Use the processor to prepare the inputs for the model. The processor combines an image processor that prepares the
image for the model by resizing and normalizing it, and a tokenizer that takes care of the text inputs.

candidate_labels = ["tree", "car", "bike", "cat"]
inputs = processor(images=image, text=candidate_labels, return_tensors="pt", padding=True)

Pass the inputs through the model, and post-process the results: