code: def test_smooth_l1_continuous_max(self):
      with self.cached_session():
          epsilon = tf.constant(1e-10, dtype=tf.float32)
          predicted = tf.constant(
              [2.375], dtype=tf.float32, name="predicted"
          )
          gold_standard = tf.constant(
              [0], dtype=tf.float32, name="gold_standard"
          )
          huber_loss_inside_delta = smooth_l1_loss(
              predicted + epsilon, gold_standard, value_thresh=0.5
          )
          huber_loss_outside_delta = smooth_l1_loss(
              predicted - epsilon, gold_standard
          )
          self.assertAlmostEqual(
              huber_loss_inside_delta.eval(),
              huber_loss_outside_delta.eval(),
          )
  
imports: from __future__ import absolute_import, print_function
  import tensorflow
  from niftynet.layer.loss_regression import LossFunction
  from niftynet.layer.loss_regression import l1_loss, l2_loss, huber_loss, smooth_l1_loss, cosine_loss
  from tests.niftynet_testcase import NiftyNetTestCase
label_desc: Use image node_id 3 for calling the SmoothL1LossTests obj's underlying member method code with example usage: obj.test_smooth_l1_continuous_max() without return types