code: def test_sens_spec_loss_by_regression(self):
      with self.cached_session():
          predicted = tf.constant(
              [[0, 10], [10, 0], [10, 0], [10, 0]],
              dtype=tf.float32,
              name="predicted",
          )
          labels = tf.constant(
              [1, 0, 0, 0], dtype=tf.int64, name="labels"
          )
          predicted, labels = [
              tf.expand_dims(x, axis=0) for x in (predicted, labels)
          ]
  
          test_loss_func = LossFunction(2, loss_type="SensSpec")
          test_loss = test_loss_func(predicted, labels)
          self.assertAlmostEqual(test_loss.eval(), 2.06106e-9)
  
imports: from __future__ import absolute_import, print_function
  import numpy
  import tensorflow
  from niftynet.layer.loss_segmentation import LossFunction, labels_to_one_hot
  from tests.niftynet_testcase import NiftyNetTestCase
label_desc: Use image node_id 1 for calling the SensitivitySpecificityTests obj's underlying member method code with example usage: obj.test_sens_spec_loss_by_regression() without return types