class_docstr: Creates dataset to use for the task of multi-label joint intent
  and slot classification with pretrained model.
  
  Converts from raw data to an instance that can be used by
  NMDataLayer.
  
  Args:
      input_file: file containing sentences + labels. The first line is header (sentence [tab] label)
          each line should be [sentence][tab][label] where label can be multiple labels separated by a comma
      slot_file: file containing slot labels, each line corresponding to slot labels for a sentence in input_file. No header.
      num_intents: total number of intents in dict.intents file
      max_seq_length: max sequence length minus 2 for [CLS] and [SEP]
      tokenizer: such as NemoBertTokenizer
      num_samples: number of samples you want to use for the dataset. If -1, use all dataset. Useful for testing.
      pad_label: pad value use for slot labels. by default, it's the neutral label.
      ignore_extra_tokens: whether to ignore extra tokens in the loss_mask.
      ignore_start_end: whether to ignore bos and eos tokens in the loss_mask.
      do_lower_case: convert query to lower case or not
docstr: 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()),
      }
  
file_path: NeMo/nemo/collections/nlp/data/intent_slot_classification/multi_label_intent_slot_classification_dataset.py
imports: from typing import Dict, Optional
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.data.intent_slot_classification import IntentSlotClassificationDataset
  from nemo.collections.nlp.data.intent_slot_classification.intent_slot_classification_dataset import get_features
  from nemo.core.neural_types import ChannelType, LabelsType, MaskType, NeuralType
label_desc: Use image node_id 1 for calling the MultiLabelIntentSlotClassificationDataset obj's underlying member method code with example usage: obj.output_types() and returns: dict