code: def test_pos_conv_act_add(data_shape, alg, quantize, use_bias):
      # conv + act + add fusion case
      class ConvActAdd(nn.HybridBlock):
          def __init__(self, use_bias, alg, **kwargs):
              super(ConvActAdd, self).__init__(**kwargs)
              self.conv0 = nn.Conv2D(
                  channels=64,
                  kernel_size=(3, 3),
                  strides=1,
                  use_bias=use_bias,
                  weight_initializer=mx.init.Xavier(magnitude=2.24),
              )
              if alg == "relu6":
                  self.act = RELU6()
              elif alg == "leakyrelu":
                  self.act = nn.LeakyReLU(0.25)
              elif alg == "gelu":
                  self.act = nn.GELU()
              elif alg == "gelu_tanh":
                  self.act = nn.GELU(approximation="tanh")
              else:
                  self.act = nn.Activation(activation=alg)
              self.conv1 = nn.Conv2D(
                  channels=64,
                  kernel_size=(3, 3),
                  strides=1,
                  use_bias=use_bias,
              )
  
          def forward(self, x):
              out = self.act(self.conv0(x)) + self.conv1(x)
              return out
  
      attrs = {
          "sg_onednn_conv_act_0": {"with_act": "true"},
          "sg_onednn_conv_add_1": {"with_sum": "true"},
      }
  
      net = ConvActAdd(use_bias, alg)
      check_fusion(net, data_shape, attrs, check_quantization=quantize)
  
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 9 for calling a global function with example usage: test_pos_conv_act_add(data_shape, alg, quantize, use_bias) and returns: out