class_docstr: Power variance function
  
  Parameters
  ----------
  power : float
      exponent used in power variance function
  
  Methods
  -------
  call
      Returns the power variance
  
  Notes
  -----
  Formulas
     V(mu) = numpy.fabs(mu)**power
  
  Aliases for Power:
  mu = Power()
  mu_squared = Power(power=2)
  mu_cubed = Power(power=3)
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docstr: Power variance function
  
  Parameters
  ----------
  mu : array_like
      mean parameters
  
  Returns
  -------
  variance : ndarray
      numpy.fabs(mu)**self.power
docstr_tok: ["Power","variance","function","Parameters","--","--","--","--","--","mu",":","array_like","mean","parameters","Returns","--","--","--","-","variance",":","ndarray","numpy.fabs","(","mu",")","*","*","self.power"]
code: def __call__(self, mu):
      """
      Power variance function
  
      Parameters
      ----------
      mu : array_like
          mean parameters
  
      Returns
      -------
      variance : ndarray
          numpy.fabs(mu)**self.power
      """
      return np.power(np.fabs(mu), self.power)
  
file_path: statsmodels/statsmodels/genmod/families/varfuncs.py
imports: import numpy
label_desc: Use image node_id 2 for calling the Power obj's underlying member method code with example usage: obj.__call__(mu) and returns: np