  the task.
In addition, we must make sure that padding token id's of the labels are not taken into account by the loss
  function. In PyTorch and Tensorflow, this can be done by replacing them with -100, which is the ignore_index
  of the CrossEntropyLoss. In Flax, one can use the decoder_attention_mask to ignore padded tokens from
  the loss (see the Flax summarization script for details). We also pass
  attention_mask as additional input to the model, which makes sure that padding tokens of the inputs are
  ignored. The code example below illustrates all of this.
```python
  from transformers import T5Tokenizer, T5ForConditionalGeneration
  import torch
tokenizer = T5Tokenizer.from_pretrained("t5-small")
  model = T5ForConditionalGeneration.from_pretrained("t5-small")
# the following 2 hyperparameters are task-specific
