def get_image_features(
        self,
        pixel_values: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> torch.FloatTensor:
        r"""
        Returns:
            image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
            applying the projection layer to the pooled output of [`OwlViTVisionModel`].

        Examples:
        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, OwlViTModel

        >>> model = OwlViTModel.from_pretrained("google/owlvit-base-patch32")
        >>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32")
        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> inputs = processor(images=image, return_tensors="pt")
        >>> image_features = model.get_image_features(**inputs)
        ```"""
        # Use OWL-ViT model's config for some fields (if specified) instead of those of vision & text components.
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        vision_outputs = self.vision_model(
            pixel_values=pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = vision_outputs[1]
        image_features = self.visual_projection(pooled_output)

        return image_features

        Returns:
            image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
            applying the projection layer to the pooled output of [`OwlViTVisionModel`].

        Examples:
        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, OwlViTModel

        >>> model = OwlViTModel.from_pretrained("google/owlvit-base-patch32")
        >>> processor = AutoProcessor.from_pretrained("google/owlvit-base-patch32")
        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)
        >>> inputs = processor(images=image, return_tensors="pt")
        >>> image_features = model.get_image_features(**inputs)
        ```
dpkd/transformers/src/transformers/models/owlvit/modeling_owlvit.py