code: def test_inference_with_prior_lora(self):
      _, prior_lora_config, _ = self.get_lora_components()
      device = "cpu"
  
      components = self.get_dummy_components()
  
      pipe = self.pipeline_class(**components)
      pipe = pipe.to(device)
  
      pipe.set_progress_bar_config(disable=None)
  
      output_no_lora = pipe(**self.get_dummy_inputs(device))
      image_embed = output_no_lora.image_embeddings
      self.assertTrue(image_embed.shape == (1, 2, 24, 24))
  
      pipe.prior.add_adapter(prior_lora_config)
      self.assertTrue(
          self.check_if_lora_correctly_set(pipe.prior),
          "Lora not correctly set in prior",
      )
  
      output_lora = pipe(**self.get_dummy_inputs(device))
      lora_image_embed = output_lora.image_embeddings
  
      self.assertTrue(image_embed.shape == lora_image_embed.shape)
  
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 17 for calling the WuerstchenPriorPipelineFastTests obj's underlying member method code with example usage: obj.test_inference_with_prior_lora() without return types