code: def get_perspective_transform3d(src: Tensor, dst: Tensor) -> Tensor:
      r"""Calculate a 3d perspective transform from four pairs of the corresponding points.
  
      The function calculates the matrix of a perspective transform so that:
  
      .. math::
  
          \begin{bmatrix}
          t_{i}x_{i}^{'} \\
          t_{i}y_{i}^{'} \\
          t_{i}z_{i}^{'} \\
          t_{i} \\
          \end{bmatrix}
          =
          \textbf{map_matrix} \cdot
          \begin{bmatrix}
          x_{i} \\
          y_{i} \\
          z_{i} \\
          1 \\
          \end{bmatrix}
  
      where
  
      .. math::
          dst(i) = (x_{i}^{'},y_{i}^{'},z_{i}^{'}), src(i) = (x_{i}, y_{i}, z_{i}), i = 0,1,2,5,7
  
      Concrete math is as below:
  
      .. math::
  
          \[ u_i =\frac{c_{00} * x_i + c_{01} * y_i + c_{02} * z_i + c_{03}}
              {c_{30} * x_i + c_{31} * y_i + c_{32} * z_i + c_{33}} \]
          \[ v_i =\frac{c_{10} * x_i + c_{11} * y_i + c_{12} * z_i + c_{13}}
              {c_{30} * x_i + c_{31} * y_i + c_{32} * z_i + c_{33}} \]
          \[ w_i =\frac{c_{20} * x_i + c_{21} * y_i + c_{22} * z_i + c_{23}}
              {c_{30} * x_i + c_{31} * y_i + c_{32} * z_i + c_{33}} \]
  
      .. math::
  
          \begin{pmatrix}
          x_0 & y_0 & z_0 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -x_0*u_0 & -y_0*u_0 & -z_0 * u_0 \\
          x_1 & y_1 & z_1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -x_1*u_1 & -y_1*u_1 & -z_1 * u_1 \\
          x_2 & y_2 & z_2 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -x_2*u_2 & -y_2*u_2 & -z_2 * u_2 \\
          x_5 & y_5 & z_5 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -x_5*u_5 & -y_5*u_5 & -z_5 * u_5 \\
          x_7 & y_7 & z_7 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -x_7*u_7 & -y_7*u_7 & -z_7 * u_7 \\
          0 & 0 & 0 & 0 & x_0 & y_0 & z_0 & 1 & 0 & 0 & 0 & 0 & -x_0*v_0 & -y_0*v_0 & -z_0 * v_0 \\
          0 & 0 & 0 & 0 & x_1 & y_1 & z_1 & 1 & 0 & 0 & 0 & 0 & -x_1*v_1 & -y_1*v_1 & -z_1 * v_1 \\
          0 & 0 & 0 & 0 & x_2 & y_2 & z_2 & 1 & 0 & 0 & 0 & 0 & -x_2*v_2 & -y_2*v_2 & -z_2 * v_2 \\
          0 & 0 & 0 & 0 & x_5 & y_5 & z_5 & 1 & 0 & 0 & 0 & 0 & -x_5*v_5 & -y_5*v_5 & -z_5 * v_5 \\
          0 & 0 & 0 & 0 & x_7 & y_7 & z_7 & 1 & 0 & 0 & 0 & 0 & -x_7*v_7 & -y_7*v_7 & -z_7 * v_7 \\
          0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & x_0 & y_0 & z_0 & 1 & -x_0*w_0 & -y_0*w_0 & -z_0 * w_0 \\
          0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & x_1 & y_1 & z_1 & 1 & -x_1*w_1 & -y_1*w_1 & -z_1 * w_1 \\
          0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & x_2 & y_2 & z_2 & 1 & -x_2*w_2 & -y_2*w_2 & -z_2 * w_2 \\
          0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & x_5 & y_5 & z_5 & 1 & -x_5*w_5 & -y_5*w_5 & -z_5 * w_5 \\
          0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & x_7 & y_7 & z_7 & 1 & -x_7*w_7 & -y_7*w_7 & -z_7 * w_7 \\
          \end{pmatrix}
  
      Args:
          src: coordinates of quadrangle vertices in the source image with shape :math:`(B, 8, 3)`.
          dst: coordinates of the corresponding quadrangle vertices in
              the destination image with shape :math:`(B, 8, 3)`.
  
