code: def create_GCN_model_sparse(graph):
      generator = FullBatchNodeGenerator(
          graph, sparse=True, method="gcn"
      )
      train_gen = generator.flow([0, 1], np.array([[1, 0], [0, 1]]))
  
      layer_sizes = [2, 2]
      gcn = GCN(
          layer_sizes=layer_sizes,
          activations=["elu", "elu"],
          generator=generator,
          dropout=0.3,
          kernel_regularizer=regularizers.l2(5e-4),
      )
  
      for layer in gcn._layers:
          layer._initializer = "ones"
      x_inp, x_out = gcn.in_out_tensors()
      keras_model = Model(inputs=x_inp, outputs=x_out)
      return gcn, keras_model, generator, 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 2 for calling a global function with example usage: create_GCN_model_sparse(graph) and returns: gcn,  keras_model,  generator,  train_gen