code: def test_ldm3d_v2(self):
      ldm3d_pipe = StableDiffusionLDM3DPipeline.from_pretrained(
          "Intel/ldm3d-4c"
      ).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
  
      expected_rgb_mean = 0.4194127
      expected_rgb_std = 0.35375586
      expected_depth_mean = 0.5638502
      expected_depth_std = 0.34686103
  
      assert rgb.shape == (1, 512, 512, 3)
      assert depth.shape == (1, 512, 512, 1)
      assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3
      assert np.abs(expected_rgb_std - rgb.std()) < 1e-3
      assert np.abs(expected_depth_mean - depth.mean()) < 1e-3
      assert np.abs(expected_depth_std - depth.std()) < 1e-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 4 for calling the StableDiffusionPipelineNightlyTests obj's underlying member method code with example usage: obj.test_ldm3d_v2() without return types