code: def eigvals(a):
      r"""
      Compute the eigenvalues of a general matrix.
  
      Main difference between `eigvals` and `eig`: the eigenvectors aren't
      returned.
  
      Parameters
      ----------
      a : (..., M, M) ndarray
          A real-valued matrix whose eigenvalues will be computed.
  
      Returns
      -------
      w : (..., M,) ndarray
          The eigenvalues, each repeated according to its multiplicity.
          They are not necessarily ordered.
  
      Raises
      ------
      MXNetError
          If the eigenvalue computation does not converge.
  
      See Also
      --------
      eig : eigenvalues and right eigenvectors of general arrays
      eigh : eigenvalues and eigenvectors of a real symmetric array.
      eigvalsh : eigenvalues of a real symmetric.
  
      Notes
      -----
      Broadcasting rules apply, see the `numpy.linalg` documentation for
      details.
  
      This is implemented using the ``_geev`` LAPACK routines which compute
      the eigenvalues and eigenvectors of general square arrays.
  
      This function differs from the original `numpy.linalg.eigvals
      <https://docs.scipy.org/doc/numpy/reference/generated/numpy.linalg.eigvals.html>`_ in
      the following way(s):
       - Does not support complex input and output.
  
      Examples
      --------
      Illustration, using the fact that the eigenvalues of a diagonal matrix
      are its diagonal elements, that multiplying a matrix on the left
      by an orthogonal matrix, `Q`, and on the right by `Q.T` (the transpose
      of `Q`), preserves the eigenvalues of the "middle" matrix.  In other words,
      if `Q` is orthogonal, then ``Q * A * Q.T`` has the same eigenvalues as
      ``A``:
      >>> from numpy import linalg as LA
      >>> x = np.random.random()
      >>> Q = np.array([[np.cos(x), -np.sin(x)], [np.sin(x), np.cos(x)]])
      >>> LA.norm(Q[0, :]), LA.norm(Q[1, :]), np.dot(Q[0, :],Q[1, :])
      (1.0, 1.0, 0.0)
  
      Now multiply a diagonal matrix by ``Q`` on one side and by ``Q.T`` on the other:
      >>> D = np.diag((-1,1))
      >>> LA.eigvals(D)
      array([-1.,  1.])
      >>> A = np.dot(Q, D)
      >>> A = np.dot(A, Q.T)
      >>> LA.eigvals(A)
      array([ 1., -1.]) # random
      """
      return _api_internal.eigvals(a)
  
file_path: mxnet/python/mxnet/ndarray/numpy/linalg.py
imports: import numpy
  from .None import _op
  from .None import _api_internal
label_desc: Use image node_id 14 for calling a global function with example usage: eigvals(a) and returns: _api_internal