Consider the neural network with two hidden layers shown in the following Figure to answer the next $8$ questions. The input layer consists of $3$ features x $=[\backslash$times $1,$ X$2,$ X$3],$ each hidden layer has two nodes and the output layer gives the probabilities g $=191,32193$; $94)$ over four classes $1,2,3$ and $4.$ The weight parameter on each arc is labeled on the figure. There are three sets of weights: a$,$ w$,.$

The weights before this pass are the following:

$1)$ Hidden layer $1$: $011=0\mathrm{.}5,$ a$12=0\mathrm{.}5,021=0\mathrm{.}7,$ a$22=0\mathrm{.}3,$ a$31=1,032=0.$

$2)$ Hidden layer $2$: w$11=0,$ w$12=1,$ w$21=0\mathrm{.}2,$ w$22=0\mathrm{.}8$

$3)$ Output layer: $312=1,$ B$23=0\mathrm{.}5,$ B$24=0\mathrm{.}5,$ all other Bjk $=0.$

Both hidden layers use a ReLU activation function g$($x$)=$ max$\{0,2\},$ and the output layer uses a linear activation function g$($x$)=2.$ In addition, we use cross entropy loss, L$($y$,$y$)=(-1$ Yk log$($k$)+(1-9)$ log$(1-9))$ where y $=[1,92,93,94]$ is a one$-$hot encoding vector representing the true class, and $$ represents the output of the network.

Based on this network, you will perform one pass of forward propagation for one data point with features $2=1,221,230\mathrm{.}1$ and true class $3.$

What is the value of $3$ for the given data point?