// inputs. If `is_alias` is set to true, the keys of the `request_inputs` are
// alias names rather than the feed names in the graph.
Status GetInputValues(
    const SignatureDef& signature,
    const ::google::protobuf::Map<std::string, ::tensorflow::TensorProto>& request_inputs,
    std::vector<std::pair<string, Tensor>>& inputs);

}  // namespace saved_model
}  // namespace tensorflow

  std::set<std::string> keys;
  for (const auto& it : map) {
    keys.insert(it.first);
  }
  return keys;
}

Status GetInputValues(
    const SignatureDef& signature,
    const ::google::protobuf::Map<std::string, ::tensorflow::TensorProto>& request_inputs,
    std::vector<std::pair<string, Tensor>>& inputs) {
  const TensorProto* tensor_proto;

    const std::vector<std::string>& signature_keys,
    const std::unordered_set<std::string>& tags,
    const QuantizationOptions& quantization_options,
    const absl::flat_hash_map<std::string, SignatureDef>& signature_def_map,
    const PyFunctionLibrary& py_function_library,
    const absl::flat_hash_map<std::string, RepresentativeDatasetFile>&
        representative_dataset_file_map_serialized);

}

// This corresponds to SignatureDef in Tensorflow SavedModel.
// The SignatureDef will be part of the SavedModel provided for conversion.
table SignatureDef {
  // Named inputs for this signature.
  inputs:[TensorMap];

  // Named outputs for this signature.
  outputs:[TensorMap];

  // Key value which was in the Tensorflow SavedModel SignatureDef map.
  signature_key:string;

  std::vector<ChunkInfo> chunks_info = std::vector<ChunkInfo>(
      chunk_metadata.chunks().begin(), chunk_metadata.chunks().end());

  ::tensorflow::protobuf::Map<std::string, SignatureDef> signature_def_map;
  SignatureDef signature_def;
  EXPECT_THAT(HashSignatureDef(signature_def_map, chunk_metadata.message(),
                               reader, chunks_info),
              IsOkAndHolds(0));
}

  oneof file {
    // Represents representative dataset saved as a .tfrecord file format.
    TfRecordFile tf_record = 1;
  }

  // [TF SavedModel] Identifies a SignatureDef which represents a single
  // logical function in a graph.
  optional string signature_key = 2;
}

// Preset config for static-range post-training quantization (PTQ).
//
// Minimal user input about representative datasets is required. Representative

#include "tensorflow/core/platform/protobuf_internal.h"

namespace tensorflow {
namespace internal {

// A SavedModel may store the name of the initialization op to run in the
// in the SignatureDef (v2) or a collection (v1). If an init_op collection
// exists, then the collection must contain exactly one op.
Status GetInitOp(const string& export_dir, const MetaGraphDef& meta_graph_def,

  }
  return tensorflow::Fingerprint64(saved_model_serialized);
}

uint64_t RegularizeAndHashSignatureDefs(
    const Map<std::string, SignatureDef>& signature_def_map) {
  // Sort `signature_def_map`, which is an unordered map from string keys to
  // SignatureDefs.
  absl::btree_map<std::string, SignatureDef> sorted_signature_defs;
  sorted_signature_defs.insert(signature_def_map.begin(),
                               signature_def_map.end());

}

// This corresponds to SignatureDef in Tensorflow SavedModel.
// The SignatureDef will be part of the SavedModel provided for conversion.
table SignatureDef {
  // Named inputs for this signature.
  inputs:[TensorMap];

  // Named outputs for this signature.
  outputs:[TensorMap];

  // Key value which was in the Tensorflow SavedModel SignatureDef map.
  signature_key:string;

// Loads a SavedModel at `saved_model_path` and converts it to `mlir::ModuleOp`.
//
// `tags` identify the `tensorflow::MetaGraphDef` to load from the SavedModel.
// Similarly, `signature_keys` identify the functions (`SignatureDef`s) to load
// within the `MetaGraphDef`. `ctx` is the `MLIRContext`, which should outlive
// the returned `ModuleOp`, thus marked with the lifetime bound attribute.