%1 = "tf.Add"(%z, %0) {__op_key = 1: i32, device = "/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %cond, %x, %y, %1 : tensor<i1>, tensor<i1>, tensor<i1>, tensor<i32>
}

// CHECK-LABEL: func @while_test_if
// CHECK-SAME: -> !tf_mlrt.tensor
func.func @while_test_if(%cond: tensor<i1>, %x: tensor<i1>, %y: tensor<i1>) -> (tensor<i32>) {

    %23 = "tf.Cast"(%22) {Truncate = false} : (tensor<i8>) -> tensor<i32>
    %24 = "tf.Maximum"(%cst_0, %arg10) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    %25 = "tf.Minimum"(%cst, %23) : (tensor<i32>, tensor<i32>) -> tensor<i32>
    %26 = "tf.ClipByValue"(%16, %24, %25) : (tensor<1x2x2x3xi32>, tensor<i32>, tensor<i32>) -> tensor<1x2x2x3xi32>
    %27 = "tf.Cast"(%26) {Truncate = false} : (tensor<1x2x2x3xi32>) -> tensor<1x2x2x3xi8>

  func.return %0 : tensor<i32>
}

// -----

func.func @unsupported_op_soft_placement_false() -> tensor<i32> {
  %0 = "tf_device.cluster"() ({
    // expected-error@+1 {{'tf.UnsupportedOp' op isn't compilable for TPU device}}
    %1 = "tf.UnsupportedOp"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
    %2 = "tf.Identity"(%1) : (tensor<i32>) -> tensor<i32>
    tf_device.return %2 : tensor<i32>

  %input_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>
  %bias_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32>
  %bias_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>
  %output_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32>
  %output_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>

  %0 = "tfl.pseudo_const"() {value = dense<[2, 2, 81]> : tensor<3xi32>} : () -> tensor<3xi32>
  %1 = "tfl.pseudo_const"() {value = dense<-1> : tensor<i32>} : () -> tensor<i32>
  %2:3 = "tfl.split_v"(%arg0, %0, %1) {num_splits = 3 : i32} : (tensor<?x13x13x85xf32>, tensor<3xi32>, tensor<i32>) -> (tensor<?x13x13x2xf32>, tensor<?x13x13x2xf32>, tensor<?x13x13x81xf32>)

  %stride = "tf.Add"(%arg2, %cst) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  %cst1 = arith.constant dense<44> : tensor<i32>
  %limit = "tf.Add"(%arg2, %cst1) : (tensor<i32>, tensor<i32>) -> tensor<i32>
  // CHECK: [[Result:%.*]]:4 = "tf.While"(%arg0, %arg1, %{{.+}}, %{{.+}} <{body = @tf.WhileRegion_body, cond = @tf.WhileRegion_cond
  %0:2 = "tf.WhileRegion"(%arg0, %arg1) (
    {
      ^bb0(%carg0: tensor<*xf32>, %carg1: tensor<i32>):

  %conv = "tfl.conv_2d"(%arg0, %w, %b) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<1x224x224x3xf32>, tensor<64x3x3x3xf32>, tensor<64xf32>) -> tensor<1x112x112x64xf32>

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 1399 : i32}} {

// CHECK-LABEL: stateless_op
func.func @stateless_op() -> tensor<i32> {
  // CHECK: %cst = "tf.Const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>
  %0 = "tf.StatelessRandomGetAlg"() {device = ""} : () -> tensor<i32>
  return %0 : tensor<i32>
}

    },
    {
      // loop body
      ^bb0(%barg0: tensor<i32>, %barg1: tensor<i32>):
      %one = arith.constant dense<1> : tensor<i32>
      %sub0 = "tf.Sub"(%barg0, %one) : (tensor<i32>, tensor<i32>) -> tensor<i32>
      %sub1 = "tf.Sub"(%barg1, %one) : (tensor<i32>, tensor<i32>) -> tensor<i32>
      "tf.Yield"(%sub0, %sub1) : (tensor<i32>, tensor<i32>) -> ()
    }

  {
    %cst = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
    %0 = "tf.AddV2"(%arg2, %cst) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>
    %1 = "tf.Identity"(%0) {device = ""} : (tensor<i32>) -> tensor<i32>