[doc] t5 incomplete example
https://huggingface.co/transformers/model_doc/t5.html#training has:
```
input_ids = tokenizer('translate English to German: The house is wonderful.', return_tensors='pt').input_ids
labels = tokenizer('Das Haus ist wunderbar.', return_tensors='pt').input_ids
# the forward function automatically creates the correct decoder_input_ids
loss = model(input_ids=input_ids, labels=labels).loss
```
which is broken unless the right model type is used. And when none if specified it typically presumes `AutoModel`, which gives: `TypeError: forward() got an unexpected keyword argument 'labels'`

So probably need to use an explicit `T5ForConditionalGeneration` and then it works.

@patrickvonplaten, @patil-suraj

A possible approach proposed here: https://github.com/huggingface/transformers/pull/12239