def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        bbox: Optional[torch.LongTensor] = None,
        image: Optional[torch.FloatTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        token_type_ids: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, TokenClassifierOutput]:
        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:

        Example:

        ```python
        >>> from transformers import AutoProcessor, LayoutLMv2ForTokenClassification, set_seed
        >>> from PIL import Image
        >>> from datasets import load_dataset

        >>> set_seed(88)

        >>> datasets = load_dataset("nielsr/funsd", split="test")
        >>> labels = datasets.features["ner_tags"].feature.names
        >>> id2label = {v: k for v, k in enumerate(labels)}

        >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr")
        >>> model = LayoutLMv2ForTokenClassification.from_pretrained(
        ...     "microsoft/layoutlmv2-base-uncased", num_labels=len(labels)
        ... )

        >>> data = datasets[0]
        >>> image = Image.open(data["image_path"]).convert("RGB")
        >>> words = data["words"]
        >>> boxes = data["bboxes"]  # make sure to normalize your bounding boxes
        >>> word_labels = data["ner_tags"]
        >>> encoding = processor(
        ...     image,
        ...     words,
        ...     boxes=boxes,
        ...     word_labels=word_labels,
        ...     padding="max_length",
        ...     truncation=True,
        ...     return_tensors="pt",
        ... )

        >>> outputs = model(**encoding)
        >>> logits, loss = outputs.logits, outputs.loss

        >>> predicted_token_class_ids = logits.argmax(-1)
        >>> predicted_tokens_classes = [id2label[t.item()] for t in predicted_token_class_ids[0]]
        >>> predicted_tokens_classes[:5]
        ['B-ANSWER', 'B-HEADER', 'B-HEADER', 'B-HEADER', 'B-HEADER']
        ```
        """

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

        outputs = self.layoutlmv2(
            input_ids=input_ids,
            bbox=bbox,
            image=image,
            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,
        )
        if input_ids is not None:
            input_shape = input_ids.size()
        else:
            input_shape = inputs_embeds.size()[:-1]

        seq_length = input_shape[1]
        # only take the text part of the output representations
        sequence_output = outputs[0][:, :seq_length]
        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            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,
        )

        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:

        Example:

        ```python
        >>> from transformers import AutoProcessor, LayoutLMv2ForTokenClassification, set_seed
        >>> from PIL import Image
        >>> from datasets import load_dataset

        >>> set_seed(88)

        >>> datasets = load_dataset("nielsr/funsd", split="test")
        >>> labels = datasets.features["ner_tags"].feature.names
        >>> id2label = {v: k for v, k in enumerate(labels)}

        >>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased", revision="no_ocr")
        >>> model = LayoutLMv2ForTokenClassification.from_pretrained(
        ...     "microsoft/layoutlmv2-base-uncased", num_labels=len(labels)
        ... )

        >>> data = datasets[0]
        >>> image = Image.open(data["image_path"]).convert("RGB")
        >>> words = data["words"]
        >>> boxes = data["bboxes"]  # make sure to normalize your bounding boxes
        >>> word_labels = data["ner_tags"]
        >>> encoding = processor(
        ...     image,
        ...     words,
        ...     boxes=boxes,
        ...     word_labels=word_labels,
        ...     padding="max_length",
        ...     truncation=True,
        ...     return_tensors="pt",
        ... )

        >>> outputs = model(**encoding)
        >>> logits, loss = outputs.logits, outputs.loss

        >>> predicted_token_class_ids = logits.argmax(-1)
        >>> predicted_tokens_classes = [id2label[t.item()] for t in predicted_token_class_ids[0]]
        >>> predicted_tokens_classes[:5]
        ['B-ANSWER', 'B-HEADER', 'B-HEADER', 'B-HEADER', 'B-HEADER']
        ```
        
dpkd/transformers/src/transformers/models/layoutlmv2/modeling_layoutlmv2.py