None batched `with_transform`, `set_transform`

**Is your feature request related to a problem? Please describe.**

A `torch.utils.data.Dataset.__getitem__` operates on a single example.
But 🤗 `Datasets.with_transform` doesn't seem to allow non-batched transform.

**Describe the solution you'd like**

Have a `batched=True` argument in `Datasets.with_transform`

**Describe alternatives you've considered**

* Convert a non-batched transform function to batched one myself. 
* Wrap a 🤗 Dataset with torch Dataset, and add a `__getitem__`. 🙄
* Have `lazy=False` in `Dataset.map`, and returns a `LazyDataset` if `lazy=True`. This way the same `map` interface can be used, and existing code can be updated with one argument change.

Comments:
Hi ! Thanks for the suggestion :)
It makes sense to me, and it can surely be implemented by wrapping the user's function to make it a batched function. However I'm not a big fan of the inconsistency it would create with `map`: `with_transform` is batched by default while `map` isn't.

Is there something you would like to contribute ? I can give you some pointers if you want

Hi @lhoestq ,
Sorry I missed your reply.

I would love to contribute. But I don't know which solution would be the best for this repo.

> However I'm not a big fan of the inconsistency it would create with map: with_transform is batched by default while map isn't.

I agree. What do you think about the alternative solutions?

> * Convert a non-batched transform function to batched one myself.

This won't be able to use torch loader multi-worker.

> * Wrap a 🤗 Dataset with torch Dataset, and add a __getitem__. 🙄

This is actually pretty simple.

```python
import torch

class LazyMapTorchDataset(torch.utils.data.Dataset):
    def __init__(self, ds, fn):
        self.ds = ds
        self.fn = fn
    def __getitem__(self, i):
        return self.fn(self.ds[i])

d = [{1:2, 2:3}, {1:3, 2:4}]
ds = LazyMapTorchDataset(d, lambda x:{k:v*2 for k,v in x.items()})
for i in range(2):
    print(f'before {d[i]}')
    print(f'after {ds[i]}')
```
```
before {1: 2, 2: 3}
after {1: 4, 2: 6}
before {1: 3, 2: 4}
after {1: 6, 2: 8}
```

But this requires converting data to torch tensor myself. And this is really similar to `.map()`, why not just use it? So I have the next solution.

> * Have lazy=False in Dataset.map, and returns a LazyDataset if lazy=True. This way the same map interface can be used, and existing code can be updated with one argument change.

I think I like this solution best.  Because `.with_transform` is entangled with `.with_format`, so seems more flexible to modify the `.map` than to modify `.with_transform`.

The usage looks nice, too.
```python
# lazy, one to one, can be parallelized via torch loader, no need to set `num_worker` beforehand.
dataset = dataset.map(fn, lazy=True, batched=False)
# collate_fn
dataloader = Dataloader(dataset.with_format('torch'), collate_fn=collate_fn, num_workers=...) 
```

There are some minor decisions like whether a lazy map should be allowed before another map, but I think we can work it out later. The implementation can probably borrow from `IterableDataset`.

I like the idea of lazy map. On the other hand we should only have either lazy map or `with_transform` (not both). That's why I'd rather stick with `with_transform` for now (but maybe we can consider it for later major releases like `datasets` v2).

I understand the issue with `with_transform` and `with_format` being exclusive, maybe we can separate them: first transform, them format.

Finally I think what's also going to be important in the end will be the addition of multiprocessing to transforms