training_count=1, 
              r_batch_size=-1
          )
  
          return inferenced_arr
docs: Args:
              sentence_list:   The list of tokenized sentences.
                               [[`token`, `token`, `token`, ...],
                               [`token`, `token`, `token`, ...],
                               [`token`, `token`, `token`, ...]]
          
          Returns:
              `np.ndarray` of tokens.
              [vector of token, vector of token, vector of token]