Fix AUROC to take advantage of known order ahead of time

[maximsch2]

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• Did you write any new necessary tests?

What does this PR do?

Fixes #219 .

PR review

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[codecov]

Codecov Report

Merging #230 (2701930) into master (33864db) will decrease coverage by 0.49%.
The diff coverage is 87.50%.

@@            Coverage Diff             @@
##           master     #230      +/-   ##
==========================================
- Coverage   96.80%   96.31%   -0.50%     
==========================================
  Files         184      184              
  Lines        6010     5997      -13     
==========================================
- Hits         5818     5776      -42     
- Misses        192      221      +29     

Flag	Coverage Δ
Linux	79.09% <87.50%> (+0.02%)	⬆️
Windows	79.09% <87.50%> (+0.02%)	⬆️
cpu	95.81% <87.50%> (-0.99%)	⬇️
gpu	96.80% <ø> (+<0.01%)	⬆️
macOS	95.81% <87.50%> (-0.99%)	⬇️
pytest	96.31% <87.50%> (-0.50%)	⬇️
python3.6	95.75% <87.50%> (+<0.01%)	⬆️
python3.8	?
python3.9	?
torch1.3.1	95.75% <87.50%> (+<0.01%)	⬆️
torch1.4.0	?
torch1.8.1	?

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
torchmetrics/functional/classification/auroc.py	66.15% <75.00%> (-20.00%)	⬇️
torchmetrics/functional/classification/auc.py	88.88% <100.00%> (+1.38%)	⬆️
torchmetrics/classification/auroc.py	87.50% <0.00%> (-5.19%)	⬇️
torchmetrics/metric.py	90.84% <0.00%> (-4.66%)	⬇️
torchmetrics/wrappers/bootstrapping.py	86.20% <0.00%> (-1.73%)	⬇️
torchmetrics/regression/r2score.py	93.33% <0.00%> (-0.22%)	⬇️
torchmetrics/regression/explained_variance.py	96.42% <0.00%> (-0.13%)	⬇️
torchmetrics/classification/accuracy.py	96.07% <0.00%> (-0.08%)	⬇️
torchmetrics/utilities/prints.py	100.00% <0.00%> (ø)
... and 193 more

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