code: def test_pos_conv_bn(use_bias, data_shape):
      # conv + bn fusion case
      class ConvBN(nn.HybridBlock):
          def __init__(self, use_bias, **kwargs):
              super(ConvBN, self).__init__(**kwargs)
              self.conv0 = nn.Conv2D(
                  channels=64,
                  kernel_size=(3, 3),
                  strides=1,
                  use_bias=use_bias,
              )
              self.bn = nn.BatchNorm()
  
          def forward(self, x):
              out = self.bn(self.conv0(x))
              return out
  
      attr = {"conv": {"with_bn": "true"}}
      net = ConvBN(use_bias)
      check_fusion(net, data_shape, attr)
  
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 16 for calling a global function with example usage: test_pos_conv_bn(use_bias, data_shape) and returns: out