def test_matrix_norms(self):
        # Not all of these are matrix norms in the most technical sense.
        np.random.seed(1234)
        for n, m in (1, 1), (1, 3), (3, 1), (4, 4), (4, 5), (5, 4):
            for t in np.single, np.double, np.csingle, np.cdouble, np.int64:
                A = 10 - np.random.randn(n, m).astype(t)
                if np.issubdtype(A.dtype, np.complexfloating):
                    A = (A + 10j * np.random.randn(n, m)).astype(t)
                    t_high = np.cdouble
                else:
                    t_high = np.double
                for order in (None, 'fro', 1, -1, 2, -2, np.inf, -np.inf):
                    actual = norm(A, ord=order)
                    desired = np.linalg.norm(A, ord=order)
                    # SciPy may return higher precision matrix norms.
                    # This is a consequence of using LAPACK.
                    if not np.allclose(actual, desired):
                        desired = np.linalg.norm(A.astype(t_high), ord=order)
                        np.assert_allclose(actual, desired)
dataset/ETHPy150Open scipy/scipy/scipy/linalg/tests/test_basic.py/TestMatrixNorms.test_matrix_norms/operator `*` -> `-`