Memory Leak in AveragePrecision

Nobody has claimed this yet.

## 🐛 Bug

The gpu memory usage keeps growing when using AveragePrecision. This doesn't happen with other metrics (Accuracy and Precision).

Is this the expected behaviour for this metric?

### To Reproduce

Code sample

```python
from torchmetrics import Accuracy, Precision, AveragePrecision
import torch

def get_memory():
return torch.cuda.memory_allocated(0) / (1024**2)

def loop_metric(metric, repetitions=20, loop_length=100):
print(type(metric).__name__)
print(f"{get_memory():.3f}MB", end="")
for i in range(repetitions):
for j in range(loop_length):
metric(pred, target)
print(f" -> {get_memory():.3f}MB", end="")
print("\n")

N_LABELS = 16
avg_p = AveragePrecision(task="multilabel", num_labels=N_LABELS).to("cuda")
prec = Precision(task="multilabel", num_labels=N_LABELS).to("cuda")
acc = Accuracy(task="multilabel", num_labels=N_LABELS).to("cuda")

BATCH_SIZE = 2056
target = torch.randint(size=(BATCH_SIZE, N_LABELS), low=0, high=2, device="cuda")
pred = torch.rand(size=(BATCH_SIZE, N_LABELS), device="cuda")

print()
loop_metric(acc)
loop_metric(prec)
loop_metric(avg_p)
```

Environment

- TorchMetrics: 1.8.2
- Python: 3.13.7
- PyTorch: 2.9.0+cu126
- Happens in Linux and Windows 10

### Additional context

I ran into a OOM error when using AveragePrecision during model training.
Using gc.collect() and torch.cuda.empty_cache() wasn't able to prevent it, and I tracked the issue down to this metric.

Is there some function that needs to be called to free this memory?

Start with the provided loop_metric reproducer and the AveragePrecision entry point, comparing its behavior with Accuracy and Precision on CUDA. Trace what state is retained across repeated metric calls and verify the result with the same environment. Done means repeated AveragePrecision calls no longer cause unbounded GPU memory growth, with a regression test covering the reproducer.

Written by the indexing model from the issue text.