code: def _log_mel_spectrogram(
      data,
      audio_sample_rate=8000,
      log_offset=0.0,
      window_length_secs=0.025,
      hop_length_secs=0.010,
      **kwargs,
  ):
      window_length_samples = int(
          round(audio_sample_rate * window_length_secs)
      )
      hop_length_samples = int(
          round(audio_sample_rate * hop_length_secs)
      )
      fft_length = 2 ** int(
          math.ceil(math.log(window_length_samples) / math.log(2.0))
      )
  
      spectrogram = _stft_magnitude(
          data,
          fft_length=fft_length,
          hop_length=hop_length_samples,
          window_length=window_length_samples,
      )
      mel_spectrogram = torch.matmul(
          spectrogram,
          _spectrogram_to_mel_matrix(
              num_spectrogram_bins=spectrogram.shape[1],
              audio_sample_rate=audio_sample_rate,
              **kwargs,
          ).to(spectrogram),
      )
      return torch.log(mel_spectrogram + log_offset)
  
file_path: audio/src/torchaudio/prototype/pipelines/_vggish/_vggish_impl.py
imports: import math
  import torch
label_desc: Use image node_id 7 for calling a global function with example usage: _log_mel_spectrogram(data, audio_sample_rate, log_offset, window_length_secs, hop_length_secs) and returns: torch