class_docstr: XNLI Dataset in a text-to-text format.
code: def __getitem__(self, idx):
      enc_query, dec_input, labels, lang = self.features[idx]
      return {
          "text_enc": enc_query,
          "text_dec": dec_input,
          "labels": labels,
          "lang": lang,
      }
  
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 2 for calling the TextToTextXNLIDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: dict