text: """Kurtosis operator."""
  import numpy
  import numpoly
  
  from .. import distributions
  from .expected import E
  from .standard_deviation import Std
  
  
  def Kurt(poly, dist=None, fisher=True, **kws):
      """
      Kurtosis operator.
  
      Element by element 4rd order statistics of a distribution or polynomial.
  
      Args:
          poly (numpoly.ndpoly, Distribution):
              Input to take kurtosis on.
          dist (Distribution):
              Defines the space the skewness is taken on. It is ignored if
              ``poly`` is a distribution.
          fisher (bool):
              If True, Fisher's definition is used (Normal -> 0.0). If False,
              Pearson's definition is used (normal -> 3.0)
  
      Returns:
          (numpy.ndarray):
              Element for element variance along ``poly``, where
              ``skewness.shape==poly.shape``.
  
      Examples:
          >>> dist = chaospy.J(chaospy.Gamma(1, 1), chaospy.Normal(0, 2))
          >>> chaospy.Kurt(dist).round(4)
          array([6., 0.])
          >>> chaospy.Kurt(dist, fisher=False).round(4)
          array([9., 3.])
          >>> q0, q1 = chaospy.variable(2)
          >>> poly = chaospy.polynomial([1, q0, q1, 10*q0*q1-1])
          >>> chaospy.Kurt(poly, dist).round(4)
          array([nan,  6.,  0., 15.])
      """
      adjust = 3 if fisher else 0
  
      if dist is None:
          dist, poly = poly, numpoly.variable(len(poly))
      poly = numpoly.set_dimensions(poly, len(dist))
      if not poly.isconstant:
          return poly.tonumpy()**4-adjust
  
      poly = poly-E(poly, dist, **kws)
      poly = numpoly.true_divide(poly, Std(poly, dist, **kws))
      return E(poly**4, dist, **kws)-adjust
  