How to use the mmcv.dump function in mmcv

else:
        model_args = cfg.model.copy()
        model_args.update(train_cfg=None, test_cfg=cfg.test_cfg)
        model_type = getattr(localizers, model_args.pop('type'))
        outputs = parallel_test(
            model_type,
            model_args,
            args.checkpoint,
            dataset,
            _data_func,
            range(args.gpus),
            workers_per_gpu=args.proc_per_gpu)

    if args.out:
        print('writing results to {}'.format(args.out))
        mmcv.dump(outputs, args.out)

    eval_type = args.eval
    if eval_type:
        print('Starting evaluate {}'.format(eval_type))

        detections = results2det(
            dataset, outputs, **cfg.test_cfg.ssn.evaluater)

        if not args.no_regression:
            print("Performing location regression")
            for cls in range(len(detections)):
                detections[cls] = {
                    k: perform_regression(v)
                    for k, v in detections[cls].items()
                }
            print("Regression finished")

os.remove(tmp_filename)

    # json load/dump with a file-like object
    with tempfile.NamedTemporaryFile(mode, delete=False) as f:
        tmp_filename = f.name
        mmcv.dump(test_obj, f, file_format=file_format)
    assert osp.isfile(tmp_filename)
    with open(tmp_filename, mode) as f:
        load_obj = mmcv.load(f, file_format=file_format)
    assert load_obj == test_obj
    os.remove(tmp_filename)

    # automatically inference the file format from the given filename
    tmp_filename = osp.join(tempfile.gettempdir(),
                            'mmcv_test_dump.' + file_format)
    mmcv.dump(test_obj, tmp_filename)
    assert osp.isfile(tmp_filename)
    load_obj = mmcv.load(tmp_filename)
    assert load_obj == test_obj
    os.remove(tmp_filename)

print('\nEvaluating {}'.format(name))
                                    outputs_ = [out[name] for out in outputs]
                                    result_file = args.out
                                    + '.{}'.format(name)
                                    result_files = dataset.results2json(
                                        outputs_, result_file)
                        eval_results = coco_eval_with_return(
                            result_files, eval_types, dataset.coco)
                        aggregated_results[corruption][
                            corruption_severity] = eval_results
                    else:
                        print('\nNo task was selected for evaluation;'
                              '\nUse --eval to select a task')

                # save results after each evaluation
                mmcv.dump(aggregated_results, eval_results_filename)

    if rank == 0:
        # print filan results
        print('\nAggregated results:')
        prints = args.final_prints
        aggregate = args.final_prints_aggregate

        if cfg.dataset_type == 'VOCDataset':
            get_results(
                eval_results_filename,
                dataset='voc',
                prints=prints,
                aggregate=aggregate)
        else:
            get_results(
                eval_results_filename,

# 32 is whitespace
        dir_tensor = torch.full((MAX_LEN, ),
                                32,
                                dtype=torch.uint8,
                                device='cuda')
        if rank == 0:
            tmpdir = tempfile.mkdtemp()
            tmpdir = torch.tensor(
                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
            dir_tensor[:len(tmpdir)] = tmpdir
        dist.broadcast(dir_tensor, 0)
        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
    else:
        mmcv.mkdir_or_exist(tmpdir)
    # dump the part result to the dir
    mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
    dist.barrier()
    # collect all parts
    if rank != 0:
        return None
    else:
        # load results of all parts from tmp dir
        part_list = []
        for i in range(world_size):
            part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
            part_list.append(mmcv.load(part_file))
        # sort the results
        ordered_results = []
        for res in zip(*part_list):
            ordered_results.extend(list(res))
        # the dataloader may pad some samples
        ordered_results = ordered_results[:size]

def _test_handler(file_format, test_obj, str_checker, mode='r+'):
    # dump to a string
    dump_str = mmcv.dump(test_obj, file_format=file_format)
    str_checker(dump_str)

    # load/dump with filenames
    tmp_filename = osp.join(tempfile.gettempdir(), 'mmcv_test_dump')
    mmcv.dump(test_obj, tmp_filename, file_format=file_format)
    assert osp.isfile(tmp_filename)
    load_obj = mmcv.load(tmp_filename, file_format=file_format)
    assert load_obj == test_obj
    os.remove(tmp_filename)

    # json load/dump with a file-like object
    with tempfile.NamedTemporaryFile(mode, delete=False) as f:
        tmp_filename = f.name
        mmcv.dump(test_obj, f, file_format=file_format)
    assert osp.isfile(tmp_filename)
    with open(tmp_filename, mode) as f:
        load_obj = mmcv.load(f, file_format=file_format)
    assert load_obj == test_obj
    os.remove(tmp_filename)

    # automatically inference the file format from the given filename

outfile_prefix (str): The filename prefix of the json files. If the
                prefix is "somepath/xxx", the json files will be named
                "somepath/xxx.bbox.json", "somepath/xxx.segm.json",
                "somepath/xxx.proposal.json".

