code: def test_squim_objective_pretrained_weights(
      lang, expected, sample_speech
  ):
      """Test that the metric scores estimated by SquimObjective Bundle is identical to the expected result."""
      bundle = SQUIM_OBJECTIVE
  
      # Get SquimObjective model
      model = bundle.get_model()
      # Create a synthetic waveform
      waveform, sample_rate = torchaudio.load(sample_speech)
      scores = model(waveform)
      for i in range(3):
          assert abs(scores[i].item() - expected[i]) < 1e-5
  
imports: import pytest
  import torchaudio
  from torchaudio.pipelines import SQUIM_OBJECTIVE, SQUIM_SUBJECTIVE
label_desc: Use image node_id 1 for calling a global function with example usage: test_squim_objective_pretrained_weights(lang, expected, sample_speech) without return types