code: def softmax(self, target, axis, name=None):
      max_axis = tf.reduce_max(target, axis, keepdims=True)
      target_exp = tf.exp(target - max_axis)
      normalize = tf.reduce_sum(target_exp, axis, keepdims=True)
      softmax = tf.div(target_exp, normalize, name)
      return softmax
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  import tensorflow
  import cv2
  import os
  import inspect
label_desc: Use image node_id 13 for calling the Network obj's underlying member method code with example usage: obj.softmax(target, axis, name) and returns: softmax