code: def test_sphinx_str():
      sphinx_doc = SphinxDocString(doc_txt)
      non_blank_line_by_line_compare(
          str(sphinx_doc),
          """
  .. index:: random
     single: random;distributions, random;gauss
  
  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
  
  .. warning::
  
      Certain warnings apply.
  
  .. seealso::
  
      :obj:`some`, :obj:`other`, :obj:`funcs`
  
      :obj:`otherfunc`
          relationship
  
  .. rubric:: 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.
  
  .. rubric:: 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.
  
  .. only:: latex
  
     [1]_, [2]_
  
  .. rubric:: 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]
  """,
      )
  
imports: import sys, textwrap
  from numpydoc.docscrape import NumpyDocString, FunctionDoc, ClassDoc
  from numpydoc.docscrape_sphinx import SphinxDocString, SphinxClassDoc
  from nose.tools import *
label_desc: Use image node_id 13 for calling a global function with example usage: test_sphinx_str() without return types