// https://github.com/tensorflow/tensorflow/blob/9bcefa44cd335c1db4a703a13da09f29ae1bbdb2/tensorflow/python/saved_model/signature_serialization.py#L181-L230
// One of the critical requirements of SignatureDef functions is that their
// inputs and outputs are "named". For example, a `.signatures` function:
// a. Requires users to pass: kwargs of all inputs:

#include "tensorflow/c/experimental/saved_model/public/function_metadata.h"

namespace tensorflow {
namespace experimental {
namespace cc {

// FunctionMetadata stores additional function information, including
// optional signaturedef feeds/fetches (for TF1-based ConcreteFunctions),
// a valid function path (for TF2-based ConcreteFunctions), and
// the types + number of inputs and outputs.
class FunctionMetadata final {

          py_function_library,
      absl::string_view src_saved_model_path,
      std::vector<std::string> signature_keys,
      std::unordered_set<std::string> tags,
      absl::flat_hash_map<std::string, tensorflow::SignatureDef>
          signature_def_map,
      absl::flat_hash_map<FunctionName, FunctionAlias> function_aliases);

  // Runs the static-range post-training quantization (PTQ) on `module_op`.
  absl::StatusOr<ModuleOp> Run(

        )

    self.assertIsNone(repr_dataset.get_num_samples(LenRaisingError()))

  @test_util.deprecated_graph_mode_only
  def test_create_feed_dict_from_input_data(self):
    signature_def = meta_graph_pb2.SignatureDef(
        inputs={'input_tensor': meta_graph_pb2.TensorInfo(name='input:0')}
    )
    rng = np.random.default_rng(seed=14)

    input_tensor_value = rng.random(size=(2, 2))

// https://github.com/tensorflow/tensorflow/blob/8ce0600f58ed84a8c84a7bbdb014d1f09e44f4c8/tensorflow/python/saved_model/load_v1_in_v2_test.py#L170-L174
// In both cases, a SignatureDef is serialized as a SignatureDef protobuf:
// https://github.com/tensorflow/tensorflow/blob/8ce0600f58ed84a8c84a7bbdb014d1f09e44f4c8/tensorflow/core/protobuf/meta_graph.proto#L260-L330
// and represents a computation defined by a TF subgraph.

    sess: session.Session,
    signature_def: meta_graph_pb2.SignatureDef,
    representative_dataset: rd.RepresentativeDataset,
) -> None:
  """Runs the representative dataset through a function for calibration.

  NOTE: This is intended to be run in graph mode (TF1).

  The function is identified by the SignatureDef.

  Args:
    sess: The Session object to run the function in.

absl::StatusOr<uint64_t> HashSignatureDef(
    const Map<std::string, ::tensorflow::SignatureDef>& signature_def_map,
    const ChunkedMessage& chunked_message,
    riegeli::RecordReader<riegeli::FdReader<>>& reader,
    const std::vector<ChunkInfo>& chunks_info) {
  uint64_t signature_def_hash = 0;
  std::vector<std::pair<std::string, ::tensorflow::SignatureDef>>
      signature_def_sorted(signature_def_map.begin(), signature_def_map.end());

#include "tsl/platform/statusor.h"

namespace mlir::quant::stablehlo {

using ::stablehlo::quantization::GetReportFilePath;
using ::stablehlo::quantization::QuantizationConfig;
using ::tensorflow::SignatureDef;
using ::tensorflow::quantization::ExportedModel;
using ::tensorflow::quantization::PyFunctionLibrary;
using ::tensorflow::quantization::RunPasses;

// weight-only quantization.
//
// `signature_keys` specify the signatures that correspond to functions to be
// quantized. `signature_def_map` connects the signature keys to
// `SignatureDef`s.
//
// Returns a non-OK status when the quantization is not successful.
// LINT.IfChange
absl::Status QuantizeWeightOnlyPtq(
    absl::string_view src_saved_model_path,
    absl::string_view dst_saved_model_path,

        `dst_saved_model_path`.

        The user should pass a serialized `QuantizationConfig` for the
        `quantization_config_serialized` argument, and a signature key ->
        serialized `SignatureDef` mapping for the `signature_def_map_serialized`
        argument.

        Raises `StatusNotOk` exception if when the run was unsuccessful.
        )pbdoc",