code: def extract_feature_hubert(
      path: str,
      device: torch.device,
      sample_rate: int,
      model: Module,
      layer_index: int,
  ) -> Tensor:
      r"""Extract HuBERT features for KMeans clustering and pseudo label prediction.
      Args:
          path (str): The file path of the audio.
          device (torch.device): The location to allocate for PyTorch Tensors.
              Options: [``torch.device('cpu')``, torch.device('cuda')``].
          sample_rate (int): The sample rate of the audio.
          model (Module): The loaded ``HuBERTPretrainModel`` model.
          layer_index (int): The index of transformer layers in
              ``torchaudio.models.HuBERTPretrainModel`` for extracting features.
              (``1`` means the first layer output).
  
      Returns:
          Tensor: The desired feature tensor of the given audio file.
      """
      waveform, sr = torchaudio.load(path)
      assert sr == sample_rate
      waveform = waveform.to(device)
      with torch.inference_mode():
          feat = model.wav2vec2.extract_features(
              waveform, num_layers=layer_index
          )[0][-1][
              0
          ]  # (time, feat_dim)
      return feat
  
file_path: audio/examples/hubert/utils/feature_utils.py
imports: import logging
  from pathlib import Path
  from typing import Optional, Tuple, Union
  import torch
  import torchaudio
  from torch import Tensor
  from torch.nn import Module
  from .common_utils import _get_feat_lens_paths
label_desc: Use image node_id 3 for calling a global function with example usage: extract_feature_hubert(path, device, sample_rate, model, layer_index) and returns: feat