def get_image_features(
        self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False
    ):
        r"""
        Args:
            pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`):
                Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained
                using [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.

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

        Examples:

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

        >>> model = FlaxCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
        >>> processor = AutoProcessor.from_pretrained("openai/clip-vit-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="np")

        >>> image_features = model.get_image_features(**inputs)
        ```"""
        pixel_values = jnp.transpose(pixel_values, (0, 2, 3, 1))

        # Handle any PRNG if needed
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        def _get_features(module, pixel_values, deterministic):
            vision_outputs = module.vision_model(pixel_values=pixel_values, deterministic=deterministic)
            pooled_output = vision_outputs[1]  # pooled_output
            image_features = module.visual_projection(pooled_output)
            return image_features

        return self.module.apply(
            {"params": params or self.params},
            jnp.array(pixel_values, dtype=jnp.float32),
            not train,
            method=_get_features,
            rngs=rngs,
        )

        Args:
            pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`):
                Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained
                using [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details.

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

        Examples:

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

        >>> model = FlaxCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
        >>> processor = AutoProcessor.from_pretrained("openai/clip-vit-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="np")

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