code: def test_model_inference_batched(self):
      # Load sample checkpoint from Tortoise TTS
      model = UnivNetModel.from_pretrained("dg845/univnet-dev")
      model.eval().to(torch_device)
  
      # Get batched noise and spectrogram inputs.
      input_speech = self.get_inputs(torch_device, num_samples=3)
  
      with torch.no_grad():
          waveform = model(**input_speech)[0]
      waveform = waveform.cpu()
  
      waveform_mean = torch.mean(waveform)
      waveform_stddev = torch.std(waveform)
      waveform_slice = waveform[-1, -9:].flatten()
  
      EXPECTED_MEAN = torch.tensor(-0.19989729)
      EXPECTED_STDDEV = torch.tensor(0.35230172)
      EXPECTED_SLICE = torch.tensor(
          [
              -0.3408,
              -0.6045,
              -0.5052,
              0.1160,
              -0.1556,
              -0.0405,
              -0.3024,
              -0.5290,
              -0.5019,
          ]
      )
  
      torch.testing.assert_close(
          waveform_mean, EXPECTED_MEAN, atol=1e-4, rtol=1e-5
      )
      torch.testing.assert_close(
          waveform_stddev, EXPECTED_STDDEV, atol=1e-4, rtol=1e-5
      )
      torch.testing.assert_close(
          waveform_slice, EXPECTED_SLICE, atol=5e-4, rtol=1e-5
      )
  
imports: import gc
  import inspect
  import random
  import unittest
  from datasets import Audio, load_dataset
  from transformers import UnivNetConfig, UnivNetFeatureExtractor
  from transformers.testing_utils import is_torch_available, require_torch, require_torch_gpu, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor
label_desc: Use image node_id 4 for calling the UnivNetModelIntegrationTests obj's underlying member method code with example usage: obj.test_model_inference_batched() without return types