bels)`, *optional*):
            Labels for computing the visual question answering loss. This tensor must be either a one-hot encoding of
            all answers that are applicable for a given example in the batch, or a soft encoding indicating which
            answers are applicable, where 1.0 is the highest score.

        Returns:

        Examples:

        ```python
        >>> from transformers import ViltProcessor, ViltForQuestionAnswering
        >>> import requests
        >>> from PIL import Image

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> text = "How many cats are there?"

        >>> processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
        >>> model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")

        >>> # prepare inputs
        >>> encoding = processor(image, text, return_tensors="pt")

        >>> # forward pass
        >>> outputs = model(**encoding)
        >>> logits = outputs.logits
        >>> idx = logits.argmax(-1).item()
        >>> print("Predicted answer:", model.config.id2label[idx])
        Predicted answer: 2
        ```Nr"