generate text with inputs_embeds (instead of input_ids) for T5.
model.generate() supports input_ids only

```
outs = model.model.generate(input_ids=batch['source_ids'],
                                    attention_mask=batch['source_mask'],
                                    output_scores=True,
                                    max_length=model.model_arguments.max_output_seq_length)
        preds_cleaned = [model.tokenizer.decode(ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
                         for ids in outs]
```

It would be good to have the functionality of generating text from embeddings.
model.forward() allows passing inputs_embeds instead of input_ids. 

One possible solution is to get the `encoder_outputs` by passing `inputs_embeds` to `encoder` and then passing that `encoder_outputs` to `.generate`, so for example

```
with torch.no_grad():
    encoder_outputs = model.get_encoder()(inputs_embeds=input_embeds)

gen_ids = model.generate(input_ids=None, encoder_outputs=encoder_outputs)
```<|||||>Thanks. 