Return Numpy Array (fixed length) Mode, in __get_item__, Instead of List
### Feature request

There are old known issues, but they can be easily forgettable problems in multiprocessing with pytorch-dataloader:

Too high usage of RAM or shared-memory in pytorch when we set num workers > 1 and returning type of dataset or dataloader is "List" or "Dict".
https://github.com/pytorch/pytorch/issues/13246 

With huggingface datasets, unfortunately, the default return type is the list, so the problem is raised too often if we do not set anything for the issue.


However, this issue can be released when the returning output is fixed in length.
Therefore, I request the mode, returning outputs with fixed length (e.g. numpy array) rather than list.

The design would be good when we load datasets as
```python
load_dataset(..., with_return_as_fixed_tensor=True)
```

### Motivation

The general solution for this issue is already in the comments:  https://github.com/pytorch/pytorch/issues/13246#issuecomment-905703662 

: Numpy or Pandas seems not to have problems, while both have the string type.
(I'm not sure that the sequence of huggingface datasets can solve this problem as well)


### Your contribution

 I'll read it ! thanks
Hi! 

You can use [`.set_format("np")`](https://huggingface.co/docs/datasets/process#format) to get NumPy arrays (or Pytorch tensors with `.set_format("torch")`) in `__getitem__`.

Also, have you been able to reproduce the linked PyTorch issue with a HF dataset?
 