code: def inertia_tensor_eigvals(image, mu=None, T=None, *, spacing=None):
      """Compute the eigenvalues of the inertia tensor of the image.
  
      The inertia tensor measures covariance of the image intensity along
      the image axes. (See `inertia_tensor`.) The relative magnitude of the
      eigenvalues of the tensor is thus a measure of the elongation of a
      (bright) object in the image.
  
      Parameters
      ----------
      image : array
          The input image.
      mu : array, optional
          The pre-computed central moments of ``image``.
      T : array, shape ``(image.ndim, image.ndim)``
          The pre-computed inertia tensor. If ``T`` is given, ``mu`` and
          ``image`` are ignored.
      spacing: tuple of float, shape (ndim,)
          The pixel spacing along each axis of the image.
  
      Returns
      -------
      eigvals : list of float, length ``image.ndim``
          The eigenvalues of the inertia tensor of ``image``, in descending
          order.
  
      Notes
      -----
      Computing the eigenvalues requires the inertia tensor of the input image.
      This is much faster if the central moments (``mu``) are provided, or,
      alternatively, one can provide the inertia tensor (``T``) directly.
      """
      if T is None:
          T = inertia_tensor(image, mu, spacing=spacing)
      eigvals = np.linalg.eigvalsh(T)
      # Floating point precision problems could make a positive
      # semidefinite matrix have an eigenvalue that is very slightly
      # negative. This can cause problems down the line, so set values
      # very near zero to zero.
      eigvals = np.clip(eigvals, 0, None, out=eigvals)
      return sorted(eigvals, reverse=True)
  
file_path: scikit-image/skimage/measure/_moments.py
imports: import itertools
  import numpy
  from .._shared.utils import _supported_float_type, check_nD
  from .None import _moments_cy
  from ._moments_analytical import moments_raw_to_central
label_desc: Use image node_id 9 for calling a global function with example usage: inertia_tensor_eigvals(image, mu, T) and returns: sorted