code: def tilt_projection(
      taux: torch.Tensor,
      tauy: torch.Tensor,
      return_inverse: bool = False,
  ) -> torch.Tensor:
      r"""Estimate the tilt projection matrix or the inverse tilt projection matrix.
  
      Args:
          taux: Rotation angle in radians around the :math:`x`-axis with shape :math:`(*, 1)`.
          tauy: Rotation angle in radians around the :math:`y`-axis with shape :math:`(*, 1)`.
          return_inverse: False to obtain the the tilt projection matrix. True for the inverse matrix.
  
      Returns:
          torch.Tensor: Inverse tilt projection matrix with shape :math:`(*, 3, 3)`.
      """
      if taux.shape != tauy.shape:
          raise ValueError(
              f"Shape of taux {taux.shape} and tauy {tauy.shape} do not match."
          )
  
      ndim: int = taux.dim()
      taux = taux.reshape(-1)
      tauy = tauy.reshape(-1)
  
      cTx = cos(taux)
      sTx = sin(taux)
      cTy = cos(tauy)
      sTy = sin(tauy)
      zero = zeros_like(cTx)
      one = ones_like(cTx)
  
      Rx = stack(
          [one, zero, zero, zero, cTx, sTx, zero, -sTx, cTx], -1
      ).reshape(-1, 3, 3)
      Ry = stack(
          [cTy, zero, -sTy, zero, one, zero, sTy, zero, cTy], -1
      ).reshape(-1, 3, 3)
      R = Ry @ Rx
  
      if return_inverse:
          invR22 = 1 / R[..., 2, 2]
          invPz = stack(
              [
                  invR22,
                  zero,
                  R[..., 0, 2] * invR22,
                  zero,
                  invR22,
                  R[..., 1, 2] * invR22,
                  zero,
                  zero,
                  one,
              ],
              -1,
          ).reshape(-1, 3, 3)
  
          inv_tilt = R.transpose(-1, -2) @ invPz
          if ndim == 0:
              inv_tilt = torch.squeeze(inv_tilt)
  
          return inv_tilt
  
      Pz = stack(
          [
              R[..., 2, 2],
              zero,
              -R[..., 0, 2],
              zero,
              R[..., 2, 2],
              -R[..., 1, 2],
              zero,
              zero,
              one,
          ],
          -1,
      ).reshape(-1, 3, 3)
  
      tilt = Pz @ R.transpose(-1, -2)
      if ndim == 0:
          tilt = torch.squeeze(tilt)
  
      return tilt
  
imports: from typing import Optional
  import torch
  from kornia.core import cos, ones_like, sin, stack, zeros_like
label_desc: Use image node_id 1 for calling a global function with example usage: tilt_projection(taux, tauy, return_inverse) and returns: tilt, inv_tilt