import tensorflow as tf
    import matplotlib.pyplot as plt

    print("Strider was here:", tf.__version__)

    # The TRAINING DATA - This defines both the problem and the solution space
    # on a business level - An Entire New Industry of Acquisition, Labeling,
    # and Structuring of this training data

    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
    x_train, x_test = x_train / 255.0, x_test / 255.0

    #  This Builds a Mechanism NOT an Algorithm
    model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),   # The input, in this case the image
    tf.keras.layers.Dense(128, activation='relu'),   # hidden
    tf.keras.layers.Dropout(.2),                     # hidden
    tf.keras.layers.Dense(10)                        # The output layer, the solution space

    #  The hidden layers are the structure that ALLOWS the
    # algorithm to work.  The algorithm is "written" into the weights using
    # back propagation and loss functions based on the training data.

    #  Training
    predictions = model(x_train[2:3]).numpy()
    loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    loss_fn(y_train[:1], predictions).numpy()

    metrics=['accuracy']), y_train, epochs=1)  # Raise this

    #  Evaluation Metrics
    evalResults = model.evaluate(x_test,y_test)

    #  Results: Wrap the Model in a probability model for "results"
    probability_model = tf.keras.Sequential([

    #  Results: Query the Model
    probResult = probability_model(x_test, training=False)   # Probability of Class Membership
    predResult = probability_model.predict(x_test)           # Model Confidence of Class Membership

    print ('\n\n')

    #  Map results into solution space
    class_names = ['Zero', 'One', 'Two', 'Three', 'Four', 'Five', 'Six', 'Seven', 'Eight', 'Nine']
    for i, logits in enumerate(predResult):  # probResults, predResults
         class_idx = tf.math.argmax(logits).numpy()
         p = tf.nn.softmax(logits)[class_idx]
         name = class_names[class_idx]

         print("Example {} prediction: {} ({:4.1f}%)".format(i, name, 100*p))
         # output image
         if i > 10:

    print ("all doneeeeeee")