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) {

  // Find the FuncOp which corresponds to current SignatureDef.
  mlir::SymbolTable sub_symbol_table(*sub_module);
  auto func_op = sub_symbol_table.lookup<mlir::func::FuncOp>(sig_def_key);
  TF_RET_CHECK(func_op)
      << "Graphdef importer should have created a function named "
      << sig_def_key << ".";

  // Use unique SignatureDef key as exported name.
  func_op->setAttr(kTfSavedModelExportedNamesAttr,

    seen_indices.insert(arg_index);
  }
  func->setAttr(
      kTfSavedModelExportedNamesAttr,
      mlir::ArrayAttr::get(
          context, {mlir::StringAttr::get(context, signature->signature_key)}));
}

// There are control nodes at each end of each control edge. For each of them,
// we store the source vertices of the incoming edges (if any) and the control

    *   Add 5D support to `SLICE` op.
    *   TFLite Supports SingatureDef:
        *   TFLiteConverter exports models with SignatureDef
        *   Interpreter supports getting a list of signatures and getting
            callable function for a given signaturedef.
    *   Add int8 support for `ReshapeV2`.
    *   Add experimental support for optimization with sparsity.