code: def test_SelfAdversarialLoss():
      lossObj = SelfAdversarialLoss(
          {"margin": 3, "alpha": 1, "reduction": "mean"}
      )
      assert (
          lossObj._loss_parameters["margin"] == 3
      ), "SelfAdversarialLoss: The internal states dont match the inputs"
      assert (
          lossObj._loss_parameters["alpha"] == 1
      ), "SelfAdversarialLoss: The internal states dont match the inputs"
  
      pos_score = tf.constant([3, -10.0], dtype=tf.float32)
      corr_score = tf.constant(
          [
              np.log(2),
              np.log(10),
              np.log(2),
              np.log(50),
              np.log(4),
              np.log(40),
          ],
          dtype=tf.float32,
      )
      loss = lossObj(pos_score, corr_score, eta=3)
      assert (
          loss.numpy()
          > np.sum(np.array([1.3552092, 9.222016], dtype=np.float32))
          - 1e-4
      ).all() and (
          loss.numpy()
          < np.sum(np.array([1.3552092, 9.222016], dtype=np.float32))
          + 1e-4
      ).all(), "SelfAdversarialLoss: Loss function outputs dont match expected outputs"
  
      lossObj = SelfAdversarialLoss(
          {"margin": 3, "alpha": 1, "reduction": "sum"}
      )
      assert (
          lossObj._loss_parameters["margin"] == 3
      ), "SelfAdversarialLoss: The internal states dont match the inputs"
      assert (
          lossObj._loss_parameters["alpha"] == 1
      ), "SelfAdversarialLoss: The internal states dont match the inputs"
  
      pos_score = tf.constant([3, -10.0], dtype=tf.float32)
      corr_score = tf.constant(
          [
              np.log(2),
              np.log(10),
              np.log(2),
              np.log(50),
              np.log(4),
              np.log(40),
          ],
          dtype=tf.float32,
      )
      loss = lossObj(pos_score, corr_score, eta=3)
      assert (
          loss.numpy()
          > np.sum(np.array([4.060676, 13.664226], dtype=np.float32))
          - 1e-4
      ).all() and (
          loss.numpy()
          < np.sum(np.array([4.060676, 13.664226], dtype=np.float32))
          + 1e-4
      ).all(), "SelfAdversarialLoss: Loss function outputs dont match expected outputs"
  
file_path: AmpliGraph/tests/ampligraph/latent_features/test_loss_functions.py
imports: from ampligraph.latent_features import loss_functions
  from ampligraph.latent_features.loss_functions import PairwiseLoss, NLLLoss, AbsoluteMarginLoss, SelfAdversarialLoss, NLLMulticlass
  import pytest
  import tensorflow
  import numpy
label_desc: Use image node_id 4 for calling a global function with example usage: test_SelfAdversarialLoss() without return types