def METHOD_NAME(self):
    device = "cpu"
    unet = self.dummy_uncond_unet
    scheduler = DDPMScheduler()
    ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
    ddpm.to(device)
    ddpm.set_progress_bar_config(disable=None)
    generator = torch.Generator(device=device).manual_seed(0)
    image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images
    generator = torch.Generator(device=device).manual_seed(0)
    image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type="numpy", return_dict=False)[0]
    image_slice = image[0, -3:, -3:, -1]
    image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
    assert image.shape == (1, 32, 32, 3)
    expected_slice = np.array(
        [9.956e-01, 5.785e-01, 4.675e-01, 9.930e-01, 0.0, 1.000, 1.199e-03, 2.648e-04, 5.101e-04]
    )
    assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2