code: def test_inference(self):
      unet = self.dummy_uncond_unet
      scheduler = ScoreSdeVeScheduler()
  
      sde_ve = ScoreSdeVePipeline(unet=unet, scheduler=scheduler)
      sde_ve.to(torch_device)
      sde_ve.set_progress_bar_config(disable=None)
  
      generator = torch.manual_seed(0)
      image = sde_ve(
          num_inference_steps=2,
          output_type="numpy",
          generator=generator,
      ).images
  
      generator = torch.manual_seed(0)
      image_from_tuple = sde_ve(
          num_inference_steps=2,
          output_type="numpy",
          generator=generator,
          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(
          [0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]
      )
  
      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/score_sde_ve/test_score_sde_ve.py
imports: import unittest
  import numpy
  import torch
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNet2DModel
  from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch, torch_device
label_desc: Use image node_id 2 for calling the ScoreSdeVeipelineFastTests obj's underlying member method code with example usage: obj.test_inference() without return types