class_docstr: Layer with the sigmoid used as an activation function. It applies
  linear transformation when the ``n_units`` parameter specified and
  sigmoid function after the transformation. When ``n_units`` is not
  specified, only sigmoid function will be applied to the input.
  
  Parameters
  ----------
  {Linear.Parameters}
  
  Methods
  -------
  {Linear.Methods}
  
  Attributes
  ----------
  {Linear.Attributes}
  
  Examples
  --------
  Logistic Regression (LR)
  
  >>> from neupy.layers import *
  >>> network = Input(10) >> Sigmoid(1)
  
  Feedforward Neural Networks (FNN)
  
  >>> from neupy.layers import *
  >>> network = Input(10) >> Sigmoid(5) >> Sigmoid(1)
  
  Convolutional Neural Networks (CNN) for Semantic Segmentation
  
  Sigmoid layer can be used in order to normalize probabilities
  per pixel in semantic classification task with two classes.
  In the example below, we have as input 32x32 image that predicts
  one of the two classes. Sigmoid normalizes raw predictions per pixel
  to the valid probabilities.
  
  >>> from neupy.layers import *
  >>> network = Input((32, 32, 1)) >> Sigmoid()
class_docstr_tok: ["Layer","with","the","sigmoid","used","as","an","activation","function",".","It","applies","linear","transformation","when","the","``","n_units","``","parameter","specified","and","sigmoid","function","after","the","transformation",".","When","``","n_units","``","is","not","specified",",","only","sigmoid","function","will","be","applied","to","the","input",".","Parameters","--","--","--","--","--","{","Linear.Parameters","}","Methods","--","--","--","-","{","Linear.Methods","}","Attributes","--","--","--","--","--","{","Linear.Attributes","}","Examples","--","--","--","--","Logistic","Regression","(","LR",")",">",">",">","from","neupy.layers","import","*",">",">",">","network","=","Input","(","10",")",">",">","Sigmoid","(","1",")","Feedforward","Neural","Networks","(","FNN",")",">",">",">","from","neupy.layers","import","*",">",">",">","network","=","Input","(","10",")",">",">","Sigmoid","(","5",")",">",">","Sigmoid","(","1",")","Convolutional","Neural","Networks","(","CNN",")","for","Semantic","Segmentation","Sigmoid","layer","can","be","used","in","order","to","normalize","probabilities","per","pixel","in","semantic","classification","task","with","two","classes",".","In","the","example","below",",","we","have","as","input","32x32","image","that","predicts","one","of","the","two","classes",".","Sigmoid","normalizes","raw","predictions","per","pixel","to","the","valid","probabilities",".",">",">",">","from","neupy.layers","import","*",">",">",">","network","=","Input","(","(","32",",","32",",","1",")",")",">",">","Sigmoid","(",")"]
code: def activation_function(self, input_value):
      return tf.nn.sigmoid(input_value)
  
imports: import numpy
  import tensorflow
  from neupy import init
  from neupy.utils import asfloat, as_tuple, tf_utils
  from neupy.exceptions import LayerConnectionError, WeightInitializationError
  from neupy.core.properties import NumberProperty, TypedListProperty, ParameterProperty, IntProperty
  from .base import BaseLayer
label_desc: Use image node_id 1 for calling the Sigmoid obj's underlying member method code with example usage: obj.activation_function(input_value) and returns: tf