# 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(

        field_checksum = FingerprintCat64(field_checksum, hash);
      }
    }
  }
  return field_checksum;
}

inline RepeatedPtrField<FieldIndex> GraphDefFieldTags() {
  // SavedModel.meta_graphs[0].graph_def
  FieldIndex meta_graph_field_tag;
  meta_graph_field_tag.set_field(2);
  FieldIndex meta_graph_index_field_tag;
  meta_graph_index_field_tag.set_index(0);
  FieldIndex graph_def_field_tag;

  }

  // 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;
}

  saved_model_proto = saved_model_loader.parse_saved_model(saved_model_path)
  for meta_graph in saved_model_proto.meta_graphs:
    if any(
        node.op.startswith('FakeQuant') for node in meta_graph.graph_def.node
    ):
      return True
    for function in meta_graph.graph_def.library.function:
      if any(node.op.startswith('FakeQuant') for node in function.node_def):
        return True

saved_model_schema_version: 1
meta_graphs {
  meta_info_def {
    stripped_op_list {
      op {
        name: "Add"
        input_arg {
          name: "x"
          type_attr: "T"
        }
        input_arg {
          name: "y"
          type_attr: "T"
        }
        output_arg {
          name: "z"
          type_attr: "T"
        }
        attr {
          name: "T"
          type: "type"

};

// 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);

  //         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.

  // 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());

//  signature_def_key - The string key of the SignatureDef map of a SavedModel:
//                      https://github.com/tensorflow/tensorflow/blob/69b08900b1e991d84bce31f3b404f5ed768f339f/tensorflow/core/protobuf/meta_graph.proto#L89
//  status - Set to OK on success and an appropriate error on failure.
// Returns:
//  If status is not OK, returns nullptr. Otherwise, returns a
//  TF_SignatureDefFunction instance. Once `model` is deleted, all