How to use the pyroomacoustics.transform.synthesis function in pyroomacoustics

def convergence_callback(Y, **kwargs):
        global SDR, SIR, ref
        from mir_eval.separation import bss_eval_sources

        if Y.shape[2] == 1:
            y = pra.transform.synthesis(
                Y[:, :, 0], framesize, framesize // 2, win=win_s
            )[:, None]
        else:
            y = pra.transform.synthesis(Y, framesize, framesize // 2, win=win_s)

        if args.algo != "blinkiva":
            new_ord = np.argsort(np.std(y, axis=0))[::-1]
            y = y[:, new_ord]

        m = np.minimum(y.shape[0] - framesize // 2, ref.shape[1])
        sdr, sir, sar, perm = bss_eval_sources(
            ref[:n_sources_target, :m, 0],
            y[framesize // 2 : m + framesize // 2, :n_sources_target].T,
        )
        SDR.append(sdr)
        SIR.append(sir)

def convergence_callback(Y, n_targets, SDR, SIR, ref, framesize, win_s, algo_name):
        from mir_eval.separation import bss_eval_sources

        if Y.shape[2] == 1:
            y = pra.transform.synthesis(
                Y[:, :, 0], framesize, framesize // 2, win=win_s
            )[:, None]
        else:
            y = pra.transform.synthesis(Y, framesize, framesize // 2, win=win_s)

        if algo_name not in parameters["overdet_algos"]:
            new_ord = np.argsort(np.std(y, axis=0))[::-1]
            y = y[:, new_ord]

        m = np.minimum(y.shape[0] - framesize // 2, ref.shape[1])

        synth[:n_targets, :m, 0] = y[framesize // 2 : m + framesize // 2, :n_targets].T

        sdr, sir, sar, perm = bss_eval_sources(
                ref[:n_targets+1, :m, 0], synth[:, :m, 0]
        )
        SDR.append(sdr[:n_targets].tolist())
        SIR.append(sir[:n_targets].tolist())

def convergence_callback(Y, n_targets, SDR, SIR, ref, framesize, win_s, algo_name):
        from mir_eval.separation import bss_eval_sources

        if Y.shape[2] == 1:
            y = pra.transform.synthesis(
                Y[:, :, 0], framesize, framesize // 2, win=win_s
            )[:, None]
        else:
            y = pra.transform.synthesis(Y, framesize, framesize // 2, win=win_s)

        if algo_name not in parameters["overdet_algos"]:
            new_ord = np.argsort(np.std(y, axis=0))[::-1]
            y = y[:, new_ord]

        m = np.minimum(y.shape[0] - framesize // 2, ref.shape[1])

        synth[:n_targets, :m, 0] = y[framesize // 2 : m + framesize // 2, :n_targets].T

        sdr, sir, sar, perm = bss_eval_sources(
                ref[:n_targets+1, :m, 0], synth[:, :m, 0]
        )

callback=convergence_callback)
    elif bss_type == 'sparseauxiva':
        # Estimate set of active frequency bins
        ratio = 0.35
        average = np.abs(np.mean(np.mean(X, axis=2), axis=0))
        k = np.int_(average.shape[0] * ratio)
        S = np.sort(np.argpartition(average, -k)[-k:])
        # Run SparseAuxIva
        Y = pra.bss.sparseauxiva(X, S, n_iter=30, proj_back=True,
                                 callback=convergence_callback)

    t_end = time.perf_counter()
    print("Time for BSS: {:.2f} s".format(t_end - t_begin))
    
    ## STFT Synthesis
    y = pra.transform.synthesis(Y, L, hop, win=win_s)

    ## Compare SDR and SIR
    y = y[L-hop:, :].T
    m = np.minimum(y.shape[1], ref.shape[1])
    sdr, sir, sar, perm = bss_eval_sources(ref[:, :m, 0], y[:, :m])
    print('SDR:', sdr)
    print('SIR:', sir)

    ## PLOT RESULTS
    import matplotlib.pyplot as plt
    plt.figure()
    plt.subplot(2,2,1)
    plt.specgram(ref[0,:,0], NFFT=1024, Fs=room.fs)
    plt.title('Source 0 (clean)')

    plt.subplot(2,2,2)