How to use the sigpy.util.randn function in sigpy

def test_L1Proj(self):
        shape = [6]
        epsilon = 1.0
        P = prox.L1Proj(shape, epsilon)
        x = util.randn(shape)
        y = P(1.0, x)
        z = 1.0 if np.linalg.norm(x, 1) > 1.0 else np.linalg.norm(x, 1)
        npt.assert_allclose(np.linalg.norm(y, 1), z)

        x = util.randn(shape) * 0.0001
        y = P(1.0, x)
        z = 1.0 if np.linalg.norm(x, 1) > 1.0 else np.linalg.norm(x, 1)
        npt.assert_allclose(np.linalg.norm(y, 1), z)

def test_Compose(self):
        shape = [5]
        I = linop.Identity(shape)
        A = linop.Compose([I, I])
        x = util.randn(shape)

        npt.assert_allclose(A(x), x)
        self.check_linop_linear(A)
        self.check_linop_adjoint(A)
        self.check_linop_pickleable(A)

def test_Conj(self):
        shape = [5]
        I = linop.Identity(shape)
        A = linop.Conj(I)
        x = util.randn(shape)

        npt.assert_allclose(A(x), x)
        self.check_linop_linear(A)
        self.check_linop_adjoint(A)
        self.check_linop_pickleable(A)

def test_RightMatMul(self):
        ishape = (5, 4, 2)
        mshape = (5, 2, 3)
        A = linop.RightMatMul(ishape, util.randn(mshape))
        self.check_linop_adjoint(A)
        self.check_linop_linear(A)
        self.check_linop_pickleable(A)

def test_monte_carlo_sure(self):
        x = np.ones([100000], dtype=np.float)
        sigma = 0.1
        noise = 0.1 * util.randn([100000], dtype=np.float)
        y = x + noise

        def f(y):
            return y

        npt.assert_allclose(
            sigma**2, util.monte_carlo_sure(f, y, sigma), atol=1e-3)

def test_Compose(self):
        shape = [5]
        I = linop.Identity(shape)
        A = linop.Compose([I, I])
        x = util.randn(shape)

        npt.assert_allclose(A(x), x)
        self.check_linop_linear(A)
        self.check_linop_adjoint(A)
        self.check_linop_pickleable(A)

def test_L1Proj(self):
        shape = [6]
        epsilon = 1.0
        P = prox.L1Proj(shape, epsilon)
        x = util.randn(shape)
        y = P(1.0, x)
        z = 1.0 if np.linalg.norm(x, 1) > 1.0 else np.linalg.norm(x, 1)
        npt.assert_allclose(np.linalg.norm(y, 1), z)

        x = util.randn(shape) * 0.0001
        y = P(1.0, x)
        z = 1.0 if np.linalg.norm(x, 1) > 1.0 else np.linalg.norm(x, 1)
        npt.assert_allclose(np.linalg.norm(y, 1), z)

def test_Identity(self):
        shape = [5]
        A = linop.Identity(shape)
        x = util.randn(shape)

        npt.assert_allclose(A(x), x)
        self.check_linop_linear(A)
        self.check_linop_adjoint(A)
        self.check_linop_unitary(A)
        self.check_linop_pickleable(A)

def test_dual_precond_LinearLeastSquares(self):
        n = 5
        _A = np.eye(n) + 0.1 * util.randn([n, n])
        A = linop.MatMul([n, 1], _A)
        x = util.randn([n, 1])
        y = A(x)
        x_lstsq = np.linalg.lstsq(_A, y, rcond=-1)[0]

        d = 1 / np.sum(abs(_A)**2, axis=1, keepdims=True).reshape([n, 1])
        x_rec = app.LinearLeastSquares(
            A,
            y,
            solver='PrimalDualHybridGradient',
            max_iter=1000,
            sigma=d, show_pbar=False).run()
        npt.assert_allclose(x_rec, x_lstsq, atol=1e-3)

def test_Identity(self):
        shape = [5]
        A = linop.Identity(shape)
        x = util.randn(shape)

        npt.assert_allclose(A(x), x)
        self.check_linop_linear(A)
        self.check_linop_adjoint(A)
        self.check_linop_unitary(A)
        self.check_linop_pickleable(A)