code: def test_for_ill_condiotioned_matrix(self):
      # Ill-conditioned triangular matrix
      C = np.array(
          [
              [1, 2, 3, 4],
              [0, 0.05, 60, 7],
              [0, 0, 0.8, 9],
              [0, 0, 0, 10],
          ]
      )
  
      # Get svd decomposition
      U, s, Vt = svd(C)
  
      # Get smallest singular value and correspondent right singular vector.
      smin_svd = s[-1]
      zmin_svd = Vt[-1, :]
  
      # Estimate smallest singular value
      smin, zmin = estimate_smallest_singular_value(C)
  
      # Check the estimation
      assert_array_almost_equal(smin, smin_svd, decimal=8)
      assert_array_almost_equal(abs(zmin), abs(zmin_svd), decimal=8)
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from scipy.optimize._trustregion_exact import estimate_smallest_singular_value, singular_leading_submatrix, IterativeSubproblem
  from scipy.linalg import svd, get_lapack_funcs, det, cho_factor, cho_solve, qr, eigvalsh, eig, norm
  from numpy.testing import assert_, assert_array_equal, assert_equal, assert_array_almost_equal, assert_array_less
label_desc: Use image node_id 1 for calling the TestEstimateSmallestSingularValue obj's underlying member method code with example usage: obj.test_for_ill_condiotioned_matrix() without return types