code: def test_finetune_asr_model(
      bundle,
      lang,
      expected,
      sample_speech,
      ctc_decoder,
  ):
      """Smoke test of downloading weights for fine-tuning models and simple transcription"""
      model = bundle.get_model().eval()
      waveform, sample_rate = torchaudio.load(sample_speech)
      emission, _ = model(waveform)
      decoder = ctc_decoder(bundle.get_labels())
      result = decoder(emission[0])
      assert result == expected
  
imports: import os
  import pytest
  import torchaudio
  from torchaudio.pipelines import HUBERT_ASR_LARGE, HUBERT_ASR_XLARGE, HUBERT_BASE, HUBERT_LARGE, HUBERT_XLARGE, VOXPOPULI_ASR_BASE_10K_DE, VOXPOPULI_ASR_BASE_10K_EN, VOXPOPULI_ASR_BASE_10K_ES, VOXPOPULI_ASR_BASE_10K_FR, VOXPOPULI_ASR_BASE_10K_IT, WAV2VEC2_ASR_BASE_100H, WAV2VEC2_ASR_BASE_10M, WAV2VEC2_ASR_BASE_960H, WAV2VEC2_ASR_LARGE_100H, WAV2VEC2_ASR_LARGE_10M, WAV2VEC2_ASR_LARGE_960H, WAV2VEC2_ASR_LARGE_LV60K_100H, WAV2VEC2_ASR_LARGE_LV60K_10M, WAV2VEC2_ASR_LARGE_LV60K_960H, WAV2VEC2_BASE, WAV2VEC2_LARGE, WAV2VEC2_LARGE_LV60K, WAV2VEC2_XLSR53, WAV2VEC2_XLSR_1B, WAV2VEC2_XLSR_300M, WAVLM_BASE, WAVLM_BASE_PLUS, WAVLM_LARGE
label_desc: Use image node_id 3 for calling a global function with example usage: test_finetune_asr_model(bundle, lang, expected, sample_speech, ctc_decoder) without return types