Enable MLIR saved model import by default in TFLiteConverterV2's saved model API

PiperOrigin-RevId: 311792366
Change-Id: I98356499c0a1eb7c740104ca4b11af5d45c4a4a1

tensorflow/lite/python/lite.py

@@ -386,13 +386,8 @@ class TFLiteConverterBase(object):
         return True
     return False
 
-  def _parse_saved_model_args(self, always_enable_saved_model_import=False):
-    """Parses SavedModel arguments from the given Keras/RNN SavedModel.
-
-    Args:
-      always_enable_saved_model_import: Bool. When the value is true, it enables
-        MLIR saved model import path regardless of checking the conditions.
-    """
+  def _parse_saved_model_args(self):
+    """Parses SavedModel arguments from the given Keras/RNN SavedModel."""
     if not self.experimental_new_converter:
       self.saved_model_dir = None
       return
@@ -405,16 +400,15 @@ class TFLiteConverterBase(object):
         # frozen graph def path.
         self.saved_model_dir = None
         return
-      if (not always_enable_saved_model_import and
-          not self._contains_function_with_implements_attr(saved_model_proto)):
+      if not self._contains_function_with_implements_attr(saved_model_proto):
         self.saved_model_dir = None
-        return
-
+      else:
         if not self._saved_model_exported_names:
           self._saved_model_exported_names = []
         self._saved_model_version = saved_model_proto.saved_model_schema_version
         if self._saved_model_version not in [1, 2]:
-        raise ValueError("SavedModel file format({0}) is not supported".format(
+          raise ValueError(
+              "SavedModel file format({0}) is not supported".format(
                   self._saved_model_version))
 
 
@@ -548,7 +542,7 @@ class TFLiteSavedModelConverterV2(TFLiteConverterBaseV2):
     self._saved_model_tags = saved_model_tags
     self._saved_model_exported_names = saved_model_exported_names
     self._trackable_obj = trackable_obj
-    self._parse_saved_model_args(always_enable_saved_model_import=True)
+    self._parse_saved_model_args()
 
   def convert(self):
     """Converts a TensorFlow GraphDef based on instance variables.