def forward(
        self,
        input_ids: Optional[Tensor] = None,
        attention_mask: Optional[Tensor] = None,
        inputs_embeds: Optional[Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[DPRReaderOutput, Tuple[Tensor, ...]]:
        r"""
        Return:

        Examples:

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

        >>> tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base")
        >>> model = DPRReader.from_pretrained("facebook/dpr-reader-single-nq-base")
        >>> encoded_inputs = tokenizer(
        ...     questions=["What is love ?"],
        ...     titles=["Haddaway"],
        ...     texts=["'What Is Love' is a song recorded by the artist Haddaway"],
        ...     return_tensors="pt",
        ... )
        >>> outputs = model(**encoded_inputs)
        >>> start_logits = outputs.start_logits
        >>> end_logits = outputs.end_logits
        >>> relevance_logits = outputs.relevance_logits
        ```
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        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:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        device = input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
            attention_mask = torch.ones(input_shape, device=device)

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

        Return:

        Examples:

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

        >>> tokenizer = DPRReaderTokenizer.from_pretrained("facebook/dpr-reader-single-nq-base")
        >>> model = DPRReader.from_pretrained("facebook/dpr-reader-single-nq-base")
        >>> encoded_inputs = tokenizer(
        ...     questions=["What is love ?"],
        ...     titles=["Haddaway"],
        ...     texts=["'What Is Love' is a song recorded by the artist Haddaway"],
        ...     return_tensors="pt",
        ... )
        >>> 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_dpr.py