class_docstr: Base task for NLP.
class_docstr_tok: ["Base","task","for","NLP","."]
docstr: Get the input pipeline function.
docstr_tok: ["Get","the","input","pipeline","function","."]
code: def get_input_pipeline(self, for_export):
      """Get the input pipeline function."""
  
      def input_pipeline_func(input_sentences):
          """This input pipeline function will be used in online inference."""
          if for_export:
              input_sentences = self.pre_process_pipeline(
                  input_sentences
              )
          batch = self.common_process_pipeline(input_sentences)
          return batch
  
      return input_pipeline_func
  
file_path: delta/delta/data/task/base_text_task.py
imports: import os
  from absl import logging
  from delta.data.task.base_task import Task
  from delta import utils
  from delta.utils.register import registers
  from delta.data.preprocess.text_ops import clean_english_str_tf
  from delta.data.preprocess.text_ops import char_cut_tf
  from delta.data.preprocess.text_ops import tokenize_sentence
  from delta.data.preprocess.text_ops import chinese_word_cut_tf
label_desc: Use image node_id 6 for calling the TextTask obj's underlying member method code with example usage: obj.get_input_pipeline(for_export) and returns: input_pipeline_func, batch