tokenizer = T5Tokenizer.from_pretrained("t5-small")
  model = T5ForConditionalGeneration.from_pretrained("t5-small")
input_ids = tokenizer("The  walks in  park", return_tensors="pt").input_ids
  labels = tokenizer(" cute dog  the ", return_tensors="pt").input_ids
  # the forward function automatically creates the correct decoder_input_ids
  loss = model(input_ids=input_ids, labels=labels).loss
  ```
If you're interested in pre-training T5 on a new corpus, check out the run_t5_mlm_flax.py script in the Examples
  directory.
Supervised training
In this setup, the input sequence and output sequence are a standard sequence-to-sequence input-output mapping.
  Suppose that we want to fine-tune the model for translation for example, and we have a training example: the input
  sequence "The house is wonderful." and output sequence "Das Haus ist wunderbar.", then they should be prepared for
