def METHOD_NAME(
    audio_list: List,
    model: torch.nn.Module,
    cfg_path: str = None,
) -> Tensor:
    """
    This function returns a single embedding for each audio clip. In this baseline
    implementation we simply summarize the temporal embeddings from
    get_timestamp_embeddings() using torch.mean().
    Args:
        audio_list: list of torch tensor audios (audios should be resampled to 16kHz).
        model: Loaded model.
        cfg_path:
    Returns:
        - embeddings, A float32 Tensor with shape
            (n_sounds, model.scene_embedding_size).
    """
    cfg_path = cfg_path or Path(__file__).parent / "config.yaml"
    device = audio_list[0].device
    # Load config file
    cfg = load_yaml_config(cfg_path)
    to_melspec = MelSpectrogram(
        sample_rate=cfg.sample_rate,
        n_fft=cfg.n_fft,
        win_length=cfg.win_length,
        hop_length=cfg.hop_length,
        n_mels=cfg.n_mels,
        f_min=cfg.f_min,
        f_max=cfg.f_max,
    ).to(device)
    stats = compute_scene_stats(audio_list, to_melspec)
    normalizer = PrecomputedNorm(stats)
    model = model.to(device)
    embeddings = generate_byols_embeddings(
        model, audio_list, to_melspec, normalizer, device
    )
    return embeddings