 to `true`
        length (int, optional): audio signal length to crop the signal
    Returns:
        x (Tensor): audio waveform of
            shape (nb_samples, nb_channels, nb_timesteps)
    &#34;&#34;&#34;

    def __init__(
        self,
        n_fft: int = 4096,
        n_hop: int = 1024,
        center: bool = False,
        sample_rate: float = 44100.0,
        window: Optional[nn.Parameter] = None,
    ) -&gt; None:
        super(TorchISTFT, self).__init__()

        self.n_fft = n_fft
        se