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.
docstr: Creates examples for the training and dev sets.
docstr_tok: ["Creates","examples","for","the","training","and","dev","sets","."]
code: def _create_examples(self, raw_features, dataset_split: str):
      """Creates examples for the training and dev sets."""
      examples = []
      for idx in range(len(raw_features)):
          ex = self.raw_features[idx].data
          user_utterance = ex["utterance"]
          intent = ex["labels"]["intent"]
          for candidate_idx, candidate_intent in enumerate(
              ex["possible_labels"]["intent"]
          ):
              guid = "{}-{}-{}".format(
                  dataset_split, idx, candidate_idx
              )
              text_a = user_utterance
              text_b = "{} {}".format(
                  self.cfg.prompt_template, candidate_intent
              )
              label = 1 if candidate_intent == intent else 0
              examples.append(
                  InputExample(
                      guid=guid,
                      text_a=text_a,
                      text_b=text_b,
                      label=label,
                  )
              )
      return examples
  
file_path: NeMo/nemo/collections/nlp/data/dialogue/dataset/dialogue_zero_shot_intent_dataset.py
imports: from typing import Dict, List, Optional, Union
  import numpy
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  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.core.neural_types import CategoricalValuesType, ChannelType, MaskType, NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 3 for calling the DialogueZeroShotIntentDataset obj's underlying member method code with example usage: obj._create_examples(raw_features, dataset_split) and returns: examples