code: def test_str():
      # doc_txt has the order of Notes and See Also sections flipped.
      # This should be handled automatically, and so, one thing this test does
      # is to make sure that See Also precedes Notes in the output.
      non_blank_line_by_line_compare(
          str(doc),
          """numpy.multivariate_normal(mean, cov, shape=None, spam=None)
  
  Draw values from a multivariate normal distribution with specified
  mean and covariance.
  
  The multivariate normal or Gaussian distribution is a generalisation
  of the one-dimensional normal distribution to higher dimensions.
  
  Parameters
  ----------
  mean : (N,) ndarray
      Mean of the N-dimensional distribution.
  
      .. math::
  
         (1+2+3)/3
  
  cov : (N, N) ndarray
      Covariance matrix of the distribution.
  shape : tuple of ints
      Given a shape of, for example, (m,n,k), m*n*k samples are
      generated, and packed in an m-by-n-by-k arrangement.  Because
      each sample is N-dimensional, the output shape is (m,n,k,N).
  
  Returns
  -------
  out : ndarray
      The drawn samples, arranged according to `shape`.  If the
      shape given is (m,n,...), then the shape of `out` is is
      (m,n,...,N).
  
      In other words, each entry ``out[i,j,...,:]`` is an N-dimensional
      value drawn from the distribution.
  list of str
      This is not a real return value.  It exists to test
      anonymous return values.
  
  Other Parameters
  ----------------
  spam : parrot
      A parrot off its mortal coil.
  
  Raises
  ------
  RuntimeError
      Some error
  
  Warns
  -----
  RuntimeWarning
      Some warning
  
  Warnings
  --------
  Certain warnings apply.
  
  See Also
  --------
  `some`_, `other`_, `funcs`_
  
  `otherfunc`_
      relationship
  
  Notes
  -----
  Instead of specifying the full covariance matrix, popular
  approximations include:
  
    - Spherical covariance (`cov` is a multiple of the identity matrix)
    - Diagonal covariance(`cov` has non-negative elements only on the diagonal)
  
  This geometrical property can be seen in two dimensions by plotting
  generated data-points:
  
  >>> mean = [0,0]
  >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis
  
  >>> x,y = multivariate_normal(mean,cov,5000).T
  >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()
  
  Note that the covariance matrix must be symmetric and non-negative
  definite.
  
  References
  ----------
  .. [1] A. Papoulis, "Probability, Random Variables, and Stochastic
         Processes," 3rd ed., McGraw-Hill Companies, 1991
  .. [2] R.O. Duda, P.E. Hart, and D.G. Stork, "Pattern Classification,"
         2nd ed., Wiley, 2001.
  
  Examples
  --------
  >>> mean = (1,2)
  >>> cov = [[1,0],[1,0]]
  >>> x = multivariate_normal(mean,cov,(3,3))
  >>> print x.shape
  (3, 3, 2)
  
  The following is probably true, given that 0.6 is roughly twice the
  standard deviation:
  
  >>> print list( (x[0,0,:] - mean) < 0.6 )
  [True, True]
  
  .. index:: random
     :refguide: random;distributions, random;gauss""",
      )
  
imports: import textwrap
  from dipy.workflows.docstring_parser import NumpyDocString
  import numpy.testing
label_desc: Use image node_id 12 for calling a global function with example usage: test_str() without return types