diff --git a/torchmetrics/classification/binned_precision_recall.py b/torchmetrics/classification/binned_precision_recall.py
index 6ba2e0a7354..f683618d234 100644
--- a/torchmetrics/classification/binned_precision_recall.py
+++ b/torchmetrics/classification/binned_precision_recall.py
@@ -34,11 +34,10 @@ def _recall_at_precision(
 
     except ValueError:
         max_recall = torch.tensor(0.0, device=recall.device, dtype=recall.dtype)
+        best_threshold = torch.tensor(0)
 
     if max_recall == 0.0:
         best_threshold = torch.tensor(1e6, device=thresholds.device, dtype=thresholds.dtype)
-    else:
-        best_threshold = torch.tensor(0)
 
     return max_recall, best_threshold
 
@@ -128,7 +127,7 @@ class BinnedPrecisionRecallCurve(Metric):
     def __init__(
         self,
         num_classes: int,
-        thresholds: Optional[Union[Tensor, List[float]]] = None,
+        thresholds: Union[float, Tensor, List[float], None] = None,
         compute_on_step: bool = True,
         dist_sync_on_step: bool = False,
         process_group: Optional[Any] = None,
@@ -187,7 +186,7 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
             self.FPs[:, i] += ((~target) & (predictions)).sum(dim=0)
             self.FNs[:, i] += ((target) & (~predictions)).sum(dim=0)
 
-    def compute(self) -> Tuple[Tensor, Tensor, Tensor]:
+    def compute(self) -> Union[Tuple[Tensor, Tensor, Tensor], Tuple[List[Tensor], List[Tensor], List[Tensor]]]:
         """Returns float tensor of size n_classes"""
         precisions = (self.TPs + METRIC_EPS) / (self.TPs + self.FPs + METRIC_EPS)
         recalls = self.TPs / (self.TPs + self.FNs + METRIC_EPS)
@@ -198,8 +197,8 @@ def compute(self) -> Tuple[Tensor, Tensor, Tensor]:
         t_zeros = torch.zeros(self.num_classes, 1, dtype=recalls.dtype, device=recalls.device)
         recalls = torch.cat([recalls, t_zeros], dim=1)
         if self.num_classes == 1:
-            return (precisions[0, :], recalls[0, :], self.thresholds)
-        return (list(precisions), list(recalls), [self.thresholds for _ in range(self.num_classes)])
+            return precisions[0, :], recalls[0, :], self.thresholds
+        return list(precisions), list(recalls), [self.thresholds for _ in range(self.num_classes)]
 
 
 class BinnedAveragePrecision(BinnedPrecisionRecallCurve):