code: def test_fc_identity_eltwise(identity_node):
      class FCIdentityEltwise(nn.HybridBlock):
          def __init__(self, identity_node, **kwargs):
              super(FCIdentityEltwise, self).__init__(**kwargs)
              self.fc1 = nn.Dense(
                  units=64,
                  use_bias=False,
                  weight_initializer=None,
                  flatten=True,
              )
              self.fc2 = nn.Dense(
                  units=64,
                  use_bias=False,
                  weight_initializer=None,
                  flatten=True,
              )
              self.identity_node = identity_node
  
          def forward(self, x):
              out = self.fc1(x)
              if self.identity_node == "copy":
                  out = mx.np.copy(out)
              else:
                  out = mx.npx.dropout(out)
              out = mx.npx.activation(out, act_type="relu")
              out = self.fc2(out)
              if self.identity_node == "copy":
                  out = mx.np.copy(out)
              else:
                  out = mx.npx.dropout(out)
              out = mx.npx.activation(out, act_type="relu")
              return out
  
      data_shape = (64, 4, 10, 10)
      attrs = {
          "sg_onednn_fully_connected_eltwise_0": {
              "with_eltwise": "true"
          },
          "sg_onednn_fully_connected_eltwise_1": {
              "with_eltwise": "true"
          },
      }
      net = FCIdentityEltwise(identity_node)
      check_fusion(net, data_shape, attrs, check_quantization=False)
  
imports: import mxnet
  import pytest
  from subgraph_common import check_fusion, check_neg_fusion, check_neg_fusion_quantized, check_quantize
  from subgraph_common import CustomNormalInit, DATA_SHAPE, TailNegBlock
  from mxnet.contrib import quantization
  from mxnet.gluon import nn
  from mxnet.test_utils import assert_almost_equal_with_err
label_desc: Use image node_id 9 for calling a global function with example usage: test_fc_identity_eltwise(identity_node) and returns: out