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

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

        Returns
        -------
        params : dict
            Contains four elements:

            * sensors_positions_matrix : numpy.ndarray
                Matrix giving the positions of each sensor in a 3D cartesian
                coordinate system. Should have shape (3, n_channels), where
                n_channels is the number of channels.
            * axis : 'x' | 'y' | 'z'
                Axis around which to rotate.
            * angles : array-like
                Array of float of shape ``(batch_size,)`` containing the
                rotation angles (in degrees) for each element of the input
                batch, sampled uniformly between ``-max_degrees``and
                ``max_degrees``.
            * spherical_splines : bool
                Whether to use spherical splines for the interpolation or not.
                When ``False``, standard scipy.interpolate.Rbf (with quadratic
                kernel) will be used (as in the original paper).
        """
        if len(batch) == 0:
            return super().get_augmentation_params(*batch)
        X = batch[0]

        u = self.rng.uniform(low=0, high=1, size=X.shape[0])
        max_degrees = self.max_degrees
        if isinstance(max_degrees, torch.Tensor):
            max_degrees = max_degrees.to(X.device)
        random_angles = (
            torch.as_tensor(u, device=X.device) * 2 * max_degrees - max_degrees
        )
        return {
            "sensors_positions_matrix": self.sensors_positions_matrix,
            "axis": self.axis,
            "angles": random_angles,
            "spherical_splines": self.spherical_splines,
        }
Return transform parameters.

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

        Returns
        -------
        params : dict
            Contains four elements:

            * sensors_positions_matrix : numpy.ndarray
                Matrix giving the positions of each sensor in a 3D cartesian
                coordinate system. Should have shape (3, n_channels), where
                n_channels is the number of channels.
            * axis : 'x' | 'y' | 'z'
                Axis around which to rotate.
            * angles : array-like
                Array of float of shape ``(batch_size,)`` containing the
                rotation angles (in degrees) for each element of the input
                batch, sampled uniformly between ``-max_degrees``and
                ``max_degrees``.
            * spherical_splines : bool
                Whether to use spherical splines for the interpolation or not.
                When ``False``, standard scipy.interpolate.Rbf (with quadratic
                kernel) will be used (as in the original paper).
        
braindecode/augmentation/transforms.py