layer, and is expected to be bound by [CLS] and a [SEP] tokens, as in BERT. The segment IDs must also be set
appropriately for the textual and visual parts.
The [BertTokenizer] is used to encode the text. A custom detector/feature extractor must be used
to get the visual embeddings. The following example notebooks show how to use VisualBERT with Detectron-like models:
VisualBERT VQA demo notebook : This notebook
  contains an example on VisualBERT VQA.
Generate Embeddings for VisualBERT (Colab Notebook) : This notebook contains
  an example on how to generate visual embeddings.
The following example shows how to get the last hidden state using [VisualBertModel]:
```python
import torch
from transformers import BertTokenizer, VisualBertModel
model = VisualBertModel.from_pretrained("uclanlp/visualbert-vqa-coco-pre")
