Improve LayoutLM
# What does this PR do?

- [x] Improve documentation of `LayoutLM`, explaining how people can normalize bounding boxes before passing them to the model, add links to the various datasets on which the model achieves state-of-the-art results, add code examples in the documentation for the various models
- [x] Add notebook to the list of community notebooks showcasing how to fine-tune `LayoutLMForTokenClassification` on the [FUNSD](https://guillaumejaume.github.io/FUNSD/) dataset (on which the model achieves SOTA results)
- [x] Add integration tests, which confirm that the model outputs the same tensors as the original implementation on the same input data
- [x] Add `LayoutLMForSequenceClassification`, which makes it possible to fine-tune LayoutLM for document image classification tasks (such as the [RVL-CLIP dataset](https://www.cs.cmu.edu/~aharley/rvl-cdip/)), extra tests included.

Fixes the following issues:
- #9228 
- #9097 
- #8866 
- #8524

## Who can review?

@LysandreJik, @patrickvonplaten, @sgugger
 

Thanks for the reviews, I've addressed all comments. There are 2 things remaining:

- in the code examples, I use both `tokenize()` and `convert_tokens_to_ids` as the bounding boxes (which are at word-level) need to be converted to token-level. Is there a better solution?
```
words = ["Hello", "world"]
normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]
tokens = []
token_boxes = []
for word, box in zip(words, normalized_word_boxes):
    word_tokens = tokenizer.tokenize(word)
    tokens.extend(word_tokens)
    token_boxes.extend([box] * len(word_tokens))
```
- according to @sgugger the input data on which the integration tests are ran are maybe too long, and black formatting causes them to be flattened vertically. Could you maybe fix this @LysandreJik? <|||||>I pushed the reformat you asked for @NielsRogge, make sure to pull before doing any more changes!<|||||>Ok thank you, so the only thing remaining is make the code examples more efficient? Is there a way to make the code block (see comment above) better?