code: def bbox_project(
      bboxes: Union[torch.Tensor, np.ndarray],
      homography_matrix: Union[torch.Tensor, np.ndarray],
      img_shape: Optional[Tuple[int, int]] = None,
  ) -> Union[torch.Tensor, np.ndarray]:
      """Geometric transformation for bbox.
  
      Args:
          bboxes (Union[torch.Tensor, np.ndarray]): Shape (n, 4) for bboxes.
          homography_matrix (Union[torch.Tensor, np.ndarray]):
              Shape (3, 3) for geometric transformation.
          img_shape (Tuple[int, int], optional): Image shape. Defaults to None.
      Returns:
          Union[torch.Tensor, np.ndarray]: Converted bboxes.
      """
      bboxes_type = type(bboxes)
      if bboxes_type is np.ndarray:
          bboxes = torch.from_numpy(bboxes)
      if isinstance(homography_matrix, np.ndarray):
          homography_matrix = torch.from_numpy(homography_matrix)
      corners = bbox2corner(bboxes)
      corners = torch.cat(
          [corners, corners.new_ones(corners.shape[0], 1)], dim=1
      )
      corners = torch.matmul(homography_matrix, corners.t()).t()
      # Convert to homogeneous coordinates by normalization
      corners = corners[:, :2] / corners[:, 2:3]
      bboxes = corner2bbox(corners)
      if img_shape is not None:
          bboxes[:, 0::2] = bboxes[:, 0::2].clamp(0, img_shape[1])
          bboxes[:, 1::2] = bboxes[:, 1::2].clamp(0, img_shape[0])
      if bboxes_type is np.ndarray:
          bboxes = bboxes.numpy()
      return bboxes
  
imports: from typing import List, Optional, Sequence, Tuple, Union
  import numpy
  import torch
  from torch import Tensor
  from mmdet.structures.bbox import BaseBoxes
label_desc: Use image node_id 15 for calling a global function with example usage: bbox_project(bboxes, homography_matrix, img_shape) and returns: bboxes