def __call__(
        self,
        html_strings=None,
        nodes=None,
        xpaths=None,
        node_labels=None,
        questions=None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = None,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs,
    ) -> BatchEncoding:
        """
        This method first forwards the `html_strings` argument to [`~MarkupLMFeatureExtractor.__call__`]. Next, it
        passes the `nodes` and `xpaths` along with the additional arguments to [`~MarkupLMTokenizer.__call__`] and
        returns the output.

        Optionally, one can also provide a `text` argument which is passed along as first sequence.

        Please refer to the docstring of the above two methods for more information.
        """
        # first, create nodes and xpaths
        if self.parse_html:
            if html_strings is None:
                raise ValueError("Make sure to pass HTML strings in case `parse_html` is set to `True`")

            if nodes is not None or xpaths is not None or node_labels is not None:
                raise ValueError(
                    "Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"
                )

            features = self.feature_extractor(html_strings)
            nodes = features["nodes"]
            xpaths = features["xpaths"]
        else:
            if html_strings is not None:
                raise ValueError("You have passed HTML strings but `parse_html` is set to `False`.")
            if nodes is None or xpaths is None:
                raise ValueError("Make sure to pass nodes and xpaths in case `parse_html` is set to `False`")

        # # second, apply the tokenizer
        if questions is not None and self.parse_html:
            if isinstance(questions, str):
                questions = [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

        encoded_inputs = self.tokenizer(
            text=questions if questions is not None else nodes,
            text_pair=nodes if questions is not None else None,
            xpaths=xpaths,
            node_labels=node_labels,
            add_special_tokens=add_special_tokens,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            return_tensors=return_tensors,
            **kwargs,
        )

        return encoded_inputs

        This method first forwards the `html_strings` argument to [`~MarkupLMFeatureExtractor.__call__`]. Next, it
        passes the `nodes` and `xpaths` along with the additional arguments to [`~MarkupLMTokenizer.__call__`] and
        returns the output.

        Optionally, one can also provide a `text` argument which is passed along as first sequence.

        Please refer to the docstring of the above two methods for more information.
        
dpkd/transformers/src/transformers/models/markuplm/processing_markuplm.py