code: def test_dnnl_sum_inplace_with_cpu_layout():
      x_shape = (32, 3, 224, 224)
      x_npy = np.ones(x_shape, dtype="float32")
      y_shape = (32, 32, 222, 222)
      y_npy = np.ones(y_shape, dtype="float32")
      x = mx.sym.Variable("x")
      y = mx.sym.Variable("y")
      z = mx.symbol.Convolution(data=x, num_filter=32, kernel=(3, 3))
      z = mx.sym.add_n(z, y)  # (DNNL data, cpu data)
      exe = z._simple_bind(ctx=mx.cpu(), x=x_shape, y=y_shape)
      out = exe.forward(is_train=False, x=x_npy, y=y_npy)[0]
      assert_almost_equal(out[0].asnumpy()[0, 0, 0], 1.0)
  
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 9 for calling a global function with example usage: test_dnnl_sum_inplace_with_cpu_layout() without return types