Dataloader in multi-threaded environments

Hi,

I've been thinking a bit about how we could implement the Dataloader pattern in v3 while still running in multi-threaded mode. Since v3 does not support Syrus's [Promise library](https://github.com/syrusakbary/promise), we need to come up with a story for batching in async mode, as well as in multi-threaded environments. There are many libraries that do not support `asyncio` and there [are many cases](https://techspot.zzzeek.org/2015/02/15/asynchronous-python-and-databases/) where it does not make sense to go fully async.

As far as I understand, the only way to batch resolver calls from a [single frame of execution](https://github.com/graphql/dataloader/blob/master/src/index.js#L220-L222) would be to use [`loop.call_soon`](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.call_soon). But since `asyncio` is [not threadsafe](https://docs.python.org/3/library/asyncio-dev.html#concurrency-and-multithreading), that means we would need to run a separate event loop in each worker thread. We would need to wrap the `graphql` call with something like this:

```py
def run_batched_query(...):
loop = asyncio.new_event_loop()
execution_future = graphql(...)
loop.run_until_complete(result_future)
return execution_future.result()
```

Is that completely crazy? If yes, do you see a less hacky way? I'm not very familiar with `asyncio` so I would love to get feedback.

Cheers