Fix memory issue in multiprocessing: Don't pickle table index
The code quality check is going to be fixed by #2265  The memory issue didn't come from `self.__dict__.copy()` but from the fact that this dict contains `_batches` which has all the batches of the table in it.
Therefore for a MemoryMappedTable all the data in `_batches` were copied in memory when pickling and this is the issue. I'm still investigating why we didn't catch this issue in the tests.
This test should have caught it but didn't:

https://github.com/huggingface/datasets/blob/3db67f5ff6cbf807b129d2b4d1107af27623b608/tests/test_table.py#L350-L353 I'll focus on the patch release and fix the test in another PR after the release Yes, I think it is better that way...
The table index is currently being pickled when doing multiprocessing, which brings all the record batches of the dataset in memory.

I fixed that by not pickling the index attributes. Therefore each process has to rebuild the index when unpickling the table.

Fix issue #2256

We'll do a patch release asap !