code: def test_fast_inference(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
      )
  
file_path: llmga/llmga/diffusers/tests/pipelines/ddpm/test_ddpm.py
imports: import unittest
  import numpy
  import torch
  from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
label_desc: Use image node_id 2 for calling the DDPMPipelineFastTests obj's underlying member method code with example usage: obj.test_fast_inference() without return types