const GraphDef& graphdef, const GraphDebugInfo& debug_info,
    const GraphImportConfig& specs, mlir::MLIRContext* context);

// Given a Graph, returns a MLIR module containing the graph, expressed with
// tf_executor dialect.
absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> ConvertGraphToMlir(
    const Graph& graph, const GraphDebugInfo& debug_info,

                      internal::FileExists(Env::Default(), debug_info_pb_path));
  if (debug_info_pb_exists) {
    GraphDebugInfo debug_info;
    TF_RETURN_IF_ERROR(
        ReadBinaryProto(Env::Default(), debug_info_pb_path, &debug_info));
    *debug_info_proto = std::make_unique<GraphDebugInfo>(std::move(debug_info));
  }
  return absl::OkStatus();
}

// it fails to convert the input.
absl::Status ConvertGraphDefToTFLiteFlatBuffer(
    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,
    const GraphDebugInfo& debug_info, const GraphDef& input,
    std::string* result);

}  // namespace tensorflow

                                      MetaGraphDef* meta_graph_def);

// Store debug info from the SavedModel export dir.
Status ReadSavedModelDebugInfoIfPresent(
    absl::string_view export_dir,
    std::unique_ptr<GraphDebugInfo>* debug_info_proto);

}  // namespace tensorflow

// flag is set to true returns a dictionary that contains string representation
// of the converted model and some statistics like arithmetic ops count.
// `debug_info_str` contains the `GraphDebugInfo` proto. When
// `enable_mlir_converter` is True, use MLIR-based conversion instead of
// TOCO conversion.
PyObject* Convert(PyObject* model_flags_proto_txt_raw,
                  PyObject* toco_flags_proto_txt_raw,

  }
  auto convert = [&]() -> Status {
    mlir::StatusScopedDiagnosticHandler status_handler(&context);
    // TODO(jpienaar): Both the graph debug info and import config should be
    // specifiable.
    GraphDebugInfo debug_info;
    switch (config.dialect) {
      case MlirDumpConfig::Dialect::kTFG: {
        TF_ASSIGN_OR_RETURN(module,
                            mlir::tfg::ImportGraphAndFunctionsToMlir(

#include "tsl/platform/errors.h"
#include "tsl/platform/statusor.h"

namespace tensorflow {

absl::Status ConvertGraphDefToTFLiteFlatBuffer(
    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,
    const GraphDebugInfo& debug_info, const GraphDef& input,
    std::string* result) {
  using ::tflite::optimize::ReducedPrecisionSupport;
  mlir::MLIRContext context;
  GraphImportConfig specs;
  mlir::quant::QuantizationSpecs quant_specs;

      flag is set to true returns a dictionary that contains string representation
      of the converted model and some statistics like arithmetic ops count.
      `debug_info_str` contains the `GraphDebugInfo` proto. When
      `enable_mlir_converter` is True, tuse MLIR-based conversion instead of
      TOCO conversion.
    )pbdoc");
  m.def(
      "ExperimentalMlirQuantizeModel",

      mlir::emitError(mlir::UnknownLoc::get(&ctx))
          << pass->name() << ": " << status.message();
      return signalPassFailure();
    }
  }

  // Convert Graph to MLIR
  GraphDebugInfo debug_info;
  GraphImportConfig specs;
  auto module_or_status =
      ConvertGraphToMlir(**options.graph, debug_info, flib_def, specs, &ctx);
  if (!module_or_status.ok()) {