code: def test_shap_e(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.images[0]
      image_slice = image[-3:, -3:].cpu().numpy()
  
      assert image.shape == (32, 16)
  
      expected_slice = np.array(
          [
              -1.0,
              0.40668195,
              0.57322013,
              -0.9469888,
              0.4283227,
              0.30348337,
              -0.81094897,
              0.74555075,
              0.15342723,
          ]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
  
imports: import gc
  import random
  import unittest
  import numpy
  import torch
  from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
  from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImg2ImgPipeline
  from diffusers.pipelines.shap_e import ShapERenderer
  from diffusers.utils.testing_utils import floats_tensor, load_image, load_numpy, nightly, require_torch_gpu, torch_device
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
label_desc: Use image node_id 11 for calling the ShapEImg2ImgPipelineFastTests obj's underlying member method code with example usage: obj.test_shap_e() without return types