code: def test_wuerstchen_prior(self):
      device = "cpu"
  
      components = self.get_dummy_components()
  
      pipe = self.pipeline_class(**components)
      pipe = pipe.to(device)
  
      pipe.set_progress_bar_config(disable=None)
  
      output = pipe(**self.get_dummy_inputs(device))
      image = output.image_embeddings
  
      image_from_tuple = pipe(
          **self.get_dummy_inputs(device), return_dict=False
      )[0]
  
      image_slice = image[0, 0, 0, -10:]
      image_from_tuple_slice = image_from_tuple[0, 0, 0, -10:]
      assert image.shape == (1, 2, 24, 24)
  
      expected_slice = np.array(
          [
              -7172.837,
              -3438.855,
              -1093.312,
              388.8835,
              -7471.467,
              -7998.1206,
              -5328.259,
              218.00089,
              -2731.5745,
              -8056.734,
          ]
      )
      assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2
      assert (
          np.abs(
              image_from_tuple_slice.flatten() - expected_slice
          ).max()
          < 5e-2
      )
  
imports: import unittest
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import DDPMWuerstchenScheduler, WuerstchenPriorPipeline
  from diffusers.loaders import AttnProcsLayers
  from diffusers.models.attention_processor import LoRAAttnProcessor, LoRAAttnProcessor2_0
  from diffusers.pipelines.wuerstchen import WuerstchenPrior
  from diffusers.utils.import_utils import is_peft_available
  from diffusers.utils.testing_utils import enable_full_determinism, require_peft_backend, skip_mps, torch_device
  from ..test_pipelines_common import PipelineTesterMixin
label_desc: Use image node_id 10 for calling the WuerstchenPriorPipelineFastTests obj's underlying member method code with example usage: obj.test_wuerstchen_prior() without return types