code: def warp_grid3d(grid: Tensor, src_homo_dst: Tensor) -> Tensor:
      r"""Compute the grid to warp the coordinates grid by the homography/ies.
  
      Args:
          grid: Unwrapped grid of the shape :math:`(1, D, H, W, 3)`.
          src_homo_dst: Homography or homographies (stacked) to
            transform all points in the grid. Shape of the homography
            has to be :math:`(1, 4, 4)` or :math:`(N, 1, 4, 4)`.
  
      Returns:
          the transformed grid of shape :math:`(N, H, W, 3)`.
      """
      batch_size: int = src_homo_dst.size(0)
      _, depth, height, width, _ = grid.size()
      # expand grid to match the input batch size
      grid = grid.expand(batch_size, -1, -1, -1, -1)  # NxDxHxWx3
      if len(src_homo_dst.shape) == 3:  # local homography case
          src_homo_dst = src_homo_dst.view(
              batch_size, 1, 4, 4
          )  # Nx1x3x3
      # perform the actual grid transformation,
      # the grid is copied to input device and casted to the same type
      flow: Tensor = transform_points(
          src_homo_dst, grid.to(src_homo_dst)
      )  # NxDxHxWx3
      return flow.view(batch_size, depth, height, width, 3)
  
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 5 for calling a global function with example usage: warp_grid3d(grid, src_homo_dst) and returns: flow