        Returns:
            dict[str: str]: Possible keys are "bbox", "segm", "proposal", and
                values are corresponding filenames.
        """
        result_files = dict()
        if isinstance(results[0], list):
            json_results = self._det2json(results)
            result_files['bbox'] = '{}.{}.json'.format(outfile_prefix, 'bbox')
            result_files['proposal'] = '{}.{}.json'.format(
                outfile_prefix, 'bbox')
            mmcv.dump(json_results, result_files['bbox'])
        elif isinstance(results[0], tuple):
            json_results = self._segm2json(results)
            result_files['bbox'] = '{}.{}.json'.format(outfile_prefix, 'bbox')
            result_files['proposal'] = '{}.{}.json'.format(
                outfile_prefix, 'bbox')
            result_files['segm'] = '{}.{}.json'.format(outfile_prefix, 'segm')
            mmcv.dump(json_results[0], result_files['bbox'])
            mmcv.dump(json_results[1], result_files['segm'])
        elif isinstance(results[0], np.ndarray):
            json_results = self._proposal2json(results)
            result_files['proposal'] = '{}.{}.json'.format(
                outfile_prefix, 'proposal')
            mmcv.dump(json_results, result_files['proposal'])
        else:
            raise TypeError('invalid type of results')
        return result_files

def results2json(dataset, results, out_file):
    result_files = dict()
    if isinstance(results[0], list):
        json_results = det2json(dataset, results)
        result_files['bbox'] = '{}.{}.json'.format(out_file, 'bbox')
        result_files['proposal'] = '{}.{}.json'.format(out_file, 'bbox')
        mmcv.dump(json_results, result_files['bbox'])
    elif isinstance(results[0], tuple):
        json_results = segm2json(dataset, results)
        result_files['bbox'] = '{}.{}.json'.format(out_file, 'bbox')
        result_files['proposal'] = '{}.{}.json'.format(out_file, 'bbox')
        result_files['segm'] = '{}.{}.json'.format(out_file, 'segm')
        mmcv.dump(json_results[0], result_files['bbox'])
        mmcv.dump(json_results[1], result_files['segm'])
    elif isinstance(results[0], np.ndarray):
        json_results = proposal2json(dataset, results)
        result_files['proposal'] = '{}.{}.json'.format(out_file, 'proposal')
        mmcv.dump(json_results, result_files['proposal'])
    else:
        raise TypeError('invalid type of results')
    return result_files

def _barrier(self, rank, world_size):
        """Due to some issues with `torch.distributed.barrier()`, we have to
        implement this ugly barrier function.
        """
        if rank == 0:
            for i in range(1, world_size):
                tmp = osp.join(self.lock_dir, '{}.pkl'.format(i))
                while not (osp.exists(tmp)):
                    time.sleep(1)
            for i in range(1, world_size):
                tmp = osp.join(self.lock_dir, '{}.pkl'.format(i))
                os.remove(tmp)
        else:
            tmp = osp.join(self.lock_dir, '{}.pkl'.format(rank))
            mmcv.dump([], tmp)
            while osp.exists(tmp):
                time.sleep(1)

"""
        result_files = dict()
        if isinstance(results[0], list):
            json_results = self._det2json(results)
            result_files['bbox'] = '{}.{}.json'.format(outfile_prefix, 'bbox')
            result_files['proposal'] = '{}.{}.json'.format(
                outfile_prefix, 'bbox')
            mmcv.dump(json_results, result_files['bbox'])
        elif isinstance(results[0], tuple):
            json_results = self._segm2json(results)
            result_files['bbox'] = '{}.{}.json'.format(outfile_prefix, 'bbox')
            result_files['proposal'] = '{}.{}.json'.format(
                outfile_prefix, 'bbox')
            result_files['segm'] = '{}.{}.json'.format(outfile_prefix, 'segm')
            mmcv.dump(json_results[0], result_files['bbox'])
            mmcv.dump(json_results[1], result_files['segm'])
        elif isinstance(results[0], np.ndarray):
            json_results = self._proposal2json(results)
            result_files['proposal'] = '{}.{}.json'.format(
                outfile_prefix, 'proposal')
            mmcv.dump(json_results, result_files['proposal'])
        else:
            raise TypeError('invalid type of results')
        return result_files