class_docstr: This class encodes and decodes a set of bounding boxes into
  the representation used for training the regressors.
  
  Args:
      weights: 4-element tuple or 6-element tuple
      boxes_xform_clip: high threshold to prevent sending too large values into torch.exp()
  
  Example:
      .. code-block:: python
  
          box_coder = BoxCoder(weights=[1., 1., 1., 1., 1., 1.])
          gt_boxes = torch.tensor([[1,2,1,4,5,6],[1,3,2,7,8,9]])
          proposals = gt_boxes + torch.rand(gt_boxes.shape)
          rel_gt_boxes = box_coder.encode_single(gt_boxes, proposals)
          gt_back = box_coder.decode_single(rel_gt_boxes, proposals)
          # We expect gt_back to be equal to gt_boxes
code: def __init__(
      self,
      weights: Sequence[float],
      boxes_xform_clip: float | None = None,
  ) -> None:
      if boxes_xform_clip is None:
          boxes_xform_clip = math.log(1000.0 / 16)
      self.spatial_dims = look_up_option(len(weights), [4, 6]) // 2
      self.weights = weights
      self.boxes_xform_clip = boxes_xform_clip
  
file_path: MONAI/monai/apps/detection/utils/box_coder.py
imports: from __future__ import annotations
  import math
  from collections.abc import Sequence
  import torch
  from torch import Tensor
  from monai.data.box_utils import COMPUTE_DTYPE, CenterSizeMode, StandardMode, convert_box_mode, is_valid_box_values
  from monai.utils.module import look_up_option
label_desc: Use image node_id 1 to create a new BoxCoder object  with example: obj = BoxCoder(weights, boxes_xform_clip)