code: def test_shap_e(self):
      expected_image = load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/shap_e/test_shap_e_np_out.npy"
      )
      pipe = ShapEPipeline.from_pretrained("openai/shap-e")
      pipe = pipe.to(torch_device)
      pipe.set_progress_bar_config(disable=None)
  
      generator = torch.Generator(device=torch_device).manual_seed(0)
  
      images = pipe(
          "a shark",
          generator=generator,
          guidance_scale=15.0,
          num_inference_steps=64,
          frame_size=64,
          output_type="np",
      ).images[0]
  
      assert images.shape == (20, 64, 64, 3)
  
      assert_mean_pixel_difference(images, expected_image)
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
  from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
  from diffusers.pipelines.shap_e import ShapERenderer
  from diffusers.utils.testing_utils import load_numpy, nightly, require_torch_gpu, torch_device
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
label_desc: Use image node_id 2 for calling the ShapEPipelineIntegrationTests obj's underlying member method code with example usage: obj.test_shap_e() without return types