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

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

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

            * sfreq : float
                Sampling frequency of the signals to be filtered.
            * bandwidth : float
                Bandwidth of the filter, i.e. distance between the low and high
                cut frequencies.
            * freqs_to_notch : array-like | None
                Array of floats of size ``(batch_size,)`` containing the center
                of the frequency band to filter out for each sample in the
                batch. Frequencies should be greater than
                ``bandwidth/2 + transition`` and lower than
                ``sfreq/2 - bandwidth/2 - transition`` (where
                ``transition = 1 Hz``).
        """
        if len(batch) == 0:
            return super().get_augmentation_params(*batch)
        X = batch[0]

        # Prevents transitions from going below 0 and above max_freq
        notched_freqs = self.rng.uniform(
            low=1 + 2 * self.bandwidth,
            high=self.max_freq - 1 - 2 * self.bandwidth,
            size=X.shape[0],
        )
        return {
            "sfreq": self.sfreq,
            "bandwidth": self.bandwidth,
            "freqs_to_notch": notched_freqs,
        }
Return transform parameters.

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

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

            * sfreq : float
                Sampling frequency of the signals to be filtered.
            * bandwidth : float
                Bandwidth of the filter, i.e. distance between the low and high
                cut frequencies.
            * freqs_to_notch : array-like | None
                Array of floats of size ``(batch_size,)`` containing the center
                of the frequency band to filter out for each sample in the
                batch. Frequencies should be greater than
                ``bandwidth/2 + transition`` and lower than
                ``sfreq/2 - bandwidth/2 - transition`` (where
                ``transition = 1 Hz``).
        
braindecode/augmentation/transforms.py