class_docstr: Cross-Leg-Table objective function.
  
  This class defines the Cross-Leg-Table [1]_ global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{CrossLegTable}}(x) = - \frac{1}{\left(\left|{e^{\left|{100
      - \frac{\sqrt{x_{1}^{2} + x_{2}^{2}}}{\pi}}\right|}
      \sin\left(x_{1}\right) \sin\left(x_{2}\right)}\right| + 1\right)^{0.1}}
  
  
  with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x) = -1`. The global minimum is found on the
  planes :math:`x_1 = 0` and :math:`x_2 = 0`
  
  ..[1] Mishra, S. Global Optimization by Differential Evolution and Particle
  Swarm Methods: Evaluation on Some Benchmark Functions Munich University,
  2006
code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
  
      self.global_optimum = [[0.0, 0.0]]
      self.fglob = -1.0
  
imports: import numpy
  from numpy import abs, asarray, cos, exp, floor, pi, sign, sin, sqrt, sum, size, tril, isnan, atleast_2d, repeat
  from numpy.testing import assert_almost_equal
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new CrossLegTable object from inherited base classes: Benchmark with example: obj = CrossLegTable(dimensions)