code: def test_burkardt_7(self):
      # This example is due to Moler and Van Loan.
      # This matrix is very close to example 5.
      # Mathematically, it has a complete set of eigenvectors.
      # Numerically, however, the calculation will be suspect.
      exp1 = np.exp(1)
      eps = np.spacing(1)
      A = np.array(
          [
              [1 + eps, 1],
              [0, 1 - eps],
          ],
          dtype=float,
      )
      desired = np.array(
          [
              [exp1, exp1],
              [0, exp1],
          ],
          dtype=float,
      )
      actual = expm(A)
      assert_allclose(actual, desired)
  
imports: from __future__ import division, print_function, absolute_import
  import math
  import numpy
  from numpy import array, eye, exp, random
  from numpy.linalg import matrix_power
  from numpy.testing import assert_allclose, assert_, assert_array_almost_equal, assert_equal, assert_array_almost_equal_nulp
  from scipy._lib._numpy_compat import suppress_warnings
  from scipy.sparse import csc_matrix, SparseEfficiencyWarning
  from scipy.sparse.construct import eye
  from scipy.sparse.linalg.matfuncs import expm, _expm, ProductOperator, MatrixPowerOperator, _onenorm_matrix_power_nnm
  from scipy.sparse.sputils import matrix
  from scipy.linalg import logm
  from scipy.special import factorial, binom
  import scipy.sparse
  import scipy.sparse.linalg
label_desc: Use image node_id 20 for calling the TestExpM obj's underlying member method code with example usage: obj.test_burkardt_7() without return types