def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
    """Test model with multiple gpus.

    This method tests model with multiple gpus and collects the results
    under two different modes: gpu and cpu modes. By setting 'gpu_collect=True'
    it encodes results to gpu tensors and use gpu communication for results
    collection. On cpu mode it saves the results on different gpus to 'tmpdir'
    and collects them by the rank 0 worker.

    Args:
        model (nn.Module): Model to be tested.
        data_loader (nn.Dataloader): Pytorch data loader.
        tmpdir (str): Path of directory to save the temporary results from
            different gpus under cpu mode.
        gpu_collect (bool): Option to use either gpu or cpu to collect results.

    Returns:
        list: The prediction results.
    """
    model.eval()
    results = []
    dataset = data_loader.dataset.mmdet_dataset
    rank, world_size = get_dist_info()
    if rank == 0:
        prog_bar = mmcv.ProgressBar(len(dataset))
    time.sleep(2)  # This line can prevent deadlock problem in some cases.
    for i, data in enumerate(data_loader):
        with torch.no_grad():
            result = model(return_loss=False, rescale=True, **data)
            # encode mask results
            if isinstance(result[0], tuple):
                result = [
                    (bbox_results, encode_mask_results(mask_results))
                    for bbox_results, mask_results in result
                ]
            # This logic is only used in panoptic segmentation test.
            elif isinstance(result[0], dict) and "ins_results" in result[0]:
                for j in range(len(result)):
                    bbox_results, mask_results = result[j]["ins_results"]
                    result[j]["ins_results"] = (
                        bbox_results,
                        encode_mask_results(mask_results),
                    )

        results.extend(result)

        if rank == 0:
            batch_size = len(result)
            for _ in range(batch_size * world_size):
                prog_bar.update()

    # collect results from all ranks
    if gpu_collect:
        results = collect_results_gpu(results, len(dataset))
    else:
        results = collect_results_cpu(results, len(dataset), tmpdir)
    return results
Test model with multiple gpus.

    This method tests model with multiple gpus and collects the results
    under two different modes: gpu and cpu modes. By setting 'gpu_collect=True'
    it encodes results to gpu tensors and use gpu communication for results
    collection. On cpu mode it saves the results on different gpus to 'tmpdir'
    and collects them by the rank 0 worker.

    Args:
        model (nn.Module): Model to be tested.
        data_loader (nn.Dataloader): Pytorch data loader.
        tmpdir (str): Path of directory to save the temporary results from
            different gpus under cpu mode.
        gpu_collect (bool): Option to use either gpu or cpu to collect results.

    Returns:
        list: The prediction results.
    
python/deeplake/integrations/mmdet/test_.py