The idea would be to allow this :
```python 
ds.to_parquet("my_dataset/ds.parquet")
reloaded = load_dataset("my_dataset")
assert ds.features == reloaded.features 
```
And it should also work with Image and Audio types (right now they're reloaded as a dict type)

This can be implemented by storing and reading the feature types in the parquet metadata, as we do for arrow files.
Reload features from Parquet metadata
I'd be happy to have a look, if nobody else has started working on this yet @lhoestq. 

It seems to me that for the `arrow` format features are currently attached as metadata [in `datasets.arrow_writer`](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/arrow_writer.py#L412)  and retrieved from the metadata at `load_dataset` time using [`datasets.features.features.from_arrow_schema`](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/features/features.py#L1602). 

This will need to be replicated for `parquet` via calls to [this api](https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_metadata.html) from `io.parquet.ParquetWriter` and `io.parquet.ParquetReader` [respectively](https://github.com/huggingface/datasets/blob/5f810b7011a8a4ab077a1847c024d2d9e267b065/src/datasets/io/parquet.py#L104).

Any other important considerations?
 Thanks @MFreidank ! That's correct :)

Reading the metadata to infer the features can be ideally done in the `parquet.py` file in `packaged_builder` when a parquet file is read. You can cast the arrow table to the schema you get from the features.arrow_schema #self-assign