class_docstr: Task class for NLU joint learning.
class_docstr_tok: ["Task","class","for","NLU","joint","learning","."]
code: def __init__(self, config, mode):
      super().__init__(config, mode)
  
      self.vocab_min_frequency = self.task_config["vocab_min_frequency"]
      self.text_vocab_file_path = self.task_config["text_vocab"]
      self.label_vocab_file_path = self.task_config["label_vocab"]
      self.max_seq_len = self.task_config["max_seq_len"]
      self.intent_num_classes = self.task_config["classes"][0][
          "num_classes"
      ]
  
      self.paths = self.data_config[mode]["paths"]
      self.paths_after_pre_process = [
          one_path + ".after" for one_path in self.paths
      ]
      self.infer_no_label = self.config["data"][utils.INFER].get(
          "infer_no_label", False
      )
      self.infer_without_label = bool(
          mode == utils.INFER and self.infer_no_label
      )
  
      self.prepare()
  
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 1 to create a new TextNLUJointTask object from inherited base classes: TextTask with example: obj = TextNLUJointTask(config, mode)