class_docstr: Mccormick function
  
  :param bounds: the box constraints to define the domain in which the function is optimized.
  :param sd: standard deviation, to generate noisy evaluations of the function.
class_docstr_tok: ["Mccormick","function",":","param","bounds",":","the","box","constraints","to","define","the","domain","in","which","the","function","is","optimized",".",":","param","sd",":","standard","deviation",",","to","generate","noisy","evaluations","of","the","function","."]
code: def __init__(self, bounds=None, sd=None):
      self.input_dim = 2
      if bounds is None:
          self.bounds = [(-1.5, 4), (-3, 4)]
      else:
          self.bounds = bounds
      self.min = [(-0.54719, -1.54719)]
      self.fmin = -1.9133
      if sd == None:
          self.sd = 0
      else:
          self.sd = sd
      self.name = "Mccormick"
  
imports: import numpy
  from ..util.general import reshape
label_desc: Use image node_id 1 to create a new mccormick object from inherited base classes: function2d with example: obj = mccormick(bounds, sd)