code: def __init__(self, channels, reverse_sum_order, **kwargs):
      super(ConvBNSum, self).__init__(**kwargs)
      self.conv0 = nn.Conv2D(
          channels=channels,
          kernel_size=(1, 1),
          strides=1,
          use_bias=False,
      )
      self.bn = nn.BatchNorm()
      self.reverse = reverse_sum_order
  
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 1 to create a new ConvBNSum object from inherited base classes: nn with example: obj = ConvBNSum(channels, reverse_sum_order)