code: def test_burkardt_8(self):
      # This matrix was an example in Wikipedia.
      exp4 = np.exp(4)
      exp16 = np.exp(16)
      A = np.array(
          [
              [21, 17, 6],
              [-5, -1, -6],
              [4, 4, 16],
          ],
          dtype=float,
      )
      desired = (
          np.array(
              [
                  [
                      13 * exp16 - exp4,
                      13 * exp16 - 5 * exp4,
                      2 * exp16 - 2 * exp4,
                  ],
                  [
                      -9 * exp16 + exp4,
                      -9 * exp16 + 5 * exp4,
                      -2 * exp16 + 2 * exp4,
                  ],
                  [16 * exp16, 16 * exp16, 4 * exp16],
              ],
              dtype=float,
          )
          * 0.25
      )
      actual = expm(A)
      assert_allclose(actual, desired)
  
imports: 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, 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 21 for calling the TestExpM obj's underlying member method code with example usage: obj.test_burkardt_8() without return types