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 the raw text to feature which is needed to run program (feed_vars).
docstr_tok: ["Convert","the","raw","text","to","feature","which","is","needed","to","run","program","(","feed_vars",")","."]
code: def _convert_text_to_feature(self, text):
      """
      Convert the raw text to feature which is needed to run program (feed_vars).
      """
      text_a = convert_to_unicode(text)
      tokens_a = self.tokenizer.tokenize(text_a)
      max_seq_len = 512
  
      # Account for [CLS] and [SEP] with "- 2"
      if len(tokens_a) > max_seq_len - 2:
          tokens_a = tokens_a[0 : (max_seq_len - 2)]
  
      tokens = []
      text_type_ids = []
      tokens.append("[CLS]")
      text_type_ids.append(0)
      for token in tokens_a:
          tokens.append(token)
          text_type_ids.append(0)
      tokens.append("[SEP]")
      text_type_ids.append(0)
  
      token_ids = self.tokenizer.convert_tokens_to_ids(tokens)
      position_ids = list(range(len(token_ids)))
      task_ids = [0] * len(token_ids)
  
      padded_token_ids, input_mask = pad_batch_data(
          [token_ids],
          max_seq_len=max_seq_len,
          pad_idx=self.pad_id,
          return_input_mask=True,
      )
      padded_text_type_ids = pad_batch_data(
          [text_type_ids], max_seq_len=max_seq_len, pad_idx=self.pad_id
      )
      padded_position_ids = pad_batch_data(
          [position_ids], max_seq_len=max_seq_len, pad_idx=self.pad_id
      )
      padded_task_ids = pad_batch_data(
          [task_ids], max_seq_len=max_seq_len, pad_idx=self.pad_id
      )
  
      feature = [
          padded_token_ids,
          padded_position_ids,
          padded_text_type_ids,
          input_mask,
          padded_task_ids,
      ]
      return feature
  
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 5 for calling the ErnieSkepSentimentAnalysis obj's underlying member method code with example usage: obj._convert_text_to_feature(text) and returns: feature