def call(
        self,
        input_ids: TFModelInputType | None = None,
        bbox: np.ndarray | tf.Tensor | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        token_type_ids: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        head_mask: np.ndarray | tf.Tensor | None = None,
        inputs_embeds: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        labels: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, TFLayoutLMForSequenceClassification
        >>> import tensorflow as tf

        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
        >>> model = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased")

        >>> words = ["Hello", "world"]
        >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

        >>> token_boxes = []
        >>> for word, box in zip(words, normalized_word_boxes):
        ...     word_tokens = tokenizer.tokenize(word)
        ...     token_boxes.extend([box] * len(word_tokens))
        >>> # add bounding boxes of cls + sep tokens
        >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

        >>> encoding = tokenizer(" ".join(words), return_tensors="tf")
        >>> input_ids = encoding["input_ids"]
        >>> attention_mask = encoding["attention_mask"]
        >>> token_type_ids = encoding["token_type_ids"]
        >>> bbox = tf.convert_to_tensor([token_boxes])
        >>> sequence_label = tf.convert_to_tensor([1])

        >>> outputs = model(
        ...     input_ids=input_ids,
        ...     bbox=bbox,
        ...     attention_mask=attention_mask,
        ...     token_type_ids=token_type_ids,
        ...     labels=sequence_label,
        ... )

        >>> loss = outputs.loss
        >>> logits = outputs.logits
        ```"""
        outputs = self.layoutlm(
            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,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )
        pooled_output = outputs[1]
        pooled_output = self.dropout(inputs=pooled_output, training=training)
        logits = self.classifier(inputs=pooled_output)
        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)

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

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

        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, TFLayoutLMForSequenceClassification
        >>> import tensorflow as tf

        >>> tokenizer = AutoTokenizer.from_pretrained("microsoft/layoutlm-base-uncased")
        >>> model = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased")

        >>> words = ["Hello", "world"]
        >>> normalized_word_boxes = [637, 773, 693, 782], [698, 773, 733, 782]

        >>> token_boxes = []
        >>> for word, box in zip(words, normalized_word_boxes):
        ...     word_tokens = tokenizer.tokenize(word)
        ...     token_boxes.extend([box] * len(word_tokens))
        >>> # add bounding boxes of cls + sep tokens
        >>> token_boxes = [[0, 0, 0, 0]] + token_boxes + [[1000, 1000, 1000, 1000]]

        >>> encoding = tokenizer(" ".join(words), return_tensors="tf")
        >>> input_ids = encoding["input_ids"]
        >>> attention_mask = encoding["attention_mask"]
        >>> token_type_ids = encoding["token_type_ids"]
        >>> bbox = tf.convert_to_tensor([token_boxes])
        >>> sequence_label = tf.convert_to_tensor([1])

        >>> outputs = model(
        ...     input_ids=input_ids,
        ...     bbox=bbox,
        ...     attention_mask=attention_mask,
        ...     token_type_ids=token_type_ids,
        ...     labels=sequence_label,
        ... )

        >>> loss = outputs.loss
        >>> logits = outputs.logits
        ```
dpkd/transformers/src/transformers/models/layoutlm/modeling_tf_layoutlm.py