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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def evaluate( references, estimates, win=1*44100, hop=1*44100, mode='v4', padding=True ):
 """BSS_EVAL images evaluation using metrics module Parameters references : np.ndarray, shape=(nsrc, nsampl, nchan) array containing true reference sources estimates : np.ndarray, shape=(nsrc, nsampl, nchan) array containing estimated sources window : int, defaults to 44100 window size in samples hop : int hop size in samples, defaults to 44100 (no overlap) mode : str BSSEval version, default to `v4` Returns ------- SDR : np.ndarray, shape=(nsrc,) vector of Signal to Distortion Ratios (SDR) ISR : np.ndarray, shape=(nsrc,) vector of Source to Spatial Distortion Image (ISR) SIR : np.ndarray, shape=(nsrc,) vector of Source to Interference Ratios (SIR) SAR : np.ndarray, shape=(nsrc,) vector of Sources to Artifacts Ratios (SAR) """


    estimates = np.array(estimates)
    references = np.array(references)

    if padding:
        references, estimates = pad_or_truncate(references, estimates)

    SDR, ISR, SIR, SAR, _ = metrics.bss_eval(
        references,
        estimates,
        compute_permutation=False,
        window=win,
        hop=hop,
        framewise_filters=(mode == "v3"),
        bsseval_sources_version=False
    )

    return SDR, ISR, SIR, SAR