%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>
    %2:2 = "tf.If"(%1, %arg0) <{else_branch = @cond_false_80, is_stateless = true, then_branch = @cond_true_70}> {Tcond = i1, Tin = [f32], Tout = [i1, f32], _lower_using_switch_merge = true, _read_only_resource_inputs = [], device = ""} : (tensor<i1>, tensor<1x4xf32>) -> (tensor<i1>, tensor<1x3xf32>)

// 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>
    %2:2 = "tf.If"(%1, %arg0) <{else_branch = @cond_false_80, is_stateless = true, then_branch = @cond_true_70}> {Tcond = i1, Tin = [f32], Tout = [i1, f32], _lower_using_switch_merge = true, _read_only_resource_inputs = [], device = ""} : (tensor<i1>, tensor<1x4xf32>) -> (tensor<i1>, tensor<1x3xf32>)

    auto original_point = rewriter.saveInsertionPoint();
    rewriter.setInsertionPointAfter(op->getParentOfType<func::FuncOp>());
    SymbolTable manager(op->getParentOfType<ModuleOp>());

    // Constructs `then_branch`, which is executed when `if_cond` evaluates to
    // true.
    auto then_branch_op =
        rewriter.create<func::FuncOp>(loc, "cond_true", func_type);

  // CHECK-SAME: device = "noodle"
  %3 = "tf.If"(%1, %2, %arg1) {then_branch = @add, else_branch = @sub, output_shapes = [#tf_type.shape<>], device = "noodle", _underscore_attr = "something", is_stateless = false} : (tensor<i1>, tensor<f32>, tensor<f32>) -> tensor<f32>

  // CHECK: %2 = "tf.If"

                    %arg1: tensor<4x!quant.uniform<u8:f32, 1.0>>) -> (tensor<4x!quant.uniform<u8:f32, 1.0>>) {
  %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>

  // 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"

      // expected-remark@above {{Successors: {2}}}
        (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>
      %if = "tf.If"(%arg1, %arg1) {
      // expected-remark@above {{ID: 1}}
          then_branch = @if_then, else_branch = @if_else, is_stateless = true}
        : (tensor<i1>, tensor<i1>) -> tensor<i1>
      "tf.AssignVariableOp"(%arg0, %r0) :
      // expected-remark@above {{ID: 2}}