def call(
        self,
        input_ids: TFModelInputType | 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,
        next_sentence_label: np.ndarray | tf.Tensor | None = None,
        training: Optional[bool] = False,
    ) -> Union[Tuple, TFMobileBertForPreTrainingOutput]:
        r"""
        Return:

        Examples:

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

        >>> tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
        >>> model = TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased")
        >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        >>> outputs = model(input_ids)
        >>> prediction_scores, seq_relationship_scores = outputs[:2]
        ```"""
        outputs = self.mobilebert(
            input_ids,
            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,
        )

        sequence_output, pooled_output = outputs[:2]
        prediction_scores = self.predictions(sequence_output)
        seq_relationship_score = self.seq_relationship(pooled_output)

        total_loss = None
        if labels is not None and next_sentence_label is not None:
            d_labels = {"labels": labels}
            d_labels["next_sentence_label"] = next_sentence_label
            total_loss = self.hf_compute_loss(labels=d_labels, logits=(prediction_scores, seq_relationship_score))

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

        return TFMobileBertForPreTrainingOutput(
            loss=total_loss,
            prediction_logits=prediction_scores,
            seq_relationship_logits=seq_relationship_score,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

        Return:

        Examples:

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

        >>> tokenizer = AutoTokenizer.from_pretrained("google/mobilebert-uncased")
        >>> model = TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased")
        >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        >>> outputs = model(input_ids)
        >>> prediction_scores, seq_relationship_scores = outputs[:2]
        ```
dpkd/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py