code: def _box_loss(
      type: str,
      box_coder: BoxCoder,
      anchors_per_image: Tensor,
      matched_gt_boxes_per_image: Tensor,
      bbox_regression_per_image: Tensor,
      cnf: Optional[Dict[str, float]] = None,
  ) -> Tensor:
      torch._assert(
          type in ["l1", "smooth_l1", "ciou", "diou", "giou"],
          f"Unsupported loss: {type}",
      )
  
      if type == "l1":
          target_regression = box_coder.encode_single(
              matched_gt_boxes_per_image, anchors_per_image
          )
          return F.l1_loss(
              bbox_regression_per_image,
              target_regression,
              reduction="sum",
          )
      elif type == "smooth_l1":
          target_regression = box_coder.encode_single(
              matched_gt_boxes_per_image, anchors_per_image
          )
          beta = (
              cnf["beta"] if cnf is not None and "beta" in cnf else 1.0
          )
          return F.smooth_l1_loss(
              bbox_regression_per_image,
              target_regression,
              reduction="sum",
              beta=beta,
          )
      else:
          bbox_per_image = box_coder.decode_single(
              bbox_regression_per_image, anchors_per_image
          )
          eps = cnf["eps"] if cnf is not None and "eps" in cnf else 1e-7
          if type == "ciou":
              return complete_box_iou_loss(
                  bbox_per_image,
                  matched_gt_boxes_per_image,
                  reduction="sum",
                  eps=eps,
              )
          if type == "diou":
              return distance_box_iou_loss(
                  bbox_per_image,
                  matched_gt_boxes_per_image,
                  reduction="sum",
                  eps=eps,
              )
          # otherwise giou
          return generalized_box_iou_loss(
              bbox_per_image,
              matched_gt_boxes_per_image,
              reduction="sum",
              eps=eps,
          )
  
imports: import math
  from collections import OrderedDict
  from typing import Dict, List, Optional, Tuple
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from torchvision.ops import complete_box_iou_loss, distance_box_iou_loss, FrozenBatchNorm2d, generalized_box_iou_loss
label_desc: Use image node_id 6 for calling a global function with example usage: _box_loss(type, box_coder, anchors_per_image, matched_gt_boxes_per_image, bbox_regression_per_image, cnf) and returns: F, F, generalized_box_iou_loss, complete_box_iou_loss, distance_box_iou_loss