$($x$1,$ x$2)$ are input features and target classes are either $+1$ or $-1$ as shown in the figure.

A$.$ What is the minimum number of hidden layers and hidden nodes required to classify the following dataset with $100\%$ accuracy using a fully connected multilayer perceptron network? Step activation functions are used at all nodes, i$.$e$.,$ output$=+1$ if total weighted input $>=$ bias b at a node, else output $=-1.$

B$.$ Show the minimal network architecture by organizing the nodes in each layer horizontally. Show the node representing x$1$ at the left on the input layer. Organize the hidden nodes in ascending order of bias at that node. Specify all weights and bias values at all nodes. Weights can be only $-2\mathrm{.}5,2\mathrm{.}5$ or $0,$ and bias $+$ve$/-$ve multiples of $2\mathrm{.}5$