def forward(self, feats: Tuple[Tensor]) -> Union[Tensor, Tuple[Tensor]]:
        """Forward the network. The input is multi scale feature maps and the
        output is the coordinates.

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

        Returns:
            Tensor: output coordinates(and sigmas[optional]).
        """
        if self.simplebaseline_head is None:
            feats = feats[-1]
            if self.final_layer is not None:
                feats = self.final_layer(feats)
        else:
            feats = self.simplebaseline_head(feats)

        heatmaps = self._flat_softmax(feats * self.beta)

        pred_x = self._linear_expectation(heatmaps, self.linspace_x)
        pred_y = self._linear_expectation(heatmaps, self.linspace_y)

        if self.debias:
            B, N, H, W = feats.shape
            C = feats.reshape(B, N, H * W).exp().sum(dim=2).reshape(B, N, 1)
            pred_x = C / (C - 1) * (pred_x - 1 / (2 * C))
            pred_y = C / (C - 1) * (pred_y - 1 / (2 * C))

        coords = torch.cat([pred_x, pred_y], dim=-1)
        return coords, heatmaps
Forward the network. The input is multi scale feature maps and the
        output is the coordinates.

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

        Returns:
            Tensor: output coordinates(and sigmas[optional]).
        
mmpose/mmpose/models/heads/regression_heads/integral_regression_head.py