code: def test_mean_hin_agg_apply():
      agg = MeanHinAggregator(
          2, act=lambda z: z, kernel_initializer="ones"
      )
      inp = [
          keras.Input(shape=(1, 2)),
          keras.Input(shape=(1, 2, 2)),
          keras.Input(shape=(1, 2, 4)),
      ]
      out = agg(inp)
      model = keras.Model(inputs=inp, outputs=out)
  
      x = [
          np.array([[[1, 1]]]),
          np.array([[[[2, 2], [2, 2]]]]),
          np.array([[[[3, 3, 3, 3], [3, 3, 3, 3]]]]),
      ]
  
      actual = model.predict(x)
      expected = np.array([[[2, 8]]])
      assert actual == pytest.approx(expected)
  
imports: import pytest
  import numpy
  import networkx
  from tensorflow import keras
  from tensorflow.keras import regularizers
  from stellargraph import StellarGraph
  from stellargraph.layer.hinsage import *
  from stellargraph.mapper import *
  from ..test_utils.graphs import example_hin_1
  from ..None import test_utils
label_desc: Use image node_id 3 for calling a global function with example usage: test_mean_hin_agg_apply() without return types