code: def test_for_the_hard_case(self):
      # `H` is chosen such that `g` is orthogonal to the
      # eigenvector associated with the smallest eigenvalue `s`.
      H = [[10, 2, 3, 4], [2, 1, 7, 1], [3, 7, 1, 7], [4, 1, 7, 2]]
      g = [6.4852641521327437, 1, 1, 1]
      s = -8.2151519874416614
  
      # Trust Radius
      trust_radius = 1
  
      # Solve Subproblem
      subprob = IterativeSubproblem(
          x=0,
          fun=lambda x: 0,
          jac=lambda x: np.array(g),
          hess=lambda x: np.array(H),
          k_easy=1e-10,
          k_hard=1e-10,
      )
      p, hits_boundary = subprob.solve(trust_radius)
  
      assert_array_almost_equal(-s, subprob.lambda_current)
  
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 2 for calling the TestIterativeSubproblem obj's underlying member method code with example usage: obj.test_for_the_hard_case() without return types