I reduced the notebook output and run time by tweaking some small things like numbers of starts, rerunning, removing PEtab model compilation output, and added seeds for reproducability. Changes per notebook:
- `amici.ipynb` -- TODO, didn't do yet
- `censored.ipynb` -- reduce n_starts 10 -> 3
- `getting_started.ipynb` -- n_starts 100 -> 10 (do we need more for a nicer waterfall plot?), added seed for reproducability, Fides verbose=logging.ERROR, create_problem verbose=False (TODO amici.swig_wrappers logging)
- `hierarchical.ipynb` -- change to only create_problem with verbose=False, set a seed for reproducability
- TODO rest...

I made the following changes in the example notebooks:
- `amici.py` -- verbose=False on model compilation, seed for reproducability, clear outputs
- `juliy.py` -- set number of starts 100 --> 20, set seed for reproducability, samples 10000 -> 2000, clear outputs
- `nonlinear_monotone` -- verbose=False on model compilation
- `ordinal` -- verbose=False on model compilation
- `petab_import` -- verbose=False on model compilation
- `sampler_study` -- verbose=False on model compilation
- `store` -- verbose=False on model compilation, fides verbose ERROR
- `synthetic_data` -- n_starts 100 -> 20, verbose=False on model compilation
- `workflow_comparison` -- verbose=False on model compilation, n_starts 25 - 10

New changes:
- `sampling_diagnostics` -- n_samples 10000 -> 1000 on both sampling
- `sampler_study` -- n_samples 1e4 -> 1e3 where not necessary to have an extremely good sampling result, add seed for reproducibility (to have a consistently low burn-in to be able to have only 1e3 samples).