Continuous inter-point constraints

[AVHopp]

This PR introduces a first variant of inter-point constraints by using the botorch-provided interface.

Here, an inter-point constraint is a constraint that acts on a full batch instead of a single recommendation. If we think of a batch recommendation of a matrix with shape batches x features, then our previous constraints would be row-wise, while these constraints allow mixed constraints across both dimensions.

This PR introduces new classes, and these classes were modeled similar to the already existing classes for continuous constraints. Some things had to be changed though resp. we need to aware:

1. Most importantly, the way of sampling points from a polytope. The reason is that get_polytope_samples is not made for including inter-point constraints, and hence a workaround was implemented. The workaround basically transforms the space in a one-dimensional space with batches * features many features, and then defines both normal and inter-point constraints over this space (see [Bug] get_polytope_samples fails for inequalities over q-batches and the actual dimension pytorch/botorch#2468). Note that this might interfere with Botorch with cardinality constraint via sampling #301 and that some alignment might be necessary.
2. The constraints in this PR always combines full columns. That is, the constraints $x_1 <=1$ would be interpreted as "the sum of $x_1$ across the whole batch needs to be smaller than 1". This can e.g. be used to limit the usage of resources, like only having 100ml of a substance available for the full batch. Also, different columns can be combined, so having $x_2 + 2*x_3 <=100$ would mean "sum of $x_1$ plus two times the sum of $x_3$ across the batch has to be smaller than 100$.
3. For now, the constraints have only been applied for continuous search spaces. The reason for this is that I want to keep this PR rather short and use it to discuss in general about the design, and to avoid any potential conflicts with Botorch with cardinality constraint via sampling #301 . However, including them in hybrid search spaces should be possible already in this PR. For discrete spaces, this still needs some investigation. So interpret this as just a first step in the direction of more :)