// CHECK:       tf.OpC
func.func @result_used_in_cond(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
  %0:2 = "tf.WhileRegion"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<*xf32>, %arg3: tensor<*xf32>):
    %1 = "tf.OpA"(%arg2) {is_stateless = true} : (tensor<*xf32>) -> tensor<i1>
    "tf.Yield"(%1) : (tensor<i1>) -> ()
  }, {
  ^bb0(%arg2: tensor<*xf32>, %arg3: tensor<*xf32>):

  "tf_device.cluster"() ({

    // CHECK: "tf.Const"() <{value = dense<(1.000000e+00,0.000000e+00)> : tensor<complex<f32>>}> : () -> tensor<complex<f32>>
    // CHECK-NOT: tf.ResourceApplyAdam

// CHECK-SAME:  ([[ARG0:%.*]]: tensor<i1>, [[ARG1:%.*]]: tensor<f32>, [[ARG2:%.*]]: tensor<f32>)
func.func @if_true_branch(%arg0: tensor<i1>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<f32> {
  // CHECK: [[INIT_TOKEN:%.*]] = mhlo.create_token

  // CHECK: [[IF:%.*]]:2 = "mhlo.if"([[ARG0]])
  %0 = "mhlo.if"(%arg0) ({
    // CHECK:      [[TRUE_SEND_TOKEN:%.*]] = "mhlo.send"([[ARG1]], [[INIT_TOKEN]])

		{name: "I64GtU", asm: "I64GtU", argLength: 2, reg: gp21, typ: "Bool"}, // arg0 > arg1 (unsigned)
		{name: "I64LeS", asm: "I64LeS", argLength: 2, reg: gp21, typ: "Bool"}, // arg0 <= arg1 (signed)
		{name: "I64LeU", asm: "I64LeU", argLength: 2, reg: gp21, typ: "Bool"}, // arg0 <= arg1 (unsigned)
		{name: "I64GeS", asm: "I64GeS", argLength: 2, reg: gp21, typ: "Bool"}, // arg0 >= arg1 (signed)

// -----

func.func private @branch_0(%arg0: tensor<!tf_type.resource>) -> tensor<*xf32>
func.func private @branch_1(%arg0: tensor<!tf_type.resource>) -> tensor<*xf32>

// CHECK-LABEL: func @testCase(%arg0: tensor<i32>, %arg1: tensor<!tf_type.resource<tensor<1x2x3xf32>>>)
func.func @testCase(%arg0: tensor<i32>, %arg1: tensor<!tf_type.resource<tensor<1x2x3xf32>>>) -> tensor<1x2x3xf32> {

// CHECK-LABEL: bias_add
func.func @bias_add(%arg0: tensor<1x10x10x32xf32>, %arg1: tensor<32xf32>) -> tensor<1x10x10x32xf32> {
  %0 = "tf.BiasAdd"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"} : (tensor<1x10x10x32xf32>, tensor<32xf32>) -> tensor<1x10x10x32xf32>
  func.return %0 : tensor<1x10x10x32xf32>

// CHECK-SAME: [[ARG0:%.*]]: tensor<f32> {tf.aliasing_output = 1 : i64},
// CHECK-SAME: [[ARG1:%.*]]: tensor<f32> {tf.aliasing_output = 2 : i64},
// CHECK-NOT: [[ARG2:%.*]]:  tensor<f32> {tf.aliasing_output = 0 : i64}
func.func @device_func_5(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> (tensor<f32>, tensor<f32>, tensor<f32>) {
  func.return %arg2, %arg1, %arg0 : tensor<f32>, tensor<f32>, tensor<f32>

func.func private @test_fuse_dynamic_dimension_ops(%arg0: tensor<?x?xi32>, %arg1: tensor<*x!tf_type.resource>, %arg2: tensor<2xi64>, %arg3: tensor<?xi64>, %arg4: tensor<?xi64>) -> tensor<*xi32> {
  // CHECK-NOT: tf._TPUCompileMlirOp
  // CHECK-NOT: tf.TPUCompileSucceededAssert
  // CHECK-NOT: tf.TPUExecuteOp
  // CHECK-NOT: tf.SetStaticDimensionBounds

  // CHECK: [[read_result:%.*]] = "tf.ReadVariableOp"(%arg1)

func.func @generator_op(%str : tensor<!tf_type.string>, %arg0: tensor<*x!tf_type.string>, %arg1: tensor<!tf_type.string>, %arg2: tensor<*xi64>, %arg3: tensor<!tf_type.string>) {
  tf_executor.graph {
    tf_executor.island {
      // CHECK: %{{.*}}, %[[CONTROL:[^ ,]*]] = tf_executor.island wraps "tf.GeneratorDataset"
      %gen0 = "tf.GeneratorDataset"(%str, %arg0, %arg1, %arg2, %arg3) {
        finalize_func = @__finalize_func_790,

CHECK:  %[[LSTM:[a-z0-9]*]] = "tfl.unidirectional_sequence_lstm"(%arg2, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg1, %arg1, %arg1, %arg1, %arg1, %arg1, %arg1, %arg0, %arg1, %[[CST_0]], %[[CST_1]], %arg0, %arg0, %arg0, %arg0) <{fused_activation_function = "NONE", time_major = true}> : (tensor<4x4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4x4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>,...