code: def standardize_empty_box(
      boxes: NdarrayOrTensor, spatial_dims: int
  ) -> NdarrayOrTensor:
      """
      When boxes are empty, this function standardize it to shape of (0,4) or (0,6).
  
      Args:
          boxes: bounding boxes, Nx4 or Nx6 or empty torch tensor or ndarray
          spatial_dims: number of spatial dimensions of the bounding boxes.
  
      Returns:
          bounding boxes with shape (N,4) or (N,6), N can be 0.
  
      Example:
          .. code-block:: python
  
              boxes = torch.ones(0,)
              standardize_empty_box(boxes, 3)
      """
      # convert numpy to tensor if needed
      boxes_t, *_ = convert_data_type(boxes, torch.Tensor)
      # handle empty box
      if boxes_t.shape[0] == 0:
          boxes_t = torch.reshape(boxes_t, [0, spatial_dims * 2])
      # convert tensor back to numpy if needed
      boxes_dst, *_ = convert_to_dst_type(src=boxes_t, dst=boxes)
      return boxes_dst
  
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 3 for calling a global function with example usage: standardize_empty_box(boxes, spatial_dims) and returns: boxes_dst