code: def test_dnnl_sum_with_dnnl_layout():
      x_shape = (32, 3, 224, 224)
      x_npy = np.ones(x_shape, dtype="float32")
      w_shape = (32, 3, 3, 3)
      w_npy = np.ones(w_shape, dtype="float32")
  
      x = mx.sym.Variable("x")
      w = mx.sym.Variable("w")
      z = mx.symbol.Convolution(
          data=x, weight=w, num_filter=32, kernel=(3, 3)
      )
      num_inputs = [2, 3, 4, 5]
      for i in num_inputs:
          inputs = []
          for _ in range(i):
              inputs.append(z)
          y = mx.sym.add_n(*inputs)  # (only DNNL data input)
          exe = y._simple_bind(ctx=mx.cpu(), x=x_shape, w=w_shape)
          out = exe.forward(
              is_train=False, x=x_npy, w=np.ones(w_shape)
          )[0]
          # conv with kernel (3,3) on ones should give result=27
          single_cov = 27.0
          assert_almost_equal(out[0].asnumpy()[0, 0, 0], single_cov * i)
  
imports: import sys
  import os
  import numpy
  import mxnet
  import pytest
  from mxnet.test_utils import rand_ndarray, assert_almost_equal
  from mxnet import gluon, context, use_np
  from mxnet.gluon import nn
  from mxnet.test_utils import *
  import itertools
label_desc: Use image node_id 8 for calling a global function with example usage: test_dnnl_sum_with_dnnl_layout() without return types