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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 shepp_logan(space, modified=False, min_pt=None, max_pt=None):
 """Standard Shepp-Logan phantom in 2 or 3 dimensions. Parameters space : `DiscreteLp` Space in which the phantom is created, must be 2- or 3-dimensional. If ``space.shape`` is 1 in an axis, a corresponding slice of the phantom is created. modified : `bool`, optional True if the modified Shepp-Logan phantom should be given. The modified phantom has greatly amplified contrast to aid visualization. min_pt, max_pt : array-like, optional If provided, use these vectors to determine the bounding box of the phantom instead of ``space.min_pt`` and ``space.max_pt``. It is currently required that ``min_pt >= space.min_pt`` and ``max_pt <= space.max_pt``, i.e., shifting or scaling outside the original space is not allowed. Providing one of them results in a shift, e.g., for ``min_pt``:: new_min_pt = min_pt new_max_pt = space.max_pt + (min_pt - space.min_pt) Providing both results in a scaled version of the phantom. See Also -------- forbild : Similar phantom but with more complexity. Only supports 2d. odl.phantom.geometric.defrise : Geometry test phantom shepp_logan_ellipsoids : Get the parameters that define this phantom odl.phantom.geometric.ellipsoid_phantom : Function for creating arbitrary ellipsoid phantoms References .. _Shepp-Logan phantom: https://en.wikipedia.org/wiki/Shepp-Logan_phantom """

    ellipsoids = shepp_logan_ellipsoids(space.ndim, modified)
    return ellipsoid_phantom(space, ellipsoids, min_pt, max_pt)