message: Add an integration tests for the new post-training quantization implementation
  And compare the results with the old implementation.
diff: @@ -266,6 +266,32 @@ class FromConcreteFunctionTest(TestModels):
  # Ensure that the quantized weights tflite model is smaller.
  self.assertLess(len(quantized_tflite), len(float_tflite))
  + @test_util.run_v2_only
  + def testNewQuantizer(self):
  + """Test the model quantized by the new converter."""
  + func, calibration_gen = self._getCalibrationQuantizeModel()
  +
  + quantized_converter = lite.TFLiteConverterV2.from_concrete_functions([func])
  + quantized_converter.target_spec.supported_ops = [
  + lite.OpsSet.TFLITE_BUILTINS_INT8
  + ]
  + quantized_converter.representative_dataset = calibration_gen
  +
  + # default quantizer
  + quantized_converter.experimental_new_quantizer = False
  + old_tflite = quantized_converter.convert()
  +
  + # new quantizer
  + quantized_converter.experimental_new_quantizer = True
  + new_tflite = quantized_converter.convert()
  +
  + for _ in range(5):
  + input_data = constant_op.constant(
  + np.random.uniform(-1, 1, size=(1, 5, 5, 3)).astype(np.float32))
  + old_value = self._evaluateTFLiteModel(old_tflite, [input_data])
  + new_value = self._evaluateTFLiteModel(new_tflite, [input_data])
  + np.testing.assert_almost_equal(old_value, new_value, 1)
  +
  @parameterized.named_parameters(
  ('EnableMlirConverter', True), # enable mlir
  ('DisableMlirConverter', False)) # disable mlir
  