%4:2 = tf_executor.island wraps "tf.Mul"(%2#1, %2#1) {T = "tfdtype$DT_INT32"} : (tensor<i32>, tensor<i32>) -> tensor<i32> loc("Multiplication")
    %5:3 = tf_executor.Merge %3#0, %4#0 : tensor<i32> {device = "", N = 2, T = "tfdtype$DT_INT32"} loc("Merge")
    tf_executor.fetch
  }
  func.return
}

  }

  func.func @while_cond_lt9(%arg0: tensor<i32>) -> tensor<i1> {
    %0 = "tf.Const"() {device = "/device:CPU:0", value = dense<9> : tensor<i32>} : () -> tensor<i32>
    %1 = "tf.Less"(%arg0, %0) {device = "/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i1>
    func.return %1 : tensor<i1>
  }

  func.func @while_body_add2(%arg0: tensor<i32>) -> tensor<i32> {

  }) {is_stateless = false} : (tensor<i32>, tensor<1xf32>) -> (tensor<i32>, tensor<1xf32>)
  func.return %0#1 : tensor<1xf32>
}

func.func @cond(%arg0: tensor<*xi32>, %arg1: tensor<*xf32>) -> tensor<i1> {
  %cst = arith.constant dense<0> : tensor<i32> loc("Const")
  %0 = "tfl.greater"(%arg0, %cst) : (tensor<*xi32>, tensor<i32>) -> tensor<i1>
  func.return %0 : tensor<i1>
}

  %0 = "tf.Rank"(%arg0) : (tensor<*xi32>) -> tensor<i32>
  %1 = "tf.Add"(%0, %arg0): (tensor<i32>, tensor<*xi32>) -> tensor<*xi32>
  func.return %1: tensor<*xi32>
}

func.func @while_cond(%arg0: tensor<*xi32>) -> tensor<*xi1> {
  %cst = arith.constant dense<10> : tensor<i32>
  %0 = "tf.Less"(%arg0, %cst) {T = i32, device = ""} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi1>
  func.return %0 : tensor<*xi1>
}

    tf_executor.graph {
      %0:2 = tf_executor.island {
        %1 = "tf.Const"() {device = "", dtype = "tfdtype$DT_INT32", value = dense<5> : tensor<i32>} : () -> tensor<i32> loc("control_const")
        tf_executor.yield %1 : tensor<i32>
      }
      // CHECK: control_output: "control_const"
      // CHECK:       control_ret {
      // CHECK-NEXT:    key: "control_const"
      // CHECK-NEXT:    value: "control_const"
      // CHECK-NEXT:  }

    %cst = "tf.Const"() {value = dense<0> : tensor<i64>} : () -> tensor<i64>
    %cst_0 = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %cst_1 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>

//go:noinline
func g(i uint64) uint64 {
	return uint64(uint32(i))
}

var sink uint64

func main() {
	for i := uint64(0); i < 1; i++ {
		i32 := int32(i - 1)
		sink = uint64((uint32(i32) << 1) ^ uint32((i32 >> 31)))
		x := g(uint64(i32))
		if x != uint64(uint32(i32)) {
			panic(x)
		}
	}

           output_shapes = [#tf_type.shape<>], output_types = [i32],
           preserve_cardinality = false, sloppy = false,
           use_inter_op_parallelism = true,
           metadata = ""} : (tensor<*x!tf_type.variant>, tensor<i32>) -> tensor<!tf_type.variant>
  %6 = "tf.BatchDatasetV2"(%5, %0, %1) {device = "",
           output_shapes = [#tf_type.shape<>],
           output_types = [i32],
           parallel_copy = false,

}

// -----

func.func @_func(%arg0: tensor<i32>) -> tensor<i32> {
  func.return %arg0 : tensor<i32>
}

func.func @testClusterFuncOpFails(%arg0: tensor<i32>) -> tensor<i32> {
   // expected-error@below {{failed TF functional to executor validation, op tf_device.cluster_func is not allowed}}
  %cluster = "tf_device.cluster_func"(%arg0) {func = @_func} : (tensor<i32>) -> tensor<i32>
 func.return %cluster : tensor<i32>
}

// -----

// CHECK-DAG: %[[CST_0:.*]] = "tf.Const"() <{value = dense<-43> : tensor<i32>}> : () -> tensor<i32>
// CHECK-DAG: %[[CST_1:.*]] = "tf.Const"() <{value = dense<0.0117647061> : tensor<f32>}> : () -> tensor<f32>
// CHECK-DAG: %[[CST_2:.*]] = "tf.Const"() <{value = dense<1.38408304E-5> : tensor<f32>}> : () -> tensor<f32>
// CHECK-DAG: %[[CST_3:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>