code: def test_predict_link_prediction():
      tf.keras.backend.clear_session()
      edge_ids_test = np.array([[1, 2], [2, 3], [1, 3]])
  
      graph = example_graph_1(feature_size=2)
  
      # base_model, keras_model, generator, train_gen
      gnn_models = [
          create_graphSAGE_model(graph, link_prediction=True),
          create_HinSAGE_model(graph, link_prediction=True),
      ]
  
      for gnn_model in gnn_models:
          keras_model = gnn_model[1]
          generator = gnn_model[2]
  
          ens = Ensemble(keras_model, n_estimators=2, n_predictions=1)
  
          ens.compile(
              optimizer=Adam(),
              loss=binary_crossentropy,
              weighted_metrics=["acc"],
          )
  
          test_gen = generator.flow(edge_ids_test)
          # Check that passing invalid parameters is handled correctly. We will not check error handling for those
          # parameters that Keras will be responsible for.
          with pytest.raises(ValueError):
              ens.predict(
                  generator=test_gen, predict_data=edge_ids_test
              )
  
          # We won't train the model instead use the initial random weights to test
          # the evaluate method.
          test_predictions = ens.predict(test_gen, summarise=True)
  
          print(
              "test_predictions shape {}".format(test_predictions.shape)
          )
          assert len(test_predictions) == len(edge_ids_test)
  
          assert test_predictions.shape[1] == 1
  
          test_predictions = ens.predict(test_gen, summarise=False)
  
          assert test_predictions.shape[0] == ens.n_estimators
          assert test_predictions.shape[1] == ens.n_predictions
          assert test_predictions.shape[2] == len(edge_ids_test)
          assert test_predictions.shape[3] == 1
  
          #
          # Repeat for BaggingEnsemble
          ens = BaggingEnsemble(
              keras_model, n_estimators=2, n_predictions=1
          )
  
          ens.compile(
              optimizer=Adam(),
              loss=binary_crossentropy,
              weighted_metrics=["acc"],
          )
  
          test_gen = generator.flow(edge_ids_test)
          # Check that passing invalid parameters is handled correctly. We will not check error handling for those
          # parameters that Keras will be responsible for.
          with pytest.raises(ValueError):
              ens.predict(
                  generator=test_gen, predict_data=edge_ids_test
              )
  
          # We won't train the model instead use the initial random weights to test
          # the evaluate method.
          test_predictions = ens.predict(test_gen, summarise=True)
  
          print(
              "test_predictions shape {}".format(test_predictions.shape)
          )
          assert len(test_predictions) == len(edge_ids_test)
  
          assert test_predictions.shape[1] == 1
  
          test_predictions = ens.predict(test_gen, summarise=False)
  
          assert test_predictions.shape[0] == ens.n_estimators
          assert test_predictions.shape[1] == ens.n_predictions
          assert test_predictions.shape[2] == len(edge_ids_test)
          assert test_predictions.shape[3] == 1
  
imports: import pandas
  import pytest
  import numpy
  import tensorflow
  from stellargraph import StellarGraph
  from stellargraph.layer import GraphSAGE, GCN, GAT, HinSAGE, link_classification, link_regression
  from stellargraph.mapper import GraphSAGENodeGenerator, FullBatchNodeGenerator, HinSAGENodeGenerator, GraphSAGELinkGenerator, HinSAGELinkGenerator
  from stellargraph.ensemble import Ensemble, BaggingEnsemble
  from tensorflow.keras import layers, Model
  from tensorflow.keras.optimizers import Adam
  from tensorflow.keras.losses import categorical_crossentropy, binary_crossentropy
label_desc: Use image node_id 13 for calling a global function with example usage: test_predict_link_prediction() without return types