Dynesty warnings added

[arrjon]

The wrapper for the dynesty sampler differs significantly to the other samplers in pyPESTO. Usually the samplers expect the objective to be the posterior and x_priors to be defined, from which they compute the likelihood if needed. I added the argument objective_type to the sampler to switch between posterior and likelihood with default set to posterior. I also added checks in the initialize function to very if x_priors is defined in the problem or not.

Furthermore, I added a warning that by default, a uniform prior with bounds specified in the problem is assumed. This warning was missing, but already mentioned in the prior_transform function.

[codecov-commenter]

Codecov Report

Attention: Patch coverage is 21.42857% with 11 lines in your changes are missing coverage. Please review.

Project coverage is 53.47%. Comparing base (b1298ea) to head (75bc446).

Files	Patch %	Lines
pypesto/sample/dynesty.py	8.33%	11 Missing :warning:

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Additional details and impacted files

```diff @@ Coverage Diff @@ ## develop #1324 +/- ## ============================================ - Coverage 81.32% 53.47% -27.86% ============================================ Files 153 153 Lines 12515 12529 +14 ============================================ - Hits 10178 6700 -3478 - Misses 2337 5829 +3492 ```

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[vwiela]

Do we want to add this flag only in the dynesty sampler? I think it would be better to add it on the objective level.

[arrjon]

My question is whether the constants are necessary? Why not define the loglikelihood of dynesty to always remove the prior in cases a prior exists? The only case when it should not remove the prior from the objective is when it does not exist, right? I might be missing something.

If you load a petab problem, that should work fine. But if you define your objective yourself (which could be the nllh) and you define x_priors as well, you would compute something wrong without giving any warnings. That is not only a problem in dynesty, but also with the other samplers, see #1325.