Apply ruff format

Author: arames

tests/ops/test_ops.py

@@ -2924,12 +2924,27 @@ def forward(self, x: Tensor) -> Tensor:
         # mean.default: global mean (dim=None), int32 input → float32 output.
         # torch promotes the operand to the requested dtype BEFORE averaging.
         (torch.randint(0, 10, (3, 4), dtype=torch.int32), None, False, torch.float32),
-        (torch.randint(-5, 5, (2, 3, 4), dtype=torch.int32), None, False, torch.float32),
+        (
+            torch.randint(-5, 5, (2, 3, 4), dtype=torch.int32),
+            None,
+            False,
+            torch.float32,
+        ),
         # mean.dim: reduce along specific dimensions, int32 input → float32.
         (torch.randint(0, 10, (3, 4), dtype=torch.int32), 1, False, torch.float32),
         (torch.randint(0, 10, (3, 4), dtype=torch.int32), 0, True, torch.float32),
-        (torch.randint(-5, 5, (2, 3, 4), dtype=torch.int32), [0, 2], False, torch.float32),
-        (torch.randint(-5, 5, (2, 3, 4), dtype=torch.int32), [0, 2], True, torch.float32),
+        (
+            torch.randint(-5, 5, (2, 3, 4), dtype=torch.int32),
+            [0, 2],
+            False,
+            torch.float32,
+        ),
+        (
+            torch.randint(-5, 5, (2, 3, 4), dtype=torch.int32),
+            [0, 2],
+            True,
+            torch.float32,
+        ),
         # dtype-agnostic: root cause is independent of the specific int operand
         # dtype or the specific float target dtype.
         (torch.randint(0, 10, (3, 4), dtype=torch.int64), 1, False, torch.float32),