How to use the biosppy.tools.smoother function in biosppy

sampling_rate = 1000
    eda = nk.eda_simulate(
        duration=30, sampling_rate=sampling_rate, scr_number=6, noise=0.01, drift=0.01, random_state=42
    )

    clean = nk.eda_clean(eda, sampling_rate=sampling_rate)
    assert len(clean) == len(eda)

    # Comparison to biosppy (https://github.com/PIA-Group/BioSPPy/blob/master/biosppy/signals/eda.py)

    eda_biosppy = nk.eda_clean(eda, sampling_rate=sampling_rate, method="biosppy")
    original, _, _ = biosppy.tools.filter_signal(
        signal=eda, ftype="butter", band="lowpass", order=4, frequency=5, sampling_rate=sampling_rate
    )

    original, _ = biosppy.tools.smoother(signal=original, kernel="boxzen", size=int(0.75 * sampling_rate), mirror=True)

    #    pd.DataFrame({"our":eda_biosppy, "biosppy":original}).plot()
    assert np.allclose((eda_biosppy - original).mean(), 0, atol=1e-5)

band='lowpass',
                                      order=4,
                                      frequency=25.,
                                      sampling_rate=sampling_rate)
    filtered, _, _ = biosppy.tools.filter_signal(signal=filtered,
                                      ftype='butter',
                                      band='highpass',
                                      order=4,
                                      frequency=3.,
                                      sampling_rate=sampling_rate)

    # diff
    dx = np.abs(np.diff(filtered, 1) * sampling_rate)

    # smoothing
    dx, _ = biosppy.tools.smoother(signal=dx, kernel='hamming', size=sm_size, mirror=True)

    # buffers
    qrspeakbuffer = np.zeros(init_ecg)
    noisepeakbuffer = np.zeros(init_ecg)
    peak_idx_test = np.zeros(init_ecg)
    noise_idx = np.zeros(init_ecg)
    rrinterval = sampling_rate * np.ones(init_ecg)

    a, b = 0, v1s
    all_peaks, _ = biosppy.tools.find_extrema(signal=dx, mode='max')
    for i in range(init_ecg):
        peaks, values = biosppy.tools.find_extrema(signal=dx[a:b], mode='max')
        try:
            ind = np.argmax(values)
        except ValueError:
            pass

- BioSPPy: https://github.com/PIA-Group/BioSPPy


    References
    -----------
    - Kim, K. H., Bang, S. W., & Kim, S. R. (2004). Emotion recognition system using short-term monitoring of physiological signals. Medical and biological engineering and computing, 42(3), 419-427.
    - Gamboa, H. (2008). Multi-Modal Behavioral Biometrics Based on HCI and Electrophysiology (Doctoral dissertation, PhD thesis, Universidade Técnica de Lisboa, Instituto Superior Técnico).
    """
    # Processing
    # ===========
    if method == "slow":
        # Compute gradient (sort of derivative)
        gradient = np.gradient(signal)
        # Smoothing
        size = int(0.1 * sampling_rate)
        smooth, _ = biosppy.tools.smoother(signal=gradient, kernel='bartlett', size=size, mirror=True)
        # Find zero-crossings
        zeros, = biosppy.tools.zero_cross(signal=smooth, detrend=True)

        # Separate onsets and peaks
        onsets = []
        peaks = []
        for i in zeros:
            if smooth[i+1] > smooth[i-1]:
                onsets.append(i)
            else:
                peaks.append(i)
    else:
        # find extrema
        peaks, _ = biosppy.tools.find_extrema(signal=signal, mode='max')
        onsets, _ = biosppy.tools.find_extrema(signal=signal, mode='min')

# Initialization
    eda = np.array(eda)
    eda_df = pd.DataFrame({"EDA_Raw": np.array(eda)})

    # Preprocessing
    # ===================
    # Filtering
    filtered, _, _ = biosppy.tools.filter_signal(signal=eda,
                                 ftype='butter',
                                 band='lowpass',
                                 order=4,
                                 frequency=5,
                                 sampling_rate=sampling_rate)

    # Smoothing
    filtered, _ = biosppy.tools.smoother(signal=filtered,
                              kernel='boxzen',
                              size=int(0.75 * sampling_rate),
                              mirror=True)
    eda_df["EDA_Filtered"] = filtered

    # Derive Phasic and Tonic
    try:
        tonic, phasic = cvxEDA(eda, sampling_rate=sampling_rate, alpha=alpha, gamma=gamma)
        eda_df["EDA_Phasic"] = phasic
        eda_df["EDA_Tonic"] = tonic
        signal = phasic
    except:
        print("NeuroKit Warning: eda_process(): Error in cvxEDA algorithm, couldn't extract phasic and tonic components. Using raw signal.")
        signal = eda

    # Skin-Conductance Responses