code: def _split_output(output, an_num, num_classes):
      """
      Split output feature map to x, y, w, h, objectness, classification
      along channel dimension
      """
      x = paddle.strided_slice(
          output,
          axes=[1],
          starts=[0],
          ends=[output.shape[1]],
          strides=[5 + num_classes],
      )
      y = paddle.strided_slice(
          output,
          axes=[1],
          starts=[1],
          ends=[output.shape[1]],
          strides=[5 + num_classes],
      )
      w = paddle.strided_slice(
          output,
          axes=[1],
          starts=[2],
          ends=[output.shape[1]],
          strides=[5 + num_classes],
      )
      h = paddle.strided_slice(
          output,
          axes=[1],
          starts=[3],
          ends=[output.shape[1]],
          strides=[5 + num_classes],
      )
      obj = paddle.strided_slice(
          output,
          axes=[1],
          starts=[4],
          ends=[output.shape[1]],
          strides=[5 + num_classes],
      )
      clss = []
      stride = output.shape[1] // an_num
      for m in range(an_num):
          clss.append(
              paddle.slice(
                  output,
                  axes=[1],
                  starts=[stride * m + 5],
                  ends=[stride * m + 5 + num_classes],
              )
          )
      cls = paddle.transpose(
          paddle.stack(clss, axis=1), perm=[0, 1, 3, 4, 2]
      )
      return (x, y, w, h, obj, cls)
  
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 1 for calling a global function with example usage: _split_output(output, an_num, num_classes) and returns: x,  y,  w,  h,  obj,  cls