[BUG] Torch doesn't support the conversion of non-writeable numpyFix non-writeable encoder issue and update tests

[cngmid]

Reference Issues/PRs

Fixes #1987

What does this implement/fix? Explain your changes.

When converting pandas objects to numpy, the resulting arrays are non-writeable if the option pd.options.mode.copy_on_write is set to True, which will be the default in future pandas versions.
PyTorch does not support non-writable tensors. It warns that "writing to this tensor will result in undefined behavior".

What should a reviewer concentrate their feedback on?

Did you add any tests for the change?

Added test function to tests/test_data/test_timeseries.py
But the real way to test it is to set globally pd.options.mode.copy_on_write = True and see whther a warning appears somewhere.

Any other comments?

I think pandas v3.0 will enforce this flag.

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 91.66667% with 1 line in your changes missing coverage. Please review.
⚠️ Please upload report for BASE (main@c78b273). Learn more about missing BASE report.

Files with missing lines	Patch %	Lines
pytorch_forecasting/data/timeseries/_timeseries.py	75.00%	1 Missing ⚠️

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #1989   +/-   ##
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  Coverage        ?   86.97%           
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  Files           ?      160           
  Lines           ?     9463           
  Branches        ?        0           
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  Hits            ?     8230           
  Misses          ?     1233           
  Partials        ?        0           

Flag	Coverage Δ
cpu	86.97% <91.66%> (?)
pytest	86.97% <91.66%> (?)

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