code: def test_audioldm2_large(self):
      audioldm_pipe = AudioLDM2Pipeline.from_pretrained(
          "cvssp/audioldm2-large"
      )
      audioldm_pipe = audioldm_pipe.to(torch_device)
      audioldm_pipe.set_progress_bar_config(disable=None)
  
      inputs = self.get_inputs(torch_device)
      audio = audioldm_pipe(**inputs).audios[0]
  
      assert audio.ndim == 1
      assert len(audio) == 81952
  
      # check the portion of the generated audio with the largest dynamic range (reduces flakiness)
      audio_slice = audio[8825:8835]
      expected_slice = np.array(
          [
              -0.1829,
              -0.1461,
              0.0759,
              -0.1493,
              -0.1396,
              0.5783,
              0.3001,
              -0.3038,
              -0.0639,
              -0.2244,
          ]
      )
      max_diff = np.abs(expected_slice - audio_slice).max()
      assert max_diff < 1e-3
  
imports: import gc
  import unittest
  import numpy
  import torch
  from transformers import ClapAudioConfig, ClapConfig, ClapFeatureExtractor, ClapModel, ClapTextConfig, GPT2Config, GPT2Model, RobertaTokenizer, SpeechT5HifiGan, SpeechT5HifiGanConfig, T5Config, T5EncoderModel, T5Tokenizer
  from diffusers import AudioLDM2Pipeline, AudioLDM2ProjectionModel, AudioLDM2UNet2DConditionModel, AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
  from diffusers.utils.testing_utils import enable_full_determinism, nightly, torch_device
  from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin
label_desc: Use image node_id 5 for calling the AudioLDM2PipelineSlowTests obj's underlying member method code with example usage: obj.test_audioldm2_large() without return types