class_docstr: The power transform as a link function:
  
  g(x) = x**power
docstr: Derivative of power transform
  
  g(x) = self.power * x**(self.power - 1)
  
  INPUTS:
     x   -- mean parameters
  
  OUTPUTS: z
     z   -- derivative of power transform of x
docstr_tok: ["Derivative","of","power","transform","g","(","x",")","=","self.power","*","x","*","*","(","self.power","-","1",")","INPUTS",":","x","--","mean","parameters","OUTPUTS",":","z","z","--","derivative","of","power","transform","of","x"]
code: def deriv(self, x):
      """
      Derivative of power transform
  
      g(x) = self.power * x**(self.power - 1)
  
      INPUTS:
         x   -- mean parameters
  
      OUTPUTS: z
         z   -- derivative of power transform of x
  
      """
  
      return self.power * np.power(x, self.power - 1)
  
imports: import numpy
  import scipy.stats
label_desc: Use image node_id 4 for calling the Power obj's underlying member method code with example usage: obj.deriv(x) and returns: unknown