Update loss.py type hints to Python 3.10+ syntax (#3501)

Modernize type hints in `ignite/metrics/loss.py` to use PEP 604 union
syntax and built-in generics, aligning with Python 3.10+ (now minimum
supported version).

## Changes

- `Union[str, torch.device]` → `str | torch.device`
- `Tuple[...]` → `tuple[...]`
- `Dict` → `dict`
- Improved `update()` signature from `Sequence[Union[torch.Tensor,
Dict]]` to `tuple[torch.Tensor, torch.Tensor] | tuple[torch.Tensor,
torch.Tensor, dict]` for better type safety
- Removed obsolete typing imports

```python
# Before
def update(self, output: Sequence[Union[torch.Tensor, Dict]]) -> None:
    if len(output) == 2:
        y_pred, y = cast(Tuple[torch.Tensor, torch.Tensor], output)
        kwargs: Dict = {}

# After  
def update(self, output: tuple[torch.Tensor, torch.Tensor] | tuple[torch.Tensor, torch.Tensor, dict]) -> None:
    if len(output) == 2:
        y_pred, y = cast(tuple[torch.Tensor, torch.Tensor], output)
        kwargs: dict = {}
```

Part of ongoing effort to modernize typing across the codebase.

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<details>

<summary>Original prompt</summary>

> Work on this issue: #3481
> Pick a python file from ignite/metrics with old typing hints and
replace them with the new typing hints. Send a PR with a single changed
file


</details>



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ignite/metrics/loss.py

@@ -1,4 +1,4 @@
-from typing import Callable, cast, Dict, Sequence, Tuple, Union
+from typing import Callable, cast
 
 import torch
 
@@ -77,7 +77,7 @@ def __init__(
         loss_fn: Callable,
         output_transform: Callable = lambda x: x,
         batch_size: Callable = len,
-        device: Union[str, torch.device] = torch.device("cpu"),
+        device: str | torch.device = torch.device("cpu"),
         skip_unrolling: bool = False,
     ):
         super(Loss, self).__init__(output_transform, device=device, skip_unrolling=skip_unrolling)
@@ -90,12 +90,12 @@ def reset(self) -> None:
         self._num_examples = 0
 
     @reinit__is_reduced
-    def update(self, output: Sequence[Union[torch.Tensor, Dict]]) -> None:
+    def update(self, output: tuple[torch.Tensor, torch.Tensor] | tuple[torch.Tensor, torch.Tensor, dict]) -> None:
         if len(output) == 2:
-            y_pred, y = cast(Tuple[torch.Tensor, torch.Tensor], output)
-            kwargs: Dict = {}
+            y_pred, y = cast(tuple[torch.Tensor, torch.Tensor], output)
+            kwargs: dict = {}
         else:
-            y_pred, y, kwargs = cast(Tuple[torch.Tensor, torch.Tensor, Dict], output)
+            y_pred, y, kwargs = cast(tuple[torch.Tensor, torch.Tensor, dict], output)
         average_loss = self._loss_fn(y_pred, y, **kwargs).detach()
 
         if len(average_loss.shape) != 0: