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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 observable(operator, rho, unfolding, complex=False):
 r"""Return an observable ammount. INPUT: - ``operator`` - An square matrix representing a hermitian operator \ in thesame basis as the density matrix. - ``rho`` - A density matrix in unfolded format, or a list of such \ density matrices. - ``unfolding`` - A mapping from matrix element indices to unfolded \ indices. 2.0 """

    if len(rho.shape) == 2:
        return np.array([observable(operator, i, unfolding) for i in rho])

    Ne = unfolding.Ne
    Mu = unfolding.Mu
    obs = 0

    if unfolding.normalized:
        rho11 = 1 - sum([rho[Mu(1, i, i)] for i in range(1, Ne)])

    for i in range(Ne):
        for k in range(Ne):
            if unfolding.real:
                if k == 0 and i == 0:
                    obs += operator[i, k]*rho11
                else:
                    if k < i:
                        u, v = (i, k)
                    else:
                        u, v = (k, i)
                    obs += operator[i, k]*rho[Mu(1, u, v)]
                    if k != i:
                        if k < i:
                            obs += 1j*operator[i, k]*rho[Mu(-1, u, v)]
                        else:
                            obs += -1j*operator[i, k]*rho[Mu(-1, u, v)]

            else:
                if k == 0 and i == 0:
                    obs += operator[i, k]*rho11
                else:
                    obs += operator[i, k]*rho[Mu(0, k, i)]

    if not complex:
        obs = np.real(obs)
    return obs