code: def test_set_ent_rel_initial_value():
      model = ScoringBasedEmbeddingModel(k=3, eta=1)
      model.compile(
          optimizer="adam",
          loss="pairwise",
          entity_relation_initializer=[
              tf.constant_initializer(
                  [
                      [10, 10, 10],
                      [10, 10, 10],
                      [10, 10, 10],
                      [10, 10, 10],
                      [10, 10, 10],
                  ]
              ),
              tf.constant_initializer([[10, 10, 10], [11, 11, 11]]),
          ],
      )
  
      model.encoding_layer.max_ent_size = 5
      model.encoding_layer.max_rel_size = 2
  
      model.encoding_layer.set_ent_rel_initial_value(
          np.array([[0, 0, 0], [1, 1, 1], [2, 2, 2]]),
          np.array([[5, 5, 5]]),
      )
      model.encoding_layer.build((10, 10))
  
      assert (
          model.encoding_layer.ent_emb.numpy()
          == np.array(
              [[0, 0, 0], [1, 1, 1], [2, 2, 2], [0, 0, 0], [0, 0, 0]]
          )
      ).all(), "EmbeddingLookupLayer (set_ent_rel_initial_value): Entity matrix not correctly initialized"
  
      assert (
          model.encoding_layer.rel_emb.numpy()
          == np.array([[5, 5, 5], [0, 0, 0]])
      ).all(), "EmbeddingLookupLayer (set_ent_rel_initial_value): Relation matrix not correctly initialized"
  
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 4 for calling a global function with example usage: test_set_ent_rel_initial_value() without return types