%2 = "tfl.add"(%arg0, %arg3) {fused_activation_function = "RELU6", per_device_costs = {CPU = 5.0 : f32, GPU = 1.0 : f32}, tac.device = "GPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>
  %3 = "tfl.pack"(%1, %2) {axis = 0 : i32, per_device_costs = {CPU = 2.0 : f32, GPU = -1.0 : f32}, values_count = 2 : i32, tac.device = "CPU"} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>
  func.return %3 : tensor<2x1xf32>
})";

  // Enable per-channel quantization for convolution weights.
  QuantizedType conv_weight_quantized_type{};

  // Assumes NHWC format, specifying the channel dimension (3) as the
  // quantized axis.
  conv_weight_quantized_type.mutable_dimension_specs()->set_dimension(3);

  // The index of weight operands passed to lifted functions for convolution
  // is 1.
  StaticRangePtq& static_range_ptq_spec =

  // CHECK-NEXT:    %[[fc:.*]]:2 = "tfl.fully_connected"(%[[stat]], %arg1,
  // CHECK-NEXT:    %[[stat1:.*]] = "quantfork.stats"(%[[fc]]#0)
  // CHECK-SAME:    <{axis = 1 : i64,
  // CHECK-SAME:      axisStats = dense<{{\[}}[-0.000000e+00, 0.000000e+00],
  // CHECK-SAME:      [-1.000000e+00, 1.000000e+00],
  // CHECK-SAME:      [-2.000000e+00, 2.000000e+00]

    %0:3 = "tf.Unpack"(%arg0) {axis = 0 : i64} : (tensor<3xi64>) -> (tensor<i64>, tensor<i64>, tensor<i64>)
    return
  }
})";

class LegalizationOpConfigTest : public ::testing::Test {
 public: