docstr: Get the input and program of the infer model
  
  Args:
       image (list(numpy.ndarray)): images data, shape of each is [H, W, C], the color space is BGR.
       use_gpu(bool): Weather to use gpu
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code: def Rumor(self, texts, use_gpu=False):
      """
      Get the input and program of the infer model
  
      Args:
           image (list(numpy.ndarray)): images data, shape of each is [H, W, C], the color space is BGR.
           use_gpu(bool): Weather to use gpu
      """
  
      # 获取数据
      def get_data(sentence):
          # 读取数据字典
          with open(
              self.directory + "/dict.txt", "r", encoding="utf-8"
          ) as f_data:
              dict_txt = eval(f_data.readlines()[0])
          dict_txt = dict(dict_txt)
          # 把字符串数据转换成列表数据
          keys = dict_txt.keys()
          data = []
          for s in sentence:
              # 判断是否存在未知字符
              if not s in keys:
                  s = "<unk>"
              data.append(int(dict_txt[s]))
          return data
  
      data = []
      for text in texts:
          text = get_data(text)
          data.append(text)
      base_shape = [[len(c) for c in data]]
      paddle.enable_static()
      place = paddle.CUDAPlace(0) if use_gpu else paddle.CPUPlace()
      exe = paddle.static.Executor(place)
      exe.run(paddle.static.default_startup_program())
      [
          infer_program,
          feeded_var_names,
          target_var,
      ] = paddle.fluid.io.load_inference_model(
          dirname=self.default_pretrained_model_path, executor=exe
      )
      # 生成预测数据
      tensor_words = paddle.fluid.create_lod_tensor(
          data, base_shape, place
      )
      # 执行预测
      result = exe.run(
          program=infer_program,
          feed={feeded_var_names[0]: tensor_words},
          fetch_list=target_var,
      )
      # 分类名称
      names = ["谣言", "非谣言"]
  
      results = []
  
      # 获取结果概率最大的label
      for i in range(len(data)):
          content = texts[i]
          lab = np.argsort(result)[0][i][-1]
  
          alltext = {
              "content": content,
              "prediction": names[lab],
              "probability": result[0][i][lab],
          }
          alltext = [alltext]
          results = results + alltext
  
      return results
  
imports: import argparse
  import ast
  import os
  import math
  import six
  import time
  from pathlib import Path
  from paddle.fluid.core import PaddleTensor, AnalysisConfig, create_paddle_predictor
  from paddlehub.module.module import runnable, serving, moduleinfo
  from paddlehub.io.parser import txt_parser
  from paddlehub.compat.module.nlp_module import DataFormatError
  import numpy
  import paddle
  import paddlehub
label_desc: Use image node_id 2 for calling the Rumorprediction obj's underlying member method code with example usage: obj.Rumor(texts, use_gpu) and returns: results, data