Avoid torchscript export for Metric forward

[ananthsub]

What does this PR do?

Fixes #4416
https://pytorch.org/docs/stable/generated/torch.jit.unused.html#torch.jit.unused

Before submitting

• Was this discussed/approved via a Github issue? (no need for typos and docs improvements)
• Did you read the contributor guideline, Pull Request section?
• Did you make sure your PR does only one thing, instead of bundling different changes together? Otherwise, we ask you to create a separate PR for every change.
• Did you make sure to update the documentation with your changes?
• Did you write any new necessary tests?
• Did you verify new and existing tests pass locally with your changes?
• If you made a notable change (that affects users), did you update the CHANGELOG?

PR review

• Is this pull request ready for review? (if not, please submit in draft mode)

Anyone in the community is free to review the PR once the tests have passed.
If we didn't discuss your PR in Github issues there's a high chance it will not be merged.

Did you have fun?

Make sure you had fun coding 🙃

[codecov]

Codecov Report

Merging #4428 into master will increase coverage by 3%.
The diff coverage is 100%.

@@           Coverage Diff           @@
##           master   #4428    +/-   ##
=======================================
+ Coverage      90%     93%    +3%     
=======================================
  Files         116     116            
  Lines        8742    8718    -24     
=======================================
+ Hits         7845    8075   +230     
+ Misses        897     643   -254     

[SkafteNicki]

Maybe add this test to tests\metrics\test_metric_lightning.py:

def test_scriptable(tmpdir):
    class TestModel(BoringModel):
        def __init__(self):
            super().__init__()
            self.metric = SumMetric()
            self.sum = 0.0

        def training_step(self, batch, batch_idx):
            x = batch
            self.metric(x.sum())
            self.sum += x.sum()
            self.log("sum", self.metric, on_epoch=True, on_step=False)
            return self.step(x)
    model = TestModel()

    script_model = torch.jit.script(model)
    # test that we can still do inference
    output = script_model(torch.randn(10, 32))

[SkafteNicki]

LGTM

[Borda]

lgtm

[Borda]

the time-outed test came from the master...