diff --git a/examples/pytorch/metapath2vec/test.py b/examples/pytorch/metapath2vec/test.py
index 7ed86685aef5..3134c386d283 100644
--- a/examples/pytorch/metapath2vec/test.py
+++ b/examples/pytorch/metapath2vec/test.py
@@ -98,9 +98,9 @@
             random_state=0, solver="lbfgs"
         ).fit(venue_training, venue_label)
         y_pred_venue = clf_venue.predict(venue_testing)
-        clf_author = LogisticRegression(
-            random_state=0, solver="lbfgs"
-        ).fit(author_training, author_label)
+        clf_author = LogisticRegression(random_state=0, solver="lbfgs").fit(
+            author_training, author_label
+        )
         y_pred_author = clf_author.predict(author_testing)
         macro_average_venue += f1_score(
             venue_true, y_pred_venue, average="macro"
diff --git a/examples/pytorch/node2vec/model.py b/examples/pytorch/node2vec/model.py
index 2dce23225d3e..282799bbe1fb 100644
--- a/examples/pytorch/node2vec/model.py
+++ b/examples/pytorch/node2vec/model.py
@@ -190,9 +190,9 @@ def evaluate(self, x_train, y_train, x_val, y_val):
 
         x_train, y_train = x_train.cpu().numpy(), y_train.cpu().numpy()
         x_val, y_val = x_val.cpu().numpy(), y_val.cpu().numpy()
-        lr = LogisticRegression(
-            solver="lbfgs", max_iter=150
-        ).fit(x_train, y_train)
+        lr = LogisticRegression(solver="lbfgs", max_iter=150).fit(
+            x_train, y_train
+        )
 
         return lr.score(x_val, y_val)