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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def circular_gaussian_kernel(sd,radius):
 """Create a 2-d Gaussian convolution kernel sd - standard deviation of the gaussian in pixels radius - build a circular kernel that convolves all points in the circle bounded by this radius """

    i,j = np.mgrid[-radius:radius+1,-radius:radius+1].astype(float) / radius
    mask = i**2 + j**2 <= 1
    i = i * radius / sd
    j = j * radius / sd

    kernel = np.zeros((2*radius+1,2*radius+1))
    kernel[mask] = np.e ** (-(i[mask]**2+j[mask]**2) /
                            (2 * sd **2))
    #
    # Normalize the kernel so that there is no net effect on a uniform image
    #
    kernel = kernel / np.sum(kernel)
    return kernel