class_docstr: Layer with the softmax activation function. It applies linear
  transformation when the ``n_units`` parameter specified and softmax
  function after the transformation. When ``n_units`` is not specified,
  only softmax 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) >> Relu(20) >> Softmax(10)
  
  Convolutional Neural Networks (CNN) for Semantic Segmentation
  
  Softmax layer can be used in order to normalize probabilities
  per pixel. In the example below, we have as input 32x32 image
  with raw prediction per each pixel for 10 different classes.
  Softmax normalizes raw predictions per pixel to the probability
  distribution.
  
  >>> from neupy.layers import *
  >>> network = Input((32, 32, 10)) >> Softmax()
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code: def activation_function(self, input_value):
      return tf.nn.softmax(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 Softmax obj's underlying member method code with example usage: obj.activation_function(input_value) and returns: tf