class_docstr: Clip the bounding boxes and the associated labels/scores to make sure they are within the image.
  There might be multiple arrays of labels/scores associated with one array of boxes.
  
  Args:
      remove_empty: whether to remove the boxes and corresponding labels that are actually empty
class_docstr_tok: ["Clip","the","bounding","boxes","and","the","associated","labels\/scores","to","make","sure","they","are","within","the","image",".","There","might","be","multiple","arrays","of","labels\/scores","associated","with","one","array","of","boxes",".","Args",":","remove_empty",":","whether","to","remove","the","boxes","and","corresponding","labels","that","are","actually","empty"]
code: def __init__(self, remove_empty: bool = False) -> None:
      self.remove_empty = remove_empty
  
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 ClipBoxToImage object from inherited base classes: Transform with example: obj = ClipBoxToImage(remove_empty)