}

// CHECK-LABEL: func @testEmpty64
func.func @testEmpty64() -> (tensor<5xi64>) {
  %0 = "tf.Const"() { value = dense<5> : tensor<i32> } : () -> tensor<i32>

  // CHECK: [[VAL:%.+]] = "tf.Const"() <{value = dense<0> : tensor<5xi64>}>
  // CHECK: return [[VAL]] : tensor<5xi64>
  %1 = "tf.Empty"(%0) : (tensor<i32>) -> (tensor<5xi64>)
  func.return %1 : tensor<5xi64>
}

// CHECK-LABEL: func @testEmptyFloat

    // CHECK-NOT: tf.BesselI0e
    %0 = "tf.BesselI0e"(%arg0) : (tensor<3xf16>) -> (tensor<3xf16>)
    %1 = "tf.BesselI0e"(%arg1) : (tensor<3xf32>) -> (tensor<3xf32>)
    %2 = "tf.BesselI0e"(%arg2) : (tensor<3xf64>) -> (tensor<3xf64>)
    func.return %0, %1, %2 : tensor<3xf16>, tensor<3xf32>, tensor<3xf64>
  }

  // CHECK-LABEL: bessel_i1e

func.func @TfAdd(%arg0: tensor<4xf64>, %arg1: tensor<4xf64>) -> tensor<4xf64> {
  %0 = "tfl.custom"(%arg0, %arg1) {
    custom_code = "FlexAdd",
    custom_option = #tfl<const_bytes : "0x03416464001412034164641A001A002A070A015412023002320000021B171414042801">
  } : (tensor<4xf64>, tensor<4xf64>) -> tensor<4xf64>

// CHECK: "tf.Add"(%arg0, %arg1) {T = f64}  : (tensor<4xf64>, tensor<4xf64>) -> tensor<4xf64>
  func.return %0 : tensor<4xf64>
}

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -emit-select-tf-ops -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(tensor<4xf64>, tensor<4xf64>) -> tensor<4xf64> {
^bb0(%arg0: tensor<4xf64>, %arg1: tensor<4xf64>):
// CHECK:  {
// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [ {
// CHECK-NEXT:    deprecated_builtin_code: 32,
// CHECK-NEXT:    custom_code: "FlexAdd",
// CHECK-NEXT:    builtin_code: CUSTOM

// -----

func.func @foldDivF64() -> tensor<2xf64> {
  %0 = stablehlo.constant dense<[2.0, 3.0]> : tensor<2xf64>
  %1 = stablehlo.constant dense<[4.0, 6.0]> : tensor<2xf64>
  %2 = stablehlo.divide %0, %1 : tensor<2xf64>
  return %2 : tensor<2xf64>
}

// CHECK-LABEL: foldDivF64
// CHECK: stablehlo.constant dense<5.000000e-01> : tensor<2xf64>

// -----

func.func @foldDivI32() -> tensor<2xi32> {

    %range_nume = "tf.Sub"(%r_max_f64, %r_min_f64) : (tensor<1xf64>, tensor<1xf64>) -> tensor<1xf64>
    %range_deno = "tf.Sub"(%i8_max_f64, %i8_min_f64) : (tensor<f64>, tensor<f64>) -> tensor<f64>

    %scale_f64 = "tf.Div"(%range_nume, %range_deno) : (tensor<1xf64>, tensor<f64>) -> tensor<1xf64>
    %scale = "tf.Cast"(%scale_f64) : (tensor<1xf64>) -> tensor<1xf32>

    // Add comparison with minimum if needed

  %98 = "tf.Reshape"(%97, %cst_2) : (tensor<7x1x8x6x1x6x1x1x18xbf16>, tensor<5xi64>) -> tensor<7x8x6x6x18xbf16>
  func.return %98 : tensor<7x8x6x6x18xbf16>

  // CHECK-DAG: %[[CST:.*]] = "tf.Const"() <{value = dense<[1, 8, 1, 1, 18]> : tensor<5xi64>}> : () -> tensor<5xi64>
  // CHECK-DAG: %[[CST1:.*]] =  "tf.Const"() <{value = dense<[7, 8, 6, 6, 18]> : tensor<5xi64>}> : () -> tensor<5xi64>

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

// CHECK-LABEL: @RewriteSendRecvOps

  %1 = "tfl.shape"(%conv_s) : (tensor<?x26x26x26x16xf32>) -> tensor<5xi64>
  %2 = "tfl.broadcast_args"(%1, %cst) : (tensor<5xi64>, tensor<1xi64>) -> tensor<5xi64>
  %broad1 = "tfl.broadcast_to"(%conv_s, %2) : (tensor<?x26x26x26x16xf32>, tensor<5xi64>) -> tensor<?x26x26x26x16xf32>
  %broad2 = "tfl.broadcast_to"(%b, %2) : (tensor<16xf32>, tensor<5xi64>) -> tensor<?x26x26x26x16xf32>

func.func @typeNotSupportedNotLegalized(%arg0: tensor<!tf_type.variant<tensor<*xf64>>>, %arg1: tensor<i32>, %arg2: tensor<*xf64>) -> tensor<!tf_type.variant<tensor<*xf64>>> {
  %0 = "tf.TensorListSetItem"(%arg0, %arg1, %arg2) : (tensor<!tf_type.variant<tensor<*xf64>>>, tensor<i32>, tensor<*xf64>) -> tensor<!tf_type.variant<tensor<*xf64>>>
  // CHECK-NOT: "tfl.custom"