out = array_ops.concat([out, ones], 0)
        elif self.same_scale_op == 'gather':
          out = array_ops.gather(out, indices=[0], axis=0)
        elif self.same_scale_op == 'max_pool':
          out = nn_ops.max_pool(out, ksize=3, strides=1, padding='SAME')
        elif self.same_scale_op == 'pad':
          paddings = array_ops.ones(
              (array_ops.rank(out), 2), dtype=dtypes.int32
          )

  %0 = "quantfork.qcast"(%arg0) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>
  %1 = "quantfork.dcast"(%0) : (tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>) -> tensor<*xf32>
  %2 = "tf.MaxPool"(%1) {data_format = "NHWC", device = "", explicit_paddings = [], ksize = [1, 2, 2, 1], padding = "VALID", strides = [1, 2, 2, 1]} : (tensor<*xf32>) -> tensor<*xf32>

	if err != nil {
		t.Error(err)
	}
}

func createTestNetwork() (*hcn.HostComputeNetwork, error) {
	network := &hcn.HostComputeNetwork{
		Type: NETWORK_TYPE_OVERLAY,
		Name: "TestOverlay",
		MacPool: hcn.MacPool{
			Ranges: []hcn.MacRange{
				{
					StartMacAddress: "00-15-5D-52-C0-00",
					EndMacAddress:   "00-15-5D-52-CF-FF",
				},
			},
		},
		Ipams: []hcn.Ipam{
			{
				Type: "Static",