code: def test_stable_diffusion_xl_img2img_euler(self):
      device = "cpu"  # ensure determinism for the device-dependent torch.Generator
      components = self.get_dummy_components()
      sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
      sd_pipe = sd_pipe.to(device)
      sd_pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs(device)
      image = sd_pipe(**inputs).images
      image_slice = image[0, -3:, -3:, -1]
  
      assert image.shape == (1, 32, 32, 3)
  
      expected_slice = np.array(
          [
              0.4664,
              0.4886,
              0.4403,
              0.6902,
              0.5592,
              0.4534,
              0.5931,
              0.5951,
              0.5224,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
  
imports: import random
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import AutoencoderKL, EulerDiscreteScheduler, StableDiffusionXLImg2ImgPipeline, UNet2DConditionModel
  from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
  from ..pipeline_params import IMAGE_TO_IMAGE_IMAGE_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
  from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 4 for calling the StableDiffusionXLImg2ImgPipelineFastTests obj's underlying member method code with example usage: obj.test_stable_diffusion_xl_img2img_euler() without return types