code: def test_saliency_init_parameters():
      graph = example_graph_1(feature_size=4)
      (
          base_model,
          keras_model_gcn,
          generator,
          train_gen,
      ) = create_GCN_model_dense(graph)
      (
          base_model_sp,
          keras_model_gcn_sp,
          generator_sp,
          train_gen_sp,
      ) = create_GCN_model_sparse(graph)
  
      keras_model_gcn.compile(
          optimizer=Adam(lr=0.1),
          loss=categorical_crossentropy,
          weighted_metrics=["acc"],
      )
  
      keras_model_gcn_sp.compile(
          optimizer=Adam(lr=0.1),
          loss=categorical_crossentropy,
          weighted_metrics=["acc"],
      )
      # Both TypeError and RuntimeError will be raised.
      # TypeError is raised due to the wrong generator type while RuntimeError is due to the wrong number of inputs for the model.
      with pytest.raises(TypeError) and pytest.raises(RuntimeError):
          IntegratedGradients(keras_model_gcn, train_gen_sp)
  
      with pytest.raises(TypeError) and pytest.raises(RuntimeError):
          IntegratedGradients(keras_model_gcn_sp, train_gen)
  
file_path: stellargraph/tests/interpretability/test_saliency_maps_gcn.py
imports: import pytest
  from stellargraph.interpretability.saliency_maps import *
  import numpy
  from stellargraph.layer import GCN
  from stellargraph.mapper import FullBatchNodeGenerator
  from tensorflow.keras import Model, regularizers
  from tensorflow.keras.optimizers import Adam
  from tensorflow.keras.losses import categorical_crossentropy
  from ..test_utils.graphs import example_graph_1_saliency_maps
label_desc: Use image node_id 4 for calling a global function with example usage: test_saliency_init_parameters() without return types