[Doctest] Fix `Blenderbot` doctest
# What does this PR do?

This PR fixes the doctest `transformers.models.blenderbot.modeling_blenderbot.BlenderbotForConditionalGeneration.forward` . Link to failing job is here: https://github.com/huggingface/transformers/actions/runs/4002271138/jobs/6869333719 

Updating the prediction with the correct result seems to be the correct fix. I am usure whether this was tested before so I cannot compare for now. 

One thing that I suspect is that we get different results across different PT versions, but not sure

cc @ydshieh 

_The documentation is not available anymore as the PR was closed or merged._<|||||>Actually, as discussed with @ydshieh offline, there seems to be a discrepency between `BlenderbotTokenizer` & `BlenderbotTokenizerFast`. 
The PR #21225 changed the docstring to use `AutoTokenizer` instead of `BlenderbotTokenizer`. This lead to loading `BlenderbotTokenizerFast`. You can reproduce the discrepency with the script below:
```python
from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast, BlenderbotForConditionalGeneration, AutoTokenizer

mname = "facebook/blenderbot-400M-distill"
model = BlenderbotForConditionalGeneration.from_pretrained(mname)

tokenizer = BlenderbotTokenizer.from_pretrained(mname)
tokenizer_fast = BlenderbotTokenizerFast.from_pretrained(mname)

def generate(tokenizer):
    UTTERANCE = "My friends are cool but they eat too many carbs."

    inputs = tokenizer([UTTERANCE], return_tensors="pt")

    NEXT_UTTERANCE = (
        "My friends are cool but they eat too many carbs.</s> <s>That's unfortunate. "
        "Are they trying to lose weight or are they just trying to be healthier?</s> "
        "<s> I'm not sure."
    )
    inputs = tokenizer([NEXT_UTTERANCE], return_tensors="pt")
    next_reply_ids = model.generate(**inputs)
    print("Bot: ", tokenizer.batch_decode(next_reply_ids, skip_special_tokens=True)[0])

generate(tokenizer)
>>> Bot:   That's too bad. Have you tried encouraging them to change their eating habits? 
generate(tokenizer_fast)
>>> Bot:   I see. Well, it's good that they're trying to change their eating habits.
``` 
I am not sure if this is a known bug or intended. Maybe the changes I proposed is not the correct fix here<|||||>Thanks for digging deeper @younesbelkada! Could you check what's the difference between the fast and slow tokenizer from the checkpoint used in this doc example? And compare the difference of `inputs = tokenizer([UTTERANCE], return_tensors="pt") between these 2 tokenizers.
`

Another similar issue (but not related to this one)
https://github.com/huggingface/transformers/pull/21254<|||||>I think it's good as we want to default to fast tokenizers (which is the reason we switched to AutoTokenizer) so the fix is the right one in my opinion.<|||||>I agree - but just thinking if we should find out what's going wrong and potentially fix the inconsistency between these 2 tokenizers (or something in our codebase).

The fix is good for me, and you can merge @younesbelkada !<|||||>Thanks everyone!
I will open an issue to describe the bug 
EDIT: https://github.com/huggingface/transformers/issues/21305