code: def test_graphsage_serialize():
      gs = GraphSAGE(
          layer_sizes=[4],
          n_samples=[2],
          bias=False,
          input_dim=2,
          multiplicity=1,
          normalize=None,
      )
  
      inp1 = keras.Input(shape=(1, 2))
      inp2 = keras.Input(shape=(2, 2))
      out = gs([inp1, inp2])
      model = keras.Model(inputs=[inp1, inp2], outputs=out)
  
      # Save model
      model_json = model.to_json()
  
      # Set all weights to one
      model_weights = [np.ones_like(w) for w in model.get_weights()]
  
      # Load model from json & set all weights
      model2 = keras.models.model_from_json(
          model_json, custom_objects={"MeanAggregator": MeanAggregator}
      )
      model2.set_weights(model_weights)
  
      # Test loaded model
      x1 = np.array([[[1, 1]]])
      x2 = np.array([[[2, 2], [3, 3]]])
      expected = np.array([[2, 2, 5, 5]])
  
      actual = model2.predict([x1, x2])
      assert expected == pytest.approx(actual)
  
imports: from tensorflow import keras
  from tensorflow.keras import initializers, regularizers
  import tensorflow
  import numpy
  import pytest
  from stellargraph.mapper import GraphSAGENodeGenerator
  from stellargraph.layer.graphsage import GraphSAGE, MeanAggregator, MaxPoolingAggregator, MeanPoolingAggregator, AttentionalAggregator
  from ..test_utils.graphs import example_graph
  from ..None import test_utils
label_desc: Use image node_id 25 for calling a global function with example usage: test_graphsage_serialize() without return types