def call(
        self,
        pixel_values: TFModelInputType | None = None,
        noise: tf.Tensor = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[TFViTMAEModelOutput, Tuple[tf.Tensor]]:
        r"""
        Returns:

        Examples:

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

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

        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base")
        >>> model = TFViTMAEModel.from_pretrained("facebook/vit-mae-base")

        >>> inputs = image_processor(images=image, return_tensors="tf")
        >>> outputs = model(**inputs)
        >>> last_hidden_states = outputs.last_hidden_state
        ```"""
        outputs = self.vit(
            pixel_values=pixel_values,
            noise=noise,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        return outputs

        Returns:

        Examples:

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

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

        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base")
        >>> model = TFViTMAEModel.from_pretrained("facebook/vit-mae-base")

        >>> inputs = image_processor(images=image, return_tensors="tf")
        >>> outputs = model(**inputs)
        >>> last_hidden_states = outputs.last_hidden_state
        ```
dpkd/transformers/src/transformers/models/vit_mae/modeling_tf_vit_mae.py