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: Add the command input options
docstr_tok: ["Add","the","command","input","options"]
code: def add_module_input_arg(self):
      """
      Add the command input options
      """
      self.arg_input_group.add_argument(
          "--input_text",
          type=str,
          default=None,
          help="data to be predicted",
      )
  
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 8 for calling the ErnieSkepSentimentAnalysis obj's underlying member method code with example usage: obj.add_module_input_arg() without return types