# CHECK-SAME: shared_name = "batch"
# CH#CK: sym_name = "predict0"

# CHECK: f = @inference_func[[post_fix]],
# CHECK-SAME: shared_name = "batch"
# CH#CK: sym_name = "predict1"

meta_graphs: {
  meta_info_def: {
    tags: ["serve", "tpu"]
  }
  graph_def: {
    node: {
      name: "input0"
      op: "Placeholder"
      attr: {
        key: "dtype"
        value: {

#include <utility>
#include <vector>

#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"

namespace tensorflow {
namespace saved_model {

std::string GetWriteVersion(const SavedModel& saved_model) {
  if (saved_model.meta_graphs_size() == 1 &&
      saved_model.meta_graphs()[0].has_object_graph_def()) {
    return "2";
  }
  return "1";
}

std::set<std::string> GetMapKeys(

  }

  // Build a map of node names to their corresponding nodes.
  //
  // See `GetGraphDefNode` for more details.
  const auto& nodes =
      tf_package.saved_model_proto_.meta_graphs(0).graph_def().node();
  for (const auto& node : nodes) {
    tf_package.graph_def_nodes_by_name_[node.name()] = &node;
  }
  return tf_package;
}

};

// Restore variable and resources in the SavedModel export dir for the
// indicated metagraph.
// The recommended way to load a saved model is to call LoadSavedModel,
// which provides an already initialized Metagraph, Session, and DebugInfo.
Status RestoreSession(const RunOptions& run_options,
                      const MetaGraphDef& meta_graph, const string& export_dir,
                      std::unique_ptr<Session>* session);

Status ValidateSingleConcreteFunction(const SavedFunction& saved_function);

// Walks through the SavedObjectGraph in metagraph, and restores all nodes
// (except "UserDefinedObjects") with their corresponding type in
// "PartiallyRevivedObjects".
Status PartiallyReviveSavedModelObjects(const MetaGraphDef& metagraph,
                                        ImmediateExecutionContext* context,

                   const std::unordered_set<std::string>& tags) {
  tensorflow::MetaGraphDef meta_graph;
  TF_RETURN_IF_ERROR(tensorflow::ReadMetaGraphDefFromSavedModel(
      saved_model_path, tags, &meta_graph));

  absl::flat_hash_map<FunctionName, FunctionAlias> function_aliases(
      meta_graph.meta_info_def().function_aliases().begin(),
      meta_graph.meta_info_def().function_aliases().end());
  return function_aliases;
}

}

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();

#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