code: def test_ldm3d_stable_diffusion(self):
      ldm3d_pipe = StableDiffusionLDM3DPipeline.from_pretrained(
          "Intel/ldm3d"
      )
      ldm3d_pipe = ldm3d_pipe.to(torch_device)
      ldm3d_pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_inputs(torch_device)
      output = ldm3d_pipe(**inputs)
      rgb, depth = output.rgb, output.depth
      rgb_slice = rgb[0, -3:, -3:, -1].flatten()
      depth_slice = rgb[0, -3:, -1].flatten()
  
      assert rgb.shape == (1, 512, 512, 3)
      assert depth.shape == (1, 512, 512)
  
      expected_slice_rgb = np.array(
          [
              0.53805465,
              0.56707305,
              0.5486515,
              0.57012236,
              0.5814511,
              0.56253487,
              0.54843014,
              0.55092263,
              0.6459706,
          ]
      )
      expected_slice_depth = np.array(
          [
              0.9263781,
              0.6678672,
              0.5486515,
              0.92202145,
              0.67831135,
              0.56253487,
              0.9241694,
              0.7551478,
              0.6459706,
          ]
      )
      assert np.abs(rgb_slice - expected_slice_rgb).max() < 3e-3
      assert np.abs(depth_slice - expected_slice_depth).max() < 3e-3
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
  from diffusers import AutoencoderKL, DDIMScheduler, PNDMScheduler, StableDiffusionLDM3DPipeline, UNet2DConditionModel
  from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
label_desc: Use image node_id 3 for calling the StableDiffusionLDM3DPipelineSlowTests obj's underlying member method code with example usage: obj.test_ldm3d_stable_diffusion() without return types