dataset.to_iterable_dataset() loses useful info like dataset features
Hi ! Oh good catch. I think the features should be passed to `IterableDataset.from_generator()` in `to_iterable_dataset()` indeed.

Setting this as a good first issue if someone would like to contribute, otherwise we can take care of it :) #self-assign seems like the feature parameter is missing from `return IterableDataset.from_generator(Dataset._iter_shards, gen_kwargs={"shards": shards})`  hence it defaults to None.
### Describe the bug

Hello,
I like the new `to_iterable_dataset` feature but I noticed something that seems to be missing.

When using `to_iterable_dataset` to transform your map style dataset into iterable dataset, you lose valuable metadata like the features. 

These metadata are useful if you want to interleave iterable datasets, cast columns etc.

### Steps to reproduce the bug

```python
dataset = load_dataset("lhoestq/demo1")["train"]
print(dataset.features)
# {'id': Value(dtype='string', id=None), 'package_name': Value(dtype='string', id=None), 'review': Value(dtype='string', id=None), 'date': Value(dtype='string', id=None), 'star': Value(dtype='int64', id=None), 'version_id': Value(dtype='int64', id=None)}
dataset = dataset.to_iterable_dataset()
print(dataset.features)
# None
```

### Expected behavior

Keep the relevant information

### Environment info

datasets==2.10.0