diff --git a/optimum/executorchruntime/modeling_executorch.py b/optimum/executorchruntime/modeling_executorch.py
index 39c75a03863..b93309f6a48 100644
--- a/optimum/executorchruntime/modeling_executorch.py
+++ b/optimum/executorchruntime/modeling_executorch.py
@@ -17,7 +17,7 @@
 import warnings
 from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import TYPE_CHECKING, List, Optional, Union
+from typing import List, Optional, Union
 
 import torch
 from executorch.extension.pybindings.portable_lib import (
@@ -35,10 +35,6 @@
 from ..modeling_base import OptimizedModel
 
 
-if TYPE_CHECKING:
-    from transformers import PretrainedConfig
-
-
 logger = logging.getLogger(__name__)
 
 
diff --git a/optimum/onnxruntime/runs/__init__.py b/optimum/onnxruntime/runs/__init__.py
index d21db2a4aca..1d982949344 100644
--- a/optimum/onnxruntime/runs/__init__.py
+++ b/optimum/onnxruntime/runs/__init__.py
@@ -110,9 +110,9 @@ def __init__(self, run_config):
         model_class = FeaturesManager.get_model_class_for_feature(get_autoclass_name(self.task))
         self.torch_model = model_class.from_pretrained(run_config["model_name_or_path"])
 
-        self.return_body["model_type"] = (
-            self.torch_model.config.model_type
-        )  # return_body is initialized in parent class
+        self.return_body[
+            "model_type"
+        ] = self.torch_model.config.model_type  # return_body is initialized in parent class
 
     def _launch_time(self, trial):
         batch_size = trial.suggest_categorical("batch_size", self.batch_sizes)