code: def model_argmax(model, samples):
    
    tfe = tf.contrib.eager
    tf_samples = tfe.Variable(samples)
    probabilities = model.get_probs(tf_samples)
  
    if samples.shape[0] == 1:
      return tf.argmax(probabilities)
    else:
      return tf.argmax(probabilities, axis=1)
docs: Helper function that computes the current class prediction
    :param samples: numpy array with input samples (dims must match x)
    :return: the argmax output of predictions, i.e. the current predicted class