def prepare_data(self):
        "Called to initialize data. Use the call to construct features"
        args = self.hparams
        processor = processors[args.task]()
        self.labels = processor.get_labels()

        for mode in ["train", "dev"]:
            cached_features_file = self._feature_file(mode)
            if os.path.exists(cached_features_file) and not args.overwrite_cache:
                logger.info("Loading features from cached file %s", cached_features_file)
            else:
                logger.info("Creating features from dataset file at %s", args.data_dir)
                examples = (
                    processor.get_dev_examples(args.data_dir)
                    if mode == "dev"
                    else processor.get_train_examples(args.data_dir)
                )
                features = convert_examples_to_features(
                    examples,
                    self.tokenizer,
                    max_length=args.max_seq_length,
                    label_list=self.labels,
                    output_mode=args.glue_output_mode,
                )
                logger.info("Saving features into cached file %s", cached_features_file)
                torch.save(features, cached_features_file)
Called to initialize data. Use the call to construct features
dpkd/transformers/examples/legacy/pytorch-lightning/run_glue.py