def __call__(self, x: np.ndarray, y: np.ndarray | None = None) -> tuple[np.ndarray, np.ndarray | None]:
        """
        Apply filter to sample `x`.

        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
                  could have different lengths. A possible example of `x` could be:
                  `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
        :param y: Labels of the sample `x`. This function does not affect them in any way.
        :return: Similar samples.
        """
        import torch

        x_preprocess = x.copy()

        # Filter one input at a time
        for i, x_preprocess_i in enumerate(tqdm(x_preprocess, desc="Apply audio filter", disable=not self.verbose)):
            if np.min(x_preprocess_i) < -1.0 or np.max(x_preprocess_i) > 1.0:  # pragma: no cover
                raise ValueError(
                    "Audio signals must be normalized to the range `[-1.0, 1.0]` to apply the audio filter function."
                )

            x_preprocess_i = torch.tensor(x_preprocess_i, device=self._device)

            with torch.no_grad():
                x_preprocess_i, _ = self.forward(x_preprocess_i)

            x_preprocess[i] = x_preprocess_i.cpu().numpy()

        return x_preprocess, y

        Apply filter to sample `x`.

        :param x: Samples of shape (nb_samples, seq_length). Note that, it is allowable that sequences in the batch
                  could have different lengths. A possible example of `x` could be:
                  `x = np.array([np.array([0.1, 0.2, 0.1, 0.4]), np.array([0.3, 0.1])])`.
        :param y: Labels of the sample `x`. This function does not affect them in any way.
        :return: Similar samples.
        
art/preprocessing/audio/l_filter/pytorch.py