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diff --git a/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/mnist_sftmax_with_saving.py b/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/mnist_sftmax_with_saving.py
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+++ b/config-model/src/test/cfg/application/ml_models/models/mnist_softmax/mnist_sftmax_with_saving.py
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+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""A very simple MNIST classifier.
+
+See extensive documentation at
+https://www.tensorflow.org/get_started/mnist/beginners
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import argparse
+import sys
+
+from tensorflow.examples.tutorials.mnist import input_data
+
+import tensorflow as tf
+
+FLAGS = None
+
+
+def main(_):
+  # Import data
+  mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True)
+
+  # Create the model
+  x = tf.placeholder(tf.float32, [None, 784])
+
+  with tf.name_scope("layer"):
+    W = tf.Variable(tf.zeros([784, 10]))
+    b = tf.Variable(tf.zeros([10]))
+    y = tf.matmul(x, W) + b
+
+
+  # Define loss and optimizer
+  y_ = tf.placeholder(tf.float32, [None, 10])
+
+  # The raw formulation of cross-entropy,
+  #
+  #   tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(tf.nn.softmax(y)),
+  #                                 reduction_indices=[1]))
+  #
+  # can be numerically unstable.
+  #
+  # So here we use tf.nn.softmax_cross_entropy_with_logits on the raw
+  # outputs of 'y', and then average across the batch.
+  cross_entropy = tf.reduce_mean(
+      tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
+  train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
+
+  sess = tf.InteractiveSession()
+  tf.global_variables_initializer().run()
+  # Train
+  for _ in range(1000):
+    batch_xs, batch_ys = mnist.train.next_batch(100)
+    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
+
+  # Test trained model
+  correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
+  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
+  print(sess.run(accuracy, feed_dict={x: mnist.test.images,
+                                      y_: mnist.test.labels}))
+
+  # Save the model
+  export_path = "saved"
+  print('Exporting trained model to ', export_path)
+  builder = tf.saved_model.builder.SavedModelBuilder(export_path)
+  signature = tf.saved_model.signature_def_utils.predict_signature_def(inputs = {'x':x}, outputs = {'y':y})
+  builder.add_meta_graph_and_variables(sess,
+                                       [tf.saved_model.tag_constants.SERVING],
+                                       signature_def_map={'serving_default':signature})
+  builder.save(as_text=True)
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser()
+  parser.add_argument('--data_dir', type=str, default='/tmp/tensorflow/mnist/input_data',
+                      help='Directory for storing input data')
+  FLAGS, unparsed = parser.parse_known_args()
+  tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)