feat: implement PatchTST v2 architecture

[amruth6002]

Reference Issues/PRs

Closes #2067

What does this implement/fix? Explain your changes.

This PR introduces the PatchTST architecture to the pytorch-forecasting v2 interface.

The implementation adapts the core "Channel Independence" patching mechanism and self-attention encoder from the original thuml implementation to align with the TslibBaseModel standards.

Key Additions:

1. Layers: Added PatchEmbedding, SelfAttnEncoder, and SelfAttnEncoderLayer to support the architecture without cross-attention components.
2. Core Model: Implemented PatchTST inside models/patchtst/_patchtst_v2.py. It correctly handles instance normalization, permuting/patching the sequences, and supports Quantile Loss resizing configurations.
3. Package Wrapper: Created PatchTST_pkg_v2 for sktime compatibility and automated testing scaffolding.
4. Tests: Added test_patchtst_v2.py verifying point forecasting, univariate flows, and quantile loss architectures via standard parameterized pytest conventions.

All pre-commit hooks and unit tests pass successfully.

[codecov]

Codecov Report

❌ Patch coverage is 87.37374% with 25 lines in your changes missing coverage. Please review.
⚠️ Please upload report for BASE (main@602ee15). Learn more about missing BASE report.

Files with missing lines	Patch %	Lines
...ch_forecasting/models/patchtst/_patchtst_pkg_v2.py	51.11%	22 Missing ⚠️
...ytorch_forecasting/models/patchtst/_patchtst_v2.py	97.46%	2 Missing ⚠️
...ch_forecasting/layers/_normalization/_transpose.py	90.00%	1 Missing ⚠️

Additional details and impacted files

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  Coverage        ?   86.64%           
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  Misses          ?     1326           
  Partials        ?        0           

Flag	Coverage Δ
cpu	86.64% <87.37%> (?)
pytest	86.64% <87.37%> (?)

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[PranavBhatP]

Please move this to the _layers folder under the correct subdirectory, if it already exists, reuse.

[amruth6002]

done , please recheck.

[PranavBhatP]

Same comment here.

[amruth6002]

done , please recheck