def METHOD_NAME(self, momentum):
    # FFT params
    n_fft = 400
    win_length = n_fft
    hop_length = n_fft // 4
    window = torch.hann_window(win_length, device=self.device)
    power = 1
    # GriffinLim params
    n_iter = 8
    waveform = get_whitenoise(device=self.device, dtype=self.dtype)
    specgram = get_spectrogram(
        waveform, n_fft=n_fft, hop_length=hop_length, power=power, win_length=win_length, window=window
    )
    result = F.griffinlim(
        specgram,
        window=window,
        n_fft=n_fft,
        hop_length=hop_length,
        win_length=win_length,
        power=power,
        n_iter=n_iter,
        momentum=momentum,
        length=waveform.size(1),
        rand_init=False,
    )
    expected = librosa.griffinlim(
        specgram[0].cpu().numpy(),
        n_iter=n_iter,
        hop_length=hop_length,
        momentum=momentum,
        init=None,
        length=waveform.size(1),
        pad_mode="reflect",
    )[None, ...]
    self.assertEqual(result, torch.from_numpy(expected), atol=5e-5, rtol=1e-07)