mmuCurve2 := trace.NewMMUCurve(mu)
		quantiles := []float64{0, 1 - .999, 1 - .99}
		for window := time.Microsecond; window < time.Second; window *= 10 {
			mud1 := mmuCurve.MUD(window, quantiles)
			mud2 := mmuCurve2.MUD(window, quantiles)
			for i := range mud1 {
				if !aeq(mud1[i], mud2[i]) {
					t.Errorf("for quantiles %v at window %v, want %v, got %v", quantiles, window, mud2, mud1)
					break
				}
			}
		}

					}
				}
			}
		}
	}

	var memstats runtime.MemStats
	runtime.ReadMemStats(&memstats)

	gcStats := debug.GCStats{
		// If stats.PauseQuantiles is non-empty, ReadGCStats fills
		// it with quantiles summarizing the distribution of pause time.
		// For example, if len(stats.PauseQuantiles) is 5, it will be
		// filled with the minimum, 25%, 50%, 75%, and maximum pause times.
		PauseQuantiles: make([]time.Duration, 5),
	}

#include "tensorflow/compiler/mlir/quantization/stablehlo/quantization_config.pb.h"
#include "tensorflow/compiler/mlir/quantization/stablehlo/quantization_options.pb.h"

namespace mlir::quant::stablehlo {

// Creates a pass that quantizes weight component of StableHLO graph.
std::unique_ptr<OperationPass<func::FuncOp>> CreateQuantizeWeightPass(
    const ::stablehlo::quantization::QuantizationComponentSpec&
        quantization_component_spec = {});

#include "tensorflow/compiler/mlir/lite/debug/debug_options.pb.h"
#include "tensorflow/compiler/mlir/lite/schema/schema_generated.h"
#include "tensorflow/lite/c/c_api_types.h"

namespace mlir {
namespace lite {

// Quantizes the input model represented as `model_buffer` and writes the result
// to the `output_buffer`. Both `model_buffer` and `output_buffer` should be a
// valid FlatBuffer format for Model supported by TFLite.
//

  return False


# TODO: b/310594193 - Export API to pip package.
def quantize_saved_model(
    src_saved_model_path: str,
    dst_saved_model_path: str,
    config: qc.QuantizationConfig,
) -> None:
  """Quantizes a saved model.

  Args:
    src_saved_model_path: Path to the directory for the source SavedModel.
    dst_saved_model_path: Path to the directory for the destination SavedModel.
    config: Quantization configuration.

	}

	return sm, nil
}

func getLatency(promAPI promv1.API, workloadName, workloadNamespace string, duration time.Duration, quantile float64) (time.Duration, error) {
	latencyQuery := fmt.Sprintf(`histogram_quantile(%f, sum(rate(%s_bucket{%s=~"%s.*", %s=~"%s.*",reporter="destination"}[%s])) by (le))`,
		quantile, reqDur, destWorkloadLabel, workloadName, destWorkloadNamespaceLabel, workloadNamespace, duration)