```
func @simpleTest(%arg0: tensor<1xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>, %arg3: tensor<1xf32>) -> tensor<2x1xf32> {
    %0 = call @func_0_GPU_FLOAT(%arg0, %arg1, %arg2) {tac.device = "GPU", tac.inference_type = "FLOAT", tac.interface_name = "func_0"} : (tensor<1xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1xf32>

  // Build the XLA computation graph.
  // "arg0" (T=DT_RESOURCE), "arg1" (T=DT_RESOURCE), "arg2" (T=DT_INT32)
  // "arg0", "arg1" -> "while" (While)
  tensorflow::Scope s = tensorflow::Scope::NewRootScope();
  Output arg0 = ops::_Arg(s.WithOpName("arg0"), DT_RESOURCE, 0);
  Output arg1 = ops::_Arg(s.WithOpName("arg1"), DT_RESOURCE, 1);
  Output arg2 = ops::_Arg(s.WithOpName("arg2"), DT_INT32, 2);
  NameAttrList cond_fn, body_fn;

  func.return %arg0: tensor<1x!tf_type.qint32>
}

// -----

// CHECK-LABEL: func @id_quint8(%arg0: tensor<1xui8>) -> tensor<1xui8> {
func.func @id_quint8(%arg0: tensor<1x!tf_type.quint8>) -> tensor<1x!tf_type.quint8> {
  // CHECK-NEXT: return %arg0 : tensor<1xui8>
  func.return %arg0: tensor<1x!tf_type.quint8>
}

// -----

// CHECK-LABEL: func @id_quint16(%arg0: tensor<1xui16>) -> tensor<1xui16> {

}

func.func private @identity(%arg0: tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>> {
  return %arg0 : tensor<!tf_type.variant<tensor<f32>>>
}

// -----

// CHECK-LABEL: @leaves_remote_calls_alone
func.func @leaves_remote_calls_alone(%arg0: tensor<!tf_type.string>, %arg1: tensor<!tf_type.string>, %arg2: tensor<i64>) {
  // CHECK: RemoteCall

  func.return %0 : tensor<1x384x32xf32>
}

// PARTIAL-LABEL: @arg_reuse_test_2
func.func @arg_reuse_test_2(%arg0: tensor<4x384x32xf32>, %arg1: tensor<3xi32>, %arg2: tensor<3xi32>) -> tensor<1x384x32xf32> attributes {tac.interface_name = "func2"} {
  %0 = "tfl.slice"(%arg0, %arg1, %arg2) : (tensor<4x384x32xf32>, tensor<3xi32>, tensor<3xi32>) -> tensor<1x384x32xf32>

    // CHECK: tf_device.replicate
    // CHECK-SAME: [%[[ARG_0]], %[[ARG_1]]] as %[[RI_0:[a-z0-9]*]]: tensor<8xi32>
    // CHECK-SAME: [%[[ARG_2]], %[[ARG_3]]] as %[[RI_1:[a-z0-9]*]]: tensor<*xi1>
    // CHECK-SAME: [%[[ARG_4]], %[[ARG_5]]] as %[[RI_2:[a-z0-9]*]]: tensor<*xi32>
    %0:2 = tf_device.replicate([%arg0, %arg1] as %ri: tensor<8xi32>, [%arg2, %arg3] as %ri2: tensor<*xi1>, [%arg4, %arg5] as %ri3: tensor<*xi32>) {n = 2 : i32} {

  func.return %0 : tensor<*xf32>
}

func.func private @add(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32>  {
  %0 = "tf.Add"(%arg0, %arg1): (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}

func.func private @mul(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32>  {

  func.func @__forward_call_369(%arg0: tensor<16x224x224x3xf32> {tf._user_specified_name = "inputs"}, %arg1: tensor<*x!tf_type.resource>, %arg2: tensor<*x!tf_type.resource>, %arg3: tensor<*x!tf_type.resource>, %arg4: tensor<*x!tf_type.resource>)...

// CHECK-LABEL: func @ifregion_then
// CHECK-SAME: (%arg0: {{.+}} {tf.device = "/TPU:0"}, %arg1: {{.+}} {tf.device = "/TPU:1"}
func.func @ifregion_then(
  %arg0: !tf_res,
  %arg1: !tf_res) {
  tf_executor.graph {
    // CHECK: tf_executor.island
    %island = tf_executor.island {
      // CHECK-NEXT: "tf.Identity"
      // CHECK-SAME: {device = "/TPU:0"}
      %id0 = "tf.Identity"(%arg0) : (!tf_res)
        -> !tf_res

  #
  # CHECK:      func {{@[a-zA-Z_0-9]+}}(
  # CHECK-SAME:   %arg0: tensor<1xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "a"]},
  # CHECK-SAME:   %arg1: tensor<2xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "b"]},
  # CHECK-SAME:   %arg2: tensor<3xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "c"]},