Add MCLMC sampler

[reubenharry]

Todos:

• [x] add tuning: without the tuning algorithm, MCLMC is next to useless
  • [x] If I add a new sampler, there is an issue discussing it already;
  • [x] We should be able to understand what the PR does from its title only;
  • [x] There is a high-level description of the changes;
  • [x] There are links to all the relevant issues, discussions and PRs;
  • [x] The branch is rebased on the latest main commit;
  • [x] Commit messages follow these guidelines;
  • [x] The code respects the current naming conventions;
  • [x] Docstrings follow the numpy style guide
  • [x] pre-commit is installed and configured on your machine, and you ran it before opening the PR;
  • [x] There are tests covering the changes;
  • [x] The doc is up-to-date; (add references to bibliography, check what you need to do for API docs)
  • [x] *If I add a new sampler** I added/updated related examples
  • [x] Remove the explore.py script
  • [x] Test that the dE and logdensity reported are correct

As per #530, this PR adds MCLMC (Microcanonical Langevin Monte Carlo) as a sampling method to BlackJax. It is an adaptation of the original repo: https://github.com/JakobRobnik/MicroCanonicalHMC/tree/master.

In particular, this is an implementation of the single chain partially momentum updating algorithm (as opposed to full updates every n steps). It also will implement (once this PR is ready) the MCLMC tuning algorithm.

[codecov[bot]]

Codecov Report

Attention: 1 lines in your changes are missing coverage. Please review.

Comparison is base (f49945d) 99.16% compared to head (70f1dd5) 99.16%.

Files	Patch %	Lines
blackjax/mcmc/mclmc.py	97.67%	1 Missing :warning:

Additional details and impacted files

```diff @@ Coverage Diff @@ ## main #586 +/- ## ======================================== Coverage 99.16% 99.16% ======================================== Files 52 54 +2 Lines 2391 2515 +124 ======================================== + Hits 2371 2494 +123 - Misses 20 21 +1 ```

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[reubenharry]

After a bit of a mammoth effort, the PR is ready for review.

One possible point of objection is that there are still some poorly named variables, now confined to certain functions in the adaptation code. These I have not renamed because I don't fully understand what all of them are doing, so some reading/talking to Jakob is in order.

But there's now a test of both the tuning and the kernel, that uses pytrees, showing that the code is pytree generic. Moreover, the implementation reproduces the original MCLMC implementation perfectly (with appropriate choices of random seeds), so other than a few details (I removed the preconditioning for now, for simplicity), it's hopefully looking good.