def forward(
        self,
        inputs: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        labels: Optional[torch.Tensor] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, PerceiverClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the optical flow loss. Indices should be in `[0, ..., config.num_labels - 1]`.

        Returns:

        Examples:

        ```python
        >>> from transformers import PerceiverForOpticalFlow
        >>> import torch

        >>> model = PerceiverForOpticalFlow.from_pretrained("deepmind/optical-flow-perceiver")

        >>> # in the Perceiver IO paper, the authors extract a 3 x 3 patch around each pixel,
        >>> # leading to 3 x 3 x 3 = 27 values for each pixel (as each pixel also has 3 color channels)
        >>> # patches have shape (batch_size, num_frames, num_channels, height, width)
        >>> # the authors train on resolutions of 368 x 496
        >>> patches = torch.randn(1, 2, 27, 368, 496)
        >>> outputs = model(inputs=patches)
        >>> logits = outputs.logits
        >>> list(logits.shape)
        [1, 368, 496, 2]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.perceiver(
            inputs=inputs,
            attention_mask=attention_mask,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        logits = outputs.logits if return_dict else outputs[0]

        loss = None
        if labels is not None:
            raise NotImplementedError("Optical flow training is not yet supported")

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return PerceiverClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )

        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the optical flow loss. Indices should be in `[0, ..., config.num_labels - 1]`.

        Returns:

        Examples:

        ```python
        >>> from transformers import PerceiverForOpticalFlow
        >>> import torch

        >>> model = PerceiverForOpticalFlow.from_pretrained("deepmind/optical-flow-perceiver")

        >>> # in the Perceiver IO paper, the authors extract a 3 x 3 patch around each pixel,
        >>> # leading to 3 x 3 x 3 = 27 values for each pixel (as each pixel also has 3 color channels)
        >>> # patches have shape (batch_size, num_frames, num_channels, height, width)
        >>> # the authors train on resolutions of 368 x 496
        >>> patches = torch.randn(1, 2, 27, 368, 496)
        >>> outputs = model(inputs=patches)
        >>> logits = outputs.logits
        >>> list(logits.shape)
        [1, 368, 496, 2]
        ```
dpkd/transformers/src/transformers/models/perceiver/modeling_perceiver.py