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 sigmoidal activation.
  
  Arguments:
      x (Tensor or optree): Input value
  
  Returns:
      Tensor or optree: Output activation
docstr_tok: ["Returns","the","sigmoidal","activation",".","Arguments",":","x","(","Tensor","or","optree",")",":","Input","value","Returns",":","Tensor","or","optree",":","Output","activation"]
code: def __call__(self, x):
      """
      Returns the sigmoidal activation.
  
      Arguments:
          x (Tensor or optree): Input value
  
      Returns:
          Tensor or optree: Output activation
      """
      return self.be.sig(x)
  
imports: from __future__ import division
  from neon.transforms.transform import Transform
label_desc: Use image node_id 3 for calling the Logistic obj's underlying member method code with example usage: obj.__call__(x) and returns: self