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>
}

  %cst_2 = arith.constant dense<[8, 6, 6, 18]> : tensor<4xi64>
  %98 = "tf.Reshape"(%97, %cst_2) : (tensor<1x8x6x1x6x1x1x18xbf16>, tensor<4xi64>) -> tensor<8x6x6x18xbf16>
  func.return %98 : tensor<8x6x6x18xbf16>

  // CHECK-DAG: %[[CST:.*]] = "tf.Const"() <{value = dense<[8, 1, 1, 18]> : tensor<4xi64>}> : () -> tensor<4xi64>
  // CHECK-DAG: %[[CST1:.*]] =  "tf.Const"() <{value = dense<[8, 6, 6, 18]> : tensor<4xi64>}> : () -> tensor<4xi64>

  // CHECK: %[[CST:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi64>}>
  // CHECK: %[[R0:.*]] = "tf.Transpose"(%[[ARG0]], %[[CST]])
  // CHECK: %[[R1:.*]] = "tf.BiasAdd"(%[[R0]], %[[ARG1]]) <{data_format = "NHWC"}> {device = ""}
  // CHECK: %[[CST_0:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}>
  // CHECK: "tf.Transpose"(%[[R1]], %[[CST_0]])

// -----

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> {

// CHECK-LABEL: func @transposeConv2D
func.func @transposeConv2D(%input: tensor<1x32x32x3xf32>, %filter: tensor<1x1x3x8xf32>) -> tensor<1x7x7x8xf32> {

  // CHECK: %[[ARG_PERM:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}>
  // CHECK: %[[ARG_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg0, %[[ARG_PERM]])

  // CHECK: %[[CONV2D:[0-9]*]] = "tf.Conv2D"(%[[ARG_TRANSPOSE]], %arg1)
  // CHECK-SAME: data_format = "NCHW"

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

// CHECK-LABEL: @RewriteSendRecvOps

  func.func @init_all_tables()
  attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"]} {
    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>

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"

  // CHECK-LABEL: init_all_tables
  func.func @init_all_tables()
  attributes {tf_saved_model.exported_names = ["__tf_saved_model_session_initializer"]} {
    %cst = arith.constant dense<[1, 2, 3, 4]> : tensor<4xi64>
    %cst_0 = arith.constant dense<["a", "b", "c", "d"]> : tensor<4x!tf_type.string>

  %1 = mhlo.constant dense<[[[[0, 1, 2, 3]], [[0, 1, 2, 3]]]]> : tensor<1x2x1x4xi32>
  %2 = "mhlo.broadcast_in_dim"(%0) <{broadcast_dimensions = dense<[0, 1, 2, 3]> : tensor<4xi64>}> : (tensor<1x1x1x4xi32>) -> tensor<1x2x1x4xi32>
  %3 = mhlo.multiply %1, %2 : tensor<1x2x1x4xi32>
  // CHECK:      return %[[RES]] : tensor<1x2x1x4xi32>
  return %3 : tensor<1x2x1x4xi32>
}