code: def extract_features(
      model,
      layer,
      splits,
      sample_pct,
      flatten,
      device="cpu",
      sample_rate=16000,
  ):
      """Extract features from audio using a pre-trained model."""
      data, num_files = fetch_data(splits, sample_pct)
      features_list = []
      id_list = []
  
      for item in tqdm.tqdm(data, total=num_files):
          wav = item["wav"]
          with torch.no_grad():
              info = torchaudio.info(wav)
              audio = sb.dataio.dataio.read_audio(wav)
              audio = torchaudio.transforms.Resample(
                  info.sample_rate,
                  sample_rate,
              )(audio)
              audio = audio.unsqueeze(0).to(device)
              feats = model.extract_features(audio)
              feats = feats[layer]
              feats = np_array(feats)
          features_list.append(feats)
          id_list.append(item["id"])
  
      if flatten:
          return np.concatenate(features_list), id_list
  
      return features_list, id_list
  
imports: import sys
  import logging
  import time
  import random
  import itertools
  import pathlib
  import joblib
  import torch
  import torchaudio
  import tqdm
  import numpy
  from sklearn.cluster import MiniBatchKMeans
  from hyperpyyaml import load_hyperpyyaml
  import speechbrain
  from ljspeech_prepare import prepare_ljspeech
  from speechbrain.lobes.models.huggingface_wav2vec import HuggingFaceWav2Vec2
label_desc: Use image node_id 5 for calling a global function with example usage: extract_features(model, layer, splits, sample_pct, flatten, device, sample_rate) and returns: features_list,  id_list, np,  id_list