[Train] Simplify single worker training

[amogkam]

Currently, Ray Train does not setup the distributed environment (torch process group, TF_CONFIG env var) if only using 1 worker.

However, this requires the user to make changes to their training code if they want to go from 1 worker to multiple workers, and has been a source of confusion in our examples:
#19506
#19761

This PR changes the behavior to setup the distributed environment regardless of the number of workers. This allows training functions that have DistributedDataParallel or MultiWorkerMirroredStrategy to still work with single worker Ray Train. This PR also adds testing the quick start code examples in the docs.

Closes #19761

Why are these changes needed?

Related issue number

Checks

• I've run scripts/format.sh to lint the changes in this PR.
• I've included any doc changes needed for https://docs.ray.io/en/master/.
• I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/
• Testing Strategy
  • Unit tests
  • Release tests
  • This PR is not tested :(

[matthewdeng]

This is awesome. Really like this pattern of running the documentation code in CI.

Can you add instructions and link this an example in [OSS] How to write an example? This is really a best-practice I think we should all be following. (Also be sure to include the trick of moving the function calls to __main__!)