  scale. To normalize, you can use the following function:
python
def normalize_bbox(bbox, width, height):
    return [
        int(1000 * (bbox[0] / width)),
        int(1000 * (bbox[1] / height)),
        int(1000 * (bbox[2] / width)),
        int(1000 * (bbox[3] / height)),
    ]
Here, width and height correspond to the width and height of the original document in which the token
occurs. Those can be obtained using the Python Image Library (PIL) library for example, as follows:
```python
from PIL import Image
image = Image.open("name_of_your_document - can be a png file, pdf, etc.")
width, height = image.size
```
For a demo which shows how to fine-tune [LayoutLMForTokenClassification] on the FUNSD dataset (a collection of annotated forms), see this notebook.
  It includes an inference part, which shows how to use Google's Tesseract on a new document.
