Support multi-worker with streaming dataset (IterableDataset).
Hi ! This is a great idea :)
I think we could have something similar to what we have in `datasets.Dataset.map`, i.e. a `num_proc` parameter that tells how many processes to spawn to parallelize the data processing. 

Regarding AUTOTUNE, this could be a nice feature as well, we could see how to add it in a second step Any update on this feature request? Not yet, I'm happy to provide some guidance if someone wants to give it a shot though.

The code that applies the `map` function is in `iterable_dataset.py`, in `MappedExamplesIterable.__iter__`
**Is your feature request related to a problem? Please describe.**
The current `.map` does not support multi-process, CPU can become bottleneck if the pre-processing is complex (e.g. t5 span masking).

**Describe the solution you'd like**
Ideally `.map` should support multi-worker like tfds, with `AUTOTUNE`.

**Describe alternatives you've considered**
A simpler solution is to shard the dataset and process it in parallel with pytorch dataloader. The shard does not need to be of equal size.
* https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset

**Additional context**
