class_docstr: GLUE 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_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 = []
      for ex_index, example in enumerate(self.examples):
          if ex_index % 10000 == 0:
              logging.info(
                  f"Writing example {ex_index} of {len(self.examples)}"
              )
  
          text_to_text_query = self.processor.get_t5_prompted_query(
              example.text_a, example.text_b
          )
          enc_query = self.tokenizer.text_to_ids(text_to_text_query)
          if len(enc_query) > self.max_seq_length:
              enc_query = enc_query[: self.max_seq_length]
          dec_query = (
              [self.tokenizer.bos_id]
              + self.tokenizer.text_to_ids(
                  self.processor.label2string(example.label)
              )
              + [self.tokenizer.eos_id]
          )
  
          dec_input = dec_query[:-1]
          labels = dec_query[1:]
  
          features.append([enc_query, dec_input, labels])
  
      return 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 7 for calling the TextToTextGLUEDataset obj's underlying member method code with example usage: obj.convert_examples_to_features() and returns: features