code: def test_audio_diffusion(self):
      device = torch_device
  
      pipe = DiffusionPipeline.from_pretrained(
          "teticio/audio-diffusion-ddim-256"
      )
      pipe = pipe.to(device)
      pipe.set_progress_bar_config(disable=None)
  
      generator = torch.Generator(device=device).manual_seed(42)
      output = pipe(generator=generator)
      audio = output.audios[0]
      image = output.images[0]
  
      assert audio.shape == (
          1,
          (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length,
      )
      assert (
          image.height == pipe.unet.config.sample_size[0]
          and image.width == pipe.unet.config.sample_size[1]
      )
      image_slice = np.frombuffer(image.tobytes(), dtype="uint8")[:10]
      expected_slice = np.array(
          [151, 167, 154, 144, 122, 134, 121, 105, 70, 26]
      )
  
      assert np.abs(image_slice.flatten() - expected_slice).max() == 0
  
file_path: llmga/llmga/diffusers/tests/pipelines/audio_diffusion/test_audio_diffusion.py
imports: import gc
  import unittest
  import numpy
  import torch
  from diffusers import AudioDiffusionPipeline, AutoencoderKL, DDIMScheduler, DDPMScheduler, DiffusionPipeline, Mel, UNet2DConditionModel, UNet2DModel
  from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
label_desc: Use image node_id 2 for calling the PipelineIntegrationTests obj's underlying member method code with example usage: obj.test_audio_diffusion() without return types