::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),
                       /*use_function*/ ::testing::Values(true, false)));
#else
INSTANTIATE_TEST_SUITE_P(
    UnifiedCAPI, CppGradients,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),

  cat > $BATS_TEST_TMPDIR/ignore_deps_for_these_packages <<EOF
//tensorflow/lite
//tensorflow/compiler/mlir/lite
//tensorflow/compiler/mlir/tfrt
//tensorflow/core/runtime_fallback
//tensorflow/core/tfrt
//tensorflow/python/kernel_tests/signal
//tensorflow/examples
//tensorflow/tools/android
//tensorflow/python/eager/benchmarks
EOF

// RUN: tf-tfrt-opt -verify-diagnostics -split-input-file -tfrt-fuse-tpu-compile-and-execute-ops -canonicalize %s | FileCheck %s --dump-input=fail --dump-input-filter=all

module attributes {tf_saved_model.semantics} {

// Test fusing _TPUCompileMlirOp and TPUExecuteOp into TPUCompileMlirAndExecuteOp.

// CHECK-LABEL: func private @test_fuse_tpu_ops
func.func private @test_fuse_tpu_ops(%arg0: tensor<*xi32>, %arg1: tensor<*x!tf_type.resource>) -> tensor<*xi32> {

// RUN: tf-tfrt-opt -split-input-file -tf-device-decompose-resource-ops -sink-variable-as-named-array %s | FileCheck %s

// -----
// Basic test: all variables tensors are for devices and sinked as named ifrt arrays
//
//
// CHECK-LABEL:  func.func @serving_default(%arg0: tensor<1x3xf32>) -> tensor<1x1xf32> {
// CHECK-NEXT:   [[HANDLE2:%.*]] = "tf.VarHandleOp"
// CHECK-NEXT:   [[KEY:%.*]], [[FUTURE:%.*]] = "tf.IfrtLoadVariable"([[HANDLE2]])

  ASSERT_EQ(-1, shape.dim_size(1));
}

INSTANTIATE_TEST_SUITE_P(
    UnifiedCppAPI, UnifiedAPI,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),
                       /*use_function*/ ::testing::Values(true, false)));
}  // namespace

      /*forward_input*/ false,
      /*tfrt*/ false);
}
// TODO(b/234067483): Investigate flakiness and re-enable.
TEST(CAPI, DISABLED_ExecuteAddAsync) {
  ExecuteAdd(
      /*async=*/true,
      /*forward_input*/ false,
      /*tfrt*/ false);
}
TEST(CAPI, ExecuteAddForward) {
  ExecuteAdd(
      /*async=*/false,
      /*forward_input*/ true,
      /*tfrt*/ false);
}

#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/status_matchers.h"
#include "tensorflow/core/platform/statusor.h"
#include "tensorflow/core/tfrt/common/create_pjrt_client_util.h"
#include "tensorflow/core/tfrt/common/pjrt_util.h"

namespace tensorflow {
namespace {

using ::testing::_;
using ::testing::ByMove;
using ::testing::Return;
using XlaDeviceExecutablePersistor =

// RUN: tf-tfrt-opt -split-input-file -verify-diagnostics -lower-to-ifrt-restore-variable %s | FileCheck %s


// -----
// single variable

// CHECK-LABEL:   func.func @restore_single() {
// CHECK-NEXT:     [[PREFIX:%.*]] = "tf.Const"() <{value = dense<"restore_ariables"> : tensor<!tf_type.string>}> : () -> tensor<!tf_type.string>
// CHECK-NEXT:     [[SLICE:%.*]] = "tf.Const"() <{value = dense<""> : tensor<1x!tf_type.string>}> : () -> tensor<1x!tf_type.string>

  // Returns:
  // If an error occurred, status->ok() will be false, and the returned
  // Tensor must not be used.
  // TODO(bmzhao): Add Runtime as an argument to this function so we can swap to
  // a TFRT backed tensor.
  // TODO(bmzhao): Add benchmarks on overhead for this function; we can
  // consider using int64_t* + length rather than vector.
  static Tensor FromBuffer(TF_DataType dtype, const std::vector<int64_t>& shape,

==============================================================================*/
#ifdef MLRT_OPS
#else
#define MLRT_OPS

include "mlir/Interfaces/SideEffectInterfaces.td"
include "tensorflow/compiler/mlir/tfrt/ir/mlrt/mlrt_dialect.td"

class Mlrt_Op<string mnemonic, list<Trait> traits = []> :
    Op<Mlrt_Dialect, mnemonic, !listconcat(traits, [IsolatedFromAbove])> {
}

def CondOp: Mlrt_Op<"cond", []> {