code: def test_inertia_tensor_eigvals():
      # Floating point precision problems could make a positive
      # semidefinite matrix have an eigenvalue that is very slightly
      # negative.  Check that we have caught and fixed this problem.
      image = np.array(
          [
              [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
              [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0],
              [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1],
          ]
      )
      # mu = np.array([[3, 0, 98], [0, 14, 0], [2, 0, 98]])
      eigvals = inertia_tensor_eigvals(image=image)
      assert min(eigvals) >= 0
  
imports: import itertools
  import numpy
  import pytest
  from scipy import ndimage
  from skimage import draw
  from skimage._shared import testing
  from skimage._shared.testing import assert_allclose, assert_almost_equal, assert_equal
  from skimage._shared.utils import _supported_float_type
  from skimage.measure import centroid, inertia_tensor, inertia_tensor_eigvals, moments, moments_central, moments_coords, moments_coords_central, moments_hu, moments_normalized
label_desc: Use image node_id 17 for calling a global function with example usage: test_inertia_tensor_eigvals() without return types