// CHECK: %[[DQ_2:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
  // CHECK: %[[DQ_3:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
  // CHECK: %[[DQ_4:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
  // CHECK: %[[DQ_5:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>

    return %result : tensor<1x1xf32>
  }
}

// -----
// Variable tensor for host can still be used.
//
// CHECK-LABEL:  func.func @serving_default(%arg0: tensor<1x3xf32>) -> (tensor<1x1xf32>, tensor<1x1xf32>) {
// CHECK:  "tf.VarHandleOp"
// CHECK-NOT:  [[VARIABLE:%.*]] = "tf.ReadVariableOp"

  // CHECK: %[[RESULT:.*]] = mhlo.reshape %[[CAST_BACK]] : (tensor<4xf16>) -> tensor<4x1xf16>
  // CHECK: return %[[RESULT]] : tensor<4x1xf16>
  %dimension = "tf.Const"() { value = dense<1> : tensor<1xi64> } : () -> tensor<1xi64>
  %0 = "tf.Mean"(%arg0, %dimension) { keep_dims = true }: (tensor<4x8xf16>, tensor<1xi64>) -> tensor<4x1xf16>
  func.return %0 : tensor<4x1xf16>
}

// -----

// CHECK-LABEL: func @mean_scalar_dim

  %0 = "tf.Cast"(%arg0) {Truncate = false, device = ""} : (tensor<1x10xf32>) -> tensor<1x10xbf16>
  %1 = "tf.MatMul"(%0, %cst) {device = "", transpose_a = false, transpose_b = false} : (tensor<1x10xbf16>, tensor<10x2xbf16>) -> tensor<1x2xbf16>
  %2 = "tf.Cast"(%1) {Truncate = false} : (tensor<1x2xbf16>) -> tensor<1x2xf32>
  %3 = "tf.IdentityN"(%2) {device = ""} : (tensor<1x2xf32>) -> tensor<1x2xf32>
  return %3 : tensor<1x2xf32>

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

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

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

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")

TEST_F(MlirTranslationTest, TranslatesMlirToGraph) {
  static constexpr char kMlirSource[] = R"(
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:           return %[[VAL_4]] : tensor<2x3xf16>
// CHECK:         }
func.func @floordiv_f16_broadcast(%arg0: tensor<2x3xf16>, %arg1: tensor<3xf16>) -> tensor<2x3xf16> {
  %0 = "chlo.broadcast_divide"(%arg0, %arg1) {broadcast_dimensions = array<i64: 1>} : (tensor<2x3xf16>, tensor<3xf16>) -> tensor<2x3xf16>

  %2 = "tfl.pseudo_const"() {value = dense<[[0.2]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %3 = "tfl.pseudo_const"() {value = dense<[[0.3]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %4 = "tfl.pseudo_const"() {value = dense<[[0.4]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
  %5 = "tfl.pseudo_const"() {value = dense<[[0.5]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>

  %3 = "tf.TPUExecute"(%arg0, %2#0, %program#0) {_producer_name = "UNKNOWN", device = "/TPU:0"} : (tensor<1x1xf32>, tensor<1x1xf32>, tensor<3x!tf_type.string>) -> tensor<1x1xf32>
  %4 = "tf.TPUExecute"(%arg0, %2#1, %program#1) {_producer_name = "UNKNOWN", device = "/TPU:1"} : (tensor<1x1xf32>, tensor<1x1xf32>, tensor<3x!tf_type.string>) -> tensor<1x1xf32>
  return %3 : tensor<1x1xf32>
}