Implement new models

[georgeyiasemis]

Implements new MRI models along with tests:

• Conjugate Gradient Network - ConjGradNet (along with baseline)
• Iterative Dual Network - IterDualNet (Similar to JointICNet but no optimisation for sensitivity maps in the model)
• Simple Residual Network - ResNet, which can be used as a denoiser (replacement of UNet or DIDN, etc)
• Variable Splitting Network - VarSplitNet, solves the variable splitting optimization problem for MRI using a DL denoiser for the denoising equation and gradient descent for the data consistency equation

Also adds complex_dot_product and complex_division transforms

[codecov]

Codecov Report

Base: 81.91% // Head: 82.92% // Increases project coverage by +1.01% 🎉

Coverage data is based on head (c02fdc9) compared to base (73eb5c6).
Patch coverage: 97.65% of modified lines in pull request are covered.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #228      +/-   ##
==========================================
+ Coverage   81.91%   82.92%   +1.01%     
==========================================
  Files          77       88      +11     
  Lines        5589     5967     +378     
==========================================
+ Hits         4578     4948     +370     
- Misses       1011     1019       +8     

Impacted Files	Coverage Δ
direct/nn/varnet/varnet.py	94.44% <ø> (ø)
direct/types.py	77.77% <64.70%> (-22.23%)	⬇️
direct/nn/varsplitnet/varsplitnet.py	95.89% <95.89%> (ø)
direct/constants.py	100.00% <100.00%> (ø)
direct/data/datasets_config.py	100.00% <100.00%> (ø)
direct/data/mri_transforms.py	97.00% <100.00%> (ø)
direct/data/transforms.py	96.31% <100.00%> (+0.68%)	⬆️
direct/engine.py	75.00% <100.00%> (ø)
direct/nn/conjgradnet/conjgrad.py	100.00% <100.00%> (ø)
direct/nn/conjgradnet/conjgradnet.py	100.00% <100.00%> (ø)
... and 9 more

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[georgeyiasemis]

ok

[jonasteuwen]

A few code quality requests

[jonatanferm]

Just a couple of comments/questions.
Don't have time to go through more now. Will look at the rest tomorrow :)

[jonatanferm]

Always hard to review when you are not well versed in the code base :)

In general I think it looks good and it's great to see some tests! Maybe some of the methods could have a couple of more tests though?

The main things I think could be improved are: added documentation to the classes where it is missing, and reduced use of "magic strings" :)

[jonasteuwen]

LGTM

[jonasteuwen]

LGTM