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: convert numpy array to PaddleTensor
docstr_tok: ["convert","numpy","array","to","PaddleTensor"]
code: def array2tensor(self, arr_data):
      """
      convert numpy array to PaddleTensor
      """
      tensor_data = core.PaddleTensor(arr_data)
      return tensor_data
  
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 3 for calling the ErnieSkepSentimentAnalysis obj's underlying member method code with example usage: obj.array2tensor(arr_data) and returns: tensor_data