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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 roberts(image, mask=None):
    '''Find edges using the Roberts algorithm

    image - the image to process
    mask  - mask of relevant points

    The algorithm returns the magnitude of the output of the two Roberts
    convolution kernels.

    The following is the canonical citation for the algorithm:
    L. Roberts Machine Perception of 3-D Solids, Optical and
    Electro-optical Information Processing, MIT Press 1965.

    The following website has a tutorial on the algorithm:
    http://homepages.inf.ed.ac.uk/rbf/HIPR2/roberts.htm
    '''
    result = np.zeros(image.shape)
    #
    # Four quadrants and two convolutions:
    #
    # q0,0 | q0,1    1  |  0  anti-diagonal
    # q1,0 | q1,1    0  | -1
    #
    # q-1,0 | q0,0   0  |  1  diagonal
    # q-1,1 | q0,1  -1  |  0
    #
    # Points near the mask edges and image edges are computed unreliably
    # so make them zero (no edge) in the result
    #
    if mask is None:
        mask = np.ones(image.shape, bool)
    big_mask = binary_erosion(mask,
                              generate_binary_structure(2,2),
                              border_value = 0)
    result[big_mask==False] = 0
    q00   = image[:,:][big_mask]
    q11   = image[1:,1:][big_mask[:-1,:-1]]
    qm11  = image[:-1,1:][big_mask[1:,:-1]]
    diagonal = q00 - qm11
    anti_diagonal = q00 - q11
    result[big_mask] = np.sqrt(diagonal*diagonal + anti_diagonal*anti_diagonal)
    return result