Consider the CNN defined by the layers in the left column below.

$(20\%)$

CONV$7-$N denotes a convolutional layer with $N$ filters with height and width equal to $7,$

padding is $3,$ and stride is $1.$

CONV$5-$N denotes a convolutional layer with $N$ filters with height and width equal to $5,$

padding is $2,$ and stride is $1.$

POOL $2$ denotes a $22$ max$-$pooling layer with stride of $2$ and $0$ padding.

FC$-$N denotes a fully connected layer with $N$ neurons.

Fill in the blanks by writing down the shape of the output volume and the number of parameters

at each layer. You can write the shapes in the NumPy format $[$e$.$g$.(128,128,3).$