diff --git a/openvino_xai/methods/black_box/aise.py b/openvino_xai/methods/black_box/aise.py
index cc5cb6be..480d2766 100644
--- a/openvino_xai/methods/black_box/aise.py
+++ b/openvino_xai/methods/black_box/aise.py
@@ -6,9 +6,9 @@
 from abc import ABC, abstractmethod
 from typing import Callable, Dict, List, Mapping, Tuple
 
-
 import numpy as np
 import openvino.runtime as ov
+from openvino.runtime.utils.data_helpers.wrappers import OVDict
 from scipy.optimize import Bounds, direct
 
 from openvino_xai.common.parameters import Task
@@ -280,7 +280,7 @@ class AISEDetection(AISEBase):
     AISE for detection models.
 
     postprocess_fn expected to return three containers: boxes (format: [x1, y1, x2, y2]), scores, labels. Without batch dim.
-    
+
     :param model: OpenVINO model.
     :type model: ov.Model
     :param postprocess_fn: Post-processing function that extract scores from IR model output.
diff --git a/tests/unit/methods/black_box/test_black_box_method.py b/tests/unit/methods/black_box/test_black_box_method.py
index 3b354219..e7747f11 100644
--- a/tests/unit/methods/black_box/test_black_box_method.py
+++ b/tests/unit/methods/black_box/test_black_box_method.py
@@ -58,6 +58,7 @@ def postprocess_det_fn(x) -> np.ndarray:
         # return x["boxes"][:, :4], x["boxes"][:, 4], x["labels"]
         return x["boxes"][0][:, :4], x["boxes"][0][:, 4], x["labels"][0]
 
+
 class TestAISEClassification(InputSampling):
     @pytest.mark.parametrize("target_indices", [[0], [0, 1]])
     def test_run(self, target_indices, fxt_data_root: Path):