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","."]
code: def __init__(self, spatial_dims: int) -> None:
      self.spatial_dims = 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 1 to create a new StandardizeEmptyBox object from inherited base classes: Transform with example: obj = StandardizeEmptyBox(spatial_dims)