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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 get_distance(region, src):
 """ Compute within-region distances from the src pixels. Parameters region : np.ndarray(shape=(m, n), dtype=bool) mask of the region src : np.ndarray(shape=(m, n), dtype=bool) mask of the source pixels to compute distances from. Returns ------- d : np.ndarray(shape=(m, n), dtype=float) approximate within-region distance from the nearest src pixel; (distances outside of the region are arbitrary). """


    dmax = float(region.size)
    d = np.full(region.shape, dmax)
    d[src] = 0
    for n in range(region.size):
        d_orth = minimum_filter(d, footprint=_ORTH2) + 1
        d_diag = minimum_filter(d, (3, 3)) + _SQRT2
        d_adj = np.minimum(d_orth[region], d_diag[region])
        d[region] = np.minimum(d_adj, d[region])
        if (d[region] < dmax).all():
            break
    return d