code: def test_inference_uncond(self):
      ldm = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
      ldm.to(torch_device)
      ldm.set_progress_bar_config(disable=None)
  
      generator = torch.manual_seed(0)
      image = ldm(
          generator=generator,
          num_inference_steps=5,
          output_type="numpy",
      ).images
  
      image_slice = image[0, -3:, -3:, -1]
  
      assert image.shape == (1, 256, 256, 3)
      expected_slice = np.array(
          [
              0.4399,
              0.44975,
              0.46825,
              0.474,
              0.4359,
              0.4581,
              0.45095,
              0.4341,
              0.4447,
          ]
      )
      tolerance = 1e-2 if torch_device != "mps" else 3e-2
  
      assert (
          np.abs(image_slice.flatten() - expected_slice).max()
          < tolerance
      )
  
file_path: llmga/llmga/diffusers/tests/pipelines/latent_diffusion/test_latent_diffusion_uncond.py
imports: import unittest
  import numpy
  import torch
  from transformers import CLIPTextConfig, CLIPTextModel
  from diffusers import DDIMScheduler, LDMPipeline, UNet2DModel, VQModel
  from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch, torch_device
label_desc: Use image node_id 1 for calling the LDMPipelineIntegrationTests obj's underlying member method code with example usage: obj.test_inference_uncond() without return types