`GeneralizedDiceScore` yields 0 scores when using `per_class=True` for samples where class is not present

@VijayVignesh1 is already working on this.

Since Sep 3, 2025.

## 🐛 Bug

The current implementation of `GeneralizedDiceScore` yields scores of `0.0` for samples that don't contain a particular class when calculating class-wise metrics via `per_class=True`.

This leads to very low dice scores, particularly for rare classes and therefore makes the dice scores between classes incomparable.

### To Reproduce

The following code sample calculates class-wise scores of `tensor([0.2500, 0.2500, 0.0000])`, even though all the predictions match the targets:

Code sample

```python
import torch
from torchmetrics.segmentation import GeneralizedDiceScore
from torchmetrics.segmentation import DiceScore

N_SAMPLES = 4
N_CLASSES = 3

target = torch.full((N_SAMPLES, N_CLASSES, 128, 128), 0, dtype=torch.int8)
preds = torch.full((N_SAMPLES, N_CLASSES, 128, 128), 0, dtype=torch.int8)

target[0, 0], preds[0, 0] = 1, 1
target[2, 1], preds[2, 1] = 1, 1

generalized_dice = GeneralizedDiceScore(num_classes=3, per_class=True, include_background=True)
print(generalized_dice(preds, target))
```

### Expected behavior

I'd expect the above code sample to return `[1.0, 1.0, nan]` for the class-wise scores (`nan` for the third class, given that this class is not present in any of the samples, therefore returning a 1.0 score might also be misleading). Also, samples where the class doesn't occur should not contribute to the dice score of that class.

### Environment

- TorchMetrics version (if build from source, add commit SHA): `1.6.0`
- Python & PyTorch Version (e.g., 1.0): `3.11.10`
- Any other relevant information such as OS (e.g., Linux): macOS 15.1.1 (24B91)

### Additional context

Very similar to issue #2850.