}
  return nullptr;
}

TEST_F(QuantizeWeightsTest, QuantizationSucceeds) {
  LoadBasicModel();
  flatbuffers::FlatBufferBuilder builder;
  auto status = QuantizeWeights(&builder, model_, 0);
  EXPECT_EQ(status, kTfLiteOk);

  const uint8_t* buffer = builder.GetBufferPointer();
  const Model* output_model = GetModel(buffer);
  ASSERT_TRUE(output_model);
}

TEST_F(QuantizeWeightsTest, QuantizationFails) {

#include "xla/shape.h"
#include "xla/shape_util.h"
#include "xla/tests/literal_test_util.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tsl/platform/errors.h"
#include "tsl/platform/statusor.h"

namespace mlir::quant::stablehlo {
namespace {

using ::testing::Test;

class ConvertTfQuantToMhloIntTest : public Test {
 protected:
  void SetUp() override {

                                                    GetShapeDeterminationFns());
}

class XlaCompilerOptionsTest : public ::testing::Test {
 protected:
  void SetUp() override {
    tensorflow::GetXlaDeviceFlags()->tf_xla_enable_xla_devices = true;
  }

  DeviceSetup device_setup_;
};

TEST_F(XlaCompilerOptionsTest, PjRtOptionsXlaDevice) {
  device_setup_.AddDevicesAndSetUp({DEVICE_XLA_GPU});

#include "tensorflow/core/framework/types.pb.h"
#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/util/tensor_bundle/tensor_bundle.h"
#include "tsl/platform/status.h"
#include "tsl/platform/status_matchers.h"

namespace tensorflow {
namespace quantization {
namespace {

using ::tensorflow::test::AsTensor;
using ::tensorflow::test::ExpectEqual;
using ::testing::IsEmpty;
using ::testing::Not;

        def header1 = includeDir.createFile("test.h")
        includePaths << includeDir

        macros << macro("TEST", '"test.h"')
        macroFunctions << macroFunction("TEST1", "TEST")
        macros << macro("TEST2", "TEST1()")
        macros << macro("TEST3", "TEST2")
        macros << unresolvableMacro("IGNORE")

        expect:
        def result = resolve(include('TEST3'))
        result.complete

  TF_DeleteFunction(funcs[0]);
}

TEST_F(CApiExperimentalFunctionTest, EmptyGraphRemoveNonExistentFunction) {
  TF_GraphRemoveFunction(func_graph_, "wrong_name", s_);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s_));
  EXPECT_EQ(string("Tried to remove non-existent function 'wrong_name'."),
            string(TF_Message(s_)));
}

TEST_F(CApiExperimentalFunctionTest, GraphRemoveNonExistentFunction) {

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/core/platform/test.h"

namespace mlir {
namespace quant {
namespace {

using ::testing::NotNull;
using ::testing::SizeIs;

using ConstantFoldingTest = ::mlir::quant::QuantizationTestBase;

TEST_F(ConstantFoldingTest, FoldLargeConstant) {
  constexpr absl::string_view kModuleCode = R"mlir(
    module {

            }
        }
        """

        when:
        succeeds taskName

        then:
        def archiveFile = archive(file("build/test.${fileExtension}"))
        archiveFile.hasDescendants('test3.txt', 'test4.txt')
        archiveFile.content('test3.txt') == 'test2'
        archiveFile.content('test4.txt') == 'test1'

        where:

      /*ret_def=*/{{"out", "out:output:0"}});
  *fdef_lib.add_function() = std::move(func);
  return fdef_lib;
}

// This tests a rewrite that only makes sense and is active in a CUDA-enabled
// build.  Specifically we check that we insert an IdentityN op to avoid extra
// device-to-host copies.
TEST_F(BuildXlaOpsTest, NoDeviceToHostCopiesForClustersWithInt32Inputs) {
  const char* kXlaDeviceName = "/job:worker/replica:0/task:0/device:GPU:0";

        assertThat(activation.getRequiredInactiveProfileIds(), containsInAnyOrder("test1", "test2"));
        assertThat(activation.getOptionalInactiveProfileIds(), containsInAnyOrder("test3", "test4"));

        activation = new ProfileActivation();
        performProfileActivation(parser.parse(options, new String[] {"-P", "-test1,+test2"}), activation);