def METHOD_NAME(test_case, placement, sbp):
    ndim = 5
    dims = [random(1, 3) * 8 for _ in range(ndim)]
    x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp)
    y = torch.nn.functional.avg_pool3d(
        x,
        kernel_size=random(1, 6).to(int),
        stride=random(1, 3).to(int),
        padding=random(1, 3).to(int),
        ceil_mode=random_bool(),
        count_include_pad=random_bool(),
    )
    return y