class_docstr: Task class for NLU joint learning.
class_docstr_tok: ["Task","class","for","NLU","joint","learning","."]
docstr: Inputs for exported model.
docstr_tok: ["Inputs","for","exported","model","."]
code: def export_inputs(self):
      """Inputs for exported model."""
      self.config["data"]["vocab_size"] = get_vocab_size(
          self.text_vocab_file_path
      )
      input_sentence = tf.placeholder(
          shape=(None,), dtype=tf.string, name="input_sentence"
      )
  
      input_pipeline_func = self.get_input_pipeline(for_export=True)
      token_ids = input_pipeline_func(input_sentence)
      token_ids_len = tf.map_fn(
          lambda x: compute_sen_lens(x, padding_token=0), token_ids
      )
  
      export_data = {
          "export_inputs": {"input_sentence": input_sentence},
          "model_inputs": {
              "input_x": token_ids,
              "input_x_len": token_ids_len,
          },
      }
  
      return export_data
  
file_path: delta/delta/data/task/text_nlu_joint_task.py
imports: import collections
  import delta.compat
  from absl import logging
  from delta.data.task.base_text_task import TextTask
  from delta.data.preprocess.text_ops import process_one_label_dataset
  from delta.data.preprocess.text_ops import process_multi_label_dataset
  from delta.data.utils.common_utils import get_file_len
  from delta.data.preprocess.text_ops import load_textline_dataset
  from delta.data.preprocess.utils import get_vocab_size
  from delta.utils.register import registers
  from delta.layers.utils import compute_sen_lens
  from delta import utils
label_desc: Use image node_id 5 for calling the TextNLUJointTask obj's underlying member method code with example usage: obj.export_inputs() and returns: export_data