seisbench/seisbench: SeisBench 0.2 - UrbanDenoiser, enhanced parallelisation, label shapes, ...

• UrbanDenoiser (https://doi.org/10.1126/sciadv.abl3564) is now available in SeisBench. It can be loaded using DeepDenoiser.from_pretrained("urban") (#83).
• The ProbabilisticLabeller now supports different label shapes, i.e., Gaussian, Triangular and Box (#67).
• SeisBench models now support local saving and loading through the newly added save and load functions. The functions ensure that not only model weights can be saved and loaded but also further model parameters, such as the component order or the sampling rate #69, #71, #86).
• The WaveformModel annotate/classify functions now support multiprocessing for annotating large datasets. Simply set the parallelism parameter in the call to these functions. The previous API based on asyncio is maintained for processing small stream objects, as multiprocessing has a higher overhead due to latency (#64, #68, #81).
• SeisBench now supports Python 3.10 (#52, #70).
• We added further example notebooks (#58, #72).
• The from_pretrained API to load pretrained models has been completely rewritten. It now supports model versioning, coming with the new function list_versions and the version_str parameter for from_pretrained. In addition, more control and transparency were added whether the function queries the SeisBench remote repository or only the local cache (#76, #77, #86).
• Added secondary keys for get_idx_from_trace_name to avoid trace name collisions for chunked data sets and multi-part datasets (#78, #84)
• In addition, v0.2 contains all bugfixes introduced in the patch versions of v0.1 and further minor fixes (#63, #85).

Thanks to everyone contributing to this release through issues, PRs and commits!