Fix bootstrapper wrapper not being reset correctly

[SkafteNicki]

What does this PR do?

Fixes #2556

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📚 Documentation preview 📚: https://torchmetrics--2574.org.readthedocs.build/en/2574/

[codecov]

Codecov Report

Attention: Patch coverage is 25.00000% with 3 lines in your changes missing coverage. Please review.

Project coverage is 34%. Comparing base (744905c) to head (ae6e6f2).
Report is 97 commits behind head on master.

Additional details and impacted files

@@           Coverage Diff            @@
##           master   #2574     +/-   ##
========================================
- Coverage      69%     34%    -34%     
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  Files         314     314             
  Lines       17669   17673      +4     
========================================
- Hits        12185    6096   -6089     
- Misses       5484   11577   +6093     

[Borda]

@SkafteNicki mind checking:

Failed example:
    from torchmetrics.classification import MulticlassAccuracy
    from torchmetrics.wrappers import BootStrapper
    # creating metrics
    wrapped_metric = BootStrapper(MulticlassAccuracy(num_classes=3))
    # sample prediction and GT
    target = torch.tensor([0, 2, 0, 2, 0, 1, 0, 2])
    preds = torch.tensor([2, 1, 2, 0, 1, 2, 2, 2])
    # showing the metric results
    print(wrapped_metric(preds, target))  # this returns a dict with label info
Expected:
    {'mean': tensor(0.1476), 'std': tensor(0.0613)}
Got:
    {'mean': tensor(0.1333), 'std': tensor(0.1554)}

[SkafteNicki]

@SkafteNicki mind checking:

Failed example:
    from torchmetrics.classification import MulticlassAccuracy
    from torchmetrics.wrappers import BootStrapper
    # creating metrics
    wrapped_metric = BootStrapper(MulticlassAccuracy(num_classes=3))
    # sample prediction and GT
    target = torch.tensor([0, 2, 0, 2, 0, 1, 0, 2])
    preds = torch.tensor([2, 1, 2, 0, 1, 2, 2, 2])
    # showing the metric results
    print(wrapped_metric(preds, target))  # this returns a dict with label info
Expected:
    {'mean': tensor(0.1476), 'std': tensor(0.0613)}
Got:
    {'mean': tensor(0.1333), 'std': tensor(0.1554)}

I assume it is due to the randomness in the bootstrapper. I try setting a seed in the docs and update the values

[Borda]

I see, so we can seed the example...