def forward(self, feats: Tuple[Tensor]) -> Tuple[Tensor, Tensor]:
        """Forward the network.

        The input is the featuremap extracted by backbone and the
        output is the simcc representation.

        Args:
            feats (Tuple[Tensor]): Multi scale feature maps.

        Returns:
            pred_x (Tensor): 1d representation of x.
            pred_y (Tensor): 1d representation of y.
        """
        if self.deconv_head is None:
            feats = feats[-1]
            if self.final_layer is not None:
                feats = self.final_layer(feats)
        else:
            feats = self.deconv_head(feats)

        # flatten the output heatmap
        x = torch.flatten(feats, 2)

        pred_x = self.mlp_head_x(x)
        pred_y = self.mlp_head_y(x)

        return pred_x, pred_y
Forward the network.

        The input is the featuremap extracted by backbone and the
        output is the simcc representation.

        Args:
            feats (Tuple[Tensor]): Multi scale feature maps.

        Returns:
            pred_x (Tensor): 1d representation of x.
            pred_y (Tensor): 1d representation of y.
        
mmpose/mmpose/models/heads/coord_cls_heads/simcc_head.py