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,
        training: Optional[bool] = False,
    ) -> Union[TFElectraForPreTrainingOutput, Tuple[tf.Tensor]]:
        r"""
        Returns:

        Examples:

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

        >>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
        >>> model = TFElectraForPreTraining.from_pretrained("google/electra-small-discriminator")
        >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        >>> outputs = model(input_ids)
        >>> scores = outputs[0]
        ```"""
        discriminator_hidden_states = self.electra(
            input_ids=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,
        )
        discriminator_sequence_output = discriminator_hidden_states[0]
        logits = self.discriminator_predictions(discriminator_sequence_output)

        if not return_dict:
            return (logits,) + discriminator_hidden_states[1:]

        return TFElectraForPreTrainingOutput(
            logits=logits,
            hidden_states=discriminator_hidden_states.hidden_states,
            attentions=discriminator_hidden_states.attentions,
        )

        Returns:

        Examples:

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

        >>> tokenizer = AutoTokenizer.from_pretrained("google/electra-small-discriminator")
        >>> model = TFElectraForPreTraining.from_pretrained("google/electra-small-discriminator")
        >>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :]  # Batch size 1
        >>> outputs = model(input_ids)
        >>> scores = outputs[0]
        ```
dpkd/transformers/src/transformers/models/electra/modeling_tf_electra.py