code: def test_ensemble_init_parameters():
      tf.keras.backend.clear_session()
  
      graph = example_graph_1(feature_size=10)
  
      (
          base_model,
          keras_model,
          generator,
          train_gen,
      ) = create_graphSAGE_model(graph)
  
      # base_model, keras_model, generator, train_gen
      gnn_models = [
          create_graphSAGE_model(graph),
          create_HinSAGE_model(graph),
          create_graphSAGE_model(graph, link_prediction=True),
          create_HinSAGE_model(graph, link_prediction=True),
          create_GCN_model(graph),
          create_GAT_model(graph),
      ]
  
      for gnn_model in gnn_models:
          base_model = gnn_model[0]
          keras_model = gnn_model[1]
  
          # Test mixed types
          with pytest.raises(ValueError):
              Ensemble(base_model, n_estimators=3, n_predictions=3)
  
          with pytest.raises(ValueError):
              Ensemble(keras_model, n_estimators=1, n_predictions=0)
  
          with pytest.raises(ValueError):
              Ensemble(keras_model, n_estimators=1, n_predictions=-3)
  
          with pytest.raises(ValueError):
              Ensemble(keras_model, n_estimators=1, n_predictions=1.7)
  
          with pytest.raises(ValueError):
              Ensemble(keras_model, n_estimators=0, n_predictions=11)
  
          with pytest.raises(ValueError):
              Ensemble(keras_model, n_estimators=-8, n_predictions=11)
  
          with pytest.raises(ValueError):
              Ensemble(keras_model, n_estimators=2.5, n_predictions=11)
  
          ens = Ensemble(keras_model, n_estimators=7, n_predictions=10)
  
          assert len(ens.models) == 7
          assert ens.n_estimators == 7
          assert ens.n_predictions == 10
  
          #
          # Repeat for BaggingEnsemble
          # Test mixed types
          with pytest.raises(ValueError):
              BaggingEnsemble(
                  base_model, n_estimators=3, n_predictions=3
              )
  
          with pytest.raises(ValueError):
              BaggingEnsemble(
                  keras_model, n_estimators=1, n_predictions=0
              )
  
          with pytest.raises(ValueError):
              BaggingEnsemble(
                  keras_model, n_estimators=1, n_predictions=-3
              )
  
          with pytest.raises(ValueError):
              BaggingEnsemble(
                  keras_model, n_estimators=1, n_predictions=1.7
              )
  
          with pytest.raises(ValueError):
              BaggingEnsemble(
                  keras_model, n_estimators=0, n_predictions=11
              )
  
          with pytest.raises(ValueError):
              BaggingEnsemble(
                  keras_model, n_estimators=-8, n_predictions=11
              )
  
          with pytest.raises(ValueError):
              BaggingEnsemble(
                  keras_model, n_estimators=2.5, n_predictions=11
              )
  
          ens = BaggingEnsemble(
              keras_model, n_estimators=7, n_predictions=10
          )
  
          assert len(ens.models) == 7
          assert ens.n_estimators == 7
          assert ens.n_predictions == 10
  
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 6 for calling a global function with example usage: test_ensemble_init_parameters() without return types