class_docstr: Decanomial objective function.
  
  This class defines the Decanomial function global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
     f_{\text{Decanomial}}(x) = 0.001 \left(\lvert{x_{2}^{4} + 12 x_{2}^{3}
     + 54 x_{2}^{2} + 108 x_{2} + 81.0}\rvert + \lvert{x_{1}^{10}
     - 20 x_{1}^{9} + 180 x_{1}^{8} - 960 x_{1}^{7} + 3360 x_{1}^{6}
     - 8064 x_{1}^{5} + 13340 x_{1}^{4} - 15360 x_{1}^{3} + 11520 x_{1}^{2}
     - 5120 x_{1} + 2624.0}\rvert\right)^{2}
  
  
  with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x) = 0` for :math:`x = [2, -3]`
  
  .. [1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
code: def fun(self, x, *args):
      self.nfev += 1
  
      val = (
          x[1] ** 4
          + 12 * x[1] ** 3
          + 54 * x[1] ** 2
          + 108 * x[1]
          + 81.0
      )
      val2 = (
          x[0] ** 10.0
          - 20 * x[0] ** 9
          + 180 * x[0] ** 8
          - 960 * x[0] ** 7
      )
      val2 += 3360 * x[0] ** 6 - 8064 * x[0] ** 5 + 13340 * x[0] ** 4
      val2 += (
          -15360 * x[0] ** 3 + 11520 * x[0] ** 2 - 5120 * x[0] + 2624
      )
      return 0.001 * (abs(val) + abs(val2)) ** 2.0
  
imports: import numpy
  from numpy import abs, cos, exp, arange, pi, sin, sqrt, sum, zeros, tanh
  from numpy.testing import assert_almost_equal
  from .go_benchmark import Benchmark
label_desc: Use image node_id 2 for calling the Decanomial obj's underlying member method code with example usage: obj.fun(x) and returns: unknown