AssertionError when using label_cols in to_tf_dataset 

## Describe the bug
An incorrect `AssertionError` is raised when using `label_cols` in `to_tf_dataset` and the label's key name is `label`.

The assertion is in this line:
https://github.com/huggingface/datasets/blob/2.4.0/src/datasets/arrow_dataset.py#L475

## Steps to reproduce the bug
```python
from datasets import load_dataset
from transformers import DefaultDataCollator

dataset = load_dataset('glue', 'mrpc', split='train')

tf_dataset = dataset.to_tf_dataset(
    columns=["sentence1", "sentence2", "idx"],
    label_cols=["label"],
    batch_size=16,
    collate_fn=DefaultDataCollator(return_tensors="tf"),
)
```

## Expected results
No assertion error.

## Actual results
```
AssertionError: in user code:

    File "/opt/conda/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 475, in split_features_and_labels  *
        assert set(features.keys()).union(labels.keys()) == set(input_batch.keys())
```

## Environment info
<!-- You can run the command `datasets-cli env` and copy-and-paste its output below. -->
- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.45.1.el8_4.ppc64le-ppc64le-with-glibc2.17
- Python version: 3.8.13
- PyArrow version: 7.0.0
- Pandas version: 1.4.3


Comments:
cc @Rocketknight1 

Hi @lehrig, this is caused by the data collator renaming "label" to "labels". If you set `label_cols=["labels"]` in the call it will work correctly. However, I agree that the cause of the bug is not obvious, so I'll see if I can make a PR to clarify things when the collator renames columns.

Thanks - and wow, that appears like a strange side-effect of the data collator. Is that really needed?

Why not make it more explicit? For example, extend `DefaultDataCollator` with an optional property `label_col_name` to be used as label column; only when it is not provided default to `labels` (and document that this happens) for backwards-compatibility? 

Haha, I honestly have no idea why our data collators rename `"label"` (the standard label column name in our datasets) to `"labels"` (the standard label column name input to our models). It's been a pain point when I design TF data pipelines, though, because I don't want to hardcode things like that - especially in `datasets`, because the renaming is something that happens purely at the `transformers` end. I don't think I could make the change in the data collators themselves at this point, because it would break backward compatibility for everything in PyTorch as well as TF.

In the most recent version of `transformers` we added a [prepare_tf_dataset](https://huggingface.co/docs/transformers/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset) method to our models which takes care of these details for you, and even chooses appropriate columns and labels for the model you're using. In future we might make that the officially recommended way to convert HF datasets to `tf.data.Dataset`.

Interesting, that'd be great especially for clarity. https://huggingface.co/docs/datasets/use_with_tensorflow#data-loading already improved clarity, yet, all those options will still confuse people. Looking forward to those advances in the hope there'll be only 1 way in the future ;)

Anyways, I am happy for the time being with the work-around you provided. Thank you!