src/transformers/models/perceiver/modeling_perceiver.py/PerceiverAttention/forward
class PerceiverAttention:    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.FloatTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        inputs: Optional[torch.FloatTensor] = None,
        inputs_mask: Optional[torch.FloatTensor] = None,
        output_attentions: Optional[bool] = False,
    ) -> Tuple[torch.Tensor]:
        self_outputs = self.self(
            hidden_states,
            attention_mask,
            head_mask,
            inputs,
            inputs_mask,
            output_attentions,
        )

        # Output projection
        attention_output = self.output(self_outputs[0])

        # Optionally include a residual to the original queries.
        # Consider omitting the residual if the semantics of query and output
        # are different, e.g. if queries are positions and outputs are pixels.
        if self.use_query_residual:
            attention_output = attention_output + hidden_states

        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them
        return outputs