integration with imbalanced-learn
You can convert any dataset to pandas to be used with imbalanced-learn using `.to_pandas()`

Otherwise if you want to keep a `Dataset` object and still use e.g. [make_imbalance](https://imbalanced-learn.org/stable/references/generated/imblearn.datasets.make_imbalance.html#imblearn.datasets.make_imbalance), you just need to pass the list of rows ids and labels:

```python
row_indices = list(range(len(dataset)))
resampled_row_indices, _ = make_imbalance(
    row_indices,
    dataset["label"],
    sampling_strategy={0: 25, 1: 50, 2: 50},
    random_state=RANDOM_STATE,
)

resampled_dataset = dataset.select(resampled_row_indices)
```
### Feature request

Wouldn't it be great if the various class balancing operations from imbalanced-learn were available as part of datasets?

### Motivation

I'm trying to use imbalanced-learn to balance a dataset, but it's not clear how to get the two to interoperate - what would be great would be some examples.  I've looked online, asked gpt-4, but so far not making much progress.

### Your contribution

If I can get this working myself I can submit a PR with example code to go in the docs