code: def _split_target(target):
      """
      split target to x, y, w, h, objectness, classification
      along dimension 2
      target is in shape [N, an_num, 6 + class_num, H, W]
      """
      tx = target[:, :, 0, :, :]
      ty = target[:, :, 1, :, :]
      tw = target[:, :, 2, :, :]
      th = target[:, :, 3, :, :]
      tscale = target[:, :, 4, :, :]
      tobj = target[:, :, 5, :, :]
      tcls = paddle.transpose(
          target[:, :, 6:, :, :], perm=[0, 1, 3, 4, 2]
      )
      tcls.stop_gradient = True
      return (tx, ty, tw, th, tscale, tobj, tcls)
  
imports: from __future__ import division
  import unittest
  import paddle
  import paddle.nn.functional
  import os
  import sys
  from ppdet.modeling.losses import YOLOv3Loss
  from ppdet.data.transform.op_helper import jaccard_overlap
  from ppdet.modeling.bbox_utils import iou_similarity
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: _split_target(target) and returns: tx,  ty,  tw,  th,  tscale,  tobj,  tcls