%1 = "tf.Identity"(%arg3) : (tensor<i32>) -> tensor<i32>
    %2 = "tf.Identity"(%arg1) : (tensor<i32>) -> tensor<i32>
    // CHECK-NEXT: %[[RESULT_2:.*]] = "tf.NotEqual"(%[[RESULT_1]], %[[ARG_1]])
    %3 = "tf.NotEqual"(%1, %2) : (tensor<i32>, tensor<i32>) -> tensor<i1>
    "tf.Yield"(%3) : (tensor<i1>) -> ()
  },  {
  ^bb0(%arg3: tensor<i32>, %arg4: tensor<*xf32>):
    %cst = arith.constant dense<1> : tensor<i32>

  %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>

!tf_res_i32 = tensor<*x!tf_type.resource<tensor<i32>>>
!tf_res_f32 = tensor<*x!tf_type.resource<tensor<f32>>>

// CHECK-LABEL: func @hoist_tail_assign
// CHECK-SAME:  ([[ARG0:%.*]]: tensor<*x!tf_type.resource<tensor<f32>>>)
func.func @hoist_tail_assign(%arg0: !tf_res_f32) {
  // CHECK: [[REPLICATE:%.*]]:4 = tf_device.replicate {n = 2 : i32}
  %replicate:2 = tf_device.replicate {n = 2 : i32} {
    // CHECK: [[OP_A:%.*]] = "tf.OpA"

  tf_executor.graph {
    %c, %control0 = tf_executor.island wraps "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %a, %control1 = tf_executor.island wraps "tf.A"() {_class = ["loc:@class"]} : () -> (tensor<2xf32>)
    %s:2, %control2 = tf_executor.island wraps "tf.Split"(%c, %a) {num_split = 2 : i32} : (tensor<i32>, tensor<2xf32>) -> (tensor<1xf32>, tensor<1xf32>)
    tf_executor.fetch
  }
  func.return
}

// -----

  %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>)

  func.return %0 : tensor<f32>
}

// CHECK-LABEL: func @collective_reduce_v2_max_id
func.func @collective_reduce_v2_max_id(%input: tensor<f32>) -> tensor<f32> {
  %group_key = "tf.Const"() { value = dense<1> : tensor<i32> } : () -> tensor<i32>
  %group_size = "tf.Const"() { value = dense<2> : tensor<i32> } : () -> tensor<i32>

  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>

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>
}