gle/vit-base-patch16-224", "bert-base-uncased"
        ... )

        >>> # contrastive training
        >>> urls = [
        ...     "http://images.cocodataset.org/val2017/000000039769.jpg",
        ...     "https://farm3.staticflickr.com/2674/5850229113_4fe05d5265_z.jpg",
        ... ]
        >>> images = [Image.open(requests.get(url, stream=True).raw) for url in urls]
        >>> inputs = processor(
        ...     text=["a photo of a cat", "a photo of a dog"], images=images, return_tensors="pt", padding=True
        ... )
        >>> outputs = model(
        ...     input_ids=inputs.input_ids,
        ...     attention_mask=inputs.attention_mask,
        ...     pixel_values=inputs.pixel_values,
        ...     return_loss=True,
        ... )
        >>> loss, logits_per_image = outputs.loss, outputs.logits_per_image  # this is the image-text similarity score

        >>> # save and load from pretrained
        >>> model.save_pretrained("vit-bert")
        >>> model = VisionTextDualEncoderModel.from_pretrained("vit-bert")

        >>> # inference
        >>> outputs = model(**inputs)
        >>> logits_per_image = outputs.logits_per_image  # this is the image-text similarity score
        >>> probs = logits_per_image.softmax(dim=1)  # we can take the softmax to get the label probabilities
        ```Nr=