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

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, TFConvNextModel
        >>> 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/convnext-tiny-224")
        >>> model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")

        >>> inputs = image_processor(images=image, return_tensors="tf")
        >>> outputs = model(**inputs)
        >>> last_hidden_states = outputs.last_hidden_state
        ```"""
        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

        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        outputs = self.convnext(
            pixel_values=pixel_values,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        if not return_dict:
            return (outputs[0],) + outputs[1:]

        return TFBaseModelOutputWithPooling(
            last_hidden_state=outputs.last_hidden_state,
            pooler_output=outputs.pooler_output,
            hidden_states=outputs.hidden_states,
        )

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, TFConvNextModel
        >>> 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/convnext-tiny-224")
        >>> model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")

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