return llvm::isa<tf_executor::EnterOp, tf_executor::ExitOp,
                   tf_executor::IslandOp, tf_executor::MergeOp,
                   tf_executor::SwitchOp>(op);
}

// Assigns all data results to a specified device.
void PopulateDeviceForOpResults(
    Operation& op, llvm::StringRef device,
    llvm::DenseMap<Value, llvm::StringRef>& value_to_device) {
  Operation* op_to_update = &op;

        tf_executor.graph {
          %control_0 = tf_executor.island wraps "tf.NoOp"() : () -> ()
        }
        return
      }

      func.func private @function_2() -> () attributes {tf._original_func_name = "__func_2"} {
        tf_executor.graph {
          %control_0 = tf_executor.island wraps "tf.NoOp"() : () -> ()
        }
        return
      }

    func.return
  }
}

// -----

module {
  // CHECK-LABEL: func @passesSingleIslandOp
  func.func @passesSingleIslandOp() {
    // CHECK: _class = ["loc:@class"]
    tf_executor.graph {
      %c, %control0 = tf_executor.island wraps "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>

  %0 = tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.Add"(%arg0, %arg1) : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
    tf_executor.fetch %outputs : tensor<*xf32>
  }
  func.return %0 : tensor<*xf32>
}

func.func @cond_false(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
  %0 = tf_executor.graph {

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main() {
  tf_executor.graph {
    // 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")

    %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>

        %4 = "tf.B"(%2) : (tensor<?xi32>) -> tensor<?xi32>
        tf_device.return %4 : tensor<?xi32>
      }) {} : () -> tensor<?xi32>

      // CHECK: tf_executor.yield %[[CLUSTER_OUTPUT]]
      tf_executor.yield %3 : tensor<?xi32>
    }
    tf_executor.fetch %1#0 : tensor<?xi32>
  }
  func.return %0 : tensor<?xi32>
}

// CHECK: func private @[[CLUSTER]]

          ExecutorDialectToFunctionalConversion> {
  void runOnOperation() override;
};

// Extracts inner ops of tf_executor.island ops in a tf_executor.graph, in the
// order of ops in tf_executor.graph.
LogicalResult LiftIslandOpInnerOpsFromGraph(tf_executor::GraphOp graph) {
  auto graph_position = graph.getOperation()->getIterator();
  Block* parent_block = graph.getOperation()->getBlock();

// CHECK-LABEL: func @control_input
func.func @control_input(%arg0 : tensor<i1>) -> tensor<i32> {
  %0:6 = tf_executor.graph {
    %1:2 = tf_executor.island wraps "tf.opA"(%arg0) {_xla_compile_device_type = "TPU", _replication_info = "cluster"} : (tensor<i1>) -> tensor<i32>

func.func @main() -> (tensor<1x2xf16>, tensor<2xf16>) {
  %graph:2 = tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<1.0> : tensor<1x2xf16>} : () -> tensor<1x2xf16> loc("const1")
// CHECK: node {
// CHECK-NEXT: name: "const1"
// CHECK-NEXT: op: "Const"
// CHECK: dtype: DT_HALF
// CHECK: half_val: 15360