Use Backpropagation method to update the weights and biases of the following neural network with one input, one hidden layer of two neurons and one output. You are required to do the forward pass and the backward pass two times by applying the inputs one by one $($i$.$e$.$ one complete epoch where you apply each input$-$output pair once$)$ to update the network weights. The activation function of the hidden and output neurons is $f(a)=$a and the learning rate is $0\mathrm{.}5(=0\mathrm{.}5).$ Report the local gradients.

$w_1=0\mathrm{.}5,w_2=0\mathrm{.}2,w_3=0\mathrm{.}1,w_4=0\mathrm{.}3$

$b_1=0\mathrm{.}1,b_2=0\mathrm{.}2,b_3=0\mathrm{.}1$

$\begin{array}{cc}\text{I}\text{n}\text{p}\text{u}\text{t}(i)& \text{O}\text{u}\text{t}\text{p}\text{u}\text{t}(o)\\ 1& 0\\ -1& 1\\ 0& 1\\ 2& 0\end{array}$