Improve T5 docs 
# What does this PR do?

This PR aims to clarify and explain some of the magic that's happening when using T5 for training/inference. It includes:
- explaining that the model automatically creates the `decoder_input_ids` based on the `labels` (a lot of people were still confused by this, see e.g. #11977, #13213 
- added code examples, to show a basic forward pass that includes the fact that padding token ids of the labels should be replaced by -100 (at least, for PyTorch, I see that for FLAX one uses the `decoder_attention_mask` to skip padding tokens), and code examples for inference (both batched/not batched)
- adding info about T5's variants, including T5v1.1, mT5 and byT5, with links to their docs.
- additional tips & tricks, based on what I found on the forum (learning rate, training on TPU, etc).

In addition, I've added T5v1.1 to the main README as well making it have its own documentation page, and some more info to the mT5 docs.