message: Disable grappler and freezing steps when MLIR SavedModel conversion path is on
diff: @@ -72,6 +72,7 @@ from tensorflow.python.framework.errors_impl import NotFoundError as _NotFoundEr
  from tensorflow.python.framework.importer import import_graph_def as _import_graph_def
  from tensorflow.python.keras.saving import saving_utils as _saving_utils
  from tensorflow.python.lib.io import file_io as _file_io
  +from tensorflow.python.saved_model import loader_impl as _loader_impl
  from tensorflow.python.saved_model import signature_constants as _signature_constants
  from tensorflow.python.saved_model import tag_constants as _tag_constants
  from tensorflow.python.saved_model.load import load as _load
  @@ -597,10 +598,29 @@ class TFLiteConverterV2(TFLiteConverterBase):
  self._parse_saved_model_args()
  # graph_def is used here to preserve the node bug information
  + if self._saved_model_dir:
  + graph = _ops.Graph()
  + saved_model = _loader_impl.SavedModelLoader(self._saved_model_dir)
  + saved_model.load_graph(graph, tags=self._saved_model_tags)
  + meta_graph = saved_model.get_meta_graph_def_from_tags(
  + self._saved_model_tags)
  + signature_def = meta_graph.signature_def[
  + _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY]
  + input_tensors = [
  + graph.get_tensor_by_name(signature_def.inputs[key].name)
  + for key in signature_def.inputs
  + ]
  + output_tensors = [
  + graph.get_tensor_by_name(signature_def.outputs[key].name)
  + for key in signature_def.outputs
  + ]
  + self._graph_def = graph_def = meta_graph.graph_def
  + else:
  frozen_func, graph_def = (
  _convert_to_constants.convert_variables_to_constants_v2_as_graph(
  self._funcs[0], lower_control_flow=False))
  self._graph_def = graph_def
  +
  input_tensors = [
  tensor for tensor in frozen_func.inputs
  if tensor.dtype != _dtypes.resource
  @@ -1231,6 +1251,7 @@ class TFLiteConverter(TFLiteConverterBase):
  "are not enabled.")
  optimized_graph = self._graph_def
  + if not self._saved_model_dir:
  # if it is not uint8 or int8 with post-training quantization, it is not
  # quantization aware training, then graph optimization is applied.
  # Graph optimization is disabled for quantization aware training.
  