code: def percent_point(self, y, V):
          
          self.check_fit()
  
          if self.theta < 0:
              return V
  
          else:
              a = np.power(y, self.theta / (-1 - self.theta))
              b = np.power(V, self.theta)
              u = np.power((a + b - 1) / b, -1 / self.theta)
              return u
docs: Compute the inverse of conditional cumulative distribution :math:`C(u|v)^-1`
  
          Args:
              y: `np.ndarray` value of :math:`C(u|v)`.
              v: `np.ndarray` given value of v.