class_docstr: Applies affine matrix to the boxes
class_docstr_tok: ["Applies","affine","matrix","to","the","boxes"]
docstr: Args:
      boxes: source bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``
      affine: affine matrix to be applied to the box coordinate
docstr_tok: ["Args",":","boxes",":","source","bounding","boxes",",","Nx4","or","Nx6","torch","tensor","or","ndarray",".","The","box","mode","is","assumed","to","be","``","StandardMode","``","affine",":","affine","matrix","to","be","applied","to","the","box","coordinate"]
code: def __call__(self, boxes: NdarrayOrTensor, affine: NdarrayOrTensor | None) -> NdarrayOrTensor:  # type: ignore
      """
      Args:
          boxes: source bounding boxes, Nx4 or Nx6 torch tensor or ndarray. The box mode is assumed to be ``StandardMode``
          affine: affine matrix to be applied to the box coordinate
      """
      if affine is None:
          return boxes
  
      return apply_affine_to_boxes(boxes, affine=affine)
  
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 for calling the AffineBox obj's underlying member method code with example usage: obj.__call__(boxes, affine) and returns: apply_affine_to_boxes, boxes