class_docstr: XNLI Dataset in a text-to-text format.
code: def __init__(
      self,
      file_name: str,
      task_name: str,
      tokenizer: TokenizerSpec,
      max_seq_length: int,
      max_seq_length_decoder: int = 128,
      use_cache: bool = True,
      prefix_override: str = None,
      lang_list: List[str] = None,
      pad_to_max_length: bool = True,
  ):
      self.lang_list = set(lang_list)
      super().__init__(
          file_name,
          task_name,
          tokenizer,
          max_seq_length,
          max_seq_length_decoder,
          use_cache,
          prefix_override,
          pad_to_max_length,
      )
      if len(lang_list) <= 0 or lang_list is None:
          raise ValueError(
              f"Found an empty or None lang_list for {self.task_name}"
          )
      self.features = self.convert_xnli_examples_to_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 1 to create a new TextToTextXNLIDataset object from inherited base classes: TextToTextGLUEDataset with example: obj = TextToTextXNLIDataset(file_name, task_name, tokenizer, max_seq_length, max_seq_length_decoder, use_cache, prefix_override, lang_list, pad_to_max_length)