Write a function that takes two arguments, a width parameter and a sigma parameter, and returns a $2$D array containing a Gaussian kernel of the desired dimension and variance. The peak of the Gaussian should be in the center of the array. Make sure to normalize the kernel such that the sum of all the elements in the array is $1.$

def genGaussianKernel$($width$,$ sigma$)$:

##########$--$WRITE YOUR CODE HERE$--$##########

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return kernel$\_2$d

def PSNR$($img$,$ img$\_$noise$)$:

##########$--$WRITE YOUR CODE HERE$--$##########

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return PSNR