How to use the simpletransformers.experimental.classification.classification_utils.convert_examples_to_features function in simpletransformers

tokenizer = self.tokenizer
        output_mode = "classification"
        args = self.args

        if not os.path.isdir(self.args["cache_dir"]):
            os.mkdir(self.args["cache_dir"])

        mode = "dev" if evaluate else "train"
        cached_features_file = os.path.join(args["cache_dir"], "cached_{}_{}_{}_{}_{}".format(mode, args["model_type"], args["max_seq_length"], self.num_labels, len(examples)))

        if os.path.exists(cached_features_file) and not args["reprocess_input_data"] and not no_cache:
            features = torch.load(cached_features_file)
            print(f"Features loaded from cache at {cached_features_file}")
        else:
            print(f"Converting to features started.")
            features = convert_examples_to_features(
                examples,
                args["max_seq_length"],
                tokenizer,
                output_mode,
                # XLNet has a CLS token at the end
                cls_token_at_end=bool(args["model_type"] in ["xlnet"]),
                cls_token=tokenizer.cls_token,
                cls_token_segment_id=2 if args["model_type"] in ["xlnet"] else 0,
                sep_token=tokenizer.sep_token,
                # RoBERTa uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
                sep_token_extra=bool(args["model_type"] in ["roberta"]),
                # PAD on the left for XLNet
                pad_on_left=bool(args["model_type"] in ["xlnet"]),
                pad_token=tokenizer.convert_tokens_to_ids([tokenizer.pad_token])[0],
                pad_token_segment_id=4 if args["model_type"] in ["xlnet"] else 0,
                process_count=process_count,