code: def test_fc_eltwise(data_shape, use_bias, flatten, alg):
      # fc + eltwise fusion case
      class FCEltwise(nn.HybridBlock):
          def __init__(self, use_bias, flatten, alg, **kwargs):
              super(FCEltwise, self).__init__(**kwargs)
              self.fc = nn.Dense(
                  units=64,
                  use_bias=use_bias,
                  flatten=flatten,
                  weight_initializer=CustomNormalInit(
                      mean=0.5, sigma=0.1, bounded=True
                  )
                  if alg == "square_root"
                  else None,
              )
              # avoid calculating square root of negative values
              self.alg = alg
  
          def forward(self, x):
              if self.alg == "square_root":
                  x = abs(x)
              fc_out = self.fc(x)
              if self.alg in [
                  "relu",
                  "sigmoid",
                  "log_sigmoid",
                  "mish",
                  "tanh",
                  "softrelu",
              ]:
                  out = mx.npx.activation(fc_out, act_type=self.alg)
              elif self.alg in ["gelu", "gelu_tanh", "elu", "leaky"]:
                  out = mx.npx.leaky_relu(fc_out, act_type=self.alg)
              elif self.alg == "square":
                  out = mx.np.square(fc_out)
              elif self.alg == "square_root":
                  out = mx.np.sqrt(fc_out)
              elif self.alg == "abs":
                  out = mx.np.abs(fc_out)
              elif self.alg == "exp":
                  out = mx.np.exp(fc_out)
              else:
                  out = mx.np.clip(fc_out, 0, 1.0)
              return out
  
      attrs = {"fc": {"with_eltwise": "true"}}
      net = FCEltwise(use_bias, flatten, alg)
      check_fusion(net, data_shape, attrs, check_quantization=flatten)
  
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 5 for calling a global function with example usage: test_fc_eltwise(data_shape, use_bias, flatten, alg) and returns: out