add training for the mitochondria tomography model

[lufre1]

Integrates the training recipe of the 'mitochondria2' model, which so far only existed outside this repository, as a first-class part of the package.

• synapse_net/training/mitochondria.py: get_mitochondria_paths for the recursive data discovery and the train/val split (optionally from a split file), and mitochondria_training, which fixes the hyperparameters of the published model. Registered as the CLI synapse_net.run_mitochondria_training.
• supervised_training: expose initial_features, mixed_precision, early_stopping and log_image_interval. The defaults keep the previous behaviour for all other callers.
• scripts/cooper/training/train_mitochondria_tomo.py plus the split file of the published run, so it can be reproduced file for file.
• test/training/init.py, without which unittest discovery skipped all of test/training. Discovery goes from 106 to 121 tests with this commit.

The recipe was taken from the init of the deployed checkpoint, not from the training script. Verified by training on the real tomograms and comparing the resulting checkpoint against the deployed one: model kwargs, loss, optimizer, scheduler, mixed_precision, early_stopping, log_image_interval and the loader kwargs all agree, and both models have the same state dict and the same 18 InstanceNorm3d layers.

Note that the percentile normalization is part of the model: the same preprocessing has to be passed to segment_mitochondria at inference time.