write an equivalent lines of code that does the same job as the following code $($ function should remain the same $)$:

$1$ import numpy as np$3$ def backward propagation $($x$,$ y$,$ predicted $\_$output, weights, learning rate$)$:$4$# Write your code hereX $=$ np$.$insert $($x$,0,1)$error $=$ y $-$ predicted $\_$outputupdated weights $=$ weights $+$ learning rate $*$ error $*$ xreturn updated weights