// CHECK-SAME:  ([[ARG0:%.+]]: tensor<i1>, [[ARG1:%.+]]: tensor<i32>, [[ARG2:%.+]]: tensor<i64>, [[ARG3:%.+]]: tensor<f32>, [[ARG4:%.+]]: tensor<f64>, [[ARG5:%.+]]: tensor<!tf_type.resource>, [[ARG6:%.+]]: tensor<!tf_type.resource>)
func.func @replace_multiple_outputs(%arg0: tensor<i1>, %arg1: tensor<i32>, %arg2: tensor<i64>, %arg3: tensor<f32>, %arg4: tensor<f64>, %arg5: tensor<!tf_type.resource>, %arg6: tensor<!tf_type.resource>) {

module {
  func.func public @main(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>, %arg2: tensor<1x100x32x4xf32>,
      %arg3: tensor<1x500x4x4xf32>, %arg4: tensor<1x500x4x4xf32>, %arg5: tensor<1x1x100x500xf32>, %arg6: tensor<f32>)
      -> tensor<1x100x32x4xf32> {

  func.return %4 : tensor<128x128x!quant.uniform<u8:f32, 0.2:125>>

// CHECK: "tfl.custom_tf"
// CHECK-NEXT: ^bb0(%arg4: tensor<128x128xf32>, %arg5: tensor<1xf32>, %arg6: tensor<1xf32>, %arg7: tensor<1xi32>):
// CHECK-NEXT:   "tf.LayerNorm"(%arg4, %arg5, %arg6, %arg7) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>

  }

  // CHECK-LABEL: func @_func
  // CHECK-SAME: %[[ARG0:.*]]: tensor<?xi32>,
  // CHECK-SAME: %[[ARG1:.*]]: tensor<?xi32> {mhlo.is_same_data_across_replicas = true}
  // CHECK-SAME: %[[ARG2:.*]]: tensor<?xi32>)
  func.func @_func(%arg0: tensor<?xi32>, %arg1: tensor<?xi32>, %arg2: tensor<?xi32>) -> tensor<?xi32> {
    %0 = "tf._D"(%arg0, %arg1) : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>
    func.return %0 : tensor<?xi32>

}

// CHECK: func @while_loop_test(%[[ARG_0:.*]]: tensor<i32>, %[[ARG_1:.*]]: tensor<i32>, %arg2: tensor<*xf32>)
func.func @while_loop_test(%arg0: tensor<i32>, %arg1: tensor<i32>, %arg2: tensor<*xf32>) {
  // CHECK-NEXT: tf.WhileRegion
  %0:2 = "tf.WhileRegion"(%arg0, %arg2) ({
  // CHECK-NEXT: bb0(%[[ARG_3:.*]]: tensor<i32>, %[[ARG_4:.*]]: tensor<*xf32>)
  ^bb0(%arg3: tensor<i32>, %arg4: tensor<*xf32>):

func.func @check_enqueue_ops_with_different_attr_disallowed(%arg0: tensor<?x2xi32>, %arg1: tensor<?x2xi32>,
  %arg2 :tensor<?x2xi32>, %arg3: tensor<?xi32>, %arg4: tensor<?xi32>, %arg5: tensor<?xi32>,
  %arg6: tensor<!tf_type.string>, %arg7: tensor<!tf_type.string>, %arg8: tensor<i1>) -> () {
  %0 = "tf.Const"() {value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>

// CHECK-LABEL: func @FusedBatchNormRelu
func.func @FusedBatchNormRelu(%arg0: tensor<8x8x8x8xf32>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>, %arg3: tensor<8xf32>, %arg4: tensor<8xf32>) -> (tensor<8x8x8x8xf32>) {
// CHECK-NEXT: %[[Y:[a-z0-9]*]], {{.*}}_FusedBatchNormEx
// CHECK-NEXT: return %[[Y]]

// CHECK-SAME: (%[[ARG0:.*]]: tensor<f32>, %[[ARG1:.*]]: tensor<f32>, %[[ARG2:.*]]: tensor<*xf32>)
func.func @test_supported_lowering_of_tf_while_region(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<*xf32>) -> (tensor<f32>){
  %0:2 = "tf.WhileRegion"(%arg0, %arg2) ({
  ^bb0(%arg3: tensor<f32>, %arg4: tensor<*xf32>):
    %1 = "tf.Identity"(%arg3) : (tensor<f32>) -> tensor<f32>

    %cst = "tf.Const"() {value = dense<127> : tensor<i32>} : () -> tensor<i32>
    %cst_0 = "tf.Const"() {value = dense<-128> : tensor<i32>} : () -> tensor<i32>
    %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x2240x2240x3xi8>) -> tensor<1x2240x2240x3xi32>

func.func @op_with_region_mixed_tfl_shlo_tfl(%arg0: tensor<7x5xf32>, %arg1 : tensor<5xf32>) -> tensor<5xf32> {
  %0 = "stablehlo.reduce"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<5xf32>, %arg3: tensor<5xf32>):
    // CHECK:      %1 = "tfl.abs"(%arg2) {fused_activation_function = "NONE"} : (tensor<5xf32>) -> tensor<5xf32>
    // CHECK-NEXT: %2 = "vhlo.add_v1"(%1, %arg2) : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32>