code: def _waveform_to_examples(data):
      # Compute log mel spectrogram features, with shape (n_frame, n_mel)
      log_mel = _log_mel_spectrogram(
          data,
          audio_sample_rate=_SAMPLE_RATE,
          log_offset=_LOG_OFFSET,
          window_length_secs=_STFT_WINDOW_LENGTH_SECONDS,
          hop_length_secs=_STFT_HOP_LENGTH_SECONDS,
          num_mel_bins=_NUM_BANDS,
          lower_edge_hertz=_MEL_MIN_HZ,
          upper_edge_hertz=_MEL_MAX_HZ,
      )
  
      # Frame features into examples, with shape (n_example, n_frame, n_mel)
      features_sample_rate = 1.0 / _STFT_HOP_LENGTH_SECONDS
      example_window_length = int(
          round(_EXAMPLE_WINDOW_SECONDS * features_sample_rate)
      )
  
      example_hop_length = int(
          round(_EXAMPLE_HOP_SECONDS * features_sample_rate)
      )
      log_mel_examples = _frame(
          log_mel,
          window_length=example_window_length,
          hop_length=example_hop_length,
      )
  
      # (n_example, 1, n_frame, n_mel)
      return log_mel_examples.unsqueeze(1)
  
file_path: audio/src/torchaudio/prototype/pipelines/_vggish/_vggish_impl.py
imports: import math
  import torch
label_desc: Use image node_id 8 for calling a global function with example usage: _waveform_to_examples(data) and returns: log_mel_examples