given the following definition of a convolutional neural network $($comments about each line of Python code highlighted in yellow before the line$),$

model $=$ Sequential$()$

#input as a $(1$x$28$x$28)$ tensor, $24(3$x$3)$ kernels, stride$=1,$ padding$=$"same" to keep input size, relu activation

model.add$($Conv$2$D $(24,$ kernel$\_$size$=(3,3),$ padding$=$"same", activation$=$"relu", input$\_$shape$=(28,28,1))$

# max pooling with pooling size $(2$x$2)$ applied as in the lecture's example

model.add$($MaxPooling$2$D $($pool$\_$size$=(2,2)))$

#input as previous layer's feature maps, no padded convolution here

model.add$($Conv$2$D $(48,$ kernel$\_$size$=(3,3),$ padding$=$"valid", activation$=$"relu"$))$

# max pooling with pooling size $(2$x$2)$ applied as in the lecture's example

model.add$($MaxPooling$2$D $($pool$\_$size$=(2,2)))$

# input as previous layer's feature maps, see description in the first layer for the rest

model.add$($Conv$2$D $(64,$ kernel$\_$size$=(3,3),$ padding$=$"same", activation$=$"relu"$))$

#max pooling with pooling size $(2$x$2)$ applied as in the lecture's example

model.add$($MaxPooling$2$D $($pool$\_$size$=(2,2)))$

#input as previous layer's feature maps, no impact on the number of the network's parameters model.add$($Flatten$())$

# $128-$wide dense layer with input as flattened vector from previous step

model.add$($Dense $(128,$ activation$=$"relu"$))$

# no impact on the number of the network's parameters

model.add$($Dropout $(0\mathrm{.}25))$

# $10-$wide output layer with input as hidden vector from previous step

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

# no impact on the number of the network's parameters

model.compile$($loss$=$"categorical$\_$crossentropy", optimizer$=$"adam", metrics$=["$accuracy$"])$

describe the network in terms of its depth, its layers' width, what it is expected to eventually calculate and say how many parameters are trainable in total elaborating on how you calculate that number.