module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> tensor<1xi32> {
      %0 = "tf.Const"() {value = dense<1000> : tensor<1xi32>} : () -> tensor<1xi32>
      func.return %0 : tensor<1xi32>
    }
  })";

absl::StatusOr<std::string> CompileMlirModule(bool compile_to_xla_hlo,
                                              const char* module_str) {

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> tensor<1xi32> {
      %0 = "tf.Const"() {value = dense<1000> : tensor<1xi32>} : () -> tensor<1xi32>
      func.return %0 : tensor<1xi32>
    }
  })";

  CreateModule(kMlirModuleStr);
  auto result = Run();

  EXPECT_TRUE(result.succeeded());
}

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> tensor<1xi32> {
      %0 = "tf.Const"() {value = dense<1000> : tensor<1xi32>} : () -> tensor<1xi32>
      return %0 : tensor<1xi32>
    }
  })";

  CellReader<int64_t> dynamism_op_counter(kDynamismOpCounterStreamzName);
  CellReader<int64_t> dynamism_function_counter(

  func.return %0 : tensor<!tf_type.variant<tensor<*xi32>>>
}

// -----

// CHECK-LABEL: listGetItem
func.func @listGetItem(%arg0: tensor<!tf_type.variant<tensor<*xi32>>>, %arg1: tensor<i32>, %arg2: tensor<2xi32>) -> tensor<2xi32> {

module {
func.func @tfInplaceUpdate(%arg0: tensor<2x1x2xf32>) -> tensor<2x1x2xf32> {
  %1 = arith.constant dense<1> : tensor<1xi32>
  %2 = arith.constant dense<2.0> : tensor<1x1x2xf32>
  %3 = "tf.InplaceUpdate"(%arg0, %1, %2) {device = ""}
    : (tensor<2x1x2xf32>, tensor<1xi32>, tensor<1x1x2xf32>) -> tensor<2x1x2xf32>
  func.return %3 : tensor<2x1x2xf32>
}
}

//CHECK: module {

module {
func.func @tfInplaceUpdate(%arg0: tensor<2x1x2xf32>) -> tensor<2x1x2xf32> {
  %1 = arith.constant dense<1> : tensor<1xi32>
  %2 = arith.constant dense<2.0> : tensor<1x1x2xf32>
  %3 = "tf.InplaceUpdate"(%arg0, %1, %2) {device = ""}
    : (tensor<2x1x2xf32>, tensor<1xi32>, tensor<1x1x2xf32>) -> tensor<2x1x2xf32>
  func.return %3 : tensor<2x1x2xf32>
}
}

//CHECK: module attributes

func.func @test_async_op_kernel_thread() -> !tfrt.chain {
  %ch0 = tfrt.new.chain

  %ch1 = tfrt_fallback_async.createop(%ch0) key(0) device("/CPU:0") "tf.Const"()
         { dtype = i32, value = dense<[2]> : tensor<1xi32> } num_args(0)
  %ch2 = tfrt_fallback_async.createop(%ch1) key(1) device("/CPU:0")
         "tf.TestAsyncTfrtAsyncThread"() {T = i32} num_args(1)
  %ch3 = tfrt_fallback_async.createop(%ch2) key(2) device("/CPU:0")

  constexpr char kMlirIllegalOpStr[] = R"(
  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main() -> tensor<1xi32> {
      %0 = "tf.DoesntExist"() : () -> tensor<1xi32>
      func.return %0 : tensor<1xi32>
    }
  })";
  CellReader<int64_t> legalize_failure_count(
      "/tensorflow/core/tf2xla/v1/mlir_failed_xla_legalize_tf_pass_count");

    %cst = "tf.Const"() <{value = dense<[1]> : tensor<1xi32>}> : () -> tensor<1xi32>
    %0 = "tf.VarHandleOp"() <{container = "", shared_name = "Variable"}> : () -> tensor<!tf_type.resource<tensor<2x3xbf16>>>
    %1 = "tf.ResourceGather"(%0, %cst) <{batch_dims = 0 : i64, validate_indices = true}> : (tensor<!tf_type.resource<tensor<2x3xbf16>>>, tensor<1xi32>) -> tensor<1x3xbf16>
    return %1: tensor<1x3xbf16>
  }

  func.return %1: tensor<10x20x30xcomplex<f32>>

// CHECK:  %[[cst:.*]] = arith.constant dense<30> : tensor<1xi32>
// CHECK:  %[[rff:.*]] = "tf.RFFT"(%arg0, %[[cst]]) : (tensor<10x20x30xf32>, tensor<1xi32>) -> tensor<10x20x30xcomplex<f32>>
}

// CHECK-LABEL: not_fold_cast_fft_to_rfft