Consider a neural network for a binary classification which has one hidden layer as shown in the figure right. We use a linear activation function h$($z$)=$ cz at hidden units and a ReLU activation function g$($z$)=$ max$[0,$z$]$ at the output unit to learn the function for P$($y $=1|$ x$,$ w$)$ where x $=($x$1,$ x$2)$ and w $=($w$1,$ w$2,...,$ w$7).$ What is the final classification boundary? $($Note that your classifier predicts yhat $=1$ if the output g$($z$)>0\mathrm{.}5,$ else yhat $=0.)$