      Returns:
          the perspective transformation with shape :math:`(B, 4, 4)`.
  
      .. note::
          This function is often used in conjunction with :func:`warp_perspective3d`.
      """
      if not isinstance(src, (Tensor)):
          raise TypeError(
              f"Input type is not a Tensor. Got {type(src)}"
          )
  
      if not isinstance(dst, (Tensor)):
          raise TypeError(
              f"Input type is not a Tensor. Got {type(dst)}"
          )
  
      if not src.shape[-2:] == (8, 3):
          raise ValueError(
              f"Inputs must be a Bx8x3 tensor. Got {src.shape}"
          )
  
      if not src.shape == dst.shape:
          raise ValueError(
              f"Inputs must have the same shape. Got {dst.shape}"
          )
  
      if not (src.shape[0] == dst.shape[0]):
          raise ValueError(
              f"Inputs must have same batch size dimension. Expect {src.shape} but got {dst.shape}"
          )
  
      if not (src.device == dst.device and src.dtype == dst.dtype):
          raise AssertionError(
              f"Expect `src` and `dst` to be in the same device (Got {src.dtype}, {dst.dtype}) "
              f"with the same dtype (Got {src.dtype}, {dst.dtype})."
          )
  
      # we build matrix A by using only 4 point correspondence. The linear
      # system is solved with the least square method, so here
      # we could even pass more correspondence
      p = []
  
      # 000, 100, 110, 101, 011
      for i in [0, 1, 2, 5, 7]:
          p.append(
              _build_perspective_param3d(src[:, i], dst[:, i], "x")
          )
          p.append(
              _build_perspective_param3d(src[:, i], dst[:, i], "y")
          )
          p.append(
              _build_perspective_param3d(src[:, i], dst[:, i], "z")
          )
  
      # A is Bx15x15
      A = stack(p, 1)
  
      # b is a Bx15x1
      b = stack(
          [
              dst[:, 0:1, 0],
              dst[:, 0:1, 1],
              dst[:, 0:1, 2],
              dst[:, 1:2, 0],
              dst[:, 1:2, 1],
              dst[:, 1:2, 2],
              dst[:, 2:3, 0],
              dst[:, 2:3, 1],
              dst[:, 2:3, 2],
              # dst[:, 3:4, 0], dst[:, 3:4, 1], dst[:, 3:4, 2],
              # dst[:, 4:5, 0], dst[:, 4:5, 1], dst[:, 4:5, 2],
              dst[:, 5:6, 0],
              dst[:, 5:6, 1],
              dst[:, 5:6, 2],
              # dst[:, 6:7, 0], dst[:, 6:7, 1], dst[:, 6:7, 2],
              dst[:, 7:8, 0],
              dst[:, 7:8, 1],
              dst[:, 7:8, 2],
          ],
          1,
      )
  
      # solve the system Ax = b
      X: Tensor = _torch_solve_cast(A, b)
  
      # create variable to return
      batch_size: int = src.shape[0]
      M = torch.empty(
          batch_size, 16, device=src.device, dtype=src.dtype
      )
      M[..., :15] = X[..., 0]
      M[..., -1].fill_(1)
  
      return M.view(-1, 4, 4)
  
imports: from __future__ import annotations
  from typing import Optional
  import torch
  import torch.nn.functional
  from kornia.core import Tensor, concatenate, ones, ones_like, stack, tan, tensor, zeros
  from kornia.core.check import KORNIA_CHECK, KORNIA_CHECK_SHAPE
  from kornia.geometry.conversions import angle_to_rotation_matrix, axis_angle_to_rotation_matrix, convert_affinematrix_to_homography, convert_affinematrix_to_homography3d, deg2rad, normalize_homography, normalize_homography3d, normalize_pixel_coordinates
  from kornia.geometry.linalg import transform_points
  from kornia.utils import create_meshgrid, create_meshgrid3d, eye_like
  from kornia.utils.helpers import _torch_inverse_cast, _torch_solve_cast
label_desc: Use image node_id 19 for calling a global function with example usage: get_perspective_transform3d(src, dst) and returns: M