[ENH] Implement RecurrentNetwork (RNN) in v2 interface

[Dev10-sys]

Reference Issues/PRs

Partially addresses #1736 and #1992.

What does this implement/fix? Explain your changes.

Adds a v2 implementation of RecurrentNetwork (RNN/LSTM/GRU) as RecurrentNetwork_v2.

• Implemented RecurrentNetwork_v2 inheriting from BaseModel supporting LSTM and GRU cell types.
• Inputs (target_past, encoder_cont) derived directly from DataModule metadata.
• Implemented RecurrentNetwork_pkg_v2 inheriting from Base_pkg with estimator tags and parameter fixtures.
• Added unit and estimator compliance tests in tests/test_models/test_rnn_v2.py.

What should a reviewer concentrate their feedback on?

• Feature tensor extraction in RecurrentNetwork_v2._build_input_tensor
• Package tags and parameter fixtures in RecurrentNetwork_pkg_v2

Did you add any tests for the change?

• Added tests/test_models/test_rnn_v2.py with check_estimator compliance and forward pass tests.

PR checklist

• Title starts with [ENH]
• Added tests (tests/test_models/test_rnn_v2.py)
• Used pre-commit hooks

[codecov]

Codecov Report

❌ Patch coverage is 92.77108% with 6 lines in your changes missing coverage. Please review.
⚠️ Please upload report for BASE (main@062aeb4). Learn more about missing BASE report.

Files with missing lines	Patch %	Lines
pytorch_forecasting/models/rnn/_rnn_v2.py	89.28%	6 Missing ⚠️

Additional details and impacted files

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  Coverage        ?   88.16%           
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Flag	Coverage Δ
cpu	88.16% <92.77%> (?)
pytest	88.16% <92.77%> (?)

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