def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: 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[Tuple, TFBaseModelOutput]:
        r"""
        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, TFT5EncoderModel

        >>> tokenizer = AutoTokenizer.from_pretrained("t5-small")
        >>> model = TFT5EncoderModel.from_pretrained("t5-small")

        >>> input_ids = tokenizer(
        ...     "Studies have been shown that owning a dog is good for you", return_tensors="tf"
        ... ).input_ids  # Batch size 1
        >>> outputs = model(input_ids)
        ```"""

        encoder_outputs = self.encoder(
            input_ids,
            attention_mask=attention_mask,
            encoder_hidden_states=None,
            encoder_attention_mask=None,
            inputs_embeds=inputs_embeds,
            head_mask=head_mask,
            past_key_values=None,
            use_cache=False,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        if not return_dict:
            return encoder_outputs

        return TFBaseModelOutput(
            last_hidden_state=encoder_outputs.last_hidden_state,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoTokenizer, TFT5EncoderModel

        >>> tokenizer = AutoTokenizer.from_pretrained("t5-small")
        >>> model = TFT5EncoderModel.from_pretrained("t5-small")

        >>> input_ids = tokenizer(
        ...     "Studies have been shown that owning a dog is good for you", return_tensors="tf"
        ... ).input_ids  # Batch size 1
        >>> outputs = model(input_ids)
        ```
dpkd/transformers/src/transformers/models/t5/modeling_tf_t5.py