Biapy

Quick patch:
- Fix identation error in some models.

**Full Changelog**: https://github.com/BiaPyX/BiaPy/compare/v3.3.8...v3.3.9

Fix:
- Separate ``SYSTEM.NUM_CPUS`` from ``SYSTEM.NUM_WORKERS``

**Full Changelog**: https://github.com/BiaPyX/BiaPy/compare/v3.3.7...v3.3.8

Changes:
- Now ``DATA.TEST.ARGMAX_TO_OUTPUT`` defaults to ``True``
- Add multi-head in instance segmentation workflow to identify the class of each instance

Fixes:
- Change rotate to scipy so it can be used with 3D images
- Change TTA to allow multiple heads as output of the network
- Fix minor error in BMZ export
- Fix edge case when using ``DATA.REFLECT_TO_COMPLETE_SHAPE``
- Reduce memory comsuption in merge functions

**Full Changelog**: https://github.com/BiaPyX/BiaPy/compare/v3.3.6...v3.3.7

Changes:
- ``AUGMENTOR.RANDOM_ROT`` and ``AUGMENTOR.ROT90`` now are implemented in BiaPy and not done through [imgaug](https://github.com/aleju/imgaug).
- Add ``TRAIN.VERBOSE`` to visualize more or less info during each batch process print

Fixes:
- Fix 4 dims length Zarr data creation during ``TEST.BY_CHUNKS``.
- Change slightly custom architectures (``MODEL.SOURCE`` == ``biapy``) so they can be converted into TorchScript via ``torch.jit.script()`` to create BMZ package.
- Fix U-Net like models for SR to depend on ``PROBLEM.SUPER_RESOLUTION.UPSCALING`` factor and allow ``MODEL.Z_DOWN`` in super-resolution workflow
- Limit number of workers per GPU for safety
- Fix crappify issues for SSL

Fix patch:
- Rename ``PROBLEM.NUM_CPUS`` to ``PROBLEM.NUM_WORKERS`` to clarify its usage.
- Speed up SSL workflow

Changes:
- Set `TEST.DET_EXCLUDE_BORDER` to `False` by default.
- Add `TEST.DET_PEAK_LOCAL_MAX_MIN_DISTANCE`.
- 3 int tuple for TEST.RESOLUTION in instance segmentation if `TEST.ANALIZE_2D_IMGS_AS_3D_STACK`.
- Prevent usage of EfficientNet architectures for 3D.
- Add `PROBLEM.INSTANCE_SEG.WATERSHED_BY_2D_SLICE`.

Fix:
- Prevent creating multiple processes to manage data if low samples are available.
- Solve EfficientNet issue with biapy backend as discussed [here](https://github.com/mrdbourke/pytorch-deep-learning/issues/696).
- Bug in instance seg when no labels are provided.
- Disable aug sample image generation if DA is disabled.
- Fix SSL bug during training due to recent changes.