def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        bbox: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        bbox_first_token_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], TokenClassifierOutput]:
        r"""

        Returns:

        Examples:

        ```python
        >>> import torch
        >>> from transformers import BrosProcessor, BrosForTokenClassification

        >>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")

        >>> model = BrosForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")

        >>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
        >>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
        >>> encoding["bbox"] = bbox

        >>> outputs = model(**encoding)
        ```"""

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.bros(
            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,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]

        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            if bbox_first_token_mask is not None:
                bbox_first_token_mask = bbox_first_token_mask.view(-1)
                loss = loss_fct(
                    logits.view(-1, self.num_labels)[bbox_first_token_mask], labels.view(-1)[bbox_first_token_mask]
                )
            else:
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))

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

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


        Returns:

        Examples:

        ```python
        >>> import torch
        >>> from transformers import BrosProcessor, BrosForTokenClassification

        >>> processor = BrosProcessor.from_pretrained("jinho8345/bros-base-uncased")

        >>> model = BrosForTokenClassification.from_pretrained("jinho8345/bros-base-uncased")

        >>> encoding = processor("Hello, my dog is cute", add_special_tokens=False, return_tensors="pt")
        >>> bbox = torch.tensor([[[0, 0, 1, 1]]]).repeat(1, encoding["input_ids"].shape[-1], 1)
        >>> encoding["bbox"] = bbox

        >>> outputs = model(**encoding)
        ```
dpkd/transformers/src/transformers/models/bros/modeling_bros.py