def forward(
        self, x: "torch.Tensor", y: "torch.Tensor" | None = None
    ) -> tuple["torch.Tensor", "torch.Tensor" | None]:
        """
        Apply filter to a single sample `x`.

        :param x: A single audio sample.
        :param y: Label of the sample `x`. This function does not affect them in any way.
        :return: Similar sample.
        """
        import torch
        import torchaudio

        if int(torchaudio.__version__.split(".")[1]) > 5:
            x_preprocess = torchaudio.functional.lfilter(
                b_coeffs=torch.tensor(self.numerator_coef, device=self._device, dtype=x.dtype),
                a_coeffs=torch.tensor(self.denominator_coef, device=self._device, dtype=x.dtype),
                waveform=x,
                clamp=False,
            )
        else:
            x_preprocess = torchaudio.functional.lfilter(
                b_coeffs=torch.tensor(self.numerator_coef, device=self._device, dtype=x.dtype),
                a_coeffs=torch.tensor(self.denominator_coef, device=self._device, dtype=x.dtype),
                waveform=x,
            )

        if self.clip_values is not None:
            x_preprocess = x_preprocess.clamp(min=self.clip_values[0], max=self.clip_values[1])

        return x_preprocess, y

        Apply filter to a single sample `x`.

        :param x: A single audio sample.
        :param y: Label of the sample `x`. This function does not affect them in any way.
        :return: Similar sample.
        
art/preprocessing/audio/l_filter/pytorch.py