func.func @func(%arg0: tensor<4xf32>) -> tensor<4xf32> {
  %cst = "tf.Const"() {value = dense<23.0> : tensor<4xf32>} : () -> tensor<4xf32>
  %0 = "tf.XlaSharding"(%arg0) { _XlaSharding = "\01\02\03"} : (tensor<4xf32>) -> tensor<4xf32>
  %1 = "tf.AddV2"(%0, %cst) : (tensor<4xf32>, tensor<4xf32>) -> (tensor<4xf32>)
  %2 = "tf.AddV2"(%cst, %1) : (tensor<4xf32>, tensor<4xf32>) -> (tensor<4xf32>)

        tensor<2xf32>, tensor<2xf32>, tensor<2xf32>,
        tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>,
        tensor<4x2xf32>, tensor<4xf32>,
        tensor<1x4xf32>, tensor<1x2xf32>,
        tensor<2xf32>, tensor<2xf32>, tensor<2xf32>, tensor<2xf32>) -> tensor<*xf32>
  %24 = "quantfork.stats"(%23) {layerStats = dense<[-1.0, 2.0]> : tensor<2xf32>} : (tensor<*xf32>) -> tensor<*xf32>

  %cst = arith.constant dense<1.0> : tensor<4xf32>
  %cst_3 = arith.constant dense<2.0> : tensor<4x3x3x3xf32>
  %0 = "tfl.conv_2d"(%arg0, %cst_3, %cst) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "RELU6", padding = "VALID", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<?x19x19x3xf32>, tensor<4x3x3x3xf32>, tensor<4xf32>) -> tensor<?x9x9x4xf32>
  func.return %0 : tensor<?x9x9x4xf32>

  func.return %0 : tensor<4xf16>
}

func.func @f32() -> tensor<4xf32> {
  // CHECK-LABEL: @f32
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf32>
  %0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32> } : () -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
}

func.func @f64() -> tensor<4xf64> {
  // CHECK-LABEL: @f64

  func.return %0 : tensor<4xf16>
}

func.func @f32() -> tensor<4xf32> {
  // CHECK-LABEL: @f32
  // CHECK: value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]> : tensor<4xf32>
  %0 = "tfl.pseudo_const"() { value = dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32> } : () -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
}

func.func @f64() -> tensor<4xf64> {
  // CHECK-LABEL: @f64

  %cst0 = mhlo.constant dense<[1, 2, 3, 4]> : tensor<4xi32>
  // CHECK: %[[BROADCAST:.*]] = "mhlo.broadcast_in_dim"(%[[CONST]]) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<4xi32>) -> tensor<1x1x2x4xi32>
  %0 = "mhlo.broadcast_in_dim"(%cst0) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<4xi32>) -> tensor<1x1x2x4xi32>

    "tfl.yield"(%2) : (tensor<8xf32>) -> ()
  }) {num_bits = 5, narrow_range = false} :  (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %rst : tensor<8xf32>

// CHECK: %[[CONSTANT:.*]] = arith.constant dense<0.000000e+00> : tensor<8xf32>
// CHECK: %[[QUANTIZE:.*]] = "tfl.quantize"(%[[CONSTANT]]) <{qtype = tensor<8x!quant.uniform<u8:f32, 1.000000e+00>>}>

  # CHECK-SAME:   %arg0: tensor<1xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "a"]},
  # CHECK-SAME:   %arg1: tensor<2xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "b"]},
  # CHECK-SAME:   %arg2: tensor<3xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "c"]},
  # CHECK-SAME:   %arg3: tensor<4xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "x"]},

        module = ir.Module.parse(module_bytecode)
        self.assertIn('stablehlo.add %arg0, %cst : tensor<4xf32>', str(module))

  def test_tf_mlir_to_stablehlo(self):
    assembly = """
      module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
        func.func @main(%arg0 : tensor<?xf32>) -> tensor<?xf32> {
          %cst = "tf.Const"() {value = dense<1.0> : tensor<f32>} : () -> tensor<f32>

  // return %[[SUM2]]
  %0 = "tf.AddN"(%arg0, %arg1, %arg2, %arg3) : (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

// CHECK-LABEL: func @addN_5
func.func @addN_5(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>, %arg2: tensor<*xf32>, %arg3: tensor<*xf32>, %arg4: tensor<*xf32>) -> tensor<*xf32> {
  // CHECK: %[[SUM0:.*]] = "tf.AddV2"(%arg0, %arg1)