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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 inverse(self, rhov, time_derivative=False):
 r"""Fold a vector into a matrix. The input of this function can be a numpy array or a sympy Matrix. If the input is understood to represent the time derivative of a density matrix, then the flag time_derivative must be set to True. [[ True True] [ True True]] [ -rho22 + 1 re(rho21) - I*im(rho21)] [ ] [re(rho21) + I*im(rho21) rho22 ] [[-0.-0.j 0.-0.j] [ 0.+0.j 0.+0.j]] """

        Ne = self.Ne
        Nrho = self.Nrho
        IJ = self.IJ

        if isinstance(rhov, np.ndarray):
            rho = np.zeros((Ne, Ne), complex)
            numeric = True
        elif isinstance(rhov, sympy.Matrix):
            rho = sympy.zeros(Ne, Ne)
            numeric = False

        for mu in range(Nrho):
            s, i, j = IJ(mu)
            if numeric:
                if s == 1:
                    rho[i, j] += rhov[mu]
                elif s == -1:
                    rho[i, j] += 1j*rhov[mu]
                elif s == 0:
                    rho[i, j] += rhov[mu]
            else:
                if s == 1:
                    rho[i, j] += rhov[mu]
                elif s == -1:
                    rho[i, j] += sympy.I*rhov[mu]
                elif s == 0:
                    rho[i, j] += rhov[mu]

        if self.lower_triangular:
            for i in range(Ne):
                for j in range(i):
                    rho[j, i] = rho[i, j].conjugate()

        if self.normalized:
            if time_derivative:
                rho[0, 0] = -sum([rho[i, i] for i in range(1, Ne)])
            else:
                rho[0, 0] = 1-sum([rho[i, i] for i in range(1, Ne)])

        return rho