merge test_probabilistic_metrics into TestAllDistrMetrics, fixes#398

[JahnaviDhanaSri]

Reference Issues/PRs

#398

What does this implement/fix? Explain your changes.

merged test_probabilistic_metrics into TestAllDistrMetrics

Does your contribution introduce a new dependency? If yes, which one?

No

What should a reviewer concentrate their feedback on?

Did you add any tests for the change?

Any other comments?

PR checklist

For all contributions

• I've added myself to the list of contributors with any new badges I've earned :-)
  How to: add yourself to the all-contributors file in the skpro root directory (not the CONTRIBUTORS.md). Common badges: code - fixing a bug, or adding code logic. doc - writing or improving documentation or docstrings. bug - reporting or diagnosing a bug (get this plus code if you also fixed the bug in the PR).maintenance - CI, test framework, release.
  See here for full badge reference
• 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.

For new estimators

• I've added the estimator to the API reference - in docs/source/api_reference/taskname.rst, follow the pattern.
• I've added one or more illustrative usage examples to the docstring, in a pydocstyle compliant Examples section.
• If the estimator relies on a soft dependency, I've set the python_dependencies tag and ensured
  dependency isolation, see the estimator dependencies guide.

[JahnaviDhanaSri]

Hi @fkiraly, I have raised the pull request could you please review it.

[fkiraly]

Can you please explain what you are doing here? I was assuming this was still a work in progress.

For example:

• the original tests still exist in their original place
• the new tests do not use the dispatch structure from the test class, you loop manually over the classes still
• I am not sure if this will work at all, given that the interval/quantile metrics follow a different interface than the probabilistic ones. Have you thought about new class vs using the one for distribution metrics?