class_docstr: Creates a dataset to use for the task of joint intent
  and slot classification with pretrained model.
  
  For a dataset to use during inference without labels, see
  IntentSlotDataset.
class_docstr_tok: ["Creates","a","dataset","to","use","for","the","task","of","joint","intent","and","slot","classification","with","pretrained","model",".","For","a","dataset","to","use","during","inference","without","labels",",","see","IntentSlotDataset","."]
docstr: Returns definitions of module output ports.
                 
docstr_tok: ["Returns","definitions","of","module","output","ports","."]
code: def output_types(self) -> Optional[Dict[str, NeuralType]]:
      """Returns definitions of module output ports."""
      return {
          "input_ids": NeuralType(("B", "T"), ChannelType()),
          "segment_ids": NeuralType(("B", "T"), ChannelType()),
          "input_mask": NeuralType(("B", "T"), MaskType()),
          "loss_mask": NeuralType(("B", "T"), MaskType()),
          "subtokens_mask": NeuralType(("B", "T"), MaskType()),
          "intent_labels": NeuralType(("B"), LabelsType()),
          "slot_labels": NeuralType(("B", "T"), LabelsType()),
      }
  
imports: from typing import Dict, Optional
  import numpy
  from nemo.collections.nlp.data.data_utils import get_stats
  from nemo.collections.nlp.data.dialogue.dataset.dialogue_dataset import DialogueDataset
  from nemo.core.neural_types import ChannelType, LabelsType, MaskType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling the DialogueBERTDataset obj's underlying member method code with example usage: obj.output_types() and returns: dict