[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

tests/unittests/retrieval/test_ndcg.py

@@ -234,15 +234,14 @@ def test_accuracy_vs_sklearn(batch_size: int, list_length: int, top_k: Optional[
     """Batched nDCG must stay within 1e-4 of sklearn across configs.
 
     See issue: https://github.com/Lightning-AI/torchmetrics/issues/2287.
+
     """
     torch.manual_seed(42)
     scores = torch.randn(batch_size, list_length)
     labels = (torch.randint(0, 2, (batch_size, list_length)) * 2 - 1).float() + 1.0
 
     fast_result = retrieval_normalized_dcg(scores, labels, top_k=top_k).item()
-    sklearn_result = float(
-        np.mean([ndcg_score([t], [p], k=top_k) for t, p in zip(labels.numpy(), scores.numpy())])
-    )
+    sklearn_result = float(np.mean([ndcg_score([t], [p], k=top_k) for t, p in zip(labels.numpy(), scores.numpy())]))
 
     assert abs(fast_result - sklearn_result) <= 1e-4, (
         f"nDCG differs from sklearn by {abs(fast_result - sklearn_result):.2e} "
@@ -254,6 +253,7 @@ def test_batched_input_matches_per_query():
     """Batched 2-D input must give the same mean nDCG as averaging per-query 1-D results.
 
     See issue: https://github.com/Lightning-AI/torchmetrics/issues/2287.
+
     """
     torch.manual_seed(42)
     preds = torch.randn(16, 50)
@@ -269,6 +269,7 @@ def test_tie_handling_explicit():
     """Tie-averaged DCG must match sklearn on inputs with explicit score ties.
 
     See issue: https://github.com/Lightning-AI/torchmetrics/issues/2287.
+
     """
     scores = torch.tensor([
         [1.0, 1.0, 0.5, 0.5, 0.1],  # two pairs of ties
@@ -280,9 +281,7 @@ def test_tie_handling_explicit():
     ])
 
     result = retrieval_normalized_dcg(scores, labels)
-    sklearn_result = float(
-        np.mean([ndcg_score([t], [p]) for t, p in zip(labels.numpy(), scores.numpy())])
-    )
+    sklearn_result = float(np.mean([ndcg_score([t], [p]) for t, p in zip(labels.numpy(), scores.numpy())]))
 
     assert isinstance(result, torch.Tensor)
     assert 0.0 <= result.item() <= 1.0