code: def test_dgcnn_smoke():
      # this is entirely implemented in terms of GCNSupervisedGraphClassification, and so it's enough
      # to validate that the functionality is composed correctly.
      dgcnn = DeepGraphCNN(
          layer_sizes=[2, 3, 4],
          activations=["relu", "relu", "relu"],
          # one graph is perfect, one graph requires padding and one requires truncation
          k=5,
          generator=generator,
      )
  
      # validate the expectations of the implementation
      assert isinstance(dgcnn, GCNSupervisedGraphClassification)
      assert isinstance(dgcnn.pooling, SortPooling)
      assert dgcnn.pool_all_layers == True
  
      # check it gives output of the expected shape
      model = tf.keras.Model(*dgcnn.in_out_tensors())
  
      preds = model.predict(generator.flow([0, 1, 2]))
      assert preds.shape == (3, (2 + 3 + 4) * 5, 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 6 for calling a global function with example usage: test_dgcnn_smoke() without return types