code: def compute_average_bass_energy(
      audio_data, sample_rate, max_bass_freq=250
  ):
      stft = librosa.stft(audio_data)
      power_spectrogram = np.abs(stft) ** 2
      frequencies = librosa.fft_frequencies(
          sr=sample_rate, n_fft=stft.shape[0]
      )
      bass_mask = frequencies <= max_bass_freq
      bass_energy = power_spectrogram[
          np.ix_(bass_mask, np.arange(power_spectrogram.shape[1]))
      ].mean()
      return bass_energy
  
imports: import logging
  import os
  import re
  from glob import glob
  from typing import Dict, List
  import librosa
  import numpy
  import torch
  import torchaudio
  import tqdm
  from encodec.utils import convert_audio
  from scipy.special import softmax
  from torch.nn import functional
  from TTS.tts.layers.bark.hubert.hubert_manager import HubertManager
  from TTS.tts.layers.bark.hubert.kmeans_hubert import CustomHubert
  from TTS.tts.layers.bark.hubert.tokenizer import HubertTokenizer
  from TTS.tts.layers.bark.load_model import clear_cuda_cache, inference_mode
label_desc: Use image node_id 9 for calling a global function with example usage: compute_average_bass_energy(audio_data, sample_rate, max_bass_freq) and returns: bass_energy