Inference flavor experiments

[mcw92]

• This PR introduces job script generation scripts for the inference flavor experiments.
• Due to OOM errors, I refactored the code to decouple the training completely from potentially building a globally shared model and evaluation. As training the classifiers is the computationally most expensive step, the locally trained subforests are dumped to file directly after the training. This prevents loss of an alredy trained model due to subsequent errors during building the globally shared model or evaluation.
• I updated the test action to use the Codecov app instead of generating and committing the badge locally using a bot. In addition, I introduced matrix testing for supported Python versions and OS types.
• I introduced separate test dependencies.

Fixes #4.

[github-actions]

Name	Stmts	Miss	Cover	Missing
specialcouscous/__init__.py	0	0	100%
specialcouscous/rf_parallel.py	105	7	93%	85-89, 237-239, 413
specialcouscous/synthetic_classification_data.py	206	49	76%	88-90, 185, 304-324, 358, 469, 471, 561-567, 585, 869-883, 1079-1135, 1202-1224
specialcouscous/train.py	203	3	99%	385-386, 512
specialcouscous/utils/__init__.py	57	29	49%	31, 81-82, 106-260
specialcouscous/utils/plot.py	129	74	43%	97, 222-247, 264-350, 366-492
specialcouscous/utils/result_handling.py	22	1	95%	79
specialcouscous/utils/slurm.py	68	62	9%	22-116, 133-149
specialcouscous/utils/timing.py	35	0	100%
TOTAL	825	225	73%

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