Add VGCN-BERT model
### Model description


HI, I am the author of [VGCN-BERT paper](https://arxiv.org/abs/2004.05707), the original implementation is in my gitlab [vgcn-bert repo](https://github.com/Louis-udm/VGCN-BERT), but recently I updated the algorithm and implemented a new version for integrating in Transformer.

> Much progress has been made recently on text classification with methods based on neural networks. In particular, models using attention mechanism such as BERT have shown to have the capability of capturing the contextual information within a sentence or document. However, their ability of capturing the global information about the vocabulary of a language is more limited. This latter is the strength of Graph Convolutional Networks (GCN). In this paper, we propose VGCN-BERT model which combines the capability of BERT with a Vocabulary Graph Convolutional Network (VGCN). Local information and global information interact through different layers of BERT, allowing them to influence mutually and to build together a final representation for classification. In our experiments on several text classification datasets, our approach outperforms BERT and GCN alone, and achieve higher effectiveness than that reported in previous studies.

I actually finished the integration of my new version and opened the PR. This new VGCN-BERT algorithm has the following improvements:
- Greatly speeds up the calculation speed of embedding vocabulary graph convolutinal network (or Word Graph embedding). Taking CoLa as an example, the new model only increases the training time by 11% compared with the base model
- Updated subgraph selection algorithm.
- Currently using DistilBert as the base model, but it is easy to migrate to other models.
- Provide two graph construction methods in vgcn_bert/modeling_graph.py (the same NPMI statistical method as the paper, and the predefined entity-relationship mapping method)

I hope that after integrating into transformers, someone can discover some more practical use case. I am ashamed to say that I have not discovered too much real use cases myself, mainly because the word-grounded graph obtained through statistical methods has limited improvement on the LLM model. I think its potential application should be when there are specific/customized graphs that need to be integrated into LLM.

### Open source status

- [X] The model implementation is available
- [X] The model weights are available

### Provide useful links for the implementation

https://arxiv.org/abs/2004.05707
https://github.com/Louis-udm/VGCN-BERT
The new implement is here:
https://huggingface.co/zhibinlu/vgcn-bert-distilbert-base-uncased