def METHOD_NAME(self, overlap_mode='crop'):
    patch_size = 10
    patch_overlap = 2
    grid_sampler = tio.inference.GridSampler(
        self.sample_subject,
        patch_size,
        patch_overlap,
    )
    patch_loader = torch.utils.data.DataLoader(grid_sampler)
    aggregator = tio.inference.GridAggregator(
        grid_sampler,
        overlap_mode=overlap_mode,
    )
    for batch in patch_loader:
        data = batch['t1'][tio.DATA].long()
        aggregator.add_batch(data, batch[tio.LOCATION])
    return aggregator