code: def encode_boxes(
      gt_boxes: Tensor, proposals: Tensor, weights: Tensor
  ) -> Tensor:
      """
      Encode a set of proposals with respect to some reference ground truth (gt) boxes.
  
      Args:
          gt_boxes: gt boxes, Nx4 or Nx6 torch tensor. The box mode is assumed to be ``StandardMode``
          proposals: boxes to be encoded, Nx4 or Nx6 torch tensor. The box mode is assumed to be ``StandardMode``
          weights: the weights for ``(cx, cy, w, h) or (cx,cy,cz, w,h,d)``
  
      Return:
          encoded gt, target of box regression that is used to convert proposals into gt_boxes, Nx4 or Nx6 torch tensor.
      """
  
      if gt_boxes.shape[0] != proposals.shape[0]:
          raise ValueError(
              "gt_boxes.shape[0] should be equal to proposals.shape[0]."
          )
      spatial_dims = look_up_option(len(weights), [4, 6]) // 2
  
      if not is_valid_box_values(gt_boxes):
          raise ValueError(
              "gt_boxes is not valid. Please check if it contains empty boxes."
          )
      if not is_valid_box_values(proposals):
          raise ValueError(
              "proposals is not valid. Please check if it contains empty boxes."
          )
  
      # implementation starts here
      ex_cccwhd: Tensor = convert_box_mode(proposals, src_mode=StandardMode, dst_mode=CenterSizeMode)  # type: ignore
      gt_cccwhd: Tensor = convert_box_mode(gt_boxes, src_mode=StandardMode, dst_mode=CenterSizeMode)  # type: ignore
      targets_dxyz = (
          weights[:spatial_dims].unsqueeze(0)
          * (gt_cccwhd[:, :spatial_dims] - ex_cccwhd[:, :spatial_dims])
          / ex_cccwhd[:, spatial_dims:]
      )
      targets_dwhd = weights[spatial_dims:].unsqueeze(0) * torch.log(
          gt_cccwhd[:, spatial_dims:] / ex_cccwhd[:, spatial_dims:]
      )
  
      targets = torch.cat((targets_dxyz, targets_dwhd), dim=1)
      # torch.log may cause NaN or Inf
      if torch.isnan(targets).any() or torch.isinf(targets).any():
          raise ValueError("targets is NaN or Inf.")
      return targets
  
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 for calling a global function with example usage: encode_boxes(gt_boxes, proposals, weights) and returns: targets