%0 = "tf.Div"(%arg1, %cst_0) : (tensor<2x2xf32>, tensor<f32>) -> tensor<2x2xf32>
    %1 = "tf.AddV2"(%0, %cst_1) : (tensor<2x2xf32>, tensor<f32>) -> tensor<2x2xf32>
    %2 = "tf.Floor"(%1) : (tensor<2x2xf32>) -> tensor<2x2xf32>
    %3 = "tf.ClipByValue"(%2, %cst_5, %cst_6) : (tensor<2x2xf32>, tensor<f32>, tensor<f32>) -> tensor<2x2xf32>
    %4 = "tf.Cast"(%3) {Truncate = false} : (tensor<2x2xf32>) -> tensor<2x2xi8>

func.func @ReorderAddWithConstant(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {
  %cst = arith.constant dense<1.0> : tensor<2x2xf32>
  %cst_1 = arith.constant dense<2.0> : tensor<2x2xf32>
  %0 = "tfl.add"(%arg0, %cst) {fused_activation_function = "NONE"} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>
  %1 = "tfl.add"(%0, %cst_1) {fused_activation_function = "NONE"} : (tensor<2x2xf32>, tensor<2x2xf32>) -> tensor<2x2xf32>

  %0 = "tf.Squeeze"(%arg0) : (tensor<1x2x2xf32>) -> tensor<2x2xf32>
  func.return %0 : tensor<2x2xf32>

// CHECK-LABEL:squeezeDefault
// CHECK:  "tfl.squeeze"(%arg0) <{squeeze_dims = []}> : (tensor<1x2x2xf32>) -> tensor<2x2xf32>
}

func.func @squeezeSingleAxis(%arg0: tensor<2x1x2xf32>) -> tensor<2x2xf32> {
  %0 = "tf.Squeeze"(%arg0) {squeeze_dims = [1]} : (tensor<2x1x2xf32>) -> tensor<2x2xf32>

func.func @NotQuantizeFloatConst() -> tensor<2x2xf32> {
  %0 = arith.constant dense<-0.1> : tensor<2x2xf32>
  %1 = "tfl.quantize"(%0) {qtype = tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>} : (tensor<2x2xf32>) -> tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>
  %2 = "tfl.dequantize"(%1) : (tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>) -> tensor<2x2xf32>
  func.return %2 : tensor<2x2xf32>

// CHECK: %0 = "tfl.pseudo_const"() <{value = dense<2> : tensor<2xi32>}> : () -> tensor<2xi32>
// CHECK: %1 = "tfl.reshape"(%arg0, %0) : (tensor<1x4xf32>, tensor<2xi32>) -> tensor<2x2xf32>

{
  "version": 3,
  "operator_codes": [
    {
      "builtin_code": "RESHAPE"
    }
  ],
  "subgraphs": [
    {
      "tensors": [
        {
          "shape": [1, 4],

  %1 = "tfl.add"(%cst_2, %cst_1) {fused_activation_function = "NONE"} : (tensor<2x2x2xf32>, tensor<  2x2xf32>) -> tensor<2x2x2xf32>
  %2 = "tfl.add"(%cst_0, %cst_2) {fused_activation_function = "NONE"} : (tensor<    2xf32>, tensor<2x2x2xf32>) -> tensor<2x2x2xf32>

  func.return %0, %1, %2 : tensor<2x2xf32>, tensor<2x2x2xf32>, tensor<2x2x2xf32>

    -> (tensor<?x2xi64>, tensor<?x2xi64>, tensor<?x!tf_type.string>, tensor<?xi64>, tensor<2xi64>, tensor<2xi64>, tensor<32xf32>, tensor<32xf32>)

  // CHECK: tfrt.return {{.*}}, [[outputs]]#0
  func.return %outputs#0 : tensor<?x2xi64>
}

// CHECK-LABEL: func @no_native

// CHECK-LABEL: testAddWithI64Broadcasting
func.func @testAddWithI64Broadcasting(tensor< 2x3xi64>, tensor<3xi64>) -> tensor<2x3xi64> {
^bb0(%arg0: tensor<2x3xi64>, %arg1: tensor<3xi64>):
  // CHECK: tfl.add(%arg0, %arg1)
  %0 = tfl.add(%arg0, %arg1) {fused_activation_function = "RELU6"} : (tensor< 2x3xi64>, tensor<3xi64>) -> tensor<2x3xi64>
  func.return %0#0 : tensor<2x3xi64>
}

// -----

// CHECK-LABEL: add_with_i32_five_dim_broadcasting

    "mhlo.return"(%10, %15) : (tensor<f32>, tensor<i32>) -> ()
  }) {dimensions = dense<2> : tensor<1xi64>} : (tensor<2x2x4xf32>, tensor<2x2x4xi32>, tensor<f32>, tensor<i32>) -> (tensor<2x2xf32>, tensor<2x2xi32>)
  func.return %4#0, %4#1 : tensor<2x2xf32>, tensor<2x2xi32>

  // CHECK-DAG:  %0 = mhlo.constant dense<0xFF800000> : tensor<f32>
  // CHECK-DAG:  %1 = mhlo.constant dense<0> : tensor<i32>

  %cst_0 = "tf.Const"() {value = dense<0.400000e+00> : tensor<2x2xf32>} : () -> tensor<2x2xf32>
  %0 = "tf.Conv2D"(%arg0, %cst) {data_format = "NHWC", dilations = [1, 1, 2, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x2x2xf32>
  %1 = "tf.Mul"(%0, %cst_0) : (tensor<1x3x2x2xf32>, tensor<2x2xf32>) -> tensor<1x3x2x2xf32>
  func.return %1 : tensor<1x3x2x2xf32>
}