docstr: Get the right rolls from the left rolls.
  
  Args:
       texts(list): the left rolls.
       use_gpu(bool): whether use gpu to predict or not
       beam_width(int): the beam search width.
  
  Returns:
       results(list): the right rolls.
docstr_tok: ["Get","the","right","rolls","from","the","left","rolls",".","Args",":","texts","(","list",")",":","the","left","rolls",".","use_gpu","(","bool",")",":","whether","use","gpu","to","predict","or","not","beam_width","(","int",")",":","the","beam","search","width",".","Returns",":","results","(","list",")",":","the","right","rolls","."]
code: def generate(self, texts, use_gpu=False, beam_width=5):
      """
      Get the right rolls from the left rolls.
  
      Args:
           texts(list): the left rolls.
           use_gpu(bool): whether use gpu to predict or not
           beam_width(int): the beam search width.
  
      Returns:
           results(list): the right rolls.
      """
      paddle.disable_static()
  
      if (
          texts
          and isinstance(texts, list)
          and all(texts)
          and all([isinstance(text, str) for text in texts])
      ):
          predicted_data = texts
      else:
          raise ValueError(
              "The input texts should be a list with nonempty string elements."
          )
      for i, text in enumerate(texts):
          for char in text:
              if not "\u4e00" <= char <= "\u9fff":
                  logger.warning(
                      "The input text: %s, contains non-Chinese characters, which may result in magic output"
                      % text
                  )
                  break
  
      if use_gpu and "CUDA_VISIBLE_DEVICES" not in os.environ:
          use_gpu = False
          logger.warning(
              "use_gpu has been set False as you didn't set the environment variable CUDA_VISIBLE_DEVICES while using use_gpu=True"
          )
  
      paddle.set_device("gpu") if use_gpu else paddle.set_device("cpu")
  
      self.model.eval()
      results = []
      for text in predicted_data:
          sample_results = []
          encode_text = self.tokenizer.encode(text)
          src_ids = paddle.to_tensor(
              encode_text["input_ids"]
          ).unsqueeze(0)
          src_sids = paddle.to_tensor(
              encode_text["token_type_ids"]
          ).unsqueeze(0)
          output_ids = beam_search_infilling(
              self.model,
              src_ids,
              src_sids,
              eos_id=self.tokenizer.vocab["[SEP]"],
              sos_id=self.tokenizer.vocab["[CLS]"],
              attn_id=self.tokenizer.vocab["[MASK]"],
              pad_id=self.tokenizer.vocab["[PAD]"],
              unk_id=self.tokenizer.vocab["[UNK]"],
              vocab_size=len(self.tokenizer.vocab),
              max_decode_len=20,
              max_encode_len=20,
              beam_width=beam_width,
              tgt_type_id=1,
          )
          output_str = self.rev_lookup(output_ids[0])
  
          for ostr in output_str.tolist():
              if "[SEP]" in ostr:
                  ostr = ostr[: ostr.index("[SEP]")]
              sample_results.append("".join(ostr))
          results.append(sample_results)
      return results
  
file_path: PaddleHub/modules/text/text_generation/ernie_gen_couplet/module.py
imports: import ast
  import json
  import argparse
  import os
  import numpy
  import paddle
  import paddlehub
  from paddlehub.module.module import runnable
  from paddlehub.module.nlp_module import DataFormatError
  from paddlehub.common.logger import logger
  from paddlehub.module.module import moduleinfo, serving
  from paddlenlp.transformers import ErnieTokenizer, ErnieForGeneration
  from ernie_gen_couplet.decode import beam_search_infilling
label_desc: Use image node_id 2 for calling the ErnieGen obj's underlying member method code with example usage: obj.generate(texts, use_gpu, beam_width) and returns: results