def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        xpath_tags_seq: Optional[torch.Tensor] = None,
        xpath_subs_seq: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoProcessor, AutoModelForTokenClassification
        >>> import torch

        >>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base")
        >>> processor.parse_html = False
        >>> model = AutoModelForTokenClassification.from_pretrained("microsoft/markuplm-base", num_labels=7)

        >>> nodes = ["hello", "world"]
        >>> xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"]
        >>> node_labels = [1, 2]
        >>> encoding = processor(nodes=nodes, xpaths=xpaths, node_labels=node_labels, return_tensors="pt")

        >>> with torch.no_grad():
        ...     outputs = model(**encoding)

        >>> loss = outputs.loss
        >>> logits = outputs.logits
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.markuplm(
            input_ids,
            xpath_tags_seq=xpath_tags_seq,
            xpath_subs_seq=xpath_subs_seq,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        prediction_scores = self.classifier(sequence_output)  # (batch_size, seq_length, node_type_size)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(
                prediction_scores.view(-1, self.config.num_labels),
                labels.view(-1),
            )

        if not return_dict:
            output = (prediction_scores,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TokenClassifierOutput(
            loss=loss,
            logits=prediction_scores,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoProcessor, AutoModelForTokenClassification
        >>> import torch

        >>> processor = AutoProcessor.from_pretrained("microsoft/markuplm-base")
        >>> processor.parse_html = False
        >>> model = AutoModelForTokenClassification.from_pretrained("microsoft/markuplm-base", num_labels=7)

        >>> nodes = ["hello", "world"]
        >>> xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span"]
        >>> node_labels = [1, 2]
        >>> encoding = processor(nodes=nodes, xpaths=xpaths, node_labels=node_labels, return_tensors="pt")

        >>> with torch.no_grad():
        ...     outputs = model(**encoding)

        >>> loss = outputs.loss
        >>> logits = outputs.logits
        ```
dpkd/transformers/src/transformers/models/markuplm/modeling_markuplm.py