Order the steps in the Residual Learning Framework that enable effective training of deeper neural networks.

First, you add shortcut connections that bypass one or more layers so the network can learn an identity mapping. Next, you define the residual function that computes the difference between the desired output and the shortcut output. Then you apply batch‑normalization and a ReLU activation after the addition to keep the signal stable. Finally, you train the whole stack with stochastic gradient descent and proper weight initialization, which lets the deeper network converge without vanishing gradients. In practice, a block might compute \(y = \text{BN}(x)+\mathcal{F}(x)\) and then pass \(y\) through ReLU before feeding it to the next block.