def call(
        self,
        input_ids: tf.Tensor | None = None,
        bbox: tf.Tensor | None = None,
        attention_mask: tf.Tensor | None = None,
        token_type_ids: tf.Tensor | None = None,
        position_ids: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        inputs_embeds: tf.Tensor | None = None,
        pixel_values: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[
        TFBaseModelOutput,
        Tuple[tf.Tensor],
        Tuple[tf.Tensor, tf.Tensor],
        Tuple[tf.Tensor, tf.Tensor, tf.Tensor],
    ]:
        r"""
        Returns:

        Examples:

        ```python
        >>> from transformers import AutoProcessor, TFAutoModel
        >>> from datasets import load_dataset

        >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
        >>> model = TFAutoModel.from_pretrained("microsoft/layoutlmv3-base")

        >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
        >>> example = dataset[0]
        >>> image = example["image"]
        >>> words = example["tokens"]
        >>> boxes = example["bboxes"]

        >>> encoding = processor(image, words, boxes=boxes, return_tensors="tf")

        >>> outputs = model(**encoding)
        >>> last_hidden_states = outputs.last_hidden_state
        ```"""

        outputs = self.layoutlmv3(
            input_ids=input_ids,
            bbox=bbox,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            pixel_values=pixel_values,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        return outputs

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoProcessor, TFAutoModel
        >>> from datasets import load_dataset

        >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
        >>> model = TFAutoModel.from_pretrained("microsoft/layoutlmv3-base")

        >>> dataset = load_dataset("nielsr/funsd-layoutlmv3", split="train")
        >>> example = dataset[0]
        >>> image = example["image"]
        >>> words = example["tokens"]
        >>> boxes = example["bboxes"]

        >>> encoding = processor(image, words, boxes=boxes, return_tensors="tf")

        >>> outputs = model(**encoding)
        >>> last_hidden_states = outputs.last_hidden_state
        ```
dpkd/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py