code: def hessian_matrix_eigvals(H_elems):
      """Compute eigenvalues of Hessian matrix.
  
      Parameters
      ----------
      H_elems : list of ndarray
          The upper-diagonal elements of the Hessian matrix, as returned
          by `hessian_matrix`.
  
      Returns
      -------
      eigs : ndarray
          The eigenvalues of the Hessian matrix, in decreasing order. The
          eigenvalues are the leading dimension. That is, ``eigs[i, j, k]``
          contains the ith-largest eigenvalue at position (j, k).
  
      Examples
      --------
      >>> from skimage.feature import hessian_matrix, hessian_matrix_eigvals
      >>> square = np.zeros((5, 5))
      >>> square[2, 2] = 4
      >>> H_elems = hessian_matrix(square, sigma=0.1, order='rc',
      ...                          use_gaussian_derivatives=False)
      >>> hessian_matrix_eigvals(H_elems)[0]
      array([[ 0.,  0.,  2.,  0.,  0.],
             [ 0.,  1.,  0.,  1.,  0.],
             [ 2.,  0., -2.,  0.,  2.],
             [ 0.,  1.,  0.,  1.,  0.],
             [ 0.,  0.,  2.,  0.,  0.]])
      """
      return _symmetric_compute_eigenvalues(H_elems)
  
imports: import functools
  import math
  from itertools import combinations_with_replacement
  import numpy
  from scipy import ndimage
  from scipy import spatial, stats
  from .._shared.filters import gaussian
  from .._shared.utils import _supported_float_type, safe_as_int, warn
  from ..transform import integral_image
  from ..util import img_as_float
  from ._hessian_det_appx import _hessian_matrix_det
  from .corner_cy import _corner_fast, _corner_moravec, _corner_orientations
  from .peak import peak_local_max
  from .util import _prepare_grayscale_input_2D, _prepare_grayscale_input_nD
label_desc: Use image node_id 9 for calling a global function with example usage: hessian_matrix_eigvals(H_elems) and returns: _symmetric_compute_eigenvalues