class_docstr: XNLI Dataset in a text-to-text format.
docstr: Converts examples into Text-to-Text batches to be used with a model like T5.
  Inputs are prefixed with a text prompt that indicates the task to perform.
docstr_tok: ["Converts","examples","into","Text-to-Text","batches","to","be","used","with","a","model","like","T5",".","Inputs","are","prefixed","with","a","text","prompt","that","indicates","the","task","to","perform","."]
code: def convert_xnli_examples_to_features(self):
      """
      Converts examples into Text-to-Text batches to be used with a model like T5.
      Inputs are prefixed with a text prompt that indicates the task to perform.
      """
      features = self.features
      lang_filtered_features = []
      for ex_index, example in enumerate(self.examples):
          language = example.guid.split("-")[1]
          if language in self.lang_list:
              lang_filtered_features.append(
                  features[ex_index] + [language]
              )
      return lang_filtered_features
  
imports: import os
  import pickle
  from typing import Dict, List, Optional, Union
  import numpy
  import torch
  from nemo.collections.common.tokenizers.tokenizer_spec import TokenizerSpec
  from nemo.collections.nlp.data.glue_benchmark.data_processors import ColaProcessor, MnliMismatchedProcessor, MnliProcessor, MrpcProcessor, QnliProcessor, QqpProcessor, RteProcessor, Sst2Processor, StsbProcessor, WnliProcessor, XNLIProcessor
  from nemo.core.classes import Dataset
  from nemo.core.neural_types import CategoricalValuesType, ChannelType, MaskType, NeuralType, RegressionValuesType
  from nemo.utils import logging
label_desc: Use image node_id 4 for calling the TextToTextXNLIDataset obj's underlying member method code with example usage: obj.convert_xnli_examples_to_features() and returns: lang_filtered_features