def METHOD_NAME(array_type):
    scale, core_start, core_end = 8, 1, 7
    cube = np.zeros((scale, scale, scale))
    cube[core_start:core_end, core_start:core_end, core_start:core_end] = torch.ones(
        core_end - core_start, core_end - core_start, core_end - core_start
    )
    cube = cube[None]
    batch_size, channels = 10, 6
    cube = np.tile(cube, (batch_size, channels, 1, 1, 1))
    return array_type(cube), array_type(expected_output_for_cube)