def get_image_features(
        self,
        pixel_values=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Returns:
            image_features (`tf.Tensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying
            the projection layer to the pooled output of [`TFCLIPVisionModel`].

        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import TFVisionTextDualEncoderModel, AutoImageProcessor

        >>> model = TFVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian", from_pt=True)
        >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> inputs = image_processor(images=image, return_tensors="np")

        >>> image_features = model.get_image_features(**inputs)
        ```"""
        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]  # pooled_output
        image_features = self.visual_projection(pooled_output)

        return image_features

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

        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import TFVisionTextDualEncoderModel, AutoImageProcessor

        >>> model = TFVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian", from_pt=True)
        >>> image_processor = AutoImageProcessor.from_pretrained("google/vit-base-patch16-224")

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> inputs = image_processor(images=image, return_tensors="np")

        >>> image_features = model.get_image_features(**inputs)
        ```
dpkd/transformers/src/transformers/models/vision_text_dual_encoder/modeling_tf_vision_text_dual_encoder.py