Add labels padding in tokenization_utils_base.py
# What does this PR do?

This PR makes `tokenizer.pad()` also pad `'labels'`.

I tried to use this:
https://github.com/huggingface/transformers/blob/8065fea87007fbf7542fc060ff8ddd0b5df567da/src/transformers/data/data_collator.py#L69 

But since labels is not padded, the result cannot turn into a tensor. `ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same lengt
h.
`
This patch solves the problem.

It seems logical to me that `tokenizer.pad()` should also pad `'labels'`. 



This portion of code is last changed in #4015 @n1t0 @thomwolf @LysandreJik 
Hi there! Thanks for your PR! I see a few problems with this approach.
1. Not all labels need to be padded. If you are doing classification (with one or multiple labels) you don't want to pad them
2. I imagine you are in a token classification problem, and in those, the number of labels is not necessarily the same as the number of tokens, as the labels are for words and tokens can be parts of words.

I think the proper fix is to create an option in `DataCollatorWithPadding` to activate label padding (so a flag `pad_labels_too` or something like that) that then pads the labels to the maximum length of the labels (so `difference` that you use here might be a different number for the labels).<|||||>Thanks for the reply!

Considering that different problem may pad labels differently, I think maybe it's better to leave it as is and use this:
```python
class MyDataCollatorWithPadding(DataCollatorWithPadding):
    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
        batch = super().__call__(features)
        # add custom label padding here
        return batch
```
Just came up with this. 😃  Not sure if it works.<|||||>Just tried it, the above code does not work, because the error is in `self.tokenizer.pad()`.
Here is the truncated trace:
```
src/transformers/data/data_collator.py", line 103, in __call__
    batch = self.tokenizer.pad(
src/transformers/tokenization_utils_base.py", line 2408, in pad
    return BatchEncoding(batch_outputs, tensor_type=return_tensors)
src/transformers/tokenization_utils_base.py", line 186, in __init__
    self.convert_to_tensors(tensor_type=tensor_type, prepend_batch_axis=prepend_batch_axis)
src/transformers/tokenization_utils_base.py", line 571, in convert_to_tensors
    raise ValueError(
ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.
```

Therefore `pad_labels_too` needs to be in `tokenizer.pad()`.
@sgugger 
> the number of labels is not necessarily the same as the number of tokens, as the labels are for words and tokens can be parts of words.

Maybe we will need a `LabelPaddingStrategy` similar to `PaddingStrategy`. But I don't know what kinds of other label padding strategies needs to be added.<|||||>I think you should use the newly pushed DataCollatorForTokenClassification from #8274.<|||||>Very nice! I guess I will close this PR.