class_docstr: Creates dataset to use for the task of joint intent
  and slot classification with pretrained model.
  This is to be used during inference only.
  It uses list of queries as the input.
  
  Args:
      queries (list): list of queries to run inference on
      max_seq_length (int): max sequence length minus 2 for [CLS] and [SEP]
      tokenizer (Tokenizer): such as NemoBertTokenizer
      pad_label (int): pad value use for slot labels.
          by default, it's the neutral label.
class_docstr_tok: ["Creates","dataset","to","use","for","the","task","of","joint","intent","and","slot","classification","with","pretrained","model",".","This","is","to","be","used","during","inference","only",".","It","uses","list","of","queries","as","the","input",".","Args",":","queries","(","list",")",":","list","of","queries","to","run","inference","on","max_seq_length","(","int",")",":","max","sequence","length","minus","2","for","[","CLS","]","and","[","SEP","]","tokenizer","(","Tokenizer",")",":","such","as","NemoBertTokenizer","pad_label","(","int",")",":","pad","value","use","for","slot","labels",".","by","default",",","it","'s","the","neutral","label","."]
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()),
      }
  
file_path: NeMo/nemo/collections/nlp/data/intent_slot_classification/intent_slot_classification_dataset.py
imports: from typing import Dict, Optional
  import numpy
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.data.data_utils import get_stats
  from nemo.core.classes import Dataset
  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 IntentSlotInferenceDataset obj's underlying member method code with example usage: obj.output_types() and returns: dict