code: def bbox_to_mask3d(
      boxes: torch.Tensor, size: tuple[int, int, int]
  ) -> torch.Tensor:
      """Convert 3D bounding boxes to masks. Covered area is 1. and the remaining is 0.
  
      Args:
          boxes: a tensor containing the coordinates of the bounding boxes to be extracted. The tensor must have the shape
              of Bx8x3, where each box is defined in the following ``clockwise`` order: front-top-left, front-top-right,
              front-bottom-right, front-bottom-left, back-top-left, back-top-right, back-bottom-right, back-bottom-left.
              The coordinates must be in the x, y, z order.
          size: depth, height and width of the masked image.
  
      Returns:
          the output mask tensor.
  
      Examples:
          >>> boxes = torch.tensor([[
          ...     [1., 1., 1.],
          ...     [2., 1., 1.],
          ...     [2., 2., 1.],
          ...     [1., 2., 1.],
          ...     [1., 1., 2.],
          ...     [2., 1., 2.],
          ...     [2., 2., 2.],
          ...     [1., 2., 2.],
          ... ]])  # 1x8x3
          >>> bbox_to_mask3d(boxes, (4, 5, 5))
          tensor([[[[[0., 0., 0., 0., 0.],
                     [0., 0., 0., 0., 0.],
                     [0., 0., 0., 0., 0.],
                     [0., 0., 0., 0., 0.],
                     [0., 0., 0., 0., 0.]],
          <BLANKLINE>
                    [[0., 0., 0., 0., 0.],
                     [0., 1., 1., 0., 0.],
                     [0., 1., 1., 0., 0.],
                     [0., 0., 0., 0., 0.],
                     [0., 0., 0., 0., 0.]],
          <BLANKLINE>
                    [[0., 0., 0., 0., 0.],
                     [0., 1., 1., 0., 0.],
                     [0., 1., 1., 0., 0.],
                     [0., 0., 0., 0., 0.],
                     [0., 0., 0., 0., 0.]],
          <BLANKLINE>
                    [[0., 0., 0., 0., 0.],
                     [0., 0., 0., 0., 0.],
                     [0., 0., 0., 0., 0.],
                     [0., 0., 0., 0., 0.],
                     [0., 0., 0., 0., 0.]]]]])
      """
      validate_bbox3d(boxes)
      mask = zeros((len(boxes), *size))
  
      mask_out = []
      # TODO: Looking for a vectorized way
      for m, box in zip(mask, boxes):
          m = m.index_fill(
              0,
              arange(
                  box[0, 2].item(),
                  box[4, 2].item() + 1,
                  device=box.device,
                  dtype=torch.long,
              ),
              torch.tensor(1, device=box.device, dtype=box.dtype),
          )
          m = m.index_fill(
              1,
              arange(
                  box[1, 1].item(),
                  box[2, 1].item() + 1,
                  device=box.device,
                  dtype=torch.long,
              ),
              torch.tensor(1, device=box.device, dtype=box.dtype),
          )
          m = m.index_fill(
              2,
              arange(
                  box[0, 0].item(),
                  box[1, 0].item() + 1,
                  device=box.device,
                  dtype=torch.long,
              ),
              torch.tensor(1, device=box.device, dtype=box.dtype),
          )
          m = m.unsqueeze(dim=0)
          m_out = ones_like(m)
          m_out = m_out * (m == 1).all(dim=2, keepdim=True).all(
              dim=1, keepdim=True
          )
          m_out = m_out * (m == 1).all(dim=3, keepdim=True).all(
              dim=1, keepdim=True
          )
          m_out = m_out * (m == 1).all(dim=2, keepdim=True).all(
              dim=3, keepdim=True
          )
          mask_out.append(m_out)
  
      return stack(mask_out, dim=0).float()
  
file_path: kornia/kornia/geometry/bbox.py
imports: from __future__ import annotations
  import warnings
  from typing import Optional
  import torch
  from kornia.core import arange, ones_like, stack, where, zeros
  from .linalg import transform_points
label_desc: Use image node_id 6 for calling a global function with example usage: bbox_to_mask3d(boxes, size) and returns: stack