code: def test_bn_relu_fusion(axis):
      dummy_data = mx.np.random.uniform(
          -1.0, 1.0, size=(32, 3, 224, 224)
      )
  
      net = mx.gluon.nn.HybridSequential()
      net.add(mx.gluon.nn.BatchNorm(axis=axis))
      net.add(mx.gluon.nn.Activation("relu"))
      net.initialize()
  
      out1 = net(dummy_data)
      out1.wait_to_read()
      net.optimize_for(dummy_data, backend="ONEDNN")
      out2 = net(dummy_data)
  
      assert_almost_equal(out1, out2)
  
imports: import copy
  import mxnet
  import pytest
  from subgraph_common import check_fusion, check_neg_fusion, check_quantize
  from subgraph_common import CustomNormalInit, DATA_SHAPE, RELU6, TailNegBlock
  from subgraph_common import DATA_SHAPE, SG_PASS_NAME, QUANTIZE_SG_PASS_NAME
  from mxnet.contrib import quantization
  from mxnet.gluon import nn
  from mxnet.test_utils import assert_almost_equal, assert_almost_equal_with_err
label_desc: Use image node_id 27 for calling a global function with example usage: test_bn_relu_fusion(axis) without return types