// Confirm that attributes that cannot be stored in the flatbuffer options
  // for a given operator are dropped silently.
  %1 = "tfl.squared_difference"(%arg0, %0) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("squared_difference")
  %2 = "tfl.mul"(%arg0, %1) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("mul")

module {
  // CHECK-LABEL: func @main
  // CHECK-SAME: %[[ARG_0:.*]]: tensor<10xf32>, %[[ARG_1:.*]]: tensor<10xf32>, %[[ARG_2:.*]]: tensor<6xi32>
  func.func @main(
      %arg0: tensor<10xf32>, %arg1: tensor<10xf32>, %arg2: tensor<6xi32>
    ) -> (tensor<10xf32>, tensor<6xi32>) {
    // CHECK: %[[CAST_0:.*]] = "tf.Cast"(%[[ARG_0]]) <{Truncate = false}> : (tensor<10xf32>) -> tensor<10xbf16>

    %add = "tf.AddV2"(%call, %call) : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
    "tf.Yield"(%call, %add) : (tensor<4xf32>, tensor<4xf32>) -> ()
  },  {
    %call_0 = func.call @test_if_else1(%arg1) : (tensor<4xf32>) -> tensor<4xf32>
    "tf.Yield"(%call_0, %call_0) : (tensor<4xf32>, tensor<4xf32>) -> ()
  }) {is_stateless = false} : (tensor<i1>) -> (tensor<*xf32>, tensor<4xf32>)

    %4 = "quantfork.stats"(%2) {layerStats = dense<[-1.36523, 3.57373]> : tensor<2xf32>} : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
    %5 = "chlo.broadcast_add"(%4, %3) : (tensor<1x2x2x2xf32>, tensor<2xf32>) -> tensor<1x2x2x2xf32>
    %6 = "quantfork.stats"(%5) {layerStats = dense<[-1.31055, 2.62842]> : tensor<2xf32>} : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>

  %0 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.svdf"(%arg0, %arg1, %arg2, %arg3, %0) {fused_activation_function = "RELU", rank = 2 : i32} : (tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
  func.return %1 : tensor<4xf32>

  %0 = "tfl.pseudo_const" () {value = dense<1.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  %1 = "tfl.mul"(%arg0, %0) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("mul")
  // tf.MyCustomOp is the result of conversion to a Custom op
  %2 = "tf.MyCustomOp"(%1, %0) {fused_activation_function = "RELU", int_attr = 2 : i32}  : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("MyCustomOp")

# CHECK: mlir_module = "\0Afunc @main(%arg0 : tensor<10xf32>, %arg1 : tensor<10xf32>) -> tensor<10x10xf32> {\0A %add = \22tf.Add\22(%arg0, %arg1) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10xf32>\0A %ret = \22magic.op\22(%add, %add) : (tensor<10xf32>, tensor<10xf32>) -> tensor<10x10xf32>\0A return %ret : tensor<10x10xf32>\0A}\0A"}> {device = ""} : (tensor<10xf32>, tensor<10xf32>) -> tensor<*xf32>

node {
  name: "x"
  op: "Placeholder"
  attr {

  %2:2 = "tf.RecvTPUEmbeddingActivations"() {_tpu_embedding_layer = "call1", config = "\0A\0B\0C\0D"} : () -> (tensor<2x2xf32>, tensor<4x4xf32>)
  func.return
}

// -----

module {
func.func @main(%arg0: tensor<2xf32>) -> tensor<2xf32> {
  %0 = "tfl.custom"(%arg0) {custom_code = "stablehlo.rsqrt", custom_option = #tfl<const_bytes : "0x00000100002401">} : (tensor<2xf32>) -> tensor<2xf32>
  func.return %0 : tensor<2xf32>
}
}

// CHECK:       module
// CHECK-NEXT:  func @main(%arg0: tensor<2xf32>) -> tensor<2xf32> {
// CHECK-NEXT:  %0 = stablehlo.rsqrt %arg0 : tensor<2xf32>
// CHECK-NEXT:  return %0 : tensor<2xf32>

func.func @main() -> (tensor<1xi32>, tensor<1xi32>) {
  %0, %1 = tf_executor.graph {
    %output_1, %token_1, %control_1 = tf_executor.NextIteration.Source : tensor<1xi32> {T = i32, device = ""}
    %output_2, %token_2, %control_2 = tf_executor.NextIteration.Source : tensor<1xi32> {T = i32, device = ""}
    tf_executor.NextIteration.Sink [%token_1] %output_1 : tensor<1xi32> {T = i32, device = ""}