Handle NaN losses as a consequence of missing data (also in DDP mode)

[observingClouds]

Describe your changes

This PR enables to run the training and evaluation also on data containing NaN, by setting the loss rate to None and communicate this across all nodes, i.e. in DDP mode.

While this PR seems to work, it requires to adjust the pytorch-lightning package slightly, by removing the following code 😬

https://github.com/Lightning-AI/pytorch-lightning/blob/df5dee674243e124a2bf34d9975dd586ff008d4b/src/lightning/pytorch/loops/optimization/automatic.py#L322-L327

🥷

Issue Link

< Link to the relevant issue or task. > (e.g. closes #00 or solves #00)

Type of change

• 🐛 Bug fix (non-breaking change that fixes an issue)
• ✨ New feature (non-breaking change that adds functionality)
• 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)
• 📖 Documentation (Addition or improvements to documentation)

Checklist before requesting a review

• My branch is up-to-date with the target branch - if not update your fork with the changes from the target branch (use pull with --rebase option if possible).
• I have performed a self-review of my code
• For any new/modified functions/classes I have added docstrings that clearly describe its purpose, expected inputs and returned values
• I have placed in-line comments to clarify the intent of any hard-to-understand passages of my code
• I have updated the README to cover introduced code changes
• I have added tests that prove my fix is effective or that my feature works
• I have given the PR a name that clearly describes the change, written in imperative form (context).
• I have requested a reviewer and an assignee (assignee is responsible for merging). This applies only if you have write access to the repo, otherwise feel free to tag a maintainer to add a reviewer and assignee.

Checklist for reviewers

Each PR comes with its own improvements and flaws. The reviewer should check the following:

• the code is readable
• the code is well tested
• the code is documented (including return types and parameters)
• the code is easy to maintain

Author checklist after completed review

• I have added a line to the CHANGELOG describing this change, in a section
  reflecting type of change (add section where missing):
  • added: when you have added new functionality
  • changed: when default behaviour of the code has been changed
  • fixes: when your contribution fixes a bug

Checklist for assignee

• PR is up to date with the base branch
• the tests pass
• author has added an entry to the changelog (and designated the change as added, changed or fixed)
• Once the PR is ready to be merged, squash commits and merge the PR.

[leifdenby]

Nice hack!