code: def model_argmax(sess, x, predictions, samples, feed=None):
    
    feed_dict = {x: samples}
    if feed is not None:
      feed_dict.update(feed)
    probabilities = sess.run(predictions, feed_dict)
  
    if samples.shape[0] == 1:
      return np.argmax(probabilities)
    else:
      return np.argmax(probabilities, axis=1)
docs: Helper function that computes the current class prediction
    :param sess: TF session
    :param x: the input placeholder
    :param predictions: the model's symbolic output
    :param samples: numpy array with input samples (dims must match x)
    :param feed: An optional dictionary that is appended to the feeding
             dictionary before the session runs. Can be used to feed
             the learning phase of a Keras model for instance.
    :return: the argmax output of predictions, i.e. the current predicted class