For the following line of code used to build a Convolutional Neural Network model, choose the correct pairing.

X$\_$train $=$ X$\_$train.reshape$(($X$\_$train.shape$[0],28,28,1))$

X$\_$train $=$ X$\_$train.astype$(\text{'}$float$32\text{'})/255\mathrm{.}0$

y$\_$train$\_$encoded $=$ tf$.$keras.utils.to$\_$categorical$($y$\_$train$)$

model $=$ Sequential$()$

model.add$($Conv$2$D$(64,(3,3),$ activation$=$'relu', padding$=$"same", input$\_$shape$=(28,28,1)))$

model.add$($MaxPooling$2$D$((2,2),$ padding $=$ 'same'$))$

model.add$($Flatten$())$

model.add$($Dense$(100,$ activation$=$'relu'$))$

model.add$($Dense$(10,$ activation$=$'softmax'$))$

A

X$\_$train $=$ X$\_$train.astype$(\text{'}$float$32\text{'})/255\mathrm{.}0$

i adding an output layer to the model

B

model.add$($Conv$2$D$(64,(3,3),$ activation$=$'relu', padding$=$"same", input$\_$shape$=(28,28,1)))$

ii Adds max$-$pooling layer

C

model.add$($MaxPooling$2$D$((2,2),$ padding $=$ 'same'$))$

iii Adds a convolutional layer

D

model.add$($Dense$(10,$ activation$=$'softmax'$))$

iv

Normalizes the data in the X$\_$train array by scaling