def get_augmentation_params(self, *batch):
        """Return transform parameters.

        Parameters
        ----------
        X : tensor.Tensor
            The data.
        y : tensor.Tensor
            The labels.

        Returns
        -------
        params : dict
            Contains

            * delta_freq : float
                The amplitude of the frequency shift (in Hz).
            * sfreq : float
                Sampling frequency of the signals to be transformed.
        """
        if len(batch) == 0:
            return super().get_augmentation_params(*batch)
        X = batch[0]

        u = torch.as_tensor(self.rng.uniform(size=X.shape[0]), device=X.device)
        max_delta_freq = self.max_delta_freq
        if isinstance(max_delta_freq, torch.Tensor):
            max_delta_freq = max_delta_freq.to(X.device)
        delta_freq = u * 2 * max_delta_freq - max_delta_freq
        return {
            "delta_freq": delta_freq,
            "sfreq": self.sfreq,
        }
Return transform parameters.

        Parameters
        ----------
        X : tensor.Tensor
            The data.
        y : tensor.Tensor
            The labels.

        Returns
        -------
        params : dict
            Contains

            * delta_freq : float
                The amplitude of the frequency shift (in Hz).
            * sfreq : float
                Sampling frequency of the signals to be transformed.
        
braindecode/augmentation/transforms.py