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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 approximate_moment( dist, K, retall=False, control_var=None, rule="F", order=1000, **kws ):
 """ Approximation method for estimation of raw statistical moments. Args: dist : Dist Distribution domain with dim=len(dist) K : numpy.ndarray The exponents of the moments of interest with shape (dim,K). control_var : Dist If provided will be used as a control variable to try to reduce the error. acc (:py:data:typing.Optional[int]):
 The order of quadrature/MCI sparse : bool If True used Smolyak's sparse grid instead of normal tensor product grid in numerical integration. rule : str Quadrature rule Key Description "G" Optiomal Gaussian quadrature from Golub-Welsch Slow for high order and composit is ignored. "E" Gauss-Legendre quadrature "C" Clenshaw-Curtis quadrature. Exponential growth rule is used when sparse is True to make the rule nested. Monte Carlo Integration Key Description "H" Halton sequence "K" Korobov set "L" Latin hypercube sampling "M" Hammersley sequence "R" (Pseudo-)Random sampling "S" Sobol sequence composite (:py:data:typing.Optional[int, numpy.ndarray]):
 If provided, composite quadrature will be used. Ignored in the case if gaussian=True. If int provided, determines number of even domain splits If array of ints, determines number of even domain splits along each axis If array of arrays/floats, determines location of splits antithetic (:py:data:typing.Optional[numpy.ndarray]):
 List of bool. Represents the axes to mirror using antithetic variable during MCI. """

    dim = len(dist)
    shape = K.shape
    size = int(K.size/dim)
    K = K.reshape(dim, size)

    if dim > 1:
        shape = shape[1:]

    X, W = quad.generate_quadrature(order, dist, rule=rule, normalize=True, **kws)

    grid = numpy.mgrid[:len(X[0]), :size]
    X = X.T[grid[0]].T
    K = K.T[grid[1]].T
    out = numpy.prod(X**K, 0)*W

    if control_var is not None:

        Y = control_var.ppf(dist.fwd(X))
        mu = control_var.mom(numpy.eye(len(control_var)))

        if (mu.size == 1) and (dim > 1):
            mu = mu.repeat(dim)

        for d in range(dim):
            alpha = numpy.cov(out, Y[d])[0, 1]/numpy.var(Y[d])
            out -= alpha*(Y[d]-mu)

    out = numpy.sum(out, -1)
    return out