def call(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: tf.Tensor | None = None,
        inputs_embeds: tf.Tensor | None = None,
        output_attentions: bool | None = None,
        output_hidden_states: bool | None = None,
        return_dict: bool | None = None,
        training: bool = False,
    ) -> TFDPRReaderOutput | Tuple[tf.Tensor, ...]:
        r"""
        Return:

        Examples:

        ```python
        >>> from transformers import TFDPRReader, DPRReaderTokenizer

        >>> tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base")
        >>> model = TFDPRReader.from_pretrained("facebook/dpr-reader-single-nq-base", from_pt=True)
        >>> encoded_inputs = tokenizer(
        ...     questions=["What is love ?"],
        ...     titles=["Haddaway"],
        ...     texts=["'What Is Love' is a song recorded by the artist Haddaway"],
        ...     return_tensors="tf",
        ... )
        >>> outputs = model(encoded_inputs)
        >>> start_logits = outputs.start_logits
        >>> end_logits = outputs.end_logits
        >>> relevance_logits = outputs.relevance_logits
        ```
        """
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = shape_list(input_ids)
        elif inputs_embeds is not None:
            input_shape = shape_list(inputs_embeds)[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        if attention_mask is None:
            attention_mask = tf.ones(input_shape, dtype=tf.dtypes.int32)

        return self.span_predictor(
            input_ids=input_ids,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        Return:

        Examples:

        ```python
        >>> from transformers import TFDPRReader, DPRReaderTokenizer

        >>> tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base")
        >>> model = TFDPRReader.from_pretrained("facebook/dpr-reader-single-nq-base", from_pt=True)
        >>> encoded_inputs = tokenizer(
        ...     questions=["What is love ?"],
        ...     titles=["Haddaway"],
        ...     texts=["'What Is Love' is a song recorded by the artist Haddaway"],
        ...     return_tensors="tf",
        ... )
        >>> outputs = model(encoded_inputs)
        >>> start_logits = outputs.start_logits
        >>> end_logits = outputs.end_logits
        >>> relevance_logits = outputs.relevance_logits
        ```
        
dpkd/transformers/src/transformers/models/dpr/modeling_tf_dpr.py