code: def get_logits_over_interval(sess, model, x_data, fgsm_params,
                               min_epsilon=-10., max_epsilon=10.,
                               num_points=21):
    
    # Get the height, width and number of channels
    height = x_data.shape[0]
    width = x_data.shape[1]
    channels = x_data.shape[2]
  
    x_data = np.expand_dims(x_data, axis=0)
    import tensorflow as tf
    from cleverhans.attacks import FastGradientMethod
  
    # Define the data placeholder
    x = tf.placeholder(dtype=tf.float32,
                       shape=[1, height,
                              width,
                              channels],
                       name='x')
    # Define adv_x
    fgsm = FastGradientMethod(model, sess=sess)
    adv_x = fgsm.generate(x, **fgsm_params)
  
    if min_epsilon > max_epsilon:
      raise ValueError('Minimum epsilon is less than maximum epsilon')
  
    eta = tf.nn.l2_normalize(adv_x - x, dim=0)
    epsilon = tf.reshape(tf.lin_space(float(min_epsilon),
                                      float(max_epsilon),
                                      num_points),
                         (num_points, 1, 1, 1))
    lin_batch = x + epsilon * eta
    logits = model.get_logits(lin_batch)
    with sess.as_default():
      log_prob_adv_array = sess.run(logits,
                                    feed_dict={x: x_data})
    return log_prob_adv_array
docs: Get logits when the input is perturbed in an interval in adv direction.
  
    Args:
        sess: Tf session
        model: Model for which we wish to get logits.
        x_data: Numpy array corresponding to single data.
                point of shape [height, width, channels].
        fgsm_params: Parameters for generating adversarial examples.
        min_epsilon: Minimum value of epsilon over the interval.
        max_epsilon: Maximum value of epsilon over the interval.
        num_points: Number of points used to interpolate.
  
    Returns:
        Numpy array containing logits.
  
    Raises:
        ValueError if min_epsilon is larger than max_epsilon.