code: def test_pool_all_layers():
      gcn_graph_model = GCNSupervisedGraphClassification(
          layer_sizes=[5, 7, 11, 1],
          activations=["relu", "relu", "relu", "relu"],
          generator=generator,
          pool_all_layers=True,
      )
  
      train_graphs = [0, 1, 2]
      train_gen = generator.flow(graphs=train_graphs, batch_size=2)
      model = tf.keras.Model(*gcn_graph_model.in_out_tensors())
  
      predictions = model.predict(train_gen)
      assert predictions.shape == (3, 5 + 7 + 11 + 1)
  
file_path: stellargraph/tests/layer/test_graph_classification.py
imports: import numpy
  import tensorflow
  from stellargraph.layer.graph_classification import *
  from stellargraph.layer import SortPooling
  from stellargraph.mapper import PaddedGraphGenerator, FullBatchNodeGenerator
  import pytest
  from ..test_utils.graphs import example_graph_random
  from ..None import test_utils
label_desc: Use image node_id 5 for calling a global function with example usage: test_pool_all_layers() without return types