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,
        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: bool = False,
        **kwargs,
    ) -> Union[Tuple[tf.Tensor], TFFunnelForPreTrainingOutput]:
        r"""
        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, TFFunnelForPreTraining
        >>> import torch

        >>> tokenizer = AutoTokenizer.from_pretrained("funnel-transformer/small")
        >>> model = TFFunnelForPreTraining.from_pretrained("funnel-transformer/small")

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
        >>> logits = model(inputs).logits
        ```"""
        discriminator_hidden_states = self.funnel(
            input_ids,
            attention_mask,
            token_type_ids,
            inputs_embeds,
            output_attentions,
            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 TFFunnelForPreTrainingOutput(
            logits=logits,
            hidden_states=discriminator_hidden_states.hidden_states,
            attentions=discriminator_hidden_states.attentions,
        )

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, TFFunnelForPreTraining
        >>> import torch

        >>> tokenizer = AutoTokenizer.from_pretrained("funnel-transformer/small")
        >>> model = TFFunnelForPreTraining.from_pretrained("funnel-transformer/small")

        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="tf")
        >>> logits = model(inputs).logits
        ```
dpkd/transformers/src/transformers/models/funnel/modeling_tf_funnel.py