class_docstr: Dataset for training a NLI model for zero shot intent recognition. Similar to GLUE/MNLI
  dataset, but allows the user to specify which columns in the data files contain the
  premise, hypothesis, and gold label.
class_docstr_tok: ["Dataset","for","training","a","NLI","model","for","zero","shot","intent","recognition",".","Similar","to","GLUE\/MNLI","dataset",",","but","allows","the","user","to","specify","which","columns","in","the","data","files","contain","the","premise",",","hypothesis",",","and","gold","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()),
          "labels": NeuralType(tuple("B"), CategoricalValuesType()),
      }
  
imports: import os
  import pickle
  from typing import Dict, List, Optional
  import torch
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.data.data_utils.data_preprocessing import DataProcessor, fill_class_weights, get_freq_weights, get_label_stats
  from nemo.collections.nlp.data.glue_benchmark.data_processors import InputExample
  from nemo.collections.nlp.data.glue_benchmark.glue_benchmark_dataset import GLUEDataset
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.core.neural_types import CategoricalValuesType, ChannelType, MaskType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling the ZeroShotIntentDataset obj's underlying member method code with example usage: obj.output_types() and returns: dict