class_docstr: Ernie_skep_sentiment_analysis module is initialize with enie_1.0_chn_large when pretraining.
  This module is finetuned on ChnSentiCorp dataset to do sentiment claasification.
  It can do sentiment analysis prediction directly, label as positive or negative.
class_docstr_tok: ["Ernie_skep_sentiment_analysis","module","is","initialize","with","enie_1.0_chn_large","when","pretraining",".","This","module","is","finetuned","on","ChnSentiCorp","dataset","to","do","sentiment","claasification",".","It","can","do","sentiment","analysis","prediction","directly",",","label","as","positive","or","negative","."]
docstr: Get the sentiment label for the predicted texts. It will be classified as positive and negative.
  Args:
      texts (list(str)): the data to be predicted.
      use_gpu (bool): Whether to use gpu or not.
  Returns:
      res (list): The result of sentiment label and probabilties.
docstr_tok: ["Get","the","sentiment","label","for","the","predicted","texts",".","It","will","be","classified","as","positive","and","negative",".","Args",":","texts","(","list","(","str",")",")",":","the","data","to","be","predicted",".","use_gpu","(","bool",")",":","Whether","to","use","gpu","or","not",".","Returns",":","res","(","list",")",":","The","result","of","sentiment","label","and","probabilties","."]
code: def predict_sentiment(self, texts=[], use_gpu=False):
      """
      Get the sentiment label for the predicted texts. It will be classified as positive and negative.
      Args:
          texts (list(str)): the data to be predicted.
          use_gpu (bool): Whether to use gpu or not.
      Returns:
          res (list): The result of sentiment label and probabilties.
      """
  
      if use_gpu:
          try:
              _places = os.environ["CUDA_VISIBLE_DEVICES"]
              int(_places[0])
          except:
              raise RuntimeError(
                  "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES as cuda_device_id."
              )
  
      results = []
      feature_list = []
      for text in texts:
          # feature.shape: [1, 512, 1]
          # batch on the first dimension
          feature = self._convert_text_to_feature(text)
          feature_list.append(feature)
  
      feature_batch = [
          np.concatenate(
              [feature[0] for feature in feature_list], axis=0
          ),
          np.concatenate(
              [feature[1] for feature in feature_list], axis=0
          ),
          np.concatenate(
              [feature[2] for feature in feature_list], axis=0
          ),
          np.concatenate(
              [feature[3] for feature in feature_list], axis=0
          ),
          np.concatenate(
              [feature[4] for feature in feature_list], axis=0
          ),
      ]
  
      inputs = [self.array2tensor(ndarray) for ndarray in feature_batch]
      output = self.predictor.run(inputs)
      probilities_list = np.array(output[0].data.float_data())
      probilities_list = probilities_list.reshape((-1, 2))
      for i, probilities in enumerate(probilities_list):
          label = self.label_map[np.argmax(probilities)]
          result = {
              "text": texts[i],
              "sentiment_label": label,
              "positive_probs": probilities[1],
              "negative_probs": probilities[0],
          }
          results.append(result)
  
      return results
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import argparse
  import ast
  import os
  import numpy
  from ernie_skep_sentiment_analysis.model.ernie import ErnieConfig
  from paddle.framework import core
  from paddlehub import TransformerModule
  from paddlehub.module.module import moduleinfo
  from paddlehub.module.module import runnable
  from paddlehub.module.module import serving
  from paddlehub.reader.batching import pad_batch_data
  from paddlehub.reader.tokenization import convert_to_unicode
  from paddlehub.reader.tokenization import FullTokenizer
label_desc: Use image node_id 4 for calling the ErnieSkepSentimentAnalysis obj's underlying member method code with example usage: obj.predict_sentiment(texts, use_gpu) and returns: results