class_docstr: When boxes are empty, this transform standardize it to shape of (0,4) or (0,6).
  
  Args:
      spatial_dims: number of spatial dimensions of the bounding boxes.
class_docstr_tok: ["When","boxes","are","empty",",","this","transform","standardize","it","to","shape","of","(","0,4",")","or","(","0,6",")",".","Args",":","spatial_dims",":","number","of","spatial","dimensions","of","the","bounding","boxes","."]
docstr: Args:
      boxes: source bounding boxes, Nx4 or Nx6 or 0xM torch tensor or ndarray.
docstr_tok: ["Args",":","boxes",":","source","bounding","boxes",",","Nx4","or","Nx6","or","0xM","torch","tensor","or","ndarray","."]
code: def __call__(self, boxes: NdarrayOrTensor) -> NdarrayOrTensor:
      """
      Args:
          boxes: source bounding boxes, Nx4 or Nx6 or 0xM torch tensor or ndarray.
      """
      return standardize_empty_box(
          boxes, spatial_dims=self.spatial_dims
      )
  
imports: from __future__ import annotations
  from typing import Any, Sequence
  import numpy
  import torch
  from monai.config.type_definitions import DtypeLike, NdarrayOrTensor, NdarrayTensor
  from monai.data.box_utils import BoxMode, clip_boxes_to_image, convert_box_mode, convert_box_to_standard_mode, get_spatial_dims, spatial_crop_boxes, standardize_empty_box
  from monai.transforms import Rotate90, SpatialCrop
  from monai.transforms.transform import Transform
  from monai.utils import ensure_tuple, ensure_tuple_rep, fall_back_tuple, look_up_option
  from monai.utils.enums import TransformBackends
  from .box_ops import apply_affine_to_boxes, convert_box_to_mask, convert_mask_to_box, flip_boxes, resize_boxes, rot90_boxes, select_labels, zoom_boxes
label_desc: Use image node_id 2 for calling the StandardizeEmptyBox obj's underlying member method code with example usage: obj.__call__(boxes) and returns: standardize_empty_box