How to use the torchaudio.functional.istft function in torchaudio

def _test_istft_of_sine(self, amplitude, L, n):
        # stft of amplitude*sin(2*pi/L*n*x) with the hop length and window size equaling L
        x = torch.arange(2 * L + 1, dtype=torch.get_default_dtype())
        sound = amplitude * torch.sin(2 * math.pi / L * x * n)
        # stft = torch.stft(sound, L, hop_length=L, win_length=L,
        #                   window=torch.ones(L), center=False, normalized=False)
        stft = torch.zeros((L // 2 + 1, 2, 2))
        stft_largest_val = (amplitude * L) / 2.0
        if n < stft.size(0):
            stft[n, :, 1] = -stft_largest_val

        if 0 <= L - n < stft.size(0):
            # symmetric about L // 2
            stft[L - n, :, 1] = stft_largest_val

        estimate = torchaudio.functional.istft(stft, L, hop_length=L, win_length=L,
                                               window=torch.ones(L), center=False, normalized=False)
        # There is a larger error due to the scaling of amplitude
        self._compare_estimate(sound, estimate, atol=1e-3)

def _test_linearity_of_istft(self, data_size, kwargs, atol=1e-6, rtol=1e-8):
        for i in range(self.number_of_trials):
            tensor1 = common_utils.random_float_tensor(i, data_size)
            tensor2 = common_utils.random_float_tensor(i * 2, data_size)
            a, b = torch.rand(2)
            istft1 = torchaudio.functional.istft(tensor1, **kwargs)
            istft2 = torchaudio.functional.istft(tensor2, **kwargs)
            istft = a * istft1 + b * istft2
            estimate = torchaudio.functional.istft(a * tensor1 + b * tensor2, **kwargs)
            self._compare_estimate(istft, estimate, atol, rtol)

for i in range(self.number_of_trials):

                # Non-batch
                sound = common_utils.random_float_tensor(i, data_size)

                stft = torch.stft(sound, **kwargs)
                estimate = torchaudio.functional.istft(stft, length=sound.size(1), **kwargs)

                self._compare_estimate(sound, estimate)

                # Batch
                stft = torch.stft(sound, **kwargs)
                stft = stft.repeat(3, 1, 1, 1, 1)
                sound = sound.repeat(3, 1, 1)

                estimate = torchaudio.functional.istft(stft, length=sound.size(1), **kwargs)
                self._compare_estimate(sound, estimate)

def _test_istft_is_inverse_of_stft(self, kwargs):
        # generates a random sound signal for each tril and then does the stft/istft
        # operation to check whether we can reconstruct signal
        for data_size in self.data_sizes:
            for i in range(self.number_of_trials):

                # Non-batch
                sound = common_utils.random_float_tensor(i, data_size)

                stft = torch.stft(sound, **kwargs)
                estimate = torchaudio.functional.istft(stft, length=sound.size(1), **kwargs)

                self._compare_estimate(sound, estimate)

                # Batch
                stft = torch.stft(sound, **kwargs)
                stft = stft.repeat(3, 1, 1, 1, 1)
                sound = sound.repeat(3, 1, 1)

                estimate = torchaudio.functional.istft(stft, length=sound.size(1), **kwargs)
                self._compare_estimate(sound, estimate)