diff --git a/supervision/tracker/byte_tracker/byte_tracker.py b/supervision/tracker/byte_tracker/byte_tracker.py
index bad675e5..b09cda36 100644
--- a/supervision/tracker/byte_tracker/byte_tracker.py
+++ b/supervision/tracker/byte_tracker/byte_tracker.py
@@ -397,7 +397,7 @@ class ByteTrack:
         # Predict the current location with KF
         Strack.multi_predict(strack_pool)
         dists = matching.iou_distance(strack_pool, detections)
-        
+
         dists = matching.fuse_score(dists, detections)
         matches, u_track, u_detection = matching.linear_assignment(
             dists, thresh=self.match_thresh
@@ -451,7 +451,7 @@ class ByteTrack:
         """Deal with unconfirmed tracks, usually tracks with only one beginning frame"""
         detections = [detections[i] for i in u_detection]
         dists = matching.iou_distance(unconfirmed, detections)
-        
+
         dists = matching.fuse_score(dists, detections)
         matches, u_unconfirmed, u_detection = matching.linear_assignment(
             dists, thresh=0.7
diff --git a/supervision/tracker/byte_tracker/matching.py b/supervision/tracker/byte_tracker/matching.py
index ba576fdd..a0c18425 100644
--- a/supervision/tracker/byte_tracker/matching.py
+++ b/supervision/tracker/byte_tracker/matching.py
@@ -1,7 +1,7 @@
 import numpy as np
 import scipy
-from scipy.spatial.distance import cdist
 from scipy.optimize import linear_sum_assignment
+from scipy.spatial.distance import cdist
 
 from supervision.tracker.byte_tracker import kalman_filter
 
@@ -42,7 +42,11 @@ def linear_assignment(cost_matrix, thresh):
     :return: matches, unmatched_a, unmatched_b
     """
     if cost_matrix.size == 0:
-        return np.empty((0, 2), dtype=int), tuple(range(cost_matrix.shape[0])), tuple(range(cost_matrix.shape[1]))
+        return (
+            np.empty((0, 2), dtype=int),
+            tuple(range(cost_matrix.shape[0])),
+            tuple(range(cost_matrix.shape[1])),
+        )
 
     cost_matrix[cost_matrix > thresh] = thresh + 1e-4
     row_ind, col_ind = linear_sum_assignment(cost_matrix)