[ENH] Add support for nn losses to ptf-v2 (Followup on #2073)

[Faakhir30]

Reference Issues/PRs

Fixes #1970

What does this implement/fix? Explain your changes.

Followups on existing PR #2073 by @Nidhicodes

By @Nidhicodes:
-Introduces NNLossAdapter, a lightweight wrapper that adapts nn.Module losses to the ptf-v2 loss/metric API.
-Enables users to pass native PyTorch losses directly to models (e.g. DLinear(loss=nn.MSELoss())) without manual wrapping.
-Ensures compatibility with BaseModel.predict() by implementing to_prediction and to_quantiles.
The adapter:
-Handles (target, weight) inputs by applying weights internally while respecting the loss’s original reduction.
-Supports multi-target predictions by splitting [B, T, N] tensors and summing losses across targets.
-Enforces point-prediction-only usage (H=1) for losses that are not horizon-aware, with clear error messages otherwise.

Further updates:

• store in self._loss instead of self.loss
• WIP

What should a reviewer concentrate their feedback on?

Did you add any tests for the change?

Any other comments?

PR checklist

• The PR title starts with either [ENH], [MNT], [DOC], or [BUG]. [BUG] - bugfix, [MNT] - CI, test framework, [ENH] - adding or improving code, [DOC] - writing or improving documentation or docstrings.
• Added/modified tests
• Used pre-commit hooks when committing to ensure that code is compliant with hooks. Install hooks with pre-commit install.
  To run hooks independent of commit, execute pre-commit run --all-files

[codecov]

Codecov Report

❌ Patch coverage is 93.00699% with 10 lines in your changes missing coverage. Please review.
⚠️ Please upload report for BASE (main@c4f5aac). Learn more about missing BASE report.

Files with missing lines	Patch %	Lines
pytorch_forecasting/metrics/nn_loss_adapter.py	84.48%	9 Missing ⚠️
pytorch_forecasting/tests/test_nn_loss_adapter.py	98.76%	1 Missing ⚠️

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #2331   +/-   ##
=======================================
  Coverage        ?   87.22%           
=======================================
  Files           ?      169           
  Lines           ?     9895           
  Branches        ?        0           
=======================================
  Hits            ?     8631           
  Misses          ?     1264           
  Partials        ?        0           

Flag	Coverage Δ
cpu	87.22% <93.00%> (?)
pytest	87.22% <93.00%> (?)

Flags with carried forward coverage won't be shown. Click here to find out more.

☔ View full report in Codecov by Harness.
📢 Have feedback on the report? Share it here.

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.