code: def jacobian_graph(predictions, x, nb_classes):
    
  
    # This function will return a list of TF gradients
    list_derivatives = []
  
    # Define the TF graph elements to compute our derivatives for each class
    for class_ind in xrange(nb_classes):
      derivatives, = tf.gradients(predictions[:, class_ind], x)
      list_derivatives.append(derivatives)
  
    return list_derivatives
docs: Create the Jacobian graph to be ran later in a TF session
    :param predictions: the model's symbolic output (linear output,
        pre-softmax)
    :param x: the input placeholder
    :param nb_classes: the number of classes the model has
    :return: