def reparameterize(self):
        """Fuse all the parameters of all branches.

        Returns:
            tuple[torch.Tensor, torch.Tensor]: Parameters after fusion of all
                branches. the first element is the weights and the second is
                the bias.
        """
        weight_3x3, bias_3x3 = self._fuse_conv_bn(self.branch_3x3)
        weight_1x1, bias_1x1 = self._fuse_conv_bn(self.branch_1x1)
        # pad a conv1x1 weight to a conv3x3 weight
        weight_1x1 = F.pad(weight_1x1, [1, 1, 1, 1], value=0)

        weight_norm, bias_norm = 0, 0
        if self.branch_norm:
            tmp_conv_bn = self._norm_to_conv3x3(self.branch_norm)
            weight_norm, bias_norm = self._fuse_conv_bn(tmp_conv_bn)

        return (weight_3x3 + weight_1x1 + weight_norm,
                bias_3x3 + bias_1x1 + bias_norm)
Fuse all the parameters of all branches.

        Returns:
            tuple[torch.Tensor, torch.Tensor]: Parameters after fusion of all
                branches. the first element is the weights and the second is
                the bias.
        
mmpretrain/models/backbones/repvgg.py