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: Returns the derivative of the logistic (sigmoid) function at y (output)
  
  Args:
      y (Tensor or OpTree): input. y = f(x)
  
  Returns:
      OpTree: Derivative of the Logistic (sigmoid)
              Returns 1 if shortcut is True.
              Returns derivative (y*(1-y)) if shortcut is False.
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code: def bprop(self, y):
      """
  
      Returns the derivative of the logistic (sigmoid) function at y (output)
  
      Args:
          y (Tensor or OpTree): input. y = f(x)
  
      Returns:
          OpTree: Derivative of the Logistic (sigmoid)
                  Returns 1 if shortcut is True.
                  Returns derivative (y*(1-y)) if shortcut is False.
  
      """
      return self.bprop_func(y)
  
imports: from __future__ import division
  from neon.transforms.transform import Transform
label_desc: Use image node_id 4 for calling the Logistic obj's underlying member method code with example usage: obj.bprop(y) and returns: self