diff --git a/python/hsml/util.py b/python/hsml/util.py
index c47733d50..6ef6d9053 100644
--- a/python/hsml/util.py
+++ b/python/hsml/util.py
@@ -232,15 +232,15 @@ def validate_metrics(metrics):
 
 def get_predictor_for_model(model, **kwargs):
     from hsml.model import Model as BaseModel
+    from hsml.predictor import Predictor as BasePredictor
     from hsml.python.model import Model as PyModel
+    from hsml.python.predictor import Predictor as PyPredictor
     from hsml.sklearn.model import Model as SkLearnModel
+    from hsml.sklearn.predictor import Predictor as SkLearnPredictor
     from hsml.tensorflow.model import Model as TFModel
+    from hsml.tensorflow.predictor import Predictor as TFPredictor
     from hsml.torch.model import Model as TorchModel
     from hsml.torch.predictor import Predictor as TorchPredictor
-    from hsml.predictor import Predictor as BasePredictor
-    from hsml.tensorflow.predictor import Predictor as TFPredictor
-    from hsml.sklearn.predictor import Predictor as SkLearnPredictor
-    from hsml.python.predictor import Predictor as PyPredictor
     if not isinstance(model, BaseModel):
         raise ValueError(
             "model is of type {}, but an instance of {} class is expected".format(
@@ -248,15 +248,15 @@ def get_predictor_for_model(model, **kwargs):
             )
         )
 
-    if type(model) == TFModel:
+    if type(model) is TFModel:
         return TFPredictor(**kwargs)
-    if type(model) == TorchModel:
+    if type(model) is TorchModel:
         return TorchPredictor(**kwargs)
-    if type(model) == SkLearnModel:
+    if type(model) is SkLearnModel:
         return SkLearnPredictor(**kwargs)
-    if type(model) == PyModel:
+    if type(model) is PyModel:
         return PyPredictor(**kwargs)
-    if type(model) == BaseModel:
+    if type(model) is BaseModel:
         return BasePredictor(  # python as default framework and model server
             model_framework=MODEL.FRAMEWORK_PYTHON,
             model_server=PREDICTOR.MODEL_SERVER_PYTHON,