code: def bbox_to_mask(
      boxes: torch.Tensor, width: int, height: int
  ) -> torch.Tensor:
      """Convert 2D 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 Bx4x2, where each box is defined in the following ``clockwise`` order: top-left, top-right, bottom-right
              and bottom-left. The coordinates must be in the x, y order.
          width: width of the masked image.
          height: height of the masked image.
  
      Returns:
          the output mask tensor.
  
      Note:
          It is currently non-differentiable.
  
      Examples:
          >>> boxes = torch.tensor([[
          ...        [1., 1.],
          ...        [3., 1.],
          ...        [3., 2.],
          ...        [1., 2.],
          ...   ]])  # 1x4x2
          >>> bbox_to_mask(boxes, 5, 5)
          tensor([[[0., 0., 0., 0., 0.],
                   [0., 1., 1., 1., 0.],
                   [0., 1., 1., 1., 0.],
                   [0., 0., 0., 0., 0.],
                   [0., 0., 0., 0., 0.]]])
      """
      validate_bbox(boxes)
      # zero padding the surroundings
      mask = zeros(
          (len(boxes), height + 2, width + 2),
          dtype=boxes.dtype,
          device=boxes.device,
      )
      # push all points one pixel off
      # in order to zero-out the fully filled rows or columns
      box_i = (boxes + 1).long()
      # set all pixels within box to 1
      for msk, bx in zip(mask, box_i):
          msk[bx[0, 1] : bx[2, 1] + 1, bx[0, 0] : bx[1, 0] + 1] = 1.0
      return mask[:, 1:-1, 1:-1]
  
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 5 for calling a global function with example usage: bbox_to_mask(boxes, width, height) and returns: mask