code: def test_controlnet_sdxl_guess(self):
      device = "cpu"
  
      components = self.get_dummy_components()
  
      sd_pipe = self.pipeline_class(**components)
      sd_pipe = sd_pipe.to(device)
  
      sd_pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs(device)
      inputs["guess_mode"] = True
  
      output = sd_pipe(**inputs)
      image_slice = output.images[0, -3:, -3:, -1]
      expected_slice = np.array(
          [
              0.5381963,
              0.4836803,
              0.45821992,
              0.5577731,
              0.51210403,
              0.4794795,
              0.59282357,
              0.5647199,
              0.43100584,
          ]
      )
  
      # make sure that it's equal
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4
  
imports: import random
  import unittest
  import numpy
  import torch
  from PIL import Image
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import AutoencoderKL, ControlNetModel, EulerDiscreteScheduler, StableDiffusionXLControlNetInpaintPipeline, UNet2DConditionModel
  from diffusers.utils.import_utils import is_xformers_available
  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_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
label_desc: Use image node_id 8 for calling the ControlNetPipelineSDXLFastTests obj's underlying member method code with example usage: obj.test_controlnet_sdxl_guess() without return types