code: def _load_checkpoint(checkpoint: str) -> torch.nn.Module:
      model = torchaudio.models.hubert_base(aux_num_out=29)
      checkpoint = torch.load(checkpoint, map_location="cpu")
      state_dict = checkpoint["state_dict"]
      new_state_dict = {}
      for k in state_dict:
          if "model.wav2vec2" in k:
              new_state_dict[
                  k.replace("model.wav2vec2.", "")
              ] = state_dict[k]
          elif "aux" in k:
              new_state_dict[k] = state_dict[k]
      model.load_state_dict(new_state_dict)
      return model
  
file_path: audio/examples/hubert/evaluate.py
imports: import argparse
  import logging
  from typing import Dict, List
  import torch
  import torch.nn.functional
  import torchaudio
  from torchaudio.models.decoder import ctc_decoder, CTCDecoder, download_pretrained_files
  from utils import _get_id2label
label_desc: Use image node_id 1 for calling a global function with example usage: _load_checkpoint(checkpoint) and returns: model