code: def test_call():
      model = ScoringBasedEmbeddingModel(k=3, eta=1)
      model.compile(
          optimizer="adam",
          loss="pairwise",
          entity_relation_initializer=[
              tf.constant_initializer(
                  [[0, 0, 0], [1, 1, 1], [2, 2, 2]]
              ),
              tf.constant_initializer([[10, 10, 10], [11, 11, 11]]),
          ],
      )
  
      model.encoding_layer.max_ent_size = 3
      model.encoding_layer.max_rel_size = 2
      model.encoding_layer.build((10, 10))
  
      out = model.encoding_layer.call(
          tf.constant([[0, 0, 1], [0, 1, 2]])
      )
  
      assert (
          out[0].numpy() == np.array([[0, 0, 0], [0, 0, 0]])
      ).all(), "EmbeddingLookupLayer (call): subject embeddings are incorrect"
      assert (
          out[1].numpy() == np.array([[10, 10, 10], [11, 11, 11]])
      ).all(), "EmbeddingLookupLayer (call): predicate embeddings are incorrect"
      assert (
          out[2].numpy() == np.array([[1, 1, 1], [2, 2, 2]])
      ).all(), (
          "EmbeddingLookupLayer (call): object embeddings are incorrect"
      )
  
file_path: AmpliGraph/tests/ampligraph/latent_features/layers/encoding/test_EmbeddingLookupLayer.py
imports: from ampligraph.latent_features.layers.encoding import EmbeddingLookupLayer
  from ampligraph.latent_features import ScoringBasedEmbeddingModel
  import pytest
  import tensorflow
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: test_call() without return types