model = self.SimpleModel()

    saved_model_save.save(model, self._input_saved_model_path)

    # Set an invalid value of -1 to QuantizationMethod.preset_method.
    options = quant_opts_pb2.QuantizationOptions(
        quantization_method=quant_opts_pb2.QuantizationMethod(preset_method=-1)
    )

    with self.assertRaises(ValueError):
      quantize_model.quantize(

  )

  if (
      quant_method.preset_method == _PresetMethod.METHOD_STATIC_RANGE_INT8
      or quant_method.preset_method == _PresetMethod.METHOD_DYNAMIC_RANGE_INT8
  ) and (len(quant_method.quantization_component_specs) != 3):
    raise ValueError('Only 3 components are needed for', quant_method)
  elif (
      quant_method.preset_method
      == _PresetMethod.METHOD_STATIC_RANGE_WEIGHT_ONLY_INT8

      quantization_options.quantization_method().preset_method(),
      quantization_options.op_set()));
  // TODO: b/260677670 - Pass quantization options as pass's inputs where
  // applicable
  pm.addPass(mlir::quant::CreateQuantizeCompositeFunctionsPass(
      quantization_options.quantization_method().preset_method(),
      quantization_options.op_set(),

    op_set_.setCallback([this](const OpSet& new_op_set) {
      quant_options_.set_op_set(new_op_set);
    });

    // Set the test quantization method to static-range.
    if (quant_options_.quantization_method().preset_method() ==
        QuantizationMethod::METHOD_UNSPECIFIED) {
      quant_options_.mutable_quantization_method()->set_preset_method(
          QuantizationMethod::METHOD_STATIC_RANGE_INT8);
    }

// Lifts the dynamic range quantizable spots as composite functions.
std::unique_ptr<OperationPass<ModuleOp>>
CreateLiftQuantizableSpotsAsFunctionsDRQPass(
    tensorflow::quantization::QuantizationMethod::PresetMethod
        quantization_method,
    tensorflow::quantization::OpSet op_set, int min_num_elements_for_weights);

// Replaces tf.CustomAggregator ops with quant.Stats ops for finalizing the
// calibration procedure.

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"

namespace mlir {
namespace quant {
namespace {

using QuantMethod =
    ::tensorflow::quantization::QuantizationMethod::PresetMethod;
using ::tensorflow::quantization::OpSet;

class LiftQuantizableSpotsAsFunctionsDRQPass
    : public PassWrapper<LiftQuantizableSpotsAsFunctionsDRQPass,
                         OperationPass<ModuleOp>> {
 public:

// The preprocess-op Pass.
//
namespace mlir {
namespace quant {

namespace {

using QuantMethod =
    ::tensorflow::quantization::QuantizationMethod::PresetMethod;
using QuantizationUnit = std::pair<Operation*, int>;
using QuantizationUnits = llvm::SetVector<QuantizationUnit>;
using ::tensorflow::quantization::OpSet;

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"
#include "tensorflow/core/util/quantization/uniform_quant_ops_attr.pb.h"

namespace mlir::quant {

using QuantMethod = tensorflow::quantization::QuantizationMethod::PresetMethod;

enum class OpType {
  kDynamicRangeOp,  // Dynamic Range kernels only have rhs attr.
  kUnaryOp,         // Unary ops have one min/max attr.
  kBinaryOp,        // Binary ops have lhs/rhs attr.

// The prepare-quantize Pass.
//
namespace mlir {
namespace quant {

namespace {

using QuantMethod = tensorflow::quantization::QuantizationMethod::PresetMethod;

// Applies prepare quantization on the model in TF dialect. This pass runs
// before the quantization pass and propagate the quantization parameters

#include "tensorflow/core/ir/importexport/convert_tensor.h"

namespace mlir {
namespace quant {
namespace {

using QuantMethod = tensorflow::quantization::QuantizationMethod::PresetMethod;
using ::tensorflow::quantization::OpSet;

constexpr absl::string_view kQuantizeCompositeFunctionsStepName =
    "_quantize_composite_functions";
constexpr StringRef kQuantizeFuncName = "quantize_i8";