code: def test_deduplication(data_shape, reverse_sum_order, model_name):
      data_nd = mx.np.random.uniform(
          -1, 1, size=data_shape, device=mx.cpu()
      )
      if model_name == "mobilenetv2_struct":
          model_dedup = MobileNetV2Struct(
              reverse_sum_order=reverse_sum_order
          )
      else:
          # channels after conv+bn should be same as input channels
          model_dedup = ConvBNSum(
              channels=data_shape[1],
              reverse_sum_order=reverse_sum_order,
          )
  
      model_dedup.initialize()
      model_no_dedup = copy.copy(model_dedup)
  
      model_dedup.optimize_for(
          data_nd,
          backend="ONEDNN",
          dedup_subgraph=True,
          skip_infer=True,
      )
      out = model_dedup(data_nd)
  
      model_dedup.optimize_for(
          data_nd,
          backend="ONEDNN",
          dedup_subgraph=False,
          skip_infer=True,
      )
      out_dedup = model_no_dedup(data_nd)
  
      assert_almost_equal(
          out.asnumpy(), out_dedup.asnumpy(), rtol=1e-3, atol=1e-1
      )
  
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 19 for calling a global function with example usage: test_deduplication(data_shape, reverse_sum_order, model_name) without return types