// CHECK:           %[[VAL_12:.*]] = "tf.AddV2"(%[[VAL_8]], %[[VAL_11]]) : (tensor<3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK:           %[[VAL_13:.*]] = "tf.Neg"(%[[VAL_12]]) : (tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK:           %[[VAL_14:.*]] = "tf.Abs"(%[[VAL_1]]) : (tensor<2x3xi32>) -> tensor<2x3xi32>

  // CHECK-NEXT:     [[SCALAR:%.*]] = "tfl.pseudo_const"() <{value = dense<10> : tensor<i32>}> : () -> tensor<i32>
  // CHECK-NEXT:     [[ADD:%.*]] = tfl.add([[SCALAR]], [[SUB]]) {fused_activation_function = "NONE"} : (tensor<i32>, tensor<3x2xi32>) -> tensor<3x2xi32>
  // CHECK-NEXT:     return [[ADD]] : tensor<3x2xi32>

  %0 = "tfl.pseudo_const" () {value = dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32>} : () -> tensor<3x2xi32> loc("Const")

func.func @main(%arg0: tensor<3x2xf32>) -> tensor<3x2xi32> {
  // CHECK-LABEL: @main
  // CHECK: "tfl.bucketize"(%arg0) <{boundaries = [0.000000e+00 : f32, 1.000000e+01 : f32, 1.000000e+02 : f32]}> : (tensor<3x2xf32>) -> tensor<3x2xi32>
  %0 = "tfl.bucketize"(%arg0) {boundaries = [0.0 : f32, 10.0 : f32, 100.0 : f32]} : (tensor<3x2xf32>) -> tensor<3x2xi32>
  func.return %0 : tensor<3x2xi32>

  // CHECK: %[[RES_INT_2:.*]] = mhlo.convert %[[RES_INT_1]] : tensor<3x2xi32>
  %result_int = "tf.Cast"(%result) {Truncate = false} : (tensor<3x2x!tf_type.qint32>) -> tensor<3x2xi32>
  // CHECK: return %[[RES_INT_2]] : tensor<3x2xi32>
  func.return %result_int : tensor<3x2xi32>

// CHECK-LABEL: main
func.func @main() -> tensor<3x2xi32> {
  // CHECK: "tfl.pseudo_const"() <{value = dense<0> : tensor<3x2xi32>}> {tfl.is_variable} : () -> tensor<3x2xi32>
  %0 = "tfl.pseudo_const"() {value = dense<0> : tensor<3x2xi32>, tfl.is_variable} : () -> tensor<3x2xi32> loc("variable")
  func.return %0 : tensor<3x2xi32>

func.func @pushTposeAfterAddSimpleWithFold(%arg0: tensor<2x3xi32>) -> tensor<3x2xi32> {
  %perm = arith.constant dense<[1, 0]> : tensor<2xi32>
  %0 = "tfl.transpose"(%arg0, %perm) : (tensor<2x3xi32>, tensor<2xi32>) -> tensor<3x2xi32>
  %cst = arith.constant dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32>
  %1 = tfl.add %0, %cst { fused_activation_function = "NONE" } : tensor<3x2xi32>
  func.return %1 : tensor<3x2xi32>
}

// -----

// Tests that int constants are not folded.

// CHECK-LABEL: transpose_int
func.func @transpose_int() -> tensor<3x2xi32> {
  %0 = stablehlo.constant dense<0> : tensor<2x3xi32>
  %1 = stablehlo.transpose %0, dims = [1, 0] : (tensor<2x3xi32>) -> tensor<3x2xi32>
  return %1 : tensor<3x2xi32>
}
// CHECK: transpose

// -----

// Tests that transposing an argument cannot be folded.

// CHECK-LABEL: transpose_arg

module attributes {tf.versions = {producer = 888 : i32}} {
func.func @testReshapeShapeInference(%arg0: tensor<3x4xi32>) -> tensor<*xi32> {
  %cst = arith.constant dense<[1, 6, 2]> : tensor<3xi32>
  // CHECK: "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<1x6x2xi32>
  %0 = "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<*xi32>
  func.return %0 : tensor<*xi32>
}
}

// -----

func.func @add_dense_dense_int_mixing_1_n() -> tensor<2x2xi32> {
  %cst_0 = arith.constant dense<[[1, 2]]> : tensor<1x2xi32>
  %cst_1 = arith.constant dense<[[3], [4]]> : tensor<2x1xi32>

  %0 = "tfl.add"(%cst_0, %cst_1) {fused_activation_function = "NONE"} : (tensor<1x2xi32>, tensor<2x1xi32>) -> tensor<2x2xi32>

  func.return %0 : tensor<2x2xi32>

func.func @RemoveRedundantUnpackPack(%arg0: tensor<2x5xf32>) -> tensor<2x5xf32> {
  %0:2 = "tfl.unpack"(%arg0) {axis = 0 : i32, num = 2 : i32} : (tensor<2x5xf32>) -> (tensor<5xf32>, tensor<5xf32>)
  %1 = "tfl.pack"(%0#0, %0#1) {axis = 0 : i32, values_count = 2 : i32} : (tensor<5xf32>, tensor<5xf32>) -> (tensor<2x5xf32>)
  func.return %1: tensor<2x5xf32>
  // CHECK-NOT: pack
  // CHECK: return %arg0 : tensor<2x5xf32>
}

// -----