public:
  SavedModelMLIRImportInput(const MetaGraphDef* meta_graph_def,
                            const GraphDebugInfo& debug_info)
      : meta_graph_def_(meta_graph_def), debug_info_(debug_info) {
    DCHECK(meta_graph_def);
  }

  virtual ~SavedModelMLIRImportInput();

  // The original MetaGraphDef of the savedmodel.
  const MetaGraphDef& meta_graph_def() const { return *meta_graph_def_; }

  // exported model is produced. It is used to copy the asset files to
  // `dst_saved_model_path`. `tags` will be attached to the saved
  // `MetaGraphDef`. `signature_def_map` will be passed to the
  // `add_meta_graph_and_variables` function, which is internally used to add a
  // `MetaGraphDef` to save to the SavedModel.
  //
  // Returns `true` if successful. Returns `std::nullopt` otherwise.
  //

// 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.

  the representative dataset for the actual data provided for inference.

  Args:
    model_dir: Path to SavedModel directory.
    tags: Collection of tags identifying the MetaGraphDef within the SavedModel.
    representative_dataset_map: A map where signature keys are mapped to
      corresponding representative datasets.

  Raises:

  // Function alias mapping for pre-calibrated SavedModel. Used to preserve
  // aliased functions.
  absl::flat_hash_map<FunctionName, FunctionAlias> function_aliases_;

  // Tags to identify the MetaGraphDef to load from a SavedModel.
  const std::unordered_set<std::string> tags_;

  const absl::flat_hash_map<std::string, tensorflow::SignatureDef>
      signature_def_map_;

}

absl::StatusOr<absl::flat_hash_map<FunctionName, FunctionAlias>>
GetFunctionAliases(absl::string_view saved_model_path,
                   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(

message WeightOnlyPtqPreset {}

// Metadata specific to the input TensorFlow SavedModel, which may be required
// to identify the specific MetaGraphDef to quantize, for example.
// Next ID: 2
message TfSavedModelConfig {
  // Set of tags that uniquely identify the `MetaGraphDef` existing in the
  // input SavedModel.
  repeated string tags = 1;
}

// Configures the graph transformation pipeline for quantization.

    absl::string_view saved_model_dir,
    const std::unordered_set<std::string>& tags,
    absl::Span<std::string> exported_names, mlir::MLIRContext* context,
    MLIRImportOptions options) {
  MetaGraphDef meta_graph_def;
  auto status =
      ReadMetaGraphDefFromSavedModel(saved_model_dir, tags, &meta_graph_def);
  if (!status.ok()) {
    LOG(ERROR) << "Failed to load saved model v1 '" << saved_model_dir

  Args:
    saved_model_path: Path to the saved model.
    signature_keys: List of keys identifying SignatureDef to retrieve. If None,
      retrieve all except the init signature.
    tags: Set of tags identifying the MetaGraphDef within the SavedModel.

  Returns:
    A map from signature_key to its SignatureDef.
  """
  if tags is None:
    tags = {tag_constants.SERVING}

  loader = saved_model_loader.SavedModelLoader(saved_model_path)

  Returns:
    A SavedModel object with TF quantization applied.

  Raises:
    ValueError: when representative_dataset is not provided for non-QAT model.
    RuntimeError: When a MetaGraphDef could not be found associated with `tags`
      in the SavedModel.
  """
  logging.info(
      'Running static range quantization on model: %s', src_saved_model_path
  )