def forward(self, pose, with_global_pose=False):
        ''' Returns the angle prior loss for the given pose
        Args:
            pose: (Bx[23 + 1] * 3) torch tensor with the axis-angle
            representation of the rotations of the joints of the SMPL model.
        Kwargs:
            with_global_pose: Whether the pose vector also contains the global
            orientation of the SMPL model. If not then the indices must be
            corrected.
        Returns:
            A sze (B) tensor containing the angle prior loss for each element
            in the batch.
        '''
        angle_prior_idxs = self.angle_prior_idxs - (not with_global_pose) * 3
        return torch.exp(pose[:, angle_prior_idxs] *
                         self.angle_prior_signs).pow(2)
 Returns the angle prior loss for the given pose
        Args:
            pose: (Bx[23 + 1] * 3) torch tensor with the axis-angle
            representation of the rotations of the joints of the SMPL model.
        Kwargs:
            with_global_pose: Whether the pose vector also contains the global
            orientation of the SMPL model. If not then the indices must be
            corrected.
        Returns:
            A sze (B) tensor containing the angle prior loss for each element
            in the batch.
        
mGPT/data/transforms/joints2rots/prior.py