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

        Parameters
        ----------
        X : tensor.Tensor
            The data.
        y : tensor.Tensor
            The labels.
        Returns
        -------
        params : dict
            Contains ...
        """
        if len(batch) == 0:
            return super().get_augmentation_params(*batch)
        X = batch[0]

        batch_size, _, n_times = X.shape

        segment_length = int((n_times * self.mask_ratio) / self.n_segments)

        assert segment_length >= 1, (
            "n_segments should be a positive integer not higher than (max_mask_ratio * window size)."
        )

        time_start = self.rng.randint(
            0, n_times - segment_length, (batch_size, self.n_segments)
        )
        time_start = torch.from_numpy(time_start)

        return {
            "time_start": time_start,
            "segment_length": segment_length,
            "n_segments": self.n_segments,
        }
Return transform parameters.

        Parameters
        ----------
        X : tensor.Tensor
            The data.
        y : tensor.Tensor
            The labels.
        Returns
        -------
        params : dict
            Contains ...
        
braindecode/augmentation/transforms.py