%1:2 = tf_executor.island wraps "tf.Identity"(%0#0) {T = "tfdtype$DT_FLOAT", device = ""} : (tensor<f32>) -> tensor<f32> loc("identity")

  // CHECK-NEXT:       }
  // CHECK-NEXT:       tensor_content: "\200\000\000\000\200\000\000\000"
  tf_executor.graph {

    %outputs_1, %control_1 = tf_executor.island wraps "tf.TPUReplicatedInput"(%outputs_0) {N = 1 : i64, T = "tfdtype$DT_FLOAT", device = "", name = "input1"} : (tensor<0xf32>) -> tensor<0xf32>

// CHECK: entry function `main` must be present

func.func @const() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "TPU:0", name = "const", dtype = "tfdtype$DT_INT32", value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
    tf_executor.fetch
  }
  func.return

func.func @main() -> (tensor<1x2xf16>, tensor<2xf16>) {
  %graph:2 = tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<1.0> : tensor<1x2xf16>} : () -> tensor<1x2xf16> loc("const1")
    %1:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<[1.0, 2.0]> : tensor<2xf16>} : () -> tensor<2xf16> loc("const2")

  func.func @main() attributes {tf.entry_function = {inputs = "", outputs = ""}} {
    tf_executor.graph {
      %0:2 = tf_executor.island wraps "tf.Const"() {device = "TPU:0", name = "const", dtype = "tfdtype$DT_INT32", value = dense<[1, 2]> : tensor<2xi32>} : () -> tensor<2xi32>
      tf_executor.fetch
    }
    func.return
  }
}

// CHECK-NOT: node

// CHECK: library
// CHECK-NEXT: function

      tf_executor.fetch
    }
    func.return
  }
  func.func @foo() {
    tf_executor.graph {
      %0:2 = tf_executor.island {
        %1 = "tf.Const"() {device = "", dtype = "tfdtype$DT_INT32", value = dense<5> : tensor<i32>} : () -> tensor<i32> loc("control_const")
        tf_executor.yield %1 : tensor<i32>
      }
      // CHECK: control_output: "control_const"
      // CHECK:       control_ret {

  // CHECK-NEXT:   }
  // CHECK:      key: "value"
  // CHECK-NEXT: value {
  // CHECK-NEXT:   s: " 0\n\000\000"
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Placeholder"() {name = "dummy", dtype = "tfdtype$DT_INT32", value = "\200\n\00\00", listvalue = ["\20\0A"]} : () -> tensor<2xi32>
    tf_executor.fetch
  }
  func.return

// CHECK-NEXT:   value {
// CHECK-NEXT:     type: DT_INT32{{$}}
// CHECK-NEXT:   }
// CHECK-NEXT: }

func.func @main() {
  tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.VariableV2"() {dtype = "tfdtype$DT_INT32", value = dense<2> : tensor<i32>, shape = #tf_type.shape<2>, container = "", shared_name = ""} : () -> tensor<!tf_type.int32ref> loc("Ref_Variable")

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

func.func @main() {
  tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_INT32", name = "Constant", value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %outputs_0, %control_1 = tf_executor.island wraps "tf.LegacyCall"(%outputs) {_tpu_replicate = "cluster", device = "", f = @foo0} : (tensor<i32>) -> tensor<i32>
    tf_executor.fetch
  }