Feature-extraction pipeline to return Tensor
# 🚀 Feature request

Actually, to code of the feature-extraction pipeline 
`transformers.pipelines.feature-extraction.FeatureExtractionPipeline l.82` return a `super().__call__(*args, **kwargs).tolist()`

Which gives a list[float] (or list[list[float]] if list[str] in input)

I guess it's to be framework agnostic, but we can specify `framework='pt'` in the pipeline config so I was expecting a `torch.tensor`. 

Could we add some logic to return tensors ? 

# Motivation 
Features will be used as input of other models, so keeping them as tensors (even better on GPU) would be profitable. 

Thanks in advance for the reply, 

Have a great day. 
Hello! Indeed, this is a valid request. Would you like to open a PR and take a stab at it?<|||||>@LysandreJik Hi, thanks for the fast reply ! 

Ok will do that :) 
I will comment here when the PR will be ready<|||||>Hi @LysandreJik is there any update on this issue? If @Ierezell didn't have time, I might be able to give a shot at it in the next days<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.<|||||>Hi!
Is this issue somewhere in consideration still?
Would be awesome to be able to get tensors from the feature extraction pipeline<|||||>I think we'd still be open to that; WDYT @Narsil?<|||||>Sure ! 

Would adding an argument `return_type= "tensors"` be OK ? That way we can enable this feature without breaking backward compatibility ?<|||||>I'm baffled as to why returning the features as a list is the default behavior in the first place... Isn't one common usage of feature extraction to provide an input to another model, which means it is preferred to keep it as a tensor?<|||||>@ajsanjoaquin 

Well it depends, not necessarily. Another very common use case is to feed it to some feature database for querying later.
Those database engines are not necessarily expecting the same kind of tensors that you are sending.

But I kind of agree that it should be at least a `numpy.array` because usually conversions between numpy and PT or TF is basically free, meaning it would be much easier to use that way.

Some `pipeline` were added a long time ago where the current situation was not as clear as today, and since we are very conservative regarding breaking changes, that can explain why some defaults are the way they are.

If/When v5 is getting prepared there would be a lot of small but breaking changes in that regard.