...     "bert-large-uncased", add_cross_attention=True, is_decoder=True, bos_token_id=101, eos_token_id=102
... )
bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)
create tokenizer...
tokenizer = BertTokenizer.from_pretrained("bert-large-uncased")
input_ids = tokenizer(
...     "This is a long article to summarize", add_special_tokens=False, return_tensors="pt"
).input_ids
labels = tokenizer("This is a short summary", return_tensors="pt").input_ids
train...
loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels).loss
loss.backward()
```
Pretrained [EncoderDecoderModel] are also directly available in the model hub, e.g.,
```python
instantiate sentence fusion model
sentence_fuser = EncoderDecoderModel.from_pretrained("google/roberta2roberta_L-24_discofuse")
