docstr: Get the robot responses of the input texts.
  
  Args:
       texts(list or str): If not in the interactive mode, texts should be a list in which every element is the chat context separated with '     '.
                           Otherwise, texts shoule be one sentence. The module can get the context automatically.
  
  Returns:
       results(list): the robot responses.
docstr_tok: ["Get","the","robot","responses","of","the","input","texts",".","Args",":","texts","(","list","or","str",")",":","If","not","in","the","interactive","mode",",","texts","should","be","a","list","in","which","every","element","is","the","chat","context","separated","with","'","'",".","Otherwise",",","texts","shoule","be","one","sentence",".","The","module","can","get","the","context","automatically",".","Returns",":","results","(","list",")",":","the","robot","responses","."]
code: def generate(self, texts):
      """
      Get the robot responses of the input texts.
  
      Args:
           texts(list or str): If not in the interactive mode, texts should be a list in which every element is the chat context separated with '\t'.
                               Otherwise, texts shoule be one sentence. The module can get the context automatically.
  
      Returns:
           results(list): the robot responses.
      """
      if not texts:
          return []
      if self._interactive_mode:
          if isinstance(texts, str):
              self.context.append(texts.strip())
              texts = [" [SEP] ".join(self.context[-self.max_turn :])]
          else:
              raise ValueError(
                  "In the interactive mode, the input data should be a string."
              )
      elif not isinstance(texts, list):
          raise ValueError(
              "If not in the interactive mode, the input data should be a list."
          )
  
      bot_responses = []
      for i, text in enumerate(texts):
          example = self.Example(
              src=text.replace("\t", " [SEP] "), data_id=0
          )
          record = self.plato_reader._convert_example_to_record(
              example, is_infer=True
          )
          data = self.plato_reader._pad_batch_records(
              [record], is_infer=True
          )
          inputs = gen_inputs(data, self.latent_type_size)
          inputs["tgt_ids"] = inputs["tgt_ids"].astype("int64")
          pred = self.model(inputs)[0]  # batch_size is 1
          bot_response = pred["response"]  # ignore data_id and score
          bot_responses.append(bot_response)
  
      if self._interactive_mode:
          self.context.append(bot_responses[0].strip())
      return bot_responses
  
imports: import argparse
  import contextlib
  import os
  import sys
  from collections import namedtuple
  import paddle
  import paddle.nn
  import paddlehub
  from .model import Plato2InferModel
  from .readers.nsp_reader import NSPReader
  from .readers.plato_reader import PlatoReader
  from .utils import gen_inputs
  from .utils.args import parse_args
  from .utils.args import str2bool
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import runnable
  from paddlehub.module.module import serving
  from paddlehub.module.nlp_module import DataFormatError
label_desc: Use image node_id 3 for calling the Plato2 obj's underlying member method code with example usage: obj.generate(texts) and returns: bot_responses, list