code: def test_stable_unclip(self):
      expected_image = load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"
      )
  
      pipe = StableUnCLIPPipeline.from_pretrained(
          "fusing/stable-unclip-2-1-l", torch_dtype=torch.float16
      )
      pipe.to(torch_device)
      pipe.set_progress_bar_config(disable=None)
      # stable unclip will oom when integration tests are run on a V100,
      # so turn on memory savings
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()
  
      generator = torch.Generator(device="cpu").manual_seed(0)
      output = pipe(
          "anime turle", generator=generator, output_type="np"
      )
  
      image = output.images[0]
  
      assert image.shape == (768, 768, 3)
  
      assert_mean_pixel_difference(image, expected_image)
  
imports: import gc
  import unittest
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, PriorTransformer, StableUnCLIPPipeline, UNet2DConditionModel
  from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
  from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, nightly, require_torch_gpu, torch_device
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, assert_mean_pixel_difference
label_desc: Use image node_id 2 for calling the StableUnCLIPPipelineIntegrationTests obj's underlying member method code with example usage: obj.test_stable_unclip() without return types