class_docstr: Logistic sigmoid activation function, :math:`f(x) = 1 / (1 + \exp(-x))`
  
  Squashes the input from range :math:`[-\infty,+\infty]` to :math:`[0, 1]`
docstr: Sets the backpropagation to use the shortcut when gradients do not
  need to be calculated.
  
  If True, a shortcut calculation is used. If False, the actual derivative
  is return during backpropagation.
  
  Arguments:
      shortcut (bool): If True, shortcut calculation will be used during backpropagation.
docstr_tok: ["Sets","the","backpropagation","to","use","the","shortcut","when","gradients","do","not","need","to","be","calculated",".","If","True",",","a","shortcut","calculation","is","used",".","If","False",",","the","actual","derivative","is","return","during","backpropagation",".","Arguments",":","shortcut","(","bool",")",":","If","True",",","shortcut","calculation","will","be","used","during","backpropagation","."]
code: def set_shortcut(self, shortcut):
      """
      Sets the backpropagation to use the shortcut when gradients do not
      need to be calculated.
  
      If True, a shortcut calculation is used. If False, the actual derivative
      is return during backpropagation.
  
      Arguments:
          shortcut (bool): If True, shortcut calculation will be used during backpropagation.
      """
      self.shortcut = shortcut
  
      if shortcut:
          self.bprop_func = lambda x: 1
      else:
          self.bprop_func = lambda x: x * (1.0 - x)
  
imports: from __future__ import division
  from neon.transforms.transform import Transform
label_desc: Use image node_id 2 for calling the Logistic obj's underlying member method code with example usage: obj.set_shortcut(shortcut) without return types