To feed images to the Transformer encoder, each image is split into a sequence of fixed-size non-overlapping patches,
which are then linearly embedded. A [CLS] token is added to serve as representation of an entire image. The authors
also add absolute position embeddings, and feed the resulting sequence of vectors to a standard Transformer encoder.
The [CLIPFeatureExtractor] can be used to resize (or rescale) and normalize images for the model.
The [CLIPTokenizer] is used to encode the text. The [CLIPProcessor] wraps
[CLIPFeatureExtractor] and [CLIPTokenizer] into a single instance to both
encode the text and prepare the images. The following example shows how to get the image-text similarity scores using
[CLIPProcessor] and [CLIPModel].
```python
from PIL import Image
import requests
from transformers import CLIPProcessor, CLIPModel
