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_conv, bias_conv = 0, 0
        for branch_conv in self.branch_conv_list:
            weight, bias = self._fuse_conv_bn(branch_conv)
            weight_conv += weight
            bias_conv += bias

        weight_scale, bias_scale = 0, 0
        if self.branch_scale is not None:
            weight_scale, bias_scale = self._fuse_conv_bn(self.branch_scale)
            # Pad scale branch kernel to match conv branch kernel size.
            pad = self.kernel_size // 2
            weight_scale = F.pad(weight_scale, [pad, pad, pad, pad])

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

        return (weight_conv + weight_scale + weight_norm,
                bias_conv + bias_scale + 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/mobileone.py