class_docstr: Layer with the hard sigmoid used as an activation function. It applies
  linear transformation when the ``n_units`` parameter specified and
  hard sigmoid function after the transformation. When ``n_units`` is
  not specified, only hard sigmoid function will be applied to the input.
  
  Parameters
  ----------
  {Linear.Parameters}
  
  Methods
  -------
  {Linear.Methods}
  
  Attributes
  ----------
  {Linear.Attributes}
  
  Examples
  --------
  Feedforward Neural Networks (FNN)
  
  >>> from neupy.layers import *
  >>> network = Input(10) >> HardSigmoid(5)
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code: def activation_function(self, input_value):
      input_value = (0.2 * input_value) + 0.5
      return tf.clip_by_value(input_value, 0.0, 1.0)
  
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 HardSigmoid obj's underlying member method code with example usage: obj.activation_function(input_value) and returns: tf