// Create a copy of `metagraph` which will be used and mutated for fingerprint
  // computation.
  FingerprintDef fingerprint_def;
  MetaGraphDef* metagraph = saved_model.mutable_meta_graphs(0);
  // Set fingerprint field #1.
  fingerprint_def.set_saved_model_checksum(HashSavedModel(saved_model));
  // Set fingerprint field #2.
  graph_regularization::SimpleDelete(*metagraph->mutable_graph_def());

};

// 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 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.

// to achieve ABI stability.
typedef struct TF_SavedModel TF_SavedModel;

// Load a SavedModel from `dirname`. We expect the SavedModel to contain a
// single Metagraph (as for those exported from TF2's `tf.saved_model.save`).
//
// Params:
//  dirname - A directory filepath that the SavedModel is at.
//  ctx - A TFE_Context containing optional load/TF runtime options.

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,

  //         to contain a single Metagraph (as for those exported from TF2's
  //         `tf.saved_model.save`). If tags != nullptr, we load the metagraph
  //         matching the tags:
  //         https://github.com/tensorflow/tensorflow/blob/428cdeda09aef81e958eeb274b83d27ad635b57b/tensorflow/core/protobuf/meta_graph.proto#L50-L56
  //  status - Set to OK on success and an appropriate error on failure.

  metagraph_def.ParseFromArray(metagraph->data, metagraph->length);
  TF_DeleteBuffer(metagraph);

  EXPECT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  CSession csession(session);

  // Retrieve the regression signature from meta graph def.
  const auto signature_def_map = metagraph_def.signature_def();
  const auto signature_def = signature_def_map.at("regress_x_to_y");

  TFE_DeleteContext(ctx);
}

// This tests running the "serving_default" SignatureDefFunction from the
// VarsAndArithmeticObjectGraph savedmodel. Here's what the signature_defs
// protobuf in the metagraph looks like:
// signature_def: {
//   key  : "serving_default"
//   value: {
//     inputs: {
//       key  : "a"
//       value: {
//         name : "serving_default_a:0"
//         dtype: DT_FLOAT

            return conflictResolver.resolveConflicts(getGraph(), ArtifactScopeEnum.compile);
        } else if (requestType.equals(MetadataResolutionRequestTypeEnum.classpathRuntime)) {
            return conflictResolver.resolveConflicts(getGraph(), ArtifactScopeEnum.runtime);
        } else if (requestType.equals(MetadataResolutionRequestTypeEnum.classpathTest)) {
            return conflictResolver.resolveConflicts(getGraph(), ArtifactScopeEnum.test);