%0 = "tf.Const"() {value = dense<2.000000e+00> : tensor<f32>} : () -> tensor<f32>
  %1 = "tf.AddV2"(%0, %0) {device = "/job:worker/replica:0/task:0/device:CPU:0"} : (tensor<f32>, tensor<f32>) -> tensor<f32>
  %2 = "tf.AddV2"(%0, %0) {device = "/job:worker/replica:0/task:0/device:CPU:1"} : (tensor<f32>, tensor<f32>) -> tensor<f32>
  func.return
}

// CHECK: func @no_change_test
func.func @no_change_test() -> ()  {

  func.func @f(
    %arg0: tensor<f32> {tf.resource_name = "resource"}
  ) attributes { tf_saved_model.exported_names = ["foo.some_func"] } {
    func.return
  }

}

// -----

module attributes {tf_saved_model.semantics, tf_saved_model.under_construction} {
  "tf_saved_model.global_tensor"() { is_mutable, sym_name = "v", type = tensor<f32>, value = dense<42.0> : tensor<f32> } : () -> ()
  // CHECK: func @f
  func.func @f(

      "tf.Yield"(%recv) : (tensor<f32>) -> ()
    }, {
      %add = "tf.Add"(%arg1, %arg1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
      "tf.Yield"(%add) : (tensor<f32>) -> ()
    }) { is_stateless = true}: (tensor<i1>) -> tensor<f32>
  // CHECK: _xla_token_input_nodes = ["_xla_token_arg_node"]

  func.return %0, %1 : tensor<f32>, tensor<f32>
}


// Next four functions are used to verify handling of a call chain.

// CHECK: %[[XLA_CALL_MODULE:.+]] = "tf.XlaCallModule"(%[[CUSTOM_AGGREGATOR_0]], %[[CST]])

}

// CHECK-LABEL: not_fold_cast_fft_to_rfft
func.func @not_fold_cast_fft_to_rfft(%arg0: tensor<10x20x30xcomplex<f64>>) -> tensor<10x20x30xcomplex<f32>> {
  %0 = "tf.Cast"(%arg0) : (tensor<10x20x30xcomplex<f64>>) -> tensor<10x20x30xcomplex<f32>>
  %1 = "tf.FFT"(%0) : (tensor<10x20x30xcomplex<f32>>) -> tensor<10x20x30xcomplex<f32>>
  func.return %1: tensor<10x20x30xcomplex<f32>>

  %0 = "tf.Conj"(%arg0) : (tensor<complex<f32>>) -> tensor<complex<f32>>
  %1 = "tf.Conj"(%0) : (tensor<complex<f32>>) -> tensor<complex<f32>>
  // CHECK: return [[ARG0]]
  func.return %1: tensor<complex<f32>>
}

// CHECK-LABEL: func @testTripleConj
// CHECK-SAME:  ([[ARG0:%.+]]: tensor<complex<f32>>)
func.func @testTripleConj(%arg0: tensor<complex<f32>>) -> tensor<complex<f32>> {

// CHECK:       ENTRY %main.{{[0-9]+}} ([[ARG0:.*]]: f32[2], [[ARG1:.*]]: f32[2]) -> (f32[2]) {
// CHECK-NEXT:    %[[ARG1]] = f32[2]{0} parameter(1)
// CHECK-NEXT:    %[[ARG0]] = f32[2]{0} parameter(0)
// CHECK-NEXT:    ROOT %tuple.{{[0-9]+}} = (f32[2]{0}) tuple(f32[2]{0} %[[ARG0]])
// CHECK-NEXT:  }

// CHECK:       // InputMapping {0, 1}
// CHECK-NEXT:  // XlaInputShape f32[2]
// CHECK-NEXT:  // XlaInputShape f32[2]
// CHECK-NEXT:  // XlaOutputShape (f32[2])

  %0 = tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.GuaranteeConst"(%arg1) {T = f32, device = ""} : (tensor<f32>) -> tensor<f32>
    %outputs_0, %control_1 = tf_executor.island wraps "tf.GuaranteeConst"(%arg2) {T = f32, device = ""} : (tensor<f32>) -> tensor<f32>

    %3 = "tf.Const"() {value = dense<2.0> : tensor<f32>} : () -> tensor<f32>
    tf_device.return %2, %3 : tensor<i32>, tensor<f32>
  }) {cluster_attr = "cluster_attr"} : () -> (tensor<i32>, tensor<f32>)
  %4 = "tf.Const"() {value = dense<3.0> : tensor<f32>} : () -> tensor<f32>
  %5 = "tf.Add"(%4, %1) : (tensor<f32>, tensor<f32>) -> tensor<f32>
  %6 = "tf.Const"() {value = dense<4> : tensor<i32>} : () -> tensor<i32>

    %0 = "tf.FakeQuantWithMinMaxArgs"(%arg0) {device = "", max = 2.000000e-01 : f32, min = -1.000000e-01 : f32, narrow_range = false, num_bits = 8 : i64} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xf32>