}

Status GetAssetFileDefs(const MetaGraphDef& meta_graph_def,
                        std::vector<AssetFileDef>* asset_file_defs) {
  // With SavedModel v2, we write asset file def into metagraph instead of
  // collection, so read from metagraph first.
  if (meta_graph_def.asset_file_def_size() > 0) {
    for (const auto& asset : meta_graph_def.asset_file_def()) {
      asset_file_defs->push_back(asset);
    }
    return absl::OkStatus();

  // "Value" attribute.
  NodeAttrMap node_attr_map = NodeToAttrMap(metagraph.graph_def());

  // These are needed for creating "Assets", by looking up their filenames.
  std::vector<AssetFileDef> assets;
  TF_RETURN_IF_ERROR(GetAssetFileDefs(metagraph, &assets));

  // Signatures are needed for determining whether a function is a
  // SignatureDefFunction or not.

#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "mlir/Support/LogicalResult.h"  // from @llvm-project
#include "tensorflow/core/protobuf/meta_graph.pb.h"

namespace mlir::quant {

// Converts arguments of the @main function that are bound to
// `tf_saved_model::AssetOp`s into regular tensor args. Returns `AsestFileDef`s
// that associates the arg with the asset.

#include <memory>
#include <string>

#include "absl/container/flat_hash_set.h"
#include "tensorflow/core/framework/graph_debug_info.pb.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include "tensorflow/core/protobuf/saved_object_graph.pb.h"
#include "tensorflow/core/protobuf/trackable_object_graph.pb.h"
#include "tensorflow/core/util/tensor_bundle/tensor_bundle.h"

namespace tensorflow {

syntax = "proto3";

package tensorflow.quantization;

import "tensorflow/core/framework/graph.proto";
import "tensorflow/core/protobuf/meta_graph.proto";
import "tensorflow/core/protobuf/saver.proto";

option cc_enable_arenas = true;

// Represents an exported TensorFlow model. It consists of a GraphDef and extra
// metadata required for building a SavedModel. This message is primarily used

#include <utility>
#include <vector>

#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor.pb.h"
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include "tensorflow/core/protobuf/saved_model.pb.h"

namespace tensorflow {
namespace saved_model {

// Utility functions for SavedModel reading and writing.

#include "tensorflow/compiler/mlir/quantization/stablehlo/quantization_config.pb.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/python/py_function_lib.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"

namespace stablehlo::quantization::pywrap {

// Function used by the pywrap_quantization module to mirror
// `::mlir::quant::stablehlo::QuantizeStaticRangePtq`.
absl::Status PywrapQuantizeStaticRangePtq(

#include "absl/status/statusor.h"
#include "tensorflow/core/framework/graph_debug_info.pb.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include "tensorflow/core/protobuf/saved_model.pb.h"

namespace tensorflow {
Status ReadSavedModel(absl::string_view export_dir,
                      SavedModel* saved_model_proto);

#include "tensorflow/compiler/mlir/quantization/stablehlo/quantization_config.pb.h"
#include "tensorflow/compiler/mlir/quantization/tensorflow/python/py_function_lib.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"

namespace stablehlo::quantization::pywrap {

using ::mlir::quant::stablehlo::QuantizeStaticRangePtq;
using ::mlir::quant::stablehlo::QuantizeWeightOnlyPtq;
using ::tensorflow::SignatureDef;

#include "tensorflow/compiler/mlir/quantization/tensorflow/quantization_options.pb.h"
#include "tensorflow/core/framework/function.pb.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"
#include "tensorflow/core/protobuf/saver.pb.h"
#include "tsl/platform/statusor.h"

namespace mlir::quant::stablehlo {

using ::stablehlo::quantization::QuantizationConfig;
using ::tensorflow::SignatureDef;