code: def test_hinsage_aggregators():
      hs = HinSAGE(
          layer_sizes=[2, 2],
          n_samples=[2, 2],
          input_neighbor_tree=[
              ("1", [1, 2]),
              ("1", [3, 4]),
              ("2", [5]),
              ("1", []),
              ("2", []),
              ("2", []),
          ],
          multiplicity=1,
          input_dim={"1": 2, "2": 4},
          aggregator=MeanHinAggregator,
          normalize="none",
          kernel_initializer="ones",
      )
  
      xin, xout = hs.in_out_tensors()
      model = keras.Model(inputs=xin, outputs=xout)
  
      x = [
          np.array([[[1, 1]]]),
          np.array([[[2, 2], [2, 2]]]),
          np.array([[[4, 4, 4, 4], [4, 4, 4, 4]]]),
          np.array([[[3, 3], [3, 3], [3, 3], [3, 3]]]),
          np.array(
              [[[6, 6, 6, 6], [6, 6, 6, 6], [6, 6, 6, 6], [6, 6, 6, 6]]]
          ),
          np.array(
              [[[9, 9, 9, 9], [9, 9, 9, 9], [9, 9, 9, 9], [9, 9, 9, 9]]]
          ),
      ]
  
      actual = model.predict(x)
      expected = np.array([[12, 35.5]])
      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 14 for calling a global function with example usage: test_hinsage_aggregators() without return types