Add transform to ensure shape multiple of N

[fepegar]

Description
Some network architectures need the input shape to be a multiple of N. For example, a U-Net with two downsampling levels will fail if the input shape is not a multiple of 4. This transforms ensures a valid shape by either cropping or padding.

Checklist

• I have read the CONTRIBUTING docs and have a developer setup (especially important are pre-commitand pytest)
• Non-breaking change (would not break existing functionality)
• Breaking change (would cause existing functionality to change)
• Tests added or modified to cover the changes
• Integration tests passed locally by running pytest
• In-line docstrings updated
• Documentation updated, tested running make html inside the docs/ folder
• This pull request is ready to be reviewed
• If the PR is ready and there are multiple commits, I have squashed them and force-pushed

[codecov]

Codecov Report

Merging #401 (ec19bc3) into master (b9ac52d) will increase coverage by 0.06%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master     #401      +/-   ##
==========================================
+ Coverage   92.42%   92.48%   +0.06%     
==========================================
  Files         117      119       +2     
  Lines        5942     5991      +49     
==========================================
+ Hits         5492     5541      +49     
  Misses        450      450              

Impacted Files	Coverage Δ
tests/transforms/test_transforms.py	100.00% <ø> (ø)
...sforms/preprocessing/test_ensure_shape_multiple.py	100.00% <100.00%> (ø)
torchio/transforms/__init__.py	100.00% <100.00%> (ø)
torchio/transforms/preprocessing/__init__.py	100.00% <100.00%> (ø)
...rms/preprocessing/spatial/ensure_shape_multiple.py	100.00% <100.00%> (ø)
torchio/transforms/transform.py	94.04% <100.00%> (+0.03%)	⬆️

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