new_contents: from cloudmesh.common.StopWatch import StopWatch
  import tensorflow as tf
  
  # start stopwatch
  import time
  StopWatch.start('install')
  
  #########################################################
  # Begin program
  #########################################################
  
  # load the mnist dataset
  mnist = tf.keras.datasets.mnist
  (x_train, y_train), (x_test, y_test) = mnist.load_data()
  
  # feature scale the data, converting from integers to floating point numbers
  x_train, x_test = x_train / 255.0, x_test / 255.0
  
  # build our model with custom layers
  model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.2),
    tf.keras.layers.Dense(10)
  ])
  
  # choose our loss function
  loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  
  # compile the model with our loss function and a selected optimizer strategy
  model.compile(optimizer='adam',
                loss=loss_fn,
                metrics=['accuracy'])
                
  # train the model
  model.fit(x_train, y_train, epochs=5)
  
  # evaluate the model
  model.evaluate(x_test,  y_test, verbose=2)
  
  #########################################################
  # End program
  #########################################################
  
  # stop stopwatch
  StopWatch.stop('install')
  StopWatch.benchmark()
  
subject: Create mnist.py