# TF/keras implement residual block

I read several papers, where they propose to implement residual blocks of ResNet as follows $$u^{k+1} = u^k - \tau K^T \sigma(K u^k),$$ where $$u^{k}$$ denotes output on k-th layer, $$\tau$$ is artificial time-step and K is convolution matrix. The ResNet with this type of blocks should be more numerically stable as with more common block: $$u^{k+1} = u^k - \tau K_2 \sigma(K_1 u^k)$$.

How does one share weights and implement $$K^T$$ operation using keras/tf?