Provide ability to load label mappings from file
I would like this also as I have been working with a dataset with hierarchical classes. In fact, I encountered this very issue when trying to define the dataset with a script. I couldn't find a work around and reverted to hard coding the class names in the readme yaml.

@david-waterworth do you envision also being able to define the hierarchical structure of the classes? @danielduckworth yes I did need to do that (but I ended up ditching datasets as it looks like this is a "wont fix").  @david-waterworth  Hmm, that's a shame. What are you using now? Also, I’m curious to know about the work you’re doing that involves hierarchical classes, if you don’t mind sharing.
### Feature request


My task is classification of a dataset containing a large label set that includes a hierarchy. Even ignoring the hierarchy I'm not able to find an example using `datasets` where the label names aren't hard-coded. This works find for classification of a handful of labels but ideally there would be a way of loading the name/id mappings required for `datasets.features.ClassLabel` from a file.

It is possible to pass a file to ClassLabel but I cannot see an easy way of using this with `GeneratorBasedBuilder` since `self._info` is called before the `dl_manager` is constructed so even if my dataset contains say `label_mappings.json` there's no way of loading it in order to construct the `datasets.DatasetInfo`

I can see other uses to accessing the `download_manager` from `self._info` - i.e. if the files contain a schema (i.e. `arrow` or `parquet` files) the `datasets.DatasetInfo` could be inferred.

The workaround that was suggested in the forum is to generate a `.py` file from the `label_mappings.json` and import it.

```
class TestDatasetBuilder(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.features.ClassLabel(names=["label_1", "label_2"]),
                }
            ),
            task_templates=[TextClassification(text_column="text", label_column="label")],
        )

    def _split_generators(self, dl_manager):
        train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
        test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
        ]

    def _generate_examples(self, filepath):
        """Generate AG News examples."""
        with open(filepath, encoding="utf-8") as csv_file:
            csv_reader = csv.DictReader(csv_file)
            for id_, row in enumerate(csv_reader):
                yield id_, row
```

### Motivation

Allow `datasets.DatasetInfo` to be generated based on the contents of the dataset. 

### Your contribution

I'm willing to work on a PR with guidence.