Integrating pytorch XLA when using multiple GPUs

Nobody has claimed this yet.

### Description & Motivation

I've experienced with pytorch XLA using multitple NVIDIA A100 GPU and I observed that in most cases training is faster. So it would be really nice to have the option to use XLA for training in pytorch lightning.

The main advantage is faster training.

### Additional context

Here is a code link : https://github.com/Dhouib-med/Test-XLA/blob/17e5b6bd6c77fffa67818462856277a57877ff3b/test_xla.py to train a simple CNN on the MNIST dataset using XLA (on 2 GPUS). The main parts where taken from https://github.com/pytorch/xla.
This wheel needs to be installed along with adequate pytorch and torchvision versions (1.11 and 0.14) https://storage.googleapis.com/tpu-pytorch/wheels/cuda/112/torch_xla-1.13-cp37-cp37m-linux_x86_64.whl
@justusschock

cc @borda @justusschock @awaelchli @carmocca @JackCaoG @steventk-g @Liyang90

Start by reading or running the linked test_xla.py example and the referenced PyTorch/XLA material, then compare its multi-GPU setup with the project’s current training integration. Confirm the compatible PyTorch, torchvision, and torch_xla versions from the issue links; done means the issue’s requested XLA training option works across multiple GPUs.

Written by the indexing model from the issue text.