code: def test_dance_diffusion(self):
      device = "cpu"  # ensure determinism for the device-dependent torch.Generator
      components = self.get_dummy_components()
      pipe = DanceDiffusionPipeline(**components)
      pipe = pipe.to(device)
      pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_dummy_inputs(device)
      output = pipe(**inputs)
      audio = output.audios
  
      audio_slice = audio[0, -3:, -3:]
  
      assert audio.shape == (1, 2, components["unet"].sample_size)
      expected_slice = np.array(
          [-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000]
      )
      assert np.abs(audio_slice.flatten() - expected_slice).max() < 1e-2
  
imports: import gc
  import unittest
  import numpy
  import torch
  from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel
  from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, skip_mps, torch_device
  from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
label_desc: Use image node_id 3 for calling the DanceDiffusionPipelineFastTests obj's underlying member method code with example usage: obj.test_dance_diffusion() without return types