Split Bijector

[vmoens]

Motivation

We introduce the Split Bijector, which allows to split a tensor in half, process one half through a sequence of transformations and normalize the other.

Changes proposed

The new class first splits the tensor, then passes the outputs to the _param_fn and then to the transform itself.
The introduction of a _forward_pre_ops and _inverse_pre_ops methods is necessary as, in the inverse case, we need to first pass the input through the transform inverse to then pass it through the convolutional layer that will give us the normalizing constants. This breaks the _param_fb(...) -> _inverse(...) logic, as we need to do something before _param_fn. As this might be the case for the forward pass too, we introduced a similar _forward_pre_ops method.

Types of changes

• Docs change / refactoring / dependency upgrade
• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)

Checklist

• My code follows the code style of this project.
• My change requires a change to the documentation.
• I have updated the documentation accordingly.
• I have read the CONTRIBUTING document.
• I have added tests to cover my changes.
• All new and existing tests passed.
• The title of my pull request is a short description of the requested changes.

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Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 98.43%. Comparing base (13cf226) to head (7b9db0a).
⚠️ Report is 18 commits behind head on main.
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Additional details and impacted files

@@            Coverage Diff             @@
##             main     #103      +/-   ##
==========================================
+ Coverage   98.39%   98.43%   +0.03%     
==========================================
  Files           6        6              
  Lines         249      255       +6     
==========================================
+ Hits          245      251       +6     
  Misses          4        4              

Flag	Coverage Δ
unittests	98.43% <100.00%> (+0.03%)	⬆️

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