Figure $4$ shows a ResNet block where the input a$[$l$]$ is a two$-$dimensional vector: $[0\mathrm{.}5,-$

$0\mathrm{.}5].$ The two layers inside the ResNet block consist of two dense layers, each having two nodes. The weight values for the first layer are $[-0\mathrm{.}3,0\mathrm{.}7]$ and $[0\mathrm{.}5,-0\mathrm{.}4],$ respectively. The weight values for the second layer are $[0\mathrm{.}7,-0\mathrm{.}3]$ and $[-0\mathrm{.}5,-0\mathrm{.}3],$ respectively. Ignore bias and use ReLu$()$ as the activation functions for all nodes.

$$ Calculate output of the first layer of the ResNet block, i$.$e$.,$ a$[$l$+1]($show calculations$)[0\mathrm{.}5$ pt$]$

$$ Calculate output of the second layer of the ResNet block, i$.$e$.,$ a$[$l$+2]($show calculations$)[0\mathrm{.}5$ pt$]$

$$ If the skip connection of the ResNet block does not exist, what is the output of the block, i$.$e$.,$

a$[$l$+2]($show calculations$)[0\mathrm{.}5$ pt$]$

$$ Explain motivation of ResNet block $($i$.$e$.,$ what is the learning objective of the ResNet block Figure $4$