How to use the squad.squad_document_utils.RawFinalResult function in squad

span_starts = span_starts.to(device)
        span_ends = span_ends.to(device)
        sequence_output = sequence_output.to(device)
        with torch.no_grad():
            batch_rerank_logits = model('rerank_inference', input_mask, span_starts=span_starts,
                                        span_ends=span_ends, sequence_input=sequence_output)
        for j, example_index in enumerate(example_indices):
            start_logits = batch_start_logits[j].detach().cpu().tolist()
            end_logits = batch_end_logits[j].detach().cpu().tolist()
            rerank_logits = batch_rerank_logits[j].detach().cpu().numpy()
            start_indexes = span_starts[j].detach().cpu().tolist()
            end_indexes = span_ends[j].detach().cpu().tolist()
            eval_feature = eval_features[example_index.item()]
            eval_rank_logit = eval_rank_logits[example_index.item()]
            unique_id = int(eval_feature.unique_id)
            all_results.append(RawFinalResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits,
                                              rank_logit=eval_rank_logit, rerank_logits=rerank_logits,
                                              start_indexes=start_indexes, end_indexes=end_indexes))

    all_predictions, all_nbest_json = write_rerank_predictions(eval_examples, eval_features, all_results, args.length_heuristic,
                                                               args.pred_rank_weight, args.pred_rerank_weight,
                                                               args.ablate_type, args.n_best_size_read,
                                                               args.max_answer_length, args.do_lower_case,
                                                               args.verbose_logging, logger)

    if write_pred:
        output_prediction_file = os.path.join(args.output_dir, "{}_predictions.json".format(type))
        output_nbest_file = os.path.join(args.output_dir, "{}_nbest_predictions.json".format(type))
        with open(output_prediction_file, "w") as writer:
            writer.write(json.dumps(all_predictions, indent=4) + "\n")
        with open(output_nbest_file, "w") as writer:
            writer.write(json.dumps(all_nbest_json, indent=4) + "\n")

span_starts = span_starts.to(device)
        span_ends = span_ends.to(device)
        sequence_output = sequence_output.to(device)
        with torch.no_grad():
            batch_rerank_logits = model('rerank_inference', input_mask, span_starts=span_starts,
                                        span_ends=span_ends, sequence_input=sequence_output)
        for j, example_index in enumerate(example_indices):
            start_logits = batch_start_logits[j].detach().cpu().tolist()
            end_logits = batch_end_logits[j].detach().cpu().tolist()
            rerank_logits = batch_rerank_logits[j].detach().cpu().numpy()
            start_indexes = span_starts[j].detach().cpu().tolist()
            end_indexes = span_ends[j].detach().cpu().tolist()
            eval_feature = eval_features[example_index.item()]
            eval_rank_logit = eval_rank_logits[example_index.item()]
            unique_id = int(eval_feature.unique_id)
            all_results.append(RawFinalResult(unique_id=unique_id, start_logits=start_logits, end_logits=end_logits,
                                              rank_logit=eval_rank_logit, rerank_logits=rerank_logits,
                                              start_indexes=start_indexes, end_indexes=end_indexes))

    all_predictions, all_nbest_json = write_rerank_predictions(eval_examples, eval_features, all_results, args.length_heuristic,
                                                               args.pred_rank_weight, args.pred_rerank_weight,
                                                               args.ablate_type, args.n_best_size_read,
                                                               args.max_answer_length, args.do_lower_case,
                                                               args.verbose_logging, logger)

    if write_pred:
        output_prediction_file = os.path.join(args.output_dir, "predictions.json")
        output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")
        with open(output_prediction_file, "w") as writer:
            writer.write(json.dumps(all_predictions, indent=4) + "\n")
        with open(output_nbest_file, "w") as writer:
            writer.write(json.dumps(all_nbest_json, indent=4) + "\n")