Please do this in python with comments also any image will do

Implement a two-dimensional Gaussian kernel with a variation (sigma) equal to 3, 5, and 10. Apply these three Gaussian kernels to imagel.png and image2.png separately, show them on the screen, discuss the differences of Gaussian operations with different sigmas (as comments on the code). Also, compare your results with question 1 and question 2: what are the differences between these three filters, what do you observe (as comments on the code)? Which filtering is the most effective in which images? Why? (For convolution operation, you can use built-in function. Do not use built-in function for Gaussian filtering.) Implement three median filters (3 by 3, 5 by 5, and 7 by 7 kernel size), and apply these filters to imagel.png and image2.png separately. Show (and discuss as comments) the resulting differences for each kernel on the screen, explain where median filters are most effective. (For convolution operation, you can use built-in function. Do not use built-in function for Median filtering.) Implement a two-dimensional Gaussian kernel with a variation (sigma) equal to 3, 5, and 10. Apply these three Gaussian kernels to imagel.png and image2.png separately, show them on the screen, discuss the differences of Gaussian operations with different sigmas (as comments on the code). Also, compare your results with question 1 and question 2: what are the differences between these three filters, what do you observe (as comments on the code)? Which filtering is the most effective in which images? Why? (For convolution operation, you can use built-in function. Do not use built-in function for Gaussian filtering.) Implement three median filters (3 by 3, 5 by 5, and 7 by 7 kernel size), and apply these filters to imagel.png and image2.png separately. Show (and discuss as comments) the resulting differences for each kernel on the screen, explain where median filters are most effective. (For convolution operation, you can use built-in function. Do not use built-in function for Median filtering.)