human
Complete the below
def _tracemin_fiedler(L, X, normalized, tol, method):

    ''''Compute the Fiedler vector of L using the TraceMIN-Fiedler algorithm.'
    '''

Write me a Python code to compute the Fiedler vector of L using the TraceMIN-Fiedler algorithm?
assistant
#Here is Python code to compute the Fiedler vector of L using the TraceMIN-Fiedler algorithm.
    n = X.shape[0]
    if normalized:
        e = sqrt(L.diagonal())
        D = spdiags((1.0 / e), [0], n, n, format='csr')
        L = ((D * L) * D)
        e *= (1.0 / norm(e, 2))
    if (not normalized):
        def project(X):
            'Make    X    orthogonal    to    the    nullspace    of    L.\n                                                '
            X = asarray(X)
            for j in range(X.shape[1]):
                X[:, j] -= (X[:, j].sum() / n)
    else:
        def project(X):
            'Make    X    orthogonal    to    the    nullspace    of    L.\n                                                '
            X = asarray(X)
            for j in range(X.shape[1]):
                X[:, j] -= (dot(X[:, j], e) * e)
    if (method is None):
        method = 'pcg'
    if (method == 'pcg'):
        def P(x):
            x -= asarray(((x * X) * X.T))[0, :]
            if (not normalized):
                x -= (x.sum() / n)
            else:
                x = daxpy(e, x, a=(- ddot(x, e)))
            return x
        solver = _PCGSolver((lambda x: P((L * P(x)))), (lambda x: (D * x)))
    elif ((method == 'chol') or (method == 'lu')):
        A = csc_matrix(L, dtype=float, copy=True)
        i = (A.indptr[1:] - A.indptr[:(-1)]).argmax()
        A[(i, i)] = float('inf')
        solver = (_CholeskySolver if (method == 'chol') else _LUSolver)(A)
    else:
        raise nx.NetworkXError('unknown    linear    system    solver.')
    Lnorm = abs(L).sum(axis=1).flatten().max()
    project(X)
    W = asmatrix(ndarray(X.shape, order='F'))
    while True:
        X = qr(X)[0]
        W[:, :] = (L * X)
        H = (X.T * W)
        (sigma, Y) = eigh(H, overwrite_a=True)
        X *= Y
        res = (dasum(((W * asmatrix(Y)[:, 0]) - (sigma[0] * X[:, 0]))) / Lnorm)
        if (res < tol):
            break
        if (method == 'pcg'):
            W *= Y
            W -= ((W.T * X) * X.T).T
            project(W)
            D = L.diagonal().astype(float)
            D += (2.0 * (asarray(X) *...