Remove param to disable new converter. New converter launched already.

PiperOrigin-RevId: 335515066

Author: karimnosseir

tensorflow_model_optimization/python/core/quantization/keras/quantize_functional_test.py

@@ -317,8 +317,7 @@ def testQuantizeSingleLayer_ProducesFullIntegerModel_TF2(
           model=quantized_model,
           output_path=quantized_tflite_file,
           is_quantized=True,
-          input_quant_params=(0., 1.),
-          experimental_new_converter=True)
+          input_quant_params=(0., 1.))
 
     interpreter = tf.lite.Interpreter(model_path=quantized_tflite_file)
     interpreter.allocate_tensors()
@@ -407,11 +406,7 @@ def testQuantizeSingleLayer_ProducesFullIntegerModel_TF1(
           is_quantized=True,
           inference_type=tf.uint8,
           inference_input_type=tf.uint8,
-          input_quant_params=(0., 1.),
-          # Set to False to throw errors when FakeQuants are
-          # not placed everywhere to create full-integer model. Errors
-          # are not thrown when set to True.
-          experimental_new_converter=False)
+          input_quant_params=(0., 1.))
 
   # pylint: enable=g-complex-comprehension,undefined-variable
 

tensorflow_model_optimization/python/core/quantization/keras/utils.py

@@ -28,8 +28,7 @@ def convert_keras_to_tflite(model,
                             is_quantized=True,
                             inference_type=None,
                             inference_input_type=None,
-                            input_quant_params=(-128., 255.),
-                            experimental_new_converter=True):
+                            input_quant_params=(-128., 255.)):
   """Convert Keras model to TFLite."""
   if custom_objects is None:
     custom_objects = {}
@@ -42,8 +41,6 @@ def convert_keras_to_tflite(model,
     converter = tf.lite.TFLiteConverter.from_keras_model_file(
         keras_file, custom_objects=custom_objects)
 
-  converter.experimental_new_converter = experimental_new_converter
-
   if is_quantized:
     if not compat.is_v1_apis():
       converter.optimizations = [tf.lite.Optimize.DEFAULT]