code: def test_dance_diffusion_fp16(self):
      device = torch_device
  
      pipe = DanceDiffusionPipeline.from_pretrained(
          "harmonai/maestro-150k", torch_dtype=torch.float16
      )
      pipe = pipe.to(device)
      pipe.set_progress_bar_config(disable=None)
  
      generator = torch.manual_seed(0)
      output = pipe(
          generator=generator,
          num_inference_steps=100,
          audio_length_in_s=4.096,
      )
      audio = output.audios
  
      audio_slice = audio[0, -3:, -3:]
  
      assert audio.shape == (1, 2, pipe.unet.sample_size)
      expected_slice = np.array(
          [-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341]
      )
  
      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 PipelineIntegrationTests obj's underlying member method code with example usage: obj.test_dance_diffusion_fp16() without return types