docstr: Get the predict result from the input texts.
  
  Args:
       texts(list): the input texts.
       use_gpu(bool): whether use gpu to predict or not
       beam_width(int): the beam search width.
  
  Returns:
       results(list): the predict result.
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code: def generate(self, texts, use_gpu=False, beam_width=5):
      """
      Get the predict result from the input texts.
  
      Args:
           texts(list): the input texts.
           use_gpu(bool): whether use gpu to predict or not
           beam_width(int): the beam search width.
  
      Returns:
           results(list): the predict result.
      """
      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."
          )
  
      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"
          )
      if use_gpu:
          place = fluid.CUDAPlace(0)
      else:
          place = fluid.CPUPlace()
  
      with fluid.dygraph.guard(place):
          self.model.eval()
          results = []
          for text in predicted_data:
              sample_results = []
              ids, sids = self.tokenizer.encode(text)
              src_ids = D.to_variable(np.expand_dims(ids, 0))
              src_sids = D.to_variable(np.expand_dims(sids, 0))
              output_ids = beam_search_infilling(
                  self.model,
                  src_ids,
                  src_sids,
                  eos_id=self.tokenizer.sep_id,
                  sos_id=self.tokenizer.cls_id,
                  attn_id=self.tokenizer.vocab["[MASK]"],
                  max_decode_len=50,
                  max_encode_len=50,
                  beam_width=beam_width,
                  tgt_type_id=1,
              )
              output_str = self.rev_lookup(output_ids[0].numpy())
  
              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
  
imports: import ast
  import json
  import paddle.fluid
  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
  import argparse
  import os
  import numpy
  import paddle.fluid.dygraph
  from reading_pictures_writing_poems_for_midautumn.MidAutumnPoetry.model.tokenizing_ernie import ErnieTokenizer
  from reading_pictures_writing_poems_for_midautumn.MidAutumnPoetry.model.decode import beam_search_infilling
  from reading_pictures_writing_poems_for_midautumn.MidAutumnPoetry.model.modeling_ernie_gen import ErnieModelForGeneration
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