ruff --fix. Again...

tests/test_quantile_ops.py

@@ -25,7 +25,7 @@ def parse_and_visit(expr_str, variables):
 
 def test_quantile_basic():
     vars = {"t": torch.tensor([0.0, 1.0, 2.0, 3.0, 4.0]), "l": [0, 1, 2, 3, 4]}
-    
+
     # Quantile (0-1)
     assert parse_and_visit("quantile(t, 0.5)", vars) == 2.0
     assert parse_and_visit("quantile(l, 0.25)", vars) == 1.0
@@ -34,7 +34,7 @@ def test_quantile_basic():
 
 def test_percentile_basic():
     vars = {"t": torch.tensor([0.0, 1.0, 2.0, 3.0, 4.0]), "l": [0, 1, 2, 3, 4]}
-    
+
     # Percentile (0-100)
     assert parse_and_visit("percentile(t, 50)", vars) == 2.0
     assert parse_and_visit("percentile(l, 25)", vars) == 1.0
@@ -43,13 +43,13 @@ def test_percentile_basic():
 
 def test_quartile_basic():
     vars = {"t": torch.tensor([0.0, 1.0, 2.0, 3.0, 4.0]), "l": [0, 1, 2, 3, 4]}
-    
+
     # Quartile (0-4)
     assert parse_and_visit("quartile(t, 2)", vars) == 2.0
     assert parse_and_visit("quartile(l, 1)", vars) == 1.0
     assert parse_and_visit("quartile(t, 4)", vars) == 4.0
     assert parse_and_visit("quartile(l, 0)", vars) == 0.0
-    
+
     # Alias
     assert parse_and_visit("quartil(t, 2)", vars) == 2.0
 
@@ -57,7 +57,7 @@ def test_tensor_queries():
     t = torch.tensor([0.0, 10.0, 20.0, 30.0, 40.0])
     q_tensor = torch.tensor([0.0, 0.5, 1.0])
     vars = {"t": t, "q": q_tensor}
-    
+
     # Quantile with tensor q
     res = parse_and_visit("quantile(t, q)", vars)
     assert isinstance(res, torch.Tensor)
@@ -74,7 +74,7 @@ def test_list_with_tensor_query():
     l = [0, 10, 20, 30, 40]
     q = torch.tensor([0.25, 0.75])
     vars = {"l": l, "q": q}
-    
+
     res = parse_and_visit("quantile(l, q)", vars)
     assert isinstance(res, torch.Tensor)
     assert torch.allclose(res, torch.tensor([10.0, 30.0]))
@@ -86,7 +86,7 @@ def test_nd_tensor_query():
     q_nd[0, 0, 1] = 1.0 # Max
     q_nd[1, 1, 1] = 0.5 # Mid
     vars = {"t": t, "q": q_nd}
-    
+
     res = parse_and_visit("quantile(t, q)", vars)
     assert res.shape == (2, 2, 2)
     assert res[0, 0, 0] == 0.0
@@ -100,7 +100,7 @@ def test_large_tensor_fallback():
     size = 1_000_000
     t = torch.rand(size)
     vars = {"t": t}
-    
+
     # Simple median
     res = parse_and_visit("quantile(t, 0.5)", vars)
     # torch.quantile(t, 0.5) should be very close to t.median()
@@ -110,7 +110,7 @@ def test_list_query():
     # percentile(t, [0, 10, 20]) -> returns a list of results
     t = torch.tensor([0.0, 10.0, 20.0, 30.0, 40.0])
     vars = {"t": t}
-    
+
     res = parse_and_visit("percentile(t, [0, 50, 100])", vars)
     assert isinstance(res, list)
     assert len(res) == 3