zfit/zfit: Binned and mixed fits

Public release of binned fits and upgrade to Python 3.10 and TensorFlow 2.9. While binned fits are now supported, they still lack some stability, public testing and features. Feedback is, as usual, very welcome! Major Features and Improvements improved data handling in constructors from_pandas (which allows now to have weights as columns, dataframes that are a superset of the obs) and from_root (obs can now be spaces and therefore cuts can be direcly applied) add hashing of unbinned datasets with a hashint attribute. None if no hash was possible. Bug fixes and small changes SimpleLoss correctly supports both functions with implicit and explicit parameters, also if they are decorated. extended sampling errored for some cases of binned PDFs. ConstantParameter errored when converted to numpy. Simultaneous binned fits could error with different binning due to a missing sum over a dimension. improved stability in loss evaluation of constraints and poisson/chi2 loss. reduce gradient evaluation time in errors for many parameters. Speedup Parameter value assignement in fits, which is most notably when the parameter update time is comparably large to the fit evaluation time, such as is the case for binned fits with many nuisance parameters. fix ipyopt was not pickleable in a fitresult treat parameters sometimes as "stateless", possibly reducing the number of retraces and reducing the memory footprint. Requirement changes nlopt and ipyopt are now optional dependencies. Python 3.10 added TensorFlow ~= 2.9.0 is now required and the corresponding TensorFlow-Probability version ~= 0.17.0 Thanks @YaniBion for discovering the bug in the extended sampling and testing the alpha release @ResStump for reporting the bug with the simultaneous binned fi