diff --git a/src/torchmetrics/functional/classification/average_precision.py b/src/torchmetrics/functional/classification/average_precision.py
index 9645c7b1097..dcc5bfde5f7 100644
--- a/src/torchmetrics/functional/classification/average_precision.py
+++ b/src/torchmetrics/functional/classification/average_precision.py
@@ -460,7 +460,9 @@ def _average_precision_compute(
         weights = weights / torch.sum(weights)
     else:
         weights = None
-    return _average_precision_compute_with_precision_recall(precision, recall, num_classes=num_classes, average=average, weights=weights)
+    return _average_precision_compute_with_precision_recall(
+        precision, recall, num_classes=num_classes, average=average, weights=weights
+    )
 
 
 def _average_precision_compute_with_precision_recall(
diff --git a/src/torchmetrics/functional/classification/precision_recall_curve.py b/src/torchmetrics/functional/classification/precision_recall_curve.py
index 8683ee4dbfe..aa63f03b950 100644
--- a/src/torchmetrics/functional/classification/precision_recall_curve.py
+++ b/src/torchmetrics/functional/classification/precision_recall_curve.py
@@ -865,11 +865,7 @@ def _precision_recall_curve_compute_multi_class(
             num_classes=1,
         )
         if target.ndim > 1:
-            prc_args.update(
-                dict(
-                    target=target[:, cls]
-                )
-            )
+            prc_args.update(dict(target=target[:, cls]))
         res = precision_recall_curve(**prc_args)
         precision.append(res[0])
         recall.append(res[1])
@@ -922,7 +918,9 @@ def _precision_recall_curve_compute(
         if num_classes == 1:
             if pos_label is None:
                 pos_label = 1
-            return _precision_recall_curve_compute_single_class(preds, target, pos_label=pos_label, sample_weights=sample_weights)
+            return _precision_recall_curve_compute_single_class(
+                preds, target, pos_label=pos_label, sample_weights=sample_weights
+            )
         return _precision_recall_curve_compute_multi_class(preds, target, num_classes=num_classes)
 
 
diff --git a/src/torchmetrics/functional/classification/roc.py b/src/torchmetrics/functional/classification/roc.py
index 9d01b2babfd..5d990e91cc8 100644
--- a/src/torchmetrics/functional/classification/roc.py
+++ b/src/torchmetrics/functional/classification/roc.py
@@ -621,10 +621,24 @@ def roc(
         return binary_roc(preds, target, thresholds=thresholds, ignore_index=ignore_index, validate_args=validate_args)
     if task == "multiclass":
         assert isinstance(num_classes, int)
-        return multiclass_roc(preds, target, num_classes=num_classes, thresholds=thresholds, ignore_index=ignore_index, validate_args=validate_args)
+        return multiclass_roc(
+            preds,
+            target,
+            num_classes=num_classes,
+            thresholds=thresholds,
+            ignore_index=ignore_index,
+            validate_args=validate_args,
+        )
     if task == "multilabel":
         assert isinstance(num_labels, int)
-        return multilabel_roc(preds, target, num_labels=num_labels, thresholds=thresholds, ignore_index=ignore_index, validate_args=validate_args)
+        return multilabel_roc(
+            preds,
+            target,
+            num_labels=num_labels,
+            thresholds=thresholds,
+            ignore_index=ignore_index,
+            validate_args=validate_args,
+        )
     raise ValueError(
         f"Expected argument `task` to either be `'binary'`, `'multiclass'` or `'multilabel'` but got {task}"
     )