[Metrics] Sklearn metrics
Do we want to include metrics of sklearn?
The following constraints would apply if we had an sklearn metric interface:

All imports would be within the class space, so sklearn would only remain an optional dependency (due to it's size)
All metrics will be provided by sklearn interface at first, but will be removed later on (once we got a native pytorch implementation)

The Advantages:

We have a pretty impressive set of metrics directly in the beginning
the maintenance effort is minimal
they're well tested

The disadvantages

For every call to a metric, we need to convert tensors to numpy
We will have to synchronise GPU(s) whenever we call a metric with inputs lying on GPU, which may be a bottleneck the users aren't aware of

My proposal

Add this layer in the beginning and try to replace as many of the metrics used there by our own :)
Explicitly state the downfalls in docs (and maybe also raise a warning on that) to ensure users are aware of this problem.

Thoughts?
cc @Borda @williamFalcon @ethanwharris @PyTorchLightning/core-contributors