Make the grid sampler compatible with the queue

[fepegar]

Resolves #513.
Related to #505, #494.

Description
This PR makes the grid sampler behave like the other samplers, so that it can be used with the queue as requested by some users (@sarthakpati, @MFaizyabAli).

The new API is not very clean, but it allows for backward compatibility with the tutorials for dense inference using the aggregator.

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 #520 (c3c0ed0) into master (68ffea7) will increase coverage by 0.03%.
The diff coverage is 83.58%.

@@            Coverage Diff             @@
##           master     #520      +/-   ##
==========================================
+ Coverage   90.44%   90.47%   +0.03%     
==========================================
  Files         132      132              
  Lines        6646     6669      +23     
==========================================
+ Hits         6011     6034      +23     
  Misses        635      635              

Impacted Files	Coverage Δ
torchio/data/sampler/grid.py	88.60% <75.67%> (ø)
torchio/data/sampler/sampler.py	95.65% <84.61%> (-4.35%)	⬇️
tests/data/inference/test_grid_sampler.py	100.00% <100.00%> (ø)
torchio/data/__init__.py	100.00% <100.00%> (ø)
torchio/data/inference/__init__.py	100.00% <100.00%> (ø)
torchio/data/inference/aggregator.py	97.72% <100.00%> (ø)
torchio/data/sampler/__init__.py	100.00% <100.00%> (ø)
torchio/data/sampler/uniform.py	94.11% <100.00%> (+7.75%)	⬆️
torchio/data/sampler/weighted.py	92.04% <100.00%> (+1.82%)	⬆️
torchio/utils.py	91.30% <0.00%> (+0.62%)	⬆️
... and 1 more

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