code: def convert_box_mode(
      boxes: NdarrayOrTensor,
      src_mode: str | BoxMode | type[BoxMode] | None = None,
      dst_mode: str | BoxMode | type[BoxMode] | None = None,
  ) -> NdarrayOrTensor:
      """
      This function converts the boxes in src_mode to the dst_mode.
  
      Args:
          boxes: source bounding boxes, Nx4 or Nx6 torch tensor or ndarray.
          src_mode: source box mode. If it is not given, this func will assume it is ``StandardMode()``.
              It follows the same format with ``mode`` in :func:`~monai.data.box_utils.get_boxmode`.
          dst_mode: target box mode. If it is not given, this func will assume it is ``StandardMode()``.
              It follows the same format with ``mode`` in :func:`~monai.data.box_utils.get_boxmode`.
  
      Returns:
          bounding boxes with target mode, with same data type as ``boxes``, does not share memory with ``boxes``
  
      Example:
          .. code-block:: python
  
              boxes = torch.ones(10,4)
              # The following three lines are equivalent
              # They convert boxes with format [xmin, ymin, xmax, ymax] to [xcenter, ycenter, xsize, ysize].
              convert_box_mode(boxes=boxes, src_mode="xyxy", dst_mode="ccwh")
              convert_box_mode(boxes=boxes, src_mode="xyxy", dst_mode=monai.data.box_utils.CenterSizeMode)
              convert_box_mode(boxes=boxes, src_mode="xyxy", dst_mode=monai.data.box_utils.CenterSizeMode())
      """
      # handle empty box
      if boxes.shape[0] == 0:
          return boxes
  
      src_boxmode = get_boxmode(src_mode)
      dst_boxmode = get_boxmode(dst_mode)
  
      # if mode not changed, deepcopy the original boxes
      if isinstance(src_boxmode, type(dst_boxmode)):
          return deepcopy(boxes)
  
      # convert box mode
      # convert numpy to tensor if needed
      boxes_t, *_ = convert_data_type(boxes, torch.Tensor)
  
      # convert boxes to corners
      corners = src_boxmode.boxes_to_corners(boxes_t)
  
      # check validity of corners
      spatial_dims = get_spatial_dims(boxes=boxes_t)
      for axis in range(0, spatial_dims):
          if (corners[spatial_dims + axis] < corners[axis]).sum() > 0:
              warnings.warn(
                  "Given boxes has invalid values. The box size must be non-negative."
              )
  
      # convert corners to boxes
      boxes_t_dst = dst_boxmode.corners_to_boxes(corners)
  
      # convert tensor back to numpy if needed
      boxes_dst, *_ = convert_to_dst_type(src=boxes_t_dst, 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 4 for calling a global function with example usage: convert_box_mode(boxes, src_mode, dst_mode) and returns: boxes_dst, boxes, deepcopy