code: def box_pair_giou(
      boxes1: NdarrayOrTensor, boxes2: NdarrayOrTensor
  ) -> NdarrayOrTensor:
      """
      Compute the generalized intersection over union (GIoU) of a pair of boxes.
      The two inputs should have the same shape and the func return an (N,) array,
      (in contrary to :func:`~monai.data.box_utils.box_giou` , which does not require the inputs to have the same
      shape and returns ``NxM`` matrix).
  
      Args:
          boxes1: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``
          boxes2: bounding boxes, same shape with boxes1. The box mode is assumed to be ``StandardMode``
  
      Returns:
          paired GIoU, with size of (N,) and same data type as ``boxes1``
  
      Reference:
          https://giou.stanford.edu/GIoU.pdf
  
      """
  
      if not isinstance(boxes1, type(boxes2)):
          warnings.warn(
              f"boxes1 is {type(boxes1)}, while boxes2 is {type(boxes2)}. The result will be {type(boxes1)}."
          )
  
      # convert numpy to tensor if needed
      boxes1_t, *_ = convert_data_type(boxes1, torch.Tensor)
      boxes2_t, *_ = convert_data_type(boxes2, torch.Tensor)
  
      if boxes1_t.shape != boxes2_t.shape:
          raise ValueError(
              "boxes1 and boxes2 should be paired and have same shape."
          )
  
      spatial_dims = get_spatial_dims(boxes=boxes1_t)
  
      # we do computation with compute_dtype to avoid overflow
      box_dtype = boxes1_t.dtype
  
      # compute area
      area1 = box_area(boxes=boxes1_t.to(dtype=COMPUTE_DTYPE))  # (N,)
      area2 = box_area(boxes=boxes2_t.to(dtype=COMPUTE_DTYPE))  # (N,)
  
      # Intersection
      # get the left top and right bottom points for the boxes pair
      lt = torch.max(
          boxes1_t[:, :spatial_dims], boxes2_t[:, :spatial_dims]
      ).to(
          dtype=COMPUTE_DTYPE
      )  # (N,spatial_dims) left top
      rb = torch.min(
          boxes1_t[:, spatial_dims:], boxes2_t[:, spatial_dims:]
      ).to(
          dtype=COMPUTE_DTYPE
      )  # (N,spatial_dims) right bottom
  
      # compute size for the intersection region for the boxes pair
      wh = (rb - lt + TO_REMOVE).clamp(min=0)  # (N,spatial_dims)
      inter = torch.prod(wh, dim=-1, keepdim=False)  # (N,)
  
      # compute IoU and convert back to original box_dtype
      union = area1 + area2 - inter
      iou = inter / (union + torch.finfo(COMPUTE_DTYPE).eps)  # (N,)
  
      # Enclosure
      # get the left top and right bottom points for the boxes pair
      lt = torch.min(
          boxes1_t[:, :spatial_dims], boxes2_t[:, :spatial_dims]
      ).to(
          dtype=COMPUTE_DTYPE
      )  # (N,spatial_dims) left top
      rb = torch.max(
          boxes1_t[:, spatial_dims:], boxes2_t[:, spatial_dims:]
      ).to(
          dtype=COMPUTE_DTYPE
      )  # (N,spatial_dims) right bottom
  
      # compute size for the enclose region for the boxes pair
      wh = (rb - lt + TO_REMOVE).clamp(min=0)  # (N,spatial_dims)
      enclosure = torch.prod(wh, dim=-1, keepdim=False)  # (N,)
  
      giou_t = iou - (enclosure - union) / (
          enclosure + torch.finfo(COMPUTE_DTYPE).eps
      )
      giou_t = giou_t.to(dtype=box_dtype)  # (N,spatial_dims)
      if torch.isnan(giou_t).any() or torch.isinf(giou_t).any():
          raise ValueError("Box GIoU is NaN or Inf.")
  
      # convert tensor back to numpy if needed
      giou, *_ = convert_to_dst_type(src=giou_t, dst=boxes1)
      return giou
  
imports: from __future__ import annotations
  import inspect
  import warnings
  from abc import ABC, abstractmethod
  from collections.abc import Callable, Sequence
  from copy import deepcopy
  import numpy
  import torch
  from monai.config.type_definitions import NdarrayOrTensor, NdarrayTensor
  from monai.utils import look_up_option
  from monai.utils.enums import BoxModeName
  from monai.utils.type_conversion import convert_data_type, convert_to_dst_type
label_desc: Use image node_id 14 for calling a global function with example usage: box_pair_giou(boxes1, boxes2) and returns: giou