code: def test_reproducible_predictions_fb15k237():
      X = load_fb15k_237()
  
      model = ScoringBasedEmbeddingModel(
          k=50, eta=1, scoring_type="TransE", seed=0
      )
  
      adam = tf.keras.optimizers.Adam(learning_rate=0.001)
      loss = PairwiseLoss(loss_params={"margin": 0.5})
  
      model.compile(
          optimizer=adam,
          loss=loss,
          entity_relation_initializer="glorot_uniform",
          entity_relation_regularizer="L2",
      )
  
      model.fit(X["train"], batch_size=30000, epochs=5, verbose=True)
  
      print(model.predict(X["test"][:2,]))
  
      assert (
          model.predict(X["test"][:2,])
          == np.array([-0.3415385, -0.4203454], dtype=np.float32).all(),
          "Prediction scores have changed.",
      )
  
file_path: AmpliGraph/tests/ampligraph/latent_features/layers/test_predictions.py
imports: import tensorflow
  import numpy
  from ampligraph.datasets import load_fb15k_237
  from ampligraph.latent_features import ScoringBasedEmbeddingModel
  from ampligraph.latent_features import PairwiseLoss
  from ampligraph.utils import save_model, restore_model
label_desc: Use image node_id 2 for calling a global function with example usage: test_reproducible_predictions_fb15k237() without return types