class_docstr: Task class for NLU joint learning.
class_docstr_tok: ["Task","class","for","NLU","joint","learning","."]
docstr: Load text data set.
docstr_tok: ["Load","text","data","set","."]
code: def load_text_dataset(self, text_ds):
      """Load text data set."""
      logging.info("Loading text dataset...")
      input_pipeline_func = self.get_input_pipeline(for_export=False)
      text_ds = text_ds.map(
          input_pipeline_func,
          num_parallel_calls=self.num_parallel_calls,
      )
      text_size_ds = text_ds.map(
          lambda x: compute_sen_lens(x, padding_token=0),
          num_parallel_calls=self.num_parallel_calls,
      )
      text_ds = tf.data.Dataset.zip((text_ds, text_size_ds))
  
      return text_ds
  
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 2 for calling the TextNLUJointTask obj's underlying member method code with example usage: obj.load_text_dataset(text_ds) and returns: text_ds