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:

// attaching a to the initializer function's type so that it is identifiable.
Location CreateInitOpLoc(MLIRContext* ctx, func::FuncOp init_func_ops) {
  const std::string init_type = GetInitializerType(init_func_ops);
  const std::string name =
      absl::StrCat(init_type, "_", init_func_ops.getName().str());
  return NameLoc::get(StringAttr::get(ctx, name));
}

void MergeInitializerFunctionOpsToMainPass::runOnOperation() {

      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();
}

// matches `initializer_type`.
bool IsInitializerType(func::FuncOp init_func_op, StringRef initializer_type) {
  auto init_type =
      init_func_op->getAttrOfType<StringAttr>(kTfSavedModelInitializerTypeAttr);
  return init_type && init_type == initializer_type;
}

// Returns the initializer function whose tf_saved_model.initializer_type

  if (!s.ok()) {
    return std::nullopt;
  }
  return attr_value->s();
}

bool HasResourceInputOrOutput(const Node& node) {
  return std::find(node.input_types().begin(), node.input_types().end(),
                   DT_RESOURCE) != node.input_types().end() ||
         std::find(node.output_types().begin(), node.output_types().end(),
                   DT_RESOURCE) != node.output_types().end();
}

#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;

absl::StatusOr<ElementsAttr> ConvertTensor(const Tensor& input_tensor,
                                           Builder* builder) {
  const auto& input_dtype = input_tensor.dtype();
  const auto& input_shape = input_tensor.shape();
  Type elt_type;
  TF_RETURN_IF_ERROR(ConvertDataType(input_dtype, *builder, &elt_type));
  SmallVector<int64_t, 4> shape;
  ConvertToMlirShape(input_shape, &shape);

    absl::Span<const int64_t> input_tensor_id,
    absl::Span<const tensorflow::DataType> input_dtypes,
    const std::function<BackwardFunction*()>& backward_function_getter,
    const std::function<void(BackwardFunction*)>& backward_function_deleter) {
  if (!ShouldRecord(input_tensor_id, input_dtypes)) {
    return;
  }
  std::vector<int64_t> ids;
  ids.reserve(input_tensor_id.size());

  // Populate inputs.
  std::vector<DataType> input_dtypes;
  TF_RETURN_IF_ERROR(GetNodeAttr(call_node->attrs(), "Tinputs", &input_dtypes));
  std::vector<NodeDefBuilder::NodeOut> inputs(input_dtypes.size());
  for (auto e : call_node->in_edges()) {
    if (e->IsControlEdge()) {
      continue;
    }

    const int input_dtypes_size = input_dtypes.size();