// CHECK:  "tf._TfrtGetResource"()
  %cond = "tf.Const"() {device = "/CPU:0", value = dense<true> : tensor<i1>} : () -> tensor<i1>
  // CHECK: tf.If
  %x = "tf.If"(%cond, %handle) {then_branch = @some_func, else_branch = @some_func, is_stateless = false} : (tensor<i1>, tensor<!tf_type.resource<tensor<i32>>>) -> tensor<i32>
  %r = "tf.AddV2"(%arg, %x) {device = "/CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
  func.return %r : tensor<i32>

  %0 = tf_executor.graph {
    %output, %control = tf_executor.island {
      // CHECK: {{%.+}}, [[IF_CONTROL:%.+]] = tf_executor.island wraps "tf.If"
      %1 = "tf.If"(%arg0, %arg1) {device = "", else_branch = @stateful_composite_op_control_else, is_stateless = false, then_branch = @stateful_composite_op_control_then} : (tensor<i1>, tensor<*x!tf_type.resource<tensor<i32>>>) -> tensor<i32>

  %3 = "tf.Less"(%2, %0) : (tensor<f32>, tensor<f32>) -> tensor<i1>
  %4 = "tf.If"(%3, %1, %2) {Tcond = i1, Tin = ["tfdtype$DT_RESOURCE", "tfdtype$DT_FLOAT"], Tout = ["tfdtype$DT_FLOAT"],
       else_branch = @cond_false, is_stateless = false,then_branch = @cond_true} :
       (tensor<i1>, tensor<!tf_type.resource<tensor<f32>>>, tensor<f32>) -> tensor<f32>
  %5 = "tf.Identity"(%4) : (tensor<f32>) -> tensor<f32>

  // expected-remark@below {{Result #1, ID 2 : 1, 2, 3, 5}}
  // expected-remark@below {{Result #2, ID 3 : 0, 1, 2, 3, 5}}
  %if:3 = "tf.If"(%read0, %arg1, %vh0) {
            then_branch = @if_then, else_branch = @if_else, is_stateless = true
          } : (tensor<32xf32>, !tf_res, !tf_res) -> (!tf_res, !tf_res, !tf_res)
  func.return
}

// expected-remark@below {{Region #0, Arg #0, ID 2 : 0, 1, 2}}

    %1 = "tf.Greater"(%0, %cst_0) {device = ""} : (tensor<f32>, tensor<f32>) -> tensor<i1>

// CHECK: [[z:%.*]] = mlrt.cond [[cond]] @then @else([[x]], [[y]])
// CHECK: return [[z]]
func.func @main(%cond: tensor<i1>, %x: tensor<i32>, %y: tensor<i32>) -> tensor<i32> {
  %z = "tf.If"(%cond, %x, %y) {then_branch = @then, else_branch = @else, is_stateless = true} : (tensor<i1>, tensor<i32>, tensor<i32>) -> tensor<i32>
  return %z: tensor<i32>
}

// -----

// Test lowering AsyncOpKernel

// CHECK-LABEL: func @main

    %1 = "tf.Greater"(%0, %cst_0) {device = ""} : (tensor<f32>, tensor<f32>) -> tensor<i1>

  %0 = "tfl.dequantize"(%arg1) : (tensor<4x!quant.uniform<u8:f32, 1.0>>) -> tensor<4xf32>
  %1 = "tf.If"(%arg0, %0) {then_branch = @testIfThen, else_branch = @testIfElse, is_stateless = false} : (tensor<i1>, tensor<4xf32>) -> tensor<4xf32>
  %2 = "tfl.quantize"(%1) {qtype = tensor<4x!quant.uniform<u8:f32, 1.0>>} : (tensor<4xf32>) -> tensor<4x!quant.uniform<u8:f32, 1.0>>

  // CHECK-NOT: tf.EmptyTensorList
  %tl = "tf.EmptyTensorList"(%elem_shape, %max_size) : (tensor<0xi32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<f32>>>
  %if_op = "tf.If"(%arg0, %tl) {then_branch = @if_then, else_branch = @if_else, is_stateless = false}
    : (tensor<i1>, tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>
  // CHECK: "tf.Slice"

  // arguments: the condition returns a bool and the two branches must return
  // the same type, which is also the return of the tf.If.
  %value = "tf.If”(%added, %mul)
             {cond: @cond_func, true_branch: @func_foo, false_branch: @func_bar}
                 : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

  return %value : tensor<*xf32>
}
```

## TensorFlow Executor Dialect