absl::StatusOr<OwningOpRef<ModuleOp>> GetMlirModuleFromString(
    absl::string_view module_string, MLIRContext* context) {
  DialectRegistry mlir_registry;
  RegisterCommonToolingDialects(mlir_registry);
  context->appendDialectRegistry(mlir_registry);

  OwningOpRef<ModuleOp> mlir_module;
  auto status =
      tensorflow::DeserializeMlirModule(module_string, context, &mlir_module);
  if (!status.ok()) {
    return status;
  }

      "tensorflow/compiler/mlir/tf2xla/internal/testdata/");
}

class LoggingHooksTest : public ::testing::Test {
 public:
  LoggingHooksTest() {
    mlir::RegisterCommonToolingDialects(registry_);
    context_.appendDialectRegistry(registry_);
    context_.loadAllAvailableDialects();
    env_ = Env::Default();

    test_group_name_ = "TestGroup";
    test_dir_ = testing::TmpDir();
    setenv("TF_DUMP_GRAPH_PREFIX", test_dir_.c_str(), 1);

}

class SessionClusterTensorflowDialectTest : public ::testing::Test {
 public:
  SessionClusterTensorflowDialectTest() {
    mlir::RegisterCommonToolingDialects(registry_);
    context_.appendDialectRegistry(registry_);
    context_.loadAllAvailableDialects();
  }

  absl::Status CreateMlirModule(std::string mlir_module_filename) {
    std::string mlir_module_path = TestDataPath() + mlir_module_filename;

}

class TensorflowDialectToExecutorTest : public ::testing::Test {
 public:
  TensorflowDialectToExecutorTest() {
    mlir::RegisterCommonToolingDialects(registry_);
    context_.appendDialectRegistry(registry_);
    context_.loadAllAvailableDialects();
  }

  absl::Status CreateMlirModule(std::string mlir_module_filename) {
    std::string mlir_module_path = TestDataPath() + mlir_module_filename;

absl::StatusOr<OwningOpRef<ModuleOp>> GetMlirModuleFromString(
    absl::string_view module_string, MLIRContext* context) {
  mlir::DialectRegistry mlir_registry;
  RegisterAllTensorFlowDialects(mlir_registry);
  context->appendDialectRegistry(mlir_registry);

  OwningOpRef<ModuleOp> mlir_module;
  auto status =
      tensorflow::DeserializeMlirModule(module_string, context, &mlir_module);
  if (!status.ok()) {
    return status;
  }

  }
  return count;
}

class TensorflowDialectToExecutorTest : public ::testing::Test {
 public:
  TensorflowDialectToExecutorTest() {
    mlir::RegisterCommonToolingDialects(registry_);
    context_.appendDialectRegistry(registry_);
    context_.loadAllAvailableDialects();
  }

  absl::Status CreateMlirModule(std::string mlir_module_filename) {
    std::string mlir_module_path = TestDataPath() + mlir_module_filename;

namespace {

std::unique_ptr<MLIRContext> CreateContext() {
  auto context = std::make_unique<MLIRContext>();
  DialectRegistry mlir_registry;
  RegisterCommonToolingDialects(mlir_registry);
  context->appendDialectRegistry(mlir_registry);
  return context;
}

TEST(IsLargeFloatTypeTest, scalars) {
  auto context = CreateContext();

  EXPECT_FALSE(IsLargeFloatType(Float8E4M3FNType::get(context.get())));

class LegalizeTfTypesTest : public Test {
 protected:
  void CreateModule(const char* module_string) {
    DialectRegistry mlir_registry;
    RegisterCommonToolingDialects(mlir_registry);
    context_.appendDialectRegistry(mlir_registry);
    TF_ASSERT_OK(
        tensorflow::DeserializeMlirModule(module_string, &context_, &module_));

    pm_ = std::make_unique<mlir::PassManager>(&context_);
    pm_->addNestedPass<mlir::func::FuncOp>(

    registry.insert<mlir::TFL::TensorFlowLiteDialect, mlir::arith::ArithDialect,
                    mlir::func::FuncDialect>();
    mlir::func::registerAllExtensions(registry);
    context->appendDialectRegistry(registry);
    source_mgr->AddNewSourceBuffer(std::move(buffer), llvm::SMLoc());
    return mlir::OwningOpRef<mlir::ModuleOp>(
        mlir::parseSourceFile<mlir::ModuleOp>(*source_mgr, context));
  }

absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> GetMlirModuleFromString(
    llvm::StringRef string, mlir::MLIRContext* context) {
  mlir::DialectRegistry mlir_registry;
  RegisterAllTensorFlowDialects(mlir_registry);
  context->appendDialectRegistry(mlir_registry);
  mlir::OwningOpRef<mlir::ModuleOp> mlir_module;
  auto status =
      tensorflow::DeserializeMlirModule(string, context, &mlir_module);
  if (!status.ok()) {
    return status;
  }