Add `concatenate_datasets` for iterable datasets
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['_The documentation is not available anymore as the PR was closed or merged._'
 'Thanks ! I addressed your comments :)\r\n\r\n> There is a slight difference in concatenate_datasets between the version for map-style datasets and the one for iterable datasets\r\n\r\nIndeed, here is what I did to fix this:\r\n\r\n- axis 0: fill missing columns with None.\r\n(I first iterate over the input datasets to infer their columns from the first examples, then I set the features of the resulting dataset to be the merged features)\r\nThis is consistent with non-streaming concatenation\r\n\r\n- axis 1: **fill the missing rows with None**, for consistency with axis 0\r\n(but let me know what you think, I can still revert this behavior and raise an error when one of the dataset runs out of examples)\r\nWe might have to align the non-streaming concatenation with this behavior though, for consistency. What do you think ?'
 'Added more comments as suggested, and some typing\r\n\r\nWhile factorizing _apply_features_types for both IterableDataset and TypedExamplesIterable, I  fixed a missing `token_per_repo_id` that was not passed to TypedExamplesIteable\r\n\r\nLet me know what you think now @mariosasko ']
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`concatenate_datasets` currently only supports lists of `datasets.Dataset`, not lists of `datasets.IterableDataset` like `interleave_datasets`

Fix https://github.com/huggingface/datasets/issues/2564

I also moved `_interleave_map_style_datasets` from combine.py to arrow_dataset.py, since the logic depends a lot on the `Dataset` object internals

And I moved `concatenate_datasets` from arrow_dataset.py to combine.py to have it with `interleave_datasets` (though it's also copied in arrow_dataset module for backward compatibility for now)
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