[Documentation] Example for LEDForConditionalGeneration does not work
The [documentation for LEDForConditionalGeneration](https://huggingface.co/transformers/model_doc/led.html#transformers.LEDForConditionalGeneration) appears to be incorrect. The same example is also used for [BartForConditionalGeneration](https://huggingface.co/transformers/model_doc/bart.html#bartforconditionalgeneration), where it works as intended. I believe that the example was just copied and not adapted, but perhaps I'm missing something? 

```python
from transformers import LEDTokenizer, LEDForConditionalGeneration
tokenizer = LEDTokenizer.from_pretrained('allenai/led-base-16384')
TXT = "My friends are <mask> but they eat too many carbs."

model = LEDForConditionalGeneration.from_pretrained('allenai/led-base-16384')
input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']
logits = model(input_ids).logits
```
Here, the last step fails with `ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds`, which as far as I can tell is not a bug, but expected, as no `decoder_input_ids`/`embeds` (or `labels`) are provided. (BART [silently generates the `decoder_input_ids` from the `input_ids`](https://github.com/huggingface/transformers/blob/master/src/transformers/models/bart/modeling_bart.py#L1151), which LED does not.)

I believe the example should look like this:
```python
input_ids = tokenizer([TXT], return_tensors='pt')['input_ids']

prediction = model.generate(input_ids)[0]
print(tokenizer.decode(prediction, skip_special_tokens=True))
# My friends are good at eating healthy but they eat too many carbs.
```
This is also a nice demonstration that LED generates more than just one token for the masked parts of the sequence.

Tagging @patrickvonplaten who contributed the model and the example.