code: def zoom_boxes(
      boxes: NdarrayTensor, zoom: Sequence[float] | float
  ) -> NdarrayTensor:
      """
      Zoom boxes
  
      Args:
          boxes: bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be StandardMode
          zoom: The zoom factor along the spatial axes.
              If a float, zoom is the same for each spatial axis.
              If a sequence, zoom should contain one value for each spatial axis.
  
      Returns:
          zoomed boxes, with same data type as ``boxes``, does not share memory with ``boxes``
  
      Example:
          .. code-block:: python
  
              boxes = torch.ones(1,4)
              zoom_boxes(boxes, zoom=[0.5,2.2]) #  will return tensor([[0.5, 2.2, 0.5, 2.2]])
      """
      spatial_dims = get_spatial_dims(boxes=boxes)
  
      # generate affine transform corresponding to ``zoom``
      affine = create_scale(
          spatial_dims=spatial_dims, scaling_factor=zoom
      )
  
      return apply_affine_to_boxes(boxes=boxes, affine=affine)
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  from copy import deepcopy
  import numpy
  import torch
  from monai.config.type_definitions import DtypeLike, NdarrayOrTensor, NdarrayTensor
  from monai.data.box_utils import COMPUTE_DTYPE, TO_REMOVE, get_spatial_dims
  from monai.transforms import Resize
  from monai.transforms.utils import create_scale
  from monai.utils import look_up_option
  from monai.utils.misc import ensure_tuple, ensure_tuple_rep
  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: zoom_boxes(boxes, zoom) and returns: apply_affine_to_boxes