def get_image_features(
        self,
        pixel_values: TFModelInputType | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> tf.Tensor:
        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 [`TFGroupViTVisionModel`].

        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, TFGroupViTModel

        >>> model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
        >>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")

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

        >>> inputs = processor(images=image, return_tensors="tf")

        >>> image_features = model.get_image_features(**inputs)
        ```"""

        image_features = self.groupvit.get_image_features(
            pixel_values=pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        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 [`TFGroupViTVisionModel`].

        Examples:

        ```python
        >>> from PIL import Image
        >>> import requests
        >>> from transformers import AutoProcessor, TFGroupViTModel

        >>> model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
        >>> processor = AutoProcessor.from_pretrained("nvidia/groupvit-gcc-yfcc")

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

        >>> inputs = processor(images=image, return_tensors="tf")

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