buildFile << """
            abstract class FilePrinter extends DefaultTask {
                $inputAnnotation
                abstract $inputType getInput()

                @OutputFile
                abstract RegularFileProperty getOutputFile()

                @TaskAction
                void doWork() {
                    println $inputToPrint

  // within the subgraph and are referenced outside the subgraph.
  llvm::SmallVector<Type> input_types =
      TypesFromValues(subgraph.FuncArguments());
  llvm::SmallVector<Type> return_types =
      TypesFromValues(subgraph.FuncOutputs());

  FunctionType function_type =
      builder.getFunctionType(input_types, return_types);

  std::string function_name = absl::StrCat("func_", subgraph.subgraph_id_);

        then:
        called
    }

    def "rule action has no inputs"() {
        when:
        def action = ruleAction({ String val -> } as Action<String>)

        then:
        action.inputTypes.empty
    }

    def "equality"() {
        def baseAction = {String val -> } as Action<String>
        def noInputsAction = ruleAction(baseAction)

        expect:

  mlir::MLIRContext* context = tfr_module_.getContext();
  llvm::SmallVector<mlir::Type, 4> input_tys, output_tys;
  mlir::Builder builder(context);
  for (auto ty : input_dtys) {
    mlir::Type elt_ty;
    TF_RETURN_IF_ERROR(ConvertDataType(ty, builder, &elt_ty));
    mlir::TensorType mlir_ty = mlir::UnrankedTensorType::get(elt_ty);
    input_tys.push_back(mlir_ty);
  }
  for (auto ty : output_dtys) {
    mlir::Type elt_ty;

    private List collector = []

    def "creates rule action for rule source"() {
        when:
        action = RuleSourceBackedRuleAction.create(ModelType.of(List), ruleSource)

        then:
        action.inputTypes == [String, Integer, Set]

        when:
        action.execute(collector, ["foo", 1, ["bar", "baz"] as Set])

        then:
        collector == ["foo", 1, "bar", "baz"]

        where:

      enable_shape_inference, unconditionally_use_set_output_shapes,
      enable_soft_placement,  set_original_tf_func_name};

  auto module_or = tensorflow::GraphdefToMlirTranslateFunction(
      input, input_arrays, input_dtypes, input_shapes, output_arrays,
      control_output_arrays, options, context);
  if (!module_or.status().ok()) return nullptr;
  return std::move(module_or).value();
}

#include "llvm/Support/CommandLine.h"

// Please see the implementation file for documentation of these options.

// Import options.
extern llvm::cl::opt<std::string> input_arrays;
extern llvm::cl::opt<std::string> input_dtypes;
extern llvm::cl::opt<std::string> input_shapes;
extern llvm::cl::opt<std::string> output_arrays;
extern llvm::cl::opt<std::string> control_output_arrays;
extern llvm::cl::opt<std::string> inference_type;

    bool use_splatted_constant, const std::vector<std::string>& extra_tf_opdefs,
    const GraphImportConfig& specs, absl::string_view debug_info_file,
    absl::string_view input_arrays, absl::string_view input_dtypes,
    absl::string_view input_shapes, absl::string_view output_arrays,
    absl::string_view control_output_arrays, llvm::SourceMgr* source_mgr,
    mlir::MLIRContext* context);

// Import options.
// NOLINTNEXTLINE
opt<std::string> input_arrays(
    "tf-input-arrays", llvm::cl::desc("Input tensor names, separated by ','"),
    llvm::cl::init(""));

// NOLINTNEXTLINE
opt<std::string> input_dtypes(
    "tf-input-data-types",
    llvm::cl::desc("(Optional) Input tensor data types, separated by ','. Use "
                   "'' if a single data type is skipped. The data type from "

    @Override
    public Type visitPrimitive(PrimitiveType t, Void unused) {
        TypeKind kind = t.getKind();
        if (kind == TypeKind.INT) {
            return Type.INT_TYPE;
        }
        if (kind == TypeKind.BYTE) {
            return Type.BYTE_TYPE;
        }
        if (kind == TypeKind.BOOLEAN) {
            return Type.BOOLEAN_TYPE;
        }