%value, %value_control = tf_executor.island wraps "tf.GetKey"() : () -> tensor<f32>
    %ret0, %ret0_control = tf_executor.island wraps "tf.SomeOp"() : () -> !tf_res
    %ret1, %ret1_control = tf_executor.island wraps "tf.SomeOp"() : () -> !tf_res
    %control_unknown = tf_executor.island wraps "tf.UnknownOp"() : () -> ()
    %key, %key_control = tf_executor.island wraps "tf.GetKey"() : () -> !tf_str

// Test that a constant stays inside an island after canonicalization

// CHECK-LABEL: func @constant_in_island
func.func @constant_in_island(%arg0 : tensor<i1>) -> tensor<f32> {
  %0 = tf_executor.graph {
// CHECK: tf_executor.island
// CHECK: tf.Const{{.*}}2.0
    %1:2 = tf_executor.island {
      %0 = "tf.Const"() {value = dense<2.000000e+00> : tensor<f32>} : () -> tensor<f32>

  func.return %result : tensor<*xi32>
}

// CHECK-LABEL: func @graph_and_more
// CHECK:  %[[RESULT:.*]] = tf_executor.graph {
// CHECK:    %[[ISLAND:.*]], %[[ISLAND_control:.*]] = tf_executor.island {
// CHECK:      tf_executor.graph {
// CHECK:        %[[ISLAND_INNER:.*]] = tf_executor.island {
// CHECK:          tf_executor.yield
// CHECK:        }
// CHECK:        tf_executor.fetch %[[ISLAND_INNER]] : !tf_executor.control
// CHECK:      }

// CHECK: size: 10

func.func @main() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<10xi32>} : () -> tensor<10xi32>
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %2:2 = tf_executor.island wraps "tf.PlaceholderWithDefault"(%1#0) {type = i32} : (tensor<i32>) -> tensor<*xi32> loc("unranked")

    // CHECK: name: "foo"
    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> (tensor<i32>) loc("foo")
    // CHECK: name: "foo{{_.*_1}}"
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<1> : tensor<i32>} : () -> (tensor<i32>) loc("foo")
    // CHECK: name: "foo1"

    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.Identity"(%arg0) {device = ""} : (tensor<2x3xi32>) -> tensor<2x3xi32>
      %outputs_0, %control_1 = tf_executor.island wraps "tf.XlaSharding"(%outputs) {_XlaSharding = "", device = "", sharding = "", unspecified_dims = []} : (tensor<2x3xi32>) -> tensor<2x3xi32>

    %0 = tf_executor.graph {
      %outputs, %control = tf_executor.island wraps "tf.Const"() {device = "", value = dense<"value"> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
      %outputs_0, %control_1 = tf_executor.island wraps "tf.Const"() {device = "", value = dense<"value"> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>

void VisitOp(GraphOp graph, Operation* op,
             llvm::SmallPtrSetImpl<Operation*>* reachable_ops,
             llvm::SmallVectorImpl<Operation*>* ops_to_visit) {
  if (auto island = llvm::dyn_cast<IslandOp>(op)) {
    mlir::visitUsedValuesDefinedAbove(
        island.getBody(), island.getBody(), [&](OpOperand* operand) {
          VisitOpOperand(graph, operand->get(), reachable_ops, ops_to_visit);
        });
  }

// RUN: tf-opt %s -tf-executor-tpu-v1-island-inlining | FileCheck %s

// Check that the nested module is inlined and erased.

module {
// CHECK-LABEL: func @func0
  func.func @func0(%arg0: tensor<i1>) -> tensor<f32> {
    %0 = tf_executor.graph {
// CHECK-NOT: PartitionedCall
// CHECK: "tf.opA"

void ClearControlInputs(Operation* op, int& num_control_inputs_removed) {
  // We only call this function for island or fetch ops. The second pair of
  // parentheses is needed for successful compilation.
  assert((isa<IslandOp, FetchOp>(op)));
  if (auto island = dyn_cast<IslandOp>(op)) {
    num_control_inputs_removed += island.getControlInputs().size();
    island.getControlInputsMutable().clear();
  } else if (auto fetch = dyn_cast<FetchOp>(op)) {